covid_audio_classification.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocess the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>id</th>\n",
       "      <th>a</th>\n",
       "      <th>covid_status</th>\n",
       "      <th>record_date</th>\n",
       "      <th>ep</th>\n",
       "      <th>g</th>\n",
       "      <th>l_c</th>\n",
       "      <th>l_l</th>\n",
       "      <th>l_s</th>\n",
       "      <th>rU</th>\n",
       "      <th>...</th>\n",
       "      <th>vacc</th>\n",
       "      <th>bd</th>\n",
       "      <th>others_resp</th>\n",
       "      <th>ftg</th>\n",
       "      <th>st</th>\n",
       "      <th>ihd</th>\n",
       "      <th>asthma</th>\n",
       "      <th>others_preexist</th>\n",
       "      <th>cld</th>\n",
       "      <th>pneumonia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>iV3Db6t1T8b7c5HQY2TwxIhjbzD3</td>\n",
       "      <td>28</td>\n",
       "      <td>healthy</td>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>y</td>\n",
       "      <td>male</td>\n",
       "      <td>India</td>\n",
       "      <td>Anantapur</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AxuYWBN0jFVLINCBqIW5aZmGCdu1</td>\n",
       "      <td>25</td>\n",
       "      <td>healthy</td>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>y</td>\n",
       "      <td>male</td>\n",
       "      <td>India</td>\n",
       "      <td>BENGALURU URBAN</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C5eIsssb9GSkaAgIfsHMHeR6fSh1</td>\n",
       "      <td>28</td>\n",
       "      <td>healthy</td>\n",
       "      <td>2020-04-24</td>\n",
       "      <td>y</td>\n",
       "      <td>female</td>\n",
       "      <td>United States</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YjbEAECMBIaZKyfqOvWy5DDImUb2</td>\n",
       "      <td>26</td>\n",
       "      <td>healthy</td>\n",
       "      <td>2020-04-23</td>\n",
       "      <td>y</td>\n",
       "      <td>male</td>\n",
       "      <td>India</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aGOvk4ji0cVqIzCs1jHnzlw2UEy2</td>\n",
       "      <td>32</td>\n",
       "      <td>healthy</td>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>y</td>\n",
       "      <td>male</td>\n",
       "      <td>India</td>\n",
       "      <td>Nalanda</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id   a covid_status record_date ep       g  \\\n",
       "0  iV3Db6t1T8b7c5HQY2TwxIhjbzD3  28      healthy  2020-04-23  y    male   \n",
       "1  AxuYWBN0jFVLINCBqIW5aZmGCdu1  25      healthy  2020-04-20  y    male   \n",
       "2  C5eIsssb9GSkaAgIfsHMHeR6fSh1  28      healthy  2020-04-24  y  female   \n",
       "3  YjbEAECMBIaZKyfqOvWy5DDImUb2  26      healthy  2020-04-23  y    male   \n",
       "4  aGOvk4ji0cVqIzCs1jHnzlw2UEy2  32      healthy  2020-04-22  y    male   \n",
       "\n",
       "             l_c              l_l             l_s rU  ... vacc   bd  \\\n",
       "0          India        Anantapur  Andhra Pradesh  n  ...  NaN  NaN   \n",
       "1          India  BENGALURU URBAN       Karnataka  n  ...  NaN  NaN   \n",
       "2  United States       Pittsburgh    Pennsylvania  n  ...  NaN  NaN   \n",
       "3          India        Bangalore       Karnataka  n  ...  NaN  NaN   \n",
       "4          India          Nalanda           Bihar  n  ...  NaN  NaN   \n",
       "\n",
       "  others_resp  ftg   st  ihd asthma  others_preexist  cld pneumonia  \n",
       "0         NaN  NaN  NaN  NaN    NaN              NaN  NaN       NaN  \n",
       "1         NaN  NaN  NaN  NaN    NaN              NaN  NaN       NaN  \n",
       "2         NaN  NaN  NaN  NaN    NaN              NaN  NaN       NaN  \n",
       "3         NaN  NaN  NaN  NaN    NaN              NaN  NaN       NaN  \n",
       "4         NaN  NaN  NaN  NaN    NaN              NaN  NaN       NaN  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "combined_data = pd.read_csv('Coswara-Data/combined_data.csv')\n",
    "combined_data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep only the columns that are needed for the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>id</th>\n",
       "      <th>covid_status</th>\n",
       "      <th>test_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>iV3Db6t1T8b7c5HQY2TwxIhjbzD3</td>\n",
       "      <td>healthy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AxuYWBN0jFVLINCBqIW5aZmGCdu1</td>\n",
       "      <td>healthy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C5eIsssb9GSkaAgIfsHMHeR6fSh1</td>\n",
       "      <td>healthy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YjbEAECMBIaZKyfqOvWy5DDImUb2</td>\n",
       "      <td>healthy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aGOvk4ji0cVqIzCs1jHnzlw2UEy2</td>\n",
       "      <td>healthy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2472</th>\n",
       "      <td>333NjqA1TfZJuICEdXSkPhVz0LA3</td>\n",
       "      <td>positive_asymp</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2473</th>\n",
       "      <td>dpA0EeRrtJUeKJjEuf7BL0AeTJZ2</td>\n",
       "      <td>positive_mild</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2474</th>\n",
       "      <td>UXhG3vgoxkWtd4Meky1nm0sRgMV2</td>\n",
       "      <td>positive_mild</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2475</th>\n",
       "      <td>A5KW9PXraNgckln1gnNaJnX6DvB2</td>\n",
       "      <td>positive_moderate</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2476</th>\n",
       "      <td>iYwmYc9CdlSuzqGwIlXNWI6eFpm1</td>\n",
       "      <td>positive_mild</td>\n",
       "      <td>p</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2477 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                id       covid_status test_status\n",
       "0     iV3Db6t1T8b7c5HQY2TwxIhjbzD3            healthy         NaN\n",
       "1     AxuYWBN0jFVLINCBqIW5aZmGCdu1            healthy         NaN\n",
       "2     C5eIsssb9GSkaAgIfsHMHeR6fSh1            healthy         NaN\n",
       "3     YjbEAECMBIaZKyfqOvWy5DDImUb2            healthy         NaN\n",
       "4     aGOvk4ji0cVqIzCs1jHnzlw2UEy2            healthy         NaN\n",
       "...                            ...                ...         ...\n",
       "2472  333NjqA1TfZJuICEdXSkPhVz0LA3     positive_asymp           p\n",
       "2473  dpA0EeRrtJUeKJjEuf7BL0AeTJZ2      positive_mild           p\n",
       "2474  UXhG3vgoxkWtd4Meky1nm0sRgMV2      positive_mild           p\n",
       "2475  A5KW9PXraNgckln1gnNaJnX6DvB2  positive_moderate           p\n",
       "2476  iYwmYc9CdlSuzqGwIlXNWI6eFpm1      positive_mild           p\n",
       "\n",
       "[2477 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data = combined_data[['id', 'covid_status', 'test_status']]\n",
    "combined_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "healthy                        1410\n",
       "positive_mild                   426\n",
       "positive_moderate               164\n",
       "no_resp_illness_exposed         161\n",
       "recovered_full                  145\n",
       "positive_asymp                   89\n",
       "resp_illness_not_identified      82\n",
       "Name: covid_status, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data['covid_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p     679\n",
       "na    312\n",
       "n     257\n",
       "Name: test_status, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data['test_status'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First let's rename 'healthy' to 'negative'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_data.loc[combined_data['covid_status'] == 'healthy', 'covid_status'] = 'negative'"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'ut' label for the `test_status` column seems to be used to indicate that the test was not validated yet, matching with the 'under_validation' label in the `covid_status` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: covid_status, dtype: int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data[combined_data['test_status'] == 'ut']['covid_status'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's remove the rows with this label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p     679\n",
       "na    312\n",
       "n     257\n",
       "Name: test_status, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data = combined_data[combined_data['test_status'] != 'ut']\n",
    "combined_data['test_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative                       197\n",
       "no_resp_illness_exposed         83\n",
       "resp_illness_not_identified     32\n",
       "Name: covid_status, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data[combined_data['test_status'] == 'na']['covid_status'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that the 'na' label for the `test_status` column, used to indicate that the test was not performed, matches in most cases with the 'negative' label in the `covid_status` column.  \n",
    "Let's used this label as a 'n' label for the `test_status` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "p    679\n",
       "n    569\n",
       "Name: test_status, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.loc[combined_data['test_status'] == 'na', 'test_status'] = 'n'\n",
    "combined_data['test_status'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's update the labels for the `covid_status` column to only have two labels: 'positive' and 'negative'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative                       1410\n",
       "positive_mild                   426\n",
       "positive_moderate               164\n",
       "no_resp_illness_exposed         161\n",
       "recovered_full                  145\n",
       "positive_asymp                   89\n",
       "resp_illness_not_identified      82\n",
       "Name: covid_status, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data['covid_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative             1683\n",
       "positive              679\n",
       "positive_mild          72\n",
       "recovered_full         20\n",
       "positive_asymp         14\n",
       "positive_moderate       9\n",
       "Name: covid_status, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.loc[combined_data['test_status'] == 'n', 'covid_status'] = 'negative'\n",
    "combined_data.loc[combined_data['test_status'] == 'p', 'covid_status'] = 'positive'\n",
    "combined_data['covid_status'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that some labels in the `covid_status` column are still present. Let's check what can be done with them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data[(combined_data['covid_status'] != 'negative') & (combined_data['covid_status'] != 'positive')]['test_status'].notna().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`test_status` column is set to NaN for all the rows with a different label in the `covid_status` column than 'positive' or 'negative'.  \n",
    "Let's switch 'positive_mild', 'positive_asymp' and 'positive_moderate' labels to 'positive', and 'recovered_full' to 'negative'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative    1703\n",
       "positive     774\n",
       "Name: covid_status, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data.loc[combined_data['covid_status'] == 'positive_mild', 'covid_status'] = 'positive'\n",
    "combined_data.loc[combined_data['covid_status'] == 'positive_moderate', 'covid_status'] = 'positive'\n",
    "combined_data.loc[combined_data['covid_status'] == 'positive_asymp', 'covid_status'] = 'positive'\n",
    "combined_data.loc[combined_data['covid_status'] == 'recovered_full', 'covid_status'] = 'negative'\n",
    "combined_data['covid_status'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For 'no_resp_illness_exposed' and 'resp_illness_not_identified', we can not deduced if the person is negative or not.  \n",
    "Let's remove these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "negative    1703\n",
       "positive     774\n",
       "Name: covid_status, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data = combined_data[(combined_data['covid_status'] == 'negative') | (combined_data['covid_status'] == 'positive')]\n",
    "combined_data['covid_status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data['covid_status'].isna().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that there is no NaN value in the `covid_status` column, we can now remove the `test_status` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>id</th>\n",
       "      <th>covid_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>iV3Db6t1T8b7c5HQY2TwxIhjbzD3</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AxuYWBN0jFVLINCBqIW5aZmGCdu1</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C5eIsssb9GSkaAgIfsHMHeR6fSh1</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YjbEAECMBIaZKyfqOvWy5DDImUb2</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aGOvk4ji0cVqIzCs1jHnzlw2UEy2</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id covid_status\n",
       "0  iV3Db6t1T8b7c5HQY2TwxIhjbzD3     negative\n",
       "1  AxuYWBN0jFVLINCBqIW5aZmGCdu1     negative\n",
       "2  C5eIsssb9GSkaAgIfsHMHeR6fSh1     negative\n",
       "3  YjbEAECMBIaZKyfqOvWy5DDImUb2     negative\n",
       "4  aGOvk4ji0cVqIzCs1jHnzlw2UEy2     negative"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_data = combined_data[combined_data.columns[~combined_data.columns.isin(['test_status'])]]\n",
    "combined_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2477"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(combined_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(negative    1089\n",
       " positive     495\n",
       " Name: covid_status, dtype: int64,\n",
       " negative    273\n",
       " positive    124\n",
       " Name: covid_status, dtype: int64,\n",
       " negative    341\n",
       " positive    155\n",
       " Name: covid_status, dtype: int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "train, test = train_test_split(combined_data, test_size=0.2, random_state=42, stratify=combined_data['covid_status'])\n",
    "train, val = train_test_split(train, test_size=0.2, random_state=42, stratify=train['covid_status'])\n",
    "\n",
    "train['covid_status'].value_counts(), val['covid_status'].value_counts(), test['covid_status'].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transform audio files to mel-spectrograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "audio_folder = 'Coswara-Data/'\n",
    "\n",
    "train_ids = train['id'].values\n",
    "val_ids = val['id'].values\n",
    "test_ids = test['id'].values\n",
    "\n",
    "train_audio_folder = []\n",
    "val_audio_folder = []\n",
    "test_audio_folder = []\n",
    "\n",
    "for folder1 in os.listdir(audio_folder):\n",
    "    if os.path.isdir(os.path.join(audio_folder, folder1)):\n",
    "        for folder2 in os.listdir(os.path.join(audio_folder, folder1)):\n",
    "            if folder2 in train_ids:\n",
    "                train_audio_folder.append(os.path.join(audio_folder, folder1, folder2))\n",
    "            elif folder2 in val_ids:\n",
    "                val_audio_folder.append(os.path.join(audio_folder, folder1, folder2))\n",
    "            elif folder2 in test_ids:\n",
    "                test_audio_folder.append(os.path.join(audio_folder, folder1, folder2))\n",
    "            \n",
    "assert len(train_audio_folder) == len(train_ids)\n",
    "assert len(val_audio_folder) == len(val_ids)\n",
    "assert len(test_audio_folder) == len(test_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leme/.local/lib/python3.10/site-packages/requests/__init__.py:102: RequestsDependencyWarning: urllib3 (1.26.9) or chardet (5.1.0)/charset_normalizer (2.0.12) doesn't match a supported version!\n",
      "  warnings.warn(\"urllib3 ({}) or chardet ({})/charset_normalizer ({}) doesn't match a supported \"\n"
     ]
    }
   ],
   "source": [
    "import librosa\n",
    "import skimage\n",
    "import numpy as np\n",
    "\n",
    "# Settings\n",
    "n_mels = 64 # Number of Mel banks to generate\n",
    "n_fft = 1024 # Interval we consider to apply FFT. Measured in # of samples\n",
    "hop_length = 441 # Sliding window for FFT. Measured in # of samples\n",
    "fmax = 22 # Maximum frequency we want to consider\n",
    "sampling_rate = 44100 # 44.1kHz sampling rate\n",
    "fmax = 22050"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/leme/.local/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchaudio\n",
    "\n",
    "def compute_SAD(sig,fs,threshold=0.0001,sad_start_end_sil_length=100, sad_margin_length=50):\n",
    "    ''' Compute threshold based sound activity '''\n",
    "    # Leading/Trailing margin\n",
    "    sad_start_end_sil_length = int(sad_start_end_sil_length*1e-3*fs)\n",
    "    # Margin around active samples\n",
    "    sad_margin_length = int(sad_margin_length*1e-3*fs)\n",
    "\n",
    "    sample_activity = np.zeros(sig.shape)\n",
    "    sample_activity[np.power(sig,2)>threshold] = 1\n",
    "    sad = np.zeros(sig.shape)\n",
    "    for i in range(sample_activity.shape[0]):\n",
    "        if sample_activity[i] == 1: sad[i-sad_margin_length:i+sad_margin_length] = 1\n",
    "    sad[0:sad_start_end_sil_length] = 0\n",
    "    sad[-sad_start_end_sil_length:] = 0\n",
    "    return sad\n",
    "\n",
    "def read_audio(file_path):\n",
    "\ttry:\n",
    "\t\ts,fs = librosa.load(file_path)\n",
    "\t\tif np.mean(s)==0 or len(s)<1024:\n",
    "\t\t\traise ValueError()\n",
    "\t\t# waveform level amplitude normalization\n",
    "\t\ts = s/np.max(np.abs(s))\n",
    "\t\tsad = compute_SAD(s,fs)\n",
    "\t\ts = s[np.where(sad==1)]\n",
    "\texcept ValueError:\n",
    "\t\ts = None\n",
    "\t\tprint(\"Read audio failed for \"+file_path)\t\t\n",
    "\treturn s\n",
    "\n",
    "def compute_logMelSpec(s):\n",
    "\t''' Feature preparation\n",
    "\tSteps:\n",
    "\t1. Apply feature extraction to waveform\n",
    "\t2. Convert amplitude to dB if required\n",
    "\t3. Append delta and delta-delta features\n",
    "\t'''\n",
    "\tF_extractor = torchaudio.transforms.MelSpectrogram(sample_rate=sampling_rate,\n",
    "                                                                            n_fft=n_fft,\n",
    "                                                                            n_mels=n_mels,\n",
    "                                                                            f_max=fmax,\n",
    "                                                                            hop_length=hop_length)\n",
    "\tF = F_extractor(torch.tensor(s))\n",
    "\tF = torchaudio.functional.amplitude_to_DB(F,multiplier=10,amin=1e-10,db_multiplier=0)\n",
    "\tFo=F\n",
    "\tFD = torchaudio.functional.compute_deltas(F)\n",
    "\tFo = torch.cat((F,FD),dim=0)\n",
    "\tFD = torchaudio.functional.compute_deltas(F)\n",
    "\tFDD = torchaudio.functional.compute_deltas(FD)\n",
    "\tFo = torch.cat((F,FD,FDD),dim=0)\n",
    "\treturn (Fo.T.numpy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_features(audio_folder, audio_name):\n",
    "    audio = read_audio(os.path.join(audio_folder, audio_name))\n",
    "    features = compute_logMelSpec(audio)\n",
    "    return features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_minmax(X, min=0.0, max=1.0):\n",
    "    X_std = (X - X.min()) / (X.max() - X.min())\n",
    "    X_scaled = X_std * (max - min) + min\n",
    "    return X_scaled\n",
    "\n",
    "def extract_melspectrogram(audio_folder, audio_name):\n",
    "    audio, sr = librosa.load(os.path.join(audio_folder, audio_name))\n",
    "    melspectrogram = librosa.feature.melspectrogram(y=audio, sr=sr, n_mels=n_mels, n_fft=n_fft, hop_length=hop_length, fmax=fmax)\n",
    "    melspectrogram = np.log(melspectrogram + 1e-9) # add small number to avoid log(0)\n",
    "    return melspectrogram\n",
    "\n",
    "def extract_melspectrogram_image(audio_folder, audio_name):\n",
    "    melspectrogram = extract_melspectrogram(audio_folder, audio_name)\n",
    "    img = scale_minmax(melspectrogram, 0, 255).astype(np.uint8)\n",
    "    img = np.flip(img, axis=0)\n",
    "    img = 255 - img\n",
    "    skimage.io.imsave(os.path.join(audio_folder, audio_name + '.png'), img, check_contrast=False)\n",
    "    return melspectrogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting melspectrogram for test set...\n"
     ]
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      "  return f(*args, **kwargs)\n",
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     ]
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      "/tmp/ipykernel_1272/3512539183.py:13: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  data = np.array(data)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting melspectrogram for train set...\n"
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    {
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      "Extracting melspectrogram for validation set...\n"
     ]
    },
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      "100%|██████████| 397/397 [02:53<00:00,  2.29it/s]\n"
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    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "audio_names = ['cough-heavy.wav', 'cough-shallow.wav']\n",
    "\n",
    "def get_data(df, audio_folder, audio_names):\n",
    "    data = []\n",
    "    for folder in tqdm(audio_folder):\n",
    "        for audio_name in audio_names:\n",
    "            try:\n",
    "                #d = extract_melspectrogram_image(folder, audio_name) # uncomment this line to save melspectrogram image\n",
    "                #d = extract_melspectrogram(folder, audio_name) # uncomment this line to save melspectrogram\n",
    "                d = get_features(folder, audio_name) # uncomment this line to save melspectrogram features\n",
    "                label = df[df['id'] == folder.split('/')[-1]]['covid_status'].values[0]\n",
    "                data.append((d, label))\n",
    "            except:\n",
    "                continue\n",
    "    data = np.array(data)\n",
    "    return data\n",
    "\n",
    "print('Extracting melspectrogram for test set...')\n",
    "test_data = get_data(test, test_audio_folder, audio_names)\n",
    "np.save('test_data.npy', test_data)\n",
    "\n",
    "print('Extracting melspectrogram for train set...')\n",
    "train_data = get_data(train, train_audio_folder, audio_names)\n",
    "np.save('train_data.npy', train_data)\n",
    "\n",
    "print('Extracting melspectrogram for validation set...')\n",
    "val_data = get_data(val, val_audio_folder, audio_names)\n",
    "np.save('val_data.npy', val_data)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create CNN model for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "train_data = np.load('train_data.npy', allow_pickle=True)\n",
    "val_data = np.load('val_data.npy', allow_pickle=True)\n",
    "test_data = np.load('test_data.npy', allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_14\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_42 (Conv2D)          (None, 128, 128, 8)       80        \n",
      "                                                                 \n",
      " max_pooling2d_42 (MaxPoolin  (None, 64, 64, 8)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_24 (Bat  (None, 64, 64, 8)        32        \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv2d_43 (Conv2D)          (None, 64, 64, 16)        1168      \n",
      "                                                                 \n",
      " max_pooling2d_43 (MaxPoolin  (None, 32, 32, 16)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_25 (Bat  (None, 32, 32, 16)       64        \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " conv2d_44 (Conv2D)          (None, 32, 32, 32)        4640      \n",
      "                                                                 \n",
      " max_pooling2d_44 (MaxPoolin  (None, 16, 16, 32)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " batch_normalization_26 (Bat  (None, 16, 16, 32)       128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_10 (Dropout)        (None, 16, 16, 32)        0         \n",
      "                                                                 \n",
      " flatten_14 (Flatten)        (None, 8192)              0         \n",
      "                                                                 \n",
      " dense_28 (Dense)            (None, 128)               1048704   \n",
      "                                                                 \n",
      " dense_29 (Dense)            (None, 2)                 258       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,055,074\n",
      "Trainable params: 1,054,962\n",
      "Non-trainable params: 112\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "def create_model():\n",
    "    model = tf.keras.Sequential([\n",
    "        tf.keras.layers.Conv2D(8, 3, activation='relu', input_shape=(128, 128, 1), padding='same'),\n",
    "        tf.keras.layers.MaxPooling2D(),\n",
    "        tf.keras.layers.BatchNormalization(),\n",
    "        tf.keras.layers.Conv2D(16, 3, activation='relu', padding='same'),\n",
    "        tf.keras.layers.MaxPooling2D(),\n",
    "        tf.keras.layers.BatchNormalization(),\n",
    "        tf.keras.layers.Conv2D(32, 3, activation='relu', padding='same'),\n",
    "        tf.keras.layers.MaxPooling2D(),\n",
    "        tf.keras.layers.BatchNormalization(),\n",
    "        tf.keras.layers.Dropout(0.2),\n",
    "        tf.keras.layers.Flatten(),\n",
    "        tf.keras.layers.Dense(128, activation='relu'),\n",
    "        tf.keras.layers.Dense(2, activation='softmax')\n",
    "    ])\n",
    "    \n",
    "    model.compile(optimizer='adam',\n",
    "                  loss='sparse_categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = create_model()\n",
    "model.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = np.array([label for _, label in train_data])\n",
    "val_labels = np.array([label for _, label in val_data])\n",
    "test_labels = np.array([label for _, label in test_data])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get and resize the mel-spectrograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import skimage\n",
    "\n",
    "def resize_melspectrogram(melspectrogram):\n",
    "    return skimage.transform.resize(melspectrogram, (128, 128))\n",
    "\n",
    "train_melspectrogram = np.array([resize_melspectrogram(melspectrogram) for melspectrogram, _ in train_data])\n",
    "val_melspectrogram = np.array([resize_melspectrogram(melspectrogram) for melspectrogram, _ in val_data])\n",
    "test_melspectrogram = np.array([resize_melspectrogram(melspectrogram) for melspectrogram, _ in test_data])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binarize the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = (train_labels == 'positive').astype(int)\n",
    "val_labels = (val_labels == 'positive').astype(int)\n",
    "test_labels = (test_labels == 'positive').astype(int)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "97/97 [==============================] - 9s 88ms/step - loss: 0.7159 - accuracy: 0.6704 - val_loss: 0.5818 - val_accuracy: 0.6917\n",
      "Epoch 2/10\n",
      "97/97 [==============================] - 8s 80ms/step - loss: 0.5409 - accuracy: 0.7122 - val_loss: 0.5770 - val_accuracy: 0.7254\n",
      "Epoch 3/10\n",
      "97/97 [==============================] - 8s 83ms/step - loss: 0.5123 - accuracy: 0.7333 - val_loss: 0.6188 - val_accuracy: 0.6438\n",
      "Epoch 4/10\n",
      "97/97 [==============================] - 8s 80ms/step - loss: 0.4435 - accuracy: 0.7797 - val_loss: 0.5825 - val_accuracy: 0.6917\n",
      "Epoch 5/10\n",
      "97/97 [==============================] - 8s 84ms/step - loss: 0.3607 - accuracy: 0.8247 - val_loss: 0.6161 - val_accuracy: 0.7111\n",
      "Epoch 6/10\n",
      "97/97 [==============================] - 8s 87ms/step - loss: 0.2612 - accuracy: 0.8892 - val_loss: 0.7093 - val_accuracy: 0.7124\n",
      "Epoch 7/10\n",
      "97/97 [==============================] - 9s 90ms/step - loss: 0.1813 - accuracy: 0.9268 - val_loss: 0.9439 - val_accuracy: 0.7241\n",
      "Epoch 8/10\n",
      "97/97 [==============================] - 7s 74ms/step - loss: 0.1106 - accuracy: 0.9637 - val_loss: 0.9942 - val_accuracy: 0.6930\n",
      "Epoch 9/10\n",
      "97/97 [==============================] - 8s 87ms/step - loss: 0.0845 - accuracy: 0.9728 - val_loss: 1.1436 - val_accuracy: 0.7111\n",
      "Epoch 10/10\n",
      "97/97 [==============================] - 8s 84ms/step - loss: 0.0698 - accuracy: 0.9770 - val_loss: 1.1658 - val_accuracy: 0.6917\n"
     ]
    }
   ],
   "source": [
    "epochs = 10\n",
    "batch_size = 32\n",
    "history = model.fit(train_melspectrogram, train_labels, batch_size=batch_size, epochs=epochs, validation_data=(val_melspectrogram, val_labels))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the training and validation accuracy/loss per epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training and Validation Loss')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs_range = range(epochs)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, loss, label='Training Loss')\n",
    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 0s 15ms/step - loss: 1.0922 - accuracy: 0.6867\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.0922260284423828, 0.6867219805717468]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_melspectrogram, test_labels)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "\n",
    "predictions = model.predict(test_melspectrogram)\n",
    "\n",
    "cm = confusion_matrix(test_labels, np.argmax(predictions, axis=1))\n",
    "sns.heatmap(cm, annot=True, fmt='d')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('covid_cough_classifier.h5')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "97cc609b13305c559618ec78a438abc56230b9381f827f22d070313b9a1f3777"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}