oat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "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_oat_trial_data():\n",
    "\n",
    "  feature_list = {\n",
    "    # demographic data\n",
    "    'oat2166.sas7bdat' : ['TRT_GRP', 'age', 'male', 'diabhx', 'finalrace', 'cursmoke', 'mihx', 'htenhx',\n",
    "                          'cerevashx', 'pvdhx', 'chfhx', 'pcihx', 'insulin', 'anginahx', 'killip24', 'white',\n",
    "                          'multi', 'collany', 'ckd', 'EFall', 'EFall50', 'iraall', 'ladall', 'cabghx', 'nyharand',\n",
    "                          'EFall40', 'thromb', 'strestst', 'stira', 'pregrade', 'nitrat1d', 'bbd',\n",
    "                          'ccbd', 'aspirind', 'ticlopd', 'clopidd', 'warfrnd', 'digoxind', 'diurd', 'spirod', 'insulind',\n",
    "                          'orhypogd', 'lipidlod', 'eitherd', 'acearbd', 'atleast1', 'atleast2', 'asporclopd', 'aspandclopd']\n",
    "  }\n",
    "  outcomes, features = datasets._load_generic_biolincc_dataset('oat2166.sas7bdat', \n",
    "                                                                   'combdays', 'combfv',\n",
    "                                                                   features=feature_list, \n",
    "                                                                   id_col='NEWID',\n",
    "                                                                   location='datasets/OAT/Ancillary/')\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": 2,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2166, 49)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ASUS\\Documents\\GitHub\\retrospective_t2e_analysis\\retrospective_t2e\\auton_survival\\gls\\datasets.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df.loc[:, col] = df[col].values.astype(str)\n",
      "C:\\Users\\ASUS\\AppData\\Local\\Temp\\ipykernel_11020\\2137431757.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  outcomes.event = outcomes.event == 1.0\n"
     ]
    },
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      "text/plain": [
       "       anginahx  nyharand  iraall  mihx  insulind  EFall40  ccbd  pcihx  \\\n",
       "NEWID                                                                     \n",
       "3.0         0.0       1.0     1.0   0.0       0.0      0.0   0.0    0.0   \n",
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       "\n",
       "       collany  orhypogd  ...  lipidlod  chfhx  aspandclopd  warfrnd  cabghx  \\\n",
       "NEWID                     ...                                                  \n",
       "3.0        1.0       0.0  ...       1.0    0.0          1.0      0.0     0.0   \n",
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       "\n",
       "       thromb  EFall50  stira diabhx  nitrat1d  \n",
       "NEWID                                           \n",
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      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the synthetic dataset\n",
    "outcomes, features = load_oat_trial_data()\n",
    "\n",
    "# Let's take a look at take the dataset\n",
    "features.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NEWID\n",
       "3.0       1\n",
       "5.0       2\n",
       "16.0      1\n",
       "20.0      1\n",
       "21.0      2\n",
       "         ..\n",
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       "9981.0    1\n",
       "9988.0    2\n",
       "Name: TRT_GRP, Length: 2166, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interventions = features['TRT_GRP']\n",
    "interventions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from preprocessing import Preprocessor\n",
    "\n",
    "cat_feats = ['male', 'diabhx', 'finalrace', 'cursmoke', 'mihx', 'htenhx',\n",
    "                          'cerevashx', 'pvdhx', 'chfhx', 'pcihx', 'insulin', 'anginahx', 'killip24', 'white',\n",
    "                          'multi', 'collany', 'EFall50', 'iraall', 'ladall', 'cabghx', 'nyharand',\n",
    "                          'EFall40', 'thromb', 'strestst', 'stira', 'pregrade', 'nitrat1d', 'bbd',\n",
    "                          'ccbd', 'aspirind', 'ticlopd', 'clopidd', 'warfrnd', 'digoxind', 'diurd', 'spirod', 'insulind',\n",
    "                          'orhypogd', 'lipidlod', 'eitherd', 'acearbd', 'atleast1', 'atleast2', 'asporclopd', 'aspandclopd',]\n",
    "num_feats = ['age', 'EFall', 'ckd']\n",
    "\n",
    "features = Preprocessor().fit_transform(features[cat_feats+num_feats], \n",
    "                                                cat_feats=cat_feats, num_feats=num_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21.0</th>\n",
       "      <td>1.678998</td>\n",
       "      <td>0.027470</td>\n",
       "      <td>-1.517877</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            age     EFall       ckd  male_1.0  diabhx_1.0  finalrace_2.0  \\\n",
       "NEWID                                                                      \n",
       "3.0   -1.517989 -1.597806 -0.113322         1           0              0   \n",
       "5.0    0.308861  1.110988 -0.009062         1           0              0   \n",
       "16.0   1.222285  0.027470 -1.076045         1           1              0   \n",
       "20.0  -0.330537 -0.243409  0.582532         1           0              0   \n",
       "21.0   1.678998  0.027470 -1.517877         1           0              0   \n",
       "\n",
       "       finalrace_3.0  finalrace_4.0  cursmoke_1.0  mihx_1.0  ...  spirod_1.0  \\\n",
       "NEWID                                                        ...               \n",
       "3.0                0              0             1         0  ...           0   \n",
       "5.0                0              0             1         0  ...           0   \n",
       "16.0               0              0             1         0  ...           0   \n",
       "20.0               0              0             1         0  ...           0   \n",
       "21.0               0              0             0         0  ...           0   \n",
       "\n",
       "       insulind_1.0  orhypogd_1.0  lipidlod_1.0  eitherd_1.0  acearbd_1.0  \\\n",
       "NEWID                                                                       \n",
       "3.0               0             0             1            1            1   \n",
       "5.0               0             0             1            0            1   \n",
       "16.0              0             1             1            1            1   \n",
       "20.0              0             0             1            1            0   \n",
       "21.0              0             0             1            0            1   \n",
       "\n",
       "       atleast1_1.0  atleast2_1.0  asporclopd_1.0  aspandclopd_1.0  \n",
       "NEWID                                                               \n",
       "3.0               1             1               1                1  \n",
       "5.0               1             0               1                0  \n",
       "16.0              1             1               1                1  \n",
       "20.0              1             1               1                1  \n",
       "21.0              1             0               1                0  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training data points: 1455\n",
      "Number of test data points: 711\n",
      "Training Data Statistics:\n",
      "Shape of covariates: (1455, 55) | times: (1455,) | events: (1455,) | interventions: (1455,)\n"
     ]
    }
   ],
   "source": [
    "# Hyper-parameters\n",
    "random_seed = 0\n",
    "test_size = 0.4\n",
    "\n",
    "# Split the synthetic data into training and testing data\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(random_seed)\n",
    "n = features.shape[0] \n",
    "\n",
    "test_idx = np.zeros(n).astype('bool')\n",
    "test_idx[np.random.randint(n, size=int(n*test_size))] = True \n",
    "\n",
    "features_tr = features.iloc[~test_idx] \n",
    "outcomes_tr = outcomes.iloc[~test_idx]\n",
    "interventions_tr = interventions[~test_idx]\n",
    "print(f'Number of training data points: {len(features_tr)}')\n",
    "\n",
    "features_te = features.iloc[test_idx] \n",
    "outcomes_te = outcomes.iloc[test_idx]\n",
    "interventions_te = interventions[test_idx]\n",
    "print(f'Number of test data points: {len(features_te)}')\n",
    "\n",
    "x_tr = features_tr.values.astype('float32')\n",
    "t_tr = outcomes_tr['time'].values.astype('float32')\n",
    "e_tr = outcomes_tr['event'].values.astype('float32')\n",
    "a_tr = interventions_tr.values.astype('float32')\n",
    "\n",
    "x_te = features_te.values.astype('float32')\n",
    "t_te = outcomes_te['time'].values.astype('float32')\n",
    "e_te = outcomes_te['event'].values.astype('float32')\n",
    "a_te = interventions_te.values.astype('float32')\n",
    "\n",
    "print('Training Data Statistics:')\n",
    "print(f'Shape of covariates: {x_tr.shape} | times: {t_tr.shape} | events: {e_tr.shape} | interventions: {a_tr.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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 = 128 \n",
    "vsize = 0.15 # size of the validation split\n",
    "patience = 3\n",
    "optimizer = \"Adam\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:08<00:00, 12.15it/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)\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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9518900343642611"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeta_train.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_list_0 = np.where(zeta_train == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_list_1 = np.where(zeta_train == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": [
    "from auton_survival import reporting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Estimate the probability of event-free survival for phenotypes using the Kaplan Meier estimator.\n",
    "ax = reporting.plot_kaplanmeier(outcomes_tr.iloc[idx_list_0], interventions_tr.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]], ['Optimal medical therapy alone', 'Percutaneous coronary intervention'], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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(outcomes_tr.iloc[idx_list_1], interventions_tr.iloc[idx_list_1])\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]], ['Optimal medical therapy alone', 'Percutaneous coronary intervention'], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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       "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 33,
     "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9549929676511955"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeta_test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": 36,
   "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(outcomes_te.iloc[idx_list_0te], interventions_te.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]], ['Optimal medical therapy alone', 'Percutaneous coronary intervention'], loc=\"upper right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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(outcomes_te.iloc[idx_list_1te], interventions_te.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]], ['Optimal medical therapy alone', 'Percutaneous coronary intervention'], 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
}