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It mines long-term dependencies between events and results into a Precise model

Project description

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

    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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