4_Feature_Analyses.ipynb

     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(df_corr_group)[2].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_groups = defaultdict(list)\n",
    "for it, row in df_corr_group[df_corr_group[2] > 0.6].iterrows():\n",
    "    grouped_groups[int(row[0])].append(int(row[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_groups_ = []\n",
    "for i, g in grouped_groups.items():\n",
    "    found = False\n",
    "    for gg in grouped_groups_:\n",
    "        if i in gg:\n",
    "            found = True\n",
    "            gg = gg | set(g)\n",
    "    if not found:\n",
    "        grouped_groups_.append(set(g + [i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{1, 3, 4}, {6, 8}, {10, 12}, {15, 20}, {16, 19}, {21, 24}, {25, 26, 27, 28}]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_groups_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temp __mean_ 632\n",
      "Test AUC: 0.662 (0.539, 0.784)\n",
      "Test AUC: 0.662 ± 0.064\n",
      "Temp slope 592\n",
      "Test AUC: 0.626 (0.500, 0.752)\n",
      "Test AUC: 0.627 ± 0.066\n",
      "Temp sample_entropy 632\n",
      "Test AUC: 0.662 (0.538, 0.788)\n",
      "Test AUC: 0.662 ± 0.063\n",
      "Temp abs 592\n",
      "Test AUC: 0.626 (0.500, 0.752)\n",
      "Test AUC: 0.627 ± 0.066\n",
      "Temp angle 592\n",
      "Test AUC: 0.626 (0.500, 0.752)\n",
      "Test AUC: 0.627 ± 0.066\n",
      "HR __mean_ 632\n",
      "Test AUC: 0.651 (0.534, 0.774)\n",
      "Test AUC: 0.651 ± 0.063\n",
      "HR slope 612\n",
      "Test AUC: 0.666 (0.545, 0.784)\n",
      "Test AUC: 0.665 ± 0.062\n",
      "HR sample_entropy 632\n",
      "Test AUC: 0.655 (0.531, 0.779)\n",
      "Test AUC: 0.654 ± 0.064\n",
      "HR abs 612\n",
      "Test AUC: 0.666 (0.545, 0.784)\n",
      "Test AUC: 0.665 ± 0.062\n",
      "HR angle 632\n",
      "Test AUC: 0.657 (0.529, 0.779)\n",
      "Test AUC: 0.657 ± 0.064\n",
      "RR __mean_ 612\n",
      "Test AUC: 0.634 (0.508, 0.758)\n",
      "Test AUC: 0.633 ± 0.065\n",
      "RR slope 632\n",
      "Test AUC: 0.656 (0.533, 0.783)\n",
      "Test AUC: 0.657 ± 0.064\n",
      "RR sample_entropy 612\n",
      "Test AUC: 0.634 (0.508, 0.758)\n",
      "Test AUC: 0.633 ± 0.065\n",
      "RR abs 632\n",
      "Test AUC: 0.664 (0.542, 0.788)\n",
      "Test AUC: 0.663 ± 0.063\n",
      "RR angle 632\n",
      "Test AUC: 0.648 (0.524, 0.771)\n",
      "Test AUC: 0.649 ± 0.063\n",
      "SysBP __mean_ 612\n",
      "Test AUC: 0.671 (0.555, 0.787)\n",
      "Test AUC: 0.672 ± 0.062\n",
      "SysBP slope 612\n",
      "Test AUC: 0.645 (0.522, 0.766)\n",
      "Test AUC: 0.644 ± 0.063\n",
      "SysBP sample_entropy 632\n",
      "Test AUC: 0.629 (0.503, 0.751)\n",
      "Test AUC: 0.630 ± 0.064\n",
      "SysBP abs 632\n",
      "Test AUC: 0.623 (0.497, 0.754)\n",
      "Test AUC: 0.623 ± 0.066\n",
      "SysBP angle 612\n",
      "Test AUC: 0.645 (0.522, 0.766)\n",
      "Test AUC: 0.644 ± 0.063\n",
      "DiaBP __mean_ 612\n",
      "Test AUC: 0.671 (0.555, 0.787)\n",
      "Test AUC: 0.672 ± 0.062\n",
      "DiaBP slope 612\n",
      "Test AUC: 0.648 (0.522, 0.776)\n",
      "Test AUC: 0.647 ± 0.065\n",
      "DiaBP sample_entropy 632\n",
      "Test AUC: 0.662 (0.536, 0.781)\n",
      "Test AUC: 0.661 ± 0.063\n",
      "DiaBP abs 632\n",
      "Test AUC: 0.655 (0.534, 0.780)\n",
      "Test AUC: 0.656 ± 0.064\n",
      "DiaBP angle 612\n",
      "Test AUC: 0.648 (0.522, 0.776)\n",
      "Test AUC: 0.647 ± 0.065\n",
      "SpO2 __mean_ 572\n",
      "Test AUC: 0.669 (0.545, 0.779)\n",
      "Test AUC: 0.667 ± 0.062\n",
      "SpO2 slope 572\n",
      "Test AUC: 0.669 (0.545, 0.779)\n",
      "Test AUC: 0.667 ± 0.062\n",
      "SpO2 sample_entropy 572\n",
      "Test AUC: 0.669 (0.545, 0.779)\n",
      "Test AUC: 0.667 ± 0.062\n",
      "SpO2 abs 572\n",
      "Test AUC: 0.669 (0.545, 0.779)\n",
      "Test AUC: 0.667 ± 0.062\n",
      "SpO2 angle 632\n",
      "Test AUC: 0.643 (0.518, 0.764)\n",
      "Test AUC: 0.642 ± 0.064\n"
     ]
    }
   ],
   "source": [
    "def boostrap_func_auc(i, y_true, y_score):\n",
    "    yte_true_b, yte_pred_b = utils.resample(y_true, y_score, replace=True, random_state=i)\n",
    "    return metrics.roc_curve(yte_true_b, yte_pred_b), metrics.roc_auc_score(yte_true_b, yte_pred_b)\n",
    "\n",
    "results_2 = {}\n",
    "boot_auc_scores_2 = {}\n",
    "for gi, (group, (variable, stat)) in enumerate(zip(feature_groups, feature_groups_desc)):\n",
    "    grouped = False\n",
    "    for gg in grouped_groups_:\n",
    "        if gi in gg:\n",
    "            grouped = True\n",
    "            to_drop = [i for i, name in enumerate(feature_names) if name in feature_names[\n",
    "                sum([feature_groups[i] for i in gg], [])]]\n",
    "            break\n",
    "    \n",
    "    if not grouped:\n",
    "        to_drop = [i for i, name in enumerate(feature_names) if name in feature_names[group]]\n",
    "    \n",
    "    to_keep = [i for i in range(652) if i not in to_drop]\n",
    "    Xtr, Xte = Xtr_all[:, to_keep], Xte_all[:, to_keep]\n",
    "    print(variable, stat, len(to_keep))\n",
    "    \n",
    "    clf = train_model(Xtr, ytr)\n",
    "\n",
    "    y_true = yte\n",
    "    y_score = clf.decision_function(Xte)\n",
    "    roc_curves, auc_scores = zip(*Parallel(n_jobs=4)(delayed(boostrap_func_auc)(i, y_true, y_score) for i in range(1000)))\n",
    "    print('Test AUC: {:.3f} ({:.3f}, {:.3f})'.format(np.median(auc_scores), np.percentile(auc_scores, 2.5), np.percentile(auc_scores, 97.5)))\n",
    "    print('Test AUC: {:.3f} ± {:.3f}'.format(np.mean(auc_scores), np.std(auc_scores)))\n",
    "\n",
    "    results_2[variable, stat] = (np.mean(auc_scores), np.std(auc_scores))\n",
    "    boot_auc_scores_2[variable, stat] = auc_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results_2 = pd.DataFrame(results_2).T\n",
    "df_results_2.columns = ['AUC_mean', 'AUC_std']\n",
    "df_results_2.index = [n_a + ' ' + n_b for n_a, n_b in df_results_2.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results_2['AUC_mean_drop'] = -(df_results_2['AUC_mean'] - 0.659)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>AUC_mean</th>\n",
       "      <th>AUC_std</th>\n",
       "      <th>AUC_mean_drop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>DiaBP __mean_</td>\n",
       "      <td>0.671861</td>\n",
       "      <td>0.061826</td>\n",
       "      <td>-0.012861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SysBP __mean_</td>\n",
       "      <td>0.671861</td>\n",
       "      <td>0.061826</td>\n",
       "      <td>-0.012861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SpO2 sample_entropy</td>\n",
       "      <td>0.666833</td>\n",
       "      <td>0.061733</td>\n",
       "      <td>-0.007833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SpO2 abs</td>\n",
       "      <td>0.666833</td>\n",
       "      <td>0.061733</td>\n",
       "      <td>-0.007833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SpO2 slope</td>\n",
       "      <td>0.666833</td>\n",
       "      <td>0.061733</td>\n",
       "      <td>-0.007833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     AUC_mean   AUC_std  AUC_mean_drop\n",
       "DiaBP __mean_        0.671861  0.061826      -0.012861\n",
       "SysBP __mean_        0.671861  0.061826      -0.012861\n",
       "SpO2 sample_entropy  0.666833  0.061733      -0.007833\n",
       "SpO2 abs             0.666833  0.061733      -0.007833\n",
       "SpO2 slope           0.666833  0.061733      -0.007833"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_results_2.sort_values(by='AUC_mean_drop').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_results_3 = pd.DataFrame(results_2).T\n",
    "df_results_3.columns = ['AUC_mean', 'AUC_std']\n",
    "df_results_3['AUC_mean_drop'] = -(df_results_3['AUC_mean'] - 0.659)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_drop_grid = pd.DataFrame()\n",
    "for i, j in df_results_3.index:\n",
    "    df_drop_grid.loc[i,j] = df_results_3.loc[(i,j), 'AUC_mean_drop']\n",
    "\n",
    "df_drop_grid = df_drop_grid.rename(\n",
    "    columns={\n",
    "        '__mean_': 'average', 'abs': 'abs($A_1$)', 'angle': 'angle($A_1$)',\n",
    "        'sample_entropy': 'entropy'\n",
    "    },\n",
    "    index={\n",
    "        'Temp': 'Temp.', 'SysBP': 'SBP', 'DiaBP': 'DBP',\n",
    "    }\n",
    ")\n",
    "df_drop_grid.index.name = 'Vital'\n",
    "df_drop_grid.columns.name = 'Trend'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Trend</th>\n",
       "      <th>average</th>\n",
       "      <th>slope</th>\n",
       "      <th>entropy</th>\n",
       "      <th>abs($A_1$)</th>\n",
       "      <th>angle($A_1$)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vital</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Temp.</td>\n",
       "      <td>-0.002578</td>\n",
       "      <td>0.032435</td>\n",
       "      <td>-0.003259</td>\n",
       "      <td>0.032435</td>\n",
       "      <td>0.032435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>HR</td>\n",
       "      <td>0.007749</td>\n",
       "      <td>-0.006428</td>\n",
       "      <td>0.005009</td>\n",
       "      <td>-0.006428</td>\n",
       "      <td>0.001755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>RR</td>\n",
       "      <td>0.025586</td>\n",
       "      <td>0.002294</td>\n",
       "      <td>0.025586</td>\n",
       "      <td>-0.004335</td>\n",
       "      <td>0.010127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SBP</td>\n",
       "      <td>-0.012861</td>\n",
       "      <td>0.014802</td>\n",
       "      <td>0.029064</td>\n",
       "      <td>0.036455</td>\n",
       "      <td>0.014802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DBP</td>\n",
       "      <td>-0.012861</td>\n",
       "      <td>0.012080</td>\n",
       "      <td>-0.001982</td>\n",
       "      <td>0.002723</td>\n",
       "      <td>0.012080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SpO2</td>\n",
       "      <td>-0.007833</td>\n",
       "      <td>-0.007833</td>\n",
       "      <td>-0.007833</td>\n",
       "      <td>-0.007833</td>\n",
       "      <td>0.017107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Trend   average     slope   entropy  abs($A_1$)  angle($A_1$)\n",
       "Vital                                                        \n",
       "Temp. -0.002578  0.032435 -0.003259    0.032435      0.032435\n",
       "HR     0.007749 -0.006428  0.005009   -0.006428      0.001755\n",
       "RR     0.025586  0.002294  0.025586   -0.004335      0.010127\n",
       "SBP   -0.012861  0.014802  0.029064    0.036455      0.014802\n",
       "DBP   -0.012861  0.012080 -0.001982    0.002723      0.012080\n",
       "SpO2  -0.007833 -0.007833 -0.007833   -0.007833      0.017107"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_drop_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
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