pm4py-core VS gensim

It's a new year 🎊 and what better way to kick off 2023 than with some Process Analytics news! January brought us exciting developments in the world of bpmn-visualization and pm4py integration 🔗. With our team working hard to connect the dots, we’re making bpmn-visualization more accessible and easier to integrate with the Process Mining ecosystem.

I have to rewrite Python's library PM4PY into C#. In fact, not whole library but only "apply" method from /pm4py/algo/discovery/alpha/algorithm.py (https://github.com/pm4py/pm4py-core/blob/release/pm4py/algo/discovery/alpha/algorithm.py).

This is our optimization problem. Now, we hope that you have an idea of what our goal is. Luckily for us, this is already implemented in a Python module called gensim. Yes, these guys are brilliant in natural language processing and we will make use of it. 🤝

Try LDA as implemented in gemsin https://github.com/RaRe-Technologies/gensim

Standout python NLP libraries include Spacy and Gensim, as well as pre-trained model availability in Hugginface. These libraries have widespread use in and support from industry and it shows. Spacy has best-in-class methods for pre-processing text for further applications. Gensim helps you manage your corpus of documents, and contains a lot of different tools for solving a common industry task, topic modeling.

Here we have to install the gensim library in a jupyter notebook to be able to use it in our project, consider the code below;

Nice work! I wonder if you're going the same challenges that gensim had for being generic in summarization.

For context:

> Despite its general-sounding name, the module will not satisfy the majority of use cases in production and is likely to waste people's time.

https://github.com/RaRe-Technologies/gensim/wiki/Migrating-f...

TextRank will work without any problems. https://radimrehurek.com/gensim/

For the topic modelling itself, I am going to use Gensim library by Radim Rehurek, which is very developer friendly and easy to use.