r4pm 0.3.1

Rust-based Process Mining

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

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 IndexLinkedOCEL for analysis functions
locel_id = bindings.index_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_locel(locel=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(event_log=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
• IndexLinkedOCEL - Indexed OCEL for analysis (required by most functions)
• SlimLinkedOCEL - Memory-efficient linked OCEL
• 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
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:

XES

import r4pm

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

OCEL

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

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

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:

1. Reads function metadata from the process_mining crate
2. Generates r4pm/bindings/ with typed Python wrappers and .pyi stubs
3. 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 API
• Edge cases and conversion caching

LICENSE

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