pls4all 1.0.1

Python binding for the pls4all C ABI — a portable PLS / NIRS engine.

pls4all

pls4all is the slim, PLS-only subset of nirs4all-methods — a thin Python binding over the portable libn4m C ABI (a C++17 PLS / NIRS engine). The wheel bundles the libn4m shared library, so pip install pls4all is self-contained; no separate native build is required. For the full method surface (preprocessing, selectors, diagnostics, augmenters, …) install the nirs4all-methods package instead — both load the same libn4m.

The binding loads libn4m with ctypes.CDLL (so the GIL is released during native calls) and exposes:

• version() / abi_version() introspection,
• a Pythonic Context and Config (RAII lifecycle wrappers),
• the PLS fit/predict surface and a scikit-learn-compatible pls4all.sklearn.PLSRegression (and the other PLS-family estimators),
• a typed Pls4allError raised on any non-OK status, carrying the context's last_error message.

Quick start

import numpy as np
import pls4all
from pls4all.sklearn import PLSRegression

print(pls4all.version())      # e.g. "0.98.0+abi.1.9.0"
print(pls4all.abi_version())  # (1, 9, 0)

rng = np.random.default_rng(0)
X = rng.standard_normal((40, 12))
y = X @ rng.standard_normal(12)

model = PLSRegression(n_components=5).fit(X, y)
print(model.predict(X).shape)  # (40,)

Low-level lifecycle, if you need it:

with pls4all.Context() as ctx, pls4all.Config() as cfg:
    cfg.algorithm = pls4all.Algorithm.PLS_REGRESSION
    cfg.solver = pls4all.Solver.SIMPLS
    cfg.n_components = 5
    # ... drive a fit through the C ABI ...

scikit-learn is an optional dependency (only pls4all.sklearn needs it); the core import pls4all works with NumPy alone.

Loading libn4m

The bundled wheel ships libn4m inside pls4all/lib/, found automatically. For development against a local build the loader searches, in order:

1. $PLS4ALL_LIB_PATH — explicit path to libn4m (most direct),
2. pls4all/lib/libn4m* next to the installed package (wheel layout),
3. <repo-root>/build/dev-release/cpp/src/libn4m* (developer convenience),
4. the standard system search path (LD_LIBRARY_PATH, macOS rpath, Windows PATH).

Building libn4m from source (developers)

cmake --preset dev-release
cmake --build --preset dev-release --parallel

This produces build/dev-release/cpp/src/libn4m.so (.dylib / .dll on macOS / Windows), which the loader rules above pick up.

See https://github.com/GBeurier/nirs4all-methods for the full project.