fix bugs

docs/Examples/01.AdaSTEM_demo.ipynb

@@ -730,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -740,6 +740,7 @@
     "        classifier=XGBClassifier(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1),\n",
     "        regressor=XGBRegressor(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1)\n",
     "    ),                                      # hurdel model for zero-inflated problem (e.g., count)\n",
+    "    task='hurdle',\n",
     "    save_gridding_plot = True,\n",
     "    ensemble_fold=10,                       # data are modeled 10 times, each time with jitter and rotation in Quadtree algo\n",
     "    min_ensemble_required=7,                # Only points covered by > 7 ensembles will be predicted\n",

docs/Examples/02.AdaSTEM_learning_curve_analysis.ipynb

@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -195,6 +195,7 @@
     "                classifier=XGBClassifier(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1),\n",
     "                regressor=XGBRegressor(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1)\n",
     "            ),\n",
+    "            task='hurdle',\n",
     "            save_gridding_plot = True,\n",
     "            ensemble_fold=10, \n",
     "            min_ensemble_required=7,\n",

docs/Examples/04.SphereAdaSTEM_demo.ipynb

@@ -721,7 +721,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -731,6 +731,7 @@
     "        classifier=XGBClassifier(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1),\n",
     "        regressor=XGBRegressor(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1)\n",
     "    ),                                      # hurdel model for zero-inflated problem (e.g., count)\n",
+    "    task='hurdle',\n",
     "    save_gridding_plot = True,\n",
     "    ensemble_fold=10,                       # data are modeled 10 times, each time with jitter and rotation in Quadtree algo\n",
     "    min_ensemble_required=7,                # Only points covered by > 7 stixels will be predicted\n",

docs/Examples/05.Hurdle_in_ada_or_ada_in_hurdle.ipynb

@@ -651,7 +651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -660,6 +660,7 @@
     "        classifier=XGBClassifier(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1),\n",
     "        regressor=XGBRegressor(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1)\n",
     "    ),\n",
+    "    task='hurdle',\n",
     "    save_gridding_plot = True,\n",
     "    ensemble_fold=10, \n",
     "    min_ensemble_required=7,\n",

docs/Examples/06.Base_model_choices.ipynb

@@ -710,6 +710,7 @@
     "                classifier=base_model_dict[base_model_name]['classifier'],\n",
     "                regressor=base_model_dict[base_model_name]['regressor']\n",
     "            ),\n",
+    "            task='hurdle',\n",
     "            save_gridding_plot = True,\n",
     "            ensemble_fold=10, \n",
     "            min_ensemble_required=7,\n",

docs/Examples/07.Optimizing_stixel_size.ipynb

@@ -581,7 +581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1154,6 +1154,7 @@
     "        classifier=XGBClassifier(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1),\n",
     "        regressor=XGBRegressor(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1)\n",
     "    ),\n",
+    "    task='hurdle',\n",
     "    save_gridding_plot = True,\n",
     "    ensemble_fold=10, \n",
     "    min_ensemble_required=7,\n",

docs/Examples/08.Lazy_loading.ipynb

@@ -678,7 +678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -691,6 +691,7 @@
     "            classifier=XGBClassifier(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1),\n",
     "            regressor=XGBRegressor(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1)\n",
     "        ),                                      # hurdel model for zero-inflated problem (e.g., count)\n",
+    "        task='hurdle',\n",
     "        save_gridding_plot = True,\n",
     "        ensemble_fold=ensemble_fold,                       # data are modeled 10 times, each time with jitter and rotation in Quadtree algo\n",
     "        min_ensemble_required=ensemble_fold-2,                # Only points covered by > 7 ensembles will be predicted\n",
@@ -720,6 +721,7 @@
     "            classifier=XGBClassifier(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1),\n",
     "            regressor=XGBRegressor(tree_method='hist',random_state=42, verbosity = 0, n_jobs=1)\n",
     "        ),                                      # hurdel model for zero-inflated problem (e.g., count)\n",
+    "        task='hurdle',\n",
     "        save_gridding_plot = True,\n",
     "        ensemble_fold=ensemble_fold,                       # data are modeled 10 times, each time with jitter and rotation in Quadtree algo\n",
     "        min_ensemble_required=ensemble_fold-2,                # Only points covered by > 7 ensembles will be predicted\n",

stemflow/model/AdaSTEM.py

@@ -1022,7 +1022,9 @@ def eval_STEM_res(
             elif task == "hurdle":
                 cls_threshold = 0
 
-        if not task == "regression":
+        if task == "regression":
+            auc, kappa, f1, precision, recall, average_precision = [np.nan] * 6
+        else:
             a = pd.DataFrame({"y_true": np.array(y_test).flatten(), "pred": np.array(y_pred).flatten()}).dropna()
 
             y_test_b = np.where(a.y_true > cls_threshold, 1, 0)
@@ -1039,9 +1041,6 @@ def eval_STEM_res(
                 recall = recall_score(y_test_b, y_pred_b)
                 average_precision = average_precision_score(y_test_b, y_pred_b)
 
-        else:
-            auc, kappa, f1, precision, recall, average_precision = [np.nan] * 6
-
         if not task == "classification":
             a = pd.DataFrame({"y_true": y_test, "pred": y_pred}).dropna()
             s_r, _ = spearmanr(np.array(a.y_true), np.array(a.pred))

stemflow/model/static_func_AdaSTEM.py

@@ -467,11 +467,12 @@ def predict_one_stixel(
 
     if pred is None:
         # Still haven't found the pred function
-        if task == "regression":
-            pred = model_x_names_tuple[0].predict(X_test_stixel[model_x_names_tuple[1]])
-        else:
+        if task == "classification":
             pred = model_x_names_tuple[0].predict_proba(X_test_stixel[model_x_names_tuple[1]], **base_model_prediction_param)
             pred = pred[:,1]
+        else:
+            pred = model_x_names_tuple[0].predict(X_test_stixel[model_x_names_tuple[1]], **base_model_prediction_param)
+
 
     res = pd.DataFrame({"index": list(X_test_stixel.index), "pred": np.array(pred).flatten()}).set_index("index")