semvec/pypi-packages huggingface dataset:
dependency-miner-pm4py 1.0.1
It mines long-term dependencies between events and results into a Precise model
License: MIT

# 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.