results_binary_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "188b45e3-9ff9-4215-88b6-d92edddd2060",
   "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",
    "import seaborn as sn\n",
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "dfda1fb6-1e24-40e1-adfd-6c49b25821c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = \"mnist\"\n",
    "#[0, 1, 2, 4, 5, -1]\n",
    "#[8, 7, 3, 9, 6, -1]\n",
    "first_class = 2\n",
    "second_class = 3\n",
    "sweep_config = \"default\"\n",
    "values_to_fetch = ['kl_bound', 'binomial_approximation_shah', 'p2l_bound', 'compression_set_size', 'validation_error', 'test_error']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a0687c19-b659-4266-89d1-db644ba1166c",
   "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",
    "size_hyperparams = tuple([len(l) for l in hps.values()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "131ef923-e1fb-4eb5-92dd-aba23e64088a",
   "metadata": {},
   "outputs": [],
   "source": [
    "api = wandb.Api(timeout=29)\n",
    "entity = \"mathieu-bazinet\"\n",
    "project = \"sweep\" + dataset + str(first_class) + str(second_class)\n",
    "runs = api.runs(entity + \"/\" + project)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f89f6a04-fcde-4377-bd18-4cf3c6ec8ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_matrix = np.ones(((len(values_to_fetch),) + size_hyperparams))\n",
    "\n",
    "for run_idx in range(len(runs)):\n",
    "    run = runs[run_idx]\n",
    "    for val_to_fetch_idx in range(len(values_to_fetch)):\n",
    "        matrix_idx = tuple([val_to_fetch_idx] + [hps[key].index(run.config[key]) for key in hps.keys()])\n",
    "        val_to_fetch = values_to_fetch[val_to_fetch_idx]\n",
    "        results_matrix[matrix_idx] = run.summary[val_to_fetch]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "6f8e980f-edaa-4239-ae10-a845908a22ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import product\n",
    "\n",
    "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))\n",
    "\n",
    "def different_length_means(list_of_list):\n",
    "    mean_list = []\n",
    "    for idx in range(max([len(l) for l in list_of_list])):\n",
    "        count = 0\n",
    "        somme = 0\n",
    "        for l in list_of_list:\n",
    "            if len(l) > idx:\n",
    "                somme += l[idx]\n",
    "                count += 1\n",
    "        mean_list.append(somme/count)\n",
    "    return np.array(mean_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "005ecf2e-7c88-4efc-aade-d20272cd79c0",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kl_bound</th>\n",
       "      <th>binomial_approximation_shah</th>\n",
       "      <th>p2l_bound</th>\n",
       "      <th>compression_set_size</th>\n",
       "      <th>validation_error</th>\n",
       "      <th>test_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mlp 0.1 0.01</th>\n",
       "      <td>0.154598</td>\n",
       "      <td>0.154169</td>\n",
       "      <td>0.041498</td>\n",
       "      <td>408.4</td>\n",
       "      <td>0.009013</td>\n",
       "      <td>0.004785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp 0.1 0.001</th>\n",
       "      <td>0.139335</td>\n",
       "      <td>0.138900</td>\n",
       "      <td>0.036207</td>\n",
       "      <td>354.2</td>\n",
       "      <td>0.008484</td>\n",
       "      <td>0.006383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp 0.1 0.005</th>\n",
       "      <td>0.145953</td>\n",
       "      <td>0.145520</td>\n",
       "      <td>0.038492</td>\n",
       "      <td>377.6</td>\n",
       "      <td>0.009028</td>\n",
       "      <td>0.004715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp 0.1 0.0001</th>\n",
       "      <td>0.130760</td>\n",
       "      <td>0.130321</td>\n",
       "      <td>0.033365</td>\n",
       "      <td>325.2</td>\n",
       "      <td>0.009592</td>\n",
       "      <td>0.006998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp 0.2 0.01</th>\n",
       "      <td>0.185745</td>\n",
       "      <td>0.185326</td>\n",
       "      <td>0.053105</td>\n",
       "      <td>528.0</td>\n",
       "      <td>0.009180</td>\n",
       "      <td>0.005747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp 0.2 0.001</th>\n",
       "      <td>0.136646</td>\n",
       "      <td>0.136210</td>\n",
       "      <td>0.035306</td>\n",
       "      <td>345.0</td>\n",
       "      <td>0.007731</td>\n",
       "      <td>0.003718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp 0.2 0.005</th>\n",
       "      <td>0.145119</td>\n",
       "      <td>0.144686</td>\n",
       "      <td>0.038181</td>\n",
       "      <td>374.4</td>\n",
       "      <td>0.009614</td>\n",
       "      <td>0.005642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp 0.2 0.0001</th>\n",
       "      <td>0.122146</td>\n",
       "      <td>0.121704</td>\n",
       "      <td>0.030558</td>\n",
       "      <td>296.6</td>\n",
       "      <td>0.008678</td>\n",
       "      <td>0.005751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.1 0.01</th>\n",
       "      <td>0.117436</td>\n",
       "      <td>0.116992</td>\n",
       "      <td>0.029062</td>\n",
       "      <td>281.4</td>\n",
       "      <td>0.003594</td>\n",
       "      <td>0.002521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.1 0.001</th>\n",
       "      <td>0.082042</td>\n",
       "      <td>0.081585</td>\n",
       "      <td>0.018583</td>\n",
       "      <td>175.8</td>\n",
       "      <td>0.003290</td>\n",
       "      <td>0.002574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.1 0.005</th>\n",
       "      <td>0.097763</td>\n",
       "      <td>0.097312</td>\n",
       "      <td>0.023087</td>\n",
       "      <td>221.0</td>\n",
       "      <td>0.003504</td>\n",
       "      <td>0.002111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.1 0.0001</th>\n",
       "      <td>0.084231</td>\n",
       "      <td>0.083775</td>\n",
       "      <td>0.019185</td>\n",
       "      <td>181.8</td>\n",
       "      <td>0.004716</td>\n",
       "      <td>0.002777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.2 0.01</th>\n",
       "      <td>0.120540</td>\n",
       "      <td>0.120098</td>\n",
       "      <td>0.030189</td>\n",
       "      <td>293.0</td>\n",
       "      <td>0.004198</td>\n",
       "      <td>0.002265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.2 0.001</th>\n",
       "      <td>0.081987</td>\n",
       "      <td>0.081530</td>\n",
       "      <td>0.018564</td>\n",
       "      <td>175.6</td>\n",
       "      <td>0.003853</td>\n",
       "      <td>0.002668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.2 0.005</th>\n",
       "      <td>0.096066</td>\n",
       "      <td>0.095614</td>\n",
       "      <td>0.022573</td>\n",
       "      <td>215.8</td>\n",
       "      <td>0.002227</td>\n",
       "      <td>0.001882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.2 0.0001</th>\n",
       "      <td>0.083000</td>\n",
       "      <td>0.082543</td>\n",
       "      <td>0.018844</td>\n",
       "      <td>178.4</td>\n",
       "      <td>0.004637</td>\n",
       "      <td>0.002835</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                kl_bound  binomial_approximation_shah  p2l_bound  \\\n",
       "mlp 0.1 0.01    0.154598                     0.154169   0.041498   \n",
       "mlp 0.1 0.001   0.139335                     0.138900   0.036207   \n",
       "mlp 0.1 0.005   0.145953                     0.145520   0.038492   \n",
       "mlp 0.1 0.0001  0.130760                     0.130321   0.033365   \n",
       "mlp 0.2 0.01    0.185745                     0.185326   0.053105   \n",
       "mlp 0.2 0.001   0.136646                     0.136210   0.035306   \n",
       "mlp 0.2 0.005   0.145119                     0.144686   0.038181   \n",
       "mlp 0.2 0.0001  0.122146                     0.121704   0.030558   \n",
       "cnn 0.1 0.01    0.117436                     0.116992   0.029062   \n",
       "cnn 0.1 0.001   0.082042                     0.081585   0.018583   \n",
       "cnn 0.1 0.005   0.097763                     0.097312   0.023087   \n",
       "cnn 0.1 0.0001  0.084231                     0.083775   0.019185   \n",
       "cnn 0.2 0.01    0.120540                     0.120098   0.030189   \n",
       "cnn 0.2 0.001   0.081987                     0.081530   0.018564   \n",
       "cnn 0.2 0.005   0.096066                     0.095614   0.022573   \n",
       "cnn 0.2 0.0001  0.083000                     0.082543   0.018844   \n",
       "\n",
       "                compression_set_size  validation_error  test_error  \n",
       "mlp 0.1 0.01                   408.4          0.009013    0.004785  \n",
       "mlp 0.1 0.001                  354.2          0.008484    0.006383  \n",
       "mlp 0.1 0.005                  377.6          0.009028    0.004715  \n",
       "mlp 0.1 0.0001                 325.2          0.009592    0.006998  \n",
       "mlp 0.2 0.01                   528.0          0.009180    0.005747  \n",
       "mlp 0.2 0.001                  345.0          0.007731    0.003718  \n",
       "mlp 0.2 0.005                  374.4          0.009614    0.005642  \n",
       "mlp 0.2 0.0001                 296.6          0.008678    0.005751  \n",
       "cnn 0.1 0.01                   281.4          0.003594    0.002521  \n",
       "cnn 0.1 0.001                  175.8          0.003290    0.002574  \n",
       "cnn 0.1 0.005                  221.0          0.003504    0.002111  \n",
       "cnn 0.1 0.0001                 181.8          0.004716    0.002777  \n",
       "cnn 0.2 0.01                   293.0          0.004198    0.002265  \n",
       "cnn 0.2 0.001                  175.6          0.003853    0.002668  \n",
       "cnn 0.2 0.005                  215.8          0.002227    0.001882  \n",
       "cnn 0.2 0.0001                 178.4          0.004637    0.002835  "
      ]
     },
     "execution_count": 94,
     "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)\n",
    "mean_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "31e3097f-dd74-4e77-b827-07849641e38b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kl_bound</th>\n",
       "      <th>binomial_approximation_shah</th>\n",
       "      <th>p2l_bound</th>\n",
       "      <th>compression_set_size</th>\n",
       "      <th>validation_error</th>\n",
       "      <th>test_error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mlp 0.1 0.01</th>\n",
       "      <td>0.006974</td>\n",
       "      <td>0.006977</td>\n",
       "      <td>0.002451</td>\n",
       "      <td>25.144383</td>\n",
       "      <td>0.003106</td>\n",
       "      <td>0.001125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp 0.1 0.001</th>\n",
       "      <td>0.003552</td>\n",
       "      <td>0.003553</td>\n",
       "      <td>0.001193</td>\n",
       "      <td>12.188519</td>\n",
       "      <td>0.002950</td>\n",
       "      <td>0.001618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp 0.1 0.005</th>\n",
       "      <td>0.008248</td>\n",
       "      <td>0.008251</td>\n",
       "      <td>0.002889</td>\n",
       "      <td>29.628365</td>\n",
       "      <td>0.002736</td>\n",
       "      <td>0.001481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp 0.1 0.0001</th>\n",
       "      <td>0.006222</td>\n",
       "      <td>0.006225</td>\n",
       "      <td>0.002050</td>\n",
       "      <td>20.903588</td>\n",
       "      <td>0.002723</td>\n",
       "      <td>0.002196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp 0.2 0.01</th>\n",
       "      <td>0.014776</td>\n",
       "      <td>0.014782</td>\n",
       "      <td>0.005758</td>\n",
       "      <td>59.521425</td>\n",
       "      <td>0.004430</td>\n",
       "      <td>0.002340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp 0.2 0.001</th>\n",
       "      <td>0.004268</td>\n",
       "      <td>0.004269</td>\n",
       "      <td>0.001432</td>\n",
       "      <td>14.628739</td>\n",
       "      <td>0.002763</td>\n",
       "      <td>0.001207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp 0.2 0.005</th>\n",
       "      <td>0.004784</td>\n",
       "      <td>0.004785</td>\n",
       "      <td>0.001650</td>\n",
       "      <td>16.894970</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.000662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp 0.2 0.0001</th>\n",
       "      <td>0.002881</td>\n",
       "      <td>0.002882</td>\n",
       "      <td>0.000924</td>\n",
       "      <td>9.393615</td>\n",
       "      <td>0.002403</td>\n",
       "      <td>0.001239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.1 0.01</th>\n",
       "      <td>0.002057</td>\n",
       "      <td>0.002058</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>6.560488</td>\n",
       "      <td>0.000991</td>\n",
       "      <td>0.001231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.1 0.001</th>\n",
       "      <td>0.004417</td>\n",
       "      <td>0.004418</td>\n",
       "      <td>0.001226</td>\n",
       "      <td>12.253979</td>\n",
       "      <td>0.000869</td>\n",
       "      <td>0.000890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.1 0.005</th>\n",
       "      <td>0.005967</td>\n",
       "      <td>0.005969</td>\n",
       "      <td>0.001762</td>\n",
       "      <td>17.753873</td>\n",
       "      <td>0.002589</td>\n",
       "      <td>0.000274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.1 0.0001</th>\n",
       "      <td>0.001747</td>\n",
       "      <td>0.001747</td>\n",
       "      <td>0.000487</td>\n",
       "      <td>4.874423</td>\n",
       "      <td>0.002309</td>\n",
       "      <td>0.000641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.2 0.01</th>\n",
       "      <td>0.016855</td>\n",
       "      <td>0.016861</td>\n",
       "      <td>0.005491</td>\n",
       "      <td>55.928526</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.000763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.2 0.001</th>\n",
       "      <td>0.003430</td>\n",
       "      <td>0.003431</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>9.478396</td>\n",
       "      <td>0.001240</td>\n",
       "      <td>0.000957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.2 0.005</th>\n",
       "      <td>0.001956</td>\n",
       "      <td>0.001956</td>\n",
       "      <td>0.000571</td>\n",
       "      <td>5.741080</td>\n",
       "      <td>0.001112</td>\n",
       "      <td>0.000558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cnn 0.2 0.0001</th>\n",
       "      <td>0.003064</td>\n",
       "      <td>0.003066</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>8.569714</td>\n",
       "      <td>0.002720</td>\n",
       "      <td>0.001141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                kl_bound  binomial_approximation_shah  p2l_bound  \\\n",
       "mlp 0.1 0.01    0.006974                     0.006977   0.002451   \n",
       "mlp 0.1 0.001   0.003552                     0.003553   0.001193   \n",
       "mlp 0.1 0.005   0.008248                     0.008251   0.002889   \n",
       "mlp 0.1 0.0001  0.006222                     0.006225   0.002050   \n",
       "mlp 0.2 0.01    0.014776                     0.014782   0.005758   \n",
       "mlp 0.2 0.001   0.004268                     0.004269   0.001432   \n",
       "mlp 0.2 0.005   0.004784                     0.004785   0.001650   \n",
       "mlp 0.2 0.0001  0.002881                     0.002882   0.000924   \n",
       "cnn 0.1 0.01    0.002057                     0.002058   0.000646   \n",
       "cnn 0.1 0.001   0.004417                     0.004418   0.001226   \n",
       "cnn 0.1 0.005   0.005967                     0.005969   0.001762   \n",
       "cnn 0.1 0.0001  0.001747                     0.001747   0.000487   \n",
       "cnn 0.2 0.01    0.016855                     0.016861   0.005491   \n",
       "cnn 0.2 0.001   0.003430                     0.003431   0.000949   \n",
       "cnn 0.2 0.005   0.001956                     0.001956   0.000571   \n",
       "cnn 0.2 0.0001  0.003064                     0.003066   0.000857   \n",
       "\n",
       "                compression_set_size  validation_error  test_error  \n",
       "mlp 0.1 0.01               25.144383          0.003106    0.001125  \n",
       "mlp 0.1 0.001              12.188519          0.002950    0.001618  \n",
       "mlp 0.1 0.005              29.628365          0.002736    0.001481  \n",
       "mlp 0.1 0.0001             20.903588          0.002723    0.002196  \n",
       "mlp 0.2 0.01               59.521425          0.004430    0.002340  \n",
       "mlp 0.2 0.001              14.628739          0.002763    0.001207  \n",
       "mlp 0.2 0.005              16.894970          0.001721    0.000662  \n",
       "mlp 0.2 0.0001              9.393615          0.002403    0.001239  \n",
       "cnn 0.1 0.01                6.560488          0.000991    0.001231  \n",
       "cnn 0.1 0.001              12.253979          0.000869    0.000890  \n",
       "cnn 0.1 0.005              17.753873          0.002589    0.000274  \n",
       "cnn 0.1 0.0001              4.874423          0.002309    0.000641  \n",
       "cnn 0.2 0.01               55.928526          0.000500    0.000763  \n",
       "cnn 0.2 0.001               9.478396          0.001240    0.000957  \n",
       "cnn 0.2 0.005               5.741080          0.001112    0.000558  \n",
       "cnn 0.2 0.0001              8.569714          0.002720    0.001141  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_matrix = results_matrix.std(1).reshape(results_matrix.shape[0],np.prod(results_matrix.shape[2:])).T\n",
    "std_df = pd.DataFrame(std_matrix, index=name_list, columns=values_to_fetch)\n",
    "\n",
    "std_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "22a5a1f0-900f-4bec-a1df-60922bd6e6c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/t7/t2tk8_dd3vsc811yh2mdwbbh0000gp/T/ipykernel_26750/1844805149.py:29: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_arr = results_matrix.mean(1)\n",
    "index = 4\n",
    "\n",
    "fig, axs = plt.subplots(2, 2)\n",
    "fig.delaxes(axs[0, 1])\n",
    "fig.subplots_adjust(right=0.8)\n",
    "cbar_ax = fig.add_axes([1, 0.12, 0.05, 0.85])\n",
    "\n",
    "cmap = 'Blues'\n",
    "vmax = mean_arr[index].max()\n",
    "vmin = mean_arr[index].min()\n",
    "\n",
    "lr_mt_arr = pd.DataFrame(mean_arr.mean(2)[index,:,:], index=hps['model_type'], columns=hps['training_lr'])\n",
    "lr_dp_arr = pd.DataFrame(mean_arr.mean(1)[index,:,:], index=hps['dropout_probability'], columns=hps['training_lr'])\n",
    "dp_mt_arr = pd.DataFrame(mean_arr.mean(3)[index,:,:], index=hps['model_type'], columns=hps['dropout_probability'])\n",
    "\n",
    "sn.heatmap(lr_dp_arr, annot=True,ax=axs[0,0],xticklabels=False, cbar=False, vmin=vmin, vmax=vmax, cmap=cmap)\n",
    "axs[0,0].set(ylabel=\"dropout probability\")\n",
    "\n",
    "sn.heatmap(lr_mt_arr, annot=True, ax=axs[1,0], cbar=False, cmap=cmap, vmin=vmin, vmax=vmax,)\n",
    "axs[1,0].set(xlabel=\"training lr\", ylabel=\"model type\")\n",
    "\n",
    "sn.heatmap(dp_mt_arr,annot=True, ax=axs[1,1], yticklabels=False, cbar=True, cmap=cmap, cbar_ax=cbar_ax, vmin=vmin, vmax=vmax,)\n",
    "axs[1,1].set(xlabel=\"dropout probability\")\n",
    "\n",
    "\n",
    "fig.suptitle(f\"Comparison of the {values_to_fetch[index].replace('_', ' ')} for the dataset {dataset + str(first_class) + str(second_class)}\")\n",
    "# Layout so plots do not overlap\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.savefig(f\"./results/binary_mnist/{dataset}_{first_class}_{second_class}_{values_to_fetch[index]}_heatmaps.jpg\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "2347fd86-2c42-431f-bb4c-9c098387f4d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model type : cnn dropout probability : 0.2 learning rate : 0.001\n",
      "Validation error: 0.0039±0.0012 Test error : 0.0027±0.0010  KL bound : 0.0820±0.0034  binomial : 0.0815±0.0034  P2L bound : 0.0186±0.0009  compression set size : 175.6000±9.4784 \n"
     ]
    }
   ],
   "source": [
    "val_of_interest = \"p2l_bound\"\n",
    "wanted_model_type = \"cnn\"\n",
    "model_df = mean_df[[wanted_model_type in idx for idx in mean_df.index]]\n",
    "std_model_df = std_df[[wanted_model_type in idx for idx in std_df.index]]\n",
    "\n",
    "model_type, dropout_probability, training_lr = correct_type_of_entry(model_df.index[model_df[val_of_interest].argmin()].split())\n",
    "\n",
    "print(\"model type :\", model_type, \"dropout probability :\", dropout_probability, \"learning rate :\", training_lr)\n",
    "\n",
    "best_val_arr = model_df.loc[model_df.index[model_df[val_of_interest].argmin()]]\n",
    "std_val_arr = std_model_df.loc[model_df.index[model_df[val_of_interest].argmin()]]\n",
    "print(f\"Validation error: {best_val_arr['validation_error']:.4f}±{std_val_arr['validation_error']:.4f} \"\n",
    "      f'Test error : {best_val_arr['test_error']:.4f}±{std_val_arr['test_error']:.4f} ', \n",
    "      f\"KL bound : {best_val_arr['kl_bound']:.4f}±{std_val_arr['kl_bound']:.4f} \",\n",
    "    f'binomial : {best_val_arr['binomial_approximation_shah']:.4f}±{std_val_arr['binomial_approximation_shah']:.4f} ',\n",
    "     f'P2L bound : {best_val_arr['p2l_bound']:.4f}±{std_val_arr['p2l_bound']:.4f} ', \n",
    "      f'compression set size : {best_val_arr['compression_set_size']:.4f}±{std_val_arr['compression_set_size']:.4f} ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1d5e48bb-ca41-4f75-8621-32687f7a6793",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best bound for run 42 happens at 50 datapoints with a bound value of 0.05458323103981838\n",
      "The best bound for run 4 happens at 90 datapoints with a bound value of 0.05806009620388122\n",
      "The best bound for run 3 happens at 110 datapoints with a bound value of 0.06520545718869367\n",
      "The best bound for run 2 happens at 70 datapoints with a bound value of 0.058688887566149046\n",
      "The best bound for run 1 happens at 100 datapoints with a bound value of 0.0653735305859402\n",
      "Mean value of the best bounds taken separately : 0.0603822405168965\n",
      "Mean value of the compression size of best bounds : 84.0\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 3200x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bounds.real_valued_bounds import compute_epsilon, kl_bound\n",
    "\n",
    "linestyles = ['-', '--', '-.', ':', (0, (3, 1, 1, 1, 1, 1))]\n",
    "colors = [\"#000000\", \"#56B4E9\", \"#009E73\", \"#0072B2\", \"#D55E00\"]\n",
    "\n",
    "plt.figure(figsize=(8,5),dpi=400)\n",
    "run_list = []\n",
    "\n",
    "for run in runs:\n",
    "    if run.config['model_type'] == model_type:\n",
    "        if run.config['training_lr'] == training_lr:\n",
    "            if run.config['dropout_probability'] == dropout_probability:\n",
    "                run_list.append(run)\n",
    "\n",
    "all_seeds_bounds = []\n",
    "max_comp_sizes = 0\n",
    "counter = 0\n",
    "best_comp_set_size = []\n",
    "for run in run_list:\n",
    "    history = run.scan_history(keys=[\"complement_error\"])\n",
    "    losses = [row[\"complement_error\"] for row in history]\n",
    "    val_sizes = run.config['data_groupsize'] * run.config['log_iterations']\n",
    "    # compression_sizes = np.array(list(range(1,len(losses)))) * val_sizes\n",
    "    compression_sizes = np.array(list(range(0,len(losses)))) * val_sizes\n",
    "    compression_sizes = np.append(compression_sizes, run.summary['compression_set_size'])\n",
    "        \n",
    "    if compression_sizes.max() > max_comp_sizes:\n",
    "        max_comp_sizes = compression_sizes.max()\n",
    "        \n",
    "    bound_list = []\n",
    "    for idx in range(len(losses)):\n",
    "        epsilon = compute_epsilon(compression_sizes[idx], 1, run.summary['train_set_size'], run.config['delta'])\n",
    "        bound = kl_bound(compression_sizes[idx], run.summary['train_set_size'], float(losses[idx]), epsilon)\n",
    "        bound_list.append(bound)\n",
    "\n",
    "    if dataset + str(first_class) + str(second_class) == \"mnist08\":\n",
    "        val_to_add = [0.5221730666235089, 0.5221730666235089, 0.5172915806790146, 0.5305361625840044, 0.5045037777077217][hps['seed'].index(run.config['seed'])]\n",
    "        bound_list = [val_to_add] +  bound_list\n",
    "    elif dataset + str(first_class) + str(second_class) == \"mnist17\":\n",
    "        val_to_add = [0.5211469745263457, 0.5211469745263457, 0.5211469745263457, 0.5042897517783912, 0.5211469745263457][hps['seed'].index(run.config['seed'])]\n",
    "        bound_list = [val_to_add] +  bound_list\n",
    "    elif dataset + str(first_class) + str(second_class) == \"mnist23\":\n",
    "        val_to_add = [0.6168213266023356, 0.5219418201057787, 0.5112311835350163, 0.5219418201057787, 0.5219418201057787][hps['seed'].index(run.config['seed'])]\n",
    "        bound_list = [val_to_add] +  bound_list\n",
    "    elif dataset + str(first_class) + str(second_class) == \"mnist49\":\n",
    "        val_to_add = [0.5221581561490893, 0.5221581561490893,0.5221581561490893,0.5221581561490893,  0.5221581561490893][hps['seed'].index(run.config['seed'])]\n",
    "        bound_list = [val_to_add] +  bound_list\n",
    "    elif dataset + str(first_class) + str(second_class) == \"mnist56\":\n",
    "        val_to_add = [0.5225730342790484, 0.5598629093475724, 0.5225730342790484, 0.5218771218723198, 0.5225730342790484][hps['seed'].index(run.config['seed'])]\n",
    "        bound_list = [val_to_add] +  bound_list\n",
    "\n",
    "\n",
    "    plt.plot(compression_sizes, bound_list,'d',\n",
    "             markevery=[np.array(bound_list).argmin()],\n",
    "             linestyle=linestyles[counter], #ms=8, #mew=1.2,\n",
    "             color=colors[counter],\n",
    "             # label=f\"Seed {run.config['seed']} (min bound : {min(bound_list):.4f})\"\n",
    "             label=f\"Seed {run.config['seed']}\"\n",
    "            )\n",
    "    best_idx = np.array(bound_list).argmin()\n",
    "    best_comp_set_size.append(compression_sizes[best_idx])\n",
    "    print(f\"The best bound for run {run.config['seed']} happens at {compression_sizes[best_idx]} datapoints with a bound value of {bound_list[best_idx]}\")\n",
    "    all_seeds_bounds.append(bound_list)\n",
    "    counter += 1\n",
    "\n",
    "plt.xlabel(\"Compression set size\")\n",
    "plt.ylabel(\"KL bound\")\n",
    "plt.xlim(0, max_comp_sizes)\n",
    "print(f\"Mean value of the best bounds taken separately : {sum([min(l) for l in all_seeds_bounds])/5}\")\n",
    "print(f\"Mean value of the compression size of best bounds : {sum(best_comp_set_size)/5}\")\n",
    "plt.legend(markerscale=0)\n",
    "plt.rcParams.update({'font.size': 15})\n",
    "# plt.title(f\"Comparison of the behavior of the bound on {dataset + str(first_class) + str(second_class)}\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"./results/binary_mnist/{dataset}_{first_class}_{second_class}_min_bound_comparison.jpg\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9505dfe9-fe14-407c-bbb2-24acf10fb932",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[23], line 28\u001b[0m\n\u001b[1;32m     26\u001b[0m losses \u001b[38;5;241m=\u001b[39m [row[val_to_fetch] \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m history]\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m val_to_fetch \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalidation_error\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(losses) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m---> 28\u001b[0m     history \u001b[38;5;241m=\u001b[39m \u001b[43mrun\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscan_history\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mval_error\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     29\u001b[0m     losses \u001b[38;5;241m=\u001b[39m [row[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mval_error\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m history]\n\u001b[1;32m     30\u001b[0m     losses\u001b[38;5;241m.\u001b[39mappend(run\u001b[38;5;241m.\u001b[39msummary[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalidation_error\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/wandb/apis/normalize.py:41\u001b[0m, in \u001b[0;36mnormalize_exceptions.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m message \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWhoa, you found a bug.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 41\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     42\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mHTTPError \u001b[38;5;28;01mas\u001b[39;00m error:\n\u001b[1;32m     43\u001b[0m     errors \u001b[38;5;241m=\u001b[39m parse_backend_error_messages(error\u001b[38;5;241m.\u001b[39mresponse)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/wandb/apis/public/runs.py:625\u001b[0m, in \u001b[0;36mRun.scan_history\u001b[0;34m(self, keys, page_size, min_step, max_step)\u001b[0m\n\u001b[1;32m    622\u001b[0m     wandb\u001b[38;5;241m.\u001b[39mtermerror(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkeys argument must be a list of strings\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    623\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m []\n\u001b[0;32m--> 625\u001b[0m last_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlastHistoryStep\u001b[49m\n\u001b[1;32m    626\u001b[0m \u001b[38;5;66;03m# set defaults for min/max step\u001b[39;00m\n\u001b[1;32m    627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m min_step \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/wandb/apis/public/runs.py:775\u001b[0m, in \u001b[0;36mRun.lastHistoryStep\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    764\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    765\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlastHistoryStep\u001b[39m(\u001b[38;5;28mself\u001b[39m):  \u001b[38;5;66;03m# noqa: N802\u001b[39;00m\n\u001b[1;32m    766\u001b[0m     query \u001b[38;5;241m=\u001b[39m gql(\n\u001b[1;32m    767\u001b[0m \u001b[38;5;250m        \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    768\u001b[0m \u001b[38;5;124;03m    query RunHistoryKeys($project: String!, $entity: String!, $name: String!) {\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    773\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    774\u001b[0m     )\n\u001b[0;32m--> 775\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_exec\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    776\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m    777\u001b[0m         response \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    778\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m response\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    779\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m response[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    780\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m response[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhistoryKeys\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    781\u001b[0m     ):\n\u001b[1;32m    782\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/wandb/apis/public/runs.py:472\u001b[0m, in \u001b[0;36mRun._exec\u001b[0;34m(self, query, **kwargs)\u001b[0m\n\u001b[1;32m    470\u001b[0m variables \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mentity\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mentity, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mproject\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mproject, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid}\n\u001b[1;32m    471\u001b[0m variables\u001b[38;5;241m.\u001b[39mupdate(kwargs)\n\u001b[0;32m--> 472\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariable_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/wandb/sdk/lib/retry.py:212\u001b[0m, in \u001b[0;36mretriable.<locals>.decorator.<locals>.wrapped_fn\u001b[0;34m(*args, **kargs)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(fn)\n\u001b[1;32m    211\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped_fn\u001b[39m(\u001b[38;5;241m*\u001b[39margs: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkargs: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m--> 212\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mretrier\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/wandb/sdk/lib/retry.py:131\u001b[0m, in \u001b[0;36mRetry.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 131\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    132\u001b[0m         \u001b[38;5;66;03m# Only print resolved attempts once every minute\u001b[39;00m\n\u001b[1;32m    133\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_iter \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m now \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_last_print \u001b[38;5;241m>\u001b[39m datetime\u001b[38;5;241m.\u001b[39mtimedelta(\n\u001b[1;32m    134\u001b[0m             minutes\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    135\u001b[0m         ):\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/wandb/apis/public/api.py:75\u001b[0m, in \u001b[0;36mRetryingClient.execute\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;129m@retry\u001b[39m\u001b[38;5;241m.\u001b[39mretriable(\n\u001b[1;32m     69\u001b[0m     retry_timedelta\u001b[38;5;241m=\u001b[39mRETRY_TIMEDELTA,\n\u001b[1;32m     70\u001b[0m     check_retry_fn\u001b[38;5;241m=\u001b[39mutil\u001b[38;5;241m.\u001b[39mno_retry_auth,\n\u001b[1;32m     71\u001b[0m     retryable_exceptions\u001b[38;5;241m=\u001b[39m(RetryError, requests\u001b[38;5;241m.\u001b[39mRequestException),\n\u001b[1;32m     72\u001b[0m )\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mexecute\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     74\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 75\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     76\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m requests\u001b[38;5;241m.\u001b[39mexceptions\u001b[38;5;241m.\u001b[39mReadTimeout:\n\u001b[1;32m     77\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m kwargs:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/wandb/vendor/gql-0.2.0/wandb_gql/client.py:52\u001b[0m, in \u001b[0;36mClient.execute\u001b[0;34m(self, document, *args, **kwargs)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mschema:\n\u001b[1;32m     50\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvalidate(document)\n\u001b[0;32m---> 52\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdocument\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     53\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result\u001b[38;5;241m.\u001b[39merrors:\n\u001b[1;32m     54\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;28mstr\u001b[39m(result\u001b[38;5;241m.\u001b[39merrors[\u001b[38;5;241m0\u001b[39m]))\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/wandb/vendor/gql-0.2.0/wandb_gql/client.py:60\u001b[0m, in \u001b[0;36mClient._get_result\u001b[0;34m(self, document, *args, **kwargs)\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_result\u001b[39m(\u001b[38;5;28mself\u001b[39m, document, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m     59\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mretries:\n\u001b[0;32m---> 60\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransport\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdocument\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     62\u001b[0m     last_exception \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     63\u001b[0m     retries_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/wandb/sdk/lib/gql_request.py:58\u001b[0m, in \u001b[0;36mGraphQLSession.execute\u001b[0;34m(self, document, variable_values, timeout)\u001b[0m\n\u001b[1;32m     51\u001b[0m data_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mjson\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_json \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     52\u001b[0m post_args \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mheaders\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[1;32m     54\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcookies\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcookies,\n\u001b[1;32m     55\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_timeout,\n\u001b[1;32m     56\u001b[0m     data_key: payload,\n\u001b[1;32m     57\u001b[0m }\n\u001b[0;32m---> 58\u001b[0m request \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msession\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpost\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpost_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     59\u001b[0m request\u001b[38;5;241m.\u001b[39mraise_for_status()\n\u001b[1;32m     61\u001b[0m result \u001b[38;5;241m=\u001b[39m request\u001b[38;5;241m.\u001b[39mjson()\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/requests/sessions.py:637\u001b[0m, in \u001b[0;36mSession.post\u001b[0;34m(self, url, data, json, **kwargs)\u001b[0m\n\u001b[1;32m    626\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\u001b[38;5;28mself\u001b[39m, url, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, json\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    627\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a POST request. Returns :class:`Response` object.\u001b[39;00m\n\u001b[1;32m    628\u001b[0m \n\u001b[1;32m    629\u001b[0m \u001b[38;5;124;03m    :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    634\u001b[0m \u001b[38;5;124;03m    :rtype: requests.Response\u001b[39;00m\n\u001b[1;32m    635\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 637\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPOST\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjson\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    585\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m    586\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m    587\u001b[0m }\n\u001b[1;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43msend_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m \u001b[43madapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m    706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/requests/adapters.py:667\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    664\u001b[0m     timeout \u001b[38;5;241m=\u001b[39m TimeoutSauce(connect\u001b[38;5;241m=\u001b[39mtimeout, read\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[1;32m    666\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 667\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murlopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    668\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    669\u001b[0m \u001b[43m        \u001b[49m\u001b[43murl\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    670\u001b[0m \u001b[43m        \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    671\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    672\u001b[0m \u001b[43m        \u001b[49m\u001b[43mredirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    673\u001b[0m \u001b[43m        \u001b[49m\u001b[43massert_same_host\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    674\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    675\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    676\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    677\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    678\u001b[0m \u001b[43m        \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    679\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    681\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    682\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(err, request\u001b[38;5;241m=\u001b[39mrequest)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/urllib3/connectionpool.py:793\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    790\u001b[0m response_conn \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m release_conn \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    792\u001b[0m \u001b[38;5;66;03m# Make the request on the HTTPConnection object\u001b[39;00m\n\u001b[0;32m--> 793\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    794\u001b[0m \u001b[43m    \u001b[49m\u001b[43mconn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    795\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    796\u001b[0m \u001b[43m    \u001b[49m\u001b[43murl\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    797\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    798\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbody\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    799\u001b[0m \u001b[43m    \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    800\u001b[0m \u001b[43m    \u001b[49m\u001b[43mchunked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    801\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    802\u001b[0m \u001b[43m    \u001b[49m\u001b[43mresponse_conn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresponse_conn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    803\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpreload_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreload_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    804\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdecode_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    805\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mresponse_kw\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    806\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    808\u001b[0m \u001b[38;5;66;03m# Everything went great!\u001b[39;00m\n\u001b[1;32m    809\u001b[0m clean_exit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/urllib3/connectionpool.py:537\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    535\u001b[0m \u001b[38;5;66;03m# Receive the response from the server\u001b[39;00m\n\u001b[1;32m    536\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 537\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[43mconn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    538\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (BaseSSLError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    539\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_timeout(err\u001b[38;5;241m=\u001b[39me, url\u001b[38;5;241m=\u001b[39murl, timeout_value\u001b[38;5;241m=\u001b[39mread_timeout)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/site-packages/urllib3/connection.py:466\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    463\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mresponse\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HTTPResponse\n\u001b[1;32m    465\u001b[0m \u001b[38;5;66;03m# Get the response from http.client.HTTPConnection\u001b[39;00m\n\u001b[0;32m--> 466\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetresponse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    468\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    469\u001b[0m     assert_header_parsing(httplib_response\u001b[38;5;241m.\u001b[39mmsg)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/http/client.py:1428\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1426\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1427\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1428\u001b[0m         \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbegin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1429\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m:\n\u001b[1;32m   1430\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/http/client.py:331\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    329\u001b[0m \u001b[38;5;66;03m# read until we get a non-100 response\u001b[39;00m\n\u001b[1;32m    330\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 331\u001b[0m     version, status, reason \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    332\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m status \u001b[38;5;241m!=\u001b[39m CONTINUE:\n\u001b[1;32m    333\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/http/client.py:292\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    291\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_read_status\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 292\u001b[0m     line \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadline\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_MAXLINE\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124miso-8859-1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    293\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(line) \u001b[38;5;241m>\u001b[39m _MAXLINE:\n\u001b[1;32m    294\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m LineTooLong(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstatus line\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/socket.py:707\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    705\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    706\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 707\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecv_into\u001b[49m\u001b[43m(\u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    708\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m timeout:\n\u001b[1;32m    709\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_timeout_occurred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/ssl.py:1252\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1248\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m flags \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   1249\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1250\u001b[0m           \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m   1251\u001b[0m           \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m)\n\u001b[0;32m-> 1252\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnbytes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1253\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1254\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mrecv_into(buffer, nbytes, flags)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.3/lib/python3.12/ssl.py:1104\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1102\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1103\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m buffer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1104\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sslobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1105\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1106\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sslobj\u001b[38;5;241m.\u001b[39mread(\u001b[38;5;28mlen\u001b[39m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from bounds.classical_bounds import binomial_approximation\n",
    "from bounds.bound_utils import zeta, log_binomial_coefficient\n",
    "from time import sleep\n",
    "\n",
    "new_vals_to_fetch = deepcopy(values_to_fetch)\n",
    "new_vals_to_fetch.append(\"complement_error\")\n",
    "new_vals_to_fetch.remove(\"p2l_bound\")\n",
    "new_vals_to_fetch.remove(\"binomial_approximation_shah\")\n",
    "early_stop_result_matrix = np.ones(((len(new_vals_to_fetch)+1,) + size_hyperparams))\n",
    "\n",
    "for run_idx in range(len(runs)):\n",
    "    sleep(10)\n",
    "    run = runs[run_idx]\n",
    "    kl_history = run.scan_history(keys=[\"kl_bound\"])\n",
    "    kl_list = [row[\"kl_bound\"] for row in kl_history]\n",
    "    kl_idx = np.argmin(kl_list)\n",
    "    \n",
    "    val_sizes = run.config['data_groupsize'] * run.config['log_iterations']\n",
    "    compression_sizes = np.array(list(range(1,len(kl_list)))) * val_sizes\n",
    "    compression_sizes = np.append(compression_sizes, run.summary['compression_set_size'])\n",
    "    \n",
    "    for val_to_fetch_idx in range(len(new_vals_to_fetch)):\n",
    "        matrix_idx = tuple([val_to_fetch_idx] + [hps[key].index(run.config[key]) for key in hps.keys()])\n",
    "        val_to_fetch = new_vals_to_fetch[val_to_fetch_idx]\n",
    "        history = run.scan_history(keys=[val_to_fetch])\n",
    "        losses = [row[val_to_fetch] for row in history]\n",
    "        if val_to_fetch == \"validation_error\" and len(losses) == 1:\n",
    "            history = run.scan_history(keys=[\"val_error\"])\n",
    "            losses = [row[\"val_error\"] for row in history]\n",
    "            losses.append(run.summary[\"validation_error\"])\n",
    "        if val_to_fetch == \"compression_set_size\":\n",
    "            losses = compression_sizes\n",
    "            # TO BE REMOVED\n",
    "        if val_to_fetch == \"test_error\":\n",
    "            if dataset + str(first_class) + str(second_class) == \"mnist49\":\n",
    "                if len(losses) == 1:\n",
    "                    history = run.scan_history(keys=[\"test_results\"])\n",
    "                    test_error = [row[\"test_results\"] for row in history]\n",
    "                    test_error.append(run.summary['test_error'])\n",
    "                    losses = test_error\n",
    "        early_stop_result_matrix[matrix_idx] = losses[kl_idx]\n",
    "\n",
    "    matrix_idx = tuple([hps[key].index(run.config[key]) for key in hps.keys()])\n",
    "    complement_error = early_stop_result_matrix[(new_vals_to_fetch.index('complement_error'),) + matrix_idx]\n",
    "    m = early_stop_result_matrix[(new_vals_to_fetch.index('compression_set_size'),) + matrix_idx]\n",
    "    n = run.summary['train_set_size']\n",
    "    k = complement_error * (n-m)\n",
    "    \n",
    "    log_delta = np.log(zeta(m)*run.config['delta']) - log_binomial_coefficient(n,m)\n",
    "    delta_prime = np.exp(log_delta)\n",
    "    early_stop_result_matrix[(-1,) + matrix_idx] = binomial_approximation(k, n-m, log_delta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3002da58-51d3-42fe-9eb8-18ee3d5b6ba8",
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop_matrix = early_stop_result_matrix.mean(1).reshape(early_stop_result_matrix.shape[0],np.prod(early_stop_result_matrix.shape[2:])).T\n",
    "l = deepcopy(new_vals_to_fetch)\n",
    "l.append(\"binomial_approximation_shah\")\n",
    "mean_early_stop_df = pd.DataFrame(early_stop_matrix, index=name_list, columns=l)\n",
    "mean_early_stop_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0aa2aed4-9d26-45b8-8b2c-a66e841d69f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_early_stop_df.to_csv(f\"./results/binary_mnist/mean_{project}.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b88b0277-2b58-474d-8a4f-54929831b512",
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stop_std = early_stop_result_matrix.std(1).reshape(early_stop_result_matrix.shape[0],np.prod(early_stop_result_matrix.shape[2:])).T\n",
    "std_early_stop_df = pd.DataFrame(early_stop_std, index=name_list, columns=l)\n",
    "mean_early_stop_df.to_csv(f\"./results/binary_mnist/std_{project}.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8833acac-5045-47cb-b862-931464586d70",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_arr = early_stop_result_matrix.mean(1)\n",
    "index = 2\n",
    "\n",
    "fig, axs = plt.subplots(2, 2)\n",
    "fig.delaxes(axs[0, 1])\n",
    "fig.subplots_adjust(right=0.8)\n",
    "cbar_ax = fig.add_axes([1, 0.12, 0.05, 0.85])\n",
    "\n",
    "cmap = 'Blues'\n",
    "vmax = mean_arr[index].max()\n",
    "vmin = mean_arr[index].min()\n",
    "\n",
    "lr_mt_arr = pd.DataFrame(mean_arr.mean(2)[index,:,:], index=hps['model_type'], columns=hps['training_lr'])\n",
    "lr_dp_arr = pd.DataFrame(mean_arr.mean(1)[index,:,:], index=hps['dropout_probability'], columns=hps['training_lr'])\n",
    "dp_mt_arr = pd.DataFrame(mean_arr.mean(3)[index,:,:], index=hps['model_type'], columns=hps['dropout_probability'])\n",
    "\n",
    "sn.heatmap(lr_dp_arr, annot=True,ax=axs[0,0],xticklabels=False, cbar=False, vmin=vmin, vmax=vmax, cmap=cmap)\n",
    "axs[0,0].set(ylabel=\"dropout probability\")\n",
    "\n",
    "sn.heatmap(lr_mt_arr, annot=True, ax=axs[1,0], cbar=False, cmap=cmap, vmin=vmin, vmax=vmax,)\n",
    "axs[1,0].set(xlabel=\"training lr\", ylabel=\"model type\")\n",
    "\n",
    "sn.heatmap(dp_mt_arr,annot=True, ax=axs[1,1], yticklabels=False, cbar=True, cmap=cmap, cbar_ax=cbar_ax, vmin=vmin, vmax=vmax,)\n",
    "axs[1,1].set(xlabel=\"dropout probability\")\n",
    "\n",
    "\n",
    "fig.suptitle(f\"Comparison of the {new_vals_to_fetch[index].replace('_', ' ')} for the dataset {dataset + str(first_class) + str(second_class)}\")\n",
    "# Layout so plots do not overlap\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.savefig(f\"./results/binary_mnist/early_stop_{dataset}_{first_class}_{second_class}_{new_vals_to_fetch[index]}_heatmaps.jpg\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e39a14a-2d2f-491a-a6cd-5658810c2a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bounds.classical_bounds import compute_classical_compression_bounds\n",
    "val_of_interest = \"kl_bound\"\n",
    "wanted_model_type = \"cnn\"\n",
    "model_df = mean_early_stop_df[[wanted_model_type in idx for idx in mean_early_stop_df.index]]\n",
    "std_model_df = std_early_stop_df[[wanted_model_type in idx for idx in std_early_stop_df.index]]\n",
    "\n",
    "model_type, dropout_probability, training_lr = correct_type_of_entry(model_df.index[model_df[val_of_interest].argmin()].split())\n",
    "\n",
    "print(\"model type :\", model_type, \"dropout probability :\", dropout_probability, \"learning rate :\", training_lr)\n",
    "\n",
    "best_val_arr = model_df.loc[model_df.index[model_df[val_of_interest].argmin()]]\n",
    "std_val_arr = std_model_df.loc[model_df.index[model_df[val_of_interest].argmin()]]\n",
    "print(f\"Complement error: {100*best_val_arr['complement_error']:.2f}±{100*std_val_arr['complement_error']:.2f} \"\n",
    "    f\"Validation error: {100*best_val_arr['validation_error']:.2f}±{100*std_val_arr['validation_error']:.2f} \"\n",
    "      f'Test error : {100*best_val_arr['test_error']:.2f}±{100*std_val_arr['test_error']:.2f} ', \n",
    "      f\"KL bound : {100*best_val_arr['kl_bound']:.2f}±{100*std_val_arr['kl_bound']:.2f} \",\n",
    "    f\"Binomial bound : {100*best_val_arr['binomial_approximation_shah']:.2f}±{100*std_val_arr['binomial_approximation_shah']:.2f} \",\n",
    "      f'compression set size : {best_val_arr['compression_set_size']:.1f}±{std_val_arr['compression_set_size']:.1f} ')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec5fdda5-3fa5-4ac7-934a-6540004f654d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"{100*best_val_arr['complement_error']:.2f}$\\\\pm${100*std_val_arr['complement_error']:.2f} & {100*best_val_arr['validation_error']:.2f}$\\\\pm${100*std_val_arr['validation_error']:.2f} & {100*best_val_arr['test_error']:.2f}$\\\\pm${100*std_val_arr['test_error']:.2f} & {100*best_val_arr['kl_bound']:.2f}$\\\\pm${100*std_val_arr['kl_bound']:.2f} & {100*best_val_arr['binomial_approximation_shah']:.2f}$\\\\pm${100*std_val_arr['binomial_approximation_shah']:.2f} & {best_val_arr['compression_set_size']:.1f}$\\\\pm${std_val_arr['compression_set_size']:.1f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f326039d-55ab-468e-a6ff-0b207a2e4f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"{4.6984:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abef1d09-51cd-4351-9963-530216f93bcf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "275a39f9-4954-4ff4-9535-1595fd12de7a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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