gum::learning::Miic Class Reference

The MIIC learning algorithm. More...

#include <Miic.h>

Inheritance diagram for gum::learning::Miic:

Collaboration diagram for gum::learning::Miic:

Public Types
enum class	ApproximationSchemeSTATE : char {   Undefined , Continue , Epsilon , Rate ,   Limit , TimeLimit , Stopped }
	The different state of an approximation scheme. More...

Public Member Functions
Miic &	operator= (const Miic &from)
Miic &	operator= (Miic &&from)
Constructors / Destructors
	Miic ()
	Miic (int maxLog)
	Miic (const Miic &from)
	Miic (Miic &&from)
	~Miic () override
Scorer injection
void	setMutualInformation (CorrectedMutualInformation &mi)
ConstraintBasedLearning interface
MixedGraph	learnSkeleton (MixedGraph graph) override
MixedGraph	learnMixedStructure (MixedGraph graph) override
Constraint setters
void	setForbiddenGraph (const gum::DiGraph &forbidGraph)
void	setMandatoryGraph (const gum::DAG &mandaGraph)
void	setMaxIndegree (gum::Size n)
void	addConstraints (const HashTable< std::pair< NodeId, NodeId >, char > &constraints)
const std::vector< Arc >	latentVariables () const
Learning — template methods (call the pure virtual interface)
PDAG	learnPDAG (MixedGraph graph)
	learns the essential graph (CPDAG)
DAG	learnDAG (MixedGraph graph)
	learns a DAG
template<GUM_Numeric GUM_SCALAR = double, typename PARAM_ESTIMATOR>
BayesNet< GUM_SCALAR >	learnBN (PARAM_ESTIMATOR &estimator, MixedGraph graph)
	learns structure then estimates parameters
Getters and setters
void	setEpsilon (double eps) override
	Given that we approximate f(t), stopping criterion on |f(t+1)-f(t)|.
double	epsilon () const override
	Returns the value of epsilon.
void	disableEpsilon () override
	Disable stopping criterion on epsilon.
void	enableEpsilon () override
	Enable stopping criterion on epsilon.
bool	isEnabledEpsilon () const override
	Returns true if stopping criterion on epsilon is enabled, false otherwise.
void	setMinEpsilonRate (double rate) override
	Given that we approximate f(t), stopping criterion on d/dt(|f(t+1)-f(t)|).
double	minEpsilonRate () const override
	Returns the value of the minimal epsilon rate.
void	disableMinEpsilonRate () override
	Disable stopping criterion on epsilon rate.
void	enableMinEpsilonRate () override
	Enable stopping criterion on epsilon rate.
bool	isEnabledMinEpsilonRate () const override
	Returns true if stopping criterion on epsilon rate is enabled, false otherwise.
void	setMaxIter (Size max) override
	Stopping criterion on number of iterations.
Size	maxIter () const override
	Returns the criterion on number of iterations.
void	disableMaxIter () override
	Disable stopping criterion on max iterations.
void	enableMaxIter () override
	Enable stopping criterion on max iterations.
bool	isEnabledMaxIter () const override
	Returns true if stopping criterion on max iterations is enabled, false otherwise.
void	setMaxTime (double timeout) override
	Stopping criterion on timeout.
double	maxTime () const override
	Returns the timeout (in seconds).
double	currentTime () const override
	Returns the current running time in second.
void	disableMaxTime () override
	Disable stopping criterion on timeout.
void	enableMaxTime () override
	Enable stopping criterion on timeout.
bool	isEnabledMaxTime () const override
	Returns true if stopping criterion on timeout is enabled, false otherwise.
void	setPeriodSize (Size p) override
	How many samples between two stopping is enable.
Size	periodSize () const override
	Returns the period size.
void	setVerbosity (bool v) override
	Set the verbosity on (true) or off (false).
bool	verbosity () const override
	Returns true if verbosity is enabled.
ApproximationSchemeSTATE	stateApproximationScheme () const override
	Returns the approximation scheme state.
Size	nbrIterations () const override
	Returns the number of iterations.
const std::vector< double > &	history () const override
	Returns the scheme history.
void	initApproximationScheme ()
	Initialise the scheme.
bool	startOfPeriod () const
	Returns true if we are at the beginning of a period (compute error is mandatory).
void	updateApproximationScheme (unsigned int incr=1)
	Update the scheme w.r.t the new error and increment steps.
Size	remainingBurnIn () const
	Returns the remaining burn in.
void	stopApproximationScheme ()
	Stop the approximation scheme.
bool	continueApproximationScheme (double error)
	Update the scheme w.r.t the new error.
Getters and setters
std::string	messageApproximationScheme () const
	Returns the approximation scheme message.

Public Attributes
Signaler< gum::NodeId, gum::NodeId, std::string, std::string >	onStructuralModification
Signaler< Size, double, double >	onProgress
	Progression, error and time.
Signaler< std::string_view >	onStop
	Criteria messageApproximationScheme.

Protected Member Functions
MIIC skeleton learning phases
void	initiation_ (CorrectedMutualInformation &mutualInformation, MixedGraph &graph, HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > &sepSet, Heap< CondRanking, GreaterPairOn2nd > &rank)
void	iteration_ (CorrectedMutualInformation &mutualInformation, MixedGraph &graph, HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > &sepSet, Heap< CondRanking, GreaterPairOn2nd > &rank)
MIIC orientation phase
void	orientationMiic_ (CorrectedMutualInformation &mutualInformation, MixedGraph &graph, const HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > &sepSet)
void	findBestContributor_ (NodeId x, NodeId y, const std::vector< NodeId > &ui, const MixedGraph &graph, CorrectedMutualInformation &mutualInformation, Heap< CondRanking, GreaterPairOn2nd > &rank)
std::vector< ProbabilisticRanking >	unshieldedTriplesMiic_ (const MixedGraph &graph, CorrectedMutualInformation &mutualInformation, const HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > &sepSet, HashTable< std::pair< NodeId, NodeId >, char > &marks)
std::vector< ProbabilisticRanking >	updateProbaTriples_ (const MixedGraph &graph, std::vector< ProbabilisticRanking > probaTriples)
Constraint checks
bool	isForbiddenArc_ (NodeId x, NodeId y) const
bool	isForbiddenEdge_ (NodeId x, NodeId y) const
bool	isMaxIndegree_ (const MixedGraph &graph, NodeId x)
bool	isArcValid_ (const MixedGraph &graph, NodeId x, NodeId y)

Protected Attributes
double	current_epsilon_
	Current epsilon.
double	last_epsilon_
	Last epsilon value.
double	current_rate_
	Current rate.
Size	current_step_
	The current step.
Timer	timer_
	The timer.
ApproximationSchemeSTATE	current_state_
	The current state.
std::vector< double >	history_
	The scheme history, used only if verbosity == true.
double	eps_
	Threshold for convergence.
bool	enabled_eps_
	If true, the threshold convergence is enabled.
double	min_rate_eps_
	Threshold for the epsilon rate.
bool	enabled_min_rate_eps_
	If true, the minimal threshold for epsilon rate is enabled.
double	max_time_
	The timeout.
bool	enabled_max_time_
	If true, the timeout is enabled.
Size	max_iter_
	The maximum iterations.
bool	enabled_max_iter_
	If true, the maximum iterations stopping criterion is enabled.
Size	burn_in_
	Number of iterations before checking stopping criteria.
Size	period_size_
	Checking criteria frequency.
bool	verbosity_
	If true, verbosity is enabled.

Private Member Functions
void	_orientingVstructureMiic_ (MixedGraph &graph, HashTable< std::pair< NodeId, NodeId >, char > &marks, NodeId x, NodeId y, NodeId z, double p1, double p2)
void	_propagatingOrientationMiic_ (MixedGraph &graph, HashTable< std::pair< NodeId, NodeId >, char > &marks, NodeId x, NodeId y, NodeId z, double p1, double p2)
bool	_isNotLatentCouple_ (NodeId x, NodeId y)
void	stopScheme_ (ApproximationSchemeSTATE new_state)
	Stop the scheme given a new state.

Private Attributes
CorrectedMutualInformation *	mi_ {nullptr}
ArcProperty< double >	_arcProbas_

Common state (accessible to subclasses)
gum::Size	_maxIndegree_ {std::numeric_limits< gum::Size >::max()}
gum::DiGraph	_forbiddenGraph_
gum::DAG	_mandatoryGraph_
HashTable< std::pair< NodeId, NodeId >, char >	_initialMarks_
std::vector< Arc >	_latentCouples_
int	_maxLog_ {100}
const std::vector< NodeId >	_emptySet_

Graph utilities
void	orientDoubleHeadedArcs_ (MixedGraph &mg)
	Builds a complete MixedGraph on the nodes of template_graph, minus edges forbidden by structural constraints. Every constraint-based skeleton phase must start from this graph so forbidden pairs are never tested by CI.
std::vector< ThreePoints >	unshieldedTriples_ (const MixedGraph &graph)
	Builds a complete MixedGraph on the nodes of template_graph, minus edges forbidden by structural constraints. Every constraint-based skeleton phase must start from this graph so forbidden pairs are never tested by CI.
void	applyStructuralConstraints_ (MixedGraph &graph)
	Builds a complete MixedGraph on the nodes of template_graph, minus edges forbidden by structural constraints. Every constraint-based skeleton phase must start from this graph so forbidden pairs are never tested by CI.
MixedGraph	initGraph_ (const MixedGraph &template_graph)
	Builds a complete MixedGraph on the nodes of template_graph, minus edges forbidden by structural constraints. Every constraint-based skeleton phase must start from this graph so forbidden pairs are never tested by CI.
gum::MeekRules	meekRules_
	Builds a complete MixedGraph on the nodes of template_graph, minus edges forbidden by structural constraints. Every constraint-based skeleton phase must start from this graph so forbidden pairs are never tested by CI.
static bool	_existsDirectedPath_ (const MixedGraph &graph, NodeId n1, NodeId n2)
	Builds a complete MixedGraph on the nodes of template_graph, minus edges forbidden by structural constraints. Every constraint-based skeleton phase must start from this graph so forbidden pairs are never tested by CI.
static bool	_existsNonTrivialDirectedPath_ (const MixedGraph &graph, NodeId n1, NodeId n2)
	Builds a complete MixedGraph on the nodes of template_graph, minus edges forbidden by structural constraints. Every constraint-based skeleton phase must start from this graph so forbidden pairs are never tested by CI.

Detailed Description

The MIIC learning algorithm.

Implements learnSkeleton() and learnMixedStructure() from the ConstraintBasedLearning interface using the MIIC algorithm: https://doi.org/10.1371/journal.pcbi.1005662

Definition at line 100 of file Miic.h.

Member Enumeration Documentation

ApproximationSchemeSTATE

enum class gum::IApproximationSchemeConfiguration::ApproximationSchemeSTATE : char

stronginherited

The different state of an approximation scheme.

Enumerator
Undefined
Continue
Epsilon
Rate
Limit
TimeLimit
Stopped

Definition at line 87 of file IApproximationSchemeConfiguration.h.

                                        : char {
      Undefined,
      Continue,
      Epsilon,
      Rate,
      Limit,
      TimeLimit,
      Stopped
    };

Constructor & Destructor Documentation

Miic() [1/4]

gum::learning::Miic::Miic

(

)

Definition at line 65 of file Miic.cpp.

: ConstraintBasedLearning() { GUM_CONSTRUCTOR(Miic); }

References gum::learning::ConstraintBasedLearning::ConstraintBasedLearning(), and Miic().

Referenced by Miic(), Miic(), Miic(), Miic(), ~Miic(), operator=(), and operator=().

Here is the call graph for this function:

Here is the caller graph for this function:

Miic() [2/4]

gum::learning::Miic::Miic ( int maxLog )

explicit

Definition at line 67 of file Miic.cpp.

: ConstraintBasedLearning(maxLog) { GUM_CONSTRUCTOR(Miic); }

References gum::learning::ConstraintBasedLearning::ConstraintBasedLearning(), and Miic().

Here is the call graph for this function:

Miic() [3/4]

gum::learning::Miic::Miic

(

const Miic &

from

)

Definition at line 69 of file Miic.cpp.

: ConstraintBasedLearning(from) { GUM_CONS_CPY(Miic); }

References gum::learning::ConstraintBasedLearning::ConstraintBasedLearning(), and Miic().

Here is the call graph for this function:

Miic() [4/4]

gum::learning::Miic::Miic

(

Miic &&

from

)

Definition at line 71 of file Miic.cpp.

: ConstraintBasedLearning(std::move(from)) { GUM_CONS_MOV(Miic); }

References gum::learning::ConstraintBasedLearning::ConstraintBasedLearning(), and Miic().

Here is the call graph for this function:

~Miic()

gum::learning::Miic::~Miic ( )

override

Definition at line 73 of file Miic.cpp.

{ GUM_DESTRUCTOR(Miic); }

References Miic().

Here is the call graph for this function:

Member Function Documentation

_existsDirectedPath_()

bool gum::learning::ConstraintBasedLearning::_existsDirectedPath_ ( const MixedGraph & graph, NodeId n1, NodeId n2 )

staticprotectedinherited

Builds a complete MixedGraph on the nodes of template_graph, minus edges forbidden by structural constraints. Every constraint-based skeleton phase must start from this graph so forbidden pairs are never tested by CI.

Definition at line 180 of file ConstraintBasedLearning.cpp.

                                                                             {
      List< NodeId > nodeFIFO;
      Set< NodeId >  mark;
 
      mark.insert(n2);
      nodeFIFO.pushBack(n2);
 
      NodeId current;
      while (!nodeFIFO.empty()) {
        current = nodeFIFO.front();
        nodeFIFO.popFront();
        for (const auto new_one: graph.parents(current)) {
          if (graph.existsArc(current, new_one)) continue;   // double arc — skip
          if (new_one == n1) return true;
          if (mark.exists(new_one)) continue;
          mark.insert(new_one);
          nodeFIFO.pushBack(new_one);
        }
      }
      return false;
    }

References gum::List< Val >::empty(), gum::Set< Key >::exists(), gum::List< Val >::front(), gum::Set< Key >::insert(), gum::List< Val >::popFront(), and gum::List< Val >::pushBack().

Referenced by _existsNonTrivialDirectedPath_(), gum::learning::Miic::_propagatingOrientationMiic_(), gum::learning::Miic::orientationMiic_(), gum::learning::CIBasedLearning::orientColliderArm_(), and orientDoubleHeadedArcs_().

Here is the call graph for this function:

Here is the caller graph for this function:

_existsNonTrivialDirectedPath_()

bool gum::learning::ConstraintBasedLearning::_existsNonTrivialDirectedPath_ ( const MixedGraph & graph, NodeId n1, NodeId n2 )

staticprotectedinherited

Builds a complete MixedGraph on the nodes of template_graph, minus edges forbidden by structural constraints. Every constraint-based skeleton phase must start from this graph so forbidden pairs are never tested by CI.

Definition at line 169 of file ConstraintBasedLearning.cpp.

                                                                                       {
      for (const auto parent: graph.parents(n2)) {
        if (graph.existsArc(n2, parent)) continue;   // double arc — skip
        if (parent == n1) continue;                  // trivial path — not counted
        if (_existsDirectedPath_(graph, n1, parent)) return true;
      }
      return false;
    }

References _existsDirectedPath_().

Referenced by gum::learning::Miic::_orientingVstructureMiic_().

Here is the call graph for this function:

Here is the caller graph for this function:

_isNotLatentCouple_()

bool gum::learning::Miic::_isNotLatentCouple_ ( NodeId x, NodeId y )

private

Definition at line 674 of file Miic.cpp.

                                                                 {
      const auto& lbeg = _latentCouples_.begin();
      const auto& lend = _latentCouples_.end();
      return (std::find(lbeg, lend, Arc(x, y)) == lend)
          && (std::find(lbeg, lend, Arc(y, x)) == lend);
    }

References gum::learning::ConstraintBasedLearning::_latentCouples_.

Referenced by _orientingVstructureMiic_().

Here is the caller graph for this function:

_orientingVstructureMiic_()

void gum::learning::Miic::_orientingVstructureMiic_ ( MixedGraph & graph, HashTable< std::pair< NodeId, NodeId >, char > & marks, NodeId x, NodeId y, NodeId z, double p1, double p2 )

private

Definition at line 331 of file Miic.cpp.

                                                                                             {
      if (marks[{x, z}] == 'o' && marks[{y, z}] == 'o') {
        if (!_existsNonTrivialDirectedPath_(graph, z, x)) {
          if (isArcValid_(graph, x, z)) {
            graph.eraseEdge(Edge(x, z));
            graph.addArc(x, z);
            GUM_SL_EMIT(x, z, "Add Arc " << x << " -> " << z, "V-structure Orientation")
            marks[{x, z}] = '>';
            if (graph.existsArc(z, x) && _isNotLatentCouple_(z, x)) {
              _latentCouples_.emplace_back(z, x);
            }
            if (!_arcProbas_.exists(Arc(x, z))) _arcProbas_.insert(Arc(x, z), p1);
          }
        } else {
          graph.eraseEdge(Edge(x, z));
          if (!_existsNonTrivialDirectedPath_(graph, x, z)) {
            if (isArcValid_(graph, z, x)) {
              graph.addArc(z, x);
              GUM_SL_EMIT(z, x, "Add Arc " << z << " -> " << x, "V-structure Orientation")
              marks[{z, x}] = '>';
            }
          }
        }
 
        if (!_existsNonTrivialDirectedPath_(graph, z, y)) {
          if (isArcValid_(graph, y, z)) {
            graph.eraseEdge(Edge(y, z));
            graph.addArc(y, z);
            GUM_SL_EMIT(y, z, "Add Arc " << y << " -> " << z, "V-structure Orientation")
            marks[{y, z}] = '>';
            if (graph.existsArc(z, y) && _isNotLatentCouple_(z, y)) {
              _latentCouples_.emplace_back(z, y);
            }
            if (!_arcProbas_.exists(Arc(y, z))) _arcProbas_.insert(Arc(y, z), p2);
          }
        } else {
          graph.eraseEdge(Edge(y, z));
          if (!_existsNonTrivialDirectedPath_(graph, y, z)) {
            if (isArcValid_(graph, z, y)) {
              graph.addArc(z, y);
              GUM_SL_EMIT(z, y, "Add Arc " << z << " -> " << y, "V-structure Orientation")
              marks[{z, y}] = '>';
            }
          }
        }
      } else if (marks[{x, z}] == '>' && marks[{y, z}] == 'o') {
        if (!_existsNonTrivialDirectedPath_(graph, z, y)) {
          if (isArcValid_(graph, y, z)) {
            graph.eraseEdge(Edge(y, z));
            graph.addArc(y, z);
            GUM_SL_EMIT(y,
                        z,
                        "Add Arc " << y << " -> " << z,
                        "V-structure Orientation | existing "
                            << x << " -> " << z << ", then orienting " << y << " -> " << z)
            marks[{y, z}] = '>';
            if (graph.existsArc(z, y) && _isNotLatentCouple_(z, y)) {
              _latentCouples_.emplace_back(z, y);
            }
            if (!_arcProbas_.exists(Arc(y, z))) _arcProbas_.insert(Arc(y, z), p2);
          }
        } else {
          graph.eraseEdge(Edge(y, z));
          if (!_existsNonTrivialDirectedPath_(graph, y, z)) {
            if (isArcValid_(graph, z, y)) {
              graph.addArc(z, y);
              GUM_SL_EMIT(z,
                          y,
                          "Add Arc " << z << " -> " << y,
                          "V-structure Orientation | existing "
                              << x << " -> " << z << ", then orienting " << z << " -> " << y)
              marks[{z, y}] = '>';
            }
          }
        }
      } else if (marks[{y, z}] == '>' && marks[{x, z}] == 'o') {
        if (!_existsNonTrivialDirectedPath_(graph, z, x)) {
          if (isArcValid_(graph, x, z)) {
            graph.eraseEdge(Edge(x, z));
            graph.addArc(x, z);
            GUM_SL_EMIT(x, z, "Add Arc " << x << " -> " << z, "V-structure Orientation")
            marks[{x, z}] = '>';
            if (graph.existsArc(z, x) && _isNotLatentCouple_(z, x)) {
              _latentCouples_.emplace_back(z, x);
            }
            if (!_arcProbas_.exists(Arc(x, z))) _arcProbas_.insert(Arc(x, z), p1);
          }
        } else {
          graph.eraseEdge(Edge(x, z));
          if (!_existsNonTrivialDirectedPath_(graph, x, z)) {
            if (isArcValid_(graph, z, x)) {
              graph.addArc(z, x);
              GUM_SL_EMIT(z, x, "Add Arc " << z << " -> " << x, "V-structure Orientation")
              marks[{z, x}] = '>';
            }
          }
        }
      }
    }

References _arcProbas_, gum::learning::ConstraintBasedLearning::_existsNonTrivialDirectedPath_(), _isNotLatentCouple_(), gum::learning::ConstraintBasedLearning::_latentCouples_, GUM_SL_EMIT, and gum::learning::ConstraintBasedLearning::isArcValid_().

Referenced by orientationMiic_().

Here is the call graph for this function:

Here is the caller graph for this function:

_propagatingOrientationMiic_()

void gum::learning::Miic::_propagatingOrientationMiic_ ( MixedGraph & graph, HashTable< std::pair< NodeId, NodeId >, char > & marks, NodeId x, NodeId y, NodeId z, double p1, double p2 )

private

Definition at line 441 of file Miic.cpp.

                                                                                                {
      if (marks[{x, z}] == '>' && marks[{y, z}] == 'o' && marks[{z, y}] != '-') {
        graph.eraseEdge(Edge(z, y));
        if (!_existsDirectedPath_(graph, y, z) && graph.parents(y).empty()) {
          if (isArcValid_(graph, z, y)) {
            graph.addArc(z, y);
            GUM_SL_EMIT(z,
                        y,
                        "Add Arc " << z << " -> " << y,
                        "Propagation MIIC (919) | existing x -> " << z << " and " << z << " - "
                                                                  << y)
            marks[{z, y}] = '>';
            marks[{y, z}] = '-';
            if (!_arcProbas_.exists(Arc(z, y))) _arcProbas_.insert(Arc(z, y), p2);
          }
        } else if (!_existsDirectedPath_(graph, z, y) && graph.parents(z).empty()) {
          if (isArcValid_(graph, y, z)) {
            graph.addArc(y, z);
            GUM_SL_EMIT(y, z, "Add Arc " << y << " -> " << z, "Propagation MIIC line 932 ")
            marks[{z, y}] = '-';
            marks[{y, z}] = '>';
            _latentCouples_.emplace_back(y, z);
            if (!_arcProbas_.exists(Arc(y, z))) _arcProbas_.insert(Arc(y, z), p2);
          }
        } else if (!_existsDirectedPath_(graph, y, z)) {
          if (isArcValid_(graph, z, y)) {
            graph.addArc(z, y);
            GUM_SL_EMIT(z, y, "Add Arc " << z << "->" << y, "Propagation MIIC 947 ")
            marks[{z, y}] = '>';
            marks[{y, z}] = '-';
            if (!_arcProbas_.exists(Arc(z, y))) _arcProbas_.insert(Arc(z, y), p2);
          }
        } else if (!_existsDirectedPath_(graph, z, y)) {
          if (isArcValid_(graph, y, z)) {
            graph.addArc(y, z);
            GUM_SL_EMIT(z, y, "Add Arc " << z << "->" << y, "Propagation MIIC 959")
            _latentCouples_.emplace_back(y, z);
            marks[{z, y}] = '-';
            marks[{y, z}] = '>';
            if (!_arcProbas_.exists(Arc(y, z))) _arcProbas_.insert(Arc(y, z), p2);
          }
        }
      } else if (marks[{y, z}] == '>' && marks[{x, z}] == 'o' && marks[{z, x}] != '-') {
        graph.eraseEdge(Edge(z, x));
        if (!_existsDirectedPath_(graph, x, z) && graph.parents(x).empty()) {
          if (isArcValid_(graph, z, x)) {
            graph.addArc(z, x);
            GUM_SL_EMIT(z, x, "Add Arc " << z << " -> " << x, "Propagation MIIC 977")
            marks[{z, x}] = '>';
            marks[{x, z}] = '-';
            if (!_arcProbas_.exists(Arc(z, x))) _arcProbas_.insert(Arc(z, x), p1);
          }
        } else if (!_existsDirectedPath_(graph, z, x) && graph.parents(z).empty()) {
          if (isArcValid_(graph, x, z)) {
            graph.addArc(x, z);
            GUM_SL_EMIT(x, z, "Add Arc " << x << "->" << z, "Propagation MIIC 990")
            marks[{z, x}] = '-';
            marks[{x, z}] = '>';
            _latentCouples_.emplace_back(x, z);
            if (!_arcProbas_.exists(Arc(x, z))) _arcProbas_.insert(Arc(x, z), p1);
          }
        } else if (!_existsDirectedPath_(graph, x, z)) {
          if (isArcValid_(graph, z, x)) {
            graph.addArc(z, x);
            GUM_SL_EMIT(z, x, "Add Arc " << z << " -> " << x, "Propagation MIIC 1004")
            marks[{z, x}] = '>';
            marks[{x, z}] = '-';
            if (!_arcProbas_.exists(Arc(z, x))) _arcProbas_.insert(Arc(z, x), p1);
          }
        } else if (!_existsDirectedPath_(graph, z, x)) {
          if (isArcValid_(graph, x, z)) {
            graph.addArc(x, z);
            GUM_SL_EMIT(x, z, "Add Arc " << x << " -> " << z, "Propagation MIIC 1016")
            marks[{z, x}] = '-';
            marks[{x, z}] = '>';
            _latentCouples_.emplace_back(x, z);
            if (!_arcProbas_.exists(Arc(x, z))) _arcProbas_.insert(Arc(x, z), p1);
          }
        }
      }
    }

References _arcProbas_, gum::learning::ConstraintBasedLearning::_existsDirectedPath_(), gum::learning::ConstraintBasedLearning::_latentCouples_, GUM_SL_EMIT, and gum::learning::ConstraintBasedLearning::isArcValid_().

Referenced by orientationMiic_().

Here is the call graph for this function:

Here is the caller graph for this function:

addConstraints()

void gum::learning::ConstraintBasedLearning::addConstraints ( const HashTable< std::pair< NodeId, NodeId >, char > & constraints )

inherited

Definition at line 124 of file ConstraintBasedLearning.cpp.

                                                                         {
      _initialMarks_ = constraints;
    }

References _initialMarks_.

applyStructuralConstraints_()

void gum::learning::ConstraintBasedLearning::applyStructuralConstraints_ ( MixedGraph & graph )

protectedinherited

Builds a complete MixedGraph on the nodes of template_graph, minus edges forbidden by structural constraints. Every constraint-based skeleton phase must start from this graph so forbidden pairs are never tested by CI.

Definition at line 250 of file ConstraintBasedLearning.cpp.

                                                                               {
      for (const auto& arc: _mandatoryGraph_.arcs()) {
        if (graph.existsEdge(arc.head(), arc.tail())) graph.eraseEdge(Edge(arc.head(), arc.tail()));
        if (!graph.existsArc(arc.tail(), arc.head())) {
          GUM_SL_EMIT(arc.tail(),
                      arc.head(),
                      "Add Arc" << arc.tail() << "->" << arc.head(),
                      "Mandatory")
          graph.addArc(arc.tail(), arc.head());
        }
      }
      for (const Arc& arc: _forbiddenGraph_.arcs()) {
        if (graph.existsEdge(Edge(arc.tail(), arc.head()))) {
          graph.eraseEdge(Edge(arc.tail(), arc.head()));
          graph.addArc(arc.head(), arc.tail());
          GUM_SL_EMIT(arc.head(),
                      arc.tail(),
                      "Add Arc" << arc.head() << "->" << arc.tail(),
                      "Forbidden in the other orientation")
        }
      }
    }

References _forbiddenGraph_, _mandatoryGraph_, and GUM_SL_EMIT.

Referenced by gum::learning::PC::learnMixedStructure(), gum::learning::FCI::learnPAG(), and gum::learning::Miic::orientationMiic_().

Here is the caller graph for this function:

continueApproximationScheme()

bool gum::ApproximationScheme::continueApproximationScheme ( double error )

inherited

Update the scheme w.r.t the new error.

Test the stopping criterion that are enabled.

Parameters

error

The new error value.

Returns

false if state become != ApproximationSchemeSTATE::Continue

Exceptions

OperationNotAllowed

Raised if state != ApproximationSchemeSTATE::Continue.

Definition at line 69 of file approximationScheme.cpp.

                                                                    {
    // For coherence, we fix the time used in the method
 
    double timer_step = timer_.step();
 
    if (enabled_max_time_) {
      if (timer_step > max_time_) {
        stopScheme_(ApproximationSchemeSTATE::TimeLimit);
        return false;
      }
    }
 
    if (!startOfPeriod()) { return true; }
 
    if (current_state_ != ApproximationSchemeSTATE::Continue) {
      GUM_ERROR(
          OperationNotAllowed,
          "state of the approximation scheme is not correct : " << messageApproximationScheme());
    }
 
    if (verbosity()) { history_.push_back(error); }
 
    if (enabled_max_iter_) {
      if (current_step_ >= max_iter_) {
        stopScheme_(ApproximationSchemeSTATE::Limit);
        return false;
      }
    }
 
    last_epsilon_    = current_epsilon_;
    current_epsilon_ = error;   // eps rate isEnabled needs it so affectation was
    // moved from eps isEnabled below
 
    if (enabled_eps_) {
      if (current_epsilon_ <= eps_) {
        stopScheme_(ApproximationSchemeSTATE::Epsilon);
        return false;
      }
    }
 
    if (last_epsilon_ >= 0.) {
      if (current_epsilon_ > .0) {
        // ! current_epsilon_ can be 0. AND epsilon
        // isEnabled can be disabled !
        current_rate_ = std::fabs((current_epsilon_ - last_epsilon_) / current_epsilon_);
      }
      // limit with current eps ---> 0 is | 1 - ( last_eps / 0 ) | --->
      // infinity the else means a return false if we isEnabled the rate below,
      // as we would have returned false if epsilon isEnabled was enabled
      else {
        current_rate_ = min_rate_eps_;
      }
 
      if (enabled_min_rate_eps_) {
        if (current_rate_ <= min_rate_eps_) {
          stopScheme_(ApproximationSchemeSTATE::Rate);
          return false;
        }
      }
    }
 
    if (stateApproximationScheme() == ApproximationSchemeSTATE::Continue) {
      if (onProgress.hasListener()) {
        GUM_EMIT3(onProgress, current_step_, current_epsilon_, timer_step);
      }
 
      return true;
    } else {
      return false;
    }
  }

References gum::IApproximationSchemeConfiguration::Continue, current_epsilon_, current_rate_, current_state_, current_step_, enabled_eps_, enabled_max_iter_, enabled_max_time_, enabled_min_rate_eps_, eps_, gum::IApproximationSchemeConfiguration::Epsilon, GUM_EMIT3, GUM_ERROR, history_, last_epsilon_, gum::IApproximationSchemeConfiguration::Limit, max_iter_, max_time_, gum::IApproximationSchemeConfiguration::messageApproximationScheme(), min_rate_eps_, gum::IApproximationSchemeConfiguration::onProgress, gum::IApproximationSchemeConfiguration::Rate, startOfPeriod(), stateApproximationScheme(), stopScheme_(), gum::IApproximationSchemeConfiguration::TimeLimit, timer_, and verbosity().

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::GreedyThickThinning::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), gum::SamplingInference< GUM_SCALAR >::loopApproxInference_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByOrderedArcs_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByRandomOrder_(), and gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceNodeToNeighbours_().

Here is the call graph for this function:

Here is the caller graph for this function:

currentTime()

INLINE double gum::ApproximationScheme::currentTime ( ) const

overridevirtualinherited

Returns the current running time in second.

Returns

Returns the current running time in second.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 137 of file approximationScheme_inl.h.

{ return timer_.step(); }

References timer_.

disableEpsilon()

INLINE void gum::ApproximationScheme::disableEpsilon ( )

overridevirtualinherited

Disable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 75 of file approximationScheme_inl.h.

{ enabled_eps_ = false; }

References enabled_eps_.

Referenced by gum::learning::EMApproximationScheme::EMApproximationScheme(), and gum::learning::EMApproximationScheme::setMinEpsilonRate().

Here is the caller graph for this function:

disableMaxIter()

INLINE void gum::ApproximationScheme::disableMaxIter ( )

overridevirtualinherited

Disable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 116 of file approximationScheme_inl.h.

{ enabled_max_iter_ = false; }

References enabled_max_iter_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing(), and gum::learning::GreedyThickThinning::GreedyThickThinning().

Here is the caller graph for this function:

disableMaxTime()

INLINE void gum::ApproximationScheme::disableMaxTime ( )

overridevirtualinherited

Disable stopping criterion on timeout.

Returns

Disable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 140 of file approximationScheme_inl.h.

{ enabled_max_time_ = false; }

References enabled_max_time_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing(), and gum::learning::GreedyThickThinning::GreedyThickThinning().

Here is the caller graph for this function:

disableMinEpsilonRate()

INLINE void gum::ApproximationScheme::disableMinEpsilonRate ( )

overridevirtualinherited

Disable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 96 of file approximationScheme_inl.h.

{ enabled_min_rate_eps_ = false; }

References enabled_min_rate_eps_.

Referenced by gum::learning::GreedyHillClimbing::GreedyHillClimbing(), gum::learning::GreedyThickThinning::GreedyThickThinning(), gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), and gum::learning::EMApproximationScheme::setEpsilon().

Here is the caller graph for this function:

enableEpsilon()

INLINE void gum::ApproximationScheme::enableEpsilon ( )

overridevirtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 78 of file approximationScheme_inl.h.

{ enabled_eps_ = true; }

References enabled_eps_.

enableMaxIter()

INLINE void gum::ApproximationScheme::enableMaxIter ( )

overridevirtualinherited

Enable stopping criterion on max iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 119 of file approximationScheme_inl.h.

{ enabled_max_iter_ = true; }

References enabled_max_iter_.

enableMaxTime()

INLINE void gum::ApproximationScheme::enableMaxTime ( )

overridevirtualinherited

Enable stopping criterion on timeout.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 143 of file approximationScheme_inl.h.

{ enabled_max_time_ = true; }

References enabled_max_time_.

enableMinEpsilonRate()

INLINE void gum::ApproximationScheme::enableMinEpsilonRate ( )

overridevirtualinherited

Enable stopping criterion on epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 99 of file approximationScheme_inl.h.

{ enabled_min_rate_eps_ = true; }

References enabled_min_rate_eps_.

Referenced by gum::learning::EMApproximationScheme::EMApproximationScheme(), and gum::GibbsBNdistance< GUM_SCALAR >::computeKL_().

Here is the caller graph for this function:

epsilon()

INLINE double gum::ApproximationScheme::epsilon ( ) const

overridevirtualinherited

Returns the value of epsilon.

Returns

Returns the value of epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 72 of file approximationScheme_inl.h.

{ return eps_; }

References eps_.

Referenced by gum::ImportanceSampling< GUM_SCALAR >::onContextualize_(), and gum::ImportanceSampling< GUM_SCALAR >::unsharpenBN_().

Here is the caller graph for this function:

findBestContributor_()

void gum::learning::Miic::findBestContributor_ ( NodeId x, NodeId y, const std::vector< NodeId > & ui, const MixedGraph & graph, CorrectedMutualInformation & mutualInformation, Heap< CondRanking, GreaterPairOn2nd > & rank )

protected

Definition at line 533 of file Miic.cpp.

                                                                                 {
      double maxP = -1.0;
      NodeId maxZ = 0;
 
      const double Ixy_ui = mutualInformation.score(x, y, ui);
 
      for (const NodeId z: graph) {
        if (z != x && z != y && std::find(ui.begin(), ui.end(), z) == ui.end()) {
          double Pnv;
          double Pb;
 
          const double Ixyz_ui    = mutualInformation.score(x, y, z, ui);
          double       calc_expo1 = -Ixyz_ui * M_LN2;
          if (calc_expo1 > _maxLog_) {
            Pnv = 0.0;
          } else if (calc_expo1 < -_maxLog_) {
            Pnv = 1.0;
          } else {
            Pnv = 1 / (1 + std::exp(calc_expo1));
          }
 
          const double Ixz_ui = mutualInformation.score(x, z, ui);
          const double Iyz_ui = mutualInformation.score(y, z, ui);
 
          calc_expo1        = -(Ixz_ui - Ixy_ui) * M_LN2;
          double calc_expo2 = -(Iyz_ui - Ixy_ui) * M_LN2;
 
          if (calc_expo1 > _maxLog_ || calc_expo2 > _maxLog_) {
            Pb = 0.0;
          } else if (calc_expo1 < -_maxLog_ && calc_expo2 < -_maxLog_) {
            Pb = 1.0;
          } else {
            double expo1, expo2;
            expo1 = (calc_expo1 < -_maxLog_) ? 0.0 : std::exp(calc_expo1);
            expo2 = (calc_expo2 < -_maxLog_) ? 0.0 : std::exp(calc_expo2);
            Pb    = 1 / (1 + expo1 + expo2);
          }
 
          const double min_pnv_pb = std::min(Pnv, Pb);
          if (min_pnv_pb > maxP) {
            maxP = min_pnv_pb;
            maxZ = z;
          }
        }
      }
      CondRanking final;
      auto        tup = new CondThreePoints{x, y, maxZ, ui};
      final.first     = tup;
      final.second    = maxP;
      rank.insert(final);
    }

References gum::learning::ConstraintBasedLearning::_maxLog_, gum::Heap< Val, Cmp >::insert(), M_LN2, and gum::learning::CorrectedMutualInformation::score().

Referenced by initiation_(), and iteration_().

Here is the call graph for this function:

Here is the caller graph for this function:

history()

INLINE const std::vector< double > & gum::ApproximationScheme::history ( ) const

overridevirtualinherited

Returns the scheme history.

Returns

Returns the scheme history.

Exceptions

OperationNotAllowed

Raised if the scheme did not performed or if verbosity is set to false.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 179 of file approximationScheme_inl.h.

                                                                       {
    if (stateApproximationScheme() == ApproximationSchemeSTATE::Undefined) {
      GUM_ERROR(OperationNotAllowed, "state of the approximation scheme is udefined")
    }
 
    if (!verbosity()) GUM_ERROR(OperationNotAllowed, "No history when verbosity=false")
 
    return history_;
  }

References GUM_ERROR, stateApproximationScheme(), and gum::IApproximationSchemeConfiguration::Undefined.

Here is the call graph for this function:

initApproximationScheme()

INLINE void gum::ApproximationScheme::initApproximationScheme ( )

inherited

Initialise the scheme.

Definition at line 190 of file approximationScheme_inl.h.

                                                           {
    current_state_   = ApproximationSchemeSTATE::Continue;
    current_step_    = 0;
    current_epsilon_ = current_rate_ = -1.0;
    history_.clear();
    timer_.reset();
  }

References ApproximationScheme(), gum::IApproximationSchemeConfiguration::Continue, current_state_, current_step_, and initApproximationScheme().

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), initApproximationScheme(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::GreedyThickThinning::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), gum::SamplingInference< GUM_SCALAR >::loopApproxInference_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInference(), and gum::SamplingInference< GUM_SCALAR >::onStateChanged_().

Here is the call graph for this function:

Here is the caller graph for this function:

initGraph_()

MixedGraph gum::learning::ConstraintBasedLearning::initGraph_ ( const MixedGraph & template_graph )

protectedinherited

Builds a complete MixedGraph on the nodes of template_graph, minus edges forbidden by structural constraints. Every constraint-based skeleton phase must start from this graph so forbidden pairs are never tested by CI.

Definition at line 273 of file ConstraintBasedLearning.cpp.

                                                                                   {
      MixedGraph graph;
      for (NodeId n: template_graph.nodes())
        graph.addNodeWithId(n);
 
      auto addEdgeIfAllowed = [&](NodeId x, NodeId y) {
        if (x > y) std::swap(x, y);
        if (isForbiddenEdge_(x, y))
          GUM_SL_EMIT(x, y, "Remove " << x << " - " << y, "Constraints : Forbidden edge")
        else graph.addEdge(x, y);
      };
 
      if (template_graph.edges().empty()) {
        // node-only template: constraint-based learning always starts from complete graph
        const std::vector< NodeId > nodes(template_graph.nodes().begin(),
                                          template_graph.nodes().end());
        for (std::size_t i = 0; i < nodes.size(); ++i)
          for (std::size_t j = i + 1; j < nodes.size(); ++j)
            addEdgeIfAllowed(nodes[i], nodes[j]);
      } else {
        for (const auto& edge: template_graph.edges())
          addEdgeIfAllowed(edge.first(), edge.second());
      }
 
      return graph;
    }

References gum::NodeGraphPart::begin(), gum::EdgeGraphPart::edges(), gum::Set< Key >::empty(), gum::NodeGraphPart::end(), GUM_SL_EMIT, isForbiddenEdge_(), and gum::NodeGraphPart::nodes().

Referenced by gum::learning::Miic::learnMixedStructure(), gum::learning::CIBasedLearning::learnSkeleton(), and gum::learning::Miic::learnSkeleton().

Here is the call graph for this function:

Here is the caller graph for this function:

initiation_()

void gum::learning::Miic::initiation_ ( CorrectedMutualInformation & mutualInformation, MixedGraph & graph, HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > & sepSet, Heap< CondRanking, GreaterPairOn2nd > & rank )

protected

Definition at line 166 of file Miic.cpp.

                                                                                                  {
      NodeId  x, y;
      EdgeSet edges      = graph.edges();
      Size    steps_init = edges.size();
 
      for (const Edge& edge: edges) {
        x = edge.first();
        y = edge.second();
 
        double Ixy = mutualInformation.score(x, y);
 
        if (Ixy <= 0) {
          graph.eraseEdge(edge);
          GUM_SL_EMIT(x,
                      y,
                      "Remove " << x << " - " << y,
                      "Independent based on Mutual Information :" << Ixy)
          sepSet.insert(std::make_pair(x, y), _emptySet_);
        } else {
          findBestContributor_(x, y, _emptySet_, graph, mutualInformation, rank);
          GUM_SL_EMIT(x,
                      y,
                      "Keep " << x << " - " << y,
                      "Dependent based on Mutual Information :" << Ixy)
        }
 
        ++current_step_;
        if (onProgress.hasListener()) {
          GUM_EMIT3(onProgress, (current_step_ * 33) / steps_init, 0., timer_.step());
        }
      }
    }

References gum::learning::ConstraintBasedLearning::_emptySet_, gum::ApproximationScheme::current_step_, findBestContributor_(), GUM_EMIT3, GUM_SL_EMIT, gum::IApproximationSchemeConfiguration::onProgress, gum::learning::CorrectedMutualInformation::score(), gum::Set< Key >::size(), and gum::ApproximationScheme::timer_.

Referenced by learnMixedStructure(), and learnSkeleton().

Here is the call graph for this function:

Here is the caller graph for this function:

isArcValid_()

bool gum::learning::ConstraintBasedLearning::isArcValid_ ( const MixedGraph & graph, NodeId x, NodeId y )

protectedinherited

Definition at line 161 of file ConstraintBasedLearning.cpp.

                                                                                         {
      return !isForbiddenArc_(x, y) && !isMaxIndegree_(graph, y);
    }

References isForbiddenArc_(), and isMaxIndegree_().

Referenced by gum::learning::Miic::_orientingVstructureMiic_(), gum::learning::Miic::_propagatingOrientationMiic_(), and gum::learning::CIBasedLearning::orientColliderArm_().

Here is the call graph for this function:

Here is the caller graph for this function:

isEnabledEpsilon()

INLINE bool gum::ApproximationScheme::isEnabledEpsilon ( ) const

overridevirtualinherited

Returns true if stopping criterion on epsilon is enabled, false otherwise.

Returns

Returns true if stopping criterion on epsilon is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 82 of file approximationScheme_inl.h.

{ return enabled_eps_; }

References enabled_eps_.

isEnabledMaxIter()

INLINE bool gum::ApproximationScheme::isEnabledMaxIter ( ) const

overridevirtualinherited

Returns true if stopping criterion on max iterations is enabled, false otherwise.

Returns

Returns true if stopping criterion on max iterations is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 123 of file approximationScheme_inl.h.

{ return enabled_max_iter_; }

References enabled_max_iter_.

isEnabledMaxTime()

INLINE bool gum::ApproximationScheme::isEnabledMaxTime ( ) const

overridevirtualinherited

Returns true if stopping criterion on timeout is enabled, false otherwise.

Returns

Returns true if stopping criterion on timeout is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 147 of file approximationScheme_inl.h.

{ return enabled_max_time_; }

References enabled_max_time_.

isEnabledMinEpsilonRate()

INLINE bool gum::ApproximationScheme::isEnabledMinEpsilonRate ( ) const

overridevirtualinherited

Returns true if stopping criterion on epsilon rate is enabled, false otherwise.

Returns

Returns true if stopping criterion on epsilon rate is enabled, false otherwise.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 103 of file approximationScheme_inl.h.

{ return enabled_min_rate_eps_; }

References enabled_min_rate_eps_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_().

Here is the caller graph for this function:

isForbiddenArc_()

bool gum::learning::ConstraintBasedLearning::isForbiddenArc_ ( NodeId x, NodeId y ) const

protectedinherited

Definition at line 149 of file ConstraintBasedLearning.cpp.

                                                                          {
      return _forbiddenGraph_.existsArc(x, y);
    }

References _forbiddenGraph_.

Referenced by gum::learning::FCI::doDdpOrientation_(), isArcValid_(), gum::learning::FCI::orientCollidersOnPAG_(), gum::learning::FCI::ruleR1_(), and gum::learning::FCI::ruleR2_().

Here is the caller graph for this function:

isForbiddenEdge_()

bool gum::learning::ConstraintBasedLearning::isForbiddenEdge_ ( NodeId x, NodeId y ) const

protectedinherited

Definition at line 153 of file ConstraintBasedLearning.cpp.

                                                                           {
      return _forbiddenGraph_.existsArc(x, y) && _forbiddenGraph_.existsArc(y, x);
    }

References _forbiddenGraph_.

Referenced by initGraph_().

Here is the caller graph for this function:

isMaxIndegree_()

bool gum::learning::ConstraintBasedLearning::isMaxIndegree_ ( const MixedGraph & graph, NodeId x )

protectedinherited

Definition at line 157 of file ConstraintBasedLearning.cpp.

                                                                                  {
      return graph.parents(x).size() >= _maxIndegree_;
    }

References _maxIndegree_.

Referenced by isArcValid_().

Here is the caller graph for this function:

iteration_()

void gum::learning::Miic::iteration_ ( CorrectedMutualInformation & mutualInformation, MixedGraph & graph, HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > & sepSet, Heap< CondRanking, GreaterPairOn2nd > & rank )

protected

Definition at line 206 of file Miic.cpp.

                                                                                                 {
      CondRanking best;
      Size        steps_init = current_step_;
      Size        steps_iter = rank.size();
 
      try {
        while (rank.top().second > 0.5) {
          best = rank.pop();
 
          const NodeId          x  = std::get< 0 >(*(best.first));
          const NodeId          y  = std::get< 1 >(*(best.first));
          const NodeId          z  = std::get< 2 >(*(best.first));
          std::vector< NodeId > ui = std::move(std::get< 3 >(*(best.first)));
 
          ui.push_back(z);
          const double i_xy_ui = mutualInformation.score(x, y, ui);
          if (i_xy_ui < 0) {
            graph.eraseEdge(Edge(x, y));
            GUM_SL_EMIT(x,
                        y,
                        "Remove " << x << " - " << y,
                        "Independent based on MutualInformation knowing Sep "
                            << ui << "Mutual information:" << i_xy_ui)
            sepSet.insert(std::make_pair(x, y), std::move(ui));
          } else {
            findBestContributor_(x, y, ui, graph, mutualInformation, rank);
          }
 
          delete best.first;
 
          ++current_step_;
          if (onProgress.hasListener()) {
            GUM_EMIT3(onProgress,
                      (current_step_ * 66) / (steps_init + steps_iter),
                      0.,
                      timer_.step());
          }
        }
      } catch (...) {}   // heap is empty
      current_step_ = steps_init + steps_iter;
      if (onProgress.hasListener()) { GUM_EMIT3(onProgress, 66, 0., timer_.step()); }
      current_step_ = steps_init + steps_iter;
    }

References gum::ApproximationScheme::current_step_, findBestContributor_(), GUM_EMIT3, GUM_SL_EMIT, gum::IApproximationSchemeConfiguration::onProgress, gum::Heap< Val, Cmp >::pop(), gum::learning::CorrectedMutualInformation::score(), gum::Heap< Val, Cmp >::size(), gum::ApproximationScheme::timer_, and gum::Heap< Val, Cmp >::top().

Referenced by learnMixedStructure(), and learnSkeleton().

Here is the call graph for this function:

Here is the caller graph for this function:

latentVariables()

const std::vector< Arc > gum::learning::ConstraintBasedLearning::latentVariables ( ) const

inherited

Definition at line 129 of file ConstraintBasedLearning.cpp.

                                                                          {
      return _latentCouples_;
    }

References _latentCouples_.

learnBN()

template<GUM_Numeric GUM_SCALAR, typename PARAM_ESTIMATOR>

BayesNet< GUM_SCALAR > gum::learning::ConstraintBasedLearning::learnBN ( PARAM_ESTIMATOR & estimator, MixedGraph graph )

inherited

learns structure then estimates parameters

Definition at line 12 of file ConstraintBasedLearning_tpl.h.

                                                                                    {
      return DAG2BNLearner::createBN< GUM_SCALAR >(estimator, learnDAG(graph));
    }

References gum::learning::DAG2BNLearner::createBN(), and learnDAG().

Here is the call graph for this function:

learnDAG()

DAG gum::learning::ConstraintBasedLearning::learnDAG ( MixedGraph graph )

inherited

learns a DAG

Definition at line 141 of file ConstraintBasedLearning.cpp.

                                                          {
      return meekRules_.propagateToDAG(learnMixedStructure(graph));
    }

References learnMixedStructure(), and meekRules_.

Referenced by learnBN().

Here is the call graph for this function:

Here is the caller graph for this function:

learnMixedStructure()

MixedGraph gum::learning::Miic::learnMixedStructure ( MixedGraph graph )

overridevirtual

Implements gum::learning::ConstraintBasedLearning.

Definition at line 143 of file Miic.cpp.

                                                         {
      if (mi_ == nullptr)
        GUM_ERROR(NullElement, "call setMutualInformation before learnMixedStructure")
      timer_.reset();
      current_step_ = 0;
 
      _latentCouples_.clear();
      graph = initGraph_(graph);
 
      Heap< CondRanking, GreaterPairOn2nd >                           rank;
      HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > sep_set;
 
      initiation_(*mi_, graph, sep_set, rank);
      iteration_(*mi_, graph, sep_set, rank);
      orientationMiic_(*mi_, graph, sep_set);
 
      return meekRules_.propagate(graph);
    }

References gum::learning::ConstraintBasedLearning::_latentCouples_, gum::ApproximationScheme::current_step_, GUM_ERROR, gum::learning::ConstraintBasedLearning::initGraph_(), initiation_(), iteration_(), gum::learning::ConstraintBasedLearning::meekRules_, mi_, orientationMiic_(), and gum::ApproximationScheme::timer_.

Here is the call graph for this function:

learnPDAG()

PDAG gum::learning::ConstraintBasedLearning::learnPDAG ( MixedGraph graph )

inherited

learns the essential graph (CPDAG)

Definition at line 137 of file ConstraintBasedLearning.cpp.

                                                            {
      return meekRules_.propagateToCPDAG(learnMixedStructure(graph));
    }

References learnMixedStructure(), and meekRules_.

Here is the call graph for this function:

learnSkeleton()

MixedGraph gum::learning::Miic::learnSkeleton ( MixedGraph graph )

overridevirtual

Implements gum::learning::ConstraintBasedLearning.

Definition at line 126 of file Miic.cpp.

                                                   {
      if (mi_ == nullptr) GUM_ERROR(NullElement, "call setMutualInformation before learnSkeleton")
      timer_.reset();
      current_step_ = 0;
 
      _latentCouples_.clear();
      graph = initGraph_(graph);
 
      Heap< CondRanking, GreaterPairOn2nd >                           rank;
      HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > sep_set;
 
      initiation_(*mi_, graph, sep_set, rank);
      iteration_(*mi_, graph, sep_set, rank);
 
      return graph;
    }

References gum::learning::ConstraintBasedLearning::_latentCouples_, gum::ApproximationScheme::current_step_, GUM_ERROR, gum::learning::ConstraintBasedLearning::initGraph_(), initiation_(), iteration_(), mi_, and gum::ApproximationScheme::timer_.

Here is the call graph for this function:

maxIter()

INLINE Size gum::ApproximationScheme::maxIter ( ) const

overridevirtualinherited

Returns the criterion on number of iterations.

Returns

Returns the criterion on number of iterations.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 113 of file approximationScheme_inl.h.

{ return max_iter_; }

References max_iter_.

maxTime()

INLINE double gum::ApproximationScheme::maxTime ( ) const

overridevirtualinherited

Returns the timeout (in seconds).

Returns

Returns the timeout (in seconds).

Implements gum::IApproximationSchemeConfiguration.

Definition at line 134 of file approximationScheme_inl.h.

{ return max_time_; }

References max_time_.

messageApproximationScheme()

std::string gum::IApproximationSchemeConfiguration::messageApproximationScheme ( ) const

inherited

Returns the approximation scheme message.

Returns

Returns the approximation scheme message.

Definition at line 64 of file IApproximationSchemeConfiguration.cpp.

                                                                                {
    switch (stateApproximationScheme()) {
      case ApproximationSchemeSTATE::Continue : return "in progress";
 
      case ApproximationSchemeSTATE::Epsilon :
        return std::format("stopped with epsilon={}", epsilon());
 
      case ApproximationSchemeSTATE::Rate :
        return std::format("stopped with rate={}", minEpsilonRate());
 
      case ApproximationSchemeSTATE::Limit :
        return std::format("stopped with max iteration={}", maxIter());
 
      case ApproximationSchemeSTATE::TimeLimit :
        return std::format("stopped with timeout={}", maxTime());
 
      case ApproximationSchemeSTATE::Stopped : return "stopped on request";
 
      case ApproximationSchemeSTATE::Undefined : return "undefined state";
    }
    return {};
  }

References Continue, Epsilon, epsilon(), Limit, maxIter(), maxTime(), minEpsilonRate(), Rate, stateApproximationScheme(), Stopped, TimeLimit, and Undefined.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::credal::InferenceEngine< GUM_SCALAR >::getApproximationSchemeMsg(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::isEnabledMaxIter().

Here is the call graph for this function:

Here is the caller graph for this function:

minEpsilonRate()

INLINE double gum::ApproximationScheme::minEpsilonRate ( ) const

overridevirtualinherited

Returns the value of the minimal epsilon rate.

Returns

Returns the value of the minimal epsilon rate.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 93 of file approximationScheme_inl.h.

{ return min_rate_eps_; }

References min_rate_eps_.

nbrIterations()

INLINE Size gum::ApproximationScheme::nbrIterations ( ) const

overridevirtualinherited

Returns the number of iterations.

Returns

Returns the number of iterations.

Exceptions

OperationNotAllowed

Raised if the scheme did not perform.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 170 of file approximationScheme_inl.h.

                                                       {
    if (stateApproximationScheme() == ApproximationSchemeSTATE::Undefined) {
      GUM_ERROR(OperationNotAllowed, "state of the approximation scheme is undefined")
    }
 
    return current_step_;
  }

References current_step_, GUM_ERROR, stateApproximationScheme(), and gum::IApproximationSchemeConfiguration::Undefined.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_().

Here is the call graph for this function:

Here is the caller graph for this function:

operator=() [1/2]

Miic & gum::learning::Miic::operator=

(

const Miic &

from

)

Definition at line 75 of file Miic.cpp.

                                        {
      ConstraintBasedLearning::operator=(from);
      return *this;
    }

References Miic(), and gum::learning::ConstraintBasedLearning::operator=().

Here is the call graph for this function:

operator=() [2/2]

Miic & gum::learning::Miic::operator=

(

Miic &&

from

)

Definition at line 80 of file Miic.cpp.

                                   {
      ConstraintBasedLearning::operator=(std::move(from));
      return *this;
    }

References Miic(), and gum::learning::ConstraintBasedLearning::operator=().

Here is the call graph for this function:

orientationMiic_()

void gum::learning::Miic::orientationMiic_ ( CorrectedMutualInformation & mutualInformation, MixedGraph & graph, const HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > & sepSet )

protected

Definition at line 257 of file Miic.cpp.

                                                                                   {
      HashTable< std::pair< NodeId, NodeId >, char > marks = _initialMarks_;
 
      applyStructuralConstraints_(graph);
 
      for (const auto& arc: _mandatoryGraph_.arcs()) {
        marks.insert({arc.tail(), arc.head()}, '>');
        marks.insert({arc.head(), arc.tail()}, '-');
      }
      for (const Arc& arc: _forbiddenGraph_.arcs()) {
        if (graph.existsArc(arc.head(), arc.tail())) {
          marks.insert({arc.tail(), arc.head()}, '-');
          marks.insert({arc.head(), arc.tail()}, '>');
        }
      }
 
      std::vector< ProbabilisticRanking > proba_triples
          = unshieldedTriplesMiic_(graph, mutualInformation, sepSet, marks);
 
      const Size steps_orient = proba_triples.size();
      Size       past_steps   = current_step_;
 
      ProbabilisticRanking best;
      if (steps_orient > 0) best = proba_triples[0];
 
      while (!proba_triples.empty() && std::max(std::get< 2 >(best), std::get< 3 >(best)) >= 0.5) {
        const NodeId x = std::get< 0 >(*std::get< 0 >(best));
        const NodeId y = std::get< 1 >(*std::get< 0 >(best));
        const NodeId z = std::get< 2 >(*std::get< 0 >(best));
 
        const double i3 = std::get< 1 >(best);
        const double p1 = std::get< 2 >(best);
        const double p2 = std::get< 3 >(best);
 
        if (i3 <= 0) {
          _orientingVstructureMiic_(graph, marks, x, y, z, p1, p2);
        } else {
          _propagatingOrientationMiic_(graph, marks, x, y, z, p1, p2);
        }
 
        delete std::get< 0 >(best);
        proba_triples.erase(proba_triples.begin());
        proba_triples = updateProbaTriples_(graph, proba_triples);
 
        if (!proba_triples.empty()) best = proba_triples[0];
 
        ++current_step_;
        if (onProgress.hasListener()) {
          GUM_EMIT3(onProgress,
                    (current_step_ * 100) / (steps_orient + past_steps),
                    0.,
                    timer_.step());
        }
      }
 
      for (auto iter = _latentCouples_.rbegin(); iter != _latentCouples_.rend(); ++iter) {
        graph.eraseArc(Arc(iter->head(), iter->tail()));
        if (_existsDirectedPath_(graph, iter->head(), iter->tail())) {
          graph.addArc(iter->head(), iter->tail());
          graph.eraseArc(Arc(iter->tail(), iter->head()));
          *iter = Arc(iter->head(), iter->tail());
        }
      }
 
      if (onProgress.hasListener()) { GUM_EMIT3(onProgress, 100, 0., timer_.step()); }
    }

References gum::learning::ConstraintBasedLearning::_existsDirectedPath_(), gum::learning::ConstraintBasedLearning::_forbiddenGraph_, gum::learning::ConstraintBasedLearning::_initialMarks_, gum::learning::ConstraintBasedLearning::_latentCouples_, gum::learning::ConstraintBasedLearning::_mandatoryGraph_, _orientingVstructureMiic_(), _propagatingOrientationMiic_(), gum::learning::ConstraintBasedLearning::applyStructuralConstraints_(), gum::ApproximationScheme::current_step_, GUM_EMIT3, gum::HashTable< Key, Val >::insert(), gum::IApproximationSchemeConfiguration::onProgress, gum::ApproximationScheme::timer_, unshieldedTriplesMiic_(), and updateProbaTriples_().

Referenced by learnMixedStructure().

Here is the call graph for this function:

Here is the caller graph for this function:

orientDoubleHeadedArcs_()

void gum::learning::ConstraintBasedLearning::orientDoubleHeadedArcs_ ( MixedGraph & mg )

protectedinherited

Builds a complete MixedGraph on the nodes of template_graph, minus edges forbidden by structural constraints. Every constraint-based skeleton phase must start from this graph so forbidden pairs are never tested by CI.

Definition at line 204 of file ConstraintBasedLearning.cpp.

                                                                        {
      gum::ArcSet L;
      for (gum::NodeId x: mg.nodes())
        for (NodeId y: mg.parents(x))
          if (mg.parents(y).contains(x)) {
            if (x > y) continue;
            else L.insert(gum::Arc(x, y));
          }
 
      if (not L.empty()) {
        while (true) {
          bool withdrawFlag_L = false;
          for (auto arc: ArcSet(L)) {
            bool tail_head        = _existsDirectedPath_(mg, arc.tail(), arc.head());
            bool head_tail        = _existsDirectedPath_(mg, arc.head(), arc.tail());
            bool withdrawFlag_arc = false;
 
            if (tail_head && !head_tail) {
              mg.eraseArc(Arc(arc.head(), arc.tail()));
              withdrawFlag_arc = true;
            } else if (!tail_head && head_tail) {
              mg.eraseArc(Arc(arc.tail(), arc.head()));
              withdrawFlag_arc = true;
            } else if (!tail_head && !head_tail) {
              mg.eraseArc(Arc(arc.head(), arc.tail()));
              withdrawFlag_arc = true;
            }
 
            if (withdrawFlag_arc) {
              L.erase(arc);
              withdrawFlag_L = true;
            }
          }
          if (L.empty()) break;
 
          // no arc was processed this pass — break cycle by erasing one arc arbitrarily
          if (!withdrawFlag_L) {
            auto arc = *L.begin();
            mg.eraseArc(Arc(arc.head(), arc.tail()));
            mg.eraseArc(Arc(arc.tail(), arc.head()));
            L.erase(arc);
          }
        }
      }
    }

References _existsDirectedPath_(), gum::Set< Key >::begin(), gum::Set< Key >::contains(), gum::Set< Key >::empty(), gum::Set< Key >::erase(), gum::ArcGraphPart::eraseArc(), gum::Set< Key >::insert(), gum::NodeGraphPart::nodes(), and gum::ArcGraphPart::parents().

Referenced by gum::learning::FCI::learnMixedStructure().

Here is the call graph for this function:

Here is the caller graph for this function:

periodSize()

INLINE Size gum::ApproximationScheme::periodSize ( ) const

overridevirtualinherited

Returns the period size.

Returns

Returns the period size.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 156 of file approximationScheme_inl.h.

{ return period_size_; }

References period_size_.

remainingBurnIn()

INLINE Size gum::ApproximationScheme::remainingBurnIn ( ) const

inherited

Returns the remaining burn in.

Returns

Returns the remaining burn in.

Definition at line 213 of file approximationScheme_inl.h.

                                                         {
    if (burn_in_ > current_step_) {
      return burn_in_ - current_step_;
    } else {
      return 0;
    }
  }

References burn_in_, and current_step_.

setEpsilon()

INLINE void gum::ApproximationScheme::setEpsilon ( double eps )

overridevirtualinherited

Given that we approximate f(t), stopping criterion on |f(t+1)-f(t)|.

If the criterion was disabled it will be enabled.

Parameters

eps	The new epsilon value.

Exceptions

OutOfBounds

Raised if eps < 0.

Implements gum::IApproximationSchemeConfiguration.

Reimplemented in gum::learning::EMApproximationScheme.

Definition at line 64 of file approximationScheme_inl.h.

                                                        {
    if (eps < 0.) { GUM_ERROR(OutOfBounds, "eps should be >=0") }
 
    eps_         = eps;
    enabled_eps_ = true;
  }

References enabled_eps_, eps_, and GUM_ERROR.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsSampling< GUM_SCALAR >::GibbsSampling(), gum::learning::GreedyHillClimbing::GreedyHillClimbing(), gum::learning::GreedyThickThinning::GreedyThickThinning(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::EMApproximationScheme::setEpsilon().

Here is the caller graph for this function:

setForbiddenGraph()

void gum::learning::ConstraintBasedLearning::setForbiddenGraph ( const gum::DiGraph & forbidGraph )

inherited

Definition at line 118 of file ConstraintBasedLearning.cpp.

                                                                                 {
      _forbiddenGraph_ = forbidGraph;
    }

References _forbiddenGraph_.

setMandatoryGraph()

void gum::learning::ConstraintBasedLearning::setMandatoryGraph ( const gum::DAG & mandaGraph )

inherited

Definition at line 114 of file ConstraintBasedLearning.cpp.

                                                                            {
      _mandatoryGraph_ = mandaGraph;
    }

References _mandatoryGraph_.

setMaxIndegree()

void gum::learning::ConstraintBasedLearning::setMaxIndegree ( gum::Size n )

inherited

Definition at line 122 of file ConstraintBasedLearning.cpp.

{ _maxIndegree_ = n; }

References _maxIndegree_.

setMaxIter()

INLINE void gum::ApproximationScheme::setMaxIter ( Size max )

overridevirtualinherited

Stopping criterion on number of iterations.

If the criterion was disabled it will be enabled.

Parameters

max	The maximum number of iterations.

Exceptions

OutOfBounds

Raised if max <= 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 106 of file approximationScheme_inl.h.

                                                      {
    if (max < 1) { GUM_ERROR(OutOfBounds, "max should be >=1") }
    max_iter_         = max;
    enabled_max_iter_ = true;
  }

References enabled_max_iter_, GUM_ERROR, and max_iter_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), and gum::SamplingInference< GUM_SCALAR >::SamplingInference().

Here is the caller graph for this function:

setMaxTime()

INLINE void gum::ApproximationScheme::setMaxTime ( double timeout )

overridevirtualinherited

Stopping criterion on timeout.

If the criterion was disabled it will be enabled.

Parameters

timeout

The timeout value in seconds.

Exceptions

OutOfBounds

Raised if timeout <= 0.0.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 127 of file approximationScheme_inl.h.

                                                            {
    if (timeout <= 0.) { GUM_ERROR(OutOfBounds, "timeout should be >0.") }
    max_time_         = timeout;
    enabled_max_time_ = true;
  }

References enabled_max_time_, GUM_ERROR, and max_time_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), and gum::SamplingInference< GUM_SCALAR >::SamplingInference().

Here is the caller graph for this function:

setMinEpsilonRate()

INLINE void gum::ApproximationScheme::setMinEpsilonRate ( double rate )

overridevirtualinherited

Given that we approximate f(t), stopping criterion on d/dt(|f(t+1)-f(t)|).

If the criterion was disabled it will be enabled

Parameters

rate	The minimal epsilon rate.

Exceptions

OutOfBounds

if rate<0

Implements gum::IApproximationSchemeConfiguration.

Reimplemented in gum::learning::EMApproximationScheme.

Definition at line 85 of file approximationScheme_inl.h.

                                                                {
    if (rate < 0) { GUM_ERROR(OutOfBounds, "rate should be >=0") }
 
    min_rate_eps_         = rate;
    enabled_min_rate_eps_ = true;
  }

References enabled_min_rate_eps_, GUM_ERROR, and min_rate_eps_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsSampling< GUM_SCALAR >::GibbsSampling(), gum::SamplingInference< GUM_SCALAR >::SamplingInference(), and gum::learning::EMApproximationScheme::setMinEpsilonRate().

Here is the caller graph for this function:

setMutualInformation()

void gum::learning::Miic::setMutualInformation

(

CorrectedMutualInformation &

mi

)

Definition at line 89 of file Miic.cpp.

{ mi_ = &mi; }

References mi_.

setPeriodSize()

INLINE void gum::ApproximationScheme::setPeriodSize ( Size p )

overridevirtualinherited

How many samples between two stopping is enable.

Parameters

p	The new period value.

Exceptions

OutOfBounds

Raised if p < 1.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 150 of file approximationScheme_inl.h.

                                                       {
    if (p < 1) { GUM_ERROR(OutOfBounds, "p should be >=1") }
 
    period_size_ = p;
  }

References GUM_ERROR.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), and gum::SamplingInference< GUM_SCALAR >::SamplingInference().

Here is the caller graph for this function:

setVerbosity()

INLINE void gum::ApproximationScheme::setVerbosity ( bool v )

overridevirtualinherited

Set the verbosity on (true) or off (false).

Parameters

v	If true, then verbosity is turned on.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 159 of file approximationScheme_inl.h.

{ verbosity_ = v; }

References verbosity_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), gum::GibbsBNdistance< GUM_SCALAR >::GibbsBNdistance(), and gum::SamplingInference< GUM_SCALAR >::SamplingInference().

Here is the caller graph for this function:

startOfPeriod()

INLINE bool gum::ApproximationScheme::startOfPeriod ( ) const

inherited

Returns true if we are at the beginning of a period (compute error is mandatory).

Returns

Returns true if we are at the beginning of a period (compute error is mandatory).

Definition at line 200 of file approximationScheme_inl.h.

                                                       {
    if (current_step_ < burn_in_) { return false; }
 
    if (period_size_ == 1) { return true; }
 
    return ((current_step_ - burn_in_) % period_size_ == 0);
  }

Referenced by continueApproximationScheme().

Here is the caller graph for this function:

stateApproximationScheme()

INLINE IApproximationSchemeConfiguration::ApproximationSchemeSTATE gum::ApproximationScheme::stateApproximationScheme ( ) const

overridevirtualinherited

Returns the approximation scheme state.

Returns

Returns the approximation scheme state.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 165 of file approximationScheme_inl.h.

                                                             {
    return current_state_;
  }

Referenced by continueApproximationScheme(), history(), and nbrIterations().

Here is the caller graph for this function:

stopApproximationScheme()

INLINE void gum::ApproximationScheme::stopApproximationScheme ( )

inherited

Stop the approximation scheme.

Definition at line 222 of file approximationScheme_inl.h.

                                                           {
    if (current_state_ == ApproximationSchemeSTATE::Continue) {
      stopScheme_(ApproximationSchemeSTATE::Stopped);
    }
  }

Referenced by gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::GreedyThickThinning::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), and gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceNodeToNeighbours_().

Here is the caller graph for this function:

stopScheme_()

INLINE void gum::ApproximationScheme::stopScheme_ ( ApproximationSchemeSTATE new_state )

privateinherited

Stop the scheme given a new state.

Parameters

new_state

The scheme new state.

Definition at line 231 of file approximationScheme_inl.h.

                                                                                 {
    if (new_state == ApproximationSchemeSTATE::Continue) { return; }
 
    if (new_state == ApproximationSchemeSTATE::Undefined) { return; }
 
    current_state_ = new_state;
    timer_.pause();
 
    if (onStop.hasListener()) { GUM_EMIT1(onStop, messageApproximationScheme()); }
  }

References gum::IApproximationSchemeConfiguration::Continue, and gum::IApproximationSchemeConfiguration::Undefined.

Referenced by continueApproximationScheme(), and gum::credal::MultipleInferenceEngine< GUM_SCALAR, LazyPropagation< GUM_SCALAR > >::disableMaxIter().

Here is the caller graph for this function:

unshieldedTriples_()

std::vector< ThreePoints > gum::learning::ConstraintBasedLearning::unshieldedTriples_ ( const MixedGraph & graph )

protectedinherited

Builds a complete MixedGraph on the nodes of template_graph, minus edges forbidden by structural constraints. Every constraint-based skeleton phase must start from this graph so forbidden pairs are never tested by CI.

Definition at line 301 of file ConstraintBasedLearning.cpp.

                                                                           {
      std::vector< ThreePoints > triples;
      for (NodeId z: graph) {
        for (NodeId x: graph.neighbours(z)) {
          for (NodeId y: graph.neighbours(z)) {
            if (y < x && !graph.existsEdge(x, y) && !graph.existsArc(x, y)
                && !graph.existsArc(y, x)) {
              triples.emplace_back(x, y, z);
            }
          }
        }
      }
      return triples;
    }

Referenced by gum::learning::FCI::orientCollidersOnPAG_(), and gum::learning::CIBasedLearning::orientUnshieldedColliders_().

Here is the caller graph for this function:

unshieldedTriplesMiic_()

std::vector< ProbabilisticRanking > gum::learning::Miic::unshieldedTriplesMiic_ ( const MixedGraph & graph, CorrectedMutualInformation & mutualInformation, const HashTable< std::pair< NodeId, NodeId >, std::vector< NodeId > > & sepSet, HashTable< std::pair< NodeId, NodeId >, char > & marks )

protected

Definition at line 594 of file Miic.cpp.

                                                                                    {
      std::vector< ProbabilisticRanking > triples;
      for (NodeId z: graph) {
        for (NodeId x: graph.neighbours(z)) {
          for (NodeId y: graph.neighbours(z)) {
            if (y < x && !graph.existsEdge(x, y)) {
              std::vector< NodeId >       ui;
              std::pair< NodeId, NodeId > key     = {x, y};
              std::pair< NodeId, NodeId > rev_key = {y, x};
              if (sepSet.exists(key)) {
                ui = sepSet[key];
              } else if (sepSet.exists(rev_key)) {
                ui = sepSet[rev_key];
              }
              const auto iter_z_place = std::find(ui.begin(), ui.end(), z);
              if (iter_z_place != ui.end()) ui.erase(iter_z_place);
 
              const double         Ixyz_ui = mutualInformation.score(x, y, z, ui);
              auto                 tup     = new ThreePoints{x, y, z};
              ProbabilisticRanking triple{tup, Ixyz_ui, 0.5, 0.5};
              triples.push_back(triple);
              if (!marks.exists({x, z})) marks.insert({x, z}, 'o');
              if (!marks.exists({z, x})) marks.insert({z, x}, 'o');
              if (!marks.exists({y, z})) marks.insert({y, z}, 'o');
              if (!marks.exists({z, y})) marks.insert({z, y}, 'o');
            }
          }
        }
      }
      triples = updateProbaTriples_(graph, triples);
      std::sort(triples.begin(), triples.end(), GreaterTupleOnLast());
      return triples;
    }

References gum::learning::CorrectedMutualInformation::score(), and updateProbaTriples_().

Referenced by orientationMiic_().

Here is the call graph for this function:

Here is the caller graph for this function:

updateApproximationScheme()

INLINE void gum::ApproximationScheme::updateApproximationScheme ( unsigned int incr = 1 )

inherited

Update the scheme w.r.t the new error and increment steps.

Parameters

incr	The new increment steps.

Definition at line 209 of file approximationScheme_inl.h.

                                                                              {
    current_step_ += incr;
  }

References current_step_.

Referenced by gum::GibbsBNdistance< GUM_SCALAR >::computeKL_(), gum::learning::GreedyHillClimbing::learnStructure(), gum::learning::GreedyThickThinning::learnStructure(), gum::learning::LocalSearchWithTabuList::learnStructure(), gum::SamplingInference< GUM_SCALAR >::loopApproxInference_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByOrderedArcs_(), gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceByRandomOrder_(), and gum::credal::CNLoopyPropagation< GUM_SCALAR >::makeInferenceNodeToNeighbours_().

Here is the caller graph for this function:

updateProbaTriples_()

std::vector< ProbabilisticRanking > gum::learning::Miic::updateProbaTriples_ ( const MixedGraph & graph, std::vector< ProbabilisticRanking > probaTriples )

protected

Definition at line 633 of file Miic.cpp.

                                                                                  {
      for (auto& triple: probaTriples) {
        NodeId x, y, z;
        x                 = std::get< 0 >(*std::get< 0 >(triple));
        y                 = std::get< 1 >(*std::get< 0 >(triple));
        z                 = std::get< 2 >(*std::get< 0 >(triple));
        const double Ixyz = std::get< 1 >(triple);
        double       Pxz  = std::get< 2 >(triple);
        double       Pyz  = std::get< 3 >(triple);
 
        if (Ixyz <= 0) {
          const double expo = std::exp(Ixyz);
          const double P0   = (1 + expo) / (1 + 3 * expo);
          if (Pxz == Pyz && Pyz == 0.5) {
            std::get< 2 >(triple) = P0;
            std::get< 3 >(triple) = P0;
          } else {
            if (graph.existsArc(x, z) && Pxz >= P0) {
              std::get< 3 >(triple) = Pxz * (1 / (1 + expo) - 0.5) + 0.5;
            } else if (graph.existsArc(y, z) && Pyz >= P0) {
              std::get< 2 >(triple) = Pyz * (1 / (1 + expo) - 0.5) + 0.5;
            }
          }
        } else {
          const double expo = std::exp(-Ixyz);
          if (graph.existsArc(x, z) && Pxz >= 0.5) {
            std::get< 3 >(triple) = Pxz * (1 / (1 + expo) - 0.5) + 0.5;
          } else if (graph.existsArc(y, z) && Pyz >= 0.5) {
            std::get< 2 >(triple) = Pyz * (1 / (1 + expo) - 0.5) + 0.5;
          }
        }
      }
      std::sort(probaTriples.begin(), probaTriples.end(), GreaterTupleOnLast());
      return probaTriples;
    }

Referenced by orientationMiic_(), and unshieldedTriplesMiic_().

Here is the caller graph for this function:

verbosity()

INLINE bool gum::ApproximationScheme::verbosity ( ) const

overridevirtualinherited

Returns true if verbosity is enabled.

Returns

Returns true if verbosity is enabled.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 161 of file approximationScheme_inl.h.

{ return verbosity_; }

References verbosity_.

Referenced by ApproximationScheme(), gum::learning::EMApproximationScheme::EMApproximationScheme(), and continueApproximationScheme().

Here is the caller graph for this function:

Member Data Documentation

_arcProbas_

ArcProperty< double > gum::learning::Miic::_arcProbas_

private

Definition at line 187 of file Miic.h.

Referenced by _orientingVstructureMiic_(), and _propagatingOrientationMiic_().

_emptySet_

const std::vector< NodeId > gum::learning::ConstraintBasedLearning::_emptySet_

protectedinherited

Definition at line 201 of file ConstraintBasedLearning.h.

Referenced by gum::learning::Miic::initiation_(), and gum::learning::CIBasedLearning::learnSkeleton().

_forbiddenGraph_

gum::DiGraph gum::learning::ConstraintBasedLearning::_forbiddenGraph_

protectedinherited

Definition at line 196 of file ConstraintBasedLearning.h.

Referenced by ConstraintBasedLearning(), ConstraintBasedLearning(), applyStructuralConstraints_(), isForbiddenArc_(), isForbiddenEdge_(), operator=(), operator=(), gum::learning::Miic::orientationMiic_(), and setForbiddenGraph().

_initialMarks_

HashTable< std::pair< NodeId, NodeId >, char > gum::learning::ConstraintBasedLearning::_initialMarks_

protectedinherited

Definition at line 198 of file ConstraintBasedLearning.h.

Referenced by ConstraintBasedLearning(), ConstraintBasedLearning(), addConstraints(), operator=(), operator=(), and gum::learning::Miic::orientationMiic_().

_latentCouples_

std::vector< Arc > gum::learning::ConstraintBasedLearning::_latentCouples_

protectedinherited

Definition at line 199 of file ConstraintBasedLearning.h.

Referenced by ConstraintBasedLearning(), ConstraintBasedLearning(), gum::learning::Miic::_isNotLatentCouple_(), gum::learning::Miic::_orientingVstructureMiic_(), gum::learning::Miic::_propagatingOrientationMiic_(), latentVariables(), gum::learning::Miic::learnMixedStructure(), gum::learning::FCI::learnPAG(), gum::learning::Miic::learnSkeleton(), operator=(), operator=(), gum::learning::Miic::orientationMiic_(), gum::learning::CIBasedLearning::orientUnshieldedColliders_(), and gum::learning::CIBasedLearning::resolveOrientConflict_().

_mandatoryGraph_

gum::DAG gum::learning::ConstraintBasedLearning::_mandatoryGraph_

protectedinherited

Definition at line 197 of file ConstraintBasedLearning.h.

Referenced by ConstraintBasedLearning(), ConstraintBasedLearning(), applyStructuralConstraints_(), operator=(), operator=(), gum::learning::Miic::orientationMiic_(), and setMandatoryGraph().

_maxIndegree_

gum::Size gum::learning::ConstraintBasedLearning::_maxIndegree_ {std::numeric_limits< gum::Size >::max()}

privateinherited

Definition at line 204 of file ConstraintBasedLearning.h.

{std::numeric_limits< gum::Size >::max()};

Referenced by ConstraintBasedLearning(), ConstraintBasedLearning(), isMaxIndegree_(), operator=(), operator=(), and setMaxIndegree().

_maxLog_

int gum::learning::ConstraintBasedLearning::_maxLog_ {100}

protectedinherited

Definition at line 200 of file ConstraintBasedLearning.h.

{100};

Referenced by ConstraintBasedLearning(), ConstraintBasedLearning(), ConstraintBasedLearning(), ConstraintBasedLearning(), gum::learning::Miic::findBestContributor_(), operator=(), and operator=().

burn_in_

Size gum::ApproximationScheme::burn_in_

protectedinherited

Number of iterations before checking stopping criteria.

Definition at line 423 of file approximationScheme.h.

Referenced by ApproximationScheme(), gum::GibbsBNdistance< GUM_SCALAR >::burnIn(), gum::GibbsSampling< GUM_SCALAR >::burnIn(), remainingBurnIn(), gum::GibbsBNdistance< GUM_SCALAR >::setBurnIn(), and gum::GibbsSampling< GUM_SCALAR >::setBurnIn().

current_epsilon_

double gum::ApproximationScheme::current_epsilon_

protectedinherited

Current epsilon.

Definition at line 378 of file approximationScheme.h.

Referenced by continueApproximationScheme().

current_rate_

double gum::ApproximationScheme::current_rate_

protectedinherited

Current rate.

Definition at line 384 of file approximationScheme.h.

Referenced by continueApproximationScheme().

current_state_

ApproximationSchemeSTATE gum::ApproximationScheme::current_state_

protectedinherited

The current state.

Definition at line 393 of file approximationScheme.h.

Referenced by ApproximationScheme(), continueApproximationScheme(), and initApproximationScheme().

current_step_

Size gum::ApproximationScheme::current_step_

protectedinherited

The current step.

Definition at line 387 of file approximationScheme.h.

Referenced by continueApproximationScheme(), initApproximationScheme(), gum::learning::Miic::initiation_(), gum::learning::SimpleMiic::initiation_(), gum::learning::Miic::iteration_(), gum::learning::SimpleMiic::iteration_(), gum::learning::Miic::learnMixedStructure(), gum::learning::SimpleMiic::learnMixedStructure(), gum::learning::Miic::learnSkeleton(), nbrIterations(), gum::learning::SimpleMiic::orientationLatents_(), gum::learning::Miic::orientationMiic_(), gum::learning::SimpleMiic::orientationMiic_(), remainingBurnIn(), and updateApproximationScheme().

enabled_eps_

bool gum::ApproximationScheme::enabled_eps_

protectedinherited

If true, the threshold convergence is enabled.

Definition at line 402 of file approximationScheme.h.

Referenced by ApproximationScheme(), continueApproximationScheme(), disableEpsilon(), enableEpsilon(), isEnabledEpsilon(), and setEpsilon().

enabled_max_iter_

bool gum::ApproximationScheme::enabled_max_iter_

protectedinherited

If true, the maximum iterations stopping criterion is enabled.

Definition at line 420 of file approximationScheme.h.

Referenced by ApproximationScheme(), continueApproximationScheme(), disableMaxIter(), enableMaxIter(), isEnabledMaxIter(), and setMaxIter().

enabled_max_time_

bool gum::ApproximationScheme::enabled_max_time_

protectedinherited

If true, the timeout is enabled.

Definition at line 414 of file approximationScheme.h.

Referenced by ApproximationScheme(), continueApproximationScheme(), disableMaxTime(), enableMaxTime(), isEnabledMaxTime(), and setMaxTime().

enabled_min_rate_eps_

bool gum::ApproximationScheme::enabled_min_rate_eps_

protectedinherited

If true, the minimal threshold for epsilon rate is enabled.

Definition at line 408 of file approximationScheme.h.

Referenced by ApproximationScheme(), continueApproximationScheme(), disableMinEpsilonRate(), enableMinEpsilonRate(), isEnabledMinEpsilonRate(), and setMinEpsilonRate().

eps_

double gum::ApproximationScheme::eps_

protectedinherited

Threshold for convergence.

Definition at line 399 of file approximationScheme.h.

Referenced by ApproximationScheme(), continueApproximationScheme(), epsilon(), and setEpsilon().

history_

std::vector< double > gum::ApproximationScheme::history_

protectedinherited

The scheme history, used only if verbosity == true.

Definition at line 396 of file approximationScheme.h.

Referenced by continueApproximationScheme().

last_epsilon_

double gum::ApproximationScheme::last_epsilon_

protectedinherited

Last epsilon value.

Definition at line 381 of file approximationScheme.h.

Referenced by continueApproximationScheme().

max_iter_

Size gum::ApproximationScheme::max_iter_

protectedinherited

The maximum iterations.

Definition at line 417 of file approximationScheme.h.

Referenced by ApproximationScheme(), continueApproximationScheme(), maxIter(), and setMaxIter().

max_time_

double gum::ApproximationScheme::max_time_

protectedinherited

The timeout.

Definition at line 411 of file approximationScheme.h.

Referenced by ApproximationScheme(), continueApproximationScheme(), maxTime(), and setMaxTime().

meekRules_

gum::MeekRules gum::learning::ConstraintBasedLearning::meekRules_

protectedinherited

Builds a complete MixedGraph on the nodes of template_graph, minus edges forbidden by structural constraints. Every constraint-based skeleton phase must start from this graph so forbidden pairs are never tested by CI.

Definition at line 176 of file ConstraintBasedLearning.h.

Referenced by learnDAG(), gum::learning::Miic::learnMixedStructure(), and learnPDAG().

mi_

CorrectedMutualInformation* gum::learning::Miic::mi_ {nullptr}

private

Definition at line 186 of file Miic.h.

{nullptr};

Referenced by learnMixedStructure(), learnSkeleton(), and setMutualInformation().

min_rate_eps_

double gum::ApproximationScheme::min_rate_eps_

protectedinherited

Threshold for the epsilon rate.

Definition at line 405 of file approximationScheme.h.

Referenced by ApproximationScheme(), continueApproximationScheme(), minEpsilonRate(), and setMinEpsilonRate().

onProgress

Signaler< Size, double, double > gum::IApproximationSchemeConfiguration::onProgress

inherited

Progression, error and time.

Definition at line 81 of file IApproximationSchemeConfiguration.h.

Referenced by gum::ApproximationScheme::continueApproximationScheme(), gum::learning::IBNLearner::distributeProgress(), gum::learning::Miic::initiation_(), gum::learning::SimpleMiic::initiation_(), gum::learning::Miic::iteration_(), gum::learning::SimpleMiic::iteration_(), gum::learning::SimpleMiic::orientationLatents_(), gum::learning::Miic::orientationMiic_(), and gum::learning::SimpleMiic::orientationMiic_().

onStop

Signaler< std::string_view > gum::IApproximationSchemeConfiguration::onStop

inherited

Criteria messageApproximationScheme.

Definition at line 84 of file IApproximationSchemeConfiguration.h.

Referenced by gum::learning::IBNLearner::distributeStop().

onStructuralModification

Signaler< gum::NodeId, gum::NodeId, std::string, std::string > gum::learning::ConstraintBasedLearning::onStructuralModification

inherited

Definition at line 145 of file ConstraintBasedLearning.h.

period_size_

Size gum::ApproximationScheme::period_size_

protectedinherited

Checking criteria frequency.

Definition at line 426 of file approximationScheme.h.

Referenced by ApproximationScheme(), and periodSize().

timer_

Timer gum::ApproximationScheme::timer_

protectedinherited

The timer.

Definition at line 390 of file approximationScheme.h.

Referenced by continueApproximationScheme(), currentTime(), gum::learning::Miic::initiation_(), gum::learning::SimpleMiic::initiation_(), gum::learning::Miic::iteration_(), gum::learning::SimpleMiic::iteration_(), gum::learning::Miic::learnMixedStructure(), gum::learning::SimpleMiic::learnMixedStructure(), gum::learning::Miic::learnSkeleton(), gum::learning::SimpleMiic::orientationLatents_(), gum::learning::Miic::orientationMiic_(), and gum::learning::SimpleMiic::orientationMiic_().

verbosity_

bool gum::ApproximationScheme::verbosity_

protectedinherited

If true, verbosity is enabled.

Definition at line 429 of file approximationScheme.h.

Referenced by ApproximationScheme(), setVerbosity(), and verbosity().

---

The documentation for this class was generated from the following files:

• agrum/BN/learning/Miic.h
• agrum/BN/learning/Miic.cpp