Refactored_Approach_2023.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Alzheimers Prediction - Refactored Approach\n",
    "\n",
    "_Predicting Cognitively Normal (CN), Mild Cognitive Impairment (MCI), or Alzheimers (AD)_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variable Descriptions\n",
    "* `directory.id`\tId assigned to imaging directories. Not from LONi data\n",
    "* `Subject (PTID)`\tParticipant ID\n",
    "* `RID`\tParticipant roster ID\n",
    "* `Image.Data.ID`\tMRI ID\n",
    "* `Modality`\tImage type\n",
    "* `Visit`\t1=screening scan\n",
    "* `Acq.Date`\tMRI date\n",
    "* `DX.bl`\tDiagnosis at baseline\n",
    "* `EXAMDATE`\tExamination Date\n",
    "* `AGE`\t    Age at baseline\n",
    "* `PTGENDER`\tSex\n",
    "* `PTEDUCAT`\tYears of Education\n",
    "* `PTETHCAT`\tEthnicity\n",
    "* `PTRACCAT`\tRace\n",
    "* `APOE4`\tAPOE4 genotype\n",
    "* `MMSE`\tMMSE score on cognitive test\n",
    "* `imputed_genotype`\tChallenge specific designation, TRUE=has imputed genotypes\n",
    "* `APOE Genotype`\tAPOE allele 1 and allele 2 genotypes\n",
    "\n",
    "### Target\n",
    "* `Dx Codes for Submission`\tThe LMCI in the ADNI data is equivalent to MCI in test. This column just converts LMCI->MCI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploration & Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>directory.id</th>\n",
       "      <th>Subject</th>\n",
       "      <th>RID</th>\n",
       "      <th>Image.Data.ID</th>\n",
       "      <th>Modality</th>\n",
       "      <th>Visit</th>\n",
       "      <th>Acq.Date</th>\n",
       "      <th>DX.bl</th>\n",
       "      <th>EXAMDATE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>imputed_genotype</th>\n",
       "      <th>APOE Genotype</th>\n",
       "      <th>Dx Codes for Submission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4702245ea294ce5d4e9b8a87027dfdf4</td>\n",
       "      <td>011_S_0003</td>\n",
       "      <td>3</td>\n",
       "      <td>32237</td>\n",
       "      <td>MRI</td>\n",
       "      <td>1</td>\n",
       "      <td>9/1/05</td>\n",
       "      <td>AD</td>\n",
       "      <td>9/12/05</td>\n",
       "      <td>81.3</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>True</td>\n",
       "      <td>3,4</td>\n",
       "      <td>AD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2e89e352af743597b2368c412e0f6de2</td>\n",
       "      <td>022_S_0004</td>\n",
       "      <td>4</td>\n",
       "      <td>64631</td>\n",
       "      <td>MRI</td>\n",
       "      <td>1</td>\n",
       "      <td>9/22/05</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>11/8/05</td>\n",
       "      <td>67.5</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>False</td>\n",
       "      <td>3,3</td>\n",
       "      <td>MCI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90419199306997753de8042f1fd55e38</td>\n",
       "      <td>011_S_0005</td>\n",
       "      <td>5</td>\n",
       "      <td>32246</td>\n",
       "      <td>MRI</td>\n",
       "      <td>1</td>\n",
       "      <td>9/2/05</td>\n",
       "      <td>CN</td>\n",
       "      <td>9/7/05</td>\n",
       "      <td>73.7</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>True</td>\n",
       "      <td>3,3</td>\n",
       "      <td>CN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d8d175ffff1e2053e6a18c5df494ccdf</td>\n",
       "      <td>100_S_0006</td>\n",
       "      <td>6</td>\n",
       "      <td>33025</td>\n",
       "      <td>MRI</td>\n",
       "      <td>1</td>\n",
       "      <td>11/15/05</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>11/29/05</td>\n",
       "      <td>80.4</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>3,3</td>\n",
       "      <td>MCI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>986e75b2e604cd44b38feb2188476fb2</td>\n",
       "      <td>011_S_0010</td>\n",
       "      <td>10</td>\n",
       "      <td>32270</td>\n",
       "      <td>MRI</td>\n",
       "      <td>1</td>\n",
       "      <td>11/7/05</td>\n",
       "      <td>AD</td>\n",
       "      <td>11/10/05</td>\n",
       "      <td>73.9</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>True</td>\n",
       "      <td>3,4</td>\n",
       "      <td>AD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       directory.id     Subject  RID  Image.Data.ID Modality  \\\n",
       "0  4702245ea294ce5d4e9b8a87027dfdf4  011_S_0003    3          32237      MRI   \n",
       "1  2e89e352af743597b2368c412e0f6de2  022_S_0004    4          64631      MRI   \n",
       "2  90419199306997753de8042f1fd55e38  011_S_0005    5          32246      MRI   \n",
       "3  d8d175ffff1e2053e6a18c5df494ccdf  100_S_0006    6          33025      MRI   \n",
       "4  986e75b2e604cd44b38feb2188476fb2  011_S_0010   10          32270      MRI   \n",
       "\n",
       "   Visit  Acq.Date DX.bl  EXAMDATE   AGE PTGENDER  PTEDUCAT         PTETHCAT  \\\n",
       "0      1    9/1/05    AD   9/12/05  81.3     Male        18  Not Hisp/Latino   \n",
       "1      1   9/22/05  LMCI   11/8/05  67.5     Male        10      Hisp/Latino   \n",
       "2      1    9/2/05    CN    9/7/05  73.7     Male        16  Not Hisp/Latino   \n",
       "3      1  11/15/05  LMCI  11/29/05  80.4   Female        13  Not Hisp/Latino   \n",
       "4      1   11/7/05    AD  11/10/05  73.9   Female        12  Not Hisp/Latino   \n",
       "\n",
       "  PTRACCAT  APOE4  MMSE imputed_genotype APOE Genotype Dx Codes for Submission  \n",
       "0    White      1    20             True           3,4                      AD  \n",
       "1    White      0    27            False           3,3                     MCI  \n",
       "2    White      0    29             True           3,3                      CN  \n",
       "3    White      0    25             True           3,3                     MCI  \n",
       "4    White      1    24             True           3,4                      AD  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/challenges/train/ADNI_Training_Q3_APOE_CollectionADNI1Complete 1Yr 1.5T_July22.2014.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(628, 19)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Small dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MCI    305\n",
       "CN     190\n",
       "AD     133\n",
       "Name: Dx Codes for Submission, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Class balance\n",
    "df['Dx Codes for Submission'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>APOE Genotype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81.3</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3,4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.5</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>3,3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73.7</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>3,3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80.4</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>3,3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>73.9</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>3,4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGE PTGENDER  PTEDUCAT         PTETHCAT PTRACCAT  APOE4  MMSE  \\\n",
       "0  81.3     Male        18  Not Hisp/Latino    White      1    20   \n",
       "1  67.5     Male        10      Hisp/Latino    White      0    27   \n",
       "2  73.7     Male        16  Not Hisp/Latino    White      0    29   \n",
       "3  80.4   Female        13  Not Hisp/Latino    White      0    25   \n",
       "4  73.9   Female        12  Not Hisp/Latino    White      1    24   \n",
       "\n",
       "  APOE Genotype  \n",
       "0           3,4  \n",
       "1           3,3  \n",
       "2           3,3  \n",
       "3           3,3  \n",
       "4           3,4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df[['AGE', 'PTGENDER', 'PTEDUCAT', 'PTETHCAT', 'PTRACCAT', 'APOE4', 'MMSE', 'APOE Genotype']]\n",
    "y_dx = df['Dx Codes for Submission']\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert this to a binary classification problem:\n",
    "\n",
    "(MCI, CN) => Not AD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "623    0\n",
       "624    0\n",
       "625    0\n",
       "626    0\n",
       "627    0\n",
       "Length: 628, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping = {'CN': 0, 'MCI': 0, 'AD': 1}\n",
    "y = pd.Series([mapping[label] for label in y_dx])\n",
    "\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3,3    276\n",
       "3,4    221\n",
       "4,4     71\n",
       "2,3     45\n",
       "2,4     13\n",
       "2,2      2\n",
       "Name: APOE Genotype, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['APOE Genotype'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AGE              0\n",
       "PTGENDER         0\n",
       "PTEDUCAT         0\n",
       "PTETHCAT         0\n",
       "PTRACCAT         0\n",
       "APOE4            0\n",
       "MMSE             0\n",
       "APOE Genotype    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No missing values\n",
    "X.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      364\n",
       "Female    264\n",
       "Name: PTGENDER, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['PTGENDER'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical variable distributions:\n",
      "\n",
      "\n",
      "Distribution of PTRACCAT\n",
      "White    584\n",
      "Black     31\n",
      "Asian     13\n",
      "Name: PTRACCAT, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Distribution of PTETHCAT\n",
      "Not Hisp/Latino    617\n",
      "Hisp/Latino          8\n",
      "Unknown              3\n",
      "Name: PTETHCAT, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Distribution of APOE Genotype\n",
      "3,3    276\n",
      "3,4    221\n",
      "4,4     71\n",
      "2,3     45\n",
      "2,4     13\n",
      "2,2      2\n",
      "Name: APOE Genotype, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Distribution of PTGENDER\n",
      "Male      364\n",
      "Female    264\n",
      "Name: PTGENDER, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Distribution of APOE4\n",
      "0    323\n",
      "1    234\n",
      "2     71\n",
      "Name: APOE4, dtype: int64\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_vars = ['AGE', 'PTEDUCAT', 'MMSE']\n",
    "cat_vars = list(set(X.columns) - set(numerical_vars))\n",
    "\n",
    "def eval_cat_vars(X, variables):\n",
    "    print('Categorical variable distributions:\\n')\n",
    "\n",
    "    for var in variables:\n",
    "        print('\\nDistribution of', var)\n",
    "\n",
    "        dist = X[var].value_counts()\n",
    "        print(dist)\n",
    "        dist.plot(kind='bar')\n",
    "        plt.show()\n",
    "        \n",
    "eval_cat_vars(X, cat_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical Var Distributions:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    628.000000\n",
      "mean      75.169586\n",
      "std        6.687429\n",
      "min       55.100000\n",
      "25%       71.200000\n",
      "50%       75.450000\n",
      "75%       80.000000\n",
      "max       89.600000\n",
      "Name: AGE, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    628.000000\n",
      "mean      15.616242\n",
      "std        2.977993\n",
      "min        6.000000\n",
      "25%       13.000000\n",
      "50%       16.000000\n",
      "75%       18.000000\n",
      "max       20.000000\n",
      "Name: PTEDUCAT, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    628.000000\n",
      "mean      26.920382\n",
      "std        2.562152\n",
      "min       20.000000\n",
      "25%       25.000000\n",
      "50%       27.000000\n",
      "75%       29.000000\n",
      "max       30.000000\n",
      "Name: MMSE, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def eval_num_vars(X, variables):\n",
    "    print('Numerical Var Distributions:\\n')\n",
    "\n",
    "    for var in variables:\n",
    "        plt.hist(X[var], bins=10)\n",
    "        plt.title(var + ' Distribution')\n",
    "        plt.show()\n",
    "\n",
    "        # descriptive stats\n",
    "        print(X[var].describe())\n",
    "        \n",
    "eval_num_vars(X, numerical_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing\n",
    "\n",
    "One-hot encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>APOE Genotype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81.3</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3,4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.5</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>3,3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73.7</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>3,3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80.4</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>3,3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>73.9</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>3,4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGE PTGENDER  PTEDUCAT         PTETHCAT PTRACCAT  APOE4  MMSE  \\\n",
       "0  81.3     Male        18  Not Hisp/Latino    White      1    20   \n",
       "1  67.5     Male        10      Hisp/Latino    White      0    27   \n",
       "2  73.7     Male        16  Not Hisp/Latino    White      0    29   \n",
       "3  80.4   Female        13  Not Hisp/Latino    White      0    25   \n",
       "4  73.9   Female        12  Not Hisp/Latino    White      1    24   \n",
       "\n",
       "  APOE Genotype  \n",
       "0           3,4  \n",
       "1           3,3  \n",
       "2           3,3  \n",
       "3           3,3  \n",
       "4           3,4  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting PTRACCAT to 1-hot encoding\n",
      "Converting PTETHCAT to 1-hot encoding\n",
      "Converting APOE Genotype to 1-hot encoding\n",
      "Converting PTGENDER to 1-hot encoding\n",
      "Converting APOE4 to 1-hot encoding\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "      <th>Hisp/Latino</th>\n",
       "      <th>Not Hisp/Latino</th>\n",
       "      <th>Unknown</th>\n",
       "      <th>2,2</th>\n",
       "      <th>2,3</th>\n",
       "      <th>2,4</th>\n",
       "      <th>3,3</th>\n",
       "      <th>3,4</th>\n",
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       "      <th>Male</th>\n",
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       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81.3</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73.7</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80.4</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGE  PTEDUCAT  MMSE  Asian  Black  White  Hisp/Latino  Not Hisp/Latino  \\\n",
       "0  81.3        18    20      0      0      1            0                1   \n",
       "1  67.5        10    27      0      0      1            1                0   \n",
       "2  73.7        16    29      0      0      1            0                1   \n",
       "3  80.4        13    25      0      0      1            0                1   \n",
       "\n",
       "   Unknown  2,2  2,3  2,4  3,3  3,4  4,4  Female  Male  0  1  2  \n",
       "0        0    0    0    0    0    1    0       0     1  0  1  0  \n",
       "1        0    0    0    0    1    0    0       0     1  1  0  0  \n",
       "2        0    0    0    0    1    0    0       0     1  1  0  0  \n",
       "3        0    0    0    0    1    0    0       1     0  1  0  0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for var in cat_vars:\n",
    "    print('Converting', var, 'to 1-hot encoding')\n",
    "    \n",
    "    #get 1-hot and replace original column with the >= 2 categories as columns\n",
    "    one_hot_df = pd.get_dummies(X[var])\n",
    "    X = pd.concat([X, one_hot_df], axis=1)\n",
    "    X = X.drop(var, axis=1)\n",
    "    \n",
    "X.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize numerical values [0, 1]"
   ]
  },
  {
   "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>AGE</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "      <th>Hisp/Latino</th>\n",
       "      <th>Not Hisp/Latino</th>\n",
       "      <th>Unknown</th>\n",
       "      <th>2,2</th>\n",
       "      <th>2,3</th>\n",
       "      <th>2,4</th>\n",
       "      <th>3,3</th>\n",
       "      <th>3,4</th>\n",
       "      <th>4,4</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.759420</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.359420</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.539130</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.544928</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        AGE  PTEDUCAT  MMSE  Asian  Black  White  Hisp/Latino  \\\n",
       "0  0.759420  0.857143   0.0      0      0      1            0   \n",
       "1  0.359420  0.285714   0.7      0      0      1            1   \n",
       "2  0.539130  0.714286   0.9      0      0      1            0   \n",
       "3  0.733333  0.500000   0.5      0      0      1            0   \n",
       "4  0.544928  0.428571   0.4      0      0      1            0   \n",
       "\n",
       "   Not Hisp/Latino  Unknown  2,2  2,3  2,4  3,3  3,4  4,4  Female  Male  0  1  \\\n",
       "0                1        0    0    0    0    0    1    0       0     1  0  1   \n",
       "1                0        0    0    0    0    1    0    0       0     1  1  0   \n",
       "2                1        0    0    0    0    1    0    0       0     1  1  0   \n",
       "3                1        0    0    0    0    1    0    0       1     0  1  0   \n",
       "4                1        0    0    0    0    0    1    0       1     0  0  1   \n",
       "\n",
       "   2  \n",
       "0  0  \n",
       "1  0  \n",
       "2  0  \n",
       "3  0  \n",
       "4  0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Fit and transform the data using the scaler\n",
    "X[['AGE', 'PTEDUCAT', 'MMSE']] = scaler.fit_transform(X[['AGE', 'PTEDUCAT', 'MMSE']])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train & Validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (502, 20)\n",
      "Test set: (126, 20)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "print('Train set:', X_train.shape)\n",
    "print('Test set:', X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    392\n",
      "1    110\n",
      "dtype: int64\n",
      "0    103\n",
      "1     23\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Careful of a little class imbalance\n",
    "print(y_train.value_counts())\n",
    "print(y_test.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, compare a few models with their default hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "332    0\n",
       "6      0\n",
       "104    0\n",
       "114    0\n",
       "482    0\n",
       "      ..\n",
       "71     0\n",
       "106    0\n",
       "270    0\n",
       "435    0\n",
       "102    1\n",
       "Length: 502, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** There was a prior subsample for a validation set that was used here to compare models. \n",
    "\n",
    "Variables: `X_valid`, `y_valid`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# lr = LogisticRegression()\n",
    "\n",
    "# # Fit the model on the training data\n",
    "# lr.fit(X_train, y_train)\n",
    "\n",
    "# # Make predictions on the validation data\n",
    "# y_pred = lr.predict(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.metrics import accuracy_score\n",
    "\n",
    "# lr_accuracy = accuracy_score(y_pred, y_valid)\n",
    "# print(f\"Logistic Regression Validation Accuracy: {lr_accuracy:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Validation Accuracy: 68.25%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "# Fit the model on the training data\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the validation data\n",
    "y_pred = rf.predict(X_valid)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "rf_accuracy = accuracy_score(y_pred, y_valid)\n",
    "print(f\"Random Forest Validation Accuracy: {rf_accuracy:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boosting Validation Accuracy: 74.60%\n"
     ]
    }
   ],
   "source": [
    "# Create a GradientBoostingClassifier object\n",
    "gb = GradientBoostingClassifier(random_state=42)\n",
    "\n",
    "# Fit the model to the training data\n",
    "gb.fit(X_train, y_train)\n",
    "\n",
    "# Predict the classes of the validation set\n",
    "y_pred = gb.predict(X_valid)\n",
    "\n",
    "gb_accuracy = accuracy_score(y_pred, y_valid)\n",
    "print(f\"Boosting Validation Accuracy: {gb_accuracy:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = gb.feature_importances_[:7]\n",
    "feat_names = X_train.columns[:7]\n",
    "feat_importances_df = pd.DataFrame({'Feature name':feat_names, 'Feature importance':feat_importances})\n",
    "feat_importances_df.sort_values(by='Feature importance', inplace=True)\n",
    "feat_importances_df.plot(kind='barh', x='Feature name', y='Feature importance')\n",
    "plt.title('Feature importances')\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Feature name')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "Unsurprisingly, cognitive test scores (`MMSE`) and age (`AGE`) are major contributors to the model. Interestingly, gender also appears to be somewhat predictive. \n",
    "\n",
    "One might wonder why its not more predictive given that roughly [2/3 of those living with Alzheimer's in the US are women.](https://www.health.harvard.edu/blog/why-are-women-more-likely-to-develop-alzheimers-disease-202201202672) Theories suggest the primary reason for this is that women tend to live longer. Thus, `AGE` being our second most important feature already captures that information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Some hyperparemeter tuning\n",
    "\n",
    "Dataset may be too small for hyperparameter tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# learning_rates = [0.01, 0.05, .1, .2]\n",
    "# n_estimators = [50, 100, 300]\n",
    "# min_samples_splits = [2, 3, 4]\n",
    "# max_depths = [2,3,4,5]\n",
    "# max_features = ['auto', 'sqrt', 'log2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# best_accuracy = float('-inf')\n",
    "# best_hparams = {\n",
    "#     'learning_rate': None,\n",
    "#     'n_estimators': None,\n",
    "#     'min_samples_split': None,\n",
    "#     'max_depth': None,\n",
    "#     'max_features': None\n",
    "# }\n",
    "\n",
    "# for learning_rate in learning_rates:\n",
    "#     for n in n_estimators:\n",
    "#         for min_samples_split in min_samples_splits:\n",
    "#             for max_depth in max_depths:\n",
    "#                 for max_feature in max_features:\n",
    "#                     #print(learning_rate, n, min_samples_split, max_depth, max_feature)\n",
    "                    \n",
    "#                     curr_gb = GradientBoostingClassifier(\n",
    "#                         learning_rate=learning_rate,\n",
    "#                         n_estimators=n,\n",
    "#                         min_samples_split=min_samples_split,\n",
    "#                         max_depth=max_depth,\n",
    "#                         max_features=max_feature,\n",
    "#                         random_state=42\n",
    "#                     )\n",
    "\n",
    "#                     # Fit the model to the training data\n",
    "#                     curr_gb.fit(X_train, y_train)\n",
    "\n",
    "#                     # Predict the classes of the validation set\n",
    "#                     y_pred = curr_gb.predict(X_valid)\n",
    "\n",
    "#                     curr_gb_accuracy = accuracy_score(y_pred, y_valid)\n",
    "                    \n",
    "#                     if curr_gb_accuracy > best_accuracy:\n",
    "#                         best_accuracy = curr_gb_accuracy\n",
    "#                         best_hparams = {\n",
    "#                             'learning_rate': learning_rate,\n",
    "#                             'n_estimators': n,\n",
    "#                             'min_samples_split': min_samples_split,\n",
    "#                             'max_depth': max_depth,\n",
    "#                             'max_features': max_feature\n",
    "#                         }\n",
    "#                         print(f'New best accuracy: {best_accuracy:.2%} with params: {best_hparams}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can also use `GridSearchCV`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
       "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "                           max_features=None, max_leaf_nodes=None,\n",
       "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                           min_samples_leaf=1, min_samples_split=2,\n",
       "                           min_weight_fraction_leaf=0.0, n_estimators=300,\n",
       "                           n_iter_no_change=None, presort='deprecated',\n",
       "                           random_state=42, subsample=1.0, tol=0.0001,\n",
       "                           validation_fraction=0.1, verbose=0,\n",
       "                           warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# Train on best hparams\n",
    "gb = GradientBoostingClassifier(n_estimators=300, random_state=42)\n",
    "\n",
    "# Fit the model to the training data\n",
    "gb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 90.48%\n"
     ]
    }
   ],
   "source": [
    "# Predict on test set\n",
    "y_pred = gb.predict(X_test)\n",
    "\n",
    "# Evaluate predictions\n",
    "test_acc = accuracy_score(y_test, y_pred)\n",
    "print(f\"Test accuracy: {test_acc:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/grantgasser/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from sklearn.metrics import precision_score, recall_score, roc_auc_score, f1_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "fix, ax = plt.subplots(figsize=(6,4))\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# plot heatmap of confusion matrix\n",
    "sns.heatmap(cm, annot=True, cmap=\"Blues\", ax=ax)\n",
    "ax.set_ylim(2, 0)\n",
    "ax.set_xlabel(\"Predicted Labels\")\n",
    "ax.set_ylabel(\"True Labels\")\n",
    "ax.set_title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[101,   2],\n",
       "       [ 10,  13]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=gb.classes_)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.87\n",
      "Recall: 0.57\n",
      "F1: 0.68\n",
      "AUC: 0.77\n"
     ]
    }
   ],
   "source": [
    "print(f'Precision: {precision_score(y_test, y_pred):.2f}')\n",
    "print(f'Recall: {recall_score(y_test, y_pred):.2f}')\n",
    "print(f'F1: {f1_score(y_test, y_pred):.2f}')\n",
    "print(f'AUC: {roc_auc_score(y_test, y_pred):.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Appears to be an issue with False Negatives (10) more so than false positives. As a general rule in medicine, it is likely better to have a false positive than a false negative. \n",
    "\n",
    "A positive yields more investigation and caution. A negative yields inaction, which is not good. _Let's lower the threshold to make the model a bit more aggressive._\n",
    "\n",
    "**Chose 0.1 with a bit of trial and error, which is somewhat cheating. Would need to do so with a validation set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "thresh = 0.1\n",
    "\n",
    "probs = gb.predict_proba(X_test)[:,1]\n",
    "y_pred = (probs > thresh).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.67\n",
      "Recall: 0.87\n",
      "F1: 0.75\n",
      "AUC: 0.89\n"
     ]
    }
   ],
   "source": [
    "print(f'Precision: {precision_score(y_test, y_pred):.2f}')\n",
    "print(f'Recall: {recall_score(y_test, y_pred):.2f}')\n",
    "print(f'F1: {f1_score(y_test, y_pred):.2f}')\n",
    "print(f'AUC: {roc_auc_score(y_test, y_pred):.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[93, 10],\n",
       "       [ 3, 20]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we're down to only 3 False Negatives\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 89.68%\n"
     ]
    }
   ],
   "source": [
    "# how does accuracy fare given our new threshold?\n",
    "print(f'Accuracy: {accuracy_score(y_test, y_pred):.2%}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Misclassifications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_missed = y_pred != y_test\n",
    "X_missed = X_test[y_missed]\n",
    "\n",
    "# in case needed\n",
    "missed_idx = X_missed.index\n",
    "\n",
    "y_test_missed = y_test[y_missed]\n",
    "y_pred_missed = y_pred[y_missed]\n",
    "y_probs_missed = probs[y_missed]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>AGE</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "      <th>Hisp/Latino</th>\n",
       "      <th>Not Hisp/Latino</th>\n",
       "      <th>Unknown</th>\n",
       "      <th>2,2</th>\n",
       "      <th>...</th>\n",
       "      <th>3,4</th>\n",
       "      <th>4,4</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>y_true</th>\n",
       "      <th>y_probs</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.991304</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.950242</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.553623</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.155638</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.811594</td>\n",
       "      <td>0.785714</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999604</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.710145</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.079563</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.617391</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.172003</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.562319</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.055878</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.269735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.710145</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.375022</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.428986</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.129599</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.652174</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.102379</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.805797</td>\n",
       "      <td>0.357143</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.104761</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.594203</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.067277</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.649275</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.112066</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         AGE  PTEDUCAT  MMSE  Asian  Black  White  Hisp/Latino  \\\n",
       "0   0.991304  0.500000   0.6      0      0      1            0   \n",
       "1   0.553623  1.000000   0.6      0      0      1            0   \n",
       "2   0.811594  0.785714   0.3      0      0      1            0   \n",
       "3   0.710145  1.000000   0.5      0      0      1            0   \n",
       "4   0.617391  0.714286   0.4      1      0      0            0   \n",
       "5   0.562319  0.928571   0.6      0      0      1            0   \n",
       "6   1.000000  0.714286   1.0      0      0      1            0   \n",
       "7   0.710145  0.714286   0.6      0      0      1            0   \n",
       "8   0.428986  0.714286   0.5      0      0      1            0   \n",
       "9   0.652174  0.714286   0.6      0      0      1            0   \n",
       "10  0.805797  0.357143   0.4      0      1      0            0   \n",
       "11  0.594203  0.428571   0.6      0      0      1            0   \n",
       "12  0.649275  0.571429   0.5      0      0      1            0   \n",
       "\n",
       "    Not Hisp/Latino  Unknown  2,2  ...  3,4  4,4  Female  Male  0  1  2  \\\n",
       "0                 1        0    0  ...    0    0       0     1  1  0  0   \n",
       "1                 1        0    0  ...    1    0       0     1  0  1  0   \n",
       "2                 1        0    0  ...    0    1       0     1  0  0  1   \n",
       "3                 1        0    0  ...    1    0       0     1  0  1  0   \n",
       "4                 1        0    0  ...    0    0       0     1  1  0  0   \n",
       "5                 1        0    0  ...    0    0       0     1  1  0  0   \n",
       "6                 1        0    0  ...    0    0       1     0  1  0  0   \n",
       "7                 1        0    0  ...    1    0       0     1  0  1  0   \n",
       "8                 1        0    0  ...    0    0       0     1  1  0  0   \n",
       "9                 1        0    0  ...    0    0       0     1  1  0  0   \n",
       "10                1        0    0  ...    1    0       1     0  0  1  0   \n",
       "11                1        0    0  ...    0    0       0     1  1  0  0   \n",
       "12                1        0    0  ...    1    0       1     0  0  1  0   \n",
       "\n",
       "    y_true   y_probs  y_pred  \n",
       "0        0  0.950242       1  \n",
       "1        0  0.155638       1  \n",
       "2        0  0.999604       1  \n",
       "3        1  0.079563       0  \n",
       "4        0  0.172003       1  \n",
       "5        1  0.055878       0  \n",
       "6        0  0.269735       1  \n",
       "7        0  0.375022       1  \n",
       "8        0  0.129599       1  \n",
       "9        0  0.102379       1  \n",
       "10       0  0.104761       1  \n",
       "11       1  0.067277       0  \n",
       "12       0  0.112066       1  \n",
       "\n",
       "[13 rows x 23 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_missed = y_test_missed.reset_index(drop=True).rename('y_true')\n",
    "y_probs_missed = pd.Series(y_probs_missed).reset_index(drop=True).rename('y_probs')\n",
    "y_pred_missed = pd.Series(y_pred_missed).reset_index(drop=True).rename('y_pred')\n",
    "\n",
    "y_concat = pd.concat([y_test_missed, y_probs_missed, y_pred_missed], axis=1)\n",
    "X_missed = pd.concat([X_missed.reset_index(drop=True), y_concat], axis=1)\n",
    "\n",
    "X_missed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "fps = X_missed[(X_missed['y_true'] == 0) & (X_missed['y_pred'] == 1)]\n",
    "fns = X_missed[(X_missed['y_true'] == 1) & (X_missed['y_pred'] == 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**False Positives**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>MMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.722029</td>\n",
       "      <td>0.678571</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             AGE   PTEDUCAT   MMSE\n",
       "count  10.000000  10.000000  10.00\n",
       "mean    0.722029   0.678571   0.55"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analyze distribution of numerical values of FPs\n",
    "fps[numerical_vars].describe()[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>MMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>502.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.582938</td>\n",
       "      <td>0.688532</td>\n",
       "      <td>0.67988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AGE    PTEDUCAT       MMSE\n",
       "count  502.000000  502.000000  502.00000\n",
       "mean     0.582938    0.688532    0.67988"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[numerical_vars].describe()[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears our false positives are on examples where `AGE` is higher and `MMSE` is lower than the means from the training data, which seem to be reasonable mistakes given that high age and low MMSE are correlated with AD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**False Negatives**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "      <th>Hisp/Latino</th>\n",
       "      <th>Not Hisp/Latino</th>\n",
       "      <th>Unknown</th>\n",
       "      <th>2,2</th>\n",
       "      <th>...</th>\n",
       "      <th>3,4</th>\n",
       "      <th>4,4</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>y_true</th>\n",
       "      <th>y_probs</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.710145</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.079563</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.562319</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.055878</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.594203</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.067277</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         AGE  PTEDUCAT  MMSE  Asian  Black  White  Hisp/Latino  \\\n",
       "3   0.710145  1.000000   0.5      0      0      1            0   \n",
       "5   0.562319  0.928571   0.6      0      0      1            0   \n",
       "11  0.594203  0.428571   0.6      0      0      1            0   \n",
       "\n",
       "    Not Hisp/Latino  Unknown  2,2  ...  3,4  4,4  Female  Male  0  1  2  \\\n",
       "3                 1        0    0  ...    1    0       0     1  0  1  0   \n",
       "5                 1        0    0  ...    0    0       0     1  1  0  0   \n",
       "11                1        0    0  ...    0    0       0     1  1  0  0   \n",
       "\n",
       "    y_true   y_probs  y_pred  \n",
       "3        1  0.079563       0  \n",
       "5        1  0.055878       0  \n",
       "11       1  0.067277       0  \n",
       "\n",
       "[3 rows x 23 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "fns"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predicting a new patient:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   AGE  PTEDUCAT  MMSE  Asian  Black  White  Hisp/Latino  Not Hisp/Latino  \\\n",
       "0   61        17    30      0      0      1            0                1   \n",
       "\n",
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       "0        0    0    0    0    0    0    0       1     0  0  0  0  "
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     "execution_count": 56,
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   ],
   "source": [
    "new_sample = new_sample = [61, 17, 30, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]\n",
    "\n",
    "new_df = pd.DataFrame([new_sample], columns=X_train.columns)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        AGE  PTEDUCAT  MMSE  Asian  Black  White  Hisp/Latino  \\\n",
       "0  0.171014  0.785714   1.0      0      0      1            0   \n",
       "\n",
       "   Not Hisp/Latino  Unknown  2,2  2,3  2,4  3,3  3,4  4,4  Female  Male  0  1  \\\n",
       "0                1        0    0    0    0    0    0    0       1     0  0  0   \n",
       "\n",
       "   2  \n",
       "0  0  "
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     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df[['AGE', 'PTEDUCAT', 'MMSE']] = scaler.transform(new_df[['AGE', 'PTEDUCAT', 'MMSE']])\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.996807, 0.003193]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb.predict_proba(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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