add getter for parameter and update tests

src/baselearner_track.cpp

@@ -53,9 +53,11 @@ void BaselearnerTrack::InsertBaselearner (blearner::Baselearner* blearner,
   // Insert new baselearner:
   blearner_vector.push_back(blearner);
 
+  std::string insert_id = blearner->GetDataIdentifier() + ": " + blearner->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_parameter_map.find(blearner->GetBaselearnerType());
+  std::map<std::string, arma::mat>::iterator it = my_parameter_map.find(insert_id);
 
   // Prune parameter by multiplying it with the learning rate:
   arma::mat parameter_temp = learning_rate * blearner->GetParameter();
@@ -65,15 +67,15 @@ void BaselearnerTrack::InsertBaselearner (blearner::Baselearner* blearner,
 
     // 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_parameter_map.insert(std::pair<std::string, arma::mat>(blearner->GetBaselearnerType(), init_parameter));
+    my_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_parameter_map[ blearner->GetBaselearnerType() ] = parameter_temp + my_parameter_map.find(blearner->GetBaselearnerType())->second;
+  my_parameter_map[ insert_id ] = parameter_temp + my_parameter_map.find(insert_id)->second;
 
 }
 

src/compboost_modules.cpp

@@ -572,16 +572,6 @@ class CompboostWrapper
     // std::cout << "<<CompboostWrapper>> Create Compboost" << std::endl;
   }
 
-  // Destructor:
-  ~CompboostWrapper ()
-  {
-    // std::cout << "Call CompboostWrapper Destructor" << std::endl;
-    delete used_logger;
-    delete used_optimizer;
-    delete eval_data;
-    delete obj;
-  }
-
   // Member functions
   void train (bool trace) 
   {
@@ -606,6 +596,28 @@ class CompboostWrapper
     );
   }
 
+  Rcpp::List getEstimatedParameter ()
+  {
+    std::map<std::string, arma::mat> parameter = obj->GetParameter();
+
+    Rcpp::List out;
+
+    for (auto &it : parameter) {
+      out[it.first] = it.second;
+    }
+    return out;
+  }
+
+  // Destructor:
+  ~CompboostWrapper ()
+  {
+    // std::cout << "Call CompboostWrapper Destructor" << std::endl;
+    // delete used_logger;
+    // delete used_optimizer;
+    // delete eval_data;
+    // delete obj;
+  }
+
 private:
 
   blearnerlist::BaselearnerList* blearner_list_ptr;
@@ -630,6 +642,7 @@ RCPP_MODULE (compboost_module)
     .method("getPrediction", &CompboostWrapper::getPrediction, "Get prediction")
     .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")
   ;
 }
 

tests/testthat/test_compboost.R

@@ -105,6 +105,22 @@ test_that("compboost does the same as mboost", {
   # ------
   expect_equal(predict(mod), cboost$getPrediction())
   expect_equal(mod$xselect(), cboost.xselect)
+  expect_equal(
+    unname(
+      unlist(
+        mod$coef()[
+          order(
+            unlist(
+              lapply(names(unlist(mod$coef()[1:3])), function (x) {
+                strsplit(x, "[.]")[[1]][2]
+              })
+            )
+          )
+          ]
+      )
+    ),
+    unname(unlist(cboost$getEstimatedParameter()))
+  )
 
   expect_equal(dim(cboost$getLoggerData()$logger_data), c(500, 2))
   expect_equal(cboost$getLoggerData()$logger_data[, 1], 1:500)

tutorials/compboost_class.R

@@ -89,5 +89,9 @@ cboost$train(trace = TRUE)
 
 # Get vector selected baselearner:
 cboost$getSelectedBaselearner()
-# cboost$getModelFrame()
+
+# Get estimated parameter:
+cboost$getEstimatedParameter()
+
+# Get logger data:
 cboost$getLoggerData()

tutorials/compboost_vs_mboost.R

@@ -78,9 +78,11 @@ cboost$train(FALSE)
 
 # Get vector selected baselearner:
 cboost$getSelectedBaselearner()
-# cboost$GetModelFrame()
+
+# Get logger data:
 cboost$getLoggerData()
 
+# Get parameter estimator:
 
 # Do the same with mboost:
 # ------------------------
@@ -113,7 +115,12 @@ all.equal(mod$xselect(), cboost.xselect)
 # ------------------------------------
 
 all.equal(predict(mod), cboost$getPrediction())
-# cboost$GetParameter()
+
+# Check if parameter are the same:
+# --------------------------------
+
+mod$coef()
+cboost$getEstimatedParameter()
 
 # Benchmark:
 # ----------