tabnetics 0.1.3

HDLSS-focused tabular learning toolkit with distribution-aware preprocessing, portfolio feature selection, and game-theoretic method aggregation.

Tabnetics

A Python toolkit for high-dimensional, low-sample-size (HDLSS) tabular classification. Tabnetics grew out of the review paper Machine learning on small size samples: A synthetic knowledge synthesis, which provided the library's initial theoretical background for small-sample learning. The library combines distribution-aware preprocessing, portfolio-based feature selection, and game-theoretic method aggregation into a single pipeline designed for settings where p >> n.

Homepage: tabnetics.org

pip install tabnetics

Licensed under Apache 2.0.

When to use Tabnetics

Tabnetics is built for tabular classification problems where the number of features greatly exceeds the number of samples:

• Transcriptomics — microarray and RNA-seq gene expression
• Proteomics and metabolomics — mass-spec feature matrices
• Other HDLSS settings — any structured tabular problem with p >> n

In these regimes the dominant failure modes are not model selection — they are unstable preprocessing, brittle feature selection, information leakage, and inflated validation estimates. Tabnetics addresses all four.

What Tabnetics adds to the HDLSS problem is not just another selector: it turns many unstable HDLSS choices into a multiplayer portfolio game. Feature-selection methods and classifier candidates are treated as competing players, oracle scores become the payoff structure, and the resulting MNPO equilibrium is used to select a robust portfolio under small-sample constraints.

Usage guide → · Methods & references → · Benchmark results → · Announcements

Call for collaboration

We are actively looking for testers, collaborators, and co-authors to help validate Tabnetics on real-world HDLSS datasets, shape the companion article, and improve the codebase. If you work with high-dimensional tabular data — transcriptomics, proteomics, metabolomics, or similar — we would love to hear from you. See the Discussions page for ongoing conversations, or open a new thread to introduce your use case.

Citation

If you use Tabnetics in research, cite the repository for the specific version you used. The library is still under active development, and a companion paper will be published after the current testing and validation cycle is complete.

Repository URL: https://github.com/klokedm/tabnetics-public

@software{kokol_tabnetics_2026,
  author = {Kokol, Marko},
  title = {Tabnetics},
  year = {2026},
  url = {https://github.com/klokedm/tabnetics-public}
}

Benchmark results

Tabnetics has been evaluated on 35 HDLSS benchmark datasets (50–2,600 samples, 500–100,000 features, 2–14 classes) drawn from OpenML, GEO, Scikit-feature, and UCSC Xena/TCGA. Across 2,800+ runs with 9 random seeds per dataset, the pipeline achieves a mean balanced accuracy of 0.80, with 12 of 35 datasets above 0.90 and 3 reaching perfect classification. The MNPO portfolio consistently outperforms single-method baselines, and distribution-aware preprocessing contributes a small but consistent positive effect. Detailed per-dataset results, statistical comparisons, and dataset provenance are available in RESULTS.md. A peer-reviewed article with full methodology and ablation studies is in preparation.

Key ideas

1. Distribution-aware preprocessing. Each feature is fitted to a parametric family (from 20+ candidates) using goodness-of-fit testing, bootstrap calibration, and L-moment prescreening. CDF-based transforms replace ad-hoc normalization.

2. Portfolio feature selection. Thirty feature-selection methods — stability selectors, copula knockoffs, tree-based importance, mutual-information filters, IPSS, HSIC-Lasso, and more — are run together. A game-theoretic oracle (MNPO — Nash Multi-Portfolio Optimization) aggregates their outputs into a single robust HDLSS feature portfolio. MNPO builds pairwise preference matrices from multiple oracles (performance, stability, complexity, etc.) and solves for a Nash equilibrium via KL-regularized mirror descent. The multiplayer game framing draws conceptual inspiration from Wu et al.'s Multiplayer Nash Preference Optimization, though the HDLSS adaptation is a distinct contribution with different players, oracles, and data regime (see BACKGROUND.md for details).

3. Regime-aware classification. An MNPO-based classifier oracle picks from regime-appropriate pools (LR, SVM, LDA, PLS-DA, NSC for extreme HDLSS; plus RF, XGBoost, CatBoost, TabPFN for moderate regimes).

4. Strict validation. All learned preprocessing and selection is train-only. HuggingFace-hosted datasets are the authoritative source. Synthetic fallback is not allowed for evidence-bearing runs.

Quick start

from tabnetics.pipeline import DistributionFeatureSelectionPipeline, DFFSConfig

config = DFFSConfig(random_seed=42)
pipeline = DistributionFeatureSelectionPipeline(config)

result = pipeline.run(X, y, dataset_name="my_dataset", seed=42)

print(f"Accuracy: {result.accuracy:.3f}")
print(f"Selected features: {result.selected_features}")

Package structure

Subpackage	Purpose
tabnetics.core	MNPO game-theoretic primitives, sklearn compatibility layer, runtime configuration
tabnetics.distribution	Univariate distribution fitting (20+ families), bootstrap GOF, CDF-based transforms
tabnetics.feature_selection	30 selection methods, MNPO portfolio aggregation, copula knockoffs, stability selectors
tabnetics.classification	Regime-aware classifier pools, MNPO classifier oracle, PLS-DA, conformal helpers
tabnetics.pipeline	End-to-end DF+FS+classification pipeline with leakage prevention
tabnetics.datasets	Dataset registry, HuggingFace/OpenML loaders, meta-feature extraction
tabnetics.domains	Domain adapters (bioinformatics prefilters, face-domain projection)
tabnetics.multiomics	Multi-block PLS-DA (DIABLO-style) and MINT batch-correction integration
tabnetics.benchmarks	Benchmark runner, method-set profiles, SOTA comparison, gaming detection
tabnetics.validation	Validation campaign planner, shard execution, promotion gates

Feature selection methods

The FeatureSelector supports 30 methods out of the box, including:

Category	Methods
Stability selectors	Lasso stability, subspace stability, decorrelated stability, cluster stability, TIGRESS
Wrapper methods	RFECV (SVM, RF, LR), Boruta
Filter methods	ANOVA F-test, mutual information, mRMR, JMI, CMIM, FCBF, Wilcoxon AUC
Tree-based	GBDT importance, TreeSHAP, random forest
Knockoff methods	Copula knockoff (D-vine, FDR-controlled via e-values), derandomized knockoffs
Embedded	OA-Elastic Net, Joint AUC+L1, HSIC-Lasso
Other	IPSS, k-TSP, OVA/ECOC wrappers, Rashomon importance

Methods are aggregated via MNPO with configurable oracle presets (minimal, perf_only, perf_complexity, full, etc.).

See BACKGROUND.md for the full list of implemented papers, USING.md for detailed usage, and RESULTS.md for benchmark results.

Installation

Core dependencies (numpy, pandas, scipy, scikit-learn):

pip install tabnetics

With optional feature-selection extras (boruta, copula support, conformal prediction):

pip install tabnetics[feature-selection-optional]

With full benchmark support (FLAML, LightGBM, XGBoost, TabPFN, etc.):

pip install tabnetics[benchmarks]

Requirements

• Python >= 3.11
• numpy, pandas, scipy, scikit-learn (core)
• See pyproject.toml for optional dependency groups

Development

git clone https://github.com/klokedm/tabnetics-public.git
cd tabnetics-public
pip install -e ".[dev]"
pytest

License

Apache 2.0 — see LICENSE.

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This documentation is auto-generated from internal notes and sources with the support of rule-based transformations and generative AI. Errors are possible — please report any issues via Discussions.