4_Feature_Analyses.ipynb

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   "cell_type": "code",
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   "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)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
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