stcnns_perf_summary_table_rotations_random.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_config = {\n",
    "    \"C\": 1,\n",
    "    \"H\": 28,\n",
    "    \"W\": 28\n",
    "}\n",
    "\n",
    "output_config = {\n",
    "    \"C\": 10\n",
    "}\n",
    "\n",
    "experiment_config = {\n",
    "    \"num_iters\": 1,\n",
    "    \"batch_size\": 32,\n",
    "    \"max_epochs\": 20,\n",
    "    \"log_every_n_steps\": 100,\n",
    "    \"patience\": 5,\n",
    "    \"rotate_train\": True,\n",
    "    \"rotate_test\": True,\n",
    "    \"seed\": 420,\n",
    "}\n",
    "\n",
    "\n",
    "def get_stcnn_hparams(\n",
    "    conv_layers: int,\n",
    "    channels: int,\n",
    "    localization_channels: int,\n",
    "    kernel_size: int = 5,\n",
    "    mode: str = \"single\",\n",
    "):\n",
    "  return {\n",
    "      \"mode\": mode,\n",
    "      \"in_channels\": input_config[\"C\"],\n",
    "      \"out_channels\": output_config[\"C\"],\n",
    "      \"channels\": channels,\n",
    "      \"localization_channels\": localization_channels,\n",
    "      \"conv_layers\": conv_layers,\n",
    "      \"kernel_size\": kernel_size,\n",
    "      \"localization_initialization_mode\": \"random\",\n",
    "      \"transformation_mode\": \"rotation\",\n",
    "  }\n",
    "\n",
    "\n",
    "optimizer_hparams = {\n",
    "    \"lr\": 1e-2,\n",
    "    \"weight_decay\": 1e-4\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics_base_path = \"results/rotations/stcnns/random-init\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from modules.MNISTModule import MNISTModule\n",
    "model_name = \"STCNN\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model_saving_helpers import get_model_save_name\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "\n",
    "from count_model_parameters import count_model_parameters\n",
    "\n",
    "\n",
    "def aggregate_model_stats(model_name, model_hparams, optimizer_hparams, experiment_config):\n",
    "  model_save_name = get_model_save_name(\n",
    "      model_name=model_name,\n",
    "      model_hparams=model_hparams,\n",
    "      optimizer_hparams=optimizer_hparams,\n",
    "      experiment_config=experiment_config\n",
    "  )\n",
    "  model = MNISTModule(model_name, model_hparams, optimizer_hparams).model\n",
    "  model_params = count_model_parameters(model)\n",
    "  million = pow(10, 3)\n",
    "\n",
    "  model_metrics_per_run = Path(\n",
    "      metrics_base_path).glob(f\"*{model_save_name}*.csv\")\n",
    "\n",
    "  model_metrics = pd.concat([pd.read_csv(experiment_metrics)\n",
    "                             for experiment_metrics in model_metrics_per_run])\n",
    "  test_metrics_subselection = model_metrics[[\n",
    "      \"test_loss\",\n",
    "      \"test_acc\",\n",
    "  ]].dropna()\n",
    "  test_metrics_means = test_metrics_subselection.mean().round(4)\n",
    "  test_metrics_stds = test_metrics_subselection.std().round(4)\n",
    "\n",
    "  test_acc_mean = test_metrics_means[\"test_acc\"]\n",
    "  test_acc_std = test_metrics_stds[\"test_acc\"]\n",
    "\n",
    "  report = pd.DataFrame({\n",
    "      \"Mode\": [model_hparams[\"mode\"]],\n",
    "      \"Conv Layers\": [model_hparams[\"conv_layers\"]],\n",
    "      \"Channels\": [model_hparams[\"channels\"]],\n",
    "      \"Localization Net Channels\": [model_hparams[\"localization_channels\"]],\n",
    "      \"Kernel Size\": [model_hparams[\"kernel_size\"]],\n",
    "      \"Params\": [f\"{np.round((model_params / million), 2)} K\"],\n",
    "      \"Test Accuracy\": [f\"{test_acc_mean} ± {test_acc_std}\"],\n",
    "  })\n",
    "\n",
    "  return report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "\n",
    "def get_max_model_stats(model_name, model_hparams, optimizer_hparams, experiment_config):\n",
    "  model_save_name = get_model_save_name(\n",
    "      model_name=model_name,\n",
    "      model_hparams=model_hparams,\n",
    "      optimizer_hparams=optimizer_hparams,\n",
    "      experiment_config=experiment_config\n",
    "  )\n",
    "  model = MNISTModule(model_name, model_hparams, optimizer_hparams).model\n",
    "  model_params = count_model_parameters(model)\n",
    "  million = pow(10, 3)\n",
    "\n",
    "  model_metrics_per_run = Path(\n",
    "      metrics_base_path).glob(f\"*{model_save_name}*.csv\")\n",
    "\n",
    "  all_metrics_files = []\n",
    "  for metrics in model_metrics_per_run:\n",
    "    run = re.search(\"-run=.\", str(metrics)).group(0)[-1]\n",
    "    read_metrics = pd.read_csv(metrics)\n",
    "    read_metrics[\"run\"] = int(run)\n",
    "    all_metrics_files.append(read_metrics.reset_index())\n",
    "\n",
    "  model_metrics = pd.concat(all_metrics_files)\n",
    "  test_metrics_subselection = model_metrics[[\n",
    "      \"run\",\n",
    "      \"test_loss\",\n",
    "      \"test_acc\",\n",
    "  ]].dropna()\n",
    "\n",
    "  best_acc_row = test_metrics_subselection[test_metrics_subselection.test_acc ==\n",
    "                                           test_metrics_subselection.test_acc.max()].reset_index()\n",
    "\n",
    "  report = pd.DataFrame({\n",
    "      \"Mode\": [model_hparams[\"mode\"]],\n",
    "      \"Conv Layers\": [model_hparams[\"conv_layers\"]],\n",
    "      \"Channels\": [model_hparams[\"channels\"]],\n",
    "      \"Localization Net Channels\": [model_hparams[\"localization_channels\"]],\n",
    "      \"Kernel Size\": [model_hparams[\"kernel_size\"]],\n",
    "      \"Params\": [f\"{np.round((model_params / million), 2)} K\"],\n",
    "      \"Best Run\": [best_acc_row[\"run\"].tolist()[0]],\n",
    "      \"Max Test Accuracy\": [best_acc_row[\"test_acc\"].tolist()[0]],\n",
    "  })\n",
    "\n",
    "  return report\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mode</th>\n",
       "      <th>Conv Layers</th>\n",
       "      <th>Channels</th>\n",
       "      <th>Localization Net Channels</th>\n",
       "      <th>Kernel Size</th>\n",
       "      <th>Params</th>\n",
       "      <th>Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>single</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>54.25 K</td>\n",
       "      <td>0.9407 ± 0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>single</td>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>209.07 K</td>\n",
       "      <td>0.9429 ± 0.0039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>single</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>110.54 K</td>\n",
       "      <td>0.9735 ± 0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>single</td>\n",
       "      <td>5</td>\n",
       "      <td>64</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>414.0 K</td>\n",
       "      <td>0.9766 ± 0.0019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>multi</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>101.88 K</td>\n",
       "      <td>0.9471 ± 0.0113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>multi</td>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>412.31 K</td>\n",
       "      <td>0.9446 ± 0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>multi</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>202.52 K</td>\n",
       "      <td>0.9713 ± 0.0034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>multi</td>\n",
       "      <td>5</td>\n",
       "      <td>64</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>821.58 K</td>\n",
       "      <td>0.9733 ± 0.0037</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mode  Conv Layers  Channels  Localization Net Channels  Kernel Size  \\\n",
       "0  single            3        32                          8            5   \n",
       "0  single            3        64                          8            5   \n",
       "0  single            5        32                         16            5   \n",
       "0  single            5        64                          8            5   \n",
       "0   multi            3        32                         17            5   \n",
       "0   multi            3        64                         35            5   \n",
       "0   multi            5        32                         18            5   \n",
       "0   multi            5        64                         37            5   \n",
       "\n",
       "     Params    Test Accuracy  \n",
       "0   54.25 K   0.9407 ± 0.007  \n",
       "0  209.07 K  0.9429 ± 0.0039  \n",
       "0  110.54 K  0.9735 ± 0.0019  \n",
       "0   414.0 K  0.9766 ± 0.0019  \n",
       "0  101.88 K  0.9471 ± 0.0113  \n",
       "0  412.31 K   0.9446 ± 0.006  \n",
       "0  202.52 K  0.9713 ± 0.0034  \n",
       "0  821.58 K  0.9733 ± 0.0037  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from model_saving_helpers import get_model_save_name\n",
    "from pathlib import Path\n",
    "\n",
    "modes = [\"single\", \"multi\"]\n",
    "convs = [3, 5]\n",
    "channels = [32, 64]\n",
    "\n",
    "loc_channels = [8, 16, 17, 32, 35, 64, 18, 37]\n",
    "\n",
    "all_models_stats = []\n",
    "\n",
    "for mode in modes:\n",
    "  for conv in convs:\n",
    "    for channel in channels:\n",
    "      for loc_channel in loc_channels:\n",
    "        model_save_name = get_model_save_name(\n",
    "            model_name=model_name,\n",
    "            model_hparams=get_stcnn_hparams(\n",
    "                conv, channel, loc_channel, mode=mode),\n",
    "            optimizer_hparams=optimizer_hparams,\n",
    "            experiment_config=experiment_config\n",
    "        )\n",
    "        model_metrics_per_run = Path(\n",
    "            metrics_base_path).glob(f\"*{model_save_name}*.csv\")\n",
    "        if len(list(model_metrics_per_run)):\n",
    "          all_models_stats.append(\n",
    "              aggregate_model_stats(\n",
    "                  model_name=model_name,\n",
    "                  model_hparams=get_stcnn_hparams(\n",
    "                      conv, channel, loc_channel, mode=mode),\n",
    "                  optimizer_hparams=optimizer_hparams,\n",
    "                  experiment_config=experiment_config\n",
    "              )\n",
    "          )\n",
    "\n",
    "all_models_stats = pd.concat(all_models_stats)\n",
    "\n",
    "all_models_stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{c|c|c|c|c|c}\n",
      "\\toprule\n",
      "  Mode &  Conv Layers &  Channels &  Localization Net Channels &  Kernel Size &   Params &   Test Accuracy \\\\\n",
      "\\midrule\n",
      "single &            3 &        32 &                          8 &            5 &  54.25 K &  0.9407 ± 0.007 \\\\\n",
      "single &            3 &        64 &                          8 &            5 & 209.07 K & 0.9429 ± 0.0039 \\\\\n",
      "single &            5 &        32 &                         16 &            5 & 110.54 K & 0.9735 ± 0.0019 \\\\\n",
      "single &            5 &        64 &                          8 &            5 &  414.0 K & 0.9766 ± 0.0019 \\\\\n",
      " multi &            3 &        32 &                         17 &            5 & 101.88 K & 0.9471 ± 0.0113 \\\\\n",
      " multi &            3 &        64 &                         35 &            5 & 412.31 K &  0.9446 ± 0.006 \\\\\n",
      " multi &            5 &        32 &                         18 &            5 & 202.52 K & 0.9713 ± 0.0034 \\\\\n",
      " multi &            5 &        64 &                         37 &            5 & 821.58 K & 0.9733 ± 0.0037 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/qw/52cxc2651nb6vmjvb6vb0gmr0000gn/T/ipykernel_32598/985651510.py:1: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  print(all_models_stats.to_latex(\n"
     ]
    }
   ],
   "source": [
    "print(all_models_stats.to_latex(\n",
    "    column_format=\"c|c|c|c|c|c\",\n",
    "    index=False)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mode</th>\n",
       "      <th>Conv Layers</th>\n",
       "      <th>Channels</th>\n",
       "      <th>Localization Net Channels</th>\n",
       "      <th>Kernel Size</th>\n",
       "      <th>Params</th>\n",
       "      <th>Best Run</th>\n",
       "      <th>Max Test Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>single</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>54.25 K</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>single</td>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>209.07 K</td>\n",
       "      <td>3</td>\n",
       "      <td>0.9474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>single</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>110.54 K</td>\n",
       "      <td>2</td>\n",
       "      <td>0.9757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>single</td>\n",
       "      <td>5</td>\n",
       "      <td>64</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>414.0 K</td>\n",
       "      <td>3</td>\n",
       "      <td>0.9783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>multi</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>101.88 K</td>\n",
       "      <td>2</td>\n",
       "      <td>0.9564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>multi</td>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "      <td>35</td>\n",
       "      <td>5</td>\n",
       "      <td>412.31 K</td>\n",
       "      <td>2</td>\n",
       "      <td>0.9490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>multi</td>\n",
       "      <td>5</td>\n",
       "      <td>32</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>202.52 K</td>\n",
       "      <td>2</td>\n",
       "      <td>0.9748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>multi</td>\n",
       "      <td>5</td>\n",
       "      <td>64</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>821.58 K</td>\n",
       "      <td>1</td>\n",
       "      <td>0.9762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Mode  Conv Layers  Channels  Localization Net Channels  Kernel Size  \\\n",
       "0  single            3        32                          8            5   \n",
       "0  single            3        64                          8            5   \n",
       "0  single            5        32                         16            5   \n",
       "0  single            5        64                          8            5   \n",
       "0   multi            3        32                         17            5   \n",
       "0   multi            3        64                         35            5   \n",
       "0   multi            5        32                         18            5   \n",
       "0   multi            5        64                         37            5   \n",
       "\n",
       "     Params  Best Run  Max Test Accuracy  \n",
       "0   54.25 K         1             0.9482  \n",
       "0  209.07 K         3             0.9474  \n",
       "0  110.54 K         2             0.9757  \n",
       "0   414.0 K         3             0.9783  \n",
       "0  101.88 K         2             0.9564  \n",
       "0  412.31 K         2             0.9490  \n",
       "0  202.52 K         2             0.9748  \n",
       "0  821.58 K         1             0.9762  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from model_saving_helpers import get_model_save_name\n",
    "from pathlib import Path\n",
    "\n",
    "modes = [\"single\", \"multi\"]\n",
    "convs = [3, 5]\n",
    "channels = [32, 64]\n",
    "\n",
    "loc_channels = [8, 16, 17, 32, 35, 64, 18, 37]\n",
    "\n",
    "all_models_stats = []\n",
    "\n",
    "for mode in modes:\n",
    "  for conv in convs:\n",
    "    for channel in channels:\n",
    "      for loc_channel in loc_channels:\n",
    "        model_save_name = get_model_save_name(\n",
    "            model_name=model_name,\n",
    "            model_hparams=get_stcnn_hparams(\n",
    "                conv, channel, loc_channel, mode=mode),\n",
    "            optimizer_hparams=optimizer_hparams,\n",
    "            experiment_config=experiment_config\n",
    "        )\n",
    "        model_metrics_per_run = Path(\n",
    "            metrics_base_path).glob(f\"*{model_save_name}*.csv\")\n",
    "        if len(list(model_metrics_per_run)):\n",
    "          all_models_stats.append(\n",
    "              get_max_model_stats(\n",
    "                  model_name=model_name,\n",
    "                  model_hparams=get_stcnn_hparams(\n",
    "                      conv, channel, loc_channel, mode=mode),\n",
    "                  optimizer_hparams=optimizer_hparams,\n",
    "                  experiment_config=experiment_config\n",
    "              )\n",
    "          )\n",
    "\n",
    "all_models_stats = pd.concat(all_models_stats)\n",
    "\n",
    "all_models_stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{c|c|c|c|c|c}\n",
      "\\toprule\n",
      "  Mode &  Conv Layers &  Channels &  Localization Net Channels &  Kernel Size &   Params &  Best Run &  Max Test Accuracy \\\\\n",
      "\\midrule\n",
      "single &            3 &        32 &                          8 &            5 &  54.25 K &         1 &             0.9482 \\\\\n",
      "single &            3 &        64 &                          8 &            5 & 209.07 K &         3 &             0.9474 \\\\\n",
      "single &            5 &        32 &                         16 &            5 & 110.54 K &         2 &             0.9757 \\\\\n",
      "single &            5 &        64 &                          8 &            5 &  414.0 K &         3 &             0.9783 \\\\\n",
      " multi &            3 &        32 &                         17 &            5 & 101.88 K &         2 &             0.9564 \\\\\n",
      " multi &            3 &        64 &                         35 &            5 & 412.31 K &         2 &             0.9490 \\\\\n",
      " multi &            5 &        32 &                         18 &            5 & 202.52 K &         2 &             0.9748 \\\\\n",
      " multi &            5 &        64 &                         37 &            5 & 821.58 K &         1 &             0.9762 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/qw/52cxc2651nb6vmjvb6vb0gmr0000gn/T/ipykernel_32598/985651510.py:1: FutureWarning: In future versions `DataFrame.to_latex` is expected to utilise the base implementation of `Styler.to_latex` for formatting and rendering. The arguments signature may therefore change. It is recommended instead to use `DataFrame.style.to_latex` which also contains additional functionality.\n",
      "  print(all_models_stats.to_latex(\n"
     ]
    }
   ],
   "source": [
    "print(all_models_stats.to_latex(\n",
    "    column_format=\"c|c|c|c|c|c\",\n",
    "    index=False)\n",
    ")\n"
   ]
  }
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