SkPM

Process mining, the scikit-learn way.

SkPM is a Python module for process mining built on top of scikit-learn and pandas, and distributed under the MIT license.

Event logs are not tables of independent rows: cases unfold over time, every event is a moment where a prediction could be made, and almost every convenient shortcut leaks the future into the past. SkPM turns an event log into a supervised learning problem without leaking it — time-aware splits, prediction targets, feature extraction and prefix encoding, all as ordinary scikit-learn transformers, so they compose with the Pipeline, ColumnTransformer and GridSearchCV you already use.

Website: https://skpm.readthedocs.io

Installation

User installation

A PyPI release is on the way. Until then, install the latest version from the repository with uv or pip:

uv venv .venv --python 3.12 && source .venv/bin/activate
uv pip install git+https://github.com/raseidi/skpm.git

Getting started

How much longer does each running case need? On a public benchmark log:

from sklearn.ensemble import HistGradientBoostingRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.pipeline import Pipeline

from skpm.event_logs import BPI20RequestForPayment
from skpm.feature_extraction import TimestampExtractor
from skpm.feature_extraction.targets import remaining_time
from skpm.model_selection import train_test_split
from skpm.sequence_encoding import Aggregation

# Payment requests from a Dutch university; downloads and caches on first use.
log = BPI20RequestForPayment()

# Split first, before extracting features: whole cases, separated in time.
train, test = train_test_split(log, strategy="unbiased")

# Hours until each case finishes. Which target to predict is your choice.
y_train = remaining_time(train, time_unit="h")
y_test = remaining_time(test, time_unit="h")

# Fitted on the training cases only, which is what keeps the estimate honest.
pipeline = Pipeline([
    ("features", TimestampExtractor(time_unit="h")),
    ("prefix", Aggregation(method="mean")),
    ("model", HistGradientBoostingRegressor()),
]).fit(train, y_train)

print(mean_absolute_error(y_test, pipeline.predict(test)))

Your own log works the same way, with no loader class and no conversion step to remember: train_test_split(pd.read_csv("my_log.csv")).

Documentation

• Quickstart — the whole workflow on one page, on synthetic data.
• User Guide — the event log, splitting, targets, features, prefix encoding, composition.
• Examples — next-activity prediction, the prefix encodings side by side, and a real BPI Challenge log end to end.
• API reference — every public class and function.

Contributing

Contributions are welcome — see CONTRIBUTING.md for how to open an issue or a pull request. SkPM follows scikit-learn's fit / transform / predict conventions, so its contributing guide applies here too.

Citation

SkPM was presented at the CoopIS 2023 demonstration track.

@inproceedings{OyamadaTJC23,
  author    = {Rafael Seidi Oyamada and
               Gabriel Marques Tavares and
               Sylvio Barbon Junior and
               Paolo Ceravolo},
  title     = {A Scikit-learn Extension Dedicated to Process Mining Purposes},
  booktitle = {Proceedings of the Demonstration Track co-located with the
               International Conference on Cooperative Information Systems 2023},
  series    = {{CEUR} Workshop Proceedings},
  publisher = {CEUR-WS.org},
  year      = {2023},
}