BNDatabaseGenerator_tpl.h

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 *       - Christophe GONZALES(_at_AMU)                                     *
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 *       - Pierre-Henri WUILLEMIN(_at_LIP6)                                 *
 *       - Christophe GONZALES(_at_AMU)                                     *
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#pragma once
 

 
#include <agrum/base/core/timer.h>
#include <agrum/BN/database/BNDatabaseGenerator.h>
 
namespace gum::learning {
 

  template < GUM_Numeric GUM_SCALAR >
  BNDatabaseGenerator< GUM_SCALAR >::BNDatabaseGenerator(const BayesNet< GUM_SCALAR >& bn) :
      _bn_(bn) {
    GUM_CONSTRUCTOR(BNDatabaseGenerator)
 
    // get the node names => they will serve as ids
    NodeId id = 0;
    for (const auto& var: _bn_.internalDag()) {
      auto name = _bn_.variable(var).name();
      _names2ids_.insert(name, var);
      ++id;
    }
    _nbVars_ = id;
    _varOrder_.resize(_nbVars_);
    _discretizedLabelMode_ = DiscretizedLabelMode::RANDOM;
    std::iota(_varOrder_.begin(), _varOrder_.end(), (Idx)0);
  }

  template < GUM_Numeric GUM_SCALAR >
  BNDatabaseGenerator< GUM_SCALAR >::~BNDatabaseGenerator() {
    GUM_DESTRUCTOR(BNDatabaseGenerator)
  }

  template < GUM_Numeric GUM_SCALAR >
  double BNDatabaseGenerator< GUM_SCALAR >::drawSamples(Size nbSamples) {
    const Instantiation inst;
    return drawSamples(nbSamples, inst);
  }

  template < GUM_Numeric GUM_SCALAR >
  double BNDatabaseGenerator< GUM_SCALAR >::drawSamples(Size                 nbSamples,
                                                        const Instantiation& evs,
                                                        int                  timeout) {
    int progress = 0;
 
    if (onProgress.hasListener()) { GUM_EMIT2(onProgress, progress, 0.0); }
 
    _database_.clear();
    _database_.resize(nbSamples);
    for (auto& row: _database_) {
      row.resize(_nbVars_);
    }
    // get the order in which the nodes will be sampled
    const auto         topOrder = _bn_.topologicalOrder();
    gum::Instantiation particule;
 
    // create instantiations in advance
    for (NodeId node = 0; node < _nbVars_; ++node)
      particule.add(_bn_.variable(node));
 
    gum::Timer timer;
    timer.reset();
 
    // perform the sampling
    _log2likelihood_ = 0;
    Idx idSample     = 0;
    while (idSample < nbSamples) {
      if (onProgress.hasListener()) {
        auto p = int((idSample * 100) / nbSamples);
        if (p != progress) {
          progress = p;
          GUM_EMIT2(onProgress, progress, timer.step());
        }
      }
      std::vector< Idx >& sample = _database_.at(idSample);
      bool                reject = false;
      for (Idx rank = 0; rank < _nbVars_; ++rank) {
        const NodeId node = topOrder[rank];
        const auto&  var  = _bn_.variable(node);
        const auto&  cpt  = _bn_.cpt(node);
 
        const double nb    = gum::randomProba();
        double       cumul = 0.0;
        for (particule.setFirstVar(var); !particule.end(); particule.incVar(var)) {
          cumul += cpt[particule];
          if (cumul >= nb) break;
        }
        if (particule.end()) particule.setLastVar(var);
 
        if ((!evs.empty()) && evs.contains(var) && (evs.val(var) != particule.val(var))) {
          reject = true;
          break;
        }
 
        sample.at(node) = particule.val(var);
        _log2likelihood_ += std::log2(_bn_.cpt(node)[particule]);
      }
      if (timeout > 0 && timer.step() > timeout) { break; }
      if (reject) { continue; }
      idSample++;
    }
 
    if (idSample > 0) {
      if (idSample < nbSamples) _database_.resize(idSample);
    } else {
      _database_.clear();
    }
    _drawnSamples_ = true;
 
    if (onProgress.hasListener()) {
      GUM_EMIT1(onStop,
                std::format("Database of size {}({}) generated in {} seconds. Log2likelihood : {}",
                            idSample,
                            nbSamples,
                            timer.step(),
                            _log2likelihood_));
    }
 
    return _log2likelihood_;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  Size BNDatabaseGenerator< GUM_SCALAR >::samplesNbRows() const {
    if (!_drawnSamples_) { GUM_ERROR(OperationNotAllowed, "drawSamples() must be called first.") }
 
    return _database_.size();
  }
 
  template < GUM_Numeric GUM_SCALAR >
  Size BNDatabaseGenerator< GUM_SCALAR >::samplesNbCols() const {
    if (!_drawnSamples_) { GUM_ERROR(OperationNotAllowed, "drawSamples() must be called first.") }
 
    return _nbVars_;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  Idx BNDatabaseGenerator< GUM_SCALAR >::samplesAt(Idx row, Idx col) const {
    if (!_drawnSamples_) { GUM_ERROR(OperationNotAllowed, "drawSamples() must be called first.") }
    return _database_.at(row).at(_varOrder_.at(col));
  }
 
  template < GUM_Numeric GUM_SCALAR >
  std::string BNDatabaseGenerator< GUM_SCALAR >::samplesLabelAt(Idx row, Idx col) const {
    if (!_drawnSamples_) { GUM_ERROR(OperationNotAllowed, "drawSamples() must be called first.") }
    const auto j = _varOrder_.at(col);
    return _label_(_database_.at(row), _bn_.variable(j), j);
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void BNDatabaseGenerator< GUM_SCALAR >::setDiscretizedLabelModeRandom() {
    _discretizedLabelMode_ = DiscretizedLabelMode::RANDOM;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void BNDatabaseGenerator< GUM_SCALAR >::setDiscretizedLabelModeMedian() {
    _discretizedLabelMode_ = DiscretizedLabelMode::MEDIAN;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  void BNDatabaseGenerator< GUM_SCALAR >::setDiscretizedLabelModeInterval() {
    _discretizedLabelMode_ = DiscretizedLabelMode::INTERVAL;
  }

  template < GUM_Numeric GUM_SCALAR >
  void BNDatabaseGenerator< GUM_SCALAR >::toCSV(std::string_view csvFileURL,
                                                bool             useLabels,
                                                bool             append,
                                                std::string      csvSeparator,
                                                bool             checkOnAppend) const {
    if (!_drawnSamples_) { GUM_ERROR(OperationNotAllowed, "drawSamples() must be called first.") }
 
    if (csvSeparator.find('\n') != std::string::npos) {
      GUM_ERROR(InvalidArgument, "csvSeparator must not contain end-line characters")
    }
 
    bool includeHeader = true;
    if (append) {
      std::ifstream csvFile(std::filesystem::path{csvFileURL});
      if (csvFile) {
        if (auto varOrder = _varOrderFromCSV_(csvFile, csvSeparator);
            checkOnAppend && varOrder != _varOrder_)
          GUM_ERROR(OperationNotAllowed,
                    "Inconsistent variable order in csvFile when appending. You "
                    "can use setVarOrderFromCSV(url) function to get the right "
                    "order. You could also set parameter checkOnAppend=false if you "
                    "know what you are doing.")
        includeHeader = false;
      }
      csvFile.close();
    }
 
 
    auto ofstreamFlag = append ? std::ofstream::app : std::ofstream::out;
 
    std::ofstream os(std::filesystem::path{csvFileURL}, ofstreamFlag);
    bool          firstCol = true;
    if (includeHeader) {
      for (const auto& i: _varOrder_) {
        if (firstCol) {
          firstCol = false;
        } else {
          os << csvSeparator;
        }
        os << _bn_.variable(i).name();
      }
    }
    os << std::endl;
 
    bool firstRow = true;
    for (const auto& row: _database_) {
      if (firstRow) {
        firstRow = false;
      } else {
        os << std::endl;
      }
      firstCol = true;
      for (const auto& i: _varOrder_) {
        if (firstCol) {
          firstCol = false;
        } else {
          os << csvSeparator;
        }
        if (useLabels) {
          const auto& v = _bn_.variable(i);
          if (v.varType() == VarType::DISCRETIZED) {
            switch (_discretizedLabelMode_) {
              case DiscretizedLabelMode::MEDIAN : os << v.numerical(row.at(i)); break;
              case DiscretizedLabelMode::RANDOM :
                os << static_cast< const IDiscretizedVariable& >(v).draw(row.at(i));
                break;
              case DiscretizedLabelMode::INTERVAL : os << v.label(row.at(i)); break;
            }
          } else {
            os << v.label(row.at(i));
          }
        } else {
          os << row[i];
        }
      }
    }
 
    os.close();
  }
 
  template < GUM_Numeric GUM_SCALAR >
  std::string BNDatabaseGenerator< GUM_SCALAR >::_label_(const std::vector< Idx >& row,
                                                         const DiscreteVariable&   v,
                                                         Idx                       i) const {
    if (v.varType() == VarType::DISCRETIZED) {
      switch (_discretizedLabelMode_) {
        case DiscretizedLabelMode::MEDIAN : return std::to_string(v.numerical(row.at(i)));
        case DiscretizedLabelMode::RANDOM :
          return std::to_string(static_cast< const IDiscretizedVariable& >(v).draw(row.at(i)));
        case DiscretizedLabelMode::INTERVAL : return v.label(row.at(i));
      }
    }
 
    return v.label(row.at(i));
  }

  template < GUM_Numeric GUM_SCALAR >
  DatabaseTable BNDatabaseGenerator< GUM_SCALAR >::toDatabaseTable(bool useLabels) const {
    if (!_drawnSamples_) GUM_ERROR(OperationNotAllowed, "proceed() must be called first.")
 
    DatabaseTable              db;
    std::vector< std::string > varNames;
    varNames.reserve(_nbVars_);
    for (const auto& i: _varOrder_) {
      varNames.push_back(_names2ids_.first(i));
    }
 
    // create the translators
    for (std::size_t i = 0; i < _nbVars_; ++i) {
      const Variable& var = _bn_.variable(_varOrder_[i]);
      db.insertTranslator(var, i);
    }
 
    if (useLabels) {
      std::vector< std::string > xrow(_nbVars_);
      for (const auto& row: _database_) {
        for (Idx i = 0; i < _nbVars_; ++i) {
          const Idx j = _varOrder_.at(i);
          xrow[i]     = _label_(row, _bn_.variable(j), j);
        }
        db.insertRow(xrow);
      }
    } else {
      std::vector< DBTranslatedValueType > translatorType(_nbVars_);
      for (std::size_t i = 0; i < _nbVars_; ++i) {
        translatorType[i] = db.translator(i).getValType();
      }
      DBRow< DBTranslatedValue > xrow(_nbVars_);
      const auto                 xmiss = gum::learning::DatabaseTable::IsMissing::False;
      for (const auto& row: _database_) {
        for (Idx i = 0; i < _nbVars_; ++i) {
          const Idx j = _varOrder_.at(i);
          if (translatorType[i] == DBTranslatedValueType::DISCRETE)
            xrow[i].discr_val = std::size_t(row.at(j));
          else xrow[i].cont_val = float(row.at(j));
        }
      }
      db.insertRow(xrow, xmiss);
    }
 
    return db;
  }

  template < GUM_Numeric GUM_SCALAR >
  std::vector< std::vector< Idx > > BNDatabaseGenerator< GUM_SCALAR >::database() const {
    if (!_drawnSamples_) GUM_ERROR(OperationNotAllowed, "drawSamples() must be called first.")
 
    auto db(_database_);
    for (Idx i = 0; i < _database_.size(); ++i) {
      for (Idx j = 0; j < _nbVars_; ++j) {
        db.at(i).at(j) = (Idx)_database_.at(i).at(_varOrder_.at(j));
      }
    }
    return db;
  }

  template < GUM_Numeric GUM_SCALAR >
  void BNDatabaseGenerator< GUM_SCALAR >::setVarOrder(const std::vector< Idx >& varOrder) {
    if (varOrder.size() != _nbVars_)
      GUM_ERROR(FatalError, "varOrder's size must be equal to the number of variables")
 
    std::vector< bool > usedVars(_nbVars_, false);
    for (const auto& i: varOrder) {
      if (i >= _nbVars_) GUM_ERROR(FatalError, "varOrder contains invalid variables")
      if (usedVars.at(i)) GUM_ERROR(FatalError, "varOrder must not have repeated variables")
      usedVars.at(i) = true;
    }
 
    if (std::find(usedVars.begin(), usedVars.end(), false) != usedVars.end()) {
      GUM_ERROR(FatalError, "varOrder must contain all variables")
    }
 
    _varOrder_ = varOrder;
  }

  template < GUM_Numeric GUM_SCALAR >
  void BNDatabaseGenerator< GUM_SCALAR >::setVarOrder(const std::vector< std::string >& varOrder) {
    std::vector< Idx > varOrderIdx;
    varOrderIdx.reserve(varOrder.size());
    for (const auto& vname: varOrder) {
      varOrderIdx.push_back(_names2ids_.second(vname));
    }
    setVarOrder(varOrderIdx);
  }

  template < GUM_Numeric GUM_SCALAR >
  void BNDatabaseGenerator< GUM_SCALAR >::setVarOrderFromCSV(std::string_view csvFileURL,
                                                             std::string_view csvSeparator) {
    setVarOrder(_varOrderFromCSV_(csvFileURL, csvSeparator));
  }

  template < GUM_Numeric GUM_SCALAR >
  void BNDatabaseGenerator< GUM_SCALAR >::setTopologicalVarOrder() {
    std::vector< Idx > varOrder;
    varOrder.reserve(_nbVars_);
    for (const auto& v: _bn_.topologicalOrder()) {
      varOrder.push_back(v);
    }
    setVarOrder(varOrder);
  }

  template < GUM_Numeric GUM_SCALAR >
  void BNDatabaseGenerator< GUM_SCALAR >::setAntiTopologicalVarOrder() {
    std::vector< Idx > varOrder;
    varOrder.reserve(_nbVars_);
    for (const auto& v: _bn_.topologicalOrder()) {
      varOrder.push_back(v);
    }
    std::reverse(varOrder.begin(), varOrder.end());
    setVarOrder(varOrder);
  }

  template < GUM_Numeric GUM_SCALAR >
  void BNDatabaseGenerator< GUM_SCALAR >::setRandomVarOrder() {
    std::vector< std::string > varOrder;
    varOrder.reserve(_bn_.size());
    for (const auto& var: _bn_.internalDag()) {
      varOrder.push_back(_bn_.variable(var).name());
    }
    std::shuffle(varOrder.begin(), varOrder.end(), gum::randomGenerator());
    setVarOrder(varOrder);
  }

  template < GUM_Numeric GUM_SCALAR >
  std::vector< Idx > BNDatabaseGenerator< GUM_SCALAR >::varOrder() const {
    return _varOrder_;
  }

  template < GUM_Numeric GUM_SCALAR >
  std::vector< std::string > BNDatabaseGenerator< GUM_SCALAR >::varOrderNames() const {
    std::vector< std::string > varNames;
    varNames.reserve(_nbVars_);
    for (const auto& i: _varOrder_) {
      varNames.push_back(_names2ids_.first(i));
    }
 
    return varNames;
  }

  template < GUM_Numeric GUM_SCALAR >
  double BNDatabaseGenerator< GUM_SCALAR >::log2likelihood() const {
    if (!_drawnSamples_) { GUM_ERROR(OperationNotAllowed, "drawSamples() must be called first.") }
    return _log2likelihood_;
  }

  template < GUM_Numeric GUM_SCALAR >
  std::vector< Idx >
      BNDatabaseGenerator< GUM_SCALAR >::_varOrderFromCSV_(std::string_view csvFileURL,
                                                           std::string_view csvSeparator) const {
    std::ifstream      csvFile(std::filesystem::path{csvFileURL});
    std::vector< Idx > varOrder;
    if (csvFile) {
      varOrder = _varOrderFromCSV_(csvFile, csvSeparator);
      csvFile.close();
    } else {
      GUM_ERROR(NotFound, "csvFileURL does not exist")
    }
 
    return varOrder;
  }

  template < GUM_Numeric GUM_SCALAR >
  std::vector< Idx >
      BNDatabaseGenerator< GUM_SCALAR >::_varOrderFromCSV_(std::ifstream&   csvFile,
                                                           std::string_view csvSeparator) const {
    std::string                line;
    std::vector< std::string > header_found;
    header_found.reserve(_nbVars_);
    while (std::getline(csvFile, line)) {
      std::size_t i   = 0;
      auto        pos = line.find(csvSeparator);
      while (pos != std::string::npos) {
        header_found.push_back(line.substr(i, pos - i));
        pos += csvSeparator.length();
        i   = pos;
        pos = line.find(csvSeparator, pos);
 
        if (pos == std::string::npos) header_found.push_back(line.substr(i, line.length()));
      }
      break;
    }
 
    std::vector< Size > varOrder;
    varOrder.reserve(_nbVars_);
 
    for (const auto& hf: header_found) {
      varOrder.push_back(_names2ids_.second(hf));
    }
 
    return varOrder;
  }
 
  template < GUM_Numeric GUM_SCALAR >
  const BayesNet< GUM_SCALAR >& BNDatabaseGenerator< GUM_SCALAR >::bn(void) {
    return _bn_;
  }
 
}   // namespace gum::learning