/****************************************************************************
 *   This file is part of the aGrUM/pyAgrum library.                        *
 *                                                                          *
 *   Copyright (c) 2005-2026 by                                             *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
 *                                                                          *
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 *   SPDX-FileCopyrightText: Copyright 2005-2026                            *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
 *   SPDX-License-Identifier: LGPL-3.0-or-later OR MIT                      *
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 *   Contact  : info_at_agrum_dot_org                                       *
 *   homepage : http://agrum.gitlab.io                                      *
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 ****************************************************************************/


/** @file
 * @brief the base class for all the scores used for learning (BIC, BDeu, etc)
 *
 * @author Christophe GONZALES(_at_AMU) and Pierre-Henri WUILLEMIN(_at_LIP6)
 */
#ifndef GUM_LEARNING_SCORE_H
#define GUM_LEARNING_SCORE_H

#include <utility>

#include <agrum/agrum.h>

#include <agrum/base/stattests/recordCounter.h>
#include <agrum/base/stattests/scoringCache.h>
#include <agrum/BN/learning/priors/prior.h>
#include <agrum/BN/learning/structureUtils/graphChange.h>

namespace gum {

  namespace learning {

    /** @class Score
     * @brief The base class for all the scores used for learning (BIC, BDeu, etc)
     * @headerfile score.h <agrum/BN/learning/scores/score.h>
     * @ingroup learning_scores
     */
    class Score: public IThreadNumberManager {
      public:
      // ##########################################################################
      /// @name Constructors / Destructors
      // ##########################################################################
      /// @{

      /// default constructor
      /** @param parser the parser used to parse the database
       * @param external_prior An prior that we add to the computation of
       * the score (this should come from expert knowledge)
       * @param ranges a set of pairs {(X1,Y1),...,(Xn,Yn)} of database's rows
       * indices. The counts are then performed only on the union of the
       * rows [Xi,Yi), i in {1,...,n}. This is useful, e.g, when performing
       * cross validation tasks, in which part of the database should be ignored.
       * An empty set of ranges is equivalent to an interval [X,Y) ranging over
       * the whole database.
       * @param nodeId2Columns a mapping from the ids of the nodes in the
       * graphical model to the corresponding column in the DatabaseTable
       * parsed by the parser. This enables estimating from a database in
       * which variable A corresponds to the 2nd column the parameters of a BN
       * in which variable A has a NodeId of 5. An empty nodeId2Columns
       * bijection means that the mapping is an identity, i.e., the value of a
       * NodeId is equal to the index of the column in the DatabaseTable.
       * @warning If nodeId2columns is not empty, then only the scores over the
       * ids belonging to this bijection can be computed: applying method
       * score() over other ids will raise exception NotFound. */
      Score(const DBRowGeneratorParser&                                 parser,
            const Prior&                                                external_prior,
            const std::vector< std::pair< std::size_t, std::size_t > >& ranges,
            const Bijection< NodeId, std::size_t >&                     nodeId2columns
            = Bijection< NodeId, std::size_t >());


      /// default constructor
      /** @param parser the parser used to parse the database
       * @param external_prior An prior that we add to the computation of
       * the score (this should come from expert knowledge)
       * @param nodeId2Columns a mapping from the ids of the nodes in the
       * graphical model to the corresponding column in the DatabaseTable
       * parsed by the parser. This enables estimating from a database in
       * which variable A corresponds to the 2nd column the parameters of a BN
       * in which variable A has a NodeId of 5. An empty nodeId2Columns
       * bijection means that the mapping is an identity, i.e., the value of a
       * NodeId is equal to the index of the column in the DatabaseTable.
       * @warning If nodeId2columns is not empty, then only the scores over the
       * ids belonging to this bijection can be computed: applying method
       * score() over other ids will raise exception NotFound. */
      Score(const DBRowGeneratorParser&             parser,
            const Prior&                            external_prior,
            const Bijection< NodeId, std::size_t >& nodeId2columns
            = Bijection< NodeId, std::size_t >());

      /// virtual copy constructor
      [[nodiscard]] virtual Score* clone() const = 0;

      /// destructor
      virtual ~Score();

      /// @}


      // ##########################################################################
      /// @name Accessors / Modifiers
      // ##########################################################################
      /// @{

      /// sets the number max of threads that can be used
      /**
       * @param nb the number max of threads to be used. If this number is set to 0, then
       * it is defaulted to aGrUM's max number of threads
       */
      void setNumberOfThreads(Size nb) override;

      /// returns the current max number of threads of the scheduler
      Size getNumberOfThreads() const override;

      /// indicates whether the user set herself the number of threads
      bool isGumNumberOfThreadsOverriden() const override;

      /** @brief changes the number min of rows a thread should process in a
       * multithreading context
       *
       * When computing score, several threads are used by record counters to
       * perform counts on the rows of the database, the MinNbRowsPerThread
       * method indicates how many rows each thread should at least process.
       * This is used to compute the number of threads actually run. This number
       * is equal to the min between the max number of threads allowed and the
       * number of records in the database divided by nb. */
      virtual void setMinNbRowsPerThread(const std::size_t nb) const;

      /// returns the minimum of rows that each thread should process
      virtual std::size_t minNbRowsPerThread() const;

      /// sets new ranges to perform the counts used by the score
      /** @param ranges a set of pairs {(X1,Y1),...,(Xn,Yn)} of database's rows
       * indices. The counts are then performed only on the union of the
       * rows [Xi,Yi), i in {1,...,n}. This is useful, e.g, when performing
       * cross validation tasks, in which part of the database should be ignored.
       * An empty set of ranges is equivalent to an interval [X,Y) ranging over
       * the whole database. */
      void setRanges(const std::vector< std::pair< std::size_t, std::size_t > >& new_ranges);

      /// reset the ranges to the one range corresponding to the whole database
      void clearRanges();

      /// returns the current ranges
      const std::vector< std::pair< std::size_t, std::size_t > >& ranges() const;

      /// returns the score of a single node
      double score(const NodeId var);

      /// returns the score of a single node given some other nodes
      /** @param var the variable on the left side of the conditioning bar
       * @param rhs_ids the set of variables on the right side of the
       * conditioning bar */
      double score(const NodeId var, const std::vector< NodeId >& rhs_ids);

      /// clears all the data structures from memory, including the cache
      void clear();

      /// clears the current cache
      void clearCache();

      /// turn on/off the use of a cache of the previously computed score
      void useCache(const bool on_off);

      /// indicates whether the score uses a cache
      bool isUsingCache() const;

      /// return the mapping between the columns of the database and the node ids
      /** @warning An empty nodeId2Columns bijection means that the mapping is
       * an identity, i.e., the value of a NodeId is equal to the index of the
       * column in the DatabaseTable. */
      const Bijection< NodeId, std::size_t >& nodeId2Columns() const;

      /// return the database used by the score
      const DatabaseTable& database() const;

      /// indicates whether the prior is compatible (meaningful) with the score
      /** The combination of some scores and priors can be meaningless. For
       * instance, adding a Dirichlet prior to the K2 score is not very
       * meaningful since K2 corresonds to a BD score with a 1-smoothing
       * prior.
       * aGrUM allows you to perform such combination, but you can check with
       * method isPriorCompatible () whether the result the score will give
       * you is meaningful or not. */
      virtual std::string isPriorCompatible() const = 0;

      /// returns the internal prior of the score
      /** Some scores include an prior. For instance, the K2 score is a BD
       * score with a Laplace Prior ( smoothing(1) ). BDeu is a BD score with
       * a N'/(r_i * q_i) prior, where N' is an effective sample size and r_i
       * is the domain size of the target variable and q_i is the domain size of
       * the Cartesian product of its parents. The goal of the score's internal
       * prior classes is to enable to account for these priors outside the
       * score, e.g., when performing parameter estimation. It is important to
       * note that, to be meaningful, a structure + parameter learning requires
       * that the same priors are taken into account during structure learning
       * and parameter learning. */
      virtual const Prior& internalPrior() const = 0;

      /// @}


      protected:
      /// 1 / log(2)
      const double one_log2_{M_LOG2E};

      /// the expert knowledge a priorwe add to the score
      Prior* prior_{nullptr};

      /// the record counter used for the counts over discrete variables
      RecordCounter counter_;

      /// the scoring cache
      ScoringCache cache_;

      /// a Boolean indicating whether we wish to use the cache
      bool use_cache_{true};

      /// an empty vector
      const std::vector< NodeId > empty_ids_;


      /// copy constructor
      Score(const Score& from);

      /// move constructor
      Score(Score&& from);

      /// copy operator
      Score& operator=(const Score& from);

      /// move operator
      Score& operator=(Score&& from);

      /// returns the score for a given IdCondSet
      /** @throws OperationNotAllowed is raised if the score does not support
       * calling method score such an idset (due to too many/too few variables
       * in the left hand side or the right hand side of the idset). */
      virtual double score_(const IdCondSet& idset) = 0;

      /// returns a counting vector where variables are marginalized from N_xyz
      /** @param X_id the id of the variable to marginalize (this is the first
       * variable in table N_xyz
       * @param N_xyz a counting vector of dimension X * cond_vars (in this order)
       */
      std::vector< double > marginalize_(const NodeId                 X_id,
                                         const std::vector< double >& N_xyz) const;
    };

  } /* namespace learning */

} /* namespace gum */

// include the inlined functions if necessary
#ifndef GUM_NO_INLINE
#  include <agrum/BN/learning/scores/score_inl.h>
#endif /* GUM_NO_INLINE */

#endif /* GUM_LEARNING_SCORE_H */
