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Oufattole / meds-torch

MIT License
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Everything is text #16

Closed teyaberg closed 1 month ago

teyaberg commented 2 months ago

Summary by CodeRabbit

coderabbitai[bot] commented 2 months ago

Walkthrough

The recent updates significantly enhance the MEDS torch models by introducing custom datasets and dataloaders for EBCL, OCP, and Strats. New model classes have been added for EBCL, OCP, Strats, and a Triplet-based GPT forecasting model, boosting data processing, training, and evaluation capabilities. Additional improvements include updated dependencies, new configuration files, and comprehensive test functions to ensure robustness.

Changes

Files Change Summaries
.gitignore, README.md Updated to ignore new PyTorch Lightning logs and include a planning document link.
src/meds_torch/dataset/... Added custom dataloaders for EBCL, OCP, and Strats datasets.
src/meds_torch/model/... Introduced new model classes for EBCL, OCP, Strats, and a Triplet-based GPT forecasting model with pretraining and evaluation methods.
src/meds_torch/embedder.py, src/meds_torch/input_encoder/text_encoder.py Added TextCodeEmbedder, AutoEmbedder, and TextObservationEmbedder for handling various embeddings.
src/meds_torch/model/architectures/... Modified classes to dynamically use configuration settings for positional embeddings.
src/meds_torch/pytorch_dataset.py Introduced new collation methods and types for handling text code and observations.
tests/test_dataloader.py, tests/test_model.py, tests/test_datamodules.py Added new test functions for text code and observation processing, with updated parameters for existing tests.
pyproject.toml Added transformers to project dependencies.
configs/... Introduced new configuration files for input encoders, specifying parameters for embedding models.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant DataLoader
    participant Model
    participant Optimizer

    User->>DataLoader: Load EBCL Dataset
    DataLoader-->>Model: Provide Batches
    Model->>Optimizer: Configure Optimizer
    Optimizer-->>Model: Optimizer Ready
    Model->>User: Training/Validation Results

    User->>DataLoader: Load OCP Dataset
    DataLoader-->>Model: Provide Batches
    Model->>Optimizer: Configure Optimizer
    Optimizer-->>Model: Optimizer Ready
    Model->>User: Training/Validation Results

Poem

In the land of code so bright,
New dataloaders take their flight,
Models train with all their might,
Optimizers join the fight.
Tests ensure the code's delight,
Dependencies set just right.
MEDS torch shines in the night! 🌟

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