{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dsm import datasets, DeepSurvivalMachines\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, t, e = datasets.load_dataset('SUPPORT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "times = np.quantile(t[e==1], [0.25, 0.5, 0.75]).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_folds = 5\n",
    "folds = list(range(cv_folds))*10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "folds = np.array(folds[:len(x)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sksurv.metrics import concordance_index_ipcw, brier_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|          | 54/10000 [00:00<00:18, 536.02it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On Fold: 0\n",
      "(7284, 44)\n",
      "Pretraining the Underlying Distributions...\n",
      "torch.Size([6192]) torch.Size([6192])\n",
      "torch.Size([6192]) torch.Size([6192])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13%|█▎        | 1312/10000 [00:02<00:13, 628.77it/s]\n",
      "  0%|          | 0/100 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.7721933727751713 -6.453758404040128\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  4%|▍         | 4/100 [00:07<02:56,  1.84s/it]\n",
      "  1%|          | 60/10000 [00:00<00:16, 594.79it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On Fold: 1\n",
      "(7284, 44)\n",
      "Pretraining the Underlying Distributions...\n",
      "torch.Size([6192]) torch.Size([6192])\n",
      "torch.Size([6192]) torch.Size([6192])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█▍        | 1438/10000 [00:02<00:13, 640.39it/s]\n",
      "  0%|          | 0/100 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.7678721461202748 -6.509151589080709\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|▋         | 7/100 [00:11<02:29,  1.61s/it]\n",
      "  1%|          | 63/10000 [00:00<00:15, 629.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On Fold: 2\n",
      "(7284, 44)\n",
      "Pretraining the Underlying Distributions...\n",
      "torch.Size([6192]) torch.Size([6192])\n",
      "torch.Size([6192]) torch.Size([6192])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 16%|█▌        | 1582/10000 [00:02<00:13, 641.78it/s]\n",
      "  0%|          | 0/100 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.7727229786575149 -6.511681205334374\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6%|▌         | 6/100 [00:10<02:38,  1.68s/it]\n",
      "  1%|          | 53/10000 [00:00<00:19, 519.58it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On Fold: 3\n",
      "(7284, 44)\n",
      "Pretraining the Underlying Distributions...\n",
      "torch.Size([6192]) torch.Size([6192])\n",
      "torch.Size([6192]) torch.Size([6192])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█▍        | 1385/10000 [00:02<00:16, 515.82it/s]\n",
      "  0%|          | 0/100 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.7719732355296647 -6.47692660327643\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|▌         | 5/100 [00:14<04:36,  2.91s/it]\n",
      "  0%|          | 40/10000 [00:00<00:25, 391.81it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On Fold: 4\n",
      "(7284, 44)\n",
      "Pretraining the Underlying Distributions...\n",
      "torch.Size([6192]) torch.Size([6192])\n",
      "torch.Size([6192]) torch.Size([6192])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 18%|█▊        | 1799/10000 [00:04<00:18, 433.18it/s]\n",
      "  0%|          | 0/100 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.7750461172086791 -6.534539612585216\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|▌         | 5/100 [00:11<03:30,  2.22s/it]\n"
     ]
    }
   ],
   "source": [
    "cis = []\n",
    "brs = []\n",
    "for fold in range(cv_folds):\n",
    "    \n",
    "    print (\"On Fold:\", fold)\n",
    "    \n",
    "    x_train, t_train, e_train = x[folds!=fold], t[folds!=fold], e[folds!=fold]\n",
    "    x_test,  t_test,  e_test  = x[folds==fold], t[folds==fold], e[folds==fold]\n",
    "    \n",
    "    print (x_train.shape)\n",
    "    \n",
    "    model = DeepSurvivalMachines(distribution='Weibull', layers=[100])\n",
    "    model.fit(x_train, t_train, e_train, iters=100, learning_rate=1e-3,batch_size=10)\n",
    "    \n",
    "    et_train = np.array([(e_train[i], t_train[i]) for i in range(len(e_train))],\n",
    "                 dtype=[('e', bool), ('t', int)])\n",
    "    \n",
    "    et_test = np.array([(e_test[i], t_test[i]) for i in range(len(e_test))],\n",
    "                 dtype=[('e', bool), ('t', int)])\n",
    "    \n",
    "    out_risk = model.predict_risk(x_test, times)\n",
    "    out_survival = model.predict_survival(x_test, times)\n",
    "\n",
    "    cis_ = []\n",
    "    for i in range(len(times)):\n",
    "        cis_.append(concordance_index_ipcw(et_train, et_test, out_risk[:,i], times[i])[0])\n",
    "    cis.append(cis_)\n",
    "\n",
    "    brs.append(brier_score(et_train, et_test, out_survival, times )[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concordance Index: [0.74546862 0.706156   0.67491647]\n",
      "Brier Score: [0.12746457 0.19834306 0.21601803]\n"
     ]
    }
   ],
   "source": [
    "print (\"Concordance Index:\", np.mean(cis,axis=0))\n",
    "print (\"Brier Score:\", np.mean(brs,axis=0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sksurv.linear_model import CoxPHSurvivalAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On Fold: 0\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "float() argument must be a string or a number, not 'StepFunction'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-34-105a4002e245>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0mcis_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconcordance_index_ipcw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0met_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0met_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout_risk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0mcis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcis_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sksurv/metrics.py\u001b[0m in \u001b[0;36mconcordance_index_ipcw\u001b[0;34m(survival_train, survival_test, estimate, tau, tied_tol)\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0msurvival_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msurvival_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m     \u001b[0mestimate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_estimate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m     \u001b[0mcens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCensoringDistributionEstimator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sksurv/metrics.py\u001b[0m in \u001b[0;36m_check_estimate\u001b[0;34m(estimate, test_time)\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_check_estimate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m     \u001b[0mestimate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mestimate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m         raise ValueError(\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     71\u001b[0m                           FutureWarning)\n\u001b[1;32m     72\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 73\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     74\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    597\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unsafe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 599\u001b[0;31m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    600\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'StepFunction'"
     ]
    }
   ],
   "source": [
    "cis = []\n",
    "for fold in range(cv_folds):\n",
    "    \n",
    "    print (\"On Fold:\", fold)\n",
    "    \n",
    "    x_train, t_train, e_train = x[folds!=fold], t[folds!=fold], e[folds!=fold]\n",
    "    x_test,  t_test,  e_test  = x[folds==fold], t[folds==fold], e[folds==fold]\n",
    "    \n",
    "    et_train = np.array([(e_train[i], t_train[i]) for i in range(len(e_train))],\n",
    "                 dtype=[('e', bool), ('t', int)])\n",
    "    et_test = np.array([(e_test[i], t_test[i]) for i in range(len(e_test))],\n",
    "                 dtype=[('e', bool), ('t', int)])\n",
    "    \n",
    "    model = CoxPHSurvivalAnalysis(alpha=1e-3)\n",
    "    model.fit(x_test, et_test)\n",
    "\n",
    "    out_risk = model.predict_survival_function(x_test)\n",
    "    \n",
    "    cis_ = []\n",
    "    for i in range(len(times)):\n",
    "        cis_.append(concordance_index_ipcw(et_train, et_test, out_risk, times[i])[0])\n",
    "    cis.append(cis_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time = 6\n",
    "int(np.where(out_risk[0].x == time)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   3,    4,    5,    6,    7,    8,    9,   10,   11,   12,   13,\n",
       "         14,   15,   16,   17,   18,   19,   20,   21,   22,   23,   24,\n",
       "         25,   26,   27,   28,   29,   30,   31,   32,   33,   34,   35,\n",
       "         36,   37,   38,   39,   40,   41,   42,   43,   44,   45,   46,\n",
       "         47,   48,   49,   50,   51,   52,   53,   54,   55,   56,   57,\n",
       "         58,   59,   60,   62,   63,   64,   65,   66,   67,   68,   69,\n",
       "         70,   71,   72,   74,   75,   77,   78,   79,   80,   81,   82,\n",
       "         83,   84,   85,   86,   88,   90,   91,   92,   93,   94,   95,\n",
       "         96,   97,   98,  100,  101,  102,  103,  104,  105,  106,  107,\n",
       "        108,  109,  110,  111,  112,  114,  116,  117,  118,  119,  120,\n",
       "        121,  122,  124,  126,  127,  128,  129,  130,  132,  133,  134,\n",
       "        136,  137,  139,  142,  143,  145,  146,  147,  148,  149,  151,\n",
       "        152,  153,  156,  157,  160,  162,  163,  164,  165,  166,  167,\n",
       "        168,  170,  171,  172,  173,  174,  176,  180,  181,  182,  183,\n",
       "        185,  186,  187,  189,  191,  193,  194,  195,  197,  198,  199,\n",
       "        200,  201,  202,  203,  204,  205,  207,  208,  212,  213,  214,\n",
       "        215,  217,  218,  220,  223,  224,  225,  227,  229,  230,  231,\n",
       "        233,  234,  235,  236,  237,  240,  242,  244,  247,  248,  251,\n",
       "        252,  253,  254,  258,  259,  260,  263,  264,  265,  266,  268,\n",
       "        269,  273,  274,  276,  277,  279,  281,  283,  287,  288,  290,\n",
       "        291,  292,  294,  295,  297,  299,  300,  303,  309,  310,  311,\n",
       "        312,  313,  314,  316,  318,  319,  320,  321,  322,  323,  324,\n",
       "        326,  328,  330,  335,  338,  339,  340,  343,  344,  346,  347,\n",
       "        348,  350,  351,  352,  353,  355,  356,  359,  360,  361,  363,\n",
       "        365,  366,  368,  370,  372,  377,  379,  380,  381,  382,  384,\n",
       "        385,  386,  387,  389,  392,  393,  394,  395,  396,  397,  399,\n",
       "        400,  401,  403,  404,  405,  406,  407,  408,  409,  410,  411,\n",
       "        413,  415,  417,  418,  420,  421,  422,  423,  425,  428,  430,\n",
       "        432,  433,  434,  435,  436,  440,  442,  444,  446,  447,  448,\n",
       "        449,  450,  451,  453,  455,  459,  460,  461,  463,  464,  465,\n",
       "        467,  468,  469,  470,  472,  473,  474,  477,  479,  482,  484,\n",
       "        485,  486,  487,  489,  491,  492,  493,  494,  496,  497,  499,\n",
       "        500,  501,  503,  504,  507,  509,  511,  513,  515,  517,  518,\n",
       "        521,  523,  524,  526,  527,  528,  529,  531,  533,  534,  536,\n",
       "        541,  542,  546,  548,  551,  552,  553,  554,  555,  557,  558,\n",
       "        560,  562,  563,  564,  566,  567,  573,  575,  576,  577,  578,\n",
       "        582,  584,  585,  586,  587,  588,  589,  591,  595,  597,  599,\n",
       "        603,  604,  605,  608,  609,  610,  613,  615,  616,  617,  618,\n",
       "        619,  620,  621,  623,  624,  626,  627,  628,  629,  631,  632,\n",
       "        633,  634,  636,  637,  641,  643,  644,  648,  649,  650,  652,\n",
       "        653,  655,  656,  657,  658,  659,  661,  662,  664,  665,  666,\n",
       "        667,  668,  669,  670,  671,  674,  675,  677,  679,  680,  682,\n",
       "        685,  686,  690,  692,  695,  702,  703,  705,  706,  707,  708,\n",
       "        709,  710,  712,  714,  716,  717,  719,  720,  721,  724,  726,\n",
       "        727,  734,  738,  741,  744,  745,  746,  747,  751,  756,  757,\n",
       "        760,  761,  763,  765,  766,  768,  770,  772,  773,  774,  776,\n",
       "        777,  779,  781,  783,  784,  786,  789,  790,  795,  797,  798,\n",
       "        799,  800,  803,  804,  807,  808,  809,  811,  812,  814,  815,\n",
       "        816,  817,  818,  819,  820,  821,  823,  824,  825,  827,  829,\n",
       "        830,  831,  833,  835,  839,  842,  844,  845,  847,  849,  851,\n",
       "        852,  853,  855,  857,  858,  861,  864,  867,  868,  869,  872,\n",
       "        873,  875,  877,  878,  879,  883,  885,  887,  889,  890,  891,\n",
       "        892,  897,  898,  904,  910,  914,  917,  918,  919,  923,  926,\n",
       "        928,  929,  930,  934,  936,  937,  940,  941,  944,  946,  950,\n",
       "        951,  954,  958,  965,  969,  970,  971,  972,  973,  977,  978,\n",
       "        982,  984,  985,  986,  987,  988,  989,  992,  996,  998,  999,\n",
       "       1000, 1006, 1009, 1011, 1012, 1017, 1018, 1021, 1022, 1023, 1029,\n",
       "       1034, 1036, 1037, 1043, 1045, 1046, 1047, 1049, 1050, 1051, 1055,\n",
       "       1059, 1060, 1063, 1064, 1068, 1070, 1072, 1073, 1074, 1075, 1078,\n",
       "       1079, 1082, 1087, 1088, 1099, 1109, 1116, 1126, 1134, 1138, 1142,\n",
       "       1162, 1164, 1172, 1174, 1177, 1185, 1201, 1212, 1213, 1224, 1227,\n",
       "       1232, 1238, 1250, 1253, 1265, 1269, 1289, 1301, 1304, 1307, 1310,\n",
       "       1312, 1320, 1321, 1326, 1327, 1328, 1342, 1344, 1345, 1346, 1347,\n",
       "       1349, 1352, 1355, 1356, 1360, 1363, 1365, 1369, 1371, 1373, 1377,\n",
       "       1379, 1380, 1382, 1384, 1385, 1388, 1391, 1392, 1396, 1398, 1401,\n",
       "       1406, 1409, 1410, 1411, 1416, 1418, 1421, 1422, 1427, 1439, 1441,\n",
       "       1442, 1444, 1449, 1452, 1455, 1458, 1466, 1467, 1474, 1475, 1484,\n",
       "       1485, 1486, 1487, 1489, 1492, 1495, 1497, 1503, 1510, 1512, 1514,\n",
       "       1517, 1518, 1519, 1521, 1530, 1531, 1534, 1539, 1542, 1543, 1547,\n",
       "       1551, 1552, 1558, 1560, 1563, 1566, 1567, 1568, 1572, 1573, 1578,\n",
       "       1579, 1593, 1596, 1599, 1600, 1605, 1610, 1613, 1614, 1618, 1622,\n",
       "       1623, 1629, 1636, 1642, 1647, 1648, 1654, 1655, 1657, 1659, 1665,\n",
       "       1670, 1671, 1676, 1677, 1681, 1683, 1686, 1688, 1689, 1691, 1697,\n",
       "       1699, 1701, 1705, 1712, 1717, 1718, 1719, 1722, 1723, 1728, 1732,\n",
       "       1733, 1734, 1739, 1740, 1742, 1745, 1747, 1748, 1761, 1763, 1767,\n",
       "       1769, 1772, 1778, 1782, 1783, 1785, 1788, 1790, 1792, 1795, 1798,\n",
       "       1801, 1807, 1812, 1814, 1819, 1820, 1823, 1825, 1826, 1830, 1845,\n",
       "       1853, 1857, 1863, 1866, 1867, 1882, 1885, 1886, 1887, 1892, 1910,\n",
       "       1911, 1915, 1916, 1918, 1921, 1928, 1938, 1940, 1944, 1945, 1948,\n",
       "       1949, 1951, 1952, 1963, 1971, 1976, 1978, 1979, 1980, 1984, 1990,\n",
       "       1992, 1995, 1998, 1999, 2001, 2007, 2009, 2010, 2012, 2014, 2016,\n",
       "       2019, 2022, 2024, 2026, 2027, 2028, 2029])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_risk[0].x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CoxPHSurvivalAnalysis(alpha=1e-3)\n",
    "model.fit(x_test, et_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.74335312, 0.7045087 , 0.68096073])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(cis,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_risk = model.predict_risk(x, times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DeepSurvivalMachinesTorch(\n",
       "  (act): SELU()\n",
       "  (embedding): Sequential(\n",
       "    (0): Linear(in_features=44, out_features=100, bias=False)\n",
       "    (1): ReLU6()\n",
       "    (2): Linear(in_features=100, out_features=100, bias=False)\n",
       "    (3): ReLU6()\n",
       "  )\n",
       "  (gate): Sequential(\n",
       "    (0): Linear(in_features=100, out_features=3, bias=False)\n",
       "  )\n",
       "  (scaleg): Sequential(\n",
       "    (0): Linear(in_features=100, out_features=3, bias=True)\n",
       "  )\n",
       "  (shapeg): Sequential(\n",
       "    (0): Linear(in_features=100, out_features=3, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.torch_model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_survival = model.predict_survival(x, times)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sksurv.metrics import brier_score, concordance_index_ipcw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "et = np.array([(e[i], t[i]) for i in range(len(e))],\n",
    "                 dtype=[('e', bool), ('t', int)])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.13039755, 0.20234974, 0.21643684])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brier_score(et, et, out_survival, times )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7519513749695589\n",
      "0.7074775823879251\n",
      "0.678728630898966\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(times)):\n",
    "    print(concordance_index_ipcw(et, et, out_risk[:,i], times[i])[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sksurv.linear_model import CoxPHSurvivalAnalysis\n",
    "\n",
    "estimator = CoxPHSurvivalAnalysis(alpha=1e-3).fit(x, et,)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "surv_funcs = estimator.predict(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.86249313,  0.16849345, -0.45380257, ..., -0.14997697,\n",
       "        0.35619347, -0.12209867])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surv_funcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6924659134706312\n",
      "0.6741630293711603\n",
      "0.6724802772351569\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(times)):\n",
    "    print(concordance_index_ipcw(et, et, surv_funcs, times[i])[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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