Source code for pm4py.algo.discovery.causal.variants.alpha

'''
    PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

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.

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but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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visit <https://www.gnu.org/licenses/>.

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

from typing import Dict, Tuple




def apply(dfg: Dict[Tuple[str, str], int]) -> Dict[Tuple[str, str], int]:
    """
    Computes a causal graph based on a directly follows graph according to the alpha miner

    Parameters
    ----------
    dfg: :class:`dict` directly follows relation, should be a dict of the form (activity,activity) -> num of occ.

    Returns
    -------
    causal_relation: :class:`dict` containing all causal relations as keys (with value 1 indicating that it holds)
    """
    causal_alpha = {}
    for f, t in dfg:
        if dfg[(f, t)] > 0:
            if (t, f) not in dfg:
                causal_alpha[(f, t)] = 1
            elif dfg[(t, f)] == 0:
                causal_alpha[(f, t)] = 1
    return causal_alpha