accord-lipid.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 sys\n",
    "sys.path.append('./')\n",
    "sys.path.append('/Users/ASUS/Documents/GitHub/retrospective_t2e_analysis/auton-survival/')\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "from tqdm import tqdm \n",
    "from examples.cmhe_demo_utils import * \n",
    "from auton_survival import reporting\n",
    "import matplotlib.pyplot as plt\n",
    "from metrics import treatment_effect\n",
    "from matplotlib.pyplot import figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Features Specified!! using default baseline features.\n",
      "(10251, 5)\n",
      "(10205, 3)\n",
      "(10251, 18)\n",
      "(10197, 5)\n",
      "(10240, 9)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ASUS\\Documents\\GitHub\\retrospective_t2e_analysis\\auton-survival\\auton_survival\\datasets_retrospective.py:14: 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\\Documents\\GitHub\\retrospective_t2e_analysis\\auton-survival\\auton_survival\\datasets_retrospective.py:746: 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'] = 1-outcomes['event']\n",
      "c:\\Users\\ASUS\\Documents\\GitHub\\retrospective_t2e_analysis\\auton-survival\\auton_survival\\datasets_retrospective.py:747: 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['time'] = outcomes['time']\n",
      "c:\\Users\\ASUS\\Documents\\GitHub\\retrospective_t2e_analysis\\auton-survival\\auton_survival\\datasets_retrospective.py:752: 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['time'] = outcomes['time']-1\n",
      "c:\\Users\\ASUS\\Documents\\GitHub\\retrospective_t2e_analysis\\auton-survival\\auton_survival\\datasets_retrospective.py:756: 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"
     ]
    }
   ],
   "source": [
    "from datasets_retrospective import load_accord_dataset\n",
    "outcomes, features, intervention, cat_feats, num_feats = load_accord_dataset(location='/Users/ASUS/Documents/GitHub/retrospective_t2e_analysis/auton-survival/auton_survival/datasets/', outcome='', intervention='Lipid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcomes.time = outcomes.time / 365.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the survival curves in the original paper\n",
    "figure(figsize=(5,5))\n",
    "ax_1 = reporting.plot_kaplanmeier(outcomes, intervention)\n",
    "h, l = ax_1.get_legend_handles_labels()\n",
    "plt.xlabel('Time (Years)')\n",
    "plt.ylabel('Event-Free Survival Probability')\n",
    "plt.legend([h[0], h[1]], ['Lipid Fibrate', 'Lipid Placebo'], loc=\"upper right\")\n",
    "plt.savefig(\"ACCORD lipid paper.pdf\", format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocess feature data\n",
    "from preprocessing import Preprocessor\n",
    "features_prep = Preprocessor().fit_transform(data=features[cat_feats+num_feats], cat_feats=cat_feats, num_feats=num_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:19<00:00,  5.07it/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(features_prep, outcomes.time, outcomes.event, intervention=='Lipid Fibrate', val_data=None, iters=iters, \n",
    "                  learning_rate=learning_rate, batch_size=batch_size, \n",
    "                  optimizer=optimizer, patience=patience)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "zeta_probs = model.predict_latent_phi(features_prep)\n",
    "zeta =  np.argmax(zeta_probs, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Baseline_History_Myocardial_Infarction',\n",
       " 'Baseline_History_Stroke',\n",
       " 'Baseline_History_Angina',\n",
       " 'Baseline_History_Coronary_Artery_Bypass_Graft',\n",
       " 'Baseline_History_Percutaneous_Coronary_Intervention',\n",
       " 'Baseline_History_Cardiovascular_Disease',\n",
       " 'Baseline_Prior_revascularization',\n",
       " 'Baseline_Lipid_Lowering_Therapy',\n",
       " 'Baseline_Smoke_status',\n",
       " 'Baseline_On_Blood_Pressure_Medication',\n",
       " 'Baseline_Not_On_Blood_Pressure_Medication',\n",
       " 'Baseline_BMI_>32',\n",
       " 'Baseline_History_Diabetes',\n",
       " 'Baseline_Hemoglobin_A1C_7.5%to11%',\n",
       " 'Baseline_Hemoglobin_A1C_7.5%to9%',\n",
       " 'Baseline_History_Albuminuria',\n",
       " 'Baseline_History_Left_Ventricular_Hypertrophy',\n",
       " 'Baseline_Low_Ankle_Brachial_Index',\n",
       " 'Sex',\n",
       " 'Race',\n",
       " 'Glycemia Trial']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_feats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White       1498\n",
       "Other        415\n",
       "Black        311\n",
       "Hispanic     236\n",
       "Name: Race, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(features.Race)[zeta == 0].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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": [
    "# Plot distribution of patients by Smoke Status\n",
    "plt.bar(['White', 'Other', 'Black', 'Hispanic'], features[zeta == 0].Race.value_counts(), color = ['grey', 'lightblue', 'maroon', 'lightgreen'],\n",
    "        width = 0.4, edgecolor='black')\n",
    "plt.xlabel(\"Race\")\n",
    "plt.ylabel(\"Number of Patients\")\n",
    "plt.savefig(\"ACCORD race harmed.pdf\", format='pdf', bbox_inches='tight')\n",
    "# plt.title(\"Distribution of Patients in Zeta 0 by Smoke Status at Baseline\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2460"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(zeta == 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2818"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(zeta == 1).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46723044397463004"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcomes[zeta == 0].event.sum() / sum(outcomes.event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.53276955602537"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcomes[zeta == 1].event.sum() / sum(outcomes.event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax2, ax1)) = plt.subplots(1, 2, figsize=(10, 5))\n",
    "reporting.plot_kaplanmeier(outcomes[zeta == 0], intervention[zeta == 0], ax=ax1)\n",
    "reporting.plot_kaplanmeier(outcomes[zeta == 1], intervention[zeta == 1], ax=ax2)\n",
    "ax2.set_title('Harmed Group')\n",
    "ax1.set_title('Benefited Group')\n",
    "for ax in fig.get_axes():\n",
    "    # ax.label_outer()\n",
    "    h, l = ax.get_legend_handles_labels()\n",
    "    ax.set_xlabel('Time (Years)')\n",
    "    ax.set_ylabel('Event-Free Survival Probability')\n",
    "    ax.legend([h[0], h[1]], ['Lipid Fibrate', 'Lipid Placebo'], loc=\"upper right\")\n",
    "plt.savefig(\"ACCORD phe.pdf\", format='pdf', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ASUS\\Documents\\GitHub\\retrospective_t2e_analysis\\auton-survival\\auton_survival\\metrics.py:107: UserWarning: WARNING: You are computing Hazard Ratios.\n",
      " Make sure you have tested the PH Assumptions.\n",
      "  warnings.warn(\"WARNING: You are computing Hazard Ratios.\\n Make sure you have tested the PH Assumptions.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrapping...  100  number of times. This may take a while. Please be Patient...\n",
      "[0.90946731 1.30464757]\n",
      "1.0836585019025158\n"
     ]
    }
   ],
   "source": [
    "hr = treatment_effect('hazard_ratio', outcomes, intervention=='Lipid Fibrate', n_bootstrap=100)\n",
    "CI = np.quantile(hr, [0.025, 0.975])\n",
    "print(CI)\n",
    "print(sum(hr)/len(hr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ASUS\\Documents\\GitHub\\retrospective_t2e_analysis\\auton-survival\\auton_survival\\metrics.py:107: UserWarning: WARNING: You are computing Hazard Ratios.\n",
      " Make sure you have tested the PH Assumptions.\n",
      "  warnings.warn(\"WARNING: You are computing Hazard Ratios.\\n Make sure you have tested the PH Assumptions.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrapping...  100  number of times. This may take a while. Please be Patient...\n",
      "[1.28401216 2.12674127]\n",
      "1.673591666627116\n"
     ]
    }
   ],
   "source": [
    "hr_zeta0 = treatment_effect('hazard_ratio', outcomes[zeta == 0], intervention[zeta == 0] == 'Lipid Fibrate', n_bootstrap=100)\n",
    "CI_zeta0 = np.quantile(hr_zeta0, [0.025, 0.975])\n",
    "print(CI_zeta0)\n",
    "print(sum(hr_zeta0)/len(hr_zeta0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ASUS\\Documents\\GitHub\\retrospective_t2e_analysis\\auton-survival\\auton_survival\\metrics.py:107: UserWarning: WARNING: You are computing Hazard Ratios.\n",
      " Make sure you have tested the PH Assumptions.\n",
      "  warnings.warn(\"WARNING: You are computing Hazard Ratios.\\n Make sure you have tested the PH Assumptions.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrapping...  100  number of times. This may take a while. Please be Patient...\n",
      "[0.55666256 0.91088539]\n",
      "0.7350877517799659\n"
     ]
    }
   ],
   "source": [
    "hr_zeta1 = treatment_effect('hazard_ratio', outcomes[zeta == 1], intervention[zeta == 1] == 'Lipid Fibrate', n_bootstrap=100)\n",
    "CI_zeta1 = np.quantile(hr_zeta1, [0.025, 0.975])\n",
    "print(CI_zeta1)\n",
    "print(sum(hr_zeta1)/len(hr_zeta1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Baseline_Alanine_Transaminase</th>\n",
       "      <th>Baseline_Estimated_Glomerular_Filtration_Rate</th>\n",
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       "      <th>Baseline_Very_low_density_lipoprotein</th>\n",
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       "      <td>0.47</td>\n",
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       "          Baseline_Alanine_Transaminase  \\\n",
       "MaskID                                    \n",
       "109052.0                           27.0   \n",
       "100041.0                           25.0   \n",
       "108663.0                           30.0   \n",
       "106707.0                           83.0   \n",
       "103485.0                           32.0   \n",
       "...                                 ...   \n",
       "109656.0                           27.0   \n",
       "106228.0                           22.0   \n",
       "103059.0                           29.0   \n",
       "104957.0                           19.0   \n",
       "105205.0                           17.0   \n",
       "\n",
       "          Baseline_Estimated_Glomerular_Filtration_Rate  Baseline_Heart_Rate  \\\n",
       "MaskID                                                                         \n",
       "109052.0                                           76.8                 81.0   \n",
       "100041.0                                           65.2                 85.0   \n",
       "108663.0                                           92.0                  NaN   \n",
       "106707.0                                          101.9                 69.0   \n",
       "103485.0                                          110.1                 97.0   \n",
       "...                                                 ...                  ...   \n",
       "109656.0                                           70.2                 49.0   \n",
       "106228.0                                           72.9                 52.0   \n",
       "103059.0                                          124.2                 59.0   \n",
       "104957.0                                           91.0                 73.0   \n",
       "105205.0                                           79.6                 71.0   \n",
       "\n",
       "          Baseline_Urine_albumin  Baseline_Total_Cholesterol  \\\n",
       "MaskID                                                         \n",
       "109052.0                    0.41                       237.0   \n",
       "100041.0                  117.00                       193.0   \n",
       "108663.0                    0.47                       186.0   \n",
       "106707.0                    3.99                       196.0   \n",
       "103485.0                    1.19                       194.0   \n",
       "...                          ...                         ...   \n",
       "109656.0                    0.68                       152.0   \n",
       "106228.0                   58.30                       159.0   \n",
       "103059.0                    0.37                       149.0   \n",
       "104957.0                    0.93                       135.0   \n",
       "105205.0                   90.30                       169.0   \n",
       "\n",
       "          Baseline_Urine_creatinine  Baseline_Seated_Diastolic_Blood_Pressure  \\\n",
       "MaskID                                                                          \n",
       "109052.0                      0.586                                      84.0   \n",
       "100041.0                      0.648                                      72.0   \n",
       "108663.0                      0.457                                       NaN   \n",
       "106707.0                      1.091                                      94.0   \n",
       "103485.0                      1.475                                      94.0   \n",
       "...                             ...                                       ...   \n",
       "109656.0                      1.708                                      71.0   \n",
       "106228.0                      0.694                                      66.0   \n",
       "103059.0                      1.312                                      81.0   \n",
       "104957.0                      1.350                                      86.0   \n",
       "105205.0                      0.646                                      81.0   \n",
       "\n",
       "          Baseline_Triglycerides  Baseline_Urine_albumin/creatinine_ratio  \\\n",
       "MaskID                                                                      \n",
       "109052.0                   199.0                                      0.7   \n",
       "100041.0                   179.0                                    180.6   \n",
       "108663.0                   158.0                                      1.0   \n",
       "106707.0                   227.0                                      3.7   \n",
       "103485.0                   232.0                                      0.8   \n",
       "...                          ...                                      ...   \n",
       "109656.0                   177.0                                      0.4   \n",
       "106228.0                   241.0                                     84.0   \n",
       "103059.0                   117.0                                      0.3   \n",
       "104957.0                   106.0                                      0.7   \n",
       "105205.0                   145.0                                    139.8   \n",
       "\n",
       "          Baseline_LDL_Cholesterol  Baseline_HDL_Cholesterol  Baseline_Age  \\\n",
       "MaskID                                                                       \n",
       "109052.0                     156.0                      41.0          63.7   \n",
       "100041.0                     126.0                      31.0          62.2   \n",
       "108663.0                     126.0                      28.0          58.5   \n",
       "106707.0                     123.0                      28.0          68.8   \n",
       "103485.0                     111.0                      37.0          55.6   \n",
       "...                            ...                       ...           ...   \n",
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       "104957.0                      79.0                      35.0          61.6   \n",
       "105205.0                      90.0                      50.0          59.4   \n",
       "\n",
       "          Baseline_Creatine_Phosphokinase  \\\n",
       "MaskID                                      \n",
       "109052.0                             83.0   \n",
       "100041.0                            221.0   \n",
       "108663.0                             56.0   \n",
       "106707.0                            610.0   \n",
       "103485.0                             74.0   \n",
       "...                                   ...   \n",
       "109656.0                            191.0   \n",
       "106228.0                            207.0   \n",
       "103059.0                             58.0   \n",
       "104957.0                             74.0   \n",
       "105205.0                            347.0   \n",
       "\n",
       "          Baseline_Seated_Systolic_Blood_Pressure  \\\n",
       "MaskID                                              \n",
       "109052.0                                    122.0   \n",
       "100041.0                                    124.0   \n",
       "108663.0                                      NaN   \n",
       "106707.0                                    171.0   \n",
       "103485.0                                    126.0   \n",
       "...                                           ...   \n",
       "109656.0                                    133.0   \n",
       "106228.0                                    135.0   \n",
       "103059.0                                    138.0   \n",
       "104957.0                                    137.0   \n",
       "105205.0                                    174.0   \n",
       "\n",
       "          Baseline_Fasting_Plasma_Glucose  \\\n",
       "MaskID                                      \n",
       "109052.0                            228.0   \n",
       "100041.0                            139.0   \n",
       "108663.0                            157.0   \n",
       "106707.0                            166.0   \n",
       "103485.0                            169.0   \n",
       "...                                   ...   \n",
       "109656.0                            106.0   \n",
       "106228.0                            200.0   \n",
       "103059.0                            126.0   \n",
       "104957.0                            133.0   \n",
       "105205.0                             93.0   \n",
       "\n",
       "          Baseline_Very_low_density_lipoprotein  Baseline_Potassium  \\\n",
       "MaskID                                                                \n",
       "109052.0                                   40.0                4.66   \n",
       "100041.0                                   36.0                4.51   \n",
       "108663.0                                   32.0                3.96   \n",
       "106707.0                                   45.0                4.35   \n",
       "103485.0                                   46.0                4.89   \n",
       "...                                         ...                 ...   \n",
       "109656.0                                   35.0                4.30   \n",
       "106228.0                                   48.0                5.18   \n",
       "103059.0                                   23.0                4.36   \n",
       "104957.0                                   21.0                4.36   \n",
       "105205.0                                   29.0                4.43   \n",
       "\n",
       "          Baseline_Serum_creatinine  \n",
       "MaskID                               \n",
       "109052.0                        0.8  \n",
       "100041.0                        1.2  \n",
       "108663.0                        0.9  \n",
       "106707.0                        0.8  \n",
       "103485.0                        0.6  \n",
       "...                             ...  \n",
       "109656.0                        1.3  \n",
       "106228.0                        1.1  \n",
       "103059.0                        0.7  \n",
       "104957.0                        0.9  \n",
       "105205.0                        1.2  \n",
       "\n",
       "[5278 rows x 18 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the numerical features\n",
    "cols_to_use2 = features.columns.difference(features_prep.columns)\n",
    "feat_num = features.loc[:, ~features.columns.isin(cols_to_use2)]\n",
    "feat_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Baseline_BMI_&gt;32_Yes</th>\n",
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       "      <th>Baseline_History_Cardiovascular_Disease_Yes</th>\n",
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       "      <th>Baseline_History_Left_Ventricular_Hypertrophy_Not Available</th>\n",
       "      <th>Baseline_History_Left_Ventricular_Hypertrophy_Yes</th>\n",
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       "      <th>Baseline_History_Percutaneous_Coronary_Intervention_Yes</th>\n",
       "      <th>...</th>\n",
       "      <th>Baseline_Low_Ankle_Brachial_Index_Not Available</th>\n",
       "      <th>Baseline_Low_Ankle_Brachial_Index_Yes</th>\n",
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       "      <th>Baseline_On_Blood_Pressure_Medication_Yes</th>\n",
       "      <th>Baseline_Prior_revascularization_Yes</th>\n",
       "      <th>Baseline_Smoke_status_Yes</th>\n",
       "      <th>Race_Hispanic</th>\n",
       "      <th>Race_Other</th>\n",
       "      <th>Race_White</th>\n",
       "      <th>Sex_Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaskID</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5278 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Baseline_BMI_>32_Yes  Baseline_History_Albuminuria_Not Available  \\\n",
       "MaskID                                                                       \n",
       "109052.0                     1                                           1   \n",
       "100041.0                     0                                           0   \n",
       "108663.0                     0                                           1   \n",
       "106707.0                     0                                           0   \n",
       "103485.0                     1                                           0   \n",
       "...                        ...                                         ...   \n",
       "109656.0                     0                                           0   \n",
       "106228.0                     1                                           0   \n",
       "103059.0                     0                                           0   \n",
       "104957.0                     1                                           0   \n",
       "105205.0                     0                                           1   \n",
       "\n",
       "          Baseline_History_Albuminuria_Yes  Baseline_History_Angina_Yes  \\\n",
       "MaskID                                                                    \n",
       "109052.0                                 0                            0   \n",
       "100041.0                                 1                            0   \n",
       "108663.0                                 0                            0   \n",
       "106707.0                                 1                            0   \n",
       "103485.0                                 0                            0   \n",
       "...                                    ...                          ...   \n",
       "109656.0                                 0                            0   \n",
       "106228.0                                 1                            0   \n",
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       "104957.0                                 0                            0   \n",
       "105205.0                                 0                            0   \n",
       "\n",
       "          Baseline_History_Cardiovascular_Disease_Yes  \\\n",
       "MaskID                                                  \n",
       "109052.0                                            0   \n",
       "100041.0                                            0   \n",
       "108663.0                                            0   \n",
       "106707.0                                            0   \n",
       "103485.0                                            0   \n",
       "...                                               ...   \n",
       "109656.0                                            0   \n",
       "106228.0                                            1   \n",
       "103059.0                                            0   \n",
       "104957.0                                            1   \n",
       "105205.0                                            0   \n",
       "\n",
       "          Baseline_History_Coronary_Artery_Bypass_Graft_Yes  \\\n",
       "MaskID                                                        \n",
       "109052.0                                                  0   \n",
       "100041.0                                                  0   \n",
       "108663.0                                                  0   \n",
       "106707.0                                                  0   \n",
       "103485.0                                                  0   \n",
       "...                                                     ...   \n",
       "109656.0                                                  0   \n",
       "106228.0                                                  0   \n",
       "103059.0                                                  0   \n",
       "104957.0                                                  0   \n",
       "105205.0                                                  0   \n",
       "\n",
       "          Baseline_History_Left_Ventricular_Hypertrophy_Not Available  \\\n",
       "MaskID                                                                  \n",
       "109052.0                                                  1             \n",
       "100041.0                                                  0             \n",
       "108663.0                                                  1             \n",
       "106707.0                                                  0             \n",
       "103485.0                                                  0             \n",
       "...                                                     ...             \n",
       "109656.0                                                  1             \n",
       "106228.0                                                  1             \n",
       "103059.0                                                  1             \n",
       "104957.0                                                  0             \n",
       "105205.0                                                  1             \n",
       "\n",
       "          Baseline_History_Left_Ventricular_Hypertrophy_Yes  \\\n",
       "MaskID                                                        \n",
       "109052.0                                                  0   \n",
       "100041.0                                                  0   \n",
       "108663.0                                                  0   \n",
       "106707.0                                                  0   \n",
       "103485.0                                                  0   \n",
       "...                                                     ...   \n",
       "109656.0                                                  0   \n",
       "106228.0                                                  0   \n",
       "103059.0                                                  0   \n",
       "104957.0                                                  0   \n",
       "105205.0                                                  0   \n",
       "\n",
       "          Baseline_History_Myocardial_Infarction_Yes  \\\n",
       "MaskID                                                 \n",
       "109052.0                                           0   \n",
       "100041.0                                           0   \n",
       "108663.0                                           0   \n",
       "106707.0                                           0   \n",
       "103485.0                                           0   \n",
       "...                                              ...   \n",
       "109656.0                                           0   \n",
       "106228.0                                           0   \n",
       "103059.0                                           0   \n",
       "104957.0                                           1   \n",
       "105205.0                                           0   \n",
       "\n",
       "          Baseline_History_Percutaneous_Coronary_Intervention_Yes  ...  \\\n",
       "MaskID                                                             ...   \n",
       "109052.0                                                  0        ...   \n",
       "100041.0                                                  0        ...   \n",
       "108663.0                                                  0        ...   \n",
       "106707.0                                                  0        ...   \n",
       "103485.0                                                  0        ...   \n",
       "...                                                     ...        ...   \n",
       "109656.0                                                  0        ...   \n",
       "106228.0                                                  1        ...   \n",
       "103059.0                                                  0        ...   \n",
       "104957.0                                                  1        ...   \n",
       "105205.0                                                  0        ...   \n",
       "\n",
       "          Baseline_Low_Ankle_Brachial_Index_Not Available  \\\n",
       "MaskID                                                      \n",
       "109052.0                                                1   \n",
       "100041.0                                                0   \n",
       "108663.0                                                1   \n",
       "106707.0                                                1   \n",
       "103485.0                                                1   \n",
       "...                                                   ...   \n",
       "109656.0                                                1   \n",
       "106228.0                                                1   \n",
       "103059.0                                                1   \n",
       "104957.0                                                0   \n",
       "105205.0                                                1   \n",
       "\n",
       "          Baseline_Low_Ankle_Brachial_Index_Yes  \\\n",
       "MaskID                                            \n",
       "109052.0                                      0   \n",
       "100041.0                                      0   \n",
       "108663.0                                      0   \n",
       "106707.0                                      0   \n",
       "103485.0                                      0   \n",
       "...                                         ...   \n",
       "109656.0                                      0   \n",
       "106228.0                                      0   \n",
       "103059.0                                      0   \n",
       "104957.0                                      0   \n",
       "105205.0                                      0   \n",
       "\n",
       "          Baseline_Not_On_Blood_Pressure_Medication_Yes  \\\n",
       "MaskID                                                    \n",
       "109052.0                                              0   \n",
       "100041.0                                              0   \n",
       "108663.0                                              0   \n",
       "106707.0                                              1   \n",
       "103485.0                                              0   \n",
       "...                                                 ...   \n",
       "109656.0                                              0   \n",
       "106228.0                                              0   \n",
       "103059.0                                              0   \n",
       "104957.0                                              0   \n",
       "105205.0                                              0   \n",
       "\n",
       "          Baseline_On_Blood_Pressure_Medication_Yes  \\\n",
       "MaskID                                                \n",
       "109052.0                                          1   \n",
       "100041.0                                          1   \n",
       "108663.0                                          1   \n",
       "106707.0                                          0   \n",
       "103485.0                                          1   \n",
       "...                                             ...   \n",
       "109656.0                                          1   \n",
       "106228.0                                          1   \n",
       "103059.0                                          1   \n",
       "104957.0                                          1   \n",
       "105205.0                                          1   \n",
       "\n",
       "          Baseline_Prior_revascularization_Yes  Baseline_Smoke_status_Yes  \\\n",
       "MaskID                                                                      \n",
       "109052.0                                     0                          0   \n",
       "100041.0                                     0                          0   \n",
       "108663.0                                     0                          0   \n",
       "106707.0                                     0                          0   \n",
       "103485.0                                     0                          0   \n",
       "...                                        ...                        ...   \n",
       "109656.0                                     0                          0   \n",
       "106228.0                                     0                          0   \n",
       "103059.0                                     0                          0   \n",
       "104957.0                                     0                          0   \n",
       "105205.0                                     0                          0   \n",
       "\n",
       "          Race_Hispanic  Race_Other  Race_White  Sex_Male  \n",
       "MaskID                                                     \n",
       "109052.0              0           0           1         0  \n",
       "100041.0              0           0           1         1  \n",
       "108663.0              0           1           0         1  \n",
       "106707.0              0           0           1         1  \n",
       "103485.0              0           0           1         0  \n",
       "...                 ...         ...         ...       ...  \n",
       "109656.0              0           0           0         1  \n",
       "106228.0              0           0           1         1  \n",
       "103059.0              0           0           1         1  \n",
       "104957.0              0           0           1         1  \n",
       "105205.0              0           0           0         1  \n",
       "\n",
       "[5278 rows x 22 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the categorical features\n",
    "cols_to_use = features_prep.columns.difference(features.columns)\n",
    "feat_cat = features_prep[cols_to_use]\n",
    "feat_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Baseline_BMI_&gt;32_Yes</th>\n",
       "      <th>Baseline_History_Albuminuria_Not Available</th>\n",
       "      <th>Baseline_History_Albuminuria_Yes</th>\n",
       "      <th>Baseline_History_Angina_Yes</th>\n",
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       "      <th>Baseline_History_Percutaneous_Coronary_Intervention_Yes</th>\n",
       "      <th>...</th>\n",
       "      <th>Baseline_Urine_albumin/creatinine_ratio</th>\n",
       "      <th>Baseline_LDL_Cholesterol</th>\n",
       "      <th>Baseline_HDL_Cholesterol</th>\n",
       "      <th>Baseline_Age</th>\n",
       "      <th>Baseline_Creatine_Phosphokinase</th>\n",
       "      <th>Baseline_Seated_Systolic_Blood_Pressure</th>\n",
       "      <th>Baseline_Fasting_Plasma_Glucose</th>\n",
       "      <th>Baseline_Very_low_density_lipoprotein</th>\n",
       "      <th>Baseline_Potassium</th>\n",
       "      <th>Baseline_Serum_creatinine</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaskID</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>109052.0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7</td>\n",
       "      <td>156.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>63.7</td>\n",
       "      <td>83.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.66</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100041.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>180.6</td>\n",
       "      <td>126.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>62.2</td>\n",
       "      <td>221.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.51</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108663.0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>58.5</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>157.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.96</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106707.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.7</td>\n",
       "      <td>123.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>610.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103485.0</th>\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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>111.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>55.6</td>\n",
       "      <td>74.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4.89</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109656.0</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>60.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>69.7</td>\n",
       "      <td>191.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4.30</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106228.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>84.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58.7</td>\n",
       "      <td>207.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>5.18</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103059.0</th>\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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>84.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>55.6</td>\n",
       "      <td>58.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104957.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7</td>\n",
       "      <td>79.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>61.6</td>\n",
       "      <td>74.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>4.36</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105205.0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>139.8</td>\n",
       "      <td>90.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>59.4</td>\n",
       "      <td>347.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>4.43</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5278 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Baseline_BMI_>32_Yes  Baseline_History_Albuminuria_Not Available  \\\n",
       "MaskID                                                                       \n",
       "109052.0                     1                                           1   \n",
       "100041.0                     0                                           0   \n",
       "108663.0                     0                                           1   \n",
       "106707.0                     0                                           0   \n",
       "103485.0                     1                                           0   \n",
       "...                        ...                                         ...   \n",
       "109656.0                     0                                           0   \n",
       "106228.0                     1                                           0   \n",
       "103059.0                     0                                           0   \n",
       "104957.0                     1                                           0   \n",
       "105205.0                     0                                           1   \n",
       "\n",
       "          Baseline_History_Albuminuria_Yes  Baseline_History_Angina_Yes  \\\n",
       "MaskID                                                                    \n",
       "109052.0                                 0                            0   \n",
       "100041.0                                 1                            0   \n",
       "108663.0                                 0                            0   \n",
       "106707.0                                 1                            0   \n",
       "103485.0                                 0                            0   \n",
       "...                                    ...                          ...   \n",
       "109656.0                                 0                            0   \n",
       "106228.0                                 1                            0   \n",
       "103059.0                                 0                            0   \n",
       "104957.0                                 0                            0   \n",
       "105205.0                                 0                            0   \n",
       "\n",
       "          Baseline_History_Cardiovascular_Disease_Yes  \\\n",
       "MaskID                                                  \n",
       "109052.0                                            0   \n",
       "100041.0                                            0   \n",
       "108663.0                                            0   \n",
       "106707.0                                            0   \n",
       "103485.0                                            0   \n",
       "...                                               ...   \n",
       "109656.0                                            0   \n",
       "106228.0                                            1   \n",
       "103059.0                                            0   \n",
       "104957.0                                            1   \n",
       "105205.0                                            0   \n",
       "\n",
       "          Baseline_History_Coronary_Artery_Bypass_Graft_Yes  \\\n",
       "MaskID                                                        \n",
       "109052.0                                                  0   \n",
       "100041.0                                                  0   \n",
       "108663.0                                                  0   \n",
       "106707.0                                                  0   \n",
       "103485.0                                                  0   \n",
       "...                                                     ...   \n",
       "109656.0                                                  0   \n",
       "106228.0                                                  0   \n",
       "103059.0                                                  0   \n",
       "104957.0                                                  0   \n",
       "105205.0                                                  0   \n",
       "\n",
       "          Baseline_History_Left_Ventricular_Hypertrophy_Not Available  \\\n",
       "MaskID                                                                  \n",
       "109052.0                                                  1             \n",
       "100041.0                                                  0             \n",
       "108663.0                                                  1             \n",
       "106707.0                                                  0             \n",
       "103485.0                                                  0             \n",
       "...                                                     ...             \n",
       "109656.0                                                  1             \n",
       "106228.0                                                  1             \n",
       "103059.0                                                  1             \n",
       "104957.0                                                  0             \n",
       "105205.0                                                  1             \n",
       "\n",
       "          Baseline_History_Left_Ventricular_Hypertrophy_Yes  \\\n",
       "MaskID                                                        \n",
       "109052.0                                                  0   \n",
       "100041.0                                                  0   \n",
       "108663.0                                                  0   \n",
       "106707.0                                                  0   \n",
       "103485.0                                                  0   \n",
       "...                                                     ...   \n",
       "109656.0                                                  0   \n",
       "106228.0                                                  0   \n",
       "103059.0                                                  0   \n",
       "104957.0                                                  0   \n",
       "105205.0                                                  0   \n",
       "\n",
       "          Baseline_History_Myocardial_Infarction_Yes  \\\n",
       "MaskID                                                 \n",
       "109052.0                                           0   \n",
       "100041.0                                           0   \n",
       "108663.0                                           0   \n",
       "106707.0                                           0   \n",
       "103485.0                                           0   \n",
       "...                                              ...   \n",
       "109656.0                                           0   \n",
       "106228.0                                           0   \n",
       "103059.0                                           0   \n",
       "104957.0                                           1   \n",
       "105205.0                                           0   \n",
       "\n",
       "          Baseline_History_Percutaneous_Coronary_Intervention_Yes  ...  \\\n",
       "MaskID                                                             ...   \n",
       "109052.0                                                  0        ...   \n",
       "100041.0                                                  0        ...   \n",
       "108663.0                                                  0        ...   \n",
       "106707.0                                                  0        ...   \n",
       "103485.0                                                  0        ...   \n",
       "...                                                     ...        ...   \n",
       "109656.0                                                  0        ...   \n",
       "106228.0                                                  1        ...   \n",
       "103059.0                                                  0        ...   \n",
       "104957.0                                                  1        ...   \n",
       "105205.0                                                  0        ...   \n",
       "\n",
       "          Baseline_Urine_albumin/creatinine_ratio  Baseline_LDL_Cholesterol  \\\n",
       "MaskID                                                                        \n",
       "109052.0                                      0.7                     156.0   \n",
       "100041.0                                    180.6                     126.0   \n",
       "108663.0                                      1.0                     126.0   \n",
       "106707.0                                      3.7                     123.0   \n",
       "103485.0                                      0.8                     111.0   \n",
       "...                                           ...                       ...   \n",
       "109656.0                                      0.4                      60.0   \n",
       "106228.0                                     84.0                      76.0   \n",
       "103059.0                                      0.3                      84.0   \n",
       "104957.0                                      0.7                      79.0   \n",
       "105205.0                                    139.8                      90.0   \n",
       "\n",
       "          Baseline_HDL_Cholesterol  Baseline_Age  \\\n",
       "MaskID                                             \n",
       "109052.0                      41.0          63.7   \n",
       "100041.0                      31.0          62.2   \n",
       "108663.0                      28.0          58.5   \n",
       "106707.0                      28.0          68.8   \n",
       "103485.0                      37.0          55.6   \n",
       "...                            ...           ...   \n",
       "109656.0                      57.0          69.7   \n",
       "106228.0                      35.0          58.7   \n",
       "103059.0                      42.0          55.6   \n",
       "104957.0                      35.0          61.6   \n",
       "105205.0                      50.0          59.4   \n",
       "\n",
       "          Baseline_Creatine_Phosphokinase  \\\n",
       "MaskID                                      \n",
       "109052.0                             83.0   \n",
       "100041.0                            221.0   \n",
       "108663.0                             56.0   \n",
       "106707.0                            610.0   \n",
       "103485.0                             74.0   \n",
       "...                                   ...   \n",
       "109656.0                            191.0   \n",
       "106228.0                            207.0   \n",
       "103059.0                             58.0   \n",
       "104957.0                             74.0   \n",
       "105205.0                            347.0   \n",
       "\n",
       "          Baseline_Seated_Systolic_Blood_Pressure  \\\n",
       "MaskID                                              \n",
       "109052.0                                    122.0   \n",
       "100041.0                                    124.0   \n",
       "108663.0                                      NaN   \n",
       "106707.0                                    171.0   \n",
       "103485.0                                    126.0   \n",
       "...                                           ...   \n",
       "109656.0                                    133.0   \n",
       "106228.0                                    135.0   \n",
       "103059.0                                    138.0   \n",
       "104957.0                                    137.0   \n",
       "105205.0                                    174.0   \n",
       "\n",
       "          Baseline_Fasting_Plasma_Glucose  \\\n",
       "MaskID                                      \n",
       "109052.0                            228.0   \n",
       "100041.0                            139.0   \n",
       "108663.0                            157.0   \n",
       "106707.0                            166.0   \n",
       "103485.0                            169.0   \n",
       "...                                   ...   \n",
       "109656.0                            106.0   \n",
       "106228.0                            200.0   \n",
       "103059.0                            126.0   \n",
       "104957.0                            133.0   \n",
       "105205.0                             93.0   \n",
       "\n",
       "          Baseline_Very_low_density_lipoprotein  Baseline_Potassium  \\\n",
       "MaskID                                                                \n",
       "109052.0                                   40.0                4.66   \n",
       "100041.0                                   36.0                4.51   \n",
       "108663.0                                   32.0                3.96   \n",
       "106707.0                                   45.0                4.35   \n",
       "103485.0                                   46.0                4.89   \n",
       "...                                         ...                 ...   \n",
       "109656.0                                   35.0                4.30   \n",
       "106228.0                                   48.0                5.18   \n",
       "103059.0                                   23.0                4.36   \n",
       "104957.0                                   21.0                4.36   \n",
       "105205.0                                   29.0                4.43   \n",
       "\n",
       "          Baseline_Serum_creatinine  \n",
       "MaskID                               \n",
       "109052.0                        0.8  \n",
       "100041.0                        1.2  \n",
       "108663.0                        0.9  \n",
       "106707.0                        0.8  \n",
       "103485.0                        0.6  \n",
       "...                             ...  \n",
       "109656.0                        1.3  \n",
       "106228.0                        1.1  \n",
       "103059.0                        0.7  \n",
       "104957.0                        0.9  \n",
       "105205.0                        1.2  \n",
       "\n",
       "[5278 rows x 40 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the features to train Decision Tree Classifier\n",
    "features_dt = pd.merge(feat_cat, feat_num, left_index=True, right_index=True, how='inner')\n",
    "features_dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training data points: 3694\n",
      "Number of test data points: 1584\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the SPRINT data into training, validation, and test data\n",
    "x_train, x_test, y_train, y_test = train_test_split(features_dt, outcomes, test_size=0.3, random_state=1, \n",
    "                                          stratify = outcomes.event + 2*(intervention=='Lipid Fibrate'))\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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the Zeta for Train data\n",
    "idxs = x_train.dropna(axis=0).index.values.astype(int)\n",
    "f_idxs = features_prep.index.values.astype(int)\n",
    "mask = np.in1d(f_idxs, idxs)\n",
    "\n",
    "# Get the Zeta for Test data\n",
    "idxs2 = x_test.dropna(axis=0).index.values.astype(int)\n",
    "f_idxs2 = features_prep.index.values.astype(int)\n",
    "mask2 = np.in1d(f_idxs2, idxs2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree # Import the decision tree namespace\n",
    "from sklearn.metrics import plot_confusion_matrix # Generate a confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=3)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and fit the model using entropy separation and a maximum depth\n",
    "# Drop NaNs because the feature columns not preprocessed i.e. \n",
    "# Numerical features have NaNs \n",
    "dt_model = tree.DecisionTreeClassifier(criterion='entropy', max_depth=3)\n",
    "dt_model.fit(x_train.dropna(axis=0), zeta[mask])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ASUS\\anaconda3\\envs\\Pandas\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1e798c11370>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the confusion matrix\n",
    "plot_confusion_matrix(dt_model, x_test.dropna(axis = 0), zeta[mask2], values_format='d', labels=[0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fit_time           0.015401\n",
       "score_time         0.005802\n",
       "test_accuracy      0.530520\n",
       "train_accuracy     0.550156\n",
       "test_precision     0.550745\n",
       "train_precision    0.785186\n",
       "test_recall        0.084663\n",
       "train_recall       0.105374\n",
       "test_f1            0.120845\n",
       "train_f1           0.154583\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the accuracy of test and train data\n",
    "from sklearn.model_selection import cross_validate\n",
    "scoring = ['accuracy','precision', 'recall', 'f1']\n",
    "scores = cross_validate(dt_model, x_train.dropna(axis = 0), zeta[mask], scoring=scoring, return_train_score=True, cv=5)\n",
    "results = pd.DataFrame(scores)\n",
    "results.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's check out the tree\n",
    "plt.figure(figsize=(20,10))\n",
    "tree.plot_tree(dt_model, feature_names=features_dt.columns, class_names=['Zeta 0', 'Zeta 1'], fontsize=10, filled=True)\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
}