{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplement 4: Interaction Terms Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source code for prediction of COVID-19 test results. This is supplemental material to publication\n",
    "\n",
    "Wojtusiak J, Bagais W, Vang J, Guralnik E, Roess A, Alemi F, \"The Role of Symptom Clusters in Triage of COVID-19 Patients,\" Quality Management in Health Care, 2022.\n",
    "\n",
    "Source code by Wejdan Bagais and Jee Vang with contribution of other authors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "from models import select_attributes\n",
    "from models import conjunction_columns\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import timeit\n",
    "\n",
    "from patsy import dmatrix, dmatrices\n",
    "\n",
    "import pickle\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import numpy as np\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run LASSO model for the 30 splits data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = timeit.default_timer()\n",
    "\n",
    "#list of inverse of regularization\n",
    "c_list = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.5,2]\n",
    "\n",
    "split_ids = []\n",
    "cs = []\n",
    "source = []\n",
    "AUCs = []\n",
    "prec = []\n",
    "rec =  []\n",
    "vars_cnt = []\n",
    "vars_lists = []\n",
    "ys_test = []\n",
    "ys_pred = []\n",
    "\n",
    "# loop over the 30 split data\n",
    "for i in range (0,30):\n",
    "    # read the data\n",
    "    tr_path = \"../data/30_splits_data/binary-transformed_tr_\"+str(i)+\".csv\"\n",
    "    ts_path= \"../data/30_splits_data/binary-transformed_ts_\"+str(i)+\".csv\"\n",
    "\n",
    "    train = pd.read_csv(tr_path)\n",
    "    test = pd.read_csv(ts_path)\n",
    "     \n",
    "    # create the single, pair, and triplets clusters\n",
    "    XT, Xt, yT, yt = conjunction_columns(train, test)\n",
    "    \n",
    "    # loop over inverse of regularization\n",
    "    for c in c_list:\n",
    "        # run the model\n",
    "        auc, recall, precision, valid_cols, yt, y_pred = select_attributes(XT, yT, Xt, yt,c)\n",
    "        # save results to the list\n",
    "        split_ids.append(i)\n",
    "        cs.append(c)\n",
    "        source.append('conjunction')\n",
    "        AUCs.append(auc)\n",
    "        prec.append(precision)\n",
    "        rec.append(recall)\n",
    "        vars_lists.append(valid_cols)\n",
    "        vars_cnt.append(len(valid_cols))\n",
    "        ys_test.append(yt.values.tolist())\n",
    "        ys_pred.append(y_pred)\n",
    "                \n",
    "        print(f'ID {i}, C={c:.2f}, AUC={auc:.5f}, Precision={precision:.5f}, Recall={recall:.5f}, cls# {len(valid_cols)}') \n",
    "\n",
    "stop = timeit.default_timer()\n",
    "print('Time: ', stop - start)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# identify the list of unique selected predictors\n",
    "unq_var = vars_lists.copy()\n",
    "for i in range(0, len(unq_var)):\n",
    "    unq_var[i] = [sub.replace(' & ', ',') for sub in unq_var[i]]\n",
    "    \n",
    "sympt_lists = []\n",
    "for i in range(0, len(unq_var)):\n",
    "    l = \",\".join(unq_var[i])\n",
    "    l2 = list(set(l.split(',')))\n",
    "    sympt_lists.append(l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ys_pred_l = []\n",
    "for i in ys_pred:\n",
    "    ys_pred_l.append(list(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe for all results\n",
    "ff = pd.DataFrame({'split_ids'    : split_ids,\n",
    "                   'cs'           : cs,\n",
    "                   'source'       : source,\n",
    "                   'AUCs'         : AUCs,\n",
    "                   'prec'         : prec,\n",
    "                   'rec'          : rec,\n",
    "                   'vars_cnt'     : vars_cnt,\n",
    "                   'vars_lists'   : vars_lists,\n",
    "                   'y_test'       : ys_test,\n",
    "                   'y_pred'       : ys_pred_l\n",
    "                  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add list of unique predictors and its count\n",
    "ff['sympt_lists'] = sympt_lists\n",
    "ff['sympt_cnt'] = ff['sympt_lists'].apply(lambda x :len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the results\n",
    "ff.to_csv('../data/results/interaction_terms.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identifying the best inverse of regularization strength value (C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = pd.pivot_table(ff, values=['AUCs', 'vars_cnt', 'sympt_cnt']\n",
    "                       , index=['cs']\n",
    "                       , aggfunc=np.mean).round(decimals =4)\n",
    "\n",
    "table['sympt_cnt'] = table['sympt_cnt'].round().astype(int)\n",
    "table['vars_cnt'] = table['vars_cnt'].round().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>AUCs</th>\n",
       "      <th>sympt_cnt</th>\n",
       "      <th>vars_cnt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.1</th>\n",
       "      <td>0.7800</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.2</th>\n",
       "      <td>0.7863</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.3</th>\n",
       "      <td>0.8040</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.4</th>\n",
       "      <td>0.8143</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.8147</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>0.8144</td>\n",
       "      <td>23</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.7</th>\n",
       "      <td>0.8138</td>\n",
       "      <td>25</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>0.8144</td>\n",
       "      <td>27</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>0.8166</td>\n",
       "      <td>28</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.8170</td>\n",
       "      <td>29</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>0.8115</td>\n",
       "      <td>33</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.8071</td>\n",
       "      <td>35</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AUCs  sympt_cnt  vars_cnt\n",
       "cs                              \n",
       "0.1  0.7800          8         5\n",
       "0.2  0.7863         12         9\n",
       "0.3  0.8040         15        14\n",
       "0.4  0.8143         17        18\n",
       "0.5  0.8147         20        25\n",
       "0.6  0.8144         23        32\n",
       "0.7  0.8138         25        38\n",
       "0.8  0.8144         27        45\n",
       "0.9  0.8166         28        52\n",
       "1.0  0.8170         29        58\n",
       "1.5  0.8115         33        94\n",
       "2.0  0.8071         35       130"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column = table[\"AUCs\"]\n",
    "best_c = column.idxmax() \n",
    "best_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# C = 1 has the highest AUC; \n",
    "# however, C = 0.4 has similar AUC values \n",
    "# with much less number of selected variables \n",
    "_C = 0.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build model based on the selected C and using all data to identify the list of predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"../data/preprocessed.csv\"\n",
    "df = pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create the single, pair, and triplets clusters\n",
    "- The purpose of the model is to identify the list of predictors. Since we already measured the performance in the previous step, we will train the model using all the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, Xt, y, yt = conjunction_columns(df, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "symptoms = df.columns.tolist()\n",
    "symptoms.remove('TestPositive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "symptom_interactions = ' + '.join(symptoms)\n",
    "\n",
    "formula = f'TestPositive ~  ({symptom_interactions}) ** 3'\n",
    "\n",
    "include_columns = symptoms + ['TestPositive']  \n",
    "y, X = dmatrices(formula, df[include_columns], return_type='dataframe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop(columns=['Intercept'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.columns  = [s.replace('_',' ') for s in X.columns]\n",
    "\n",
    "X.columns = X.columns.str.replace(\":\", \" & \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k-fold cross-validation, k=24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xy_pickle = 'BinaryDataX_pairs_triples.p'\n",
    "pickle.dump({'X': X, 'y': y}, open(Xy_pickle, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_validation(fold, tr, te, c= _C):\n",
    "    data = pickle.load(open(Xy_pickle, 'rb'))\n",
    "    X, y = data['X'], data['y']\n",
    "    \n",
    "    X_tr, X_te = X.iloc[tr], X.iloc[te]\n",
    "    y_tr, y_te = y.iloc[tr].values.ravel(), y.iloc[te].values.ravel()\n",
    "    \n",
    "    print(f'fold {fold:02}')\n",
    "        \n",
    "    regressor = LogisticRegression(penalty='l1', solver='saga', C=c, n_jobs=-1, max_iter=5000*2)\n",
    "    regressor.fit(X_tr, y_tr)\n",
    "    \n",
    "    y_pr = regressor.predict_proba(X_te)[:,1]\n",
    "    \n",
    "    score = roc_auc_score(y_te, y_pr)\n",
    "    print(f'fold {fold:02}, score={score:.5f}')\n",
    "    return score, regressor.coef_[0]\n",
    "    \n",
    "\n",
    "skf = StratifiedKFold(n_splits=24, shuffle=True, random_state=37)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = Parallel(n_jobs=-1)(delayed(do_validation)(fold, tr, te, _C) \n",
    "                              for fold, (tr, te) in enumerate(skf.split(X, y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = pd.Series([score for score, _ in outputs])\n",
    "coefs = pd.DataFrame([coef for _, coef in outputs], columns=X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coefficients from k-fold cross-validation that is consistent 95% of the time\n",
    "- Consistent means in same direction and not absent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_profile(df, col):\n",
    "    s = df[col]\n",
    "    \n",
    "    s_pos = s[s > 0]\n",
    "    s_neg = s[s < 0]\n",
    "    \n",
    "    n = df.shape[0]\n",
    "    p_pos = len(s_pos) / n\n",
    "    p_neg = len(s_neg) / n\n",
    "    \n",
    "    return {\n",
    "        'field': col,\n",
    "        'n_pos': len(s_pos),\n",
    "        'n_neg': len(s_neg),\n",
    "        'pct_pos': p_pos, \n",
    "        'pct_neg': p_neg, \n",
    "        'is_valid': 1 if p_pos >= 0.95 or p_neg >= 0.95 else 0\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_coefs = pd.DataFrame([get_profile(coefs, c) for c in coefs.columns]).sort_values(['is_valid'], ascending=False)\n",
    "valid_coefs = valid_coefs[valid_coefs.is_valid == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.4, max_iter=10000, n_jobs=-1, penalty='l1',\n",
       "                   random_state=37, solver='saga')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_cols = list(valid_coefs.field)\n",
    "regressor = LogisticRegression(penalty='l1', solver='saga', C= _C, n_jobs=-1, \n",
    "                               max_iter=5000*2, random_state=37)\n",
    "regressor.fit(X[valid_cols], y.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regressor.predict_proba(X[valid_cols])[:,1]\n",
    "\n",
    "t = X[valid_cols].copy()\n",
    "t['y_pred'] = y_pred\n",
    "t['y_actual'] = y\n",
    "t.to_csv(\"../data/results/prediction_interaction_terms.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = pd.Series(regressor.coef_[0], valid_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "\n",
    "i = pd.Series([regressor.intercept_[0]], index=['intercept'])\n",
    "s = pd.concat([c[c > 0], c[c < 0]]).sort_index()\n",
    "s = pd.concat([i, s])\n",
    "color = ['r' if v > 0 else 'b' for v in s]\n",
    "\n",
    "ax = s.plot(kind='bar', color=color, figsize=(20, 4))\n",
    "_ = ax.set_title(f'Logistic Regression, validated auc={scores.mean():.5f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s_odds = np.exp(s)\n",
    "color = ['r' if v > 1 else 'b' for v in s_odds]\n",
    "\n",
    "ax = s_odds.plot(kind='bar', color=color, figsize=(20, 4))\n",
    "_ = ax.set_title(f'Logistic Regression, coefficient odds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tabular output of coefficients with odds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>coefficient</th>\n",
       "      <th>coefficient_odds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>intercept</th>\n",
       "      <td>-1.7669</td>\n",
       "      <td>0.1709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cough &amp; Age_18_to_29 &amp; Female</th>\n",
       "      <td>0.3608</td>\n",
       "      <td>1.4345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cough &amp; Fatigue &amp; Non_Hispanic_or_Latino</th>\n",
       "      <td>-0.2603</td>\n",
       "      <td>0.7708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cough &amp; Fever &amp; Runny_nose</th>\n",
       "      <td>0.5409</td>\n",
       "      <td>1.7175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cough &amp; Headaches &amp; Race_White</th>\n",
       "      <td>0.6375</td>\n",
       "      <td>1.8917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cough &amp; Headaches &amp; Runny_nose</th>\n",
       "      <td>0.5911</td>\n",
       "      <td>1.8059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cough &amp; Loss_of_appetite &amp; Race_White</th>\n",
       "      <td>0.4745</td>\n",
       "      <td>1.6073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cough &amp; Loss_of_smell &amp; Loss_of_taste</th>\n",
       "      <td>0.9050</td>\n",
       "      <td>2.4718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cough &amp; Loss_of_taste &amp; Fever</th>\n",
       "      <td>0.3199</td>\n",
       "      <td>1.3769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cough &amp; Loss_of_taste &amp; Runny_nose</th>\n",
       "      <td>0.0550</td>\n",
       "      <td>1.0566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fatigue &amp; Chest_pain &amp; Race_White</th>\n",
       "      <td>1.5554</td>\n",
       "      <td>4.7370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fever &amp; Age_30_and_over</th>\n",
       "      <td>-0.4427</td>\n",
       "      <td>0.6423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Headaches &amp; Chills &amp; Race_White</th>\n",
       "      <td>0.6258</td>\n",
       "      <td>1.8698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Headaches &amp; Muscle_aches</th>\n",
       "      <td>0.6852</td>\n",
       "      <td>1.9841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Headaches &amp; Pinkeye &amp; Non_Hispanic_or_Latino</th>\n",
       "      <td>0.7138</td>\n",
       "      <td>2.0418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History_of_respiratory_symptoms &amp; Cough &amp; Muscle_aches</th>\n",
       "      <td>-0.6269</td>\n",
       "      <td>0.5342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History_of_respiratory_symptoms &amp; Cough &amp; Race_White</th>\n",
       "      <td>0.5975</td>\n",
       "      <td>1.8175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History_of_respiratory_symptoms &amp; Cough &amp; Runny_nose</th>\n",
       "      <td>0.1993</td>\n",
       "      <td>1.2206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>History_of_respiratory_symptoms &amp; Muscle_aches &amp; Age_30_and_over</th>\n",
       "      <td>-0.8643</td>\n",
       "      <td>0.4213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loss_of_taste &amp; Headaches &amp; Non_Hispanic_or_Latino</th>\n",
       "      <td>0.3909</td>\n",
       "      <td>1.4783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Numbness &amp; Non_Hispanic_or_Latino</th>\n",
       "      <td>-0.5733</td>\n",
       "      <td>0.5637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runny_nose &amp; Non_Hispanic_or_Latino</th>\n",
       "      <td>-0.9138</td>\n",
       "      <td>0.4010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sore_throat &amp; Chills &amp; Female</th>\n",
       "      <td>-0.2131</td>\n",
       "      <td>0.8081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sore_throat &amp; Fever &amp; Runny_nose</th>\n",
       "      <td>0.4726</td>\n",
       "      <td>1.6041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    coefficient  \\\n",
       "intercept                                               -1.7669   \n",
       "Cough & Age_18_to_29 & Female                            0.3608   \n",
       "Cough & Fatigue & Non_Hispanic_or_Latino                -0.2603   \n",
       "Cough & Fever & Runny_nose                               0.5409   \n",
       "Cough & Headaches & Race_White                           0.6375   \n",
       "Cough & Headaches & Runny_nose                           0.5911   \n",
       "Cough & Loss_of_appetite & Race_White                    0.4745   \n",
       "Cough & Loss_of_smell & Loss_of_taste                    0.9050   \n",
       "Cough & Loss_of_taste & Fever                            0.3199   \n",
       "Cough & Loss_of_taste & Runny_nose                       0.0550   \n",
       "Fatigue & Chest_pain & Race_White                        1.5554   \n",
       "Fever & Age_30_and_over                                 -0.4427   \n",
       "Headaches & Chills & Race_White                          0.6258   \n",
       "Headaches & Muscle_aches                                 0.6852   \n",
       "Headaches & Pinkeye & Non_Hispanic_or_Latino             0.7138   \n",
       "History_of_respiratory_symptoms & Cough & Muscl...      -0.6269   \n",
       "History_of_respiratory_symptoms & Cough & Race_...       0.5975   \n",
       "History_of_respiratory_symptoms & Cough & Runny...       0.1993   \n",
       "History_of_respiratory_symptoms & Muscle_aches ...      -0.8643   \n",
       "Loss_of_taste & Headaches & Non_Hispanic_or_Latino       0.3909   \n",
       "Numbness & Non_Hispanic_or_Latino                       -0.5733   \n",
       "Runny_nose & Non_Hispanic_or_Latino                     -0.9138   \n",
       "Sore_throat & Chills & Female                           -0.2131   \n",
       "Sore_throat & Fever & Runny_nose                         0.4726   \n",
       "\n",
       "                                                    coefficient_odds  \n",
       "intercept                                                     0.1709  \n",
       "Cough & Age_18_to_29 & Female                                 1.4345  \n",
       "Cough & Fatigue & Non_Hispanic_or_Latino                      0.7708  \n",
       "Cough & Fever & Runny_nose                                    1.7175  \n",
       "Cough & Headaches & Race_White                                1.8917  \n",
       "Cough & Headaches & Runny_nose                                1.8059  \n",
       "Cough & Loss_of_appetite & Race_White                         1.6073  \n",
       "Cough & Loss_of_smell & Loss_of_taste                         2.4718  \n",
       "Cough & Loss_of_taste & Fever                                 1.3769  \n",
       "Cough & Loss_of_taste & Runny_nose                            1.0566  \n",
       "Fatigue & Chest_pain & Race_White                             4.7370  \n",
       "Fever & Age_30_and_over                                       0.6423  \n",
       "Headaches & Chills & Race_White                               1.8698  \n",
       "Headaches & Muscle_aches                                      1.9841  \n",
       "Headaches & Pinkeye & Non_Hispanic_or_Latino                  2.0418  \n",
       "History_of_respiratory_symptoms & Cough & Muscl...            0.5342  \n",
       "History_of_respiratory_symptoms & Cough & Race_...            1.8175  \n",
       "History_of_respiratory_symptoms & Cough & Runny...            1.2206  \n",
       "History_of_respiratory_symptoms & Muscle_aches ...            0.4213  \n",
       "Loss_of_taste & Headaches & Non_Hispanic_or_Latino            1.4783  \n",
       "Numbness & Non_Hispanic_or_Latino                             0.5637  \n",
       "Runny_nose & Non_Hispanic_or_Latino                           0.4010  \n",
       "Sore_throat & Chills & Female                                 0.8081  \n",
       "Sore_throat & Fever & Runny_nose                              1.6041  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\n",
    "    'coefficient': s,\n",
    "    'coefficient_odds': s_odds\n",
    "}).round(decimals=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 2)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(pd.DataFrame({\n",
    "    'coefficient': s,\n",
    "    'coefficient_odds': s_odds\n",
    "}).round(decimals=4)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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