r2py_rpart
A Python port of the R package rpart (Recursive Partitioning and Regression Trees). r2py_rpart reimplements rpart's original C recursive-partitioning engine — compiled directly from a lightly adapted copy of the C sources and driven from Python via cffi — and translates rpart's R-level functions (formula handling, pruning, printing, plotting, prediction, cross-validation, ...) into Python with numpy/pandas, aiming for output that matches R's rpart as closely as possible.
Trees are fit using the same recursive-partitioning algorithm as R's rpart: classification, regression (anova), Poisson/exponential (survival), and user-defined splitting methods are all supported, along with cost-complexity pruning, surrogate splits for missing data, and cross-validated complexity tables.
Installation
pip install r2py_rpart
Prebuilt wheels are published for Python 3.10–3.14 on Linux (x86_64/aarch64), macOS (Intel/Apple Silicon), and Windows (x86_64). If a wheel isn't available for your platform, pip will build from source, which requires a C/C++ compiler (GCC, Clang, or MinGW-w64 on Windows) — no R installation is needed.
Quick start
import pandas as pd
from r2py_rpart import rpart, print_rpart, printcp, prune, plot_rpart, text_rpart
iris = pd.read_csv("iris.csv")
iris["species"] = iris["species"].astype("category")
# Classification tree
fit = rpart("species ~ .", data=iris, method="class", control={"cp": 0.0001, "minsplit": 5})
print_rpart(fit)
printcp(fit)
# Prune with a chosen complexity parameter
pruned = prune(fit, cp=0.02)
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(8, 8))
plot_rpart(pruned, ax=ax)
text_rpart(pruned, ax=ax)
fig.savefig("tree.pdf")
See examples/classification_tree.py and examples/regression_tree.py for complete, runnable end-to-end examples (each paired with the equivalent R script and side-by-side R/Python output for comparison).
API overview
r2py_rpart mirrors the public functions of R's rpart package by name:
| Purpose | Function(s) |
|---|---|
| Fit a tree | rpart, rpart_control |
| Prune | prune, prune_rpart, snip_rpart, snip_rpart_mouse |
| Predict | predict_rpart, pred_rpart, xpred_rpart |
| Inspect / summarize | print_rpart, summary_rpart, printcp, path_rpart, labels_rpart |
| Plot | plot_rpart, text_rpart, plotcp, meanvar_rpart, post_rpart, rsq_rpart |
| Diagnostics | residuals_rpart, importance, roc_rpart |
Testing
pip install -e . pytest
pytest tests/
Many tests compare r2py_rpart output against R's rpart running via rpy2, so an R installation with the rpart package is required to run the full parity suite.
License
r2py_rpart is a derivative work of R's rpart package and is distributed, like its upstream, under the GNU General Public License v2 or later (GPL-2.0-or-later). See LICENSE for the full text and NOTICE for attribution to the original rpart authors.
Authors
- Yufei Cai (ycai9@nd.edu)
- Jun Li (jun.li@nd.edu)
University of Notre Dame
1 maintainer
Release files for r2py-rpart 0.1.0
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Source distribution (sdist)
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| r2py_rpart-0.1.0.tar.gz | 786.5 kB | Details |
Built distributions (wheels)
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