multiple_linear_regression-AM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tuxml\n",
    "import pandas as pd \n",
    "\n",
    "df = tuxml.load_dataset()\n",
    "df.replace(2, 0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "        OPENVSWITCH  TEXTSEARCH_FSM  NETFILTER_XT_MATCH_TCPMSS  MPLS  NFC_HCI  \\\n",
       "109097            0               0                          0     0        0   \n",
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       "\n",
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       "109097                        0             0          0   \n",
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       "\n",
       "        NETFILTER_XT_MATCH_U32  OPENVSWITCH_GRE  ...  LZO-vmlinux      LZO  \\\n",
       "109097                       0                0  ...      2863640   644621   \n",
       "58621                        0                0  ...      2777624   558712   \n",
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       "99232                        0                0  ...      4248440  2027905   \n",
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       "\n",
       "        LZ4-bzImage  LZ4-vmlinux      LZ4     cid  nbyes  nbno  nbmodule  \\\n",
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       "\n",
       "        nbyesmodule  \n",
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       "\n",
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"vmlinux\")[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       DEBUG_INFO  nbyes  DEBUG_INFO_REDUCED  DEBUG_INFO_SPLIT  \\\n",
       "14758           0   1219                   0                 0   \n",
       "14759           0   1166                   0                 0   \n",
       "14760           0   1410                   0                 0   \n",
       "14761           0   1924                   0                 0   \n",
       "14762           0   2435                   0                 0   \n",
       "\n",
       "       X86_NEED_RELOCS  RANDOMIZE_BASE  UBSAN_SANITIZE_ALL  KASAN_OUTLINE  \\\n",
       "14758                1               1                   0              1   \n",
       "14759                0               0                   0              0   \n",
       "14760                0               0                   0              0   \n",
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       "\n",
       "       KASAN  UBSAN_ALIGNMENT  ...  RSI_DEBUGFS  CRYPTO_RNG_DEFAULT  \\\n",
       "14758      1                0  ...            0                   1   \n",
       "14759      0                1  ...            0                   1   \n",
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       "14761      0                1  ...            0                   1   \n",
       "14762      0                0  ...            0                   1   \n",
       "\n",
       "       CRYPTO_ECB  CRYPTO_WORKQUEUE  HISAX_HFC_SX  USB_GSPCA_VC032X  \\\n",
       "14758           1                 1             0                 0   \n",
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       "14761           1                 1             0                 0   \n",
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       "\n",
       "       HISAX_ENTERNOW_PCI  CRYPTO_HASH2  HISAX_DEBUG  IP_NF_ARP_MANGLE  \n",
       "14758                   0             1            0                 0  \n",
       "14759                   0             1            0                 0  \n",
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       "\n",
       "[5 rows x 9239 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_importance = pd.read_csv(\"feature_importanceRF.csv\", header=None)\n",
    "df_importance.rename(columns={0 : 'option', 1 : 'importance' }, inplace=True)\n",
    "# df_importance = df[df_importance[:100][\"Unnamed: 0\"].values]\n",
    "df_importance = df[df_importance[\"option\"].values] # [:100]\n",
    "df_importance.head()\n",
    "# df_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "additional_ft = [\"nbyes\", \"nbno\", \"nbmodule\", \"nbyesmodule\"]\n",
    "# very ad-hoc, not consistent with feature importance\n",
    "# df.drop(columns=[\"nbno\", \"nbmodule\", \"nbyesmodule\"], inplace=True) # should be a parameter of the experiment (whether or not we keep nbyes, etc.)\n",
    "df.drop(columns=additional_ft, inplace=True) # should be a parameter of the experiment (whether or not we keep nbyes, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#learning a model by using linear regression\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "def run_regressorML(reg, test_size, size_target, x_train, x_test):\n",
    "    assert(size_target in size_methods)\n",
    "    reg.fit(x_train, y_train)\n",
    "    y_pred = reg.predict(x_test)\n",
    "    #y_pred = reg.intercept_ + np.sum(reg.coef_ * x_test.values, axis=1)\n",
    "    dfErrors = pd.DataFrame({\"error\":(y_pred - y_test).abs(), \"% error\":((y_pred - y_test)/y_test).abs()*100})\n",
    "    return dfErrors[\"% error\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ft_importances(coef_, col):\n",
    "    importanceSeries = pd.Series(coef_, index=col.values)\n",
    "    return importanceSeries[importanceSeries != 0].abs().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_result  = pd.DataFrame(columns = ['algorithm_name', 'ft_dummyfication', 'origin_ft_selection', 'ft_selection', 'hyperparameters', 'size_target', 'additional_ft', 'test_size', 'coef_order', 'accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "0.9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2508405585768832.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3753217315168896.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3366868257702528.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2959196153665408.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3666365506755968.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2663260043677440.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2536931440084096.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2662400850424704.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2446100075888256.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3180307603528064.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2430638621514496.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4382584320276480.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3554504713133312.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2963936519909504.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2630651175670400.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2787390760186752.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4003904327764096.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4180564612463360.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2853347695927424.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3094836071810304.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4850391079933376.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2508405585768832.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3753217315168896.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3366868257702528.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2959196153665408.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3666365506755968.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2663260043677440.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2536931440084096.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2662400850424704.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2446100075888256.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3180307603528064.0, tolerance: 2109515452783953.2\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2430638621514496.0, tolerance: 2406499802831159.5\n",
      "  positive)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4382584320276480.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3554504713133312.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2963936519909504.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2630651175670400.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2787390760186752.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4003904327764096.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4180564612463360.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2853347695927424.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 3094836071810304.0, tolerance: 2406499802831159.5\n",
      "  positive)\n",
      "/usr/local/lib/python3.7/site-packages/sklearn/linear_model/coordinate_descent.py:475: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4850391079933376.0, tolerance: 2406499802831159.5\n",
      "  positive)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "\n",
    "target = \"vmlinux\"\n",
    "paramsLasso = {'alpha': [25,10,4,2,1.0,0.8,0.5,0.3,0.2,0.1,0.05,0.02,0.01],\n",
    "               'max_iter': [1, 5, 10, 100, 1000],}\n",
    "paramsRidge = {'alpha': [25,10,4,2,1.0,0.8,0.5,0.3,0.2,0.1,0.05,0.02,0.01]}\n",
    "paramsENet = {'alpha': [25,10,4,2,1.0,0.8,0.5,0.3,0.2,0.1,0.05,0.02,0.01],\n",
    "              'l1_ratio': [0.0, 0.5, 1.0, 0.1],\n",
    "              'max_iter': [1, 5, 10, 100, 1000],}\n",
    "\n",
    "for ft_dummyfication in [False]: # [False, True]:\n",
    "    if ft_dummyfication:\n",
    "        #Creating dummy variables in pandas\n",
    "        df = pd.get_dummies(df, columns=list(df.drop(columns=[\"cid\"]).drop(columns=size_methods).drop(columns=additional_ft).columns.values))\n",
    "        df_importance = pd.get_dummies(df_importance, columns=list(df_importance.drop(columns=[\"nbyes\", \"nbno\", \"nbyesmodule\"]).columns.values))\n",
    "        \n",
    "    ft_selection =  {'None': df.drop(columns=[\"cid\"]).drop(columns=size_methods).columns,\n",
    "                    # 'RandomForest': df_importance.columns[:100],\n",
    "                     # 'RandomForest200': df_importance.columns[:200],\n",
    "                     # 'RandomForest1000': df_importance.columns[:1000],\n",
    "                    }\n",
    "        \n",
    "    for origin_ft_selection in ft_selection:\n",
    "        print(origin_ft_selection)\n",
    "        # for size in [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]:\n",
    "        for size in [0.9]:\n",
    "            print(size)\n",
    "\n",
    "            x_train, x_test, y_train, y_test = train_test_split(df[ft_selection[origin_ft_selection]], df[target], test_size=size, random_state=42)  \n",
    "            # x_train, y_train = np.array(x_train.values), np.array(y_train.values)\n",
    "\n",
    "            model = {'LinearRegression': LinearRegression(),\n",
    "                    # 'Lasso': GridSearchCV(linear_model.Lasso(), param_grid=paramsLasso, cv=3).fit(x_train, y_train).best_estimator_,\n",
    "                     'LassoCV': linear_model.LassoCV(cv=2, n_jobs=-1).fit(x_train, y_train),                    \n",
    "                     # 'Ridge': GridSearchCV(linear_model.Ridge(), param_grid=paramsRidge, cv=3).fit(x_train, y_train).best_estimator_,\n",
    "                     'RidgeCV': linear_model.RidgeCV(cv=2, n_jobs=-1).fit(x_train, y_train),  \n",
    "                    # 'ElasticNet': GridSearchCV(ElasticNet(), param_grid=paramsENet, scoring='r2', cv=3).fit(x_train, y_train).best_estimator_,\n",
    "                    'ElasticNetCV': linear_model.ElasticNetCV(cv=2, n_jobs=-1).fit(x_train, y_train),\n",
    "                   # 'PolynomialRegression': LinearRegression()\n",
    "                    }\n",
    "        \n",
    "            for key in model:\n",
    "                if (key == 'PolynomialRegression') and (origin_ft_selection == 'None'):\n",
    "                    print(\"It doesn't scale\")\n",
    "                else:\n",
    "                    if (key == 'PolynomialRegression'):\n",
    "                        #for 2 options we have: 𝑓(𝑥₁, 𝑥₂) = 𝑏₀ + 𝑏₁𝑥₁ + 𝑏₂𝑥₂ + 𝑏₃𝑥₁² + 𝑏₄𝑥₁𝑥₂ + 𝑏₅𝑥₂²\n",
    "                        pf = PolynomialFeatures(degree=2, include_bias=False)\n",
    "                        x_train_pf = pf.fit_transform(x_train)\n",
    "                        x_test_pf = pf.fit_transform(x_test)\n",
    "                        res_model = run_regressorML(model[key], test_size=size, size_target=target, x_train=x_train_pf, x_test=x_test_pf)\n",
    "                        # model[key].coef_ = model[key].coef_[:100]\n",
    "                        importanceSeries = pd.Series(model[key].coef_, index=pf.get_feature_names(x_train.columns))\n",
    "                        coef_order = importanceSeries[importanceSeries != 0].abs().sort_values(ascending = False)\n",
    "                    else:\n",
    "                        res_model = run_regressorML(model[key], test_size=size, size_target=target, x_train=x_train, x_test=x_test)\n",
    "                        coef_order = ft_importances(model[key].coef_, col=ft_selection[origin_ft_selection])\n",
    "                   \n",
    "                        \n",
    "                    df_result.loc[len(df_result)] = [key, ft_dummyfication, origin_ft_selection, ft_selection[origin_ft_selection], model[key], target, additional_ft, size, coef_order, res_model]\n",
    "\n",
    "df_result.to_csv(\"results.csv\", header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_result['coef_order']\n",
    "df_result.to_pickle(\"results.pkl\")\n",
    "ftimportances = pd.DataFrame(columns=['option', 'importance'])\n",
    "a = 0\n",
    "for o, coeff in df_result['coef_order'].iloc[0].iteritems():\n",
    "    ftimportances.loc[a] = [o, coeff]\n",
    "    a = a + 1\n",
    "ftimportances.to_csv('feature_importanceLasso09.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Unnamed: 0</th>\n",
       "      <th>algorithm_name</th>\n",
       "      <th>ft_dummyfication</th>\n",
       "      <th>origin_ft_selection</th>\n",
       "      <th>ft_selection</th>\n",
       "      <th>hyperparameters</th>\n",
       "      <th>size_target</th>\n",
       "      <th>additional_ft</th>\n",
       "      <th>test_size</th>\n",
       "      <th>coef_order</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>LassoCV</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>Index(['OPENVSWITCH', 'TEXTSEARCH_FSM', 'NETFI...</td>\n",
       "      <td>LassoCV(alphas=None, copy_X=True, cv=2, eps=0....</td>\n",
       "      <td>vmlinux</td>\n",
       "      <td>['nbyes', 'nbno', 'nbmodule', 'nbyesmodule']</td>\n",
       "      <td>0.9</td>\n",
       "      <td>DEBUG_INFO               2.103632e+08\\nDEBUG_I...</td>\n",
       "      <td>count    83224.000000\\nmean        34.129817\\n...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 algorithm_name  ft_dummyfication origin_ft_selection  \\\n",
       "0           0        LassoCV             False                None   \n",
       "\n",
       "                                        ft_selection  \\\n",
       "0  Index(['OPENVSWITCH', 'TEXTSEARCH_FSM', 'NETFI...   \n",
       "\n",
       "                                     hyperparameters size_target  \\\n",
       "0  LassoCV(alphas=None, copy_X=True, cv=2, eps=0....     vmlinux   \n",
       "\n",
       "                                  additional_ft  test_size  \\\n",
       "0  ['nbyes', 'nbno', 'nbmodule', 'nbyesmodule']        0.9   \n",
       "\n",
       "                                          coef_order  \\\n",
       "0  DEBUG_INFO               2.103632e+08\\nDEBUG_I...   \n",
       "\n",
       "                                            accuracy  \n",
       "0  count    83224.000000\\nmean        34.129817\\n...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = pd.read_csv('results.csv')\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62\n",
      "DEBUG_INFO               2.103632e+08\n",
      "DEBUG_INFO_SPLIT         1.046844e+08\n",
      "DEBUG_INFO_REDUCED       9.293008e+07\n",
      "UBSAN_SANITIZE_ALL       3.725765e+07\n",
      "RANDOMIZE_BASE           3.721600e+07\n",
      "KASAN                    1.718221e+07\n",
      "GCOV_PROFILE_ALL         1.614553e+07\n",
      "XFS_DEBUG                7.165541e+06\n",
      "DRM_I915                 6.134273e+06\n",
      "IP_SCTP                  3.312451e+06\n",
      "ASUS_WIRELESS            2.993991e+06\n",
      "UBSAN_ALIGNMENT          2.770776e+06\n",
      "CFG80211                 2.559556e+06\n",
      "JME                      2.558470e+06\n",
      "SFC_FALCON               2.516995e+06\n",
      "BT                       2.481396e+06\n",
      "PCI_STUB                 2.201425e+06\n",
      "NET_SWITCHDEV            2.071991e+06\n",
      "DRM_AMDGPU               1.980267e+06\n",
      "SCSI_SAS_ATTRS           1.965855e+06\n",
      "SENSORS_I5K_AMB          1.963881e+06\n",
      "CONSTRUCTORS             1.932630e+06\n",
      "SND_HWDEP                1.877632e+06\n",
      "KCOV_INSTRUMENT_ALL      1.824779e+06\n",
      "TCM_PSCSI                1.772911e+06\n",
      "DRM                      1.753895e+06\n",
      "FB_CARMINE               1.734688e+06\n",
      "SP5100_TCO               1.730010e+06\n",
      "TARGET_CORE              1.709490e+06\n",
      "DP83867_PHY              1.653706e+06\n",
      "                             ...     \n",
      "VIRT_DRIVERS             2.999554e+04\n",
      "MUTEX_SPIN_ON_OWNER      2.925713e+04\n",
      "I2C_DESIGNWARE_PCI       2.891793e+04\n",
      "SENSORS_EMC6W201         2.819072e+04\n",
      "IOSCHED_CFQ              2.483057e+04\n",
      "HIGH_RES_TIMERS          2.317124e+04\n",
      "MFD_MENF21BMC            2.190486e+04\n",
      "BLK_SCSI_REQUEST         2.016512e+04\n",
      "HID_DRAGONRISE           1.997355e+04\n",
      "SCHED_HRTICK             1.971932e+04\n",
      "QUEUED_SPINLOCKS         1.133023e+04\n",
      "PARTITION_ADVANCED       9.498107e+03\n",
      "CRYPTO_TGR192            6.382082e+03\n",
      "TREE_RCU                 5.867678e+03\n",
      "DVB_TDA10048             5.436912e+03\n",
      "RCU_STALL_COMMON         2.171857e+03\n",
      "X86_NEED_RELOCS          1.828877e+03\n",
      "CHR_DEV_SCH              1.496758e+03\n",
      "DRM_NOUVEAU_BACKLIGHT    1.231827e+03\n",
      "HW_CONSOLE               1.118928e+03\n",
      "QUEUED_RWLOCKS           3.622742e+02\n",
      "CRYPTO_842               8.784026e+01\n",
      "SPARSEMEM_VMEMMAP        2.103924e-08\n",
      "RCU_NEED_SEGCBLIST       3.300368e-09\n",
      "RWSEM_SPIN_ON_OWNER      2.475276e-09\n",
      "DEFAULT_CFQ              1.706736e-09\n",
      "LOCK_SPIN_ON_OWNER       1.031365e-09\n",
      "GENERIC_IRQ_MIGRATION    8.250921e-10\n",
      "SMP                      8.250921e-10\n",
      "GENERIC_PENDING_IRQ      6.188191e-10\n",
      "Length: 285, dtype: float64\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>option</th>\n",
       "      <th>coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DEBUG_INFO</td>\n",
       "      <td>2.103632e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DEBUG_INFO_SPLIT</td>\n",
       "      <td>1.046844e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DEBUG_INFO_REDUCED</td>\n",
       "      <td>9.293008e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UBSAN_SANITIZE_ALL</td>\n",
       "      <td>3.725765e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RANDOMIZE_BASE</td>\n",
       "      <td>3.721600e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KASAN</td>\n",
       "      <td>1.718221e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GCOV_PROFILE_ALL</td>\n",
       "      <td>1.614553e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>XFS_DEBUG</td>\n",
       "      <td>7.165541e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DRM_I915</td>\n",
       "      <td>6.134273e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>IP_SCTP</td>\n",
       "      <td>3.312451e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ASUS_WIRELESS</td>\n",
       "      <td>2.993991e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UBSAN_ALIGNMENT</td>\n",
       "      <td>2.770776e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CFG80211</td>\n",
       "      <td>2.559556e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>JME</td>\n",
       "      <td>2.558470e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SFC_FALCON</td>\n",
       "      <td>2.516995e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BT</td>\n",
       "      <td>2.481396e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PCI_STUB</td>\n",
       "      <td>2.201425e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NET_SWITCHDEV</td>\n",
       "      <td>2.071991e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>DRM_AMDGPU</td>\n",
       "      <td>1.980267e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SCSI_SAS_ATTRS</td>\n",
       "      <td>1.965855e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SENSORS_I5K_AMB</td>\n",
       "      <td>1.963881e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CONSTRUCTORS</td>\n",
       "      <td>1.932630e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SND_HWDEP</td>\n",
       "      <td>1.877632e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>KCOV_INSTRUMENT_ALL</td>\n",
       "      <td>1.824779e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>TCM_PSCSI</td>\n",
       "      <td>1.772911e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>DRM</td>\n",
       "      <td>1.753895e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>FB_CARMINE</td>\n",
       "      <td>1.734688e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SP5100_TCO</td>\n",
       "      <td>1.730010e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>TARGET_CORE</td>\n",
       "      <td>1.709490e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>DP83867_PHY</td>\n",
       "      <td>1.653706e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>MUTEX_SPIN_ON_OWNER</td>\n",
       "      <td>2.925713e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>I2C_DESIGNWARE_PCI</td>\n",
       "      <td>2.891793e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>SENSORS_EMC6W201</td>\n",
       "      <td>2.819072e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>IOSCHED_CFQ</td>\n",
       "      <td>2.483057e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>HIGH_RES_TIMERS</td>\n",
       "      <td>2.317124e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>MFD_MENF21BMC</td>\n",
       "      <td>2.190486e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>BLK_SCSI_REQUEST</td>\n",
       "      <td>2.016512e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>HID_DRAGONRISE</td>\n",
       "      <td>1.997355e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>SCHED_HRTICK</td>\n",
       "      <td>1.971932e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>QUEUED_SPINLOCKS</td>\n",
       "      <td>1.133023e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>PARTITION_ADVANCED</td>\n",
       "      <td>9.498107e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>CRYPTO_TGR192</td>\n",
       "      <td>6.382082e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>TREE_RCU</td>\n",
       "      <td>5.867678e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>DVB_TDA10048</td>\n",
       "      <td>5.436912e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>RCU_STALL_COMMON</td>\n",
       "      <td>2.171857e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>X86_NEED_RELOCS</td>\n",
       "      <td>1.828877e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>CHR_DEV_SCH</td>\n",
       "      <td>1.496758e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>DRM_NOUVEAU_BACKLIGHT</td>\n",
       "      <td>1.231827e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>HW_CONSOLE</td>\n",
       "      <td>1.118928e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>QUEUED_RWLOCKS</td>\n",
       "      <td>3.622742e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>CRYPTO_842</td>\n",
       "      <td>8.784026e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>SPARSEMEM_VMEMMAP</td>\n",
       "      <td>2.103924e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>RCU_NEED_SEGCBLIST</td>\n",
       "      <td>3.300368e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>RWSEM_SPIN_ON_OWNER</td>\n",
       "      <td>2.475276e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>DEFAULT_CFQ</td>\n",
       "      <td>1.706736e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>LOCK_SPIN_ON_OWNER</td>\n",
       "      <td>1.031365e-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>GENERIC_IRQ_MIGRATION</td>\n",
       "      <td>8.250921e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>SMP</td>\n",
       "      <td>8.250921e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>GENERIC_PENDING_IRQ</td>\n",
       "      <td>6.188191e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Length:</td>\n",
       "      <td>285,</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   option   coefficient\n",
       "0              DEBUG_INFO  2.103632e+08\n",
       "1        DEBUG_INFO_SPLIT  1.046844e+08\n",
       "2      DEBUG_INFO_REDUCED  9.293008e+07\n",
       "3      UBSAN_SANITIZE_ALL  3.725765e+07\n",
       "4          RANDOMIZE_BASE  3.721600e+07\n",
       "5                   KASAN  1.718221e+07\n",
       "6        GCOV_PROFILE_ALL  1.614553e+07\n",
       "7               XFS_DEBUG  7.165541e+06\n",
       "8                DRM_I915  6.134273e+06\n",
       "9                 IP_SCTP  3.312451e+06\n",
       "10          ASUS_WIRELESS  2.993991e+06\n",
       "11        UBSAN_ALIGNMENT  2.770776e+06\n",
       "12               CFG80211  2.559556e+06\n",
       "13                    JME  2.558470e+06\n",
       "14             SFC_FALCON  2.516995e+06\n",
       "15                     BT  2.481396e+06\n",
       "16               PCI_STUB  2.201425e+06\n",
       "17          NET_SWITCHDEV  2.071991e+06\n",
       "18             DRM_AMDGPU  1.980267e+06\n",
       "19         SCSI_SAS_ATTRS  1.965855e+06\n",
       "20        SENSORS_I5K_AMB  1.963881e+06\n",
       "21           CONSTRUCTORS  1.932630e+06\n",
       "22              SND_HWDEP  1.877632e+06\n",
       "23    KCOV_INSTRUMENT_ALL  1.824779e+06\n",
       "24              TCM_PSCSI  1.772911e+06\n",
       "25                    DRM  1.753895e+06\n",
       "26             FB_CARMINE  1.734688e+06\n",
       "27             SP5100_TCO  1.730010e+06\n",
       "28            TARGET_CORE  1.709490e+06\n",
       "29            DP83867_PHY  1.653706e+06\n",
       "..                    ...           ...\n",
       "31    MUTEX_SPIN_ON_OWNER  2.925713e+04\n",
       "32     I2C_DESIGNWARE_PCI  2.891793e+04\n",
       "33       SENSORS_EMC6W201  2.819072e+04\n",
       "34            IOSCHED_CFQ  2.483057e+04\n",
       "35        HIGH_RES_TIMERS  2.317124e+04\n",
       "36          MFD_MENF21BMC  2.190486e+04\n",
       "37       BLK_SCSI_REQUEST  2.016512e+04\n",
       "38         HID_DRAGONRISE  1.997355e+04\n",
       "39           SCHED_HRTICK  1.971932e+04\n",
       "40       QUEUED_SPINLOCKS  1.133023e+04\n",
       "41     PARTITION_ADVANCED  9.498107e+03\n",
       "42          CRYPTO_TGR192  6.382082e+03\n",
       "43               TREE_RCU  5.867678e+03\n",
       "44           DVB_TDA10048  5.436912e+03\n",
       "45       RCU_STALL_COMMON  2.171857e+03\n",
       "46        X86_NEED_RELOCS  1.828877e+03\n",
       "47            CHR_DEV_SCH  1.496758e+03\n",
       "48  DRM_NOUVEAU_BACKLIGHT  1.231827e+03\n",
       "49             HW_CONSOLE  1.118928e+03\n",
       "50         QUEUED_RWLOCKS  3.622742e+02\n",
       "51             CRYPTO_842  8.784026e+01\n",
       "52      SPARSEMEM_VMEMMAP  2.103924e-08\n",
       "53     RCU_NEED_SEGCBLIST  3.300368e-09\n",
       "54    RWSEM_SPIN_ON_OWNER  2.475276e-09\n",
       "55            DEFAULT_CFQ  1.706736e-09\n",
       "56     LOCK_SPIN_ON_OWNER  1.031365e-09\n",
       "57  GENERIC_IRQ_MIGRATION  8.250921e-10\n",
       "58                    SMP  8.250921e-10\n",
       "59    GENERIC_PENDING_IRQ  6.188191e-10\n",
       "60                Length:          285,\n",
       "\n",
       "[61 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# oblige to reparse \n",
    "coeffs = res.iloc[0]['coef_order']\n",
    "coef = pd.DataFrame(columns=['option', 'coefficient'])\n",
    "i = 0\n",
    "print(len(coeffs.splitlines()))\n",
    "for coeff in coeffs.splitlines():\n",
    "    print(coeff)\n",
    "    optcoeff = [c for c in coeff.split(' ') if c is not '']\n",
    "    if len(optcoeff) == 1:\n",
    "        continue\n",
    "    elif len(optcoeff) == 3:\n",
    "        opt = optcoeff[0] + \" \" + optcoeff[1]\n",
    "        coeff = optcoeff[2]\n",
    "    else: # standard: one option, one coeff\n",
    "        opt = optcoeff[0]\n",
    "        coeff = optcoeff[1]\n",
    "    coef.loc[i] = (opt, coeff)\n",
    "    i = i + 1\n",
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/matplotlib/__init__.py:886: MatplotlibDeprecationWarning: \n",
      "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n",
      "  \"found relative to the 'datapath' directory.\".format(key))\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'order_lr' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-3e40a6b78500>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0morder_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder_lasso\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder_ridge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder_en\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mfig1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Coeficient of feature importance for all algorithms'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'order_lr' is not defined"
     ]
    }
   ],
   "source": [
    "# Boxplot\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "data = [order_lr, order_lasso, order_ridge, order_en]\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.set_title('Coeficient of feature importance for all algorithms')\n",
    "ax1.boxplot(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res.iloc[1]['hyperparameters']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'nbyes' in coef['option']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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