sentence_length_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import json\n",
    "\n",
    "from functools import reduce\n",
    "import re\n",
    "import numpy as np\n",
    "import nltk\n",
    "import json\n",
    "from pprint import pprint as pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize(sent):\n",
    "    \"\"\"Returns the tokens of a sequece\"\"\"\n",
    "    tokens = nltk.word_tokenize(sent)\n",
    "    tokens = [w.lower() for w in tokens]\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/SciQ dataset/train.json', 'r') as rf:\n",
    "    train = json.load(rf)\n",
    "with open('data/SciQ dataset/test.json', 'r') as rf:\n",
    "    test = json.load(rf)\n",
    "with open('data/SciQ dataset/valid.json', 'r') as rf:\n",
    "    valid = json.load(rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_question_lengths = []\n",
    "all_support_lengths = []\n",
    "\n",
    "for sample in train+test+valid:\n",
    "    question_length = len(tokenize(sample['question']))\n",
    "    support_length = len(tokenize(sample['support']))\n",
    "    all_question_lengths.append(question_length)\n",
    "    all_support_lengths.append(support_length)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0FlgEnJ7q3wecCDQDrwHnAETEKkmXADNTvYsjYlXBWMzMrINUe43jR5J2AH4G3BIRaystEBEvAAeVKX8ZOLZMeQDntrGuicDEKmM1M7MaqqqpKiI+DHyK7FrDbEk/l3RcTSMzM7MuqeprHBGxAPgmcD7wEeBaSfMl/VOtgjMzs66nqsQh6YOSrgKeA44BTo6ID6Txq2oYn5mZdTHVXuP4d+B64BsR8XqpMCJekvTNmkRmZmZdUrWJ4+PA6xHxJoCkbYAdI+K1iLipZtGZmVmXU+01jgeAnXLTPVOZmZl1M9Umjh0jYn1pIo33rE1IZmbWlVWbOF5t9XyMDwGvt1PfzMy2UtVe4zgPuEPSS4CA9wCfrFlUZmbWZVWVOCJipqQDgP1T0fMR8ffahWVmZl1VkW7VDwUGp2WGSyIibqxJVGZm1mVV28nhTcB+wBzgzVQcgBOHmVk3U+0ZRxMwLHVEaGZm3Vi1d1U9Q3ZB3MzMurlqzzj6AvMkPQFsKBVGxCk1icrMzLqsahPHRbUMwszMGke1t+P+VtJ7gaER8YCknsC2tQ3NzMy6omq7Vf8sMAX4cSoaAPyiVkGZmVnXVe3F8XOBI4F18PZDnfasZkFJ20p6StI9aXqIpMclNUu6XdL2qXyHNN2c5g/OreOCVP68pOOr3z0zM+to1SaODRHxRmlCUg+y33FU48tkD4AquRy4KiLeB6wGxqbyscDqVH5VqoekYcAZwIHAKLLnn7uZzMysTqpNHL+V9A1gp/Ss8TuAX1ZaSNJAsmd5XJ+mRfbUwCmpyiTg1DQ+Ok2T5h+b6o8GbouIDRHxItAMHFZl3GZm1sGqTRzjgRbgaeBzwH1kzx+v5Grg68BbaXoPYE1EbEzTS8iul5D+LgZI89em+m+Xl1nGzMw6WbV3Vb0F/CQNVZF0ErAyImZLOnrzwquepHHAOIB99tmn1pszM+u2qu2r6kXKXNOIiH3bWexI4BRJJwI7ArsB1wC9JPVIZxUDgaWp/lJgELAkXUPZHXg5V16SXyYfywRgAkBTU5O7RjEzq5Fqm6qayHrHPRT4MHAtcHN7C0TEBRExMCIGk13cfjAiPgU8BJyWqo0BpqbxaWmaNP/B1DfWNOCMdNfVEGAo8ESVcZuZWQerKnFExMu5YWlEXE120XtznA98RVIz2TWMG1L5DcAeqfwrZNdViIhngcnAPODXwLkR8ea71mpmZp2i2qaq4bnJbcjOQKp+lkdEPAw8nMZfoMxdURHxN+ATbSx/KXBptdszM7PaqfbD/we58Y3AQuD0Do+mmxs8/t66bHfhZZt78mhm3VG1d1V9tNaBmJlZY6i2qeor7c2PiCs7JhwzM+vqijwB8FCyO5wATia7s2lBLYIyM7Ouq9rEMRAYHhGvAEi6CLg3Ij5dq8DMzKxrqvZ3HP2BN3LTb6QyMzPrZqo947gReELS3Wn6VDZ1SGhmZt1ItXdVXSrpV2S/Ggc4JyKeql1YZmbWVVXbVAXQE1gXEdeQ9Sc1pEYxmZlZF1bto2MvJOsq5IJUtB0V+qoyM7OtU7VnHP8InAK8ChARLwG71iooMzPruqpNHG+knmoDQNLOtQvJzMy6smoTx2RJPyZ7lsZngQco8FAnMzPbelR7V9UV6Vnj64D9gW9HxPSaRmZmZl1SxcQhaVvggdTRoZOFmVk3V7GpKj006S1Ju3dCPGZm1sVV+8vx9cDTkqaT7qwCiIgv1SQqMzPrsqpNHHelwczMurl2E4ekfSLi/0eE+6UyMzOg8jWOX5RGJN1ZZMWSdpT0hKQ/SXpW0ndS+RBJj0tqlnS7pO1T+Q5pujnNH5xb1wWp/HlJxxeJw8zMOlalxKHc+L4F170BOCYiDgIOBkZJGgFcDlwVEe8DVgNjU/2xwOpUflWqh6RhwBnAgcAo4EfpTi8zM6uDSokj2hivKDLr0+R2aQjgGGBKKp9E1kU7wGg2ddU+BThWklL5bRGxISJeBJqBw4rEYmZmHadS4jhI0jpJrwAfTOPrJL0iaV2llUvaVtIcYCXZb0D+AqyJiI2pyhJgQBofACwGSPPXAnvky8ssk9/WOEmzJM1qaWmpFJqZmW2mdi+OR8QWNQml34AcLKkXcDdwwJasr8K2JgATAJqamgqdHZmZWfWKPI9js0XEGuAh4Aiy/q5KCWsgsDSNLwUGAaT5uwMv58vLLGNmZp2sZolDUr90poGknYDjgOfIEshpqdoYYGoan5amSfMfTD3yTgPOSHddDQGGAk/UKm4zM2tftT8A3Bx7AZPSHVDbAJMj4h5J84DbJH0XeAq4IdW/AbhJUjOwiuxOKiLiWUmTgXnARuDc1ARmZmZ1ULPEERFzgUPKlL9AmbuiIuJvwCfaWNelwKUdHaOZmRXXKdc4zMxs6+HEYWZmhThxmJlZIU4cZmZWiBOHmZkV4sRhZmaFOHGYmVkhThxmZlaIE4eZmRXixGFmZoU4cZiZWSFOHGZmVogTh5mZFeLEYWZmhThxmJlZIU4cZmZWiBOHmZkV4sRhZmaF1CxxSBok6SFJ8yQ9K+nLqbyPpOmSFqS/vVO5JF0rqVnSXEnDc+sak+ovkDSmVjGbmVlltTzj2Ah8NSKGASOAcyUNA8YDMyJiKDAjTQOcAAxNwzjgOsgSDXAhcDjZs8ovLCUbMzPrfDVLHBGxLCKeTOOvAM8BA4DRwKRUbRJwahofDdwYmceAXpL2Ao4HpkfEqohYDUwHRtUqbjMza1+nXOOQNBg4BHgc6B8Ry9Ks5UD/ND4AWJxbbEkqa6u89TbGSZolaVZLS0uHxm9mZpvUPHFI2gW4EzgvItbl50VEANER24mICRHRFBFN/fr164hVmplZGTVNHJK2I0sat0TEXal4RWqCIv1dmcqXAoNyiw9MZW2Vm5lZHdTyrioBNwDPRcSVuVnTgNKdUWOAqbnys9LdVSOAtalJ635gpKTe6aL4yFRmZmZ10KOG6z4S+GfgaUlzUtk3gMuAyZLGAouA09O8+4ATgWbgNeAcgIhYJekSYGaqd3FErKph3GZm1o6aJY6I+D2gNmYfW6Z+AOe2sa6JwMSOi87MzDaXfzluZmaFOHGYmVkhThxmZlaIE4eZmRXixGFmZoU4cZiZWSFOHGZmVogTh5mZFeLEYWZmhThxmJlZIU4cZmZWiBOHmZkV4sRhZmaFOHGYmVkhThxmZlaIE4eZmRXixGFmZoU4cZiZWSE1SxySJkpaKemZXFkfSdMlLUh/e6dySbpWUrOkuZKG55YZk+ovkDSmVvGamVl1avbMceBnwA+BG3Nl44EZEXGZpPFp+nzgBGBoGg4HrgMOl9QHuBBoAgKYLWlaRKyuYdzdzuDx99Zt2wsv+3jdtm1mm6dmZxwR8QiwqlXxaGBSGp8EnJorvzEyjwG9JO0FHA9Mj4hVKVlMB0bVKmYzM6uss69x9I+IZWl8OdA/jQ8AFufqLUllbZWbmVmd1O3ieEQEWfNTh5A0TtIsSbNaWlo6arVmZtZKZyeOFakJivR3ZSpfCgzK1RuYytoqf5eImBARTRHR1K9fvw4P3MzMMp2dOKYBpTujxgBTc+VnpburRgBrU5PW/cBISb3THVgjU5mZmdVJze6qknQrcDTQV9ISsrujLgMmSxoLLAJOT9XvA04EmoHXgHMAImKVpEuAmanexRHR+oK7mZl1opoljog4s41Zx5apG8C5baxnIjCxA0MzM7Mt4F+Om5lZIU4cZmZWiBOHmZkV4sRhZmaFOHGYmVkhThxmZlaIE4eZmRVSy27VzSqqV5fu7s7dbPP5jMPMzApx4jAzs0KcOMzMrBAnDjMzK8SJw8zMCvFdVdYt1etuLvAdXdb4fMZhZmaFOHGYmVkhThxmZlaIr3GYdTL/Wt4anc84zMyskIY545A0CrgG2Ba4PiIuq3NIZg3Fd5JZR2mIMw5J2wL/AZwADAPOlDSsvlGZmXVPjXLGcRjQHBEvAEi6DRgNzKtrVGZWlXqe7dTD1n6G1SiJYwCwODe9BDg8X0HSOGBcmlwv6fkt2F5f4K9bsHy9Of76cvz1Vff4dfkWLV7P+N9bTaVGSRwVRcQEYEJHrEvSrIho6oh11YPjry/HX1+Ov/Ya4hoHsBQYlJsemMrMzKyTNUrimAkMlTRE0vbAGcC0OsdkZtYtNURTVURslPR/gPvJbsedGBHP1nCTHdLkVUeOv74cf305/hpTRNQ7BjMzayCN0lRlZmZdhBOHmZkV4sSRI2mUpOclNUsaX+94ypE0UdJKSc/kyvpImi5pQfrbO5VL0rVpf+ZKGl6/yN+OdZCkhyTNk/SspC+n8obYB0k7SnpC0p9S/N9J5UMkPZ7ivD3dxIGkHdJ0c5o/uJ7xl0jaVtJTku5J0w0Tv6SFkp6WNEfSrFTWEO+fFFMvSVMkzZf0nKQjGil+cOJ4WwN1a/IzYFSrsvHAjIgYCsxI05Dty9A0jAOu66QY27MR+GpEDANGAOem49wo+7ABOCYiDgIOBkZJGgFcDlwVEe8DVgNjU/2xwOpUflWq1xV8GXguN91o8X80Ig7O/d6hUd4/kPW59+uIOAA4iOx1aKT4ISI8ZDcIHAHcn5u+ALig3nG1Eetg4Jnc9PPAXml8L+D5NP5j4Mxy9brKAEwFjmvEfQB6Ak+S9WLwV6BH6/cS2Z2AR6TxHqme6hz3QLIPp2OAewA1WPwLgb6tyhri/QPsDrzY+hg2SvylwWccm5Tr1mRAnWIpqn9ELEvjy4H+abxL71Nq9jgEeJwG2ofUzDMHWAlMB/4CrImIjalKPsa340/z1wJ7dG7E73I18HXgrTS9B40VfwC/kTQ7dTUEjfP+GQK0AD9NTYXXS9qZxokfcFPVVieyryVd/h5rSbsAdwLnRcS6/Lyuvg8R8WZEHEz2zf0w4IA6h1Q1SScBKyNidr1j2QJHRcRwsmaccyX9Q35mF3//9ACGA9dFxCHAq2xqlgK6fPyAE0deI3drskLSXgDp78pU3iX3SdJ2ZEnjloi4KxU31D4ARMQa4CGypp1ekko/qM3H+Hb8af7uwMudHGrekcApkhYCt5E1V11D48RPRCxNf1cCd5Ml70Z5/ywBlkTE42l6ClkiaZT4ASeOvEbu1mQaMCaNjyG7blAqPyvdmTECWJs7Ha4LSQJuAJ6LiCtzsxpiHyT1k9Qrje9Edn3mObIEclqq1jr+0n6dBjyYvlHWRURcEBEDI2Iw2Xv8wYj4FA0Sv6SdJe1aGgdGAs/QIO+fiFgOLJa0fyo6luzxEA0R/9vqfZGlKw3AicCfydqs/1+942kjxluBZcDfyb69jCVrc54BLAAeAPqkuiK7U+wvwNNAUxeI/yiy0/C5wJw0nNgo+wB8EHgqxf8M8O1Uvi/wBNAM3AHskMp3TNPNaf6+9X4NcvtyNHBPI8Wf4vxTGp4t/Z82yvsnxXQwMCu9h34B9G6k+CPCXY6YmVkxbqoyM7NCnDjMzKwQJw4zMyvEicPMzApx4jAzs0KcOGyrJml9jdd/tqS9c9MLJfWtsMzRktam3l3nS7qiljGadTQnDrMtczawd6VKZfwusm5LDgFOknRkh0ZlVkNOHNbtpF9/3ylpZhqOTOUXKXveycOSXpD0pdwy31L2rJbfS7pV0tcknQY0Abeks4edUvUvSnoyPTOi3X6sIuJ1sh9BDkjbOUzSH1MHeH8o/cI4ndncJenX6ZkN/5aLbaykPyt7TshPJP2wwn5+JMU7J21n1w47uNY91PsXiB481HIA1pcp+zlZR3kA+5B1fwJwEfAHYAegL1mfTNsBh5J9uO8I7Er2696vpWUeJvdrXrIuv7+Yxr8AXF9m+0ez6RfbvYHZwHvS9G5s6t78Y8Cdafxs4AWyvqJ2BBaR9WG0d9pmnxTr74AfVtjPXwJHpvFdStvz4KHaodSpmVl38jFgWNZtFgC7pd56Ae6NiA3ABkkrybq3PhKYGhF/A/4m6ZcV1l/quHE28E9t1PmwpD+RPaDn6sj6MIIsMUySNJSsa5btcsvMiIi1AJLmAe8lS3C/jYhVqfwO4P0V9vNR4EpJtwB3RcSSCvtj9g5OHNYdbQOMSIngbekDdkOu6E0273+ktI72lv9dRJwkaQjwmKTJETEHuAR4KCL+UdnzSh4us95qYys1ST/nAAABMklEQVS7n8Blku4l6yPsUUnHR8T8SjtlVuJrHNYd/Qb4YmlC0sEV6j8KnKzseeO7ACfl5r1C1ny1WSLiReAy4PxUtDubus0+u4pVzAQ+Iql36vb8f+bmld1PSftFxNMRcXlavmGeJ2JdgxOHbe16SlqSG74CfAlokjQ3Nfn87/ZWEBEzybq3ngv8iqyX0rVp9s+A/2x1cbyo/wT+IZ1h/BvwfUlPUcXZTmTPpvgeWc+1j5Jd7yjF1tZ+nifpGUlzyXpZ/tVmxm3dlHvHNauCpF0iYr2knsAjwLiIeLLeccE7YutB9mCjiRFxd73jsq2Xr3GYVWeCpGFkdzRN6ipJI7lI0sfIYvsN2TMezGrGZxxmZlaIr3GYmVkhThxmZlaIE4eZmRXixGFmZoU4cZiZWSH/BTdmLeixm/i1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(all_support_lengths)\n",
    "plt.title(\"Histogram plot of length of Support\")\n",
    "plt.xlabel(\"Length Ranges\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(all_question_lengths)\n",
    "plt.title(\"Histogram plot of length of Question\")\n",
    "plt.xlabel(\"Length Ranges\")\n",
    "plt.ylabel(\"Frequency\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}