mit_exp_debug.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating data\n",
      "start_date 2008-10-01 00:00:00  len(df_current)  1926\n",
      "new interval is 2008-10-01 00:00:00 2008-11-01 00:00:00\n",
      "start_date 2008-11-01 00:00:00  len(df_current)  1241\n",
      "new interval is 2008-11-01 00:00:00 2008-12-01 00:00:00\n",
      "start_date 2008-12-01 00:00:00  len(df_current)  1525\n",
      "new interval is 2008-12-01 00:00:00 2009-01-01 00:00:00\n",
      "start_date 2009-01-01 00:00:00  len(df_current)  1944\n",
      "new interval is 2009-01-01 00:00:00 2009-02-01 00:00:00\n",
      "start_date 2009-02-01 00:00:00  len(df_current)  1024\n",
      "new interval is 2009-02-01 00:00:00 2009-03-01 00:00:00\n",
      "start_date 2009-03-01 00:00:00  len(df_current)  1068\n",
      "new interval is 2009-03-01 00:00:00 2009-04-01 00:00:00\n",
      "start_date 2009-04-01 00:00:00  len(df_current)  730\n",
      "new interval is 2009-04-01 00:00:00 2009-05-01 00:00:00\n",
      "start_date 2009-05-01 00:00:00  len(df_current)  741\n",
      "new interval is 2009-05-01 00:00:00 2009-06-01 00:00:00\n",
      "start_date 2009-06-01 00:00:00  len(df_current)  369\n",
      "new interval is 2009-06-01 00:00:00 2009-07-01 00:00:00\n",
      "start_date 2009-07-01 00:00:00  len(df_current)  0\n",
      "beta_nodes {65, 2, 3, 68, 69, 5, 10, 13, 14, 24, 26, 27, 30, 32, 35, 36, 38, 39, 45, 46, 53, 55, 60}\n",
      "Generating data\n",
      "start_date 2008-10-01 00:00:00  len(df_current)  1926\n",
      "new interval is 2008-10-01 00:00:00 2008-11-01 00:00:00\n",
      "start_date 2008-11-01 00:00:00  len(df_current)  1241\n",
      "new interval is 2008-11-01 00:00:00 2008-12-01 00:00:00\n",
      "start_date 2008-12-01 00:00:00  len(df_current)  1525\n",
      "new interval is 2008-12-01 00:00:00 2009-01-01 00:00:00\n",
      "start_date 2009-01-01 00:00:00  len(df_current)  1944\n",
      "new interval is 2009-01-01 00:00:00 2009-02-01 00:00:00\n",
      "start_date 2009-02-01 00:00:00  len(df_current)  1024\n",
      "new interval is 2009-02-01 00:00:00 2009-03-01 00:00:00\n",
      "start_date 2009-03-01 00:00:00  len(df_current)  1068\n",
      "new interval is 2009-03-01 00:00:00 2009-04-01 00:00:00\n",
      "start_date 2009-04-01 00:00:00  len(df_current)  730\n",
      "new interval is 2009-04-01 00:00:00 2009-05-01 00:00:00\n",
      "start_date 2009-05-01 00:00:00  len(df_current)  741\n",
      "new interval is 2009-05-01 00:00:00 2009-06-01 00:00:00\n",
      "start_date 2009-06-01 00:00:00  len(df_current)  369\n",
      "new interval is 2009-06-01 00:00:00 2009-07-01 00:00:00\n",
      "start_date 2009-07-01 00:00:00  len(df_current)  0\n"
     ]
    }
   ],
   "source": [
    "run -i real_data_experiment.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[27], [], [25, 33], [], [24, 32, 43], [17, 24, 41, 43]]\n"
     ]
    }
   ],
   "source": [
    "isolated = []\n",
    "for G in GT:\n",
    "    isolated.append([x for x in nx.isolates(G)])\n",
    "print(isolated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "{1: {}, 38: {}}\n"
     ]
    }
   ],
   "source": [
    "print(len(GT[0][1]))\n",
    "print(GT[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[27]\n"
     ]
    }
   ],
   "source": [
    "isolated1 = []\n",
    "for nd in GT[0].nodes():\n",
    "    if len(GT[0][nd]) < 1:\n",
    "        isolated1.append(nd)\n",
    "print(isolated1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i=0\n",
    "nx.draw(GT[i], \n",
    "        pos=nx.spring_layout(GT[i]), \n",
    "        with_labels=True, \n",
    "        node_color=[ x[1]['group'][0] * 1.0 / 2 for x in GT[i].nodes(data=True) ])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#temp delete\n",
    "from collections import Counter\n",
    "nodecounts = []\n",
    "edgecounts = []\n",
    "for G in GT:\n",
    "    nodecounts.append(len(G.nodes()))\n",
    "    edgecounts.append(len(G.edges()))\n",
    "    gtrue = {x[0]:x[1]['group'][0] for x in GT[0].nodes(data=True)} #only works for fixed group\n",
    "    community_sizes = Counter([gtrue[i] for i in gtrue])\n",
    "glog = {'gtrue':gtrue, 'nodecounts': nodecounts, 'edgecounts': edgecounts, 'community_sizes': community_sizes}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[46, 46, 46, 46, 46, 46]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glog['nodecounts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[82, 68, 67, 68, 61, 55]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glog['edgecounts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GT[5].nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}