diff --git a/src/baybe_playground.ipynb b/src/baybe_playground.ipynb
index 7bb01e5..7a4b998 100644
--- a/src/baybe_playground.ipynb
+++ b/src/baybe_playground.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,58 +33,25 @@
     "\n",
     "parameters = [\n",
     "    NumericalContinuousParameter('schwefel1', bounds=(-500,500)),\n",
-    "    NumericalContinuousParameter('schwefel2', bounds=(-500,500))\n",
-    "\n",
     "]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from baybe.recommenders import SequentialGreedyRecommender\n",
-    "\n",
-    "recommender = SequentialGreedyRecommender()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from baybe.searchspace import SearchSpace\n",
-    "\n",
-    "searchspace = SearchSpace.from_product(parameters)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from baybe import Campaign\n",
-    "\n",
-    "campaign = Campaign(searchspace, objective, recommender)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['__annotations__',\n",
+       "['__abstractmethods__',\n",
+       " '__annotations__',\n",
        " '__attrs_attrs__',\n",
        " '__attrs_own_setattr__',\n",
-       " '__attrs_types_resolved__',\n",
        " '__class__',\n",
+       " '__class_getitem__',\n",
        " '__delattr__',\n",
+       " '__dict__',\n",
        " '__dir__',\n",
        " '__doc__',\n",
        " '__eq__',\n",
@@ -101,6 +68,7 @@
        " '__module__',\n",
        " '__ne__',\n",
        " '__new__',\n",
+       " '__parameters__',\n",
        " '__reduce__',\n",
        " '__reduce_ex__',\n",
        " '__repr__',\n",
@@ -111,60 +79,122 @@
        " '__str__',\n",
        " '__subclasshook__',\n",
        " '__weakref__',\n",
-       " '_cached_recommendation',\n",
-       " '_measurements_exp',\n",
-       " '_measurements_parameters_comp',\n",
-       " '_measurements_targets_comp',\n",
-       " '_validate_strategy',\n",
-       " '_validate_tolerance_flag',\n",
-       " 'add_measurements',\n",
-       " 'from_config',\n",
-       " 'from_dict',\n",
-       " 'from_json',\n",
-       " 'measurements',\n",
-       " 'n_batches_done',\n",
-       " 'n_fits_done',\n",
-       " 'numerical_measurements_must_be_within_tolerance',\n",
-       " 'objective',\n",
-       " 'parameters',\n",
-       " 'recommend',\n",
-       " 'recommender',\n",
-       " 'searchspace',\n",
-       " 'strategy',\n",
-       " 'targets',\n",
-       " 'to_dict',\n",
-       " 'to_json',\n",
-       " 'validate_config']"
+       " '_abc_impl',\n",
+       " '_is_protocol',\n",
+       " '_is_runtime_protocol',\n",
+       " '_recommend_continuous',\n",
+       " '_recommend_discrete',\n",
+       " '_recommend_hybrid',\n",
+       " '_recommend_with_discrete_parts',\n",
+       " 'allow_recommending_already_measured',\n",
+       " 'allow_repeated_recommendations',\n",
+       " 'compatibility',\n",
+       " 'recommend']"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dir(campaign)"
+    "import baybe.recommenders\n",
+    "\n",
+    "dir(baybe.recommenders.pure.RandomRecommender)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from baybe.recommenders import (\n",
+    "    SequentialGreedyRecommender, \n",
+    "    SequentialMetaRecommender, \n",
+    "    RandomRecommender\n",
+    ")\n",
+    "\n",
+    "recommender = RandomRecommender()#SequentialMetaRecommender([SequentialGreedyRecommender(), RandomRecommender()])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from baybe.searchspace import SearchSpace\n",
+    "\n",
+    "searchspace = SearchSpace.from_product(parameters)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from baybe import Campaign\n",
+    "\n",
+    "campaign = Campaign(searchspace, objective, recommender)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "can only convert an array of size 1 to a Python scalar",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[16], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mcampaign\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecommend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m display(df)\n",
-      "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\baybe\\campaign.py:298\u001b[0m, in \u001b[0;36mCampaign.recommend\u001b[1;34m(self, batch_size, batch_quantity)\u001b[0m\n\u001b[0;32m    295\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_measurements_exp\u001b[38;5;241m.\u001b[39mfillna({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFitNr\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_fits_done}, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    297\u001b[0m \u001b[38;5;66;03m# Get the recommended search space entries\u001b[39;00m\n\u001b[1;32m--> 298\u001b[0m rec \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecommender\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecommend\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    299\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msearchspace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    300\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    301\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_measurements_parameters_comp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    302\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_measurements_targets_comp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    303\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    305\u001b[0m \u001b[38;5;66;03m# Cache the recommendations\u001b[39;00m\n\u001b[0;32m    306\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cached_recommendation \u001b[38;5;241m=\u001b[39m rec\u001b[38;5;241m.\u001b[39mcopy()\n",
-      "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\baybe\\recommenders\\pure\\bayesian\\base.py:139\u001b[0m, in \u001b[0;36mBayesianRecommender.recommend\u001b[1;34m(self, searchspace, batch_size, train_x, train_y)\u001b[0m\n\u001b[0;32m    136\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _ONNX_INSTALLED \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msurrogate_model, CustomONNXSurrogate):\n\u001b[0;32m    137\u001b[0m     CustomONNXSurrogate\u001b[38;5;241m.\u001b[39mvalidate_compatibility(searchspace)\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msetup_acquisition_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43msearchspace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_x\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_y\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    141\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecommend(searchspace, batch_size, train_x, train_y)\n",
-      "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\baybe\\recommenders\\pure\\bayesian\\base.py:94\u001b[0m, in \u001b[0;36mBayesianRecommender.setup_acquisition_function\u001b[1;34m(self, searchspace, train_x, train_y)\u001b[0m\n\u001b[0;32m     89\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m train_x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m train_y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     90\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\n\u001b[0;32m     91\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBayesian recommenders do not support empty training data yet.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     92\u001b[0m     )\n\u001b[1;32m---> 94\u001b[0m best_f \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_y\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     95\u001b[0m surrogate_model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit(searchspace, train_x, train_y)\n\u001b[0;32m     96\u001b[0m acquisition_function_cls \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_acquisition_function_cls()\n",
-      "File \u001b[1;32mc:\\Users\\d23895jm\\AppData\\Local\\anaconda3\\envs\\BO\\lib\\site-packages\\pandas\\core\\base.py:418\u001b[0m, in \u001b[0;36mIndexOpsMixin.item\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m    417\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(\u001b[38;5;28mself\u001b[39m))\n\u001b[1;32m--> 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcan only convert an array of size 1 to a Python scalar\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "\u001b[1;31mValueError\u001b[0m: can only convert an array of size 1 to a Python scalar"
-     ]
+     "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>schwefel1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>356.991347</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-309.004730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-55.674007</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    schwefel1\n",
+       "0  356.991347\n",
+       "1 -309.004730\n",
+       "2  -55.674007"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -174,10 +204,151 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from schwefel_functions import schwefel_1d\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "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>schwefel1</th>\n",
+       "      <th>Yield</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>356.991347</td>\n",
+       "      <td>403.045423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-309.004730</td>\n",
+       "      <td>123.758482</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-55.674007</td>\n",
+       "      <td>470.423643</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    schwefel1       Yield\n",
+       "0  356.991347  403.045423\n",
+       "1 -309.004730  123.758482\n",
+       "2  -55.674007  470.423643"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Yield'] = df['schwefel1'].apply(schwefel_1d)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "campaign.add_measurements(df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\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>schwefel1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-428.432368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-347.173847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>395.345514</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    schwefel1\n",
+       "0 -428.432368\n",
+       "1 -347.173847\n",
+       "2  395.345514"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "campaign.recommend(batch_size=3)"
+   ]
   }
  ],
  "metadata": {