ltiao/LCBench.ipynb

## LCBench.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6217d943-6141-47f3-98ab-ed5f964c187a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy.stats import geom\n",
    "from matplotlib.colors import LogNorm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f05416a2-4104-4c33-aabe-7adf8d8a2d18",
   "metadata": {},
   "outputs": [],
   "source": [
    "ASPECT = 0.5 * (1 + np.sqrt(5))\n",
    "WIDTH = 4.\n",
    "HEIGHT = WIDTH / ASPECT\n",
    "DPI = 300\n",
    "rc = {\n",
    "    \"figure.figsize\": (WIDTH, HEIGHT),\n",
    "    \"figure.dpi\": DPI,\n",
    "    # \"font.serif\": [\"Palatino\"],\n",
    "    \"text.usetex\": False,\n",
    "    \"savefig.dpi\": DPI,\n",
    "    \"savefig.transparent\": True\n",
    "}\n",
    "sns.set(context=\"paper\", style=\"ticks\", palette=\"deep\", font=\"serif\", rc=rc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e111dcc8-3db0-4bb4-9485-90bb7fed6d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 42\n",
    "\n",
    "random_state = np.random.RandomState(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "f8b2a1f6-182c-4f46-96e2-ef0de26e6628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20,), (256,))"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qs = np.linspace(1e-3, 1-1e-3, 20)\n",
    "ps = np.logspace(-3, -2, 256)\n",
    "qs.shape, ps.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "c9583eaf-194a-4f16-a9f4-4faaf3d192e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.001     , 0.05352632, 0.10605263, 0.15857895, 0.21110526,\n",
       "       0.26363158, 0.31615789, 0.36868421, 0.42121053, 0.47373684,\n",
       "       0.52626316, 0.57878947, 0.63131579, 0.68384211, 0.73636842,\n",
       "       0.78889474, 0.84142105, 0.89394737, 0.94647368, 0.999     ])"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "2c708f6c-3f2f-4058-af2c-46f9089c597d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 256)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = geom.ppf(qs.reshape(-1, 1), p=ps)\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "90e45b3b-4339-4ee2-953a-afecdf5c2c6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = []\n",
    "for i, q in enumerate(qs):\n",
    "    frames.append(pd.DataFrame(dict(q=q, p=ps, k=a[i])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "063cbe9e-f2cc-422c-9ff4-61a8cf9ac721",
   "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>q</th>\n",
       "      <th>p</th>\n",
       "      <th>k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001009</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001018</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001027</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001037</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>0.999</td>\n",
       "      <td>0.009645</td>\n",
       "      <td>713.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>0.999</td>\n",
       "      <td>0.009733</td>\n",
       "      <td>707.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>0.999</td>\n",
       "      <td>0.009821</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>0.999</td>\n",
       "      <td>0.009910</td>\n",
       "      <td>694.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>0.999</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>688.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5120 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         q         p      k\n",
       "0    0.001  0.001000    1.0\n",
       "1    0.001  0.001009    1.0\n",
       "2    0.001  0.001018    1.0\n",
       "3    0.001  0.001027    1.0\n",
       "4    0.001  0.001037    1.0\n",
       "..     ...       ...    ...\n",
       "251  0.999  0.009645  713.0\n",
       "252  0.999  0.009733  707.0\n",
       "253  0.999  0.009821  700.0\n",
       "254  0.999  0.009910  694.0\n",
       "255  0.999  0.010000  688.0\n",
       "\n",
       "[5120 rows x 3 columns]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat(frames, axis=\"index\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "7cf506f2-bdee-4413-981a-b18d5b2eae39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x741.641 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "sns.scatterplot(x=\"p\",\n",
    "             y='k',\n",
    "             hue='q', palette=\"crest\",\n",
    "             # hue_norm=LogNorm(),\n",
    "             linewidth=.75, alpha=.9,\n",
    "             data=data, ax=ax)\n",
    "\n",
    "ax.set_xscale('log')\n",
    "# ax.set_yscale('log')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "1fe5addb-452d-4e57-8378-8171c9b379ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256, 256)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "aad2ff74-6769-4d9f-88a9-696d0c995c80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256, 256)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P, Q = np.meshgrid(ps, qs)\n",
    "Q.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "5221325f-5452-474d-aa83-bdb83c71aef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x741.641 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# sns.scatterplot(x=\"p\",\n",
    "#              y='q',\n",
    "#              hue='k', palette=\"crest\",\n",
    "#              hue_norm=LogNorm(),\n",
    "#              linewidth=.75, alpha=.9,\n",
    "#              data=data, ax=ax)\n",
    "\n",
    "contours = ax.pcolormesh(*np.broadcast_arrays(ps, qs.reshape(-1, 1)), a, norm=LogNorm(), cmap=\"Spectral\")\n",
    "\n",
    "fig.colorbar(contours, ax=ax)\n",
    "\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "8dfb66d9-2727-4b98-85f1-701f9db1f4f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256,)"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps = np.logspace(-3, -.1, 256)\n",
    "ps.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "fad5a512-e730-4c74-814f-07a98fa7ad7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20,)"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ks = np.arange(1, 100, 5)\n",
    "ks.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "f4007e57-bfcf-4778-9b48-01d1c7f1bfcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 256)"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 1. - geom.cdf(ks.reshape(-1, 1), p=ps)\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "1264fe43-11e3-46b4-b3e9-49fa7c5cfdd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = []\n",
    "for i, k in enumerate(ks):\n",
    "    frames.append(pd.DataFrame(dict(q=a[i], p=ps, k=k)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "806cf9bc-b5fa-4fdd-890e-1dcf07921c97",
   "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>q</th>\n",
       "      <th>p</th>\n",
       "      <th>k</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.999000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.998973</td>\n",
       "      <td>0.001027</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.998946</td>\n",
       "      <td>0.001054</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.998918</td>\n",
       "      <td>0.001082</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.998890</td>\n",
       "      <td>0.001110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.715336</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.734315</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.753798</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.773798</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.794328</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5120 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            q         p   k\n",
       "0    0.999000  0.001000   1\n",
       "1    0.998973  0.001027   1\n",
       "2    0.998946  0.001054   1\n",
       "3    0.998918  0.001082   1\n",
       "4    0.998890  0.001110   1\n",
       "..        ...       ...  ..\n",
       "251  0.000000  0.715336  96\n",
       "252  0.000000  0.734315  96\n",
       "253  0.000000  0.753798  96\n",
       "254  0.000000  0.773798  96\n",
       "255  0.000000  0.794328  96\n",
       "\n",
       "[5120 rows x 3 columns]"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat(frames, axis=\"index\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "058a3a15-127d-4524-8fac-85a1cf455889",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x741.641 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "sns.lineplot(x=\"p\",\n",
    "             y='q',\n",
    "             hue='k', palette=\"crest\",\n",
    "             # hue_norm=LogNorm(),\n",
    "             linewidth=.75, alpha=.9,\n",
    "             data=data, ax=ax)\n",
    "\n",
    "ax.set_xscale('log')\n",
    "# ax.set_yscale('log')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a14db23e-e613-4cc2-8eb8-77eae2499050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(459.0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rv.ppf(.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "ae0d0e7b-575f-401a-bc8d-162186f71cbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X_grid = np.arange(*rv.ppf([1e-2, 1.-1e-2]), step=1)\n",
    "X_grid = np.arange(50, step=1) + 1\n",
    "X_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "6fa11f8d-3535-4557-919d-08feb92ed06e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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v1KhRyszMNHxtQLA6BnDtvyR6MxR+ScA4hw8f1s0336z6+npNmjRJL7zwghISErqde/ToUe3YscP3xqWznJwc39derzeg1+NDhw75NYbuWAOQpMrKSv2///f/dN999/Xpus5HaHb3BpvnLIyUlpamKVOmBPS/cePG+V17ySWXdJnTeecF7wkQCj/+8Y919913q66uLuBrTpw44Te+5JJLuszh9bV77OhAn1x11VXasWOHJKmqqkplZWVdtqx25PF4tG/fPt84JyfHsGZRgJFycnL0yiuvSPr7L4mODfa6MxR+ScAYR44cCTjkkKT/+q//0hNPPKFvfOMb+vnPf97l53PmzNGIESN8b77feeedXp+v7X8Blc5/EJ0zZ04//zUwq8bGRr355ptyOBy677775HA4Arqu860qKSkpXebwnIWRfvOb3wQ8t6ioSDfeeKNvvHTpUn3zm9+84DW8J0AobN++XWfOnNHixYsD3ol04MAB39d2u13Tp0/vMofX1+7xiRN9smDBAr9kfNu2bRecv3fvXl+6HRUVpfz8/JCuD+iv9l8S7d55551erxkKvyQQvNLSUt18882qq6tTRkZGryFHIBwOh98b9+3bt8vtdvc4v62tzRdSS9JNN90U8IdYDD0ulyug18B2HZ9bkvSlL32pyxyesxhMeE+AUHrzzTcDmvfxxx/7nbIyb948xcTEdJnH62v3CDrQJ3a7XXfddZdvvGHDhh6bNHm9Xj366KO+cW5uriZMmBDiFQL9wy8JhMLRo0d10003qba2Vunp6YaEHO1uueUWX62qqiq9/PLLPc595ZVXVFVVJUlKSEggdEav1q5d2+WozO784Q9/0MGDB33jBQsWdDmlrR3PWQwWvCdAKL388ss6derUBed4vV49+OCDvnFUVJTuuOOOHufz+toVQQf67Nprr1Vubq6k8/fl3nnnnV0a5Xi9Xq1evdp3f/nkyZO1fPnyAV8r0Bf8koCRysrKdNNNN/mOgjt69KiysrI0efLkXv/3xBNP9Fo/Li5Ov/rVrxQVdf4u1F/84hcqKirqMu/dd9/VL37xC0nn3yj96le/4shD9OrIkSO69dZbL3jixO9//3u/fh6jR4/Wf/zHf/Q4n+csBhPeEyBUGhsb9b3vfU+VlZXd/ry5uVkrV67UW2+95fvef/zHf2jSpEk91uT1tSt6dKBf7r//fjU2Nmrnzp16//33tXDhQi1evFipqamqqanR1q1b9eGHH0qSJk2apKeffrpLV3Yg0rT/kvje976ntrY2/eIXv1B6erpmz57tN28o/ZJA/7388suqqakJ6WPMmTNHDz74oO677z41NzcrPz9fX//61333/r733nv64x//KLfbrWHDhumBBx5gOzV6FBcXp4svvtgXzu3du1fz58/X/PnzNX36dF188cVqa2vTJ598ojfeeEMlJSW+aydNmqS1a9cqKSnpgo/BcxaDBe8JYLQJEyb4/gj80Ucf6Wtf+5rmz5+vmTNnKiEhQefOndNHH32k1157zbfjw+Fw6Ec/+pG+/e1v91qf11d/Fm/HrjlAH23atEnr1q3rNpF0Op3Kzc3VsmXLupwBDQRrxYoVftulW1tb/Y48TkxM7NJF/ZlnntGYMWN6rb1t2zbdd999OnfunGw2W6+/JK677jqD/lUwkzVr1ujFF18MqkZPzUg7Ky0t1c9//nPt3btXHo/H72dWq1VZWVm69957lZGREdR6YH4ul0uvv/66du7cqT179nTZsdnZlClTdP3112vx4sWy2+0BPw7PWYTK7373O7/dF01NTfrkk09840suucTviM558+b53ZbdGe8JYKT9+/frj3/8o958802dPHmyx3nDhg3T1Vdfre9973sXPPihO7y+nkfQgaB5vV4VFxervLxcNTU1GjlypJKTkzVr1iwCDoTMDTfcoHfffbdP1xQWFnZ7GkB3+CWBwejkyZN6//33fW+evvCFL2j69OndHkcH9KatrU0ff/yxjh49qpqaGp09e1ZWq1VxcXFKSkrS1KlTNWrUqKAeg+csjPb4448HdPtfu0ACZd4TIBQqKyt15MgRnTx5Uo2NjbLb7XI6nZo4caL+4R/+Ieg+L0P99ZWgAwAuYKj/kgAAAOfxngAYPAg6AAAAAACAaXDqCgAAAAAAMA2CDgAAAAAAYBoEHQAAAAAAwDQIOgAAAAAAgGkQdAAAAAAAANMg6AAAAAAAAKZB0AEAAAAAAEyDoAMAAAAAAJgGQQcAAAAAADANgg4AAAAAAGAaBB0AAAAAAMA0CDoAAAAAAIBpEHQAAAAAAADTIOgAAAAAAACmQdABAAAAAABMg6ADAAAAAACYBkEHAAAAAAAwDYIOAAAAAABgGgQdAAAAAADANAg6AAAAAACAaRB0AAAAAAAA0yDoAAAAAAAApkHQAQAAAAAATIOgAwAAAAAAmAZBBwAAAAAAMA2CDgAAAAAAYBoEHQAAAAAAwDQIOgAAAAAAgGkQdAAAAAAAANMg6AAAAAAAAKZB0AEAAAAAAEyDoAMAAAAAAJgGQQcAAAAAADANgg4AAAAAAGAaBB0AAAAAAMA0/v8PxLDvxAEfFQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x741.641 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.scatter(X_grid, np.add.accumulate(geom.pmf(X_grid, p=.1)))\n",
    "# ax.scatter(X_grid, 1. - geom.cdf(X_grid, p=.1))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "383fa0f4-2fc6-4ed2-900a-9462e307187d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.49159683480686955)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1. - 0.5 * np.exp(1/(60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e7f11e1-d256-47c6-a881-6360f7dd12ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.6050060671375366)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1. - geom(.01).cdf(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "cfd940f9-8ae8-4990-a859-87f025598ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.6050060671375366)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - rv.cdf(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "89bfd973-bf71-42d3-9db9-42631409cab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(5.0)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geom.ppf(.4, p=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "413ab162-3aeb-471a-a6e2-250cd64c9eb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5563a78-b4a2-4935-af52-10c2ea3c6fef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6accfb0a-8d3c-4ace-9dc0-c0d218da3914",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(0.3949939328624633)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rv.pmf(X_grid).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "12924602-fad8-4fd7-a990-5a33771bd9a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50, 15, 35, 46, 50, 50, 23, 50, 50,  5, 50, 19,  7, 50, 50, 50, 37,\n",
       "       11, 50, 50, 13, 50,  4, 50, 30, 50, 38, 50, 50, 21, 50, 50, 50, 50,\n",
       "       50, 50, 10, 22,  5, 40, 49, 32, 50, 44, 33, 50, 16, 50,  8, 50, 50,\n",
       "       23,  1, 50, 50, 50, 50,  8, 45, 13, 50, 50, 40,  7, 38, 40, 50, 50,\n",
       "       50, 50, 13, 50, 50, 50, 50, 50, 50, 50,  3, 12,  4, 50, 38, 50, 50,\n",
       "       29, 50, 50, 26,  8, 35, 18, 50, 50, 50, 50, 50, 21, 50, 50])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.minimum(rv.rvs(size=(100,), random_state=random_state), 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1058d8cd-2a4d-4b3f-8e92-5ac88dbb4ec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 16, 20, 26, 73, 15,  8, 31,  7, 12,  2,  1, 65, 36, 24, 11,  4,\n",
       "        4,  6, 16, 25, 22,  7, 61, 27, 16, 19, 11,  6,  9, 28,  1,  3,  1,\n",
       "        1, 38, 24, 13,  3, 14, 13,  4, 12, 10, 19, 20,  1, 10, 20, 14, 38,\n",
       "       21,  4,  2, 21,  1, 18, 55, 17, 10, 21, 12, 16, 56, 10, 64, 46,  5,\n",
       "        2,  3,  1,  2, 23,  2,  8, 37,  1, 33,  7,  3, 24, 20, 41, 26, 32,\n",
       "        7,  4, 28, 33, 91, 11, 10, 30,  9, 53, 39, 11, 28, 28,  3])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_state.geometric(p=.05, size=(100,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "61c68560-1fdc-42b4-ad3f-3b4a8fcea076",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "68dbeb7f-c14f-436b-8b53-28af0fb3774d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"data_2k_lw.json\", \"r\") as infile:\n",
    "    dct = json.load(infile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "5c8c9b7e-5c42-40ab-b978-9edb897ed770",
   "metadata": {},
   "outputs": [],
   "source": [
    "records = []\n",
    "for dataset_name, bar in dct.items():\n",
    "    records.append(bar['0']['results'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "51382602-b8e8-4f23-b818-fc0c09f4ee34",
   "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>model_parameters</th>\n",
       "      <th>final_train_cross_entropy</th>\n",
       "      <th>final_train_accuracy</th>\n",
       "      <th>final_train_balanced_accuracy</th>\n",
       "      <th>final_val_cross_entropy</th>\n",
       "      <th>final_val_accuracy</th>\n",
       "      <th>final_val_balanced_accuracy</th>\n",
       "      <th>final_test_cross_entropy</th>\n",
       "      <th>final_test_accuracy</th>\n",
       "      <th>final_test_balanced_accuracy</th>\n",
       "      <th>OpenML_task_id</th>\n",
       "      <th>test_split</th>\n",
       "      <th>budget</th>\n",
       "      <th>seed</th>\n",
       "      <th>instances</th>\n",
       "      <th>classes</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>93913</td>\n",
       "      <td>0.097975</td>\n",
       "      <td>97.898347</td>\n",
       "      <td>0.970575</td>\n",
       "      <td>0.093814</td>\n",
       "      <td>97.589860</td>\n",
       "      <td>0.944003</td>\n",
       "      <td>0.356219</td>\n",
       "      <td>97.535885</td>\n",
       "      <td>0.944212</td>\n",
       "      <td>168868</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>76000</td>\n",
       "      <td>2</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46740</td>\n",
       "      <td>0.679274</td>\n",
       "      <td>76.755728</td>\n",
       "      <td>0.559910</td>\n",
       "      <td>0.630763</td>\n",
       "      <td>76.731990</td>\n",
       "      <td>0.544323</td>\n",
       "      <td>0.659876</td>\n",
       "      <td>77.325689</td>\n",
       "      <td>0.558814</td>\n",
       "      <td>34539</td>\n",
       "      <td>0.330007</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>32769</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>48205</td>\n",
       "      <td>0.613444</td>\n",
       "      <td>85.161290</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.611427</td>\n",
       "      <td>86.928105</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>0.656600</td>\n",
       "      <td>83.259912</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>167104</td>\n",
       "      <td>0.328986</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>690</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>931065</td>\n",
       "      <td>0.447136</td>\n",
       "      <td>84.526765</td>\n",
       "      <td>0.846668</td>\n",
       "      <td>0.468136</td>\n",
       "      <td>83.608993</td>\n",
       "      <td>0.836728</td>\n",
       "      <td>1.702352</td>\n",
       "      <td>84.610390</td>\n",
       "      <td>0.843751</td>\n",
       "      <td>189908</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>70000</td>\n",
       "      <td>10</td>\n",
       "      <td>784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>106219</td>\n",
       "      <td>0.411249</td>\n",
       "      <td>84.681875</td>\n",
       "      <td>0.819309</td>\n",
       "      <td>0.345584</td>\n",
       "      <td>84.117221</td>\n",
       "      <td>0.698728</td>\n",
       "      <td>0.490613</td>\n",
       "      <td>84.551515</td>\n",
       "      <td>0.694260</td>\n",
       "      <td>3945</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>58753</td>\n",
       "      <td>0.332028</td>\n",
       "      <td>86.263595</td>\n",
       "      <td>0.860771</td>\n",
       "      <td>0.335127</td>\n",
       "      <td>86.358104</td>\n",
       "      <td>0.861677</td>\n",
       "      <td>0.481766</td>\n",
       "      <td>85.974559</td>\n",
       "      <td>0.857375</td>\n",
       "      <td>168335</td>\n",
       "      <td>0.330007</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>130064</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>48205</td>\n",
       "      <td>0.414427</td>\n",
       "      <td>79.288483</td>\n",
       "      <td>0.809022</td>\n",
       "      <td>0.415673</td>\n",
       "      <td>79.842593</td>\n",
       "      <td>0.815885</td>\n",
       "      <td>0.524324</td>\n",
       "      <td>79.754297</td>\n",
       "      <td>0.813380</td>\n",
       "      <td>126025</td>\n",
       "      <td>0.329982</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>48842</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>46154</td>\n",
       "      <td>0.671111</td>\n",
       "      <td>58.426948</td>\n",
       "      <td>0.587272</td>\n",
       "      <td>0.670239</td>\n",
       "      <td>58.394405</td>\n",
       "      <td>0.586985</td>\n",
       "      <td>0.679574</td>\n",
       "      <td>58.534694</td>\n",
       "      <td>0.588444</td>\n",
       "      <td>189354</td>\n",
       "      <td>0.330001</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>539383</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>66957</td>\n",
       "      <td>0.636707</td>\n",
       "      <td>64.570695</td>\n",
       "      <td>0.645701</td>\n",
       "      <td>0.638318</td>\n",
       "      <td>64.227141</td>\n",
       "      <td>0.642280</td>\n",
       "      <td>0.657192</td>\n",
       "      <td>64.436432</td>\n",
       "      <td>0.644360</td>\n",
       "      <td>189866</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>425240</td>\n",
       "      <td>2</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>48791</td>\n",
       "      <td>0.423275</td>\n",
       "      <td>81.621089</td>\n",
       "      <td>0.815308</td>\n",
       "      <td>0.431465</td>\n",
       "      <td>81.844553</td>\n",
       "      <td>0.820540</td>\n",
       "      <td>0.526126</td>\n",
       "      <td>81.158255</td>\n",
       "      <td>0.803247</td>\n",
       "      <td>126029</td>\n",
       "      <td>0.329986</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>45211</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>45275</td>\n",
       "      <td>0.644849</td>\n",
       "      <td>73.511905</td>\n",
       "      <td>0.739754</td>\n",
       "      <td>0.639546</td>\n",
       "      <td>62.650602</td>\n",
       "      <td>0.672952</td>\n",
       "      <td>0.664549</td>\n",
       "      <td>62.601626</td>\n",
       "      <td>0.636175</td>\n",
       "      <td>167184</td>\n",
       "      <td>0.328877</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>748</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>46457</td>\n",
       "      <td>1.192222</td>\n",
       "      <td>67.096774</td>\n",
       "      <td>0.666201</td>\n",
       "      <td>1.236570</td>\n",
       "      <td>61.096606</td>\n",
       "      <td>0.608783</td>\n",
       "      <td>1.347492</td>\n",
       "      <td>58.947368</td>\n",
       "      <td>0.627373</td>\n",
       "      <td>189905</td>\n",
       "      <td>0.329861</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1728</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4020475</td>\n",
       "      <td>0.525473</td>\n",
       "      <td>74.712171</td>\n",
       "      <td>0.747188</td>\n",
       "      <td>0.570179</td>\n",
       "      <td>70.784641</td>\n",
       "      <td>0.707778</td>\n",
       "      <td>0.617843</td>\n",
       "      <td>70.246085</td>\n",
       "      <td>0.702461</td>\n",
       "      <td>168908</td>\n",
       "      <td>0.330011</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>5418</td>\n",
       "      <td>2</td>\n",
       "      <td>1636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1108579</td>\n",
       "      <td>2.142003</td>\n",
       "      <td>32.783505</td>\n",
       "      <td>0.348109</td>\n",
       "      <td>2.163496</td>\n",
       "      <td>24.686192</td>\n",
       "      <td>0.239674</td>\n",
       "      <td>2.193896</td>\n",
       "      <td>26.685393</td>\n",
       "      <td>0.253823</td>\n",
       "      <td>167185</td>\n",
       "      <td>0.329630</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1080</td>\n",
       "      <td>9</td>\n",
       "      <td>856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>56558</td>\n",
       "      <td>1.067724</td>\n",
       "      <td>49.307525</td>\n",
       "      <td>0.441987</td>\n",
       "      <td>1.057233</td>\n",
       "      <td>48.299639</td>\n",
       "      <td>0.426574</td>\n",
       "      <td>1.080707</td>\n",
       "      <td>49.221729</td>\n",
       "      <td>0.437760</td>\n",
       "      <td>167201</td>\n",
       "      <td>0.329988</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>67557</td>\n",
       "      <td>3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>61272</td>\n",
       "      <td>0.752298</td>\n",
       "      <td>61.104764</td>\n",
       "      <td>0.707323</td>\n",
       "      <td>0.932549</td>\n",
       "      <td>60.835111</td>\n",
       "      <td>0.703640</td>\n",
       "      <td>1.659874</td>\n",
       "      <td>61.119050</td>\n",
       "      <td>0.710865</td>\n",
       "      <td>7593</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>581012</td>\n",
       "      <td>7</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>49963</td>\n",
       "      <td>0.655188</td>\n",
       "      <td>65.401786</td>\n",
       "      <td>0.713087</td>\n",
       "      <td>0.666215</td>\n",
       "      <td>65.315315</td>\n",
       "      <td>0.666682</td>\n",
       "      <td>0.682844</td>\n",
       "      <td>61.515152</td>\n",
       "      <td>0.658730</td>\n",
       "      <td>167161</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>208385</td>\n",
       "      <td>4.970359</td>\n",
       "      <td>2.727672</td>\n",
       "      <td>0.027033</td>\n",
       "      <td>4.970577</td>\n",
       "      <td>2.730928</td>\n",
       "      <td>0.027219</td>\n",
       "      <td>5.870127</td>\n",
       "      <td>2.817783</td>\n",
       "      <td>0.027453</td>\n",
       "      <td>189873</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>416188</td>\n",
       "      <td>355</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>967239</td>\n",
       "      <td>1.850840</td>\n",
       "      <td>45.090614</td>\n",
       "      <td>0.489209</td>\n",
       "      <td>1.886234</td>\n",
       "      <td>35.969248</td>\n",
       "      <td>0.372941</td>\n",
       "      <td>1.936126</td>\n",
       "      <td>35.454211</td>\n",
       "      <td>0.374050</td>\n",
       "      <td>168910</td>\n",
       "      <td>0.330096</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>8237</td>\n",
       "      <td>7</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>85922</td>\n",
       "      <td>4.352067</td>\n",
       "      <td>6.868038</td>\n",
       "      <td>0.042986</td>\n",
       "      <td>4.160308</td>\n",
       "      <td>7.242456</td>\n",
       "      <td>0.046408</td>\n",
       "      <td>4.590835</td>\n",
       "      <td>7.055543</td>\n",
       "      <td>0.044551</td>\n",
       "      <td>168329</td>\n",
       "      <td>0.330005</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>65196</td>\n",
       "      <td>100</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>52307</td>\n",
       "      <td>0.639955</td>\n",
       "      <td>64.202299</td>\n",
       "      <td>0.640642</td>\n",
       "      <td>0.638036</td>\n",
       "      <td>64.464945</td>\n",
       "      <td>0.642805</td>\n",
       "      <td>0.659958</td>\n",
       "      <td>64.198294</td>\n",
       "      <td>0.640833</td>\n",
       "      <td>167200</td>\n",
       "      <td>0.329995</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>98050</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>60521</td>\n",
       "      <td>1.055260</td>\n",
       "      <td>56.399819</td>\n",
       "      <td>0.562938</td>\n",
       "      <td>1.021260</td>\n",
       "      <td>55.622772</td>\n",
       "      <td>0.544983</td>\n",
       "      <td>1.229691</td>\n",
       "      <td>56.130573</td>\n",
       "      <td>0.539986</td>\n",
       "      <td>168330</td>\n",
       "      <td>0.330001</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>83733</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>86295</td>\n",
       "      <td>0.614242</td>\n",
       "      <td>74.607916</td>\n",
       "      <td>0.748194</td>\n",
       "      <td>0.606263</td>\n",
       "      <td>76.701967</td>\n",
       "      <td>0.766032</td>\n",
       "      <td>0.650085</td>\n",
       "      <td>76.321138</td>\n",
       "      <td>0.760956</td>\n",
       "      <td>189862</td>\n",
       "      <td>0.329759</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2984</td>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>46010</td>\n",
       "      <td>0.732083</td>\n",
       "      <td>65.644416</td>\n",
       "      <td>0.697469</td>\n",
       "      <td>0.762353</td>\n",
       "      <td>65.721493</td>\n",
       "      <td>0.692251</td>\n",
       "      <td>0.918260</td>\n",
       "      <td>66.484111</td>\n",
       "      <td>0.701399</td>\n",
       "      <td>189909</td>\n",
       "      <td>0.329994</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>44819</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>50256</td>\n",
       "      <td>0.583750</td>\n",
       "      <td>77.613516</td>\n",
       "      <td>0.699813</td>\n",
       "      <td>0.594632</td>\n",
       "      <td>78.586724</td>\n",
       "      <td>0.691920</td>\n",
       "      <td>0.618030</td>\n",
       "      <td>77.122302</td>\n",
       "      <td>0.689279</td>\n",
       "      <td>167181</td>\n",
       "      <td>0.329540</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2109</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>54651</td>\n",
       "      <td>0.576036</td>\n",
       "      <td>85.296167</td>\n",
       "      <td>0.852968</td>\n",
       "      <td>0.587494</td>\n",
       "      <td>83.734088</td>\n",
       "      <td>0.838373</td>\n",
       "      <td>0.632781</td>\n",
       "      <td>84.060721</td>\n",
       "      <td>0.841023</td>\n",
       "      <td>167149</td>\n",
       "      <td>0.329787</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>3196</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>109799</td>\n",
       "      <td>1.934677</td>\n",
       "      <td>67.892977</td>\n",
       "      <td>0.686981</td>\n",
       "      <td>1.940322</td>\n",
       "      <td>69.751693</td>\n",
       "      <td>0.706411</td>\n",
       "      <td>2.253402</td>\n",
       "      <td>73.333333</td>\n",
       "      <td>0.719490</td>\n",
       "      <td>167152</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2000</td>\n",
       "      <td>10</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>78677</td>\n",
       "      <td>0.156897</td>\n",
       "      <td>93.717277</td>\n",
       "      <td>0.940001</td>\n",
       "      <td>0.164591</td>\n",
       "      <td>93.780344</td>\n",
       "      <td>0.939140</td>\n",
       "      <td>0.391885</td>\n",
       "      <td>93.669217</td>\n",
       "      <td>0.938591</td>\n",
       "      <td>126026</td>\n",
       "      <td>0.329987</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>34465</td>\n",
       "      <td>2</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>50256</td>\n",
       "      <td>0.692173</td>\n",
       "      <td>52.303529</td>\n",
       "      <td>0.522787</td>\n",
       "      <td>0.692369</td>\n",
       "      <td>51.213786</td>\n",
       "      <td>0.511955</td>\n",
       "      <td>0.692557</td>\n",
       "      <td>52.018248</td>\n",
       "      <td>0.519926</td>\n",
       "      <td>167083</td>\n",
       "      <td>0.329994</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>96320</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>45568</td>\n",
       "      <td>0.501101</td>\n",
       "      <td>75.556472</td>\n",
       "      <td>0.768807</td>\n",
       "      <td>0.523075</td>\n",
       "      <td>75.230126</td>\n",
       "      <td>0.766337</td>\n",
       "      <td>0.579869</td>\n",
       "      <td>75.490746</td>\n",
       "      <td>0.771152</td>\n",
       "      <td>167190</td>\n",
       "      <td>0.329941</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>5404</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>50138</td>\n",
       "      <td>1.542066</td>\n",
       "      <td>51.398264</td>\n",
       "      <td>0.509589</td>\n",
       "      <td>1.534266</td>\n",
       "      <td>53.228963</td>\n",
       "      <td>0.525627</td>\n",
       "      <td>1.862325</td>\n",
       "      <td>48.950131</td>\n",
       "      <td>0.499325</td>\n",
       "      <td>189906</td>\n",
       "      <td>0.329870</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2310</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>48087</td>\n",
       "      <td>0.289619</td>\n",
       "      <td>96.781380</td>\n",
       "      <td>0.841701</td>\n",
       "      <td>0.232018</td>\n",
       "      <td>96.709295</td>\n",
       "      <td>0.784348</td>\n",
       "      <td>1.316259</td>\n",
       "      <td>96.839080</td>\n",
       "      <td>0.830675</td>\n",
       "      <td>146212</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>58000</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>49963</td>\n",
       "      <td>0.530035</td>\n",
       "      <td>83.913043</td>\n",
       "      <td>0.838935</td>\n",
       "      <td>0.534039</td>\n",
       "      <td>83.245150</td>\n",
       "      <td>0.832392</td>\n",
       "      <td>0.611009</td>\n",
       "      <td>80.946746</td>\n",
       "      <td>0.809769</td>\n",
       "      <td>189865</td>\n",
       "      <td>0.329820</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>5124</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>49973</td>\n",
       "      <td>1.339445</td>\n",
       "      <td>40.105541</td>\n",
       "      <td>0.407986</td>\n",
       "      <td>1.346927</td>\n",
       "      <td>44.148936</td>\n",
       "      <td>0.439649</td>\n",
       "      <td>1.376051</td>\n",
       "      <td>35.483871</td>\n",
       "      <td>0.330263</td>\n",
       "      <td>167168</td>\n",
       "      <td>0.329787</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>846</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>99251</td>\n",
       "      <td>1.461395</td>\n",
       "      <td>46.511806</td>\n",
       "      <td>0.482414</td>\n",
       "      <td>1.452463</td>\n",
       "      <td>46.040487</td>\n",
       "      <td>0.473609</td>\n",
       "      <td>2.088805</td>\n",
       "      <td>46.531206</td>\n",
       "      <td>0.474935</td>\n",
       "      <td>168331</td>\n",
       "      <td>0.330012</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>58310</td>\n",
       "      <td>10</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    model_parameters  final_train_cross_entropy  final_train_accuracy  \\\n",
       "0              93913                   0.097975             97.898347   \n",
       "1              46740                   0.679274             76.755728   \n",
       "2              48205                   0.613444             85.161290   \n",
       "3             931065                   0.447136             84.526765   \n",
       "4             106219                   0.411249             84.681875   \n",
       "5              58753                   0.332028             86.263595   \n",
       "6              48205                   0.414427             79.288483   \n",
       "7              46154                   0.671111             58.426948   \n",
       "8              66957                   0.636707             64.570695   \n",
       "9              48791                   0.423275             81.621089   \n",
       "10             45275                   0.644849             73.511905   \n",
       "11             46457                   1.192222             67.096774   \n",
       "12           4020475                   0.525473             74.712171   \n",
       "13           1108579                   2.142003             32.783505   \n",
       "14             56558                   1.067724             49.307525   \n",
       "15             61272                   0.752298             61.104764   \n",
       "16             49963                   0.655188             65.401786   \n",
       "17            208385                   4.970359              2.727672   \n",
       "18            967239                   1.850840             45.090614   \n",
       "19             85922                   4.352067              6.868038   \n",
       "20             52307                   0.639955             64.202299   \n",
       "21             60521                   1.055260             56.399819   \n",
       "22             86295                   0.614242             74.607916   \n",
       "23             46010                   0.732083             65.644416   \n",
       "24             50256                   0.583750             77.613516   \n",
       "25             54651                   0.576036             85.296167   \n",
       "26            109799                   1.934677             67.892977   \n",
       "27             78677                   0.156897             93.717277   \n",
       "28             50256                   0.692173             52.303529   \n",
       "29             45568                   0.501101             75.556472   \n",
       "30             50138                   1.542066             51.398264   \n",
       "31             48087                   0.289619             96.781380   \n",
       "32             49963                   0.530035             83.913043   \n",
       "33             49973                   1.339445             40.105541   \n",
       "34             99251                   1.461395             46.511806   \n",
       "\n",
       "    final_train_balanced_accuracy  final_val_cross_entropy  \\\n",
       "0                        0.970575                 0.093814   \n",
       "1                        0.559910                 0.630763   \n",
       "2                        0.846747                 0.611427   \n",
       "3                        0.846668                 0.468136   \n",
       "4                        0.819309                 0.345584   \n",
       "5                        0.860771                 0.335127   \n",
       "6                        0.809022                 0.415673   \n",
       "7                        0.587272                 0.670239   \n",
       "8                        0.645701                 0.638318   \n",
       "9                        0.815308                 0.431465   \n",
       "10                       0.739754                 0.639546   \n",
       "11                       0.666201                 1.236570   \n",
       "12                       0.747188                 0.570179   \n",
       "13                       0.348109                 2.163496   \n",
       "14                       0.441987                 1.057233   \n",
       "15                       0.707323                 0.932549   \n",
       "16                       0.713087                 0.666215   \n",
       "17                       0.027033                 4.970577   \n",
       "18                       0.489209                 1.886234   \n",
       "19                       0.042986                 4.160308   \n",
       "20                       0.640642                 0.638036   \n",
       "21                       0.562938                 1.021260   \n",
       "22                       0.748194                 0.606263   \n",
       "23                       0.697469                 0.762353   \n",
       "24                       0.699813                 0.594632   \n",
       "25                       0.852968                 0.587494   \n",
       "26                       0.686981                 1.940322   \n",
       "27                       0.940001                 0.164591   \n",
       "28                       0.522787                 0.692369   \n",
       "29                       0.768807                 0.523075   \n",
       "30                       0.509589                 1.534266   \n",
       "31                       0.841701                 0.232018   \n",
       "32                       0.838935                 0.534039   \n",
       "33                       0.407986                 1.346927   \n",
       "34                       0.482414                 1.452463   \n",
       "\n",
       "    final_val_accuracy  final_val_balanced_accuracy  final_test_cross_entropy  \\\n",
       "0            97.589860                     0.944003                  0.356219   \n",
       "1            76.731990                     0.544323                  0.659876   \n",
       "2            86.928105                     0.861542                  0.656600   \n",
       "3            83.608993                     0.836728                  1.702352   \n",
       "4            84.117221                     0.698728                  0.490613   \n",
       "5            86.358104                     0.861677                  0.481766   \n",
       "6            79.842593                     0.815885                  0.524324   \n",
       "7            58.394405                     0.586985                  0.679574   \n",
       "8            64.227141                     0.642280                  0.657192   \n",
       "9            81.844553                     0.820540                  0.526126   \n",
       "10           62.650602                     0.672952                  0.664549   \n",
       "11           61.096606                     0.608783                  1.347492   \n",
       "12           70.784641                     0.707778                  0.617843   \n",
       "13           24.686192                     0.239674                  2.193896   \n",
       "14           48.299639                     0.426574                  1.080707   \n",
       "15           60.835111                     0.703640                  1.659874   \n",
       "16           65.315315                     0.666682                  0.682844   \n",
       "17            2.730928                     0.027219                  5.870127   \n",
       "18           35.969248                     0.372941                  1.936126   \n",
       "19            7.242456                     0.046408                  4.590835   \n",
       "20           64.464945                     0.642805                  0.659958   \n",
       "21           55.622772                     0.544983                  1.229691   \n",
       "22           76.701967                     0.766032                  0.650085   \n",
       "23           65.721493                     0.692251                  0.918260   \n",
       "24           78.586724                     0.691920                  0.618030   \n",
       "25           83.734088                     0.838373                  0.632781   \n",
       "26           69.751693                     0.706411                  2.253402   \n",
       "27           93.780344                     0.939140                  0.391885   \n",
       "28           51.213786                     0.511955                  0.692557   \n",
       "29           75.230126                     0.766337                  0.579869   \n",
       "30           53.228963                     0.525627                  1.862325   \n",
       "31           96.709295                     0.784348                  1.316259   \n",
       "32           83.245150                     0.832392                  0.611009   \n",
       "33           44.148936                     0.439649                  1.376051   \n",
       "34           46.040487                     0.473609                  2.088805   \n",
       "\n",
       "    final_test_accuracy  final_test_balanced_accuracy  OpenML_task_id  \\\n",
       "0             97.535885                      0.944212          168868   \n",
       "1             77.325689                      0.558814           34539   \n",
       "2             83.259912                      0.820173          167104   \n",
       "3             84.610390                      0.843751          189908   \n",
       "4             84.551515                      0.694260            3945   \n",
       "5             85.974559                      0.857375          168335   \n",
       "6             79.754297                      0.813380          126025   \n",
       "7             58.534694                      0.588444          189354   \n",
       "8             64.436432                      0.644360          189866   \n",
       "9             81.158255                      0.803247          126029   \n",
       "10            62.601626                      0.636175          167184   \n",
       "11            58.947368                      0.627373          189905   \n",
       "12            70.246085                      0.702461          168908   \n",
       "13            26.685393                      0.253823          167185   \n",
       "14            49.221729                      0.437760          167201   \n",
       "15            61.119050                      0.710865            7593   \n",
       "16            61.515152                      0.658730          167161   \n",
       "17             2.817783                      0.027453          189873   \n",
       "18            35.454211                      0.374050          168910   \n",
       "19             7.055543                      0.044551          168329   \n",
       "20            64.198294                      0.640833          167200   \n",
       "21            56.130573                      0.539986          168330   \n",
       "22            76.321138                      0.760956          189862   \n",
       "23            66.484111                      0.701399          189909   \n",
       "24            77.122302                      0.689279          167181   \n",
       "25            84.060721                      0.841023          167149   \n",
       "26            73.333333                      0.719490          167152   \n",
       "27            93.669217                      0.938591          126026   \n",
       "28            52.018248                      0.519926          167083   \n",
       "29            75.490746                      0.771152          167190   \n",
       "30            48.950131                      0.499325          189906   \n",
       "31            96.839080                      0.830675          146212   \n",
       "32            80.946746                      0.809769          189865   \n",
       "33            35.483871                      0.330263          167168   \n",
       "34            46.531206                      0.474935          168331   \n",
       "\n",
       "    test_split  budget  seed  instances  classes  features  \n",
       "0     0.330000      50     1      76000        2       170  \n",
       "1     0.330007      50     1      32769        2         9  \n",
       "2     0.328986      50     1        690        2        14  \n",
       "3     0.330000      50     1      70000       10       784  \n",
       "4     0.330000      50     1      50000        2       230  \n",
       "5     0.330007      50     1     130064        2        50  \n",
       "6     0.329982      50     1      48842        2        14  \n",
       "7     0.330001      50     1     539383        2         7  \n",
       "8     0.330000      50     1     425240        2        78  \n",
       "9     0.329986      50     1      45211        2        16  \n",
       "10    0.328877      50     1        748        2         4  \n",
       "11    0.329861      50     1       1728        4         6  \n",
       "12    0.330011      50     1       5418        2      1636  \n",
       "13    0.329630      50     1       1080        9       856  \n",
       "14    0.329988      50     1      67557        3        42  \n",
       "15    0.330000      50     1     581012        7        54  \n",
       "16    0.330000      50     1       1000        2        20  \n",
       "17    0.330000      50     1     416188      355        60  \n",
       "18    0.330096      50     1       8237        7       800  \n",
       "19    0.330005      50     1      65196      100        27  \n",
       "20    0.329995      50     1      98050        2        28  \n",
       "21    0.330001      50     1      83733        4        54  \n",
       "22    0.329759      50     1       2984        2       144  \n",
       "23    0.329994      50     1      44819        3         6  \n",
       "24    0.329540      50     1       2109        2        21  \n",
       "25    0.329787      50     1       3196        2        36  \n",
       "26    0.330000      50     1       2000       10       216  \n",
       "27    0.329987      50     1      34465        2       118  \n",
       "28    0.329994      50     1      96320        2        21  \n",
       "29    0.329941      50     1       5404        2         5  \n",
       "30    0.329870      50     1       2310        7        16  \n",
       "31    0.330000      50     1      58000        7         9  \n",
       "32    0.329820      50     1       5124        2        20  \n",
       "33    0.329787      50     1        846        4        18  \n",
       "34    0.330012      50     1      58310       10       180  "
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(records)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "9286ce80-f28a-48f2-a382-3d8f5c360a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['APSFailure', 'Amazon_employee_access', 'Australian', 'Fashion-MNIST', 'KDDCup09_appetency', 'MiniBooNE', 'adult', 'airlines', 'albert', 'bank-marketing', 'blood-transfusion-service-center', 'car', 'christine', 'cnae-9', 'connect-4', 'covertype', 'credit-g', 'dionis', 'fabert', 'helena', 'higgs', 'jannis', 'jasmine', 'jungle_chess_2pcs_raw_endgame_complete', 'kc1', 'kr-vs-kp', 'mfeat-factors', 'nomao', 'numerai28.6', 'phoneme', 'segment', 'shuttle', 'sylvine', 'vehicle', 'volkert'])"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dct.keys()a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "79c9abb5-a36e-4314-9f90-0d90c30be881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batch_size</th>\n",
       "      <th>imputation_strategy</th>\n",
       "      <th>learning_rate_scheduler</th>\n",
       "      <th>loss</th>\n",
       "      <th>network</th>\n",
       "      <th>max_dropout</th>\n",
       "      <th>normalization_strategy</th>\n",
       "      <th>optimizer</th>\n",
       "      <th>cosine_annealing_T_max</th>\n",
       "      <th>cosine_annealing_eta_min</th>\n",
       "      <th>activation</th>\n",
       "      <th>max_units</th>\n",
       "      <th>mlp_shape</th>\n",
       "      <th>num_layers</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>momentum</th>\n",
       "      <th>weight_decay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71</td>\n",
       "      <td>mean</td>\n",
       "      <td>cosine_annealing</td>\n",
       "      <td>cross_entropy_weighted</td>\n",
       "      <td>shapedmlpnet</td>\n",
       "      <td>0.025926</td>\n",
       "      <td>standardize</td>\n",
       "      <td>sgd</td>\n",
       "      <td>50</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>relu</td>\n",
       "      <td>293</td>\n",
       "      <td>funnel</td>\n",
       "      <td>3</td>\n",
       "      <td>0.001824</td>\n",
       "      <td>0.213252</td>\n",
       "      <td>0.020473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>457</td>\n",
       "      <td>mean</td>\n",
       "      <td>cosine_annealing</td>\n",
       "      <td>cross_entropy_weighted</td>\n",
       "      <td>shapedmlpnet</td>\n",
       "      <td>0.547232</td>\n",
       "      <td>standardize</td>\n",
       "      <td>sgd</td>\n",
       "      <td>50</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>relu</td>\n",
       "      <td>950</td>\n",
       "      <td>funnel</td>\n",
       "      <td>4</td>\n",
       "      <td>0.012393</td>\n",
       "      <td>0.164114</td>\n",
       "      <td>0.097628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>352</td>\n",
       "      <td>mean</td>\n",
       "      <td>cosine_annealing</td>\n",
       "      <td>cross_entropy_weighted</td>\n",
       "      <td>shapedmlpnet</td>\n",
       "      <td>0.331980</td>\n",
       "      <td>standardize</td>\n",
       "      <td>sgd</td>\n",
       "      <td>50</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>relu</td>\n",
       "      <td>623</td>\n",
       "      <td>funnel</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.391250</td>\n",
       "      <td>0.052986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>329</td>\n",
       "      <td>mean</td>\n",
       "      <td>cosine_annealing</td>\n",
       "      <td>cross_entropy_weighted</td>\n",
       "      <td>shapedmlpnet</td>\n",
       "      <td>0.968541</td>\n",
       "      <td>standardize</td>\n",
       "      <td>sgd</td>\n",
       "      <td>50</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>relu</td>\n",
       "      <td>712</td>\n",
       "      <td>funnel</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000499</td>\n",
       "      <td>0.102648</td>\n",
       "      <td>0.043053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>mean</td>\n",
       "      <td>cosine_annealing</td>\n",
       "      <td>cross_entropy_weighted</td>\n",
       "      <td>shapedmlpnet</td>\n",
       "      <td>0.501875</td>\n",
       "      <td>standardize</td>\n",
       "      <td>sgd</td>\n",
       "      <td>50</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>relu</td>\n",
       "      <td>252</td>\n",
       "      <td>funnel</td>\n",
       "      <td>True</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.165046</td>\n",
       "      <td>0.041857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>155</td>\n",
       "      <td>mean</td>\n",
       "      <td>cosine_annealing</td>\n",
       "      <td>cross_entropy_weighted</td>\n",
       "      <td>shapedmlpnet</td>\n",
       "      <td>0.635370</td>\n",
       "      <td>standardize</td>\n",
       "      <td>sgd</td>\n",
       "      <td>50</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>relu</td>\n",
       "      <td>325</td>\n",
       "      <td>funnel</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000246</td>\n",
       "      <td>0.438175</td>\n",
       "      <td>0.084043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>490</td>\n",
       "      <td>mean</td>\n",
       "      <td>cosine_annealing</td>\n",
       "      <td>cross_entropy_weighted</td>\n",
       "      <td>shapedmlpnet</td>\n",
       "      <td>0.728061</td>\n",
       "      <td>standardize</td>\n",
       "      <td>sgd</td>\n",
       "      <td>50</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>relu</td>\n",
       "      <td>237</td>\n",
       "      <td>funnel</td>\n",
       "      <td>4</td>\n",
       "      <td>0.009034</td>\n",
       "      <td>0.364982</td>\n",
       "      <td>0.066421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>166</td>\n",
       "      <td>mean</td>\n",
       "      <td>cosine_annealing</td>\n",
       "      <td>cross_entropy_weighted</td>\n",
       "      <td>shapedmlpnet</td>\n",
       "      <td>0.363454</td>\n",
       "      <td>standardize</td>\n",
       "      <td>sgd</td>\n",
       "      <td>50</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>relu</td>\n",
       "      <td>236</td>\n",
       "      <td>funnel</td>\n",
       "      <td>3</td>\n",
       "      <td>0.037966</td>\n",
       "      <td>0.496414</td>\n",
       "      <td>0.017774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>460</td>\n",
       "      <td>mean</td>\n",
       "      <td>cosine_annealing</td>\n",
       "      <td>cross_entropy_weighted</td>\n",
       "      <td>shapedmlpnet</td>\n",
       "      <td>0.043747</td>\n",
       "      <td>standardize</td>\n",
       "      <td>sgd</td>\n",
       "      <td>50</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>relu</td>\n",
       "      <td>711</td>\n",
       "      <td>funnel</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000971</td>\n",
       "      <td>0.108313</td>\n",
       "      <td>0.043999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>57</td>\n",
       "      <td>mean</td>\n",
       "      <td>cosine_annealing</td>\n",
       "      <td>cross_entropy_weighted</td>\n",
       "      <td>shapedmlpnet</td>\n",
       "      <td>0.404549</td>\n",
       "      <td>standardize</td>\n",
       "      <td>sgd</td>\n",
       "      <td>50</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>relu</td>\n",
       "      <td>408</td>\n",
       "      <td>funnel</td>\n",
       "      <td>4</td>\n",
       "      <td>0.093697</td>\n",
       "      <td>0.281682</td>\n",
       "      <td>0.052186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      batch_size imputation_strategy learning_rate_scheduler  \\\n",
       "0             71                mean        cosine_annealing   \n",
       "1            457                mean        cosine_annealing   \n",
       "2            352                mean        cosine_annealing   \n",
       "3            329                mean        cosine_annealing   \n",
       "4             16                mean        cosine_annealing   \n",
       "...          ...                 ...                     ...   \n",
       "1995         155                mean        cosine_annealing   \n",
       "1996         490                mean        cosine_annealing   \n",
       "1997         166                mean        cosine_annealing   \n",
       "1998         460                mean        cosine_annealing   \n",
       "1999          57                mean        cosine_annealing   \n",
       "\n",
       "                        loss       network  max_dropout  \\\n",
       "0     cross_entropy_weighted  shapedmlpnet     0.025926   \n",
       "1     cross_entropy_weighted  shapedmlpnet     0.547232   \n",
       "2     cross_entropy_weighted  shapedmlpnet     0.331980   \n",
       "3     cross_entropy_weighted  shapedmlpnet     0.968541   \n",
       "4     cross_entropy_weighted  shapedmlpnet     0.501875   \n",
       "...                      ...           ...          ...   \n",
       "1995  cross_entropy_weighted  shapedmlpnet     0.635370   \n",
       "1996  cross_entropy_weighted  shapedmlpnet     0.728061   \n",
       "1997  cross_entropy_weighted  shapedmlpnet     0.363454   \n",
       "1998  cross_entropy_weighted  shapedmlpnet     0.043747   \n",
       "1999  cross_entropy_weighted  shapedmlpnet     0.404549   \n",
       "\n",
       "     normalization_strategy optimizer  cosine_annealing_T_max  \\\n",
       "0               standardize       sgd                      50   \n",
       "1               standardize       sgd                      50   \n",
       "2               standardize       sgd                      50   \n",
       "3               standardize       sgd                      50   \n",
       "4               standardize       sgd                      50   \n",
       "...                     ...       ...                     ...   \n",
       "1995            standardize       sgd                      50   \n",
       "1996            standardize       sgd                      50   \n",
       "1997            standardize       sgd                      50   \n",
       "1998            standardize       sgd                      50   \n",
       "1999            standardize       sgd                      50   \n",
       "\n",
       "      cosine_annealing_eta_min activation  max_units mlp_shape num_layers  \\\n",
       "0                 1.000000e-08       relu        293    funnel          3   \n",
       "1                 1.000000e-08       relu        950    funnel          4   \n",
       "2                 1.000000e-08       relu        623    funnel       True   \n",
       "3                 1.000000e-08       relu        712    funnel          3   \n",
       "4                 1.000000e-08       relu        252    funnel       True   \n",
       "...                        ...        ...        ...       ...        ...   \n",
       "1995              1.000000e-08       relu        325    funnel          2   \n",
       "1996              1.000000e-08       relu        237    funnel          4   \n",
       "1997              1.000000e-08       relu        236    funnel          3   \n",
       "1998              1.000000e-08       relu        711    funnel          2   \n",
       "1999              1.000000e-08       relu        408    funnel          4   \n",
       "\n",
       "      learning_rate  momentum  weight_decay  \n",
       "0          0.001824  0.213252      0.020473  \n",
       "1          0.012393  0.164114      0.097628  \n",
       "2          0.000210  0.391250      0.052986  \n",
       "3          0.000499  0.102648      0.043053  \n",
       "4          0.000267  0.165046      0.041857  \n",
       "...             ...       ...           ...  \n",
       "1995       0.000246  0.438175      0.084043  \n",
       "1996       0.009034  0.364982      0.066421  \n",
       "1997       0.037966  0.496414      0.017774  \n",
       "1998       0.000971  0.108313      0.043999  \n",
       "1999       0.093697  0.281682      0.052186  \n",
       "\n",
       "[2000 rows x 17 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([foo['config'] for foo in dct['Australian'].values()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ec18ea45-dfaa-41f7-acfc-5d037ca7a77b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>time</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.853955</td>\n",
       "      <td>1</td>\n",
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       "      <td>58.823528</td>\n",
       "      <td>0.683689</td>\n",
       "      <td>0.678622</td>\n",
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       "      <td>57.268723</td>\n",
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       "      <th>2</th>\n",
       "      <td>2.064345</td>\n",
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       "      <td>59.677418</td>\n",
       "      <td>58.823528</td>\n",
       "      <td>0.680672</td>\n",
       "      <td>0.675738</td>\n",
       "      <td>0.563674</td>\n",
       "      <td>0.548654</td>\n",
       "      <td>59.471367</td>\n",
       "      <td>0.686409</td>\n",
       "      <td>0.533357</td>\n",
       "      <td>0.001824</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.438759</td>\n",
       "      <td>3</td>\n",
       "      <td>0.682095</td>\n",
       "      <td>60.645161</td>\n",
       "      <td>58.169933</td>\n",
       "      <td>0.677709</td>\n",
       "      <td>0.672974</td>\n",
       "      <td>0.574782</td>\n",
       "      <td>0.542702</td>\n",
       "      <td>59.471367</td>\n",
       "      <td>0.685187</td>\n",
       "      <td>0.533357</td>\n",
       "      <td>0.001823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.784480</td>\n",
       "      <td>4</td>\n",
       "      <td>0.678999</td>\n",
       "      <td>61.935482</td>\n",
       "      <td>58.169933</td>\n",
       "      <td>0.674849</td>\n",
       "      <td>0.670170</td>\n",
       "      <td>0.588867</td>\n",
       "      <td>0.542702</td>\n",
       "      <td>61.233479</td>\n",
       "      <td>0.683923</td>\n",
       "      <td>0.552799</td>\n",
       "      <td>0.001817</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.135232</td>\n",
       "      <td>5</td>\n",
       "      <td>0.675939</td>\n",
       "      <td>64.838707</td>\n",
       "      <td>58.823528</td>\n",
       "      <td>0.671878</td>\n",
       "      <td>0.667410</td>\n",
       "      <td>0.619467</td>\n",
       "      <td>0.549948</td>\n",
       "      <td>63.436123</td>\n",
       "      <td>0.682720</td>\n",
       "      <td>0.578841</td>\n",
       "      <td>0.001808</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.512886</td>\n",
       "      <td>6</td>\n",
       "      <td>0.672858</td>\n",
       "      <td>66.774193</td>\n",
       "      <td>60.130718</td>\n",
       "      <td>0.669112</td>\n",
       "      <td>0.664650</td>\n",
       "      <td>0.641138</td>\n",
       "      <td>0.564441</td>\n",
       "      <td>65.638763</td>\n",
       "      <td>0.681485</td>\n",
       "      <td>0.602099</td>\n",
       "      <td>0.001796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.856942</td>\n",
       "      <td>7</td>\n",
       "      <td>0.669829</td>\n",
       "      <td>68.387100</td>\n",
       "      <td>61.437908</td>\n",
       "      <td>0.666286</td>\n",
       "      <td>0.661944</td>\n",
       "      <td>0.658744</td>\n",
       "      <td>0.577640</td>\n",
       "      <td>66.079292</td>\n",
       "      <td>0.680194</td>\n",
       "      <td>0.608699</td>\n",
       "      <td>0.001780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.200388</td>\n",
       "      <td>8</td>\n",
       "      <td>0.667041</td>\n",
       "      <td>68.709679</td>\n",
       "      <td>63.398693</td>\n",
       "      <td>0.663530</td>\n",
       "      <td>0.659326</td>\n",
       "      <td>0.662265</td>\n",
       "      <td>0.599379</td>\n",
       "      <td>67.841408</td>\n",
       "      <td>0.678963</td>\n",
       "      <td>0.629532</td>\n",
       "      <td>0.001760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.548642</td>\n",
       "      <td>9</td>\n",
       "      <td>0.664280</td>\n",
       "      <td>72.258064</td>\n",
       "      <td>67.320259</td>\n",
       "      <td>0.660837</td>\n",
       "      <td>0.656742</td>\n",
       "      <td>0.700998</td>\n",
       "      <td>0.644151</td>\n",
       "      <td>68.281937</td>\n",
       "      <td>0.677826</td>\n",
       "      <td>0.633349</td>\n",
       "      <td>0.001738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12.891976</td>\n",
       "      <td>10</td>\n",
       "      <td>0.661370</td>\n",
       "      <td>73.548386</td>\n",
       "      <td>69.934639</td>\n",
       "      <td>0.658190</td>\n",
       "      <td>0.654227</td>\n",
       "      <td>0.716172</td>\n",
       "      <td>0.671843</td>\n",
       "      <td>69.162994</td>\n",
       "      <td>0.676696</td>\n",
       "      <td>0.643766</td>\n",
       "      <td>0.001712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.259831</td>\n",
       "      <td>11</td>\n",
       "      <td>0.658686</td>\n",
       "      <td>75.161293</td>\n",
       "      <td>71.895424</td>\n",
       "      <td>0.655560</td>\n",
       "      <td>0.651724</td>\n",
       "      <td>0.733233</td>\n",
       "      <td>0.693582</td>\n",
       "      <td>70.044052</td>\n",
       "      <td>0.675548</td>\n",
       "      <td>0.655574</td>\n",
       "      <td>0.001682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.676542</td>\n",
       "      <td>12</td>\n",
       "      <td>0.655967</td>\n",
       "      <td>75.483871</td>\n",
       "      <td>74.509804</td>\n",
       "      <td>0.653014</td>\n",
       "      <td>0.649365</td>\n",
       "      <td>0.738389</td>\n",
       "      <td>0.722567</td>\n",
       "      <td>70.484581</td>\n",
       "      <td>0.674516</td>\n",
       "      <td>0.663566</td>\n",
       "      <td>0.001650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>17.055972</td>\n",
       "      <td>13</td>\n",
       "      <td>0.653364</td>\n",
       "      <td>75.806450</td>\n",
       "      <td>76.470589</td>\n",
       "      <td>0.650577</td>\n",
       "      <td>0.647094</td>\n",
       "      <td>0.743000</td>\n",
       "      <td>0.744306</td>\n",
       "      <td>70.925110</td>\n",
       "      <td>0.673515</td>\n",
       "      <td>0.670165</td>\n",
       "      <td>0.001615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18.428269</td>\n",
       "      <td>14</td>\n",
       "      <td>0.650893</td>\n",
       "      <td>76.774193</td>\n",
       "      <td>76.470589</td>\n",
       "      <td>0.648395</td>\n",
       "      <td>0.644918</td>\n",
       "      <td>0.753563</td>\n",
       "      <td>0.745600</td>\n",
       "      <td>73.568283</td>\n",
       "      <td>0.672508</td>\n",
       "      <td>0.702807</td>\n",
       "      <td>0.001577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19.802392</td>\n",
       "      <td>15</td>\n",
       "      <td>0.648534</td>\n",
       "      <td>77.419357</td>\n",
       "      <td>76.470589</td>\n",
       "      <td>0.646036</td>\n",
       "      <td>0.642743</td>\n",
       "      <td>0.761150</td>\n",
       "      <td>0.745600</td>\n",
       "      <td>74.008812</td>\n",
       "      <td>0.671479</td>\n",
       "      <td>0.708015</td>\n",
       "      <td>0.001537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21.174268</td>\n",
       "      <td>16</td>\n",
       "      <td>0.646189</td>\n",
       "      <td>79.354836</td>\n",
       "      <td>77.124184</td>\n",
       "      <td>0.643794</td>\n",
       "      <td>0.640603</td>\n",
       "      <td>0.782277</td>\n",
       "      <td>0.752847</td>\n",
       "      <td>75.330399</td>\n",
       "      <td>0.670466</td>\n",
       "      <td>0.725032</td>\n",
       "      <td>0.001494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>22.541935</td>\n",
       "      <td>17</td>\n",
       "      <td>0.643941</td>\n",
       "      <td>79.677422</td>\n",
       "      <td>78.431374</td>\n",
       "      <td>0.641646</td>\n",
       "      <td>0.638543</td>\n",
       "      <td>0.785798</td>\n",
       "      <td>0.767340</td>\n",
       "      <td>76.651985</td>\n",
       "      <td>0.669478</td>\n",
       "      <td>0.740657</td>\n",
       "      <td>0.001448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23.910560</td>\n",
       "      <td>18</td>\n",
       "      <td>0.641737</td>\n",
       "      <td>81.290321</td>\n",
       "      <td>80.392159</td>\n",
       "      <td>0.639526</td>\n",
       "      <td>0.636564</td>\n",
       "      <td>0.803404</td>\n",
       "      <td>0.789079</td>\n",
       "      <td>77.533043</td>\n",
       "      <td>0.668556</td>\n",
       "      <td>0.751073</td>\n",
       "      <td>0.001401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>25.274473</td>\n",
       "      <td>19</td>\n",
       "      <td>0.639642</td>\n",
       "      <td>82.580643</td>\n",
       "      <td>81.045753</td>\n",
       "      <td>0.637512</td>\n",
       "      <td>0.634627</td>\n",
       "      <td>0.817488</td>\n",
       "      <td>0.796325</td>\n",
       "      <td>78.854622</td>\n",
       "      <td>0.667643</td>\n",
       "      <td>0.766698</td>\n",
       "      <td>0.001352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>26.622701</td>\n",
       "      <td>20</td>\n",
       "      <td>0.637644</td>\n",
       "      <td>82.903229</td>\n",
       "      <td>81.045753</td>\n",
       "      <td>0.635556</td>\n",
       "      <td>0.632777</td>\n",
       "      <td>0.821009</td>\n",
       "      <td>0.796325</td>\n",
       "      <td>79.295151</td>\n",
       "      <td>0.666754</td>\n",
       "      <td>0.770515</td>\n",
       "      <td>0.001301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>27.987362</td>\n",
       "      <td>21</td>\n",
       "      <td>0.635651</td>\n",
       "      <td>82.903229</td>\n",
       "      <td>81.045753</td>\n",
       "      <td>0.633709</td>\n",
       "      <td>0.631058</td>\n",
       "      <td>0.821009</td>\n",
       "      <td>0.796325</td>\n",
       "      <td>79.735680</td>\n",
       "      <td>0.665938</td>\n",
       "      <td>0.775724</td>\n",
       "      <td>0.001248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>29.360464</td>\n",
       "      <td>22</td>\n",
       "      <td>0.633863</td>\n",
       "      <td>82.903229</td>\n",
       "      <td>81.045753</td>\n",
       "      <td>0.631999</td>\n",
       "      <td>0.629376</td>\n",
       "      <td>0.821554</td>\n",
       "      <td>0.796325</td>\n",
       "      <td>80.616737</td>\n",
       "      <td>0.665141</td>\n",
       "      <td>0.786140</td>\n",
       "      <td>0.001194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30.727140</td>\n",
       "      <td>23</td>\n",
       "      <td>0.632123</td>\n",
       "      <td>83.225807</td>\n",
       "      <td>81.699348</td>\n",
       "      <td>0.630258</td>\n",
       "      <td>0.627761</td>\n",
       "      <td>0.825075</td>\n",
       "      <td>0.803571</td>\n",
       "      <td>80.176208</td>\n",
       "      <td>0.664345</td>\n",
       "      <td>0.782323</td>\n",
       "      <td>0.001139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>32.138760</td>\n",
       "      <td>24</td>\n",
       "      <td>0.630417</td>\n",
       "      <td>83.225807</td>\n",
       "      <td>82.352943</td>\n",
       "      <td>0.628599</td>\n",
       "      <td>0.626208</td>\n",
       "      <td>0.825075</td>\n",
       "      <td>0.810818</td>\n",
       "      <td>80.616737</td>\n",
       "      <td>0.663587</td>\n",
       "      <td>0.787532</td>\n",
       "      <td>0.001083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>33.500488</td>\n",
       "      <td>25</td>\n",
       "      <td>0.628845</td>\n",
       "      <td>83.548386</td>\n",
       "      <td>83.660133</td>\n",
       "      <td>0.627047</td>\n",
       "      <td>0.624794</td>\n",
       "      <td>0.828597</td>\n",
       "      <td>0.825311</td>\n",
       "      <td>80.616737</td>\n",
       "      <td>0.662908</td>\n",
       "      <td>0.788923</td>\n",
       "      <td>0.001026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34.846305</td>\n",
       "      <td>26</td>\n",
       "      <td>0.627316</td>\n",
       "      <td>84.193550</td>\n",
       "      <td>84.313728</td>\n",
       "      <td>0.625636</td>\n",
       "      <td>0.623419</td>\n",
       "      <td>0.835094</td>\n",
       "      <td>0.832557</td>\n",
       "      <td>81.057266</td>\n",
       "      <td>0.662238</td>\n",
       "      <td>0.794132</td>\n",
       "      <td>0.000969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>36.202874</td>\n",
       "      <td>27</td>\n",
       "      <td>0.625902</td>\n",
       "      <td>84.838707</td>\n",
       "      <td>84.313728</td>\n",
       "      <td>0.624252</td>\n",
       "      <td>0.622165</td>\n",
       "      <td>0.842136</td>\n",
       "      <td>0.832557</td>\n",
       "      <td>81.057266</td>\n",
       "      <td>0.661658</td>\n",
       "      <td>0.794132</td>\n",
       "      <td>0.000912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>37.566040</td>\n",
       "      <td>28</td>\n",
       "      <td>0.624623</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>84.967323</td>\n",
       "      <td>0.622998</td>\n",
       "      <td>0.621025</td>\n",
       "      <td>0.845657</td>\n",
       "      <td>0.839803</td>\n",
       "      <td>81.057266</td>\n",
       "      <td>0.661126</td>\n",
       "      <td>0.794132</td>\n",
       "      <td>0.000855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>38.919389</td>\n",
       "      <td>29</td>\n",
       "      <td>0.623400</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>84.967323</td>\n",
       "      <td>0.621849</td>\n",
       "      <td>0.619931</td>\n",
       "      <td>0.845657</td>\n",
       "      <td>0.839803</td>\n",
       "      <td>81.497795</td>\n",
       "      <td>0.660617</td>\n",
       "      <td>0.799340</td>\n",
       "      <td>0.000798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>40.280173</td>\n",
       "      <td>30</td>\n",
       "      <td>0.622183</td>\n",
       "      <td>84.838707</td>\n",
       "      <td>84.967323</td>\n",
       "      <td>0.620752</td>\n",
       "      <td>0.618918</td>\n",
       "      <td>0.842681</td>\n",
       "      <td>0.839803</td>\n",
       "      <td>81.497795</td>\n",
       "      <td>0.660157</td>\n",
       "      <td>0.799340</td>\n",
       "      <td>0.000741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>41.642956</td>\n",
       "      <td>31</td>\n",
       "      <td>0.621135</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.274513</td>\n",
       "      <td>0.619781</td>\n",
       "      <td>0.617985</td>\n",
       "      <td>0.846202</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>81.497795</td>\n",
       "      <td>0.659721</td>\n",
       "      <td>0.799340</td>\n",
       "      <td>0.000685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>42.992093</td>\n",
       "      <td>32</td>\n",
       "      <td>0.620213</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.274513</td>\n",
       "      <td>0.618812</td>\n",
       "      <td>0.617115</td>\n",
       "      <td>0.846202</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>82.378853</td>\n",
       "      <td>0.659313</td>\n",
       "      <td>0.809757</td>\n",
       "      <td>0.000630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>44.352772</td>\n",
       "      <td>33</td>\n",
       "      <td>0.619270</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.274513</td>\n",
       "      <td>0.617943</td>\n",
       "      <td>0.616310</td>\n",
       "      <td>0.846202</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>82.378853</td>\n",
       "      <td>0.658933</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.000576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>45.722915</td>\n",
       "      <td>34</td>\n",
       "      <td>0.618413</td>\n",
       "      <td>85.483871</td>\n",
       "      <td>86.274513</td>\n",
       "      <td>0.617158</td>\n",
       "      <td>0.615591</td>\n",
       "      <td>0.849723</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>82.378853</td>\n",
       "      <td>0.658595</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.000524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>47.092343</td>\n",
       "      <td>35</td>\n",
       "      <td>0.617658</td>\n",
       "      <td>85.483871</td>\n",
       "      <td>86.274513</td>\n",
       "      <td>0.616448</td>\n",
       "      <td>0.614938</td>\n",
       "      <td>0.849723</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>82.378853</td>\n",
       "      <td>0.658278</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.000473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>48.473442</td>\n",
       "      <td>36</td>\n",
       "      <td>0.617017</td>\n",
       "      <td>85.483871</td>\n",
       "      <td>86.274513</td>\n",
       "      <td>0.615797</td>\n",
       "      <td>0.614361</td>\n",
       "      <td>0.849723</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>82.378853</td>\n",
       "      <td>0.658011</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.000423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>49.862231</td>\n",
       "      <td>37</td>\n",
       "      <td>0.616372</td>\n",
       "      <td>85.483871</td>\n",
       "      <td>86.274513</td>\n",
       "      <td>0.615235</td>\n",
       "      <td>0.613850</td>\n",
       "      <td>0.849723</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>82.378853</td>\n",
       "      <td>0.657779</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.000376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>51.222697</td>\n",
       "      <td>38</td>\n",
       "      <td>0.615909</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.614735</td>\n",
       "      <td>0.613389</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>82.819382</td>\n",
       "      <td>0.657558</td>\n",
       "      <td>0.816357</td>\n",
       "      <td>0.000331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>52.600034</td>\n",
       "      <td>39</td>\n",
       "      <td>0.615386</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.614280</td>\n",
       "      <td>0.612987</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.657357</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>53.977758</td>\n",
       "      <td>40</td>\n",
       "      <td>0.615051</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.613897</td>\n",
       "      <td>0.612647</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.657187</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>55.345247</td>\n",
       "      <td>41</td>\n",
       "      <td>0.614658</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.613555</td>\n",
       "      <td>0.612359</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.657045</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>56.719481</td>\n",
       "      <td>42</td>\n",
       "      <td>0.614390</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.613278</td>\n",
       "      <td>0.612122</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656934</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>58.072700</td>\n",
       "      <td>43</td>\n",
       "      <td>0.614081</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.613054</td>\n",
       "      <td>0.611930</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656846</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>59.420171</td>\n",
       "      <td>44</td>\n",
       "      <td>0.613918</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612866</td>\n",
       "      <td>0.611773</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656770</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>60.778419</td>\n",
       "      <td>45</td>\n",
       "      <td>0.613752</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612718</td>\n",
       "      <td>0.611649</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656708</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>62.170808</td>\n",
       "      <td>46</td>\n",
       "      <td>0.613609</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612602</td>\n",
       "      <td>0.611561</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656665</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>63.532418</td>\n",
       "      <td>47</td>\n",
       "      <td>0.613569</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612521</td>\n",
       "      <td>0.611501</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656637</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>64.899219</td>\n",
       "      <td>48</td>\n",
       "      <td>0.613489</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612468</td>\n",
       "      <td>0.611462</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656618</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>66.289350</td>\n",
       "      <td>49</td>\n",
       "      <td>0.613536</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612433</td>\n",
       "      <td>0.611439</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656606</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>67.664944</td>\n",
       "      <td>50</td>\n",
       "      <td>0.613477</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612414</td>\n",
       "      <td>0.611429</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656601</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>69.060279</td>\n",
       "      <td>51</td>\n",
       "      <td>0.613444</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612406</td>\n",
       "      <td>0.611427</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656600</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time  epoch  Train/loss  Train/train_accuracy  Train/val_accuracy  \\\n",
       "0    0.000232      0    0.689201             58.064518           58.169933   \n",
       "1    0.853955      1    0.688285             58.387096           58.823528   \n",
       "2    2.064345      2    0.685270             59.677418           58.823528   \n",
       "3    3.438759      3    0.682095             60.645161           58.169933   \n",
       "4    4.784480      4    0.678999             61.935482           58.169933   \n",
       "5    6.135232      5    0.675939             64.838707           58.823528   \n",
       "6    7.512886      6    0.672858             66.774193           60.130718   \n",
       "7    8.856942      7    0.669829             68.387100           61.437908   \n",
       "8   10.200388      8    0.667041             68.709679           63.398693   \n",
       "9   11.548642      9    0.664280             72.258064           67.320259   \n",
       "10  12.891976     10    0.661370             73.548386           69.934639   \n",
       "11  14.259831     11    0.658686             75.161293           71.895424   \n",
       "12  15.676542     12    0.655967             75.483871           74.509804   \n",
       "13  17.055972     13    0.653364             75.806450           76.470589   \n",
       "14  18.428269     14    0.650893             76.774193           76.470589   \n",
       "15  19.802392     15    0.648534             77.419357           76.470589   \n",
       "16  21.174268     16    0.646189             79.354836           77.124184   \n",
       "17  22.541935     17    0.643941             79.677422           78.431374   \n",
       "18  23.910560     18    0.641737             81.290321           80.392159   \n",
       "19  25.274473     19    0.639642             82.580643           81.045753   \n",
       "20  26.622701     20    0.637644             82.903229           81.045753   \n",
       "21  27.987362     21    0.635651             82.903229           81.045753   \n",
       "22  29.360464     22    0.633863             82.903229           81.045753   \n",
       "23  30.727140     23    0.632123             83.225807           81.699348   \n",
       "24  32.138760     24    0.630417             83.225807           82.352943   \n",
       "25  33.500488     25    0.628845             83.548386           83.660133   \n",
       "26  34.846305     26    0.627316             84.193550           84.313728   \n",
       "27  36.202874     27    0.625902             84.838707           84.313728   \n",
       "28  37.566040     28    0.624623             85.161293           84.967323   \n",
       "29  38.919389     29    0.623400             85.161293           84.967323   \n",
       "30  40.280173     30    0.622183             84.838707           84.967323   \n",
       "31  41.642956     31    0.621135             85.161293           86.274513   \n",
       "32  42.992093     32    0.620213             85.161293           86.274513   \n",
       "33  44.352772     33    0.619270             85.161293           86.274513   \n",
       "34  45.722915     34    0.618413             85.483871           86.274513   \n",
       "35  47.092343     35    0.617658             85.483871           86.274513   \n",
       "36  48.473442     36    0.617017             85.483871           86.274513   \n",
       "37  49.862231     37    0.616372             85.483871           86.274513   \n",
       "38  51.222697     38    0.615909             85.161293           86.928108   \n",
       "39  52.600034     39    0.615386             85.161293           86.928108   \n",
       "40  53.977758     40    0.615051             85.161293           86.928108   \n",
       "41  55.345247     41    0.614658             85.161293           86.928108   \n",
       "42  56.719481     42    0.614390             85.161293           86.928108   \n",
       "43  58.072700     43    0.614081             85.161293           86.928108   \n",
       "44  59.420171     44    0.613918             85.161293           86.928108   \n",
       "45  60.778419     45    0.613752             85.161293           86.928108   \n",
       "46  62.170808     46    0.613609             85.161293           86.928108   \n",
       "47  63.532418     47    0.613569             85.161293           86.928108   \n",
       "48  64.899219     48    0.613489             85.161293           86.928108   \n",
       "49  66.289350     49    0.613536             85.161293           86.928108   \n",
       "50  67.664944     50    0.613477             85.161293           86.928108   \n",
       "51  69.060279     51    0.613444             85.161293           86.928108   \n",
       "\n",
       "    Train/train_cross_entropy  Train/val_cross_entropy  \\\n",
       "0                    0.684482                 0.681390   \n",
       "1                    0.683689                 0.678622   \n",
       "2                    0.680672                 0.675738   \n",
       "3                    0.677709                 0.672974   \n",
       "4                    0.674849                 0.670170   \n",
       "5                    0.671878                 0.667410   \n",
       "6                    0.669112                 0.664650   \n",
       "7                    0.666286                 0.661944   \n",
       "8                    0.663530                 0.659326   \n",
       "9                    0.660837                 0.656742   \n",
       "10                   0.658190                 0.654227   \n",
       "11                   0.655560                 0.651724   \n",
       "12                   0.653014                 0.649365   \n",
       "13                   0.650577                 0.647094   \n",
       "14                   0.648395                 0.644918   \n",
       "15                   0.646036                 0.642743   \n",
       "16                   0.643794                 0.640603   \n",
       "17                   0.641646                 0.638543   \n",
       "18                   0.639526                 0.636564   \n",
       "19                   0.637512                 0.634627   \n",
       "20                   0.635556                 0.632777   \n",
       "21                   0.633709                 0.631058   \n",
       "22                   0.631999                 0.629376   \n",
       "23                   0.630258                 0.627761   \n",
       "24                   0.628599                 0.626208   \n",
       "25                   0.627047                 0.624794   \n",
       "26                   0.625636                 0.623419   \n",
       "27                   0.624252                 0.622165   \n",
       "28                   0.622998                 0.621025   \n",
       "29                   0.621849                 0.619931   \n",
       "30                   0.620752                 0.618918   \n",
       "31                   0.619781                 0.617985   \n",
       "32                   0.618812                 0.617115   \n",
       "33                   0.617943                 0.616310   \n",
       "34                   0.617158                 0.615591   \n",
       "35                   0.616448                 0.614938   \n",
       "36                   0.615797                 0.614361   \n",
       "37                   0.615235                 0.613850   \n",
       "38                   0.614735                 0.613389   \n",
       "39                   0.614280                 0.612987   \n",
       "40                   0.613897                 0.612647   \n",
       "41                   0.613555                 0.612359   \n",
       "42                   0.613278                 0.612122   \n",
       "43                   0.613054                 0.611930   \n",
       "44                   0.612866                 0.611773   \n",
       "45                   0.612718                 0.611649   \n",
       "46                   0.612602                 0.611561   \n",
       "47                   0.612521                 0.611501   \n",
       "48                   0.612468                 0.611462   \n",
       "49                   0.612433                 0.611439   \n",
       "50                   0.612414                 0.611429   \n",
       "51                   0.612406                 0.611427   \n",
       "\n",
       "    Train/train_balanced_accuracy  Train/val_balanced_accuracy  \\\n",
       "0                        0.546068                     0.541408   \n",
       "1                        0.549589                     0.548654   \n",
       "2                        0.563674                     0.548654   \n",
       "3                        0.574782                     0.542702   \n",
       "4                        0.588867                     0.542702   \n",
       "5                        0.619467                     0.549948   \n",
       "6                        0.641138                     0.564441   \n",
       "7                        0.658744                     0.577640   \n",
       "8                        0.662265                     0.599379   \n",
       "9                        0.700998                     0.644151   \n",
       "10                       0.716172                     0.671843   \n",
       "11                       0.733233                     0.693582   \n",
       "12                       0.738389                     0.722567   \n",
       "13                       0.743000                     0.744306   \n",
       "14                       0.753563                     0.745600   \n",
       "15                       0.761150                     0.745600   \n",
       "16                       0.782277                     0.752847   \n",
       "17                       0.785798                     0.767340   \n",
       "18                       0.803404                     0.789079   \n",
       "19                       0.817488                     0.796325   \n",
       "20                       0.821009                     0.796325   \n",
       "21                       0.821009                     0.796325   \n",
       "22                       0.821554                     0.796325   \n",
       "23                       0.825075                     0.803571   \n",
       "24                       0.825075                     0.810818   \n",
       "25                       0.828597                     0.825311   \n",
       "26                       0.835094                     0.832557   \n",
       "27                       0.842136                     0.832557   \n",
       "28                       0.845657                     0.839803   \n",
       "29                       0.845657                     0.839803   \n",
       "30                       0.842681                     0.839803   \n",
       "31                       0.846202                     0.854296   \n",
       "32                       0.846202                     0.854296   \n",
       "33                       0.846202                     0.854296   \n",
       "34                       0.849723                     0.854296   \n",
       "35                       0.849723                     0.854296   \n",
       "36                       0.849723                     0.854296   \n",
       "37                       0.849723                     0.854296   \n",
       "38                       0.846747                     0.861542   \n",
       "39                       0.846747                     0.861542   \n",
       "40                       0.846747                     0.861542   \n",
       "41                       0.846747                     0.861542   \n",
       "42                       0.846747                     0.861542   \n",
       "43                       0.846747                     0.861542   \n",
       "44                       0.846747                     0.861542   \n",
       "45                       0.846747                     0.861542   \n",
       "46                       0.846747                     0.861542   \n",
       "47                       0.846747                     0.861542   \n",
       "48                       0.846747                     0.861542   \n",
       "49                       0.846747                     0.861542   \n",
       "50                       0.846747                     0.861542   \n",
       "51                       0.846747                     0.861542   \n",
       "\n",
       "    Train/test_result  Train/test_cross_entropy  Train/test_balanced_accuracy  \\\n",
       "0           57.268723                  0.688986                      0.508707   \n",
       "1           57.268723                  0.687684                      0.508707   \n",
       "2           59.471367                  0.686409                      0.533357   \n",
       "3           59.471367                  0.685187                      0.533357   \n",
       "4           61.233479                  0.683923                      0.552799   \n",
       "5           63.436123                  0.682720                      0.578841   \n",
       "6           65.638763                  0.681485                      0.602099   \n",
       "7           66.079292                  0.680194                      0.608699   \n",
       "8           67.841408                  0.678963                      0.629532   \n",
       "9           68.281937                  0.677826                      0.633349   \n",
       "10          69.162994                  0.676696                      0.643766   \n",
       "11          70.044052                  0.675548                      0.655574   \n",
       "12          70.484581                  0.674516                      0.663566   \n",
       "13          70.925110                  0.673515                      0.670165   \n",
       "14          73.568283                  0.672508                      0.702807   \n",
       "15          74.008812                  0.671479                      0.708015   \n",
       "16          75.330399                  0.670466                      0.725032   \n",
       "17          76.651985                  0.669478                      0.740657   \n",
       "18          77.533043                  0.668556                      0.751073   \n",
       "19          78.854622                  0.667643                      0.766698   \n",
       "20          79.295151                  0.666754                      0.770515   \n",
       "21          79.735680                  0.665938                      0.775724   \n",
       "22          80.616737                  0.665141                      0.786140   \n",
       "23          80.176208                  0.664345                      0.782323   \n",
       "24          80.616737                  0.663587                      0.787532   \n",
       "25          80.616737                  0.662908                      0.788923   \n",
       "26          81.057266                  0.662238                      0.794132   \n",
       "27          81.057266                  0.661658                      0.794132   \n",
       "28          81.057266                  0.661126                      0.794132   \n",
       "29          81.497795                  0.660617                      0.799340   \n",
       "30          81.497795                  0.660157                      0.799340   \n",
       "31          81.497795                  0.659721                      0.799340   \n",
       "32          82.378853                  0.659313                      0.809757   \n",
       "33          82.378853                  0.658933                      0.811148   \n",
       "34          82.378853                  0.658595                      0.811148   \n",
       "35          82.378853                  0.658278                      0.811148   \n",
       "36          82.378853                  0.658011                      0.811148   \n",
       "37          82.378853                  0.657779                      0.811148   \n",
       "38          82.819382                  0.657558                      0.816357   \n",
       "39          83.259911                  0.657357                      0.820173   \n",
       "40          83.259911                  0.657187                      0.820173   \n",
       "41          83.259911                  0.657045                      0.820173   \n",
       "42          83.259911                  0.656934                      0.820173   \n",
       "43          83.259911                  0.656846                      0.820173   \n",
       "44          83.259911                  0.656770                      0.820173   \n",
       "45          83.259911                  0.656708                      0.820173   \n",
       "46          83.259911                  0.656665                      0.820173   \n",
       "47          83.259911                  0.656637                      0.820173   \n",
       "48          83.259911                  0.656618                      0.820173   \n",
       "49          83.259911                  0.656606                      0.820173   \n",
       "50          83.259911                  0.656601                      0.820173   \n",
       "51          83.259911                  0.656600                      0.820173   \n",
       "\n",
       "    Train/lr  \n",
       "0   0.000000  \n",
       "1   0.001824  \n",
       "2   0.001824  \n",
       "3   0.001823  \n",
       "4   0.001817  \n",
       "5   0.001808  \n",
       "6   0.001796  \n",
       "7   0.001780  \n",
       "8   0.001760  \n",
       "9   0.001738  \n",
       "10  0.001712  \n",
       "11  0.001682  \n",
       "12  0.001650  \n",
       "13  0.001615  \n",
       "14  0.001577  \n",
       "15  0.001537  \n",
       "16  0.001494  \n",
       "17  0.001448  \n",
       "18  0.001401  \n",
       "19  0.001352  \n",
       "20  0.001301  \n",
       "21  0.001248  \n",
       "22  0.001194  \n",
       "23  0.001139  \n",
       "24  0.001083  \n",
       "25  0.001026  \n",
       "26  0.000969  \n",
       "27  0.000912  \n",
       "28  0.000855  \n",
       "29  0.000798  \n",
       "30  0.000741  \n",
       "31  0.000685  \n",
       "32  0.000630  \n",
       "33  0.000576  \n",
       "34  0.000524  \n",
       "35  0.000473  \n",
       "36  0.000423  \n",
       "37  0.000376  \n",
       "38  0.000331  \n",
       "39  0.000288  \n",
       "40  0.000247  \n",
       "41  0.000209  \n",
       "42  0.000174  \n",
       "43  0.000142  \n",
       "44  0.000113  \n",
       "45  0.000087  \n",
       "46  0.000064  \n",
       "47  0.000045  \n",
       "48  0.000029  \n",
       "49  0.000016  \n",
       "50  0.000007  \n",
       "51  0.000002  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = pd.DataFrame(dct['Australian']['0']['log'])\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "07649331-92b4-4d99-bd71-8ae0e08e7167",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model_parameters': 48205,\n",
       " 'final_train_cross_entropy': 0.6134444175227995,\n",
       " 'final_train_accuracy': 85.16129032258064,\n",
       " 'final_train_balanced_accuracy': 0.846747149564051,\n",
       " 'final_val_cross_entropy': 0.6114265322685242,\n",
       " 'final_val_accuracy': 86.9281045751634,\n",
       " 'final_val_balanced_accuracy': 0.8615424430641823,\n",
       " 'final_test_cross_entropy': 0.6565998792648315,\n",
       " 'final_test_accuracy': 83.25991189427313,\n",
       " 'final_test_balanced_accuracy': 0.8201733460559797,\n",
       " 'OpenML_task_id': 167104,\n",
       " 'test_split': 0.3289855072463768,\n",
       " 'budget': 50,\n",
       " 'seed': 1,\n",
       " 'instances': 690,\n",
       " 'classes': 2,\n",
       " 'features': 14}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dct['Australian']['0']['results']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7392da12-2cec-4faa-87c1-e31992f10f49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>epoch</th>\n",
       "      <th>Train/loss</th>\n",
       "      <th>Train/train_accuracy</th>\n",
       "      <th>Train/val_accuracy</th>\n",
       "      <th>Train/train_cross_entropy</th>\n",
       "      <th>Train/val_cross_entropy</th>\n",
       "      <th>Train/train_balanced_accuracy</th>\n",
       "      <th>Train/val_balanced_accuracy</th>\n",
       "      <th>Train/test_result</th>\n",
       "      <th>Train/test_cross_entropy</th>\n",
       "      <th>Train/test_balanced_accuracy</th>\n",
       "      <th>Train/lr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000232</td>\n",
       "      <td>0</td>\n",
       "      <td>0.689201</td>\n",
       "      <td>58.064518</td>\n",
       "      <td>58.169933</td>\n",
       "      <td>0.684482</td>\n",
       "      <td>0.681390</td>\n",
       "      <td>0.546068</td>\n",
       "      <td>0.541408</td>\n",
       "      <td>57.268723</td>\n",
       "      <td>0.688986</td>\n",
       "      <td>0.508707</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.853955</td>\n",
       "      <td>1</td>\n",
       "      <td>0.688285</td>\n",
       "      <td>58.387096</td>\n",
       "      <td>58.823528</td>\n",
       "      <td>0.683689</td>\n",
       "      <td>0.678622</td>\n",
       "      <td>0.549589</td>\n",
       "      <td>0.548654</td>\n",
       "      <td>57.268723</td>\n",
       "      <td>0.687684</td>\n",
       "      <td>0.508707</td>\n",
       "      <td>0.001824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.064345</td>\n",
       "      <td>2</td>\n",
       "      <td>0.685270</td>\n",
       "      <td>59.677418</td>\n",
       "      <td>58.823528</td>\n",
       "      <td>0.680672</td>\n",
       "      <td>0.675738</td>\n",
       "      <td>0.563674</td>\n",
       "      <td>0.548654</td>\n",
       "      <td>59.471367</td>\n",
       "      <td>0.686409</td>\n",
       "      <td>0.533357</td>\n",
       "      <td>0.001824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.438759</td>\n",
       "      <td>3</td>\n",
       "      <td>0.682095</td>\n",
       "      <td>60.645161</td>\n",
       "      <td>58.169933</td>\n",
       "      <td>0.677709</td>\n",
       "      <td>0.672974</td>\n",
       "      <td>0.574782</td>\n",
       "      <td>0.542702</td>\n",
       "      <td>59.471367</td>\n",
       "      <td>0.685187</td>\n",
       "      <td>0.533357</td>\n",
       "      <td>0.001823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.784480</td>\n",
       "      <td>4</td>\n",
       "      <td>0.678999</td>\n",
       "      <td>61.935482</td>\n",
       "      <td>58.169933</td>\n",
       "      <td>0.674849</td>\n",
       "      <td>0.670170</td>\n",
       "      <td>0.588867</td>\n",
       "      <td>0.542702</td>\n",
       "      <td>61.233479</td>\n",
       "      <td>0.683923</td>\n",
       "      <td>0.552799</td>\n",
       "      <td>0.001817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.135232</td>\n",
       "      <td>5</td>\n",
       "      <td>0.675939</td>\n",
       "      <td>64.838707</td>\n",
       "      <td>58.823528</td>\n",
       "      <td>0.671878</td>\n",
       "      <td>0.667410</td>\n",
       "      <td>0.619467</td>\n",
       "      <td>0.549948</td>\n",
       "      <td>63.436123</td>\n",
       "      <td>0.682720</td>\n",
       "      <td>0.578841</td>\n",
       "      <td>0.001808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.512886</td>\n",
       "      <td>6</td>\n",
       "      <td>0.672858</td>\n",
       "      <td>66.774193</td>\n",
       "      <td>60.130718</td>\n",
       "      <td>0.669112</td>\n",
       "      <td>0.664650</td>\n",
       "      <td>0.641138</td>\n",
       "      <td>0.564441</td>\n",
       "      <td>65.638763</td>\n",
       "      <td>0.681485</td>\n",
       "      <td>0.602099</td>\n",
       "      <td>0.001796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.856942</td>\n",
       "      <td>7</td>\n",
       "      <td>0.669829</td>\n",
       "      <td>68.387100</td>\n",
       "      <td>61.437908</td>\n",
       "      <td>0.666286</td>\n",
       "      <td>0.661944</td>\n",
       "      <td>0.658744</td>\n",
       "      <td>0.577640</td>\n",
       "      <td>66.079292</td>\n",
       "      <td>0.680194</td>\n",
       "      <td>0.608699</td>\n",
       "      <td>0.001780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.200388</td>\n",
       "      <td>8</td>\n",
       "      <td>0.667041</td>\n",
       "      <td>68.709679</td>\n",
       "      <td>63.398693</td>\n",
       "      <td>0.663530</td>\n",
       "      <td>0.659326</td>\n",
       "      <td>0.662265</td>\n",
       "      <td>0.599379</td>\n",
       "      <td>67.841408</td>\n",
       "      <td>0.678963</td>\n",
       "      <td>0.629532</td>\n",
       "      <td>0.001760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.548642</td>\n",
       "      <td>9</td>\n",
       "      <td>0.664280</td>\n",
       "      <td>72.258064</td>\n",
       "      <td>67.320259</td>\n",
       "      <td>0.660837</td>\n",
       "      <td>0.656742</td>\n",
       "      <td>0.700998</td>\n",
       "      <td>0.644151</td>\n",
       "      <td>68.281937</td>\n",
       "      <td>0.677826</td>\n",
       "      <td>0.633349</td>\n",
       "      <td>0.001738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12.891976</td>\n",
       "      <td>10</td>\n",
       "      <td>0.661370</td>\n",
       "      <td>73.548386</td>\n",
       "      <td>69.934639</td>\n",
       "      <td>0.658190</td>\n",
       "      <td>0.654227</td>\n",
       "      <td>0.716172</td>\n",
       "      <td>0.671843</td>\n",
       "      <td>69.162994</td>\n",
       "      <td>0.676696</td>\n",
       "      <td>0.643766</td>\n",
       "      <td>0.001712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.259831</td>\n",
       "      <td>11</td>\n",
       "      <td>0.658686</td>\n",
       "      <td>75.161293</td>\n",
       "      <td>71.895424</td>\n",
       "      <td>0.655560</td>\n",
       "      <td>0.651724</td>\n",
       "      <td>0.733233</td>\n",
       "      <td>0.693582</td>\n",
       "      <td>70.044052</td>\n",
       "      <td>0.675548</td>\n",
       "      <td>0.655574</td>\n",
       "      <td>0.001682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.676542</td>\n",
       "      <td>12</td>\n",
       "      <td>0.655967</td>\n",
       "      <td>75.483871</td>\n",
       "      <td>74.509804</td>\n",
       "      <td>0.653014</td>\n",
       "      <td>0.649365</td>\n",
       "      <td>0.738389</td>\n",
       "      <td>0.722567</td>\n",
       "      <td>70.484581</td>\n",
       "      <td>0.674516</td>\n",
       "      <td>0.663566</td>\n",
       "      <td>0.001650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>17.055972</td>\n",
       "      <td>13</td>\n",
       "      <td>0.653364</td>\n",
       "      <td>75.806450</td>\n",
       "      <td>76.470589</td>\n",
       "      <td>0.650577</td>\n",
       "      <td>0.647094</td>\n",
       "      <td>0.743000</td>\n",
       "      <td>0.744306</td>\n",
       "      <td>70.925110</td>\n",
       "      <td>0.673515</td>\n",
       "      <td>0.670165</td>\n",
       "      <td>0.001615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18.428269</td>\n",
       "      <td>14</td>\n",
       "      <td>0.650893</td>\n",
       "      <td>76.774193</td>\n",
       "      <td>76.470589</td>\n",
       "      <td>0.648395</td>\n",
       "      <td>0.644918</td>\n",
       "      <td>0.753563</td>\n",
       "      <td>0.745600</td>\n",
       "      <td>73.568283</td>\n",
       "      <td>0.672508</td>\n",
       "      <td>0.702807</td>\n",
       "      <td>0.001577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19.802392</td>\n",
       "      <td>15</td>\n",
       "      <td>0.648534</td>\n",
       "      <td>77.419357</td>\n",
       "      <td>76.470589</td>\n",
       "      <td>0.646036</td>\n",
       "      <td>0.642743</td>\n",
       "      <td>0.761150</td>\n",
       "      <td>0.745600</td>\n",
       "      <td>74.008812</td>\n",
       "      <td>0.671479</td>\n",
       "      <td>0.708015</td>\n",
       "      <td>0.001537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>21.174268</td>\n",
       "      <td>16</td>\n",
       "      <td>0.646189</td>\n",
       "      <td>79.354836</td>\n",
       "      <td>77.124184</td>\n",
       "      <td>0.643794</td>\n",
       "      <td>0.640603</td>\n",
       "      <td>0.782277</td>\n",
       "      <td>0.752847</td>\n",
       "      <td>75.330399</td>\n",
       "      <td>0.670466</td>\n",
       "      <td>0.725032</td>\n",
       "      <td>0.001494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>22.541935</td>\n",
       "      <td>17</td>\n",
       "      <td>0.643941</td>\n",
       "      <td>79.677422</td>\n",
       "      <td>78.431374</td>\n",
       "      <td>0.641646</td>\n",
       "      <td>0.638543</td>\n",
       "      <td>0.785798</td>\n",
       "      <td>0.767340</td>\n",
       "      <td>76.651985</td>\n",
       "      <td>0.669478</td>\n",
       "      <td>0.740657</td>\n",
       "      <td>0.001448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23.910560</td>\n",
       "      <td>18</td>\n",
       "      <td>0.641737</td>\n",
       "      <td>81.290321</td>\n",
       "      <td>80.392159</td>\n",
       "      <td>0.639526</td>\n",
       "      <td>0.636564</td>\n",
       "      <td>0.803404</td>\n",
       "      <td>0.789079</td>\n",
       "      <td>77.533043</td>\n",
       "      <td>0.668556</td>\n",
       "      <td>0.751073</td>\n",
       "      <td>0.001401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>25.274473</td>\n",
       "      <td>19</td>\n",
       "      <td>0.639642</td>\n",
       "      <td>82.580643</td>\n",
       "      <td>81.045753</td>\n",
       "      <td>0.637512</td>\n",
       "      <td>0.634627</td>\n",
       "      <td>0.817488</td>\n",
       "      <td>0.796325</td>\n",
       "      <td>78.854622</td>\n",
       "      <td>0.667643</td>\n",
       "      <td>0.766698</td>\n",
       "      <td>0.001352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>26.622701</td>\n",
       "      <td>20</td>\n",
       "      <td>0.637644</td>\n",
       "      <td>82.903229</td>\n",
       "      <td>81.045753</td>\n",
       "      <td>0.635556</td>\n",
       "      <td>0.632777</td>\n",
       "      <td>0.821009</td>\n",
       "      <td>0.796325</td>\n",
       "      <td>79.295151</td>\n",
       "      <td>0.666754</td>\n",
       "      <td>0.770515</td>\n",
       "      <td>0.001301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>27.987362</td>\n",
       "      <td>21</td>\n",
       "      <td>0.635651</td>\n",
       "      <td>82.903229</td>\n",
       "      <td>81.045753</td>\n",
       "      <td>0.633709</td>\n",
       "      <td>0.631058</td>\n",
       "      <td>0.821009</td>\n",
       "      <td>0.796325</td>\n",
       "      <td>79.735680</td>\n",
       "      <td>0.665938</td>\n",
       "      <td>0.775724</td>\n",
       "      <td>0.001248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>29.360464</td>\n",
       "      <td>22</td>\n",
       "      <td>0.633863</td>\n",
       "      <td>82.903229</td>\n",
       "      <td>81.045753</td>\n",
       "      <td>0.631999</td>\n",
       "      <td>0.629376</td>\n",
       "      <td>0.821554</td>\n",
       "      <td>0.796325</td>\n",
       "      <td>80.616737</td>\n",
       "      <td>0.665141</td>\n",
       "      <td>0.786140</td>\n",
       "      <td>0.001194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30.727140</td>\n",
       "      <td>23</td>\n",
       "      <td>0.632123</td>\n",
       "      <td>83.225807</td>\n",
       "      <td>81.699348</td>\n",
       "      <td>0.630258</td>\n",
       "      <td>0.627761</td>\n",
       "      <td>0.825075</td>\n",
       "      <td>0.803571</td>\n",
       "      <td>80.176208</td>\n",
       "      <td>0.664345</td>\n",
       "      <td>0.782323</td>\n",
       "      <td>0.001139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>32.138760</td>\n",
       "      <td>24</td>\n",
       "      <td>0.630417</td>\n",
       "      <td>83.225807</td>\n",
       "      <td>82.352943</td>\n",
       "      <td>0.628599</td>\n",
       "      <td>0.626208</td>\n",
       "      <td>0.825075</td>\n",
       "      <td>0.810818</td>\n",
       "      <td>80.616737</td>\n",
       "      <td>0.663587</td>\n",
       "      <td>0.787532</td>\n",
       "      <td>0.001083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>33.500488</td>\n",
       "      <td>25</td>\n",
       "      <td>0.628845</td>\n",
       "      <td>83.548386</td>\n",
       "      <td>83.660133</td>\n",
       "      <td>0.627047</td>\n",
       "      <td>0.624794</td>\n",
       "      <td>0.828597</td>\n",
       "      <td>0.825311</td>\n",
       "      <td>80.616737</td>\n",
       "      <td>0.662908</td>\n",
       "      <td>0.788923</td>\n",
       "      <td>0.001026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34.846305</td>\n",
       "      <td>26</td>\n",
       "      <td>0.627316</td>\n",
       "      <td>84.193550</td>\n",
       "      <td>84.313728</td>\n",
       "      <td>0.625636</td>\n",
       "      <td>0.623419</td>\n",
       "      <td>0.835094</td>\n",
       "      <td>0.832557</td>\n",
       "      <td>81.057266</td>\n",
       "      <td>0.662238</td>\n",
       "      <td>0.794132</td>\n",
       "      <td>0.000969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>36.202874</td>\n",
       "      <td>27</td>\n",
       "      <td>0.625902</td>\n",
       "      <td>84.838707</td>\n",
       "      <td>84.313728</td>\n",
       "      <td>0.624252</td>\n",
       "      <td>0.622165</td>\n",
       "      <td>0.842136</td>\n",
       "      <td>0.832557</td>\n",
       "      <td>81.057266</td>\n",
       "      <td>0.661658</td>\n",
       "      <td>0.794132</td>\n",
       "      <td>0.000912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>37.566040</td>\n",
       "      <td>28</td>\n",
       "      <td>0.624623</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>84.967323</td>\n",
       "      <td>0.622998</td>\n",
       "      <td>0.621025</td>\n",
       "      <td>0.845657</td>\n",
       "      <td>0.839803</td>\n",
       "      <td>81.057266</td>\n",
       "      <td>0.661126</td>\n",
       "      <td>0.794132</td>\n",
       "      <td>0.000855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>38.919389</td>\n",
       "      <td>29</td>\n",
       "      <td>0.623400</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>84.967323</td>\n",
       "      <td>0.621849</td>\n",
       "      <td>0.619931</td>\n",
       "      <td>0.845657</td>\n",
       "      <td>0.839803</td>\n",
       "      <td>81.497795</td>\n",
       "      <td>0.660617</td>\n",
       "      <td>0.799340</td>\n",
       "      <td>0.000798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>40.280173</td>\n",
       "      <td>30</td>\n",
       "      <td>0.622183</td>\n",
       "      <td>84.838707</td>\n",
       "      <td>84.967323</td>\n",
       "      <td>0.620752</td>\n",
       "      <td>0.618918</td>\n",
       "      <td>0.842681</td>\n",
       "      <td>0.839803</td>\n",
       "      <td>81.497795</td>\n",
       "      <td>0.660157</td>\n",
       "      <td>0.799340</td>\n",
       "      <td>0.000741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>41.642956</td>\n",
       "      <td>31</td>\n",
       "      <td>0.621135</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.274513</td>\n",
       "      <td>0.619781</td>\n",
       "      <td>0.617985</td>\n",
       "      <td>0.846202</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>81.497795</td>\n",
       "      <td>0.659721</td>\n",
       "      <td>0.799340</td>\n",
       "      <td>0.000685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>42.992093</td>\n",
       "      <td>32</td>\n",
       "      <td>0.620213</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.274513</td>\n",
       "      <td>0.618812</td>\n",
       "      <td>0.617115</td>\n",
       "      <td>0.846202</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>82.378853</td>\n",
       "      <td>0.659313</td>\n",
       "      <td>0.809757</td>\n",
       "      <td>0.000630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>44.352772</td>\n",
       "      <td>33</td>\n",
       "      <td>0.619270</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.274513</td>\n",
       "      <td>0.617943</td>\n",
       "      <td>0.616310</td>\n",
       "      <td>0.846202</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>82.378853</td>\n",
       "      <td>0.658933</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.000576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>45.722915</td>\n",
       "      <td>34</td>\n",
       "      <td>0.618413</td>\n",
       "      <td>85.483871</td>\n",
       "      <td>86.274513</td>\n",
       "      <td>0.617158</td>\n",
       "      <td>0.615591</td>\n",
       "      <td>0.849723</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>82.378853</td>\n",
       "      <td>0.658595</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.000524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>47.092343</td>\n",
       "      <td>35</td>\n",
       "      <td>0.617658</td>\n",
       "      <td>85.483871</td>\n",
       "      <td>86.274513</td>\n",
       "      <td>0.616448</td>\n",
       "      <td>0.614938</td>\n",
       "      <td>0.849723</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>82.378853</td>\n",
       "      <td>0.658278</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.000473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>48.473442</td>\n",
       "      <td>36</td>\n",
       "      <td>0.617017</td>\n",
       "      <td>85.483871</td>\n",
       "      <td>86.274513</td>\n",
       "      <td>0.615797</td>\n",
       "      <td>0.614361</td>\n",
       "      <td>0.849723</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>82.378853</td>\n",
       "      <td>0.658011</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.000423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>49.862231</td>\n",
       "      <td>37</td>\n",
       "      <td>0.616372</td>\n",
       "      <td>85.483871</td>\n",
       "      <td>86.274513</td>\n",
       "      <td>0.615235</td>\n",
       "      <td>0.613850</td>\n",
       "      <td>0.849723</td>\n",
       "      <td>0.854296</td>\n",
       "      <td>82.378853</td>\n",
       "      <td>0.657779</td>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.000376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>51.222697</td>\n",
       "      <td>38</td>\n",
       "      <td>0.615909</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.614735</td>\n",
       "      <td>0.613389</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>82.819382</td>\n",
       "      <td>0.657558</td>\n",
       "      <td>0.816357</td>\n",
       "      <td>0.000331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>52.600034</td>\n",
       "      <td>39</td>\n",
       "      <td>0.615386</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.614280</td>\n",
       "      <td>0.612987</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.657357</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>53.977758</td>\n",
       "      <td>40</td>\n",
       "      <td>0.615051</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.613897</td>\n",
       "      <td>0.612647</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.657187</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>55.345247</td>\n",
       "      <td>41</td>\n",
       "      <td>0.614658</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.613555</td>\n",
       "      <td>0.612359</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.657045</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>56.719481</td>\n",
       "      <td>42</td>\n",
       "      <td>0.614390</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.613278</td>\n",
       "      <td>0.612122</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656934</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>58.072700</td>\n",
       "      <td>43</td>\n",
       "      <td>0.614081</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.613054</td>\n",
       "      <td>0.611930</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656846</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>59.420171</td>\n",
       "      <td>44</td>\n",
       "      <td>0.613918</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612866</td>\n",
       "      <td>0.611773</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656770</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>60.778419</td>\n",
       "      <td>45</td>\n",
       "      <td>0.613752</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612718</td>\n",
       "      <td>0.611649</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656708</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>62.170808</td>\n",
       "      <td>46</td>\n",
       "      <td>0.613609</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612602</td>\n",
       "      <td>0.611561</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656665</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>63.532418</td>\n",
       "      <td>47</td>\n",
       "      <td>0.613569</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612521</td>\n",
       "      <td>0.611501</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656637</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>64.899219</td>\n",
       "      <td>48</td>\n",
       "      <td>0.613489</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612468</td>\n",
       "      <td>0.611462</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656618</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>66.289350</td>\n",
       "      <td>49</td>\n",
       "      <td>0.613536</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612433</td>\n",
       "      <td>0.611439</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656606</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>67.664944</td>\n",
       "      <td>50</td>\n",
       "      <td>0.613477</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612414</td>\n",
       "      <td>0.611429</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656601</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>69.060279</td>\n",
       "      <td>51</td>\n",
       "      <td>0.613444</td>\n",
       "      <td>85.161293</td>\n",
       "      <td>86.928108</td>\n",
       "      <td>0.612406</td>\n",
       "      <td>0.611427</td>\n",
       "      <td>0.846747</td>\n",
       "      <td>0.861542</td>\n",
       "      <td>83.259911</td>\n",
       "      <td>0.656600</td>\n",
       "      <td>0.820173</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         time  epoch  Train/loss  Train/train_accuracy  Train/val_accuracy  \\\n",
       "0    0.000232      0    0.689201             58.064518           58.169933   \n",
       "1    0.853955      1    0.688285             58.387096           58.823528   \n",
       "2    2.064345      2    0.685270             59.677418           58.823528   \n",
       "3    3.438759      3    0.682095             60.645161           58.169933   \n",
       "4    4.784480      4    0.678999             61.935482           58.169933   \n",
       "5    6.135232      5    0.675939             64.838707           58.823528   \n",
       "6    7.512886      6    0.672858             66.774193           60.130718   \n",
       "7    8.856942      7    0.669829             68.387100           61.437908   \n",
       "8   10.200388      8    0.667041             68.709679           63.398693   \n",
       "9   11.548642      9    0.664280             72.258064           67.320259   \n",
       "10  12.891976     10    0.661370             73.548386           69.934639   \n",
       "11  14.259831     11    0.658686             75.161293           71.895424   \n",
       "12  15.676542     12    0.655967             75.483871           74.509804   \n",
       "13  17.055972     13    0.653364             75.806450           76.470589   \n",
       "14  18.428269     14    0.650893             76.774193           76.470589   \n",
       "15  19.802392     15    0.648534             77.419357           76.470589   \n",
       "16  21.174268     16    0.646189             79.354836           77.124184   \n",
       "17  22.541935     17    0.643941             79.677422           78.431374   \n",
       "18  23.910560     18    0.641737             81.290321           80.392159   \n",
       "19  25.274473     19    0.639642             82.580643           81.045753   \n",
       "20  26.622701     20    0.637644             82.903229           81.045753   \n",
       "21  27.987362     21    0.635651             82.903229           81.045753   \n",
       "22  29.360464     22    0.633863             82.903229           81.045753   \n",
       "23  30.727140     23    0.632123             83.225807           81.699348   \n",
       "24  32.138760     24    0.630417             83.225807           82.352943   \n",
       "25  33.500488     25    0.628845             83.548386           83.660133   \n",
       "26  34.846305     26    0.627316             84.193550           84.313728   \n",
       "27  36.202874     27    0.625902             84.838707           84.313728   \n",
       "28  37.566040     28    0.624623             85.161293           84.967323   \n",
       "29  38.919389     29    0.623400             85.161293           84.967323   \n",
       "30  40.280173     30    0.622183             84.838707           84.967323   \n",
       "31  41.642956     31    0.621135             85.161293           86.274513   \n",
       "32  42.992093     32    0.620213             85.161293           86.274513   \n",
       "33  44.352772     33    0.619270             85.161293           86.274513   \n",
       "34  45.722915     34    0.618413             85.483871           86.274513   \n",
       "35  47.092343     35    0.617658             85.483871           86.274513   \n",
       "36  48.473442     36    0.617017             85.483871           86.274513   \n",
       "37  49.862231     37    0.616372             85.483871           86.274513   \n",
       "38  51.222697     38    0.615909             85.161293           86.928108   \n",
       "39  52.600034     39    0.615386             85.161293           86.928108   \n",
       "40  53.977758     40    0.615051             85.161293           86.928108   \n",
       "41  55.345247     41    0.614658             85.161293           86.928108   \n",
       "42  56.719481     42    0.614390             85.161293           86.928108   \n",
       "43  58.072700     43    0.614081             85.161293           86.928108   \n",
       "44  59.420171     44    0.613918             85.161293           86.928108   \n",
       "45  60.778419     45    0.613752             85.161293           86.928108   \n",
       "46  62.170808     46    0.613609             85.161293           86.928108   \n",
       "47  63.532418     47    0.613569             85.161293           86.928108   \n",
       "48  64.899219     48    0.613489             85.161293           86.928108   \n",
       "49  66.289350     49    0.613536             85.161293           86.928108   \n",
       "50  67.664944     50    0.613477             85.161293           86.928108   \n",
       "51  69.060279     51    0.613444             85.161293           86.928108   \n",
       "\n",
       "    Train/train_cross_entropy  Train/val_cross_entropy  \\\n",
       "0                    0.684482                 0.681390   \n",
       "1                    0.683689                 0.678622   \n",
       "2                    0.680672                 0.675738   \n",
       "3                    0.677709                 0.672974   \n",
       "4                    0.674849                 0.670170   \n",
       "5                    0.671878                 0.667410   \n",
       "6                    0.669112                 0.664650   \n",
       "7                    0.666286                 0.661944   \n",
       "8                    0.663530                 0.659326   \n",
       "9                    0.660837                 0.656742   \n",
       "10                   0.658190                 0.654227   \n",
       "11                   0.655560                 0.651724   \n",
       "12                   0.653014                 0.649365   \n",
       "13                   0.650577                 0.647094   \n",
       "14                   0.648395                 0.644918   \n",
       "15                   0.646036                 0.642743   \n",
       "16                   0.643794                 0.640603   \n",
       "17                   0.641646                 0.638543   \n",
       "18                   0.639526                 0.636564   \n",
       "19                   0.637512                 0.634627   \n",
       "20                   0.635556                 0.632777   \n",
       "21                   0.633709                 0.631058   \n",
       "22                   0.631999                 0.629376   \n",
       "23                   0.630258                 0.627761   \n",
       "24                   0.628599                 0.626208   \n",
       "25                   0.627047                 0.624794   \n",
       "26                   0.625636                 0.623419   \n",
       "27                   0.624252                 0.622165   \n",
       "28                   0.622998                 0.621025   \n",
       "29                   0.621849                 0.619931   \n",
       "30                   0.620752                 0.618918   \n",
       "31                   0.619781                 0.617985   \n",
       "32                   0.618812                 0.617115   \n",
       "33                   0.617943                 0.616310   \n",
       "34                   0.617158                 0.615591   \n",
       "35                   0.616448                 0.614938   \n",
       "36                   0.615797                 0.614361   \n",
       "37                   0.615235                 0.613850   \n",
       "38                   0.614735                 0.613389   \n",
       "39                   0.614280                 0.612987   \n",
       "40                   0.613897                 0.612647   \n",
       "41                   0.613555                 0.612359   \n",
       "42                   0.613278                 0.612122   \n",
       "43                   0.613054                 0.611930   \n",
       "44                   0.612866                 0.611773   \n",
       "45                   0.612718                 0.611649   \n",
       "46                   0.612602                 0.611561   \n",
       "47                   0.612521                 0.611501   \n",
       "48                   0.612468                 0.611462   \n",
       "49                   0.612433                 0.611439   \n",
       "50                   0.612414                 0.611429   \n",
       "51                   0.612406                 0.611427   \n",
       "\n",
       "    Train/train_balanced_accuracy  Train/val_balanced_accuracy  \\\n",
       "0                        0.546068                     0.541408   \n",
       "1                        0.549589                     0.548654   \n",
       "2                        0.563674                     0.548654   \n",
       "3                        0.574782                     0.542702   \n",
       "4                        0.588867                     0.542702   \n",
       "5                        0.619467                     0.549948   \n",
       "6                        0.641138                     0.564441   \n",
       "7                        0.658744                     0.577640   \n",
       "8                        0.662265                     0.599379   \n",
       "9                        0.700998                     0.644151   \n",
       "10                       0.716172                     0.671843   \n",
       "11                       0.733233                     0.693582   \n",
       "12                       0.738389                     0.722567   \n",
       "13                       0.743000                     0.744306   \n",
       "14                       0.753563                     0.745600   \n",
       "15                       0.761150                     0.745600   \n",
       "16                       0.782277                     0.752847   \n",
       "17                       0.785798                     0.767340   \n",
       "18                       0.803404                     0.789079   \n",
       "19                       0.817488                     0.796325   \n",
       "20                       0.821009                     0.796325   \n",
       "21                       0.821009                     0.796325   \n",
       "22                       0.821554                     0.796325   \n",
       "23                       0.825075                     0.803571   \n",
       "24                       0.825075                     0.810818   \n",
       "25                       0.828597                     0.825311   \n",
       "26                       0.835094                     0.832557   \n",
       "27                       0.842136                     0.832557   \n",
       "28                       0.845657                     0.839803   \n",
       "29                       0.845657                     0.839803   \n",
       "30                       0.842681                     0.839803   \n",
       "31                       0.846202                     0.854296   \n",
       "32                       0.846202                     0.854296   \n",
       "33                       0.846202                     0.854296   \n",
       "34                       0.849723                     0.854296   \n",
       "35                       0.849723                     0.854296   \n",
       "36                       0.849723                     0.854296   \n",
       "37                       0.849723                     0.854296   \n",
       "38                       0.846747                     0.861542   \n",
       "39                       0.846747                     0.861542   \n",
       "40                       0.846747                     0.861542   \n",
       "41                       0.846747                     0.861542   \n",
       "42                       0.846747                     0.861542   \n",
       "43                       0.846747                     0.861542   \n",
       "44                       0.846747                     0.861542   \n",
       "45                       0.846747                     0.861542   \n",
       "46                       0.846747                     0.861542   \n",
       "47                       0.846747                     0.861542   \n",
       "48                       0.846747                     0.861542   \n",
       "49                       0.846747                     0.861542   \n",
       "50                       0.846747                     0.861542   \n",
       "51                       0.846747                     0.861542   \n",
       "\n",
       "    Train/test_result  Train/test_cross_entropy  Train/test_balanced_accuracy  \\\n",
       "0           57.268723                  0.688986                      0.508707   \n",
       "1           57.268723                  0.687684                      0.508707   \n",
       "2           59.471367                  0.686409                      0.533357   \n",
       "3           59.471367                  0.685187                      0.533357   \n",
       "4           61.233479                  0.683923                      0.552799   \n",
       "5           63.436123                  0.682720                      0.578841   \n",
       "6           65.638763                  0.681485                      0.602099   \n",
       "7           66.079292                  0.680194                      0.608699   \n",
       "8           67.841408                  0.678963                      0.629532   \n",
       "9           68.281937                  0.677826                      0.633349   \n",
       "10          69.162994                  0.676696                      0.643766   \n",
       "11          70.044052                  0.675548                      0.655574   \n",
       "12          70.484581                  0.674516                      0.663566   \n",
       "13          70.925110                  0.673515                      0.670165   \n",
       "14          73.568283                  0.672508                      0.702807   \n",
       "15          74.008812                  0.671479                      0.708015   \n",
       "16          75.330399                  0.670466                      0.725032   \n",
       "17          76.651985                  0.669478                      0.740657   \n",
       "18          77.533043                  0.668556                      0.751073   \n",
       "19          78.854622                  0.667643                      0.766698   \n",
       "20          79.295151                  0.666754                      0.770515   \n",
       "21          79.735680                  0.665938                      0.775724   \n",
       "22          80.616737                  0.665141                      0.786140   \n",
       "23          80.176208                  0.664345                      0.782323   \n",
       "24          80.616737                  0.663587                      0.787532   \n",
       "25          80.616737                  0.662908                      0.788923   \n",
       "26          81.057266                  0.662238                      0.794132   \n",
       "27          81.057266                  0.661658                      0.794132   \n",
       "28          81.057266                  0.661126                      0.794132   \n",
       "29          81.497795                  0.660617                      0.799340   \n",
       "30          81.497795                  0.660157                      0.799340   \n",
       "31          81.497795                  0.659721                      0.799340   \n",
       "32          82.378853                  0.659313                      0.809757   \n",
       "33          82.378853                  0.658933                      0.811148   \n",
       "34          82.378853                  0.658595                      0.811148   \n",
       "35          82.378853                  0.658278                      0.811148   \n",
       "36          82.378853                  0.658011                      0.811148   \n",
       "37          82.378853                  0.657779                      0.811148   \n",
       "38          82.819382                  0.657558                      0.816357   \n",
       "39          83.259911                  0.657357                      0.820173   \n",
       "40          83.259911                  0.657187                      0.820173   \n",
       "41          83.259911                  0.657045                      0.820173   \n",
       "42          83.259911                  0.656934                      0.820173   \n",
       "43          83.259911                  0.656846                      0.820173   \n",
       "44          83.259911                  0.656770                      0.820173   \n",
       "45          83.259911                  0.656708                      0.820173   \n",
       "46          83.259911                  0.656665                      0.820173   \n",
       "47          83.259911                  0.656637                      0.820173   \n",
       "48          83.259911                  0.656618                      0.820173   \n",
       "49          83.259911                  0.656606                      0.820173   \n",
       "50          83.259911                  0.656601                      0.820173   \n",
       "51          83.259911                  0.656600                      0.820173   \n",
       "\n",
       "    Train/lr  \n",
       "0   0.000000  \n",
       "1   0.001824  \n",
       "2   0.001824  \n",
       "3   0.001823  \n",
       "4   0.001817  \n",
       "5   0.001808  \n",
       "6   0.001796  \n",
       "7   0.001780  \n",
       "8   0.001760  \n",
       "9   0.001738  \n",
       "10  0.001712  \n",
       "11  0.001682  \n",
       "12  0.001650  \n",
       "13  0.001615  \n",
       "14  0.001577  \n",
       "15  0.001537  \n",
       "16  0.001494  \n",
       "17  0.001448  \n",
       "18  0.001401  \n",
       "19  0.001352  \n",
       "20  0.001301  \n",
       "21  0.001248  \n",
       "22  0.001194  \n",
       "23  0.001139  \n",
       "24  0.001083  \n",
       "25  0.001026  \n",
       "26  0.000969  \n",
       "27  0.000912  \n",
       "28  0.000855  \n",
       "29  0.000798  \n",
       "30  0.000741  \n",
       "31  0.000685  \n",
       "32  0.000630  \n",
       "33  0.000576  \n",
       "34  0.000524  \n",
       "35  0.000473  \n",
       "36  0.000423  \n",
       "37  0.000376  \n",
       "38  0.000331  \n",
       "39  0.000288  \n",
       "40  0.000247  \n",
       "41  0.000209  \n",
       "42  0.000174  \n",
       "43  0.000142  \n",
       "44  0.000113  \n",
       "45  0.000087  \n",
       "46  0.000064  \n",
       "47  0.000045  \n",
       "48  0.000029  \n",
       "49  0.000016  \n",
       "50  0.000007  \n",
       "51  0.000002  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a0f5aa3-5ea5-4186-ba31-d614dd4ce9b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APSFailure</th>\n",
       "      <th>Amazon_employee_access</th>\n",
       "      <th>Australian</th>\n",
       "      <th>Fashion-MNIST</th>\n",
       "      <th>KDDCup09_appetency</th>\n",
       "      <th>MiniBooNE</th>\n",
       "      <th>adult</th>\n",
       "      <th>airlines</th>\n",
       "      <th>albert</th>\n",
       "      <th>bank-marketing</th>\n",
       "      <th>...</th>\n",
       "      <th>kr-vs-kp</th>\n",
       "      <th>mfeat-factors</th>\n",
       "      <th>nomao</th>\n",
       "      <th>numerai28.6</th>\n",
       "      <th>phoneme</th>\n",
       "      <th>segment</th>\n",
       "      <th>shuttle</th>\n",
       "      <th>sylvine</th>\n",
       "      <th>vehicle</th>\n",
       "      <th>volkert</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'log': {'time': [0.000195503234863, 3.6340391...</td>\n",
       "      <td>{'log': {'time': [0.00026798248291, 1.72883129...</td>\n",
       "      <td>{'log': {'time': [0.00023245811462400002, 0.85...</td>\n",
       "      <td>{'log': {'time': [0.00028920173645000005, 9.52...</td>\n",
       "      <td>{'log': {'time': [0.00021195411682100001, 2.90...</td>\n",
       "      <td>{'log': {'time': [0.00046157836914, 5.86666536...</td>\n",
       "      <td>{'log': {'time': [0.00027728080749500004, 2.22...</td>\n",
       "      <td>{'log': {'time': [0.00043225288391100005, 16.1...</td>\n",
       "      <td>{'log': {'time': [0.00044178962707500005, 14.8...</td>\n",
       "      <td>{'log': {'time': [0.000255107879638, 2.1391329...</td>\n",
       "      <td>...</td>\n",
       "      <td>{'log': {'time': [0.000258684158325, 0.9336907...</td>\n",
       "      <td>{'log': {'time': [0.00028657913208, 0.92955923...</td>\n",
       "      <td>{'log': {'time': [0.00022459030151300002, 2.02...</td>\n",
       "      <td>{'log': {'time': [0.000352621078491, 3.5299777...</td>\n",
       "      <td>{'log': {'time': [0.00020289421081500002, 0.99...</td>\n",
       "      <td>{'log': {'time': [0.00029730796813900003, 0.91...</td>\n",
       "      <td>{'log': {'time': [0.000220775604248, 2.5275578...</td>\n",
       "      <td>{'log': {'time': [0.00033760070800700005, 0.97...</td>\n",
       "      <td>{'log': {'time': [0.00037980079650800003, 0.87...</td>\n",
       "      <td>{'log': {'time': [0.00038671493530200004, 3.04...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'log': {'time': [0.000524759292602, 6.9341192...</td>\n",
       "      <td>{'log': {'time': [0.00042772293090800005, 3.12...</td>\n",
       "      <td>{'log': {'time': [0.000370025634765, 0.9656958...</td>\n",
       "      <td>{'log': {'time': [0.00046706199645900006, 9.74...</td>\n",
       "      <td>{'log': {'time': [0.000335931777954, 5.1948654...</td>\n",
       "      <td>{'log': {'time': [0.00045728683471600004, 10.1...</td>\n",
       "      <td>{'log': {'time': [0.000430107116699, 4.1559793...</td>\n",
       "      <td>{'log': {'time': [0.00021862983703600002, 38.6...</td>\n",
       "      <td>{'log': {'time': [0.000230073928833, 33.415742...</td>\n",
       "      <td>{'log': {'time': [0.000421524047851, 4.0013418...</td>\n",
       "      <td>...</td>\n",
       "      <td>{'log': {'time': [0.000226020812988, 1.0877950...</td>\n",
       "      <td>{'log': {'time': [0.00021076202392500003, 1.01...</td>\n",
       "      <td>{'log': {'time': [0.000617504119873, 3.3951654...</td>\n",
       "      <td>{'log': {'time': [0.00048041343688900003, 7.55...</td>\n",
       "      <td>{'log': {'time': [0.00020313262939400002, 1.22...</td>\n",
       "      <td>{'log': {'time': [0.00021910667419400002, 1.07...</td>\n",
       "      <td>{'log': {'time': [0.000254869461059, 4.8665730...</td>\n",
       "      <td>{'log': {'time': [0.00022506713867100002, 1.25...</td>\n",
       "      <td>{'log': {'time': [0.00032567977905200004, 0.93...</td>\n",
       "      <td>{'log': {'time': [0.00047039985656700003, 5.62...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'log': {'time': [0.00019097328186, 2.38047766...</td>\n",
       "      <td>{'log': {'time': [0.00055980682373, 1.32991147...</td>\n",
       "      <td>{'log': {'time': [0.000284194946289, 0.8494393...</td>\n",
       "      <td>{'log': {'time': [0.00043320655822700005, 3.89...</td>\n",
       "      <td>{'log': {'time': [0.00020265579223600002, 2.07...</td>\n",
       "      <td>{'log': {'time': [0.00022506713867100002, 3.03...</td>\n",
       "      <td>{'log': {'time': [0.00021409988403300003, 1.60...</td>\n",
       "      <td>{'log': {'time': [0.000179290771484, 9.7384555...</td>\n",
       "      <td>{'log': {'time': [0.00044035911560000003, 9.03...</td>\n",
       "      <td>{'log': {'time': [0.0001962184906, 1.566128253...</td>\n",
       "      <td>...</td>\n",
       "      <td>{'log': {'time': [0.00022435188293400002, 0.89...</td>\n",
       "      <td>{'log': {'time': [0.000213384628295, 0.8868896...</td>\n",
       "      <td>{'log': {'time': [0.000266551971435, 1.5262763...</td>\n",
       "      <td>{'log': {'time': [0.000435590744018, 2.4533412...</td>\n",
       "      <td>{'log': {'time': [0.00022459030151300002, 0.91...</td>\n",
       "      <td>{'log': {'time': [0.000248908996582, 0.8669002...</td>\n",
       "      <td>{'log': {'time': [0.000261068344116, 1.7755198...</td>\n",
       "      <td>{'log': {'time': [0.00042414665222100006, 0.90...</td>\n",
       "      <td>{'log': {'time': [0.000252485275268, 0.8463735...</td>\n",
       "      <td>{'log': {'time': [0.00018787384033200002, 2.09...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'log': {'time': [0.00043773651123000005, 4.34...</td>\n",
       "      <td>{'log': {'time': [0.00043225288391100005, 2.08...</td>\n",
       "      <td>{'log': {'time': [0.000397443771362, 0.9221093...</td>\n",
       "      <td>{'log': {'time': [0.000464439392089, 7.3254776...</td>\n",
       "      <td>{'log': {'time': [0.00046277046203600005, 3.49...</td>\n",
       "      <td>{'log': {'time': [0.00021386146545400003, 5.92...</td>\n",
       "      <td>{'log': {'time': [0.000420570373535, 2.6804733...</td>\n",
       "      <td>{'log': {'time': [0.00020742416381800002, 21.6...</td>\n",
       "      <td>{'log': {'time': [0.000213384628295, 19.567517...</td>\n",
       "      <td>{'log': {'time': [0.00041580200195300005, 2.57...</td>\n",
       "      <td>...</td>\n",
       "      <td>{'log': {'time': [0.00041341781616200003, 0.97...</td>\n",
       "      <td>{'log': {'time': [0.001196146011352, 0.9619770...</td>\n",
       "      <td>{'log': {'time': [0.000428915023803, 2.3731176...</td>\n",
       "      <td>{'log': {'time': [0.000298976898193, 4.4447009...</td>\n",
       "      <td>{'log': {'time': [0.00023913383483800002, 1.05...</td>\n",
       "      <td>{'log': {'time': [0.0006189346313470001, 0.986...</td>\n",
       "      <td>{'log': {'time': [0.000602960586547, 3.0916359...</td>\n",
       "      <td>{'log': {'time': [0.00037074089050200004, 1.10...</td>\n",
       "      <td>{'log': {'time': [0.000375032424926, 0.8953502...</td>\n",
       "      <td>{'log': {'time': [0.000250339508056, 3.9802963...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'log': {'time': [0.00019907951354900002, 3.70...</td>\n",
       "      <td>{'log': {'time': [0.00040817260742100005, 1.85...</td>\n",
       "      <td>{'log': {'time': [0.00018334388732900003, 0.83...</td>\n",
       "      <td>{'log': {'time': [0.000425100326538, 4.9804036...</td>\n",
       "      <td>{'log': {'time': [0.000411987304687, 2.9354200...</td>\n",
       "      <td>{'log': {'time': [0.00028681755065900004, 5.21...</td>\n",
       "      <td>{'log': {'time': [0.000439167022705, 2.4441220...</td>\n",
       "      <td>{'log': {'time': [0.000468492507934, 18.130872...</td>\n",
       "      <td>{'log': {'time': [0.00026249885559, 15.8644056...</td>\n",
       "      <td>{'log': {'time': [0.000249862670898, 2.2413945...</td>\n",
       "      <td>...</td>\n",
       "      <td>{'log': {'time': [0.000413179397583, 0.9637236...</td>\n",
       "      <td>{'log': {'time': [0.000412940979003, 0.9367494...</td>\n",
       "      <td>{'log': {'time': [0.00044274330139100005, 2.09...</td>\n",
       "      <td>{'log': {'time': [0.00045013427734300006, 3.92...</td>\n",
       "      <td>{'log': {'time': [0.00034785270690900004, 1.01...</td>\n",
       "      <td>{'log': {'time': [0.00023841857910100002, 0.89...</td>\n",
       "      <td>{'log': {'time': [0.000302553176879, 2.7762589...</td>\n",
       "      <td>{'log': {'time': [0.000186443328857, 0.9915802...</td>\n",
       "      <td>{'log': {'time': [0.00057053565979, 0.87369918...</td>\n",
       "      <td>{'log': {'time': [0.00036478042602500004, 3.11...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>{'log': {'time': [0.00019431114196700002, 2.96...</td>\n",
       "      <td>{'log': {'time': [0.00021076202392500003, 1.51...</td>\n",
       "      <td>{'log': {'time': [0.000416994094848, 0.8588693...</td>\n",
       "      <td>{'log': {'time': [0.00047373771667400005, 6.77...</td>\n",
       "      <td>{'log': {'time': [0.00028681755065900004, 2.57...</td>\n",
       "      <td>{'log': {'time': [0.00033164024353, 3.90463566...</td>\n",
       "      <td>{'log': {'time': [0.00021862983703600002, 1.93...</td>\n",
       "      <td>{'log': {'time': [0.000221729278564, 12.292574...</td>\n",
       "      <td>{'log': {'time': [0.000230312347412, 11.577726...</td>\n",
       "      <td>{'log': {'time': [0.000311136245727, 1.7990512...</td>\n",
       "      <td>...</td>\n",
       "      <td>{'log': {'time': [0.000169992446899, 0.8282887...</td>\n",
       "      <td>{'log': {'time': [0.00020241737365700002, 0.93...</td>\n",
       "      <td>{'log': {'time': [0.000195741653442, 1.8008894...</td>\n",
       "      <td>{'log': {'time': [0.00022888183593700002, 2.94...</td>\n",
       "      <td>{'log': {'time': [0.00018382072448700003, 0.93...</td>\n",
       "      <td>{'log': {'time': [0.000431060791015, 0.8981275...</td>\n",
       "      <td>{'log': {'time': [0.000199794769287, 2.0428426...</td>\n",
       "      <td>{'log': {'time': [0.000196695327758, 0.9090476...</td>\n",
       "      <td>{'log': {'time': [0.000319004058837, 0.8578341...</td>\n",
       "      <td>{'log': {'time': [0.00020313262939400002, 2.58...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>{'log': {'time': [0.00019264221191400002, 3.05...</td>\n",
       "      <td>{'log': {'time': [0.00021362304687500003, 1.56...</td>\n",
       "      <td>{'log': {'time': [0.000195264816284, 0.8566269...</td>\n",
       "      <td>{'log': {'time': [0.00018477439880300003, 7.40...</td>\n",
       "      <td>{'log': {'time': [0.00020217895507800002, 2.48...</td>\n",
       "      <td>{'log': {'time': [0.00021433830261200003, 3.93...</td>\n",
       "      <td>{'log': {'time': [0.00020217895507800002, 1.91...</td>\n",
       "      <td>{'log': {'time': [0.000195980072021, 13.135707...</td>\n",
       "      <td>{'log': {'time': [0.000249624252319, 12.575221...</td>\n",
       "      <td>{'log': {'time': [0.000205993652343, 1.8610863...</td>\n",
       "      <td>...</td>\n",
       "      <td>{'log': {'time': [0.00023674964904700003, 0.93...</td>\n",
       "      <td>{'log': {'time': [0.000195503234863, 0.8777751...</td>\n",
       "      <td>{'log': {'time': [0.000304460525512, 1.7229654...</td>\n",
       "      <td>{'log': {'time': [0.00036478042602500004, 3.03...</td>\n",
       "      <td>{'log': {'time': [0.000195026397705, 0.9689574...</td>\n",
       "      <td>{'log': {'time': [0.00021386146545400003, 0.89...</td>\n",
       "      <td>{'log': {'time': [0.00020289421081500002, 2.21...</td>\n",
       "      <td>{'log': {'time': [0.00020742416381800002, 0.95...</td>\n",
       "      <td>{'log': {'time': [0.00021457672119100003, 0.88...</td>\n",
       "      <td>{'log': {'time': [0.00021147727966300001, 2.57...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>{'log': {'time': [0.000200271606445, 3.1272635...</td>\n",
       "      <td>{'log': {'time': [0.00019741058349600002, 1.54...</td>\n",
       "      <td>{'log': {'time': [0.000206470489501, 0.8706564...</td>\n",
       "      <td>{'log': {'time': [0.00023317337036100002, 7.93...</td>\n",
       "      <td>{'log': {'time': [0.000196695327758, 2.5018320...</td>\n",
       "      <td>{'log': {'time': [0.00021266937255800001, 4.01...</td>\n",
       "      <td>{'log': {'time': [0.000199794769287, 1.9518892...</td>\n",
       "      <td>{'log': {'time': [0.00021457672119100003, 13.2...</td>\n",
       "      <td>{'log': {'time': [0.00018882751464800002, 11.1...</td>\n",
       "      <td>{'log': {'time': [0.00024724006652800004, 1.94...</td>\n",
       "      <td>...</td>\n",
       "      <td>{'log': {'time': [0.000227689743041, 0.9283785...</td>\n",
       "      <td>{'log': {'time': [0.000213146209716, 0.9279336...</td>\n",
       "      <td>{'log': {'time': [0.00030970573425200004, 1.87...</td>\n",
       "      <td>{'log': {'time': [0.00021529197692800003, 3.09...</td>\n",
       "      <td>{'log': {'time': [0.000195026397705, 0.9952492...</td>\n",
       "      <td>{'log': {'time': [0.00022983551025300002, 0.91...</td>\n",
       "      <td>{'log': {'time': [0.000195503234863, 2.2739052...</td>\n",
       "      <td>{'log': {'time': [0.000216007232666, 0.9660992...</td>\n",
       "      <td>{'log': {'time': [0.000174760818481, 0.7847044...</td>\n",
       "      <td>{'log': {'time': [0.000200271606445, 2.7035379...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>{'log': {'time': [0.00028681755065900004, 3.19...</td>\n",
       "      <td>{'log': {'time': [0.000363111495971, 1.5427975...</td>\n",
       "      <td>{'log': {'time': [0.000191688537597, 0.8607888...</td>\n",
       "      <td>{'log': {'time': [0.000186443328857, 5.7755744...</td>\n",
       "      <td>{'log': {'time': [0.00019788742065400002, 2.45...</td>\n",
       "      <td>{'log': {'time': [0.00039935111999500004, 3.91...</td>\n",
       "      <td>{'log': {'time': [0.000216960906982, 1.9152915...</td>\n",
       "      <td>{'log': {'time': [0.00118064880371, 12.4446895...</td>\n",
       "      <td>{'log': {'time': [0.000201702117919, 12.095380...</td>\n",
       "      <td>{'log': {'time': [0.00021028518676700003, 1.78...</td>\n",
       "      <td>...</td>\n",
       "      <td>{'log': {'time': [0.000218152999877, 0.9913084...</td>\n",
       "      <td>{'log': {'time': [0.000216722488403, 0.9631505...</td>\n",
       "      <td>{'log': {'time': [0.000205993652343, 1.7549741...</td>\n",
       "      <td>{'log': {'time': [0.000473022460937, 2.7412514...</td>\n",
       "      <td>{'log': {'time': [0.00018501281738200003, 0.94...</td>\n",
       "      <td>{'log': {'time': [0.0005304813385, 0.915136098...</td>\n",
       "      <td>{'log': {'time': [0.00022959709167400002, 2.14...</td>\n",
       "      <td>{'log': {'time': [0.00020885467529200002, 0.97...</td>\n",
       "      <td>{'log': {'time': [0.00023245811462400002, 0.86...</td>\n",
       "      <td>{'log': {'time': [0.000527143478393, 2.6925821...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>{'log': {'time': [0.000205039978027, 5.0630719...</td>\n",
       "      <td>{'log': {'time': [0.000191926956176, 2.1427121...</td>\n",
       "      <td>{'log': {'time': [0.00018119812011700001, 0.79...</td>\n",
       "      <td>{'log': {'time': [0.000196456909179, 11.889720...</td>\n",
       "      <td>{'log': {'time': [0.000326633453369, 3.8678622...</td>\n",
       "      <td>{'log': {'time': [0.00042080879211400004, 6.81...</td>\n",
       "      <td>{'log': {'time': [0.000191450119018, 2.8229281...</td>\n",
       "      <td>{'log': {'time': [0.000326633453369, 35.775121...</td>\n",
       "      <td>{'log': {'time': [0.00020742416381800002, 22.0...</td>\n",
       "      <td>{'log': {'time': [0.00036573410034100004, 2.83...</td>\n",
       "      <td>...</td>\n",
       "      <td>{'log': {'time': [0.00021004676818800003, 1.02...</td>\n",
       "      <td>{'log': {'time': [0.00020933151245100003, 0.95...</td>\n",
       "      <td>{'log': {'time': [0.000305891036987, 2.9291291...</td>\n",
       "      <td>{'log': {'time': [0.00020694732666000002, 5.09...</td>\n",
       "      <td>{'log': {'time': [0.000205278396606, 1.0645167...</td>\n",
       "      <td>{'log': {'time': [0.000199794769287, 0.9920957...</td>\n",
       "      <td>{'log': {'time': [0.00018572807312, 3.18579006...</td>\n",
       "      <td>{'log': {'time': [0.00046277046203600005, 1.07...</td>\n",
       "      <td>{'log': {'time': [0.00022363662719700002, 0.90...</td>\n",
       "      <td>{'log': {'time': [0.00019407272338800002, 4.14...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             APSFailure  \\\n",
       "0     {'log': {'time': [0.000195503234863, 3.6340391...   \n",
       "1     {'log': {'time': [0.000524759292602, 6.9341192...   \n",
       "2     {'log': {'time': [0.00019097328186, 2.38047766...   \n",
       "3     {'log': {'time': [0.00043773651123000005, 4.34...   \n",
       "4     {'log': {'time': [0.00019907951354900002, 3.70...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.00019431114196700002, 2.96...   \n",
       "1996  {'log': {'time': [0.00019264221191400002, 3.05...   \n",
       "1997  {'log': {'time': [0.000200271606445, 3.1272635...   \n",
       "1998  {'log': {'time': [0.00028681755065900004, 3.19...   \n",
       "1999  {'log': {'time': [0.000205039978027, 5.0630719...   \n",
       "\n",
       "                                 Amazon_employee_access  \\\n",
       "0     {'log': {'time': [0.00026798248291, 1.72883129...   \n",
       "1     {'log': {'time': [0.00042772293090800005, 3.12...   \n",
       "2     {'log': {'time': [0.00055980682373, 1.32991147...   \n",
       "3     {'log': {'time': [0.00043225288391100005, 2.08...   \n",
       "4     {'log': {'time': [0.00040817260742100005, 1.85...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.00021076202392500003, 1.51...   \n",
       "1996  {'log': {'time': [0.00021362304687500003, 1.56...   \n",
       "1997  {'log': {'time': [0.00019741058349600002, 1.54...   \n",
       "1998  {'log': {'time': [0.000363111495971, 1.5427975...   \n",
       "1999  {'log': {'time': [0.000191926956176, 2.1427121...   \n",
       "\n",
       "                                             Australian  \\\n",
       "0     {'log': {'time': [0.00023245811462400002, 0.85...   \n",
       "1     {'log': {'time': [0.000370025634765, 0.9656958...   \n",
       "2     {'log': {'time': [0.000284194946289, 0.8494393...   \n",
       "3     {'log': {'time': [0.000397443771362, 0.9221093...   \n",
       "4     {'log': {'time': [0.00018334388732900003, 0.83...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.000416994094848, 0.8588693...   \n",
       "1996  {'log': {'time': [0.000195264816284, 0.8566269...   \n",
       "1997  {'log': {'time': [0.000206470489501, 0.8706564...   \n",
       "1998  {'log': {'time': [0.000191688537597, 0.8607888...   \n",
       "1999  {'log': {'time': [0.00018119812011700001, 0.79...   \n",
       "\n",
       "                                          Fashion-MNIST  \\\n",
       "0     {'log': {'time': [0.00028920173645000005, 9.52...   \n",
       "1     {'log': {'time': [0.00046706199645900006, 9.74...   \n",
       "2     {'log': {'time': [0.00043320655822700005, 3.89...   \n",
       "3     {'log': {'time': [0.000464439392089, 7.3254776...   \n",
       "4     {'log': {'time': [0.000425100326538, 4.9804036...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.00047373771667400005, 6.77...   \n",
       "1996  {'log': {'time': [0.00018477439880300003, 7.40...   \n",
       "1997  {'log': {'time': [0.00023317337036100002, 7.93...   \n",
       "1998  {'log': {'time': [0.000186443328857, 5.7755744...   \n",
       "1999  {'log': {'time': [0.000196456909179, 11.889720...   \n",
       "\n",
       "                                     KDDCup09_appetency  \\\n",
       "0     {'log': {'time': [0.00021195411682100001, 2.90...   \n",
       "1     {'log': {'time': [0.000335931777954, 5.1948654...   \n",
       "2     {'log': {'time': [0.00020265579223600002, 2.07...   \n",
       "3     {'log': {'time': [0.00046277046203600005, 3.49...   \n",
       "4     {'log': {'time': [0.000411987304687, 2.9354200...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.00028681755065900004, 2.57...   \n",
       "1996  {'log': {'time': [0.00020217895507800002, 2.48...   \n",
       "1997  {'log': {'time': [0.000196695327758, 2.5018320...   \n",
       "1998  {'log': {'time': [0.00019788742065400002, 2.45...   \n",
       "1999  {'log': {'time': [0.000326633453369, 3.8678622...   \n",
       "\n",
       "                                              MiniBooNE  \\\n",
       "0     {'log': {'time': [0.00046157836914, 5.86666536...   \n",
       "1     {'log': {'time': [0.00045728683471600004, 10.1...   \n",
       "2     {'log': {'time': [0.00022506713867100002, 3.03...   \n",
       "3     {'log': {'time': [0.00021386146545400003, 5.92...   \n",
       "4     {'log': {'time': [0.00028681755065900004, 5.21...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.00033164024353, 3.90463566...   \n",
       "1996  {'log': {'time': [0.00021433830261200003, 3.93...   \n",
       "1997  {'log': {'time': [0.00021266937255800001, 4.01...   \n",
       "1998  {'log': {'time': [0.00039935111999500004, 3.91...   \n",
       "1999  {'log': {'time': [0.00042080879211400004, 6.81...   \n",
       "\n",
       "                                                  adult  \\\n",
       "0     {'log': {'time': [0.00027728080749500004, 2.22...   \n",
       "1     {'log': {'time': [0.000430107116699, 4.1559793...   \n",
       "2     {'log': {'time': [0.00021409988403300003, 1.60...   \n",
       "3     {'log': {'time': [0.000420570373535, 2.6804733...   \n",
       "4     {'log': {'time': [0.000439167022705, 2.4441220...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.00021862983703600002, 1.93...   \n",
       "1996  {'log': {'time': [0.00020217895507800002, 1.91...   \n",
       "1997  {'log': {'time': [0.000199794769287, 1.9518892...   \n",
       "1998  {'log': {'time': [0.000216960906982, 1.9152915...   \n",
       "1999  {'log': {'time': [0.000191450119018, 2.8229281...   \n",
       "\n",
       "                                               airlines  \\\n",
       "0     {'log': {'time': [0.00043225288391100005, 16.1...   \n",
       "1     {'log': {'time': [0.00021862983703600002, 38.6...   \n",
       "2     {'log': {'time': [0.000179290771484, 9.7384555...   \n",
       "3     {'log': {'time': [0.00020742416381800002, 21.6...   \n",
       "4     {'log': {'time': [0.000468492507934, 18.130872...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.000221729278564, 12.292574...   \n",
       "1996  {'log': {'time': [0.000195980072021, 13.135707...   \n",
       "1997  {'log': {'time': [0.00021457672119100003, 13.2...   \n",
       "1998  {'log': {'time': [0.00118064880371, 12.4446895...   \n",
       "1999  {'log': {'time': [0.000326633453369, 35.775121...   \n",
       "\n",
       "                                                 albert  \\\n",
       "0     {'log': {'time': [0.00044178962707500005, 14.8...   \n",
       "1     {'log': {'time': [0.000230073928833, 33.415742...   \n",
       "2     {'log': {'time': [0.00044035911560000003, 9.03...   \n",
       "3     {'log': {'time': [0.000213384628295, 19.567517...   \n",
       "4     {'log': {'time': [0.00026249885559, 15.8644056...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.000230312347412, 11.577726...   \n",
       "1996  {'log': {'time': [0.000249624252319, 12.575221...   \n",
       "1997  {'log': {'time': [0.00018882751464800002, 11.1...   \n",
       "1998  {'log': {'time': [0.000201702117919, 12.095380...   \n",
       "1999  {'log': {'time': [0.00020742416381800002, 22.0...   \n",
       "\n",
       "                                         bank-marketing  ...  \\\n",
       "0     {'log': {'time': [0.000255107879638, 2.1391329...  ...   \n",
       "1     {'log': {'time': [0.000421524047851, 4.0013418...  ...   \n",
       "2     {'log': {'time': [0.0001962184906, 1.566128253...  ...   \n",
       "3     {'log': {'time': [0.00041580200195300005, 2.57...  ...   \n",
       "4     {'log': {'time': [0.000249862670898, 2.2413945...  ...   \n",
       "...                                                 ...  ...   \n",
       "1995  {'log': {'time': [0.000311136245727, 1.7990512...  ...   \n",
       "1996  {'log': {'time': [0.000205993652343, 1.8610863...  ...   \n",
       "1997  {'log': {'time': [0.00024724006652800004, 1.94...  ...   \n",
       "1998  {'log': {'time': [0.00021028518676700003, 1.78...  ...   \n",
       "1999  {'log': {'time': [0.00036573410034100004, 2.83...  ...   \n",
       "\n",
       "                                               kr-vs-kp  \\\n",
       "0     {'log': {'time': [0.000258684158325, 0.9336907...   \n",
       "1     {'log': {'time': [0.000226020812988, 1.0877950...   \n",
       "2     {'log': {'time': [0.00022435188293400002, 0.89...   \n",
       "3     {'log': {'time': [0.00041341781616200003, 0.97...   \n",
       "4     {'log': {'time': [0.000413179397583, 0.9637236...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.000169992446899, 0.8282887...   \n",
       "1996  {'log': {'time': [0.00023674964904700003, 0.93...   \n",
       "1997  {'log': {'time': [0.000227689743041, 0.9283785...   \n",
       "1998  {'log': {'time': [0.000218152999877, 0.9913084...   \n",
       "1999  {'log': {'time': [0.00021004676818800003, 1.02...   \n",
       "\n",
       "                                          mfeat-factors  \\\n",
       "0     {'log': {'time': [0.00028657913208, 0.92955923...   \n",
       "1     {'log': {'time': [0.00021076202392500003, 1.01...   \n",
       "2     {'log': {'time': [0.000213384628295, 0.8868896...   \n",
       "3     {'log': {'time': [0.001196146011352, 0.9619770...   \n",
       "4     {'log': {'time': [0.000412940979003, 0.9367494...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.00020241737365700002, 0.93...   \n",
       "1996  {'log': {'time': [0.000195503234863, 0.8777751...   \n",
       "1997  {'log': {'time': [0.000213146209716, 0.9279336...   \n",
       "1998  {'log': {'time': [0.000216722488403, 0.9631505...   \n",
       "1999  {'log': {'time': [0.00020933151245100003, 0.95...   \n",
       "\n",
       "                                                  nomao  \\\n",
       "0     {'log': {'time': [0.00022459030151300002, 2.02...   \n",
       "1     {'log': {'time': [0.000617504119873, 3.3951654...   \n",
       "2     {'log': {'time': [0.000266551971435, 1.5262763...   \n",
       "3     {'log': {'time': [0.000428915023803, 2.3731176...   \n",
       "4     {'log': {'time': [0.00044274330139100005, 2.09...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.000195741653442, 1.8008894...   \n",
       "1996  {'log': {'time': [0.000304460525512, 1.7229654...   \n",
       "1997  {'log': {'time': [0.00030970573425200004, 1.87...   \n",
       "1998  {'log': {'time': [0.000205993652343, 1.7549741...   \n",
       "1999  {'log': {'time': [0.000305891036987, 2.9291291...   \n",
       "\n",
       "                                            numerai28.6  \\\n",
       "0     {'log': {'time': [0.000352621078491, 3.5299777...   \n",
       "1     {'log': {'time': [0.00048041343688900003, 7.55...   \n",
       "2     {'log': {'time': [0.000435590744018, 2.4533412...   \n",
       "3     {'log': {'time': [0.000298976898193, 4.4447009...   \n",
       "4     {'log': {'time': [0.00045013427734300006, 3.92...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.00022888183593700002, 2.94...   \n",
       "1996  {'log': {'time': [0.00036478042602500004, 3.03...   \n",
       "1997  {'log': {'time': [0.00021529197692800003, 3.09...   \n",
       "1998  {'log': {'time': [0.000473022460937, 2.7412514...   \n",
       "1999  {'log': {'time': [0.00020694732666000002, 5.09...   \n",
       "\n",
       "                                                phoneme  \\\n",
       "0     {'log': {'time': [0.00020289421081500002, 0.99...   \n",
       "1     {'log': {'time': [0.00020313262939400002, 1.22...   \n",
       "2     {'log': {'time': [0.00022459030151300002, 0.91...   \n",
       "3     {'log': {'time': [0.00023913383483800002, 1.05...   \n",
       "4     {'log': {'time': [0.00034785270690900004, 1.01...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.00018382072448700003, 0.93...   \n",
       "1996  {'log': {'time': [0.000195026397705, 0.9689574...   \n",
       "1997  {'log': {'time': [0.000195026397705, 0.9952492...   \n",
       "1998  {'log': {'time': [0.00018501281738200003, 0.94...   \n",
       "1999  {'log': {'time': [0.000205278396606, 1.0645167...   \n",
       "\n",
       "                                                segment  \\\n",
       "0     {'log': {'time': [0.00029730796813900003, 0.91...   \n",
       "1     {'log': {'time': [0.00021910667419400002, 1.07...   \n",
       "2     {'log': {'time': [0.000248908996582, 0.8669002...   \n",
       "3     {'log': {'time': [0.0006189346313470001, 0.986...   \n",
       "4     {'log': {'time': [0.00023841857910100002, 0.89...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.000431060791015, 0.8981275...   \n",
       "1996  {'log': {'time': [0.00021386146545400003, 0.89...   \n",
       "1997  {'log': {'time': [0.00022983551025300002, 0.91...   \n",
       "1998  {'log': {'time': [0.0005304813385, 0.915136098...   \n",
       "1999  {'log': {'time': [0.000199794769287, 0.9920957...   \n",
       "\n",
       "                                                shuttle  \\\n",
       "0     {'log': {'time': [0.000220775604248, 2.5275578...   \n",
       "1     {'log': {'time': [0.000254869461059, 4.8665730...   \n",
       "2     {'log': {'time': [0.000261068344116, 1.7755198...   \n",
       "3     {'log': {'time': [0.000602960586547, 3.0916359...   \n",
       "4     {'log': {'time': [0.000302553176879, 2.7762589...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.000199794769287, 2.0428426...   \n",
       "1996  {'log': {'time': [0.00020289421081500002, 2.21...   \n",
       "1997  {'log': {'time': [0.000195503234863, 2.2739052...   \n",
       "1998  {'log': {'time': [0.00022959709167400002, 2.14...   \n",
       "1999  {'log': {'time': [0.00018572807312, 3.18579006...   \n",
       "\n",
       "                                                sylvine  \\\n",
       "0     {'log': {'time': [0.00033760070800700005, 0.97...   \n",
       "1     {'log': {'time': [0.00022506713867100002, 1.25...   \n",
       "2     {'log': {'time': [0.00042414665222100006, 0.90...   \n",
       "3     {'log': {'time': [0.00037074089050200004, 1.10...   \n",
       "4     {'log': {'time': [0.000186443328857, 0.9915802...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.000196695327758, 0.9090476...   \n",
       "1996  {'log': {'time': [0.00020742416381800002, 0.95...   \n",
       "1997  {'log': {'time': [0.000216007232666, 0.9660992...   \n",
       "1998  {'log': {'time': [0.00020885467529200002, 0.97...   \n",
       "1999  {'log': {'time': [0.00046277046203600005, 1.07...   \n",
       "\n",
       "                                                vehicle  \\\n",
       "0     {'log': {'time': [0.00037980079650800003, 0.87...   \n",
       "1     {'log': {'time': [0.00032567977905200004, 0.93...   \n",
       "2     {'log': {'time': [0.000252485275268, 0.8463735...   \n",
       "3     {'log': {'time': [0.000375032424926, 0.8953502...   \n",
       "4     {'log': {'time': [0.00057053565979, 0.87369918...   \n",
       "...                                                 ...   \n",
       "1995  {'log': {'time': [0.000319004058837, 0.8578341...   \n",
       "1996  {'log': {'time': [0.00021457672119100003, 0.88...   \n",
       "1997  {'log': {'time': [0.000174760818481, 0.7847044...   \n",
       "1998  {'log': {'time': [0.00023245811462400002, 0.86...   \n",
       "1999  {'log': {'time': [0.00022363662719700002, 0.90...   \n",
       "\n",
       "                                                volkert  \n",
       "0     {'log': {'time': [0.00038671493530200004, 3.04...  \n",
       "1     {'log': {'time': [0.00047039985656700003, 5.62...  \n",
       "2     {'log': {'time': [0.00018787384033200002, 2.09...  \n",
       "3     {'log': {'time': [0.000250339508056, 3.9802963...  \n",
       "4     {'log': {'time': [0.00036478042602500004, 3.11...  \n",
       "...                                                 ...  \n",
       "1995  {'log': {'time': [0.00020313262939400002, 2.58...  \n",
       "1996  {'log': {'time': [0.00021147727966300001, 2.57...  \n",
       "1997  {'log': {'time': [0.000200271606445, 2.7035379...  \n",
       "1998  {'log': {'time': [0.000527143478393, 2.6925821...  \n",
       "1999  {'log': {'time': [0.00019407272338800002, 4.14...  \n",
       "\n",
       "[2000 rows x 35 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_json(\"data_2k_lw.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e777cee-275f-418c-87c7-b189fdb6bfa7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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No results found