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) – the model
fields (Dict[str,int]) – Dict of name,position in the file
csv_name (str) – name of the csv file
- Raises
gum.DatabaseError – if a BN variable is not in fields
- Returns
return a dictionary of position for BN variables in fields
- Return type
Dict[int,str]
- pyAgrum.lib.bn2scores.computeScores(bn_name, csv_name, visible=False)
Compute scores (likelihood, aic, bic, mdl, etc.) from a bn w.r.t to a csv
- Parameters
bn_name (pyAgrum.BayesNet | str) – a gum.BayesianNetwork or a filename for a BN
csv_name (str) – a filename for the CSV database
visible (bool) – do we show the progress
- Returns
percentDatabaseUsed,scores
- Return type
Tuple[float,Dict[str,float]]
- 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
name1 (str or pyAgrum.BayesNet) – a BN or a filename for reference
name2 (str or pyAgrum.BayesNet) – another BN or antoher filename for comparison
- dotDiff()
Return a pydot 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 pydot is not installed, this function just returns None
- Returns
the result dot graph or None if pydot can not be imported
- Return type
pydot.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]
- pyAgrum.lib.bn_vs_bn.graphDiff(bnref, bncmp, noStyle=False)
Return a pydot graph that compares the arcs of bnref to bncmp. graphDiff allows bncmp to have less nodes than bnref. (this is not the case in GraphicalBNComparator.dotDiff())
- if noStyle is False use 4 styles (fixed in pyAgrum.config) :
the arc is common for both
the arc is common but inverted in _bn2
the arc is added in _bn2
the arc is removed in _bn2
See graphDiffLegend() to add a legend to the graph. .. warning:: if pydot is not installed, this function just returns None
- Returns
the result dot graph or None if pydot can not be imported
- Return type
pydot.Dot
- pyAgrum.lib.bn_vs_bn.graphDiffLegend()