Update pzmm_binary_classification_model_import notebook to include model card generation

Author: djm21

examples/pzmm_binary_classification_model_import.ipynb

@@ -740,7 +740,7 @@
    ],
    "source": [
     "import getpass\n",
-    "def write_model_stats(x_train, y_train, test_predict, test_proba, y_test, model, path):\n",
+    "def write_model_stats(x_train, y_train, test_predict, test_proba, y_test, model, path, prefix):\n",
     "    # Calculate train predictions\n",
     "    train_predict = model.predict(x_train)\n",
     "    train_proba = model.predict_proba(x_train)\n",
@@ -757,6 +757,20 @@
     "        test_data=test_data, \n",
     "        json_path=path\n",
     "    )\n",
+    "\n",
+    "    full_training_data = pd.concat([y_train.reset_index(drop=True), x_train.reset_index(drop=True)], axis=1)\n",
+    "\n",
+    "    pzmm.JSONFiles.generate_model_card(\n",
+    "        model_prefix=prefix,\n",
+    "        model_files = path,\n",
+    "        algorithm = str(type(model).__name__),\n",
+    "        train_data = full_training_data,\n",
+    "        train_predictions=train_predict,\n",
+    "        target_type='classification',\n",
+    "        target_value=1,\n",
+    "        interval_vars=predictor_columns,\n",
+    "        selection_statistic='_RASE_',\n",
+    "    )\n",
     "        \n",
     "username = getpass.getpass()\n",
     "password = getpass.getpass()\n",
@@ -766,8 +780,8 @@
     "\n",
     "test_predict = [y_dtc_predict, y_rfc_predict, y_gbc_predict]\n",
     "test_proba = [y_dtc_proba, y_rfc_proba, y_gbc_proba]\n",
-    "for (mod, pred, proba, path) in zip(model, test_predict, test_proba, zip_folder):\n",
-    "    write_model_stats(x_train, y_train, pred, proba, y_test, mod, path)"
+    "for (mod, pred, proba, path, prefix) in zip(model, test_predict, test_proba, zip_folder, model_prefix):\n",
+    "    write_model_stats(x_train, y_train, pred, proba, y_test, mod, path, prefix)"
    ]
   },
   {