Voter_Targeting_LogReg.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOuY6KFtgn9ZREMDUI/aoWe",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ellenwterry/PoliticalAnalysis/blob/main/Voter_Targeting_LogReg.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load Libraries"
      ],
      "metadata": {
        "id": "yUOsjDzaufik"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from scipy import stats\n",
        "import patsy\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "import random\n",
        "!pip install nest-asyncio\n",
        "import nest_asyncio\n",
        "nest_asyncio.apply()\n",
        "\n",
        "\n",
        "!pip install pystan==3.7.0\n",
        "#!pip install pystan\n",
        "!pip install corner\n",
        "import stan\n",
        "\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "!pip install geopy\n",
        "from geopy.geocoders import Nominatim\n",
        "import matplotlib.pyplot as plt\n",
        "!pip install pygris\n",
        "# import matplotlib.pyplot as plt\n",
        "from pygris import core_based_statistical_areas\n",
        "from pygris import tracts\n",
        "\n",
        "from google.colab import files\n",
        "\n",
        "\n",
        "import geopandas as gpd\n",
        "import folium\n",
        "# from google.colab import files\n"
      ],
      "metadata": {
        "id": "G9vYtDP_eAij"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Get Data from Github Site:"
      ],
      "metadata": {
        "id": "ss-RECEruwjo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ---------- Get Data from Github ---------- #\n",
        "\n",
        "url = 'https://raw.githubusercontent.com/ellenwterry/PoliticalAnalysis/main/VoteBase.csv'\n",
        "VoteBase = pd.read_csv(url)"
      ],
      "metadata": {
        "id": "nEk2b3mGgIXh"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tidy Data"
      ],
      "metadata": {
        "id": "DYdBz2E5u1Va"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ---------- Clean up data ---------- #\n",
        "\n",
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "\n",
        "le.fit(VoteBase['Sex'])\n",
        "\n",
        "codes = {'NR':0, 'M': 1, 'F': 2}\n",
        "VoteBase['Sex'] = VoteBase['Sex'].map(codes)\n",
        "\n",
        "VoteBase['Age']=VoteBase.Age.astype('int32')\n",
        "\n",
        "#VoteBase['LastPrimary'] = le.transform(VoteBase['LastPrimary'])\n",
        "codes = {'NR':0, 'R': 1, 'D':2}\n",
        "VoteBase['LastPrimary'] = VoteBase['LastPrimary'].map(codes)\n",
        "\n",
        "#VoteBase['Education'] = le.transform(VoteBase['Education'])\n",
        "codes = {'NR':0, 'HS': 1, 'Some College':2, 'Bachelor':3, 'Masters':4, 'Doctorate':5}\n",
        "VoteBase['Education'] = VoteBase['Education'].map(codes)\n",
        "\n",
        "#VoteBase['HHIncome'] = le.transform(VoteBase['HHIncome'])\n",
        "codes = {'NR':0, 'Under 50k': 1, '50k-100k':2, '100k-200k':3, '200k-300k':4, '300k-500k':5, 'Over 500k':6}\n",
        "VoteBase['HHIncome'] = VoteBase['HHIncome'].map(codes)\n",
        "\n",
        "#VoteBase['ReligiousAffil'] = le.transform(VoteBase['ReligiousAffil'])\n",
        "codes = {'NR':0,'Protestant': 1, 'Catholic':2, 'Other':3, 'None':4}\n",
        "VoteBase['ReligiousAffil'] = VoteBase['ReligiousAffil'].map(codes)\n",
        "\n",
        "#VoteBase['Support24'] = le.transform(VoteBase['Support24'])\n",
        "codes = {'R':0, 'D': 1}\n",
        "VoteBase['Support24'] = VoteBase['Support24'].map(codes)\n",
        "# NOTE: NAs were excluded from sample so that algorithms could score using logistic scale - 2nd pass will use imputed values\n",
        "\n",
        "#VoteBase['TopIssue'] = le.transform(VoteBase['TopIssue'])\n",
        "codes = {'NR':0, 'RFree':1, 'Parents':2, 'Crime':3, 'Economy':4, 'Womens':5, 'Education':6, 'Environment':7, 'Democracy':8}\n",
        "VoteBase['TopIssue'] = VoteBase['TopIssue'].map(codes)\n",
        "\n",
        "# This is for the second data source (later)\n",
        "codes = {'NS':0, 'NR':1,'Signed':2}\n",
        "VoteBase['RRPetition'] = VoteBase['RRPetition'].map(codes)"
      ],
      "metadata": {
        "id": "bOha1ArygIhY"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create a Model Matrix without intercept column (because the algorithm separates the intecept for multinomial problems), and split into train and test."
      ],
      "metadata": {
        "id": "4Si6D2VNwcFU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(316)\n",
        "VoteMatrix = patsy.dmatrix('Age + Sex + Education + HHIncome+ ReligiousAffil + LastPrimary + TopIssue -1', VoteBase)\n",
        "yArray = np.array(VoteBase['Support24'])\n",
        "rows = VoteMatrix.shape[0]\n",
        "tstInd = np.random.randint(0, rows, size=100)\n",
        "tstMatrix = VoteMatrix[tstInd]\n",
        "yTst = yArray[tstInd]\n",
        "trnMatrix = np.delete(VoteMatrix, tstInd, axis=0)\n",
        "yTrn = np.delete(yArray, tstInd, axis=0)"
      ],
      "metadata": {
        "id": "z1Yw01BYCYWz"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train Model, produce probabilities and store theta (array of coefficients). This is the model predictions"
      ],
      "metadata": {
        "id": "g5WoIiVg7Iki"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression(max_iter=1000)\n",
        "model.fit(trnMatrix, yTrn)\n",
        "Pred = model.predict(tstMatrix)\n",
        "ModelProbs = pd.DataFrame(model.predict_proba(tstMatrix))\n",
        "theta = np.matrix(model.coef_)\n",
        "intercept = model.intercept_"
      ],
      "metadata": {
        "id": "VpRrP2aqgX0C"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "View coefficients: (0.00590519,  0.07741354,  0.28367684,  0.79278793,  0.99741254, 0.37823278,  0.12781814)\n",
        "\n",
        "bias: -4.71900663"
      ],
      "metadata": {
        "id": "nTxSQcnTxmc9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.coef_"
      ],
      "metadata": {
        "id": "iaWD3UCkmY-J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a840dab-efdc-4aa1-f422-370c68be447a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.00590519,  0.07741354,  0.28367684,  0.79278793,  0.99741254,\n",
              "         0.37823278,  0.12781814]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9wJe9fg-TSo",
        "outputId": "4cf36b96-d509-4fca-a48d-02f7ae6ebf50"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-4.71900663])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Confirm using Equation, comparing with model probs"
      ],
      "metadata": {
        "id": "7Y4vLTFUv5pE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def stable_sigmoid(x):\n",
        "  # Using np.where to avoid numerical overflow or underflow.\n",
        "  return np.where(x >= 0, 1 / (1 + np.exp(-x)), np.exp(x) / (1 + np.exp(x)))\n",
        "\n",
        "EQProbs = stable_sigmoid((np.dot(model.coef_,tstMatrix[:,].transpose())+ model.intercept_).transpose())"
      ],
      "metadata": {
        "id": "eN4HzVnImVYb"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now lets take a look at the Test data in a DataFrame, cmparing model probs with equation probs :"
      ],
      "metadata": {
        "id": "QKRtCLfiG15f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tstDF = VoteBase.iloc[tstInd,:].copy()\n",
        "tstDF['ModelProbs'] = ModelProbs[1].tolist()\n",
        "tstDF['EQProbs'] = EQProbs[:].astype(float)\n",
        "tstDF\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "CdUSoL4zAWYr",
        "outputId": "eb11a5c7-691f-4887-a627-e84add0f92a5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID        Name  Sex  Age  LastPrimary  Latitude  Longitude  Education  \\\n",
              "829    830   Voter 830    2   32            0  41.09766  -73.61686          2   \n",
              "830    831   Voter 831    1   35            0  41.02575  -73.66299          2   \n",
              "1632  1633  Voter 1633    1   85            0  41.01395  -73.65489          3   \n",
              "920    921   Voter 921    2   51            0  41.05441  -73.56803          2   \n",
              "708    709   Voter 709    1   65            0  41.08071  -73.63603          2   \n",
              "...    ...         ...  ...  ...          ...       ...        ...        ...   \n",
              "671    672   Voter 672    2   33            0  41.09276  -73.64826          2   \n",
              "922    923   Voter 923    1   51            0  41.08903  -73.65623          0   \n",
              "1121  1122  Voter 1122    2   72            0  41.00559  -73.63517          3   \n",
              "1273  1274  Voter 1274    1   90            0  41.02769  -73.58716          3   \n",
              "1418  1419  Voter 1419    2   45            0  41.03307  -73.63751          3   \n",
              "\n",
              "      HHIncome  ReligiousAffil  Support24  TopIssue  RRPetition  ModelProbs  \\\n",
              "829          2               1          1         0           2    0.167590   \n",
              "830          2               1          0         0           0    0.154735   \n",
              "1632         3               3          1         5           0    0.847766   \n",
              "920          3               1          0         0           0    0.284507   \n",
              "708          2               1          1         0           0    0.132954   \n",
              "...        ...             ...        ...       ...         ...         ...   \n",
              "671          2               1          0         0           2    0.166768   \n",
              "922          3               1          0         0           0    0.172647   \n",
              "1121         3               2          0         0           0    0.558448   \n",
              "1273         3               2          0         0           0    0.512787   \n",
              "1418         3               3          1         7           0    0.907748   \n",
              "\n",
              "       EQProbs  \n",
              "829   0.167590  \n",
              "830   0.154735  \n",
              "1632  0.847766  \n",
              "920   0.284507  \n",
              "708   0.132954  \n",
              "...        ...  \n",
              "671   0.166768  \n",
              "922   0.172647  \n",
              "1121  0.558448  \n",
              "1273  0.512787  \n",
              "1418  0.907748  \n",
              "\n",
              "[100 rows x 15 columns]"
            ],
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              "\n",
              "  <div id=\"df-f47a2626-7a8f-4fe1-a20c-52971842b209\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>LastPrimary</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "      <th>Education</th>\n",
              "      <th>HHIncome</th>\n",
              "      <th>ReligiousAffil</th>\n",
              "      <th>Support24</th>\n",
              "      <th>TopIssue</th>\n",
              "      <th>RRPetition</th>\n",
              "      <th>ModelProbs</th>\n",
              "      <th>EQProbs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>829</th>\n",
              "      <td>830</td>\n",
              "      <td>Voter 830</td>\n",
              "      <td>2</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>41.09766</td>\n",
              "      <td>-73.61686</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.167590</td>\n",
              "      <td>0.167590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>830</th>\n",
              "      <td>831</td>\n",
              "      <td>Voter 831</td>\n",
              "      <td>1</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>41.02575</td>\n",
              "      <td>-73.66299</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.154735</td>\n",
              "      <td>0.154735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1632</th>\n",
              "      <td>1633</td>\n",
              "      <td>Voter 1633</td>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>0</td>\n",
              "      <td>41.01395</td>\n",
              "      <td>-73.65489</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.847766</td>\n",
              "      <td>0.847766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>920</th>\n",
              "      <td>921</td>\n",
              "      <td>Voter 921</td>\n",
              "      <td>2</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>41.05441</td>\n",
              "      <td>-73.56803</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.284507</td>\n",
              "      <td>0.284507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>708</th>\n",
              "      <td>709</td>\n",
              "      <td>Voter 709</td>\n",
              "      <td>1</td>\n",
              "      <td>65</td>\n",
              "      <td>0</td>\n",
              "      <td>41.08071</td>\n",
              "      <td>-73.63603</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.132954</td>\n",
              "      <td>0.132954</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>671</th>\n",
              "      <td>672</td>\n",
              "      <td>Voter 672</td>\n",
              "      <td>2</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>41.09276</td>\n",
              "      <td>-73.64826</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.166768</td>\n",
              "      <td>0.166768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>922</th>\n",
              "      <td>923</td>\n",
              "      <td>Voter 923</td>\n",
              "      <td>1</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>41.08903</td>\n",
              "      <td>-73.65623</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.172647</td>\n",
              "      <td>0.172647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1121</th>\n",
              "      <td>1122</td>\n",
              "      <td>Voter 1122</td>\n",
              "      <td>2</td>\n",
              "      <td>72</td>\n",
              "      <td>0</td>\n",
              "      <td>41.00559</td>\n",
              "      <td>-73.63517</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.558448</td>\n",
              "      <td>0.558448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1273</th>\n",
              "      <td>1274</td>\n",
              "      <td>Voter 1274</td>\n",
              "      <td>1</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>41.02769</td>\n",
              "      <td>-73.58716</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.512787</td>\n",
              "      <td>0.512787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1418</th>\n",
              "      <td>1419</td>\n",
              "      <td>Voter 1419</td>\n",
              "      <td>2</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>41.03307</td>\n",
              "      <td>-73.63751</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0.907748</td>\n",
              "      <td>0.907748</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 15 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-color: transparent;\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_29d587b7-6dd6-4598-96a6-2ea783319a3e\">\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('tstDF')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#id_29d587b7-6dd6-4598-96a6-2ea783319a3e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('tstDF');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tstDF",
              "summary": "{\n  \"name\": \"tstDF\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 741,\n        \"min\": 7,\n        \"max\": 2490,\n        \"num_unique_values\": 98,\n        \"samples\": [\n          1773,\n          7,\n          923\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 98,\n        \"samples\": [\n          \"Voter 1773\",\n          \"Voter 7\",\n          \"Voter 923\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 50,\n        \"samples\": [\n          36,\n          99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastPrimary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02611662249498306,\n        \"min\": 41.00047,\n        \"max\": 41.13875,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          41.10917,\n          41.04044\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.038853383172504787,\n        \"min\": -73.69957,\n        \"max\": -73.45137,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          -73.60417,\n          -73.58322\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HHIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ReligiousAffil\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Support24\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TopIssue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RRPetition\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ModelProbs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3545728527336168,\n        \"min\": 0.011603453637696106,\n        \"max\": 0.97020918440922,\n        \"num_unique_values\": 98,\n        \"samples\": [\n          0.8510556753304499,\n          0.5235985679704407\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EQProbs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3545728527336168,\n        \"min\": 0.011603453637696107,\n        \"max\": 0.97020918440922,\n        \"num_unique_values\": 98,\n        \"samples\": [\n          0.8510556753304499,\n          0.5235985679704407\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "OK, that looks good - just one last check - compare predicted class with the voter response in the initial survey"
      ],
      "metadata": {
        "id": "2d3OQWKAAHOQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# just to reality check class assignment accuracy\n",
        "tstDF.loc[tstDF['Support24'] ==1].count()\n",
        "SupportCnt = tstDF.query('Support24 == 1').shape[0]\n",
        "ClassCnt = tstDF.query('Support24 == 1 & EQProbs > .5' ).shape[0]\n",
        "ClassCnt/SupportCnt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zUkCv8H8wPM",
        "outputId": "0fa861ea-39f3-4e95-c637-6d7cf9a8d400"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8085106382978723"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's pull a selected voter and change their data (intervention), observing the effect - changing the Top Issue on voter 921 to 5 (womens healthcare) and see how much that would change EQprob"
      ],
      "metadata": {
        "id": "OER4NnueH8qS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tstDF.loc[tstDF['ID'] == 921]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "lx3U9b8GIwXI",
        "outputId": "05459ed2-e850-4deb-931d-a624158762a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      ID       Name  Sex  Age  LastPrimary  Latitude  Longitude  Education  \\\n",
              "920  921  Voter 921    2   51            0  41.05441  -73.56803          2   \n",
              "\n",
              "     HHIncome  ReligiousAffil  Support24  TopIssue  RRPetition  ModelProbs  \\\n",
              "920         3               1          0         0           0    0.284507   \n",
              "\n",
              "      EQProbs  \n",
              "920  0.284507  "
            ],
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              "      <th>EQProbs</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>920</th>\n",
              "      <td>921</td>\n",
              "      <td>Voter 921</td>\n",
              "      <td>2</td>\n",
              "      <td>51</td>\n",
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              "summary": "{\n  \"name\": \"tstDF\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 921,\n        \"max\": 921,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          921\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Voter 921\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastPrimary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 41.05441,\n        \"max\": 41.05441,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          41.05441\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": -73.56803,\n        \"max\": -73.56803,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -73.56803\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HHIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 3,\n        \"max\": 3,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 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            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What happens if we can move Voter 921 to a TopIssue of 5\n",
        "\n",
        "tstVoter = tstDF.loc[tstDF['ID'] == 921].to_numpy()\n",
        "#pull the columns from the dataframe that we used to model\n",
        "tstVoter = tstVoter[:, [3, 2, 7, 8, 9, 4, 10]].astype(float)\n",
        "# change top issue from 0 to 5 (simulating a change in position after a canvassing conversation - intervention)\n",
        "tstVoter[0,6] = 5\n",
        "ProbNew = stable_sigmoid((np.dot(model.coef_,tstVoter[0].transpose())+ model.intercept_).transpose())\n",
        "ProbNew\n"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "l6cxge8IXEjB",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.42968812])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We've estimated an intevention here, moving the probability of voting D from .28% to 42% which is getting close to a D vote. Just based on this, it seems like it would make sense to target some voters and have conversations about womens healthcare.\n",
        "\n",
        "So, let's step back and look at the voter universe:"
      ],
      "metadata": {
        "id": "QqwFiBRQJSV2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "AllProbs = stable_sigmoid((np.dot(model.coef_,VoteMatrix[:,].transpose())+ model.intercept_).transpose())\n",
        "AllDF = VoteBase.copy()\n",
        "AllDF['EQProbs'] = AllProbs[:].astype(float)\n",
        "AllDF\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Oe_qpBnbQHjw",
        "outputId": "249bb3da-2e3d-43de-8cf5-94af02065e0a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ID        Name  Sex  Age  LastPrimary  Latitude  Longitude  Education  \\\n",
              "0        1     Voter 1    1   51            0  41.00544  -73.62954          2   \n",
              "1        2     Voter 2    1   52            0  41.04027  -73.63011          2   \n",
              "2        3     Voter 3    2   54            0  41.05734  -73.65044          3   \n",
              "3        4     Voter 4    1   59            0  41.03255  -73.60403          2   \n",
              "4        5     Voter 5    1   36            0  41.06684  -73.57837          2   \n",
              "...    ...         ...  ...  ...          ...       ...        ...        ...   \n",
              "2493  2494  Voter 2494    2   62            0  41.07108  -73.63842          4   \n",
              "2494  2495  Voter 2495    2   62            2  41.03758  -73.57932          3   \n",
              "2495  2496  Voter 2496    2   65            2  41.01605  -73.58949          3   \n",
              "2496  2497  Voter 2497    1   67            2  40.99031  -73.65605          0   \n",
              "2497  2498  Voter 2498    2   90            2  41.02249  -73.57073          3   \n",
              "\n",
              "      HHIncome  ReligiousAffil  Support24  TopIssue  RRPetition   EQProbs  \n",
              "0            3               1          0         0           0  0.269015  \n",
              "1            2               1          0         0           0  0.142056  \n",
              "2            6               1          1         4           0  0.903208  \n",
              "3            3               1          0         0           0  0.259827  \n",
              "4            3               4          0         0           0  0.889061  \n",
              "...        ...             ...        ...       ...         ...       ...  \n",
              "2493         3               4          1         5           1  0.961265  \n",
              "2494         3               0          1         5           1  0.424250  \n",
              "2495         6               1          1         0           1  0.917860  \n",
              "2496         6               0          1         0           1  0.616792  \n",
              "2497         3               4          1         0           1  0.946842  \n",
              "\n",
              "[2498 rows x 14 columns]"
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              "      <th></th>\n",
              "      <th>ID</th>\n",
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              "      <th>0</th>\n",
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              "      <td>Voter 1</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
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              "      <td>4</td>\n",
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              "      <td>59</td>\n",
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              "      <td>-73.60403</td>\n",
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              "      <td>36</td>\n",
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              "      <td>41.06684</td>\n",
              "      <td>-73.57837</td>\n",
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              "      <td>-73.57932</td>\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-26aec44c-3312-4c58-be0e-2cf4cedbd076 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-26aec44c-3312-4c58-be0e-2cf4cedbd076');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_2c11cd8b-aa97-4faf-9f31-e2fb58fd356d\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
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              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('AllDF')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('AllDF');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "AllDF",
              "summary": "{\n  \"name\": \"AllDF\",\n  \"rows\": 2498,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 721,\n        \"min\": 1,\n        \"max\": 2498,\n        \"num_unique_values\": 2498,\n        \"samples\": [\n          2294,\n          1865,\n          903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2498,\n        \"samples\": [\n          \"Voter 2294\",\n          \"Voter 1865\",\n          \"Voter 903\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 80,\n        \"samples\": [\n          75,\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastPrimary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.025668932771855203,\n        \"min\": 40.99031,\n        \"max\": 41.20214,\n        \"num_unique_values\": 1672,\n        \"samples\": [\n          41.03876,\n          41.09038\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03609867078460551,\n        \"min\": -73.71461,\n        \"max\": -73.22756,\n        \"num_unique_values\": 1751,\n        \"samples\": [\n          -73.66495,\n          -73.61965\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HHIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ReligiousAffil\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Support24\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TopIssue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          6,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RRPetition\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EQProbs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3514851703584381,\n        \"min\": 0.011603453637696107,\n        \"max\": 0.9987096380022641,\n        \"num_unique_values\": 2056,\n        \"samples\": [\n          0.018340540039213094,\n          0.8359165924939586\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "OK, this shoud be the beginning potential of our universe, let's take a look:"
      ],
      "metadata": {
        "id": "v5-IywlUJ6qk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# lets back up and look at the whole picture\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Histogram(x=AllDF['EQProbs'], marker_color='#378796', opacity=0.05, name = \"Vote Prob\"))\n",
        "fig.update_layout(barmode='overlay')\n",
        "fig.update_traces(opacity=0.9, nbinsx=100)\n",
        "fig.update_layout(\n",
        "    autosize=False,\n",
        "    width=800,\n",
        "    height=400,\n",
        "    plot_bgcolor = \"white\",\n",
        "    xaxis=dict(title='Probability of Vote',),\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "dYs5oMjJHmgD",
        "outputId": "b21a7f1a-508e-4822-e143-de19dcf64fd3"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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    {
      "cell_type": "markdown",
      "source": [
        "So again, let's stress the idea of dealing with continous outcomes. Those clusters around 20%-50% are possibly D votes, and 50%-70% are possibly R votes - it's up to you and you should be aware of the differences in the voter base.\n",
        "\n",
        "Now,let's look at this from a TopIssue perspectve, highlighting voters who responded that \"womens healthcare\" is most important:"
      ],
      "metadata": {
        "id": "t65xwCzkKMpU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "WmnVote = AllDF.query('TopIssue == 5' )\n",
        "\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Histogram(x=AllDF['EQProbs'], marker_color='#DEDCDC', opacity=0.05, name = \"AllVoters\"))\n",
        "fig.add_trace(go.Histogram(x=WmnVote['EQProbs'], marker_color='#94CDD7', opacity=0.1, name = \"Women Healthcare Voters\"))\n",
        "fig.update_layout(barmode='overlay')\n",
        "fig.update_traces(opacity=0.9, nbinsx=100)\n",
        "fig.update_layout(\n",
        "    autosize=False,\n",
        "    width=800,\n",
        "    height=400,\n",
        "    plot_bgcolor = \"white\",\n",
        "    xaxis=dict(title='Probability of Vote',),\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "HzP691HNPO5j",
        "outputId": "99738111-b1d4-4fa9-b10f-2e7433b3b082",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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    {
      "cell_type": "markdown",
      "source": [
        "What this tells us is that the WH voters are more likely to have stated support for D in the election - but there are WH voters that stated support for R voters, and overall, a lot of voters that we just don't know much about.\n",
        "\n",
        "Maybe we should be targeting women voters greater than 20% but less than 60% that did not respond that Womens Healthcare is their top issue"
      ],
      "metadata": {
        "id": "AXf3XaNILcvZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SegmentDF = AllDF.query('EQProbs > .2 & EQProbs <.6' )\n",
        "Target = AllDF.query('TopIssue !=5 & EQProbs > .2 & EQProbs <.6 & Sex ==2' )"
      ],
      "metadata": {
        "id": "l90kL6wMLwnu"
      },
      "execution_count": 14,
      "outputs": []
    },
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      "cell_type": "code",
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          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9YF45xNxS4P",
        "outputId": "4b7d59ac-b350-4f55-d7ef-6333e2c5353c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "220"
            ]
          },
          "metadata": {},
          "execution_count": 16
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      ]
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      "cell_type": "code",
      "source": [
        "fig = go.Figure()\n",
        "fig.add_trace(go.Histogram(x=SegmentDF['EQProbs'], marker_color='#DEDCDC', opacity=0.05, name = \"AllVoters\"))\n",
        "fig.add_trace(go.Histogram(x=Target['EQProbs'], marker_color='#94CDD7', opacity=0.1, name = \"Women Not Responding WH\"))\n",
        "fig.update_layout(barmode='overlay')\n",
        "fig.update_traces(opacity=0.9, nbinsx=100)\n",
        "fig.update_layout(\n",
        "    autosize=False,\n",
        "    width=800,\n",
        "    height=400,\n",
        "    plot_bgcolor = \"white\",\n",
        "    xaxis=dict(title='Probability of Vote',),\n",
        ")\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "4L2FKTeLUV2a",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
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      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
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        "So, there are 220 women voters that did not respond that womens healthcare was the top concern in their voting choices. We can obviously target further - e.g., using correlations between other values and variables to improve effects. Seems like special interest events and canvassing could spur some interesting conversations, don't you think?   \n",
        "\n",
        "Summariing:\n",
        "\n",
        "We built a probabilistic, explanatory model of voters, and targeted a likely group of persuadable voters based on gender ('sex' in this data) and survey responses because we tested an intervention and found that it could have a significant effect on voter outcome (btw these are strong hints of causality - an acyclic graph with distribution effects would be confirmation).  "
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