main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SPH6004 Assignment1 Huang Zefan A0149880R E0014784"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## headers and imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#general imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "#data processing imports\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "#feature selection imports\n",
    "from sklearn.model_selection import train_test_split\n",
    "from genetic_selection import GeneticSelectionCV\n",
    "\n",
    "#learning imports\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "#metrics\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, RocCurveDisplay\n",
    "\n",
    "#plotting figure imports\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data preprocessing\n",
    "\n",
    "we have to deal with data that is imperfect and unsuitable for use in training. the following steps are taken\n",
    "\n",
    "1. dealing with data with subpar quality\n",
    "  * filtering out data that does not lie within sensible ranges (bad entry)\n",
    "  * removing patient entries with large percentage of fields empty (poor entry)\n",
    "  * removing fields with large percentage of missing patient entries (poor field)\n",
    "2. transforming data for training\n",
    "  * encoding categories into numerical values as model inputs (binary, multiple categories)\n",
    "  * imputing values to replace NaNs\n",
    "  * normalizing data to deal with different ranges\n",
    "  \n",
    "\n",
    "### dealing with subpar data quality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50920, 162)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "aki\n",
       "0    16860\n",
       "2    16288\n",
       "1     9838\n",
       "3     7934\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"./data.csv\")\n",
    "print(data.shape)\n",
    "\n",
    "data.aki.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                  min              max\n",
      "id                                           30000153         39999810\n",
      "aki                                                 0                3\n",
      "gender                                              F                M\n",
      "admission_age                               18.002527       102.865603\n",
      "race                    AMERICAN INDIAN/ALASKA NATIVE  WHITE - RUSSIAN\n",
      "heart_rate_min                                    1.0            167.0\n",
      "heart_rate_max                                   36.0            280.0\n",
      "heart_rate_mean                                  28.5       174.740741\n",
      "sbp_min                                         0.108            195.0\n",
      "sbp_max                                          34.0            357.0\n",
      "sbp_mean                                         32.0       215.857143\n",
      "dbp_min                                           1.0            113.0\n",
      "dbp_max                                          17.0            291.0\n",
      "dbp_mean                                         17.0            136.0\n",
      "mbp_min                                           1.0            133.0\n",
      "mbp_max                                          25.0            299.0\n",
      "mbp_mean                                    24.071429       159.470588\n",
      "resp_rate_min                                     1.0             48.0\n",
      "resp_rate_max                                     6.0             69.0\n",
      "resp_rate_mean                                    6.0             48.0\n",
      "temperature_min                                  15.0             40.0\n",
      "temperature_max                                 26.67             42.3\n",
      "temperature_mean                                26.67           40.055\n",
      "spo2_min                                          1.0            100.0\n",
      "spo2_max                                         42.0            100.0\n",
      "spo2_mean                                        35.0            100.0\n",
      "glucose_min                                      0.12         999999.0\n",
      "glucose_max                                      34.0         999999.0\n",
      "glucose_mean                                     34.0         999999.0\n",
      "lactate_min                                       0.0             25.4\n",
      "lactate_max                                       0.0             89.0\n",
      "ph_min                                           6.41             7.64\n",
      "ph_max                                           6.49             7.89\n",
      "so2_min                                          1.68            100.0\n",
      "so2_max                                          1.68            100.0\n",
      "po2_min                                           0.0            610.0\n",
      "po2_max                                           6.0            795.0\n",
      "pco2_min                                          0.0            214.0\n",
      "pco2_max                                          8.0            246.0\n",
      "aado2_min                                        17.0            675.0\n",
      "aado2_max                                        50.0            675.0\n",
      "aado2_calc_min                                 -430.6           653.75\n",
      "aado2_calc_max                                -281.75           660.25\n",
      "pao2fio2ratio_min                                 0.0      1368.571429\n",
      "pao2fio2ratio_max                                19.0      2242.857143\n",
      "baseexcess_min                                 -414.0             32.0\n",
      "baseexcess_max                                 -414.0             50.0\n",
      "bicarbonate_min                                   3.0             97.0\n",
      "bicarbonate_max                                   3.0             98.0\n",
      "totalco2_min                                      1.0             65.0\n",
      "totalco2_max                                      1.0             80.0\n",
      "hematocrit_min                                    0.0             59.0\n",
      "hematocrit_max                                    9.0             63.0\n",
      "hemoglobin_min                                    0.0             19.8\n",
      "hemoglobin_max                                    3.0             78.0\n",
      "carboxyhemoglobin_min                             0.0             11.0\n",
      "carboxyhemoglobin_max                             0.0             11.0\n",
      "methemoglobin_min                                 0.0              8.9\n",
      "methemoglobin_max                                 0.0             91.0\n",
      "temperature_min.1                                10.0             40.8\n",
      "temperature_max.1                                10.0             48.2\n",
      "chloride_min                                      3.4            149.0\n",
      "chloride_max                                      3.4            405.0\n",
      "calcium_min                                      0.21              2.5\n",
      "calcium_max                                      0.34             39.0\n",
      "glucose_min.1                                    0.12            866.0\n",
      "glucose_max.1                                     3.8           1689.0\n",
      "potassium_min                                     0.6             10.4\n",
      "potassium_max                                     1.0            538.0\n",
      "sodium_min                                       1.36            186.0\n",
      "sodium_max                                        5.1           1036.0\n",
      "hematocrit_min.1                                  4.3             65.6\n",
      "hematocrit_max.1                                 12.5             71.0\n",
      "hemoglobin_min.1                                  1.3             19.4\n",
      "hemoglobin_max.1                                  3.5             23.9\n",
      "platelets_min                                     5.0           2001.0\n",
      "platelets_max                                     5.0           2947.0\n",
      "wbc_min                                           0.1            385.9\n",
      "wbc_max                                           0.1            572.5\n",
      "albumin_min                                       0.3              6.0\n",
      "albumin_max                                       0.6              6.0\n",
      "globulin_min                                      0.6              7.3\n",
      "globulin_max                                      0.6              7.3\n",
      "total_protein_min                                 2.0             16.4\n",
      "total_protein_max                                 2.0             16.4\n",
      "aniongap_min                                     -9.0             49.0\n",
      "aniongap_max                                      3.0             89.0\n",
      "bicarbonate_min.1                                 2.0             49.0\n",
      "bicarbonate_max.1                                 5.0             50.0\n",
      "bun_min                                           1.0            224.0\n",
      "bun_max                                           1.0            260.0\n",
      "calcium_min.1                                     0.8             20.7\n",
      "calcium_max.1                                     2.6             79.2\n",
      "chloride_min.1                                   46.0            150.0\n",
      "chloride_max.1                                   67.0            155.0\n",
      "glucose_min.2                                     5.0            692.0\n",
      "glucose_max.2                                     7.0           2440.0\n",
      "sodium_min.1                                     74.0            175.0\n",
      "sodium_max.1                                     97.0            185.0\n",
      "potassium_min.1                                   0.8              9.6\n",
      "potassium_max.1                                   1.6             26.5\n",
      "abs_basophils_min                                 0.0             4.92\n",
      "abs_basophils_max                                 0.0             5.28\n",
      "abs_eosinophils_min                               0.0           24.118\n",
      "abs_eosinophils_max                               0.0           24.118\n",
      "abs_lymphocytes_min                               0.0           424.53\n",
      "abs_lymphocytes_max                               0.0          519.849\n",
      "abs_monocytes_min                                 0.0            64.45\n",
      "abs_monocytes_max                                 0.0            86.72\n",
      "abs_neutrophils_min                               0.0          165.722\n",
      "abs_neutrophils_max                               0.0          165.722\n",
      "atyps_min                                         0.0             41.0\n",
      "atyps_max                                         0.0             56.0\n",
      "bands_min                                         0.0             63.0\n",
      "bands_max                                         0.0             66.0\n",
      "imm_granulocytes_min                              0.0              5.0\n",
      "imm_granulocytes_max                              0.0              5.0\n",
      "metas_min                                         0.0             24.0\n",
      "metas_max                                         0.0             33.0\n",
      "nrbc_min                                          0.0            448.0\n",
      "nrbc_max                                          0.0            448.0\n",
      "d_dimer_min                                     148.0          20067.0\n",
      "d_dimer_max                                     148.0          20067.0\n",
      "fibrinogen_min                                   26.0           1466.0\n",
      "fibrinogen_max                                   26.0           1466.0\n",
      "thrombin_min                                     12.3            150.0\n",
      "thrombin_max                                     12.3            150.0\n",
      "inr_min                                           0.6             26.7\n",
      "inr_max                                           0.6             27.4\n",
      "pt_min                                            8.0            150.0\n",
      "pt_max                                            8.0            150.0\n",
      "ptt_min                                           2.7            150.0\n",
      "ptt_max                                          16.6            150.0\n",
      "alt_min                                           1.0          13330.0\n",
      "alt_max                                           1.0          61854.0\n",
      "alp_min                                           5.0           2703.0\n",
      "alp_max                                          11.0           2875.0\n",
      "ast_min                                           2.0          26790.0\n",
      "ast_max                                           3.0          42606.0\n",
      "amylase_min                                       2.0           2715.0\n",
      "amylase_max                                       2.0           4938.0\n",
      "bilirubin_total_min                               0.1             67.8\n",
      "bilirubin_total_max                               0.1             78.0\n",
      "bilirubin_direct_min                              0.1             63.7\n",
      "bilirubin_direct_max                              0.1             63.7\n",
      "bilirubin_indirect_min                            0.1             22.8\n",
      "bilirubin_indirect_max                            0.1             22.8\n",
      "ck_cpk_min                                        5.0         472680.0\n",
      "ck_cpk_max                                        5.0         591950.0\n",
      "ck_mb_min                                         1.0            673.0\n",
      "ck_mb_max                                         1.0            673.0\n",
      "ggt_min                                           6.0           2720.0\n",
      "ggt_max                                           6.0           2720.0\n",
      "ld_ldh_min                                       31.0          22890.0\n",
      "ld_ldh_max                                       31.0          63325.0\n",
      "gcs_min                                           3.0             15.0\n",
      "gcs_motor                                         1.0              6.0\n",
      "gcs_verbal                                        0.0              5.0\n",
      "gcs_eyes                                          1.0              4.0\n",
      "gcs_unable                                        0.0              1.0\n",
      "height                                          122.0            226.0\n",
      "weight_admit                                      1.0           5864.0\n"
     ]
    }
   ],
   "source": [
    "#get data ranges\n",
    "col_max = data.max()\n",
    "col_min = data.min()\n",
    "col_range = pd.concat([col_min, col_max], axis = 1)\n",
    "col_range.columns = [\"min\", \"max\"]\n",
    "print(col_range.to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "anomalous inputs such as: glucodse_min, glucose_max, glucose_mean and weight_admit to be filtered out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150.3344393519077 6340.018017427811\n",
      "19170.38849163534\n",
      "True\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#helper func to viz specified data field e.g. glucose_min\n",
    "\n",
    "plot_var_name = \"glucose_min\"\n",
    "plot_var_data = data[plot_var_name].copy()\n",
    "print(plot_var_data.mean(), plot_var_data.std())\n",
    "plot_var_data_max = plot_var_data.mean() + (plot_var_data.std() * 3)\n",
    "print(plot_var_data_max)\n",
    "plot_var_data[plot_var_data > plot_var_data_max] = np.nan\n",
    "plot_var_data.plot.hist(bins=500)\n",
    "\n",
    "#check whether majority of admissable data has been captured\n",
    "print(plot_var_data_max > plot_var_data.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "glucose_min 0.12 True 1016.0 True\n",
      "glucose_max 34.0 True 2440.0 True\n",
      "glucose_mean 34.0 True 279.2 True\n",
      "weight_admit 1.0 True 190.3 True\n"
     ]
    }
   ],
   "source": [
    "#do previous cell in place over identified fields\n",
    "anomalous_data_fields = [\"glucose_min\", \"glucose_max\", \"glucose_mean\", \"weight_admit\"]\n",
    "for field in anomalous_data_fields:\n",
    "    field_data = data[field]\n",
    "    field_data_mean = field_data.mean()\n",
    "    field_data_std = field_data.std()\n",
    "    field_data_range = [field_data_mean - 3 * field_data_std, field_data_mean + 3 * field_data_std]\n",
    "    data.loc[(field_data < field_data_range[0]) | (field_data > field_data_range[1])] = np.nan\n",
    "    print(field, field_data.min(), field_data.min() > field_data_range[0], field_data.max(), field_data.max() < field_data_range[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "check quality of each data input for missing fields per patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "null_entry_count = data.isna().sum(axis = 1)\n",
    "num_col = len(data.columns)\n",
    "null_entry_count_percent = null_entry_count/num_col\n",
    "plot = null_entry_count_percent.plot.hist(bins=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#try removing invalid data\n",
    "null_entry_remove = data.copy()\n",
    "null_entry_remove = null_entry_remove.dropna()\n",
    "len(null_entry_remove)\n",
    "\n",
    "#aaaaand finding out there is not a single entry with no invalid data :("
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "deciding to take 80% threshold for completeness of a patient's total fields as a high-pass threshold for usability of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "688"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.dropna(axis = 0, thresh = len(data.columns) * 0.8)\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "688 entries remaining after removing low-quality entries\n",
    "\n",
    "now to check quality of features for the remaining entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "null_feature_count = data.isna().sum(axis = 0)\n",
    "num_row = len(data.index)\n",
    "null_feature_count_percent = null_feature_count/num_row\n",
    "plot = null_feature_count_percent.plot.hist(bins=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "deciding to take 80% as threshold for completeness of a feature for usability of data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "118"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.dropna(axis = 1, thresh = len(data.index) * 0.8)\n",
    "data = data.drop(\"id\", axis=1)\n",
    "\n",
    "len(data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "100 features remaining with more than 80% completeness chosen\n",
    "\n",
    "Final data size is 688 * 118"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(688, 118)\n",
      "aki\n",
      "gender\n",
      "admission_age\n",
      "race\n",
      "heart_rate_min\n",
      "heart_rate_max\n",
      "heart_rate_mean\n",
      "sbp_min\n",
      "sbp_max\n",
      "sbp_mean\n",
      "dbp_min\n",
      "dbp_max\n",
      "dbp_mean\n",
      "mbp_min\n",
      "mbp_max\n",
      "mbp_mean\n",
      "resp_rate_min\n",
      "resp_rate_max\n",
      "resp_rate_mean\n",
      "temperature_min\n",
      "temperature_max\n",
      "temperature_mean\n",
      "spo2_min\n",
      "spo2_max\n",
      "spo2_mean\n",
      "glucose_min\n",
      "glucose_max\n",
      "glucose_mean\n",
      "lactate_min\n",
      "lactate_max\n",
      "ph_min\n",
      "ph_max\n",
      "so2_min\n",
      "so2_max\n",
      "po2_min\n",
      "po2_max\n",
      "pco2_min\n",
      "pco2_max\n",
      "aado2_calc_min\n",
      "aado2_calc_max\n",
      "pao2fio2ratio_min\n",
      "pao2fio2ratio_max\n",
      "baseexcess_min\n",
      "baseexcess_max\n",
      "totalco2_min\n",
      "totalco2_max\n",
      "hematocrit_min\n",
      "hematocrit_max\n",
      "hemoglobin_min\n",
      "hemoglobin_max\n",
      "chloride_min\n",
      "chloride_max\n",
      "calcium_min\n",
      "calcium_max\n",
      "glucose_min.1\n",
      "glucose_max.1\n",
      "potassium_min\n",
      "potassium_max\n",
      "sodium_min\n",
      "sodium_max\n",
      "hematocrit_min.1\n",
      "hematocrit_max.1\n",
      "hemoglobin_min.1\n",
      "hemoglobin_max.1\n",
      "platelets_min\n",
      "platelets_max\n",
      "wbc_min\n",
      "wbc_max\n",
      "albumin_min\n",
      "albumin_max\n",
      "aniongap_min\n",
      "aniongap_max\n",
      "bicarbonate_min.1\n",
      "bicarbonate_max.1\n",
      "bun_min\n",
      "bun_max\n",
      "calcium_min.1\n",
      "calcium_max.1\n",
      "chloride_min.1\n",
      "chloride_max.1\n",
      "glucose_min.2\n",
      "glucose_max.2\n",
      "sodium_min.1\n",
      "sodium_max.1\n",
      "potassium_min.1\n",
      "potassium_max.1\n",
      "abs_basophils_min\n",
      "abs_basophils_max\n",
      "abs_eosinophils_min\n",
      "abs_eosinophils_max\n",
      "abs_lymphocytes_min\n",
      "abs_lymphocytes_max\n",
      "abs_monocytes_min\n",
      "abs_monocytes_max\n",
      "abs_neutrophils_min\n",
      "abs_neutrophils_max\n",
      "fibrinogen_min\n",
      "fibrinogen_max\n",
      "inr_min\n",
      "inr_max\n",
      "pt_min\n",
      "pt_max\n",
      "ptt_min\n",
      "ptt_max\n",
      "alt_min\n",
      "alt_max\n",
      "alp_min\n",
      "alp_max\n",
      "ast_min\n",
      "ast_max\n",
      "bilirubin_total_min\n",
      "bilirubin_total_max\n",
      "gcs_min\n",
      "gcs_motor\n",
      "gcs_verbal\n",
      "gcs_eyes\n",
      "gcs_unable\n",
      "weight_admit\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aki</th>\n",
       "      <th>gender</th>\n",
       "      <th>admission_age</th>\n",
       "      <th>race</th>\n",
       "      <th>heart_rate_min</th>\n",
       "      <th>heart_rate_max</th>\n",
       "      <th>heart_rate_mean</th>\n",
       "      <th>sbp_min</th>\n",
       "      <th>sbp_max</th>\n",
       "      <th>sbp_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>ast_min</th>\n",
       "      <th>ast_max</th>\n",
       "      <th>bilirubin_total_min</th>\n",
       "      <th>bilirubin_total_max</th>\n",
       "      <th>gcs_min</th>\n",
       "      <th>gcs_motor</th>\n",
       "      <th>gcs_verbal</th>\n",
       "      <th>gcs_eyes</th>\n",
       "      <th>gcs_unable</th>\n",
       "      <th>weight_admit</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>3.0</td>\n",
       "      <td>F</td>\n",
       "      <td>20.909196</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>94.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>115.031250</td>\n",
       "      <td>31.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>113.062500</td>\n",
       "      <td>...</td>\n",
       "      <td>83.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>1.9</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.5</td>\n",
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       "      <th>270</th>\n",
       "      <td>3.0</td>\n",
       "      <td>M</td>\n",
       "      <td>28.544966</td>\n",
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       "      <td>59.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>75.487179</td>\n",
       "      <td>73.0</td>\n",
       "      <td>141.0</td>\n",
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       "      <td>95.0</td>\n",
       "      <td>111.0</td>\n",
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       "      <td>108.0</td>\n",
       "      <td>199.0</td>\n",
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       "      <td>43.0</td>\n",
       "      <td>59.0</td>\n",
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       "      <td>14.0</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>190.0</td>\n",
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       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>3.0</td>\n",
       "      <td>M</td>\n",
       "      <td>69.752532</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>85.196970</td>\n",
       "      <td>67.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>108.303030</td>\n",
       "      <td>...</td>\n",
       "      <td>186.0</td>\n",
       "      <td>276.0</td>\n",
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       "      <td>0.7</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
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       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>1.0</td>\n",
       "      <td>F</td>\n",
       "      <td>74.883919</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>46.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>61.958333</td>\n",
       "      <td>100.5</td>\n",
       "      <td>179.0</td>\n",
       "      <td>135.791667</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>72.0</td>\n",
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       "      <th>50728</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>52.430798</td>\n",
       "      <td>UNKNOWN</td>\n",
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       "      <td>139.0</td>\n",
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       "      <td>78.0</td>\n",
       "      <td>156.0</td>\n",
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       "      <td>66.0</td>\n",
       "      <td>86.0</td>\n",
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       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.6</td>\n",
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       "    <tr>\n",
       "      <th>50746</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>57.976938</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>87.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>106.160000</td>\n",
       "      <td>94.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>122.720000</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>36.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.8</td>\n",
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       "      <th>50758</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>64.966034</td>\n",
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       "      <td>84.0</td>\n",
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       "      <td>92.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>105.806452</td>\n",
       "      <td>...</td>\n",
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       "      <td>49.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.5</td>\n",
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       "      <th>50891</th>\n",
       "      <td>2.0</td>\n",
       "      <td>F</td>\n",
       "      <td>68.097968</td>\n",
       "      <td>UNABLE TO OBTAIN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>80.869565</td>\n",
       "      <td>95.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>128.413043</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.4</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>50917</th>\n",
       "      <td>0.0</td>\n",
       "      <td>F</td>\n",
       "      <td>55.399450</td>\n",
       "      <td>WHITE</td>\n",
       "      <td>68.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>83.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>688 rows × 118 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       aki gender  admission_age              race  heart_rate_min  \\\n",
       "129    3.0      F      20.909196             OTHER            94.0   \n",
       "270    3.0      M      28.544966             WHITE            59.0   \n",
       "289    3.0      F      55.870361             WHITE            95.0   \n",
       "324    3.0      M      69.752532           UNKNOWN            64.0   \n",
       "366    1.0      F      74.883919             WHITE            46.0   \n",
       "...    ...    ...            ...               ...             ...   \n",
       "50728  1.0      M      52.430798           UNKNOWN           100.0   \n",
       "50746  1.0      F      57.976938           UNKNOWN            87.0   \n",
       "50758  1.0      M      64.966034             WHITE            63.0   \n",
       "50891  2.0      F      68.097968  UNABLE TO OBTAIN            63.0   \n",
       "50917  0.0      F      55.399450             WHITE            68.0   \n",
       "\n",
       "       heart_rate_max  heart_rate_mean  sbp_min  sbp_max    sbp_mean  ...  \\\n",
       "129             136.0       115.031250     31.0    156.0  113.062500  ...   \n",
       "270              86.0        75.487179     73.0    141.0  115.583333  ...   \n",
       "289             111.0        99.636364    108.0    199.0  147.578947  ...   \n",
       "324             110.0        85.196970     67.0    152.0  108.303030  ...   \n",
       "366              70.0        61.958333    100.5    179.0  135.791667  ...   \n",
       "...               ...              ...      ...      ...         ...  ...   \n",
       "50728           139.0       116.444444     78.0    156.0  115.435484  ...   \n",
       "50746           126.0       106.160000     94.0    161.0  122.720000  ...   \n",
       "50758            84.0        73.419355     92.0    117.0  105.806452  ...   \n",
       "50891            93.0        80.869565     95.0    168.0  128.413043  ...   \n",
       "50917           162.0        91.666667     83.0    132.0  113.000000  ...   \n",
       "\n",
       "       ast_min  ast_max  bilirubin_total_min  bilirubin_total_max  gcs_min  \\\n",
       "129       83.0    152.0                  1.9                  2.5     15.0   \n",
       "270     1660.0   2082.0                  0.4                  0.7     15.0   \n",
       "289       43.0     59.0                  0.4                  0.6     14.0   \n",
       "324      186.0    276.0                  0.3                  0.7     15.0   \n",
       "366       38.0     38.0                  0.4                  0.4     15.0   \n",
       "...        ...      ...                  ...                  ...      ...   \n",
       "50728     66.0     86.0                  1.1                  3.5     13.0   \n",
       "50746     25.0     36.0                  0.2                  0.5     11.0   \n",
       "50758     49.0     49.0                  0.2                  0.2     15.0   \n",
       "50891     22.0     22.0                  0.4                  0.4     15.0   \n",
       "50917    210.0    210.0                  0.4                  0.4      3.0   \n",
       "\n",
       "       gcs_motor  gcs_verbal  gcs_eyes  gcs_unable  weight_admit  \n",
       "129          6.0         5.0       4.0         0.0          95.5  \n",
       "270          1.0         0.0       1.0         1.0         100.0  \n",
       "289          6.0         4.0       4.0         0.0         190.0  \n",
       "324          6.0         0.0       3.0         1.0          80.0  \n",
       "366          1.0         0.0       1.0         1.0          72.0  \n",
       "...          ...         ...       ...         ...           ...  \n",
       "50728        6.0         4.0       3.0         0.0          93.6  \n",
       "50746        6.0         2.0       3.0         0.0          71.8  \n",
       "50758        1.0         0.0       1.0         1.0          67.5  \n",
       "50891        1.0         0.0       1.0         1.0         100.0  \n",
       "50917        1.0         1.0       1.0         0.0          80.0  \n",
       "\n",
       "[688 rows x 118 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data.shape)\n",
    "#features remaining\n",
    "for col in data.columns:\n",
    "    print(col)\n",
    "data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### encode categories, clean up NANs, and normalizing \n",
    "\n",
    "from observation only categorical data columns are gender and race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['F' 'M']\n",
      "['OTHER' 'WHITE' 'UNKNOWN' 'HISPANIC/LATINO - PUERTO RICAN'\n",
      " 'ASIAN - CHINESE' 'BLACK/CARIBBEAN ISLAND' 'BLACK/AFRICAN AMERICAN'\n",
      " 'WHITE - OTHER EUROPEAN' 'BLACK/AFRICAN' 'HISPANIC/LATINO - MEXICAN'\n",
      " 'UNABLE TO OBTAIN' 'ASIAN' 'HISPANIC/LATINO - DOMINICAN'\n",
      " 'BLACK/CAPE VERDEAN' 'HISPANIC OR LATINO' 'HISPANIC/LATINO - COLUMBIAN'\n",
      " 'PATIENT DECLINED TO ANSWER' 'WHITE - RUSSIAN'\n",
      " 'HISPANIC/LATINO - HONDURAN' 'NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER'\n",
      " 'PORTUGUESE' 'ASIAN - SOUTH EAST ASIAN'\n",
      " 'HISPANIC/LATINO - CENTRAL AMERICAN' 'HISPANIC/LATINO - CUBAN'\n",
      " 'WHITE - EASTERN EUROPEAN' 'MULTIPLE RACE/ETHNICITY']\n"
     ]
    }
   ],
   "source": [
    "print(data.gender.unique())\n",
    "print(data.race.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some unusable data in race category with 'UNKNOWN', 'UNABLE TO OBTAIN', 'PATIENT DECLINED TO ANSWER' that should be discarded as well\n",
    "\n",
    "Will be classifying aki into a binary class as well, with False or 0 included classes of {0} and True or 1 to represent classes of {1, 2, 3}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50728</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50746</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50758</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50891</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50917</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>688 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender  aki_bin  aki  race_unknown  race_black  race_white  race_asian  \\\n",
       "129         0        1  3.0             0           0           0           0   \n",
       "270         1        1  3.0             0           0           1           0   \n",
       "289         0        1  3.0             0           0           1           0   \n",
       "324         1        1  3.0             1           0           0           0   \n",
       "366         0        1  1.0             0           0           1           0   \n",
       "...       ...      ...  ...           ...         ...         ...         ...   \n",
       "50728       1        1  1.0             1           0           0           0   \n",
       "50746       0        1  1.0             1           0           0           0   \n",
       "50758       1        1  1.0             0           0           1           0   \n",
       "50891       0        1  2.0             1           0           0           0   \n",
       "50917       0        0  0.0             0           0           1           0   \n",
       "\n",
       "       race_hispanic  race_native  race_other  \n",
       "129                0            0           1  \n",
       "270                0            0           0  \n",
       "289                0            0           0  \n",
       "324                0            0           0  \n",
       "366                0            0           0  \n",
       "...              ...          ...         ...  \n",
       "50728              0            0           0  \n",
       "50746              0            0           0  \n",
       "50758              0            0           0  \n",
       "50891              0            0           0  \n",
       "50917              0            0           0  \n",
       "\n",
       "[688 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def encode_aki(aki):\n",
    "    return 0 if aki == 0 else 1\n",
    "\n",
    "def encode_gender(gender):\n",
    "    return 0 if gender == \"F\" else 1\n",
    "\n",
    "def encode_race(race):\n",
    "    if \"UNKNOWN\" in race or \"UNABLE TO OBTAIN\" in race or \"PATIENT DECLINED TO ANSWER\" in race:\n",
    "        return 0\n",
    "    if \"BLACK\" in race:\n",
    "        return 1\n",
    "    if \"WHITE\" in race:\n",
    "        return 2\n",
    "    if \"ASIAN\" in race:\n",
    "        return 3\n",
    "    if \"HISPANIC\" in race:\n",
    "        return 4\n",
    "    if \"NATIVE\" in race:\n",
    "        return 5\n",
    "    else: #for all others\n",
    "        return 6\n",
    "\n",
    "data_category = pd.DataFrame()\n",
    "data_category[\"gender\"] = data.gender.map(encode_gender)\n",
    "data = data.drop(\"gender\", axis = 1)\n",
    "data_category[\"aki_bin\"] = data.aki.map(encode_aki)\n",
    "data_category[\"aki\"] = data.aki.copy()\n",
    "data = data.drop(\"aki\", axis = 1)\n",
    "\n",
    "data_category[\"race_unknown\"] = (data.race.map(encode_race) == 0).astype(int)\n",
    "data_category[\"race_black\"] = (data.race.map(encode_race) == 1).astype(int)\n",
    "data_category[\"race_white\"] = (data.race.map(encode_race) == 2).astype(int)\n",
    "data_category[\"race_asian\"] = (data.race.map(encode_race) == 3).astype(int)\n",
    "data_category[\"race_hispanic\"] = (data.race.map(encode_race) == 4).astype(int)\n",
    "data_category[\"race_native\"] = (data.race.map(encode_race) == 5).astype(int)\n",
    "data_category[\"race_other\"] = (data.race.map(encode_race) == 6).astype(int)\n",
    "data = data.drop(\"race\", axis = 1)\n",
    "data_category\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "cleaning up nans via imputing using sklearn.impute.SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>admission_age</th>\n",
       "      <th>heart_rate_min</th>\n",
       "      <th>heart_rate_max</th>\n",
       "      <th>heart_rate_mean</th>\n",
       "      <th>sbp_min</th>\n",
       "      <th>sbp_max</th>\n",
       "      <th>sbp_mean</th>\n",
       "      <th>dbp_min</th>\n",
       "      <th>dbp_max</th>\n",
       "      <th>dbp_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>ast_min</th>\n",
       "      <th>ast_max</th>\n",
       "      <th>bilirubin_total_min</th>\n",
       "      <th>bilirubin_total_max</th>\n",
       "      <th>gcs_min</th>\n",
       "      <th>gcs_motor</th>\n",
       "      <th>gcs_verbal</th>\n",
       "      <th>gcs_eyes</th>\n",
       "      <th>gcs_unable</th>\n",
       "      <th>weight_admit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.909196</td>\n",
       "      <td>94.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>115.031250</td>\n",
       "      <td>31.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>113.062500</td>\n",
       "      <td>20.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>66.187500</td>\n",
       "      <td>...</td>\n",
       "      <td>83.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.544966</td>\n",
       "      <td>59.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>75.487179</td>\n",
       "      <td>73.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>115.583333</td>\n",
       "      <td>51.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>74.226190</td>\n",
       "      <td>...</td>\n",
       "      <td>1660.0</td>\n",
       "      <td>2082.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55.870361</td>\n",
       "      <td>95.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>99.636364</td>\n",
       "      <td>108.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>147.578947</td>\n",
       "      <td>46.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>65.894737</td>\n",
       "      <td>...</td>\n",
       "      <td>43.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.752532</td>\n",
       "      <td>64.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>85.196970</td>\n",
       "      <td>67.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>108.303030</td>\n",
       "      <td>46.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>64.772727</td>\n",
       "      <td>...</td>\n",
       "      <td>186.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.7</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.883919</td>\n",
       "      <td>46.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>61.958333</td>\n",
       "      <td>100.5</td>\n",
       "      <td>179.0</td>\n",
       "      <td>135.791667</td>\n",
       "      <td>20.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>33.104167</td>\n",
       "      <td>...</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>72.0</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>683</th>\n",
       "      <td>52.430798</td>\n",
       "      <td>100.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>116.444444</td>\n",
       "      <td>78.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>115.435484</td>\n",
       "      <td>41.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>63.193548</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>57.976938</td>\n",
       "      <td>87.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>106.160000</td>\n",
       "      <td>94.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>122.720000</td>\n",
       "      <td>57.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70.240000</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>64.966034</td>\n",
       "      <td>63.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>73.419355</td>\n",
       "      <td>92.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>105.806452</td>\n",
       "      <td>50.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>66.645161</td>\n",
       "      <td>...</td>\n",
       "      <td>49.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>68.097968</td>\n",
       "      <td>63.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>80.869565</td>\n",
       "      <td>95.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>128.413043</td>\n",
       "      <td>50.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>69.086957</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>55.399450</td>\n",
       "      <td>68.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>91.666667</td>\n",
       "      <td>83.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>43.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>210.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>688 rows × 115 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     admission_age  heart_rate_min  heart_rate_max  heart_rate_mean  sbp_min  \\\n",
       "0        20.909196            94.0           136.0       115.031250     31.0   \n",
       "1        28.544966            59.0            86.0        75.487179     73.0   \n",
       "2        55.870361            95.0           111.0        99.636364    108.0   \n",
       "3        69.752532            64.0           110.0        85.196970     67.0   \n",
       "4        74.883919            46.0            70.0        61.958333    100.5   \n",
       "..             ...             ...             ...              ...      ...   \n",
       "683      52.430798           100.0           139.0       116.444444     78.0   \n",
       "684      57.976938            87.0           126.0       106.160000     94.0   \n",
       "685      64.966034            63.0            84.0        73.419355     92.0   \n",
       "686      68.097968            63.0            93.0        80.869565     95.0   \n",
       "687      55.399450            68.0           162.0        91.666667     83.0   \n",
       "\n",
       "     sbp_max    sbp_mean  dbp_min  dbp_max   dbp_mean  ...  ast_min  ast_max  \\\n",
       "0      156.0  113.062500     20.0    104.0  66.187500  ...     83.0    152.0   \n",
       "1      141.0  115.583333     51.0     89.0  74.226190  ...   1660.0   2082.0   \n",
       "2      199.0  147.578947     46.0    128.0  65.894737  ...     43.0     59.0   \n",
       "3      152.0  108.303030     46.0     86.0  64.772727  ...    186.0    276.0   \n",
       "4      179.0  135.791667     20.0     47.0  33.104167  ...     38.0     38.0   \n",
       "..       ...         ...      ...      ...        ...  ...      ...      ...   \n",
       "683    156.0  115.435484     41.0    134.0  63.193548  ...     66.0     86.0   \n",
       "684    161.0  122.720000     57.0     90.0  70.240000  ...     25.0     36.0   \n",
       "685    117.0  105.806452     50.0     76.0  66.645161  ...     49.0     49.0   \n",
       "686    168.0  128.413043     50.0    141.0  69.086957  ...     22.0     22.0   \n",
       "687    132.0  113.000000     43.0     86.0  65.000000  ...    210.0    210.0   \n",
       "\n",
       "     bilirubin_total_min  bilirubin_total_max  gcs_min  gcs_motor  gcs_verbal  \\\n",
       "0                    1.9                  2.5     15.0        6.0         5.0   \n",
       "1                    0.4                  0.7     15.0        1.0         0.0   \n",
       "2                    0.4                  0.6     14.0        6.0         4.0   \n",
       "3                    0.3                  0.7     15.0        6.0         0.0   \n",
       "4                    0.4                  0.4     15.0        1.0         0.0   \n",
       "..                   ...                  ...      ...        ...         ...   \n",
       "683                  1.1                  3.5     13.0        6.0         4.0   \n",
       "684                  0.2                  0.5     11.0        6.0         2.0   \n",
       "685                  0.2                  0.2     15.0        1.0         0.0   \n",
       "686                  0.4                  0.4     15.0        1.0         0.0   \n",
       "687                  0.4                  0.4      3.0        1.0         1.0   \n",
       "\n",
       "     gcs_eyes  gcs_unable  weight_admit  \n",
       "0         4.0         0.0          95.5  \n",
       "1         1.0         1.0         100.0  \n",
       "2         4.0         0.0         190.0  \n",
       "3         3.0         1.0          80.0  \n",
       "4         1.0         1.0          72.0  \n",
       "..        ...         ...           ...  \n",
       "683       3.0         0.0          93.6  \n",
       "684       3.0         0.0          71.8  \n",
       "685       1.0         1.0          67.5  \n",
       "686       1.0         1.0         100.0  \n",
       "687       1.0         0.0          80.0  \n",
       "\n",
       "[688 rows x 115 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
    "imputer.fit(data)\n",
    "columns = data.columns\n",
    "data_numerical = pd.DataFrame(imputer.transform(data), columns = columns)\n",
    "data_numerical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now normalize normally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>admission_age</th>\n",
       "      <th>heart_rate_min</th>\n",
       "      <th>heart_rate_max</th>\n",
       "      <th>heart_rate_mean</th>\n",
       "      <th>sbp_min</th>\n",
       "      <th>sbp_max</th>\n",
       "      <th>sbp_mean</th>\n",
       "      <th>dbp_min</th>\n",
       "      <th>dbp_max</th>\n",
       "      <th>dbp_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>gender</th>\n",
       "      <th>aki_bin</th>\n",
       "      <th>aki</th>\n",
       "      <th>race_unknown</th>\n",
       "      <th>race_black</th>\n",
       "      <th>race_white</th>\n",
       "      <th>race_asian</th>\n",
       "      <th>race_hispanic</th>\n",
       "      <th>race_native</th>\n",
       "      <th>race_other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.305718</td>\n",
       "      <td>0.909144</td>\n",
       "      <td>0.799067</td>\n",
       "      <td>1.076249</td>\n",
       "      <td>-2.504011</td>\n",
       "      <td>0.256650</td>\n",
       "      <td>0.202519</td>\n",
       "      <td>-1.754452</td>\n",
       "      <td>0.634761</td>\n",
       "      <td>0.542099</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.864083</td>\n",
       "      <td>-0.923298</td>\n",
       "      <td>-1.342736</td>\n",
       "      <td>-1.050038</td>\n",
       "      <td>-0.283678</td>\n",
       "      <td>-0.304576</td>\n",
       "      <td>0.378238</td>\n",
       "      <td>0.826360</td>\n",
       "      <td>-0.018739</td>\n",
       "      <td>1.383293</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.283646</td>\n",
       "      <td>0.961500</td>\n",
       "      <td>-0.271835</td>\n",
       "      <td>0.248465</td>\n",
       "      <td>1.566599</td>\n",
       "      <td>1.865497</td>\n",
       "      <td>2.608540</td>\n",
       "      <td>0.410100</td>\n",
       "      <td>1.680362</td>\n",
       "      <td>0.511463</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.519266</td>\n",
       "      <td>-0.661521</td>\n",
       "      <td>-0.314671</td>\n",
       "      <td>-0.527942</td>\n",
       "      <td>-0.600869</td>\n",
       "      <td>0.106990</td>\n",
       "      <td>-0.129247</td>\n",
       "      <td>0.410100</td>\n",
       "      <td>-0.149439</td>\n",
       "      <td>0.394053</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.816053</td>\n",
       "      <td>-1.603920</td>\n",
       "      <td>-2.028113</td>\n",
       "      <td>-1.777485</td>\n",
       "      <td>1.170111</td>\n",
       "      <td>1.117196</td>\n",
       "      <td>1.786890</td>\n",
       "      <td>-1.754452</td>\n",
       "      <td>-1.848540</td>\n",
       "      <td>-2.919846</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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>0</td>\n",
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       "      <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>683</th>\n",
       "      <td>-0.482583</td>\n",
       "      <td>1.223277</td>\n",
       "      <td>0.927575</td>\n",
       "      <td>1.152236</td>\n",
       "      <td>-0.019353</td>\n",
       "      <td>0.256650</td>\n",
       "      <td>0.367932</td>\n",
       "      <td>-0.006160</td>\n",
       "      <td>1.941762</td>\n",
       "      <td>0.228802</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>-0.161807</td>\n",
       "      <td>0.542656</td>\n",
       "      <td>0.370706</td>\n",
       "      <td>0.599241</td>\n",
       "      <td>0.826488</td>\n",
       "      <td>0.443725</td>\n",
       "      <td>0.875710</td>\n",
       "      <td>1.325872</td>\n",
       "      <td>0.024827</td>\n",
       "      <td>0.966165</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>0.242426</td>\n",
       "      <td>-0.713876</td>\n",
       "      <td>-1.428408</td>\n",
       "      <td>-1.161226</td>\n",
       "      <td>0.720758</td>\n",
       "      <td>-1.202537</td>\n",
       "      <td>-0.303275</td>\n",
       "      <td>0.743108</td>\n",
       "      <td>-0.585106</td>\n",
       "      <td>0.589990</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>0.423570</td>\n",
       "      <td>-0.713876</td>\n",
       "      <td>-1.042884</td>\n",
       "      <td>-0.760627</td>\n",
       "      <td>0.879353</td>\n",
       "      <td>0.705630</td>\n",
       "      <td>1.272552</td>\n",
       "      <td>0.743108</td>\n",
       "      <td>2.246729</td>\n",
       "      <td>0.845507</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>-0.310883</td>\n",
       "      <td>-0.452099</td>\n",
       "      <td>1.912804</td>\n",
       "      <td>-0.180066</td>\n",
       "      <td>0.244972</td>\n",
       "      <td>-0.641311</td>\n",
       "      <td>0.198162</td>\n",
       "      <td>0.160344</td>\n",
       "      <td>-0.149439</td>\n",
       "      <td>0.417835</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>688 rows × 125 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     admission_age  heart_rate_min  heart_rate_max  heart_rate_mean   sbp_min  \\\n",
       "0        -2.305718        0.909144        0.799067         1.076249 -2.504011   \n",
       "1        -1.864083       -0.923298       -1.342736        -1.050038 -0.283678   \n",
       "2        -0.283646        0.961500       -0.271835         0.248465  1.566599   \n",
       "3         0.519266       -0.661521       -0.314671        -0.527942 -0.600869   \n",
       "4         0.816053       -1.603920       -2.028113        -1.777485  1.170111   \n",
       "..             ...             ...             ...              ...       ...   \n",
       "683      -0.482583        1.223277        0.927575         1.152236 -0.019353   \n",
       "684      -0.161807        0.542656        0.370706         0.599241  0.826488   \n",
       "685       0.242426       -0.713876       -1.428408        -1.161226  0.720758   \n",
       "686       0.423570       -0.713876       -1.042884        -0.760627  0.879353   \n",
       "687      -0.310883       -0.452099        1.912804        -0.180066  0.244972   \n",
       "\n",
       "      sbp_max  sbp_mean   dbp_min   dbp_max  dbp_mean  ...  gender  aki_bin  \\\n",
       "0    0.256650  0.202519 -1.754452  0.634761  0.542099  ...       0        1   \n",
       "1   -0.304576  0.378238  0.826360 -0.018739  1.383293  ...       1        1   \n",
       "2    1.865497  2.608540  0.410100  1.680362  0.511463  ...       0        1   \n",
       "3    0.106990 -0.129247  0.410100 -0.149439  0.394053  ...       1        1   \n",
       "4    1.117196  1.786890 -1.754452 -1.848540 -2.919846  ...       0        1   \n",
       "..        ...       ...       ...       ...       ...  ...     ...      ...   \n",
       "683  0.256650  0.367932 -0.006160  1.941762  0.228802  ...       1        1   \n",
       "684  0.443725  0.875710  1.325872  0.024827  0.966165  ...       0        1   \n",
       "685 -1.202537 -0.303275  0.743108 -0.585106  0.589990  ...       1        1   \n",
       "686  0.705630  1.272552  0.743108  2.246729  0.845507  ...       0        1   \n",
       "687 -0.641311  0.198162  0.160344 -0.149439  0.417835  ...       0        0   \n",
       "\n",
       "     aki  race_unknown  race_black  race_white  race_asian  race_hispanic  \\\n",
       "0    3.0             0           0           0           0              0   \n",
       "1    3.0             0           0           1           0              0   \n",
       "2    3.0             0           0           1           0              0   \n",
       "3    3.0             1           0           0           0              0   \n",
       "4    1.0             0           0           1           0              0   \n",
       "..   ...           ...         ...         ...         ...            ...   \n",
       "683  1.0             1           0           0           0              0   \n",
       "684  1.0             1           0           0           0              0   \n",
       "685  1.0             0           0           1           0              0   \n",
       "686  2.0             1           0           0           0              0   \n",
       "687  0.0             0           0           1           0              0   \n",
       "\n",
       "     race_native  race_other  \n",
       "0              0           1  \n",
       "1              0           0  \n",
       "2              0           0  \n",
       "3              0           0  \n",
       "4              0           0  \n",
       "..           ...         ...  \n",
       "683            0           0  \n",
       "684            0           0  \n",
       "685            0           0  \n",
       "686            0           0  \n",
       "687            0           0  \n",
       "\n",
       "[688 rows x 125 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numerical = (data_numerical - data_numerical.mean()) / data_numerical.std()\n",
    "data_category.reset_index(drop=True, inplace=True)\n",
    "data = pd.concat([data_numerical, data_category], axis = 1, join=\"inner\")\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now we are ready to conduct feature selection\n",
    "\n",
    "## feature selection\n",
    "\n",
    "as observed from data fields, there are many fields with similar characteristic such as property_min, property_max and property_mean. we first have to identify and drop features that have high degree of correlation to control the effects of multiple correlated fields affecting the estimator\n",
    "\n",
    "different features maybe useful for different estimation, an generic function that is agnostic to different estimators needs to be built to select and return the features to be used in the estimator. a genetic selection function from sklearn-genetic will be selected (huehuehue)\n",
    "\n",
    "### removing highly correlated fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_bin = data.aki_bin.copy().to_frame()\n",
    "y_cat = data.aki.copy().to_frame()\n",
    "x = data.copy()\n",
    "x = x.drop(\"aki\", axis = 1)\n",
    "x = x.drop(\"aki_bin\", axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure out AKI split between positive and negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aki_bin\n",
      "1          613\n",
      "0           75\n",
      "Name: count, dtype: int64\n",
      "aki\n",
      "3.0    349\n",
      "2.0    176\n",
      "1.0     88\n",
      "0.0     75\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y_bin.value_counts())\n",
    "print(y_cat.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "now to split between training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_bin_train, x_bin_test, y_bin_train, y_bin_test = train_test_split(x, y_bin, test_size=0.3, random_state=666)\n",
    "x_cat_train, x_cat_test, y_cat_train, y_cat_test = train_test_split(x, y_cat, test_size=0.3, random_state=666)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "check self correlation to get rid of non differentiating features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(481, 123)\n",
      "(123, 123)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "is_bin = True\n",
    "\n",
    "if is_bin == True:\n",
    "    x_train = x_bin_train\n",
    "    x_test = x_bin_test\n",
    "    y_train = y_bin_train\n",
    "    y_test = y_bin_test\n",
    "else:\n",
    "    x_train = x_cat_train\n",
    "    x_test = x_cat_test\n",
    "    y_train = y_cat_train\n",
    "    y_test = y_cat_test\n",
    "\n",
    "print(x_train.shape)\n",
    "\n",
    "x_corr = x.corr().abs()\n",
    "print(x_corr.shape)\n",
    "sns.heatmap(x_corr, cmap = \"viridis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(123, 123)\n",
      "15006\n",
      "7503\n",
      "170\n",
      "79 ['heart_rate_max', 'heart_rate_mean', 'sbp_mean', 'dbp_min', 'mbp_min', 'sbp_max', 'dbp_max', 'mbp_mean', 'dbp_mean', 'resp_rate_mean', 'resp_rate_max', 'temperature_mean', 'temperature_max', 'spo2_mean', 'spo2_max', 'glucose_mean', 'glucose_min.2', 'glucose_max', 'glucose_max.1', 'glucose_max.2', 'glucose_min.1', 'lactate_max', 'baseexcess_min', 'totalco2_min', 'aniongap_min', 'aniongap_max', 'bicarbonate_min.1', 'ph_min', 'baseexcess_max', 'po2_min', 'pao2fio2ratio_min', 'pao2fio2ratio_max', 'pco2_min', 'aado2_calc_min', 'aado2_calc_max', 'bicarbonate_max.1', 'totalco2_max', 'hematocrit_max', 'hemoglobin_min', 'hemoglobin_max', 'hematocrit_min.1', 'hematocrit_max.1', 'hemoglobin_min.1', 'hemoglobin_max.1', 'chloride_max', 'sodium_min', 'chloride_min.1', 'chloride_max.1', 'sodium_max', 'calcium_max.1', 'potassium_max', 'potassium_min.1', 'potassium_max.1', 'sodium_min.1', 'sodium_max.1', 'platelets_max', 'wbc_max', 'abs_lymphocytes_min', 'abs_lymphocytes_max', 'abs_monocytes_min', 'abs_monocytes_max', 'albumin_max', 'bun_max', 'abs_basophils_max', 'abs_eosinophils_min', 'abs_eosinophils_max', 'abs_neutrophils_max', 'fibrinogen_max', 'inr_max', 'pt_min', 'pt_max', 'alt_max', 'ast_min', 'ast_max', 'alp_max', 'bilirubin_total_max', 'gcs_unable', 'gcs_verbal', 'gcs_eyes']\n"
     ]
    }
   ],
   "source": [
    "x_corr = x_corr[x_corr != 1.00].abs()\n",
    "\n",
    "print(x_corr.shape)\n",
    "x_corr_stack = x_corr.stack()\n",
    "print(len(x_corr_stack))\n",
    "x_corr_stack = x_corr_stack.drop_duplicates()\n",
    "print(len(x_corr_stack))\n",
    "x_corr_stack = x_corr_stack[x_corr_stack > 0.5]\n",
    "print(len(x_corr_stack))\n",
    "\n",
    "high_corr_features_pairs = list(x_corr_stack.index)\n",
    "high_corr_features_uniques = []\n",
    "high_corr_features = []\n",
    "\n",
    "#get one feature out of a group with high correlation to each other\n",
    "for pair in high_corr_features_pairs:\n",
    "    first, second = pair\n",
    "    if second == None:\n",
    "        continue\n",
    "    if first not in high_corr_features_uniques and second not in high_corr_features_uniques:\n",
    "        high_corr_features.append(first)\n",
    "        high_corr_features_uniques.append(first)\n",
    "        high_corr_features_uniques.append(second)\n",
    "    elif first not in high_corr_features_uniques:\n",
    "        high_corr_features_uniques.append(first)\n",
    "    elif second not in high_corr_features_uniques:\n",
    "        high_corr_features_uniques.append(second)\n",
    "    else:\n",
    "        continue\n",
    "for feature in high_corr_features_uniques:\n",
    "    if feature not in high_corr_features and \"race\" in feature:\n",
    "        high_corr_features.append(feature)\n",
    "\n",
    "features_to_drop = [feature for feature in high_corr_features_uniques if feature not in high_corr_features]\n",
    "print(len(features_to_drop), features_to_drop)\n",
    "# x_train = x_train.drop(columns=features_to_drop)\n",
    "# x_test = x_test.drop(columns=features_to_drop)\n",
    "# Disabled self correlation filtering for better performance due to low number of candidate features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(481, 123)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we have narrowed down to 41 distinct features that can be now used as candidate features to be selected for use in our estimator models\n",
    "\n",
    "manual pre-processing done\n",
    "\n",
    "#### feature selection function and training functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_select(model_name, classifier, dataset):\n",
    "    x_train, y_train = dataset\n",
    "    #parameters for genetic selection\n",
    "    n_population = 100\n",
    "    crossover_proba = 0.8\n",
    "    mutation_proba = 0.2\n",
    "    n_generations = 5\n",
    "    tournament_size = 10\n",
    "\n",
    "    feature_selector = GeneticSelectionCV(\n",
    "        classifier,\n",
    "        cv=10,\n",
    "        scoring=\"accuracy\",\n",
    "        n_jobs=-1,\n",
    "        n_population=n_population,\n",
    "        crossover_proba=crossover_proba,\n",
    "        mutation_proba=mutation_proba,\n",
    "        crossover_independent_proba=0.5,\n",
    "        mutation_independent_proba=0.05,\n",
    "        n_generations=n_generations,\n",
    "        tournament_size=tournament_size,\n",
    "        n_gen_no_change=5,\n",
    "        caching=True,\n",
    "    )\n",
    "\n",
    "    start_time = time.time()\n",
    "    feature_selector = feature_selector.fit(x_train, y_train.values.ravel())\n",
    "    time_taken = time.time() - start_time\n",
    "    features_selected_bool = feature_selector.support_\n",
    "    features_selected = x_train.columns[features_selected_bool]\n",
    "    print(model_name, \"Time Taken for Feature Selection:\", round(time_taken, 1), \"seconds\")\n",
    "    print(\"Selected Features:\", features_selected)\n",
    "    return features_selected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function for training and pretty printing training and test scores\n",
    "def train(model_name, classifier, dataset):\n",
    "    train, test = dataset\n",
    "    x_train, y_train = train\n",
    "    x_test, y_test = test\n",
    "\n",
    "    start_time = time.time()\n",
    "\n",
    "    #start test\n",
    "    classifier = classifier.fit(x_train, y_train.values.ravel())\n",
    "    y_train_prediction =  classifier.predict(x_train)\n",
    "    y_test_prediction = classifier.predict(x_test)\n",
    "\n",
    "    time_taken = time.time() - start_time\n",
    "\n",
    "    #metrics for training set\n",
    "    f1_score_train = accuracy_score(y_train, y_train_prediction)\n",
    "    auroc_train = roc_auc_score(y_train, y_train_prediction)\n",
    "\n",
    "    #metrics for test set\n",
    "    f1_score_test = accuracy_score(y_test, y_test_prediction)\n",
    "    auroc_test = roc_auc_score(y_test, y_test_prediction)\n",
    "\n",
    "    print(model_name, \"Training Scores - F1:\", round(f1_score_train, 3), \"and AUROC: \", round(auroc_train, 3))\n",
    "    print(model_name, \"Test Scores - F1:\", round(f1_score_test, 3), \"and AUROC: \", round(auroc_test, 3))\n",
    "    print(model_name, \"Time Taken for Training:\", round(time_taken, 1), \"seconds\")\n",
    "    return auroc_train, auroc_test, f1_score_train, f1_score_test, classifier, y_test_prediction, y_train_prediction\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature and Model Selection with Parameter Sweep\n",
    "\n",
    "with feature selection function, models for training can be established\n",
    "\n",
    "classification models that were went thru in SPH6004 includes {Logistic Regression, Decision Tree, Random Forest, Adaboost, Gradient Boost, SVM}\n",
    "\n",
    "to enable finding of the best estimator for different models, we will setup parameter sweeps to undergo a process of trial and error, A FAMOUS MACHINE LEARNING TECHNIQUE, to help with finding the best possible model with parameter combination "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#setup models and parameter sweeps\n",
    "\n",
    "#default values\n",
    "\n",
    "model_names = [\"Logistic Regression\", \"Decision Tree\", \"Random Forest\", \"Adaboost\", \"Gradient Boost\", \"Support Vector Machine\"]\n",
    "\n",
    "# for debugging\n",
    "# model_names = [\"Logistic Regression\", \"Decision Tree\"]\n",
    "\n",
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(solver=\"liblinear\"),\n",
    "    \"Decision Tree\": DecisionTreeClassifier(criterion=\"entropy\"),\n",
    "    \"Random Forest\": RandomForestClassifier(),\n",
    "    \"Adaboost\": AdaBoostClassifier(),\n",
    "    \"Gradient Boost\": GradientBoostingClassifier(),\n",
    "    \"Support Vector Machine\": SVC()\n",
    "}\n",
    "\n",
    "#param sweep values\n",
    "logistic_regression_params = {\"C\": [0.1, 1, 10, 100], \"penalty\": [\"l1\", \"l2\"]}\n",
    "decision_tree_params = {\"max_depth\":[3, 5, 7]}\n",
    "random_forest_params = {\"max_depth\":[3, 5, 7], \"n_estimators\": [50, 100, 500], \"max_samples\": [100, 1000, 3515]}\n",
    "adaboost_params = {\"learning_rate\": [0.001, 0.01, 0.1, 1], \"n_estimators\": [10, 50 , 100, 500], \"estimator\": [DecisionTreeClassifier(max_depth= 3, class_weight = \"balanced\"), DecisionTreeClassifier(max_depth= 5, class_weight= \"balanced\"), DecisionTreeClassifier(max_depth= 7, class_weight= \"balanced\")]}\n",
    "gradient_boost_params = {\"learning_rate\": [0.001, 0.01, 0.1, 1], \"n_estimators\": [10, 50, 100, 500], 'subsample': [0.5, 0.7, 1.0], 'max_depth': [3, 5, 7]}\n",
    "svm_params = {\"C\":[100, 10, 1.0, 0.1, 0.01] , \"kernel\":[\"linear\", \"poly\", \"rbf\", \"sigmoid\"]}\n",
    "\n",
    "#setup param grid for grid search\n",
    "param_grid = {\n",
    "    \"Logistic Regression\": [logistic_regression_params],\n",
    "    \"Decision Tree\": [decision_tree_params],\n",
    "    \"Random Forest\": [random_forest_params],\n",
    "    \"Adaboost\": [adaboost_params],\n",
    "    \"Gradient Boost\": [gradient_boost_params],\n",
    "    \"Support Vector Machine\": [svm_params]\n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "###########################\t Logistic Regression Baseline        ###########################\n",
      "Logistic Regression Training Scores - F1: 0.973 and AUROC:  0.883\n",
      "Logistic Regression Test Scores - F1: 0.865 and AUROC:  0.582\n",
      "Logistic Regression Time Taken for Training: 0.0 seconds\n",
      "###########################\t Decision Tree Baseline              ###########################\n",
      "Decision Tree Training Scores - F1: 1.0 and AUROC:  1.0\n",
      "Decision Tree Test Scores - F1: 0.855 and AUROC:  0.595\n",
      "Decision Tree Time Taken for Training: 0.0 seconds\n",
      "###########################\t Random Forest Baseline              ###########################\n",
      "Random Forest Training Scores - F1: 1.0 and AUROC:  1.0\n",
      "Random Forest Test Scores - F1: 0.894 and AUROC:  0.522\n",
      "Random Forest Time Taken for Training: 0.3 seconds\n",
      "###########################\t Adaboost Baseline                   ###########################\n",
      "Adaboost Training Scores - F1: 1.0 and AUROC:  1.0\n",
      "Adaboost Test Scores - F1: 0.879 and AUROC:  0.59\n",
      "Adaboost Time Taken for Training: 0.2 seconds\n",
      "###########################\t Gradient Boost Baseline             ###########################\n",
      "Gradient Boost Training Scores - F1: 0.998 and AUROC:  0.99\n",
      "Gradient Boost Test Scores - F1: 0.865 and AUROC:  0.562\n",
      "Gradient Boost Time Taken for Training: 0.8 seconds\n",
      "###########################\t Support Vector Machine Baseline     ###########################\n",
      "Support Vector Machine Training Scores - F1: 0.915 and AUROC:  0.606\n",
      "Support Vector Machine Test Scores - F1: 0.889 and AUROC:  0.5\n",
      "Support Vector Machine Time Taken for Training: 0.0 seconds\n"
     ]
    }
   ],
   "source": [
    "#run baseline test\n",
    "\n",
    "total_start_time = time.time()\n",
    "\n",
    "classifiers_baseline = {}\n",
    "auroc_train_baseline = {}\n",
    "auroc_test_baseline = {}\n",
    "prediction_baseline = {}\n",
    "f1_train_baseline = {}\n",
    "f1_test_baseline = {}\n",
    "\n",
    "for model_name in model_names:\n",
    "    print (\"###########################\\t\", model_name, \"Baseline\", \" \"*(25-len(model_name)), \"###########################\")\n",
    "    model = models[model_name]\n",
    "    dataset = [[x_train, y_train], [x_test, y_test]]\n",
    "    auroc_train, auroc_test, f1_score_train, f1_score_test, classifier, prediction_test, prediction_train = train(model_name, model, dataset)\n",
    "\n",
    "    classifiers_baseline[model_name] = classifier\n",
    "    auroc_train_baseline[model_name] = auroc_train\n",
    "    auroc_test_baseline[model_name] = auroc_test\n",
    "    prediction_baseline[model_name] = [prediction_test, prediction_train]\n",
    "    f1_train_baseline[model_name] = f1_score_train\n",
    "    f1_test_baseline[model_name] = f1_score_test\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "###########################\t Logistic Regression Feature Selection        ###########################\n",
      "Logistic Regression Time Taken for Feature Selection: 2.8 seconds\n",
      "Selected Features: Index(['sbp_max', 'sbp_mean', 'dbp_min', 'mbp_min', 'spo2_min', 'spo2_max',\n",
      "       'glucose_max', 'lactate_min', 'ph_max', 'so2_min', 'po2_min',\n",
      "       'pco2_min', 'pco2_max', 'aado2_calc_min', 'baseexcess_min',\n",
      "       'baseexcess_max', 'hemoglobin_min', 'glucose_min.1', 'sodium_min',\n",
      "       'hematocrit_min.1', 'platelets_max', 'bicarbonate_max.1', 'bun_min',\n",
      "       'bun_max', 'calcium_min.1', 'glucose_min.2', 'glucose_max.2',\n",
      "       'sodium_min.1', 'abs_lymphocytes_min', 'abs_lymphocytes_max',\n",
      "       'abs_monocytes_min', 'abs_neutrophils_min', 'abs_neutrophils_max',\n",
      "       'inr_max', 'pt_max', 'ptt_min', 'alp_max', 'gcs_motor', 'gcs_eyes',\n",
      "       'weight_admit', 'race_asian'],\n",
      "      dtype='object')\n",
      "Logistic Regression Training Scores - F1: 0.938 and AUROC:  0.72\n",
      "Logistic Regression Test Scores - F1: 0.894 and AUROC:  0.579\n",
      "Logistic Regression Time Taken for Training: 0.0 seconds\n",
      "###########################\t Decision Tree Feature Selection              ###########################\n",
      "Decision Tree Time Taken for Feature Selection: 7.3 seconds\n",
      "Selected Features: Index(['admission_age', 'heart_rate_min', 'heart_rate_max', 'heart_rate_mean',\n",
      "       'sbp_max', 'sbp_mean', 'dbp_min', 'dbp_mean', 'mbp_min', 'mbp_max',\n",
      "       'mbp_mean', 'resp_rate_min', 'resp_rate_max', 'temperature_min',\n",
      "       'temperature_max', 'spo2_max', 'spo2_mean', 'glucose_mean',\n",
      "       'lactate_min', 'lactate_max', 'ph_min', 'ph_max', 'so2_min', 'so2_max',\n",
      "       'po2_min', 'po2_max', 'aado2_calc_max', 'pao2fio2ratio_max',\n",
      "       'baseexcess_max', 'totalco2_min', 'hematocrit_max', 'chloride_min',\n",
      "       'chloride_max', 'glucose_min.1', 'potassium_min', 'sodium_min',\n",
      "       'sodium_max', 'hematocrit_min.1', 'hematocrit_max.1',\n",
      "       'hemoglobin_max.1', 'platelets_min', 'wbc_max', 'albumin_min',\n",
      "       'aniongap_max', 'bicarbonate_min.1', 'bicarbonate_max.1', 'bun_min',\n",
      "       'calcium_max.1', 'chloride_max.1', 'glucose_min.2', 'glucose_max.2',\n",
      "       'sodium_min.1', 'sodium_max.1', 'potassium_min.1',\n",
      "       'abs_neutrophils_min', 'fibrinogen_min', 'inr_min', 'pt_max', 'ptt_min',\n",
      "       'alt_max', 'alp_min', 'ast_min', 'ast_max', 'bilirubin_total_min',\n",
      "       'bilirubin_total_max', 'gcs_verbal', 'gcs_eyes', 'gcs_unable', 'gender',\n",
      "       'race_unknown', 'race_asian', 'race_other'],\n",
      "      dtype='object')\n",
      "Decision Tree Training Scores - F1: 1.0 and AUROC:  1.0\n",
      "Decision Tree Test Scores - F1: 0.816 and AUROC:  0.535\n",
      "Decision Tree Time Taken for Training: 0.0 seconds\n",
      "###########################\t Random Forest Feature Selection              ###########################\n",
      "Random Forest Time Taken for Feature Selection: 79.2 seconds\n",
      "Selected Features: Index(['dbp_min', 'dbp_mean', 'mbp_max', 'mbp_mean', 'resp_rate_mean',\n",
      "       'temperature_mean', 'po2_min', 'hematocrit_max', 'hemoglobin_min',\n",
      "       'chloride_min', 'calcium_max', 'hematocrit_min.1', 'wbc_max',\n",
      "       'bicarbonate_min.1', 'bicarbonate_max.1', 'calcium_max.1',\n",
      "       'glucose_max.2', 'abs_eosinophils_max', 'fibrinogen_max', 'inr_max',\n",
      "       'alt_max', 'alp_min', 'gcs_eyes', 'weight_admit', 'race_native'],\n",
      "      dtype='object')\n",
      "Random Forest Training Scores - F1: 1.0 and AUROC:  1.0\n",
      "Random Forest Test Scores - F1: 0.879 and AUROC:  0.533\n",
      "Random Forest Time Taken for Training: 0.1 seconds\n",
      "###########################\t Adaboost Feature Selection                   ###########################\n",
      "Adaboost Time Taken for Feature Selection: 68.9 seconds\n",
      "Selected Features: Index(['heart_rate_max', 'heart_rate_mean', 'sbp_min', 'sbp_max', 'sbp_mean',\n",
      "       'dbp_min', 'dbp_max', 'mbp_max', 'mbp_mean', 'resp_rate_min',\n",
      "       'resp_rate_max', 'resp_rate_mean', 'temperature_max',\n",
      "       'temperature_mean', 'spo2_min', 'spo2_mean', 'glucose_min',\n",
      "       'lactate_min', 'so2_max', 'po2_min', 'po2_max', 'pco2_min',\n",
      "       'aado2_calc_max', 'baseexcess_max', 'hematocrit_max', 'hemoglobin_min',\n",
      "       'chloride_min', 'chloride_max', 'calcium_min', 'glucose_min.1',\n",
      "       'potassium_min', 'potassium_max', 'sodium_min', 'platelets_min',\n",
      "       'platelets_max', 'wbc_min', 'wbc_max', 'albumin_min', 'bun_max',\n",
      "       'calcium_min.1', 'chloride_max.1', 'glucose_max.2', 'sodium_max.1',\n",
      "       'abs_basophils_min', 'abs_eosinophils_max', 'abs_lymphocytes_max',\n",
      "       'abs_monocytes_max', 'fibrinogen_min', 'inr_min', 'pt_max', 'alp_max',\n",
      "       'ast_min', 'ast_max', 'bilirubin_total_min', 'gcs_min', 'gcs_motor',\n",
      "       'gcs_eyes', 'gcs_unable', 'weight_admit', 'gender', 'race_unknown',\n",
      "       'race_white', 'race_hispanic', 'race_other'],\n",
      "      dtype='object')\n",
      "Adaboost Training Scores - F1: 0.994 and AUROC:  0.971\n",
      "Adaboost Test Scores - F1: 0.879 and AUROC:  0.59\n",
      "Adaboost Time Taken for Training: 0.1 seconds\n",
      "###########################\t Gradient Boost Feature Selection             ###########################\n",
      "Gradient Boost Time Taken for Feature Selection: 239.9 seconds\n",
      "Selected Features: Index(['heart_rate_mean', 'sbp_min', 'sbp_max', 'sbp_mean', 'dbp_min',\n",
      "       'dbp_max', 'dbp_mean', 'mbp_max', 'resp_rate_min', 'resp_rate_max',\n",
      "       'resp_rate_mean', 'temperature_max', 'spo2_max', 'glucose_max',\n",
      "       'lactate_max', 'ph_max', 'so2_min', 'po2_min', 'pco2_max',\n",
      "       'pao2fio2ratio_max', 'baseexcess_min', 'baseexcess_max', 'totalco2_min',\n",
      "       'totalco2_max', 'hematocrit_min', 'hemoglobin_min', 'hemoglobin_max',\n",
      "       'calcium_max', 'potassium_min', 'hematocrit_min.1', 'hematocrit_max.1',\n",
      "       'hemoglobin_min.1', 'hemoglobin_max.1', 'platelets_min',\n",
      "       'platelets_max', 'wbc_min', 'wbc_max', 'albumin_max', 'aniongap_min',\n",
      "       'aniongap_max', 'bicarbonate_min.1', 'bicarbonate_max.1', 'bun_max',\n",
      "       'calcium_max.1', 'chloride_min.1', 'chloride_max.1', 'glucose_max.2',\n",
      "       'sodium_min.1', 'sodium_max.1', 'abs_eosinophils_min',\n",
      "       'abs_eosinophils_max', 'abs_monocytes_max', 'abs_neutrophils_max',\n",
      "       'inr_max', 'pt_max', 'ptt_max', 'alp_min', 'ast_max',\n",
      "       'bilirubin_total_max', 'gcs_min', 'gcs_motor', 'gcs_verbal', 'gcs_eyes',\n",
      "       'gcs_unable', 'weight_admit', 'race_unknown', 'race_black',\n",
      "       'race_asian', 'race_hispanic', 'race_native'],\n",
      "      dtype='object')\n",
      "Gradient Boost Training Scores - F1: 0.992 and AUROC:  0.962\n",
      "Gradient Boost Test Scores - F1: 0.865 and AUROC:  0.543\n",
      "Gradient Boost Time Taken for Training: 0.4 seconds\n",
      "###########################\t Support Vector Machine Feature Selection     ###########################\n",
      "Support Vector Machine Time Taken for Feature Selection: 3.3 seconds\n",
      "Selected Features: Index(['temperature_mean', 'spo2_min', 'ph_max', 'glucose_max.1',\n",
      "       'hemoglobin_max.1'],\n",
      "      dtype='object')\n",
      "Support Vector Machine Training Scores - F1: 0.898 and AUROC:  0.529\n",
      "Support Vector Machine Test Scores - F1: 0.889 and AUROC:  0.5\n",
      "Support Vector Machine Time Taken for Training: 0.0 seconds\n"
     ]
    }
   ],
   "source": [
    "#run feature selection\n",
    "\n",
    "classifiers_feature = {}\n",
    "features_selected = {}\n",
    "auroc_train_feature = {}\n",
    "auroc_test_feature = {}\n",
    "prediction_feature = {}\n",
    "f1_train_feature = {}\n",
    "f1_test_feature = {}\n",
    "\n",
    "for model_name in model_names:\n",
    "    print (\"###########################\\t\", model_name, \"Feature Selection\", \" \"*(25-len(model_name)), \"###########################\")\n",
    "    training_set = [x_train, y_train]\n",
    "    model = classifiers_baseline[model_name]\n",
    "    features = feature_select(model_name, model, training_set)\n",
    "\n",
    "    x_train_features = x_train[features]\n",
    "    x_test_features = x_test[features]\n",
    "    training_set_features = [x_train_features, y_train]\n",
    "    test_set_features = [x_test_features, y_test]\n",
    "    dataset_features = [training_set_features, test_set_features]\n",
    "    auroc_train, auroc_test, f1_score_train, f1_score_test, classifier, prediction_test, prediction_train = train(model_name, model, dataset_features)\n",
    "\n",
    "    classifiers_feature[model_name] = classifier\n",
    "    features_selected[model_name] = features\n",
    "    auroc_train_feature[model_name] = auroc_train\n",
    "    auroc_test_feature[model_name] = auroc_test\n",
    "    prediction_feature[model_name] = [prediction_test, prediction_train]\n",
    "    f1_test_feature[model_name] = f1_score_test\n",
    "    f1_train_feature[model_name] = f1_score_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "###########################\t Logistic Regression Grid Search        ###########################\n",
      "Logistic Regression Training Scores - F1: 0.892 and AUROC:  0.5\n",
      "Logistic Regression Test Scores - F1: 0.889 and AUROC:  0.5\n",
      "Logistic Regression Time Taken for Training: 1.2 seconds\n",
      "Best Parameters for Logistic Regression : {'C': 10, 'penalty': 'l1'}\n",
      "###########################\t Decision Tree Grid Search              ###########################\n",
      "Decision Tree Training Scores - F1: 0.904 and AUROC:  0.575\n",
      "Decision Tree Test Scores - F1: 0.889 and AUROC:  0.5\n",
      "Decision Tree Time Taken for Training: 0.1 seconds\n",
      "Best Parameters for Decision Tree : {'max_depth': 3}\n",
      "###########################\t Random Forest Grid Search              ###########################\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ziggy/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:425: FitFailedWarning: \n",
      "180 fits failed out of a total of 270.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "9 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/ensemble/_forest.py\", line 408, in fit\n",
      "    n_samples_bootstrap = _get_n_samples_bootstrap(\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/ensemble/_forest.py\", line 120, in _get_n_samples_bootstrap\n",
      "    raise ValueError(msg.format(n_samples, max_samples))\n",
      "ValueError: `max_samples` must be <= n_samples=432 but got value 1000\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "81 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/ensemble/_forest.py\", line 408, in fit\n",
      "    n_samples_bootstrap = _get_n_samples_bootstrap(\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/ensemble/_forest.py\", line 120, in _get_n_samples_bootstrap\n",
      "    raise ValueError(msg.format(n_samples, max_samples))\n",
      "ValueError: `max_samples` must be <= n_samples=433 but got value 1000\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "9 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/ensemble/_forest.py\", line 408, in fit\n",
      "    n_samples_bootstrap = _get_n_samples_bootstrap(\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/ensemble/_forest.py\", line 120, in _get_n_samples_bootstrap\n",
      "    raise ValueError(msg.format(n_samples, max_samples))\n",
      "ValueError: `max_samples` must be <= n_samples=432 but got value 3515\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "81 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\", line 729, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/base.py\", line 1152, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/ensemble/_forest.py\", line 408, in fit\n",
      "    n_samples_bootstrap = _get_n_samples_bootstrap(\n",
      "  File \"/home/ziggy/.local/lib/python3.8/site-packages/sklearn/ensemble/_forest.py\", line 120, in _get_n_samples_bootstrap\n",
      "    raise ValueError(msg.format(n_samples, max_samples))\n",
      "ValueError: `max_samples` must be <= n_samples=433 but got value 3515\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "/home/ziggy/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py:979: UserWarning: One or more of the test scores are non-finite: [0.57004614 0.60778331 0.61317276        nan        nan        nan\n",
      "        nan        nan        nan 0.60266704 0.57671835 0.5865873\n",
      "        nan        nan        nan        nan        nan        nan\n",
      " 0.58200812 0.58150609 0.57799003        nan        nan        nan\n",
      "        nan        nan        nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Training Scores - F1: 0.892 and AUROC:  0.5\n",
      "Random Forest Test Scores - F1: 0.889 and AUROC:  0.5\n",
      "Random Forest Time Taken for Training: 2.8 seconds\n",
      "Best Parameters for Random Forest : {'max_depth': 3, 'max_samples': 100, 'n_estimators': 500}\n",
      "###########################\t Adaboost Grid Search                   ###########################\n",
      "Adaboost Training Scores - F1: 0.709 and AUROC:  0.659\n",
      "Adaboost Test Scores - F1: 0.696 and AUROC:  0.562\n",
      "Adaboost Time Taken for Training: 14.0 seconds\n",
      "Best Parameters for Adaboost : {'estimator': DecisionTreeClassifier(class_weight='balanced', max_depth=3), 'learning_rate': 0.001, 'n_estimators': 10}\n",
      "###########################\t Gradient Boost Grid Search             ###########################\n",
      "Gradient Boost Training Scores - F1: 0.892 and AUROC:  0.5\n",
      "Gradient Boost Test Scores - F1: 0.889 and AUROC:  0.5\n",
      "Gradient Boost Time Taken for Training: 62.7 seconds\n",
      "Best Parameters for Gradient Boost : {'learning_rate': 0.001, 'max_depth': 3, 'n_estimators': 50, 'subsample': 0.7}\n",
      "###########################\t Support Vector Machine Grid Search     ###########################\n",
      "Support Vector Machine Training Scores - F1: 0.892 and AUROC:  0.5\n",
      "Support Vector Machine Test Scores - F1: 0.889 and AUROC:  0.5\n",
      "Support Vector Machine Time Taken for Training: 0.6 seconds\n",
      "Best Parameters for Support Vector Machine : {'C': 0.1, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "classifiers_optimized = {}\n",
    "auroc_train_optimized = {}\n",
    "auroc_test_optimized = {}\n",
    "prediction_optimized = {}\n",
    "f1_train_optimized = {}\n",
    "f1_test_optimized = {}\n",
    "\n",
    "for model_name in model_names:\n",
    "    print (\"###########################\\t\", model_name, \"Grid Search\", \" \"*(25-len(model_name)), \"###########################\")\n",
    "    model = classifiers_feature[model_name]\n",
    "    features = features_selected[model_name]\n",
    "    x_train_features = x_train[features]\n",
    "    x_test_features = x_test[features]\n",
    "    training_set_features = [x_train_features, y_train]\n",
    "    test_set_features = [x_test_features, y_test]\n",
    "    dataset_optimization = [training_set_features, test_set_features]\n",
    "\n",
    "    grid_search_classifier = GridSearchCV(model, param_grid[model_name], cv = 10, scoring='roc_auc', n_jobs=-1)\n",
    "\n",
    "    auroc_train, auroc_test, f1_score_train, f1_score_test, classifier, prediction_test, prediction_train = train(model_name, grid_search_classifier, dataset_features)\n",
    "    print(\"Best Parameters for\", model_name,\":\", classifier.best_params_)\n",
    "    \n",
    "    classifiers_optimized[model_name] = classifier\n",
    "    auroc_train_optimized[model_name] = auroc_train\n",
    "    auroc_test_optimized[model_name] = auroc_test\n",
    "    prediction_optimized[model_name] = [prediction_test, prediction_train]\n",
    "    f1_train_optimized[model_name] = f1_score_train\n",
    "    f1_test_optimized[model_name] = f1_score_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "484.982871055603\n"
     ]
    }
   ],
   "source": [
    "#cmd test\n",
    "total_time_taken = time.time() - total_start_time\n",
    "print(total_time_taken)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Approx runtime is 485s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Data\n",
    "\n",
    "### AUROC Figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "#consolidate data\n",
    "prediction_results = {\"Baseline\": prediction_baseline, \"Feature Selection\": prediction_feature, \"Optimization\": prediction_optimized}\n",
    "auroc_train_results = {\"Baseline\": auroc_train_baseline, \"Feature Selection\": auroc_train_feature, \"Optimization\": auroc_train_optimized}\n",
    "auroc_test_results = {\"Baseline\": auroc_test_baseline, \"Feature Selection\": auroc_test_feature, \"Optimization\": auroc_test_optimized}\n",
    "f1_test_results = {\"Baseline\": f1_test_baseline, \"Feature Selection\": f1_test_feature, \"Optimization\": f1_test_optimized}\n",
    "f1_train_results = {\"Baseline\": f1_train_baseline, \"Feature Selection\": f1_train_feature, \"Optimization\": f1_train_optimized}\n",
    "test_names = [\"Baseline\", \"Feature Selection\", \"Optimization\"]\n",
    "\n",
    "#plotting AUROC\n",
    "fig, axs = plt.subplots(2, 3, figsize=(24,6))\n",
    "\n",
    "for i in range(3):\n",
    "    test_name = test_names[i]\n",
    "    prediction_models = prediction_results[test_name]\n",
    "    auroc_train_models = auroc_train_results[test_name]\n",
    "    auroc_test_models = auroc_test_results[test_name]\n",
    "    \n",
    "    for model_name in model_names:\n",
    "        prediction_test, prediction_train = prediction_models[model_name]\n",
    "        auroc_train = auroc_train_models[model_name]\n",
    "        auroc_test = auroc_test_models[model_name]\n",
    "        RocCurveDisplay.from_predictions(\n",
    "            y_true = y_train, \n",
    "            y_pred = prediction_train, \n",
    "            ax = axs[0][i], \n",
    "            label = test_name + \" - \" + model_name + \" Train - AUROC=\" + str(round(auroc_train,3))\n",
    "        )\n",
    "        RocCurveDisplay.from_predictions(\n",
    "            y_true = y_test, \n",
    "            y_pred = prediction_test, \n",
    "            ax = axs[1][i], \n",
    "            label = test_name + \" - \" + model_name + \" Test - AUROC=\" + str(round(auroc_test,3)) \n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F1 Score Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Baseline': {'Logistic Regression': 0.972972972972973,\n",
       "  'Decision Tree': 1.0,\n",
       "  'Random Forest': 1.0,\n",
       "  'Adaboost': 1.0,\n",
       "  'Gradient Boost': 0.997920997920998,\n",
       "  'Support Vector Machine': 0.9147609147609148},\n",
       " 'Feature Selection': {'Logistic Regression': 0.9376299376299376,\n",
       "  'Decision Tree': 1.0,\n",
       "  'Random Forest': 1.0,\n",
       "  'Adaboost': 0.9937629937629938,\n",
       "  'Gradient Boost': 0.9916839916839917,\n",
       "  'Support Vector Machine': 0.8981288981288982},\n",
       " 'Optimization': {'Logistic Regression': 0.8918918918918919,\n",
       "  'Decision Tree': 0.9043659043659044,\n",
       "  'Random Forest': 0.8918918918918919,\n",
       "  'Adaboost': 0.7089397089397089,\n",
       "  'Gradient Boost': 0.8918918918918919,\n",
       "  'Support Vector Machine': 0.8918918918918919}}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_train_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Baseline': {'Logistic Regression': 0.8647342995169082,\n",
       "  'Decision Tree': 0.855072463768116,\n",
       "  'Random Forest': 0.893719806763285,\n",
       "  'Adaboost': 0.8792270531400966,\n",
       "  'Gradient Boost': 0.8647342995169082,\n",
       "  'Support Vector Machine': 0.8888888888888888},\n",
       " 'Feature Selection': {'Logistic Regression': 0.893719806763285,\n",
       "  'Decision Tree': 0.8164251207729468,\n",
       "  'Random Forest': 0.8792270531400966,\n",
       "  'Adaboost': 0.8792270531400966,\n",
       "  'Gradient Boost': 0.8647342995169082,\n",
       "  'Support Vector Machine': 0.8888888888888888},\n",
       " 'Optimization': {'Logistic Regression': 0.8888888888888888,\n",
       "  'Decision Tree': 0.8888888888888888,\n",
       "  'Random Forest': 0.8888888888888888,\n",
       "  'Adaboost': 0.6956521739130435,\n",
       "  'Gradient Boost': 0.8888888888888888,\n",
       "  'Support Vector Machine': 0.8888888888888888}}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_test_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# echo area echo area"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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",
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