pytrees-rs

pytrees-rs learns anytime optimal decision trees. It is written mostly in Rust and comes with a Python wrapper that follows the scikit-learn API.

An optimal tree is the one with the lowest training error among all trees of a given depth. Finding it can take a long time, so the optimal searches can run as anytime searches: they return a good tree quickly and keep improving it until it is proven optimal or the time limit is reached. status_ tells you which.

The estimators behave like any scikit-learn estimator: they can be cloned, pickled and used in Pipeline, GridSearchCV or cross_val_score. The fitted tree is in tree_, with scikit-learn's layout, and a row goes left when x[feature] <= threshold.

Installation

pip install pytrees-rs

Wheels are provided for Linux, macOS and Windows, for Python 3.10 and later. To build from source you need a Rust toolchain (1.77 or later):

git clone https://github.com/haroldks/pytrees-rs.git
cd pytrees-rs
pip install .

Quick start

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from pytrees import ConTreeClassifier

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

clf = ConTreeClassifier(max_depth=3, min_sup=5).fit(X_train, y_train)
print(clf.status_)              # "optimal", or "time_limit" if it ran out of time
print(clf.train_error_)         # training misclassifications
print(clf.score(X_test, y_test))
print(clf.to_dot())             # the tree in Graphviz format

DL8.5 and LGDT need binary features (0 or 1). A Binarizer or KBinsDiscretizer with one-hot output in a Pipeline takes care of that:

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import KBinsDiscretizer
from pytrees import DL85Classifier

model = make_pipeline(
    KBinsDiscretizer(n_bins=4, encode="onehot-dense"),
    DL85Classifier(max_depth=3, min_sup=5, max_time=60),
)
model.fit(X_train, y_train)

Anytime search

fit_anytime works like fit, but calls you back each time the search finds a better tree:

from pytrees import DL85Classifier
from pytrees.rules import DiscrepancyRule

X_bin = KBinsDiscretizer(n_bins=4, encode="onehot-dense").fit_transform(X)
clf = DL85Classifier(
    max_depth=5,
    heuristic="information_gain",
    discrepancy=DiscrepancyRule(),   # limited discrepancy search
    max_time=120,
)
clf.fit_anytime(X_bin, y, callback=lambda error, seconds, status: print(seconds, error, status))

ConTreeClassifier has the same method, and use_lds=True makes its plain fit anytime too.

Custom error functions

DL8.5 finds the tree that minimises the sum of its leaf errors, and the error of a leaf does not have to be the number of misclassified rows. Pass your own as error_function: it receives the class counts of a leaf (or its row indices, with error_function_input="indices") and returns the error and the predicted class. Class-dependent costs, sample weights and clustering objectives (DL85Cluster works this way) all fit:

import numpy as np

y_bin = (y == 2).astype(int)   # is it Iris virginica?
costs = np.array([1.0, 5.0])   # missing a virginica costs five times more

def cost_sensitive(class_counts):
    counts = np.asarray(class_counts, dtype=float)
    per_prediction = [(costs * counts).sum() - costs[k] * counts[k] for k in range(len(counts))]
    best = int(np.argmin(per_prediction))
    return per_prediction[best], best

clf = DL85Classifier(max_depth=3, error_function=cost_sensitive).fit(X_bin, y_bin)

Documentation

• User guide: installation, every estimator and its parameters, the anytime search rules, custom error functions and the command line tools.
• Rust API: the dtrees-rs and contree-rs crates.

Repository layout

The Python package is built from a Cargo workspace:

To work on it:

cargo test --workspace                     # Rust tests
pip install maturin && maturin develop     # build the Python package in place
pytest python/tests                        # Python tests

Publications

The algorithms in this repository come from the following papers. If you use them in your work, please cite the relevant one.

• H. Kiossou, P. Schaus, S. Nijssen and V. R. Houndji. Time Constrained DL8.5 Using Limited Discrepancy Search. ECML PKDD 2022, LNCS 13717, pp. 443-459. doi:10.1007/978-3-031-26419-1_27 (DL85Classifier with DiscrepancyRule)
• H. Kiossou, P. Schaus, S. Nijssen and G. Aglin. Efficient Lookahead Decision Trees. IDA 2024, pp. 133-144. doi:10.1007/978-3-031-58553-1_11 (LGDTClassifier)
• H. Kiossou and P. Schaus. A Generic Complete Anytime Beam Search for Optimal Decision Tree. IDA 2026. doi:10.1007/978-3-032-23833-7_8, arXiv:2508.06064 (the search rules of DL85Classifier: CA-DL8.5)
• H. Kiossou, P. Schaus and S. Nijssen. Anytime Optimal Decision Tree Learning with Continuous Features. ECML PKDD 2026. arXiv:2601.14765 (ConTreeClassifier with use_lds=True)

They build on:

• G. Aglin, S. Nijssen and P. Schaus. Learning Optimal Decision Trees Using Caching Branch-and-Bound Search. AAAI 2020. (DL8.5; the original implementation is pydl8.5.)
• E. Demirović, A. Lukina, E. Hebrard, J. Chan, J. Bailey, C. Leckie, K. Ramamohanarao and P. J. Stuckey. MurTree: Optimal Decision Trees via Dynamic Programming and Search. JMLR 23, 2022. (The depth-2 solver.)
• C. E. Briţa, J. G. M. van der Linden and E. Demirović. Optimal Classification Trees for Continuous Feature Data Using Dynamic Programming with Branch-and-Bound. AAAI 2025. (ConTree; the original implementation is ConSol-Lab/contree.)

License

MIT; see LICENSE.