RedQueen-demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f68dee2a160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "np.set_printoptions(suppress=True, precision=3)\n",
    "\n",
    "sns.set(style='ticks', palette='Set2')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import redqueen.utils as U\n",
    "import redqueen.opt_runs as OR\n",
    "import redqueen.opt_model as OM\n",
    "import decorated_options as Deco"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RedQueen demo expects data in the following format:\n",
    "\n",
    "```\n",
    "{\n",
    "  \"walls\": {\n",
    "    \"1\": [1,2,3,4,5], # These time-stamps do not contain posts by our user.\n",
    "    \"2\": [1,4,5]\n",
    "  },\n",
    "  \"broadcasts\": {\n",
    "    \"redqueen\": [1, 4],\n",
    "    \"poisson\": [1, 3]\n",
    "  }\n",
    "}\n",
    "```\n",
    "\n",
    "However, this shifts the task of calculating performance to the JS library, which will require re-implementation and potential bugs. Hence, the performance data should be included with this data.\n",
    "\n",
    "```\n",
    "{\n",
    "  \"walls\": {\n",
    "    \"1\": [1,2,3,4,5],\n",
    "    \"2\": [1,4,5]\n",
    "  },\n",
    "  \"broadcasts\": {\n",
    "    \"redqueen\": {\n",
    "        \"post_times\": [1, 4],\n",
    "        \"performance\": {\n",
    "            \"<metric>\": [(<timestamp>, <perf>)]\n",
    "        }\n",
    "     }, \n",
    "    \"poisson\": {\n",
    "        \"post_times\": [1, 3],\n",
    "        \"performance\": {\n",
    "            \"<metric>\": [(<timestamp>, <perf>)]\n",
    "        }\n",
    "    },\n",
    "  }\n",
    "}\n",
    "```\n",
    "\n",
    "The `<metric>` could be `perf_top_{1,5,10}`, `avg_rank`, `r_2_int`, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Step 1: Create a SimOpts which will generate raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_opts_1_follower = OM.SimOpts(\n",
    "    src_id=0,\n",
    "    end_time=100,\n",
    "    s=np.array([1]),\n",
    "    q=1.0,\n",
    "    other_sources=[('Hawkes', {'src_id': 1, 'seed': 1, 'l_0': 1.0, 'alpha': 1.0, 'beta': 5.0})],\n",
    "    sink_ids=[1000],\n",
    "    edge_list=[(0, 1000), (1, 1000)]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Step 2: Simulate the walls along with different strategies for our broadcaster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 66.7 ms, sys: 0 ns, total: 66.7 ms\n",
      "Wall time: 63.1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "seed = 1\n",
    "opt_mgr = sim_opts_1_follower.create_manager_with_opt(seed)\n",
    "opt_mgr.run_dynamic()\n",
    "opt_df = opt_mgr.state.get_dataframe()\n",
    "num_opt_tweets = U.num_tweets_of(opt_df, broadcaster_id=0)\n",
    "perf_opt = {\n",
    "    'type': 'Opt',\n",
    "    'seed': seed,\n",
    "    'capacity': num_opt_tweets,\n",
    "    'q': sim_opts_1_follower.q\n",
    "}\n",
    "OR.add_perf(perf_opt, opt_df, sim_opts_1_follower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 100 ms, sys: 6.67 ms, total: 107 ms\n",
      "Wall time: 103 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "seed = 9\n",
    "poisson_mgr = sim_opts_1_follower.create_manager_with_poisson(seed, capacity=num_opt_tweets)\n",
    "poisson_mgr.run_dynamic()\n",
    "poisson_df = poisson_mgr.state.get_dataframe()\n",
    "num_poisson_tweets = U.num_tweets_of(opt_df, broadcaster_id=0)\n",
    "perf_poisson = {\n",
    "    'type': 'Poisson',\n",
    "    'seed': seed,\n",
    "    'capacity': num_poisson_tweets,\n",
    "    'q': sim_opts_1_follower.q\n",
    "}\n",
    "OR.add_perf(perf_poisson, poisson_df, sim_opts_1_follower)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Step 3: Calculate the performance and save JSON output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_opt_tweets = 78.0, num_poisson_tweets = 78.0\n"
     ]
    }
   ],
   "source": [
    "print('num_opt_tweets = {}, num_poisson_tweets = {}'\n",
    "      .format(U.num_tweets_of(opt_df, 0), U.num_tweets_of(poisson_df, 0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg_rank_opt = 66.8116376874034, avg_rank_poisson = 201.3461740580856\n"
     ]
    }
   ],
   "source": [
    "print('avg_rank_opt = {}, avg_rank_poisson = {}'\n",
    "      .format(U.average_rank(opt_df, sim_opts=sim_opts_1_follower), \n",
    "              U.average_rank(poisson_df, sim_opts=sim_opts_1_follower)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'avg_rank': 66.811637687403405,\n",
       " 'capacity': 78.0,\n",
       " 'num_events': 78,\n",
       " 'r_2': 138.15727122691501,\n",
       " 's': 1.0,\n",
       " 'seed': 1,\n",
       " 'top_1': 56.517519051405124,\n",
       " 'type': 'Opt',\n",
       " 'world_events': 154}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "top_1_opt = 56.517519051405124, top_1_poisson = 39.68728199394901\n"
     ]
    }
   ],
   "source": [
    "print('top_1_opt = {}, top_1_poisson = {}'\n",
    "      .format(perf_opt['top_1'], perf_poisson['top_1']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output JSON structure, for reference:\n",
    "\n",
    "```\n",
    "{\n",
    "  \"walls\": {\n",
    "    \"1\": [1,2,3,4,5],\n",
    "    \"2\": [1,4,5]\n",
    "  },\n",
    "  \"broadcasts\": {\n",
    "    \"redqueen\": {\n",
    "        \"post_times\": [1, 4],\n",
    "        \"performance\": {\n",
    "            \"<metric>\": [(<timestamp>, <perf>)]\n",
    "        }\n",
    "     }, \n",
    "    \"poisson\": {\n",
    "        \"post_times\": [1, 3],\n",
    "        \"performance\": {\n",
    "            \"<metric>\": [(<timestamp>, <perf>)]\n",
    "        }\n",
    "    },\n",
    "  }\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'avg_rank': 66.811637687403405,\n",
       " 'capacity': 78.0,\n",
       " 'num_events': 78,\n",
       " 'r_2': 138.15727122691501,\n",
       " 's': 1.0,\n",
       " 'seed': 1,\n",
       " 'top_1': 56.517519051405124,\n",
       " 'type': 'Opt',\n",
       " 'world_events': 154}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
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       " (92.43259374906944, 1.0),\n",
       " (92.688401693441762, 0.0),\n",
       " (97.534197669148725, 1.0),\n",
       " (98.281644625145205, 2.0),\n",
       " (98.711015901747615, 3.0),\n",
       " (98.723651374330075, 0.0),\n",
       " (98.867159699035994, 1.0),\n",
       " (98.887565402411113, 2.0),\n",
       " (98.941855529764169, 0.0),\n",
       " (99.383829095017916, 1.0),\n",
       " (99.870707195853313, 2.0)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = U.rank_of_src_in_df(opt_df, 0).mean(1)\n",
    "list(zip(tmp.index, tmp.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "@Deco.optioned('opts')\n",
    "def perf_to_json(dfs, names, src_id, sink_ids, end_time):\n",
    "    \"\"\"Produce a dictionary which captures performance for the demo app.\"\"\"        \n",
    "    \n",
    "    # Assumes that the walls across the data frames are the same.\n",
    "    eg_df = dfs[0]\n",
    "    \n",
    "    walls = {}\n",
    "    for sink_id in sink_ids:\n",
    "        walls[sink_id] = eg_df[(eg_df.src_id != src_id) & (eg_df.sink_id == sink_id)].t.tolist()\n",
    "    \n",
    "    broadcasts = {}\n",
    "    for df, name in zip(dfs, names):\n",
    "        r_t = U.rank_of_src_in_df(df, src_id)\n",
    "        avg_rank = r_t.mean(1)\n",
    "        time_at_top = np.where(r_t < 1.0, 1.0, 0.0).mean(1)\n",
    "        dt = np.diff(np.concatenate([r_t.index.values, [end_time]]))\n",
    "        broadcasts[name] = {\n",
    "            'post_times': df[df.src_id == src_id].t.unique().tolist(),\n",
    "            'performance': {\n",
    "                'avg_rank': list(zip(avg_rank.index, avg_rank.values)),\n",
    "                'time_at_top': list(zip(avg_rank.index, np.cumsum(time_at_top * dt)))\n",
    "            }\n",
    "        }\n",
    "        \n",
    "    return {\n",
    "        'walls': walls,\n",
    "        'broadcasts': broadcasts\n",
    "    }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "example_1 = perf_to_json([opt_df, poisson_df], ['redqueen', 'poisson'], \n",
    "                         opts=Deco.Options(**sim_opts_1_follower.get_dict()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.482480948594869"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U.time_in_top_k(opt_df, src_id=1, K=1, sim_opts=sim_opts_1_follower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "    (82.209274547601595, 41.881746947247677),\n",
       "    (82.509779025603621, 41.881746947247677),\n",
       "    (82.730943945759904, 42.478843124744813),\n",
       "    (83.32804012325704, 42.478843124744813),\n",
       "    (83.474749653837705, 43.892758701116364),\n",
       "    (84.888665230209256, 43.892758701116364),\n",
       "    (84.950453073412163, 45.357038605721115),\n",
       "    (86.414732978016914, 45.357038605721115),\n",
       "    (86.544094170301648, 45.584533866357376),\n",
       "    (86.771589430937908, 45.584533866357376),\n",
       "    (86.817164242370723, 47.521628236664114),\n",
       "    (88.754258612677461, 47.521628236664114),\n",
       "    (88.867980799995053, 51.086241185738501),\n",
       "    (92.43259374906944, 51.086241185738501),\n",
       "    (92.688401693441762, 55.932037161445464),\n",
       "    (97.534197669148725, 55.932037161445464),\n",
       "    (98.281644625145205, 55.932037161445464),\n",
       "    (98.711015901747615, 55.932037161445464),\n",
       "    (98.723651374330075, 56.075545486151384),\n",
       "    (98.867159699035994, 56.075545486151384),\n",
       "    (98.887565402411113, 56.075545486151384),\n",
       "    (98.941855529764169, 56.517519051405131),\n",
       "    (99.383829095017916, 56.517519051405131),\n",
       "    (99.870707195853313, 56.517519051405131)]},\n",
       "  'post_times': [0.0,\n",
       "   0.6192120893471,\n",
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       "   1.288598312080455,\n",
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       "   53.1328355360868,\n",
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       "   69.22035638823832,\n",
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       "   70.90646836345958,\n",
       "   71.46909729647396,\n",
       "   71.80415239474138,\n",
       "   73.75685225908943,\n",
       "   74.57423103022877,\n",
       "   78.18178379064065,\n",
       "   78.5853799936759,\n",
       "   79.67723627345721,\n",
       "   82.7309439457599,\n",
       "   83.4747496538377,\n",
       "   84.95045307341216,\n",
       "   86.54409417030165,\n",
       "   86.81716424237072,\n",
       "   88.86798079999505,\n",
       "   92.68840169344176,\n",
       "   98.72365137433007,\n",
       "   98.94185552976417]}}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_1['broadcasts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import json\n",
    "with open('data/example2.json', 'w') as f:\n",
    "    json.dump(example_1, f, indent=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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