correct changepoint notebook

Author: Thomas Morzadec

notebooks/regression/ts-changepoint.ipynb

@@ -245,7 +245,7 @@
     "print(\"EnbPI, with no partial_fit, width optimization\")\n",
     "mapie_enbpi = mapie_enbpi.fit(X_train, y_train)\n",
     "y_pred_npfit, y_pis_npfit = mapie_enbpi.predict(\n",
-    "    X_test, alpha=alpha, ensemble=True, beta_optimize=True\n",
+    "    X_test, alpha=alpha, ensemble=True, optimize_beta=True\n",
     ")\n",
     "coverage_npfit = regression_coverage_score(\n",
     "    y_test, y_pis_npfit[:, 0, 0], y_pis_npfit[:, 1, 0]\n",
@@ -273,6 +273,24 @@
      "text": [
       "EnbPI with partial_fit, width optimization\n"
      ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [9]\u001b[0m, in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(gap, \u001b[38;5;28mlen\u001b[39m(X_test), gap):\n\u001b[1;32m     10\u001b[0m     mapie_enbpi\u001b[38;5;241m.\u001b[39mpartial_fit(\n\u001b[1;32m     11\u001b[0m         X_test\u001b[38;5;241m.\u001b[39miloc[(step \u001b[38;5;241m-\u001b[39m gap):step, :],\n\u001b[1;32m     12\u001b[0m         y_test\u001b[38;5;241m.\u001b[39miloc[(step \u001b[38;5;241m-\u001b[39m gap):step],\n\u001b[1;32m     13\u001b[0m     )\n\u001b[1;32m     14\u001b[0m     (\n\u001b[1;32m     15\u001b[0m         y_pred_pfit[step:step \u001b[38;5;241m+\u001b[39m gap],\n\u001b[1;32m     16\u001b[0m         y_pis_pfit[step:step \u001b[38;5;241m+\u001b[39m gap, :, :],\n\u001b[0;32m---> 17\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[43mmapie_enbpi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     18\u001b[0m \u001b[43m        \u001b[49m\u001b[43mX_test\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m:\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mgap\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     19\u001b[0m \u001b[43m        \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     20\u001b[0m \u001b[43m        \u001b[49m\u001b[43mensemble\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m     21\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptimize_beta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m     22\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     23\u001b[0m coverage_pfit \u001b[38;5;241m=\u001b[39m regression_coverage_score(\n\u001b[1;32m     24\u001b[0m     y_test, y_pis_pfit[:, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m], y_pis_pfit[:, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     25\u001b[0m )\n\u001b[1;32m     26\u001b[0m width_pfit \u001b[38;5;241m=\u001b[39m regression_mean_width_score(\n\u001b[1;32m     27\u001b[0m     y_pis_pfit[:, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m], y_pis_pfit[:, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     28\u001b[0m )\n",
+      "File \u001b[0;32m~/Missions/MAPIE/mapie/time_series_regression.py:263\u001b[0m, in \u001b[0;36mMapieTimeSeriesRegressor.predict\u001b[0;34m(self, X, ensemble, alpha, optimize_beta)\u001b[0m\n\u001b[1;32m    261\u001b[0m     y_pred_up \u001b[38;5;241m=\u001b[39m y_pred[:, np\u001b[38;5;241m.\u001b[39mnewaxis] \u001b[38;5;241m+\u001b[39m higher_quantiles\n\u001b[1;32m    262\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 263\u001b[0m     y_pred_multi \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_pred_multi\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    264\u001b[0m     pred \u001b[38;5;241m=\u001b[39m aggregate_all(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magg_function, y_pred_multi)\n\u001b[1;32m    265\u001b[0m     lower_bounds, upper_bounds \u001b[38;5;241m=\u001b[39m pred, pred\n",
+      "File \u001b[0;32m~/Missions/MAPIE/mapie/regression.py:477\u001b[0m, in \u001b[0;36mMapieRegressor._pred_multi\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    462\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_pred_multi\u001b[39m(\u001b[38;5;28mself\u001b[39m, X: ArrayLike) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDArray:\n\u001b[1;32m    463\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    464\u001b[0m \u001b[38;5;124;03m    Return a prediction per train sample for each test sample, by\u001b[39;00m\n\u001b[1;32m    465\u001b[0m \u001b[38;5;124;03m    aggregation with matrix  ``k_``.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    474\u001b[0m \u001b[38;5;124;03m        NDArray of shape (n_samples_test, n_samples_train)\u001b[39;00m\n\u001b[1;32m    475\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    476\u001b[0m     y_pred_multi \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcolumn_stack(\n\u001b[0;32m--> 477\u001b[0m         [e\u001b[38;5;241m.\u001b[39mpredict(X) \u001b[38;5;28;01mfor\u001b[39;00m e \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimators_]\n\u001b[1;32m    478\u001b[0m     )\n\u001b[1;32m    479\u001b[0m     \u001b[38;5;66;03m# At this point, y_pred_multi is of shape\u001b[39;00m\n\u001b[1;32m    480\u001b[0m     \u001b[38;5;66;03m# (n_samples_test, n_estimators_). The method\u001b[39;00m\n\u001b[1;32m    481\u001b[0m     \u001b[38;5;66;03m# ``_aggregate_with_mask`` fits it to the right size\u001b[39;00m\n\u001b[1;32m    482\u001b[0m     \u001b[38;5;66;03m# thanks to the shape of k_.\u001b[39;00m\n\u001b[1;32m    484\u001b[0m     y_pred_multi \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_aggregate_with_mask(y_pred_multi, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mk_)\n",
+      "File \u001b[0;32m~/Missions/MAPIE/mapie/regression.py:477\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    462\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_pred_multi\u001b[39m(\u001b[38;5;28mself\u001b[39m, X: ArrayLike) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m NDArray:\n\u001b[1;32m    463\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    464\u001b[0m \u001b[38;5;124;03m    Return a prediction per train sample for each test sample, by\u001b[39;00m\n\u001b[1;32m    465\u001b[0m \u001b[38;5;124;03m    aggregation with matrix  ``k_``.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    474\u001b[0m \u001b[38;5;124;03m        NDArray of shape (n_samples_test, n_samples_train)\u001b[39;00m\n\u001b[1;32m    475\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    476\u001b[0m     y_pred_multi \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcolumn_stack(\n\u001b[0;32m--> 477\u001b[0m         [\u001b[43me\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m e \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimators_]\n\u001b[1;32m    478\u001b[0m     )\n\u001b[1;32m    479\u001b[0m     \u001b[38;5;66;03m# At this point, y_pred_multi is of shape\u001b[39;00m\n\u001b[1;32m    480\u001b[0m     \u001b[38;5;66;03m# (n_samples_test, n_estimators_). The method\u001b[39;00m\n\u001b[1;32m    481\u001b[0m     \u001b[38;5;66;03m# ``_aggregate_with_mask`` fits it to the right size\u001b[39;00m\n\u001b[1;32m    482\u001b[0m     \u001b[38;5;66;03m# thanks to the shape of k_.\u001b[39;00m\n\u001b[1;32m    484\u001b[0m     y_pred_multi \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_aggregate_with_mask(y_pred_multi, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mk_)\n",
+      "File \u001b[0;32m~/miniconda3_64/envs/mapie/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:974\u001b[0m, in \u001b[0;36mForestRegressor.predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    972\u001b[0m \u001b[38;5;66;03m# Parallel loop\u001b[39;00m\n\u001b[1;32m    973\u001b[0m lock \u001b[38;5;241m=\u001b[39m threading\u001b[38;5;241m.\u001b[39mLock()\n\u001b[0;32m--> 974\u001b[0m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    975\u001b[0m \u001b[43m    \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    976\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    977\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m_joblib_parallel_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequire\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msharedmem\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    978\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    979\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_accumulate_prediction\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43my_hat\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    980\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43me\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestimators_\u001b[49m\n\u001b[1;32m    981\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    983\u001b[0m y_hat \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimators_)\n\u001b[1;32m    985\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y_hat\n",
+      "File \u001b[0;32m~/miniconda3_64/envs/mapie/lib/python3.10/site-packages/joblib/parallel.py:1046\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1043\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdispatch_one_batch(iterator):\n\u001b[1;32m   1044\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterating \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_original_iterator \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1046\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdispatch_one_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m   1047\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m   1049\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pre_dispatch \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m n_jobs \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   1050\u001b[0m     \u001b[38;5;66;03m# The iterable was consumed all at once by the above for loop.\u001b[39;00m\n\u001b[1;32m   1051\u001b[0m     \u001b[38;5;66;03m# No need to wait for async callbacks to trigger to\u001b[39;00m\n\u001b[1;32m   1052\u001b[0m     \u001b[38;5;66;03m# consumption.\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniconda3_64/envs/mapie/lib/python3.10/site-packages/joblib/parallel.py:822\u001b[0m, in \u001b[0;36mParallel.dispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    814\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m    815\u001b[0m     \u001b[38;5;66;03m# to ensure an even distribution of the workolad between workers,\u001b[39;00m\n\u001b[1;32m    816\u001b[0m     \u001b[38;5;66;03m# we look ahead in the original iterators more than batch_size\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    819\u001b[0m     \u001b[38;5;66;03m# queue, _ready_batches, that is looked-up prior to re-consuming\u001b[39;00m\n\u001b[1;32m    820\u001b[0m     \u001b[38;5;66;03m# tasks from the origal iterator.\u001b[39;00m\n\u001b[1;32m    821\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 822\u001b[0m         tasks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ready_batches\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    823\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m queue\u001b[38;5;241m.\u001b[39mEmpty:\n\u001b[1;32m    824\u001b[0m         \u001b[38;5;66;03m# slice the iterator n_jobs * batchsize items at a time. If the\u001b[39;00m\n\u001b[1;32m    825\u001b[0m         \u001b[38;5;66;03m# slice returns less than that, then the current batchsize puts\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    828\u001b[0m         \u001b[38;5;66;03m# accordingly to distribute evenly the last items between all\u001b[39;00m\n\u001b[1;32m    829\u001b[0m         \u001b[38;5;66;03m# workers.\u001b[39;00m\n\u001b[1;32m    830\u001b[0m         n_jobs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cached_effective_n_jobs\n",
+      "File \u001b[0;32m~/miniconda3_64/envs/mapie/lib/python3.10/queue.py:154\u001b[0m, in \u001b[0;36mQueue.get\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    151\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munfinished_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    152\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnot_empty\u001b[38;5;241m.\u001b[39mnotify()\n\u001b[0;32m--> 154\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget\u001b[39m(\u001b[38;5;28mself\u001b[39m, block\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    155\u001b[0m     \u001b[38;5;124;03m'''Remove and return an item from the queue.\u001b[39;00m\n\u001b[1;32m    156\u001b[0m \n\u001b[1;32m    157\u001b[0m \u001b[38;5;124;03m    If optional args 'block' is true and 'timeout' is None (the default),\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[38;5;124;03m    in that case).\u001b[39;00m\n\u001b[1;32m    164\u001b[0m \u001b[38;5;124;03m    '''\u001b[39;00m\n\u001b[1;32m    165\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnot_empty:\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
     }
    ],
    "source": [
@@ -282,7 +300,7 @@
     "y_pred_pfit = np.zeros(y_pred_npfit.shape)\n",
     "y_pis_pfit = np.zeros(y_pis_npfit.shape)\n",
     "y_pred_pfit[:gap], y_pis_pfit[:gap, :, :] = mapie_enbpi.predict(\n",
-    "    X_test.iloc[:gap, :], alpha=alpha, ensemble=True\n",
+    "    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, optimize_beta=True\n",
     ")\n",
     "for step in range(gap, len(X_test), gap):\n",
     "    mapie_enbpi.partial_fit(\n",
@@ -295,7 +313,8 @@
     "    ) = mapie_enbpi.predict(\n",
     "        X_test.iloc[step:(step + gap), :],\n",
     "        alpha=alpha,\n",
-    "        ensemble=True\n",
+    "        ensemble=True,\n",
+    "        optimize_beta=True\n",
     "    )\n",
     "coverage_pfit = regression_coverage_score(\n",
     "    y_test, y_pis_pfit[:, 0, 0], y_pis_pfit[:, 1, 0]\n",
@@ -314,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -712,9 +731,9 @@
    "formats": "ipynb,md"
   },
   "kernelspec": {
-   "display_name": "mapie-notebooks",
+   "display_name": "mapie",
    "language": "python",
-   "name": "mapie-notebooks"
+   "name": "mapie"
   },
   "language_info": {
    "codemirror_mode": {