Bachelor_analysis_NN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HnpDssuPjhzk",
    "outputId": "7da65f9c-d1d6-4c4f-e4c2-f6ccb46a8239"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "Pj1WgCQcjwRu",
    "outputId": "f3a4ce65-0daf-4f73-d6c9-d43f23db41eb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-41b2978a-cc76-4e77-8546-4632f73b054a\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>Job category</th>\n",
       "      <th>Race</th>\n",
       "      <th>Place</th>\n",
       "      <th>Season</th>\n",
       "      <th>1-on-1 week</th>\n",
       "      <th>FIR?</th>\n",
       "      <th>Joke Entrance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DeAnna Pappas</td>\n",
       "      <td>25</td>\n",
       "      <td>South</td>\n",
       "      <td>REALTOR</td>\n",
       "      <td>White</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jenni Croft</td>\n",
       "      <td>27</td>\n",
       "      <td>Midwest</td>\n",
       "      <td>DANCER</td>\n",
       "      <td>White</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bettina Bell[2]</td>\n",
       "      <td>27</td>\n",
       "      <td>Mid atlantic</td>\n",
       "      <td>REALTOR</td>\n",
       "      <td>White</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sheena Stewart[3]</td>\n",
       "      <td>23</td>\n",
       "      <td>West</td>\n",
       "      <td>EXECUTIVE</td>\n",
       "      <td>White</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hillary Reisinger[4]</td>\n",
       "      <td>27</td>\n",
       "      <td>Mid atlantic</td>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>White</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>Casandra Suarez[45]</td>\n",
       "      <td>25</td>\n",
       "      <td>West</td>\n",
       "      <td>POLITICS</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>Corrinne Jones[48]</td>\n",
       "      <td>22</td>\n",
       "      <td>East</td>\n",
       "      <td>MARKETING</td>\n",
       "      <td>White</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>Emani Curl[49]</td>\n",
       "      <td>25</td>\n",
       "      <td>West</td>\n",
       "      <td>REALTOR</td>\n",
       "      <td>Black</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>Kimberly Courneya[28]</td>\n",
       "      <td>28</td>\n",
       "      <td>Northwest</td>\n",
       "      <td>RECRUITER</td>\n",
       "      <td>Black</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Saneh Ste. Claire[50]</td>\n",
       "      <td>25</td>\n",
       "      <td>South</td>\n",
       "      <td>SCIENCE/TECHNOLOGY</td>\n",
       "      <td>White</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gimmick</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>422 rows × 10 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41b2978a-cc76-4e77-8546-4632f73b054a')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-41b2978a-cc76-4e77-8546-4632f73b054a button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-41b2978a-cc76-4e77-8546-4632f73b054a');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                      Name  Age      Hometown        Job category      Race  \\\n",
       "0            DeAnna Pappas   25         South             REALTOR     White   \n",
       "1              Jenni Croft   27       Midwest              DANCER     White   \n",
       "2          Bettina Bell[2]   27  Mid atlantic             REALTOR     White   \n",
       "3        Sheena Stewart[3]   23          West           EXECUTIVE     White   \n",
       "4     Hillary Reisinger[4]   27  Mid atlantic             MEDICAL     White   \n",
       "..                     ...  ...           ...                 ...       ...   \n",
       "417    Casandra Suarez[45]   25          West            POLITICS  Hispanic   \n",
       "418     Corrinne Jones[48]   22          East           MARKETING     White   \n",
       "419         Emani Curl[49]   25          West             REALTOR     Black   \n",
       "420  Kimberly Courneya[28]   28     Northwest           RECRUITER     Black   \n",
       "421  Saneh Ste. Claire[50]   25         South  SCIENCE/TECHNOLOGY     White   \n",
       "\n",
       "     Place  Season   1-on-1 week FIR? Joke Entrance  \n",
       "0        2       11          NaN  NaN           NaN  \n",
       "1        2       11          NaN  Yes           NaN  \n",
       "2        3       11          NaN  NaN           NaN  \n",
       "3        4       11          NaN  NaN           NaN  \n",
       "4        5       11          NaN  NaN           NaN  \n",
       "..     ...      ...          ...  ...           ...  \n",
       "417     25       25          NaN  NaN           NaN  \n",
       "418     25       25          NaN  NaN           NaN  \n",
       "419     25       25          NaN  NaN           NaN  \n",
       "420     25       25          NaN  NaN           NaN  \n",
       "421     25       25          NaN  NaN       Gimmick  \n",
       "\n",
       "[422 rows x 10 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('drive/MyDrive/DSFP_data/Bachelor analysis.csv')\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "Edmw4UO_lLeW",
    "outputId": "b66e9495-5c8c-4dad-feed-1cbde8cfa71e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-5fa25c52-9209-4064-ab80-64ef742d0e75\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Place</th>\n",
       "      <th>Season</th>\n",
       "      <th>1-on-1 week</th>\n",
       "      <th>FIR</th>\n",
       "      <th>INTERNATIONAL</th>\n",
       "      <th>NE</th>\n",
       "      <th>NW</th>\n",
       "      <th>SE</th>\n",
       "      <th>SW</th>\n",
       "      <th>Race_0</th>\n",
       "      <th>Race_1</th>\n",
       "      <th>Race_2</th>\n",
       "      <th>Race_3</th>\n",
       "      <th>Race_4</th>\n",
       "      <th>CORPORATE</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>POLITICS</th>\n",
       "      <th>TRADES</th>\n",
       "      <th>TRADITIONAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>422 rows × 20 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5fa25c52-9209-4064-ab80-64ef742d0e75')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-5fa25c52-9209-4064-ab80-64ef742d0e75 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-5fa25c52-9209-4064-ab80-64ef742d0e75');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "     Age  Place  Season   1-on-1 week  FIR  INTERNATIONAL  NE  NW  SE  SW  \\\n",
       "0     25      2       11          0.0    0              0   0   0   1   0   \n",
       "1     27      2       11          0.0    1              0   1   0   0   0   \n",
       "2     27      3       11          0.0    0              0   1   0   0   0   \n",
       "3     23      4       11          0.0    0              0   0   1   0   0   \n",
       "4     27      5       11          0.0    0              0   1   0   0   0   \n",
       "..   ...    ...      ...          ...  ...            ...  ..  ..  ..  ..   \n",
       "417   25     25       25          0.0    0              0   0   1   0   0   \n",
       "418   22     25       25          0.0    0              0   1   0   0   0   \n",
       "419   25     25       25          0.0    0              0   0   1   0   0   \n",
       "420   28     25       25          0.0    0              0   0   1   0   0   \n",
       "421   25     25       25          0.0    0              0   0   0   1   0   \n",
       "\n",
       "     Race_0  Race_1  Race_2  Race_3  Race_4  CORPORATE  OTHER  POLITICS  \\\n",
       "0         1       0       0       0       0          0      0         0   \n",
       "1         1       0       0       0       0          0      0         0   \n",
       "2         1       0       0       0       0          0      0         0   \n",
       "3         1       0       0       0       0          1      0         0   \n",
       "4         1       0       0       0       0          0      0         0   \n",
       "..      ...     ...     ...     ...     ...        ...    ...       ...   \n",
       "417       0       0       0       1       0          0      0         1   \n",
       "418       1       0       0       0       0          1      0         0   \n",
       "419       0       1       0       0       0          0      0         0   \n",
       "420       0       1       0       0       0          1      0         0   \n",
       "421       1       0       0       0       0          0      0         0   \n",
       "\n",
       "     TRADES  TRADITIONAL  \n",
       "0         1            0  \n",
       "1         1            0  \n",
       "2         1            0  \n",
       "3         0            0  \n",
       "4         0            1  \n",
       "..      ...          ...  \n",
       "417       0            0  \n",
       "418       0            0  \n",
       "419       1            0  \n",
       "420       0            0  \n",
       "421       0            1  \n",
       "\n",
       "[422 rows x 20 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_clean = data.copy()\n",
    "for col in [\"Hometown\", \"Job category\", \"Race\", \"Joke Entrance\"]:\n",
    "  # print( {name:i for i, name in enumerate(data[col].unique())} )\n",
    "  data_clean = data_clean.replace( {name:i for i, name in enumerate(data[col].unique())} )\n",
    "\n",
    "data_clean['FIR'] = np.where( data_clean['FIR?'] == 'Yes' , 1 , 0)\n",
    "\n",
    "data_clean = data_clean.drop(columns=['Name','FIR?', 'Joke Entrance'])\n",
    "\n",
    "new_job_cat = {\n",
    "    'POLITICS':['NEWS', 'MILLITARY', 'POLITICS'],\n",
    "    'CORPORATE':['MARKETING', 'PR', 'RECRUITER', 'ASSISTANT', 'EXECUTIVE', 'SALES', 'FINANCE'],\n",
    "    'TRADES':['RESTAURANT JOB', 'WAITRESS', 'BEAUTY', 'ACTRESS/MODEL', 'DANCER', 'REALTOR', 'EVENT PLANNER', 'FITNESS', 'NANNY'],\n",
    "    'TRADITIONAL':['TEACHER','DENTIST', 'LAWYER', 'MEDICAL', 'SCIENCE/TECHNOLOGY'],\n",
    "    'OTHER':['STUDENTT', 'STUDENT', 'WRITER', 'PASTOR', 'ART', 'NONE', 'JOKE', 'FLIGHT ATTENDANT'],\n",
    "}\n",
    "\n",
    "job_categories = data['Job category'].to_numpy()\n",
    "new_job_categories = []\n",
    "for name in job_categories:\n",
    "  for key, val in new_job_cat.items():\n",
    "    if name in val:\n",
    "      new_name = key\n",
    "  new_job_categories.append( new_name )\n",
    "\n",
    "data_clean['Job category'] = np.array(new_job_categories)\n",
    "\n",
    "\n",
    "new_hometown_cat = {\n",
    "    'INTERNATIONAL':['Canada','International'],\n",
    "    'NE':['Midwestt','East','Midwest','Mid atlantic'],\n",
    "    'NW':['West','Northwest'],\n",
    "    'SE':['South',],\n",
    "    'SW':['Southwest']\n",
    "}\n",
    "home_categories = data['Hometown'].to_numpy()\n",
    "new_home_categories = []\n",
    "for name in home_categories:\n",
    "  for key, val in new_hometown_cat.items():\n",
    "    if name in val:\n",
    "      new_name = key\n",
    "  new_home_categories.append( new_name )\n",
    "\n",
    "data_clean['Hometown'] = np.array(new_home_categories)\n",
    "\n",
    "\n",
    "one_hot_dfs = [data_clean]\n",
    "col_name_one_hot = ['Hometown', 'Race', 'Job category']\n",
    "\n",
    "for name in col_name_one_hot:\n",
    "  num_un = data_clean[name].nunique()\n",
    "  one_hot = pd.get_dummies( data_clean[name] )\n",
    "  one_hot.rename( columns={i:name+f'_{i}' for i in range(num_un)}, inplace=True)\n",
    "  one_hot_dfs.append( one_hot )\n",
    "\n",
    "\n",
    "combined_df = pd.concat( one_hot_dfs, axis=1 )\n",
    "combined_df = combined_df.drop(columns=['Hometown','Job category', 'Race'])\n",
    "\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "3_aXzNLgROns",
    "outputId": "7a11ec15-ad85-4e63-c5a0-14026245cb43"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5bd057ea90>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD5CAYAAAA+0W6bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAP3klEQVR4nO3ccWxd5XnH8e+zBFQUswQGu4sCm9mKKqFEpcsV6wSrrkGtWKmWTEJREaqcLZX3R6mYxqRmlaZ20qqFarTrH9O0DBieRGsiCktE2m0oi8f2RxkxsAbIWBkzKxYk60goRtWqtM/+8Ekxjp17r32vndfn+5Ein/Pec6+fx+/1z8evz0lkJpKk8vzUShcgSVocA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVBrOzkoIjYA9wKbgQR+G3gReAgYBCaBHZl58lyvc9lll+Xg4CBvv/0269atW0LZZatz/3XuHerdv70vvveJiYnvZeblZz2QmW3/AaPAJ6vtC4ENwBeB3dXYbuDudq+zdevWzMw8fPhw1lmd+69z75n17t/eFw84kvNkatsllIhYD3wIuK8K/B9m5ilgWxXsZwJ++6J/vEiSutbJGvhVwP8Afx0Rz0TEvRGxDmhk5mvVMa8DjX4VKUk6W2SbW+kjogl8C7g+M5+MiK8A3wc+nZkbZh13MjMvmef5I8AIQKPR2Do2Nsb09DQDAwO97KMode6/zr1Dvfu398X3PjQ0NJGZzbMemG9dJd+9/v1zwOSs/V8DDjLzR8yN1dhG4MV2r+Ua+Iw691/n3jPr3b+9Lx6LXQPPzNeB70bE+6qhm4AXgAPAcDU2DOxf9I8XSVLXOrqMEPg08GBEXAi8DPwWM+vn+yJiF/AKsKM/JUqS5tNRgGfms8DZ6y8zZ+OSpBXgnZiSVCgDXJIK1ekauKQ+Gdx9sKvjJ/fc0qdKVBrPwCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVCgDXJIKZYBLUqEMcEkqlAEuSYVa28lBETEJvAX8CDidmc2IuBR4CBgEJoEdmXmyP2VKkubq5gx8KDOvzcxmtb8bOJSZVwOHqn1J0jJZyhLKNmC02h4Fti+9HElSpyIz2x8U8V/ASSCBv8zMvRFxKjM3VI8HcPLM/pznjgAjAI1GY+vY2BjT09MMDAz0so+i1Ln/OvcO8/d/dOrNrl5jy6b1vSxp2dR57pfa+9DQ0MSs1Y+f6GgNHLghM6ci4meBxyPi32c/mJkZEfP+JMjMvcBegGazma1Wi/HxcVqtVncdrCJ17r/OvcP8/e/cfbCr15i8vdX2mPNRnee+X713tISSmVPVxxPAo8B1wPGI2AhQfTzR8+okSQtqG+ARsS4iLj6zDXwEeA44AAxXhw0D+/tVpCTpbJ0soTSAR2eWuVkLfDUz/y4ingL2RcQu4BVgR//KlCTN1TbAM/Nl4P3zjP8vcFM/ipIkteedmJJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVCgDXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoToO8IhYExHPRMRj1f5VEfFkRLwUEQ9FxIX9K1OSNFc3Z+B3Asdm7d8NfDkz3wucBHb1sjBJ0rl1FOARcQVwC3BvtR/AjcDD1SGjwPZ+FChJml9kZvuDIh4G/gS4GPh9YCfwrersm4i4EvhmZm6e57kjwAhAo9HYOjY2xvT0NAMDAz1rojR17r/OvcP8/R+derOr19iyaX0vS1o2dZ77pfY+NDQ0kZnNueNr2z0xIj4GnMjMiYhodfuJM3MvsBeg2Wxmq9VifHycVqvrl1o16tx/nXuH+fvfuftgV68xeXur7THnozrPfb96bxvgwPXAb0TER4H3AD8NfAXYEBFrM/M0cAUw1fPqJEkLarsGnpl/kJlXZOYg8HHgHzPzduAwcGt12DCwv29VSpLOspTrwD8D/F5EvAT8DHBfb0qSJHWikyWUn8jMcWC82n4ZuK73JUmSOuGdmJJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVCgDXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgrVNsAj4j0R8a8R8W8R8XxE/FE1flVEPBkRL0XEQxFxYf/LlSSd0ckZ+P8BN2bm+4FrgZsj4oPA3cCXM/O9wElgV//KlCTN1TbAc8Z0tXtB9S+BG4GHq/FRYHtfKpQkzaujNfCIWBMRzwIngMeB/wROZebp6pBXgU39KVGSNJ/IzM4PjtgAPAr8IfBAtXxCRFwJfDMzN8/znBFgBKDRaGwdGxtjenqagYGBXtRfpDr3X+feYf7+j0692dVrbNm0vpclLZs6z/1Sex8aGprIzObc8bXdvEhmnoqIw8CvAhsiYm11Fn4FMLXAc/YCewGazWa2Wi3Gx8dptVrd9rBq1Ln/OvcO8/e/c/fBrl5j8vZW22POR3We+3713slVKJdXZ95ExEXAh4FjwGHg1uqwYWB/z6uTJC2okzPwjcBoRKxhJvD3ZeZjEfECMBYRfww8A9zXxzolLZPBbn8j2HNLnypRO20DPDO/DXxgnvGXgev6UZQkqT3vxJSkQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVCgDXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCtU2wCPiyog4HBEvRMTzEXFnNX5pRDweEd+pPl7S/3IlSWd0cgZ+GrgrM68BPgh8KiKuAXYDhzLzauBQtS9JWiZtAzwzX8vMp6vtt4BjwCZgGzBaHTYKbO9XkZKks0Vmdn5wxCDwBLAZ+O/M3FCNB3DyzP6c54wAIwCNRmPr2NgY09PTDAwMLL36QtWp/6NTb75rv3ERHP/Bwsdv2bS+zxWtrPnmfu7XqJ1+f436VU+d3vdzLbX3oaGhicxszh3vOMAjYgD4J+ALmflIRJyaHdgRcTIzz7kO3mw288iRI4yPj9NqtbrrYBWpU/+Duw++a/+uLae55+jaBY+f3HNLv0taUfPN/dyvUTv9/hr1q546ve/nWmrvETFvgHd0FUpEXAB8HXgwMx+pho9HxMbq8Y3AiUVXJ0nqWidXoQRwH3AsM78066EDwHC1PQzs7315kqSFLPy77DuuBz4BHI2IZ6uxzwJ7gH0RsQt4BdjRnxKlhZ1vyw/Scmob4Jn5L0As8PBNvS1HktQp78SUpEIZ4JJUqE7WwLVCXN9VCTp9n9615TQ7q2N9r/aGZ+CSVCgDXJIK5RLKMjrzq+bsXyVL0u2SzvnIZSmtJp6BS1KhDHBJKpQBLkmFWrVr4K51SvVVl+9/z8AlqVAGuCQVatUuoXTLS+Sk81e/vz9L/d7xDFySCmWAS1KhDHBJKlQxa+CrYY1aknrJM3BJKpQBLkmFKmYJRVoJpV5epv7q9n3xwM3r+lKHZ+CSVCgDXJIKZYBLUqFcA68xL82UyuYZuCQVygCXpEIZ4JJUqLZr4BFxP/Ax4ERmbq7GLgUeAgaBSWBHZp7sX5mqC9fl68F57o1OzsAfAG6eM7YbOJSZVwOHqn1J0jJqG+CZ+QTwxpzhbcBotT0KbO9xXZKkNiIz2x8UMQg8NmsJ5VRmbqi2Azh5Zn+e544AIwCNRmPr2NgY09PTDAwMdFXo0ak3uzr+fNa4CI7/YKWrWBl17h160/+WTet7U8wC+vW9Vue5v2r9mq4zb7ahoaGJzGzOHV/ydeCZmRGx4E+BzNwL7AVoNpvZarUYHx+n1Wp19Xl2rqI1s7u2nOaeo/W8BL/OvUNv+p+8vdWbYhbQr++1Os/9Azev6zrzOrHYq1COR8RGgOrjid6VJEnqxGID/AAwXG0PA/t7U44kqVOdXEb4NaAFXBYRrwKfA/YA+yJiF/AKsKOfRUp6h5fg6Yy2AZ6Zty3w0E09rkWS1AXvxJSkQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVygCXpEIZ4JJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFcoAl6RCGeCSVCgDXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklSoJQV4RNwcES9GxEsRsbtXRUmS2lt0gEfEGuDPgV8HrgFui4hrelWYJOnclnIGfh3wUma+nJk/BMaAbb0pS5LUzlICfBPw3Vn7r1ZjkqRlEJm5uCdG3ArcnJmfrPY/AfxKZt4x57gRYKTafR/wInAZ8L3FFr0K1Ln/OvcO9e7f3hfvFzLz8rmDa5fwglPAlbP2r6jG3iUz9wJ7Z49FxJHMbC7hcxetzv3XuXeod//23vvel7KE8hRwdURcFREXAh8HDvSmLElSO4s+A8/M0xFxB/D3wBrg/sx8vmeVSZLOaSlLKGTmN4BvLOKpe9sfsqrVuf869w717t/ee2zRf8SUJK0sb6WXpEIte4DX+fb7iJiMiKMR8WxEHFnpevotIu6PiBMR8dyssUsj4vGI+E718ZKVrLFfFuj98xExVc3/sxHx0ZWssV8i4sqIOBwRL0TE8xFxZzVel7lfqP+ez/+yLqFUt9//B/BhZm78eQq4LTNfWLYiVlBETALNzKzFtbAR8SFgGvibzNxcjX0ReCMz91Q/wC/JzM+sZJ39sEDvnwemM/NPV7K2fouIjcDGzHw6Ii4GJoDtwE7qMfcL9b+DHs//cp+Be/t9jWTmE8Abc4a3AaPV9igzb+xVZ4HeayEzX8vMp6vtt4BjzNylXZe5X6j/nlvuAK/77fcJ/ENETFR3qNZRIzNfq7ZfBxorWcwKuCMivl0tsazKJYTZImIQ+ADwJDWc+zn9Q4/n3z9iLq8bMvOXmfkfHD9V/ZpdWzmzfleny6D+Avgl4FrgNeCelS2nvyJiAPg68LuZ+f3Zj9Vh7ufpv+fzv9wB3tHt96tVZk5VH08AjzKzpFQ3x6s1wjNrhSdWuJ5lk5nHM/NHmflj4K9YxfMfERcwE14PZuYj1XBt5n6+/vsx/8sd4LW9/T4i1lV/0CAi1gEfAZ4797NWpQPAcLU9DOxfwVqW1Znwqvwmq3T+IyKA+4BjmfmlWQ/VYu4X6r8f87/sN/JUl878Ge/cfv+FZS1ghUTELzJz1g0zd8B+dbX3HhFfA1rM/E9sx4HPAX8L7AN+HngF2JGZq+6PfQv03mLm1+cEJoHfmbUmvGpExA3APwNHgR9Xw59lZh24DnO/UP+30eP5905MSSqUf8SUpEIZ4JJUKANckgplgEtSoQxwSSqUAS5JhTLAJalQBrgkFer/AdFWz7bnM3HnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined_df.Place.hist(bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qw6Sc9o9jnYy"
   },
   "outputs": [],
   "source": [
    "# combined_df.drop(columns=['Season '], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 480
    },
    "id": "3lcZXXFFnvUG",
    "outputId": "18d85254-2a14-43f0-8d46-e6ed2658672c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16    58\n",
      "19    29\n",
      "10    20\n",
      "12    19\n",
      "6     18\n",
      "Name: Place, dtype: int64\n",
      "16    29\n",
      "19    29\n",
      "10    20\n",
      "12    19\n",
      "6     18\n",
      "Name: Place, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5bcdbe7410>"
      ]
     },
     "execution_count": 24,
     "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": [
    "print( combined_df.Place.value_counts().head() )\n",
    "\n",
    "# Place 16 is over represented in the data set, lets try and reduce this to a 28 (compared to 58)\n",
    "\n",
    "locs_to_remove = np.random.choice(np.where(combined_df.Place == 16)[0], size=37)\n",
    "\n",
    "combined_df.drop(index=locs_to_remove, inplace=True)\n",
    "\n",
    "print( combined_df.Place.value_counts().head() )\n",
    "\n",
    "combined_df.Place.hist(bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 411
    },
    "id": "EhS34bzPDxOh",
    "outputId": "dd497761-deb3-49f3-c22d-0544604960dc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-b9db0151-fccf-45b8-82e2-e92ad1cebc61\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>1-on-1 week</th>\n",
       "      <th>FIR</th>\n",
       "      <th>INTERNATIONAL</th>\n",
       "      <th>NE</th>\n",
       "      <th>NW</th>\n",
       "      <th>SE</th>\n",
       "      <th>SW</th>\n",
       "      <th>Race_0</th>\n",
       "      <th>Race_1</th>\n",
       "      <th>Race_2</th>\n",
       "      <th>Race_3</th>\n",
       "      <th>Race_4</th>\n",
       "      <th>CORPORATE</th>\n",
       "      <th>OTHER</th>\n",
       "      <th>POLITICS</th>\n",
       "      <th>TRADES</th>\n",
       "      <th>TRADITIONAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7340027</th>\n",
       "      <td>21</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7340028</th>\n",
       "      <td>21</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7340029</th>\n",
       "      <td>21</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7340030</th>\n",
       "      <td>21</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7340031</th>\n",
       "      <td>21</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7340032 rows × 18 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b9db0151-fccf-45b8-82e2-e92ad1cebc61')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-b9db0151-fccf-45b8-82e2-e92ad1cebc61 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-b9db0151-fccf-45b8-82e2-e92ad1cebc61');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "         Age  1-on-1 week  FIR  INTERNATIONAL  NE  NW  SE  SW  Race_0  Race_1  \\\n",
       "0         25          0.0    0              0   0   0   1   0       1       0   \n",
       "1         25          0.0    0              0   0   0   1   0       1       0   \n",
       "2         25          0.0    0              0   0   0   1   0       1       0   \n",
       "3         25          0.0    0              0   0   0   1   0       1       0   \n",
       "4         25          0.0    0              0   0   0   1   0       1       0   \n",
       "...      ...          ...  ...            ...  ..  ..  ..  ..     ...     ...   \n",
       "7340027   21          5.0    1              1   1   1   0   1       0       1   \n",
       "7340028   21          5.0    1              1   1   1   0   1       0       1   \n",
       "7340029   21          5.0    1              1   1   1   0   1       0       1   \n",
       "7340030   21          5.0    1              1   1   1   0   1       0       1   \n",
       "7340031   21          5.0    1              1   1   1   0   1       0       1   \n",
       "\n",
       "         Race_2  Race_3  Race_4  CORPORATE  OTHER  POLITICS  TRADES  \\\n",
       "0             0       0       0          0      0         0       1   \n",
       "1             0       0       0          0      0         0       1   \n",
       "2             0       0       0          0      0         0       0   \n",
       "3             0       0       0          0      0         0       0   \n",
       "4             0       0       0          0      0         1       1   \n",
       "...         ...     ...     ...        ...    ...       ...     ...   \n",
       "7340027       1       1       1          1      1         0       0   \n",
       "7340028       1       1       1          1      1         1       1   \n",
       "7340029       1       1       1          1      1         1       1   \n",
       "7340030       1       1       1          1      1         1       0   \n",
       "7340031       1       1       1          1      1         1       0   \n",
       "\n",
       "         TRADITIONAL  \n",
       "0                  0  \n",
       "1                  1  \n",
       "2                  0  \n",
       "3                  1  \n",
       "4                  0  \n",
       "...              ...  \n",
       "7340027            1  \n",
       "7340028            0  \n",
       "7340029            1  \n",
       "7340030            0  \n",
       "7340031            1  \n",
       "\n",
       "[7340032 rows x 18 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all possible combinations to see which combo is best\n",
    "determine = pd.DataFrame(index=pd.MultiIndex.from_product([combined_df[col].unique() for col in combined_df.columns.drop(['Place', 'Season '])], \n",
    "                                                          names=combined_df.columns.drop(['Place', 'Season ']))).reset_index()\n",
    "\n",
    "determine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gJcDnp8y8qoD",
    "outputId": "b4daf8ad-547d-48ec-ed2b-faac07e1d6a0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age',\n",
       " 'Season ',\n",
       " '1-on-1 week',\n",
       " 'FIR',\n",
       " 'INTERNATIONAL',\n",
       " 'NE',\n",
       " 'NW',\n",
       " 'SE',\n",
       " 'SW',\n",
       " 'Race_0',\n",
       " 'Race_1',\n",
       " 'Race_2',\n",
       " 'Race_3',\n",
       " 'Race_4',\n",
       " 'CORPORATE',\n",
       " 'OTHER',\n",
       " 'POLITICS',\n",
       " 'TRADES',\n",
       " 'TRADITIONAL']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_names = list(combined_df.columns)\n",
    "input_names.remove('Place')\n",
    "\n",
    "input_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BZegWxYDl58I"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "labels = combined_df['Place'].to_numpy()\n",
    "\n",
    "inputs = combined_df[input_names].to_numpy()\n",
    "\n",
    "labels = torch.tensor( labels )\n",
    "inputs = torch.tensor( inputs )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X6pZpjUMugdN",
    "outputId": "9916ec00-2631-418f-e504-3a280d5a1d69"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(labels.numpy()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n_RP2XlomrZD",
    "outputId": "9186eaf6-1012-4bcf-aaba-e62fd552b6b0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLP(\n",
       "  (hiddenlayer): Linear(in_features=10, out_features=20, bias=True)\n",
       "  (mid_layer_0): Linear(in_features=20, out_features=20, bias=True)\n",
       "  (outputlayer): Linear(in_features=20, out_features=25, bias=True)\n",
       "  (batch_norm_layer): BatchNorm1d(20, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (sigmoid): Sigmoid()\n",
       "  (softmax): Softmax(dim=None)\n",
       "  (relu): ReLU()\n",
       "  (leaky_relu): LeakyReLU(negative_slope=0.01)\n",
       ")"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class MLP(torch.nn.Module):\n",
    "\n",
    "      # this defines the model\n",
    "        def __init__(self, input_size, hidden_size, n_hidden_layers):\n",
    "            super(MLP, self).__init__()\n",
    "            self.input_size = input_size\n",
    "            self.hidden_size  = hidden_size\n",
    "            self.n_hidden_layers = n_hidden_layers\n",
    "\n",
    "            self.hiddenlayer = torch.nn.Linear(self.input_size, self.hidden_size)\n",
    "            \n",
    "            for i in range(self.n_hidden_layers):\n",
    "                setattr( self, f'mid_layer_{i}', torch.nn.Linear(self.hidden_size, self.hidden_size) )\n",
    "            \n",
    "            # self.hiddenlayer_mid = torch.nn.Linear(self.hidden_size, self.hidden_size)\n",
    "\n",
    "            self.outputlayer = torch.nn.Linear(self.hidden_size, 25) # number of classes, Places\n",
    "\n",
    "            # Normalizations\n",
    "            # self.batch_norm_layer_input = torch.nn.BatchNorm1d(self.input_size) \n",
    "            self.batch_norm_layer = torch.nn.BatchNorm1d(self.hidden_size)\n",
    "\n",
    "            # some nonlinear options\n",
    "            self.sigmoid = torch.nn.Sigmoid() # binary\n",
    "            self.softmax = torch.nn.Softmax()\n",
    "            self.relu = torch.nn.ReLU()\n",
    "            self.leaky_relu = torch.nn.LeakyReLU(0.01)\n",
    "\n",
    "        def forward(self, x):\n",
    "            layer1 = self.hiddenlayer(x)\n",
    "            activation = self.leaky_relu(layer1)\n",
    "\n",
    "            for i in range(self.n_hidden_layers):\n",
    "                _hiddenlayer = getattr(self, f'mid_layer_{i}')\n",
    "                layer = _hiddenlayer(activation)\n",
    "                activation = self.leaky_relu( self.batch_norm_layer(layer) )\n",
    "\n",
    "            # layer2 = self.hiddenlayer_mid(activation)\n",
    "            # activation = self.leaky_relu( self.batch_norm_layer(layer2) )\n",
    "\n",
    "            layer3 = self.outputlayer(activation)\n",
    "            output = self.softmax( layer3 )\n",
    "            return output\n",
    "\n",
    "MLP(10, 20, 1) # test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 551
    },
    "id": "dA1OE3YasRhx",
    "outputId": "01401749-fa45-4473-f1c3-ac7de2f6537c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP(\n",
      "  (hiddenlayer): Linear(in_features=19, out_features=19, bias=True)\n",
      "  (mid_layer_0): Linear(in_features=19, out_features=19, bias=True)\n",
      "  (outputlayer): Linear(in_features=19, out_features=25, bias=True)\n",
      "  (batch_norm_layer): BatchNorm1d(19, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (sigmoid): Sigmoid()\n",
      "  (softmax): Softmax(dim=None)\n",
      "  (relu): ReLU()\n",
      "  (leaky_relu): LeakyReLU(negative_slope=0.01)\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/70000 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:42: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
      "100%|██████████| 70000/70000 [02:28<00:00, 471.48it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6108325\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEtCAYAAAASkvd7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAASdAAAEnQB3mYfeAAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAca0lEQVR4nO3df2xd533f8c+X8o86XBOKppwmEAOSkmqvUpSNZIUBdRarphI3WOeskiiga4FhraklwTBgjkW5GJYNQytRjvfPtniUFwwYChf6NaRZkcWTbEudkT9sWpk5q7VikdRMLc4kSqIT04plk9/9cc6hDo/OvZf33nN5z718vwCCvOfH5cMj8nz0/DjPY+4uAACSWupdAABAPhEQAIBUBAQAIBUBAQBIRUAAAFIREACAVAQEACAVAQEASEVAAABS3VHvAuSBmX1C0hckTUu6WefiAECt3CWpU9IZd3+31MEEROALkv683oUAgBXyqKTvlTqIgAhMS9J3v/tdbdy4sd5lAYCauHDhgr7yla9I4T2vFAIicFOSNm7cqM2bN9e7LABQa8tqSqeTGgCQioAAAKQiIAAAqQgIAEAqOqmrMDUzp6Nj07p0/YbWr71Hg/2d6u5orXexACATBESFjo5Na/+JcS3EVmwdPTOhgzu3arC/s34FA4CM0MRUgamZOe0/Ma7kct7u0v4T47o4M1efggFAhgiIChwdm9aCS4l8kEtacOnI2LKeQQGAXCMgKnDp+o2q9gNAI8hNH4SZtUkaDF/2SWqXNOzukyXOGwm/7JF0LTxntmYFlbR+7T1V7QeARpCnGsSzktrc/bC771Vwsz9Z7IQwHA64+7C771YQKi/UuqCD/Z1qMcmS5ZHUYtIeOqkBNIE8BYQkbYh9/ZqknrBmcZtw+5Ck/tjmA5J6zWygdkWUujtadXDnVlkiIcykkZ1b1cVQVwBNIDdNTGENIK5P0uwymoviARI1R/VkVrACBvs7ta2rXUdiz0Hs6e8kHAA0jdwERFysPyIZGovC4Fib2BwFQ8F+CzO7T9K6xOYNaceW0tXRquFHHqjkVADIvdwFhJntk7RHQWfzqTJP3ytpssR5X5P0zUrLBwCrRd76IOTuh9y9T9JuMxtd7nlm1qug1rGjxKHflrQl8fFohcUFgKaVuxpEzIikk2Z2bJk1iRFJfaWGxbr7ZUmX49ss2dsMAMhPDcLMjoW1gEh0oy9VI1BY09gbhUOhkU8AgOXLRUCEwbBLS4esRjf5iRLnDkkaTYRDTYe5AsBqkIuAcPezko67++HY5j0KOpwPS0GIJPskwucd+hQ8L7ErDItnVWQUEwBgefLUB/FYbNoMKRiy2pd4PWhm8ak0oieth+JvlPJMBQCgTLkJiPCmP1xk/3FJxxPb6F0GgBrJRRMTACB/CAgAQCoCAgCQioAAAKQiIAAAqQgIAEAqAgIAkIqAAACkIiAAAKkICABAKgICAJCKgAAApCIgAACpCAgAQCoCAgCQioAAAKQiIAAAqQgIAEAqAgIAkIqAAACkIiAAAKkICABAKgICAJCKgAAApCIgAACpCAgAQCoCAgCQioAAAKQiIAAAqQgIAEAqAgIAkIqAAACkIiAAAKkICABAKgICAJCKgAAApCIgAACpCAgAQCoCAgCQioAAAKQiIAAAqQgIAEAqAgIAkIqAAACkIiAAAKnuqHcBGtnUzJyOjk3r0vUbWr/2Hg32d6q7o7XexQKATBAQFTo6Nq39J8a14Le2jZ6Z0MGdWzXY31m/ggFARmhiqsDUzJz2nxiX+9Lt7tL+E+O6ODNXn4IBQIYIiAocHZvWgkuJfJBLWnDpyNh0PYoFAJkiICpw6fqNqvYDQCMgICqwfu09Ve0HgEZAQFRgsL9TLSZZYrtJajFpD53UAJoAAVGB7o5WHdy5VZZICDNpZOdWdTHUFUATYJhrhQb7O7Wtq11HYs9B7OnvJBwANA0CogpdHa0afuSBehcDAGoiNwFhZm2SBsOXfZLaJQ27+2SJ84ZiLze4+3CNiggAq0qe+iCeldTm7ofdfa+ka5JOFjshDIcN4TmHJZ00s2MrUFYAaHp5CghJ2hD7+jVJPWHNopBhSUeiF+5+StJAiXMAAMuQmyYmd9+d2NQnadbdZ9OOD0OgR1KyCeqagqaqw5kXEgBWkbzVICQt6Y9IhkZcf4Hts1paEwEAVCA3NYiIme2TtEdBB/WpIoe2SVKBGkbBJiYzu0/SusRmAgUAEnIXEO5+SNIhMztpZn1hh3WWvibpmxm/JwA0nVw2MYVGJA2Z2UCB/bPSYnNUXFu0r4BvS9qS+Hi0uqICQPPJTUCY2TEz641tijqfdxQ4JdrfntjeLmmi0Pdx98vufi7+Uex4AFitchEQYTDs0tKO56hmkHrzDh+gm9Tt/Q1tkor1XQAAliEXAeHuZyUdDx92i+yRNBltM7NeMxtNnDoiabGPImyOOlXq6WsAQGl56qR+zMxGYq97FDwLEX89aGbD0cgldz9sZvvCkU+zCp6qLtQkBQAoQ24CIrzpF5xHyd2PSzqesv1QLcsFAKtVLpqYAAD5Q0AAAFIREACAVAQEACAVAQEASEVAAABSERAAgFQEBAAgFQEBAEhFQAAAUlUdEGb2hJkdMLO/Fb4+YmZvhZ+7qn1/AEB9ZFWDOOju/8vMDkrqdfdN7r5HwRTeAIAGlEVAzLr7u+HXQ5LiU3K/m3I8AKABZBEQVyXJzLoVLNYTn3HVM3h/AEAdZBEQG8zsG5KOKVis56KZdZvZM7p9tTcAQIOoej0Id3/KzB6WNOXuJ8LNuxQsB0oTEwA0qEwWDHL3FxKvn8rifQEA9cMwVwBAKoa5AgBSMcwVAJCKYa4AgFQMcwUApGKYKwAgVWbDXMPRTM+Hm866+5NZvDcAoD6qDggz+4Sk1xTUGCbDzX1m9pakPnf/WbXfAwCw8rKoQeyXtMPdp+IbzaxH0oikr2bwPQAAKyyLTurJZDhIkrvHaxQAgAaTRUAUG8ransH7AwDqIIuAWGtmv5PcaGZ/KMkyeH8AQB1UHRDhxHxfNLP5cA6mt8xsXkG/xP7qiwgAqIeshrn+EzM7LKlPwcNxp9z9R1m8d95Nzczp6Ni0Ll2/ofVr79Fgf6e6O1rrXSwAqFomASFJ7n5W0tn4NjPrcveLWX2PvDk6Nq39J8a1EOuFGT0zoYM7t2qwv7N+BQOADGQ1m2shIzV+/7qZmpnT/hPj8kQXvbu0/8S4Ls7M1adgAJCRsgLCzP62mV1d5seCmni676Nj01rw24dwuaQFl46MTdejWACQmXKbmCYVPDW9dxnHmqT/WHaJGsSl6zeq2g8AeVdWQLj7u2Y2nPZgXBozG66sWPm3fu09Ve0HgLwruw+inNFJzTySabC/Uy12+4MeJqnFpD10UgNocLXupG5a3R2tOrhzqyyREGbSyM6t6mKoK4AGl9kw19VosL9T27radST2HMSe/k7CAUBTICCq1NXRquFHHqh3MQAgczQxAQBSERAAgFQEBAAgFQEBAEhFQAAAUhEQAIBUBAQAIBUBAQBIRUAAAFIREACAVAQEACAVczFlYGpmTkdjE/YN9neqmwn7ADQ4AqJKR8emtf/EuBZia4+OnpnQwZ1bNciaEAAaGE1MVZiamdP+E+PyxMLU7tL+E+O6ODNXn4IBQAYIiCocHZvWgkuJfJBLWnDpyNh0PYoFAJkgIKpw6fqNqvYDQJ7lqg/CzEbCL3skXZM07O6zJc4ZktQWvrxX0oFS52Rl/dp7qtoPAHmWm4AIw2Hx5m5mxyS9IKmvyDlDkk65+2T4uk3SMUk7al/iYMnR0TMT8kQzkylYm3oPndQAGlgumpjCG/uQpP7Y5gOSes1soMipu6NwkKQwXGbD96u57o5WHdy5VWZLt5tJIzu3sjY1gIaWmxpEKH5jj278PUWObzezUXffG9vWs1JNTFJQi9jW1a4jsecg9vR3Eg4AGl4uAiK8oa9NbI6CYVKFDUs6aWb9knZL2ivpsexLWFxXR6uGH3lgpb8tANRULgKigL2SJt39VKED3P2Ume1W0O8wIemQu58t9qZmdp+kdYnNG6otLE9TA2g2uQwIM+uVNKgiHdThcW0KahprJY1I2mdmPe6+u8hpX5P0zazKKvE0NYDmlItO6hQjkvriHdAFHHP3Q+4+G/ZD7JC0q0TH9rclbUl8PFppQXmaGkCzyl1AmNmopL2Joatpx/VKWtIZHTZHDavIMFd3v+zu5+IfCpqnKsLT1ACaVa4CInyuYTQRDoVqA4VGKk2qiht+uXiaGkCzyk1AhM1CfZJ6zGxXGBbPKhzFZGa9Ye1CkhSGSJuZJYfB7nD3wytVbp6mBtCs8tRJfTL8PBTfGOtw7pE0aGbx6Td2S3rSgifVriqYamN4Bcq6iKepATSr3ASEu1uJ/cclHU9sm9UKB0JS9DR1sqOap6kBNLrcBEQj42lqAM2IgMgIT1MDaDa56aQGAOQLNYgMMd0GgGZCQGSE6TYANBuamDLAdBsAmhEBkQGm2wDQjGhiykBW023QhwEgTwiIDGQx3QZ9GADyhiamDAz2d6qlyHPgH7+7eA7ThwEgjwiIDHR3tOrxL95fcP+h588XvcnThwEgjwiIjFy6/n7BfS7p8F8WXvuIKcMB5BEBkZFXL14vuv8H594puI8pwwHkEQGxQq7NfahnXrqQui/qw0h2Y5iklgaZMnxqZk4jP3hT//TPfqSRH7ypKfpNgIbHKKaMbOtq14XL7xU9ZuT585q+fkM//+CjJcNYG33KcEZgAc3JPDl0ZhUys82S3njjjTe0efPmit5jamZO2791uqxzWkxLbqIXZ+YabsrwqZk5Pfz06YILJr34+EO5/xmA1eLcuXPasmWLJG1x93OljqcGkZHujlZtv3+dXjp/ZdnnRMNYt3W1q6ujtSGnDI9GYCW5gp/vyNh0w/1MAAL0QWToX/52ebWPZhjGyggsoHkREBnq7mjVE18q/DxEIT+cuFqD0qwMRmABzYuAyNjXt2/U3+luL+uc16dn9fJby2+aypNmGIEFIB0BUQMHdm4t+5zf+84r6vs3J/VH//V/N9QQ0WgEliUSolFGYAEojE7qGujuaNUfPtit//TyVFnnXZ27qedeeVt/9urbGmmgIaKD/Z3a1tXecCOwABRHQNTIv/h7v6b/O3tD//2Nn5Z9bnJ0UyNoxBFYAIqjiamGnvm9Pv3pH2zTpz/xS2Wf2+ijmwA0PgKixh7ctE4/fPJhnf7GQ/r4L5VXYXvpzcs1KhUAlEZArJCujlb9h3/YW9Y5b/7056wFAaBuCIgV9PlN67S7f31Z59DMBKBeCIgV9tSuz5XVL/HmT39e4xIBQDoCog6ifok//YNtJY89/eZlHaUWAaAOCIg6enDTupJTc7ik4eOsSw1g5fEcRJ19fftGtShYK6IQl7T9W6d15xrTHWta1HVvq5788gP6/KZ1K1ZOAKsPNYgc+Or2jfrNB+4reoxLujnvev/mvP7qnZ/p97/zip44/vrKFBDAqkQNIifu/5Vf1otlPvdwbOySzpy/ooG/+Uk99nd71F3mU9dTM3M6GpseI1rhDgAkAiI3Bvs79czpibLPu/zzD/TcK2/ruVfe1h0twSyqCx7MpHrnmha5pI/mFxa3rWkxzS+4PlpYugKcxDKhAJYiIHKikhXpkj5auPX1vEsfLiws2R9sK7zE7IJL+46P619975zW/Y279RsbOyqqmQBoDvRB5Ei5K9LVyvs35/V/rr2v5155W7/59GmG2QKrFAGRI9E04XnizjBbYLWiiSlnqpkmvFaiYbZ3tOi2vozo9d13rtG9rTRLAc2EGkQORdOE/9qnflkfu2uN7szBv5JL+nAh6seQfvGRL3n93gc0SwHNhhpETj24aZ2+/89uPQh3dGxaw8fHbxt5lEeNuOARgNvl4P+mWI7B/k699I2H9LvbPqPPtH9Md7RY6ZPqiAWPgMZHDaKBdHW06k9+57OLr19+64r+5Pt/rYtX39dH8wtqaZHm52/1C9x5R4vk0ofx5yDW2OIxUtBEVCuXrt+o3ZsDqDkCooElm6EqcXFmTk//j/M6/eMreu8XH2XahLV+7T0ZvhuAlUZArHJdHa36d78brHQ3NTOn7d86ncn7tpi0hyeygYZGHwQWdXe06tCuraq2d8NMGtm5lQ5qoMFRg8ASg/2d2tbVrsN/OamXL8zo+vs35QuuBfeCfRnRcxAfu3ONbs67bs4v6I+//9c68+MrevyL9/NMBNCgCAjcJtkZvhxPHH9dx8YuLb5+/+a8/mL8Hf3F+Dt64kv36+vbN2ZdTAA1RkCgav/zrStLwiHpqefPq0XBuhdIx9TryCMCAlX7tyd/XPKYQ8+f12999lP0S6Q4Ojat/SfGFZ9ol6nXkQd0UqNq/+9nH5Q8xsWDc2mmZua0/8S4PDG+OJp6/R//51c0xUSJqBNqEKjaJz9+t34yW/qhuB9OXF2B0jSWo2PTKrJEh148f0Uvnj+te1vv0pc2/woTITaJRmlSpAaBqv3zHb+6rONen57Vy29VviBSM1ru0+ZX527quVfe1vZvndaj//5lahUN7OjYtB5++rSeOT2h//b6T/TM6Qk9nNMJLs2TddtVyMw2S3rjjTfe0ObN+Vi0p9EkRzEV02LB0qhp04YXWyZ1JY5Z6e9/c35hyUqA5TBJd7TU/5rl7Zh6f/9ix8wvSGn/3Kbg+aEXH3+opv10586d05YtWyRpi7ufK3U8ASECIisvv3VFj/2XMd34sMI7HrDKffWhDRp+5IGavX+5AUEfBDLz4KZ1+ke/0a1nTk/UuyhAQ3rm9IQOn5koWhOZX3DdsaZFXfe26skvP6DPb6puPrZi6INAphiWCVSn0KJc73+4oBsfLujmvOv9m/P6q3d+pt//zit64vjrNStLrmoQZjYSftkj6ZqkYXefXcZ5+2IvZ939cC3Kh9K6O1r1mfZ79PY1pvoGVsKxsUt69HOf1oM1qEnkpgYRhsMBdx92992S2iW9sIzzTko66+6HJJ2SNGpmbbUtLYr5439Q3jQdAKrz9DIeVq1ELgIivKEPSeqPbT4gqdfMBoqcN6SgxnAq3DQp6dByah2onc9vWqfd/evrXYyGZpJ+e+un1HrXmnoXBQ1gOQ+rViJXTUyS4v/znww/9xQ5flhS1CylMBiGa1AulOmpXZ/To5/7tPYdH9dP3v1FvYvTUEzSoV1btTvsz3n5rStcRxT1yY/fXZP3zUVAhDf2tYnNUTBMqrAeabEPYlbSBgXNVAVrEGZ2n6RkY92GsgqMZXlw0zr98MmHdXFmbnH68KvvfaCbH80XnDa81DKptT6mXt/fZfrYXWv0hV9dp8e/eP+SsfDJ6/jS+cu6+t4Hml/wXFyzvB1T7++/nGOkoOM5K48v82HVcuUiIArYK2ky1ny0hJn1hl9ucPfhcNuAgn6LviLv+zVJ38yyoCiukunDcTuuY3N55qULGnn+fNXvM9i/viYd1FJOAyK8+Q+q+I2+Pfz8arTB3U+ZWa+ZDRQKFknflnQssW2DpD+vtLwAUK6vbt+o3/rsp8quXX8Uew7ij778QM3CQcppQCjoV+hz92LNS5OJz5FZSb0KRjTdxt0vS7oc32ZW7SKbAFC+vNcKczGKKc7MRiXtjcKh0JDVWHi0p+0HAFQnVwERDlsdTYRDwWGuCmoJyVFObZLO1qaEALB65CYgwg7mPkk9ZrYrDItnFTYhhX0Lo4nTRiTtjr3HLgUPzRXqfwAALFOe+iBOhp+H4hvDp6qloKYwaGaL02+EndJtYXDMSmpz92Id24XcJUkXLlyorOQA0ABi97i7lnM8031LMrO/L0YxAVg9HnX375U6iICQZGafkPQFSdOSbpZ5ejRE9lFJzHNdGa5hNriO2Wjm63iXpE5JZ9z93VIH56mJqW7CC1UyTdPEhshOLGcBDtyOa5gNrmM2VsF1/NFyD8xNJzUAIF8ICABAKgICAJCKgKjeFUn/OvyMynANs8F1zAbXMcQoJgBAKmoQAIBUBAQAIBUBAQBIxYNyQM6Ec4uNxNdDCSevjCyuorhS+7E60UldIf6gAmY2En7ZI+mapOH4muDc2MoTzmp8UsHPEs1kPKTbl9bdG01kWev9jSRcnz4y6+6HY/v4XSyXu/NR5oeCGWdHYq8HJB2rd7nqcB1GFMygG70+Jum15V6nWu9vxA9J+yS5pJ7YtglJvYnjrkfXvtb7G+VDQbAOhF/3htcx+hn5Xazkmta7AI340Sx/UFVeg7bwZx6IbYv+KKM/Um5s5V3TofC6LgZE7HVb4tiJxPE12V/va1LmtYvfsNsSN2x+Fyv4oJO6TOEqdz26fS3sa5IGV75EdRdfEja6Jj2lrlOt91fyg9STmfVKmvRY81yov8ApswpmHa31/kYxrFtrysjdZ/1Wkxm/ixWik7p8zfIHVZXwRrY2sTla/nVS3NjKNeDuh1K2t0mL1zttX633N4oeabEPIvodOBD+XPwuVogaRPma5Q+qFvYq+F/wKXFjW7ZwqdzDJQ9EqrD2JQUdw4c86Jg+KemFcDu/ixUiIJCJ8I90UNKOepelkYTNE4VuLlLwv9DF42Lawn213t8I2sPPr0Ybwv+k9IYjslAhmpjKt/gHlfijbqQ/qFoYkdTnt8bul7pOtd7fKAYl9ZnZr4evoxv1iJm9Kul4+LpdS3+udgUdo5M13t8IJhOfI7MKBk6clfhdrAQBUb5m+IPKVPhg116/NW6/TbW/cTXFv4PHxulLi9duSMHzJNH1nNTtTRVtkk65+2Qt91f6c62k8GeQbtUkkvhdrBBNTGUK/2gb+g8qS+HDQaOJcBgodZ1qvb+6n6qu0m5yIwr6dyQtPsh2KlZbq/X+RnBKtwZJRNokneV3sXIERGWa4Q+qauHP3adgWOuuMCye1a3/UXFjK0MUtuHL0ejJ3LCWMWFm+8JtO9x9sa+n1vsbxIikxSe/w47/s2FfRLSf38UyMdVGhWKP9M+qWR6rL5OZpf7yuLvFjil6nWq9H6tHGAo7FPwutLn73sR+fhfLREAAAFLRxAQASEVAAABSERAAgFQEBAAgFQEBAEhFQAAAUhEQAIBUBAQAIBUBATQQM+uJrX8A1BQBATSIcH6fY7q1EA5QU0y1ATSQsPbwgrsnl3sFMkcNAmgsDb0ADRoLAQEASMWKckAZzGxEwSph0VrSh8Jppp8Mtw1LWlw+NDnldPge+3SrJrBB0oHkmtThMfdKuhp+XnKMmfVIGgi/56+7+24BGaMPAlgmMzupYPW84+HrUUmvufvhsAP5pILFdk6F+/dJ2uPufbH3GJV0MvYePeHrDYljFIVL/HV4/ISk3bH3OCnpWHL5UqBaNDEByxB2Dg9EN+XQSd1aReyapNnYCmZy90OSesMaRvQeQ/H3iJarjBabCQNgSMEKZZHXwu8VF1/KclJBTQTIFE1MwPIMSJqNbvahHkljJc47q6DJ6bikft1ajjVuUsFKaIck9UqLwaHw69tqBokmqVndvh4yUDUCAihDogZRLm7iaCg0MQHLc0pSm5ktucmHTULF9Ep6NfYeacf3KKhpKPqcfN9lfB8gcwQEsAzuflZBM9GT0bYwLAZih7XFp8EI+xVORbWO6D3MbCh2TI+knmiB+7Bp6bCC0VBx0fdJq4VQM0FNMIoJKEM4zFUKRhIt9g9ETzhLekxSu4Kb9r3Rjb/Ae1xV0Lk8XGKYazScdkBBcAwoCKthSdEQWykYCnsom58UICCATDAFBpoRTUxANtrrXQAgawQEUKWw6WdEQR/EaLIjG2hUNDEBAFJRgwAApCIgAACpCAgAQCoCAgCQioAAAKQiIAAAqQgIAEAqAgIAkIqAAACkIiAAAKn+P5+HDjNvpPpfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 405.6x304.2 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "# train the model\n",
    "def train_model(training_data, training_labels, test_data,test_labels, model):\n",
    "  # define the optimization\n",
    "  # criterion = torch.nn.MSELoss() # output layer size = 1\n",
    "  # criterion = torch.nn.BCELoss() # output layer size = 25\n",
    "  criterion = torch.nn.CrossEntropyLoss() # weighted costs \n",
    "\n",
    "  # def mean_power_err_loss(input, target, power=3):\n",
    "  #   return torch.mean(torch.abs(input - target) ** power)\n",
    "  # criterion = mean_power_err_loss\n",
    "\n",
    "  # optimizer = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.9)\n",
    "\n",
    "  optimizer = torch.optim.Adam( model.parameters(), lr=1e-3, betas=(0.9, 0.999) )\n",
    "  # Increase the number of epochs for your \"final\" run\n",
    "  epochs, losses = [], []\n",
    "  for epoch in tqdm(range(70_000)):\n",
    "\n",
    "    # clear the gradient\n",
    "    optimizer.zero_grad()\n",
    "    # compute the model output\n",
    "    myoutput = model(training_data.float())\n",
    "\n",
    "    # calculate loss\n",
    "    loss = criterion(myoutput, training_labels.float())\n",
    "\n",
    "    # credit assignment\n",
    "    loss.backward()\n",
    "    # update model weights\n",
    "    optimizer.step()\n",
    "\n",
    "    if epoch%200 == 0:\n",
    "      epochs.append(epoch)\n",
    "      losses.append(loss.detach().numpy())\n",
    "\n",
    "  # ADD PLOT\n",
    "  fig, ax = plt.subplots(1,1, dpi=120)\n",
    "  ax.scatter(epochs, losses)\n",
    "  ax.set_ylabel('loss')\n",
    "  ax.set_xlabel('epoch')\n",
    "  print(losses[-1])\n",
    "  return fig, ax, model\n",
    "\n",
    "\n",
    "# convert ranks or contestants into probability array \n",
    "p_labels = torch.zeros( (len(labels),25), dtype=torch.long)\n",
    "for i, rank in enumerate(labels):\n",
    "  p_labels[i,rank-1] = 1\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(inputs, \n",
    "                                                    p_labels, \n",
    "                                                    # np.reshape(labels, (len(labels),1)), \n",
    "                                                    test_size=0.33, train_size=0.67, random_state=12345 )\n",
    "\n",
    "num_feat = X_train.numpy().shape[1]\n",
    "model = MLP( input_size=num_feat, hidden_size=num_feat, n_hidden_layers=1 )\n",
    "print(model)\n",
    "fig, ax, model = train_model( X_train, y_train, X_test, y_test, model )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 462
    },
    "id": "elkBSz00tVBv",
    "outputId": "c14332be-6551-4a24-a2b9-1ab04bd33b22"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:42: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x450 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09841991341991342 0.04\n"
     ]
    }
   ],
   "source": [
    "pred = model( X_test.float() ).detach().numpy()\n",
    "\n",
    "# plt.scatter( y_train, np.argmax(pred, axis=1) )\n",
    "# plt.scatter( np.argmax(y_test, axis=1), np.argmax(pred, axis=1) )\n",
    "# plt.show()\n",
    "\n",
    "fig, subs = plt.subplots(nrows=1,ncols=2, figsize=(6,3), gridspec_kw={'width_ratios': [1,2]},dpi=150)\n",
    "subs[0].imshow( pred, aspect='equal', extent=(0,25, 0,50), cmap='RdPu')\n",
    "\n",
    "ind=0\n",
    "\n",
    "dat = subs[1].scatter( np.argmax(pred, axis=1), np.argmax(y_test, axis=1), c=X_test.numpy()[:,ind], label=input_names[ind] )\n",
    "subs[1].plot( np.arange(0,25), np.arange(0,25), '-k', zorder=0 )\n",
    "subs[1].grid(alpha=0.25)\n",
    "fig.colorbar(dat, label=input_names[ind])\n",
    "plt.show()\n",
    "\n",
    "\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "score = balanced_accuracy_score( np.argmax(y_test, axis=1),  np.argmax(pred, axis=1) )\n",
    "print( score, 1/25 )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Qu24_mOFsSVt",
    "outputId": "4e71c355-3b57-4cb9-82c3-fff008c4f0c4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0,Age',\n",
       " '1,Season ',\n",
       " '2,1-on-1 week',\n",
       " '3,FIR',\n",
       " '4,INTERNATIONAL',\n",
       " '5,NE',\n",
       " '6,NW',\n",
       " '7,SE',\n",
       " '8,SW',\n",
       " '9,Race_0',\n",
       " '10,Race_1',\n",
       " '11,Race_2',\n",
       " '12,Race_3',\n",
       " '13,Race_4',\n",
       " '14,CORPORATE',\n",
       " '15,OTHER',\n",
       " '16,POLITICS',\n",
       " '17,TRADES',\n",
       " '18,TRADITIONAL']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[f\"{i},{val}\" for i, val in enumerate(input_names)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3ooX4BfbnuNp"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import numpy as np\n",
    "\n",
    "def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):\n",
    "    new_cmap = colors.LinearSegmentedColormap.from_list(\n",
    "        'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),\n",
    "        cmap(np.linspace(minval, maxval, n)))\n",
    "    return new_cmap\n",
    "\n",
    "# arr = np.linspace(0, 50, 100).reshape((10, 10))\n",
    "# fig, ax = plt.subplots(ncols=2)\n",
    "\n",
    "# cmap = plt.get_cmap('jet')\n",
    "# new_cmap = truncate_colormap(cmap, 0.2, 0.8)\n",
    "# ax[0].imshow(arr, interpolation='nearest', cmap=cmap)\n",
    "# ax[1].imshow(arr, interpolation='nearest', cmap=new_cmap)\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HABAR7-0I16Y"
   },
   "outputs": [],
   "source": [
    "# install latex for matplotlib\n",
    "!sudo apt install cm-super dvipng texlive-latex-extra texlive-latex-recommended\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 863
    },
    "id": "z35Ux_iytVZY",
    "outputId": "69bea827-682a-461b-9bd2-bed631745ff6"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1050x1050 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "import matplotlib.patches as patches\n",
    "\n",
    "plt.style.use('drive/MyDrive/DSFP_data/mpl_style.txt')\n",
    "\n",
    "\n",
    "cm = confusion_matrix( np.argmax(y_test, axis=1),  np.argmax(pred, axis=1) )\n",
    "\n",
    "disp = ConfusionMatrixDisplay( confusion_matrix=cm, display_labels=[i for i in range(1,26)] )\n",
    "\n",
    "fig, ax = plt.subplots( 1, 1, figsize=(7,7), dpi=150)\n",
    "\n",
    "cmap = plt.get_cmap('RdPu')\n",
    "custom_cmap = truncate_colormap(cmap, 0.0, 0.8)\n",
    "\n",
    "disp.plot(ax = ax, cmap=custom_cmap, colorbar=False)\n",
    "\n",
    "plt.colorbar( mappable=disp.im_, ax=ax, fraction=0.046, pad=0.04 )\n",
    "\n",
    "for i in range(25):\n",
    "  rect = patches.Rectangle( (i-.5, i-.5), 1, 1, linewidth=1, edgecolor='k', facecolor='none')\n",
    "  # Add the patch to the Axes\n",
    "  ax.add_patch(rect)\n",
    "\n",
    "ax.set_xlabel( 'Predicted Place', fontsize=12,)\n",
    "ax.set_ylabel( 'True Place', fontsize=12,)\n",
    "\n",
    "# plt.savefig('drive/MyDrive/DSFP_data/NN_conf_matr.pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GFkoa2wC0Tjz"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "A73IHXsUXEPX"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a9FQCTaiLfdp",
    "outputId": "76a5ca11-f702-44e9-b4eb-cfbb090f98cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([132, 19])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4e-gyCQcL0vB",
    "outputId": "2e5b0a6d-4149-4d9e-bb9a-a1c5a8b088c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-1.0810,  1.3962,  2.5343,  0.5350,  0.6050],\n",
      "        [ 0.9558,  0.3176,  0.7493, -1.8300,  0.4218],\n",
      "        [-0.9194,  0.4368, -2.6578, -0.4051,  0.1625]], requires_grad=True) \n",
      " tensor([3, 0, 1])\n"
     ]
    }
   ],
   "source": [
    "# Example of target with class indices\n",
    "loss = torch.nn.CrossEntropyLoss()\n",
    "input = torch.randn(3, 5, requires_grad=True)\n",
    "target = torch.empty(3, dtype=torch.long).random_(5)\n",
    "\n",
    "print(input, '\\n', target)\n",
    "\n",
    "output = loss(input, target)\n",
    "output.backward()\n",
    "\n",
    "# Example of target with class probabilities\n",
    "input = torch.randn(3, 5, requires_grad=True)\n",
    "target = torch.randn(3, 5).softmax(dim=1)\n",
    "\n",
    "output = loss(input, target)\n",
    "\n",
    "output.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZgQc65FxdWIT",
    "outputId": "d3f5b584-e55a-4562-9c24-4f97a6dfb7e8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.,\n",
       "       24., 25.])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique( X_train[:,1].numpy() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7gpYiNS2eJNe",
    "outputId": "0fd35cd5-2a7c-4b56-b5d7-94502e410aa3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.041952290185935694"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import balanced_accuracy_score\n",
    "\n",
    "balanced_accuracy_score( np.random.randint(low=1, high=25, size=600_000),  np.random.randint(low=1, high=25, size=600_000) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ogYdVzx2eXfX",
    "outputId": "f0cbd6de-9aa1-4925-8cbf-5e806c9a4443"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 656
    },
    "id": "gZfYl8gpVtAG",
    "outputId": "ece1b6d5-5939-410a-baa7-12309196d6f5"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,2))\n",
    "plt.hist( combined_df.Place, bins=25, histtype='step', color='pink', linewidth=2 )\n",
    "plt.grid(alpha=0.1)\n",
    "plt.xticks( np.arange(1,26,2) )\n",
    "plt.xlabel('Place')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# plt.savefig('drive/MyDrive/DSFP_data/NN_places_hist.pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 704
    },
    "id": "4LSV5fewaDtP",
    "outputId": "1d9fd222-17da-4895-f2a7-4bb1e9e8eb75"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:42: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 750x750 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pred = model( X_test.float() ).detach().numpy()\n",
    "\n",
    "fig, subs = plt.subplots(nrows=1,ncols=2, figsize=(3,3),dpi=250)\n",
    "\n",
    "locs = np.argsort( np.argmax(y_test.numpy(), axis=1) )\n",
    "\n",
    "subs[0].imshow( pred[locs,:] , cmap='RdPu', extent=(0,25, 0,130) )\n",
    "subs[1].imshow( y_test.numpy()[locs,:],  cmap='bone', extent=(0,25, 0,130) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xwBJlkMexVgz",
    "outputId": "39b695a3-ec8b-46e9-8391-bedd6abeac6b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(130, 25)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hIa2q3nnxZIa"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Bachelor_analysis.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}