Metadata-Version: 2.1
Name: dependency-miner-pm4py
Version: 1.0.1
Summary: It mines long-term dependencies between events and results into a Precise model
Home-page: https://github.com/AshwiniJogbhat/dependency-miner-pm4py
Author: Ashwini Jogbhat
Author-email: ashwini.jogbhat@rwth-aachen.de
License: MIT
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
        
        ## Installation
        
        ```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)
        
        ## License
        
        Copyright (c) 2021 Ashwini Jogbhat
        
        This repository is licensed under the MIT license. See LICENSE for details.
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
