SL_testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import SnakesLadders as SL\n",
    "from pprint import PrettyPrinter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "from scipy import stats # sub-package\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "pp = PrettyPrinter(16)  # 16 is the column width of the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "game = SL.GameFSM(16)\n",
    "\n",
    "# Make ladders\n",
    "game.all_states[2].link = 10\n",
    "game.all_states[8].link = 14\n",
    "\n",
    "# Make snakes\n",
    "game.all_states[11].link = 4\n",
    "game.all_states[15].links = 6\n",
    "\n",
    "game.make_state_kinds()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GameFSM(position: 0/16)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[               {'die': 4, 'end': 4, 'kind': 'B', 'start': 0},\n",
      "                {'die': 4, 'end': 14, 'kind': 'L', 'start': 4},\n",
      "                {'die': 3, 'end': 16, 'kind': 'B', 'start': 14}]\n",
      "3\n",
      "(0, 1)\n"
     ]
    }
   ],
   "source": [
    "game.run()\n",
    "pp.pprint(game.records)\n",
    "print(SL.count_moves(game.records))\n",
    "print(SL.count_snakes_and_ladders(game.records))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GameFSM(position: 16/16)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find average number of moves in 10000 games (variable `num_runs`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.6127\n"
     ]
    }
   ],
   "source": [
    "tot_moves = 0\n",
    "all_moves = []\n",
    "num_runs = 10000\n",
    "for i in range(num_runs):\n",
    "    game.run()\n",
    "    moves = SL.count_moves(game.records)\n",
    "    tot_moves += moves\n",
    "    all_moves.append(moves)\n",
    "print(tot_moves/num_runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(all_moves)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'moves': all_moves})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>moves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.612700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.240714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              moves\n",
       "count  10000.000000\n",
       "mean       5.612700\n",
       "std        2.240714\n",
       "min        3.000000\n",
       "25%        4.000000\n",
       "50%        5.000000\n",
       "75%        7.000000\n",
       "max       19.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>moves</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
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       "      <th>17</th>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3</td>\n",
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       "      <th>24</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9970</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9971</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9972</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9973</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9974</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9975</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9976</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9985</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9986</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      moves\n",
       "0         7\n",
       "1         5\n",
       "2         6\n",
       "3         4\n",
       "4         8\n",
       "5         6\n",
       "6         6\n",
       "7         4\n",
       "8         6\n",
       "9         9\n",
       "10        5\n",
       "11        4\n",
       "12        6\n",
       "13        6\n",
       "14        7\n",
       "15        3\n",
       "16        6\n",
       "17        9\n",
       "18        4\n",
       "19        3\n",
       "20        4\n",
       "21        4\n",
       "22        8\n",
       "23        3\n",
       "24        4\n",
       "25       10\n",
       "26        5\n",
       "27        7\n",
       "28        7\n",
       "29        3\n",
       "...     ...\n",
       "9970      8\n",
       "9971     12\n",
       "9972      5\n",
       "9973      3\n",
       "9974      5\n",
       "9975      6\n",
       "9976      6\n",
       "9977      4\n",
       "9978      7\n",
       "9979      5\n",
       "9980      4\n",
       "9981     10\n",
       "9982      6\n",
       "9983      4\n",
       "9984     15\n",
       "9985      5\n",
       "9986      7\n",
       "9987      6\n",
       "9988      5\n",
       "9989      3\n",
       "9990     12\n",
       "9991      4\n",
       "9992      4\n",
       "9993      7\n",
       "9994      5\n",
       "9995      8\n",
       "9996     12\n",
       "9997      4\n",
       "9998      4\n",
       "9999     10\n",
       "\n",
       "[10000 rows x 1 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['moves'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "move_counts = df['moves'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2384"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(move_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We want as many bins as there are integers between the lowest and highest number of moves found in the data!**\n",
    "`keys` is the method of the pd.Series object that pulls out those numbers. This approach is more robust than simply taking `len(move_counts)` in case some # moves are not represented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0842F110>]], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tcbt9+dgiYG/H+N58zKznhoeH644wqRRzOVMxTcpU1dFB7gSamc1Cc2Z4vzFJCyNiLF/q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\nD41LHrgrvCBMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x83ec550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=max(move_counts.keys())-min(move_counts.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8562270>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MnnVmdjHwu+R0z0VAP+DHAO6+DJgCLANmAZdnvJtfAdwBrARWVVUA8kVnBdXffvvBqaeG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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x83ec4b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.linspace(min(move_counts.keys()), max(move_counts.keys()))\n",
    "plt.plot(xs, max(move_counts)*stats.norm.pdf(xs, df.mean(), df.std())/stats.norm.pdf(df.mean(), df.mean(), df.std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "moves    5.6127\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We want to transpose our data records:**\n",
    "\n",
    "{'die': 4, 'end': 4, 'kind': 'B', 'start': 0},\n",
    "{'die': 3, 'end': 7, 'kind': 'B', 'start': 4}]\n",
    "\n",
    "**mapping to**\n",
    "\n",
    "{'die': [4, 3],\n",
    " 'end': [4, 7],\n",
    " 'kind': ['B', 'B'],\n",
    " 'start': [0, 4]}\n",
    " \n",
    " I.e., convert list of dicts to dict of lists, and make that into a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def convert_records(records):\n",
    "    \"\"\"\n",
    "    records is a list of dicts\n",
    "    \"\"\"\n",
    "    d = {'die': [],\n",
    "        'start': [],\n",
    "        'end': [],\n",
    "        'kind': []\n",
    "        }\n",
    "    for recdict in records:\n",
    "        for k in ('die', 'start', 'end', 'kind'):\n",
    "            d[k].append(recdict[k])\n",
    "    return pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "moves_df = convert_records(game.records)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this is just one individual game's records!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "moves_df.to_csv('moves.csv')"
   ]
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}

def multi_run(n):
    """n is number of times to run game, where game is a globally defined GameFSM object.
    Returns list of dataframes created from move records (using convert_records)
    """
    all_data = []
    for i in range(n):
        game.run()
        all_data.append(convert_records(game.records))
    return all_data