Python+Rust implementation of the Probabilistic Principal Component Analysis model
Project description
Probabilistic Principal Component Analysis (PPCA) model
This project implements a PPCA model implemented in Rust for Python using pyO3
and maturin
.
Installing
This package is available in PyPI!
pip install ppca-rs
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 (convenient edition!):
model: PPCAModel = PPCATrainer(dataset).train(state_size=10, n_iters=10)
# And now, here is a free sample of what you can do:
# Extrapolates the missing values with the most probable values:
extrapolated: Dataset = model.extrapolate(dataset)
# Smooths (removes noise from) samples and fills in missing values:
extrapolated: Dataset = model.filter_extrapolate(dataset)
# ... go back to numpy:
eextrapolated_np = extrapolated.numpy()
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!
Project details
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