[Test()] public void TestNonEmptyRowIDs()
{
var matrix = new SparseBooleanMatrix();
for (int i = 0; i < 5; i++)
{
if (i != 2 && i != 3)
{
matrix[i, 1] = true;
matrix[i, 4] = true;
}
}
Assert.AreEqual(3, matrix.NonEmptyRowIDs.Count);
var rowIDs = matrix.NonEmptyRowIDs;
var rowIDsEnum = rowIDs.GetEnumerator();
rowIDsEnum.MoveNext();
Assert.AreEqual(0, rowIDsEnum.Current);
rowIDsEnum.MoveNext();
Assert.AreEqual(1, rowIDsEnum.Current);
rowIDsEnum.MoveNext();
Assert.AreEqual(4, rowIDsEnum.Current);
Assert.IsFalse(rowIDsEnum.MoveNext());
}
[Test()] public void TestNonEmptyColumnIDs()
{
var matrix = new SparseBooleanMatrix();
for (int i = 0; i < 5; i++)
{
if (i != 2 && i != 3)
{
matrix[1, i] = true;
matrix[4, i] = true;
}
}
Assert.AreEqual(3, matrix.NonEmptyColumnIDs.Count);
ICollection <int> colIDs = matrix.NonEmptyColumnIDs;
var colIDsEnum = colIDs.GetEnumerator();
colIDsEnum.MoveNext();
Assert.AreEqual(0, colIDsEnum.Current);
colIDsEnum.MoveNext();
Assert.AreEqual(1, colIDsEnum.Current);
colIDsEnum.MoveNext();
Assert.AreEqual(4, colIDsEnum.Current);
Assert.IsFalse(colIDsEnum.MoveNext());
}
/// <summary>Read binary attribute data from a StreamReader</summary>
/// <remarks>
/// The expected (sparse) line format is:
/// ENTITY_ID tab/space/comma ATTRIBUTE_ID
/// for the relations that hold.
/// </remarks>
/// <param name="reader">a StreamReader to be read from</param>
/// <param name="mapping">the mapping object for the given entity type</param>
/// <returns>the attribute data</returns>
static public IBooleanMatrix Read(StreamReader reader, IMapping mapping)
{
var matrix = new SparseBooleanMatrix();
string line;
while ((line = reader.ReadLine()) != null)
{
// ignore empty lines
if (line.Length == 0)
{
continue;
}
string[] tokens = line.Split(Constants.SPLIT_CHARS);
if (tokens.Length != 2)
{
throw new FormatException("Expected exactly 2 columns: " + line);
}
int entity_id = mapping.ToInternalID(tokens[0]);
int attr_id = int.Parse(tokens[1]);
matrix[entity_id, attr_id] = true;
}
return(matrix);
}
[Test()] public void TestCreate()
{
var sparse_boolean_matrix = new SparseBooleanMatrix();
sparse_boolean_matrix[0, 1] = true;
sparse_boolean_matrix[0, 4] = true;
sparse_boolean_matrix[1, 0] = true;
sparse_boolean_matrix[1, 2] = true;
sparse_boolean_matrix[1, 4] = true;
sparse_boolean_matrix[3, 1] = true;
sparse_boolean_matrix[3, 3] = true;
sparse_boolean_matrix[3, 4] = true;
var correlation_matrix = new BinaryCosine(sparse_boolean_matrix.NumberOfRows);
correlation_matrix.ComputeCorrelations(sparse_boolean_matrix);
Assert.AreEqual(4, correlation_matrix.NumberOfRows);
Assert.IsTrue(correlation_matrix.IsSymmetric);
Assert.AreEqual(1 / Math.Sqrt(6), correlation_matrix[0, 1], DELTA);
Assert.AreEqual(1 / Math.Sqrt(6), correlation_matrix[1, 0], DELTA);
Assert.AreEqual(1 / 3d, correlation_matrix[1, 3], DELTA);
Assert.AreEqual(0f, correlation_matrix[2, 0]);
Assert.AreEqual(0f, correlation_matrix[2, 1]);
Assert.AreEqual(1f, correlation_matrix[2, 2]);
Assert.AreEqual(0f, correlation_matrix[2, 3]);
Assert.AreEqual(0f, correlation_matrix[0, 2]);
Assert.AreEqual(0f, correlation_matrix[1, 2]);
Assert.AreEqual(0f, correlation_matrix[3, 2]);
}
/// <summary>Read binary attribute data from a StreamReader</summary>
/// <param name="reader">a StreamReader to be read from</param>
/// <param name="mapping">the mapping object for the given entity type</param>
/// <returns>the attribute data</returns>
static public SparseBooleanMatrix Read(StreamReader reader, IEntityMapping mapping)
{
var matrix = new SparseBooleanMatrix();
var split_chars = new char[] { '\t', ' ' };
string line;
while (!reader.EndOfStream)
{
line = reader.ReadLine();
// ignore empty lines
if (line.Length == 0)
{
continue;
}
string[] tokens = line.Split(split_chars);
if (tokens.Length != 2)
{
throw new IOException("Expected exactly two columns: " + line);
}
int entity_id = mapping.ToInternalID(int.Parse(tokens[0]));
int attr_id = int.Parse(tokens[1]);
matrix[entity_id, attr_id] = true;
}
return(matrix);
}
[Test()] public void TestCreateMatrix()
{
var matrix = new SparseBooleanMatrix();
var other_matrix = matrix.CreateMatrix(2, 2);
Assert.IsInstanceOfType(matrix.GetType(), other_matrix);
}
[Test()] public void TestNumberOfRows()
{
var matrix = new SparseBooleanMatrix();
for (int i = 0; i < 5; i++)
{
matrix[i, 1] = true;
matrix[i, 4] = true;
}
Assert.AreEqual(5, matrix.NumberOfRows);
}
// TODO generalize more to save code ...
// TODO generalize that normal protocol is just an instance of this? Only if w/o performance penalty ...
/// <summary>For a given user and the test dataset, return a dictionary of items filtered by attributes</summary>
/// <param name="user_id">the user ID</param>
/// <param name="test">the test dataset</param>
/// <param name="item_attributes"></param>
/// <returns>a dictionary containing a mapping from attribute IDs to collections of item IDs</returns>
static public Dictionary<int, ICollection<int>> GetFilteredItems(int user_id, IPosOnlyFeedback test,
SparseBooleanMatrix item_attributes)
{
var filtered_items = new Dictionary<int, ICollection<int>>();
foreach (int item_id in test.UserMatrix[user_id])
foreach (int attribute_id in item_attributes[item_id])
if (filtered_items.ContainsKey(attribute_id))
filtered_items[attribute_id].Add(item_id);
else
filtered_items[attribute_id] = new HashSet<int>() { item_id };
return filtered_items;
}
[Test()] public void TestNumberOfEntries()
{
var matrix = new SparseBooleanMatrix();
for (int i = 0; i < 5; i++)
{
if (i != 2 && i != 4)
{
matrix[i, 1] = true;
matrix[i, 4] = false;
}
}
Assert.AreEqual(3, matrix.NumberOfEntries);
}
public override void Train()
{
filtered_items_by_user = new Dictionary <int, ICollection <int> > [MaxUserID + 1];
items_by_attribute = (SparseBooleanMatrix)item_attributes.Transpose();
Console.Error.WriteLine("max_user_id {0} max_item_id {1}", MaxUserID, MaxItemID);
for (int u = 0; u < filtered_items_by_user.Count; u++)
{
filtered_items_by_user[u] = ItemsFiltered.GetFilteredItems(u, Feedback, ItemAttributes);
}
base.Train();
}
public MediaLiteRatingPredictor(MyMediaLite.IRecommender recommender, IEnumerable <Relation> relations)
: this(recommender)
{
if (recommender is SocialMF)
{
_relations = new SparseBooleanMatrix();
foreach (var con in relations)
{
_relations[_usersMap.ToInternalID(con.UserId), _usersMap.ToInternalID(con.ConnectedId)] = true;
}
((SocialMF)recommender).UserRelation = _relations;
}
}
[Test()] public void TestIsSymmetric()
{
var matrix = new SparseBooleanMatrix();
Assert.IsTrue(matrix.IsSymmetric);
matrix[1, 1] = true;
Assert.IsTrue(matrix.IsSymmetric);
matrix[2, 1] = true;
Assert.IsFalse(matrix.IsSymmetric);
matrix[1, 2] = true;
Assert.IsTrue(matrix.IsSymmetric);
matrix[2, 1] = false;
Assert.IsFalse(matrix.IsSymmetric);
}
[Test()] public void TestNumEntriesByColumn()
{
var matrix = new SparseBooleanMatrix();
for (int i = 0; i < 5; i++)
{
if (i != 2 && i != 3)
{
matrix[i, 1] = true;
matrix[i, 4] = true;
}
}
Assert.AreEqual(0, matrix.NumEntriesByColumn(0));
Assert.AreEqual(3, matrix.NumEntriesByColumn(1));
Assert.AreEqual(0, matrix.NumEntriesByColumn(2));
Assert.AreEqual(0, matrix.NumEntriesByColumn(3));
Assert.AreEqual(3, matrix.NumEntriesByColumn(4));
}
/// <summary>Read binary attribute data from an IDataReader, e.g. a database via DbDataReader</summary>
/// <param name="reader">an IDataReader to be read from</param>
/// <param name="mapping">the mapping object for the given entity type</param>
/// <returns>the attribute data</returns>
static public SparseBooleanMatrix Read(IDataReader reader, IEntityMapping mapping)
{
if (reader.FieldCount < 2)
{
throw new IOException("Expected at least two columns.");
}
var matrix = new SparseBooleanMatrix();
while (!reader.Read())
{
int entity_id = mapping.ToInternalID(reader.GetInt32(0));
int attr_id = reader.GetInt32(1);
matrix[entity_id, attr_id] = true;
}
return(matrix);
}
[Test()] public void TestNonEmptyRows()
{
var matrix = new SparseBooleanMatrix();
for (int i = 0; i < 5; i++)
{
if (i != 2)
{
matrix[i, 1] = true;
matrix[i, 4] = true;
}
}
Assert.IsTrue(matrix[0, 1]);
IList <KeyValuePair <int, HashSet <int> > > nonEmptyRows = matrix.NonEmptyRows;
Assert.AreEqual(4, nonEmptyRows.Count);
// TODO test contents
}
[Test()] public void TestGetEntriesByRow()
{
var matrix = new SparseBooleanMatrix();
for (int i = 0; i < 5; i++)
{
if (i != 2 && i != 3)
{
matrix[i, 1] = true;
matrix[i, 4] = true;
}
}
Assert.AreEqual(2, matrix.GetEntriesByRow(0).Count);
Assert.AreEqual(2, matrix.GetEntriesByRow(1).Count);
Assert.AreEqual(0, matrix.GetEntriesByRow(2).Count);
Assert.AreEqual(0, matrix.GetEntriesByRow(3).Count);
Assert.AreEqual(2, matrix.GetEntriesByRow(4).Count);
}
[Test()] public void TestCreate()
{
// create test objects
var sparse_boolean_matrix = new SparseBooleanMatrix();
sparse_boolean_matrix[0, 1] = true;
sparse_boolean_matrix[0, 4] = true;
sparse_boolean_matrix[1, 0] = true;
sparse_boolean_matrix[1, 2] = true;
sparse_boolean_matrix[1, 4] = true;
sparse_boolean_matrix[3, 1] = true;
sparse_boolean_matrix[3, 3] = true;
sparse_boolean_matrix[3, 4] = true;
// test
var correlation_matrix = BinaryCosine.Create(sparse_boolean_matrix);
Assert.AreEqual(Math.Round(1 / Math.Sqrt(6), 4), Math.Round(correlation_matrix[0, 1], 4));
Assert.AreEqual(Math.Round(1 / Math.Sqrt(6), 4), Math.Round(correlation_matrix[1, 0], 4));
Assert.AreEqual(Math.Round(1 / 3d, 4), Math.Round(correlation_matrix[1, 3], 4));
}
[Test()] public void TestComputeCorrelations()
{
var sparse_boolean_matrix = new SparseBooleanMatrix();
sparse_boolean_matrix[0, 1] = true;
sparse_boolean_matrix[0, 4] = true;
sparse_boolean_matrix[1, 0] = true;
sparse_boolean_matrix[1, 2] = true;
sparse_boolean_matrix[1, 4] = true;
sparse_boolean_matrix[3, 1] = true;
sparse_boolean_matrix[3, 3] = true;
sparse_boolean_matrix[3, 4] = true;
var correlation = new BinaryCosine(4);
correlation.ComputeCorrelations(sparse_boolean_matrix);
Assert.AreEqual(1 / Math.Sqrt(6), correlation[0, 1], DELTA);
Assert.AreEqual(1 / Math.Sqrt(6), correlation[1, 0], DELTA);
Assert.AreEqual(1 / 3d, correlation[1, 3], DELTA);
}
[Test()] public void TestRemoveColumn()
{
var matrix = new SparseBooleanMatrix();
for (int i = 0; i < 5; i++)
{
if (i != 2 && i != 4)
{
matrix[i, 1] = true;
matrix[i, 4] = true;
}
}
matrix[2, 2] = true;
matrix.RemoveColumn(2);
Assert.IsTrue(matrix[0, 3]);
Assert.IsTrue(matrix[1, 3]);
Assert.IsTrue(matrix[3, 3]);
Assert.IsTrue(matrix[1, 1]);
}
/// <summary>Read binary relation data from an IDataReader, e.g. a database via DbDataReader</summary>
/// <param name="reader">an IDataReader to be read from</param>
/// <param name="mapping">the mapping object for the given entity type</param>
/// <returns>the relation data</returns>
static public IBooleanMatrix Read(IDataReader reader, IMapping mapping)
{
if (reader.FieldCount < 2)
{
throw new FormatException("Expected at least 2 columns.");
}
var matrix = new SparseBooleanMatrix();
Func <string> get_e1_id = reader.GetStringGetter(0);
Func <string> get_e2_id = reader.GetStringGetter(1);
while (!reader.Read())
{
int entity1_id = mapping.ToInternalID(get_e1_id());
int entity2_id = mapping.ToInternalID(get_e2_id());
matrix[entity1_id, entity2_id] = true;
}
return(matrix);
}
[Test()] public void TestOverlapCount()
{
var matrix = new SparseBooleanMatrix();
matrix[2, 2] = true;
matrix[2, 5] = true;
matrix[4, 3] = true;
matrix[4, 6] = true;
matrix[5, 1] = true;
matrix[5, 5] = true;
var overlapMatrix = new SparseBooleanMatrix();
overlapMatrix[2, 1] = true;
overlapMatrix[2, 5] = true; // same entry
overlapMatrix[4, 4] = true;
overlapMatrix[4, 6] = true; // same entry
overlapMatrix[5, 2] = true;
overlapMatrix[5, 5] = true; // same entry
Assert.AreEqual(3, matrix.Overlap(overlapMatrix));
}
[Test()] public void TestRemoveColumns()
{
var matrix = new SparseBooleanMatrix();
for (int i = 0; i < 7; i++)
{
if (i != 2 && i != 4)
{
matrix[i, 1] = true;
matrix[i, 4] = true;
}
}
matrix[2, 2] = true;
matrix[2, 5] = true;
matrix[4, 3] = true;
int[] delete_columns = { 2, 4 };
matrix.RemoveColumn(delete_columns);
// test the new columns
Assert.IsTrue(matrix[4, 2]);
Assert.IsTrue(matrix[2, 3]);
Assert.IsFalse(matrix[1, 3]);
Assert.IsFalse(matrix[4, 3]);
}
///
public override void Train()
{
base.Train();
CreateSimilarityMatrix(Similarity);
if (correlation is RatingCorrelationMatrix)
{
((RatingCorrelationMatrix)correlation).ComputeCorrelations(ratings, Entity);
}
if (correlation is BinaryDataCorrelationMatrix)
{
this.entity_data = new SparseBooleanMatrix();
if (Entity == EntityType.USER)
{
for (int i = 0; i < ratings.Count; i++)
{
entity_data[ratings.Users[i], ratings.Items[i]] = true;
}
}
else if (Entity == EntityType.ITEM)
{
for (int i = 0; i < ratings.Count; i++)
{
entity_data[ratings.Items[i], ratings.Users[i]] = true;
}
}
else
{
throw new ArgumentException("Unknown entity type: " + Entity);
}
((BinaryDataCorrelationMatrix)correlation).ComputeCorrelations(entity_data);
}
}
[Test()] public void TestTranspose()
{
var matrix = new SparseBooleanMatrix();
for (int i = 0; i < 7; i++)
{
if (i != 2 && i != 4)
{
matrix[i, 1] = true;
matrix[i, 4] = true;
}
}
matrix[2, 2] = true;
matrix[2, 5] = true;
matrix[4, 3] = true;
// transpose the matrix
var transposed_matrix = (IBooleanMatrix)matrix.Transpose();
// test the transposed matrix
Assert.IsTrue(transposed_matrix[1, 0]);
Assert.IsTrue(transposed_matrix[4, 6]);
Assert.IsFalse(transposed_matrix[3, 1]);
Assert.IsFalse(transposed_matrix[5, 4]);
}
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));
}
protected void Run(string[] args)
{
Console.WriteLine("WISER-RecSys começou");
options = new OptionSet()
{
// string-valued options
{ "arquivo=", v => arquivo = v },
{ "measures=", v => measures = v },
{ "recommender-options=", v => recommender_options += " " + v },
{ "help", v => show_help = v != null },
};
eval_measures = ItemRecommendationEvaluationResults.DefaultMeasuresToShow;
IList <string> extra_args = options.Parse(args);
if (show_help)
{
Usage(0);
}
//eval
if (measures != null)
{
eval_measures = measures.Split(' ', ',');
}
//Rodar o de vocs
//
training_file = "training.data";
test_file = "test.data";
training_partial_file = "training.partial.data";
test_partial_file = "test.partial.data";
for (int i = 0; i < arquivos.Length; i++)
{
MyMediaLite.Random.Seed = 1;
item_attributes_file = "movie_" + arquivos[i] + ".dat_saida";
user_mapping.Add(new Mapping());
item_mapping.Add(new Mapping());
//Setup recommender
recommenders.Add("BPRMFAttr".CreateItemRecommender());
recommenders[i].Configure(recommender_options, (string msg) =>
{
Console.Error.WriteLine(msg); Environment.Exit(-1);
});
// item attributes
if (recommenders[i] is IItemAttributeAwareRecommender && item_attributes_file == null)
{
Abort("Recommender expects --item-attributes=FILE.");
}
if (item_attributes_file != null)
{
item_attributes.Add(AttributeData.Read(item_attributes_file, item_mapping[i]));
}
if (recommenders[i] is IItemAttributeAwareRecommender)
{
((IItemAttributeAwareRecommender)recommenders[i]).ItemAttributes = item_attributes[i];
}
IBooleanMatrix lista_vazia = new SparseBooleanMatrix();
if (recommenders[i] is IUserAttributeAwareRecommender)
{
((IUserAttributeAwareRecommender)recommenders[i]).UserAttributes = lista_vazia;
}
// training data
training_data.Add(ItemData.Read(training_file, user_mapping[i], item_mapping[i], false));
test_data.Add(ItemData.Read(test_file, user_mapping[i], item_mapping[i], false));
test_users.Add(test_data[i].AllUsers);
//Probe
training_probe_data.Add(ItemData.Read(training_partial_file, user_mapping[i], item_mapping[i], false));
test_probe_data.Add(ItemData.Read(test_partial_file, user_mapping[i], item_mapping[i], false));
if (recommenders[i] is MyMediaLite.ItemRecommendation.ItemRecommender)
{
((ItemRecommender)recommenders[i]).Feedback = training_probe_data[i];
}
//Trainar
Console.WriteLine("Vamos ao probe training");
var train_time_span = Wrap.MeasureTime(delegate() { recommenders[i].Train(); });
Console.WriteLine("training_time " + train_time_span + " ");
}
Evaluation evaluation = new Evaluation(recommenders, test_probe_data, training_probe_data);
//Probe learn
Console.WriteLine("Probe learn started");
TimeSpan time_span = Wrap.MeasureTime(delegate() { evaluation.EvaluateProbe(test_users, user_mapping, item_mapping); });
Console.WriteLine(" Probe learn time: " + time_span);
for (int i = 0; i < arquivos.Length; i++)
{
MyMediaLite.Random.Seed = 1;
item_attributes_file = "movie_" + arquivos[i] + ".dat_saida";
//Setup recommender
recommenders[i] = "BPRMFAttr".CreateItemRecommender();
recommenders[i].Configure(recommender_options, (string msg) => { Console.Error.WriteLine(msg); Environment.Exit(-1); });
// item attributes
if (recommenders[i] is IItemAttributeAwareRecommender && item_attributes_file == null)
{
Abort("Recommender expects --item-attributes=FILE.");
}
if (recommenders[i] is IItemAttributeAwareRecommender)
{
((IItemAttributeAwareRecommender)recommenders[i]).ItemAttributes = item_attributes[i];
}
IBooleanMatrix lista_vazia = new SparseBooleanMatrix();
if (recommenders[i] is IUserAttributeAwareRecommender)
{
((IUserAttributeAwareRecommender)recommenders[i]).UserAttributes = lista_vazia;
}
if (recommenders[i] is MyMediaLite.ItemRecommendation.ItemRecommender)
{
((ItemRecommender)recommenders[i]).Feedback = training_data[i];
}
//Trainar
Console.WriteLine("Agora ao treino normal");
var train_time_span = Wrap.MeasureTime(delegate() { recommenders[i].Train(); });
Console.WriteLine("training_time " + train_time_span + " ");
}
var results = evaluation.Evaluate(test_data, training_data, test_users, user_mapping, item_mapping);
foreach (EvaluationResults result in results)
{
Console.WriteLine(result.ToString());
}
Console.WriteLine("Press any key to continue...");
Console.ReadKey();
}
private void IterateBatch(IList <int> rating_indices, bool update_user, bool update_item)
{
SetupLoss();
SparseBooleanMatrix user_reverse_connections = (SparseBooleanMatrix)user_connections.Transpose();
// I. compute gradients
var user_factors_gradient = new Matrix <float>(user_factors.dim1, user_factors.dim2);
var item_factors_gradient = new Matrix <float>(item_factors.dim1, item_factors.dim2);
var user_bias_gradient = new float[user_factors.dim1];
var item_bias_gradient = new float[item_factors.dim1];
// I.1 prediction error
foreach (int index in rating_indices)
{
int user_id = ratings.Users[index];
int item_id = ratings.Items[index];
// prediction
float score = global_bias + user_bias[user_id] + item_bias[item_id];
score += DataType.MatrixExtensions.RowScalarProduct(user_factors, user_id, item_factors, item_id);
double sig_score = 1 / (1 + Math.Exp(-score));
float prediction = (float)(MinRating + sig_score * rating_range_size);
float error = prediction - ratings[index];
float gradient_common = compute_gradient_common(sig_score, error);
user_bias_gradient[user_id] += gradient_common;
item_bias_gradient[item_id] += gradient_common;
for (int f = 0; f < NumFactors; f++)
{
float u_f = user_factors[user_id, f];
float i_f = item_factors[item_id, f];
user_factors_gradient.Inc(user_id, f, gradient_common * i_f);
item_factors_gradient.Inc(item_id, f, gradient_common * u_f);
}
}
// I.2 L2 regularization
// biases
for (int u = 0; u < user_bias_gradient.Length; u++)
{
user_bias_gradient[u] += user_bias[u] * RegU * BiasReg;
}
for (int i = 0; i < item_bias_gradient.Length; i++)
{
item_bias_gradient[i] += item_bias[i] * RegI * BiasReg;
}
// latent factors
for (int u = 0; u < user_factors_gradient.dim1; u++)
{
for (int f = 0; f < user_factors_gradient.dim2; f++)
{
user_factors_gradient.Inc(u, f, user_factors[u, f] * RegU);
}
}
for (int i = 0; i < item_factors_gradient.dim1; i++)
{
for (int f = 0; f < item_factors_gradient.dim2; f++)
{
item_factors_gradient.Inc(i, f, item_factors[i, f] * RegI);
}
}
// I.3 social network regularization -- see eq. (13) in the paper
if (SocialRegularization != 0)
{
for (int u = 0; u < user_factors_gradient.dim1; u++)
{
var sum_connections = new float[NumFactors];
float bias_sum_connections = 0;
int num_connections = user_connections[u].Count;
foreach (int v in user_connections[u])
{
bias_sum_connections += user_bias[v];
for (int f = 0; f < sum_connections.Length; f++)
{
sum_connections[f] += user_factors[v, f];
}
}
if (num_connections != 0)
{
user_bias_gradient[u] += social_regularization * (user_bias[u] - bias_sum_connections / num_connections);
for (int f = 0; f < user_factors_gradient.dim2; f++)
{
user_factors_gradient.Inc(u, f, social_regularization * (user_factors[u, f] - sum_connections[f] / num_connections));
}
}
foreach (int v in user_reverse_connections[u])
{
float trust_v = (float)1 / user_connections[v].Count;
float neg_trust_times_reg = -social_regularization * trust_v;
float bias_diff = 0;
var factor_diffs = new float[NumFactors];
foreach (int w in user_connections[v])
{
bias_diff -= user_bias[w];
for (int f = 0; f < factor_diffs.Length; f++)
{
factor_diffs[f] -= user_factors[w, f];
}
}
bias_diff *= trust_v; // normalize
bias_diff += user_bias[v];
user_bias_gradient[u] += neg_trust_times_reg * bias_diff;
for (int f = 0; f < factor_diffs.Length; f++)
{
factor_diffs[f] *= trust_v; // normalize
factor_diffs[f] += user_factors[v, f];
user_factors_gradient.Inc(u, f, neg_trust_times_reg * factor_diffs[f]);
}
}
}
}
// II. apply gradient descent step
if (update_user)
{
for (int user_id = 0; user_id < user_factors_gradient.dim1; user_id++)
{
user_bias[user_id] -= user_bias_gradient[user_id] * LearnRate * BiasLearnRate;
}
user_factors_gradient.Multiply(-LearnRate);
user_factors.Inc(user_factors_gradient);
}
if (update_item)
{
for (int item_id = 0; item_id < item_factors_gradient.dim1; item_id++)
{
item_bias[item_id] -= item_bias_gradient[item_id] * LearnRate * BiasLearnRate;
}
item_factors_gradient.Multiply(-LearnRate);
item_factors.Inc(item_factors_gradient);
}
}
/// <summary>Evaluation for rankings of filtered items</summary>
/// <remarks>
/// </remarks>
/// <param name="recommender">item recommender</param>
/// <param name="test">test cases</param>
/// <param name="train">training data</param>
/// <param name="item_attributes">the item attributes to be used for filtering</param>
/// <param name="relevant_users">a collection of integers with all relevant users</param>
/// <param name="relevant_items">a collection of integers with all relevant items</param>
/// <returns>a dictionary containing the evaluation results</returns>
static public Dictionary<string, double> Evaluate(
IItemRecommender recommender,
IPosOnlyFeedback test,
IPosOnlyFeedback train,
SparseBooleanMatrix item_attributes,
ICollection<int> relevant_users,
ICollection<int> relevant_items)
{
if (train.Overlap(test) > 0)
Console.Error.WriteLine("WARNING: Overlapping train and test data");
SparseBooleanMatrix items_by_attribute = (SparseBooleanMatrix) item_attributes.Transpose();
// compute evaluation measures
double auc_sum = 0;
double map_sum = 0;
double prec_5_sum = 0;
double prec_10_sum = 0;
double prec_15_sum = 0;
double ndcg_sum = 0;
// for counting the users and the evaluation lists
int num_lists = 0;
int num_users = 0;
int last_user_id = -1;
foreach (int user_id in relevant_users)
{
var filtered_items = GetFilteredItems(user_id, test, item_attributes);
foreach (int attribute_id in filtered_items.Keys)
{
// TODO optimize this a bit, currently it is quite naive
var relevant_filtered_items = new HashSet<int>(items_by_attribute[attribute_id]);
relevant_filtered_items.IntersectWith(relevant_items);
var correct_items = new HashSet<int>(filtered_items[attribute_id]);
correct_items.IntersectWith(relevant_filtered_items);
// the number of items that are really relevant for this user
var relevant_items_in_train = new HashSet<int>(train.UserMatrix[user_id]);
relevant_items_in_train.IntersectWith(relevant_filtered_items);
int num_eval_items = relevant_filtered_items.Count - relevant_items_in_train.Count();
// skip all users that have 0 or #relevant_filtered_items test items
if (correct_items.Count == 0)
continue;
if (num_eval_items - correct_items.Count == 0)
continue;
// counting stats
num_lists++;
if (last_user_id != user_id)
{
last_user_id = user_id;
num_users++;
}
// evaluation
int[] prediction = Prediction.PredictItems(recommender, user_id, relevant_filtered_items);
auc_sum += Items.AUC(prediction, correct_items, train.UserMatrix[user_id]);
map_sum += Items.MAP(prediction, correct_items, train.UserMatrix[user_id]);
ndcg_sum += Items.NDCG(prediction, correct_items, train.UserMatrix[user_id]);
prec_5_sum += Items.PrecisionAt(prediction, correct_items, train.UserMatrix[user_id], 5);
prec_10_sum += Items.PrecisionAt(prediction, correct_items, train.UserMatrix[user_id], 10);
prec_15_sum += Items.PrecisionAt(prediction, correct_items, train.UserMatrix[user_id], 15);
if (prediction.Length != relevant_filtered_items.Count)
throw new Exception("Not all items have been ranked.");
if (num_lists % 1000 == 0)
Console.Error.Write(".");
if (num_lists % 20000 == 0)
Console.Error.WriteLine();
}
}
var result = new Dictionary<string, double>();
result.Add("AUC", auc_sum / num_lists);
result.Add("MAP", map_sum / num_lists);
result.Add("NDCG", ndcg_sum / num_lists);
result.Add("prec@5", prec_5_sum / num_lists);
result.Add("prec@10", prec_10_sum / num_lists);
result.Add("prec@15", prec_15_sum / num_lists);
result.Add("num_users", num_users);
result.Add("num_lists", num_lists);
result.Add("num_items", relevant_items.Count);
return result;
}
///
public override void LearnAttributeToFactorMapping()
{
random = Util.Random.GetInstance();
// create helper data structure
this.data_item = new SparseBooleanMatrix();
for (int i = 0; i < ratings.Count; i++)
{
data_item[ratings.Items[i], ratings.Users[i]] = true;
}
// create attribute-to-factor weight matrix
this.attribute_to_factor = new Matrix <double>(NumItemAttributes + 1, NumFactors + 1);
// account for regression bias term, and the item bias that we want to model
// store the results of the different runs in the following array
var old_attribute_to_factor = new Matrix <double> [num_init_mapping];
Console.Error.WriteLine("Will use {0} examples ...", num_iter_mapping * MaxItemID);
var old_rmse_per_factor = new double[num_init_mapping][];
for (int h = 0; h < num_init_mapping; h++)
{
MatrixUtils.InitNormal(attribute_to_factor, InitMean, InitStdev);
Console.Error.WriteLine("----");
for (int i = 0; i < num_iter_mapping * MaxItemID; i++)
{
IterateMapping();
}
old_attribute_to_factor[h] = new Matrix <double>(attribute_to_factor);
old_rmse_per_factor[h] = ComputeMappingFit();
}
var min_rmse_per_factor = new double[NumFactors + 1];
for (int i = 0; i <= NumFactors; i++)
{
min_rmse_per_factor[i] = Double.MaxValue;
}
var best_factor_init = new int[NumFactors + 1];
// find best factor mappings:
for (int i = 0; i < num_init_mapping; i++)
{
for (int j = 0; j <= NumFactors; j++)
{
if (old_rmse_per_factor[i][j] < min_rmse_per_factor[j])
{
min_rmse_per_factor[j] = old_rmse_per_factor[i][j];
best_factor_init[j] = i;
}
}
}
// set the best weight combinations for each factor mapping
for (int i = 0; i <= NumFactors; i++)
{
Console.Error.WriteLine("Factor {0}, pick {1}", i, best_factor_init[i]);
attribute_to_factor.SetColumn(i,
old_attribute_to_factor[best_factor_init[i]].GetColumn(i)
);
}
}
