TiesandStrengths.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json, csv, gzip\n",
    "import pandas as pd\n",
    "from collections import defaultdict\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_auths = pd.read_csv('./high.csv', header=0)\n",
    "high_authors = set(high_auths['author'].tolist())\n",
    "\n",
    "low_auths = pd.read_csv('./low.csv', header=0)\n",
    "low_authors = set(low_auths['author'].tolist())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read first CSV to initiate dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "frame1 = pd.read_csv('./ties/2016_01.csv', header=None, names=['author', 'subreddit', 'name', 'parent_id'])\n",
    "frame = frame1.loc[frame1['author']!='AutoModerator']\n",
    "nameset = set(frame['name'].tolist())\n",
    "parentset = set(frame['parent_id'].tolist())\n",
    "ids = nameset.intersection(parentset)\n",
    "Fcomments = frame[['author', 'name']]\n",
    "Frelation = frame[['name', 'parent_id']]\n",
    "comments = Fcomments.set_index('name').to_dict()['author']\n",
    "relation = Frelation.set_index('parent_id').to_dict()['name']\n",
    "#parent = Fparent.set_index('name').to_dict()['author']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#testcell\n",
    "d = {1: \"one\", 2: \"three\"}\n",
    "d1 = {3: \"two\"}\n",
    "\n",
    "# updates the value of key 2\n",
    "d.update(d1)\n",
    "print(d)\n",
    "\n",
    "d1 = {3: \"three\"}\n",
    "\n",
    "# adds element with key 3\n",
    "d.update(d1)\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read CSV as pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary sizes:  367166 327819\n",
      "Dictionary sizes:  681224 608739\n",
      "Dictionary sizes:  995899 890565\n",
      "Dictionary sizes:  1302516 1165255\n",
      "Dictionary sizes:  1602691 1433832\n",
      "Dictionary sizes:  1912384 1712754\n",
      "Dictionary sizes:  2297993 2060240\n",
      "Dictionary sizes:  2760972 2468101\n",
      "Dictionary sizes:  3250439 2899131\n",
      "Dictionary sizes:  3877674 3440326\n",
      "Dictionary sizes:  4680504 4121558\n"
     ]
    }
   ],
   "source": [
    "#comments = dict()\n",
    "#relation = dict()\n",
    "\n",
    "years = [2016]\n",
    "for year in years:\n",
    "    for month in range(2,13):\n",
    "        print \"Dictionary sizes: \", len(comments), len(relation)\n",
    "        fname = './ties/' + str(year) + '_' + str('{:02d}'.format(month)) + '.csv'\n",
    "        frame1 = pd.read_csv(fname, header=None, names=['author', 'subreddit', 'name', 'parent_id'])\n",
    "        frame = frame1.loc[frame1['author']!='AutoModerator']\n",
    "        nameset = set(frame['name'].tolist())\n",
    "        parentset = set(frame['parent_id'].tolist())\n",
    "\n",
    "        ids = ids.union(nameset.intersection(parentset))\n",
    "        Fcomments = frame[['author', 'name']]\n",
    "        Frelation = frame[['name', 'parent_id']]\n",
    "        comments.update(Fcomments.set_index('name').to_dict()['author'])\n",
    "        relation.update(Frelation.set_index('parent_id').to_dict()['name'])\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary sizes:  5646736 4925025\n",
      "Dictionary sizes:  6686796 5804848\n",
      "Dictionary sizes:  7837350 6770010\n",
      "Dictionary sizes:  8854297 7612693\n",
      "Dictionary sizes:  9834377 8435969\n",
      "Dictionary sizes:  10815811 9264346\n",
      "Dictionary sizes:  11863538 10137755\n",
      "Dictionary sizes:  12913986 11031139\n",
      "Dictionary sizes:  13479839 11556235\n",
      "Dictionary sizes:  14765500 12616296\n",
      "Dictionary sizes:  16010966 13696333\n",
      "Dictionary sizes:  17338485 14805090\n"
     ]
    }
   ],
   "source": [
    "years = [2017]\n",
    "for year in years:\n",
    "    for month in range(1,13):\n",
    "        print \"Dictionary sizes: \", len(comments), len(relation)\n",
    "        fname = './ties/' + str(year) + '_' + str('{:02d}'.format(month)) + '.csv'\n",
    "        frame1 = pd.read_csv(fname, header=None, names=['author', 'subreddit', 'name', 'parent_id'])\n",
    "        frame = frame1.loc[frame1['author']!='AutoModerator']\n",
    "        nameset = set(frame['name'].tolist())\n",
    "        parentset = set(frame['parent_id'].tolist())\n",
    "        ids = ids.union(nameset.intersection(parentset))\n",
    "        #ids = nameset.intersection(parentset)\n",
    "        Fcomments = frame[['author', 'name']]\n",
    "        Frelation = frame[['name', 'parent_id']]\n",
    "        comments.update(Fcomments.set_index('name').to_dict()['author'])\n",
    "        relation.update(Frelation.set_index('parent_id').to_dict()['name'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "D = nx.DiGraph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ids = comments.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "edgelist = []\n",
    "for interaction_id in ids:\n",
    "    replier = comments[interaction_id]\n",
    "    author = comments[relation[interaction_id]]\n",
    "    #edgelist.append((author, replier))\n",
    "    if replier in high_authors or author in high_authors:\n",
    "        D.add_edge(replier, author, color='red')\n",
    "    elif replier in low_authors or author in low_authors:\n",
    "        D.add_edge(replier, author, color='green')\n",
    "    else:\n",
    "        D.add_edge(replier, author, color='blue')\n",
    "        \n",
    "    #print author, ', ', replier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#H = nx.DiGraph(G)\n",
    "from networkx.drawing.nx_agraph import graphviz_layout\n",
    "import holoviews as hv\n",
    "\n",
    "#hv.extension('bokeh')\n",
    "\n",
    "#%opts Graph [width=1000 height=1000]\n",
    "'''\n",
    "a_names = list(G.nodes)\n",
    "#G.nodes[a_names[0]]['color'] = 'blue'\n",
    "\n",
    "for name in a_names:\n",
    "    if name in high_authors:\n",
    "        G.nodes[name]['color'] = 'blue'\n",
    "        #print \"high\"\n",
    "    elif name in low_authors:\n",
    "        G.nodes[name]['color'] = 'green'\n",
    "    else:\n",
    "        G.nodes[name]['color'] = 'red'\n",
    "        G.nodes[name]['alpha'] = 1\n",
    "        G.nodes[name]['size'] = 1\n",
    "        \n",
    "color_map = []\n",
    "Gnodes = list(G.nodes)\n",
    "for i in range(len(Gnodes)):\n",
    "    if Gnodes[i] in high_authors:\n",
    "        \n",
    "        G.nodes[i]['color'] = 'blue'\n",
    "    elif Gnodes[i] in low_authors:\n",
    "        #n = G.get_node(node)\n",
    "        G.nodes[i]['color'] = 'green'\n",
    "    else:\n",
    "        #n = G.get_node(node)\n",
    "        G.nodes[i]['color'] = 'grey'\n",
    "\n",
    "'''\n",
    "\n",
    "\n",
    "'''\n",
    "'''\n",
    "padding = dict(x=(-1.2, 1.2), y=(-1.2, 1.2))\n",
    "\n",
    "hv.Graph.from_networkx(D, nx.layout.random_layout)#.redim.range(**padding)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(1,figsize=(100,100)) \n",
    "#%matplotlib\n",
    "nx.draw(D,with_labels = True, node_size=10,font_size=6, alpha=0.6)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from networkx.algorithms.reciprocity import reciprocity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_network = set(high_authors).union(set(D.nodes).difference(set(low_authors)))\n",
    "low_network = set(low_authors).union(set(D.nodes).difference(set(high_authors)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_recp = reciprocity(D, high_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "low_recp = reciprocity(D, low_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "h_recp = []\n",
    "l_recp = []\n",
    "for author in high_authors:\n",
    "    try:\n",
    "        h_recp.append(high_recp[author])\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "for author in low_authors:\n",
    "    try:\n",
    "        l_recp.append(low_recp[author])\n",
    "    except:\n",
    "        pass\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f29a0c87a90>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "sb.set(style=\"whitegrid\")\n",
    "sb.set(rc={'figure.figsize':(15,5)})\n",
    "sb.distplot(h_recp, kde=False, rug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f28072bbb90>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set(style=\"whitegrid\")\n",
    "sb.set(rc={'figure.figsize':(15,5)})\n",
    "sb.distplot(l_recp, kde=False, rug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.545314543787\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print np.mean(h_recp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.513271035379\n"
     ]
    }
   ],
   "source": [
    "print np.mean(l_recp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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