tabnetics 0.1.0

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 combines distribution-aware preprocessing, portfolio-based feature selection, and game-theoretic method aggregation into a single pipeline designed for settings where p >> n.

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.

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

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 feature set.

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.