7. Multi-Agents Simulations/20191112 - Chicken game.ipynb

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   "source": [
    "# Base Data Science snippet\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import time\n",
    "from tqdm import tqdm_notebook\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
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     "end_time": "2019-12-18T01:28:43.556671Z",
     "start_time": "2019-12-18T01:28:43.427984Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from simulations.chicken_game import *\n",
    "agents = [Player(i) for i in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-18T01:28:43.894733Z",
     "start_time": "2019-12-18T01:28:43.768072Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Player(id=0, status=0, x=4),\n",
       " Player(id=1, status=0, x=58),\n",
       " Player(id=2, status=0, x=15),\n",
       " Player(id=3, status=0, x=78),\n",
       " Player(id=4, status=0, x=83),\n",
       " Player(id=5, status=0, x=71),\n",
       " Player(id=6, status=0, x=22),\n",
       " Player(id=7, status=0, x=73),\n",
       " Player(id=8, status=0, x=43),\n",
       " Player(id=9, status=0, x=34)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
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   "source": [
    "agents"
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   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
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     "end_time": "2019-12-18T01:28:46.306282Z",
     "start_time": "2019-12-18T01:28:46.185603Z"
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   },
   "outputs": [],
   "source": [
    "env = ChickenGame(agents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-18T01:28:46.743114Z",
     "start_time": "2019-12-18T01:28:46.619444Z"
    }
   },
   "outputs": [],
   "source": [
    "xs = env[\"x\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-18T01:28:49.202533Z",
     "start_time": "2019-12-18T01:28:49.082853Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.spatial import distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-18T01:28:49.426965Z",
     "start_time": "2019-12-18T01:28:49.307252Z"
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   },
   "outputs": [],
   "source": [
    "xsa = np.concatenate([xs[:,np.newaxis],np.zeros((len(xs),1))],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-18T01:28:49.829856Z",
     "start_time": "2019-12-18T01:28:49.701199Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-18T01:34:40.964834Z",
     "start_time": "2019-12-18T01:34:40.843160Z"
    }
   },
   "outputs": [],
   "source": [
    "nn = NearestNeighbors(metric = \"euclidean\",n_neighbors = 5,radius = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
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     "end_time": "2019-12-18T01:34:41.302930Z",
     "start_time": "2019-12-18T01:34:41.166297Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='auto', leaf_size=30, metric='euclidean',\n",
       "                 metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                 radius=100)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "nn.fit(xsa)"
   ]
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   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-18T01:35:07.039076Z",
     "start_time": "2019-12-18T01:35:06.907427Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 998 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "distance,ind = nn.kneighbors(xsa)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
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     "end_time": "2019-12-18T01:35:45.266837Z",
     "start_time": "2019-12-18T01:35:45.130167Z"
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    {
     "data": {
      "text/plain": [
       "array([[0, 2, 6, 9, 8],\n",
       "       [1, 5, 7, 8, 3],\n",
       "       [2, 6, 0, 9, 8],\n",
       "       [3, 7, 4, 5, 1],\n",
       "       [4, 3, 7, 5, 1],\n",
       "       [5, 7, 3, 4, 1],\n",
       "       [6, 2, 9, 0, 8],\n",
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     "execution_count": 78,
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   "execution_count": 76,
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     "end_time": "2019-12-18T01:36:32.341053Z",
     "start_time": "2019-12-18T01:36:32.221377Z"
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       " array([0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2,\n",
       "        3, 4, 0, 1, 2, 3, 0, 1, 2, 3, 4, 0, 1, 2, 0, 1, 2, 3], dtype=int64))"
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     "execution_count": 76,
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   "source": [
    "np.where(distance < 20)"
   ]
  },
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     "end_time": "2019-12-18T01:36:14.193605Z",
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     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-75-2a0c44e637dd>, line 1)",
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     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-75-2a0c44e637dd>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    ind[*np.where(distance < 20)]\u001b[0m\n\u001b[1;37m        ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
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    "ind[*np.where(distance < 20)]"
   ]
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      "Wall time: 0 ns\n"
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   "source": [
    "%%time\n",
    "interactions = env.interactions()"
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   "cell_type": "code",
   "execution_count": 57,
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     "end_time": "2019-12-18T01:34:12.064379Z",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "xs = env.get(\"x\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-18T00:49:36.578217Z",
     "start_time": "2019-12-18T00:49:36.475466Z"
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   "outputs": [
    {
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    "xs"
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  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-17T23:55:21.844988Z",
     "start_time": "2019-12-17T23:55:21.222655Z"
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    "render = []\n",
    "statuses = []\n",
    "for i in tqdm_notebook(range(100)):\n",
    "    env.step()\n",
    "    render.append(env.render())\n",
    "    statuses.append(env.get(\"status\"))\n",
    "#     print(len(env.agents),env.render())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-17T23:55:24.095967Z",
     "start_time": "2019-12-17T23:55:23.991247Z"
    }
   },
   "outputs": [],
   "source": [
    "test = pd.concat(statuses,axis = 1).T"
   ]
  }
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