Data Facts.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linked Books Deep Reference Parsing\n",
    "\n",
    "This notebook contains a brief analysis of the dataset used for this project.\n",
    "\n",
    "> The data is expected to be in the **dataset** folder in root\n",
    "> * Training dataset -> clean_train.txt\n",
    "> * Testing dataset -> clean_test.txt\n",
    "> * Validation dataset -> clean_valid.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:43.865640Z",
     "start_time": "2018-02-20T17:45:43.547716Z"
    }
   },
   "outputs": [],
   "source": [
    "### IMPORTS\n",
    "\n",
    "# Matplot\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "# Numpy\n",
    "import numpy as np\n",
    "\n",
    "# Counter for frequencies count\n",
    "from collections import Counter\n",
    "\n",
    "# Better print for arrays\n",
    "import pprint\n",
    "\n",
    "# Sort dict\n",
    "from collections import OrderedDict\n",
    "import operator\n",
    "from operator import itemgetter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "\n",
    "Load in memory the dataset. \n",
    "\n",
    "The dataset is composed of words and the associated tags (Section `2.1 Dataset` of the paper). \n",
    "In the dataset, each word is associated with four tags, but in the context of this paper, the second tag is discarded.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:43.916844Z",
     "start_time": "2018-02-20T17:45:43.867489Z"
    }
   },
   "outputs": [],
   "source": [
    "def load_data(filepath):\n",
    "    \"\"\"\n",
    "        Load and return the data stored in the given filepath.\n",
    "        The data is structured as follows (CoNNL format): \n",
    "            Each line contains four columns separated by a single space. \n",
    "            Each word has been put on a separate line and there is an empty line after each sentence. \n",
    "            The first item on each line is a word, the second, third and fourth are tags associated to the word.\n",
    "        \n",
    "        Data example:\n",
    "            The sentence \"L. Antonielli, Iprefetti dell' Italia napoleonica, Bologna 1983.\" is represented in the dataset as:\n",
    "            \n",
    "                L author b-secondary b-r\n",
    "                . author i-secondary i-r\n",
    "                Antonielli author i-secondary i-r\n",
    "                , author i-secondary i-r\n",
    "                Iprefetti title i-secondary i-r\n",
    "                dell title i-secondary i-r\n",
    "                ’ title i-secondary i-r\n",
    "                Italia title i-secondary i-r\n",
    "                napoleonica title i-secondary i-r\n",
    "                , title i-secondary i-r\n",
    "                Bologna publicationplace i-secondary i-r\n",
    "                1983 year e-secondary i-r\n",
    "                . year e-secondary e-r\n",
    "                \n",
    "                \n",
    "        :param filepath: Path to the data\n",
    "        :return: Four arrays: The first one contains sentences (one array of words per sentence) and the other threes are arrays of tags.\n",
    "    \"\"\"\n",
    "\n",
    "    # Arrays to return\n",
    "    words = []\n",
    "    tags_1 = []\n",
    "    tags_2 = []\n",
    "    tags_3 = []\n",
    "\n",
    "    word = tags1 = tags2 = tags3 = []\n",
    "    with open (filepath, \"r\") as file:\n",
    "        for line in file:\n",
    "            if 'DOCSTART' not in line: #Do not take the first line into consideration\n",
    "                # Check if empty line\n",
    "                if line in ['\\n', '\\r\\n']:\n",
    "                    # Append line\n",
    "                    words.append(word)\n",
    "                    tags_1.append(tags1)\n",
    "                    tags_2.append(tags2)\n",
    "                    tags_3.append(tags3)\n",
    "\n",
    "                    # Reset\n",
    "                    word = []\n",
    "                    tags1 = []\n",
    "                    tags2 = []\n",
    "                    tags3 = []\n",
    "\n",
    "                else:\n",
    "                    # Split the line into words, tag #1, tag #2, tag #3\n",
    "                    w = line[:-1].split(\" \")\n",
    "                    word.append(w[0])\n",
    "                    tags1.append(w[1])\n",
    "                    tags2.append(w[2])\n",
    "                    tags3.append(w[3])\n",
    "\n",
    "    return words,tags_1,tags_2,tags_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:45.580479Z",
     "start_time": "2018-02-20T17:45:43.918844Z"
    }
   },
   "outputs": [],
   "source": [
    "# Training data\n",
    "train_w, train_t1, train_t2, train_t3 = load_data(\"dataset/clean_train.txt\")\n",
    "\n",
    "# Testing data\n",
    "test_w, test_t1, test_t2, test_t3 = load_data(\"dataset/clean_test.txt\")\n",
    "\n",
    "# Validation data\n",
    "validation_w, validation_t1, validation_t2, validation_t3 = load_data(\"dataset/clean_valid.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The variable **train_w** is an array of array, where is array corresponds to a line in a document. For each word in a line, the associated tag for `Task 1: Reference components` is in variable **train_t1**. The tags for `Task 2: Reference typology` are in variable **train_t2** and the tags for `Task 3: Reference span` in variable **train_t3**. The same logic is used for testing data (**test_**) and validation data (**validation_**)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data facts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:45.598735Z",
     "start_time": "2018-02-20T17:45:45.581985Z"
    }
   },
   "outputs": [],
   "source": [
    "words = [train_w, test_w, validation_w]\n",
    "\n",
    "\n",
    "def plot_list(values,\n",
    "              size,\n",
    "              title,\n",
    "              xlabel,\n",
    "              ylabel,\n",
    "              y=[\"Training\", \"Testing\", \"Validation\"]):\n",
    "    values.reverse()\n",
    "    y.reverse()\n",
    "    plt.figure(figsize=size)\n",
    "    plt.barh(range(len(values)), values, align='center')\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.yticks(range(len(y)), y)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:45.854763Z",
     "start_time": "2018-02-20T17:45:45.600601Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training sentences   : 49616\n",
      "Number of testing sentences    : 2258\n",
      "Number of validation sentences : 2436\n"
     ]
    },
    {
     "data": {
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HmzQgO3Xq1Pb1woULc8cdd+Tb3/52unbtmksvvTTNzc1vX9CffehOTU1NWltb13nsbbbZ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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa38507f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = [len(data) for data in words]\n",
    "\n",
    "print(\"{0:<30} : {1:.0f}\".format(\"Number of training sentences\", values[0]))\n",
    "print(\"{0:<30} : {1:.0f}\".format(\"Number of testing sentences\", values[1]))\n",
    "print(\"{0:<30} : {1:.0f}\".format(\"Number of validation sentences\", values[2]))\n",
    "\n",
    "plot_list(values, (15, 5), \"Number of sentences\", \"\", \"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total number of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:46.177058Z",
     "start_time": "2018-02-20T17:45:45.857237Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of training words           : 828394\n",
      "Total number of testing words            : 30876\n",
      "Total number of validation words         : 27177\n"
     ]
    },
    {
     "data": {
      "image/png": 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gAQAAKEWABAAAoBQBEgAAgFIESAAAAEoRIAEAAChFgAQAAKAUARIAAIBSBEgA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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa385076908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = [len([x for y in data for x in y]) for data in words]\n",
    "\n",
    "print(\"{0:<40} : {1:.0f}\".format(\"Total number of training words\", values[0]))\n",
    "print(\"{0:<40} : {1:.0f}\".format(\"Total number of testing words\", values[1]))\n",
    "print(\n",
    "    \"{0:<40} : {1:.0f}\".format(\"Total number of validation words\", values[2]))\n",
    "\n",
    "plot_list(values, (15, 5), \"Total number of words\", \"\", \"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average number of words per sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:46.456402Z",
     "start_time": "2018-02-20T17:45:46.179196Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average number of words per sentence in training data        : 16.70,  (max: 73)\n",
      "Average number of words per sentence in testing data         : 13.67,  (max: 30)\n",
      "Average number of words per sentence in validation data      : 11.16,  (max: 35)\n"
     ]
    },
    {
     "data": {
      "image/png": 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BSdLBgwd5iMg19tVXX2nlypV69tlnCe/X0G+//abc3Fz9v//3/9Sr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\nACsESAAAAACAlf8PmDPZtztIOKAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa382dfa5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def avg(x):\n",
    "    return sum(x) / len(x)\n",
    "\n",
    "\n",
    "values = [avg([len(x) for x in data]) for data in words]\n",
    "\n",
    "print(\"{0:<60} : {1:.2f},  (max: {2})\".format(\n",
    "    \"Average number of words per sentence in training data\", values[0],\n",
    "    max([len(x) for x in words[0]])))\n",
    "print(\"{0:<60} : {1:.2f},  (max: {2})\".format(\n",
    "    \"Average number of words per sentence in testing data\", values[1],\n",
    "    max([len(x) for x in words[1]])))\n",
    "print(\"{0:<60} : {1:.2f},  (max: {2})\".format(\n",
    "    \"Average number of words per sentence in validation data\", values[2],\n",
    "    max([len(x) for x in words[2]])))\n",
    "\n",
    "plot_list(values, (15, 5), \"Average number of words per sentence\", \"\", \"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most used words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:46.749904Z",
     "start_time": "2018-02-20T17:45:46.458385Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====== Training ======\n",
      "20 most commomn words in Training dataset :\n",
      "[('.', 94146),\n",
      " (',', 89818),\n",
      " ('-', 21674),\n",
      " ('(', 15631),\n",
      " ('di', 13826),\n",
      " ('.,', 10515),\n",
      " ('e', 10147),\n",
      " ('in', 9131),\n",
      " (')', 8364),\n",
      " ('’', 6789),\n",
      " ('p', 6729),\n",
      " ('pp', 6676),\n",
      " ('Venezia', 5932),\n",
      " ('del', 5658),\n",
      " (';', 5508),\n",
      " (':', 4372),\n",
      " ('a', 4341),\n",
      " ('A', 4305),\n",
      " ('),', 3953),\n",
      " ('«', 3906)]\n",
      "\n",
      "====== Testing ======\n",
      "20 most commomn words in Testing dataset :\n",
      "[(',', 3111),\n",
      " ('.', 2564),\n",
      " ('-', 886),\n",
      " ('di', 621),\n",
      " ('.,', 486),\n",
      " ('Venezia', 480),\n",
      " ('in', 429),\n",
      " ('e', 400),\n",
      " ('(', 328),\n",
      " (':', 257),\n",
      " ('’', 256),\n",
      " ('del', 211),\n",
      " ('a', 194),\n",
      " ('della', 186),\n",
      " ('«', 178),\n",
      " ('),', 177),\n",
      " ('»,', 156),\n",
      " ('of', 153),\n",
      " ('G', 150),\n",
      " ('and', 148)]\n",
      "\n",
      "====== Validation ======\n",
      "20 most commomn words in Validation dataset :\n",
      "[(',', 2990),\n",
      " ('.', 2261),\n",
      " ('-', 1024),\n",
      " ('di', 654),\n",
      " ('Venezia', 561),\n",
      " ('e', 361),\n",
      " ('in', 359),\n",
      " ('’', 350),\n",
      " ('(', 319),\n",
      " ('a', 204),\n",
      " ('del', 202),\n",
      " (':', 196),\n",
      " ('.,', 190),\n",
      " ('),', 189),\n",
      " ('pp', 170),\n",
      " ('della', 164),\n",
      " ('La', 126),\n",
      " ('=', 123),\n",
      " ('»,', 117),\n",
      " ('«', 114)]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def most_used_words(data, name, n=20):\n",
    "    print(\"====== {} ======\".format(name))\n",
    "    w = [x for y in data for x in y]\n",
    "    cnt = Counter(w)\n",
    "\n",
    "    print(n, \"most commomn words in {0} dataset :\".format(name))\n",
    "    pprint.pprint(cnt.most_common(n))\n",
    "    print()\n",
    "\n",
    "\n",
    "most_used_words(train_w, \"Training\")\n",
    "most_used_words(test_w, \"Testing\")\n",
    "most_used_words(validation_w, \"Validation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Words Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:48.771961Z",
     "start_time": "2018-02-20T17:45:46.758248Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====== Training ======\n",
      "  Words appearing 1 time  =>  34848 (58.08%)\n",
      "  Words appearing 2 time  =>  8236 (13.73%)\n",
      "  Words appearing 3 time  =>  3892 (6.49%)\n",
      "  Words appearing 4 time  =>  2333 (3.89%)\n",
      "  Words appearing 5 time  =>  1523 (2.54%)\n",
      "  Words appearing 6 time  =>  1222 (2.04%)\n",
      "  Words appearing 7 time  =>  832 (1.39%)\n",
      "  Words appearing 8 time  =>  697 (1.16%)\n",
      "  Words appearing 9 time  =>  564 (0.94%)\n",
      "  Words appearing 10 time  =>  453 (0.76%)\n",
      "  ...\n",
      "  Words appearing +100 time  =>  711 (1.19%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Bf/EXf5ErrrgidXV1qa+vz9atW/Paa69l+/btefXVV7NgwYJs2bIls2fPTpLs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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa38303c6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====== Testing ======\n",
      "  Words appearing 1 time  =>  4814 (67.71%)\n",
      "  Words appearing 2 time  =>  977 (13.74%)\n",
      "  Words appearing 3 time  =>  392 (5.51%)\n",
      "  Words appearing 4 time  =>  230 (3.23%)\n",
      "  Words appearing 5 time  =>  134 (1.88%)\n",
      "  Words appearing 6 time  =>  92 (1.29%)\n",
      "  Words appearing 7 time  =>  58 (0.82%)\n",
      "  Words appearing 8 time  =>  43 (0.60%)\n",
      "  Words appearing 9 time  =>  41 (0.58%)\n",
      "  Words appearing 10 time  =>  35 (0.49%)\n",
      "  ...\n",
      "  Words appearing +100 time  =>  27 (0.38%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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AFEDcAQAAFEDcAQAAFEDcAQAAFEDcAQAAFEDcAQAAFEDcAQAAFEDcAQAAFEDcAQAAFEDc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\nMrnpegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa382baba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====== Validation ======\n",
      "  Words appearing 1 time  =>  4038 (65.53%)\n",
      "  Words appearing 2 time  =>  933 (15.14%)\n",
      "  Words appearing 3 time  =>  379 (6.15%)\n",
      "  Words appearing 4 time  =>  200 (3.25%)\n",
      "  Words appearing 5 time  =>  130 (2.11%)\n",
      "  Words appearing 6 time  =>  77 (1.25%)\n",
      "  Words appearing 7 time  =>  61 (0.99%)\n",
      "  Words appearing 8 time  =>  41 (0.67%)\n",
      "  Words appearing 9 time  =>  23 (0.37%)\n",
      "  Words appearing 10 time  =>  32 (0.52%)\n",
      "  ...\n",
      "  Words appearing +100 time  =>  22 (0.36%)\n"
     ]
    },
    {
     "data": {
      "image/png": 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fmQAAANzAJr1y95uGhoby/PPPZ/Xq1fnJT36Sp556KkeOHMmqVavyyU9+MgsWLEi9Xs+a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\ndwAAAAX4/ywb6+Nvheb9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa383058048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def words_frequency(data, name):\n",
    "    print(\"====== {} ======\".format(name))\n",
    "    w = [x for y in data for x in y]\n",
    "    cnt = Counter(w)\n",
    "    wf = list(cnt.values())\n",
    "    wf = [x if x < 100 else 100 for x in wf]\n",
    "\n",
    "    for i in range(1, 11):\n",
    "        y = len([x for x in wf if x == i])\n",
    "        print(\"  Words appearing {0} time  =>  {1} ({2:.2f}%)\".format(\n",
    "            i, y, 100 * y / len(wf)))\n",
    "\n",
    "    print(\"  ...\")\n",
    "    i = 100\n",
    "    y = len([x for x in wf if x == i])\n",
    "    print(\"  Words appearing +{0} time  =>  {1} ({2:.2f}%)\".format(\n",
    "        i, y, 100 * y / len(wf)))\n",
    "\n",
    "    plt.figure(figsize=(15, 7))\n",
    "    plt.hist(wf, bins=100)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "words_frequency(train_w, \"Training\")\n",
    "words_frequency(test_w, \"Testing\")\n",
    "words_frequency(validation_w, \"Validation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:48.777417Z",
     "start_time": "2018-02-20T17:45:48.773613Z"
    }
   },
   "outputs": [],
   "source": [
    "tags1 = [train_t1, test_t1, validation_t1]\n",
    "tags2 = [train_t2, test_t2, validation_t2]\n",
    "tags3 = [train_t3, test_t3, validation_t3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:49.918387Z",
     "start_time": "2018-02-20T17:45:48.779109Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tags for Task 1: \n",
      "  abbreviation\n",
      "  archivalreference\n",
      "  archive_lib\n",
      "  attachment\n",
      "  author\n",
      "  box\n",
      "  cartulation\n",
      "  column\n",
      "  conjunction\n",
      "  date\n",
      "  filza\n",
      "  folder\n",
      "  foliation\n",
      "  numbered_ref\n",
      "  o\n",
      "  pagination\n",
      "  publicationnumber-year\n",
      "  publicationplace\n",
      "  publicationspecifications\n",
      "  publisher\n",
      "  ref\n",
      "  registry\n",
      "  series\n",
      "  title\n",
      "  tomo\n",
      "  volume\n",
      "  year\n",
      "\n",
      "Tags for Task 2: \n",
      "  b-meta-annotation\n",
      "  b-primary\n",
      "  b-secondary\n",
      "  e-meta-annotation\n",
      "  e-primary\n",
      "  e-secondary\n",
      "  i-meta-annotation\n",
      "  i-primary\n",
      "  i-secondary\n",
      "  o\n",
      "\n",
      "Tags for Task 3: \n",
      "  b-r\n",
      "  e-r\n",
      "  i-r\n",
      "  o\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Tags for Task 1: \")\n",
    "print('\\n'.join(\n",
    "    [\"  \" + x for x in np.unique([x for d in tags1 for y in d for x in y])]))\n",
    "print()\n",
    "\n",
    "print(\"Tags for Task 2: \")\n",
    "print('\\n'.join(\n",
    "    [\"  \" + x for x in np.unique([x for d in tags2 for y in d for x in y])]))\n",
    "print()\n",
    "\n",
    "print(\"Tags for Task 3: \")\n",
    "print('\\n'.join(\n",
    "    [\"  \" + x for x in np.unique([x for d in tags3 for y in d for x in y])]))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:51.730083Z",
     "start_time": "2018-02-20T17:45:49.921371Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==== Task 1 =====\n",
      "Number of unique tags in training data for Task 1       : 27.00\n",
      "Number of unique tags in testing data for Task 1        : 25.00\n",
      "Number of unique tags in validation data for Task 1     : 24.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAEoRkAAAAJQiIAEAAChFQAIAAFCKgAQAAKAUAQkAAEApAhIAAIBSBCQAAAClCEgAAABK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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa3810a7b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "==== Task 2 =====\n",
      "Number of unique tags in training data for Task 2       : 10.00\n",
      "Number of unique tags in testing data for Task 2        : 10.00\n",
      "Number of unique tags in validation data for Task 2     : 10.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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rrsmmTZu26USSZLfddmv472bNmm1xpuHDh2f48OHbfCwAAICiamtrs3r16nKP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\nAABQiIAEAACgEAEJAABAIf8f08WoSMEGp0YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa37df66550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "==== Task 3 =====\n",
      "Number of unique tags in training data for Task 3       : 4.00\n",
      "Number of unique tags in testing data for Task 3        : 4.00\n",
      "Number of unique tags in validation data for Task 3     : 4.00\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAIBSBEgAAABKESABAAAoRYAEAACgFAESAACAUgRIAAAAShEgAQAAKEWABAAAoBQBEgAA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\nAAAoRYAEAACgFAESAACAUv4fN7JyXiXigRwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa37df4fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i, tags in enumerate([tags1, tags2, tags3]):\n",
    "    values = [len(np.unique([x for y in d for x in y])) for d in tags]\n",
    "    print(\"==== Task\", 1 + i, \"=====\")\n",
    "    print(\"{0:<55} : {1:.2f}\".format(\n",
    "        \"Number of unique tags in training data for Task {}\".format(\n",
    "            i + 1), values[0]))\n",
    "    print(\"{0:<55} : {1:.2f}\".format(\n",
    "        \"Number of unique tags in testing data for Task {}\".format(\n",
    "            i + 1), values[1]))\n",
    "    print(\"{0:<55} : {1:.2f}\".format(\n",
    "        \"Number of unique tags in validation data for Task {}\".format(\n",
    "            i + 1), values[2]))\n",
    "    plot_list(values, (15, 5), \"Number of tags for Task {}\".format(i + 1), \"\",\n",
    "              \"\")\n",
    "    print()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Some tags are present in the Training data but will never be tested for Task 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average tags in sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:52.493702Z",
     "start_time": "2018-02-20T17:45:51.736624Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average unique tags per sentence in training data - Task 1   : 3.30,  (max: 10)\n",
      "Average unique tags per sentence in testing data -Task 1     : 2.91,  (max: 7)\n",
      "Average unique tags per sentence in validation data - Task 1 : 2.49,  (max: 7)\n"
     ]
    }
   ],
   "source": [
    "values = [[len(np.unique(s)) for s in d] for d in tags1]\n",
    "\n",
    "print(\"{0:<60} : {1:.2f},  (max: {2})\".format(\n",
    "    \"Average unique tags per sentence in training data - Task 1\",\n",
    "    avg(values[0]), max(values[0])))\n",
    "print(\"{0:<60} : {1:.2f},  (max: {2})\".format(\n",
    "    \"Average unique tags per sentence in testing data -Task 1\",\n",
    "    avg(values[1]), max(values[1])))\n",
    "print(\"{0:<60} : {1:.2f},  (max: {2})\".format(\n",
    "    \"Average unique tags per sentence in validation data - Task 1\",\n",
    "    avg(values[2]), max(values[2])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tags frequency - Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:56.260293Z",
     "start_time": "2018-02-20T17:45:52.496282Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LYRhMnDiRjh07snfvXt55551s644ZM4aCBQsSEBDAhAkTuHTpkvkLSf3792fOnDk89dRT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa37de71278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa37d579908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EBASob9++6tu3r4wxevjhh5WWlqbff/9d27dv18iRI1WuXDk9+uij6tq1q6ZPn66QkBCN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\nhw4dFBoamtul4Cbi4+O1ZMm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w/vycdClET58+XXa7XWFhYd6feQsLC9PgwYM1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\nvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa37dcc6ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, t in enumerate([train_t1, train_t2, train_t3]):\n",
    "    x = np.hstack(t)\n",
    "    unique, counts = np.unique(x, return_counts=True)\n",
    "    values = dict(zip(unique, counts))\n",
    "    values = OrderedDict(sorted(values.items(), key=itemgetter(1)))\n",
    "\n",
    "    plt.figure(figsize=(15, 10))\n",
    "    plt.barh(range(len(values)), values.values(), align='center')\n",
    "    plt.title(\"Frequency of labels per tokens - Task {}\".format(i + 1))\n",
    "    plt.xlabel(\"Number of tokens\")\n",
    "    plt.ylabel(\"Labels\")\n",
    "    plt.yticks(range(len(values)), list(map(lambda x: \"{0} : (Nbr: {1} - {2:.1f}%)\".format(x[0],x[1], 100*x[1]/sum(values.values())), values.items())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label per position in sequence - Training Data - Task 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:56.289604Z",
     "start_time": "2018-02-20T17:45:56.263968Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def label_per_position(data, n=4):\n",
    "    for position in range(n):\n",
    "        dd = dict()\n",
    "        for s in data:\n",
    "            if len(s) > position:\n",
    "                dd[s[position]] = dd.get(s[position], 0) + 1\n",
    "\n",
    "        values = OrderedDict(\n",
    "            sorted(dd.items(), key=itemgetter(1), reverse=True))\n",
    "\n",
    "        # Print plot\n",
    "        plt.figure(figsize=(20, 5))\n",
    "        plt.title(\"Number of labels in position {}\".format(position + 1))\n",
    "        plt.ylabel(\"Number of tokens\")\n",
    "        plt.xlabel(\"Labels\")\n",
    "        plt.bar(range(len(values)), values.values(), align='center')\n",
    "        plt.xticks(range(len(values)), values.keys(), fontsize=12, rotation=90)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:58.692438Z",
     "start_time": "2018-02-20T17:45:56.292397Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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fK1asUFnr06cPgoODCSlq+QFtPXFSLpeDVFe1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa376f59550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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4XdEAzYcQxcXFKCgogJWVlcaJNSmOHDmClJQU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Z599lnFxcWppqZGJ06cUE5OjkaNGmV0PAAAgA5hKAXA1Hw+n7Zt2yan06kePXoYHQcdUFhYKJfL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\nCDqGUgAAAAAAAAi6/wOUpjrszfrl/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa376f77208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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rBE9LS0NNTQ3RayOtJh6Nxxafz8epU6eIG/rN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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa382b35ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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xyMvLg6Ojo9p3SLrrp7CwEDKZDEVFRfD39+fu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa382b8cb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_per_position(train_t1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Where are labels located in sequence ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:45:58.774234Z",
     "start_time": "2018-02-20T17:45:58.695257Z"
    }
   },
   "outputs": [],
   "source": [
    "def position_for_label(data, label):\n",
    "    dd = {}\n",
    "    dd2 = {}\n",
    "    for d in data:\n",
    "        for i, l in enumerate(d):\n",
    "            if l == label:\n",
    "                dd[i] = dd.get(i, 0) + 1\n",
    "                j = round(i / len(d), 1)\n",
    "                dd2[j] = dd2.get(j, 0) + 1\n",
    "\n",
    "    values = OrderedDict(sorted(dd.items(), key=itemgetter(0), reverse=False))\n",
    "    values2 = OrderedDict(\n",
    "        sorted(dd2.items(), key=itemgetter(0), reverse=False))\n",
    "\n",
    "    # Print plots\n",
    "    plt.figure(figsize=(20, 5))\n",
    "    plt.title(\"Number of labels per position - Label: {}\".format(label))\n",
    "    plt.ylabel(\"Number of tokens\")\n",
    "    plt.xlabel(\"Position in sequence\")\n",
    "    plt.bar(range(len(values)), values.values(), align='center')\n",
    "    plt.xticks(range(len(values)), values.keys(), fontsize=12, rotation=90)\n",
    "    plt.show()\n",
    "\n",
    "    plt.figure(figsize=(20, 5))\n",
    "    plt.title(\"Number of labels per position - Label: {}\".format(label))\n",
    "    plt.ylabel(\"Number of tokens\")\n",
    "    plt.xlabel(\"Position in sequence\")\n",
    "    plt.bar(range(len(values2)), values2.values(), align='center')\n",
    "    plt.xticks(\n",
    "        range(len(values2)),\n",
    "        [\"Begin\", \"\", \"\", \"\", \"\", \"Middle\", \"\", \"\", \"\", \"\", \"End\"],\n",
    "        fontsize=12,\n",
    "        rotation=90)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:46:00.346155Z",
     "start_time": "2018-02-20T17:45:58.778048Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WatFKxtOuJTExkYiICHr37s3NmzdZvXo1n3/+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\nIYQQQgjRYGTwSQghhBBCCCGEEEI0mP8D1Uo7V3UroYoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa382eb4c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa37dac1dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "position_for_label(train_t1, \"pagination\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Inside the training data, the `pagination` tokens are mostly at the end of the line/sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-02-20T17:46:03.485482Z",
     "start_time": "2018-02-20T17:46:00.352614Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cTDF+/Hi+/PLLR6rlYdTW1vLbb7890h0lDAYD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\nCCGEEEKYzf8BR6MdvYBeypwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa382ccb9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa37d9af7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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HH5k3b57qu40+PeKufv36yuWbSUlJpKenmzX6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa37d9c8ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa3c62d4550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "position_for_label(train_t3, \"b-r\")\n",
    "\n",
    "position_for_label(train_t3, \"e-r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The **begin** tags are mostly concentrated at the begin of the line, while **end** tags are at the end."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}