It mines long-term dependencies between events and results into a Precise model
Creating Precise Models by Discovering Long-term Dependencies in Process Trees
Given a log path and set of parameters, the dependency_miner algorithm is responsible for discovering long-term dependencies between the events and results into a precise Petri net by repairing the free-choice Petri net which includes the discovered rules. Added set of rules and computed evaluation metrics are returned.
Call miner(logpath, support, confidence, lift, soundness) It takes as input
1. log_path (str): Path of event log 2. support (str): Threshold value for support measure 3. confidence (str): Threshold value for confidence measure 4. lift (str): Threshold value for lift measure, default min value = 1 5. sound (str) : Soundness requirement if user wants sound model , "Yes/No"
The resulting precise Petri net can be found in the current location with the same name as that of input event log in .pnml and .svg format
pip install dependency_miner_pm4py
How to use it?
Install dependency_miner_pm4py package. Following, from dependency_miner.ltminer import miner
Example: log_path = "<path>\<file>.xes" support = "0.2" confidence = "0.3" lift = "1.0" sound = "Yes" miner(log_path, support, confidence, lift, sound)
Copyright (c) 2021 Ashwini Jogbhat
This repository is licensed under the MIT license. See LICENSE for details.
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