election_county_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>CS 4780/5780 Final Project: </h2>\n",
    "<h3>Election Result Prediction for US Counties</h3>\n",
    "\n",
    "Jerry Sun, Peter Wu, Shihao Cao (js2746, plw53, sfc72)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Introduction:</h3>\n",
    "\n",
    "<p> The final project is about conducting a real-world machine learning project on your own, with everything that is involved. Unlike in the programming projects 1-5, where we gave you all the scaffolding and you just filled in the blanks, you now start from scratch. The programming project provide templates for how to do this, and the most recent video lectures summarize some of the tricks you will need (e.g. feature normalization, feature construction). So, this final project brings realism to how you will use machine learning in the real world.  </p>\n",
    "\n",
    "The task you will work on is forecasting election results. Economic and sociological factors have been widely used when making predictions on the voting results of US elections. Economic and sociological factors vary a lot among counties in the United States. In addition, as you may observe from the election map of recent elections, neighbor counties show similar patterns in terms of the voting results. In this project you will bring the power of machine learning to make predictions for the county-level election results using Economic and sociological factors and the geographic structure of US counties. </p>\n",
    "<p>\n",
    "\n",
    "<h3>Your Task:</h3>\n",
    "Plase read the project description PDF file carefully and make sure you write your code and answers to all the questions in this Jupyter Notebook. Your answers to the questions are a large portion of your grade for this final project. Please import the packages in this notebook and cite any references you used as mentioned in the project description. You need to print this entire Jupyter Notebook as a PDF file and submit to Gradescope and also submit the ipynb runnable version to Canvas for us to run.\n",
    "\n",
    "<h3>Due Date:</h3>\n",
    "The final project dataset and template jupyter notebook will be due on <strong>December 15th</strong> . Note that <strong>no late submissions will be accepted</strong>  and you cannot use any of your unused slip days before.\n",
    "</p>"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png; width=\"100\";](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Part 1: Basics</h2><p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>1.1 Import:</h3><p>\n",
    "Please import necessary packages to use. Note that learning and using packages are recommended but not required for this project. Some official tutorial for suggested packacges includes:\n",
    "    \n",
    "https://scikit-learn.org/stable/tutorial/basic/tutorial.html\n",
    "    \n",
    "https://pytorch.org/tutorials/\n",
    "    \n",
    "https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html\n",
    "<p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split # Split train and validation sets of the train data\n",
    "from sklearn.neighbors import KNeighborsClassifier # Import KNN model for first basic solution\n",
    "from sklearn import svm # Import SVM since it's good at predicting binary labels - second basic solution\n",
    "from sklearn.model_selection import cross_val_score # uses K-fold cross-val by default.\n",
    "from sklearn.metrics import make_scorer # Import scorer for use in cross-validation scoring (weighted accuracy)\n",
    "import warnings # Remove Warning print statements\n",
    "warnings.filterwarnings('ignore') # Ignore "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>1.2 Weighted Accuracy:</h3><p>\n",
    "Since our dataset labels are heavily biased, you need to use the following function to compute weighted accuracy throughout your training and validation process and we use this for testing on Kaggle.\n",
    "<p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weighted_accuracy(pred, true):\n",
    "    assert(len(pred) == len(true))\n",
    "    num_labels = len(true)\n",
    "    num_pos = sum(true)\n",
    "    num_neg = num_labels - num_pos\n",
    "    frac_pos = num_pos/num_labels\n",
    "    weight_pos = 1/frac_pos\n",
    "    weight_neg = 1/(1-frac_pos)\n",
    "    num_pos_correct = 0\n",
    "    num_neg_correct = 0\n",
    "    for pred_i, true_i in zip(pred, true):\n",
    "        num_pos_correct += (pred_i == true_i and true_i == 1)\n",
    "        num_neg_correct += (pred_i == true_i and true_i == 0)\n",
    "    \n",
    "    d_err = num_pos - num_pos_correct\n",
    "    r_err = num_neg - num_neg_correct\n",
    "    weighted_accuracy = ((weight_pos * num_pos_correct) \n",
    "                         + (weight_neg * num_neg_correct))/((weight_pos * num_pos) + (weight_neg * num_neg))\n",
    "    return weighted_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Part 2: Baseline Solution</h2><p>\n",
    "Note that your code should be commented well and in part 2.4 you can refer to your comments. (e.g. Here is SVM, \n",
    "Here is validation for SVM, etc). Also, we recommend that you do not to use 2012 dataset and the graph dataset to reach the baseline accuracy for 68% in this part, a basic solution with only 2016 dataset and reasonable model selection will be enough, it will be great if you explore thee graph and possibly 2012 dataset in Part 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>2.1 Preprocessing and Feature Extraction:</h3><p>\n",
    "Given the training dataset and graph information, you need to correctly preprocess the dataset (e.g. feature normalization). For baseline solution in this part, you might not need to introduce extra features to reach the baseline test accuracy.\n",
    "<p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preprocessing any train dataframe (2016/2012 train)\n",
    "def preprocess(df):    \n",
    "    '''Preprocesses the training data  \n",
    "    Args: \n",
    "     df: input dataframe\n",
    "    Returns: \n",
    "      df with a Winner indicator column and normalized feature columns\n",
    "    '''\n",
    "    # Binary Indicator of Dem or Gop winner of a county\n",
    "    df['Winner'] = 0\n",
    "    for x in range(0, len(df)):    \n",
    "        if df['DEM'][x] > df['GOP'][x]:\n",
    "            df['Winner'][x] = 1\n",
    "        else:\n",
    "            df['Winner'][x] = 0\n",
    "   \n",
    "    # Max normalization - Divide by the largest absolute valued feature value\n",
    "    norm_cols = ['MedianIncome','MigraRate', 'BirthRate', 'DeathRate', 'BachelorRate', 'UnemploymentRate']\n",
    "    for col in norm_cols:\n",
    "        factor = max(np.abs(df[col]))\n",
    "        df[col] = df[col]/factor\n",
    "    return df\n",
    "\n",
    "# Preprocessing any test dataframe (2016/2012 test)\n",
    "def test_preprocess(df):\n",
    "    '''Preprocesses the training data  \n",
    "    Args: \n",
    "     df: input dataframe\n",
    "    Returns: \n",
    "      df with normalized feature columns\n",
    "    '''\n",
    "    # Max normalization - Divide by the largest absolute valued feature value\n",
    "    norm_cols = ['MedianIncome','MigraRate', 'BirthRate', 'DeathRate', 'BachelorRate', 'UnemploymentRate']\n",
    "    for col in norm_cols:\n",
    "        factor = max(np.abs(df[col]))\n",
    "        df[col] = df[col]/factor\n",
    "    return df\n",
    "\n",
    "# Package a list of predictions for submission\n",
    "def package(preds, df):\n",
    "    '''Packages the predictions for submission using the sample data frame\n",
    "    Args: \n",
    "     preds: input list of predictions\n",
    "     df: sample dataframe\n",
    "    Returns: \n",
    "      df with predictions substituted in for sample predictions\n",
    "    '''\n",
    "    df['Result'][0:] = preds.astype(int)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>County</th>\n",
       "      <th>DEM</th>\n",
       "      <th>GOP</th>\n",
       "      <th>MedianIncome</th>\n",
       "      <th>MigraRate</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>DeathRate</th>\n",
       "      <th>BachelorRate</th>\n",
       "      <th>UnemploymentRate</th>\n",
       "      <th>Winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18019</td>\n",
       "      <td>Clark County, IN</td>\n",
       "      <td>18791</td>\n",
       "      <td>30012</td>\n",
       "      <td>0.385093</td>\n",
       "      <td>0.045287</td>\n",
       "      <td>0.439863</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.280161</td>\n",
       "      <td>0.174274</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6035</td>\n",
       "      <td>Lassen County, CA</td>\n",
       "      <td>2026</td>\n",
       "      <td>6533</td>\n",
       "      <td>0.369908</td>\n",
       "      <td>-0.170055</td>\n",
       "      <td>0.316151</td>\n",
       "      <td>0.220280</td>\n",
       "      <td>0.160858</td>\n",
       "      <td>0.286307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40081</td>\n",
       "      <td>Lincoln County, OK</td>\n",
       "      <td>2423</td>\n",
       "      <td>10838</td>\n",
       "      <td>0.333663</td>\n",
       "      <td>-0.012015</td>\n",
       "      <td>0.391753</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.202413</td>\n",
       "      <td>0.219917</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31153</td>\n",
       "      <td>Sarpy County, NE</td>\n",
       "      <td>27704</td>\n",
       "      <td>44649</td>\n",
       "      <td>0.552519</td>\n",
       "      <td>0.085028</td>\n",
       "      <td>0.487973</td>\n",
       "      <td>0.174825</td>\n",
       "      <td>0.537534</td>\n",
       "      <td>0.120332</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28055</td>\n",
       "      <td>Issaquena County, MS</td>\n",
       "      <td>395</td>\n",
       "      <td>298</td>\n",
       "      <td>0.200261</td>\n",
       "      <td>-0.118299</td>\n",
       "      <td>0.336770</td>\n",
       "      <td>0.185315</td>\n",
       "      <td>0.089812</td>\n",
       "      <td>0.580913</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>36009</td>\n",
       "      <td>Cattaraugus County, NY</td>\n",
       "      <td>8717</td>\n",
       "      <td>18668</td>\n",
       "      <td>0.343395</td>\n",
       "      <td>-0.032348</td>\n",
       "      <td>0.384880</td>\n",
       "      <td>0.377622</td>\n",
       "      <td>0.256032</td>\n",
       "      <td>0.248963</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>55031</td>\n",
       "      <td>Douglas County, WI</td>\n",
       "      <td>11342</td>\n",
       "      <td>9657</td>\n",
       "      <td>0.373972</td>\n",
       "      <td>-0.024030</td>\n",
       "      <td>0.336770</td>\n",
       "      <td>0.356643</td>\n",
       "      <td>0.321716</td>\n",
       "      <td>0.215768</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>27065</td>\n",
       "      <td>Kanabec County, MN</td>\n",
       "      <td>2327</td>\n",
       "      <td>5230</td>\n",
       "      <td>0.381453</td>\n",
       "      <td>0.014787</td>\n",
       "      <td>0.347079</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.195710</td>\n",
       "      <td>0.269710</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>17139</td>\n",
       "      <td>Moultrie County, IL</td>\n",
       "      <td>1476</td>\n",
       "      <td>4453</td>\n",
       "      <td>0.426769</td>\n",
       "      <td>-0.085028</td>\n",
       "      <td>0.443299</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.261394</td>\n",
       "      <td>0.190871</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>20185</td>\n",
       "      <td>Stafford County, KS</td>\n",
       "      <td>303</td>\n",
       "      <td>1485</td>\n",
       "      <td>0.342503</td>\n",
       "      <td>-0.073013</td>\n",
       "      <td>0.443299</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.320375</td>\n",
       "      <td>0.153527</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1555 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS                  County    DEM    GOP  MedianIncome  MigraRate  \\\n",
       "0     18019        Clark County, IN  18791  30012      0.385093   0.045287   \n",
       "1      6035       Lassen County, CA   2026   6533      0.369908  -0.170055   \n",
       "2     40081      Lincoln County, OK   2423  10838      0.333663  -0.012015   \n",
       "3     31153        Sarpy County, NE  27704  44649      0.552519   0.085028   \n",
       "4     28055    Issaquena County, MS    395    298      0.200261  -0.118299   \n",
       "...     ...                     ...    ...    ...           ...        ...   \n",
       "1550  36009  Cattaraugus County, NY   8717  18668      0.343395  -0.032348   \n",
       "1551  55031      Douglas County, WI  11342   9657      0.373972  -0.024030   \n",
       "1552  27065      Kanabec County, MN   2327   5230      0.381453   0.014787   \n",
       "1553  17139     Moultrie County, IL   1476   4453      0.426769  -0.085028   \n",
       "1554  20185     Stafford County, KS    303   1485      0.342503  -0.073013   \n",
       "\n",
       "      BirthRate  DeathRate  BachelorRate  UnemploymentRate  Winner  \n",
       "0      0.439863   0.384615      0.280161          0.174274       0  \n",
       "1      0.316151   0.220280      0.160858          0.286307       0  \n",
       "2      0.391753   0.409091      0.202413          0.219917       0  \n",
       "3      0.487973   0.174825      0.537534          0.120332       0  \n",
       "4      0.336770   0.185315      0.089812          0.580913       1  \n",
       "...         ...        ...           ...               ...     ...  \n",
       "1550   0.384880   0.377622      0.256032          0.248963       0  \n",
       "1551   0.336770   0.356643      0.321716          0.215768       1  \n",
       "1552   0.347079   0.363636      0.195710          0.269710       0  \n",
       "1553   0.443299   0.454545      0.261394          0.190871       0  \n",
       "1554   0.443299   0.423077      0.320375          0.153527       0  \n",
       "\n",
       "[1555 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocess training 2016 dataset (keep original intact and unchanged)\n",
    "rawdf16 = pd.read_csv('train_2016.csv', sep=',', thousands=',',encoding='unicode_escape')\n",
    "df16 = preprocess(rawdf16.copy())\n",
    "df16\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>County</th>\n",
       "      <th>DEM</th>\n",
       "      <th>GOP</th>\n",
       "      <th>MedianIncome</th>\n",
       "      <th>MigraRate</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>DeathRate</th>\n",
       "      <th>BachelorRate</th>\n",
       "      <th>UnemploymentRate</th>\n",
       "      <th>Winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18019</td>\n",
       "      <td>Clark County, IN</td>\n",
       "      <td>20775</td>\n",
       "      <td>25422</td>\n",
       "      <td>0.413372</td>\n",
       "      <td>0.000832</td>\n",
       "      <td>0.458042</td>\n",
       "      <td>0.431111</td>\n",
       "      <td>0.273611</td>\n",
       "      <td>0.284672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6035</td>\n",
       "      <td>Lassen County, CA</td>\n",
       "      <td>3044</td>\n",
       "      <td>7261</td>\n",
       "      <td>0.399213</td>\n",
       "      <td>-0.164725</td>\n",
       "      <td>0.314685</td>\n",
       "      <td>0.355556</td>\n",
       "      <td>0.179167</td>\n",
       "      <td>0.456204</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40081</td>\n",
       "      <td>Lincoln County, OK</td>\n",
       "      <td>3265</td>\n",
       "      <td>9542</td>\n",
       "      <td>0.371206</td>\n",
       "      <td>-0.023295</td>\n",
       "      <td>0.395105</td>\n",
       "      <td>0.493333</td>\n",
       "      <td>0.181944</td>\n",
       "      <td>0.182482</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31153</td>\n",
       "      <td>Sarpy County, NE</td>\n",
       "      <td>24709</td>\n",
       "      <td>40318</td>\n",
       "      <td>0.572738</td>\n",
       "      <td>0.066556</td>\n",
       "      <td>0.555944</td>\n",
       "      <td>0.217778</td>\n",
       "      <td>0.508333</td>\n",
       "      <td>0.142336</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28055</td>\n",
       "      <td>Issaquena County, MS</td>\n",
       "      <td>479</td>\n",
       "      <td>302</td>\n",
       "      <td>0.242874</td>\n",
       "      <td>0.018303</td>\n",
       "      <td>0.300699</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>0.108333</td>\n",
       "      <td>0.642336</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>36009</td>\n",
       "      <td>Cattaraugus County, NY</td>\n",
       "      <td>11695</td>\n",
       "      <td>15406</td>\n",
       "      <td>0.360048</td>\n",
       "      <td>-0.062396</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.448889</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.339416</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>55031</td>\n",
       "      <td>Douglas County, WI</td>\n",
       "      <td>14855</td>\n",
       "      <td>7698</td>\n",
       "      <td>0.366363</td>\n",
       "      <td>-0.054077</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.404444</td>\n",
       "      <td>0.304167</td>\n",
       "      <td>0.251825</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>27065</td>\n",
       "      <td>Kanabec County, MN</td>\n",
       "      <td>3586</td>\n",
       "      <td>4319</td>\n",
       "      <td>0.371879</td>\n",
       "      <td>-0.122296</td>\n",
       "      <td>0.248252</td>\n",
       "      <td>0.364444</td>\n",
       "      <td>0.186111</td>\n",
       "      <td>0.328467</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>17139</td>\n",
       "      <td>Moultrie County, IL</td>\n",
       "      <td>2142</td>\n",
       "      <td>3781</td>\n",
       "      <td>0.427624</td>\n",
       "      <td>0.006656</td>\n",
       "      <td>0.430070</td>\n",
       "      <td>0.528889</td>\n",
       "      <td>0.215278</td>\n",
       "      <td>0.259124</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>20185</td>\n",
       "      <td>Stafford County, KS</td>\n",
       "      <td>403</td>\n",
       "      <td>1382</td>\n",
       "      <td>0.363563</td>\n",
       "      <td>-0.038270</td>\n",
       "      <td>0.360140</td>\n",
       "      <td>0.568889</td>\n",
       "      <td>0.281944</td>\n",
       "      <td>0.167883</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1555 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS                  County    DEM    GOP  MedianIncome  MigraRate  \\\n",
       "0     18019        Clark County, IN  20775  25422      0.413372   0.000832   \n",
       "1      6035       Lassen County, CA   3044   7261      0.399213  -0.164725   \n",
       "2     40081      Lincoln County, OK   3265   9542      0.371206  -0.023295   \n",
       "3     31153        Sarpy County, NE  24709  40318      0.572738   0.066556   \n",
       "4     28055    Issaquena County, MS    479    302      0.242874   0.018303   \n",
       "...     ...                     ...    ...    ...           ...        ...   \n",
       "1550  36009  Cattaraugus County, NY  11695  15406      0.360048  -0.062396   \n",
       "1551  55031      Douglas County, WI  14855   7698      0.366363  -0.054077   \n",
       "1552  27065      Kanabec County, MN   3586   4319      0.371879  -0.122296   \n",
       "1553  17139     Moultrie County, IL   2142   3781      0.427624   0.006656   \n",
       "1554  20185     Stafford County, KS    403   1382      0.363563  -0.038270   \n",
       "\n",
       "      BirthRate  DeathRate  BachelorRate  UnemploymentRate  Winner  \n",
       "0      0.458042   0.431111      0.273611          0.284672       0  \n",
       "1      0.314685   0.355556      0.179167          0.456204       0  \n",
       "2      0.395105   0.493333      0.181944          0.182482       0  \n",
       "3      0.555944   0.217778      0.508333          0.142336       0  \n",
       "4      0.300699   0.320000      0.108333          0.642336       1  \n",
       "...         ...        ...           ...               ...     ...  \n",
       "1550   0.409091   0.448889      0.250000          0.339416       0  \n",
       "1551   0.384615   0.404444      0.304167          0.251825       1  \n",
       "1552   0.248252   0.364444      0.186111          0.328467       0  \n",
       "1553   0.430070   0.528889      0.215278          0.259124       0  \n",
       "1554   0.360140   0.568889      0.281944          0.167883       0  \n",
       "\n",
       "[1555 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocess training 2012 dataset (keep original intact and unchanged)\n",
    "rawdf12 = pd.read_csv('train_2012.csv', sep=',', thousands=',',encoding='unicode_escape')\n",
    "df12 = preprocess(rawdf12.copy())\n",
    "df12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>12713</th>\n",
       "      <td>78010</td>\n",
       "      <td>78010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12714</th>\n",
       "      <td>78020</td>\n",
       "      <td>78020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12715</th>\n",
       "      <td>78020</td>\n",
       "      <td>78030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12716</th>\n",
       "      <td>78030</td>\n",
       "      <td>78030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12717 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         SRC    DST\n",
       "0       1001   1001\n",
       "1       1001   1021\n",
       "2       1001   1047\n",
       "3       1001   1051\n",
       "4       1001   1085\n",
       "...      ...    ...\n",
       "12712  72153  72153\n",
       "12713  78010  78010\n",
       "12714  78020  78020\n",
       "12715  78020  78030\n",
       "12716  78030  78030\n",
       "\n",
       "[12717 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in graph data\n",
    "dfgraph = pd.read_csv('graph.csv', sep=',', encoding='unicode_escape')\n",
    "dfgraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>FIPS</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17059</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6103</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42047</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47147</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39039</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>55095</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>34035</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>42107</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>48293</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>37087</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1555 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS  Result\n",
       "0     17059       0\n",
       "1      6103       0\n",
       "2     42047       0\n",
       "3     47147       0\n",
       "4     39039       0\n",
       "...     ...     ...\n",
       "1550  55095       0\n",
       "1551  34035       0\n",
       "1552  42107       0\n",
       "1553  48293       0\n",
       "1554  37087       0\n",
       "\n",
       "[1555 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in Sample dataframe\n",
    "dfsample = pd.read_csv('sampleSubmission.csv', sep=',', encoding='unicode_escape')\n",
    "dfsample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>County</th>\n",
       "      <th>MedianIncome</th>\n",
       "      <th>MigraRate</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>DeathRate</th>\n",
       "      <th>BachelorRate</th>\n",
       "      <th>UnemploymentRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17059</td>\n",
       "      <td>Gallatin County, IL</td>\n",
       "      <td>0.335782</td>\n",
       "      <td>-0.050548</td>\n",
       "      <td>0.378947</td>\n",
       "      <td>0.363372</td>\n",
       "      <td>0.126115</td>\n",
       "      <td>0.381910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6103</td>\n",
       "      <td>Tehama County, CA</td>\n",
       "      <td>0.343839</td>\n",
       "      <td>0.008584</td>\n",
       "      <td>0.449123</td>\n",
       "      <td>0.302326</td>\n",
       "      <td>0.197452</td>\n",
       "      <td>0.356784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42047</td>\n",
       "      <td>Elk County, PA</td>\n",
       "      <td>0.417452</td>\n",
       "      <td>-0.044349</td>\n",
       "      <td>0.340351</td>\n",
       "      <td>0.377907</td>\n",
       "      <td>0.236943</td>\n",
       "      <td>0.276382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47147</td>\n",
       "      <td>Robertson County, TN</td>\n",
       "      <td>0.495506</td>\n",
       "      <td>0.035289</td>\n",
       "      <td>0.445614</td>\n",
       "      <td>0.281977</td>\n",
       "      <td>0.236943</td>\n",
       "      <td>0.206030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39039</td>\n",
       "      <td>Defiance County, OH</td>\n",
       "      <td>0.442326</td>\n",
       "      <td>-0.025274</td>\n",
       "      <td>0.389474</td>\n",
       "      <td>0.296512</td>\n",
       "      <td>0.212739</td>\n",
       "      <td>0.246231</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>55095</td>\n",
       "      <td>Polk County, WI</td>\n",
       "      <td>0.454619</td>\n",
       "      <td>0.006676</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.313953</td>\n",
       "      <td>0.259873</td>\n",
       "      <td>0.231156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>34035</td>\n",
       "      <td>Somerset County, NJ</td>\n",
       "      <td>0.886754</td>\n",
       "      <td>-0.009061</td>\n",
       "      <td>0.354386</td>\n",
       "      <td>0.209302</td>\n",
       "      <td>0.694268</td>\n",
       "      <td>0.206030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>42107</td>\n",
       "      <td>Schuylkill County, PA</td>\n",
       "      <td>0.426136</td>\n",
       "      <td>-0.000477</td>\n",
       "      <td>0.326316</td>\n",
       "      <td>0.389535</td>\n",
       "      <td>0.206369</td>\n",
       "      <td>0.311558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>48293</td>\n",
       "      <td>Limestone County, TX</td>\n",
       "      <td>0.333647</td>\n",
       "      <td>0.002384</td>\n",
       "      <td>0.463158</td>\n",
       "      <td>0.328488</td>\n",
       "      <td>0.178344</td>\n",
       "      <td>0.281407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>37087</td>\n",
       "      <td>Haywood County, NC</td>\n",
       "      <td>0.383700</td>\n",
       "      <td>0.071531</td>\n",
       "      <td>0.354386</td>\n",
       "      <td>0.366279</td>\n",
       "      <td>0.318471</td>\n",
       "      <td>0.226131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1555 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS                 County  MedianIncome  MigraRate  BirthRate  \\\n",
       "0     17059    Gallatin County, IL      0.335782  -0.050548   0.378947   \n",
       "1      6103      Tehama County, CA      0.343839   0.008584   0.449123   \n",
       "2     42047         Elk County, PA      0.417452  -0.044349   0.340351   \n",
       "3     47147   Robertson County, TN      0.495506   0.035289   0.445614   \n",
       "4     39039    Defiance County, OH      0.442326  -0.025274   0.389474   \n",
       "...     ...                    ...           ...        ...        ...   \n",
       "1550  55095        Polk County, WI      0.454619   0.006676   0.333333   \n",
       "1551  34035    Somerset County, NJ      0.886754  -0.009061   0.354386   \n",
       "1552  42107  Schuylkill County, PA      0.426136  -0.000477   0.326316   \n",
       "1553  48293   Limestone County, TX      0.333647   0.002384   0.463158   \n",
       "1554  37087     Haywood County, NC      0.383700   0.071531   0.354386   \n",
       "\n",
       "      DeathRate  BachelorRate  UnemploymentRate  \n",
       "0      0.363372      0.126115          0.381910  \n",
       "1      0.302326      0.197452          0.356784  \n",
       "2      0.377907      0.236943          0.276382  \n",
       "3      0.281977      0.236943          0.206030  \n",
       "4      0.296512      0.212739          0.246231  \n",
       "...         ...           ...               ...  \n",
       "1550   0.313953      0.259873          0.231156  \n",
       "1551   0.209302      0.694268          0.206030  \n",
       "1552   0.389535      0.206369          0.311558  \n",
       "1553   0.328488      0.178344          0.281407  \n",
       "1554   0.366279      0.318471          0.226131  \n",
       "\n",
       "[1555 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocess testing 2016 dataset (keep original intact and unchanged)\n",
    "rawtest16 = pd.read_csv('test_2016_no_label.csv', sep=',', thousands=\",\", encoding='unicode_escape')\n",
    "test16 = test_preprocess(rawtest16.copy())\n",
    "test16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FIPS</th>\n",
       "      <th>County</th>\n",
       "      <th>MedianIncome</th>\n",
       "      <th>MigraRate</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>DeathRate</th>\n",
       "      <th>BachelorRate</th>\n",
       "      <th>UnemploymentRate</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17059</td>\n",
       "      <td>Gallatin County, IL</td>\n",
       "      <td>0.314441</td>\n",
       "      <td>-0.128660</td>\n",
       "      <td>0.299003</td>\n",
       "      <td>0.570796</td>\n",
       "      <td>0.142477</td>\n",
       "      <td>0.351464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6103</td>\n",
       "      <td>Tehama County, CA</td>\n",
       "      <td>0.318161</td>\n",
       "      <td>-0.031056</td>\n",
       "      <td>0.378738</td>\n",
       "      <td>0.420354</td>\n",
       "      <td>0.187750</td>\n",
       "      <td>0.573222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42047</td>\n",
       "      <td>Elk County, PA</td>\n",
       "      <td>0.401674</td>\n",
       "      <td>-0.026619</td>\n",
       "      <td>0.322259</td>\n",
       "      <td>0.548673</td>\n",
       "      <td>0.205060</td>\n",
       "      <td>0.292887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47147</td>\n",
       "      <td>Robertson County, TN</td>\n",
       "      <td>0.445897</td>\n",
       "      <td>-0.039929</td>\n",
       "      <td>0.475083</td>\n",
       "      <td>0.415929</td>\n",
       "      <td>0.225033</td>\n",
       "      <td>0.292887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39039</td>\n",
       "      <td>Defiance County, OH</td>\n",
       "      <td>0.382532</td>\n",
       "      <td>-0.057675</td>\n",
       "      <td>0.385382</td>\n",
       "      <td>0.433628</td>\n",
       "      <td>0.217044</td>\n",
       "      <td>0.322176</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>55095</td>\n",
       "      <td>Polk County, WI</td>\n",
       "      <td>0.412586</td>\n",
       "      <td>-0.099379</td>\n",
       "      <td>0.318937</td>\n",
       "      <td>0.371681</td>\n",
       "      <td>0.255659</td>\n",
       "      <td>0.334728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>34035</td>\n",
       "      <td>Somerset County, NJ</td>\n",
       "      <td>0.788239</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.345515</td>\n",
       "      <td>0.300885</td>\n",
       "      <td>0.692410</td>\n",
       "      <td>0.309623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>42107</td>\n",
       "      <td>Schuylkill County, PA</td>\n",
       "      <td>0.366433</td>\n",
       "      <td>0.007098</td>\n",
       "      <td>0.328904</td>\n",
       "      <td>0.584071</td>\n",
       "      <td>0.198402</td>\n",
       "      <td>0.405858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>48293</td>\n",
       "      <td>Limestone County, TX</td>\n",
       "      <td>0.300998</td>\n",
       "      <td>0.011535</td>\n",
       "      <td>0.451827</td>\n",
       "      <td>0.469027</td>\n",
       "      <td>0.181092</td>\n",
       "      <td>0.288703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>37087</td>\n",
       "      <td>Haywood County, NC</td>\n",
       "      <td>0.330078</td>\n",
       "      <td>0.007098</td>\n",
       "      <td>0.328904</td>\n",
       "      <td>0.526549</td>\n",
       "      <td>0.316911</td>\n",
       "      <td>0.380753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1555 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS                 County  MedianIncome  MigraRate  BirthRate  \\\n",
       "0     17059    Gallatin County, IL      0.314441  -0.128660   0.299003   \n",
       "1      6103      Tehama County, CA      0.318161  -0.031056   0.378738   \n",
       "2     42047         Elk County, PA      0.401674  -0.026619   0.322259   \n",
       "3     47147   Robertson County, TN      0.445897  -0.039929   0.475083   \n",
       "4     39039    Defiance County, OH      0.382532  -0.057675   0.385382   \n",
       "...     ...                    ...           ...        ...        ...   \n",
       "1550  55095        Polk County, WI      0.412586  -0.099379   0.318937   \n",
       "1551  34035    Somerset County, NJ      0.788239   0.000000   0.345515   \n",
       "1552  42107  Schuylkill County, PA      0.366433   0.007098   0.328904   \n",
       "1553  48293   Limestone County, TX      0.300998   0.011535   0.451827   \n",
       "1554  37087     Haywood County, NC      0.330078   0.007098   0.328904   \n",
       "\n",
       "      DeathRate  BachelorRate  UnemploymentRate  \n",
       "0      0.570796      0.142477          0.351464  \n",
       "1      0.420354      0.187750          0.573222  \n",
       "2      0.548673      0.205060          0.292887  \n",
       "3      0.415929      0.225033          0.292887  \n",
       "4      0.433628      0.217044          0.322176  \n",
       "...         ...           ...               ...  \n",
       "1550   0.371681      0.255659          0.334728  \n",
       "1551   0.300885      0.692410          0.309623  \n",
       "1552   0.584071      0.198402          0.405858  \n",
       "1553   0.469027      0.181092          0.288703  \n",
       "1554   0.526549      0.316911          0.380753  \n",
       "\n",
       "[1555 rows x 8 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocess testing 2012 dataset (keep original intact and unchanged)\n",
    "rawtest12 = pd.read_csv('test_2012_no_label.csv', sep=',', thousands=\",\", encoding='unicode_escape')\n",
    "test12 = test_preprocess(rawtest12.copy())\n",
    "test12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>2.2 Use At Least Two Training Algorithms from class:</h3><p>\n",
    "You need to use at least two training algorithms from class. You can use your code from previous projects or any packages you imported in part 1.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic Model 1 - K-Nearest Neighbors\n",
    "\n",
    "def knn_classifier(k_grid, distance_metrics, train_x, train_y):\n",
    "    '''Returns KNN parameters for classifying county election results.\n",
    "\n",
    "    Args: \n",
    "      k_grid: interval for grid searching number of neighbors\n",
    "      distance_metrics: array of distance metric strings for the K-nearest neighbor classifier\n",
    "      train_x: feature vectors for each county in training set\n",
    "      train_y: labels for each county in training set\n",
    "    Returns: \n",
    "      array of length 3 containing best accuracy and values of C and gamma that gave that accuracy.\n",
    "    '''\n",
    "    # Initialize best array to store our best calculated hyperparameters as well as the best accuracy so far.\n",
    "    best = [0, 0, 'euclidean']\n",
    "    # Create sklearn scorer based on the weighted accuracy function given\n",
    "    scorer = make_scorer(weighted_accuracy)\n",
    "    \n",
    "    # Grid search the hyperparameters\n",
    "    for metric in distance_metrics:\n",
    "        for k in k_grid:\n",
    "            knn_model = KNeighborsClassifier(n_neighbors=k, metric=metric)\n",
    "            # 10-Fold Cross validation via Sklearn\n",
    "            scores = cross_val_score(knn_model, train_x, train_y, cv=10, scoring=scorer)\n",
    "            # Cache this specific model with its hyperparameters if the weighted accuracy is best.\n",
    "            if np.mean(scores) > best[0]:\n",
    "                    best[0], best[1], best[2] = np.mean(scores), k, metric\n",
    "    return best\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic Model 2 - SVM. \n",
    "def train_SVM(c_grid, g_grid, train_x, train_y):\n",
    "    '''Trains an SVM using scikit-learn by conducting a grid search and cross-validation\n",
    "       to find optimal hyperparameter values based on given intervals.\n",
    "       \n",
    "    Args: \n",
    "      c_grid: interval for grid searching C values\n",
    "      g_grid: interval for grid searching gamma values\n",
    "      train_x: feature vectors for each county in training set\n",
    "      train_y: labels for each county in training set\n",
    "    Returns: \n",
    "      array of length 3 containing best accuracy and values of C and gamma that gave that accuracy.\n",
    "    '''\n",
    "    \n",
    "    # Create sklearn scorer based on the weighted accuracy function given\n",
    "    scorer = make_scorer(weighted_accuracy)\n",
    "    # Initialize best array to store our best calculated hyperparameters as well as the best accuracy so far.\n",
    "    best = [0, -1, -1]\n",
    "    # Grid search the hyperparameters\n",
    "    for c in c_grid:\n",
    "        for g in g_grid:\n",
    "            # We suspect the data to be nonlinear and RBF is a consistent choice for this purpose.\n",
    "            svm_model = svm.SVC(kernel='rbf', C=c, gamma=g)\n",
    "            # 10-Fold Cross validation via Sklearn\n",
    "            scores = cross_val_score(svm_model, train_x, train_y, cv=10, scoring=scorer)\n",
    "            # Cache this specific model with its hyperparameters if the weighted accuracy is best.\n",
    "            if np.mean(scores) > best[0]:\n",
    "                best[0], best[1], best[2] = np.mean(scores), c, g\n",
    "    return best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>2.3 Training, Validation and Model Selection:</h3><p>\n",
    "You need to split your data to a training set and validation set or performing a cross-validation for model selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Partition data into train for features and labels, and test labels for predictions\n",
    "train_x = np.array(df16.iloc[0:,4:10])\n",
    "train_y = np.array(df16.iloc[0:,10])\n",
    "test_x = np.array(test16.iloc[0:,2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8309138534813879, 4, 'minkowski']\n"
     ]
    }
   ],
   "source": [
    "# K-Nearest Neighbors algorithm\n",
    "\n",
    "# Grid search using these grids in the KNN model. For use with cross-validation.\n",
    "ks = range(1, 6, 1)\n",
    "distance_metrics = ['minkowski', 'manhattan', 'chebyshev', 'euclidean', 'seuclidean']\n",
    "\n",
    "# Cross-validate on hyperparameters for model selection\n",
    "best = knn_classifier(ks, distance_metrics, train_x, train_y)\n",
    "\n",
    "print(best)\n",
    "knn_model = KNeighborsClassifier(n_neighbors=best[1], metric=best[2])\n",
    "knn_model.fit(train_x, train_y)\n",
    "preds_knn = knn_model.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8570917136918862, 1290.159155092349, 0.5]\n"
     ]
    }
   ],
   "source": [
    "# SVM algorithm with 10-fold cross validation + grid search to find good hyperparameters.\n",
    "\n",
    "# Grid search using these intervals for C and gamma in the SVM model. For use with cross-validation.\n",
    "cs = (2.0**np.linspace(5,11,10))\n",
    "gammas = (np.linspace(0.5,7,10))\n",
    "\n",
    "# Cross-validate on hyperparameters for model selection\n",
    "best = train_SVM(cs, gammas, train_x, train_y)\n",
    "\n",
    "print(best)\n",
    "svm_model = svm.SVC(kernel='rbf', C=best[1], gamma=best[2])\n",
    "svm_model.fit(train_x, train_y)\n",
    "preds_svm = svm_model.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1412\n",
      "1     143\n",
      "Name: Result, dtype: int64\n",
      "0    1384\n",
      "1     171\n",
      "Name: Result, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Package predictions for both basic models into a csv for submission\n",
    "\n",
    "submission_knn = package(preds_knn, dfsample.copy())\n",
    "submission_svm = package(preds_svm, dfsample.copy())\n",
    "\n",
    "submission_knn.to_csv('submission_knn.csv',index = False)\n",
    "submission_svm.to_csv('submission_svm.csv',index = False)\n",
    "\n",
    "\n",
    "# Check number of DEM and GOP counties predicted for further validation information\n",
    "submiss_knn = pd.read_csv('submission_knn.csv', sep=',', encoding='unicode_escape')\n",
    "series_knn = submiss_knn['Result'].value_counts()\n",
    "submiss_svm = pd.read_csv('submission_svm.csv', sep=',', encoding='unicode_escape')\n",
    "series_svm = submiss_svm['Result'].value_counts()\n",
    "\n",
    "print(series_knn) \n",
    "print(series_svm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>2.4 Explanation in Words:</h3><p>\n",
    "    You need to answer the following questions in the markdown cell after this cell:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4.1 How did you preprocess the dataset and features?\n",
    "\n",
    "For the basic section we kept things simple and only used the primary 6 features given to us. All we did was normalized these features all by dividing by the absolute max value. We also tried more standard techniques such as min/max as well as standardization normalization techniques as shown in class but neither worked as well as just normal “max” normalization when we fed the data into our basic models  (SVM, KNN) for validation. This could be due to information loss with some feature columns having negative values prior to normalization that no longer have negative values post normalization. Aside from that, we created a target column that consisted of labeling whether a county had more DEM or GOP votes as specified in the handout.\n",
    "\n",
    "#### 2.4.2 Which two learning methods from class did you choose and why did you made the choices?\n",
    "\n",
    "We chose the K-Nearest Neighbors and Support Vector Machines learning methods. \n",
    "\n",
    "We first used **K-Nearest Neighbors** due to its simplicity and ease of use for empirical risk minimization, as well as the fact that we expect data points in the test set with similar features to have similar labels. Due to the KNN algorithm, it inherently assigns the same label to features with respect to its k neighbors according to some distance metric. The dataset is also relatively small, so the algorithm will not be bogged down by too much inefficiency. Also, the feature dimensionality is not so large, so the algorithm will not suffer from the “curse of dimensionality”.\n",
    "\n",
    "We also used the **SVM**, specifically with the Radial Basis Function (RBF) kernel as our second basic model, because we expect our data to be nonlinearly separable, and a simple linear classifier would just not cut it. The RBF kernel is specifically used as it relates close points to likely have the same label, with a similar rationale to the use of KNN. By using a kernel to find a hyperplane within this kernel space to separate the data into classes, we can cross validate if needed on the SVM’s hyperparameters and choose the right model from our cross-validation.\n",
    "\n",
    "\n",
    "#### 2.4.3 How did you do the model selection?\n",
    "\n",
    "We performed model selection through a grid search on hyperparameters for both of the learning methods. For the KNN, this was done through passing a range of numbers for the value of `k` as well as the distance function for use in the algorithm. For this distance function, we consulted a paper “Effects of Distance Measure Choice on KNN Classifier Performance” by Prasath et al. [1], which suggested a few distance metrics included with scikit-learn that outperformed others in a study. \n",
    "\n",
    "For the SVM, this was done through passing evenly (power and linearly) spaced numbers through a specified interval using NumPy for each of the values of the hyperparameters we were evaluating, `C` and `gamma`. We consulted the scikit-learn documentation for these parameters to find typical values to use [2]. As for the actual cross-validation, we made use of `sklearn`’s `model_selection` package and specifically the `cross_val_score` on 10 folds, taking the mean of the scores on each of these partitions and caching the best-performing model to be selected for use on the test data. We decided on 10 folds as it doesn't take up too much time to compute results but also trains on 90% of the data available in each fold, making it a close approximation to training on all 100% of the data as we do before predicting on the test data. \n",
    "\n",
    "\n",
    "#### 2.4.4 Does the test performance reach a given baseline 68% performance? (Please include a screenshot of Kaggle Submission)\n",
    "\n",
    "Yes. Here is a screenshot of both our submission scores for KNN and SVM solutions (both >68% performance)\n",
    "\n",
    "\n",
    "\n",
    "![kaggle_basic_screenshot](kaggle_submission_basic.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Part 3: Creative Solution</h2><p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.1 Open-ended Code:</h3><p>\n",
    "You may follow the steps in part 2 again but making innovative changes like creating new features, using new training algorithms, etc. Make sure you explain everything clearly in part 3.2. Note that reaching the 75% creative baseline is only a small portion of this part. Any creative ideas will receive most points as long as they are reasonable and clearly explained."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.1 Import Statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Basic + Tensorflow imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import Model, layers, regularizers\n",
    "import logging\n",
    "tf.get_logger().setLevel(logging.ERROR) # suppress TF warnings from being printed\n",
    "tf.random.set_seed(123)\n",
    "from IPython.display import clear_output\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import KFold\n",
    "from IPython.display import clear_output\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.1.1 Import Statements Pre + Post Processing Setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_class_weights(df):\n",
    "    '''\n",
    "    Generate class weights\n",
    "    '''\n",
    "    df['Weight'] = 0.0\n",
    "    # rationale: training data has roughly 5.9 ratio of GOP counties to DEM\n",
    "    d_weight = 5.0 \n",
    "    r_weight = 1.0\n",
    "    # assign weights accordingly\n",
    "    for x in range(0, len(df)):    \n",
    "        if df['Winner'][x] == 1:\n",
    "            df['Weight'][x] = d_weight\n",
    "        else:\n",
    "            df['Weight'][x] = r_weight  \n",
    "    return df\n",
    "\n",
    "def gen_labels(df):\n",
    "    '''\n",
    "    Binary Indicator of Dem or GOP winner of a county\n",
    "    '''\n",
    "    df['Winner'] = 0\n",
    "    for x in range(0, len(df)):    \n",
    "        if df['DEM'][x] > df['GOP'][x]:\n",
    "            df['Winner'][x] = 1\n",
    "        else:\n",
    "            df['Winner'][x] = 0 \n",
    "    return df\n",
    "\n",
    "def norm_preprocess(df):\n",
    "    '''\n",
    "    Max normalization - Divide by the largest absolute valued feature value\n",
    "\n",
    "    '''\n",
    "    norm_cols = ['MedianIncome','MigraRate', 'BirthRate', 'DeathRate', 'BachelorRate', 'UnemploymentRate']\n",
    "    for col in norm_cols:\n",
    "        factor = max(np.abs(df[col]))\n",
    "        df[col] = df[col] / factor\n",
    "    return df\n",
    "\n",
    "def ratio_preprocess(df):\n",
    "    '''\n",
    "    NOT USED\n",
    "    \n",
    "    Generate labels that are the ratio of dem voters to total voters\n",
    "    '''\n",
    "    df['Winner'] = 0.0\n",
    "    for x in range(0, len(df)):    \n",
    "        d = float(df['DEM'][x])\n",
    "        r = df['GOP'][x]\n",
    "        total_pop = d + r\n",
    "        mult = 1\n",
    "        if d > r:\n",
    "            mult = 1\n",
    "        df['Winner'][x] = mult * d / total_pop\n",
    "    return df\n",
    "\n",
    "def cthresh(preds, thresh = 0.5):\n",
    "    '''\n",
    "    Threshold a set of predictions based on the threshold\n",
    "    '''\n",
    "    preds = (preds > thresh).astype(int)\n",
    "    return preds\n",
    "\n",
    "def cpackage(preds, df, thresh = 0.5):\n",
    "    '''\n",
    "    Threshold a set of predictions as a dataframe, based on the threshold\n",
    "    '''\n",
    "    df['Result'][0:] = (preds > thresh).astype(int)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.1.2 Data Setup\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-read train/test data from given CSVs and preprocess the test data\n",
    "rawdf16 = pd.read_csv('train_2016.csv', sep=',', thousands=',',encoding='unicode_escape')\n",
    "df16 = gen_labels(rawdf16)\n",
    "rawdf12 = pd.read_csv('train_2012.csv', sep=',', thousands=',',encoding='unicode_escape')\n",
    "df12 = gen_labels(rawdf12)\n",
    "dfgraph = pd.read_csv('graph.csv', sep=',', encoding='unicode_escape')\n",
    "dfsample = pd.read_csv('sampleSubmission.csv', sep=',', encoding='unicode_escape')\n",
    "rawtest16 = pd.read_csv('test_2016_no_label.csv', sep=',', thousands=\",\", encoding='unicode_escape')\n",
    "test16 = test_preprocess(rawtest16)\n",
    "rawtest12 = pd.read_csv('test_2012_no_label.csv', sep=',', thousands=\",\", encoding='unicode_escape')\n",
    "test12 = test_preprocess(rawtest12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.2 Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph_process(d16, graph, result, result12):\n",
    "    '''\n",
    "    Preprocesses the given basic preprocessed dataframe (train or test) and extracts features from the graph data\n",
    "    adds them to the dataframe\n",
    "    Args: \n",
    "      d16: some preprocessed 2016 dataframe (train or test)\n",
    "      graph: graph dataframe\n",
    "      result: 2016 preprocessed dataframe with election results in a column\n",
    "      result12: 2012 preprocessed dataframe with election results from 2012 in a column\n",
    "    Returns: \n",
    "    Dataframe with graph data extracted into columns\n",
    "    '''\n",
    "    # Adds extra features to the dataframe based on the graph structure of counties\n",
    "    # Note: Graph DST for each SRC is sorted least to greatest, other neighbors < DST FIPS can occur but with that DST as SRC\n",
    "    neigh_exist, neigh_vote, neigh_swing, neigh_pop = [], [], [], []\n",
    "    for row in range(len(d16)):\n",
    "        # Get neighboring counties for this particular SRC\n",
    "        neighbors = np.array(dfgraph[dfgraph['SRC'] == d16['FIPS'][row]]['DST'][1:])\n",
    "        # Get neighboring counties that have this SRC as a DST\n",
    "        neighbors = np.append(neighbors,np.array(dfgraph[dfgraph['DST'] == d16['FIPS'][row]]['SRC'][:-1]))\n",
    "        prop, swing = [], []\n",
    "        dem, gop = 0, 0\n",
    "        for neigh in neighbors:\n",
    "            if len(result[result['FIPS'] == neigh]) > 0:\n",
    "                # Compute the proportion of neighbor counties that voted DEM\n",
    "                prop = np.append(prop, np.array(result[result['FIPS'] == neigh]['Winner']))\n",
    "                # Compute proportion of neighbors that swung from 2012 -> 2016                \n",
    "                swing = np.append(swing, np.array(np.abs(np.array(result[result['FIPS'] == neigh]['Winner'])[0] - np.array(result12[result12['FIPS'] == neigh]['Winner'])[0])))\n",
    "                # Add number of DEM votes in neighbors\n",
    "                dem += np.array(result[result['FIPS'] == neigh]['DEM'])[0]\n",
    "                # Add number of GOP votes in neighbors\n",
    "                gop += np.array(result[result['FIPS'] == neigh]['GOP'])[0]\n",
    "        if len(prop) == 0:\n",
    "            # If no neighbors could be found in 2016, assign a feature value of 0 to represent it and 0.5 everything else\n",
    "            neigh_vote.append(0.5)\n",
    "            neigh_exist.append(0)\n",
    "            neigh_swing.append(0.5)\n",
    "            neigh_pop.append(0.5)\n",
    "        else:\n",
    "            # Append previously found values\n",
    "            neigh_vote.append(np.mean(prop))\n",
    "            neigh_exist.append(1)\n",
    "            neigh_swing.append(np.mean(swing))\n",
    "            # Append proportion of DEM voters in neighbors\n",
    "            neigh_pop.append(dem / (dem + gop))\n",
    "\n",
    "    # Add feature columns to training data\n",
    "    d16['neigh_vote'] = np.array(neigh_vote)\n",
    "    d16['neigh_exist'] = np.array(neigh_exist)\n",
    "    d16['neigh_swing'] = np.array(neigh_swing)\n",
    "    d16['neigh_pop'] = np.array(neigh_pop)\n",
    "    return d16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FIPS                      int64\n",
      "County                   object\n",
      "DEM                       int64\n",
      "GOP                       int64\n",
      "MedianIncome            float64\n",
      "MigraRate               float64\n",
      "BirthRate               float64\n",
      "DeathRate               float64\n",
      "BachelorRate            float64\n",
      "UnemploymentRate        float64\n",
      "neigh_vote              float64\n",
      "neigh_exist               int64\n",
      "neigh_swing             float64\n",
      "neigh_pop               float64\n",
      "StateCode                object\n",
      "MedianIncome2012        float64\n",
      "MigraRate2012           float64\n",
      "BirthRate2012           float64\n",
      "DeathRate2012           float64\n",
      "BachelorRate2012        float64\n",
      "UnemploymentRate2012    float64\n",
      "MedianIncomeDiff        float64\n",
      "MigraRateDiff           float64\n",
      "BirthRateDiff           float64\n",
      "DeathRateDiff           float64\n",
      "BachelorRateDiff        float64\n",
      "UnemploymentRateDiff    float64\n",
      "Weight                  float64\n",
      "Winner                    int64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
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       "      <th>GOP</th>\n",
       "      <th>MedianIncome</th>\n",
       "      <th>MigraRate</th>\n",
       "      <th>BirthRate</th>\n",
       "      <th>DeathRate</th>\n",
       "      <th>BachelorRate</th>\n",
       "      <th>UnemploymentRate</th>\n",
       "      <th>...</th>\n",
       "      <th>BachelorRate2012</th>\n",
       "      <th>UnemploymentRate2012</th>\n",
       "      <th>MedianIncomeDiff</th>\n",
       "      <th>MigraRateDiff</th>\n",
       "      <th>BirthRateDiff</th>\n",
       "      <th>DeathRateDiff</th>\n",
       "      <th>BachelorRateDiff</th>\n",
       "      <th>UnemploymentRateDiff</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18019</td>\n",
       "      <td>Clark County, IN</td>\n",
       "      <td>18791</td>\n",
       "      <td>30012</td>\n",
       "      <td>0.385093</td>\n",
       "      <td>0.045287</td>\n",
       "      <td>0.439863</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.280161</td>\n",
       "      <td>0.174274</td>\n",
       "      <td>...</td>\n",
       "      <td>0.273611</td>\n",
       "      <td>0.284672</td>\n",
       "      <td>0.116587</td>\n",
       "      <td>0.073282</td>\n",
       "      <td>-0.032967</td>\n",
       "      <td>0.110169</td>\n",
       "      <td>0.063492</td>\n",
       "      <td>-0.734694</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6035</td>\n",
       "      <td>Lassen County, CA</td>\n",
       "      <td>2026</td>\n",
       "      <td>6533</td>\n",
       "      <td>0.369908</td>\n",
       "      <td>-0.170055</td>\n",
       "      <td>0.316151</td>\n",
       "      <td>0.220280</td>\n",
       "      <td>0.160858</td>\n",
       "      <td>0.286307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.179167</td>\n",
       "      <td>0.456204</td>\n",
       "      <td>0.100885</td>\n",
       "      <td>0.021374</td>\n",
       "      <td>0.021978</td>\n",
       "      <td>-0.144068</td>\n",
       "      <td>-0.047619</td>\n",
       "      <td>-1.142857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40081</td>\n",
       "      <td>Lincoln County, OK</td>\n",
       "      <td>2423</td>\n",
       "      <td>10838</td>\n",
       "      <td>0.333663</td>\n",
       "      <td>-0.012015</td>\n",
       "      <td>0.391753</td>\n",
       "      <td>0.409091</td>\n",
       "      <td>0.202413</td>\n",
       "      <td>0.219917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.181944</td>\n",
       "      <td>0.182482</td>\n",
       "      <td>0.033367</td>\n",
       "      <td>0.022901</td>\n",
       "      <td>0.010989</td>\n",
       "      <td>0.050847</td>\n",
       "      <td>0.105820</td>\n",
       "      <td>0.061224</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31153</td>\n",
       "      <td>Sarpy County, NE</td>\n",
       "      <td>27704</td>\n",
       "      <td>44649</td>\n",
       "      <td>0.552519</td>\n",
       "      <td>0.085028</td>\n",
       "      <td>0.487973</td>\n",
       "      <td>0.174825</td>\n",
       "      <td>0.537534</td>\n",
       "      <td>0.120332</td>\n",
       "      <td>...</td>\n",
       "      <td>0.508333</td>\n",
       "      <td>0.142336</td>\n",
       "      <td>0.272866</td>\n",
       "      <td>0.018321</td>\n",
       "      <td>-0.186813</td>\n",
       "      <td>0.008475</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>-0.204082</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28055</td>\n",
       "      <td>Issaquena County, MS</td>\n",
       "      <td>395</td>\n",
       "      <td>298</td>\n",
       "      <td>0.200261</td>\n",
       "      <td>-0.118299</td>\n",
       "      <td>0.336770</td>\n",
       "      <td>0.185315</td>\n",
       "      <td>0.089812</td>\n",
       "      <td>0.580913</td>\n",
       "      <td>...</td>\n",
       "      <td>0.108333</td>\n",
       "      <td>0.642336</td>\n",
       "      <td>-0.084137</td>\n",
       "      <td>-0.229008</td>\n",
       "      <td>0.131868</td>\n",
       "      <td>-0.161017</td>\n",
       "      <td>-0.058201</td>\n",
       "      <td>-0.734694</td>\n",
       "      <td>5.0</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>36009</td>\n",
       "      <td>Cattaraugus County, NY</td>\n",
       "      <td>8717</td>\n",
       "      <td>18668</td>\n",
       "      <td>0.343395</td>\n",
       "      <td>-0.032348</td>\n",
       "      <td>0.384880</td>\n",
       "      <td>0.377622</td>\n",
       "      <td>0.256032</td>\n",
       "      <td>0.248963</td>\n",
       "      <td>...</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.339416</td>\n",
       "      <td>0.148384</td>\n",
       "      <td>0.061069</td>\n",
       "      <td>-0.054945</td>\n",
       "      <td>0.059322</td>\n",
       "      <td>0.058201</td>\n",
       "      <td>-0.673469</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1551</th>\n",
       "      <td>55031</td>\n",
       "      <td>Douglas County, WI</td>\n",
       "      <td>11342</td>\n",
       "      <td>9657</td>\n",
       "      <td>0.373972</td>\n",
       "      <td>-0.024030</td>\n",
       "      <td>0.336770</td>\n",
       "      <td>0.356643</td>\n",
       "      <td>0.321716</td>\n",
       "      <td>0.215768</td>\n",
       "      <td>...</td>\n",
       "      <td>0.304167</td>\n",
       "      <td>0.251825</td>\n",
       "      <td>0.295154</td>\n",
       "      <td>0.059542</td>\n",
       "      <td>-0.131868</td>\n",
       "      <td>0.093220</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>-0.346939</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1552</th>\n",
       "      <td>27065</td>\n",
       "      <td>Kanabec County, MN</td>\n",
       "      <td>2327</td>\n",
       "      <td>5230</td>\n",
       "      <td>0.381453</td>\n",
       "      <td>0.014787</td>\n",
       "      <td>0.347079</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.195710</td>\n",
       "      <td>0.269710</td>\n",
       "      <td>...</td>\n",
       "      <td>0.186111</td>\n",
       "      <td>0.328467</td>\n",
       "      <td>0.310464</td>\n",
       "      <td>0.248855</td>\n",
       "      <td>0.329670</td>\n",
       "      <td>0.186441</td>\n",
       "      <td>0.063492</td>\n",
       "      <td>-0.510204</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553</th>\n",
       "      <td>17139</td>\n",
       "      <td>Moultrie County, IL</td>\n",
       "      <td>1476</td>\n",
       "      <td>4453</td>\n",
       "      <td>0.426769</td>\n",
       "      <td>-0.085028</td>\n",
       "      <td>0.443299</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.261394</td>\n",
       "      <td>0.190871</td>\n",
       "      <td>...</td>\n",
       "      <td>0.215278</td>\n",
       "      <td>0.259124</td>\n",
       "      <td>0.287347</td>\n",
       "      <td>-0.152672</td>\n",
       "      <td>0.065934</td>\n",
       "      <td>0.093220</td>\n",
       "      <td>0.211640</td>\n",
       "      <td>-0.510204</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>20185</td>\n",
       "      <td>Stafford County, KS</td>\n",
       "      <td>303</td>\n",
       "      <td>1485</td>\n",
       "      <td>0.342503</td>\n",
       "      <td>-0.073013</td>\n",
       "      <td>0.443299</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.320375</td>\n",
       "      <td>0.153527</td>\n",
       "      <td>...</td>\n",
       "      <td>0.281944</td>\n",
       "      <td>0.167883</td>\n",
       "      <td>0.124918</td>\n",
       "      <td>-0.050382</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>-0.059322</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>-0.183673</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1555 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       FIPS                  County    DEM    GOP  MedianIncome  MigraRate  \\\n",
       "0     18019        Clark County, IN  18791  30012      0.385093   0.045287   \n",
       "1      6035       Lassen County, CA   2026   6533      0.369908  -0.170055   \n",
       "2     40081      Lincoln County, OK   2423  10838      0.333663  -0.012015   \n",
       "3     31153        Sarpy County, NE  27704  44649      0.552519   0.085028   \n",
       "4     28055    Issaquena County, MS    395    298      0.200261  -0.118299   \n",
       "...     ...                     ...    ...    ...           ...        ...   \n",
       "1550  36009  Cattaraugus County, NY   8717  18668      0.343395  -0.032348   \n",
       "1551  55031      Douglas County, WI  11342   9657      0.373972  -0.024030   \n",
       "1552  27065      Kanabec County, MN   2327   5230      0.381453   0.014787   \n",
       "1553  17139     Moultrie County, IL   1476   4453      0.426769  -0.085028   \n",
       "1554  20185     Stafford County, KS    303   1485      0.342503  -0.073013   \n",
       "\n",
       "      BirthRate  DeathRate  BachelorRate  UnemploymentRate  ...  \\\n",
       "0      0.439863   0.384615      0.280161          0.174274  ...   \n",
       "1      0.316151   0.220280      0.160858          0.286307  ...   \n",
       "2      0.391753   0.409091      0.202413          0.219917  ...   \n",
       "3      0.487973   0.174825      0.537534          0.120332  ...   \n",
       "4      0.336770   0.185315      0.089812          0.580913  ...   \n",
       "...         ...        ...           ...               ...  ...   \n",
       "1550   0.384880   0.377622      0.256032          0.248963  ...   \n",
       "1551   0.336770   0.356643      0.321716          0.215768  ...   \n",
       "1552   0.347079   0.363636      0.195710          0.269710  ...   \n",
       "1553   0.443299   0.454545      0.261394          0.190871  ...   \n",
       "1554   0.443299   0.423077      0.320375          0.153527  ...   \n",
       "\n",
       "      BachelorRate2012  UnemploymentRate2012  MedianIncomeDiff  MigraRateDiff  \\\n",
       "0             0.273611              0.284672          0.116587       0.073282   \n",
       "1             0.179167              0.456204          0.100885       0.021374   \n",
       "2             0.181944              0.182482          0.033367       0.022901   \n",
       "3             0.508333              0.142336          0.272866       0.018321   \n",
       "4             0.108333              0.642336         -0.084137      -0.229008   \n",
       "...                ...                   ...               ...            ...   \n",
       "1550          0.250000              0.339416          0.148384       0.061069   \n",
       "1551          0.304167              0.251825          0.295154       0.059542   \n",
       "1552          0.186111              0.328467          0.310464       0.248855   \n",
       "1553          0.215278              0.259124          0.287347      -0.152672   \n",
       "1554          0.281944              0.167883          0.124918      -0.050382   \n",
       "\n",
       "     BirthRateDiff  DeathRateDiff  BachelorRateDiff  UnemploymentRateDiff  \\\n",
       "0        -0.032967       0.110169          0.063492             -0.734694   \n",
       "1         0.021978      -0.144068         -0.047619             -1.142857   \n",
       "2         0.010989       0.050847          0.105820              0.061224   \n",
       "3        -0.186813       0.008475          0.185185             -0.204082   \n",
       "4         0.131868      -0.161017         -0.058201             -0.734694   \n",
       "...            ...            ...               ...                   ...   \n",
       "1550     -0.054945       0.059322          0.058201             -0.673469   \n",
       "1551     -0.131868       0.093220          0.111111             -0.346939   \n",
       "1552      0.329670       0.186441          0.063492             -0.510204   \n",
       "1553      0.065934       0.093220          0.211640             -0.510204   \n",
       "1554      0.285714      -0.059322          0.190476             -0.183673   \n",
       "\n",
       "      Weight  Winner  \n",
       "0        1.0       0  \n",
       "1        1.0       0  \n",
       "2        1.0       0  \n",
       "3        1.0       0  \n",
       "4        5.0       1  \n",
       "...      ...     ...  \n",
       "1550     1.0       0  \n",
       "1551     5.0       1  \n",
       "1552     1.0       0  \n",
       "1553     1.0       0  \n",
       "1554     1.0       0  \n",
       "\n",
       "[1555 rows x 29 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cpreprocess(d16, d12, graph, test=False, ratio = False):\n",
    "    '''\n",
    "    Preprocesses the given basic preprocessed dataframe (train or test) and extracts features from the 2012 data\n",
    "    adds them to the dataframe in addition to some other creative features such as one-hot encoding state markers\n",
    "    Args: \n",
    "      d16: some preprocessed 2016 dataframe (train or test)\n",
    "      d12: some preprocessed 2012 dataframe (train or test)      \n",
    "      graph: graph dataframe\n",
    "      test: boolean of whether or not d16 and d12 are test dataframes\n",
    "    Returns: \n",
    "    Dataframe with creative features and preprocessing\n",
    "    '''\n",
    "    # Preprocess raw dataframes\n",
    "    creative = norm_preprocess(d16.copy())\n",
    "    # Add graph features\n",
    "    creative = graph_process(creative.copy(), graph.copy(), df16.copy(), df12.copy())\n",
    "    # Set up the Hot Encode Categorical State Column for Tensorflow to do it for us :)\n",
    "    states = []\n",
    "    for row in range(len(creative)):\n",
    "        states.append(creative['County'][row][-2:])\n",
    "    # A column with values of all state abbreviations \n",
    "    creative['StateCode'] = np.array(states)\n",
    "    # Incorporate 2012->2016 changes in data and Standard Normalize them\n",
    "    norm_cols = ['MedianIncome','MigraRate', 'BirthRate', 'DeathRate', 'BachelorRate', 'UnemploymentRate']\n",
    "    \n",
    "    # GENERATE 2012 COLS\n",
    "    for cols in norm_cols:\n",
    "        delta = np.array(d12[cols])\n",
    "        name = cols + '2012'\n",
    "        factor = max(delta)\n",
    "        creative[name] = delta/np.abs(factor)\n",
    "    \n",
    "    # GENERATE DIFF COLS\n",
    "    for cols in norm_cols:\n",
    "        delta = np.array(d16[cols]) - np.array(d12[cols])\n",
    "        name = cols + 'Diff'\n",
    "        factor = max(delta)\n",
    "        creative[name] = delta/np.abs(factor)\n",
    "                \n",
    "    if not test:\n",
    "        if ratio:\n",
    "            creative = ratio_preprocess(creative)\n",
    "        else:\n",
    "            creative = gen_labels(creative)\n",
    "            creative = creative.astype({'Winner': 'int64'})\n",
    "        \n",
    "        # GENERATE CLASS WEIGHTS\n",
    "        creative = gen_class_weights(creative)\n",
    "        \n",
    "        # get a list of the columns\n",
    "        col_list = list(creative.columns)\n",
    "        w_idx = col_list.index('Winner')\n",
    "        del col_list[w_idx]\n",
    "        col_list.append('Winner')\n",
    "\n",
    "        creative = creative.reindex(columns=col_list)\n",
    "        print(creative.dtypes)\n",
    "    return creative\n",
    "\n",
    "traindf = cpreprocess(rawdf16, rawdf12, dfgraph, ratio = False)\n",
    "testdf = cpreprocess(rawtest16, rawtest12, dfgraph, test = True)\n",
    "traindf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Choose Data Being Sent To Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.1\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)\n",
    "\n",
    "# Trim unique identifier labels\n",
    "trimmed_data_frame = traindf.iloc[0:,4:]\n",
    "\n",
    "# Define sets of training data to use for the model\n",
    "CATEGORICAL_COLUMNS = ['StateCode']\n",
    "NUMERIC_COLUMNS = ['MedianIncome', 'MigraRate', 'BirthRate', 'DeathRate', 'BachelorRate', 'UnemploymentRate']\n",
    "\n",
    "NUMERIC_COLUMNS_2012 = []\n",
    "NUMERIC_COLUMNS_DIFF = []\n",
    "NUMERIC_GRAPH_EXTEND = []\n",
    "\n",
    "NUMERIC_COLUMNS_2012 = [x+'2012' for x in NUMERIC_COLUMNS]\n",
    "NUMERIC_COLUMNS_DIFF = [x+'Diff' for x in NUMERIC_COLUMNS]\n",
    "NUMERIC_GRAPH_EXTEND = ['neigh_vote', 'neigh_swing', 'neigh_pop']\n",
    "\n",
    "NUMERIC_COLUMNS += NUMERIC_COLUMNS_2012\n",
    "# We are not using Diff since we found it negatively affected performance\n",
    "# NUMERIC_COLUMNS += NUMERIC_COLUMNS_DIFF \n",
    "NUMERIC_COLUMNS += NUMERIC_GRAPH_EXTEND\n",
    "\n",
    "# Handy block of code that lets us remove columns at will\n",
    "REMOVE = ['MigraRate', 'MigraRate2012', 'BirthRate']\n",
    "NUMERIC_COLUMNS = [x for x in NUMERIC_COLUMNS if x not in REMOVE]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Define Model Helper Functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model util functions\n",
    "def one_hot_cat_column(feature_name, vocab):\n",
    "    '''\n",
    "    From tensorflow tutorial: One hot encoder helper function\n",
    "    '''\n",
    "    return tf.feature_column.indicator_column(\n",
    "        tf.feature_column.categorical_column_with_vocabulary_list(feature_name,\n",
    "                                                 vocab))\n",
    "\n",
    "def make_input_fn(X, y, n_epochs=None, shuffle=True):\n",
    "    '''\n",
    "    From tensorflow tutorial: wrap data for training, or for validation\n",
    "    '''\n",
    "    if y is not None:\n",
    "        NUM_EXAMPLES = len(y)\n",
    "    else:\n",
    "        NUM_EXAMPLES = 1555\n",
    "        \n",
    "    def input_fn():\n",
    "        dataset = tf.data.Dataset.from_tensor_slices((dict(X), y))\n",
    "        \n",
    "        if shuffle:\n",
    "            dataset = dataset.shuffle(NUM_EXAMPLES)\n",
    "        # For training, cycle thru dataset as many times as need (n_epochs=None).\n",
    "        dataset = dataset.repeat(n_epochs)\n",
    "        # In memory training doesn't use batching.\n",
    "        dataset = dataset.batch(NUM_EXAMPLES)\n",
    "        return dataset\n",
    "    return input_fn\n",
    "\n",
    "def generate_mini_batch(entire_data_frame, num_rows):\n",
    "    '''\n",
    "    Generate a training and validation mini batch from entire_data_frame\n",
    "    \n",
    "    Both batches haave size num_rows\n",
    "    '''\n",
    "    local_copy = entire_data_frame.copy()\n",
    "    subdf = local_copy.sample(n = num_rows*2, replace = False)\n",
    "\n",
    "    # Winner is in last col\n",
    "    label_idx = len(subdf.columns) - 1\n",
    "\n",
    "    sub_x = subdf.iloc[0:,:label_idx]\n",
    "    sub_y = subdf.iloc[0:,label_idx]\n",
    "    \n",
    "    split = num_rows\n",
    "    \n",
    "    valid_x = sub_x.iloc[split:,:]\n",
    "    valid_y = sub_y.iloc[split:]    \n",
    "    sub_x = sub_x.iloc[:split,:]\n",
    "    sub_y = sub_y.iloc[:split]\n",
    "    \n",
    "    return sub_x, sub_y, valid_x, valid_y\n",
    "\n",
    "def info_weighted_accuracy(pred, true):\n",
    "    '''\n",
    "    Returns weighted accuracy, as well as statistics on raw errors.\n",
    "    \n",
    "    weighted_accuracy, democrat_error, republican_error,\n",
    "    total_errors, number_labels\n",
    "    '''\n",
    "    assert(len(pred) == len(true))\n",
    "    num_labels = len(true)\n",
    "    num_pos = sum(true)\n",
    "    num_neg = num_labels - num_pos\n",
    "    frac_pos = num_pos/num_labels\n",
    "    weight_pos = 1/frac_pos\n",
    "    weight_neg = 1/(1-frac_pos)\n",
    "    num_pos_correct = 0\n",
    "    num_neg_correct = 0\n",
    "    for pred_i, true_i in zip(pred, true):\n",
    "        num_pos_correct += (pred_i == true_i and true_i == 1)\n",
    "        num_neg_correct += (pred_i == true_i and true_i == 0)\n",
    "    \n",
    "    d_err = num_pos - num_pos_correct\n",
    "    r_err = num_neg - num_neg_correct\n",
    "    t_e = d_err+r_err\n",
    "        \n",
    "    weighted_accuracy = ((weight_pos * num_pos_correct) \n",
    "                         + (weight_neg * num_neg_correct))/((weight_pos * num_pos) + (weight_neg * num_neg))\n",
    "    return weighted_accuracy,d_err,r_err,t_e,num_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Train the Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ *** ] STEP MAX: 50\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 100\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 150\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 200\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 250\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 300\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 350\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 400\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 450\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 500\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 550\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 600\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 650\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 700\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 750\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 800\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 850\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 900\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 950\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 1000\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 1050\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 1100\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 1150\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 1200\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 1250\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 1300\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 1350\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 1400\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 1450\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 1500\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 1550\n",
      "[ $$$ ] TRAINING\n",
      "[ *** ] STEP MAX: 1600\n",
      "[ $$$ ] TRAINING\n",
      "[ $$$ ] EVAL RESULTS:\n",
      "accuracy                   0.903846\n",
      "accuracy_baseline          0.551282\n",
      "auc                        0.972359\n",
      "auc_precision_recall       0.964845\n",
      "average_loss               0.210356\n",
      "label/mean                 0.448718\n",
      "loss                       0.328156\n",
      "precision                  0.876712\n",
      "prediction/mean            0.446501\n",
      "recall                     0.914286\n",
      "global_step             1600.000000\n",
      "dtype: float64\n",
      "[ $ ] VALIDATION ERROR STATS: \n",
      "D ERR: 6; R ERR: 45 TOTAL ERR: 51 on n = 500 labels\n",
      "[ $ ] WEIGHTED ACCURACY ON VALIDATION: \n",
      "0.9048172757475084\n",
      "[ $ ] VALIDATION ACCURACY OVER TIME: \n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ $$$ ] TEST\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setup the feature columns for ingest into the model, given t\n",
    "feature_columns = []\n",
    "for feature_name in CATEGORICAL_COLUMNS:\n",
    "  # Need to one-hot encode categorical features.\n",
    "  vocabulary = traindf[feature_name].unique()\n",
    "  feature_columns.append(one_hot_cat_column(feature_name, vocabulary))\n",
    "\n",
    "for feature_name in NUMERIC_COLUMNS:\n",
    "  feature_columns.append(tf.feature_column.numeric_column(feature_name,\n",
    "                                           dtype=tf.float32))\n",
    "\n",
    "# Initialize the model\n",
    "est = tf.estimator.DNNLinearCombinedClassifier(\n",
    "    linear_feature_columns=feature_columns,\n",
    "    linear_optimizer = 'Adam',\n",
    "    dnn_optimizer = 'Adam',\n",
    "    dnn_feature_columns=feature_columns,  \n",
    "    dnn_activation_fn = tf.nn.relu,\n",
    "    dnn_hidden_units=[15],\n",
    "    weight_column = 'Weight',\n",
    "    dnn_dropout = None,\n",
    "    batch_norm = False\n",
    ")\n",
    "\n",
    "# Setup variables that need to be initialized\n",
    "n = 0\n",
    "accs = []\n",
    "ns = []\n",
    "\n",
    "# setup input function for kaggle submission generation\n",
    "test_x = testdf.iloc[0:,2:]\n",
    "test_input_fn = make_input_fn(test_x.copy(), None, shuffle=False, n_epochs=1)\n",
    "\n",
    "# MINI BATCH SETUP\n",
    "batch_size = 500\n",
    "indices = [x for x in range(1555)]\n",
    "\n",
    "while True:\n",
    "    incr = 50 # Number of steps to train on a given batch\n",
    "    n += incr\n",
    "\n",
    "    print(f\"[ *** ] STEP MAX: {n}\")\n",
    "\n",
    "    # BATCH SELECTOR  \n",
    "    batch_x, batch_y, valid_x, valid_y = generate_mini_batch(trimmed_data_frame, num_rows = batch_size)\n",
    "    train_input_fn = make_input_fn(batch_x, batch_y, shuffle=True)\n",
    "    eval_input_fn  = make_input_fn(valid_x, valid_y, shuffle=False, n_epochs=1)\n",
    "    \n",
    "    print(\"[ $$$ ] TRAINING\")\n",
    "    # Train up to n steps\n",
    "    est.train(train_input_fn, max_steps=n)\n",
    "    \n",
    "    # Have the model predict on a validation set to estimate performance\n",
    "    eval_pred_dicts = list(est.predict(eval_input_fn))\n",
    "    result = est.evaluate(eval_input_fn)\n",
    "\n",
    "    # Given the prediction dictionaries, process them into parseable form\n",
    "    eval_preds = pd.Series([pred['probabilities'][1] for pred in eval_pred_dicts])\n",
    "    eval_submission = cthresh(eval_preds, thresh = 0.5)\n",
    "    \n",
    "    # Calculate weighted accuracy, and error statistics on validation\n",
    "    w_acc,d_err,r_err,total_err,n_lab = info_weighted_accuracy(eval_submission, valid_y)\n",
    "    \n",
    "    # Append to list of running weighted accuracies\n",
    "    accs.append(w_acc)\n",
    "    ns.append(n)\n",
    "           \n",
    "    # Generate prediction on the test set\n",
    "    pred_dicts = list(est.predict(test_input_fn))\n",
    "    \n",
    "    # Extract the prediction from model output\n",
    "    preds = pd.Series([pred['probabilities'][1] for pred in pred_dicts])\n",
    "\n",
    "    submission = cpackage(preds, dfsample.copy(), thresh = 0.5)\n",
    " \n",
    "    # Package submission \n",
    "    name = 'creative/submission2_r.csv'\n",
    "    submission.to_csv(name ,index = False)\n",
    "    submiss = pd.read_csv(name, sep=',', encoding='unicode_escape')\n",
    "\n",
    "    # EARLY STOPPING and print outputs\n",
    "    if n >= 1600:\n",
    "        print(\"[ $$$ ] EVAL RESULTS:\")\n",
    "        print(pd.Series(result))\n",
    "\n",
    "        print(\"[ $ ] VALIDATION ERROR STATS: \")\n",
    "        print(f\"D ERR: {d_err}; R ERR: {r_err} TOTAL ERR: {total_err} on n = {n_lab} labels\")\n",
    "\n",
    "        print(\"[ $ ] WEIGHTED ACCURACY ON VALIDATION: \")\n",
    "        print(w_acc)\n",
    "\n",
    "\n",
    "        # Create a graph over time of validation accuracy\n",
    "        print(\"[ $ ] VALIDATION ACCURACY OVER TIME: \")\n",
    "        plt.plot(ns,accs)\n",
    "        plt.show()\n",
    "\n",
    "        print(\"[ $$$ ] TEST\")\n",
    "        # Create histogram of the predictions\n",
    "        preds.plot(kind='hist', bins=20, title='predicted probabilities')\n",
    "\n",
    "        # Create a histogram of submission statistics\n",
    "        submission.iloc[0:,1].plot(kind='hist', bins=20, title='predicted probabilities')\n",
    "        plt.show()\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.2 Explanation in Words:</h3><p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You need to answer the following questions in a markdown cell after this cell:\n",
    "\n",
    "#### 3.2.1 How much did you manage to improve performance on the test set compared to part 2? Did you reach the 75% accuracy for the test in Kaggle? (Please include a screenshot of Kaggle Submission)\n",
    "\n",
    "We managed to improve significantly (by 14%) on our performance from the basic solution. We did reach the 75% accuracy threshold for the test accuracy in Kaggle, as seen in the following screenshot:\n",
    "\n",
    "![kaggle_creative_screenshot](kaggle_submission_creative.png)\n",
    "\n",
    "#### 3.2.2 Please explain in detail how you achieved this and what you did specifically and why you tried this.\n",
    "\n",
    "<ins>Feature Engineering:<ins>\n",
    "    \n",
    "Features played a large role in allowing us to increase our model’s accuracy. For the creative section we decided to incorporate the 2012 training data as well as the graph data. We added the 6 generic features from 2012 training to get 12 features total (6 previously). We also calculated the difference in value between each of the 6 features from 2012 to 2016 (2012 values subtracted from 2016 values) to get an additional 6 features for 18 total. We also then parsed the county information in the 2016 training and testing data frames in order to create state codes that represented which state each county was in. After one-hot encoding this feature, we had a total of 68 features. From there, we took a look at the graph data and came up with a few more features. \n",
    "\n",
    "We thought it’d be helpful to find out what proportion of neighboring counties voted for DEM for any given county as well as what proportion of these neighboring counties swung their vote to the opposing party from 2012 to 2016. We also found the proportion of DEM votes out of all votes in neighboring counties. The last feature created was in response to the cases where we couldn’t find any neighboring counties in the 2016 or 2012 training data for any given county to make these features from, and so we encoded a feature that was 1 if neighboring counties existed at all and 0 if we could find none of them. We would then encode default 0.5 values for the other features if this feature was 0 and hypothesized that the model would take those features with a grain of salt if the existence of neighbors feature was 0. \n",
    "\n",
    "From this we reached a total of 72 features. However, after some training and model selection that is discussed later, we implemented backward selection (as well as took a look at the correlation matrix generated by Pandas to help us make these educated guesses) to remove 9 features: MigraRate (2016), MigraRate (2012), and BirthRate (2016) (we also recognize that the correlation matrix only displays independent linear correlations and doesn’t reflect more complex and dependent features such as the neighbors’ existence feature that derives meaning from the other graph features). We found these features to be of little help and in some cases detrimental to our model performance. This brought the total to 69 features. Afterwards, we did some more backward feature selection and discovered that all 6 of the features derived from the difference between 2016 and 2012 values were detrimental to our model and were thus removed. Thus, we removed a total of 9 features and performed the same normalization procedure as before in basic, resulting in a total of 63 features.  \n",
    "\n",
    "<ins>DNN Linear CombinedClassifer:<ins>\n",
    "  \n",
    "We had a lot of success early on with a SVM on the data set, so we wanted to use a DNNLinearCombinedClassifier from tensorflow so that it could easily already have a linear portion of the model ready to go to help classify things. Then, once the model was later into its training cycles, the tuned ReLU functions could help grab the edge case counties. We only had one hidden layer as we felt that multiple hidden layers would easily overfit onto our training data. Similarly, we found that 15 ReLU nodes in the hidden layer was optimal for getting the best performance out of our model as well. As for the learning parameters, we stuck with the Adam optimizer as it not only performed the best out of the optimizers we tried (SGD, Adagrad), but was also recommended and considered a popular/default choice in industry [2].  \n",
    "\n",
    "<ins>Weighting:<ins>\n",
    "\n",
    "Since on average, there seems to be about 5 times as many Republican counties than Democrat counties, we weigh errors in Democratic counties 5 times more than Republican county errors to prioritize minimizing Democratic errors. By weighting classes differently, we bake into the algorithm this extra information about their likelihoods of occurring and alter the cost associated with each class [3].\n",
    "\n",
    "<ins>Batching:<ins>\n",
    "\n",
    "We had a strong feeling that we were overfitting on our training set, so we wanted to train on batches of our data instead. This allows the model to essentially be exposed to groups of data that could be more representative of the test set. We chose a batch size of 500 samples. While this is not very “mini” batching, we chose a large batch size to try and make results more consistent across multiple runs.\n",
    "\n",
    "<ins>Validation:<ins>\n",
    "\n",
    "After training on each batch of data, we ran validation on another randomly sampled batch of data that was disjoint from the most recent batch of training data. We run this validation after every training batch to get a live sense of how the model might actually perform on an arbitrary test set. Lastly, we also print out a set of statistics about the number of errors we make. This lets us know if we’re over predicting or underpredicting certain counties on a given validation batch\n",
    "\n",
    "<ins>EarlyStopping:<ins>\n",
    "    \n",
    "We stop the training of the model early as we believe that it's likely that if we trained for any further on the given dataset, we might start overfitting on the data. This seems to be a few hundred steps after our validation curve begins to slow down significantly. At the moment 1600 seems like the best stopping point.\n",
    "\n",
    "<ins>Submission Statistics:<ins>\n",
    "    \n",
    "We keep track of what the data looks like before we send it to the Creative Kaggle, to see what kinds of submissions tend to look like, and try and correlate its properties with its performance on Kaggle. This includes understanding how many DEM and GOP errors we’re making on our validation runs.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Part 4: Kaggle Submission</h2><p>\n",
    "You need to generate a prediction CSV using the following cell from your trained model and submit the direct output of your code to Kaggle. The CSV shall contain TWO column named exactly \"FIPS\" and \"Result\" and 1555 total rows excluding the column names, \"FIPS\" column shall contain FIPS of counties with same order as in the test_2016_no_label.csv while \"Result\" column shall contain the 0 or 1 prdicaitons for corresponding columns. A sample predication file can be downloaded from Kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This was done in previous sections but here it is copy/pasted.\n",
    "#Packaging submission method\n",
    "def package(preds, df):\n",
    "    '''Packages the predictions for submission using the sample data frame\n",
    "    Args: \n",
    "     preds: input list of predictions\n",
    "     df: sample dataframe\n",
    "    Returns: \n",
    "      df with predictions substituted in for sample predictions\n",
    "    '''\n",
    "    df['Result'][0:] = preds.astype(int)\n",
    "    return df\n",
    "# Basic\n",
    "submission_knn = package(preds_knn, dfsample.copy())\n",
    "submission_svm = package(preds_svm, dfsample.copy())\n",
    "\n",
    "submission_knn.to_csv('submission_knn.csv',index = False)\n",
    "submission_svm.to_csv('submission_svm.csv',index = False)\n",
    "# Creative\n",
    "name = 'creative/submission2_r.csv'\n",
    "submission.to_csv(name ,index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Part 5: Resources and Literature Used</h2>\n",
    "\n",
    "### Basic\n",
    "[1] https://arxiv.org/pdf/1708.04321.pdf\n",
    "\n",
    "[2] https://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html\n",
    "\n",
    "### Creative\n",
    "[1] https://www.tensorflow.org/api_docs/python/tf \n",
    "\n",
    "[2] https://towardsdatascience.com/learning-parameters-part-5-65a2f3583f7d \n",
    "\n",
    "[3] https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28 \n"
   ]
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