tree_based.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5cb6d8db-724d-4564-8ddc-bd8d835f9a53",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mathieubazinet/.pyenv/versions/3.12.3/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import wandb\n",
    "import yaml\n",
    "from utilities.utils import correct_type_of_entry\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import product\n",
    "from copy import deepcopy\n",
    "import seaborn as sn\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "404fe45d-afd1-4bf9-9577-b8f957cfd6bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = \"powerplant\"\n",
    "sweep_config = \"tree\"\n",
    "min_max_val = {'concrete':{'min_val':2.33, 'max_val':82.6},\n",
    "               'parkinson':{'min_val':5.0377, 'max_val':39.511}, \n",
    "               'airfoil':{'min_val':103.38, 'max_val':140.987}, \n",
    "               'powerplant':{'min_val':420.26, 'max_val':495.76},\n",
    "               'infrared':{'min_val':35.75, 'max_val':39.3}}\n",
    "min_max_val_10_percent = {dts:{\"min_val\": max(0,min_max_val[dts]['min_val'] - 0.1*(min_max_val[dts]['max_val'] - min_max_val[dts]['min_val'])),\n",
    "                              \"max_val\":min_max_val[dts]['max_val'] + 0.1*(min_max_val[dts]['max_val'] - min_max_val[dts]['min_val'])} for dts in min_max_val.keys()}\n",
    "values_to_fetch = ['kl_bound', 'min_val_catoni_bound', 'compression_set_size', 'complement_loss', 'validation_loss', 'test_loss', 'min_val_linear_bound']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "362b1c1e-7f1c-4fe7-84b4-5784db310a0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sweep_config_name = \"./configs/sweep_configs/\" + sweep_config + \".yaml\"\n",
    "with open(sweep_config_name) as file:\n",
    "    sweep_configuration = yaml.safe_load(file)\n",
    "    \n",
    "hps = {}\n",
    "for key, item in sweep_configuration['parameters'].items():\n",
    "    if item.get('values', None) is not None:\n",
    "        hps[key] = correct_type_of_entry(item['values'])\n",
    "        # if key == \"seed\":\n",
    "        #     hps['early_stopping_patience'] = [10,20]\n",
    "\n",
    "size_hyperparams = tuple([len(l) for l in hps.values()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7fe90226-daee-4fe0-b810-f18afba7216a",
   "metadata": {},
   "outputs": [],
   "source": [
    "api = wandb.Api()\n",
    "entity = \"mathieu-bazinet\"\n",
    "project = \"sweep\" + dataset\n",
    "runs = api.runs(entity + \"/\" + project)\n",
    "\n",
    "new_runs = []\n",
    "for run in runs:\n",
    "    if run.createdAt[:-4] > '2024-10-23T9:22':\n",
    "        new_runs.append(run)\n",
    "runs = new_runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b6ef98ed-c664-4593-b7ab-720bbc501065",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bounds.real_valued_bounds import compute_epsilon, linear_bound, kl_bound\n",
    "results_matrix = np.ones(((len(values_to_fetch)+1,) + size_hyperparams))\n",
    "\n",
    "for run_idx in range(len(runs)):\n",
    "    run = runs[run_idx]\n",
    "    if run.config['model_type'] == sweep_config:\n",
    "        for val_to_fetch_idx in range(len(values_to_fetch)):\n",
    "            try:\n",
    "                matrix_idx = tuple([val_to_fetch_idx] + [hps[key].index(run.config[key]) for key in hps.keys()])\n",
    "            except ValueError:\n",
    "                continue\n",
    "            except KeyError as e:\n",
    "                continue\n",
    "            val_to_fetch = values_to_fetch[val_to_fetch_idx]\n",
    "            try:\n",
    "                results_matrix[matrix_idx] = run.summary[val_to_fetch]\n",
    "            except KeyError:\n",
    "                results_matrix[matrix_idx] = [row[val_to_fetch] for row in run.scan_history(keys=[val_to_fetch])][-1]\n",
    "        try:\n",
    "            matrix_idx = tuple([hps[key].index(run.config[key]) for key in hps.keys()])\n",
    "            complement_error = results_matrix[(values_to_fetch.index('complement_loss'),) + matrix_idx]\n",
    "            m = results_matrix[(values_to_fetch.index('compression_set_size'),) + matrix_idx]\n",
    "            n = run.summary['train_set_size']\n",
    "            \n",
    "            bounded_val_error = complement_error / (min_max_val_10_percent[dataset]['max_val'] - min_max_val_10_percent[dataset]['min_val'])\n",
    "\n",
    "            epsilon = compute_epsilon(m, 1, n, run.config['delta'])\n",
    "            results_matrix[(0,) + matrix_idx] = (min_max_val_10_percent[dataset]['max_val'] - min_max_val_10_percent[dataset]['min_val']) * kl_bound(m, n, bounded_val_error, epsilon)\n",
    "\n",
    "            epsilon = compute_epsilon(m, 1, n, run.config['delta'] / 20)\n",
    "    \n",
    "            linear_bound_parameters = np.logspace(-5, 1, 20) # np.logspace(-10, 10, 20)\n",
    "            linear_bounds = np.array([linear_bound(m, n, complement_error, epsilon, t, min_max_val[dataset]['min_val'], min_max_val[dataset]['max_val']) for t in linear_bound_parameters])\n",
    "            results_matrix[(-1,) + matrix_idx] = np.min(linear_bounds)\n",
    "        except KeyError:\n",
    "            continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ddefab9a-1f7a-4448-9437-4e3e01a4639b",
   "metadata": {},
   "outputs": [],
   "source": [
    "hp_list = list(hps.values())[1:]\n",
    "params_product = list(product(*hp_list))\n",
    "name_list = []\n",
    "idx_list = []\n",
    "for params in params_product:\n",
    "    name = \"\"\n",
    "    for p in params:\n",
    "        name += str(p) + \" \"\n",
    "    name_list.append(name[:-1])\n",
    "    idx = ()\n",
    "    for p_idx in range(len(params)):\n",
    "        p_key = list(hps.keys())[1:][p_idx]\n",
    "        idx += (hps[p_key].index(params[p_idx]),)\n",
    "    idx_list.append(tuple(idx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "586e1c55-5253-4c55-95bb-ea491c5c6a4f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kl_bound</th>\n",
       "      <th>min_val_catoni_bound</th>\n",
       "      <th>compression_set_size</th>\n",
       "      <th>complement_loss</th>\n",
       "      <th>validation_loss</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>min_val_linear_bound</th>\n",
       "      <th>linear_bound</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5 2 1 0.0</th>\n",
       "      <td>28.127865</td>\n",
       "      <td>29.090425</td>\n",
       "      <td>70.2</td>\n",
       "      <td>15.354145</td>\n",
       "      <td>15.590007</td>\n",
       "      <td>15.393940</td>\n",
       "      <td>30.138334</td>\n",
       "      <td>27.825265</td>\n",
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       "    <tr>\n",
       "      <th>5 2 1 0.05</th>\n",
       "      <td>28.712799</td>\n",
       "      <td>29.515797</td>\n",
       "      <td>66.8</td>\n",
       "      <td>16.072329</td>\n",
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       "      <td>30.556501</td>\n",
       "      <td>28.243432</td>\n",
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       "    <tr>\n",
       "      <th>5 2 1 0.1</th>\n",
       "      <td>27.721288</td>\n",
       "      <td>28.544636</td>\n",
       "      <td>92.2</td>\n",
       "      <td>14.023319</td>\n",
       "      <td>14.315376</td>\n",
       "      <td>14.043023</td>\n",
       "      <td>30.396654</td>\n",
       "      <td>27.617541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 1 0.2</th>\n",
       "      <td>28.697322</td>\n",
       "      <td>29.819129</td>\n",
       "      <td>83.6</td>\n",
       "      <td>15.069580</td>\n",
       "      <td>15.247607</td>\n",
       "      <td>14.997541</td>\n",
       "      <td>30.969350</td>\n",
       "      <td>28.346362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 1 0.5</th>\n",
       "      <td>27.957281</td>\n",
       "      <td>28.445931</td>\n",
       "      <td>102.0</td>\n",
       "      <td>13.849456</td>\n",
       "      <td>14.144338</td>\n",
       "      <td>13.950987</td>\n",
       "      <td>30.747083</td>\n",
       "      <td>27.635473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 1 1</th>\n",
       "      <td>29.037135</td>\n",
       "      <td>29.161930</td>\n",
       "      <td>44.0</td>\n",
       "      <td>18.041484</td>\n",
       "      <td>18.126605</td>\n",
       "      <td>18.193799</td>\n",
       "      <td>30.470288</td>\n",
       "      <td>28.290763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 1 2</th>\n",
       "      <td>27.472204</td>\n",
       "      <td>28.005153</td>\n",
       "      <td>99.6</td>\n",
       "      <td>13.627126</td>\n",
       "      <td>13.696582</td>\n",
       "      <td>13.959556</td>\n",
       "      <td>30.461752</td>\n",
       "      <td>27.350142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 2 0.0</th>\n",
       "      <td>24.509603</td>\n",
       "      <td>25.864247</td>\n",
       "      <td>125.4</td>\n",
       "      <td>9.388352</td>\n",
       "      <td>9.576035</td>\n",
       "      <td>9.497299</td>\n",
       "      <td>28.811118</td>\n",
       "      <td>25.497439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 2 0.05</th>\n",
       "      <td>24.550999</td>\n",
       "      <td>25.904529</td>\n",
       "      <td>122.6</td>\n",
       "      <td>9.548557</td>\n",
       "      <td>9.752170</td>\n",
       "      <td>9.722638</td>\n",
       "      <td>28.752273</td>\n",
       "      <td>25.551779</td>\n",
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       "    <tr>\n",
       "      <th>5 2 2 0.1</th>\n",
       "      <td>25.100820</td>\n",
       "      <td>26.199236</td>\n",
       "      <td>143.6</td>\n",
       "      <td>9.025257</td>\n",
       "      <td>9.243180</td>\n",
       "      <td>9.193870</td>\n",
       "      <td>29.738632</td>\n",
       "      <td>25.930200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 2 0.2</th>\n",
       "      <td>24.901567</td>\n",
       "      <td>26.468065</td>\n",
       "      <td>110.4</td>\n",
       "      <td>10.348714</td>\n",
       "      <td>10.497557</td>\n",
       "      <td>10.500740</td>\n",
       "      <td>28.607691</td>\n",
       "      <td>25.690534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 2 0.5</th>\n",
       "      <td>24.739244</td>\n",
       "      <td>25.882623</td>\n",
       "      <td>143.8</td>\n",
       "      <td>8.853422</td>\n",
       "      <td>9.016999</td>\n",
       "      <td>9.167431</td>\n",
       "      <td>29.189028</td>\n",
       "      <td>25.736039</td>\n",
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       "    <tr>\n",
       "      <th>5 2 2 1</th>\n",
       "      <td>25.221399</td>\n",
       "      <td>26.421807</td>\n",
       "      <td>86.6</td>\n",
       "      <td>11.950741</td>\n",
       "      <td>11.955553</td>\n",
       "      <td>12.014152</td>\n",
       "      <td>28.179804</td>\n",
       "      <td>25.646424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 2 2</th>\n",
       "      <td>24.893545</td>\n",
       "      <td>26.100856</td>\n",
       "      <td>88.6</td>\n",
       "      <td>11.581490</td>\n",
       "      <td>11.738276</td>\n",
       "      <td>11.815354</td>\n",
       "      <td>27.983293</td>\n",
       "      <td>25.394314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 3 0.0</th>\n",
       "      <td>24.640797</td>\n",
       "      <td>25.470330</td>\n",
       "      <td>96.8</td>\n",
       "      <td>11.518954</td>\n",
       "      <td>11.738035</td>\n",
       "      <td>11.796274</td>\n",
       "      <td>28.132580</td>\n",
       "      <td>25.158605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 3 0.05</th>\n",
       "      <td>24.728433</td>\n",
       "      <td>25.565192</td>\n",
       "      <td>97.2</td>\n",
       "      <td>11.566502</td>\n",
       "      <td>11.791009</td>\n",
       "      <td>11.829385</td>\n",
       "      <td>28.210759</td>\n",
       "      <td>25.220957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 3 0.1</th>\n",
       "      <td>24.002690</td>\n",
       "      <td>24.877875</td>\n",
       "      <td>89.2</td>\n",
       "      <td>11.361427</td>\n",
       "      <td>11.650790</td>\n",
       "      <td>11.662710</td>\n",
       "      <td>27.523185</td>\n",
       "      <td>24.719890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 3 0.2</th>\n",
       "      <td>23.613857</td>\n",
       "      <td>24.441320</td>\n",
       "      <td>87.2</td>\n",
       "      <td>11.366361</td>\n",
       "      <td>11.585687</td>\n",
       "      <td>11.585582</td>\n",
       "      <td>26.789134</td>\n",
       "      <td>24.232449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 3 0.5</th>\n",
       "      <td>23.462767</td>\n",
       "      <td>24.585143</td>\n",
       "      <td>60.4</td>\n",
       "      <td>12.330890</td>\n",
       "      <td>12.565544</td>\n",
       "      <td>12.482891</td>\n",
       "      <td>26.126857</td>\n",
       "      <td>24.036215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 3 1</th>\n",
       "      <td>23.453451</td>\n",
       "      <td>24.559207</td>\n",
       "      <td>60.0</td>\n",
       "      <td>12.347486</td>\n",
       "      <td>12.590631</td>\n",
       "      <td>12.492693</td>\n",
       "      <td>26.109303</td>\n",
       "      <td>24.018660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 2 3 2</th>\n",
       "      <td>24.376698</td>\n",
       "      <td>25.221981</td>\n",
       "      <td>68.8</td>\n",
       "      <td>12.654862</td>\n",
       "      <td>12.845725</td>\n",
       "      <td>12.875193</td>\n",
       "      <td>27.145424</td>\n",
       "      <td>24.838481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 1 0.0</th>\n",
       "      <td>29.454240</td>\n",
       "      <td>29.519625</td>\n",
       "      <td>25.6</td>\n",
       "      <td>20.556684</td>\n",
       "      <td>20.759188</td>\n",
       "      <td>20.790138</td>\n",
       "      <td>30.741924</td>\n",
       "      <td>28.917929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 1 0.05</th>\n",
       "      <td>29.455009</td>\n",
       "      <td>29.520369</td>\n",
       "      <td>25.6</td>\n",
       "      <td>20.557335</td>\n",
       "      <td>20.758650</td>\n",
       "      <td>20.791408</td>\n",
       "      <td>30.742576</td>\n",
       "      <td>28.918580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 1 0.1</th>\n",
       "      <td>29.454699</td>\n",
       "      <td>29.520054</td>\n",
       "      <td>25.6</td>\n",
       "      <td>20.557053</td>\n",
       "      <td>20.758222</td>\n",
       "      <td>20.791061</td>\n",
       "      <td>30.742293</td>\n",
       "      <td>28.918298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 1 0.2</th>\n",
       "      <td>29.612618</td>\n",
       "      <td>29.915509</td>\n",
       "      <td>32.8</td>\n",
       "      <td>20.131537</td>\n",
       "      <td>20.366161</td>\n",
       "      <td>20.405086</td>\n",
       "      <td>30.986696</td>\n",
       "      <td>29.162700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 1 0.5</th>\n",
       "      <td>29.191724</td>\n",
       "      <td>29.487460</td>\n",
       "      <td>32.8</td>\n",
       "      <td>19.749167</td>\n",
       "      <td>20.078234</td>\n",
       "      <td>19.945011</td>\n",
       "      <td>30.604326</td>\n",
       "      <td>28.780331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 1 1</th>\n",
       "      <td>28.957203</td>\n",
       "      <td>29.262403</td>\n",
       "      <td>33.0</td>\n",
       "      <td>19.526006</td>\n",
       "      <td>19.852372</td>\n",
       "      <td>19.665796</td>\n",
       "      <td>30.398729</td>\n",
       "      <td>28.574733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 1 2</th>\n",
       "      <td>29.109684</td>\n",
       "      <td>29.523065</td>\n",
       "      <td>34.8</td>\n",
       "      <td>19.576314</td>\n",
       "      <td>19.879980</td>\n",
       "      <td>19.754827</td>\n",
       "      <td>30.605138</td>\n",
       "      <td>28.781142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 2 0.0</th>\n",
       "      <td>24.509603</td>\n",
       "      <td>25.864247</td>\n",
       "      <td>125.4</td>\n",
       "      <td>9.388352</td>\n",
       "      <td>9.576035</td>\n",
       "      <td>9.497299</td>\n",
       "      <td>28.811118</td>\n",
       "      <td>25.497439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 2 0.05</th>\n",
       "      <td>24.550999</td>\n",
       "      <td>25.904529</td>\n",
       "      <td>122.6</td>\n",
       "      <td>9.548557</td>\n",
       "      <td>9.752170</td>\n",
       "      <td>9.722638</td>\n",
       "      <td>28.752273</td>\n",
       "      <td>25.551779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 2 0.1</th>\n",
       "      <td>25.100820</td>\n",
       "      <td>26.199236</td>\n",
       "      <td>143.6</td>\n",
       "      <td>9.025257</td>\n",
       "      <td>9.243180</td>\n",
       "      <td>9.193870</td>\n",
       "      <td>29.738632</td>\n",
       "      <td>25.930200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 2 0.2</th>\n",
       "      <td>24.901567</td>\n",
       "      <td>26.468065</td>\n",
       "      <td>110.4</td>\n",
       "      <td>10.348714</td>\n",
       "      <td>10.497557</td>\n",
       "      <td>10.500740</td>\n",
       "      <td>28.607691</td>\n",
       "      <td>25.690534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 2 0.5</th>\n",
       "      <td>24.739244</td>\n",
       "      <td>25.882623</td>\n",
       "      <td>143.8</td>\n",
       "      <td>8.853422</td>\n",
       "      <td>9.016999</td>\n",
       "      <td>9.167431</td>\n",
       "      <td>29.189028</td>\n",
       "      <td>25.736039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 2 1</th>\n",
       "      <td>25.221399</td>\n",
       "      <td>26.421807</td>\n",
       "      <td>86.6</td>\n",
       "      <td>11.950741</td>\n",
       "      <td>11.955553</td>\n",
       "      <td>12.014152</td>\n",
       "      <td>28.179804</td>\n",
       "      <td>25.646424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 2 2</th>\n",
       "      <td>24.893545</td>\n",
       "      <td>26.100856</td>\n",
       "      <td>88.6</td>\n",
       "      <td>11.581490</td>\n",
       "      <td>11.738276</td>\n",
       "      <td>11.815354</td>\n",
       "      <td>27.983293</td>\n",
       "      <td>25.394314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 3 0.0</th>\n",
       "      <td>24.640797</td>\n",
       "      <td>25.470330</td>\n",
       "      <td>96.8</td>\n",
       "      <td>11.518954</td>\n",
       "      <td>11.738035</td>\n",
       "      <td>11.796274</td>\n",
       "      <td>28.132580</td>\n",
       "      <td>25.158605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 3 0.05</th>\n",
       "      <td>24.728433</td>\n",
       "      <td>25.565192</td>\n",
       "      <td>97.2</td>\n",
       "      <td>11.566502</td>\n",
       "      <td>11.791009</td>\n",
       "      <td>11.829385</td>\n",
       "      <td>28.210759</td>\n",
       "      <td>25.220957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 3 0.1</th>\n",
       "      <td>24.002690</td>\n",
       "      <td>24.877875</td>\n",
       "      <td>89.2</td>\n",
       "      <td>11.361427</td>\n",
       "      <td>11.650790</td>\n",
       "      <td>11.662710</td>\n",
       "      <td>27.523185</td>\n",
       "      <td>24.719890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 3 0.2</th>\n",
       "      <td>23.613857</td>\n",
       "      <td>24.441320</td>\n",
       "      <td>87.2</td>\n",
       "      <td>11.366361</td>\n",
       "      <td>11.585687</td>\n",
       "      <td>11.585582</td>\n",
       "      <td>26.789134</td>\n",
       "      <td>24.232449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 3 0.5</th>\n",
       "      <td>23.462767</td>\n",
       "      <td>24.585143</td>\n",
       "      <td>60.4</td>\n",
       "      <td>12.330890</td>\n",
       "      <td>12.565544</td>\n",
       "      <td>12.482891</td>\n",
       "      <td>26.126857</td>\n",
       "      <td>24.036215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 3 1</th>\n",
       "      <td>23.453451</td>\n",
       "      <td>24.559207</td>\n",
       "      <td>60.0</td>\n",
       "      <td>12.347486</td>\n",
       "      <td>12.590631</td>\n",
       "      <td>12.492693</td>\n",
       "      <td>26.109303</td>\n",
       "      <td>24.018660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 3 3 2</th>\n",
       "      <td>24.376698</td>\n",
       "      <td>25.221981</td>\n",
       "      <td>68.8</td>\n",
       "      <td>12.654862</td>\n",
       "      <td>12.845725</td>\n",
       "      <td>12.875193</td>\n",
       "      <td>27.145424</td>\n",
       "      <td>24.838481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 1 0.0</th>\n",
       "      <td>27.103869</td>\n",
       "      <td>27.617486</td>\n",
       "      <td>46.2</td>\n",
       "      <td>16.670506</td>\n",
       "      <td>16.829754</td>\n",
       "      <td>16.845969</td>\n",
       "      <td>28.985931</td>\n",
       "      <td>26.951957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 1 0.05</th>\n",
       "      <td>27.126658</td>\n",
       "      <td>27.596559</td>\n",
       "      <td>40.2</td>\n",
       "      <td>17.352624</td>\n",
       "      <td>17.508951</td>\n",
       "      <td>17.486833</td>\n",
       "      <td>28.970321</td>\n",
       "      <td>27.025229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 1 0.1</th>\n",
       "      <td>27.126093</td>\n",
       "      <td>27.595996</td>\n",
       "      <td>40.2</td>\n",
       "      <td>17.352130</td>\n",
       "      <td>17.508586</td>\n",
       "      <td>17.486252</td>\n",
       "      <td>28.969828</td>\n",
       "      <td>27.024736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 1 0.2</th>\n",
       "      <td>27.125130</td>\n",
       "      <td>27.595016</td>\n",
       "      <td>40.2</td>\n",
       "      <td>17.351254</td>\n",
       "      <td>17.507338</td>\n",
       "      <td>17.485003</td>\n",
       "      <td>28.968952</td>\n",
       "      <td>27.023860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 1 0.5</th>\n",
       "      <td>27.880118</td>\n",
       "      <td>27.936578</td>\n",
       "      <td>61.6</td>\n",
       "      <td>17.182268</td>\n",
       "      <td>17.323052</td>\n",
       "      <td>17.382390</td>\n",
       "      <td>30.020106</td>\n",
       "      <td>27.641185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 1 1</th>\n",
       "      <td>27.830656</td>\n",
       "      <td>28.214701</td>\n",
       "      <td>42.6</td>\n",
       "      <td>17.843828</td>\n",
       "      <td>17.948617</td>\n",
       "      <td>18.050599</td>\n",
       "      <td>29.657634</td>\n",
       "      <td>27.611212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 1 2</th>\n",
       "      <td>27.315972</td>\n",
       "      <td>27.560347</td>\n",
       "      <td>34.0</td>\n",
       "      <td>17.859937</td>\n",
       "      <td>18.020946</td>\n",
       "      <td>18.022037</td>\n",
       "      <td>28.947060</td>\n",
       "      <td>27.034182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 2 0.0</th>\n",
       "      <td>24.509603</td>\n",
       "      <td>25.864247</td>\n",
       "      <td>125.4</td>\n",
       "      <td>9.388352</td>\n",
       "      <td>9.576035</td>\n",
       "      <td>9.497299</td>\n",
       "      <td>28.811118</td>\n",
       "      <td>25.497439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 2 0.05</th>\n",
       "      <td>24.550999</td>\n",
       "      <td>25.904529</td>\n",
       "      <td>122.6</td>\n",
       "      <td>9.548557</td>\n",
       "      <td>9.752170</td>\n",
       "      <td>9.722638</td>\n",
       "      <td>28.752273</td>\n",
       "      <td>25.551779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 2 0.1</th>\n",
       "      <td>25.100820</td>\n",
       "      <td>26.199236</td>\n",
       "      <td>143.6</td>\n",
       "      <td>9.025257</td>\n",
       "      <td>9.243180</td>\n",
       "      <td>9.193870</td>\n",
       "      <td>29.738632</td>\n",
       "      <td>25.930200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 2 0.2</th>\n",
       "      <td>24.901567</td>\n",
       "      <td>26.468065</td>\n",
       "      <td>110.4</td>\n",
       "      <td>10.348714</td>\n",
       "      <td>10.497557</td>\n",
       "      <td>10.500740</td>\n",
       "      <td>28.607691</td>\n",
       "      <td>25.690534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 2 0.5</th>\n",
       "      <td>24.739244</td>\n",
       "      <td>25.882623</td>\n",
       "      <td>143.8</td>\n",
       "      <td>8.853422</td>\n",
       "      <td>9.016999</td>\n",
       "      <td>9.167431</td>\n",
       "      <td>29.189028</td>\n",
       "      <td>25.736039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 2 1</th>\n",
       "      <td>25.221399</td>\n",
       "      <td>26.421807</td>\n",
       "      <td>86.6</td>\n",
       "      <td>11.950741</td>\n",
       "      <td>11.955553</td>\n",
       "      <td>12.014152</td>\n",
       "      <td>28.179804</td>\n",
       "      <td>25.646424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 2 2</th>\n",
       "      <td>24.893545</td>\n",
       "      <td>26.100856</td>\n",
       "      <td>88.6</td>\n",
       "      <td>11.581490</td>\n",
       "      <td>11.738276</td>\n",
       "      <td>11.815354</td>\n",
       "      <td>27.983293</td>\n",
       "      <td>25.394314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 3 0.0</th>\n",
       "      <td>24.640797</td>\n",
       "      <td>25.470330</td>\n",
       "      <td>96.8</td>\n",
       "      <td>11.518954</td>\n",
       "      <td>11.738035</td>\n",
       "      <td>11.796274</td>\n",
       "      <td>28.132580</td>\n",
       "      <td>25.158605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 3 0.05</th>\n",
       "      <td>24.728433</td>\n",
       "      <td>25.565192</td>\n",
       "      <td>97.2</td>\n",
       "      <td>11.566502</td>\n",
       "      <td>11.791009</td>\n",
       "      <td>11.829385</td>\n",
       "      <td>28.210759</td>\n",
       "      <td>25.220957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 3 0.1</th>\n",
       "      <td>24.002690</td>\n",
       "      <td>24.877875</td>\n",
       "      <td>89.2</td>\n",
       "      <td>11.361427</td>\n",
       "      <td>11.650790</td>\n",
       "      <td>11.662710</td>\n",
       "      <td>27.523185</td>\n",
       "      <td>24.719890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 3 0.2</th>\n",
       "      <td>23.613857</td>\n",
       "      <td>24.441320</td>\n",
       "      <td>87.2</td>\n",
       "      <td>11.366361</td>\n",
       "      <td>11.585687</td>\n",
       "      <td>11.585582</td>\n",
       "      <td>26.789134</td>\n",
       "      <td>24.232449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 3 0.5</th>\n",
       "      <td>23.462767</td>\n",
       "      <td>24.585143</td>\n",
       "      <td>60.4</td>\n",
       "      <td>12.330890</td>\n",
       "      <td>12.565544</td>\n",
       "      <td>12.482891</td>\n",
       "      <td>26.126857</td>\n",
       "      <td>24.036215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 3 1</th>\n",
       "      <td>23.453451</td>\n",
       "      <td>24.559207</td>\n",
       "      <td>60.0</td>\n",
       "      <td>12.347486</td>\n",
       "      <td>12.590631</td>\n",
       "      <td>12.492693</td>\n",
       "      <td>26.109303</td>\n",
       "      <td>24.018660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5 4 3 2</th>\n",
       "      <td>24.376698</td>\n",
       "      <td>25.221981</td>\n",
       "      <td>68.8</td>\n",
       "      <td>12.654862</td>\n",
       "      <td>12.845725</td>\n",
       "      <td>12.875193</td>\n",
       "      <td>27.145424</td>\n",
       "      <td>24.838481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 1 0.0</th>\n",
       "      <td>28.950399</td>\n",
       "      <td>29.335181</td>\n",
       "      <td>52.6</td>\n",
       "      <td>17.272253</td>\n",
       "      <td>17.472361</td>\n",
       "      <td>17.370635</td>\n",
       "      <td>30.500252</td>\n",
       "      <td>28.320727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 1 0.05</th>\n",
       "      <td>28.152034</td>\n",
       "      <td>28.376347</td>\n",
       "      <td>46.6</td>\n",
       "      <td>17.066069</td>\n",
       "      <td>17.282653</td>\n",
       "      <td>17.204039</td>\n",
       "      <td>29.732705</td>\n",
       "      <td>27.553180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 1 0.1</th>\n",
       "      <td>28.168411</td>\n",
       "      <td>28.392329</td>\n",
       "      <td>46.6</td>\n",
       "      <td>17.080169</td>\n",
       "      <td>17.295019</td>\n",
       "      <td>17.216866</td>\n",
       "      <td>29.746805</td>\n",
       "      <td>27.567280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 1 0.2</th>\n",
       "      <td>28.648484</td>\n",
       "      <td>28.999443</td>\n",
       "      <td>83.8</td>\n",
       "      <td>15.500173</td>\n",
       "      <td>15.763193</td>\n",
       "      <td>15.553725</td>\n",
       "      <td>30.692365</td>\n",
       "      <td>28.046797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 1 0.5</th>\n",
       "      <td>28.461605</td>\n",
       "      <td>29.102539</td>\n",
       "      <td>58.8</td>\n",
       "      <td>16.419778</td>\n",
       "      <td>16.685802</td>\n",
       "      <td>16.733153</td>\n",
       "      <td>30.203758</td>\n",
       "      <td>28.024233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 1 1</th>\n",
       "      <td>28.686387</td>\n",
       "      <td>29.607692</td>\n",
       "      <td>72.4</td>\n",
       "      <td>15.888103</td>\n",
       "      <td>16.282005</td>\n",
       "      <td>15.949159</td>\n",
       "      <td>30.804123</td>\n",
       "      <td>28.324904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 1 2</th>\n",
       "      <td>27.674196</td>\n",
       "      <td>28.403281</td>\n",
       "      <td>71.6</td>\n",
       "      <td>15.107312</td>\n",
       "      <td>15.418566</td>\n",
       "      <td>15.275471</td>\n",
       "      <td>29.955553</td>\n",
       "      <td>27.494531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 2 0.0</th>\n",
       "      <td>26.059995</td>\n",
       "      <td>27.550413</td>\n",
       "      <td>100.2</td>\n",
       "      <td>12.283261</td>\n",
       "      <td>12.230745</td>\n",
       "      <td>12.577014</td>\n",
       "      <td>29.265740</td>\n",
       "      <td>26.663792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 2 0.05</th>\n",
       "      <td>25.903502</td>\n",
       "      <td>27.183695</td>\n",
       "      <td>91.2</td>\n",
       "      <td>12.638985</td>\n",
       "      <td>12.685849</td>\n",
       "      <td>12.874296</td>\n",
       "      <td>28.956971</td>\n",
       "      <td>26.378685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 2 0.1</th>\n",
       "      <td>26.244482</td>\n",
       "      <td>27.564515</td>\n",
       "      <td>92.2</td>\n",
       "      <td>12.847659</td>\n",
       "      <td>12.888097</td>\n",
       "      <td>13.097399</td>\n",
       "      <td>29.220424</td>\n",
       "      <td>26.625018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 2 0.2</th>\n",
       "      <td>25.914952</td>\n",
       "      <td>27.316656</td>\n",
       "      <td>85.6</td>\n",
       "      <td>12.867023</td>\n",
       "      <td>12.901560</td>\n",
       "      <td>13.147951</td>\n",
       "      <td>28.828266</td>\n",
       "      <td>26.401238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 2 0.5</th>\n",
       "      <td>26.507223</td>\n",
       "      <td>27.974688</td>\n",
       "      <td>88.0</td>\n",
       "      <td>13.196361</td>\n",
       "      <td>13.284520</td>\n",
       "      <td>13.511970</td>\n",
       "      <td>29.359235</td>\n",
       "      <td>26.892286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 2 1</th>\n",
       "      <td>26.025565</td>\n",
       "      <td>27.237926</td>\n",
       "      <td>82.8</td>\n",
       "      <td>13.121573</td>\n",
       "      <td>13.282392</td>\n",
       "      <td>13.398284</td>\n",
       "      <td>28.837450</td>\n",
       "      <td>26.353180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 2 2</th>\n",
       "      <td>26.584472</td>\n",
       "      <td>27.487119</td>\n",
       "      <td>92.4</td>\n",
       "      <td>13.180405</td>\n",
       "      <td>13.229448</td>\n",
       "      <td>13.217505</td>\n",
       "      <td>29.610395</td>\n",
       "      <td>26.772213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 3 0.0</th>\n",
       "      <td>23.403339</td>\n",
       "      <td>24.413853</td>\n",
       "      <td>72.8</td>\n",
       "      <td>11.658473</td>\n",
       "      <td>11.826613</td>\n",
       "      <td>12.003534</td>\n",
       "      <td>26.488509</td>\n",
       "      <td>24.149707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 3 0.05</th>\n",
       "      <td>23.403872</td>\n",
       "      <td>24.414051</td>\n",
       "      <td>72.8</td>\n",
       "      <td>11.658892</td>\n",
       "      <td>11.827333</td>\n",
       "      <td>12.005349</td>\n",
       "      <td>26.488929</td>\n",
       "      <td>24.150127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 3 0.1</th>\n",
       "      <td>24.122719</td>\n",
       "      <td>25.124202</td>\n",
       "      <td>75.8</td>\n",
       "      <td>12.104134</td>\n",
       "      <td>12.332254</td>\n",
       "      <td>12.438467</td>\n",
       "      <td>27.127998</td>\n",
       "      <td>24.571312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 3 0.2</th>\n",
       "      <td>23.580819</td>\n",
       "      <td>24.455800</td>\n",
       "      <td>77.0</td>\n",
       "      <td>11.685213</td>\n",
       "      <td>11.897092</td>\n",
       "      <td>12.004878</td>\n",
       "      <td>26.702259</td>\n",
       "      <td>24.145574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 3 0.5</th>\n",
       "      <td>23.801237</td>\n",
       "      <td>24.794985</td>\n",
       "      <td>67.2</td>\n",
       "      <td>12.252310</td>\n",
       "      <td>12.440687</td>\n",
       "      <td>12.637441</td>\n",
       "      <td>26.612804</td>\n",
       "      <td>24.355339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 3 1</th>\n",
       "      <td>23.898266</td>\n",
       "      <td>24.827508</td>\n",
       "      <td>70.0</td>\n",
       "      <td>12.209920</td>\n",
       "      <td>12.447829</td>\n",
       "      <td>12.583422</td>\n",
       "      <td>26.792351</td>\n",
       "      <td>24.420210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 2 3 2</th>\n",
       "      <td>23.825510</td>\n",
       "      <td>24.703784</td>\n",
       "      <td>73.8</td>\n",
       "      <td>11.952316</td>\n",
       "      <td>12.319606</td>\n",
       "      <td>12.280509</td>\n",
       "      <td>26.860282</td>\n",
       "      <td>24.444470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 1 0.0</th>\n",
       "      <td>31.272756</td>\n",
       "      <td>31.318149</td>\n",
       "      <td>28.8</td>\n",
       "      <td>21.780305</td>\n",
       "      <td>21.697652</td>\n",
       "      <td>21.870918</td>\n",
       "      <td>32.390580</td>\n",
       "      <td>30.477702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 1 0.05</th>\n",
       "      <td>31.299913</td>\n",
       "      <td>31.344976</td>\n",
       "      <td>28.8</td>\n",
       "      <td>21.804341</td>\n",
       "      <td>21.705267</td>\n",
       "      <td>21.882545</td>\n",
       "      <td>32.414617</td>\n",
       "      <td>30.501739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 1 0.1</th>\n",
       "      <td>31.149865</td>\n",
       "      <td>31.204485</td>\n",
       "      <td>29.2</td>\n",
       "      <td>21.634048</td>\n",
       "      <td>21.568076</td>\n",
       "      <td>21.674572</td>\n",
       "      <td>32.284297</td>\n",
       "      <td>30.371419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 1 0.2</th>\n",
       "      <td>31.126337</td>\n",
       "      <td>31.180832</td>\n",
       "      <td>29.2</td>\n",
       "      <td>21.613434</td>\n",
       "      <td>21.541599</td>\n",
       "      <td>21.655091</td>\n",
       "      <td>32.263683</td>\n",
       "      <td>30.350805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 1 0.5</th>\n",
       "      <td>31.245503</td>\n",
       "      <td>31.302449</td>\n",
       "      <td>29.2</td>\n",
       "      <td>21.726782</td>\n",
       "      <td>21.840941</td>\n",
       "      <td>21.749442</td>\n",
       "      <td>32.377031</td>\n",
       "      <td>30.464153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 1 1</th>\n",
       "      <td>30.108443</td>\n",
       "      <td>30.159304</td>\n",
       "      <td>25.2</td>\n",
       "      <td>21.186875</td>\n",
       "      <td>21.219307</td>\n",
       "      <td>21.316309</td>\n",
       "      <td>31.332141</td>\n",
       "      <td>29.508146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 1 2</th>\n",
       "      <td>29.003366</td>\n",
       "      <td>28.997995</td>\n",
       "      <td>28.6</td>\n",
       "      <td>19.720062</td>\n",
       "      <td>19.687161</td>\n",
       "      <td>19.927511</td>\n",
       "      <td>30.310207</td>\n",
       "      <td>28.397329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 2 0.0</th>\n",
       "      <td>26.059995</td>\n",
       "      <td>27.550413</td>\n",
       "      <td>100.2</td>\n",
       "      <td>12.283261</td>\n",
       "      <td>12.230745</td>\n",
       "      <td>12.577014</td>\n",
       "      <td>29.265740</td>\n",
       "      <td>26.663792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 2 0.05</th>\n",
       "      <td>25.903502</td>\n",
       "      <td>27.183695</td>\n",
       "      <td>91.2</td>\n",
       "      <td>12.638985</td>\n",
       "      <td>12.685849</td>\n",
       "      <td>12.874296</td>\n",
       "      <td>28.956971</td>\n",
       "      <td>26.378685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 2 0.1</th>\n",
       "      <td>26.244482</td>\n",
       "      <td>27.564515</td>\n",
       "      <td>92.2</td>\n",
       "      <td>12.847659</td>\n",
       "      <td>12.888097</td>\n",
       "      <td>13.097399</td>\n",
       "      <td>29.220424</td>\n",
       "      <td>26.625018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 2 0.2</th>\n",
       "      <td>25.914952</td>\n",
       "      <td>27.316656</td>\n",
       "      <td>85.6</td>\n",
       "      <td>12.867023</td>\n",
       "      <td>12.901560</td>\n",
       "      <td>13.147951</td>\n",
       "      <td>28.828266</td>\n",
       "      <td>26.401238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 2 0.5</th>\n",
       "      <td>26.507223</td>\n",
       "      <td>27.974688</td>\n",
       "      <td>88.0</td>\n",
       "      <td>13.196361</td>\n",
       "      <td>13.284520</td>\n",
       "      <td>13.511970</td>\n",
       "      <td>29.359235</td>\n",
       "      <td>26.892286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 2 1</th>\n",
       "      <td>26.025565</td>\n",
       "      <td>27.237926</td>\n",
       "      <td>82.8</td>\n",
       "      <td>13.121573</td>\n",
       "      <td>13.282392</td>\n",
       "      <td>13.398284</td>\n",
       "      <td>28.837450</td>\n",
       "      <td>26.353180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 2 2</th>\n",
       "      <td>26.584472</td>\n",
       "      <td>27.487119</td>\n",
       "      <td>92.4</td>\n",
       "      <td>13.180405</td>\n",
       "      <td>13.229448</td>\n",
       "      <td>13.217505</td>\n",
       "      <td>29.610395</td>\n",
       "      <td>26.772213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 3 0.0</th>\n",
       "      <td>23.403339</td>\n",
       "      <td>24.413853</td>\n",
       "      <td>72.8</td>\n",
       "      <td>11.658473</td>\n",
       "      <td>11.826613</td>\n",
       "      <td>12.003534</td>\n",
       "      <td>26.488509</td>\n",
       "      <td>24.149707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 3 0.05</th>\n",
       "      <td>23.403872</td>\n",
       "      <td>24.414051</td>\n",
       "      <td>72.8</td>\n",
       "      <td>11.658892</td>\n",
       "      <td>11.827333</td>\n",
       "      <td>12.005349</td>\n",
       "      <td>26.488929</td>\n",
       "      <td>24.150127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 3 0.1</th>\n",
       "      <td>24.122719</td>\n",
       "      <td>25.124202</td>\n",
       "      <td>75.8</td>\n",
       "      <td>12.104134</td>\n",
       "      <td>12.332254</td>\n",
       "      <td>12.438467</td>\n",
       "      <td>27.127998</td>\n",
       "      <td>24.571312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 3 0.2</th>\n",
       "      <td>23.580819</td>\n",
       "      <td>24.455800</td>\n",
       "      <td>77.0</td>\n",
       "      <td>11.685213</td>\n",
       "      <td>11.897092</td>\n",
       "      <td>12.004878</td>\n",
       "      <td>26.702259</td>\n",
       "      <td>24.145574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 3 0.5</th>\n",
       "      <td>23.801237</td>\n",
       "      <td>24.794985</td>\n",
       "      <td>67.2</td>\n",
       "      <td>12.252310</td>\n",
       "      <td>12.440687</td>\n",
       "      <td>12.637441</td>\n",
       "      <td>26.612804</td>\n",
       "      <td>24.355339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 3 1</th>\n",
       "      <td>23.898266</td>\n",
       "      <td>24.827508</td>\n",
       "      <td>70.0</td>\n",
       "      <td>12.209920</td>\n",
       "      <td>12.447829</td>\n",
       "      <td>12.583422</td>\n",
       "      <td>26.792351</td>\n",
       "      <td>24.420210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 3 3 2</th>\n",
       "      <td>23.825510</td>\n",
       "      <td>24.703784</td>\n",
       "      <td>73.8</td>\n",
       "      <td>11.952316</td>\n",
       "      <td>12.319606</td>\n",
       "      <td>12.280509</td>\n",
       "      <td>26.860282</td>\n",
       "      <td>24.444470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 1 0.0</th>\n",
       "      <td>26.775542</td>\n",
       "      <td>27.428841</td>\n",
       "      <td>53.0</td>\n",
       "      <td>15.927359</td>\n",
       "      <td>16.059246</td>\n",
       "      <td>16.037344</td>\n",
       "      <td>28.811743</td>\n",
       "      <td>26.560977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 1 0.05</th>\n",
       "      <td>26.639708</td>\n",
       "      <td>26.802218</td>\n",
       "      <td>31.6</td>\n",
       "      <td>17.446736</td>\n",
       "      <td>17.530784</td>\n",
       "      <td>17.644897</td>\n",
       "      <td>28.316779</td>\n",
       "      <td>26.403901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 1 0.1</th>\n",
       "      <td>26.639462</td>\n",
       "      <td>26.801968</td>\n",
       "      <td>31.6</td>\n",
       "      <td>17.446513</td>\n",
       "      <td>17.530556</td>\n",
       "      <td>17.644475</td>\n",
       "      <td>28.316556</td>\n",
       "      <td>26.403678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 1 0.2</th>\n",
       "      <td>26.649406</td>\n",
       "      <td>26.812212</td>\n",
       "      <td>31.6</td>\n",
       "      <td>17.455431</td>\n",
       "      <td>17.536157</td>\n",
       "      <td>17.648748</td>\n",
       "      <td>28.325473</td>\n",
       "      <td>26.412595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 1 0.5</th>\n",
       "      <td>27.437872</td>\n",
       "      <td>27.735080</td>\n",
       "      <td>36.0</td>\n",
       "      <td>17.814050</td>\n",
       "      <td>17.863830</td>\n",
       "      <td>18.026574</td>\n",
       "      <td>29.076612</td>\n",
       "      <td>27.163734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 1 1</th>\n",
       "      <td>27.356560</td>\n",
       "      <td>27.664689</td>\n",
       "      <td>36.0</td>\n",
       "      <td>17.751194</td>\n",
       "      <td>17.830489</td>\n",
       "      <td>17.987025</td>\n",
       "      <td>29.013756</td>\n",
       "      <td>27.100878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 1 2</th>\n",
       "      <td>26.553588</td>\n",
       "      <td>26.878973</td>\n",
       "      <td>43.8</td>\n",
       "      <td>16.657776</td>\n",
       "      <td>16.829766</td>\n",
       "      <td>16.867114</td>\n",
       "      <td>28.568013</td>\n",
       "      <td>26.471764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 2 0.0</th>\n",
       "      <td>26.059995</td>\n",
       "      <td>27.550413</td>\n",
       "      <td>100.2</td>\n",
       "      <td>12.283261</td>\n",
       "      <td>12.230745</td>\n",
       "      <td>12.577014</td>\n",
       "      <td>29.265740</td>\n",
       "      <td>26.663792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 2 0.05</th>\n",
       "      <td>25.903502</td>\n",
       "      <td>27.183695</td>\n",
       "      <td>91.2</td>\n",
       "      <td>12.638985</td>\n",
       "      <td>12.685849</td>\n",
       "      <td>12.874296</td>\n",
       "      <td>28.956971</td>\n",
       "      <td>26.378685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 2 0.1</th>\n",
       "      <td>26.244482</td>\n",
       "      <td>27.564515</td>\n",
       "      <td>92.2</td>\n",
       "      <td>12.847659</td>\n",
       "      <td>12.888097</td>\n",
       "      <td>13.097399</td>\n",
       "      <td>29.220424</td>\n",
       "      <td>26.625018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 2 0.2</th>\n",
       "      <td>25.914952</td>\n",
       "      <td>27.316656</td>\n",
       "      <td>85.6</td>\n",
       "      <td>12.867023</td>\n",
       "      <td>12.901560</td>\n",
       "      <td>13.147951</td>\n",
       "      <td>28.828266</td>\n",
       "      <td>26.401238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 2 0.5</th>\n",
       "      <td>26.507223</td>\n",
       "      <td>27.974688</td>\n",
       "      <td>88.0</td>\n",
       "      <td>13.196361</td>\n",
       "      <td>13.284520</td>\n",
       "      <td>13.511970</td>\n",
       "      <td>29.359235</td>\n",
       "      <td>26.892286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 2 1</th>\n",
       "      <td>26.025565</td>\n",
       "      <td>27.237926</td>\n",
       "      <td>82.8</td>\n",
       "      <td>13.121573</td>\n",
       "      <td>13.282392</td>\n",
       "      <td>13.398284</td>\n",
       "      <td>28.837450</td>\n",
       "      <td>26.353180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 2 2</th>\n",
       "      <td>26.584472</td>\n",
       "      <td>27.487119</td>\n",
       "      <td>92.4</td>\n",
       "      <td>13.180405</td>\n",
       "      <td>13.229448</td>\n",
       "      <td>13.217505</td>\n",
       "      <td>29.610395</td>\n",
       "      <td>26.772213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 3 0.0</th>\n",
       "      <td>23.403339</td>\n",
       "      <td>24.413853</td>\n",
       "      <td>72.8</td>\n",
       "      <td>11.658473</td>\n",
       "      <td>11.826613</td>\n",
       "      <td>12.003534</td>\n",
       "      <td>26.488509</td>\n",
       "      <td>24.149707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 3 0.05</th>\n",
       "      <td>23.403872</td>\n",
       "      <td>24.414051</td>\n",
       "      <td>72.8</td>\n",
       "      <td>11.658892</td>\n",
       "      <td>11.827333</td>\n",
       "      <td>12.005349</td>\n",
       "      <td>26.488929</td>\n",
       "      <td>24.150127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 3 0.1</th>\n",
       "      <td>24.122719</td>\n",
       "      <td>25.124202</td>\n",
       "      <td>75.8</td>\n",
       "      <td>12.104134</td>\n",
       "      <td>12.332254</td>\n",
       "      <td>12.438467</td>\n",
       "      <td>27.127998</td>\n",
       "      <td>24.571312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 3 0.2</th>\n",
       "      <td>23.580819</td>\n",
       "      <td>24.455800</td>\n",
       "      <td>77.0</td>\n",
       "      <td>11.685213</td>\n",
       "      <td>11.897092</td>\n",
       "      <td>12.004878</td>\n",
       "      <td>26.702259</td>\n",
       "      <td>24.145574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 3 0.5</th>\n",
       "      <td>23.801237</td>\n",
       "      <td>24.794985</td>\n",
       "      <td>67.2</td>\n",
       "      <td>12.252310</td>\n",
       "      <td>12.440687</td>\n",
       "      <td>12.637441</td>\n",
       "      <td>26.612804</td>\n",
       "      <td>24.355339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 3 1</th>\n",
       "      <td>23.898266</td>\n",
       "      <td>24.827508</td>\n",
       "      <td>70.0</td>\n",
       "      <td>12.209920</td>\n",
       "      <td>12.447829</td>\n",
       "      <td>12.583422</td>\n",
       "      <td>26.792351</td>\n",
       "      <td>24.420210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10 4 3 2</th>\n",
       "      <td>23.825510</td>\n",
       "      <td>24.703784</td>\n",
       "      <td>73.8</td>\n",
       "      <td>11.952316</td>\n",
       "      <td>12.319606</td>\n",
       "      <td>12.280509</td>\n",
       "      <td>26.860282</td>\n",
       "      <td>24.444470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              kl_bound  min_val_catoni_bound  compression_set_size  \\\n",
       "5 2 1 0.0    28.127865             29.090425                  70.2   \n",
       "5 2 1 0.05   28.712799             29.515797                  66.8   \n",
       "5 2 1 0.1    27.721288             28.544636                  92.2   \n",
       "5 2 1 0.2    28.697322             29.819129                  83.6   \n",
       "5 2 1 0.5    27.957281             28.445931                 102.0   \n",
       "5 2 1 1      29.037135             29.161930                  44.0   \n",
       "5 2 1 2      27.472204             28.005153                  99.6   \n",
       "5 2 2 0.0    24.509603             25.864247                 125.4   \n",
       "5 2 2 0.05   24.550999             25.904529                 122.6   \n",
       "5 2 2 0.1    25.100820             26.199236                 143.6   \n",
       "5 2 2 0.2    24.901567             26.468065                 110.4   \n",
       "5 2 2 0.5    24.739244             25.882623                 143.8   \n",
       "5 2 2 1      25.221399             26.421807                  86.6   \n",
       "5 2 2 2      24.893545             26.100856                  88.6   \n",
       "5 2 3 0.0    24.640797             25.470330                  96.8   \n",
       "5 2 3 0.05   24.728433             25.565192                  97.2   \n",
       "5 2 3 0.1    24.002690             24.877875                  89.2   \n",
       "5 2 3 0.2    23.613857             24.441320                  87.2   \n",
       "5 2 3 0.5    23.462767             24.585143                  60.4   \n",
       "5 2 3 1      23.453451             24.559207                  60.0   \n",
       "5 2 3 2      24.376698             25.221981                  68.8   \n",
       "5 3 1 0.0    29.454240             29.519625                  25.6   \n",
       "5 3 1 0.05   29.455009             29.520369                  25.6   \n",
       "5 3 1 0.1    29.454699             29.520054                  25.6   \n",
       "5 3 1 0.2    29.612618             29.915509                  32.8   \n",
       "5 3 1 0.5    29.191724             29.487460                  32.8   \n",
       "5 3 1 1      28.957203             29.262403                  33.0   \n",
       "5 3 1 2      29.109684             29.523065                  34.8   \n",
       "5 3 2 0.0    24.509603             25.864247                 125.4   \n",
       "5 3 2 0.05   24.550999             25.904529                 122.6   \n",
       "5 3 2 0.1    25.100820             26.199236                 143.6   \n",
       "5 3 2 0.2    24.901567             26.468065                 110.4   \n",
       "5 3 2 0.5    24.739244             25.882623                 143.8   \n",
       "5 3 2 1      25.221399             26.421807                  86.6   \n",
       "5 3 2 2      24.893545             26.100856                  88.6   \n",
       "5 3 3 0.0    24.640797             25.470330                  96.8   \n",
       "5 3 3 0.05   24.728433             25.565192                  97.2   \n",
       "5 3 3 0.1    24.002690             24.877875                  89.2   \n",
       "5 3 3 0.2    23.613857             24.441320                  87.2   \n",
       "5 3 3 0.5    23.462767             24.585143                  60.4   \n",
       "5 3 3 1      23.453451             24.559207                  60.0   \n",
       "5 3 3 2      24.376698             25.221981                  68.8   \n",
       "5 4 1 0.0    27.103869             27.617486                  46.2   \n",
       "5 4 1 0.05   27.126658             27.596559                  40.2   \n",
       "5 4 1 0.1    27.126093             27.595996                  40.2   \n",
       "5 4 1 0.2    27.125130             27.595016                  40.2   \n",
       "5 4 1 0.5    27.880118             27.936578                  61.6   \n",
       "5 4 1 1      27.830656             28.214701                  42.6   \n",
       "5 4 1 2      27.315972             27.560347                  34.0   \n",
       "5 4 2 0.0    24.509603             25.864247                 125.4   \n",
       "5 4 2 0.05   24.550999             25.904529                 122.6   \n",
       "5 4 2 0.1    25.100820             26.199236                 143.6   \n",
       "5 4 2 0.2    24.901567             26.468065                 110.4   \n",
       "5 4 2 0.5    24.739244             25.882623                 143.8   \n",
       "5 4 2 1      25.221399             26.421807                  86.6   \n",
       "5 4 2 2      24.893545             26.100856                  88.6   \n",
       "5 4 3 0.0    24.640797             25.470330                  96.8   \n",
       "5 4 3 0.05   24.728433             25.565192                  97.2   \n",
       "5 4 3 0.1    24.002690             24.877875                  89.2   \n",
       "5 4 3 0.2    23.613857             24.441320                  87.2   \n",
       "5 4 3 0.5    23.462767             24.585143                  60.4   \n",
       "5 4 3 1      23.453451             24.559207                  60.0   \n",
       "5 4 3 2      24.376698             25.221981                  68.8   \n",
       "10 2 1 0.0   28.950399             29.335181                  52.6   \n",
       "10 2 1 0.05  28.152034             28.376347                  46.6   \n",
       "10 2 1 0.1   28.168411             28.392329                  46.6   \n",
       "10 2 1 0.2   28.648484             28.999443                  83.8   \n",
       "10 2 1 0.5   28.461605             29.102539                  58.8   \n",
       "10 2 1 1     28.686387             29.607692                  72.4   \n",
       "10 2 1 2     27.674196             28.403281                  71.6   \n",
       "10 2 2 0.0   26.059995             27.550413                 100.2   \n",
       "10 2 2 0.05  25.903502             27.183695                  91.2   \n",
       "10 2 2 0.1   26.244482             27.564515                  92.2   \n",
       "10 2 2 0.2   25.914952             27.316656                  85.6   \n",
       "10 2 2 0.5   26.507223             27.974688                  88.0   \n",
       "10 2 2 1     26.025565             27.237926                  82.8   \n",
       "10 2 2 2     26.584472             27.487119                  92.4   \n",
       "10 2 3 0.0   23.403339             24.413853                  72.8   \n",
       "10 2 3 0.05  23.403872             24.414051                  72.8   \n",
       "10 2 3 0.1   24.122719             25.124202                  75.8   \n",
       "10 2 3 0.2   23.580819             24.455800                  77.0   \n",
       "10 2 3 0.5   23.801237             24.794985                  67.2   \n",
       "10 2 3 1     23.898266             24.827508                  70.0   \n",
       "10 2 3 2     23.825510             24.703784                  73.8   \n",
       "10 3 1 0.0   31.272756             31.318149                  28.8   \n",
       "10 3 1 0.05  31.299913             31.344976                  28.8   \n",
       "10 3 1 0.1   31.149865             31.204485                  29.2   \n",
       "10 3 1 0.2   31.126337             31.180832                  29.2   \n",
       "10 3 1 0.5   31.245503             31.302449                  29.2   \n",
       "10 3 1 1     30.108443             30.159304                  25.2   \n",
       "10 3 1 2     29.003366             28.997995                  28.6   \n",
       "10 3 2 0.0   26.059995             27.550413                 100.2   \n",
       "10 3 2 0.05  25.903502             27.183695                  91.2   \n",
       "10 3 2 0.1   26.244482             27.564515                  92.2   \n",
       "10 3 2 0.2   25.914952             27.316656                  85.6   \n",
       "10 3 2 0.5   26.507223             27.974688                  88.0   \n",
       "10 3 2 1     26.025565             27.237926                  82.8   \n",
       "10 3 2 2     26.584472             27.487119                  92.4   \n",
       "10 3 3 0.0   23.403339             24.413853                  72.8   \n",
       "10 3 3 0.05  23.403872             24.414051                  72.8   \n",
       "10 3 3 0.1   24.122719             25.124202                  75.8   \n",
       "10 3 3 0.2   23.580819             24.455800                  77.0   \n",
       "10 3 3 0.5   23.801237             24.794985                  67.2   \n",
       "10 3 3 1     23.898266             24.827508                  70.0   \n",
       "10 3 3 2     23.825510             24.703784                  73.8   \n",
       "10 4 1 0.0   26.775542             27.428841                  53.0   \n",
       "10 4 1 0.05  26.639708             26.802218                  31.6   \n",
       "10 4 1 0.1   26.639462             26.801968                  31.6   \n",
       "10 4 1 0.2   26.649406             26.812212                  31.6   \n",
       "10 4 1 0.5   27.437872             27.735080                  36.0   \n",
       "10 4 1 1     27.356560             27.664689                  36.0   \n",
       "10 4 1 2     26.553588             26.878973                  43.8   \n",
       "10 4 2 0.0   26.059995             27.550413                 100.2   \n",
       "10 4 2 0.05  25.903502             27.183695                  91.2   \n",
       "10 4 2 0.1   26.244482             27.564515                  92.2   \n",
       "10 4 2 0.2   25.914952             27.316656                  85.6   \n",
       "10 4 2 0.5   26.507223             27.974688                  88.0   \n",
       "10 4 2 1     26.025565             27.237926                  82.8   \n",
       "10 4 2 2     26.584472             27.487119                  92.4   \n",
       "10 4 3 0.0   23.403339             24.413853                  72.8   \n",
       "10 4 3 0.05  23.403872             24.414051                  72.8   \n",
       "10 4 3 0.1   24.122719             25.124202                  75.8   \n",
       "10 4 3 0.2   23.580819             24.455800                  77.0   \n",
       "10 4 3 0.5   23.801237             24.794985                  67.2   \n",
       "10 4 3 1     23.898266             24.827508                  70.0   \n",
       "10 4 3 2     23.825510             24.703784                  73.8   \n",
       "\n",
       "             complement_loss  validation_loss  test_loss  \\\n",
       "5 2 1 0.0          15.354145        15.590007  15.393940   \n",
       "5 2 1 0.05         16.072329        16.346961  16.097926   \n",
       "5 2 1 0.1          14.023319        14.315376  14.043023   \n",
       "5 2 1 0.2          15.069580        15.247607  14.997541   \n",
       "5 2 1 0.5          13.849456        14.144338  13.950987   \n",
       "5 2 1 1            18.041484        18.126605  18.193799   \n",
       "5 2 1 2            13.627126        13.696582  13.959556   \n",
       "5 2 2 0.0           9.388352         9.576035   9.497299   \n",
       "5 2 2 0.05          9.548557         9.752170   9.722638   \n",
       "5 2 2 0.1           9.025257         9.243180   9.193870   \n",
       "5 2 2 0.2          10.348714        10.497557  10.500740   \n",
       "5 2 2 0.5           8.853422         9.016999   9.167431   \n",
       "5 2 2 1            11.950741        11.955553  12.014152   \n",
       "5 2 2 2            11.581490        11.738276  11.815354   \n",
       "5 2 3 0.0          11.518954        11.738035  11.796274   \n",
       "5 2 3 0.05         11.566502        11.791009  11.829385   \n",
       "5 2 3 0.1          11.361427        11.650790  11.662710   \n",
       "5 2 3 0.2          11.366361        11.585687  11.585582   \n",
       "5 2 3 0.5          12.330890        12.565544  12.482891   \n",
       "5 2 3 1            12.347486        12.590631  12.492693   \n",
       "5 2 3 2            12.654862        12.845725  12.875193   \n",
       "5 3 1 0.0          20.556684        20.759188  20.790138   \n",
       "5 3 1 0.05         20.557335        20.758650  20.791408   \n",
       "5 3 1 0.1          20.557053        20.758222  20.791061   \n",
       "5 3 1 0.2          20.131537        20.366161  20.405086   \n",
       "5 3 1 0.5          19.749167        20.078234  19.945011   \n",
       "5 3 1 1            19.526006        19.852372  19.665796   \n",
       "5 3 1 2            19.576314        19.879980  19.754827   \n",
       "5 3 2 0.0           9.388352         9.576035   9.497299   \n",
       "5 3 2 0.05          9.548557         9.752170   9.722638   \n",
       "5 3 2 0.1           9.025257         9.243180   9.193870   \n",
       "5 3 2 0.2          10.348714        10.497557  10.500740   \n",
       "5 3 2 0.5           8.853422         9.016999   9.167431   \n",
       "5 3 2 1            11.950741        11.955553  12.014152   \n",
       "5 3 2 2            11.581490        11.738276  11.815354   \n",
       "5 3 3 0.0          11.518954        11.738035  11.796274   \n",
       "5 3 3 0.05         11.566502        11.791009  11.829385   \n",
       "5 3 3 0.1          11.361427        11.650790  11.662710   \n",
       "5 3 3 0.2          11.366361        11.585687  11.585582   \n",
       "5 3 3 0.5          12.330890        12.565544  12.482891   \n",
       "5 3 3 1            12.347486        12.590631  12.492693   \n",
       "5 3 3 2            12.654862        12.845725  12.875193   \n",
       "5 4 1 0.0          16.670506        16.829754  16.845969   \n",
       "5 4 1 0.05         17.352624        17.508951  17.486833   \n",
       "5 4 1 0.1          17.352130        17.508586  17.486252   \n",
       "5 4 1 0.2          17.351254        17.507338  17.485003   \n",
       "5 4 1 0.5          17.182268        17.323052  17.382390   \n",
       "5 4 1 1            17.843828        17.948617  18.050599   \n",
       "5 4 1 2            17.859937        18.020946  18.022037   \n",
       "5 4 2 0.0           9.388352         9.576035   9.497299   \n",
       "5 4 2 0.05          9.548557         9.752170   9.722638   \n",
       "5 4 2 0.1           9.025257         9.243180   9.193870   \n",
       "5 4 2 0.2          10.348714        10.497557  10.500740   \n",
       "5 4 2 0.5           8.853422         9.016999   9.167431   \n",
       "5 4 2 1            11.950741        11.955553  12.014152   \n",
       "5 4 2 2            11.581490        11.738276  11.815354   \n",
       "5 4 3 0.0          11.518954        11.738035  11.796274   \n",
       "5 4 3 0.05         11.566502        11.791009  11.829385   \n",
       "5 4 3 0.1          11.361427        11.650790  11.662710   \n",
       "5 4 3 0.2          11.366361        11.585687  11.585582   \n",
       "5 4 3 0.5          12.330890        12.565544  12.482891   \n",
       "5 4 3 1            12.347486        12.590631  12.492693   \n",
       "5 4 3 2            12.654862        12.845725  12.875193   \n",
       "10 2 1 0.0         17.272253        17.472361  17.370635   \n",
       "10 2 1 0.05        17.066069        17.282653  17.204039   \n",
       "10 2 1 0.1         17.080169        17.295019  17.216866   \n",
       "10 2 1 0.2         15.500173        15.763193  15.553725   \n",
       "10 2 1 0.5         16.419778        16.685802  16.733153   \n",
       "10 2 1 1           15.888103        16.282005  15.949159   \n",
       "10 2 1 2           15.107312        15.418566  15.275471   \n",
       "10 2 2 0.0         12.283261        12.230745  12.577014   \n",
       "10 2 2 0.05        12.638985        12.685849  12.874296   \n",
       "10 2 2 0.1         12.847659        12.888097  13.097399   \n",
       "10 2 2 0.2         12.867023        12.901560  13.147951   \n",
       "10 2 2 0.5         13.196361        13.284520  13.511970   \n",
       "10 2 2 1           13.121573        13.282392  13.398284   \n",
       "10 2 2 2           13.180405        13.229448  13.217505   \n",
       "10 2 3 0.0         11.658473        11.826613  12.003534   \n",
       "10 2 3 0.05        11.658892        11.827333  12.005349   \n",
       "10 2 3 0.1         12.104134        12.332254  12.438467   \n",
       "10 2 3 0.2         11.685213        11.897092  12.004878   \n",
       "10 2 3 0.5         12.252310        12.440687  12.637441   \n",
       "10 2 3 1           12.209920        12.447829  12.583422   \n",
       "10 2 3 2           11.952316        12.319606  12.280509   \n",
       "10 3 1 0.0         21.780305        21.697652  21.870918   \n",
       "10 3 1 0.05        21.804341        21.705267  21.882545   \n",
       "10 3 1 0.1         21.634048        21.568076  21.674572   \n",
       "10 3 1 0.2         21.613434        21.541599  21.655091   \n",
       "10 3 1 0.5         21.726782        21.840941  21.749442   \n",
       "10 3 1 1           21.186875        21.219307  21.316309   \n",
       "10 3 1 2           19.720062        19.687161  19.927511   \n",
       "10 3 2 0.0         12.283261        12.230745  12.577014   \n",
       "10 3 2 0.05        12.638985        12.685849  12.874296   \n",
       "10 3 2 0.1         12.847659        12.888097  13.097399   \n",
       "10 3 2 0.2         12.867023        12.901560  13.147951   \n",
       "10 3 2 0.5         13.196361        13.284520  13.511970   \n",
       "10 3 2 1           13.121573        13.282392  13.398284   \n",
       "10 3 2 2           13.180405        13.229448  13.217505   \n",
       "10 3 3 0.0         11.658473        11.826613  12.003534   \n",
       "10 3 3 0.05        11.658892        11.827333  12.005349   \n",
       "10 3 3 0.1         12.104134        12.332254  12.438467   \n",
       "10 3 3 0.2         11.685213        11.897092  12.004878   \n",
       "10 3 3 0.5         12.252310        12.440687  12.637441   \n",
       "10 3 3 1           12.209920        12.447829  12.583422   \n",
       "10 3 3 2           11.952316        12.319606  12.280509   \n",
       "10 4 1 0.0         15.927359        16.059246  16.037344   \n",
       "10 4 1 0.05        17.446736        17.530784  17.644897   \n",
       "10 4 1 0.1         17.446513        17.530556  17.644475   \n",
       "10 4 1 0.2         17.455431        17.536157  17.648748   \n",
       "10 4 1 0.5         17.814050        17.863830  18.026574   \n",
       "10 4 1 1           17.751194        17.830489  17.987025   \n",
       "10 4 1 2           16.657776        16.829766  16.867114   \n",
       "10 4 2 0.0         12.283261        12.230745  12.577014   \n",
       "10 4 2 0.05        12.638985        12.685849  12.874296   \n",
       "10 4 2 0.1         12.847659        12.888097  13.097399   \n",
       "10 4 2 0.2         12.867023        12.901560  13.147951   \n",
       "10 4 2 0.5         13.196361        13.284520  13.511970   \n",
       "10 4 2 1           13.121573        13.282392  13.398284   \n",
       "10 4 2 2           13.180405        13.229448  13.217505   \n",
       "10 4 3 0.0         11.658473        11.826613  12.003534   \n",
       "10 4 3 0.05        11.658892        11.827333  12.005349   \n",
       "10 4 3 0.1         12.104134        12.332254  12.438467   \n",
       "10 4 3 0.2         11.685213        11.897092  12.004878   \n",
       "10 4 3 0.5         12.252310        12.440687  12.637441   \n",
       "10 4 3 1           12.209920        12.447829  12.583422   \n",
       "10 4 3 2           11.952316        12.319606  12.280509   \n",
       "\n",
       "             min_val_linear_bound  linear_bound  \n",
       "5 2 1 0.0               30.138334     27.825265  \n",
       "5 2 1 0.05              30.556501     28.243432  \n",
       "5 2 1 0.1               30.396654     27.617541  \n",
       "5 2 1 0.2               30.969350     28.346362  \n",
       "5 2 1 0.5               30.747083     27.635473  \n",
       "5 2 1 1                 30.470288     28.290763  \n",
       "5 2 1 2                 30.461752     27.350142  \n",
       "5 2 2 0.0               28.811118     25.497439  \n",
       "5 2 2 0.05              28.752273     25.551779  \n",
       "5 2 2 0.1               29.738632     25.930200  \n",
       "5 2 2 0.2               28.607691     25.690534  \n",
       "5 2 2 0.5               29.189028     25.736039  \n",
       "5 2 2 1                 28.179804     25.646424  \n",
       "5 2 2 2                 27.983293     25.394314  \n",
       "5 2 3 0.0               28.132580     25.158605  \n",
       "5 2 3 0.05              28.210759     25.220957  \n",
       "5 2 3 0.1               27.523185     24.719890  \n",
       "5 2 3 0.2               26.789134     24.232449  \n",
       "5 2 3 0.5               26.126857     24.036215  \n",
       "5 2 3 1                 26.109303     24.018660  \n",
       "5 2 3 2                 27.145424     24.838481  \n",
       "5 3 1 0.0               30.741924     28.917929  \n",
       "5 3 1 0.05              30.742576     28.918580  \n",
       "5 3 1 0.1               30.742293     28.918298  \n",
       "5 3 1 0.2               30.986696     29.162700  \n",
       "5 3 1 0.5               30.604326     28.780331  \n",
       "5 3 1 1                 30.398729     28.574733  \n",
       "5 3 1 2                 30.605138     28.781142  \n",
       "5 3 2 0.0               28.811118     25.497439  \n",
       "5 3 2 0.05              28.752273     25.551779  \n",
       "5 3 2 0.1               29.738632     25.930200  \n",
       "5 3 2 0.2               28.607691     25.690534  \n",
       "5 3 2 0.5               29.189028     25.736039  \n",
       "5 3 2 1                 28.179804     25.646424  \n",
       "5 3 2 2                 27.983293     25.394314  \n",
       "5 3 3 0.0               28.132580     25.158605  \n",
       "5 3 3 0.05              28.210759     25.220957  \n",
       "5 3 3 0.1               27.523185     24.719890  \n",
       "5 3 3 0.2               26.789134     24.232449  \n",
       "5 3 3 0.5               26.126857     24.036215  \n",
       "5 3 3 1                 26.109303     24.018660  \n",
       "5 3 3 2                 27.145424     24.838481  \n",
       "5 4 1 0.0               28.985931     26.951957  \n",
       "5 4 1 0.05              28.970321     27.025229  \n",
       "5 4 1 0.1               28.969828     27.024736  \n",
       "5 4 1 0.2               28.968952     27.023860  \n",
       "5 4 1 0.5               30.020106     27.641185  \n",
       "5 4 1 1                 29.657634     27.611212  \n",
       "5 4 1 2                 28.947060     27.034182  \n",
       "5 4 2 0.0               28.811118     25.497439  \n",
       "5 4 2 0.05              28.752273     25.551779  \n",
       "5 4 2 0.1               29.738632     25.930200  \n",
       "5 4 2 0.2               28.607691     25.690534  \n",
       "5 4 2 0.5               29.189028     25.736039  \n",
       "5 4 2 1                 28.179804     25.646424  \n",
       "5 4 2 2                 27.983293     25.394314  \n",
       "5 4 3 0.0               28.132580     25.158605  \n",
       "5 4 3 0.05              28.210759     25.220957  \n",
       "5 4 3 0.1               27.523185     24.719890  \n",
       "5 4 3 0.2               26.789134     24.232449  \n",
       "5 4 3 0.5               26.126857     24.036215  \n",
       "5 4 3 1                 26.109303     24.018660  \n",
       "5 4 3 2                 27.145424     24.838481  \n",
       "10 2 1 0.0              30.500252     28.320727  \n",
       "10 2 1 0.05             29.732705     27.553180  \n",
       "10 2 1 0.1              29.746805     27.567280  \n",
       "10 2 1 0.2              30.692365     28.046797  \n",
       "10 2 1 0.5              30.203758     28.024233  \n",
       "10 2 1 1                30.804123     28.324904  \n",
       "10 2 1 2                29.955553     27.494531  \n",
       "10 2 2 0.0              29.265740     26.663792  \n",
       "10 2 2 0.05             28.956971     26.378685  \n",
       "10 2 2 0.1              29.220424     26.625018  \n",
       "10 2 2 0.2              28.828266     26.401238  \n",
       "10 2 2 0.5              29.359235     26.892286  \n",
       "10 2 2 1                28.837450     26.353180  \n",
       "10 2 2 2                29.610395     26.772213  \n",
       "10 2 3 0.0              26.488509     24.149707  \n",
       "10 2 3 0.05             26.488929     24.150127  \n",
       "10 2 3 0.1              27.127998     24.571312  \n",
       "10 2 3 0.2              26.702259     24.145574  \n",
       "10 2 3 0.5              26.612804     24.355339  \n",
       "10 2 3 1                26.792351     24.420210  \n",
       "10 2 3 2                26.860282     24.444470  \n",
       "10 3 1 0.0              32.390580     30.477702  \n",
       "10 3 1 0.05             32.414617     30.501739  \n",
       "10 3 1 0.1              32.284297     30.371419  \n",
       "10 3 1 0.2              32.263683     30.350805  \n",
       "10 3 1 0.5              32.377031     30.464153  \n",
       "10 3 1 1                31.332141     29.508146  \n",
       "10 3 1 2                30.310207     28.397329  \n",
       "10 3 2 0.0              29.265740     26.663792  \n",
       "10 3 2 0.05             28.956971     26.378685  \n",
       "10 3 2 0.1              29.220424     26.625018  \n",
       "10 3 2 0.2              28.828266     26.401238  \n",
       "10 3 2 0.5              29.359235     26.892286  \n",
       "10 3 2 1                28.837450     26.353180  \n",
       "10 3 2 2                29.610395     26.772213  \n",
       "10 3 3 0.0              26.488509     24.149707  \n",
       "10 3 3 0.05             26.488929     24.150127  \n",
       "10 3 3 0.1              27.127998     24.571312  \n",
       "10 3 3 0.2              26.702259     24.145574  \n",
       "10 3 3 0.5              26.612804     24.355339  \n",
       "10 3 3 1                26.792351     24.420210  \n",
       "10 3 3 2                26.860282     24.444470  \n",
       "10 4 1 0.0              28.811743     26.560977  \n",
       "10 4 1 0.05             28.316779     26.403901  \n",
       "10 4 1 0.1              28.316556     26.403678  \n",
       "10 4 1 0.2              28.325473     26.412595  \n",
       "10 4 1 0.5              29.076612     27.163734  \n",
       "10 4 1 1                29.013756     27.100878  \n",
       "10 4 1 2                28.568013     26.471764  \n",
       "10 4 2 0.0              29.265740     26.663792  \n",
       "10 4 2 0.05             28.956971     26.378685  \n",
       "10 4 2 0.1              29.220424     26.625018  \n",
       "10 4 2 0.2              28.828266     26.401238  \n",
       "10 4 2 0.5              29.359235     26.892286  \n",
       "10 4 2 1                28.837450     26.353180  \n",
       "10 4 2 2                29.610395     26.772213  \n",
       "10 4 3 0.0              26.488509     24.149707  \n",
       "10 4 3 0.05             26.488929     24.150127  \n",
       "10 4 3 0.1              27.127998     24.571312  \n",
       "10 4 3 0.2              26.702259     24.145574  \n",
       "10 4 3 0.5              26.612804     24.355339  \n",
       "10 4 3 1                26.792351     24.420210  \n",
       "10 4 3 2                26.860282     24.444470  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reshaped_matrix = results_matrix.mean(1).reshape(results_matrix.shape[0],np.prod(results_matrix.shape[2:])).T\n",
    "mean_df = pd.DataFrame(reshaped_matrix, index=name_list, columns=(values_to_fetch + ['linear_bound']))\n",
    "mean_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8208a26c-0757-4619-b190-31cc29481ece",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "reshaped_std = results_matrix.std(1).reshape(results_matrix.shape[0],np.prod(results_matrix.shape[2:])).T\n",
    "std_df = pd.DataFrame(reshaped_std, index=name_list, columns=(values_to_fetch + ['linear_bound']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5e772a7a-cf87-41bf-b8e6-50f5f256d4e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.0, 5.0, 2.0, 1.0, 0.0]\n",
      "Complement loss: 1.0000±0.0000  Validation loss: 1.0000±0.0000 Test loss : 1.0000±0.0000  KL bound : 1.0000±0.0000  Catoni bound : 1.0000±0.0000 Linear bound : 1.0000±0.0000 compression set size : 1.0000±0.0000 \n"
     ]
    }
   ],
   "source": [
    "val_of_interest = \"validation_loss\"\n",
    "best_params = correct_type_of_entry(mean_df.index[mean_df[val_of_interest].argmin()].split())\n",
    "\n",
    "print(best_params)\n",
    "best_val_arr = mean_df.loc[mean_df.index[mean_df[val_of_interest].argmin()]]\n",
    "std_val_arr = std_df.loc[mean_df.index[mean_df[val_of_interest].argmin()]]\n",
    "print(\n",
    "    f\"Complement loss: {best_val_arr['complement_loss']:.4f}±{std_val_arr['complement_loss']:.4f} \",\n",
    "    f\"Validation loss: {best_val_arr['validation_loss']:.4f}±{std_val_arr['validation_loss']:.4f} \"\n",
    "      f'Test loss : {best_val_arr['test_loss']:.4f}±{std_val_arr['test_loss']:.4f} ', \n",
    "      f\"KL bound : {best_val_arr['kl_bound']:.4f}±{std_val_arr['kl_bound']:.4f} \",\n",
    "        f\"Catoni bound : {best_val_arr['min_val_catoni_bound']:.4f}±{std_val_arr['min_val_catoni_bound']:.4f}\",\n",
    "    f\"Linear bound : {best_val_arr['min_val_linear_bound']:.4f}±{std_val_arr['min_val_linear_bound']:.4f}\",\n",
    "      f'compression set size : {best_val_arr['compression_set_size']:.4f}±{std_val_arr['compression_set_size']:.4f} ',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fbe105d1-b86d-4879-ae79-d0102d9544d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.0, 5.0, 2.0, 1.0, 0.0]\n",
      "Complement loss: 1.00±0.00  Validation loss: 1.00±0.00 Test loss : 1.00±0.00  KL bound : 1.00±0.00  Catoni bound : 1.00±0.00 Linear bound : 1.00±0.00 compression set size : 1.00±0.00 \n"
     ]
    }
   ],
   "source": [
    "val_of_interest = \"kl_bound\"\n",
    "# val_of_interest = \"validation_loss\"\n",
    "best_params = correct_type_of_entry(mean_df.index[mean_df[val_of_interest].argmin()].split())\n",
    "print(best_params)\n",
    "best_val_arr = mean_df.loc[mean_df.index[mean_df[val_of_interest].argmin()]]\n",
    "std_val_arr = std_df.loc[mean_df.index[mean_df[val_of_interest].argmin()]]\n",
    "print(\n",
    "    f\"Complement loss: {best_val_arr['complement_loss']:.2f}±{std_val_arr['complement_loss']:.2f} \",\n",
    "    f\"Validation loss: {best_val_arr['validation_loss']:.2f}±{std_val_arr['validation_loss']:.2f} \"\n",
    "      f'Test loss : {best_val_arr['test_loss']:.2f}±{std_val_arr['test_loss']:.2f} ', \n",
    "      f\"KL bound : {best_val_arr['kl_bound']:.2f}±{std_val_arr['kl_bound']:.2f} \",\n",
    "        f\"Catoni bound : {best_val_arr['min_val_catoni_bound']:.2f}±{std_val_arr['min_val_catoni_bound']:.2f}\",\n",
    "    f\"Linear bound : {best_val_arr['linear_bound']:.2f}±{std_val_arr['linear_bound']:.2f}\",\n",
    "      f'compression set size : {best_val_arr['compression_set_size']:.2f}±{std_val_arr['compression_set_size']:.2f} ',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "1706c782-a84d-4b2c-bc22-384ae7e7ae4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/t7/t2tk8_dd3vsc811yh2mdwbbh0000gp/T/ipykernel_32473/653062687.py:53: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./results/trees/powerplant_tree_validation_loss_heatmaps.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = 4\n",
    "mean_arr = results_matrix.mean(1)[index]\n",
    "key_list = list(hps.keys())[1:]\n",
    "\n",
    "fig, axs = plt.subplots(len(key_list)-1, len(key_list)-1, figsize=(10,10))\n",
    "for i in range(0,len(key_list)-1):\n",
    "    for j in range(i+1,len(key_list)-1):\n",
    "        fig.delaxes(axs[i, j])\n",
    "\n",
    "fig.subplots_adjust(right=0.8)\n",
    "\n",
    "cmap = 'Blues'\n",
    "vmax = mean_arr.max()\n",
    "vmin = mean_arr.min()\n",
    "\n",
    "for i in range(0,len(key_list)-1):\n",
    "    for j in range(i+1,len(key_list)):\n",
    "        index_list = list(range(len(key_list)))\n",
    "        index_list.remove(i)\n",
    "        index_list.remove(j)\n",
    "        index_list = index_list[::-1]\n",
    "        new_arr = mean_arr.mean(index_list[0])\n",
    "        for k in range(1,len(index_list)):\n",
    "            new_arr = new_arr.mean(index_list[k])\n",
    "        df = pd.DataFrame(new_arr.T, index=hps[key_list[j]], columns=hps[key_list[i]])\n",
    "        if i != 0:\n",
    "            yticklabels = False\n",
    "        else:\n",
    "            yticklabels = True\n",
    "            axs[j-1,i].set(xlabel=key_list[j])\n",
    "        if j+1 != len(key_list):\n",
    "            xticklabels = False\n",
    "        else:\n",
    "            xticklabels = True\n",
    "            axs[j-1,i].set(ylabel=key_list[i])\n",
    "            \n",
    "        if j == 1 and i == 0:\n",
    "            cbar_ax = fig.add_axes([1, 0.12, 0.05, 0.85])\n",
    "            cbar = True\n",
    "        else:\n",
    "            cbar_ax = None\n",
    "            cbar = False\n",
    "        \n",
    "        sn.heatmap(df, annot=True,fmt=\".2f\", ax=axs[j-1,i], cbar=cbar, vmin=vmin, vmax=vmax,\n",
    "                cmap=cmap, xticklabels=xticklabels, yticklabels=yticklabels, cbar_ax=cbar_ax)\n",
    "        if j+1 == len(key_list):\n",
    "            axs[j-1, i].set(xlabel=key_list[i])\n",
    "        if i == 0:\n",
    "            axs[j-1, i].set(ylabel=key_list[j])\n",
    "\n",
    "fig.suptitle(f\"Comparison of the {values_to_fetch[index].replace(\"_\", \" \")} for the dataset {dataset} with a {sweep_config}\")\n",
    "# # Layout so plots do not overlap\n",
    "fig.tight_layout()\n",
    "fig.align_labels()\n",
    "plt.savefig(f\"./results/trees/{dataset}_{sweep_config}_{values_to_fetch[index]}_heatmaps.jpg\", bbox_inches='tight')\n",
    "print(f\"./results/trees/{dataset}_{sweep_config}_{values_to_fetch[index]}_heatmaps.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ebc352ca-b2f9-417e-bf2e-e905d99a222e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/t7/t2tk8_dd3vsc811yh2mdwbbh0000gp/T/ipykernel_32473/1206765030.py:53: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./results/trees/powerplant_tree_kl_bound_heatmaps.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = 0\n",
    "mean_arr = results_matrix.mean(1)[index]\n",
    "key_list = list(hps.keys())[1:]\n",
    "\n",
    "fig, axs = plt.subplots(len(key_list)-1, len(key_list)-1, figsize=(10,10))\n",
    "for i in range(0,len(key_list)-1):\n",
    "    for j in range(i+1,len(key_list)-1):\n",
    "        fig.delaxes(axs[i, j])\n",
    "\n",
    "fig.subplots_adjust(right=0.8)\n",
    "\n",
    "cmap = 'Blues'\n",
    "vmax = mean_arr.max()\n",
    "vmin = mean_arr.min()\n",
    "\n",
    "for i in range(0,len(key_list)-1):\n",
    "    for j in range(i+1,len(key_list)):\n",
    "        index_list = list(range(len(key_list)))\n",
    "        index_list.remove(i)\n",
    "        index_list.remove(j)\n",
    "        index_list = index_list[::-1]\n",
    "        new_arr = mean_arr.mean(index_list[0])\n",
    "        for k in range(1,len(index_list)):\n",
    "            new_arr = new_arr.mean(index_list[k])\n",
    "        df = pd.DataFrame(new_arr.T, index=hps[key_list[j]], columns=hps[key_list[i]])\n",
    "        if i != 0:\n",
    "            yticklabels = False\n",
    "        else:\n",
    "            yticklabels = True\n",
    "            axs[j-1,i].set(xlabel=key_list[j])\n",
    "        if j+1 != len(key_list):\n",
    "            xticklabels = False\n",
    "        else:\n",
    "            xticklabels = True\n",
    "            axs[j-1,i].set(ylabel=key_list[i])\n",
    "            \n",
    "        if j == 1 and i == 0:\n",
    "            cbar_ax = fig.add_axes([1, 0.12, 0.05, 0.85])\n",
    "            cbar = True\n",
    "        else:\n",
    "            cbar_ax = None\n",
    "            cbar = False\n",
    "        \n",
    "        sn.heatmap(df, annot=True,fmt=\".2f\", ax=axs[j-1,i], cbar=cbar, vmin=vmin, vmax=vmax,\n",
    "                cmap=cmap, xticklabels=xticklabels, yticklabels=yticklabels, cbar_ax=cbar_ax)\n",
    "        if j+1 == len(key_list):\n",
    "            axs[j-1, i].set(xlabel=key_list[i])\n",
    "        if i == 0:\n",
    "            axs[j-1, i].set(ylabel=key_list[j])\n",
    "\n",
    "fig.suptitle(f\"Comparison of the {values_to_fetch[index].replace(\"_\", \" \")} for the dataset {dataset} with a {sweep_config}\")\n",
    "# # Layout so plots do not overlap\n",
    "fig.tight_layout()\n",
    "fig.align_labels()\n",
    "plt.savefig(f\"./results/trees/{dataset}_{sweep_config}_{values_to_fetch[index]}_heatmaps.jpg\", bbox_inches='tight')\n",
    "print(f\"./results/trees/{dataset}_{sweep_config}_{values_to_fetch[index]}_heatmaps.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a51f63a-97a9-4ef9-aede-79b137660443",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "685f9a94-1cae-498e-8b73-9ba84c9d3147",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf4e1903-e470-47d5-8149-57febe37a30d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}