structuralMetrics.h

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 *   Copyright (c) 2005-2026 by                                             *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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#ifndef GUM_LEARNING_STRUCTURAL_METRICS_H
#define GUM_LEARNING_STRUCTURAL_METRICS_H
 
#include <agrum/base/graphs/PDAG.h>
#include <agrum/BN/BayesNet.h>
 
namespace gum {

  class StructuralMetrics {
    public:
    // ##########################################################################
    // ##########################################################################

    StructuralMetrics();

    ~StructuralMetrics();

 
    // ##########################################################################
    // ##########################################################################
    void compare(const DiGraph& ref, const DiGraph& test);
    void compare(const UndiGraph& ref, const UndiGraph& test);
    void compare(const PDAG& ref, const PDAG& test);
    template < typename GS1, typename GS2 >
    void compare(const BayesNet< GS1 >& ref, const BayesNet< GS2 >& test);
    template < GUM_Numeric GUM_SCALAR >
    void compare(const BayesNet< GUM_SCALAR >& ref, const PDAG& test);
    template < GUM_Numeric GUM_SCALAR >
    void compare(const PDAG& ref, const BayesNet< GUM_SCALAR >& test);

    double tp_skeleton() const;
    double fp_skeleton() const;
    double fn_skeleton() const;
    double tn_skeleton() const;
    double precision_skeleton() const;
    double recall_skeleton() const;
    double f_score_skeleton() const;
    double shd_skeleton() const;

    double tp() const;
    double fp() const;
    double fn() const;
    double tn() const;
    double precision() const;
    double recall() const;
    double f_score() const;
    double shd() const;
 
    // Causal measure for DAGs
    double sid(const DAG& ref, const DAG& test) const;
    template < typename GS1, typename GS2 >
    double sid(const BayesNet< GS1 >& ref, const BayesNet< GS2 >& test) const;
 
    private:
    /*    __________________________________________________
     *  |   \predicted |           |           |           |
     *  |true\         |    -->    |    ---    |     X     |
     *  |--------------|-----------|-----------|-----------|
     *  |      -->     | true arc  |wrong edge |wrong none |
     *  |              | mis. arc  |      arc  |      arc  |
     *  |--------------|-----------|-----------|-----------|
     *  |      ---     |wrong arc  | true edge |wrong none |
     *  |              |      edge |           |      edge |
     *  |--------------|-----------|-----------|-----------|
     *  |       X      |wrong arc  |wrong edge | true none |
     *  |              |      none |      none |           |
     *  | _____________|___________|___________|___________|
     *
     */
    double _true_edge_, _true_arc_, _true_none_;
    double _wrong_edge_arc_, _wrong_edge_none_;
    double _wrong_arc_edge_, _wrong_arc_none_, _misoriented_arc_;
    double _wrong_none_edge_, _wrong_none_arc_;
    // double  _precision_,  _recall_,  _f_score_;

    Size _size_;
  };
 
} /* namespace gum */

#include <agrum/BN/algorithms/structuralMetrics_tpl.h>
 
#endif /* GUM_LEARNING_STRUCTURAL_METRICS_H */