Compare/UA_CompareUVIC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WALK\t: Web Archives for Longitudinal Research\n",
    "\n",
    "\"WALK will enable present and future humanities and social science scholars to access, interpret and curate born-digital primary resources that document our recent past.\"\n",
    "\n",
    "This is a demo/tutorial for the **University of Victoria** on our up-to-now explorations on Web Archives.\n",
    "\n",
    "Over the summer, I have had the pleasure of working with Dr. Ian Milligan and Nick Ruest, building a tool for conducting analytics on the Web Archive collections. This has involved working with [Warcbase](http://lintool.github.io/warcbase-docs/) in order to extract important data from the collections and using Python including the pandas / numpy libraries via Anaconda, a framework intended for analytics-style work.\n",
    "\n",
    "I have called this tool / library *Compare* because it uses Warcbase derivative data from the archives / WARC files.\n",
    "\n",
    "Compare uses either Simple (CA) or Multiple Correspondence Analysis (MCA) to explore relationships among different collections of data. It is like factor analysis.  I have a demo of Simple CA [using parliamentary committees and MPs as a sample.](https://github.com/web-archive-group/WALK/blob/master/Scripts/compare_collection/UnderstandingMCA.ipynb). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#First let's import the necessary libraries.\n",
    "%matplotlib inline\n",
    "from Compare import Compare # The Compare class\n",
    "import os #file operations\n",
    "from collections import defaultdict #complex dictionaries\n",
    "import matplotlib.pyplot as plt #plotting library\n",
    "from mpl_toolkits.mplot3d import Axes3D #for 3d graphs\n",
    "import copy #need deepcopy() for working with the dictionaries.\n",
    "\n",
    "####   Uncomment the items below if you want \n",
    "####   to use D3 for output.\n",
    "\n",
    "#import mpld3\n",
    "#mpld3.enable_notebook()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While many questions could be answered using MCA, we have decided to focus on a simple problem statement: ***Can we conduct an evaluation of a collection of web archives using data analytics?*** For this demo, I have included a number of collections:\n",
    "\n",
    "- Two 'external' collections (UofA prairie provinces & UFT Canadian Labour Unions)\n",
    "- Three Twitter crawls.\n",
    "- University of Victoria collections (10 in all)\n",
    "\n",
    "We have also included some dummy collections based on the most popular websites, categorized by media, social media, government, tech industry and NGOs.\n",
    "\n",
    "While there are a number of ways we could compare the collections, this example will use web domain names (urls). Often MCA tries to compare both factors (collections *and* urls) but including the urls would be too difficult. (Besides, do we really need to understand why www.youtube.com is in multiple collections?)  The urls will be represented by off-white squares and no labels to keep them from confusing the analysis.\n",
    "\n",
    "We assume that libraries will continue not to count format as a selection criteria, meaning that web archives are selected on the same criteria as books, journals or any other source. We have decided to focus on the following principles of collection management, however:\n",
    "\n",
    "- Coverage / Scope\n",
    "  + What are the best ways to evaluate coverage?\n",
    "- Accuracy\n",
    "  + Can we detect problems (eg. ineffective crawls)\n",
    "- Relevance\n",
    "  + Is a historian likely to find something unique or interesting in the collection?\n",
    "- Dynamics\n",
    "  + How has the collection changed from crawl to crawl?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UVIC_trans_web-urls.txt\n",
      "UVIC_VI_Gov\n",
      "WAHR_exln42-all.txt\n",
      "UVIC_Websites\n",
      "WAHR_panamapapers-urls.txt\n",
      "UVIC_anarchist_archives-urls.txt\n",
      "UVIC_environmental_organizations_and_resources_of_bc-urls.txt\n",
      "UVIC_bc_teachers_labour_dispute_2014-urls.txt\n",
      "UVIC_VI_News\n",
      "WAHR_ymmfire-urls.txt\n",
      "UVIC_50thanniversary\n",
      "UVIC_DigitalScholarship\n",
      "DUMMY\n",
      "DUMMY_GOVERNMENT\n",
      "SOCIALMEDIADUMMY\n",
      "ORGDUMMY\n",
      "TECHDUMMY\n",
      "DUMMY_MEDIA\n",
      "UVIC_Calendar\n",
      "UVIC_Faculty\n",
      "UVIC_Research_Centres\n"
     ]
    }
   ],
   "source": [
    "# The output below takes the derivative files from the folder \"UFT/\" and puts them into a python dictionary (array)\n",
    "# for later use.  I have included two of these. One including the Heritage Community Foundation's collection and one\n",
    "# not.\n",
    "\n",
    "path = \"UVIC/\"\n",
    "\n",
    "def processCollection (path):\n",
    "    #initialise vars:\n",
    "    urls = []\n",
    "    #establish the data folder\n",
    "    for filename in os.listdir(path):\n",
    "        with open(path+filename, \"r\") as file:\n",
    "            print (filename) #see the filenames available.\n",
    "            urls.append(list({(filename[0:15], line.translate(str.maketrans(')',\" \")).split(\",\")[0][2:6], line.translate(str.maketrans(')',\" \")).split(\",\")[1].strip()) for line in file.readlines()}))\n",
    "    return(urls)\n",
    "\n",
    "#newdict = defaultdict(dict)\n",
    "newdict = defaultdict(lambda: defaultdict(list))\n",
    "newdict2 = defaultdict(lambda: defaultdict(list))\n",
    "PC = processCollection(path)\n",
    "#print(list(zip(PC[0])))\n",
    "#print(list(zip(PC[0][0])))\n",
    "#print (**collect)\n",
    "for collect in PC:\n",
    "    for coll, date, url in collect:\n",
    "        newdict[date][coll].append(url)\n",
    "\n",
    "# newdict will provide all the data like so:\n",
    "\n",
    "#newdict2 eliminates collections in if-then statement.\n",
    "for collect in PC:\n",
    "    for coll, date, url in collect:\n",
    "        if coll == 'ALBERTA_heritag' or coll=='DUMMY_GOVERNMEN' or coll == 'ALBERTA_hcf_onl' or coll==\"DUMMY_MEDIA\" or coll==\"DUMMY\" or coll== \"ORGDUMMY\" or coll== \"TECHDUMMY\" or coll== \"SOCIALMEDIADUMM\" or coll==\"GOVDUMMY\":\n",
    "            pass\n",
    "        else:\n",
    "            newdict2[date][coll].append(url)\n",
    "\n",
    "#{'DATE': {'COLLECTION': ['url1.com', 'url2.com', 'etc']}}\n",
    "#\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Produce a dictionary output that creates a list of outputs suitable for analysis by date.\n",
    "##\n",
    "## collection_var[-1] would analyze all the links together until the latest year (2016). collection_var[-2]\n",
    "## would analyze everything up to t-1 (2015).\n",
    "##\n",
    "## Our hope for the future is that the data could be used in an animation, showing changes over time. But for now, \n",
    "## we will just show the progress.\n",
    "\n",
    "def add_two_collections (col1, col2):\n",
    "    # This takes two collections and combines them into one.\n",
    "    col_1 = col1.copy()\n",
    "    for coll, values in col2.items():\n",
    "        #print(values)\n",
    "        try:\n",
    "            col_1[coll] = set(col_1[coll])\n",
    "            col_1[coll].update(set(values)) \n",
    "            col_1[coll] = list(col_1[coll])\n",
    "        except KeyError:\n",
    "            col_1[coll] = list(values)       \n",
    "    return col_1\n",
    "\n",
    "def reduce_collections (dictionary):\n",
    "    dict_list = []\n",
    "    fulllist = {}\n",
    "    dict2 = copy.deepcopy(dictionary)\n",
    "    for x, y in sorted(dict2.items()):\n",
    "        #print(x)\n",
    "        n = dictionary.pop(x)\n",
    "        if len(dict_list) < 1:\n",
    "            dict_list.append(n)\n",
    "        #print(n)\n",
    "        else:\n",
    "            dict_list.append((add_two_collections(dict_list[-1], n)))\n",
    "        #print(dict_list)\n",
    "    return(dict_list)\n",
    "\n",
    "def removekey(d, key):\n",
    "    r = dict(d)\n",
    "    del r[key]\n",
    "    return r\n",
    "\n",
    "collection_var = reduce_collections (copy.deepcopy(newdict2))\n",
    "\n",
    "# Collection var is a list of dictionaries starting from the earliest to the latest. The later dictionaries\n",
    "# are accumulations of the former.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we will start with the earliest date (2014). Eleven collections have data listed for these dates.\n",
    "\n",
    "When there are fewer than 3 collections, you get a venn diagram showing the cross-connections. You can also extract data using V2_AB object or V3_ABC (if there are three items). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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Xmq6/UKNTkMajhF/D8kYzyzjn9jvn9jrnXnLOPe+8/cCfAu+o/MwHgNecc99z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\nkoJURKRK/x9z8uVV0aWDcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9937198080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = Compare({\"happy\": [\"ecstatic\", \"bursting\", \"nostalgic\"], \"sad\": [\"down\", \"depressed\", \"nostalgic\"]}, LABEL_BOTH_FACTORS=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 2014, we have eleven collections and move to correspondence analysis.  \n",
    "\n",
    "To analyse, I suggest a three pronged approach.\n",
    "\n",
    "- HA: What does the horizontal axis mean?\n",
    "- VA: What does the vertical axis mean?\n",
    "- QUA: Can we name the quadrants?\n",
    "- CLUS: What sources have clustered together\n",
    "\n",
    "HA-VA-QUA-CLUS!\n",
    "\n",
    "We can speculate, for instance, that the horizontal axis represents collections specific to UVIC and collections curated by UVIC about other issues.  The vertical axis, may have something to do with the differences between Vancouver Island local news and the other collections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['UVIC_Calendar', 'UVIC_Faculty', 'UVIC_trans_web-', 'UVIC_bc_teacher', 'UVIC_VI_Gov', 'UVIC_environmen', 'UVIC_Research_C', 'UVIC_50thannive', 'UVIC_VI_News', 'UVIC_DigitalSch', 'UVIC_anarchist_']\n",
      "[ 0.16606834  0.13785419  0.13304039  0.06120577  0.05600275  0.05021047\n",
      "  0.03928356  0.03538299  0.02170198  0.01326725]\n"
     ]
    },
    {
     "data": {
      "image/png": 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lsWLFCnr37o21FoC0tDTn/t27dwegbt26HD58uMCvRkRKNrX0iciVY/dueP99WLbMEf7S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\nPgCADgh9AAAdEPoAADrw/wE7vRwZC5N37wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f997119b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "UVIC = Compare(collection_var[-3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to this graph, a strong distinction exists for the VI News collection. Adding a dummy collection puts VI NEWS and trans_web on the same vertical axis but both collections have few connections to the MEDIA DUMMY.  This probably means that the \"news\" sites do not link frequently to the more popular news media sites.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['UVIC_Calendar', 'UVIC_Faculty', 'UVIC_VI_Gov', 'UVIC_50thannive', 'MEDIADUMMY', 'UVIC_DigitalSch', 'UVIC_bc_teacher', 'UVIC_environmen', 'UVIC_Research_C', 'UVIC_VI_News', 'UVIC_trans_web-', 'UVIC_anarchist_']\n",
      "[ 0.18835742  0.16179544  0.13806671  0.13314245  0.0592297   0.05543951\n",
      "  0.05119499  0.03845224  0.03224291  0.02300074  0.01314313]\n"
     ]
    },
    {
     "data": {
      "image/png": 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RREdH8/333wOuySEAnTp1AlxdtXXq1CEwMBCAESNGEBkZSWRkJO3atSMyMtLjGkRERC42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\n8NT/B0hjrmov3O7TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f993718a7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#collection_var[-3] = removekey(collection_var[-3], 'SOCIALMEDIA')\n",
    "collection_var[-3]['MEDIADUMMY'] = newdict['2015']['DUMMY_MEDIA']\n",
    "\n",
    "UVIC = Compare(collection_var[-3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Going to 2015 (which is a cumulative look at the collections, so includes both 2014 & 2015) the level of difference among the collections reduces by a lot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['UVIC_Calendar', 'UVIC_Websites', 'UVIC_Faculty', 'UVIC_VI_Gov', 'UVIC_50thannive', 'UVIC_DigitalSch', 'UVIC_bc_teacher', 'UVIC_environmen', 'UVIC_Research_C', 'UVIC_VI_News', 'UVIC_trans_web-', 'UVIC_anarchist_']\n",
      "[ 0.14071597  0.09441055  0.07598815  0.06486787  0.04751476  0.03813857\n",
      "  0.03256712  0.03088208  0.0262362   0.02348788  0.00812743]\n"
     ]
    },
    {
     "data": {
      "image/png": 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NmwD48ccf+eKLL5y5TOs6Z8yYwS233MKePXvYu3cvn3zyicM1SelSSBSpwsr7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\n5n1FRL/M3NzRem+BI0mS+pSmpiaOPfZYFixYsLV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nz0YXIKnu/g54PjPHRkQ/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\negAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9934913588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "UVIC = Compare(collection_var[-2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moving to 2016, we now see the WAHR Twitter collections included, which greatly expand the difference among the \n",
    "collections overall. So the collections are distinct from Twitter collections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['UVIC_Calendar', 'UVIC_50thannive', 'UVIC_Faculty', 'UVIC_VI_Gov', 'UVIC_Websites', 'WAHR_ymmfire-ur', 'WAHR_panamapape', 'UVIC_trans_web-', 'UVIC_bc_teacher', 'UVIC_environmen', 'UVIC_Research_C', 'WAHR_exln42-all', 'UVIC_VI_News', 'UVIC_DigitalSch', 'UVIC_anarchist_']\n",
      "[ 0.61910505  0.14563931  0.09950897  0.09221756  0.08459989  0.06969057\n",
      "  0.05774344  0.049607    0.04744617  0.03502615  0.03461494  0.02806694\n",
      "  0.0161937   0.00545049]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ChQsZMGCAxyGvLIMHDyYpKQlwhL8hQ4b8osfp1q0b7du3Z9y4cQBs2rTJGSTvuusu1q1b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\niDiIgj8RERERB1HwJyIiIuIg/w+8RWQlQSyo8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f993659a780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "UVIC = Compare(collection_var[-1])\n",
    "#print(\"There are \"+ str(len(UVIC.V2_AB)) + \" members in common. They are : \" + str(UVIC.V2_AB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing the Twitter examples, again we see that the collections are not very distinct in terms of url exchange."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['UVIC_Calendar', 'UVIC_Websites', 'UVIC_Faculty', 'UVIC_VI_Gov', 'UVIC_50thannive', 'UVIC_DigitalSch', 'UVIC_bc_teacher', 'UVIC_environmen', 'UVIC_Research_C', 'UVIC_VI_News', 'UVIC_trans_web-', 'UVIC_anarchist_']\n",
      "[ 0.1561991   0.0979351   0.07218339  0.06522017  0.05474628  0.03977227\n",
      "  0.03558125  0.03248739  0.02590215  0.02458546  0.01160857]\n"
     ]
    },
    {
     "data": {
      "image/png": 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LO3fubE+FVrduXXr06EFubi4tW7Zk69atnDhxgvXr1zNs2DD7wz07O9vev3fv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\nIVGSJEmF/w+9dfQiqTKPbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9934b34f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Compare.Compare at 0x7f9934b58da0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collection_var[-1] = removekey(collection_var[-1], 'WAHR_ymmfire-ur')\n",
    "collection_var[-1] = removekey(collection_var[-1], 'WAHR_panamapape')\n",
    "collection_var[-1] = removekey(collection_var[-1], 'WAHR_exln42-all')\n",
    "Compare(collection_var[-1])\n",
    "#print(\"There are \"+ str(len(UFT.V2_AB)) + \" members in common. They are : \" + str(UFT.V2_AB))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Conclusion*: The web archive collections are quite similar in terms of url content. More research is necessary to determine why, but analysis of collection policies regarding web archives may be beneficial to decide whether the current approach is meeting organizational needs. The lack of distinction among the collections is an area of curiosity, due to a lack of internal diversity among the sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "TestDict1 = {'2009': {'c1': {'lk1', 'lk2', 'lk3'},\n",
    "                     'c2': {'lk1', 'lk10', 'lk20', 'lk2'},\n",
    "                     'c3': {'lk3', 'lk10', 'lk33', 'lk4'}},\n",
    "            '2010': {'c1': {'lk3', 'lk5', 'lk6'},\n",
    "                    'c3': {'lk10', 'lk9', 'lk7'}},\n",
    "            '2011': {'c1': {'lk3', 'lk5', 'lk6'},\n",
    "                    'c4': {'lk1', 'lk2', 'lk3'}},\n",
    "            '2012': {'c1': {'lk1', 'lk99', 'lk6'}}\n",
    "           }\n",
    "\n",
    "#print(list(zip(*zip(TestDict['2009'])))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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