Supplemental_Analyses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi'] = 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from joblib import dump, load\n",
    "np.random.seed(42)\n",
    "random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = './data/'\n",
    "with np.load('data/Xy.npz') as f:\n",
    "    X = f['X']\n",
    "    y = f['y']\n",
    "    y34 = f['y34']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform temporal split of data into train/test sets\n",
    "pop = pd.read_csv(data_dir + 'population/d10_with_vitals.csv').set_index('BMT_ID')\n",
    "\n",
    "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify hyperparameters and cv parameters\n",
    "base_estimator = LogisticRegression(penalty='l2', class_weight='balanced', solver='liblinear')\n",
    "param_grid = {\n",
    "    'C': [10. ** n for n in range(-6, 7)],\n",
    "    'penalty': ['l2'],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alternative label definition\n",
    "{0,1,2} -> negative, {3,4} -> positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.31790123456790126, 0.13580246913580246)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.mean(), y34.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data4/tangsp/venv/lib/python3.7/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "Xtr, Xte = X[:split_idx], X[split_idx:]\n",
    "ytr, yte = y34[:split_idx], y34[split_idx:]\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', n_jobs=5,\n",
    ")\n",
    "clf.fit(Xtr, ytr)\n",
    "test_score = metrics.roc_auc_score(yte, clf.decision_function(Xte))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                  \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test AUC: 0.596 (0.423, 0.761)\n"
     ]
    }
   ],
   "source": [
    "y_true = yte\n",
    "y_score = clf.decision_function(Xte)\n",
    "\n",
    "def boostrap_func(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",
    "roc_curves, auc_scores = zip(*Parallel(n_jobs=4)(delayed(boostrap_func)(i, y_true, y_score) for i in tqdm(range(1000), leave=False)))\n",
    "print('Test AUC: {:.3f} ({:.3f}, {:.3f})'.format(np.median(auc_scores), np.percentile(auc_scores, 2.5), np.percentile(auc_scores, 97.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=<sklearn.model_selection._split.RepeatedStratifiedKFold object at 0x7f7e38c43310>,\n",
       "                   error_score='raise-deprecating',\n",
       "                   estimator=RandomForestClassifier(bootstrap=True,\n",
       "                                                    class_weight=None,\n",
       "                                                    criterion='gini',\n",
       "                                                    max_depth=None,\n",
       "                                                    max_features='auto',\n",
       "                                                    max_leaf_nodes=None,\n",
       "                                                    min_impurity_decrease=0.0,\n",
       "                                                    min_impurity_split=None,\n",
       "                                                    min_samples_leaf=1,\n",
       "                                                    min_sample...\n",
       "                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f7e385a4110>,\n",
       "                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f7e385acf10>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f7e385a4090>},\n",
       "                   pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "                   return_train_score=False, scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtr, Xte = X[:split_idx], X[split_idx:]\n",
    "ytr, yte = y[:split_idx], y[split_idx:]\n",
    "\n",
    "cv_splits, cv_repeat = 5, 20\n",
    "cv = model_selection.RepeatedStratifiedKFold(cv_splits, cv_repeat, random_state=0)\n",
    "clf = RandomizedSearchCV(\n",
    "        RandomForestClassifier(), \n",
    "        {\n",
    "            \"criterion\": [\"gini\", \"entropy\"],\n",
    "            \"max_depth\": [4, 8, 16, 32, None],\n",
    "            \"max_features\": scipy.stats.randint(1, 100),\n",
    "            \"min_samples_split\": scipy.stats.randint(2, 11),\n",
    "            \"min_samples_leaf\": scipy.stats.randint(1, 11),\n",
    "            \"n_estimators\": scipy.stats.randint(50,500),\n",
    "            \"bootstrap\": [True],\n",
    "        },\n",
    "        n_iter=10,\n",
    "        cv=cv,\n",
    "        scoring='roc_auc',\n",
    "        n_jobs=5,\n",
    "    )\n",
    "\n",
    "clf.fit(Xtr, ytr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_score = metrics.roc_auc_score(yte, clf.predict_proba(Xte)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                  \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test AUC: 0.651 (0.525, 0.768)\n"
     ]
    }
   ],
   "source": [
    "y_true = yte\n",
    "y_score = clf.predict_proba(Xte)[:,1]\n",
    "\n",
    "def boostrap_func(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",
    "_, auc_scores_rf = zip(*Parallel(n_jobs=4)(delayed(boostrap_func)(i, y_true, y_score) for i in tqdm(range(1000), leave=False)))\n",
    "print('Test AUC: {:.3f} ({:.3f}, {:.3f})'.format(np.median(auc_scores_rf), np.percentile(auc_scores_rf, 2.5), np.percentile(auc_scores_rf, 97.5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                  \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test AUC: 0.658 (0.536, 0.784)\n"
     ]
    }
   ],
   "source": [
    "clf_main = load('data/model_combined.joblib')\n",
    "y_score = clf_main.predict_proba(Xte)[:,1]\n",
    "_, auc_scores_main = zip(*Parallel(n_jobs=4)(delayed(boostrap_func)(i, y_true, y_score) for i in tqdm(range(1000), leave=False)))\n",
    "print('Test AUC: {:.3f} ({:.3f}, {:.3f})'.format(\n",
    "    np.median(auc_scores_main), np.percentile(auc_scores_main, 2.5), np.percentile(auc_scores_main, 97.5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.782"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# H0: proposed == baseline\n",
    "# H1: proposed != baseline\n",
    "# resampling test, two-sided p-value\n",
    "2 * min(\n",
    "    (np.array(auc_scores_main) < np.array(auc_scores_rf)).mean(),\n",
    "    (np.array(auc_scores_main) > np.array(auc_scores_rf)).mean()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kaplan Meier plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn import utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtr, Xte = X[:split_idx], X[split_idx:]\n",
    "ytr, yte = y[:split_idx], y[split_idx:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop = pd.read_csv(data_dir + 'population/d10_with_vitals.csv').set_index('BMT_ID')\n",
    "labels = pd.read_csv(data_dir + 'prep/label.csv').set_index('BMT_ID')\n",
    "IDs = pop.index[split_idx:]\n",
    "onset = labels.loc[IDs, 'GVHD_onset_day'].replace(np.nan, np.inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IntervalIndex([(0.234, 0.539], (0.539, 0.74]],\n",
      "              closed='right',\n",
      "              dtype='interval[float64]')\n"
     ]
    }
   ],
   "source": [
    "clf = load('data/model_combined.joblib')\n",
    "threshold = np.percentile(clf.predict_proba(Xtr)[:,1], [0, 70, 100])\n",
    "y_prob = clf.predict_proba(Xte)[:,1]\n",
    "y_group = pd.cut(y_prob, threshold)\n",
    "print(y_group.categories)\n",
    "y_group = y_group.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard errors calculated via the Greenwood's formula\n",
    "# http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_Survival/BS704_Survival4.html\n",
    "def calculate_survival_curve_se(onset_m, times):\n",
    "    surv_probs = [(onset_m > 0).mean()]\n",
    "    stderrs = [0]\n",
    "    quotients = []\n",
    "    for day in sorted(times):\n",
    "        if np.isfinite(day):\n",
    "            Nt, Dt = (onset_m >= day).sum(), (onset_m == day).sum()\n",
    "            St = (onset_m > day).mean()\n",
    "            quotients.append(Dt / (Nt * (Nt - Dt)))\n",
    "            surv_probs.append(St)\n",
    "            stderrs.append(St * np.sqrt(np.sum(quotients)))\n",
    "    return np.array(surv_probs), np.array(stderrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = onset.unique()\n",
    "times = times[times <= 105]\n",
    "times = sorted(times)\n",
    "\n",
    "for group in [0,1]:\n",
    "    m = (y_group == group)\n",
    "    o_m = onset[m]\n",
    "    surv_probs, stderrs = calculate_survival_curve_se(o_m, times)\n",
    "    CI = [\n",
    "        np.clip(surv_probs - 1.96 * stderrs, 0, 1), \n",
    "        np.clip(surv_probs + 1.96 * stderrs, 0, 1),\n",
    "    ]\n",
    "    plt.plot([0] + times, surv_probs)\n",
    "    plt.fill_between([0] + times, CI[0], CI[1], alpha=0.12)\n",
    "\n",
    "plt.xlim(0,100)\n",
    "plt.legend(labels=['low risk', 'high risk'])\n",
    "plt.xlabel('Days post-transplant')\n",
    "plt.ylabel('Grade II-IV aGVHD-free probability')\n",
    "plt.savefig('fig/kaplan_meier.svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering results\n",
    "\n",
    "`'Temp-slope', 'Temp-abs(A1)', 'Temp-angle(A1)', 'SBP-abs(A1)'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.cluster import KMeans, SpectralClustering\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "extracted_features = pd.read_csv('data/ts_features.csv', index_col='id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>DiaBP_dt=1_max__fft_coefficient__coeff_1__attr_\"abs\"</th>\n",
       "      <th>DiaBP_dt=1_max__fft_coefficient__coeff_1__attr_\"angle\"</th>\n",
       "      <th>DiaBP_dt=1_max__linear_trend__attr_\"slope\"</th>\n",
       "      <th>DiaBP_dt=1_max__mean</th>\n",
       "      <th>DiaBP_dt=1_max__sample_entropy</th>\n",
       "      <th>DiaBP_dt=1_mean__fft_coefficient__coeff_1__attr_\"abs\"</th>\n",
       "      <th>DiaBP_dt=1_mean__fft_coefficient__coeff_1__attr_\"angle\"</th>\n",
       "      <th>DiaBP_dt=1_mean__linear_trend__attr_\"slope\"</th>\n",
       "      <th>DiaBP_dt=1_mean__mean</th>\n",
       "      <th>DiaBP_dt=1_mean__sample_entropy</th>\n",
       "      <th>...</th>\n",
       "      <th>Temp_dt=1_min__fft_coefficient__coeff_1__attr_\"abs\"</th>\n",
       "      <th>Temp_dt=1_min__fft_coefficient__coeff_1__attr_\"angle\"</th>\n",
       "      <th>Temp_dt=1_min__linear_trend__attr_\"slope\"</th>\n",
       "      <th>Temp_dt=1_min__mean</th>\n",
       "      <th>Temp_dt=1_min__sample_entropy</th>\n",
       "      <th>Temp_dt=1_std__fft_coefficient__coeff_1__attr_\"abs\"</th>\n",
       "      <th>Temp_dt=1_std__fft_coefficient__coeff_1__attr_\"angle\"</th>\n",
       "      <th>Temp_dt=1_std__linear_trend__attr_\"slope\"</th>\n",
       "      <th>Temp_dt=1_std__mean</th>\n",
       "      <th>Temp_dt=1_std__sample_entropy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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",
       "      <th>201406001</th>\n",
       "      <td>13.711126</td>\n",
       "      <td>-39.840250</td>\n",
       "      <td>-1.151515</td>\n",
       "      <td>80.8</td>\n",
       "      <td>1.504077</td>\n",
       "      <td>12.511950</td>\n",
       "      <td>-60.302991</td>\n",
       "      <td>-0.771727</td>\n",
       "      <td>70.326825</td>\n",
       "      <td>2.420368</td>\n",
       "      <td>...</td>\n",
       "      <td>0.567878</td>\n",
       "      <td>73.555730</td>\n",
       "      <td>0.021481</td>\n",
       "      <td>36.518889</td>\n",
       "      <td>1.860752</td>\n",
       "      <td>0.263259</td>\n",
       "      <td>69.530757</td>\n",
       "      <td>0.018092</td>\n",
       "      <td>0.220617</td>\n",
       "      <td>2.420368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201406002</th>\n",
       "      <td>3.452240</td>\n",
       "      <td>42.925589</td>\n",
       "      <td>-0.066667</td>\n",
       "      <td>77.5</td>\n",
       "      <td>2.420368</td>\n",
       "      <td>17.946614</td>\n",
       "      <td>-14.951144</td>\n",
       "      <td>-0.530707</td>\n",
       "      <td>69.379524</td>\n",
       "      <td>2.014903</td>\n",
       "      <td>...</td>\n",
       "      <td>0.164019</td>\n",
       "      <td>-59.464582</td>\n",
       "      <td>0.009091</td>\n",
       "      <td>36.316667</td>\n",
       "      <td>2.420368</td>\n",
       "      <td>0.265981</td>\n",
       "      <td>100.908363</td>\n",
       "      <td>0.004882</td>\n",
       "      <td>0.268865</td>\n",
       "      <td>2.708050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201406003</th>\n",
       "      <td>14.437545</td>\n",
       "      <td>161.710499</td>\n",
       "      <td>-0.151515</td>\n",
       "      <td>86.9</td>\n",
       "      <td>2.708050</td>\n",
       "      <td>18.338949</td>\n",
       "      <td>-169.211341</td>\n",
       "      <td>0.150459</td>\n",
       "      <td>76.578379</td>\n",
       "      <td>1.860752</td>\n",
       "      <td>...</td>\n",
       "      <td>0.501112</td>\n",
       "      <td>-73.426276</td>\n",
       "      <td>-0.042088</td>\n",
       "      <td>36.172222</td>\n",
       "      <td>2.420368</td>\n",
       "      <td>0.431178</td>\n",
       "      <td>-149.344978</td>\n",
       "      <td>-0.000899</td>\n",
       "      <td>0.284152</td>\n",
       "      <td>2.197225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201406004</th>\n",
       "      <td>39.672864</td>\n",
       "      <td>-56.532935</td>\n",
       "      <td>-0.939394</td>\n",
       "      <td>78.7</td>\n",
       "      <td>2.014903</td>\n",
       "      <td>21.591328</td>\n",
       "      <td>-62.275730</td>\n",
       "      <td>-0.655020</td>\n",
       "      <td>67.781822</td>\n",
       "      <td>1.504077</td>\n",
       "      <td>...</td>\n",
       "      <td>1.444653</td>\n",
       "      <td>145.906924</td>\n",
       "      <td>0.045118</td>\n",
       "      <td>35.911111</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>0.420941</td>\n",
       "      <td>-3.505990</td>\n",
       "      <td>-0.006831</td>\n",
       "      <td>0.225669</td>\n",
       "      <td>1.727221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201406005</th>\n",
       "      <td>46.236218</td>\n",
       "      <td>-83.519009</td>\n",
       "      <td>-2.454545</td>\n",
       "      <td>77.7</td>\n",
       "      <td>2.014903</td>\n",
       "      <td>45.041080</td>\n",
       "      <td>-100.861026</td>\n",
       "      <td>-1.821655</td>\n",
       "      <td>69.535720</td>\n",
       "      <td>2.708050</td>\n",
       "      <td>...</td>\n",
       "      <td>2.181057</td>\n",
       "      <td>78.501122</td>\n",
       "      <td>0.113805</td>\n",
       "      <td>36.288889</td>\n",
       "      <td>2.420368</td>\n",
       "      <td>0.512131</td>\n",
       "      <td>118.454862</td>\n",
       "      <td>0.030110</td>\n",
       "      <td>0.271239</td>\n",
       "      <td>2.014903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 120 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DiaBP_dt=1_max__fft_coefficient__coeff_1__attr_\"abs\"  \\\n",
       "id                                                                \n",
       "201406001                                          13.711126      \n",
       "201406002                                           3.452240      \n",
       "201406003                                          14.437545      \n",
       "201406004                                          39.672864      \n",
       "201406005                                          46.236218      \n",
       "\n",
       "           DiaBP_dt=1_max__fft_coefficient__coeff_1__attr_\"angle\"  \\\n",
       "id                                                                  \n",
       "201406001                                         -39.840250        \n",
       "201406002                                          42.925589        \n",
       "201406003                                         161.710499        \n",
       "201406004                                         -56.532935        \n",
       "201406005                                         -83.519009        \n",
       "\n",
       "           DiaBP_dt=1_max__linear_trend__attr_\"slope\"  DiaBP_dt=1_max__mean  \\\n",
       "id                                                                            \n",
       "201406001                                   -1.151515                  80.8   \n",
       "201406002                                   -0.066667                  77.5   \n",
       "201406003                                   -0.151515                  86.9   \n",
       "201406004                                   -0.939394                  78.7   \n",
       "201406005                                   -2.454545                  77.7   \n",
       "\n",
       "           DiaBP_dt=1_max__sample_entropy  \\\n",
       "id                                          \n",
       "201406001                        1.504077   \n",
       "201406002                        2.420368   \n",
       "201406003                        2.708050   \n",
       "201406004                        2.014903   \n",
       "201406005                        2.014903   \n",
       "\n",
       "           DiaBP_dt=1_mean__fft_coefficient__coeff_1__attr_\"abs\"  \\\n",
       "id                                                                 \n",
       "201406001                                          12.511950       \n",
       "201406002                                          17.946614       \n",
       "201406003                                          18.338949       \n",
       "201406004                                          21.591328       \n",
       "201406005                                          45.041080       \n",
       "\n",
       "           DiaBP_dt=1_mean__fft_coefficient__coeff_1__attr_\"angle\"  \\\n",
       "id                                                                   \n",
       "201406001                                         -60.302991         \n",
       "201406002                                         -14.951144         \n",
       "201406003                                        -169.211341         \n",
       "201406004                                         -62.275730         \n",
       "201406005                                        -100.861026         \n",
       "\n",
       "           DiaBP_dt=1_mean__linear_trend__attr_\"slope\"  DiaBP_dt=1_mean__mean  \\\n",
       "id                                                                              \n",
       "201406001                                    -0.771727              70.326825   \n",
       "201406002                                    -0.530707              69.379524   \n",
       "201406003                                     0.150459              76.578379   \n",
       "201406004                                    -0.655020              67.781822   \n",
       "201406005                                    -1.821655              69.535720   \n",
       "\n",
       "           DiaBP_dt=1_mean__sample_entropy  ...  \\\n",
       "id                                          ...   \n",
       "201406001                         2.420368  ...   \n",
       "201406002                         2.014903  ...   \n",
       "201406003                         1.860752  ...   \n",
       "201406004                         1.504077  ...   \n",
       "201406005                         2.708050  ...   \n",
       "\n",
       "           Temp_dt=1_min__fft_coefficient__coeff_1__attr_\"abs\"  \\\n",
       "id                                                               \n",
       "201406001                                           0.567878     \n",
       "201406002                                           0.164019     \n",
       "201406003                                           0.501112     \n",
       "201406004                                           1.444653     \n",
       "201406005                                           2.181057     \n",
       "\n",
       "           Temp_dt=1_min__fft_coefficient__coeff_1__attr_\"angle\"  \\\n",
       "id                                                                 \n",
       "201406001                                          73.555730       \n",
       "201406002                                         -59.464582       \n",
       "201406003                                         -73.426276       \n",
       "201406004                                         145.906924       \n",
       "201406005                                          78.501122       \n",
       "\n",
       "           Temp_dt=1_min__linear_trend__attr_\"slope\"  Temp_dt=1_min__mean  \\\n",
       "id                                                                          \n",
       "201406001                                   0.021481            36.518889   \n",
       "201406002                                   0.009091            36.316667   \n",
       "201406003                                  -0.042088            36.172222   \n",
       "201406004                                   0.045118            35.911111   \n",
       "201406005                                   0.113805            36.288889   \n",
       "\n",
       "           Temp_dt=1_min__sample_entropy  \\\n",
       "id                                         \n",
       "201406001                       1.860752   \n",
       "201406002                       2.420368   \n",
       "201406003                       2.420368   \n",
       "201406004                       2.197225   \n",
       "201406005                       2.420368   \n",
       "\n",
       "           Temp_dt=1_std__fft_coefficient__coeff_1__attr_\"abs\"  \\\n",
       "id                                                               \n",
       "201406001                                           0.263259     \n",
       "201406002                                           0.265981     \n",
       "201406003                                           0.431178     \n",
       "201406004                                           0.420941     \n",
       "201406005                                           0.512131     \n",
       "\n",
       "           Temp_dt=1_std__fft_coefficient__coeff_1__attr_\"angle\"  \\\n",
       "id                                                                 \n",
       "201406001                                          69.530757       \n",
       "201406002                                         100.908363       \n",
       "201406003                                        -149.344978       \n",
       "201406004                                          -3.505990       \n",
       "201406005                                         118.454862       \n",
       "\n",
       "           Temp_dt=1_std__linear_trend__attr_\"slope\"  Temp_dt=1_std__mean  \\\n",
       "id                                                                          \n",
       "201406001                                   0.018092             0.220617   \n",
       "201406002                                   0.004882             0.268865   \n",
       "201406003                                  -0.000899             0.284152   \n",
       "201406004                                  -0.006831             0.225669   \n",
       "201406005                                   0.030110             0.271239   \n",
       "\n",
       "           Temp_dt=1_std__sample_entropy  \n",
       "id                                        \n",
       "201406001                       2.420368  \n",
       "201406002                       2.708050  \n",
       "201406003                       2.197225  \n",
       "201406004                       1.727221  \n",
       "201406005                       2.014903  \n",
       "\n",
       "[5 rows x 120 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [\n",
    "    c for c in extracted_features.columns\n",
    "    if c.startswith('Temp') or c.startswith('SysBP')\n",
    "]\n",
    "cols = [\n",
    "    c for c in cols\n",
    "    if \\\n",
    "    ('Temp' in c and 'slope' in c) or \\\n",
    "    ('Temp' in c and 'fft_coefficient__coeff_1__attr_\"abs\"' in c) or \\\n",
    "    ('Temp' in c and 'fft_coefficient__coeff_1__attr_\"angle\"' in c) or \\\n",
    "    ('SysBP' in c and 'fft_coefficient__coeff_1__attr_\"abs\"' in c)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "XX = extracted_features[cols].values\n",
    "scaler = preprocessing.StandardScaler()\n",
    "XX = scaler.fit_transform(XX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(324, 16)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(2, random_state=0).fit(XX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusterings = [np.nan, np.nan]\n",
    "scores = [np.nan, np.nan]\n",
    "for k in range(2,11):\n",
    "    cluster = KMeans(k, random_state=0, n_init=20, n_jobs=-1).fit(XX)\n",
    "    clusterings.append(cluster)\n",
    "    scores.append(cluster.inertia_)\n",
    "#     purities, sizes = [], []\n",
    "#     for c in range(k):\n",
    "#         m = (cluster.labels_ == c)\n",
    "#         yc = y[m].mean()\n",
    "#         purities.append(max(yc, 1-yc))\n",
    "#         sizes.append(sum(m) / len(y))\n",
    "#     purity = np.array(purities) @ np.array(sizes)\n",
    "#     scores.append(purity)\n",
    "\n",
    "# plt.plot(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 3\n",
    "cluster = clusterings[k]\n",
    "pred = cluster.fit_predict(XX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "purities, majority = [], []\n",
    "for c in range(k):\n",
    "    m = (cluster.labels_ == c)\n",
    "    yc = y[m].mean()\n",
    "    purities.append(max(yc, 1-yc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.scatter(*pca.transform(XX).T, c=y, s=5, cmap='bwr')\n",
    "X_pca = pca.transform(XX)\n",
    "for i in range(k):\n",
    "    plt.scatter(*X_pca[pred == i].T, c=[plt.get_cmap(\"tab10\")(i)], s=5, \n",
    "                label='Cluster {}, negative cases: {:.1%}'.format(i+1, purities[i]))\n",
    "\n",
    "# plt.scatter(*X_pca.T, c=[plt.get_cmap(\"tab10\")(ci) for ci in pred], s=5)\n",
    "\n",
    "# Mark negative cases with lighter color\n",
    "plt.scatter(*X_pca.T, c=[{0: '#ffffff88', 1: '#00000000'}[yi] for yi in y], s=5)\n",
    "\n",
    "plt.xlabel('First principle component')\n",
    "plt.ylabel('Second principle component')\n",
    "plt.legend(fontsize=8)\n",
    "plt.axis('square')\n",
    "plt.savefig('fig/cluster.svg', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtr, Xte = X[:split_idx], X[split_idx:]\n",
    "ytr, yte = y[:split_idx], y[split_idx:]"
   ]
  },