# To be filled by the author(s) at the time of submission
# -------------------------------------------------------

# Title of the article:
#  - For a successful replication, it should be prefixed with "[Re]"
#  - For a failed replication, it should be prefixed with "[¬Re]"
#  - For other article types, no instruction (but please, not too long)
# Change the default title
title: "[Re] Bandit Theory and Thompson Sampling-guided Directed Evolution for Sequence Optimization"

# List of authors with name, orcid number, email and affiliation
# Affiliation "*" means contact author (required even for single-authored papers)
authors:
  - name: Luka Žontar
    orcid: 0009-0004-8918-843X
    email: luka.zontar@outlook.com
    affiliations: 1,*

# List of affiliations with code (corresponding to author affiliations), name
# and address. You can also use these affiliations to add text such as "Equal
# contributions" as name (with no address).
affiliations:
  - code:    1
    name:    University of Ljubljana
    address: Ljubljana, Slovenia


# List of keywords (adding the programming language might be a good idea)
keywords:  rescience c, machine learning, optimization, bandit learning, Thompson sampling, Bayesian regret, evolution, python

# Code URL and DOI/SWH (url is mandatory for replication, doi after acceptance)
# You can get a DOI for your code from Zenodo, or an SWH identifier from
# Software Heritage.
#   see https://guides.github.com/activities/citable-code/
code:
  - url: https://github.com/lukazontar/RC2022-Bandit-Theory-and-Thompson-Sampling-Guided-Directed-Evolution-for-Sequence-Optimization
  - doi: 
  - swh: swh:1:dir:7cffa7d64bb51bac56c43f657e2e3746a2539915

# Data URL and DOI (optional if no data)
data:
  - url:
  - doi:

# Information about the original article that has been replicated
replication:
 - cite: Hui Yuan, Chengzhuo Ni, Huazheng Wang, Xuezhou Zhang, Le Cong, Csaba Szepesvári, Mengdi Wang. "Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization" Conference on Neural Information Processing Systems. NeurIPS, 2022.
 - bib:  yuan2022bandit
 - url:  https://proceedings.neurips.cc/paper_files/paper/2022/file/fa3c139cf8084de7bfd944f1c90c8695-Paper-Conference.pdf
 - doi:  10.48550/arXiv.2206.02092

# Don't forget to surround abstract with double quotes
abstract: "The paper presents a novel DE approach using Thompson Sampling and Bandit theory, TS-DE. Our reproducibility study aims to confirm the 5 main claims of the original paper, including sublinear Bayesian regret, improved performance compared to basic DE, robustness to mutation rate changes, initial diversification, and the concentration of the population to the optimal value in later iterations, and iterative distribution shift towards optimal population fitness. Finally, we provide a reproducible environment to support the main claims of the original paper along with the source code of the proposed approach and all experiments, comprehensive documentation, and unit tests.

No code was available beforehand for this article, thus we re-implemented the proposed approach by meticulously following the comprehensive explanations of the process in the original article. The experiments were run on a personal computer.

We managed to reproduce all the experiments supporting the main claims of the original article. Additionally, we add uncertainty quantification to the results as we believe this is a crucial part to confirm any of the claims. Finally, we present the exploration-exploitation trade-off experiment in a more robust manner leveraging the nucleotide diversity metric to gain additional insight into how the proposed algorithm works.

With comprehensive explanations in the original article, it was relatively easy to rewrite the main concepts from pseudo-code to Python code. Experiments were clearly explained with all the necessary hyperparameters.

Since no source code was available, every detail missing from the original article resulted in additional research and trial and error experimentation. To conclude, we recommend several improvements to the authors of the studied paper that could additionally improve the quality of their outstanding contribution. Some of the recommendations include better explanations of how the optimal solution is calculated, on which population the PCA is fitted, how to use certain variables and lastly improved documentation on how the basic DE was implemented."

# Bibliography file (yours)
bibliography: bibliography.bib

# Type of the article
# Type can be:
#  * Editorial
#  * Letter
#  * Replication
type: Replication

# Scientific domain of the article (e.g. Computational Neuroscience)
#  (one domain only & try to be not overly specific)
domain: ML Reproducibility Challenge 2022

# Coding language (main one only if several)
language: Python


# To be filled by the author(s) after acceptance
# -----------------------------------------------------------------------------

# For example, the URL of the GitHub issue where review actually occured
review:
  - url: https://openreview.net/forum?id=NE_x1dpz-Q

contributors:
  - name: 
    orcid:
    role: editor
  - name: Anonymous Reviewers
    orcid:
    role: reviewer
  - name:
    orcid:
    role: reviewer


# This information will be provided by the editor
dates:
  - received:
  - accepted:
  - published:


# This information will be provided by the editor
article:
  - number: 1
  - doi:
  - url:

# This information will be provided by the editor
journal:
  - name: "ReScience C"
  - issn: 2430-3658
  - volume: 9
  - issue: 2