TUXML-basic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3020: DtypeWarning: Columns (1150,6015,6026,6717,7350,7676,7726,9949,10442) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "some configurations may have X86_32 (coz we have tested/tried some options and there are in the database) -- we removed them of course\n",
      "count     11538\n",
      "unique        2\n",
      "top           y\n",
      "freq      11532\n",
      "Name: X86_64, dtype: object\n",
      "Original size (#configs/#options) of the dataset (11532, 12798)\n",
      "Number of options with only one value (eg always y): (3398, 1)\n",
      "Non tri-state value options (eg string or integer or hybrid values): (146, 1) \n",
      "Predictor variables: 12652\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 11532 entries, 0 to 11537\n",
      "Columns: 12798 entries, cid to NETWORK_FILESYSTEMS\n",
      "dtypes: float64(24), int64(240), object(12534)\n",
      "memory usage: 8.9 GB\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# './config_bdd50K.csv' # ./config_bdd10K.csv' # './config_bdd.csv' # \n",
    "TUXML_CSV_FILENAME= './config_bdd10K.csv' # \"/mnt/temp_dd/igrida-fs1/macher/config_bdd30To102K.csv\" # \"config_bdd83K.csv\" # 'config_bdd1K.csv' # # \"./config_bdd.csv\" # \n",
    "\n",
    "# sanity check CSV\n",
    "with open(TUXML_CSV_FILENAME, \"r\") as file:\n",
    "    k = file.readline()\n",
    "    t = k.split(\",\")\n",
    "    s = set(t)\n",
    "    assert(len(t) == len(s)) # unique number of options/features/column names\n",
    "\n",
    "# parsing for real with pandas \n",
    "rawtuxdata = pd.read_csv(open(TUXML_CSV_FILENAME, \"r\"))\n",
    "print(\"some configurations may have X86_32 (coz we have tested/tried some options and there are in the database) -- we removed them of course\")\n",
    "# we only keep X86_64 configurations\n",
    "#rawtuxdata = rawtuxdata[rawtuxdata['X86_64'] == 'y'] ### TODO: I've impression it's not the most effective way (wrt memory) to filter \n",
    "if 'X86_64' in rawtuxdata.columns:\n",
    "    print(rawtuxdata['X86_64'].describe())\n",
    "    rawtuxdata.query(\"X86_64 == 'y'\", inplace=True) # only keep them\n",
    "\n",
    "\n",
    "basic_head = [\"cid\", \"time\", \"date\"] # \"compile\"\n",
    "size_methods = [\"vmlinux\", \"GZIP-bzImage\", \"GZIP-vmlinux\", \"GZIP\", \"BZIP2-bzImage\", \n",
    "              \"BZIP2-vmlinux\", \"BZIP2\", \"LZMA-bzImage\", \"LZMA-vmlinux\", \"LZMA\", \"XZ-bzImage\", \"XZ-vmlinux\", \"XZ\", \n",
    "              \"LZO-bzImage\", \"LZO-vmlinux\", \"LZO\", \"LZ4-bzImage\", \"LZ4-vmlinux\", \"LZ4\"]\n",
    "\n",
    "\n",
    "### basic stats about options and remove of unique values \n",
    "## could be improved \n",
    "\n",
    "tri_state_values = ['y', 'n', 'm']\n",
    "\n",
    "ftuniques = []\n",
    "freq_ymn_features = []\n",
    "non_tristate_options = []\n",
    "\n",
    "for col in rawtuxdata:\n",
    "    ft = rawtuxdata[col]    \n",
    "    # eg always \"y\"\n",
    "    if len(ft.unique()) == 1:\n",
    "        ftuniques.append(col)\n",
    "    # only tri-state values (y, n, m) (possible TODO: handle numerical/string options)    \n",
    "    elif all(x in tri_state_values for x in ft.unique()):     #len(ft.unique()) == 3: \n",
    "        freq = ft.value_counts(normalize=True)\n",
    "        freqy = 0\n",
    "        freqn = 0\n",
    "        freqm = 0\n",
    "        if ('y' in freq.index):\n",
    "            freqy = freq['y']\n",
    "        if ('n' in freq.index):\n",
    "            freqn = freq['n']\n",
    "        if ('m' in freq.index):\n",
    "            freqm = freq['m']\n",
    "        freq_ymn_features.append((col, freqy, freqm, freqn))\n",
    "    else:\n",
    "        if not (col in size_methods): \n",
    "            non_tristate_options.append(col)\n",
    "        \n",
    "\n",
    "### TODO: we want to keep all quantitative values!\n",
    "# non_tristate_options.remove('LZO') # ('vmlinux')\n",
    "\n",
    "# we want to keep measurements (that are not tristate ;)) \n",
    "# non_tristate_options = list(set(non_tristate_options) - set(size_methods))\n",
    "\n",
    "#### print options with unique values\n",
    "# options with only one value eg always \"y\"\n",
    "#i = 0\n",
    "#for ft in ftuniques:\n",
    "#    print(ft + \" (\" + str(i) + \")\")\n",
    "#    i = i + 1\n",
    "\n",
    "print(\"Original size (#configs/#options) of the dataset \" + str(rawtuxdata.shape))\n",
    "print (\"Number of options with only one value (eg always y): \" + str(pd.DataFrame(ftuniques).shape))\n",
    "\n",
    "# maybe we can drop options with only one unique value (no interest for machine learning)\n",
    "# TODO: maybe we can rely on more traditional feature reduction techniques\n",
    "# TODO: need to think about *when* to apply the removal \n",
    "\n",
    "#rawtuxdata.drop(columns=ftuniques,inplace=True) \n",
    "\n",
    "## non_tristate_options include basic stuff like date, time, cid but also string/numerical options\n",
    "print (\"Non tri-state value options (eg string or integer or hybrid values): \" \n",
    "       + str(pd.DataFrame(non_tristate_options).shape) + \" \") \n",
    "#      + str(pd.DataFrame(non_tristate_options)))\n",
    "\n",
    "\n",
    "print (\"Predictor variables: \" + str(rawtuxdata.drop(columns=non_tristate_options).columns.size))\n",
    "# frequency of y, m, and n values \n",
    "#plt.figure()\n",
    "#pd.DataFrame(freq_ymn_features, columns=[\"feature\", \"freqy\", \"freqm\", \"freqn\"]).plot(kind='hist', alpha=0.8) #plot()\n",
    "#plt.show()\n",
    "\n",
    "\n",
    "rawtuxdata.info(memory_usage='deep')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11532 before the removal of some entries (those with same configurations)\n",
      "11521 after the removal of some entries (those with same configurations)\n"
     ]
    }
   ],
   "source": [
    "# remove entries with same configurations\n",
    "print(str(len(rawtuxdata)) + \" before the removal of some entries (those with same configurations)\")\n",
    "# tuxdata.drop_duplicates(subset=tuxdata.columns.difference(['vmlinux']), inplace=True)\n",
    "rawtuxdata.drop_duplicates(subset=rawtuxdata.columns.difference(size_methods).difference(basic_head), inplace=True)\n",
    "print(str(len(rawtuxdata)) + \" after the removal of some entries (those with same configurations)\")\n",
    "\n",
    "#n_failures = len(tuxdata[~np.isnan(tuxdata['vmlinux'])])\n",
    "#n_failures = len(rawtuxdata.query(\"vmlinux != -1\")) #len(tuxdata[np.isnan(tuxdata['vmlinux'])])\n",
    "#print(str(n_failures) + \" non-failures out of \" + str(len(rawtuxdata)))\n",
    "\n",
    "#tuxdata = tuxdata[~np.isnan(tuxdata['vmlinux'])]\n",
    "#rawtuxdata = rawtuxdata[rawtuxdata['vmlinux'] != -1] #tuxdata[~np.isnan(tuxdata['vmlinux'])]\n",
    "# print(str(len(rawtuxdata)) + \" after the removal of configurations that do NOT compile\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cid_to_remove=74459 # compilation time is very low (2 sec)\n",
    "cid_to_remove2=30698 # size is 0! \n",
    "\n",
    "rawtuxdata = rawtuxdata[rawtuxdata.cid != cid_to_remove]\n",
    "rawtuxdata = rawtuxdata[rawtuxdata.cid != cid_to_remove2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "compilation failures: 695 out of 11520 ( 6.032986111111112 %)\n"
     ]
    }
   ],
   "source": [
    "compilation_failures = len(rawtuxdata.query(\"vmlinux == -1\").index)\n",
    "compilation_successes = len(rawtuxdata.query(\"vmlinux != -1\").index)\n",
    "n_compilations = len(rawtuxdata.index)\n",
    "assert(compilation_successes + compilation_failures == n_compilations)\n",
    "compilation_failure_percentage = (compilation_failures / n_compilations) * 100\n",
    "print(\"compilation failures:\", compilation_failures, \"out of\", n_compilations, \"(\", compilation_failure_percentage, \"%)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nbyes(row):\n",
    "    return sum(row == \"y\")                    \n",
    "rawtuxdata['nbyes'] = rawtuxdata.apply(nbyes, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cid</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>vmlinux</th>\n",
       "      <th>GZIP-bzImage</th>\n",
       "      <th>GZIP-vmlinux</th>\n",
       "      <th>GZIP</th>\n",
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       "      <th>BZIP2</th>\n",
       "      <th>...</th>\n",
       "      <th>APDS9960</th>\n",
       "      <th>ARCH_SUPPORTS_INT128</th>\n",
       "      <th>SLABINFO</th>\n",
       "      <th>MICROCODE_AMD</th>\n",
       "      <th>ISDN_DRV_HISAX</th>\n",
       "      <th>CHARGER_BQ24190</th>\n",
       "      <th>SND_SOC_NAU8825</th>\n",
       "      <th>BH1750</th>\n",
       "      <th>NETWORK_FILESYSTEMS</th>\n",
       "      <th>nbyes</th>\n",
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       "  </thead>\n",
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       "      <th>9789</th>\n",
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       "      <td>2018-07-05 16:07:21</td>\n",
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       "      <td>n</td>\n",
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       "      <td>n</td>\n",
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       "      <th>9554</th>\n",
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       "      <td>2018-07-05 09:25:11</td>\n",
       "      <td>134.2110</td>\n",
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       "      <td>5465552</td>\n",
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       "      <th>9796</th>\n",
       "      <td>74465</td>\n",
       "      <td>2018-07-05 17:11:54</td>\n",
       "      <td>225.9910</td>\n",
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       "      <td>y</td>\n",
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       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10861</th>\n",
       "      <td>75533</td>\n",
       "      <td>2018-07-07 08:58:57</td>\n",
       "      <td>123.1040</td>\n",
       "      <td>18422024</td>\n",
       "      <td>2876368</td>\n",
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       "      <td>739</td>\n",
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       "    <tr>\n",
       "      <th>8691</th>\n",
       "      <td>73356</td>\n",
       "      <td>2018-07-04 18:42:26</td>\n",
       "      <td>328.9470</td>\n",
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       "      <td>2018-07-03 02:31:48</td>\n",
       "      <td>182.0430</td>\n",
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       "    <tr>\n",
       "      <th>7656</th>\n",
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       "      <td>2018-07-03 22:22:58</td>\n",
       "      <td>166.5090</td>\n",
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       "      <th>2671</th>\n",
       "      <td>67181</td>\n",
       "      <td>2018-06-28 03:23:46</td>\n",
       "      <td>253.0940</td>\n",
       "      <td>25261080</td>\n",
       "      <td>2629584</td>\n",
       "      <td>4715648</td>\n",
       "      <td>2485188</td>\n",
       "      <td>6709200</td>\n",
       "      <td>8794704</td>\n",
       "      <td>2446299</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>776</td>\n",
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       "    <tr>\n",
       "      <th>10028</th>\n",
       "      <td>74700</td>\n",
       "      <td>2018-07-06 19:06:49</td>\n",
       "      <td>167.5530</td>\n",
       "      <td>17921840</td>\n",
       "      <td>3494864</td>\n",
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       "      <td>9658960</td>\n",
       "      <td>3309054</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10306</th>\n",
       "      <td>74978</td>\n",
       "      <td>2018-07-06 23:44:39</td>\n",
       "      <td>183.4520</td>\n",
       "      <td>23933464</td>\n",
       "      <td>4501968</td>\n",
       "      <td>6587856</td>\n",
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       "      <td>8376784</td>\n",
       "      <td>10462120</td>\n",
       "      <td>4113125</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>782</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 12799 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         cid                 date      time    vmlinux  GZIP-bzImage  \\\n",
       "9789   74458  2018-07-05 16:07:21   28.7108    7317008        646608   \n",
       "9554   74219  2018-07-05 09:25:11  134.2110   13729408       3380176   \n",
       "9796   74465  2018-07-05 17:11:54  225.9910   48912544       6660048   \n",
       "10861  75533  2018-07-07 08:58:57  123.1040   18422024       2876368   \n",
       "8691   73356  2018-07-04 18:42:26  328.9470   27475232       3973072   \n",
       "6699   71322  2018-07-03 02:31:48  182.0430   28913520       3347408   \n",
       "7656   72297  2018-07-03 22:22:58  166.5090  147221608       2487248   \n",
       "2671   67181  2018-06-28 03:23:46  253.0940   25261080       2629584   \n",
       "10028  74700  2018-07-06 19:06:49  167.5530   17921840       3494864   \n",
       "10306  74978  2018-07-06 23:44:39  183.4520   23933464       4501968   \n",
       "\n",
       "       GZIP-vmlinux     GZIP  BZIP2-bzImage  BZIP2-vmlinux    BZIP2  ...    \\\n",
       "9789        2733176   501222        4722128        6808144   458568  ...     \n",
       "9554        5465552  3233544        7439312        9524136  3173721  ...     \n",
       "9796        8746104  6515085       10166224       12251728  5903503  ...     \n",
       "10861       4962984  2675913        7037904        9123968  2720465  ...     \n",
       "8691        6060712  3773051        8196048       10283136  3878106  ...     \n",
       "6699        5432784  3198622        7394256        9479080  3129856  ...     \n",
       "7656        4574072  2274640        6632400        8718672  2301220  ...     \n",
       "2671        4715648  2485188        6709200        8794704  2446299  ...     \n",
       "10028       5579904  3349254        7574480        9658960  3309054  ...     \n",
       "10306       6587856  4355568        8376784       10462120  4113125  ...     \n",
       "\n",
       "       APDS9960  ARCH_SUPPORTS_INT128  SLABINFO  MICROCODE_AMD  \\\n",
       "9789          n                     y         n              n   \n",
       "9554          m                     y         n              n   \n",
       "9796          n                     y         y              n   \n",
       "10861         n                     y         n              n   \n",
       "8691          n                     y         n              y   \n",
       "6699          m                     y         n              n   \n",
       "7656          m                     y         n              n   \n",
       "2671          n                     y         n              n   \n",
       "10028         n                     y         n              y   \n",
       "10306         n                     y         y              n   \n",
       "\n",
       "       ISDN_DRV_HISAX  CHARGER_BQ24190  SND_SOC_NAU8825  BH1750  \\\n",
       "9789                n                n                n       n   \n",
       "9554                n                n                n       m   \n",
       "9796                n                m                n       n   \n",
       "10861               n                m                n       n   \n",
       "8691                n                n                n       n   \n",
       "6699                n                m                n       m   \n",
       "7656                n                n                n       n   \n",
       "2671                n                n                n       n   \n",
       "10028               n                n                n       m   \n",
       "10306               n                n                n       n   \n",
       "\n",
       "       NETWORK_FILESYSTEMS  nbyes  \n",
       "9789                     n    240  \n",
       "9554                     n    710  \n",
       "9796                     n    730  \n",
       "10861                    n    739  \n",
       "8691                     n    739  \n",
       "6699                     n    765  \n",
       "7656                     n    771  \n",
       "2671                     n    776  \n",
       "10028                    n    776  \n",
       "10306                    n    782  \n",
       "\n",
       "[10 rows x 12799 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawtuxdata.sort_values(by=\"nbyes\")[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rawtuxdata.query(\"X86_64 == 'y'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9789    240\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tinykernel of 4.13.3: size of binary is 7317008\n",
    "rawtuxdata.query(\"vmlinux == 7317008\").apply(nbyes, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>cid</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>vmlinux</th>\n",
       "      <th>GZIP-bzImage</th>\n",
       "      <th>GZIP-vmlinux</th>\n",
       "      <th>GZIP</th>\n",
       "      <th>BZIP2-bzImage</th>\n",
       "      <th>BZIP2-vmlinux</th>\n",
       "      <th>BZIP2</th>\n",
       "      <th>...</th>\n",
       "      <th>APDS9960</th>\n",
       "      <th>ARCH_SUPPORTS_INT128</th>\n",
       "      <th>SLABINFO</th>\n",
       "      <th>MICROCODE_AMD</th>\n",
       "      <th>ISDN_DRV_HISAX</th>\n",
       "      <th>CHARGER_BQ24190</th>\n",
       "      <th>SND_SOC_NAU8825</th>\n",
       "      <th>BH1750</th>\n",
       "      <th>NETWORK_FILESYSTEMS</th>\n",
       "      <th>nbyes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9789</th>\n",
       "      <td>74458</td>\n",
       "      <td>2018-07-05 16:07:21</td>\n",
       "      <td>28.7108</td>\n",
       "      <td>7317008</td>\n",
       "      <td>646608</td>\n",
       "      <td>2733176</td>\n",
       "      <td>501222</td>\n",
       "      <td>4722128</td>\n",
       "      <td>6808144</td>\n",
       "      <td>458568</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 12799 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        cid                 date     time  vmlinux  GZIP-bzImage  \\\n",
       "9789  74458  2018-07-05 16:07:21  28.7108  7317008        646608   \n",
       "\n",
       "      GZIP-vmlinux    GZIP  BZIP2-bzImage  BZIP2-vmlinux   BZIP2  ...    \\\n",
       "9789       2733176  501222        4722128        6808144  458568  ...     \n",
       "\n",
       "      APDS9960  ARCH_SUPPORTS_INT128  SLABINFO  MICROCODE_AMD  ISDN_DRV_HISAX  \\\n",
       "9789         n                     y         n              n               n   \n",
       "\n",
       "      CHARGER_BQ24190  SND_SOC_NAU8825  BH1750  NETWORK_FILESYSTEMS  nbyes  \n",
       "9789                n                n       n                    n    240  \n",
       "\n",
       "[1 rows x 12799 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tinykernel\n",
    "rawtuxdata.query(\"vmlinux == 7317008\") #.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>cid</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>vmlinux</th>\n",
       "      <th>GZIP-bzImage</th>\n",
       "      <th>GZIP-vmlinux</th>\n",
       "      <th>GZIP</th>\n",
       "      <th>BZIP2-bzImage</th>\n",
       "      <th>BZIP2-vmlinux</th>\n",
       "      <th>BZIP2</th>\n",
       "      <th>...</th>\n",
       "      <th>APDS9960</th>\n",
       "      <th>ARCH_SUPPORTS_INT128</th>\n",
       "      <th>SLABINFO</th>\n",
       "      <th>MICROCODE_AMD</th>\n",
       "      <th>ISDN_DRV_HISAX</th>\n",
       "      <th>CHARGER_BQ24190</th>\n",
       "      <th>SND_SOC_NAU8825</th>\n",
       "      <th>BH1750</th>\n",
       "      <th>NETWORK_FILESYSTEMS</th>\n",
       "      <th>nbyes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9789</th>\n",
       "      <td>74458</td>\n",
       "      <td>2018-07-05 16:07:21</td>\n",
       "      <td>28.7108</td>\n",
       "      <td>7317008</td>\n",
       "      <td>646608</td>\n",
       "      <td>2733176</td>\n",
       "      <td>501222</td>\n",
       "      <td>4722128</td>\n",
       "      <td>6808144</td>\n",
       "      <td>458568</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11051</th>\n",
       "      <td>75723</td>\n",
       "      <td>2018-07-07 12:03:34</td>\n",
       "      <td>248.0520</td>\n",
       "      <td>10856456</td>\n",
       "      <td>2420688</td>\n",
       "      <td>4506744</td>\n",
       "      <td>2277016</td>\n",
       "      <td>6500304</td>\n",
       "      <td>8585808</td>\n",
       "      <td>2239508</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4339</th>\n",
       "      <td>68849</td>\n",
       "      <td>2018-06-28 07:28:08</td>\n",
       "      <td>148.5210</td>\n",
       "      <td>11035016</td>\n",
       "      <td>2675664</td>\n",
       "      <td>4761040</td>\n",
       "      <td>2527981</td>\n",
       "      <td>6751184</td>\n",
       "      <td>8836008</td>\n",
       "      <td>2486907</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>65182</td>\n",
       "      <td>2018-06-27 22:21:51</td>\n",
       "      <td>146.2500</td>\n",
       "      <td>11098984</td>\n",
       "      <td>2417104</td>\n",
       "      <td>4502992</td>\n",
       "      <td>2270281</td>\n",
       "      <td>6513104</td>\n",
       "      <td>8598440</td>\n",
       "      <td>2246106</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3557</th>\n",
       "      <td>68067</td>\n",
       "      <td>2018-06-28 05:35:08</td>\n",
       "      <td>261.6930</td>\n",
       "      <td>11100872</td>\n",
       "      <td>2900432</td>\n",
       "      <td>4985976</td>\n",
       "      <td>2754582</td>\n",
       "      <td>6967760</td>\n",
       "      <td>9052752</td>\n",
       "      <td>2705747</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3073</th>\n",
       "      <td>67583</td>\n",
       "      <td>2018-06-28 04:22:49</td>\n",
       "      <td>264.7150</td>\n",
       "      <td>11123472</td>\n",
       "      <td>3036112</td>\n",
       "      <td>5121488</td>\n",
       "      <td>2888239</td>\n",
       "      <td>7054288</td>\n",
       "      <td>9139112</td>\n",
       "      <td>2787111</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10107</th>\n",
       "      <td>74779</td>\n",
       "      <td>2018-07-06 20:24:27</td>\n",
       "      <td>151.6130</td>\n",
       "      <td>11168320</td>\n",
       "      <td>2474960</td>\n",
       "      <td>4559992</td>\n",
       "      <td>2327284</td>\n",
       "      <td>6562768</td>\n",
       "      <td>8647248</td>\n",
       "      <td>2298254</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4009</th>\n",
       "      <td>68519</td>\n",
       "      <td>2018-06-28 06:39:57</td>\n",
       "      <td>121.5050</td>\n",
       "      <td>11264264</td>\n",
       "      <td>2733008</td>\n",
       "      <td>4818040</td>\n",
       "      <td>2589098</td>\n",
       "      <td>6808528</td>\n",
       "      <td>8893008</td>\n",
       "      <td>2543209</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>67447</td>\n",
       "      <td>2018-06-28 04:02:33</td>\n",
       "      <td>225.8730</td>\n",
       "      <td>11288296</td>\n",
       "      <td>2658256</td>\n",
       "      <td>4744656</td>\n",
       "      <td>2509337</td>\n",
       "      <td>6709200</td>\n",
       "      <td>8795048</td>\n",
       "      <td>2446212</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>1174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>65392</td>\n",
       "      <td>2018-06-27 22:53:17</td>\n",
       "      <td>173.1320</td>\n",
       "      <td>11316840</td>\n",
       "      <td>2696144</td>\n",
       "      <td>4781176</td>\n",
       "      <td>2550515</td>\n",
       "      <td>6763472</td>\n",
       "      <td>8847952</td>\n",
       "      <td>2500002</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>64949</td>\n",
       "      <td>2018-06-27 21:47:59</td>\n",
       "      <td>135.0970</td>\n",
       "      <td>11330376</td>\n",
       "      <td>2647504</td>\n",
       "      <td>4732024</td>\n",
       "      <td>2502541</td>\n",
       "      <td>6690256</td>\n",
       "      <td>8774224</td>\n",
       "      <td>2423913</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>1099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5459</th>\n",
       "      <td>69969</td>\n",
       "      <td>2018-06-28 11:09:18</td>\n",
       "      <td>307.0370</td>\n",
       "      <td>11355552</td>\n",
       "      <td>3346384</td>\n",
       "      <td>5432784</td>\n",
       "      <td>3198880</td>\n",
       "      <td>7413712</td>\n",
       "      <td>9499560</td>\n",
       "      <td>3150893</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>1054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3405</th>\n",
       "      <td>67915</td>\n",
       "      <td>2018-06-28 05:12:06</td>\n",
       "      <td>163.9440</td>\n",
       "      <td>11371928</td>\n",
       "      <td>2757584</td>\n",
       "      <td>4842616</td>\n",
       "      <td>2610449</td>\n",
       "      <td>6837200</td>\n",
       "      <td>8921680</td>\n",
       "      <td>2572452</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>1032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>65951</td>\n",
       "      <td>2018-06-28 00:18:29</td>\n",
       "      <td>138.9740</td>\n",
       "      <td>11381304</td>\n",
       "      <td>2683856</td>\n",
       "      <td>4768888</td>\n",
       "      <td>2540023</td>\n",
       "      <td>6697936</td>\n",
       "      <td>8782416</td>\n",
       "      <td>2434628</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4767</th>\n",
       "      <td>69277</td>\n",
       "      <td>2018-06-28 08:33:05</td>\n",
       "      <td>148.4320</td>\n",
       "      <td>11398632</td>\n",
       "      <td>2998224</td>\n",
       "      <td>5084624</td>\n",
       "      <td>2852762</td>\n",
       "      <td>7061456</td>\n",
       "      <td>9147304</td>\n",
       "      <td>2798094</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>y</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6406</th>\n",
       "      <td>71029</td>\n",
       "      <td>2018-07-02 21:40:34</td>\n",
       "      <td>165.5230</td>\n",
       "      <td>11514448</td>\n",
       "      <td>2638800</td>\n",
       "      <td>4724176</td>\n",
       "      <td>2491054</td>\n",
       "      <td>6702032</td>\n",
       "      <td>8786856</td>\n",
       "      <td>2436987</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6003</th>\n",
       "      <td>70577</td>\n",
       "      <td>2018-06-30 20:50:56</td>\n",
       "      <td>272.0820</td>\n",
       "      <td>11532960</td>\n",
       "      <td>3142608</td>\n",
       "      <td>5227984</td>\n",
       "      <td>2993506</td>\n",
       "      <td>7197648</td>\n",
       "      <td>9282472</td>\n",
       "      <td>2931082</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>1115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7764</th>\n",
       "      <td>72408</td>\n",
       "      <td>2018-07-04 00:03:52</td>\n",
       "      <td>335.1350</td>\n",
       "      <td>11556216</td>\n",
       "      <td>2663376</td>\n",
       "      <td>4748416</td>\n",
       "      <td>2517327</td>\n",
       "      <td>6697936</td>\n",
       "      <td>8782416</td>\n",
       "      <td>2432940</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10343</th>\n",
       "      <td>75015</td>\n",
       "      <td>2018-07-07 00:26:52</td>\n",
       "      <td>315.7570</td>\n",
       "      <td>11606352</td>\n",
       "      <td>3010512</td>\n",
       "      <td>5096912</td>\n",
       "      <td>2864924</td>\n",
       "      <td>7040976</td>\n",
       "      <td>9126824</td>\n",
       "      <td>2776937</td>\n",
       "      <td>...</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>1107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10569</th>\n",
       "      <td>75241</td>\n",
       "      <td>2018-07-07 04:18:28</td>\n",
       "      <td>227.6770</td>\n",
       "      <td>11655584</td>\n",
       "      <td>3281872</td>\n",
       "      <td>5367248</td>\n",
       "      <td>3134062</td>\n",
       "      <td>7320528</td>\n",
       "      <td>9405352</td>\n",
       "      <td>3053864</td>\n",
       "      <td>...</td>\n",
       "      <td>m</td>\n",
       "      <td>y</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "      <td>n</td>\n",
       "      <td>1072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 12799 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         cid                 date      time   vmlinux  GZIP-bzImage  \\\n",
       "9789   74458  2018-07-05 16:07:21   28.7108   7317008        646608   \n",
       "11051  75723  2018-07-07 12:03:34  248.0520  10856456       2420688   \n",
       "4339   68849  2018-06-28 07:28:08  148.5210  11035016       2675664   \n",
       "672    65182  2018-06-27 22:21:51  146.2500  11098984       2417104   \n",
       "3557   68067  2018-06-28 05:35:08  261.6930  11100872       2900432   \n",
       "3073   67583  2018-06-28 04:22:49  264.7150  11123472       3036112   \n",
       "10107  74779  2018-07-06 20:24:27  151.6130  11168320       2474960   \n",
       "4009   68519  2018-06-28 06:39:57  121.5050  11264264       2733008   \n",
       "2937   67447  2018-06-28 04:02:33  225.8730  11288296       2658256   \n",
       "882    65392  2018-06-27 22:53:17  173.1320  11316840       2696144   \n",
       "439    64949  2018-06-27 21:47:59  135.0970  11330376       2647504   \n",
       "5459   69969  2018-06-28 11:09:18  307.0370  11355552       3346384   \n",
       "3405   67915  2018-06-28 05:12:06  163.9440  11371928       2757584   \n",
       "1441   65951  2018-06-28 00:18:29  138.9740  11381304       2683856   \n",
       "4767   69277  2018-06-28 08:33:05  148.4320  11398632       2998224   \n",
       "6406   71029  2018-07-02 21:40:34  165.5230  11514448       2638800   \n",
       "6003   70577  2018-06-30 20:50:56  272.0820  11532960       3142608   \n",
       "7764   72408  2018-07-04 00:03:52  335.1350  11556216       2663376   \n",
       "10343  75015  2018-07-07 00:26:52  315.7570  11606352       3010512   \n",
       "10569  75241  2018-07-07 04:18:28  227.6770  11655584       3281872   \n",
       "\n",
       "       GZIP-vmlinux     GZIP  BZIP2-bzImage  BZIP2-vmlinux    BZIP2  ...    \\\n",
       "9789        2733176   501222        4722128        6808144   458568  ...     \n",
       "11051       4506744  2277016        6500304        8585808  2239508  ...     \n",
       "4339        4761040  2527981        6751184        8836008  2486907  ...     \n",
       "672         4502992  2270281        6513104        8598440  2246106  ...     \n",
       "3557        4985976  2754582        6967760        9052752  2705747  ...     \n",
       "3073        5121488  2888239        7054288        9139112  2787111  ...     \n",
       "10107       4559992  2327284        6562768        8647248  2298254  ...     \n",
       "4009        4818040  2589098        6808528        8893008  2543209  ...     \n",
       "2937        4744656  2509337        6709200        8795048  2446212  ...     \n",
       "882         4781176  2550515        6763472        8847952  2500002  ...     \n",
       "439         4732024  2502541        6690256        8774224  2423913  ...     \n",
       "5459        5432784  3198880        7413712        9499560  3150893  ...     \n",
       "3405        4842616  2610449        6837200        8921680  2572452  ...     \n",
       "1441        4768888  2540023        6697936        8782416  2434628  ...     \n",
       "4767        5084624  2852762        7061456        9147304  2798094  ...     \n",
       "6406        4724176  2491054        6702032        8786856  2436987  ...     \n",
       "6003        5227984  2993506        7197648        9282472  2931082  ...     \n",
       "7764        4748416  2517327        6697936        8782416  2432940  ...     \n",
       "10343       5096912  2864924        7040976        9126824  2776937  ...     \n",
       "10569       5367248  3134062        7320528        9405352  3053864  ...     \n",
       "\n",
       "       APDS9960  ARCH_SUPPORTS_INT128  SLABINFO  MICROCODE_AMD  \\\n",
       "9789          n                     y         n              n   \n",
       "11051         n                     y         y              n   \n",
       "4339          n                     y         n              n   \n",
       "672           m                     y         n              n   \n",
       "3557          n                     y         n              n   \n",
       "3073          n                     y         n              n   \n",
       "10107         m                     y         n              n   \n",
       "4009          n                     y         n              n   \n",
       "2937          n                     y         n              n   \n",
       "882           n                     y         n              n   \n",
       "439           n                     y         n              n   \n",
       "5459          n                     y         y              n   \n",
       "3405          n                     y         n              n   \n",
       "1441          n                     y         n              n   \n",
       "4767          m                     y         y              n   \n",
       "6406          n                     y         n              n   \n",
       "6003          m                     y         n              n   \n",
       "7764          m                     y         n              n   \n",
       "10343         n                     y         n              y   \n",
       "10569         m                     y         n              n   \n",
       "\n",
       "       ISDN_DRV_HISAX  CHARGER_BQ24190  SND_SOC_NAU8825  BH1750  \\\n",
       "9789                n                n                n       n   \n",
       "11051               n                m                n       n   \n",
       "4339                n                m                n       n   \n",
       "672                 n                n                n       m   \n",
       "3557                n                y                m       m   \n",
       "3073                n                n                n       n   \n",
       "10107               n                n                n       n   \n",
       "4009                n                y                n       n   \n",
       "2937                n                n                n       m   \n",
       "882                 n                n                n       n   \n",
       "439                 n                n                n       n   \n",
       "5459                n                m                n       n   \n",
       "3405                n                y                n       m   \n",
       "1441                n                n                n       n   \n",
       "4767                n                m                n       n   \n",
       "6406                n                n                n       n   \n",
       "6003                n                n                n       n   \n",
       "7764                n                m                m       m   \n",
       "10343               n                n                n       m   \n",
       "10569               n                m                n       m   \n",
       "\n",
       "       NETWORK_FILESYSTEMS  nbyes  \n",
       "9789                     n    240  \n",
       "11051                    n    903  \n",
       "4339                     n    935  \n",
       "672                      n    879  \n",
       "3557                     n    972  \n",
       "3073                     y    960  \n",
       "10107                    n    801  \n",
       "4009                     n    920  \n",
       "2937                     n   1174  \n",
       "882                      n    883  \n",
       "439                      n   1099  \n",
       "5459                     n   1054  \n",
       "3405                     n   1032  \n",
       "1441                     n    930  \n",
       "4767                     n    961  \n",
       "6406                     n    980  \n",
       "6003                     n   1115  \n",
       "7764                     n    929  \n",
       "10343                    n   1107  \n",
       "10569                    n   1072  \n",
       "\n",
       "[20 rows x 12799 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawtuxdata.query(\"vmlinux != -1\").sort_values(by=\"vmlinux\")[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "n    5771\n",
       "y    5749\n",
       "Name: COMPILE_TEST, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rawtuxdata['COMPILE_TEST'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# debugging some old entries\n",
    "for k, v in rawtuxdata['LZO'].iteritems():\n",
    "    if isinstance(v, str):\n",
    "        print (rawtuxdata.loc[k]['cid'], v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, False, False, False, False, False, False, False, False, False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'X86_64' in ftuniques, 'DEBUG_INFO' in ftuniques, 'GCOV_PROFILE_ALL' in ftuniques, 'KASAN' in ftuniques, 'UBSAN_SANITIZE_ALL' in ftuniques, 'RELOCATABLE' in ftuniques, 'XFS_DEBUG' in ftuniques, 'AIC7XXX_BUILD_FIRMWARE' in ftuniques, 'AIC79XX_BUILD_FIRMWARE' in ftuniques, 'WANXL_BUILD_FIRMWARE' in ftuniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(n    11202\n",
       " y      318\n",
       " Name: SLOB, dtype: int64, n    11090\n",
       " y      430\n",
       " Name: CC_OPTIMIZE_FOR_SIZE, dtype: int64, y    5956\n",
       " n    5564\n",
       " Name: OPTIMIZE_INLINING, dtype: int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tinyconfig \n",
    "rawtuxdata['SLOB'].value_counts(), rawtuxdata['CC_OPTIMIZE_FOR_SIZE'].value_counts(), rawtuxdata['OPTIMIZE_INLINING'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         cid RELOCATABLE\n",
      "2      64512           y\n",
      "4      64514           y\n",
      "6      64516           y\n",
      "7      64517           y\n",
      "8      64518           y\n",
      "10     64520           y\n",
      "11     64521           y\n",
      "12     64522           y\n",
      "14     64524           y\n",
      "15     64525           y\n",
      "16     64526           y\n",
      "17     64527           y\n",
      "18     64528           y\n",
      "21     64531           y\n",
      "24     64534           y\n",
      "25     64535           y\n",
      "26     64536           y\n",
      "28     64538           y\n",
      "29     64539           y\n",
      "30     64540           y\n",
      "31     64541           y\n",
      "34     64544           y\n",
      "35     64545           y\n",
      "36     64546           y\n",
      "37     64547           y\n",
      "38     64548           y\n",
      "40     64550           y\n",
      "42     64552           y\n",
      "43     64553           y\n",
      "46     64556           y\n",
      "...      ...         ...\n",
      "11484  76156           y\n",
      "11485  76157           y\n",
      "11488  76160           y\n",
      "11489  76161           y\n",
      "11493  76165           y\n",
      "11495  76167           y\n",
      "11498  76170           y\n",
      "11502  76174           y\n",
      "11503  76175           y\n",
      "11506  76178           y\n",
      "11507  76179           y\n",
      "11508  76180           y\n",
      "11509  76181           y\n",
      "11510  76182           y\n",
      "11512  76184           y\n",
      "11513  76185           y\n",
      "11514  76186           y\n",
      "11517  76189           y\n",
      "11519  76191           y\n",
      "11520  76192           y\n",
      "11521  76193           y\n",
      "11522  76194           y\n",
      "11523  76195           y\n",
      "11524  76196           y\n",
      "11525  76197           y\n",
      "11529  76201           y\n",
      "11531  76203           y\n",
      "11532  76204           y\n",
      "11536  76208           y\n",
      "11537  76209           y\n",
      "\n",
      "[6328 rows x 2 columns] (6328, 12799)\n"
     ]
    }
   ],
   "source": [
    "if 'RELOCATABLE' in rawtuxdata.columns:\n",
    "    print(rawtuxdata.query(\"RELOCATABLE == 'y'\")[['cid', 'RELOCATABLE']], rawtuxdata.query(\"RELOCATABLE == 'y'\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data exploration\n"
     ]
    }
   ],
   "source": [
    "print(\"Data exploration\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AIC7XXX_BUILD_FIRMWARE (without 79XX) 221 AIC79XX_BUILD_FIRMWARE (without X7XX) 236\n",
      "AIC7XXX_BUILD_FIRMWARE or AIC79XX_BUILD_FIRMWARE 553\n",
      "Without the 2 major configuration bugs, caused by 2 individual options, it remains 142\n",
      "WANXL_BUILD_FIRMWARE 69\n",
      "There is apparently one subtle case with WANXL_BUILD_FIRMWARE=y causing **NO** configuration bug (with VIDEO_RCAR_VIN) 69\n",
      "still tracking 73 configuration bugs\n",
      "let's now investigate GENERIC_ALLOCATOR\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(29, 44)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BUGS EXPLORATION\n",
    "def bug_exploration():\n",
    "    rawtuxdata.query(\"AIC7XXX_BUILD_FIRMWARE == 'y'\")[['cid', 'vmlinux']]\n",
    "    rawtuxdata.query(\"AIC79XX_BUILD_FIRMWARE == 'y'\")[['cid', 'vmlinux']]\n",
    "    rawtuxdata.query(\"WANXL_BUILD_FIRMWARE == 'y'\")[['cid', 'vmlinux']]\n",
    "    rawtuxdata.query(\"GENERIC_ALLOCATOR == 'n' & DRM_VBOXVIDEO == 'y'\")[['cid', 'vmlinux']]\n",
    "    rawtuxdata.query(\"GENERIC_ALLOCATOR == 'y' & DRM_VBOXVIDEO == 'y'\")[['cid', 'vmlinux']]\n",
    "    rawtuxdata.query(\"GENERIC_ALLOCATOR == 'n' & DRM_VBOXVIDEO == 'm'\")[['cid', 'vmlinux']]\n",
    "    return rawtuxdata.query(\"DRM_VBOXVIDEO == 'y'\")[['cid', 'vmlinux']]\n",
    "\n",
    "#bug_exploration()\n",
    "\n",
    "\n",
    "# y or n values\n",
    "n_AIC7XXX_alone = len(rawtuxdata.query(\"AIC7XXX_BUILD_FIRMWARE == 'y'\").index)\n",
    "n_AIC79XX_alone = len(rawtuxdata.query(\"AIC79XX_BUILD_FIRMWARE == 'y'\").index)\n",
    "# masking effects!\n",
    "n_AIC_both = len(rawtuxdata.query(\"AIC79XX_BUILD_FIRMWARE == 'y' & AIC7XXX_BUILD_FIRMWARE == 'y'\").index)\n",
    "n_AIC_or = len(rawtuxdata.query(\"AIC7XXX_BUILD_FIRMWARE == 'y' | AIC79XX_BUILD_FIRMWARE == 'y'\").index)\n",
    "print(\"AIC7XXX_BUILD_FIRMWARE (without 79XX)\", n_AIC7XXX_alone - n_AIC_both, \"AIC79XX_BUILD_FIRMWARE (without X7XX)\", n_AIC79XX_alone - n_AIC_both)\n",
    "print(\"AIC7XXX_BUILD_FIRMWARE or AIC79XX_BUILD_FIRMWARE\",  n_AIC_or)\n",
    "print(\"Without the 2 major configuration bugs, caused by 2 individual options, it remains\", len(rawtuxdata.query(\"AIC7XXX_BUILD_FIRMWARE != 'y' & AIC79XX_BUILD_FIRMWARE != 'y' & vmlinux == -1\").index))\n",
    "# masking effects!\n",
    "# option WANXL_BUILD_FIRMWARE responsible of many bugs, except... 1\n",
    "n_wanxl = len(rawtuxdata.query(\"WANXL_BUILD_FIRMWARE == 'y' & (AIC7XXX_BUILD_FIRMWARE != 'y' & AIC79XX_BUILD_FIRMWARE != 'y')\").index)\n",
    "print(\"WANXL_BUILD_FIRMWARE\", n_wanxl)\n",
    "# same as previously, but we only keep compilation failures... and surprise:\n",
    "n_wanxl_failure = len(rawtuxdata.query(\"WANXL_BUILD_FIRMWARE == 'y' & (AIC7XXX_BUILD_FIRMWARE != 'y' & AIC79XX_BUILD_FIRMWARE != 'y' & vmlinux == -1)\").index)\n",
    "print(\"There is apparently one subtle case with WANXL_BUILD_FIRMWARE=y causing **NO** configuration bug (with VIDEO_RCAR_VIN)\", n_wanxl_failure) # len(rawtuxdata.query(\"GENERIC_ALLOCATOR == 'y' & vmlinux == -1\").index)\n",
    "\n",
    "assert ((compilation_failures - (n_AIC_or + n_wanxl_failure)) == len(rawtuxdata.query(\"WANXL_BUILD_FIRMWARE != 'y' & AIC7XXX_BUILD_FIRMWARE != 'y' & AIC79XX_BUILD_FIRMWARE != 'y' & vmlinux == -1\").index))\n",
    "print(\"still tracking\", (compilation_failures - (n_AIC_or + n_wanxl_failure)), \"configuration bugs\")\n",
    "print(\"let's now investigate GENERIC_ALLOCATOR\")\n",
    "len(rawtuxdata.query(\"GENERIC_ALLOCATOR == 'y' & WANXL_BUILD_FIRMWARE != 'y' & AIC7XXX_BUILD_FIRMWARE != 'y' & AIC79XX_BUILD_FIRMWARE != 'y' & vmlinux == -1\").index), len(rawtuxdata.query(\"GENERIC_ALLOCATOR == 'n' & WANXL_BUILD_FIRMWARE != 'y' & AIC7XXX_BUILD_FIRMWARE != 'y' & AIC79XX_BUILD_FIRMWARE != 'y' & vmlinux == -1\").index)\n",
    "#len(rawtuxdata.query(\"WANXL_BUILD_FIRMWARE != 'y' & AIC7XXX_BUILD_FIRMWARE != 'y' & AIC79XX_BUILD_FIRMWARE != 'y' & vmlinux == -1\").index)\n",
    "#len(rawtuxdata.query(\"vmlinux == -1\").index)\n",
    "\n",
    "#n_AIC_or + n_wanxl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([64884, 71679, 67029, 65748, 73309, 73387, 66910, 75995, 66445,\n",
       "       69795])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Juliana \n",
    "rawtuxdata.query(\"AIC7XXX_BUILD_FIRMWARE == 'y'\").sample(n=10)['cid'].values\n",
    "len(rawtuxdata.query(\"AIC7XXX_BUILD_FIRMWARE == 'y'\").index)\n",
    "\n",
    "# Djamel \n",
    "rawtuxdata.query(\"AIC79XX_BUILD_FIRMWARE == 'y'\").sample(n=10)['cid'].values\n",
    "len(rawtuxdata.query(\"AIC79XX_BUILD_FIRMWARE == 'y'\").index)\n",
    "\n",
    "# Arnaud \n",
    "rawtuxdata.query(\"WANXL_BUILD_FIRMWARE == 'y' & (AIC7XXX_BUILD_FIRMWARE != 'y' & AIC79XX_BUILD_FIRMWARE != 'y')\").sample(n=10)['cid'].values\n",
    "rawtuxdata.query(\"WANXL_BUILD_FIRMWARE == 'y'\").sample(n=10)['cid'].values\n",
    "\n",
    "# Mathieu \n",
    "# GENERIC_ALLOCATOR == 'y' + DRM_VBOXVIDEO\n",
    "# interestingly: 'm' (module) does not lead to a bug! < 1.5\n",
    "# ['vmlinux', 'cid']\n",
    "rawtuxdata.query(\"GENERIC_ALLOCATOR == 'n' & DRM_VBOXVIDEO == 'y'\").sample(n=10)['cid'].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        cid                 date     time  vmlinux  GZIP-bzImage  \\\n",
      "3066  67576  2018-06-28 04:21:58  275.427       -1            -1   \n",
      "\n",
      "      GZIP-vmlinux  GZIP  BZIP2-bzImage  BZIP2-vmlinux  BZIP2  ...    \\\n",
      "3066            -1    -1             -1             -1     -1  ...     \n",
      "\n",
      "      APDS9960  ARCH_SUPPORTS_INT128  SLABINFO  MICROCODE_AMD  ISDN_DRV_HISAX  \\\n",
      "3066         y                     y         n              n               n   \n",
      "\n",
      "      CHARGER_BQ24190  SND_SOC_NAU8825  BH1750  NETWORK_FILESYSTEMS  nbyes  \n",
      "3066                y                n       y                    n   2446  \n",
      "\n",
      "[1 rows x 12799 columns]\n",
      "Empty DataFrame\n",
      "Columns: [cid, date, time, vmlinux, GZIP-bzImage, GZIP-vmlinux, GZIP, BZIP2-bzImage, BZIP2-vmlinux, BZIP2, LZMA-bzImage, LZMA-vmlinux, LZMA, XZ-bzImage, XZ-vmlinux, XZ, LZO-bzImage, LZO-vmlinux, LZO, LZ4-bzImage, LZ4-vmlinux, LZ4, X86_LOCAL_APIC, OPENVSWITCH, TEXTSEARCH_FSM, LOCKDEP_SUPPORT, GENERIC_CLOCKEVENTS_MIN_ADJUST, NETFILTER_XT_MATCH_TCPMSS, MPLS, NFC_HCI, STACKTRACE_SUPPORT, GENERIC_CMOS_UPDATE, NETFILTER_XT_MATCH_TIME, NET_MPLS_GSO, NFC_SHDLC, ARCH_MMAP_RND_BITS_MIN, NETFILTER_XT_MATCH_U32, OPENVSWITCH_GRE, NFC_TRF7970A, GENERIC_CPU_AUTOPROBE, GENERIC_EARLY_IOREMAP, IP_SET_MAX, OPENVSWITCH_VXLAN, NFC_MEI_PHY, INTEL_MEI, VXLAN, GENERIC_FIND_FIRST_BIT, ARCH_MMAP_RND_BITS_MAX, IP_SET_BITMAP_IP, OPENVSWITCH_GENEVE, NFC_SIM, GENERIC_IOMAP, GENERIC_IRQ_EFFECTIVE_AFF_MASK, IP_SET_BITMAP_IPMAC, GENEVE, SMP, IP_SET_BITMAP_PORT, VSOCKETS, NFC_PORT100, ARCH_MMAP_RND_COMPAT_BITS_MIN, GENERIC_IRQ_MIGRATION, IP_SET_HASH_IP, VMWARE_VMCI_VSOCKETS, NFC_FDP, ARCH_MMAP_RND_COMPAT_BITS_MAX, GENERIC_IRQ_PROBE, IP_SET_HASH_IPMARK, NFC_FDP_I2C, VMWARE_VMCI, SBUS, GENERIC_IRQ_SHOW, IP_SET_HASH_IPPORT, VIRTIO_VSOCKETS, NFC_PN544, NEED_DMA_MAP_STATE, GENERIC_PENDING_IRQ, IP_SET_HASH_IPPORTIP, VIRTIO_VSOCKETS_COMMON, NFC_PN544_I2C, GENERIC_SMP_IDLE_THREAD, NETLINK_DIAG, NFC_PN544_MEI, INTEL_IOMMU, IP_SET_HASH_IPPORTNET, MPLS_ROUTING, NFC_PN533, DMA_API_DEBUG, GENERIC_STRNCPY_FROM_USER, SWIOTLB, IP_SET_HASH_IPMAC, MPLS_IPTUNNEL, NFC_PN533_USB, GENERIC_STRNLEN_USER, NEED_SG_DMA_LENGTH, GENERIC_TIME_VSYSCALL, IP_SET_HASH_MAC, HSR, NFC_PN533_I2C, X86_POWERNOW_K7_ACPI, BINFMT_SHARED_FLAT, ...]\n",
      "Index: []\n",
      "\n",
      "[0 rows x 12799 columns]\n"
     ]
    },
    {
     "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>cid</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>vmlinux</th>\n",
       "      <th>GZIP-bzImage</th>\n",
       "      <th>GZIP-vmlinux</th>\n",
       "      <th>GZIP</th>\n",
       "      <th>BZIP2-bzImage</th>\n",
       "      <th>BZIP2-vmlinux</th>\n",
       "      <th>BZIP2</th>\n",
       "      <th>...</th>\n",
       "      <th>APDS9960</th>\n",
       "      <th>ARCH_SUPPORTS_INT128</th>\n",
       "      <th>SLABINFO</th>\n",
       "      <th>MICROCODE_AMD</th>\n",
       "      <th>ISDN_DRV_HISAX</th>\n",
       "      <th>CHARGER_BQ24190</th>\n",
       "      <th>SND_SOC_NAU8825</th>\n",
       "      <th>BH1750</th>\n",
       "      <th>NETWORK_FILESYSTEMS</th>\n",
       "      <th>nbyes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 12799 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [cid, date, time, vmlinux, GZIP-bzImage, GZIP-vmlinux, GZIP, BZIP2-bzImage, BZIP2-vmlinux, BZIP2, LZMA-bzImage, LZMA-vmlinux, LZMA, XZ-bzImage, XZ-vmlinux, XZ, LZO-bzImage, LZO-vmlinux, LZO, LZ4-bzImage, LZ4-vmlinux, LZ4, X86_LOCAL_APIC, OPENVSWITCH, TEXTSEARCH_FSM, LOCKDEP_SUPPORT, GENERIC_CLOCKEVENTS_MIN_ADJUST, NETFILTER_XT_MATCH_TCPMSS, MPLS, NFC_HCI, STACKTRACE_SUPPORT, GENERIC_CMOS_UPDATE, NETFILTER_XT_MATCH_TIME, NET_MPLS_GSO, NFC_SHDLC, ARCH_MMAP_RND_BITS_MIN, NETFILTER_XT_MATCH_U32, OPENVSWITCH_GRE, NFC_TRF7970A, GENERIC_CPU_AUTOPROBE, GENERIC_EARLY_IOREMAP, IP_SET_MAX, OPENVSWITCH_VXLAN, NFC_MEI_PHY, INTEL_MEI, VXLAN, GENERIC_FIND_FIRST_BIT, ARCH_MMAP_RND_BITS_MAX, IP_SET_BITMAP_IP, OPENVSWITCH_GENEVE, NFC_SIM, GENERIC_IOMAP, GENERIC_IRQ_EFFECTIVE_AFF_MASK, IP_SET_BITMAP_IPMAC, GENEVE, SMP, IP_SET_BITMAP_PORT, VSOCKETS, NFC_PORT100, ARCH_MMAP_RND_COMPAT_BITS_MIN, GENERIC_IRQ_MIGRATION, IP_SET_HASH_IP, VMWARE_VMCI_VSOCKETS, NFC_FDP, ARCH_MMAP_RND_COMPAT_BITS_MAX, GENERIC_IRQ_PROBE, IP_SET_HASH_IPMARK, NFC_FDP_I2C, VMWARE_VMCI, SBUS, GENERIC_IRQ_SHOW, IP_SET_HASH_IPPORT, VIRTIO_VSOCKETS, NFC_PN544, NEED_DMA_MAP_STATE, GENERIC_PENDING_IRQ, IP_SET_HASH_IPPORTIP, VIRTIO_VSOCKETS_COMMON, NFC_PN544_I2C, GENERIC_SMP_IDLE_THREAD, NETLINK_DIAG, NFC_PN544_MEI, INTEL_IOMMU, IP_SET_HASH_IPPORTNET, MPLS_ROUTING, NFC_PN533, DMA_API_DEBUG, GENERIC_STRNCPY_FROM_USER, SWIOTLB, IP_SET_HASH_IPMAC, MPLS_IPTUNNEL, NFC_PN533_USB, GENERIC_STRNLEN_USER, NEED_SG_DMA_LENGTH, GENERIC_TIME_VSYSCALL, IP_SET_HASH_MAC, HSR, NFC_PN533_I2C, X86_POWERNOW_K7_ACPI, BINFMT_SHARED_FLAT, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 12799 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# masking effect\n",
    "print(rawtuxdata.query(\"GENERIC_ALLOCATOR == 'n' & DRM_VBOXVIDEO == 'y' & AIC79XX_BUILD_FIRMWARE == 'y'\"))\n",
    "print(rawtuxdata.query(\"GENERIC_ALLOCATOR == 'n' & DRM_VBOXVIDEO == 'y' & AIC7XXX_BUILD_FIRMWARE == 'y'\"))\n",
    "rawtuxdata.query(\"GENERIC_ALLOCATOR == 'n' & DRM_VBOXVIDEO == 'y' & WANXL_BUILD_FIRMWARE == 'y'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rawtuxdata[rawtuxdata['X86_64'] == 'n']\n",
    "#rawtuxdata.query(\"X86_64 == 'n'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rawtuxdata[(rawtuxdata['DEBUG_INFO'] == 'n') & (rawtuxdata['GCOV_PROFILE_ALL'] == 'n') & (rawtuxdata['KASAN'] == 'n') & (rawtuxdata['MODULES'] == 'y')]\n",
    "# rawtuxdata.query(\"(DEBUG_INFO == 'n') & (GCOV_PROFILE_ALL == 'n') & (KASAN == 'n') & (MODULES == 'y')\")\n",
    "#rawtuxdata.query(\"(DEBUG_INFO == 'n') & (GCOV_PROFILE_ALL == 'n') & (KASAN == 'n')\").shape, rawtuxdata.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rawtuxdata[rawtuxdata['vmlinux'] == 1168072][['cid', 'CC_OPTIMIZE_FOR_SIZE', 'DEBUG_INFO_DWARF4', 'KASAN', 'UBSAN_ALIGNMENT', 'X86_NEED_RELOCS', 'RANDOMIZE_BASE', 'GCOV_PROFILE_ALL', 'UBSAN_SANITIZE_ALL', 'DEBUG_INFO', 'MODULES', 'DEBUG_INFO_REDUCED', 'DEBUG_INFO_SPLIT']]\n",
    "#tiny_data = rawtuxdata.query(\"vmlinux == 1168072\") #tiny config for X86_32\n",
    "#if (len(tiny_data) > 0):\n",
    "#    print(tiny_data[['cid', 'CC_OPTIMIZE_FOR_SIZE', 'DEBUG_INFO_DWARF4', 'KASAN', 'UBSAN_ALIGNMENT', 'X86_NEED_RELOCS', 'RANDOMIZE_BASE', 'GCOV_PROFILE_ALL', 'UBSAN_SANITIZE_ALL', 'DEBUG_INFO', 'MODULES', 'DEBUG_INFO_REDUCED', 'DEBUG_INFO_SPLIT']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rawtuxdata[rawtuxdata['vmlinux'] == -1]\n",
    "#rawtuxdata.query(\"vmlinux == -1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rawtuxdata[rawtuxdata['vmlinux'] == 1168072]['MODULES']\n",
    "#rawtuxdata.query(\"vmlinux == 1168072\")['MODULES'] #tiny config for X86_32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      30880200\n",
       "1      53875520\n",
       "2      18772696\n",
       "3      24147496\n",
       "4     230285584\n",
       "5      69676088\n",
       "6      35061024\n",
       "7      89176752\n",
       "8      32526392\n",
       "9            -1\n",
       "10     39675592\n",
       "11     66906576\n",
       "12     27363432\n",
       "13     53800928\n",
       "14     47768152\n",
       "15     89392680\n",
       "16     45488728\n",
       "17           -1\n",
       "18     20366504\n",
       "19     13354128\n",
       "Name: vmlinux, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# playing a bit with the data \n",
    "rawtuxdata.dtypes\n",
    "# 'DEBUG_INFOO' in list(pd.DataFrame(non_tristate_options)[0]) # \n",
    "# tuxdata['DEBUG_INFO'].unique()\n",
    "#tuxdata['OUTPUT_FORMAT'].dtypes\n",
    "#tuxdata['DEFAULT_HOSTNAME'].unique()\n",
    "\n",
    "#rawtuxdata[:5]\n",
    "rawtuxdata[:20]['vmlinux']\n",
    "#tuxdata[:5]['CONFIG_DEBUG_INFO']\n",
    "#tuxdata['ARCH_HAS_SG_CHAIN'].unique()\n",
    "#tuxdata['ARCH_HAS_SG_CHAIN'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rawtuxdata.shape, rawtuxdata.query(\"vmlinux != -1\").shape"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking: top (20)\n",
      "1. feature AIC79XX_BUILD_FIRMWARE 2547 (0.464406)\n",
      "2. feature AIC7XXX_BUILD_FIRMWARE 1799 (0.315865)\n",
      "3. feature WANXL_BUILD_FIRMWARE 10533 (0.105716)\n",
      "4. feature DRM_VBOXVIDEO 8022 (0.058193)\n",
      "5. feature SND_SOC_TLV320AIC3X 6989 (0.006322)\n",
      "6. feature UBSAN_SANITIZE_ALL 2731 (0.004704)\n",
      "7. feature FORTIFY_SOURCE 5353 (0.004456)\n",
      "8. feature GENERIC_ALLOCATOR 11935 (0.003805)\n",
      "9. feature USB_WUSB_CBAF 8765 (0.003191)\n",
      "10. feature KEYBOARD_XTKBD 7524 (0.002705)\n",
      "11. feature SENSORS_W83L786NG 7674 (0.002705)\n",
      "12. feature SPEAKUP_SYNTH_APOLLO 1794 (0.002061)\n",
      "13. feature VIDEO_MUX 12571 (0.001731)\n",
      "14. feature MLX5_INFINIBAND 9819 (0.001729)\n",
      "15. feature MPLS_IPTUNNEL 67 (0.001724)\n",
      "16. feature INTEL_MIC_HOST 7934 (0.001716)\n",
      "17. feature HYPERV_NET 12518 (0.001716)\n",
      "18. feature DVB_LGS8GXX 706 (0.001700)\n",
      "19. feature USB_SI4713 4399 (0.001685)\n",
      "20. feature USB_F_MIDI 10687 (0.001672)\n",
      "Accuracy score: 0.99\n",
      "Precision score: 1.00\n",
      "Recall score: 1.00\n",
      "F1 score: 1.00\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import *\n",
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingClassifier\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn import linear_model\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
    "from sklearn import tree\n",
    "import graphviz \n",
    "\n",
    "\n",
    "LEARN_COMPILATION_SUCCESS = True # costly in time and space \n",
    "compilation_status_column_name = 'compile_success'\n",
    "\n",
    "def encode_data_compilation(rawtuxdata):\n",
    "    lae = LabelEncoder()\n",
    "    # we save quantitative values we want (here vmlinux, TODO: generalize)\n",
    "    # the key idea is that the labelling encoder should not be applied to this kind of values (only to predictor variables!)\n",
    "    # vml = rawtuxdata['LZO'] # rawtuxdata['vmlinux'] \n",
    "    o_sizes = rawtuxdata[size_methods]\n",
    "\n",
    "    # we remove non tri state options, but TODO there are perhaps some interesting options (numerical or string) here\n",
    "    #tuxdata = rawtuxdata.drop(columns=non_tristate_options).drop(columns=['vmlinux']).apply(le.fit_transform)\n",
    "    tuxdata_for_compilation = rawtuxdata.drop(columns=non_tristate_options).drop(columns=size_methods).apply(lae.fit_transform)\n",
    "\n",
    "    #tuxdata['vmlinux'] = vml \n",
    "    tuxdata_for_compilation[size_methods] = o_sizes\n",
    "    # we can ue vmlinux since it has been restored thanks to previous line\n",
    "    tuxdata_for_compilation[compilation_status_column_name] = tuxdata_for_compilation['vmlinux'] != -1\n",
    "    return tuxdata_for_compilation\n",
    "\n",
    "def learn_compilation_success(tuxdata_for_compilation):\n",
    "    TESTING_SIZE=0.1 \n",
    "    X_train, X_test, y_train, y_test = train_test_split(tuxdata_for_compilation.drop(columns=size_methods).drop(columns=compilation_status_column_name), tuxdata_for_compilation[compilation_status_column_name], test_size=TESTING_SIZE, random_state=0)  \n",
    "    clf = tree.DecisionTreeClassifier() #GradientBoostingClassifier(n_estimators=100) #RandomForestRegressor(n_estimators=100) #   #GradientBoostingRegressor(n_estimators=100)  \n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    importances = clf.feature_importances_\n",
    "    indices = np.argsort(importances)[::-1]    \n",
    "\n",
    "    TOP_FT_IMPORTANCE=20\n",
    "    print(\"Feature ranking: \" + \"top (\" + str(TOP_FT_IMPORTANCE) + \")\")\n",
    "    for f in range(TOP_FT_IMPORTANCE): # len(indices)\n",
    "        print(\"%d. feature %s %d (%f)\" % (f + 1, tuxdata_for_compilation.columns[indices[f]], indices[f], importances[indices[f]]))\n",
    "   \n",
    "    \n",
    "    dot_data = tree.export_graphviz(clf, out_file=None, \n",
    "                         feature_names=tuxdata_for_compilation.drop(columns=size_methods).drop(columns=compilation_status_column_name).columns,  \n",
    "                         filled=True, rounded=True,\n",
    "                         special_characters=True)  \n",
    "    graph = graphviz.Source(dot_data)  \n",
    "    graph.render(\"TUXML_compilation_failures\")\n",
    "    \n",
    "    acc = accuracy_score (y_test, y_pred)\n",
    "    prec = precision_score (y_test, y_pred)\n",
    "    reca = recall_score (y_test, y_pred)\n",
    "    f1 = f1_score (y_test, y_pred)\n",
    "    print(\"Accuracy score: %.2f\" % (acc))\n",
    "    print(\"Precision score: %.2f\" % (prec))\n",
    "    print(\"Recall score: %.2f\" % (reca))\n",
    "    print(\"F1 score: %.2f\" % (f1))\n",
    "\n",
    "if (LEARN_COMPILATION_SUCCESS):\n",
    "    tuxdata_for_compilation = encode_data_compilation(rawtuxdata)\n",
    "    tuxdata_for_compilation [compilation_status_column_name].describe()\n",
    "    learn_compilation_success(tuxdata_for_compilation)\n",
    "    del tuxdata_for_compilation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rawtuxdata.query(\"vmlinux == -1\")[['cid', 'AIC7XXX_BUILD_FIRMWARE', 'AIC79XX_BUILD_FIRMWARE', 'IPVTAP', 'WANXL_BUILD_FIRMWARE', 'TCIC']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# aka MAPE\n",
    "def mean_relative_error(y_true, y_pred): \n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawtuxdata.query(\"vmlinux == 1168072\") # tinyconfig with X86_32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawtuxdata.query(\"vmlinux == 7317008\") # tiny config for X86_64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawtuxdata.query(\"vmlinux == 7317008\").iloc[0].value_counts() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "pd.DataFrame(rawtuxdata['vmlinux']).plot.box()\n",
    "plt.show(block=False)\n",
    "\n",
    "plt.figure()\n",
    "pd.DataFrame(rawtuxdata['LZO']).plot.box()\n",
    "plt.show(block=False)\n",
    "\n",
    "plt.figure()\n",
    "pd.DataFrame(rawtuxdata['BZIP2']).plot.box()\n",
    "plt.show(block=False)\n",
    "\n",
    "\n",
    "rawtuxdata['vmlinux'].describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawtuxdata['vmlinux'].sort_values()[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "\n",
    "def color_negative_positive(val, pcolor=\"green\", ncolor=\"red\"):\n",
    "    \"\"\"\n",
    "    Takes a scalar and returns a string with\n",
    "    the css property `'color: red'` for negative\n",
    "    strings, black otherwise.\n",
    "    \"\"\"\n",
    "    color = pcolor if val > 0 else ncolor \n",
    "    if val == 0:\n",
    "        color = 'black' \n",
    "    return 'color: %s' % color\n",
    "\n",
    "compress_methods = [\"GZIP\", \"BZIP2\", \"LZMA\", \"XZ\", \"LZO\", \"LZ4\"]\n",
    "def compareCompress(size_measure_of_interest): #\"\" # \"-vmlinux\" #\"-bzImage\" # prefix\n",
    "    rCompressDiff = pd.DataFrame(index=list(map(lambda c: c + \"o\", compress_methods)) , columns=compress_methods) \n",
    "    for compress_method in compress_methods:\n",
    "        for compress_method2 in compress_methods:\n",
    "            rCompressDiff.loc[compress_method + \"o\"][compress_method2] = (np.mean(rawtuxdata[compress_method + size_measure_of_interest] / rawtuxdata[compress_method2 + size_measure_of_interest]) * 100) - 100\n",
    "    return rCompressDiff\n",
    "\n",
    "#cmy = sns.light_palette(\"red\", as_cmap=True)\n",
    "compareCompress(\"\").style.set_caption('Difference (average in percentage) per compression methods').applymap(color_negative_positive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "compareCompress(\"-bzImage\").style.set_caption('Difference (average in percentage) per compression methods, bzImage').applymap(color_negative_positive)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "compareCompress(\"-vmlinux\").style.set_caption('Difference (average in percentage) per compression methods, vmlinux').applymap(color_negative_positive)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
    "pd.DataFrame.corr(rawtuxdata[size_methods]).style.set_caption('Correlations between size measures').background_gradient(cmap=cm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from category_encoders import *\n",
    "from sklearn.preprocessing import *\n",
    "\n",
    "## class to integer encoding (y, n, m)\n",
    "\n",
    "## note: we also remove non-tristate-options\n",
    "# \"in place\" is to avoid memory burden (having two dfs in memory)\n",
    "\n",
    "# encode labels with value between 0 and n_classes-1.\n",
    "le = LabelEncoder()\n",
    "# 2/3. FIT AND TRANSFORM\n",
    "vml = rawtuxdata[size_methods]\n",
    "\n",
    "# we remove non tri state options, but TODO there are perhaps some interesting options (numerical or string) here\n",
    "#tuxdata = rawtuxdata.drop(columns=non_tristate_options).drop(columns=['vmlinux']).apply(le.fit_transform)\n",
    "rawtuxdata = rawtuxdata.drop(columns=non_tristate_options).drop(columns=size_methods).apply(le.fit_transform)\n",
    "\n",
    "#tuxdata['vmlinux'] = vml \n",
    "rawtuxdata[size_methods] = vml\n",
    " \n",
    "rawtuxdata.shape, rawtuxdata.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### takes a while\n",
    "# One-Hot-Encoding \n",
    "#from sklearn.preprocessing import *\n",
    "\n",
    "#enc = OneHotEncoder()\n",
    "#o_sizes = rawtuxdata[size_methods]\n",
    "#oh_tuxdata = enc.fit_transform(rawtuxdata)\n",
    "#oh_tuxdata.shape, o_sizes.shape\n",
    "# rawtuxdata.drop(columns=non_tristate_options).drop(columns=size_methods).apply(enc.fit_transform)\n",
    "#oh_tuxdata[size_methods] = o_sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DUMMY (with Pandas)\n",
    "\n",
    "#o_sizes = tuxdata[size_methods]\n",
    "#tuxdata_dummy = pd.get_dummies(rawtuxdata.drop(columns=size_methods), columns=rawtuxdata.drop(columns=size_methods).columns)\n",
    "#tuxdata_dummy[size_methods] = o_sizes\n",
    "#tuxdata_dummy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data exploration (again)\n",
    "#print(rawtuxdata['UBSAN_SANITIZE_ALL'].value_counts(), rawtuxdata['COMPILE_TEST'].value_counts(), rawtuxdata['NOHIGHMEM'].value_counts(), rawtuxdata['OPTIMIZE_INLINING'].value_counts(), rawtuxdata['SLOB'].value_counts(), rawtuxdata['CC_OPTIMIZE_FOR_SIZE'].value_counts(), sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from enum import Enum\n",
    "class LearningStrategy(Enum):\n",
    "    LINEAR = 1\n",
    "    AUTOML = 2\n",
    "    ML = 3\n",
    "    DT = 4 # decision tree\n",
    "    MULTIML = 5 # multi output \n",
    "    CVML = 6 # cross validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# https://ramhiser.com/post/2018-03-25-feature-selection-with-scikit-learn-pipeline/\n",
    "class PipelineRFE(Pipeline):\n",
    "\n",
    "    def fit(self, X, y=None, **fit_params):\n",
    "        super(PipelineRFE, self).fit(X, y, **fit_params)\n",
    "        self.feature_importances_ = self.steps[-1][-1].feature_importances_\n",
    "        return self"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "NO_ENCODED_VALUE = le.transform(['n'])[0] \n",
    "YES_ENCODED_VALUE = le.transform(['y'])[0] \n",
    "M_ENCODED_VALUE = le.classes_.size # last value, we know that\n",
    "\n",
    "def mkNoOption(option_name):\n",
    "    return \"(\" + option_name + \" == \" + str(NO_ENCODED_VALUE) + \")\"\n",
    "\n",
    "def mkNoOptions(l_options):\n",
    "    r = []\n",
    "    for option_name in l_options:\n",
    "        r.append(mkNoOption(option_name))\n",
    "        \n",
    "    return ' & '.join(r)\n",
    "\n",
    "mkNoOptions(['DEBUG_INFO', 'GCOV_PROFILE_ALL', 'KASAN', 'UBSAN_SANITIZE_ALL', 'RELOCATABLE', 'XFS_DEBUG'])\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#le.classes_\n",
    "#le.transform(['y'])[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def nbyes(row):\n",
    "    return sum(row == YES_ENCODED_VALUE)\n",
    "\n",
    "def nbno(row):\n",
    "    return sum(row == NO_ENCODED_VALUE)\n",
    "\n",
    "def nbmodule(row):\n",
    "    return sum(row == M_ENCODED_VALUE)\n",
    "                    \n",
    "rawtuxdata['nbyes'] = rawtuxdata.apply(nbyes, axis=1)\n",
    "rawtuxdata['nbno'] = rawtuxdata.apply(nbno, axis=1)\n",
    "rawtuxdata['nbmodule'] = rawtuxdata.apply(nbmodule, axis=1)\n",
    "rawtuxdata['nbyesmodule'] = rawtuxdata['nbyes'] + rawtuxdata['nbmodule']\n",
    "rawtuxdata.sort_values(by='nbyes', ascending=False)[['vmlinux', 'nbyes', 'nbno', 'nbyesmodule']]\n",
    "\n",
    "#rawtuxdata.query(\"vmlinux == 7317008\").iloc[0].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats\n",
    "scipy.stats.pearsonr(rawtuxdata['nbyes'], rawtuxdata['vmlinux'])\n",
    "#, scipy.stats.pearsonr(rawtuxdata['nbyesmodule'], rawtuxdata['vmlinux']), scipy.stats.pearsonr(rawtuxdata['nbno'], rawtuxdata['vmlinux'])\n",
    "\n",
    "# scipy.stats.pearsonr\n",
    "# size_and_nbs = rawtuxdata.query(mkNoOptions(['DEBUG_INFO', 'GCOV_PROFILE_ALL', 'KASAN', 'UBSAN_SANITIZE_ALL', 'RELOCATABLE', 'XFS_DEBUG']))[['vmlinux', 'nbyes', 'nbno', 'nbyesmodule']]\n",
    "# scipy.stats.pearsonr(size_and_nbs['vmlinux'], size_and_nbs['nbyes'])\n",
    "        \n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.linear_model import LassoCV, RidgeCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn import linear_model\n",
    "from sklearn import svm\n",
    "from sklearn.decomposition import PCA\n",
    "from tpot import TPOTRegressor\n",
    "from sklearn.linear_model import LassoLarsCV\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.pipeline import make_pipeline, make_union\n",
    "from tpot.builtins import StackingEstimator\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "#from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# domain knowledge: intuitively the more you have activated options...\n",
    "# there is no correlation between these (hidden) predictors and size\n",
    "nbdk_features = ['nbyes', 'nbno', 'nbmodule', 'nbyesmodule'] \n",
    "def prefilter_data(tuxdata):  \n",
    "    return rawtuxdata\n",
    "    #return rawtuxdata.drop(columns=nbdk_features)\n",
    "    #return rawtuxdata.query(mkNoOption(\"DEBUG_INFO\"))\n",
    "    #return rawtuxdata.query(mkNoOption(\"DEBUG_INFO\") + \" & \" + mkNoOption(\"GCOV_PROFILE_ALL\") + \" & \" + mkNoOption(\"KASAN\") + \" & \" + mkNoOption(\"UBSAN_SANITIZE_ALL\") + \" & \" + mkNoOption(\"RELOCATABLE\") + \" & \" + mkNoOption(\"XFS_DEBUG\"))\n",
    "    #return rawtuxdata.query(mkNoOptions(['DEBUG_INFO', 'GCOV_PROFILE_ALL', 'KASAN', 'UBSAN_SANITIZE_ALL', 'RELOCATABLE', 'XFS_DEBUG'])) #.drop(columns=nbdk_features)  \n",
    "                \n",
    "\n",
    "def regLearning(tuxdata, kindOfLearning=LearningStrategy.ML):\n",
    " \n",
    "    TESTING_SIZE=0.6 # 0.9 means 10% for training, 90% for testing\n",
    "    size_of_interest = \"vmlinux\" # could be LZO, BZIP, etc. \n",
    "    PRINT_FEATURE_IMPORTANCES = True\n",
    "   \n",
    "       \n",
    "    #X_train, X_test, y_train, y_test = train_test_split(tuxdata[(tuxdata['DEBUG_INFO'] == le.transform(['n'])[0])].drop(columns=size_methods), tuxdata[(tuxdata['DEBUG_INFO'] == le.transform(['n'])[0])][size_of_interest], test_size=TESTING_SIZE, random_state=0)  \n",
    "    print (\"Warning: prefiltering on DEBUG_INFO=n GCOV_PROFILE_ALL=n KASAN=n ....\")   \n",
    "    X_train, X_test, y_train, y_test = train_test_split(prefilter_data(tuxdata).drop(columns=size_methods), prefilter_data(tuxdata)[size_of_interest], test_size=TESTING_SIZE, random_state=0)  \n",
    "  \n",
    "    # multi output\n",
    "    #X_train, X_test, y_train, y_test = train_test_split(tuxdata.drop(columns=size_methods), tuxdata[size_methods], test_size=TESTING_SIZE, random_state=0)  \n",
    "\n",
    "    # train_test_split(tuxdata.drop(columns=['vmlinux']), tuxdata['vmlinux'], test_size=TESTING_SIZE, random_state=0)  \n",
    "\n",
    "    #clf = RandomForestRegressor(n_estimators=100) \n",
    "\n",
    "    if kindOfLearning == LearningStrategy.LINEAR:\n",
    "        regr =  linear_model.Lasso() # svm.SVC(kernel='linear') # linear_model.Ridge(alpha=.1) #  # linear_model.Lasso() # linear_model.SGDRegressor() #LinearRegression() # SGDRegressor or linear_model.Lasso()\n",
    "        regr.fit(X_train, y_train)\n",
    "        y_pred = regr.predict(X_test)\n",
    "\n",
    "    elif kindOfLearning == LearningStrategy.AUTOML:\n",
    "\n",
    "\n",
    "        tpot_config = {\n",
    "\n",
    "            'sklearn.linear_model.ElasticNetCV': {\n",
    "                'l1_ratio': np.arange(0.0, 1.01, 0.05),\n",
    "                'tol': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1]\n",
    "            },\n",
    "\n",
    "            'sklearn.ensemble.ExtraTreesRegressor': {\n",
    "                'n_estimators': [100],\n",
    "                'max_features': np.arange(0.05, 1.01, 0.05),\n",
    "                'min_samples_split': range(2, 21),\n",
    "                'min_samples_leaf': range(1, 21),\n",
    "                'bootstrap': [True, False]\n",
    "            },\n",
    "\n",
    "            'sklearn.ensemble.GradientBoostingRegressor': {\n",
    "                'n_estimators': [100],\n",
    "                'loss': [\"ls\", \"lad\", \"huber\", \"quantile\"],\n",
    "                'learning_rate': [1e-3, 1e-2, 1e-1, 0.5, 1.],\n",
    "                'max_depth': range(1, 11),\n",
    "                'min_samples_split': range(2, 21),\n",
    "                'min_samples_leaf': range(1, 21),\n",
    "                'subsample': np.arange(0.05, 1.01, 0.05),\n",
    "                'max_features': np.arange(0.05, 1.01, 0.05),\n",
    "                'alpha': [0.75, 0.8, 0.85, 0.9, 0.95, 0.99]\n",
    "            },\n",
    "\n",
    "            'sklearn.ensemble.AdaBoostRegressor': {\n",
    "                'n_estimators': [100],\n",
    "                'learning_rate': [1e-3, 1e-2, 1e-1, 0.5, 1.],\n",
    "                'loss': [\"linear\", \"square\", \"exponential\"],\n",
    "                'max_depth': range(1, 11)\n",
    "            },\n",
    "\n",
    "            'sklearn.tree.DecisionTreeRegressor': {\n",
    "                'max_depth': range(1, 11),\n",
    "                'min_samples_split': range(2, 21),\n",
    "                'min_samples_leaf': range(1, 21)\n",
    "            },\n",
    "\n",
    "            'sklearn.neighbors.KNeighborsRegressor': {\n",
    "                'n_neighbors': range(1, 101),\n",
    "                'weights': [\"uniform\", \"distance\"],\n",
    "                'p': [1, 2]\n",
    "            },\n",
    "\n",
    "            'sklearn.linear_model.LassoLarsCV': {\n",
    "                'normalize': [True, False]\n",
    "            },\n",
    "\n",
    "            'sklearn.svm.LinearSVR': {\n",
    "                'loss': [\"epsilon_insensitive\", \"squared_epsilon_insensitive\"],\n",
    "                'dual': [True, False],\n",
    "                'tol': [1e-5, 1e-4, 1e-3, 1e-2, 1e-1],\n",
    "                'C': [1e-4, 1e-3, 1e-2, 1e-1, 0.5, 1., 5., 10., 15., 20., 25.],\n",
    "                'epsilon': [1e-4, 1e-3, 1e-2, 1e-1, 1.]\n",
    "            },\n",
    "\n",
    "            'sklearn.ensemble.RandomForestRegressor': {\n",
    "                'n_estimators': [100],\n",
    "                'max_features': np.arange(0.05, 1.01, 0.05),\n",
    "                'min_samples_split': range(2, 21),\n",
    "                'min_samples_leaf': range(1, 21),\n",
    "                'bootstrap': [True, False]\n",
    "            },\n",
    "\n",
    "            'sklearn.linear_model.RidgeCV': {\n",
    "            },\n",
    "\n",
    "            #'xgboost.XGBRegressor': {\n",
    "            #    'n_estimators': [100],\n",
    "            #    'max_depth': range(1, 11),\n",
    "            #    'learning_rate': [1e-3, 1e-2, 1e-1, 0.5, 1.],\n",
    "            #    'subsample': np.arange(0.05, 1.01, 0.05),\n",
    "            #    'min_child_weight': range(1, 21),\n",
    "            #    'nthread': [1]\n",
    "            #}     \n",
    "        }\n",
    "\n",
    "        tpot = TPOTRegressor(generations=5, population_size=50, verbosity=3, config_dict=tpot_config, scoring='neg_mean_absolute_error')\n",
    "        #tpot = TPOTRegressor(generations=2, population_size=5, verbosity=3, config_dict=tpot_config, scoring='neg_mean_absolute_error')\n",
    "        tpot.fit(X_train, y_train)\n",
    "        y_pred = tpot.predict(X_test)\n",
    "        print(tpot.score(X_test, y_test))\n",
    "        print(tpot.evaluated_individuals_)\n",
    "        tpot.export('tpot_boston_pipeline.py')\n",
    "        \n",
    "    elif kindOfLearning == LearningStrategy.DT:\n",
    "        clf = tree.DecisionTreeRegressor() #GradientBoostingClassifier(n_estimators=100) #RandomForestRegressor(n_estimators=100) #   #GradientBoostingRegressor(n_estimators=100)  \n",
    "        clf.fit(X_train, y_train)\n",
    "        y_pred = clf.predict(X_test)\n",
    "        dot_data = tree.export_graphviz(clf, out_file=None, \n",
    "                         feature_names=tuxdata.drop(columns=size_methods).columns,  \n",
    "                         filled=True, rounded=True,\n",
    "                         special_characters=True)  \n",
    "        graph = graphviz.Source(dot_data)  \n",
    "        graph.render(\"TUXML-DT-\" + size_of_interest)\n",
    "        if PRINT_FEATURE_IMPORTANCES:\n",
    "            importances = clf.feature_importances_\n",
    "            indices = np.argsort(importances)[::-1]    \n",
    "\n",
    "            TOP_FT_IMPORTANCE=100\n",
    "            print(\"Feature ranking: \" + \"top (\" + str(TOP_FT_IMPORTANCE) + \")\")\n",
    "            for f in range(TOP_FT_IMPORTANCE): # len(indices)\n",
    "                print(\"%d. feature %s %d (%f)\" % (f + 1, X_train.columns[indices[f]], indices[f], importances[indices[f]]))\n",
    "\n",
    "\n",
    "    elif (kindOfLearning == LearningStrategy.CVML):\n",
    "        \n",
    "        tuned_parameters = [{\n",
    "                    'n_estimators': [200, 300],  \n",
    "                   # 'alpha': [0.5, 0.6, 0.7, 0.75], The alpha-quantile of the huber loss function and the quantile loss function. Only if loss='huber' or loss='quantile'.\n",
    "                   'learning_rate': [1e-1],\n",
    "                   'max_depth': range(1, 6) # 11)\n",
    "                   # 'learning_rate': [1e-3, 1e-2, 1e-1, 0.5, 1.],\n",
    "                   # 'loss': [\"ls\", \"lad\", \"huber\", \"quantile\"],\n",
    "                }]   \n",
    "\n",
    "        clf = GridSearchCV(GradientBoostingRegressor(), tuned_parameters, cv=5,\n",
    "                           scoring='neg_mean_absolute_error', verbose=5) # n_jobs=-1\n",
    "        clf.fit(X_train, y_train)\n",
    "\n",
    "        print(\"Best parameters set found on development set:\")\n",
    "        print()\n",
    "        print(clf.best_params_)\n",
    "        print()\n",
    "        print(\"Grid scores on development set:\")\n",
    "        print()\n",
    "        means = clf.cv_results_['mean_test_score']\n",
    "        stds = clf.cv_results_['std_test_score']\n",
    "        for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
    "            print(\"%0.3f (+/-%0.03f) for %r\"\n",
    "                  % (mean, std * 2, params))\n",
    "        print()\n",
    "\n",
    "        print(\"Detailed classification report:\")\n",
    "        print()\n",
    "        print(\"The model is trained on the full development set.\")\n",
    "        print(\"The scores are computed on the full evaluation set.\")\n",
    "        print()\n",
    "        y_pred = clf.predict(X_test)\n",
    "        \n",
    "    else:\n",
    "        assert (kindOfLearning == LearningStrategy.ML)\n",
    "        #clf = XGBRegressor(learning_rate=0.1, max_depth=3, min_child_weight=8, n_estimators=100, nthread=1, subsample=0.2)\n",
    "        N_ESTIMATORS = 1000\n",
    "        clf = GradientBoostingRegressor(n_estimators=N_ESTIMATORS, max_depth=4) # RandomForestRegressor(n_estimators=100) # #tree.DecisionTreeRegressor()  #GradientBoostingRegressor(n_estimators=100) # KNeighborsRegressor() #RandomForestRegressor(n_estimators=100) # linear_model.SGDRegressor(alpha=0.15, max_iter=200)\n",
    "        # #LassoLarsCV() # MLPRegressor() # GradientBoostingRegressor(n_estimators=100) # ExtraTreesRegressor(n_estimators=100) #RandomForestRegressor(n_estimators=100) # ExtraTreesRegressor(n_estimators=100) #  #   GradientBoostingRegressor(n_estimators=100) # \n",
    "        # \n",
    "        #estimator = RandomForestRegressor(n_estimators=100) # RidgeCV(alphas=[1000.0]) # LassoCV(tol = 0.001) #   #  # RandomForestRegressor(n_estimators=100) #LassoCV() #RidgeCV(alphas=[2000.0]) # LassoCV()\n",
    "        #clf = PipelineRFE([ # Pipeline([\n",
    "        #  ('feature_selection', SelectFromModel(estimator)), # tol = 0.001\n",
    "        #  ('regression', GradientBoostingRegressor(n_estimators=100))\n",
    "        #])\n",
    "        #clf = PipelineRFE([\n",
    "          #('reduce_dim', PCA()),\n",
    "        #  ('feature_selection', SelectFromModel(estimator)), # tol = 0.001\n",
    "        #  ('regression', GradientBoostingRegressor(n_estimators=100))\n",
    "        #])\n",
    "        #clf = make_pipeline(\n",
    "        #    StackingEstimator(estimator=LassoLarsCV(normalize=False)),\n",
    "        #    StackingEstimator(estimator=RandomForestRegressor(bootstrap=True, max_features=0.6500000000000001, min_samples_leaf=10, min_samples_split=2, n_estimators=100)),\n",
    "        #    KNeighborsRegressor(n_neighbors=82, p=2, weights=\"distance\")\n",
    "        #)\n",
    "        #clf = make_pipeline(\n",
    "        #    StackingEstimator(estimator=LassoLarsCV(normalize=True)),\n",
    "        #    RidgeCV()\n",
    "        #)\n",
    "\n",
    "\n",
    "        clf.fit(X_train, y_train)\n",
    "        y_pred = clf.predict(X_test)\n",
    "        \n",
    "        \n",
    "        # #############################################################################\n",
    "        # Plot training deviance\n",
    "\n",
    "        # compute test set deviance\n",
    "        test_score = np.zeros((N_ESTIMATORS,), dtype=np.float64)\n",
    "\n",
    "        for i, y_pred in enumerate(clf.staged_predict(X_test)):\n",
    "            test_score[i] = clf.loss_(y_test, y_pred)\n",
    "\n",
    "        plt.figure(figsize=(12, 6))\n",
    "        plt.subplot(1, 2, 1)\n",
    "        plt.title('Deviance')\n",
    "        plt.plot(np.arange(N_ESTIMATORS) + 1, clf.train_score_, 'b-',\n",
    "                 label='Training Set Deviance')\n",
    "        plt.plot(np.arange(N_ESTIMATORS) + 1, test_score, 'r-',\n",
    "                 label='Test Set Deviance')\n",
    "        plt.legend(loc='upper right')\n",
    "        plt.xlabel('Boosting Iterations')\n",
    "        plt.ylabel('Deviance')\n",
    "        \n",
    "        if PRINT_FEATURE_IMPORTANCES:\n",
    "            importances = clf.feature_importances_\n",
    "            indices = np.argsort(importances)[::-1]    \n",
    "\n",
    "            TOP_FT_IMPORTANCE=100\n",
    "            print(\"Feature ranking: \" + \"top (\" + str(TOP_FT_IMPORTANCE) + \")\")\n",
    "            for f in range(TOP_FT_IMPORTANCE): # len(indices)\n",
    "                print(\"%d. feature %s %d (%f)\" % (f + 1, X_train.columns[indices[f]], indices[f], importances[indices[f]]))\n",
    "        \n",
    "            # #############################################################################\n",
    "            # Plot feature importance\n",
    "            #feature_importance = clf.feature_importances_\n",
    "            # make importances relative to max importance\n",
    "            #feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
    "            #sorted_idx = np.argsort(feature_importance)\n",
    "            #pos = np.arange(sorted_idx.shape[0]) + .5\n",
    "            #plt.subplot(1, 2, 2)\n",
    "            #plt.barh(pos, feature_importance[sorted_idx], align='center')\n",
    "            #plt.yticks(pos, X_train.columns[sorted_idx])\n",
    "            #plt.xlabel('Relative Importance')\n",
    "            #plt.title('Variable Importance')\n",
    "            #plt.show()\n",
    "\n",
    "    # Plot the feature importances of the forest\n",
    "    #plt.figure()\n",
    "    #plt.title(\"Feature importances for size of vmlinux\")\n",
    "    #plt.bar(range(tuxdata.shape[1]), importances[indices], color=\"r\", align=\"center\")\n",
    "    #plt.xticks(range(tuxdata.shape[1]), indices)\n",
    "    #plt.xlim([-1, tuxdata.shape[1]])\n",
    "    #plt.show()\n",
    "    mae = mean_absolute_error (y_test, y_pred)# , multioutput='raw_values')\n",
    "    mse = mean_squared_error (y_test, y_pred) #, multioutput='raw_values') \n",
    "    r2 = r2_score (y_test, y_pred) #, multioutput='raw_values') \n",
    "    mre = mean_relative_error (y_test, y_pred)\n",
    "\n",
    "    ONE_MEGABYTE = 1048576\n",
    "\n",
    "    print(\"Prediction score (MAE): %.2f\" % (mae / ONE_MEGABYTE))\n",
    "    print(\"Prediction score (MSE): %.2f\" % (mse / ONE_MEGABYTE))\n",
    "    print(\"Prediction score (R2): %.2f\" % (r2))\n",
    "    print(\"Prediction score (MRE): %.2f\" % (mre))\n",
    "    return clf #y_pred, y_test\n",
    "    \n",
    "# rawtuxdata\n",
    "regr = regLearning(rawtuxdata, LearningStrategy.CVML)\n",
    "#regr = regLearning(tuxdata_reduced, LearningStrategy.ML)\n",
    "\n",
    "#regLearning(tuxdata_dummy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#re[56589]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.ensemble import ExtraTreesClassifier\n",
    "#from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "#model = SelectFromModel(regr, prefit=True)\n",
    "#tuxdata_reduced = model.transform(prefilter_data(rawtuxdata).drop(columns=size_methods))\n",
    "#tuxdata_reduced.shape, prefilter_data(rawtuxdata).drop(columns=size_methods).shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LassoCV, RidgeCV\n",
    "from sklearn.svm import LinearSVR\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.feature_selection import RFECV, RFE\n",
    "\n",
    "\n",
    "#alphas=[0.1, 1.0, 10.0, 100.0, 500.0, 750.0, 1000.0, 2000.0, 2500.0, 3000.0, 5000.0, 10000.0]\n",
    "#selector = RidgeCV(alphas=[0.1, 1.0, 10.0, 100.0, 500.0, 750.0, 1000.0, 2000.0, 2500.0, 3000.0, 5000.0, 10000.0]) # LassoCV(tol = 0.001) # RidgeCV(alphas=[2000.0])  # \n",
    "#lass = selector #SelectFromModel(selector) #  RFECV(estimator=selector, step=1, scoring='neg_mean_squared_error') # \n",
    "#lass = RFE(estimator=selector, step=1)\n",
    "#lass.fit(X_train, y_train)\n",
    "#tuxdata_reduced_lass = lass.transform(tuxdata.drop(columns=size_methods))\n",
    "#tuxdata_reduced_lass.shape, tuxdata.shape  \n",
    "#lass.alpha_ \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.decomposition import PCA\n",
    "\n",
    "#pca = PCA(n_components=100)\n",
    "#pca.fit(X_train, y_train)\n",
    "\n",
    "#tuxdata_reduced_pca = pca.transform(tuxdata.drop(columns=size_methods))\n",
    "#tuxdata_reduced_pca.shape, tuxdata.shape  \n",
    "\n",
    "#pca.components_.shape\n",
    "\n",
    "#plt.matshow(pca.components_, cmap='viridis')\n",
    "#plt.yticks([0, 1], [\"First component\", \"Second component\"])\n",
    "#plt.colorbar()\n",
    "#plt.xticks(range(len(X_train.columns)),\n",
    "#           X_train.columns, rotation=60, ha='left')\n",
    "#plt.xlabel(\"Feature\")\n",
    "#plt.ylabel(\"Principal components\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ft_vals = ['y', 'n'] \n",
    "tri_state_values = ['y', 'n', 'm']\n",
    "all(x in tri_state_values for x in ft_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for tux1 in tuxdata:\n",
    "#    ft1 = tuxdata[tux1]\n",
    "#    for tux2 in tuxdata:\n",
    "#        if (tux1 != tux2):\n",
    "#            if (ft1.all() == tuxdata[tux2].all()):\n",
    "#                print (\"feature \" + str(tux1) + \" always have the same values than \" + str(tux2))\n",
    "            \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#provisoire = pd.read_csv(open('provisoire.csv', \"r\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#provisoire[['cid','CC_OPTIMIZE_FOR_SIZE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rawtuxdata.columns[6015] #Columns (1150,6015,6026,7676,7726)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#size_methods = [\"vmlinux\", \"GZIP-bzImage\", \"GZIP-vmlinux\", \"GZIP\", \"BZIP2-bzImage\", \n",
    "#              \"BZIP2-vmlinux\", \"BZIP2\", \"LZMA-bzImage\", \"LZMA-vmlinux\", \"LZMA\", \"XZ-bzImage\", \"XZ-vmlinux\", \"XZ\", \n",
    "#              \"LZO-bzImage\", \"LZO-vmlinux\", \"LZO\", \"LZ4-bzImage\", \"LZ4-vmlinux\", \"LZ4\"]\n",
    "#size_methods_without_soi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import h2o\n",
    "#from h2o.automl import H2OAutoML\n",
    "#h2o.init()\n",
    "#df = h2o.import_file(TUXML_CSV_FILENAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splits = df.split_frame(ratios = [0.8], seed = 1)\n",
    "#train = splits[0]\n",
    "#test = splits[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y = size_of_interest\n",
    "#aml = H2OAutoML(max_runtime_secs = 36000, seed = 1, project_name = \"tuxlearning\")\n",
    "#aml.train(y = y, training_frame = train, leaderboard_frame = test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#aml.leaderboard.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pred = aml.predict(test)\n",
    "#pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#perf = aml.leader.model_performance(test)\n",
    "#perf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#h2o.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import category_encoders as ce\n",
    "\n",
    "#colmatters = list(tuxdata.columns)\n",
    "#for s in size_methods:\n",
    "#    colmatters.remove(s)\n",
    "    \n",
    "# colmatters.remove(size_methods)\n",
    "#encoder = ce.OneHotEncoder(cols=colmatters) #cols=tuxdata.drop(columns=size_methods).columns\n",
    "\n",
    "#o_sizes = tuxdata[size_methods]\n",
    "#encoder.fit(tuxdata.drop(columns=size_methods))\n",
    "#tuxdata_dummy2 = encoder.transform(tuxdata.drop(columns=size_methods))\n",
    "#tuxdata_dummy2[size_methods] = o_sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rawtuxdata[rawtuxdata['vmlinux'] == 1168072]#['MODULES']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tuxdata_dummy2.shape, tuxdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rawtuxdata[(rawtuxdata['MODULES'] == 'y')]['vmlinux'].describe(), rawtuxdata[(rawtuxdata['MODULES'] == 'n')]['vmlinux'].describe()\n",
    "#rawtuxdata[(rawtuxdata['UBSAN_SANITIZE_ALL'] == 'y')]\n",
    "# [['cid', 'CC_OPTIMIZE_FOR_SIZE', 'DEBUG_INFO_DWARF4', 'KASAN', 'UBSAN_ALIGNMENT', 'X86_NEED_RELOCS', 'RANDOMIZE_BASE', 'GCOV_PROFILE_ALL', 'UBSAN_SANITIZE_ALL', 'DEBUG_INFO', 'MODULES', 'DEBUG_INFO_REDUCED', 'DEBUG_INFO_SPLIT']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rawtuxdata.info(memory_usage='deep')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from numpy import inf\n",
    "#lalgos = {'ExtraTreesRegressor(LinearSVR(input_matrix, LinearSVR__C=20.0, LinearSVR__dual=False, LinearSVR__epsilon=0.01, LinearSVR__loss=squared_epsilon_insensitive, LinearSVR__tol=0.1), ExtraTreesRegressor__bootstrap=False, ExtraTreesRegressor__max_features=0.1, ExtraTreesRegressor__min_samples_leaf=7, ExtraTreesRegressor__min_samples_split=13, ExtraTreesRegressor__n_estimators=100)': {'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 2, 'internal_cv_score': -3167974.080136995}, 'RidgeCV(input_matrix)': {'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 1, 'internal_cv_score': -2459378.2937241266}, 'XGBRegressor(LinearSVR(input_matrix, LinearSVR__C=20.0, LinearSVR__dual=False, LinearSVR__epsilon=0.0001, LinearSVR__loss=squared_epsilon_insensitive, LinearSVR__tol=0.001), XGBRegressor__learning_rate=0.5, XGBRegressor__max_depth=6, XGBRegressor__min_child_weight=10, XGBRegressor__n_estimators=100, XGBRegressor__nthread=1, XGBRegressor__subsample=0.5)': {'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 2, 'internal_cv_score': -3349726.33}, 'RidgeCV(RandomForestRegressor(input_matrix, RandomForestRegressor__bootstrap=False, RandomForestRegressor__max_features=0.15000000000000002, RandomForestRegressor__min_samples_leaf=4, RandomForestRegressor__min_samples_split=3, RandomForestRegressor__n_estimators=100))': {'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 2, 'internal_cv_score': -2992925.3310339055}, 'ElasticNetCV(CombineDFs(input_matrix, input_matrix), ElasticNetCV__l1_ratio=0.1, ElasticNetCV__tol=0.01)': {'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 1, 'internal_cv_score': -inf}, 'GradientBoostingRegressor(input_matrix, GradientBoostingRegressor__alpha=0.8, GradientBoostingRegressor__learning_rate=1.0, GradientBoostingRegressor__loss=ls, GradientBoostingRegressor__max_depth=3, GradientBoostingRegressor__max_features=0.35000000000000003, GradientBoostingRegressor__min_samples_leaf=20, GradientBoostingRegressor__min_samples_split=9, GradientBoostingRegressor__n_estimators=100, GradientBoostingRegressor__subsample=0.7500000000000001)': {'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 1, 'internal_cv_score': -4822495.9475317}, 'DecisionTreeRegressor(input_matrix, DecisionTreeRegressor__max_depth=4, DecisionTreeRegressor__min_samples_leaf=14, DecisionTreeRegressor__min_samples_split=13)': {'generation': 0, 'mutation_count': 0, 'crossover_count': 0, 'predecessor': ('ROOT',), 'operator_count': 1, 'internal_cv_score': -3920416.0042674863}, 'XGBRegressor(CombineDFs(input_matrix, input_matrix), XGBRegressor__learning_rate=0.001, XGBRegressor__max_depth=7, XGBRegressor__min_child_weight=7, 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GradientBoostingRegressor__n_estimators=100, GradientBoostingRegressor__subsample=0.6000000000000001))',), 'operator_count': 2, 'internal_cv_score': -2468953.551811531}, 'RidgeCV(RidgeCV(CombineDFs(input_matrix, input_matrix)))': {'generation': 'INVALID', 'mutation_count': 2, 'crossover_count': 0, 'predecessor': ('RidgeCV(CombineDFs(input_matrix, input_matrix))',), 'operator_count': 2, 'internal_cv_score': -2468671.3485777183}, 'XGBRegressor(input_matrix, XGBRegressor__learning_rate=0.1, XGBRegressor__max_depth=3, XGBRegressor__min_child_weight=12, XGBRegressor__n_estimators=100, XGBRegressor__nthread=1, XGBRegressor__subsample=0.2)': {'generation': 'INVALID', 'mutation_count': 4, 'crossover_count': 0, 'predecessor': ('XGBRegressor(input_matrix, XGBRegressor__learning_rate=0.1, XGBRegressor__max_depth=3, XGBRegressor__min_child_weight=10, XGBRegressor__n_estimators=100, XGBRegressor__nthread=1, XGBRegressor__subsample=0.2)',), 'operator_count': 1, 'internal_cv_score': -2168788.3624242423}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dalgo = pd.DataFrame(columns=['algo', 'score'])\n",
    "#i = 0\n",
    "#for lalgo in lalgos:\n",
    "#    i = i + 1\n",
    "#    dalgo.loc[i] = lalgo, lalgos[lalgo]['internal_cv_score']\n",
    "#    dalgo['algo'] = lalgo\n",
    "#    dalgo['score'] = lalgos[lalgo]['internal_cv_score']\n",
    "#    print(lalgo, lalgos[lalgo]['internal_cv_score'])\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dalgo.sort_values(by=\"score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#(dalgo.query(\"score != inf\")['score'] / 1048576).plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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