ppca-py 1.0.1

Probabilistic PCA (PPCA) with missing-data support - fast C++ core, clean Python API

ppca-cpp

Probabilistic PCA (PPCA) with missing-data support — fast C++ core, clean Python API.

Overview

ppca-cpp implements Probabilistic Principal Component Analysis (PPCA) as described by Tipping & Bishop (1999), with a focus on speed, usability, and robust handling of missing data. The core is written in C++ (Armadillo + CARMA + pybind11), exposed via a simple Python interface.

Key Features

• Handles missing values natively: No need for manual imputation—just use np.nan for missing entries.
• Familiar API: Drop-in replacement for scikit-learn PCA with attributes like components_, explained_variance_, etc.
• Probabilistic modeling: Compute log-likelihoods, posterior latent variable distributions, multiple imputations, and more.
• Fast and scalable: Optimized C++ backend for large datasets.
• Flexible: Supports both batch and online (mini-batch) EM.

Quick Start

pip install ppca-py

Note: pre-built wheels are produced only for Linux and macOS (CI builds target ubuntu-latest and macos-latest). On other platforms (e.g. Windows) you will need to build from source (see further below).

Usage example:

import numpy as np
from ppca import PPCA

X_train = np.random.randn(600, 10) + 0.1  # (n_samples, n_features)
X_train[::7, 3] = np.nan                  # missing values
X_test = np.random.randn(100, 10) + 0.1
X_test[::7, 2] = np.nan                   # missing values

model = PPCA(n_components=3, batch_size=200)
model.fit(X_train)

mZ, covZ = model.posterior_latent(X_test) # latent representation
mX, covX = model.likelihood(mZ)           # reconstruction
ll = model.score_samples(X_test)          # data log likelihood

# multiple imputation (return shape: (n_draws, n_samples, n_features))
X_imputed = model.sample_missing(X_test, n_draws=5)

# estimate of components, mean and noise variance
print("Components:", model.components_)
print("Mean:", model.mean_)
print("Noise variance:", model.noise_variance_)

For a short PPCA reference doc see docs/ppca.md, and some usage examples are provided in examples/.

Installation from Source

Minimum requirements

• CMake >= 3.18
• Python >= 3.9 (+ development headers)
• C++17-capable compiler (clang on macOS, gcc on Linux, MSVC on Windows)
• BLAS/LAPACK implementation (OpenBLAS, MKL, or Accelerate on macOS)
• git (to fetch submodules)
• Network access (CMake will download Armadillo into extern by default) or provide extern/armadillo-<version>/ or a system Armadillo install

Quick install (fresh clone)

git clone https://github.com/brdav/ppca-cpp.git
cd ppca-cpp
git submodule update --init --recursive   # ensure extern/carma is present
python -m pip install .                   # build and install

Editable install for development

git clone https://github.com/brdav/ppca-cpp.git
cd ppca-cpp
git submodule update --init --recursive
python -m pip install -e '.[dev]'         # editable install
pre-commit install                        # optional: register hooks

Note: Builds on Windows are untested in CI. You can attempt a Windows build but expect manual steps.

Internals

PPCA uses an Expectation-Maximization (EM) algorithm to learn parameters through maximum likelihood estimation. For details see the reference paper listed below. The equations for the EM algorithm in the presence of missing values are shown in docs/equations.md.

Citing

If you use this code academically, cite the original PPCA paper:

• M. Tipping & C. Bishop. Probabilistic Principal Component Analysis. JRSS B, 1999.

You may also reference the library name or URL.

License

MIT License — see LICENSE.

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Questions or requests? Open an issue.