Docs update with osa_tool

[Roman223]

No description provided.

[Copilot]

Pull Request Overview

This PR implements comprehensive documentation updates using OSA (Open Source Assistant) tool for the WGAN-GP with Meta-Feature Statistics project. The update relocates utility functions from the root level to the wgan_gp module, adds extensive docstrings throughout the codebase, and creates a complete MkDocs-based documentation system.

Key Changes

• Moves utils.py from root to wgan_gp/utils.py with enhanced documentation
• Adds comprehensive docstrings to all modules following Google style
• Creates MkDocs configuration and documentation structure
• Updates dependencies and adds CI/CD workflows for documentation

Reviewed Changes

Copilot reviewed 18 out of 20 changed files in this pull request and generated 5 comments.

Show a summary per file

File	Description
wgan_gp/utils.py	New utility module with comprehensive functions and docstrings
wgan_gp/training.py	Enhanced training classes with detailed documentation
wgan_gp/pymfe_to_torch.py	Meta-feature extraction with improved error handling
wgan_gp/models.py	Neural network architectures with complete docstrings
wgan_gp/main.py	Updated imports and configuration parameters
utils.py	Removed - functionality moved to wgan_gp/utils.py
osa_mkdocs.yml	MkDocs configuration for documentation generation
osa_docs/	Documentation source files with module references
README.md	Comprehensive project documentation
req.txt	Updated dependencies for documentation tools

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[Copilot]

Line 510 assigns .values to real_data_scaled_no_anomalies, converting it to a numpy array. Line 511 then tries to access .index on this numpy array, which will cause an AttributeError since numpy arrays don't have an .index attribute.

Suggested change

	].values
	real_data_scaled_no_anomalies = real_data_scaled_no_anomalies.values
	y = pd.DataFrame(y[real_data_scaled_no_anomalies.index], columns=["target"])
	]
	y = pd.DataFrame(y.loc[real_data_scaled_no_anomalies.index], columns=["target"])
	real_data_scaled_no_anomalies = real_data_scaled_no_anomalies.values

[Copilot]

The epochs value has been reduced from 1000 to 10, which may be too low for meaningful training. This appears to be a testing/debugging configuration that should likely be reverted to a higher value for actual training.

Suggested change

	epochs=10,
	epochs=1000,

[Copilot]

The change from list format [.1, 2.] to float 0.1 for mfs_lambda alters the loss computation logic. Ensure this change is intentional and that the training logic in TrainerModified._generator_train_iteration properly handles both float and list cases.