alaska_2022.ipynb

{
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   "source": [
    "# 2022 Alaska Primary  Special General Election\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "424484e8",
   "metadata": {},
   "source": [
    "* The results for the 2022 Special General Eleciton in Alaska can be downloaded here [https://www.elections.alaska.gov/election-results/e/?id=22sspg](https://www.elections.alaska.gov/election-results/e/?id=22sspg)\n",
    "\n",
    "* There were 4 candidates (including Write-Ins) and  192,542 voters: \n",
    "\n",
    "    1. Nick Begich ($N$)\n",
    "    2. Sarah Palin ($P$)\n",
    "    3. Mary S. Peltola ($M$)\n",
    "    4. Write-Ins ($W$)\n",
    "    \n",
    "* The Ranked Choice winner is Mary S. Peltola. After removing the Write-Ins, Begich loses in the first round leaving Palin in Peltola.  \n",
    "\n",
    "* Analyses of this election can be found at the following blog posts: \n",
    "\n",
    "    * [https://electionscience.org/commentary-analysis/rcv-fools-palin-voters-into-electing-a-progressive-democrat/](https://electionscience.org/commentary-analysis/rcv-fools-palin-voters-into-electing-a-progressive-democrat/).\n",
    "    * [https://fairvote.org/alaska_rcv_analysis/](https://fairvote.org/alaska_rcv_analysis/)\n",
    "\n",
    "* One difficulty when converting a CVR file to a profile is rankings is how to truncate a ballot when a voter *overvotes* (places more than two candidates at the same rank).  The rules used to truncate ballots in the Alaska election can be found [here](https://casetext.com/statute/alaska-statutes/title-15-elections/chapter-1515-elections-and-ballots/section-1515350-general-procedure-for-ballot-count?searchWithin=true&listingIndexId=alaska-statutes&q=ranked%20choice%20voting&type=statute&sort=relevance&p=1&tab=keyword&jxs=).  The two key rules are to truncate ballots at the first overvote and truncate ballots if the voter skips two rankings. \n",
    "\n",
    "    See [https://github.com/jirin1a/alaska_irv_2022/](https://github.com/jirin1a/alaska_irv_2022/) for another way to process the cvr file. \n",
    "\n",
    "* The Condorcet winner was Begich, but was not elected by Ranked Choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "326529a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pref_voting.profiles_with_ties import *\n",
    "from pref_voting.voting_methods import *\n",
    "import json\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fc100ef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open('./edata/alaska_2022/alaska_ranked_choice_2022_cvr.json')\n",
    "data = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2bb018a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def _truncation(ranking, max_rank, truncate_at_overvote=True, truncate_when_skip = False, truncate_when_skipped_two=False): \n",
    "    new_ranking = dict()\n",
    "    prev_ranked_cand = 0\n",
    "    for rnk in range(1, max_rank+1):\n",
    "        cands_at_rank = [c for c in ranking.keys() if ranking[c] == rnk]\n",
    "        if len(cands_at_rank) == 1: \n",
    "            new_ranking[cands_at_rank[0]] = rnk\n",
    "            prev_ranked_cand = rnk\n",
    "        elif len(cands_at_rank) == 0:\n",
    "            if truncate_when_skip: \n",
    "                return new_ranking\n",
    "            if truncate_when_skipped_two and rnk - prev_ranked_cand == 2: \n",
    "                return new_ranking\n",
    "        elif len(cands_at_rank) > 0: \n",
    "            if truncate_at_overvote: \n",
    "                return new_ranking\n",
    "            else: \n",
    "                for c in cands_at_rank: \n",
    "                    new_ranking[c] = rnk\n",
    "    return new_ranking\n",
    "    \n",
    "def process_cvr_data(cvr_data, \n",
    "                     election_id, \n",
    "                     cmap, \n",
    "                     max_rank, \n",
    "                     ignore_ambiguous_marks = True, \n",
    "                     truncation_type = \"simple\", \n",
    "                     verbose=False): \n",
    "    \n",
    "    truncation_rules = {\n",
    "        \n",
    "        \"Alaska\":  lambda ranking, max_rank: _truncation(ranking, \n",
    "                                                         max_rank, \n",
    "                                                         truncate_at_overvote=True, \n",
    "                                                         truncate_when_skip=False,           \n",
    "                                                         truncate_when_skipped_two=True),\n",
    "        \n",
    "        \"San Francisco\": lambda ranking, max_rank: _truncation(ranking,  max_rank,   \n",
    "                                                               truncate_at_overvote=True, \n",
    "                                                               truncate_when_skip=False, \n",
    "                                                               truncate_when_skipped_two=False),\n",
    "        \n",
    "        \"simple\": lambda ranking, max_rank: _truncation(ranking, \n",
    "                                                        max_rank, \n",
    "                                                        truncate_at_overvote=False,\n",
    "                                                        truncate_when_skip=False, \n",
    "                                                        truncate_when_skipped_two=False)    \n",
    "    }\n",
    "    \n",
    "    report = {\n",
    "        \"num_ballots_with_ambiguous_marks\": 0,\n",
    "        \"num_overvotes\": 0,\n",
    "        \"num_ballots_with_skipped_ranks\": 0,\n",
    "        \"num_ballots_with_multiple_cand_ranks\": 0,\n",
    "        \"num_empty_ballots\": 0,\n",
    "        \"num_bullet_votes\": 0,\n",
    "        \"num_rank_one\": 0,\n",
    "        \"num_truncated_ranking\": 0\n",
    "    }\n",
    "    num_ambiguous_marks = 0\n",
    "    rankings = list()\n",
    "    for c in cvr_data['Contests']:\n",
    "        if c[\"Id\"] == election_id:\n",
    "            has_ambiguous_mark = False\n",
    "            ranking = {}\n",
    "\n",
    "            if any([int(_m[\"Rank\"]) > 4 for _m in c[\"Marks\"] if not _m[\"IsAmbiguous\"]]): \n",
    "                print(c[\"Marks\"])\n",
    "                print(\"\\n----\\n\")\n",
    "            for m in c[\"Marks\"]: \n",
    "                if m[\"IsAmbiguous\"]: \n",
    "                    has_ambiguous_mark = True\n",
    "                    num_ambiguous_marks += 1\n",
    "                    if not ignore_ambiguous_marks: \n",
    "                        # check if candidate is double ranked, but ignore write ins (candidate id 214)\n",
    "                        if m[\"CandidateId\"] != 214 and m[\"CandidateId\"] in ranking.keys(): \n",
    "                            report[\"num_ballots_with_multiple_cand_ranks\"] += 1\n",
    "                            if int(m[\"Rank\"]) < ranking[m[\"CandidateId\"]]: \n",
    "                                # keep the highest rank\n",
    "                                ranking[m[\"CandidateId\"]] = int(m[\"Rank\"])\n",
    "                        else: \n",
    "                            ranking[m[\"CandidateId\"]] = int(m[\"Rank\"])\n",
    "                else: \n",
    "                    # check if candidate is double ranked, but ignore write ins (candidate id 214)\n",
    "                    if m[\"CandidateId\"] != 214 and m[\"CandidateId\"] in ranking.keys():  \n",
    "                        report[\"num_ballots_with_multiple_cand_ranks\"] += 1\n",
    "                        if int(m[\"Rank\"]) < ranking[m[\"CandidateId\"]]: \n",
    "                            # keep the highest rank\n",
    "                            ranking[m[\"CandidateId\"]] = int(m[\"Rank\"])\n",
    "                    else: \n",
    "                        ranking[m[\"CandidateId\"]] = int(m[\"Rank\"])\n",
    "            if has_ambiguous_mark: \n",
    "                report[\"num_ballots_with_ambiguous_marks\"] += 1\n",
    "            ranks = list(ranking.values())\n",
    "\n",
    "            if len(ranks) == 0: \n",
    "                report[\"num_empty_ballots\"] += 1\n",
    "\n",
    "            if len(list(set(ranks))) != len(ranks): \n",
    "                report[\"num_overvotes\"] += 1\n",
    "\n",
    "            if len(ranks) > 0 and len(list(set(ranks))) != max(ranks): \n",
    "                report[\"num_ballots_with_skipped_ranks\"] += 1\n",
    "\n",
    "            if ranks == [1]: \n",
    "                report[\"num_bullet_votes\"] += 1\n",
    "\n",
    "            if len(list(set(ranks))) == 1: \n",
    "                report[\"num_rank_one\"] += 1\n",
    "\n",
    "            truncated_ranking = truncation_rules[truncation_type](ranking, max_rank)\n",
    "\n",
    "            if ranking == truncated_ranking: \n",
    "                rankings.append(ranking)\n",
    "            else: \n",
    "                report[\"num_truncated_ranking\"] += 1\n",
    "                rankings.append(truncated_ranking)\n",
    "                        \n",
    "    if verbose: \n",
    "        print(f'''The number of ballots: {len(rankings)}\n",
    "        The number of empty ballots: {report[\"num_empty_ballots\"]}\n",
    "        The number of ambiguous marks: {num_ambiguous_marks}\n",
    "        The number of ballots with ambiguous marks: {report[\"num_ballots_with_ambiguous_marks\"]}\n",
    "        The number of ballots with multiple ranks for a candidate: {report[\"num_ballots_with_multiple_cand_ranks\"]}\n",
    "        The number of ballots with overvote: {report[\"num_overvotes\"]}\n",
    "        The number of ballots with a skipped ranking: {report[\"num_ballots_with_skipped_ranks\"]}\n",
    "        The number of bullet votes: {report[\"num_bullet_votes\"]}\n",
    "        The number of ballots with one ranking: {report[\"num_rank_one\"]}\n",
    "        The number of ballots that were truncated: {report[\"num_truncated_ranking\"]}      \n",
    "        ''')\n",
    "        \n",
    "    profile = ProfileWithTies(rankings, cmap=cmap, candidates=sorted(cmap.keys()))\n",
    "    \n",
    "    if verbose: \n",
    "        print(\"After processing the ballots:\\n\")\n",
    "        profile.report()\n",
    "    return profile\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "edd66d4b",
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   "source": [
    "def display_instant_runoff_results(prof): \n",
    "    \"\"\"\n",
    "    Display the instant runoff results, ignoring any overvotes.   \n",
    "    \n",
    "    Note that, in principle, this could give a different answer than first truncating the ballots at any overvotes, \n",
    "    then finding the irv winner(s).\n",
    "    \"\"\"\n",
    "    rounds = list()\n",
    "    \n",
    "    remaining_candidates = prof.candidates\n",
    "    \n",
    "    while len(remaining_candidates) > 1: \n",
    "        reduced_prof = prof.remove_candidates([c for c in prof.candidates if c not in remaining_candidates])\n",
    "        \n",
    "        pl_scores = reduced_prof.plurality_scores_ignoring_overvotes()\n",
    "        lowest_pl_score = min(pl_scores.values())\n",
    "        cands_to_remove = [c for c in pl_scores.keys() if pl_scores[c] == lowest_pl_score]\n",
    "        \n",
    "        sorted_pl_scores = sorted(pl_scores.values(), reverse=True)\n",
    "        sorted_cands = sorted(pl_scores.items(), reverse=True, key=lambda cpl: cpl[1])\n",
    "        rounds.append('\\n'.join([f\"\\t{prof.cmap[c]} plurality score: {pl_score}\" for c, pl_score in sorted_cands]) + '\\n' + f'\\tThe candidate removed during this round: {reduced_prof.cmap[cands_to_remove[0]]}')\n",
    "        remaining_candidates = [c for c in remaining_candidates if c not in cands_to_remove]\n",
    "        \n",
    "    for ridx, r in enumerate(rounds): \n",
    "        print(f\"Instant Runoff round {ridx +1}\")\n",
    "        print(r)\n",
    "        \n",
    "    print(f\"The IRV winner is {prof.cmap[remaining_candidates[0]]}\")\n",
    "    \n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b8c5d7a8",
   "metadata": {},
   "source": [
    "## Alaska Truncation Rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "29389e5d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alaska rules for truncating ballots\n",
      "\n",
      "\n",
      "Ignoring ambiguous marks\n",
      "\n",
      "The number of ballots: 192289\n",
      "        The number of empty ballots: 2776\n",
      "        The number of ambiguous marks: 281\n",
      "        The number of ballots with ambiguous marks: 173\n",
      "        The number of ballots with multiple ranks for a candidate: 8899\n",
      "        The number of ballots with overvote: 661\n",
      "        The number of ballots with a skipped ranking: 6267\n",
      "        The number of bullet votes: 51037\n",
      "        The number of ballots with one ranking: 51612\n",
      "        The number of ballots that were truncated: 1245      \n",
      "        \n",
      "After processing the ballots:\n",
      "\n",
      "There are 4 candidates and 192289 rankings:  \n",
      "        The number of empty rankings: 3199\n",
      "        The number of rankings with ties: 0\n",
      "        The number of linear orders: 10093\n",
      "        The number of truncated linear orders: 178997\n",
      "        \n",
      "The number of rankings with skipped ranks: 5374\n",
      "        \n",
      "        \n",
      "Margin graph using extended strict preference\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Do not ignore ambiguous marks\n",
      "\n",
      "The number of ballots: 192289\n",
      "        The number of empty ballots: 2773\n",
      "        The number of ambiguous marks: 281\n",
      "        The number of ballots with ambiguous marks: 173\n",
      "        The number of ballots with multiple ranks for a candidate: 9037\n",
      "        The number of ballots with overvote: 747\n",
      "        The number of ballots with a skipped ranking: 6299\n",
      "        The number of bullet votes: 51017\n",
      "        The number of ballots with one ranking: 51615\n",
      "        The number of ballots that were truncated: 1330      \n",
      "        \n",
      "After processing the ballots:\n",
      "\n",
      "There are 4 candidates and 192289 rankings:  \n",
      "        The number of empty rankings: 3235\n",
      "        The number of rankings with ties: 0\n",
      "        The number of linear orders: 10088\n",
      "        The number of truncated linear orders: 178966\n",
      "        \n",
      "The number of rankings with skipped ranks: 5384\n",
      "        \n",
      "        \n",
      "Margin graph using extended strict preference\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "election_id = 69\n",
    "cmap = {215: \"N\", 217:\"S\", 218: \"M\", 214: \"W\"}\n",
    "max_rank = 4\n",
    "rc_winner = 218\n",
    "\n",
    "print(\"Alaska rules for truncating ballots\\n\")\n",
    "\n",
    "print(\"\\nIgnoring ambiguous marks\\n\")\n",
    "prof1 = process_cvr_data(data, \n",
    "                         election_id, \n",
    "                         cmap, \n",
    "                         max_rank,\n",
    "                         ignore_ambiguous_marks = True, \n",
    "                         truncation_type = \"Alaska\",\n",
    "                         verbose=True)\n",
    "\n",
    "\n",
    "prof1.use_extended_strict_preference()\n",
    "\n",
    "print(\"Margin graph using extended strict preference\")\n",
    "prof1.display_margin_graph()\n",
    "\n",
    "print()\n",
    "# uncomment to see the rankings \n",
    "# print(\"The anonymous profile of rankings: \")\n",
    "# prof1.display_rankings()\n",
    "\n",
    "    \n",
    "print(\"\\nDo not ignore ambiguous marks\\n\")\n",
    "\n",
    "prof2 = process_cvr_data(data, \n",
    "                         election_id, \n",
    "                         cmap, \n",
    "                         max_rank,\n",
    "                         ignore_ambiguous_marks = False, \n",
    "                         truncation_type = \"Alaska\",\n",
    "                         verbose=True)\n",
    "\n",
    "\n",
    "prof2.use_extended_strict_preference()\n",
    "\n",
    "print(\"Margin graph using extended strict preference\")\n",
    "prof2.display_margin_graph()\n",
    "\n",
    "# uncomment to see the rankings \n",
    "#print(\"The anonymous profile of rankings: \")\n",
    "#prof2.display_rankings()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "42f3f062",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-------+-------+-------+-------+-------+------+-------+------+\n",
      "| 47404 | 23736 | 21260 | 27054 | 11280 | 15463 | 4648 | 34052 | 3654 |\n",
      "+-------+-------+-------+-------+-------+-------+------+-------+------+\n",
      "|   M   |   M   |   S   |   N   |   N   |   N   |  M   |   S   |  S   |\n",
      "|   N   |  N S  |  N M  |   S   |  S M  |   M   |  S   |   N   |  M   |\n",
      "|   S   |       |       |   M   |       |   S   |  N   |   M   |  N   |\n",
      "+-------+-------+-------+-------+-------+-------+------+-------+------+\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# anonymous rankings after removing candidate W (214)\n",
    "\n",
    "_reduced_prof = prof2.remove_candidates([214])\n",
    "_reduced_prof.remove_empty_rankings()\n",
    "\n",
    "reduced_prof = _reduced_prof.add_unranked_candidates()\n",
    "\n",
    "reduced_prof.display()\n",
    "reduced_prof.display_margin_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d1fc137d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ProfileWithTies([{218: 1, 215: 2, 217: 3}, {218: 1, 215: 2, 217: 2}, {217: 1, 215: 2, 218: 2}, {215: 1, 217: 2, 218: 3}, {215: 1, 217: 2, 218: 2}, {215: 1, 218: 2, 217: 3}, {218: 1, 217: 2, 215: 3}, {217: 1, 215: 2, 218: 3}, {217: 1, 218: 2, 215: 3}], rcounts=[47404, 23736, 21260, 27054, 11280, 15463, 4648, 34052, 3654], cmap={215: 'N', 217: 'S', 218: 'M', 214: 'W'})\n"
     ]
    }
   ],
   "source": [
    "print(reduced_prof.description())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ba189f83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instant Runoff round 1\n",
      "\tM plurality score: 74815\n",
      "\tS plurality score: 58544\n",
      "\tN plurality score: 52774\n",
      "\tW plurality score: 2921\n",
      "\tThe candidate removed during this round: W\n",
      "Instant Runoff round 2\n",
      "\tM plurality score: 75788\n",
      "\tS plurality score: 58966\n",
      "\tN plurality score: 53797\n",
      "\tThe candidate removed during this round: N\n",
      "Instant Runoff round 3\n",
      "\tM plurality score: 91251\n",
      "\tS plurality score: 86020\n",
      "\tThe candidate removed during this round: S\n",
      "The IRV winner is M\n"
     ]
    }
   ],
   "source": [
    "display_instant_runoff_results(prof2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "85d21776",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Condorcet winner is N.\n",
      "Split Cycle winner is {N}\n",
      "Stable Voting winner is {N}\n",
      "Minimax winner is {N}\n",
      "Beat Path winner is {N}\n",
      "Copeland winner is {N}\n",
      "\n",
      "The Condorcet loser is W.\n"
     ]
    }
   ],
   "source": [
    "cw = prof2.condorcet_winner()\n",
    "\n",
    "if cw is None: \n",
    "    print(\"There is no Condorcet winner.\")\n",
    "else: \n",
    "    print(f\"The Condorcet winner is {cmap[cw]}.\")\n",
    "\n",
    "split_cycle.display(prof2)\n",
    "stable_voting.display(prof2)\n",
    "minimax.display(prof2)\n",
    "beat_path.display(prof2)\n",
    "copeland.display(prof2)\n",
    "\n",
    "print()\n",
    "\n",
    "cl = prof2.condorcet_loser()\n",
    "\n",
    "if cl is None: \n",
    "    print(\"There is no Condorcet loser.\")\n",
    "else: \n",
    "    print(f\"The Condorcet loser is {cmap[cl]}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "944cccb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instant Runoff (Truncated Linear Orders) winner is {M}\n",
      "The margins of M over each other candidate is: \n",
      "\tThe margin of M over W is 100528\n",
      "\tThe margin of M over N is -8407\n",
      "\tThe margin of M over S is 5231\n"
     ]
    }
   ],
   "source": [
    "instant_runoff_for_truncated_linear_orders.display(prof2)\n",
    "irv_winner = instant_runoff_for_truncated_linear_orders(prof2)[0]\n",
    "print(f\"The margins of {cmap[irv_winner]} over each other candidate is: \")\n",
    "for c in prof2.candidates: \n",
    "    if c != rc_winner:\n",
    "        print(f\"\\tThe margin of {cmap[irv_winner]} over {cmap[c]} is {prof2.margin(rc_winner, c)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "706e89d8",
   "metadata": {},
   "source": [
    "## Borda winners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3eee9065",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Symmetric Borda:  N\n",
      "Domination Borda:  N\n",
      "Weak Domination Borda:  N\n",
      "Non-Domination Borda:  N\n"
     ]
    }
   ],
   "source": [
    "print(\"Symmetric Borda: \", \n",
    "      \", \".join([prof1.cmap[c] for c in borda_for_profile_with_ties(prof1, borda_scores = symmetric_borda_scores)]))    \n",
    "print(\"Domination Borda: \", \n",
    "      \", \".join([prof1.cmap[c] for c in borda_for_profile_with_ties(prof1, borda_scores = domination_borda_scores)]))    \n",
    "\n",
    "print(\"Weak Domination Borda: \", \n",
    "      \", \".join([prof1.cmap[c] for c in borda_for_profile_with_ties(prof1, borda_scores = weak_domination_borda_scores)]))    \n",
    "\n",
    "print(\"Non-Domination Borda: \", \n",
    "      \", \".join([prof1.cmap[c] for c in borda_for_profile_with_ties(prof1, borda_scores = non_domination_borda_scores)]))    \n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8ada3b3e",
   "metadata": {},
   "source": [
    "## Failure of Negative Involvement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fe3a5e6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-------+-------+-------+-------+-------+------+-------+------+\n",
      "| 47404 | 23736 | 21260 | 27054 | 11280 | 15463 | 4648 | 34052 | 3654 |\n",
      "+-------+-------+-------+-------+-------+-------+------+-------+------+\n",
      "|   M   |   M   |   S   |   N   |   N   |   N   |  M   |   S   |  S   |\n",
      "|   N   |       |       |   S   |       |   M   |  S   |   N   |  M   |\n",
      "|   S   |       |       |   M   |       |   S   |  N   |   M   |  N   |\n",
      "+-------+-------+-------+-------+-------+-------+------+-------+------+\n",
      "Instant Runoff (Truncated Linear Orders) winner is {M}\n"
     ]
    }
   ],
   "source": [
    "init_prof, cmap = reduced_prof.truncate_overvotes()\n",
    "init_prof.display()\n",
    "\n",
    "instant_runoff_for_truncated_linear_orders.display(init_prof)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c6f69f06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After removing 5,170 voters with the ranking S N M, the winner is N\n",
      "+-------+-------+-------+-------+-------+-------+------+-------+------+\n",
      "| 47404 | 23736 | 21260 | 27054 | 11280 | 15463 | 4648 | 28882 | 3654 |\n",
      "+-------+-------+-------+-------+-------+-------+------+-------+------+\n",
      "|   M   |   M   |   S   |   N   |   N   |   N   |  M   |   S   |  S   |\n",
      "|   N   |       |       |   S   |       |   M   |  S   |   N   |  M   |\n",
      "|   S   |       |       |   M   |       |   S   |  N   |   M   |  N   |\n",
      "+-------+-------+-------+-------+-------+-------+------+-------+------+\n",
      "Instant Runoff (Truncated Linear Orders) winner is {N}\n",
      "\n",
      "So, N wins without 5,170 voters that rank M is last place.   With these voters in the election, their last place candidate M wins the election according to instant runoff voting. \n"
     ]
    }
   ],
   "source": [
    "rankings, counts = init_prof.rankings_counts\n",
    "\n",
    "print(\"After removing 5,170 voters with the ranking S N M, the winner is N\")\n",
    "new_counts = [c for c in counts]\n",
    "new_counts[7] = new_counts[7] - 5170\n",
    "new_prof = ProfileWithTies([r.rmap for r in rankings], rcounts=new_counts, cmap = reduced_prof.cmap)\n",
    "new_prof.display()\n",
    "instant_runoff_for_truncated_linear_orders.display(new_prof)\n",
    "\n",
    "print(\"\\nSo, N wins without 5,170 voters that rank M is last place.   With these voters in the election, their last place candidate M wins the election according to instant runoff voting. \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d082be37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5c4d1272e134b0c8c928baaf398a012",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/34053 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 215\n",
    "\n",
    "num_negative_involvement_violations = 0\n",
    "\n",
    "min_num_cands = 34052\n",
    "max_num_cands = 0\n",
    "\n",
    "for num_to_remove in tqdm(range(0, 34053)): \n",
    "    rankings, counts = init_prof.rankings_counts\n",
    "    new_counts = [c for c in counts]\n",
    "    new_counts[7] = new_counts[7] - num_to_remove\n",
    "    new_prof = ProfileWithTies([r.rmap for r in rankings], rcounts=new_counts, cmap = reduced_prof.cmap)\n",
    "    ws_without_voters = instant_runoff_for_truncated_linear_orders(new_prof)\n",
    "    if [N] ==  ws_without_voters: \n",
    "        num_negative_involvement_violations += 1\n",
    "        if num_to_remove < min_num_cands: \n",
    "            min_num_cands = num_to_remove\n",
    "        if num_to_remove > max_num_cands: \n",
    "            max_num_cands = num_to_remove\n",
    "\n",
    "print(f\"{num_negative_involvement_violations} violations of Negative Involvement\")\n",
    "\n",
    "print(f\"A minimum of {min_num_cands} or a maximum of {max_num_cands} voters demonstrate a violation of Negative Involvement\")\n",
    "\n",
    "\n",
    "for num_to_remove in tqdm(range(min_num_cands, max_num_cands+1)): \n",
    "    rankings, counts = init_prof.rankings_counts\n",
    "    new_counts = [c for c in counts]\n",
    "    new_counts[7] = new_counts[7] - num_to_remove\n",
    "    new_prof = ProfileWithTies([r.rmap for r in rankings], rcounts=new_counts, cmap = reduced_prof.cmap)\n",
    "    ws_without_voters = instant_runoff_for_truncated_linear_orders(new_prof)\n",
    "    not_all_counterexamples = False\n",
    "    if [N] !=  ws_without_voters: \n",
    "        not_all_counterexamples = True\n",
    "        print(f\"{num_to_remove} does not demonstrate a vioaltion of Negative Involvement.\")\n",
    "    \n",
    "if not_all_counterexamples: \n",
    "    print(f\"Not all groups of voters between {min_num_cands} to {max_num_cands} voters demonstrate a violation of Negative Involvement\")\n",
    "else:\n",
    "    print(f\"Removing any group of voters between {min_num_cands} to {max_num_cands} voters demonstrate a violation of Negative Involvement\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "351a0946",
   "metadata": {},
   "source": [
    "## Do not truncate ballots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9e875ac3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do not truncate ballots\n",
      "\n",
      "Ignoring ambiguous marks\n",
      "The number of ballots: 192289\n",
      "        The number of empty ballots: 2776\n",
      "        The number of ambiguous marks: 281\n",
      "        The number of ballots with ambiguous marks: 173\n",
      "        The number of ballots with multiple ranks for a candidate: 8899\n",
      "        The number of ballots with overvote: 661\n",
      "        The number of ballots with a skipped ranking: 6267\n",
      "        The number of bullet votes: 51037\n",
      "        The number of ballots with one ranking: 51612\n",
      "        The number of ballots that were truncated: 0      \n",
      "        \n",
      "After processing the ballots:\n",
      "\n",
      "There are 4 candidates and 192289 rankings:  \n",
      "        The number of empty rankings: 2776\n",
      "        The number of rankings with ties: 661\n",
      "        The number of linear orders: 10093\n",
      "        The number of truncated linear orders: 178759\n",
      "        \n",
      "The number of rankings with skipped ranks: 6267\n",
      "        \n",
      "        \n",
      "Margin graph using extended strict preference\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Do not ignore ambiguous marks\n",
      "The number of ballots: 192289\n",
      "        The number of empty ballots: 2773\n",
      "        The number of ambiguous marks: 281\n",
      "        The number of ballots with ambiguous marks: 173\n",
      "        The number of ballots with multiple ranks for a candidate: 9037\n",
      "        The number of ballots with overvote: 747\n",
      "        The number of ballots with a skipped ranking: 6299\n",
      "        The number of bullet votes: 51017\n",
      "        The number of ballots with one ranking: 51615\n",
      "        The number of ballots that were truncated: 0      \n",
      "        \n",
      "After processing the ballots:\n",
      "\n",
      "There are 4 candidates and 192289 rankings:  \n",
      "        The number of empty rankings: 2773\n",
      "        The number of rankings with ties: 747\n",
      "        The number of linear orders: 10088\n",
      "        The number of truncated linear orders: 178681\n",
      "        \n",
      "The number of rankings with skipped ranks: 6299\n",
      "        \n",
      "        \n",
      "Margin graph using extended strict preference\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "election_id = 69\n",
    "cmap = {215: \"N\", 217:\"S\", 218: \"M\", 214: \"W\"}\n",
    "max_rank = 4\n",
    "\n",
    "print(\"Do not truncate ballots\\n\")\n",
    "\n",
    "print(\"Ignoring ambiguous marks\")\n",
    "prof1 = process_cvr_data(data, \n",
    "                         election_id, \n",
    "                         cmap, \n",
    "                         max_rank,\n",
    "                         ignore_ambiguous_marks = True, \n",
    "                         truncation_type = \"simple\",\n",
    "                         verbose=True)\n",
    "\n",
    "prof1.use_extended_strict_preference()\n",
    "\n",
    "print(\"Margin graph using extended strict preference\")\n",
    "prof1.display_margin_graph()\n",
    "\n",
    "## uncomment to display rankings\n",
    "#prof1.display_rankings()\n",
    "    \n",
    "print(\"\\nDo not ignore ambiguous marks\")\n",
    "\n",
    "prof2 = process_cvr_data(data, \n",
    "                         election_id, \n",
    "                         cmap, \n",
    "                         max_rank,\n",
    "                         ignore_ambiguous_marks=False, \n",
    "                         truncation_type=\"simple\",\n",
    "                         verbose=True)\n",
    "\n",
    "prof2.use_extended_strict_preference()\n",
    "\n",
    "print(\"Margin graph using extended strict preference\")\n",
    "prof2.display_margin_graph()\n",
    "\n",
    "## uncomment to display rankings\n",
    "#prof2.display_rankings()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "17bbec9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Instant Runoff round 1\n",
      "\tM plurality score: 74835\n",
      "\tS plurality score: 58580\n",
      "\tN plurality score: 52798\n",
      "\tW plurality score: 2952\n",
      "\tThe candidate removed during this round: W\n",
      "Instant Runoff round 2\n",
      "\tM plurality score: 75855\n",
      "\tS plurality score: 59048\n",
      "\tN plurality score: 53861\n",
      "\tThe candidate removed during this round: N\n",
      "Instant Runoff round 3\n",
      "\tM plurality score: 91434\n",
      "\tS plurality score: 86314\n",
      "\tThe candidate removed during this round: S\n",
      "The IRV winner is M\n"
     ]
    }
   ],
   "source": [
    "display_instant_runoff_results(prof2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dc46c2a0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Condorcet winner is N.\n",
      "Split Cycle winner is {N}\n",
      "Stable Voting winner is {N}\n",
      "Minimax winner is {N}\n",
      "Beat Path winner is {N}\n",
      "Copeland winner is {N}\n",
      "\n",
      "The Condorcet loser is W.\n"
     ]
    }
   ],
   "source": [
    "cw = prof2.condorcet_winner()\n",
    "\n",
    "if cw is None: \n",
    "    print(\"There is no Condorcet winner.\")\n",
    "else: \n",
    "    print(f\"The Condorcet winner is {cmap[cw]}.\")\n",
    "\n",
    "split_cycle.display(prof2)\n",
    "stable_voting.display(prof2)\n",
    "minimax.display(prof2)\n",
    "beat_path.display(prof2)\n",
    "copeland.display(prof2)\n",
    "\n",
    "print()\n",
    "\n",
    "cl = prof2.condorcet_loser()\n",
    "\n",
    "if cl is None: \n",
    "    print(\"There is no Condorcet loser.\")\n",
    "else: \n",
    "    print(f\"The Condorcet loser is {cmap[cl]}.\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "b8960e1d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "39124188",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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