diff --git a/feature_importance/fi_config/mdi_local/two_subgroups_linear_sims/dgp.py b/feature_importance/fi_config/mdi_local/two_subgroups_linear_sims/dgp.py
index 6d6bcf2..b1b5152 100644
--- a/feature_importance/fi_config/mdi_local/two_subgroups_linear_sims/dgp.py
+++ b/feature_importance/fi_config/mdi_local/two_subgroups_linear_sims/dgp.py
@@ -2,14 +2,14 @@
 sys.path.append("../..")
 from feature_importance.scripts.simulations_util import *
 
-
-X_DGP = sample_real_X
+### Update start for local MDI+
+X_DGP = sample_normal_X
 X_PARAMS_DICT = {
-    "fpath": "/mnt/d/local_MDI+/imodels-experiments/data/X_splicing_cleaned.csv",
-    "sample_row_n": None,
-    "sample_col_n": None
+    "n": 1200,
+    "d": 50,
+    "mean": 0,
+    "scale": 1
 }
-### Update start for local MDI+
 Y_DGP = linear_model_two_groups
 Y_PARAMS_DICT = {
     "beta": 1,
diff --git a/feature_importance/fi_config/mdi_local/two_subgroups_linear_sims/models.py b/feature_importance/fi_config/mdi_local/two_subgroups_linear_sims/models.py
index fb93f05..aff8710 100644
--- a/feature_importance/fi_config/mdi_local/two_subgroups_linear_sims/models.py
+++ b/feature_importance/fi_config/mdi_local/two_subgroups_linear_sims/models.py
@@ -10,7 +10,7 @@
 ]
 
 FI_ESTIMATORS = [
-    # [FIModelConfig('MDI_all_stumps', MDI_local_all_stumps, model_type='tree')],
+    [FIModelConfig('MDI_all_stumps', MDI_local_all_stumps, model_type='tree')],
     [FIModelConfig('MDI_sub_stumps', MDI_local_sub_stumps, model_type='tree')],
     [FIModelConfig('TreeSHAP', tree_shap_local, model_type='tree')],
     [FIModelConfig('Permutation', permutation_local, model_type='tree')],
diff --git a/feature_importance/scripts/competing_methods_local.py b/feature_importance/scripts/competing_methods_local.py
index b8e35e9..46bbd44 100644
--- a/feature_importance/scripts/competing_methods_local.py
+++ b/feature_importance/scripts/competing_methods_local.py
@@ -106,7 +106,7 @@ def MDI_local_sub_stumps(X, y, fit, scoring_fns="auto", return_stability_scores=
     rf_plus_model.fit(X, y)
 
     try:
-        mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, local_scoring_fns=mean_squared_error)
+        mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, local_scoring_fns=mean_squared_error, version = "zach")
         if return_stability_scores:
             stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25)
     except ValueError as e:
@@ -123,6 +123,7 @@ def MDI_local_sub_stumps(X, y, fit, scoring_fns="auto", return_stability_scores=
     # if return_stability_scores:
     #     mdi_plus_scores = pd.concat([mdi_plus_scores, stability_scores], axis=1)
     result = mdi_plus_scores["local"]
+    print(result)
     # Convert the array to a DataFrame
     result_table = pd.DataFrame(result, columns=[f'Feature_{i}' for i in range(num_features)])
 
@@ -146,6 +147,7 @@ def MDI_local_all_stumps(X, y, fit, scoring_fns="auto", return_stability_scores=
                          Var: variable name
                          Importance: MDI+ score
     """
+    num_samples, num_features = X.shape
 
     if isinstance(fit, RegressorMixin):
         RFPlus = RandomForestPlusRegressor
@@ -157,7 +159,7 @@ def MDI_local_all_stumps(X, y, fit, scoring_fns="auto", return_stability_scores=
     rf_plus_model = RFPlus(rf_model=fit, **kwargs)
     rf_plus_model.fit(X, y)
     try:
-        mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, local_scoring_fns=mean_squared_error)
+        mdi_plus_scores = rf_plus_model.get_mdi_plus_scores(X=X, y=y, local_scoring_fns=mean_squared_error, version = "tiffany")
         if return_stability_scores:
             stability_scores = rf_plus_model.get_mdi_plus_stability_scores(B=25)
     except ValueError as e:
@@ -174,6 +176,7 @@ def MDI_local_all_stumps(X, y, fit, scoring_fns="auto", return_stability_scores=
     # if return_stability_scores:
     #     mdi_plus_scores = pd.concat([mdi_plus_scores, stability_scores], axis=1)
     result = mdi_plus_scores["local"]
+    print(result)
     # Convert the array to a DataFrame
     result_table = pd.DataFrame(result, columns=[f'Feature_{i}' for i in range(num_features)])