ppca-rs 0.1.0

Python+Rust implementation of the Probabilistic Principal Component Analysis model

Probabilistic Principal Component Analysis model

This project implements a PPCA model implemented in Rust for Python using pyO3 and maturin.

Installing

PyPI package comming soon:

pip install ppca_rs # hopefully!

Quick example

import numpy as np
from ppca_rs import Dataset, PPCATrainer, PPCA

samples: np.ndarray

# Create your dataset from a rank 2 np.ndarray, where each line is a sample.
# Use non-finite values (`inf`s and `nan`) to signal masked values
dataset = Dataset(samples)

# Train the model:
model: PPCAModel = PPCATrainer(dataset).train(state_size=10, n_iters=10)


# And now, let's have fun!

# Extrapolates the missing values with the most probable values:
model.extrapolate(dataset)

# Smooths (removes noise from) samples and fills in missing values:
model.filter_extrapolate(dataset)

Building from soure

Prerequisites

You will need Rust, which can be installed locally (i.e., without sudo) and you will also need maturin, which can be installed by

pip install maturin

pipenv is also a good idea if you are going to mess around with it locally. At least, you need a venv set, otherwise, maturin will complain with you.

Installing it locally

Check the Makefile for the available commands (or just type make). To install it locally, do

make install    # optional: i=python.version (e.g, `i=3.9`)

Messing around and testing

To mess around, inside a virtual environment (a Pipfile is provided for the pipenv lovers), do

maturin develop  # use the flag --release to unlock superspeed!

This will install the package locally as is from source.

How do I use this stuff?

See the examples in the examples folder. Also, all functions are type hinted and commented. If you are using pylance or mypy, it should be easy to navigate.

Is it faster than the pure Python implemetation you made?

You bet!