07_27_grid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6551c1a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4b513a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>category</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0002-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0002-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0002-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0600-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>0600-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>0602-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>0603-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>0604-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>211 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       filename  category  class\n",
       "0    0001-2.wav   healthy      0\n",
       "1    0002-1.wav   healthy      0\n",
       "2    0002-2.wav   healthy      0\n",
       "3    0002-3.wav   healthy      0\n",
       "4    0002-4.wav   healthy      0\n",
       "..          ...       ...    ...\n",
       "206  0600-2.wav  wheezing      1\n",
       "207  0600-3.wav  wheezing      1\n",
       "208  0602-3.wav  wheezing      1\n",
       "209  0603-1.wav  wheezing      1\n",
       "210  0604-1.wav  wheezing      1\n",
       "\n",
       "[211 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catholic_file = 'data/data_train.csv'\n",
    "df = pd.read_csv(Catholic_file)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c8e13f5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>relative_path</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/0001-2.wav</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/0002-2.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/0002-3.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/0002-4.wav</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "  relative_path  class\n",
       "0   /0001-2.wav      0\n",
       "1   /0002-1.wav      0\n",
       "2   /0002-2.wav      0\n",
       "3   /0002-3.wav      0\n",
       "4   /0002-4.wav      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['relative_path'] = '/' + df['filename'].astype(str)\n",
    "df = df[['relative_path', 'class']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7cf51ed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'data/train'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6f50c63",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math, random\n",
    "import torch\n",
    "import torchaudio\n",
    "from torchaudio import transforms\n",
    "from IPython.display import Audio\n",
    "\n",
    "class Breath_sound_Util():\n",
    "  \n",
    "  def open(audio_file):\n",
    "    sig, sr = torchaudio.load(audio_file)\n",
    "    sig = torchaudio.functional.gain(sig, gain_db=5.0)\n",
    "    sig = torchaudio.functional.dither(sig)\n",
    "    sig = torchaudio.functional.lowpass_biquad(sig, sr, cutoff_freq=3000)\n",
    "    sig = torchaudio.functional.highpass_biquad(sig, sr, cutoff_freq=2000)\n",
    "    \n",
    "    return (sig, sr)\n",
    "\n",
    "  def resample(aud, newsr):\n",
    "    sig, sr = aud\n",
    "    \n",
    "    if (sr == newsr):\n",
    "     return aud\n",
    "\n",
    "    num_channels = sig.shape[0]\n",
    "    resig = torchaudio.transforms.Resample(sr, newsr)(sig[:1,:])\n",
    "    if (num_channels > 1):\n",
    "      retwo = torchaudio.transforms.Resample(sr, newsr)(sig[1:,:])\n",
    "      resig = torch.cat([resig, retwo])\n",
    "\n",
    "    return ((resig, newsr))\n",
    "  \n",
    "\n",
    "  def pad(aud, max_ms):\n",
    "    sig, sr = aud\n",
    "    num_rows, sig_len = sig.shape\n",
    "    max_len = sr//1000 * max_ms\n",
    "   \n",
    "    if (sig_len > max_len):\n",
    "      sig = sig[:,:max_len]\n",
    " \n",
    "    elif (sig_len < max_len):\n",
    "\n",
    "      repeated = []\n",
    "      repeated.append(sig)\n",
    "      required_len = max_len - sig_len\n",
    "\n",
    "      while required_len > sig_len : \n",
    "        repeated.append(sig)\n",
    "        require_len -= sig_len\n",
    "      repeated.append(sig[:, :required_len])\n",
    " \n",
    "      sig = torch.cat(repeated, 1)\n",
    "\n",
    "    return (sig, sr)\n",
    "\n",
    "\n",
    "  def time_shift(aud, shift_limit):\n",
    "    sig,sr = aud\n",
    "    _, sig_len = sig.shape\n",
    "    shift_amt = int(random.random() * shift_limit * sig_len)\n",
    "    return (sig.roll(shift_amt), sr)\n",
    "\n",
    "  def spectro_gram(aud, n_mels=64, n_fft=1024, hop_len=None):\n",
    "    sig,sr = aud\n",
    "    aud = torchaudio.transforms.TimeStretch(hop_length=2048, fixed_rate=5000000)\n",
    "    top_db = 80\n",
    "    \n",
    "    spec = transforms.MelSpectrogram(sr, n_fft=n_fft, hop_length=hop_len, n_mels=n_mels)(sig)\n",
    "    spec = transforms.AmplitudeToDB(top_db=top_db)(spec)\n",
    "    return (spec)\n",
    "\n",
    "  def spectro_augment(spec, max_mask_pct=0.1, n_freq_masks=1, n_time_masks=1):\n",
    "    _, n_mels, n_steps = spec.shape\n",
    "    mask_value = spec.mean()\n",
    "    aug_spec = spec\n",
    "\n",
    "    freq_mask_param = max_mask_pct * n_mels\n",
    "    for _ in range(n_freq_masks):\n",
    "      aug_spec = transforms.FrequencyMasking(freq_mask_param)(aug_spec, mask_value)\n",
    "\n",
    "    time_mask_param = max_mask_pct * n_steps\n",
    "    for _ in range(n_time_masks):\n",
    "      aug_spec = transforms.TimeMasking(time_mask_param)(aug_spec, mask_value)\n",
    "\n",
    "    return aug_spec\n",
    "    print(aug_spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bd301cca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader, Dataset, random_split\n",
    "import torchaudio\n",
    "import torchvision\n",
    "\n",
    "class breathDS(Dataset):\n",
    "    \n",
    "  def __init__(self, df, data_path):\n",
    "    self.df = df\n",
    "    self.data_path = str(data_path)\n",
    "    self.duration = 4000\n",
    "    self.sr = 48000\n",
    "    self.shift_pct = 0.4\n",
    "            \n",
    "  def __len__(self):\n",
    "    return len(self.df)    \n",
    "    \n",
    "  def __getitem__(self, idx):\n",
    "    audio_file = self.data_path + self.df.loc[idx, 'relative_path']\n",
    "    class_id = self.df.loc[idx, 'class']\n",
    "    aud = Breath_sound_Util.open(audio_file)\n",
    "    reaud = Breath_sound_Util.resample(aud, self.sr)\n",
    "    dur_aud = Breath_sound_Util.pad(reaud, self.duration)\n",
    "    shift_aud = Breath_sound_Util.time_shift(dur_aud, self.shift_pct)\n",
    "    sgram = Breath_sound_Util.spectro_gram(shift_aud, n_mels=64, n_fft=1024, hop_len=None)\n",
    "    aug_sgram = Breath_sound_Util.spectro_augment(sgram, max_mask_pct=0.1, n_freq_masks=2, n_time_masks=2)\n",
    "\n",
    "    \n",
    "    return aug_sgram, class_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0ee0571e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import random_split\n",
    "\n",
    "brds = breathDS(df, data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43312ca8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train_dl = torch.utils.data.DataLoader(brds, batch_size=16, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7faa2a90",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filename</th>\n",
       "      <th>category</th>\n",
       "      <th>class</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0041-1.wav</td>\n",
       "      <td>healthy</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0041-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0041-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0041-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0042-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0042-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0042-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0042-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0043-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0043-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0043-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0043-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0044-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0044-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0044-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0063-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0063-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0070-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0108-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0261-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0411-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0515-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0533-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0533-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0535-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0588-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0588-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0592-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0599-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0599-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0599-3.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0602-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0603-2.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0614-1.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0615-4.wav</td>\n",
       "      <td>healthy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0606-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0606-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0607-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0607-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0609-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0611-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0611-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0611-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0611-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0612-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0613-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0613-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0614-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0614-3.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0614-4.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0615-1.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0615-2.wav</td>\n",
       "      <td>wheezing</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      filename  category  class\n",
       "0   0041-1.wav   healthy      0\n",
       "1   0041-2.wav   healthy      0\n",
       "2   0041-3.wav   healthy      0\n",
       "3   0041-4.wav   healthy      0\n",
       "4   0042-1.wav   healthy      0\n",
       "5   0042-2.wav   healthy      0\n",
       "6   0042-3.wav   healthy      0\n",
       "7   0042-4.wav   healthy      0\n",
       "8   0043-1.wav   healthy      0\n",
       "9   0043-2.wav   healthy      0\n",
       "10  0043-3.wav   healthy      0\n",
       "11  0043-4.wav   healthy      0\n",
       "12  0044-2.wav   healthy      0\n",
       "13  0044-3.wav   healthy      0\n",
       "14  0044-4.wav   healthy      0\n",
       "15  0063-2.wav   healthy      0\n",
       "16  0063-3.wav   healthy      0\n",
       "17  0070-1.wav   healthy      0\n",
       "18  0108-2.wav   healthy      0\n",
       "19  0261-1.wav   healthy      0\n",
       "20  0411-4.wav   healthy      0\n",
       "21  0515-2.wav   healthy      0\n",
       "22  0533-3.wav   healthy      0\n",
       "23  0533-4.wav   healthy      0\n",
       "24  0535-4.wav   healthy      0\n",
       "25  0588-1.wav   healthy      0\n",
       "26  0588-4.wav   healthy      0\n",
       "27  0592-2.wav   healthy      0\n",
       "28  0599-1.wav   healthy      0\n",
       "29  0599-2.wav   healthy      0\n",
       "30  0599-3.wav   healthy      0\n",
       "31  0602-2.wav   healthy      0\n",
       "32  0603-2.wav   healthy      0\n",
       "33  0614-1.wav   healthy      0\n",
       "34  0615-4.wav   healthy      0\n",
       "35  0606-1.wav  wheezing      1\n",
       "36  0606-2.wav  wheezing      1\n",
       "37  0607-2.wav  wheezing      1\n",
       "38  0607-3.wav  wheezing      1\n",
       "39  0609-4.wav  wheezing      1\n",
       "40  0611-1.wav  wheezing      1\n",
       "41  0611-2.wav  wheezing      1\n",
       "42  0611-3.wav  wheezing      1\n",
       "43  0611-4.wav  wheezing      1\n",
       "44  0612-1.wav  wheezing      1\n",
       "45  0613-2.wav  wheezing      1\n",
       "46  0613-3.wav  wheezing      1\n",
       "47  0614-2.wav  wheezing      1\n",
       "48  0614-3.wav  wheezing      1\n",
       "49  0614-4.wav  wheezing      1\n",
       "50  0615-1.wav  wheezing      1\n",
       "51  0615-2.wav  wheezing      1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Catholic_file1 = 'data/data_val.csv'\n",
    "df1 = pd.read_csv(Catholic_file1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6ae0285c",
   "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>relative_path</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/0041-1.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/0041-2.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/0041-3.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/0041-4.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/0042-1.wav</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  relative_path  class\n",
       "0   /0041-1.wav      0\n",
       "1   /0041-2.wav      0\n",
       "2   /0041-3.wav      0\n",
       "3   /0041-4.wav      0\n",
       "4   /0042-1.wav      0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['relative_path'] = '/' + df1['filename'].astype(str)\n",
    "df1 = df1[['relative_path', 'class']]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0650f6ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = 'data/val'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a62fac9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class breathDS1(Dataset):\n",
    "    \n",
    "  def __init__(self, df1, data_path):\n",
    "    self.df1 = df1\n",
    "    self.data_path = str(data_path)\n",
    "    self.duration = 4000\n",
    "    self.sr = 48000\n",
    "    self.shift_pct = 0.4\n",
    "            \n",
    "  def __len__(self):\n",
    "    return len(self.df1)    \n",
    "    \n",
    "  def __getitem__(self, idx):\n",
    "    audio_file = self.data_path + self.df1.loc[idx, 'relative_path']\n",
    "    class_id = self.df1.loc[idx, 'class']\n",
    "    aud = Breath_sound_Util.open(audio_file)\n",
    "    reaud = Breath_sound_Util.resample(aud, self.sr)\n",
    "    dur_aud = Breath_sound_Util.pad(reaud, self.duration)\n",
    "    shift_aud = Breath_sound_Util.time_shift(dur_aud, self.shift_pct)\n",
    "    sgram = Breath_sound_Util.spectro_gram(shift_aud, n_mels=64, n_fft=1024, hop_len=None)\n",
    "    aug_sgram = Breath_sound_Util.spectro_augment(sgram, max_mask_pct=0.1, n_freq_masks=2, n_time_masks=2)\n",
    "\n",
    "    \n",
    "    return aug_sgram, class_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "04a635a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "brds1 = breathDS1(df1, data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1f9959de",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_dl = torch.utils.data.DataLoader(brds1, batch_size=16, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0bb011b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.nn import init\n",
    "\n",
    "# --------------------------------------------------------------\n",
    "# 호흡음의 Healthy, Wheezing을 판단하는 Binary Classification Model\n",
    "# --------------------------------------------------------------\n",
    "\n",
    "class WhoWheezing(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        conv_layers = []\n",
    "        self.conv1 = nn.Conv2d(1, 8, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.bn1 = nn.BatchNorm2d(8)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv1.bias.data.zero_()\n",
    "        conv_layers += [self.conv1, self.relu1, self.bn1]\n",
    "\n",
    "        self.conv2 = nn.Conv2d(8, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        self.relu2 = nn.ReLU()\n",
    "        self.bn2 = nn.BatchNorm2d(16)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv2.bias.data.zero_()\n",
    "        conv_layers += [self.conv2, self.relu2, self.bn2]\n",
    "\n",
    "        self.conv3 = nn.Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        self.relu3 = nn.ReLU()\n",
    "        self.bn3 = nn.BatchNorm2d(32)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv3.bias.data.zero_()\n",
    "        conv_layers += [self.conv3, self.relu3, self.bn3]\n",
    "\n",
    "        self.conv4 = nn.Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        self.relu4 = nn.ReLU()\n",
    "        self.bn4 = nn.BatchNorm2d(64)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv4.bias.data.zero_()\n",
    "        conv_layers += [self.conv4, self.relu4, self.bn4]\n",
    "\n",
    "        self.ap = nn.AdaptiveAvgPool2d(output_size=1)\n",
    "        self.dropout = nn.Dropout(0.4)\n",
    "        self.lin = nn.Linear(in_features=64, out_features=2)\n",
    "        self.dropout = nn.Dropout(0.4)\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "\n",
    "        self.conv = nn.Sequential(*conv_layers)\n",
    " \n",
    "    def forward(self, x):\n",
    "        x = self.conv(x)\n",
    "        x = self.ap(x)\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        x = self.lin(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.sigmoid(x)\n",
    "        return x\n",
    "\n",
    "Model1 = WhoWheezing()\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "Model1 = Model1.to(device)\n",
    "next(Model1.parameters()).device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "de07a447",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchcontrib.optim import SWA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "082f754b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, Loss: 0.6933, Accuracy: 0.5498\n",
      "Epoch: 1, Loss: 0.6345, Accuracy: 0.8483\n",
      "Epoch: 2, Loss: 0.5857, Accuracy: 0.8389\n",
      "Epoch: 3, Loss: 0.5617, Accuracy: 0.8389\n",
      "Epoch: 4, Loss: 0.5604, Accuracy: 0.8436\n",
      "Epoch: 5, Loss: 0.5231, Accuracy: 0.9005\n",
      "Epoch: 6, Loss: 0.5114, Accuracy: 0.9194\n",
      "Epoch: 7, Loss: 0.5185, Accuracy: 0.8957\n",
      "Epoch: 8, Loss: 0.4993, Accuracy: 0.9052\n",
      "Epoch: 9, Loss: 0.5065, Accuracy: 0.8910\n",
      "Epoch: 10, Loss: 0.5116, Accuracy: 0.8957\n",
      "Epoch: 11, Loss: 0.4932, Accuracy: 0.9194\n",
      "Epoch: 12, Loss: 0.5077, Accuracy: 0.8768\n",
      "Epoch: 13, Loss: 0.4882, Accuracy: 0.9052\n",
      "Epoch: 14, Loss: 0.4922, Accuracy: 0.9289\n",
      "Epoch: 15, Loss: 0.4933, Accuracy: 0.8957\n",
      "Epoch: 16, Loss: 0.4873, Accuracy: 0.9194\n",
      "Epoch: 17, Loss: 0.4740, Accuracy: 0.8910\n",
      "Epoch: 18, Loss: 0.4825, Accuracy: 0.9242\n",
      "Epoch: 19, Loss: 0.5000, Accuracy: 0.9147\n",
      "Epoch: 20, Loss: 0.4614, Accuracy: 0.9336\n",
      "Epoch: 21, Loss: 0.4754, Accuracy: 0.9147\n",
      "Epoch: 22, Loss: 0.4934, Accuracy: 0.9005\n",
      "Epoch: 23, Loss: 0.4806, Accuracy: 0.9194\n",
      "Epoch: 24, Loss: 0.4826, Accuracy: 0.9100\n",
      "Epoch: 25, Loss: 0.4938, Accuracy: 0.8815\n",
      "Epoch: 26, Loss: 0.4903, Accuracy: 0.8910\n",
      "Epoch: 27, Loss: 0.4817, Accuracy: 0.9147\n",
      "Epoch: 28, Loss: 0.5088, Accuracy: 0.9052\n",
      "Epoch: 29, Loss: 0.4988, Accuracy: 0.9100\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import numpy as np\n",
    "import sklearn.metrics as metrics\n",
    "\n",
    "\n",
    "def training(model, train_dl, num_epochs):\n",
    "  criterion = nn.CrossEntropyLoss()\n",
    "  optimizer = torch.optim.Adam(model.parameters(),lr=0.001)\n",
    "  scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.001,\n",
    "                                                steps_per_epoch=int(len(train_dl)),\n",
    "                                                epochs=num_epochs,\n",
    "                                                anneal_strategy='linear')\n",
    "\n",
    "  for epoch in range(num_epochs):\n",
    "    running_loss = 0.0\n",
    "    correct_prediction = 0\n",
    "    total_prediction = 0\n",
    "\n",
    "    for i, data in enumerate(train_dl):\n",
    "        inputs, labels = data[0].to(device), data[1].to(device)\n",
    "        inputs_m, inputs_s = inputs.mean(), inputs.std()\n",
    "        inputs = (inputs - inputs_m) / inputs_s\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        scheduler.step()\n",
    "        running_loss += loss.item()\n",
    "        _, prediction = torch.max(outputs,1)\n",
    "        correct_prediction += (prediction == labels).sum().item()\n",
    "        total_prediction += prediction.shape[0]\n",
    "\n",
    "    num_batches = len(train_dl)\n",
    "    avg_loss = running_loss / num_batches\n",
    "    acc = correct_prediction/total_prediction\n",
    "    print(f'Epoch: {epoch}, Loss: {avg_loss:.4f}, Accuracy: {acc:.4f}')\n",
    "\n",
    "  print('Finished Training')\n",
    "    \n",
    "num_epochs=30\n",
    "training(Model1, train_dl, num_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3114486",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "37c6d372",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6923, Total items: 52\n"
     ]
    }
   ],
   "source": [
    "def inference (model, val_dl):\n",
    "  correct_prediction = 0\n",
    "  total_prediction = 0\n",
    "\n",
    "  with torch.no_grad():\n",
    "    for data in val_dl:\n",
    "      \n",
    "      inputs, labels = data[0].to(device), data[1].to(device)\n",
    "      inputs_m, inputs_s = inputs.mean(), inputs.std()\n",
    "      inputs = (inputs - inputs_m) / inputs_s\n",
    "      outputs = model(inputs)\n",
    "      _, prediction = torch.max(outputs,1)\n",
    "      \n",
    "      correct_prediction += (prediction == labels).sum().item()\n",
    "      total_prediction += prediction.shape[0]\n",
    "    \n",
    "  acc = correct_prediction/total_prediction\n",
    "  print(f'Accuracy: {acc:.4f}, Total items: {total_prediction}')\n",
    "  \n",
    "inference(Model1, val_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8f1e85f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8553aadc",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-17-578dc92b27d0>, line 9)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-17-578dc92b27d0>\"\u001b[0;36m, line \u001b[0;32m9\u001b[0m\n\u001b[0;31m    X, y = make_classification(1000, 20 n_informative=10, random_state=0)\u001b[0m\n\u001b[0m                                                    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import make_classification\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from skorch import NeuralNetClassifier\n",
    "\n",
    "\n",
    "X, y = make_classification(1000, 20 n_informative=10, random_state=0)\n",
    "X = X.astype(np.float32)\n",
    "y = y.astype(np.int64)\n",
    "\n",
    "class WhoWheezing(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        conv_layers = []\n",
    "        self.conv1 = nn.Conv2d(1, 8, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.bn1 = nn.BatchNorm2d(8)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv1.bias.data.zero_()\n",
    "        conv_layers += [self.conv1, self.relu1, self.bn1]\n",
    "\n",
    "        self.conv2 = nn.Conv2d(8, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        self.relu2 = nn.ReLU()\n",
    "        self.bn2 = nn.BatchNorm2d(16)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv2.bias.data.zero_()\n",
    "        conv_layers += [self.conv2, self.relu2, self.bn2]\n",
    "\n",
    "        self.conv3 = nn.Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        self.relu3 = nn.ReLU()\n",
    "        self.bn3 = nn.BatchNorm2d(32)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv3.bias.data.zero_()\n",
    "        conv_layers += [self.conv3, self.relu3, self.bn3]\n",
    "\n",
    "        self.conv4 = nn.Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        self.relu4 = nn.ReLU()\n",
    "        self.bn4 = nn.BatchNorm2d(64)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv4.bias.data.zero_()\n",
    "        conv_layers += [self.conv4, self.relu4, self.bn4]\n",
    "\n",
    "        self.ap = nn.AdaptiveAvgPool2d(output_size=1)\n",
    "        self.dropout = nn.Dropout(0.4)\n",
    "        self.lin = nn.Linear(in_features=64, out_features=2)\n",
    "        self.dropout = nn.Dropout(0.4)\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "\n",
    "        self.conv = nn.Sequential(*conv_layers)\n",
    " \n",
    "    def forward(self, x):\n",
    "        x = self.conv(x)\n",
    "        x = self.ap(x)\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        x = self.lin(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.sigmoid(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "net = NeuralNetClassifier(\n",
    "    Model1,\n",
    "    max_epochs=100,\n",
    "    lr=0.1,\n",
    "    # Shuffle training data on each epoch\n",
    "    iterator_train__shuffle=True,\n",
    ")\n",
    "\n",
    "net.fit(X, y)\n",
    "y_proba = net.predict_proba(X)\n",
    "y_proba = net.predict_proba(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e60801d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using the example from the README\n",
    "import numpy as np\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data.dataloader import default_collate\n",
    "\n",
    "from skorch import NeuralNetClassifier\n",
    "from skorch.dataset import Dataset\n",
    "from skorch.dataset import CVSplit\n",
    "\n",
    "X, y = make_classification(1000, 20, n_informative=10, random_state=0)\n",
    "X = X.astype(np.float32)\n",
    "y = y.astype(np.int64)\n",
    "\n",
    "'''class MyModule(nn.Module):\n",
    "    def __init__(self, num_units=10, nonlin=F.relu):\n",
    "        super(MyModule, self).__init__()\n",
    "\n",
    "        self.dense0 = nn.Linear(20, num_units)\n",
    "        self.nonlin = nonlin\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.dense1 = nn.Linear(num_units, 10)\n",
    "        self.output = nn.Linear(10, 2)\n",
    "\n",
    "    def forward(self, X, **kwargs):\n",
    "        X = self.nonlin(self.dense0(X))\n",
    "        X = self.dropout(X)\n",
    "        X = F.relu(self.dense1(X))\n",
    "        X = F.softmax(self.output(X), dim=-1)\n",
    "        return X'''\n",
    "\n",
    "class WhoWheezing(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        conv_layers = []\n",
    "        self.conv1 = nn.Conv2d(1, 8, kernel_size=(3, 3), stride=(2, 2), padding=(2, 2))\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.bn1 = nn.BatchNorm2d(8)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv1.bias.data.zero_()\n",
    "        conv_layers += [self.conv1, self.relu1, self.bn1]\n",
    "\n",
    "        self.conv2 = nn.Conv2d(8, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        self.relu2 = nn.ReLU()\n",
    "        self.bn2 = nn.BatchNorm2d(16)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv2.bias.data.zero_()\n",
    "        conv_layers += [self.conv2, self.relu2, self.bn2]\n",
    "\n",
    "        self.conv3 = nn.Conv2d(16, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        self.relu3 = nn.ReLU()\n",
    "        self.bn3 = nn.BatchNorm2d(32)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv3.bias.data.zero_()\n",
    "        conv_layers += [self.conv3, self.relu3, self.bn3]\n",
    "\n",
    "        self.conv4 = nn.Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n",
    "        self.relu4 = nn.ReLU()\n",
    "        self.bn4 = nn.BatchNorm2d(64)\n",
    "        init.kaiming_normal_(self.conv1.weight, a=0.2)\n",
    "        self.conv4.bias.data.zero_()\n",
    "        conv_layers += [self.conv4, self.relu4, self.bn4]\n",
    "\n",
    "        self.ap = nn.AdaptiveAvgPool2d(output_size=1)\n",
    "        self.dropout = nn.Dropout(0.4)\n",
    "        self.lin = nn.Linear(in_features=64, out_features=2)\n",
    "        self.dropout = nn.Dropout(0.4)\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "\n",
    "        self.conv = nn.Sequential(*conv_layers)\n",
    " \n",
    "    def forward(self, x):\n",
    "        x = self.conv(x)\n",
    "        x = self.ap(x)\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        x = self.lin(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.sigmoid(x)\n",
    "        return x\n",
    "\n",
    "# pack the data into a Dataset\n",
    "'''class MyDataset(Dataset):\n",
    "    def __init__(self, X, y):\n",
    "        self.X = X\n",
    "        self.y = y\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.X)\n",
    "        \n",
    "    def __getitem__(self, i):\n",
    "        Xi = self.X[i]\n",
    "        yi = self.y[i]\n",
    "        return self.transform(Xi, yi)'''\n",
    "\n",
    "class breathDS(Dataset):\n",
    "    \n",
    "  def __init__(self, df, data_path):\n",
    "    self.df = df\n",
    "    self.data_path = str(data_path)\n",
    "    self.duration = 4000\n",
    "    self.sr = 48000\n",
    "    self.shift_pct = 0.4\n",
    "            \n",
    "  def __len__(self):\n",
    "    return len(self.df)    \n",
    "    \n",
    "  def __getitem__(self, idx):\n",
    "    audio_file = self.data_path + self.df.loc[idx, 'relative_path']\n",
    "    class_id = self.df.loc[idx, 'class']\n",
    "    aud = Breath_sound_Util.open(audio_file)\n",
    "    reaud = Breath_sound_Util.resample(aud, self.sr)\n",
    "    dur_aud = Breath_sound_Util.pad(reaud, self.duration)\n",
    "    shift_aud = Breath_sound_Util.time_shift(dur_aud, self.shift_pct)\n",
    "    sgram = Breath_sound_Util.spectro_gram(shift_aud, n_mels=64, n_fft=1024, hop_len=None)\n",
    "    aug_sgram = Breath_sound_Util.spectro_augment(sgram, max_mask_pct=0.1, n_freq_masks=2, n_time_masks=2)\n",
    "\n",
    "    return self.transform(aug_sgram, class_id)\n",
    "    #return aug_sgram, class_id\n",
    "\n",
    "#ds = MyDataset(df1, data_path)\n",
    "ds = breathDS1(df, data_path)\n",
    "\n",
    "class SliceDatasetX(Dataset):\n",
    "    \"\"\"Helper class that wraps a torch dataset to make it work with sklearn\"\"\"\n",
    "    def __init__(self, dataset, collate_fn=default_collate):\n",
    "        self.dataset = dataset\n",
    "        self.collate_fn = collate_fn\n",
    "\n",
    "        self._indices = list(range(len(self.dataset)))\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.dataset)\n",
    "        \n",
    "    @property\n",
    "    def shape(self):\n",
    "        return len(self),\n",
    "    \n",
    "    def __getitem__(self, i):\n",
    "        if isinstance(i, (int, np.integer)):\n",
    "            Xb = self.transform(*self.dataset[i])[0]\n",
    "            return Xb\n",
    "        \n",
    "        if isinstance(i, slice):\n",
    "            i = self._indices[i]\n",
    "\n",
    "        Xb = self.collate_fn([self.transform(*self.dataset[j])[0] for j in i])\n",
    "        return Xb\n",
    "    \n",
    "\n",
    "params = {\n",
    "    'lr': [0.001, 0.0001, 0.00001],\n",
    "    'max_epochs': [30, 40, 50, 60, 70, 80],\n",
    "    'batch_size': [4, 8, 16, 32, 64]\n",
    "}\n",
    "\n",
    "net = NeuralNetClassifier(\n",
    "    WhoWheezing,\n",
    "    max_epochs=50,\n",
    "    lr=0.001,\n",
    "    batch_size=16,\n",
    "    iterator_train__shuffle=True,\n",
    "    verbose=0,\n",
    "    train_split=None\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cb655f0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "211"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c4343cef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3,\n",
       "             estimator=<class 'skorch.classifier.NeuralNetClassifier'>[uninitialized](\n",
       "  module=<class '__main__.WhoWheezing'>,\n",
       "),\n",
       "             param_grid={'batch_size': [4, 8, 16, 32, 64],\n",
       "                         'lr': [0.001, 0.0001, 1e-05],\n",
       "                         'max_epochs': [30, 40, 50, 60, 70, 80]},\n",
       "             refit=False, scoring='accuracy')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs = GridSearchCV(net, params, refit=False, cv=3, scoring='accuracy')\n",
    "\n",
    "# we have to extract the target data, otherwise sklearn will complain\n",
    "y_from_ds = np.asarray([ds[i][1] for i in range(len(ds))])\n",
    "# wrap the dataset into the new helper class\n",
    "ds_sliceable = SliceDatasetX(ds)\n",
    "\n",
    "gs.fit(ds_sliceable , y_from_ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7171e25f",
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_batch_size</th>\n",
       "      <th>param_lr</th>\n",
       "      <th>param_max_epochs</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.392112</td>\n",
       "      <td>0.019546</td>\n",
       "      <td>0.016569</td>\n",
       "      <td>0.000539</td>\n",
       "      <td>4</td>\n",
       "      <td>0.001</td>\n",
       "      <td>30</td>\n",
       "      <td>{'batch_size': 4, 'lr': 0.001, 'max_epochs': 30}</td>\n",
       "      <td>0.802817</td>\n",
       "      <td>0.914286</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>0.891415</td>\n",
       "      <td>0.065046</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.750510</td>\n",
       "      <td>0.095295</td>\n",
       "      <td>0.016684</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>4</td>\n",
       "      <td>0.001</td>\n",
       "      <td>40</td>\n",
       "      <td>{'batch_size': 4, 'lr': 0.001, 'max_epochs': 40}</td>\n",
       "      <td>0.760563</td>\n",
       "      <td>0.914286</td>\n",
       "      <td>0.971429</td>\n",
       "      <td>0.882093</td>\n",
       "      <td>0.089044</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.275710</td>\n",
       "      <td>0.130940</td>\n",
       "      <td>0.016574</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>4</td>\n",
       "      <td>0.001</td>\n",
       "      <td>50</td>\n",
       "      <td>{'batch_size': 4, 'lr': 0.001, 'max_epochs': 50}</td>\n",
       "      <td>0.774648</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>0.971429</td>\n",
       "      <td>0.901073</td>\n",
       "      <td>0.089586</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.738872</td>\n",
       "      <td>0.102512</td>\n",
       "      <td>0.016618</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>4</td>\n",
       "      <td>0.001</td>\n",
       "      <td>60</td>\n",
       "      <td>{'batch_size': 4, 'lr': 0.001, 'max_epochs': 60}</td>\n",
       "      <td>0.774648</td>\n",
       "      <td>0.942857</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905835</td>\n",
       "      <td>0.095652</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.107062</td>\n",
       "      <td>0.111557</td>\n",
       "      <td>0.016833</td>\n",
       "      <td>0.000645</td>\n",
       "      <td>4</td>\n",
       "      <td>0.001</td>\n",
       "      <td>70</td>\n",
       "      <td>{'batch_size': 4, 'lr': 0.001, 'max_epochs': 70}</td>\n",
       "      <td>0.788732</td>\n",
       "      <td>0.985714</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924816</td>\n",
       "      <td>0.096402</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.519905</td>\n",
       "      <td>0.094682</td>\n",
       "      <td>0.014494</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>64</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>40</td>\n",
       "      <td>{'batch_size': 64, 'lr': 1e-05, 'max_epochs': 40}</td>\n",
       "      <td>0.591549</td>\n",
       "      <td>0.328571</td>\n",
       "      <td>0.728571</td>\n",
       "      <td>0.549564</td>\n",
       "      <td>0.165976</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>7.543148</td>\n",
       "      <td>0.085940</td>\n",
       "      <td>0.014163</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>64</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>50</td>\n",
       "      <td>{'batch_size': 64, 'lr': 1e-05, 'max_epochs': 50}</td>\n",
       "      <td>0.647887</td>\n",
       "      <td>0.771429</td>\n",
       "      <td>0.128571</td>\n",
       "      <td>0.515962</td>\n",
       "      <td>0.278531</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>8.653281</td>\n",
       "      <td>0.044723</td>\n",
       "      <td>0.014388</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>64</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>60</td>\n",
       "      <td>{'batch_size': 64, 'lr': 1e-05, 'max_epochs': 60}</td>\n",
       "      <td>0.563380</td>\n",
       "      <td>0.885714</td>\n",
       "      <td>0.271429</td>\n",
       "      <td>0.573508</td>\n",
       "      <td>0.250883</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>9.594540</td>\n",
       "      <td>0.095816</td>\n",
       "      <td>0.014315</td>\n",
       "      <td>0.000268</td>\n",
       "      <td>64</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>70</td>\n",
       "      <td>{'batch_size': 64, 'lr': 1e-05, 'max_epochs': 70}</td>\n",
       "      <td>0.323944</td>\n",
       "      <td>0.814286</td>\n",
       "      <td>0.871429</td>\n",
       "      <td>0.669886</td>\n",
       "      <td>0.245728</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>10.687073</td>\n",
       "      <td>0.128322</td>\n",
       "      <td>0.014635</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>64</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>80</td>\n",
       "      <td>{'batch_size': 64, 'lr': 1e-05, 'max_epochs': 80}</td>\n",
       "      <td>0.563380</td>\n",
       "      <td>0.185714</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.321127</td>\n",
       "      <td>0.171696</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        6.392112      0.019546         0.016569        0.000539   \n",
       "1        7.750510      0.095295         0.016684        0.000503   \n",
       "2        9.275710      0.130940         0.016574        0.000424   \n",
       "3       10.738872      0.102512         0.016618        0.000303   \n",
       "4       12.107062      0.111557         0.016833        0.000645   \n",
       "..            ...           ...              ...             ...   \n",
       "85       6.519905      0.094682         0.014494        0.000225   \n",
       "86       7.543148      0.085940         0.014163        0.000325   \n",
       "87       8.653281      0.044723         0.014388        0.000407   \n",
       "88       9.594540      0.095816         0.014315        0.000268   \n",
       "89      10.687073      0.128322         0.014635        0.000471   \n",
       "\n",
       "   param_batch_size param_lr param_max_epochs  \\\n",
       "0                 4    0.001               30   \n",
       "1                 4    0.001               40   \n",
       "2                 4    0.001               50   \n",
       "3                 4    0.001               60   \n",
       "4                 4    0.001               70   \n",
       "..              ...      ...              ...   \n",
       "85               64  0.00001               40   \n",
       "86               64  0.00001               50   \n",
       "87               64  0.00001               60   \n",
       "88               64  0.00001               70   \n",
       "89               64  0.00001               80   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0    {'batch_size': 4, 'lr': 0.001, 'max_epochs': 30}           0.802817   \n",
       "1    {'batch_size': 4, 'lr': 0.001, 'max_epochs': 40}           0.760563   \n",
       "2    {'batch_size': 4, 'lr': 0.001, 'max_epochs': 50}           0.774648   \n",
       "3    {'batch_size': 4, 'lr': 0.001, 'max_epochs': 60}           0.774648   \n",
       "4    {'batch_size': 4, 'lr': 0.001, 'max_epochs': 70}           0.788732   \n",
       "..                                                ...                ...   \n",
       "85  {'batch_size': 64, 'lr': 1e-05, 'max_epochs': 40}           0.591549   \n",
       "86  {'batch_size': 64, 'lr': 1e-05, 'max_epochs': 50}           0.647887   \n",
       "87  {'batch_size': 64, 'lr': 1e-05, 'max_epochs': 60}           0.563380   \n",
       "88  {'batch_size': 64, 'lr': 1e-05, 'max_epochs': 70}           0.323944   \n",
       "89  {'batch_size': 64, 'lr': 1e-05, 'max_epochs': 80}           0.563380   \n",
       "\n",
       "    split1_test_score  split2_test_score  mean_test_score  std_test_score  \\\n",
       "0            0.914286           0.957143         0.891415        0.065046   \n",
       "1            0.914286           0.971429         0.882093        0.089044   \n",
       "2            0.957143           0.971429         0.901073        0.089586   \n",
       "3            0.942857           1.000000         0.905835        0.095652   \n",
       "4            0.985714           1.000000         0.924816        0.096402   \n",
       "..                ...                ...              ...             ...   \n",
       "85           0.328571           0.728571         0.549564        0.165976   \n",
       "86           0.771429           0.128571         0.515962        0.278531   \n",
       "87           0.885714           0.271429         0.573508        0.250883   \n",
       "88           0.814286           0.871429         0.669886        0.245728   \n",
       "89           0.185714           0.214286         0.321127        0.171696   \n",
       "\n",
       "    rank_test_score  \n",
       "0                 7  \n",
       "1                11  \n",
       "2                 5  \n",
       "3                 3  \n",
       "4                 1  \n",
       "..              ...  \n",
       "85               69  \n",
       "86               73  \n",
       "87               67  \n",
       "88               60  \n",
       "89               90  \n",
       "\n",
       "[90 rows x 14 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(gs.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "07bc9c27",
   "metadata": {},
   "outputs": [],
   "source": [
    "del gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1fb26bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "# parameter 넣어줄 값들 dict 형태로 정의해주기\n",
    "h_para = {'max_depth':[1,2,3], 'min_samples_split':[2,3]}\n",
    "\n",
    "grid_dtree = GridSearchCV(model, param_grid=h_para,\n",
    "                         cv=5, refit=True, return_train_score=True)\n",
    "# GridSearchCV 인자설명\n",
    "# cv = 하나의 파라미터 쌍으로 모델링할 때 train, test 교차검증을 3번실시하겠다는 뜻\n",
    "# refit=True : GridSearch한 후 가장 최고로 좋은 파라미터로 학습시켜 놓겠다.\n",
    "# ㄴ> 이것 때문에 애초에 GridSearchCV 적용한 객체만으로 최적의 파라미터 적용된 모델로드 가능\n",
    "\n",
    "# GridSearch 하면서 모든 파라미터값들에 대해 학습 수행\n",
    "grid_dtree.fit(train_x, train_y)\n",
    "\n",
    "# 각 파라미터값들에 대한 모델 결과값들이 cv_results_ 객체에 할당됨\n",
    "scores_df = pd.DataFrame(grid_dtree.cv_results_)\n",
    "\n",
    "# score 결과값(ndarray형태로 할당됨) 중 특정 칼럼들만 가져오기 \n",
    "scores_df[['params', 'mean_test_score', 'rank_test_score', \n",
    "           'split0_test_score', 'split1_test_score', 'split2_test_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68fcd8c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "505ba94f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9a29f5ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ray import tune\n",
    "from ray.tune import CLIReporter\n",
    "from ray.tune.schedulers import ASHAScheduler\n",
    "\n",
    "config = {\n",
    "    \"l1\": tune.sample_from(lambda _: 2**np.random.randint(2, 9)),\n",
    "    \"l2\": tune.sample_from(lambda _: 2**np.random.randint(2, 9)),\n",
    "    \"lr\": tune.loguniform(1e-4, 1e-1),\n",
    "    \"batch_size\": tune.choice([2, 4, 8, 16])\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "10976b8a",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'partial' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-44c26ff95213>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# ...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m result = tune.run(\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_cifar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mresources_per_trial\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"gpu\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mgpus_per_trial\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'partial' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4dc3a355",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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