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)

Module contents