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Off-Policy Evaluation for Large Action Spaces via Embeddings

This repository contains the code used for the experiments in "Off-Policy Evaluation for Large Action Spaces via Embeddings (ICML2022)" by Yuta Saito and Thorsten Joachims.

A generic implementation of the proposed MIPS estimator is available via Open Bandit Pipeline, so researchers can use the same implementation of our method easily for future research.

Abstract

Off-policy evaluation (OPE) in contextual bandits has seen rapid adoption in real-world systems, since it enables offline evaluation of new policies using only historic log data. Unfortunately, when the number of actions is large, existing OPE estimators -- most of which are based on inverse propensity score weighting -- degrade severely and can suffer from extreme bias and variance. This foils the use of OPE in many applications from recommender systems to language models. To overcome this issue, we propose a new OPE estimator that leverages marginalized importance weights when action embeddings provide structure in the action space. We characterize the bias, variance, and mean squared error of the proposed estimator and analyze the conditions under which the action embedding provides statistical benefits over conventional estimators. In addition to the theoretical analysis, we find that the empirical performance improvement can be substantial, enabling reliable OPE even when existing estimators collapse due to a large number of actions.

Citation

@inproceedings{saito2022off,
  title={Off-Policy Evaluation for Large Action Spaces via Embeddings},
  author={Saito, Yuta and Joachims, Thorsten},
  booktitle={Proceedings of the 39th International Conference on Machine Learning},
  pages={19089--19122},
  year={2022},
  organization={PMLR}
}

Requirements and Setup

The Python environment is built using poetry. You can build the same environment as in our experiments by cloning the repository and running poetry install directly under the folder (if you have not installed poetry yet, please run pip install poetry first).

# clone the repository
git clone https://github.com/usaito/icml2022-mips.git
cd src

# install poetry
pip install poetry

# build the environment with poetry
poetry install

The versions of Python and necessary packages are specified as follows (from pyproject.toml).

[tool.poetry.dependencies]
python = ">=3.9,<3.10"
obp = "0.5.5"
scikit-learn = "1.0.2"
pandas = "1.3.5"
scipy = "1.7.3"
numpy = "^1.22.4"
matplotlib = "^3.5.2"
seaborn = "^0.11.2"
hydra-core = "1.0.7"

Running the Code

The experimental workflow is implemented using Hydra. The commands needed to reproduce the experiments are summarized below. Please move under the src directly first and then run the commands. The experimental results (including the corresponding figures) will be stored in the logs/ directory.

Section 4.1: Synthetic Data

cd src


# How does MIPS perform with varying number of actions?
poetry run python synthetic/main_n_actions.py setting.beta=-1,0,1 setting.eps=0.05,0.8 -m


# How does MIPS perform with varying sample sizes?
poetry run python synthetic/main_n_val.py setting.beta=-1,0,1 setting.eps=0.05,0.8 -m


# How does MIPS perform with varying numbers of deficient actions?
poetry run python synthetic/main_n_def_actions.py setting.beta=-1,0,1 setting.eps=0.05,0.8 -m


# How does MIPS perform with varying number of unobserved embedding dimensions?
poetry run python synthetic/main_n_unobs_cat_dim.py setting.n_cat_per_dim=2 setting.n_cat_dim=20 setting.beta=-1,0,1 setting.eps=0.05,0.8 -m


# How data-driven embedding selection affects the performance of MIPS?
poetry run python synthetic/main_n_val.py setting.n_cat_per_dim=2 setting.n_cat_dim=20 setting.embed_selection=True


# How does MIPS perform with varying logging policies? (in the Appendix)
poetry run python synthetic/main_beta.py


# How does MIPS perform with varying evaluation policies? (in the Appendix)
poetry run python synthetic/main_eps.py


# How does MIPS perform with varying noise levels? (in the Appendix)
poetry run python synthetic/main_noise.py

Section 4.2: Real-World Data

To run the real-world experiment, please download the Open Bandit Dataset (about 11GB) and place it as ./src/real/open_bandit_dataset/. Then, run the following command. It may take a few days.

cd src

poetry run python real/main.py setting.sample_size=10000,50000,100000,500000 -m