Removed unnessesary features calculation

Author: ulya-tkch

src/experiments/adjusted-OOD-scores/1_Evaluate_All_OOD_Experiments.ipynb

@@ -162,7 +162,7 @@
    "id": "a2932f76-a8c7-4009-bff1-f672da79923a",
    "metadata": {},
    "source": [
-    "## Save the pred_probs and features used for OOD scoring"
+    "## Save the pred_probs used for OOD scoring"
    ]
   },
   {
@@ -214,21 +214,6 @@
     "    in_test_pred_probs = in_predictor_loaded.predict_proba(data=in_test_dataset, as_pandas=False)\n",
     "    out_test_pred_probs = in_predictor_loaded.predict_proba(data=out_test_dataset, as_pandas=False)\n",
     "    \n",
-    "    # Get LEARNED embeddings\n",
-    "    print(\"  Extracting learned embeddings...\")\n",
-    "    in_train_features = \\\n",
-    "        np.stack(\n",
-    "            in_predictor_loaded.predict_feature(data=in_train_dataset, as_pandas=False)[:, 0]\n",
-    "        )\n",
-    "    in_test_features = \\\n",
-    "        np.stack(\n",
-    "            in_predictor_loaded.predict_feature(data=in_test_dataset, as_pandas=False)[:, 0]\n",
-    "        )\n",
-    "    out_test_features = \\\n",
-    "        np.stack(\n",
-    "            in_predictor_loaded.predict_feature(data=out_test_dataset, as_pandas=False)[:, 0]\n",
-    "        )    \n",
-    "    \n",
     "    # Save files here\n",
     "    out_folder = f\"./model_{model}_experiment_in_{in_dataset}_out_{out_dataset}/\"\n",
     "    \n",
@@ -241,10 +226,6 @@
     "    np.save(out_folder + \"in_test_pred_probs.npy\", in_test_pred_probs)\n",
     "    np.save(out_folder + \"out_test_pred_probs.npy\", out_test_pred_probs)\n",
     "    \n",
-    "    np.save(out_folder + \"in_train_features.npy\", in_train_features)\n",
-    "    np.save(out_folder + \"in_test_features.npy\", in_test_features)\n",
-    "    np.save(out_folder + \"out_test_features.npy\", out_test_features)\n",
-    "    \n",
     "    np.save(out_folder + \"in_train_dataset_class_labels.npy\", in_train_dataset_class_labels)\n",
     "    np.save(out_folder + \"in_test_dataset_class_labels.npy\", in_test_dataset_class_labels)"
    ]
@@ -254,7 +235,7 @@
    "id": "47837b90-a36f-4c50-a966-33f09b2bb31a",
    "metadata": {},
    "source": [
-    "## Run OOD scoring on loaded pred_probs and features"
+    "## Run OOD scoring on loaded pred_probs"
    ]
   },
   {
@@ -309,16 +290,8 @@
     "    in_test_pred_probs = np.load(out_folder + \"in_test_pred_probs.npy\")\n",
     "    out_test_pred_probs = np.load(out_folder + \"out_test_pred_probs.npy\")\n",
     "    \n",
-    "    in_train_features = np.load(out_folder + \"in_train_features.npy\")\n",
-    "    in_test_features = np.load(out_folder + \"in_test_features.npy\", )\n",
-    "    out_test_features = np.load(out_folder + \"out_test_features.npy\")\n",
-    "    \n",
     "    in_train_dataset_class_labels = np.load(out_folder + \"in_train_dataset_class_labels.npy\")\n",
     "    in_test_dataset_class_labels = np.load(out_folder + \"in_test_dataset_class_labels.npy\")\n",
-    "\n",
-    "    # Combine pred_probs and features for TEST dataset\n",
-    "    test_pred_probs = np.vstack([in_test_pred_probs, out_test_pred_probs])\n",
-    "    test_features = np.vstack([in_test_features, out_test_features]) # LEARNED embeddings\n",
     "    \n",
     "    # Create OOD binary labels (1 = out-of-distribution)\n",
     "    in_labels = np.zeros(shape=len(in_test_pred_probs))\n",