aimhigh stratify 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('./')\n",
    "sys.path.append('/Users/ASUS/Documents/GitHub/retrospective_t2e_analysis/retrospective_t2e/')\n",
    "\n",
    "from gls import datasets\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def load_aimhigh_trial_data():\n",
    "\n",
    "  feature_list = {\n",
    "    # demographic data\n",
    "    'patient.sas7bdat' : ['Age', 'Sex', 'ethn', 'Race', 'HxDiab', 'Smoke', 'MetabSyn', 'BMI', 'SysBP', 'DiasBP', \n",
    "                        'LDL_C_Base', 'HDL_C_Base', 'Trig_Base', 'Glucose_Base', 'Lpa', 'RandAssi']\n",
    "  }\n",
    "  outcomes, features = datasets._load_generic_biolincc_dataset('patient.sas7bdat', \n",
    "                                                                   'ETPrimaryA6', 'VSDthA6',\n",
    "                                                                   features=feature_list, \n",
    "                                                                   id_col='PatientKey',\n",
    "                                                                   location='datasets/AIMHIGH/')\n",
    "\n",
    "  # Convert Censoring Indicator to Binary\n",
    "  # ie. 1 = Event, 0 = Censored\n",
    "  outcomes.event = outcomes.event == 1.0 \n",
    "  \n",
    "  NaNindex = pd.isna(outcomes.time)\n",
    "  outcomes = outcomes.loc[~NaNindex]\n",
    "  features = features.loc[~NaNindex]\n",
    "\n",
    "  return outcomes, features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ASUS\\anaconda3\\envs\\Pandas\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import torch\n",
    "from tqdm import tqdm\n",
    "from examples.cmhe_demo_utils import * "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4273, 16)\n"
     ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>DiasBP</th>\n",
       "      <th>Glucose_Base</th>\n",
       "      <th>Lpa</th>\n",
       "      <th>ethn</th>\n",
       "      <th>Sex</th>\n",
       "      <th>HDL_C_Base</th>\n",
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       "      <th>BMI</th>\n",
       "      <th>HxDiab</th>\n",
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      "text/plain": [
       "             Age  DiasBP  Glucose_Base   Lpa  ethn  Sex  HDL_C_Base  SysBP  \\\n",
       "PatientKey                                                                   \n",
       "1.0         69.0    62.0         111.0  17.4   1.0  1.0        33.0  143.0   \n",
       "2.0         69.0    72.0         102.0  50.5   1.0  1.0        38.0  116.0   \n",
       "3.0         58.0    58.0         172.0   2.8   1.0  1.0        35.0  107.0   \n",
       "7.0         58.0    95.0         106.0   2.8   1.0  1.0        30.0  117.0   \n",
       "9.0         56.0    77.0         106.0  54.6   1.0  0.0        27.0  108.0   \n",
       "\n",
       "             BMI  HxDiab  RandAssi  Smoke  LDL_C_Base  MetabSyn  Trig_Base  \\\n",
       "PatientKey                                                                   \n",
       "1.0         36.3     1.0       0.0    4.0        75.0       1.0      140.0   \n",
       "2.0         30.9     0.0       0.0    1.0        70.0       1.0      192.0   \n",
       "3.0         38.1     1.0       1.0    1.0        48.0       1.0      252.0   \n",
       "7.0         28.7     0.0       1.0    4.0        69.0       1.0      327.0   \n",
       "9.0         39.5     0.0       1.0    4.0        62.0       1.0      145.0   \n",
       "\n",
       "            Race  \n",
       "PatientKey        \n",
       "1.0          5.0  \n",
       "2.0          5.0  \n",
       "3.0          5.0  \n",
       "7.0          5.0  \n",
       "9.0          5.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the synthetic dataset\n",
    "outcomes, x = load_aimhigh_trial_data()\n",
    "\n",
    "# Let's take a look at take the dataset\n",
    "x.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from preprocessing import Preprocessor\n",
    "\n",
    "cat_feats = ['Race', 'Smoke', 'MetabSyn', 'HxDiab', 'Sex', 'ethn', 'RandAssi']\n",
    "num_feats = ['Age', 'Lpa', 'SysBP', 'LDL_C_Base', 'BMI', 'Glucose_Base', 'DiasBP', 'Trig_Base', 'HDL_C_Base']\n",
    "\n",
    "features = Preprocessor().fit_transform(x[cat_feats+num_feats], cat_feats=cat_feats, num_feats=num_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "                 Age       Lpa     SysBP  LDL_C_Base       BMI  Glucose_Base  \\\n",
       "PatientKey                                                                     \n",
       "1.0         0.610827 -0.670381  0.903030    0.038767  0.953478      0.019726   \n",
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       "9.0        -0.879210 -0.247614 -1.243165   -0.525717  1.554222     -0.202334   \n",
       "\n",
       "              DiasBP  Trig_Base  HDL_C_Base  Race_5.0  Race_8.0  Smoke_2.0  \\\n",
       "PatientKey                                                                   \n",
       "1.0        -1.263547  -0.639595   -0.306123         1         0          0   \n",
       "2.0        -0.241422   0.139126    0.584388         1         0          0   \n",
       "3.0        -1.672397   1.037650    0.050081         1         0          0   \n",
       "7.0         2.109466   2.160805   -0.840429         1         0          0   \n",
       "9.0         0.269641  -0.564718   -1.374735         1         0          0   \n",
       "\n",
       "            Smoke_3.0  Smoke_4.0  MetabSyn_1.0  MetabSyn_2.0  HxDiab_1.0  \\\n",
       "PatientKey                                                                 \n",
       "1.0                 0          1             1             0           1   \n",
       "2.0                 0          0             1             0           0   \n",
       "3.0                 0          0             1             0           1   \n",
       "7.0                 0          1             1             0           0   \n",
       "9.0                 0          1             1             0           0   \n",
       "\n",
       "            Sex_1.0  ethn_2.0  RandAssi_1.0  \n",
       "PatientKey                                   \n",
       "1.0               1         0             0  \n",
       "2.0               1         0             0  \n",
       "3.0               1         0             1  \n",
       "7.0               1         0             1  \n",
       "9.0               0         0             1  "
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "interventions = x['RandAssi']\n",
    "interventions"
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  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyper-parameters to train model\n",
    "k = 1 # number of underlying base survival phenotypes\n",
    "g = 2 # number of underlying treatment effect phenotypes.\n",
    "layers = [] # number of neurons in each hidden layer.\n",
    "\n",
    "random_seed = 3\n",
    "iters = 100 # number of training epochs\n",
    "learning_rate = 0.001\n",
    "batch_size = 256 \n",
    "vsize = 0.15 # size of the validation split\n",
    "patience = 5\n",
    "optimizer = \"Adam\"\n",
    "gate_l2_penalty = 1e-3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training data points: 1707\n",
      "Number of test data points: 1707\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into train and test sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(features, outcomes, test_size=0.5, random_state=1, stratify = outcomes.event + 2*interventions) \n",
    "\n",
    "print(f'Number of training data points: {len(x_train)}')\n",
    "print(f'Number of test data points: {len(x_test)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_tr = (x_train.loc[:, x_train.columns != 'RandAssi_1.0']).values.astype('float32')\n",
    "t_tr = y_train['time'].values.astype('float32')\n",
    "e_tr = y_train['event'].values.astype('float32')\n",
    "a_tr = x_train['RandAssi_1.0'].values.astype('float32')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_te = (x_test.loc[:, x_test.columns != 'RandAssi_1.0']).values.astype('float32')\n",
    "t_te = y_test['time'].values.astype('float32')\n",
    "e_te = y_test['event'].values.astype('float32')\n",
    "a_te = x_test['RandAssi_1.0'].values.astype('float32')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/100 [00:00<?, ?it/s]c:\\Users\\ASUS\\Documents\\GitHub\\retrospective_t2e_analysis\\retrospective_t2e\\auton_survival\\models\\cmhe\\cmhe_utilities.py:121: RuntimeWarning: invalid value encountered in log\n",
      "  probs.append(np.log(event_probs))\n",
      "100%|██████████| 100/100 [00:06<00:00, 14.92it/s]\n"
     ]
    }
   ],
   "source": [
    "from models.cmhe import DeepCoxMixturesHeterogenousEffects\n",
    "\n",
    "torch.manual_seed(random_seed)\n",
    "np.random.seed(random_seed)\n",
    "\n",
    "# Instantiate the CMHE model\n",
    "model = DeepCoxMixturesHeterogenousEffects(random_seed=10, k=k, g=g, layers=layers, gate_l2_penalty=gate_l2_penalty)\n",
    "\n",
    "model = model.fit(x_tr, t_tr, e_tr, a_tr, vsize=vsize, val_data=None, iters=iters, \n",
    "                  learning_rate=learning_rate, batch_size=batch_size, \n",
    "                  optimizer=optimizer, patience=patience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 0, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeta_probs_train = model.predict_latent_phi(x_tr)\n",
    "zeta_train =  np.argmax(zeta_probs_train, axis=1)\n",
    "zeta_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8863503222026948"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeta_train.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_list_0 = np.where(zeta_train == 0)\n",
    "idx_list_1 = np.where(zeta_train == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from auton_survival import reporting\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the probability of event-free survival for phenotypes using the Kaplan Meier estimator.\n",
    "ax = reporting.plot_kaplanmeier(y_train.iloc[idx_list_0], x_train['RandAssi_1.0'].iloc[idx_list_0])\n",
    "h, l = ax.get_legend_handles_labels()\n",
    "plt.xlabel('Time (Days)')\n",
    "plt.ylabel('Event-Free Survival Probability')\n",
    "plt.legend([h[0], h[1]], ['Placebo plus Statin', 'Niacin plus Statin'], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the probability of event-free survival for phenotypes using the Kaplan Meier estimator.\n",
    "ax2 = reporting.plot_kaplanmeier(y_train.iloc[idx_list_1], x_train['RandAssi_1.0'].iloc[idx_list_1])\n",
    "h, l = ax2.get_legend_handles_labels()\n",
    "plt.xlabel('Time (Days)')\n",
    "plt.ylabel('Event-Free Survival Probability')\n",
    "plt.legend([h[0], h[1]], ['Placebo plus Statin', 'Niacin plus Statin'], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeta_probs_test = model.predict_latent_phi(x_te)\n",
    "zeta_test =  np.argmax(zeta_probs_test, axis=1)\n",
    "zeta_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9068541300527241"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeta_test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_list_0te = np.where(zeta_test == 0)\n",
    "idx_list_1te = np.where(zeta_test == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the probability of event-free survival for phenotypes using the Kaplan Meier estimator.\n",
    "ax3 = reporting.plot_kaplanmeier(y_test.iloc[idx_list_0te], x_test['RandAssi_1.0'].iloc[idx_list_0te])\n",
    "h, l = ax3.get_legend_handles_labels()\n",
    "plt.xlabel('Time (Days)')\n",
    "plt.ylabel('Event-Free Survival Probability')\n",
    "plt.legend([h[0], h[1]], ['Placebo plus Statin', 'Niacin plus Statin'], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Estimate the probability of event-free survival for phenotypes using the Kaplan Meier estimator.\n",
    "ax4 = reporting.plot_kaplanmeier(y_test.iloc[idx_list_1te], x_test['RandAssi_1.0'].iloc[idx_list_1te])\n",
    "h, l = ax4.get_legend_handles_labels()\n",
    "plt.xlabel('Time (Days)')\n",
    "plt.ylabel('Event-Free Survival Probability')\n",
    "plt.legend([h[0], h[1]], ['Placebo plus Statin', 'Niacin plus Statin'], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = [0.9, 0.8, 0.7, 0.6]\n",
    "zeta_train_qt = np.quantile(zeta_probs_train[:, 0], q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_list_qt10 = np.where(zeta_probs_train[:, 0] >= zeta_train_qt[0])\n",
    "idx_list_qt20 = np.where(zeta_probs_train[:, 0] >= zeta_train_qt[1])\n",
    "idx_list_qt30 = np.where(zeta_probs_train[:, 0] >= zeta_train_qt[2])\n",
    "idx_list_qt40 = np.where(zeta_probs_train[:, 0] >= zeta_train_qt[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeta_probs_test = model.predict_latent_phi(x_te)\n",
    "zeta_test =  np.argmax(zeta_probs_test, axis=1)\n",
    "zeta_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "zeta_test_qt = np.quantile(zeta_probs_test[:, 0], q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_list_qt10te = np.where(zeta_probs_test[:, 0] >= zeta_test_qt[0])\n",
    "idx_list_qt20te = np.where(zeta_probs_test[:, 0] >= zeta_test_qt[1])\n",
    "idx_list_qt30te = np.where(zeta_probs_test[:, 0] >= zeta_test_qt[2])\n",
    "idx_list_qt40te = np.where(zeta_probs_test[:, 0] >= zeta_test_qt[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# zeta 1\n",
    "zeta_train_qt1 = np.quantile(zeta_probs_train[:, 1], q)\n",
    "idx_list_qt10_1 = np.where(zeta_probs_train[:, 1] >= zeta_train_qt1[0])\n",
    "idx_list_qt20_1 = np.where(zeta_probs_train[:, 1] >= zeta_train_qt1[1])\n",
    "idx_list_qt30_1 = np.where(zeta_probs_train[:, 1] >= zeta_train_qt1[2])\n",
    "idx_list_qt40_1 = np.where(zeta_probs_train[:, 1] >= zeta_train_qt1[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# zeta 1\n",
    "zeta_test_qt1 = np.quantile(zeta_probs_test[:, 1], q)\n",
    "idx_list_qt10te_1 = np.where(zeta_probs_test[:, 1] >= zeta_test_qt1[0])\n",
    "idx_list_qt20te_1 = np.where(zeta_probs_test[:, 1] >= zeta_test_qt1[1])\n",
    "idx_list_qt30te_1 = np.where(zeta_probs_test[:, 1] >= zeta_test_qt1[2])\n",
    "idx_list_qt40te_1 = np.where(zeta_probs_test[:, 1] >= zeta_test_qt1[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "lifelines.fitters.kaplan_meier_fitter.KaplanMeierFitter.plot() got multiple values for keyword argument 'ax'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\ASUS\\Documents\\GitHub\\retrospective_t2e_analysis\\retrospective_t2e\\auton_survival\\aimhigh stratify.ipynb Cell 23'\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/ASUS/Documents/GitHub/retrospective_t2e_analysis/retrospective_t2e/auton_survival/aimhigh%20stratify.ipynb#ch0000020?line=0'>1</a>\u001b[0m fig, ((ax5, ax13), (ax6, ax14), (ax7, ax15), (ax8, ax16)) \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39msubplots(\u001b[39m4\u001b[39m, \u001b[39m2\u001b[39m, figsize\u001b[39m=\u001b[39m (\u001b[39m20\u001b[39m, \u001b[39m20\u001b[39m))\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/ASUS/Documents/GitHub/retrospective_t2e_analysis/retrospective_t2e/auton_survival/aimhigh%20stratify.ipynb#ch0000020?line=1'>2</a>\u001b[0m fig\u001b[39m.\u001b[39msuptitle(\u001b[39m'\u001b[39m\u001b[39mTrain Data\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/ASUS/Documents/GitHub/retrospective_t2e_analysis/retrospective_t2e/auton_survival/aimhigh%20stratify.ipynb#ch0000020?line=2'>3</a>\u001b[0m reporting\u001b[39m.\u001b[39;49mplot_kaplanmeier(y_train\u001b[39m.\u001b[39;49miloc[idx_list_qt10], x_train[\u001b[39m'\u001b[39;49m\u001b[39mRandAssi_1.0\u001b[39;49m\u001b[39m'\u001b[39;49m]\u001b[39m.\u001b[39;49miloc[idx_list_qt10], ax\u001b[39m=\u001b[39;49max5)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/ASUS/Documents/GitHub/retrospective_t2e_analysis/retrospective_t2e/auton_survival/aimhigh%20stratify.ipynb#ch0000020?line=3'>4</a>\u001b[0m reporting\u001b[39m.\u001b[39mplot_kaplanmeier(y_train\u001b[39m.\u001b[39miloc[idx_list_qt20], x_train[\u001b[39m'\u001b[39m\u001b[39mRandAssi_1.0\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39miloc[idx_list_qt20], ax\u001b[39m=\u001b[39max6)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/ASUS/Documents/GitHub/retrospective_t2e_analysis/retrospective_t2e/auton_survival/aimhigh%20stratify.ipynb#ch0000020?line=4'>5</a>\u001b[0m reporting\u001b[39m.\u001b[39mplot_kaplanmeier(y_train\u001b[39m.\u001b[39miloc[idx_list_qt30], x_train[\u001b[39m'\u001b[39m\u001b[39mRandAssi_1.0\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39miloc[idx_list_qt30], ax\u001b[39m=\u001b[39max7) \n",
      "File \u001b[1;32mc:\\Users/ASUS/Documents/GitHub/retrospective_t2e_analysis/retrospective_t2e\\auton_survival\\reporting.py:44\u001b[0m, in \u001b[0;36mplot_kaplanmeier\u001b[1;34m(outcomes, groups, plot_counts, **kwargs)\u001b[0m\n\u001b[0;32m     40\u001b[0m   \u001b[39mif\u001b[39;00m pd\u001b[39m.\u001b[39misna(group): \u001b[39mcontinue\u001b[39;00m\n\u001b[0;32m     42\u001b[0m   curves[group] \u001b[39m=\u001b[39m KaplanMeierFitter()\u001b[39m.\u001b[39mfit(outcomes[groups\u001b[39m==\u001b[39mgroup][\u001b[39m'\u001b[39m\u001b[39mtime\u001b[39m\u001b[39m'\u001b[39m],\n\u001b[0;32m     43\u001b[0m                                           outcomes[groups\u001b[39m==\u001b[39mgroup][\u001b[39m'\u001b[39m\u001b[39mevent\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[1;32m---> 44\u001b[0m   ax \u001b[39m=\u001b[39m curves[group]\u001b[39m.\u001b[39mplot(label\u001b[39m=\u001b[39mgroup, ax\u001b[39m=\u001b[39max, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m     46\u001b[0m \u001b[39mif\u001b[39;00m plot_counts:\n\u001b[0;32m     47\u001b[0m   add_at_risk_counts(\u001b[39miter\u001b[39m([curves[group] \u001b[39mfor\u001b[39;00m group \u001b[39min\u001b[39;00m curves]), ax\u001b[39m=\u001b[39max)\n",
      "\u001b[1;31mTypeError\u001b[0m: lifelines.fitters.kaplan_meier_fitter.KaplanMeierFitter.plot() got multiple values for keyword argument 'ax'"
     ]
    },
    {
     "data": {
      "image/png": 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zX9xOJDp9oFPC08/x6us333L24urBtdbvnetF+/pxs7XWwzPz2ur26rzqzWutu2bmxurEWut49aPtXG54sp2bLr18P2eCp4Mtz73vqZ5R/ezmXvG/s9a69tCGhiNgy3MP2GNbnnu3V39zZu6u/qT61rWWq9fhCdry/Htd9SMz80/buYn1q1wYAE/MzLy1nX/8uGhzv683Vp9atdb6oXbu//Wy6mT1UPXqrd7XuQkAAADAfn/cDAAAAICnAJEIAAAAAJEIAAAAAJEIAAAAgEQiAAAAABKJAAAAAEgkAgAAACCRCAAAAIDq/wLnNqeamC6N2AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax5, ax13), (ax6, ax14), (ax7, ax15), (ax8, ax16)) = plt.subplots(4, 2, figsize= (20, 20))\n",
    "fig.suptitle('Train Data')\n",
    "reporting.plot_kaplanmeier(y_train.iloc[idx_list_qt10], x_train['RandAssi_1.0'].iloc[idx_list_qt10], ax=ax5)\n",
    "reporting.plot_kaplanmeier(y_train.iloc[idx_list_qt20], x_train['RandAssi_1.0'].iloc[idx_list_qt20], ax=ax6)\n",
    "reporting.plot_kaplanmeier(y_train.iloc[idx_list_qt30], x_train['RandAssi_1.0'].iloc[idx_list_qt30], ax=ax7) \n",
    "reporting.plot_kaplanmeier(y_train.iloc[idx_list_qt40], x_train['RandAssi_1.0'].iloc[idx_list_qt40], ax=ax8)\n",
    "reporting.plot_kaplanmeier(y_train.iloc[idx_list_qt10_1], x_train['RandAssi_1.0'].iloc[idx_list_qt10_1], ax=ax13)\n",
    "reporting.plot_kaplanmeier(y_train.iloc[idx_list_qt20_1], x_train['RandAssi_1.0'].iloc[idx_list_qt20_1], ax=ax14)\n",
    "reporting.plot_kaplanmeier(y_train.iloc[idx_list_qt30_1], x_train['RandAssi_1.0'].iloc[idx_list_qt30_1], ax=ax15)\n",
    "reporting.plot_kaplanmeier(y_train.iloc[idx_list_qt40_1], x_train['RandAssi_1.0'].iloc[idx_list_qt40_1], ax=ax16) \n",
    "for ax in fig.get_axes():\n",
    "    ax.label_outer()\n",
    "    h, l = ax.get_legend_handles_labels()\n",
    "    ax.set_xlabel('Time (Days)')\n",
    "    ax.set_ylabel('Event-Free Survival Probability')\n",
    "    ax.legend([h[0], h[1]], ['Placebo plus Statin', 'Niacin plus Statin'], loc=\"upper right\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('Pandas')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "f08cc569b594e1720561ae63e75656c07c548e4f90e451cb99794b898c100aec"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}