path: root/Metrics/Metrics-Calculation/metrics_plot/type_analysis/src/Node Type Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import glob\n",
    "lib_path = os.path.abspath(os.path.join('..', '..', 'utils'))\n",
    "sys.path.append(lib_path)\n",
    "import matplotlib.pyplot as plt\n",
    "from GraphType import GraphStat\n",
    "import readCSV as reader\n",
    "import constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getModels(folderName, numberOfModels):\n",
    "    filenames = reader.readmultiplefiles(folderName, numberOfModels, False)\n",
    "    graphStats = [GraphStat(filename) for filename in filenames]\n",
    "    return graphStats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drawTypeDistributions(folderName, numberOfModels):\n",
    "    graphStats = getModels(folderName, numberOfModels)\n",
    "    typeMap = {}\n",
    "    keys = set()\n",
    "    for g in graphStats:\n",
    "        keys = keys.union(set(g.nodeTypeStat.keys()))\n",
    "    for key in keys:\n",
    "        typeMap[key] = [float(g.nodeTypeStat.get(key,0)) for g in graphStats]\n",
    "    print(list(typeMap.keys()))\n",
    "    for i, key in enumerate(typeMap.keys()):\n",
    "        plt.figure(i)\n",
    "        plt.hist(typeMap[key], range = (0,1), bins=50)\n",
    "        plt.title(key)\n",
    "        plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['EPackage', 'EAttribute', 'EEnumLiteral', 'EGenericType', 'EAnnotation', 'EOperation', 'EEnum', 'ETypeParameter', 'EParameter', 'EReference', 'EClass', 'EStringToStringMapEntry', 'EDataType']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JR+JIw30HsLktbwZuHGq/uJ01czawb3r6RpK0eFbM1iHJtcDrgFVJJoH/AFwOXJ9kC/AwcGHrfhNwHrALeAa4ZAFqliTNYtZwr6o3H2LTxhn6FnDpfIuSJM2P31CVpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdWrHUBSy29Zd94pDbHrr8jYtYiSQtHI/cJalD3R65H+4Ifa6P8Yhe0nLTbbiPk6EvablxWkaSOrQgR+5JzgHeAxwHvL+qLl+I/RytjmRKaCa+M5B0pMYe7kmOA94L/CAwCXw+yY6q+tK497XUxhXiC71fXySkY89CHLm/GthVVQ8CJPkIsAnoLtwX2rhePBbjRWiuLyBz/RzDzz2kuVmIcF8D7B5anwRec2CnJFuBrW316SRfPsL9rQIeO8LHLldH3ZhzxYL/nBnHPK79HqWOuud5ETjmuXnpoTYsRLhnhrY6qKFqG7Bt3jtLdlbVxHx/znLimI8NjvnYsFBjXoizZSaBdUPra4E9C7AfSdIhLES4fx7YkORlSY4HLgJ2LMB+JEmHMPZpmap6NskvAZ9icCrkB6rqi+Pez5B5T+0sQ4752OCYjw0LMuZUHTQdLkla5vyGqiR1yHCXpA4tm3BPck6SLyfZleSyGbY/P8l1bfsdSdYvfpXjNcKY35bkS0nuTXJLkkOe87pczDbmoX4XJKkky/60uVHGnOTH23P9xSQfXuwax22E3+2XJLk1yV3t9/u8pahzXJJ8IMneJPcdYnuSXNX+Pe5Ncta8d1pVR/2NwQezfwOcDhwP3AOccUCffw38blu+CLhuqetehDG/HviOtvyLx8KYW78XAp8BbgcmlrruRXieNwB3ASe19VOWuu5FGPM24Bfb8hnAQ0td9zzH/M+Bs4D7DrH9POCTDL4ndDZwx3z3uVyO3J+7pEFV/QMwfUmDYZuA7W35BmBjkpm+ULVczDrmqrq1qp5pq7cz+E7BcjbK8wzwn4DfAr6xmMUtkFHG/HPAe6vqSYCq2rvINY7bKGMu4EVt+USW+XdlquozwBOH6bIJuKYGbgdWJjltPvtcLuE+0yUN1hyqT1U9C+wDXrwo1S2MUcY8bAuDV/7lbNYxJ3kVsK6qPr6YhS2gUZ7nlwMvT/LnSW5vV11dzkYZ828AP5lkErgJeMvilLZk5vr/fVbL5Y91jHJJg5Eue7CMjDyeJD8JTAD/YkErWniHHXOS5wFXAj+9WAUtglGe5xUMpmZex+Dd2WeTfF9VPbXAtS2UUcb8ZuD3q+pdSb4f+GAb8zcXvrwlMfb8Wi5H7qNc0uC5PklWMHgrd7i3QUe7kS7jkOQHgHcCb6qqv1+k2hbKbGN+IfB9wG1JHmIwN7ljmX+oOurv9o1V9Y9V9bfAlxmE/XI1ypi3ANcDVNVfACcwuMBWr8Z+2ZblEu6jXNJgB7C5LV8AfLraJxXL1KxjblMUv8cg2Jf7PCzMMuaq2ldVq6pqfVWtZ/A5w5uqaufSlDsWo/xu/zGDD89JsorBNM2Di1rleI0y5oeBjQBJXsEg3KcWtcrFtQO4uJ01czawr6oemddPXOpPkefwafN5wF8z+JT9na3tPzL4zw2DJ/+jwC7gc8DpS13zIoz5T4FHgbvbbcdS17zQYz6g720s87NlRnyeA7ybwd9E+AJw0VLXvAhjPgP4cwZn0twN/NBS1zzP8V4LPAL8I4Oj9C3ALwC/MPQcv7f9e3xhHL/XXn5Akjq0XKZlJElzYLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDv1/r5fejWS3rgkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawTypeDistributions('../input/human/', 1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "drawTypeDistributions('../input/viatra_75/', 50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extract Human Models with size [90, 101]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extractModel():\n",
    "    from shutil import copy\n",
    "    list_of_files = reader.readmultiplefiles('../input/humanOutput/', 5000)\n",
    "    num_nodes_list =[]\n",
    "    human_size_dic = {}\n",
    "    for file in list_of_files:\n",
    "        contents = reader.readcsvfile(file)\n",
    "        num_of_node = contents[constants.NUMBER_NODES]\n",
    "        # human_size_dic[file] = int(num_of_node[0])\n",
    "        # num_nodes_list.append(int(num_of_node[0]))\n",
    "        if 90 <= num_of_node[0] <= 110:\n",
    "            copy(file, '../input/human_output_100/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multinomial Distribution Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999999999999998\n",
      "{'EAttribute': 0.23539778449144008, 'EClass': 0.30996978851963747, 'EReference': 0.33081570996978854, 'EPackage': 0.012789526686807653, 'EAnnotation': 0.002517623363544813, 'EEnumLiteral': 0.07275931520644502, 'EEnum': 0.013645518630412891, 'EDataType': 0.004028197381671702, 'EParameter': 0.005941591137965764, 'EGenericType': 0.002014098690835851, 'EOperation': 0.009415911379657605, 'ETypeParameter': 0.0007049345417925478}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "graphStats = getModels('../input/human_30_500_no_xml/', 1500)\n",
    "totalNodes = 0\n",
    "typeMap = {}\n",
    "for g in graphStats:\n",
    "    gKeys = g.nodeTypeStat.keys()\n",
    "    size = g.numNodes[0]\n",
    "    totalNodes += size\n",
    "    for key in gKeys:\n",
    "        curNum = typeMap.get(key, 0)\n",
    "        typeMap[key] = curNum + float(g.nodeTypeStat[key]) * size\n",
    "        \n",
    "for key in typeMap.keys():\n",
    "    typeMap[key] /= totalNodes\n",
    "print(sum(typeMap.values()))\n",
    "print(typeMap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chiSquareMultinomialTest(freq, prob):\n",
    "    freq = np.array(freq)\n",
    "    prob = np.array(prob)\n",
    "    size = sum(freq)\n",
    "    e = prob * size\n",
    "    return stats.chisquare(freq, e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def typeDistributionTest(g):\n",
    "    size = g.numNodes[0]\n",
    "    freq = []\n",
    "    prob = []\n",
    "    for key in typeMap.keys():\n",
    "        value = float(g.nodeTypeStat.get(key, 0))\n",
    "        freq.append(np.round(value * size))\n",
    "        prob.append(typeMap[key])\n",
    "    test = chiSquareMultinomialTest(freq, prob)\n",
    "    return test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7.26359261e+01 6.82490355e-02]\n",
      "[3.31848655e+01 1.59841378e-03]\n"
     ]
    }
   ],
   "source": [
    "test = [typeDistributionTest(graphStats[i]) for i in range(len(graphStats))]\n",
    "print(np.mean(test, 0))\n",
    "print(np.median(test, 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node Counts Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from shutil import copyfile\n",
    "import statistics \n",
    "\n",
    "graphStats = getModels('../input/human/', 1500)\n",
    "sizes = []\n",
    "filenames = reader.readmultiplefiles('../input/human/', 1500, False)\n",
    "count = 1\n",
    "for filename in filenames:\n",
    "    graphStat = GraphStat(filename)\n",
    "    size = graphStat.numNodes[0]\n",
    "    if size >= 30 and size <= 500 and not ('EAnnotation' in graphStat.nodeTypeStat.keys() and 'EStringToStringMapEntry' in graphStat.nodeTypeStat.keys()):\n",
    "        copyfile(filename, filename.replace('human', 'human_30_500_no_xml'))\n",
    "        \n",
    "        \n",
    "# for g in graphStats:\n",
    "#     size = g.numNodes[0]\n",
    "#     if size >= 30 and size <= 500 and not ('EAnnotation' in g.nodeTypeStat.keys() and 'EStringToStringMapEntry' in g.nodeTypeStat.keys()):\n",
    "#         sizes.append(g.numNodes[0])\n",
    "# print(max(sizes))\n",
    "# print(min(sizes))\n",
    "# print(statistics.mean(sizes))\n",
    "# print(len(sizes))\n",
    "# plt.hist(sizes, bins=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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