106 check passing initialized modules #109
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Improve code readability with consistent formatting for conditions, argument alignment, and tensor initialization. Add precise type annotations for `_load_instructions` method to enhance type safety and clarity.
Simplified and modernized the codebase by removing hardcoded classifiers and regressors like LogisticRegression, LinearRegression, and MultiLayerPerceptron. Introduced flexible base classes, dynamic module handling, and incremental capabilities for targets, features, and classes in classification and regression tasks. This improves maintainability and extensibility of the code while ensuring support for complex use cases.
This commit improves overall code readability by adjusting spacing, aligning imports, and refining incosistent formatting like inline comments and function definitions. No functional changes were made; these updates enhance maintainability and coding style adherence across multiple modules.
…mple notebooks for consistency
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lucasczz
approved these changes
Mar 21, 2025
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Hi @kulbachcedric,
Looks great! Thanks for all the work on this!
Passing initialized modules is definitely more convenient.
Cheers
Lucas
…ved classification
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Hi @lucasczz and @hoanganhngo610,
I finally managed to refactor the code base to enable passing initialized PyTorch Modules (and some structural changes). What do you think about the changes, where in the future, we would remove and replace the old classes with the *Initialized classes.
The documentation is still missing in many parts, but I think from the coding side it is a first draft to get feedback :-)
Best
Cedric