Updated paths

explainability/compute_shap_inter_vals.py

@@ -16,10 +16,10 @@ from sklearn.tree import DecisionTreeClassifier
 # --------------------------------------------------------------------------------------------------------
 def read_test_data(attribute_names):
     # Load test data
-    X_test_pre = np.load('../gen_train_data/data/output/pre/X_test_pre.npy', allow_pickle=True)
-    y_test_pre = np.load('../gen_train_data/data/output/pre/y_test_pre.npy', allow_pickle=True)
-    X_test_post = np.load('../gen_train_data/data/output/post/X_test_post.npy', allow_pickle=True)
-    y_test_post = np.load('../gen_train_data/data/output/post/y_test_post.npy', allow_pickle=True)
+    X_test_pre = np.load('../gen_train_data/output/pre/X_test_pre.npy', allow_pickle=True)
+    y_test_pre = np.load('../gen_train_data/output/pre/y_test_pre.npy', allow_pickle=True)
+    X_test_post = np.load('../gen_train_data/output/post/X_test_post.npy', allow_pickle=True)
+    y_test_post = np.load('../gen_train_data/output/post/y_test_post.npy', allow_pickle=True)
 
     # Type conversion needed    
     data_dic = {
@@ -36,7 +36,7 @@ if __name__ == "__main__":
     # Setup
     # --------------------------------------------------------------------------------------------------------
     # Retrieve attribute names in order
-    attribute_names = list(np.load('../gen_train_data/data/output/attributes.npy', allow_pickle=True))
+    attribute_names = list(np.load('../gen_train_data/output/attributes.npy', allow_pickle=True))
     # Reading data
     data_dic = read_test_data(attribute_names)
     method_names = {

explainability/compute_shap_vals.py

@@ -16,10 +16,10 @@ from sklearn.tree import DecisionTreeClassifier
 # --------------------------------------------------------------------------------------------------------
 def read_test_data(attribute_names):
     # Load test data
-    X_test_pre = np.load('../gen_train_data/data/output/pre/X_test_pre.npy', allow_pickle=True)
-    y_test_pre = np.load('../gen_train_data/data/output/pre/y_test_pre.npy', allow_pickle=True)
-    X_test_post = np.load('../gen_train_data/data/output/post/X_test_post.npy', allow_pickle=True)
-    y_test_post = np.load('../gen_train_data/data/output/post/y_test_post.npy', allow_pickle=True)
+    X_test_pre = np.load('../gen_train_data/output/pre/X_test_pre.npy', allow_pickle=True)
+    y_test_pre = np.load('../gen_train_data/output/pre/y_test_pre.npy', allow_pickle=True)
+    X_test_post = np.load('../gen_train_data/output/post/X_test_post.npy', allow_pickle=True)
+    y_test_post = np.load('../gen_train_data/output/post/y_test_post.npy', allow_pickle=True)
 
     # Type conversion needed    
     data_dic = {
@@ -36,7 +36,7 @@ if __name__ == "__main__":
     # Setup
     # --------------------------------------------------------------------------------------------------------
     # Retrieve attribute names in order
-    attribute_names = list(np.load('../gen_train_data/data/output/attributes.npy', allow_pickle=True))
+    attribute_names = list(np.load('../gen_train_data/output/attributes.npy', allow_pickle=True))
     # Reading data
     data_dic = read_test_data(attribute_names)
     method_names = {

explainability/fit_final_models.py

@@ -18,22 +18,22 @@ from sklearn.tree import DecisionTreeClassifier
 # --------------------------------------------------------------------------------------------------------
 def read_training_data(attribute_names):
     # Load ORIGINAL training data
-    X_train_pre = np.load('../gen_train_data/data/output/pre/X_train_pre.npy', allow_pickle=True)
-    y_train_pre = np.load('../gen_train_data/data/output/pre/y_train_pre.npy', allow_pickle=True)
-    X_train_post = np.load('../gen_train_data/data/output/post/X_train_post.npy', allow_pickle=True)
-    y_train_post = np.load('../gen_train_data/data/output/post/y_train_post.npy', allow_pickle=True)
+    X_train_pre = np.load('../gen_train_data/output/pre/X_train_pre.npy', allow_pickle=True)
+    y_train_pre = np.load('../gen_train_data/output/pre/y_train_pre.npy', allow_pickle=True)
+    X_train_post = np.load('../gen_train_data/output/post/X_train_post.npy', allow_pickle=True)
+    y_train_post = np.load('../gen_train_data/output/post/y_train_post.npy', allow_pickle=True)
 
     # Load oversampled training data
-    X_train_over_pre = np.load('../gen_train_data/data/output/pre/X_train_over_pre.npy', allow_pickle=True)
-    y_train_over_pre = np.load('../gen_train_data/data/output/pre/y_train_over_pre.npy', allow_pickle=True)
-    X_train_over_post = np.load('../gen_train_data/data/output/post/X_train_over_post.npy', allow_pickle=True)
-    y_train_over_post = np.load('../gen_train_data/data/output/post/y_train_over_post.npy', allow_pickle=True)
+    X_train_over_pre = np.load('../gen_train_data/output/pre/X_train_over_pre.npy', allow_pickle=True)
+    y_train_over_pre = np.load('../gen_train_data/output/pre/y_train_over_pre.npy', allow_pickle=True)
+    X_train_over_post = np.load('../gen_train_data/output/post/X_train_over_post.npy', allow_pickle=True)
+    y_train_over_post = np.load('../gen_train_data/output/post/y_train_over_post.npy', allow_pickle=True)
 
     # Load undersampled training data
-    X_train_under_pre = np.load('../gen_train_data/data/output/pre/X_train_under_pre.npy', allow_pickle=True)
-    y_train_under_pre = np.load('../gen_train_data/data/output/pre/y_train_under_pre.npy', allow_pickle=True)
-    X_train_under_post = np.load('../gen_train_data/data/output/post/X_train_under_post.npy', allow_pickle=True)
-    y_train_under_post = np.load('../gen_train_data/data/output/post/y_train_under_post.npy', allow_pickle=True)
+    X_train_under_pre = np.load('../gen_train_data/output/pre/X_train_under_pre.npy', allow_pickle=True)
+    y_train_under_pre = np.load('../gen_train_data/output/pre/y_train_under_pre.npy', allow_pickle=True)
+    X_train_under_post = np.load('../gen_train_data/output/post/X_train_under_post.npy', allow_pickle=True)
+    y_train_under_post = np.load('../gen_train_data/output/post/y_train_under_post.npy', allow_pickle=True)
 
     # Type conversion needed    
     data_dic = {
@@ -111,7 +111,7 @@ if __name__ == "__main__":
     # Setup
     # --------------------------------------------------------------------------------------------------------
     # Retrieve attribute names in order
-    attribute_names = list(np.load('../gen_train_data/data/output/attributes.npy', allow_pickle=True))
+    attribute_names = list(np.load('../gen_train_data/output/attributes.npy', allow_pickle=True))
     # Reading data
     data_dic = read_training_data(attribute_names)
     method_names = {

explainability/shap_plots.ipynb

@@ -35,13 +35,13 @@
    "outputs": [],
    "source": [
     "# Retrieve attribute names in order\n",
-    "attribute_names = list(np.load('../gen_train_data/data/output/attributes.npy', allow_pickle=True))\n",
+    "attribute_names = list(np.load('../gen_train_data/output/attributes.npy', allow_pickle=True))\n",
     "\n",
     "# Load test data\n",
-    "X_test_pre = np.load('../gen_train_data/data/output/pre/X_test_pre.npy', allow_pickle=True)\n",
-    "y_test_pre = np.load('../gen_train_data/data/output/pre/y_test_pre.npy', allow_pickle=True)\n",
-    "X_test_post = np.load('../gen_train_data/data/output/post/X_test_post.npy', allow_pickle=True)\n",
-    "y_test_post = np.load('../gen_train_data/data/output/post/y_test_post.npy', allow_pickle=True)\n",
+    "X_test_pre = np.load('../gen_train_data/output/pre/X_test_pre.npy', allow_pickle=True)\n",
+    "y_test_pre = np.load('../gen_train_data/output/pre/y_test_pre.npy', allow_pickle=True)\n",
+    "X_test_post = np.load('../gen_train_data/output/post/X_test_post.npy', allow_pickle=True)\n",
+    "y_test_post = np.load('../gen_train_data/output/post/y_test_post.npy', allow_pickle=True)\n",
     "\n",
     "# Type conversion needed    \n",
     "data_dic = {\n",