wids-datathon-2021-xgb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/widsdatathon2021data/SampleSubmissionWiDS2021.csv\n",
      "/kaggle/input/widsdatathon2021data/SolutionTemplateWiDS2021.csv\n",
      "/kaggle/input/widsdatathon2021data/DataDictionaryWiDS2021.csv\n",
      "/kaggle/input/widsdatathon2021data/UnlabeledWiDS2021.csv\n",
      "/kaggle/input/widsdatathon2021data/TrainingWiDS2021.csv\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "        \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>encounter_id</th>\n",
       "      <th>hospital_id</th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>elective_surgery</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>hospital_admit_source</th>\n",
       "      <th>...</th>\n",
       "      <th>h1_pao2fio2ratio_max</th>\n",
       "      <th>h1_pao2fio2ratio_min</th>\n",
       "      <th>aids</th>\n",
       "      <th>cirrhosis</th>\n",
       "      <th>hepatic_failure</th>\n",
       "      <th>immunosuppression</th>\n",
       "      <th>leukemia</th>\n",
       "      <th>lymphoma</th>\n",
       "      <th>solid_tumor_with_metastasis</th>\n",
       "      <th>diabetes_mellitus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>22.732803</td>\n",
       "      <td>0</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>M</td>\n",
       "      <td>180.3</td>\n",
       "      <td>Floor</td>\n",
       "      <td>...</td>\n",
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       "      <td>246060</td>\n",
       "      <td>81</td>\n",
       "      <td>77.0</td>\n",
       "      <td>27.421875</td>\n",
       "      <td>0</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>F</td>\n",
       "      <td>160.0</td>\n",
       "      <td>Floor</td>\n",
       "      <td>...</td>\n",
       "      <td>51.0</td>\n",
       "      <td>51.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>276985</td>\n",
       "      <td>118</td>\n",
       "      <td>25.0</td>\n",
       "      <td>31.952749</td>\n",
       "      <td>0</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>F</td>\n",
       "      <td>172.7</td>\n",
       "      <td>Emergency Department</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>262220</td>\n",
       "      <td>118</td>\n",
       "      <td>81.0</td>\n",
       "      <td>22.635548</td>\n",
       "      <td>1</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>F</td>\n",
       "      <td>165.1</td>\n",
       "      <td>Operating Room</td>\n",
       "      <td>...</td>\n",
       "      <td>337.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>201746</td>\n",
       "      <td>33</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>M</td>\n",
       "      <td>188.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 181 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  encounter_id  hospital_id   age        bmi  elective_surgery  \\\n",
       "0           1        214826          118  68.0  22.732803                 0   \n",
       "1           2        246060           81  77.0  27.421875                 0   \n",
       "2           3        276985          118  25.0  31.952749                 0   \n",
       "3           4        262220          118  81.0  22.635548                 1   \n",
       "4           5        201746           33  19.0        NaN                 0   \n",
       "\n",
       "   ethnicity gender  height hospital_admit_source  ... h1_pao2fio2ratio_max  \\\n",
       "0  Caucasian      M   180.3                 Floor  ...                  NaN   \n",
       "1  Caucasian      F   160.0                 Floor  ...                 51.0   \n",
       "2  Caucasian      F   172.7  Emergency Department  ...                  NaN   \n",
       "3  Caucasian      F   165.1        Operating Room  ...                337.0   \n",
       "4  Caucasian      M   188.0                   NaN  ...                  NaN   \n",
       "\n",
       "   h1_pao2fio2ratio_min aids cirrhosis  hepatic_failure  immunosuppression  \\\n",
       "0                   NaN    0         0                0                  0   \n",
       "1                  51.0    0         0                0                  0   \n",
       "2                   NaN    0         0                0                  0   \n",
       "3                 337.0    0         0                0                  0   \n",
       "4                   NaN    0         0                0                  0   \n",
       "\n",
       "   leukemia  lymphoma  solid_tumor_with_metastasis  diabetes_mellitus  \n",
       "0         0         0                            0                  1  \n",
       "1         0         0                            0                  1  \n",
       "2         0         0                            0                  0  \n",
       "3         0         0                            0                  0  \n",
       "4         0         0                            0                  0  \n",
       "\n",
       "[5 rows x 181 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data\n",
    "train_df = pd.read_csv(\"../input/widsdatathon2021data/TrainingWiDS2021.csv\")\n",
    "test_df = pd.read_csv(\"../input/widsdatathon2021data/UnlabeledWiDS2021.csv\")\n",
    "data_dictionary_df = pd.read_csv(\"../input/widsdatathon2021data/DataDictionaryWiDS2021.csv\")\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop unnamed first column, encounter_id and hospital_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>bmi</th>\n",
       "      <th>elective_surgery</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>hospital_admit_source</th>\n",
       "      <th>icu_admit_source</th>\n",
       "      <th>icu_id</th>\n",
       "      <th>icu_stay_type</th>\n",
       "      <th>...</th>\n",
       "      <th>h1_pao2fio2ratio_max</th>\n",
       "      <th>h1_pao2fio2ratio_min</th>\n",
       "      <th>aids</th>\n",
       "      <th>cirrhosis</th>\n",
       "      <th>hepatic_failure</th>\n",
       "      <th>immunosuppression</th>\n",
       "      <th>leukemia</th>\n",
       "      <th>lymphoma</th>\n",
       "      <th>solid_tumor_with_metastasis</th>\n",
       "      <th>diabetes_mellitus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>68.0</td>\n",
       "      <td>22.732803</td>\n",
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       "      <td>M</td>\n",
       "      <td>180.3</td>\n",
       "      <td>Floor</td>\n",
       "      <td>Floor</td>\n",
       "      <td>92</td>\n",
       "      <td>admit</td>\n",
       "      <td>...</td>\n",
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       "      <th>1</th>\n",
       "      <td>77.0</td>\n",
       "      <td>27.421875</td>\n",
       "      <td>0</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>F</td>\n",
       "      <td>160.0</td>\n",
       "      <td>Floor</td>\n",
       "      <td>Floor</td>\n",
       "      <td>90</td>\n",
       "      <td>admit</td>\n",
       "      <td>...</td>\n",
       "      <td>51.0</td>\n",
       "      <td>51.0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.0</td>\n",
       "      <td>31.952749</td>\n",
       "      <td>0</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>F</td>\n",
       "      <td>172.7</td>\n",
       "      <td>Emergency Department</td>\n",
       "      <td>Accident &amp; Emergency</td>\n",
       "      <td>93</td>\n",
       "      <td>admit</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>81.0</td>\n",
       "      <td>22.635548</td>\n",
       "      <td>1</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>F</td>\n",
       "      <td>165.1</td>\n",
       "      <td>Operating Room</td>\n",
       "      <td>Operating Room / Recovery</td>\n",
       "      <td>92</td>\n",
       "      <td>admit</td>\n",
       "      <td>...</td>\n",
       "      <td>337.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>M</td>\n",
       "      <td>188.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Accident &amp; Emergency</td>\n",
       "      <td>91</td>\n",
       "      <td>admit</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 178 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age        bmi  elective_surgery  ethnicity gender  height  \\\n",
       "0  68.0  22.732803                 0  Caucasian      M   180.3   \n",
       "1  77.0  27.421875                 0  Caucasian      F   160.0   \n",
       "2  25.0  31.952749                 0  Caucasian      F   172.7   \n",
       "3  81.0  22.635548                 1  Caucasian      F   165.1   \n",
       "4  19.0        NaN                 0  Caucasian      M   188.0   \n",
       "\n",
       "  hospital_admit_source           icu_admit_source  icu_id icu_stay_type  ...  \\\n",
       "0                 Floor                      Floor      92         admit  ...   \n",
       "1                 Floor                      Floor      90         admit  ...   \n",
       "2  Emergency Department       Accident & Emergency      93         admit  ...   \n",
       "3        Operating Room  Operating Room / Recovery      92         admit  ...   \n",
       "4                   NaN       Accident & Emergency      91         admit  ...   \n",
       "\n",
       "  h1_pao2fio2ratio_max  h1_pao2fio2ratio_min  aids  cirrhosis  \\\n",
       "0                  NaN                   NaN     0          0   \n",
       "1                 51.0                  51.0     0          0   \n",
       "2                  NaN                   NaN     0          0   \n",
       "3                337.0                 337.0     0          0   \n",
       "4                  NaN                   NaN     0          0   \n",
       "\n",
       "   hepatic_failure  immunosuppression  leukemia  lymphoma  \\\n",
       "0                0                  0         0         0   \n",
       "1                0                  0         0         0   \n",
       "2                0                  0         0         0   \n",
       "3                0                  0         0         0   \n",
       "4                0                  0         0         0   \n",
       "\n",
       "   solid_tumor_with_metastasis  diabetes_mellitus  \n",
       "0                            0                  1  \n",
       "1                            0                  1  \n",
       "2                            0                  0  \n",
       "3                            0                  0  \n",
       "4                            0                  0  \n",
       "\n",
       "[5 rows x 178 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.drop(columns=[\"Unnamed: 0\", \"encounter_id\", \"hospital_id\"], axis=1, inplace=True)\n",
    "test_submission = pd.DataFrame(test_df[\"encounter_id\"])\n",
    "test_df.drop(columns=[\"Unnamed: 0\", \"encounter_id\", \"hospital_id\"], axis=1, inplace=True)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training dataset shape: (130157, 178)\n",
      "Training dataset shape: (10234, 177)\n"
     ]
    }
   ],
   "source": [
    "print(f'Training dataset shape: {train_df.shape}')\n",
    "print(f'Training dataset shape: {test_df.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop duplicate rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 rows were dropped.\n"
     ]
    }
   ],
   "source": [
    "l0 = train_df.shape[0]\n",
    "train_df.drop_duplicates(inplace=True)\n",
    "l1 = train_df.shape[0]\n",
    "print(f'{l0-l1} rows were dropped.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check proportion of missing values in each column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Total    Percent\n",
      "h1_bilirubin_min  119853  92.089067\n",
      "h1_bilirubin_max  119853  92.089067\n",
      "h1_albumin_max    118997  91.431359\n",
      "h1_albumin_min    118997  91.431359\n",
      "h1_lactate_max    118459  91.017987\n",
      "...                  ...        ...\n",
      "d1_sysbp_min         263   0.202076\n",
      "d1_heartrate_max     254   0.195161\n",
      "d1_heartrate_min     254   0.195161\n",
      "icu_admit_source     232   0.178257\n",
      "gender                58   0.044564\n",
      "\n",
      "[160 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "def count_missing_values(dataframe):\n",
    "    \n",
    "    total = dataframe.isnull().sum()\n",
    "    percent = (dataframe.isnull().sum())*100/(len(dataframe))\n",
    "    missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
    "    missing_data = missing_data[missing_data['Total']>0]\n",
    "    missing_data.sort_values('Total', ascending=False, inplace=True)\n",
    "    print(missing_data)\n",
    "    \n",
    "    return \n",
    "\n",
    "count_missing_values(train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "160 out of 177 features have missing values. The highest proportion of missing values in a column is >92%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['diabetes_mellitus'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target variable does not have any missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop columns having more than a certain percentage of missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(130149, 146)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thr = 0.8\n",
    "to_drop = train_df.columns[train_df.isnull().sum()>thr*len(train_df)]\n",
    "train_df.drop(columns=to_drop, axis=1, inplace=True)\n",
    "test_df.drop(columns=to_drop, axis=1, inplace=True)\n",
    "train_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute pair-wise correlation between numeric columns and drop one if this is greater than a threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "f = plt.figure(figsize=(10, 10))\n",
    "plt.matshow(train_df.corr(), fignum=f.number)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'h1_inr_min', 'h1_inr_max', 'paco2_for_ph_apache'}\n"
     ]
    }
   ],
   "source": [
    "def correlation(dataframe, threshold):\n",
    "    to_drop = set() # Set of all the names of deleted columns\n",
    "    corr_matrix = dataframe.corr()\n",
    "    for i in range(len(corr_matrix.columns)):\n",
    "        for j in range(i):\n",
    "            if (corr_matrix.iloc[i, j] >= threshold) and (corr_matrix.columns[j] not in to_drop):\n",
    "                column = corr_matrix.columns[i] # getting the name of column\n",
    "                to_drop.add(column)\n",
    "\n",
    "    return to_drop\n",
    "\n",
    "to_drop = correlation(train_df, 1)\n",
    "print(to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(130149, 143)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.drop(columns=to_drop, axis=1, inplace=True)\n",
    "test_df.drop(columns=to_drop, axis=1, inplace=True)\n",
    "train_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check readmission status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['readmission_status'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This column has only one unique value. So we can drop it from the train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop(columns=['readmission_status'], axis=1, inplace=True)\n",
    "test_df.drop(columns=['readmission_status'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separate categorical and numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ethnicity', 'gender', 'hospital_admit_source', 'icu_admit_source', 'icu_stay_type', 'icu_type']\n"
     ]
    }
   ],
   "source": [
    "cat_columns = train_df.dtypes[train_df.dtypes == \"object\"].index.tolist()\n",
    "numerical_columns = train_df.dtypes[train_df.dtypes != \"object\"].index.tolist()\n",
    "numerical_columns.remove('diabetes_mellitus')\n",
    "print(cat_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impute missing categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ethnicity                 1579\n",
       "gender                      58\n",
       "hospital_admit_source    33190\n",
       "icu_admit_source           232\n",
       "icu_stay_type                0\n",
       "icu_type                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[cat_columns].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['ethnicity'] = train_df['ethnicity'].fillna('Unknown')\n",
    "train_df['gender'] = train_df['gender'].fillna('Unknown')\n",
    "train_df['icu_admit_source'] = train_df['icu_admit_source'].fillna('Unknown')\n",
    "train_df['hospital_admit_source'] = train_df['hospital_admit_source'].fillna('Unknown')\n",
    "\n",
    "test_df['ethnicity'] = test_df['ethnicity'].fillna('Unknown')\n",
    "test_df['gender'] = test_df['gender'].fillna('Unknown')\n",
    "test_df['icu_admit_source'] = test_df['icu_admit_source'].fillna('Unknown')\n",
    "test_df['hospital_admit_source'] = test_df['hospital_admit_source'].fillna('Unknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_df[cat_columns] = train_df[cat_columns].fillna(train_df[cat_columns].mode().loc[0])\n",
    "#test_df[cat_columns] = test_df[cat_columns].fillna(train_df[cat_columns].mode().loc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impute missing numerical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impute height and weight according to gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_female_height = train_df[train_df.gender == 'F'].height.mean(skipna = True)\n",
    "avg_male_height = train_df[train_df.gender == 'M'].height.mean(skipna = True)\n",
    "avg_female_weight = train_df[train_df.gender == 'F'].weight.mean(skipna = True)\n",
    "avg_male_weight = train_df[train_df.gender == 'M'].weight.mean(skipna = True)\n",
    "\n",
    "avg_pop_height = 0.5*(avg_female_height + avg_male_height)\n",
    "avg_pop_weight = 0.5*(avg_female_weight + avg_male_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df[\"height\"] = np.where((train_df.height.isna() & (train_df.gender == 'F')), \n",
    "                              avg_female_height, train_df[\"height\"])\n",
    "train_df[\"height\"] = np.where((train_df.height.isna() & (train_df.gender == 'M')), \n",
    "                              avg_male_height, train_df[\"height\"])\n",
    "train_df[\"height\"] = np.where((train_df.height.isna() & (train_df.gender.isna())), \n",
    "                              avg_pop_height, train_df[\"height\"])\n",
    "\n",
    "train_df[\"weight\"] = np.where((train_df.weight.isna() & (train_df.gender == 'F')), \n",
    "                              avg_female_weight, train_df[\"weight\"])\n",
    "train_df[\"weight\"] = np.where((train_df.weight.isna() & (train_df.gender == 'M')), \n",
    "                              avg_male_weight, train_df[\"weight\"])\n",
    "train_df[\"weight\"] = np.where((train_df.weight.isna() & (train_df.gender.isna())), \n",
    "                              avg_pop_weight, train_df[\"weight\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df[\"height\"] = np.where((test_df.height.isna() & (test_df.gender == 'F')), \n",
    "                              avg_female_height, test_df[\"height\"])\n",
    "test_df[\"height\"] = np.where((test_df.height.isna() & (test_df.gender == 'M')), \n",
    "                              avg_male_height, test_df[\"height\"])\n",
    "test_df[\"height\"] = np.where((test_df.height.isna() & (test_df.gender.isna())), \n",
    "                              avg_pop_height, test_df[\"height\"])\n",
    "\n",
    "test_df[\"weight\"] = np.where((test_df.weight.isna() & (test_df.gender == 'F')), \n",
    "                              avg_female_weight, test_df[\"weight\"])\n",
    "test_df[\"weight\"] = np.where((test_df.weight.isna() & (test_df.gender == 'M')), \n",
    "                              avg_male_weight, test_df[\"weight\"])\n",
    "test_df[\"weight\"] = np.where((test_df.weight.isna() & (test_df.gender.isna())), \n",
    "                              avg_pop_weight, test_df[\"weight\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impute missing blood oxygen levels with standard values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['d1_spo2_max'] = train_df['d1_spo2_max'].fillna(95)\n",
    "train_df['d1_spo2_min'] = train_df['d1_spo2_min'].fillna(90)\n",
    "\n",
    "test_df['d1_spo2_max'] = test_df['d1_spo2_max'].fillna(95)\n",
    "test_df['d1_spo2_min'] = test_df['d1_spo2_min'].fillna(90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impute remaining missing numerical values with medians of the respective columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df[numerical_columns] = train_df[numerical_columns].fillna(train_df[numerical_columns].median())\n",
    "test_df[numerical_columns] = test_df[numerical_columns].fillna(train_df[numerical_columns].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate missing BMI from height and weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['bmi'] = (train_df['weight']*10000)/(train_df['height']**2)\n",
    "test_df['bmi'] = (test_df['weight']*10000)/(test_df['height']**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### New features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_df['agi'] = train_df['weight']/train_df['age']\n",
    "#test_df['agi'] = test_df['weight']/test_df['age']\n",
    "\n",
    "#train_df['abmi'] = train_df['age']/train_df['bmi']#\n",
    "#test_df['abmi'] = test_df['age']/test_df['bmi']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separate features and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y = train_df[\"diabetes_mellitus\"]\n",
    "train_df.drop(\"diabetes_mellitus\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-hot encoding of categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in cat_columns:\n",
    "    # Training data\n",
    "    dummies = pd.get_dummies(train_df[column], prefix=column, drop_first=True)\n",
    "    train_df = pd.concat([train_df, dummies],axis=1)\n",
    "    train_df.drop(column, axis=1, inplace=True)\n",
    "    # Test data\n",
    "    dummies = pd.get_dummies(test_df[column], prefix=column, drop_first=True)\n",
    "    test_df = pd.concat([test_df, dummies],axis=1)\n",
    "    test_df.drop(column, axis=1, inplace=True)\n",
    "    \n",
    "# Get missing columns in the training test\n",
    "missing_cols = set(train_df.columns) - set(test_df.columns)\n",
    "# Add a missing column in test set with default value equal to 0\n",
    "for c in missing_cols:\n",
    "    test_df[c] = 0\n",
    "    \n",
    "# Ensure the order of columns in the test set is in the same order than in train set\n",
    "test_df = test_df[train_df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(130149, 172)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SMOTE on training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['elective_surgery', 'icu_id', 'apache_post_operative', 'arf_apache', 'intubated_apache', 'ventilated_apache', 'aids', 'cirrhosis', 'hepatic_failure', 'immunosuppression', 'leukemia', 'lymphoma', 'solid_tumor_with_metastasis']\n"
     ]
    }
   ],
   "source": [
    "cat_cols = []\n",
    "cat_cols_index = []\n",
    "for i, col in enumerate(train_df.columns):\n",
    "    if train_df[col].dtypes == np.object or train_df[col].dtypes == np.int64:\n",
    "        cat_cols.append(col)\n",
    "        cat_cols_index.append(i)\n",
    "        \n",
    "print(cat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from imblearn.over_sampling import SMOTENC\n",
    "\n",
    "#smote = SMOTENC(categorical_features = cat_cols_index, random_state = 7, sampling_strategy = 0.6)\n",
    "\n",
    "## Transform the dataset\n",
    "#train_df, train_y = smote.fit_resample(train_df, train_y)\n",
    "#train_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost Hyper-parameter tuning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from xgboost import XGBClassifier \n",
    "#from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#xgboost = XGBClassifier(tree_method='gpu_hist', subsample=0.5, colsample_bytree=0.5, eval_metric='auc')\n",
    "#params = {\n",
    "#         'learning_rate': [0.1, 0.05],\n",
    "#         'n_estimators': [100, 500],\n",
    "#         'max_depth': [3, 6],\n",
    "#          }\n",
    "#clf = GridSearchCV(xgboost, params, scoring='roc_auc', cv=5, verbose=0)\n",
    "#best_xgboost = clf.fit(train_df, train_y)\n",
    "#print('Best learning rate:', best_xgboost.best_estimator_.get_params()['learning_rate'])\n",
    "#print('Best number of estimators:', best_xgboost.best_estimator_.get_params()['n_estimators'])\n",
    "#print('Best maximum depth:', best_xgboost.best_estimator_.get_params()['max_depth'])\n",
    "#print('Best subsample:', best_xgboost.best_estimator_.get_params()['subsample'])\n",
    "#print('Best colsample by tree:', best_xgboost.best_estimator_.get_params()['colsample_bytree'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "K_FOLDS = 5\n",
    "\n",
    "def train_model_kfold_cv(X, y, X_test):\n",
    "    \n",
    "    skf = StratifiedKFold(n_splits = K_FOLDS, random_state = 1, shuffle = True)\n",
    "    index = 0\n",
    "    model_predict = np.zeros((len(X_test), 2))\n",
    "    \n",
    "    for train, val in skf.split(X, y):\n",
    "        \n",
    "        X_train_cv, X_val_cv = X.values[train], X.values[val]\n",
    "        y_train_cv, y_val_cv = y.values[train], y.values[val]\n",
    "        \n",
    "        xgboost = XGBClassifier(learning_rate=0.1, n_estimators=500, tree_method='gpu_hist', eval_metric='auc')\n",
    "        \n",
    "        print('--------------------------------------------------')\n",
    "        print(f'Training on fold {index+1}')\n",
    "        print('--------------------------------------------------')\n",
    "        \n",
    "        xgboost.fit(X_train_cv, y_train_cv)\n",
    "        y_preds = xgboost.predict(X_val_cv)\n",
    "        score = roc_auc_score(y_preds, y_val_cv)\n",
    "        \n",
    "        print(f'AUROC score on fold {index+1}: {score}')\n",
    "        \n",
    "        prediction = xgboost.predict_proba(X_test.values[:])\n",
    "        model_predict+= prediction/K_FOLDS\n",
    "        index += 1\n",
    "        \n",
    "    return model_predict    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "Training on fold 1\n",
      "--------------------------------------------------\n",
      "AUROC score on fold 1: 0.75387987696682\n",
      "--------------------------------------------------\n",
      "Training on fold 2\n",
      "--------------------------------------------------\n",
      "AUROC score on fold 2: 0.7534745738901032\n",
      "--------------------------------------------------\n",
      "Training on fold 3\n",
      "--------------------------------------------------\n",
      "AUROC score on fold 3: 0.7521688475813686\n",
      "--------------------------------------------------\n",
      "Training on fold 4\n",
      "--------------------------------------------------\n",
      "AUROC score on fold 4: 0.7571380243804193\n",
      "--------------------------------------------------\n",
      "Training on fold 5\n",
      "--------------------------------------------------\n",
      "AUROC score on fold 5: 0.7643198579748354\n"
     ]
    }
   ],
   "source": [
    "predictions = train_model_kfold_cv(train_df, train_y, test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encounter_id</th>\n",
       "      <th>diabetes_mellitus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>144740</td>\n",
       "      <td>0.058391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141990</td>\n",
       "      <td>0.084536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>142038</td>\n",
       "      <td>0.095640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>138628</td>\n",
       "      <td>0.037514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141682</td>\n",
       "      <td>0.441355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10229</th>\n",
       "      <td>143750</td>\n",
       "      <td>0.073678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10230</th>\n",
       "      <td>143813</td>\n",
       "      <td>0.067194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10231</th>\n",
       "      <td>137126</td>\n",
       "      <td>0.034888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10232</th>\n",
       "      <td>135652</td>\n",
       "      <td>0.002488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10233</th>\n",
       "      <td>136852</td>\n",
       "      <td>0.002216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10234 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       encounter_id  diabetes_mellitus\n",
       "0            144740           0.058391\n",
       "1            141990           0.084536\n",
       "2            142038           0.095640\n",
       "3            138628           0.037514\n",
       "4            141682           0.441355\n",
       "...             ...                ...\n",
       "10229        143750           0.073678\n",
       "10230        143813           0.067194\n",
       "10231        137126           0.034888\n",
       "10232        135652           0.002488\n",
       "10233        136852           0.002216\n",
       "\n",
       "[10234 rows x 2 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_submission[\"diabetes_mellitus\"] = predictions[:, 1]\n",
    "test_submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_submission.to_csv(\"xgb_v39.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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