skein-glm 0.6.0

Weighted structured nonconvex sparse models (Python + Rust)

skein

Weighted structured nonconvex sparse models. Rust core + Python API.

Documentation: the docs site has the full conceptual reference (penalties, datafits, weights, backends), porting guides for glmnet / ncvreg / grpreg, worked examples, and an auto-generated API reference. Hosted on Read the Docs once the project is connected (config in .readthedocs.yaml); preview locally with mkdocs serve. CI builds it --strict on every PR.

skein targets a niche that's well-served in R (grpreg, ncvreg) but missing in Python at production quality: nonconvex group-structured penalties (group MCP, group SCAD, sparse-group nonconvex) with first-class support for weights along three axes — per-sample, per-feature, and per-group.

Status

v0.1 development. Core algorithms and the headline GLM family are in place; design-matrix backends (sparse, mmap, chunked) are next. See ROADMAP.md for the full plan.

Done so far:

• Solvers — production CD core (path solver, strong rule + KKT verification, gap-safe screening, Anderson acceleration); group block-CD with LLA outer loop for nonconvex group penalties; Rayon-parallel group sweeps; operator-norm Lipschitz via power iteration.
• Datafits — least squares, binomial logistic, Poisson (log link), Cox PH (Breslow ties). All glued together by a GlmDatafit trait that exposes a weighted-LS surrogate; the M1/M2 inner solvers absorb every GLM unchanged.
• Penalties — MCP, SCAD, group lasso, group MCP, sparse-group lasso, sparse-group MCP. Per-feature and per-group weights honored throughout.
• Python — sklearn-compatible estimators for every (datafit × penalty) combination; type stubs; warm-started λ-paths; standardization with original-scale coef_ / intercept_ recovery (dense backend).
• Graphical models — sparse precision matrix estimation (GraphicalLasso / GraphicalMCP / GraphicalSCAD) and joint estimation across K related populations (JointGraphicalLasso / JointGraphicalMCP, Danaher–Wang–Witten 2014 group form via ADMM), with EBIC tuning. Nonconvex penalties on edges close the shrinkage-bias gap that sklearn.covariance.GraphicalLasso and R's glasso / qgraph / bootnet leave open.

M8 (Distribution & DX) is done: CI + cibuildwheel + Read the Docs + 25-page mkdocs site (concepts + R-porting + extending + examples + API ref) + R numerical regression suite vs glmnet/ncvreg/grpreg + stable Rust API contract. The library is pip install-able once published, documented end-to-end, and pinned against R reference fits so we don't silently drift.

Coming next: algorithmic features — M5.x adaptive weights and stability selection are the next high-value milestones; both leverage the existing per-feature/per-group weight axes that are already wired through every solver.

Layout

crates/skein-core/   pure Rust: traits + algorithms (no Python)
crates/skein-py/     PyO3 bindings (cdylib → skein_glm._core)
python/skein/        sklearn-compatible estimators + ABCs for extensions
tests/               pytest smoke tests
benches/             criterion (Rust) + asv (Python)

The Rust traits (DesignMatrix, Datafit, GlmDatafit, Penalty, GroupPenalty) and their Python ABC mirrors (skein.penalties.Penalty, etc.) are the extension surface for downstream per-paper projects.

Quick start

import numpy as np
from skein import MCPPathRegressor, LogisticGroupMCPPathRegressor, CoxMCPRegressor

# Nonconvex sparse least squares with a λ-path.
rng = np.random.default_rng(0)
n, p = 200, 50
X = rng.standard_normal((n, p))
y = X[:, :3] @ np.array([1.5, -2.0, 0.8]) + 0.1 * rng.standard_normal(n)
model = MCPPathRegressor(gamma=3.0, n_lambdas=50, standardize=True).fit(X, y)
print(model.coefs_[-1, :5], model.intercepts_[-1])

# Logistic + group MCP via LLA, with sklearn-style predict/predict_proba.
groups = np.repeat(np.arange(p // 5), 5)  # 5 features per group
y_bin = (X[:, :3].sum(axis=1) > 0).astype(float)
clf = LogisticGroupMCPPathRegressor(groups=groups, gamma=3.0, n_lambdas=20).fit(X, y_bin)
proba = clf.predict_proba(X)  # shape (n, n_lambdas)

# Cox PH with right-censored survival data.
time = rng.exponential(1.0 / np.exp(X[:, :3].sum(axis=1)))
event = rng.uniform(size=n) < 0.7
cox = CoxMCPRegressor(lambda_=0.01, gamma=3.0).fit(X, time, event.astype(float))
risk = cox.predict(X)  # prognostic index η

Every regressor follows the same (datafit) × (penalty) × ({,Path}Regressor) naming scheme. The path variants warm-start across λ; their coefs_ / intercepts_ (where applicable) are 2D arrays indexed by λ.

Performance

skein is benchmarked against sklearn / skglm / celer / glmnet / ncvreg on shared λ-grids via the harness under benches/. Headline numbers (Apple M1, 16 GB; median of N timed trials after a warm-up):

scenario	size	skein	next-fastest comparator
Lasso LS — deep	medium (n=10k, p=1k)	1.17 s	sklearn 0.125 s
Lasso LS — sparse	medium	0.78 s	sklearn 0.099 s
MCP LS — deep	medium	1.37 s	skglm 3.35 s
MCP LS — sparse	medium	0.75 s	ncvreg 1.17 s
MCP LS — deep	large (n=100k, p=10k)	510 s	skglm 666 s
MCP LS — sparse	large	497 s	skglm 702 s
SCAD LS — deep	medium	1.78 s	ncvreg 7.99 s
SCAD LS — sparse	medium	0.90 s	ncvreg 1.86 s

skein is the fastest on every nonconvex row across every size; on convex lasso/LS the sklearn Cython lasso_path remains the floor at ~8–9× faster on the medium bench. See docs/benchmarks/mcp_ls.md and docs/benchmarks/scad_ls.md for the full nonconvex write-ups (correctness matrices + methodology + per-size tables) and docs/perf/lasso_ls_profile.md for the lasso/LS profiling work that drove M10.

Reproduce with python benches/run.py --scenarios mcp_ls mcp_ls_sparse --sizes small,medium.

Build

# Rust core only (fast iteration on algorithms)
cargo test -p skein-core

# Full Python package (requires maturin in your env)
maturin develop --release
pytest

License

MIT.