skein-glm 0.10.0

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. Built with Sphinx + Furo and hosted on Read the Docs (config in .readthedocs.yaml); preview locally with sphinx-build -b html docs docs/_build/html. CI builds it with -W (warnings = errors) 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

v0.9 — the research-grade release. Closes the inference axis across all four mainstream GLM families (debiased Cox lasso joins LS / logistic / Poisson), adds edge-level FDR / FWER / MB stability control on graphical models, ships polychoric / polyserial preprocessing for ordinal Likert data, and finishes the M13 / M14c perf work — every GLM × group penalty (plain + sparse-group) now runs native, no LLA wrappers underneath any prox-Newton outer. M13.8 (post-v0.9) ports celer's gap-safe screening + Anderson dual extrapolation to the GLM prox-Newton surrogate, closing the F-series gap that M10 left LS-only — 3–8× wall-clock on logistic_lasso v2 cells. M5 model selection + inference + threaded CV folds + M11 graphical lasso (single + joint) + M12 hardening all carried over from v0.8. See ROADMAP.md for the full plan and the open M14b software-paper milestone.

Done so far:

• Solvers — production CD core (path solver, strong rule + KKT verification, gap-safe screening, Anderson acceleration, M13.1 saturation bypass, M13.2 cross-λ gradient cache); GLM prox-Newton paths run the same celer-style screening on the weighted-LS surrogate (M13.8: gap-safe sphere + Anderson dual extrapolation + adaptive 0.3 × prev_outer_pgd inner tol + weighted strong-convexity correction r²=2·gap·max(w)/n — 2.2–8.2× wall-clock on logistic_lasso v2 cells); group block-CD with native non-convex prox for group MCP (M13.4b for LS, M13.4c for logistic / Poisson / Cox) and an LLA outer loop for the remaining sparse-group MCP / SCAD families (M13.4 Phase 2.3 weight-space short-circuit); Rayon-parallel group sweeps; operator-norm Lipschitz via power iteration.
• Datafits — least squares, binomial logistic, Poisson (log link, with offsets), Cox PH (Breslow + Efron ties), multinomial softmax, Huber. All glued together by a GlmDatafit trait that exposes a weighted-LS surrogate; the M1/M2 inner solvers absorb every GLM unchanged.
• Penalties — lasso, MCP, SCAD, elastic net, bridge |β|^q, group lasso, group MCP, group SCAD, group elastic net, sparse-group lasso, sparse-group MCP, sparse-group SCAD. Per-feature and per-group weights honored throughout.
• Design-matrix backends — DenseMatrix, SparseCSC, lazy Standardized<D>, MmapMatrix (f64 + f32), row-block Chunked<C>, Augmented<D>, MultiTaskDesign<D> — all behind one trait, freely composable.
• Python — sklearn-compatible estimators for every (datafit × penalty) combination (~150 classes); type stubs; warm-started λ-paths; standardization with original-scale coef_ / intercept_ recovery on dense and sparse.
• Model selection + inference (M5 + M14a) — K-fold CV across every *PathCV class (threaded folds via PyO3 GIL release, ~2.3–2.5× speedup); AIC/BIC/EBIC tuning; stability selection (MB bootstrap); debiased / desparsified lasso for LS + binomial + Poisson + Cox with Wald CIs and p-values (Cox added in M14a — no mainstream Python package has it).
• Graphical models (M11 + M14a) — 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, bootnet-style bootstrap edge stability, and edge-level Benjamini–Hochberg FDR / Bonferroni / Holm FWER / Meinshausen–Bühlmann stability bound (M14a — no other graphical-models package controls error rates at the edge level). Nonconvex penalties on edges close the shrinkage-bias gap that sklearn.covariance.GraphicalLasso and R's glasso / qgraph / bootnet leave open.
• Network psychometrics pipeline (M14a) — polychoric / polyserial correlations (Olsson 1979 two-step ML) for ordinal Likert data via polychoric_correlation / polyserial_correlation / polychoric_covariance_matrix. The end-to-end polychoric_correlation → GraphicalMCP (EBIC-tuned) → GraphicalBootstrap.fdr_threshold(...) worked example in docs/examples/psychometrics.md is the closeout for the M11.1 psychometrics-replication exit criterion.
• Distribution + docs (M8) + hardening (M12) — CI + cibuildwheel + Read the Docs + Sphinx site (concepts + R-porting + extending + examples + API ref) + R numerical regression suite vs glmnet / ncvreg / grpreg + stable Rust API contract. M12 added penalty + datafit unit-test coverage, an integration test directory, a CI smoke job for the PyO3 layer, and an R-fixture gate.

Coming next: M14b (software paper) — run the full benches/v2 GLM + graphical headline matrix and draft the JMLR-MLOSS / JOSS manuscript from the figures + tables that already auto-generate. M14c shipped: scalar LLA weight short-circuit (bridge / adaptive / multitask), native sparse-group MCP BCD for logistic / Poisson / Cox, and an at-scale R-fixture tier (n=500, p=100) for cross-package regression gating.

Layout

crates/skein-core/   pure Rust: traits + algorithms (no Python)
crates/skein-py/     PyO3 bindings (cdylib → skein_glm._core)
python/skein_glm/    sklearn-compatible estimators + ABCs for extensions
tests/               pytest suite (Rust extension required)
benches/             v1 cross-package harness (skein vs sklearn / skglm / celer / glmnet / ncvreg / grpreg)
benches/v2/          publication-quality Snakemake suite backing the paper
crates/skein-core/benches/   internal Rust criterion microbenches
paper/               figure + table bundle regenerated by benches/v2
docs/                Sphinx site (Read the Docs)

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

Quick start

import numpy as np
from skein_glm 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 (native non-convex BCD), 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):

Each scenario is run in two regimes that name what the solution does at the tail of the λ-path, not the path geometry:

• dense — λ_min/λ_max = 1e-3; the active set saturates near the smallest λ (typical "I want the full path including the over-fit tail" usage).
• sparse — λ_min/λ_max = 5e-2; the path stops near support recovery, support stays small throughout.

scenario	size	skein	next-fastest comparator
Lasso LS — dense	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 — dense	medium	1.37 s	skglm 3.35 s
MCP LS — sparse	medium	0.75 s	ncvreg 1.17 s
MCP LS — dense	large (n=100k, p=10k)	510 s	skglm 666 s
MCP LS — sparse	large	497 s	skglm 702 s
SCAD LS — dense	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. The publication-quality benchmark suite under benches/v2/ drives the paper figures and tables; see docs/benchmarks/index.md for the layered overview.

Build

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

# Full Python package (requires maturin in your env). Always pass the
# BLAS feature flag — without it ndarray's matvec / rmatvec / dot fall
# back to a naive Rust loop and the GLM hot path is ~3× slower. The
# shipped PyPI wheels are built this way; building from source without
# the flag will not match published benchmark numbers.
maturin develop --release --features=blas-accelerate   # macOS
maturin develop --release --features=blas-openblas     # Linux
pytest

See docs/installation.md for from-source and development installs, and CLAUDE.md for the contributor quickstart (pre-PR checks, solver-change pre-flight protocol, etc.).

License

MIT.