Data_Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_pred_data(file_name):\n",
    "    \"\"\"\n",
    "    Read file with preprocessed data\n",
    "    \"\"\"\n",
    "    quetions, pred_questions, answers, pred_answers = [], [], [], []\n",
    "    with open(file_name, 'r') as csvfile:\n",
    "        file_info = csv.reader(csvfile)\n",
    "        # Store the information\n",
    "        for i, line in enumerate(file_info):\n",
    "            if i == 0: continue\n",
    "            quetions.append(line[0].strip().decode(\"utf-8\"))\n",
    "            pred_questions.append(line[1].strip().decode(\"utf-8\").split(\" \"))\n",
    "            answers.append(line[2].strip().decode(\"utf-8\"))\n",
    "            pred_answers.append(line[3].strip().decode(\"utf-8\").split(\" \"))\n",
    "\n",
    "            # Counter for test\n",
    "            if i > 100000: break\n",
    "\n",
    "    return quetions, pred_questions, answers, pred_answers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data....\n"
     ]
    }
   ],
   "source": [
    "print(\"loading data....\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, questions, _, answers = read_pred_data(\"Data/simple_pred_QA-pair.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_len = [len(s) for s in questions]\n",
    "a_len = [len(s) for s in answers]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.805981940180597 1 93\n"
     ]
    }
   ],
   "source": [
    "print np.mean(q_len), np.min(q_len), np.max(q_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.001629983700163 1 428\n"
     ]
    }
   ],
   "source": [
    "print np.mean(a_len), np.min(a_len), np.max(a_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1.4049e+04, 3.7287e+04, 2.1800e+04, 9.8730e+03, 5.3790e+03,\n",
       "        3.4210e+03, 2.1490e+03, 1.4400e+03, 9.7000e+02, 7.2500e+02,\n",
       "        6.5000e+02, 4.5200e+02, 3.9200e+02, 2.5200e+02, 2.1700e+02,\n",
       "        1.9600e+02, 1.2700e+02, 7.7000e+01, 1.0300e+02, 5.8000e+01,\n",
       "        6.5000e+01, 5.0000e+01, 5.0000e+01, 5.1000e+01, 4.9000e+01,\n",
       "        3.8000e+01, 3.2000e+01, 2.3000e+01, 2.0000e+01, 6.0000e+00]),\n",
       " array([ 1.        ,  4.06666667,  7.13333333, 10.2       , 13.26666667,\n",
       "        16.33333333, 19.4       , 22.46666667, 25.53333333, 28.6       ,\n",
       "        31.66666667, 34.73333333, 37.8       , 40.86666667, 43.93333333,\n",
       "        47.        , 50.06666667, 53.13333333, 56.2       , 59.26666667,\n",
       "        62.33333333, 65.4       , 68.46666667, 71.53333333, 74.6       ,\n",
       "        77.66666667, 80.73333333, 83.8       , 86.86666667, 89.93333333,\n",
       "        93.        ]),\n",
       " <a list of 30 Patch objects>)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109f51d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(q_len, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([8.4817e+04, 1.1345e+04, 2.4250e+03, 7.9400e+02, 3.0100e+02,\n",
       "        1.2500e+02, 7.9000e+01, 3.9000e+01, 1.8000e+01, 1.1000e+01,\n",
       "        1.2000e+01, 5.0000e+00, 1.1000e+01, 5.0000e+00, 2.0000e+00,\n",
       "        2.0000e+00, 2.0000e+00, 2.0000e+00, 1.0000e+00, 0.0000e+00,\n",
       "        0.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00, 2.0000e+00,\n",
       "        0.0000e+00, 1.0000e+00, 0.0000e+00, 0.0000e+00, 1.0000e+00]),\n",
       " array([  1.        ,  15.23333333,  29.46666667,  43.7       ,\n",
       "         57.93333333,  72.16666667,  86.4       , 100.63333333,\n",
       "        114.86666667, 129.1       , 143.33333333, 157.56666667,\n",
       "        171.8       , 186.03333333, 200.26666667, 214.5       ,\n",
       "        228.73333333, 242.96666667, 257.2       , 271.43333333,\n",
       "        285.66666667, 299.9       , 314.13333333, 328.36666667,\n",
       "        342.6       , 356.83333333, 371.06666667, 385.3       ,\n",
       "        399.53333333, 413.76666667, 428.        ]),\n",
       " <a list of 30 Patch objects>)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10feee8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(a_len, bins=30, color = 'steelblue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}