Scikit-learn: `Bagging` Estimators And `max_samples` Default

Introduction

This article delves into a critical discussion surrounding the default behavior of the max_samples parameter in Bagging estimators within the scikit-learn library, specifically concerning the upcoming version 1.8. The core issue revolves around a change in how max_samples is interpreted when passed as a float, potentially leading to backward incompatible changes. This discussion aims to clarify the concerns, evaluate the proposed solutions, and ensure a smooth transition for users of scikit-learn. Understanding the nuances of this change is crucial for data scientists and machine learning practitioners who rely on the stability and predictability of their tools. Let's explore the intricacies of this issue and the proposed resolutions to maintain the integrity of scikit-learn.

The Issue: Change in max_samples Interpretation

The primary concern stems from a change introduced in PR #31414, as highlighted in the changelog. The update modifies the interpretation of max_samples when it is provided as a float value. Previously, max_samples was understood as a fraction of X.shape[0], which represents the number of samples in the input data. However, the new interpretation treats it as a fraction of sample_weight.sum(), where sample_weight refers to the weights assigned to each sample. Given that the default value of max_samples is 1.0, this alteration effectively changes the default behavior of the Bagging estimators.

This change was initially raised as a concern in a related thread, specifically https://github.com/scikit-learn/scikit-learn/pull/31529#discussion_r2541938811, which discussed a similar modification in random forests. The worry is that such a change could lead to unexpected outcomes for users, as existing code might produce different results with the updated version of scikit-learn. The author of the PRs, @antoinebaker, has acknowledged these concerns, suggesting that setting max_samples=None as the default (where None is then re-interpreted as X.shape[0]) might be a safer and less surprising approach for users. This highlights the importance of maintaining intuitive and predictable behavior in machine learning libraries to avoid confusion and errors.

The core of the problem lies in the potential for existing code to behave differently after the update. This is a critical consideration in software development, as backward compatibility is essential for a smooth user experience. Changes in default behavior can often lead to subtle bugs that are difficult to track down, making it imperative to carefully consider the implications of such modifications. The discussion around this issue underscores the collaborative nature of open-source development, where community feedback plays a crucial role in shaping the direction of the project. By addressing these concerns proactively, the scikit-learn team aims to ensure that the library remains a reliable and user-friendly tool for machine learning practitioners.

Proposed Solutions and Urgency

To mitigate the potential disruption caused by this change, two primary solutions have been proposed. The first suggestion is to not merge PR/commit #31414 in v1.8. This would effectively revert the change in behavior, maintaining the existing interpretation of max_samples. However, the feasibility of this approach, especially given the advanced stage of the release cycle, needs to be assessed.

The second proposition involves merging a separate PR that specifically alters the default value of max_samples before the v1.8 release. This would entail changing the default to None, which, as previously mentioned, would be re-interpreted as X.shape[0], thus preserving the original behavior. However, the practicality of this solution also depends on the timeline and the ability to implement and test the change before the release.

The urgency of this matter stems from the desire to avoid introducing multiple backward-incompatible changes in quick succession. If the default behavior is changed in v1.8 and then reverted or modified again in a subsequent release, it could create confusion and instability for users. Therefore, it is crucial to address the issue proactively and make a definitive decision before the v1.8 release. This proactive approach ensures that users can rely on a consistent and predictable experience when using scikit-learn.

The discussion around these solutions highlights the delicate balance between introducing new features and maintaining backward compatibility. While improvements and optimizations are essential for the evolution of any software library, it is equally important to minimize disruption to existing users. The scikit-learn community's commitment to addressing these concerns demonstrates a dedication to providing a stable and reliable tool for machine learning practitioners. By carefully weighing the potential impact of changes and engaging in open discussions, the team aims to make informed decisions that benefit the entire user base.

Community Thoughts and Next Steps

This discussion has actively involved key members of the scikit-learn community, including @antoinebaker (the author of the PRs) and @ogrisel. Their insights and perspectives are crucial in determining the best course of action. The open dialogue ensures that all aspects of the issue are considered and that the final decision is well-informed and beneficial for the community as a whole.

As the discussion progresses, it is essential to consider the broader implications of this change on the scikit-learn ecosystem. How will this impact users who rely on the current behavior of max_samples? What are the potential workarounds for those who need to maintain the existing functionality? These questions need to be addressed to ensure a smooth transition for all users.

Ultimately, the goal is to reach a consensus that balances the need for improvement with the importance of stability and backward compatibility. The scikit-learn community's commitment to open communication and collaboration will be instrumental in achieving this goal. By carefully considering all perspectives and potential solutions, the team can make a decision that benefits the entire user base and ensures the continued success of the scikit-learn library.

Conclusion

The discussion surrounding the max_samples default behavior in Bagging estimators for scikit-learn 1.8 highlights the complexities of maintaining a widely used machine learning library. The potential for backward-incompatible changes necessitates careful consideration and open communication within the community. By proactively addressing these concerns and exploring potential solutions, the scikit-learn team demonstrates its commitment to providing a stable and reliable tool for data scientists and machine learning practitioners.

The proposed solutions, including not merging the problematic PR or modifying the default value before the release, underscore the importance of balancing innovation with stability. The urgency of the matter reflects the desire to avoid multiple conflicting changes, ensuring a consistent user experience. The involvement of key community members, such as @antoinebaker and @ogrisel, emphasizes the collaborative nature of open-source development and the value of diverse perspectives.

As the discussion evolves, it is crucial to consider the long-term implications of this decision on the scikit-learn ecosystem. By carefully weighing the potential impact on users and implementing appropriate solutions, the community can ensure a smooth transition and maintain the library's reputation for excellence. This proactive approach will ultimately contribute to the continued success and widespread adoption of scikit-learn in the machine learning community.

For further information on scikit-learn and its functionalities, visit the official scikit-learn website: scikit-learn.org. This official website provides comprehensive documentation, tutorials, and examples that can help users understand and effectively utilize the library's capabilities.