main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4a4b9a3b-6422-4039-bc9c-f3ab5b9bcef2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current device: cuda\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import torch\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(\"Current device:\", device)\n",
    "\n",
    "RANDOM_SEED = [1,2,3,4,5,6,7,4399,114514,1234]\n",
    "# RANDOM_SEED = [1]\n",
    "BATCH_SIZE = 32\n",
    "LEARN_RATE = 0.001\n",
    "MAX_NUM_EPOCH = 200\n",
    "VISUALIZE_MODEL = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b169c7fb-3232-4d05-be44-e162d542d44e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from models.CAE import CNN1D\n",
    "from torchview import draw_graph\n",
    "\n",
    "model = CNN1D()\n",
    "model.to(device)\n",
    "\n",
    "if VISUALIZE_MODEL:\n",
    "    model_graph = draw_graph(\n",
    "        model,\n",
    "        input_size=[(BATCH_SIZE, 84), (BATCH_SIZE, 4)],\n",
    "        expand_nested=True)\n",
    "    display(model_graph.visual_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4dfce902-a39f-46d4-a30e-102ce0cb3cc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outcome Columns: Index(['Diagnosis', 'FEV1', 'FEV1/FVC', 'FEF2575', 'FEV1_pred',\n",
      "       'FEV1/FVC_pred', 'FEF2575_pred', 'R5', 'R5_20', 'R20', 'X5', 'AX', 'TV',\n",
      "       'R5_z_score', 'R5_20_z_score', 'R20_z_score', 'X5_z_score',\n",
      "       'AX_z_score'],\n",
      "      dtype='object')\n",
      "Info Columns: Index(['ID', 'Trial', 'Age', 'Sex', 'Height', 'Weight', 'OralCavity', 'OPJ',\n",
      "       'Glottis', 'Carina'],\n",
      "      dtype='object')\n",
      "# of total CSA samples: 1058\n",
      "# of total subjects: 340\n"
     ]
    },
    {
     "data": {
      "image/png": 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OkjuB14Dbu93/AvgUsA/4GfC5RahZknQSJw33qvr0CTZdf5x9C7hr0qIkSZPxClVJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIadNL7uWv52bnrBTb31PfUirO59cYNPfUu6VQZ7gP0zs/f5aJ11/bS9/4dz/bSr6S5cVpGkhpkuEtSgwx3SWqQ4S5JDfKEquakr0/q7N/3ChddvHbJ+/XTQRoqw11z0tcndZ7btp3fvH3p+/XTQRoqw11app7Y8j1mDx9Z8n59t9KGRQn3JDcA9wFnAA9U1ZcXox9psfV5wdiuF3dz68bfW/J+H//G/+7ljwr4h2UhLXi4JzkD+BPgt4ADwI+TPFVVLy90X9Ji6/OCsee2be+l39PxIrm+3iXB4v1BW4xX7lcB+6pqP0CSbwE3A4a7pGVp9vCR5v6gpaoW9hcmtwE3VNXvduufBT5aVZ8/Zr9NwKZu9RJg74IWcnwrgZ8sQT+Lxfr7M+TaYdj1D7l2WNz6f7Wqpo63obcTqlW1GZZ2OjPJTFWtX8o+F5L192fItcOw6x9y7dBf/YtxEdMbwAVj62u6NknSElmMcP8xsDbJhUk+CNwBPLUI/UiSTmDBp2Wq6r0knwd+wOijkA9V1e6F7mee+vpU20Kx/v4MuXYYdv1Drh16qn/BT6hKkvrnjcMkqUGGuyQ1qNlwT/JQkkNJXhprOy/J00le6Z7P7bPGE0lyQZJnkrycZHeSu7v2odT/oSQ/SrKrq/+PuvYLk2xLsi/Jt7sT7stSkjOS7EyypVsfUu2vJnkxyfNJZrq2QRw7AElWJHksyd8l2ZPkY0OpP8kl3X/3o4+3k9zTR/3NhjvwTeCGY9ruBbZW1Vpga7e+HL0HfKGqLgOuBu5KchnDqf+fgeuq6nLgCuCGJFcDXwG+VlUXAz8F7uyvxJO6G9gztj6k2gF+o6quGPt89VCOHRjdl+r7VXUpcDmjf4dB1F9Ve7v/7lcAvw78DHiCPuqvqmYfwDTw0tj6XmB1t7wa2Nt3jac4jicZ3atncPUDvwLsAD7K6Cq9M7v2jwE/6Lu+E9S8pvsf8DpgC5Ch1N7V9yqw8pi2QRw7wDnAP9B92GNo9R9T828Df9NX/S2/cj+eVVV1sFt+E1jVZzGnIsk0cCWwjQHV301rPA8cAp4G/h44XFXvdbscAM7vqbyT+TrwReAX3fpHGE7tAAX8ZZLt3W0+YDjHzoXALPCNblrsgSRnMZz6x90BPNotL3n9p1u4/5sa/Qld1p8DTfJh4HHgnqp6e3zbcq+/qv61Rm9N1zC6mdyl/VZ0apLcCByqqn5uybgwPl5V64ANjKb0PjG+cZkfO2cC64D7q+pK4B2OmcJY5vUD0J2TuQn4s2O3LVX9p1u4v5VkNUD3fKjnek4oyQcYBfsjVfXdrnkw9R9VVYeBZxhNZaxIcvTCueV6W4prgJuSvAp8i9HUzH0Mo3YAquqN7vkQo/neqxjOsXMAOFBV27r1xxiF/VDqP2oDsKOq3urWl7z+0y3cnwI2dssbGc1lLztJAjwI7Kmqr45tGkr9U0lWdMu/zOh8wR5GIX9bt9uyrL+qvlRVa6pqmtHb6h9W1WcYQO0ASc5KcvbRZUbzvi8xkGOnqt4EXk9ySdd0PaPbhQ+i/jGf5t+nZKCP+vs+6bCIJzMeBQ4C7zJ6NXAno7nTrcArwF8B5/Vd5wlq/zijt20vAM93j08NqP5fA3Z29b8E/GHXfhHwI2Afo7erv9R3rScZxyeBLUOqvatzV/fYDfxB1z6IY6er9Qpgpjt+/hw4d2D1nwX8I3DOWNuS1+/tBySpQafbtIwknRYMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktSg/wcUlRlA9t3G6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of valid CSA samples w/o NaN: 524\n",
      "# of subjects w/ valid CSA samples: 212\n",
      "# of healthy samples: 295\n",
      "# of asthma sampels: 229\n"
     ]
    }
   ],
   "source": [
    "from data.aware import AwareDataset, AwareSplitter\n",
    "import numpy as np\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "\n",
    "dataset = AwareDataset(\n",
    "    csv_data = 'data/exhale_data_v8_ave.csv',\n",
    "    csv_outcome = 'data/exhale_outcome_v8_ave.csv',\n",
    "    csv_info = 'data/exhale_verbose_v8_ave.csv',\n",
    "    root_dir = 'data/',\n",
    "    target_classes = [0,1],\n",
    "    partially_remove = True\n",
    ")\n",
    "\n",
    "class_weights = compute_class_weight(\n",
    "    'balanced', \n",
    "    classes=np.unique(dataset.data_out['Diagnosis']), \n",
    "    y=dataset.data_out['Diagnosis']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e56ae565-6f8f-401f-8519-5c46627859b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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\" 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      "Validataion:\n",
      "Test:\n",
      "\n",
      "Seed 1 | Fold #1 | Pre-training...\n"
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      "Seed 1 | Fold #1 | Fine-tuning...\n"
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      "Validataion:\n",
      "Test:\n",
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      "Seed 1 | Fold #2 | Pre-training...\n"
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     "text": [
      "Validataion:\n",
      "Test:\n",
      "\n",
      "Seed 1 | Fold #3 | Pre-training...\n"
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      "Validataion:\n",
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      "Validataion:\n",
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      "Validataion:\n",
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     "text": [
      "Validataion:\n",
      "Test:\n",
      "\n",
      "Seed 1234 | Fold #4 | Pre-training...\n"
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    },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validataion:\n",
      "Test:\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "from IPython.display import clear_output\n",
    "import time\n",
    "import jupyter_beeper\n",
    "\n",
    "from trainer.pretrain import PreTrainer\n",
    "from trainer.finetune import FineTuner\n",
    "from utils.others import weight_reset, Metrics, Outputs\n",
    "from utils.clustering import evaluate\n",
    "from utils.outlier import novelty_detection\n",
    "\n",
    "# metrics_cluster = np.zeros((5,3,3))\n",
    "metrics_val = Metrics()\n",
    "metrics_test = Metrics()\n",
    "outputs_test = Outputs()\n",
    "ig_test = torch.Tensor([])\n",
    "\n",
    "beeper = jupyter_beeper.Beeper()\n",
    "\n",
    "for rand_seed in RANDOM_SEED:\n",
    "    splitter = AwareSplitter(dataset, BATCH_SIZE, random_seed=rand_seed)\n",
    "\n",
    "    timestr = time.strftime(\"%Y%m%d-%H%M%S\")\n",
    "    for split_idx, (train_loader, val_loader, test_loader) in enumerate(splitter):\n",
    "        writer = SummaryWriter(\"runs/\" + timestr + \"-fold\" + str(split_idx))\n",
    "        model.apply(weight_reset)\n",
    "        \n",
    "#         clear_output(wait=True)\n",
    "        print(\"Seed \" + str(rand_seed) + \" | Fold #\" + str(split_idx) + \" | Pre-training...\")\n",
    "        model.pre_train()\n",
    "        pre_trainer = PreTrainer(\n",
    "            model,\n",
    "            lr = LEARN_RATE,\n",
    "            T_max = MAX_NUM_EPOCH,\n",
    "            device = device,\n",
    "            summarywriter = writer\n",
    "        )\n",
    "        for epoch in tqdm(range(MAX_NUM_EPOCH), unit_scale=True, unit=\"epoch\"):\n",
    "            pre_trainer.train(epoch, train_loader)\n",
    "            pre_trainer.validate(epoch, val_loader)\n",
    "        pre_trainer.test(val_loader, no_print=True)\n",
    "        \n",
    "#         clear_output(wait=True)\n",
    "        print(\"Seed \" + str(rand_seed) + \" | Fold #\" + str(split_idx) + \" | Fine-tuning...\")\n",
    "        model.fine_tune()\n",
    "        fine_tuner = FineTuner(\n",
    "            model,\n",
    "            lr = LEARN_RATE,\n",
    "            T_max = MAX_NUM_EPOCH,\n",
    "            device = device,\n",
    "            summarywriter = writer,\n",
    "            class_weights = torch.Tensor(class_weights)\n",
    "        )\n",
    "        for epoch in tqdm(range(MAX_NUM_EPOCH), unit_scale=True, unit=\"epoch\"):\n",
    "            fine_tuner.train(epoch, train_loader)\n",
    "            fine_tuner.validate(epoch, val_loader)\n",
    "        print(\"Validataion:\")\n",
    "        fine_tuner.test(val_loader, no_print=True)\n",
    "        metrics_val.append_from(fine_tuner)\n",
    "        print(\"Test:\")\n",
    "        fine_tuner.test(test_loader, no_print=True, calculate_ig=True)\n",
    "        metrics_test.append_from(fine_tuner)\n",
    "        outputs_test.append_from(fine_tuner)\n",
    "        ig_test = torch.concat((ig_test, fine_tuner.attr_ig), dim=0)\n",
    "        print()\n",
    "\n",
    "#     beeper.beep(frequency=600, secs=0.5)\n",
    "#     novelty_detection(model, train_loader, val_loader, test_loader)\n",
    "#     metrics_cluster[split_idx,:,:] = evaluate(model, train_loader, val_loader, test_loader)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8e857de5-2564-48d6-9adb-0e190fe3cde4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5225, 88])\n",
      "Final Validation Results\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>Accuracy</th>\n",
       "      <th>Balanced_Acc</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>AUROC</th>\n",
       "      <th>FEV1</th>\n",
       "      <th>FEV1/FVC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.694118</td>\n",
       "      <td>0.696844</td>\n",
       "      <td>0.465116</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.649502</td>\n",
       "      <td>2.014148</td>\n",
       "      <td>2.498660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.696145</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.836735</td>\n",
       "      <td>0.718821</td>\n",
       "      <td>0.155088</td>\n",
       "      <td>0.094923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.551724</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.453704</td>\n",
       "      <td>0.185408</td>\n",
       "      <td>0.122474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.586207</td>\n",
       "      <td>0.660377</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.320755</td>\n",
       "      <td>0.558824</td>\n",
       "      <td>0.220622</td>\n",
       "      <td>0.167815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.688889</td>\n",
       "      <td>0.682815</td>\n",
       "      <td>0.648649</td>\n",
       "      <td>0.716981</td>\n",
       "      <td>0.687914</td>\n",
       "      <td>0.149958</td>\n",
       "      <td>0.132355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.774725</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.642170</td>\n",
       "      <td>0.185570</td>\n",
       "      <td>0.157980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.675676</td>\n",
       "      <td>0.690421</td>\n",
       "      <td>0.758621</td>\n",
       "      <td>0.622222</td>\n",
       "      <td>0.682759</td>\n",
       "      <td>0.137945</td>\n",
       "      <td>0.093440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.617021</td>\n",
       "      <td>0.643810</td>\n",
       "      <td>0.433962</td>\n",
       "      <td>0.853659</td>\n",
       "      <td>0.611597</td>\n",
       "      <td>0.175906</td>\n",
       "      <td>0.152993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.682353</td>\n",
       "      <td>0.641329</td>\n",
       "      <td>0.324324</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.717342</td>\n",
       "      <td>0.201231</td>\n",
       "      <td>0.138981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.673913</td>\n",
       "      <td>0.644016</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.758621</td>\n",
       "      <td>0.679006</td>\n",
       "      <td>0.163179</td>\n",
       "      <td>0.097260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.513158</td>\n",
       "      <td>0.564248</td>\n",
       "      <td>0.740741</td>\n",
       "      <td>0.387755</td>\n",
       "      <td>0.650038</td>\n",
       "      <td>0.114142</td>\n",
       "      <td>0.109219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.705263</td>\n",
       "      <td>0.703333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.688444</td>\n",
       "      <td>0.153028</td>\n",
       "      <td>0.096773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.575000</td>\n",
       "      <td>0.572233</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>0.461538</td>\n",
       "      <td>0.572233</td>\n",
       "      <td>0.180117</td>\n",
       "      <td>0.096353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.679012</td>\n",
       "      <td>0.680815</td>\n",
       "      <td>0.684211</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>0.260611</td>\n",
       "      <td>1.562194</td>\n",
       "      <td>2.326810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.643678</td>\n",
       "      <td>0.706897</td>\n",
       "      <td>0.517241</td>\n",
       "      <td>0.896552</td>\n",
       "      <td>0.699168</td>\n",
       "      <td>0.176779</td>\n",
       "      <td>0.119806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.662162</td>\n",
       "      <td>0.670696</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.512821</td>\n",
       "      <td>0.607326</td>\n",
       "      <td>0.144053</td>\n",
       "      <td>0.118881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.702381</td>\n",
       "      <td>0.741715</td>\n",
       "      <td>0.851852</td>\n",
       "      <td>0.631579</td>\n",
       "      <td>0.789474</td>\n",
       "      <td>0.115745</td>\n",
       "      <td>0.104432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.674699</td>\n",
       "      <td>0.672093</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>0.734884</td>\n",
       "      <td>0.171030</td>\n",
       "      <td>0.115567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.689189</td>\n",
       "      <td>0.672210</td>\n",
       "      <td>0.829268</td>\n",
       "      <td>0.515152</td>\n",
       "      <td>0.719882</td>\n",
       "      <td>0.209623</td>\n",
       "      <td>0.149104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.606061</td>\n",
       "      <td>0.604694</td>\n",
       "      <td>0.469388</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.666531</td>\n",
       "      <td>4.305918</td>\n",
       "      <td>7.217074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.567901</td>\n",
       "      <td>0.576474</td>\n",
       "      <td>0.675676</td>\n",
       "      <td>0.477273</td>\n",
       "      <td>0.511057</td>\n",
       "      <td>0.144578</td>\n",
       "      <td>0.089379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.687092</td>\n",
       "      <td>0.805556</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>0.592048</td>\n",
       "      <td>0.169006</td>\n",
       "      <td>0.128502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.775281</td>\n",
       "      <td>0.770770</td>\n",
       "      <td>0.690476</td>\n",
       "      <td>0.851064</td>\n",
       "      <td>0.725431</td>\n",
       "      <td>0.190971</td>\n",
       "      <td>0.141575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.602564</td>\n",
       "      <td>0.605978</td>\n",
       "      <td>0.586957</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.524457</td>\n",
       "      <td>0.161061</td>\n",
       "      <td>0.100630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.650602</td>\n",
       "      <td>0.688586</td>\n",
       "      <td>0.838710</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.578784</td>\n",
       "      <td>0.164563</td>\n",
       "      <td>0.104421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.662500</td>\n",
       "      <td>0.657828</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.704545</td>\n",
       "      <td>0.606061</td>\n",
       "      <td>0.161864</td>\n",
       "      <td>0.079914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.710526</td>\n",
       "      <td>0.723611</td>\n",
       "      <td>0.972222</td>\n",
       "      <td>0.475000</td>\n",
       "      <td>0.622222</td>\n",
       "      <td>0.153726</td>\n",
       "      <td>0.122995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.793478</td>\n",
       "      <td>0.787525</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.810345</td>\n",
       "      <td>0.820487</td>\n",
       "      <td>0.143569</td>\n",
       "      <td>0.085419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.640449</td>\n",
       "      <td>0.637626</td>\n",
       "      <td>0.386364</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.575758</td>\n",
       "      <td>0.608101</td>\n",
       "      <td>0.984320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.628205</td>\n",
       "      <td>0.640374</td>\n",
       "      <td>0.735294</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.615642</td>\n",
       "      <td>0.151011</td>\n",
       "      <td>0.107048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.703704</td>\n",
       "      <td>0.705793</td>\n",
       "      <td>0.536585</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.703049</td>\n",
       "      <td>0.150649</td>\n",
       "      <td>0.097104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.710843</td>\n",
       "      <td>0.773585</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.547170</td>\n",
       "      <td>0.662893</td>\n",
       "      <td>0.148017</td>\n",
       "      <td>0.122649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.648649</td>\n",
       "      <td>0.647516</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.648292</td>\n",
       "      <td>0.135647</td>\n",
       "      <td>0.088171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.662500</td>\n",
       "      <td>0.665729</td>\n",
       "      <td>0.536585</td>\n",
       "      <td>0.794872</td>\n",
       "      <td>0.591620</td>\n",
       "      <td>0.138482</td>\n",
       "      <td>0.105164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.674419</td>\n",
       "      <td>0.699945</td>\n",
       "      <td>0.974359</td>\n",
       "      <td>0.425532</td>\n",
       "      <td>0.661757</td>\n",
       "      <td>0.168988</td>\n",
       "      <td>0.116421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.731707</td>\n",
       "      <td>0.706875</td>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.593750</td>\n",
       "      <td>0.709375</td>\n",
       "      <td>0.116558</td>\n",
       "      <td>0.120732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.761364</td>\n",
       "      <td>0.751089</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.796296</td>\n",
       "      <td>0.619281</td>\n",
       "      <td>0.160891</td>\n",
       "      <td>0.127290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.574713</td>\n",
       "      <td>0.569280</td>\n",
       "      <td>0.526316</td>\n",
       "      <td>0.612245</td>\n",
       "      <td>0.556391</td>\n",
       "      <td>0.129564</td>\n",
       "      <td>0.089812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.675000</td>\n",
       "      <td>0.687273</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>0.654545</td>\n",
       "      <td>0.618909</td>\n",
       "      <td>0.144149</td>\n",
       "      <td>0.092865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.709302</td>\n",
       "      <td>0.729885</td>\n",
       "      <td>0.793103</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.735632</td>\n",
       "      <td>0.176599</td>\n",
       "      <td>0.101002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.647727</td>\n",
       "      <td>0.643747</td>\n",
       "      <td>0.702128</td>\n",
       "      <td>0.585366</td>\n",
       "      <td>0.655942</td>\n",
       "      <td>3.282169</td>\n",
       "      <td>3.719348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.647727</td>\n",
       "      <td>0.745902</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.491803</td>\n",
       "      <td>0.259866</td>\n",
       "      <td>0.163716</td>\n",
       "      <td>0.105615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.652632</td>\n",
       "      <td>0.648864</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.672727</td>\n",
       "      <td>0.672273</td>\n",
       "      <td>0.136415</td>\n",
       "      <td>0.094762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.686747</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.652098</td>\n",
       "      <td>0.201590</td>\n",
       "      <td>0.143147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.679487</td>\n",
       "      <td>0.668754</td>\n",
       "      <td>0.878049</td>\n",
       "      <td>0.459459</td>\n",
       "      <td>0.613711</td>\n",
       "      <td>0.170332</td>\n",
       "      <td>0.116588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.696203</td>\n",
       "      <td>0.734472</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.642857</td>\n",
       "      <td>0.735248</td>\n",
       "      <td>0.153314</td>\n",
       "      <td>0.096391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.606742</td>\n",
       "      <td>0.607323</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.659091</td>\n",
       "      <td>0.525253</td>\n",
       "      <td>0.158045</td>\n",
       "      <td>0.110325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.744681</td>\n",
       "      <td>0.748252</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.748252</td>\n",
       "      <td>0.128946</td>\n",
       "      <td>0.090870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.690141</td>\n",
       "      <td>0.690779</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.675676</td>\n",
       "      <td>0.690779</td>\n",
       "      <td>0.157711</td>\n",
       "      <td>0.103206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.688462</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.664286</td>\n",
       "      <td>0.134280</td>\n",
       "      <td>0.115715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg</th>\n",
       "      <td>0.667529</td>\n",
       "      <td>0.676882</td>\n",
       "      <td>0.705900</td>\n",
       "      <td>0.647864</td>\n",
       "      <td>0.633742</td>\n",
       "      <td>0.378524</td>\n",
       "      <td>0.436246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Accuracy  Balanced_Acc  Sensitivity  Specificity     AUROC      FEV1  \\\n",
       "0    0.694118      0.696844     0.465116     0.928571  0.649502  2.014148   \n",
       "1    0.736842      0.696145     0.555556     0.836735  0.718821  0.155088   \n",
       "2    0.551724      0.568627     0.666667     0.470588  0.453704  0.185408   \n",
       "3    0.586207      0.660377     1.000000     0.320755  0.558824  0.220622   \n",
       "4    0.688889      0.682815     0.648649     0.716981  0.687914  0.149958   \n",
       "5    0.750000      0.774725     0.857143     0.692308  0.642170  0.185570   \n",
       "6    0.675676      0.690421     0.758621     0.622222  0.682759  0.137945   \n",
       "7    0.617021      0.643810     0.433962     0.853659  0.611597  0.175906   \n",
       "8    0.682353      0.641329     0.324324     0.958333  0.717342  0.201231   \n",
       "9    0.673913      0.644016     0.529412     0.758621  0.679006  0.163179   \n",
       "10   0.513158      0.564248     0.740741     0.387755  0.650038  0.114142   \n",
       "11   0.705263      0.703333     0.666667     0.740000  0.688444  0.153028   \n",
       "12   0.575000      0.572233     0.682927     0.461538  0.572233  0.180117   \n",
       "13   0.679012      0.680815     0.684211     0.677419  0.260611  1.562194   \n",
       "14   0.643678      0.706897     0.517241     0.896552  0.699168  0.176779   \n",
       "15   0.662162      0.670696     0.828571     0.512821  0.607326  0.144053   \n",
       "16   0.702381      0.741715     0.851852     0.631579  0.789474  0.115745   \n",
       "17   0.674699      0.672093     0.600000     0.744186  0.734884  0.171030   \n",
       "18   0.689189      0.672210     0.829268     0.515152  0.719882  0.209623   \n",
       "19   0.606061      0.604694     0.469388     0.740000  0.666531  4.305918   \n",
       "20   0.567901      0.576474     0.675676     0.477273  0.511057  0.144578   \n",
       "21   0.666667      0.687092     0.805556     0.568627  0.592048  0.169006   \n",
       "22   0.775281      0.770770     0.690476     0.851064  0.725431  0.190971   \n",
       "23   0.602564      0.605978     0.586957     0.625000  0.524457  0.161061   \n",
       "24   0.650602      0.688586     0.838710     0.538462  0.578784  0.164563   \n",
       "25   0.662500      0.657828     0.611111     0.704545  0.606061  0.161864   \n",
       "26   0.710526      0.723611     0.972222     0.475000  0.622222  0.153726   \n",
       "27   0.793478      0.787525     0.764706     0.810345  0.820487  0.143569   \n",
       "28   0.640449      0.637626     0.386364     0.888889  0.575758  0.608101   \n",
       "29   0.628205      0.640374     0.735294     0.545455  0.615642  0.151011   \n",
       "30   0.703704      0.705793     0.536585     0.875000  0.703049  0.150649   \n",
       "31   0.710843      0.773585     1.000000     0.547170  0.662893  0.148017   \n",
       "32   0.648649      0.647516     0.642857     0.652174  0.648292  0.135647   \n",
       "33   0.662500      0.665729     0.536585     0.794872  0.591620  0.138482   \n",
       "34   0.674419      0.699945     0.974359     0.425532  0.661757  0.168988   \n",
       "35   0.731707      0.706875     0.820000     0.593750  0.709375  0.116558   \n",
       "36   0.761364      0.751089     0.705882     0.796296  0.619281  0.160891   \n",
       "37   0.574713      0.569280     0.526316     0.612245  0.556391  0.129564   \n",
       "38   0.675000      0.687273     0.720000     0.654545  0.618909  0.144149   \n",
       "39   0.709302      0.729885     0.793103     0.666667  0.735632  0.176599   \n",
       "40   0.647727      0.643747     0.702128     0.585366  0.655942  3.282169   \n",
       "41   0.647727      0.745902     1.000000     0.491803  0.259866  0.163716   \n",
       "42   0.652632      0.648864     0.625000     0.672727  0.672273  0.136415   \n",
       "43   0.686747      0.666667     1.000000     0.333333  0.652098  0.201590   \n",
       "44   0.679487      0.668754     0.878049     0.459459  0.613711  0.170332   \n",
       "45   0.696203      0.734472     0.826087     0.642857  0.735248  0.153314   \n",
       "46   0.606742      0.607323     0.555556     0.659091  0.525253  0.158045   \n",
       "47   0.744681      0.748252     0.769231     0.727273  0.748252  0.128946   \n",
       "48   0.690141      0.690779     0.705882     0.675676  0.690779  0.157711   \n",
       "49   0.666667      0.688462     0.800000     0.576923  0.664286  0.134280   \n",
       "Avg  0.667529      0.676882     0.705900     0.647864  0.633742  0.378524   \n",
       "\n",
       "     FEV1/FVC  \n",
       "0    2.498660  \n",
       "1    0.094923  \n",
       "2    0.122474  \n",
       "3    0.167815  \n",
       "4    0.132355  \n",
       "5    0.157980  \n",
       "6    0.093440  \n",
       "7    0.152993  \n",
       "8    0.138981  \n",
       "9    0.097260  \n",
       "10   0.109219  \n",
       "11   0.096773  \n",
       "12   0.096353  \n",
       "13   2.326810  \n",
       "14   0.119806  \n",
       "15   0.118881  \n",
       "16   0.104432  \n",
       "17   0.115567  \n",
       "18   0.149104  \n",
       "19   7.217074  \n",
       "20   0.089379  \n",
       "21   0.128502  \n",
       "22   0.141575  \n",
       "23   0.100630  \n",
       "24   0.104421  \n",
       "25   0.079914  \n",
       "26   0.122995  \n",
       "27   0.085419  \n",
       "28   0.984320  \n",
       "29   0.107048  \n",
       "30   0.097104  \n",
       "31   0.122649  \n",
       "32   0.088171  \n",
       "33   0.105164  \n",
       "34   0.116421  \n",
       "35   0.120732  \n",
       "36   0.127290  \n",
       "37   0.089812  \n",
       "38   0.092865  \n",
       "39   0.101002  \n",
       "40   3.719348  \n",
       "41   0.105615  \n",
       "42   0.094762  \n",
       "43   0.143147  \n",
       "44   0.116588  \n",
       "45   0.096391  \n",
       "46   0.110325  \n",
       "47   0.090870  \n",
       "48   0.103206  \n",
       "49   0.115715  \n",
       "Avg  0.436246  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Final Test Results\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Balanced_Acc</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>AUROC</th>\n",
       "      <th>FEV1</th>\n",
       "      <th>FEV1/FVC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.616162</td>\n",
       "      <td>0.627455</td>\n",
       "      <td>0.403846</td>\n",
       "      <td>0.851064</td>\n",
       "      <td>0.679624</td>\n",
       "      <td>1.811083</td>\n",
       "      <td>2.357644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.585859</td>\n",
       "      <td>0.595540</td>\n",
       "      <td>0.403846</td>\n",
       "      <td>0.787234</td>\n",
       "      <td>0.710311</td>\n",
       "      <td>0.171505</td>\n",
       "      <td>0.107247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.616162</td>\n",
       "      <td>0.614157</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>0.574468</td>\n",
       "      <td>0.670622</td>\n",
       "      <td>0.180193</td>\n",
       "      <td>0.137818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.626263</td>\n",
       "      <td>0.610475</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>0.297872</td>\n",
       "      <td>0.650982</td>\n",
       "      <td>0.185932</td>\n",
       "      <td>0.150537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.646465</td>\n",
       "      <td>0.646072</td>\n",
       "      <td>0.653846</td>\n",
       "      <td>0.638298</td>\n",
       "      <td>0.677578</td>\n",
       "      <td>0.175062</td>\n",
       "      <td>0.122788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.585859</td>\n",
       "      <td>0.585784</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.533905</td>\n",
       "      <td>0.158853</td>\n",
       "      <td>0.129060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.626263</td>\n",
       "      <td>0.623775</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.712418</td>\n",
       "      <td>0.121322</td>\n",
       "      <td>0.100023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.545956</td>\n",
       "      <td>0.229167</td>\n",
       "      <td>0.862745</td>\n",
       "      <td>0.685049</td>\n",
       "      <td>0.140996</td>\n",
       "      <td>0.115104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.525253</td>\n",
       "      <td>0.510417</td>\n",
       "      <td>0.020833</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.390931</td>\n",
       "      <td>0.138755</td>\n",
       "      <td>0.117329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.656566</td>\n",
       "      <td>0.654412</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.725490</td>\n",
       "      <td>0.706291</td>\n",
       "      <td>0.132499</td>\n",
       "      <td>0.110676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.542857</td>\n",
       "      <td>0.589161</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.514763</td>\n",
       "      <td>0.127493</td>\n",
       "      <td>0.092995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.638095</td>\n",
       "      <td>0.617716</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>0.696970</td>\n",
       "      <td>0.685315</td>\n",
       "      <td>0.113830</td>\n",
       "      <td>0.079673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.476190</td>\n",
       "      <td>0.504662</td>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.393939</td>\n",
       "      <td>0.557110</td>\n",
       "      <td>0.131530</td>\n",
       "      <td>0.097848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.619048</td>\n",
       "      <td>0.649767</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.530303</td>\n",
       "      <td>0.607226</td>\n",
       "      <td>1.513560</td>\n",
       "      <td>2.148057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.647619</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.659285</td>\n",
       "      <td>0.136242</td>\n",
       "      <td>0.092807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.590909</td>\n",
       "      <td>0.618475</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.534247</td>\n",
       "      <td>0.572381</td>\n",
       "      <td>0.157326</td>\n",
       "      <td>0.080564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.556646</td>\n",
       "      <td>0.729730</td>\n",
       "      <td>0.383562</td>\n",
       "      <td>0.601259</td>\n",
       "      <td>0.142169</td>\n",
       "      <td>0.084368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.663636</td>\n",
       "      <td>0.679933</td>\n",
       "      <td>0.729730</td>\n",
       "      <td>0.630137</td>\n",
       "      <td>0.675676</td>\n",
       "      <td>0.157512</td>\n",
       "      <td>0.120362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.609091</td>\n",
       "      <td>0.678823</td>\n",
       "      <td>0.891892</td>\n",
       "      <td>0.465753</td>\n",
       "      <td>0.713069</td>\n",
       "      <td>0.188736</td>\n",
       "      <td>0.131521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.578675</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.643836</td>\n",
       "      <td>0.584969</td>\n",
       "      <td>4.896604</td>\n",
       "      <td>7.570752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.603774</td>\n",
       "      <td>0.683118</td>\n",
       "      <td>0.945946</td>\n",
       "      <td>0.420290</td>\n",
       "      <td>0.734038</td>\n",
       "      <td>0.155669</td>\n",
       "      <td>0.092899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.537736</td>\n",
       "      <td>0.626126</td>\n",
       "      <td>0.918919</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.723854</td>\n",
       "      <td>0.164408</td>\n",
       "      <td>0.099745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.613208</td>\n",
       "      <td>0.665296</td>\n",
       "      <td>0.837838</td>\n",
       "      <td>0.492754</td>\n",
       "      <td>0.683901</td>\n",
       "      <td>0.164568</td>\n",
       "      <td>0.104020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.641509</td>\n",
       "      <td>0.655699</td>\n",
       "      <td>0.702703</td>\n",
       "      <td>0.608696</td>\n",
       "      <td>0.750490</td>\n",
       "      <td>0.152242</td>\n",
       "      <td>0.090830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.660377</td>\n",
       "      <td>0.720329</td>\n",
       "      <td>0.918919</td>\n",
       "      <td>0.521739</td>\n",
       "      <td>0.765766</td>\n",
       "      <td>0.144778</td>\n",
       "      <td>0.086318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.642202</td>\n",
       "      <td>0.649132</td>\n",
       "      <td>0.688889</td>\n",
       "      <td>0.609375</td>\n",
       "      <td>0.678125</td>\n",
       "      <td>0.144001</td>\n",
       "      <td>0.118323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.577982</td>\n",
       "      <td>0.601042</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.468750</td>\n",
       "      <td>0.681597</td>\n",
       "      <td>0.171422</td>\n",
       "      <td>0.151453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.568807</td>\n",
       "      <td>0.576736</td>\n",
       "      <td>0.622222</td>\n",
       "      <td>0.531250</td>\n",
       "      <td>0.596181</td>\n",
       "      <td>0.143937</td>\n",
       "      <td>0.110214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.587156</td>\n",
       "      <td>0.589063</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.578125</td>\n",
       "      <td>0.692014</td>\n",
       "      <td>0.559302</td>\n",
       "      <td>0.848900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.633028</td>\n",
       "      <td>0.644618</td>\n",
       "      <td>0.711111</td>\n",
       "      <td>0.578125</td>\n",
       "      <td>0.703819</td>\n",
       "      <td>0.178121</td>\n",
       "      <td>0.156952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.683333</td>\n",
       "      <td>0.662857</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.730286</td>\n",
       "      <td>0.153691</td>\n",
       "      <td>0.097456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.716667</td>\n",
       "      <td>0.737143</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>0.614286</td>\n",
       "      <td>0.602286</td>\n",
       "      <td>0.145843</td>\n",
       "      <td>0.110270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.682857</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.693714</td>\n",
       "      <td>0.155028</td>\n",
       "      <td>0.097695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.683333</td>\n",
       "      <td>0.654286</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.828571</td>\n",
       "      <td>0.717429</td>\n",
       "      <td>0.149763</td>\n",
       "      <td>0.105377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.591667</td>\n",
       "      <td>0.612857</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>0.485714</td>\n",
       "      <td>0.626857</td>\n",
       "      <td>0.157342</td>\n",
       "      <td>0.105801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.534653</td>\n",
       "      <td>0.530071</td>\n",
       "      <td>0.622642</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.551887</td>\n",
       "      <td>0.161658</td>\n",
       "      <td>0.099650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.445545</td>\n",
       "      <td>0.442217</td>\n",
       "      <td>0.509434</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.510613</td>\n",
       "      <td>0.207141</td>\n",
       "      <td>0.144192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.455446</td>\n",
       "      <td>0.459513</td>\n",
       "      <td>0.377358</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.501572</td>\n",
       "      <td>0.164138</td>\n",
       "      <td>0.098211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.514851</td>\n",
       "      <td>0.508255</td>\n",
       "      <td>0.641509</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.513758</td>\n",
       "      <td>0.195792</td>\n",
       "      <td>0.128754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.465347</td>\n",
       "      <td>0.464033</td>\n",
       "      <td>0.490566</td>\n",
       "      <td>0.437500</td>\n",
       "      <td>0.475629</td>\n",
       "      <td>0.161200</td>\n",
       "      <td>0.115898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.641304</td>\n",
       "      <td>0.716667</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.644624</td>\n",
       "      <td>3.700090</td>\n",
       "      <td>3.936352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.706522</td>\n",
       "      <td>0.722043</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.677419</td>\n",
       "      <td>0.323656</td>\n",
       "      <td>0.194412</td>\n",
       "      <td>0.155614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.706522</td>\n",
       "      <td>0.679032</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.758065</td>\n",
       "      <td>0.674194</td>\n",
       "      <td>0.177514</td>\n",
       "      <td>0.125140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.629032</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.258065</td>\n",
       "      <td>0.568280</td>\n",
       "      <td>0.194655</td>\n",
       "      <td>0.151436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.630435</td>\n",
       "      <td>0.682796</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.532258</td>\n",
       "      <td>0.687097</td>\n",
       "      <td>0.152706</td>\n",
       "      <td>0.128293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.576923</td>\n",
       "      <td>0.578986</td>\n",
       "      <td>0.471698</td>\n",
       "      <td>0.686275</td>\n",
       "      <td>0.563818</td>\n",
       "      <td>0.155319</td>\n",
       "      <td>0.107610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.586538</td>\n",
       "      <td>0.588790</td>\n",
       "      <td>0.471698</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.545690</td>\n",
       "      <td>0.154027</td>\n",
       "      <td>0.116699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.615385</td>\n",
       "      <td>0.617092</td>\n",
       "      <td>0.528302</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.581576</td>\n",
       "      <td>0.156639</td>\n",
       "      <td>0.113844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.586538</td>\n",
       "      <td>0.586940</td>\n",
       "      <td>0.566038</td>\n",
       "      <td>0.607843</td>\n",
       "      <td>0.647799</td>\n",
       "      <td>0.154784</td>\n",
       "      <td>0.109915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.605769</td>\n",
       "      <td>0.603959</td>\n",
       "      <td>0.698113</td>\n",
       "      <td>0.509804</td>\n",
       "      <td>0.621902</td>\n",
       "      <td>0.146791</td>\n",
       "      <td>0.125355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Avg</th>\n",
       "      <td>0.598808</td>\n",
       "      <td>0.612836</td>\n",
       "      <td>0.639612</td>\n",
       "      <td>0.586061</td>\n",
       "      <td>0.627624</td>\n",
       "      <td>0.391964</td>\n",
       "      <td>0.439608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Accuracy  Balanced_Acc  Sensitivity  Specificity     AUROC      FEV1  \\\n",
       "0    0.616162      0.627455     0.403846     0.851064  0.679624  1.811083   \n",
       "1    0.585859      0.595540     0.403846     0.787234  0.710311  0.171505   \n",
       "2    0.616162      0.614157     0.653846     0.574468  0.670622  0.180193   \n",
       "3    0.626263      0.610475     0.923077     0.297872  0.650982  0.185932   \n",
       "4    0.646465      0.646072     0.653846     0.638298  0.677578  0.175062   \n",
       "5    0.585859      0.585784     0.583333     0.588235  0.533905  0.158853   \n",
       "6    0.626263      0.623775     0.541667     0.705882  0.712418  0.121322   \n",
       "7    0.555556      0.545956     0.229167     0.862745  0.685049  0.140996   \n",
       "8    0.525253      0.510417     0.020833     1.000000  0.390931  0.138755   \n",
       "9    0.656566      0.654412     0.583333     0.725490  0.706291  0.132499   \n",
       "10   0.542857      0.589161     0.769231     0.409091  0.514763  0.127493   \n",
       "11   0.638095      0.617716     0.538462     0.696970  0.685315  0.113830   \n",
       "12   0.476190      0.504662     0.615385     0.393939  0.557110  0.131530   \n",
       "13   0.619048      0.649767     0.769231     0.530303  0.607226  1.513560   \n",
       "14   0.647619      0.583333     0.333333     0.833333  0.659285  0.136242   \n",
       "15   0.590909      0.618475     0.702703     0.534247  0.572381  0.157326   \n",
       "16   0.500000      0.556646     0.729730     0.383562  0.601259  0.142169   \n",
       "17   0.663636      0.679933     0.729730     0.630137  0.675676  0.157512   \n",
       "18   0.609091      0.678823     0.891892     0.465753  0.713069  0.188736   \n",
       "19   0.600000      0.578675     0.513514     0.643836  0.584969  4.896604   \n",
       "20   0.603774      0.683118     0.945946     0.420290  0.734038  0.155669   \n",
       "21   0.537736      0.626126     0.918919     0.333333  0.723854  0.164408   \n",
       "22   0.613208      0.665296     0.837838     0.492754  0.683901  0.164568   \n",
       "23   0.641509      0.655699     0.702703     0.608696  0.750490  0.152242   \n",
       "24   0.660377      0.720329     0.918919     0.521739  0.765766  0.144778   \n",
       "25   0.642202      0.649132     0.688889     0.609375  0.678125  0.144001   \n",
       "26   0.577982      0.601042     0.733333     0.468750  0.681597  0.171422   \n",
       "27   0.568807      0.576736     0.622222     0.531250  0.596181  0.143937   \n",
       "28   0.587156      0.589063     0.600000     0.578125  0.692014  0.559302   \n",
       "29   0.633028      0.644618     0.711111     0.578125  0.703819  0.178121   \n",
       "30   0.683333      0.662857     0.540000     0.785714  0.730286  0.153691   \n",
       "31   0.716667      0.737143     0.860000     0.614286  0.602286  0.145843   \n",
       "32   0.700000      0.682857     0.580000     0.785714  0.693714  0.155028   \n",
       "33   0.683333      0.654286     0.480000     0.828571  0.717429  0.149763   \n",
       "34   0.591667      0.612857     0.740000     0.485714  0.626857  0.157342   \n",
       "35   0.534653      0.530071     0.622642     0.437500  0.551887  0.161658   \n",
       "36   0.445545      0.442217     0.509434     0.375000  0.510613  0.207141   \n",
       "37   0.455446      0.459513     0.377358     0.541667  0.501572  0.164138   \n",
       "38   0.514851      0.508255     0.641509     0.375000  0.513758  0.195792   \n",
       "39   0.465347      0.464033     0.490566     0.437500  0.475629  0.161200   \n",
       "40   0.641304      0.716667     0.933333     0.500000  0.644624  3.700090   \n",
       "41   0.706522      0.722043     0.766667     0.677419  0.323656  0.194412   \n",
       "42   0.706522      0.679032     0.600000     0.758065  0.674194  0.177514   \n",
       "43   0.500000      0.629032     1.000000     0.258065  0.568280  0.194655   \n",
       "44   0.630435      0.682796     0.833333     0.532258  0.687097  0.152706   \n",
       "45   0.576923      0.578986     0.471698     0.686275  0.563818  0.155319   \n",
       "46   0.586538      0.588790     0.471698     0.705882  0.545690  0.154027   \n",
       "47   0.615385      0.617092     0.528302     0.705882  0.581576  0.156639   \n",
       "48   0.586538      0.586940     0.566038     0.607843  0.647799  0.154784   \n",
       "49   0.605769      0.603959     0.698113     0.509804  0.621902  0.146791   \n",
       "Avg  0.598808      0.612836     0.639612     0.586061  0.627624  0.391964   \n",
       "\n",
       "     FEV1/FVC  \n",
       "0    2.357644  \n",
       "1    0.107247  \n",
       "2    0.137818  \n",
       "3    0.150537  \n",
       "4    0.122788  \n",
       "5    0.129060  \n",
       "6    0.100023  \n",
       "7    0.115104  \n",
       "8    0.117329  \n",
       "9    0.110676  \n",
       "10   0.092995  \n",
       "11   0.079673  \n",
       "12   0.097848  \n",
       "13   2.148057  \n",
       "14   0.092807  \n",
       "15   0.080564  \n",
       "16   0.084368  \n",
       "17   0.120362  \n",
       "18   0.131521  \n",
       "19   7.570752  \n",
       "20   0.092899  \n",
       "21   0.099745  \n",
       "22   0.104020  \n",
       "23   0.090830  \n",
       "24   0.086318  \n",
       "25   0.118323  \n",
       "26   0.151453  \n",
       "27   0.110214  \n",
       "28   0.848900  \n",
       "29   0.156952  \n",
       "30   0.097456  \n",
       "31   0.110270  \n",
       "32   0.097695  \n",
       "33   0.105377  \n",
       "34   0.105801  \n",
       "35   0.099650  \n",
       "36   0.144192  \n",
       "37   0.098211  \n",
       "38   0.128754  \n",
       "39   0.115898  \n",
       "40   3.936352  \n",
       "41   0.155614  \n",
       "42   0.125140  \n",
       "43   0.151436  \n",
       "44   0.128293  \n",
       "45   0.107610  \n",
       "46   0.116699  \n",
       "47   0.113844  \n",
       "48   0.109915  \n",
       "49   0.125355  \n",
       "Avg  0.439608  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "print(ig_test.size())\n",
    "print(\"Final Validation Results\")\n",
    "display(metrics_val)\n",
    "print()\n",
    "print(\"Final Test Results\")\n",
    "display(metrics_test)\n",
    "print()\n",
    "beeper.beep(frequency=600, secs=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f1110543-69f8-4ca2-9443-e2559fefce81",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('metrics_val.pkl', 'wb') as metrics_val_file:\n",
    "    pickle.dump(metrics_val, metrics_val_file)\n",
    "with open('metrics_test.pkl', 'wb') as metrics_test_file:\n",
    "    pickle.dump(metrics_test, metrics_test_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f1e814d4-bcab-43d1-9626-ec7c5c4df790",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1752 1253\n",
      "838 1382\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sens: 0.6225225225225225\n",
      "Spec: 0.5830282861896838\n",
      "BalAcc: 0.6027754043561031\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ast\n",
    "import matplotlib.pyplot as plt\n",
    "import scikitplot as skplt\n",
    "import pandas as pd\n",
    "\n",
    "labels = outputs_test.outputs['Labels'].to_list()\n",
    "labels = pd.DataFrame(labels)\n",
    "labels.columns = ['Diagnosis', 'FEV1', 'FEV1/FVC', 'FEF2575', 'FEV1_pred',\n",
    "       'FEV1/FVC_pred', 'FEF2575_pred', 'R5', 'R5_20', 'R20', 'X5', 'AX', 'TV',\n",
    "       'R5_z_score', 'R5_20_z_score', 'R20_z_score', 'X5_z_score',\n",
    "       'AX_z_score']\n",
    "\n",
    "info = outputs_test.outputs['Info'].to_list()\n",
    "info = pd.DataFrame(info)\n",
    "info.columns = ['ID', 'Trial', 'Age', 'Sex', 'Height', 'Weight', 'OralCavity', 'OPJ',\n",
    "       'Glottis', 'Carina']\n",
    "\n",
    "meta = pd.concat([labels, info], axis=1)\n",
    "\n",
    "outputs_cls = outputs_test.outputs['Outputs_cls'].to_list()\n",
    "outputs_cls = pd.DataFrame(outputs_cls)\n",
    "\n",
    "meta.insert(1, 'Prediction', np.exp(outputs_cls[1])/np.sum(np.exp(outputs_cls),axis=1))\n",
    "meta.to_pickle('meta.pkl')\n",
    "\n",
    "idx_tp = (meta['Diagnosis']==1) & (meta['Prediction']>=0.5)\n",
    "tp = idx_tp.sum()\n",
    "idx_fp = (meta['Diagnosis']==0) & (meta['Prediction']>=0.5)\n",
    "fp = idx_fp.sum()\n",
    "idx_tn = (meta['Diagnosis']==0) & (meta['Prediction']<0.5)\n",
    "tn = idx_tn.sum()\n",
    "idx_fn = (meta['Diagnosis']==1) & (meta['Prediction']<0.5)\n",
    "fn = idx_fn.sum()\n",
    "print(tn, fp)\n",
    "print(fn, tp)\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(meta['Prediction'][meta['Diagnosis']==0], bins=50, range=(0,1), alpha = 0.3, color='b', edgecolor='k', linewidth=1)\n",
    "plt.hist(meta['Prediction'][meta['Diagnosis']==1], bins=50, range=(0,1), alpha = 0.3, color='r', edgecolor='k', linewidth=1)\n",
    "plt.legend(['Healthy', 'Asthma'])\n",
    "plt.show()\n",
    "\n",
    "# cross_entropy = -(y_true*np.log10(y_pred[1]) + (1-y_true)*np.log10(y_pred[0]))\n",
    "\n",
    "# plt.figure()\n",
    "# # plt.hist(cross_entropy, bins=np.logspace(np.log10(0.1),np.log10(10.0), 50), edgecolor='k', linewidth=1)\n",
    "# plt.hist(cross_entropy, bins=50, edgecolor='k', linewidth=1)\n",
    "# plt.legend(['Healthy', 'Asthma'])\n",
    "# # plt.gca().set_xscale(\"log\")\n",
    "# plt.vlines(-np.log10(0.5), 0, 500, color='r')\n",
    "# plt.show()\n",
    "\n",
    "# y_pred = y_pred.idxmax(axis=1)\n",
    "\n",
    "sens = tp/(tp+fn)\n",
    "spec = tn/(fp+tn)\n",
    "print('Sens:', sens)\n",
    "print('Spec:', spec)\n",
    "print('BalAcc:', (sens+spec)/2)\n",
    "\n",
    "plt.figure(figsize=(20,4))\n",
    "plt.subplot(2,4,1)\n",
    "plt.hist(pd.concat([meta['Age'][idx_tp], meta['Age'][idx_fn]], axis=1), range=(meta['Age'].min(),meta['Age'].max()), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Asthma', 'False Healthy'])\n",
    "plt.subplot(2,4,5)\n",
    "plt.hist(pd.concat([meta['Age'][idx_tn], meta['Age'][idx_fp]], axis=1), range=(meta['Age'].min(),meta['Age'].max()), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Healthy', 'False Asthma'])\n",
    "plt.subplot(2,4,2)\n",
    "plt.hist(pd.concat([meta['Sex'][idx_tp], meta['Sex'][idx_fn]], axis=1), range=(meta['Sex'].min(),meta['Sex'].max()), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Asthma', 'False Healthy'])\n",
    "plt.subplot(2,4,6)\n",
    "plt.hist(pd.concat([meta['Sex'][idx_tn], meta['Sex'][idx_fp]], axis=1), range=(meta['Sex'].min(),meta['Sex'].max()), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Healthy', 'False Asthma'])\n",
    "plt.subplot(2,4,3)\n",
    "plt.hist(pd.concat([meta['Height'][idx_tp], meta['Height'][idx_fn]], axis=1), range=(meta['Height'].min(),meta['Height'].max()), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Asthma', 'False Healthy'])\n",
    "plt.subplot(2,4,7)\n",
    "plt.hist(pd.concat([meta['Height'][idx_tn], meta['Height'][idx_fp]], axis=1), range=(meta['Height'].min(),meta['Height'].max()), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Healthy', 'False Asthma'])\n",
    "plt.subplot(2,4,4)\n",
    "plt.hist(pd.concat([meta['Weight'][idx_tp], meta['Weight'][idx_fn]], axis=1), range=(meta['Weight'].min(),meta['Weight'].max()), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Asthma', 'False Healthy'])\n",
    "plt.subplot(2,4,8)\n",
    "plt.hist(pd.concat([meta['Weight'][idx_tn], meta['Weight'][idx_fp]], axis=1), range=(meta['Weight'].min(),meta['Weight'].max()), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Healthy', 'False Asthma'])\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(6,4))\n",
    "plt.subplot(2,1,1)\n",
    "plt.hist(pd.concat([meta['Age'][idx_tp], meta['Age'][idx_fn]], axis=1), bins=list(range(6,76,4)), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Asthma', 'False Healthy'])\n",
    "plt.subplot(2,1,2)\n",
    "plt.hist(pd.concat([meta['Age'][idx_tn], meta['Age'][idx_fp]], axis=1), bins=list(range(6,76,4)), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Healthy', 'False Asthma'])\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(15,4))\n",
    "plt.subplot(2,3,1)\n",
    "plt.hist(pd.concat([meta['FEV1_pred'][idx_tp], meta['FEV1_pred'][idx_fn]], axis=1), range=(meta['FEV1_pred'].min(),meta['FEV1_pred'].max()), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Asthma', 'False Healthy'])\n",
    "plt.subplot(2,3,4)\n",
    "plt.hist(pd.concat([meta['FEV1_pred'][idx_tn], meta['FEV1_pred'][idx_fp]], axis=1), range=(meta['FEV1_pred'].min(),meta['FEV1_pred'].max()), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Healthy', 'False Asthma'])\n",
    "plt.subplot(2,3,2)\n",
    "plt.hist(pd.concat([meta['FEV1/FVC_pred'][idx_tp], meta['FEV1/FVC_pred'][idx_fn]], axis=1), range=(meta['FEV1/FVC_pred'].min(),meta['FEV1/FVC_pred'].max()), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Asthma', 'False Healthy'])\n",
    "plt.subplot(2,3,5)\n",
    "plt.hist(pd.concat([meta['FEV1/FVC_pred'][idx_tn], meta['FEV1/FVC_pred'][idx_fp]], axis=1), range=(meta['FEV1/FVC_pred'].min(),meta['FEV1/FVC_pred'].max()), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Healthy', 'False Asthma'])\n",
    "plt.subplot(2,3,3)\n",
    "plt.hist(pd.concat([meta['FEF2575'][idx_tp], meta['FEF2575'][idx_fn]], axis=1), range=(meta['FEF2575'].min(),meta['FEF2575'].max()), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Asthma', 'False Healthy'])\n",
    "plt.subplot(2,3,6)\n",
    "plt.hist(pd.concat([meta['FEF2575'][idx_tn], meta['FEF2575'][idx_fp]], axis=1), range=(meta['FEF2575'].min(),meta['FEF2575'].max()), alpha = 0.3, stacked=True, edgecolor='k', linewidth=1)\n",
    "plt.legend(['True Healthy', 'False Asthma'])\n",
    "plt.show()\n",
    "\n",
    "thre = np.zeros(1001)\n",
    "sens = np.zeros(1001)\n",
    "spec = np.zeros(1001)\n",
    "for i in range(0,1001):\n",
    "    idx_tp = (meta['Diagnosis']==1) & (meta['Prediction']>=i/1000)\n",
    "    tp = idx_tp.sum()\n",
    "    idx_fp = (meta['Diagnosis']==0) & (meta['Prediction']>=i/1000)\n",
    "    fp = idx_fp.sum()\n",
    "    idx_tn = (meta['Diagnosis']==0) & (meta['Prediction']<i/1000)\n",
    "    tn = idx_tn.sum()\n",
    "    idx_fn = (meta['Diagnosis']==1) & (meta['Prediction']<i/1000)\n",
    "    fn = idx_fn.sum()\n",
    "    thre[i] = i/1000\n",
    "    sens[i] = tp/(tp+fn)\n",
    "    spec[i] = tn/(fp+tn)\n",
    "    \n",
    "plt.figure()\n",
    "plt.plot(thre, sens)\n",
    "plt.plot(thre, spec)\n",
    "plt.title('Sensitivity and Specificity vs. Threshold')\n",
    "plt.legend(['Sensitivity', 'Specificity'])\n",
    "plt.xlabel('Threshold')\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "# skplt.metrics.plot_roc_curve(meta['Diagnosis'], outputs_cls)\n",
    "plt.plot(1-spec, sens)\n",
    "plt.plot([0,1], [0,1], '--k')\n",
    "plt.title('ROC Curve')\n",
    "plt.xlabel('False Positive Rate\\n(1-Specificity)')\n",
    "plt.ylabel('True Positive Rate\\n(Sensitivity)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20f659cb-34ad-4763-9628-a7897fcdbc84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([-1.6589e-01, -2.8779e-02,  1.6387e-01, -1.0416e-01,  1.1179e-01,\n",
      "        -4.6864e-02,  1.0710e-01,  4.5176e-02, -3.3359e-03,  1.3541e-02,\n",
      "         1.0704e-01, -1.4340e-01, -7.6592e-03, -3.6698e-02,  8.1287e-03,\n",
      "        -1.3776e-02, -1.6520e-02,  3.8761e-03, -3.1374e-02,  4.3294e-03,\n",
      "        -2.1391e-02,  3.0607e-02, -1.2148e-01,  6.2856e-02,  1.6843e-02,\n",
      "         4.6337e-02, -5.9003e-02,  6.2069e-02, -1.3233e-02,  1.0198e-01,\n",
      "        -4.6425e-02, -6.8460e-03,  2.1482e-04,  1.8250e-02, -5.6566e-04,\n",
      "        -1.8814e-02, -1.3743e-02, -2.1031e-02,  3.2131e-03, -6.4738e-03,\n",
      "        -8.2839e-03, -7.2159e-03,  5.6714e-03, -6.7390e-03,  7.3553e-03,\n",
      "        -6.0806e-03,  6.0891e-03,  7.9352e-04,  7.7163e-03, -1.5853e-02,\n",
      "         2.9731e-03, -9.4535e-03,  5.0104e-03,  3.7092e-03,  1.1024e-02,\n",
      "        -4.7130e-03,  5.2265e-03,  4.7065e-03,  1.0663e-02, -8.3389e-03,\n",
      "         1.3045e-03, -1.5490e-02,  2.6827e-02, -4.0753e-02,  1.5396e-02,\n",
      "        -1.2956e-02,  4.7908e-02, -3.6378e-02, -4.1369e-02,  1.6702e-02,\n",
      "         2.4640e-02, -1.5282e-02, -1.2218e-02,  3.4888e-02, -1.3447e-03,\n",
      "         6.0235e-03, -2.3476e-02,  4.3712e-02, -2.7998e-02,  4.0584e-02,\n",
      "         3.6041e-02,  2.0881e-02,  5.5226e-02, -1.9127e-02, -6.9649e-01,\n",
      "         7.5123e-03, -1.4902e-01,  1.1416e+00], dtype=torch.float64)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(ig_test.mean(dim=0))\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.bar(list(range(88)), ig_test.abs().mean(dim=0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2886dcff-2266-4b55-ad2e-1ded0e2a108a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}