nesy4ppm 0.1
Python library to perform learning and suffix prediction with background knowledge under concept drifts.
Requires Python: >=3.9.7
Keywords: python, process mining, predictive process monitoring, suffix prediction, background knowledge

NeSy4PPM documentation
======================

NeSy4PPM is the first Python package designed for both single-attribute (e.g., activity) and multi-attribute (e.g., activity and resource) suffix prediction in predictive process monitoring. It implements a Neuro-Symbolic (NeSy) system that integrates neural models with various types of symbolic background knowledge (BK), enabling accurate and compliant predictions even under concept drift.

NeSy4PPM offers the following key features:

1. **Symbolic knowledge integration**: supports declarative and procedural BK, including DECLARE, MP-DECLARE (multi-perspective DECLARE), ProbDECLARE (probabilistic DECLARE), and Petri nets.

2. **Flexible learning**: provides multiple prefix encoding methods and supports LSTM (Long Short-Term Memory) and Transformer architectures.

3. **Drift-aware prediction**: contextualizes neural predictions using BK in real-time, enhancing prediction accuracy and compliance in dynamic environments.

Installation
============

We recommend using a virtual environment to avoid conflicts between your local Python setup and required libraries. You can use `Conda <https://conda.io>`_ or `venv <https://docs.python.org/3/library/venv.html>`_:

1. Clone/download the NeSy4PPM project.
2. Activate your virtual environment.
3. Install the dependencies listed in ``requirements.txt`` using:: pip install -r requirements.txt

Alternatively, you can install NeSy4PPM directly from `PyPi <https://pypi.org/project/nesy4ppm/>`_.

For ProbDECLARE BK conformance checking, install the `Lydia <https://github.com/whitemech/lydia>`_ backend using Docker:

1. `Install Docker <https://www.docker.com/get-started>`_
2. Pull the Lydia image:

   .. code-block:: bash

      docker pull whitemech/lydia:latest


3. Make the Docker image executable under the name ``lydia``. On Linux and macOS machines, the following commands should work:

   .. code-block:: bash

      echo '#!/usr/bin/env sh' > lydia
      echo 'docker run -v$(pwd):/home/default whitemech/lydia lydia "$@"' >> lydia
      sudo chmod u+x lydia
      sudo mv lydia /usr/local/bin/


More information can be found at `Logaut repository <https://github.com/whitemech/logaut>`_.

Tutorials
=========

Here, you will find tutorials explaining how to utilize the different functionalities of NeSy4PPM.
All tutorials can be downloaded and run from our
`Github. <https://github.com/JamilaOUKHARIJANE/NeSy4PPM/tree/master/docs/source/tutorials>`_ The tutorials use the `Helpdesk log <https://data.4tu.nl/articles/dataset/Dataset_belonging_to_the_help_desk_log_of_an_Italian_Company/12675977>`_ as a running example.

Repository Structure
====================
- ``NeSy4PPM/learning``: contains the implementation of event log encoding and Neural Networks model training.
- ``NeSy4PPM/ProbDeclmonitor``: contains the implementation of suffix prediction using a contextualized Neural predictions with BK.
- ``tests/``: provides evaluation scripts for assessing the NeSy4PPM prediction performance.
- ``docs/tutorials/``: contains step-by-step guides and examples to help users get started with NeSy4PPM.