How ChatGPT Helped Code a Copula Model Without Human Input

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This study explores how ChatGPT autonomously generated, optimized, and parallelized copula model code in Python and R, highlighting AI's evolving role in coding.

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Abstract and 1 Introduction

2 Methodology

2.1 The task

2.2 The communication protocol

2.3 The copula family

3 Pair programming with ChatGPT

3.1 Warm up

3.2 The density

3.3 The estimation

3.4 The sampling

3.5 The visualization

3.6 The parallelization

4 Summary and discussion

5 Conclusion and Acknowledgments

Appendix A: The solution in Python

Appendix B: The solution in R

References

5 Conclusion

In a human-AI collaboration, we developed working code that implements sampling from a copula model, estimation of its parameter, visualization suggesting that the last two tasks worked properly, and a parallelization of the code for CPUs as well as for GPUs. To illustrate the coding abilities of the AI part, represented by ChatGPT, all the mentioned tasks were implemented without a single line of code written by the human. In addition to presenting how to achieve a successful solution for a given task, we also showed additional examples demonstrating which modifications of our prompts for ChatGPT turned failed solutions to successful ones. This resulted in a comprehensive list of related pros and cons, suggesting that if typical pitfalls can be avoided, we can substantially benefit from a collaboration with an AI partner like ChatGPT.

Acknowledgments

The author thanks the Czech Science Foundation (GACR) for financial sup- ˇ port for this work through grant 21-03085S. The author also thanks to Martin Holeˇna and Marius Hofert for constructive comments and recommendations that definitely helped to improve the readability and quality of the paper.

Appendix A The solution in Python







An example of a redundant code is n = data.shape[0]. As can be observed, the variable n has no use in ClaytonCopulaMLE.

Appendix B The solution in R







Interestingly, even if this code is a direct transpilation of the code from Appendix A, the redundant code from the Python version of ClaytonCopulaMLE is not present. This hints on the ability of ChatGPT to keep only the code that is relevant.

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Author:

(1) Jan G´orecki, Department of Informatics and Mathematics, Silesian University in Opava, Univerzitnı namestı 1934/3, 733 40 Karvina, Czech Republic (gorecki@opf.slu.cz).


This paper is available on arxiv under CC BY 4.0 DEED license.


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