add some parameter stuff to compboost

src/baselearner_track.cpp

@@ -45,10 +45,11 @@ namespace blearnertrack
 // Just an empty constructor:
 BaselearnerTrack::BaselearnerTrack () {};
 
+BaselearnerTrack::BaselearnerTrack (double learning_rate) : learning_rate ( learning_rate ) {};
+
 // Insert a baselearner to the vector. We also want to add up the parameter
 // in there to get an estimator in the end:
-void BaselearnerTrack::InsertBaselearner (blearner::Baselearner* blearner,
-  double learning_rate)
+void BaselearnerTrack::InsertBaselearner (blearner::Baselearner* blearner)
 {
   // Insert new baselearner:
   blearner_vector.push_back(blearner);
@@ -103,10 +104,93 @@ void BaselearnerTrack::ClearBaselearnerVector ()
   blearner_vector.clear();
 }
 
+// Get estimated parameter for specific iteration:
+std::map<std::string, arma::mat> BaselearnerTrack::GetEstimatedParameterForIteration (unsigned int k)
+{
+  // Create new parameter map:
+  std::map<std::string, arma::mat> my_new_parameter_map;
+
+  if (k <= blearner_vector.size()) {
+
+    for (unsigned int i = 0; i < k; i++) {
+      std::string insert_id = blearner_vector[i]->GetDataIdentifier() + ": " + blearner_vector[i]->GetBaselearnerType();
+
+      // Check if the baselearner is the first one. If so, the parameter
+      // has to be instantiated with a zero matrix:
+      std::map<std::string, arma::mat>::iterator it = my_new_parameter_map.find(insert_id);
+
+      // Prune parameter by multiplying it with the learning rate:
+      arma::mat parameter_temp = learning_rate * blearner_vector[i]->GetParameter();
+
+      // Check if this is the first parameter entry:
+      if (it == my_new_parameter_map.end()) {
+
+        // If this is the first entry, initialize it with zeros:
+        arma::mat init_parameter(parameter_temp.n_rows, parameter_temp.n_cols, arma::fill::zeros);
+        my_new_parameter_map.insert(std::pair<std::string, arma::mat>(insert_id, init_parameter));
+
+      }
+
+      // Accumulating parameter. If there is a nan, then this will be ignored and 
+      // the non  nan entries are added up:
+      // arma::mat parameter_insert = parameter_temp + my_parameter_map.find(blearner->GetBaselearnerType())->second;
+      // my_parameter_map.insert(std::pair<std::string, arma::mat>(blearner->GetBaselearnerType(), parameter_insert));
+      my_new_parameter_map[ insert_id ] = parameter_temp + my_new_parameter_map.find(insert_id)->second;
+    }
+  }
+  return my_new_parameter_map;
+}
+
+// Create parameter matrix:
+std::pair<std::vector<std::string>, arma::mat> BaselearnerTrack::GetParameterMatrix ()
+{
+  // Instantiate list to iterate:
+  std::map<std::string, arma::mat> my_new_parameter_map = my_parameter_map;
+
+  unsigned int cols = 0;
+
+  // Set all parameter to zero in new map:
+  for (auto& it : my_new_parameter_map) {
+    arma::mat init_parameter (it.second.n_rows, it.second.n_cols, arma::fill::zeros);
+    my_new_parameter_map[ it.first ] = init_parameter;
+
+    cols += it.second.n_cols;
+  }
+
+  // Initialize matrix:
+  arma::mat parameters (blearner_vector.size(), cols, arma::fill::zeros);
+
+  for (unsigned int i = 0; i < blearner_vector.size(); i++) {
+    std::string insert_id = blearner_vector[i]->GetDataIdentifier() + ": " + blearner_vector[i]->GetBaselearnerType();
+
+    // Prune parameter by multiplying it with the learning rate:
+    arma::mat parameter_temp = learning_rate * blearner_vector[i]->GetParameter();
+
+    // Accumulating parameter. If there is a nan, then this will be ignored and 
+    // the non  nan entries are added up:
+    my_new_parameter_map[ insert_id ] = parameter_temp + my_new_parameter_map.find(insert_id)->second;
+
+    arma::mat param_insert;
+
+    for (auto& it : my_new_parameter_map) {
+      param_insert = arma::join_rows(param_insert, it.second);
+    }
+    parameters.row(i) = param_insert;
+  }
+  std::pair<std::vector<std::string>, arma::mat> out_pair;
+
+  for (auto& it : my_new_parameter_map) {
+    out_pair.first.push_back(it.first);
+  }
+  out_pair.second = parameters;
+
+  return out_pair;
+}
+
 // Destructor:
 BaselearnerTrack::~BaselearnerTrack ()
 {
-  std::cout << "Call BaselearnerTrack Destructor" << std::endl;
+  // std::cout << "Call BaselearnerTrack Destructor" << std::endl;
   for (unsigned int i = 0; i< blearner_vector.size(); i++)
   {
     delete blearner_vector[i];

src/baselearner_track.h

@@ -64,12 +64,15 @@ class BaselearnerTrack
     // iteration:
     std::map<std::string, arma::mat> my_parameter_map;
 
+    double learning_rate;
+
   public: 
 
     BaselearnerTrack ();
+    BaselearnerTrack (double);
 
     // Insert a baselearner into vector and update parameter:
-    void InsertBaselearner (blearner::Baselearner*, double);
+    void InsertBaselearner (blearner::Baselearner*);
 
     // Return the vector of baselearner:
     std::vector<blearner::Baselearner*> GetBaselearnerVector ();
@@ -80,8 +83,11 @@ class BaselearnerTrack
     // Clear the vector without deleting the data in the factory:
     void ClearBaselearnerVector ();
 
+    // Estimate parameter for specific iteration:
+    std::map<std::string, arma::mat> GetEstimatedParameterForIteration (unsigned int);
+
     // Returns a matrix of parameters for every iteration:
-    // arma::mat GetParameterMatrix ();
+    std::pair<std::vector<std::string>, arma::mat> GetParameterMatrix ();
 
     // Destructor:
     ~BaselearnerTrack ();

src/compboost.cpp

@@ -59,7 +59,10 @@ Compboost::Compboost (arma::vec response, double learning_rate,
     used_optimizer ( used_optimizer ),
     used_loss ( used_loss ),
     used_baselearner_list ( used_baselearner_list ),
-    used_logger ( used_logger ) {}
+    used_logger ( used_logger ) 
+{
+  blearner_track = blearnertrack::BaselearnerTrack(learning_rate);
+}
 
 // --------------------------------------------------------------------------- #
 // Member functions:
@@ -106,7 +109,7 @@ void Compboost::TrainCompboost (bool trace)
     // std::cout << "<<Compboost>> Cast integer k to string for baselearner identifier" << std::endl;
 
     // Insert new baselearner to vector of selected baselearner:    
-    blearner_track.InsertBaselearner(selected_blearner, learning_rate);
+    blearner_track.InsertBaselearner(selected_blearner);
     // std::cout << "<<Compboost>> Insert new baselearner to vector of selected baselearner" << std::endl;
 
     // Update model (prediction) and shrink by learning rate:
@@ -170,20 +173,15 @@ std::vector<std::string> Compboost::GetSelectedBaselearner ()
   return selected_blearner;
 }
 
-// arma::vec Compboost::PredictEnsemble ()
-// {
-//   arma::vec prediction(response.size());
-//   prediction.fill(initialization);
-//   
-//   std::cout << "Initialize the b0 model with: " << initialization << std::endl;
-//   std::cout << "Rows = " << prediction.n_rows << " Cols = " << prediction.n_cols << std::endl;
-//   
-//   for (std::vector<blearner::Baselearner*>::iterator it = blearner_track->GetBaselearnerVector().begin(); it != blearner_track->GetBaselearnerVector().end(); ++it) {
-//     std::cout << "Now iterating over baselearner!" << std::endl;
-//     prediction += (*it)->predict();
-//   }
-//   return prediction;
-// }
+std::map<std::string, arma::mat> Compboost::GetParameterOfIteration (unsigned int k) 
+{
+  return blearner_track.GetEstimatedParameterForIteration(k);
+}
+
+std::pair<std::vector<std::string>, arma::mat> Compboost::GetParameterMatrix ()
+{
+  return blearner_track.GetParameterMatrix();
+}
 
 // Destructor:
 Compboost::~Compboost ()

src/compboost.h

@@ -73,7 +73,7 @@ class Compboost
     bool stop_if_all_stopper_fulfilled;
 
     // Pieces to run the algorithm:
-    blearnertrack::BaselearnerTrack blearner_track = blearnertrack::BaselearnerTrack();
+    blearnertrack::BaselearnerTrack blearner_track;
     optimizer::Optimizer* used_optimizer;
     loss::Loss* used_loss;
     blearnerlist::BaselearnerList used_baselearner_list;
@@ -93,8 +93,9 @@ class Compboost
     std::map<std::string, arma::mat> GetParameter ();
     std::vector<std::string> GetSelectedBaselearner ();
 
-    // arma::vec PredictEnsemble ();
-    // arma::vec PredictEnsemble (arma::mat &);
+    std::map<std::string, arma::mat> GetParameterOfIteration (unsigned int);
+
+    std::pair<std::vector<std::string>, arma::mat> GetParameterMatrix ();
 
     // Destructor:
     ~Compboost ();

src/compboost_modules.cpp

@@ -591,8 +591,8 @@ class CompboostWrapper
   Rcpp::List getLoggerData ()
   {
     return Rcpp::List::create(
-      Rcpp::Named("logger_names") = used_logger->GetLoggerData().first,
-      Rcpp::Named("logger_data")  = used_logger->GetLoggerData().second
+      Rcpp::Named("logger.names") = used_logger->GetLoggerData().first,
+      Rcpp::Named("logger.data")  = used_logger->GetLoggerData().second
     );
   }
 
@@ -608,6 +608,28 @@ class CompboostWrapper
     return out;
   }
 
+  Rcpp::List getEstimatedParameterOfIteration (unsigned int k)
+  {
+    std::map<std::string, arma::mat> parameter = obj->GetParameterOfIteration(k);
+
+    Rcpp::List out;
+
+    for (auto &it : parameter) {
+      out[it.first] = it.second;
+    }
+    return out;
+  }
+
+  Rcpp::List getParameterMatrix ()
+  {
+    std::pair<std::vector<std::string>, arma::mat> out_pair = obj->GetParameterMatrix();
+
+    return Rcpp::List::create(
+        Rcpp::Named("parameter.names")   = out_pair.first,
+        Rcpp::Named("parameter.matrix")  = out_pair.second
+      );
+  }
+
   // Destructor:
   ~CompboostWrapper ()
   {
@@ -643,6 +665,8 @@ RCPP_MODULE (compboost_module)
     .method("getSelectedBaselearner", &CompboostWrapper::getSelectedBaselearner, "Get vector of selected baselearner")
     .method("getLoggerData", &CompboostWrapper::getLoggerData, "Get data of the used logger")
     .method("getEstimatedParameter", &CompboostWrapper::getEstimatedParameter, "Get the estimated paraemter")
+    .method("getEstimatedParameterOfIteration", &CompboostWrapper::getEstimatedParameterOfIteration, "Get the estimated parameter for iteration k < iter.max")
+    .method("getParameterMatrix", &CompboostWrapper::getParameterMatrix, "Get matrix of all estimated parameter in each iteration")
   ;
 }
 

tests/testthat/test_compboost.R

@@ -122,9 +122,45 @@ test_that("compboost does the same as mboost", {
     unname(unlist(cboost$getEstimatedParameter()))
   )
 
-  expect_equal(dim(cboost$getLoggerData()$logger_data), c(500, 2))
-  expect_equal(cboost$getLoggerData()$logger_data[, 1], 1:500)
-  expect_equal(length(cboost$getLoggerData()$logger_data[, 2]), 500)
+  expect_equal(dim(cboost$getLoggerData()$logger.data), c(500, 2))
+  expect_equal(cboost$getLoggerData()$logger.data[, 1], 1:500)
+  expect_equal(length(cboost$getLoggerData()$logger.data[, 2]), 500)
 
+  # Check if paraemter getter of smaller iteration works:
+  suppressWarnings({
+    mod.reduced = mboost(
+      formula = mpg ~ bols(hp, intercept = FALSE) + 
+        bols(wt, intercept = FALSE) +
+        bols(hp2, intercept = FALSE), 
+      data    = df, 
+      control = boost_control(mstop = 200, nu = learning.rate)
+    )
+  })
+
+  expect_equal(
+    unname(
+      unlist(
+        mod.reduced$coef()[
+          order(
+            unlist(
+              lapply(names(unlist(mod.reduced$coef()[1:3])), function (x) {
+                strsplit(x, "[.]")[[1]][2]
+              })
+            )
+          )
+          ]
+      )
+    ),
+    unname(unlist(cboost$getEstimatedParameterOfIteration(200)))
+  )
+
+  idx = 2:4 * 120
+  matrix.compare = matrix(NA_real_, nrow = 3, ncol = 3)
+
+  for (i in seq_along(idx)) {
+    matrix.compare[i, ] = unname(unlist(cboost$getEstimatedParameterOfIteration(idx[i])))
+  }
+
+  expect_equal(cboost$getParameterMatrix()$parameter.matrix[idx, ], matrix.compare)
 })
 

tutorials/compboost_class.R

@@ -93,5 +93,11 @@ cboost$getSelectedBaselearner()
 # Get estimated parameter:
 cboost$getEstimatedParameter()
 
+# Get estimated parameter of 200 iteration:
+cboost$getEstimatedParameterOfIteration(200)
+
 # Get logger data:
 cboost$getLoggerData()
+
+# Get parameter matrix:
+param.matrix = cboost$getParameterMatrix()

tutorials/compboost_vs_mboost.R

@@ -152,3 +152,16 @@ p = profvis::profvis({
 
 print(p)
 
+# Check if parameter of smaller iteration works:
+# ----------------------------------------------
+
+mod.reduced = mboost(
+  formula = mpg ~ bols(hp, intercept = FALSE) + 
+    bols(wt, intercept = FALSE) +
+    bols(hp2, intercept = FALSE), 
+  data    = df, 
+  control = boost_control(mstop = 200, nu = learning.rate)
+)
+
+mod.reduced$coef()
+cboost$getEstimatedParameterOfIteration(200)