fix

benchmark/NBspecific_18/NBspecific_18_reproduced.ipynb

@@ -1770,6 +1770,13 @@
     "print(y_pred)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 18,

benchmark/tensorflow_5/tensorflow_5_fixed.ipynb

@@ -269,45 +269,13 @@
     "print(len(anime_ids))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2023-12-07T11:40:24.564887Z",
-     "iopub.status.busy": "2023-12-07T11:40:24.564123Z",
-     "iopub.status.idle": "2023-12-07T11:40:33.326829Z",
-     "shell.execute_reply": "2023-12-07T11:40:33.325539Z",
-     "shell.execute_reply.started": "2023-12-07T11:40:24.564840Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Buggy --\n",
-    "# # Encoding categorical data\n",
-    "# user_ids = rating_df[\"user_id\"].unique().tolist()[:1000]\n",
-    "# user2user_encoded = {x: i for i, x in enumerate(user_ids)}\n",
-    "# user_encoded2user = {i: x for i, x in enumerate(user_ids)}\n",
-    "# rating_df[\"user\"] = rating_df[\"user_id\"].map(user2user_encoded)\n",
-    "# n_users = len(user2user_encoded)\n",
-    "\n",
-    "# anime_ids = rating_df[\"anime_id\"].unique().tolist()[:1000]\n",
-    "# anime2anime_encoded = {x: i for i, x in enumerate(anime_ids)}\n",
-    "# anime_encoded2anime = {i: x for i, x in enumerate(anime_ids)}\n",
-    "# rating_df[\"anime\"] = rating_df[\"anime_id\"].map(anime2anime_encoded)\n",
-    "# n_animes = len(anime2anime_encoded)\n",
-    "\n",
-    "# print(\"Num of users: {}, Num of animes: {}\".format(n_users, n_animes))\n",
-    "# print(\"Min rating: {}, Max rating: {}\".format(min(rating_df['rating']), max(rating_df['rating'])))"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Fix-------------------user is and anime id doi not match\n",
+    "# fix-------------------user ids and anime ids do not match\n",
     "# Encoding categorical data\n",
     "user_ids = rating_df[\"user_id\"].unique().tolist()\n",
     "anime_ids = rating_df[\"anime_id\"].unique().tolist()\n",
@@ -323,7 +291,24 @@
     "\n",
     "\n",
     "n_users = rating_df[\"user\"].nunique()\n",
-    "n_animes = rating_df[\"anime\"].nunique()"
+    "n_animes = rating_df[\"anime\"].nunique()\n",
+    "\n",
+    "# buggy --\n",
+    "# # Encoding categorical data\n",
+    "# user_ids = rating_df[\"user_id\"].unique().tolist()[:1000]\n",
+    "# user2user_encoded = {x: i for i, x in enumerate(user_ids)}\n",
+    "# user_encoded2user = {i: x for i, x in enumerate(user_ids)}\n",
+    "# rating_df[\"user\"] = rating_df[\"user_id\"].map(user2user_encoded)\n",
+    "# n_users = len(user2user_encoded)\n",
+    "\n",
+    "# anime_ids = rating_df[\"anime_id\"].unique().tolist()[:1000]\n",
+    "# anime2anime_encoded = {x: i for i, x in enumerate(anime_ids)}\n",
+    "# anime_encoded2anime = {i: x for i, x in enumerate(anime_ids)}\n",
+    "# rating_df[\"anime\"] = rating_df[\"anime_id\"].map(anime2anime_encoded)\n",
+    "# n_animes = len(anime2anime_encoded)\n",
+    "\n",
+    "# print(\"Num of users: {}, Num of animes: {}\".format(n_users, n_animes))\n",
+    "# print(\"Min rating: {}, Max rating: {}\".format(min(rating_df['rating']), max(rating_df['rating'])))"
    ]
   },
   {