wikipii_dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "wikipii_dataset.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ratmcu/wikipiifed/blob/master/wikipii_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2N5iHt4vG_zX",
        "colab_type": "code",
        "outputId": "e96d6972-b37e-4c62-9291-0f50e1246dad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!pip install wget\n",
        "import os\n",
        "import wget\n",
        "import logging\n",
        "logger = logging.getLogger()\n",
        "logger.setLevel(logging.INFO)\n",
        "try:\n",
        "    import colabimport\n",
        "except:\n",
        "    colabimporturl = 'https://github.com/ratmcu/colaboratory_import/raw/master/colabimport.py'\n",
        "    filename = colabimporturl.split(\"/\")[-1].split(\"?\")[0]\n",
        "    if os.path.isfile(filename):\n",
        "        os.remove(filename)\n",
        "    wget.download(colabimporturl)\n",
        "    import colabimport\n",
        "    \n",
        "colabimport.get_notebook('https://github.com/ratmcu/wiki_ner/blob/master/conll_tagged_ne.ipynb?raw=true')\n",
        "from conll_tagged_ne import WikiConLLTagger\n",
        "class HashableTupleAnnotations(tuple):\n",
        "    def __hash__(self):\n",
        "        return hash(tuple(sorted([self[0:1],self[1:2]])))\n",
        "class hashabledict(dict):\n",
        "    def __hash__(self):\n",
        "        return hash(tuple(sorted(self.items())))\n",
        "!pip install spacy\n",
        "!python -m spacy download en_core_web_sm\n",
        "import spacy\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "import re\n",
        "!pip install pyahocorasick\n",
        "!pip install fuzzyset\n",
        "from ahocorasick import Automaton\n",
        "import fuzzyset\n",
        "from operator import itemgetter, attrgetter\n",
        "import pandas as pd\n",
        "import pickle\n",
        "import urllib\n",
        "from bs4 import BeautifulSoup\n",
        "import numpy as np\n",
        "import json"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting wget\n",
            "  Downloading https://files.pythonhosted.org/packages/47/6a/62e288da7bcda82b935ff0c6cfe542970f04e29c756b0e147251b2fb251f/wget-3.2.zip\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-cp36-none-any.whl size=9682 sha256=3e97526a9db92f5a1b59cf36ae2db060aff8e48922026723967ea36935627571\n",
            "  Stored in directory: /root/.cache/pip/wheels/40/15/30/7d8f7cea2902b4db79e3fea550d7d7b85ecb27ef992b618f3f\n",
            "Successfully built wget\n",
            "Installing collected packages: wget\n",
            "Successfully installed wget-3.2\n",
            "importing Jupyter notebook from conll_tagged_ne.ipynb\n",
            "Requirement already satisfied: wget in /usr/local/lib/python3.6/dist-packages (3.2)\n",
            "importing Jupyter notebook from reusable_annotator.ipynb\n",
            "Collecting pyahocorasick\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f4/9f/f0d8e8850e12829eea2e778f1c90e3c53a9a799b7f412082a5d21cd19ae1/pyahocorasick-1.4.0.tar.gz (312kB)\n",
            "\u001b[K     |████████████████████████████████| 317kB 3.4MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pyahocorasick\n",
            "  Building wheel for pyahocorasick (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyahocorasick: filename=pyahocorasick-1.4.0-cp36-cp36m-linux_x86_64.whl size=81696 sha256=d0a31afb4c095f58a8d8f2dac7856349915c89b6f04d87e64f208762a2fe3860\n",
            "  Stored in directory: /root/.cache/pip/wheels/0a/90/61/87a55f5b459792fbb2b7ba6b31721b06ff5cf6bde541b40994\n",
            "Successfully built pyahocorasick\n",
            "Installing collected packages: pyahocorasick\n",
            "Successfully installed pyahocorasick-1.4.0\n",
            "Collecting fuzzyset\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2e/78/7509f3efbb6acbcf842d7bdbd9a919ca8c0ed248123bdd8c57f08497e0dd/fuzzyset-0.0.19.tar.gz (377kB)\n",
            "\u001b[K     |████████████████████████████████| 378kB 3.4MB/s \n",
            "\u001b[?25hCollecting python-levenshtein\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/42/a9/d1785c85ebf9b7dfacd08938dd028209c34a0ea3b1bcdb895208bd40a67d/python-Levenshtein-0.12.0.tar.gz (48kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 4.9MB/s \n",
            "\u001b[?25hCollecting texttable\n",
            "  Downloading https://files.pythonhosted.org/packages/ec/b1/8a1c659ce288bf771d5b1c7cae318ada466f73bd0e16df8d86f27a2a3ee7/texttable-1.6.2-py2.py3-none-any.whl\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from python-levenshtein->fuzzyset) (46.3.0)\n",
            "Building wheels for collected packages: fuzzyset, python-levenshtein\n",
            "  Building wheel for fuzzyset (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fuzzyset: filename=fuzzyset-0.0.19-cp36-cp36m-linux_x86_64.whl size=165729 sha256=2b929612399c88e76f535d64f54a3f63468b8309e3e273cd6afb0740fb66cc8f\n",
            "  Stored in directory: /root/.cache/pip/wheels/d8/36/9a/8f1cac047c7c3b03dce3d5434ed0088bfd8da8aeca615dfb4c\n",
            "  Building wheel for python-levenshtein (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-levenshtein: filename=python_Levenshtein-0.12.0-cp36-cp36m-linux_x86_64.whl size=144798 sha256=56708145b0c5613d03b0e368afa3a6d6ae00649359debba57769f1f546a948e4\n",
            "  Stored in directory: /root/.cache/pip/wheels/de/c2/93/660fd5f7559049268ad2dc6d81c4e39e9e36518766eaf7e342\n",
            "Successfully built fuzzyset python-levenshtein\n",
            "Installing collected packages: python-levenshtein, texttable, fuzzyset\n",
            "Successfully installed fuzzyset-0.0.19 python-levenshtein-0.12.0 texttable-1.6.2\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "importing Jupyter notebook from info_box.ipynb\n",
            "Requirement already satisfied: pyahocorasick in /usr/local/lib/python3.6/dist-packages (1.4.0)\n",
            "Requirement already satisfied: fuzzyset in /usr/local/lib/python3.6/dist-packages (0.0.19)\n",
            "Requirement already satisfied: texttable in /usr/local/lib/python3.6/dist-packages (from fuzzyset) (1.6.2)\n",
            "Requirement already satisfied: python-levenshtein in /usr/local/lib/python3.6/dist-packages (from fuzzyset) (0.12.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from python-levenshtein->fuzzyset) (46.3.0)\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "Requirement already satisfied: wget in /usr/local/lib/python3.6/dist-packages (3.2)\n",
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            "Requirement already satisfied: regex in /usr/local/lib/python3.6/dist-packages (2019.12.20)\n",
            "Requirement already satisfied: en_core_web_sm==2.2.5 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz#egg=en_core_web_sm==2.2.5 in /usr/local/lib/python3.6/dist-packages (2.2.5)\n",
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            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_sm')\n",
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            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (4.41.1)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.6.0)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.6.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2020.4.5.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2.9)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.1.0)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_sm')\n",
            "Requirement already satisfied: pyahocorasick in /usr/local/lib/python3.6/dist-packages (1.4.0)\n",
            "Requirement already satisfied: fuzzyset in /usr/local/lib/python3.6/dist-packages (0.0.19)\n",
            "Requirement already satisfied: texttable in /usr/local/lib/python3.6/dist-packages (from fuzzyset) (1.6.2)\n",
            "Requirement already satisfied: python-levenshtein in /usr/local/lib/python3.6/dist-packages (from fuzzyset) (0.12.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from python-levenshtein->fuzzyset) (46.3.0)\n",
            "Requirement already satisfied: spacy in /usr/local/lib/python3.6/dist-packages (2.2.4)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy) (2.0.3)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.1.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy) (46.3.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.18.4)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (4.41.1)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.0.2)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (0.6.0)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (7.4.0)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.0.0)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (0.4.1)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy) (2.23.0)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy) (3.0.2)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy) (1.0.2)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy) (1.6.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2020.4.5.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy) (3.1.0)\n",
            "Requirement already satisfied: en_core_web_sm==2.2.5 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz#egg=en_core_web_sm==2.2.5 in /usr/local/lib/python3.6/dist-packages (2.2.5)\n",
            "Requirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.6/dist-packages (from en_core_web_sm==2.2.5) (2.2.4)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.6.0)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.0.3)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.23.0)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.2)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.2)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (4.41.1)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (46.3.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.18.4)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (7.4.0)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.6.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2020.4.5.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2.9)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.1.0)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_sm')\n",
            "Requirement already satisfied: pyahocorasick in /usr/local/lib/python3.6/dist-packages (1.4.0)\n",
            "Requirement already satisfied: fuzzyset in /usr/local/lib/python3.6/dist-packages (0.0.19)\n",
            "Requirement already satisfied: python-levenshtein in /usr/local/lib/python3.6/dist-packages (from fuzzyset) (0.12.0)\n",
            "Requirement already satisfied: texttable in /usr/local/lib/python3.6/dist-packages (from fuzzyset) (1.6.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from python-levenshtein->fuzzyset) (46.3.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-dzSuf9-vd2",
        "colab_type": "text"
      },
      "source": [
        "## Collecting page links "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yxeSyi9Wx-gO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "quote_page = 'https://en.wikipedia.org/w/index.php?title=Category:Living_people'\n",
        "page = urllib.request.urlopen(quote_page)\n",
        "soup = BeautifulSoup(page, 'html.parser')\n",
        "links = []\n",
        "i = 0\n",
        "while i  < 10:\n",
        "    content_big = soup.find('div', attrs={'class': 'mw-content-ltr'}) # or infobox biography vcard\n",
        "    link_list = content_big.find('div', attrs={'class': 'mw-category'}) # or infobox biography vcard\n",
        "    links.extend(link_list.find_all('a'))\n",
        "    print(len(links))\n",
        "    next_page = None\n",
        "    for j, aa in enumerate(soup.find_all('a')):\n",
        "        if aa.text == 'next page':\n",
        "            next_page = aa.get('href')\n",
        "            if j%100 == 0:\n",
        "                print(aa.get('href')[:10])\n",
        "            page = urllib.request.urlopen('https://en.wikipedia.org' + next_page)\n",
        "            soup = BeautifulSoup(page, 'html.parser')\n",
        "            break\n",
        "    if next_page == None:\n",
        "        break\n",
        "    i = i + 1\n",
        " \n",
        "links"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "20Ly5Pr8NP5F",
        "colab_type": "text"
      },
      "source": [
        "# Mass Dataset "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vWT-vVCAtFy",
        "colab_type": "code",
        "outputId": "9b182fec-d20d-46ac-ec6e-51ef38b365a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import multiprocessing\n",
        "import os\n",
        "import time\n",
        "# split a list into evenly sized chunks\n",
        "!pip install wget\n",
        "import wget\n",
        "import logging\n",
        "import pickle\n",
        "import ast\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import urllib\n",
        "from bs4 import BeautifulSoup\n",
        "import traceback\n",
        "import re\n",
        "\n",
        "try:\n",
        "    import colabimport\n",
        "except:\n",
        "    colabimporturl = 'https://github.com/ratmcu/colaboratory_import/raw/master/colabimport.py'\n",
        "    filename = colabimporturl.split(\"/\")[-1].split(\"?\")[0]\n",
        "    if os.path.isfile(filename):\n",
        "        os.remove(filename)\n",
        "    wget.download(colabimporturl)\n",
        "    import colabimport\n",
        "colabimport.get_notebook('https://github.com/ratmcu/wiki_ner/blob/master/reusable_annotator.ipynb?raw=true')\n",
        "colabimport.get_notebook('https://github.com/ratmcu/wiki_ner/blob/master/info_box.ipynb?raw=true')\n",
        "# import io, os, sys, types\n",
        "from reusable_annotator import PageContents\n",
        "from info_box import InfoCard, PrivateEntities\n",
        "# exit()\n",
        "if not os.path.isfile('./Living_people.pkl'):\n",
        "    wget.download('https://github.com/ratmcu/wiki_ner/blob/master/Living_people.pkl?raw=true')\n",
        "\n",
        "logdir = 'data_frames'\n",
        "if not os.path.exists(logdir): os.makedirs(logdir)\n",
        "random_test_params = { 'test':True, 'samples': 40}"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: wget in /usr/local/lib/python3.6/dist-packages (3.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZkdUFkfwGsrh",
        "colab_type": "text"
      },
      "source": [
        "# Scoring pages and choosing the best ones\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EeWhx6_4Yf5Z",
        "colab_type": "text"
      },
      "source": [
        "## selecting the row names to be scored"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8p68dnSMVsJY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#filter the row entry\n",
        "def rowEntryFilter(lst):\n",
        "    entries = set()\n",
        "#     reg = re.compile('[0-9]+$', re.MULTILINE)\n",
        "#     reg = re.compile('')\n",
        "#     reg = re.compile('[0-9*]')\n",
        "    for entry in lst:\n",
        "#         match = reg.match(entry)\n",
        "        if any(d in entry for d in'0123456789'): # remove the clutter a bit by removing rows with digits\n",
        "            # print(entry)\n",
        "            continue  \n",
        "        entries.add(entry)\n",
        "    return entries"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZu-HFwq3ZEx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tarfile\n",
        "if not os.path.exists('900k_living_people_ner_presence.tar.gz'):\n",
        "    wget.download('https://github.com/ratmcu/wiki_ner/blob/master/900k_living_people_ner_presence.tar.gz?raw=true')\n",
        "tar = tarfile.open('900k_living_people_ner_presence.tar.gz', mode='r')\n",
        "tar.extractall('./900k_living_people_ner_presence')\n",
        "tar.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJ_mAvaRbC9F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "paths = sorted([os.path.join(f[0], name) for f in os.walk('./900k_living_people_ner_presence') \n",
        "                if len(f[2])!=0 for name in f[2] if os.path.splitext(name)[-1] == '.csv'],\n",
        "               key=lambda path: int(path.split('_')[-1].split('.')[0]))\n",
        "paths = paths[:8201]\n",
        "# int(paths[0].split('_')[-1].split('.')[0])\n",
        "# presence_frames_raw =  pd.DataFrame()\n",
        "entity_rows = set()\n",
        "for path in paths:\n",
        "#     df = pd.read_csv(path).fillna(0)\n",
        "    df = pd.read_csv(path)\n",
        "    entity_rows.update(df.columns.tolist())\n",
        "#     presence_frames_raw = presence_frames_raw.append(df,ignore_index = True)\n",
        "# presence_frames_raw = presence_frames_raw.fillna(0)\n",
        "# 'Home\\xa0town' in set(presence_frames_raw.columns.tolist())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iOFyBZjrebqd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ss = rowEntryFilter(entity_rows)\n",
        "with open('entity_rows.pkl', 'wb') as f:\n",
        "    pickle.dump(ss, f)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e_o_kdj1eoAT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if not os.path.exists('entity_rows.pkl'):\n",
        "    wget.download('https://github.com/ratmcu/wiki_ner/blob/master/entity_rows.pkl?raw=true')\n",
        "with open('entity_rows.pkl', 'rb') as f:\n",
        "    ss = pickle.load(f)\n",
        "fltrd = rowEntryFilter(ss)\n",
        "with open('entity_rows.txt', 'w') as f:\n",
        "    for entry in fltrd:\n",
        "        f.writelines(entry+'\\n')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KAGesD4bZIGS",
        "colab_type": "code",
        "outputId": "0fe4aea2-9ca8-48a6-e9f4-1aee303620c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        }
      },
      "source": [
        "#if the rows mentioned in the following text following exists in a link score of one is given to each presence\n",
        "if not os.path.exists('selected_entity_rows.txt'):\n",
        "    wget.download('https://github.com/ratmcu/wiki_ner/blob/master/selected_entity_rows.txt?raw=true')\n",
        "selected_entity_rows = open('selected_entity_rows.txt', 'r').read().splitlines()\n",
        "selected_entity_rows = set(selected_entity_rows)\n",
        "selected_entity_rows.add('Alma\\xa0mater')\n",
        "selected_entity_rows\n",
        "# chosen_entity_rows = {'Alma mater', 'Alma\\xa0mater', 'Born', 'Born:', \n",
        "#                 'College', 'Education','Parent(s)', 'Parents',\n",
        "#                 'High school', 'High school:', 'Spouse(s)',\n",
        "#                 'Place of birth', }\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Alma mater',\n",
              " 'Alma\\\\xa0mater',\n",
              " 'Almat mater',\n",
              " 'Alma\\xa0mater',\n",
              " 'Birth name',\n",
              " 'Born',\n",
              " 'Born:',\n",
              " 'Children',\n",
              " 'College',\n",
              " 'College(s)',\n",
              " 'Colleges',\n",
              " 'Date of birth',\n",
              " 'Education',\n",
              " 'Father',\n",
              " 'Father’s name',\n",
              " 'Full name',\n",
              " 'High school',\n",
              " 'High school:',\n",
              " 'Home town',\n",
              " 'Law School',\n",
              " 'Mother',\n",
              " 'Mother’s name',\n",
              " 'Name',\n",
              " 'Name(s)',\n",
              " 'Parent',\n",
              " 'Parent(s)',\n",
              " 'Parents',\n",
              " 'Place of birth',\n",
              " 'School',\n",
              " 'Schools',\n",
              " 'Spouse',\n",
              " 'Spouse(s)',\n",
              " 'Spouses'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6yFeEV1f_SMy",
        "colab_type": "text"
      },
      "source": [
        "## Scoring pages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_i-9sTyR1-f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def scorePages(page_summary):\n",
        "    '''Give a score to each entry by the columns with the ones'''\n",
        "    score_list = []\n",
        "#     selected_entity_rows = {'Alma mater', 'Alma\\xa0mater', 'Born', 'Born:', \n",
        "#                 'College', 'Education','Parent(s)', 'Parents',\n",
        "#                 'High school', 'High school:', 'Spouse(s)',\n",
        "#                 'Place of birth'}\n",
        "    \n",
        "    for idx, row in enumerate(page_summary.iterrows()):\n",
        "        frame = row[1]\n",
        "        score = 0\n",
        "        for key in frame.keys():\n",
        "            if key in {'link'}:\n",
        "                continue\n",
        "#             print(frame[key],key)\n",
        "            elif key in selected_entity_rows:\n",
        "                score = score + frame[key]\n",
        "# #         score_df = score_df.append(pd.DataFrame({'index': [idx], 'score':score, 'NAME': name }))\n",
        "# #         score_df = score_df.append(pd.DataFrame({'INDEX': [idx], 'SCORE':score }))    \n",
        "        score_list.append(score)\n",
        "    return pd.DataFrame({'SCORE': score_list })\n",
        "\n",
        "# paths = paths[:101]\n",
        "paths = paths[:8201]\n",
        "presence_score_df =  pd.DataFrame()\n",
        "for path in paths:\n",
        "    df = pd.read_csv(path).fillna(0).iloc[:,1:]\n",
        "#     print(df['link'])\n",
        "    df = pd.DataFrame({'LINK': df['link'] }).assign(SCORE=scorePages(df))\n",
        "    presence_score_df = presence_score_df.append(df, ignore_index = True)\n",
        "presence_score_df = presence_score_df.sort_values(by='SCORE', ascending = False)\n",
        "# presence_frames.to_csv('presence_frames.csv')\n",
        "# presence_frames.drop_duplicates(subset='link')\n",
        "# from inspect import currentframe, getsourcelines, getframeinfo\n",
        "# frameinfo  = getframeinfo(currentframe())\n",
        "# framelines = getsourcelines(currentframe())\n",
        "# ll = [print(line[:-1]) for line in framelines[0]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEdv54Py2ar5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "presence_score_df.to_csv('entity_presence_score.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XMWPRnfRxEFo",
        "colab_type": "text"
      },
      "source": [
        "## Analysis of the Entity presence inside the info box"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qp5JOXPTY_5C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if not os.path.exists('entity_presence_score.csv'):\n",
        "    wget.download('https://github.com/ratmcu/wiki_ner/blob/master/entity_presence_score.csv?raw=true')\n",
        "\n",
        "entity_presence_score_df = pd.read_csv('entity_presence_score.csv')\n",
        "# sanity_check_samples_1 = entity_presence_score_df.sample(n=10)\n",
        "# print(sanity_check_samples_1.iloc[:,0].iloc[0], sanity_check_samples_1.iloc[:,1].iloc[0])\n",
        "# print(sanity_check_samples_1.iloc[0]['LINK'], sanity_check_samples_1.iloc[0][0])\n",
        "# sanity_check_samples_2 = pd.read_csv(paths[sanity_check_samples_1.iloc[0][0]//100])\n",
        "# print(sanity_check_samples_2.iloc[sanity_check_samples_1.iloc[0][0]%100]['link'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GMuihSks6Tj_",
        "colab_type": "code",
        "outputId": "fcd38866-eeef-4577-cf3f-5deae98fe69f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "entity_presence_score_df.iloc[:,1:].plot.hist(by='SCORE',bins=7)\n",
        "histogram_df = entity_presence_score_df.iloc[:,1:].groupby('SCORE')\n",
        "# entity_presence_score_df.plot.hist(by='SCORE',bins = max(entity_presence_score_df['SCORE']), alpha=0.5)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cpzwaGsObnqm",
        "colab_type": "text"
      },
      "source": [
        "## Score vs the number of entries\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QCQwIpihuzl5",
        "outputId": "9383f155-f49e-4c5a-dfef-af9bc56845e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        }
      },
      "source": [
        "# histogram_df.groupby('SCORE').count()\n",
        "histogram_df=entity_presence_score_df.iloc[:,1:].groupby('SCORE').count()\n",
        "histogram_df"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LINK</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SCORE</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>23513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>256418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>134091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>132703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>19723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>2978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6.0</th>\n",
              "      <td>406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.0</th>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         LINK\n",
              "SCORE        \n",
              "0.0     23513\n",
              "1.0    256418\n",
              "2.0    134091\n",
              "3.0    132703\n",
              "4.0     19723\n",
              "5.0      2978\n",
              "6.0       406\n",
              "7.0        17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "57ZzWa73nX5e",
        "colab_type": "text"
      },
      "source": [
        "## Testing entity extraction from the pages with 7 relavant rows in the info box"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_yPQlrX9WcI",
        "colab_type": "code",
        "outputId": "f8e6e5e5-785a-48f0-dee1-d24a9573b7f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        }
      },
      "source": [
        "logger.setLevel(logging.INFO)\n",
        "indices = entity_presence_score_df.iloc[:,1:].groupby('SCORE').groups[7.0]\n",
        "for index in indices:\n",
        "    url = 'http://en.wikipedia.org'+entity_presence_score_df.iloc[index]['LINK']\n",
        "    try:\n",
        "        urllib.request.urlopen(url, timeout = 5)\n",
        "    except:\n",
        "        raise Exception('page not found!!!!!!!!!')\n",
        "    pg = PageContents(url)\n",
        "    \n",
        "    if not pg.table:\n",
        "        raise Exception('unable to find a info table in the page')\n",
        "# pg = PageContents('https://en.wikipedia.org/wiki/Donald_Trump')\n",
        "    else:\n",
        "        print(url)\n",
        "        info_card = InfoCard(pg)\n",
        "        print(info_card.info_table)\n",
        "        print(PrivateEntities(info_card).entity_dict)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "http://en.wikipedia.org/wiki/Carl_Roberts_(diplomat)\n",
            "{'Preceded by': ['Ronald Michael Sanders'], 'Succeeded by': ['Althea Allison Vanderpoole Banahene'], 'Born': ['', '1948-10-13', ')', 'October 13, 1948', '', \"St. John's, Antigua and Barbuda\"], 'Spouse(s)': ['m 1974, Pauline Margaret A.'], 'Children': ['two sons one daughter, Chalene to Brian Gagalager)'], 'Mother': ['Audrey E. Roberts'], 'Father': ['Arthur E. S. Roberts'], 'Education': ['Antigua Grammar School'], 'Alma mater': ['', '1982:', 'Bachelor of Engineering', 'Hons magna cum laude of the', 'Northeastern University', 'Boston', '', '1995', 'Master of Business Administration', 'of the', 'University of the West Indies', ',', 'Barbados', '.', '', '2004:', 'Doctor of Business Administration', 'of the', 'University of Bradford', '.', '']}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': [\"St. John's\"]}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1948-10-13', 'October 13, 1948']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': [\"St. John's, Antigua and Barbuda\"]}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': []}], 'SPOUSES': [{'dict_key': 'Spouse(s)', 'type': 'PERSON', 'entity_list': []}], 'PARENTS': [{'dict_key': 'Father', 'type': 'PERSON', 'entity_list': ['Arthur E. S. Roberts']}, {'dict_key': 'Mother', 'type': 'PERSON', 'entity_list': ['Audrey E. Roberts']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': []}, {'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': ['Northeastern University', 'Business Administration', 'University of the West Indies', 'Business Administration', 'University of Bradford']}]}\n",
            "http://en.wikipedia.org/wiki/Unmesh_Bhaiyyasaheb_Patil\n",
            "{'Preceded by': ['Rajiv Anil Deshmukh, NCP'], 'Born': ['24 June 1978'], 'Nationality': ['Indian'], 'Political party': ['Bharatiya Janata Party'], 'Spouse(s)': ['Sampada Unmesh Patil'], 'Children': ['Srushti, Swami and Samarth'], 'Mother': ['Mangala Patil'], 'Father': ['Bhaiyasaheb Patil'], 'Education': ['B.E Chemical'], 'Alma mater': ['North Maharashtra'], 'Occupation': ['Politician'], 'Website': ['www.unmeshpatil.in']}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': []}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['24 June 1978']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': []}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': ['Srushti, Swami and Samarth']}], 'SPOUSES': [{'dict_key': 'Spouse(s)', 'type': 'PERSON', 'entity_list': ['Sampada Unmesh Patil']}], 'PARENTS': [{'dict_key': 'Father', 'type': 'PERSON', 'entity_list': ['Bhaiyasaheb Patil']}, {'dict_key': 'Mother', 'type': 'PERSON', 'entity_list': ['Mangala Patil']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': []}, {'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': []}]}\n",
            "http://en.wikipedia.org/wiki/Kantilal_Bhuria\n",
            "{'Preceded by': ['Dileep Singh Bhuria'], 'Succeeded by': ['Ram Vilas Paswan'], 'Constituency': ['Ratlam'], 'Prime Minister': ['Manmohan Singh'], 'Born': ['', '1950-06-01', ')', '1 June 1950', '', 'Jhabua', ',', 'Madhya Bharat', ',', 'India'], 'Nationality': ['India'], 'Political party': ['Indian National Congress'], 'Spouse(s)': ['Kalpana Bhuria'], 'Children': ['2 sons', 'Sandeep Bhuria', 'Vikrant Bhuria'], 'Mother': ['Ladki Bai'], 'Father': ['Nanu Ram Bhuria'], 'Residence': ['Jhabua'], 'Education': ['M. A.', ',', 'LLB'], 'Alma mater': ['Chandrashekhar Azad College, Jhabua'], 'Profession': ['Politician']}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': ['Jhabua', 'Madhya Bharat']}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1950-06-01', '1 June 1950']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': ['Jhabua', 'Madhya Bharat', 'India']}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': ['Sandeep Bhuria', 'Vikrant Bhuria']}], 'SPOUSES': [{'dict_key': 'Spouse(s)', 'type': 'PERSON', 'entity_list': ['Kalpana Bhuria']}], 'PARENTS': [{'dict_key': 'Father', 'type': 'PERSON', 'entity_list': ['Nanu Ram']}, {'dict_key': 'Mother', 'type': 'PERSON', 'entity_list': ['Ladki Bai']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': ['LLB']}, {'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': []}]}\n",
            "http://en.wikipedia.org/wiki/Stephen_Colbert\n",
            "{'Birth name': ['Stephen Tyrone Colbert'], 'Born': ['', '1964-05-13', ')', 'May 13, 1964', '', 'Washington, D.C.', ', U.S.'], 'Medium': ['Television, theater, film, books'], 'Education': ['Porter-Gaud School'], 'Alma mater': ['Northwestern University'], 'Years active': ['1984–present'], 'Genres': ['', 'Political', '/', 'news satire', '', 'improvisational comedy', '', 'black comedy', '', 'character comedy', '', 'sketch comedy', '', 'surreal humor', ''], 'Subject(s)': ['', 'American politics', '', 'American culture', '', 'political punditry', '', 'pop culture', '', 'current events', '', 'mass media/news media', '', 'civil rights', '', 'religion', '', 'social awkwardness', ''], 'Spouse': ['Evelyn McGee', 'm.', '', '1993', ')'], 'Children': ['3'], 'Parent(s)': ['James William Colbert Jr.'], 'Relative(s)': ['Elizabeth Colbert Busch', ''], 'Signature': []}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': []}, {'dict_key': 'Birth name', 'type': 'PERSON', 'entity_list': ['Stephen Tyrone Colbert']}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1964-05-13', 'May 13, 1964']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': ['Washington, D.C.']}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': []}], 'SPOUSES': [{'dict_key': 'Spouse', 'type': 'PERSON', 'entity_list': ['Evelyn McGee']}], 'PARENTS': [{'dict_key': 'Parent(s)', 'type': 'PERSON', 'entity_list': ['James William Colbert Jr.']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': ['Porter-Gaud School']}, {'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': ['Northwestern University']}]}\n",
            "http://en.wikipedia.org/wiki/Rubab_Sayda\n",
            "{'Preceded by': ['Padamsen Chaudhary'], 'Succeeded by': ['Kamal Kishor'], 'Constituency': ['Bahraich'], 'Born': ['', '1950-06-15', ')', '15 June 1950', '', 'Meerut', ',', 'Uttar Pradesh'], 'Political party': ['SP'], 'Spouse(s)': ['Waqar Ahmad Shah'], 'Children': ['Yasar Shah', 'and Dr.Alvira Shah'], 'Mother': ['Noorjahan'], 'Father': ['Razaulhaq'], 'Residence': ['Qazipura', 'Bahraich'], 'Education': ['M.A., B.Ed.'], 'Alma mater': ['Meerut University']}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': ['Meerut']}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1950-06-15', '15 June 1950']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': ['Meerut', 'Uttar Pradesh']}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': ['Yasar Shah', 'Alvira Shah']}], 'SPOUSES': [{'dict_key': 'Spouse(s)', 'type': 'PERSON', 'entity_list': ['Waqar Ahmad Shah']}], 'PARENTS': [{'dict_key': 'Father', 'type': 'PERSON', 'entity_list': ['Razaulhaq']}, {'dict_key': 'Mother', 'type': 'PERSON', 'entity_list': ['Noorjahan']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': []}, {'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': ['Meerut University']}]}\n",
            "http://en.wikipedia.org/wiki/Kees_Meeuws\n",
            "{'Birth name': ['Kees Junior Meeuws'], 'Date of birth': ['', '1974-07-26', ')', '26 July 1974', ''], 'Place of birth': ['Auckland', ', New Zealand'], 'Height': ['1', '⁄', '2', 'in)'], 'Weight': [], 'School': ['Kelston Boys High School, Rutherford College'], 'Notable relative(s)': ['Johan, Roberta, Syliva, Gloria, Monique and Johnny Meeuws.'], 'Spouse': ['Juanita Meeuws'], 'Children': ['Javier, Cayne, Tayla, Eva, Inez and Serg.'], 'Position(s)': ['', 'Prop'], 'All Black No.': ['', '977'], 'Years': ['', 'Team'], '2004-2006  2006-2007  2007-2008  2008-2009': ['', 'Castres', 'Agen', 'Castres', 'Scarlets'], '1996  1997–2001  2002–2004  2009–2011': ['', 'Auckland', '', '', 'Otago', '', '', 'Auckland', '', '', 'Otago'], '1997–20012002–2004': ['', 'Highlanders', 'Blues'], '1998–2004': ['', 'New Zealand'], 'Other names': ['Casey'], 'Parent(s)': ['Cornelius and Rebecca Meeuws']}\n",
            "{'NAME': [{'dict_key': 'Birth name', 'type': 'PERSON', 'entity_list': []}], 'BIRTH_DATE': [{'dict_key': 'Date of birth', 'type': 'DATE', 'entity_list': ['1974-07', '26 July 1974']}], 'BIRTH_PLACE': [{'dict_key': 'Place of birth', 'type': 'GPE', 'entity_list': []}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': ['Javier, Cayne, Tayla, Eva, Inez and Serg.']}], 'SPOUSES': [{'dict_key': 'Spouse', 'type': 'PERSON', 'entity_list': ['Juanita Meeuws']}], 'PARENTS': [{'dict_key': 'Parent(s)', 'type': 'PERSON', 'entity_list': ['Cornelius']}], 'EDUCATION': [{'dict_key': 'School', 'type': 'ORG', 'entity_list': ['Kelston Boys High School']}]}\n",
            "http://en.wikipedia.org/wiki/Basori_Singh_Masram\n",
            "{'Preceded by': ['Faggan Singh Kulaste'], 'Succeeded by': ['Faggan Singh Kulaste'], 'Constituency': ['Bajag'], 'Born': ['', '1945-11-29', ')', '29 November 1945', 'Dugariya, District. Mandla Pradesh)'], 'Political party': ['Indian National Congress'], 'Spouse(s)': ['Jeera Bai', 'm.', '1970)'], 'Children': ['2 daughters and 2 sons'], 'Mother': ['Bhaddabai Masram'], 'Father': ['Dhiraj Singh Masram'], 'Residence': [], 'Education': ['Under Matriculate'], 'Alma mater': ['Higher Secondary School, Bichhiya']}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': []}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1945-11-29']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': []}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': []}], 'SPOUSES': [{'dict_key': 'Spouse(s)', 'type': 'PERSON', 'entity_list': ['Jeera Bai']}], 'PARENTS': [{'dict_key': 'Father', 'type': 'PERSON', 'entity_list': ['Dhiraj Singh Masram']}, {'dict_key': 'Mother', 'type': 'PERSON', 'entity_list': ['Bhaddabai Masram']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': []}, {'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': ['Higher Secondary School']}]}\n",
            "http://en.wikipedia.org/wiki/Craig_Roberts_Stapleton\n",
            "{'President': ['George W. Bush'], 'Preceded by': ['John Shattuck'], 'Succeeded by': ['William J. Cabaniss'], 'Born': ['1945', 'Kansas City, Missouri', ', U.S.'], 'Political party': ['Republican'], 'Spouse(s)': ['Dorothy Walker Stapleton'], 'Children': ['2'], 'Mother': ['Katherine Histed Hall'], 'Father': ['Benjamin Franklin Stapleton Jr.'], 'Education': ['Phillips Exeter Academy'], 'Alma mater': ['Harvard University'], 'Occupation': ['Diplomat', ',', 'Businessman']}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': []}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1945']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': ['Kansas City, Missouri']}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': []}], 'SPOUSES': [{'dict_key': 'Spouse(s)', 'type': 'PERSON', 'entity_list': ['Dorothy Walker Stapleton']}], 'PARENTS': [{'dict_key': 'Father', 'type': 'PERSON', 'entity_list': ['Benjamin Franklin Stapleton Jr.']}, {'dict_key': 'Mother', 'type': 'PERSON', 'entity_list': ['Katherine Histed Hall']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': ['Phillips Exeter Academy']}, {'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': ['Harvard University']}]}\n",
            "http://en.wikipedia.org/wiki/Tamim_Iqbal\n",
            "{'Full name': ['Tamim Iqbal Khan'], 'Born': ['', '1989-03-20', ')', '20 March 1989', '', 'Chittagong, Bangladesh'], 'Nickname': ['Tamim'], 'Height': [], 'Batting': ['Left handed'], 'Bowling': ['Right-arm off-break'], 'Role': ['Opening batsman'], 'Relations': ['Akram Khan', '', 'Nafees Iqbal', ''], 'National side': ['Bangladesh', ''], 'Test debut (cap 51)': ['New Zealand'], 'Last Test': ['Zimbabwe'], 'ODI debut (cap 83)': ['Zimbabwe'], 'Last ODI': ['Zimbabwe'], 'T20I debut (cap 17)': ['Kenya'], 'Last T20I': ['Zimbabwe'], '2007–present': ['Chittagong Division'], '2011': ['Nottinghamshire'], '2012': ['Wayamba United'], '2013': ['St Lucia Zouks'], '2012–2013': ['Wellington'], '2012/13': ['Pune Warriors India'], '2015–2016': ['Chittagong Vikings'], '2016–2018': ['Peshawar Zalmi'], '2017[2]': ['Essex'], '2017': ['Speen Ghar Tigers'], '2017–2019': ['Comilla Victorians'], '2018–present': ['Nangarhar Leopards'], '2019–present': ['Dhaka Platoon'], 'Spouse(s)': ['Ayesha Siddiqa'], 'Children': ['1 son & 1 daughter'], 'Mother': ['Nusrat Iqbal'], 'Father': ['Iqbal Khan'], 'Residence': ['Dhaka, Bangladesh'], 'Education': ['Sunshine Grammar School'], 'Occupation': ['Cricketer'], 'Net worth': ['US$30', 'million']}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': []}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1989-03-20', '20 March 1989']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': ['Chittagong, Bangladesh']}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': []}], 'SPOUSES': [{'dict_key': 'Spouse(s)', 'type': 'PERSON', 'entity_list': ['Ayesha Siddiqa']}], 'PARENTS': [{'dict_key': 'Father', 'type': 'PERSON', 'entity_list': ['Iqbal Khan']}, {'dict_key': 'Mother', 'type': 'PERSON', 'entity_list': ['Nusrat Iqbal']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': ['Sunshine Grammar School']}]}\n",
            "http://en.wikipedia.org/wiki/Mark_Harmon\n",
            "{'Born': ['Thomas Mark Harmon', '', '1951-09-02', ')', 'September 2, 1951', '', 'Burbank, California', ', U.S.'], 'Alma mater': ['UCLA', ', B.A. 1974'], 'Occupation': ['Actor', ',', 'television producer', ',', 'television director'], 'Years active': ['1970–present'], 'Spouse(s)': ['Pam Dawber', '', 'm.', '1987)'], 'Children': ['2'], 'Parent(s)': ['', 'Tom Harmon', '', 'Elyse Knox', ''], 'Position': ['Quarterback'], 'Major': ['Communication'], 'College': ['', 'Pierce College', '', '', 'UCLA', '', '1972', '–', '1973', ')', ''], 'High school': ['Harvard-Westlake'], 'Height': [], 'Weight': []}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': ['Thomas Mark Harmon']}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1951-09-02', 'September 2, 1951']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': ['Burbank, California']}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': []}], 'SPOUSES': [{'dict_key': 'Spouse(s)', 'type': 'PERSON', 'entity_list': ['Pam Dawber']}], 'PARENTS': [{'dict_key': 'Parent(s)', 'type': 'PERSON', 'entity_list': ['Tom Harmon', 'Elyse Knox']}], 'EDUCATION': [{'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': ['UCLA', 'B.A. 1974']}, {'dict_key': 'High school', 'type': 'ORG', 'entity_list': ['Harvard-Westlake']}, {'dict_key': 'College', 'type': 'ORG', 'entity_list': ['Pierce College', 'UCLA']}]}\n",
            "http://en.wikipedia.org/wiki/Gajendra_Singh_Rajukhedi\n",
            "{'Preceded by': ['Chhatar Singh Darbar'], 'Succeeded by': ['Savitri Thakur'], 'Constituency': ['Dhar'], 'Born': ['', '1964-12-11', ')', '11 December 1964', '', 'Rajukhedi, Dhar'], 'Political party': ['Indian National Congress'], 'Spouse(s)': ['Gayatri Singh', 'm.', '1990)'], 'Children': ['2 daughters and 2 sons'], 'Mother': ['Ramkunwar Devi'], 'Father': ['Onkar Singh'], 'Residence': [], 'Education': ['B. A.', '', 'LLB'], 'Alma mater': ['Devi Ahilya University'], 'Profession': ['Politician', ',', 'Advocate']}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': []}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1964-12-11', '11 December 1964']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': ['Rajukhedi, Dhar']}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': []}], 'SPOUSES': [{'dict_key': 'Spouse(s)', 'type': 'PERSON', 'entity_list': ['Gayatri Singh']}], 'PARENTS': [{'dict_key': 'Father', 'type': 'PERSON', 'entity_list': ['Onkar Singh']}, {'dict_key': 'Mother', 'type': 'PERSON', 'entity_list': ['Ramkunwar Devi']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': ['LLB']}, {'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': ['Devi Ahilya University']}]}\n",
            "http://en.wikipedia.org/wiki/Gajendra_Patel\n",
            "{'Preceded by': ['Subhash Patel'], 'Constituency': ['Khargone'], 'Born': ['', '1975-02-16', ')', '16 February 1975', '', 'Barwani', ',', 'Madhya Pradesh'], 'Nationality': ['Indian'], 'Political party': ['Bharatiya Janata Party'], 'Spouse(s)': ['Basanti Patel'], 'Children': ['2'], 'Mother': ['Sushila Bai Patel'], 'Father': ['Umrao Singh Patel'], 'Education': ['BA', 'LL.B.'], 'Alma mater': ['Government College, Barwani', 'Indore Christian College,', 'Indore'], 'Profession': ['Businessperson', 'Agriculturist']}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': ['Barwani']}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1975-02-16', '16 February 1975']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': ['Barwani', 'Madhya Pradesh']}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': []}], 'SPOUSES': [{'dict_key': 'Spouse(s)', 'type': 'PERSON', 'entity_list': ['Basanti Patel']}], 'PARENTS': [{'dict_key': 'Father', 'type': 'PERSON', 'entity_list': ['Umrao Singh Patel']}, {'dict_key': 'Mother', 'type': 'PERSON', 'entity_list': ['Sushila Bai Patel']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': []}, {'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': ['Government College', 'Indore Christian College']}]}\n",
            "http://en.wikipedia.org/wiki/William_Legge,_10th_Earl_of_Dartmouth\n",
            "{'Leader': ['Nigel Farage', 'Diane James', 'Paul Nuttall', 'Steve Crowther', '', '', 'Henry Bolton'], 'Preceded by': ['Gerald Legge'], 'Succeeded by': ['House of Lords Act 1999'], 'Born': ['', '1949-09-23', ')', '23 September 1949', '', 'Westminster', ',', 'London', ', England'], 'Political party': ['Independent', 'UK Independence Party', '', ''], 'Other politicalaffiliations': ['Conservative', '', '2007)'], 'Spouse(s)': ['Fiona Campbell', 'm.', '', '2009', ')'], 'Children': ['1'], 'Mother': ['Raine McCorquodale'], 'Father': ['Gerald Legge, 9th Earl of Dartmouth'], 'Education': ['Eton College'], 'Alma mater': ['Christ Church, Oxford', 'Harvard Business School'], 'Website': ['williamdartmouth', '.com']}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': ['Westminster']}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1949-09-23', '23 September 1949']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': ['Westminster', 'London']}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': []}], 'SPOUSES': [{'dict_key': 'Spouse(s)', 'type': 'PERSON', 'entity_list': ['Fiona Campbell']}], 'PARENTS': [{'dict_key': 'Father', 'type': 'PERSON', 'entity_list': ['Gerald Legge']}, {'dict_key': 'Mother', 'type': 'PERSON', 'entity_list': ['Raine McCorquodale']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': ['Eton College']}, {'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': ['Christ Church', 'Harvard Business School']}]}\n",
            "http://en.wikipedia.org/wiki/Ajay_Arjun_Singh\n",
            "{'Succeeded by': ['Arjun Singh'], 'Preceded by': ['Arjun Singh'], 'Constituency': ['Churhat'], 'Born': ['', '1955-09-23', ')', '23 September 1955', '', 'Prayagraj', ',', 'Uttar Pradesh', ',', 'India'], 'Nationality': ['Indian'], 'Political party': ['Indian National Congress'], 'Spouse(s)': ['Suniti Singh'], 'Children': ['1 son, 2 daughters'], 'Mother': ['Saroj Kumari'], 'Father': ['Arjun Singh'], 'Residence': ['Churhat', ',', 'Sidhi district'], 'Education': ['MA', 'in Economics'], 'Alma mater': ['Barkatullah University'], 'Profession': ['Activist', ',', 'Politician'], 'Website': ['http://ajaysinghrahul.in']}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': ['Prayagraj']}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1955-09-23', '23 September 1955']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': ['Prayagraj', 'Uttar Pradesh', 'India']}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': []}], 'SPOUSES': [{'dict_key': 'Spouse(s)', 'type': 'PERSON', 'entity_list': ['Suniti Singh']}], 'PARENTS': [{'dict_key': 'Father', 'type': 'PERSON', 'entity_list': ['Arjun Singh']}, {'dict_key': 'Mother', 'type': 'PERSON', 'entity_list': ['Saroj Kumari']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': []}, {'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': ['Barkatullah University']}]}\n",
            "http://en.wikipedia.org/wiki/Christopher_Lewis_(priest)\n",
            "{'Church': ['Church of England'], 'Diocese': ['Oxford'], 'In office': ['2003–2014'], 'Predecessor': ['John Drury'], 'Successor': ['Martyn Percy'], 'Other posts': ['Dean of St Albans', ''], 'Ordination': ['1973', '1974'], 'Birth name': ['Christopher Andrew Lewis'], 'Born': ['', '1944-02-04', ')', '4 February 1944', ''], 'Nationality': ['British'], 'Denomination': ['Anglicanism'], 'Parents': ['Admiral', 'Sir Andrew Lewis'], 'Spouse': ['Rhona Jane Martindale', 'm.', '1970)'], 'Children': ['Three'], 'Education': ['Marlborough College'], 'Alma mater': ['University of Bristol', '', '', 'Corpus Christi College, Cambridge', '', '', 'Westcott House, Cambridge'], 'Service/branch': ['Royal Navy'], 'Years of service': ['1961–1966'], 'Rank': ['Lieutenant'], 'Battles/wars': ['Indonesia–Malaysia confrontation']}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': []}, {'dict_key': 'Birth name', 'type': 'PERSON', 'entity_list': ['Christopher Andrew Lewis']}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1944-02-04', '4 February 1944']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': []}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': ['Three']}], 'SPOUSES': [{'dict_key': 'Spouse', 'type': 'PERSON', 'entity_list': ['Rhona Jane Martindale']}], 'PARENTS': [{'dict_key': 'Parents', 'type': 'PERSON', 'entity_list': ['Admiral', 'Andrew Lewis']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': ['Marlborough College']}, {'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': ['University of Bristol', 'Corpus Christi College']}]}\n",
            "http://en.wikipedia.org/wiki/Ho_Kah_Leong\n",
            "{'Born': ['', '1937-09-21', ')', 'September 21, 1937', ''], 'Citizenship': ['Singapore'], 'Nationality': ['Singaporean'], 'Political party': [\"People's Action Party\"], 'Spouse(s)': ['Grace Chan Lai Kiew'], 'Children': ['4'], 'Mother': ['Wong Kaw Moey'], 'Father': ['Ho Foo Sen'], 'Education': ['Bachelor of Science'], 'Alma mater': ['Nanyang University']}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': []}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1937-09-21', 'September 21, 1937']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': []}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': []}], 'SPOUSES': [{'dict_key': 'Spouse(s)', 'type': 'PERSON', 'entity_list': ['Grace Chan Lai Kiew']}], 'PARENTS': [{'dict_key': 'Father', 'type': 'PERSON', 'entity_list': ['Ho Foo Sen']}, {'dict_key': 'Mother', 'type': 'PERSON', 'entity_list': ['Wong Kaw Moey']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': []}, {'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': ['Nanyang University']}]}\n",
            "http://en.wikipedia.org/wiki/Victor_de_Waal\n",
            "{'Church': ['Church of England'], 'Installed': ['1976'], 'Term ended': ['1986'], 'Predecessor': ['Ian White-Thomson'], 'Successor': ['John Simpson'], 'Birth name': ['Victor de Waal'], 'Born': ['', '1929-02-02', ')', '2 February 1929', ''], 'Denomination': ['Anglicanism'], 'Parents': ['Hendrik de Waal', 'Elisabeth von Ephrussi'], 'Spouse': ['Esther Aline Lowndes-Moir'], 'Children': ['Alex de Waal', 'Edmund de Waal', 'Thomas de Waal'], 'Education': ['Tonbridge School'], 'Alma mater': ['Pembroke College, Cambridge']}\n",
            "{'NAME': [{'dict_key': 'Born', 'type': 'PERSON', 'entity_list': []}, {'dict_key': 'Birth name', 'type': 'PERSON', 'entity_list': ['Victor de Waal']}], 'BIRTH_DATE': [{'dict_key': 'Born', 'type': 'DATE', 'entity_list': ['1929-02-02', '2 February 1929']}], 'BIRTH_PLACE': [{'dict_key': 'Born', 'type': 'GPE', 'entity_list': []}], 'CHILDREN': [{'dict_key': 'Children', 'type': 'PERSON', 'entity_list': ['Alex de Waal', 'Edmund de Waal', 'Thomas de Waal']}], 'SPOUSES': [{'dict_key': 'Spouse', 'type': 'PERSON', 'entity_list': ['Esther Aline']}], 'PARENTS': [{'dict_key': 'Parents', 'type': 'PERSON', 'entity_list': ['Hendrik de Waal', 'Elisabeth von Ephrussi']}], 'EDUCATION': [{'dict_key': 'Education', 'type': 'ORG', 'entity_list': ['Tonbridge School']}, {'dict_key': 'Alma mater', 'type': 'ORG', 'entity_list': ['Pembroke College']}]}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5M6Brc05b30o",
        "colab_type": "text"
      },
      "source": [
        "import the tagging mechanism"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UoRp3_o2i4bU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import logging\n",
        "logger = logging.getLogger()\n",
        "logger.setLevel(logging.INFO)\n",
        "colabimport.get_notebook('https://github.com/ratmcu/wiki_ner/blob/master/conll_tagged_ne.ipynb?raw=true')\n",
        "from conll_tagged_ne import WikiConLLTagger"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AoaVOVvNMAuG",
        "colab_type": "text"
      },
      "source": [
        "# Dataset creation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efic43qWKCcp",
        "colab_type": "code",
        "outputId": "4615e9be-e676-460f-a595-c43b60ea5a2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import os\n",
        "import logging\n",
        "logger = logging.getLogger()\n",
        "logger.setLevel(logging.INFO)\n",
        "!pip install wget\n",
        "import wget\n",
        "try:\n",
        "    import colabimport\n",
        "except:\n",
        "    colabimporturl = 'https://github.com/ratmcu/colaboratory_import/raw/master/colabimport.py'\n",
        "    filename = colabimporturl.split(\"/\")[-1].split(\"?\")[0]\n",
        "    if os.path.isfile(filename):\n",
        "        os.remove(filename)\n",
        "    wget.download(colabimporturl)\n",
        "    import colabimport\n",
        "# import importlib \n",
        "# importlib.reload(conll_tagged_ne) \n",
        "# from conll_tagged_ne import WikiConLLTagger    \n",
        "colabimport.get_notebook('https://github.com/ratmcu/wiki_ner/blob/master/conll_tagged_ne.ipynb?raw=true')\n",
        "from conll_tagged_ne import WikiConLLTagger\n",
        "import pandas as pd\n",
        "import urllib\n",
        "import multiprocessing\n",
        "import time\n",
        "# split a list into evenly sized chunks\n",
        "#!pip install wget\n",
        "import wget\n",
        "import pickle\n",
        "import ast\n",
        "import numpy as np\n",
        "from bs4 import BeautifulSoup\n",
        "import traceback"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: wget in /usr/local/lib/python3.6/dist-packages (3.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JgBR4y3kA1Iy",
        "colab_type": "text"
      },
      "source": [
        "## creating annotated files from urls"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vi84G3TyBGSd",
        "colab_type": "text"
      },
      "source": [
        "scrapes, annotate and store the pages with multiple workers , shown here is a sample of the process for few randomly chosen entries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "djuBR3oWRV-y",
        "colab_type": "code",
        "outputId": "eb5089c4-d0ec-4358-d6bd-d359123c8a5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "csv_url = 'https://github.com/ratmcu/wiki_ner/blob/master/entity_presence_score.csv?raw=true'\n",
        "csv_filename = csv_url.split(\"/\")[-1].split(\"?\")[0]\n",
        "if os.path.isfile(csv_filename):\n",
        "        os.remove(csv_filename)\n",
        "wget.download(csv_url)\n",
        "entity_presence_score_df = pd.read_csv(csv_filename)\n",
        "\n",
        "# import matplotlib.pyplot as plt\n",
        "# entity_presence_score_df.iloc[:,1:].plot.hist(by='SCORE',bins=7)\n",
        "# histogram_df = entity_presence_score_df.iloc[:,1:].groupby('SCORE')\n",
        "\n",
        "indices_list = []\n",
        "indices_list.extend(entity_presence_score_df.iloc[:,1:].groupby('SCORE').groups[4.0])\n",
        "indices_list.extend(entity_presence_score_df.iloc[:,1:].groupby('SCORE').groups[5.0])\n",
        "indices_list.extend(entity_presence_score_df.iloc[:,1:].groupby('SCORE').groups[6.0])\n",
        "indices_list.extend(entity_presence_score_df.iloc[:,1:].groupby('SCORE').groups[7.0])\n",
        "print(indices_list)\n",
        "data = []\n",
        "for index in indices_list:\n",
        "    data.append((index, 'http://en.wikipedia.org' +entity_presence_score_df.iloc[index]['LINK']))\n",
        "# print(data)\n",
        "# exit(0)\n",
        "save_dir = 'scrapes'\n",
        "if not os.path.exists(save_dir): os.makedirs(save_dir)\n",
        "\n",
        "def get_annotations(url):\n",
        "    try:\n",
        "#     from conll_tagged_ne import WikiConLLTagger\n",
        "        tagger = WikiConLLTagger(url[1])\n",
        "        df = pd.DataFrame(data = tagger.place_tags()).copy()\n",
        "        summary_dict = {'URL':url[1]}\n",
        "        summary = tagger.tag_factory.get_tag_summary()\n",
        "        [ summary_dict.update({key:list([summary[key]])}) for key in summary.keys()]\n",
        "        summary_df = pd.DataFrame(data = summary_dict)\n",
        "        # print(summary_df)\n",
        "        return url[0], df, summary_df, url[1].split('/')[-1]\n",
        "#     return url[0], pd.DataFrame(), pd.DataFrame(), url[1].split('/')[-1]\n",
        "\n",
        "    except Exception as e:\n",
        "        # print(traceback.format_exc())\n",
        "        # print(\"failed to annotate the page!\")\n",
        "        # print(url)    \n",
        "        pass\n",
        "\n",
        "def chunks(l, n):\n",
        "    return [l[i:i+n] for i in range(0, len(l), n)]\n",
        "\n",
        "def do_job(in_queue, queue):\n",
        "    import logging\n",
        "    logger = logging.getLogger()\n",
        "    logger.setLevel(logging.INFO)\n",
        "    while True:\n",
        "        if in_queue.empty(): return\n",
        "        url_list = in_queue.get()\n",
        "        for url in url_list:\n",
        "            queue.put(get_annotations(url))\n",
        "        \n",
        "def save_chunk_to_disk(saver_queue, event):\n",
        "    while True:\n",
        "            try:\n",
        "                index, annot_df, summary_df, name = saver_queue.get(block=True, timeout=1)\n",
        "                if not os.path.exists(save_dir + '/' + name): os.makedirs(save_dir + '/' + name)\n",
        "                annot_df.to_csv(save_dir + '/' + name + '/' + 'annot_csv_{0}.csv'.format(index))    \n",
        "                # print('saved file: annot_csv_{0}.csv'.format(index))\n",
        "                summary_df.to_csv(save_dir + '/' + name + '/' + 'summary_df_{0}.csv'.format(index))    \n",
        "                # print('saved file: summary_df_{0}.csv'.format(index))\n",
        "#             except Exception as e:\n",
        "#                 print(traceback.format_exc())\n",
        "            except:\n",
        "                pass\n",
        "            if event.is_set(): break\n",
        "    # print('done saving files to the disk')\n",
        "             \n",
        "def dispatch_jobs(data, worker_number):\n",
        "    category_data_frame =  pd.DataFrame()\n",
        "    total = len(data)\n",
        "    chunk_size = 100 #total // job_number\n",
        "    #chunk_size = total // worker_number\n",
        "    slice = chunks(data, chunk_size)\n",
        "\n",
        "    saver_queue = multiprocessing.Queue(1000)\n",
        "    event = multiprocessing.Event()\n",
        "    disk_save_mp = multiprocessing.Process(target=save_chunk_to_disk, args=(saver_queue, event))\n",
        "    workers = []\n",
        "    in_queues = []\n",
        "    \n",
        "    # print('queued data')    \n",
        "    in_queue = multiprocessing.Queue(10000)\n",
        "    for worker in range(worker_number):\n",
        "        j = multiprocessing.Process(target=do_job, args=(in_queue, saver_queue))\n",
        "        workers.append(j)\n",
        "    \n",
        "    queue_number = 0\n",
        "    \n",
        "    for i, s in enumerate(slice):\n",
        "        in_queue.put(s)\n",
        "        \n",
        "    print('workers created')    \n",
        "    \n",
        "    for worker in workers:\n",
        "        time.sleep(1)\n",
        "        worker.start()\n",
        "    disk_save_mp.start()    \n",
        "    print('workers started')\n",
        "    queue_empty = False\n",
        "    running = any(p.is_alive() for p in workers)\n",
        "    while running:\n",
        "        #save_chunk_to_disk(saver_queue.get(block=True))\n",
        "        running = any(p.is_alive() for p in workers)\n",
        "    print('all input queues drained')\n",
        "    while not saver_queue.empty(): pass    \n",
        "    event.set()\n",
        "    disk_save_mp.join()  \n",
        "    return\n",
        "\n",
        "import random\n",
        "# data_r = random.choices(data, k=500)\n",
        "data_r = data[:500]\n",
        "start_time = time.perf_counter()\n",
        "print(os.cpu_count())\n",
        "cpu_count = os.cpu_count()\n",
        "dispatch_jobs(data_r, cpu_count)\n",
        "end_time = time.perf_counter()      # 2\n",
        "run_time = end_time - start_time \n",
        "print(run_time)\n",
        "import tarfile\n",
        "tar =  tarfile.open('dataset.tar.gz', mode='w:gz')\n",
        "tar.add('scrapes')\n",
        "tar.close()\n"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3401, 3402, 3403, 3404, 3405, 3406, 3407, 3408, 3409, 3410, 3411, 3412, 3413, 3414, 3415, 3416, 3417, 3418, 3419, 3420, 3421, 3422, 3423, 3424, 3425, 3426, 3427, 3428, 3429, 3430, 3431, 3432, 3433, 3434, 3435, 3436, 3437, 3438, 3439, 3440, 3441, 3442, 3443, 3444, 3445, 3446, 3447, 3448, 3449, 3450, 3451, 3452, 3453, 3454, 3455, 3456, 3457, 3458, 3459, 3460, 3461, 3462, 3463, 3464, 3465, 3466, 3467, 3468, 3469, 3470, 3471, 3472, 3473, 3474, 3475, 3476, 3477, 3478, 3479, 3480, 3481, 3482, 3483, 3484, 3485, 3486, 3487, 3488, 3489, 3490, 3491, 3492, 3493, 3494, 3495, 3496, 3497, 3498, 3499, 3500, 3501, 3502, 3503, 3504, 3505, 3506, 3507, 3508, 3509, 3510, 3511, 3512, 3513, 3514, 3515, 3516, 3517, 3518, 3519, 3520, 3521, 3522, 3523, 3524, 3525, 3526, 3527, 3528, 3529, 3530, 3531, 3532, 3533, 3534, 3535, 3536, 3537, 3538, 3539, 3540, 3541, 3542, 3543, 3544, 3545, 3546, 3547, 3548, 3549, 3550, 3551, 3552, 3553, 3554, 3555, 3556, 3557, 3558, 3559, 3560, 3561, 3562, 3563, 3564, 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2922, 2923, 2924, 2925, 2926, 2927, 2928, 2929, 2930, 2931, 2932, 2933, 2934, 2935, 2936, 2937, 2938, 2939, 2940, 2941, 2942, 2943, 2944, 2945, 2946, 2947, 2948, 2949, 2950, 2951, 2952, 2953, 2954, 2955, 2956, 2957, 2958, 2959, 2960, 2961, 2962, 2963, 2964, 2965, 2966, 2967, 2968, 2969, 2970, 2971, 2972, 2973, 2974, 2975, 2976, 2977, 2978, 2979, 2980, 2981, 2982, 2983, 2984, 2985, 2986, 2987, 2988, 2989, 2990, 2991, 2992, 2993, 2994, 2995, 2996, 2997, 2998, 2999, 3000, 3001, 3002, 3003, 3004, 3005, 3006, 3007, 3008, 3009, 3010, 3011, 3012, 3013, 3014, 3015, 3016, 3017, 3018, 3019, 3020, 3021, 3022, 3023, 3024, 3025, 3026, 3027, 3028, 3029, 3030, 3031, 3032, 3033, 3034, 3035, 3036, 3037, 3038, 3039, 3040, 3041, 3042, 3043, 3044, 3045, 3046, 3047, 3048, 3049, 3050, 3051, 3052, 3053, 3054, 3055, 3056, 3057, 3058, 3059, 3060, 3061, 3062, 3063, 3064, 3065, 3066, 3067, 3068, 3069, 3070, 3071, 3072, 3073, 3074, 3075, 3076, 3077, 3078, 3079, 3080, 3081, 3082, 3083, 3084, 3085, 3086, 3087, 3088, 3089, 3090, 3091, 3092, 3093, 3094, 3095, 3096, 3097, 3098, 3099, 3100, 3101, 3102, 3103, 3104, 3105, 3106, 3107, 3108, 3109, 3110, 3111, 3112, 3113, 3114, 3115, 3116, 3117, 3118, 3119, 3120, 3121, 3122, 3123, 3124, 3125, 3126, 3127, 3128, 3129, 3130, 3131, 3132, 3133, 3134, 3135, 3136, 3137, 3138, 3139, 3140, 3141, 3142, 3143, 3144, 3145, 3146, 3147, 3148, 3149, 3150, 3151, 3152, 3153, 3154, 3155, 3156, 3157, 3158, 3159, 3160, 3161, 3162, 3163, 3164, 3165, 3166, 3167, 3168, 3169, 3170, 3171, 3172, 3173, 3174, 3175, 3176, 3177, 3178, 3179, 3180, 3181, 3182, 3183, 3184, 3185, 3186, 3187, 3188, 3189, 3190, 3191, 3192, 3193, 3194, 3195, 3196, 3197, 3198, 3199, 3200, 3201, 3202, 3203, 3204, 3205, 3206, 3207, 3208, 3209, 3210, 3211, 3212, 3213, 3214, 3215, 3216, 3217, 3218, 3219, 3220, 3221, 3222, 3223, 3224, 3225, 3226, 3227, 3228, 3229, 3230, 3231, 3232, 3233, 3234, 3235, 3236, 3237, 3238, 3239, 3240, 3241, 3242, 3243, 3244, 3245, 3246, 3247, 3248, 3249, 3250, 3251, 3252, 3253, 3254, 3255, 3256, 3257, 3258, 3259, 3260, 3261, 3262, 3263, 3264, 3265, 3266, 3267, 3268, 3269, 3270, 3271, 3272, 3273, 3274, 3275, 3276, 3277, 3278, 3279, 3280, 3281, 3282, 3283, 3284, 3285, 3286, 3287, 3288, 3289, 3290, 3291, 3292, 3293, 3294, 3295, 3296, 3297, 3298, 3299, 3300, 3301, 3302, 3303, 3304, 3305, 3306, 3307, 3308, 3309, 3310, 3311, 3312, 3313, 3314, 3315, 3316, 3317, 3318, 3319, 3320, 3321, 3322, 3323, 3324, 3325, 3326, 3327, 3328, 3329, 3330, 3331, 3332, 3333, 3334, 3335, 3336, 3337, 3338, 3339, 3340, 3341, 3342, 3343, 3344, 3345, 3346, 3347, 3348, 3349, 3350, 3351, 3352, 3353, 3354, 3355, 3356, 3357, 3358, 3359, 3360, 3361, 3362, 3363, 3364, 3365, 3366, 3367, 3368, 3369, 3370, 3371, 3372, 3373, 3374, 3375, 3376, 3377, 3378, 3379, 3380, 3381, 3382, 3383, 3384, 3385, 3386, 3387, 3388, 3389, 3390, 3391, 3392, 3393, 3394, 3395, 3396, 3397, 3398, 3399, 3400, 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, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]\n",
            "4\n",
            "workers created\n",
            "workers started\n",
            "all input queues drained\n",
            "1408.0506993389997\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kzsMaus4Hj-q",
        "colab_type": "text"
      },
      "source": [
        "## creating text file dataset from csv files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZOeHwmbmy6P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tarfile\n",
        "tar =  tarfile.open('dataset.tar.gz', mode='r:gz')\n",
        "tar.extractall('./dataset')\n",
        "tar.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fQD-o4bKnZyt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "paths_summary = sorted([os.path.join(f[0], name) for f in os.walk('./dataset') \n",
        "                if len(f[2])!=0 for name in f[2] if os.path.splitext(name)[-1] == '.csv' and name.split('_')[0]=='summary'],\n",
        "               key=lambda path: int(path.split('_')[-1].split('.')[0]))\n",
        "\n",
        "paths_annot = sorted([os.path.join(f[0], name) for f in os.walk('./dataset') \n",
        "                if len(f[2])!=0 for name in f[2] if os.path.splitext(name)[-1] == '.csv' and name.split('_')[0]=='annot'],\n",
        "               key=lambda path: int(path.split('_')[-1].split('.')[0]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XzdFeeunv-C",
        "colab_type": "code",
        "outputId": "b790e647-864d-4daf-b5d8-c2e95ffa1766",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(paths_annot), len(paths_summary)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(493, 493)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0YuFcEBpXS4",
        "colab_type": "code",
        "outputId": "6ba52746-1fc8-46d2-be21-2b0dbc3b0c27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        }
      },
      "source": [
        "import pandas as pd\n",
        "summary_df = pd.DataFrame()\n",
        "import random\n",
        "# paths = random.choices(paths_summary, k=100)\n",
        "# for path in paths:\n",
        "for path in paths_summary:\n",
        "    df = pd.read_csv(path).iloc[:,1:].assign(idx=int(path.split('_')[-1].split('.')[0]))\n",
        "#     if df.iloc[:,1:]['BP'][0]!=0:\n",
        "#         summary_df = summary_df.append(df, ignore_index = True)\n",
        "    summary_df = summary_df.append(df, ignore_index = True)\n",
        "print(summary_df['URL'])\n",
        "summary_df.iloc[:,1:]"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0             http://en.wikipedia.org/wiki/Cynthia_Hogan\n",
            "1        http://en.wikipedia.org/wiki/Arvind_Singh_Mewar\n",
            "2                http://en.wikipedia.org/wiki/Pete_Olson\n",
            "3              http://en.wikipedia.org/wiki/Gene_Hackman\n",
            "4      http://en.wikipedia.org/wiki/Prince_Faisal_bin...\n",
            "                             ...                        \n",
            "488        http://en.wikipedia.org/wiki/Jeffrey_Goldberg\n",
            "489           http://en.wikipedia.org/wiki/Ricardo_Anaya\n",
            "490              http://en.wikipedia.org/wiki/David_Soul\n",
            "491           http://en.wikipedia.org/wiki/Etilda_Gjonaj\n",
            "492          http://en.wikipedia.org/wiki/James_Suckling\n",
            "Name: URL, Length: 493, dtype: object\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BD</th>\n",
              "      <th>NBD</th>\n",
              "      <th>CH</th>\n",
              "      <th>PR</th>\n",
              "      <th>SP</th>\n",
              "      <th>NPE</th>\n",
              "      <th>BP</th>\n",
              "      <th>NGPE</th>\n",
              "      <th>ED</th>\n",
              "      <th>NORG</th>\n",
              "      <th>SENTS</th>\n",
              "      <th>idx</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>14</td>\n",
              "      <td>11</td>\n",
              "      <td>3401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
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              "      <td>4</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>3402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>78</td>\n",
              "      <td>0</td>\n",
              "      <td>44</td>\n",
              "      <td>2</td>\n",
              "      <td>103</td>\n",
              "      <td>94</td>\n",
              "      <td>3403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>94</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>97</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>3</td>\n",
              "      <td>37</td>\n",
              "      <td>105</td>\n",
              "      <td>3404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>11</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>3405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>488</th>\n",
              "      <td>1</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "      <td>78</td>\n",
              "      <td>86</td>\n",
              "      <td>3896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>489</th>\n",
              "      <td>0</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>22</td>\n",
              "      <td>3897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490</th>\n",
              "      <td>2</td>\n",
              "      <td>67</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>33</td>\n",
              "      <td>2</td>\n",
              "      <td>40</td>\n",
              "      <td>76</td>\n",
              "      <td>3898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>491</th>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>3899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>492</th>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>32</td>\n",
              "      <td>45</td>\n",
              "      <td>3900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>493 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     BD  NBD  CH  PR  SP  NPE  BP  NGPE  ED  NORG  SENTS   idx\n",
              "0     2    3   0   0   0    8   0     6   2    14     11  3401\n",
              "1     0    7   3   1   4   19   0    30   0     0     38  3402\n",
              "2     1   52   0   0   1   78   0    44   2   103     94  3403\n",
              "3     1   94   0   0   2   97   0    30   3    37    105  3404\n",
              "4     1   23   0   2  11   31   0    16   0     0     35  3405\n",
              "..   ..  ...  ..  ..  ..  ...  ..   ...  ..   ...    ...   ...\n",
              "488   1   30   0   0   1  100   0    59   1    78     86  3896\n",
              "489   0   23   0   0   0   15   0    12   0    42     22  3897\n",
              "490   2   67   0   0   5   62   0    33   2    40     76  3898\n",
              "491   1   10   0   0   1    7   0     9   1     5     14  3899\n",
              "492   1   24   2   0   0   24   0    25   4    32     45  3900\n",
              "\n",
              "[493 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yV98Aq394BnQ",
        "colab_type": "text"
      },
      "source": [
        "## splitting the dataset to the test/valid/train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mA5XNhO3yVA-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scored_summary = summary_df.assign(SCORE = summary_df[['BD','CH', 'PR', 'SP','BP', 'ED']].apply(lambda x: x>0, axis=1, result_type='broadcast').sum(axis=1))\n",
        "scored_summary.to_csv('scored_summary.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MjLvCWymc4hQ",
        "colab_type": "text"
      },
      "source": [
        "## Final score summary for the chosen random entries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RF-VS_dBCJa8",
        "colab_type": "code",
        "outputId": "edef6dbd-1e12-4aa8-c2ce-18d9ac11cd3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "scored_summary_sorted = scored_summary.sort_values(by='SCORE', ascending = False)\n",
        "scored_summary_sorted.groupby('SCORE').count()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>URL</th>\n",
              "      <th>BD</th>\n",
              "      <th>NBD</th>\n",
              "      <th>CH</th>\n",
              "      <th>PR</th>\n",
              "      <th>SP</th>\n",
              "      <th>NPE</th>\n",
              "      <th>BP</th>\n",
              "      <th>NGPE</th>\n",
              "      <th>ED</th>\n",
              "      <th>NORG</th>\n",
              "      <th>SENTS</th>\n",
              "      <th>idx</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SCORE</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "      <td>139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "      <td>171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       URL   BD  NBD   CH   PR   SP  NPE   BP  NGPE   ED  NORG  SENTS  idx\n",
              "SCORE                                                                     \n",
              "0       47   47   47   47   47   47   47   47    47   47    47     47   47\n",
              "1       84   84   84   84   84   84   84   84    84   84    84     84   84\n",
              "2      139  139  139  139  139  139  139  139   139  139   139    139  139\n",
              "3      171  171  171  171  171  171  171  171   171  171   171    171  171\n",
              "4       51   51   51   51   51   51   51   51    51   51    51     51   51\n",
              "5        1    1    1    1    1    1    1    1     1    1     1      1    1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QZuKCQcnLMdu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "f2aec7e3-52e2-4aea-edcb-d2836a0755b9"
      },
      "source": [
        "scored_summary_sorted"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>URL</th>\n",
              "      <th>BD</th>\n",
              "      <th>NBD</th>\n",
              "      <th>CH</th>\n",
              "      <th>PR</th>\n",
              "      <th>SP</th>\n",
              "      <th>NPE</th>\n",
              "      <th>BP</th>\n",
              "      <th>NGPE</th>\n",
              "      <th>ED</th>\n",
              "      <th>NORG</th>\n",
              "      <th>SENTS</th>\n",
              "      <th>idx</th>\n",
              "      <th>SCORE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>http://en.wikipedia.org/wiki/Dianne_Feinstein</td>\n",
              "      <td>1</td>\n",
              "      <td>152</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>87</td>\n",
              "      <td>0</td>\n",
              "      <td>86</td>\n",
              "      <td>2</td>\n",
              "      <td>233</td>\n",
              "      <td>191</td>\n",
              "      <td>3746</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>http://en.wikipedia.org/wiki/Paul_Anderson_(ju...</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>16</td>\n",
              "      <td>14</td>\n",
              "      <td>3684</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>http://en.wikipedia.org/wiki/Stephen_King</td>\n",
              "      <td>2</td>\n",
              "      <td>157</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>198</td>\n",
              "      <td>0</td>\n",
              "      <td>75</td>\n",
              "      <td>3</td>\n",
              "      <td>132</td>\n",
              "      <td>304</td>\n",
              "      <td>3467</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>http://en.wikipedia.org/wiki/Angel_P%C3%A9rez_...</td>\n",
              "      <td>2</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>18</td>\n",
              "      <td>27</td>\n",
              "      <td>3745</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>http://en.wikipedia.org/wiki/Bill_Pullman</td>\n",
              "      <td>1</td>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>44</td>\n",
              "      <td>3743</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>http://en.wikipedia.org/wiki/Simon_Keogh</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3561</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>http://en.wikipedia.org/wiki/D%27Arcy_Rae</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>3562</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>363</th>\n",
              "      <td>http://en.wikipedia.org/wiki/Satish_Hiremath</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>3769</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>http://en.wikipedia.org/wiki/Magnus_Lund</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>3447</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>310</th>\n",
              "      <td>http://en.wikipedia.org/wiki/Adam_Doughty</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>3715</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>493 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   URL  BD  ...   idx  SCORE\n",
              "340      http://en.wikipedia.org/wiki/Dianne_Feinstein   1  ...  3746      5\n",
              "279  http://en.wikipedia.org/wiki/Paul_Anderson_(ju...   1  ...  3684      4\n",
              "65           http://en.wikipedia.org/wiki/Stephen_King   2  ...  3467      4\n",
              "339  http://en.wikipedia.org/wiki/Angel_P%C3%A9rez_...   2  ...  3745      4\n",
              "338          http://en.wikipedia.org/wiki/Bill_Pullman   1  ...  3743      4\n",
              "..                                                 ...  ..  ...   ...    ...\n",
              "158           http://en.wikipedia.org/wiki/Simon_Keogh   0  ...  3561      0\n",
              "159          http://en.wikipedia.org/wiki/D%27Arcy_Rae   0  ...  3562      0\n",
              "363       http://en.wikipedia.org/wiki/Satish_Hiremath   0  ...  3769      0\n",
              "45            http://en.wikipedia.org/wiki/Magnus_Lund   0  ...  3447      0\n",
              "310          http://en.wikipedia.org/wiki/Adam_Doughty   0  ...  3715      0\n",
              "\n",
              "[493 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U9KyeDQwFnkf",
        "colab_type": "text"
      },
      "source": [
        "score of 5 as test\\\n",
        "score of 4 as eval"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4w8idK3FbrU",
        "colab_type": "code",
        "outputId": "f63b10d3-31ba-4035-a5a9-5eea9194ca49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "scored_summary_sorted.groupby('SCORE').groups[5.0]"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([340], dtype='int64')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1najaY3ntGgX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def toConllTxt(path, save_file = None):\n",
        "    df = pd.read_csv(path)\n",
        "#     dir_path, _  = os.path.split(path)\n",
        "    if not save_file:\n",
        "        save_file = os.path.join(os.path.split(path)[0], '%s.txt' % path.split('/')[-1])\n",
        "    with open(save_file, 'w') as file:    \n",
        "        for i, row in enumerate(df.iterrows()):\n",
        "            if (row[1]['words'] == '\\n' and row[1]['tags'] == '\\n'):\n",
        "                file.write('\\n')\n",
        "            else:\n",
        "                try:\n",
        "                    file.write(row[1]['words']+' ')\n",
        "                except:\n",
        "                    file.write( str(row[1]['words']) + ' ')\n",
        "                file.write(row[1]['tags']+'\\n')\n",
        "    return save_file"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8-nAgL-BmtpN",
        "colab_type": "text"
      },
      "source": [
        "#### test set creation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wGfHDpfqmG6O",
        "colab_type": "code",
        "outputId": "2eb04972-9708-470f-883d-b0d9ec4d322e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#experiment_code\n",
        "test_indices = scored_summary_sorted.groupby('SCORE').groups[5.0]\n",
        "print(sorted(test_indices))\n",
        "paths_annot = sorted([os.path.join(f[0], name) for f in os.walk('./dataset') \n",
        "                if len(f[2])!=0 for name in f[2] if os.path.splitext(name)[-1] == '.csv' and name.split('_')[0]=='annot'],\n",
        "               key=lambda path: int(path.split('_')[-1].split('.')[0]))\n",
        "# test_paths = [path for path in paths_annot if int(path.split('_')[-1].split('.')[0]) in test_indices]\n",
        "test_paths_idx = [idx for idx in scored_summary_sorted.iloc[test_indices]['idx']]\n",
        "test_paths = [path for path in paths_annot if int(path.split('_')[-1].split('.')[0]) in test_paths_idx]\n",
        "\n",
        "print(test_paths)\n",
        "test_data_folder = './dataset_txt/test'\n",
        "if not os.path.exists(test_data_folder): os.makedirs(test_data_folder)\n",
        "import pandas as pd\n",
        "for i, path in enumerate(test_paths):\n",
        "    # print(path.split('/')[-2])\n",
        "    save_file = os.path.join(test_data_folder, '%s.txt' % path.split('/')[-1].split('.')[0])\n",
        "    txt_path = toConllTxt(path, save_file)\n",
        "    # print(txt_path)\n"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[340]\n",
            "['./dataset/scrapes/John_Boehner/annot_csv_3615.csv']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bh0VUr7J2YVe",
        "colab_type": "text"
      },
      "source": [
        "#### evaluation set creation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FfCbwgKpiTlv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "c4b3a00b-0c3a-4239-d45b-2b15081923d3"
      },
      "source": [
        "#experiment_code\n",
        "eval_indices = scored_summary_sorted.groupby('SCORE').groups[4.0]\n",
        "print(sorted(eval_indices))\n",
        "paths_annot = sorted([os.path.join(f[0], name) for f in os.walk('./dataset') \n",
        "                if len(f[2])!=0 for name in f[2] if os.path.splitext(name)[-1] == '.csv' and name.split('_')[0]=='annot'],\n",
        "               key=lambda path: int(path.split('_')[-1].split('.')[0]))\n",
        "# test_paths = [path for path in paths_annot if int(path.split('_')[-1].split('.')[0]) in test_indices]\n",
        "eval_paths_idx = [idx for idx in scored_summary_sorted.iloc[eval_indices]['idx']]\n",
        "eval_paths = [path for path in paths_annot if int(path.split('_')[-1].split('.')[0]) in eval_paths_idx]\n",
        "\n",
        "print(eval_paths)\n",
        "eval_data_folder = './dataset_txt/eval'\n",
        "if not os.path.exists(eval_data_folder): os.makedirs(eval_data_folder)\n",
        "import pandas as pd\n",
        "for i, path in enumerate(eval_paths):\n",
        "    # print(path.split('/')[-2])\n",
        "    save_file = os.path.join(eval_data_folder, '%s.txt' % path.split('/')[-1].split('.')[0])\n",
        "    txt_path = toConllTxt(path, save_file)\n",
        "    # print(txt_path)\n"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[6, 13, 39, 49, 59, 65, 81, 83, 108, 111, 122, 125, 126, 133, 135, 152, 163, 183, 214, 216, 230, 235, 240, 241, 253, 261, 263, 279, 281, 303, 304, 315, 321, 326, 335, 338, 339, 343, 346, 357, 379, 383, 389, 392, 402, 416, 432, 439, 462, 466, 474]\n",
            "['./dataset/scrapes/Greg_Mottola/annot_csv_3425.csv', './dataset/scrapes/Sadie_Coles/annot_csv_3428.csv', './dataset/scrapes/Vop_Osili/annot_csv_3432.csv', './dataset/scrapes/John_C._Metzler_Jr./annot_csv_3439.csv', './dataset/scrapes/Princess_Firyal/annot_csv_3448.csv', './dataset/scrapes/Dude_Harlino/annot_csv_3457.csv', './dataset/scrapes/Noel_Dempsey/annot_csv_3458.csv', './dataset/scrapes/Davon_Raubenheimer/annot_csv_3460.csv', './dataset/scrapes/Andrew_Forrest/annot_csv_3466.csv', './dataset/scrapes/Mart%C3%ADn_Landajo/annot_csv_3469.csv', './dataset/scrapes/Bidzina_Ivanishvili/annot_csv_3483.csv', './dataset/scrapes/Bryan_Avila/annot_csv_3498.csv', './dataset/scrapes/Christopher_Stalford/annot_csv_3557.csv', './dataset/scrapes/L%C3%A9one-No%C3%ABlle_Meyer/annot_csv_3567.csv', './dataset/scrapes/Gil_Gerard/annot_csv_3578.csv', './dataset/scrapes/Whetu_Douglas/annot_csv_3580.csv', './dataset/scrapes/Philippa_Perry/annot_csv_3584.csv', './dataset/scrapes/Kyle_Chandler/annot_csv_3585.csv', './dataset/scrapes/Mark_Anderson_(Minnesota_politician)/annot_csv_3591.csv', './dataset/scrapes/Kwabena_Darko/annot_csv_3594.csv', './dataset/scrapes/Dave_Freudenthal/annot_csv_3596.csv', './dataset/scrapes/Prem_Singh_Chandumajra/annot_csv_3601.csv', './dataset/scrapes/Louise_Markus/annot_csv_3606.csv', './dataset/scrapes/Jennifer_Runyon/annot_csv_3609.csv', './dataset/scrapes/Marlin_Stutzman/annot_csv_3619.csv', './dataset/scrapes/Richard_H._Anderson_(businessman)/annot_csv_3628.csv', './dataset/scrapes/Coco_Brandolini_d%27Adda/annot_csv_3631.csv', './dataset/scrapes/Cristiana_Brandolini_d%27Adda/annot_csv_3635.csv', './dataset/scrapes/Gayle_Kingery/annot_csv_3655.csv', './dataset/scrapes/Robert_Fox_(producer)/annot_csv_3663.csv', './dataset/scrapes/Yelena_Mizulina/annot_csv_3666.csv', './dataset/scrapes/Nathan_Hannay/annot_csv_3667.csv', './dataset/scrapes/Steve_Stark/annot_csv_3673.csv', './dataset/scrapes/Graham_Kings/annot_csv_3678.csv', './dataset/scrapes/Peter_Marks/annot_csv_3685.csv', './dataset/scrapes/Gregory_Chaney/annot_csv_3693.csv', './dataset/scrapes/John_Dougall_(Utah_politician)/annot_csv_3696.csv', './dataset/scrapes/Kevin_Hogan_(politician)/annot_csv_3697.csv', './dataset/scrapes/Larry_Stutts/annot_csv_3710.csv', './dataset/scrapes/Grzegorz_Schetyna/annot_csv_3716.csv', './dataset/scrapes/Christina_Rau/annot_csv_3723.csv', './dataset/scrapes/Neil_Coles/annot_csv_3781.csv', './dataset/scrapes/Bruce_Feiler/annot_csv_3789.csv', './dataset/scrapes/Jim_Banks/annot_csv_3795.csv', './dataset/scrapes/Rodney_Anderson_(Texas_politician)/annot_csv_3796.csv', './dataset/scrapes/Conor_O%27Shea/annot_csv_3813.csv', './dataset/scrapes/Barry_Gardiner/annot_csv_3830.csv', './dataset/scrapes/Erika_Renee_Land/annot_csv_3857.csv', './dataset/scrapes/Halvor_Nordhaug/annot_csv_3863.csv', './dataset/scrapes/Nickolay_Mladenov/annot_csv_3887.csv', './dataset/scrapes/Akua_Sena_Dansua/annot_csv_3895.csv']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nT494gDNCw0O",
        "colab_type": "text"
      },
      "source": [
        "#### training set creation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2kqXPed67qH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_indices = scored_summary_sorted.groupby('SCORE').groups[3.0]\n",
        "# print([key for key in scored_summary_sorted.groupby('SCORE').groups.keys()])\n",
        "keys = [key for key in scored_summary_sorted.groupby('SCORE').groups.keys()]\n",
        "training_indices = []\n",
        "for key in keys[:-2]:\n",
        "    # print(key)\n",
        "    training_indices.extend(scored_summary_sorted.groupby('SCORE').groups[keys[key]])\n",
        "# len(training_indices)\n",
        "# training_paths = [path for path in paths_annot if int(path.split('_')[-1].split('.')[0]) in training_indices]\n",
        "\n",
        "training_paths_idx = [idx for idx in scored_summary_sorted.iloc[training_indices]['idx']]\n",
        "training_paths = [path for path in paths_annot if int(path.split('_')[-1].split('.')[0]) in training_paths_idx]\n",
        "\n",
        "training_data_folder = './dataset_txt/training'\n",
        "if not os.path.exists(training_data_folder): os.makedirs(training_data_folder)\n",
        "import pandas as pd\n",
        "for i, path in enumerate(training_paths):\n",
        "    # print(path.split('/')[-2])\n",
        "    save_file = os.path.join(training_data_folder, '%s.txt' % path.split('/')[-1].split('.')[0])\n",
        "    txt_path = toConllTxt(path, save_file)\n",
        "    # print(txt_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-GupSeRFWvlu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tarfile\n",
        "tar =  tarfile.open('dataset_txt.tar.gz', mode='w:gz')\n",
        "tar.add('./dataset_txt')\n",
        "tar.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzoq9W7SFXOe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!rm dataset_txt.tar.gz\n",
        "wget.download('https://archive.org/download/wikiner_dataset_csv.tar/wikiner_dataset_txt.tar.gz')\n",
        "tar = tarfile.open('wikiner_dataset_txt.tar.gz', mode='r')\n",
        "tar.extractall('./dataset_txt')\n",
        "tar.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpHyVA_gNwHZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "colabimport.get_notebook('https://github.com/ratmcu/wiki_ner/blob/master/conll_tagged_ne.ipynb?raw=true')\n",
        "from conll_tagged_ne import WikiConLLTagger"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "acL2OzyNpArT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#experiment_code\n",
        "import os\n",
        "paths_annot = sorted([os.path.join(f[0], name) for f in os.walk('./dataset_txt') \n",
        "                if len(f[2])!=0 for name in f[2] if os.path.splitext(name)[-1] == '.txt' and name.split('_')[0]=='annot'],\n",
        "               key=lambda path: int(path.split('_')[-1].split('.')[0]))\n",
        "training_files = [path for path in paths_annot if 'training' in path.split('/')]\n",
        "eval_files     = [path for path in paths_annot if 'eval' in path.split('/')]\n",
        "test_files     = [path for path in paths_annot if 'test' in path.split('/')]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "INJK415_emDK",
        "colab_type": "code",
        "outputId": "fc65532e-585e-4e51-85ff-229d2eea1fcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "#experiment_code\n",
        "from torch.utils import data\n",
        "eval_dataset = NerDataset(eval_files[0])\n",
        "_ = [eval_dataset.append(NerDataset(eval_file)) for eval_file in eval_files[1:]]\n",
        "\n",
        "test_dataset = NerDataset(test_files[0])\n",
        "_ = [test_dataset.append(NerDataset(test_file)) for test_file in test_files[1:]]\n",
        "\n",
        "train_dataset = NerDataset(training_files[0])\n",
        "_ = [train_dataset.append(NerDataset(train_file)) for train_file in training_files[1:]]\n",
        "\n",
        "eval_iter = data.DataLoader(dataset=eval_dataset,\n",
        "                              batch_size=1,\n",
        "                              shuffle=True,\n",
        "                              num_workers=16,\n",
        "                              collate_fn=pad)\n",
        "\n",
        "test_iter = data.DataLoader(dataset=test_dataset,\n",
        "                              batch_size=32,\n",
        "                              shuffle=True,\n",
        "                              num_workers=16,\n",
        "                              collate_fn=pad)\n",
        "\n",
        "train_iter = data.DataLoader(dataset=train_dataset,\n",
        "                              batch_size=1,\n",
        "                              shuffle=True,\n",
        "                              num_workers=16,\n",
        "                              collate_fn=pad)\n",
        "print(len(train_iter))\n",
        "print(len(eval_iter))\n",
        "print(len(test_iter))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "77703\n",
            "12267\n",
            "65\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}