r4pm

Python bindings for the Rust4PM Project: Process mining in Python with the speed of Rust

This library provides basic import/export of XES/OCEL event data, as well as other exposed functionality from the Rust4PM project (e.g., process discovery algorithms).

Features

Fast XES/OCEL Import/Export: Efficient Rust-based import and export of .xes, .xes.gz, and OCEL2 (.xml/.json) files
Auto-Generated Bindings: All process_mining functions automatically exposed with full IDE support (autocomplete, type hints, docs)
Registry System: Manage data objects and convert between types as needed
Polars DataFrames: Polars facilitates the fast transfer of event data from Python to Rust and vice versa

Installation

pip install r4pm         # default
pip install r4pm-full    # additionally bundles DuckDB (~15 MiB larger)

Both provide the r4pm import package, so install one or the other. r4pm-full adds the DuckDB bindings (stream_ocel_to_duckdb, read_consolidated_ocel_from_duckdb, ...), nothing else differs.

import r4pm

r4pm.__variant__                 # "default" or "full"
r4pm.__features__                # optional features compiled in
r4pm.has_feature("ocel-duckdb")

Quick Start

from r4pm import bindings
import r4pm

# Load an OCEL file - returns a registry ID
ocel_id = r4pm.import_item('OCEL', 'data/orders.xml')

# Convert to SlimLinkedOCEL for analysis functions
locel_id = bindings.slim_link_ocel(ocel=ocel_id)

# Get statistics
num = bindings.num_events(ocel=locel_id)
print(f"Events: {num}")

# Discover object-centric DFG
dfg = bindings.discover_dfg_from_ocel(locel_id)
print(f"Discovered DFG for {len(dfg['object_type_to_dfg'])} object types")

# For case-centric event logs:
log_id = r4pm.import_item('EventLog', 'data/log.xes')
case_dfg = bindings.discover_dfg(log_id)

How It Works

Auto-Generated Bindings

All functions from the process_mining Rust library are automatically discovered and exposed as Python functions with:

Full type hints for IDE autocomplete
Automatic documentation from Rust docs
Type validation via JSON schemas

The bindings are organized by module (mirroring the Rust crate structure):

from r4pm import bindings

# Top-level access to all functions
bindings.discover_dfg(event_log=log_id)
bindings.num_events(ocel=locel_id)

# Or use submodules for organization
from r4pm.bindings.discovery.case_centric import dfg
dfg.discover_dfg(event_log=log_id)

Bindings are automatically generated during the Rust build via build.rs.

Registry System

Data is managed through a registry that holds different object types:

OCEL - Raw OCEL data
SlimLinkedOCEL - Memory-efficient linked OCEL (required by most functions)
IndexLinkedOCEL - Indexed OCEL for analysis
EventLog - Case-centric event log
EventLogActivityProjection - Activity-projected log for discovery

# Load files into registry
ocel_id = r4pm.import_item('OCEL', 'file.xml')
log_id = r4pm.import_item('EventLog', 'file.xes')

# Convert between types (either like this or using r4pm.convert_item)
locel_id = bindings.index_link_ocel(ocel=ocel_id)
proj_id = bindings.log_to_activity_projection(log=log_id)

# List registry contents
for item in r4pm.list_items():
    print(f"{item['id']}: {item['type']}")

Simple Import/Export API

For direct DataFrame operations without the registry, use the df submodule.

XES

import r4pm

# Import returns (DataFrame, log_attributes_json)
xes, attrs = r4pm.df.import_xes("file.xes", date_format="%Y-%m-%d")
r4pm.df.export_xes(xes, "test_data/output.xes")

OCEL

# Returns dict with DataFrames: events, objects, relations, o2o, object_changes
ocel = r4pm.df.import_ocel("file.xml")
print(ocel['events'].shape)
r4pm.df.export_ocel(ocel, "export.xml")

# PM4Py integration (requires pm4py)
ocel_pm4py = r4pm.df.import_ocel_pm4py("file.xml")
print(ocel['events'].shape)
r4pm.df.export_ocel_pm4py(ocel_pm4py, "export.xml")

Petri Nets & Alignments

A Petri net is a plain JSON-compatible dict (r4pm.petri_net.PetriNet). Import/export PNML, convert to/from PM4Py, and compute alignment-based fitness with the fast Rust alignment implementation.

import pm4py
import r4pm
from r4pm import petri_net
from r4pm.bindings.conformance.case_centric.alignments import align_variants, compute_fitness

LOG = "test_data/Sepsis Cases - Event Log.xes.gz"

# 1. Discover a Petri net with PM4Py (Inductive Miner infrequent, 0.2 noise threshold)
log = pm4py.read_xes(LOG)
net, im, fm = pm4py.discover_petri_net_inductive(log, noise_threshold=0.2)

# 2. Convert the PM4Py net (+ markings) to an r4pm Petri net dict
rnet = petri_net.from_pm4py(net, im, fm)
# petri_net.export_pnml(rnet, "model.pnml")     # write PNML
# rnet = petri_net.import_pnml("model.pnml")    # or read PNML directly

# 3. Load the log into the registry
log_id = r4pm.import_item("EventLog", LOG)

# 4. Align all variants with the Rust binding and compute fitness
#    (the EventLog id is auto-projected to activity variants)
align_res = align_variants(rnet, log_id)
fitness = compute_fitness(align_res, rnet)
print(fitness)
# {'log_fitness': 0.962, 'average_fitness': 0.907,
#  'perfectly_fitting_frac': 0.626, 'total_costs': 573}

Development

Setup

# Install Rust: https://rustup.rs/
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Create virtual environment
python -m venv .venv
source .venv/bin/activate

# Install in development mode
pip install maturin
maturin develop --release

How Bindings Are Generated

Python bindings are automatically generated during the Rust build via build.rs. Thus, bindings are always in sync with the Rust code and do not require manual regeneration.

The build script:

Reads function metadata from the process_mining crate
Generates r4pm/bindings/ with typed Python wrappers and .pyi stubs
Organizes functions by their Rust module structure

Building for Release

maturin build --release  # Creates wheels in target/wheels/

The wheel automatically includes the generated bindings.

Running Tests

# Run comprehensive test suite
python test_all.py

# Run simple example
python example.py

The test suite (test_all.py) covers:

Automatic type conversion (positional & keyword arguments)
Process discovery (DFG, OC-Declare)
Registry operations (CRUD, DataFrames, export)
Simple Import/Export DataFrame (df) API
Edge cases and conversion caching

LICENSE

This package is licensed under either Apache License Version 2.0 or MIT License at your option.

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