notebooks/dummy_vs_no_brown_players.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dummy player VS Personalized player"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we show a basic example of a 2-players simulation using the default player (a.k.a \"dummy\" player). The dummy player uses a set of hard-coded rules in the Player class, reproducing a somewhat \"naive\" behaviour. A dummy player always buys (when cash is sufficient), without following any particular strategy, nor looking at his opponent's situation.\n",
    "\n",
    "Successively we provide a simple example to show how to modify the behaviour of a player, changing the class functions that determine how the player acts when falling in a particular cell. In particular, we will create a simple player that never buys brown roads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.chdir('..')\n",
    "\n",
    "from monosim.player import Player\n",
    "from monosim.board import get_board, get_roads, get_properties, get_community_chest_cards, get_bank\n",
    "import random\n",
    "import pandas as pd\n",
    "from scipy.stats import binom_test\n",
    "from tqdm import tqdm\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dummy player"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We run 10K simulations where at each iteration the seed of the random value is changed. This will modify the values of the dice used in each game, providing different games results. At the end of the simulation, we compare the number of times each player has won the game. We randomize which player starts first to further avoid bias in the results.\n",
    "\n",
    "Note: We set a maximum number of turns equal to 1000 because there are situations in which the game doesn't end. This is possible in the game of Monopoly, though the number of times this happens might depend on the implementation as well. For now, we analyze results filtering out the occurrences where the game didn't end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:31<00:00, 317.68it/s]\n"
     ]
    }
   ],
   "source": [
    "list_results = []\n",
    "\n",
    "for seed in tqdm(range(0, 10000)):\n",
    "    random.seed(seed)\n",
    "    \n",
    "    # ############## SET GAME (board, players, etc.) ##############\n",
    "    bank = get_bank()\n",
    "    dict_board = get_board()\n",
    "    dict_roads = get_roads()\n",
    "    dict_properties = get_properties()\n",
    "    dict_community_chest_cards = get_community_chest_cards()\n",
    "    community_cards_deck = list(dict_community_chest_cards.keys())\n",
    "    player1 = Player('player1', 1, bank, dict_board, dict_roads, dict_properties, community_cards_deck)\n",
    "    player2 = Player('player2', 2, bank, dict_board, dict_roads, dict_properties, community_cards_deck)\n",
    "    \n",
    "    player1.meet_other_players([player2])\n",
    "    player2.meet_other_players([player1])\n",
    "\n",
    "    list_players = [player1, player2]\n",
    "    random.shuffle(list_players)  # Shuffle so that player1 doesn't always start first\n",
    "\n",
    "    idx_count = 0\n",
    "    while not player1.has_lost() and not player2.has_lost() and idx_count < 1000:\n",
    "        for player in list_players:\n",
    "            player.play()\n",
    "        idx_count += 1\n",
    "    if player1.has_lost():\n",
    "        list_results.append((seed, 'player1_lost', list_players[0]._name))\n",
    "    elif player2.has_lost():\n",
    "        list_results.append((seed, 'player2_lost', list_players[0]._name))\n",
    "    else:\n",
    "        list_results.append((seed, 'nobody_lost', list_players[0]._name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "player 1 started 4320 times and lost 4272 times\n",
      "player 2 started 4359 times and lost 4407 times\n",
      "nobody lost 1321 times\n"
     ]
    }
   ],
   "source": [
    "df_res = pd.DataFrame(list_results, columns=['seed', 'lost_player', 'starting_player'])\n",
    "n_lost_p1 = df_res[df_res.lost_player == 'player1_lost'].shape[0]\n",
    "n_lost_p2 = df_res[df_res.lost_player == 'player2_lost'].shape[0]\n",
    "count_start_p1 = df_res[(df_res.starting_player == 'player1') & (df_res.lost_player != 'nobody_lost')].shape[0]\n",
    "count_start_p2 = df_res[(df_res.starting_player == 'player2') & (df_res.lost_player != 'nobody_lost')].shape[0]\n",
    "\n",
    "print('player 1 started {} times and lost {} times'.format(count_start_p1, n_lost_p1))\n",
    "print('player 2 started {} times and lost {} times'.format(count_start_p2, n_lost_p2))\n",
    "print('nobody lost {} times'.format(df_res[df_res.lost_player == 'nobody_lost'].shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To assess whether one player is stronger than the other one, we run a binobial test. The Null hyphosis is there player 1 is as good as player 2, which can also be espressed as: \"Player 1 wins 50% of the times\". The previous statement must be true also for Player 2, hence a two sided binomial test is run. It can be noticed that the p-value of the test is > 0.05, hence we're not able to reject the null hypothesis. This means that we cannot prove statistically that Player 1 > Player 2 (or viceversa), so we continue assuming that Player 1 and Player 2 are equally strong.\n",
    "\n",
    "So far this makes sense considering that the implementation for Player 1 and 2 is identical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15032553580484911"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binom_test(x=n_lost_p1, n=n_lost_p1+n_lost_p2, p=1/2, alternative='two-sided')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To further illustrate that the two players are equally strong, we plot the number of times each player has won the game after N games. The blue and orange lines represent the number of times that player 1 and 2 won respectively. It can be notice how the two lines overlap through time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "list_agg = []\n",
    "\n",
    "for seed in range(0, 10000, 1000):\n",
    "    df_tmp = df_res.iloc[:seed]\n",
    "    n_win_p1 = df_tmp[df_tmp.lost_player == 'player2_lost'].shape[0]\n",
    "    n_win_p2 = df_tmp[df_tmp.lost_player == 'player1_lost'].shape[0]\n",
    "    n_nobody_lost = df_tmp[df_tmp.lost_player == 'nobody_lost'].shape[0]\n",
    "    list_agg.append((seed, n_win_p1, n_win_p2, n_nobody_lost))\n",
    "\n",
    "df_agg = pd.DataFrame(list_agg, columns=['seed', 'n_win_p1', 'n_win_p2', 'n_nobody_lost'])\n",
    "ax = df_agg.set_index('seed').plot(figsize=(14, 8), linewidth=3)\n",
    "ax.set_title('Number of victories per Player per number of games', fontsize=28)\n",
    "ax.set_xlabel('Number of games played', fontsize=22)\n",
    "ax.set_ylabel('Number of games won', fontsize=22)\n",
    "ax.legend(fontsize=18)\n",
    "\n",
    "fig = ax.get_figure()\n",
    "fig.savefig('images/dummy_vs_dummy.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No-brown player"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we modify the behaviour of one of the two players. In particular, one of the two players will never buy brown roads. To change the player's behaviour we modify the function buy(), responsible for buying roads. The are several ways to modify the Player methods:\n",
    "\n",
    "1. Use Decorators\n",
    "1. Inherit new class from class Player, modifying the methods\n",
    "3. Use types.MethodType\n",
    "\n",
    "\n",
    "Here we use the first approach, which is the most concise, however the other solutions are shown at the end of the notebook. We decorate the function buy(), adding an initial check, which avoids a road acquisition if the color is brown. The new decorated class is called Player_no_brown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from https://stackoverflow.com/questions/24768400/how-would-one-decorate-an-inherited-method-in-the-child-class\n",
    "def modify_buy(buy):\n",
    "    def _wrapper(self, dict_road_info, road_name):\n",
    "        if dict_road_info['color'] == 'brown':\n",
    "            pass\n",
    "        else:\n",
    "            buy(self, dict_road_info, road_name)\n",
    "    return _wrapper\n",
    "\n",
    "class Player_no_brown(Player):\n",
    "    buy = modify_buy(Player.buy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:32<00:00, 310.18it/s]\n"
     ]
    }
   ],
   "source": [
    "list_results = []\n",
    "\n",
    "for seed in tqdm(range(0, 10000)):\n",
    "    random.seed(seed)\n",
    "    bank = get_bank()\n",
    "    dict_board = get_board()\n",
    "    dict_roads = get_roads()\n",
    "    dict_properties = get_properties()\n",
    "    dict_community_chest_cards = get_community_chest_cards()\n",
    "    community_cards_deck = list(dict_community_chest_cards.keys())\n",
    "    player1 = Player_no_brown('player1', 1, bank, dict_board, dict_roads, dict_properties, community_cards_deck)\n",
    "    player2 = Player('player2', 2, bank, dict_board, dict_roads, dict_properties, community_cards_deck)\n",
    "\n",
    "    player1.meet_other_players([player2])\n",
    "    player2.meet_other_players([player1])\n",
    "\n",
    "    list_players = [player1, player2]\n",
    "    random.shuffle(list_players)  # Shuffle so that player1 doesn't always start first\n",
    "\n",
    "    idx_count = 0\n",
    "    while not player1.has_lost() and not player2.has_lost() and idx_count < 1000:\n",
    "        for player in list_players:\n",
    "            player.play()\n",
    "        idx_count += 1\n",
    "\n",
    "    if player1.has_lost():\n",
    "        list_results.append((seed, 'player1_lost', list_players[0]._name))\n",
    "    elif player2.has_lost():\n",
    "        list_results.append((seed, 'player2_lost', list_players[0]._name))\n",
    "    else:\n",
    "        list_results.append((seed, 'nobody_lost', list_players[0]._name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "player 1 started 4336 times and lost 4678 times\n",
      "player 2 started 4362 times and lost 4020 times\n",
      "nobody lost 1302 times\n"
     ]
    }
   ],
   "source": [
    "df_res = pd.DataFrame(list_results, columns=['seed', 'lost_player', 'starting_player'])\n",
    "n_lost_p1 = df_res[df_res.lost_player == 'player1_lost'].shape[0]\n",
    "n_lost_p2 = df_res[df_res.lost_player == 'player2_lost'].shape[0]\n",
    "count_start_p1 = df_res[(df_res.starting_player == 'player1') & (df_res.lost_player != 'nobody_lost')].shape[0]\n",
    "count_start_p2 = df_res[(df_res.starting_player == 'player2') & (df_res.lost_player != 'nobody_lost')].shape[0]\n",
    "\n",
    "print('player 1 started {} times and lost {} times'.format(count_start_p1, n_lost_p1))\n",
    "print('player 2 started {} times and lost {} times'.format(count_start_p2, n_lost_p2))\n",
    "print('nobody lost {} times'.format(df_res[df_res.lost_player == 'nobody_lost'].shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case the p-value of the binomial test is lower than 0.05, so we reject the null hypothesis. This means that the two players are not equally good, in particular, Player 2 is stronger than Player 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8166707613709124e-12"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binom_test(x=n_lost_p1, n=n_lost_p1+n_lost_p2, p=1/2, alternative='two-sided')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "list_agg = []\n",
    "\n",
    "for seed in range(0, 10000, 100):\n",
    "    df_tmp = df_res.iloc[:seed]\n",
    "    n_win_p1 = df_tmp[df_tmp.lost_player == 'player2_lost'].shape[0]\n",
    "    n_win_p2 = df_tmp[df_tmp.lost_player == 'player1_lost'].shape[0]\n",
    "    n_nobody_lost = df_tmp[df_tmp.lost_player == 'nobody_lost'].shape[0]\n",
    "    list_agg.append((seed, n_win_p1, n_win_p2, n_nobody_lost))\n",
    "\n",
    "df_agg = pd.DataFrame(list_agg, columns=['seed', 'n_win_p1', 'n_win_p2', 'n_nobody_lost'])\n",
    "ax = df_agg.set_index('seed').plot(figsize=(14, 8), linewidth=3)\n",
    "\n",
    "ax.set_title('Number of victories - Dummy Player vs no-brown Player', fontsize=28)\n",
    "ax.set_xlabel('Number of games played', fontsize=22)\n",
    "ax.set_ylabel('Number of games won', fontsize=22)\n",
    "ax.legend(fontsize=18)\n",
    "\n",
    "fig = ax.get_figure()\n",
    "fig.savefig('images/dummy_vs_no_brown.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appendix A (change class Player methods behaviours)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we show to alternative approaches to change players' behavior, modifying the Player class methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Inherit new class**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The class Player_no_brown is inherited from the class player, and the function buy() is copied, adding a special case for when the player ends in a road with color \"brown\". Alternatively, the function buy_or_bid() can be modified, to return \"bid\" instead of \"buy\" when the property has color=\"brown\". (NOTE: this would be a hack as the bid() function has not been inplemented yet, so it's not a suggested solution)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Player_no_brown(Player):\n",
    "    def buy(self, dict_road_info, road_name):\n",
    "        \"\"\" Buy road\n",
    "\n",
    "        :param dict_road_info: (dictionary) Road information\n",
    "        :param road_name: (String) Road name\n",
    "        :return:\n",
    "        \"\"\"\n",
    "\n",
    "        if dict_road_info['color'] == 'brown':\n",
    "            pass\n",
    "        else:\n",
    "            road_price = dict_road_info['price']\n",
    "            # enough money?\n",
    "            if self._cash < road_price:\n",
    "                raise Exception('Player {} does not have enough money'.format(self._name))\n",
    "            # pay bank\n",
    "            self._bank['cash'] += road_price\n",
    "            self._cash -= road_price\n",
    "            # exchange ownership\n",
    "            dict_road_info['belongs_to'] = self._name\n",
    "            self._list_owned_roads.append(road_name)\n",
    "            self._dict_owned_houses_hotels[road_name] = (0, 0)\n",
    "            # mortgage value\n",
    "            self._properties_total_mortgageable_amount += dict_road_info['mortgage_value']\n",
    "\n",
    "            color = dict_road_info['color']\n",
    "            count_roads_of_color = 0\n",
    "            for road in self._list_owned_roads:\n",
    "                if color == self._dict_roads[road]['color']:\n",
    "                    count_roads_of_color += 1\n",
    "            if color == 'brown' and count_roads_of_color == 2:\n",
    "                self._dict_owned_colors[color] = True\n",
    "            elif color == 'light_blue' and count_roads_of_color == 3:\n",
    "                self._dict_owned_colors[color] = True\n",
    "            elif color == 'purple' and count_roads_of_color == 3:\n",
    "                self._dict_owned_colors[color] = True\n",
    "            elif color == 'orange' and count_roads_of_color == 3:\n",
    "                self._dict_owned_colors[color] = True\n",
    "            elif color == 'red' and count_roads_of_color == 3:\n",
    "                self._dict_owned_colors[color] = True\n",
    "            elif color == 'yellow' and count_roads_of_color == 3:\n",
    "                self._dict_owned_colors[color] = True\n",
    "            elif color == 'green' and count_roads_of_color == 3:\n",
    "                self._dict_owned_colors[color] = True\n",
    "            elif color == 'blue' and count_roads_of_color == 2:\n",
    "                self._dict_owned_colors[color] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Use types.MethodType**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the library types, we can substitute an object method using the function MethodType. See example below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import types\n",
    "\n",
    "def buy_new(self, dict_road_info, road_name):\n",
    "    \"\"\" Buy road\n",
    "\n",
    "    :param dict_road_info: (dictionary) Road information\n",
    "    :param road_name: (String) Road name\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    \n",
    "    if dict_road_info['color'] == 'brown':\n",
    "        pass\n",
    "    else:\n",
    "        road_price = dict_road_info['price']\n",
    "        # enough money?\n",
    "        if self._cash < road_price:\n",
    "            raise Exception('Player {} does not have enough money'.format(self._name))\n",
    "        # pay bank\n",
    "        self._bank['cash'] += road_price\n",
    "        self._cash -= road_price\n",
    "        # exchange ownership\n",
    "        dict_road_info['belongs_to'] = self._name\n",
    "        self._list_owned_roads.append(road_name)\n",
    "        self._dict_owned_houses_hotels[road_name] = (0, 0)\n",
    "        # mortgage value\n",
    "        self._properties_total_mortgageable_amount += dict_road_info['mortgage_value']\n",
    "\n",
    "        color = dict_road_info['color']\n",
    "        count_roads_of_color = 0\n",
    "        for road in self._list_owned_roads:\n",
    "            if color == self._dict_roads[road]['color']:\n",
    "                count_roads_of_color += 1\n",
    "        if color == 'brown' and count_roads_of_color == 2:\n",
    "            self._dict_owned_colors[color] = True\n",
    "        elif color == 'light_blue' and count_roads_of_color == 3:\n",
    "            self._dict_owned_colors[color] = True\n",
    "        elif color == 'purple' and count_roads_of_color == 3:\n",
    "            self._dict_owned_colors[color] = True\n",
    "        elif color == 'orange' and count_roads_of_color == 3:\n",
    "            self._dict_owned_colors[color] = True\n",
    "        elif color == 'red' and count_roads_of_color == 3:\n",
    "            self._dict_owned_colors[color] = True\n",
    "        elif color == 'yellow' and count_roads_of_color == 3:\n",
    "            self._dict_owned_colors[color] = True\n",
    "        elif color == 'green' and count_roads_of_color == 3:\n",
    "            self._dict_owned_colors[color] = True\n",
    "        elif color == 'blue' and count_roads_of_color == 2:\n",
    "            self._dict_owned_colors[color] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:32<00:00, 312.35it/s]\n"
     ]
    }
   ],
   "source": [
    "list_results = []\n",
    "\n",
    "for seed in tqdm(range(0, 10000)):\n",
    "    random.seed(seed)\n",
    "    bank = get_bank()\n",
    "    dict_board = get_board()\n",
    "    dict_roads = get_roads()\n",
    "    dict_properties = get_properties()\n",
    "    dict_community_chest_cards = get_community_chest_cards()\n",
    "    community_cards_deck = list(dict_community_chest_cards.keys())\n",
    "    player1 = Player('player1', 1, bank, dict_board, dict_roads, dict_properties, community_cards_deck)\n",
    "    player2 = Player('player2', 2, bank, dict_board, dict_roads, dict_properties, community_cards_deck)\n",
    "    \n",
    "    player1.buy = types.MethodType(buy_new, player1)\n",
    "\n",
    "    player1.meet_other_players([player2])\n",
    "    player2.meet_other_players([player1])\n",
    "\n",
    "    list_players = [player1, player2]\n",
    "    random.shuffle(list_players)  # Shuffle so that player1 doesn't always start first\n",
    "\n",
    "    idx_count = 0\n",
    "    while not player1.has_lost() and not player2.has_lost() and idx_count < 1000:\n",
    "        for player in list_players:\n",
    "            player.play()\n",
    "        idx_count += 1\n",
    "\n",
    "    if player1.has_lost():\n",
    "        list_results.append((seed, 'player1_lost', list_players[0]._name))\n",
    "    elif player2.has_lost():\n",
    "        list_results.append((seed, 'player2_lost', list_players[0]._name))\n",
    "    else:\n",
    "        list_results.append((seed, 'nobody_lost', list_players[0]._name))"
   ]
  }
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