Tutorial Notebooks

notebooks/.ipynb_checkpoints/load_clean-checkpoint.ipynb

@@ -0,0 +1,135 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a tutorial on loading in an election dataset and cleaning ballots.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from votekit.cvr_loaders import load_blt\n",
+    "import votekit.cleaning as clean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's first load in our cvr into a preference profile\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# need to make the scottish election data importable\n",
+    "\n",
+    "pp, seats = load_blt(\"...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can clean the ballots from this election.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we want to remove a candidate, we can call remove_noncands() from the package as shown below.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "cleaned_pp = clean.remove_noncands(pp, [\"Graham HUTCHISON (C)\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also write our own cleaning rule with the helper function clean_profile(). The following example is a cleaning rule truncates the ballot to n-ranks.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from votekit.cleaning import clean_profile\n",
+    "from votekit.pref_profile import PreferenceProfile\n",
+    "from votekit.ballot import Ballot\n",
+    "\n",
+    "\n",
+    "def truncate(n: int, pp: PreferenceProfile):\n",
+    "    def truncate_ballot(ballot: Ballot):\n",
+    "        return Ballot(ranking=ballot.ranking[:n], weight=ballot.weight)\n",
+    "\n",
+    "    pp_clean = clean_profile(pp=pp, clean_ballot_func=truncate_ballot)\n",
+    "    return pp_clean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cleaned_pp = truncate(n=3, pp=pp)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Our preference profile is now cleaned, and we can save it as an csv for future use.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cleaned_pp.to_csv('path/to/save')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/diff_bg_viz.ipynb → notebooks/ballot_generator.ipynb

@@ -16,9 +16,7 @@
    "outputs": [],
    "source": [
     "import votekit.ballot_generator as bg\n",
-    "from votekit.plots.profile_plots import plot_summary_stats\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np"
+    "from votekit.plots.profile_plots import plot_summary_stats"
    ]
   },
   {

notebooks/election_simulation.ipynb

@@ -0,0 +1,176 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This tutorial shows you how to simulate elections using VoteKit.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'votekit.elections'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[12], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# from votekit.utils import make_ballot\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mvotekit\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39melections\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39melection_types\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39melections\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mvotekit\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpref_profile\u001b[39;00m \u001b[39mimport\u001b[39;00m PreferenceProfile\n\u001b[1;32m      4\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mvotekit\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39melections\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mtransfers\u001b[39;00m \u001b[39mimport\u001b[39;00m fractional_transfer\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'votekit.elections'"
+     ]
+    }
+   ],
+   "source": [
+    "# from votekit.utils import make_ballot\n",
+    "import votekit.elections.election_types as elections\n",
+    "from votekit.pref_profile import PreferenceProfile\n",
+    "from votekit.elections.transfers import fractional_transfer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's first build our preference profile with synthetic ballots\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b1 = make_ballot(ranking=[\"A\", \"D\", \"E\", \"C\", \"B\"], weight=18)\n",
+    "b2 = make_ballot(ranking=[\"B\", \"E\", \"D\", \"C\", \"A\"], weight=12)\n",
+    "b3 = make_ballot(ranking=[\"C\", \"B\", \"E\", \"D\", \"A\"], weight=10)\n",
+    "b4 = make_ballot(ranking=[\"D\", \"C\", \"E\", \"B\", \"A\"], weight=4)\n",
+    "b5 = make_ballot(ranking=[\"E\", \"B\", \"D\", \"C\", \"A\"], weight=4)\n",
+    "b6 = make_ballot(ranking=[\"E\", \"C\", \"D\", \"B\", \"A\"], weight=2)\n",
+    "pp = PreferenceProfile(ballots=[b1, b2, b3, b4, b5, b6])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can define our set of elections to simulate.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_seats = 1\n",
+    "election_borda = elections.Borda(pp, seats=num_seats, score_vector=None)\n",
+    "election_irv = elections.STV(pp, fractional_transfer, seats=num_seats)\n",
+    "election_plurality = elections.Plurality(\n",
+    "    pp, seats=num_seats, ballot_ties=False)\n",
+    "election_seq = elections.SequentialRCV(pp, seats=num_seats)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's run the elections. Running the elections will generate an election state, from which we can get the winners.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "election_state_borda = election_borda.run_election()\n",
+    "election_state_irv = election_irv.run_election()\n",
+    "election_state_plurality = election_plurality.run_election()\n",
+    "election_state_seq = election_seq.run_election()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outcome_borda = election_state_borda.get_all_winners()\n",
+    "outcome_irv = election_state_irv.get_all_winners()\n",
+    "outcome_plurality = election_state_plurality.get_all_winners()\n",
+    "outcome_seq = election_state_seq.get_all_winners()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We should expect different results for different elections.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(outcome_borda)\n",
+    "print(outcome_irv)\n",
+    "print(outcome_plurality)\n",
+    "print(outcome_seq)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "csci1470 Python 3.9.12",
+   "language": "python",
+   "name": "csci1470"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}