comapre two models

models/compare_models.py

@@ -0,0 +1,55 @@
+import pandas
+from catboost_model import CAT_BOOST
+from lightgbm_model import LIGHT_GBM
+
+data_frame = pandas.read_csv("./data/Ketamine_icp_no_missing.csv")
+
+cat_boost_results = pandas.read_csv("./cat_boost_tuning_results.csv")
+lgbm_results = pandas.read_csv("./lightgbm_tuning_results.csv")
+
+
+def get_best_params(data_frame, metrics=["f2_class1", "f1_class1"]):
+    max_f2 = cat_boost_results[metrics[0]].max()
+
+    best_f2_rows = cat_boost_results[cat_boost_results[metrics[0]] == max_f2]
+
+    best_row = best_f2_rows.loc[best_f2_rows[metrics[1]].idxmax()]
+
+    return best_row.to_dict()
+
+
+cat_boost_best_params = get_best_params(cat_boost_results)
+cat_boost_model = CAT_BOOST(data_frame, params=cat_boost_best_params)
+cat_boost_model.fit()
+cat_test_metrics = cat_boost_model.eval()
+print(cat_test_metrics)
+
+x_test, y_test = cat_boost_model.x_test, cat_boost_model.y_test
+
+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(x_test, y_test)
+print(lgbm_test_metrics)
+
+import pandas as pd
+
+
+def clean_metrics(metrics):
+    return {k: float(v) if hasattr(v, "item") else v for k, v in metrics.items()}
+
+
+cat_test_metrics_clean = clean_metrics(cat_test_metrics)
+lgbm_test_metrics_clean = clean_metrics(lgbm_test_metrics)
+
+comparison_df = pd.DataFrame(
+    [
+        {"model": "catboost", **cat_test_metrics_clean},
+        {"model": "lightgbm", **lgbm_test_metrics_clean},
+    ]
+)
+
+comparison_filename = "comparison_catboost_lightgbm.csv"
+comparison_df.to_csv(comparison_filename, index=False)
+
+print(f"Comparison saved to: {comparison_filename}")