VBPCApy

Variational Bayesian PCA (Ilin & Raiko, 2010) with support for missing data, sparse masks, optional bias terms, and an orthogonal post-rotation to a PCA basis. The implementation follows the original MATLAB reference while adding Python-native APIs, fast C++ extensions, and runtime autotuning.

Documentation · API Reference · Tutorials

Statement of need

Missing values are common in scientific and industrial tabular datasets, but many analysis pipelines either impute first (masking uncertainty) or drop incomplete samples. VBPCApy models missingness directly and exposes posterior uncertainty outputs alongside reconstructions, enabling uncertainty-aware latent-factor analysis in a single reproducible Python API.

Installation

From PyPI (pre-built wheels for Python 3.11–3.14, Linux/macOS/Windows):

pip install vbpca-py

With plotting support:

pip install vbpca-py[plot]

See the installation guide for building from source and Eigen setup.

Quick start

import numpy as np
from vbpca_py import VBPCA

# 50 features, 200 samples
x = np.random.randn(50, 200)
mask = np.ones_like(x)  # 1 = observed, 0 = missing

model = VBPCA(n_components=5, maxiters=100)
scores = model.fit_transform(x, mask=mask)
recon = model.reconstruction_
var = model.variance_

More examples: quickstart, dense PCA tutorial, missing data & model selection, genomics dosage data, and sparse data.

Features

• Dense or sparse data with explicit missing-entry masks
• Optional bias estimation and rotation to PCA-aligned solution
• Posterior covariances for scores and loadings; held-out probe RMS
• C++ extensions with runtime autotune for threading and memory
• Missing-aware preprocessing: one-hot, standard/minmax scaling, log, power, winsorize, auto-routing (AutoEncoder)
• Preflight data diagnostics via check_data() / DataReport
• scikit-learn-compatible estimator (fit/transform/inverse_transform, get_params/set_params, cloning) — scikit-learn is an optional dependency
• Model selection via select_n_components and cross_validate_components
• Configurable convergence: subspace angle, RMS/cost plateau, ELBO, curvature, composite rules, patience, with per-criterion enable/disable and custom ordering
• Convergence diagnostics (n_iter_, converged_, convergence_reason_, learning_curve_, best-probe state metadata) and calibrated predictive_variance_ (includes observation noise)
• recommend_config(n, p, priority) — regime-aware default hyperparameters from a surrogate trade study

See the concept guides and API reference for full details.

Categorical component selection

Dense AutoEncoder and MissingAwareOneHotEncoder fits expose an encoding_schema_ snapshot (EncodingSchema / EncodedVariable, exported from vbpca_py). Pass it to CV to hold out whole variable cells and decode single-binary, dropped-reference, centered and weighted indicators before categorical scoring:

from vbpca_py import AutoEncoder, CVConfig, cross_validate_components

encoder = AutoEncoder(
    column_types=["categorical", "categorical", "continuous"],
    drop="first",
    block_weighting="equal_variance",
    handle_unknown="raise",
)
Z = encoder.fit_transform(X)  # samples x encoded features
best_k, results = cross_validate_components(
    Z.T,
    components=range(5),
    config=CVConfig(encoding_schema=encoder.encoding_schema_, metric="brier"),
)

Continuous and ordinal variables contribute to encoded probe RMS, but are excluded from nominal categorical scores. Gaussian reconstructions are decoded, clipped at 1e-6 and normalized as approximate category probabilities; this is not a calibrated categorical likelihood. Equivalent decoded predictions have equivalent scores; refitting different encodings can yield different predictions. Schema-free categorical selection requires full unweighted one-hot blocks and rejects singleton groups and transformed targets. feature_groups alone cannot identify binary versus numeric columns.

This API splits an already encoded matrix. A schema describes the transform; use the raw-table API below when learning preprocessing within CV folds. Unknown categories cannot be distinguished from a dropped reference when handle_unknown="ignore"; use "raise" when scoring new raw data. See the model-selection guide and benchmark validation note.

Raw-table component selection

cross_validate_raw_components accepts samples × original variables, with NaNs or an explicit observation mask. It holds out original variable cells before fitting means, scales, categorical centering and block weights. A fresh AutoEncoder is fitted once per fold and reused across every candidate rank, including first-minimum early stopping.

from vbpca_py import AutoEncoder, CVConfig, cross_validate_raw_components

def make_encoder():
    return AutoEncoder(
        column_types=["categorical", "continuous", "ordinal"],
        # External codebook; do not derive this from held-out values.
        column_levels=[["no", "yes"], None, ["low", "medium", "high"]],
        mean_center_ohe=True,
        block_weighting="equal_variance",
        handle_unknown="raise",
    )

best_k, results, folds = cross_validate_raw_components(
    X, encoder_factory=make_encoder, components=range(4),
    config=CVConfig(n_splits=3, metric="brier", seed=11),
    maxiters=200,
)

Declare column kinds explicitly. column_levels=None (or a None entry) learns levels from training cells; a held-only unknown level raises an error. External nominal vocabularies and ordinal orders support declared levels absent from training. Each returned PreprocessedFold exposes original training/holdout masks, encoded matrices, the fitted encoder and its schema; prepare_raw_cv_folds constructs these without fitting candidate models.

Results include nominal Brier/log/accuracy scores and rmse_variable_j for each numeric input column: continuous values in input units, ordinal values in unrounded level-index units. Numeric summaries report contributing fold counts; encoded prms is also retained. This predicts cells of the same samples; sample-wise sklearn CV evaluates new samples. Run the complete mixed-data example with just example-raw-cv, or python scripts/example_raw_cell_cv.py after installing the package.

Development

The 0.4.6 release validation describes the paired fitting-compatibility check and package/adoption gates. General scoring and benchmark validation commands are in scripts/benchmark_study.md.

git clone https://github.com/yoavram-lab/VBPCApy.git
cd VBPCApy
uv sync --extra dev --extra plot
just ci          # lint + typecheck + test
just docs-serve  # local docs preview

See CONTRIBUTING.md for guidelines.

The trade-a-* analysis recipes require a local trade-study checkout. After syncing the environment, install its full extras (including adaptive optimisation, surrogate fitting and sensitivity analysis):

# Default: trade-study is checked out alongside VBPCApy.
just trade-a-install-adaptive

# Override the checkout location, including paths containing spaces.
TRADE_STUDY_PATH="/path/to/trade study" just trade-a-install-adaptive

Citation

If you use this package in your research, please cite:

@software{vbpca_py2026,
  author = {Macdonald, Joshua and Naim, Shany and Ram, Yoav},
  title = {{VBPCApy}: Variational Bayesian PCA with Missing Data Support},
  year = {2026},
  url = {https://github.com/yoavram-lab/VBPCApy},
  version = {0.4.6},
}

@article{ilin2010practical,
  title={Practical Approaches to Principal Component Analysis in the Presence of Missing Values},
  author={Ilin, Alexander and Raiko, Tapani},
  journal={Journal of Machine Learning Research},
  volume={11},
  pages={1957--2000},
  year={2010}
}

See CITATION.cff for machine-readable metadata.

License

MIT — see LICENSE.