dapqa-fast-mf

Fast implementations of SVD and SVD++ matrix factorization algorithms.
Powered by Numba.

Usage

Models available are SVD, SVDpp.
All the parameters are set via constructor.

Input data contain two columns -- user_id and item_id.
Output data contain ratings.

IMPORTANT
Data must be sorted by user_id.
To sort data, there is a helper function dfmf.util.sort_by_user.

Example usage:

import numpy as np
from sklearn.model_selection import train_test_split

from dfmf.model import SVD
from dfmf.util import sort_by_user

data = np.genfromtxt('my-fancy-dataset.csv', delimiter=',')
X, y = data[:, 0:2], data[:, 2]

X_train, X_test, y_train, y_test = sort_by_user(
    *train_test_split(X, y, test_size=0.2)
)

model = SVD()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

Benchmarking

To run benchmarks, execute

python -m pytest benchmark --benchmark-autosave

in a virtual environment console in the root directory.
As in raw pytest, keywords can be used to filter benchmarks. For example, only ml100k benchmarks can be run using

python -m pytest benchmark --benchmark-autosave -k ml100k

To view or compare existing benchmark results, run

py.test-benchmark compare .benchmarks/Windows-CPython-3.9-64bit/saved-file-name.json