{
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"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import random\n",
"import scipy.stats\n",
"import pickle, os, time\n",
"import itertools\n",
"from datetime import datetime, timedelta\n",
"from collections import Counter, defaultdict, namedtuple\n",
"from PIL import Image\n",
"import yaml\n",
"from tqdm import tqdm\n",
"\n",
"from sklearn import preprocessing, model_selection, metrics, utils\n",
"from sklearn.linear_model import LogisticRegression\n",
"from tqdm import tqdm\n",
"from joblib import Parallel, delayed\n",
"from sklearn.base import clone\n",
"\n",
"import seaborn as sns\n",
"from matplotlib import pyplot as plt\n",
"\n",
"data_dir = '/data/GVHD/'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%config InlineBackend.figure_format = 'svg'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Helper functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model training"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def train_model(Xtr, ytr):\n",
" np.random.seed(42)\n",
" random.seed(42)\n",
" \n",
" # Specify hyperparameters and cv parameters\n",
" base_estimator = LogisticRegression(\n",
" penalty='l2', \n",
" class_weight='balanced', \n",
" solver='liblinear'\n",
" )\n",
" param_grid = {\n",
" 'C': [10. ** n for n in range(-6, 7)],\n",
" 'penalty': ['l2'],\n",
" }\n",
" \n",
" cv_splits, cv_repeat = 5, 20\n",
" cv = model_selection.RepeatedStratifiedKFold(cv_splits, cv_repeat, random_state=0)\n",
" clf = model_selection.GridSearchCV(\n",
" clone(base_estimator), param_grid, \n",
" cv=cv, scoring='roc_auc', iid=False, n_jobs=5,\n",
" )\n",
" clf.fit(Xtr, ytr)\n",
" return clf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def boostrap_func_all(i, y_true, y_prob, threshold):\n",
" fpr, tpr, thresholds = metrics.roc_curve(y_true, y_prob)\n",
" y_true_b, y_prob_b = utils.resample(y_true, y_prob, replace=True, random_state=i)\n",
" y_pred_b = (y_prob_b > threshold)\n",
" tpr_cutoff = metrics.recall_score(y_true_b, y_pred_b)\n",
" idx = (np.abs(tpr - tpr_cutoff)).argmin()\n",
" \n",
" return (\n",
" metrics.roc_auc_score(y_true_b, y_prob_b), # AUC\n",
" tpr[idx], # sensitivity\n",
" 1-fpr[idx], # specificity\n",
" metrics.precision_score(y_true_b, y_pred_b), # positive predictive value\n",
" )\n",
"\n",
"def boostrap_func_confusion(i, y_true, y_prob, threshold):\n",
" fpr, tpr, thresholds = metrics.roc_curve(y_true, y_prob)\n",
" y_true_b, y_prob_b = utils.resample(y_true, y_prob, replace=True, random_state=i)\n",
" y_pred_b = (y_prob_b > threshold)\n",
" tpr_cutoff = metrics.recall_score(y_true_b, y_pred_b)\n",
" idx = (np.abs(tpr - tpr_cutoff)).argmin()\n",
" \n",
" return metrics.confusion_matrix(y_true_b, y_pred_b).ravel()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def evaluate_model(clf, Xte, yte, Xtr=None, threshold_p=55, verbose=True):\n",
" y_true = yte\n",
" y_score = clf.decision_function(Xte)\n",
" y_prob = clf.predict_proba(Xte)[:,1]\n",
" \n",
" fpr, tpr, thresholds = metrics.roc_curve(y_true, y_score)\n",
" test_auc = metrics.roc_auc_score(y_true, y_score)\n",
" \n",
" # Picking a risk threshold based on training set if possible\n",
" if Xtr is not None:\n",
" if verbose: print('Risk threshold based on train set')\n",
" threshold = np.percentile(clf.predict_proba(Xtr)[:,1], threshold_p)\n",
" else:\n",
" if verbose: print('Risk threshold based on test set')\n",
" threshold = np.percentile(y_score, 55)\n",
" if verbose: print('p_Threshold', threshold)\n",
" \n",
" if verbose: print()\n",
" if verbose: print('Confusion matrix (95%CI lower, upper)')\n",
" y_pred = (y_prob > threshold)\n",
" conf_mat = metrics.confusion_matrix(y_true, y_pred)\n",
" if verbose: print(conf_mat) # Rows: actual 0, actual 1; Cols: predicted 0, predicted 1\n",
" \n",
" confmats = [boostrap_func_confusion(i, y_true, y_prob, threshold) for i in range(1000)]\n",
" confmats_ = np.asarray(confmats)\n",
" if verbose: print(np.percentile(confmats_, 2.5, axis=0).reshape(2,2))\n",
" if verbose: print(np.percentile(confmats_, 97.5, axis=0).reshape(2,2))\n",
"\n",
" if verbose: print()\n",
" if verbose: print('scores')\n",
" tpr_ = metrics.recall_score(y_true, y_pred)\n",
" idx = (np.abs(tpr - tpr_)).argmin()\n",
" if verbose: print('AUROC={:.3f}'.format(test_auc))\n",
" if verbose: print('TPR={:.3f}, FPR={:.3f} PPV={:.3f}'.format(\n",
" tpr[idx], fpr[idx],\n",
" metrics.precision_score(y_true, y_pred)))\n",
"\n",
" if verbose: print()\n",
" if verbose: print('scores (95%CI lower, upper)')\n",
" auc_scores, sensitivities, specificities, ppvs = zip(*[boostrap_func_all(i, y_true, y_prob, threshold) for i in range(1000)])\n",
" if verbose: print('Test AUC {:.3f} ({:.3f}, {:.3f})'.format(np.median(auc_scores), np.percentile(auc_scores, 2.5), np.percentile(auc_scores, 97.5)))\n",
" if verbose: print('Test AUC {:.3f} ± {:.3f}'.format(np.mean(auc_scores), np.std(auc_scores)))\n",
" if verbose: print('sens.\\t {:.1%} ({:.1%}, {:.1%})'.format(np.mean(sensitivities), np.percentile(sensitivities, 2.5), np.percentile(sensitivities, 97.5)))\n",
" if verbose: print('spec.\\t {:.1%} ({:.1%}, {:.1%})'.format(np.mean(specificities), np.percentile(specificities, 2.5), np.percentile(specificities, 97.5)))\n",
" if verbose: print('prec.\\t {:.1%} ({:.1%}, {:.1%})'.format(np.mean(ppvs), np.percentile(ppvs, 2.5), np.percentile(ppvs, 97.5)))\n",
" \n",
" return test_auc, auc_scores, fpr, tpr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data loading"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"pop = pd.read_csv(data_dir + 'population/d10_with_vitals.csv').set_index('BMT_ID')\n",
"extracted_features = pd.read_csv('data/ts_features.csv', index_col='id')\n",
"df_label = pop.join(pd.read_csv(data_dir + 'prep/label.csv', index_col='BMT_ID'), how='left')\n",
"df_features = pd.read_csv('data/df_features.csv', index_col='id')\n",
"feature_names = df_features.columns"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"with np.load('data/Xy.npz') as f:\n",
" X = f['X']\n",
" y = f['y']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Temporal split"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"split_date = 201701001\n",
"split_idx = -85\n",
"\n",
"assert (pop[:split_idx].index < split_date).all()\n",
"assert (pop[split_idx:].index >= split_date).all()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"Xtr_all, Xte_all = X[:split_idx], X[split_idx:]\n",
"ytr, yte = y[:split_idx], y[split_idx:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Main model"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"Xtr, Xte = Xtr_all, Xte_all\n",
"clf = train_model(Xtr, ytr)\n",
"auc_main, auc_scores_main, fpr_main, tpr_main = evaluate_model(clf, Xte, yte, Xtr, 55, verbose=False)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'C': 0.01, 'penalty': 'l2'}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf.best_params_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sensitivity Analyses"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Omitting specific subsets"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"df_corr = df_features.corr().abs()\n",
"np.fill_diagonal(df_corr.values, 0)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"feature_groups = []\n",
"feature_groups_desc = []\n",
"for variable, stat in itertools.product(\n",
" ['Temp', 'HR', 'RR', 'SysBP', 'DiaBP', 'SpO2'],\n",
" ['__mean_', 'slope', 'sample_entropy', 'abs', 'angle']\n",
"):\n",
" to_drop = [i for i, name in enumerate(feature_names) if name.startswith(variable) and stat in name]\n",
" feature_groups.append(to_drop)\n",
" feature_groups_desc.append((variable, stat))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"df_corr_group = []\n",
"for (i1, g1), (i2, g2) in itertools.product(enumerate(feature_groups), enumerate(feature_groups)):\n",
"# print(i1,i2)\n",
"# if i1 < i2:\n",
" if i1 != i2:\n",
" co = df_corr.iloc[g1, g2].max().max()\n",
" df_corr_group.append((i1,i2,co))\n",
"\n",
"df_corr_group = pd.DataFrame(df_corr_group)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
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"execution_count": 18,
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"source": [
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},
{
"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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</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",
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],
"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": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
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"\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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"source": [
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},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
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]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
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"fig = plt.figure(figsize=(3.2*1.2, 2*1.2))\n",
"df_drop_row.sort_values().plot.barh(color='silver', edgecolor='k')\n",
"plt.axvline(0, c='k')\n",
"plt.xlabel('Feature importance')\n",
"plt.xlim(-0.005, 0.02)\n",
"plt.tight_layout()\n",
"plt.savefig('fig/drop_imp_vital.pdf')"
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},
{
"cell_type": "code",
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"source": [
"fig = plt.figure(figsize=(3.5*1.2,2*1.2))\n",
"df_drop_col.sort_values().plot.barh(color='silver', edgecolor='k')\n",
"plt.axvline(0, c='k')\n",
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]
},
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