test: add failing test for large dataset performance (#420)

[ANAMASGARD]

• Test documents performance degradation with >10K observations
• check_model() currently takes 5+ minutes on 25K dataset
• Test expects completion in <30 seconds
• Initially FAILS as expected (proving bug exists)

This test will pass after implementing data sampling in plot.check_model().

In the Next Commit I will Push the Code with all the necessary Fixes .

Relates to #420

[gemini-code-assist]

Summary of Changes

Hello @ANAMASGARD, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a comprehensive set of R tests designed to expose and quantify a significant performance bottleneck in the check_model() function when processing large datasets. By establishing a failing test case that demonstrates the current slow execution, it lays the groundwork for future performance improvements, specifically through the implementation of data sampling, while also ensuring that sampling does not negatively impact smaller datasets or visual accuracy.

Highlights

• Performance Degradation Test: Introduces a new test file (test-performance_large_datasets.R) to specifically highlight and reproduce a performance issue in check_model() with datasets exceeding 10,000 observations.
• Expected Failure & Future Fix: The test is designed to initially fail (taking over 5 minutes for a 25K dataset, expecting <30 seconds) to confirm the existence of the performance bug, with the expectation that it will pass once data sampling is implemented in plot.check_model().
• Sampling Behavior Tests: Includes additional tests to ensure that data sampling, once implemented, preserves visual fidelity for small datasets, significantly speeds up plotting for large datasets, and that medium datasets (below the sampling threshold) still perform reasonably well.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request adds a new test file to document and test a performance issue with check_model() on large datasets. The new tests are designed to fail initially and will pass once the performance fix is implemented. My review focuses on improving the clarity and maintainability of the new test code. I've suggested renaming a misleadingly named test and an associated cleanup, as well as using a more specific variable name to avoid confusion. Overall, this is a good step towards addressing the performance problem.