pm4py.algo.discovery.causal.variants package¶
Submodules¶
pm4py.algo.discovery.causal.variants.alpha module¶
This module contains code that allows us to compute a causal graph, according to the alpha miner. It expects a dictionary of the form (activity,activity) > num of occ. A causal relation holds between activity a and b, written as a>b, if dfg(a,b) > 0 and dfg(b,a) = 0.

pm4py.algo.discovery.causal.variants.alpha.
apply
(dfg)[source]¶ Computes a causal graph based on a directly follows graph according to the alpha miner
 Parameters
dfg (
dict
directly follows relation, should be a dict of the form (activity,activity) > num of occ.) Returns
causal_relation
 Return type
dict
containing all causal relations as keys (with value 1 indicating that it holds)
pm4py.algo.discovery.causal.variants.heuristic module¶

pm4py.algo.discovery.causal.variants.heuristic.
apply
(dfg)[source]¶ Computes a causal graph based on a directly follows graph according to the heuristics miner
 Parameters
dfg (
dict
directly follows relation, should be a dict of the form (activity,activity) > num of occ.) Returns
return: dictionary containing all causal relations as keys (with value inbetween 1 and 1 indicating that
how strong it holds)