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(*args)
Available ructors:

BNLearner(filename) -> BNLearner

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

BNLearner(learner) -> BNLearner

Parameters:
  • filename (str) – the file to learn from
  • src (pyAgrum.BayesNet) – the Bayesian network used to find thoes modalities
  • parse_database (bool) – if true, the modalities specified by the user will be considered as a superset of the modalities of the variables.
  • learner (pyAgrum.BNLearner) – the BNLearner to copy
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

chi2(BNLearner self, str var1, str var2, Vector_string knw)

chi2(BNLearner self, str var1, str var2) -> PyObject *

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
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 in parameter 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)
hasMissingValues(BNLearner self)

Indicates wether 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 > >

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, DAG dag) -> BayesNet learnParameters(BNLearner self, bool take_into_account_score=True) -> BayesNet learnParameters(BNLearner self) -> 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
setAprioriWeight(BNLearner self, double weight)

Set the weigth of the prior

Parameters:weight (double) – the apriori weight
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
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.

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