VarSculpt

VarSculpt provides fast binning, MDLP discretization and distribution diagnostics for Python and R. Both interfaces call the same C++17 core so numerical results and interval conventions remain aligned across languages.

The project is under active development. The first public Python release is version 0.1.0.

Python installation

python -m pip install varsculpt

Install plotting support with:

python -m pip install "varsculpt[plot]"

Python example

import numpy as np
import varsculpt

rng = np.random.default_rng(123)
x = rng.normal(size=1_000)
y = (x + rng.normal(scale=0.7, size=x.size) > 0).astype(int)
x[::100] = np.nan

result = varsculpt.binning(x, y, method="mdlp", params={"minobs": 20})
print(result.splits)
print(result.summary())

comparison = varsculpt.compare_distributions(
    x[:500], x[500:], splits=result.splits
)
print(comparison.summary())
print("PSI:", comparison.psi, "KS:", comparison.ks)
comparison.plot(kind="density")

Numeric intervals follow (left, right]. Missing values are excluded when learning cuts and retained in a dedicated Missing bin in summaries, transformations and distribution comparisons.

Main features

  • Supervised MDLP discretization.
  • Equal-width bins over full, quantile-trimmed or IQR-based ranges.
  • Equal-frequency quantile bins and explicit custom cuts.
  • Binary and continuous-target summaries.
  • Missing-aware transformations and fuzzy memberships.
  • PSI and KS distribution comparison on learned or reused cuts.
  • Categorical summaries, ordering helpers and Lorenz/Gini diagnostics.

The complete interface is documented in docs/BINNING_API.md. Executable examples are available under examples/python and examples/r.

Interactive help documents arguments, return classes, methods and examples:

help(varsculpt.binning)
help(varsculpt.BinningResult.transform)
help(varsculpt.compare_distributions)

?binning
?transform.varsculpt_binning
?compare_distributions
methods(class = "varsculpt_binning")

R package

The repository also contains the varsculpt R package, backed by the same core. Build and install its self-contained source package with:

bash tools/make_cran_tar_v3.sh
R CMD INSTALL dist/varsculpt_*.tar.gz

Development and validation

Create a development environment and install the project from the repository root:

python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ".[dev,plot]"

The complete validation builds the C++ core, Python wheel and R source package in isolated temporary installations, runs their test suites, performs R CMD check, and verifies cross-language numerical equality:

bash tools/build_and_test_all.sh

Build Python distributions directly with:

python -m build
python -m twine check dist/*

Release process

Releases are built by GitHub Actions. A manual run publishes a candidate to TestPyPI; publishing a GitHub Release whose tag matches the project version publishes the validated artifacts to PyPI after approval of the protected pypi environment. Authentication uses PyPI Trusted Publishing, so no long-lived API token is stored in GitHub.

Detailed maintainer instructions are in docs/RELEASING.md.

License

VarSculpt is distributed under the Apache License 2.0. See NOTICE for attribution information.

Project links

Data verified by PyPI on Aug 24, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Latest release

1 maintainer

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Avatar for tararea from gravatar.com tararea

Credits

Author: Maria Calderon

GitHub Statistics

Data verified by PyPI on Aug 24, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.8

Provides Extra

plot dev

Tags

binning discretization mdlp psi feature-engineering

Classifiers

Report project as malware

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