Set up for cmake script to automatically insert Doxygen comments as python doc_strings

src/descriptor_identifier/Model/Model.hpp

@@ -27,6 +27,8 @@
 #endif
 
 typedef std::shared_ptr<ModelNode> model_node_ptr;
+
+// DocString: cls_model
 /**
  * @brief Class to store the models found from SISSO
  *
@@ -64,6 +66,7 @@ public:
      */
     Model(std::vector<double> prop_train, std::vector<double> prop_test, std::vector<model_node_ptr> feats, std::vector<int> task_sizes_train, std::vector<int> task_sizes_test);
 
+    // DocString: model_init_str
     /**
      * @brief Construct a model from a training output file
      * @details Reads in all of the data from the output file and recreates the model object
@@ -72,6 +75,7 @@ public:
      */
     Model(std::string train_file);
 
+    // DocString: model_init_str_str
     /**
      * @brief Construct a model from a training and testing output file
      * @details Reads in all of the data from the output files and recreates the model object
@@ -92,6 +96,7 @@ public:
      */
     std::vector<std::string> populate_model(std::string filename, bool train);
 
+    // DocString: model_str
     /**
      * @brief Convert the model to a string
 
@@ -100,46 +105,53 @@ public:
     std::string toString() const;
 
     /**
-     * @brief Accessor function to _prop_test_est
+     * @brief Accessor function to the estimated property values
      */
     inline std::vector<double> predict(){return _prop_test_est;}
 
     /**
-     * @brief Accessor function to _prop_train_est
+     * @brief Accessor function to predict property values
      */
     inline std::vector<double> predict_train(){return _prop_train_est;}
 
     /**
-     * @brief Copy the error into a new array
+     *  @brief Copy the error into a new array
      *
      * @param res pointer to the beginning of the vector to store the residual
      */
     inline void copy_error(double* res){std::copy_n(_train_error.data(), _n_samp_train, res);}
 
+    // DocString: model_rmse
     /**
      * @brief The training rmse of the model
      */
     inline double rmse(){return util_funcs::norm(_train_error.data(), _n_samp_train) / std::sqrt(static_cast<double>(_n_samp_train));}
+
+    // DocString: model_test_rmse
     /**
      * @brief The testing rmse of the model
      */
     inline double test_rmse(){return util_funcs::norm(_test_error.data(), _n_samp_test) / std::sqrt(static_cast<double>(_n_samp_test));}
 
+    // DocString: model_n_samp_train
     /**
      * @brief Total number of samples being trained on
      */
     inline int n_samp_train(){return _n_samp_train;}
 
+    // DocString: model_n_samp_test
     /**
      * @brief Total number of samples being tested
      */
     inline int n_samp_test(){return _n_samp_test;}
 
+    // DocString: model_n_dim
     /**
      * @brief The dimensionality of the data
      */
     inline int n_dim(){return _n_dim;}
 
+    // DocString: model_max_ae
     /**
      * @brief The max Absolute error of the training data
      */
@@ -148,6 +160,7 @@ public:
         return std::abs(*std::max_element(_train_error.data(), _train_error.data() + _n_samp_train, [](double d1, double d2){return std::abs(d1) < std::abs(d2);}));
     }
 
+    // DocString: model_test_max_ae
     /**
      * @brief The max Absolute error of the testing data
      */
@@ -166,6 +179,7 @@ public:
     void to_file(std::string filename, bool train = true, std::vector<int> test_inds = {});
 
     #ifdef PY_BINDINGS
+        // DocString: model_coefs
         /**
          * @brief Python Accessor functions to the coefficient array
          * @return The coefficients as a python list
@@ -178,6 +192,7 @@ public:
             return coef_lst;
         }
 
+        // DocString: model_feats
         /**
          * @brief Python Accessor functions to the features
          * @return A python list containing all of the features
@@ -190,38 +205,44 @@ public:
             return feat_lst;
         }
 
+        // DocString: model_prop_train_est
         /**
-         * @brief Python Accessor function to _prop_train_est
+         * @brief Python Accessor function to the estimation of the property
          * @return _prop_train_est as a numpy ndarray
          */
         inline np::ndarray prop_train_est(){return python_conv_utils::to_ndarray<double>(_prop_train_est);}
 
+        // DocString: model_prop_test_est
         /**
-         * @brief Python Accessor function to _prop_test_est
+         * @brief Python Accessor function to the estimation of the properties test values
          * @return _prop_test_est as a numpy ndarray
          */
         inline np::ndarray prop_test_est(){return python_conv_utils::to_ndarray<double>(_prop_test_est);}
 
+        // DocString: model_prop_train
         /**
-         * @brief Python Accessor function to _prop_train
+         * @brief Python Accessor function to the property to be learned
          * @return _prop_train as a numpy ndarray
          */
         inline np::ndarray prop_train(){return python_conv_utils::to_ndarray<double>(_prop_train);}
 
+        // DocString: model_prop_test
         /**
-         * @brief Python Accessor function to _prop_test
+         * @brief Python Accessor function to the test values for the property
          * @return _prop_test as a numpy ndarray
          */
         inline np::ndarray prop_test(){return python_conv_utils::to_ndarray<double>(_prop_test);}
 
+        // DocString: model_train_error
         /**
-         * @brief Python Accessor function to _train_error
+         * @brief Python Accessor function to the training error
          * @return _train_error as a numpy ndarray
          */
         inline np::ndarray train_error(){return python_conv_utils::to_ndarray<double>(_train_error);}
 
+        // DocString: model_test_error
         /**
-         * @brief Python Accessor function to _test_error
+         * @brief Python Accessor function to the test error
          * @return _test_error as a numpy ndarray
          */
         inline np::ndarray test_error(){return python_conv_utils::to_ndarray<double>(_test_error);}

src/descriptor_identifier/SISSORegressor.hpp

@@ -18,6 +18,7 @@
     namespace py = boost::python;
 #endif
 
+// DocString: cls_sisso_reg
 /**
  * @brief SISSO Regressor class, performs the SISSO algorithm and stores all selected models
  *
@@ -140,18 +141,21 @@ public:
      */
     inline std::vector<std::vector<Model>> models(){return _models;}
 
+    // DocString: sisso_reg_n_samp
     /**
-     * @brief Acessor function for n_samp
+     * @brief Acessor function to the number of samples per feature
      */
     inline int n_samp(){return _n_samp;}
 
+    // DocString: sisso_reg_n_dim
     /**
-     * @brief Acessor function for n_dim
+     * @brief Acessor function to the maximum model dimensionality
      */
     inline int n_dim(){return _n_dim;}
 
+    // DocString: sisso_reg_n_residual
     /**
-     * @brief Acessor function for n_residual
+     * @brief Acessor function to the number of residuals each iteration of SIS acts on
      */
     inline int n_residual(){return _n_residual;}
 
@@ -180,6 +184,7 @@ public:
             int n_residual
         );
 
+        // DocString: sisso_reg_init_list
         /**
          * @brief Constructor for the Regressor that takes in python objects (cpp definition in <python/descriptor_identifier/SISSORegressor.cpp)
          *
@@ -203,38 +208,43 @@ public:
             int n_residual
         );
 
+        // DocString: sisso_reg_models_py
         /**
-         * @brief Python Accessor function to models (cpp definition in <python/descriptor_identifier/SISSORegressor.cpp)
+         * @brief Python Accessor function to the selected model (cpp definition in <python/descriptor_identifier/SISSORegressor.cpp)
          * @return models as a python list
          */
         py::list models_py();
 
+        // DocString: sisso_reg_prop_py
         /**
-         * @brief Python Accessor function to prop
+         * @brief Python Accessor function to the property to be learned
          * @return prop as a numpy array
          */
         inline np::ndarray prop_py(){return python_conv_utils::to_ndarray<double>(_prop);}
 
+        // DocString: sisso_reg_prop_test_py
         /**
-         * @brief Python Accessor function to prop_test
+         * @brief Python Accessor function to the test values for the property
          * @return prop_test as a numpy array
          */
         inline np::ndarray prop_test_py(){return python_conv_utils::to_ndarray<double>(_prop_test);}
 
+        // DocString: sisso_reg_task_sizes_train
         /**
-         * @brief Python Accessor function to task_sizes_train
+         * @brief Python Accessor function to the number of samples per task in the training set
          * @return task_sizes_train as a python list
          */
         inline py::list task_sizes_train(){python_conv_utils::to_list<int>(_task_sizes_train);}
 
+        // DocString: sisso_reg_task_sizes_test
         /**
-         * @brief Python Accessor function to task_sizes_test
+         * @brief Python Accessor function to the number of samples per task in the test set
          * @return task_sizes_test as a python list
          */
         inline py::list task_sizes_test(){python_conv_utils::to_list<int>(_task_sizes_test);}
 
         /**
-         * @brief Python Accessor function to error
+         * @brief Python Accessor function to the error associated with the last tested model
          * @return error as a numpy array
          */
         inline np::ndarray error_py(){return python_conv_utils::to_ndarray<double>(_error);}

src/feature_creation/feature_space/FeatureSpace.hpp

@@ -28,6 +28,7 @@
     namespace py = boost::python;
 #endif
 
+// DocString: cls_feat_space
 /**
  * @brief Feature Space for SISSO calculations
  * @details Stores and performs all feature calculations for SIS
@@ -93,6 +94,11 @@ public:
         double max_abs_feat_val=1e50
     );
 
+    /**
+     * @brief Initialize the feature set given a property vector
+     *
+     * @param prop The property trying to be learned
+     */
     void initialize_fs(std::vector<double> prop);
 
     /**
@@ -102,77 +108,85 @@ public:
     void generate_feature_space(std::vector<double>& prop);
 
     /**
-     * @brief Accessor function for _phi_selected
+     * @brief Accessor function to the selected feature space
      */
     inline std::vector<node_ptr> phi_selected(){return _phi_selected;};
 
     /**
-     * @brief Accessor function for _phi
+     * @brief Accessor function to the full feature space
      */
     inline std::vector<node_ptr> phi(){return _phi;};
 
     /**
-     * @brief Accessor function for _phi_0
+     * @brief Accessor function to the initial feature space
      */
     inline std::vector<node_ptr> phi0(){return _phi_0;};
 
     /**
-     * @brief Accessor function for _scores
+     * @brief Accessor function to the vector of projection scores for SIS
      */
     inline std::vector<double> scores(){return _scores;}
 
     /**
-     * @brief Accessor function for _mpi_comm
+     * @brief Accessor function to the MPI Communicator
      */
     inline std::shared_ptr<MPI_Interface> mpi_comm(){return _mpi_comm;}
 
     /**
-     * @brief Accessor function for _mpi_comm
+     * @brief Accessor function to the vector storing the number of samples in each task
      */
     inline std::vector<int> task_sizes(){return _task_sizes;}
 
+    // DocString: feat_space_feature_space_file
     /**
-     * @brief Accessor function for _feature_space_file
+     * @brief Accessor function to the feature space filename
      */
     inline std::string feature_space_file(){return _feature_space_file;}
-
+    // DocString: feat_space_l_bound
     /**
-     * @brief Accessor function for _l_bound
+     * @brief Accessor function to the minimum absolute value of the feature
      */
     inline double l_bound(){return _l_bound;}
 
+    // DocString: feat_space_u_bound
     /**
-     * @brief Accessor function for _u_bound
+     * @brief Accessor function to the maximum absolute value of the feature
      */
     inline double u_bound(){return _u_bound;}
 
+    // DocString: feat_space_max_phi
     /**
-     * @brief Accessor function for _max_phi
+     * @brief Accessor function to the maximum rung of the feature space
      */
     inline int max_phi(){return _max_phi;}
 
+    // DocString: feat_space_n_sis_select
     /**
-     * @brief Accessor function for _n_sis_select
+     * @brief Accessor function to the number of features selected in each SIS step
      */
     inline int n_sis_select(){return _n_sis_select;}
 
+    // DocString: feat_space_n_samp
     /**
-     * @brief Accessor function for _n_samp
+     * @brief Accessor function to the number of samples per feature
      */
     inline int n_samp(){return _n_samp;}
 
+    // DocString: feat_space_n_feat
     /**
-     * @brief Accessor function for _n_feat
+     * @brief Accessor function to the number of features in the feature space
      */
     inline int n_feat(){return _n_feat;}
 
+    // DocString: feat_space_n_rung_store
     /**
-     * @brief Accessor function for _n_rung_store
+     * @brief Accessor function to the number of rungs whose feature training data is stored in memory
      */
     inline int n_rung_store(){return _n_rung_store;}
 
+    // DocString: feat_space_n_rung_generate
     /**
-     * @brief Accessor function for _n_rung_generate
+     * @brief Accessor function to the number of rungs to be generated on the fly during SIS
      */
     inline int n_rung_generate(){return _n_rung_generate;}
 
@@ -234,6 +248,7 @@ public:
      */
     void sis(std::vector<double>& prop);
 
+    // DocString: feat_space_feat_in_phi
     /**
      * @brief Is a feature in this process' _phi?
      *
@@ -300,6 +315,7 @@ public:
             double max_abs_feat_val=1e50
         );
 
+        // DocString: feat_space_sis_arr
         /**
          * @brief Wrapper function for SIS using a numpy array
          *
@@ -310,6 +326,8 @@ public:
             std::vector<double> prop_vec = python_conv_utils::from_ndarray<double>(prop);
             sis(prop_vec);
         }
+
+        // DocString: feat_space_sis_list
         /**
          * @brief Wrapper function for SIS using a python list
          *
@@ -321,38 +339,44 @@ public:
             sis(prop_vec);
         }
 
+        // DocString: feat_space_phi_selected_py
         /**
-         * @brief Python Accesor function to _phi_selected (cpp definition in <python/feature_creation/FeatureSpace.cpp>)
+         * @brief Python Accesor function to the selected feature space (cpp definition in <python/feature_creation/FeatureSpace.cpp>)
          * @return _phi_selected as a python list
          */
         py::list phi_selected_py();
 
+        // DocString: feat_space_phi0_py
         /**
-         * @brief Python Accesor function to _phi0 (cpp definition in <python/feature_creation/FeatureSpace.cpp>)
+         * @brief Python Accesor function to the initial feature space (cpp definition in <python/feature_creation/FeatureSpace.cpp>)
          * @return _phi0 as a python list
          */
         py::list phi0_py();
 
+        // DocString: feat_space_scores_py
         /**
-         * @brief Python Accesor function to _scores
+         * @brief Python Accesor function to the vector of projection scores for SIS
          * @return _scores as a numpy array
          */
         inline np::ndarray scores_py(){return python_conv_utils::to_ndarray<double>(_scores);};
 
+        // DocString: feat_space_task_sizes_py
         /**
-         * @brief Python Accesor function to _task_sizes
+         * @brief Python Accesor function to the vector storing the number of samples in each task
          * @return _task_sizes as a python list
          */
         inline py::list task_sizes_py(){return python_conv_utils::to_list<int>(_task_sizes);};
 
+        // DocString: feat_space_allowed_ops_py
         /**
-         * @brief Python Accesor function to _allowed_ops
+         * @brief Python Accesor function to the list of allowed operator nodes
          * @return _allowed_ops as a python list
          */
         inline py::list allowed_ops_py(){return python_conv_utils::to_list<std::string>(_allowed_ops);}
 
+        // DocString: feat_space_start_gen_py
         /**
-         * @brief Python Accesor function to _start_gen
+         * @brief Python Accesor function to list storing when each generation starts in phi
          * @return _start_gen as a python list
          */
         inline py::list start_gen_py(){return python_conv_utils::to_list<int>(_start_gen);}

src/feature_creation/node/FeatureNode.hpp

@@ -24,6 +24,7 @@
     namespace py = boost::python;
 #endif
 
+// DocString: cls_feat_node
 /**
  * @brief Node that describe the leaves of the operator graph (Initial features in Phi_0)
  */
@@ -125,16 +126,19 @@ public:
      */
     ~FeatureNode();
 
+    // DocString: feat_node_expr_1
     /**
      * @brief Get the string expression used to represent the primary feature
      */
     inline std::string expr(){return _expr;}
 
+    // DocString: feat_node_expr_const
     /**
      * @brief Get the string expression used to represent the primary feature
      */
     inline std::string expr()const{return _expr;}
 
+    // DocString: feat_node_unit
     /**
      * @brief The unit of the primary feature
      */
@@ -150,6 +154,7 @@ public:
      */
     inline std::vector<double> test_value(){return _test_value;}
 
+    // DocString: feat_node_set_value
     /**
      * @brief Set the value for the feature inside of the value storage arrays
      *
@@ -157,6 +162,7 @@ public:
      */
     inline void set_value(int offset = -1){std::copy_n(_value.data(), _n_samp, value_ptr());}
 
+    // DocString: feat_node_set_test_value
     /**
      * @brief Set the test values for the feature inside of the value storage arrays
      *
@@ -164,11 +170,13 @@ public:
      */
     inline void set_test_value(int offset = -1){if(!_selected) std::copy_n(_test_value.data(), _n_test_samp, test_value_ptr());}
 
+    // DocString: feat_node_is_nan
     /**
      * @brief Check if the feature contains NaN
      */
     inline bool is_nan(){return std::any_of(value_ptr(), value_ptr() + _n_samp, [](double d){return !std::isfinite(d);});}
 
+    // DocString: feat_node_is_const
     /**
      * @brief Check if feature is constant
      */
@@ -198,6 +206,7 @@ public:
     inline double* test_value_ptr(int offset = 0){return node_value_arrs::get_test_value_ptr(_arr_ind, _feat_ind, offset);}
 
 
+    // DocString: feat_node_rung
     /**
      * @brief return the rung of the feature
      *

src/feature_creation/node/ModelNode.hpp

@@ -11,6 +11,7 @@
 
 #include <feature_creation/node/FeatureNode.hpp>
 
+// DocString: cls_model_node
 /**
  * @brief Node that describe the leaves of the operator graph (Initial features in Phi_0)
  */
@@ -84,6 +85,7 @@ public:
      */
     ~ModelNode();
 
+    // DocString: model_node_set_value
     /**
      * @brief Set the value for the feature inside of the value storage arrays
      *
@@ -91,6 +93,7 @@ public:
      */
     inline void set_value(int offset = -1){return;}
 
+    // DocString: model_node_set_test_value
     /**
      * @brief Set the test values for the feature inside of the value storage arrays
      *
@@ -98,11 +101,13 @@ public:
      */
     inline void set_test_value(int offset = -1){return;}
 
+    // DocString: model_node_is_nan
     /**
      * @brief Check if the feature contains NaN
      */
     inline bool is_nan(){return false;}
 
+    // DocString: model_node_is_const
     /**
      * @brief Check if feature is constant
      */
@@ -128,6 +133,7 @@ public:
     inline double* test_value_ptr(int offset = -1){return _test_value.data();}
 
 
+    // DocString: model_node_rung
     /**
      * @brief return the rung of the feature
      *

src/feature_creation/node/Node.hpp

@@ -31,6 +31,7 @@
     namespace np = boost::python::numpy;
 #endif
 
+// DocString: cls_node
 /**
  * @brief Base class for a Node
  * @details Class used to describe a Node on the feature graph. Features are treated as an operation graph, these are the nodes on that graph.
@@ -113,6 +114,7 @@ public:
      */
     virtual ~Node();
 
+    // DocString: node_reindex_1
     /**
      * @brief Reindex the feature
      * @details re-index the feature to be continuous
@@ -122,6 +124,7 @@ public:
      */
     inline void reindex(int ind){_feat_ind = ind; _arr_ind = ind;}
 
+    // DocString: node_reindex_2
     /**
      * @brief Reindex the feature
      * @details re-index the feature to be continuous
@@ -131,26 +134,31 @@ public:
      */
     inline void reindex(int ind, int arr_ind){_feat_ind = ind; _arr_ind = arr_ind;}
 
+    // DocString: node_samp
     /**
      * @brief Acesssor function to get the number of samples
      */
     inline int n_samp(){return _n_samp;}
 
+    // DocString: node_test_samp
     /**
      * @brief Acesssor function to get the number of samples in the test set
      */
     inline int n_test_samp(){return _n_test_samp;}
 
+    // DocString: node_feat_ind
     /**
      * @brief Accessor function to get the feature ind
      */
     inline int feat_ind(){return _feat_ind;}
 
+    // DocString: node_arr_ind
     /**
      * @brief Accessor function to get the feature array index
      */
     inline int arr_ind(){return _arr_ind;}
 
+    // DocString: node_selected
     /**
      * @brief Accessor function to _selected
      * @return True if this feature was selected
@@ -164,6 +172,7 @@ public:
      */
     inline void set_selected(bool sel){_selected = sel;}
 
+    // DocString: node_d_mat_ind
     /**
      * @brief  Setter function for _d_mat_ind
      *
@@ -176,11 +185,13 @@ public:
      */
     inline int d_mat_ind(){return _d_mat_ind;}
 
+    // DocString: node_expr
     /**
      * @brief Get the expression for the overall feature (From root node down)