irec 1.2.4

Reinforcement Learning Recommender Systems Framework

Introduction

For Python >= 3.9

Reinforcement Learning Recommender Systems Framework

Main features:

• Several state-of-the-art reinforcement learning models for the recommendation scenario
• Novelty, coverage and much more different type of online metrics
• Integration with the most used datasets for evaluating recommendation systems
• Flexible configuration
• Modular and reusable design
• Contains multiple evaluation policies currently used in the literature to evaluate reinforcement learning models
• Online Learning and Reinforcement Learning models
• Metrics and metrics evaluators are awesome to evaluate recommender systems in different ways

Also, we provide a amazing application created using the IRec library (under the app/ folder) that can be used to setup a experiment under 5~ minutes with parallel processes, log registry and results views. The main features are:

• Powerful application to run any reinforcement learning experiment powered by MLflow
• Entire pipeline of execution is fully parallelized
• Log registry
• Results views
• Statistical test
• Extensible environment

Install

Install with pip:

pip install irec

Examples

Under app/ folder is a example of a application using irec and mlflow, where different experiments can be run with easy using existing recommender systems.

Check this example of a execution using the example application:

cd app
metrics=(Hits Precision Recall);
models=(Random MostPopular UCB ThompsonSampling EGreedy);
metric_evaluator="IterationsMetricEvaluator"
bases=("Netflix 10k" "Good Books" "Yahoo Music 10k");
# run agents
./run_agent_best.py --dataset_loaders "${bases[@]}" --agents "${models[@]}"

# evaluate agents using the metrics and metric evaluator defined
./eval_agent_best.py --dataset_loaders "${bases[@]}"\
--agents "${models[@]}" --metrics "${metrics[@]}"\
--metric_evaluator="$metric_evaluator"

# print latex table with results and statistical test
./print_latex_table_results.py --dataset_loaders "${bases[@]}"\
--agents "${models[@]}" --metrics "${metrics[@]}"\
--metric_evaluator="$metric_evaluator"

API

For writing anything new to the library (e.g., value function, agent, etc) read the documentation.

Contributing

All contributions are welcome! Just open a pull request.