Metadata-Version: 2.1
Name: da4py
Version: 0.0.1
Summary: da4py implements state-of-the-art Process Mining methods over SAT encoding. An Ocaml version is Darksider.
Home-page: https://github.com/BoltMaud/da4py
Author: Boltenhagen Mathilde
Author-email: boltenhagen@lsv.fr
License: UNKNOWN
Description: >Author : Boltenhagen Mathilde<br>
        >Date : 09.2019<br>
        
        ## INTRODUCTION 
        
        This project implements **Process Mining** algorithms with  _SAT encodings_ to get optimal results in case of verification problems.
        Boolean formulas are first created, then converted to CNF and solved with SAT solvers, thanks to  `pysat`.
        This librairy used `pm4py` Objects. 
        
        The project is a translation of the Ocaml version `darksider` created by Thomas Chatain and Mathilde Boltenhagen. 
        
        ### Scientific papers
        
        - _Encoding Conformance Checking Artefacts in SAT_ by Mathilde Boltenhagen, Thomas Chatain, Josep Carmona <br>
        - _Anti-alignments in conformance checking–the dark side of process models_ by Thomas Chatain, Josep Carmona
        
        #### To be implemented soon
        
        - (Ocaml version exists) _Generalized Alignment-Based Trace Clustering of Process Behavior_ by Mathilde Boltenhagen, Thomas Chatain, Josep Carmona
         
        ## ENVIRONNEMENT
        
         `python 3.7.x `
         
        ## EXAMPLE OF USE 
        
        
         ```
          > from pm4py.objects.petri import importer
          > from pm4py.objects.log.importer.xes import factory as xes_importer
          > from da4py.src.main.conformanceArtefacts import ConformanceArtefacts
          
          # get the data with pm4py 
          > net, m0, mf = importer.pnml.import_net("./medium/CloseToM8.pnml")
          > log = xes_importer.import_log("./medium/CloseToM8.xes")
        
          # da4py has a common class for the different artefacts
          > artefacts =  ConformanceArtefacts(size_of_run = 6, max_d = 13)
          
          # launch a multi-alignment
          > artefacts.multiAlignment(net,m0,mf,log)
        
         ```
        
        # FOLDERS 
        <pre>
        ┬  
        ├ src : python code
        ├ examples : data and example files
        └ ...
        </pre>
        
        ## ACKNOWLEDGEMENT 
        
        Affiliations : LSV, CNRS, ENS Paris-Saclay, Inria, Université Paris-Saclay
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
