raise an error if the model in TabPFNClassifier doesn't have encoder …

[LeoGrin]

…attribute to prevent silent behavior difference with our base model

Motivation and Context

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Public API Changes

• No Public API changes
• Yes, Public API changes (Details below)

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How Has This Been Tested?

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Checklist

• The changes have been tested locally.
• Documentation has been updated (if the public API or usage changes).
• A entry has been added to CHANGELOG.md (if relevant for users).
• The code follows the project's style guidelines.
• I have considered the impact of these changes on the public API.

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[gemini-code-assist]

Summary of Changes

Hello @LeoGrin, 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 focuses on enhancing the robustness and predictability of the TabPFNClassifier by introducing explicit error checks for critical model components. My changes ensure that if a model passed to update_encoder_params lacks expected attributes like encoder or remove_outliers on its normalization layer, an immediate ValueError is raised. This prevents silent failures or divergent behavior, making the system more reliable and easier to debug.

Highlights

• Improved Error Handling: I've updated the update_encoder_params function in src/tabpfn/utils.py to explicitly raise a ValueError if the provided model object does not possess an encoder attribute. Previously, the function would silently return, potentially leading to unexpected behavior or silent differences in TabPFNClassifier.
• Enhanced Robustness: Within the same update_encoder_params function, I've added another validation step. It now checks if the identified InputNormalizationEncoderStep object has a remove_outliers attribute. If this critical attribute is missing, a ValueError is raised, preventing potential runtime errors when attempting to modify it.

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[Copilot]

Pull Request Overview

This PR adds validation to the update_encoder_params function to ensure the model has the required encoder and normalization layer attributes. Instead of silently returning when the model lacks an encoder, it now raises explicit errors to prevent unexpected behavior differences with the base model.

• Replaces silent return with ValueError when model lacks encoder attribute
• Adds validation for the remove_outliers attribute on InputNormalizationEncoderStep
• Includes TODO comment about finding a less hacky approach for setting changes

[gemini-code-assist]

Code Review

This pull request improves the robustness of the TabPFN models by adding explicit checks for required attributes on the model and its encoder, raising errors instead of failing silently. This is a valuable change.

My feedback focuses on making the new error messages more generic. Since the update_encoder_params function is a utility shared by both TabPFNClassifier and TabPFNRegressor, the error messages should not be specific to just the classifier. This will improve clarity and maintainability.