diff --git a/docs/source/benchmarks/AutoKeras_HTGR.ipynb b/docs/source/benchmarks/AutoKeras_HTGR.ipynb
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@@ -0,0 +1,1699 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c7a85cf3-369a-4bd2-ac30-40af8dc8394a",
+   "metadata": {},
+   "source": [
+    "# AutoKeras Benchmark: HTGR Micro-Core Quadrant Power\n",
+    "\n",
+    "**Input**\n",
+    "\n",
+    "- `theta1`: Angle of control drum in quadrant 1 (radians) \n",
+    "- `theta2`: Angle of control drum in quadrant 1 (radians) \n",
+    "- `theta3`: Angle of control drum in quadrant 2 (radians)  \n",
+    "- `theta4`: Angle of control drum in quadrant 2 (radians)\n",
+    "- `theta5`: Angle of control drum in quadrant 3 (radians)\n",
+    "- `theta6`: Angle of control drum in quadrant 3 (radians)\n",
+    "- `theta7`: Angle of control drum in quadrant 4 (radians)  \n",
+    "- `theta8`: Angle of control drum in quadrant 4 (radians)  \n",
+    "\n",
+    "**Output** \n",
+    "\n",
+    "- `fluxQ1` : Neutron flux in quadrant 1 ($\\frac{neutrons}{cm^{2} s}$)\n",
+    "- `fluxQ2` : Neutron flux in quadrant 2 ($\\frac{neutrons}{cm^{2} s}$)\n",
+    "- `fluxQ3` : Neutron flux in quadrant 3 ($\\frac{neutrons}{cm^{2} s}$)\n",
+    "- `fluxQ4` : Neutron flux in quadrant 4 ($\\frac{neutrons}{cm^{2} s}$)\n",
+    "\n",
+    "\n",
+    "We will be benchmarking the complete HTGR dataset of 3004 samples using H2O ML (version 3.46.0.5) in efforts to compare pyMAISE to other industry standard ML benchmarking frameworks. We will be following the same procedures we did in the original HTGR example, first extending the dataset to 3004 samples using symmetry, and then training and evaluating to compare results. Since Keras is a deep-learning framework, this benchmark will follow all the procedures we set for the FNN in the original HTGR example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "32b0c9d0-c21e-4bb8-9018-17b56a1e0ff1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Packages\n",
+    "import time\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Set display option to show all rows and columns\n",
+    "pd.set_option('display.max_rows', None)\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "\n",
+    "# Set the width of the columns\n",
+    "pd.set_option('display.width', None)\n",
+    "\n",
+    "# See the full content of each column\n",
+    "pd.set_option('display.max_colwidth', None)\n",
+    "\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import uniform, randint\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import Normalizer, MinMaxScaler\n",
+    "# Plot settings\n",
+    "matplotlib_settings = {\n",
+    "    \"font.size\": 12,\n",
+    "    \"legend.fontsize\": 11,\n",
+    "    \"figure.figsize\": (8, 8)\n",
+    "}\n",
+    "plt.rcParams.update(**matplotlib_settings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "762b042e-77f9-4b93-a846-5cfcec082d0b",
+   "metadata": {},
+   "source": [
+    "## Processing the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa66a223-826f-4ccf-bc72-424e97a8fdfd",
+   "metadata": {},
+   "source": [
+    "First, we will load the raw data into a dataframe and print it out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b1545ffc-bce4-4638-91df-448454c91cca",
+   "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>sample number</th>\n",
+       "      <th>cpu_time</th>\n",
+       "      <th>runtime</th>\n",
+       "      <th>k</th>\n",
+       "      <th>fluxQ1</th>\n",
+       "      <th>fluxQ2</th>\n",
+       "      <th>fluxQ3</th>\n",
+       "      <th>fluxQ4</th>\n",
+       "      <th>k_uncert</th>\n",
+       "      <th>flux_runcertQ1</th>\n",
+       "      <th>flux_runcertQ2</th>\n",
+       "      <th>flux_runcertQ3</th>\n",
+       "      <th>flux_runcertQ4</th>\n",
+       "      <th>fissQ1</th>\n",
+       "      <th>fissQ2</th>\n",
+       "      <th>fissQ3</th>\n",
+       "      <th>fissQ4</th>\n",
+       "      <th>fissEQ1</th>\n",
+       "      <th>fissEQ2</th>\n",
+       "      <th>fissEQ3</th>\n",
+       "      <th>fissEQ4</th>\n",
+       "      <th>fiss_runcertQ1</th>\n",
+       "      <th>fiss_runcertQ2</th>\n",
+       "      <th>fiss_runcertQ3</th>\n",
+       "      <th>fiss_runcertQ4</th>\n",
+       "      <th>fissE_runcertQ1</th>\n",
+       "      <th>fissE_runcertQ2</th>\n",
+       "      <th>fissE_runcertQ3</th>\n",
+       "      <th>fissE_runcertQ4</th>\n",
+       "      <th>theta1</th>\n",
+       "      <th>theta2</th>\n",
+       "      <th>theta3</th>\n",
+       "      <th>theta4</th>\n",
+       "      <th>theta5</th>\n",
+       "      <th>theta6</th>\n",
+       "      <th>theta7</th>\n",
+       "      <th>theta8</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sample_00000</td>\n",
+       "      <td>4260.0</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>0.998328</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.590000e+19</td>\n",
+       "      <td>2.670000e+19</td>\n",
+       "      <td>2.560000e+19</td>\n",
+       "      <td>0.00019</td>\n",
+       "      <td>0.00112</td>\n",
+       "      <td>0.00111</td>\n",
+       "      <td>0.00111</td>\n",
+       "      <td>0.00108</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>2751290</td>\n",
+       "      <td>2751060</td>\n",
+       "      <td>2749270</td>\n",
+       "      <td>2750450</td>\n",
+       "      <td>0.00060</td>\n",
+       "      <td>0.00060</td>\n",
+       "      <td>0.00063</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>0.00060</td>\n",
+       "      <td>0.00060</td>\n",
+       "      <td>0.00063</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>5.919526</td>\n",
+       "      <td>2.369503</td>\n",
+       "      <td>2.923656</td>\n",
+       "      <td>4.488987</td>\n",
+       "      <td>3.683212</td>\n",
+       "      <td>4.008905</td>\n",
+       "      <td>4.970368</td>\n",
+       "      <td>2.987966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sample_00001</td>\n",
+       "      <td>2570.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>0.988522</td>\n",
+       "      <td>2.550000e+19</td>\n",
+       "      <td>2.530000e+19</td>\n",
+       "      <td>2.510000e+19</td>\n",
+       "      <td>2.510000e+19</td>\n",
+       "      <td>0.00025</td>\n",
+       "      <td>0.00142</td>\n",
+       "      <td>0.00148</td>\n",
+       "      <td>0.00154</td>\n",
+       "      <td>0.00150</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>2750610</td>\n",
+       "      <td>2750210</td>\n",
+       "      <td>2750150</td>\n",
+       "      <td>2750110</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00077</td>\n",
+       "      <td>0.00084</td>\n",
+       "      <td>0.00074</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00077</td>\n",
+       "      <td>0.00084</td>\n",
+       "      <td>0.00074</td>\n",
+       "      <td>2.162380</td>\n",
+       "      <td>0.273624</td>\n",
+       "      <td>0.927741</td>\n",
+       "      <td>4.595586</td>\n",
+       "      <td>2.598824</td>\n",
+       "      <td>0.170167</td>\n",
+       "      <td>2.124048</td>\n",
+       "      <td>4.980209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sample_00002</td>\n",
+       "      <td>2590.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>1.004610</td>\n",
+       "      <td>2.570000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>0.00025</td>\n",
+       "      <td>0.00167</td>\n",
+       "      <td>0.00163</td>\n",
+       "      <td>0.00161</td>\n",
+       "      <td>0.00165</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>2748870</td>\n",
+       "      <td>2749690</td>\n",
+       "      <td>2752250</td>\n",
+       "      <td>2751840</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00077</td>\n",
+       "      <td>0.00086</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00077</td>\n",
+       "      <td>0.00086</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.450100</td>\n",
+       "      <td>0.006301</td>\n",
+       "      <td>2.512217</td>\n",
+       "      <td>3.313864</td>\n",
+       "      <td>1.913458</td>\n",
+       "      <td>3.582252</td>\n",
+       "      <td>0.280764</td>\n",
+       "      <td>4.888595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sample_00003</td>\n",
+       "      <td>2580.0</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>0.991892</td>\n",
+       "      <td>2.570000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>2.560000e+19</td>\n",
+       "      <td>0.00025</td>\n",
+       "      <td>0.00197</td>\n",
+       "      <td>0.00193</td>\n",
+       "      <td>0.00195</td>\n",
+       "      <td>0.00200</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.470000e+16</td>\n",
+       "      <td>2748920</td>\n",
+       "      <td>2750720</td>\n",
+       "      <td>2749330</td>\n",
+       "      <td>2746220</td>\n",
+       "      <td>0.00082</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00078</td>\n",
+       "      <td>0.00082</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00078</td>\n",
+       "      <td>0.461105</td>\n",
+       "      <td>4.825628</td>\n",
+       "      <td>3.771356</td>\n",
+       "      <td>2.599278</td>\n",
+       "      <td>2.056019</td>\n",
+       "      <td>0.007332</td>\n",
+       "      <td>1.106786</td>\n",
+       "      <td>5.504671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sample_00004</td>\n",
+       "      <td>2570.0</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>0.985047</td>\n",
+       "      <td>2.540000e+19</td>\n",
+       "      <td>2.620000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>0.00025</td>\n",
+       "      <td>0.00167</td>\n",
+       "      <td>0.00167</td>\n",
+       "      <td>0.00172</td>\n",
+       "      <td>0.00169</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>2748910</td>\n",
+       "      <td>2753130</td>\n",
+       "      <td>2747870</td>\n",
+       "      <td>2752420</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00081</td>\n",
+       "      <td>0.00082</td>\n",
+       "      <td>0.00083</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00081</td>\n",
+       "      <td>0.00082</td>\n",
+       "      <td>0.00083</td>\n",
+       "      <td>5.248202</td>\n",
+       "      <td>3.549416</td>\n",
+       "      <td>3.333632</td>\n",
+       "      <td>3.907310</td>\n",
+       "      <td>2.095312</td>\n",
+       "      <td>5.585145</td>\n",
+       "      <td>3.774253</td>\n",
+       "      <td>2.480120</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  sample number  cpu_time  runtime         k        fluxQ1        fluxQ2  \\\n",
+       "0  sample_00000    4260.0    200.0  0.998328  2.580000e+19  2.590000e+19   \n",
+       "1  sample_00001    2570.0    130.0  0.988522  2.550000e+19  2.530000e+19   \n",
+       "2  sample_00002    2590.0    130.0  1.004610  2.570000e+19  2.580000e+19   \n",
+       "3  sample_00003    2580.0    129.0  0.991892  2.570000e+19  2.580000e+19   \n",
+       "4  sample_00004    2570.0    129.0  0.985047  2.540000e+19  2.620000e+19   \n",
+       "\n",
+       "         fluxQ3        fluxQ4  k_uncert  flux_runcertQ1  flux_runcertQ2  \\\n",
+       "0  2.670000e+19  2.560000e+19   0.00019         0.00112         0.00111   \n",
+       "1  2.510000e+19  2.510000e+19   0.00025         0.00142         0.00148   \n",
+       "2  2.520000e+19  2.520000e+19   0.00025         0.00167         0.00163   \n",
+       "3  2.520000e+19  2.560000e+19   0.00025         0.00197         0.00193   \n",
+       "4  2.580000e+19  2.520000e+19   0.00025         0.00167         0.00167   \n",
+       "\n",
+       "   flux_runcertQ3  flux_runcertQ4        fissQ1        fissQ2        fissQ3  \\\n",
+       "0         0.00111         0.00108  8.490000e+16  8.490000e+16  8.480000e+16   \n",
+       "1         0.00154         0.00150  8.490000e+16  8.490000e+16  8.490000e+16   \n",
+       "2         0.00161         0.00165  8.480000e+16  8.480000e+16  8.490000e+16   \n",
+       "3         0.00195         0.00200  8.480000e+16  8.490000e+16  8.480000e+16   \n",
+       "4         0.00172         0.00169  8.480000e+16  8.490000e+16  8.480000e+16   \n",
+       "\n",
+       "         fissQ4  fissEQ1  fissEQ2  fissEQ3  fissEQ4  fiss_runcertQ1  \\\n",
+       "0  8.490000e+16  2751290  2751060  2749270  2750450         0.00060   \n",
+       "1  8.490000e+16  2750610  2750210  2750150  2750110         0.00076   \n",
+       "2  8.490000e+16  2748870  2749690  2752250  2751840         0.00076   \n",
+       "3  8.470000e+16  2748920  2750720  2749330  2746220         0.00082   \n",
+       "4  8.490000e+16  2748910  2753130  2747870  2752420         0.00080   \n",
+       "\n",
+       "   fiss_runcertQ2  fiss_runcertQ3  fiss_runcertQ4  fissE_runcertQ1  \\\n",
+       "0         0.00060         0.00063         0.00062          0.00060   \n",
+       "1         0.00077         0.00084         0.00074          0.00076   \n",
+       "2         0.00077         0.00086         0.00080          0.00076   \n",
+       "3         0.00076         0.00080         0.00078          0.00082   \n",
+       "4         0.00081         0.00082         0.00083          0.00080   \n",
+       "\n",
+       "   fissE_runcertQ2  fissE_runcertQ3  fissE_runcertQ4    theta1    theta2  \\\n",
+       "0          0.00060          0.00063          0.00062  5.919526  2.369503   \n",
+       "1          0.00077          0.00084          0.00074  2.162380  0.273624   \n",
+       "2          0.00077          0.00086          0.00080  0.450100  0.006301   \n",
+       "3          0.00076          0.00080          0.00078  0.461105  4.825628   \n",
+       "4          0.00081          0.00082          0.00083  5.248202  3.549416   \n",
+       "\n",
+       "     theta3    theta4    theta5    theta6    theta7    theta8  \n",
+       "0  2.923656  4.488987  3.683212  4.008905  4.970368  2.987966  \n",
+       "1  0.927741  4.595586  2.598824  0.170167  2.124048  4.980209  \n",
+       "2  2.512217  3.313864  1.913458  3.582252  0.280764  4.888595  \n",
+       "3  3.771356  2.599278  2.056019  0.007332  1.106786  5.504671  \n",
+       "4  3.333632  3.907310  2.095312  5.585145  3.774253  2.480120  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "cwd = os.getcwd()\n",
+    "new_cwd = cwd.replace(\"/docs/source/benchmarks\", \"/pyMAISE/datasets\")\n",
+    "\n",
+    "# Define the full path to the microreactor.csv file\n",
+    "csv_path = os.path.join(new_cwd, 'microreactor.csv')\n",
+    "\n",
+    "# Load the CSV file into a pandas DataFrame\n",
+    "raw_data = pd.read_csv(csv_path)\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e31d3b9-d7a4-4ad4-95d1-f0e258b5cddf",
+   "metadata": {},
+   "source": [
+    "We are then going to create input and output dataframes by defining our input and output variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e40bda25-dcf9-4604-a3eb-ccd069ba8662",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the input DataFrame with theta values\n",
+    "input_columns = ['theta1', 'theta2', 'theta3', 'theta4', 'theta5', 'theta6', 'theta7', 'theta8']\n",
+    "inputs = raw_data[input_columns]\n",
+    "\n",
+    "# Create the output DataFrame with flux values\n",
+    "output_columns = ['fluxQ1', 'fluxQ2', 'fluxQ3', 'fluxQ4']\n",
+    "outputs = raw_data[output_columns]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d688ceeb-e9fc-4f1c-af4c-b8364f2ec17e",
+   "metadata": {},
+   "source": [
+    "Below, we print out the results for input and output then also create a combined dataset with both."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "87def8e9-4377-4467-a39b-53dc1bbc4089",
+   "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>theta1</th>\n",
+       "      <th>theta2</th>\n",
+       "      <th>theta3</th>\n",
+       "      <th>theta4</th>\n",
+       "      <th>theta5</th>\n",
+       "      <th>theta6</th>\n",
+       "      <th>theta7</th>\n",
+       "      <th>theta8</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.919526</td>\n",
+       "      <td>2.369503</td>\n",
+       "      <td>2.923656</td>\n",
+       "      <td>4.488987</td>\n",
+       "      <td>3.683212</td>\n",
+       "      <td>4.008905</td>\n",
+       "      <td>4.970368</td>\n",
+       "      <td>2.987966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.162380</td>\n",
+       "      <td>0.273624</td>\n",
+       "      <td>0.927741</td>\n",
+       "      <td>4.595586</td>\n",
+       "      <td>2.598824</td>\n",
+       "      <td>0.170167</td>\n",
+       "      <td>2.124048</td>\n",
+       "      <td>4.980209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.450100</td>\n",
+       "      <td>0.006301</td>\n",
+       "      <td>2.512217</td>\n",
+       "      <td>3.313864</td>\n",
+       "      <td>1.913458</td>\n",
+       "      <td>3.582252</td>\n",
+       "      <td>0.280764</td>\n",
+       "      <td>4.888595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.461105</td>\n",
+       "      <td>4.825628</td>\n",
+       "      <td>3.771356</td>\n",
+       "      <td>2.599278</td>\n",
+       "      <td>2.056019</td>\n",
+       "      <td>0.007332</td>\n",
+       "      <td>1.106786</td>\n",
+       "      <td>5.504671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.248202</td>\n",
+       "      <td>3.549416</td>\n",
+       "      <td>3.333632</td>\n",
+       "      <td>3.907310</td>\n",
+       "      <td>2.095312</td>\n",
+       "      <td>5.585145</td>\n",
+       "      <td>3.774253</td>\n",
+       "      <td>2.480120</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     theta1    theta2    theta3    theta4    theta5    theta6    theta7  \\\n",
+       "0  5.919526  2.369503  2.923656  4.488987  3.683212  4.008905  4.970368   \n",
+       "1  2.162380  0.273624  0.927741  4.595586  2.598824  0.170167  2.124048   \n",
+       "2  0.450100  0.006301  2.512217  3.313864  1.913458  3.582252  0.280764   \n",
+       "3  0.461105  4.825628  3.771356  2.599278  2.056019  0.007332  1.106786   \n",
+       "4  5.248202  3.549416  3.333632  3.907310  2.095312  5.585145  3.774253   \n",
+       "\n",
+       "     theta8  \n",
+       "0  2.987966  \n",
+       "1  4.980209  \n",
+       "2  4.888595  \n",
+       "3  5.504671  \n",
+       "4  2.480120  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "293ab317-d7e1-4fcf-aa94-5e3758e5a22a",
+   "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>fluxQ1</th>\n",
+       "      <th>fluxQ2</th>\n",
+       "      <th>fluxQ3</th>\n",
+       "      <th>fluxQ4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.590000e+19</td>\n",
+       "      <td>2.670000e+19</td>\n",
+       "      <td>2.560000e+19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.550000e+19</td>\n",
+       "      <td>2.530000e+19</td>\n",
+       "      <td>2.510000e+19</td>\n",
+       "      <td>2.510000e+19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.570000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.570000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>2.560000e+19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.540000e+19</td>\n",
+       "      <td>2.620000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         fluxQ1        fluxQ2        fluxQ3        fluxQ4\n",
+       "0  2.580000e+19  2.590000e+19  2.670000e+19  2.560000e+19\n",
+       "1  2.550000e+19  2.530000e+19  2.510000e+19  2.510000e+19\n",
+       "2  2.570000e+19  2.580000e+19  2.520000e+19  2.520000e+19\n",
+       "3  2.570000e+19  2.580000e+19  2.520000e+19  2.560000e+19\n",
+       "4  2.540000e+19  2.620000e+19  2.580000e+19  2.520000e+19"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outputs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3dfa175a-b363-4c23-ba85-d80d515128a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     theta1    theta2    theta3    theta4    theta5    theta6    theta7  \\\n",
+      "0  5.919526  2.369503  2.923656  4.488987  3.683212  4.008905  4.970368   \n",
+      "1  2.162380  0.273624  0.927741  4.595586  2.598824  0.170167  2.124048   \n",
+      "2  0.450100  0.006301  2.512217  3.313864  1.913458  3.582252  0.280764   \n",
+      "3  0.461105  4.825628  3.771356  2.599278  2.056019  0.007332  1.106786   \n",
+      "4  5.248202  3.549416  3.333632  3.907310  2.095312  5.585145  3.774253   \n",
+      "\n",
+      "     theta8        fluxQ1        fluxQ2        fluxQ3        fluxQ4  \n",
+      "0  2.987966  2.580000e+19  2.590000e+19  2.670000e+19  2.560000e+19  \n",
+      "1  4.980209  2.550000e+19  2.530000e+19  2.510000e+19  2.510000e+19  \n",
+      "2  4.888595  2.570000e+19  2.580000e+19  2.520000e+19  2.520000e+19  \n",
+      "3  5.504671  2.570000e+19  2.580000e+19  2.520000e+19  2.560000e+19  \n",
+      "4  2.480120  2.540000e+19  2.620000e+19  2.580000e+19  2.520000e+19  \n"
+     ]
+    }
+   ],
+   "source": [
+    "combined_df = pd.concat([inputs, outputs], axis=1)\n",
+    "print(combined_df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7532d981-6683-42b2-87d5-fc4a18e9b613",
+   "metadata": {},
+   "source": [
+    "Now it is time to extend the dataset to 3004 samples. This is done in the same way as in the original HTGR, replicating the same steps below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ee65be9b-4c39-4e8f-8952-5f4e8b7bb814",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Credit to mult_sym and g21 from https://github.com/deanrp2/MicroControl/blob/main/pmdata/utils.py#L51\n",
+    "theta_cols = [f\"theta{i + 1}\" for i in range(8)]\n",
+    "flux_cols = [f\"fluxQ{i + 1}\" for i in range(4)]\n",
+    "\n",
+    "def mult_samples(data):\n",
+    "    # Create empty arrays\n",
+    "    ht = xr.DataArray(\n",
+    "        np.zeros(data.shape), \n",
+    "        coords={\n",
+    "            \"index\": [f\"{idx}_h\" for idx in data.coords[\"index\"].values],\n",
+    "            \"variable\": data.coords[\"variable\"],\n",
+    "        },\n",
+    "    )\n",
+    "    vt = xr.DataArray(\n",
+    "        np.zeros(data.shape), \n",
+    "        coords={\n",
+    "            \"index\": [f\"{idx}_v\" for idx in data.coords[\"index\"].values],\n",
+    "            \"variable\": data.coords[\"variable\"],\n",
+    "        },\n",
+    "    )\n",
+    "    rt = xr.DataArray(\n",
+    "        np.zeros(data.shape),     \n",
+    "        coords={\n",
+    "            \"index\": [f\"{idx}_r\" for idx in data.coords[\"index\"].values],\n",
+    "            \"variable\": data.coords[\"variable\"],\n",
+    "        },\n",
+    "    )\n",
+    "\n",
+    "    # Swap drum positions\n",
+    "    hkey = [f\"theta{i}\" for i in np.array([3, 2, 1, 0, 7, 6, 5, 4], dtype=int) + 1]\n",
+    "    vkey = [f\"theta{i}\" for i in np.array([7, 6, 5, 4, 3, 2, 1, 0], dtype=int) + 1]\n",
+    "    rkey = [f\"theta{i}\" for i in np.array([4, 5, 6, 7, 0, 1, 2, 3], dtype=int) + 1]\n",
+    "\n",
+    "    ht.loc[:, hkey] = data.loc[:, theta_cols].values\n",
+    "    vt.loc[:, vkey] = data.loc[:, theta_cols].values\n",
+    "    rt.loc[:, rkey] = data.loc[:, theta_cols].values\n",
+    "\n",
+    "    # Adjust angles\n",
+    "    ht.loc[:, hkey] = (3 * np.pi - ht.loc[:, hkey].loc[:, hkey]) % (2 * np.pi)\n",
+    "    vt.loc[:, vkey] = (2 * np.pi - vt.loc[:, hkey].loc[:, vkey]) % (2 * np.pi)\n",
+    "    rt.loc[:, rkey] = (np.pi + rt.loc[:, hkey].loc[:, rkey]) % (2 * np.pi)\n",
+    "\n",
+    "    # Fill quadrant tallies\n",
+    "    hkey = [2, 1, 4, 3]\n",
+    "    vkey = [4, 3, 2, 1]\n",
+    "    rkey = [3, 4, 1, 2]\n",
+    "\n",
+    "    ht.loc[:, [f\"fluxQ{i}\" for i in hkey]] = data.loc[:, flux_cols].values\n",
+    "    vt.loc[:, [f\"fluxQ{i}\" for i in vkey]] = data.loc[:, flux_cols].values\n",
+    "    rt.loc[:, [f\"fluxQ{i}\" for i in rkey]] = data.loc[:, flux_cols].values\n",
+    "\n",
+    "    sym_data = xr.concat([data, ht, vt, rt], dim=\"index\").sortby(\"index\")\n",
+    "    \n",
+    "    # Normalize fluxes\n",
+    "    sym_data.loc[:, flux_cols].values = Normalizer().transform(sym_data.loc[:, flux_cols].values)\n",
+    "    \n",
+    "    # Convert global coordinate system to local\n",
+    "    loc_offsets = np.array(\n",
+    "        [3.6820187359906447, 4.067668586955522, 2.2155167202240653 - np.pi, 2.6011665711889425 - np.pi, \n",
+    "         0.5404260824008517, 0.9260759333657285, 5.3571093738138575 - np.pi, 5.742759224778734 - np.pi]\n",
+    "    )\n",
+    "\n",
+    "    # Apply correct 0 point\n",
+    "    sym_data.loc[:, theta_cols] = sym_data.loc[:, theta_cols] - loc_offsets + 2 * np.pi\n",
+    "\n",
+    "    # Reverse necessary angles\n",
+    "    sym_data.loc[:, [f\"theta{i}\" for i in [3,4,5,6]]] *= -1\n",
+    "\n",
+    "    # Scale all to [0, 2 * np.pi]\n",
+    "    sym_data.loc[:, theta_cols] = sym_data.loc[:, theta_cols] % (2 * np.pi)\n",
+    "        \n",
+    "    return sym_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5e9375ce-4349-4565-9338-c8e6cefe17fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data, test_data = train_test_split(combined_df, test_size=0.3)\n",
+    "\n",
+    "# Convert to xarray DataArray and specify the index as a coordinate\n",
+    "train_data_xr = xr.DataArray(\n",
+    "    train_data.values,\n",
+    "    coords={\"index\": train_data.index, \"variable\": train_data.columns},\n",
+    "    dims=[\"index\", \"variable\"]\n",
+    ")\n",
+    "test_data_xr = xr.DataArray(\n",
+    "    test_data.values,\n",
+    "    coords={\"index\": test_data.index, \"variable\": test_data.columns},\n",
+    "    dims=[\"index\", \"variable\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "638ecb25-2331-47d6-84b6-52664a22a91a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Multiplied training shape: (2100, 12), Multiplied testing shape: (904, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "sym_train_data = mult_samples(train_data_xr)\n",
+    "sym_test_data = mult_samples(test_data_xr)\n",
+    "print(f\"Multiplied training shape: {sym_train_data.shape}, Multiplied testing shape: {sym_test_data.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c26d2e3-75f2-4cca-b508-aab658976a7f",
+   "metadata": {},
+   "source": [
+    "As seen above, we end up with data the same size as the original HTGR. Below, we are going to Min-Max the X_data and normalize the y_data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "954fc374-8596-4f39-abe6-a340489fe57d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Min-Max scaling data \n",
+    "def scale_data(train_data, test_data, scaler):\n",
+    "    train_data.values = scaler.fit_transform(\n",
+    "        train_data.values.reshape(-1, train_data.shape[-1])\n",
+    "    ).reshape(train_data.shape)\n",
+    "    test_data.values = scaler.transform(\n",
+    "        test_data.values.reshape(-1, test_data.shape[-1])\n",
+    "    ).reshape(test_data.shape)\n",
+    "    \n",
+    "    # Return data\n",
+    "    return train_data, test_data, scaler\n",
+    "\n",
+    "xtrain_arr, xtest_arr , _ = scale_data(sym_train_data.loc[:, theta_cols], sym_test_data.loc[:, theta_cols], MinMaxScaler())\n",
+    "ytrain_arr, ytest_arr, _ = scale_data(sym_train_data.loc[:, flux_cols], sym_test_data.loc[:, flux_cols], Normalizer(norm=\"l1\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bbbecbde-b745-48f4-a7d7-81cc88e2373c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xtrain = xtrain_arr.to_pandas()\n",
+    "xtest = xtest_arr.to_pandas()\n",
+    "ytrain = ytrain_arr.to_pandas()\n",
+    "ytest = ytest_arr.to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78b1ff10-3f19-469d-8daa-8ed8a715bc1f",
+   "metadata": {},
+   "source": [
+    "## Benchmark with AutoKeras"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aeffb798-f8f6-4fde-ab16-34292fffb2f0",
+   "metadata": {},
+   "source": [
+    "After preprocessing, we are going to now train an AutoKeras model on the data. First, we will import the necessary libraries. We will be using the CPU only for these tasks since we did the same for HTGR."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "feeb8520-f582-48c3-9374-e106e5751e50",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-10-24 15:23:38.896153: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
+      "2024-10-24 15:23:38.927502: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
+      "2024-10-24 15:23:38.928279: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
+      "2024-10-24 15:23:39.453443: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Import things required from Keras\n",
+    "import autokeras as ak\n",
+    "import tensorflow as tf\n",
+    "import keras_tuner\n",
+    "import tensorflow.keras.backend as K"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de007566-4f60-43a3-8bc7-c70cd8a59706",
+   "metadata": {},
+   "source": [
+    "Below we define the R2 metric so that we can train our AutoKeras model to maximize validation R2 score. We will use a bayesian tuner as in the pyMAISE example and also use MSE as our loss. We are going to try a max of 50 models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0f21133b-be43-49bb-be33-b6a09c739659",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Custom R2 metric\n",
+    "def r2_score(y_true, y_pred):\n",
+    "    ss_res = K.sum(K.square(y_true - y_pred))\n",
+    "    ss_tot = K.sum(K.square(y_true - K.mean(y_true)))\n",
+    "    return 1 - ss_res / (ss_tot + K.epsilon())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b43187ae-6863-4860-add2-1d292145efbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regressor = ak.StructuredDataRegressor(\n",
+    "    max_trials=50, \n",
+    "    overwrite=True,\n",
+    "    loss='mean_squared_error',\n",
+    "    directory='HTGR_Keras_model',\n",
+    "    metrics=[r2_score, \"mean_absolute_error\", \"mean_squared_error\", \"mean_absolute_percentage_error\"],\n",
+    "    objective=keras_tuner.Objective('val_r2_score', direction='max'),\n",
+    "    tuner='bayesian',\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6138391f-a099-4ff8-b988-5321f42328cb",
+   "metadata": {},
+   "source": [
+    "We are going to train our training dataset, setting epochs to 50 as in the pyMAISE example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "69c41b34-f9c3-4bdb-b15a-646bd4476fc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Trial 50 Complete [00h 00m 02s]\n",
+      "val_r2_score: 0.19001974165439606\n",
+      "\n",
+      "Best val_r2_score So Far: 0.8381330966949463\n",
+      "Total elapsed time: 00h 03m 11s\n",
+      "Epoch 1/50\n",
+      "66/66 [==============================] - 0s 877us/step - loss: 0.0118 - r2_score: -588.4028 - mean_absolute_error: 0.0678 - mean_squared_error: 0.0118 - mean_absolute_percentage_error: 27.1121\n",
+      "Epoch 2/50\n",
+      "66/66 [==============================] - 0s 702us/step - loss: 3.0753e-04 - r2_score: -17.7216 - mean_absolute_error: 0.0135 - mean_squared_error: 3.0753e-04 - mean_absolute_percentage_error: 5.3787\n",
+      "Epoch 3/50\n",
+      "66/66 [==============================] - 0s 643us/step - loss: 4.2264e-05 - r2_score: -1.4148 - mean_absolute_error: 0.0051 - mean_squared_error: 4.2264e-05 - mean_absolute_percentage_error: 2.0216\n",
+      "Epoch 4/50\n",
+      "66/66 [==============================] - 0s 674us/step - loss: 1.8311e-05 - r2_score: -0.0337 - mean_absolute_error: 0.0033 - mean_squared_error: 1.8311e-05 - mean_absolute_percentage_error: 1.3298\n",
+      "Epoch 5/50\n",
+      "66/66 [==============================] - 0s 666us/step - loss: 1.3242e-05 - r2_score: 0.2497 - mean_absolute_error: 0.0028 - mean_squared_error: 1.3242e-05 - mean_absolute_percentage_error: 1.1091\n",
+      "Epoch 6/50\n",
+      "66/66 [==============================] - 0s 679us/step - loss: 1.0701e-05 - r2_score: 0.3943 - mean_absolute_error: 0.0025 - mean_squared_error: 1.0701e-05 - mean_absolute_percentage_error: 0.9950\n",
+      "Epoch 7/50\n",
+      "66/66 [==============================] - 0s 677us/step - loss: 9.1322e-06 - r2_score: 0.4803 - mean_absolute_error: 0.0023 - mean_squared_error: 9.1322e-06 - mean_absolute_percentage_error: 0.9141\n",
+      "Epoch 8/50\n",
+      "66/66 [==============================] - 0s 700us/step - loss: 8.5413e-06 - r2_score: 0.5132 - mean_absolute_error: 0.0022 - mean_squared_error: 8.5413e-06 - mean_absolute_percentage_error: 0.8819\n",
+      "Epoch 9/50\n",
+      "66/66 [==============================] - 0s 657us/step - loss: 7.6287e-06 - r2_score: 0.5686 - mean_absolute_error: 0.0021 - mean_squared_error: 7.6287e-06 - mean_absolute_percentage_error: 0.8292\n",
+      "Epoch 10/50\n",
+      "66/66 [==============================] - 0s 646us/step - loss: 6.8944e-06 - r2_score: 0.6081 - mean_absolute_error: 0.0020 - mean_squared_error: 6.8944e-06 - mean_absolute_percentage_error: 0.7862\n",
+      "Epoch 11/50\n",
+      "66/66 [==============================] - 0s 607us/step - loss: 6.3508e-06 - r2_score: 0.6373 - mean_absolute_error: 0.0019 - mean_squared_error: 6.3508e-06 - mean_absolute_percentage_error: 0.7528\n",
+      "Epoch 12/50\n",
+      "66/66 [==============================] - 0s 588us/step - loss: 5.8693e-06 - r2_score: 0.6634 - mean_absolute_error: 0.0018 - mean_squared_error: 5.8693e-06 - mean_absolute_percentage_error: 0.7229\n",
+      "Epoch 13/50\n",
+      "66/66 [==============================] - 0s 660us/step - loss: 5.4902e-06 - r2_score: 0.6835 - mean_absolute_error: 0.0018 - mean_squared_error: 5.4902e-06 - mean_absolute_percentage_error: 0.7001\n",
+      "Epoch 14/50\n",
+      "66/66 [==============================] - 0s 644us/step - loss: 5.2062e-06 - r2_score: 0.6982 - mean_absolute_error: 0.0017 - mean_squared_error: 5.2062e-06 - mean_absolute_percentage_error: 0.6848\n",
+      "Epoch 15/50\n",
+      "66/66 [==============================] - 0s 684us/step - loss: 4.9959e-06 - r2_score: 0.7088 - mean_absolute_error: 0.0017 - mean_squared_error: 4.9959e-06 - mean_absolute_percentage_error: 0.6761\n",
+      "Epoch 16/50\n",
+      "66/66 [==============================] - 0s 662us/step - loss: 4.7923e-06 - r2_score: 0.7199 - mean_absolute_error: 0.0017 - mean_squared_error: 4.7923e-06 - mean_absolute_percentage_error: 0.6668\n",
+      "Epoch 17/50\n",
+      "66/66 [==============================] - 0s 647us/step - loss: 4.5585e-06 - r2_score: 0.7333 - mean_absolute_error: 0.0016 - mean_squared_error: 4.5585e-06 - mean_absolute_percentage_error: 0.6526\n",
+      "Epoch 18/50\n",
+      "66/66 [==============================] - 0s 655us/step - loss: 4.3578e-06 - r2_score: 0.7448 - mean_absolute_error: 0.0016 - mean_squared_error: 4.3578e-06 - mean_absolute_percentage_error: 0.6394\n",
+      "Epoch 19/50\n",
+      "66/66 [==============================] - 0s 650us/step - loss: 4.2260e-06 - r2_score: 0.7522 - mean_absolute_error: 0.0016 - mean_squared_error: 4.2260e-06 - mean_absolute_percentage_error: 0.6311\n",
+      "Epoch 20/50\n",
+      "66/66 [==============================] - 0s 642us/step - loss: 4.1386e-06 - r2_score: 0.7570 - mean_absolute_error: 0.0016 - mean_squared_error: 4.1386e-06 - mean_absolute_percentage_error: 0.6262\n",
+      "Epoch 21/50\n",
+      "66/66 [==============================] - 0s 649us/step - loss: 4.0887e-06 - r2_score: 0.7596 - mean_absolute_error: 0.0016 - mean_squared_error: 4.0887e-06 - mean_absolute_percentage_error: 0.6243\n",
+      "Epoch 22/50\n",
+      "66/66 [==============================] - 0s 688us/step - loss: 4.0519e-06 - r2_score: 0.7618 - mean_absolute_error: 0.0016 - mean_squared_error: 4.0519e-06 - mean_absolute_percentage_error: 0.6230\n",
+      "Epoch 23/50\n",
+      "66/66 [==============================] - 0s 676us/step - loss: 4.0262e-06 - r2_score: 0.7633 - mean_absolute_error: 0.0016 - mean_squared_error: 4.0262e-06 - mean_absolute_percentage_error: 0.6218\n",
+      "Epoch 24/50\n",
+      "66/66 [==============================] - 0s 692us/step - loss: 4.0130e-06 - r2_score: 0.7640 - mean_absolute_error: 0.0016 - mean_squared_error: 4.0130e-06 - mean_absolute_percentage_error: 0.6214\n",
+      "Epoch 25/50\n",
+      "66/66 [==============================] - 0s 702us/step - loss: 3.9824e-06 - r2_score: 0.7658 - mean_absolute_error: 0.0016 - mean_squared_error: 3.9824e-06 - mean_absolute_percentage_error: 0.6190\n",
+      "Epoch 26/50\n",
+      "66/66 [==============================] - 0s 703us/step - loss: 3.9355e-06 - r2_score: 0.7685 - mean_absolute_error: 0.0015 - mean_squared_error: 3.9355e-06 - mean_absolute_percentage_error: 0.6154\n",
+      "Epoch 27/50\n",
+      "66/66 [==============================] - 0s 653us/step - loss: 3.8757e-06 - r2_score: 0.7721 - mean_absolute_error: 0.0015 - mean_squared_error: 3.8757e-06 - mean_absolute_percentage_error: 0.6108\n",
+      "Epoch 28/50\n",
+      "66/66 [==============================] - 0s 648us/step - loss: 3.8415e-06 - r2_score: 0.7740 - mean_absolute_error: 0.0015 - mean_squared_error: 3.8415e-06 - mean_absolute_percentage_error: 0.6084\n",
+      "Epoch 29/50\n",
+      "66/66 [==============================] - 0s 662us/step - loss: 4.0811e-06 - r2_score: 0.7586 - mean_absolute_error: 0.0016 - mean_squared_error: 4.0811e-06 - mean_absolute_percentage_error: 0.6297\n",
+      "Epoch 30/50\n",
+      "66/66 [==============================] - 0s 656us/step - loss: 5.0505e-06 - r2_score: 0.7008 - mean_absolute_error: 0.0018 - mean_squared_error: 5.0505e-06 - mean_absolute_percentage_error: 0.7035\n",
+      "Epoch 31/50\n",
+      "66/66 [==============================] - 0s 654us/step - loss: 4.2491e-06 - r2_score: 0.7461 - mean_absolute_error: 0.0016 - mean_squared_error: 4.2491e-06 - mean_absolute_percentage_error: 0.6380\n",
+      "Epoch 32/50\n",
+      "66/66 [==============================] - 0s 662us/step - loss: 4.5959e-06 - r2_score: 0.7270 - mean_absolute_error: 0.0017 - mean_squared_error: 4.5959e-06 - mean_absolute_percentage_error: 0.6716\n",
+      "Epoch 33/50\n",
+      "66/66 [==============================] - 0s 675us/step - loss: 4.4099e-06 - r2_score: 0.7368 - mean_absolute_error: 0.0017 - mean_squared_error: 4.4099e-06 - mean_absolute_percentage_error: 0.6588\n",
+      "Epoch 34/50\n",
+      "66/66 [==============================] - 0s 744us/step - loss: 4.3533e-06 - r2_score: 0.7422 - mean_absolute_error: 0.0016 - mean_squared_error: 4.3533e-06 - mean_absolute_percentage_error: 0.6535\n",
+      "Epoch 35/50\n",
+      "66/66 [==============================] - 0s 706us/step - loss: 3.9833e-06 - r2_score: 0.7647 - mean_absolute_error: 0.0016 - mean_squared_error: 3.9833e-06 - mean_absolute_percentage_error: 0.6234\n",
+      "Epoch 36/50\n",
+      "66/66 [==============================] - 0s 688us/step - loss: 4.1743e-06 - r2_score: 0.7523 - mean_absolute_error: 0.0016 - mean_squared_error: 4.1743e-06 - mean_absolute_percentage_error: 0.6385\n",
+      "Epoch 37/50\n",
+      "66/66 [==============================] - 0s 652us/step - loss: 3.9515e-06 - r2_score: 0.7644 - mean_absolute_error: 0.0016 - mean_squared_error: 3.9515e-06 - mean_absolute_percentage_error: 0.6202\n",
+      "Epoch 38/50\n",
+      "66/66 [==============================] - 0s 656us/step - loss: 3.8314e-06 - r2_score: 0.7731 - mean_absolute_error: 0.0015 - mean_squared_error: 3.8314e-06 - mean_absolute_percentage_error: 0.6101\n",
+      "Epoch 39/50\n",
+      "66/66 [==============================] - 0s 647us/step - loss: 3.7276e-06 - r2_score: 0.7800 - mean_absolute_error: 0.0015 - mean_squared_error: 3.7276e-06 - mean_absolute_percentage_error: 0.6007\n",
+      "Epoch 40/50\n",
+      "66/66 [==============================] - 0s 626us/step - loss: 3.6322e-06 - r2_score: 0.7858 - mean_absolute_error: 0.0015 - mean_squared_error: 3.6322e-06 - mean_absolute_percentage_error: 0.5921\n",
+      "Epoch 41/50\n",
+      "66/66 [==============================] - 0s 638us/step - loss: 3.6070e-06 - r2_score: 0.7872 - mean_absolute_error: 0.0015 - mean_squared_error: 3.6070e-06 - mean_absolute_percentage_error: 0.5903\n",
+      "Epoch 42/50\n",
+      "66/66 [==============================] - 0s 649us/step - loss: 3.5930e-06 - r2_score: 0.7881 - mean_absolute_error: 0.0015 - mean_squared_error: 3.5930e-06 - mean_absolute_percentage_error: 0.5889\n",
+      "Epoch 43/50\n",
+      "66/66 [==============================] - 0s 639us/step - loss: 3.5808e-06 - r2_score: 0.7888 - mean_absolute_error: 0.0015 - mean_squared_error: 3.5808e-06 - mean_absolute_percentage_error: 0.5879\n",
+      "Epoch 44/50\n",
+      "66/66 [==============================] - 0s 637us/step - loss: 3.5752e-06 - r2_score: 0.7891 - mean_absolute_error: 0.0015 - mean_squared_error: 3.5752e-06 - mean_absolute_percentage_error: 0.5877\n",
+      "Epoch 45/50\n",
+      "66/66 [==============================] - 0s 680us/step - loss: 3.5628e-06 - r2_score: 0.7898 - mean_absolute_error: 0.0015 - mean_squared_error: 3.5628e-06 - mean_absolute_percentage_error: 0.5864\n",
+      "Epoch 46/50\n",
+      "66/66 [==============================] - 0s 643us/step - loss: 3.5550e-06 - r2_score: 0.7902 - mean_absolute_error: 0.0015 - mean_squared_error: 3.5550e-06 - mean_absolute_percentage_error: 0.5858\n",
+      "Epoch 47/50\n",
+      "66/66 [==============================] - 0s 680us/step - loss: 3.5386e-06 - r2_score: 0.7912 - mean_absolute_error: 0.0015 - mean_squared_error: 3.5386e-06 - mean_absolute_percentage_error: 0.5842\n",
+      "Epoch 48/50\n",
+      "66/66 [==============================] - 0s 726us/step - loss: 3.5296e-06 - r2_score: 0.7916 - mean_absolute_error: 0.0015 - mean_squared_error: 3.5296e-06 - mean_absolute_percentage_error: 0.5836\n",
+      "Epoch 49/50\n",
+      "66/66 [==============================] - 0s 748us/step - loss: 3.5197e-06 - r2_score: 0.7922 - mean_absolute_error: 0.0015 - mean_squared_error: 3.5197e-06 - mean_absolute_percentage_error: 0.5826\n",
+      "Epoch 50/50\n",
+      "66/66 [==============================] - 0s 684us/step - loss: 3.5116e-06 - r2_score: 0.7926 - mean_absolute_error: 0.0015 - mean_squared_error: 3.5116e-06 - mean_absolute_percentage_error: 0.5823\n",
+      "INFO:tensorflow:Assets written to: HTGR_Keras_model/structured_data_regressor/best_model/assets\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Assets written to: HTGR_Keras_model/structured_data_regressor/best_model/assets\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0x778897f98070>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "regressor.fit(xtrain, ytrain, epochs=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6924a786-4c9a-4c41-944e-c6f4539366da",
+   "metadata": {},
+   "source": [
+    "Now that the best model was chosen, we are going to load that model in and train for another 250 epochs, totaling 300 for the best model (as done in the original HTGR benchmark)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7cc507b1-50c4-45cf-a8ab-b86f2ad128c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/250\n",
+      "66/66 [==============================] - 0s 823us/step - loss: 1.1730e-05 - r2_score: 0.3397 - mean_absolute_error: 0.0026 - mean_squared_error: 1.1730e-05 - mean_absolute_percentage_error: 1.0311\n",
+      "Epoch 2/250\n",
+      "66/66 [==============================] - 0s 837us/step - loss: 2.0769e-05 - r2_score: -0.1766 - mean_absolute_error: 0.0035 - mean_squared_error: 2.0769e-05 - mean_absolute_percentage_error: 1.3966\n",
+      "Epoch 3/250\n",
+      "66/66 [==============================] - 0s 850us/step - loss: 2.1443e-05 - r2_score: -0.2214 - mean_absolute_error: 0.0037 - mean_squared_error: 2.1443e-05 - mean_absolute_percentage_error: 1.4618\n",
+      "Epoch 4/250\n",
+      "66/66 [==============================] - 0s 844us/step - loss: 2.3893e-05 - r2_score: -0.3473 - mean_absolute_error: 0.0039 - mean_squared_error: 2.3893e-05 - mean_absolute_percentage_error: 1.5590\n",
+      "Epoch 5/250\n",
+      "66/66 [==============================] - 0s 841us/step - loss: 2.7886e-05 - r2_score: -0.5823 - mean_absolute_error: 0.0042 - mean_squared_error: 2.7886e-05 - mean_absolute_percentage_error: 1.6658\n",
+      "Epoch 6/250\n",
+      "66/66 [==============================] - 0s 856us/step - loss: 2.0310e-05 - r2_score: -0.1354 - mean_absolute_error: 0.0036 - mean_squared_error: 2.0310e-05 - mean_absolute_percentage_error: 1.4413\n",
+      "Epoch 7/250\n",
+      "66/66 [==============================] - 0s 838us/step - loss: 1.9783e-05 - r2_score: -0.1373 - mean_absolute_error: 0.0036 - mean_squared_error: 1.9783e-05 - mean_absolute_percentage_error: 1.4269\n",
+      "Epoch 8/250\n",
+      "66/66 [==============================] - 0s 853us/step - loss: 1.9997e-05 - r2_score: -0.1140 - mean_absolute_error: 0.0036 - mean_squared_error: 1.9997e-05 - mean_absolute_percentage_error: 1.4221\n",
+      "Epoch 9/250\n",
+      "66/66 [==============================] - 0s 861us/step - loss: 1.3156e-05 - r2_score: 0.2597 - mean_absolute_error: 0.0029 - mean_squared_error: 1.3156e-05 - mean_absolute_percentage_error: 1.1519\n",
+      "Epoch 10/250\n",
+      "66/66 [==============================] - 0s 853us/step - loss: 1.2673e-05 - r2_score: 0.2925 - mean_absolute_error: 0.0029 - mean_squared_error: 1.2673e-05 - mean_absolute_percentage_error: 1.1425\n",
+      "Epoch 11/250\n",
+      "66/66 [==============================] - 0s 856us/step - loss: 1.1320e-05 - r2_score: 0.3547 - mean_absolute_error: 0.0027 - mean_squared_error: 1.1320e-05 - mean_absolute_percentage_error: 1.0638\n",
+      "Epoch 12/250\n",
+      "66/66 [==============================] - 0s 850us/step - loss: 1.7579e-05 - r2_score: 0.0105 - mean_absolute_error: 0.0033 - mean_squared_error: 1.7579e-05 - mean_absolute_percentage_error: 1.3339\n",
+      "Epoch 13/250\n",
+      "66/66 [==============================] - 0s 841us/step - loss: 2.8620e-05 - r2_score: -0.5916 - mean_absolute_error: 0.0042 - mean_squared_error: 2.8620e-05 - mean_absolute_percentage_error: 1.6747\n",
+      "Epoch 14/250\n",
+      "66/66 [==============================] - 0s 859us/step - loss: 2.3218e-05 - r2_score: -0.2942 - mean_absolute_error: 0.0038 - mean_squared_error: 2.3218e-05 - mean_absolute_percentage_error: 1.5351\n",
+      "Epoch 15/250\n",
+      "66/66 [==============================] - 0s 868us/step - loss: 1.5039e-05 - r2_score: 0.1588 - mean_absolute_error: 0.0031 - mean_squared_error: 1.5039e-05 - mean_absolute_percentage_error: 1.2287\n",
+      "Epoch 16/250\n",
+      "66/66 [==============================] - 0s 833us/step - loss: 1.4347e-05 - r2_score: 0.1999 - mean_absolute_error: 0.0030 - mean_squared_error: 1.4347e-05 - mean_absolute_percentage_error: 1.2112\n",
+      "Epoch 17/250\n",
+      "66/66 [==============================] - 0s 831us/step - loss: 6.4573e-06 - r2_score: 0.6426 - mean_absolute_error: 0.0020 - mean_squared_error: 6.4573e-06 - mean_absolute_percentage_error: 0.8060\n",
+      "Epoch 18/250\n",
+      "66/66 [==============================] - 0s 828us/step - loss: 6.9523e-06 - r2_score: 0.6101 - mean_absolute_error: 0.0021 - mean_squared_error: 6.9523e-06 - mean_absolute_percentage_error: 0.8549\n",
+      "Epoch 19/250\n",
+      "66/66 [==============================] - 0s 801us/step - loss: 1.0543e-05 - r2_score: 0.4175 - mean_absolute_error: 0.0026 - mean_squared_error: 1.0543e-05 - mean_absolute_percentage_error: 1.0311\n",
+      "Epoch 20/250\n",
+      "66/66 [==============================] - 0s 856us/step - loss: 1.0120e-05 - r2_score: 0.4364 - mean_absolute_error: 0.0025 - mean_squared_error: 1.0120e-05 - mean_absolute_percentage_error: 1.0064\n",
+      "Epoch 21/250\n",
+      "66/66 [==============================] - 0s 859us/step - loss: 8.0319e-06 - r2_score: 0.5354 - mean_absolute_error: 0.0023 - mean_squared_error: 8.0319e-06 - mean_absolute_percentage_error: 0.9013\n",
+      "Epoch 22/250\n",
+      "66/66 [==============================] - 0s 850us/step - loss: 6.1896e-06 - r2_score: 0.6550 - mean_absolute_error: 0.0020 - mean_squared_error: 6.1896e-06 - mean_absolute_percentage_error: 0.7917\n",
+      "Epoch 23/250\n",
+      "66/66 [==============================] - 0s 847us/step - loss: 4.8192e-06 - r2_score: 0.7342 - mean_absolute_error: 0.0017 - mean_squared_error: 4.8192e-06 - mean_absolute_percentage_error: 0.6967\n",
+      "Epoch 24/250\n",
+      "66/66 [==============================] - 0s 817us/step - loss: 4.9022e-06 - r2_score: 0.7239 - mean_absolute_error: 0.0018 - mean_squared_error: 4.9022e-06 - mean_absolute_percentage_error: 0.7049\n",
+      "Epoch 25/250\n",
+      "66/66 [==============================] - 0s 849us/step - loss: 4.8883e-06 - r2_score: 0.7275 - mean_absolute_error: 0.0018 - mean_squared_error: 4.8883e-06 - mean_absolute_percentage_error: 0.7010\n",
+      "Epoch 26/250\n",
+      "66/66 [==============================] - 0s 827us/step - loss: 4.0721e-06 - r2_score: 0.7706 - mean_absolute_error: 0.0016 - mean_squared_error: 4.0721e-06 - mean_absolute_percentage_error: 0.6322\n",
+      "Epoch 27/250\n",
+      "66/66 [==============================] - 0s 851us/step - loss: 3.8901e-06 - r2_score: 0.7803 - mean_absolute_error: 0.0016 - mean_squared_error: 3.8901e-06 - mean_absolute_percentage_error: 0.6268\n",
+      "Epoch 28/250\n",
+      "66/66 [==============================] - 0s 846us/step - loss: 2.7553e-06 - r2_score: 0.8442 - mean_absolute_error: 0.0013 - mean_squared_error: 2.7553e-06 - mean_absolute_percentage_error: 0.5315\n",
+      "Epoch 29/250\n",
+      "66/66 [==============================] - 0s 840us/step - loss: 1.9081e-06 - r2_score: 0.8919 - mean_absolute_error: 0.0011 - mean_squared_error: 1.9081e-06 - mean_absolute_percentage_error: 0.4377\n",
+      "Epoch 30/250\n",
+      "66/66 [==============================] - 0s 857us/step - loss: 3.6230e-06 - r2_score: 0.7950 - mean_absolute_error: 0.0015 - mean_squared_error: 3.6230e-06 - mean_absolute_percentage_error: 0.6036\n",
+      "Epoch 31/250\n",
+      "66/66 [==============================] - 0s 865us/step - loss: 2.0756e-06 - r2_score: 0.8844 - mean_absolute_error: 0.0011 - mean_squared_error: 2.0756e-06 - mean_absolute_percentage_error: 0.4598\n",
+      "Epoch 32/250\n",
+      "66/66 [==============================] - 0s 845us/step - loss: 2.8928e-06 - r2_score: 0.8370 - mean_absolute_error: 0.0014 - mean_squared_error: 2.8928e-06 - mean_absolute_percentage_error: 0.5489\n",
+      "Epoch 33/250\n",
+      "66/66 [==============================] - 0s 862us/step - loss: 3.3605e-06 - r2_score: 0.8091 - mean_absolute_error: 0.0014 - mean_squared_error: 3.3605e-06 - mean_absolute_percentage_error: 0.5766\n",
+      "Epoch 34/250\n",
+      "66/66 [==============================] - 0s 852us/step - loss: 1.4918e-06 - r2_score: 0.9157 - mean_absolute_error: 9.7481e-04 - mean_squared_error: 1.4918e-06 - mean_absolute_percentage_error: 0.3898\n",
+      "Epoch 35/250\n",
+      "66/66 [==============================] - 0s 852us/step - loss: 1.5459e-06 - r2_score: 0.9135 - mean_absolute_error: 9.7493e-04 - mean_squared_error: 1.5459e-06 - mean_absolute_percentage_error: 0.3898\n",
+      "Epoch 36/250\n",
+      "66/66 [==============================] - 0s 870us/step - loss: 8.7077e-07 - r2_score: 0.9511 - mean_absolute_error: 7.4017e-04 - mean_squared_error: 8.7077e-07 - mean_absolute_percentage_error: 0.2960\n",
+      "Epoch 37/250\n",
+      "66/66 [==============================] - 0s 876us/step - loss: 1.1337e-06 - r2_score: 0.9359 - mean_absolute_error: 8.3815e-04 - mean_squared_error: 1.1337e-06 - mean_absolute_percentage_error: 0.3352\n",
+      "Epoch 38/250\n",
+      "66/66 [==============================] - 0s 853us/step - loss: 1.1169e-06 - r2_score: 0.9373 - mean_absolute_error: 8.3447e-04 - mean_squared_error: 1.1169e-06 - mean_absolute_percentage_error: 0.3337\n",
+      "Epoch 39/250\n",
+      "66/66 [==============================] - 0s 867us/step - loss: 1.2407e-06 - r2_score: 0.9299 - mean_absolute_error: 8.8925e-04 - mean_squared_error: 1.2407e-06 - mean_absolute_percentage_error: 0.3557\n",
+      "Epoch 40/250\n",
+      "66/66 [==============================] - 0s 854us/step - loss: 7.4405e-07 - r2_score: 0.9579 - mean_absolute_error: 6.8152e-04 - mean_squared_error: 7.4405e-07 - mean_absolute_percentage_error: 0.2725\n",
+      "Epoch 41/250\n",
+      "66/66 [==============================] - 0s 849us/step - loss: 7.3416e-07 - r2_score: 0.9585 - mean_absolute_error: 6.7495e-04 - mean_squared_error: 7.3416e-07 - mean_absolute_percentage_error: 0.2699\n",
+      "Epoch 42/250\n",
+      "66/66 [==============================] - 0s 866us/step - loss: 5.9292e-07 - r2_score: 0.9667 - mean_absolute_error: 6.0965e-04 - mean_squared_error: 5.9292e-07 - mean_absolute_percentage_error: 0.2438\n",
+      "Epoch 43/250\n",
+      "66/66 [==============================] - 0s 856us/step - loss: 6.2165e-07 - r2_score: 0.9648 - mean_absolute_error: 6.2460e-04 - mean_squared_error: 6.2165e-07 - mean_absolute_percentage_error: 0.2498\n",
+      "Epoch 44/250\n",
+      "66/66 [==============================] - 0s 865us/step - loss: 5.6013e-07 - r2_score: 0.9684 - mean_absolute_error: 5.9545e-04 - mean_squared_error: 5.6013e-07 - mean_absolute_percentage_error: 0.2381\n",
+      "Epoch 45/250\n",
+      "66/66 [==============================] - 0s 871us/step - loss: 5.1000e-07 - r2_score: 0.9715 - mean_absolute_error: 5.6583e-04 - mean_squared_error: 5.1000e-07 - mean_absolute_percentage_error: 0.2263\n",
+      "Epoch 46/250\n",
+      "66/66 [==============================] - 0s 831us/step - loss: 4.8273e-07 - r2_score: 0.9728 - mean_absolute_error: 5.5196e-04 - mean_squared_error: 4.8273e-07 - mean_absolute_percentage_error: 0.2207\n",
+      "Epoch 47/250\n",
+      "66/66 [==============================] - 0s 856us/step - loss: 4.4158e-07 - r2_score: 0.9752 - mean_absolute_error: 5.2453e-04 - mean_squared_error: 4.4158e-07 - mean_absolute_percentage_error: 0.2098\n",
+      "Epoch 48/250\n",
+      "66/66 [==============================] - 0s 857us/step - loss: 4.0537e-07 - r2_score: 0.9774 - mean_absolute_error: 5.0433e-04 - mean_squared_error: 4.0537e-07 - mean_absolute_percentage_error: 0.2017\n",
+      "Epoch 49/250\n",
+      "66/66 [==============================] - 0s 858us/step - loss: 3.7559e-07 - r2_score: 0.9789 - mean_absolute_error: 4.8237e-04 - mean_squared_error: 3.7559e-07 - mean_absolute_percentage_error: 0.1929\n",
+      "Epoch 50/250\n",
+      "66/66 [==============================] - 0s 862us/step - loss: 3.5761e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7267e-04 - mean_squared_error: 3.5761e-07 - mean_absolute_percentage_error: 0.1891\n",
+      "Epoch 51/250\n",
+      "66/66 [==============================] - 0s 853us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 52/250\n",
+      "66/66 [==============================] - 0s 853us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 53/250\n",
+      "66/66 [==============================] - 0s 811us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 54/250\n",
+      "66/66 [==============================] - 0s 750us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 55/250\n",
+      "66/66 [==============================] - 0s 857us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 56/250\n",
+      "66/66 [==============================] - 0s 854us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 57/250\n",
+      "66/66 [==============================] - 0s 859us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 58/250\n",
+      "66/66 [==============================] - 0s 857us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 59/250\n",
+      "66/66 [==============================] - 0s 847us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 60/250\n",
+      "66/66 [==============================] - 0s 860us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 61/250\n",
+      "66/66 [==============================] - 0s 862us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 62/250\n",
+      "66/66 [==============================] - 0s 853us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 63/250\n",
+      "66/66 [==============================] - 0s 853us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 64/250\n",
+      "66/66 [==============================] - 0s 857us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 65/250\n",
+      "66/66 [==============================] - 0s 859us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 66/250\n",
+      "66/66 [==============================] - 0s 856us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 67/250\n",
+      "66/66 [==============================] - 0s 828us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 68/250\n",
+      "66/66 [==============================] - 0s 841us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 69/250\n",
+      "66/66 [==============================] - 0s 814us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 70/250\n",
+      "66/66 [==============================] - 0s 853us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 71/250\n",
+      "66/66 [==============================] - 0s 861us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 72/250\n",
+      "66/66 [==============================] - 0s 815us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 73/250\n",
+      "66/66 [==============================] - 0s 844us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 74/250\n",
+      "66/66 [==============================] - 0s 869us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 75/250\n",
+      "66/66 [==============================] - 0s 855us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 76/250\n",
+      "66/66 [==============================] - 0s 850us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 77/250\n",
+      "66/66 [==============================] - 0s 865us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 78/250\n",
+      "66/66 [==============================] - 0s 834us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 79/250\n",
+      "66/66 [==============================] - 0s 833us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 80/250\n",
+      "66/66 [==============================] - 0s 804us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 81/250\n",
+      "66/66 [==============================] - 0s 838us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 82/250\n",
+      "66/66 [==============================] - 0s 844us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 83/250\n",
+      "66/66 [==============================] - 0s 849us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 84/250\n",
+      "66/66 [==============================] - 0s 857us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 85/250\n",
+      "66/66 [==============================] - 0s 852us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 86/250\n",
+      "66/66 [==============================] - 0s 848us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 87/250\n",
+      "66/66 [==============================] - 0s 836us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 88/250\n",
+      "66/66 [==============================] - 0s 837us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 89/250\n",
+      "66/66 [==============================] - 0s 845us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 90/250\n",
+      "66/66 [==============================] - 0s 819us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 91/250\n",
+      "66/66 [==============================] - 0s 868us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 92/250\n",
+      "66/66 [==============================] - 0s 863us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 93/250\n",
+      "66/66 [==============================] - 0s 875us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 94/250\n",
+      "66/66 [==============================] - 0s 848us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 95/250\n",
+      "66/66 [==============================] - 0s 842us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 96/250\n",
+      "66/66 [==============================] - 0s 892us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 97/250\n",
+      "66/66 [==============================] - 0s 841us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 98/250\n",
+      "66/66 [==============================] - 0s 856us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 99/250\n",
+      "66/66 [==============================] - 0s 856us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 100/250\n",
+      "66/66 [==============================] - 0s 860us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 101/250\n",
+      "66/66 [==============================] - 0s 845us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 102/250\n",
+      "66/66 [==============================] - 0s 844us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 103/250\n",
+      "66/66 [==============================] - 0s 851us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 104/250\n",
+      "66/66 [==============================] - 0s 848us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 105/250\n",
+      "66/66 [==============================] - 0s 848us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 106/250\n",
+      "66/66 [==============================] - 0s 842us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 107/250\n",
+      "66/66 [==============================] - 0s 842us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 108/250\n",
+      "66/66 [==============================] - 0s 834us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 109/250\n",
+      "66/66 [==============================] - 0s 836us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 110/250\n",
+      "66/66 [==============================] - 0s 845us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 111/250\n",
+      "66/66 [==============================] - 0s 847us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 112/250\n",
+      "66/66 [==============================] - 0s 859us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 113/250\n",
+      "66/66 [==============================] - 0s 854us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 114/250\n",
+      "66/66 [==============================] - 0s 832us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 115/250\n",
+      "66/66 [==============================] - 0s 806us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 116/250\n",
+      "66/66 [==============================] - 0s 767us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 117/250\n",
+      "66/66 [==============================] - 0s 853us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 118/250\n",
+      "66/66 [==============================] - 0s 834us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 119/250\n",
+      "66/66 [==============================] - 0s 838us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 120/250\n",
+      "66/66 [==============================] - 0s 832us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 121/250\n",
+      "66/66 [==============================] - 0s 831us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 122/250\n",
+      "66/66 [==============================] - 0s 849us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 123/250\n",
+      "66/66 [==============================] - 0s 861us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 124/250\n",
+      "66/66 [==============================] - 0s 713us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 125/250\n",
+      "66/66 [==============================] - 0s 712us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 126/250\n",
+      "66/66 [==============================] - 0s 720us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 127/250\n",
+      "66/66 [==============================] - 0s 730us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 128/250\n",
+      "66/66 [==============================] - 0s 815us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 129/250\n",
+      "66/66 [==============================] - 0s 772us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 130/250\n",
+      "66/66 [==============================] - 0s 868us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 131/250\n",
+      "66/66 [==============================] - 0s 824us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 132/250\n",
+      "66/66 [==============================] - 0s 833us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 133/250\n",
+      "66/66 [==============================] - 0s 842us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 134/250\n",
+      "66/66 [==============================] - 0s 735us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 135/250\n",
+      "66/66 [==============================] - 0s 827us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 136/250\n",
+      "66/66 [==============================] - 0s 835us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 137/250\n",
+      "66/66 [==============================] - 0s 885us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 138/250\n",
+      "66/66 [==============================] - 0s 876us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 139/250\n",
+      "66/66 [==============================] - 0s 859us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 140/250\n",
+      "66/66 [==============================] - 0s 835us/step - loss: 3.5663e-07 - r2_score: 0.9797 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 141/250\n",
+      "66/66 [==============================] - 0s 831us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 142/250\n",
+      "66/66 [==============================] - 0s 819us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 143/250\n",
+      "66/66 [==============================] - 0s 834us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 144/250\n",
+      "66/66 [==============================] - 0s 820us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 145/250\n",
+      "66/66 [==============================] - 0s 839us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 146/250\n",
+      "66/66 [==============================] - 0s 825us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 147/250\n",
+      "66/66 [==============================] - 0s 835us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 148/250\n",
+      "66/66 [==============================] - 0s 828us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 149/250\n",
+      "66/66 [==============================] - 0s 820us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 150/250\n",
+      "66/66 [==============================] - 0s 816us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 151/250\n",
+      "66/66 [==============================] - 0s 818us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 152/250\n",
+      "66/66 [==============================] - 0s 740us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 153/250\n",
+      "66/66 [==============================] - 0s 697us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 154/250\n",
+      "66/66 [==============================] - 0s 702us/step - loss: 3.5663e-07 - r2_score: 0.9797 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 155/250\n",
+      "66/66 [==============================] - 0s 828us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 156/250\n",
+      "66/66 [==============================] - 0s 829us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 157/250\n",
+      "66/66 [==============================] - 0s 828us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 158/250\n",
+      "66/66 [==============================] - 0s 825us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 159/250\n",
+      "66/66 [==============================] - 0s 811us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 160/250\n",
+      "66/66 [==============================] - 0s 838us/step - loss: 3.5663e-07 - r2_score: 0.9797 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 161/250\n",
+      "66/66 [==============================] - 0s 800us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 162/250\n",
+      "66/66 [==============================] - 0s 807us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 163/250\n",
+      "66/66 [==============================] - 0s 832us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 164/250\n",
+      "66/66 [==============================] - 0s 868us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 165/250\n",
+      "66/66 [==============================] - 0s 866us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 166/250\n",
+      "66/66 [==============================] - 0s 819us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 167/250\n",
+      "66/66 [==============================] - 0s 848us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 168/250\n",
+      "66/66 [==============================] - 0s 851us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 169/250\n",
+      "66/66 [==============================] - 0s 879us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 170/250\n",
+      "66/66 [==============================] - 0s 873us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 171/250\n",
+      "66/66 [==============================] - 0s 872us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 172/250\n",
+      "66/66 [==============================] - 0s 883us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 173/250\n",
+      "66/66 [==============================] - 0s 847us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 174/250\n",
+      "66/66 [==============================] - 0s 843us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 175/250\n",
+      "66/66 [==============================] - 0s 812us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 176/250\n",
+      "66/66 [==============================] - 0s 829us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 177/250\n",
+      "66/66 [==============================] - 0s 831us/step - loss: 3.5663e-07 - r2_score: 0.9802 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 178/250\n",
+      "66/66 [==============================] - 0s 835us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 179/250\n",
+      "66/66 [==============================] - 0s 840us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 180/250\n",
+      "66/66 [==============================] - 0s 853us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 181/250\n",
+      "66/66 [==============================] - 0s 844us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 182/250\n",
+      "66/66 [==============================] - 0s 842us/step - loss: 3.5663e-07 - r2_score: 0.9797 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 183/250\n",
+      "66/66 [==============================] - 0s 840us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 184/250\n",
+      "66/66 [==============================] - 0s 867us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 185/250\n",
+      "66/66 [==============================] - 0s 864us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 186/250\n",
+      "66/66 [==============================] - 0s 774us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 187/250\n",
+      "66/66 [==============================] - 0s 714us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 188/250\n",
+      "66/66 [==============================] - 0s 764us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 189/250\n",
+      "66/66 [==============================] - 0s 765us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 190/250\n",
+      "66/66 [==============================] - 0s 784us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 191/250\n",
+      "66/66 [==============================] - 0s 785us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 192/250\n",
+      "66/66 [==============================] - 0s 781us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 193/250\n",
+      "66/66 [==============================] - 0s 824us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 194/250\n",
+      "66/66 [==============================] - 0s 835us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 195/250\n",
+      "66/66 [==============================] - 0s 828us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 196/250\n",
+      "66/66 [==============================] - 0s 806us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 197/250\n",
+      "66/66 [==============================] - 0s 816us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 198/250\n",
+      "66/66 [==============================] - 0s 826us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 199/250\n",
+      "66/66 [==============================] - 0s 830us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 200/250\n",
+      "66/66 [==============================] - 0s 833us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 201/250\n",
+      "66/66 [==============================] - 0s 834us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 202/250\n",
+      "66/66 [==============================] - 0s 820us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 203/250\n",
+      "66/66 [==============================] - 0s 840us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 204/250\n",
+      "66/66 [==============================] - 0s 853us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 205/250\n",
+      "66/66 [==============================] - 0s 860us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 206/250\n",
+      "66/66 [==============================] - 0s 861us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 207/250\n",
+      "66/66 [==============================] - 0s 857us/step - loss: 3.5663e-07 - r2_score: 0.9797 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 208/250\n",
+      "66/66 [==============================] - 0s 842us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 209/250\n",
+      "66/66 [==============================] - 0s 855us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 210/250\n",
+      "66/66 [==============================] - 0s 869us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 211/250\n",
+      "66/66 [==============================] - 0s 825us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 212/250\n",
+      "66/66 [==============================] - 0s 824us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 213/250\n",
+      "66/66 [==============================] - 0s 822us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 214/250\n",
+      "66/66 [==============================] - 0s 845us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 215/250\n",
+      "66/66 [==============================] - 0s 861us/step - loss: 3.5663e-07 - r2_score: 0.9801 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 216/250\n",
+      "66/66 [==============================] - 0s 850us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 217/250\n",
+      "66/66 [==============================] - 0s 866us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 218/250\n",
+      "66/66 [==============================] - 0s 874us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 219/250\n",
+      "66/66 [==============================] - 0s 853us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 220/250\n",
+      "66/66 [==============================] - 0s 854us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 221/250\n",
+      "66/66 [==============================] - 0s 779us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 222/250\n",
+      "66/66 [==============================] - 0s 825us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 223/250\n",
+      "66/66 [==============================] - 0s 823us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 224/250\n",
+      "66/66 [==============================] - 0s 829us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 225/250\n",
+      "66/66 [==============================] - 0s 840us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 226/250\n",
+      "66/66 [==============================] - 0s 822us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 227/250\n",
+      "66/66 [==============================] - 0s 848us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 228/250\n",
+      "66/66 [==============================] - 0s 844us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 229/250\n",
+      "66/66 [==============================] - 0s 828us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 230/250\n",
+      "66/66 [==============================] - 0s 745us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 231/250\n",
+      "66/66 [==============================] - 0s 811us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 232/250\n",
+      "66/66 [==============================] - 0s 823us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 233/250\n",
+      "66/66 [==============================] - 0s 804us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 234/250\n",
+      "66/66 [==============================] - 0s 782us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 235/250\n",
+      "66/66 [==============================] - 0s 787us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 236/250\n",
+      "66/66 [==============================] - 0s 816us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 237/250\n",
+      "66/66 [==============================] - 0s 786us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 238/250\n",
+      "66/66 [==============================] - 0s 812us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 239/250\n",
+      "66/66 [==============================] - 0s 802us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 240/250\n",
+      "66/66 [==============================] - 0s 819us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 241/250\n",
+      "66/66 [==============================] - 0s 816us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 242/250\n",
+      "66/66 [==============================] - 0s 813us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 243/250\n",
+      "66/66 [==============================] - 0s 827us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 244/250\n",
+      "66/66 [==============================] - 0s 816us/step - loss: 3.5663e-07 - r2_score: 0.9798 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 245/250\n",
+      "66/66 [==============================] - 0s 848us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 246/250\n",
+      "66/66 [==============================] - 0s 852us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 247/250\n",
+      "66/66 [==============================] - 0s 832us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 248/250\n",
+      "66/66 [==============================] - 0s 785us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 249/250\n",
+      "66/66 [==============================] - 0s 824us/step - loss: 3.5663e-07 - r2_score: 0.9800 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n",
+      "Epoch 250/250\n",
+      "66/66 [==============================] - 0s 823us/step - loss: 3.5663e-07 - r2_score: 0.9799 - mean_absolute_error: 4.7200e-04 - mean_squared_error: 3.5663e-07 - mean_absolute_percentage_error: 0.1888\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.History at 0x77879c210d00>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Train another 250 epochs ontop of the 50 beforehand using the best model\n",
+    "best_model = tf.keras.models.load_model('./HTGR_Keras_model/structured_data_regressor/best_model', custom_objects={'r2_score': r2_score})\n",
+    "best_model.fit(xtrain, ytrain, epochs=250)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "400c97b2-47d5-465a-9bfb-d81870b44491",
+   "metadata": {},
+   "source": [
+    "Now that the best model is fully trained, we can predict on our testing dataset and generate the results below. We can see that we obtain very similar results as the FNN in the original example with r2 for both around 0.97. The same can be said about the other metrics such as MSE."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "97b55546-e9cf-4284-bd01-039d35099e0f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "29/29 [==============================] - 0s 682us/step - loss: 4.4791e-07 - r2_score: 0.9724 - mean_absolute_error: 5.3476e-04 - mean_squared_error: 4.4791e-07 - mean_absolute_percentage_error: 0.2140\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate the model on the test data\n",
+    "results = best_model.evaluate(xtest, ytest)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.20"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/benchmarks/AutoSklearn_HTGR.ipynb b/docs/source/benchmarks/AutoSklearn_HTGR.ipynb
new file mode 100644
index 0000000..4ad1a0b
--- /dev/null
+++ b/docs/source/benchmarks/AutoSklearn_HTGR.ipynb
@@ -0,0 +1,2069 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "414809d1-9891-4c30-82f4-cca981e53435",
+   "metadata": {},
+   "source": [
+    "# AutoSkLearn Benchmark: HTGR Micro-Core Quadrant Power\n",
+    "\n",
+    "**Input**\n",
+    "\n",
+    "- `theta1`: Angle of control drum in quadrant 1 (radians) \n",
+    "- `theta2`: Angle of control drum in quadrant 1 (radians) \n",
+    "- `theta3`: Angle of control drum in quadrant 2 (radians)  \n",
+    "- `theta4`: Angle of control drum in quadrant 2 (radians)\n",
+    "- `theta5`: Angle of control drum in quadrant 3 (radians)\n",
+    "- `theta6`: Angle of control drum in quadrant 3 (radians)\n",
+    "- `theta7`: Angle of control drum in quadrant 4 (radians)  \n",
+    "- `theta8`: Angle of control drum in quadrant 4 (radians)  \n",
+    "\n",
+    "**Output** \n",
+    "\n",
+    "- `fluxQ1` : Neutron flux in quadrant 1 ($\\frac{neutrons}{cm^{2} s}$)\n",
+    "- `fluxQ2` : Neutron flux in quadrant 2 ($\\frac{neutrons}{cm^{2} s}$)\n",
+    "- `fluxQ3` : Neutron flux in quadrant 3 ($\\frac{neutrons}{cm^{2} s}$)\n",
+    "- `fluxQ4` : Neutron flux in quadrant 4 ($\\frac{neutrons}{cm^{2} s}$)\n",
+    "\n",
+    "\n",
+    "We will be benchmarking the complete HTGR dataset of 3004 samples using H2O ML (version 3.46.0.5) in efforts to compare pyMAISE to other industry standard ML benchmarking frameworks. We will be following the same procedures we did in the original HTGR example, first extending the dataset to 3004 samples using symmetry, and then training and evaluating to compare results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "32b0c9d0-c21e-4bb8-9018-17b56a1e0ff1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Packages\n",
+    "import time\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Set display option to show all rows and columns\n",
+    "pd.set_option('display.max_rows', None)\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "\n",
+    "# Set the width of the columns\n",
+    "pd.set_option('display.width', None)\n",
+    "\n",
+    "# See the full content of each column\n",
+    "pd.set_option('display.max_colwidth', None)\n",
+    "\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import uniform, randint\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import Normalizer, MinMaxScaler\n",
+    "# Plot settings\n",
+    "matplotlib_settings = {\n",
+    "    \"font.size\": 12,\n",
+    "    \"legend.fontsize\": 11,\n",
+    "    \"figure.figsize\": (8, 8)\n",
+    "}\n",
+    "plt.rcParams.update(**matplotlib_settings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "762b042e-77f9-4b93-a846-5cfcec082d0b",
+   "metadata": {},
+   "source": [
+    "## Processing the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa66a223-826f-4ccf-bc72-424e97a8fdfd",
+   "metadata": {},
+   "source": [
+    "First, we will load the raw data into a dataframe and print it out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b1545ffc-bce4-4638-91df-448454c91cca",
+   "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>sample number</th>\n",
+       "      <th>cpu_time</th>\n",
+       "      <th>runtime</th>\n",
+       "      <th>k</th>\n",
+       "      <th>fluxQ1</th>\n",
+       "      <th>fluxQ2</th>\n",
+       "      <th>fluxQ3</th>\n",
+       "      <th>fluxQ4</th>\n",
+       "      <th>k_uncert</th>\n",
+       "      <th>flux_runcertQ1</th>\n",
+       "      <th>flux_runcertQ2</th>\n",
+       "      <th>flux_runcertQ3</th>\n",
+       "      <th>flux_runcertQ4</th>\n",
+       "      <th>fissQ1</th>\n",
+       "      <th>fissQ2</th>\n",
+       "      <th>fissQ3</th>\n",
+       "      <th>fissQ4</th>\n",
+       "      <th>fissEQ1</th>\n",
+       "      <th>fissEQ2</th>\n",
+       "      <th>fissEQ3</th>\n",
+       "      <th>fissEQ4</th>\n",
+       "      <th>fiss_runcertQ1</th>\n",
+       "      <th>fiss_runcertQ2</th>\n",
+       "      <th>fiss_runcertQ3</th>\n",
+       "      <th>fiss_runcertQ4</th>\n",
+       "      <th>fissE_runcertQ1</th>\n",
+       "      <th>fissE_runcertQ2</th>\n",
+       "      <th>fissE_runcertQ3</th>\n",
+       "      <th>fissE_runcertQ4</th>\n",
+       "      <th>theta1</th>\n",
+       "      <th>theta2</th>\n",
+       "      <th>theta3</th>\n",
+       "      <th>theta4</th>\n",
+       "      <th>theta5</th>\n",
+       "      <th>theta6</th>\n",
+       "      <th>theta7</th>\n",
+       "      <th>theta8</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sample_00000</td>\n",
+       "      <td>4260.0</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>0.998328</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.590000e+19</td>\n",
+       "      <td>2.670000e+19</td>\n",
+       "      <td>2.560000e+19</td>\n",
+       "      <td>0.00019</td>\n",
+       "      <td>0.00112</td>\n",
+       "      <td>0.00111</td>\n",
+       "      <td>0.00111</td>\n",
+       "      <td>0.00108</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>2751290</td>\n",
+       "      <td>2751060</td>\n",
+       "      <td>2749270</td>\n",
+       "      <td>2750450</td>\n",
+       "      <td>0.00060</td>\n",
+       "      <td>0.00060</td>\n",
+       "      <td>0.00063</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>0.00060</td>\n",
+       "      <td>0.00060</td>\n",
+       "      <td>0.00063</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>5.919526</td>\n",
+       "      <td>2.369503</td>\n",
+       "      <td>2.923656</td>\n",
+       "      <td>4.488987</td>\n",
+       "      <td>3.683212</td>\n",
+       "      <td>4.008905</td>\n",
+       "      <td>4.970368</td>\n",
+       "      <td>2.987966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sample_00001</td>\n",
+       "      <td>2570.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>0.988522</td>\n",
+       "      <td>2.550000e+19</td>\n",
+       "      <td>2.530000e+19</td>\n",
+       "      <td>2.510000e+19</td>\n",
+       "      <td>2.510000e+19</td>\n",
+       "      <td>0.00025</td>\n",
+       "      <td>0.00142</td>\n",
+       "      <td>0.00148</td>\n",
+       "      <td>0.00154</td>\n",
+       "      <td>0.00150</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>2750610</td>\n",
+       "      <td>2750210</td>\n",
+       "      <td>2750150</td>\n",
+       "      <td>2750110</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00077</td>\n",
+       "      <td>0.00084</td>\n",
+       "      <td>0.00074</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00077</td>\n",
+       "      <td>0.00084</td>\n",
+       "      <td>0.00074</td>\n",
+       "      <td>2.162380</td>\n",
+       "      <td>0.273624</td>\n",
+       "      <td>0.927741</td>\n",
+       "      <td>4.595586</td>\n",
+       "      <td>2.598824</td>\n",
+       "      <td>0.170167</td>\n",
+       "      <td>2.124048</td>\n",
+       "      <td>4.980209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sample_00002</td>\n",
+       "      <td>2590.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>1.004610</td>\n",
+       "      <td>2.570000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>0.00025</td>\n",
+       "      <td>0.00167</td>\n",
+       "      <td>0.00163</td>\n",
+       "      <td>0.00161</td>\n",
+       "      <td>0.00165</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>2748870</td>\n",
+       "      <td>2749690</td>\n",
+       "      <td>2752250</td>\n",
+       "      <td>2751840</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00077</td>\n",
+       "      <td>0.00086</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00077</td>\n",
+       "      <td>0.00086</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.450100</td>\n",
+       "      <td>0.006301</td>\n",
+       "      <td>2.512217</td>\n",
+       "      <td>3.313864</td>\n",
+       "      <td>1.913458</td>\n",
+       "      <td>3.582252</td>\n",
+       "      <td>0.280764</td>\n",
+       "      <td>4.888595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sample_00003</td>\n",
+       "      <td>2580.0</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>0.991892</td>\n",
+       "      <td>2.570000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>2.560000e+19</td>\n",
+       "      <td>0.00025</td>\n",
+       "      <td>0.00197</td>\n",
+       "      <td>0.00193</td>\n",
+       "      <td>0.00195</td>\n",
+       "      <td>0.00200</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.470000e+16</td>\n",
+       "      <td>2748920</td>\n",
+       "      <td>2750720</td>\n",
+       "      <td>2749330</td>\n",
+       "      <td>2746220</td>\n",
+       "      <td>0.00082</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00078</td>\n",
+       "      <td>0.00082</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00078</td>\n",
+       "      <td>0.461105</td>\n",
+       "      <td>4.825628</td>\n",
+       "      <td>3.771356</td>\n",
+       "      <td>2.599278</td>\n",
+       "      <td>2.056019</td>\n",
+       "      <td>0.007332</td>\n",
+       "      <td>1.106786</td>\n",
+       "      <td>5.504671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sample_00004</td>\n",
+       "      <td>2570.0</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>0.985047</td>\n",
+       "      <td>2.540000e+19</td>\n",
+       "      <td>2.620000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>0.00025</td>\n",
+       "      <td>0.00167</td>\n",
+       "      <td>0.00167</td>\n",
+       "      <td>0.00172</td>\n",
+       "      <td>0.00169</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>2748910</td>\n",
+       "      <td>2753130</td>\n",
+       "      <td>2747870</td>\n",
+       "      <td>2752420</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00081</td>\n",
+       "      <td>0.00082</td>\n",
+       "      <td>0.00083</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00081</td>\n",
+       "      <td>0.00082</td>\n",
+       "      <td>0.00083</td>\n",
+       "      <td>5.248202</td>\n",
+       "      <td>3.549416</td>\n",
+       "      <td>3.333632</td>\n",
+       "      <td>3.907310</td>\n",
+       "      <td>2.095312</td>\n",
+       "      <td>5.585145</td>\n",
+       "      <td>3.774253</td>\n",
+       "      <td>2.480120</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  sample number  cpu_time  runtime         k        fluxQ1        fluxQ2  \\\n",
+       "0  sample_00000    4260.0    200.0  0.998328  2.580000e+19  2.590000e+19   \n",
+       "1  sample_00001    2570.0    130.0  0.988522  2.550000e+19  2.530000e+19   \n",
+       "2  sample_00002    2590.0    130.0  1.004610  2.570000e+19  2.580000e+19   \n",
+       "3  sample_00003    2580.0    129.0  0.991892  2.570000e+19  2.580000e+19   \n",
+       "4  sample_00004    2570.0    129.0  0.985047  2.540000e+19  2.620000e+19   \n",
+       "\n",
+       "         fluxQ3        fluxQ4  k_uncert  flux_runcertQ1  flux_runcertQ2  \\\n",
+       "0  2.670000e+19  2.560000e+19   0.00019         0.00112         0.00111   \n",
+       "1  2.510000e+19  2.510000e+19   0.00025         0.00142         0.00148   \n",
+       "2  2.520000e+19  2.520000e+19   0.00025         0.00167         0.00163   \n",
+       "3  2.520000e+19  2.560000e+19   0.00025         0.00197         0.00193   \n",
+       "4  2.580000e+19  2.520000e+19   0.00025         0.00167         0.00167   \n",
+       "\n",
+       "   flux_runcertQ3  flux_runcertQ4        fissQ1        fissQ2        fissQ3  \\\n",
+       "0         0.00111         0.00108  8.490000e+16  8.490000e+16  8.480000e+16   \n",
+       "1         0.00154         0.00150  8.490000e+16  8.490000e+16  8.490000e+16   \n",
+       "2         0.00161         0.00165  8.480000e+16  8.480000e+16  8.490000e+16   \n",
+       "3         0.00195         0.00200  8.480000e+16  8.490000e+16  8.480000e+16   \n",
+       "4         0.00172         0.00169  8.480000e+16  8.490000e+16  8.480000e+16   \n",
+       "\n",
+       "         fissQ4  fissEQ1  fissEQ2  fissEQ3  fissEQ4  fiss_runcertQ1  \\\n",
+       "0  8.490000e+16  2751290  2751060  2749270  2750450         0.00060   \n",
+       "1  8.490000e+16  2750610  2750210  2750150  2750110         0.00076   \n",
+       "2  8.490000e+16  2748870  2749690  2752250  2751840         0.00076   \n",
+       "3  8.470000e+16  2748920  2750720  2749330  2746220         0.00082   \n",
+       "4  8.490000e+16  2748910  2753130  2747870  2752420         0.00080   \n",
+       "\n",
+       "   fiss_runcertQ2  fiss_runcertQ3  fiss_runcertQ4  fissE_runcertQ1  \\\n",
+       "0         0.00060         0.00063         0.00062          0.00060   \n",
+       "1         0.00077         0.00084         0.00074          0.00076   \n",
+       "2         0.00077         0.00086         0.00080          0.00076   \n",
+       "3         0.00076         0.00080         0.00078          0.00082   \n",
+       "4         0.00081         0.00082         0.00083          0.00080   \n",
+       "\n",
+       "   fissE_runcertQ2  fissE_runcertQ3  fissE_runcertQ4    theta1    theta2  \\\n",
+       "0          0.00060          0.00063          0.00062  5.919526  2.369503   \n",
+       "1          0.00077          0.00084          0.00074  2.162380  0.273624   \n",
+       "2          0.00077          0.00086          0.00080  0.450100  0.006301   \n",
+       "3          0.00076          0.00080          0.00078  0.461105  4.825628   \n",
+       "4          0.00081          0.00082          0.00083  5.248202  3.549416   \n",
+       "\n",
+       "     theta3    theta4    theta5    theta6    theta7    theta8  \n",
+       "0  2.923656  4.488987  3.683212  4.008905  4.970368  2.987966  \n",
+       "1  0.927741  4.595586  2.598824  0.170167  2.124048  4.980209  \n",
+       "2  2.512217  3.313864  1.913458  3.582252  0.280764  4.888595  \n",
+       "3  3.771356  2.599278  2.056019  0.007332  1.106786  5.504671  \n",
+       "4  3.333632  3.907310  2.095312  5.585145  3.774253  2.480120  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "cwd = os.getcwd()\n",
+    "new_cwd = cwd.replace(\"/docs/source/benchmarks\", \"/pyMAISE/datasets\")\n",
+    "\n",
+    "# Define the full path to the microreactor.csv file\n",
+    "csv_path = os.path.join(new_cwd, 'microreactor.csv')\n",
+    "\n",
+    "# Load the CSV file into a pandas DataFrame\n",
+    "raw_data = pd.read_csv(csv_path)\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e31d3b9-d7a4-4ad4-95d1-f0e258b5cddf",
+   "metadata": {},
+   "source": [
+    "We are then going to create input and output dataframes by defining our input and output variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e40bda25-dcf9-4604-a3eb-ccd069ba8662",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the input DataFrame with theta values\n",
+    "input_columns = ['theta1', 'theta2', 'theta3', 'theta4', 'theta5', 'theta6', 'theta7', 'theta8']\n",
+    "inputs = raw_data[input_columns]\n",
+    "\n",
+    "# Create the output DataFrame with flux values\n",
+    "output_columns = ['fluxQ1', 'fluxQ2', 'fluxQ3', 'fluxQ4']\n",
+    "outputs = raw_data[output_columns]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d688ceeb-e9fc-4f1c-af4c-b8364f2ec17e",
+   "metadata": {},
+   "source": [
+    "Below, we print out the results for input and output then also create a combined dataset with both."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "87def8e9-4377-4467-a39b-53dc1bbc4089",
+   "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>theta1</th>\n",
+       "      <th>theta2</th>\n",
+       "      <th>theta3</th>\n",
+       "      <th>theta4</th>\n",
+       "      <th>theta5</th>\n",
+       "      <th>theta6</th>\n",
+       "      <th>theta7</th>\n",
+       "      <th>theta8</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.919526</td>\n",
+       "      <td>2.369503</td>\n",
+       "      <td>2.923656</td>\n",
+       "      <td>4.488987</td>\n",
+       "      <td>3.683212</td>\n",
+       "      <td>4.008905</td>\n",
+       "      <td>4.970368</td>\n",
+       "      <td>2.987966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.162380</td>\n",
+       "      <td>0.273624</td>\n",
+       "      <td>0.927741</td>\n",
+       "      <td>4.595586</td>\n",
+       "      <td>2.598824</td>\n",
+       "      <td>0.170167</td>\n",
+       "      <td>2.124048</td>\n",
+       "      <td>4.980209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.450100</td>\n",
+       "      <td>0.006301</td>\n",
+       "      <td>2.512217</td>\n",
+       "      <td>3.313864</td>\n",
+       "      <td>1.913458</td>\n",
+       "      <td>3.582252</td>\n",
+       "      <td>0.280764</td>\n",
+       "      <td>4.888595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.461105</td>\n",
+       "      <td>4.825628</td>\n",
+       "      <td>3.771356</td>\n",
+       "      <td>2.599278</td>\n",
+       "      <td>2.056019</td>\n",
+       "      <td>0.007332</td>\n",
+       "      <td>1.106786</td>\n",
+       "      <td>5.504671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.248202</td>\n",
+       "      <td>3.549416</td>\n",
+       "      <td>3.333632</td>\n",
+       "      <td>3.907310</td>\n",
+       "      <td>2.095312</td>\n",
+       "      <td>5.585145</td>\n",
+       "      <td>3.774253</td>\n",
+       "      <td>2.480120</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     theta1    theta2    theta3    theta4    theta5    theta6    theta7  \\\n",
+       "0  5.919526  2.369503  2.923656  4.488987  3.683212  4.008905  4.970368   \n",
+       "1  2.162380  0.273624  0.927741  4.595586  2.598824  0.170167  2.124048   \n",
+       "2  0.450100  0.006301  2.512217  3.313864  1.913458  3.582252  0.280764   \n",
+       "3  0.461105  4.825628  3.771356  2.599278  2.056019  0.007332  1.106786   \n",
+       "4  5.248202  3.549416  3.333632  3.907310  2.095312  5.585145  3.774253   \n",
+       "\n",
+       "     theta8  \n",
+       "0  2.987966  \n",
+       "1  4.980209  \n",
+       "2  4.888595  \n",
+       "3  5.504671  \n",
+       "4  2.480120  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "293ab317-d7e1-4fcf-aa94-5e3758e5a22a",
+   "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>fluxQ1</th>\n",
+       "      <th>fluxQ2</th>\n",
+       "      <th>fluxQ3</th>\n",
+       "      <th>fluxQ4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.590000e+19</td>\n",
+       "      <td>2.670000e+19</td>\n",
+       "      <td>2.560000e+19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.550000e+19</td>\n",
+       "      <td>2.530000e+19</td>\n",
+       "      <td>2.510000e+19</td>\n",
+       "      <td>2.510000e+19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.570000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.570000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>2.560000e+19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.540000e+19</td>\n",
+       "      <td>2.620000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         fluxQ1        fluxQ2        fluxQ3        fluxQ4\n",
+       "0  2.580000e+19  2.590000e+19  2.670000e+19  2.560000e+19\n",
+       "1  2.550000e+19  2.530000e+19  2.510000e+19  2.510000e+19\n",
+       "2  2.570000e+19  2.580000e+19  2.520000e+19  2.520000e+19\n",
+       "3  2.570000e+19  2.580000e+19  2.520000e+19  2.560000e+19\n",
+       "4  2.540000e+19  2.620000e+19  2.580000e+19  2.520000e+19"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outputs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3dfa175a-b363-4c23-ba85-d80d515128a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     theta1    theta2    theta3    theta4    theta5    theta6    theta7  \\\n",
+      "0  5.919526  2.369503  2.923656  4.488987  3.683212  4.008905  4.970368   \n",
+      "1  2.162380  0.273624  0.927741  4.595586  2.598824  0.170167  2.124048   \n",
+      "2  0.450100  0.006301  2.512217  3.313864  1.913458  3.582252  0.280764   \n",
+      "3  0.461105  4.825628  3.771356  2.599278  2.056019  0.007332  1.106786   \n",
+      "4  5.248202  3.549416  3.333632  3.907310  2.095312  5.585145  3.774253   \n",
+      "\n",
+      "     theta8        fluxQ1        fluxQ2        fluxQ3        fluxQ4  \n",
+      "0  2.987966  2.580000e+19  2.590000e+19  2.670000e+19  2.560000e+19  \n",
+      "1  4.980209  2.550000e+19  2.530000e+19  2.510000e+19  2.510000e+19  \n",
+      "2  4.888595  2.570000e+19  2.580000e+19  2.520000e+19  2.520000e+19  \n",
+      "3  5.504671  2.570000e+19  2.580000e+19  2.520000e+19  2.560000e+19  \n",
+      "4  2.480120  2.540000e+19  2.620000e+19  2.580000e+19  2.520000e+19  \n"
+     ]
+    }
+   ],
+   "source": [
+    "combined_df = pd.concat([inputs, outputs], axis=1)\n",
+    "print(combined_df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7532d981-6683-42b2-87d5-fc4a18e9b613",
+   "metadata": {},
+   "source": [
+    "Now it is time to extend the dataset to 3004 samples. This is done in the same way as in the original HTGR, replicating the same steps below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ee65be9b-4c39-4e8f-8952-5f4e8b7bb814",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Credit to mult_sym and g21 from https://github.com/deanrp2/MicroControl/blob/main/pmdata/utils.py#L51\n",
+    "theta_cols = [f\"theta{i + 1}\" for i in range(8)]\n",
+    "flux_cols = [f\"fluxQ{i + 1}\" for i in range(4)]\n",
+    "\n",
+    "def mult_samples(data):\n",
+    "    # Create empty arrays\n",
+    "    ht = xr.DataArray(\n",
+    "        np.zeros(data.shape), \n",
+    "        coords={\n",
+    "            \"index\": [f\"{idx}_h\" for idx in data.coords[\"index\"].values],\n",
+    "            \"variable\": data.coords[\"variable\"],\n",
+    "        },\n",
+    "    )\n",
+    "    vt = xr.DataArray(\n",
+    "        np.zeros(data.shape), \n",
+    "        coords={\n",
+    "            \"index\": [f\"{idx}_v\" for idx in data.coords[\"index\"].values],\n",
+    "            \"variable\": data.coords[\"variable\"],\n",
+    "        },\n",
+    "    )\n",
+    "    rt = xr.DataArray(\n",
+    "        np.zeros(data.shape),     \n",
+    "        coords={\n",
+    "            \"index\": [f\"{idx}_r\" for idx in data.coords[\"index\"].values],\n",
+    "            \"variable\": data.coords[\"variable\"],\n",
+    "        },\n",
+    "    )\n",
+    "\n",
+    "    # Swap drum positions\n",
+    "    hkey = [f\"theta{i}\" for i in np.array([3, 2, 1, 0, 7, 6, 5, 4], dtype=int) + 1]\n",
+    "    vkey = [f\"theta{i}\" for i in np.array([7, 6, 5, 4, 3, 2, 1, 0], dtype=int) + 1]\n",
+    "    rkey = [f\"theta{i}\" for i in np.array([4, 5, 6, 7, 0, 1, 2, 3], dtype=int) + 1]\n",
+    "\n",
+    "    ht.loc[:, hkey] = data.loc[:, theta_cols].values\n",
+    "    vt.loc[:, vkey] = data.loc[:, theta_cols].values\n",
+    "    rt.loc[:, rkey] = data.loc[:, theta_cols].values\n",
+    "\n",
+    "    # Adjust angles\n",
+    "    ht.loc[:, hkey] = (3 * np.pi - ht.loc[:, hkey].loc[:, hkey]) % (2 * np.pi)\n",
+    "    vt.loc[:, vkey] = (2 * np.pi - vt.loc[:, hkey].loc[:, vkey]) % (2 * np.pi)\n",
+    "    rt.loc[:, rkey] = (np.pi + rt.loc[:, hkey].loc[:, rkey]) % (2 * np.pi)\n",
+    "\n",
+    "    # Fill quadrant tallies\n",
+    "    hkey = [2, 1, 4, 3]\n",
+    "    vkey = [4, 3, 2, 1]\n",
+    "    rkey = [3, 4, 1, 2]\n",
+    "\n",
+    "    ht.loc[:, [f\"fluxQ{i}\" for i in hkey]] = data.loc[:, flux_cols].values\n",
+    "    vt.loc[:, [f\"fluxQ{i}\" for i in vkey]] = data.loc[:, flux_cols].values\n",
+    "    rt.loc[:, [f\"fluxQ{i}\" for i in rkey]] = data.loc[:, flux_cols].values\n",
+    "\n",
+    "    sym_data = xr.concat([data, ht, vt, rt], dim=\"index\").sortby(\"index\")\n",
+    "    \n",
+    "    # Normalize fluxes\n",
+    "    sym_data.loc[:, flux_cols].values = Normalizer().transform(sym_data.loc[:, flux_cols].values)\n",
+    "    \n",
+    "    # Convert global coordinate system to local\n",
+    "    loc_offsets = np.array(\n",
+    "        [3.6820187359906447, 4.067668586955522, 2.2155167202240653 - np.pi, 2.6011665711889425 - np.pi, \n",
+    "         0.5404260824008517, 0.9260759333657285, 5.3571093738138575 - np.pi, 5.742759224778734 - np.pi]\n",
+    "    )\n",
+    "\n",
+    "    # Apply correct 0 point\n",
+    "    sym_data.loc[:, theta_cols] = sym_data.loc[:, theta_cols] - loc_offsets + 2 * np.pi\n",
+    "\n",
+    "    # Reverse necessary angles\n",
+    "    sym_data.loc[:, [f\"theta{i}\" for i in [3,4,5,6]]] *= -1\n",
+    "\n",
+    "    # Scale all to [0, 2 * np.pi]\n",
+    "    sym_data.loc[:, theta_cols] = sym_data.loc[:, theta_cols] % (2 * np.pi)\n",
+    "        \n",
+    "    return sym_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5e9375ce-4349-4565-9338-c8e6cefe17fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data, test_data = train_test_split(combined_df, test_size=0.3)\n",
+    "\n",
+    "# Convert to xarray DataArray and specify the index as a coordinate\n",
+    "train_data_xr = xr.DataArray(\n",
+    "    train_data.values,\n",
+    "    coords={\"index\": train_data.index, \"variable\": train_data.columns},\n",
+    "    dims=[\"index\", \"variable\"]\n",
+    ")\n",
+    "test_data_xr = xr.DataArray(\n",
+    "    test_data.values,\n",
+    "    coords={\"index\": test_data.index, \"variable\": test_data.columns},\n",
+    "    dims=[\"index\", \"variable\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "638ecb25-2331-47d6-84b6-52664a22a91a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Multiplied training shape: (2100, 12), Multiplied testing shape: (904, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "sym_train_data = mult_samples(train_data_xr)\n",
+    "sym_test_data = mult_samples(test_data_xr)\n",
+    "print(f\"Multiplied training shape: {sym_train_data.shape}, Multiplied testing shape: {sym_test_data.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "175bf86e-f9ca-41d2-bfeb-f384db7ebbd2",
+   "metadata": {},
+   "source": [
+    "As seen above, we end up with data the same size as the original HTGR. Below, we are going to Min-Max the X_data and normalize the y_data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3273166b-5d63-491b-872e-f8fe45b33e55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Min-Max scaling data \n",
+    "def scale_data(train_data, test_data, scaler):\n",
+    "    train_data.values = scaler.fit_transform(\n",
+    "        train_data.values.reshape(-1, train_data.shape[-1])\n",
+    "    ).reshape(train_data.shape)\n",
+    "    test_data.values = scaler.transform(\n",
+    "        test_data.values.reshape(-1, test_data.shape[-1])\n",
+    "    ).reshape(test_data.shape)\n",
+    "    \n",
+    "    # Return data\n",
+    "    return train_data, test_data, scaler\n",
+    "\n",
+    "xtrain_arr, xtest_arr , _ = scale_data(sym_train_data.loc[:, theta_cols], sym_test_data.loc[:, theta_cols], MinMaxScaler())\n",
+    "ytrain_arr, ytest_arr, _ = scale_data(sym_train_data.loc[:, flux_cols], sym_test_data.loc[:, flux_cols], Normalizer(norm=\"l1\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bbbecbde-b745-48f4-a7d7-81cc88e2373c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xtrain = xtrain_arr.to_pandas()\n",
+    "xtest = xtest_arr.to_pandas()\n",
+    "ytrain = ytrain_arr.to_pandas()\n",
+    "ytest = ytest_arr.to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78b1ff10-3f19-469d-8daa-8ed8a715bc1f",
+   "metadata": {},
+   "source": [
+    "## Benchmark with Auto-Sklearn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa37b505-f4f3-412c-bf8c-743e43091894",
+   "metadata": {},
+   "source": [
+    "Now that preprocessing is done, it is time to use the Auto-Sklearn framework. We are going to first import the necessary libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "feeb8520-f582-48c3-9374-e106e5751e50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import things required from Autosklearn\n",
+    "from autosklearn.regression import AutoSklearnRegressor\n",
+    "from sklearn.multioutput import MultiOutputRegressor\n",
+    "import autosklearn.metrics as metrics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48a3079b-e14b-4a35-899e-5089be28d8ba",
+   "metadata": {},
+   "source": [
+    "We are going to set all the settings for auto-sklearn. Since pyMAISE took around 3 hours to complete parameter finding, we are going to cap it around the same. We will set ensemble_size=0 and ensemble_class=None since auto-sklearn automatically tries to create ensembles while we want single estimators. All available cores will be used (n_jobs=-1) and we also set cross-validation on with 5 folds as done in the orignal HTGR. Finally, we will only allow auto-sklearn to pick from decision trees, random forest, and k nearest neighbors, because these are what we used in the original HTGR notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b43187ae-6863-4860-add2-1d292145efbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "auto_reg = AutoSklearnRegressor(\n",
+    "    n_jobs=-1,                         # Number of parallel jobs to run\n",
+    "    initial_configurations_via_metalearning=0,  # Start from scratch\n",
+    "    resampling_strategy='cv',         # Cross-validation\n",
+    "    resampling_strategy_arguments={'folds': 5},  # Set number of folds for cross-validation\n",
+    "    memory_limit=None,  # No memory limit \n",
+    "    time_left_for_this_task= (3*60*60), #3 hrs\n",
+    "    ensemble_class=None, #Disable ensemble building or use SingleBest to obtain only use the single best model instead of an ensemble\n",
+    "    ensemble_kwargs={\n",
+    "      \"ensemble_size\": 0,  \n",
+    "    },\n",
+    "    include={\n",
+    "        'regressor': [\n",
+    "            \"decision_tree\",  # Decision Tree Regressor\n",
+    "            \"random_forest\",  # Random Forest Regressor\n",
+    "            \"k_nearest_neighbors\"  # K-Nearest Neighbors Regressor\n",
+    "        ],\n",
+    "    },\n",
+    "    metric= metrics.r2,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "119a87c3-82c0-4957-a1fa-5177ee6103d1",
+   "metadata": {},
+   "source": [
+    "We then run the parameter selection using .fit() on the training dataset. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "69c41b34-f9c3-4bdb-b15a-646bd4476fc8",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[WARNING] [2024-10-28 12:08:03,691:Client-AutoML(1):a6f6378d-9546-11ef-895b-08bfb876ce9f] Time limit for a single run is higher than total time limit. Capping the limit for a single run to the total time given to SMAC (10799.725003)\n",
+      "[WARNING] [2024-10-28 12:08:03,691:Client-AutoML(1):a6f6378d-9546-11ef-895b-08bfb876ce9f] Capping the per_run_time_limit to 5399.0 to have time for a least 2 models in each process.\n",
+      "RunKey(config_id=1, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.2900568491999885, time=19.91672372817993, status=<StatusType.SUCCESS: 1>, starttime=1730131684.1462064, endtime=1730131704.0797384, additional_info={'duration': 19.056434631347656, 'num_run': 2, 'train_loss': 0.04029638749909272, 'configuration_origin': 'Default'})\n",
+      "RunKey(config_id=2, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8384699802929064, time=2.1806697845458984, status=<StatusType.SUCCESS: 1>, starttime=1730131684.1624343, endtime=1730131686.3918867, additional_info={'duration': 2.0688865184783936, 'num_run': 3, 'train_loss': 0.8053840389443043, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=3, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.384726235043597, time=18.248084545135498, status=<StatusType.SUCCESS: 1>, starttime=1730131684.1739662, endtime=1730131702.4649675, additional_info={'duration': 17.89402747154236, 'num_run': 4, 'train_loss': 0.17119662056234314, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=4, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5796637303452743, time=2.2823591232299805, status=<StatusType.SUCCESS: 1>, starttime=1730131684.186885, endtime=1730131686.5062137, additional_info={'duration': 2.0846080780029297, 'num_run': 5, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=5, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7880954568665353, time=42.44270157814026, status=<StatusType.SUCCESS: 1>, starttime=1730131684.203846, endtime=1730131726.6999652, additional_info={'duration': 42.083160161972046, 'num_run': 6, 'train_loss': 0.7004836872008685, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=6, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.34218534585593297, time=2.1860620975494385, status=<StatusType.SUCCESS: 1>, starttime=1730131684.22709, endtime=1730131686.4427252, additional_info={'duration': 2.0185253620147705, 'num_run': 7, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=7, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3749726836450267, time=2.137798309326172, status=<StatusType.SUCCESS: 1>, starttime=1730131684.28946, endtime=1730131686.4756796, additional_info={'duration': 1.9605095386505127, 'num_run': 8, 'train_loss': 0.22934264774173083, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=8, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.49901288108449565, time=2.4752774238586426, status=<StatusType.SUCCESS: 1>, starttime=1730131684.34862, endtime=1730131686.8570623, additional_info={'duration': 2.1951615810394287, 'num_run': 9, 'train_loss': 0.4694808131251653, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=9, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9830170181719693, time=2.286241054534912, status=<StatusType.SUCCESS: 1>, starttime=1730131684.411472, endtime=1730131686.7369866, additional_info={'duration': 2.1596057415008545, 'num_run': 10, 'train_loss': 0.9775439028412796, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=10, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9986509626873799, time=16.262723922729492, status=<StatusType.SUCCESS: 1>, starttime=1730131684.4369354, endtime=1730131700.733726, additional_info={'duration': 16.131059885025024, 'num_run': 11, 'train_loss': 0.9936789893867666, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=11, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5023013073971373, time=2.4479031562805176, status=<StatusType.SUCCESS: 1>, starttime=1730131684.682807, endtime=1730131687.1774964, additional_info={'duration': 2.3701794147491455, 'num_run': 12, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=12, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7095774942045953, time=23.061662197113037, status=<StatusType.SUCCESS: 1>, starttime=1730131684.7420447, endtime=1730131707.8486025, additional_info={'duration': 22.711967706680298, 'num_run': 13, 'train_loss': 0.4993059515274468, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=13, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5506603132939794, time=1.5055489540100098, status=<StatusType.SUCCESS: 1>, starttime=1730131684.7925603, endtime=1730131686.3247366, additional_info={'duration': 1.4206504821777344, 'num_run': 14, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=14, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7989129182689403, time=3.620234966278076, status=<StatusType.SUCCESS: 1>, starttime=1730131684.8268454, endtime=1730131688.4748158, additional_info={'duration': 3.4484217166900635, 'num_run': 15, 'train_loss': 0.700772283747443, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=15, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9155423218210066, time=2.302638292312622, status=<StatusType.SUCCESS: 1>, starttime=1730131684.8486538, endtime=1730131687.2592928, additional_info={'duration': 2.195991039276123, 'num_run': 16, 'train_loss': 0.6795086356460653, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=16, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.6946427822113037, status=<StatusType.CRASHED: 3>, starttime=1730131684.884792, endtime=1730131685.6599777, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=17, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6320193768456901, time=12.358214855194092, status=<StatusType.SUCCESS: 1>, starttime=1730131684.9411502, endtime=1730131697.3582704, additional_info={'duration': 11.984260320663452, 'num_run': 18, 'train_loss': 0.3877997927768567, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=18, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.34562837778591904, time=1.7244338989257812, status=<StatusType.SUCCESS: 1>, starttime=1730131684.9622455, endtime=1730131686.7078488, additional_info={'duration': 1.539421558380127, 'num_run': 19, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=19, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0009529391556706, time=1.4993984699249268, status=<StatusType.SUCCESS: 1>, starttime=1730131684.9969099, endtime=1730131686.585379, additional_info={'duration': 1.3704631328582764, 'num_run': 20, 'train_loss': 0.9990387813588392, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=20, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.6186099052429199, status=<StatusType.CRASHED: 3>, starttime=1730131685.0823624, endtime=1730131685.7798645, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=21, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6054507767960581, time=5.870704412460327, status=<StatusType.SUCCESS: 1>, starttime=1730131685.1028333, endtime=1730131691.0331295, additional_info={'duration': 5.344543695449829, 'num_run': 22, 'train_loss': 0.4362824267565229, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=22, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0042607832685957, time=1.2788646221160889, status=<StatusType.SUCCESS: 1>, starttime=1730131685.2798796, endtime=1730131686.65135, additional_info={'duration': 1.1612122058868408, 'num_run': 23, 'train_loss': 0.9948551961429163, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=23, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9463938193534196, time=1.1075189113616943, status=<StatusType.SUCCESS: 1>, starttime=1730131685.3169086, endtime=1730131686.4793158, additional_info={'duration': 1.0030848979949951, 'num_run': 24, 'train_loss': 0.931586883991931, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=24, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5093304845607235, time=2.4513282775878906, status=<StatusType.SUCCESS: 1>, starttime=1730131685.3435688, endtime=1730131687.822922, additional_info={'duration': 2.3097774982452393, 'num_run': 25, 'train_loss': 0.2629677744262694, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=25, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.2001557102459262, time=490.21865487098694, status=<StatusType.SUCCESS: 1>, starttime=1730131685.3699417, endtime=1730132175.7638855, additional_info={'duration': 489.2046010494232, 'num_run': 26, 'train_loss': 0.1850454773280708, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=26, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.77867711292647, time=1.2650096416473389, status=<StatusType.SUCCESS: 1>, starttime=1730131685.4208543, endtime=1730131686.7068548, additional_info={'duration': 1.0566620826721191, 'num_run': 27, 'train_loss': 0.48475160494653147, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=27, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9803532362521936, time=2.0268614292144775, status=<StatusType.SUCCESS: 1>, starttime=1730131685.551544, endtime=1730131687.6073518, additional_info={'duration': 1.8785223960876465, 'num_run': 28, 'train_loss': 0.9670098069742993, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=28, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7078992601335613, time=30.458622455596924, status=<StatusType.SUCCESS: 1>, starttime=1730131685.582615, endtime=1730131716.0986328, additional_info={'duration': 30.04286503791809, 'num_run': 29, 'train_loss': 0.5826073552618224, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=29, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7544670422033513, time=36.970075368881226, status=<StatusType.SUCCESS: 1>, starttime=1730131685.6292253, endtime=1730131722.6366842, additional_info={'duration': 36.74270272254944, 'num_run': 30, 'train_loss': 0.5535628713969669, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=30, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.001150411516183, time=1.282437801361084, status=<StatusType.SUCCESS: 1>, starttime=1730131685.823683, endtime=1730131687.1388693, additional_info={'duration': 1.1262366771697998, 'num_run': 31, 'train_loss': 0.9995235582626723, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=31, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.40943455696105957, status=<StatusType.CRASHED: 3>, starttime=1730131687.8610122, endtime=1730131688.3015747, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=32, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.34876894278818754, time=1.2484874725341797, status=<StatusType.SUCCESS: 1>, starttime=1730131688.7165952, endtime=1730131690.014003, additional_info={'duration': 1.165806770324707, 'num_run': 33, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=33, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6832084859686542, time=0.9455907344818115, status=<StatusType.SUCCESS: 1>, starttime=1730131689.6725192, endtime=1730131690.639873, additional_info={'duration': 0.8643403053283691, 'num_run': 34, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=34, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.358699289403956, time=2.093303680419922, status=<StatusType.SUCCESS: 1>, starttime=1730131690.5839498, endtime=1730131692.712912, additional_info={'duration': 2.0230233669281006, 'num_run': 35, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=35, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.527672364656663, time=145.84615015983582, status=<StatusType.SUCCESS: 1>, starttime=1730131690.6210086, endtime=1730131836.4911115, additional_info={'duration': 145.53389048576355, 'num_run': 36, 'train_loss': 0.47062869833432164, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=36, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6953100387440079, time=108.67911338806152, status=<StatusType.SUCCESS: 1>, starttime=1730131690.733198, endtime=1730131799.4716768, additional_info={'duration': 108.46694493293762, 'num_run': 37, 'train_loss': 0.590674815094981, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=37, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.3846263885498047, status=<StatusType.CRASHED: 3>, starttime=1730131690.8141143, endtime=1730131691.234448, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=38, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3360605061630527, time=0.9857776165008545, status=<StatusType.SUCCESS: 1>, starttime=1730131691.9559138, endtime=1730131692.9737422, additional_info={'duration': 0.9036645889282227, 'num_run': 39, 'train_loss': 0.24090505734121578, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=39, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.38426744611012487, time=0.9523208141326904, status=<StatusType.SUCCESS: 1>, starttime=1730131693.085498, endtime=1730131694.073574, additional_info={'duration': 0.8841004371643066, 'num_run': 40, 'train_loss': 0.31716518285916, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=40, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.33523191830687726, time=1.3034284114837646, status=<StatusType.SUCCESS: 1>, starttime=1730131693.20333, endtime=1730131694.5447807, additional_info={'duration': 1.2324843406677246, 'num_run': 41, 'train_loss': 0.22749656240191807, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=41, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3869952405339103, time=1.3047256469726562, status=<StatusType.SUCCESS: 1>, starttime=1730131694.1451683, endtime=1730131695.4788313, additional_info={'duration': 1.219193458557129, 'num_run': 42, 'train_loss': 0.20793190319489335, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=42, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.45238093327993245, time=1.3784732818603516, status=<StatusType.SUCCESS: 1>, starttime=1730131695.593738, endtime=1730131697.017159, additional_info={'duration': 1.2387502193450928, 'num_run': 43, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=43, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4233942185028259, time=249.7198522090912, status=<StatusType.SUCCESS: 1>, starttime=1730131695.7470038, endtime=1730131945.559777, additional_info={'duration': 249.35816025733948, 'num_run': 44, 'train_loss': 0.23399837967634252, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=44, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.46376842389768624, time=10.01272439956665, status=<StatusType.SUCCESS: 1>, starttime=1730131695.7980304, endtime=1730131705.8540506, additional_info={'duration': 9.77421760559082, 'num_run': 45, 'train_loss': 0.3910393923245552, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=45, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.01881597721154, time=1.2158746719360352, status=<StatusType.SUCCESS: 1>, starttime=1730131695.845189, endtime=1730131697.1086526, additional_info={'duration': 1.0760736465454102, 'num_run': 46, 'train_loss': 0.11692352406894874, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=46, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.34812362892892507, time=0.9525089263916016, status=<StatusType.SUCCESS: 1>, starttime=1730131696.8574598, endtime=1730131697.845448, additional_info={'duration': 0.8913414478302002, 'num_run': 47, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=47, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9942071000970732, time=0.8934400081634521, status=<StatusType.SUCCESS: 1>, starttime=1730131696.8976865, endtime=1730131697.8279266, additional_info={'duration': 0.7627582550048828, 'num_run': 48, 'train_loss': 0.9808940042507115, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=48, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.45238025656639597, time=1.193946123123169, status=<StatusType.SUCCESS: 1>, starttime=1730131698.0213969, endtime=1730131699.2484837, additional_info={'duration': 1.1134188175201416, 'num_run': 49, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=49, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5180764338120147, time=167.75931787490845, status=<StatusType.SUCCESS: 1>, starttime=1730131698.1937044, endtime=1730131865.9604373, additional_info={'duration': 167.44343042373657, 'num_run': 50, 'train_loss': 0.45015504920923854, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=50, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3450535630361454, time=1.1472415924072266, status=<StatusType.SUCCESS: 1>, starttime=1730131699.4245076, endtime=1730131700.604392, additional_info={'duration': 1.0707457065582275, 'num_run': 51, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=51, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4902130295365384, time=1.0484521389007568, status=<StatusType.SUCCESS: 1>, starttime=1730131699.4830234, endtime=1730131700.560586, additional_info={'duration': 0.9073596000671387, 'num_run': 52, 'train_loss': 0.1782957091794225, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=52, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3360605061630527, time=0.8712081909179688, status=<StatusType.SUCCESS: 1>, starttime=1730131700.8236523, endtime=1730131701.7364874, additional_info={'duration': 0.8091316223144531, 'num_run': 53, 'train_loss': 0.24090505734121578, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=53, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3601652389599189, time=0.8807291984558105, status=<StatusType.SUCCESS: 1>, starttime=1730131701.9133332, endtime=1730131702.8260722, additional_info={'duration': 0.8070809841156006, 'num_run': 54, 'train_loss': 0.2590655201389451, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=54, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5939570317092794, time=1.3139927387237549, status=<StatusType.SUCCESS: 1>, starttime=1730131703.1898286, endtime=1730131704.5332277, additional_info={'duration': 1.2310974597930908, 'num_run': 55, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=55, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.44708027209939555, time=5.57986855506897, status=<StatusType.SUCCESS: 1>, starttime=1730131703.2320094, endtime=1730131708.8741724, additional_info={'duration': 5.221940040588379, 'num_run': 56, 'train_loss': 0.301573079497872, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=56, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.46037065250123654, time=338.39932775497437, status=<StatusType.SUCCESS: 1>, starttime=1730131703.3196447, endtime=1730132041.7245977, additional_info={'duration': 337.5826222896576, 'num_run': 57, 'train_loss': 0.3644488133402136, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=57, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.3960566520690918, status=<StatusType.CRASHED: 3>, starttime=1730131704.93388, endtime=1730131705.3828146, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=58, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8055782770355248, time=1.2906091213226318, status=<StatusType.SUCCESS: 1>, starttime=1730131705.0618176, endtime=1730131706.392293, additional_info={'duration': 1.2006754875183105, 'num_run': 59, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=59, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.41453123092651367, status=<StatusType.CRASHED: 3>, starttime=1730131705.093586, endtime=1730131705.5357497, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=60, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7466872789584132, time=25.391066312789917, status=<StatusType.SUCCESS: 1>, starttime=1730131705.2221196, endtime=1730131730.6511052, additional_info={'duration': 25.29489493370056, 'num_run': 61, 'train_loss': 0.43287170005987546, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=61, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3448169647999628, time=1.340954065322876, status=<StatusType.SUCCESS: 1>, starttime=1730131706.3452663, endtime=1730131707.7251048, additional_info={'duration': 1.260624647140503, 'num_run': 62, 'train_loss': 0.2899247345049795, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=62, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.34876894278818754, time=1.4071967601776123, status=<StatusType.SUCCESS: 1>, starttime=1730131707.2641459, endtime=1730131708.6980984, additional_info={'duration': 1.3174221515655518, 'num_run': 63, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=63, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7166899213850925, time=1.2429678440093994, status=<StatusType.SUCCESS: 1>, starttime=1730131707.3059094, endtime=1730131708.6828089, additional_info={'duration': 1.100881576538086, 'num_run': 64, 'train_loss': 0.3323141418656858, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=64, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.36161198773994296, time=0.8830592632293701, status=<StatusType.SUCCESS: 1>, starttime=1730131708.4910543, endtime=1730131709.4116104, additional_info={'duration': 0.7996082305908203, 'num_run': 65, 'train_loss': 0.2459417209586878, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=65, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6678042895273055, time=1.1815404891967773, status=<StatusType.SUCCESS: 1>, starttime=1730131709.728191, endtime=1730131710.9541912, additional_info={'duration': 1.0951783657073975, 'num_run': 66, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=66, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7859029430111728, time=32.47009515762329, status=<StatusType.SUCCESS: 1>, starttime=1730131709.8602152, endtime=1730131742.3652787, additional_info={'duration': 32.29305577278137, 'num_run': 67, 'train_loss': 0.742256097175936, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=67, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7434614005095644, time=2.5994210243225098, status=<StatusType.SUCCESS: 1>, starttime=1730131709.9278197, endtime=1730131712.5610619, additional_info={'duration': 2.4909472465515137, 'num_run': 68, 'train_loss': 0.03308979498127307, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=68, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.33673760691702037, time=1.1383638381958008, status=<StatusType.SUCCESS: 1>, starttime=1730131711.372693, endtime=1730131712.5489657, additional_info={'duration': 1.0678446292877197, 'num_run': 69, 'train_loss': 0.27579922125678463, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=69, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.3926079273223877, status=<StatusType.CRASHED: 3>, starttime=1730131711.4879618, endtime=1730131711.9251118, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=70, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.2906200639257448, time=1.1558401584625244, status=<StatusType.SUCCESS: 1>, starttime=1730131711.5699217, endtime=1730131712.7656806, additional_info={'duration': 1.0421128273010254, 'num_run': 71, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=71, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7070422197019967, time=1.027764081954956, status=<StatusType.SUCCESS: 1>, starttime=1730131711.7622535, endtime=1730131712.8170862, additional_info={'duration': 0.9624283313751221, 'num_run': 72, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=72, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3583561011503458, time=1.3646538257598877, status=<StatusType.SUCCESS: 1>, starttime=1730131713.1746612, endtime=1730131714.5690815, additional_info={'duration': 1.2757012844085693, 'num_run': 73, 'train_loss': 0.3146239689888405, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=73, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.34221847251553783, time=1.2165191173553467, status=<StatusType.SUCCESS: 1>, starttime=1730131715.0818841, endtime=1730131716.3336887, additional_info={'duration': 1.14503812789917, 'num_run': 74, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=74, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9998207368651174, time=1.1247329711914062, status=<StatusType.SUCCESS: 1>, starttime=1730131715.3964157, endtime=1730131716.563729, additional_info={'duration': 1.0062320232391357, 'num_run': 75, 'train_loss': 0.9921008144124325, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=75, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6828951441115079, time=1.0525577068328857, status=<StatusType.SUCCESS: 1>, starttime=1730131715.4591844, endtime=1730131716.5588217, additional_info={'duration': 0.9126050472259521, 'num_run': 76, 'train_loss': 0.6502065624236861, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=76, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5928682818631803, time=2.08971905708313, status=<StatusType.SUCCESS: 1>, starttime=1730131715.5720916, endtime=1730131717.6977262, additional_info={'duration': 1.9635064601898193, 'num_run': 77, 'train_loss': 0.5557732459307265, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=77, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9719241073688837, time=1.0210142135620117, status=<StatusType.SUCCESS: 1>, starttime=1730131715.7037663, endtime=1730131716.8037136, additional_info={'duration': 0.928955078125, 'num_run': 78, 'train_loss': 0.9575367333386182, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=78, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6327145992736571, time=0.841778039932251, status=<StatusType.SUCCESS: 1>, starttime=1730131715.8065128, endtime=1730131716.7441971, additional_info={'duration': 0.7706029415130615, 'num_run': 79, 'train_loss': 0.22167416424169828, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=79, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3851693165660507, time=0.9027125835418701, status=<StatusType.SUCCESS: 1>, starttime=1730131717.1820729, endtime=1730131718.116972, additional_info={'duration': 0.8325164318084717, 'num_run': 80, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=80, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.37170236707502746, time=0.9152071475982666, status=<StatusType.SUCCESS: 1>, starttime=1730131718.2773638, endtime=1730131719.2245002, additional_info={'duration': 0.8414402008056641, 'num_run': 81, 'train_loss': 0.3075196637461244, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=81, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3278792479948627, time=1.000579833984375, status=<StatusType.SUCCESS: 1>, starttime=1730131719.32635, endtime=1730131720.3612556, additional_info={'duration': 0.9322762489318848, 'num_run': 82, 'train_loss': 0.2508562614855161, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=82, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.324902817941749, time=0.9742865562438965, status=<StatusType.SUCCESS: 1>, starttime=1730131720.4792547, endtime=1730131721.4918423, additional_info={'duration': 0.8989872932434082, 'num_run': 83, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=83, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.39202244412594517, time=1.2543902397155762, status=<StatusType.SUCCESS: 1>, starttime=1730131721.8133693, endtime=1730131723.1029537, additional_info={'duration': 1.1871120929718018, 'num_run': 84, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=84, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8219896127908086, time=24.893535375595093, status=<StatusType.SUCCESS: 1>, starttime=1730131721.92427, endtime=1730131746.86724, additional_info={'duration': 24.803057432174683, 'num_run': 85, 'train_loss': 0.7337136574625015, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=85, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.32769087358126514, time=1.3124618530273438, status=<StatusType.SUCCESS: 1>, starttime=1730131723.1818058, endtime=1730131724.518066, additional_info={'duration': 1.1249420642852783, 'num_run': 86, 'train_loss': 0.25098303421140666, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=86, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3625597814823588, time=1.1570115089416504, status=<StatusType.SUCCESS: 1>, starttime=1730131724.1879287, endtime=1730131725.392341, additional_info={'duration': 1.0838799476623535, 'num_run': 87, 'train_loss': 0.2698023122201036, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=87, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3536992345171449, time=1.0612728595733643, status=<StatusType.SUCCESS: 1>, starttime=1730131724.2323823, endtime=1730131725.4080575, additional_info={'duration': 0.996558666229248, 'num_run': 88, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=88, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5215780794118123, time=18.05314302444458, status=<StatusType.SUCCESS: 1>, starttime=1730131724.3582795, endtime=1730131742.4867249, additional_info={'duration': 17.69792866706848, 'num_run': 89, 'train_loss': 0.3818180922556567, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=89, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3539411064840441, time=1.314927101135254, status=<StatusType.SUCCESS: 1>, starttime=1730131725.8935828, endtime=1730131727.246365, additional_info={'duration': 1.2287547588348389, 'num_run': 90, 'train_loss': 0.2863883469187096, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=90, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9812237865829693, time=1.3312935829162598, status=<StatusType.SUCCESS: 1>, starttime=1730131726.0481832, endtime=1730131727.4065201, additional_info={'duration': 1.2299373149871826, 'num_run': 91, 'train_loss': 0.9759196679905193, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=91, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3299777323610481, time=1.5557174682617188, status=<StatusType.SUCCESS: 1>, starttime=1730131727.441773, endtime=1730131729.0409322, additional_info={'duration': 1.4783661365509033, 'num_run': 92, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=92, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4078880733180702, time=14.119436979293823, status=<StatusType.SUCCESS: 1>, starttime=1730131727.5858464, endtime=1730131741.7265906, additional_info={'duration': 13.869559049606323, 'num_run': 93, 'train_loss': 0.27144248667556453, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=93, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9749125484930652, time=117.1388783454895, status=<StatusType.SUCCESS: 1>, starttime=1730131727.7154691, endtime=1730131844.9248354, additional_info={'duration': 116.92895650863647, 'num_run': 94, 'train_loss': 0.8256957924331756, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=94, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.5050191879272461, status=<StatusType.CRASHED: 3>, starttime=1730131729.353059, endtime=1730131729.8634303, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=95, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8145679283033271, time=101.41959190368652, status=<StatusType.SUCCESS: 1>, starttime=1730131729.4155035, endtime=1730131830.9564488, additional_info={'duration': 101.1081771850586, 'num_run': 96, 'train_loss': 0.5198919626879773, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=96, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0000252503586693, time=0.9229791164398193, status=<StatusType.SUCCESS: 1>, starttime=1730131729.5194101, endtime=1730131730.4782803, additional_info={'duration': 0.8120889663696289, 'num_run': 97, 'train_loss': 0.9923349022279282, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=97, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.344310998916626, status=<StatusType.CRASHED: 3>, starttime=1730131729.6756928, endtime=1730131730.0595307, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=98, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8168589504574708, time=941.6014602184296, status=<StatusType.SUCCESS: 1>, starttime=1730131729.8166187, endtime=1730132671.4853659, additional_info={'duration': 941.1361379623413, 'num_run': 99, 'train_loss': 0.6184229269007198, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=99, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3604575837580516, time=0.9023301601409912, status=<StatusType.SUCCESS: 1>, starttime=1730131731.323884, endtime=1730131732.2599502, additional_info={'duration': 0.8241748809814453, 'num_run': 100, 'train_loss': 0.2961617249389412, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=100, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3159054528179776, time=1.3609235286712646, status=<StatusType.SUCCESS: 1>, starttime=1730131732.7504077, endtime=1730131734.1957781, additional_info={'duration': 1.297271966934204, 'num_run': 101, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=101, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3233823086352832, time=1.4884626865386963, status=<StatusType.SUCCESS: 1>, starttime=1730131734.387058, endtime=1730131735.916634, additional_info={'duration': 1.4101440906524658, 'num_run': 102, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=102, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3963614271301332, time=3.0725367069244385, status=<StatusType.SUCCESS: 1>, starttime=1730131734.542163, endtime=1730131737.6877747, additional_info={'duration': 2.9653894901275635, 'num_run': 103, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=103, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5679107937629844, time=443.6645040512085, status=<StatusType.SUCCESS: 1>, starttime=1730131734.6848402, endtime=1730132178.39186, additional_info={'duration': 443.40414571762085, 'num_run': 104, 'train_loss': 0.44849716759821345, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=104, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9958956457845508, time=40.49518609046936, status=<StatusType.SUCCESS: 1>, starttime=1730131734.7419872, endtime=1730131775.3331838, additional_info={'duration': 40.40274620056152, 'num_run': 105, 'train_loss': 0.9966378339673714, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=105, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0011874916557153, time=13.459668397903442, status=<StatusType.SUCCESS: 1>, starttime=1730131734.8592563, endtime=1730131748.45066, additional_info={'duration': 13.377630472183228, 'num_run': 106, 'train_loss': 0.9993942563315205, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=106, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5568864681722188, time=1.1201801300048828, status=<StatusType.SUCCESS: 1>, starttime=1730131735.11117, endtime=1730131736.2664154, additional_info={'duration': 1.0427443981170654, 'num_run': 107, 'train_loss': 0.2865558019439745, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=107, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.330153631099281, time=1.5238134860992432, status=<StatusType.SUCCESS: 1>, starttime=1730131736.89651, endtime=1730131738.4604335, additional_info={'duration': 1.440497875213623, 'num_run': 108, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=108, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3869003016123685, time=1.5757026672363281, status=<StatusType.SUCCESS: 1>, starttime=1730131737.074272, endtime=1730131738.6941273, additional_info={'duration': 1.4557232856750488, 'num_run': 109, 'train_loss': 0.20766894179751383, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=109, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6753641295360917, time=1.7073171138763428, status=<StatusType.SUCCESS: 1>, starttime=1730131737.2077239, endtime=1730131738.9614882, additional_info={'duration': 1.5606043338775635, 'num_run': 110, 'train_loss': 1.6007328795808463e-11, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=110, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.098807099774207, time=22.19131875038147, status=<StatusType.SUCCESS: 1>, starttime=1730131737.3050685, endtime=1730131759.603534, additional_info={'duration': 22.080262660980225, 'num_run': 111, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=111, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.5594606399536133, status=<StatusType.CRASHED: 3>, starttime=1730131737.426416, endtime=1730131738.0223453, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=112, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6036918529900355, time=14.885790586471558, status=<StatusType.SUCCESS: 1>, starttime=1730131737.6153772, endtime=1730131752.5437498, additional_info={'duration': 14.775940418243408, 'num_run': 113, 'train_loss': 0.5773317766670518, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=113, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.48935749989746746, time=324.18829798698425, status=<StatusType.SUCCESS: 1>, starttime=1730131737.7460172, endtime=1730132061.9442925, additional_info={'duration': 323.7452850341797, 'num_run': 114, 'train_loss': 0.4159406372553512, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=114, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3151698946399589, time=1.1455779075622559, status=<StatusType.SUCCESS: 1>, starttime=1730131739.2730591, endtime=1730131740.4616332, additional_info={'duration': 1.0542497634887695, 'num_run': 115, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=115, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9835786133771975, time=0.9385280609130859, status=<StatusType.SUCCESS: 1>, starttime=1730131739.4595287, endtime=1730131740.4373553, additional_info={'duration': 0.8608558177947998, 'num_run': 116, 'train_loss': 0.9757939851522085, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=116, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.35038028624655754, time=1.194657802581787, status=<StatusType.SUCCESS: 1>, starttime=1730131741.2194908, endtime=1730131742.4635184, additional_info={'duration': 1.1001636981964111, 'num_run': 117, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=117, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4091900195654611, time=1.195849895477295, status=<StatusType.SUCCESS: 1>, starttime=1730131742.9833598, endtime=1730131744.209417, additional_info={'duration': 1.1089847087860107, 'num_run': 118, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=118, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.18531902823741017, time=192.93640804290771, status=<StatusType.SUCCESS: 1>, starttime=1730131743.1862833, endtime=1730131936.1669042, additional_info={'duration': 192.54274702072144, 'num_run': 119, 'train_loss': 0.07082501706348521, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=119, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.47650477700710736, time=1.7193138599395752, status=<StatusType.SUCCESS: 1>, starttime=1730131744.7686374, endtime=1730131746.5315516, additional_info={'duration': 1.6308972835540771, 'num_run': 120, 'train_loss': 0.45887538942366124, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=120, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3196682604838863, time=0.9684553146362305, status=<StatusType.SUCCESS: 1>, starttime=1730131746.616889, endtime=1730131747.6195633, additional_info={'duration': 0.9091145992279053, 'num_run': 121, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=121, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.45798134311097555, time=1.1683602333068848, status=<StatusType.SUCCESS: 1>, starttime=1730131748.424324, endtime=1730131749.6389198, additional_info={'duration': 1.0927670001983643, 'num_run': 122, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=122, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.33098519992019115, time=1.4909322261810303, status=<StatusType.SUCCESS: 1>, starttime=1730131750.3031387, endtime=1730131751.8406727, additional_info={'duration': 1.405813217163086, 'num_run': 123, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=123, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4133949337897835, time=1.604787826538086, status=<StatusType.SUCCESS: 1>, starttime=1730131752.0051699, endtime=1730131753.6528397, additional_info={'duration': 1.5147066116333008, 'num_run': 124, 'train_loss': 0.31342863092083584, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=124, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4117133226758115, time=1.8266844749450684, status=<StatusType.SUCCESS: 1>, starttime=1730131753.4130807, endtime=1730131755.2812428, additional_info={'duration': 1.7497847080230713, 'num_run': 125, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=125, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3232830853861171, time=1.6139893531799316, status=<StatusType.SUCCESS: 1>, starttime=1730131755.0714812, endtime=1730131756.7316859, additional_info={'duration': 1.5310382843017578, 'num_run': 126, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=126, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6823696920517033, time=10.379870891571045, status=<StatusType.SUCCESS: 1>, starttime=1730131755.2730691, endtime=1730131765.7023325, additional_info={'duration': 10.10700249671936, 'num_run': 127, 'train_loss': 0.617874518839648, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=127, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8225563505521308, time=3.5361809730529785, status=<StatusType.SUCCESS: 1>, starttime=1730131755.3901236, endtime=1730131758.9677193, additional_info={'duration': 3.4298315048217773, 'num_run': 128, 'train_loss': 0.5625120960895387, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=128, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7041856886472175, time=8.134911298751831, status=<StatusType.SUCCESS: 1>, starttime=1730131755.5998077, endtime=1730131763.7445104, additional_info={'duration': 7.965929269790649, 'num_run': 129, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=129, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9719241073688837, time=0.7699153423309326, status=<StatusType.SUCCESS: 1>, starttime=1730131755.7139554, endtime=1730131756.523833, additional_info={'duration': 0.6945486068725586, 'num_run': 130, 'train_loss': 0.9575367333386182, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=130, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6367752857850971, time=8.57502794265747, status=<StatusType.SUCCESS: 1>, starttime=1730131755.826181, endtime=1730131764.4545538, additional_info={'duration': 8.23215913772583, 'num_run': 131, 'train_loss': 0.23914607416351613, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=131, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3365983226283337, time=1.4617257118225098, status=<StatusType.SUCCESS: 1>, starttime=1730131757.4956717, endtime=1730131759.000216, additional_info={'duration': 1.3722028732299805, 'num_run': 132, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=132, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3200620318880034, time=1.3509297370910645, status=<StatusType.SUCCESS: 1>, starttime=1730131759.573783, endtime=1730131760.971584, additional_info={'duration': 1.2691423892974854, 'num_run': 133, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=133, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3661896395850519, time=1.4786581993103027, status=<StatusType.SUCCESS: 1>, starttime=1730131761.5067647, endtime=1730131763.0328715, additional_info={'duration': 1.3911097049713135, 'num_run': 134, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=134, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.3293311595916748, status=<StatusType.CRASHED: 3>, starttime=1730131761.7243006, endtime=1730131762.0952888, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=135, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3630378608919866, time=1.2519209384918213, status=<StatusType.SUCCESS: 1>, starttime=1730131763.3122065, endtime=1730131764.6093352, additional_info={'duration': 1.149472951889038, 'num_run': 136, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=136, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8344518908461421, time=3535.5691571235657, status=<StatusType.SUCCESS: 1>, starttime=1730131763.4880433, endtime=1730135299.1278248, additional_info={'duration': 3535.2265117168427, 'num_run': 137, 'train_loss': 0.6094128585372119, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=137, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3232180080729587, time=1.1481852531433105, status=<StatusType.SUCCESS: 1>, starttime=1730131765.1094174, endtime=1730131766.3070433, additional_info={'duration': 1.072356939315796, 'num_run': 138, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=138, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.47702873330441814, time=508.93085646629333, status=<StatusType.SUCCESS: 1>, starttime=1730131766.5956492, endtime=1730132275.6078167, additional_info={'duration': 508.60692405700684, 'num_run': 139, 'train_loss': 0.4027978573647104, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=139, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.32754225892197375, time=1.5622868537902832, status=<StatusType.SUCCESS: 1>, starttime=1730131768.5854728, endtime=1730131770.1808364, additional_info={'duration': 1.4723763465881348, 'num_run': 140, 'train_loss': 0.24989850605078465, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=140, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9963607772353932, time=0.7810182571411133, status=<StatusType.SUCCESS: 1>, starttime=1730131768.6476967, endtime=1730131769.5339706, additional_info={'duration': 0.710686445236206, 'num_run': 141, 'train_loss': 0.9827276751845039, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=141, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.34797342101688233, time=1.3055589199066162, status=<StatusType.SUCCESS: 1>, starttime=1730131769.7703652, endtime=1730131771.1122463, additional_info={'duration': 1.2337148189544678, 'num_run': 142, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=142, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.44597387313842773, status=<StatusType.CRASHED: 3>, starttime=1730131769.9271145, endtime=1730131770.4520822, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=143, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.440653618214462, time=0.8274035453796387, status=<StatusType.SUCCESS: 1>, starttime=1730131770.0657098, endtime=1730131770.9296627, additional_info={'duration': 0.6752943992614746, 'num_run': 144, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=144, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.37560914891291014, time=1.2350924015045166, status=<StatusType.SUCCESS: 1>, starttime=1730131771.6039724, endtime=1730131772.881092, additional_info={'duration': 1.1475105285644531, 'num_run': 145, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=145, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6617502336487544, time=20.12889838218689, status=<StatusType.SUCCESS: 1>, starttime=1730131771.8556652, endtime=1730131792.026945, additional_info={'duration': 19.894287824630737, 'num_run': 146, 'train_loss': 0.5590547521332851, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=146, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3446109863181677, time=1.4383103847503662, status=<StatusType.SUCCESS: 1>, starttime=1730131773.505281, endtime=1730131774.9910328, additional_info={'duration': 1.349890947341919, 'num_run': 147, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=147, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6447441874410941, time=134.7184772491455, status=<StatusType.SUCCESS: 1>, starttime=1730131773.706309, endtime=1730131908.4907918, additional_info={'duration': 134.37406635284424, 'num_run': 148, 'train_loss': 0.4445344244278515, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=148, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5130605292280712, time=18.320104598999023, status=<StatusType.SUCCESS: 1>, starttime=1730131775.9617786, endtime=1730131794.3366444, additional_info={'duration': 18.072619438171387, 'num_run': 149, 'train_loss': 0.3826257948466463, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=149, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5235085728240785, time=1.585763692855835, status=<StatusType.SUCCESS: 1>, starttime=1730131777.7089581, endtime=1730131779.3429189, additional_info={'duration': 1.4466886520385742, 'num_run': 150, 'train_loss': 0.510675374975967, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=150, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3690865312170128, time=2.0596506595611572, status=<StatusType.SUCCESS: 1>, starttime=1730131779.333292, endtime=1730131781.4452205, additional_info={'duration': 1.9525210857391357, 'num_run': 151, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=151, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3378974836605804, time=1282.8783090114594, status=<StatusType.SUCCESS: 1>, starttime=1730131779.5238073, endtime=1730133062.6119528, additional_info={'duration': 1282.1243016719818, 'num_run': 152, 'train_loss': 0.08551990433748928, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=152, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3319233038605166, time=1.8384943008422852, status=<StatusType.SUCCESS: 1>, starttime=1730131781.2824078, endtime=1730131783.160881, additional_info={'duration': 1.7542786598205566, 'num_run': 153, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=153, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7870723614913989, time=2.4444165229797363, status=<StatusType.SUCCESS: 1>, starttime=1730131781.445395, endtime=1730131783.93929, additional_info={'duration': 2.183514356613159, 'num_run': 154, 'train_loss': 0.5510444862878241, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=154, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3200523983347784, time=1.3965911865234375, status=<StatusType.SUCCESS: 1>, starttime=1730131783.3038945, endtime=1730131784.7401452, additional_info={'duration': 1.3049898147583008, 'num_run': 155, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=155, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.36045425773980855, time=2.1822259426116943, status=<StatusType.SUCCESS: 1>, starttime=1730131785.5588791, endtime=1730131787.7773926, additional_info={'duration': 2.081044912338257, 'num_run': 156, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=156, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.32896804506020877, time=1.4457166194915771, status=<StatusType.SUCCESS: 1>, starttime=1730131787.118862, endtime=1730131788.6128778, additional_info={'duration': 1.3165547847747803, 'num_run': 157, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=157, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7557904946343927, time=15.718546867370605, status=<StatusType.SUCCESS: 1>, starttime=1730131789.004647, endtime=1730131804.824109, additional_info={'duration': 14.708069324493408, 'num_run': 158, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=158, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3555106623512349, time=0.9525880813598633, status=<StatusType.SUCCESS: 1>, starttime=1730131791.0689344, endtime=1730131792.0696423, additional_info={'duration': 0.875683069229126, 'num_run': 159, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=159, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.350278988989521, time=1.225189447402954, status=<StatusType.SUCCESS: 1>, starttime=1730131793.463095, endtime=1730131794.7334816, additional_info={'duration': 1.1469087600708008, 'num_run': 160, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=160, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7773499164782721, time=34.62732458114624, status=<StatusType.SUCCESS: 1>, starttime=1730131793.7061024, endtime=1730131828.3745034, additional_info={'duration': 34.37496018409729, 'num_run': 161, 'train_loss': 0.6830352965819368, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=161, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3149232499221708, time=1.458460807800293, status=<StatusType.SUCCESS: 1>, starttime=1730131795.7679222, endtime=1730131797.2720563, additional_info={'duration': 1.365891695022583, 'num_run': 162, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=162, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3605580226029409, time=1.4822444915771484, status=<StatusType.SUCCESS: 1>, starttime=1730131797.263027, endtime=1730131798.7920086, additional_info={'duration': 1.3105244636535645, 'num_run': 163, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=163, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0001732187736931, time=1.1832163333892822, status=<StatusType.SUCCESS: 1>, starttime=1730131797.4111514, endtime=1730131798.6383643, additional_info={'duration': 1.0568170547485352, 'num_run': 164, 'train_loss': 0.998199985078662, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=164, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9080339081172809, time=1.3425233364105225, status=<StatusType.SUCCESS: 1>, starttime=1730131797.6679642, endtime=1730131799.048245, additional_info={'duration': 1.20339035987854, 'num_run': 165, 'train_loss': 0.7329349118742715, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=165, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8657775748488843, time=1.3652803897857666, status=<StatusType.SUCCESS: 1>, starttime=1730131797.8428013, endtime=1730131799.2556138, additional_info={'duration': 1.2059636116027832, 'num_run': 166, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=166, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.36729745125633145, time=1.606764554977417, status=<StatusType.SUCCESS: 1>, starttime=1730131799.6127572, endtime=1730131801.2603743, additional_info={'duration': 1.526404857635498, 'num_run': 167, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=167, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9803532362521936, time=1.9085288047790527, status=<StatusType.SUCCESS: 1>, starttime=1730131799.900318, endtime=1730131801.8419225, additional_info={'duration': 1.789245367050171, 'num_run': 168, 'train_loss': 0.9670098069742993, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=168, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.1576929180527593, time=1.1589293479919434, status=<StatusType.SUCCESS: 1>, starttime=1730131799.981986, endtime=1730131801.1905067, additional_info={'duration': 1.0380113124847412, 'num_run': 169, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=169, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6219431774852988, time=5.355991840362549, status=<StatusType.SUCCESS: 1>, starttime=1730131800.229316, endtime=1730131805.7002013, additional_info={'duration': 5.105924367904663, 'num_run': 170, 'train_loss': 0.5402635134965148, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=170, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3182752892463827, time=1.3164002895355225, status=<StatusType.SUCCESS: 1>, starttime=1730131801.6100843, endtime=1730131802.9648883, additional_info={'duration': 1.2296464443206787, 'num_run': 171, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=171, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.34024436857651774, time=1.0994384288787842, status=<StatusType.SUCCESS: 1>, starttime=1730131803.439818, endtime=1730131804.575299, additional_info={'duration': 1.0247983932495117, 'num_run': 172, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=172, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3205660271058399, time=1.5240707397460938, status=<StatusType.SUCCESS: 1>, starttime=1730131804.9449823, endtime=1730131806.5194576, additional_info={'duration': 1.4043922424316406, 'num_run': 173, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=173, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.29976630210876465, status=<StatusType.CRASHED: 3>, starttime=1730131805.2656817, endtime=1730131805.5714412, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=174, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.37717789725541123, time=0.9905009269714355, status=<StatusType.SUCCESS: 1>, starttime=1730131806.773599, endtime=1730131807.8013165, additional_info={'duration': 0.906158447265625, 'num_run': 175, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=175, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9813988695742026, time=3.020604133605957, status=<StatusType.SUCCESS: 1>, starttime=1730131807.0598297, endtime=1730131810.1224868, additional_info={'duration': 2.925626039505005, 'num_run': 176, 'train_loss': 0.9757642190324234, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=176, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3928024260001242, time=1.2207324504852295, status=<StatusType.SUCCESS: 1>, starttime=1730131808.921212, endtime=1730131810.1839902, additional_info={'duration': 1.1348097324371338, 'num_run': 177, 'train_loss': 0.20841277475808934, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=177, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.36545372009277344, status=<StatusType.CRASHED: 3>, starttime=1730131809.2051651, endtime=1730131809.6083858, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=178, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.3246936798095703, status=<StatusType.CRASHED: 3>, starttime=1730131809.3740797, endtime=1730131809.7356646, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=179, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.359809604329501, time=1.3923826217651367, status=<StatusType.SUCCESS: 1>, starttime=1730131811.2845578, endtime=1730131812.7206135, additional_info={'duration': 1.3073999881744385, 'num_run': 180, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=180, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7320385473071808, time=26.037713527679443, status=<StatusType.SUCCESS: 1>, starttime=1730131811.5678284, endtime=1730131837.6493611, additional_info={'duration': 25.73993754386902, 'num_run': 181, 'train_loss': 0.6765307421957875, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=181, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5015431208822216, time=3.533672332763672, status=<StatusType.SUCCESS: 1>, starttime=1730131813.7641666, endtime=1730131817.3457582, additional_info={'duration': 3.4399847984313965, 'num_run': 182, 'train_loss': 0.4110809156241686, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=182, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3183134449581656, time=1.379807472229004, status=<StatusType.SUCCESS: 1>, starttime=1730131815.584572, endtime=1730131817.0074713, additional_info={'duration': 1.2877781391143799, 'num_run': 183, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=183, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4924083605410808, time=6.800521612167358, status=<StatusType.SUCCESS: 1>, starttime=1730131817.712536, endtime=1730131824.5510015, additional_info={'duration': 6.700932264328003, 'num_run': 184, 'train_loss': 0.3937151123008292, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=184, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3707321412112434, time=1.330477237701416, status=<StatusType.SUCCESS: 1>, starttime=1730131817.9569483, endtime=1730131819.3302746, additional_info={'duration': 1.1908395290374756, 'num_run': 185, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=185, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.45606255531311035, status=<StatusType.CRASHED: 3>, starttime=1730131818.2056537, endtime=1730131818.6934965, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=186, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9564483328750459, time=34.22235155105591, status=<StatusType.SUCCESS: 1>, starttime=1730131818.3807132, endtime=1730131852.6627433, additional_info={'duration': 33.86446833610535, 'num_run': 187, 'train_loss': 0.8362151613045026, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=187, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.3486809730529785, status=<StatusType.CRASHED: 3>, starttime=1730131818.6612847, endtime=1730131819.0643554, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=188, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.33437697000824734, time=1.3872668743133545, status=<StatusType.SUCCESS: 1>, starttime=1730131820.783915, endtime=1730131822.2142165, additional_info={'duration': 1.2985827922821045, 'num_run': 189, 'train_loss': 0.2671415034082147, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=189, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.447943271661034, time=1.1458027362823486, status=<StatusType.SUCCESS: 1>, starttime=1730131823.0874078, endtime=1730131824.2782147, additional_info={'duration': 1.066176176071167, 'num_run': 190, 'train_loss': 0.1695913857841436, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=190, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7307929019301611, time=0.9254248142242432, status=<StatusType.SUCCESS: 1>, starttime=1730131823.3843145, endtime=1730131824.3412762, additional_info={'duration': 0.8145327568054199, 'num_run': 191, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=191, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.35459998567683587, time=1.3274095058441162, status=<StatusType.SUCCESS: 1>, starttime=1730131824.8219078, endtime=1730131826.1956568, additional_info={'duration': 1.2376587390899658, 'num_run': 192, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=192, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6511615544794758, time=30.75944709777832, status=<StatusType.SUCCESS: 1>, starttime=1730131825.0755339, endtime=1730131855.9856865, additional_info={'duration': 30.33117938041687, 'num_run': 193, 'train_loss': 0.33180090487873615, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=193, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6527731066456371, time=728.0590753555298, status=<StatusType.SUCCESS: 1>, starttime=1730131825.3331914, endtime=1730132553.4957774, additional_info={'duration': 727.674266576767, 'num_run': 194, 'train_loss': 0.3624550999566125, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=194, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.36077022552490234, status=<StatusType.CRASHED: 3>, starttime=1730131825.5461164, endtime=1730131825.9784403, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=195, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3869889163540675, time=1.601184606552124, status=<StatusType.SUCCESS: 1>, starttime=1730131827.5239604, endtime=1730131829.1740875, additional_info={'duration': 1.518761157989502, 'num_run': 196, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=196, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.33259458568389066, time=1.3890926837921143, status=<StatusType.SUCCESS: 1>, starttime=1730131829.4047368, endtime=1730131830.8491879, additional_info={'duration': 1.2988650798797607, 'num_run': 197, 'train_loss': 0.2674548369410936, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=197, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4041766202465588, time=1.181731939315796, status=<StatusType.SUCCESS: 1>, starttime=1730131831.4224699, endtime=1730131832.6366458, additional_info={'duration': 1.0976834297180176, 'num_run': 198, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=198, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3594850881791011, time=1.4048616886138916, status=<StatusType.SUCCESS: 1>, starttime=1730131833.081794, endtime=1730131834.5372553, additional_info={'duration': 1.3141052722930908, 'num_run': 199, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=199, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3350718801906416, time=1.2802798748016357, status=<StatusType.SUCCESS: 1>, starttime=1730131834.7165065, endtime=1730131836.037438, additional_info={'duration': 1.1983006000518799, 'num_run': 200, 'train_loss': 0.267441335302897, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=200, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.45316997664162917, time=0.8828291893005371, status=<StatusType.SUCCESS: 1>, starttime=1730131836.3015935, endtime=1730131837.2189536, additional_info={'duration': 0.8016133308410645, 'num_run': 201, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=201, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4299440592943201, time=1654.3484153747559, status=<StatusType.SUCCESS: 1>, starttime=1730131837.818946, endtime=1730133492.3744147, additional_info={'duration': 1653.9716701507568, 'num_run': 202, 'train_loss': 0.2696555266289174, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=202, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.34102082194819217, time=1.4259395599365234, status=<StatusType.SUCCESS: 1>, starttime=1730131839.6498399, endtime=1730131841.4293745, additional_info={'duration': 1.2853069305419922, 'num_run': 203, 'train_loss': 0.2808050698621845, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=203, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3772901007566063, time=1.462477684020996, status=<StatusType.SUCCESS: 1>, starttime=1730131841.7062125, endtime=1730131843.2176447, additional_info={'duration': 1.3810358047485352, 'num_run': 204, 'train_loss': 0.3420319295151019, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=204, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.36004336381888363, time=0.9792375564575195, status=<StatusType.SUCCESS: 1>, starttime=1730131843.5229182, endtime=1730131844.550891, additional_info={'duration': 0.905242919921875, 'num_run': 205, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=205, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.31310606002807617, status=<StatusType.CRASHED: 3>, starttime=1730131845.1874094, endtime=1730131845.54179, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=206, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7654490731802479, time=1.0087249279022217, status=<StatusType.SUCCESS: 1>, starttime=1730131845.4086442, endtime=1730131846.461784, additional_info={'duration': 0.8745002746582031, 'num_run': 207, 'train_loss': 0.5958791839826011, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=207, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7028224765812844, time=7.572280168533325, status=<StatusType.SUCCESS: 1>, starttime=1730131845.6452525, endtime=1730131853.288799, additional_info={'duration': 7.338720321655273, 'num_run': 208, 'train_loss': 0.5892758845153349, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=208, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.3706212043762207, status=<StatusType.CRASHED: 3>, starttime=1730131845.8280196, endtime=1730131846.2694411, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=209, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.3098311424255371, status=<StatusType.CRASHED: 3>, starttime=1730131845.9885118, endtime=1730131846.3550375, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=210, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3149232499221708, time=1.342559814453125, status=<StatusType.SUCCESS: 1>, starttime=1730131847.3869426, endtime=1730131848.781184, additional_info={'duration': 1.2501163482666016, 'num_run': 211, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=211, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7042183528259331, time=8.422430515289307, status=<StatusType.SUCCESS: 1>, starttime=1730131847.593972, endtime=1730131856.0631416, additional_info={'duration': 8.117490768432617, 'num_run': 212, 'train_loss': 0.6365644956062586, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=212, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.004267114008022, time=0.8479533195495605, status=<StatusType.SUCCESS: 1>, starttime=1730131847.820823, endtime=1730131848.6946797, additional_info={'duration': 0.7749865055084229, 'num_run': 213, 'train_loss': 0.989333131387137, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=213, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7536562947184734, time=14.213295936584473, status=<StatusType.SUCCESS: 1>, starttime=1730131848.0374298, endtime=1730131862.3053246, additional_info={'duration': 13.884350776672363, 'num_run': 214, 'train_loss': 0.6970599505209598, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=214, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8037471303809882, time=119.88613939285278, status=<StatusType.SUCCESS: 1>, starttime=1730131850.327642, endtime=1730131970.2241418, additional_info={'duration': 118.98648595809937, 'num_run': 215, 'train_loss': 0.7192343713948216, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=215, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3161497973386622, time=1.009894847869873, status=<StatusType.SUCCESS: 1>, starttime=1730131852.323499, endtime=1730131853.3647091, additional_info={'duration': 0.925438404083252, 'num_run': 216, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=216, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4211853014249066, time=1.3815038204193115, status=<StatusType.SUCCESS: 1>, starttime=1730131853.966239, endtime=1730131855.3906713, additional_info={'duration': 1.2883319854736328, 'num_run': 217, 'train_loss': 2.1886048529040637e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=217, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3492753437150838, time=1.3256211280822754, status=<StatusType.SUCCESS: 1>, starttime=1730131855.816757, endtime=1730131857.1893084, additional_info={'duration': 1.2398877143859863, 'num_run': 218, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=218, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.35478440725168453, time=2.2278575897216797, status=<StatusType.SUCCESS: 1>, starttime=1730131856.0414832, endtime=1730131858.4157395, additional_info={'duration': 2.052393674850464, 'num_run': 219, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=219, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3278307114963227, time=1.6898539066314697, status=<StatusType.SUCCESS: 1>, starttime=1730131857.7440317, endtime=1730131859.4504855, additional_info={'duration': 1.3186616897583008, 'num_run': 220, 'train_loss': 0.24962222799510206, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=220, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.48766989443019076, time=3.284461259841919, status=<StatusType.SUCCESS: 1>, starttime=1730131858.020326, endtime=1730131861.3343992, additional_info={'duration': 3.127558708190918, 'num_run': 221, 'train_loss': 0.40390300514276867, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=221, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4587967899831781, time=1.4559717178344727, status=<StatusType.SUCCESS: 1>, starttime=1730131858.2691066, endtime=1730131859.7699697, additional_info={'duration': 1.369997501373291, 'num_run': 222, 'train_loss': 0.17129055717403496, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=222, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5414213248918279, time=4.648283004760742, status=<StatusType.SUCCESS: 1>, starttime=1730131858.510806, endtime=1730131863.202345, additional_info={'duration': 4.572124004364014, 'num_run': 223, 'train_loss': 4.2311969693309416e-05, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=223, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9803532362521936, time=1.9395606517791748, status=<StatusType.SUCCESS: 1>, starttime=1730131858.8066914, endtime=1730131860.7936068, additional_info={'duration': 1.7671236991882324, 'num_run': 224, 'train_loss': 0.9670098069742993, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=224, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.31751481931817177, time=1.5308833122253418, status=<StatusType.SUCCESS: 1>, starttime=1730131861.0347366, endtime=1730131862.6057796, additional_info={'duration': 1.4400601387023926, 'num_run': 225, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=225, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3312353325537264, time=1.2181103229522705, status=<StatusType.SUCCESS: 1>, starttime=1730131862.8917522, endtime=1730131864.1506133, additional_info={'duration': 1.138171672821045, 'num_run': 226, 'train_loss': 0.258836047659215, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=226, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4638814873988157, time=1.3744511604309082, status=<StatusType.SUCCESS: 1>, starttime=1730131864.9184217, endtime=1730131866.3236961, additional_info={'duration': 1.277395248413086, 'num_run': 227, 'train_loss': 0.4076435293074707, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=227, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.25831086901693584, time=163.2507848739624, status=<StatusType.SUCCESS: 1>, starttime=1730131865.2676938, endtime=1730132028.5320501, additional_info={'duration': 162.91316556930542, 'num_run': 228, 'train_loss': 0.20171303191880227, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=228, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0016318427050421, time=1.022467851638794, status=<StatusType.SUCCESS: 1>, starttime=1730131865.5099275, endtime=1730131866.5470796, additional_info={'duration': 0.8670694828033447, 'num_run': 229, 'train_loss': 0.9986677092923107, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=229, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9371683504440271, time=1.3918914794921875, status=<StatusType.SUCCESS: 1>, starttime=1730131865.7892904, endtime=1730131867.2246165, additional_info={'duration': 1.3002238273620605, 'num_run': 230, 'train_loss': 0.5463455491991316, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=230, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5583476419721128, time=1.0714895725250244, status=<StatusType.SUCCESS: 1>, starttime=1730131866.0515726, endtime=1730131867.1568205, additional_info={'duration': 0.9911458492279053, 'num_run': 231, 'train_loss': 0.4353664591641825, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=231, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.686183107525539, time=19.75433850288391, status=<StatusType.SUCCESS: 1>, starttime=1730131866.3763149, endtime=1730131886.1539187, additional_info={'duration': 19.58475613594055, 'num_run': 232, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=232, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5657875189547009, time=15.882946252822876, status=<StatusType.SUCCESS: 1>, starttime=1730131866.5959566, endtime=1730131882.5192401, additional_info={'duration': 15.775931119918823, 'num_run': 233, 'train_loss': 0.5248127639057425, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=233, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3563895171286582, time=1.3310484886169434, status=<StatusType.SUCCESS: 1>, starttime=1730131868.2409751, endtime=1730131869.61829, additional_info={'duration': 1.241645097732544, 'num_run': 234, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=234, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.40207172042882466, time=23.59189748764038, status=<StatusType.SUCCESS: 1>, starttime=1730131870.7726028, endtime=1730131894.3983366, additional_info={'duration': 23.16693639755249, 'num_run': 235, 'train_loss': 0.07133130532289433, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=235, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3159054528179776, time=1.1993284225463867, status=<StatusType.SUCCESS: 1>, starttime=1730131872.588836, endtime=1730131873.8244593, additional_info={'duration': 1.1166725158691406, 'num_run': 236, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=236, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.2121279371376601, time=93.92264795303345, status=<StatusType.SUCCESS: 1>, starttime=1730131872.7958064, endtime=1730131966.724191, additional_info={'duration': 92.93996596336365, 'num_run': 237, 'train_loss': 0.1138347240599171, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=237, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.41386789995169393, time=6.2552266120910645, status=<StatusType.SUCCESS: 1>, starttime=1730131874.3319714, endtime=1730131880.633158, additional_info={'duration': 5.95781946182251, 'num_run': 238, 'train_loss': 0.21300633951095604, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=238, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0012444331830286, time=0.92645263671875, status=<StatusType.SUCCESS: 1>, starttime=1730131874.5901644, endtime=1730131875.5658386, additional_info={'duration': 0.8478436470031738, 'num_run': 239, 'train_loss': 0.9980408284718505, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=239, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.32069958217102895, time=1.5597310066223145, status=<StatusType.SUCCESS: 1>, starttime=1730131876.4555554, endtime=1730131878.0680022, additional_info={'duration': 1.4604706764221191, 'num_run': 240, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=240, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4189466226687297, time=1.741593837738037, status=<StatusType.SUCCESS: 1>, starttime=1730131876.8191626, endtime=1730131878.6897933, additional_info={'duration': 1.659219741821289, 'num_run': 241, 'train_loss': 0.21864388440541246, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=241, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7860912749352983, time=44.7621009349823, status=<StatusType.SUCCESS: 1>, starttime=1730131877.0826404, endtime=1730131921.963508, additional_info={'duration': 44.43219470977783, 'num_run': 242, 'train_loss': 0.7191442379426154, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=242, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.45238093327993245, time=1.2754547595977783, status=<StatusType.SUCCESS: 1>, starttime=1730131878.7426214, endtime=1730131880.0588305, additional_info={'duration': 1.1931695938110352, 'num_run': 243, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=243, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7960393854974478, time=1860.725204706192, status=<StatusType.SUCCESS: 1>, starttime=1730131879.053908, endtime=1730133739.950147, additional_info={'duration': 1860.3978643417358, 'num_run': 244, 'train_loss': 0.6067691744408081, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=244, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3303301421266633, time=1.6561522483825684, status=<StatusType.SUCCESS: 1>, starttime=1730131881.552768, endtime=1730131883.2584398, additional_info={'duration': 1.5420434474945068, 'num_run': 245, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=245, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3205968437631332, time=1.5467026233673096, status=<StatusType.SUCCESS: 1>, starttime=1730131883.703936, endtime=1730131885.2909012, additional_info={'duration': 1.4504671096801758, 'num_run': 246, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=246, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.37040426976322166, time=2.433030605316162, status=<StatusType.SUCCESS: 1>, starttime=1730131885.788361, endtime=1730131888.2705283, additional_info={'duration': 2.3308074474334717, 'num_run': 247, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=247, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.37022388437264003, time=1.076587438583374, status=<StatusType.SUCCESS: 1>, starttime=1730131887.7018251, endtime=1730131888.8205929, additional_info={'duration': 0.989586591720581, 'num_run': 248, 'train_loss': 2.1948221018419645e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=248, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.34152892125680284, time=1.5290718078613281, status=<StatusType.SUCCESS: 1>, starttime=1730131889.444896, endtime=1730131891.0723536, additional_info={'duration': 1.4474949836730957, 'num_run': 249, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=249, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0013303416090253, time=0.7400896549224854, status=<StatusType.SUCCESS: 1>, starttime=1730131889.9014065, endtime=1730131890.673045, additional_info={'duration': 0.6630969047546387, 'num_run': 250, 'train_loss': 0.9992118255247728, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=250, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.37258637126938177, time=1114.3014435768127, status=<StatusType.SUCCESS: 1>, starttime=1730131892.2295969, endtime=1730133006.6285765, additional_info={'duration': 1113.5573313236237, 'num_run': 251, 'train_loss': 0.1928398656370808, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=251, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.32924894504681645, time=1.377711296081543, status=<StatusType.SUCCESS: 1>, starttime=1730131894.216253, endtime=1730131895.6367526, additional_info={'duration': 1.2902991771697998, 'num_run': 252, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=252, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.4302093982696533, status=<StatusType.CRASHED: 3>, starttime=1730131894.4612253, endtime=1730131894.9069548, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=253, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5396561547713978, time=10.54937195777893, status=<StatusType.SUCCESS: 1>, starttime=1730131894.7453508, endtime=1730131905.3543324, additional_info={'duration': 10.251718997955322, 'num_run': 254, 'train_loss': 0.3393901210915615, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=254, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7140103679424827, time=25.520652055740356, status=<StatusType.SUCCESS: 1>, starttime=1730131896.9265916, endtime=1730131922.5074897, additional_info={'duration': 25.216304540634155, 'num_run': 255, 'train_loss': 0.3594282719194681, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=255, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.44314802624830263, time=21.032240629196167, status=<StatusType.SUCCESS: 1>, starttime=1730131899.176924, endtime=1730131920.225764, additional_info={'duration': 20.70920419692993, 'num_run': 256, 'train_loss': 0.23464547959145454, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=256, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8824142686144267, time=56.25225615501404, status=<StatusType.SUCCESS: 1>, starttime=1730131899.3999658, endtime=1730131956.6754615, additional_info={'duration': 55.87198352813721, 'num_run': 257, 'train_loss': 0.6095972075367443, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=257, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3365432176301056, time=1.6415557861328125, status=<StatusType.SUCCESS: 1>, starttime=1730131901.355536, endtime=1730131903.0486848, additional_info={'duration': 1.5517933368682861, 'num_run': 258, 'train_loss': 0.2701544242476589, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=258, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.5298819541931152, status=<StatusType.CRASHED: 3>, starttime=1730131901.6111152, endtime=1730131902.189589, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=259, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3207967301654252, time=1.5525484085083008, status=<StatusType.SUCCESS: 1>, starttime=1730131904.1899154, endtime=1730131905.7650704, additional_info={'duration': 1.4426751136779785, 'num_run': 260, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=260, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.359809604329501, time=1.1905367374420166, status=<StatusType.SUCCESS: 1>, starttime=1730131906.2629867, endtime=1730131907.4853306, additional_info={'duration': 1.1028881072998047, 'num_run': 261, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=261, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4479271600999669, time=5.547640085220337, status=<StatusType.SUCCESS: 1>, starttime=1730131906.6063097, endtime=1730131912.1830847, additional_info={'duration': 5.435950517654419, 'num_run': 262, 'train_loss': 0.40145548631629974, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=262, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.31797883630482965, time=1.5728507041931152, status=<StatusType.SUCCESS: 1>, starttime=1730131908.8019338, endtime=1730131910.4498348, additional_info={'duration': 1.4830069541931152, 'num_run': 263, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=263, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9803532362521936, time=1.8578603267669678, status=<StatusType.SUCCESS: 1>, starttime=1730131909.1834686, endtime=1730131911.073069, additional_info={'duration': 1.7695667743682861, 'num_run': 264, 'train_loss': 0.9670098069742993, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=264, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3149232499221708, time=1.374859094619751, status=<StatusType.SUCCESS: 1>, starttime=1730131911.1804872, endtime=1730131912.5945938, additional_info={'duration': 1.2861504554748535, 'num_run': 265, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=265, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6054880632755083, time=1.6828515529632568, status=<StatusType.SUCCESS: 1>, starttime=1730131911.471244, endtime=1730131913.2061462, additional_info={'duration': 1.477212905883789, 'num_run': 266, 'train_loss': 6.025535626008604e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=266, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.49973329299584685, time=1.5074093341827393, status=<StatusType.SUCCESS: 1>, starttime=1730131911.7966964, endtime=1730131913.4091706, additional_info={'duration': 1.4266712665557861, 'num_run': 267, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=267, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4604835953595177, time=1.4807617664337158, status=<StatusType.SUCCESS: 1>, starttime=1730131912.0225196, endtime=1730131913.549122, additional_info={'duration': 1.2620720863342285, 'num_run': 268, 'train_loss': 0.17180336253847645, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=268, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.36197308088058394, time=0.9769752025604248, status=<StatusType.SUCCESS: 1>, starttime=1730131914.086366, endtime=1730131915.0999422, additional_info={'duration': 0.8989813327789307, 'num_run': 269, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=269, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.37703375891008295, time=1.5070860385894775, status=<StatusType.SUCCESS: 1>, starttime=1730131916.0123847, endtime=1730131917.5342262, additional_info={'duration': 1.30503249168396, 'num_run': 270, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=270, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0073202694200236, time=1.1338741779327393, status=<StatusType.SUCCESS: 1>, starttime=1730131916.740227, endtime=1730131917.906069, additional_info={'duration': 0.9515612125396729, 'num_run': 271, 'train_loss': 0.9904069439450209, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=271, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8356061190930459, time=9.583913087844849, status=<StatusType.SUCCESS: 1>, starttime=1730131917.0134106, endtime=1730131926.650406, additional_info={'duration': 9.308004140853882, 'num_run': 272, 'train_loss': 0.7098543462788951, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=272, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6514748629448057, time=315.82280802726746, status=<StatusType.SUCCESS: 1>, starttime=1730131917.3458266, endtime=1730132233.2757432, additional_info={'duration': 315.3277521133423, 'num_run': 273, 'train_loss': 0.36526429115089604, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=273, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.110941886902, status=<StatusType.TIMEOUT: 2>, starttime=1730131917.6953697, endtime=1730137317.859717, additional_info={'error': 'Timeout', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=274, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3402582260853708, time=1834.0495681762695, status=<StatusType.SUCCESS: 1>, starttime=1730131919.7435594, endtime=1730133753.80431, additional_info={'duration': 1830.824991941452, 'num_run': 275, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=275, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.5205740928649902, status=<StatusType.CRASHED: 3>, starttime=1730131920.091402, endtime=1730131920.6180367, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=276, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.45820749969599955, time=2.8493223190307617, status=<StatusType.SUCCESS: 1>, starttime=1730131920.3665593, endtime=1730131923.2504144, additional_info={'duration': 2.7441446781158447, 'num_run': 277, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=277, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8661705894240238, time=1.229705572128296, status=<StatusType.SUCCESS: 1>, starttime=1730131920.7238266, endtime=1730131921.9820857, additional_info={'duration': 1.064617395401001, 'num_run': 278, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=278, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.5542585849761963, status=<StatusType.CRASHED: 3>, starttime=1730131921.0141885, endtime=1730131921.627939, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=279, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0015584294965003, time=1.124192476272583, status=<StatusType.SUCCESS: 1>, starttime=1730131921.2756245, endtime=1730131922.5564036, additional_info={'duration': 0.9528722763061523, 'num_run': 280, 'train_loss': 0.9973455297816861, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=280, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4370292079670304, time=2175.552343606949, status=<StatusType.SUCCESS: 1>, starttime=1730131923.2967565, endtime=1730134099.083421, additional_info={'duration': 2175.1446130275726, 'num_run': 281, 'train_loss': 0.16338932341575593, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=281, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.45489697972731036, time=2114.0866091251373, status=<StatusType.SUCCESS: 1>, starttime=1730131925.6840458, endtime=1730134039.8765879, additional_info={'duration': 2113.744130373001, 'num_run': 282, 'train_loss': 0.2074745827657071, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=282, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4847041264832738, time=15.13670539855957, status=<StatusType.SUCCESS: 1>, starttime=1730131927.8063633, endtime=1730131942.9483328, additional_info={'duration': 14.572242975234985, 'num_run': 283, 'train_loss': 0.2869273632399871, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=283, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9423694453517811, time=1.3054149150848389, status=<StatusType.SUCCESS: 1>, starttime=1730131928.1235235, endtime=1730131929.4716916, additional_info={'duration': 1.0872173309326172, 'num_run': 284, 'train_loss': 0.7619189453486314, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=284, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3207967301654252, time=1.549448013305664, status=<StatusType.SUCCESS: 1>, starttime=1730131930.182313, endtime=1730131931.7837524, additional_info={'duration': 1.4607696533203125, 'num_run': 285, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=285, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4929100907751912, time=18.310147285461426, status=<StatusType.SUCCESS: 1>, starttime=1730131932.0711334, endtime=1730131950.3960965, additional_info={'duration': 17.3088321685791, 'num_run': 286, 'train_loss': 0.23294043565417075, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=286, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.39475794562960276, time=1.6280488967895508, status=<StatusType.SUCCESS: 1>, starttime=1730131933.9123425, endtime=1730131935.67955, additional_info={'duration': 1.50482177734375, 'num_run': 287, 'train_loss': 0.3646179515251827, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=287, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9276683340262778, time=6.829512357711792, status=<StatusType.SUCCESS: 1>, starttime=1730131934.4915447, endtime=1730131941.496262, additional_info={'duration': 6.407671689987183, 'num_run': 288, 'train_loss': 0.9102535848665728, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=288, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.36138392787744766, time=1.7331199645996094, status=<StatusType.SUCCESS: 1>, starttime=1730131934.7192097, endtime=1730131936.6037397, additional_info={'duration': 1.4636049270629883, 'num_run': 289, 'train_loss': 0.22159319354817827, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=289, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9343411504924665, time=70.80782914161682, status=<StatusType.SUCCESS: 1>, starttime=1730131934.7653036, endtime=1730132005.6558, additional_info={'duration': 70.15241360664368, 'num_run': 290, 'train_loss': 0.8516767433799047, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=290, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.831061731570588, time=395.0931634902954, status=<StatusType.SUCCESS: 1>, starttime=1730131935.2675972, endtime=1730132330.4358027, additional_info={'duration': 394.72222900390625, 'num_run': 291, 'train_loss': 0.5715355672738534, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=291, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3520450524160149, time=1.903475046157837, status=<StatusType.SUCCESS: 1>, starttime=1730131938.9255126, endtime=1730131940.8436286, additional_info={'duration': 1.5777699947357178, 'num_run': 292, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=292, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.43218578603026797, time=3.4392333030700684, status=<StatusType.SUCCESS: 1>, starttime=1730131939.505779, endtime=1730131942.9720228, additional_info={'duration': 3.2313644886016846, 'num_run': 293, 'train_loss': 0.4025269108072007, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=293, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4275524381161785, time=2119.3282158374786, status=<StatusType.SUCCESS: 1>, starttime=1730131943.286519, endtime=1730134062.787797, additional_info={'duration': 2118.780239343643, 'num_run': 294, 'train_loss': 0.08312396442686013, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=294, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7159761609963641, time=3.9633097648620605, status=<StatusType.SUCCESS: 1>, starttime=1730131943.6937232, endtime=1730131947.7958214, additional_info={'duration': 3.7190511226654053, 'num_run': 295, 'train_loss': 0.5706068589369522, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=295, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8414534982896552, time=11.295703887939453, status=<StatusType.SUCCESS: 1>, starttime=1730131943.9095416, endtime=1730131955.3037899, additional_info={'duration': 10.686607837677002, 'num_run': 296, 'train_loss': 0.775783882004085, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=296, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.39705490864769044, time=26.399755716323853, status=<StatusType.SUCCESS: 1>, starttime=1730131947.3333366, endtime=1730131973.7991102, additional_info={'duration': 25.6644024848938, 'num_run': 297, 'train_loss': 0.04237597431634017, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=297, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.28896116884719825, time=23.833491325378418, status=<StatusType.SUCCESS: 1>, starttime=1730131951.1799798, endtime=1730131975.040509, additional_info={'duration': 22.854469776153564, 'num_run': 298, 'train_loss': 0.040270841961851045, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=298, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3061995700642788, time=918.9119243621826, status=<StatusType.SUCCESS: 1>, starttime=1730131954.0425143, endtime=1730132873.3296597, additional_info={'duration': 915.9528684616089, 'num_run': 299, 'train_loss': 0.043233092945967395, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=299, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3530530328189507, time=9.166755676269531, status=<StatusType.SUCCESS: 1>, starttime=1730131957.534109, endtime=1730131966.763814, additional_info={'duration': 8.442517280578613, 'num_run': 300, 'train_loss': 0.16010221212630296, 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=300, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.6525440216064453, status=<StatusType.CRASHED: 3>, starttime=1730131957.872257, endtime=1730131958.5557702, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=301, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6037423813970162, time=2.6901798248291016, status=<StatusType.SUCCESS: 1>, starttime=1730131958.0919871, endtime=1730131960.8128417, additional_info={'duration': 2.238619089126587, 'num_run': 302, 'train_loss': 0.5695666897391877, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=302, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.33602287910373296, time=1.6280622482299805, status=<StatusType.SUCCESS: 1>, starttime=1730131958.5473685, endtime=1730131960.2041185, additional_info={'duration': 1.2635834217071533, 'num_run': 303, 'train_loss': 0.24183045471418774, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=303, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.157414197922, status=<StatusType.TIMEOUT: 2>, starttime=1730131962.5794978, endtime=1730137362.791789, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=304, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9938027930009553, time=11.707446098327637, status=<StatusType.SUCCESS: 1>, starttime=1730131963.0735354, endtime=1730131974.7843146, additional_info={'duration': 11.022143840789795, 'num_run': 305, 'train_loss': 0.9868919623448775, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=305, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.5984442234039307, status=<StatusType.CRASHED: 3>, starttime=1730131963.4742255, endtime=1730131964.0762448, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=306, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15938339242687102, time=77.05167317390442, status=<StatusType.SUCCESS: 1>, starttime=1730131966.8133502, endtime=1730132043.8876956, additional_info={'duration': 76.14293551445007, 'num_run': 307, 'train_loss': 0.02783318539732678, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=307, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4823955404992214, time=2.421067714691162, status=<StatusType.SUCCESS: 1>, starttime=1730131967.8396175, endtime=1730131970.2639825, additional_info={'duration': 1.9403407573699951, 'num_run': 308, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=308, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7683153410684832, time=1.839953899383545, status=<StatusType.SUCCESS: 1>, starttime=1730131968.2094314, endtime=1730131970.0602808, additional_info={'duration': 1.2664234638214111, 'num_run': 309, 'train_loss': 0.677509787127088, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=309, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16126115102739605, time=63.82725119590759, status=<StatusType.SUCCESS: 1>, starttime=1730131972.3437119, endtime=1730132036.2678185, additional_info={'duration': 62.87881398200989, 'num_run': 310, 'train_loss': 0.05410711253400176, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=310, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3132042479815008, time=17.423026084899902, status=<StatusType.SUCCESS: 1>, starttime=1730131975.6855752, endtime=1730131993.1880596, additional_info={'duration': 16.96508526802063, 'num_run': 311, 'train_loss': 0.11919505281296242, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=311, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7866063064734516, time=434.5774254798889, status=<StatusType.SUCCESS: 1>, starttime=1730131976.0431771, endtime=1730132410.6243606, additional_info={'duration': 433.3114094734192, 'num_run': 312, 'train_loss': 0.4939532766073023, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=312, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.19814277392852067, time=316.061158657074, status=<StatusType.SUCCESS: 1>, starttime=1730131979.2953382, endtime=1730132295.499796, additional_info={'duration': 315.40993309020996, 'num_run': 313, 'train_loss': 0.043245145970220264, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=313, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.33752942336559016, time=931.2359187602997, status=<StatusType.SUCCESS: 1>, starttime=1730131982.3991702, endtime=1730132913.794885, additional_info={'duration': 930.7667412757874, 'num_run': 314, 'train_loss': 0.14281146791328508, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=314, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.27311474528548985, time=28.051026582717896, status=<StatusType.SUCCESS: 1>, starttime=1730131986.0527692, endtime=1730132014.7437325, additional_info={'duration': 27.525790452957153, 'num_run': 315, 'train_loss': 0.22164625423766726, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=315, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7692852004705985, time=19.48932671546936, status=<StatusType.SUCCESS: 1>, starttime=1730131986.5195816, endtime=1730132006.08472, additional_info={'duration': 19.12547731399536, 'num_run': 316, 'train_loss': 0.6782656529613058, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=316, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6034026866728309, time=2.3728954792022705, status=<StatusType.SUCCESS: 1>, starttime=1730131986.9876354, endtime=1730131989.36416, additional_info={'duration': 1.9406421184539795, 'num_run': 317, 'train_loss': 0.5676747031200751, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=317, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6832079200711627, time=2.024501085281372, status=<StatusType.SUCCESS: 1>, starttime=1730131991.6122136, endtime=1730131993.640137, additional_info={'duration': 1.568882942199707, 'num_run': 318, 'train_loss': 0.4927692505918616, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=318, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9047728261871801, time=67.21804094314575, status=<StatusType.SUCCESS: 1>, starttime=1730131993.824608, endtime=1730132061.0800877, additional_info={'duration': 66.6051516532898, 'num_run': 319, 'train_loss': 0.3012322505927628, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=319, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3130767542271559, time=19.688048601150513, status=<StatusType.SUCCESS: 1>, starttime=1730131997.989096, endtime=1730132017.6878748, additional_info={'duration': 18.842084169387817, 'num_run': 320, 'train_loss': 0.11914831952369369, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=320, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.5576114654541016, status=<StatusType.CRASHED: 3>, starttime=1730132006.7315412, endtime=1730132007.3375633, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=321, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.44187016372937266, time=3.060021162033081, status=<StatusType.SUCCESS: 1>, starttime=1730132007.3754604, endtime=1730132010.5201151, additional_info={'duration': 2.7558815479278564, 'num_run': 322, 'train_loss': 0.22912617206105107, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=322, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4286465048251804, time=14.050910949707031, status=<StatusType.SUCCESS: 1>, starttime=1730132011.221999, endtime=1730132025.3357391, additional_info={'duration': 13.576942920684814, 'num_run': 323, 'train_loss': 0.3407747968154884, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=323, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9935047992552688, time=2.1735503673553467, status=<StatusType.SUCCESS: 1>, starttime=1730132011.6434786, endtime=1730132013.8202078, additional_info={'duration': 1.639190435409546, 'num_run': 324, 'train_loss': 0.9897464125968682, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=324, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.7522525787353516, status=<StatusType.CRASHED: 3>, starttime=1730132015.824635, endtime=1730132016.5801816, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=325, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.118959903717, status=<StatusType.TIMEOUT: 2>, starttime=1730132018.6259716, endtime=1730137418.8077435, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=326, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3441781963772232, time=127.19581627845764, status=<StatusType.SUCCESS: 1>, starttime=1730132021.404946, endtime=1730132148.679794, additional_info={'duration': 126.66058039665222, 'num_run': 327, 'train_loss': 0.23001959492815763, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=327, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6879261625987799, time=130.73681497573853, status=<StatusType.SUCCESS: 1>, starttime=1730132024.6724744, endtime=1730132155.5310814, additional_info={'duration': 130.14137530326843, 'num_run': 328, 'train_loss': 0.2874338692690565, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=328, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4309944127516915, time=11.713550329208374, status=<StatusType.SUCCESS: 1>, starttime=1730132028.7754843, endtime=1730132040.551779, additional_info={'duration': 11.286941051483154, 'num_run': 329, 'train_loss': 0.32432760581688946, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=329, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3569698102839968, time=216.20728254318237, status=<StatusType.SUCCESS: 1>, starttime=1730132032.5754852, endtime=1730132248.8531485, additional_info={'duration': 215.5087444782257, 'num_run': 330, 'train_loss': 0.14369090796154022, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=330, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.21304145889814965, time=193.62156915664673, status=<StatusType.SUCCESS: 1>, starttime=1730132039.0834076, endtime=1730132232.7558956, additional_info={'duration': 193.19741678237915, 'num_run': 331, 'train_loss': 0.1405520244080779, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=331, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3277218359561854, time=110.81938290596008, status=<StatusType.SUCCESS: 1>, starttime=1730132043.701713, endtime=1730132154.6758041, additional_info={'duration': 109.6586766242981, 'num_run': 332, 'train_loss': 0.045046275236703355, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=332, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.5078384876251221, status=<StatusType.CRASHED: 3>, starttime=1730132046.7453988, endtime=1730132047.6479576, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search (sorted)'})\n",
+      "RunKey(config_id=333, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8911942742175478, time=11.570662498474121, status=<StatusType.SUCCESS: 1>, starttime=1730132047.239522, endtime=1730132058.8917723, additional_info={'duration': 10.931766986846924, 'num_run': 334, 'train_loss': 0.7893716623380219, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=334, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=1.013770341873169, status=<StatusType.CRASHED: 3>, starttime=1730132050.3630679, endtime=1730132051.380443, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=335, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.20970886879980305, time=238.7375452518463, status=<StatusType.SUCCESS: 1>, starttime=1730132055.3754888, endtime=1730132294.1917799, additional_info={'duration': 238.10551166534424, 'num_run': 336, 'train_loss': 0.126492224837386, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=336, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.6253163814544678, status=<StatusType.CRASHED: 3>, starttime=1730132059.7634854, endtime=1730132060.4597497, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=337, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.131689071655, status=<StatusType.TIMEOUT: 2>, starttime=1730132065.733428, endtime=1730137466.1397095, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=338, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.265374422073, status=<StatusType.TIMEOUT: 2>, starttime=1730132068.911535, endtime=1730137469.3916523, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=339, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1665307216625357, time=4681.019154548645, status=<StatusType.SUCCESS: 1>, starttime=1730132072.311484, endtime=1730136753.4279532, additional_info={'duration': 4679.865807056427, 'num_run': 340, 'train_loss': 0.04387887128658525, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=340, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.204829454422, status=<StatusType.TIMEOUT: 2>, starttime=1730132152.053321, endtime=1730137552.4448996, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=341, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.208717823029, status=<StatusType.TIMEOUT: 2>, starttime=1730132158.4020493, endtime=1730137558.675693, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=342, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.115519762039, status=<StatusType.TIMEOUT: 2>, starttime=1730132161.9979885, endtime=1730137562.198608, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=343, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.1857080459595, status=<StatusType.TIMEOUT: 2>, starttime=1730132181.3154995, endtime=1730137581.5517025, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=344, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.5811686515808105, status=<StatusType.CRASHED: 3>, starttime=1730132181.9043024, endtime=1730132182.533445, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=345, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.40252011922175207, time=3676.329810857773, status=<StatusType.SUCCESS: 1>, starttime=1730132186.3435018, endtime=1730135862.7557662, additional_info={'duration': 3675.846122264862, 'num_run': 346, 'train_loss': 0.3537516183221934, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=346, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.686494911299176, time=1.9038326740264893, status=<StatusType.SUCCESS: 1>, starttime=1730132233.3251626, endtime=1730132235.4037633, additional_info={'duration': 1.6707735061645508, 'num_run': 347, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=347, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3765594511696565, time=69.73648357391357, status=<StatusType.SUCCESS: 1>, starttime=1730132236.2114902, endtime=1730132306.219732, additional_info={'duration': 69.21348142623901, 'num_run': 348, 'train_loss': 0.22704480252668857, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=348, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5904298144010436, time=2.249669075012207, status=<StatusType.SUCCESS: 1>, starttime=1730132236.7434764, endtime=1730132239.0359912, additional_info={'duration': 2.015343427658081, 'num_run': 349, 'train_loss': 0.5682945862871303, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=349, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7909985980632308, time=1355.3694984912872, status=<StatusType.SUCCESS: 1>, starttime=1730132240.9875035, endtime=1730133596.4637432, additional_info={'duration': 1354.9082868099213, 'num_run': 350, 'train_loss': 0.6315541138984483, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=350, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5783465514537269, time=729.3973531723022, status=<StatusType.SUCCESS: 1>, starttime=1730132249.46749, endtime=1730132978.9918845, additional_info={'duration': 728.6102070808411, 'num_run': 351, 'train_loss': 0.17499879556457504, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=351, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6982335919994478, time=1.574798583984375, status=<StatusType.SUCCESS: 1>, starttime=1730132276.1024103, endtime=1730132277.7357855, additional_info={'duration': 1.2490651607513428, 'num_run': 352, 'train_loss': 0.5069890425402226, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=352, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.2377898693085, status=<StatusType.TIMEOUT: 2>, starttime=1730132282.0231726, endtime=1730137682.2786617, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=353, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.23837719377078667, time=54.02158188819885, status=<StatusType.SUCCESS: 1>, starttime=1730132297.4674885, endtime=1730132351.5718517, additional_info={'duration': 53.58997082710266, 'num_run': 354, 'train_loss': 0.17996267194133184, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=354, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0012120555697295, time=1.793562650680542, status=<StatusType.SUCCESS: 1>, starttime=1730132298.347495, endtime=1730132300.1797693, additional_info={'duration': 1.180654525756836, 'num_run': 355, 'train_loss': 0.9992870126405125, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=355, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4206694490075003, time=4058.694053888321, status=<StatusType.SUCCESS: 1>, starttime=1730132305.3349447, endtime=1730136364.0719259, additional_info={'duration': 4058.2528207302094, 'num_run': 356, 'train_loss': 0.37175497219210596, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=356, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.5289130210876465, status=<StatusType.CRASHED: 3>, starttime=1730132309.2439244, endtime=1730132309.8118057, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=357, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.39402952889506593, time=579.8655083179474, status=<StatusType.SUCCESS: 1>, starttime=1730132314.1434906, endtime=1730132894.1008418, additional_info={'duration': 579.2639000415802, 'num_run': 358, 'train_loss': 0.2169491749296479, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=358, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7533236511538999, time=1.4560449123382568, status=<StatusType.SUCCESS: 1>, starttime=1730132331.447497, endtime=1730132332.9837718, additional_info={'duration': 1.2689554691314697, 'num_run': 359, 'train_loss': 0.4043712096864146, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=359, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4489432362600704, time=1.3655900955200195, status=<StatusType.SUCCESS: 1>, starttime=1730132333.7874808, endtime=1730132335.1997495, additional_info={'duration': 1.1326172351837158, 'num_run': 360, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=360, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.413734912872, status=<StatusType.TIMEOUT: 2>, starttime=1730132338.8153102, endtime=1730137739.328723, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=361, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9382552063283447, time=1421.363538980484, status=<StatusType.SUCCESS: 1>, starttime=1730132351.9095502, endtime=1730133773.5999427, additional_info={'duration': 1420.636057138443, 'num_run': 362, 'train_loss': 0.30542878351661185, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=362, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.797855487132373, time=47.76365828514099, status=<StatusType.SUCCESS: 1>, starttime=1730132411.5155964, endtime=1730132459.352827, additional_info={'duration': 46.90505003929138, 'num_run': 363, 'train_loss': 0.709390292999741, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=363, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.35721884257855707, time=645.6175630092621, status=<StatusType.SUCCESS: 1>, starttime=1730132464.2714715, endtime=1730133110.0219922, additional_info={'duration': 644.9226622581482, 'num_run': 364, 'train_loss': 0.13181233911523027, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=364, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.2913359613097147, time=586.031937122345, status=<StatusType.SUCCESS: 1>, starttime=1730132559.2738628, endtime=1730133145.4320304, additional_info={'duration': 585.6550934314728, 'num_run': 365, 'train_loss': 0.1636693002303054, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=365, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.19530719304727154, time=250.39286994934082, status=<StatusType.SUCCESS: 1>, starttime=1730132676.3598871, endtime=1730132926.8638186, additional_info={'duration': 249.8200159072876, 'num_run': 366, 'train_loss': 0.10445624053330684, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=366, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9273718743204271, time=9.557998418807983, status=<StatusType.SUCCESS: 1>, starttime=1730132874.0821383, endtime=1730132883.6718473, additional_info={'duration': 9.207713842391968, 'num_run': 367, 'train_loss': 0.9107226072094159, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=367, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6762373983553167, time=13.89174747467041, status=<StatusType.SUCCESS: 1>, starttime=1730132884.3749185, endtime=1730132898.327829, additional_info={'duration': 13.762458801269531, 'num_run': 368, 'train_loss': 0.6553836321301911, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=368, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7357030661388585, time=19.388594388961792, status=<StatusType.SUCCESS: 1>, starttime=1730132895.4243963, endtime=1730132915.0917375, additional_info={'duration': 18.65124273300171, 'num_run': 369, 'train_loss': 0.6722848433089915, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=369, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8454804463222142, time=498.3211908340454, status=<StatusType.SUCCESS: 1>, starttime=1730132899.8038666, endtime=1730133398.2157407, additional_info={'duration': 497.8835380077362, 'num_run': 370, 'train_loss': 0.5996161094996606, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=370, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.2058009547917269, time=344.17985248565674, status=<StatusType.SUCCESS: 1>, starttime=1730132918.655349, endtime=1730133263.1834955, additional_info={'duration': 343.35151863098145, 'num_run': 371, 'train_loss': 0.12065392844981387, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=371, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.2017645003425628, time=349.16762232780457, status=<StatusType.SUCCESS: 1>, starttime=1730132922.2733946, endtime=1730133271.6758316, additional_info={'duration': 348.4308648109436, 'num_run': 372, 'train_loss': 0.11181719636894025, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=372, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4641646278865734, time=1.6153697967529297, status=<StatusType.SUCCESS: 1>, starttime=1730132927.9856472, endtime=1730132929.651995, additional_info={'duration': 1.400460958480835, 'num_run': 373, 'train_loss': 0.44173068049916697, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=373, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6196224019586094, time=706.5670578479767, status=<StatusType.SUCCESS: 1>, starttime=1730132930.3314867, endtime=1730133637.0028243, additional_info={'duration': 705.7228212356567, 'num_run': 374, 'train_loss': 0.49900591987128834, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=374, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.17206459943896035, time=125.58693623542786, status=<StatusType.SUCCESS: 1>, starttime=1730132985.6154728, endtime=1730133111.2055795, additional_info={'duration': 124.69453644752502, 'num_run': 375, 'train_loss': 0.07135353071054942, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=375, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.655886936173172, time=299.849515914917, status=<StatusType.SUCCESS: 1>, starttime=1730133007.3275747, endtime=1730133307.2400112, additional_info={'duration': 299.04971742630005, 'num_run': 376, 'train_loss': 0.5207913356897711, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=376, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3020678977947301, time=191.78431606292725, status=<StatusType.SUCCESS: 1>, starttime=1730133069.2046068, endtime=1730133261.2998512, additional_info={'duration': 191.02623319625854, 'num_run': 377, 'train_loss': 0.14772176892239675, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=377, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.953509299044151, time=4.42298698425293, status=<StatusType.SUCCESS: 1>, starttime=1730133110.727568, endtime=1730133115.15359, additional_info={'duration': 4.155273914337158, 'num_run': 378, 'train_loss': 0.9235508278536041, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=378, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3605982018310038, time=2.4282970428466797, status=<StatusType.SUCCESS: 1>, starttime=1730133111.996726, endtime=1730133114.5126932, additional_info={'duration': 2.203995943069458, 'num_run': 379, 'train_loss': 0.21997406082607543, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=379, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8256656896607739, time=30.926647663116455, status=<StatusType.SUCCESS: 1>, starttime=1730133114.862421, endtime=1730133145.7921371, additional_info={'duration': 30.371176958084106, 'num_run': 380, 'train_loss': 0.4969489279293113, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=380, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.2531379710898052, time=205.273047208786, status=<StatusType.SUCCESS: 1>, starttime=1730133117.8199966, endtime=1730133323.2197459, additional_info={'duration': 204.80819964408875, 'num_run': 381, 'train_loss': 0.10748965642884603, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=381, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6819053632778811, time=607.0080580711365, status=<StatusType.SUCCESS: 1>, starttime=1730133145.885035, endtime=1730133752.9957945, additional_info={'duration': 606.0425209999084, 'num_run': 382, 'train_loss': 0.4695082700932002, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=382, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.459890127182, status=<StatusType.TIMEOUT: 2>, starttime=1730133146.7303574, endtime=1730138547.4517236, additional_info={'error': 'Timeout', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=383, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3789985002189631, time=3746.259877681732, status=<StatusType.SUCCESS: 1>, starttime=1730133265.4676166, endtime=1730137011.8519876, additional_info={'duration': 3745.77534866333, 'num_run': 384, 'train_loss': 0.3357399702945232, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=384, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0003425756718494, time=2.7251484394073486, status=<StatusType.SUCCESS: 1>, starttime=1730133266.347487, endtime=1730133269.0965173, additional_info={'duration': 2.078221321105957, 'num_run': 385, 'train_loss': 0.9919048271715719, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=385, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15982717824897727, time=114.01991558074951, status=<StatusType.SUCCESS: 1>, starttime=1730133276.000452, endtime=1730133390.199844, additional_info={'duration': 112.0861828327179, 'num_run': 386, 'train_loss': 0.02801726417207764, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=386, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.37178456802274135, time=2.357982873916626, status=<StatusType.SUCCESS: 1>, starttime=1730133276.7308102, endtime=1730133279.1757517, additional_info={'duration': 2.150465488433838, 'num_run': 387, 'train_loss': 0.31049214676741616, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=387, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6348552441000797, time=303.2173352241516, status=<StatusType.SUCCESS: 1>, starttime=1730133279.8277254, endtime=1730133583.0785964, additional_info={'duration': 302.642915725708, 'num_run': 388, 'train_loss': 0.5770313622083955, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=388, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.20362299019726518, time=398.354861497879, status=<StatusType.SUCCESS: 1>, starttime=1730133311.7978463, endtime=1730133710.247827, additional_info={'duration': 397.936984539032, 'num_run': 389, 'train_loss': 0.11495586879788336, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=389, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.623524608371618, time=1558.864863395691, status=<StatusType.SUCCESS: 1>, starttime=1730133327.8034883, endtime=1730134886.7718282, additional_info={'duration': 1557.653315782547, 'num_run': 390, 'train_loss': 0.4780802026816655, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=390, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.5454282760620117, status=<StatusType.CRASHED: 3>, starttime=1730133391.219612, endtime=1730133391.7681553, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=391, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4513518610715226, time=1620.2512142658234, status=<StatusType.SUCCESS: 1>, starttime=1730133392.5257602, endtime=1730135012.8518226, additional_info={'duration': 1619.9195449352264, 'num_run': 392, 'train_loss': 0.30999488880990844, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=392, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.141657590866, status=<StatusType.TIMEOUT: 2>, starttime=1730133403.2754242, endtime=1730138803.471818, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=393, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9803532362521936, time=5.386500597000122, status=<StatusType.SUCCESS: 1>, starttime=1730133493.0618613, endtime=1730133498.5358326, additional_info={'duration': 4.947210073471069, 'num_run': 394, 'train_loss': 0.9670098069742993, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=394, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0001167119859957, time=3.1733574867248535, status=<StatusType.SUCCESS: 1>, starttime=1730133499.695528, endtime=1730133502.9438376, additional_info={'duration': 2.92622709274292, 'num_run': 395, 'train_loss': 0.9985077335613654, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=395, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.43219493423756583, time=29.092735528945923, status=<StatusType.SUCCESS: 1>, starttime=1730133507.820238, endtime=1730133536.963308, additional_info={'duration': 28.27604913711548, 'num_run': 396, 'train_loss': 0.1329468234525592, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=396, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6017562396118755, time=381.3932065963745, status=<StatusType.SUCCESS: 1>, starttime=1730133537.5073793, endtime=1730133918.991222, additional_info={'duration': 380.87774300575256, 'num_run': 397, 'train_loss': 0.5136730910366407, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=397, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.158790676172633, time=99.67721438407898, status=<StatusType.SUCCESS: 1>, starttime=1730133588.8156173, endtime=1730133688.6557918, additional_info={'duration': 98.94565391540527, 'num_run': 398, 'train_loss': 0.02776565473353032, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=398, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6421212094397929, time=4.053883790969849, status=<StatusType.SUCCESS: 1>, starttime=1730133599.7121658, endtime=1730133603.8197773, additional_info={'duration': 3.878950595855713, 'num_run': 399, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=399, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.28471664898742427, time=1425.3717591762543, status=<StatusType.SUCCESS: 1>, starttime=1730133608.371492, endtime=1730135034.1038523, additional_info={'duration': 1425.040466785431, 'num_run': 400, 'train_loss': 0.2249984464943275, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=400, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3936777153104477, time=3437.262741088867, status=<StatusType.SUCCESS: 1>, starttime=1730133645.8314977, endtime=1730137083.1677854, additional_info={'duration': 3436.968278646469, 'num_run': 401, 'train_loss': 0.33895957822382244, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=401, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.3006246089935, status=<StatusType.TIMEOUT: 2>, starttime=1730133689.3734171, endtime=1730139089.9270334, additional_info={'error': 'Timeout', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=402, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.32286351647568956, time=85.32363700866699, status=<StatusType.SUCCESS: 1>, starttime=1730133714.0934389, endtime=1730133799.4880452, additional_info={'duration': 84.72856211662292, 'num_run': 403, 'train_loss': 0.16979508954179404, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=403, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.17731682431446183, time=410.6685299873352, status=<StatusType.SUCCESS: 1>, starttime=1730133743.3806622, endtime=1730134154.1520607, additional_info={'duration': 409.68050599098206, 'num_run': 404, 'train_loss': 0.043853646321030726, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=404, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8768902204274981, time=4.849644660949707, status=<StatusType.SUCCESS: 1>, starttime=1730133754.1434813, endtime=1730133759.155782, additional_info={'duration': 4.266313552856445, 'num_run': 405, 'train_loss': 0.4427702057574483, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=405, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.000894786481162, time=1.9657235145568848, status=<StatusType.SUCCESS: 1>, starttime=1730133754.8471818, endtime=1730133756.8957756, additional_info={'duration': 1.7244226932525635, 'num_run': 406, 'train_loss': 0.9990009596625067, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=406, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16243544572232932, time=4595.82125210762, status=<StatusType.SUCCESS: 1>, starttime=1730133769.2838764, endtime=1730138365.1838636, additional_info={'duration': 4594.53257060051, 'num_run': 407, 'train_loss': 0.02851085648083891, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=407, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9968636350586805, time=7.092851877212524, status=<StatusType.SUCCESS: 1>, starttime=1730133769.9994862, endtime=1730133777.09958, additional_info={'duration': 6.598189115524292, 'num_run': 408, 'train_loss': 0.9912580971881425, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=408, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.881257005026501, time=877.0000493526459, status=<StatusType.SUCCESS: 1>, starttime=1730133775.0438793, endtime=1730134652.1599097, additional_info={'duration': 876.3717231750488, 'num_run': 409, 'train_loss': 0.7473229865824492, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=409, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.20829966034728747, time=3821.179568052292, status=<StatusType.SUCCESS: 1>, starttime=1730133781.6803474, endtime=1730137603.5302804, additional_info={'duration': 3820.7889342308044, 'num_run': 410, 'train_loss': 0.13282788032601398, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=410, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16243041851604845, time=4462.835522890091, status=<StatusType.SUCCESS: 1>, starttime=1730133804.5294552, endtime=1730138267.8623629, additional_info={'duration': 4462.150607347488, 'num_run': 411, 'train_loss': 0.02847277019541115, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=411, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7764305516995869, time=2.29742431640625, status=<StatusType.SUCCESS: 1>, starttime=1730133919.8834872, endtime=1730133922.23579, additional_info={'duration': 1.866403579711914, 'num_run': 412, 'train_loss': 0.5432421107075902, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=412, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.255792617798, status=<StatusType.TIMEOUT: 2>, starttime=1730133927.5966718, endtime=1730139328.1596622, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=413, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4305252389147163, time=12.992591619491577, status=<StatusType.SUCCESS: 1>, starttime=1730134043.759633, endtime=1730134056.8118498, additional_info={'duration': 12.679530143737793, 'num_run': 414, 'train_loss': 0.34149816687959084, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=414, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.788295780214111, time=2.8914546966552734, status=<StatusType.SUCCESS: 1>, starttime=1730134057.803605, endtime=1730134060.7758777, additional_info={'duration': 2.630171298980713, 'num_run': 415, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=415, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4085582650518551, time=3596.6969861984253, status=<StatusType.SUCCESS: 1>, starttime=1730134065.455545, endtime=1730137662.1870368, additional_info={'duration': 3596.1802232265472, 'num_run': 416, 'train_loss': 0.3600415166718886, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=416, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3636593410925032, time=13.155220031738281, status=<StatusType.SUCCESS: 1>, starttime=1730134066.0840006, endtime=1730134079.32394, additional_info={'duration': 12.402637720108032, 'num_run': 417, 'train_loss': 0.2119643320521778, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=417, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5035647320236329, time=8.644861221313477, status=<StatusType.SUCCESS: 1>, starttime=1730134079.9276376, endtime=1730134088.6161776, additional_info={'duration': 8.410552024841309, 'num_run': 418, 'train_loss': 0.2591949429602469, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=418, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.8518562316894531, status=<StatusType.CRASHED: 3>, starttime=1730134089.9556267, endtime=1730134091.0158017, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=419, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=1.1374807357788086, status=<StatusType.CRASHED: 3>, starttime=1730134094.9034994, endtime=1730134096.1037483, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=420, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.22611117363, status=<StatusType.TIMEOUT: 2>, starttime=1730134101.0235765, endtime=1730139501.307668, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=421, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8837313404481464, time=18.079864501953125, status=<StatusType.SUCCESS: 1>, starttime=1730134101.7597039, endtime=1730134119.9077609, additional_info={'duration': 17.50476884841919, 'num_run': 422, 'train_loss': 0.7768273628561173, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=422, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16243519992310146, time=4410.760857105255, status=<StatusType.SUCCESS: 1>, starttime=1730134125.3556638, endtime=1730138536.2060144, additional_info={'duration': 4409.643367052078, 'num_run': 423, 'train_loss': 0.02851085648083891, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=423, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9976790478483069, time=1.3322334289550781, status=<StatusType.SUCCESS: 1>, starttime=1730134154.7555964, endtime=1730134156.2958195, additional_info={'duration': 1.122518539428711, 'num_run': 424, 'train_loss': 0.9975240083677005, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=424, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6141177314003632, time=28.20485758781433, status=<StatusType.SUCCESS: 1>, starttime=1730134156.847555, endtime=1730134185.0958238, additional_info={'duration': 27.612672328948975, 'num_run': 425, 'train_loss': 0.5936062602336447, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=425, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.477021694183, status=<StatusType.TIMEOUT: 2>, starttime=1730134189.8594873, endtime=1730139590.3677828, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=426, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9428882210416568, time=69.16849303245544, status=<StatusType.SUCCESS: 1>, starttime=1730134652.772491, endtime=1730134722.0297985, additional_info={'duration': 68.41974472999573, 'num_run': 427, 'train_loss': 0.7056649464240243, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=427, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16094190210938353, time=110.79329180717468, status=<StatusType.SUCCESS: 1>, starttime=1730134731.807485, endtime=1730134842.6979053, additional_info={'duration': 109.92942810058594, 'num_run': 428, 'train_loss': 0.03505795093812254, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=428, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16445286667445597, time=4314.720821619034, status=<StatusType.SUCCESS: 1>, starttime=1730134847.880552, endtime=1730139162.7757978, additional_info={'duration': 4313.657867431641, 'num_run': 429, 'train_loss': 0.03638392008099903, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=429, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3964240814928599, time=2.6393046379089355, status=<StatusType.SUCCESS: 1>, starttime=1730134888.019605, endtime=1730134890.8358667, additional_info={'duration': 2.2517404556274414, 'num_run': 430, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=430, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16633102009095896, time=4232.235923528671, status=<StatusType.SUCCESS: 1>, starttime=1730134898.4610705, endtime=1730139130.7958243, additional_info={'duration': 4231.504233837128, 'num_run': 431, 'train_loss': 0.04396639151323059, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=431, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15859609874386515, time=117.30952310562134, status=<StatusType.SUCCESS: 1>, starttime=1730135017.0834801, endtime=1730135134.5278208, additional_info={'duration': 115.88160872459412, 'num_run': 432, 'train_loss': 0.0219368718418719, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=432, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3549243535989531, time=146.6687047481537, status=<StatusType.SUCCESS: 1>, starttime=1730135038.4524798, endtime=1730135185.223764, additional_info={'duration': 145.957754611969, 'num_run': 433, 'train_loss': 0.09314274205964464, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=433, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16294726526509928, time=86.5321912765503, status=<StatusType.SUCCESS: 1>, starttime=1730135145.9235067, endtime=1730135232.55817, additional_info={'duration': 85.84972858428955, 'num_run': 434, 'train_loss': 0.03976948631968567, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=434, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4036640149755143, time=21.89738416671753, status=<StatusType.SUCCESS: 1>, starttime=1730135185.8314884, endtime=1730135207.7917926, additional_info={'duration': 21.674440145492554, 'num_run': 435, 'train_loss': 0.21901538230501583, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=435, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5565016641492753, time=2.612560987472534, status=<StatusType.SUCCESS: 1>, starttime=1730135208.7322412, endtime=1730135211.4318001, additional_info={'duration': 1.8584914207458496, 'num_run': 436, 'train_loss': 0.4741203561829423, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=436, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.138301054629384, time=1.2284140586853027, status=<StatusType.SUCCESS: 1>, starttime=1730135212.2525525, endtime=1730135213.5708544, additional_info={'duration': 1.0496697425842285, 'num_run': 437, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=437, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8785125598553204, time=2.593463659286499, status=<StatusType.SUCCESS: 1>, starttime=1730135218.253944, endtime=1730135220.897243, additional_info={'duration': 2.119126319885254, 'num_run': 438, 'train_loss': 0.7281581157879766, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=438, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.463007450104, status=<StatusType.TIMEOUT: 2>, starttime=1730135225.850008, endtime=1730140626.447825, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=439, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7338120486657329, time=6.475821018218994, status=<StatusType.SUCCESS: 1>, starttime=1730135233.4790025, endtime=1730135240.022741, additional_info={'duration': 6.264721870422363, 'num_run': 440, 'train_loss': 0.4687922376088711, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=440, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7155901962682073, time=4.045429944992065, status=<StatusType.SUCCESS: 1>, starttime=1730135241.2314904, endtime=1730135245.3438349, additional_info={'duration': 3.8611960411071777, 'num_run': 441, 'train_loss': 0.5016026985092774, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=441, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.158790676172633, time=116.43740963935852, status=<StatusType.SUCCESS: 1>, starttime=1730135249.4795053, endtime=1730135366.071789, additional_info={'duration': 115.25813007354736, 'num_run': 442, 'train_loss': 0.02776565473353032, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=442, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4167314371969326, time=3705.7335875034332, status=<StatusType.SUCCESS: 1>, starttime=1730135303.583523, endtime=1730139009.3598323, additional_info={'duration': 3705.1708307266235, 'num_run': 443, 'train_loss': 0.36592593671416307, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=443, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.254393577576, status=<StatusType.TIMEOUT: 2>, starttime=1730135372.619565, endtime=1730140772.9517703, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=444, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5399.344662666321, status=<StatusType.TIMEOUT: 2>, starttime=1730135867.2204897, endtime=1730141267.6237872, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=445, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16183708711265696, time=95.62559151649475, status=<StatusType.SUCCESS: 1>, starttime=1730136368.3010767, endtime=1730136464.027786, additional_info={'duration': 94.85270690917969, 'num_run': 446, 'train_loss': 0.039457899509026347, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=446, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=1.3279075622558594, status=<StatusType.CRASHED: 3>, starttime=1730136465.2732553, endtime=1730136466.7557569, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=447, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4413625365761584, time=306.09012055397034, status=<StatusType.SUCCESS: 1>, starttime=1730136471.3540723, endtime=1730136777.5057228, additional_info={'duration': 305.3469252586365, 'num_run': 448, 'train_loss': 0.3183811399926516, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=448, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8252439040597466, time=19.901418924331665, status=<StatusType.SUCCESS: 1>, starttime=1730136754.3594937, endtime=1730136774.3238142, additional_info={'duration': 19.523394107818604, 'num_run': 449, 'train_loss': 0.7792006539496237, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=449, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15906002184571294, time=108.16244220733643, status=<StatusType.SUCCESS: 1>, starttime=1730136779.0663943, endtime=1730136887.359866, additional_info={'duration': 107.42453932762146, 'num_run': 450, 'train_loss': 0.027855232100945558, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=450, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6898366832743861, time=2171.8046724796295, status=<StatusType.SUCCESS: 1>, starttime=1730136780.2073407, endtime=1730138952.119762, additional_info={'duration': 2171.282940864563, 'num_run': 451, 'train_loss': 0.38651492155653233, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=451, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.17399699604288554, time=390.4246554374695, status=<StatusType.SUCCESS: 1>, starttime=1730136892.5476086, endtime=1730137283.1077738, additional_info={'duration': 389.064998626709, 'num_run': 452, 'train_loss': 0.029648477168236067, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=452, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.2202376135083281, time=173.46558332443237, status=<StatusType.SUCCESS: 1>, starttime=1730137016.7634819, endtime=1730137190.2685707, additional_info={'duration': 170.52774024009705, 'num_run': 453, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=453, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.158790676172633, time=98.61770462989807, status=<StatusType.SUCCESS: 1>, starttime=1730137087.1016035, endtime=1730137185.819931, additional_info={'duration': 97.30907368659973, 'num_run': 454, 'train_loss': 0.02776565473353032, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=454, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3456634932760174, time=189.94613552093506, status=<StatusType.SUCCESS: 1>, starttime=1730137190.680625, endtime=1730137380.839966, additional_info={'duration': 188.22576069831848, 'num_run': 455, 'train_loss': 0.012006288574594803, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=455, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.339438188372869, time=2.36724853515625, status=<StatusType.SUCCESS: 1>, starttime=1730137191.4817572, endtime=1730137193.8998096, additional_info={'duration': 2.1351876258850098, 'num_run': 456, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=456, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16133260916269287, time=88.64736104011536, status=<StatusType.SUCCESS: 1>, starttime=1730137200.2835307, endtime=1730137288.9346483, additional_info={'duration': 87.5641074180603, 'num_run': 457, 'train_loss': 0.03522336551797185, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=457, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=1.1918528079986572, status=<StatusType.CRASHED: 3>, starttime=1730137284.035588, endtime=1730137285.3840463, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=458, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5183.143266439438, status=<StatusType.TIMEOUT: 2>, starttime=1730137289.1864011, endtime=1730142473.3480732, additional_info={'error': 'Timeout', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=459, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16183708711265696, time=57.60501432418823, status=<StatusType.SUCCESS: 1>, starttime=1730137294.0675008, endtime=1730137351.7678285, additional_info={'duration': 56.95669627189636, 'num_run': 460, 'train_loss': 0.039457899509026347, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=460, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4046260455886722, time=13.448662757873535, status=<StatusType.SUCCESS: 1>, starttime=1730137319.0197685, endtime=1730137332.6118426, additional_info={'duration': 13.02459454536438, 'num_run': 461, 'train_loss': 0.2939295975388354, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=461, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.160955995452485, time=98.00339841842651, status=<StatusType.SUCCESS: 1>, starttime=1730137339.5756571, endtime=1730137437.689967, additional_info={'duration': 96.79263496398926, 'num_run': 462, 'train_loss': 0.0349364730304079, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=462, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.593718301336926, time=643.357360124588, status=<StatusType.SUCCESS: 1>, starttime=1730137352.5995154, endtime=1730137996.0398734, additional_info={'duration': 642.260294675827, 'num_run': 463, 'train_loss': 0.3627609006107309, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=463, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15927724075825864, time=97.2648057937622, status=<StatusType.SUCCESS: 1>, starttime=1730137367.880508, endtime=1730137465.267788, additional_info={'duration': 96.07861328125, 'num_run': 464, 'train_loss': 0.027786942621129396, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=464, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.38636983199434205, time=2.332179307937622, status=<StatusType.SUCCESS: 1>, starttime=1730137382.0337484, endtime=1730137384.4450567, additional_info={'duration': 2.0954346656799316, 'num_run': 465, 'train_loss': 0.2047569471383039, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=465, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.2898139271002603, time=97.51664686203003, status=<StatusType.SUCCESS: 1>, starttime=1730137389.7403657, endtime=1730137487.2605026, additional_info={'duration': 95.27416729927063, 'num_run': 466, 'train_loss': 0.06646621311121753, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=466, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7849590554043075, time=56.64216136932373, status=<StatusType.SUCCESS: 1>, starttime=1730137419.7423656, endtime=1730137476.4358487, additional_info={'duration': 55.83682179450989, 'num_run': 467, 'train_loss': 0.7361689438406572, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=467, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=5034.575000047684, status=<StatusType.TIMEOUT: 2>, starttime=1730137438.39837, endtime=1730142474.0012102, additional_info={'error': 'Timeout', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=468, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15947081181321346, time=100.54971766471863, status=<StatusType.SUCCESS: 1>, starttime=1730137470.4914873, endtime=1730137571.1758723, additional_info={'duration': 99.28303837776184, 'num_run': 469, 'train_loss': 0.02788257875774358, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=469, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4263520285393642, time=2.0254945755004883, status=<StatusType.SUCCESS: 1>, starttime=1730137471.6514862, endtime=1730137473.6997426, additional_info={'duration': 1.795776605606079, 'num_run': 470, 'train_loss': 0.3781252905140642, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=470, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9803532362521936, time=4.053464651107788, status=<StatusType.SUCCESS: 1>, starttime=1730137472.5714855, endtime=1730137476.7277749, additional_info={'duration': 3.843958616256714, 'num_run': 471, 'train_loss': 0.9670098069742993, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=471, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.18389547036542742, time=26.51871681213379, status=<StatusType.SUCCESS: 1>, starttime=1730137488.2355163, endtime=1730137514.903936, additional_info={'duration': 25.388238430023193, 'num_run': 472, 'train_loss': 0.025537687877268955, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=472, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.704993486404419, status=<StatusType.CRASHED: 3>, starttime=1730137489.1956222, endtime=1730137490.0038123, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=473, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.28485735527184, time=107.93842816352844, status=<StatusType.SUCCESS: 1>, starttime=1730137494.5658507, endtime=1730137602.6639144, additional_info={'duration': 107.1670274734497, 'num_run': 474, 'train_loss': 0.04797688663942344, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=474, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4922390190370809, time=4.06214714050293, status=<StatusType.SUCCESS: 1>, starttime=1730137495.2348616, endtime=1730137499.3998237, additional_info={'duration': 3.8584351539611816, 'num_run': 475, 'train_loss': 0.4132108977331197, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=475, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7869931551037251, time=81.89583683013916, status=<StatusType.SUCCESS: 1>, starttime=1730137496.3286493, endtime=1730137578.6398385, additional_info={'duration': 81.29806733131409, 'num_run': 476, 'train_loss': 0.7132691993993858, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=476, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16414976645577206, time=104.9677197933197, status=<StatusType.SUCCESS: 1>, starttime=1730137505.389148, endtime=1730137610.360022, additional_info={'duration': 103.70806169509888, 'num_run': 477, 'train_loss': 0.04929169846134101, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=477, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6567720052544983, time=779.8931794166565, status=<StatusType.SUCCESS: 1>, starttime=1730137515.7635005, endtime=1730138295.7840729, additional_info={'duration': 779.003570318222, 'num_run': 478, 'train_loss': 0.39582687703899544, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=478, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16137537189795512, time=75.26965951919556, status=<StatusType.SUCCESS: 1>, starttime=1730137557.0635028, endtime=1730137632.447896, additional_info={'duration': 73.97713017463684, 'num_run': 479, 'train_loss': 0.03921961107303713, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=479, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.3616256112398024, time=3.27252197265625, status=<StatusType.SUCCESS: 1>, starttime=1730137559.5402799, endtime=1730137562.816527, additional_info={'duration': 2.7589619159698486, 'num_run': 480, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=480, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.2272080586397943, time=21.994113206863403, status=<StatusType.SUCCESS: 1>, starttime=1730137568.0509257, endtime=1730137590.0482047, additional_info={'duration': 19.884936094284058, 'num_run': 481, 'train_loss': 0.03130238230514766, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=481, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.5811007022857666, status=<StatusType.CRASHED: 3>, starttime=1730137569.0077775, endtime=1730137569.9387176, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=482, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.17767604709850737, time=73.0810718536377, status=<StatusType.SUCCESS: 1>, starttime=1730137573.6678736, endtime=1730137646.8238003, additional_info={'duration': 72.3423342704773, 'num_run': 483, 'train_loss': 0.08490313512659585, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=483, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7018970272946863, time=92.88826441764832, status=<StatusType.SUCCESS: 1>, starttime=1730137574.429568, endtime=1730137667.320962, additional_info={'duration': 92.40506386756897, 'num_run': 484, 'train_loss': 0.5977093658293529, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=484, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.697115633773503, time=1.6220002174377441, status=<StatusType.SUCCESS: 1>, starttime=1730137580.0669193, endtime=1730137581.731977, additional_info={'duration': 1.4326395988464355, 'num_run': 485, 'train_loss': 0.4762996969839116, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=485, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1964539902486704, time=27.834489345550537, status=<StatusType.SUCCESS: 1>, starttime=1730137587.3665466, endtime=1730137615.204659, additional_info={'duration': 26.785298824310303, 'num_run': 486, 'train_loss': 0.034975295857640476, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=486, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.19113320616462184, time=74.57321810722351, status=<StatusType.SUCCESS: 1>, starttime=1730137593.3155909, endtime=1730137667.976884, additional_info={'duration': 74.0951635837555, 'num_run': 487, 'train_loss': 0.11207743980990906, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=487, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0747877245443473, time=16.0535147190094, status=<StatusType.SUCCESS: 1>, starttime=1730137594.2475147, endtime=1730137610.304258, additional_info={'duration': 15.23857593536377, 'num_run': 488, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=488, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7129713654592895, time=5.737447500228882, status=<StatusType.SUCCESS: 1>, starttime=1730137603.6474938, endtime=1730137609.3885553, additional_info={'duration': 5.515939474105835, 'num_run': 489, 'train_loss': 0.5015780253379676, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=489, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.20772120085407106, time=61.47645807266235, status=<StatusType.SUCCESS: 1>, starttime=1730137609.8835835, endtime=1730137671.3666532, additional_info={'duration': 60.07972574234009, 'num_run': 490, 'train_loss': 0.049920387489978735, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=490, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.18066691907979696, time=83.06654000282288, status=<StatusType.SUCCESS: 1>, starttime=1730137615.3584645, endtime=1730137698.4328144, additional_info={'duration': 81.91393637657166, 'num_run': 491, 'train_loss': 0.09053293928301648, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=491, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15938339242687102, time=116.40914821624756, status=<StatusType.SUCCESS: 1>, starttime=1730137620.6435294, endtime=1730137737.2158885, additional_info={'duration': 115.31037139892578, 'num_run': 492, 'train_loss': 0.02783318539732678, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=492, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.17887642660840478, time=329.18228363990784, status=<StatusType.SUCCESS: 1>, starttime=1730137624.5196154, endtime=1730137953.8820546, additional_info={'duration': 328.5870521068573, 'num_run': 493, 'train_loss': 0.05092002539560572, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=493, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1797026170266538, time=75.23512935638428, status=<StatusType.SUCCESS: 1>, starttime=1730137629.3395572, endtime=1730137704.6482964, additional_info={'duration': 74.39672565460205, 'num_run': 494, 'train_loss': 0.08902950571254648, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=494, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6564960210862798, time=47.310981035232544, status=<StatusType.SUCCESS: 1>, starttime=1730137640.4787607, endtime=1730137687.792977, additional_info={'duration': 45.85315752029419, 'num_run': 495, 'train_loss': 0.2691841884551523, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=495, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4656483813577986, time=2.8663454055786133, status=<StatusType.SUCCESS: 1>, starttime=1730137648.2275412, endtime=1730137651.343649, additional_info={'duration': 2.618953227996826, 'num_run': 496, 'train_loss': 0.4266756570349034, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=496, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8392886874554787, time=43.07662105560303, status=<StatusType.SUCCESS: 1>, starttime=1730137653.0271149, endtime=1730137696.1075737, additional_info={'duration': 41.9316041469574, 'num_run': 497, 'train_loss': 0.4996183192563026, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=497, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.18507556967843686, time=289.5275876522064, status=<StatusType.SUCCESS: 1>, starttime=1730137666.0052965, endtime=1730137955.5362587, additional_info={'duration': 288.39448618888855, 'num_run': 498, 'train_loss': 0.07075404509240728, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=498, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6678042895273055, time=1.46128249168396, status=<StatusType.SUCCESS: 1>, starttime=1730137670.8155084, endtime=1730137672.3877733, additional_info={'duration': 1.2320282459259033, 'num_run': 499, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=499, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7384473291389584, time=16.15591049194336, status=<StatusType.SUCCESS: 1>, starttime=1730137671.4624023, endtime=1730137687.791921, additional_info={'duration': 15.436177253723145, 'num_run': 500, 'train_loss': 0.4207042890986537, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=500, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.873255729675293, status=<StatusType.CRASHED: 3>, starttime=1730137672.599484, endtime=1730137673.476369, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=501, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1900633530183918, time=33.05734419822693, status=<StatusType.SUCCESS: 1>, starttime=1730137677.075509, endtime=1730137710.327781, additional_info={'duration': 32.29138803482056, 'num_run': 502, 'train_loss': 0.058806738203776514, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=502, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7290807469414229, time=27.42209243774414, status=<StatusType.SUCCESS: 1>, starttime=1730137677.7836165, endtime=1730137705.3117619, additional_info={'duration': 26.93136763572693, 'num_run': 503, 'train_loss': 0.5318907840498382, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=503, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9792589467111223, time=3.7961201667785645, status=<StatusType.SUCCESS: 1>, starttime=1730137683.2193146, endtime=1730137687.0725553, additional_info={'duration': 3.5725772380828857, 'num_run': 504, 'train_loss': 0.9683009868244387, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=504, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15938339242687102, time=102.69776368141174, status=<StatusType.SUCCESS: 1>, starttime=1730137691.943268, endtime=1730137794.7443583, additional_info={'duration': 101.82681822776794, 'num_run': 505, 'train_loss': 0.02783318539732678, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=505, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.44898033142089844, status=<StatusType.CRASHED: 3>, starttime=1730137692.7199342, endtime=1730137693.219769, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=506, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16254764353651197, time=101.05991816520691, status=<StatusType.SUCCESS: 1>, starttime=1730137696.2315297, endtime=1730137797.38941, additional_info={'duration': 100.4435658454895, 'num_run': 507, 'train_loss': 0.0422801265707943, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=507, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5600918910346109, time=2.8049373626708984, status=<StatusType.SUCCESS: 1>, starttime=1730137696.9880204, endtime=1730137699.8397732, additional_info={'duration': 2.3354032039642334, 'num_run': 508, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=508, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5424778462385886, time=8.369385719299316, status=<StatusType.SUCCESS: 1>, starttime=1730137697.7595394, endtime=1730137706.155834, additional_info={'duration': 7.8084797859191895, 'num_run': 509, 'train_loss': 0.47677350260842316, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=509, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=4767.503161430359, status=<StatusType.TIMEOUT: 2>, starttime=1730137704.643517, endtime=1730142473.1762416, additional_info={'error': 'Timeout', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=510, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7862940273657877, time=444.4200530052185, status=<StatusType.SUCCESS: 1>, starttime=1730137705.349044, endtime=1730138149.907825, additional_info={'duration': 443.9493217468262, 'num_run': 511, 'train_loss': 0.5118753366212418, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=511, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.31041413124038936, time=89.05326771736145, status=<StatusType.SUCCESS: 1>, starttime=1730137711.4715593, endtime=1730137800.895793, additional_info={'duration': 87.96976661682129, 'num_run': 512, 'train_loss': 0.04273797074148771, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=512, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.17113402495250812, time=109.28037786483765, status=<StatusType.SUCCESS: 1>, starttime=1730137716.5923927, endtime=1730137825.9318874, additional_info={'duration': 108.62851309776306, 'num_run': 513, 'train_loss': 0.06955674112709453, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=513, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.394220826041634, time=3.2296388149261475, status=<StatusType.SUCCESS: 1>, starttime=1730137717.4714906, endtime=1730137720.7718205, additional_info={'duration': 2.6961750984191895, 'num_run': 514, 'train_loss': 0.356766411416199, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=514, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5432863535028513, time=3.2009849548339844, status=<StatusType.SUCCESS: 1>, starttime=1730137718.0401173, endtime=1730137721.24422, additional_info={'duration': 2.3987202644348145, 'num_run': 515, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=515, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9678756737692461, time=1.4894111156463623, status=<StatusType.SUCCESS: 1>, starttime=1730137721.4315217, endtime=1730137722.9241629, additional_info={'duration': 1.2738285064697266, 'num_run': 516, 'train_loss': 0.9529918306400684, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=516, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1607424133379998, time=98.72212076187134, status=<StatusType.SUCCESS: 1>, starttime=1730137726.007519, endtime=1730137824.8998165, additional_info={'duration': 97.28869366645813, 'num_run': 517, 'train_loss': 0.03497432569938121, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=517, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15924684693958457, time=82.24547052383423, status=<StatusType.SUCCESS: 1>, starttime=1730137730.379505, endtime=1730137812.9118779, additional_info={'duration': 81.32738065719604, 'num_run': 518, 'train_loss': 0.027953911544807777, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=518, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5660946521268728, time=2.4032418727874756, status=<StatusType.SUCCESS: 1>, starttime=1730137738.361907, endtime=1730137740.9251466, additional_info={'duration': 2.163569688796997, 'num_run': 519, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=519, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15824383311823362, time=89.45005011558533, status=<StatusType.SUCCESS: 1>, starttime=1730137746.2103217, endtime=1730137835.6786754, additional_info={'duration': 88.08401799201965, 'num_run': 520, 'train_loss': 0.021921765780297055, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=520, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.19880430615602412, time=72.05140924453735, status=<StatusType.SUCCESS: 1>, starttime=1730137751.4016302, endtime=1730137823.4562523, additional_info={'duration': 71.60279631614685, 'num_run': 521, 'train_loss': 0.12417364397083025, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=521, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9781787136933495, time=27.58636236190796, status=<StatusType.SUCCESS: 1>, starttime=1730137795.299509, endtime=1730137823.0158484, additional_info={'duration': 26.7492196559906, 'num_run': 522, 'train_loss': 0.9647189590447242, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=522, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15764259747097162, time=114.06624984741211, status=<StatusType.SUCCESS: 1>, starttime=1730137805.9666185, endtime=1730137920.036512, additional_info={'duration': 112.08936333656311, 'num_run': 523, 'train_loss': 0.021877904460763008, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=523, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9113075151426239, time=1.922842264175415, status=<StatusType.SUCCESS: 1>, starttime=1730137806.7184544, endtime=1730137808.7961571, additional_info={'duration': 1.7376682758331299, 'num_run': 524, 'train_loss': 0.8721556245955311, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=524, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7454720125916277, time=30.79218316078186, status=<StatusType.SUCCESS: 1>, starttime=1730137812.6687567, endtime=1730137843.587736, additional_info={'duration': 30.17294716835022, 'num_run': 525, 'train_loss': 0.6311001529472443, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=525, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.79075809338753, time=8.853278636932373, status=<StatusType.SUCCESS: 1>, starttime=1730137816.4235144, endtime=1730137825.2999742, additional_info={'duration': 8.636192560195923, 'num_run': 526, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=526, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8582061772366221, time=1.7679810523986816, status=<StatusType.SUCCESS: 1>, starttime=1730137824.1007323, endtime=1730137825.923839, additional_info={'duration': 1.542414903640747, 'num_run': 527, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=527, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.2208982849521247, time=40.714067459106445, status=<StatusType.SUCCESS: 1>, starttime=1730137829.9881935, endtime=1730137870.8052976, additional_info={'duration': 40.02342963218689, 'num_run': 528, 'train_loss': 0.05671968877632831, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=528, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9086444907096881, time=324.2134487628937, status=<StatusType.SUCCESS: 1>, starttime=1730137831.2635021, endtime=1730138155.551894, additional_info={'duration': 323.71017837524414, 'num_run': 529, 'train_loss': 0.7644493966871552, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=529, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.17703201485196657, time=71.80053043365479, status=<StatusType.SUCCESS: 1>, starttime=1730137835.7839556, endtime=1730137907.6678143, additional_info={'duration': 71.01997828483582, 'num_run': 530, 'train_loss': 0.08281724369890203, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=530, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5296186126202274, time=1730.730882883072, status=<StatusType.SUCCESS: 1>, starttime=1730137836.2896278, endtime=1730139567.1080182, additional_info={'duration': 1729.855830192566, 'num_run': 531, 'train_loss': 0.37935103386168684, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=531, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.674393079857287, time=25.0936918258667, status=<StatusType.SUCCESS: 1>, starttime=1730137836.9029598, endtime=1730137862.0681074, additional_info={'duration': 24.41447925567627, 'num_run': 532, 'train_loss': 0.40873725575866804, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=532, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1711793668701419, time=87.00378918647766, status=<StatusType.SUCCESS: 1>, starttime=1730137842.8410654, endtime=1730137930.167828, additional_info={'duration': 86.38762092590332, 'num_run': 533, 'train_loss': 0.06972421748072927, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=533, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4442390120490989, time=118.50809860229492, status=<StatusType.SUCCESS: 1>, starttime=1730137847.7884326, endtime=1730137966.7535777, additional_info={'duration': 117.37987089157104, 'num_run': 534, 'train_loss': 0.07609536635224916, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=534, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6110079710362828, time=13.51779818534851, status=<StatusType.SUCCESS: 1>, starttime=1730137863.1515174, endtime=1730137876.7957506, additional_info={'duration': 12.88959288597107, 'num_run': 535, 'train_loss': 0.5164809157626133, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=535, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7079495987226643, time=2.3414766788482666, status=<StatusType.SUCCESS: 1>, starttime=1730137871.7914927, endtime=1730137874.1957812, additional_info={'duration': 2.1369826793670654, 'num_run': 536, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=536, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15809333364964623, time=76.04494118690491, status=<StatusType.SUCCESS: 1>, starttime=1730137880.0555234, endtime=1730137956.2078052, additional_info={'duration': 74.4136393070221, 'num_run': 537, 'train_loss': 0.038454705806852084, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=537, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15859609874386515, time=90.05917620658875, status=<StatusType.SUCCESS: 1>, starttime=1730137886.1555016, endtime=1730137976.3517694, additional_info={'duration': 89.0643310546875, 'num_run': 538, 'train_loss': 0.0219368718418719, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=538, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15809348088515868, time=101.50002598762512, status=<StatusType.SUCCESS: 1>, starttime=1730137911.823506, endtime=1730138013.4879928, additional_info={'duration': 99.72790431976318, 'num_run': 539, 'train_loss': 0.021842621759545856, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=539, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.17208173652401046, time=87.00535464286804, status=<StatusType.SUCCESS: 1>, starttime=1730137928.6275163, endtime=1730138015.7319028, additional_info={'duration': 86.36204195022583, 'num_run': 540, 'train_loss': 0.07110814706252241, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=540, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0013161587940715, time=2.426640033721924, status=<StatusType.SUCCESS: 1>, starttime=1730137933.2463474, endtime=1730137935.7477958, additional_info={'duration': 1.8198940753936768, 'num_run': 541, 'train_loss': 0.9991338189982634, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=541, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9719241073688837, time=1.6272354125976562, status=<StatusType.SUCCESS: 1>, starttime=1730137938.1017308, endtime=1730137939.7917917, additional_info={'duration': 1.424400806427002, 'num_run': 542, 'train_loss': 0.9575367333386182, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=542, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4582423137144459, time=2.6215603351593018, status=<StatusType.SUCCESS: 1>, starttime=1730137940.8433409, endtime=1730137943.5146816, additional_info={'duration': 1.870551586151123, 'num_run': 543, 'train_loss': 0.42052324925496787, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=543, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7540148434426079, time=15.629543781280518, status=<StatusType.SUCCESS: 1>, starttime=1730137947.7714944, endtime=1730137963.404141, additional_info={'duration': 14.863282203674316, 'num_run': 544, 'train_loss': 0.6996103477897848, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=544, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.357852726187158, time=2.8398241996765137, status=<StatusType.SUCCESS: 1>, starttime=1730137955.5595262, endtime=1730137958.555789, additional_info={'duration': 2.4441187381744385, 'num_run': 545, 'train_loss': 0.3113375340862682, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=545, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8137544989272812, time=88.55287051200867, status=<StatusType.SUCCESS: 1>, starttime=1730137961.8680437, endtime=1730138050.432458, additional_info={'duration': 85.10235571861267, 'num_run': 546, 'train_loss': 0.6634199326029626, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=546, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0117074426616086, time=1.3418431282043457, status=<StatusType.SUCCESS: 1>, starttime=1730137962.8835094, endtime=1730137964.5158257, additional_info={'duration': 1.1553449630737305, 'num_run': 547, 'train_loss': 0.9945717509090768, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=547, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3440374400604992, time=88.34469747543335, status=<StatusType.SUCCESS: 1>, starttime=1730137967.9670172, endtime=1730138056.4717789, additional_info={'duration': 87.62255907058716, 'num_run': 548, 'train_loss': 0.15285702697815604, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=548, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=1.1454527378082275, status=<StatusType.CRASHED: 3>, starttime=1730137969.0955005, endtime=1730137970.2519112, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=549, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3487433196020176, time=80.29711294174194, status=<StatusType.SUCCESS: 1>, starttime=1730137975.7850657, endtime=1730138056.5679395, additional_info={'duration': 79.49332404136658, 'num_run': 550, 'train_loss': 0.16297901824917482, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=550, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5404639951602687, time=1.5614171028137207, status=<StatusType.SUCCESS: 1>, starttime=1730137976.787484, endtime=1730137978.432697, additional_info={'duration': 1.3160712718963623, 'num_run': 551, 'train_loss': 0.41404111710393504, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=551, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.17979879951309105, time=61.80119752883911, status=<StatusType.SUCCESS: 1>, starttime=1730137982.0954845, endtime=1730138043.9918659, additional_info={'duration': 61.116321325302124, 'num_run': 552, 'train_loss': 0.09377547153640393, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=552, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3179989488874315, time=87.2928478717804, status=<StatusType.SUCCESS: 1>, starttime=1730137987.2242258, endtime=1730138074.611873, additional_info={'duration': 86.28773021697998, 'num_run': 553, 'train_loss': 0.06793353698200764, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=553, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.157938545769232, time=97.05729413032532, status=<StatusType.SUCCESS: 1>, starttime=1730137992.7674487, endtime=1730138089.9598234, additional_info={'duration': 96.07344007492065, 'num_run': 554, 'train_loss': 0.021849316675241752, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=554, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.31335144994595904, time=104.36296081542969, status=<StatusType.SUCCESS: 1>, starttime=1730138001.8971937, endtime=1730138106.4399493, additional_info={'duration': 103.04150032997131, 'num_run': 555, 'train_loss': 0.04302259230726853, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=555, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.35188524097863416, time=2.3731260299682617, status=<StatusType.SUCCESS: 1>, starttime=1730138014.5676067, endtime=1730138017.0478275, additional_info={'duration': 2.1475775241851807, 'num_run': 556, 'train_loss': 0.30305717828743395, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=556, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15780025705709877, time=101.46120262145996, status=<StatusType.SUCCESS: 1>, starttime=1730138021.187984, endtime=1730138122.8398094, additional_info={'duration': 100.43453764915466, 'num_run': 557, 'train_loss': 0.02186984513724699, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=557, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3153281053883705, time=109.88823747634888, status=<StatusType.SUCCESS: 1>, starttime=1730138024.8807063, endtime=1730138134.89131, additional_info={'duration': 109.06632924079895, 'num_run': 558, 'train_loss': 0.043240245920716534, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=558, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.9968950748443604, status=<StatusType.CRASHED: 3>, starttime=1730138044.771657, endtime=1730138045.8599594, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=559, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5462623015992207, time=491.87230944633484, status=<StatusType.SUCCESS: 1>, starttime=1730138054.6806738, endtime=1730138546.562134, additional_info={'duration': 491.13653898239136, 'num_run': 560, 'train_loss': 0.41477837747064883, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=560, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9719241073688837, time=1.5540592670440674, status=<StatusType.SUCCESS: 1>, starttime=1730138055.490954, endtime=1730138057.4278576, additional_info={'duration': 1.3421218395233154, 'num_run': 561, 'train_loss': 0.9575367333386182, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=561, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7273535471052354, time=1.7018630504608154, status=<StatusType.SUCCESS: 1>, starttime=1730138062.4911573, endtime=1730138064.1962905, additional_info={'duration': 1.4250097274780273, 'num_run': 562, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=562, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7180628010658738, time=1095.2757639884949, status=<StatusType.SUCCESS: 1>, starttime=1730138063.4569209, endtime=1730139158.8025293, additional_info={'duration': 1094.4352169036865, 'num_run': 563, 'train_loss': 0.5437212345768268, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=563, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1576423409294126, time=90.9866349697113, status=<StatusType.SUCCESS: 1>, starttime=1730138068.5023615, endtime=1730138159.5878227, additional_info={'duration': 89.95465278625488, 'num_run': 564, 'train_loss': 0.021877904460763008, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=564, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16227294555655286, time=128.1899929046631, status=<StatusType.SUCCESS: 1>, starttime=1730138074.7509074, endtime=1730138203.10381, additional_info={'duration': 126.43870854377747, 'num_run': 565, 'train_loss': 0.002185292917820592, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=565, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1768899926494161, time=101.38276815414429, status=<StatusType.SUCCESS: 1>, starttime=1730138079.918187, endtime=1730138181.417803, additional_info={'duration': 100.6234359741211, 'num_run': 566, 'train_loss': 0.08304321612490355, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=566, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.17322055510591683, time=357.581552028656, status=<StatusType.SUCCESS: 1>, starttime=1730138095.263489, endtime=1730138452.9799044, additional_info={'duration': 356.6742527484894, 'num_run': 567, 'train_loss': 0.023864179336322924, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=567, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.43154641970113516, time=50.000946044921875, status=<StatusType.SUCCESS: 1>, starttime=1730138107.011667, endtime=1730138157.3878992, additional_info={'duration': 48.81912016868591, 'num_run': 568, 'train_loss': 0.18428850516768142, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=568, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.6357734203338623, status=<StatusType.CRASHED: 3>, starttime=1730138123.9282293, endtime=1730138124.6725593, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=569, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15809575944716064, time=106.8084352016449, status=<StatusType.SUCCESS: 1>, starttime=1730138129.8366942, endtime=1730138236.6964571, additional_info={'duration': 104.82222390174866, 'num_run': 570, 'train_loss': 0.021842621759545856, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=570, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7313481205633989, time=14.421617031097412, status=<StatusType.SUCCESS: 1>, starttime=1730138135.7035267, endtime=1730138150.128382, additional_info={'duration': 14.199990034103394, 'num_run': 571, 'train_loss': 0.5235059616123664, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=571, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15794066447597524, time=117.51272797584534, status=<StatusType.SUCCESS: 1>, starttime=1730138155.4355254, endtime=1730138273.1157904, additional_info={'duration': 116.58771324157715, 'num_run': 572, 'train_loss': 0.021849316675241752, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=572, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15380235706183557, time=96.05484986305237, status=<StatusType.SUCCESS: 1>, starttime=1730138160.2181385, endtime=1730138256.3718107, additional_info={'duration': 94.74548029899597, 'num_run': 573, 'train_loss': 0.026875092571459525, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=573, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.48191588411234976, time=2.753464937210083, status=<StatusType.SUCCESS: 1>, starttime=1730138161.3515012, endtime=1730138164.1081157, additional_info={'duration': 1.9742958545684814, 'num_run': 574, 'train_loss': 0.44762790475451536, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=574, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1768395520332769, time=87.20156145095825, status=<StatusType.SUCCESS: 1>, starttime=1730138165.9515, endtime=1730138253.2478034, additional_info={'duration': 86.73672556877136, 'num_run': 575, 'train_loss': 0.08239728154842087, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=575, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.5575380325317383, status=<StatusType.CRASHED: 3>, starttime=1730138166.8074977, endtime=1730138167.3682804, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=576, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9998137401295561, time=2.718034029006958, status=<StatusType.SUCCESS: 1>, starttime=1730138167.6869915, endtime=1730138170.408162, additional_info={'duration': 2.2817211151123047, 'num_run': 577, 'train_loss': 0.996404550641622, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=577, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1603191565935503, time=68.06672430038452, status=<StatusType.SUCCESS: 1>, starttime=1730138172.9702146, endtime=1730138241.1478605, additional_info={'duration': 66.3451361656189, 'num_run': 578, 'train_loss': 0.022206750948135425, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=578, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15822401490107416, time=98.19662475585938, status=<StatusType.SUCCESS: 1>, starttime=1730138178.6556516, endtime=1730138277.019699, additional_info={'duration': 96.46350908279419, 'num_run': 579, 'train_loss': 0.021882912051566512, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=579, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6113300374956766, time=183.20931601524353, status=<StatusType.SUCCESS: 1>, starttime=1730138186.0275164, endtime=1730138369.24034, additional_info={'duration': 182.13276171684265, 'num_run': 580, 'train_loss': 0.44508580662146735, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=580, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.3539560176703168, time=27.977452278137207, status=<StatusType.SUCCESS: 1>, starttime=1730138204.0475094, endtime=1730138232.1317904, additional_info={'duration': 27.436424493789673, 'num_run': 581, 'train_loss': 0.16073241707068386, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=581, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5167911250402434, time=2.6795060634613037, status=<StatusType.SUCCESS: 1>, starttime=1730138233.0774403, endtime=1730138235.8319025, additional_info={'duration': 2.4865787029266357, 'num_run': 582, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=582, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15894280774762282, time=108.6328992843628, status=<StatusType.SUCCESS: 1>, starttime=1730138241.6195004, endtime=1730138350.8678184, additional_info={'duration': 107.85605430603027, 'num_run': 583, 'train_loss': 0.027734717770414433, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=583, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5014820886929753, time=121.42833948135376, status=<StatusType.SUCCESS: 1>, starttime=1730138242.772544, endtime=1730138364.2318556, additional_info={'duration': 120.24380612373352, 'num_run': 584, 'train_loss': 0.12776440700098984, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=584, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9500783032178436, time=18.155381679534912, status=<StatusType.SUCCESS: 1>, starttime=1730138243.5490847, endtime=1730138261.738875, additional_info={'duration': 17.98090100288391, 'num_run': 585, 'train_loss': 0.759898378742752, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=585, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9766290955928654, time=30.37718677520752, status=<StatusType.SUCCESS: 1>, starttime=1730138254.111603, endtime=1730138284.5478, additional_info={'duration': 30.19184970855713, 'num_run': 586, 'train_loss': 0.9644258178046772, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=586, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.67871668334786, time=26.245131731033325, status=<StatusType.SUCCESS: 1>, starttime=1730138257.3395143, endtime=1730138283.7238898, additional_info={'duration': 25.821334838867188, 'num_run': 587, 'train_loss': 0.5688642630500093, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=587, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8822302807409397, time=1.5637924671173096, status=<StatusType.SUCCESS: 1>, starttime=1730138262.513208, endtime=1730138264.0997245, additional_info={'duration': 1.3709948062896729, 'num_run': 588, 'train_loss': 0.752124795353984, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=588, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5375424436572669, time=2.1452314853668213, status=<StatusType.SUCCESS: 1>, starttime=1730138267.791558, endtime=1730138269.9403188, additional_info={'duration': 1.8101294040679932, 'num_run': 589, 'train_loss': 0.5225823267213228, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=589, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15809348088515868, time=104.47142934799194, status=<StatusType.SUCCESS: 1>, starttime=1730138274.0250964, endtime=1730138378.5399823, additional_info={'duration': 102.87710642814636, 'num_run': 590, 'train_loss': 0.021842621759545856, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=590, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.17812807803624447, time=32.71090650558472, status=<StatusType.SUCCESS: 1>, starttime=1730138280.4635131, endtime=1730138313.2678347, additional_info={'duration': 31.924221515655518, 'num_run': 591, 'train_loss': 0.04535765927102012, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=591, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.56872016220619, time=47.10951638221741, status=<StatusType.SUCCESS: 1>, starttime=1730138281.7715044, endtime=1730138328.9678614, additional_info={'duration': 46.815412759780884, 'num_run': 592, 'train_loss': 0.39620716290721497, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=592, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15965132613546043, time=80.44954252243042, status=<StatusType.SUCCESS: 1>, starttime=1730138287.0394857, endtime=1730138367.4921834, additional_info={'duration': 78.96408987045288, 'num_run': 593, 'train_loss': 0.02210134341455987, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=593, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15789457433504342, time=96.27356004714966, status=<StatusType.SUCCESS: 1>, starttime=1730138293.8154998, endtime=1730138390.2959108, additional_info={'duration': 95.2920355796814, 'num_run': 594, 'train_loss': 0.021890098177939188, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=594, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9825775884809539, time=2.3339457511901855, status=<StatusType.SUCCESS: 1>, starttime=1730138294.7350547, endtime=1730138297.1118674, additional_info={'duration': 2.1628286838531494, 'num_run': 595, 'train_loss': 0.9774926779508111, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=595, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=1.3690595626831055, status=<StatusType.CRASHED: 3>, starttime=1730138301.6956303, endtime=1730138303.0684996, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=596, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15780025705709877, time=97.79783296585083, status=<StatusType.SUCCESS: 1>, starttime=1730138307.8226023, endtime=1730138405.7717936, additional_info={'duration': 96.18201994895935, 'num_run': 597, 'train_loss': 0.02186984513724699, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=597, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15243622870363188, time=102.20542049407959, status=<StatusType.SUCCESS: 1>, starttime=1730138312.6074336, endtime=1730138414.9477816, additional_info={'duration': 100.73458433151245, 'num_run': 598, 'train_loss': 0.02110804688968375, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=598, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1829154557640492, time=21.06775665283203, status=<StatusType.SUCCESS: 1>, starttime=1730138319.5648966, endtime=1730138340.675814, additional_info={'duration': 20.11522150039673, 'num_run': 599, 'train_loss': 0.05339374681484119, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=599, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1584339072194586, time=96.77067685127258, status=<StatusType.SUCCESS: 1>, starttime=1730138337.0662777, endtime=1730138434.0319028, additional_info={'duration': 95.47145819664001, 'num_run': 600, 'train_loss': 0.0218242550447846, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=600, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15779812764302006, time=94.3339192867279, status=<StatusType.SUCCESS: 1>, starttime=1730138350.9190505, endtime=1730138445.419853, additional_info={'duration': 93.4324107170105, 'num_run': 601, 'train_loss': 0.02186984513724699, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=601, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7046820903465176, time=6.060030698776245, status=<StatusType.SUCCESS: 1>, starttime=1730138351.8956127, endtime=1730138358.0296168, additional_info={'duration': 5.313286542892456, 'num_run': 602, 'train_loss': 0.5337453897396474, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=602, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6924778617246665, time=2.270728826522827, status=<StatusType.SUCCESS: 1>, starttime=1730138358.8765066, endtime=1730138361.1997578, additional_info={'duration': 2.0842275619506836, 'num_run': 603, 'train_loss': 0.4686136326487553, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=603, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4214053585470946, time=20.26810908317566, status=<StatusType.SUCCESS: 1>, starttime=1730138362.1194937, endtime=1730138382.53181, additional_info={'duration': 19.83757519721985, 'num_run': 604, 'train_loss': 0.2946702284095649, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=604, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.44307633709059485, time=14.196272611618042, status=<StatusType.SUCCESS: 1>, starttime=1730138367.9672563, endtime=1730138382.3198917, additional_info={'duration': 13.769175291061401, 'num_run': 605, 'train_loss': 0.3397743239825338, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=605, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7621463881167255, time=12.959310531616211, status=<StatusType.SUCCESS: 1>, starttime=1730138368.7492273, endtime=1730138381.7838275, additional_info={'duration': 12.115406274795532, 'num_run': 606, 'train_loss': 0.6480839259083792, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=606, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15809348088515868, time=97.19347667694092, status=<StatusType.SUCCESS: 1>, starttime=1730138372.8675141, endtime=1730138470.0645502, additional_info={'duration': 95.86160039901733, 'num_run': 607, 'train_loss': 0.021842621759545856, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=607, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15300781641175445, time=98.45238304138184, status=<StatusType.SUCCESS: 1>, starttime=1730138377.922641, endtime=1730138476.3789928, additional_info={'duration': 95.16060996055603, 'num_run': 608, 'train_loss': 0.02119792671604426, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=608, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7817427658202716, time=11.522916316986084, status=<StatusType.SUCCESS: 1>, starttime=1730138380.3036299, endtime=1730138391.830691, additional_info={'duration': 10.894416809082031, 'num_run': 609, 'train_loss': 0.7421349157871053, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=609, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6110284893347738, time=11.054157972335815, status=<StatusType.SUCCESS: 1>, starttime=1730138387.8675003, endtime=1730138399.0018787, additional_info={'duration': 10.694445133209229, 'num_run': 610, 'train_loss': 0.4844576014992737, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=610, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.20900176916061294, time=142.44526863098145, status=<StatusType.SUCCESS: 1>, starttime=1730138394.931584, endtime=1730138537.3805342, additional_info={'duration': 140.09388494491577, 'num_run': 611, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=611, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=1.1201789379119873, status=<StatusType.CRASHED: 3>, starttime=1730138400.7635949, endtime=1730138401.9278405, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=612, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15809575944716064, time=106.18003225326538, status=<StatusType.SUCCESS: 1>, starttime=1730138405.875515, endtime=1730138512.2121632, additional_info={'duration': 104.79626274108887, 'num_run': 613, 'train_loss': 0.021842621759545856, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=613, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15780025705709877, time=123.58546686172485, status=<StatusType.SUCCESS: 1>, starttime=1730138412.2875047, endtime=1730138536.0864737, additional_info={'duration': 121.76217341423035, 'num_run': 614, 'train_loss': 0.02186984513724699, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=614, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15243622870363188, time=98.4746162891388, status=<StatusType.SUCCESS: 1>, starttime=1730138416.6076605, endtime=1730138515.259785, additional_info={'duration': 97.50693988800049, 'num_run': 615, 'train_loss': 0.02110804688968375, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=615, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7288212360495234, time=9.907116651535034, status=<StatusType.SUCCESS: 1>, starttime=1730138417.8015947, endtime=1730138427.739764, additional_info={'duration': 9.47846531867981, 'num_run': 616, 'train_loss': 0.667678007839119, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=616, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15779812764302006, time=121.49471616744995, status=<StatusType.SUCCESS: 1>, starttime=1730138423.5503173, endtime=1730138545.2717674, additional_info={'duration': 120.21463298797607, 'num_run': 617, 'train_loss': 0.02186984513724699, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=617, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1556802928989014, time=98.0480604171753, status=<StatusType.SUCCESS: 1>, starttime=1730138428.8999014, endtime=1730138527.151773, additional_info={'duration': 96.99150919914246, 'num_run': 618, 'train_loss': 0.03401303142957353, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=618, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15243622870363188, time=100.82516121864319, status=<StatusType.SUCCESS: 1>, starttime=1730138433.1316054, endtime=1730138534.1118643, additional_info={'duration': 99.49090838432312, 'num_run': 619, 'train_loss': 0.02110804688968375, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=619, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15780025705709877, time=112.53235149383545, status=<StatusType.SUCCESS: 1>, starttime=1730138438.848595, endtime=1730138551.3845167, additional_info={'duration': 111.21867871284485, 'num_run': 620, 'train_loss': 0.02186984513724699, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=620, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9803532362521936, time=4.098639726638794, status=<StatusType.SUCCESS: 1>, starttime=1730138446.8182452, endtime=1730138450.9918036, additional_info={'duration': 3.783452272415161, 'num_run': 621, 'train_loss': 0.9670098069742993, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=621, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6923008794100027, time=14.777697563171387, status=<StatusType.SUCCESS: 1>, starttime=1730138451.8115077, endtime=1730138466.6678023, additional_info={'duration': 14.421421766281128, 'num_run': 622, 'train_loss': 0.591079529125382, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=622, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15780025705709877, time=107.06533026695251, status=<StatusType.SUCCESS: 1>, starttime=1730138458.3394108, endtime=1730138565.5838683, additional_info={'duration': 105.33426547050476, 'num_run': 623, 'train_loss': 0.02186984513724699, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=623, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15863433514468844, time=73.02100658416748, status=<StatusType.SUCCESS: 1>, starttime=1730138471.5075152, endtime=1730138544.7161763, additional_info={'duration': 72.12932658195496, 'num_run': 624, 'train_loss': 0.04139880918696118, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=624, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4004876229234801, time=16.243547916412354, status=<StatusType.SUCCESS: 1>, starttime=1730138472.6716383, endtime=1730138488.9998062, additional_info={'duration': 12.24629282951355, 'num_run': 625, 'train_loss': 0.35195870167394583, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=625, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7250219367763615, time=61.11998105049133, status=<StatusType.SUCCESS: 1>, starttime=1730138482.4436104, endtime=1730138543.7878933, additional_info={'duration': 60.31873965263367, 'num_run': 626, 'train_loss': 0.5779550083864334, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=626, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.8365614414215088, status=<StatusType.CRASHED: 3>, starttime=1730138489.9316363, endtime=1730138490.77229, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=627, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.6784162521362305, status=<StatusType.CRASHED: 3>, starttime=1730138496.2257636, endtime=1730138497.0677998, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=628, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15345336991103875, time=95.32528066635132, status=<StatusType.SUCCESS: 1>, starttime=1730138505.4792702, endtime=1730138600.963797, additional_info={'duration': 94.17156028747559, 'num_run': 629, 'train_loss': 0.021221102189346742, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=629, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.45238025656639597, time=2.7174057960510254, status=<StatusType.SUCCESS: 1>, starttime=1730138513.2995143, endtime=1730138516.1238515, additional_info={'duration': 2.3652431964874268, 'num_run': 630, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=630, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.320440304869569, time=82.65839457511902, status=<StatusType.SUCCESS: 1>, starttime=1730138521.3433425, endtime=1730138604.459891, additional_info={'duration': 81.94300842285156, 'num_run': 631, 'train_loss': 0.06835045541894576, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=631, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.43002893157175714, time=178.95651173591614, status=<StatusType.SUCCESS: 1>, starttime=1730138522.0965521, endtime=1730138701.1565032, additional_info={'duration': 178.50917601585388, 'num_run': 632, 'train_loss': 0.2554119932517738, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=632, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.22002390939419245, time=57.72355079650879, status=<StatusType.SUCCESS: 1>, starttime=1730138531.7956398, endtime=1730138589.5274487, additional_info={'duration': 56.85853552818298, 'num_run': 633, 'train_loss': 0.15955276071606178, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=633, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9999879317294782, time=2.101696014404297, status=<StatusType.SUCCESS: 1>, starttime=1730138535.0429447, endtime=1730138537.1481612, additional_info={'duration': 1.6900851726531982, 'num_run': 634, 'train_loss': 0.9983058301910358, 'configuration_origin': 'Random Search'})\n",
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+      "RunKey(config_id=635, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.36700303088909286, time=1.8712918758392334, status=<StatusType.SUCCESS: 1>, starttime=1730138543.2418103, endtime=1730138545.1750891, additional_info={'duration': 1.2940618991851807, 'num_run': 636, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=636, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.43469394681404727, time=1116.0654299259186, status=<StatusType.SUCCESS: 1>, starttime=1730138543.908984, endtime=1730139660.054821, additional_info={'duration': 1115.684986591339, 'num_run': 637, 'train_loss': 0.3118062774805232, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=637, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3922.1197311878204, status=<StatusType.TIMEOUT: 2>, starttime=1730138549.739039, endtime=1730142472.98272, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=638, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.36153348039504124, time=1.5869557857513428, status=<StatusType.SUCCESS: 1>, starttime=1730138550.8294814, endtime=1730138552.475116, additional_info={'duration': 1.3616721630096436, 'num_run': 639, 'train_loss': 0.22156688518543807, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=639, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9990773194053126, time=1.4390220642089844, status=<StatusType.SUCCESS: 1>, starttime=1730138551.8413148, endtime=1730138553.3357728, additional_info={'duration': 1.1719098091125488, 'num_run': 640, 'train_loss': 0.9986880329909847, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=640, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5079691442041289, time=21.681487560272217, status=<StatusType.SUCCESS: 1>, starttime=1730138552.951497, endtime=1730138574.6797092, additional_info={'duration': 21.014363050460815, 'num_run': 641, 'train_loss': 0.3735074054199126, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=641, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15968352070539787, time=58.95452952384949, status=<StatusType.SUCCESS: 1>, starttime=1730138558.4795616, endtime=1730138617.5479362, additional_info={'duration': 58.050865173339844, 'num_run': 642, 'train_loss': 0.03512103513427978, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=642, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.15780025705709877, time=94.83105945587158, status=<StatusType.SUCCESS: 1>, starttime=1730138564.585856, endtime=1730138659.5596967, additional_info={'duration': 92.5076220035553, 'num_run': 643, 'train_loss': 0.02186984513724699, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=643, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16833672014912798, time=297.28859305381775, status=<StatusType.SUCCESS: 1>, starttime=1730138569.8082864, endtime=1730138867.315867, additional_info={'duration': 296.1648817062378, 'num_run': 644, 'train_loss': 0.023260736554806826, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=644, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3898.9738688468933, status=<StatusType.TIMEOUT: 2>, starttime=1730138573.7683613, endtime=1730142473.8055966, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=645, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.32232949004017863, time=2.2825369834899902, status=<StatusType.SUCCESS: 1>, starttime=1730138574.7675157, endtime=1730138577.1677709, additional_info={'duration': 2.0682170391082764, 'num_run': 646, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=646, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1576423409294126, time=98.93737053871155, status=<StatusType.SUCCESS: 1>, starttime=1730138580.899547, endtime=1730138680.0879421, additional_info={'duration': 97.47779083251953, 'num_run': 647, 'train_loss': 0.021877904460763008, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=647, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5891328988266233, time=3164.2150678634644, status=<StatusType.SUCCESS: 1>, starttime=1730138581.8733923, endtime=1730141746.1678305, additional_info={'duration': 3163.6416144371033, 'num_run': 648, 'train_loss': 0.5246681468409247, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=648, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3889.7781171798706, status=<StatusType.TIMEOUT: 2>, starttime=1730138583.2635536, endtime=1730142474.0586474, additional_info={'error': 'Timeout', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=649, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9990464137311776, time=2.3284854888916016, status=<StatusType.SUCCESS: 1>, starttime=1730138584.4476628, endtime=1730138586.8438797, additional_info={'duration': 1.6753692626953125, 'num_run': 650, 'train_loss': 0.9964573390775326, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=650, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5789497250253552, time=3.3174383640289307, status=<StatusType.SUCCESS: 1>, starttime=1730138585.3115096, endtime=1730138588.695857, additional_info={'duration': 2.9842538833618164, 'num_run': 651, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=651, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6143337090178996, time=583.2014491558075, status=<StatusType.SUCCESS: 1>, starttime=1730138587.927601, endtime=1730139171.2118506, additional_info={'duration': 582.2950971126556, 'num_run': 652, 'train_loss': 0.533782358758845, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=652, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7751070738248059, time=46.332491397857666, status=<StatusType.SUCCESS: 1>, starttime=1730138589.8765619, endtime=1730138636.3125248, additional_info={'duration': 45.7314875125885, 'num_run': 653, 'train_loss': 0.6994159521458121, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=653, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3878.3050792217255, status=<StatusType.TIMEOUT: 2>, starttime=1730138594.1556606, endtime=1730142473.6306224, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=654, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7575775852931098, time=25.261871337890625, status=<StatusType.SUCCESS: 1>, starttime=1730138601.8187244, endtime=1730138627.1597753, additional_info={'duration': 24.923763513565063, 'num_run': 655, 'train_loss': 0.6350449100652433, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=655, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4946861644961452, time=2.314474582672119, status=<StatusType.SUCCESS: 1>, starttime=1730138605.5606406, endtime=1730138607.9479756, additional_info={'duration': 2.0523386001586914, 'num_run': 656, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=656, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3856.5397067070007, status=<StatusType.TIMEOUT: 2>, starttime=1730138615.7843733, endtime=1730142473.3639455, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=657, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16712758478368855, time=42.78387713432312, status=<StatusType.SUCCESS: 1>, starttime=1730138622.531988, endtime=1730138665.9103384, additional_info={'duration': 41.401691913604736, 'num_run': 658, 'train_loss': 0.029680884475777658, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=658, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9548700046255407, time=17.346516847610474, status=<StatusType.SUCCESS: 1>, starttime=1730138628.0221217, endtime=1730138645.4997952, additional_info={'duration': 17.130966186523438, 'num_run': 659, 'train_loss': 0.9427298477381664, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=659, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5340185530178532, time=2.815795660018921, status=<StatusType.SUCCESS: 1>, starttime=1730138636.999622, endtime=1730138640.4518828, additional_info={'duration': 2.459843635559082, 'num_run': 660, 'train_loss': 0.4386770347738337, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=660, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5130434199178219, time=2.6854567527770996, status=<StatusType.SUCCESS: 1>, starttime=1730138641.6674442, endtime=1730138644.4279149, additional_info={'duration': 2.4855949878692627, 'num_run': 661, 'train_loss': 0.49905334319032024, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=661, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6098848987469481, time=4.172922611236572, status=<StatusType.SUCCESS: 1>, starttime=1730138645.831647, endtime=1730138650.0878277, additional_info={'duration': 3.9492151737213135, 'num_run': 662, 'train_loss': 0.376185281246738, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=662, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16025508025373114, time=57.2963707447052, status=<StatusType.SUCCESS: 1>, starttime=1730138650.3847625, endtime=1730138707.7877553, additional_info={'duration': 56.72063374519348, 'num_run': 663, 'train_loss': 0.035373394209696894, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=663, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3816.1808199882507, status=<StatusType.TIMEOUT: 2>, starttime=1730138655.9240952, endtime=1730142473.1512792, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=664, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.33669405025194676, time=1.5931236743927002, status=<StatusType.SUCCESS: 1>, starttime=1730138661.3035252, endtime=1730138663.0077693, additional_info={'duration': 1.363203525543213, 'num_run': 665, 'train_loss': 0.2759556081172324, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=665, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.5893397331237793, status=<StatusType.CRASHED: 3>, starttime=1730138666.46262, endtime=1730138667.0917962, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=666, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8575565218907907, time=95.97343373298645, status=<StatusType.SUCCESS: 1>, starttime=1730138667.3915036, endtime=1730138763.427784, additional_info={'duration': 95.65154147148132, 'num_run': 667, 'train_loss': 0.7665299289427223, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=667, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.6254096646381906, time=5.2106921672821045, status=<StatusType.SUCCESS: 1>, starttime=1730138668.9661493, endtime=1730138674.2715945, additional_info={'duration': 4.986806392669678, 'num_run': 668, 'train_loss': 0.21904909547777954, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=668, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3792.140979528427, status=<StatusType.TIMEOUT: 2>, starttime=1730138680.3515282, endtime=1730142473.6302617, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=669, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.628936810084331, time=2.991807460784912, status=<StatusType.SUCCESS: 1>, starttime=1730138681.4435015, endtime=1730138684.505507, additional_info={'duration': 2.766103744506836, 'num_run': 670, 'train_loss': 0.6049862189819513, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=670, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9719241073688837, time=1.1770966053009033, status=<StatusType.SUCCESS: 1>, starttime=1730138686.5993817, endtime=1730138687.8318014, additional_info={'duration': 1.0049700736999512, 'num_run': 671, 'train_loss': 0.9575367333386182, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=671, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0066365939244826, time=1.3981552124023438, status=<StatusType.SUCCESS: 1>, starttime=1730138689.0462506, endtime=1730138690.4999182, additional_info={'duration': 1.2425570487976074, 'num_run': 672, 'train_loss': 0.9898385362764222, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=672, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3777.125908136368, status=<StatusType.TIMEOUT: 2>, starttime=1730138695.380391, endtime=1730142473.6290274, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=673, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3765.1777822971344, status=<StatusType.TIMEOUT: 2>, starttime=1730138706.591206, endtime=1730142472.9819927, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=674, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3759.134523153305, status=<StatusType.TIMEOUT: 2>, starttime=1730138712.798466, endtime=1730142472.9837098, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=675, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3704.5223722457886, status=<StatusType.TIMEOUT: 2>, starttime=1730138768.108387, endtime=1730142473.6848927, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=676, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5061382203849807, time=1.292341947555542, status=<StatusType.SUCCESS: 1>, starttime=1730138804.4836981, endtime=1730138805.896842, additional_info={'duration': 1.1593496799468994, 'num_run': 677, 'train_loss': 0.4126345973537928, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=677, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3657.5974085330963, status=<StatusType.TIMEOUT: 2>, starttime=1730138815.3566685, endtime=1730142473.9989705, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=678, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.1607230242442244, time=100.54818153381348, status=<StatusType.SUCCESS: 1>, starttime=1730138874.780728, endtime=1730138975.4358351, additional_info={'duration': 99.96988415718079, 'num_run': 679, 'train_loss': 0.0538120619914136, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=679, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.19456580529354245, time=83.32136344909668, status=<StatusType.SUCCESS: 1>, starttime=1730138958.399507, endtime=1730139041.7878182, additional_info={'duration': 82.0855438709259, 'num_run': 680, 'train_loss': 0.11996259493973044, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=680, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5470268437113235, time=16.20524835586548, status=<StatusType.SUCCESS: 1>, starttime=1730138976.8410769, endtime=1730138993.1317773, additional_info={'duration': 15.771668672561646, 'num_run': 681, 'train_loss': 0.40125438611513503, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=681, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7097236735374971, time=151.92142057418823, status=<StatusType.SUCCESS: 1>, starttime=1730138994.3435094, endtime=1730139146.335757, additional_info={'duration': 151.14505195617676, 'num_run': 682, 'train_loss': 0.5477387889018702, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=682, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.001006982342661, time=2.060957908630371, status=<StatusType.SUCCESS: 1>, starttime=1730139010.8756158, endtime=1730139013.0198746, additional_info={'duration': 1.8819820880889893, 'num_run': 683, 'train_loss': 0.9968261373338154, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=683, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.16052125301666392, time=84.47256255149841, status=<StatusType.SUCCESS: 1>, starttime=1730139018.9956555, endtime=1730139103.5756245, additional_info={'duration': 83.5867178440094, 'num_run': 684, 'train_loss': 0.0536595422928154, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=684, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9803532362521936, time=3.7174582481384277, status=<StatusType.SUCCESS: 1>, starttime=1730139042.5274963, endtime=1730139046.2481334, additional_info={'duration': 3.365211248397827, 'num_run': 685, 'train_loss': 0.9670098069742993, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=685, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3417.1485595703125, status=<StatusType.TIMEOUT: 2>, starttime=1730139055.1044571, endtime=1730142473.292522, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=686, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3377.075088262558, status=<StatusType.TIMEOUT: 2>, starttime=1730139094.9377246, endtime=1730142473.0880527, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=687, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.6925365924835205, status=<StatusType.CRASHED: 3>, starttime=1730139108.9716258, endtime=1730139109.6918745, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=688, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3356.6039922237396, status=<StatusType.TIMEOUT: 2>, starttime=1730139116.40364, endtime=1730142474.0455718, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=689, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.336334037386454, time=2.071906328201294, status=<StatusType.SUCCESS: 1>, starttime=1730139131.897001, endtime=1730139134.0717597, additional_info={'duration': 1.491302490234375, 'num_run': 690, 'train_loss': 0.2754139683431801, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=690, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3330.4921791553497, status=<StatusType.TIMEOUT: 2>, starttime=1730139141.948875, endtime=1730142473.629792, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=691, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3321.763662338257, status=<StatusType.TIMEOUT: 2>, starttime=1730139151.2101092, endtime=1730142473.9916267, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=692, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3312.5251684188843, status=<StatusType.TIMEOUT: 2>, starttime=1730139159.850463, endtime=1730142473.417757, additional_info={'error': 'Timeout', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=693, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9938878651919518, time=1.2485873699188232, status=<StatusType.SUCCESS: 1>, starttime=1730139166.8963432, endtime=1730139168.185991, additional_info={'duration': 1.0628623962402344, 'num_run': 694, 'train_loss': 0.9837551218293361, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=694, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3297.9048035144806, status=<StatusType.TIMEOUT: 2>, starttime=1730139174.5515447, endtime=1730142473.6250458, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=695, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.4215878082114788, time=1068.7610216140747, status=<StatusType.SUCCESS: 1>, starttime=1730139175.847613, endtime=1730140244.703781, additional_info={'duration': 1067.9203705787659, 'num_run': 696, 'train_loss': 0.28846029999060996, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=696, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=3138.5401351451874, status=<StatusType.TIMEOUT: 2>, starttime=1730139334.0034833, endtime=1730142473.6294923, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=697, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5025083702606448, time=338.7453513145447, status=<StatusType.SUCCESS: 1>, starttime=1730139502.2315154, endtime=1730139841.0689473, additional_info={'duration': 337.7662432193756, 'num_run': 698, 'train_loss': 0.28908629191997975, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=698, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.9771388751893748, time=2.7382490634918213, status=<StatusType.SUCCESS: 1>, starttime=1730139568.5520914, endtime=1730139571.3677816, additional_info={'duration': 2.5313198566436768, 'num_run': 699, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=699, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=2897.7267501354218, status=<StatusType.TIMEOUT: 2>, starttime=1730139575.2476237, endtime=1730142474.0113761, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=700, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.645223005179572, time=15.188000440597534, status=<StatusType.SUCCESS: 1>, starttime=1730139591.7675922, endtime=1730139607.0080328, additional_info={'duration': 14.940159797668457, 'num_run': 701, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=701, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.8285510079772482, time=61.60978722572327, status=<StatusType.SUCCESS: 1>, starttime=1730139608.3227854, endtime=1730139670.0357645, additional_info={'duration': 60.72871971130371, 'num_run': 702, 'train_loss': 0.5688064425119275, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=702, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.948959610999725, time=2.451796054840088, status=<StatusType.SUCCESS: 1>, starttime=1730139661.2956486, endtime=1730139663.7918754, additional_info={'duration': 1.606043815612793, 'num_run': 703, 'train_loss': 0.9415385944396177, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=703, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=2800.1511425971985, status=<StatusType.TIMEOUT: 2>, starttime=1730139671.9462178, endtime=1730142473.1245763, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=704, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.7339003572103554, time=1.3861048221588135, status=<StatusType.SUCCESS: 1>, starttime=1730139672.8389335, endtime=1730139674.272036, additional_info={'duration': 1.2010343074798584, 'num_run': 705, 'train_loss': 2.1794122062601675e-12, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=705, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.5980346551520336, time=2.3928892612457275, status=<StatusType.SUCCESS: 1>, starttime=1730139675.37563, endtime=1730139677.827846, additional_info={'duration': 2.1787874698638916, 'num_run': 706, 'train_loss': 0.5750573361473897, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=706, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=2790.997569799423, status=<StatusType.TIMEOUT: 2>, starttime=1730139681.7876685, endtime=1730142473.9885962, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=707, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.41789377577051673, time=996.0817632675171, status=<StatusType.SUCCESS: 1>, starttime=1730139846.8552625, endtime=1730140843.071903, additional_info={'duration': 994.8789558410645, 'num_run': 708, 'train_loss': 0.0739601981004205, 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=708, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.6053919792175293, status=<StatusType.CRASHED: 3>, starttime=1730140246.031503, endtime=1730140246.712077, additional_info={'traceback': 'Traceback (most recent call last):\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/__init__.py\", line 55, in fit_predict_try_except_decorator\\n    return ta(queue=queue, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 1407, in eval_cv\\n    evaluator.fit_predict_and_loss(iterative=iterative)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 535, in fit_predict_and_loss\\n    ) = self._partial_fit_and_predict_standard(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/train_evaluator.py\", line 900, in _partial_fit_and_predict_standard\\n    _fit_and_suppress_warnings(self.logger, model, X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/evaluation/abstract_evaluator.py\", line 188, in _fit_and_suppress_warnings\\n    model.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 124, in fit\\n    X, fit_params = self.fit_transformer(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/base.py\", line 136, in fit_transformer\\n    Xt = self._fit(X, y, **fit_params_steps)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 303, in _fit\\n    X, fitted_transformer = fit_transform_one_cached(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/memory.py\", line 312, in __call__\\n    return self.func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 756, in _fit_transform_one\\n    res = transformer.fit(X, y, **fit_params).transform(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/feature_type.py\", line 216, in fit\\n    self.column_transformer.fit(X, y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 470, in fit\\n    self.fit_transform(X, y=y)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 507, in fit_transform\\n    result = self._fit_transform(X, y, _fit_transform_one)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/compose/_column_transformer.py\", line 434, in _fit_transform\\n    return Parallel(n_jobs=self.n_jobs)(\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1918, in __call__\\n    return output if self.return_generator else list(output)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/joblib/parallel.py\", line 1847, in _get_sequential_output\\n    res = func(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/utils/fixes.py\", line 222, in __call__\\n    return self.function(*args, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 754, in _fit_transform_one\\n    res = transformer.fit_transform(X, y, **fit_params)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/pipeline.py\", line 389, in fit_transform\\n    return last_step.fit(Xt, y,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/base.py\", line 473, in fit\\n    return self.choice.fit(X, y, **kwargs)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/autosklearn/pipeline/components/data_preprocessing/rescaling/abstract_rescaling.py\", line 27, in fit\\n    self.preprocessor.fit(X)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3046, in fit\\n    self._fit(X, y=y, force_transform=False)\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3053, in _fit\\n    X = self._check_input(X, in_fit=True, check_positive=True,\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/sklearn/preprocessing/_data.py\", line 3274, in _check_input\\n    with np.warnings.catch_warnings():\\n  File \"/home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/numpy/__init__.py\", line 320, in __getattr__\\n    raise AttributeError(\"module {!r} has no attribute \"\\nAttributeError: module \\'numpy\\' has no attribute \\'warnings\\'\\n', 'error': 'AttributeError(\"module \\'numpy\\' has no attribute \\'warnings\\'\")', 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=709, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=2220.1611688137054, status=<StatusType.TIMEOUT: 2>, starttime=1730140252.3235795, endtime=1730142473.630805, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=710, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0005269082287749, time=1.7602019309997559, status=<StatusType.SUCCESS: 1>, starttime=1730140627.6917143, endtime=1730140629.4799592, additional_info={'duration': 1.3214006423950195, 'num_run': 711, 'train_loss': 0.994492802099496, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=711, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=1832.177506685257, status=<StatusType.TIMEOUT: 2>, starttime=1730140639.467515, endtime=1730142472.687729, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=712, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=1693.4997928142548, status=<StatusType.TIMEOUT: 2>, starttime=1730140778.7900925, endtime=1730142473.3259535, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=713, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=1624.552755355835, status=<StatusType.TIMEOUT: 2>, starttime=1730140848.0483336, endtime=1730142473.6424897, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=714, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=1197.1927227973938, status=<StatusType.TIMEOUT: 2>, starttime=1730141274.80416, endtime=1730142473.0407054, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=715, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=0.36019551847262343, time=14.960763931274414, status=<StatusType.SUCCESS: 1>, starttime=1730141747.4035673, endtime=1730141762.44792, additional_info={'duration': 13.945124864578247, 'num_run': 716, 'train_loss': 0.2228424410207964, 'configuration_origin': 'Random Search'})\n",
+      "RunKey(config_id=716, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=704.3024723529816, status=<StatusType.TIMEOUT: 2>, starttime=1730141768.4235692, endtime=1730142473.7600305, additional_info={'error': 'Timeout', 'configuration_origin': 'Local Search'})\n",
+      "RunKey(config_id=717, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.0, status=<StatusType.STOP: 8>, starttime=1730142473.1741295, endtime=1730142473.1741295, additional_info={})\n",
+      "RunKey(config_id=718, instance_id='{\"task_id\": \"a6f6378d-9546-11ef-895b-08bfb876ce9f\"}', seed=0, budget=0.0) RunValue(cost=1.0, time=0.0, status=<StatusType.STOP: 8>, starttime=1730142473.8041618, endtime=1730142473.804162, additional_info={})\n",
+      "[WARNING] [2024-10-28 15:07:54,770:Client-AutoML(1):a6f6378d-9546-11ef-895b-08bfb876ce9f] No valid ensemble was created. Please check the logfile for errors. Default to the best individual estimator:(1, 598, 0.0)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AutoSklearnRegressor(ensemble_class=None, ensemble_kwargs={'ensemble_size': 0},\n",
+       "                     include={'regressor': ['decision_tree', 'random_forest',\n",
+       "                                            'k_nearest_neighbors']},\n",
+       "                     initial_configurations_via_metalearning=0,\n",
+       "                     memory_limit=None, metric=r2, n_jobs=-1,\n",
+       "                     per_run_time_limit=34560, resampling_strategy='cv',\n",
+       "                     resampling_strategy_arguments={'folds': 5},\n",
+       "                     time_left_for_this_task=10800)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto_reg.fit(xtrain, ytrain)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f160ea4-7de6-4875-af32-5de514e449b2",
+   "metadata": {},
+   "source": [
+    "We then refit (actually training this time) on the entire dataset since the previous was using cross validation to pick the best hyperparameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d2a1f75d-a865-4686-9424-c75f5760bde6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AutoSklearnRegressor(ensemble_class=None, ensemble_kwargs={'ensemble_size': 0},\n",
+       "                     include={'regressor': ['decision_tree', 'random_forest',\n",
+       "                                            'k_nearest_neighbors']},\n",
+       "                     initial_configurations_via_metalearning=0,\n",
+       "                     memory_limit=None, metric=r2, n_jobs=-1,\n",
+       "                     per_run_time_limit=34560, resampling_strategy='cv',\n",
+       "                     resampling_strategy_arguments={'folds': 5},\n",
+       "                     time_left_for_this_task=10800)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "auto_reg.refit(xtrain, ytrain)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "461de8d3-5df6-46ae-bdf0-ec1bdeb90bc8",
+   "metadata": {},
+   "source": [
+    "Now that training is done, we can show the best models below for this task. As shown, we can see that auto_sklearn just chose random forest models for this task, possibly because they performed the best on this dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "b47648b0-58f8-4e68-abf1-9dccfdaa61bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          rank  ensemble_weight           type      cost   duration\n",
+      "model_id                                                           \n",
+      "598          1              1.0  random_forest  0.152436  102.20542\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(auto_reg.leaderboard())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e85cf61-014c-491b-a37f-385566b30cb6",
+   "metadata": {},
+   "source": [
+    "We can show all the different models and their rankings below, showing each of their configurations. Note that auto-sklearn returns a pipeline that actually preprocesses the data first. This pipeline has 3 preprocessing steps: Imputation (not needed here), Scaling (already completed), and Polynomial Degree fitting. To keep our benchmark consistent with the pyMAISE example, we will take the hyperparameters learnt here and make a new pipeline in the next section without imputation and scaling."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "b38e8a31-6c60-4c0d-8d05-da52225b289d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(1.0,\n",
+       "  SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'regressor:__choice__': 'random_forest', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'False', 'feature_preprocessor:polynomial:interaction_only': 'False', 'regressor:random_forest:bootstrap': 'True', 'regressor:random_forest:criterion': 'mse', 'regressor:random_forest:max_depth': 'None', 'regressor:random_forest:max_features': 0.9654893761894044, 'regressor:random_forest:max_leaf_nodes': 'None', 'regressor:random_forest:min_impurity_decrease': 0.0, 'regressor:random_forest:min_samples_leaf': 1, 'regressor:random_forest:min_samples_split': 2, 'regressor:random_forest:min_weight_fraction_leaf': 0.0},\n",
+       "  dataset_properties={\n",
+       "    'task': 5,\n",
+       "    'sparse': False,\n",
+       "    'multioutput': True,\n",
+       "    'target_type': 'regression',\n",
+       "    'signed': False}))]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#print(auto_reg.show_models())\n",
+    "auto_reg.get_models_with_weights()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b32e9d9-141a-461d-84b9-d4bf691e242c",
+   "metadata": {},
+   "source": [
+    "Finally it is time to benchmark the results for HTGR. We will rebuild the pipeline with the learned hyperparameters as stated above to align with the same methodolgy as in the pyMAISE example. Below, we print out the best results for R2, MAE, RMSE, and MAPE. We can see that the results for pyMAISE and auto-sklearn are almost identical at around 0.70 $R^2$. The other metrics also line up similarly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "2a2e7892-8f17-4c05-9b5c-849d82f92d2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score: 0.7053476042225316\n",
+      "MAE score: 0.001872428722826578\n",
+      "RMSE score: 0.002374621226524507\n",
+      "MAPE score: 0.007475683139485854\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.metrics import r2_score, mean_absolute_error, mean_absolute_percentage_error, mean_squared_error\n",
+    "import numpy as np\n",
+    "\n",
+    "# Define the pipeline with all preprocessing steps and the model\n",
+    "pipeline = Pipeline([\n",
+    "    ('poly', PolynomialFeatures(degree=2, include_bias=False, interaction_only=False)),  # Polynomial features\n",
+    "    ('regressor', RandomForestRegressor(\n",
+    "        bootstrap=True,\n",
+    "        criterion='mse',\n",
+    "        max_depth=None,\n",
+    "        max_features=0.9655,  # Close to 0.9654893761894044\n",
+    "        max_leaf_nodes=None,\n",
+    "        min_impurity_decrease=0.0,\n",
+    "        min_samples_leaf=1,\n",
+    "        min_samples_split=2,\n",
+    "        min_weight_fraction_leaf=0.0,\n",
+    "        random_state=42\n",
+    "    ))\n",
+    "])\n",
+    "\n",
+    "# Fit the pipeline on the training data\n",
+    "pipeline.fit(xtrain, ytrain)\n",
+    "\n",
+    "# Make predictions\n",
+    "predictions = pipeline.predict(xtest)\n",
+    "\n",
+    "# Evaluate the model\n",
+    "print(\"R2 score:\", r2_score(ytest, predictions))\n",
+    "print(\"MAE score:\", mean_absolute_error(ytest, predictions))\n",
+    "print(\"RMSE score:\", np.sqrt(mean_squared_error(ytest, predictions)))\n",
+    "print(\"MAPE score:\", mean_absolute_percentage_error(ytest, predictions))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f864623c-ab83-41d7-90ce-2f90bc106de0",
+   "metadata": {},
+   "source": [
+    "We can also optionally print out the configuration space below which shows all the hyperparameter spaces which we trained out. This gives us an idea for what decisions Auto-Sklearn made when running this example. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "fbe9127a-6b0c-488a-82cc-d266b796b92d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Configuration space object:\n",
+      "  Hyperparameters:\n",
+      "    data_preprocessor:__choice__, Type: Categorical, Choices: {feature_type}, Default: feature_type\n",
+      "    data_preprocessor:feature_type:numerical_transformer:imputation:strategy, Type: Categorical, Choices: {mean, median, most_frequent}, Default: mean\n",
+      "    data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__, Type: Categorical, Choices: {minmax, none, normalize, power_transformer, quantile_transformer, robust_scaler, standardize}, Default: standardize\n",
+      "    data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles, Type: UniformInteger, Range: [10, 2000], Default: 1000\n",
+      "    data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution, Type: Categorical, Choices: {normal, uniform}, Default: normal\n",
+      "    data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max, Type: UniformFloat, Range: [0.7, 0.999], Default: 0.75\n",
+      "    data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min, Type: UniformFloat, Range: [0.001, 0.3], Default: 0.25\n",
+      "    feature_preprocessor:__choice__, Type: Categorical, Choices: {extra_trees_preproc_for_regression, fast_ica, feature_agglomeration, kernel_pca, kitchen_sinks, no_preprocessing, nystroem_sampler, pca, polynomial, random_trees_embedding}, Default: no_preprocessing\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:bootstrap, Type: Categorical, Choices: {True, False}, Default: False\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:criterion, Type: Categorical, Choices: {mse, friedman_mse, mae}, Default: mse\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:max_depth, Type: Constant, Value: None\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:max_features, Type: UniformFloat, Range: [0.1, 1.0], Default: 1.0\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes, Type: Constant, Value: None\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf, Type: Constant, Value: 0.0\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:n_estimators, Type: Constant, Value: 100\n",
+      "    feature_preprocessor:fast_ica:algorithm, Type: Categorical, Choices: {parallel, deflation}, Default: parallel\n",
+      "    feature_preprocessor:fast_ica:fun, Type: Categorical, Choices: {logcosh, exp, cube}, Default: logcosh\n",
+      "    feature_preprocessor:fast_ica:n_components, Type: UniformInteger, Range: [10, 2000], Default: 100\n",
+      "    feature_preprocessor:fast_ica:whiten, Type: Categorical, Choices: {False, True}, Default: False\n",
+      "    feature_preprocessor:feature_agglomeration:affinity, Type: Categorical, Choices: {euclidean, manhattan, cosine}, Default: euclidean\n",
+      "    feature_preprocessor:feature_agglomeration:linkage, Type: Categorical, Choices: {ward, complete, average}, Default: ward\n",
+      "    feature_preprocessor:feature_agglomeration:n_clusters, Type: UniformInteger, Range: [2, 400], Default: 25\n",
+      "    feature_preprocessor:feature_agglomeration:pooling_func, Type: Categorical, Choices: {mean, median, max}, Default: mean\n",
+      "    feature_preprocessor:kernel_pca:coef0, Type: UniformFloat, Range: [-1.0, 1.0], Default: 0.0\n",
+      "    feature_preprocessor:kernel_pca:degree, Type: UniformInteger, Range: [2, 5], Default: 3\n",
+      "    feature_preprocessor:kernel_pca:gamma, Type: UniformFloat, Range: [3.0517578125e-05, 8.0], Default: 0.01, on log-scale\n",
+      "    feature_preprocessor:kernel_pca:kernel, Type: Categorical, Choices: {poly, rbf, sigmoid, cosine}, Default: rbf\n",
+      "    feature_preprocessor:kernel_pca:n_components, Type: UniformInteger, Range: [10, 2000], Default: 100\n",
+      "    feature_preprocessor:kitchen_sinks:gamma, Type: UniformFloat, Range: [3.0517578125e-05, 8.0], Default: 1.0, on log-scale\n",
+      "    feature_preprocessor:kitchen_sinks:n_components, Type: UniformInteger, Range: [50, 10000], Default: 100, on log-scale\n",
+      "    feature_preprocessor:nystroem_sampler:coef0, Type: UniformFloat, Range: [-1.0, 1.0], Default: 0.0\n",
+      "    feature_preprocessor:nystroem_sampler:degree, Type: UniformInteger, Range: [2, 5], Default: 3\n",
+      "    feature_preprocessor:nystroem_sampler:gamma, Type: UniformFloat, Range: [3.0517578125e-05, 8.0], Default: 0.1, on log-scale\n",
+      "    feature_preprocessor:nystroem_sampler:kernel, Type: Categorical, Choices: {poly, rbf, sigmoid, cosine}, Default: rbf\n",
+      "    feature_preprocessor:nystroem_sampler:n_components, Type: UniformInteger, Range: [50, 10000], Default: 100, on log-scale\n",
+      "    feature_preprocessor:pca:keep_variance, Type: UniformFloat, Range: [0.5, 0.9999], Default: 0.9999\n",
+      "    feature_preprocessor:pca:whiten, Type: Categorical, Choices: {False, True}, Default: False\n",
+      "    feature_preprocessor:polynomial:degree, Type: UniformInteger, Range: [2, 3], Default: 2\n",
+      "    feature_preprocessor:polynomial:include_bias, Type: Categorical, Choices: {True, False}, Default: True\n",
+      "    feature_preprocessor:polynomial:interaction_only, Type: Categorical, Choices: {False, True}, Default: False\n",
+      "    feature_preprocessor:random_trees_embedding:bootstrap, Type: Categorical, Choices: {True, False}, Default: True\n",
+      "    feature_preprocessor:random_trees_embedding:max_depth, Type: UniformInteger, Range: [2, 10], Default: 5\n",
+      "    feature_preprocessor:random_trees_embedding:max_leaf_nodes, Type: Constant, Value: None\n",
+      "    feature_preprocessor:random_trees_embedding:min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1\n",
+      "    feature_preprocessor:random_trees_embedding:min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2\n",
+      "    feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf, Type: Constant, Value: 1.0\n",
+      "    feature_preprocessor:random_trees_embedding:n_estimators, Type: UniformInteger, Range: [10, 100], Default: 10\n",
+      "    regressor:__choice__, Type: Categorical, Choices: {decision_tree, k_nearest_neighbors, random_forest}, Default: random_forest\n",
+      "    regressor:decision_tree:criterion, Type: Categorical, Choices: {mse, friedman_mse, mae}, Default: mse\n",
+      "    regressor:decision_tree:max_depth_factor, Type: UniformFloat, Range: [0.0, 2.0], Default: 0.5\n",
+      "    regressor:decision_tree:max_features, Type: Constant, Value: 1.0\n",
+      "    regressor:decision_tree:max_leaf_nodes, Type: Constant, Value: None\n",
+      "    regressor:decision_tree:min_impurity_decrease, Type: Constant, Value: 0.0\n",
+      "    regressor:decision_tree:min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1\n",
+      "    regressor:decision_tree:min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2\n",
+      "    regressor:decision_tree:min_weight_fraction_leaf, Type: Constant, Value: 0.0\n",
+      "    regressor:k_nearest_neighbors:n_neighbors, Type: UniformInteger, Range: [1, 100], Default: 1, on log-scale\n",
+      "    regressor:k_nearest_neighbors:p, Type: Categorical, Choices: {1, 2}, Default: 2\n",
+      "    regressor:k_nearest_neighbors:weights, Type: Categorical, Choices: {uniform, distance}, Default: uniform\n",
+      "    regressor:random_forest:bootstrap, Type: Categorical, Choices: {True, False}, Default: True\n",
+      "    regressor:random_forest:criterion, Type: Categorical, Choices: {mse, friedman_mse, mae}, Default: mse\n",
+      "    regressor:random_forest:max_depth, Type: Constant, Value: None\n",
+      "    regressor:random_forest:max_features, Type: UniformFloat, Range: [0.1, 1.0], Default: 1.0\n",
+      "    regressor:random_forest:max_leaf_nodes, Type: Constant, Value: None\n",
+      "    regressor:random_forest:min_impurity_decrease, Type: Constant, Value: 0.0\n",
+      "    regressor:random_forest:min_samples_leaf, Type: UniformInteger, Range: [1, 20], Default: 1\n",
+      "    regressor:random_forest:min_samples_split, Type: UniformInteger, Range: [2, 20], Default: 2\n",
+      "    regressor:random_forest:min_weight_fraction_leaf, Type: Constant, Value: 0.0\n",
+      "  Conditions:\n",
+      "    data_preprocessor:feature_type:numerical_transformer:imputation:strategy | data_preprocessor:__choice__ == 'feature_type'\n",
+      "    data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__ | data_preprocessor:__choice__ == 'feature_type'\n",
+      "    data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles | data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__ == 'quantile_transformer'\n",
+      "    data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution | data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__ == 'quantile_transformer'\n",
+      "    data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max | data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__ == 'robust_scaler'\n",
+      "    data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min | data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__ == 'robust_scaler'\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:bootstrap | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression'\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:criterion | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression'\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:max_depth | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression'\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:max_features | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression'\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:max_leaf_nodes | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression'\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:min_samples_leaf | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression'\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:min_samples_split | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression'\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:min_weight_fraction_leaf | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression'\n",
+      "    feature_preprocessor:extra_trees_preproc_for_regression:n_estimators | feature_preprocessor:__choice__ == 'extra_trees_preproc_for_regression'\n",
+      "    feature_preprocessor:fast_ica:algorithm | feature_preprocessor:__choice__ == 'fast_ica'\n",
+      "    feature_preprocessor:fast_ica:fun | feature_preprocessor:__choice__ == 'fast_ica'\n",
+      "    feature_preprocessor:fast_ica:n_components | feature_preprocessor:fast_ica:whiten == 'True'\n",
+      "    feature_preprocessor:fast_ica:whiten | feature_preprocessor:__choice__ == 'fast_ica'\n",
+      "    feature_preprocessor:feature_agglomeration:affinity | feature_preprocessor:__choice__ == 'feature_agglomeration'\n",
+      "    feature_preprocessor:feature_agglomeration:linkage | feature_preprocessor:__choice__ == 'feature_agglomeration'\n",
+      "    feature_preprocessor:feature_agglomeration:n_clusters | feature_preprocessor:__choice__ == 'feature_agglomeration'\n",
+      "    feature_preprocessor:feature_agglomeration:pooling_func | feature_preprocessor:__choice__ == 'feature_agglomeration'\n",
+      "    feature_preprocessor:kernel_pca:coef0 | feature_preprocessor:kernel_pca:kernel in {'poly', 'sigmoid'}\n",
+      "    feature_preprocessor:kernel_pca:degree | feature_preprocessor:kernel_pca:kernel == 'poly'\n",
+      "    feature_preprocessor:kernel_pca:gamma | feature_preprocessor:kernel_pca:kernel in {'poly', 'rbf'}\n",
+      "    feature_preprocessor:kernel_pca:kernel | feature_preprocessor:__choice__ == 'kernel_pca'\n",
+      "    feature_preprocessor:kernel_pca:n_components | feature_preprocessor:__choice__ == 'kernel_pca'\n",
+      "    feature_preprocessor:kitchen_sinks:gamma | feature_preprocessor:__choice__ == 'kitchen_sinks'\n",
+      "    feature_preprocessor:kitchen_sinks:n_components | feature_preprocessor:__choice__ == 'kitchen_sinks'\n",
+      "    feature_preprocessor:nystroem_sampler:coef0 | feature_preprocessor:nystroem_sampler:kernel in {'poly', 'sigmoid'}\n",
+      "    feature_preprocessor:nystroem_sampler:degree | feature_preprocessor:nystroem_sampler:kernel == 'poly'\n",
+      "    feature_preprocessor:nystroem_sampler:gamma | feature_preprocessor:nystroem_sampler:kernel in {'poly', 'rbf', 'sigmoid'}\n",
+      "    feature_preprocessor:nystroem_sampler:kernel | feature_preprocessor:__choice__ == 'nystroem_sampler'\n",
+      "    feature_preprocessor:nystroem_sampler:n_components | feature_preprocessor:__choice__ == 'nystroem_sampler'\n",
+      "    feature_preprocessor:pca:keep_variance | feature_preprocessor:__choice__ == 'pca'\n",
+      "    feature_preprocessor:pca:whiten | feature_preprocessor:__choice__ == 'pca'\n",
+      "    feature_preprocessor:polynomial:degree | feature_preprocessor:__choice__ == 'polynomial'\n",
+      "    feature_preprocessor:polynomial:include_bias | feature_preprocessor:__choice__ == 'polynomial'\n",
+      "    feature_preprocessor:polynomial:interaction_only | feature_preprocessor:__choice__ == 'polynomial'\n",
+      "    feature_preprocessor:random_trees_embedding:bootstrap | feature_preprocessor:__choice__ == 'random_trees_embedding'\n",
+      "    feature_preprocessor:random_trees_embedding:max_depth | feature_preprocessor:__choice__ == 'random_trees_embedding'\n",
+      "    feature_preprocessor:random_trees_embedding:max_leaf_nodes | feature_preprocessor:__choice__ == 'random_trees_embedding'\n",
+      "    feature_preprocessor:random_trees_embedding:min_samples_leaf | feature_preprocessor:__choice__ == 'random_trees_embedding'\n",
+      "    feature_preprocessor:random_trees_embedding:min_samples_split | feature_preprocessor:__choice__ == 'random_trees_embedding'\n",
+      "    feature_preprocessor:random_trees_embedding:min_weight_fraction_leaf | feature_preprocessor:__choice__ == 'random_trees_embedding'\n",
+      "    feature_preprocessor:random_trees_embedding:n_estimators | feature_preprocessor:__choice__ == 'random_trees_embedding'\n",
+      "    regressor:decision_tree:criterion | regressor:__choice__ == 'decision_tree'\n",
+      "    regressor:decision_tree:max_depth_factor | regressor:__choice__ == 'decision_tree'\n",
+      "    regressor:decision_tree:max_features | regressor:__choice__ == 'decision_tree'\n",
+      "    regressor:decision_tree:max_leaf_nodes | regressor:__choice__ == 'decision_tree'\n",
+      "    regressor:decision_tree:min_impurity_decrease | regressor:__choice__ == 'decision_tree'\n",
+      "    regressor:decision_tree:min_samples_leaf | regressor:__choice__ == 'decision_tree'\n",
+      "    regressor:decision_tree:min_samples_split | regressor:__choice__ == 'decision_tree'\n",
+      "    regressor:decision_tree:min_weight_fraction_leaf | regressor:__choice__ == 'decision_tree'\n",
+      "    regressor:k_nearest_neighbors:n_neighbors | regressor:__choice__ == 'k_nearest_neighbors'\n",
+      "    regressor:k_nearest_neighbors:p | regressor:__choice__ == 'k_nearest_neighbors'\n",
+      "    regressor:k_nearest_neighbors:weights | regressor:__choice__ == 'k_nearest_neighbors'\n",
+      "    regressor:random_forest:bootstrap | regressor:__choice__ == 'random_forest'\n",
+      "    regressor:random_forest:criterion | regressor:__choice__ == 'random_forest'\n",
+      "    regressor:random_forest:max_depth | regressor:__choice__ == 'random_forest'\n",
+      "    regressor:random_forest:max_features | regressor:__choice__ == 'random_forest'\n",
+      "    regressor:random_forest:max_leaf_nodes | regressor:__choice__ == 'random_forest'\n",
+      "    regressor:random_forest:min_impurity_decrease | regressor:__choice__ == 'random_forest'\n",
+      "    regressor:random_forest:min_samples_leaf | regressor:__choice__ == 'random_forest'\n",
+      "    regressor:random_forest:min_samples_split | regressor:__choice__ == 'random_forest'\n",
+      "    regressor:random_forest:min_weight_fraction_leaf | regressor:__choice__ == 'random_forest'\n",
+      "  Forbidden Clauses:\n",
+      "    (Forbidden: feature_preprocessor:feature_agglomeration:affinity in {'cosine', 'manhattan'} && Forbidden: feature_preprocessor:feature_agglomeration:linkage == 'ward')\n",
+      "    (Forbidden: regressor:__choice__ == 'decision_tree' && Forbidden: feature_preprocessor:__choice__ == 'kitchen_sinks')\n",
+      "    (Forbidden: regressor:__choice__ == 'decision_tree' && Forbidden: feature_preprocessor:__choice__ == 'kernel_pca')\n",
+      "    (Forbidden: regressor:__choice__ == 'decision_tree' && Forbidden: feature_preprocessor:__choice__ == 'nystroem_sampler')\n",
+      "    (Forbidden: regressor:__choice__ == 'k_nearest_neighbors' && Forbidden: feature_preprocessor:__choice__ == 'kitchen_sinks')\n",
+      "    (Forbidden: regressor:__choice__ == 'k_nearest_neighbors' && Forbidden: feature_preprocessor:__choice__ == 'kernel_pca')\n",
+      "    (Forbidden: regressor:__choice__ == 'k_nearest_neighbors' && Forbidden: feature_preprocessor:__choice__ == 'nystroem_sampler')\n",
+      "    (Forbidden: regressor:__choice__ == 'random_forest' && Forbidden: feature_preprocessor:__choice__ == 'kitchen_sinks')\n",
+      "    (Forbidden: regressor:__choice__ == 'random_forest' && Forbidden: feature_preprocessor:__choice__ == 'kernel_pca')\n",
+      "    (Forbidden: regressor:__choice__ == 'random_forest' && Forbidden: feature_preprocessor:__choice__ == 'nystroem_sampler')\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(auto_reg.get_configuration_space(xtrain, ytrain))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.20"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/benchmarks/H2O_HTGR.ipynb b/docs/source/benchmarks/H2O_HTGR.ipynb
new file mode 100644
index 0000000..a095aa3
--- /dev/null
+++ b/docs/source/benchmarks/H2O_HTGR.ipynb
@@ -0,0 +1,1656 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5519d711-f26c-4d74-a03d-3372e4cf2e00",
+   "metadata": {},
+   "source": [
+    "# H2O Benchmark: HTGR Micro-Core Quadrant Power\n",
+    "\n",
+    "**Input**\n",
+    "\n",
+    "- `theta1`: Angle of control drum in quadrant 1 (radians) \n",
+    "- `theta2`: Angle of control drum in quadrant 1 (radians) \n",
+    "- `theta3`: Angle of control drum in quadrant 2 (radians)  \n",
+    "- `theta4`: Angle of control drum in quadrant 2 (radians)\n",
+    "- `theta5`: Angle of control drum in quadrant 3 (radians)\n",
+    "- `theta6`: Angle of control drum in quadrant 3 (radians)\n",
+    "- `theta7`: Angle of control drum in quadrant 4 (radians)  \n",
+    "- `theta8`: Angle of control drum in quadrant 4 (radians)  \n",
+    "\n",
+    "**Output** \n",
+    "\n",
+    "- `fluxQ1` : Neutron flux in quadrant 1 ($\\frac{neutrons}{cm^{2} s}$)\n",
+    "- `fluxQ2` : Neutron flux in quadrant 2 ($\\frac{neutrons}{cm^{2} s}$)\n",
+    "- `fluxQ3` : Neutron flux in quadrant 3 ($\\frac{neutrons}{cm^{2} s}$)\n",
+    "- `fluxQ4` : Neutron flux in quadrant 4 ($\\frac{neutrons}{cm^{2} s}$)\n",
+    "\n",
+    "\n",
+    "We will be benchmarking the complete HTGR dataset of 3004 samples using H2O ML (version 3.46.0.5) in efforts to compare pyMAISE to other industry standard ML benchmarking frameworks. We will be following the same procedures we did in the original HTGR example, first extending the dataset to 3004 samples using symmetry, and then training and evaluating to compare results. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "32b0c9d0-c21e-4bb8-9018-17b56a1e0ff1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Packages\n",
+    "import time\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Set display option to show all rows and columns\n",
+    "pd.set_option('display.max_rows', None)\n",
+    "pd.set_option('display.max_columns', None)\n",
+    "\n",
+    "# Set the width of the columns\n",
+    "pd.set_option('display.width', None)\n",
+    "\n",
+    "# See the full content of each column\n",
+    "pd.set_option('display.max_colwidth', None)\n",
+    "\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import uniform, randint\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import Normalizer, MinMaxScaler\n",
+    "# Plot settings\n",
+    "matplotlib_settings = {\n",
+    "    \"font.size\": 12,\n",
+    "    \"legend.fontsize\": 11,\n",
+    "    \"figure.figsize\": (8, 8)\n",
+    "}\n",
+    "plt.rcParams.update(**matplotlib_settings)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "762b042e-77f9-4b93-a846-5cfcec082d0b",
+   "metadata": {},
+   "source": [
+    "## Processing the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa66a223-826f-4ccf-bc72-424e97a8fdfd",
+   "metadata": {},
+   "source": [
+    "First, we will load the raw data into a dataframe and print it out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b1545ffc-bce4-4638-91df-448454c91cca",
+   "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>sample number</th>\n",
+       "      <th>cpu_time</th>\n",
+       "      <th>runtime</th>\n",
+       "      <th>k</th>\n",
+       "      <th>fluxQ1</th>\n",
+       "      <th>fluxQ2</th>\n",
+       "      <th>fluxQ3</th>\n",
+       "      <th>fluxQ4</th>\n",
+       "      <th>k_uncert</th>\n",
+       "      <th>flux_runcertQ1</th>\n",
+       "      <th>flux_runcertQ2</th>\n",
+       "      <th>flux_runcertQ3</th>\n",
+       "      <th>flux_runcertQ4</th>\n",
+       "      <th>fissQ1</th>\n",
+       "      <th>fissQ2</th>\n",
+       "      <th>fissQ3</th>\n",
+       "      <th>fissQ4</th>\n",
+       "      <th>fissEQ1</th>\n",
+       "      <th>fissEQ2</th>\n",
+       "      <th>fissEQ3</th>\n",
+       "      <th>fissEQ4</th>\n",
+       "      <th>fiss_runcertQ1</th>\n",
+       "      <th>fiss_runcertQ2</th>\n",
+       "      <th>fiss_runcertQ3</th>\n",
+       "      <th>fiss_runcertQ4</th>\n",
+       "      <th>fissE_runcertQ1</th>\n",
+       "      <th>fissE_runcertQ2</th>\n",
+       "      <th>fissE_runcertQ3</th>\n",
+       "      <th>fissE_runcertQ4</th>\n",
+       "      <th>theta1</th>\n",
+       "      <th>theta2</th>\n",
+       "      <th>theta3</th>\n",
+       "      <th>theta4</th>\n",
+       "      <th>theta5</th>\n",
+       "      <th>theta6</th>\n",
+       "      <th>theta7</th>\n",
+       "      <th>theta8</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sample_00000</td>\n",
+       "      <td>4260.0</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>0.998328</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.590000e+19</td>\n",
+       "      <td>2.670000e+19</td>\n",
+       "      <td>2.560000e+19</td>\n",
+       "      <td>0.00019</td>\n",
+       "      <td>0.00112</td>\n",
+       "      <td>0.00111</td>\n",
+       "      <td>0.00111</td>\n",
+       "      <td>0.00108</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>2751290</td>\n",
+       "      <td>2751060</td>\n",
+       "      <td>2749270</td>\n",
+       "      <td>2750450</td>\n",
+       "      <td>0.00060</td>\n",
+       "      <td>0.00060</td>\n",
+       "      <td>0.00063</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>0.00060</td>\n",
+       "      <td>0.00060</td>\n",
+       "      <td>0.00063</td>\n",
+       "      <td>0.00062</td>\n",
+       "      <td>5.919526</td>\n",
+       "      <td>2.369503</td>\n",
+       "      <td>2.923656</td>\n",
+       "      <td>4.488987</td>\n",
+       "      <td>3.683212</td>\n",
+       "      <td>4.008905</td>\n",
+       "      <td>4.970368</td>\n",
+       "      <td>2.987966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sample_00001</td>\n",
+       "      <td>2570.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>0.988522</td>\n",
+       "      <td>2.550000e+19</td>\n",
+       "      <td>2.530000e+19</td>\n",
+       "      <td>2.510000e+19</td>\n",
+       "      <td>2.510000e+19</td>\n",
+       "      <td>0.00025</td>\n",
+       "      <td>0.00142</td>\n",
+       "      <td>0.00148</td>\n",
+       "      <td>0.00154</td>\n",
+       "      <td>0.00150</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>2750610</td>\n",
+       "      <td>2750210</td>\n",
+       "      <td>2750150</td>\n",
+       "      <td>2750110</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00077</td>\n",
+       "      <td>0.00084</td>\n",
+       "      <td>0.00074</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00077</td>\n",
+       "      <td>0.00084</td>\n",
+       "      <td>0.00074</td>\n",
+       "      <td>2.162380</td>\n",
+       "      <td>0.273624</td>\n",
+       "      <td>0.927741</td>\n",
+       "      <td>4.595586</td>\n",
+       "      <td>2.598824</td>\n",
+       "      <td>0.170167</td>\n",
+       "      <td>2.124048</td>\n",
+       "      <td>4.980209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sample_00002</td>\n",
+       "      <td>2590.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>1.004610</td>\n",
+       "      <td>2.570000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>0.00025</td>\n",
+       "      <td>0.00167</td>\n",
+       "      <td>0.00163</td>\n",
+       "      <td>0.00161</td>\n",
+       "      <td>0.00165</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>2748870</td>\n",
+       "      <td>2749690</td>\n",
+       "      <td>2752250</td>\n",
+       "      <td>2751840</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00077</td>\n",
+       "      <td>0.00086</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00077</td>\n",
+       "      <td>0.00086</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.450100</td>\n",
+       "      <td>0.006301</td>\n",
+       "      <td>2.512217</td>\n",
+       "      <td>3.313864</td>\n",
+       "      <td>1.913458</td>\n",
+       "      <td>3.582252</td>\n",
+       "      <td>0.280764</td>\n",
+       "      <td>4.888595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sample_00003</td>\n",
+       "      <td>2580.0</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>0.991892</td>\n",
+       "      <td>2.570000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>2.560000e+19</td>\n",
+       "      <td>0.00025</td>\n",
+       "      <td>0.00197</td>\n",
+       "      <td>0.00193</td>\n",
+       "      <td>0.00195</td>\n",
+       "      <td>0.00200</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.470000e+16</td>\n",
+       "      <td>2748920</td>\n",
+       "      <td>2750720</td>\n",
+       "      <td>2749330</td>\n",
+       "      <td>2746220</td>\n",
+       "      <td>0.00082</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00078</td>\n",
+       "      <td>0.00082</td>\n",
+       "      <td>0.00076</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00078</td>\n",
+       "      <td>0.461105</td>\n",
+       "      <td>4.825628</td>\n",
+       "      <td>3.771356</td>\n",
+       "      <td>2.599278</td>\n",
+       "      <td>2.056019</td>\n",
+       "      <td>0.007332</td>\n",
+       "      <td>1.106786</td>\n",
+       "      <td>5.504671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sample_00004</td>\n",
+       "      <td>2570.0</td>\n",
+       "      <td>129.0</td>\n",
+       "      <td>0.985047</td>\n",
+       "      <td>2.540000e+19</td>\n",
+       "      <td>2.620000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>0.00025</td>\n",
+       "      <td>0.00167</td>\n",
+       "      <td>0.00167</td>\n",
+       "      <td>0.00172</td>\n",
+       "      <td>0.00169</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>8.480000e+16</td>\n",
+       "      <td>8.490000e+16</td>\n",
+       "      <td>2748910</td>\n",
+       "      <td>2753130</td>\n",
+       "      <td>2747870</td>\n",
+       "      <td>2752420</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00081</td>\n",
+       "      <td>0.00082</td>\n",
+       "      <td>0.00083</td>\n",
+       "      <td>0.00080</td>\n",
+       "      <td>0.00081</td>\n",
+       "      <td>0.00082</td>\n",
+       "      <td>0.00083</td>\n",
+       "      <td>5.248202</td>\n",
+       "      <td>3.549416</td>\n",
+       "      <td>3.333632</td>\n",
+       "      <td>3.907310</td>\n",
+       "      <td>2.095312</td>\n",
+       "      <td>5.585145</td>\n",
+       "      <td>3.774253</td>\n",
+       "      <td>2.480120</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  sample number  cpu_time  runtime         k        fluxQ1        fluxQ2  \\\n",
+       "0  sample_00000    4260.0    200.0  0.998328  2.580000e+19  2.590000e+19   \n",
+       "1  sample_00001    2570.0    130.0  0.988522  2.550000e+19  2.530000e+19   \n",
+       "2  sample_00002    2590.0    130.0  1.004610  2.570000e+19  2.580000e+19   \n",
+       "3  sample_00003    2580.0    129.0  0.991892  2.570000e+19  2.580000e+19   \n",
+       "4  sample_00004    2570.0    129.0  0.985047  2.540000e+19  2.620000e+19   \n",
+       "\n",
+       "         fluxQ3        fluxQ4  k_uncert  flux_runcertQ1  flux_runcertQ2  \\\n",
+       "0  2.670000e+19  2.560000e+19   0.00019         0.00112         0.00111   \n",
+       "1  2.510000e+19  2.510000e+19   0.00025         0.00142         0.00148   \n",
+       "2  2.520000e+19  2.520000e+19   0.00025         0.00167         0.00163   \n",
+       "3  2.520000e+19  2.560000e+19   0.00025         0.00197         0.00193   \n",
+       "4  2.580000e+19  2.520000e+19   0.00025         0.00167         0.00167   \n",
+       "\n",
+       "   flux_runcertQ3  flux_runcertQ4        fissQ1        fissQ2        fissQ3  \\\n",
+       "0         0.00111         0.00108  8.490000e+16  8.490000e+16  8.480000e+16   \n",
+       "1         0.00154         0.00150  8.490000e+16  8.490000e+16  8.490000e+16   \n",
+       "2         0.00161         0.00165  8.480000e+16  8.480000e+16  8.490000e+16   \n",
+       "3         0.00195         0.00200  8.480000e+16  8.490000e+16  8.480000e+16   \n",
+       "4         0.00172         0.00169  8.480000e+16  8.490000e+16  8.480000e+16   \n",
+       "\n",
+       "         fissQ4  fissEQ1  fissEQ2  fissEQ3  fissEQ4  fiss_runcertQ1  \\\n",
+       "0  8.490000e+16  2751290  2751060  2749270  2750450         0.00060   \n",
+       "1  8.490000e+16  2750610  2750210  2750150  2750110         0.00076   \n",
+       "2  8.490000e+16  2748870  2749690  2752250  2751840         0.00076   \n",
+       "3  8.470000e+16  2748920  2750720  2749330  2746220         0.00082   \n",
+       "4  8.490000e+16  2748910  2753130  2747870  2752420         0.00080   \n",
+       "\n",
+       "   fiss_runcertQ2  fiss_runcertQ3  fiss_runcertQ4  fissE_runcertQ1  \\\n",
+       "0         0.00060         0.00063         0.00062          0.00060   \n",
+       "1         0.00077         0.00084         0.00074          0.00076   \n",
+       "2         0.00077         0.00086         0.00080          0.00076   \n",
+       "3         0.00076         0.00080         0.00078          0.00082   \n",
+       "4         0.00081         0.00082         0.00083          0.00080   \n",
+       "\n",
+       "   fissE_runcertQ2  fissE_runcertQ3  fissE_runcertQ4    theta1    theta2  \\\n",
+       "0          0.00060          0.00063          0.00062  5.919526  2.369503   \n",
+       "1          0.00077          0.00084          0.00074  2.162380  0.273624   \n",
+       "2          0.00077          0.00086          0.00080  0.450100  0.006301   \n",
+       "3          0.00076          0.00080          0.00078  0.461105  4.825628   \n",
+       "4          0.00081          0.00082          0.00083  5.248202  3.549416   \n",
+       "\n",
+       "     theta3    theta4    theta5    theta6    theta7    theta8  \n",
+       "0  2.923656  4.488987  3.683212  4.008905  4.970368  2.987966  \n",
+       "1  0.927741  4.595586  2.598824  0.170167  2.124048  4.980209  \n",
+       "2  2.512217  3.313864  1.913458  3.582252  0.280764  4.888595  \n",
+       "3  3.771356  2.599278  2.056019  0.007332  1.106786  5.504671  \n",
+       "4  3.333632  3.907310  2.095312  5.585145  3.774253  2.480120  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "cwd = os.getcwd()\n",
+    "new_cwd = cwd.replace(\"/docs/source/benchmarks\", \"/pyMAISE/datasets\")\n",
+    "\n",
+    "# Define the full path to the microreactor.csv file\n",
+    "csv_path = os.path.join(new_cwd, 'microreactor.csv')\n",
+    "\n",
+    "# Load the CSV file into a pandas DataFrame\n",
+    "raw_data = pd.read_csv(csv_path)\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e31d3b9-d7a4-4ad4-95d1-f0e258b5cddf",
+   "metadata": {},
+   "source": [
+    "We are then going to create input and output dataframes by defining our input and output variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e40bda25-dcf9-4604-a3eb-ccd069ba8662",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the input DataFrame with theta values\n",
+    "input_columns = ['theta1', 'theta2', 'theta3', 'theta4', 'theta5', 'theta6', 'theta7', 'theta8']\n",
+    "inputs = raw_data[input_columns]\n",
+    "\n",
+    "# Create the output DataFrame with flux values\n",
+    "output_columns = ['fluxQ1', 'fluxQ2', 'fluxQ3', 'fluxQ4']\n",
+    "outputs = raw_data[output_columns]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d688ceeb-e9fc-4f1c-af4c-b8364f2ec17e",
+   "metadata": {},
+   "source": [
+    "Below, we print out the results for input and output then also create a combined dataset with both."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "87def8e9-4377-4467-a39b-53dc1bbc4089",
+   "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>theta1</th>\n",
+       "      <th>theta2</th>\n",
+       "      <th>theta3</th>\n",
+       "      <th>theta4</th>\n",
+       "      <th>theta5</th>\n",
+       "      <th>theta6</th>\n",
+       "      <th>theta7</th>\n",
+       "      <th>theta8</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.919526</td>\n",
+       "      <td>2.369503</td>\n",
+       "      <td>2.923656</td>\n",
+       "      <td>4.488987</td>\n",
+       "      <td>3.683212</td>\n",
+       "      <td>4.008905</td>\n",
+       "      <td>4.970368</td>\n",
+       "      <td>2.987966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.162380</td>\n",
+       "      <td>0.273624</td>\n",
+       "      <td>0.927741</td>\n",
+       "      <td>4.595586</td>\n",
+       "      <td>2.598824</td>\n",
+       "      <td>0.170167</td>\n",
+       "      <td>2.124048</td>\n",
+       "      <td>4.980209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.450100</td>\n",
+       "      <td>0.006301</td>\n",
+       "      <td>2.512217</td>\n",
+       "      <td>3.313864</td>\n",
+       "      <td>1.913458</td>\n",
+       "      <td>3.582252</td>\n",
+       "      <td>0.280764</td>\n",
+       "      <td>4.888595</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.461105</td>\n",
+       "      <td>4.825628</td>\n",
+       "      <td>3.771356</td>\n",
+       "      <td>2.599278</td>\n",
+       "      <td>2.056019</td>\n",
+       "      <td>0.007332</td>\n",
+       "      <td>1.106786</td>\n",
+       "      <td>5.504671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.248202</td>\n",
+       "      <td>3.549416</td>\n",
+       "      <td>3.333632</td>\n",
+       "      <td>3.907310</td>\n",
+       "      <td>2.095312</td>\n",
+       "      <td>5.585145</td>\n",
+       "      <td>3.774253</td>\n",
+       "      <td>2.480120</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     theta1    theta2    theta3    theta4    theta5    theta6    theta7  \\\n",
+       "0  5.919526  2.369503  2.923656  4.488987  3.683212  4.008905  4.970368   \n",
+       "1  2.162380  0.273624  0.927741  4.595586  2.598824  0.170167  2.124048   \n",
+       "2  0.450100  0.006301  2.512217  3.313864  1.913458  3.582252  0.280764   \n",
+       "3  0.461105  4.825628  3.771356  2.599278  2.056019  0.007332  1.106786   \n",
+       "4  5.248202  3.549416  3.333632  3.907310  2.095312  5.585145  3.774253   \n",
+       "\n",
+       "     theta8  \n",
+       "0  2.987966  \n",
+       "1  4.980209  \n",
+       "2  4.888595  \n",
+       "3  5.504671  \n",
+       "4  2.480120  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "293ab317-d7e1-4fcf-aa94-5e3758e5a22a",
+   "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>fluxQ1</th>\n",
+       "      <th>fluxQ2</th>\n",
+       "      <th>fluxQ3</th>\n",
+       "      <th>fluxQ4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.590000e+19</td>\n",
+       "      <td>2.670000e+19</td>\n",
+       "      <td>2.560000e+19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.550000e+19</td>\n",
+       "      <td>2.530000e+19</td>\n",
+       "      <td>2.510000e+19</td>\n",
+       "      <td>2.510000e+19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.570000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2.570000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "      <td>2.560000e+19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2.540000e+19</td>\n",
+       "      <td>2.620000e+19</td>\n",
+       "      <td>2.580000e+19</td>\n",
+       "      <td>2.520000e+19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         fluxQ1        fluxQ2        fluxQ3        fluxQ4\n",
+       "0  2.580000e+19  2.590000e+19  2.670000e+19  2.560000e+19\n",
+       "1  2.550000e+19  2.530000e+19  2.510000e+19  2.510000e+19\n",
+       "2  2.570000e+19  2.580000e+19  2.520000e+19  2.520000e+19\n",
+       "3  2.570000e+19  2.580000e+19  2.520000e+19  2.560000e+19\n",
+       "4  2.540000e+19  2.620000e+19  2.580000e+19  2.520000e+19"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outputs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3dfa175a-b363-4c23-ba85-d80d515128a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     theta1    theta2    theta3    theta4    theta5    theta6    theta7  \\\n",
+      "0  5.919526  2.369503  2.923656  4.488987  3.683212  4.008905  4.970368   \n",
+      "1  2.162380  0.273624  0.927741  4.595586  2.598824  0.170167  2.124048   \n",
+      "2  0.450100  0.006301  2.512217  3.313864  1.913458  3.582252  0.280764   \n",
+      "3  0.461105  4.825628  3.771356  2.599278  2.056019  0.007332  1.106786   \n",
+      "4  5.248202  3.549416  3.333632  3.907310  2.095312  5.585145  3.774253   \n",
+      "\n",
+      "     theta8        fluxQ1        fluxQ2        fluxQ3        fluxQ4  \n",
+      "0  2.987966  2.580000e+19  2.590000e+19  2.670000e+19  2.560000e+19  \n",
+      "1  4.980209  2.550000e+19  2.530000e+19  2.510000e+19  2.510000e+19  \n",
+      "2  4.888595  2.570000e+19  2.580000e+19  2.520000e+19  2.520000e+19  \n",
+      "3  5.504671  2.570000e+19  2.580000e+19  2.520000e+19  2.560000e+19  \n",
+      "4  2.480120  2.540000e+19  2.620000e+19  2.580000e+19  2.520000e+19  \n"
+     ]
+    }
+   ],
+   "source": [
+    "combined_df = pd.concat([inputs, outputs], axis=1)\n",
+    "print(combined_df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7532d981-6683-42b2-87d5-fc4a18e9b613",
+   "metadata": {},
+   "source": [
+    "Now it is time to extend the dataset to 3004 samples. This is done in the same way as in the original HTGR, replicating the same steps below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ee65be9b-4c39-4e8f-8952-5f4e8b7bb814",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Credit to mult_sym and g21 from https://github.com/deanrp2/MicroControl/blob/main/pmdata/utils.py#L51\n",
+    "theta_cols = [f\"theta{i + 1}\" for i in range(8)]\n",
+    "flux_cols = [f\"fluxQ{i + 1}\" for i in range(4)]\n",
+    "\n",
+    "def mult_samples(data):\n",
+    "    # Create empty arrays\n",
+    "    ht = xr.DataArray(\n",
+    "        np.zeros(data.shape), \n",
+    "        coords={\n",
+    "            \"index\": [f\"{idx}_h\" for idx in data.coords[\"index\"].values],\n",
+    "            \"variable\": data.coords[\"variable\"],\n",
+    "        },\n",
+    "    )\n",
+    "    vt = xr.DataArray(\n",
+    "        np.zeros(data.shape), \n",
+    "        coords={\n",
+    "            \"index\": [f\"{idx}_v\" for idx in data.coords[\"index\"].values],\n",
+    "            \"variable\": data.coords[\"variable\"],\n",
+    "        },\n",
+    "    )\n",
+    "    rt = xr.DataArray(\n",
+    "        np.zeros(data.shape),     \n",
+    "        coords={\n",
+    "            \"index\": [f\"{idx}_r\" for idx in data.coords[\"index\"].values],\n",
+    "            \"variable\": data.coords[\"variable\"],\n",
+    "        },\n",
+    "    )\n",
+    "\n",
+    "    # Swap drum positions\n",
+    "    hkey = [f\"theta{i}\" for i in np.array([3, 2, 1, 0, 7, 6, 5, 4], dtype=int) + 1]\n",
+    "    vkey = [f\"theta{i}\" for i in np.array([7, 6, 5, 4, 3, 2, 1, 0], dtype=int) + 1]\n",
+    "    rkey = [f\"theta{i}\" for i in np.array([4, 5, 6, 7, 0, 1, 2, 3], dtype=int) + 1]\n",
+    "\n",
+    "    ht.loc[:, hkey] = data.loc[:, theta_cols].values\n",
+    "    vt.loc[:, vkey] = data.loc[:, theta_cols].values\n",
+    "    rt.loc[:, rkey] = data.loc[:, theta_cols].values\n",
+    "\n",
+    "    # Adjust angles\n",
+    "    ht.loc[:, hkey] = (3 * np.pi - ht.loc[:, hkey].loc[:, hkey]) % (2 * np.pi)\n",
+    "    vt.loc[:, vkey] = (2 * np.pi - vt.loc[:, hkey].loc[:, vkey]) % (2 * np.pi)\n",
+    "    rt.loc[:, rkey] = (np.pi + rt.loc[:, hkey].loc[:, rkey]) % (2 * np.pi)\n",
+    "\n",
+    "    # Fill quadrant tallies\n",
+    "    hkey = [2, 1, 4, 3]\n",
+    "    vkey = [4, 3, 2, 1]\n",
+    "    rkey = [3, 4, 1, 2]\n",
+    "\n",
+    "    ht.loc[:, [f\"fluxQ{i}\" for i in hkey]] = data.loc[:, flux_cols].values\n",
+    "    vt.loc[:, [f\"fluxQ{i}\" for i in vkey]] = data.loc[:, flux_cols].values\n",
+    "    rt.loc[:, [f\"fluxQ{i}\" for i in rkey]] = data.loc[:, flux_cols].values\n",
+    "\n",
+    "    sym_data = xr.concat([data, ht, vt, rt], dim=\"index\").sortby(\"index\")\n",
+    "    \n",
+    "    # Normalize fluxes\n",
+    "    sym_data.loc[:, flux_cols].values = Normalizer().transform(sym_data.loc[:, flux_cols].values)\n",
+    "    \n",
+    "    # Convert global coordinate system to local\n",
+    "    loc_offsets = np.array(\n",
+    "        [3.6820187359906447, 4.067668586955522, 2.2155167202240653 - np.pi, 2.6011665711889425 - np.pi, \n",
+    "         0.5404260824008517, 0.9260759333657285, 5.3571093738138575 - np.pi, 5.742759224778734 - np.pi]\n",
+    "    )\n",
+    "\n",
+    "    # Apply correct 0 point\n",
+    "    sym_data.loc[:, theta_cols] = sym_data.loc[:, theta_cols] - loc_offsets + 2 * np.pi\n",
+    "\n",
+    "    # Reverse necessary angles\n",
+    "    sym_data.loc[:, [f\"theta{i}\" for i in [3,4,5,6]]] *= -1\n",
+    "\n",
+    "    # Scale all to [0, 2 * np.pi]\n",
+    "    sym_data.loc[:, theta_cols] = sym_data.loc[:, theta_cols] % (2 * np.pi)\n",
+    "        \n",
+    "    return sym_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5e9375ce-4349-4565-9338-c8e6cefe17fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data, test_data = train_test_split(combined_df, test_size=0.3)\n",
+    "\n",
+    "# Convert to xarray DataArray and specify the index as a coordinate\n",
+    "train_data_xr = xr.DataArray(\n",
+    "    train_data.values,\n",
+    "    coords={\"index\": train_data.index, \"variable\": train_data.columns},\n",
+    "    dims=[\"index\", \"variable\"]\n",
+    ")\n",
+    "test_data_xr = xr.DataArray(\n",
+    "    test_data.values,\n",
+    "    coords={\"index\": test_data.index, \"variable\": test_data.columns},\n",
+    "    dims=[\"index\", \"variable\"]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "638ecb25-2331-47d6-84b6-52664a22a91a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Multiplied training shape: (2100, 12), Multiplied testing shape: (904, 12)\n"
+     ]
+    }
+   ],
+   "source": [
+    "sym_train_data = mult_samples(train_data_xr)\n",
+    "sym_test_data = mult_samples(test_data_xr)\n",
+    "print(f\"Multiplied training shape: {sym_train_data.shape}, Multiplied testing shape: {sym_test_data.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df4eda46-66b5-46bc-8034-b05cc46d2011",
+   "metadata": {},
+   "source": [
+    "As seen above, we end up with data the same size as the original HTGR. Below, we are going to Min-Max the X_data and normalize the y_data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3273166b-5d63-491b-872e-f8fe45b33e55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Min-Max scaling data \n",
+    "def scale_data(train_data, test_data, scaler):\n",
+    "    train_data.values = scaler.fit_transform(\n",
+    "        train_data.values.reshape(-1, train_data.shape[-1])\n",
+    "    ).reshape(train_data.shape)\n",
+    "    test_data.values = scaler.transform(\n",
+    "        test_data.values.reshape(-1, test_data.shape[-1])\n",
+    "    ).reshape(test_data.shape)\n",
+    "    \n",
+    "    # Return data\n",
+    "    return train_data, test_data, scaler\n",
+    "\n",
+    "xtrain_arr, xtest_arr , _ = scale_data(sym_train_data.loc[:, theta_cols], sym_test_data.loc[:, theta_cols], MinMaxScaler())\n",
+    "ytrain_arr, ytest_arr, _ = scale_data(sym_train_data.loc[:, flux_cols], sym_test_data.loc[:, flux_cols], Normalizer(norm=\"l1\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bbbecbde-b745-48f4-a7d7-81cc88e2373c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xtrain = xtrain_arr.to_pandas()\n",
+    "xtest = xtest_arr.to_pandas()\n",
+    "ytrain = ytrain_arr.to_pandas()\n",
+    "ytest = ytest_arr.to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "78b1ff10-3f19-469d-8daa-8ed8a715bc1f",
+   "metadata": {},
+   "source": [
+    "## Benchmark with H20ML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5867ec22-1ead-4f68-922d-06dbb936c8d4",
+   "metadata": {},
+   "source": [
+    "Now that all the data is preprocessed in the same fashion as the original HTGR example, it is time to use H2OML to obtain results. Below, we import the necessary libraries from H2O and initialize the H2O instance for the next tasks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "feeb8520-f582-48c3-9374-e106e5751e50",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n",
+      "Attempting to start a local H2O server...\n",
+      "  Java Version: openjdk version \"11.0.24\" 2024-07-16; OpenJDK Runtime Environment (build 11.0.24+8-post-Ubuntu-1ubuntu322.04); OpenJDK 64-Bit Server VM (build 11.0.24+8-post-Ubuntu-1ubuntu322.04, mixed mode, sharing)\n",
+      "  Starting server from /home/schidige/anaconda3/envs/h2oML/lib/python3.8/site-packages/h2o/backend/bin/h2o.jar\n",
+      "  Ice root: /tmp/tmpqy9lzok0\n",
+      "  JVM stdout: /tmp/tmpqy9lzok0/h2o_schidige_started_from_python.out\n",
+      "  JVM stderr: /tmp/tmpqy9lzok0/h2o_schidige_started_from_python.err\n",
+      "  Server is running at http://127.0.0.1:54321\n",
+      "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<style>\n",
+       "\n",
+       "#h2o-table-2.h2o-container {\n",
+       "  overflow-x: auto;\n",
+       "}\n",
+       "#h2o-table-2 .h2o-table {\n",
+       "  /* width: 100%; */\n",
+       "  margin-top: 1em;\n",
+       "  margin-bottom: 1em;\n",
+       "}\n",
+       "#h2o-table-2 .h2o-table caption {\n",
+       "  white-space: nowrap;\n",
+       "  caption-side: top;\n",
+       "  text-align: left;\n",
+       "  /* margin-left: 1em; */\n",
+       "  margin: 0;\n",
+       "  font-size: larger;\n",
+       "}\n",
+       "#h2o-table-2 .h2o-table thead {\n",
+       "  white-space: nowrap; \n",
+       "  position: sticky;\n",
+       "  top: 0;\n",
+       "  box-shadow: 0 -1px inset;\n",
+       "}\n",
+       "#h2o-table-2 .h2o-table tbody {\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "#h2o-table-2 .h2o-table th,\n",
+       "#h2o-table-2 .h2o-table td {\n",
+       "  text-align: right;\n",
+       "  /* border: 1px solid; */\n",
+       "}\n",
+       "#h2o-table-2 .h2o-table tr:nth-child(even) {\n",
+       "  /* background: #F5F5F5 */\n",
+       "}\n",
+       "\n",
+       "</style>      \n",
+       "<div id=\"h2o-table-2\" class=\"h2o-container\">\n",
+       "  <table class=\"h2o-table\">\n",
+       "    <caption></caption>\n",
+       "    <thead></thead>\n",
+       "    <tbody><tr><td>H2O_cluster_uptime:</td>\n",
+       "<td>01 secs</td></tr>\n",
+       "<tr><td>H2O_cluster_timezone:</td>\n",
+       "<td>America/New_York</td></tr>\n",
+       "<tr><td>H2O_data_parsing_timezone:</td>\n",
+       "<td>UTC</td></tr>\n",
+       "<tr><td>H2O_cluster_version:</td>\n",
+       "<td>3.46.0.5</td></tr>\n",
+       "<tr><td>H2O_cluster_version_age:</td>\n",
+       "<td>1 month and 22 days</td></tr>\n",
+       "<tr><td>H2O_cluster_name:</td>\n",
+       "<td>H2O_from_python_schidige_rvz83h</td></tr>\n",
+       "<tr><td>H2O_cluster_total_nodes:</td>\n",
+       "<td>1</td></tr>\n",
+       "<tr><td>H2O_cluster_free_memory:</td>\n",
+       "<td>29.97 Gb</td></tr>\n",
+       "<tr><td>H2O_cluster_total_cores:</td>\n",
+       "<td>32</td></tr>\n",
+       "<tr><td>H2O_cluster_allowed_cores:</td>\n",
+       "<td>32</td></tr>\n",
+       "<tr><td>H2O_cluster_status:</td>\n",
+       "<td>locked, healthy</td></tr>\n",
+       "<tr><td>H2O_connection_url:</td>\n",
+       "<td>http://127.0.0.1:54321</td></tr>\n",
+       "<tr><td>H2O_connection_proxy:</td>\n",
+       "<td>{\"http\": null, \"https\": null}</td></tr>\n",
+       "<tr><td>H2O_internal_security:</td>\n",
+       "<td>False</td></tr>\n",
+       "<tr><td>Python_version:</td>\n",
+       "<td>3.8.20 final</td></tr></tbody>\n",
+       "  </table>\n",
+       "</div>\n"
+      ],
+      "text/plain": [
+       "--------------------------  -------------------------------\n",
+       "H2O_cluster_uptime:         01 secs\n",
+       "H2O_cluster_timezone:       America/New_York\n",
+       "H2O_data_parsing_timezone:  UTC\n",
+       "H2O_cluster_version:        3.46.0.5\n",
+       "H2O_cluster_version_age:    1 month and 22 days\n",
+       "H2O_cluster_name:           H2O_from_python_schidige_rvz83h\n",
+       "H2O_cluster_total_nodes:    1\n",
+       "H2O_cluster_free_memory:    29.97 Gb\n",
+       "H2O_cluster_total_cores:    32\n",
+       "H2O_cluster_allowed_cores:  32\n",
+       "H2O_cluster_status:         locked, healthy\n",
+       "H2O_connection_url:         http://127.0.0.1:54321\n",
+       "H2O_connection_proxy:       {\"http\": null, \"https\": null}\n",
+       "H2O_internal_security:      False\n",
+       "Python_version:             3.8.20 final\n",
+       "--------------------------  -------------------------------"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import h2o\n",
+    "from h2o.automl import H2OAutoML\n",
+    "\n",
+    "# Step 1: Initialize H2O\n",
+    "h2o.init()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e61836d-dc0a-4728-a1fc-bf7c2da232d3",
+   "metadata": {},
+   "source": [
+    "After that, we are going to put each of our dataset splits on the connection through H2OFrame. This lets H2O access our data and use it for training/testing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "b43187ae-6863-4860-add2-1d292145efbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n",
+      "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Assuming xtrain, xtest, ytrain, ytest are Pandas DataFrames\n",
+    "xtrain_h2o = h2o.H2OFrame(xtrain)\n",
+    "xtest_h2o = h2o.H2OFrame(xtest)\n",
+    "ytrain_h2o = h2o.H2OFrame(ytrain)\n",
+    "ytest_h2o = h2o.H2OFrame(ytest)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ba7de8d-de63-4c04-bdb4-daaac02aa83b",
+   "metadata": {},
+   "source": [
+    "We will then create a combined set of train and test datasets and also set the target and feature variables in lists. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ca2947e7-0a5f-4cc1-900c-cfc91085024f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Combine features and targets for training\n",
+    "train_h2o = xtrain_h2o.cbind(ytrain_h2o)\n",
+    "test_h2o = xtest_h2o.cbind(ytest_h2o)\n",
+    "\n",
+    "# Specify the column names for the targets\n",
+    "targets = ytrain.columns.tolist()  # List of target columns\n",
+    "features = xtrain.columns.tolist()  # List of feature columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b26dfe3f-1f2d-4209-b892-890258ea6bc9",
+   "metadata": {},
+   "source": [
+    "It is now time to train our model. H2O is not natively multi-output while this problem has us predicting 4 variables simultaneously. This means we can't use H2O out of the box on this dataset. Instead we will naively extend the capacities of H2O by have it train a different model for each target outcome. Below, we do training an H2OAutoML model on each taget independently and then storing it inside a dictionary. It is also important to note that H2O natively does cross validation while training with a 5 fold split."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "5a850ef2-9e15-40f1-bb39-61193b69f78e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AutoML progress: |███████████████████████████████████████████████████████████████| (done) 100%\n",
+      "AutoML progress: |███████████████████████████████████████████████████████████████| (done) 100%\n",
+      "AutoML progress: |███████████████████████████████████████████████████████████████| (done) 100%\n",
+      "AutoML progress: |███████████████████████████████████████████████████████████████| (done) 100%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Dictionary to store models\n",
+    "aml_models = {}\n",
+    "\n",
+    "for target in targets:\n",
+    "    # Initialize AutoML\n",
+    "    aml = H2OAutoML(max_models=20, seed=1234)\n",
+    "\n",
+    "    # Train AutoML for each target\n",
+    "    aml.train(x=features, y=target, training_frame=train_h2o)\n",
+    "\n",
+    "    # Store the model\n",
+    "    aml_models[target] = aml\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb297dcf-cec6-4242-962b-518b10e1a0f7",
+   "metadata": {},
+   "source": [
+    "After training, we can test our models below on the testing dataset we set aside earlier. Note that the $R^2$ for all the models are quite similar to the FNN's performance on HTGR, showing similar results. The same can be said about RMSE and MAE. It is also important to note that there are 4 different sets of results here for each target variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "38d687e9-5883-4810-8c16-5d8cc48ffecc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
+      "Evaluation for target: fluxQ1\n",
+      "ModelMetricsRegression: gbm\n",
+      "** Reported on test data. **\n",
+      "\n",
+      "MSE: 4.008380111192609e-07\n",
+      "RMSE: 0.0006331176913649317\n",
+      "MAE: 0.0005025689597728681\n",
+      "RMSLE: 0.0005058321307694414\n",
+      "Mean Residual Deviance: 4.008380111192609e-07\n",
+      "R² on test set: 0.9776496656942096\n",
+      "--------------------------------------------------\n",
+      "gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
+      "Evaluation for target: fluxQ2\n",
+      "ModelMetricsRegression: gbm\n",
+      "** Reported on test data. **\n",
+      "\n",
+      "MSE: 3.8885122709052564e-07\n",
+      "RMSE: 0.0006235793671141835\n",
+      "MAE: 0.000492043480224217\n",
+      "RMSLE: 0.0004982041393286098\n",
+      "Mean Residual Deviance: 3.8885122709052564e-07\n",
+      "R² on test set: 0.9783180370134837\n",
+      "--------------------------------------------------\n",
+      "gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
+      "Evaluation for target: fluxQ3\n",
+      "ModelMetricsRegression: gbm\n",
+      "** Reported on test data. **\n",
+      "\n",
+      "MSE: 4.0225058972030825e-07\n",
+      "RMSE: 0.0006342322837260086\n",
+      "MAE: 0.0005049802613484237\n",
+      "RMSLE: 0.0005068485553018549\n",
+      "Mean Residual Deviance: 4.0225058972030825e-07\n",
+      "R² on test set: 0.977570901697126\n",
+      "--------------------------------------------------\n",
+      "gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%\n",
+      "Evaluation for target: fluxQ4\n",
+      "ModelMetricsRegression: gbm\n",
+      "** Reported on test data. **\n",
+      "\n",
+      "MSE: 4.011371711788862e-07\n",
+      "RMSE: 0.000633353906736894\n",
+      "MAE: 0.0004977111311085677\n",
+      "RMSLE: 0.0005061076449568598\n",
+      "Mean Residual Deviance: 4.011371711788862e-07\n",
+      "R² on test set: 0.9776329848227056\n",
+      "--------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluate models for each target\n",
+    "for target in targets:\n",
+    "    # Make predictions\n",
+    "    predictions = aml_models[target].leader.predict(test_h2o)\n",
+    "\n",
+    "    # Evaluate model performance\n",
+    "    performance = aml_models[target].leader.model_performance(test_h2o)\n",
+    "    print(f\"Evaluation for target: {target}\")\n",
+    "    print(performance)\n",
+    "    print(f\"R² on test set: {performance.r2()}\")\n",
+    "    print('-' * 50)  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddd08c0b-d543-451a-84a0-1932bf71ef47",
+   "metadata": {},
+   "source": [
+    "All that is left now is to print out the model parameters for all the models trained in a leaderboard style (best to worst). We can see that H2O models that are used were mainly Gradient Boosting and XGBoost models. Again, we are printing the leaderboard for each target variable. However, we can see that each leaderboard gives pretty consistant results across all the targets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "534984a9-cae3-43fa-8823-cf4a1c32ba64",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Leaderboard for target: fluxQ1\n",
+      "model_id                                           rmse          mse          mae        rmsle    mean_residual_deviance\n",
+      "GBM_1_AutoML_1_20241023_32520               0.000685361  4.69719e-07  0.000542796  0.000547656               4.69719e-07\n",
+      "GBM_grid_1_AutoML_1_20241023_32520_model_2  0.000766287  5.87195e-07  0.00060316   0.000612268               5.87195e-07\n",
+      "GBM_2_AutoML_1_20241023_32520               0.000945019  8.93061e-07  0.000742318  0.000755141               8.93061e-07\n",
+      "GBM_3_AutoML_1_20241023_32520               0.000973695  9.48082e-07  0.000769139  0.000778094               9.48082e-07\n",
+      "XGBoost_3_AutoML_1_20241023_32520           0.000985956  9.72109e-07  0.000778139  0.000788122               9.72109e-07\n",
+      "GBM_5_AutoML_1_20241023_32520               0.000996767  9.93544e-07  0.000779383  0.000796456               9.93544e-07\n",
+      "GBM_4_AutoML_1_20241023_32520               0.00102334   1.04723e-06  0.000806216  0.000817664               1.04723e-06\n",
+      "XGBoost_1_AutoML_1_20241023_32520           0.00106348   1.13098e-06  0.000832568  0.000849964               1.13098e-06\n",
+      "GBM_grid_1_AutoML_1_20241023_32520_model_1  0.00108547   1.17825e-06  0.000862658  0.000867518               1.17825e-06\n",
+      "XGBoost_2_AutoML_1_20241023_32520           0.00117195   1.37348e-06  0.000923484  0.000936506               1.37348e-06\n",
+      "[22 rows x 6 columns]\n",
+      "\n",
+      "Best model for fluxQ1: Model Details\n",
+      "=============\n",
+      "H2OGradientBoostingEstimator : Gradient Boosting Machine\n",
+      "Model Key: GBM_1_AutoML_1_20241023_32520\n",
+      "\n",
+      "\n",
+      "Model Summary: \n",
+      "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
+      "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
+      "    214                214                         47211                  5            12           6.64019       11            15            12.9019\n",
+      "\n",
+      "ModelMetricsRegression: gbm\n",
+      "** Reported on train data. **\n",
+      "\n",
+      "MSE: 1.6331278682565502e-07\n",
+      "RMSE: 0.00040411976792240073\n",
+      "MAE: 0.0003163858609540122\n",
+      "RMSLE: 0.00032308420724847767\n",
+      "Mean Residual Deviance: 1.6331278682565502e-07\n",
+      "\n",
+      "ModelMetricsRegression: gbm\n",
+      "** Reported on cross-validation data. **\n",
+      "\n",
+      "MSE: 4.697191557378037e-07\n",
+      "RMSE: 0.0006853606027032804\n",
+      "MAE: 0.0005427964198011339\n",
+      "RMSLE: 0.0005476564684272209\n",
+      "Mean Residual Deviance: 4.697191557378037e-07\n",
+      "\n",
+      "Cross-Validation Metrics Summary: \n",
+      "                        mean         sd           cv_1_valid    cv_2_valid    cv_3_valid    cv_4_valid    cv_5_valid\n",
+      "----------------------  -----------  -----------  ------------  ------------  ------------  ------------  ------------\n",
+      "aic                     nan          0            nan           nan           nan           nan           nan\n",
+      "loglikelihood           nan          0            nan           nan           nan           nan           nan\n",
+      "mae                     0.000542796  1.31243e-05  0.000538211   0.000530016   0.000560481   0.00055243    0.000532844\n",
+      "mean_residual_deviance  4.69719e-07  2.33523e-08  4.54558e-07   4.60519e-07   4.87936e-07   5.00324e-07   4.45259e-07\n",
+      "mse                     4.69719e-07  2.33523e-08  4.54558e-07   4.60519e-07   4.87936e-07   5.00324e-07   4.45259e-07\n",
+      "r2                      0.973282     0.00149219   0.973758      0.974979      0.971066      0.972635      0.973969\n",
+      "residual_deviance       4.69719e-07  2.33523e-08  4.54558e-07   4.60519e-07   4.87936e-07   5.00324e-07   4.45259e-07\n",
+      "rmse                    0.000685192  1.69773e-05  0.000674209   0.000678615   0.000698524   0.000707336   0.000667277\n",
+      "rmsle                   0.00054752   1.36616e-05  0.000538584   0.000542178   0.000558484   0.000565184   0.00053317\n",
+      "\n",
+      "Scoring History: \n",
+      "     timestamp            duration    number_of_trees    training_rmse           training_mae            training_deviance\n",
+      "---  -------------------  ----------  -----------------  ----------------------  ----------------------  ----------------------\n",
+      "     2024-10-23 03:25:24  0.865 sec   0.0                0.004201010689797002    0.0033778119158177195   1.764849081578869e-05\n",
+      "     2024-10-23 03:25:24  0.889 sec   5.0                0.0034190857716209617   0.0027252461512883505   1.1690147513700905e-05\n",
+      "     2024-10-23 03:25:24  0.911 sec   10.0               0.002810227376203113    0.002228595025482632    7.897377905961435e-06\n",
+      "     2024-10-23 03:25:24  0.929 sec   15.0               0.0023530311674819487   0.0018721228412219456   5.536755675141463e-06\n",
+      "     2024-10-23 03:25:24  0.955 sec   20.0               0.00203453340498974     0.0016157419340951103   4.139326176019145e-06\n",
+      "     2024-10-23 03:25:24  0.980 sec   25.0               0.0017813600224910193   0.0014127596432254427   3.173243529729205e-06\n",
+      "     2024-10-23 03:25:24  0.999 sec   30.0               0.0015817458323410948   0.00125214146006675     2.5019198781284225e-06\n",
+      "     2024-10-23 03:25:24  1.010 sec   35.0               0.001407358676304971    0.0011104385696706317   1.98065844377088e-06\n",
+      "     2024-10-23 03:25:24  1.017 sec   40.0               0.0012673626148236852   0.000994884031159537    1.6062079974527286e-06\n",
+      "     2024-10-23 03:25:24  1.025 sec   45.0               0.0011565484099844263   0.0009059004130817595   1.3376042246375045e-06\n",
+      "---  ---                  ---         ---                ---                     ---                     ---\n",
+      "     2024-10-23 03:25:24  1.166 sec   170.0              0.00043863169837880735  0.00034314684924625216  1.9239776682267702e-07\n",
+      "     2024-10-23 03:25:24  1.172 sec   175.0              0.00043411767239254917  0.0003398880007721129   1.8845815348352463e-07\n",
+      "     2024-10-23 03:25:24  1.178 sec   180.0              0.0004298357716996795   0.0003370365216618493   1.8475879063265902e-07\n",
+      "     2024-10-23 03:25:24  1.183 sec   185.0              0.0004259582440229696   0.00033383660373233616  1.8144042565113172e-07\n",
+      "     2024-10-23 03:25:24  1.189 sec   190.0              0.0004221228339103973   0.00033074041917210533  1.7818768690854488e-07\n",
+      "     2024-10-23 03:25:24  1.194 sec   195.0              0.00041788367495367907  0.0003273369584764753   1.746267657927921e-07\n",
+      "     2024-10-23 03:25:24  1.200 sec   200.0              0.0004136387071495793   0.00032392779276484536  1.7109698005237546e-07\n",
+      "     2024-10-23 03:25:24  1.206 sec   205.0              0.00041072468443775024  0.00032179740213212515  1.686947664064895e-07\n",
+      "     2024-10-23 03:25:24  1.211 sec   210.0              0.00040694037215320046  0.0003185727908497765   1.6560046648818527e-07\n",
+      "     2024-10-23 03:25:24  1.216 sec   214.0              0.00040411976792240073  0.0003163858609540122   1.6331278682565502e-07\n",
+      "[44 rows x 7 columns]\n",
+      "\n",
+      "\n",
+      "Variable Importances: \n",
+      "variable    relative_importance    scaled_importance    percentage\n",
+      "----------  ---------------------  -------------------  ------------\n",
+      "theta1      0.0627416              1                    0.379852\n",
+      "theta2      0.0548633              0.874434             0.332155\n",
+      "theta5      0.017366               0.276786             0.105138\n",
+      "theta6      0.014772               0.235441             0.0894328\n",
+      "theta7      0.00608472             0.0969806            0.0368383\n",
+      "theta4      0.00589191             0.0939076            0.035671\n",
+      "theta8      0.00173475             0.0276491            0.0105026\n",
+      "theta3      0.0017196              0.0274077            0.0104108\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "\n",
+      "Leaderboard for target: fluxQ2\n",
+      "model_id                                           rmse          mse          mae        rmsle    mean_residual_deviance\n",
+      "GBM_1_AutoML_2_20241023_32739               0.000666645  4.44415e-07  0.000528096  0.000532684               4.44415e-07\n",
+      "GBM_grid_1_AutoML_2_20241023_32739_model_2  0.00075398   5.68486e-07  0.000591372  0.000602509               5.68486e-07\n",
+      "GBM_2_AutoML_2_20241023_32739               0.000944374  8.91842e-07  0.00073735   0.000754506               8.91842e-07\n",
+      "GBM_3_AutoML_2_20241023_32739               0.000957021  9.1589e-07   0.000756335  0.000764697               9.1589e-07\n",
+      "GBM_5_AutoML_2_20241023_32739               0.0009706    9.42065e-07  0.00075692   0.000775477               9.42065e-07\n",
+      "XGBoost_3_AutoML_2_20241023_32739           0.000984195  9.68639e-07  0.00078467   0.000786657               9.68639e-07\n",
+      "GBM_4_AutoML_2_20241023_32739               0.00100781   1.01569e-06  0.000798048  0.000805267               1.01569e-06\n",
+      "XGBoost_1_AutoML_2_20241023_32739           0.00102376   1.04809e-06  0.000808626  0.000818355               1.04809e-06\n",
+      "GBM_grid_1_AutoML_2_20241023_32739_model_1  0.00111303   1.23885e-06  0.00088144   0.000889503               1.23885e-06\n",
+      "XGBoost_2_AutoML_2_20241023_32739           0.00116277   1.35202e-06  0.000921097  0.000929546               1.35202e-06\n",
+      "[22 rows x 6 columns]\n",
+      "\n",
+      "Best model for fluxQ2: Model Details\n",
+      "=============\n",
+      "H2OGradientBoostingEstimator : Gradient Boosting Machine\n",
+      "Model Key: GBM_1_AutoML_2_20241023_32739\n",
+      "\n",
+      "\n",
+      "Model Summary: \n",
+      "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
+      "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
+      "    232                232                         51225                  5            10           6.73707       11            15            12.9009\n",
+      "\n",
+      "ModelMetricsRegression: gbm\n",
+      "** Reported on train data. **\n",
+      "\n",
+      "MSE: 1.5099336540128997e-07\n",
+      "RMSE: 0.00038857864763943215\n",
+      "MAE: 0.00030885128038270134\n",
+      "RMSLE: 0.0003106507832008333\n",
+      "Mean Residual Deviance: 1.5099336540128997e-07\n",
+      "\n",
+      "ModelMetricsRegression: gbm\n",
+      "** Reported on cross-validation data. **\n",
+      "\n",
+      "MSE: 4.4441549201724677e-07\n",
+      "RMSE: 0.0006666449519926231\n",
+      "MAE: 0.0005280959649242026\n",
+      "RMSLE: 0.0005326838418565784\n",
+      "Mean Residual Deviance: 4.4441549201724677e-07\n",
+      "\n",
+      "Cross-Validation Metrics Summary: \n",
+      "                        mean         sd           cv_1_valid    cv_2_valid    cv_3_valid    cv_4_valid    cv_5_valid\n",
+      "----------------------  -----------  -----------  ------------  ------------  ------------  ------------  ------------\n",
+      "aic                     nan          0            nan           nan           nan           nan           nan\n",
+      "loglikelihood           nan          0            nan           nan           nan           nan           nan\n",
+      "mae                     0.000528096  2.70101e-05  0.000509151   0.000553827   0.000490432   0.000544519   0.00054255\n",
+      "mean_residual_deviance  4.44415e-07  3.30293e-08  4.37549e-07   4.86273e-07   3.95044e-07   4.48174e-07   4.55039e-07\n",
+      "mse                     4.44415e-07  3.30293e-08  4.37549e-07   4.86273e-07   3.95044e-07   4.48174e-07   4.55039e-07\n",
+      "r2                      0.974713     0.00278013   0.976243      0.971341      0.978574      0.973339      0.974066\n",
+      "residual_deviance       4.44415e-07  3.30293e-08  4.37549e-07   4.86273e-07   3.95044e-07   4.48174e-07   4.55039e-07\n",
+      "rmse                    0.000666271  2.49577e-05  0.000661474   0.000697333   0.000628525   0.000669458   0.000674566\n",
+      "rmsle                   0.000532382  2.00328e-05  0.000528159   0.000557246   0.000502127   0.000535247   0.000539133\n",
+      "\n",
+      "Scoring History: \n",
+      "     timestamp            duration    number_of_trees    training_rmse           training_mae            training_deviance\n",
+      "---  -------------------  ----------  -----------------  ----------------------  ----------------------  ----------------------\n",
+      "     2024-10-23 03:27:40  0.420 sec   0.0                0.0042010106897970015   0.0033778119158177165   1.7648490815788678e-05\n",
+      "     2024-10-23 03:27:40  0.426 sec   5.0                0.0034189064007153075   0.002755580430939084    1.1688920976852099e-05\n",
+      "     2024-10-23 03:27:40  0.431 sec   10.0               0.002753995370508964    0.00219750185807546     7.584490500784807e-06\n",
+      "     2024-10-23 03:27:40  0.437 sec   15.0               0.0023605017980835455   0.0018795494664283026   5.571968738755651e-06\n",
+      "     2024-10-23 03:27:40  0.442 sec   20.0               0.002005577924567542    0.001596005984715053    4.02234281151265e-06\n",
+      "     2024-10-23 03:27:40  0.447 sec   25.0               0.0017670810062046143   0.001399007070632208    3.122575282489112e-06\n",
+      "     2024-10-23 03:27:40  0.452 sec   30.0               0.0015582377414062273   0.0012330717416036696   2.4281048587427803e-06\n",
+      "     2024-10-23 03:27:40  0.458 sec   35.0               0.0013934382528751693   0.0010996492136092413   1.941670164575804e-06\n",
+      "     2024-10-23 03:27:40  0.463 sec   40.0               0.0012580393440821257   0.0009919860462347667   1.582662991258585e-06\n",
+      "     2024-10-23 03:27:40  0.468 sec   45.0               0.0011436701653217791   0.0008994359984284356   1.3079814470471458e-06\n",
+      "---  ---                  ---         ---                ---                     ---                     ---\n",
+      "     2024-10-23 03:27:40  0.621 sec   190.0              0.00041807011294505157  0.0003314097438539778   1.7478261933788817e-07\n",
+      "     2024-10-23 03:27:40  0.626 sec   195.0              0.0004143686431392978   0.00032852509901637125  1.7170137241710275e-07\n",
+      "     2024-10-23 03:27:40  0.632 sec   200.0              0.00041075333746739683  0.00032629044283004035  1.6871830424060517e-07\n",
+      "     2024-10-23 03:27:40  0.637 sec   205.0              0.0004072874678658136   0.0003237103848230271   1.6588308148054612e-07\n",
+      "     2024-10-23 03:27:40  0.642 sec   210.0              0.00040341464243058307  0.0003207209777264368   1.6274337372739517e-07\n",
+      "     2024-10-23 03:27:40  0.648 sec   215.0              0.0003996149487542951   0.00031803920155479797  1.596921072678979e-07\n",
+      "     2024-10-23 03:27:40  0.654 sec   220.0              0.00039626481175479046  0.0003153943234965915   1.570258010350595e-07\n",
+      "     2024-10-23 03:27:40  0.659 sec   225.0              0.00039280958057331304  0.0003124307450794038   1.542993665901821e-07\n",
+      "     2024-10-23 03:27:40  0.665 sec   230.0              0.0003897637814427752   0.00030984618834086825  1.519158053245714e-07\n",
+      "     2024-10-23 03:27:40  0.668 sec   232.0              0.00038857864763943215  0.00030885128038270134  1.5099336540128997e-07\n",
+      "[48 rows x 7 columns]\n",
+      "\n",
+      "\n",
+      "Variable Importances: \n",
+      "variable    relative_importance    scaled_importance    percentage\n",
+      "----------  ---------------------  -------------------  ------------\n",
+      "theta4      0.0632612              1                    0.381511\n",
+      "theta3      0.0543378              0.858944             0.327696\n",
+      "theta8      0.0177624              0.28078              0.10712\n",
+      "theta7      0.0147892              0.23378              0.0891895\n",
+      "theta6      0.006291               0.0994448            0.0379393\n",
+      "theta1      0.00605141             0.0956576            0.0364944\n",
+      "theta5      0.00167385             0.0264593            0.0100945\n",
+      "theta2      0.00165073             0.0260939            0.00995511\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "\n",
+      "Leaderboard for target: fluxQ3\n",
+      "model_id                                           rmse          mse          mae        rmsle    mean_residual_deviance\n",
+      "GBM_1_AutoML_3_20241023_32951               0.00066506   4.42305e-07  0.000522491  0.000531438               4.42305e-07\n",
+      "GBM_grid_1_AutoML_3_20241023_32951_model_2  0.000759034  5.76133e-07  0.000591843  0.000606594               5.76133e-07\n",
+      "GBM_2_AutoML_3_20241023_32951               0.000946011  8.94937e-07  0.000741802  0.000755897               8.94937e-07\n",
+      "GBM_5_AutoML_3_20241023_32951               0.000969073  9.39103e-07  0.000768166  0.000774454               9.39103e-07\n",
+      "GBM_3_AutoML_3_20241023_32951               0.000979783  9.59974e-07  0.000764376  0.000783008               9.59974e-07\n",
+      "GBM_4_AutoML_3_20241023_32951               0.000994735  9.89497e-07  0.000786269  0.000794846               9.89497e-07\n",
+      "XGBoost_3_AutoML_3_20241023_32951           0.00100103   1.00206e-06  0.00079418   0.000800109               1.00206e-06\n",
+      "XGBoost_1_AutoML_3_20241023_32951           0.00106398   1.13205e-06  0.00084634   0.000850548               1.13205e-06\n",
+      "GBM_grid_1_AutoML_3_20241023_32951_model_1  0.00111174   1.23596e-06  0.000882908  0.000888529               1.23596e-06\n",
+      "XGBoost_2_AutoML_3_20241023_32951           0.0011786    1.38911e-06  0.000923643  0.000942039               1.38911e-06\n",
+      "[22 rows x 6 columns]\n",
+      "\n",
+      "Best model for fluxQ3: Model Details\n",
+      "=============\n",
+      "H2OGradientBoostingEstimator : Gradient Boosting Machine\n",
+      "Model Key: GBM_1_AutoML_3_20241023_32951\n",
+      "\n",
+      "\n",
+      "Model Summary: \n",
+      "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
+      "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
+      "    234                234                         51841                  5            10           6.73077       11            15            12.9615\n",
+      "\n",
+      "ModelMetricsRegression: gbm\n",
+      "** Reported on train data. **\n",
+      "\n",
+      "MSE: 1.493456961269383e-07\n",
+      "RMSE: 0.0003864527087845786\n",
+      "MAE: 0.00030561133509590514\n",
+      "RMSLE: 0.00030899687989079105\n",
+      "Mean Residual Deviance: 1.493456961269383e-07\n",
+      "\n",
+      "ModelMetricsRegression: gbm\n",
+      "** Reported on cross-validation data. **\n",
+      "\n",
+      "MSE: 4.42304993025337e-07\n",
+      "RMSE: 0.0006650601424122009\n",
+      "MAE: 0.0005224908919147636\n",
+      "RMSLE: 0.0005314384086717635\n",
+      "Mean Residual Deviance: 4.42304993025337e-07\n",
+      "\n",
+      "Cross-Validation Metrics Summary: \n",
+      "                        mean         sd           cv_1_valid    cv_2_valid    cv_3_valid    cv_4_valid    cv_5_valid\n",
+      "----------------------  -----------  -----------  ------------  ------------  ------------  ------------  ------------\n",
+      "aic                     nan          0            nan           nan           nan           nan           nan\n",
+      "loglikelihood           nan          0            nan           nan           nan           nan           nan\n",
+      "mae                     0.000522491  2.18906e-05  0.000548144   0.00054168    0.000505729   0.000498076   0.000518825\n",
+      "mean_residual_deviance  4.42305e-07  4.03101e-08  4.86119e-07   4.83604e-07   4.17081e-07   3.9745e-07    4.27271e-07\n",
+      "mse                     4.42305e-07  4.03101e-08  4.86119e-07   4.83604e-07   4.17081e-07   3.9745e-07    4.27271e-07\n",
+      "r2                      0.974731     0.00362993   0.96933       0.973208      0.977859      0.978083      0.975175\n",
+      "residual_deviance       4.42305e-07  4.03101e-08  4.86119e-07   4.83604e-07   4.17081e-07   3.9745e-07    4.27271e-07\n",
+      "rmse                    0.000664511  3.02221e-05  0.000697223   0.000695417   0.000645818   0.000630436   0.000653659\n",
+      "rmsle                   0.000530996  2.42349e-05  0.000557269   0.000555726   0.000515868   0.000503701   0.000522417\n",
+      "\n",
+      "Scoring History: \n",
+      "     timestamp            duration    number_of_trees    training_rmse           training_mae            training_deviance\n",
+      "---  -------------------  ----------  -----------------  ----------------------  ----------------------  ----------------------\n",
+      "     2024-10-23 03:29:52  0.414 sec   0.0                0.004201010689797002    0.0033778119158177186   1.764849081578869e-05\n",
+      "     2024-10-23 03:29:52  0.420 sec   5.0                0.0033117992293671544   0.0026528380314509072   1.0968014135636877e-05\n",
+      "     2024-10-23 03:29:52  0.425 sec   10.0               0.0027753717087465675   0.002217293516511009    7.702688121710842e-06\n",
+      "     2024-10-23 03:29:52  0.430 sec   15.0               0.002348862808689713    0.0018691390256086985   5.5171564940457265e-06\n",
+      "     2024-10-23 03:29:52  0.435 sec   20.0               0.002043271002061544    0.0016216694386232467   4.174956387865586e-06\n",
+      "     2024-10-23 03:29:52  0.440 sec   25.0               0.0017947391354647246   0.001424937972000667    3.2210885643686673e-06\n",
+      "     2024-10-23 03:29:52  0.445 sec   30.0               0.00159472235896177     0.0012614684516475314   2.5431394021725925e-06\n",
+      "     2024-10-23 03:29:52  0.450 sec   35.0               0.001427722266175823    0.0011272070166610537   2.0383908693342274e-06\n",
+      "     2024-10-23 03:29:52  0.455 sec   40.0               0.001294380138889908    0.0010192357500394184   1.6754199439526574e-06\n",
+      "     2024-10-23 03:29:52  0.460 sec   45.0               0.001172273388192027    0.0009192545144330888   1.3742248966632152e-06\n",
+      "---  ---                  ---         ---                ---                     ---                     ---\n",
+      "     2024-10-23 03:29:52  0.618 sec   190.0              0.0004187731351611232   0.0003299681984242939   1.7537093873267633e-07\n",
+      "     2024-10-23 03:29:52  0.623 sec   195.0              0.00041523292467442733  0.00032747069994608564  1.7241838173367864e-07\n",
+      "     2024-10-23 03:29:52  0.629 sec   200.0              0.00041115355874123615  0.0003241806087039766   1.6904724886558312e-07\n",
+      "     2024-10-23 03:29:52  0.635 sec   205.0              0.00040638562548642393  0.00032032632402011327  1.65149276601992e-07\n",
+      "     2024-10-23 03:29:52  0.640 sec   210.0              0.0004025827204136496   0.0003177240916660854   1.6207284677565477e-07\n",
+      "     2024-10-23 03:29:52  0.646 sec   215.0              0.00039956708282097055  0.00031522626678148904  1.5965385367406035e-07\n",
+      "     2024-10-23 03:29:52  0.652 sec   220.0              0.0003957602695231215   0.0003126263121763865   1.5662619093301377e-07\n",
+      "     2024-10-23 03:29:53  0.657 sec   225.0              0.0003921905502061792   0.0003098773530551365   1.5381342767102558e-07\n",
+      "     2024-10-23 03:29:53  0.663 sec   230.0              0.00038911633471645643  0.00030744881857009163  1.5141152194316935e-07\n",
+      "     2024-10-23 03:29:53  0.667 sec   234.0              0.0003864527087845786   0.00030561133509590514  1.493456961269383e-07\n",
+      "[48 rows x 7 columns]\n",
+      "\n",
+      "\n",
+      "Variable Importances: \n",
+      "variable    relative_importance    scaled_importance    percentage\n",
+      "----------  ---------------------  -------------------  ------------\n",
+      "theta5      0.0627204              1                    0.379668\n",
+      "theta6      0.0547693              0.873229             0.331537\n",
+      "theta1      0.0169435              0.270143             0.102565\n",
+      "theta2      0.0149594              0.238509             0.0905544\n",
+      "theta8      0.00610147             0.0972804            0.0369343\n",
+      "theta3      0.00607011             0.0967804            0.0367444\n",
+      "theta4      0.00191                0.0304526            0.0115619\n",
+      "theta7      0.00172383             0.0274844            0.0104349\n",
+      "----------------------------------------------------------------------------------------------------\n",
+      "\n",
+      "Leaderboard for target: fluxQ4\n",
+      "model_id                                           rmse          mse          mae        rmsle    mean_residual_deviance\n",
+      "GBM_1_AutoML_4_20241023_33207               0.000661857  4.38054e-07  0.000523309  0.000528991               4.38054e-07\n",
+      "GBM_grid_1_AutoML_4_20241023_33207_model_2  0.000759653  5.77073e-07  0.000600732  0.000606976               5.77073e-07\n",
+      "GBM_2_AutoML_4_20241023_33207               0.000930048  8.6499e-07   0.000737675  0.000743301               8.6499e-07\n",
+      "GBM_5_AutoML_4_20241023_33207               0.000965944  9.33048e-07  0.000764493  0.000771864               9.33048e-07\n",
+      "GBM_3_AutoML_4_20241023_33207               0.000976795  9.54128e-07  0.000777304  0.000780649               9.54128e-07\n",
+      "GBM_4_AutoML_4_20241023_33207               0.00100016   1.00032e-06  0.000801409  0.000799579               1.00032e-06\n",
+      "XGBoost_3_AutoML_4_20241023_33207           0.00102218   1.04484e-06  0.000811577  0.000817006               1.04484e-06\n",
+      "GBM_grid_1_AutoML_4_20241023_33207_model_1  0.00109561   1.20036e-06  0.000869224  0.000875739               1.20036e-06\n",
+      "XGBoost_1_AutoML_4_20241023_33207           0.00112215   1.25923e-06  0.000884476  0.000897058               1.25923e-06\n",
+      "XGBoost_2_AutoML_4_20241023_33207           0.00113872   1.29667e-06  0.000894447  0.000910263               1.29667e-06\n",
+      "[22 rows x 6 columns]\n",
+      "\n",
+      "Best model for fluxQ4: Model Details\n",
+      "=============\n",
+      "H2OGradientBoostingEstimator : Gradient Boosting Machine\n",
+      "Model Key: GBM_1_AutoML_4_20241023_33207\n",
+      "\n",
+      "\n",
+      "Model Summary: \n",
+      "    number_of_trees    number_of_internal_trees    model_size_in_bytes    min_depth    max_depth    mean_depth    min_leaves    max_leaves    mean_leaves\n",
+      "--  -----------------  --------------------------  ---------------------  -----------  -----------  ------------  ------------  ------------  -------------\n",
+      "    242                242                         53527                  5            10           6.7562        11            15            12.9339\n",
+      "\n",
+      "ModelMetricsRegression: gbm\n",
+      "** Reported on train data. **\n",
+      "\n",
+      "MSE: 1.4928759705323814e-07\n",
+      "RMSE: 0.0003863775317655494\n",
+      "MAE: 0.00030924480585824875\n",
+      "RMSLE: 0.00030897086341989167\n",
+      "Mean Residual Deviance: 1.4928759705323814e-07\n",
+      "\n",
+      "ModelMetricsRegression: gbm\n",
+      "** Reported on cross-validation data. **\n",
+      "\n",
+      "MSE: 4.3805439122159745e-07\n",
+      "RMSE: 0.0006618567754594625\n",
+      "MAE: 0.000523309145664202\n",
+      "RMSLE: 0.0005289910747630794\n",
+      "Mean Residual Deviance: 4.3805439122159745e-07\n",
+      "\n",
+      "Cross-Validation Metrics Summary: \n",
+      "                        mean         sd           cv_1_valid    cv_2_valid    cv_3_valid    cv_4_valid    cv_5_valid\n",
+      "----------------------  -----------  -----------  ------------  ------------  ------------  ------------  ------------\n",
+      "aic                     nan          0            nan           nan           nan           nan           nan\n",
+      "loglikelihood           nan          0            nan           nan           nan           nan           nan\n",
+      "mae                     0.000523309  3.30866e-05  0.00050479    0.000551298   0.0005243     0.000558059   0.000478099\n",
+      "mean_residual_deviance  4.38054e-07  5.10323e-08  4.06417e-07   4.80216e-07   4.40655e-07   4.92915e-07   3.70068e-07\n",
+      "mse                     4.38054e-07  5.10323e-08  4.06417e-07   4.80216e-07   4.40655e-07   4.92915e-07   3.70068e-07\n",
+      "r2                      0.975189     0.00210843   0.975949      0.973051      0.976248      0.972955      0.977742\n",
+      "residual_deviance       4.38054e-07  5.10323e-08  4.06417e-07   4.80216e-07   4.40655e-07   4.92915e-07   3.70068e-07\n",
+      "rmse                    0.000660943  3.88706e-05  0.000637509   0.000692977   0.000663819   0.000702079   0.000608332\n",
+      "rmsle                   0.000528265  3.09804e-05  0.000509871   0.000553647   0.000530504   0.000561128   0.000486175\n",
+      "\n",
+      "Scoring History: \n",
+      "     timestamp            duration    number_of_trees    training_rmse           training_mae            training_deviance\n",
+      "---  -------------------  ----------  -----------------  ----------------------  ----------------------  ----------------------\n",
+      "     2024-10-23 03:32:08  0.437 sec   0.0                0.004201010689797002    0.0033778119158177195   1.764849081578869e-05\n",
+      "     2024-10-23 03:32:08  0.443 sec   5.0                0.0032377035514388115   0.0026033101834001997   1.0482724286999494e-05\n",
+      "     2024-10-23 03:32:08  0.448 sec   10.0               0.0027126304259239955   0.0021702216991356443   7.358363827648598e-06\n",
+      "     2024-10-23 03:32:08  0.454 sec   15.0               0.0023150232359903885   0.0018431942022982097   5.35933258317541e-06\n",
+      "     2024-10-23 03:32:08  0.459 sec   20.0               0.0020154108985627845   0.0016047244057768868   4.061881090045651e-06\n",
+      "     2024-10-23 03:32:08  0.464 sec   25.0               0.001783390970047642    0.0014166246851285299   3.180483352047469e-06\n",
+      "     2024-10-23 03:32:08  0.469 sec   30.0               0.001590353971752259    0.0012585039862564632   2.5292257554681854e-06\n",
+      "     2024-10-23 03:32:08  0.474 sec   35.0               0.0014283456511595693   0.0011312026707899002   2.040171299186454e-06\n",
+      "     2024-10-23 03:32:08  0.480 sec   40.0               0.0012980488231495303   0.001027571154492242    1.6849307472798806e-06\n",
+      "     2024-10-23 03:32:08  0.485 sec   45.0               0.00117923036729206     0.0009335457072371528   1.3905842591437663e-06\n",
+      "---  ---                  ---         ---                ---                     ---                     ---\n",
+      "     2024-10-23 03:32:08  0.649 sec   200.0              0.00041755541477453016  0.00033326026229631335  1.7435252440752994e-07\n",
+      "     2024-10-23 03:32:08  0.655 sec   205.0              0.0004138400513103363   0.00033017737524850026  1.712635880685418e-07\n",
+      "     2024-10-23 03:32:08  0.661 sec   210.0              0.0004101445372666396   0.0003273545134635199   1.6821854144966592e-07\n",
+      "     2024-10-23 03:32:08  0.666 sec   215.0              0.0004062373085734939   0.00032462202367328463  1.6502875087703608e-07\n",
+      "     2024-10-23 03:32:08  0.671 sec   220.0              0.0004024412510854033   0.00032183952984355746  1.6195896057518462e-07\n",
+      "     2024-10-23 03:32:08  0.677 sec   225.0              0.00039844592860989865  0.0003188148992402213   1.5875915802580445e-07\n",
+      "     2024-10-23 03:32:08  0.683 sec   230.0              0.00039493119718597957  0.00031597646928968885  1.5597065051075106e-07\n",
+      "     2024-10-23 03:32:08  0.688 sec   235.0              0.00039116385521705167  0.00031291855233056207  1.5300916162826656e-07\n",
+      "     2024-10-23 03:32:08  0.694 sec   240.0              0.0003876882931526777   0.0003102123382545653   1.5030221264763657e-07\n",
+      "     2024-10-23 03:32:08  0.696 sec   242.0              0.0003863775317655494   0.00030924480585824875  1.4928759705323814e-07\n",
+      "[50 rows x 7 columns]\n",
+      "\n",
+      "\n",
+      "Variable Importances: \n",
+      "variable    relative_importance    scaled_importance    percentage\n",
+      "----------  ---------------------  -------------------  ------------\n",
+      "theta8      0.0633288              1                    0.385543\n",
+      "theta7      0.054432               0.859515             0.33138\n",
+      "theta4      0.0169941              0.268348             0.10346\n",
+      "theta3      0.014081               0.222347             0.0857244\n",
+      "theta2      0.00624284             0.0985783            0.0380062\n",
+      "theta5      0.0057615              0.0909776            0.0350758\n",
+      "theta1      0.00179929             0.0284118            0.010954\n",
+      "theta6      0.0016191              0.0255666            0.00985701\n",
+      "----------------------------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "for target, aml in aml_models.items():\n",
+    "    print(f\"\\nLeaderboard for target: {target}\")\n",
+    "    print(aml.leaderboard)\n",
+    "    print(f\"Best model for {target}: {aml.leader}\")\n",
+    "    print('-' * 100)  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3c9aed6-712f-4860-892a-21613617f8c7",
+   "metadata": {},
+   "source": [
+    "All that is left is to shutdown the cluster since we initalized it earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e18982bb-2f86-4c23-87d0-6ee13b7ec40b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "h2o.cluster.shutdown()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.20"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}