ropwr

library1.2.0pypypi✓ verified 85d ago

RoPWR (Robust Piecewise Regression) is a Python library that implements robust piecewise regression using convex optimization techniques. It provides a flexible framework to model non-linear relationships with segments, supporting various solvers via CVXPY. The current version is 1.2.0, with releases occurring every few months, primarily driven by feature additions, improvements, and dependency updates.

data ai-ml

pip install ropwr

github ↗package ↗

INSTALL

IMPORT

SIG · ROPWR

ropwr

datapythonv1.2.0

Install

12.2s avg

Import

—

Disk

378MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.2.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.940 runs

build_error

glibc

py 3.10–3.940 runs

installs and imports cleanly · install 12.2s · import 0.000s · 324MB

378MB installed

● package 378MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

RobustPWRegression

✓ from ropwr import RobustPWRegression

✗ from ropwr import RoPWR

This quickstart demonstrates how to instantiate, fit, and predict with the `RoPWR` model. It creates a simple dataset with two linear segments and fits a `RoPWR` model with two segments. The 'Clarabel' solver is specified, which is efficient for SOCP problems.

import numpy as np from ropwr import RoPWR # Generate some synthetic data np.random.seed(0) X = np.linspace(0, 10, 100).reshape(-1, 1) y = np.where(X < 5, 2 * X, -X + 15).flatten() + np.random.normal(0, 1, 100) # Initialize and fit the RoPWR model with 2 segments model = RoPWR(n_segments=2, solver='Clarabel', random_state=42) model.fit(X, y) # Make predictions y_pred = model.predict(X) print(f"Predicted y for X[0]: {y_pred[0]:.2f}") print(f"R^2 score: {model.score(X, y):.2f}")

Debug

Known issues

gotchaIncompatibility with scikit-learn 1.6+ due to `force_all_finite` deprecation.

fix

Upgrade ropwr to version 1.2.0 or higher. If unable to upgrade ropwr, downgrade scikit-learn to <1.6.

affects: <1.2.0

gotchaOlder versions of RoPWR might experience OSQP solver convergence issues, especially with complex problems.

fix

Upgrade ropwr to version 0.4.0 or higher. If using an older version, try specifying a different solver (e.g., `solver='Clarabel'` or `solver='SCS'`) or use `solver='auto'`.

affects: <0.4.0

gotchaThe `max_iter` parameter for controlling solver iterations was introduced in v1.0.0. Attempting to use it with older versions will raise an AttributeError.

fix

Upgrade ropwr to version 1.0.0 or higher to utilize the `max_iter` parameter.

affects: <1.0.0

gotchaKey features like enforcing continuous derivatives at split points and extrapolation methods were added in v1.0.0.

fix

Ensure ropwr is version 1.0.0 or higher if your application requires these features. Refer to the documentation for how to enable continuous derivatives or specific extrapolation methods.

affects: <1.0.0

Upgrade

Version history

1.2.0latest on PyPI · released Jan 23, 2026

Audit

Dependencies

numpyrequiredCore numerical operations and array handling.

pandasrequiredData manipulation, often used for input data.

scikit-learnrequiredMachine learning utilities, estimators, and data preprocessing.

cvxpyrequiredConvex optimization problem formulation and solving; core engine for RoPWR.

clarabeloptionalDefault SOCP solver for CVXPY, recommended for performance.

scsoptionalA conic-problem solver for CVXPY.

osqpoptionalAn operator splitting solver for quadratic programs.

Agent activity

15 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/guillermo-navas-palencia/ropwr ↗

packagehttps://pypi.org/project/ropwr/ ↗