other pyAgrum.lib modules

bn2roc

The purpose of this module is to provide tools for building ROC and PR from Bayesian Network.

pyAgrum.lib.bn2roc.showPR(bn, csv_name, target, label, show_progress=True, show_fig=True, save_fig=False, with_labels=True, significant_digits=10)

Compute the ROC curve and save the result in the folder of the csv file.

Parameters:
  • bn (pyAgrum.BayesNet) – a Bayesian network
  • csv_name (str) – a csv filename
  • target (str) – the target
  • label (str) – the target label
  • show_progress (bool) – indicates if the progress bar must be printed
  • save_fig – save the result ?
  • show_fig – plot the resuls ?
  • with_labels – labels in csv ?
  • significant_digits – number of significant digits when computing probabilities
pyAgrum.lib.bn2roc.showROC(bn, csv_name, target, label, show_progress=True, show_fig=True, save_fig=False, with_labels=True, significant_digits=10)

Compute the ROC curve and save the result in the folder of the csv file.

Parameters:
  • bn (pyAgrum.BayesNet) – a Bayesian network
  • csv_name (str) – a csv filename
  • target (str) – the target
  • label (str) – the target label
  • show_progress (bool) – indicates if the progress bar must be printed
  • save_fig – save the result
  • show_fig – plot the resuls
  • with_labels – labels in csv
  • significant_digits – number of significant digits when computing probabilities
pyAgrum.lib.bn2roc.showROC_PR(bn, csv_name, target, label, show_progress=True, show_fig=True, save_fig=False, with_labels=True, show_ROC=True, show_PR=True, significant_digits=10)

Compute the ROC curve and save the result in the folder of the csv file.

Parameters:
  • bn (pyAgrum.BayesNet) – a Bayesian network
  • csv_name (str) – a csv filename
  • target (str) – the target
  • label (str) – the target label
  • show_progress (bool) – indicates if the progress bar must be printed
  • save_fig – save the result
  • show_fig – plot the resuls
  • with_labels – labels in csv
  • show_ROC (bool) – whether we show the ROC figure
  • show_PR (bool) – whether we show the PR figure
  • significant_digits – number of significant digits when computing probabilitie
Returns:

(pointsROC, thresholdROC, pointsPR, thresholdPR)
Return type:

tuple

bn2scores

The purpose of this module is to provide tools for computing different scores from a BN.

pyAgrum.lib.bn2scores.checkCompatibility(bn, fields, csv_name)

check if the variables of the bn are in the fields

Parameters:
  • bn – gum.BayesNet
  • fields – Dict of name,position in the file
  • csv_name – name of the csv file

@throw gum.DatabaseError if a BN variable is not in fields

Returns:return a dictionary of position for BN variables in fields
pyAgrum.lib.bn2scores.computeScores(bn_name, csv_name, visible=False, transforme_label=False)

Compute scores from a bn w.r.t to a csv :param bn_name: a gum.BayesianNetwork or a filename for a BN :param csv_name: a filename for the CSV database :param visible: do we show the progress :param transforme_label: do we adapt from labels to id :return: percentDatabaseUsed,scores

pyAgrum.lib.bn2scores.lines_count(filename)

count lines in a file

bn_vs_bn

The purpose of this module is to provide tools for comaring different BNs.

class pyAgrum.lib.bn_vs_bn.GraphicalBNComparator(name1, name2, delta=1e-06)

Bases: object

BNGraphicalComparator allows to compare in multiple way 2 BNs…The smallest assumption is that the names of the variables are the same in the 2 BNs. But some comparisons will have also to check the type and domainSize of the variables. The bns have not exactly the same role : _bn1 is rather the referent model for the comparison whereas _bn2 is the compared one to the referent model.

Parameters:
dotDiff()

Return a pydotplus graph that compares the arcs of _bn1 (reference) with those of self._bn2. full black line: the arc is common for both full red line: the arc is common but inverted in _bn2 dotted black line: the arc is added in _bn2 dotted red line: the arc is removed in _bn2

Warning

if pydotplus is not installed, this function just returns None

Returns:the result dot graph or None if pydotplus can not be imported
Return type:pydotplus.Dot
equivalentBNs()

Check if the 2 BNs are equivalent :

  • same variables
  • same graphical structure
  • same parameters
Returns:“OK” if bn are the same, a description of the error otherwise
Return type:str
hamming()

Compute hamming and structural hamming distance

Hamming distance is the difference of edges comparing the 2 skeletons, and Structural Hamming difference is the difference comparing the cpdags, including the arcs’ orientation.

Returns:A dictionnary containing ‘hamming’,’structural hamming’
Return type:dict[double,double]
scores()

Compute Precision, Recall, F-score for self._bn2 compared to self._bn1

precision and recall are computed considering BN1 as the reference

Fscor is 2*(recall* precision)/(recall+precision) and is the weighted average of Precision and Recall.

dist2opt=square root of (1-precision)^2+(1-recall)^2 and represents the euclidian distance to the ideal point (precision=1, recall=1)

Returns:A dictionnary containing ‘precision’, ‘recall’, ‘fscore’, ‘dist2opt’ and so on.
Return type:dict[str,double]
skeletonScores()

Compute Precision, Recall, F-score for skeletons of self._bn2 compared to self._bn1

precision and recall are computed considering BN1 as the reference

Fscor is 2*(recall* precision)/(recall+precision) and is the weighted average of Precision and Recall.

dist2opt=square root of (1-precision)^2+(1-recall)^2 and represents the euclidian distance to the ideal point (precision=1, recall=1)

Returns:A dictionnary containing ‘precision’, ‘recall’, ‘fscore’, ‘dist2opt’ and so on.
Return type:dict[str,double]