01_inspecting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 1: Inspecting the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this first assignment, we will get to know the dataset that we will be using throughout. You can find the assignment tasks at the bottom of this document.\n",
    "\n",
    "Our dataset consists of short texts (article abstracts) from the [PubMed](https://www.ncbi.nlm.nih.gov/pubmed/) database of scientific publications in the Life Science domain. As the full dataset consists of millions of documents, we are using just a small subset, namely all publications that contain the word \"allergy\" in their title or abstract. You can download that dataset in the form of four files (`allergy_Summaries.pkl.bz2`, etc.) from Canvas. Save these four files in a directory called `data` (do not unpack them, but save them as .bz2 files!), which should be a sub-directory of the one that contains this notebook file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing some Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import namedtuple, defaultdict, Counter\n",
    "import pickle, bz2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Summaries_file = 'data/allergy_Summaries.pkl.bz2'\n",
    "Summaries = pickle.load( bz2.BZ2File( Summaries_file, 'rb' ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make it easier to access the data, we convert here paper entries into [named tuples](http://docs.python.org/3/library/collections.html#collections.namedtuple). This will allow us to refer to fields by keyword (like `var.year`), rather than index (like `var[2]`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "paper = namedtuple( 'paper', ['title', 'authors', 'year', 'doi'] )\n",
    "\n",
    "for (id, paper_info) in Summaries.items():\n",
    "    Summaries[id] = paper( *paper_info )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "paper(title='Forum: Immunology: Allergy challenged.', authors=['Artis D', 'Maizels RM', 'Finkelman FD'], year=2012, doi='10.1038/484458a')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Summaries[22538601]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Forum: Immunology: Allergy challenged.'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Summaries[22538601].title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two lines below will install [matplotlib](http://matplotlib.org/) and [NumPy](http://www.numpy.org/), if they are not installed already (if that doesn't work, you might have to download and install them from [here](http://matplotlib.org/downloads.html) and [here](http://sourceforge.net/projects/numpy/files/NumPy/1.10.1/)). We will need these libraries for drawing diagrams and analyzing the data below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /Users/andrewphilipharrison/anaconda3/lib/python3.7/site-packages (3.1.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/andrewphilipharrison/anaconda3/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/andrewphilipharrison/anaconda3/lib/python3.7/site-packages (from matplotlib) (1.1.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/andrewphilipharrison/anaconda3/lib/python3.7/site-packages (from matplotlib) (2.4.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /Users/andrewphilipharrison/anaconda3/lib/python3.7/site-packages (from matplotlib) (2.8.0)\n",
      "Requirement already satisfied: numpy>=1.11 in /Users/andrewphilipharrison/anaconda3/lib/python3.7/site-packages (from matplotlib) (1.16.4)\n",
      "Requirement already satisfied: six in /Users/andrewphilipharrison/anaconda3/lib/python3.7/site-packages (from cycler>=0.10->matplotlib) (1.12.0)\n",
      "Requirement already satisfied: setuptools in /Users/andrewphilipharrison/anaconda3/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib) (41.0.1)\n",
      "Requirement already satisfied: numpy in /Users/andrewphilipharrison/anaconda3/lib/python3.7/site-packages (1.16.4)\n"
     ]
    }
   ],
   "source": [
    "! pip install --user matplotlib\n",
    "! pip install --user numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# show plots inline within the notebook\n",
    "%matplotlib inline\n",
    "# set plots' resolution\n",
    "plt.rcParams['savefig.dpi'] = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Papers per Year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will try to find out how many papers in the dataset were published per year. We are using the [Counter](https://docs.python.org/3/library/collections.html#collections.Counter) class for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of papers in the dataset per year for the past decade:\n",
      "[(2011, 2276), (2012, 2244), (2013, 2458), (2014, 2769), (2015, 2796), (2016, 2965), (2017, 3068), (2018, 3114), (2019, 3005), (2020, 4)]\n"
     ]
    }
   ],
   "source": [
    "paper_years = [ p.year for p in Summaries.values() ]\n",
    "papers_per_year = sorted( Counter(paper_years).items() )\n",
    "\n",
    "print('Number of papers in the dataset per year for the past decade:')\n",
    "print(papers_per_year[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtering results, to obain only papers since 1940:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of papers in the dataset published since 1940:\n",
      "73646\n"
     ]
    }
   ],
   "source": [
    "papers_per_year_since_1940 = [ (y,count) for (y,count) in papers_per_year if y >= 1940 ]\n",
    "years_since_1940 = [ y for (y,count) in papers_per_year_since_1940 ]\n",
    "nr_papers_since_1940 = [ count for (y,count) in papers_per_year_since_1940 ]\n",
    "\n",
    "print('Number of papers in the dataset published since 1940:')\n",
    "print(sum(nr_papers_since_1940))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a bar plot to visualize the results (using [matplotlib.pyplot.bar](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.bar)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x=years_since_1940, height=nr_papers_since_1940, width=1.0)\n",
    "plt.xlim(1940, 2019)\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('number of papers');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can get the same result by plotting it as a histogram with [matplotlib.pyplot.hist](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist( x=[p.year for p in Summaries.values()], bins=range(1940,2021) );\n",
    "plt.xlim(1940, 2020)\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('number of papers');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Papers per Author"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will obtain the distribution characterizing the number of papers published by an author."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flattening the list of lists of authors:\n",
    "flat_author_list = [ auth for paper in Summaries.values() for auth in paper.authors ]\n",
    "\n",
    "nr_papers_by_author = Counter( flat_author_list )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of authors in the dataset with distinct names:\n",
      "137755\n"
     ]
    }
   ],
   "source": [
    "print('Number of authors in the dataset with distinct names:')\n",
    "print(len(nr_papers_by_author))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 50 authors with greatest number of papers:\n",
      "[('Valenta R', 335), ('Sampson HA', 313), ('Bousquet J', 270), ('Bruze M', 259), ('Canonica GW', 248), ('Johansen JD', 240), ('Passalacqua G', 232), ('Demoly P', 231), ('Menné T', 219), ('Ring J', 208), ('Wahn U', 193), ('Burks AW', 185), ('Sicherer SH', 181), ('Vieths S', 181), ('Moneret-Vautrin DA', 181), ('Niggemann B', 167), ('Wüthrich B', 162), ('Sheikh A', 161), ('van Ree R', 158), ('Andersen KE', 158), ('Beyer K', 157), ('Asero R', 157), ('Akdis CA', 155), ('Allen KJ', 154), ('Bindslev-Jensen C', 154), ('Muraro A', 152), ('Ciprandi G', 152), ('Incorvaia C', 151), ('Fiocchi A', 149), ('Roberts G', 146), ('Kraft D', 146), ('Ebisawa M', 145), ('Wood RA', 144), ('Uter W', 140), ('Wang J', 137), ('Ebner C', 137), ('Haahtela T', 132), ('Thyssen JP', 132), ('Björkstén B', 129), ('Papadopoulos NG', 128), ('Zuberbier T', 128), ('Lack G', 128), ('Zhang Y', 127), ('Knulst AC', 126), ('Wickman M', 124), ('Warner JO', 122), ('Kimber I', 120), ('Ferreira F', 119), ('Poulsen LK', 118), ('Goossens A', 118)]\n"
     ]
    }
   ],
   "source": [
    "print('Top 50 authors with greatest number of papers:')\n",
    "print(sorted(nr_papers_by_author.items(), key=lambda i:i[1], reverse=True)[:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can create a histogram to visualize the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist( x=list(nr_papers_by_author.values()), bins=range(51), log=True )\n",
    "plt.xlabel('number of papers authored')\n",
    "plt.ylabel('number of authors');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Authors per Paper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can make a similar analysis for the number of authors per paper:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=[ len(p.authors) for p in Summaries.values() ], bins=range(20), align='left', density=True)\n",
    "plt.xlabel('number of authors in one paper')\n",
    "plt.ylabel('fraction of papers')\n",
    "plt.xlim(-0.5, 15.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Words in Titles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we can have a first look into the words we find in titles (applying a very naive word splitting method for now):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of distinct words in the paper titles:\n",
      "63474\n"
     ]
    }
   ],
   "source": [
    "words = [ word.lower() for paper in Summaries.values() for word in paper.title.split(' ') ]\n",
    "word_counts = Counter(words)\n",
    "\n",
    "print('Number of distinct words in the paper titles:')\n",
    "print(len(word_counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your name:** Andrew Harrison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1\n",
    "\n",
    "Create a Python dictionary object that returns sets of author names for a given year. Name this dictionary `authors_at_year`. (You can use a [*defaultdict*](https://docs.python.org/3/library/collections.html#collections.defaultdict) with a default value of [*set*](https://docs.python.org/3/library/stdtypes.html#set-types-set-frozenset).) Demonstrate the working of this dictionary by showing the author set for the year 1935."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Cooke RA', 'Jay HM', 'Martin EG', 'Stull A', 'Hebald S', 'Barnard JH'}\n"
     ]
    }
   ],
   "source": [
    "authors_at_year = defaultdict(set)\n",
    "for paper in Summaries.values():\n",
    "    for author in paper.authors:\n",
    "        authors_at_year[paper.year].add(author)\n",
    "print(authors_at_year[1935])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2\n",
    "\n",
    "Based on the dictionary `authors_at_year` from exercise 1 above, create a plot for the years from 1940 until now that shows how many authors published at least one paper in the given particular year. (You can retrieve the number of unique items in a set `s` with `len(s)`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of authors publishing >=1 paper')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nr_authors_per_year_since_1940 = []\n",
    "for year in years_since_1940:\n",
    "#     print(year, len(authors_at_year[year]))\n",
    "    nr_authors_per_year_since_1940.append(len(authors_at_year[year]))\n",
    "# print(nr_authors_per_year_since_1940)\n",
    "\n",
    "plt.bar(x=years_since_1940, height=nr_authors_per_year_since_1940, width=1.0)\n",
    "plt.xlim(1940, 2019)\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('number of authors publishing >=1 paper')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3\n",
    "\n",
    "Print out the top 10 most often occurring words in the papers' titles together with their frequency (in descending order with respect to the frequency). Use the data structures created above (in the sections before the tasks)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "of : 52379\n",
      "in : 38590\n",
      "and : 35612\n",
      "the : 26613\n",
      "allergy : 16954\n",
      "to : 15350\n",
      "a : 15314\n",
      "with : 12125\n",
      "allergic : 8569\n",
      "for : 8192\n"
     ]
    }
   ],
   "source": [
    "most_frequent_10 = word_counts.most_common(10)\n",
    "for word in most_frequent_10: print(word[0],\":\",word[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4\n",
    "\n",
    "Calculate and plot (e.g. using [plt.plot](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot)) a graph of the frequency of the 100 most frequent words in titles of papers, from most frequent to least frequent. (For readability, don't show the actual words.) Make again use of the data structures created above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of times used')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "most_frequent_100 = word_counts.most_common(100)\n",
    "most_frequent_word = []\n",
    "most_frequent_count = []\n",
    "for most in most_frequent_100:\n",
    "    most_frequent_word.append(most[0])\n",
    "    most_frequent_count.append(most[1])\n",
    "\n",
    "plt.plot(most_frequent_word, most_frequent_count)\n",
    "# plt.bar(x=most_frequent_word, height=most_frequent_count, width=1.0)\n",
    "plt.xlabel('word')\n",
    "plt.xticks([])\n",
    "plt.ylabel('number of times used')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5\n",
    "\n",
    "What do you notice when looking at the top 10 most frequent words? Explain all types of words we find in this top-10 and why."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** [Eight of the words: 'of, in, and, the, to, a, with, for' are stop words. Words that will take up memory, and computation cycles, but don't add anything to your search (or to NLP in machine learning, for example, NLTK lists all of these as standard english stop words). You should thus remove these words from your indexing efforts. The other two words 'allergy, allergic' are expected, as this dataset is a subset of pubmed publications containing the word \"allergy\" in their title or abstract. Allergic being simply the adjective of the noun allergy, and would inherently be highly likely to be used. You could use word stemming to further reduce space used i.e. just using allergy]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Submit the answers to the assignment via Canvas as a modified version of this Notebook file (file with `.ipynb` extension) that includes your code and your answers.\n",
    "\n",
    "Before submitting, restart the kernel and re-run the complete code (**Kernel > Restart & Run All**), and then check whether your assignment code still works as expected.\n",
    "\n",
    "Don't forget to add your name, and remember that the assignments have to be done individually and group submissions are **not allowed**."
   ]
  }
 ],
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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