Repository: ourownstory/neural_prophet

Issue:
cross-validation demo error
When I was studying the cross-validation page in the official document, I found that the following code would report an error, prompting "['SmoothL1Loss'] not in index". The output of the test metrics does not include these items. Is it because the version update has been modified?
```
metrics_train = pd.DataFrame(columns=METRICS)
metrics_test = pd.DataFrame(columns=METRICS)

for df_train, df_test in folds:
    m = NeuralProphet(**params)
    train = m.fit(df=df_train, freq="MS")
    test = m.test(df=df_test)
    metrics_train = metrics_train.append(train[METRICS].iloc[-1])
    metrics_test = metrics_test.append(test[METRICS].iloc[-1]
```
test metrics like this
![image](https://user-images.githubusercontent.com/43956952/227927644-973bec3d-3695-44d4-a1f2-0b1a7667801f.png)



Discussion:
@CrazyBalthazar thanks for reporting the issue, I can confirm it. we'll get back to you with a fix.
@karl-richter seems the return values of the metrics in our `test` function changed with the lightning migration. do you have any idea how to retrieve the prior metrics for `test` calls?
Hi, we are planning to include this in our V1 release in three weeks, but have no capacity work on this. If someone want to take over this issue, you are welcome to do so

Resolving patch:
diff --git a/neuralprophet/time_net.py b/neuralprophet/time_net.py
--- a/neuralprophet/time_net.py
+++ b/neuralprophet/time_net.py
@@ -793,6 +793,9 @@ def test_step(self, batch, batch_idx):
         loss, reg_loss = self.loss_func(inputs, predicted, targets)
         # Metrics
         if self.metrics_enabled:
+            predicted_denorm = self.denormalize(predicted[:, :, 0])
+            target_denorm = self.denormalize(targets.squeeze(dim=2))
+            self.log_dict(self.metrics_val(predicted_denorm, target_denorm), **self.log_args)
             self.log("Loss_test", loss, **self.log_args)
             self.log("RegLoss_test", reg_loss, **self.log_args)
 


Test patch:
diff --git a/tutorials/feature-use/test_and_crossvalidate.ipynb b/tutorials/feature-use/test_and_crossvalidate.ipynb
--- a/tutorials/feature-use/test_and_crossvalidate.ipynb
+++ b/tutorials/feature-use/test_and_crossvalidate.ipynb
@@ -18,14 +18,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "80400b6d-ca57-47ba-9dc5-0da3885ab6b1",
    "metadata": {},
    "outputs": [],
    "source": [
     "if \"google.colab\" in str(get_ipython()):\n",
-    "    !pip install git+https://github.com/ourownstory/neural_prophet.git # may take a while\n",
-    "    #!pip install neuralprophet # much faster, but may not have the latest upgrades/bugfixes\n",
+    "    #!pip install git+https://github.com/ourownstory/neural_prophet.git # may take a while\n",
+    "    !pip install neuralprophet # much faster, but may not have the latest upgrades/bugfixes\n",
     "\n",
     "import pandas as pd\n",
     "from neuralprophet import NeuralProphet, set_log_level\n",
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "90e2ee7e-6798-4a0c-a37f-c3b7325d9c70",
    "metadata": {},
    "outputs": [],
@@ -73,17 +73,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "170af980-1830-4909-800a-b3e4f0ccc4f9",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                        \r"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -105,28 +98,30 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>SmoothL1Loss</th>\n",
-       "      <th>MAE</th>\n",
-       "      <th>RMSE</th>\n",
+       "      <th>MAE_val</th>\n",
+       "      <th>RMSE_val</th>\n",
+       "      <th>Loss_test</th>\n",
+       "      <th>RegLoss_test</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.005307</td>\n",
-       "      <td>26.848057</td>\n",
-       "      <td>32.760937</td>\n",
+       "      <td>18.830694</td>\n",
+       "      <td>23.04147</td>\n",
+       "      <td>0.00274</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "   SmoothL1Loss        MAE       RMSE\n",
-       "0      0.005307  26.848057  32.760937"
+       "     MAE_val  RMSE_val  Loss_test  RegLoss_test\n",
+       "0  18.830694  23.04147    0.00274           0.0"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -154,28 +149,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "47d23260-ad96-4196-aba1-7eb9fd2e6b21",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                        \r"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "m = NeuralProphet(seasonality_mode=\"multiplicative\", learning_rate=0.1)\n",
     "metrics_train2 = m.fit(df=df, freq=\"MS\")\n",
@@ -313,12 +290,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "id": "fbf84c93-dc7c-406d-941a-87ae5b8adafa",
    "metadata": {},
    "outputs": [],
    "source": [
-    "METRICS = [\"SmoothL1Loss\", \"MAE\", \"RMSE\"]\n",
+    "METRICS = [\"MAE\", \"RMSE\"]\n",
+    "METRICS_VAL = [\"MAE_val\", \"RMSE_val\"]\n",
     "params = {\"seasonality_mode\": \"multiplicative\", \"learning_rate\": 0.1}\n",
     "\n",
     "df = pd.read_csv(data_location + \"air_passengers.csv\")\n",
@@ -327,35 +305,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "b585c737-08f6-468e-890a-558348f6b9ee",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                        \r"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "metrics_train = pd.DataFrame(columns=METRICS)\n",
-    "metrics_test = pd.DataFrame(columns=METRICS)\n",
+    "metrics_test = pd.DataFrame(columns=METRICS_VAL)\n",
     "\n",
     "for df_train, df_test in folds:\n",
     "    m = NeuralProphet(**params)\n",
     "    train = m.fit(df=df_train, freq=\"MS\")\n",
     "    test = m.test(df=df_test)\n",
     "    metrics_train = metrics_train.append(train[METRICS].iloc[-1])\n",
-    "    metrics_test = metrics_test.append(test[METRICS].iloc[-1])"
+    "    metrics_test = metrics_test.append(test[METRICS_VAL].iloc[-1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "id": "e09fc84b-3e11-47be-aa88-024b0c27c8a1",
    "metadata": {
     "tags": []
@@ -382,49 +352,44 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>SmoothL1Loss</th>\n",
-       "      <th>MAE</th>\n",
-       "      <th>RMSE</th>\n",
+       "      <th>MAE_val</th>\n",
+       "      <th>RMSE_val</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>0.009792</td>\n",
-       "      <td>21.090633</td>\n",
-       "      <td>26.227701</td>\n",
+       "      <td>22.738665</td>\n",
+       "      <td>27.219829</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>0.008580</td>\n",
-       "      <td>7.266532</td>\n",
-       "      <td>9.003498</td>\n",
+       "      <td>5.473991</td>\n",
+       "      <td>5.156014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
-       "      <td>0.003340</td>\n",
-       "      <td>10.293222</td>\n",
-       "      <td>13.339386</td>\n",
+       "      <td>16.465864</td>\n",
+       "      <td>20.199633</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>0.023061</td>\n",
-       "      <td>26.983458</td>\n",
-       "      <td>34.758419</td>\n",
+       "      <td>30.117323</td>\n",
+       "      <td>32.755363</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "      SmoothL1Loss        MAE       RMSE\n",
-       "mean      0.009792  21.090633  26.227701\n",
-       "std       0.008580   7.266532   9.003498\n",
-       "min       0.003340  10.293222  13.339386\n",
-       "max       0.023061  26.983458  34.758419"
+       "        MAE_val   RMSE_val\n",
+       "mean  22.738665  27.219829\n",
+       "std    5.473991   5.156014\n",
+       "min   16.465864  20.199633\n",
+       "max   30.117323  32.755363"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -448,18 +413,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "f23fd06b-2f0c-4f2c-92fa-5ad28e286d4c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Importing plotly failed. Interactive plots will not work.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from neuralprophet.benchmark import Dataset, NeuralProphetModel, SimpleExperiment, CrossValidationExperiment"
    ]
@@ -475,39 +432,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "id": "c7d050b7-2b8e-40d1-ba4d-e43e22a64c48",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fb260098c8ea489186b3d268b6628065",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/107 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "052d56ecaad5483f9c82c565329c060c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/107 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ts = Dataset(df=pd.read_csv(data_location + \"air_passengers.csv\"), name=\"air_passengers\", freq=\"MS\")\n",
     "params = {\"seasonality_mode\": \"multiplicative\"}\n",
@@ -560,95 +488,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "id": "9a5812bd-45d5-4a45-af9f-8dac25524e15",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dc921d64bf8d4e9db22b1c535aac7465",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/107 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5eba2ce8155447a6b5c3177b1d4367fd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/107 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3618c693fbda4148912fb3dd9274f989",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/108 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3c64478c367445308bcb368d9f74ab2c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/108 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "536037272e07411d9a7dbabfdd9edb22",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b180271ce8184fbf98306b9aff2fe3f2",
-       "version_major": 2,
-       "version_minor": 0
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ts = Dataset(df=pd.read_csv(data_location + \"air_passengers.csv\"), name=\"air_passengers\", freq=\"MS\")\n",
     "params = {\"seasonality_mode\": \"multiplicative\"}\n",
@@ -712,18 +555,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "id": "8e342fef-b57d-4ab8-8550-f9311cb0e032",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                        \r"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "m = NeuralProphet(seasonality_mode=\"multiplicative\", learning_rate=0.1)\n",
     "\n",
@@ -745,83 +580,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "id": "635e284c-b3b7-477c-ada0-9a131912137f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>SmoothL1Loss</th>\n",
-       "      <th>MAE</th>\n",
-       "      <th>RMSE</th>\n",
-       "      <th>split</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>563</th>\n",
-       "      <td>0.000379</td>\n",
-       "      <td>5.379905</td>\n",
-       "      <td>6.704538</td>\n",
-       "      <td>train1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>491</th>\n",
-       "      <td>0.000318</td>\n",
-       "      <td>6.277920</td>\n",
-       "      <td>7.928728</td>\n",
-       "      <td>train2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.005016</td>\n",
-       "      <td>18.140518</td>\n",
-       "      <td>25.140703</td>\n",
-       "      <td>validate</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.005475</td>\n",
-       "      <td>27.335314</td>\n",
-       "      <td>33.275681</td>\n",
-       "      <td>test</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     SmoothL1Loss        MAE       RMSE     split\n",
-       "563      0.000379   5.379905   6.704538    train1\n",
-       "491      0.000318   6.277920   7.928728    train2\n",
-       "0        0.005016  18.140518  25.140703  validate\n",
-       "0        0.005475  27.335314  33.275681      test"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "metrics_train1[\"split\"] = \"train1\"\n",
     "metrics_train2[\"split\"] = \"train2\"\n",
@@ -842,12 +604,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 8,
    "id": "dac90c4f-63e7-42f2-b49e-f6b3177e070f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "METRICS = [\"SmoothL1Loss\", \"MAE\", \"RMSE\"]\n",
+    "METRICS = [\"MAE\", \"RMSE\"]\n",
+    "METRICS_VAL = [\"MAE_val\", \"RMSE_val\"]\n",
     "params = {\"seasonality_mode\": \"multiplicative\", \"learning_rate\": 0.1}\n",
     "\n",
     "df = pd.read_csv(data_location + \"air_passengers.csv\")\n",
@@ -858,107 +621,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "id": "96d8154e-c77a-4836-ad92-9b205e88b268",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                                                                                                        \r"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "metrics_train1 = pd.DataFrame(columns=METRICS)\n",
-    "metrics_val = pd.DataFrame(columns=METRICS)\n",
+    "metrics_val = pd.DataFrame(columns=METRICS_VAL)\n",
     "for df_train1, df_val in folds_val:\n",
     "    m = NeuralProphet(**params)\n",
     "    train1 = m.fit(df=df_train, freq=\"MS\")\n",
     "    val = m.test(df=df_val)\n",
     "    metrics_train1 = metrics_train1.append(train1[METRICS].iloc[-1])\n",
-    "    metrics_val = metrics_val.append(val[METRICS].iloc[-1])\n",
+    "    metrics_val = metrics_val.append(val[METRICS_VAL].iloc[-1])\n",
     "\n",
     "metrics_train2 = pd.DataFrame(columns=METRICS)\n",
-    "metrics_test = pd.DataFrame(columns=METRICS)\n",
+    "metrics_test = pd.DataFrame(columns=METRICS_val)\n",
     "for df_train2, df_test in folds_test:\n",
     "    m = NeuralProphet(**params)\n",
     "    train2 = m.fit(df=df_train2, freq=\"MS\")\n",
     "    test = m.test(df=df_test)\n",
     "    metrics_train2 = metrics_train2.append(train2[METRICS].iloc[-1])\n",
-    "    metrics_test = metrics_test.append(test[METRICS].iloc[-1])"
+    "    metrics_test = metrics_test.append(test[METRICS_VAL].iloc[-1])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "c5ea1cbc-fbb2-46b1-9a43-4547f8b6113a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>SmoothL1Loss</th>\n",
-       "      <th>MAE</th>\n",
-       "      <th>RMSE</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>0.000265</td>\n",
-       "      <td>6.721306</td>\n",
-       "      <td>8.426241</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.000021</td>\n",
-       "      <td>0.091057</td>\n",
-       "      <td>0.133246</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      SmoothL1Loss       MAE      RMSE\n",
-       "mean      0.000265  6.721306  8.426241\n",
-       "std       0.000021  0.091057  0.133246"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "metrics_train2.describe().loc[[\"mean\", \"std\"]]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 10,
    "id": "13f96119-df3c-4a0d-adeb-f2816ff88587",
    "metadata": {
     "tags": []
@@ -985,35 +686,32 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>SmoothL1Loss</th>\n",
-       "      <th>MAE</th>\n",
-       "      <th>RMSE</th>\n",
+       "      <th>MAE_val</th>\n",
+       "      <th>RMSE_val</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>0.009150</td>\n",
-       "      <td>30.096512</td>\n",
-       "      <td>31.593293</td>\n",
+       "      <td>30.366895</td>\n",
+       "      <td>31.763364</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>0.006674</td>\n",
-       "      <td>12.822483</td>\n",
-       "      <td>13.912322</td>\n",
+       "      <td>13.052365</td>\n",
+       "      <td>14.098237</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "      SmoothL1Loss        MAE       RMSE\n",
-       "mean      0.009150  30.096512  31.593293\n",
-       "std       0.006674  12.822483  13.912322"
+       "        MAE_val   RMSE_val\n",
+       "mean  30.366895  31.763364\n",
+       "std   13.052365  14.098237"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1024,64 +722,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 15,
    "id": "6c6327f8-bcb3-4454-bc9f-a1fe26f8bea6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>SmoothL1Loss</th>\n",
-       "      <th>MAE</th>\n",
-       "      <th>RMSE</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>0.001057</td>\n",
-       "      <td>14.137106</td>\n",
-       "      <td>15.214975</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.001216</td>\n",
-       "      <td>7.679862</td>\n",
-       "      <td>7.979069</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      SmoothL1Loss        MAE       RMSE\n",
-       "mean      0.001057  14.137106  15.214975\n",
-       "std       0.001216   7.679862   7.979069"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "metrics_test.describe().loc[[\"mean\", \"std\"]]"
    ]
@@ -1108,9 +752,9 @@
    "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
   },
   "kernelspec": {
-   "display_name": "np-dev",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "np-dev"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1122,7 +766,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,