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bnaul/election_forecast.ipynb

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election_forecast.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bayesian presidential election forecasting model\n",
"Here we'll consider a simple Bayesian model for the 2016 presidential election based on polling data aggregated by FiveThirtyEight."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we'll download the polling data in CSV format and do some re-formatting:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import datetime\n",
"import pymc3 as pm\n",
"\n",
"\n",
"LATEST_POLL_DATE = datetime.datetime(2016, 10, 28)\n",
"polls = pd.read_csv('http://projects.fivethirtyeight.com/general-model/president_general_polls_2016.csv',\n",
" parse_dates=['startdate', 'enddate'])\n",
"polls = polls[polls.enddate > LATEST_POLL_DATE]\n",
"polls = polls[['state', 'adjpoll_clinton', 'adjpoll_trump']]\n",
"\n",
"# Remove national polls and combine polls for individual congressional districts\n",
"polls = polls[polls.state != 'U.S.']\n",
"polls.state = polls.state.replace(' CD.*', '', regex=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll also need the number of electoral college votes for each state in order to make predictions about the overall winner:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>abbr</th>\n",
" <th>state</th>\n",
" <th>votes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AL</td>\n",
" <td>Alabama</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AK</td>\n",
" <td>Alaska</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AZ</td>\n",
" <td>Arizona</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AR</td>\n",
" <td>Arkansas</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>CA</td>\n",
" <td>California</td>\n",
" <td>55</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" abbr state votes\n",
"0 AL Alabama 9\n",
"1 AK Alaska 3\n",
"2 AZ Arizona 11\n",
"3 AR Arkansas 6\n",
"4 CA California 55"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"states = pd.read_csv('electoral_votes.csv')\n",
"states.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to make sure we have a decent sample size, we'll count the number of polls per state:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>abbr</th>\n",
" <th>state</th>\n",
" <th>votes</th>\n",
" <th>poll_count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AL</td>\n",
" <td>Alabama</td>\n",
" <td>9</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>MN</td>\n",
" <td>Minnesota</td>\n",
" <td>10</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>MS</td>\n",
" <td>Mississippi</td>\n",
" <td>6</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>MT</td>\n",
" <td>Montana</td>\n",
" <td>3</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>NE</td>\n",
" <td>Nebraska</td>\n",
" <td>5</td>\n",
" <td>15</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" abbr state votes poll_count\n",
"0 AL Alabama 9 15\n",
"23 MN Minnesota 10 15\n",
"24 MS Mississippi 6 15\n",
"26 MT Montana 3 15\n",
"27 NE Nebraska 5 15"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"counts_by_state = polls.state.value_counts()\n",
"states = states.merge(pd.DataFrame({'poll_count': counts_by_state}), left_on='state', right_index=True)\n",
"states.sort_values(by='poll_count').head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll use the following simple model for our polling data: \n",
"\n",
"- Let $\\mu_C$ and $\\mu_T$ represent Clinton's and Trump's national levels of support, respectively\n",
"- Let $\\mu_C^{(i)}$ and $\\mu_T^{(i)}$ represent their support within state $i$\n",
"- We'll then treat each poll from state $i$ as a sample from a normal distribution centered around each candidate's support there"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To more easily link states with their model parameters, we'll add an index column `i`:"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>state</th>\n",
" <th>adjpoll_clinton</th>\n",
" <th>adjpoll_trump</th>\n",
" <th>i</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Wisconsin</td>\n",
" <td>45.53756</td>\n",
" <td>40.78262</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Wisconsin</td>\n",
" <td>45.47334</td>\n",
" <td>40.64487</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Wisconsin</td>\n",
" <td>42.35514</td>\n",
" <td>41.58504</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Wisconsin</td>\n",
" <td>46.04616</td>\n",
" <td>35.19124</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Wisconsin</td>\n",
" <td>42.28715</td>\n",
" <td>41.87758</td>\n",
" <td>49</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state adjpoll_clinton adjpoll_trump i\n",
"0 Wisconsin 45.53756 40.78262 49\n",
"1 Wisconsin 45.47334 40.64487 49\n",
"2 Wisconsin 42.35514 41.58504 49\n",
"3 Wisconsin 46.04616 35.19124 49\n",
"4 Wisconsin 42.28715 41.87758 49"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"states['i'] = states.index\n",
"polls = polls.merge(states[['state', 'i']], on='state')\n",
"polls.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we're ready to define our `pymc3` model:"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with pm.Model() as model:\n",
" # Prior distributions for national- and state-level support\n",
" mu_C = pm.Normal('mu_C', mu=42., sd=2.)\n",
" mu_C_state = pm.Normal('mu_C_state', mu=mu_C, sd=10., shape=51)\n",
" mu_T = pm.Normal('mu_T', mu=42., sd=2.)\n",
" mu_T_state = pm.Normal('mu_T_state', mu=mu_T, sd=10., shape=51)\n",
"\n",
" # Select the appropriate state-level variable for each poll\n",
" mu_C_poll = pm.Deterministic('mu_C_poll', mu_C_state[polls.i])\n",
" mu_T_poll = pm.Deterministic('mu_T_poll', mu_T_state[polls.i])\n",
" \n",
" # Gaussian likelihood for polling data\n",
" C_polls = pm.Normal('C_polls', mu=mu_C_poll, sd=10., observed=polls.adjpoll_clinton)\n",
" T_polls = pm.Normal('T_polls', mu=mu_T_poll, sd=10., observed=polls.adjpoll_trump)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our goal is to model posterior distribution of parameters given observed data:\n",
"1. Choose a MCMC sampler (defaults are NUTS/Metropolis-Hastings)\n",
"2. Run chain for a while to reduce correlation with starting value (\"burn-in\")\n",
"3. Take every $n$th step as samples from the approximate posterior (\"thinning\")"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 100000/100000 [00:37<00:00, 2650.11it/s]\n"
]
}
],
"source": [
"N = int(1e5)\n",
"burn_in_frac = 0.1\n",
"thin_factor = 5\n",
"\n",
"with model:\n",
" step = pm.Metropolis()\n",
" trace = pm.sample(N, step=step, njobs=1)\n",
" \n",
"burned_in = trace[int(burn_in_frac * N):]\n",
"samples = burned_in[::thin_factor]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll use the `chorogrid` tool (https://github.com/Prooffreader/chorogrid) to visualize results using a map of the U.S.:"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!pip install -q chorogrid\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns\n",
"import os\n",
"import chorogrid as cg\n",
"\n",
"\n",
"def us_choropleth(state_df, column, fenceposts=None, map_type='map'):\n",
" \"\"\"Draw a U.S. map color-coded by `state_df[column]`.\"\"\"\n",
" # Hard-coded FiveThirtyEight color palette\n",
" colors = ['#fc6b5e', '#fd998a', '#fec2b6', '#fee7e2', '#e2edfb', '#b3d4f5', '#7fbaf0', '#33a2e9']\n",
" mybin = cg.Colorbin(state_df[column], colors, proportional=True, decimals=None)\n",
" mybin.set_decimals(2)\n",
" if fenceposts is not None:\n",
" mybin.fenceposts = fenceposts\n",
" mybin.recalc(fenceposts=False)\n",
" colors_by_state = mybin.colors_out\n",
" font_colors_by_state = mybin.complements\n",
" legend_colors = mybin.colors_in\n",
" legend_labels = mybin.labels\n",
" usa_states_file = os.path.join(os.path.dirname(cg.__file__), 'databases', 'usa_states.csv')\n",
" grid = cg.Chorogrid(usa_states_file, state_df.abbr, colors_by_state)\n",
" if map_type == 'map':\n",
" grid.draw_map(spacing_dict={'legend_offset': [-150, -25]})\n",
" elif map_type == 'hex':\n",
" grid.draw_hex(spacing_dict={'legend_offset': [-150, -25]})\n",
" else:\n",
" raise NotImplementedError(\"Unrecognized map type.\")\n",
" grid.done(show=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our estimate of the probability that Clinton/Trump wins a given state is simply the number of realizations in which that candidate's share of the vote is higher:"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"metadata": {},
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}
],
"source": [
"states['prob_clinton'] = (samples['mu_C_state'] > samples['mu_T_state']).mean(axis=0)\n",
"us_choropleth(states, 'prob_clinton', map_type='map')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also choose a random realization and plot the resulting map:"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"source": [
"j = np.random.randint(len(samples))\n",
"states['vote_diff'] = samples['mu_C_state'][j] - samples['mu_T_state'][j]\n",
"us_choropleth(states, 'vote_diff', fenceposts=np.linspace(-20, 20, 8), map_type='map')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we only care about who wins the electoral college, then we should calculate the number of votes in each sample and examine that distribution:"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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2VS6ccy0tI2lra22KeJopFhiaeNate5Z77/09AMcccyzjxiV7E7PTTO9PM8UCiifPmikW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\nIplQUiEiIiKZ+P9oDFGehtIAwAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f8c1be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"clinton_votes = np.sum(np.atleast_2d(states.votes)\n",
" * (samples['mu_C_state'] > samples['mu_T_state']), axis=1)\n",
"trump_votes = np.sum(np.atleast_2d(states.votes)\n",
" * (samples['mu_C_state'] < samples['mu_T_state']), axis=1)\n",
"\n",
"n, bins, patches = plt.hist(clinton_votes, bins=np.arange(200, 380), normed=True, color='#33a2e9')\n",
"bin_centers = 0.5 * (bins[:-1] + bins[1:])\n",
"for c, p in zip(bin_centers, patches):\n",
" if c < 270:\n",
" plt.setp(p, 'facecolor', '#fc6b5e')\n",
"plt.axvline(270, color='k', ls='dashed')\n",
"plt.title(\"Clinton win probability = {:2.1f}%\".format(100 * (clinton_votes > 270).mean()));"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `forestplot` function from `pmyc3` can show the distribution of the vote for each state:"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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7E7Bynod0OkU6nSrnkCErk0m/5laSpb/it3RpilWrBvd3euHCNPPnV9HRsWXH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\nSZLa6n8BeaI5HY+y8lUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112d18da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12, 12))\n",
"pm.forestplot(samples, varnames=['mu_C_state'], ylabels=states.state);"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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