Learning¶

pyAgrum encloses all the learning processes for Bayesian network in a simple class BNLearner. This class gives access directly to the complete learning algorithm and theirs parameters (such as prior, scores, constraints, etc.) but also proposes low-level functions that eases the work on developping new learning algorithms (for instance, compute chi2 or conditioanl likelihood on the database, etc.).

class pyAgrum.BNLearner(filename)¶

Parameters:

filename (str) – the file to learn from

BNLearner(filename,src,parse_database=False) -> BNLearner

Parameters:

filename (str) – the file to learn from
src (pyAgrum.BayesNet) – the Bayesian network used to find those modalities
parse_database (bool) – if True, the modalities specified by the user will be considered as a superset of the modalities of the variables.

BNLearner(learner) -> BNLearner

Parameters:

learner (pyAgrum.BNLearner) – the BNLearner to copy

G2(BNLearner self, str var1, str var2, Vector_string knw={})¶

G2 computes the G2 statistic and pvalue for two columns, given a list of other columns.

Parameters:	name1 (str) – the name of the first column name2 (str) – the name of the second column knowing ([str]) – the list of names of conditioning columns
Returns:	the G2 statistic and the associated p-value as a Tuple
Return type:	statistic,pvalue

addForbiddenArc(BNLearner self, Arc arc)¶

addForbiddenArc(BNLearner self, int tail, int head) addForbiddenArc(BNLearner self, str tail, str head)

The arc in parameters won’t be added.

Parameters:	arc (pyAgrum.Arc) – an arc head – a variable’s id (int) tail – a variable’s id (int) head – a variable’s name (str) tail – a variable’s name (str)

addMandatoryArc(BNLearner self, Arc arc)¶

addMandatoryArc(BNLearner self, int tail, int head) addMandatoryArc(BNLearner self, str tail, str head)

Allow to add prior structural knowledge.

Parameters:	arc (pyAgrum.Arc) – an arc head – a variable’s id (int) tail – a variable’s id (int) head – a variable’s name (str) tail – a variable’s name (str)
Raises:	`gum.InvalidDirectedCycle` – If the added arc creates a directed cycle in the DAG

addPossibleEdge(BNLearner self, Edge edge)¶: addPossibleEdge(BNLearner self, int tail, int head) addPossibleEdge(BNLearner self, str tail, str head)

chi2(BNLearner self, str var1, str var2, Vector_string knw={})¶

chi2 computes the chi2 statistic and pvalue for two columns, given a list of other columns.

Parameters:	name1 (str) – the name of the first column name2 (str) – the name of the second column knowing ([str]) – the list of names of conditioning columns
Returns:	the chi2 statistic and the associated p-value as a Tuple
Return type:	statistic,pvalue

currentTime(BNLearner self)¶

Returns:	get the current running time in second (double)
Return type:	double

databaseWeight(BNLearner self)¶

epsilon(BNLearner self)¶

Returns:	the value of epsilon
Return type:	double

eraseForbiddenArc(BNLearner self, Arc arc)¶

eraseForbiddenArc(BNLearner self, int tail, int head) eraseForbiddenArc(BNLearner self, str tail, str head)

Allow the arc to be added if necessary.

Parameters:	arc (pyAgrum) – an arc head – a variable’s id (int) tail – a variable’s id (int) head – a variable’s name (str) tail – a variable’s name (str)

eraseMandatoryArc(BNLearner self, Arc arc)¶

eraseMandatoryArc(BNLearner self, int tail, int head) eraseMandatoryArc(BNLearner self, str tail, str head)

Parameters:	arc (pyAgrum) – an arc head – a variable’s id (int) tail – a variable’s id (int) head – a variable’s name (str) tail – a variable’s name (str)

erasePossibleEdge(BNLearner self, Edge edge)¶

erasePossibleEdge(BNLearner self, int tail, int head) erasePossibleEdge(BNLearner self, str tail, str head)

Allow the 2 arcs to be added if necessary.

Parameters:	arc (pyAgrum) – an arc head – a variable’s id (int) tail – a variable’s id (int) head – a variable’s name (str) tail – a variable’s name (str)

hasMissingValues(BNLearner self)¶

Indicates whether there are missing values in the database.

Returns:	True if there are some missing values in the database.
Return type:	bool

history(BNLearner self)¶

Returns:	the scheme history
Return type:	tuple
Raises:	`gum.OperationNotAllowed` – If the scheme did not performed or if verbosity is set to false

idFromName(BNLearner self, str var_name)¶

Parameters:	var_names (str) – a variable’s name
Returns:	the column id corresponding to a variable name
Return type:	int
Raises:	`gum.MissingVariableInDatabase` – If a variable of the BN is not found in the database.

latentVariables(BNLearner self)¶

latentVariables(BNLearner self) -> vector< pyAgrum.Arc,allocator< pyAgrum.Arc > > const

Warning

learner must be using 3off2 or MIIC algorithm

Returns:	the list of latent variables
Return type:	list

learnBN(BNLearner self)¶

learn a BayesNet from a file (must have read the db before)

Returns:	the learned BayesNet
Return type:	pyAgrum.BayesNet

learnDAG(BNLearner self)¶

learn a structure from a file

Returns:	the learned DAG
Return type:	pyAgrum.DAG

learnMixedStructure(BNLearner self)¶

Warning

learner must be using 3off2 or MIIC algorithm

Returns:	the learned structure as an EssentialGraph
Return type:	pyAgrum.EssentialGraph

learnParameters(BNLearner self, DAG dag, bool take_into_account_score=True)¶

learnParameters(BNLearner self, bool take_into_account_score=True) -> BayesNet

learns a BN (its parameters) when its structure is known.

Parameters:	dag (pyAgrum.DAG) – bn (pyAgrum.BayesNet) – take_into_account_score (bool) – The dag passed in argument may have been learnt from a structure learning. In this case, if the score used to learn the structure has an implicit apriori (like K2 which has a 1-smoothing apriori), it is important to also take into account this implicit apriori for parameter learning. By default, if a score exists, we will learn parameters by taking into account the apriori specified by methods useAprioriXXX () + the implicit apriori of the score, else we just take into account the apriori specified by useAprioriXXX ()
Returns:	the learned BayesNet
Return type:	pyAgrum.BayesNet
Raises:	`gum.MissingVariableInDatabase` – If a variable of the BN is not found in the database `gum.UnknownLabelInDatabase` – If a label is found in the database that do not correspond to the variable

logLikelihood(BNLearner self, vector< int, allocator< int > > vars, vector< int, allocator< int > > knowing={})¶

logLikelihood(BNLearner self, vector< int,allocator< int > > vars) -> double logLikelihood(BNLearner self, Vector_string vars, Vector_string knowing={}) -> double logLikelihood(BNLearner self, Vector_string vars) -> double

logLikelihood computes the log-likelihood for the columns in vars, given the columns in the list knowing (optional)

Parameters:	vars (List[str]) – the name of the columns of interest knowing (List[str]) – the (optional) list of names of conditioning columns
Returns:	the log-likelihood (base 2)
Return type:	double

maxIter(BNLearner self)¶

Returns:	the criterion on number of iterations
Return type:	int

maxTime(BNLearner self)¶

Returns:	the timeout(in seconds)
Return type:	double

messageApproximationScheme(BNLearner self)¶

Returns:	the approximation scheme message
Return type:	str

minEpsilonRate(BNLearner self)¶

Returns:	the value of the minimal epsilon rate
Return type:	double

nameFromId(BNLearner self, int id)¶

Parameters:	id – a node id
Returns:	the variable’s name
Return type:	str

names(BNLearner self)¶

Returns:	the names of the variables in the database
Return type:	List[str]

nbCols(BNLearner self)¶

Return the nimber of columns in the database

Returns:	the number of columns in the database
Return type:	int

nbRows(BNLearner self)¶

Return the number of row in the database

Returns:	the number of rows in the database
Return type:	int

nbrIterations(BNLearner self)¶

Returns:	the number of iterations
Return type:	int

periodSize(BNLearner self)¶

Returns:	the number of samples between 2 stopping
Return type:	int
Raises:	`gum.OutOfLowerBound` – If p<1

recordWeight(BNLearner self, size_t i)¶

setAprioriWeight(weight)¶: Deprecated methods in BNLearner for pyAgrum>0.14.0

setDatabaseWeight(BNLearner self, double new_weight)¶

Set the database weight.

Parameters:	weight (double) – the database weight

setEpsilon(BNLearner self, double eps)¶

Parameters:	eps (double) – the epsilon we want to use
Raises:	`gum.OutOfLowerBound` – If eps<0

setInitialDAG(BNLearner self, DAG g)¶

Parameters:	dag (pyAgrum.DAG) – an initial DAG structure

setMaxIndegree(BNLearner self, int max_indegree)¶

setMaxIter(BNLearner self, int max)¶

Parameters:	max (int) – the maximum number of iteration
Raises:	`gum.OutOfLowerBound` – If max <= 1

setMaxTime(BNLearner self, double timeout)¶

Parameters:	tiemout (double) – stopping criterion on timeout (in seconds)
Raises:	`gum.OutOfLowerBound` – If timeout<=0.0

setMinEpsilonRate(BNLearner self, double rate)¶

Parameters:	rate (double) – the minimal epsilon rate

setPeriodSize(BNLearner self, int p)¶

Parameters:	p (int) – number of samples between 2 stopping
Raises:	`gum.OutOfLowerBound` – If p<1

setPossibleSkeleton(BNLearner self, UndiGraph skeleton)¶

setRecordWeight(BNLearner self, size_t i, double weight)¶

setSliceOrder(BNLearner self, PyObject * l)¶

setSliceOrder(BNLearner self, pyAgrum.NodeProperty< int > slice_order) setSliceOrder(BNLearner self, vector< vector< str,allocator< str > >,allocator< vector< str,allocator< str > > > > slices)

Set a partial order on the nodes.

Parameters:	l (list) – a list of sequences (composed of ids of rows or string)

setVerbosity(BNLearner self, bool v)¶

Parameters:	v (bool) – verbosity

use3off2(BNLearner self)¶: Indicate that we wish to use 3off2.

useAprioriBDeu(BNLearner self, double weight=1)¶

useAprioriBDeu(BNLearner self)

The BDeu apriori adds weight to all the cells of the counting tables. In other words, it adds weight rows in the database with equally probable values.

Parameters:	weight (double) – the apriori weight

useAprioriDirichlet(BNLearner self, str filename, double weight=1)¶: useAprioriDirichlet(BNLearner self, str filename)

useAprioriSmoothing(BNLearner self, double weight=1)¶: useAprioriSmoothing(BNLearner self)

useEM(BNLearner self, double epsilon)¶

Indicates if we use EM for parameter learning.

Parameters:	epsilon (double) – if epsilon=0.0 then EM is not used if epsilon>0 then EM is used and stops when the sum of the cumulative squared error on parameters is les than epsilon.

useGreedyHillClimbing(BNLearner self)¶

useK2(BNLearner self, PyObject * l)¶

useK2(BNLearner self, pyAgrum.Sequence< int > order) useK2(BNLearner self, vector< int,allocator< int > > order)

Indicate that we wish to use K2.

Parameters:	order (list) – a list of ids

useLocalSearchWithTabuList(BNLearner self, int tabu_size=100, int nb_decrease=2)¶

useLocalSearchWithTabuList(BNLearner self, int tabu_size=100) useLocalSearchWithTabuList(BNLearner self)

Indicate that we wish to use a local search with tabu list

Parameters:	tabu_size (int) – The size of the tabu list nb_decrease (int) – The max number of changes decreasing the score consecutively that we allow to apply

useMDL(BNLearner self)¶: Indicate that we wish to use the MDL correction for 3off2 or MIIC

useMIIC(BNLearner self)¶: Indicate that we wish to use MIIC.

useNML(BNLearner self)¶: Indicate that we wish to use the NML correction for 3off2 or MIIC

useNoApriori(BNLearner self)¶

useNoCorr(BNLearner self)¶: Indicate that we wish to use the NoCorr correction for 3off2 or MIIC

useScoreAIC(BNLearner self)¶

useScoreBD(BNLearner self)¶

useScoreBDeu(BNLearner self)¶

useScoreBIC(BNLearner self)¶

useScoreK2(BNLearner self)¶

useScoreLog2Likelihood(BNLearner self)¶

verbosity(BNLearner self)¶

Returns:	True if the verbosity is enabled
Return type:	bool