Wilson 3D road network dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da2f1945",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-10 18:01:56.974325: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from bayesian_benchmarks.data import Wilson_3droad\n",
    "from gpflow_decomposed.benchmarking.plotting import WIDTH, HEIGHT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb925dc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "rc = {\n",
    "    \"figure.figsize\": (WIDTH, HEIGHT),\n",
    "    \"figure.dpi\": 300,\n",
    "    \"font.serif\": [\"Palatino\"],\n",
    "    \"text.usetex\": True,\n",
    "}\n",
    "sns.set(context=\"talk\", style=\"ticks\", palette=\"crest\", font=\"serif\", rc=rc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99a6a1e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "split = 0\n",
    "frac = 0.5\n",
    "\n",
    "seed = 8888\n",
    "random_state = np.random.RandomState(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bb8ec495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bayesian_benchmarks.data.Wilson_3droad at 0x7f6d7426f9d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = Wilson_3droad(split=split)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f44f4dc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <td>-0.652020</td>\n",
       "      <td>1.146774</td>\n",
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       "      <td>0.142015</td>\n",
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       "      <td>0.865307</td>\n",
       "      <td>0.011288</td>\n",
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       "      <td>-0.006274</td>\n",
       "      <td>-0.045162</td>\n",
       "      <td>-0.772770</td>\n",
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       "      <td>-0.578184</td>\n",
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       "      <td>1.251058</td>\n",
       "      <td>-0.932163</td>\n",
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       "      <th>43485</th>\n",
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       "<p>43488 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "              0         1         2       hue\n",
       "0      0.868575  0.765707  1.558066 -0.419957\n",
       "1     -1.911345 -1.488496 -0.652020  1.146774\n",
       "2      0.142015  1.227965  0.865307  0.011288\n",
       "3     -0.505180  1.050859  1.666834 -0.883993\n",
       "4      0.324750 -0.006274 -0.045162 -0.772770\n",
       "...         ...       ...       ...       ...\n",
       "43483 -0.437499  0.614072 -1.246918 -0.578184\n",
       "43484 -1.986455  1.286251  1.251058 -0.932163\n",
       "43485  1.230195 -1.982785 -1.363562 -0.199199\n",
       "43486 -0.241771  0.417908 -0.177831  0.364876\n",
       "43487  1.337289  0.884293  0.385982  4.331796\n",
       "\n",
       "[43488 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(data=dataset.X_test).assign(hue=dataset.Y_test.squeeze(axis=-1))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1324ceb7",
   "metadata": {},
   "outputs": [
    {
     "data": {