pls_logistic — PLS-logistic regression

Group: Classification & GLM · Registry tolerance: 1e-08

Description

PLS-Logistic — Logistic regression on PLS scores

Registry note — sklearn PLSRegression -> LogisticRegression pipeline vs pls4all’s single-pass PLS + softmax IRLS. Latent decompositions differ; parity is qualitative.

Parameters

Name	Type	Default	Notes
n_components	int	3	registry benchmark cell value
n_classes	int	3	registry benchmark cell value

Explanations

Bibliographic source

Bastien, P., Esposito Vinzi, V. & Tenenhaus, M. (2005). PLS generalised linear regression. Computational Statistics & Data Analysis 48(1), 17–46. Verified primary link: https://doi.org/10.1016/j.csda.2004.02.005.

Mathematical principle

The implementation extracts a fixed PLS score matrix, then fits a ridge-regularized multinomial logistic model on those scores with Newton updates and a softmax likelihood. Unlike iterative PLS-GLR variants, it does not recompute PLS directions from an IRLS working response.

Appropriate uses

Multiclass probabilistic classification with a linear softmax model on a fixed PLS score representation.

Limits and validation

PLS scores are fitted once before Newton optimization rather than recomputed from each IRLS working response; ridge and component count need validation.

Implementation

Probabilities are produced by the multinomial softmax head in cpp/src/core/pls_logistic.cpp. Component count, regularization, and convergence settings all affect calibration.

Sources and provenance

Current implementation: cpp/src/core/pls_logistic.cpp.

API and bindings

C ABI (ABI 2): n4m_estimators_pls_logistic_fit. Use the linked public header for the exact signature, configuration, and result handles.

Python: no current AST-verified public n4m re-export was found for this method. The linked C ABI above is the documented surface in this checkout.

R (source-verified): pls_logistic_fit(X, y_labels, n_components, n_classes = NULL).

The source signature has additional required inputs, so no example call is fabricated.

MATLAB / Octave (source-verified): pls_logistic(X, y_labels, n_components, n_classes).

The source signature has additional required inputs, so no example call is fabricated.

Registry parity references 📐

• 📐 ref.python_scikit_learn (python · python) — scikit-learn 1.8.0 · strict (rmse_rel ≤ 1e-08) — sklearn PLSRegression -> LogisticRegression pipeline. pls4all’s PLS-Logistic does a single PLS + softmax IRLS in C; sklearn fits PLS on one-hot Y, then a multinomial LogisticRegression on the scores. Both are valid PLS-logistic pipelines but the latent decompositions differ; parity is on the decision-score shape.

Benchmarks

Archived measurement identity. Backend labels in this table are the raw IDs recorded when the benchmark ran (including historical pls4all.* IDs). They preserve measurement provenance and do not describe a current public Python, R, or MATLAB binding; use the source-verified API and bindings section above for current entry points.

Adaptive wall-clock per cell measured against full_matrix.csv. Only backends that implement this method are listed; libraries without the method are omitted.

Verdict  ·  ✓ ref / ≈ ref / ~ shape mark a reference-gate pass at strict / relaxed / qualitative tolerance  ·  ✓ bind = archived binding-harness result agrees with the C++ baseline  ·  ⇄ cross-check = documented by-design selector/RNG/model, noncanonical API/facade convention, or secondary oracle  ·  ✗ divergent  ·  ⚠ error  ·  — not run. The fastest backend per column is marked 🏆.

Reference gate: strict — numeric equivalence (rmse_rel_tol ≤ 1e-08).

Rows tagged with 📐 are the canonical parity references for this method (declared in parity_timing.registry). C++ and external rows show reference parity; archived language-harness rows show binding parity against the C++ backend. Hover the icon for role and tolerance band.

1 thread

Backend	Parity	200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp	✓ ref 6e-16	3.14 ms🏆
Python · archived pls4all benchmark
pls4all.python	✓ bind	3.34 ms
Python · external
📐ref.python_scikit_learn	source	3.44 ms

3 threads

Backend	Parity	200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp	✓ ref 6e-16	3.20 ms🏆
Python · archived pls4all benchmark
pls4all.python	✓ bind	3.81 ms
Python · external
📐ref.python_scikit_learn	source	23.2 ms

10 threads

Backend	Parity	200×40 (ms)
C++ native · libn4m
pls4all.cpp.blas+omp	✓ ref 6e-16	4.74 ms
Python · archived pls4all benchmark
pls4all.python	✓ bind	4.36 ms🏆
Python · external
📐ref.python_scikit_learn	source	5.41 ms

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See also: benchmark overview · methods index · interactive dashboard