[ENG-259]

[safaricd]

Change Description

No telemetry events were recorded for TabPFN-TS using the TabPFN API client library. This is because the decorators were expecting tabpfn to run, however tabpfn is never used then.

[gemini-code-assist]

Summary of Changes

Hello @safaricd, 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 addresses a critical bug preventing telemetry events from being recorded for TabPFN-TS models by enhancing the task inference mechanism to correctly recognize time series regression. The change ensures that usage data for these models is accurately captured, which is vital for monitoring and analysis. Additionally, the project version has been updated, and a minor cleanup was performed in the interactive prompt handling.

Highlights

• Telemetry Fix for TabPFN-TS: Resolved an issue where telemetry events were not being recorded for TabPFN-TS models because the decorator logic failed to correctly identify tabpfn_time_series as a regression task.
• Version Update: The project version has been incremented from 0.2.2 to 0.2.4 in pyproject.toml.
• Changelog Entry: A new entry has been added to CHANGELOG.md documenting the telemetry fix for TabPFN-TS.
• Minor Code Cleanup: A redundant print(on_retry_message) statement was removed from the interactive prompt parsing logic.

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[gemini-code-assist]

Code Review

The pull request addresses an issue where telemetry events were not being recorded for TabPFN-TS when using the TabPFN API client library. The changes include updating the version number in pyproject.toml and CHANGELOG.md, adding a condition to infer the task for tabpfn_time_series in decorators.py, and removing an unnecessary print statement in base.py. The review focuses on the correctness of the added condition and the removal of the print statement.

[gemini-code-assist]

This change introduces a new condition to infer the task for tabpfn_time_series. It's high severity because it directly impacts the telemetry data being collected, and any inaccuracies here would lead to incorrect data analysis. It's important to ensure that this condition accurately reflects the task type for time series models.