{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "961e18c7",
   "metadata": {},
   "source": [
    "# Data Loading & Initial Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "90b87909",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score, f1_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a3786215",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import NSUDH dataset\n",
    "df = pd.read_csv(\"../data/NSDUH_2023_Tab.txt\", \n",
    "                 sep=\"\\t\", \n",
    "                 low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b17fa3f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of        QUESTID2    FILEDATE     ANALWT2_C  VESTR_C  VEREP  PDEN10  COUTYP4  \\\n",
       "0      10000053  03/25/2025   3276.469874    40031      2       2        2   \n",
       "1      10000679  03/25/2025  15630.082955    40021      2       2        3   \n",
       "2      10001208  03/25/2025   4018.172390    40043      1       2        2   \n",
       "3      10001260  03/25/2025  10711.709540    40030      2       2        2   \n",
       "4      10001588  03/25/2025   8195.104779    40023      2       2        2   \n",
       "...         ...         ...           ...      ...    ...     ...      ...   \n",
       "56700  50556120  03/25/2025    417.630119    40018      2       2        2   \n",
       "56701  50557151  03/25/2025   7625.717934    40017      1       2        2   \n",
       "56702  50558694  03/25/2025  23556.083908    40042      1       1        1   \n",
       "56703  50558696  03/25/2025   5193.882625    40040      2       1        1   \n",
       "56704  50558785  03/25/2025   2676.234296    40027      1       2        2   \n",
       "\n",
       "       MAIIN102  AIIND102  AGE3  ...  COSUTELE2  COSUAPTDL2  COSURXDL2  \\\n",
       "0             2         2    10  ...        3.0         3.0        3.0   \n",
       "1             2         2     9  ...        3.0         3.0        3.0   \n",
       "2             2         2     9  ...        3.0         3.0        3.0   \n",
       "3             2         2     1  ...        2.0         2.0        2.0   \n",
       "4             2         2    10  ...        3.0         3.0        3.0   \n",
       "...         ...       ...   ...  ...        ...         ...        ...   \n",
       "56700         2         2     9  ...        NaN         NaN        NaN   \n",
       "56701         2         2    11  ...        3.0         3.0        3.0   \n",
       "56702         2         2    10  ...        2.0         2.0        2.0   \n",
       "56703         2         2    11  ...        3.0         3.0        3.0   \n",
       "56704         2         2     3  ...        2.0         2.0        2.0   \n",
       "\n",
       "       COSUSVHLT2  COHCTELE2  COHCAPTDL2  COHCRXDL2  COHCSVHLT2  LANGVER  \\\n",
       "0             3.0        3.0         3.0        3.0         3.0        1   \n",
       "1             3.0        3.0         3.0        3.0         3.0        1   \n",
       "2             3.0        3.0         3.0        3.0         3.0        1   \n",
       "3             2.0        2.0         2.0        2.0         2.0        1   \n",
       "4             3.0        1.0         3.0        3.0         3.0        1   \n",
       "...           ...        ...         ...        ...         ...      ...   \n",
       "56700         NaN        NaN         NaN        NaN         NaN        2   \n",
       "56701         3.0        2.0         2.0        2.0         2.0        1   \n",
       "56702         2.0        2.0         2.0        2.0         2.0        1   \n",
       "56703         3.0        2.0         2.0        2.0         2.0        1   \n",
       "56704         2.0        2.0         2.0        2.0         2.0        1   \n",
       "\n",
       "       GQTYPE2  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2          NaN  \n",
       "3          NaN  \n",
       "4          NaN  \n",
       "...        ...  \n",
       "56700      NaN  \n",
       "56701      NaN  \n",
       "56702      NaN  \n",
       "56703      NaN  \n",
       "56704      NaN  \n",
       "\n",
       "[56705 rows x 2636 columns]>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking Data\n",
    "df.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ce756b8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SRCCLFRSED     99.871264\n",
       "SRCFRSEDNM     99.871264\n",
       "GQTYPE2        99.850101\n",
       "SRCSEDNM2      99.728419\n",
       "SRCFRTRQNM     99.370426\n",
       "                 ...    \n",
       "SEXIDENT22      0.000000\n",
       "SPEAKENGL       0.000000\n",
       "LVLDIFSEE2      0.000000\n",
       "LVLDIFHEAR2     0.000000\n",
       "LVLDIFWALK2     0.000000\n",
       "Length: 2636, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_summary = df.isna().mean() * 100\n",
    "missing_summary.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6247efb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for duplicate rows\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6e5829ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45133, 2636)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtering dataset for relevant age group (18 and above)\n",
    "'''I used AGE3 instead of CATAG7 because AGE3 is the final edited age variable in NSDUH. It’s the most accurate age measure because it incorporates multiple consistency checks—birthdate, roster age, screener age, and internal corrections. CATAG7 is just a categorical recode derived from AGE3. For filtering out respondents under 18, it’s better to filter at the source (AGE3) and let the model work with the true final age before any recoding.\n",
    "\n",
    "AGE3 Len : 2 RECODE - FINAL EDITED AGE\n",
    "Freq Pct\n",
    "1 = Respondent is 12 or 13 years old\n",
    "2 = Respondent is 14 or 15 years old\n",
    "3 = Respondent is 16 or 17 years old\n",
    "4 = Respondent is between 18 and 20 years old\n",
    "5 = Respondent is between 21 and 23 years old\n",
    "6 = Respondent is 24 or 25 years old\n",
    "7 = Respondent is between 26 and 29 years old\n",
    "8 = Respondent is between 30 and 34 years old\n",
    "9 = Respondent is between 35 and 49 years old\n",
    "10 = Respondent is between 50 and 64 years old\n",
    "11 = Respondent is 65 years old or older'''\n",
    "\n",
    "df_no_age = df[df['AGE3'] >= 4]   \n",
    "df_no_age.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "59e10e60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94 72183\n",
      "97 44145\n",
      "98 1898217\n",
      "85 9487\n",
      "994 7015\n",
      "997 3857\n",
      "998 33083\n",
      "985 5817\n",
      "9994 4675\n",
      "9997 1899\n",
      "9998 20242\n",
      "9985 1774\n",
      "99 17222835\n",
      "999 1267661\n",
      "9999 1453080\n",
      "89 4185\n",
      "989 154\n",
      "9989 1332\n"
     ]
    }
   ],
   "source": [
    "# Checking how many error codes are present in the dataset\n",
    "'''Code\tMeaning\tShould Convert to NaN?\n",
    "94 / 994 / 9994\tDon't know\n",
    "97 / 997 / 9997\tRefused\n",
    "98 / 998 / 9998\tBlank / Not answered\n",
    "85 / 985 / 9985\tBad / inconsistent data\n",
    "99 / 999 / 9999\tLegitimate skip\t\n",
    "89 / 989 / 9989\tLegitimate skip - logically assigned'''\n",
    "\n",
    "nan_codes = [\n",
    "    94, 97, 98, 85,\n",
    "    994, 997, 998, 985,\n",
    "    9994, 9997, 9998, 9985,\n",
    "    99, 999, 9999,\n",
    "    89, 989, 9989\n",
    "]\n",
    "\n",
    "for code in nan_codes:\n",
    "    count = (df_no_age == code).sum().sum()\n",
    "    print(code, count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8c22802b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the following error codes to NaN\n",
    "\n",
    "nan_codes = [\n",
    "    94, 97, 98, 85,\n",
    "    994, 997, 998, 985,\n",
    "    9994, 9997, 9998, 9985,\n",
    "    99, 999, 9999,\n",
    "    89, 989, 9989\n",
    "]\n",
    "\n",
    "df_ErrorCode_NaN = df_no_age.replace(nan_codes, np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b7f8f370",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94 0\n",
      "97 0\n",
      "98 0\n",
      "85 0\n",
      "994 0\n",
      "997 0\n",
      "998 0\n",
      "985 0\n",
      "9994 0\n",
      "9997 0\n",
      "9998 0\n",
      "9985 0\n",
      "99 0\n",
      "999 0\n",
      "9999 0\n",
      "89 0\n",
      "989 0\n",
      "9989 0\n"
     ]
    }
   ],
   "source": [
    "nan_codes = [\n",
    "    94, 97, 98, 85,\n",
    "    994, 997, 998, 985,\n",
    "    9994, 9997, 9998, 9985,\n",
    "    99, 999, 9999,\n",
    "    89, 989, 9989\n",
    "]\n",
    "\n",
    "for code in nan_codes:\n",
    "    count = (df_ErrorCode_NaN == code).sum().sum()\n",
    "    print(code, count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9fb5f29d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for any NaNs in the Target Variable (IRAMDEYR)\n",
    "df_ErrorCode_NaN['IRAMDEYR'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "ee14e6bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows before: 56705\n",
      "Rows after : 45133\n",
      "Rows removed: 11572\n"
     ]
    }
   ],
   "source": [
    "# 2. DROP rows where the target is NaN\n",
    "df_dropped_targetNaN = df_ErrorCode_NaN.dropna(subset=['IRAMDEYR'])\n",
    "\n",
    "before = df.shape[0]\n",
    "after = df_dropped_targetNaN.shape[0]\n",
    "\n",
    "print(\"Rows before:\", before)\n",
    "print(\"Rows after :\", after)\n",
    "print(\"Rows removed:\", before - after)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9214f5b3",
   "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>QUESTID2</th>\n",
       "      <th>ANALWT2_C</th>\n",
       "      <th>VESTR_C</th>\n",
       "      <th>VEREP</th>\n",
       "      <th>PDEN10</th>\n",
       "      <th>COUTYP4</th>\n",
       "      <th>MAIIN102</th>\n",
       "      <th>AIIND102</th>\n",
       "      <th>AGE3</th>\n",
       "      <th>SERVICE</th>\n",
       "      <th>...</th>\n",
       "      <th>COMHSVHLT2</th>\n",
       "      <th>COSUTELE2</th>\n",
       "      <th>COSUAPTDL2</th>\n",
       "      <th>COSURXDL2</th>\n",
       "      <th>COSUSVHLT2</th>\n",
       "      <th>COHCTELE2</th>\n",
       "      <th>COHCAPTDL2</th>\n",
       "      <th>COHCRXDL2</th>\n",
       "      <th>COHCSVHLT2</th>\n",
       "      <th>LANGVER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.513300e+04</td>\n",
       "      <td>45133.000000</td>\n",
       "      <td>45133.000000</td>\n",
       "      <td>45133.000000</td>\n",
       "      <td>45133.000000</td>\n",
       "      <td>45133.000000</td>\n",
       "      <td>45133.000000</td>\n",
       "      <td>45133.000000</td>\n",
       "      <td>45133.000000</td>\n",
       "      <td>45108.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>43333.000000</td>\n",
       "      <td>43354.000000</td>\n",
       "      <td>43364.000000</td>\n",
       "      <td>43362.000000</td>\n",
       "      <td>43347.000000</td>\n",
       "      <td>43378.000000</td>\n",
       "      <td>43375.000000</td>\n",
       "      <td>43380.000000</td>\n",
       "      <td>43347.00000</td>\n",
       "      <td>45133.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.027962e+07</td>\n",
       "      <td>5706.297779</td>\n",
       "      <td>40025.494849</td>\n",
       "      <td>1.429774</td>\n",
       "      <td>1.628631</td>\n",
       "      <td>1.713602</td>\n",
       "      <td>1.984579</td>\n",
       "      <td>1.984357</td>\n",
       "      <td>7.816697</td>\n",
       "      <td>1.948967</td>\n",
       "      <td>...</td>\n",
       "      <td>2.410772</td>\n",
       "      <td>2.533884</td>\n",
       "      <td>2.537450</td>\n",
       "      <td>2.563004</td>\n",
       "      <td>2.578518</td>\n",
       "      <td>1.968809</td>\n",
       "      <td>2.036749</td>\n",
       "      <td>2.196358</td>\n",
       "      <td>2.26424</td>\n",
       "      <td>1.042519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.172008e+07</td>\n",
       "      <td>8511.336263</td>\n",
       "      <td>14.499402</td>\n",
       "      <td>0.495049</td>\n",
       "      <td>0.586271</td>\n",
       "      <td>0.730958</td>\n",
       "      <td>0.123222</td>\n",
       "      <td>0.124090</td>\n",
       "      <td>2.194038</td>\n",
       "      <td>0.220068</td>\n",
       "      <td>...</td>\n",
       "      <td>0.592895</td>\n",
       "      <td>0.618880</td>\n",
       "      <td>0.603669</td>\n",
       "      <td>0.563047</td>\n",
       "      <td>0.542894</td>\n",
       "      <td>0.761610</td>\n",
       "      <td>0.720678</td>\n",
       "      <td>0.611436</td>\n",
       "      <td>0.56382</td>\n",
       "      <td>0.201772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000005e+07</td>\n",
       "      <td>1.516955</td>\n",
       "      <td>40001.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.013831e+07</td>\n",
       "      <td>973.740425</td>\n",
       "      <td>40013.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.027958e+07</td>\n",
       "      <td>2697.651023</td>\n",
       "      <td>40025.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.039094e+07</td>\n",
       "      <td>6788.874141</td>\n",
       "      <td>40038.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.055870e+07</td>\n",
       "      <td>118941.430150</td>\n",
       "      <td>40050.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 2634 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           QUESTID2      ANALWT2_C       VESTR_C         VEREP        PDEN10  \\\n",
       "count  4.513300e+04   45133.000000  45133.000000  45133.000000  45133.000000   \n",
       "mean   3.027962e+07    5706.297779  40025.494849      1.429774      1.628631   \n",
       "std    1.172008e+07    8511.336263     14.499402      0.495049      0.586271   \n",
       "min    1.000005e+07       1.516955  40001.000000      1.000000      1.000000   \n",
       "25%    2.013831e+07     973.740425  40013.000000      1.000000      1.000000   \n",
       "50%    3.027958e+07    2697.651023  40025.000000      1.000000      2.000000   \n",
       "75%    4.039094e+07    6788.874141  40038.000000      2.000000      2.000000   \n",
       "max    5.055870e+07  118941.430150  40050.000000      2.000000      3.000000   \n",
       "\n",
       "            COUTYP4      MAIIN102      AIIND102          AGE3       SERVICE  \\\n",
       "count  45133.000000  45133.000000  45133.000000  45133.000000  45108.000000   \n",
       "mean       1.713602      1.984579      1.984357      7.816697      1.948967   \n",
       "std        0.730958      0.123222      0.124090      2.194038      0.220068   \n",
       "min        1.000000      1.000000      1.000000      4.000000      1.000000   \n",
       "25%        1.000000      2.000000      2.000000      6.000000      2.000000   \n",
       "50%        2.000000      2.000000      2.000000      8.000000      2.000000   \n",
       "75%        2.000000      2.000000      2.000000      9.000000      2.000000   \n",
       "max        3.000000      2.000000      2.000000     11.000000      2.000000   \n",
       "\n",
       "       ...    COMHSVHLT2     COSUTELE2    COSUAPTDL2     COSURXDL2  \\\n",
       "count  ...  43333.000000  43354.000000  43364.000000  43362.000000   \n",
       "mean   ...      2.410772      2.533884      2.537450      2.563004   \n",
       "std    ...      0.592895      0.618880      0.603669      0.563047   \n",
       "min    ...      1.000000      1.000000      1.000000      1.000000   \n",
       "25%    ...      2.000000      2.000000      2.000000      2.000000   \n",
       "50%    ...      2.000000      3.000000      3.000000      3.000000   \n",
       "75%    ...      3.000000      3.000000      3.000000      3.000000   \n",
       "max    ...      3.000000      3.000000      3.000000      3.000000   \n",
       "\n",
       "         COSUSVHLT2     COHCTELE2    COHCAPTDL2     COHCRXDL2   COHCSVHLT2  \\\n",
       "count  43347.000000  43378.000000  43375.000000  43380.000000  43347.00000   \n",
       "mean       2.578518      1.968809      2.036749      2.196358      2.26424   \n",
       "std        0.542894      0.761610      0.720678      0.611436      0.56382   \n",
       "min        1.000000      1.000000      1.000000      1.000000      1.00000   \n",
       "25%        2.000000      1.000000      2.000000      2.000000      2.00000   \n",
       "50%        3.000000      2.000000      2.000000      2.000000      2.00000   \n",
       "75%        3.000000      3.000000      3.000000      3.000000      3.00000   \n",
       "max        3.000000      3.000000      3.000000      3.000000      3.00000   \n",
       "\n",
       "            LANGVER  \n",
       "count  45133.000000  \n",
       "mean       1.042519  \n",
       "std        0.201772  \n",
       "min        1.000000  \n",
       "25%        1.000000  \n",
       "50%        1.000000  \n",
       "75%        1.000000  \n",
       "max        2.000000  \n",
       "\n",
       "[8 rows x 2634 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dropped_targetNaN.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "709bd39d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YOLOSEV         45133\n",
       "YODSCEV         45133\n",
       "YODPREV         45133\n",
       "YOWRCHR         45133\n",
       "YOWRDST         45133\n",
       "                ...  \n",
       "FLVVAPMON           0\n",
       "FLVVAPYR            0\n",
       "IREDUHIGHST2        0\n",
       "IIMARIT             0\n",
       "MAIIN102            0\n",
       "Length: 2636, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dropped_targetNaN.isna().sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3bc84cfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7,\n",
       " ['WTPOUND2',\n",
       "  'ANALWT2_C',\n",
       "  'FILEDATE',\n",
       "  'VESTR_C',\n",
       "  'QUESTID2',\n",
       "  'WTANSWER',\n",
       "  'VEREP'])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop ID, weight, design, and metadata variables\n",
    "cols_to_drop = []\n",
    "\n",
    "# Unique identifiers\n",
    "cols_to_drop += [c for c in df_dropped_targetNaN.columns if 'CASEID' in c or 'QUESTID' in c]\n",
    "\n",
    "# Weight variables\n",
    "cols_to_drop += [c for c in df_dropped_targetNaN.columns if 'WT' in c or 'WGT' in c or 'ANALWT' in c]\n",
    "\n",
    "# Sampling design variables\n",
    "cols_to_drop += [c for c in df_dropped_targetNaN.columns if 'VESTR' in c or 'VEREP' in c or 'ESTRAT' in c]\n",
    "\n",
    "# Interview month/day/time variables\n",
    "cols_to_drop += [c for c in df_dropped_targetNaN.columns if c.startswith('INTV_')]\n",
    "\n",
    "# Meta / administration variables\n",
    "cols_to_drop += [c for c in df_dropped_targetNaN.columns if c.endswith('_A') or c.endswith('_E')]\n",
    "\n",
    "# Year and file metadata\n",
    "cols_to_drop += [c for c in ['YEAR', 'FILEDATE', 'QUARTER'] if c in df_dropped_targetNaN.columns]\n",
    "\n",
    "# Remove duplicates in the list\n",
    "cols_to_drop = list(set(cols_to_drop))\n",
    "\n",
    "len(cols_to_drop), cols_to_drop[:20]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c9844535",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7,\n",
       " ['QUESTID2',\n",
       "  'FILEDATE',\n",
       "  'ANALWT2_C',\n",
       "  'VESTR_C',\n",
       "  'VEREP',\n",
       "  'WTANSWER',\n",
       "  'WTPOUND2'])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_patterns = [\n",
    "    r'^CASEID', r'^QUESTID', r'^PANEL', r'^VERSION', \n",
    "    r'^INTV', r'^FILE', r'^YEAR', \n",
    "    r'^WT', r'WGT', r'WEIGHT', r'ANALWT', \n",
    "    r'^VESTR', r'^VEREP',\n",
    "    r'_A$', r'_E$', r'_ORIG$', r'_R$', r'_RC$'\n",
    "]\n",
    "\n",
    "import re\n",
    "\n",
    "cols_to_drop = [\n",
    "    c for c in df_dropped_targetNaN.columns\n",
    "    if any(re.search(pat, c) for pat in drop_patterns)\n",
    "]\n",
    "\n",
    "len(cols_to_drop), cols_to_drop[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5ee28b7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Removed_Columns = df_dropped_targetNaN.drop(columns=cols_to_drop, errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6eb87539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, [])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_patterns = [\n",
    "    r'^CASEID', r'^QUESTID', r'^PANEL', r'^VERSION', \n",
    "    r'^INTV', r'^FILE', r'^YEAR', \n",
    "    r'^WT', r'WGT', r'WEIGHT', r'ANALWT', \n",
    "    r'^VESTR', r'^VEREP',\n",
    "    r'_A$', r'_E$', r'_ORIG$', r'_R$', r'_RC$'\n",
    "]\n",
    "\n",
    "import re\n",
    "\n",
    "cols_to_drop2 = [\n",
    "    c for c in df_Removed_Columns.columns\n",
    "    if any(re.search(pat, c) for pat in drop_patterns)\n",
    "]\n",
    "\n",
    "len(cols_to_drop2), cols_to_drop2[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "56aa1ad8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of variables with >99% missingness: 282\n",
      "\n",
      "Variables with >99% missingness:\n",
      "['EDUSCKEST', 'EDUSKPEST', 'HLCALLFG', 'HLCALL99', 'BKPOSTOB', 'BKOTHOF2', 'ALTOTFG', 'ALFQFLG', 'ALDYSFG', 'MJFQFLG', 'BLRECFL2', 'BLNT30C1', 'BLNT30C2', 'RSNOMRJ', 'RSNMRJMO', 'CCTOTFG', 'CCFQFLG', 'CRTOTFG', 'CRFQFLG', 'HRTOTFG', 'HRFQFLG', 'HALTOTFG', 'HALFQFLG', 'INHTOTFG', 'INHFQFLG', 'METOTFG', 'MEFQFLG', 'PNRNORXFG', 'TRQNORXFG', 'STMNORXFG', 'SEDNORXFG', 'SRCSEDNM2', 'SRCFRPNRNM', 'SRCFRTRQNM', 'SRCFRSEDNM', 'SRCCLFRPNR', 'SRCCLFRTRQ', 'SRCCLFRSED', 'OTCFLAG', 'OTDGNDLA', 'OTDGNDLB', 'OTDGNDLC', 'OTDGNDLD', 'OTDGNDLE', 'SUNTINSCV', 'SUNTENCV', 'SUNTNOOPN', 'YEATNDYR', 'YEHMSLYR', 'YESCHFLT', 'YESCHWRK', 'YESCHIMP', 'YESCHINT', 'YETCGJOB', 'YELSTGRD', 'YECIGFRNDOF2', 'YECIGNEXTYR2', 'YESTSCIG', 'YESTSMJ', 'YESTSALC', 'YESTSDNK', 'YEPCHKHW', 'YEPHLPHW', 'YEPCHORE', 'YEPLMTTV', 'YEPLMTSN', 'YEPGDJOB', 'YEPPROUD', 'YEYARGUP', 'YEYFGTSW', 'YEYFGTGP', 'YEYHGUN', 'YEYSELL', 'YEYSTOLE', 'YEYATTAK', 'YEPPKCIG', 'YEPMJEVR', 'YEPMJMO', 'YEPALDLY', 'YEGPKCIG', 'YEGMJEVR', 'YEGMJMO', 'YEGALDLY', 'YEFPKCIG', 'YEFMJEVR', 'YEFMJMO', 'YEFALDLY', 'YETLKNON', 'YETLKPAR', 'YETLKBGF', 'YETLKOTA', 'YETLKSOP', 'YEPRTDNG', 'YEPRBSLV', 'YEVIOPRV', 'YEDGPRGP', 'YESLFHLP', 'YEPRGSTD', 'YESCHACT', 'YECOMACT', 'YEFAIACT', 'YEOTHACT', 'YEDECLAS', 'YEDERGLR', 'YEDESPCL', 'YEPVNTYR', 'YERLGSVC', 'YERLGIMP', 'YERLDCSN', 'YERLFRND', 'YUSUITHK', 'YUCOSUITHK', 'YUSUIPLN', 'YUCOSUIPLN', 'SCHFELT', 'TCHGJOB', 'AVGGRADE', 'STNDSCIG', 'STNDSMJ', 'STNDALC', 'STNDDNK', 'PARCHKHW', 'PARHLPHW', 'PRCHORE2', 'PRLMTTV2', 'PARLMTSN', 'PRGDJOB2', 'PRPROUD2', 'ARGUPAR', 'YOFIGHT2', 'YOGRPFT2', 'YOHGUN2', 'YOSELL2', 'YOSTOLE2', 'YOATTAK2', 'PRPKCIG2', 'PRMJEVR2', 'PRMJMO', 'PRALDLY2', 'YFLPKCG2', 'YFLTMRJ2', 'YFLMJMO', 'YFLADLY2', 'FRDPCIG2', 'FRDMEVR2', 'FRDMJMON', 'FRDADLY2', 'TALKPROB', 'PRTALK3', 'PRBSOLV2', 'PREVIOL2', 'PRVDRGO2', 'GRPCNSL2', 'PREGPGM2', 'YTHACT2', 'DRPRVME3', 'ANYEDUC3', 'RLGATTD', 'RLGIMPT', 'RLGDCSN', 'RLGFRND', 'YUSUITHKYR', 'YUCOSUITHK2', 'YUSUIPLNYR', 'YUCOSUIPLN2', 'YODPREV', 'YODSCEV', 'YOLOSEV', 'YODPDISC', 'YODPLSIN', 'YODSLSIN', 'YOLSI2WK', 'YODPR2WK', 'YOWRHRS', 'YOWRDST', 'YOWRCHR', 'YOWRIMP', 'YODPPROB', 'YOWRPROB', 'YOWRAGE', 'YOWRDEPR', 'YOWRDISC', 'YOWRLSIN', 'YOWRPLSR', 'YOWRELES', 'YOWREMOR', 'YOWRGAIN', 'YOWRGROW', 'YOWRPREG', 'YOWRGNL2', 'YOWRLOSE', 'YOWRDIET', 'YOWRLSL2', 'YOWRSLEP', 'YOWRSMOR', 'YOWRENRG', 'YOWRSLOW', 'YOWRSLNO', 'YOWRJITT', 'YOWRJINO', 'YOWRTHOT', 'YOWRCONC', 'YOWRDCSN', 'YOWRNOGD', 'YOWRWRTH', 'YO_MDEA1', 'YO_MDEA2', 'YO_MDEA3', 'YO_MDEA4', 'YO_MDEA5', 'YO_MDEA6', 'YO_MDEA7', 'YO_MDEA8', 'YODSMMDE', 'YOPBINTF', 'YOPBDLYA', 'YOPBRMBR', 'YOPBAGE', 'YOPBNUM', 'YOPB2WK', 'YOPSHMGT', 'YOPSWORK', 'YOPSRELS', 'YOPSSOC', 'YOPSDAYS', 'YOSEEDOC', 'YOFAMDOC', 'YOOTHDOC', 'YOPSYCH', 'YOPSYMD', 'YOSOCWRK', 'YOCOUNS', 'YOOTHMHP', 'YONURSE', 'YORELIG', 'YOHERBAL', 'YOOTHHLP', 'YOTMTNOW', 'YORX12MO', 'YORXNOW', 'YORXHLP', 'YOTMTHLP', 'YMDELT', 'YMDEYR', 'YMDEAUD5YR', 'YMIUD5YANY', 'YMSUD5YANY', 'YMDERSUD5ANY', 'YMDESUD5ANYO', 'YTXMDEYR', 'YRXMDEYR', 'YMDETXRX', 'YDOCMDE', 'YOMDMDE', 'YPSY1MDE', 'YPSY2MDE', 'YSOCMDE', 'YCOUNMDE', 'YOMHMDE', 'YNURSMDE', 'YRELMDE', 'YHBCHMDE', 'YHLTMDE', 'YALTMDE', 'YMDEHPRX', 'YMDEHPO', 'YMDERXO2', 'YMDEHARX', 'YSDSHOME', 'YSDSWRK', 'YSDSREL', 'YSDSSOC', 'YSDSOVRL', 'MDEIMPY', 'YMDEIMAD5YR', 'YMIMI5YANY', 'YMIMR5YANY', 'YMIMS5YANY', 'CIRROSAGE', 'HEPBCAGE', 'HIVAIDSAG', 'GQTYPE2']\n",
      "\n",
      "Preview (first 20):\n",
      "EDUSCKEST     99.915804\n",
      "EDUSKPEST     99.960118\n",
      "HLCALLFG      99.953471\n",
      "HLCALL99      99.953471\n",
      "BKPOSTOB     100.000000\n",
      "BKOTHOF2      99.519199\n",
      "ALTOTFG       99.339729\n",
      "ALFQFLG       99.295416\n",
      "ALDYSFG       99.960118\n",
      "MJFQFLG       99.550218\n",
      "BLRECFL2      99.915804\n",
      "BLNT30C1      99.884785\n",
      "BLNT30C2      99.802805\n",
      "RSNOMRJ       99.922451\n",
      "RSNMRJMO      99.898079\n",
      "CCTOTFG       99.995569\n",
      "CCFQFLG       99.953471\n",
      "CRTOTFG       99.995569\n",
      "CRFQFLG       99.991137\n",
      "HRTOTFG       99.986706\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Checking to see which variables have >99% missingess\n",
    "na_percent = df_Removed_Columns.isna().mean() * 100\n",
    "\n",
    "# Filter columns with > 99% missing\n",
    "cols_over_99 = na_percent[na_percent > 99]\n",
    "\n",
    "# Print count\n",
    "print(\"Number of variables with >99% missingness:\", len(cols_over_99))\n",
    "\n",
    "# Print the variable names\n",
    "print(\"\\nVariables with >99% missingness:\")\n",
    "print(cols_over_99.index.tolist())\n",
    "\n",
    "# Optional: show top 20 instead of the whole list\n",
    "print(\"\\nPreview (first 20):\")\n",
    "print(cols_over_99.head(20))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "1da00727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45133, 2347)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at how often these variables are non-missing among people with IRAMDEYR=1\n",
    "df_99 = df_Removed_Columns.drop(columns=cols_over_99.index)\n",
    "df_99.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b088c501",
   "metadata": {},
   "source": [
    "Split X and Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e8b9b2f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Missing Values (NaNs):\n",
    "-Many variables had missing values due to survey skip patterns or special response codes.\n",
    "-For numeric variables, missing values were replaced with the median of the column. The median is robust to outliers and preserves the typical value without disproportionately influencing the results.\n",
    "-For categorical variables, missing values were replaced with the mode (most frequent category), which preserves the dominant response and minimizes distortion.\n",
    "-This ensures that all columns are usable for tree-based algorithms without introducing extreme bias, especially important for feature importance ranking.\n",
    "\n",
    "Encoding Categorical Variables:\n",
    "-Tree-based models like RandomForest cannot directly process string categories.\n",
    "-We converted categorical variables to numeric codes using LabelEncoder, which assigns each category a unique integer. Importantly, RandomForest treats these codes as discrete categories, not as ordered values, so this transformation does not bias splits or feature importance.\n",
    "\n",
    "Rationale:\n",
    "-These steps allow us to use the full set of variables in a first-pass feature importance analysis with RandomForest.\n",
    "-Alternative models like LightGBM or CatBoost can handle missing values natively, but median/mode imputation provides a simple, consistent approach for exploratory feature ranking.\n",
    "-No other cleaning (e.g., dropping additional columns) is required for this stage, as we have already removed identifiers, survey weights, timestamps, and variables with extremely high missingness.'''\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Separate target\n",
    "TARGET = \"IRAMDEYR\"\n",
    "y = df_99[TARGET]\n",
    "X = df_99.drop(columns=[TARGET])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e36f1615",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numeric columns: 2346\n",
      "Categorical columns: 0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Identify numeric vs categorical\n",
    "numeric_cols = X.select_dtypes(include=[np.number]).columns.tolist()\n",
    "categorical_cols = X.select_dtypes(exclude=[np.number]).columns.tolist()\n",
    "\n",
    "print(f\"Numeric columns: {len(numeric_cols)}\")\n",
    "print(f\"Categorical columns: {len(categorical_cols)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "993805b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute missing values\n",
    "\n",
    "# Numeric -> median\n",
    "for col in numeric_cols:\n",
    "    median_val = X[col].median()\n",
    "    X[col] = X[col].fillna(median_val)\n",
    "\n",
    "# Categorical -> mode\n",
    "for col in categorical_cols:\n",
    "    mode_val = X[col].mode()[0]\n",
    "    X[col] = X[col].fillna(mode_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "35b97c96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Any NaNs left in the dataset? False\n",
      "Series([], dtype: int64)\n",
      "Total NaNs in the dataset: 0\n"
     ]
    }
   ],
   "source": [
    "# Check if any NaNs remain\n",
    "print(\"Any NaNs left in the dataset?\", X.isna().any().any())\n",
    "\n",
    "# Count of NaNs per column (should all be 0)\n",
    "nan_counts = X.isna().sum()\n",
    "print(nan_counts[nan_counts > 0])  # this will print nothing if there are no NaNs\n",
    "\n",
    "total_nans = X.isna().sum().sum()\n",
    "print(\"Total NaNs in the dataset:\", total_nans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "cc62039f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode categorical variables\n",
    "le_dict = {}  # save encoders in case needed later\n",
    "for col in categorical_cols:\n",
    "    le = LabelEncoder()\n",
    "    X[col] = le.fit_transform(X[col])\n",
    "    le_dict[col] = le"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "64f57aa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine X and y for later use\n",
    "df_prepared = pd.concat([X, y], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "673ed86e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Any NaNs left in the dataset? False\n",
      "[]\n",
      "Total NaNs in the dataset: 0\n"
     ]
    }
   ],
   "source": [
    "# Check if any NaNs remain\n",
    "print(\"Any NaNs left in the dataset?\", y.isna().any().any())\n",
    "\n",
    "# Count of NaNs per column (should all be 0)\n",
    "nan_counts = y.isna().sum()\n",
    "print(nan_counts[nan_counts > 0])  # this will print nothing if there are no NaNs\n",
    "\n",
    "total_nans = y.isna().sum().sum()\n",
    "print(\"Total NaNs in the dataset:\", total_nans)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7727e047",
   "metadata": {},
   "source": [
    "# Feature Importance / Tiering\n",
    "\n",
    "Correlation only works on linear continuous relationships. Our dataset is mostly categorical and ordinal, so Pearson correlation wouldn’t capture any useful signal. That’s why the papers and frameworks use model-based feature selection, like LASSO, ElasticNet, RFE, and Random Forest. Those methods work regardless of whether the relationship is linear or categorical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f2213f6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing target leakage features that was used to impute / recode the target IRAMDEYR\n",
    "# IRAMDEYR → AMDEYR → AMDELT → ADSMMDEA → MDE symptom items + fallback items + timing\n",
    "\n",
    "leakage_vars = [\n",
    "\n",
    "    # Target + imputation\n",
    "    \"IRAMDEYR\", \"IIAMDEYR\",\n",
    "\n",
    "    # Pre-imputation past-year MDE\n",
    "    \"AMDEYR\",\n",
    "\n",
    "    # Lifetime MDE + imputation\n",
    "    \"AMDELT\", \"IRAMDELT\", \"IIAMDELT\",\n",
    "\n",
    "    # MDE with impairment (logically dependent on AMDEYR)\n",
    "    \"AMDEIMP\", \"IRAMDEIMP\", \"IIAMDEIMP\",\n",
    "\n",
    "    # DSM summary\n",
    "    \"ADSMMDEA\",\n",
    "\n",
    "    # Raw DSM symptom items (if present)\n",
    "    \"D_MDEA1\", \"D_MDEA2\", \"D_MDEA3\", \"D_MDEA4\", \"D_MDEA5\",\n",
    "    \"D_MDEA6\", \"D_MDEA7\", \"D_MDEA8\", \"D_MDEA9\",\n",
    "\n",
    "    # Symptom recodes (one-digit suffixes, if present)\n",
    "    \"AD_MDEA1\", \"AD_MDEA2\", \"AD_MDEA3\", \"AD_MDEA4\",\n",
    "    \"AD_MDEA5\", \"AD_MDEA6\", \"AD_MDEA7\", \"AD_MDEA8\",\n",
    "\n",
    "    # Symptom recodes (true DSM flags: two-digit suffix)\n",
    "    \"AD_MDEA11\", \"AD_MDEA21\", \"AD_MDEA31\", \"AD_MDEA41\",\n",
    "    \"AD_MDEA51\", \"AD_MDEA61\", \"AD_MDEA71\", \"AD_MDEA81\", \"AD_MDEA91\",\n",
    "\n",
    "    # Fallback/gating items\n",
    "    \"ADPB2WK\",\n",
    "    \"ADDPREV\", \"ADDSCEV\", \"ADLOSEV\", \"ADLSI2WK\", \"ADDPR2WK\",\n",
    "    \"ADWRHRS\", \"ADWRDST\", \"ADWRCHR\", \"ADWRIMP\", \"ADDPPROB\",\n",
    "\n",
    "    # Soft-leakage variables\n",
    "    \"ARXMDEYR\",   # received prescription meds\n",
    "    \"ATXMDEYR\",   # received counseling/therapy\n",
    "    \"AHLTMDE\",     # told by provider they have depression\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "dd235744",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['IRAMDEYR', 'IIAMDEYR', 'AMDEYR', 'AMDELT', 'IRAMDELT', 'IIAMDELT', 'AMDEIMP', 'IRAMDEIMP', 'IIAMDEIMP', 'ADSMMDEA', 'D_MDEA1', 'D_MDEA2', 'D_MDEA3', 'D_MDEA4', 'D_MDEA5', 'D_MDEA6', 'D_MDEA7', 'D_MDEA8', 'D_MDEA9', 'AD_MDEA1', 'AD_MDEA2', 'AD_MDEA3', 'AD_MDEA4', 'AD_MDEA5', 'AD_MDEA6', 'AD_MDEA7', 'AD_MDEA8', 'AD_MDEA11', 'AD_MDEA21', 'AD_MDEA31', 'AD_MDEA41', 'AD_MDEA51', 'AD_MDEA61', 'AD_MDEA71', 'AD_MDEA81', 'AD_MDEA91', 'ADPB2WK', 'ADDPREV', 'ADDSCEV', 'ADLOSEV', 'ADLSI2WK', 'ADDPR2WK', 'ADWRHRS', 'ADWRDST', 'ADWRCHR', 'ADWRIMP', 'ADDPPROB', 'ARXMDEYR', 'ATXMDEYR', 'AHLTMDE']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(leakage_vars)\n",
    "len(leakage_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "633dee57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45133, 2315)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_no_leakage = X.drop(columns=[col for col in leakage_vars if col != 'IRAMDEYR'], errors='ignore')\n",
    "X_no_leakage.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "d13baec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
       "            <td class=\"value\">500</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
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       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('criterion',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">criterion&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_depth&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_split',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
       "            <td class=\"value\">1</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "            <td class=\"param\">max_features&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('bootstrap',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">oob_score&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">-1</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">random_state&nbsp;</td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
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       "    \n",
       "\n",
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       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">warm_start&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">class_weight&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "\n",
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       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
       "            <td class=\"value\">0.0</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_samples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">max_samples&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=500, n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf_model_no_leakage = RandomForestClassifier(\n",
    "    n_estimators=500,\n",
    "    max_depth=None,\n",
    "    random_state=42,\n",
    "    n_jobs=-1\n",
    ")\n",
    "\n",
    "rf_model_no_leakage.fit(X_no_leakage, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9db4932",
   "metadata": {},
   "source": [
    "# EVERYTHING BELOW THIS IS WIP, AND SHOULD NOT BE EDITED"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca222ead",
   "metadata": {},
   "source": [
    "Tiering with Tier 0 + Tiers 1–4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ed02a4f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature counts per tier:\n",
      "Tier 0 (Top 100): 100 features\n",
      "Tier 1: 5 features\n",
      "Tier 2: 12 features\n",
      "Tier 3: 29 features\n",
      "Tier 4: 52 features\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agila\\AppData\\Local\\Temp\\ipykernel_16380\\2494543076.py:47: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agila\\AppData\\Local\\Temp\\ipykernel_16380\\2494543076.py:47: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agila\\AppData\\Local\\Temp\\ipykernel_16380\\2494543076.py:47: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agila\\AppData\\Local\\Temp\\ipykernel_16380\\2494543076.py:47: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\agila\\AppData\\Local\\Temp\\ipykernel_16380\\2494543076.py:47: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''Tier 0 serves a different role from Tiers 1-4.\n",
    "Tier 0 is a master candidate list for Phase 2 (clinical review), so a simple “top 100 ranked features” is the right method.\n",
    "\n",
    "Tiers 1-4 are performance-driven tiers that reflect different cumulative contributions of importance. They should remain based on percentage thresholds, because that structure tests how predictive performance scales with increasingly informative subsets.\n",
    "\n",
    "So Tier 0 = fixed-K;\n",
    "Tiers 1-4 = cumulative importance thresholds.'''\n",
    "\n",
    "# Get feature importances from your Random Forest model (already leakage-clean)\n",
    "importances = pd.Series(\n",
    "    rf_model_no_leakage.feature_importances_,\n",
    "    index=X_no_leakage.columns\n",
    ").sort_values(ascending=False)\n",
    "\n",
    "# Tier 0: Top-100 features\n",
    "K = 100\n",
    "tier0 = importances.head(K).index.tolist()\n",
    "\n",
    "# Tiers 1–4 (cumulative thresholds)\n",
    "cumulative_importance = importances.cumsum()\n",
    "\n",
    "tier1 = importances[cumulative_importance <= 0.25].index.tolist()\n",
    "tier2 = importances[cumulative_importance <= 0.45].index.tolist()\n",
    "tier3 = importances[cumulative_importance <= 0.60].index.tolist()\n",
    "tier4 = importances[cumulative_importance <= 0.70].index.tolist()\n",
    "\n",
    "# Combine into dictionary\n",
    "tiers = {\n",
    "    'Tier 0 (Top 100)': tier0,\n",
    "    'Tier 1': tier1,\n",
    "    'Tier 2': tier2,\n",
    "    'Tier 3': tier3,\n",
    "    'Tier 4': tier4\n",
    "}\n",
    "\n",
    "# Print tier sizes\n",
    "print(\"Feature counts per tier:\")\n",
    "for tier_name, features in tiers.items():\n",
    "    print(f\"{tier_name}: {len(features)} features\")\n",
    "\n",
    "# Plot feature importances for each tier\n",
    "for tier_name, features in tiers.items():\n",
    "    if len(features) == 0:\n",
    "        continue\n",
    "        \n",
    "    plt.figure(figsize=(10, 12))\n",
    "    sns.barplot(\n",
    "        x=importances[features].values,\n",
    "        y=importances[features].index,\n",
    "        palette='viridis'\n",
    "    )\n",
    "    plt.title(f\"{tier_name} Feature Importances ({len(features)} features)\")\n",
    "    plt.xlabel(\"Importance\")\n",
    "    plt.ylabel(\"Feature\")\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4d81fa1",
   "metadata": {},
   "source": [
    "# Logistic Regression Baseline (With Weights + Metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92feaf80",
   "metadata": {},
   "source": [
    "Re-Cleaning Dataset for Logistics Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "ce6a7f0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45133, 2636)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dropped_targetNaN.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "563140e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_patterns_with_weight = [\n",
    "    r'^CASEID', r'^QUESTID', r'^PANEL', r'^VERSION',\n",
    "    r'^INTV', r'^FILE', r'^YEAR',\n",
    "    r'^WT(?!ANSWER$)(?!POUND.*$)',  # Drop WT* except WTANSWER/WTPOUND (we drop separately)\n",
    "    r'WGT', r'WEIGHT',\n",
    "    r'^VESTR', r'^VEREP', r'^ESTRAT',\n",
    "    r'_A$', r'_E$', r'_ORIG$', r'_R$', r'_RC$'\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "67677159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, ['WTPOUND2', 'FILEDATE', 'VESTR_C', 'QUESTID2', 'WTANSWER', 'VEREP'])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify columns to drop without weight variable\n",
    "cols_to_drop_with_weight = [\n",
    "    c for c in df_dropped_targetNaN.columns\n",
    "    if any(re.search(pat, c) for pat in drop_patterns_with_weight)\n",
    "]\n",
    "\n",
    "# Never drop ANALWT2_C\n",
    "cols_to_drop_with_weight = [c for c in cols_to_drop if c != \"ANALWT2_C\"]\n",
    "\n",
    "# Also manually drop these (we know they must go)\n",
    "manual_dropping = [\n",
    "    \"WTANSWER\", \"WTPOUND2\",  # useless weight components\n",
    "    \"QUESTID2\", \"FILEDATE\",\n",
    "    \"VESTR_C\", \"VEREP\"       # replicate/strata → DROP\n",
    "]\n",
    "\n",
    "cols_to_drop_with_weight += manual_dropping\n",
    "\n",
    "# Remove duplicates\n",
    "cols_to_drop_with_weight = list(set(cols_to_drop_with_weight))\n",
    "\n",
    "len(cols_to_drop_with_weight), cols_to_drop_with_weight[:30]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "cda592d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_removed_columns_LR = df_dropped_targetNaN.drop(columns=cols_to_drop_with_weight, errors='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "68cc8d32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of variables with >99% missingness: 282\n",
      "\n",
      "Variables with >99% missingness:\n",
      "['EDUSCKEST', 'EDUSKPEST', 'HLCALLFG', 'HLCALL99', 'BKPOSTOB', 'BKOTHOF2', 'ALTOTFG', 'ALFQFLG', 'ALDYSFG', 'MJFQFLG', 'BLRECFL2', 'BLNT30C1', 'BLNT30C2', 'RSNOMRJ', 'RSNMRJMO', 'CCTOTFG', 'CCFQFLG', 'CRTOTFG', 'CRFQFLG', 'HRTOTFG', 'HRFQFLG', 'HALTOTFG', 'HALFQFLG', 'INHTOTFG', 'INHFQFLG', 'METOTFG', 'MEFQFLG', 'PNRNORXFG', 'TRQNORXFG', 'STMNORXFG', 'SEDNORXFG', 'SRCSEDNM2', 'SRCFRPNRNM', 'SRCFRTRQNM', 'SRCFRSEDNM', 'SRCCLFRPNR', 'SRCCLFRTRQ', 'SRCCLFRSED', 'OTCFLAG', 'OTDGNDLA', 'OTDGNDLB', 'OTDGNDLC', 'OTDGNDLD', 'OTDGNDLE', 'SUNTINSCV', 'SUNTENCV', 'SUNTNOOPN', 'YEATNDYR', 'YEHMSLYR', 'YESCHFLT', 'YESCHWRK', 'YESCHIMP', 'YESCHINT', 'YETCGJOB', 'YELSTGRD', 'YECIGFRNDOF2', 'YECIGNEXTYR2', 'YESTSCIG', 'YESTSMJ', 'YESTSALC', 'YESTSDNK', 'YEPCHKHW', 'YEPHLPHW', 'YEPCHORE', 'YEPLMTTV', 'YEPLMTSN', 'YEPGDJOB', 'YEPPROUD', 'YEYARGUP', 'YEYFGTSW', 'YEYFGTGP', 'YEYHGUN', 'YEYSELL', 'YEYSTOLE', 'YEYATTAK', 'YEPPKCIG', 'YEPMJEVR', 'YEPMJMO', 'YEPALDLY', 'YEGPKCIG', 'YEGMJEVR', 'YEGMJMO', 'YEGALDLY', 'YEFPKCIG', 'YEFMJEVR', 'YEFMJMO', 'YEFALDLY', 'YETLKNON', 'YETLKPAR', 'YETLKBGF', 'YETLKOTA', 'YETLKSOP', 'YEPRTDNG', 'YEPRBSLV', 'YEVIOPRV', 'YEDGPRGP', 'YESLFHLP', 'YEPRGSTD', 'YESCHACT', 'YECOMACT', 'YEFAIACT', 'YEOTHACT', 'YEDECLAS', 'YEDERGLR', 'YEDESPCL', 'YEPVNTYR', 'YERLGSVC', 'YERLGIMP', 'YERLDCSN', 'YERLFRND', 'YUSUITHK', 'YUCOSUITHK', 'YUSUIPLN', 'YUCOSUIPLN', 'SCHFELT', 'TCHGJOB', 'AVGGRADE', 'STNDSCIG', 'STNDSMJ', 'STNDALC', 'STNDDNK', 'PARCHKHW', 'PARHLPHW', 'PRCHORE2', 'PRLMTTV2', 'PARLMTSN', 'PRGDJOB2', 'PRPROUD2', 'ARGUPAR', 'YOFIGHT2', 'YOGRPFT2', 'YOHGUN2', 'YOSELL2', 'YOSTOLE2', 'YOATTAK2', 'PRPKCIG2', 'PRMJEVR2', 'PRMJMO', 'PRALDLY2', 'YFLPKCG2', 'YFLTMRJ2', 'YFLMJMO', 'YFLADLY2', 'FRDPCIG2', 'FRDMEVR2', 'FRDMJMON', 'FRDADLY2', 'TALKPROB', 'PRTALK3', 'PRBSOLV2', 'PREVIOL2', 'PRVDRGO2', 'GRPCNSL2', 'PREGPGM2', 'YTHACT2', 'DRPRVME3', 'ANYEDUC3', 'RLGATTD', 'RLGIMPT', 'RLGDCSN', 'RLGFRND', 'YUSUITHKYR', 'YUCOSUITHK2', 'YUSUIPLNYR', 'YUCOSUIPLN2', 'YODPREV', 'YODSCEV', 'YOLOSEV', 'YODPDISC', 'YODPLSIN', 'YODSLSIN', 'YOLSI2WK', 'YODPR2WK', 'YOWRHRS', 'YOWRDST', 'YOWRCHR', 'YOWRIMP', 'YODPPROB', 'YOWRPROB', 'YOWRAGE', 'YOWRDEPR', 'YOWRDISC', 'YOWRLSIN', 'YOWRPLSR', 'YOWRELES', 'YOWREMOR', 'YOWRGAIN', 'YOWRGROW', 'YOWRPREG', 'YOWRGNL2', 'YOWRLOSE', 'YOWRDIET', 'YOWRLSL2', 'YOWRSLEP', 'YOWRSMOR', 'YOWRENRG', 'YOWRSLOW', 'YOWRSLNO', 'YOWRJITT', 'YOWRJINO', 'YOWRTHOT', 'YOWRCONC', 'YOWRDCSN', 'YOWRNOGD', 'YOWRWRTH', 'YO_MDEA1', 'YO_MDEA2', 'YO_MDEA3', 'YO_MDEA4', 'YO_MDEA5', 'YO_MDEA6', 'YO_MDEA7', 'YO_MDEA8', 'YODSMMDE', 'YOPBINTF', 'YOPBDLYA', 'YOPBRMBR', 'YOPBAGE', 'YOPBNUM', 'YOPB2WK', 'YOPSHMGT', 'YOPSWORK', 'YOPSRELS', 'YOPSSOC', 'YOPSDAYS', 'YOSEEDOC', 'YOFAMDOC', 'YOOTHDOC', 'YOPSYCH', 'YOPSYMD', 'YOSOCWRK', 'YOCOUNS', 'YOOTHMHP', 'YONURSE', 'YORELIG', 'YOHERBAL', 'YOOTHHLP', 'YOTMTNOW', 'YORX12MO', 'YORXNOW', 'YORXHLP', 'YOTMTHLP', 'YMDELT', 'YMDEYR', 'YMDEAUD5YR', 'YMIUD5YANY', 'YMSUD5YANY', 'YMDERSUD5ANY', 'YMDESUD5ANYO', 'YTXMDEYR', 'YRXMDEYR', 'YMDETXRX', 'YDOCMDE', 'YOMDMDE', 'YPSY1MDE', 'YPSY2MDE', 'YSOCMDE', 'YCOUNMDE', 'YOMHMDE', 'YNURSMDE', 'YRELMDE', 'YHBCHMDE', 'YHLTMDE', 'YALTMDE', 'YMDEHPRX', 'YMDEHPO', 'YMDERXO2', 'YMDEHARX', 'YSDSHOME', 'YSDSWRK', 'YSDSREL', 'YSDSSOC', 'YSDSOVRL', 'MDEIMPY', 'YMDEIMAD5YR', 'YMIMI5YANY', 'YMIMR5YANY', 'YMIMS5YANY', 'CIRROSAGE', 'HEPBCAGE', 'HIVAIDSAG', 'GQTYPE2']\n",
      "\n",
      "Preview (first 20):\n",
      "EDUSCKEST      99.915804\n",
      "EDUSKPEST      99.960118\n",
      "HLCALLFG       99.953471\n",
      "HLCALL99       99.953471\n",
      "BKPOSTOB      100.000000\n",
      "                 ...    \n",
      "YMIMS5YANY    100.000000\n",
      "CIRROSAGE      99.725256\n",
      "HEPBCAGE       99.058339\n",
      "HIVAIDSAG      99.736335\n",
      "GQTYPE2        99.811668\n",
      "Length: 282, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Checking to see which variables have >99% missingess\n",
    "na_percent = df_removed_columns_LR.isna().mean() * 100\n",
    "\n",
    "# Filter columns with > 99% missing\n",
    "cols_over_99_LR = na_percent[na_percent > 99]\n",
    "\n",
    "# Print count\n",
    "print(\"Number of variables with >99% missingness:\", len(cols_over_99_LR))\n",
    "\n",
    "# Print the variable names\n",
    "print(\"\\nVariables with >99% missingness:\")\n",
    "print(cols_over_99_LR.index.tolist())\n",
    "\n",
    "# Optional: show top 20 instead of the whole list\n",
    "print(\"\\nPreview (first 20):\")\n",
    "print(cols_over_99_LR.head(300))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f63b34ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45133, 2348)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_99_LR = df_removed_columns_LR.drop(columns=cols_over_99_LR.index)\n",
    "df_99_LR.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "24838c43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped leakage columns: ['IIAMDEYR', 'AMDEYR', 'AMDELT', 'IRAMDELT', 'IIAMDELT', 'AMDEIMP', 'IRAMDEIMP', 'IIAMDEIMP', 'ADSMMDEA', 'AD_MDEA1', 'AD_MDEA2', 'AD_MDEA3', 'AD_MDEA4', 'AD_MDEA5', 'AD_MDEA6', 'AD_MDEA7', 'AD_MDEA8', 'ADPB2WK', 'ADDPREV', 'ADDSCEV', 'ADLOSEV', 'ADLSI2WK', 'ADDPR2WK', 'ADWRHRS', 'ADWRDST', 'ADWRCHR', 'ADWRIMP', 'ADDPPROB', 'ARXMDEYR', 'ATXMDEYR', 'AHLTMDE']\n"
     ]
    }
   ],
   "source": [
    "# Remove target leakage features that was used to impute / recode the target IRAMDEYR\n",
    "\n",
    "leakage_vars_LR = [\n",
    "\n",
    "    # Target + imputation\n",
    "    #\"IRAMDEYR\", \"IIAMDEYR\",\n",
    "    \"IIAMDEYR\",\n",
    "\n",
    "    # Pre-imputation past-year MDE\n",
    "    \"AMDEYR\",\n",
    "\n",
    "    # Lifetime MDE + imputation\n",
    "    \"AMDELT\", \"IRAMDELT\", \"IIAMDELT\",\n",
    "\n",
    "    # MDE with impairment (logically dependent on AMDEYR)\n",
    "    \"AMDEIMP\", \"IRAMDEIMP\", \"IIAMDEIMP\",\n",
    "\n",
    "    # DSM summary\n",
    "    \"ADSMMDEA\",\n",
    "\n",
    "    # Raw DSM symptom items (if present)\n",
    "    \"D_MDEA1\", \"D_MDEA2\", \"D_MDEA3\", \"D_MDEA4\", \"D_MDEA5\",\n",
    "    \"D_MDEA6\", \"D_MDEA7\", \"D_MDEA8\", \"D_MDEA9\",\n",
    "\n",
    "    # Symptom recodes (one-digit suffixes, if present)\n",
    "    \"AD_MDEA1\", \"AD_MDEA2\", \"AD_MDEA3\", \"AD_MDEA4\",\n",
    "    \"AD_MDEA5\", \"AD_MDEA6\", \"AD_MDEA7\", \"AD_MDEA8\",\n",
    "\n",
    "    # Symptom recodes (true DSM flags: two-digit suffix)\n",
    "    \"AD_MDEA11\", \"AD_MDEA21\", \"AD_MDEA31\", \"AD_MDEA41\",\n",
    "    \"AD_MDEA51\", \"AD_MDEA61\", \"AD_MDEA71\", \"AD_MDEA81\", \"AD_MDEA91\",\n",
    "\n",
    "    # Fallback/gating items\n",
    "    \"ADPB2WK\",\n",
    "    \"ADDPREV\", \"ADDSCEV\", \"ADLOSEV\", \"ADLSI2WK\", \"ADDPR2WK\",\n",
    "    \"ADWRHRS\", \"ADWRDST\", \"ADWRCHR\", \"ADWRIMP\", \"ADDPPROB\",\n",
    "\n",
    "    # Soft-leakage variables\n",
    "    \"ARXMDEYR\",   # received prescription meds\n",
    "    \"ATXMDEYR\",   # received counseling/therapy\n",
    "    \"AHLTMDE\",     # told by provider they have depression\n",
    "]\n",
    "\n",
    "# Drop only those leakage columns that actually exist in this PUF\n",
    "cols_to_drop_leakaging = [c for c in leakage_vars_LR if c in df_99_LR.columns]\n",
    "df_no_leakage_LR = df_99_LR.drop(columns=cols_to_drop_leakaging)\n",
    "print(\"Dropped leakage columns:\", cols_to_drop_leakaging)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "55aa5e3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45133, 2317)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_leakage_LR.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b23283a",
   "metadata": {},
   "source": [
    "Define Target, Weight, and Base Feature Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "012223c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base X shape: (45133, 2315)\n",
      "y positive rate: 0.115\n"
     ]
    }
   ],
   "source": [
    "# Columns\n",
    "TARGET_COL = \"IRAMDEYR\"\n",
    "WEIGHT_COL = \"ANALWT2_C\"\n",
    "\n",
    "# Sanity checks\n",
    "assert TARGET_COL in df_no_leakage_LR.columns, \"IRAMDEYR not found in df_no_leakage_LR\"\n",
    "assert WEIGHT_COL in df_no_leakage_LR.columns, \"ANALWT2_C not found in df_no_leakage_LR\"\n",
    "\n",
    "# Raw target and weights\n",
    "y_raw = df_no_leakage_LR[TARGET_COL]\n",
    "w     = df_no_leakage_LR[WEIGHT_COL]\n",
    "\n",
    "# Binary-encode target: 1 = past-year MDE, 0 = no MDE\n",
    "y = (y_raw == 1).astype(int)\n",
    "\n",
    "# Features = all columns except target + weight\n",
    "X = df_no_leakage_LR.drop(columns=[TARGET_COL, WEIGHT_COL], errors='ignore')\n",
    "\n",
    "print(\"Base X shape:\", X.shape)\n",
    "print(\"y positive rate:\", y.mean().round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7ba4192",
   "metadata": {},
   "source": [
    "Tier 1 and Subset Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "acc1fe3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tier 1: 5 features\n",
      "X_tier shape: (45133, 5)\n"
     ]
    }
   ],
   "source": [
    "# Tier to evaluate\n",
    "tier_name = \"Tier 1\"\n",
    "tier_features = tiers[tier_name]\n",
    "\n",
    "# Keep only those columns in X\n",
    "X_tier = X[tier_features].copy()\n",
    "\n",
    "print(f\"{tier_name}: {len(tier_features)} features\")\n",
    "print(\"X_tier shape:\", X_tier.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f361e16",
   "metadata": {},
   "source": [
    "Train / Test Split (with Weights Kept Aligned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "1d40cc4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape: (36106, 5) (36106,)\n",
      "Test shape: (9027, 5) (9027,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test, w_train, w_test = train_test_split(\n",
    "    X_tier, y, w,\n",
    "    test_size=0.2,\n",
    "    random_state=42,\n",
    "    stratify=y\n",
    ")\n",
    "\n",
    "print(\"Train shape:\", X_train.shape, y_train.shape)\n",
    "print(\"Test shape:\",  X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06fb5537",
   "metadata": {},
   "source": [
    "Define Preprocessing (Median Impute + Scaling for Numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "19e6a5ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''Here we define numeric vs categorical and build a ColumnTransformer that:\n",
    "-Imputes numeric with median, scales with StandardScaler\n",
    "-Imputes categorical with mode, encodes with OneHotEncoder'''\n",
    "\n",
    "'''Why median is better for NSDUH over mean imputation as NSDUH variables are mostly:\n",
    "-Ordinal categories (0,1,2,3)\n",
    "-Non-normal/long-tailed distributions\n",
    "-Heavily skewed (e.g., hours, days, counts)\n",
    "-Integer-coded survey recodes\n",
    "-Mean imputation pushes values toward the wrong part of the distribution\n",
    "-Median imputation better reflects the actual underlying \"typical\" respondent\n",
    "-Median preserves the ordinal structure\n",
    "-This prevents distortion of the predictors and reduces bias in logistic regression.'''\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# All numeric\n",
    "numeric_cols = X_train.columns.tolist()\n",
    "categorical_cols = []\n",
    "\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='median')),\n",
    "    ('scaler',  StandardScaler())\n",
    "])\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_cols)\n",
    "    ],\n",
    "    remainder='drop'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4fa7d9d",
   "metadata": {},
   "source": [
    "Building Logistic Regression Pipeline + CV Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "c27c8367",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n",
      "Best params: {'model__C': 1.0, 'model__class_weight': None}\n",
      "Best CV ROC AUC: 0.8406564536348655\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import StratifiedKFold, GridSearchCV\n",
    "\n",
    "log_reg_pipe = Pipeline(steps=[\n",
    "    ('preprocess', preprocessor),\n",
    "    ('model', LogisticRegression(\n",
    "        max_iter=2000,\n",
    "        solver='liblinear'\n",
    "    ))\n",
    "])\n",
    "\n",
    "param_grid = {\n",
    "    'model__C': [0.01, 0.1, 1.0, 10.0],\n",
    "    'model__class_weight': [None, 'balanced']\n",
    "}\n",
    "\n",
    "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "grid = GridSearchCV(\n",
    "    estimator=log_reg_pipe,\n",
    "    param_grid=param_grid,\n",
    "    cv=cv,\n",
    "    scoring='roc_auc',\n",
    "    n_jobs=1,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "print(\"Best params:\", grid.best_params_)\n",
    "print(\"Best CV ROC AUC:\", grid.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80cc004b",
   "metadata": {},
   "source": [
    "Refit best model on full training set with survey weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f7492331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    font-family: monospace;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td.value pre {\n",
       "    color:rgb(255, 94, 0) !important;\n",
       "    background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  [&#x27;ADPSDAYS&#x27;, &#x27;ASDSSOC2&#x27;,\n",
       "                                                   &#x27;ADPSHMGT&#x27;, &#x27;ADPSRELS&#x27;,\n",
       "                                                   &#x27;ADPSWORK&#x27;])])),\n",
       "                (&#x27;model&#x27;,\n",
       "                 LogisticRegression(max_iter=2000, solver=&#x27;liblinear&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('steps',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">steps&nbsp;</td>\n",
       "            <td class=\"value\">[(&#x27;preprocess&#x27;, ...), (&#x27;model&#x27;, ...)]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transform_input',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">transform_input&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('memory',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">memory&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>preprocess: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocess: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocess__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transformers',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">transformers&nbsp;</td>\n",
       "            <td class=\"value\">[(&#x27;num&#x27;, ...)]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('remainder',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">remainder&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;drop&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('sparse_threshold',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">sparse_threshold&nbsp;</td>\n",
       "            <td class=\"value\">0.3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transformer_weights',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">transformer_weights&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose_feature_names_out',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">verbose_feature_names_out&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('force_int_remainder_cols',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">force_int_remainder_cols&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>num</div></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocess__num__\"><pre>[&#x27;ADPSDAYS&#x27;, &#x27;ASDSSOC2&#x27;, &#x27;ADPSHMGT&#x27;, &#x27;ADPSRELS&#x27;, &#x27;ADPSWORK&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocess__num__imputer__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('missing_values',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">missing_values&nbsp;</td>\n",
       "            <td class=\"value\">nan</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('strategy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">strategy&nbsp;</td>\n",
       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fill_value',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">fill_value&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">copy&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('add_indicator',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocess__num__scaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">copy&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">with_mean&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">with_std&nbsp;</td>\n",
       "            <td class=\"value\">True</td>\n",
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       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"model__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
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       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">dual&nbsp;</td>\n",
       "            <td class=\"value\">False</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "            <td class=\"param\">tol&nbsp;</td>\n",
       "            <td class=\"value\">0.0001</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">C&nbsp;</td>\n",
       "            <td class=\"value\">1.0</td>\n",
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       "    \n",
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       "            <td><i class=\"copy-paste-icon\"\n",
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       "            <td class=\"param\">fit_intercept&nbsp;</td>\n",
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       "            <td><i class=\"copy-paste-icon\"\n",
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       "            <td class=\"value\">1</td>\n",
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       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "\n",
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       "            <td><i class=\"copy-paste-icon\"\n",
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       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">max_iter&nbsp;</td>\n",
       "            <td class=\"value\">2000</td>\n",
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       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('multi_class',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">verbose&nbsp;</td>\n",
       "            <td class=\"value\">0</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "            <td class=\"param\">warm_start&nbsp;</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "                          this.parentElement.nextElementSibling)\"\n",
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       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
       "            <td class=\"value\">None</td>\n",
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       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
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       "            <td class=\"param\">l1_ratio&nbsp;</td>\n",
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       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
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       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
       "    const paramName = element.parentElement.nextElementSibling.textContent.trim();\n",
       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
       "\n",
       "    element.setAttribute('title', fullParamName);\n",
       "});\n",
       "</script></body>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocess',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(strategy='median')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler())]),\n",
       "                                                  ['ADPSDAYS', 'ASDSSOC2',\n",
       "                                                   'ADPSHMGT', 'ADPSRELS',\n",
       "                                                   'ADPSWORK'])])),\n",
       "                ('model',\n",
       "                 LogisticRegression(max_iter=2000, solver='liblinear'))])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Refit best estimator WITH survey weights (correct way)\n",
    "best_log_reg = grid.best_estimator_\n",
    "\n",
    "best_log_reg.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    model__sample_weight=w_train\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d55b80fb",
   "metadata": {},
   "source": [
    "Predicting on test set (Probabilities + Class Labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "bc749f43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Probability for class 1 (MDE)\n",
    "y_proba = best_log_reg.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Hard labels using 0.5 threshold\n",
    "y_pred = (y_proba >= 0.5).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d782826d",
   "metadata": {},
   "source": [
    "Compute Weighted Metrics: Accuracy, Sensitivity, Specificity, PPV, NPV, F1, F2, ROC AUC, PR AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "793b9b90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Tier 1 — Weighted Test Metrics ===\n",
      "Accuracy:              0.972\n",
      "Sensitivity (Recall):  0.635\n",
      "Specificity:           1.000\n",
      "PPV (Precision):       0.991\n",
      "NPV:                   0.971\n",
      "F1:                    0.774\n",
      "F2:                    0.684\n",
      "ROC AUC:               0.829\n",
      "PR AUC:                0.836\n",
      "Confusion matrix (weighted):\n",
      "  TN: 47082423.8,  FP: 21888.1\n",
      "  FN: 1426902.7,  TP: 2477163.9\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    recall_score,\n",
    "    precision_score,\n",
    "    f1_score,\n",
    "    fbeta_score,\n",
    "    roc_auc_score,\n",
    "    precision_recall_curve,\n",
    "    auc,\n",
    "    confusion_matrix\n",
    ")\n",
    "\n",
    "# Confusion matrix with weights\n",
    "tn, fp, fn, tp = confusion_matrix(\n",
    "    y_test, y_pred,\n",
    "    sample_weight=w_test\n",
    ").ravel()\n",
    "\n",
    "# Basic metrics\n",
    "accuracy    = accuracy_score(y_test, y_pred, sample_weight=w_test)\n",
    "sensitivity = recall_score(y_test, y_pred, sample_weight=w_test)          # TPR / recall\n",
    "specificity = tn / (tn + fp) if (tn + fp) > 0 else np.nan\n",
    "\n",
    "ppv = precision_score(y_test, y_pred, sample_weight=w_test)               # Positive Predictive Value\n",
    "npv = tn / (tn + fn) if (tn + fn) > 0 else np.nan                         # Negative Predictive Value\n",
    "\n",
    "f1 = f1_score(y_test, y_pred, sample_weight=w_test)\n",
    "f2 = fbeta_score(y_test, y_pred, beta=2, sample_weight=w_test)\n",
    "\n",
    "# ROC AUC (weighted)\n",
    "roc_auc = roc_auc_score(y_test, y_proba, sample_weight=w_test)\n",
    "\n",
    "# PR AUC (via precision-recall curve)\n",
    "precisions, recalls, _ = precision_recall_curve(\n",
    "    y_test, y_proba,\n",
    "    sample_weight=w_test\n",
    ")\n",
    "pr_auc = auc(recalls, precisions)\n",
    "\n",
    "print(f\"=== {tier_name} — Weighted Test Metrics ===\")\n",
    "print(f\"Accuracy:              {accuracy:.3f}\")\n",
    "print(f\"Sensitivity (Recall):  {sensitivity:.3f}\")\n",
    "print(f\"Specificity:           {specificity:.3f}\")\n",
    "print(f\"PPV (Precision):       {ppv:.3f}\")\n",
    "print(f\"NPV:                   {npv:.3f}\")\n",
    "print(f\"F1:                    {f1:.3f}\")\n",
    "print(f\"F2:                    {f2:.3f}\")\n",
    "print(f\"ROC AUC:               {roc_auc:.3f}\")\n",
    "print(f\"PR AUC:                {pr_auc:.3f}\")\n",
    "print(\"Confusion matrix (weighted):\")\n",
    "print(f\"  TN: {tn:.1f},  FP: {fp:.1f}\")\n",
    "print(f\"  FN: {fn:.1f},  TP: {tp:.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d661efa",
   "metadata": {},
   "source": [
    "Bootstrapped 95% CI for ROC AUC and F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "a8d70214",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC 95% CI: [0.795, 0.861]\n",
      "F1      95% CI: [0.737,  0.807]\n"
     ]
    }
   ],
   "source": [
    "n_bootstrap = 500   # can bump to 1000 if laptop handles it\n",
    "rng = np.random.RandomState(42)\n",
    "\n",
    "roc_values = []\n",
    "f1_values  = []\n",
    "\n",
    "y_test_arr = y_test.to_numpy()\n",
    "w_test_arr = w_test.to_numpy()\n",
    "\n",
    "for i in range(n_bootstrap):\n",
    "    # resample indices with replacement\n",
    "    idx = rng.randint(0, len(y_test_arr), len(y_test_arr))\n",
    "    \n",
    "    y_b   = y_test_arr[idx]\n",
    "    proba_b = y_proba[idx]\n",
    "    pred_b  = y_pred[idx]\n",
    "    w_b   = w_test_arr[idx]\n",
    "\n",
    "    # ROC AUC\n",
    "    try:\n",
    "        roc_values.append(\n",
    "            roc_auc_score(y_b, proba_b, sample_weight=w_b)\n",
    "        )\n",
    "    except ValueError:\n",
    "        # can happen if bootstrap sample has only one class\n",
    "        continue\n",
    "\n",
    "    # F1\n",
    "    f1_values.append(\n",
    "        f1_score(y_b, pred_b, sample_weight=w_b)\n",
    "    )\n",
    "\n",
    "roc_ci = (np.percentile(roc_values, 2.5), np.percentile(roc_values, 97.5))\n",
    "f1_ci  = (np.percentile(f1_values, 2.5),  np.percentile(f1_values, 97.5))\n",
    "\n",
    "print(f\"ROC AUC 95% CI: [{roc_ci[0]:.3f}, {roc_ci[1]:.3f}]\")\n",
    "print(f\"F1      95% CI: [{f1_ci[0]:.3f},  {f1_ci[1]:.3f}]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "105eaffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import StratifiedKFold, GridSearchCV, train_test_split\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score, recall_score, precision_score, f1_score, fbeta_score,\n",
    "    roc_auc_score, precision_recall_curve, auc, confusion_matrix\n",
    ")\n",
    "\n",
    "def evaluate_tier(tier_name, tier_features, X, y, w):\n",
    "\n",
    "    print(f\"\\n=== Running Logistic Regression for {tier_name} ({len(tier_features)} features) ===\")\n",
    "\n",
    "    # Subset X to this tier\n",
    "    X_tier = X[tier_features].copy()\n",
    "\n",
    "    # Train-test split\n",
    "    X_train, X_test, y_train, y_test, w_train, w_test = train_test_split(\n",
    "        X_tier, y, w,\n",
    "        test_size=0.2,\n",
    "        random_state=42,\n",
    "        stratify=y\n",
    "    )\n",
    "\n",
    "    # Preprocessing (median impute + scaling)\n",
    "    numeric_cols = X_train.columns.tolist()\n",
    "    numeric_transformer = Pipeline(steps=[\n",
    "        ('imputer', SimpleImputer(strategy='median')),\n",
    "        ('scaler', StandardScaler())\n",
    "    ])\n",
    "\n",
    "    preprocessor = ColumnTransformer(\n",
    "        transformers=[('num', numeric_transformer, numeric_cols)],\n",
    "        remainder='drop'\n",
    "    )\n",
    "\n",
    "    # Logistic Regression Pipeline + CV\n",
    "    log_reg_pipe = Pipeline(steps=[\n",
    "        ('preprocess', preprocessor),\n",
    "        ('model', LogisticRegression(\n",
    "            max_iter=2000,\n",
    "            solver='liblinear'\n",
    "        ))\n",
    "    ])\n",
    "\n",
    "    param_grid = {\n",
    "        'model__C': [0.01, 0.1, 1.0, 10.0],\n",
    "        'model__class_weight': [None, 'balanced']\n",
    "    }\n",
    "\n",
    "    cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
    "\n",
    "    grid = GridSearchCV(\n",
    "        estimator=log_reg_pipe,\n",
    "        param_grid=param_grid,\n",
    "        cv=cv,\n",
    "        scoring='roc_auc',\n",
    "        n_jobs=1,\n",
    "        verbose=0\n",
    "    )\n",
    "\n",
    "    grid.fit(X_train, y_train)\n",
    "\n",
    "    best_model = grid.best_estimator_\n",
    "\n",
    "    # Refit using survey weights\n",
    "    best_model.fit(X_train, y_train, model__sample_weight=w_train)\n",
    "\n",
    "    # Predict\n",
    "    y_proba = best_model.predict_proba(X_test)[:, 1]\n",
    "    y_pred = (y_proba >= 0.5).astype(int)\n",
    "\n",
    "    # Weighted metrics\n",
    "    tn, fp, fn, tp = confusion_matrix(y_test, y_pred, sample_weight=w_test).ravel()\n",
    "\n",
    "    metrics = {\n",
    "        \"Tier\": tier_name,\n",
    "        \"Feature_Count\": len(tier_features),\n",
    "        \"Accuracy\": accuracy_score(y_test, y_pred, sample_weight=w_test),\n",
    "        \"Sensitivity\": recall_score(y_test, y_pred, sample_weight=w_test),\n",
    "        \"Specificity\": tn / (tn + fp),\n",
    "        \"PPV\": precision_score(y_test, y_pred, sample_weight=w_test),\n",
    "        \"NPV\": tn / (tn + fn),\n",
    "        \"F1\": f1_score(y_test, y_pred, sample_weight=w_test),\n",
    "        \"F2\": fbeta_score(y_test, y_pred, beta=2, sample_weight=w_test),\n",
    "        \"ROC_AUC\": roc_auc_score(y_test, y_proba, sample_weight=w_test),\n",
    "        \"PR_AUC\": auc(*precision_recall_curve(\n",
    "            y_test, y_proba, sample_weight=w_test\n",
    "        )[1::-1]),\n",
    "        \"TN\": tn, \"FP\": fp, \"FN\": fn, \"TP\": tp\n",
    "    }\n",
    "\n",
    "    print(f\"Finished {tier_name} ✓\")\n",
    "\n",
    "    return metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0b7e10a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Running Logistic Regression for Tier 0 (Top 100) (100 features) ===\n",
      "Finished Tier 0 (Top 100) ✓\n",
      "\n",
      "=== Running Logistic Regression for Tier 1 (5 features) ===\n",
      "Finished Tier 1 ✓\n",
      "\n",
      "=== Running Logistic Regression for Tier 2 (12 features) ===\n",
      "Finished Tier 2 ✓\n",
      "\n",
      "=== Running Logistic Regression for Tier 3 (29 features) ===\n",
      "Finished Tier 3 ✓\n",
      "\n",
      "=== Running Logistic Regression for Tier 4 (52 features) ===\n",
      "Finished Tier 4 ✓\n"
     ]
    },
    {
     "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>Tier</th>\n",
       "      <th>Feature_Count</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Sensitivity</th>\n",
       "      <th>Specificity</th>\n",
       "      <th>PPV</th>\n",
       "      <th>NPV</th>\n",
       "      <th>F1</th>\n",
       "      <th>F2</th>\n",
       "      <th>ROC_AUC</th>\n",
       "      <th>PR_AUC</th>\n",
       "      <th>TN</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>TP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tier 0 (Top 100)</td>\n",
       "      <td>100</td>\n",
       "      <td>0.995128</td>\n",
       "      <td>0.991536</td>\n",
       "      <td>0.995425</td>\n",
       "      <td>0.947268</td>\n",
       "      <td>0.999296</td>\n",
       "      <td>0.968897</td>\n",
       "      <td>0.982354</td>\n",
       "      <td>0.999676</td>\n",
       "      <td>0.997569</td>\n",
       "      <td>4.688882e+07</td>\n",
       "      <td>215490.696164</td>\n",
       "      <td>3.304401e+04</td>\n",
       "      <td>3.871023e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tier 1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.971597</td>\n",
       "      <td>0.634509</td>\n",
       "      <td>0.999535</td>\n",
       "      <td>0.991241</td>\n",
       "      <td>0.970585</td>\n",
       "      <td>0.773737</td>\n",
       "      <td>0.683721</td>\n",
       "      <td>0.829097</td>\n",
       "      <td>0.835851</td>\n",
       "      <td>4.708242e+07</td>\n",
       "      <td>21888.125341</td>\n",
       "      <td>1.426903e+06</td>\n",
       "      <td>2.477164e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tier 2</td>\n",
       "      <td>12</td>\n",
       "      <td>0.985758</td>\n",
       "      <td>0.970803</td>\n",
       "      <td>0.986997</td>\n",
       "      <td>0.860880</td>\n",
       "      <td>0.997554</td>\n",
       "      <td>0.912543</td>\n",
       "      <td>0.946628</td>\n",
       "      <td>0.997614</td>\n",
       "      <td>0.982910</td>\n",
       "      <td>4.649183e+07</td>\n",
       "      <td>612482.848786</td>\n",
       "      <td>1.139883e+05</td>\n",
       "      <td>3.790078e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tier 3</td>\n",
       "      <td>29</td>\n",
       "      <td>0.985917</td>\n",
       "      <td>0.971832</td>\n",
       "      <td>0.987085</td>\n",
       "      <td>0.861812</td>\n",
       "      <td>0.997640</td>\n",
       "      <td>0.913522</td>\n",
       "      <td>0.947637</td>\n",
       "      <td>0.998514</td>\n",
       "      <td>0.987752</td>\n",
       "      <td>4.649595e+07</td>\n",
       "      <td>608366.414519</td>\n",
       "      <td>1.099692e+05</td>\n",
       "      <td>3.794097e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tier 4</td>\n",
       "      <td>52</td>\n",
       "      <td>0.986358</td>\n",
       "      <td>0.969399</td>\n",
       "      <td>0.987763</td>\n",
       "      <td>0.867826</td>\n",
       "      <td>0.997439</td>\n",
       "      <td>0.915805</td>\n",
       "      <td>0.947226</td>\n",
       "      <td>0.998456</td>\n",
       "      <td>0.987261</td>\n",
       "      <td>4.652790e+07</td>\n",
       "      <td>576409.973102</td>\n",
       "      <td>1.194673e+05</td>\n",
       "      <td>3.784599e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Tier  Feature_Count  Accuracy  Sensitivity  Specificity  \\\n",
       "0  Tier 0 (Top 100)            100  0.995128     0.991536     0.995425   \n",
       "1            Tier 1              5  0.971597     0.634509     0.999535   \n",
       "2            Tier 2             12  0.985758     0.970803     0.986997   \n",
       "3            Tier 3             29  0.985917     0.971832     0.987085   \n",
       "4            Tier 4             52  0.986358     0.969399     0.987763   \n",
       "\n",
       "        PPV       NPV        F1        F2   ROC_AUC    PR_AUC            TN  \\\n",
       "0  0.947268  0.999296  0.968897  0.982354  0.999676  0.997569  4.688882e+07   \n",
       "1  0.991241  0.970585  0.773737  0.683721  0.829097  0.835851  4.708242e+07   \n",
       "2  0.860880  0.997554  0.912543  0.946628  0.997614  0.982910  4.649183e+07   \n",
       "3  0.861812  0.997640  0.913522  0.947637  0.998514  0.987752  4.649595e+07   \n",
       "4  0.867826  0.997439  0.915805  0.947226  0.998456  0.987261  4.652790e+07   \n",
       "\n",
       "              FP            FN            TP  \n",
       "0  215490.696164  3.304401e+04  3.871023e+06  \n",
       "1   21888.125341  1.426903e+06  2.477164e+06  \n",
       "2  612482.848786  1.139883e+05  3.790078e+06  \n",
       "3  608366.414519  1.099692e+05  3.794097e+06  \n",
       "4  576409.973102  1.194673e+05  3.784599e+06  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = []\n",
    "\n",
    "for tier_name, tier_features in tiers.items():\n",
    "    metrics = evaluate_tier(tier_name, tier_features, X, y, w)\n",
    "    results.append(metrics)\n",
    "\n",
    "# Convert to DataFrame for comparison\n",
    "results_df = pd.DataFrame(results)\n",
    "\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10c5a08d",
   "metadata": {},
   "source": [
    "# Results Summary\n",
    "\n",
    "## What you found:\n",
    "Tier 1 (5 features) works but misses a lot of true MDE cases:\n",
    "- Sensitivity = 0.63 → It catches 63% of real depression cases.\n",
    "- Specificity = 1.00 → It never falsely marks healthy cases as depression.\n",
    "- It’s “ultra-conservative”—great for avoiding false alarms, but not good for screening.\n",
    "\n",
    "Tier 2–4 (12–52 features) perform extremely well:\n",
    "- Sensitivity ≈ 0.97 → They catch 97% of all real MDE cases.\n",
    "- Specificity ≈ 0.987 → They correctly identify healthy people 98.7% of the time.\n",
    "- AUC ≈ 0.998 → Near-perfect discrimination.\n",
    "- These models perform at the same level as the best clinical screeners used in psychiatry research, and in some aspects even better.\n",
    "\n",
    "Tier 0 (100 features) is the maximum-possible-performance tier:\n",
    "- It achieves nearly perfect separation: AUC 0.9997\n",
    "- But it uses too many predictors to be practical.\n",
    "\n",
    "A model using about 29–52 variables (Tier 3–4) can detect past-year major depression with extremely high accuracy: around 97% sensitivity and 99% specificity. These tiers offer nearly the same predictive value as using 100 features, but with far fewer inputs—meaning they can be adapted into clinical workflows.\n",
    "\n",
    "Final verdict: Using only 12–30 variables, the model can detect depression cases with about 97% sensitivity and 99% specificity. This is comparable to or better than many psychiatric screening tools. The smallest tier (5 variables) is much more conservative and misses cases, so it isn’t suitable for screening. The larger tiers show that adding more contextual variables dramatically improves ability to identify patients with depression.Using only 12–30 variables, the model can detect depression cases with about 97% sensitivity and 99% specificity. This is comparable to or better than many psychiatric screening tools. The smallest tier (5 variables) is much more conservative and misses cases, so it isn’t suitable for screening. The larger tiers show that adding more contextual variables dramatically improves ability to identify patients with depression.\n",
    "\n",
    "\n",
    "## Detailed Technical Analysis\n",
    "### Tier 0 (Top 100 features)\n",
    "Best possible / upper bound:\n",
    "- Accuracy: 0.995\n",
    "- Sensitivity: 0.992\n",
    "- Specificity: 0.995\n",
    "- ROC AUC: 0.9997\n",
    "- PR AUC: 0.9975\n",
    "\n",
    "Interpretation:\n",
    "- The model almost perfectly separates depressed vs. non-depressed.\n",
    "- This shows that NSDUH data contains very strong signal for detecting IRAMDEYR, even after removing leakage.\n",
    "- But this is not deployable—too large, not clinically feasible.\n",
    "\n",
    "### Tier 1 (5 features)\n",
    "- Accuracy: 0.972\n",
    "- Sensitivity: 0.635\n",
    "- Specificity: 0.9995\n",
    "- ROC AUC: 0.829\n",
    "\n",
    "Interpretation:\n",
    "- Very high specificity → almost no false positives\n",
    "- Low sensitivity → misses many real cases\n",
    "- It’s not good enough for a screening use case but fine for confirmatory use.\n",
    "- Why?: With only 5 predictors, the model doesn’t capture enough variation in clinical presentations.\n",
    "\n",
    "### Tier 2 (12 features)\n",
    "- Sensitivity: 0.971\n",
    "- Specificity: 0.987\n",
    "- ROC AUC: 0.9976\n",
    "\n",
    "Interpretation:\n",
    "- Massive jump in sensitivity (+33% over Tier 1).\n",
    "- This shows that around 10–15 features are enough to fully capture the underlying construct.\n",
    "- Tier 2 is already extremely strong.\n",
    "\n",
    "### Tier 3 (29 features)\n",
    "- Sensitivity: 0.972\n",
    "- Specificity: 0.987\n",
    "- ROC AUC: 0.9985\n",
    "\n",
    "Interpretation:\n",
    "- Performance increases slightly over Tier 2.\n",
    "- This is likely the clinically optimal tradeoff between performance and complexity.\n",
    "\n",
    "### Tier 4 (52 features)\n",
    "- Sensitivity: 0.969\n",
    "- Specificity: 0.988\n",
    "- ROC AUC: 0.9984\n",
    "\n",
    "Interpretation:\n",
    "- Very similar to Tier 3.\n",
    "- You gain almost no performance beyond ~30 features."
   ]
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