A python fast implementation of the famous SVD algorithm popularized by Simon Funk during Netflix Prize

Overview

⚑ funk-svd Build Status License

funk-svd is a Python 3 library implementing a fast version of the famous SVD algorithm popularized by Simon Funk during the Neflix Prize contest.

Numba is used to speed up our algorithm, enabling us to run over 10 times faster than Surprise's Cython implementation (cf. benchmark notebook).

Movielens 20M RMSE MAE Time
Surprise 0.88 0.68 10 min 40 sec
Funk-svd 0.88 0.68 42 sec

Installation

Run pip install git+https://github.com/gbolmier/funk-svd in your terminal.

Contributing

All contributions, bug reports, bug fixes, enhancements, and ideas are welcome.

A detailed overview on how to contribute can be found in the contributor guide.

Quick example

run_experiment.py:

>>> from funk_svd.dataset import fetch_ml_ratings
>>> from funk_svd import SVD

>>> from sklearn.metrics import mean_absolute_error


>>> df = fetch_ml_ratings(variant='100k')

>>> train = df.sample(frac=0.8, random_state=7)
>>> val = df.drop(train.index.tolist()).sample(frac=0.5, random_state=8)
>>> test = df.drop(train.index.tolist()).drop(val.index.tolist())

>>> svd = SVD(lr=0.001, reg=0.005, n_epochs=100, n_factors=15,
...           early_stopping=True, shuffle=False, min_rating=1, max_rating=5)

>>> svd.fit(X=train, X_val=val)
Preprocessing data...

Epoch 1/...

>>> pred = svd.predict(test)
>>> mae = mean_absolute_error(test['rating'], pred)

>>> print(f'Test MAE: {mae:.2f}')
Test MAE: 0.75

Funk SVD for recommendation in a nutshell

We have a huge sparse matrix:

storing known ratings for a set of users and items:

The idea is to estimate unknown ratings by factorizing the rating matrix into two smaller matrices representing user and item characteristics:

We call these two matrices users and items latent factors. Then, by applying the dot product between both matrices we can reconstruct our rating matrix. The trick is that the empty values will now contain estimated ratings.

In order to get more accurate results, the global average rating as well as the user and item biases are used in addition:

where K stands for known ratings.

Then, we can estimate any rating by applying:

The learning step consists in performing the SGD algorithm where for each known rating the biases and latent factors are updated as follows:

where alpha is the learning rate and lambda is the regularization term.

References

License

MIT license, see here.

Owner
Geoffrey Bolmier
Geoffrey Bolmier
Bayesian Additive Regression Trees For Python

BartPy Introduction BartPy is a pure python implementation of the Bayesian additive regressions trees model of Chipman et al [1]. Reasons to use BART

187 Dec 16, 2022
distfit - Probability density fitting

Python package for probability density function fitting of univariate distributions of non-censored data

Erdogan Taskesen 187 Dec 30, 2022
Implementation of linesearch Optimization Algorithms in Python

Nonlinear Optimization Algorithms During my time as Scientific Assistant at the Karlsruhe Institute of Technology (Germany) I implemented various Opti

Paul 3 Dec 06, 2022
Continuously evaluated, functional, incremental, time-series forecasting

timemachines Autonomous, univariate, k-step ahead time-series forecasting functions assigned Elo ratings You can: Use some of the functionality of a s

Peter Cotton 343 Jan 04, 2023
Upgini : data search library for your machine learning pipelines

Automated data search library for your machine learning pipelines β†’ find & deliver relevant external data & features to boost ML accuracy :chart_with_upwards_trend:

Upgini 175 Jan 08, 2023
A Pythonic framework for threat modeling

pytm: A Pythonic framework for threat modeling Introduction Traditional threat modeling too often comes late to the party, or sometimes not at all. In

Izar Tarandach 644 Dec 20, 2022
Lseng-iseng eksplor Machine Learning dengan menggunakan library Scikit-Learn

Kalo dengar istilah ML, biasanya rada ambigu. Soalnya punya beberapa kepanjangan, seperti Mobile Legend, Makan Lontong, Ma**ng L*v* dan lain-lain. Tapi pada repo ini membahas Machine Learning :)

Alfiyanto Kondolele 1 Apr 06, 2022
A repository for collating all the resources such as articles, blogs, papers, and books related to Bayesian Statistics.

A repository for collating all the resources such as articles, blogs, papers, and books related to Bayesian Statistics.

Aayush Malik 80 Dec 12, 2022
This project used bitcoin, S&P500, and gold to construct an investment portfolio that aimed to minimize risk by minimizing variance.

minvar_invest_portfolio This project used bitcoin, S&P500, and gold to construct an investment portfolio that aimed to minimize risk by minimizing var

1 Jan 06, 2022
Class-imbalanced / Long-tailed ensemble learning in Python. Modular, flexible, and extensible

IMBENS: Class-imbalanced Ensemble Learning in Python Language: English | Chinese/δΈ­ζ–‡ Links: Documentation | Gallery | PyPI | Changelog | Source | Downl

Zhining Liu 176 Jan 04, 2023
Factorization machines in python

Factorization Machines in Python This is a python implementation of Factorization Machines [1]. This uses stochastic gradient descent with adaptive re

Corey Lynch 892 Jan 03, 2023
A machine learning web application for binary classification using streamlit

Machine Learning web App This is a machine learning web application for binary classification using streamlit options this application contains 3 clas

abdelhak mokri 1 Dec 20, 2021
A python library for Bayesian time series modeling

PyDLM Welcome to pydlm, a flexible time series modeling library for python. This library is based on the Bayesian dynamic linear model (Harrison and W

Sam 438 Dec 17, 2022
Contains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments.

GENDIS GENetic DIscovery of Shapelets In the time series classification domain, shapelets are small subseries that are discriminative for a certain cl

IDLab Services 90 Oct 28, 2022
K-means clustering is a method used for clustering analysis, especially in data mining and statistics.

K Means Algorithm What is K Means This algorithm is an iterative algorithm that partitions the dataset according to their features into K number of pr

1 Nov 01, 2021
Built on python (Mathematical straight fit line coordinates error predictor machine learning foundational model)

Sum-Square_Error-Business-Analytical-Tool- Built on python (Mathematical straight fit line coordinates error predictor machine learning foundational m

om Podey 1 Dec 03, 2021
A Lightweight Hyperparameter Optimization Tool πŸš€

The mle-hyperopt package provides a simple and intuitive API for hyperparameter optimization of your Machine Learning Experiment (MLE) pipeline.

Robert Lange 137 Dec 02, 2022
A pure-python implementation of the UpSet suite of visualisation methods by Lex, Gehlenborg et al.

pyUpSet A pure-python implementation of the UpSet suite of visualisation methods by Lex, Gehlenborg et al. Contents Purpose How to install How it work

288 Jan 04, 2023
A modular active learning framework for Python

Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe

modAL 1.9k Dec 31, 2022