Restricted Decision Trees (RDT)
A native C++ implementation of Restricted Decision Trees — a tree-based algorithm that sits between the rigidity of oblivious trees and the flexibility of standard decision trees, exposed to Python via nanobind.
What is a Restricted Decision Tree?
In a Restricted Decision Tree, all nodes at the same depth use the same feature for splitting, but each node can have a different threshold. This constraint provides:
- Structure: more interpretable than standard decision trees — the feature chosen at each depth is a natural, depth-ordered importance ranking.
- Flexibility: more adaptable than oblivious trees (which use the same feature and threshold at every node in a depth).
- Staged decisions: each depth can correspond to one information source (e.g. a KYC vendor check, a medical test panel, a fraud-investigation stage) — useful when a real deployment pipeline is itself staged.
Tree Type Comparison
| Tree Type | Feature Selection | Threshold Selection | Structure |
|---|---|---|---|
| Standard Decision Tree | Different feature per node | Different threshold per node | Unrestricted |
| Restricted Decision Tree | Same feature per depth | Different threshold per node | Semi-structured |
| Oblivious Decision Tree | Same feature per depth | Same threshold per depth | Highly structured |
Features
- ✅ Classification (binary & multi-class) and regression
- ✅ Multiple splitting criteria: Gini, entropy, MSE, MAE
- ✅ Probability predictions (
predict_proba) - ✅ Interactive D3.js tree visualization, plain-text tree printing, and JSON export
- ✅ Native C++ core — validated for exact output parity (predictions, per-depth feature choice, probabilities) against the project's original Cython reference implementation, and substantially faster in practice
Install
pip install restricted-decision-trees
Quick start
import numpy as np
from rdt import RDT
X = np.array([[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]])
y = np.array([0, 0, 0, 1, 1, 1], dtype=float)
model = RDT(task="classification", criterion="gini", max_depth=3)
model.fit(X, y)
model.predict(X) # array([0., 0., 0., 1., 1., 1.])
model.predict_proba(X) # per-class probabilities
model.depth_features_ # {0: 0} -- feature index chosen at each depth
For regression, pass task="regression" (criterion defaults to "mse";
"mae" is also available).
Visualizing a tree
from rdt.visualize import print_tree, visualize_d3_observable, export_tree_dict
print_tree(model, feature_names=["x"])
# Interactive, collapsible D3.js visualization -- writes an HTML file you
# open in a browser (click nodes to expand/collapse, scroll/drag to zoom+pan).
visualize_d3_observable(model, y, feature_names=["x"], filename="tree.html")
# Or get the same tree structure + per-node stats as a plain dict/JSON,
# for a custom visualization of your own.
tree_dict = export_tree_dict(model, y, feature_names=["x"])
See examples/basic_usage.py and examples/d3_visualization_demo.py for
fuller, runnable examples.
API reference
rdt.RDT(task="classification", criterion=None, max_depth=5, min_samples_split=2, min_samples_leaf=1)
Parameters
task—"classification"or"regression".criterion— splitting criterion. Defaults to"gini"for classification or"mse"for regression. Classification accepts"gini"or"entropy"; regression accepts"mse"or"mae".max_depth— maximum tree depth.min_samples_split— minimum samples required to split a node.min_samples_leaf— minimum samples required in a leaf node.
Methods
fit(X, y)— build the tree from training data.predict(X)— predict target values.predict_proba(X)— predict class probabilities (classification only).
Attributes (after fitting)
depth_features_—dict[int, int], the feature index chosen at each depth.n_features_— number of features seen duringfit.classes_,n_classes_— unique class labels and their count (classification only).root— the fitted tree's rootNode(.is_leaf,.split_feature,.split_threshold,.value,.class_counts,.left,.right,.indices) for direct programmatic inspection.
rdt.visualize
print_tree(model, feature_names=None)— print an indented text representation of a fitted tree.export_tree_dict(model, y, feature_names=None, class_names=None) -> dict— export the tree as a nested dict with per-node statistics (sample count, impurity, target average, leaf purity).visualize_d3_observable(model, y, feature_names=None, class_names=None, filename="rdt_tree_d3.html", width=1200, height=800) -> str— render an interactive, collapsible D3.js tree to an HTML file (and return the HTML string).
How it works
For each depth, every candidate feature is scored across all currently active nodes at that depth (summing each node's best achievable impurity improvement for that feature); the feature with the highest total wins and is locked in for the whole depth, and the per-node thresholds already found while scoring it are reused directly as the final split points:
For each depth level:
1. For every candidate feature:
for every active node at this depth:
find that node's best threshold for this feature
total_improvement = sum of improvements across all nodes
2. Choose the feature with the highest total_improvement; lock it in.
3. Split every node at this depth using the threshold already found
for the winning feature.
Package structure
restricted-decision-trees/
├── README.md
├── LICENSE
├── pyproject.toml, CMakeLists.txt # nanobind + scikit-build-core build
├── core/include/rdt/ # header-only C++ algorithm core
├── src/bindings.cpp # nanobind bindings
├── python/rdt/
│ ├── __init__.py # RDT (sklearn-style wrapper)
│ └── visualize.py # print_tree / D3 viz / JSON export
├── tests/ # pytest unit tests
└── examples/
├── basic_usage.py
└── d3_visualization_demo.py
License
GPL-3.0 — see LICENSE.
1 maintainer
Metadata
1 maintainer
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