Fix keras functional 759 #1156
Open
Fix keras functional 759 #1156
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Hi @rasbt,
This Pull Request addresses the issue reported in #759, where the bias_variance_decomp function fails when using Keras models built with the Functional API or in certain TensorFlow versions where the class name is not strictly 'Sequential'.
Key Changes:
Broadened Class Check: Added 'Model' to the list of recognized Keras-like classes (Sequential, Functional, Model) to ensure proper handling of Functional API models.
Improved Logic: Refined the if-else block to decouple the .fit() and .predict() calls. This prevents AttributeError on estimators that return a History object (like Keras) instead of the estimator itself.
Dimensions Preserved: Maintained the original Scikit-learn logic in the else block to ensure that existing unit tests for non-Keras estimators continue to pass without dimension issues.
Verification:
Manual Test: Verified with a Keras Functional API model (it now executes without errors).
Unit Tests: Ran pytest mlxtend/evaluate/tests/test_bias_variance_decomp.py and all tests passed.
Linting: Formatted the code using isort, black, and flake8 (or ruff) to comply with the project's style guide.
Fixes #759