Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting positive and negative links \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import graph_tool as gt\n",
    "import graph_tool.topology as top\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline \n",
    "from operator import itemgetter\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "from collections import Counter\n",
    "from __future__ import division"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wikipedia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We perform the same experiments for the wikipedia signed graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = \"wikiElec.ElecBs3.csv\"\n",
    "g = gt.load_graph_from_csv(\"./data/{}\".format(filename), directed=True, eprop_types=['int'], eprop_names=['weight'], string_vals=True)\n",
    "g.is_directed()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "triad_features  = pickle.load(open(\"./data/wiki2Elec.ElecBs3.csv.triad_features.pk\".format(filename), 'rb'))\n",
    "degree_features = pickle.load(open(\"./data/wiki2Elec.ElecBs3.csv.graph_features.pk\".format(filename), 'rb'))\n",
    "common_neighbors = pickle.load(open(\"./data/wiki2Elec.ElecBs3.csv.common_neighbors.pk\".format(filename), 'rb'))\n",
    "counts_3 = pickle.load(open(\"./data/wiki2Elec.ElecBs3.csv.paths_of_length_3_counts.pk\".format(filename), 'rb'))\n",
    "counts_4 = pickle.load(open(\"./data/wiki2Elec.ElecBs3.csv.paths_of_length_4_counts.pk\".format(filename), 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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       " (3394, 3685): [25, 24],\n",
       " (315, 316): [0, 2],\n",
       " (2908, 3301): [13, 29],\n",
       " (5176, 4350): [36, 26],\n",
       " (1890, 2621): [24, 19],\n",
       " (47, 3949): [0, 0],\n",
       " (5141, 4672): [41, 49],\n",
       " (3972, 5256): [3, 5],\n",
       " (642, 863): [6, 17],\n",
       " (2761, 4552): [26, 41],\n",
       " (1174, 2617): [8, 14],\n",
       " (958, 701): [0, 0],\n",
       " (4463, 5921): [19, 37],\n",
       " (6151, 3984): [10, 11],\n",
       " (28, 3150): [27, 104],\n",
       " (1095, 2556): [14, 16],\n",
       " (318, 3172): [12, 12],\n",
       " (1306, 1613): [14, 65],\n",
       " (1519, 5548): [11, 6],\n",
       " (1002, 6053): [5, 2],\n",
       " (6786, 6876): [2, 2],\n",
       " (2758, 3146): [5, 36],\n",
       " (4566, 4048): [10, 14],\n",
       " (4180, 1675): [7, 14],\n",
       " (318, 318): [0, 0],\n",
       " (2758, 1585): [10, 43],\n",
       " (4547, 3045): [3, 2],\n",
       " (2457, 2090): [13, 31],\n",
       " (5239, 5267): [5, 0],\n",
       " (2467, 5324): [6, 13],\n",
       " (3156, 3128): [14, 8],\n",
       " (546, 1704): [14, 22],\n",
       " (5783, 6173): [25, 30],\n",
       " (1519, 2431): [19, 21],\n",
       " (6146, 5185): [13, 38],\n",
       " (624, 1404): [10, 28],\n",
       " (917, 1343): [3, 9],\n",
       " (697, 2004): [5, 22],\n",
       " (665, 546): [1, 13],\n",
       " (916, 2226): [19, 20],\n",
       " (1062, 2831): [13, 16],\n",
       " (3605, 6195): [5, 10],\n",
       " (898, 1591): [3, 11],\n",
       " (3109, 3551): [11, 5],\n",
       " (6878, 5199): [3, 3],\n",
       " (1131, 1919): [7, 14],\n",
       " (5, 82): [2, 1],\n",
       " (430, 2381): [3, 1],\n",
       " (6182, 6585): [5, 4],\n",
       " (3739, 2607): [19, 24],\n",
       " (164, 757): [4, 4],\n",
       " (5129, 5765): [1, 1],\n",
       " (3329, 3519): [9, 16],\n",
       " (23, 1682): [7, 35],\n",
       " (1415, 6403): [11, 27],\n",
       " (2600, 2691): [6, 19],\n",
       " (4310, 4813): [8, 14],\n",
       " (5149, 6161): [6, 30],\n",
       " (2463, 2323): [11, 38],\n",
       " (3287, 2518): [8, 18],\n",
       " (4602, 3644): [22, 21],\n",
       " (1699, 1288): [8, 6],\n",
       " (2896, 3446): [1, 2],\n",
       " (3330, 3873): [18, 30],\n",
       " (2763, 3150): [34, 98],\n",
       " (1205, 1003): [6, 24],\n",
       " (2463, 3956): [25, 43],\n",
       " (1938, 3464): [19, 33],\n",
       " (1890, 1701): [3, 14],\n",
       " (656, 1432): [12, 18],\n",
       " (2338, 1627): [10, 28],\n",
       " (1625, 2204): [30, 36],\n",
       " (1375, 1643): [12, 13],\n",
       " (792, 807): [4, 21],\n",
       " (3173, 2348): [20, 20],\n",
       " (422, 4970): [5, 7],\n",
       " (6396, 6403): [2, 3],\n",
       " (4228, 4291): [4, 15],\n",
       " (1062, 5179): [10, 17],\n",
       " (4897, 6173): [28, 31],\n",
       " (954, 4275): [10, 16],\n",
       " (2427, 2838): [30, 51],\n",
       " (951, 6585): [27, 32],\n",
       " (2204, 2430): [31, 53],\n",
       " (5786, 4216): [10, 20],\n",
       " (105, 4482): [11, 28],\n",
       " (4430, 5134): [9, 9],\n",
       " (2377, 2838): [45, 52],\n",
       " (5652, 5694): [1, 1],\n",
       " (3082, 2127): [8, 23],\n",
       " (286, 764): [1, 4],\n",
       " (641, 915): [1, 19],\n",
       " (6556, 142): [0, 0],\n",
       " (2203, 962): [0, 2],\n",
       " (913, 1523): [3, 13],\n",
       " (285, 1096): [11, 48],\n",
       " (5769, 4482): [12, 25],\n",
       " (99, 3306): [26, 32],\n",
       " (1140, 1172): [0, 5],\n",
       " (5038, 5736): [10, 5],\n",
       " (5127, 6916): [13, 16],\n",
       " (6055, 6149): [10, 15],\n",
       " (1002, 321): [7, 22],\n",
       " (1471, 1555): [5, 21],\n",
       " (1394, 1151): [18, 25],\n",
       " (2833, 2864): [35, 43],\n",
       " (2260, 2331): [6, 24],\n",
       " (1068, 1064): [2, 2],\n",
       " (4603, 1655): [13, 35],\n",
       " (2164, 2956): [26, 60],\n",
       " (19, 598): [2, 5],\n",
       " (139, 168): [6, 6],\n",
       " (5792, 2733): [5, 10],\n",
       " (1979, 1682): [1, 20],\n",
       " (4149, 4867): [30, 38],\n",
       " (2241, 2226): [0, 0],\n",
       " (2270, 2044): [22, 46],\n",
       " (4615, 4990): [7, 8],\n",
       " (701, 546): [8, 32],\n",
       " (1035, 1294): [17, 26],\n",
       " (5586, 4616): [20, 20],\n",
       " (284, 794): [5, 25],\n",
       " (1675, 4210): [9, 18],\n",
       " (3135, 2357): [0, 0],\n",
       " (3148, 2761): [13, 31],\n",
       " (3111, 3108): [14, 30],\n",
       " (76, 724): [7, 14],\n",
       " (23, 1471): [12, 33],\n",
       " (1365, 1184): [14, 28],\n",
       " (373, 1730): [19, 30],\n",
       " (3155, 1080): [9, 17],\n",
       " (1830, 2434): [5, 8],\n",
       " (5713, 2233): [7, 22],\n",
       " ...}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0623824943944\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "from __future__ import division\n",
    "x=Counter(common_neighbors.values())[0]/(sum(common_neighbors.values()) )\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, y = [], []\n",
    "for edge in g.edges():\n",
    "    e = (edge.source().__hash__(), edge.target().__hash__())\n",
    "    features = ( triad_features[e][:]) #0..15 \n",
    "    features.extend( degree_features[e][:] ) #16,17,18,19,20,21\n",
    "    features.extend( [common_neighbors[e]] ) #22 \n",
    "    try:\n",
    "        features.extend( counts_3[e] ) #23 24 \n",
    "    except KeyError:\n",
    "        features.extend( [0, 0] )\n",
    "    try:\n",
    "        features.extend( counts_4[e] ) #25 26\n",
    "    except KeyError:\n",
    "        features.extend( [0, 0] )    \n",
    "    X.append( features[:] )\n",
    "    y.append( g.edge_properties['weight'][e] )\n",
    "X = np.array(X)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(107080, 27)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_all     = X.copy()\n",
    "X_triads  = np.take(X, range( 0, 16), axis=1)\n",
    "X_deg      = np.take(X, range(16, 22), axis=1)    \n",
    "X_deg_tr   = np.take(X, range(16, 23), axis=1)    \n",
    "X_triangles   = np.take(X, range(23, 24), axis=1)  \n",
    "X_leskovec     = np.take(X, range( 0, 23), axis=1)  \n",
    "X_paths_3       = np.take(X, range(23, 25), axis=1)  \n",
    "X_degrees_paths_3       = np.take(X, range(16, 22)+range(23, 25), axis=1)  \n",
    "X_paths_4       = np.take(X, range(25, 27), axis=1)  \n",
    "X_degrees_paths_4       = np.take(X, range(16, 22)+range(25, 27), axis=1)  \n",
    "X_p3_p4  = np.take(X, range(23, 27), axis=1)  \n",
    "\n",
    "\n",
    "feature_set_names = [\\\n",
    "                     \"X_all   \",\n",
    "                     \"X_triads\",    \n",
    "                     \"X_deg\",   \n",
    "                     \"X_deg_tr \", \n",
    "                    \"X_triangles\",\n",
    "                     \"X_leskovec\",    \n",
    "                     \"X_paths_3\",\n",
    "                     \"X_degrees_paths_3\",\n",
    "                     \"X_paths_4\",\n",
    "                     \"X_degrees_paths_4\",\n",
    "                     \"X_p3_p4\"\n",
    "                    ]\n",
    "feature_sets = [\\\n",
    "                     X_all   ,\n",
    "                     X_triads,    \n",
    "                     X_deg,\n",
    "                     X_deg_tr ,     \n",
    "                     X_triangles, \n",
    "                     X_leskovec   ,    \n",
    "                     X_paths_3     ,\n",
    "                     X_degrees_paths_3,\n",
    "                     X_paths_4,\n",
    "                     X_degrees_paths_4,\n",
    "                     X_p3_p4\n",
    "                    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_names = ['t1', 't2','t3','t4','t5','t6','t7','t8','t9','t10','t11',\\\n",
    "                 't12','t13','t14','t15','t16',\\\n",
    "                 'd+in(v)', 'd-in(v)', 'd+out(u)', 'd-out(u)', 'din(v)', 'dout(u)', 'C(u,v)',\\\n",
    "                 'neg 3 path', 'pos 3 path',\\\n",
    "                'neg 4 path', 'pos 4 path']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "positive_candidates = np.where(y == 1)[0]\n",
    "negative_candidates = np.where(y ==  -1)[0]\n",
    "if( len(positive_candidates) >  len(negative_candidates) ):\n",
    "    positives = np.random.choice(positive_candidates, size=len(negative_candidates))\n",
    "    negatives = negative_candidates\n",
    "else:\n",
    "    positives = positive_candidates\n",
    "    negatives = np.random.choice(negative_candidates, size=len(positive_candidates))\n",
    "    \n",
    "balanced = np.concatenate([positives, negatives])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23305, 23305)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(negatives), len(positives)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_all   \t0.81828125489069703\n",
      "X_triads\t0.57065006453418476\n",
      "X_deg\t0.79908043850103938\n",
      "X_deg_tr \t0.80041049633324302\n",
      "X_triangles\t0.55536840641990859\n",
      "X_leskovec\t0.8096573704299026\n",
      "X_paths_3\t0.74608646660148803\n",
      "X_degrees_paths_3\t0.80755417281168351\n",
      "X_paths_4\t0.71982713860396275\n",
      "X_degrees_paths_4\t0.80251326126862599\n",
      "X_p3_p4\t0.75724365383163661\n"
     ]
    }
   ],
   "source": [
    "scores = [0.5]\n",
    "for x, name in zip(feature_sets, feature_set_names):\n",
    "    score = np.mean(cross_val_score(LogisticRegression(), x[balanced], y[balanced], n_jobs=-1, cv=10))\n",
    "    print(name+\"\\t\"+repr(score))\n",
    "    scores.append(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### At-Least-Embeddedness experiments\n",
    "#### Embeddedness $\\geq 10$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "e10 = [x[-1] > 9 for x in X_leskovec] #common neighbors\n",
    "embedness10 = np.where(e10)\n",
    "y_e10 = y[embedness10]\n",
    "\n",
    "possible = np.where(y_e10 == 1)[0]\n",
    "negatives = np.where(y_e10 ==  -1)[0]\n",
    "positives = np.random.choice(possible, size=len(negatives))\n",
    "balanced = np.concatenate([positives, negatives])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_all   \t0.83693876621898478\n",
      "X_triads\t0.78763335609682072\n",
      "X_deg\t0.81724227938386829\n",
      "X_deg_tr \t0.81846209879353515\n",
      "X_triangles\t0.7789188861066848\n",
      "X_leskovec\t0.82595553532134447\n",
      "X_paths_3\t0.80174884285605896\n",
      "X_degrees_paths_3\t0.82909628955155923\n",
      "X_paths_4\t0.79529888458911913\n",
      "X_degrees_paths_4\t0.82613976781242893\n",
      "X_p3_p4\t0.8041915168070416\n"
     ]
    }
   ],
   "source": [
    "scores_e10 = [0.5]\n",
    "for x, name in zip(feature_sets, feature_set_names):\n",
    "    score = np.mean(cross_val_score(LogisticRegression(), x[balanced], y[balanced], n_jobs=-1, cv=10))\n",
    "    print(name+\"\\t\"+repr(score))\n",
    "    scores_e10.append(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Embeddedness $\\geq 25$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_all   \t0.85544116646635993\n",
      "X_triads\t0.81821583255856145\n",
      "X_deg\t0.84875167237352311\n",
      "X_deg_tr \t0.84779926982471243\n",
      "X_triangles\t0.80963513911880081\n",
      "X_leskovec\t0.85207827841901174\n",
      "X_paths_3\t0.83780584593754948\n",
      "X_degrees_paths_3\t0.8530624277194494\n",
      "X_paths_4\t0.82062631805709874\n",
      "X_degrees_paths_4\t0.84590353522755612\n",
      "X_p3_p4\t0.83734098278872549\n"
     ]
    }
   ],
   "source": [
    "e25 = [x[-1] >= 25 for x in X_leskovec] #common neighbors\n",
    "embedness25 = np.where(e25)\n",
    "y_e25 = y[embedness25]\n",
    "\n",
    "possible = np.where(y_e25 == 1)[0]\n",
    "negatives = np.where(y_e25 ==  -1)[0]\n",
    "positives = np.random.choice(possible, size=len(negatives))\n",
    "balanced = np.concatenate([positives, negatives])\n",
    "\n",
    "scores_e25 = [0.5]\n",
    "for x, name in zip(feature_sets, feature_set_names):\n",
    "    score = np.mean(cross_val_score(LogisticRegression(), x[balanced], y[balanced], n_jobs=-1, cv=10))\n",
    "    print(name+\"\\t\"+repr(score))\n",
    "    scores_e25.append(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Embeddedness $\\geq 50$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_all   \t0.89562044921633954\n",
      "X_triads\t0.86955345756715618\n",
      "X_deg\t0.89012145707351187\n",
      "X_deg_tr \t0.89010243119832155\n",
      "X_triangles\t0.86540581677567974\n",
      "X_leskovec\t0.89561993500349657\n",
      "X_paths_3\t0.87088526883047435\n",
      "X_degrees_paths_3\t0.90110041548397712\n",
      "X_paths_4\t0.870866757168127\n",
      "X_degrees_paths_4\t0.894250071989798\n",
      "X_p3_p4\t0.87630815747254098\n"
     ]
    }
   ],
   "source": [
    "e50 = [x[-1] >= 50 for x in X_leskovec] #common neighbors\n",
    "embedness50 = np.where(e50)\n",
    "y_e50 = y[embedness50]\n",
    "\n",
    "possible = np.where(y_e50 == 1)[0]\n",
    "negatives = np.where(y_e50 ==  -1)[0]\n",
    "positives = np.random.choice(possible, size=len(negatives))\n",
    "balanced = np.concatenate([positives, negatives])\n",
    "\n",
    "scores_e50 = [0.5]\n",
    "for x, name in zip(feature_sets, feature_set_names):\n",
    "    score = np.mean(cross_val_score(LogisticRegression(), x[balanced], y[balanced], n_jobs=-1, cv=10))\n",
    "    print(name+\"\\t\"+repr(score))\n",
    "    scores_e50.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p0qQLpaWlhxcvXnxmxYoVuRs2bGioqamp3r179+jW1laqrq5OTUtL66qoqKjZ\nuHHj0TVr1kyI1G57eztuv/32SWvXrj1WW1t7sK6uLiUvL6/Nqrw/bQGGOOuoUaM6S0tLD69bt+7o\nyy+/nB7J1qampqQNGzaMKy0tramtrT24Z8+eD8zKgt+7kpKS411dXbjnnnuy8/PzC5YtW3bpjh07\nRrS0tISLZZpw7NixYRMnTuw+5CorK+uLhoaGsKCpL2xFvMxcTUSNMFTlQUTZzKz48IwEwvQPM1Qz\nS59WFxCsMaT8g9LCXieIbsUgXFNJbZaTtWaWTv8qIwp7B9LFXFs1xb+OYxpImGuixRwOB0IAsGXL\nllF1dXWeRYsW5QFAZ2cnzZ49u7mlpYWam5uTH3rooZMA4PF4upYsWdKYk5PTDgApKSldSUlJOHv2\n7JAnnnjiOADMmTOnpampKTlSu5s3bx49efLk1nnz5rUAQGFhYavH4+myKu9PW83NzUnNzc1DHn74\n4ZMA0N7eTiNHjuy0agcAkpOT0dbWlrRixYqsO++888zVV1/d0tzcnNS7LPi983q9F7xe76lVq1ad\n2rlz54jVq1dnvfTSS5kvvvjiR0uXLj2LPjAT/yWiqBWB+wyGiOhbMG6VTYCxMpQD4BCAsE1MQj+x\n/MMMaAzlhizrq9/oqk4TDYAte51gru2aiu0FbNkb+JDU59/Ah5cCYtK/7tobD/71BS40+FcJ0QZC\nNveAlZWVeVavXt2wcuXKECG6N998M7WwsPD8kCFDAACVlZWp99xzzykA+PDDD4eOHTu2vaysLCUn\nJ+dCSkoKA8Dbb7+dWlBQ0Bqp3fvuu2/CrFmzuoOKAwcOXHTNNdc0V1RUeMzK+9PWgQMHUrxeb0ty\nshEmVFRUeLxeb6tVOwCQlpbWdejQoept27aNvPvuu3OWLl16+qc//WmjWVngZ5599tkxO3fuHFVT\nU+O5/PLLz99///3Hb7jhhs8zMjI67bz32dnZIStB9fX1wyZMmNBu52eDsbOB+lEAfwaglpm/BGA+\ngLei7UiIQMQ/TOMAs4BW10qs1DCZqtFE8wUubNirFw0acDbs1e/fgWtm5Y4LlRrplhuJSf8q1AiL\nBf92f3CrsdcHO8kIbvjXJv0JhGxuDh8/fnz7G2+8MaKz0/j8fvfddz1dXV14//33PV6vtzVQ79Ch\nQ57Zs2e3AsB7772XWlBQ0LJ//37PsWPHhre2ttK5c+eSHnvssQkrV648Gand9PT0jurqag9gBFyv\nvfbamCuCtHqWAAAgAElEQVSuuKLFqrw/bZWVlXmmTZvWHSRVVVWlzpo1q8WqHQCorKwcPmLEiK7l\ny5c3LViw4FxbW1uSWVnwe9fU1DRk1apVJ+rq6qq3b99+5K677mqyGwgBwDXXXHP+yJEjKTU1NcPa\n2troN7/5zZhFixb1uaLUGzu3ydqZ+QwRJRFREjPvJaK/i7YjIQIOLdUOiJBvQM5rDPkQXVaXchTb\nG3Mosjewn8SH/m1WV0Yi+rf7g1vd32/M+Dda+hsIdW8Oj8y99957et++fWl5eXnelJSUrilTprS+\n+uqrH1dWVnpmz559HgBaWlqora0tKTMzsxMAysvLPdOmTWstLy9PXbRo0Zni4uKpbW1tSQ888MDx\n+fPnn4/U7vLlyz9bsGDB5KlTpxbk5eW1paWldcycObMtOzu73ay8P21t2rQpfc6cOecDNtbW1nqK\niopai4qKWs3aAYCSkpLxpaWlF6empnbm5+e3bd269cgdd9yR07ss+L3buXPn6BdeeGFs7/d0zZo1\nDUuWLOkOar75zW9+6Z133klrampKvuSSS6b/9Kc//XTlypWnhw4dinXr1n1y3XXX5Xd2duK22247\nXVxc3GbDyyHYCYbOEtHFAP4AYAsRnQLQEW1HQn8xNIYCWl2Ba0cJ+wb0YwBPONa8D9FMpBo0lRTb\nGx099ur3rzPb/nyIZjOzm/51Y5ujRv92f3A7b29M+bcP0i9J77DMALMR2PSun35Jep+fdxdffDHv\n3r07TLNz06ZN9YHr1NRUbmhoqAw8f/LJJ3tnO31qt93x48d3VFZWHrJb3p+2nn/++frg5/X19d1j\nN2sHAHbs2HHETlkwf/zjH2sjvR7gtddes7xpecstt5y75ZZbztlpxwo7t8m+DaAFwEoAuwF8COCb\nA+lUsEuPxpBerS5nNdHsT6QaNKQ02GufUHv1+9eZWxqDx7+6b+Fo9m/3iofz9saMf21w+sTpcmYu\ndepx+sTpcrdtEtRjJ7X+PDN3MXMHM29m5o3M7H74H/eEambp0+oCgjWGnMh8sTeRWmuiOYYNe4NR\nm/VjrZmlNssJcFozK3r/KtTrEv9ChSbaXFu1cv3/69aAE4SBY2dlSHAFc40wR4l4D93QNXLipN65\ntmrFhr0B1Gt1mWuEOYUvcBHRXuO91u9fhfpzNuwFxL9q0OBfQVCEBEMxS7imkuPY2EyoT6srNuwF\ndGl1qbPXB7ub1Xv02OLCv4Ate8W/DtO9k0ODfwVBEbaCISLyENEU1YMREHPaZHGl1ZVAWmyi1WWN\n+NdBYlCbTBD6Q5/BEBF9E0AZjM3TIKKZRLRT9cASkpiYWEbD0Bhq7i7Ro+XkFqH2uiNq2WxWGDU+\n2NnMHAv+dcZee4TbOxD/Wp6rBMSIf4FI/nU0IDLdrA7o9a8gOIOdlaESALNhiO2AmcsQ00eMDlIs\nJxbdOj/mGmF6spwAt+3Vp8UWIFQzayDY+6A018zS61+denvh9g7Ev711uky1ukLQ7V8gkn8d87Fl\nMoK5vfr2LQlC/7ATDHUw84Dy94U+sJxYNGgMhcEwjvEPR30WW4+9wZOn2ok01F4lgRCgxb/2PiiD\n7Q3tW32Wk7m9+vyr8G8pZvwLRPKvY8FQlP5Vf6q6IAwMO8FQFRHdBmAIEU0moqcB/NFO40R0HREd\nJqIPiCjsMBciWk9EZf5HLRFFfYR2XGCZ5aRBM8uUgMaQImzYG1jS1zORKrYX6MNe8+AzWubarhkb\n9saDf32Bi0HiX8cC3ij9K8FQZHbt2pV2ww03DOhs/sWLF+ds27ZtpN3y/rTlFh988MHQOXPm5E+a\nNKnwy1/+cuGjjz4acmL1xIkTp+Xn5xdMnTq1wOv1XtafPuycQH0vjJzJCwC2AtgDI20gIkQ0BMAv\nAFwLoB7Ae0S0k5kPBuow88qg+vcCuDyq0Q9yfIELyywnDZpZlig8oM6GvQEtNsA4uTdwrQ7FB/JF\ntFehZpYl7ts72P3rQ19ZXZr9G7J5O7b8q2zF1YSMjKwZZ8402Plss0V6+sSO06frlR68eODAAc/M\nmTNb+q5pTXV1deqTTz4Zdoq1VXl/2nILv+RG/Ve+8pWWpqampMsvv7zg61//+udFRUXdshv79u2r\nHT9+fL/VMeysDE1h5tXMfIX/8SAz29H9mA3gA2b+iJm/ALANxmnWVnwXwL/ZaDcu8MFOeqxGTaWY\nyOqKU3stEXuVERNZXZrtdT0ZITY04IxAiGGc9QQgbJdX8G6vUUHPzevbDaxqamqGzZ8/P8/r9V42\nbdq0y8rLy4cDwMKFCyctXbo0u6ioaMqECROm7dmz5+Ibb7wxNzc317t48eIcAKioqEhtaGgYOn36\n9KlZWVnTdu3aldZXuxUVFcOLioqm5OfnFzzyyCNjGxsbh+bl5bVblfenrWuvvTbvvvvum1BUVDQl\nIyNjxiuvvJIWqR0AePrpp9MLCwsvy8/PLygqKppiVRbg5ptvzn388cfHVlVVDYcFOTk57V/5ylda\nAGD06NFdeXl5rZ988skwq/r9wU4w9A9EVENEjxJRYRRtTwRwLOh5vb8sDCLKgfER+V9RtD9o8cFu\neuyPVQ/FwHQideOQ8R57A1ps67FeU5aTm/Yq1syKUXsHq399sLuZWbN/QzZvx5Z/3dlAHen9D7w/\nq23Wj8yFCxdo2bJluRs3bqyvqqo69OCDD3766KOPjgeAmpoaz6RJky6UlpYeXrx48ZkVK1bkbtiw\noaGmpqZ69+7do1tbW6m6ujo1LS2tq6Kiombjxo1H16xZMyFSu+3t7bj99tsnrV279lhtbe3Burq6\nlLy8vDar8v60BRjirKNGjeosLS09vG7duqMvv/xyeiRbm5qakjZs2DCutLS0pra29uCePXs+MCsL\nfu9KSkqOd3V14Z577snOz88vWLZs2aU7duwY0dLSQjDh8OHDww4ePJh6zTXX/Cm4fP78+ZMLCwsv\nW7t2bb80YezIccyD8TffCOA5IqokogdttG1mCJuUAcCtALYzc6dpQ0TLiWg/Ee1vbGy00XVsYz8r\nRINmlulE6pbGUI+9erXY3LZX4S2NGLZ3sPo3+r9fTf7tXnG1tldtUGLtX3f2DAWPJzg4DH5/Vtio\n3zdbtmwZVVdX51m0aFHe1KlTCx588MGs4cOHd7W0tFBzc3PyQw89dBIAPB5P15IlSxpzcnLaU1JS\nOCUlpSspKQlnz54d8sQTTxwHgDlz5rQ0NTUlR2p38+bNoydPntw6b968FgAoLCxs9Xq9LVbl/Wmr\nubk5qbm5ecjDDz98EgDa29tp5MiRnVbtAEBycjLa2tqSVqxYkfWHP/whNSMjo9OsLPi983q9Fx56\n6KFTe/furXvqqafq33nnnbRbbrnly9u3bw/bs3Tu3Lmkm266Ke+pp546NmbMmK5A+VtvvVVz8ODB\nQ7/73e/qNm3aNPa3v/3txVE5EDYPXWTmE8y8EcDdMM4cetjGj9UDuDToeRZMVHn93IoIt8iY+Tlm\nLmbm4szMTDtDjmnsZ4U4rzEUgulEaq0Rpv7bXY+9+rTYQu3V+w1WsX+BPuwN7Vunf4NRm8XmvCZa\nzPz9AhbJCLn+/8P9qzYosfavO8FQ8HgyYPwu9P59SLNRv2/Kyso8q1evbqipqTlYU1NzsK6urnrL\nli2flJaWegoLC88PGTIEAFBZWZl65ZVXngeADz/8cOjYsWPby8rKUnJyci6kpKQwALz99tupBQUF\nrZHaraio8MyaNat7j9GBAwcumjFjRqtVeX/aOnDgQIrX621JTjbuElZUVHi8Xm+rVTsAkJaW1nXo\n0KHqq6666k933313zlNPPZVpVhb83j377LNjrr/++klTpkwp3Lp165j777//+IkTJ8qXLl0aklB1\n4cIFuv766/O+853vfPa9730v5LXc3Nx2AJg4cWLH9ddff/btt9++yJbjgrBz6OJlRFRCRFUA/hFG\nJlmWjbbfAzCZiL5ERMNgBDxhhzX6T7YeDeDtqEY+iJkbVW2FOj+mE6m5Rpi+9Fh37dW/pB+qmeU4\nEe3tea/1+td5e32BCxv26tFiC6Dbv0Ak/6r3sbm9OjdQhxIYz9/D2B/Ul2acWf2+GT9+fPsbb7wx\norPTWPR49913PV1dXXj//fc9Xq+3NVDv0KFDntmzZ7cCwHvvvZdaUFDQsn//fs+xY8eGt7a20rlz\n55Iee+yxCStXrjwZqd309PSO6upqDwC8+eabqa+99tqYK664osWqvD9tlZWVeaZNm9YdJFVVVaXO\nmjWrxaodAKisrBw+YsSIruXLlzctWLDgXFtbW5JZWfB719TUNGTVqlUn6urqqrdv337krrvuauq9\netTV1YVbb701Jz8/v62kpORk8Guff/55UlNTU1Lgeu/evSOmT5/eiiixszHslzBWbRYws+3d5czc\nQUQ/hJF9NgTAi8xcTUQ/B7CfmQOB0XcBbGNmq1togipMJ9LzYdWCJ1L1WT/O4wtc2LC3JOifvglc\noZ5TTPrXWXt9iJSMEG5vfPsX6J3sq19iJtb0ydIA/MT/6E/9vleH7r333tP79u1Ly8vL86akpHRN\nmTKl9dVXX/24srLSM3v27PMA0NLSQm1tbUmZmZmdAFBeXu6ZNm1aa3l5eeqiRYvOFBcXT21ra0t6\n4IEHjs+fP/98pHaXL1/+2YIFCyZPnTq1IC8vry0tLa1j5syZbdnZ2e1m5f1pa9OmTelz5szp/gOq\nra31FBUVtRYVFbWatQMAJSUl40tLSy9OTU3tzM/Pb9u6deuRO+64I6d3WfB7t3PnztEvvPBCSKo8\nAKxZs6ZhyZIlZwHgjTfeuPiVV15Jnzx5cuvUqVMLAOCRRx5puOWWW87V19cn33jjjV8GgM7OTlq0\naNGZm2++OepvIH0GQ8z8Z9E2GvSzrwN4vVfZw72el/S3/bgkJrK6eogXrS4AUWuxKfkWHRNZXaHE\ng39/DWCezZ8R/zpITGSxhZKePrHjzBlyNLW+rzoXX3wx7969+6Pe5Zs2baoPXKempnJDQ0Nl4PmT\nTz55olf1sMUGq3bHjx/fUVlZechueX/aev755+uDn9fX13eP3awdANixY8cRO2XB/PGPf6yN9DoA\nfO1rX/sTM5vKExQUFHxx+PDhg2avRYPlLwwR/YqZFxNRJUI3PhMAZubpA+1c6EXIxDIawCoA97g2\nHEDnROq8vT5E/0EJ6NLqCrbXrW/SPXpsev3rjL0+RLOZGQjYK/51iOA9eJsB49aSm/YaqD4TSIhP\nIu0Z+pH//28A+GbQI/BccJKwLBhzjTB9GEG4Pq0u5+2N7oMy9EuH+iwnc40wffRoSOn3rzP2Ru/f\nnt8t8e8AMU1GcNNeQRgYlsEQMx/3X/4VMx8NfgD4Kz3DSxBMJ5ZQzSx9Wl1AsMaQvonUWhOtv0S3\nYhCuqaQ2y8laM0unf5URhb0D6WKurZriX8cxTUbQoAEnCIqwk1p/rUnZQqcHktBYanX1aAzp1epS\nrIlmw14nmGu7pgYNOBv26vevs8En0Ndmdbf967y93Yh/oUXzThAUEWnP0D0wVoDyiKgi6KU02BRq\nFWxiqdUFBA4w06vVpUYTzRe4sGGvXjRowNmwV79/ndXM8sHOZnU3/atQIywW/Nu9eTvR/CsIAyfS\njvutAH4L4y8r+CjkZmb+TOmoEo0BaRU7REgWjPMaQz5El9WlHMX2xhya/BvtZnVlJKJ/uzdvJ4B/\nBcFhIu0ZOsfMRwD8XwCfBe0XaieiOboGKBjHwuvV6nJWE80Hu1psgBZNJcX2RkePvfr964xmlg/R\nbGZ207+KNcJM0ejf7m8bztsbU/4VBAXY2TP0zwCCBdHOQ8smCyFYQ0evVpezmmj2J1INmlka7LVP\nqL36/evMLY3B41/dt3A0+7d7xdV5e2PGv4KgCDvBEAWfDs3MXbB3crUwIEI1s/RpdQHBGj1OZL7Y\nm0itNdEcw4a9wajN+rHWzFKb5QQ4rZkVvX8V6nWJf6FCE22urVq5/v8V67HFIbt27Uq74YYbBrSB\nYPHixTnbtm0LEze1Ku9PW24yceLEafn5+QVTp04t8Hq9lwW/tn379hG5ubne7Oxs789+9rNx/Wnf\nTjD0ERHdR0RD/Y8fATA9fVJwEnONMEexzOoCAro9Tmh1zbVVKzbsDaA+66cvjaSB4QtcRLTXeK/1\n+1eh/pwNewHxrxo0+NcGEzImzCCiIqceEzImzFA95gMHDnhmzpzZ0ndNa6qrq1MDOmR2yvvTltvs\n27evtqam5mBVVVX3idkdHR1YuXJl9uuvv15bW1tbvWPHjjGlpaUp0bZtJxi6G8D/AtAAQ4l+DoDl\n0XYkRMt5GBpDCk9zjZjVZRA4qVf9hBob9gK6RC3V2euD3c3qRt9x41/Alr3iX4fp3qyuwb82OH7m\nePJIjMR6rMfePv6tx3r0Vff4meO27oTU1NQMmz9/fp7X671s2rRpl5WXlw8HgIULF05aunRpdlFR\n0ZQJEyZM27Nnz8U33nhjbm5urnfx4sU5AFBRUZHa0NAwdPr06VOzsrKm7dq1K62vdisqKoYXFRVN\nyc/PL3jkkUfGNjY2Ds3Ly2u3Ku9PW9dee23efffdN6GoqGhKRkbGjFdeeSUtUjsA8PTTT6cXFhZe\nlp+fX1BUVDTFqizAzTffnPv444+PraqqGo5+4PP5LsrJyblQUFDwRUpKCt90002fbd++fVS07fQZ\nDDHzKWa+lZnHMvMlzHwbM5/qz6AFG8SYNlmwdIGSCTXG7I0XLbZo5KLixr82Ef86SAxqkwH2T853\nan67cOECLVu2LHfjxo31VVVVhx588MFPH3300fEAUFNT45k0adKF0tLSw4sXLz6zYsWK3A0bNjTU\n1NRU7969e3RraytVV1enpqWldVVUVNRs3Ljx6Jo1ayZEare9vR233377pLVr1x6rra09WFdXl5KX\nl9dmVd6ftgBDnHXUqFGdpaWlh9etW3f05ZdfTo9ka1NTU9KGDRvGlZaW1tTW1h7cs2fPB2ZlIb4q\nKTne1dWFe+65Jzs/P79g2bJll+7YsWNES0tLmELu/PnzJxcWFl62du3a7s1px44dGzZx4sTuFMqs\nrKwvGhoahkXrw0jnDD3AzH9PRE8jVJsMAMDM90XbmdAHMTGxhGuE6dFycotQe90RtWx2pHkf7Gxm\nDtfM0u9fZ+y1R7i98e1foLdGmDLx4TBtsgA6/WtONP7t/f7053djy5Yto+rq6jyLFi3KAwz19Nmz\nZze3tLRQc3Nz8kMPPXQSADweT9eSJUsac3Jy2gEgJSWlKykpCWfPnh3yxBNPHAeAOXPmtDQ1NSVH\nanfz5s2jJ0+e3Dpv3rwWACgsLGz1eDxdVuX9aau5uTmpubl5yMMPP3wSANrb22nkyJGdVu0AQHJy\nMtra2pJWrFiRdeedd565+uqrW5qbm5N6lwW/d16v94LX6z21atWqUzt37hyxevXqrJdeeinzxRdf\n/Gjp0qVnA/Xeeuutmtzc3PaGhobkr371q/mFhYVtCxcu/FPQluZuiCi8sA8irQwF7snthyE20/sh\nOIlpFgyg/6021wjTk+UEuG2vPi22AKGaWQPB3geluWaWXv/q1NsLtze+/QtE8q9jPrZMRjC3V9++\npf4x0BWisrIyz+rVqxtqamoO1tTUHKyrq6vesmXLJ6WlpZ7CwsLzQ4YMAQBUVlamXnnllecB4MMP\nPxw6duzY9rKyspScnJwLKSkpDABvv/12akFBQWukdisqKjyzZs3qDioOHDhw0YwZM1qtyvvT1oED\nB1K8Xm9LcrKxZlJRUeHxer2tVu0AQFpaWtehQ4eqr7rqqj/dfffdOU899VSmWVnwe/fss8+Ouf76\n6ydNmTKlcOvWrWPuv//+4ydOnCgPDoQAIDc3tx0AJk6c2HH99defffvtty8CgOzs7JCVoPr6+mET\nJkxoj9aHkc4Zes3//2azR7QdCRGwnFg0aAyFYa0Rpj6LrcdefVpsofYq+aAEtPjX3geltWaW+iwn\nc3v1+Vfh31LM+BeI5F/HgqEo/ategmTgDCQgGj9+fPsbb7wxorOzEwDw7rvverq6uvD+++97vF5v\na6DeoUOHPLNnz24FgPfeey+1oKCgZf/+/Z5jx44Nb21tpXPnziU99thjE1auXHkyUrvp6ekd1dXV\nHgB48803U1977bUxV1xxRYtVeX/aKisr80ybNq07SKqqqkqdNWtWi1U7AFBZWTl8xIgRXcuXL29a\nsGDBuba2tiSzsuD3rqmpaciqVatO1NXVVW/fvv3IXXfd1ZSRkdEZXOfzzz9PampqSgpc7927d8T0\n6dNbAeCaa645f+TIkZSampphbW1t9Jvf/GbMokWLQgIpO0S6TfYaTG6PBWDmb0XbmWCBZZaTW8c5\nBTSGctU0b8Pe4G+x6idSxfYCfdh7Gk6czzLXds3YsDfwgTOY/esLXAwS/zoW8Ebp32A5oVimv7cU\n77333tP79u1Ly8vL86akpHRNmTKl9dVXX/24srLSM3v27PMA0NLSQm1tbUmZmZmdAFBeXu6ZNm1a\na3l5eeqiRYvOFBcXT21ra0t64IEHjs+fP/98pHaXL1/+2YIFCyZPnTq1IC8vry0tLa1j5syZbdnZ\n2e1m5f1pa9OmTelz5sw5H7CxtrbWU1RU1FpUVNRq1g4AlJSUjC8tLb04NTW1Mz8/v23r1q1H7rjj\njpzeZcHv3c6dO0e/8MILY3u/p2vWrGlYsmTJWQCor69PvvHGG78MGLflFi1adObmm2/+HACGDh2K\ndevWfXLdddfld3Z24rbbbjtdXFzcFoXbARhnCJm/QHSN//ImAOMAvOx//l0AR5j5Z9F25gTFxcW8\nf//gPceCiLojTB/8R9t/D6ETSwlgxKGjYSzzA3uxN6SdeZgHK9/ZHUfY33p3v921BtxvsL1Gi7Bl\nb7B+U+B6wP2W9KrU3W9PLaff58BYQvru7jdgL4f1PdD3GTCxubvfkBoa+w21N4AT/o3cb0gNx+z1\nwYgJTlv2q9m/H8O/d4eDa6j5Gy7pVakEAEbByr8zMdOpv6VSZg5ZfiovLz8yY8aM7uXdCRkTZtjN\nALPDJaMv6Tjx2Ylyp9oT3KO8vDxjxowZuWavWf7CMPM+ACCiR5n56qCXXiOiPzg7xMTDBzvpsRo1\nlWIiqytO7bVE7FWGxqwua60uzfa6nowQGxpwn57+VAIXIWrsnDOUSUSTAk+I6EsAMiPUF/rAB7vp\nsZo0s0wnUjc0hnrsLYFOLTbAXXsVa2bFqL2D1b8+2N3MrNm/IZu3Y8u/sb6BWhDsBEMrAfiIyEdE\nPgB74a6y5aDHflaIBs0s04nULY2hHnsDWV16spzctlehZlYM2ztY/Rv9368m/3avuFrbqzYosfbv\nYNhALSQ2dg5d3A1gMoAf+R9TmHmP6oHFM/azQpzXGArBdCK11ghT/+2ux159Wmyh9ur9BqvYv0Af\n9ob2rdO/wajNYnNeEy1m/n4Bi2SEXP//4f5VG5RY+1dxMNTV1dUVdkCfIATj/x3psnq9z2CIiFIB\n/ATAD5m5HEA2EX3DuSEmHnOjqq1Q58d0IjXXCNOXHuuuvfqX9EM1sxwnor0977Ve/zpvry9wYcNe\nPVpsAXT7F4jkX/U+NrdX2ZEVBlWNjY0jJSASrOjq6qLGxsaRAKqs6tjZcf9LGCdpXel/Xg/jY2XX\ngEcouIvpRHo+rFrwRDoY0mN74wtc2LB3oCfR9g+Fek4x6V9n7fUhUjJCuL3x7V/A0AjrQb/EjF59\nso6OjrtOnDjx/IkTJ7ywt/VDSDy6AFR1dHTcZVXBTjCUx8y3ENF3AYCZW4lIInBVxERWVw/xotUF\nIGotNiXfomMiqyuUePBv5KyuUMS/DhIDWWxFRUWnAMi5d8KAsBNFf0FEHvgPryCiPAAX7DRORNcR\n0WEi+oCITHcDE9FiIjpIRNVEtNX2yOORkIllNAyNIXd1fvRNpM7b60P0opaALq2uWPCv0Xc8+Heu\nrZ8w+hb/OkTYZnW37RWE/mMnGFoDYDeAS4loC4DfA3igrx8ioiEAfgFgIYACAN8looJedSbDUFG8\nipkLkchZamETi7lGmD4MXSN9E6nz9kb3QRmqiaY+y8lcI0wfPRpS+v3rjL3R+7fnd0v8O0BMkxHc\ntFcQBkbEYMh/O6wGxinUfwHg3wAUM7PPRtuzAXzAzB8x8xcAtgH4dq86fwngF8zcBADMfCqq0ccL\nphNLqGaWPq0uIFhjSN9Eaq2J1l+iWzEI11RSm+VkrZml07/KiMLegXQx11ZN8a/jmCYjaNCAEwRF\nRAyG2Dg7/RVmPsPM/8HMu5jZ7ifWRADHgp7X+8uCyQeQT0RvEdE7RHSd7ZHHE5ZaXQGNoZ4sJz1Z\nP4o10WzY6wRzbdfUoAFnw179/nU2+AT62qzutn+dt7cb8S+c9q8g6MTOBup3iOgKZn4vyrbNNln3\nFqZJhnGG0VwAWQDeJCIvM4cozhLRcgDLASA7OzvKYQwCTCeWAMYBZsGZPsG6XWp4UkmrvsCFDXv1\nosbeEGzYq9+/6Y626oOdzepu+tdZe0OIBf92b95ONP8KwsCxs2doHoyA6EMiqiCiSiKqsPFz9QAu\nDXqeBeBTkzqvMnM7M38M4DCM4CgEZn6OmYuZuTgzMw6VQGxkOSknJAvGeY0hH6LL6lKOYntjDk3+\ndV0eK0Ai+rf7zU8A/wqCw9hZGVrYz7bfAzDZr2XWAOBWALf1qvMKgO8C+BciyoBx2+yjfvYXpxga\nQwGtrsC1o4Slx/4YwBOONe9DNOnPGjSVFNsbHT326vevM5pZPkSzmdlN/yrWCDNFo3+/A79qvfP2\nxpR/BUEBlitDRJRCRD+Gcfr0dQAamPlo4NFXw8zcAeCHAPYAOATgV8xcTUQ/J6LAmRB7AJwhooMw\nNM9+wszy19RNj8aQXq0uZzXR7E+kGjSzNNhrn1B79fvXmVsag8e/um/haPZv94qr8/bGjH8FQRGR\nbpNza10AAB6WSURBVJNthpEWUAljdWhdtI0z8+vMnM/Mecz8uL/sYWbe6b9mZv5rZi5g5mnMvK0f\nNsQpoZpZ+rS6gGCNIScyX+xNpNaaaI5hw95g1Gb9WGtmqc1yApzWzIrevwr1usS/UKGJNtdWrVz/\n/4r12ARBAZGCoQJmXsLMzwK4GcCfaxqTAMBKI8xRLLO6gICukRNaXXNt1YoNewOoz/ox1whzCl/g\nIqK9xnut378K9eds2AuIf9Wgwb+CoIhIwVB74MJ/y0vQynkYGkMKdX4iZnUZBE7qVT+hxoa9gC5R\nS3X2+mB3s7rRd9z4F7Blr/jXYbo3q2vwryAoIlIwNIOIPvc/mgFMD1wTkcKv7wlOjGmTBUsXKJlQ\nY8zeeNFiiybrJ278axPxr4PEgDaZIDiBZTDEzEOYeYT/kcbMyUHXbmlExDcxMbGEayrp0XJyi1B7\n3RG1dEbPyQc7m5ljwb869avC7Y1v/wKR/OtoQGS6WR0QfTJhMGLnnCFBB5YTizM6P9mXXAIi6n5Y\nY64RpifLCdCvaxRqrz4ttgChmlkDwd4Hpblmll7/6vwuFW5vfPsXiORfx3xsmYxgbq++fUuC0D8k\nGIoFLCcW5zSGjp06hb0wjgDvfQx4KNYaYeqz2Hrs1afFFmqvkg9KQLl/A13M7bOWtWaW+iwnc3v1\n+VehZlbM+BeI5F/HgqEo/ategkQQBoYEQ7GAZZaTs5pZc23XVKwxZMNevVpsGjSVItrrjIbUXNs1\nY8PeePCvL3AxSPzrWMAbpX8lGBJiHTsnUAuK8AUuLLOcNGhmWaLwgDob9urVYgOUH8gX0V6FmlmW\nuG/vYPevD31ldWn2b8jm7djyr7IVV0FwCFkZcgkf7KTHatRUiomsrji11xKxVxkxkdWl2V7XkxES\nQANOiFskGHIBH+ymx/5Y9VAMTCdSN1RReuwNaLGtx3pNWU7O2Js7blzIRvXIm9UD9irWzIo5/xr2\nDkb/AtFkdWn2b8jm7djyr2ygFmIdCYZcwH5WiAbNLNOJ1C2NoR579WqxOWfv0ZMnkQHY3KyuQTMr\nJv1r2DsY/Qv05+9Xk3+7V1yt7VUblFj7V/YMCbGOBEMuYD8rxHmNoRBMJ1JrjTD13+567NWnxRZq\nrz4tNkC5f4E+7A3tW6d/g1Gbxea8Jlps+xeI5F+1QYm1fyUYEmIdCYZcYG5UtRXq/JhOpOYaYfrS\nY921V58WW4BQzSzHiWhvz3ut17/O2+sLXNiwN779C0Tyr3ofm9srG6iFWEeCoUTGdCIN11Qa7Omx\nvsCFDXv1abEFo1DPKSb966y9PkRKRgi3N779C0Tyrx4JEtEnEwYfEgzFGjGR1dVDvGh1AUg8LTab\nxIN/Y0qrK8ZwR2JGEAYXEgzFEiETS7imkhvom0idt9eH6D8oAV1aXbHg3x49tsHu37m2fsLoW/zr\nEGGb1d22VxD6jwRDsULYxGKuEaYPQ9dI30TqvL3RfVCGaqKpz3Iy1wjTR4+GlH7/OmNv9P7t+d0S\n/w4Q02QEN+0VhIEhwVAsYDqxhGpm6dPqAoI1hvRNpNaaaP0luhWDcE0ltVlO1ppZOv2rjCjsHUgX\nc23VFP86jmkyggYNOEFQhARDsYClVlePxpBerS5nNdHCsGGvE8y1XVOxvYAte/X719ngE+hrs7rb\n/nXe3m7Ev9CieScIihBtsljAUqsLCBxgplerS40mmi9wYcNevWjQgLNhr37/OquZ5YOdzepu+leh\nRlgs+Ld783ai+VcQBo6sDMUCNrKclBOSBeO8xpAP0WV1KUexvTGHJv/GTGJRIvq3+81PAP8KgsNI\nMBTzGBpDerW6nNVE8yGaiVSDppJie6Ojx179/nVGM8uHaDYzu+lfxRphpmj0b/e3DeftjSn/CoIC\nJBiKaXo0hvRqdTmriWZ/ItWgmaXBXvuE2qvfv87c0hg8/tV9C0ezf7tXXJ23N2b8KwiKkGAoZgnV\nzNKn1QUEawzp0+qy1kRzDBv2BqM268daM0ttlhPgtGZW9P5VqNcl/oUKTbS5tmrl+v9XrMcmCApQ\nGgwR0XVEdJiIPiCisK/fRPQXRNRIRGX+x10qxzO4MNcIcxTLrC4goGukT6srNuwNoD7rx1wjzCl8\ngYuI9hrvtX7/KtSfs2EvIP5Vgwb/CoIilAVDRDQEwC8ALARQAOC7RFRgUvXfmXmm//G8qvEMPsI1\nlZzCF7iImNVloE+rS5293diwF9Cl1aXWv/Y2q/foscWFfwFb9op/HaZ7s7oG/wqCIlSuDM0G8AEz\nf8TMXwDYBuDbCvuLD0SrSx0JpMUmWl3WiH8dRLTJhDhBZTA0EcCxoOf1/rLeLCKiCiLaTkSXKhxP\n7KN4YvHBzkQarqmkR8vJLULtdUfU0hk9Jx/sbGaOBf/q1K8Ktze+/QtE8q+jAZHpZnVA9MmEwYjK\nYIhMyrjX89cA5DLzdAD/CWCzaUNEy4loPxHtb2xsdGRwuePGgYi6H0OGDwl5TkRISkoJeT48aXhY\nnexx2Y6Mx3picU7nx95Eaq4RpifLCdCvaxRqrz4ttgChmlkDIXr/9rzXev2rU28v3N749i8Qyb+O\n+dgyGcHcXn37lgShf6gMhuoBBK/0ZAH4NLgCM59h5gv+p5tgsfOOmZ9j5mJmLs7MzHRkcEdPngQD\n3Y+uL7qAEoQ8jKH1aAx9wV9gb69/x04eM2k9SiwnFmc1huxNpNYaYeqz2Hrs1afFFmqvkg9KIEb9\nG9q3+iwnc3v1+VehZlbM+BeI5F/HgqEo/ategkQQBobKYOg9AJOJ6EtENAzArQB2BlcgovFBT78F\n4JDC8QwAxZo7lllOzmpmzbVd03179WqxqbPXF7iIaK8zGlJzbdfUoCFlw17xb3TMtV3T2l7HAt4o\n/SvBkBDrKNMmY+YOIvohgD0AhgB4kZmriejnAPYz804A9xHRtwB0APgMwF+oGs/Acf7ANl/gwjLL\nSYNmliUKD6izYa9eLTZAlX8jb1bXoJllieIDCG3YK/51mJDN27HlX2UrroLgEEqFWpn5dQCv9yp7\nOOh6FYBVKsfQb2Iiq0ujplJMZHXFj70+9OzxmGdZK37stUf82OtDDPrX9WSEBNCAE+IWOYHaDNOJ\nxTnNHR/spsdq0sxSbK99euzVq8UGqPLv3Ig1NWlmxZx/DXvFvw5hunk7tvwrG6iFWEeCod6YTizO\nau7YzwrRoJmlwV779NirV4vNbf8qvKURk/417BX/OoBpMoK1vWqDEmv/yp4hIdaRYCgY04klVDNL\nn1YXoEJjKAQb9gaj/ttdj736tNji2L9AH/aG9q3Tv8GozWJzXhMttv0LRPKv2qDE2r8SDAmxjgRD\nwZhOLKGaWfq0ugIo1PmxYW8Afemx7trrjn8V6rFFtLfnvdbrX+ft9QUubNgb3/4FIvlXvY/N7ZUN\n1EKsI8EQ+srqCtUY0qfVpQEb9gI6J1I1+AIXMetfhXpOMelfZ+31IVIyQri98e1fIJJ/9UiQiD6Z\nMPhI+GDIB9HqikS8aHUBSDz/2iQe/BtTWl0xhjsSM4IwuEjoYMiH6CdSQJeWU7imkhvom0idt9cH\n8W9kevTYBrt/59r6CaNv8a9DhG1Wd9teQeg/CR0MRTeRhmpmqc+CMdcI04dhr76J1Hl7B49/deux\nAcEaUvr964y90fu353dL/DtATJMR3LRXEAZGQgdD0X2jDNfcUZvlFKqZpU+rCwi2V99Eaq2J1l9i\nxr+BwVjaG9q3Tv8qIwp7B9LFXFs1xb+OY5qMoEEDThAUkdDB0FzbNZ3VCAvDUqurR2NIr1aX+/Y6\nwVzbNRXbC9iyV79/nQ0+gb42q7vtX+ft7Ub8Cy2ad4KgCKVyHPGDYo0wS60uIHCAmV6tLjX2+gIX\nNuzViwYNOBv26vevs5pZPtjZrO6mfxVqhMWCf7s3byeafwVh4BAzuz2GqCCiRhhHrKoiA0q/Qkq/\nMdC39Cv9Sr+Ds98cZs7U3KeQAAy6YEg1RLSfmbXf9E60ft3sW/qVfqXfwduvIKggofcMCYIgCIIg\nSDAkCIIgCEJCI8FQOM9Jv3Hft/Qr/Uq/g7dfQXAc2TMkCIIgCEJCIytDgiAIgiAkNBIMCYIgCIKQ\n0EgwlMAQ0UgiKnB7HIIgCILgJhIMJRhE9HsiGkFEowFUAthKRP/H7XElCkQ03aV+l7vRrxC/ENG3\n3B6DIDiFBEMAiKiYiP4fER0gogoiqiSiCrfHpYgxzPw5gJsAbGbmmQC+5uaAiOgbLvWb4UK3P3ah\nTwAg5R0QzSaiK/zXBUT010T0dQ39jiOicf7rTCK6iYgKVfdrMo5/1d2nv98nNPRxU6/HIgDPBZ6r\n7l8QVCPaZAZbAPwExkpJl8tjUU0yEWXCkBl62O3B+LkCwC4X+n0ZwHUqGiYiAjCOmY8HlzPz91X0\n1xfM/KzK9oloDYCFMH6/3gAwB4ac1U+J6HJmflxRvysA/NS4pL8D8BcAqgE8SUR/z8wvKOp3Z+8i\nAPOIaBQAMLOSVRMi2mjS7x1EdLG/3/tU9AvgVwB2AziFnsD6IgDfBMAAfqOoX0HQgqTWAyCi/2bm\nr7g9Dh0Q0a0wgqD/ZublRDQJwHpm/rbLQ4s7iKiUmYs09vfXkV5n5n9Q2HclgJkAhgM4ASCLmT8n\nIg+A/2FmJbcH/f3OAeCBoVn4ZWY+4b8NvNe/8qmi3wMADgJ4HkYwQAD+DcCtAMDM+xT1Ww8jyPwd\neoKStQD+xt/vZkX9XgHgKQDbATzDzExEHzOzpWSrIAwmZGXIYA0RPQ/g9wAuBAqZOe6+7TDzNgDb\ngp5/BEBbIGSxpH4OQCUzn1LU5xAAB5h5hor2I/AuEc1i5gOa+kvT1I8ZHczcCaCFiD7034oFM7cS\nkcrV1nZmbgnq94S/3yYiUvlNrxjAjwCsBvATZi4jolZVQVAQlwF4FMaK5k+YuYGI1qgKggIw83tE\ndC2AewH8FxH9LYwgUBDiAgmGDJYBmApgKHpuk8XV0i8RrUeEyYuZI64qOMidAK4EsNf/fC6AdwDk\nE9HPmfklpztk5k4iOkhEE5m5wen2e0NEyczcAeArAP6SiD4EcB7GN3lm5lkq+mXmR1S0a5MviCjV\nH5h0r4YR0UiovfXcRURDmbkdwPVB/aZA4Z5IZu4CsJ6Ifu3//yQ0zKfM3Azgx0RUBOBlIvoPaNr7\n6bf5/xLRdgDrdfQpCLqQYMhgBjNPc3sQiqny//9nALww9gAAwM0A3tM4ji4AlzHzSQAgoksA/DOM\nWx1/AOB4MOQnA8AhInobRmACAGBmFZs/3wUwC8ANCtruE38gcCeAQgApgXLF+5WuZuYL/n6Cg5+h\nAL6nsN+b4A/ymbk+qDwdwP0K+0VQn98housBfK66v6B+S4noqwD+CsB/6+jTv9cwB8AHzLxYR5+C\noAvZMwSAiDbB2Ddz0O2xqIaI/gvA1/zfpEFEwwDsZuavauq/Mjjw9G80rmRmLxG9z8yXK+p3vlk5\nM/9eQV/K7LDZ/68B1AC4DcDPAdwO4BAz/0hhnykA7gbwZRiJCC/4V8eUQ0Q3BPpl5j2a+gy2twLA\niy7YW8HMv9PU510AngDwIYAvAVjOzL03kQvCoEWCIQBEdAhAHoCPYewZCtzOcOVMGJUQ0WEAc5j5\nrP/5KBgbXKdo6v+fAGQD+LW/6GYAx2Bk8+1i5nkO9/c7Zl7gZJs2+qwHYLlZWeVGZn//7zPz5URU\nwczTiWgogD0qA14i+ncA7QDehJFVdlRl8BXU7z/BWAH7I4D5AF5j5kc19Nvb3iPMrPzYBBftrQIw\nj5kb/UkXW5j5StX9CoIu5DaZgZL0ajOI6DVE3ruj+iCz/wOgjIj+0//8qwAeU9xnMD+AcWvjKzCC\nzs0AdrARlTsaCPnJVNBmXwwBcDE0nO1jQbv//7NE5IWR3ZWruM+CwIofEb0A41ahDq6GcZu7k4hS\nYQQnyoMDJJ69XzBzI2AkXRDRcA19CoI2JBgCwMxHiWgGgD/3F73JzOWKulurqF1bMPPzRPRbGHuH\nAPz/9u4+1rKqPuP49ylMBYaCIoMvUCBMoS3QDLQMClaKKdRAbROoTSTQKirQFESgTYMWOoTENGmb\nCmItUtSi6R9qcaYOIExNUbQFKS8KQrXIe1KqwMiLM7zN8PSPtc+dy+UyEnrXXmf2fj7JzczeJ3PX\nb92bc9Zv9lrrt1jRx6LiWe1b0k3A47a/2n2gbw88WanJHTdXFK7SjsGHbJ9f4fu+XJd0W8vPAb5M\n+fmeW7nNSQKG7Q1l9rMXz3a72LC9Xv01PLb+7janxtELrivWN4roRabJAEkfBE5i0+6xY4BLbF/U\nLqp6uh0+S3nh4tr/6Kntk4CTKZWwl0ram1K3ZN41PQvQ3qPAvzD/UxrXWFTces1QC5I2smlhuih1\nf9azacp5h0rtrgd+MKvdpd111anuEfZ3s4vga2/tj6gtyRCgcvTGIbbXddeLgetrfLB0ReLm+6H3\nsk5J0nspu2x2pSx0XQ7cYPvwmu3Oav/bwMGUdUoHdvdesKh6gdu7pdZW9s20uZPttX22OVaS9tjc\n67bv7yuWPoytvxF9yTRZIWDjrOuN1Fvv0eQcrlnOpBSMu972W1XOcDqnx/afsf3s5Om+pK2pW7yt\n93U7SYT6M7bBf1r6K+lDtv+ydRwRCyUHtRafAb4l6TxJ51GKAFY508j2/fN9AbsBf1ajzTmetv0U\nlG31tu+gFJzsy9clfRjYtqto+0VgdcX2/qDi955K8y1uHcuCV0mXt46hTw37+/uN2o2oIskQM1ud\nTwTWAj8GTrR9Qe12JR0g6a8k3UfZ0fW92m0CD3Xb6VcD13Qfpj/sod2Js4GHKVN0pwBXUfHJlO3v\nzr4eyWB5/cu8N0R7tQ6gZ2Prb0QVo54mk7TTrMv7uq+Z12pMd0jah3KY43HAo8DnKWu3amwrf5FZ\nW/fP7QoR7ghc2UfbXfvPS1oFrJps1e3ZYAcPSa+nrAXbVtKBbJoi3AHYrllglUnaffJXYJGkn+/+\nju0HmgVWSav+SrqXTYfSvkHSPWxa6zjY91WMw6gXUM95c+9OeSok4NXAA65wIrPKoZXfAN5n+wfd\nvXtqf5hIWmx7naR5d7m4O1izYvsCVgCnUX7Gk3VaF9Xehj5n8LiSUiRvcINlt+PnPZQ1YTfNeukJ\n4LJKZQSak3Qtm97HB1GOl5kM0r1UVu/TNPR3jDsmY9hGnQxNSLoY+LLtq7rro4AjbC/42UaSjqE8\nGToUuJpygvylNRKvOe1+xfZRkh5k0wfpzJ+2d9/sN/j/t38mcDSljP+93b29KOeSXW272sGP0zB4\n9EnS79kew3Tgi4xtkG7V37H9nGP4kgwBkm62/Wtz7t1k+6CKbS6mHOR5HKUK9GXASlc8a6h7OvMG\n2/9Tq43NtH0rcKTtR+bcXwKs6euDdQwf4t102UeAN3YJ8L6U0hFVNgVMkzH8fmdrmAx93PZpfbcb\nUUsWUBePSDpH0p6S9pD055T1PNXYXmf7n2y/g7KT7NuUxcU12zR1d25tzqK5iRBAt25oUYN4huwz\nwDXAG7vr/waqn5s1JS5sHUDPmvQ3iVAMTZKh4jjKGVYrgVXALt29Xthea/uTPU3Z3Cip1yKEnWdf\n4WsLbQyD5c62vwA8D+W4CF5YR2uwbP9j6xj61Gd/JR0saXn3930lnSXp6L7aj6hp1LvJJrpdY9VP\n2G5J0tbdoPjrwEmS7qYcJzBZO1M7QVomab5F2mLWsSC1jWSwXCfptXTFLCW9GXi8bUj9k/QO21e0\njqMvNfsraQVl48HWkv4VeBPwNeBsSQfa/kiNdiP6kmSIme3uf0o52XvmZzKwxbU3Ar9KWafUO9tb\ntWh3cwY8WJ5FOaB1qaR/pzz1fGfbkJpYDgzx9/tSavb3ncABwKuA/wV2s/2EpL8GvkVZoxaxxUoy\nVHwRuBi4lOFOJ0y2kt/dOpApMsjB0vYtkn4D+EXK7/37tp/7Kf9scGyvaB1Dnyr3d4PtjcB6SXdP\nSnHYfqorFxKxRUsyVGyw/fetg6hsiaSzXurFrgr3qAx5sOymRO9oHUdfJB07z+3Hgdtt/6jveGpr\n0N9nJW1nez0ws/NW0o50a9MitmRJhorVkv6YsoD6mcnNgR24uRWwPQ0OLp0GYxssR+h9wCHAtd31\n4ZQzBveRdL7tz7UKrJK++3uY7WegVJGfdX8R8O4Fbiuid6kzxEwl6rkGVWJe0i09LJKeWpKu5CUG\nD2CIg+WoSFoNvN/2D7vr11EKer4fuM72/i3jW2h991fSNsAfAb9AOVfwU93Tx4hByJMhoHb15ykx\nyidCszwP/PI8g8ebgOuAwSRDXZXzf7P9eHf9auBw26vaRlbVnpPfbedHwD6210oa4nqpvvt7GfAc\n5Siho4B9GfgO3BiXJEMdSftT3uAz27xtf7ZdRAvuN1sH0NiYBssVtldOLmw/1m2NHnIy9A1JV1A2\nQ0DZ/XRdV+n9sXZhVdN3f/e1/SsAkj5F2Z0aMRhJhpipoXE4JRm6ivI/n28Cg0mGBrb+6ZUY02A5\nXzHVob/XTwWOpdTREuVJxuVd1fW3tQyskr77O/MfBtsbysk+EcORNUOApNuBZcCttpd1UyiX2v6d\nxqHFAunOZZs9eHyTTYPHoEj6NCXB+ztK4cUPAK+x/Z6WcdUmaQ9gb9tflbQdsJXtJ1vHVUuf/ZW0\nkVKkFcr7Z1tgPZuKtu5Qo92Ivgz9f4sv19O2n5e0QdIOlCmUwSyejvJpLekm4PFZg8f2wBAHyw8A\n5wKf767XAOe0C6c+SScBJwM7AUuBXSm1wwY5Pdx3f6exaGrEQhp9MtQ9MbitW2T6D8DNwE/InPig\njGmwtL2OckzC9rZ/0jqenpwKHEyphoztuyTt0jakqsbW34iqRn9QazdNcoDtx2xfDBwJvNv2iY1D\ni4V1KvAWYFI59y7KgbyDI+lQSXcCd3bXyyR9onFYtT1je+bAX0lb053NNlBj629EVaNPhjo3TE5j\ntn2f7dtaBxQLbkyDx0eBtwOPAtj+DnBY04jq+7qkDwPbSjqSslB+deOYahpbfyOqSjJUvA24XtLd\nkm6TdLukJETDMqrBw/aDc24N9cy9ibOBhykFAU+h7Aod8jqpsfU3oqrsJmNmV8aL2L6/71iiDkk/\nQznC4LcoO2CuoewYHNwbQNI/A38LfBx4M3A6cJDtdzUNrDJJSwBsP9w6lj6Mrb8RNSUZitEYy+Ah\naWfgQuAISuK3Bvig7UebBlZBtwFiBXAapa+iPAW7yPb5LWOrYWz9jehLpsli0FScJ+kR4HvA9yU9\nLOkvWsdWkWwfb/t1tnexfcIQE6HOGZSF8cttv9b2TpQjVt4i6cy2oVUxtv5G9CJPhmLQugHiaOBk\n2/d29/ainEt2te2PtoyvBkl3AfdS6gxdbntoFbZnSLoVONL2I3PuLwHW2D6wTWR1jK2/EX3Jk6EY\nuj8EjpskQgC27wFO6F4bHNt7UxbT7gfcIukKSSc0DquWRXMTA5iZCl3UIJ7axtbfiF4kGYqhG+Xg\nYftG22dRCvOtpZxdNUTPvsLXtlRj629EL0ZfgToGb3SDR3ekzDHAuyjVtldSkqIhWibpiXnuC9im\n72B6MLb+RvQia4Zi0OYcMPmCl4BtbA/u6ZCke4FVwBdsX986noiIaZdkKGJgJKk7mPbnKCfOjOV8\nsoiIVyRrhiKGZ79u19F3gTsl3Sxp/9ZBRURMqyRDEcNzCXCW7T1s7w78SXcvIiLmkWQoYngW2752\ncmH7a8DiduFEREy37CaLGJ57JJ0LfK67PoFShDEiIuaRJ0MRw/NeYAnwpe5rZ+DEphFFREyx7CaL\nGBhJe9q+b8695bb/s1FIERFTLU+GIobnS5J2nVxIOgz4dMN4IiKmWpKhiOE5BVgl6fWSjgY+Rjms\nNiIi5pFpsogBknQI8EngaeC3u7PYIiJiHkmGIgZC0mpg9ht6X+Ah4McAtn+3RVwREdMuW+sjhuNv\nWgcQEbElypOhiIGRtBh4yvbzkvYBfgn4iu3nGocWETGVkgxFDIykm4G3Aq8BbgBuAtbbPr5pYBER\nUyq7ySKGR7bXA8cCF9k+BtivcUwREVMryVDE8KjbTXY8cGV3b6uG8URETLUkQxHDcwbwIWCl7Tsk\n7QVc+1P+TUTEaGXNUMRASVpse13rOCIipl2eDEUMjKRDJN0J/Fd3vUzSJxqHFRExtZIMRQzPBcDb\ngUcBbH8HOKxpRBERUyzJUMQA2X5wzq2NTQKJiNgCpAJ1xPA8KOlQwJJ+FjidbsosIiJeLAuoIwZG\n0s7AhcARgIA1wOm21zYNLCJiSiUZihgBSWfYvqB1HBER0yjJUMQISHrA9u6t44iImEZZQB0xDmod\nQETEtEoyFDEOeQQcEfESspssYiAkPcn8SY+AbXsOJyJii5E1QxERETFqmSaLiIiIUUsyFBEREaOW\nZCgiIiJGLclQREREjFqSoYiIiBi1JEMRERExav8H4Qeq+pHldtsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13b820550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_set_names = [\\\n",
    "                     \"All   \",\n",
    "                     \"Triads\",    \n",
    "                     \"Deg.\",   \n",
    "                     \"Deg.+Tr.\", \n",
    "                    \"Tr.\",\n",
    "                     \"Leskovec et al.\",    \n",
    "                     \"P3\",\n",
    "                     \"Deg.+P3\",\n",
    "                     \"P4\",\n",
    "                     \"Deg.+P4\",\n",
    "                     \"P3+P4\"\n",
    "                    ]\n",
    "\n",
    "\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline \n",
    "index = np.arange(len(feature_set_names) +1) # for random\n",
    "bar_width = 0.15\n",
    "\n",
    "rects1 = plt.bar(index, height=scores, width=bar_width, color='r', edgecolor='black', hatch=\"/\", label='$embeddedness \\geq 0$')\n",
    "rects2 = plt.bar(index + bar_width, scores_e10, bar_width, color='g',edgecolor='black', hatch=\"//\", label='$embeddedness \\geq 10$')\n",
    "rects3 = plt.bar(index +2*bar_width, scores_e25, bar_width, color='b', edgecolor='black', hatch=\"O\",label='$embeddedness \\geq 25$')\n",
    "rects4 = plt.bar(index +3*bar_width, scores_e50, bar_width, color='m', edgecolor='black', hatch=\"x\",label='$embeddedness \\geq 50$')\n",
    "\n",
    "plt.xticks( index + bar_width, ['random'] + feature_set_names, rotation='vertical')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.tight_layout()\n",
    "plt.ylim(0.45, 1)\n",
    "plt.ylabel(\"Predictive accuracy\")\n",
    "#plt.xlabel(\"Sets of features\")\n",
    "plt.title(\"Wikipedia\")\n",
    "plt.savefig(\"wikipedia_logistic_regression_compare_features.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SzpSIeHOF/Z0KrAxcBEzO+1gEWAfYFngzcERE/K6NstckfYHtQPpSn1Eo+0Xg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1460d6690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_of_iterations = 10\n",
    "weights = np.zeros(X_all.shape[1])\n",
    "s = 0\n",
    "for _ in range(num_of_iterations):\n",
    "    lg = LogisticRegression(n_jobs=-2)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_all, y)\n",
    "    #ss = StandardScaler()\n",
    "    #X_train = ss.fit_transform(X_train)\n",
    "    lg.fit(X_train, y_train)\n",
    "    weights += lg.coef_[0]\n",
    "    #s += lg.score(ss.transform(X_test), y_test)\n",
    "weights /= num_of_iterations\n",
    "s/num_of_iterations\n",
    "\n",
    "f_names_sorted, weights_sorted = zip(*sorted((list(zip(feature_names, weights))), key=itemgetter(1), reverse=True))\n",
    "x_plot = plt.arange(len(f_names_sorted))\n",
    "plt.bar(x_plot, weights_sorted, label='$embeddedness \\geq 0$')\n",
    "plt.xticks(x_plot, f_names_sorted, rotation='vertical')\n",
    "plt.ylabel(\"Log. Reg. Coefficients\")\n",
    "\n",
    "plt.title(\"Wikipedia \\n $Embeddedness \\geq 0$\")\n",
    "plt.savefig(\"wikipedia_logistic_regression_coefficients.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Equal-to-Embeddedness experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_zero, y_zero = [], []\n",
    "for edge in g.edges():\n",
    "    e = (edge.source().__hash__(), edge.target().__hash__())\n",
    "    if common_neighbors[e] > 0:\n",
    "        continue\n",
    "    if sum(triad_features[e]) > 0:\n",
    "        continue\n",
    "    features = triad_features[e][:]\n",
    "    features.extend( degree_features[e] )\n",
    "    features.extend( [common_neighbors[e]] )\n",
    "    try:\n",
    "        features.extend( counts_3[e] )\n",
    "    except KeyError:\n",
    "        features.extend( [0, 0] )\n",
    "    try:\n",
    "        features.extend( counts_4[e] )\n",
    "    except KeyError:\n",
    "        features.extend( [0, 0] )\n",
    "    \n",
    "    X_zero.append( features[:] )\n",
    "    y_zero.append( g.edge_properties['weight'][e] )\n",
    "X_zero = np.array(X_zero)\n",
    "y_zero = np.array(y_zero)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = X_zero.copy()\n",
    "X_all     = X_zero.copy() \n",
    "X_triads  = np.take(X, range( 0, 16), axis=1)\n",
    "X_deg      = np.take(X, range(16, 22), axis=1)    \n",
    "X_deg_tr   = np.take(X, range(16, 23), axis=1)    \n",
    "X_leskovec     = np.take(X, range( 0, 23), axis=1) \n",
    "X_paths_3       = np.take(X, range(23, 25), axis=1) \n",
    "X_degrees_paths_3       = np.take(X, range(16, 22)+range(23, 25), axis=1)   \n",
    "X_paths_4       = np.take(X, range(25, 27), axis=1)  \n",
    "X_degrees_paths_4       = np.take(X, range(16, 22)+range(25, 27), axis=1)  \n",
    "X_p3_p4  = np.take(X, range(23, 27), axis=1)  \n",
    "\n",
    "\n",
    "feature_set_names = [\\\n",
    "                     \"X_all   \",\n",
    "                     \"X_triads\",    \n",
    "                     \"X_deg\",   \n",
    "                     \"X_deg_tr \", \n",
    "                    \"X_triangles\",\n",
    "                     \"X_leskovec\",    \n",
    "                     \"X_paths_3\",\n",
    "                     \"X_degrees_paths_3\",\n",
    "                     \"X_paths_4\",\n",
    "                     \"X_degrees_paths_4\",\n",
    "                     \"X_p3_p4\"\n",
    "                    ]\n",
    "feature_sets = [\\\n",
    "                     X_all   ,\n",
    "                     X_triads,    \n",
    "                     X_deg,\n",
    "                     X_deg_tr ,     \n",
    "                     X_triangles, \n",
    "                     X_leskovec   ,    \n",
    "                     X_paths_3     ,\n",
    "                     X_degrees_paths_3,\n",
    "                     X_paths_4,\n",
    "                     X_degrees_paths_4,\n",
    "                     X_p3_p4\n",
    "                    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_all   \t0.80978042364509384\n",
      "X_triads\t0.5\n",
      "X_deg\t0.80140631102534066\n",
      "X_deg_tr \t0.80140631102534066\n",
      "X_triangles\t0.47745419509729592\n",
      "X_leskovec\t0.80140631102534066\n",
      "X_paths_3\t0.72349731201323308\n",
      "X_degrees_paths_3\t0.80879520182874987\n",
      "X_paths_4\t0.69701285983412586\n",
      "X_degrees_paths_4\t0.8052338203873457\n",
      "X_p3_p4\t0.73558704941760289\n"
     ]
    }
   ],
   "source": [
    "possible = np.where(y_zero == 1)[0]\n",
    "negatives = np.where(y_zero ==  -1)[0]\n",
    "positives = np.random.choice(possible, size=len(negatives))\n",
    "balanced = np.concatenate([positives, negatives])\n",
    "\n",
    "scores_zero = [0.5]\n",
    "for x, name in zip(feature_sets, feature_set_names):\n",
    "    score = np.mean(cross_val_score(LogisticRegression(), x[balanced], y_zero[balanced], n_jobs=-1, cv=10))\n",
    "    print(name+\"\\t\"+repr(score))\n",
    "    scores_zero.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gZQCSnkHZ721hz6WPiIjo0Eti+yZwX9vxA9W5UVWtuv2AucDVlNGPV0o6RFJrma4PAu+S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\nEQMliS0iIgbK/wd9tMHEePV3/AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1312b3e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = np.arange(len(feature_set_names) +1) # for random\n",
    "bar_width = 0.35\n",
    "rects0 = plt.bar(index, height=scores_zero, width=bar_width, color='blue', edgecolor='black')\n",
    "\n",
    "plt.xticks( index + bar_width, ['random'] + feature_set_names, rotation='vertical')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.tight_layout()\n",
    "plt.ylim(0.45, 1)\n",
    "plt.ylabel(\"Predictive accuracy\")\n",
    "plt.title(\"Wikipedia\\n Embeddedness = 0 \")\n",
    "plt.savefig(\"wikipedia_logistic_regression_embed_0.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('pos 3 path', 0.096749467261956365),\n",
       " ('d+in(v)', 0.026817924754469639),\n",
       " ('pos 4 path', 0.026216621255857636),\n",
       " ('d+out(u)', 0.010384958551824166),\n",
       " ('t1', 0.0),\n",
       " ('t2', 0.0),\n",
       " ('t3', 0.0),\n",
       " ('t4', 0.0),\n",
       " ('t5', 0.0),\n",
       " ('t6', 0.0),\n",
       " ('t7', 0.0),\n",
       " ('t8', 0.0),\n",
       " ('t9', 0.0),\n",
       " ('t10', 0.0),\n",
       " ('t11', 0.0),\n",
       " ('t12', 0.0),\n",
       " ('t13', 0.0),\n",
       " ('t14', 0.0),\n",
       " ('t15', 0.0),\n",
       " ('t16', 0.0),\n",
       " ('C(u,v)', 0.0),\n",
       " ('dout(u)', -0.0060748537538082188),\n",
       " ('din(v)', -0.0076945456058722453),\n",
       " ('d-out(u)', -0.016459812305222483),\n",
       " ('d-in(v)', -0.03451247036123048),\n",
       " ('neg 4 path', -0.041078187029212523),\n",
       " ('neg 3 path', -0.062303814501209684)]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#average coefficeints after 10 runs\n",
    "\n",
    "\n",
    "weights = np.zeros(X_all.shape[1])\n",
    "for _ in range(10):\n",
    "    lg = LogisticRegression(n_jobs=-2)\n",
    "    ss = StandardScaler()\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_all, y_zero)\n",
    "    lg.fit(ss.fit_transform(X_train), y_train)\n",
    "    lg.fit(X_train, y_train)\n",
    "    weights += lg.coef_[0]\n",
    "weights /= 10\n",
    "\n",
    "\n",
    "f_names_sorted, weights_sorted = zip(*sorted((list(zip(feature_names, weights))), key=itemgetter(1), reverse=True))\n",
    "list(zip(f_names_sorted, weights_sorted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pGVAi4qsAklaKiOc7XyUzMxuK2lkP5S2S7ibNt4WkrSX9uOM1MzOzIaWdTvlTgD2BJwHy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\nvNKuOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1312b30d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_plot = plt.arange(len(f_names_sorted))\n",
    "plt.bar(x_plot, weights_sorted, label='$embeddedness = 0$')\n",
    "plt.ylabel(\"Log. Reg. Coefficients\")\n",
    "plt.xticks(x_plot, f_names_sorted, rotation='vertical')\n",
    "plt.title(\"Wikipedia \\n $Embeddedness = 0$\")\n",
    "\n",
    "plt.savefig(\"wikipedia_logistic_regression_coefficients.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Ucidata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = \"ucidata.txt\"\n",
    "g = gt.load_graph_from_csv(\"./data/{}\".format(filename), directed=True, eprop_types=['int'], eprop_names=['weight'], string_vals=True)\n",
    "g.is_directed()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "triad_features  = pickle.load(open(\"./data/ucidata.txt_triad_features.pk\", 'rb'))\n",
    "degree_features = pickle.load(open(\"./data/ucidata.txt_degree_features.pk\", 'rb'))\n",
    "common_neighbors = pickle.load(open(\"./data/ucidata.txt_common_neighbors.pk\", 'rb'))\n",
    "counts_3 = pickle.load(open(\"./data/ucidata.txt_paths_of_length_3.pk\", 'rb'))\n",
    "counts_4 = pickle.load(open(\"./data/ucidata.txt_paths_of_length_4.pk\", 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(0, 1): 5,\n",
       " (0, 2): 3,\n",
       " (0, 3): 1,\n",
       " (0, 4): 3,\n",
       " (0, 5): 4,\n",
       " (0, 11): 3,\n",
       " (0, 14): 4,\n",
       " (0, 15): 5,\n",
       " (1, 2): 2,\n",
       " (1, 4): 4,\n",
       " (1, 5): 4,\n",
       " (1, 8): 4,\n",
       " (1, 9): 2,\n",
       " (1, 14): 5,\n",
       " (1, 15): 4,\n",
       " (2, 3): 2,\n",
       " (2, 5): 4,\n",
       " (2, 6): 2,\n",
       " (2, 7): 3,\n",
       " (3, 7): 1,\n",
       " (4, 6): 0,\n",
       " (4, 8): 2,\n",
       " (4, 13): 1,\n",
       " (4, 14): 4,\n",
       " (4, 15): 4,\n",
       " (5, 6): 5,\n",
       " (5, 7): 4,\n",
       " (5, 8): 3,\n",
       " (5, 10): 6,\n",
       " (5, 11): 5,\n",
       " (5, 12): 4,\n",
       " (5, 15): 5,\n",
       " (6, 7): 4,\n",
       " (6, 10): 4,\n",
       " (6, 11): 3,\n",
       " (6, 12): 2,\n",
       " (7, 10): 3,\n",
       " (7, 11): 4,\n",
       " (7, 13): 1,\n",
       " (8, 9): 4,\n",
       " (8, 10): 4,\n",
       " (8, 12): 4,\n",
       " (8, 14): 5,\n",
       " (9, 10): 3,\n",
       " (9, 12): 3,\n",
       " (9, 14): 4,\n",
       " (10, 11): 5,\n",
       " (10, 12): 6,\n",
       " (10, 14): 5,\n",
       " (10, 15): 4,\n",
       " (11, 13): 2,\n",
       " (11, 14): 3,\n",
       " (11, 15): 5,\n",
       " (12, 13): 1,\n",
       " (12, 14): 4,\n",
       " (12, 15): 4,\n",
       " (13, 15): 3,\n",
       " (14, 15): 6}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, y = [], []\n",
    "for edge in g.edges():\n",
    "    e = (edge.source().__hash__(), edge.target().__hash__())\n",
    "    features = ( triad_features[e][:]) #0..15 \n",
    "    features.extend( degree_features[e][:] ) #16,17,18,19,20,21\n",
    "    features.extend( [common_neighbors[e]] ) #22 \n",
    "    try:\n",
    "        features.extend( counts_3[e] ) #23 24 \n",
    "    except KeyError:\n",
    "        features.extend( [0, 0] )\n",
    "    try:\n",
    "        features.extend( counts_4[e] ) #25 26\n",
    "    except KeyError:\n",
    "        features.extend( [0, 0] )    \n",
    "    X.append( features[:] )\n",
    "    y.append( g.edge_properties['weight'][e] )\n",
    "X = np.array(X)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_all     = X.copy()\n",
    "X_triads  = np.take(X, range( 0, 16), axis=1)\n",
    "X_deg      = np.take(X, range(16, 22), axis=1)    \n",
    "X_deg_tr   = np.take(X, range(16, 23), axis=1)    \n",
    "X_triangles   = np.take(X, range(23, 24), axis=1)  \n",
    "X_leskovec     = np.take(X, range( 0, 23), axis=1)  \n",
    "X_paths_3       = np.take(X, range(23, 25), axis=1)  \n",
    "X_degrees_paths_3       = np.take(X, range(16, 22)+range(23, 25), axis=1)  \n",
    "X_paths_4       = np.take(X, range(25, 27), axis=1)  \n",
    "X_degrees_paths_4       = np.take(X, range(16, 22)+range(25, 27), axis=1)  \n",
    "X_p3_p4  = np.take(X, range(23, 27), axis=1)  \n",
    "\n",
    "\n",
    "feature_set_names = [\\\n",
    "                     \"X_all   \",\n",
    "                     \"X_triads\",    \n",
    "                     \"X_deg\",   \n",
    "                     \"X_deg_tr \", \n",
    "                    \"X_triangles\",\n",
    "                     \"X_leskovec\",    \n",
    "                     \"X_paths_3\",\n",
    "                     \"X_degrees_paths_3\",\n",
    "                     \"X_paths_4\",\n",
    "                     \"X_degrees_paths_4\",\n",
    "                     \"X_p3_p4\"\n",
    "                    ]\n",
    "feature_sets = [\\\n",
    "                     X_all   ,\n",
    "                     X_triads,    \n",
    "                     X_deg,\n",
    "                     X_deg_tr ,     \n",
    "                     X_triangles, \n",
    "                     X_leskovec   ,    \n",
    "                     X_paths_3     ,\n",
    "                     X_degrees_paths_3,\n",
    "                     X_paths_4,\n",
    "                     X_degrees_paths_4,\n",
    "                     X_p3_p4\n",
    "                    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_names = ['t1', 't2','t3','t4','t5','t6','t7','t8','t9','t10','t11',\\\n",
    "                 't12','t13','t14','t15','t16',\\\n",
    "                 'd+in(v)', 'd-in(v)', 'd+out(u)', 'd-out(u)', 'din(v)', 'dout(u)', 'C(u,v)',\\\n",
    "                 'neg 3 path', 'pos 3 path',\\\n",
    "                'neg 4 path', 'pos 4 path']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "positive_candidates = np.where(y == 1)[0]\n",
    "negative_candidates = np.where(y ==  -1)[0]\n",
    "if( len(positive_candidates) >  len(negative_candidates) ):\n",
    "    positives = np.random.choice(positive_candidates, size=len(negative_candidates))\n",
    "    negatives = negative_candidates\n",
    "else:\n",
    "    positives = positive_candidates\n",
    "    negatives = np.random.choice(negative_candidates, size=len(positive_candidates))\n",
    "    \n",
    "balanced = np.concatenate([positives, negatives])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29, 29)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(positives), len(negatives)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_all   \t0.96666666666666679\n",
      "X_triads\t0.98333333333333339\n",
      "X_deg\t0.76666666666666661\n",
      "X_deg_tr \t0.75\n",
      "X_triangles\t0.79166666666666674\n",
      "X_leskovec\t0.98333333333333339\n",
      "X_paths_3\t0.80833333333333324\n",
      "X_degrees_paths_3\t0.86666666666666681\n",
      "X_paths_4\t0.62499999999999989\n",
      "X_degrees_paths_4\t0.77500000000000002\n",
      "X_p3_p4\t0.77500000000000013\n"
     ]
    }
   ],
   "source": [
    "scores = [0.5]\n",
    "for x, name in zip(feature_sets, feature_set_names):\n",
    "    score = np.mean(cross_val_score(LogisticRegression(), x[balanced], y[balanced], n_jobs=-1, cv=10))\n",
    "    print(name+\"\\t\"+repr(score))\n",
    "    scores.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jWyTpEEmrVa9DgUV1FywiImIqeklsB1Lmi/wry7rsH1BnoSIiIqZqwsRm+wbb+9p+uO0N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\nyts0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146bd1a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "index = np.arange(len(feature_set_names) +1) # for random\n",
    "bar_width = 0.35\n",
    "rects0 = plt.bar(index, height=scores, width=bar_width, color='blue', edgecolor='black')\n",
    "\n",
    "plt.xticks( index + bar_width, ['random'] + feature_set_names, rotation='vertical')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.tight_layout()\n",
    "plt.ylim(0.45, 1)\n",
    "plt.ylabel(\"Predictive accuracy\")\n",
    "plt.title(\"UCIdata\\n Embeddedness = 0 \")\n",
    "plt.savefig(\"uci_logistic_regression_embed_0.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_zero, y_zero = [], []\n",
    "for edge in g.edges():\n",
    "    e = (edge.source().__hash__(), edge.target().__hash__())\n",
    "    if common_neighbors[e] > 0:\n",
    "        continue\n",
    "    if sum(triad_features[e]) > 0:\n",
    "        continue\n",
    "    features = triad_features[e][:]\n",
    "    features.extend( degree_features[e] )\n",
    "    features.extend( [common_neighbors[e]] )\n",
    "    try:\n",
    "        features.extend( counts_3[e] )\n",
    "    except KeyError:\n",
    "        features.extend( [0, 0] )\n",
    "    try:\n",
    "        features.extend( counts_4[e] )\n",
    "    except KeyError:\n",
    "        features.extend( [0, 0] )\n",
    "    \n",
    "    X_zero.append( features[:] )\n",
    "    y_zero.append( g.edge_properties['weight'][e] )\n",
    "X_zero = np.array(X_zero)\n",
    "y_zero = np.array(y_zero)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = X_zero.copy()\n",
    "X_all     = X_zero.copy() \n",
    "X_triads  = np.take(X, range( 0, 16), axis=1)\n",
    "X_deg      = np.take(X, range(16, 22), axis=1)    \n",
    "X_deg_tr   = np.take(X, range(16, 23), axis=1)    \n",
    "X_leskovec     = np.take(X, range( 0, 23), axis=1) \n",
    "X_paths_3       = np.take(X, range(23, 25), axis=1) \n",
    "X_degrees_paths_3       = np.take(X, range(16, 22)+range(23, 25), axis=1)   \n",
    "X_paths_4       = np.take(X, range(25, 27), axis=1)  \n",
    "X_degrees_paths_4       = np.take(X, range(16, 22)+range(25, 27), axis=1)  \n",
    "X_p3_p4  = np.take(X, range(23, 27), axis=1)  \n",
    "\n",
    "\n",
    "feature_set_names = [\\\n",
    "                     \"X_all   \",\n",
    "                     \"X_triads\",    \n",
    "                     \"X_deg\",   \n",
    "                     \"X_deg_tr \", \n",
    "                    \"X_triangles\",\n",
    "                     \"X_leskovec\",    \n",
    "                     \"X_paths_3\",\n",
    "                     \"X_degrees_paths_3\",\n",
    "                     \"X_paths_4\",\n",
    "                     \"X_degrees_paths_4\",\n",
    "                     \"X_p3_p4\"\n",
    "                    ]\n",
    "feature_sets = [\\\n",
    "                     X_all   ,\n",
    "                     X_triads,    \n",
    "                     X_deg,\n",
    "                     X_deg_tr ,     \n",
    "                     X_triangles, \n",
    "                     X_leskovec   ,    \n",
    "                     X_paths_3     ,\n",
    "                     X_degrees_paths_3,\n",
    "                     X_paths_4,\n",
    "                     X_degrees_paths_4,\n",
    "                     X_p3_p4\n",
    "                    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "29\n",
      "Does not make sense to perform this experiment for the ucidata since it is small\n"
     ]
    }
   ],
   "source": [
    "possible = np.where(y_zero == 1)[0]\n",
    "negatives = np.where(y_zero ==  -1)[0]\n",
    "print (len(negatives))\n",
    "print (len(positives))\n",
    "positives = np.random.choice(possible, size=len(negatives))\n",
    "balanced = np.concatenate([positives, negatives])\n",
    "print \"Does not make sense to perform this experiment for the ucidata since it is small\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ERExEL4ltX8p8kX+jdAjZnqqHYkRExFTTywDtWygdPyIiIqa8Ma/YJH1H0lpt24+V9O16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\nGChJbBERMVD+P1bPhQCBrOc/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13faf7710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "index = np.arange(len(feature_set_names) +1) # for random\n",
    "bar_width = 0.35\n",
    "rects0 = plt.bar(index, height=scores_zero, width=bar_width, color='blue', edgecolor='black')\n",
    "\n",
    "plt.xticks( index + bar_width, ['random'] + feature_set_names, rotation='vertical')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.tight_layout()\n",
    "plt.ylim(0.45, 1)\n",
    "plt.ylabel(\"Predictive accuracy\")\n",
    "plt.title(\"UCIdata\\n Embeddedness = 0 \")\n",
    "plt.savefig(\"uci_logistic_regression_embed_0.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}