DBRowGeneratorEM_tpl.h

Go to the documentation of this file.

/****************************************************************************
 *   This file is part of the aGrUM/pyAgrum library.                        *
 *                                                                          *
 *   Copyright (c) 2005-2026 by                                             *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
 *                                                                          *
 *   The aGrUM/pyAgrum library is free software; you can redistribute it    *
 *   and/or modify it under the terms of either :                           *
 *                                                                          *
 *    - the GNU Lesser General Public License as published by               *
 *      the Free Software Foundation, either version 3 of the License,      *
 *      or (at your option) any later version,                              *
 *    - the MIT license (MIT),                                              *
 *    - or both in dual license, as here.                                   *
 *                                                                          *
 *   (see https://agrum.gitlab.io/articles/dual-licenses-lgplv3mit.html)    *
 *                                                                          *
 *   This aGrUM/pyAgrum library is distributed in the hope that it will be  *
 *   useful, but WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,          *
 *   INCLUDING BUT NOT LIMITED TO THE WARRANTIES MERCHANTABILITY or FITNESS *
 *   FOR A PARTICULAR PURPOSE  AND NONINFRINGEMENT. IN NO EVENT SHALL THE   *
 *   AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER *
 *   LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,        *
 *   ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR  *
 *   OTHER DEALINGS IN THE SOFTWARE.                                        *
 *                                                                          *
 *   See LICENCES for more details.                                         *
 *                                                                          *
 *   SPDX-FileCopyrightText: Copyright 2005-2026                            *
 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
 *   SPDX-License-Identifier: LGPL-3.0-or-later OR MIT                      *
 *                                                                          *
 *   Contact  : info_at_agrum_dot_org                                       *
 *   homepage : http://agrum.gitlab.io                                      *
 *   gitlab   : https://gitlab.com/agrumery/agrum                           *
 *                                                                          *
 ****************************************************************************/
 
#pragma once
 

#include <agrum/base/database/DBRowGeneratorEM.h>   // to ease IDE parser
#include <agrum/base/database/DBRowGeneratorIdentity.h>
 
#ifndef DOXYGEN_SHOULD_SKIP_THIS
 
namespace gum {
 
  namespace learning {

    template < GUM_Numeric GUM_SCALAR >
    DBRowGeneratorEM< GUM_SCALAR >::DBRowGeneratorEM(
        const std::vector< DBTranslatedValueType >& column_types,
        const BayesNet< GUM_SCALAR >&               bn,
        const Bijection< NodeId, std::size_t >&     nodeId2columns) :
        DBRowGeneratorWithBN< GUM_SCALAR >(column_types,
                                           bn,
                                           DBRowGeneratorGoal::ONLY_REMOVE_MISSING_VALUES,
                                           nodeId2columns),
        _filled_row1_(bn.size(), 1.0), _filled_row2_(bn.size(), 1.0) {
      setBayesNet(bn);
 
      GUM_CONSTRUCTOR(DBRowGeneratorEM);
    }

    template < GUM_Numeric GUM_SCALAR >
    DBRowGeneratorEM< GUM_SCALAR >::DBRowGeneratorEM(const DBRowGeneratorEM< GUM_SCALAR >& from) :
        DBRowGeneratorWithBN< GUM_SCALAR >(from), _input_row_(from._input_row_),
        _missing_cols_(from._missing_cols_), _nb_miss_(from._nb_miss_),
        _joint_proba_(from._joint_proba_), _filled_row1_(from._filled_row1_),
        _filled_row2_(from._filled_row2_), _use_filled_row1_(from._use_filled_row1_),
        _original_weight_(from._original_weight_) {
      if (from._joint_inst_ != nullptr) {
        _joint_inst_              = new Instantiation(_joint_proba_);
        const auto&       var_seq = _joint_inst_->variablesSequence();
        const std::size_t size    = var_seq.size();
        for (std::size_t i = std::size_t(0); i < size; ++i) {
          _joint_inst_->chgVal(Idx(i), from._joint_inst_->val(i));
        }
      }
 
      GUM_CONS_CPY(DBRowGeneratorEM);
    }

    template < GUM_Numeric GUM_SCALAR >
    DBRowGeneratorEM< GUM_SCALAR >::DBRowGeneratorEM(DBRowGeneratorEM< GUM_SCALAR >&& from) noexcept
        :
        DBRowGeneratorWithBN< GUM_SCALAR >(std::move(from)), _input_row_(from._input_row_),
        _missing_cols_(std::move(from._missing_cols_)), _nb_miss_(from._nb_miss_),
        _joint_proba_(std::move(from._joint_proba_)), _filled_row1_(std::move(from._filled_row1_)),
        _filled_row2_(std::move(from._filled_row2_)), _use_filled_row1_(from._use_filled_row1_),
        _original_weight_(from._original_weight_) {
      if (from._joint_inst_ != nullptr) {
        _joint_inst_              = new Instantiation(_joint_proba_);
        const auto&       var_seq = _joint_inst_->variablesSequence();
        const std::size_t size    = var_seq.size();
        for (std::size_t i = std::size_t(0); i < size; ++i) {
          _joint_inst_->chgVal(Idx(i), from._joint_inst_->val(i));
        }
      }
 
      GUM_CONS_MOV(DBRowGeneratorEM);
    }

    template < GUM_Numeric GUM_SCALAR >
    DBRowGeneratorEM< GUM_SCALAR >* DBRowGeneratorEM< GUM_SCALAR >::clone() const {
      return new DBRowGeneratorEM< GUM_SCALAR >(*this);
    }

    template < GUM_Numeric GUM_SCALAR >
    DBRowGeneratorEM< GUM_SCALAR >::~DBRowGeneratorEM() {
      if (_joint_inst_ != nullptr) delete _joint_inst_;
      GUM_DESTRUCTOR(DBRowGeneratorEM);
    }

    template < GUM_Numeric GUM_SCALAR >
    DBRowGeneratorEM< GUM_SCALAR >&
        DBRowGeneratorEM< GUM_SCALAR >::operator=(const DBRowGeneratorEM< GUM_SCALAR >& from) {
      if (this != &from) {
        DBRowGeneratorWithBN< GUM_SCALAR >::operator=(from);
        _input_row_       = from._input_row_;
        _missing_cols_    = from._missing_cols_;
        _nb_miss_         = from._nb_miss_;
        _joint_proba_     = from._joint_proba_;
        _filled_row1_     = from._filled_row1_;
        _filled_row2_     = from._filled_row2_;
        _use_filled_row1_ = from._use_filled_row1_;
        _original_weight_ = from._original_weight_;
 
        if (_joint_inst_ != nullptr) {
          delete _joint_inst_;
          _joint_inst_ = nullptr;
        }
 
        if (from._joint_inst_ != nullptr) {
          _joint_inst_              = new Instantiation(_joint_proba_);
          const auto&       var_seq = _joint_inst_->variablesSequence();
          const std::size_t size    = var_seq.size();
          for (std::size_t i = std::size_t(0); i < size; ++i) {
            _joint_inst_->chgVal(Idx(i), from._joint_inst_->val(i));
          }
        }
      }
 
      return *this;
    }

    template < GUM_Numeric GUM_SCALAR >
    DBRowGeneratorEM< GUM_SCALAR >&
        DBRowGeneratorEM< GUM_SCALAR >::operator=(DBRowGeneratorEM< GUM_SCALAR >&& from) {
      if (this != &from) {
        DBRowGeneratorWithBN< GUM_SCALAR >::operator=(std::move(from));
        _input_row_       = from._input_row_;
        _missing_cols_    = std::move(from._missing_cols_);
        _nb_miss_         = from._nb_miss_;
        _joint_proba_     = std::move(from._joint_proba_);
        _filled_row1_     = std::move(from._filled_row1_);
        _filled_row2_     = std::move(from._filled_row2_);
        _use_filled_row1_ = from._use_filled_row1_;
        _original_weight_ = from._original_weight_;
 
        if (_joint_inst_ != nullptr) {
          delete _joint_inst_;
          _joint_inst_ = nullptr;
        }
 
        if (from._joint_inst_ != nullptr) {
          _joint_inst_              = new Instantiation(_joint_proba_);
          const auto&       var_seq = _joint_inst_->variablesSequence();
          const std::size_t size    = var_seq.size();
          for (std::size_t i = std::size_t(0); i < size; ++i) {
            _joint_inst_->chgVal(Idx(i), from._joint_inst_->val(i));
          }
        }
      }
 
      return *this;
    }

    template < GUM_Numeric GUM_SCALAR >
    const DBRow< DBTranslatedValue >& DBRowGeneratorEM< GUM_SCALAR >::generate() {
      this->decreaseRemainingRows();
 
      // if everything is observed, return the input row
      if (_input_row_ != nullptr) return *_input_row_;
 
      if (_use_filled_row1_) {
        // get the weight of the row from the joint probability
        _filled_row1_.setWeight(_joint_proba_.get(*_joint_inst_) * _original_weight_);
 
        // fill the values of the row
        for (std::size_t i = std::size_t(0); i < _nb_miss_; ++i)
          _filled_row1_[_missing_cols_[i]].discr_val = _joint_inst_->val(i);
 
        _joint_inst_->inc();
        _use_filled_row1_ = false;
 
        return _filled_row1_;
      } else {
        // get the weight of the row from the joint probability
        _filled_row2_.setWeight(_joint_proba_.get(*_joint_inst_) * _original_weight_);
 
        // fill the values of the row
        for (std::size_t i = std::size_t(0); i < _nb_miss_; ++i)
          _filled_row2_[_missing_cols_[i]].discr_val = _joint_inst_->val(i);
 
        _joint_inst_->inc();
        _use_filled_row1_ = true;
 
        return _filled_row2_;
      }
    }

    template < GUM_Numeric GUM_SCALAR >
    std::size_t
        DBRowGeneratorEM< GUM_SCALAR >::computeRows_(const DBRow< DBTranslatedValue >& row) {
      // check if there are unobserved values among the columns of interest.
      // If this is the case, save in _missing_cols_ all the columns with unobserved values
      bool        found_unobserved = false;
      const auto& xrow             = row.row();
      for (const auto col: this->columns_of_interest_) {
        switch (this->column_types_[col]) {
          case DBTranslatedValueType::DISCRETE :
            if (xrow[col].discr_val == std::numeric_limits< std::size_t >::max()) {
              if (!found_unobserved) {
                _missing_cols_.clear();
                found_unobserved = true;
              }
              _missing_cols_.push_back(col);
            }
            break;
 
          case DBTranslatedValueType::CONTINUOUS :
            GUM_ERROR(NotImplementedYet,
                      "The BDRowGeneratorEM does not handle yet continuous "
                          << "variables. But the variable in column" << col << " is continuous.");
            break;
 
          default :
            GUM_ERROR(NotImplementedYet,
                      "DBTranslatedValueType " << int(this->column_types_[col])
                                               << " is not supported yet");
        }
      }
 
      // if there is no unobserved value, make the _input_row_ point to the row
      if (!found_unobserved) {
        _input_row_ = &row;
        return std::size_t(1);
      }
 
      _input_row_       = nullptr;
      _nb_miss_         = _missing_cols_.size();
      _original_weight_ = row.weight();
 
      // here, there are missing symbols, so we should compute the distribution
      // of the missing values. For this purpose, we use Variable Elimination
      VariableElimination< GUM_SCALAR > ve(this->bn_);
 
      // add the targets and fill the output row with the observed values
      NodeSet target_set(_nb_miss_);
      if (this->nodeId2columns_.empty()) {
        std::size_t i        = std::size_t(0);
        bool        end_miss = false;
        for (const auto col: this->columns_of_interest_) {
          if (!end_miss && (col == _missing_cols_[i])) {
            target_set.insert(NodeId(col));
            ++i;
            if (i == _nb_miss_) end_miss = true;
          } else {
            _filled_row1_[col].discr_val = xrow[col].discr_val;
            _filled_row2_[col].discr_val = xrow[col].discr_val;
          }
        }
      } else {
        std::size_t i        = std::size_t(0);
        bool        end_miss = false;
        for (const auto col: this->columns_of_interest_) {
          if (!end_miss && (col == _missing_cols_[i])) {
            target_set.insert(this->nodeId2columns_.first(col));
            ++i;
            if (i == _nb_miss_) end_miss = true;
          } else {
            _filled_row1_[col].discr_val = xrow[col].discr_val;
            _filled_row2_[col].discr_val = xrow[col].discr_val;
          }
        }
      }
 
      ve.addJointTarget(target_set);
 
      // add the evidence and the target into variable elimination
      const std::size_t row_size = xrow.size();
      if (this->nodeId2columns_.empty()) {
        for (std::size_t col = std::size_t(0); col < row_size; ++col) {
          switch (this->column_types_[col]) {
            case DBTranslatedValueType::DISCRETE :
              // only observed values are evidence
              if (xrow[col].discr_val != std::numeric_limits< std::size_t >::max()) {
                ve.addEvidence(NodeId(col), xrow[col].discr_val);
              }
              break;
 
            case DBTranslatedValueType::CONTINUOUS :
              GUM_ERROR(NotImplementedYet,
                        "The BDRowGeneratorEM does not handle yet continuous "
                            << "variables. But the variable in column" << col << " is continuous.");
              break;
 
            default :
              GUM_ERROR(NotImplementedYet,
                        "DBTranslatedValueType " << int(this->column_types_[col])
                                                 << " is not supported yet");
          }
        }
      } else {
        for (std::size_t col = std::size_t(0); col < row_size; ++col) {
          switch (this->column_types_[col]) {
            case DBTranslatedValueType::DISCRETE :
              // only observed values are evidence
              if (xrow[col].discr_val != std::numeric_limits< std::size_t >::max()) {
                ve.addEvidence(this->nodeId2columns_.first(col), xrow[col].discr_val);
              }
              break;
 
            case DBTranslatedValueType::CONTINUOUS :
              GUM_ERROR(NotImplementedYet,
                        "The BDRowGeneratorEM does not handle yet continuous "
                            << "variables. But the variable in column" << col << " is continuous.");
              break;
 
            default :
              GUM_ERROR(NotImplementedYet,
                        "DBTranslatedValueType " << int(this->column_types_[col])
                                                 << " is not supported yet");
          }
        }
      }
 
      // get the tensor of the target set
      Tensor< GUM_SCALAR >& pot
          = const_cast< Tensor< GUM_SCALAR >& >(ve.jointPosterior(target_set));
      _joint_proba_ = std::move(pot);
      if (_joint_inst_ != nullptr) delete _joint_inst_;
      _joint_inst_ = new Instantiation(_joint_proba_);
 
      // get the mapping between variables of the joint proba and the
      // columns in the database
      const auto& var_sequence = _joint_proba_.variablesSequence();
      if (this->nodeId2columns_.empty()) {
        for (std::size_t i = std::size_t(0); i < _nb_miss_; ++i) {
          _missing_cols_[i] = std::size_t(this->bn_->nodeId(*(var_sequence[i])));
        }
      } else {
        for (std::size_t i = std::size_t(0); i < _nb_miss_; ++i) {
          _missing_cols_[i] = this->nodeId2columns_.second(this->bn_->nodeId(*(var_sequence[i])));
        }
      }
 
      return std::size_t(_joint_proba_.domainSize());
    }

    template < GUM_Numeric GUM_SCALAR >
    void DBRowGeneratorEM< GUM_SCALAR >::setBayesNet(const BayesNet< GUM_SCALAR >& new_bn) {
      // check that if nodeId2columns is not empty, then all the columns
      // correspond to nodes of the BN
      if (!this->nodeId2columns_.empty()) {
        const DAG& dag = new_bn.internalDag();
        for (auto iter = this->nodeId2columns_.begin(); iter != this->nodeId2columns_.end();
             ++iter) {
          if (!dag.existsNode(iter.first())) {
            GUM_ERROR(InvalidNode,
                      "Column " << iter.second() << " of the database is associated to Node ID "
                                << iter.first()
                                << ", which does not belong to the Bayesian network");
          }
        }
      }
 
      DBRowGeneratorWithBN< GUM_SCALAR >::setBayesNet(new_bn);
 
      // we determine the size of the filled rows
      std::size_t size = std::size_t(0);
      if (this->nodeId2columns_.empty()) {
        // here, we know that the DAG's node IDs correspond precisely to column indices
        // in the database. So we can safely set the size of _filled_row_ to the highest
        // node ID in the graph
        for (auto node: new_bn.internalDag())
          if (std::size_t(node) > size) size = std::size_t(node);
      } else {
        for (auto iter = this->nodeId2columns_.begin(); iter != this->nodeId2columns_.end();
             ++iter) {
          if (iter.second() > size) size = iter.second();
        }
      }
      _filled_row1_.resize(size + 1);
      _filled_row2_.resize(size + 1);
    }
 
  } /* namespace learning */
 
} /* namespace gum */
 
#endif /* DOXYGEN_SHOULD_SKIP_THIS */