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

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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