Optimize XGBoost with Optuna on Zarr Data

A solution that optimizes an XGBoost model using Optuna, data from a Zarr zip store, and performs 10-fold cross-validation.

Arguments

--input_zarr_path

Path to the input Zarr zip store containing the features and labels. (default value: PARAMETER_VALUE)

--output_model_path

Path for the output joblib file containing the trained XGBoost model. (default value: PARAMETER_VALUE)

--best_params_path

Path for the output file containing the best parameters from Optuna. (default value: PARAMETER_VALUE)

--n_splits

Number of splits for cross-validation. (default value: PARAMETER_VALUE)

--subset_size

Total number of points for balanced subset. (default value: PARAMETER_VALUE)

--seed

Random seed for reproducibility. (default value: PARAMETER_VALUE)

--num_trials

Number of Optuna trials to run. (default value: PARAMETER_VALUE)

--objective_function

Objective function to optimize. Options are: accuracy, f1, precision, recall. (default value: PARAMETER_VALUE)

Please follow this link for details on how to install and run this solution.