api_complexity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "685f645a-ccf2-43b0-b8e2-59bc30bea6bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xml.etree.ElementTree as ET\n",
    "src_ = \"{http://www.srcML.org/srcML/src}\"\n",
    "\n",
    "api_names = [\"Sienna\", \"Azureus\", \"Prefuse\", \"JackRabbit\", \"Jung\", \"jEdit\"]\n",
    "api_oc_sum = []\n",
    "api_oc_mean = []\n",
    "api_is_sum = []\n",
    "api_is_mean = []\n",
    "api_il_sum = []\n",
    "api_il_mean = []\n",
    "\n",
    "api_n_files = []\n",
    "api_n_blank_lines = []\n",
    "api_n_comments = []\n",
    "api_n_lines_of_code = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62719dd8-026b-421b-a47d-f7344f7053e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics\n",
    "\n",
    "def strength(parameter):\n",
    "    par = str(parameter.text).lower()\n",
    "        \n",
    "    if par == \"boolean\" or \"exception\" in par or \"throwable\" in par:\n",
    "        return 0\n",
    "    elif par in [\"int\", \"char\", \"byte\", \"field\", \"long\", \"short\"]:\n",
    "        return 1\n",
    "    elif par in [\"real\", \"double\", \"float\"]:\n",
    "        return 2\n",
    "    elif par in [\"string\", \"list\", \"json\", \"iterable\", \"date\"] or \"array\" in par or \"set\" in par:\n",
    "        return 3\n",
    "    elif par == \"object\" or \"class\" in par or \"map\" in par:\n",
    "        return 6\n",
    "    elif \"file\" in par:\n",
    "        return 10\n",
    "    else:\n",
    "        #print(par)\n",
    "        return -1\n",
    "\n",
    "# Count API operation complexity and interface size\n",
    "def count_api_oc_and_is(): \n",
    "    api_oc = []\n",
    "    api_is = []\n",
    "    api_il = []\n",
    "\n",
    "    for class_ in root:\n",
    "\n",
    "        total_function_operation_complexity = 0\n",
    "        total_function_interface_size = 0\n",
    "        total_function_interaction_level = 0\n",
    "        \n",
    "        total_attribute_size = 0\n",
    "        for attribute in class_.findall(\".//\" + src_ + \"block/\" + src_ + \"decl_stmt\"):\n",
    "            attribute_type = attribute.findall(\".//\" + src_ + \"decl/\" + src_ + \"type/\" + src_ + \"name\")\n",
    "            if str(attribute_type[0].text) == \"None\":\n",
    "                val = strength(attribute_type[0][0])\n",
    "            else:\n",
    "                val = strength(attribute_type[0])\n",
    "                \n",
    "            if(val>=0):\n",
    "                 total_attribute_size += val\n",
    "                            \n",
    "        for function in class_.findall(\".//\" + src_ + \"function\"):\n",
    "            n_parameters = 0\n",
    "            function_operation_complexity = 0\n",
    "\n",
    "            parameter_types = function.findall(\".//\" + src_ + \"parameter_list/\" + src_ + \"parameter/\" + src_ + \"decl/\" +\n",
    "                                   src_ + \"type/\" + src_ + \"name\")\n",
    "            for par_type in parameter_types:\n",
    "                val = 0\n",
    "                if str(par_type.text) == \"None\":\n",
    "                    val = strength(par_type[0])\n",
    "                else:\n",
    "                    val = strength(par_type)\n",
    "                    \n",
    "                if val>=0:\n",
    "                    function_operation_complexity += val\n",
    "                    n_parameters += 1\n",
    "                    \n",
    "            total_function_operation_complexity += function_operation_complexity\n",
    "            total_function_interface_size += function_operation_complexity + n_parameters\n",
    "            total_function_interaction_level += total_attribute_size *  function_operation_complexity\n",
    "\n",
    "        api_oc.append(total_function_operation_complexity)\n",
    "        api_is.append(total_function_interface_size)\n",
    "        api_il.append(total_function_interaction_level)\n",
    "    \n",
    "    #print(api_oc)\n",
    "    print(\"API OC - Summation:\", sum(api_oc), \", Mean:\", statistics.mean(api_oc))\n",
    "    api_oc_sum.append(sum(api_oc))\n",
    "    api_oc_mean.append(statistics.mean(api_oc))\n",
    "    \n",
    "    #print(api_is)\n",
    "    print(\"API IS - Summation:\", sum(api_is), \", Mean:\", statistics.mean(api_is))\n",
    "    api_is_sum.append(sum(api_is))\n",
    "    api_is_mean.append(statistics.mean(api_is))\n",
    "    \n",
    "    #print(api_il)\n",
    "    print(\"API IL - Summation:\", sum(api_il), \", Mean:\", statistics.mean(api_il))\n",
    "    api_il_sum.append(sum(api_il))\n",
    "    api_il_mean.append(statistics.mean(api_il))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e48be060-1f02-4eaa-9519-9b2c48b788d8",
   "metadata": {},
   "source": [
    "## APIs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1bff3ee-1451-4d5f-b1f0-4be44089739a",
   "metadata": {},
   "source": [
    "### Siena\n",
    "https://github.com/mandubian/siena"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c93f5772-7c05-47f7-b1af-9bb985cea90b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API OC - Summation: 4686 , Mean: 28.059880239520957\n",
      "API IS - Summation: 6271 , Mean: 37.550898203592816\n",
      "API IL - Summation: 88833 , Mean: 531.934131736527\n"
     ]
    }
   ],
   "source": [
    "tree = ET.parse('siena.xml')\n",
    "root = tree.getroot()\n",
    "\n",
    "# Values come from cloc\n",
    "api_n_files.append(167)\n",
    "api_n_blank_lines.append(4186)\n",
    "api_n_comments.append(5456)\n",
    "api_n_lines_of_code.append(19944)\n",
    "\n",
    "count_api_oc_and_is()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e00b4e22-b25d-4c51-9337-6b3ad813da98",
   "metadata": {},
   "source": [
    "### Azureus\n",
    "https://sourceforge.net/projects/azureus/files/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "565ef4d5-b2d4-4f09-84cb-331e80838b7f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API OC - Summation: 53114 , Mean: 14.676429953025698\n",
      "API IS - Summation: 81489 , Mean: 22.51699364465322\n",
      "API IL - Summation: 1872688 , Mean: 517.4600718430505\n"
     ]
    }
   ],
   "source": [
    "tree = ET.parse('azureus.xml')\n",
    "root = tree.getroot()\n",
    "\n",
    "#Values come from cloc\n",
    "api_n_files.append(3619)\n",
    "api_n_blank_lines.append(233435)\n",
    "api_n_comments.append(129623)\n",
    "api_n_lines_of_code.append(633247)\n",
    "\n",
    "count_api_oc_and_is()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "481d2a15-af1c-4b6f-8e87-924ff4856415",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Prefuse\n",
    "https://github.com/prefuse/Prefuse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a333fcb5-afb1-4f71-b6e6-0b0f2f5f959c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API OC - Summation: 5820 , Mean: 14.441687344913152\n",
      "API IS - Summation: 8890 , Mean: 22.05955334987593\n",
      "API IL - Summation: 64428 , Mean: 159.8709677419355\n"
     ]
    }
   ],
   "source": [
    "tree = ET.parse('prefuse.xml')\n",
    "root = tree.getroot()\n",
    "\n",
    "#Values come from cloc\n",
    "api_n_files.append(445)\n",
    "api_n_blank_lines.append(9128)\n",
    "api_n_comments.append(27672)\n",
    "api_n_lines_of_code.append(42574)\n",
    "\n",
    "count_api_oc_and_is()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67a81956-6c89-4710-b855-b517c0ceae31",
   "metadata": {},
   "source": [
    "### Apache Jackrabbit\n",
    "https://github.com/apache/jackrabbit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30b77035-c8fc-4bc9-861c-70a5a29a4a3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API OC - Summation: 24975 , Mean: 7.981783317353787\n",
      "API IS - Summation: 40985 , Mean: 13.098434004474273\n",
      "API IL - Summation: 357086 , Mean: 114.12144455097476\n"
     ]
    }
   ],
   "source": [
    "tree = ET.parse('jackrabbit.xml')\n",
    "root = tree.getroot()\n",
    "\n",
    "#Values come from cloc\n",
    "api_n_files.append(3408)\n",
    "api_n_blank_lines.append(67897)\n",
    "api_n_comments.append(214618)\n",
    "api_n_lines_of_code.append(362089)\n",
    "\n",
    "count_api_oc_and_is()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cd92754-b383-4f76-989b-ad4e098b0f1a",
   "metadata": {},
   "source": [
    "### Jung\n",
    "https://github.com/jrtom/jung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90f877cf-a138-4577-8879-53efadd71593",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API OC - Summation: 2002 , Mean: 4.570776255707763\n",
      "API IS - Summation: 3023 , Mean: 6.901826484018265\n",
      "API IL - Summation: 16713 , Mean: 38.157534246575345\n"
     ]
    }
   ],
   "source": [
    "tree = ET.parse('jung.xml')\n",
    "root = tree.getroot()\n",
    "\n",
    "#Values come from cloc\n",
    "api_n_files.append(494)\n",
    "api_n_blank_lines.append(8310)\n",
    "api_n_comments.append(15021)\n",
    "api_n_lines_of_code.append(41305)\n",
    "\n",
    "count_api_oc_and_is()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eff2010-268b-4a48-bd3a-84b0ff4294a7",
   "metadata": {},
   "source": [
    "### jEdit\n",
    "https://github.com/albfan/jEdit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "580f09be-c1f1-420a-bdcc-8fca8e88d83c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "API OC - Summation: 10415 , Mean: 18.08159722222222\n",
      "API IS - Summation: 15688 , Mean: 27.23611111111111\n",
      "API IL - Summation: 234411 , Mean: 406.9635416666667\n"
     ]
    }
   ],
   "source": [
    "tree = ET.parse('jedit.xml')\n",
    "root = tree.getroot()\n",
    "\n",
    "#Values come from cloc\n",
    "api_n_files.append(575)\n",
    "api_n_blank_lines.append(20434)\n",
    "api_n_comments.append(48619)\n",
    "api_n_lines_of_code.append(119275)\n",
    "\n",
    "count_api_oc_and_is()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c60126c-1e1e-4b28-b2cc-8d305c9e191d",
   "metadata": {},
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c9b6d1c2-86fc-435f-98d0-949b811edc66",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>api_oc_sum</th>\n",
       "      <th>api_oc_mean</th>\n",
       "      <th>api_is_sum</th>\n",
       "      <th>api_is_mean</th>\n",
       "      <th>api_il_sum</th>\n",
       "      <th>api_il_mean</th>\n",
       "      <th>api_n_files</th>\n",
       "      <th>api_n_blank_lines</th>\n",
       "      <th>api_n_comments</th>\n",
       "      <th>api_n_lines_of_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sienna</td>\n",
       "      <td>4686</td>\n",
       "      <td>28.059880</td>\n",
       "      <td>6271</td>\n",
       "      <td>37.550898</td>\n",
       "      <td>88833</td>\n",
       "      <td>531.934132</td>\n",
       "      <td>167</td>\n",
       "      <td>4186</td>\n",
       "      <td>5456</td>\n",
       "      <td>19944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Azureus</td>\n",
       "      <td>53114</td>\n",
       "      <td>14.676430</td>\n",
       "      <td>81489</td>\n",
       "      <td>22.516994</td>\n",
       "      <td>1872688</td>\n",
       "      <td>517.460072</td>\n",
       "      <td>3619</td>\n",
       "      <td>233435</td>\n",
       "      <td>129623</td>\n",
       "      <td>633247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prefuse</td>\n",
       "      <td>5820</td>\n",
       "      <td>14.441687</td>\n",
       "      <td>8890</td>\n",
       "      <td>22.059553</td>\n",
       "      <td>64428</td>\n",
       "      <td>159.870968</td>\n",
       "      <td>445</td>\n",
       "      <td>9128</td>\n",
       "      <td>27672</td>\n",
       "      <td>42574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JackRabbit</td>\n",
       "      <td>24975</td>\n",
       "      <td>7.981783</td>\n",
       "      <td>40985</td>\n",
       "      <td>13.098434</td>\n",
       "      <td>357086</td>\n",
       "      <td>114.121445</td>\n",
       "      <td>3408</td>\n",
       "      <td>67897</td>\n",
       "      <td>214618</td>\n",
       "      <td>362089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jung</td>\n",
       "      <td>2002</td>\n",
       "      <td>4.570776</td>\n",
       "      <td>3023</td>\n",
       "      <td>6.901826</td>\n",
       "      <td>16713</td>\n",
       "      <td>38.157534</td>\n",
       "      <td>494</td>\n",
       "      <td>8310</td>\n",
       "      <td>15021</td>\n",
       "      <td>41305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>jEdit</td>\n",
       "      <td>10415</td>\n",
       "      <td>18.081597</td>\n",
       "      <td>15688</td>\n",
       "      <td>27.236111</td>\n",
       "      <td>234411</td>\n",
       "      <td>406.963542</td>\n",
       "      <td>575</td>\n",
       "      <td>20434</td>\n",
       "      <td>48619</td>\n",
       "      <td>119275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  api_oc_sum  api_oc_mean  api_is_sum  api_is_mean  api_il_sum  \\\n",
       "0      Sienna        4686    28.059880        6271    37.550898       88833   \n",
       "1     Azureus       53114    14.676430       81489    22.516994     1872688   \n",
       "2     Prefuse        5820    14.441687        8890    22.059553       64428   \n",
       "3  JackRabbit       24975     7.981783       40985    13.098434      357086   \n",
       "4        Jung        2002     4.570776        3023     6.901826       16713   \n",
       "5       jEdit       10415    18.081597       15688    27.236111      234411   \n",
       "\n",
       "   api_il_mean  api_n_files  api_n_blank_lines  api_n_comments  \\\n",
       "0   531.934132          167               4186            5456   \n",
       "1   517.460072         3619             233435          129623   \n",
       "2   159.870968          445               9128           27672   \n",
       "3   114.121445         3408              67897          214618   \n",
       "4    38.157534          494               8310           15021   \n",
       "5   406.963542          575              20434           48619   \n",
       "\n",
       "   api_n_lines_of_code  \n",
       "0                19944  \n",
       "1               633247  \n",
       "2                42574  \n",
       "3               362089  \n",
       "4                41305  \n",
       "5               119275  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame({'Name': api_names, 'api_oc_sum': api_oc_sum, 'api_oc_mean': api_oc_mean, 'api_is_sum': api_is_sum, 'api_is_mean': api_is_mean,\n",
    "                  'api_il_sum': api_il_sum, 'api_il_mean': api_il_mean, 'api_n_files': api_n_files, 'api_n_blank_lines': api_n_blank_lines,\n",
    "                  'api_n_comments': api_n_comments, 'api_n_lines_of_code': api_n_lines_of_code})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b1c1ff9-86db-44f9-a310-e34958e69632",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>api_oc_sum_by_lc</th>\n",
       "      <th>api_oc_mean</th>\n",
       "      <th>api_is_sum_by_lc</th>\n",
       "      <th>api_is_mean</th>\n",
       "      <th>api_il_sum_by_lc</th>\n",
       "      <th>api_il_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sienna</td>\n",
       "      <td>0.234958</td>\n",
       "      <td>28.059880</td>\n",
       "      <td>0.314430</td>\n",
       "      <td>37.550898</td>\n",
       "      <td>4.454122</td>\n",
       "      <td>531.934132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Azureus</td>\n",
       "      <td>0.083876</td>\n",
       "      <td>14.676430</td>\n",
       "      <td>0.128684</td>\n",
       "      <td>22.516994</td>\n",
       "      <td>2.957279</td>\n",
       "      <td>517.460072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prefuse</td>\n",
       "      <td>0.136703</td>\n",
       "      <td>14.441687</td>\n",
       "      <td>0.208813</td>\n",
       "      <td>22.059553</td>\n",
       "      <td>1.513318</td>\n",
       "      <td>159.870968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JackRabbit</td>\n",
       "      <td>0.068975</td>\n",
       "      <td>7.981783</td>\n",
       "      <td>0.113190</td>\n",
       "      <td>13.098434</td>\n",
       "      <td>0.986183</td>\n",
       "      <td>114.121445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jung</td>\n",
       "      <td>0.048469</td>\n",
       "      <td>4.570776</td>\n",
       "      <td>0.073187</td>\n",
       "      <td>6.901826</td>\n",
       "      <td>0.404624</td>\n",
       "      <td>38.157534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>jEdit</td>\n",
       "      <td>0.087319</td>\n",
       "      <td>18.081597</td>\n",
       "      <td>0.131528</td>\n",
       "      <td>27.236111</td>\n",
       "      <td>1.965299</td>\n",
       "      <td>406.963542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  api_oc_sum_by_lc  api_oc_mean  api_is_sum_by_lc  api_is_mean  \\\n",
       "0      Sienna          0.234958    28.059880          0.314430    37.550898   \n",
       "1     Azureus          0.083876    14.676430          0.128684    22.516994   \n",
       "2     Prefuse          0.136703    14.441687          0.208813    22.059553   \n",
       "3  JackRabbit          0.068975     7.981783          0.113190    13.098434   \n",
       "4        Jung          0.048469     4.570776          0.073187     6.901826   \n",
       "5       jEdit          0.087319    18.081597          0.131528    27.236111   \n",
       "\n",
       "   api_il_sum_by_lc  api_il_mean  \n",
       "0          4.454122   531.934132  \n",
       "1          2.957279   517.460072  \n",
       "2          1.513318   159.870968  \n",
       "3          0.986183   114.121445  \n",
       "4          0.404624    38.157534  \n",
       "5          1.965299   406.963542  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"api_oc_sum_by_lc\"] = df[\"api_oc_sum\"]/df[\"api_n_lines_of_code\"]\n",
    "df[\"api_is_sum_by_lc\"] = df[\"api_is_sum\"]/df[\"api_n_lines_of_code\"]\n",
    "df[\"api_il_sum_by_lc\"] = df[\"api_il_sum\"]/df[\"api_n_lines_of_code\"]\n",
    "df2 = df[[\"Name\", \"api_oc_sum_by_lc\", \"api_oc_mean\", \"api_is_sum_by_lc\", \"api_is_mean\", \"api_il_sum_by_lc\", \"api_il_mean\"]]\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "68627240-baad-41cd-b368-d66f949dcf91",
   "metadata": {},
   "outputs": [
    {
     "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>Name</th>\n",
       "      <th>api_oc_sum_by_lc</th>\n",
       "      <th>api_oc_mean</th>\n",
       "      <th>api_is_sum_by_lc</th>\n",
       "      <th>api_is_mean</th>\n",
       "      <th>api_il_sum_by_lc</th>\n",
       "      <th>api_il_mean</th>\n",
       "      <th>Clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sienna</td>\n",
       "      <td>0.234958</td>\n",
       "      <td>28.059880</td>\n",
       "      <td>0.314430</td>\n",
       "      <td>37.550898</td>\n",
       "      <td>4.454122</td>\n",
       "      <td>531.934132</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Azureus</td>\n",
       "      <td>0.083876</td>\n",
       "      <td>14.676430</td>\n",
       "      <td>0.128684</td>\n",
       "      <td>22.516994</td>\n",
       "      <td>2.957279</td>\n",
       "      <td>517.460072</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prefuse</td>\n",
       "      <td>0.136703</td>\n",
       "      <td>14.441687</td>\n",
       "      <td>0.208813</td>\n",
       "      <td>22.059553</td>\n",
       "      <td>1.513318</td>\n",
       "      <td>159.870968</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>JackRabbit</td>\n",
       "      <td>0.068975</td>\n",
       "      <td>7.981783</td>\n",
       "      <td>0.113190</td>\n",
       "      <td>13.098434</td>\n",
       "      <td>0.986183</td>\n",
       "      <td>114.121445</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jung</td>\n",
       "      <td>0.048469</td>\n",
       "      <td>4.570776</td>\n",
       "      <td>0.073187</td>\n",
       "      <td>6.901826</td>\n",
       "      <td>0.404624</td>\n",
       "      <td>38.157534</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>jEdit</td>\n",
       "      <td>0.087319</td>\n",
       "      <td>18.081597</td>\n",
       "      <td>0.131528</td>\n",
       "      <td>27.236111</td>\n",
       "      <td>1.965299</td>\n",
       "      <td>406.963542</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  api_oc_sum_by_lc  api_oc_mean  api_is_sum_by_lc  api_is_mean  \\\n",
       "0      Sienna          0.234958    28.059880          0.314430    37.550898   \n",
       "1     Azureus          0.083876    14.676430          0.128684    22.516994   \n",
       "2     Prefuse          0.136703    14.441687          0.208813    22.059553   \n",
       "3  JackRabbit          0.068975     7.981783          0.113190    13.098434   \n",
       "4        Jung          0.048469     4.570776          0.073187     6.901826   \n",
       "5       jEdit          0.087319    18.081597          0.131528    27.236111   \n",
       "\n",
       "   api_il_sum_by_lc  api_il_mean  Clusters  \n",
       "0          4.454122   531.934132         1  \n",
       "1          2.957279   517.460072         1  \n",
       "2          1.513318   159.870968         0  \n",
       "3          0.986183   114.121445         0  \n",
       "4          0.404624    38.157534         0  \n",
       "5          1.965299   406.963542         1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn.cluster as cluster\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "kmeans = cluster.KMeans(n_clusters=2, init=\"k-means++\")\n",
    "kmeans = kmeans.fit(df2[[\"api_oc_sum_by_lc\", \"api_oc_mean\", \"api_is_sum_by_lc\", \"api_is_mean\", \"api_il_sum_by_lc\", \"api_il_mean\"]])\n",
    "df2['Clusters'] = kmeans.labels_\n",
    "#df2['Clusters'] = df2['Clusters'].replace({0: \"Low\"})\n",
    "#sns.scatterplot(x=\"api_oc_sum_by_lc\", y=\"api_is_sum_by_lc\", hue = 'Clusters', data = df2)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f2b9da4d-d668-4134-a2ad-98dc23774bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1128.75x1080 with 27 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.pairplot(df2, vars=df2[[\"api_oc_sum_by_lc\", \"api_oc_mean\", \"api_is_sum_by_lc\", \"api_is_mean\", \"api_il_sum_by_lc\", \"api_il_mean\"]], hue = 'Clusters', corner=True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}