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rvandewater / YAIB

🧪Yet Another ICU Benchmark: a holistic framework for the standardization of clinical prediction model experiments. Provide custom datasets, cohorts, prediction tasks, endpoints, preprocessing, and models. Paper: https://arxiv.org/abs/2306.05109
https://github.com/rvandewater/YAIB/wiki
MIT License
50 stars 9 forks source link

Version release based on practice Cassandra use #155

Open rvandewater opened 2 days ago

rvandewater commented 2 days ago

Updates from the cassandra project currently include:

Summary by CodeRabbit

coderabbitai[bot] commented 2 days ago

Walkthrough

The changes in this pull request introduce new configuration files for various classifiers, including Balanced Random Forest, CatBoost, and XGBoost, along with modifications to existing configurations for models like GRU, Random Forest, and Transformer. Additionally, significant updates were made to support data handling using the Polars library, enhancing data loading and preprocessing functionalities. New scripts for benchmarking and job scheduling on HPC clusters were added, while several files were created for new deep learning architectures, including RNNs and Transformers. Various updates were also made to enhance hyperparameter tuning and model training processes.

Changes

File Path Change Summary
configs/prediction_models/BRFClassifier.gin New file for Balanced Random Forest Classifier configuration with hyperparameters.
configs/prediction_models/CBClassifier.gin New file for CatBoost classifier configuration with hyperparameters.
configs/prediction_models/GRU.gin Updates to GRU model configuration, including learning rate and hidden dimensions.
configs/prediction_models/RFClassifier.gin Modifications to Random Forest Classifier configuration, adjusting hyperparameters.
configs/prediction_models/RUSBClassifier.gin New file for RUSBClassifier configuration with hyperparameters.
configs/prediction_models/TCN.gin Updates to TCN model configuration, including learning rate and kernel size.
configs/prediction_models/Transformer.gin Modifications to Transformer model configuration, updating learning rate and hyperparameters.
configs/prediction_models/XGBClassifier.gin New file for XGBoost classifier configuration with hyperparameters.
configs/prediction_models/common/DLCommon.gin Updates to deep learning common configurations, including import statements and epochs.
configs/prediction_models/common/MLCommon.gin Modifications to machine learning common configurations, updating import statements.
configs/tasks/BinaryClassification.gin Modifications to binary classification task configuration, enhancing preprocessing.
configs/tasks/CassClassification.gin New file for a classification task in deep learning with specific parameters.
configs/tasks/DatasetImputation.gin Modifications to data imputation task configuration, including data loading settings.
configs/tasks/Regression.gin Modifications to regression task configuration, enhancing data loading.
configs/tasks/common/Dataloader.gin New configuration settings for dataset classes for improved data handling.
configs/tasks/common/PredictionTaskVariables.gin New configuration section for modality mapping in prediction tasks.
docs/adding_model/RNN.gin New file for RNN model configuration settings.
docs/adding_model/instructions.md Updated guidelines for adding new models to the YAIB framework.
docs/adding_model/rnn.py New file defining RNN model using PyTorch.
environment.yml Updated pip dependency specification to a version range.
experiments/benchmark_cass.yml New configuration for a benchmarking experiment with execution parameters.
experiments/charhpc_wandb_sweep.sh New Bash script for executing a WandB sweep on HPC cluster.
experiments/charhpc_wandb_sweep_cpu.sh New shell script for executing a job on HPC cluster.
experiments/slurm_base_char_sc.sh New Bash script for submitting binary classification jobs.
icu_benchmarks/cross_validation.py Enhancements to error handling in the execute_repeated_cv function.
icu_benchmarks/data/loader.py Significant modifications for Polars data manipulation, including new classes and renaming.
icu_benchmarks/data/preprocessor.py Major updates to implement new preprocessing classes and methods for Polars.
icu_benchmarks/data/split_process_data.py Enhancements for Polars data manipulation with new methods added.
icu_benchmarks/models/constants.py New import statement for confusion matrix and updates to metrics dictionaries.
icu_benchmarks/models/custom_metrics.py Added function for computing confusion matrices.
icu_benchmarks/models/dl_models.py Deleted file with various neural network architectures.
icu_benchmarks/models/dl_models/rnn.py New file for RNN architectures with multiple classes.
icu_benchmarks/models/dl_models/tcn.py New class for Temporal Convolutional Network.
icu_benchmarks/models/dl_models/transformer.py New file defining Transformer and LocalTransformer classes.
icu_benchmarks/models/ml_models/catboost.py New file for CatBoost classification tasks.
icu_benchmarks/models/ml_models/imblearn.py New classes for imbalanced learning tasks.
icu_benchmarks/models/ml_models/lgbm.py New file for LightGBM model wrappers.
icu_benchmarks/models/ml_models/sklearn.py Significant modifications, including removal of LightGBM wrapper classes.
icu_benchmarks/models/ml_models/xgboost.py New file for XGBoost classification tasks.
icu_benchmarks/models/train.py Updates to replace pandas with Polars for data handling.
icu_benchmarks/models/utils.py Added functionality related to scoring and label smoothing.
icu_benchmarks/models/wrappers.py Enhanced metrics integration and data handling.
icu_benchmarks/run.py Enhancements to configurability and robustness.
icu_benchmarks/run_utils.py Updates to configuration and logging functionality.
icu_benchmarks/tuning/hyperparameters.py Enhanced hyperparameter tuning functionality.
requirements.txt Updates to package versions and dependencies.

Poem

In the garden of code, new blooms arise,
Classifiers sprout, reaching for the skies.
With tweaks and turns, the models grow,
RNNs and TCNs, putting on a show.
Data flows freely, like rivers run,
In the world of AI, we’ve just begun! 🐇✨

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