kantun 0.0.2

Lightweight, model-agnostic hyperparameter tuning. Works standalone or alongside KANBoost.

Kantun

A lightweight, model-agnostic hyperparameter tuning library.

Built as a companion to KANBoost, but with zero hard dependency on it — Kantun works with any estimator that follows a scikit-learn-like fit/predict/predict_proba interface. Use it with KANBoost, with RandomForestClassifier, with your own custom model — your choice.

Why a separate package?

Not everyone using KANBoost needs hyperparameter search, and not everyone doing hyperparameter search needs KANBoost. Splitting them keeps each library's dependency footprint minimal and lets Kantun be useful on its own.

Install

pip install -r requirements.txt
pip install -e .

To also tune KANBoost models, install it separately:

pip install -e ../kanboost_project   # local, or: pip install kanboost (once published to PyPI)
# repo: https://github.com/tuamah/kanboost

Quickstart

from kantun import KantunSearch
from kanboost import KANBoostClassifier

param_space = {
    "n_estimators": [30, 60, 100],
    "learning_rate": [0.1, 0.2, 0.3],
    "kan_hidden": [3, 4, 6],
    "kan_grid": [2, 3],
}

search = KantunSearch(
    KANBoostClassifier,
    param_space,
    n_iter=10,
    cv=3,
    scoring="auc",
)
search.fit(X, y)

print(search.best_params_, search.best_score_)
best_model = search.best_estimator_          # ready to use
results_df = search.results_dataframe()       # sorted leaderboard

Works with any sklearn-style estimator, not just KANBoost

from sklearn.ensemble import RandomForestClassifier
from kantun import KantunSearch

search = KantunSearch(
    RandomForestClassifier,
    {"n_estimators": [50, 100], "max_depth": [3, 5, None]},
    n_iter=5, cv=3, scoring="f1",
    use_eval_set=False,   # RandomForestClassifier.fit() has no eval_set kwarg
)
search.fit(X, y)

How it decides whether to use early stopping

KantunSearch inspects the target model class's fit() signature. If it finds an eval_set parameter (as KANBoost's estimators do), it automatically passes eval_set=(X_val, y_val) on each fold so early stopping kicks in during the search itself. You can override this with use_eval_set=True/False explicitly.

Supported scoring

• Classification: "auc" (default), "f1", "accuracy"
• Regression: "neg_mse" (default), "neg_mae"

Search types

• search_type="random" (default): samples n_iter random combinations
• search_type="grid": tries every combination in param_distributions

License

MIT — see LICENSE.