Update docstrings for feature creation step for the python API

src/feature_creation/node/FeatureNode.hpp

@@ -74,27 +74,27 @@ public:
         const bool set_val=true
     );
     #ifdef PY_BINDINGS
-    // DocString: feat_node_init_list
+    // DocString: feat_node_init_arr
     /**
-     * @brief Constructs a feature node using numpy arrays (cpp definition in "python/feature_creation/FeatureNode.cpp)
+     * @brief Constructs a feature node using numpy arrays (cpp definition in python/feature_creation/FeatureNode.cpp)
      *
-     * @param feat_ind index of the feature
-     * @param expr Expression for the feature
-     * @param value Value of the feature for each sample
-     * @param test_value Value of the feature for each test sample
-     * @param unit Unit of the feature
+     * @param feat_ind (int) index of the feature
+     * @param expr (str) Expression for the feature
+     * @param value (np.ndarray) Value of the feature for each sample
+     * @param test_value (np.ndarray) Value of the feature for each test sample
+     * @param unit (Unit) Unit of the feature
      */
     FeatureNode(unsigned long int feat_ind, std::string expr, np::ndarray value, np::ndarray test_value, Unit unit);
 
-    // DocString: feat_node_init_arr
+    // DocString: feat_node_init_list
     /**
-     * @brief Constructs a feature node using Python lists (cpp definition in "python/feature_creation/FeatureNode.cpp)
+     * @brief Constructs a feature node using Python lists (cpp definition in python/feature_creation/FeatureNode.cpp)
      *
-     * @param feat_ind index of the feature
-     * @param expr Expression for the feature
-     * @param value Value of the feature for each sample
-     * @param test_value Value of the feature for each test sample
-     * @param unit Unit of the feature
+     * @param feat_ind (int) index of the feature
+     * @param expr (str) Expression for the feature
+     * @param value (list) Value of the feature for each sample
+     * @param test_value (list) Value of the feature for each test sample
+     * @param unit (Unit) Unit of the feature
      */
     FeatureNode(unsigned long int feat_ind, std::string expr, py::list value, py::list test_value, Unit unit);
     #endif
@@ -147,17 +147,13 @@ public:
 
     // DocString: feat_node_x_in_expr
     /**
-     * @brief Get the list of feature expressions
-     * @return vector storing the expressions for all primary features that show up in feature in the order they appear in the postfix notation
+     * @brief A vector storing the expressions for all primary features in the order they appear in the postfix expression
      */
     virtual inline std::vector<std::string> get_x_in_expr_list() const {return std::vector<std::string>(1, _expr);}
 
     // DocString: feat_node_n_leaves
     /**
-     * @brief return the number of leaves in the feature
-     *
-     * @param cur_n_leaves The current number of primary features that are inside the feature
-     * @return total number of leaves of the binary expression tree
+     * @brief Get the number of primary features (non-unique) this feature contains (The number of leaves of the Binary Expression Tree)
      */
     virtual inline int n_leaves(int cur_n_leaves = 0) const {return cur_n_leaves + 1;}
 
@@ -171,13 +167,13 @@ public:
 
     // DocString: feat_node_expr_const
     /**
-     * @brief Get the string expression used to represent the primary feature
+     * @brief Get the expression for the feature (From root node down)
      */
     inline std::string expr()const{return _expr;}
 
     // DocString: feat_node_latex_expr
     /**
-     * @brief Get the latexified expression for the overall feature (From root node down)
+     * @brief Get the valid LaTeX expression that represents the feature
      */
     inline std::string get_latex_expr() const {return str_utils::latexify(_expr);}
 
@@ -188,21 +184,23 @@ public:
     inline Unit unit() const {return _unit;}
 
     /**
-     * @brief Get the training data of the feature
+     * @brief An array containing the values of the training set samples for the feature
+
      */
     inline std::vector<double> value() const {return _value;}
 
     /**
-     * @brief Get the test data for the feature
+     * @brief An array containing the values of the test set samples for the feature
+
      */
     inline std::vector<double> test_value() const {return _test_value;}
 
     // DocString: feat_node_set_value
     /**
-     * @brief Set the values of the training data for the feature inside of the value storage arrays
+     * @brief Set the value of all training samples for the feature inside the central data storage array
      *
-     * @param offset(int) Key to determine which part of the temporary storage array to look into
-     * @param for_comp(bool) If true then the evaluation is for comparing features
+     * @param offset (int) Where the current node is in the binary expression tree relative to other nodes at the same depth
+     * @param for_comp(bool) If true then the evaluation is used for comparing features
      */
     inline void set_value(int offset=-1, const bool for_comp=false) const
     {
@@ -211,10 +209,10 @@ public:
 
     // DocString: feat_node_set_test_value
     /**
-     * @brief Set the values of the test data for the feature inside of the value storage arrays
+     * @brief Set the value of all test samples for the feature inside the central data storage array
      *
-     * @param offset(int) Key to determine which part of the temporary storage array to look into
-     * @param for_comp(bool) If true then the evaluation is for comparing features
+     * @param offset (int) Where the current node is in the binary expression tree relative to other nodes at the same depth
+     * @param for_comp(bool) If true then the evaluation is used for comparing features
      */
     inline void set_test_value(int offset=-1, const bool for_comp=false) const
     {
@@ -226,7 +224,9 @@ public:
 
     // DocString: feat_node_is_nan
     /**
-     * @brief Check if the feature contains NaN
+     * @brief Check if the feature has a NaN value in it
+     *
+     * @return True if one of the values of the feature is a NaN
      */
     inline bool is_nan() const
     {
@@ -235,7 +235,9 @@ public:
 
     // DocString: feat_node_is_const
     /**
-     * @brief Check if feature is constant
+     * @brief Check if feature is constant for one of the tasks
+     *
+     * @return True if the feature is constant for all samples in any task
      */
     bool is_const() const;
 
@@ -247,8 +249,8 @@ public:
     /**
      * @brief The pointer to where the feature's training data is stored
      *
-     * @param offset(int) Key to determine which part of the temporary storage array to look into
-     * @param for_comp(bool) If true then the evaluation is for comparing features
+     * @param offset (int) Where the current node is in the binary expression tree relative to other nodes at the same depth
+     * @param for_comp (bool) If true then the evaluation is used for comparing features
      */
     virtual inline double* value_ptr(int offset=-1, const bool for_comp=false) const
     {
@@ -258,8 +260,8 @@ public:
     /**
      * @brief The pointer to where the feature's test data is stored
      *
-     * @param offset(int) Key to determine which part of the temporary storage array to look into
-     * @param for_comp(bool) If true then the evaluation is for comparing features
+     * @param offset (int) Where the current node is in the binary expression tree relative to other nodes at the same depth
+     * @param for_comp (bool) If true then the evaluation is used for comparing features
      */
     virtual inline double* test_value_ptr(int offset=-1, const bool for_comp=false) const
     {
@@ -268,9 +270,8 @@ public:
 
     // DocString: feat_node_rung
     /**
-     * @brief return the rung of the feature
+     * @brief return the rung of the feature (Height of the binary expression tree + 1)
      *
-     * @param cur_rung The rung current rung of the feature tree (used to recursively calculate rung)
      */
     inline int rung(int cur_rung = 0) const {return cur_rung;}
 
@@ -318,18 +319,17 @@ public:
         return str_utils::matlabify(_expr);
     }
 
-    //DocString: feat_node_nfeats
+    // DocString: feat_node_nfeats
     /**
      * @brief Number of features used for an operator node
-     * @return the number of features for an operator node
      */
     inline int n_feats() const {return 0;}
 
-    //DocString: feat_node_feat
+    // DocString: feat_node_feat
     /**
      * @brief Return the ind node_ptr in the operator node's feat list
      *
-     * @param ind the index of the node to access
+     * @param ind (int) the index of the node to access
      * @return the ind feature in feature_list
      */
     inline node_ptr feat(const int ind) const
@@ -379,7 +379,7 @@ public:
      */
     inline void set_parameters(const double* params){};
 
-    //DocString: feat_node_get_params
+    // DocString: feat_node_get_params
     /**
      * @brief Solve the non-linear optimization to set the parameters
      * @details Fits the data points from _feats->value_ptr and prop to get the parameters for the feature
@@ -392,49 +392,49 @@ public:
      * @brief returns the number of parameters for this feature
      *
      * @param n_cur the current number of parameters for the feature
-     * @param depth the current depth of the node on the Binary expression tree
+     * @param depth the current depth of the node on the binary expression tree
      * @return the number of parameters (_params.size())
      */
     inline int n_params(int n_cur=0, int depth = 1) const {return n_cur;};
 
     /**
-     * @brief Set the values of the training data for the feature inside of the value storage arrays
+     * @brief Set the value of all test samples for the feature inside the central data storage array
      *
-     * @param offset(int) Key to determine which part of the temporary storage array to look into
-     * @param params pointer to the parameter values
-     * @param for_comp(bool) If true then the evaluation is for comparing features
-     * @param depth the current depth of the node on the Binary expression tree
+     * @param offset (int) Where the current node is in the binary expression tree relative to other nodes at the same depth
+     * @param params pointer to the non-linear parameter values
+     * @param for_comp(bool) If true then the evaluation is used for comparing features
+     * @param depth the current depth of the node on the binary expression tree
      */
     inline void set_value(const double* params, int offset=-1, const bool for_comp=false, const int depth=1) const {set_value(offset);};
 
     /**
      * @brief The pointer to where the feature's training data is stored
      *
-     * @param offset(int) Key to determine which part of the temporary storage array to look into
-     * @param params pointer to the parameter values
-     * @param for_comp(bool) If true then the evaluation is for comparing features
-     * @param depth the current depth of the node on the Binary expression tree
+     * @param offset (int) Where the current node is in the binary expression tree relative to other nodes at the same depth
+     * @param params pointer to the non-linear parameter values
+     * @param for_comp (bool) If true then the evaluation is used for comparing features
+     * @param depth the current depth of the node on the binary expression tree
      * @returns the pointer to the feature's data
      */
     inline double* value_ptr(const double* params, int offset=-1, const bool for_comp=false, const int depth=1) const {return value_ptr(offset);};
 
     /**
-     * @brief Set the values of the test data for the feature inside of the value storage arrays
+     * @brief Set the value of all test samples for the feature inside the central data storage array
      *
-     * @param offset(int) Key to determine which part of the temporary storage array to look into
-     * @param params pointer to the parameter values
-     * @param for_comp(bool) If true then the evaluation is for comparing features
-     * @param depth the current depth of the node on the Binary expression tree
+     * @param offset (int) Where the current node is in the binary expression tree relative to other nodes at the same depth
+     * @param params pointer to the non-linear parameter values
+     * @param for_comp (bool) If true then the evaluation is used for comparing features
+     * @param depth the current depth of the node on the binary expression tree
      */
     inline void set_test_value(const double* params, int offset=-1, const bool for_comp=false, const int depth=1) const {set_test_value(offset);};
 
     /**
      * @brief The pointer to where the feature's test data is stored
      *
-     * @param offset(int) Key to determine which part of the temporary storage array to look into
-     * @param params pointer to the parameter values
-     * @param for_comp(bool) If true then the evaluation is for comparing features
-     * @param depth the current depth of the node on the Binary expression tree
+     * @param offset (int) Where the current node is in the binary expression tree relative to other nodes at the same depth
+     * @param params pointer to the non-linear parameter values
+     * @param for_comp (bool) If true then the evaluation is used for comparing features
+     * @param depth the current depth of the node on the binary expression tree
      * @returns the pointer to the feature's data
      */
     inline double* test_value_ptr(const double* params, int offset=-1, const bool for_comp=false, const int depth=1) const {return test_value_ptr(offset);};
@@ -443,16 +443,16 @@ public:
      * @brief The expression of the feature
      *
      * @param params parameter values for non-linear operations
-     * @param depth the current depth of the node on the Binary expression tree
+     * @param depth the current depth of the node on the binary expression tree
      * @return feature expression
      */
     inline std::string expr(const double* params, const int depth = 1) const {return _expr;};
 
     /**
-     * @brief Get the latexified expression for the overall feature (From root node down)
+     * @brief Get the valid LaTeX expression that represents the feature
      *
      * @param params parameter values for non-linear operations
-     * @param depth the current depth of the node on the Binary expression tree
+     * @param depth the current depth of the node on the binary expression tree
      * @return the latexified expression
      */
     inline std::string get_latex_expr(const double* params, const int depth=1) const {return str_utils::latexify(_expr);}
@@ -462,7 +462,7 @@ public:
      * @brief Get the string that corresponds to the code needed to evaluate the node in matlab
      *
      * @param params parameter values for non-linear operations
-     * @param depth the current depth of the node on the Binary expression tree
+     * @param depth the current depth of the node on the binary expression tree
      * @return The matlab code for the feature
      */
     virtual inline std::string matlab_fxn_expr(const double* params, const int depth=1) const
@@ -476,7 +476,7 @@ public:
      * @param lb pointer to the lower bounds data
      * @param ub pointer to the upper bounds data
      * @param from_parent How many parameters are between the start of this node's parameters and its parent
-     * @param depth the current depth of the node on the Binary expression tree
+     * @param depth the current depth of the node on the binary expression tree
      */
     inline void set_bounds(double* lb, double* ub, const int depth=1) const {};
 
@@ -484,7 +484,7 @@ public:
      * @brief Set the bounds for the nl parameterization
      *
      * @param params pointer to the parameters vector
-     * @param depth the current depth of the node on the Binary expression tree
+     * @param depth the current depth of the node on the binary expression tree
      */
     virtual void initialize_params(double* params, const int depth = 1) const {};
 

src/feature_creation/node/ModelNode.hpp

@@ -76,7 +76,7 @@ public:
      * @param matlab_fxn_expr The code to evaluate the feature in matlab
      * @param value Value of the feature for each sample
      * @param test_value Value of the feature for each test sample
-     * @param x_in_expr_list //!< vector storing the expressions for all primary features that show up in feature in the order they appear in the postfix notation
+     * @param x_in_expr_list vector storing the expressions for all primary features that show up in feature in the order they appear in the postfix notation
      * @param unit Unit of the feature
      */
     ModelNode(
@@ -96,7 +96,7 @@ public:
     /**
      * @brief Copy constructor from general node_ptr
      *
-     * @param in_node Node to be copied
+     * @param in_node (Node) Node to be copied
      */
     ModelNode(node_ptr in_node);
 
@@ -193,17 +193,14 @@ public:
 
     // DocString: model_node_x_in_expr
     /**
-     * @brief get the list of feature expressions for each of the leaves
-     * @return Vector containing the expression for each leaf in the binary expression tree
+     * @brief A vector storing the expressions for all primary features in the order they appear in the postfix expression
      */
     inline std::vector<std::string> get_x_in_expr_list() const {return _x_in_expr_list;}
 
     // DocString: model_node_n_leaves
     /**
-     * @brief return the number of leaves in the feature
+     * @brief The number of primary features (non-unique) this feature contains (The number of leaves of the Binary Expression Tree)
      *
-     * @param cur_n_leaves The current number of primary features that are inside the feature
-     * @return total number of leaves of the binary expression tree
      */
     inline int n_leaves(int n_cur_leaves = 0) const {return _n_leaves;}
 
@@ -239,31 +236,35 @@ public:
 
     // DocString: model_node_set_value
     /**
-     * @brief Set the values of the training data for the feature inside of the value storage arrays
+     * @brief Set the value of all training samples for the feature inside the central data storage array
      *
-     * @param offset(int) Key to determine which part of the temporary storage array to look into
-     * @param for_comp(bool) If true then the evaluation is for comparing features
+     * @param offset (int) Where the current node is in the binary expression tree relative to other nodes at the same depth
+     * @param for_comp(bool) If true then the evaluation is used for comparing features
      */
     inline void set_value(int offset=-1, const bool for_comp=false) const {return;}
 
     // DocString: model_node_set_test_value
     /**
-     * @brief Set the values of the test data for the feature inside of the value storage arrays
+     * @brief Set the value of all test samples for the feature inside the central data storage array
      *
-     * @param offset(int) Key to determine which part of the temporary storage array to look into
-     * @param for_comp(bool) If true then the evaluation is for comparing features
+     * @param offset (int) Where the current node is in the binary expression tree relative to other nodes at the same depth
+     * @param for_comp(bool) If true then the evaluation is used for comparing features
      */
     inline void set_test_value(int offset=-1, const bool for_comp=false) const {return;}
 
     // DocString: model_node_is_nan
     /**
-     * @brief Check if the feature contains NaN
+     * @brief Check if the feature has a NaN value in it
+     *
+     * @return True if one of the values of the feature is a NaN
      */
     inline bool is_nan() const {return false;}
 
     // DocString: model_node_is_const
     /**
-     * @brief Check if feature is constant
+     * @brief Check if feature is constant for one of the tasks
+     *
+     * @return True if the feature is constant for all samples in any task
      */
     inline bool is_const() const {return false;}
 
@@ -275,24 +276,24 @@ public:
     /**
      * @brief The pointer to where the feature's training data is stored
      *
-     * @param offset(int) Key to determine which part of the temporary storage array to look into
-     * @param for_comp(bool) If true then the evaluation is for comparing features
+     * @param offset (int) Where the current node is in the binary expression tree relative to other nodes at the same depth
+     * @param for_comp (bool) If true then the evaluation is used for comparing features
      */
     inline double* value_ptr(int offset=-1, const bool for_comp=false){return _value.data();}
 
     /**
      * @brief The pointer to where the feature's test data is stored
      *
-     * @param offset(int) Key to determine which part of the temporary storage array to look into
-     * @param for_comp(bool) If true then the evaluation is for comparing features
+     * @param offset (int) Where the current node is in the binary expression tree relative to other nodes at the same depth
+     * @param for_comp (bool) If true then the evaluation is used for comparing features
      */
     inline double* test_value_ptr(int offset=-1, const bool for_comp=false){return _test_value.data();}
 
     /**
      * @brief The pointer to where the feature's training data is stored
      *
-     * @param offset(int) Key to determine which part of the temporary storage array to look into
-     * @param for_comp(bool) If true then the evaluation is for comparing features
+     * @param offset (int) Where the current node is in the binary expression tree relative to other nodes at the same depth
+     * @param for_comp (bool) If true then the evaluation is used for comparing features
      */
     inline double* value_ptr(int offset=-1, const bool for_comp=false) const
     {
@@ -303,8 +304,8 @@ public:
     /**
      * @brief The pointer to where the feature's test data is stored
      *
-     * @param offset(int) Key to determine which part of the temporary storage array to look into
-     * @param for_comp(bool) If true then the evaluation is for comparing features
+     * @param offset (int) Where the current node is in the binary expression tree relative to other nodes at the same depth
+     * @param for_comp (bool) If true then the evaluation is used for comparing features
      */
     inline double* test_value_ptr(int offset=-1, const bool for_comp=false) const
     {
@@ -314,9 +315,8 @@ public:
 
     // DocString: model_node_rung
     /**
-     * @brief return the rung of the feature
+     * @brief return the rung of the feature (Height of the binary expression tree + 1)
      *
-     * @param cur_rung(int) The rung current rung of the feature tree (used to recursively calculate rung)
      */
     inline int rung(int cur_rung = 0) const {return _rung;}
 
@@ -341,7 +341,7 @@ public:
 
     // DocString: model_node_latex_expr
     /**
-     * @brief Get the latexified expression for the overall feature (From root node down)
+     * @brief Get the valid LaTeX expression that represents the feature
      */
     inline std::string get_latex_expr() const {return _latex_expr.substr(1, _latex_expr.size() - 2);}
 
@@ -376,11 +376,65 @@ public:
     void update_div_mult_leaves(std::map<std::string, double>& div_mult_leaves, const double fact, double& expected_abs_tot) const;
 
     #ifdef PY_BINDINGS
+    // DocString: model_node_init_list
+    /**
+     * @brief Constructs a feature node
+     *
+     * @param feat_ind (int) index of the feature
+     * @param rung (int) the rung of the feature
+     * @param expr (str) Expression for the feature
+     * @param latex_expr (str) The LaTeXified expression for the feature
+     * @param matlab_fxn_expr (str) The code to evaluate the feature in matlab
+     * @param value (list) Value of the feature for each sample
+     * @param test_value (list) Value of the feature for each test sample
+     * @param x_in_expr_list (list) //!< vector storing the expressions for all primary features that show up in feature in the order they appear in the postfix notation
+     * @param unit (Unit) Unit of the feature
+     */
+    ModelNode(
+        const unsigned long int feat_ind,
+        const unsigned long int rung,
+        const std::string expr,
+        const std::string latex_expr,
+        const std::string expr_postfix,
+        const std::string matlab_fxn_expr,
+        const py::list value,
+        const py::list test_value,
+        const py::list x_in_expr_list,
+        const Unit unit
+    );
+
+    // DocString: model_node_init_arr
+    /**
+     * @brief Constructs a feature node
+     *
+     * @param feat_ind (int) index of the feature
+     * @param rung (int) the rung of the feature
+     * @param expr (str) Expression for the feature
+     * @param latex_expr (str) The LaTeXified expression for the feature
+     * @param matlab_fxn_expr (str) The code to evaluate the feature in matlab
+     * @param value (np.ndarray) Value of the feature for each sample
+     * @param test_value (np.ndarray) Value of the feature for each test sample
+     * @param x_in_expr_list (list) //!< vector storing the expressions for all primary features that show up in feature in the order they appear in the postfix notation
+     * @param unit (Unit) Unit of the feature
+     */
+    ModelNode(
+        const unsigned long int feat_ind,
+        const unsigned long int rung,
+        const std::string expr,
+        const std::string latex_expr,
+        const std::string expr_postfix,
+        const std::string matlab_fxn_expr,
+        const np::ndarray value,
+        const np::ndarray test_value,
+        const py::list x_in_expr_list,
+        const Unit unit
+    );
+
     // DocString: model_node_eval_arr
     /**
      * @brief Evaluate the model for a new point
      *
-     * @param x_in The data point to evaluate the model (order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
+     * @param x_in (np.ndarray) The data point to evaluate the model (order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
      * @return The prediction of the model for a given data point
      */
     inline double eval_py(np::ndarray x_in){return eval(python_conv_utils::from_ndarray<double>(x_in));}
@@ -389,7 +443,7 @@ public:
     /**
      * @brief Evaluate the model for a new point
      *
-     * @param x_in The data point to evaluate the model (order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
+     * @param x_in (list) The data point to evaluate the model (order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
      * @return The prediction of the model for a given data point
      */
     inline double eval_py(py::list x_in){return eval(python_conv_utils::from_list<double>(x_in));}
@@ -398,7 +452,7 @@ public:
     /**
      * @brief Evaluate the model for a new point
      *
-     * @param x_in_dct Dictionary describing the new point (\"feature expr\": value)
+     * @param x_in (dict) Dictionary describing the new point (\"feature expr\": value)
      * @return The prediction of the model for a given data point
      */
     inline double eval_py(py::dict x_in){return eval(python_conv_utils::from_dict<std::string, double>(x_in));}
@@ -407,7 +461,7 @@ public:
     /**
      * @brief Evaluate the model for a set of new points
      *
-     * @param x_in The set data for a set of new data points (size of n_feature x n_points, and order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
+     * @param x_in (np.ndarray) The set data for a set of new data points (size of n_feature x n_points, and order the same as appending the results of _feats[nn]->get_x_in_expr_list() for all feature)
      * @return The prediction of the model for a given data point
      */
     np::ndarray eval_many_py(np::ndarray x_in);
@@ -416,7 +470,7 @@ public:
     /**
      * @brief Evaluate the model for a set of new points
      *
-     * @param x_in_dct The set of data points to evaluate the model. Keys must be strings representing feature expressions and vectors must be the same length
+     * @param x_in (dict) The set of data points to evaluate the model. Keys must be strings representing feature expressions and vectors must be the same length
      * @return The prediction of the model for a given data point
      */
     np::ndarray eval_many_py(py::dict x_in);
@@ -434,7 +488,7 @@ public:
      * @brief Get the string that corresponds to the code needed to evaluate the node in matlab
      *
      * @param params parameter values for non-linear operations
-     * @param depth the current depth of the node on the Binary expression tree
+     * @param depth the current depth of the node on the binary expression tree
      * @return The matlab code for the feature
      */
     inline std::string matlab_fxn_expr(const double* params, const int depth=1) const

src/feature_creation/node/Node.hpp

@@ -126,35 +126,33 @@ public:
 
     // DocString: node_x_in_expr
     /**
-     * @brief Get the list of feature expressions
-     * @return vector storing the expressions for all primary features that show up in feature in the order they appear in the postfix notation
+     * @brief A vector storing the expressions for all primary features in the order they appear in the postfix expression
      */
     virtual std::vector<std::string> get_x_in_expr_list() const = 0;
 
     // DocString: node_n_leaves
     /**
-     * @brief return the number of leaves in the feature
+     * @brief The number of primary features (non-unique) this feature contains (The number of leaves of the Binary Expression Tree)
      *
-     * @param cur_n_leaves The current number of primary features that are inside the feature
-     * @return total number of leaves of the binary expression tree
+     * @param cur_n_leaves (int) A recurisve counting variable
+     * @return The number of leaves of the Binary Expression Tree
      */
     virtual int n_leaves(const int cur_n_leaves = 0) const = 0;
 
     // DocString: node_reindex_1
     /**
-     * @brief Reindex the feature
-     * @details re-index the feature to be continuous
+     * @brief Reset _feat_ind and _arr_ind to ind
      *
-     * @param ind(int) the new feature and array index
+     * @param ind (int) the new value of _feat_ind and _arr_ind
      */
     inline void reindex(const unsigned long int ind){_feat_ind = ind; _arr_ind = ind;}
 
     // DocString: node_reindex_2
     /**
-     * @brief Reindex the feature
-     * @details re-index the feature to be continuous
+     * @brief Reset _feat_ind and _arr_ind to feat_ind and arr_ind, respectively
      *
-     * @param ind(int) the new feature and array index
+     * @param feat_ind (int) the new value of _feat_ind
+     * @param arr_ind (int) the new value of _arr_ind
      */
     inline void reindex(const unsigned long int feat_ind, const unsigned long int arr_ind){_feat_ind = feat_ind; _arr_ind = arr_ind;}
 
@@ -166,13 +164,13 @@ public:
      */
     virtual unsigned long long sort_score(unsigned int max_ind) const = 0;