query_count_vs_graph_size.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "from mwu import main_routine as mwu, MAX_MU, RANDOM\n",
    "from synthetic_data import (load_data_by_gtype, GRID, KRONECKER_HIER, KRONECKER_PERI, KRONECKER_RAND,\n",
    "                            PL_TREE, ER, BARABASI)\n",
    "from experiment_utils import experiment_mwu_multiple_rounds, experiment_dog_multiple_rounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def counts_to_stat(counts):\n",
    "    s = pd.Series(list(filter(lambda c: c != False, counts)))\n",
    "    return s.describe().to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2-2\n",
      "fail to load er/2-2\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Cannot infer number of levels from empty list",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-70-15023d0c3997>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[0mindices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dog'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber_of_nodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMultiIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_tuples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'method'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'graph size'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_records\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/cloud-user/code/source_finding/venv/lib/python3.5/site-packages/pandas/indexes/multi.py\u001b[0m in \u001b[0;36mfrom_tuples\u001b[0;34m(cls, tuples, sortorder, names)\u001b[0m\n\u001b[1;32m    971\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtuples\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    972\u001b[0m             \u001b[0;31m# I think this is right? Not quite sure...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Cannot infer number of levels from empty list'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    974\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtuples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Cannot infer number of levels from empty list"
     ]
    }
   ],
   "source": [
    "n_rounds = 100\n",
    "size_params = ['2-{}'.format(i) for i in range(2, 5)]\n",
    "gtype = ER\n",
    "fracition = 0.01\n",
    "epsilon = 0.7\n",
    "sampling_method = 'late_nodes'\n",
    "check_neighbor_threshold = 0.01\n",
    "\n",
    "rows = []\n",
    "indices = []\n",
    "for size_param in size_params:\n",
    "    print(size_param)\n",
    "    try:\n",
    "        g, time_probas, node2id, id2node = load_data_by_gtype(gtype, size_param)\n",
    "    except IOError:\n",
    "        print('fail to load {}/{}'.format(gtype, size_param))\n",
    "        break\n",
    "    def mwu_wrapper(method):\n",
    "        return experiment_mwu_multiple_rounds(n_rounds, g, node2id, id2node, time_probas,\n",
    "                                              fraction=fracition, epsilon=epsilon,\n",
    "                                              sampling_method=sampling_method,\n",
    "                                              query_selection_method=method, \n",
    "                                              check_neighbor_threshold=check_neighbor_threshold,\n",
    "                                              max_iter=g.number_of_nodes())\n",
    "    \n",
    "    \n",
    "    counts = mwu_wrapper(MAX_MU)\n",
    "    rows.append(counts_to_stat(counts))    \n",
    "    indices.append((MAX_MU, g.number_of_nodes()))\n",
    "\n",
    "    counts = mwu_wrapper(RANDOM)\n",
    "    rows.append(counts_to_stat(counts))    \n",
    "    indices.append((RANDOM, g.number_of_nodes()))\n",
    "    \n",
    "    counts = experiment_dog_multiple_rounds(n_rounds, g, fracition, sampling_method)\n",
    "    rows.append(counts_to_stat(counts))\n",
    "    indices.append(('dog', g.number_of_nodes()))\n",
    "    \n",
    "index = pd.MultiIndex.from_tuples(indices, names=('method', 'graph size'))\n",
    "df = pd.DataFrame.from_records(rows, index=index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df.to_pickle('data/{}/performance.pkl'.format(gtype))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_pickle('data/{}/performance.pkl'.format(KRONECKER_HIER))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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></th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>count</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th>graph size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max_mu</th>\n",
       "      <th>255</th>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>19.510417</td>\n",
       "      <td>2.0</td>\n",
       "      <td>18.070619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random</th>\n",
       "      <th>255</th>\n",
       "      <td>24.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>99.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>38.545455</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.688789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog</th>\n",
       "      <th>255</th>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>22.230000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.563480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    25%   50%   75%  count    max       mean   min        std\n",
       "method graph size                                                            \n",
       "max_mu 255         10.0  14.0  21.0   96.0  115.0  19.510417   2.0  18.070619\n",
       "random 255         24.0  33.0  43.5   99.0  130.0  38.545455  16.0  20.688789\n",
       "dog    255         20.0  22.0  25.0  100.0   32.0  22.230000   2.0   4.563480"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-84-67d9b01912bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mlb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'25%'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mub\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'75%'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mub\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'o-'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"log\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnonposx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'clip'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbasex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/cloud-user/code/source_finding/venv/lib/python3.5/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    601\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 603\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/cloud-user/code/source_finding/venv/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   2167\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2168\u001b[0m             return self._engine.get_value(s, k,\n\u001b[0;32m-> 2169\u001b[0;31m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[1;32m   2170\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2171\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'integer'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3557)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3240)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4279)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:8564)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:8508)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADcRJREFUeJzt3X+M5HV9x/HnC65oqqKpRJscP2ILiuEfxAT5o9FpIb3D\noNc/rOUaKzWE2CbYxIYE+xd78S/sH7QGG7Qhppaaa20TpRYipnSMREEiXrSXO+5o9bwDQiLVkJho\nKLz7xw53y3Zu57t7s7N3b56PZJP5znz2O598Mvu873xnv3upKiRJPZ211ROQJG0eIy9JjRl5SWrM\nyEtSY0Zekhoz8pLU2MzIJ7k7yTNJvr/GmE8nOZxkX5LL5ztFSdJGDTmS/zyw42QPJrkW+M2qugT4\nKHDXnOYmSTpFMyNfVQ8BP11jyC7gC5OxjwCvT/Lm+UxPknQq5nFOfjtwdMX2k5P7JElbbB6Rz5T7\n/FsJknQa2DaHfRwDLlixfT7w1LSBSYy/JG1AVU07oJ5p6JF8mH7EDnAv8GGAJFcBP6uqZ062o6ry\nq4rbbrtty+dwuny5Fq6Fa7H216mYeSSf5IvACHhjkh8DtwHnLPe6PldV9yV5b5IngJ8DHzmlGUmS\n5mZm5KvqDweMuXk+05EkzZNXvG6R0Wi01VM4bbgWJ7gWJ7gW85FTPd+zridLapHPJ0kdJKE2+YNX\nSdIZyMhLUmNGXpIaM/KS1JiRl6TGjLwkNWbkJakxIy9JjRl5SWrMyEtSY0Zekhoz8pLUmJGXpMaM\nvCQ1ZuQlqTEjL0mNGXlJaszIS1JjRl6SGjPyktSYkZekxoy8JDVm5CWpMSMvSY0ZeUlqzMhLUmNG\nXpIaM/KS1JiRl6TGjLwkNWbkJakxIy9JjRl5SWrMyEtSY4Min2RnkoNJDiW5dcrjFyR5MMljSfYl\nuXb+U5UkrVeqau0ByVnAIeBq4CngUeD6qjq4Ysxngceq6rNJ3g7cV1VvmbKvmvV8kqSXS0JVZSPf\nO+RI/krgcFUdqarngb3ArlVjXgTOndx+A/DkRiYjSZqvbQPGbAeOrtg+xnL4V9oDPJDkz4BfBa6Z\nz/QkSadiSOSnvUVYfc5lN/D5qrojyVXAPcBl03a2tLR0/PZoNGI0Gg2aqCS9UozHY8bj8Vz2NeSc\n/FXAUlXtnGx/Aqiqun3FmP8EdlTVk5Pt/wLeVVU/WbUvz8lL0jpt9jn5R4GLk1yU5BzgeuDeVWOO\nMDlFM/ng9VWrAy9JWryZka+qF4CbgQeA/cDeqjqQZE+S6ybDbgFuSrIP+Afghs2asCRpuJmna+b6\nZJ6ukaR12+zTNZKkM5SRl6TGjLwkNWbkJakxIy9JjRl5SWrMyEtSY0Zekhoz8pLUmJGXpMaMvCQ1\nZuQlqTEjL0mNGXlJaszIS1JjRl6SGjPyktSYkZekxoy8JDVm5CWpMSMvSY0ZeUlqzMhLUmNGXpIa\nM/KS1JiRl6TGjLwkNWbkJakxIy9JjRl5SWrMyEtSY0Zekhoz8pLUmJGXpMaMvCQ1NijySXYmOZjk\nUJJbTzLmg0n2J/lBknvmO01J0kakqtYekJwFHAKuBp4CHgWur6qDK8ZcDPwj8NtV9VyS86rqJ1P2\nVbOeT5L0ckmoqmzke4ccyV8JHK6qI1X1PLAX2LVqzE3AZ6rqOYBpgZckLd6QyG8Hjq7YPja5b6W3\nAm9L8lCSbyXZMa8JSpI2btuAMdPeIqw+57INuBh4N3Ah8M0kl710ZC9J2hpDIn+M5XC/5HyWz82v\nHvPtqnoR+FGSx4FLgO+u3tnS0tLx26PRiNFotL4ZS1Jz4/GY8Xg8l30N+eD1bOBxlj94fRr4DrC7\nqg6sGLNjct8fJzmP5bhfXlU/XbUvP3iVpHXa1A9eq+oF4GbgAWA/sLeqDiTZk+S6yZivAc8m2Q/8\nO3DL6sBLkhZv5pH8XJ/MI3lJWrfN/hVKSdIZyshLUmNGXpIaM/KS1JiRl6TGjLwkNWbkJakxIy9J\njRl5SWrMyEtSY0Zekhoz8pLUmJGXpMaMvCQ1ZuQlqTEjL0mNGXlJaszIS1JjRl6SGjPyktSYkZek\nxoy8JDVm5CWpMSMvSY0ZeUlqzMhLUmNGXpIaM/KS1JiRl6TGjLwkNWbkJakxIy9JjRl5SWrMyEtS\nY0Zekhoz8pLU2KDIJ9mZ5GCSQ0luXWPcB5K8mOSK+U1RkrRRMyOf5CzgTmAHcBmwO8mlU8a9FvgY\n8PC8JylJ2pghR/JXAoer6khVPQ/sBXZNGfdJ4Hbgl3OcnyTpFAyJ/Hbg6IrtY5P7jktyOXB+Vd03\nx7lJkk7RtgFjMuW+Ov5gEuAO4IYZ3yNJWrAhkT8GXLhi+3zgqRXbr2P5XP14EvxfB76S5P1V9djq\nnS0tLR2/PRqNGI1G65+1JDU2Ho8Zj8dz2Veqau0BydnA48DVwNPAd4DdVXXgJOP/A/jzqvrelMdq\n1vNJkl4uCVW1oTMkM8/JV9ULwM3AA8B+YG9VHUiyJ8l1074FT9dI0mlh5pH8XJ/MI3lJWrdNPZKX\nJJ25jLwkNWbkJakxIy9JjRl5SWrMyEtSY0Zekhoz8pLUmJGXpMaMvCQ1ZuQlqTEjL0mNGXlJaszI\nS1JjRl6SGjPyktSYkZekxoy8JDVm5CWpMSMvSY0ZeUlqzMhLUmNGXpIaM/KS1JiRl6TGjLwkNWbk\nJakxIy9JjRl5SWrMyEtSY0Zekhoz8pLUmJGXpMaMvCQ1ZuQlqTEjL0mNDYp8kp1JDiY5lOTWKY9/\nPMn+JPuSfD3JBfOfqiRpvWZGPslZwJ3ADuAyYHeSS1cNewx4Z1VdDvwL8Jfznqgkaf2GHMlfCRyu\nqiNV9TywF9i1ckBVfaOqfjHZfBjYPt9pSpI2YkjktwNHV2wfY+2I3wjcfyqTkiTNx7YBYzLlvpo6\nMPkQ8E7gPSfb2dLS0vHbo9GI0Wg0YAqS9MoxHo8Zj8dz2Veqpvb6xIDkKmCpqnZOtj8BVFXdvmrc\nNcBfA++uqmdPsq+a9XySpJdLQlVNO+CeacjpmkeBi5NclOQc4Hrg3lUTeAdwF/D+kwVekrR4MyNf\nVS8ANwMPAPuBvVV1IMmeJNdNhn0KeA3wpSTfS/LlTZuxJGmwmadr5vpknq6RpHXb7NM1kqQzlJGX\npMaMvCQ1ZuQlqTEjL0mNGXlJaszIS1JjRl6SGjPyktSYkZekxoy8JDVm5CWpMSMvSY0ZeUlqzMhL\nUmNGXpIaM/KS1JiRl6TGjLwkNWbkJakxIy9JjRl5SWrMyEtSY0Zekhoz8pLUmJGXpMaMvCQ1ZuQl\nqTEjL0mNGXlJaszIS1JjRl6SGjPyktSYkZekxoy8JDU2KPJJdiY5mORQklunPH5Okr1JDif5dpIL\n5z9VSdJ6zYx8krOAO4EdwGXA7iSXrhp2I/A/VXUJ8FfAp+Y90W7G4/FWT+G04Vqc4Fqc4FrMx5Aj\n+SuBw1V1pKqeB/YCu1aN2QX83eT2PwNXz2+KPfkCPsG1OMG1OMG1mI8hkd8OHF2xfWxy39QxVfUC\n8LMkvzaXGUqSNmxI5DPlvpoxJlPGSJIWLFVrtzjJVcBSVe2cbH8CqKq6fcWY+ydjHklyNvB0Vb1p\nyr4MvyRtQFVNO+CeaduAMY8CFye5CHgauB7YvWrMvwI3AI8Avw88OM9JSpI2Zmbkq+qFJDcDD7B8\neufuqjqQZA/waFV9Fbgb+Pskh4FnWf6HQJK0xWaerpEknbk25YpXL546YcBafDzJ/iT7knw9yQVb\nMc9FmLUWK8Z9IMmLSa5Y5PwWachaJPng5LXxgyT3LHqOizLgZ+SCJA8meWzyc3LtVsxzsyW5O8kz\nSb6/xphPT7q5L8nlg3ZcVXP9YvkfjieAi4BfAfYBl64a86fA30xu/wGwd97zOB2+Bq7Fe4BXT27/\nySt5LSbjXgt8A/gWcMVWz3sLXxcXA98Fzp1sn7fV897Ctfgs8NHJ7bcDP9zqeW/SWvwWcDnw/ZM8\nfi3wb5Pb7wIeHrLfzTiS9+KpE2auRVV9o6p+Mdl8mP9/DUIXQ14XAJ8Ebgd+ucjJLdiQtbgJ+ExV\nPQdQVT9Z8BwXZchavAicO7n9BuDJBc5vYarqIeCnawzZBXxhMvYR4PVJ3jxrv5sReS+eOmHIWqx0\nI3D/ps5o68xci8nbz/Or6r5FTmwLDHldvBV4W5KHknwryY6FzW6xhqzFHuCPkhwFvgp8bEFzO92s\nXqsnGXBQOORXKNfLi6dOGLIWywOTDwHvZPn0TUdrrkWSAHew/Ku4a31PB0NeF9tYPmXzbuBC4JtJ\nLnvpyL6RIWuxG/h8Vd0xuW7nHpb/jtYrzeCerLQZR/LHWH5RvuR84KlVY44CFwBMLp46t6rWepty\nphqyFiS5BvgL4H2Tt6wdzVqL17H8gztO8kPgKuArTT98HfK6OAZ8paperKofAY8Dlyxmegs1ZC1u\nBP4JoKoeBl6d5LzFTO+0coxJNyem9mS1zYj88YunkpzD8u/M37tqzEsXT8EaF081MHMtkrwDuAt4\nf1U9uwVzXJQ116KqnquqN1XVb1TVW1j+fOJ9VfXYFs13Mw35Gfky8DsAk6BdAvz3Qme5GEPW4ghw\nDUCStwOvavwZRTj5O9h7gQ/D8b9E8LOqembWDud+uqa8eOq4gWvxKeA1wJcmpyyOVNXvbd2sN8fA\ntXjZt9D0dM2QtaiqryX53ST7gf8Fbun4bnfg6+IW4G+TfJzlD2FvOPkez1xJvgiMgDcm+TFwG3AO\ny39G5nNVdV+S9yZ5Avg58JFB+538Oo4kqSH/+z9JaszIS1JjRl6SGjPyktSYkZekxoy8JDVm5CWp\nMSMvSY39H2x3icMl2bwzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faa09073d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "methods, x = df.index.levels\n",
    "for m in methods:\n",
    "    y = df['50%'][m]\n",
    "    lb = df['25%'][m]\n",
    "    ub = df['75%'][m]\n",
    "    ax.errorbar(x, y, yerr=np.array([lb, ub]), fmt='o-')\n",
    "    ax.set_xscale(\"log\", nonposx='clip', basex=2)\n",
    "    ax.set_xlim(2**3, 2**9)\n",
    "ax.set_xlabel('graph size')\n",
    "ax.set_ylabel('query count')\n",
    "ax.set_title('mean query count vs graph size on {} graphs'.format(gtype))\n",
    "ax.legend(methods, loc='upper left')"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}