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

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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 Optional, Dict, Any, Union, Tuple

[docs]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