'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
PM4Py is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with PM4Py. If not, see <https://www.gnu.org/licenses/>.
'''
from typing import Optional, Dict, Any, Union, Tuple
[docs]def apply(dfg: Dict[Tuple[str, str], int]) -> Dict[Tuple[str, str], float]:
"""
Computes a causal graph based on a directly follows graph according to the heuristics miner
Parameters
----------
dfg: :class:`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)
"""
causal_heur = {}
for (f, t) in dfg:
if (f, t) not in causal_heur:
rev = dfg[(t, f)] if (t, f) in dfg else 0
causal_heur[(f, t)] = float((dfg[(f, t)] - rev) / (dfg[(f, t)] + rev + 1))
causal_heur[(t, f)] = -1 * causal_heur[(f, t)]
return causal_heur