asymtree 0.1.7

Decision tree classifier with centroid-asymmetry split criterion

asymtree

A scikit-learn compatible decision tree classifier that rewards splits where one side's centroid is pulled far from the splitting boundary — centroid asymmetry.

Motivation

Standard decision trees choose splits purely on impurity (Gini / entropy). A split that perfectly separates classes but places both centroids equidistant from the boundary is treated the same as one where nearly all of one class is packed tightly on one side. asymtree exposes this asymmetry as an explicit objective, so you can tune how aggressively the tree favors interpretable, one-sided splits.

Mathematical background

For a candidate split on feature k at threshold t, with left samples
L = {x : x_k ≤ t} and right samples R = {x : x_k > t}, define:

asymmetry(k, t) = max(t − μ_L, μ_R − t) / (x_k_max − x_k_min)

where μ_L and μ_R are the feature-k means on each side. The denominator normalises by the feature range so scores are comparable across features and always lie in [0, 1].

Two combination strategies

Additive — score the split as a weighted sum:

score(k, t) = ΔGini(k, t) + λ · asymmetry(k, t)

Lexicographic — among all splits within ε of the best Gini improvement, pick the one with the highest asymmetry. Purity and asymmetry are fully decoupled.

Efficient implementation

μ_L and μ_R are maintained as running sums while the threshold scan moves left to right, giving the same O(n) cost per feature as the standard split search — no extra pass over the data.

Installation

pip install asymtree

Requirements: Python ≥ 3.9, scikit-learn ≥ 1.4, numpy ≥ 1.21.
A C compiler and Cython ≥ 3.0 are needed to build from source.

Quick start

from asymtree import AsymmetryDecisionTreeClassifier

# Additive mode: impurity + 0.5 × asymmetry
clf = AsymmetryDecisionTreeClassifier(
    max_depth=4,
    lambda_=0.5,
    lexicographic=False,
    random_state=42,
)
clf.fit(X_train, y_train)
print(clf.score(X_test, y_test))

# Lexicographic mode: purity first, asymmetry breaks ties
clf_lexico = AsymmetryDecisionTreeClassifier(
    max_depth=4,
    eps_impurity=1e-3,
    lexicographic=True,
    random_state=42,
)
clf_lexico.fit(X_train, y_train)

Parameters

Parameter	Type	Default	Description
lambda_	float	1.0	Weight of the asymmetry term (additive mode only).
eps_impurity	float	1e-4	Tolerance band for lexicographic tiebreaking.
lexicographic	bool	False	If True, use lexicographic mode.
max_depth	int | None	None	Maximum tree depth.
min_samples_split	int	2	Minimum samples to split a node.
min_samples_leaf	int	1	Minimum samples in a leaf.
max_features	int | float | str | None	None	Number of features to consider per split.
random_state	int | None	None	Random seed.

All other DecisionTreeClassifier parameters are forwarded unchanged.

Compatibility

AsymmetryDecisionTreeClassifier is a drop-in replacement for sklearn.tree.DecisionTreeClassifier and works with all sklearn utilities: cross-validation, pipelines, clone, GridSearchCV, plot_tree, etc.

from sklearn.model_selection import GridSearchCV

param_grid = {"max_depth": [3, 4, 5], "lambda_": [0.1, 0.5, 1.0]}
gs = GridSearchCV(AsymmetryDecisionTreeClassifier(), param_grid, cv=5)
gs.fit(X_train, y_train)

Running the tests

pip install pytest
pytest tests/

License

MIT