refactor paper benchmarking notebook and add more metrics

docs/paper/benchmark.py

@@ -0,0 +1,163 @@
+from typing import List
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from boruta import BorutaPy
+from sklearn.feature_selection import RFE
+from sklearn.metrics import accuracy_score, f1_score
+from sklearn.model_selection import train_test_split
+import xgboost as xgb
+import time
+from shap_select import shap_select
+import hisel
+from shap_selection import feature_selection
+from skfeature.function.information_theoretical_based import MRMR
+
+RANDOM_SEED = 42
+np.random.seed(RANDOM_SEED)
+
+# Global XGBoost parameters for consistency
+XGB_PARAMS = {
+    "objective": "binary:logistic",
+    "eval_metric": "logloss",
+    "verbosity": 0,
+    "seed": RANDOM_SEED,
+    "nthread": 1,
+}
+
+
+# Define common XGBoost model
+def train_xgboost(X_train, y_train):
+    dtrain = xgb.DMatrix(X_train, label=y_train)
+    xgb_model = xgb.train(XGB_PARAMS, dtrain, num_boost_round=100)
+    return xgb_model
+
+
+def predict_xgboost(xgb_model, X_val):
+    dval = xgb.DMatrix(X_val)
+    y_pred = (xgb_model.predict(dval) > 0.5).astype(int)
+    return y_pred
+
+
+# HISEL feature selection using MRMR
+def hisel_feature_selection(xgb_model, X_train, X_val, y_train, y_val, n_features):
+    return hisel.feature_selection.select_features(X_train, y_train)
+
+
+def shap_selection(xgb_model, X_train, X_val, y_train, y_val, n_features) -> List[str]:
+    selected_shap_selection, _ = feature_selection.shap_select(
+        xgb_model, X_train, X_val, X_train.columns, agnostic=False
+    )
+    selected_shap_selection = selected_shap_selection[:n_features]  # Why 15?
+    return selected_shap_selection
+
+
+def shap_select_selection(
+    xgb_model, X_train, X_val, y_train, y_val, n_features
+) -> List[str]:
+    shap_features, _ = shap_select(
+        xgb_model,
+        X_val,
+        y_val,
+        task="binary",
+        alpha=1e-6,
+        threshold=0.05,
+        return_extended_data=True,
+    )
+    selected_features = shap_features[shap_features["selected"] == 1][
+        "feature name"
+    ].tolist()
+    return selected_features
+
+
+def no_selection(xgb_model, X_train, X_val, y_train, y_val, n_features) -> List[str]:
+    return list(X_train.columns)
+
+
+def rfe_selection(xgb_model, X_train, X_val, y_train, y_val, n_features) -> List[str]:
+    rfe = RFE(
+        xgb.XGBClassifier(**XGB_PARAMS, use_label_encoder=False),
+        n_features_to_select=n_features,
+    )
+    rfe.fit(X_train, y_train)
+    selected_rfe = X_train.columns[rfe.support_]
+    return selected_rfe
+
+
+def boruta_selection(
+    xgb_model, X_train, X_val, y_train, y_val, n_features
+) -> List[str]:
+    rf_model = xgb.XGBClassifier(**XGB_PARAMS, use_label_encoder=False)
+    boruta_selector = BorutaPy(rf_model, n_estimators=100, random_state=RANDOM_SEED)
+    boruta_selector.fit(X_train.values, y_train.values)
+    selected_boruta = X_train.columns[boruta_selector.support_].tolist()
+    return selected_boruta
+
+
+method_dict = {
+    "No selection": no_selection,
+    "shap-select": shap_select_selection,
+    "shap-selection": shap_selection,
+    "HISEL": hisel_feature_selection,
+    "Boruta": boruta_selection,
+    "RFE": rfe_selection,
+}
+
+
+# Run experiments with different feature selection methods and shap-select p-values
+def run_experiments(X_train, X_val, X_test, y_train, y_val, y_test):
+    results = []
+    pretrained_model = None
+
+    for name, fun in method_dict.items():
+        print(f"\n--- {name} ---")
+        start_time = time.time()
+        selected = fun(pretrained_model, X_train, X_val, y_train, y_val, n_features=15)
+
+        runtime = time.time() - start_time
+        print(
+            f"{name} completed in {runtime:.2f} seconds with {len(selected)} features."
+        )
+
+        this_model = train_xgboost(X_train[selected], y_train)
+
+        if name == "No selection":
+            pretrained_model = this_model
+
+        y_pred = predict_xgboost(this_model, X_test[selected])
+        results.append(
+            {
+                "Method": name,
+                "Selected Features": selected,
+                "Accuracy": accuracy_score(y_test, y_pred),
+                "F1 Score": f1_score(y_test, y_pred),
+                "Runtime (s)": runtime,
+            }
+        )
+
+    #     assert set(X_train.columns) == set(selected_hisel), "Feature sets differ!"
+
+    results_df = pd.DataFrame(results)
+    print("\n--- Experiment Results ---")
+    print(results_df)
+    return results_df, pretrained_model
+
+
+if __name__ == "__main__":
+    print("Loading dataset...")
+    df = pd.read_csv("creditcard.csv")
+    X = df.drop(columns=["Class"])
+    y = df["Class"]
+    # Perform a 60-20-20 split for train, validation, and test sets
+    X_train_full, X_test, y_train_full, y_test = train_test_split(
+        X, y, test_size=0.2, random_state=RANDOM_SEED
+    )
+    X_train, X_val, y_train, y_val = train_test_split(
+        X_train_full, y_train_full, test_size=0.25, random_state=RANDOM_SEED
+    )
+
+    results_df, trained_model = run_experiments(
+        X_train, X_val, X_test, y_train, y_val, y_test
+    )
+    print(results_df)
+    print("yay!")