Abstract base for CI-test-based causal structure learning (PC, FCI). More...

#include <CIBasedLearning.h>

Inheritance diagram for gum::learning::CIBasedLearning:

Collaboration diagram for gum::learning::CIBasedLearning:

Classes
struct	SepSetEntry_

Public Types
enum class	UCPriority : uint8_t { Standard , Sorted }
	Controls how collider candidates are ordered before orientation. More...
enum class	ApproximationSchemeSTATE : char { Undefined , Continue , Epsilon , Rate , Limit , TimeLimit , Stopped }
	The different state of an approximation scheme. More...

Public Member Functions
Constructors / Destructors
	CIBasedLearning ()
	CIBasedLearning (const CIBasedLearning &)
	CIBasedLearning (CIBasedLearning &&) noexcept
	~CIBasedLearning () override
CIBasedLearning &	operator= (const CIBasedLearning &)
CIBasedLearning &	operator= (CIBasedLearning &&) noexcept
CI test injection
void	setIndependenceTest (IndependenceTest &test)
	inject independence test (non-owning: caller manages lifetime)
Parameterisation
void	setAlpha (double alpha)
	maximum conditioning set size; Size(-1) = unlimited (default)
double	alpha () const
	maximum conditioning set size; Size(-1) = unlimited (default)
void	setMaxCondSetSize (Size max_k)
	maximum conditioning set size; Size(-1) = unlimited (default)
void	setStable (bool stable)
	stable mode: defer edge removals until end of each depth (default: true)
void	setExhaustiveSepSet (bool exhaustive)
	exhaustive sepset mode: accumulate union of all separating sets found at each depth, rather than stopping at the first (default: false)
bool	exhaustiveSepSet () const
	maximum conditioning set size; Size(-1) = unlimited (default)
void	setUCPriority (UCPriority p)
	collider candidate ordering (default: Standard)
UCPriority	ucPriority () const
	maximum conditioning set size; Size(-1) = unlimited (default)
Learning
MixedGraph	learnSkeleton (MixedGraph graph) override
	Phase 1: skeleton discovery via conditional independence tests.
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
Collider orientation (shared between PC and FCI Phase 2/4)
MixedGraph	orientUnshieldedColliders_ (MixedGraph graph)
	orient unshielded colliders in graph and return the updated graph
void	orientColliderArm_ (MixedGraph &graph, NodeId src, NodeId dst)
	orient one arm (src → dst) of a collider; calls resolveOrientConflict_ on conflict
virtual void	resolveOrientConflict_ (NodeId src, NodeId dst)
	conflict hook: called when orientation cannot proceed (arc blocked or reversed). Default: push Arc(src,dst) to latentCouples (PC behaviour). FCI override: no-op (leave circle mark).
Pure virtual interface — subclasses must implement
virtual MixedGraph	learnMixedStructure (MixedGraph graph)=0
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.
Shared state
IndependenceTest *	test_ {nullptr}
double	alpha_ = 0.05
Size	maxCondSetSize_ = Size(-1)
bool	stable_ = true
bool	exhaustiveSepSet_ = false
UCPriority	ucPriority_ = UCPriority::Standard
HashTable< std::pair< NodeId, NodeId >, SepSetEntry_ >	sepSet_

Private Member Functions
void	stopScheme_ (ApproximationSchemeSTATE new_state)
	Stop the scheme given a new state.

Graph utilities
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.
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.
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.

Common state (accessible to subclasses)
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_
gum::Size	_maxIndegree_ {std::numeric_limits< gum::Size >::max()}

Detailed Description

Abstract base for CI-test-based causal structure learning (PC, FCI).

Provides:

Independence test injection and parameterisation (alpha, stable, maxCondSetSize).
learnSkeleton() — PC-stable skeleton discovery with separation-set tracking.
orientUnshieldedColliders_() — collider orientation on MixedGraph.
resolveOrientConflict_() virtual hook — called when orientation conflicts; default records the conflicting pair in latentCouples (PC behaviour). FCI override is a no-op (circle marks are kept).

Definition at line 81 of file CIBasedLearning.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
    };

◆ UCPriority

enum class gum::learning::CIBasedLearning::UCPriority : uint8_t

strong

Controls how collider candidates are ordered before orientation.

Enumerator
Standard	process triples in natural traversal order
Sorted	descending p-value order (strongest evidence first)

Definition at line 84 of file CIBasedLearning.h.

                            : uint8_t {
        Standard,   
        Sorted,     
      };

Constructor & Destructor Documentation

◆ CIBasedLearning() [1/3]

gum::learning::CIBasedLearning::CIBasedLearning ( )

default

Referenced by CIBasedLearning(), CIBasedLearning(), gum::learning::FCI::FCI(), gum::learning::PC::PC(), ~CIBasedLearning(), operator=(), and operator=().

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◆ CIBasedLearning() [2/3]

gum::learning::CIBasedLearning::CIBasedLearning ( const CIBasedLearning & )

default

References CIBasedLearning().

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◆ CIBasedLearning() [3/3]

gum::learning::CIBasedLearning::CIBasedLearning ( CIBasedLearning && from )

noexcept

Definition at line 63 of file CIBasedLearning.cpp.

                                                                    :
        ConstraintBasedLearning(std::move(from)), test_(from.test_), alpha_(from.alpha_),
        maxCondSetSize_(from.maxCondSetSize_), stable_(from.stable_),
        exhaustiveSepSet_(from.exhaustiveSepSet_), ucPriority_(from.ucPriority_),
        sepSet_(std::move(from.sepSet_)) {
      from.test_ = nullptr;
    }

References CIBasedLearning(), gum::learning::ConstraintBasedLearning::ConstraintBasedLearning(), alpha_, exhaustiveSepSet_, maxCondSetSize_, sepSet_, stable_, test_, and ucPriority_.

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◆ ~CIBasedLearning()

gum::learning::CIBasedLearning::~CIBasedLearning ( )

overridedefault

References CIBasedLearning(), alpha(), exhaustiveSepSet(), learnSkeleton(), setAlpha(), setExhaustiveSepSet(), setIndependenceTest(), setMaxCondSetSize(), setStable(), setUCPriority(), and ucPriority().

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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_().

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◆ _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_().

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◆ 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_.

◆ alpha()

double gum::learning::CIBasedLearning::alpha ( ) const

maximum conditioning set size; Size(-1) = unlimited (default)

Definition at line 108 of file CIBasedLearning.cpp.

108{ return alpha_; }

References alpha_.

Referenced by ~CIBasedLearning(), and setAlpha().

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◆ 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_().

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◆ 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_().

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◆ 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.

137{ 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.

75{ enabled_eps_ = false; }

References enabled_eps_.

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

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◆ 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.

116{ enabled_max_iter_ = false; }

References enabled_max_iter_.

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

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◆ 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.

140{ enabled_max_time_ = false; }

References enabled_max_time_.

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

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◆ 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.

96{ 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().

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◆ enableEpsilon()

INLINE void gum::ApproximationScheme::enableEpsilon ( )

overridevirtualinherited

Enable stopping criterion on epsilon.

Implements gum::IApproximationSchemeConfiguration.

Definition at line 78 of file approximationScheme_inl.h.

78{ 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.

119{ 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.

143{ 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.

99{ enabled_min_rate_eps_ = true; }

References enabled_min_rate_eps_.

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

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◆ 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.

72{ return eps_; }

References eps_.

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

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◆ exhaustiveSepSet()

bool gum::learning::CIBasedLearning::exhaustiveSepSet ( ) const

maximum conditioning set size; Size(-1) = unlimited (default)

Definition at line 116 of file CIBasedLearning.cpp.

116{ return exhaustiveSepSet_; }

References exhaustiveSepSet_.

Referenced by ~CIBasedLearning().

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◆ 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.

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◆ 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_().

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◆ 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().

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◆ 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_().

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◆ 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.

82{ 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.

123{ 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.

147{ 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.

103{ return enabled_min_rate_eps_; }

References enabled_min_rate_eps_.

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

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◆ 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_().

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◆ 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_().

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◆ 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_().

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◆ 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().

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◆ 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().

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◆ learnMixedStructure()

virtual MixedGraph gum::learning::ConstraintBasedLearning::learnMixedStructure ( MixedGraph graph )

protectedpure virtualinherited

Implemented in gum::learning::FCI, gum::learning::Miic, and gum::learning::PC.

Referenced by learnDAG(), and learnPDAG().

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◆ 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_.

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◆ learnSkeleton()

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

overridevirtual

Phase 1: skeleton discovery via conditional independence tests.

Implements gum::learning::ConstraintBasedLearning.

Definition at line 126 of file CIBasedLearning.cpp.

                                                              {
      graph = initGraph_(graph);
      sepSet_.clear();
 
      Size d = 0;
      while (d <= maxCondSetSize_) {
        std::vector< Edge > edges(graph.edges().begin(), graph.edges().end());
 
        bool                anyTested = false;
        std::vector< Edge > toRemove;
 
        for (const Edge& edge: edges) {
          if (!graph.existsEdge(edge)) { continue; }
 
          const NodeId X = edge.first();
          const NodeId Y = edge.second();
 
          [&]() {
            if (d == 0) {
              anyTested         = true;
              const double pval = test_->statistics(X, Y, _emptySet_).second;
              if (pval <= alpha_) { return; }
              if (stable_) {
                toRemove.push_back(edge);
              } else {
                graph.eraseEdge(edge);
              }
              sepSet_.insert({X, Y}, {{}, pval});
              sepSet_.insert({Y, X}, {{}, pval});
              return;
            }
 
            bool               exh_found = false;
            std::set< NodeId > exh_union;
            double             exh_pval = 0.0;
 
            for (int dir = 0; dir < 2; ++dir) {
              const NodeId A = (dir == 0) ? X : Y;
              const NodeId B = (dir == 0) ? Y : X;
 
              std::vector< NodeId > adj;
              for (NodeId nb: graph.neighbours(A)) {
                if (nb != B) { adj.push_back(nb); }
              }
 
              if (static_cast< Size >(adj.size()) < d) { continue; }
              anyTested = true;
 
              std::vector< std::size_t > idx(d);
              std::iota(idx.begin(), idx.end(), std::size_t(0));
 
              for (;;) {
                std::vector< NodeId > cond(d);
                for (Size i = 0; i < d; ++i) {
                  cond[i] = adj[idx[i]];
                }
 
                const double pval = test_->statistics(A, B, cond).second;
                if (pval > alpha_) {
                  if (!exhaustiveSepSet_) {
                    if (stable_) {
                      toRemove.push_back(edge);
                    } else {
                      graph.eraseEdge(edge);
                    }
                    sepSet_.insert({X, Y}, {cond, pval});
                    sepSet_.insert({Y, X}, {cond, pval});
                    return;
                  }
                  exh_found = true;
                  if (exh_pval == 0.0) { exh_pval = pval; }
                  for (const NodeId n: cond) {
                    exh_union.insert(n);
                  }
                }
 
                int i = static_cast< int >(d) - 1;
                while (i >= 0 && idx[i] == adj.size() - d + static_cast< std::size_t >(i)) {
                  --i;
                }
                if (i < 0) { break; }
                ++idx[i];
                for (int j = i + 1; j < static_cast< int >(d); ++j) {
                  idx[j] = idx[j - 1] + 1;
                }
              }
            }
 
            if (exh_found) {
              if (stable_) {
                toRemove.push_back(edge);
              } else {
                graph.eraseEdge(edge);
              }
              const std::vector< NodeId > union_vec(exh_union.begin(), exh_union.end());
              sepSet_.insert({X, Y}, {union_vec, exh_pval});
              sepSet_.insert({Y, X}, {union_vec, exh_pval});
            }
          }();
        }
 
        if (stable_) {
          for (const Edge& e: toRemove) {
            if (graph.existsEdge(e)) { graph.eraseEdge(e); }
          }
        }
 
        if (!anyTested) { break; }
        ++d;
      }
 
      return graph;
    }

References gum::learning::ConstraintBasedLearning::_emptySet_, alpha_, exhaustiveSepSet_, gum::learning::ConstraintBasedLearning::initGraph_(), maxCondSetSize_, sepSet_, stable_, and test_.

Referenced by ~CIBasedLearning(), gum::learning::PC::learnMixedStructure(), and gum::learning::FCI::learnPAG().

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◆ 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.

113{ 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.

134{ 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().

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◆ 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.

93{ 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_().

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◆ operator=() [1/2]

CIBasedLearning & gum::learning::CIBasedLearning::operator= ( CIBasedLearning && from )

noexcept

Definition at line 85 of file CIBasedLearning.cpp.

                                                                             {
      if (this != &from) {
        ConstraintBasedLearning::operator=(std::move(from));
        test_             = from.test_;
        alpha_            = from.alpha_;
        maxCondSetSize_   = from.maxCondSetSize_;
        stable_           = from.stable_;
        exhaustiveSepSet_ = from.exhaustiveSepSet_;
        ucPriority_       = from.ucPriority_;
        sepSet_           = std::move(from.sepSet_);
        from.test_        = nullptr;
      }
      return *this;
    }

References CIBasedLearning(), alpha_, exhaustiveSepSet_, maxCondSetSize_, gum::learning::ConstraintBasedLearning::operator=(), sepSet_, stable_, test_, and ucPriority_.

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◆ operator=() [2/2]

CIBasedLearning & gum::learning::CIBasedLearning::operator= ( const CIBasedLearning & from )

Definition at line 71 of file CIBasedLearning.cpp.

                                                                         {
      if (this != &from) {
        ConstraintBasedLearning::operator=(from);
        test_             = from.test_;
        alpha_            = from.alpha_;
        maxCondSetSize_   = from.maxCondSetSize_;
        stable_           = from.stable_;
        exhaustiveSepSet_ = from.exhaustiveSepSet_;
        ucPriority_       = from.ucPriority_;
        sepSet_           = from.sepSet_;
      }
      return *this;
    }

References CIBasedLearning(), alpha_, exhaustiveSepSet_, maxCondSetSize_, gum::learning::ConstraintBasedLearning::operator=(), sepSet_, stable_, test_, and ucPriority_.

Referenced by gum::learning::FCI::operator=(), gum::learning::FCI::operator=(), gum::learning::PC::operator=(), and gum::learning::PC::operator=().

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◆ orientColliderArm_()

void gum::learning::CIBasedLearning::orientColliderArm_	(	MixedGraph &	graph,
		NodeId	src,
		NodeId	dst )

protected

orient one arm (src → dst) of a collider; calls resolveOrientConflict_ on conflict

Definition at line 244 of file CIBasedLearning.cpp.

                                                                                      {
      if (graph.existsEdge(src, dst)) {
        if (isArcValid_(graph, src, dst) && !_existsDirectedPath_(graph, dst, src)) {
          graph.eraseEdge(Edge(src, dst));
          graph.addArc(src, dst);
          GUM_SL_EMIT(src, dst, "Orient " << src << " -> " << dst, "V-structure collider");
        } else {
          resolveOrientConflict_(src, dst);
          GUM_SL_EMIT(src,
                      dst,
                      "Conflict " << src << " -> " << dst,
                      "V-structure blocked by constraint");
        }
      } else if (graph.existsArc(dst, src)) {
        resolveOrientConflict_(src, dst);
        GUM_SL_EMIT(src, dst, "Conflict " << src << " -> " << dst, "V-structure conflict: latent?");
      }
      // existsArc(src, dst) → consistent, skip
    }

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

Referenced by orientUnshieldedColliders_().

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◆ 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().

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◆ orientUnshieldedColliders_()

MixedGraph gum::learning::CIBasedLearning::orientUnshieldedColliders_ ( MixedGraph graph )

protected

orient unshielded colliders in graph and return the updated graph

Definition at line 268 of file CIBasedLearning.cpp.

                                                                           {
      _latentCouples_.clear();
 
      const auto triples = unshieldedTriples_(graph);
 
      const auto isCollider = [&](NodeId X, NodeId Y, NodeId Z) -> bool {
        if (!sepSet_.exists({X, Y})) { return false; }
        const SepSetEntry_& entry = sepSet_[{X, Y}];
        return std::find(entry.cond.begin(), entry.cond.end(), Z) == entry.cond.end();
      };
 
      if (ucPriority_ == UCPriority::Standard) {
        for (const ThreePoints& tp: triples) {
          const NodeId X = std::get< 0 >(tp);
          const NodeId Y = std::get< 1 >(tp);
          const NodeId Z = std::get< 2 >(tp);
          if (!isCollider(X, Y, Z)) { continue; }
          orientColliderArm_(graph, X, Z);
          orientColliderArm_(graph, Y, Z);
        }
      } else {
        struct Candidate {
          NodeId X, Y, Z;
          double pval;
        };
 
        std::vector< Candidate > candidates;
        candidates.reserve(triples.size());
 
        for (const ThreePoints& tp: triples) {
          const NodeId X = std::get< 0 >(tp);
          const NodeId Y = std::get< 1 >(tp);
          const NodeId Z = std::get< 2 >(tp);
          if (!isCollider(X, Y, Z)) { continue; }
          candidates.push_back({X, Y, Z, sepSet_[{X, Y}].pval});
        }
 
        std::sort(candidates.begin(), candidates.end(), [](const Candidate& a, const Candidate& b) {
          return a.pval > b.pval;
        });
 
        for (const auto& [X, Y, Z, pval]: candidates) {
          orientColliderArm_(graph, X, Z);
          orientColliderArm_(graph, Y, Z);
        }
      }
 
      return graph;
    }

References gum::learning::ConstraintBasedLearning::_latentCouples_, gum::learning::CIBasedLearning::SepSetEntry_::cond, orientColliderArm_(), sepSet_, Standard, ucPriority_, and gum::learning::ConstraintBasedLearning::unshieldedTriples_().

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

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◆ 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.

156{ return period_size_; }

gum::ApproximationScheme::period_size_

Size period_size_

Checking criteria frequency.

Definition approximationScheme.h:426

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_.

◆ resolveOrientConflict_()

void gum::learning::CIBasedLearning::resolveOrientConflict_	(	NodeId	src,
		NodeId	dst )

protectedvirtual

conflict hook: called when orientation cannot proceed (arc blocked or reversed). Default: push Arc(src,dst) to latentCouples (PC behaviour). FCI override: no-op (leave circle mark).

Reimplemented in gum::learning::FCI.

Definition at line 264 of file CIBasedLearning.cpp.

                                                                       {
      _latentCouples_.push_back(Arc(src, dst));
    }

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

Referenced by orientColliderArm_().

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◆ setAlpha()

void gum::learning::CIBasedLearning::setAlpha ( double alpha )

maximum conditioning set size; Size(-1) = unlimited (default)

Definition at line 106 of file CIBasedLearning.cpp.

106{ alpha_ = alpha; }

gum::learning::CIBasedLearning::alpha

double alpha() const

maximum conditioning set size; Size(-1) = unlimited (default)

Definition CIBasedLearning.cpp:108

References alpha(), and alpha_.

Referenced by ~CIBasedLearning().

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◆ 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().

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◆ setExhaustiveSepSet()

void gum::learning::CIBasedLearning::setExhaustiveSepSet ( bool exhaustive )

exhaustive sepset mode: accumulate union of all separating sets found at each depth, rather than stopping at the first (default: false)

Definition at line 114 of file CIBasedLearning.cpp.

114{ exhaustiveSepSet_ = exhaustive; }

References exhaustiveSepSet_.

Referenced by ~CIBasedLearning().

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◆ setForbiddenGraph()

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

inherited

Definition at line 118 of file ConstraintBasedLearning.cpp.

                                                                                 {
      _forbiddenGraph_ = forbidGraph;
    }

References _forbiddenGraph_.

◆ setIndependenceTest()

void gum::learning::CIBasedLearning::setIndependenceTest ( IndependenceTest & test )

inject independence test (non-owning: caller manages lifetime)

Definition at line 104 of file CIBasedLearning.cpp.

104{ test_ = &test; }

References test_.

Referenced by ~CIBasedLearning().

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◆ setMandatoryGraph()

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

inherited

Definition at line 114 of file ConstraintBasedLearning.cpp.

                                                                            {
      _mandatoryGraph_ = mandaGraph;
    }

References _mandatoryGraph_.

◆ setMaxCondSetSize()

void gum::learning::CIBasedLearning::setMaxCondSetSize ( Size max_k )

maximum conditioning set size; Size(-1) = unlimited (default)

Definition at line 110 of file CIBasedLearning.cpp.

110{ maxCondSetSize_ = max_k; }

References maxCondSetSize_.

Referenced by ~CIBasedLearning().

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◆ setMaxIndegree()

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

inherited

Definition at line 122 of file ConstraintBasedLearning.cpp.

122{ _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().

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◆ 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().

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◆ 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().

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◆ 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().

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◆ setStable()

void gum::learning::CIBasedLearning::setStable ( bool stable )

stable mode: defer edge removals until end of each depth (default: true)

Definition at line 112 of file CIBasedLearning.cpp.

112{ stable_ = stable; }

References stable_.

Referenced by ~CIBasedLearning().

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◆ setUCPriority()

void gum::learning::CIBasedLearning::setUCPriority ( UCPriority p )

collider candidate ordering (default: Standard)

Definition at line 118 of file CIBasedLearning.cpp.

118{ ucPriority_ = p; }

References ucPriority_.

Referenced by ~CIBasedLearning().

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◆ 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.

159{ verbosity_ = v; }

gum::ApproximationScheme::verbosity_

bool verbosity_

If true, verbosity is enabled.

Definition approximationScheme.h:429

References verbosity_.

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

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◆ 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().

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◆ 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().

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◆ 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_().

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◆ 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().

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◆ ucPriority()

CIBasedLearning::UCPriority gum::learning::CIBasedLearning::ucPriority ( ) const

maximum conditioning set size; Size(-1) = unlimited (default)

Definition at line 120 of file CIBasedLearning.cpp.

120{ return ucPriority_; }

References ucPriority_.

Referenced by ~CIBasedLearning().

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◆ 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_().

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◆ 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_().

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◆ 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.

161{ return verbosity_; }

References verbosity_.

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

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Member Data Documentation

◆ _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.

204{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.

200{100};

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

◆ alpha_

double gum::learning::CIBasedLearning::alpha_ = 0.05

protected

Definition at line 181 of file CIBasedLearning.h.

Referenced by CIBasedLearning(), alpha(), gum::learning::FCI::doDdpOrientation_(), learnSkeleton(), operator=(), operator=(), gum::learning::FCI::possibleDSepPhase_(), and setAlpha().

◆ 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().

◆ exhaustiveSepSet_

bool gum::learning::CIBasedLearning::exhaustiveSepSet_ = false

protected

Definition at line 184 of file CIBasedLearning.h.

Referenced by CIBasedLearning(), exhaustiveSepSet(), learnSkeleton(), operator=(), operator=(), and setExhaustiveSepSet().

◆ 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().

◆ maxCondSetSize_

Size gum::learning::CIBasedLearning::maxCondSetSize_ = Size(-1)

protected

Definition at line 182 of file CIBasedLearning.h.

Referenced by CIBasedLearning(), learnSkeleton(), operator=(), operator=(), gum::learning::FCI::possibleDSepPhase_(), and setMaxCondSetSize().

◆ 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().

◆ 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().

◆ sepSet_

HashTable< std::pair< NodeId, NodeId >, SepSetEntry_ > gum::learning::CIBasedLearning::sepSet_

protected

Definition at line 187 of file CIBasedLearning.h.

Referenced by CIBasedLearning(), gum::learning::FCI::doDdpOrientation_(), learnSkeleton(), operator=(), operator=(), gum::learning::FCI::orientCollidersOnPAG_(), orientUnshieldedColliders_(), gum::learning::FCI::possibleDSepPhase_(), and gum::learning::FCI::ruleR3_().

◆ stable_

bool gum::learning::CIBasedLearning::stable_ = true

protected

Definition at line 183 of file CIBasedLearning.h.

Referenced by CIBasedLearning(), learnSkeleton(), operator=(), operator=(), and setStable().

◆ test_

IndependenceTest* gum::learning::CIBasedLearning::test_ {nullptr}

protected

Definition at line 180 of file CIBasedLearning.h.

180{nullptr};

Referenced by CIBasedLearning(), gum::learning::FCI::doDdpOrientation_(), learnSkeleton(), operator=(), operator=(), gum::learning::FCI::possibleDSepPhase_(), and setIndependenceTest().

◆ 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_().

◆ ucPriority_

UCPriority gum::learning::CIBasedLearning::ucPriority_ = UCPriority::Standard

protected

Definition at line 185 of file CIBasedLearning.h.

Referenced by CIBasedLearning(), operator=(), operator=(), gum::learning::FCI::orientCollidersOnPAG_(), orientUnshieldedColliders_(), setUCPriority(), and ucPriority().

◆ 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/CIBasedLearning.h
agrum/BN/learning/CIBasedLearning.cpp

Classes

Public Types

Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

Private Member Functions

Graph utilities

Common state (accessible to subclasses)

Detailed Description

Member Enumeration Documentation

◆ ApproximationSchemeSTATE

◆ UCPriority

Constructor & Destructor Documentation

◆ CIBasedLearning() [1/3]

◆ CIBasedLearning() [2/3]

◆ CIBasedLearning() [3/3]

◆ ~CIBasedLearning()

Member Function Documentation

◆ _existsDirectedPath_()

◆ _existsNonTrivialDirectedPath_()

◆ addConstraints()

◆ alpha()

◆ applyStructuralConstraints_()

◆ continueApproximationScheme()

◆ currentTime()

◆ disableEpsilon()

◆ disableMaxIter()

◆ disableMaxTime()

◆ disableMinEpsilonRate()

◆ enableEpsilon()

◆ enableMaxIter()

◆ enableMaxTime()

◆ enableMinEpsilonRate()

◆ epsilon()

◆ exhaustiveSepSet()

◆ history()

◆ initApproximationScheme()

◆ initGraph_()

◆ isArcValid_()

◆ isEnabledEpsilon()

◆ isEnabledMaxIter()

◆ isEnabledMaxTime()

◆ isEnabledMinEpsilonRate()

◆ isForbiddenArc_()

◆ isForbiddenEdge_()

◆ isMaxIndegree_()

◆ latentVariables()

◆ learnBN()

◆ learnDAG()

◆ learnMixedStructure()

◆ learnPDAG()

◆ learnSkeleton()

◆ maxIter()

◆ maxTime()

◆ messageApproximationScheme()

◆ minEpsilonRate()

◆ nbrIterations()

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ orientColliderArm_()

◆ orientDoubleHeadedArcs_()

◆ orientUnshieldedColliders_()

◆ periodSize()

◆ remainingBurnIn()

◆ resolveOrientConflict_()

◆ setAlpha()

◆ setEpsilon()

◆ setExhaustiveSepSet()

◆ setForbiddenGraph()

◆ setIndependenceTest()

◆ setMandatoryGraph()

◆ setMaxCondSetSize()

◆ setMaxIndegree()

◆ setMaxIter()

◆ setMaxTime()

◆ setMinEpsilonRate()

◆ setPeriodSize()

◆ setStable()

◆ setUCPriority()