import pandas

from models.lightgbm_model import LIGHT_GBM

data_frame = pandas.read_csv("./data/Ketamine_icp_no_x.csv")

lgbm_results = pandas.read_csv("./lightgbm_tuning_results_no_x.csv")


def get_best_params(results, metrics=["f2_class1", "f1_class1"]):
    max_f2 = results[metrics[0]].max()

    best_f2_rows = results[results[metrics[0]] == max_f2]

    best_row = best_f2_rows.loc[best_f2_rows[metrics[1]].idxmax()]

    return best_row.to_dict()


lgbm_best_params = get_best_params(lgbm_results)
lgbm_model = LIGHT_GBM(data_frame, params=lgbm_best_params)
lgbm_model.fit()
lgbm_test_metrics = lgbm_model.eval()
print(lgbm_test_metrics)


def clean_metrics(metrics):
    return {k: float(v) if hasattr(v, "item") else v for k, v in metrics.items()}


lgbm_test_metrics_clean = clean_metrics(lgbm_test_metrics)

comparison_df = pandas.DataFrame(
    [
        {"model": "lightgbm", **lgbm_test_metrics_clean},
    ]
)

comparison_filename = "comparison_catboost_lightgbm_no_x.csv"
comparison_df.to_csv(comparison_filename, index=False)

print(f"Comparison saved to: {comparison_filename}")