discover_inductive: Petri net discovery algorithms

In fmannhardt/pm4py: Interface to the 'PM4py' Process Mining Library

Petri net discovery algorithms

Description

PM4PY discovery algorithms that discover a Petri net and its initial and final marking. Currently the Inductive Miner and the Alpha Miner are implemented.

Usage

discover_inductive(
  log,
  multi_processing = FALSE,
  noise_threshold = 0,
  convert = TRUE
)

## S3 method for class 'log'
discover_inductive(
  log,
  multi_processing = FALSE,
  noise_threshold = 0,
  convert = TRUE
)

Arguments

log	log: Object of class log or derivatives (grouped_log, eventlog,
multi_processing	logical (default FALSE): Disables if FALSE, enables if TRUE multiprocessing in inductive miner.
noise_threshold	numeric (default: 0): noise threshold. For Inductive Miner currently only variant_inductive_imdfb is supported.
convert	logical (default: TRUE): TRUE to automatically convert Python objects to their R equivalent. If you pass FALSE you can do manual conversion using the r-py-conversion function.

Value

A named list with elements petrinet, initial_marking, and final_marking or the original Python object.

Methods (by class)

• discover_inductive(log): Discover Inductive Miner model based on event log

Examples

## Not run: 
library(pm4py)
library(eventdataR)

discover_inductive(patients)


## End(Not run)

fmannhardt/pm4py documentation built on July 21, 2023, 10:55 p.m.