Replace xgboost - #3
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This reverts commit 8a01454.
…with updated model configurations and calibration methods
- Updated `calib_frac` in `train_config.yaml` from 0.1 to 0.06 for improved calibration. - Reduced `num_epochs` from 300 to 200 in `train_config.yaml` to optimize training duration. - Fixed checkpoint directory path in `train_config.yaml` by removing the extra slash. - Added a blank line in `train_step.py` for better code readability. - Deleted obsolete test file `test_estimint_nn.py` which was not integrated into the package.
- Moved checkpoint saving logic to a separate function `save_checkpoint` in `checkpoint.py`. - Updated `train_model` function in `train_base.py` to utilize the new checkpointing function. - Removed unused checkpoint session management code for clarity. - Introduced a new YAML configuration file for sweep parameters to facilitate hyperparameter tuning.
…ning - Added `types.py` to define `ModelFactory` and `ModelArtifact` protocols. - Created `export_config.yaml` for model export configurations. - Updated `train_config.yaml` to include dynamic naming for output files based on predictor and target. - Refactored feature handling in `features.py` to dynamically include the predictor in the feature list. - Modified `preprocess.py` to fit feature scalers based on the updated feature list. - Enhanced metrics computation in `metrics.py` to utilize the `ModelArtifact` protocol. - Implemented a factory method in `rqs.py` for model instantiation from configuration. - Updated `train_base.py` to use the new model factory method and removed redundant feature list handling. - Created `calibrate.py` for conformal calibration offset calculations.
absternator
marked this pull request as draft
July 17, 2026 14:59
- Introduced a new sweep configuration file `hbr_eir.yaml` for hyperparameter tuning using Bayesian optimization. - Implemented `model_export.py` to export trained models along with their scalers and configuration for sharing. - Created `hub.py` to facilitate loading model artifacts from local or Hugging Face repositories. - Added logging for training processes in `train_base.log` to capture model training details and performance metrics.
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the following is WIP.
Currently have done training not exported and setup hugging face pipeline