pm4py.algo.discovery.causal.variants package

Submodules

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

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/>.

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

Return type

dict containing all causal relations as keys (with value 1 indicating that it holds)

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

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/>.

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)

Module contents

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/>.