update shared ptr ...

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: compboost
 Type: Package
 Title: C++ Implementation of Component-Wise Boosting
-Version: 0.1.0
+Version: 0.1.1
 Authors@R: c(
   person(given = "Daniel", family = "Schalk", email = "daniel.schalk@stat.uni-muenchen.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-0950-1947")),
   person(given = "Janek", family = "Thomas", email = "janek.thomas@stat.uni-muenchen.de", role = "aut", comment = c(ORCID = "0000-0003-4511-6245")),

NEWS.md

@@ -1,3 +1,5 @@
+## compboost 0.1.1
+
 - **23.01.2019** \
   **Style**: Change `.` to `_`, e.g. change `n.knots` to `n_knots`, to be more consistent with `C++` syntax.  
 

R/RcppExports.R

@@ -1060,7 +1060,7 @@ NULL
 #'
 #' oob_list = list(data_source1, data_source2)
 #'
-#' set_seed(123)
+#' set.seed(123)
 #' y_oob = rnorm(10)
 #'
 #' # Used loss:

README.Rmd

@@ -42,7 +42,7 @@ For an introduction and overview about the functionality visit the [project page
 #### CRAN version:
 
 ```r
-instlal.packages("compboost")
+install.packages("compboost")
 ```
 
 #### Developer version:

README.md

@@ -30,10 +30,10 @@ install.packages("compboost")
 devtools::install_github("schalkdaniel/compboost")
 ```
 
-Examples [![Binder](http://mybinder.org/badge.svg)](http://beta.mybinder.org/v2/gh/schalkdaniel/compboost/master?filepath=./binder/tutorial_intro.ipynb)
---------------------------------------------------------------------------------------------------------------------------------------------------------
+Examples
+--------
 
-This examples are rendered using <code>compboost 0.1.0</code>.
+This examples are rendered using <code>compboost 0.1.1</code>.
 
 To be as flexible as possible one should use the `R6` API do define base-learner, losses, stopping criteria, or optimizer as desired. Another option is to use wrapper functions as described on the [project page](https://schalkdaniel.github.io/compboost/).
 
@@ -42,23 +42,23 @@ library(compboost)
 
 # Check installed version:
 packageVersion("compboost")
-#> [1] '0.1.0'
+#> [1] '0.1.1'
 
 # Load data set with binary classification task:
 data(PimaIndiansDiabetes, package = "mlbench")
 # Create categorical feature:
-PimaIndiansDiabetes$pregnant.cat = ifelse(PimaIndiansDiabetes$pregnant == 0, "no", "yes")
+PimaIndiansDiabetes$pregnant_cat = ifelse(PimaIndiansDiabetes$pregnant == 0, "no", "yes")
 
 # Define Compboost object:
 cboost = Compboost$new(data = PimaIndiansDiabetes, target = "diabetes", loss = LossBinomial$new())
 cboost
+#> 
 #> Component-Wise Gradient Boosting
 #> 
 #> Trained on PimaIndiansDiabetes with target diabetes
 #> Number of base-learners: 0
 #> Learning rate: 0.05
 #> Iterations: 0
-#> Positive class: neg
 #> 
 #> LossBinomial Loss:
 #> 
@@ -67,41 +67,40 @@ cboost
 #> 
 
 # Add p-spline base-learner with default parameter:
-cboost$addBaselearner(feature = "pressure", id = "spline", bl.factory = BaselearnerPSpline)
+cboost$addBaselearner(feature = "pressure", id = "spline", bl_factory = BaselearnerPSpline)
 
 # Add another p-spline learner with custom parameters:
-cboost$addBaselearner(feature = "age", id = "spline", bl.factory = BaselearnerPSpline, degree = 3, 
-  n.knots = 10, penalty = 4, differences = 2)
+cboost$addBaselearner(feature = "age", id = "spline", bl_factory = BaselearnerPSpline, degree = 3, 
+  n_knots = 10, penalty = 4, differences = 2)
 
 # Add categorical feature (as single linear base-learner):
-cboost$addBaselearner(feature = "pregnant.cat", id = "category", bl.factory = BaselearnerPolynomial,
+cboost$addBaselearner(feature = "pregnant_cat", id = "category", bl_factory = BaselearnerPolynomial,
   degree = 1, intercept = FALSE)
 
 # Check all registered base-learner:
 cboost$getBaselearnerNames()
 #> [1] "pressure_spline"           "age_spline"               
-#> [3] "pregnant.cat_yes_category" "pregnant.cat_no_category"
+#> [3] "pregnant_cat_yes_category" "pregnant_cat_no_category"
 
 # Train model:
 cboost$train(1000L, trace = 200L)
-#>    1/1000: risk = 0.66
-#>  200/1000: risk = 0.58
-#>  400/1000: risk = 0.57
-#>  600/1000: risk = 0.57
-#>  800/1000: risk = 0.57
-#> 1000/1000: risk = 0.57
+#>    1/1000   risk = 0.68  
+#>  200/1000   risk = 0.65  
+#>  400/1000   risk = 0.64  
+#>  600/1000   risk = 0.64  
+#>  800/1000   risk = 0.64  
+#> 1000/1000   risk = 0.64  
 #> 
 #> 
 #> Train 1000 iterations in 0 Seconds.
-#> Final risk based on the train set: 0.57
+#> Final risk based on the train set: 0.64
 cboost
 #> Component-Wise Gradient Boosting
 #> 
 #> Trained on PimaIndiansDiabetes with target diabetes
 #> Number of base-learners: 4
 #> Learning rate: 0.05
 #> Iterations: 1000
-#> Positive class: neg
 #> Offset: 0.3118
 #> 
 #> LossBinomial Loss:
@@ -112,20 +111,20 @@ cboost
 
 cboost$getBaselearnerNames()
 #> [1] "pressure_spline"           "age_spline"               
-#> [3] "pregnant.cat_yes_category" "pregnant.cat_no_category"
+#> [3] "pregnant_cat_yes_category" "pregnant_cat_no_category"
 
-selected.features = cboost$getSelectedBaselearner()
-table(selected.features)
-#> selected.features
-#>               age_spline pregnant.cat_no_category          pressure_spline 
-#>                      434                      150                      416
+selected_features = cboost$getSelectedBaselearner()
+table(selected_features)
+#> selected_features
+#>               age_spline pregnant_cat_no_category          pressure_spline 
+#>                      448                      130                      422
 
 params = cboost$getEstimatedCoef()
 str(params)
 #> List of 4
-#>  $ age_spline              : num [1:14, 1] 2.99 1.501 0.588 -0.535 -0.119 ...
-#>  $ pregnant.cat_no_category: num [1, 1] -0.299
-#>  $ pressure_spline         : num [1:24, 1] -0.8087 -0.4274 -0.0602 0.2226 0.3368 ...
+#>  $ age_spline              : num [1:14, 1] 3.717 1.5152 0.669 -0.6627 -0.0277 ...
+#>  $ pregnant_cat_no_category: num [1, 1] -0.41
+#>  $ pressure_spline         : num [1:24, 1] -0.969 -0.403 0.14 0.537 0.644 ...
 #>  $ offset                  : num 0.312
 
 cboost$train(3000)
@@ -141,7 +140,6 @@ cboost$plot("age_spline", iters = c(100, 500, 1000, 2000, 3000)) +
 <p align="center">
 <img src="Readme_files/cboost-1.png" width="70%" />
 </p>
-
 Benchmark
 ---------
 

src/baselearner.cpp

@@ -28,15 +28,15 @@ namespace blearner {
 
 // Copy (or initialize) the members in new copied class:
 void Baselearner::copyMembers (const arma::mat& parameter0,
-  const std::string& blearner_identifier0, data::Data* data0)
+  const std::string& blearner_identifier0, std::shared_ptr<data::Data> data0)
 {
   parameter = parameter0;
   blearner_identifier = blearner_identifier0;
   data_ptr = data0;
 }
 
 // Set the data pointer:
-void Baselearner::setData (data::Data* data)
+void Baselearner::setData (std::shared_ptr<data::Data> data)
 {
   data_ptr = data;
 }
@@ -106,7 +106,7 @@ Baselearner::~Baselearner ()
 // BaselearnerPolynomial:
 // -----------------------
 
-BaselearnerPolynomial::BaselearnerPolynomial (data::Data* data, const std::string& identifier,
+BaselearnerPolynomial::BaselearnerPolynomial (std::shared_ptr<data::Data> data, const std::string& identifier,
   const unsigned int& degree, const bool& intercept)
   : degree ( degree ),
     intercept ( intercept )
@@ -185,7 +185,7 @@ arma::mat BaselearnerPolynomial::predict () const
     return data_ptr->getData() * parameter;
   }
 }
-arma::mat BaselearnerPolynomial::predict (data::Data* newdata) const
+arma::mat BaselearnerPolynomial::predict (std::shared_ptr<data::Data> newdata) const
 {
   return instantiateData(newdata->getData()) * parameter;
 }
@@ -222,7 +222,7 @@ BaselearnerPolynomial::~BaselearnerPolynomial () {}
  * Finally we get a \f$9 - (p + 1)\f$ splines for which we can calculate the
  * base.
  *
- * \param data `data::Data*` Target data used for training etc.
+ * \param data `std::shared_ptr<data::Data>` Target data used for training etc.
  * \param identifier `std::string` Identifier for one specific baselearner
  * \param degree `unsigned int` Polynomial degree of the splines
  * \param n_knots `unsigned int` Number of inner knots used
@@ -233,7 +233,7 @@ BaselearnerPolynomial::~BaselearnerPolynomial () {}
  *   penalty matrix.
  */
 
-BaselearnerPSpline::BaselearnerPSpline (data::Data* data, const std::string& identifier,
+BaselearnerPSpline::BaselearnerPSpline (std::shared_ptr<data::Data> data, const std::string& identifier,
   const unsigned int& degree, const unsigned int& n_knots, const double& penalty,
   const unsigned int& differences, const bool& use_sparse_matrices)
   : degree ( degree ),
@@ -327,11 +327,11 @@ arma::mat BaselearnerPSpline::predict () const
 /**
  * \brief Predict on newdata
  *
- * \param newdata `data::Data*` new source data object
+ * \param newdata `std::shared_ptr<data::Data>` new source data object
  *
  * \returns `arma::mat` of predicted values
  */
-arma::mat BaselearnerPSpline::predict (data::Data* newdata) const
+arma::mat BaselearnerPSpline::predict (std::shared_ptr<data::Data> newdata) const
 {
   return instantiateData(newdata->getData()) * parameter;
 }
@@ -344,7 +344,7 @@ BaselearnerPSpline::~BaselearnerPSpline () {}
 // BaselearnerCustom:
 // -----------------------
 
-BaselearnerCustom::BaselearnerCustom (data::Data* data, const std::string& identifier,
+BaselearnerCustom::BaselearnerCustom (std::shared_ptr<data::Data> data, const std::string& identifier,
   Rcpp::Function instantiateDataFun, Rcpp::Function trainFun, Rcpp::Function predictFun,
   Rcpp::Function extractParameter)
   : instantiateDataFun ( instantiateDataFun ),
@@ -392,7 +392,7 @@ arma::mat BaselearnerCustom::predict () const
   return Rcpp::as<arma::mat>(out);
 }
 
-arma::mat BaselearnerCustom::predict (data::Data* newdata) const
+arma::mat BaselearnerCustom::predict (std::shared_ptr<data::Data> newdata) const
 {
   Rcpp::NumericMatrix out = predictFun(model, instantiateData(newdata->getData()));
   return Rcpp::as<arma::mat>(out);
@@ -405,7 +405,7 @@ BaselearnerCustom::~BaselearnerCustom () {}
 // BaselearnerCustomCpp:
 // -----------------------
 
-BaselearnerCustomCpp::BaselearnerCustomCpp (data::Data* data, const std::string& identifier,
+BaselearnerCustomCpp::BaselearnerCustomCpp (std::shared_ptr<data::Data> data, const std::string& identifier,
   SEXP instantiateDataFun0, SEXP trainFun0, SEXP predictFun0)
 {
   // Called from parent class 'Baselearner':
@@ -457,7 +457,7 @@ arma::mat BaselearnerCustomCpp::predict () const
   return predictFun (data_ptr->getData(), parameter);
 }
 
-arma::mat BaselearnerCustomCpp::predict (data::Data* newdata) const
+arma::mat BaselearnerCustomCpp::predict (std::shared_ptr<data::Data> newdata) const
 {
   arma::mat temp_mat = instantiateData(newdata->getData());
   return predictFun (temp_mat, parameter);

src/baselearner.h

@@ -44,7 +44,7 @@ class Baselearner
   arma::mat getParameter () const;
 
   virtual arma::mat predict () const = 0;
-  virtual arma::mat predict (data::Data*) const = 0;
+  virtual arma::mat predict (std::shared_ptr<data::Data>) const = 0;
 
   // Specify how the data has to be transformed. E. g. for splines a mapping
   // to the higher dimension space. The overloading function with the
@@ -54,14 +54,14 @@ class Baselearner
   // Clone function (in some places needed e.g. "optimizer.cpp") and a copy
   // function which is called by clone to avoid copy and pasting of the
   // protected members:
-  void copyMembers (const arma::mat&, const std::string&, data::Data*);
+  void copyMembers (const arma::mat&, const std::string&, std::shared_ptr<data::Data>);
   virtual Baselearner* clone () = 0;
 
   // Within 'setData' the pointer will be setted, while 'instantiateData'
   // overwrite the object on which 'data_ptr' points. This guarantees that
   // the data is just stored once in the factory and then called by reference
   // within the baselearner:
-  void setData (data::Data*);
+  void setData (std::shared_ptr<data::Data>);
   // arma::mat getData () const;
 
   // Get data identifier stored within the data object:
@@ -86,7 +86,7 @@ class Baselearner
   arma::mat parameter;
   std::string blearner_identifier;
   std::string blearner_type;
-  data::Data* data_ptr;
+  std::shared_ptr<data::Data> data_ptr;
   // std::string data_identifier;
 
 };
@@ -112,7 +112,7 @@ class BaselearnerPolynomial : public Baselearner
 public:
 
   // (data pointer, data identifier, baselearner identifier, degree)
-  BaselearnerPolynomial (data::Data*, const std::string&, const unsigned int&, const bool&);
+  BaselearnerPolynomial (std::shared_ptr<data::Data>, const std::string&, const unsigned int&, const bool&);
 
   Baselearner* clone ();
 
@@ -121,7 +121,7 @@ class BaselearnerPolynomial : public Baselearner
 
   void train (const arma::mat&);
   arma::mat predict () const;
-  arma::mat predict (data::Data*) const;
+  arma::mat predict (std::shared_ptr<data::Data>) const;
 
   ~BaselearnerPolynomial ();
 
@@ -169,7 +169,7 @@ class BaselearnerPSpline : public Baselearner
 
 public:
   /// Default constructor of `BaselearnerPSpline` class
-  BaselearnerPSpline (data::Data*, const std::string&, const unsigned int&,
+  BaselearnerPSpline (std::shared_ptr<data::Data>, const std::string&, const unsigned int&,
     const unsigned int&, const double&, const unsigned int&, const bool&);
 
   /// Clean copy of baselearner
@@ -185,7 +185,7 @@ class BaselearnerPSpline : public Baselearner
   arma::mat predict () const;
 
   /// Predict on newdata
-  arma::mat predict (data::Data*) const;
+  arma::mat predict (std::shared_ptr<data::Data>) const;
 
 
   /// Destructor
@@ -216,7 +216,7 @@ class BaselearnerCustom : public Baselearner
   // (data pointer, data identifier, baselearner identifier, R function for
   // data instantiation, R function for training, R function for prediction,
   // R function to extract parameter):
-  BaselearnerCustom (data::Data*, const std::string&, Rcpp::Function,
+  BaselearnerCustom (std::shared_ptr<data::Data>, const std::string&, Rcpp::Function,
     Rcpp::Function, Rcpp::Function, Rcpp::Function);
 
   // Copy constructor:
@@ -227,7 +227,7 @@ class BaselearnerCustom : public Baselearner
 
   void train (const arma::mat&);
   arma::mat predict () const;
-  arma::mat predict (data::Data*) const;
+  arma::mat predict (std::shared_ptr<data::Data>) const;
 
   ~BaselearnerCustom ();
 
@@ -262,7 +262,7 @@ class BaselearnerCustomCpp : public Baselearner
   // (data pointer, data identifier, baselearner identifier, R function for
   // data instantiation, R function for training, R function for prediction,
   // R function to extract parameter):
-  BaselearnerCustomCpp (data::Data*, const std::string&, SEXP, SEXP, SEXP);
+  BaselearnerCustomCpp (std::shared_ptr<data::Data>, const std::string&, SEXP, SEXP, SEXP);
 
   // Copy constructor:
   Baselearner* clone ();
@@ -272,7 +272,7 @@ class BaselearnerCustomCpp : public Baselearner
 
   void train (const arma::mat&);
   arma::mat predict () const;
-  arma::mat predict (data::Data*) const;
+  arma::mat predict (std::shared_ptr<data::Data>) const;
 
   ~BaselearnerCustomCpp ();
 };