{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4dfd68f5",
   "metadata": {},
   "source": [
    "# Jupyter - Day 22 - Section 002\n",
    "# Lec 22 - Step Functions for Classification\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90aa0b0",
   "metadata": {},
   "source": [
    "We're going to try again with the step functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4ea3a4b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Everyone's favorite standard imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import time\n",
    "\n",
    "\n",
    "# ML imports we've used previously\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import statsmodels.api as sm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f9a257",
   "metadata": {},
   "source": [
    "## Loading in the data\n",
    "\n",
    "We're going to use the `Wage` data used in the book, so note that many of your plots can be checked by looking at figures in the book."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3ceeb83a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>maritl</th>\n",
       "      <th>race</th>\n",
       "      <th>education</th>\n",
       "      <th>region</th>\n",
       "      <th>jobclass</th>\n",
       "      <th>health</th>\n",
       "      <th>health_ins</th>\n",
       "      <th>logwage</th>\n",
       "      <th>wage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>231655</th>\n",
       "      <td>2006</td>\n",
       "      <td>18</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>1. Never Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>1. &lt; HS Grad</td>\n",
       "      <td>2. Middle Atlantic</td>\n",
       "      <td>1. Industrial</td>\n",
       "      <td>1. &lt;=Good</td>\n",
       "      <td>2. No</td>\n",
       "      <td>4.318063</td>\n",
       "      <td>75.043154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86582</th>\n",
       "      <td>2004</td>\n",
       "      <td>24</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>1. Never Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>4. College Grad</td>\n",
       "      <td>2. Middle Atlantic</td>\n",
       "      <td>2. Information</td>\n",
       "      <td>2. &gt;=Very Good</td>\n",
       "      <td>2. No</td>\n",
       "      <td>4.255273</td>\n",
       "      <td>70.476020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161300</th>\n",
       "      <td>2003</td>\n",
       "      <td>45</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>3. Some College</td>\n",
       "      <td>2. Middle Atlantic</td>\n",
       "      <td>1. Industrial</td>\n",
       "      <td>1. &lt;=Good</td>\n",
       "      <td>1. Yes</td>\n",
       "      <td>4.875061</td>\n",
       "      <td>130.982177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155159</th>\n",
       "      <td>2003</td>\n",
       "      <td>43</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>3. Asian</td>\n",
       "      <td>4. College Grad</td>\n",
       "      <td>2. Middle Atlantic</td>\n",
       "      <td>2. Information</td>\n",
       "      <td>2. &gt;=Very Good</td>\n",
       "      <td>1. Yes</td>\n",
       "      <td>5.041393</td>\n",
       "      <td>154.685293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11443</th>\n",
       "      <td>2005</td>\n",
       "      <td>50</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>4. Divorced</td>\n",
       "      <td>1. White</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>2. Middle Atlantic</td>\n",
       "      <td>2. Information</td>\n",
       "      <td>1. &lt;=Good</td>\n",
       "      <td>1. Yes</td>\n",
       "      <td>4.318063</td>\n",
       "      <td>75.043154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        year  age      sex            maritl      race        education  \\\n",
       "231655  2006   18  1. Male  1. Never Married  1. White     1. < HS Grad   \n",
       "86582   2004   24  1. Male  1. Never Married  1. White  4. College Grad   \n",
       "161300  2003   45  1. Male        2. Married  1. White  3. Some College   \n",
       "155159  2003   43  1. Male        2. Married  3. Asian  4. College Grad   \n",
       "11443   2005   50  1. Male       4. Divorced  1. White       2. HS Grad   \n",
       "\n",
       "                    region        jobclass          health health_ins  \\\n",
       "231655  2. Middle Atlantic   1. Industrial       1. <=Good      2. No   \n",
       "86582   2. Middle Atlantic  2. Information  2. >=Very Good      2. No   \n",
       "161300  2. Middle Atlantic   1. Industrial       1. <=Good     1. Yes   \n",
       "155159  2. Middle Atlantic  2. Information  2. >=Very Good     1. Yes   \n",
       "11443   2. Middle Atlantic  2. Information       1. <=Good     1. Yes   \n",
       "\n",
       "         logwage        wage  \n",
       "231655  4.318063   75.043154  \n",
       "86582   4.255273   70.476020  \n",
       "161300  4.875061  130.982177  \n",
       "155159  5.041393  154.685293  \n",
       "11443   4.318063   75.043154  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../../DataSets/Wage.csv', index_col =0 )\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "903ebb82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3000 entries, 231655 to 453557\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   year        3000 non-null   int64  \n",
      " 1   age         3000 non-null   int64  \n",
      " 2   sex         3000 non-null   object \n",
      " 3   maritl      3000 non-null   object \n",
      " 4   race        3000 non-null   object \n",
      " 5   education   3000 non-null   object \n",
      " 6   region      3000 non-null   object \n",
      " 7   jobclass    3000 non-null   object \n",
      " 8   health      3000 non-null   object \n",
      " 9   health_ins  3000 non-null   object \n",
      " 10  logwage     3000 non-null   float64\n",
      " 11  wage        3000 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(8)\n",
      "memory usage: 304.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bc159e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>age</th>\n",
       "      <th>logwage</th>\n",
       "      <th>wage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3000.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2005.791000</td>\n",
       "      <td>42.414667</td>\n",
       "      <td>4.653905</td>\n",
       "      <td>111.703608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.026167</td>\n",
       "      <td>11.542406</td>\n",
       "      <td>0.351753</td>\n",
       "      <td>41.728595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2003.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>20.085537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>33.750000</td>\n",
       "      <td>4.447158</td>\n",
       "      <td>85.383940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2006.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>4.653213</td>\n",
       "      <td>104.921507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>4.857332</td>\n",
       "      <td>128.680488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2009.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>5.763128</td>\n",
       "      <td>318.342430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year          age      logwage         wage\n",
       "count  3000.000000  3000.000000  3000.000000  3000.000000\n",
       "mean   2005.791000    42.414667     4.653905   111.703608\n",
       "std       2.026167    11.542406     0.351753    41.728595\n",
       "min    2003.000000    18.000000     3.000000    20.085537\n",
       "25%    2004.000000    33.750000     4.447158    85.383940\n",
       "50%    2006.000000    42.000000     4.653213   104.921507\n",
       "75%    2008.000000    51.000000     4.857332   128.680488\n",
       "max    2009.000000    80.000000     5.763128   318.342430"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68b20448",
   "metadata": {},
   "source": [
    "Here's the plot we used multiple times in class to look at a single variable:  `age` vs `wage`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b90464f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.age[df.wage <=250], df.wage[df.wage<=250],marker = '*', label = '< 250')\n",
    "plt.scatter(df.age[df.wage >250], df.wage[df.wage>250], label = '> 250')\n",
    "plt.legend()\n",
    "\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Wage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5058b601",
   "metadata": {},
   "source": [
    "## Classification version of step functions\n",
    "\n",
    "Now we can try out the classification version of the problem. Let's build the classifier that predicts whether a person of a given age will make more than $250,000. You already made the matrix of step function features, so we just have to hand it to `LogisticRegression` to do its thing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a5670a1",
   "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>[18.0, 33.5)</th>\n",
       "      <th>[33.5, 49.0)</th>\n",
       "      <th>[49.0, 64.5)</th>\n",
       "      <th>[64.5, 80.062)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>231655</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86582</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161300</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155159</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11443</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        [18.0, 33.5)  [33.5, 49.0)  [49.0, 64.5)  [64.5, 80.062)\n",
       "231655             1             0             0               0\n",
       "86582              1             0             0               0\n",
       "161300             0             1             0               0\n",
       "155159             0             1             0               0\n",
       "11443              0             0             1               0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same matrix of step function features as last time.\n",
    "df_cut, bins = pd.cut(df.age, 4, retbins = True, right = False)\n",
    "df_steps_dummies = pd.get_dummies(df_cut) # This gives us entries with true/false\n",
    "df_steps = df_steps_dummies.apply(lambda x: x * 1) # This converts those to either 0 or 1.\n",
    "df_steps.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7ebeb7ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=48824)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=48824)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(random_state=48824)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "y = np.array(df.wage>250) #<--- this makes sure I \n",
    "                          #     just have true/false input\n",
    "                          #     so that we're doing classification\n",
    "clf = LogisticRegression(random_state=48824)\n",
    "clf.fit(df_steps_dummies,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "37b70e07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the same step features for the x-values we want to draw\n",
    "t_age = pd.Series(np.linspace(20,80,100))\n",
    "t_df_cut = pd.cut(t_age, bins, right = False) #<-- I'm explicitly passing the same bins learned above so tha the procedure is the same. \n",
    "t_dummies = pd.get_dummies(t_df_cut)\n",
    "t_step = t_dummies.apply(lambda x: x * 1)\n",
    "\n",
    "# Predict on these to get the line we can draw\n",
    "f = clf.predict_proba(t_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d873bad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "below = df.age[df.wage <=250]\n",
    "above = df.age[df.wage >250]\n",
    "\n",
    "# Comment this out to see the function better\n",
    "# plt.scatter(above,np.ones(above.shape[0]),marker = '|', color = 'orange')\n",
    "# plt.scatter(below,np.zeros(below.shape[0]),marker = '|', color = 'blue')\n",
    "\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('P[Wage >= 250]')\n",
    "plt.plot(t_age,f[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66f79113",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "-----\n",
    "### Congratulations, we're done!\n",
    "Initially created by Dr. Liz Munch, modified by Dr. Lianzhang Bao, Michigan State University\n",
    "\n",
    "<a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc/4.0/\"><img alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc/4.0/88x31.png\" /></a><br />This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc/4.0/\">Creative Commons Attribution-NonCommercial 4.0 International License</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc69e323",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}