final_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Adolescent Drug Use with Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align:center\"><img src='img/stopdrugs.png'/></div>\n",
    "\n",
    "#### Author: [Jamie Dowat](mailto:jamie_dowat44@yahoo.com)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***********\n",
    "#### The drug problem in the United States is widespread and growing:\n",
    "\n",
    "> [Mortality rates](https://jamanetwork.com/journals/jama/article-abstract/2674665) from drug use disorders increased nationally and in every county between 1980 and 2014, but the relative increase varied from 8.2% to 8369.7%.\n",
    "\n",
    "************\n",
    "> No suprise, but more and more research is showing the level of damage drugs do to the brain. Check out this diagram from Amen Clinics:\n",
    "\n",
    "<div style=\"text-align:center\"><img src='img/amenbrain.png'/></div>\n",
    "\n",
    "************\n",
    "\n",
    "> [**Adolescents**](https://www.drugabuse.gov/news-events/news-releases/2021/03/younger-age-of-first-cann[…]ssociated-with-faster-development-of-substance-use-disorders) are especially vulnerable. After a year since first use of drugs:\n",
    "        * 11.2% of adolescents had prescription opioid use disorder versus 6.9% of young adults. \n",
    "        * 13.9% of adolescents had prescription stimulant use disorder versus 3.9% of young adults. \n",
    "        * 11.2% of adolescents had prescription tranquilizer use disorder versus 4.7% of young adults.\n",
    "        \n",
    "***********        \n",
    "\n",
    "> The author of the above study on adolescents noted: ***“This [study] underscores the importance of drug use prevention and screening for substance use or misuse among adolescents and young adults. Offering timely treatment and support to young people who need it must be a public health priority.”***\n",
    "\n",
    "***********\n",
    "\n",
    "## POTENTIAL STAKEHOLDER & DEPLOYMENT:\n",
    "\n",
    "In terms of real-life value, this model mirrors much of the work being done by the NIDA and SAMHSA. This model has potential to serve as a barometer for high schools, who can implement the survey anonymously and gain some high-level data about the state of their school and be able to more precisely analyze trends amongst their students and measure their prevention efforts against any changes seen in the model.\n",
    "\n",
    "## DATA: [Monitoring the Future](www.monitoringthefuture.org)\n",
    "\n",
    "<p float=\"right\">\n",
    "    <img src=\"img/nida.png\" width=\"100\" />\n",
    "    <img src=\"img/mtf.png\" width=\"100\" />\n",
    "    <img src=\"img/samhsa.png\" width=\"100\" />\n",
    "</p>\n",
    "\n",
    "\n",
    "Monitoring the Future began in 1975 as an attempt to collect and study drug use and feelings toward drug use amongst adolescents (8th-12th). This project specifically focuses on survey responses from 12th graders (since data for younger cohorts was not all open source), totaling to approximately 360,000 high school seniors across 133 schools nationwide.\n",
    "For more information about the design and implementation of the study, click [here](http://monitoringthefuture.org/purpose.html).\n",
    "About 6 different forms of the survey are distributed, each with slightly different questions.\n",
    "\n",
    "**Questions** in this dataset are limited to the following:\n",
    "* Demographic information\n",
    "* Drug Use Information\n",
    "\n",
    "To download the dataset used in this notebook, click [here](https://www.kaggle.com/jamiedowat/monitoring-the-future-12th-grade-drug-use-survey).\n",
    "\n",
    "## MODELING:\n",
    "\n",
    "The following models were implemented, along with [Boruta feature selection](https://towardsdatascience.com/boruta-explained-the-way-i-wish-someone-explained-it-to-me-4489d70e154a) and Bayesian Optimization.\n",
    "* Logistic Regression\n",
    "* Decision Trees\n",
    "* Random Forests\n",
    "* XGBoost\n",
    "\n",
    "The **final model** produced an accuracy of **80.6%** and a recall of **72%** on the hold-out set.\n",
    "\n",
    "**Recall** was given extra attention as a metric, since false negatives (predicting no drug use when drug use is present) are predictions that would potentially cause the most harm.\n",
    "\n",
    "***********\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we'll take a look at the original survey. The \"core dataset\" combines certain questions from all 6 versions of the survey, approximating to about 250 questions (features)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "survey = pd.read_csv('data/modeling_data/mtf.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yr_adminst</th>\n",
       "      <th>sampling_weight</th>\n",
       "      <th>region</th>\n",
       "      <th>msa</th>\n",
       "      <th>smsa</th>\n",
       "      <th>siblings</th>\n",
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       "      <th>evr_drink</th>\n",
       "      <th>alcohol_lifetime_freq</th>\n",
       "      <th>...</th>\n",
       "      <th>hgh_yr_freq</th>\n",
       "      <th>pwd_alc_yr_freq</th>\n",
       "      <th>hookah_month_freq</th>\n",
       "      <th>evr_vape</th>\n",
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       "      <th>vape_flav_lifetime_freq</th>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Regularly Now</td>\n",
       "      <td>2+ Packs</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>40+</td>\n",
       "      <td>...</td>\n",
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       "      <td>1995</td>\n",
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       "      <td>Regularly Now</td>\n",
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       "      <td>40+</td>\n",
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       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995</td>\n",
       "      <td>0.6</td>\n",
       "      <td>South</td>\n",
       "      <td>0</td>\n",
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       "      <td>1995</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Midwest</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "   yr_adminst  sampling_weight   region  msa  smsa siblings evr_smoked_cig  \\\n",
       "0        1995              0.6  Midwest    1     1        1          Never   \n",
       "1        1995              0.6     West    0     1        2  Regularly Now   \n",
       "2        1995              0.9  Midwest    0     1        1  Regularly Now   \n",
       "3        1995              0.6    South    0     0       3+          Never   \n",
       "4        1995              0.9  Midwest    1     1     None          Never   \n",
       "\n",
       "  cig_month_freq evr_drink alcohol_lifetime_freq  ... hgh_yr_freq  \\\n",
       "0           None   Unknown                     0  ...     Unknown   \n",
       "1       2+ Packs   Unknown                   40+  ...     Unknown   \n",
       "2        2 Packs   Unknown                   40+  ...     Unknown   \n",
       "3           None   Unknown                     0  ...     Unknown   \n",
       "4           None   Unknown                     0  ...     Unknown   \n",
       "\n",
       "  pwd_alc_yr_freq hookah_month_freq evr_vape vape_marj_lifetime_freq  \\\n",
       "0         Unknown           Unknown  Unknown                 Unknown   \n",
       "1         Unknown           Unknown  Unknown                 Unknown   \n",
       "2         Unknown           Unknown  Unknown                 Unknown   \n",
       "3         Unknown           Unknown  Unknown                 Unknown   \n",
       "4         Unknown           Unknown  Unknown                 Unknown   \n",
       "\n",
       "  vape_marj_yr_freq vape_marj_month_freq vape_flav_lifetime_freq  \\\n",
       "0           Unknown              Unknown                 Unknown   \n",
       "1           Unknown              Unknown                 Unknown   \n",
       "2           Unknown              Unknown                 Unknown   \n",
       "3           Unknown              Unknown                 Unknown   \n",
       "4           Unknown              Unknown                 Unknown   \n",
       "\n",
       "  vape_flav_yr_freq vape_flav_month_freq  \n",
       "0           Unknown              Unknown  \n",
       "1           Unknown              Unknown  \n",
       "2           Unknown              Unknown  \n",
       "3           Unknown              Unknown  \n",
       "4           Unknown              Unknown  \n",
       "\n",
       "[5 rows x 210 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         30756\n",
       "40+       27832\n",
       "10-19X    16308\n",
       "20-39X    14748\n",
       "3-5X      14194\n",
       "6-9X      12665\n",
       "1-2X      10532\n",
       "Name: alcohol_lifetime_freq, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.alcohol_lifetime_freq.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src import eda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting with some timely trends, it seems that marijuana usage has remained relatively the same.\n",
    "\n",
    "***Ignoring the data-based anamoly in 2018, of course.**\n",
    "\n",
    "As seen in the graph, students were asked the following about various drugs:\n",
    "   * How many times have you consumed [insert drug here]...\n",
    "        * in the past **month**?\n",
    "        * in the past **year**?\n",
    "        * in your **lifetime**?\n",
    "        \n",
    "Students were given the following options as **responses**:\n",
    "   * 0 Occasions\n",
    "   * 1-2X\n",
    "   * 3-5X\n",
    "   * 6-9X\n",
    "   * 10-29X\n",
    "   * 20-39X\n",
    "   * 40+ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eda.marijuana_use_by_year(survey)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Though 2018 is acting up again, **cigarette use** is clearly on the decline. In conjunction with this decrease they have also found a recent [rise in **vaping**](https://www.youtube.com/watch?v=6hN5FKYkHSQ). However, since they only added questions about vape use in **2017**, I have excluded them from this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eda.cigarette_use_by_year(survey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unknown         14482\n",
       "Occasionally        7\n",
       "1-2x                7\n",
       "Never               6\n",
       "Name: evr_smoked_cig, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survey[survey['yr_adminst']==2018]['evr_smoked_cig'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that drugs other than marijuana have also decreased in use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eda.other_drug_use_by_year(survey)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Marijuana is, as seen here, the overriding majority in terms of drug choice. This began the debate on what *exactly* the target should be for this machine learning model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eda.drug_popularity(survey)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the same dataset as the `survey` one we just opened, but this one contains the pre-engineering target. After played with different targets (marijuana only, excluding marijuana), I decided to narrow in on what would be considered more \"regular users\". The target was essentially an engineered version of the survey question:\n",
    "\n",
    "> Have you used any illicit drug (including marijuana) in the past ***year***?\n",
    "\n",
    "I found the target to be too broad when originally modeling with a target where the student could have tried a drug any *any point* in their lifetime. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/modeling_data/mtf_with_target.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>yr_adminst</th>\n",
       "      <th>sampling_weight</th>\n",
       "      <th>region</th>\n",
       "      <th>msa</th>\n",
       "      <th>smsa</th>\n",
       "      <th>evr_smoked_cig</th>\n",
       "      <th>cig_month_freq</th>\n",
       "      <th>alcohol_lifetime_freq</th>\n",
       "      <th>alcohol_yr_freq</th>\n",
       "      <th>alcohol_2weeks</th>\n",
       "      <th>...</th>\n",
       "      <th>work_hrs</th>\n",
       "      <th>work_pay</th>\n",
       "      <th>other_income</th>\n",
       "      <th>rec_time</th>\n",
       "      <th>date_freq</th>\n",
       "      <th>drive_freq</th>\n",
       "      <th>12mo_r_tcktd</th>\n",
       "      <th>12mo_accidents</th>\n",
       "      <th>num_drugs_yr</th>\n",
       "      <th>binary_drug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1995</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Never</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>30+</td>\n",
       "      <td>126-175</td>\n",
       "      <td>0</td>\n",
       "      <td>4-5</td>\n",
       "      <td>2-3x Month</td>\n",
       "      <td>51-100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1995</td>\n",
       "      <td>0.6</td>\n",
       "      <td>West</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Regularly Now</td>\n",
       "      <td>2+ Packs</td>\n",
       "      <td>40+</td>\n",
       "      <td>40+</td>\n",
       "      <td>10+X</td>\n",
       "      <td>...</td>\n",
       "      <td>5 or less</td>\n",
       "      <td>21-35</td>\n",
       "      <td>126-175</td>\n",
       "      <td>&lt;1</td>\n",
       "      <td>Never</td>\n",
       "      <td>1-10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1995</td>\n",
       "      <td>1.8</td>\n",
       "      <td>South</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Never</td>\n",
       "      <td>None</td>\n",
       "      <td>3-5X</td>\n",
       "      <td>3-5X</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>11-15</td>\n",
       "      <td>51-75</td>\n",
       "      <td>6-10</td>\n",
       "      <td>2</td>\n",
       "      <td>1x Month</td>\n",
       "      <td>11-50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995</td>\n",
       "      <td>0.9</td>\n",
       "      <td>South</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1-2x</td>\n",
       "      <td>None</td>\n",
       "      <td>40+</td>\n",
       "      <td>6-9X</td>\n",
       "      <td>Once</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2-3x Month</td>\n",
       "      <td>51-100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995</td>\n",
       "      <td>0.9</td>\n",
       "      <td>NorthEast</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Never</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11-20</td>\n",
       "      <td>2</td>\n",
       "      <td>Never</td>\n",
       "      <td>51-100</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   yr_adminst  sampling_weight     region  msa  smsa evr_smoked_cig  \\\n",
       "0        1995              0.6    Midwest    1     1          Never   \n",
       "1        1995              0.6       West    0     1  Regularly Now   \n",
       "2        1995              1.8      South    1     1          Never   \n",
       "3        1995              0.9      South    0     1           1-2x   \n",
       "4        1995              0.9  NorthEast    0     1          Never   \n",
       "\n",
       "  cig_month_freq alcohol_lifetime_freq alcohol_yr_freq alcohol_2weeks  ...  \\\n",
       "0           None                     0               0           None  ...   \n",
       "1       2+ Packs                   40+             40+           10+X  ...   \n",
       "2           None                  3-5X            3-5X           None  ...   \n",
       "3           None                   40+            6-9X           Once  ...   \n",
       "4           None                     0               0           None  ...   \n",
       "\n",
       "    work_hrs  work_pay other_income rec_time   date_freq drive_freq  \\\n",
       "0        30+   126-175            0      4-5  2-3x Month     51-100   \n",
       "1  5 or less     21-35      126-175       <1       Never       1-10   \n",
       "2      11-15     51-75         6-10        2    1x Month      11-50   \n",
       "3          0         0            0        2  2-3x Month     51-100   \n",
       "4          0         0        11-20        2       Never     51-100   \n",
       "\n",
       "  12mo_r_tcktd  12mo_accidents num_drugs_yr binary_drug  \n",
       "0            0               0            0           0  \n",
       "1            0               0            9           1  \n",
       "2            0               1            0           0  \n",
       "3            0               0            1           1  \n",
       "4            0               1            0           0  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Kzp078/XXX1OrVq0XntOzPbjcqLY9+8XDzc2N3bt3ExoaSkREBBs3bgTA2dmZCRMm0KdPH43zunz5sjpR6PLgwQP1/6veE11XJKt+54xF1xeHZ4dxDdGqVSt27dqVZ52TJ0/i6+tLVlYW7dq1o1u3bpQpUwYLCwvOnTtHZGSk+iIs0P99NpZne6DP/n9+Pp+GnquKjY2NQR2k1zoZAkyfPp3IyEhmzJhBjx49dNZR/fLl9qG8d++ezsRmCjdv3tTqXWVnZ5OWlkaZMmXUZap4jhw5YtC3G9Ddm8qLqq2UlBSd21VXk5rrPYqLiyMrKws7OzudE965nV9eP2ddiVz1ft+8eVPn8XIrf1nNmzenSZMm/PLLL8yePZv9+/eTlpbG6NGjKVq0qEGxqH5mqp+N6r8jR47km2++0SseS0tLAgMDCQwM5NatWxw7dozw8HB++eUXzp8/T1xc3AsvAFG1q/oSo4vqD+HziaNly5Zs3LiRx48fEx8fz4EDB1i6dCkBAQHY2NjQvn179fF79Oih9zUCqn1u3bqlNVSm+p1TjfK8bubNm0dGRgY7duzQmtebP38+kZGRWvvo8z6bWn4+n/k5VzD87+BrO2eoUrNmTYYNG8bVq1c1Lud9lmqO7saNG1rbrly5YrYeD0BsbKxW2dGjR8nKytK4V61FixYAJp0PVVG1e+TIEa1tWVlZxMXFAdCkSROTx5KTk8O8efMADP62mtfPOT4+XqtMdd4nT57U2aNRnbcpDBs2jEePHrFhwwZWrlyJQqFQX3aui67PTXZ2tjpG1bm4ublhYWGR78+Nra0tvr6+rFy5krZt25KYmMjff//9wv1UX9jyWiVFtS23L3fFihXD3d2dyZMnq3t/qh5SnTp1KFeunPoqUH2oPq+6Pteq37nX1ZUrVyhfvrzOC1x0fVaeldf7DP+/R5tXLz+/8vP5fJlzNcRrnwzh6aX+5cqVIzQ0VOewYZ06dShbtiyRkZEaQ5EZGRlaK4eYWkhIiMYcwqNHj5g5cybw9OIKlQEDBlCuXDnmzJmjcwI+JydH7+WSXsTHx4fy5cuzdetW9VylyuLFi0lKSqJ9+/Ymny+8desWQ4cO5ciRI1SrVo0xY8YYtL9qLnHdunUaf+hu3LjB3LlztepXq1aN1q1bc+XKFVasWKGx7cCBA0afL3xWnz59KFu2LD/88AOxsbF06NAhz/mbw4cPs2fPHo2ypUuXkpiYSJs2bdSXwNva2uLv78+ZM2eYO3euzj/4iYmJJCUlAU/vbTx06JDGpfvwdJhf1cvTZ9WVHj16ULZsWXbv3s2hQ4e0tq9bt45z585Rs2ZN3nrrLXX50aNHdX4ZVf2eqq5ytLKy4sMPPyQ5OZkJEyaQkZGhtU9ycrLGBWWqVVxCQ0M1eqzP/s69rhwdHblz5w5//PGHRvnq1auJiorSqq/v+wyoh49NccGcoZ9PMPxc8+u1HyYFKF++PGPGjMn1CrEiRYowYsQIQkJCaNu2LT169CArK4vo6GgqV65s1qGSOnXq4OHhoXGfYWJiIl26dFFfiQpPP5CrV6/m/fffp1OnTrRr14569ephYWHBjRs3OHnyJGlpabkObRqidOnSLFiwgMGDB+Pj40Pv3r2pVq0a8fHx/Prrr9jb2/Pdd9+9dDvPUk2e5+TkqJdji4uLIzMzEzc3N3766SeDr2Rs3rw5np6eHD16FC8vL9q2bcvNmzfZs2cPHTt21NljnDdvHl26dGHMmDHs27ePRo0akZSUREREBN27dycyMtKoV9qplCxZkn79+qlHM3TdTvGsrl278v7779OjRw9q1arFH3/8wb59+yhfvrzW4gQhISFcuXKFb775ho0bN+Lh4YGdnR3//vsvFy9e5PTp0yxbtowaNWqQkZFBr169cHR0pHnz5jg4OPDo0SMOHjzIhQsX6NatG3Xr1n3h+ZQrV45FixYxbNgw3n77bTp16kTDhg3Jzs7m1KlTxMbGUrZsWX766SeNK0EXLFhAdHQ0rVu3pkaNGpQqVYq///5bvUDBs+/L+PHj+eOPP1i+fDl79uyhTZs2VKlShVu3bnH58mWOHz/O1KlT1ReUeXh48OGHH7J06VLeeustjd85a2vrfK+e9CoIDAwkKiqKbt260bt3b8qWLcuZM2eIi4tTX0fxLEPe5zZt2mBhYcHMmTP5+++/1SMu48aNM0rshnw+83Ou+fVGJEN4emlzWFiY+hLz533xxReULFmSVatWsXLlSuzt7fHz81PfzGkuK1euJCQkhE2bNpGcnEzlypWZOHEin332mdYYd7t27Thy5AgLFiwgKiqKY8eOUbRoUSpVqkSbNm3w9fU1Wlw+Pj7s3buX0NBQoqKiuHfvHvb29gwdOpRx48YZ/QuDanimaNGilC5dGgcHB9577z18fX3x8vLKdwJav349X375JZGRkSxduhQnJydmzpxJhw4d1PdJPqtevXrs37+fmTNncvjwYWJiYmjYsCFr167l4sWLREZGaszlGtP777+vviG8e/fuedbt2bMngwcPJjQ0lH379mFlZUXPnj2ZNm2a1q0YZcuWZdeuXaxcuZItW7awY8cOHj16hJ2dHbVq1eKbb76hQ4cOAJQqVYoZM2YQExPDiRMn2LVrF6VLl6ZmzZrMnz+f999/X+/z6dGjB9HR0SxYsIAjR45w6NAhLCwsqFq1KsOHD+fjjz/Wuok7ICAAa2trTp06xfHjx8nKyqJKlSoMGzZMfVuPSpEiRVi/fj0bN25k/fr17N27l4cPH2JjY0P16tWZPHmy1r2hc+bMoXbt2oSFhbFy5UoqVKhAjx49mDp1qlEWiSgonTp14ueff2bevHls27YNCwsL3Nzc2LFjB0lJSVoJwpD3uW7duixevJgff/yRZcuWqa/cNFYyNOTzmZ9zzS9Fenq68sXVhCh8hg8fzubNmzl58qTGCi/Gsm7dOoKCghg7dqzGihu66ixcuFBjGF0IYVxvxJyhEPmVk5Ojc6j50KFDhIeHU69ePZMkwqysLBYuXIiVlZXWDf9CCPN7Y4ZJhciPzMxMGjZsSJs2bXB2dsbKyorz588THR1N0aJFc73BO7+OHTtGbGwsR44c4a+//mL48OE6V9cQQpiXJENRqBUpUoQhQ4YQExPDqVOn+O+//6hYsSK9e/dm9OjRRr+d5ODBg8yZM4fy5cszaNCg1/6qRiHeFDJnKIQQotCTOUMhhBCFniRDIYQQhZ4kQyGEEIWeJEMTSUhIKOgQ1CQW3V6VWF6VOEBiyY3EoturFMvLkmQohBCi0JNkKIQQotCTZCiEEKLQk2QohBCi0JNkKIQQotCTZCiEEKLQk2QohBCi0JNkKIQQotCTp1a8wC5Pz3zvezEf+/gcPZrv9oQQQuSP9AyFEEIUepIMhRBCFHqSDIUQQhR6BZ4Mw8LCcHFxwd7ennbt2nH0BXNmSqWSRYsW0aJFC+zs7Khbty7Tp083T7BCCCHeSAV6AU14eDgTJ04kNDQUDw8PwsLC8Pf3Jy4uDgcHB537TJ48mb179zJz5kwaNmzI3bt3SUlJMXPkQggh3iQFmgwXLlxI//79GTRoEAAhISFERUWxfPlypk2bplU/ISGBpUuXEhsbS926dc0drhBCiDdUgQ2TZmZmEh8fj5eXl0a5l5cXx48f17lPZGQkNWrU4MCBAzRp0oTGjRvz0UcfcevWLXOELIQQ4g1VYD3D1NRUsrOzsbW11Si3tbXl5s2bOvdJSkri+vXrhIeHs2jRIhQKBVOnTuW9995j//79WFjozu2v0wMoTRXrq/QeSCzaXpU4QGLJjcSiW0HE4uzsbPRj6p0MMzIyePjwITY2Nuqy27dvs3r1atLT0+nVqxdubm4GB6BQKDReK5VKrTKVnJwcHj9+zJIlS6hduzYAS5YsoXnz5pw+fZrmzZvr3O9l3rj83Dj/MkzxQ05ISDDJcfNDYnl14wCJJTcSi26vUiwvS+9k+Nlnn/H3339z6NAhAB4+fEjHjh25du0aAIsXL2bHjh14eHjodbyKFStiaWmp1Qu8ffu2Vm9Rxd7eHisrK3UiBHBycsLKyoobN27kmgyFEEKIvOg9ZxgXF0e3bt3Ur7ds2cK1a9fYsmULFy5coG7dusybN0/vhosWLYqrqyvR0dEa5dHR0bi7u+vcx8PDg6ysLBITE9VlSUlJZGVl5Xr1qRBCCPEieifDlJQUqlatqn69e/duWrZsSceOHbGzs2PAgAGcPXvWoMaDgoJYv349q1ev5sKFC0yYMIHk5GSGDBkCwIwZM/D19VXXb9++PU2aNCEoKIjff/+d33//naCgIJo3b07Tpk0NalsIIYRQ0XuYtFSpUqSnpwOQlZXF0aNHCQwMVG8vUaIE9+/fN6hxPz8/0tLSCAkJISUlhfr167Np0yYcHR0BSE5O1ugFWlhYsHHjRiZMmICPjw/FixenQ4cOfP3117lePCOEEEK8iN7JsGnTpqxZs4a2bduye/duHjx4QNeuXdXbExMTsbOzMziAgIAAAgICdG5bvHixVlmlSpVYtWqVwe0IIYQQudE7GU6ePBk/Pz86dOiAUqnE19dXY2hy586duc71CSGEEK8yvZOhq6srJ0+e5Pjx45QpU4Y2bdqot6WnpxMQEEDr1q1NEqQQQghhSnpPtMXGxqJUKunevbtGIgSwtrbG39+fe/fuGT1AIYQQwtT0ToY9e/bUug3iWYcPH6Znz55GCUoIIYQwJ72ToVKpzHN7ZmamXNEphBDitZTnnOG9e/e4e/eu+nVaWhrXr1/Xqpeens7WrVupXLmy8SMUQgghTCzPZLho0SLmzp0LPF1DdNKkSUyaNElnXaVSydSpU40foRBCCGFieSbD9u3bU7x4cZRKJTNnzsTPz4/GjRtr1FEoFJQsWZKmTZvK2qBCCCFeS3kmQw8PD/XC248fP8bX15cGDRqYJTAhhBDCXPS+z3DixImmjEMIIYQoMAY93Fd1oUxSUhJ37tzRusJUoVCwYMECowYohBBCmJreyfDQoUMMHDiQ+/fvU6ZMGaytrbXq5PZQXiGEEOJVpncy/OKLLyhfvjy7du3SuohGCCGEeJ3pfZf8pUuXCAwMlEQohBDijaN3MqxevTqPHj0yZSxCCCFEgdA7GX7++eesWLGCO3fumDIeIYQQwuz0njNMSUmhQoUKNGvWjLfffptq1aphaWmpUUehUPDJJ58YPUghhBDClPROhtOnT1f//4oVK3TWkWQohBDidaR3Mvz9999NGYcQQghRYPROho6OjqaMQwghhCgwBq1AA3D9+nViY2O5deuWeu4wKyuLO3fuUL58eaysDD6kEEIIUaAMylxffPEFS5cuJTs7G4VCgYuLC9WqVeO///6jWbNmTJw4kaCgIFPFKoQQQpiE3rdW/PDDDyxevJigoCB++eUXjXVJy5Yti4+PDzt37jRJkEIIIYQp6Z0MV61axbvvvsuMGTN0rkLTsGFDLl++bNTghBBCCHPQOxneuHEDT0/PXLeXKVOGu3fvGhxAWFgYLi4u2Nvb065dO44ePZpr3atXr2Jtba3178CBAwa3K4QQQqjoPWdYoUIFkpOTc93+559/UrlyZYMaDw8PZ+LEiYSGhuLh4UFYWBj+/v7ExcXh4OCQ635bt26lUaNG6tfly5c3qF0hhBDiWXr3DL29vVm1ahWpqala237//XfWrl2Lj4+PQY0vXLiQ/v37M2jQIOrWrUtISAj29vYsX748z/0qVKiAvb29+l/RokUNalcIIYR4lt7J8IsvvsDCwgJPT0+mT5+OQqFg3bp1DB06lM6dO1OlShXGjRund8OZmZnEx8fj5eWlUe7l5cXx48fz3HfgwIHUrl2bLl26sH37dr3bFEIIIXTROxna29tz8OBBunbtyo4dO1AqlWzevJkDBw7Qt29f9u3bp/OBv7lJTU0lOzsbW1tbjXJbW1tu3rypc5/SpUvz1VdfsWLFCjZv3kzbtm0ZMmQIGzdu1LtdIYQQ4nmK9PR05Yurabt9+zY5OTnY2NhgYaF3TlX7999/qV+/PpGRkRoX5syePZutW7dy8uRJvY4zZswYjh07lueFNwkJCQbHp3Jx0KB875sfdVatMmt7QgjxunF2djb6MfO9XIyNjc1LNVyxYkUsLS21eoG3b9/W6i3mxc3NjXXr1uVZ52XeuIv53jN/TPFDTkhIMMlx80NieXXjAIklNxKLbq9SLC8r12Q4Z84cgw+mUCgYP368XnWLFi2Kq6sr0dHR9O7dW10eHR2Nr6+v3m2eO3cOe3t7Q0MVQggh1HJNhrNnz9YqUygUABqrz6jKlUqlQckQICgoiBEjRuDm5oa7uzvLly8nOTmZIUOGADBjxgxOnTpFREQEAOvXr6dIkSK4uLhgYWHBnj17CAsL03i8lBBCCGGoXJPh80+0T0lJwd/fn3r16hEYGEjt2rWBp93kxYsXc+HCBTZv3mxQ435+fqSlpRESEkJKSgr169dn06ZN6idkJCcnk5iYqLHPvHnzuH79OpaWljg5ObFgwQL69u1rULtCCCHEs/S+gGbQoEHk5OSwZs0andvff/99LC0tWfWGXQCyK49Vd0zBJ48LgfLrVRrXl1he3ThAYsmNxKLbqxTLy9L7MtDo6Gjat2+f6/YOHToQHR1tjJiEEEIIs9I7GVpaWvLHH3/kuv3s2bP5usVCCCGEKGh6Zy9fX19Wr15NaGgo9+/fV5ffv3+fefPmsXbtWnr16mWSIIUQQghT0vs+w1mzZpGYmMisWbMIDg7Gzs4OhUJBSkoK2dnZtGrVilmzZpkyViGEEMIk9E6GZcqUISIigt27d7Nv3z6uX7+OUqnE29sbb29vunXrZso4hRBCCJMxeAWabt26SeITQgjxRpErXoQQQhR6evcMXVxc1CvQ5EahUBAfH/+yMQkhhBBmpXcybNWqlVYyzM7O5tq1a5w4cYL69evj4uJi9ACFEEIIU9M7GS5evDjXbfHx8fTp04evv/7aKEEJIYQQ5mSUOUNXV1cGDx4sC2YLIYR4LRntApoqVapw/vx5Yx1OCCGEMBujJMMnT56wdetWgx7KK4QQQrwq9J4zDAoK0ll+9+5dTp48yc2bN5k7d67RAhNCCCHMRe9kePjwYa2rSRUKBdbW1nh6ejJ48GDatWtn9ACFEEIIU9M7GZ47d86UcQghhBAFRu85ww0bNnD16tVct1+9epUNGzYYJSghhBDCnPROhkFBQZw4cSLX7adOncp1XlEIIYR4lemdDJVKZZ7bMzIysLS0fOmAhBBCCHPLc87w+vXrXLt2Tf364sWLxMbGatVLT09nxYoVVK9e3fgRCiGEECaWZzJct24dc+bMQaFQoFAoCA0NJTQ0VKueUqnEwsKC77//3mSBCiGEEKaSZzLs1asXderUQalUEhAQQEBAAG+99ZZGHYVCQcmSJWnSpAmVKlUyabBCCCGEKeSZDOvXr0/9+vUBePz4Ma1atZKhUCGEEG8cve8z7N+/v1bZ//73P5KTk3FycsLa2tqYcQkhhBBmk+fVpL/99htz5szh1q1bGuUpKSn06NGDxo0b07lzZ5ydnZk2bZpJAxVCCCFMJc9kuGzZMtasWaO1AHdQUBCxsbG89dZbBAUFUa9ePX788cd83XQfFhaGi4sL9vb2tGvXjqNHj+q13+XLl6lWrRpVq1Y1uE0hhBDiWS/sGfr4+GiUJSYmEhUVhZeXF7t27eKrr77i119/pUGDBqxZs8agxsPDw5k4cSJjxozh8OHDtGzZEn9/f65fv57nfpmZmQwdOhRPT0+D2hNCCCF0yTMZpqSk4OzsrFG2d+9eFAoFgwcPVpcVKVKEPn368NdffxnU+MKFC+nfvz+DBg2ibt26hISEYG9vz/Lly/Pcb9q0aTRs2JBevXoZ1J4QQgihywtXoHl+5RnVkmytWrXSKLe1tSUjI0PvhjMzM4mPj8fLy0uj3MvLi+PHj+e63969e9m7dy9z5szRuy0hhBAiL3leTVqzZk3i4uIYPnw4AFlZWcTExFCnTh0qVKigUTc1NRUbGxu9G05NTSU7O1trPtLW1pabN2/q3Cc5OZlPP/2UNWvWUKZMGb3bSkhI0LtuQTNVrK/SeyCxaHtV4gCJJTcSi24FEcvzI5bGkGcy7NevH1OmTMHZ2RlPT0+2bNlCamoqH330kVbd2NhYateubXAAzz8jUalUapWpfPjhhwwdOpQWLVoY1MbLvHEX871n/pjih5yQkGCS4+aHxPLqxgESS24kFt1epVheVp7JcNiwYURHR6uXZFMqlbRp00br6RTXr1/n119/5csvv9S74YoVK2JpaanVC7x9+7ZWb1Hl8OHDxMbGqodIlUolOTk5VKxYkdDQUI15TCGEEEJfeSbDIkWKsHHjRs6cOUNiYiKOjo40b95cq96TJ08ICwvTmkfMS9GiRXF1dSU6OprevXury6Ojo/H19dW5z/O3XURGRhIaGkpUVBRVqlTRu20hhBDiWXqtQNO0aVOaNm2a6/ZatWpRq1YtgxsPCgpixIgRuLm54e7uzvLly0lOTmbIkCEAzJgxg1OnThEREQFAgwYNNPY/c+YMFhYWWuVCCCGEIfRejs0U/Pz8SEtLIyQkhJSUFOrXr8+mTZtwdHQEnl4wk5iYWJAhCiGEKAQKNBkC6qdh6LJ48eI89x0wYAADBgwwRVhCCCEKEb2fdC+EEEK8qSQZCiGEKPRyTYYbNmzg6tWr5oxFCCGEKBC5JsOgoCD10msAFSpUYPPmzWYJSgghhDCnXJNh2bJluXPnjvr182uUCiGEEG+KXK8mbd68OXPnzuXq1auULVsWgB07dnDlypVcD6ZQKBg/frzxoxRCCCFMKNdkOG/ePEaNGsWSJUvIzs5GoVCwY8cOduzYkevBJBkKIYR4HeWaDGvUqMHOnTvJyckhNTWVOnXqMG/ePHr27GnO+IQQQgiTe+FN9xYWFtja2jJhwgTeeust7OzszBGXEEIIYTZ6r0AzceJE9f/fvXuXGzduAFCtWjXKlStn/MiEEEIIMzHopvvTp0/TrVs3atWqRZs2bWjTpg21atWie/funD592lQxCiGEECald8/w1KlT+Pj4UKRIET744APq1q2LUqnk4sWLbNmyBR8fH3bt2kWzZs1MGa8QQghhdHonw1mzZmFra8u+ffuoXLmyxrbx48fj7e3NrFmzCA8PN3qQQgghhCnpPUz622+/MXToUK1ECFC5cmWGDh3KyZMnjRqcEEIIYQ56J0OlUomlpWXuB7KwkFVqhBBCvJb0ToZNmzZl5cqVGku0qdy5c4dVq1bJfKEQQojXkt5zhl988QW9e/emefPm9O/fH2dnZwAuXrzIzz//zP3791m0aJHJAhVCCCFMRe9k+NZbbxEeHs7kyZNZsGCBxjZXV1e+/vprPDw8jB6gEEIIYWp6J0OAVq1acfDgQW7evMm1a9cAcHR0lFVphBBCvNYMSoYqdnZ2kgCFEEK8MQxagUYIIYR4E0kyFEIIUehJMhRCCFHoSTIUQghR6EkyFEIIUegZnAzj4uKYO3cu48aN49KlSwA8fPiQU6dOce/ePYMDCAsLw8XFBXt7e9q1a8fRo0dzrXv+/Hl69OiBs7Mz9vb2NGnShJkzZ5KZmWlwu0IIIYSK3rdWZGZmMnToUCIjI1EqlSgUCnr06EHt2rWxtLSkT58+BAUFMXbsWL0bDw8PZ+LEiYSGhuLh4UFYWBj+/v7ExcXh4OCgVb9o0aL069cPFxcXypUrxx9//MGnn35KVlYWM2fO1LtdIYQQ4ll69wyDg4PZu3cvISEhnDx5UmNR7uLFi9O7d292795tUOMLFy6kf//+DBo0iLp16xISEoK9vT3Lly/XWb9WrVoMGDCAxo0b4+joSPfu3fH39+fYsWMGtSuEEEI8S+9kuHnzZgYPHsywYcOoUKGC1nZnZ2eSkpL0bjgzM5P4+Hi8vLw0yr28vDh+/Lhex7hy5QpRUVG0atVK73aFEEKI5+k9THrr1i0aN26c6/ZixYrx8OFDvRtOTU0lOzsbW1tbjXJbW1tu3ryZ577e3t78/vvvPH78mEGDBvHll1/mWT8hIUHvuAqaqWJ9ld4DiUXbqxIHSCy5kVh0K4hYVA+KMCa9k6G9vX2ePb9Tp05RvXp1gwNQKBQar1XzkXlZvnw5Dx484I8//uDLL7/ku+++4/PPP8+1/su8cRfzvWf+mOKHnJCQYJLj5ofE8urGARJLbiQW3V6lWF6W3snQ19eXFStW0L9/f/UwqSpp7d69m82bNxt08UzFihWxtLTU6gXevn1bq7f4vGrVqgFQr149srOz+eSTT/jkk0+wssrXUqtCCCEKOb3nDCdMmICDgwPt2rUjICAAhULB/Pnz6dSpEwMGDMDV1ZVPP/1U74aLFi2Kq6sr0dHRGuXR0dG4u7vrfZycnByysrLIzs7Wex8hhBDiWXp3pcqUKcO+fftYuHAhv/zyC8WLFycuLo6aNWsyadIkPv74Y4oXL25Q40FBQYwYMQI3Nzfc3d1Zvnw5ycnJDBkyBIAZM2Zw6tQpIiIiAPj5558pXrw4DRo0oGjRopw5c4aZM2fSq1cvihUrZlDbQgghhIpB44rFixdnzJgxjBkzxiiN+/n5kZaWRkhICCkpKdSvX59Nmzbh6OgIQHJyMomJif8/WCsr5s+fz5UrV1AqlTg4OBAQEMDIkSONEo8QQojCqcAn2QICAggICNC5bfHixRqv+/TpQ58+fcwRlhBCiEJE72QYFBSU53aFQkHx4sWpUqUKbdu2pXnz5i8dnBBCCGEOeifDw4cP8+jRI27fvg2AtbU1SqWSu3fvAmBjY0NOTg5paWkoFAo6d+7MqlWrDJ5HFEIIIcxN76tJt23bRrFixRg7diyXL18mMTGRpKQkLl++zJgxYyhevDj79u0jMTGRzz//nH379jF79mxTxi6EEEIYhUG3Vnh5eTF58mSN5dgqVKjAlClTaN++PePHj8fa2popU6bQq1cvtm3bZpKghRBCCGPSOxnGxcXh6uqa6/amTZtqrCnaunVrkpOTXyo4IYQQwhz0ToYlS5bM8+kQsbGxlChRQv06IyOD0qVLv1x0QgghhBnonQz9/f3ZunUr48aN4+LFi2RlZZGVlcXFixcZO3Ys27Ztw9/fX13/0KFD1K1b1yRBCyGEEMak99Wk06ZN4+bNm4SFhbFs2TL1uqRKpRKlUomfnx/Tp08H4NGjR7Ro0cKgZdWEEEKIgqJ3MixWrBhhYWF88sknHDhwgOvXrwPg4OBAp06dcHFxUdctXrw4EyZMMH60QgghhAkYvAKNi4uLRuITQgghXnd6zxkKIYQQbyqDeobR0dH8+OOPxMfHc/fuXZRKpVadtLQ0owUnhBBCmIPePcPdu3fTp08f/vnnH95++21ycnLo06cP77zzDsWLF6dRo0aMHz/elLEKIYQQJqF3zzA0NJTGjRtz4MAB7t69y/LlyxkwYADt2rUjKSmJTp064eTkZMpYhRBCCJPQu2f4559/4u/vj5WVFZaWlgDqp8vXqFGDoUOH8u2335omSiGEEMKE9E6GxYoVU68wU6pUKRQKBbdu3VJvr1q1qsaDeIUQQojXhd7DpDVr1uTChQsAFClShLp167Jjxw769u0LQGRkJJUqVTJNlAKAXZ6e+d73Yj728Tl6NN/tCSHE60TvnmGnTp3Ytm0bT548ASAwMJBdu3bRrFkzmjVrxr59+xg6dKjJAhVCCCFMRe+e4fjx4wkMDMTK6ukuH3zwASVLlmTbtm1YWloybtw4+vXrZ7JAhRBCCFPROxkWKVJE4zmGAH369KFPnz5GD0oIIYQwJ72HSZs0aUJkZGSu2/fs2UOTJk2MEpQQQghhTnonw2vXrvHw4cNctz98+FC9eLcQQgjxOjFobVLVY5t0uXTpEmXKlHnpgIQQQghzy3POcP369WzYsEH9et68eaxatUqrXnp6On/99RddunQxfoRCCCGEieWZDB8+fEhKSor69d27d8nJydGoo1AoKFmyJIMGDWLixImmiVIIIYQwoTyT4fDhwxk+fDjw9DmGs2fPpnv37kYNICwsjB9++IGUlBTq1atHcHAwnrncXB4TE8OiRYs4ffo09+7do2bNmgQGBjJw4ECjxiSEEKJw0fvWirNnzxq98fDwcCZOnEhoaCgeHh6EhYXh7+9PXFwcDg4OWvVPnDhBw4YN+fTTT6lUqRJRUVGMHj2a4sWL4+/vb/T4hBBCFA4GP+n+/v373Lhxgzt37uh8nmGrVq30PtbChQvp378/gwYNAiAkJISoqCiWL1/OtGnTtOqPGTNG4/WwYcOIiYkhIiJCkqEQQoh80zsZ3rlzhwkTJrBt2zb10yqepVQqUSgUej/cNzMzk/j4eD7++GONci8vL44fP65vWNy/f58qVaroXV8IIYR4nt7J8LPPPmPnzp0MHz6cVq1aYW1t/VINp6amkp2dja2trUa5ra0tN2/e1OsYe/bs4dChQ+zduzfPegkJCfmO09xepVhNFUthOEdDvSpxgMSSG4lFt4KIxdnZ2ejH1DsZHjhwgBEjRvD1118bNYDn711U9TBfJC4ujuHDhzNnzhzc3NzyrPsyb1x+nvbwMvKK9VWKJb8SEhJMctz8eFVieVXiAIklNxKLbq9SLC9L75vuixYtatQn2VesWBFLS0utXuDt27e1eovPO3bsGP7+/kyaNIlhw4YZLSYhhBCFk97JsFevXuzfv99oDRctWhRXV1eio6M1yqOjo3F3d891v9jYWPz9/Rk/fjwjR440WjxCCCEKL72T4ccff0xycjIfffQRJ0+eJDk5mVu3bmn9M0RQUBDr169n9erVXLhwgQkTJpCcnMyQIUMAmDFjBr6+vur6MTEx+Pv7M2TIEN59911SUlJISUnh9u3bBrUrhBBCPEvvOUM3NzcUCgXx8fFs2rQp13r6Xk0K4OfnR1paGiEhIaSkpFC/fn02bdqEo6MjAMnJySQmJqrrr1+/nv/++48ff/yRH3/8UV3u4ODAuXPn9G5XCCGEeJZBD/fV58IWQwUEBBAQEKBz2+LFi7VeP18mhBBCvCy9k+GkSZNMGYcQQghRYAx6hJNKdnY2aWlpZGVlGTseIYQQwuwMSoanT5+md+/eVKlShdq1axMbGws8vYH+3Xff5dChQyYJUgghhDAlvZPhiRMn6N69O4mJibz33nsa65JWrFiRBw8esGbNGpMEKYQQQpiS3snwq6++wsnJiePHj/Pll19qbW/Tpg2//fabUYMTQgghzEHvZHj69Gnef/99ihcvrvOq0qpVq2o8CFgIIYR4XeidDC0sLLCwyL16SkoKJUqUMEpQQgghhDnpnQxdXV3Zs2ePzm2ZmZls3ryZli1bGi0wIYQQwlz0Toaff/45hw8fZtSoUerVXpKTkzlw4AC+vr4kJiZqPXxXCCGEeB3ofdN9hw4dWLJkCePGjWP9+vUABAYGolQqKVeuHGFhYbRo0cJkgQohhBCmoncyBOjTpw/du3cnOjqay5cvk5OTQ82aNenYsSOlS5c2VYxCCCGESRmUDAFKliyJj4+PKWIRQgghCoTec4aRkZGMGzcu1+3jxo3L9QIbIYQQ4lWmdzL88ccf+e+//3Ld/ujRI77//nujBCWEEEKYk97J8K+//sLV1TXX7U2aNOH8+fPGiEkIIYQwK72TYVZWFhkZGbluz8jI4PHjx0YJSgghhDAnvZNhgwYNiIiIICcnR2tbTk4OERER1KtXz6jBCSGEEOagdzL86KOPOHXqFP369SM+Pp7Hjx/z+PFj4uPj6d+/P6dOnWLEiBGmjFUIIYQwCb1vrXjnnXdITEwkODiY/fv3A6BQKFAqlSgUCiZMmEDfvn1NFqgQQghhKgbdZzh27Fj69OnDjh07SEpKQqlUUrNmTXr27EmNGjVMFKIQQghhWnolw4yMDN5991369u3L+++/z8cff2zquIQQQgiz0WvOsESJEvz+++9kZ2ebOh4hhBDC7PS+gKZ169YcPXrUlLEIIYQQBULvZDhnzhxOnz7N1KlTSUpK0nmLhRBCCPE60jsZtmjRgmvXrrFw4UKaNWuGnZ0dlStX1vhXpUoVgwMICwvDxcUFe3t72rVrl2fv89GjRwQGBuLp6YmNjY0sGC6EEMIo9L6a9O2330ahUBi18fDwcCZOnEhoaCgeHh6EhYXh7+9PXFwcDg4OWvWzs7MpXrw4H374Ifv27ePu3btGjUcIIUThpHcyXLx4sdEbX7hwIf3792fQoEEAhISEEBUVxfLly5k2bZpW/VKlSvHtt98C8Oeff0oyFEIIYRR6D5MaW2ZmJvHx8Xh5eWmUe3l5cfz48QKKSgghRGFkUDK8du0an3zyCa6urjg4OHDkyBEAUlNTGTNmDPHx8XofKzU1lezsbGxtbTXKbW1tuXnzpiFhCSGEEC9F72HSCxcu0LVrV3JycmjevDnXrl1T33dYsWJFTp48yePHj1mwYIFBATw/D6la3s2YEhISjHo8U3qVYjVVLIXhHA31qsQBEktuJBbdCiIWZ2dnox9T72Q4bdo0ypQpw4EDB7C0tKR27doa2729vfnll1/0brhixYpYWlpq9QJv376t1Vt8WS/zxl00Yhz6yCvWVymW/EpISDDJcfPjVYnlVYkDJJbcSCy6vUqxvCy9h0mPHj1KQEAAdnZ2OntuDg4O/Pvvv3o3XLRoUVxdXYmOjtYoj46Oxt3dXe/jCCGEEC9L755hVlYWpUqVynX7nTt3sLS0NKjxoKAgRowYgZubG+7u7ixfvpzk5GSGDBkCwIwZMzh16hQRERHqfc6fP09mZiapqak8fPiQs2fPAuDi4mJQ20IIIYSK3smwQYMGxMTEMGzYMK1tSqWSHTt24OrqalDjfn5+pKWlERISQkpKCvXr12fTpk04OjoCkJycTGJiosY+/v7+XL9+Xf26bdu2AKSnpxvUthBCCKGidzIMDAwkICCAuXPn4ufnBzx9wv3FixcJDg7mzJkzbNy40eAAAgICCAgI0LlN172N586dM7gNIYQQIi8GPdz3+vXrfP3118yePVtdBmBpacmsWbPo3LmzaaIUQgghTMigh/uOHj2aPn36EBERwZUrV8jJyaFmzZr4+vpSvXp1U8UohBBCmNQLk+Hjx4+JjIwkKSmJChUq0KVLF0aOHGmO2IQQQgizyDMZpqSk0L17dxITE1EqlcDT9UE3btxIq1atzBKgEEIIYWp53mc4a9YskpKSGDlyJBs3biQ4OJhixYoxfvx4c8UnhBBCmFyePcNff/2Vfv36MWvWLHWZnZ0dAQEB/O9//6Nq1aomD1AIIYQwtTx7hikpKVqrwXh4eKBUKrlx44ZJAxNCCCHMJc9kqHqY7rNUrx89emS6qIQQQggzeuHVpElJSZw6dUr9+t69e8DTBVpLly6tVd/Nzc2I4YlX1S5Pz3zvm58Fx32OHs13e0II8SIvTIbBwcEEBwdrlT9/EY3q0UtpaWnGi04IIYQwgzyT4cKFC80VhxBCCFFg8kyG/fv3N1ccQgghRIHR+3mGQgghxJtKkqEQQohCT5KhEEKIQk+SoRBCiEJPkqEQQohCT5KhEEKIQs+gh/sK8SqS1XCEEC9LeoZCCCEKPUmGQgghCj1JhkIIIQo9mTMUwkhk7lKI15f0DIUQQhR60jMU4g0kvVQhDFPgPcOwsDBcXFywt7enXbt2HH3BL9Wff/5J9+7dqVSpEvXr12fOnDkolUozRSuEEOJNVKA9w/DwcCZOnEhoaCgeHh6EhYXh7+9PXFwcDg4OWvXv3bvH22+/jaenJ7/++isJCQkEBQVRsmRJPv744wI4AyHEi0gvVbwOCjQZLly4kP79+zNo0CAAQkJCiIqKYvny5UybNk2r/ubNm8nIyGDx4sWUKFGCBg0acPHiRRYtWsSoUaNQKBTmPgUhxGtEErPIjSI9Pb1AxhgzMzOpXLkyy5Yto3fv3urysWPH8tdffxEZGam1z4gRI7hz5w6bNm1Sl50+fRovLy/i4+OpUaOGGSIXQgjxpimwOcPU1FSys7OxtbXVKLe1teXmzZs697l586bO+qptQgghRH4U+AU0zw9tKpXKPIc7ddXXVS6EEELoq8CSYcWKFbG0tNTq0d2+fVur96diZ2ensz6Q6z5CCCHEixRYMixatCiurq5ER0drlEdHR+Pu7q5zn5YtW3Ls2DEePXqkUb9y5cpUr17dpPEKIYR4cxXoMGlQUBDr169n9erVXLhwgQkTJpCcnMyQIUMAmDFjBr6+vur6ffr0oUSJEowcOZK//vqLiIgIvvvuO0aOHCnDpEIIIfKtQJOhn58fwcHBhISE0KZNG+Li4ti0aROOjo4AJCcnk5iYqK5frlw5tm3bxr///kuHDh0YN24cQUFBjBo1qqBOQYuhiwiYSmxsLO+99x7169fH2tqadevWFUgc8+fPp0OHDjg4OODk5ETfvn3566+/CiSWn376CU9PTxwcHHBwcKBz587s3bu3QGJ5XmhoKNbW1owbN87sbQcHB2Ntba3xr06dOmaPQyU5OZmPPvoIJycn7O3tcXd358iRI2aPo3Hjxlrvi7W1Ne+++65Z48jOzmbWrFnqvysuLi7MmjWLrKwss8ahcv/+fSZOnEijRo2oVKkS3t7enD59ukBiMaYCX44tICCAgIAAndsWL16sVdawYUN2795t6rDyxdBFBEzp4cOHNGjQgH79+vHRRx+Zte1nHTlyhGHDhtGsWTOUSiXffPMNvXv35vjx45QvX96ssVSpUoUZM2bg5ORETk4OGzZsYMCAARw8eJBGjRqZNZZnnTx5klWrVtGwYcMCi8HZ2ZmdO3eqX1taWhZIHOnp6XTp0gUPDw82bdpExYoVuXr1aoFcExAdHU12drb6dXJyMu3bt9e4FcwcvvvuO8LCwli8eDENGjTgzz//JDAwkKJFizJ+/HizxgLwySef8Oeff7J48WKqVq3Kxo0b6d27N3FxcVSpUsXs8RhLgd1n+Cbq2LEjDRs25IcfflCXNWvWjF69eulcRMBcqlatyty5cxkwYECBxaDy4MEDHB0dWbduHd26dSvocKhRowbTpk1TD82b2927d2nXrh3ff/89c+fOpUGDBoSEhJg1huDgYCIiIjh27JhZ29Vl5syZxMbGvjI99mfNmzePH374gfPnz1OyZEmztdu3b1/Kly/P//3f/6nLPvroI+7cucPGjRvNFgdARkYG1apVY/Xq1fj4+KjL27VrR+fOnZkyZYpZ4zGmAr+14k2RmZlJfHw8Xl5eGuVeXl4cP368gKJ69Tx48ICcnBysra0LNI7s7Gy2bt3Kw4cPadmyZYHFMXr0aHr16kW7du0KLAaApKQk6tevj4uLC0OHDiUpKalA4ti1axdubm4MGTKE2rVr07p1a5YuXVrg6w8rlUrWrFlD3759zZoIATw8PDhy5AgXLz5dA+f8+fPExMTQuXNns8YBkJWVRXZ2NsWLF9coL1GixCvxZeplFPgw6ZsiP4sIFEYTJ06kcePGBZaA/vzzT7y9vXn06BGlSpVi7dq1BTY8uWrVKq5cucKSJUsKpH2V5s2bs2jRIpydnbl9+zYhISF4e3sTFxdHhQoVzBpLUlISy5YtY+TIkYwePZpz584xYcIEAD788EOzxvKs6Ohorl69ysCBA83e9ujRo3nw4AHu7u5YWlqSlZXF2LFjc51eMqUyZcrQsmVL5s2bR/369bG3t2fLli2cOHGCWrVqmT0eY5JkaGSGLiJQmHzxxRfExcWxZ8+eApuTcnZ2JiYmhrt37xIREUFgYCA7d+6kQYMGZo0jISGBmTNnsnv3booWLWrWtp/3fA+jefPmuLq6sn79erNfnJaTk0PTpk3V0wpNmjThypUrhIWFFWgyXLVqFc2aNcPFxcXsbYeHh/Pzzz8TFhZGvXr1OHfuHBMnTsTR0ZEPPvjA7PEsWbKEoKAgGjRogKWlJU2aNKFPnz78/vvvZo/FmCQZGkl+FhEoTCZNmkR4eDg7duwo0DVkixYtqv4G27RpU06fPs2iRYtYsGCBWeM4ceIEqampvPXWW+qy7Oxsjh49yvLly/nnn38oVqyYWWNSKV26NPXq1ePKlStmb9ve3p66detqlNWpU4cbN26YPRaVW7duERkZybx58wqk/S+//JJRo0bxzjvvAE8vIrx+/TrffvttgSTDmjVrEhkZycOHD7l//z6VKlViyJAhr/293jJnaCT5WUSgsJgwYQJbtmwhIiKiQC/Z1yUnJ4fMzEyzt+vj48PRo0eJiYlR/2vatCnvvPMOMTExBdpbfPToEQkJCdjb25u9bQ8PDy5duqRRdunSJbNfjf2s9evXU6xYMfz8/Aqk/f/++09rJMXS0pKcnJwCiUelVKlSVKpUifT0dKKioujevXuBxvOypGdoREFBQYwYMQI3Nzfc3d1Zvny5xiIC5vTgwQP1N/ucnBxu3LjB2bNnKV++vFn/sIwdO5aNGzeydu1arK2tSUlJAZ7+IpUuXdpscQBMnz4db29vqlatyoMHD9iyZQtHjhzReAqKuajuWXtWyZIlKV++vNmHbKdMmULXrl2pVq2aes7wv//+o1+/fmaNA2DkyJF4e3szb948/Pz8OHv2LEuXLmXq1KlmjwWeTnOsXr0aPz8/ypQpUyAxdO3ale+++47q1atTr149zp49y8KFC3nvvfcKJJ6oqChycnJwdnYmMTGRqVOn4uzs/Epcrf4y5NYKIwsLC+P7778nJSWF+vXr880339CqVSuzxxETE0PPnj21yvv166fz/k1Tye2q0QkTJjBp0iSzxQEQGBhITEwMN2/epGzZsjRs2JBPPvmEjh07mjWO3Pj4+BTIrRVDhw7l6NGjpKamYmNjQ/PmzZk8eTL16tUzaxwqe/fuZebMmVy6dIlq1aoxfPhwRowYUSBz74cPH8bX15eoqCjc3NzM3j48vcn966+/ZufOndy+fRt7e3veeecdxo8fr3VVpzls27aNGTNm8M8//1C+fHl8fX2ZMmUK5cqVM3ssxiTJUAghRKEnc4ZCCCEKPUmGQgghCj1JhkIIIQo9SYZCCCEKPUmGQgghCj1JhkIIIQo9SYZCGJG1tTWfffZZQYeht8TERPr06UP16tUL9CHQQhQ0SYbitbJu3Tqsra2xs7PTuV7lO++8Q+PGjQsgstfTxx9/zOnTp5k4cSJLlizJc4GIZ5/8Xr58eRwdHfH09OTTTz/lt99+M2PUQhifLMcmXkuZmZnMnz+f+fPnF3Qor63s7GyOHTvG8OHDCQwM1Gsf1ao98HTJvwsXLrB9+3ZWrVpFYGAgwcHBpgxZCJORZCheS40bN2bt2rV8/vnnVKtWraDDMSulUsnjx49feimutLQ0srOzDVpGq1KlSvTt21ejbMaMGQQEBLB48WKcnJzyfM5ednY22dnZBf7YKiGeJ8Ok4rX0+eefA7ywZ3j16tVc58IaN26s0SNSDcEeOXKEL774gtq1a+Po6EhQUBCPHj3i4cOHjB49mlq1auHo6MjYsWPJysrS2W54eDju7u7Y29vj6enJ3r17tercu3ePKVOm0LhxY+zs7GjUqBHTp0/n8ePHGvVU85C//PILnp6e2NnZsXXr1jzP+9ixY/Ts2ZOqVatSrVo1evfurTGUGRwcjLOzMwBz5szRuXC4vkqUKMGSJUsoX7488+fPVz+VXvXef/vtt4SFhdGsWTPs7Ow4fvw4MTExWFtbExMTo3U8a2trrR7msWPH6NixI/b29jRq1Ijvv/9evfj71atX1fXi4+Px9/fHycmJSpUq0aRJE0aMGMHDhw/zdW6i8JCeoXgtVatWjf79+5ukdzhp0iRsbGyYMGEC8fHxrFu3jpIlS5KUlESJEiWYPHkyhw8fJiwsjFq1ajFy5EiN/Y8fP862bdsYMWIEpUuXZtWqVQwYMIDt27er5+QyMjLo0aMHV69eZfDgwdSsWZNz586xYMECLl68yPr16zWOeezYMbZv387w4cOxt7fP81FYsbGxvP3221SpUoWxY8eSk5PDihUr8PHxYdeuXTRv3pyePXtiY2PDuHHj6NGjh85F3Q1RunRpevTowZo1azh//jz169dXb9u0aRMPHjxg8ODBlC5dmkqVKpGcnKz3sc+dO4efnx8VKlRg3LhxFC1alFWrVlGyZEmNerdv3+btt9+mYsWKfPrpp1hbW3Pjxg12797Nw4cPKVWq1Eudo3izSTIUr60xY8awfv16o88dVqxYkfDwcPVTEq5du0ZYWBj+/v4sXboUgGHDhuHu7s7atWu1kuFff/3F3r171c+xHDBgAM2aNWPGjBns27cPgEWLFpGQkMDBgwc1HmZbv359xo4dy9GjR/H09FSXX7hwgUOHDun1pPXJkydTqlQpDhw4gI2NDfD0aSUtW7ZkypQp7Nmzh0aNGmFra8u4ceNo2LCh1tBnfqgSYGJiokYyvHbtGqdOnaJSpUrqMkOS4TfffENOTg67d+/G0dERePqePv8UiePHj3Pnzh3Cw8Np2rSpuvyLL77I1/mIwkWGScVry8HBQd07NOaT0N9//32NxwU1b94cpVLJwIEDNeq5ubmRmJiotX/Tpk01HuhcoUIF/P39OXHiBOnp6cDTx+C4u7tjY2NDamqq+l/79u2Bp48Oepa7u7teiTAlJYX4+Hj69eunToQAVapUoU+fPhw/flwdg7Gpnk/54MEDjXIfHx+NRGiI7OxsDh48SLdu3dSJEJ5+YfH399eoq3re4J49e3jy5Em+2hOFlyRD8VobM2YM8OK5Q0M8P+RatmzZXMszMjK05vicnJy0jqkqu379OgCXL1/m4MGDODk5afxr3rw58HTI71k1atTQK/Zr164B6BxGrVu3LkqlUh2DsamS4PMPbdY3dl1u3bpFRkZGnu+pSps2bejZsydz5syhVq1a9O3bl5UrV2olZyF0kWFS8Vp7tneouqjmWXk9EDYnJ0dnuaWlpc5yCwvd3x1VF4zk1ebzdXJycmjbtq3OmOFpT+5ZJUqU0FnPEM/HYGx///03ALVq1dIo1xV7bj+X7OxsvdvT9b6vWbOGU6dOsWfPHg4ePMjo0aMJDQ0lKioKOzs7vY8tCh9JhuK19+zc4fPKly8PwN27dzXKHz9+bNC8lSEuXbqkVXblyhXgafIGqFmzJg8ePFAPixqLaijx4sWLWtsSEhJQKBTqGIzpwYMH7Ny5k2rVqmnMgeZGdeXq8z8XVc9WxdbWlhIlSnD58mWtY6je0+e5ubnh5ubG5MmT2b9/P/7+/qxevZqxY8fqeTaiMJJhUvHae7Z3+L///U9jW5kyZbCxsdG6hH/58uUG9UIMcebMGU6cOKF+nZaWxubNm2nRooU6Cfj5+XH69GkiIyO19s/IyMj30J69vT2urq78/PPPpKamqsv//fdfNm/ejLu7e75vochNRkYGI0aM4M6dO4wZMybP3riKo6MjlpaWWj8X1QVKKpaWlrRv357du3drJMrU1FQ2b96sUTc9PV2rt9ikSRP1NiHyIj1D8UZQ9Q7Pnz+v1fMZPHgw8+bNY+TIkbRo0YIzZ85w6NAhKlasaJJYGjRoQN++ffnwww/Vt1bcv3+fL7/8Ul3n448/Zt++fQwcOJB3330XNzc3Hj9+zKVLl9i2bZs6eebH119/Te/evenUqRODBg1CqVSybNkynjx5wldfffVS55acnMzGjRsBePjwIefPn2f79u2kpKQwatQohgwZotdxypYtyzvvvENYWBgKhQJnZ2diYmJISkrSqjtp0iR+/fVXunXrxtChQylSpAirVq3C0dGR9PR0dfJdv349YWFh9OjRg5o1a5KRkcG6deuwtLSkV69eL3Xe4s0nyVC8ERwcHBgwYAArVqzQ2jZ27FjS0tIIDw/nl19+oXXr1mzfvv2l763Ljbu7O23atGH27NkkJSXh5OTE2rVradOmjbpOiRIliIiI4Pvvvyc8PJytW7dSqlQpatSoQWBgoPqG+Pxo1aoV27dv55tvvmHu3LkoFAqaN2/OihUr8p1gVf78809GjBiBQqGgTJkyVK1ala5du/LBBx9o3erwInPmzCErK4u1a9diYWGBt7c3W7ZsoXbt2hr1XFxcCA8PZ+rUqcyZMwc7OzuGDx9O8eLFOXv2rHolnlatWnHmzBm2bdvGzZs3KVOmDC4uLsydO/elz1u8+RTp6emmnVUXQggTmDBhAqtWreJ///tfrhc9CaEvmTMUQrzyMjIyNF7fvn2bjRs34unpKYlQGIUMkwohXnkuLi68++67ODs78++//7JmzRoePnzI+PHjCzo08YaQZCiEeOV5e3uzY8cObt68iZWVFa6urixduhQPD4+CDk28IWTOUAghRKEnc4ZCCCEKPUmGQgghCj1JhkIIIQo9SYZCCCEKPUmGQgghCj1JhkIIIQq9/wd5sVdBtM/rxgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eda.number_of_drugs_used_past_year(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upon examination of the graph below, I deemed it balance enough not to do any SMOTING (especially since, as we'll see soon, these models don't seem to have a problem predicting the positive/drug use class).\n",
    "\n",
    "* DRUG USE - CLASS 1 \n",
    "* NO DRUG USE - CLASS 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eda.class_distribution(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After engineering this target using multiple drug use questions, we have to get rid of those columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.modeling import drug_cols\n",
    "data.drop(drug_cols, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('binary_drug', axis=1)\n",
    "y = data.binary_drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For plotting confusion matrices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tr_pp = pd.read_csv('data/modeling_data/X_tr_pp.csv')\n",
    "y_tr_pp = pd.read_csv('data/modeling_data/y_tr_pp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This class, which you can check out in the `src` folder, will keep track of `accuracy`, and `recall` (along with some other metrics) each time we run a new model (as long as you don't restart the kernal, all of your results will be stored in the `.history` attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.modeling import ModelHistory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = ModelHistory(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***********\n",
    "### First Simple Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "fsm_lr = LogisticRegression(max_iter=10000, C=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_recall, val_prec, val_acc, roc_auc, lr1 = history.kfold_validation(fsm_lr, 'LogReg 1 AllFeatures', ['sampling_weight'], X_train.drop('sampling_weight', axis=1).columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first three models here all seem to have a penchant for acquiring a **lot** of **false positives**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f84247b5850>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(lr1, X_tr_pp, y_tr_pp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "fsm_dt = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_recall, val_prec, val_acc, roc_auc, dt1 = history.kfold_validation(fsm_dt, 'DecTree 1 AllFeatures', ['sampling_weight'], X_train.drop('sampling_weight', axis=1).columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f8425b4a9a0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(dt1, X_tr_pp, y_tr_pp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "fsm_rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_recall, val_prec, val_acc, roc_auc, rf1 = history.kfold_validation(fsm_rf, 'RandFor 1 AllFeatures', ['sampling_weight'], X_train.drop('sampling_weight', axis=1).columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f837e4ef4c0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(rf1, X_tr_pp, y_tr_pp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***********\n",
    "### GridSearch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can start to tune some hyperparameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(max_iter=10000, C=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = {'penalty': ['l1', 'l2', 'elasticnet'],\n",
    "       'tol': [0.0001, 0.00005, 0.00001],\n",
    "       'class_weight': [{0:0.7, 1:1}, None]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "lrgs = GridSearchCV(lr, grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_recall, val_prec, val_acc, roc_auc, lr2 = history.kfold_validation(lrgs, 'LogReg 2 AllFeatures', ['sampling_weight'], X_train.drop('sampling_weight', axis=1).columns)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression ended up favoring its **default** parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class_weight': None, 'penalty': 'l2', 'tol': 0.0001}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr2.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = {'criterion': ['gini', 'entropy'],\n",
    "       'max_depth': [5,15,30],\n",
    "       'min_samples_leaf': [1, 20, 40]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtgs = GridSearchCV(dt, grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_recall, val_prec, val_acc, roc_auc, dt2 = history.kfold_validation(dtgs, 'DecTree 2 AllFeatures', ['sampling_weight'], X_train.drop('sampling_weight', axis=1).columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini', 'max_depth': 15, 'min_samples_leaf': 40}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt2.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = {'n_estimators': [40,100],\n",
    "       'max_depth': [5,20,None],\n",
    "       'min_samples_leaf': [1, 20], \n",
    "       'bootstrap': [True, False]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfgs = GridSearchCV(dt, grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_recall, val_prec, val_acc, roc_auc, rf2 = history.kfold_validation(rfgs, 'RandFor 2 AllFeatures', ['sampling_weight'], X_train.drop('sampling_weight', axis=1).columns)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': False, 'max_depth': 20, 'min_samples_leaf': 1, 'n_estimators': 100}\n"
     ]
    }
   ],
   "source": [
    "rf2.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***********\n",
    "### Feature Selection: Boruta & Phi-K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Phi-K** is a package that adapts Pearson's Correlation Coefficient to be able to accomodate not only categorical variables, but variables of mixed types. For the documentation, click [here](https://phik.readthedocs.io/en/latest/tutorials.html). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import phik\n",
    "from src.eda import best_correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to start by examining the correlations amongst all the variables (including the target, `binary_drug`). The Phi-K coefficient, also denoted **Φ**, aims to correct incorrect \"asymptotic\" assumptions of statistical significance seen in the X-squared contingency test, and is also able to capture non-linear relationships."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix = data.drop('sampling_weight', axis=1).phik_matrix(interval_cols=['yr_adminst'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrs = best_correlations(corr_matrix)[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To validate these coefficient values, we'll use the `phik` method, `significance_matrix()`, to look for features with the highest significance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "signif_matrix = data.drop('sampling_weight', axis=1).significance_matrix(interval_cols=['yr_adminst'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "signifs = best_correlations(signif_matrix).iloc[61:80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "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>cc</th>\n",
       "      <th>cc_signif</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pairs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(alcohol_yr_freq, alcohol_lifetime_freq)</th>\n",
       "      <td>0.915190</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(academic_self_rating, intelligence_self_rating)</th>\n",
       "      <td>0.879218</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(tech_school_after_hs, desire_tech_school)</th>\n",
       "      <td>0.853378</td>\n",
       "      <td>205.246271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(desire_2yrcoll, 2yrcoll_after_hs)</th>\n",
       "      <td>0.851864</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(military_after_hs, desire_military)</th>\n",
       "      <td>0.848997</td>\n",
       "      <td>195.092427</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        cc   cc_signif\n",
       "pairs                                                                 \n",
       "(alcohol_yr_freq, alcohol_lifetime_freq)          0.915190         NaN\n",
       "(academic_self_rating, intelligence_self_rating)  0.879218         NaN\n",
       "(tech_school_after_hs, desire_tech_school)        0.853378  205.246271\n",
       "(desire_2yrcoll, 2yrcoll_after_hs)                0.851864         NaN\n",
       "(military_after_hs, desire_military)              0.848997  195.092427"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_eval = corrs.join(signifs, on=corrs.index, rsuffix=\"_signif\")\n",
    "feature_eval.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Plotting distribution of correlation values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cc', ylabel='Count'>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(feature_eval.phik)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Distribution of significance values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cc_signif', ylabel='Count'>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(feature_eval.phik_signif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "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>cc</th>\n",
       "      <th>cc_signif</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pairs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(tech_school_after_hs, desire_tech_school)</th>\n",
       "      <td>0.853378</td>\n",
       "      <td>205.246271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2yrcoll_after_hs, tech_school_after_hs)</th>\n",
       "      <td>0.693962</td>\n",
       "      <td>201.490563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(alcohol_lifetime_freq, binary_drug)</th>\n",
       "      <td>0.503510</td>\n",
       "      <td>201.641182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(alcohol_yr_freq, evr_smoked_cig)</th>\n",
       "      <td>0.414042</td>\n",
       "      <td>204.328780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  cc   cc_signif\n",
       "pairs                                                           \n",
       "(tech_school_after_hs, desire_tech_school)  0.853378  205.246271\n",
       "(2yrcoll_after_hs, tech_school_after_hs)    0.693962  201.490563\n",
       "(alcohol_lifetime_freq, binary_drug)        0.503510  201.641182\n",
       "(alcohol_yr_freq, evr_smoked_cig)           0.414042  204.328780"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_eval[feature_eval['phik_signif']>200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": true
   },
   "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>cc</th>\n",
       "      <th>cc_signif</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pairs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(desire_4yrcoll, 4yrcoll_after_hs)</th>\n",
       "      <td>0.776309</td>\n",
       "      <td>178.474915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(tech_school_after_hs, 4yrcoll_after_hs)</th>\n",
       "      <td>0.631231</td>\n",
       "      <td>168.859587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(cig_month_freq, alcohol_lifetime_freq)</th>\n",
       "      <td>0.496440</td>\n",
       "      <td>170.764672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(area_type, smsa)</th>\n",
       "      <td>0.463581</td>\n",
       "      <td>169.659827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(school_missed_ditched, skipped_class)</th>\n",
       "      <td>0.410799</td>\n",
       "      <td>166.408879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(evr_smoked_cig, alcohol_2weeks)</th>\n",
       "      <td>0.309633</td>\n",
       "      <td>150.490580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                cc   cc_signif\n",
       "pairs                                                         \n",
       "(desire_4yrcoll, 4yrcoll_after_hs)        0.776309  178.474915\n",
       "(tech_school_after_hs, 4yrcoll_after_hs)  0.631231  168.859587\n",
       "(cig_month_freq, alcohol_lifetime_freq)   0.496440  170.764672\n",
       "(area_type, smsa)                         0.463581  169.659827\n",
       "(school_missed_ditched, skipped_class)    0.410799  166.408879\n",
       "(evr_smoked_cig, alcohol_2weeks)          0.309633  150.490580"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_eval[feature_eval['phik_signif']<180]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After reviewing the correlation and significance values, I decided to try and eliminate all the redundant columns (since, as the dataframe shows, similar questions are heavily correlated to each other). For example, there is a separate feature that asks if **desire** to go to a 4 year college, and another question that asks if you **plan** to go to a 4 year college."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_mask = ['sampling_weight', 'region', 'evr_smoked_cig', 'alcohol_2weeks', '4yrcoll_after_hs', 'work_pay', \n",
    "           'has_father', 'avg_grade', 'drive_freq', '12mo_r_tcktd', 'relig_attd', 'rec_time']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make sure the `ModelHistory` object has the new subsetting dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "history.X_train = history.X_train[col_mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can try our first try at feature selection!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_fs = LogisticRegression(max_iter=10000, C=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_recall, val_prec, val_acc, roc_auc, lr3 = history.kfold_validation(lr_fs, 'LogReg 3 PhikFeatures', ['sampling_weight'], col_mask[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tr_mp = pd.read_csv('data/modeling_data/X_train_mp.csv')\n",
    "y_tr_cm = pd.read_csv('data/modeling_data/y_train_cm.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, it seems this decrease in features has lead to a significant **decrease** in performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f837e54aa90>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(lr3, X_tr_mp, y_tr_cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_fs = DecisionTreeClassifier(criterion='gini', max_depth=15, min_samples_leaf=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_recall, val_prec, val_acc, roc_auc, dt3 = history.kfold_validation(dt_fs, 'DecTree 3 PhikFeatures', ['sampling_weight'], col_mask[1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f837eceb760>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BN2sXx9SpU5k4cSLdu3enV69erF27losXLxITE9OcaTWYW9sK4t85Q+s2Ogo0an750Z7pDweR+7/C+WmyO9a2eqYuzMbxfw+qxI8NMI6BrqxQ4Wj7Ews/Oo2tvZ6881akfdOaDW94otdX/UOtKFfRb7iWJ/95EStrA7nZ1vz3I1c2vePRbNd9t8i7YEXilPZcuazGybWSTt2usvw/v+L5v5Zt/IrfWbuwLYv+4Ueh1hKPduWMj7vA8KfyjMf4Yp2bycMu88Z1BN7HepkTg6KqCvAz87KxstLz+jQ/ykosCOxSwuJNJ3H1qroxqFbDq+tO89aLPvxzRBDWdnoiHq16UKWlaelPEjb7orGrV68mKSmJnJwcQkJCWLhwIX369Kk1XhaNvXvcCX3QjUEWjW24p36Or3P/2tDEJjmvUjT7TcIJEyYwYcLdXXSFEA0jXRxCCKFQUqCFEEKhpEALIYRCSYEWQgiFagljnesiBVoIoVjSghZCCIWSAi2EEAolBVoIIRRKCrQQQiiUTqYbFUIIZWrpLeiW/eNJCKFoBoOqzpc5li5dirOzM3Fxcdcd10BiYiKdOnXCy8uLYcOGcfz4cZPPlZWVERcXR0BAAN7e3kRHR5Odbbpor1arJTY2Fj8/P/z8/IiNjUWr1ZrEZGVlERUVhbe3NwEBAcyaNYvy8nKzcpcCLYRQLL1BVeerPgcPHuSDDz4gNDTUZHtSUhLJycksWrSIXbt24e7uzsiRIyksvDZfe3x8PNu2bWPNmjVs376dwsJCoqKi0OmurRE6YcIEjhw5wqZNm0hJSeHIkSNMnDjRuF+n0xEVFUVRURHbt29nzZo1bN26lTlz5ph1/VKghRCKdSst6IKCAp555hneeustnJ2drzumgRUrVjB9+nRGjBhB586dWbFiBUVFRaSkpBg/++GHH/LKK68QERFBWFgYK1eu5Oeff2b37t0AZGZm8vXXX7N8+XJ69epFz549WbZsGTt37uTEiRMA7Nq1i+PHj7Ny5UrCwsKIiIhgwYIFrFu3jitX6l/LstY+6I8//rjeD9dk7Ni7e1pFIcTtcyt90H8U4H79+rF48WLj9jNnzpCTk8OAAQOM2+zs7AgPDyctLY2YmBgyMjKoqKgwifHx8SE4OJi0tDQiIyNJT0/HwcGBXr16GWN69+6Nvb09aWlpBAUFkZ6eTnBwMD4+PsaYyMhIysrKyMjIoG/fvnVeQ60FesqUKTf3bVC1vqAUaCFEY7mZ5cSu98EHH3D69GlWrlxZbV9OTg5AjQtWX7hwAYDc3FzUajWurq7VYv5Ypi83NxdXV1eTNVRVKhVubm4mMTeep7bl/mpSa4E+fPhwvR8WQoim1JC5OE6cOMErr7zCf//7X6yta18G7mYXrK4ppqZ4c2Lq2n69Wgu0n59fvR8WQoimZO5Ijeulp6ej0Wj485//bNym0+nYt28fa9eu5cCBA0BV6/b6rofrF6z28PBAp9Oh0Whwc3MziQkPDzfG5OXlmRRkg8GARqMxOU5aWppJfhqNBp1OZ9bi2Dd9k7CkpIR9+/axZcsW8vLy6v+AEEI0UENGcQwbNox9+/axZ88e4+u+++5j9OjR7Nmzh8DAQDw9PUlNTTV+prS0lP379xv7k8PCwrCysjKJyc7OJjMz0xjTs2dPioqKSE9PN8akp6dTXFxsEpOZmWkyPC81NRUbGxvCwsLqvf6belDl3XffZeHChRQVFQHw+eef069fPzQaDffddx8JCQmMHz/+Zg4phBC1+mOx5Jvh7OxsMmoDoFWrVri4uNC5c2cAJk+ezNKlSwkKCiIwMJDXX38de3t7xowZA4CTkxPjxo1j/vz5uLu74+Liwpw5cwgNDaV///4ABAcHM3DgQGbMmEFSUhIGg4EZM2YwePBggoKCABgwYAAhISFMmjSJhIQE8vPzmT9/PuPHj6d169b1XovZLegNGzYQHx/PwIEDeeuttzBc13vv6upKREQEn3/+ubmHE0KIejXGgyo1mTZtGlOmTCEuLo6IiAguXrzI5s2bcXR0NMYsXLiQhx9+mJiYGIYMGYK9vT2ffPIJavW1lcZXrVrFvffey6hRoxg9ejT33nuvyY1JtVrNxo0badWqFUOGDCEmJoaHH36YhIQEs/I0uwWdnJzM4MGDWbt2LZcvX662PywsjFWrVpl7OCGEqFdjPer9xRdfmLxXqVTEx8cTH1/7quG2trYsWbKEJUuW1Brj4uLCe++9V+e5fX192bhx480l/D9mt6BPnTrF4MGDa93v6uqKRqNpUBJCCFETg6Hu193O7Ba0o6MjBQUFte4/deqUyd1OIYS4VbfSjXE3MLsF3bdvXzZs2EBZWVm1fdnZ2XzwwQcMHDiwUZMTQrRsTdUHfacwuwU9d+5cIiMj6d+/P48++igqlYqvvvqK1NRU3n//faysrJg1a1ZT5iqEaGFaQC9GncxuQQcEBLBjxw68vLxYtGgRBoOB5ORkkpKS+NOf/sSOHTto165dU+YqhGhhpAV9E4KDg/n888/RarWcPn0avV5P+/btpe9ZCNE0WngTukErqjg7O9OtW7fGzkUIIUy0hFZyXW6qQGu1Wt5++22+/PJLsrKygKoxfoMGDWLq1Km4uLg0SZJCiJapIU8S3k3M7oM+efIk4eHhLF26lMrKSh544AH69OlDZWUlS5cuJTw83DhJtRBCNAqDqu7XXc7sFnRcXBxFRUVs2bKl2iTT3377LePGjWP27Nls3ry50ZMUQrRMLeFhlLqY3YJOS0tj0qRJNa4A0K9fPyZOnGicxk8IIRqFoZ7XXc7sFrSTk1O1GaKuV9MMUkIIcSta+k1Cs1vQ48aNY/369Sar3v6hoKCA9evXM27cuEZNTgjRwkkLumY3Th16zz33oFKp6NGjB2PHjiUgIAComoPjk08+wd3d3TgHqhBCNIaW3oKutUA/9dRTqFQq47zP1/85KSmpWnxubi6xsbHGCa+FEOKWtYBWcl1qLdDbtm27nXkIIUQNpAVdowceeOB25iGEENVJC1oIIRSqhT9JeFMF+tKlS3z44YdkZGRQUFCAXq832a9Sqdi6dWujJiiEaLla+oMqZhfoX375hWHDhlFcXEzHjh05fvw4nTp1QqvVcuHCBTp06CDTjQohGlcLL9Bmj4N++eWXsbKy4sCBA2zduhWDwUBiYiLHjh1j1apVaLVaXn311abMVQjR0rTwuTjMLtD79+8nJiaG9u3bY2FR9bE/ht2NGTOGUaNGMW/evKbJUgjRIqkMdb/udmYX6IqKCtq2bQtULUcOmCwi26VLFw4dOtTI6QkhWrQW/iSh2QXax8eHs2fPAmBnZ4eXlxfp6enG/ceOHcPe3r7xMxRCtFwtvIvD7JuEDz74INu3b2fu3LkAPPbYY7zzzjtcuXIFvV7Pxo0bZS4OIUTjagGt5LqYXaCnT59O3759KS0txdbWljlz5nDlyhU+//xz1Go1UVFRcpNQCNG4pECbx9fXF19fX+N7Gxsbli9fzvLly5siLyGEkALd3AkIIUStWkA/c11qLdAff/xxgw44duzYBicjhBDXU+nrj7mb1Vqgp0yZctMHU6lUUqCFEKKR1FqgDx8+fDvzMJvKyrq5U2hyLeEaAQJSJjZ3CrdFSu+7/1oPN9EArpbwMEpdai3Qfn5+tzMPIYSoTvqghRBCoVp4C9rsJwmFEOK2a+Cj3qtWrSI8PNw4PPihhx5i586d1w77v8neOnXqhJeXF8OGDeP48eMmxygrKyMuLo6AgAC8vb2Jjo4mOzvbJEar1RIbG4ufnx9+fn7Exsai1WpNYrKysoiKisLb25uAgABmzZpFeXm5WZcvBVoIoVgNnSzJ29ubBQsW8O2335Kamkrfvn154okn+Omnn4CqdVWTk5NZtGgRu3btwt3dnZEjR1JYWGg8Rnx8PNu2bWPNmjVs376dwsJCoqKi0Ol0xpgJEyZw5MgRNm3aREpKCkeOHGHixGv3G3Q6HVFRURQVFbF9+3bWrFnD1q1bmTNnjlnXLwVaCKFcDWxBDxs2jIceeoiAgAACAwOZN28eDg4OHDx4EIPBwIoVK5g+fTojRoygc+fOrFixgqKiIlJSUoCqieA+/PBDXnnlFSIiIggLC2PlypX8/PPP7N69G4DMzEy+/vprli9fTq9evejZsyfLli1j586dnDhxAoBdu3Zx/PhxVq5cSVhYGBERESxYsIB169Zx5cqVei9fCrQQQrkaYTY7nU7HZ599RnFxMT179uTMmTPk5OQwYMAAY4ydnR3h4eGkpaUBkJGRQUVFhUmMj48PwcHBxpj09HQcHBzo1auXMaZ3797Y29ubxAQHB+Pj42OMiYyMpKysjIyMjHpzl5uEQgjFupVhdj///DODBg2itLQUe3t71q9fT2hoqLF4uru7m8S7u7tz4cIFAHJzc1Gr1bi6ulaLyc3NNca4urqiUl0baaJSqXBzczOJufE8rq6uqNVqY0xdbqoFffbsWZ577jnCwsLw9fXl+++/B0Cj0fD888+b9RNBCCHMplfV/apDUFAQe/bs4euvv+bpp59m8uTJHDt2zLj/+sIKVTcOb9x2oxtjaoo3J6au7dczu0BnZmbSr18/tmzZQseOHSkuLjZ2lru6unLw4EFWr15t7uGEEKJet7KiirW1NQEBAdx333289NJLdOnShXfeeQdPT0+Aai3YvLw8Y2vXw8MDnU6HRqOpMyYvL8+4shRUFWeNRmMSc+N5NBoNOp2uWsu6JmYX6JdeeglHR0cOHjzIe++9Z5IUwKBBgzhw4IC5hxNCiPo14ooqer2e8vJy/P398fT0JDU11bivtLSU/fv3G/uTw8LCsLKyMonJzs4mMzPTGNOzZ0+KiopMFi5JT0+nuLjYJCYzM9NkeF5qaio2NjaEhYXVm7PZfdD79u1j5syZeHh4cPny5Wr7fX19jf03QgjRGBraB/3yyy8zaNAg2rVrZxyd8f333/Ppp5+iUqmYPHkyS5cuJSgoiMDAQF5//XXs7e0ZM2YMAE5OTowbN4758+fj7u6Oi4sLc+bMITQ0lP79+wMQHBzMwIEDmTFjBklJSRgMBmbMmMHgwYMJCgoCYMCAAYSEhDBp0iQSEhLIz89n/vz5jB8/ntatW9d7HWYX6MrKyjqXtMrPz0etVpt7OCGEqF8DC3ROTg6xsbHk5ubSunVrQkNDSUlJITIyEoBp06ZRUlJCXFwcWq2W7t27s3nzZhwdHY3HWLhwIWq1mpiYGEpLS+nbty/vvvuuSZ1btWoVs2fPZtSoUQAMHTqUxYsXG/er1Wo2btzIzJkzGTJkCLa2towZM4aEhASzrsPsAt25c2f27NnD008/XW2fwWBg27ZtZjXZhRDCbA0s0CtWrKhzv0qlIj4+nvj4+FpjbG1tWbJkCUuWLKk1xsXFhffee6/Oc/n6+rJx48a6E66F2X3QkydPZsuWLSxevNjYxaHX6/n111956qmnOHToEM8++2yDkhBCiJrcyk3Cu4HZLejRo0eTlZXFv/71L1577TXjNqhqxickJPDQQw81TZZCCNEC3dSDKtOnT2fMmDFs3bqV06dPo9fr6dChA8OHD8ff37+pchRCtFQtoJVcl5t+ktDHx6dBq60IIcTNagndGHWRR72FEMolaxKax8XFxaxHE2saIy2EEA0hLWgzzZo1q1qB1ul0nDlzhv/+978EBgYyePDgRk9QCNGCSYE2T13jBc+fP8/AgQO55557GiUpIYQAaUE3ynzQ3t7exMTEmDxBI4QQt6wR5+K4EzXaTUJnZ2d+++23xjqcEEK0iCJcl0Yp0Hl5eXzwwQf4+fk1xuGEEAKQLg6zC/QjjzxS4/aCggJ+/fVXKioqWLt2baMlJoQQ0oI2k16vrzaKQ6VS4e/vT0REBOPHj6djx46NnqAQogWTAm2eL774oinzEEKIalp6F4dZozhKSkp45JFHWL9+fVPnI4QQRip93a+7nVkF2s7OjsOHDxvXIBRCiNuihQ+zM3sc9AMPPMC+ffuaMhchhDAlBdo8ixYt4scff2TevHn8/vvv6PUt4PcLIUSzUtXzutvVeZPw448/Jjw8HH9/f+6//34MBgPJyckkJydjYWGBlZWVSbxKpeL8+fNNmrAQogVpAa3kutRZoKdOncrKlSvx9/dn5MiRZs1mJ4QQjaWlj+Kos0AbDNe+nfoWYRRCiEYnBVoIIRRKCnTdpFtDCNFcpIujHlOnTuXZZ58162Byk1AI0aikQNete/futG/f/jakIoQQpqQFXY+YmBgee+yx25GLEEKYaAmPc9dFbhIKIZRLWtBCCKFQUqCFEEKZpA+6Dvn5+bcrDyGEqE4KtBBCKJPK0LIrtBRoIYRytez6LAVaCKFcLb0P2uz5oIUQ4rZr4IT9b7zxBhEREfj6+tKxY0eioqI4duyY6aENBhITE+nUqRNeXl4MGzaM48ePm8SUlZURFxdHQEAA3t7eREdHk52dbRKj1WqJjY3Fz88PPz8/YmNj0Wq1JjFZWVlERUXh7e1NQEAAs2bNory8vN7LlwIthFAslaHuV22+//57nn76aXbu3MnWrVuxtLTk0UcfNRn4kJSURHJyMosWLWLXrl24u7szcuRICgsLjTHx8fFs27aNNWvWsH37dgoLC4mKijJZ/m/ChAkcOXKETZs2kZKSwpEjR5g4caJxv06nIyoqiqKiIrZv386aNWvYunUrc+bMqff6pYtDCKFYDX2ScPPmzSbvV65ciZ+fHwcOHGDo0KEYDAZWrFjB9OnTGTFiBFA1pXJQUBApKSnExMRQUFDAhx9+SHJyMhEREcbjdOnShd27dxMZGUlmZiZff/01O3bsoFevXgAsW7aMoUOHcuLECYKCgti1axfHjx/n6NGj+Pj4ALBgwQKee+455s2bR+vWrWu9DmlBCyGUq5HWJCwqKkKv1+Ps7AzAmTNnyMnJYcCAAcYYOzs7wsPDSUtLAyAjI4OKigqTGB8fH4KDg40x6enpODg4GIszQO/evbG3tzeJCQ4ONhZngMjISMrKysjIyKgzb2lBCyEUq7FuEr7wwgt06dKFnj17ApCTkwOAu7u7SZy7uzsXLlwAIDc3F7Vajaura7WY3NxcY4yrq6vJtMwqlQo3NzeTmBvP4+rqilqtNsbURgq0EEK5GmEc9IsvvsiBAwfYsWMHarXaZN+N890bDIZ658C/MaameHNi6tr+BynQjejJ6dk8OcN0PuzLuZY8fv99ADi7VfD0C1l063sF+9Y6fkpz4J2X/Dn/u60x3tJCS9yy09z3QAGtHPSc/92GTSvbkvrvqp/iXXtfYfHGzBrP/6/JHdmzvU0TXd3dz+m7izjty8VSUwZAedtWXB7szdVQF9Dpcf3POeyPa7HKK0Nvq6YkqDV5j/hS2cam+sEMBrxXZGL/SwFHE73AvmqzpaaMNjuzaXXiCuor5ehaW1N4XxsuD/HBYG3a4+h4MA+XXRewyinBYKOmuLMzOeM6NvXXoCi32oKOj49n8+bNbNu2zWTaZE9PT6CqdXt910NeXp6xtevh4YFOp0Oj0eDm5mYSEx4ebozJy8szKcgGgwGNRmNynD+6O/6g0WjQ6XTVWtY3kgLdyLJO2jIrupPxvd54s9fAS6tOoNerWPBMIFcL1YyakEPihkxiB95LWUnVT/b2bVZgG1jCgmeCKNBYEj6kqmBfOm/NT+mOHPs/B8b2CDM554i/5zD87zkc3O10ey7yLlXpbE3ecD8q3G3BYKB1eh7eq05wNu5eKttYY3uumMuD2lHWrhUWpTrcPz+L94pfOPtCV1CbtoScd10Ei+qtI+ucElR6A7l/bU+5uy3WOSV4fvIb6quV5EYHGOOcvr1Im6/OkzfCj9L2Dqgq9Fjnljb5d6A4t1CgZ8+ezebNm/nPf/7DPffcY7LP398fT09PUlNT6datGwClpaXs37+fV155BYCwsDCsrKxITU01TrmcnZ1NZmamsc+5Z8+eFBUVkZ6ebtyWnp5OcXGxSczrr79OdnY27dq1AyA1NRUbGxvCwsLqvIZmvUm4d+9eoqOjCQkJwdnZmQ0bNjRnOo1Cp1ORf8nK+Cq4bAVAuw5lhHQr5u25/vx62IFzp+14a44/NrZ6IkZcNn7e3vpXtq3zIDPDgYtZtmxe5cWl89YEhxUBUFlhYXL8/EtWPDA0n91bXSm9qq4xJ2Ge4q5tuNrZmQp3Wyo87NA87Ive1gLb3wvR21mSPTWEom6uVHjaUebvQG5Ue2xySrHOKTE5js3ZIpy/vUjOEwHVznG1szM5T3bkaogzlW62XA114fKgdjhkXBv+ZXG1ErdtWeQ82ZHC+92ocLel3LsVRWEt77cjlb7uV21mzpzJRx99xOrVq3F2diYnJ4ecnByKiqr+H6lUKiZPnszy5cvZunUrx44dY8qUKdjb2zNmzBgAnJycGDduHPPnz2f37t0cPnyYiRMnEhoaSv/+/QEIDg5m4MCBzJgxg4MHD5Kens6MGTMYPHgwQUFBAAwYMICQkBAmTZrE4cOH2b17N/Pnz2f8+PF1juCAZm5BFxcX07lzZ8aOHcukSZOaM5VG4+VXxvq0DCorVPxyyIH3F7fjYpYtVtZV/5oqyq61qgwGFRXlKkJ7FLLjk6pfdYrKg+n78BkOfOVCUYGaXgO1OLlWcuj7mlvHXXtfwadjKYumVS8G4hboDTgcuoxFmZ7SDo41hliUVv16pLe79oNRVarD64OT5EZ1QOdoZdapLEp16FpdO0arXwpAb0BdWIHfvw6jLtVR6u/ApUf9qHSzreNId6EGtqBXr14NYBxC94fZs2cTHx8PwLRp0ygpKSEuLg6tVkv37t3ZvHkzjo7X/r4XLlyIWq0mJiaG0tJS+vbty7vvvmvSl71q1Spmz57NqFGjABg6dCiLFy827ler1WzcuJGZM2cyZMgQbG1tGTNmDAkJCfVeR7MW6EGDBjFo0CAApkyZ0pypNIpfMuxZ+nwHzp2yxdm1grHPXuCNzceZ+FAXsk7ZkpNlzd9nZbN8dntKr1ow8ukc3L0raONRYTzGb5rnMBieZ9PhQ1RWVBXw154N4PSxVjWec+jjlzj1sx0njtrfrsu8q1mfv4rvGz+jqtSjt1FzfkIQ5d41fPeVetz+fZaie52pdLnWB+2x8TeuhjhzNdTZrPNZXi7DZdcFLj/kbdxmpSlDZYA2O7O5NMofnb0lrjuy8XnrOGfmdMVg3XJ+U2poH/SNT/LVeGyVivj4eGPBromtrS1LlixhyZIltca4uLjw3nvv1XkuX19fNm7cWG9ON5Jx0I3oh93O7PmiDb/90opDe52Y/1QQFhbw0Jg8dJUWvDopkLZ+paQcOcSWX/6PP/35CumpTuj011rV3q034eRSyQuPB/PsI51JWelF3Bu/0SHkarXzOThV0mdwPv/92ON2XuZdrdzDlrOzu5D1z1AK+njgtf401udv+O51BrzWncKiRGfSjeGYfgmb81fJG+Fn1rnUVypot+IXrgY7oY3wurbDYEClM3BptD9XOztT5u/AxfEdURdVYP+TthGu8g5iMNT9usvdcTcJF+6d0dwp3BS1QwIjZ7SlR8zTABQAGdlXsVBVYhHYmhCPeVwtD2Dh3his1Tl4OM5A0zaRvy72Nx5DZ/0vXv3MnbP5sSbH9nDYjqX1zzwweR7hk2puYYtb8FdYPfUTev0Mo0f1B0BXqeeTuVvJyYdn1j6Fo5uDMXzTzi84dLGEe2b9YNymBz6es5WBXbyZtOpJ4/bCvCJWT/0Ej1B/ohOGo7a81lb64dIRPvvPOd76ywCcPK/1US7xOMUwRx/69r72UMTdTtYkvMO82GdZc6dgNisbPR98f4L/fqjlozer5+3dvpRVu07xr0lqftyzjPbBV3n3S1j+5HrO/Hqt4P5rXRZ5OTksizM9xsqvjvLNV44snbmyya+lKfy69L7mTqFe7bT5VBqK+PjAbtDpafv+SawvlHDu2RD2nfwBTl6LVfeyQt2li8nn/V87yl+ei+Dt1hq+PrC7Kq6gHJ+3jlPe1o69jzjx+Q/fmXzGylBCe+AfO1IpCa6696Aq0xGQe4W1hed484DpTUklOPy/G2KNraXPZnfHFWglmzDnLGlfO5N73gZn1woef+48NnY6vv6sagzlg3+5TEG+JbnnrGnfqYTJL51l/5cu/Lin6j9h1ilbSis8+UfCGVb9y5fCfEv+PFjLfQ9eYcEE0/8AoT0K8b+nlKT49rf7Mu9arlvPUhzqTKWzDRZlOhx/yMPu5BXOTwwGnYG2a09ie7aI87HBoAL1larZyPS2lhisLdA5W6Nztq52XCdPRyrti4H/Fec3j1PpZMWlUf6oi6/df9A5WIGFigoPO4q6uOC++Qy5UR3Q26lp899sdI6WFN/rfFu+C8VoAd0YdZEC3YjcvCp44a3TtHappOCyJb8ccmDGyM7kZlfdRGrjUUHsvLM4u1VyOdeKbza78tGb124O6SotOKWZRcHlBBasOYGdfdWDKm/M7EDaN84m5xoy9hJnTthy7IeaRxiIm2d5pQKvdadQX6lAb6em3LsV5ycFczXEGUtNGQ5Hq4bC+S35yeRzF58IoLBX3Q8c/KHVLwVYXyrF+lIpHV7KMNn320thVLpW/Vu5OK4j7pvP4P1eJhigJMCR7KkhLeoGIUgLulkLdFFREadPnwZAr9dz7tw5jhw5gouLC76+vs2ZWoO89mzdT3lted+TLe971hlTVtmWhEmB9Z5r6fMyrK6x5TxZ+99fpasNJ968+b7fE2/2okvvTnDgIgCFvdzNKuYGWzW5jweQ+3gL/3tu4QW6WUdxHDp0iL59+9K3b19KSkpITEykb9++LFy4sDnTEkIoREPng75bNGsL+sEHHzRrvKIQooXSt4AqXAfpgxZCKFfLrs9SoIUQytUSujHqIgVaCKFcMsxOCCGUSVrQQgihUCq5SSiEEAolc3EIIYQyqaQPWgghFKpl12cp0EIIBZMWtBBCKJOM4hBCCKWSFrQQQiiTrKgihBBKJS1oIYRQqJZdn6VACyGUS6Vv2X0cUqCFEMrVsuuzFGghhHLJk4RCCKFUUqCFEEKhpEALIYRCSR+0EEIok/RBCyGEUkmBFkIIhZICLYQQCiUFWgghlEmla9kF2qK5ExBCiFoZDHW/arF3716io6MJCQnB2dmZDRs23HBYA4mJiXTq1AkvLy+GDRvG8ePHTWLKysqIi4sjICAAb29voqOjyc7ONonRarXExsbi5+eHn58fsbGxaLVak5isrCyioqLw9vYmICCAWbNmUV5ebtblS4EWQiiX3lD3qxbFxcV07tyZ1157DTs7u2r7k5KSSE5OZtGiRezatQt3d3dGjhxJYWGhMSY+Pp5t27axZs0atm/fTmFhIVFRUeh0OmPMhAkTOHLkCJs2bSIlJYUjR44wceJE436dTkdUVBRFRUVs376dNWvWsHXrVubMmWPW5UsXhxBCuRrYBz1o0CAGDRoEwJQpU244pIEVK1Ywffp0RowYAcCKFSsICgoiJSWFmJgYCgoK+PDDD0lOTiYiIgKAlStX0qVLF3bv3k1kZCSZmZl8/fXX7Nixg169egGwbNkyhg4dyokTJwgKCmLXrl0cP36co0eP4uPjA8CCBQt47rnnmDdvHq1bt67zOqQFLYRQrgZ2cdTlzJkz5OTkMGDAAOM2Ozs7wsPDSUtLAyAjI4OKigqTGB8fH4KDg40x6enpODg4GIszQO/evbG3tzeJCQ4ONhZngMjISMrKysjIyKg3V2lBCyGUqwlGceTk5ADg7u5ust3d3Z0LFy4AkJubi1qtxtXVtVpMbm6uMcbV1RWVSmXcr1KpcHNzM4m58Tyurq6o1WpjTF2kQAshlKuOfuZbdX1hhaqujxu33ejGmJrizYmpa/v1pItDCKFcBn3drwbw9PQEqNaCzcvLM7Z2PTw80Ol0aDSaOmPy8vIwXNfKNxgMaDQak5gbz6PRaNDpdNVa1jWRAi2EUK4m6IP29/fH09OT1NRU47bS0lL2799v7E8OCwvDysrKJCY7O5vMzExjTM+ePSkqKiI9Pd0Yk56eTnFxsUlMZmamyfC81NRUbGxsCAsLqzdX6eIQQihXA7s4ioqKOH36dNUh9HrOnTvHkSNHcHFxwdfXl8mTJ7N06VKCgoIIDAzk9ddfx97enjFjxgDg5OTEuHHjmD9/Pu7u7ri4uDBnzhxCQ0Pp378/AMHBwQwcOJAZM2aQlJSEwWBgxowZDB48mKCgIAAGDBhASEgIkyZNIiEhgfz8fObPn8/48ePrHcEBUqCFEErWwDUJDx06xCOPPGJ8n5iYSGJiImPHjmXFihVMmzaNkpIS4uLi0Gq1dO/enc2bN+Po6Gj8zMKFC1Gr1cTExFBaWkrfvn159913UavVxphVq1Yxe/ZsRo0aBcDQoUNZvHixcb9arWbjxo3MnDmTIUOGYGtry5gxY0hISDDrOlRarfaOepbyMY8p9QfdwRbuncGLfZY1dxq3xa9L72vuFG6LlN79GXNgd3On0aQOj3umSY4b3Xlunfs/OWZeobtTSQtaCKFcsqq3EEIolMxmJ4QQCiUFWgghFKoJH1S5E0iBFkIolqGBD6PcLaRACyGUS1rQQgihUNIHLYQQCiXD7IQQQqGkBS2EEMpkuG55qZZICrQQQrnkJqEQQiiUDLMTQghlMkgLWgghFKqFt6DvuOlGhRCipZAlr4QQQqGkQAshhEJJgRZCCIWSAi2EEAolBVoIIRRKCrRCrF69mq5du+Lp6Um/fv3Yt29fc6ckbsHevXuJjo4mJCQEZ2dnNmzY0NwpiTuQFGgF2Lx5My+88ALPP/883333HT179uSxxx4jKyuruVMTDVRcXEznzp157bXXsLOza+50xB1KxkErQGRkJKGhobz55pvGbd26dWPEiBG89NJLzZiZaAzt2rVj8eLFPPHEE82dirjDSAu6mZWXl5ORkcGAAQNMtg8YMIC0tLRmykoIoQRSoJuZRqNBp9Ph7u5ust3d3Z3c3NxmykoIoQRSoBVCpVKZvDcYDNW2CSFaFinQzczV1RW1Wl2ttZyXl1etVS2EaFmkQDcza2trwsLCSE1NNdmemppKr169mikrIYQSyHSjCjB16lQmTpxI9+7d6dWrF2vXruXixYvExMQ0d2qigYqKijh9+jQAer2ec+fOceTIEVxcXPD19W3m7MSdQobZKcTq1atJSkoiJyeHkJAQFi5cSJ8+fZo7LdFAe/bs4ZFHHqm2fezYsaxYsaIZMhJ3IinQQgihUNIHLYQQCiUFWgghFEoKtBBCKJQUaCGEUCgp0EIIoVBSoIUQQqGkQAsAEhMTcXZ2NtnWpUsXJk+e3DwJ1cLZ2ZnExMRGi7vRmTNncHZ2ZtmyZQ1Jr9HzES2bFGgF2LBhA87OzsaXq6srnTt35h//+AcXL15s7vRuSlFREYmJiezZs6e5UxHijiePeivICy+8QIcOHSgrK+PAgQN89NFH7N27l3379jXLqhw//PADFhY39zO8uLiYRYsWAfDggw82RVpCtBhSoBUkMjKS+++/H4Dx48fj4uJCcnIy27dvZ/To0TV+5urVq7Rq1apJ8rGxsWmS4wohzCNdHArWt29fAH7//XcAJk+ejKenJ2fPnuXxxx/Hz8+Pxx57zBj/2WefERkZSdu2bfHz8yMqKopffvml2nF37txJnz598PT0pHv37qxbt67G89fUB11eXs6SJUu4//778fDwICgoiLFjx3L8+HHOnDlDcHAwAIsWLTJ22Vx/jIsXLzJt2jQ6deqEh4cH3bp1IykpCYPBdMaBK1euMG3aNNq3b4+vry/jxo27pe6e/Px85s6dS3h4OD4+PrRr146HH36YAwcO1PqZlStX0rVrV7y8vBg4cCA//PBDtRhzr0eIhpAWtIL99ttvALRp08a4Ta/XM2rUKLp168aCBQtQq9UALF++nJdffplHHnmE6OhoiouLWb16NYMHD+bbb7+lffv2AHz77bc8/vjjBAQEMGfOHEpLS3n11Vfx9PSsNx+9Xs/YsWP55ptvGD58OM888wwlJSXs2bOHjIwMhg8fzpIlS4iLi+Phhx82ThbUoUMHAC5dusTAgQOprKzkb3/7G15eXuzfv5+XXnqJCxcu8NprrwFVixU8+eST7Nmzh3HjxtGlSxd2795t8sPoZv3+++9s2bKFESNGEBAQQEFBAevWrWPEiBGkpqbSuXNnk/hNmzZRUFDA008/jV6vZ/Xq1Tz66KPs3r2bwMDAm7oeIRpKCrSCXLlyBY1GQ2lpKWlpaSxevBg7OzsGDx5sjKmoqGDQoEEsXLjQuC0rK4uEhARmz55NfHy8cXt0dDQ9e/bk9ddf5+233wZg/vz5ODs78+WXX+Li4gLAiBEjCA8Prze/jz/+mG+++Ya5c+cyc+ZM4/Zp06YZV4AZPnw4cXFxhIaGEhUVZfL5hIQEysrK2Lt3Lx4eHgDExMTg5eXF22+/zeTJk/H392fHjh189913vPjii8yaNQuAZ555hmeeeYajR4/e7NcKQOfOncnIyDD+QAP4+9//zv3338+7775rsmAvwMmTJzl48CD+/v4APProo/Tu3ZvXXnuN1atX39T1CNFQ0sWhIKNHj6Zjx46Ehoby1FNP4enpycaNG/H29jaJmzBhgsn7bdu2UVlZyejRo9FoNMaXlZUVPXr04LvvvgMgJyeHw4cPEx0dbSzOAMHBwURGRtab39atW3FycuLZZ5+ttq++5bkMBgNbtmxh8ODBqNVqkzwjIyPR6/Xs3bsXqOqCsbCwYOLEiSbHuJUhfzY2NsbiXFpayuXLl9Hr9XTv3p2MjIxq8UOHDjUproGBgURGRvLVV1/d9PUI0VDSglaQRYsWERwcjI2NDT4+Pvj4+FQrfBYWFvj5+ZlsO3XqFAA9e/as8bh/3EQ8e/YsAEFBQdViAgMD+fLLL+vM77fffiMwMLBBNw/z8vLQarWsX7+e9evX1xoDVb8ReHh44OTkVC3HhtLr9SQlJfH+++9z5swZk301tXI7duxY47adO3dSUFBAeXm52dcjRENJgVaQbt26GUdx1MbKygpLS9O/Nr1eD0BKSkq1fYBxqNwfN65qau2ac1PrVhay/SPHMWPG8OSTT9YYExAQcMvnqc3y5ct55ZVXGDt2LHPnzqVNmzao1WreeOMNY1//9er7jm7meoRoKCnQd4E/bsL5+PjQqVOnWuP+aCn++uuv1fb90QqvS0BAAGlpaZSXl2NtbV1jTG2F1c3NjdatW1NZWUn//v3rPI+fnx+7d++moKDApBV98uTJenOszebNm3nggQeqrWZS29N9NZ3r9OnTODk54eTkhIODg9nXI0RDSR/0XWD48OFYWlqSmJhobNld749ftT09PenatSuffPIJ+fn5xv2ZmZl88803Zp1Hq9WSnJxcbd8frcs/ulO0Wq3JfrVazfDhw/nPf/5TY59vQUEBFRUVAAwaNAi9Xs/KlStNYm5lqSi1Wl3tt4S0tDTS09NrjN+xY4dJV8jJkyf55ptvGDhw4E1fjxANJS3ou0D79u1ZsGABc+bMYeDAgTzyyCO4uLiQlZXFl19+SY8ePYxzSyxYsIDRo0czaNAgxo8fT0lJCatWrSIkJISffvqpzvNER0fz6aefsmDBAg4fPkyfPn0oLS3l+++/Z+TIkURHR+Pg4EBQUBCbN28mMDCQNm3a4O/vT48ePXj55ZfZu3cvQ4YMYdy4cXTu3JnCwkKOHTvGtm3b+PHHH/H09GTo0KH06dOHxMREzp07R9euXUlNTa3Wd3wzhg4dymuvvcbEiRMJDw/n1KlTvP/++3Tq1ImioqJq8R07duQvf/kLEyZMQK/Xs2rVKmxsbJg9e7YxxtzrEaKhpEDfJaZOnUpgYCBvvfUWb7zxBpWVlbRt25bevXszbtw4Y1xERAQbNmzg1Vdf5dVXX8XX15d58+aRnZ1db4FWq9Vs3LiRpUuXkpKSwhdffIGLiws9evQgLCzMGJecnEx8fDxz586lrKyMsWPH0qNHD9zc3Pjmm29YsmQJX3zxBe+//z5OTk4EBgbywgsvGEeWqFQqPvroI+bOncu///1vPv/8c/r168emTZsICQlp0Pfzz3/+k5KSEjZt2sSWLVsICQlh7dq1fPbZZ3z//ffV4h977DFatWpFcnIyOTk53HvvvSxcuJB77rnHGGPu9QjRULJorBBCKJT0QQshhEJJgRZCCIWSAi2EEAolBVoIIRRKCrQQQiiUFGghhFAoKdBCCKFQUqCFEEKhpEALIYRCSYEWQgiF+v98XxpWsDzoUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(dt3, X_tr_mp, y_tr_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Let's try a different kind of feature selection with our `RandomForestClassifier`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install boruta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from boruta import BorutaPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_b = RandomForestClassifier(bootstrap=False, max_depth=20, min_samples_leaf=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`BorutaPy` is a wrapper for the RandomForest algorithm, that attaches a \"randomized\" duplicate dataset to the original and ranks the original features. For a great article explaining the process, click [here](https://towardsdatascience.com/boruta-explained-the-way-i-wish-someone-explained-it-to-me-4489d70e154a)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "boruta = BorutaPy(\n",
    "                 estimator = rf_b,\n",
    "                 n_estimators = 'auto',\n",
    "                 max_iter = 25, \n",
    "                 perc = 80\n",
    "                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BorutaPy(estimator=RandomForestClassifier(bootstrap=False, max_depth=20,\n",
       "                                          n_estimators=67,\n",
       "                                          random_state=RandomState(MT19937) at 0x7FCE85894440),\n",
       "         max_iter=25, n_estimators='auto', perc=80,\n",
       "         random_state=RandomState(MT19937) at 0x7FCE85894440)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boruta.fit(np.array(X_tr_pp), np.array(y_tr_pp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Features in the \"green area\" have passed the maximum shadow feature importance \"threshold\". The threshold can be adjusted with the param `perc` (percentile). \n",
    "\n",
    "Features in the \"blue area\" are considered to be in the real of uncertainty, and ultimately up to the user to decided whether to keep or discard the feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "green_area = X_tr_pp.columns[boruta.support_]\n",
    "blue_area = X_tr_pp.columns[boruta.support_weak_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns within Area of Acceptance\n",
      " Index(['x0_2019', 'x1_Midwest', 'x1_NorthEast', 'x1_South', 'x3_0', 'x3_1',\n",
      "       'x4_1-2x', 'x4_Never', 'x4_Occasionally', 'x4_Regularly Now',\n",
      "       'x4_Regularly in Past', 'x5_1 Pack', 'x5_1-5 Cigarettes', 'x5_2 Packs',\n",
      "       'x5_<1 Cigarettes', 'x5_None', 'x6_0', 'x6_1-2X', 'x6_10-19X',\n",
      "       'x6_20-39X', 'x6_3-5X', 'x6_40+', 'x6_6-9X', 'x7_0', 'x7_1-2X',\n",
      "       'x7_10-19X', 'x7_20-39X', 'x7_3-5X', 'x7_40+', 'x7_6-9X', 'x8_3-5X',\n",
      "       'x8_6-9X', 'x8_None', 'x8_Once', 'x8_Twice', 'x9_Female', 'x9_Male',\n",
      "       'x10_Country', 'x10_Farm', 'x12_No', 'x12_Yes', 'x14_Yes',\n",
      "       'x15_Graduated HS', 'x16_Graduated HS', 'x17_Yes/Nearly All',\n",
      "       'x18_Conservative', 'x18_Liberal', 'x18_Very Liberal',\n",
      "       'x19_Every Week+', 'x19_Never', 'x19_Rarely', 'x20_A Little Important',\n",
      "       'x20_Not Important', 'x20_Pretty Important', 'x20_Very Important',\n",
      "       'x21_Above Average', 'x21_Average', 'x22_Average', 'x23_0', 'x24_0',\n",
      "       'x24_1', 'x25_0', 'x26_0', 'x26_1-2', 'x26_3-5', 'x27_A+',\n",
      "       'x30_Definitely Won't', 'x31_Definitely Will', 'x37_No', 'x40_0',\n",
      "       'x41_0', 'x42_1', 'x42_2', 'x42_4-5', 'x42_6-7', 'x43_Never', 'x45_0',\n",
      "       'x46_0', 'sampling_weight'],\n",
      "      dtype='object')\n",
      "Columns within Area of Irresolution\n",
      " Index([], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(\"Columns within Area of Acceptance\\n\", green_area)\n",
    "print(\"Columns within Area of Irresolution\\n\", blue_area)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code here is going to help display the feature rankings in a more readable format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_features():       \n",
    "    labeled_cols = []\n",
    "    cols = X_train.drop('sampling_weight', axis=1).columns\n",
    "    for i in range(len(X_tr_pp.columns)-1):\n",
    "        ind = X_tr_pp.columns[i].split('_')[0]\n",
    "        col = X_tr_pp.columns[i]\n",
    "\n",
    "        if ind == X_tr_pp.columns[i].split('_')[0]:\n",
    "            labeled_cols.append(cols[int(ind[1:])])\n",
    "    return labeled_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('x0_2019', 1, 'yr_adminst'),\n",
       " ('x1_Midwest', 1, 'region'),\n",
       " ('x1_NorthEast', 1, 'region'),\n",
       " ('x1_South', 1, 'region'),\n",
       " ('x3_0', 1, 'smsa'),\n",
       " ('x3_1', 1, 'smsa'),\n",
       " ('x4_1-2x', 1, 'evr_smoked_cig'),\n",
       " ('x4_Never', 1, 'evr_smoked_cig'),\n",
       " ('x4_Occasionally', 1, 'evr_smoked_cig'),\n",
       " ('x4_Regularly Now', 1, 'evr_smoked_cig'),\n",
       " ('x4_Regularly in Past', 1, 'evr_smoked_cig'),\n",
       " ('x5_1 Pack', 1, 'cig_month_freq'),\n",
       " ('x5_1-5 Cigarettes', 1, 'cig_month_freq'),\n",
       " ('x5_2 Packs', 1, 'cig_month_freq'),\n",
       " ('x5_<1 Cigarettes', 1, 'cig_month_freq'),\n",
       " ('x5_None', 1, 'cig_month_freq'),\n",
       " ('x6_0', 1, 'alcohol_lifetime_freq'),\n",
       " ('x6_1-2X', 1, 'alcohol_lifetime_freq'),\n",
       " ('x6_10-19X', 1, 'alcohol_lifetime_freq'),\n",
       " ('x6_20-39X', 1, 'alcohol_lifetime_freq'),\n",
       " ('x6_3-5X', 1, 'alcohol_lifetime_freq'),\n",
       " ('x6_40+', 1, 'alcohol_lifetime_freq'),\n",
       " ('x6_6-9X', 1, 'alcohol_lifetime_freq'),\n",
       " ('x7_0', 1, 'alcohol_yr_freq'),\n",
       " ('x7_1-2X', 1, 'alcohol_yr_freq'),\n",
       " ('x7_10-19X', 1, 'alcohol_yr_freq'),\n",
       " ('x7_20-39X', 1, 'alcohol_yr_freq'),\n",
       " ('x7_3-5X', 1, 'alcohol_yr_freq'),\n",
       " ('x7_40+', 1, 'alcohol_yr_freq'),\n",
       " ('x7_6-9X', 1, 'alcohol_yr_freq'),\n",
       " ('x8_3-5X', 1, 'alcohol_2weeks'),\n",
       " ('x8_6-9X', 1, 'alcohol_2weeks'),\n",
       " ('x8_None', 1, 'alcohol_2weeks'),\n",
       " ('x8_Once', 1, 'alcohol_2weeks'),\n",
       " ('x8_Twice', 1, 'alcohol_2weeks'),\n",
       " ('x9_Female', 1, 'sex'),\n",
       " ('x9_Male', 1, 'sex'),\n",
       " ('x10_Country', 1, 'area_type'),\n",
       " ('x10_Farm', 1, 'area_type'),\n",
       " ('x12_No', 1, 'has_father'),\n",
       " ('x12_Yes', 1, 'has_father'),\n",
       " ('x14_Yes', 1, 'has_siblings'),\n",
       " ('x15_Graduated HS', 1, 'father_educ_lvl'),\n",
       " ('x16_Graduated HS', 1, 'mother_educ_lvl'),\n",
       " ('x17_Yes/Nearly All', 1, 'mother_employed'),\n",
       " ('x18_Conservative', 1, 'political_value_type'),\n",
       " ('x18_Liberal', 1, 'political_value_type'),\n",
       " ('x18_Very Liberal', 1, 'political_value_type'),\n",
       " ('x19_Every Week+', 1, 'relig_attd'),\n",
       " ('x19_Never', 1, 'relig_attd'),\n",
       " ('x19_Rarely', 1, 'relig_attd'),\n",
       " ('x20_A Little Important', 1, 'relig_importance'),\n",
       " ('x20_Not Important', 1, 'relig_importance'),\n",
       " ('x20_Pretty Important', 1, 'relig_importance'),\n",
       " ('x20_Very Important', 1, 'relig_importance'),\n",
       " ('x21_Above Average', 1, 'academic_self_rating'),\n",
       " ('x21_Average', 1, 'academic_self_rating'),\n",
       " ('x22_Average', 1, 'intelligence_self_rating'),\n",
       " ('x23_0', 1, 'school_missed_illness'),\n",
       " ('x24_0', 1, 'school_missed_ditched'),\n",
       " ('x24_1', 1, 'school_missed_ditched'),\n",
       " ('x25_0', 1, 'school_missed_other'),\n",
       " ('x26_0', 1, 'skipped_class'),\n",
       " ('x26_1-2', 1, 'skipped_class'),\n",
       " ('x26_3-5', 1, 'skipped_class'),\n",
       " ('x27_A+', 1, 'avg_grade'),\n",
       " (\"x30_Definitely Won't\", 1, '2yrcoll_after_hs'),\n",
       " ('x31_Definitely Will', 1, '4yrcoll_after_hs'),\n",
       " ('x37_No', 1, 'desire_gradsch'),\n",
       " ('x40_0', 1, 'work_pay'),\n",
       " ('x41_0', 1, 'other_income'),\n",
       " ('x42_1', 1, 'rec_time'),\n",
       " ('x42_2', 1, 'rec_time'),\n",
       " ('x42_4-5', 1, 'rec_time'),\n",
       " ('x42_6-7', 1, 'rec_time'),\n",
       " ('x43_Never', 1, 'date_freq'),\n",
       " ('x45_0', 1, '12mo_r_tcktd'),\n",
       " ('x46_0', 1, '12mo_accidents'),\n",
       " (\"x28_Definitely Won't\", 2, 'tech_school_after_hs'),\n",
       " ('x32_Probably Will', 3, 'gradsch_after_hs'),\n",
       " (\"x29_Definitely Won't\", 4, 'military_after_hs'),\n",
       " ('x10_Small Town', 5, 'area_type'),\n",
       " (\"x18_Don't Know\", 6, 'political_value_type'),\n",
       " ('x16_College Grad', 7, 'mother_educ_lvl'),\n",
       " ('x18_Moderate', 9, 'political_value_type'),\n",
       " ('x22_Above Average', 9, 'intelligence_self_rating'),\n",
       " ('x37_Yes', 9, 'desire_gradsch'),\n",
       " ('x14_No', 11, 'has_siblings'),\n",
       " ('x40_126-175', 12, 'work_pay'),\n",
       " ('x39_0', 13, 'work_hrs'),\n",
       " ('x32_Definitely Will', 14, 'gradsch_after_hs'),\n",
       " ('x45_1', 14, '12mo_r_tcktd'),\n",
       " ('x31_Probably Will', 17, '4yrcoll_after_hs'),\n",
       " ('x44_51-100', 17, 'drive_freq'),\n",
       " ('x46_1', 17, '12mo_accidents'),\n",
       " ('x24_2', 19, 'school_missed_ditched'),\n",
       " ('x42_<1', 20, 'rec_time'),\n",
       " ('x24_3', 21, 'school_missed_ditched'),\n",
       " ('x24_4-5', 22, 'school_missed_ditched'),\n",
       " (\"x32_Probably Won't\", 23, 'gradsch_after_hs'),\n",
       " ('x15_College Grad', 24, 'father_educ_lvl'),\n",
       " ('x42_3', 25, 'rec_time'),\n",
       " ('x44_11-50', 26, 'drive_freq'),\n",
       " ('x22_Slightly Above Average', 27, 'intelligence_self_rating'),\n",
       " ('x2_1', 28, 'msa'),\n",
       " ('x2_0', 29, 'msa'),\n",
       " ('x21_Slightly Above Average', 30, 'academic_self_rating'),\n",
       " ('x45_2', 31, '12mo_r_tcktd'),\n",
       " ('x16_Some College', 32, 'mother_educ_lvl'),\n",
       " ('x44_101-200', 32, 'drive_freq'),\n",
       " ('x40_76-125', 34, 'work_pay'),\n",
       " ('x27_B+', 35, 'avg_grade'),\n",
       " ('x27_A-', 36, 'avg_grade'),\n",
       " (\"x32_Definitely Won't\", 37, 'gradsch_after_hs'),\n",
       " ('x25_1', 38, 'school_missed_other'),\n",
       " ('x27_B-', 38, 'avg_grade'),\n",
       " ('x44_200+', 40, 'drive_freq'),\n",
       " ('x43_2-3x Week', 41, 'date_freq'),\n",
       " ('x17_Yes/Some', 42, 'mother_employed'),\n",
       " ('x15_Some College', 43, 'father_educ_lvl'),\n",
       " ('x27_B', 44, 'avg_grade'),\n",
       " ('x35_Yes', 44, 'desire_2yrcoll'),\n",
       " (\"x28_Probably Won't\", 46, 'tech_school_after_hs'),\n",
       " ('x19_1-2x Month', 47, 'relig_attd'),\n",
       " ('x43_2-3x Month', 47, 'date_freq'),\n",
       " ('x30_Probably Will', 49, '2yrcoll_after_hs'),\n",
       " ('x35_No', 50, 'desire_2yrcoll'),\n",
       " ('x36_Yes', 51, 'desire_4yrcoll'),\n",
       " ('x36_No', 52, 'desire_4yrcoll'),\n",
       " ('x17_No', 53, 'mother_employed'),\n",
       " ('x23_1', 55, 'school_missed_illness'),\n",
       " (\"x30_Probably Won't\", 55, '2yrcoll_after_hs'),\n",
       " ('x43_1x Month', 55, 'date_freq'),\n",
       " ('x30_Definitely Will', 57, '2yrcoll_after_hs'),\n",
       " ('x17_Yes/Most', 58, 'mother_employed'),\n",
       " ('x41_11-20', 59, 'other_income'),\n",
       " (\"x29_Probably Won't\", 60, 'military_after_hs'),\n",
       " ('x27_C+', 61, 'avg_grade'),\n",
       " ('x43_1x Week', 61, 'date_freq'),\n",
       " ('x10_Medium City', 63, 'area_type'),\n",
       " ('x39_16-20', 64, 'work_hrs'),\n",
       " ('x43_3x Week+', 64, 'date_freq'),\n",
       " ('x39_21-25', 66, 'work_hrs'),\n",
       " ('x16_Graduate School', 67, 'mother_educ_lvl'),\n",
       " ('x15_Graduate School', 68, 'father_educ_lvl'),\n",
       " ('x0_1995', 69, 'yr_adminst'),\n",
       " ('x34_No', 70, 'desire_military'),\n",
       " ('x34_Yes', 71, 'desire_military'),\n",
       " ('x10_Suburb of Medium City', 72, 'area_type'),\n",
       " ('x33_No', 72, 'desire_tech_school'),\n",
       " ('x33_Yes', 74, 'desire_tech_school'),\n",
       " ('x27_C', 75, 'avg_grade'),\n",
       " ('x41_21-35', 76, 'other_income'),\n",
       " ('x39_30+', 77, 'work_hrs'),\n",
       " ('x26_6-10', 78, 'skipped_class'),\n",
       " ('x23_2', 79, 'school_missed_illness'),\n",
       " ('x25_2', 79, 'school_missed_other'),\n",
       " ('x0_2015', 81, 'yr_adminst'),\n",
       " ('x13_No', 81, 'has_mother'),\n",
       " ('x13_Yes', 83, 'has_mother'),\n",
       " ('x11_Single', 85, 'marital_status'),\n",
       " ('x39_11-15', 85, 'work_hrs'),\n",
       " ('x44_0', 85, 'drive_freq'),\n",
       " ('x15_Some High School', 87, 'father_educ_lvl'),\n",
       " ('x40_176+', 88, 'work_pay'),\n",
       " ('x39_26-30', 89, 'work_hrs'),\n",
       " ('x28_Probably Will', 90, 'tech_school_after_hs'),\n",
       " ('x41_6-10', 91, 'other_income'),\n",
       " ('x40_51-75', 92, 'work_pay'),\n",
       " ('x0_1997', 93, 'yr_adminst'),\n",
       " ('x0_1998', 94, 'yr_adminst'),\n",
       " ('x28_Definitely Will', 95, 'tech_school_after_hs'),\n",
       " ('x0_1996', 96, 'yr_adminst'),\n",
       " ('x22_Far Above Average', 97, 'intelligence_self_rating'),\n",
       " ('x0_2014', 98, 'yr_adminst'),\n",
       " ('x39_6-10', 98, 'work_hrs'),\n",
       " (\"x31_Definitely Won't\", 100, '4yrcoll_after_hs'),\n",
       " ('x10_Large City', 101, 'area_type'),\n",
       " ('x10_Suburb of Large City', 102, 'area_type'),\n",
       " ('x23_3', 102, 'school_missed_illness'),\n",
       " ('x39_5 or less', 104, 'work_hrs'),\n",
       " (\"x31_Probably Won't\", 105, '4yrcoll_after_hs'),\n",
       " ('x41_36-50', 106, 'other_income'),\n",
       " ('x11_Engaged', 107, 'marital_status'),\n",
       " ('x18_Very Conservative', 108, 'political_value_type'),\n",
       " ('x16_Some High School', 109, 'mother_educ_lvl'),\n",
       " ('x29_Probably Will', 110, 'military_after_hs'),\n",
       " ('x46_2', 111, '12mo_accidents'),\n",
       " ('x21_Far Above Average', 112, 'academic_self_rating'),\n",
       " ('x0_2011', 113, 'yr_adminst'),\n",
       " ('x44_1-10', 114, 'drive_freq'),\n",
       " ('x10_City >500,000 (Vry Large)', 115, 'area_type'),\n",
       " ('x25_3', 115, 'school_missed_other'),\n",
       " ('x0_2012', 118, 'yr_adminst'),\n",
       " ('x29_Definitely Will', 118, 'military_after_hs'),\n",
       " ('x40_36-50', 118, 'work_pay'),\n",
       " ('x45_3', 118, '12mo_r_tcktd'),\n",
       " ('x0_2006', 121, 'yr_adminst'),\n",
       " ('x0_2010', 121, 'yr_adminst'),\n",
       " ('x23_4-5', 123, 'school_missed_illness'),\n",
       " ('x21_Slightly Below Avg', 124, 'academic_self_rating'),\n",
       " ('x0_2003', 125, 'yr_adminst'),\n",
       " ('x0_2002', 126, 'yr_adminst'),\n",
       " ('x0_2005', 126, 'yr_adminst'),\n",
       " ('x0_2007', 128, 'yr_adminst'),\n",
       " ('x0_2004', 129, 'yr_adminst'),\n",
       " ('x25_4-5', 130, 'school_missed_other'),\n",
       " ('x10_Suburb of Vry Lrg City', 131, 'area_type'),\n",
       " ('x41_1-5', 131, 'other_income'),\n",
       " ('x18_Radical', 133, 'political_value_type'),\n",
       " ('x0_2009', 134, 'yr_adminst'),\n",
       " ('x0_2008', 135, 'yr_adminst'),\n",
       " ('x22_Slightly Below Avg', 136, 'intelligence_self_rating'),\n",
       " ('x41_51-75', 136, 'other_income'),\n",
       " ('x38_Yes', 138, 'desire_none'),\n",
       " ('x38_No', 139, 'desire_none'),\n",
       " ('x0_1999', 140, 'yr_adminst'),\n",
       " ('x8_10+X', 141, 'alcohol_2weeks'),\n",
       " ('x41_76-125', 142, 'other_income'),\n",
       " ('x0_2000', 143, 'yr_adminst'),\n",
       " ('x40_21-35', 143, 'work_pay'),\n",
       " ('x0_2001', 145, 'yr_adminst'),\n",
       " ('x15_Grade School', 145, 'father_educ_lvl'),\n",
       " ('x41_126-175', 147, 'other_income'),\n",
       " ('x27_C-', 148, 'avg_grade'),\n",
       " ('x40_11-20', 149, 'work_pay'),\n",
       " ('x24_11+', 150, 'school_missed_ditched'),\n",
       " ('x45_4+', 151, '12mo_r_tcktd'),\n",
       " ('x11_Married', 152, 'marital_status'),\n",
       " ('x16_Grade School', 153, 'mother_educ_lvl'),\n",
       " ('x1_West', 154, 'region'),\n",
       " ('x24_6-10', 155, 'school_missed_ditched'),\n",
       " ('x40_6-10', 155, 'work_pay'),\n",
       " ('x23_6-10', 157, 'school_missed_illness'),\n",
       " ('x21_Below Avg', 158, 'academic_self_rating'),\n",
       " ('x25_6-10', 158, 'school_missed_other'),\n",
       " ('x26_11-20', 160, 'skipped_class'),\n",
       " ('x41_176+', 161, 'other_income'),\n",
       " ('x22_Below Avg', 162, 'intelligence_self_rating'),\n",
       " ('x46_3', 163, '12mo_accidents'),\n",
       " ('x11_Separated/Divorced', 164, 'marital_status'),\n",
       " ('x27_D', 165, 'avg_grade'),\n",
       " ('x26_21+', 166, 'skipped_class'),\n",
       " ('x25_11+', 167, 'school_missed_other'),\n",
       " ('x40_1-5', 168, 'work_pay'),\n",
       " ('x23_11+', 169, 'school_missed_illness'),\n",
       " ('x21_Far Below', 170, 'academic_self_rating'),\n",
       " ('x5_1.5 Packs', 171, 'cig_month_freq'),\n",
       " ('x22_Far Below', 172, 'intelligence_self_rating'),\n",
       " ('x46_4+', 173, '12mo_accidents'),\n",
       " ('x5_2+ Packs', 174, 'cig_month_freq')]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = list(zip(X_tr_pp.columns, boruta.ranking_, label_features()))\n",
    "sorted(features, key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_b = RandomForestClassifier(bootstrap=False, max_depth=20, min_samples_leaf=1, n_estimators=67)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "history.X_train = X_train\n",
    "history.y_train = y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll implement `BorukaPy`'s feature recommendations by setting the class's `feature_mask` parameter to `True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_recall, val_prec, val_acc, roc_auc, rf3 = history.kfold_validation(rf_b, 'RandFor 3 Boruta', ['sampling_weight'], X_train.drop('sampling_weight', axis=1).columns, feature_mask=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Bayesian Optimization** was performed on the newly subsetted dataset, then fed into an XGBoost Classifier.\n",
    "\n",
    "Here is the function used to tune the model (courtesy of [this](https://towardsdatascience.com/hyperparameter-optimization-in-gradient-boosting-packages-with-bayesian-optimization-aaf1b27e7b90) tutorial):\n",
    "\n",
    "```\n",
    "def xgb_classifier(n_estimators, max_depth, reg_alpha,\n",
    "                   reg_lambda, min_child_weight,\n",
    "                   gamma):\n",
    "    params = {\"booster\": 'gbtree',\n",
    "              \"objective\" : \"binary:logistic\",\n",
    "              \"eval_metric\" : \"auc\",\n",
    "              \"is_unbalance\": True,\n",
    "              \"n_estimators\": int(n_estimators),\n",
    "              \"max_depth\" : int(max_depth),\n",
    "              \"reg_alpha\" : reg_alpha,\n",
    "              \"reg_lambda\" : reg_lambda,\n",
    "              \"gamma\": gamma,\n",
    "              \"num_threads\" : 20,\n",
    "              \"min_child_weight\" : int(min_child_weight),\n",
    "              \"learning_rate\" : 0.01,\n",
    "              \"subsample_freq\" : 5,\n",
    "              \"seed\" : 1,\n",
    "              \"verbosity\" : 1,}\n",
    "    train_data = xgb.DMatrix(X_train_mp, y_train_mask)\n",
    "    cv_result = xgb.cv(params,\n",
    "                       train_data,\n",
    "                       100,\n",
    "                       early_stopping_rounds=50,\n",
    "                       stratified=True,\n",
    "                       nfold=3)\n",
    "    return cv_result['test-auc-mean'].iloc[-1]\n",
    "    \n",
    "\n",
    "xgbBO = BayesianOptimization(xgb_classifier, {  \"n_estimators\": (10, 40),\n",
    "                                                'max_depth': (5, 40),\n",
    "                                                'reg_alpha': (0.0, 0.1),\n",
    "                                                'reg_lambda': (0.0, 0.1),\n",
    "                                                'min_child_weight': (1, 10),\n",
    "                                                \"gamma\": (0, 7)\n",
    "                                                })\n",
    "\n",
    "```\n",
    "\n",
    "Here are the final parameters, after 3 iterations:\n",
    "\n",
    "```\n",
    "{'gamma': 6.736014399746335,\n",
    " 'max_depth': 33.020125031565684,\n",
    " 'min_child_weight': 5.656535788440871,\n",
    " 'n_estimators': 37.21788952899237,\n",
    " 'reg_alpha': 0.00650572377979447,\n",
    " 'reg_lambda': 0.04279772663980502}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metric Analysis - Choosing Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall:\n",
      "0.7053596765006076\n",
      "0.7092368757779071\n",
      "0.6273912691106032\n",
      "\n",
      "Accuracy:\n",
      "0.800773468046516\n",
      "0.8015211664123942\n",
      "0.7740288474019903\n"
     ]
    }
   ],
   "source": [
    "print('Recall:')\n",
    "print(np.mean(history.history['LogReg 1 AllFeatures']['recall']))\n",
    "print(np.mean(history.history['LogReg 2 AllFeatures']['recall']))\n",
    "print(np.mean(history.history['LogReg 3 PhikFeatures']['recall']))\n",
    "print('\\nAccuracy:')\n",
    "print(np.mean(history.history['LogReg 1 AllFeatures']['accuracy']))\n",
    "print(np.mean(history.history['LogReg 2 AllFeatures']['accuracy']))\n",
    "print(np.mean(history.history['LogReg 3 PhikFeatures']['accuracy']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall:\n",
      "0.6137241462122492\n",
      "0.6785599037040121\n",
      "0.844\n",
      "\n",
      "Accuracy:\n",
      "0.7064097011664019\n",
      "0.7772956061232417\n",
      "0.368\n"
     ]
    }
   ],
   "source": [
    "print('Recall:')\n",
    "print(np.mean(history.history['DecTree 1 AllFeatures']['recall']))\n",
    "print(np.mean(history.history['DecTree 2 AllFeatures']['recall']))\n",
    "print(np.mean(history.history['DecTree 3 PhikFeatures']['recall']))\n",
    "print('\\nAccuracy:')\n",
    "print(np.mean(history.history['DecTree 1 AllFeatures']['accuracy']))\n",
    "print(np.mean(history.history['DecTree 2 AllFeatures']['accuracy']))\n",
    "print(np.mean(history.history['DecTree 3 PhikFeatures']['accuracy']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall:\n",
      "0.6917706582624985\n",
      "0.6911416052617457\n",
      "0.6926038085203876\n",
      "\n",
      "Accuracy:\n",
      "0.7924293047145735\n",
      "0.7923013342779548\n",
      "0.7911304407707604\n"
     ]
    }
   ],
   "source": [
    "print('Recall:')\n",
    "print(np.mean(history.history['RandFor 1 AllFeatures']['recall']))\n",
    "print(np.mean(history.history['RandFor 2 AllFeatures']['recall']))\n",
    "print(np.mean(history.history['RandFor 3 Boruta']['recall']))\n",
    "print('\\nAccuracy:')\n",
    "print(np.mean(history.history['RandFor 1 AllFeatures']['accuracy']))\n",
    "print(np.mean(history.history['RandFor 2 AllFeatures']['accuracy']))\n",
    "print(np.mean(history.history['RandFor 3 Boruta']['accuracy']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGB Score:\n",
      "\n",
      " 0.766629314\n"
     ]
    }
   ],
   "source": [
    "print('XGB Score:\\n\\n', np.mean(history.history['XGB 1 PhikFeatures']['test_score']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although `RandomForest` was close, `LogisticRegression` won by a small margin in both **accuracy** and **recall**.\n",
    "\n",
    "###### Let's fit the final model to the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.1, max_iter=10000)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = LogisticRegression(max_iter=10000, C=0.1)\n",
    "final.fit(X_tr_pp, y_tr_pp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, recall_score, roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### TRAINING SET RESULTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "tr_preds = final.predict(X_tr_pp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f839bb72730>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SrAhNej02rXAm/7YZT//1JnaOxdg5Gvdc69UD+yZFNPcsuWF4O8eMt0a1JjfbjHfWXSD/thnZ6TncrF+PhnZazOuXjD59tc4R3065WFnr+Hl/Q9a868rLb13Fxrak+B79rhE3b9TDt+Nt6lvqOH7IhvVLmvLMCxnUt5CZHA/i6PeN+OvENK5frs+lJEtaP5bH0HE32LOlsSHmqWc13LpZj9Qr5rTyyWf8vBSO7LQ13BTU6Ruw69PGhM26iia9Hrcy1Yx7+yoXTlty7MCd4jsoNJ1TPzUgL1dNh+7ZhM2+yroFTcm9ZbrzoKUHXYcmTpzIuHHj6NixI126dGHdunVcv36d0NDQukyrytKvmRP5Wktu3VRj61BM2w63WfH17zi7Ve2mzpkTDTj9f9YAvPyk7//2RgOPsTj2LI8HlvSQkxIbsGGZC/m5Zrh5FvDG4mR6D880nEdtrufrjxz59zsW6HTQtEUhY6ZdY9Df0muyuSbpw1nNeGn6df4eeQU7h2Juppmzc5MDG5ffuQHb2LmIce9cxc6xmJtp9di9xZ5PVhjfoF35jitaLbwVc4n6VjoSDzZkySR3o965t/9tRr95HUtrHVfOWvCv6W7s+bwxpszUnySss5uEf1izZg1RUVGkpqbi4+PDggULjG4a3qthXqeHmN3D9/OVTxU/zl5T/gxj0DUhMmEG4QGLKg/8E6utMeiXT4ZXeHydX2StXFcp6vwmYVhYGGFhYXWdhhBCgWSIQwghFEoKtBBCKJQUaCGEUCgp0EIIoVCmPg/60V8OSgjxp1UTy40uW7YMOzs7pk2bZtin1+uJjIykbdu2uLi4MGDAAE6fPm30uYKCAqZNm4aHhweurq6MHDmSlJQUoxiNRsPYsWNxd3fH3d2dsWPHotFojGKSk5MJCQnB1dUVDw8Ppk+fTmGh8dLD5ZECLYRQrAct0D/++CMff/wxfn5+RvujoqKIjo5m0aJF7N27FycnJ4YMGUJ2drYhJjw8nO3bt7N27Vp27NhBdnY2ISEhaLV3ns4NCwvjxIkTbNmyhdjYWE6cOMG4ceMMx7VaLSEhIeTk5LBjxw7Wrl3Ltm3biIiIqFL7pUALIRTrQQp0VlYWr776Ku+//z52dnaG/Xq9npiYGCZPnszgwYPx9fUlJiaGnJwcYmNjDZ/dsGED8+bNIygoCH9/f1atWsXJkyfZt28fAElJSezevZsVK1bQpUsXAgICWL58Obt27TIs6rZ3715Onz7NqlWr8Pf3JygoiLlz57J+/Xpu3bpVafulQAshFOtBCvQfBbhHjx5G+y9dukRqaqrRUsdWVlYEBgYaljpOTEykqKjIKMbNzQ1vb29DTEJCAjY2NnTpcmdFya5du2JtbW0U4+3tjZubmyEmODiYgoICEhMTK22/3CQUQiiWtppvTfn44485f/48q1atKnUsNTUVoNSibE5OTly7VrIGd1paGmq1GgcHh1IxfyzwlpaWhoODg9HqmyqVCkdHR6OYe69T3kJxZZECLYRQrOpMsztz5gzz5s3j22+/pX798leHvN+ljsuKKSu+KjEV7b+bDHEIIRRLr1dVuJUlISGBjIwM/vKXv+Dg4ICDgwOHDh1izZo1ODg40LhxyQJUFS113KRJE7RaLRkZGRXGpKeno9ffWc5Ir9eTkZFhFHPvdTIyMtBqtVVaVlkKtBBCsaozBj1gwAAOHz7MgQMHDNsTTzzBsGHDOHDgAJ6enjg7OxMXF2f4TH5+PkeOHDGMJ/v7+2Nubm4Uk5KSQlJSkiEmICCAnJwcEhLuvLghISGB3Nxco5ikpCSj6XlxcXFYWFjg7+9faftliEMIoVjl9ZIrYmdnZzRrA6BBgwbY29vj61uyrO+ECRNYtmwZXl5eeHp6snTpUqytrRk+fDgAtra2jB49mjlz5uDk5IS9vT0RERH4+fnRs2dPALy9venduzdTpkwhKioKvV7PlClT6Nu3r+EVYL169cLHx4fx48czf/58MjMzmTNnDmPGjKFRo0aVtqXcAv3pp5/e9zcGYNQo01gqUwhR+2rrUe9JkyaRl5fHtGnT0Gg0dOzYka1bt9Kw4Z0XKCxYsAC1Wk1oaCj5+fl0796dlStXolbfWaN69erVzJgxg6FDhwLQv39/Fi9ebDiuVqvZvHkzU6dOpV+/flhaWjJ8+HDmz59fpTzLXQ/a3t7+vhutUqm4efPmfX/ufsh60I8OWQ/60VFb60H3Plzx92134Ixaua5SlNuDPn78+MPMQwghSjH1tTjKLdDu7u4PMw8hhCilOmPQj5L7vkmYl5fHsWPHuHHjBt26dcPR0bE28hJCCJNfbvS+ptmtXLkSb29vnn32WUJDQzl58iRQMq/P3d2d9evX10qSQgjTpNOpKtwedVUu0Js2bSI8PJzevXvz/vvvG03OdnBwICgoiC+++KJWkhRCmKbqPKjyKKnyEEd0dDR9+/Zl3bp1Zc7U8Pf3Z/Xq1TWanBDCtMkQRxWdO3eOvn37lnvcwcGh1GORQgjxIPT6irdHXZV70A0bNiQrK6vc4+fOnZMbhkKIGmUKwxgVqXIPunv37mzatImCgoJSx1JSUvj444/p3bt3jSYnhDBtMgZdRbNmzSI4OJiePXvy3HPPoVKp+P7774mLi+Ojjz7C3Nyc6dOn12auQggTYwKjGBWqcg/aw8ODnTt34uLiwqJFi9Dr9URHRxMVFcXjjz/Ozp07adasWW3mKoQwMdKDvg/e3t588cUXaDQazp8/j06no2XLljL2LISoHSbeha7WcqN2dnZ06NChpnMRQggjptBLrsh9FWiNRsMHH3zAd999R3JyMgDNmzenT58+TJw4sVor4AkhRHlM4WnBilR5DPrs2bMEBgaybNkyiouLefLJJ+nWrRvFxcUsW7aMwMBAw6vGhRCiRuhVFW+PuCr3oKdNm0ZOTg5fffUV3bt3Nzr2ww8/MHr0aGbMmMHWrVtrPEkhhGkyhYdRKlLlHnR8fDzjx48vVZwBevTowbhx4zh69GiNJieEMHH6SrZHXJV70La2tqXe83W3st4DJoQQD8LUbxJWuQc9evRoNm7cSHZ2dqljWVlZbNy4kdGjR9dockIIEyc96LLdu3RomzZtUKlUdOrUiVGjRuHh4QGUrMHx2Wef4eTkZHiTrRBC1ART70GXW6BffvllVCqVYd3nu/87KiqqVHxaWhpjx441vLZcCCEemAn0kitSboHevn37w8xDCCHKID3oMj355JMPMw8hhChNetBCCKFQJv4k4X0V6Bs3brBhwwYSExPJyspCp9MZHVepVGzbtq1GExRCmC5Tf1ClygX6t99+Y8CAAeTm5tK6dWtOnz5N27Zt0Wg0XLt2jVatWslyo0KImmXiBbrK86DfeecdzM3NOXr0KNu2bUOv1xMZGcmpU6dYvXo1Go2Gd999tzZzFUKYGhNfi6PKBfrIkSOEhobSsmVLzMxKPvbHtLvhw4czdOhQZs+eXTtZCiFMkkpf8faoq3KBLioqomnTpgBYWloCGL1Etl27dhw7dqyG0xNCmDQTf5KwygXazc2Ny5cvA2BlZYWLiwsJCQmG46dOncLa2rrmMxRCmC4TH+Ko8k3Cp556ih07djBr1iwARowYwYcffsitW7fQ6XRs3rxZ1uIQQtQsE+glV6TKBXry5Ml0796d/Px8LC0tiYiI4NatW3zxxReo1WpCQkLkJqEQomZJga6a5s2b07x5c8PXFhYWrFixghUrVtRGXkIIIQW6rhMQQohymcA4c0XKLdCffvpptU44atSoaicjhBB3U+kqj3mUlVugX3vttfs+mUqlkgIthBA1pNwCffz48YeZR5U9065XXadQq+Z/++i38Q8pM9rWdQoPTcqMwLpO4U/JFB5GqUi5Bdrd3f1h5iGEEKXJGLQQQiiU9KCFEEKhpEALIYQymfoYdJXX4hBCiIeumoslrV69msDAQMMDdk8//TS7du26c9r/LZfctm1bXFxcGDBgAKdPnzY6R0FBAdOmTcPDwwNXV1dGjhxJSkqKUYxGo2Hs2LG4u7vj7u7O2LFj0Wg0RjHJycmEhITg6uqKh4cH06dPp7CwsErNlwIthFCuahZoV1dX5s6dyw8//EBcXBzdu3fnhRde4NdffwUgKiqK6OhoFi1axN69e3FycmLIkCFkZ2cbzhEeHs727dtZu3YtO3bsIDs7m5CQELRarSEmLCyMEydOsGXLFmJjYzlx4gTjxo0zHNdqtYSEhJCTk8OOHTtYu3Yt27ZtIyIiokrNlwIthFCs6q4HPWDAAJ5++mk8PDzw9PRk9uzZ2NjY8OOPP6LX64mJiWHy5MkMHjwYX19fYmJiyMnJITY2FihZSnnDhg3MmzePoKAg/P39WbVqFSdPnmTfvn0AJCUlsXv3blasWEGXLl0ICAhg+fLl7Nq1izNnzgCwd+9eTp8+zapVq/D39ycoKIi5c+eyfv16bt26VWn776tAX758mTfeeAN/f3+aN2/OwYMHAcjIyODNN98kMTHxfk4nhBAV06kq3qpAq9Xy+eefk5ubS0BAAJcuXSI1NZVeve48b2BlZUVgYCDx8fEAJCYmUlRUZBTj5uaGt7e3ISYhIQEbGxu6dOliiOnatSvW1tZGMd7e3ri5uRligoODKSgoqFK9rPJNwqSkJPr164dOp6NTp05cvnzZ0NV3cHDgxx9/pKCggA8++KCqpxRCiAo9yE3CkydP0qdPH/Lz87G2tmbjxo34+fkZiqeTk5NRvJOTE9euXQMgLS0NtVqNg4NDqZi0tDRDjIODAyrVnV8UKpUKR0dHo5h7r+Pg4IBarTbEVKTKBfrtt9+mYcOG7N69G7Vajaenp9HxPn368OWXX1b1dEIIUbkHKNBeXl4cOHCArKwstm3bxoQJE/j6668Nx+8urFBy4/DefaXSuSemrPiqxFS0/25VHuI4fPgwYWFhNGnSpMwTN2/e3PDbRwghasKDvJOwfv36eHh48MQTT/D222/Trl07PvzwQ5ydnQFK9WDT09MNvd0mTZqg1WrJyMioMCY9Pd3wblYoKc4ZGRlGMfdeJyMjA61WW6pnXZYqF+ji4uIKX2mVmZmJWq2u6umEEKJyNfhOQp1OR2FhIS1atMDZ2Zm4uDjDsfz8fI4cOWIYT/b398fc3NwoJiUlhaSkJENMQEAAOTk5Rq/+S0hIIDc31ygmKSnJaHpeXFwcFhYW+Pv7V5pzlYc4fH19OXDgAK+88kqpY3q9nu3bt1fpgkIIUWXVHOJ455136NOnD82aNTPMzjh48CD//e9/UalUTJgwgWXLluHl5YWnpydLly7F2tqa4cOHA2Bra8vo0aOZM2cOTk5O2NvbExERgZ+fHz179gTA29ub3r17M2XKFKKiotDr9UyZMoW+ffvi5eUFQK9evfDx8WH8+PHMnz+fzMxM5syZw5gxY2jUqFGl7ahygZ4wYQJhYWEsXryYoUOHAiW/kX7//XciIyM5duwYmzdvvt/voxBClKu6NwlTU1MZO3YsaWlpNGrUCD8/P2JjYwkODgZg0qRJ5OXlMW3aNDQaDR07dmTr1q00bNjQcI4FCxagVqsJDQ0lPz+f7t27s3LlSqORgtWrVzNjxgxDTezfvz+LFy82HFer1WzevJmpU6fSr18/LC0tGT58OPPnz69a+zUaTZW/BStWrOCf//wnWq3WaCBcrVYzd+7caq0hfb9CPKfX+jXq0vxvX2VW/9V1ncZDcTnMNJYbXT/sKcZ8fqCu06hVR2a+XCvnDfjXugqPJ7xRO9dVivtai2Py5MkMHz6cbdu2cf78eXQ6Ha1atWLQoEG0aNGitnIUQpgqE1+L474XS3Jzc3soPWUhhDD1xZJkNTshhHLJOwmrxt7evkoTq2/evPlACQkhxB+kB11F06dPL1WgtVotly5d4ttvv8XT05O+ffvWeIJCCBMmBbpqwsPDyz129epVevfuTZs2bWokKSGEAOlB18hyo66uroSGhhrN/xNCiAdWg08S/hnV2E1COzs7Lly4UFOnE0IIkyjCFamRAp2ens7HH3+Mu7t7TZxOCCEAGeKocoEeOHBgmfuzsrL4/fffKSoqYt26ip/6EUKI+yIFump0Ol2pWRwqlYoWLVoQFBTEmDFjaN26dY0nKIQwYVKgq+abb76pzTyEEKIUUx/iqNIsjry8PAYOHMjGjRtrOx8hhDBQ6SreHnVVKtBWVlYcP37c6HXjQghR60x8ml2V50E/+eSTHD58uDZzEUIIY1Kgq2bRokX8/PPPzJ49m4sXL6LTmcDfF0KIOqWqZHvUVXiT8NNPPyUwMJAWLVrQuXNn9Ho90dHRREdHY2Zmhrm5uVG8SqXi6tWrtZqwEMKEmEAvuSIVFuiJEyeyatUqWrRowZAhQ6q0mp0QQtQUU5/FUWGBvvt14jExMbWejBBCGJECLYQQCiUFumIyrCGEqCsyxFGJiRMn8vrrr1fpZHKTUAhRo6RAV6xjx460bNnyIaQihBDGpAddidDQUEaMGPEwchFCCCOm8Dh3ReQmoRBCuaQHLYQQCiUFWgghlEnGoCuQmZn5sPIQQojSpEALIYQyqfSmXaGlQAshlMu067MUaCGEcskYtBBCKJUUaCGEUCbpQQshhELJk4RCCKFU0oMWQghlkiEOIYRQKpkHLarrsY4ahr50GU/fbBydC3lvVlt2f9XUcDww+Ab9R6Tg6ZODbeMiZoT688tP9kbneOPt33i8SyaNnQrJv61Gb3WL5q3UJF+wNsSEvHqRzk9l4OGdg2UDHc+0CyqVy7gZZ/B9IosWnrlkptcntN9faq/hj6iwTj/T2/M8Le00FGrVnLjuzIrDXTib4WCI+XVS2a9++/S4H//c1x2A4Y+d4pk2Z2jbJJ1GFoX8eq1tqfixnf+Pp1pextspnQbmxTwWNcHouL1VHgv77sHLIQM7y3xu5lkRd74lUYe7kFNoUYOtVjZT70Gb1XUCf2aWDbRcOmvNqkVe5OeV/lZaWmk5nWjL6iWe5Z7jzKmGvDfLh3GDA5g1/nFAzz9XJ6Kud+fuiHl9HYd2O/Hlxublnkdlpmf3Nhf2bHd+oDaZss5uV/nsxGO8uGUIr2wdhFZnxpoh22lkkW+I6bH6JaNt4rb+AOw6c+dnbFmviMOXm/Ph0U7lXstcrWX32VZsPNa+zOM6vYo951rx9+3PMGD980R834suzVN4J/iHGmrtn4S+ku0RV6c96EOHDvH+++9z/Phxrl27RnR0NC+88EJdpnRffjrgwE8HSnpX/5j/W6nje792AaCRXWG55/h2SzPDf6ddhWu3+uLj/DMubvmkXGwAwMZoDwC6PZ1W7nlWRrYBYOhLl+nwF1lDpTrGffms0dczvwvm6Pi1POF6nR8utAQg43YDo5ggj4tcyLTlpxRXw76NiY8D4Nek/J9X9NEAAJ72PFfm8ax8S/77i5/h62vZDdl8wo+wzseq3qBHgKnP4qjTHnRubi6+vr4sXLgQKyurukxFESystDRu8ANpVy1ITbGs63RMnrV5IWozPbcKyh5SsDIvon+bs3z+q2+t5+JknUtvzwv8dMW18uBHSTV70O+99x5BQUE0b96c1q1bExISwqlTp4xPrdcTGRlJ27ZtcXFxYcCAAZw+fdoopqCggGnTpuHh4YGrqysjR44kJSXFKEaj0TB27Fjc3d1xd3dn7NixaDQao5jk5GRCQkJwdXXFw8OD6dOnU1hYfsftD3VaoPv06cOcOXMYPHgwZmamO9oyICSFz+P380XCfmwtEwkP86e4yHS/H0oxs8chTqc5cvxa2cNGA7zPUF+t5avT3rWWw+J+3/Pja6uJC1tPbqE5s74vff/hUabSV7yV5+DBg7zyyivs2rWLbdu2Ua9ePZ577jmjFTqjoqKIjo5m0aJF7N27FycnJ4YMGUJ2drYhJjw8nO3bt7N27Vp27NhBdnY2ISEhaLVaQ0xYWBgnTpxgy5YtxMbGcuLECcaNG2c4rtVqCQkJIScnhx07drB27Vq2bdtGREREpe2XKqAAcd848/qITkz/2xPkFzflrWUnsbDUVv5BUWumPXWIDq7XmPJNX3T6sv+ZDH/sFHvOtSIzr/b++lu0vxt//XQ4r2/vh5vtLWb0OFRr11Ikvb7irRxbt27lxRdfxNfXFz8/P1atWkV6ejpHjx7932n1xMTEMHnyZAYPHoyvry8xMTHk5OQQGxsLQFZWFhs2bGDevHkEBQXh7+/PqlWrOHnyJPv27QMgKSmJ3bt3s2LFCrp06UJAQADLly9n165dnDlzBoC9e/dy+vRpVq1ahb+/P0FBQcydO5f169dz69atCpv/p5vFMf/bV+s6hTLVtzzM0H/0oOf4HqWOqc1uAYd4ZfGz5BRU/OfwhYxi2nu/yvJdfty8/ZTRMTureOBkhd+DJjZfY2fznWK/T38GzWw30LjBZX6/MZcFTzcrM8bK/CI+zjewMAtj/bB2ZcY0MD8PfA7A+mFPlRljZ1Uf+K7c43fTagP4a7t5+DhOoEjrUGn8o6CmxqBzcnLQ6XTY2dkBcOnSJVJTU+nVq5chxsrKisDAQOLj4wkNDSUxMZGioiKjGDc3N7y9vYmPjyc4OJiEhARsbGzo0qWLIaZr165YW1sTHx+Pl5cXCQkJeHt74+bmZogJDg6moKCAxMREunfvXm7ef7oCPav/6rpOoUyfxxez9b0f2P3V76WONbIr5LMDsHb61/zyU8U9oH9++zeKCor4Kmovu7Ya33js9nQaEe9V/D0Y+tJlBo7KUez36W6Xw0pPP6trM7sfpEXjswzbNJjzmeeB82XGzQraT0PLhgz5JBM4UGaMX5M0No8q+e8xn5cd87TnOZYPKP/43Tq6XuXjETB91xGSs2yr0pyH5shMr1o5b01Ns5s5cybt2rUjIKDk5mxqaioATk5ORnFOTk5cu3YNgLS0NNRqNQ4ODqVi0tLSDDEODg6oVKo7OatUODo6GsXcex0HBwfUarUhpjx/ugKtJJZWxbi65wGgUulxapqPh3c22Vnm3LhuiU2jIpo0zce6YTEAru555GbXIzO9PpkZFjRtfptuT98g8Whjsm6a4+hcQCuHKIqKzEj44c7/FE4u+TS0LcLZtWS6l4d3yRjZ1ctW5OeV/AibNr+NVQMtDk0KqGeuM8RcPmdNcbGMZFVFRM/9DGz7O2983Y+sAgscGtwG4HaROXlF5oY4y3pFDPA+w3/+zx9QlTqPQ4PbODa4TQt7TUm8+RW8HdO5lm3DrYKSm78uDbOxtSigWaOSn5O3YzoAl7NsySsyp0eri9ha5nMqzYnbheZ4OmTy5pNHSLzmrLjiXKtq4EGVt956i6NHj7Jz507UarXRsbsLa8nl9KX2lU5JX6ogVyemov1/kAL9ALz8sln0n0TD16MnXmT0xIt8/5ULy2f50DUo3Wj63aS5SQBs+rAlm2JaUVRoRvvOGoaOSca6UTGajPro9B2Z/mIHMjPuzBx48e8XeHrwdcPXH8T+BGD04MukuUm076wpFfO3vl1JuyozZKpi1OMnAVg3bLvR/g+PduLD+M6Gr/u1OYeVeRFfnCr7L4CQdid5retPhq89HZfw+QsQ8V0QX50u+czfu/7Ic75JhpjPX9gCQGjsIH5MaUZBcT3+2u4UHo0zqa/Wcj3bhj3nWrH2pw4109g/iQftQYeHh7N161a2b99Oy5YtDfudnUtu/KalpRkNPaSnpxt6u02aNEGr1ZKRkYGjo6NRTGBgoCEmPT3dqCDr9XoyMjKMzhMfH2+UV0ZGBlqttlTP+l51WqBzcnI4f77kT0idTseVK1c4ceIE9vb2NG9e/kMZSvHLT/ZlPtX3h91fNTV6svBe6amWzJnwuNG++d++ypULxsMTy2f5sHyWT4W5zHz5iSpkLCpy79N85fnyVFu+LKc4A3wY39mooK8f9lSpIYxZ3/di1ve97v2owdFkN44mu5V73GQ8QIGeMWMGW7du5euvv6ZNmzZGx1q0aIGzszNxcXF06FDySy8/P58jR44wb948APz9/TE3NycuLo4RI0YAkJKSQlJSkmHMOSAggJycHBISEgz7EhISyM3NNYpZunQpKSkpNGtWck8jLi4OCwsL/P39K2xDnRboY8eOMXDgQMPXkZGRREZGMmrUKGJiyn6kVghhOqrbg546dSqbN29m48aN2NnZGcacra2tsbGxQaVSMWHCBJYtW4aXlxeenp4sXboUa2trhg8fDoCtrS2jR49mzpw5ODk5YW9vT0REBH5+fvTs2RMAb29vevfuzZQpU4iKikKv1zNlyhT69u2Ll1fJuHyvXr3w8fFh/PjxzJ8/n8zMTObMmcOYMWNo1KhRhe2o0wL91FNPlZrQLYQQBrrqVeg1a9YAMHjwYKP9M2bMIDw8HIBJkyaRl5fHtGnT0Gg0dOzYka1bt9KwYUND/IIFC1Cr1YSGhpKfn0/37t1ZuXKl0Vj26tWrmTFjBkOHDgWgf//+LF682HBcrVazefNmpk6dSr9+/bC0tGT48OHMnz+/0nbIGLQQQrmq2YOuSsdPpVIRHh5uKNhlsbS0ZMmSJSxZsqTcGHt7e/79739XeK3mzZuzefPmSnO6lxRoIYRimfpqdlKghRDKJetBCyGEMkkPWgghFEpVzZuEjwop0EII5TLx9aClQAshFEslY9BCCKFQpl2fpUALIRRMetBCCKFMMotDCCGUSnrQQgihTKb+Vm8p0EII5ZIetBBCKJRp12cp0EII5VLpTHuMQwq0EEK5TLs+S4EWQiiXPEkohBBKJQVaCCEUSgq0EEIolIxBCyGEMskYtBBCKJUUaCGEUCgp0EIIoVBSoIUQQplUWinQQgihTNKDFkIIhZK3egshhEJJD1oIIRRKCrQQQiiUFGghhFAoGYMWQgiF0pv2YhxSoIUQyiVDHEIIoVAyxCGEEAol7yQUQgiFkiEOIYRQKOlBCyGEQkkPWgghFEoKtBBCKJTM4hBCCGXSm/iDKmZ1nYAQQpRLp694K8ehQ4cYOXIkPj4+2NnZsWnTJqPjer2eyMhI2rZti4uLCwMGDOD06dNGMQUFBUybNg0PDw9cXV0ZOXIkKSkpRjEajYaxY8fi7u6Ou7s7Y8eORaPRGMUkJycTEhKCq6srHh4eTJ8+ncLCwio1Xwq0EEK59PqKt3Lk5ubi6+vLwoULsbKyKnU8KiqK6OhoFi1axN69e3FycmLIkCFkZ2cbYsLDw9m+fTtr165lx44dZGdnExISglarNcSEhYVx4sQJtmzZQmxsLCdOnGDcuHGG41qtlpCQEHJyctixYwdr165l27ZtREREVKn5MsQhhFCuak6z69OnD3369AHgtddeMzqm1+uJiYlh8uTJDB48GICYmBi8vLyIjY0lNDSUrKwsNmzYQHR0NEFBQQCsWrWKdu3asW/fPoKDg0lKSmL37t3s3LmTLl26ALB8+XL69+/PmTNn8PLyYu/evZw+fZpffvkFNzc3AObOncsbb7zB7NmzadSoUYXtkB60EEK5qtmDrsilS5dITU2lV69ehn1WVlYEBgYSHx8PQGJiIkVFRUYxbm5ueHt7G2ISEhKwsbExFGeArl27Ym1tbRTj7e1tKM4AwcHBFBQUkJiYWGmu0oMWQiiW/q7hhJqSmpoKgJOTk9F+Jycnrl27BkBaWhpqtRoHB4dSMWlpaYYYBwcHVCqV4bhKpcLR0dEo5t7rODg4oFarDTEVkQIthFCuWpxmd3dhhZKhj3v33evemLLiqxJT0f67yRCHEEK59LqKt2pwdnYGKNWDTU9PN/R2mzRpglarJSMjo8KY9PR09HcNtej1ejIyMoxi7r1ORkYGWq22VM+6LFKghRCKpdfpK9yqo0WLFjg7OxMXF2fYl5+fz5EjRwzjyf7+/pibmxvFpKSkkJSUZIgJCAggJyeHhIQEQ0xCQgK5ublGMUlJSUbT8+Li4rCwsMDf37/SXGWIQwihXNXsJefk5HD+/HkAdDodV65c4cSJE9jb29O8eXMmTJjAsmXL8PLywtPTk6VLl2Jtbc3w4cMBsLW1ZfTo0cyZMwcnJyfs7e2JiIjAz8+Pnj17AuDt7U3v3r2ZMmUKUVFR6PV6pkyZQt++ffHy8gKgV69e+Pj4MH78eObPn09mZiZz5sxhzJgxlc7gAFBpNBrTfpZSCPHIOXDgAAMHDiy1f9SoUcTExKDX61m4cCEfffQRGo2Gjh07snTpUnx9fQ2x+fn5zJ49m9jYWPLz8+nevTvLli0zmpGRmZnJjBkz+PbbbwHo378/ixcvxs7OzhCTnJzM1KlT2b9/P5aWlgwfPpz58+djYWFRaTukQAshhELJGLQQQiiUFGghhFAoKdBCCKFQUqCFEEKhpEArxJo1a2jfvj3Ozs706NGDw4cP13VK4gFUttylEFUhBVoBtm7dysyZM3nzzTfZv38/AQEBjBgxguTk5LpOTVRTZctdClEVMs1OAYKDg/Hz8+Nf//qXYV+HDh0YPHgwb7/9dh1mJmpCs2bNWLx4MS+88EJdpyL+ZKQHXccKCwtJTEw0WtYQSp5A+mPJQiGEaZICXcfKWzjl7mUNhRCmSQq0QlRn6UMhxKNNCnQdK2/x7ruXNRRCmCYp0HWsfv36+Pv7Gy1rCCVLEt79Kh0hhOmR5UYVYOLEiYwbN46OHTvSpUsX1q1bx/Xr1wkNDa3r1EQ1VbbcpRBVIdPsFGLNmjVERUWRmpqKj48PCxYsoFu3bnWdlqimypa7FKIqpEALIYRCyRi0EEIolBRoIYRQKCnQQgihUFKghRBCoaRACyGEQkmBFkIIhZICLQCIjIw0elU8QLt27ZgwYULdJFQOOzs7IiMjayzuXpcuXcLOzo7ly5dXJ70az0eYNinQCrBp0ybs7OwMm4ODA76+vvz973/n+vXrdZ3efcnJySEyMpIDBw7UdSpC/OnJo94KMnPmTFq1akVBQQFHjx7lk08+4dChQxw+fLhO3srx008/YWZ2f7/Dc3NzWbRoEQBPPfVUbaQlhMmQAq0gwcHBdO7cGYAxY8Zgb29PdHQ0O3bsYNiwYWV+5vbt2zRo0KBW8rGwsKiV8wohqkaGOBSse/fuAFy8eBGACRMm4OzszOXLl3n++edxd3dnxIgRhvjPP/+c4OBgmjZtiru7OyEhIfz222+lzrtr1y66deuGs7MzHTt2ZP369WVev6wx6MLCQpYsWULnzp1p0qQJXl5ejBo1itOnT3Pp0iW8vb0BWLRokWHI5u5zXL9+nUmTJtG2bVuaNGlChw4diIqKQq83XnHg1q1bTJo0iZYtW9K8eXNGjx79QMM9mZmZzJo1i8DAQNzc3GjWrBnPPvssR48eLfczq1aton379ri4uNC7d29++umnUjFVbY8Q1SE9aAW7cOECAI0bNzbs0+l0DB06lA4dOjB37lzUajUAK1as4J133mHgwIGMHDmS3Nxc1qxZQ9++ffnhhx9o2bIlAD/88APPP/88Hh4eREREkJ+fz7vvvouzs3Ol+eh0OkaNGsWePXsYNGgQr776Knl5eRw4cIDExEQGDRrEkiVLmDZtGs8++6xhsaBWrVoBcOPGDXr37k1xcTEvvfQSLi4uHDlyhLfffptr166xcOFCoORlBS+++CIHDhxg9OjRtGvXjn379hn9MrpfFy9e5KuvvmLw4MF4eHiQlZXF+vXrGTx4MHFxcfj6+hrFb9myhaysLF555RV0Oh1r1qzhueeeY9++fXh6et5Xe4SoLinQCnLr1i0yMjLIz88nPj6exYsXY2VlRd++fQ0xRUVF9OnThwULFhj2JScnM3/+fGbMmEF4eLhh/8iRIwkICGDp0qV88MEHAMyZMwc7Ozu+++477O3tARg8eDCBgYGV5vfpp5+yZ88eZs2axdSpUw37J02aZHgDzKBBg5g2bRp+fn6EhIQYfX7+/PkUFBRw6NAhmjRpAkBoaCguLi588MEHTJgwgRYtWrBz507279/PW2+9xfTp0wF49dVXefXVV/nll1/u99sKgK+vL4mJiYZfaAB/+9vf6Ny5MytXrjR6YS/A2bNn+fHHH2nRogUAzz33HF27dmXhwoWsWbPmvtojRHXJEIeCDBs2jNatW+Pn58fLL7+Ms7MzmzdvxtXV1SguLCzM6Ovt27dTXFzMsGHDyMjIMGzm5uZ06tSJ/fv3A5Camsrx48cZOXKkoTgDeHt7ExwcXGl+27Ztw9bWltdff73Uscpez6XX6/nqq6/o27cvarXaKM/g4GB0Oh2HDh0CSoZgzMzMGDdunNE5HmTKn4WFhaE45+fnc/PmTXQ6HR07diQxMbFUfP/+/Y2Kq6enJ8HBwXz//ff33R4hqkt60AqyaNEivL29sbCwwM3NDTc3t1KFz8zMDHd3d6N9586dAyAgIKDM8/5xE/Hy5csAeHl5lYrx9PTku+++qzC/Cxcu4OnpWa2bh+np6Wg0GjZu3MjGjRvLjYGSvwiaNGmCra1tqRyrS6fTERUVxUcffcSlS5eMjpXVy23dunWZ+3bt2kVWVhaFhYVVbo8Q1SUFWkE6dOhgmMVRHnNzc+rVM/6x6XQ6AGJjY0sdAwxT5f64cVVWb7cqN7Ue5EW2f+Q4fPhwXnzxxTJjPDw8Hvg65VmxYgXz5s1j1KhRzJo1i8aNG6NWq3nvvfcMY/13q+x7dD/tEaK6pEA/Av64Cefm5kbbtm3Ljfujp/j777+XOvZHL7wiHh4exMfHU1hYSP369cuMKa+wOjo60qhRI4qLi+nZs2eF13F3d2ffvn1kZWUZ9aLPnj1baY7l2bp1K08++WSpt5mU93RfWdc6f/48tra22NraYmNjU+X2CFFdMgb9CBg0aBD16tUjMjLS0LO72x9/ajs7O9O+fXs+++wzMjMzDceTkpLYs2dPla6j0WiIjo4udeyP3uUfwykajcbouFqtZtCgQXz99ddljvlmZWVRVFQEQJ8+fdDpdKxatcoo5kFeFaVWq0v9lRAfH09CQkKZ8Tt37jQaCjl79ix79uyhd+/e990eIapLetCPgJYtWzJ37lwiIiLo3bs3AwcOxN7enuTkZL777js6depkWFti7ty5DBs2jD59+jBmzBjy8vJYvXo1Pj4+/PrrrxVeZ+TIkfz3v/9l7ty5HD9+nG7dupGfn8/BgwcZMmQII0eOxMbGBi8vL7Zu3YqnpyeNGzemRYsWdOrUiXfeeYdDhw7Rr18/Ro8eja+vL9nZ2Zw6dYrt27fz888/4+zsTP/+/enWrRuRkZFcuXKF9u3bExcXV2rs+H7079+fhQsXMm7cOAIDAzl37hwfffQRbdu2JScnp1R869ateeaZZwgLC0On07F69WosLCyYMWOGIaaq7RGiuqRAPyImTpyIp6cn77//Pu+99x7FxcU0bdqUrl27Mnr0aENcUFAQmzZt4t133+Xdd9+lefPmzJ49m5SUlEoLtFqtZvPmzSxbtozY2Fi++eYb7O3t6dSpE/7+/oa46OhowsPDmTVrFgUFBYwaNYpOnTrh6OjInj17WLJkCd988w0fffQRtra2eHp6MnPmTMPMEpVKxSeffMKsWbP48ssv+eKLL+jRowdbtmzBx8enWt+ff/zjH+Tl5bFlyxa++uorfHx8WLduHZ9//jkHDx4sFT9ixAgaNGhAdHQ0qampPPbYYyxYsIA2bdoYYqraHiGqS14aK4QQCiVj0EIIoVBSoIUQQqGkQAshhEJJgRZCCIWSAi2EEAolBVoIIRRKCrQQQiiUFGghhFAoKdBCCKFQUqCFEEKh/h+E9aAxgaNeRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(final, X_tr_pp, y_tr_pp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8010390837170859"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_tr_pp, tr_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7056266916510514"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_tr_pp, tr_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### TEST SET RESULTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_te_pp = pd.read_csv('data/modeling_data/X_te_pp.csv')\n",
    "y_te_pp = pd.read_csv('data/modeling_data/y_te_pp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "te_preds = final.predict(X_te_pp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f837ec84a60>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(final, X_te_pp, y_te_pp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8060770653756839"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_te_pp, te_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7203495630461922"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_te_pp, te_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### TOP 3 Postive Features:\n",
    "* `YEAR=2019` - `1.122`\n",
    "* `YEAR=2015` - `0.714`\n",
    "* `Consumed Alcohol 40+ times in the past year` - `0.655`\n",
    "\n",
    "###### TOP 3 Negative Features:\n",
    "* `Consumed NO Alcohol in the past year` - `-1.044`\n",
    "* `Never Smoked Cigarette` - `-1`\n",
    "* `Grew up on a Farm` - `-0.577`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Features are sorted in ascending order..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('x7_0', -1.0444238919067246, 'alcohol_yr_freq'),\n",
       " ('x4_Never', -1.0002862094560194, 'evr_smoked_cig'),\n",
       " ('x10_Farm', -0.5769442716134606, 'area_type'),\n",
       " ('x6_0', -0.5292576884390817, 'alcohol_lifetime_freq'),\n",
       " ('x0_1995', -0.5172839065439283, 'yr_adminst'),\n",
       " ('x7_1-2X', -0.4827856151482851, 'alcohol_yr_freq'),\n",
       " ('x0_1998', -0.4688949744583935, 'yr_adminst'),\n",
       " ('x27_A+', -0.45049541635200846, 'avg_grade'),\n",
       " ('x5_None', -0.42471051161853496, 'cig_month_freq'),\n",
       " ('x0_1999', -0.41391917672518236, 'yr_adminst'),\n",
       " ('x0_1996', -0.40899752439927467, 'yr_adminst'),\n",
       " ('x0_2000', -0.4074725664497595, 'yr_adminst'),\n",
       " ('x0_1997', -0.3904223239703652, 'yr_adminst'),\n",
       " ('x18_Very Conservative', -0.3901011549128239, 'political_value_type'),\n",
       " ('x26_0', -0.323227102650196, 'skipped_class'),\n",
       " ('x6_1-2X', -0.28232717574067656, 'alcohol_lifetime_freq'),\n",
       " ('x27_A-', -0.27054340887020634, 'avg_grade'),\n",
       " ('x24_0', -0.26408151037198024, 'school_missed_ditched'),\n",
       " ('x8_None', -0.2575588733897159, 'alcohol_2weeks'),\n",
       " ('x18_Conservative', -0.23121201776125122, 'political_value_type'),\n",
       " ('x46_0', -0.23056733034192914, '12mo_accidents'),\n",
       " ('x0_2001', -0.21024838804703644, 'yr_adminst'),\n",
       " ('x29_Definitely Will', -0.19819423790434768, 'military_after_hs'),\n",
       " ('x22_Far Below', -0.1832556679458219, 'intelligence_self_rating'),\n",
       " ('x0_2002', -0.1790786555002979, 'yr_adminst'),\n",
       " ('x27_B+', -0.17024542719522526, 'avg_grade'),\n",
       " ('x4_1-2x', -0.1545562364065721, 'evr_smoked_cig'),\n",
       " ('x42_1', -0.15434314241465985, 'rec_time'),\n",
       " ('x45_0', -0.1533191322660309, '12mo_r_tcktd'),\n",
       " ('x19_Every Week+', -0.15202695345453304, 'relig_attd'),\n",
       " ('x20_Very Important', -0.14710787676193562, 'relig_importance'),\n",
       " ('x44_200+', -0.14636685398818383, 'drive_freq'),\n",
       " ('x0_2004', -0.14623290396490563, 'yr_adminst'),\n",
       " ('x42_<1', -0.1456028725767086, 'rec_time'),\n",
       " ('x10_Country', -0.13996009837845255, 'area_type'),\n",
       " ('x0_2003', -0.13058825166403948, 'yr_adminst'),\n",
       " ('x0_2007', -0.1253894311012046, 'yr_adminst'),\n",
       " ('x22_Slightly Below Avg', -0.12329894007870888, 'intelligence_self_rating'),\n",
       " ('x6_3-5X', -0.12262796197554499, 'alcohol_lifetime_freq'),\n",
       " ('x23_0', -0.11931194791786333, 'school_missed_illness'),\n",
       " ('x5_<1 Cigarettes', -0.11832271899694295, 'cig_month_freq'),\n",
       " ('x46_1', -0.11445578227716466, '12mo_accidents'),\n",
       " ('x7_3-5X', -0.10702167159415683, 'alcohol_yr_freq'),\n",
       " ('x11_Married', -0.10542725744898765, 'marital_status'),\n",
       " (\"x18_Don't Know\", -0.10416037807629933, 'political_value_type'),\n",
       " ('x24_1', -0.10004602166582474, 'school_missed_ditched'),\n",
       " ('x1_Midwest', -0.09789358699910064, 'region'),\n",
       " ('x16_Grade School', -0.09754866732977949, 'mother_educ_lvl'),\n",
       " ('x12_Yes', -0.09736463953753875, 'has_father'),\n",
       " ('x0_2006', -0.09622605477332417, 'yr_adminst'),\n",
       " ('x16_Graduated HS', -0.09276382490131338, 'mother_educ_lvl'),\n",
       " ('x8_10+X', -0.09163843599645877, 'alcohol_2weeks'),\n",
       " ('x39_6-10', -0.08836832947491889, 'work_hrs'),\n",
       " ('x38_No', -0.08711230056320729, 'desire_none'),\n",
       " ('x3_0', -0.08631244308385001, 'smsa'),\n",
       " ('x25_6-10', -0.08614153417994991, 'school_missed_other'),\n",
       " ('x22_Below Avg', -0.08237736749285114, 'intelligence_self_rating'),\n",
       " (\"x31_Definitely Won't\", -0.08100170554029455, '4yrcoll_after_hs'),\n",
       " ('x27_B', -0.0793575983666155, 'avg_grade'),\n",
       " ('x41_0', -0.07743765624342994, 'other_income'),\n",
       " ('x40_1-5', -0.07573809097721791, 'work_pay'),\n",
       " ('x18_Moderate', -0.07468520485551473, 'political_value_type'),\n",
       " ('x42_2', -0.07429463996084654, 'rec_time'),\n",
       " ('x22_Average', -0.06546797610309578, 'intelligence_self_rating'),\n",
       " ('x25_3', -0.06225489235778328, 'school_missed_other'),\n",
       " ('x32_Definitely Will', -0.062150299410017165, 'gradsch_after_hs'),\n",
       " ('x43_Never', -0.062067474088192076, 'date_freq'),\n",
       " ('x13_Yes', -0.05772109179116275, 'has_mother'),\n",
       " ('x41_6-10', -0.056964986101052106, 'other_income'),\n",
       " ('x43_3x Week+', -0.05659899692882359, 'date_freq'),\n",
       " ('x36_No', -0.045451767072860035, 'desire_4yrcoll'),\n",
       " ('x20_Pretty Important', -0.04541442152437895, 'relig_importance'),\n",
       " ('x17_No', -0.044954799893322316, 'mother_employed'),\n",
       " ('x21_Far Above Average', -0.04452402471446309, 'academic_self_rating'),\n",
       " ('x26_1-2', -0.04346185428308131, 'skipped_class'),\n",
       " ('x0_2008', -0.04216647310482258, 'yr_adminst'),\n",
       " ('x2_1', -0.04037455947241352, 'msa'),\n",
       " ('x31_Definitely Will', -0.03941031106976739, '4yrcoll_after_hs'),\n",
       " ('x37_No', -0.03913467557878213, 'desire_gradsch'),\n",
       " ('x23_1', -0.03761304323702738, 'school_missed_illness'),\n",
       " ('x39_0', -0.03752240396449846, 'work_hrs'),\n",
       " ('x28_Definitely Will', -0.037424033671166285, 'tech_school_after_hs'),\n",
       " ('x15_Grade School', -0.03615301948775429, 'father_educ_lvl'),\n",
       " ('x39_5 or less', -0.03475352196964149, 'work_hrs'),\n",
       " ('x39_30+', -0.029852746637921843, 'work_hrs'),\n",
       " ('x41_21-35', -0.0294576056733512, 'other_income'),\n",
       " ('x8_Once', -0.02806605868419583, 'alcohol_2weeks'),\n",
       " ('x40_176+', -0.027632786026221167, 'work_pay'),\n",
       " ('x44_101-200', -0.027438206358857478, 'drive_freq'),\n",
       " ('x28_Probably Will', -0.026648986338004384, 'tech_school_after_hs'),\n",
       " ('x43_2-3x Week', -0.02607316220155871, 'date_freq'),\n",
       " ('x34_Yes', -0.024998391616829287, 'desire_military'),\n",
       " ('x11_Engaged', -0.024169439164170584, 'marital_status'),\n",
       " ('x21_Far Below', -0.02245468666815223, 'academic_self_rating'),\n",
       " ('x41_76-125', -0.021884958754372952, 'other_income'),\n",
       " ('x33_Yes', -0.021121532539523483, 'desire_tech_school'),\n",
       " ('x1_South', -0.02051268634484258, 'region'),\n",
       " ('x14_Yes', -0.01575807044556364, 'has_siblings'),\n",
       " (\"x30_Definitely Won't\", -0.01547207304578854, '2yrcoll_after_hs'),\n",
       " ('x40_0', -0.013350168890570968, 'work_pay'),\n",
       " ('x0_2005', -0.01307563372590367, 'yr_adminst'),\n",
       " ('x40_11-20', -0.010959512043609083, 'work_pay'),\n",
       " ('x15_College Grad', -0.009736445595105593, 'father_educ_lvl'),\n",
       " ('x19_1-2x Month', -0.009174332963048367, 'relig_attd'),\n",
       " ('x17_Yes/Some', -0.00914328755531431, 'mother_employed'),\n",
       " ('x35_Yes', -0.008417935734313505, 'desire_2yrcoll'),\n",
       " (\"x30_Probably Won't\", -0.008217147222622236, '2yrcoll_after_hs'),\n",
       " ('x21_Above Average', -0.005453451282690494, 'academic_self_rating'),\n",
       " ('x21_Average', -0.005145875970308614, 'academic_self_rating'),\n",
       " ('x24_6-10', -0.00457337799706087, 'school_missed_ditched'),\n",
       " ('x30_Definitely Will', -0.004246795209749706, '2yrcoll_after_hs'),\n",
       " ('x46_2', -0.0031054641835899397, '12mo_accidents'),\n",
       " ('x32_Probably Will', -0.00210842412208829, 'gradsch_after_hs'),\n",
       " ('x9_Male', -0.00193584174422137, 'sex'),\n",
       " ('x23_11+', -0.0016974198734483691, 'school_missed_illness'),\n",
       " ('x40_126-175', -0.0005517280413543515, 'work_pay'),\n",
       " ('x9_Female', 0.0007508064075052652, 'sex'),\n",
       " ('x15_Some College', 0.0009343114217596406, 'father_educ_lvl'),\n",
       " ('x40_21-35', 0.0016161742216206428, 'work_pay'),\n",
       " ('x23_2', 0.0019243699694299224, 'school_missed_illness'),\n",
       " ('x15_Graduated HS', 0.002905859089823506, 'father_educ_lvl'),\n",
       " ('x41_11-20', 0.0036311667278919966, 'other_income'),\n",
       " ('x25_0', 0.003871537083730752, 'school_missed_other'),\n",
       " ('x45_4+', 0.0044213203987085396, '12mo_r_tcktd'),\n",
       " ('x21_Slightly Above Average', 0.004601774572857727, 'academic_self_rating'),\n",
       " ('x24_3', 0.0050222024238184585, 'school_missed_ditched'),\n",
       " ('x24_2', 0.005187145457705919, 'school_missed_ditched'),\n",
       " ('x35_No', 0.007232900397461582, 'desire_2yrcoll'),\n",
       " ('x40_36-50', 0.008857992677376535, 'work_pay'),\n",
       " ('x39_11-15', 0.009484852302483611, 'work_hrs'),\n",
       " ('x21_Below Avg', 0.011009259561859634, 'academic_self_rating'),\n",
       " ('x14_No', 0.014573035108701815, 'has_siblings'),\n",
       " (\"x28_Probably Won't\", 0.014713201152559842, 'tech_school_after_hs'),\n",
       " ('x15_Some High School', 0.015166312368676669, 'father_educ_lvl'),\n",
       " ('x40_76-125', 0.01752105502116157, 'work_pay'),\n",
       " ('x41_36-50', 0.017673451088462445, 'other_income'),\n",
       " ('x40_51-75', 0.017982267650243917, 'work_pay'),\n",
       " (\"x29_Definitely Won't\", 0.019189793333349702, 'military_after_hs'),\n",
       " ('x41_1-5', 0.019471486603714616, 'other_income'),\n",
       " ('x25_4-5', 0.0195060875199164, 'school_missed_other'),\n",
       " ('x42_3', 0.019724120699187577, 'rec_time'),\n",
       " ('x33_No', 0.01993649720277798, 'desire_tech_school'),\n",
       " ('x44_51-100', 0.020014455965393085, 'drive_freq'),\n",
       " (\"x32_Probably Won't\", 0.020755405857058068, 'gradsch_after_hs'),\n",
       " ('x45_1', 0.021274218938496545, '12mo_r_tcktd'),\n",
       " ('x34_No', 0.023813356279938223, 'desire_military'),\n",
       " ('x19_Rarely', 0.0244369008301906, 'relig_attd'),\n",
       " ('x17_Yes/Nearly All', 0.02468469902666867, 'mother_employed'),\n",
       " ('x16_College Grad', 0.025612106813128675, 'mother_educ_lvl'),\n",
       " ('x15_Graduate School', 0.025697946865729866, 'father_educ_lvl'),\n",
       " ('x30_Probably Will', 0.02675098014134121, '2yrcoll_after_hs'),\n",
       " ('x41_51-75', 0.02726402990578019, 'other_income'),\n",
       " ('x17_Yes/Most', 0.02822835308517007, 'mother_employed'),\n",
       " ('x11_Single', 0.028302484692313706, 'marital_status'),\n",
       " ('x45_2', 0.028361925686169322, '12mo_r_tcktd'),\n",
       " ('x5_1-5 Cigarettes', 0.02877487254152604, 'cig_month_freq'),\n",
       " ('x23_6-10', 0.030829740245924594, 'school_missed_illness'),\n",
       " ('x43_1x Week', 0.030967171917112176, 'date_freq'),\n",
       " ('x16_Some College', 0.031221180700548586, 'mother_educ_lvl'),\n",
       " ('x39_16-20', 0.031626712408969686, 'work_hrs'),\n",
       " ('x25_11+', 0.03370081008108379, 'school_missed_other'),\n",
       " ('x43_1x Month', 0.03663157499324181, 'date_freq'),\n",
       " ('x37_Yes', 0.03794964024200434, 'desire_gradsch'),\n",
       " ('x2_0', 0.03918952413555341, 'msa'),\n",
       " ('x20_A Little Important', 0.04061670369395791, 'relig_importance'),\n",
       " ('x25_2', 0.041462462056206455, 'school_missed_other'),\n",
       " ('x44_1-10', 0.04185687177534901, 'drive_freq'),\n",
       " ('x10_Suburb of Vry Lrg City', 0.04193646603508071, 'area_type'),\n",
       " (\"x32_Definitely Won't\", 0.04231828233828008, 'gradsch_after_hs'),\n",
       " ('x39_26-30', 0.04327008102071022, 'work_hrs'),\n",
       " ('x26_11-20', 0.04363113012493004, 'skipped_class'),\n",
       " ('x36_Yes', 0.044266731735955245, 'desire_4yrcoll'),\n",
       " ('x23_4-5', 0.045794821524393896, 'school_missed_illness'),\n",
       " ('x16_Graduate School', 0.04644397940792976, 'mother_educ_lvl'),\n",
       " ('x22_Slightly Above Average',\n",
       "  0.04794702259280192,\n",
       "  'intelligence_self_rating'),\n",
       " (\"x28_Definitely Won't\", 0.04817478351990988, 'tech_school_after_hs'),\n",
       " ('x25_1', 0.048670494459961515, 'school_missed_other'),\n",
       " ('x0_2009', 0.0487924078726151, 'yr_adminst'),\n",
       " (\"x31_Probably Won't\", 0.04950736605902609, '4yrcoll_after_hs'),\n",
       " ('x44_11-50', 0.049703535692345986, 'drive_freq'),\n",
       " ('x27_B-', 0.04993450924405891, 'avg_grade'),\n",
       " ('x6_6-9X', 0.05271648562867177, 'alcohol_lifetime_freq'),\n",
       " ('x1_West', 0.055134574774039984, 'region'),\n",
       " ('x8_Twice', 0.055320315313012805, 'alcohol_2weeks'),\n",
       " ('x13_No', 0.056536056454419295, 'has_mother'),\n",
       " ('x41_126-175', 0.05800795408205347, 'other_income'),\n",
       " ('x41_176+', 0.05851208302749662, 'other_income'),\n",
       " ('x10_Small Town', 0.06069580288307936, 'area_type'),\n",
       " ('x21_Slightly Below Avg', 0.06078196916412549, 'academic_self_rating'),\n",
       " ('x44_0', 0.06104516157714231, 'drive_freq'),\n",
       " ('x26_6-10', 0.06156932263130565, 'skipped_class'),\n",
       " (\"x29_Probably Won't\", 0.06181232879703695, 'military_after_hs'),\n",
       " ('x5_2 Packs', 0.06200534642034228, 'cig_month_freq'),\n",
       " ('x1_NorthEast', 0.06208666323311126, 'region'),\n",
       " ('x31_Probably Will', 0.069719615214191, '4yrcoll_after_hs'),\n",
       " ('x10_City >500,000 (Vry Large)', 0.07032042937452752, 'area_type'),\n",
       " ('x43_2-3x Month', 0.07595585097131853, 'date_freq'),\n",
       " ('x23_3', 0.07888844395180135, 'school_missed_illness'),\n",
       " ('x40_6-10', 0.08106976107172532, 'work_pay'),\n",
       " ('x26_3-5', 0.08388953515417745, 'skipped_class'),\n",
       " ('x3_1', 0.08512740774722437, 'smsa'),\n",
       " ('x16_Some High School', 0.08585018997265502, 'mother_educ_lvl'),\n",
       " ('x38_Yes', 0.08592726522635022, 'desire_none'),\n",
       " ('x24_4-5', 0.09453808536879092, 'school_missed_ditched'),\n",
       " ('x12_No', 0.09617960420063842, 'has_father'),\n",
       " ('x10_Suburb of Large City', 0.09745878507272022, 'area_type'),\n",
       " ('x45_3', 0.09807663190584615, '12mo_r_tcktd'),\n",
       " ('x11_Separated/Divorced', 0.10010917658397003, 'marital_status'),\n",
       " ('x5_1.5 Packs', 0.1028510494399794, 'cig_month_freq'),\n",
       " ('x18_Liberal', 0.1035504505824983, 'political_value_type'),\n",
       " ('x39_21-25', 0.10493032097795106, 'work_hrs'),\n",
       " ('x29_Probably Will', 0.11600708043717245, 'military_after_hs'),\n",
       " ('x27_C+', 0.11637358278822217, 'avg_grade'),\n",
       " ('x10_Large City', 0.11831108071096748, 'area_type'),\n",
       " ('x22_Above Average', 0.12351536919649578, 'intelligence_self_rating'),\n",
       " ('x19_Never', 0.1355793502505848, 'relig_attd'),\n",
       " ('x7_6-9X', 0.14221516686474875, 'alcohol_yr_freq'),\n",
       " ('x5_1 Pack', 0.1469015256722513, 'cig_month_freq'),\n",
       " ('x20_Not Important', 0.1507205592556796, 'relig_importance'),\n",
       " ('x8_6-9X', 0.15444862121441363, 'alcohol_2weeks'),\n",
       " ('x10_Suburb of Medium City', 0.15896503210594734, 'area_type'),\n",
       " ('x8_3-5X', 0.1663093962060953, 'alcohol_2weeks'),\n",
       " ('x6_10-19X', 0.1675458128184387, 'alcohol_lifetime_freq'),\n",
       " ('x10_Medium City', 0.16803173847278405, 'area_type'),\n",
       " ('x42_4-5', 0.16918756580194907, 'rec_time'),\n",
       " ('x46_3', 0.17343212990076878, '12mo_accidents'),\n",
       " ('x46_4+', 0.1735114115652519, '12mo_accidents'),\n",
       " ('x26_21+', 0.17641393368610367, 'skipped_class'),\n",
       " ('x27_C', 0.17799007779694542, 'avg_grade'),\n",
       " ('x42_6-7', 0.1841439331143769, 'rec_time'),\n",
       " ('x5_2+ Packs', 0.20131540120454422, 'cig_month_freq'),\n",
       " ('x4_Occasionally', 0.20777076249561427, 'evr_smoked_cig'),\n",
       " ('x27_C-', 0.21510019376956294, 'avg_grade'),\n",
       " ('x0_2010', 0.24589110947578946, 'yr_adminst'),\n",
       " ('x24_11+', 0.26276844144769024, 'school_missed_ditched'),\n",
       " ('x22_Far Above Average', 0.2817525244944278, 'intelligence_self_rating'),\n",
       " ('x6_20-39X', 0.31972122674337605, 'alcohol_lifetime_freq'),\n",
       " ('x7_10-19X', 0.3317013727128379, 'alcohol_yr_freq'),\n",
       " ('x18_Radical', 0.3380945136715312, 'political_value_type'),\n",
       " ('x18_Very Liberal', 0.357328756015146, 'political_value_type'),\n",
       " ('x4_Regularly in Past', 0.3631583263525524, 'evr_smoked_cig'),\n",
       " ('x0_2011', 0.3800380034474571, 'yr_adminst'),\n",
       " ('x0_2012', 0.38266356167486937, 'yr_adminst'),\n",
       " ('x6_40+', 0.3930442656280201, 'alcohol_lifetime_freq'),\n",
       " ('x27_D', 0.4100584518483733, 'avg_grade'),\n",
       " ('x7_20-39X', 0.5036351338947056, 'alcohol_yr_freq'),\n",
       " ('x4_Regularly Now', 0.5827283216776209, 'evr_smoked_cig'),\n",
       " ('x0_2014', 0.6550510128297548, 'yr_adminst'),\n",
       " ('x7_40+', 0.655494469839989, 'alcohol_yr_freq'),\n",
       " ('x0_2015', 0.7145695435550995, 'yr_adminst'),\n",
       " ('x0_2019', 1.1218055902357884, 'yr_adminst')]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = list(zip(X_tr_pp.columns, final.coef_[0], label_features()))\n",
    "sorted(features, key=lambda x: x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next Steps\n",
    "* Further subsetting of the dataset and experimentation with XGBoost hyperparameter tuning.\n",
    "* Implementing Recursive Feature Elimination.\n",
    "* Using a Neural Network.\n",
    "* Be able to feed in **raw data** from the Monitoring the Future study and produce predictions via an **app**.\n",
    "\n",
    "### Sources\n",
    "\n",
    "[History of Drug Abuse](https://dualdiagnosis.org/the-history-of-drug-abuse-and-how-its-changed/)\n",
    "\n",
    "[American Drug Use Trends](https://unityrehab.com/blog/american-drug-use-trend-on-the-rise/)\n",
    "\n",
    "[Younger Age of first Cannabis Use - More likely to develop Substance Abuse Disorder](https://www.drugabuse.gov/news-events/news-releases/2021/03/younger-age-of-first-cann[…]ssociated-with-faster-development-of-substance-use-disorders)\n",
    "\n",
    "[More Drug Abuse Trends](https://www.drugabuse.gov/drug-topics/trends-statistics)\n",
    "\n",
    "[CDC Drug Use Stats](https://www.cdc.gov/nchs/products/databriefs/db329.html)\n"
   ]
  }
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