Release 0.0.5

[mmcdermott]

Summary by CodeRabbit

• New Features

  • Added documentation for "Tokenization and Tensorization" to guide users in data preparation for MEDS models.
  • Integrated a new navigation entry in documentation for easy access to the tokenization guide.

• Bug Fixes

  • Enhanced output file path clarity in data processing by explicitly appending file types.
  • Improved error handling and logging during data merging processes for better tracking.

• Tests

  • Updated testing function to ensure no unexpected output files are generated, enhancing test reliability.

[coderabbitai[bot]]

[!CAUTION]

Review failed

The pull request is closed.

Walkthrough

The recent updates enhance the MEDS framework by introducing comprehensive documentation on tokenization and tensorization, improving data processing logic in patient data handling, and enhancing testing functions to ensure output integrity. These changes promote clarity and efficiency, ensuring that the system effectively prepares complex medical data for deep learning applications while maintaining robust validation in testing.

Changes

Files	Change Summary
docs/tokenization_tensorization.md	Introduced a guide on tokenization and tensorization for MEDS models, detailing methodologies, definitions, and strategies.
mkdocs.yml	Added a navigation link for "Tokenization & Tensorization" to the documentation.
src/MEDS_transforms/reshard_to_split.py	Simplified output path construction, streamlined sub-sharding logic, improved logging, and retained error handling for empty datasets.
tests/transform_tester_base.py	Enhanced single_stage_transform_tester with a new parameter for output validation, improving test reliability.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant Tokenization
    participant Tensorization
    participant MEDS_Model

    User->>Tokenization: Prepare data
    Tokenization->>Tensorization: Convert to tensors
    Tensorization->>MEDS_Model: Input data for training
    MEDS_Model-->>User: Return training results

🐇 In the garden where data grows,
A rabbit hops where knowledge flows.
With tokens and tensors, bright and new,
MEDS models learn with a vibrant view!
So let's celebrate this wondrous change,
In the world of data, it's time to arrange! 🌱✨

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