Inspired by gbolmler implementation of SVD using numba. This repo contains reimplementation of kNN and common matrix factorization methods using numba library to accelarate numpy operations. Numba is a cool library and you need to give this a shot for future implementation using numpy.

The algorithms in this repo are tested on the Movielens 20M Dataset.

This is a big dataset. In order to extract the dataset to get a smaller dataset, first you need to download MovieLens 20M and save it on your computer, for example, to movielens20M folder. Then you need to create a folder movilens-sample for the new sampling dataset.

On utils/sample_movielens.py you can change the parameter to your like.

if __name__ == "__main__":
    sample_movielens(
       "movielens20M",
       "movielens-sample",
       sample_size=1000
    )

where "movielens20M" is the folder contains MovieLens 20M Dataset, "movielens-sample" is the folder contains new extracted dataset. Size of the extracted dataset can be changed via sample_size.

The algorithms in this repo are also tested on the Netflix Prize dataset. Published by Netflix, the dataset contains a training set of 100 million ratings, which includes a probe set of 1 million ratings. However, the qualifying dataset has not been published anywhere (to my knowledge).

For that reason, the scipt in utils/split_netflix_dataset.py first uses the probe set as the validation set, then split the remaining ratings into training set and testing set. The output contains 3 distinct files, rating_train.csv, rating_test.csv, rating_val.csv just like MovieLens 20M, and can be loaded into the algorithms using utils/DataLoader.

Folder /examples contains test runs on MovieLens dataset.

Compare to NicolasHug/Surprise, the runtime of kNNBaseline using Pearson similarity scores is much faster (817s compared to 3166s of Surprise on MovieLens 20M dataset).