eals 0.9.1

eALS - Element-wise Alternating Least Squares

eALS - Element-wise Alternating Least Squares

A Python implementation of the element-wise alternating least squares (eALS) for fast online matrix factorization proposed by arXiv:1708.05024.

Prerequisites

• Python >= 3.8

Installation

pip install eals

Usage

from eals import ElementwiseAlternatingLeastSquares, load_model

# Batch training
model = ElementwiseAlternatingLeastSquares()
model.fit(rating_data)

# Learned latent vectors
model.user_factors
model.item_factors

# Online training for new data
model.update_model(user_id, item_id)

# Save and load the model
model.save("model.joblib")
model = load_model("model.joblib")

See the examples directory for complete examples.

Development

Setup development environment

git clone https://github.com/newspicks/eals.git
cd eals
poetry run pip install -U pip
poetry install

Tests

poetry run pytest

Set USE_NUMBA=0 for faster testing without numba JIT overhead.

USE_NUMBA=0 poetry run pytest

To run tests against all supported Python versions, use tox.

poetry run tox