fixed an error in tree builing with pretrained apc + ordered rejectio…

…n counts in detectron

MED3pa/detectron/experiment.py

@@ -122,8 +122,8 @@ def save(self, file_path: str, file_name: str = 'detectron_results', save_config
             json.dump(self.test_results, file, indent=4)
 
         counts_dict = {}
-        counts_dict['reference'] = self.cal_record.rejected_counts().tolist()
-        counts_dict['test'] = self.test_record.rejected_counts().tolist()
+        counts_dict['reference'] = np.sort(self.cal_record.rejected_counts()).tolist()
+        counts_dict['test'] = np.sort(self.test_record.rejected_counts()).tolist()
 
         file_name_path_counts = os.path.join(file_path, 'rejection_counts.json')
         with open(file_name_path_counts, 'w') as file:

MED3pa/med3pa/tree.py

@@ -80,15 +80,23 @@ def build_tree(self, dtr: DecisionTreeRegressorModel, X: DataFrame, y: Series, n
             curr_node = _TreeNode(value=node_value, value_max=node_max, samples_ratio=node_samples_ratio,
                                   node_id=self.nb_nodes, path=path)
             return curr_node
-
+        
         node_thresh = dtr.model.tree_.threshold[node_id]
         node_feature_id = dtr.model.tree_.feature[node_id]
         node_feature = self.features[node_feature_id]
 
+        # Check if the split would result in an empty set, if so, stop the recursion
+        if y[X[node_feature] <= node_thresh].size == 0 or y[X[node_feature] > node_thresh].size == 0:
+            print("split would results in an empty data section")
+            curr_node = _TreeNode(value=node_value, value_max=node_max, samples_ratio=node_samples_ratio,
+                                node_id=self.nb_nodes, path=path)
+            return curr_node
+
         curr_path = list(path)  # Copy the current path to avoid modifying the original list
         curr_node = _TreeNode(value=node_value, value_max=node_max, samples_ratio=node_samples_ratio,
                               threshold=node_thresh, feature=node_feature, feature_id=node_feature_id,
                               node_id=self.nb_nodes, path=curr_path)
+
 
         # Update paths for child nodes
         left_path = curr_path + [f"{node_feature} <= {node_thresh}"]

setup.py

@@ -9,7 +9,7 @@
 
 setup(
     name="MED3pa",
-    version="0.1.20",
+    version="0.1.21",
     author="MEDomics consortium",
     author_email="medomics.info@gmail.com",
     description="Python Open-source package for ensuring robust and reliable ML models deployments",

tutorials/detectron_tutorial.ipynb

@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,73 +87,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "running seeds: 100%|██████████| 100/100 [00:13<00:00,  7.57it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Detectron execution on reference set completed.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "running seeds: 100%|██████████| 100/100 [00:14<00:00,  7.01it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Detectron execution on testing set completed.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "running seeds: 100%|██████████| 100/100 [00:24<00:00,  4.15it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Detectron execution on reference set completed.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "running seeds: 100%|██████████| 100/100 [00:14<00:00,  6.89it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Detectron execution on testing set completed.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from MED3pa.detectron import DetectronExperiment\n",
     "\n",
@@ -172,35 +108,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'shift_probability': 0.5777777777777777,\n",
-       "  'test_statistic': 7.6,\n",
-       "  'baseline_mean': 7.4,\n",
-       "  'baseline_std': 1.2631530214330944,\n",
-       "  'significance_description': {'unsignificant shift': 56.148148148148145,\n",
-       "   'small': 18.432098765432098,\n",
-       "   'moderate': 12.506172839506172,\n",
-       "   'large': 12.91358024691358},\n",
-       "  'Strategy': 'enhanced_disagreement_strategy'},\n",
-       " {'p_value': 0.22518291651155886,\n",
-       "  'u_statistic': 4696.5,\n",
-       "  'significance_description': {'unsignificant shift': 55.33,\n",
-       "   'small': 15.459999999999999,\n",
-       "   'moderate': 19.56,\n",
-       "   'large': 9.65},\n",
-       "  'Strategy': 'mannwhitney_strategy'}]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "from MED3pa.detectron.strategies import *\n",
     "\n",
@@ -220,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -238,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [