python

mlw

A grammar of machine learning workflows for Python. Four verbs prevent data leakage by construction. 16 algorithms, 11 Rust-native.

Paper · R · Rust engine · epagogy.ai

Install

pip install mlw                       # core (11 Rust-native algorithms)
pip install "mlw[xgboost]"            # + XGBoost
pip install "mlw[all]"                # everything

Python 3.10+. Also available: lightgbm, catboost, plots, optuna, dev.

Quickstart

import ml

data = ml.dataset("churn")
s = ml.split(data, "churn", seed=42)

lb = ml.screen(s, "churn", seed=42)          # rank all algorithms
model = ml.fit(s.train, "churn", seed=42)
ml.evaluate(model, s.valid)                   # iterate freely

final = ml.fit(s.dev, "churn", seed=42)       # retrain on train+valid
ml.assess(final, test=s.test)                 # once — second call errors

Why ml

The evaluate/assess boundary. evaluate runs on validation data — call it as often as you like. assess runs on held-out test data and locks after one use. No discipline required; the API makes leakage inexpressible. This encodes the protocol from Hastie, Tibshirani & Friedman (ESL, Ch. 7).

Three-way split with .dev. Train (60%), valid (20%), test (20%). s.dev = train + valid combined for the final refit before assessment.

47 verbs, one import. From check_data and split through tune, stack, explain, drift, and shelf. Everything returns plain objects you can inspect, compare, or serialize.

168 datasets. tips and flights are bundled. The rest download from OpenML on first use and cache locally.

Highlights

Tune. Random, Bayesian (mlw[optuna]), or grid search.

tuned = ml.tune(s.train, "churn", algorithm="xgboost", seed=42, n_trials=50)
ml.evaluate(tuned, s.valid)

Ship gate. Hard pass/fail contracts before deployment.

ml.validate(final, test=s.test, rules={"accuracy": ">0.85"})

Drift. Catch distribution shift before users notice.

ml.drift(reference=s.train, new=live_data).shifted

Algorithms

16 families. engine="auto" picks Rust when available. engine="sklearn" forces scikit-learn fallback.

Algorithm	String	Engine	Clf	Reg
Random Forest	"random_forest"	Rust	Y	Y
Extra Trees	"extra_trees"	Rust	Y	Y
Gradient Boosting	"gradient_boosting"	Rust	Y	Y
Hist. Gradient Boosting	"histgradient"	Rust	Y	Y
Decision Tree	"decision_tree"	Rust	Y	Y
Ridge	"linear"	Rust	·	Y
Logistic	"logistic"	Rust	Y	·
Elastic Net	"elastic_net"	Rust	·	Y
KNN	"knn"	Rust	Y	Y
Naive Bayes	"naive_bayes"	Rust	Y	·
AdaBoost	"adaboost"	Rust	Y	·
SVM	"svm"	Rust	Y	Y
XGBoost	"xgboost"	optional	Y	Y
LightGBM	"lightgbm"	optional	Y	Y
CatBoost	"catboost"	optional	Y	Y
TabPFN	"tabpfn"	optional	Y	·

Citation

Roth, S. (2026). A Grammar of Machine Learning Workflows.
doi:10.5281/zenodo.19023838

License

MIT. Simon Roth, 2026.