ReSyPE: Recommender System in Python Environment

Introduction

ReSyPE (pronounced recipe) is a Python library built for both practitioners and researchers that wish to quickly develop and deploy ML-based recommender systems.

The library provides an end-to-end pipeline that includes:

1. Loading transaction, user feature, and item feature datasets
2. Interchangable methods for user and item clustering
3. Modular framework for machine learning models
4. Iterative and decomposition-based techniques

Installation

pip install resype

Setup

The package uses Python 3 and virtualenv to manage the environment. Once you've cloned this repo run this from the root of the repo to create the environment:

conda env create -f environment.yml
source activate resype

Getting Started

import pandas as pd
import numpy as np
from resype.collab_filtering import CollabFilteringModel

# load transaction list
transaction_list = pd.read_csv("sample_data/ratings.csv")
transaction_list = transaction_list[['userId', 'movieId', 'rating']]
transaction_list = transaction_list.sample(20)
transaction_list.columns = ["user_id", 'item_id', 'rating']

re = CollabFilteringModel(transaction_list)

# construct utlity matrix
re.construct_utility_matrix()

# import sklearn Model
from sklearn.neural_network import MLPClassifier, MLPRegressor
mlp1 = MLPRegressor(hidden_layer_sizes=(50, 50))

# fit and predict
re.fit(mlp1, method='iterative')

# recommend
user_list = [0, 1, 2] # indices
top_n = 10
re.get_rec(user_list, top_n)

Comparison with Surprise

Performance