pm4py.algo.discovery.causal.variants package#

PM4Py – A Process Mining Library for Python

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program 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 Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

Submodules#

pm4py.algo.discovery.causal.variants.alpha module#

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.algo.discovery.causal.variants.alpha.apply(dfg: Dict[Tuple[str, str], int]) → Dict[Tuple[str, str], int][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: dict containing all causal relations as keys (with value 1 indicating that it holds)

pm4py.algo.discovery.causal.variants.heuristic module#

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

pm4py.algo.discovery.causal.variants.heuristic.apply(dfg: Dict[Tuple[str, str], int]) → Dict[Tuple[str, str], float][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)