A python library for implementing a recommender system

Overview

python-recsys

A python library for implementing a recommender system.

Installation

Dependencies

python-recsys is build on top of Divisi2, with csc-pysparse (Divisi2 also requires NumPy, and uses Networkx).

python-recsys also requires SciPy.

To install the dependencies do something like this (Ubuntu):

sudo apt-get install python-scipy python-numpy
sudo apt-get install python-pip
sudo pip install csc-pysparse networkx divisi2

# If you don't have pip installed then do:
# sudo easy_install csc-pysparse
# sudo easy_install networkx
# sudo easy_install divisi2

Download

Download python-recsys from github.

Install

tar xvfz python-recsys.tar.gz
cd python-recsys
sudo python setup.py install

Example

  1. Load Movielens dataset:
from recsys.algorithm.factorize import SVD
svd = SVD()
svd.load_data(filename='./data/movielens/ratings.dat',
            sep='::',
            format={'col':0, 'row':1, 'value':2, 'ids': int})
  1. Compute Singular Value Decomposition (SVD), M=U Sigma V^t:
k = 100
svd.compute(k=k,
            min_values=10,
            pre_normalize=None,
            mean_center=True,
            post_normalize=True,
            savefile='/tmp/movielens')
  1. Get similarity between two movies:
ITEMID1 = 1    # Toy Story (1995)
ITEMID2 = 2355 # A bug's life (1998)

svd.similarity(ITEMID1, ITEMID2)
# 0.67706936677315799
  1. Get movies similar to Toy Story:
svd.similar(ITEMID1)

# Returns: <ITEMID, Cosine Similarity Value>
[(1,    0.99999999999999978), # Toy Story
 (3114, 0.87060391051018071), # Toy Story 2
 (2355, 0.67706936677315799), # A bug's life
 (588,  0.5807351496754426),  # Aladdin
 (595,  0.46031829709743477), # Beauty and the Beast
 (1907, 0.44589398718134365), # Mulan
 (364,  0.42908159895574161), # The Lion King
 (2081, 0.42566581277820803), # The Little Mermaid
 (3396, 0.42474056361935913), # The Muppet Movie
 (2761, 0.40439361857585354)] # The Iron Giant
  1. Predict the rating a user (USERID) would give to a movie (ITEMID):
MIN_RATING = 0.0
MAX_RATING = 5.0
ITEMID = 1
USERID = 1

svd.predict(ITEMID, USERID, MIN_RATING, MAX_RATING)
# Predicted value 5.0

svd.get_matrix().value(ITEMID, USERID)
# Real value 5.0
  1. Recommend (non-rated) movies to a user:
svd.recommend(USERID, is_row=False) #cols are users and rows are items, thus we set is_row=False

# Returns: <ITEMID, Predicted Rating>
[(2905, 5.2133848204673416), # Shaggy D.A., The
 (318,  5.2052108435956033), # Shawshank Redemption, The
 (2019, 5.1037438278755474), # Seven Samurai (The Magnificent Seven)
 (1178, 5.0962756861447023), # Paths of Glory (1957)
 (904,  5.0771405690055724), # Rear Window (1954)
 (1250, 5.0744156653222436), # Bridge on the River Kwai, The
 (858,  5.0650911066862907), # Godfather, The
 (922,  5.0605327279819408), # Sunset Blvd.
 (1198, 5.0554543765500419), # Raiders of the Lost Ark
 (1148, 5.0548789542105332)] # Wrong Trousers, The
  1. Which users should see Toy Story? (e.g. which users -that have not rated Toy Story- would give it a high rating?)
svd.recommend(ITEMID)

# Returns: <USERID, Predicted Rating>
[(283,  5.716264440514446),
 (3604, 5.6471765418323141),
 (5056, 5.6218800339214496),
 (446,  5.5707524860615738),
 (3902, 5.5494529168484652),
 (4634, 5.51643364021289),
 (3324, 5.5138903299082802),
 (4801, 5.4947999354188548),
 (1131, 5.4941438045650068),
 (2339, 5.4916048051511659)]

Documentation

Documentation and examples available here.

To create the HTML documentation files from doc/source do:

cd doc
make html

HTML files are created here:

doc/build/html/index.html
Owner
Oscar Celma
I used to code. Now I barely remember how to do it
Oscar Celma
TLDR: Twin Learning for Dimensionality Reduction

TLDR (Twin Learning for Dimensionality Reduction) is an unsupervised dimensionality reduction method that combines neighborhood embedding learning with the simplicity and effectiveness of recent self

NAVER 105 Dec 28, 2022
The aim of the game, as in the original one, is to find a specific image from a group of different images of a person's face

GUESS WHO Main Links: [Github] [App] Related Links: [CLIP] [Celeba] The aim of the game, as in the original one, is to find a specific image from a gr

Arnau - DIMAI 3 Jan 04, 2022
Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach

This repository holds the implementation for paper Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach Download our preproc

Qitian Wu 42 Dec 27, 2022
Equivariant CNNs for the sphere and SO(3) implemented in PyTorch

Equivariant CNNs for the sphere and SO(3) implemented in PyTorch

Jonas Köhler 893 Dec 28, 2022
PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

943 Jan 07, 2023
ServiceX Transformer that converts flat ROOT ntuples into columnwise data

ServiceX_Uproot_Transformer ServiceX Transformer that converts flat ROOT ntuples into columnwise data Usage You can invoke the transformer from the co

Vis 0 Jan 20, 2022
Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.

Torch-template-for-deep-learning Pytorch implementations of some **classical backbone CNNs, data enhancement, torch loss, attention, visualization and

Li Shengyan 270 Dec 31, 2022
A learning-based data collection tool for human segmentation

FullBodyFilter A Learning-Based Data Collection Tool For Human Segmentation Contents Documentation Source Code and Scripts Overview of Project Usage O

Robert Jiang 4 Jun 24, 2022
[ICCV 2021 Oral] PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers

PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers Created by Xumin Yu*, Yongming Rao*, Ziyi Wang, Zuyan Liu, Jiwen Lu, Jie Zhou

Xumin Yu 317 Dec 26, 2022
Boosted neural network for tabular data

XBNet - Xtremely Boosted Network Boosted neural network for tabular data XBNet is an open source project which is built with PyTorch which tries to co

Tushar Sarkar 175 Jan 04, 2023
Unofficial implementation of Proxy Anchor Loss for Deep Metric Learning

Proxy Anchor Loss for Deep Metric Learning Unofficial pytorch, tensorflow and mxnet implementations of Proxy Anchor Loss for Deep Metric Learning. Not

Geonmo Gu 3 Jun 09, 2021
Non-Vacuous Generalisation Bounds for Shallow Neural Networks

This package requires jax, tensorflow, and numpy. Either tensorflow or scikit-learn can be used for loading data. To run in a nix-shell with required

Felix Biggs 0 Feb 04, 2022
An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and Machine Learning.

ALgorithmic_Trading_with_ML An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and

1 Mar 14, 2022
Object detection and instance segmentation toolkit based on PaddlePaddle.

Object detection and instance segmentation toolkit based on PaddlePaddle.

9.3k Jan 02, 2023
Source code of the paper "Deep Learning of Latent Variable Models for Industrial Process Monitoring".

Source code of the paper "Deep Learning of Latent Variable Models for Industrial Process Monitoring".

Xiangyin Kong 7 Nov 08, 2022
Anti-UAV base on PaddleDetection

Paddle-Anti-UAV Anti-UAV base on PaddleDetection Background UAVs are very popular and we can see them in many public spaces, such as parks and playgro

Qingzhong Wang 2 Apr 20, 2022
Object Depth via Motion and Detection Dataset

ODMD Dataset ODMD is the first dataset for learning Object Depth via Motion and Detection. ODMD training data are configurable and extensible, with ea

Brent Griffin 172 Dec 21, 2022
Unsupervised Image to Image Translation with Generative Adversarial Networks

Unsupervised Image to Image Translation with Generative Adversarial Networks Paper: Unsupervised Image to Image Translation with Generative Adversaria

Hao 71 Oct 30, 2022
PyArmadillo: an alternative approach to linear algebra in Python

PyArmadillo is a linear algebra library for the Python language, with an emphasis on ease of use.

Terry Zhuo 58 Oct 11, 2022
Implementation of the Transformer variant proposed in "Transformer Quality in Linear Time"

FLASH - Pytorch Implementation of the Transformer variant proposed in the paper Transformer Quality in Linear Time Install $ pip install FLASH-pytorch

Phil Wang 209 Dec 28, 2022