https_brute_force.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HTTPS Brute Force detection using Network Traffic Classification based on Single Flow Time Series Analysis \n",
    "\n",
    "Problem: binary detection of HTTPS Brute Force\n",
    "\n",
    "Raw data from: HTTPS Brute-force [1]\n",
    "\n",
    "-----------------------------------------------------------------------\n",
    "[1] Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import collections\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "#Import Random Forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.svm import SVC\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from collections import Counter\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler, NearMiss\n",
    "\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../cnsm_final_datasets/https_brute_force.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "        Unnamed: 0                                             DST_IP  \\\n",
       "0                0  39118CC8C45401711E837CFE8D9F982596F7BCEA55A320...   \n",
       "1                1  39118CC8C45401711E837CFE8D9F982596F7BCEA55A320...   \n",
       "2                2  39118CC8C45401711E837CFE8D9F982596F7BCEA55A320...   \n",
       "3                3  39118CC8C45401711E837CFE8D9F982596F7BCEA55A320...   \n",
       "4                4  39118CC8C45401711E837CFE8D9F982596F7BCEA55A320...   \n",
       "...            ...                                                ...   \n",
       "923375      923375  2B4079E7083A44B9EB26A3D62B508BDEE0A1695B303E2C...   \n",
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       "\n",
       "                                                   SRC_IP  PACKETS  \\\n",
       "0       28598F178AE82C013D63BCB803ECF5076F0ADEAE3CF59A...        3   \n",
       "1       632A7854237232035C4AA219F02B6BFAD2FEDA784DA0A1...        3   \n",
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       "...                                                   ...      ...   \n",
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       "\n",
       "        PACKETS_REV  BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  \\\n",
       "0                 6    120       6128  1.601465e+09  1.601465e+09       443   \n",
       "1                 4    120       2651  1.601465e+09  1.601465e+09       443   \n",
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       "3                 2   1970       1360  1.601465e+09  1.601465e+09       443   \n",
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       "...             ...    ...        ...           ...           ...       ...   \n",
       "923375          139  16409     144272  1.601636e+09  1.601636e+09       443   \n",
       "923376           23   2128      16552  1.601636e+09  1.601636e+09       443   \n",
       "923377           22   2489      16500  1.601636e+09  1.601636e+09       443   \n",
       "923378           22   2497      16500  1.601636e+09  1.601636e+09       443   \n",
       "923379           22   2542      16500  1.601636e+09  1.601636e+09       443   \n",
       "\n",
       "        ... SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0       ...               NaN          0.006667                NaN   \n",
       "1       ...               NaN          0.006667                NaN   \n",
       "2       ...               NaN          0.006667                NaN   \n",
       "3       ...               NaN          0.006667                NaN   \n",
       "4       ...               NaN          0.006667                NaN   \n",
       "...     ...               ...               ...                ...   \n",
       "923375  ...      -2061.982151          0.006667           0.498886   \n",
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       "923378  ...               NaN          0.006667                NaN   \n",
       "923379  ...               NaN          0.006667                NaN   \n",
       "\n",
       "        SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0                   NaN             NaN             NaN            NaN   \n",
       "1                   NaN             NaN             NaN            NaN   \n",
       "2                   NaN             NaN             NaN            NaN   \n",
       "3                   NaN             NaN             NaN            NaN   \n",
       "4                   NaN             NaN             NaN            NaN   \n",
       "...                 ...             ...             ...            ...   \n",
       "923375         0.327262         -0.1827        1.855315       0.005522   \n",
       "923376              NaN             NaN             NaN            NaN   \n",
       "923377         0.278471             NaN       10.699371       8.491211   \n",
       "923378              NaN             NaN             NaN            NaN   \n",
       "923379              NaN             NaN             NaN            NaN   \n",
       "\n",
       "        SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0                 0.000000               NaN  False  \n",
       "1                 0.000000               NaN  False  \n",
       "2                 0.000000               NaN  False  \n",
       "3                 0.000000               NaN  False  \n",
       "4                 0.000000               NaN  False  \n",
       "...                    ...               ...    ...  \n",
       "923375           -0.070924          0.999992   True  \n",
       "923376            0.000000               NaN   True  \n",
       "923377            0.145225          0.698817   True  \n",
       "923378            0.000000               NaN   True  \n",
       "923379            0.000000               NaN   True  \n",
       "\n",
       "[923380 rows x 84 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_faetures = [\n",
    "    \"PACKETS\",\n",
    "    \"PACKETS_REV\",\n",
    "    \"BYTES\",\n",
    "    \"BYTES_REV\",\n",
    "]\n",
    "\n",
    "statistics_features = [\n",
    "    \"MEAN\",\n",
    "    \"MEDIAN\",\n",
    "    \"STDEV\",\n",
    "    \"VAR\",\n",
    "    \"BURSTINESS\",\n",
    "    \"Q1\",\n",
    "    \"Q3\",\n",
    "    \"MIN\",\n",
    "    \"MAX\",\n",
    "    \"MIN_MINUS_MAX\",\n",
    "    \"MODE\",\n",
    "    \"COEFFICIENT_OF_VARIATION\",\n",
    "    \"AVERAGE_DISPERSION\",\n",
    "    \"PERCENT_DEVIATION\",\n",
    "    \"ROOT_MEAN_SQUARE\",\n",
    "    \"PERCENT_BELOW_MEAN\",\n",
    "    \"PERCENT_ABOVE_MEAN\",\n",
    "    \"PEARSON_SK1_SKEWNESS\",\n",
    "    \"PEARSON_SK2_SKEWNESS\",\n",
    "    \"FISHER_MI_3_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_g1_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_G1_SKEWNESS\",\n",
    "    \"GALTON_SKEWNESS\",\n",
    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "]\n",
    "distribution_features = [\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "]\n",
    "time_features = [\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    ]\n",
    "behavior_features = [\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"DIRECTIONS\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"PERIODICITY\",\n",
    "    \"VAL\",\n",
    "    \"TIME\",\n",
    "]\n",
    "frequency_features = [\n",
    "    \"MIN_POWER\",\n",
    "    \"MAX_POWER\",\n",
    "    \"MIN_POWER_FREQ\",\n",
    "    \"MAX_POWER_FREQ\",\n",
    "    \"POWER_MEAN\",\n",
    "    \"POWER_STD\",\n",
    "    \"POWER_MODE\",\n",
    "    \"SPECTRAL_ENERGY\",\n",
    "    \"SPECTRAL_ENTROPY\",\n",
    "    \"SPECTRAL_KURTOSIS\",\n",
    "    \"SPECTRAL_SKEWNESS\",\n",
    "    \"SPECTRAL_ROLLOFF\",\n",
    "    \"SPECTRAL_CENTROID\",\n",
    "    \"SPECTRAL_SPREAD\",\n",
    "    \"SPECTRAL_SLOPE\",\n",
    "    \"SPECTRAL_CREST\",\n",
    "    \"SPECTRAL_FLUX\",\n",
    "    \"SPECTRAL_BANDWIDTH\",\n",
    "    \"PERIODICITY_SCDF\",\n",
    "]\n",
    "\n",
    "features = [\n",
    "    \"PACKETS\",\n",
    "    \"PACKETS_REV\",\n",
    "    \"BYTES\",\n",
    "    \"BYTES_REV\",\n",
    "    \"MEAN\",\n",
    "    \"MEDIAN\",\n",
    "    \"STDEV\",\n",
    "    \"VAR\",\n",
    "    \"BURSTINESS\",\n",
    "    \"Q1\",\n",
    "    \"Q3\",\n",
    "    \"MIN\",\n",
    "    \"MAX\",\n",
    "    \"MIN_MINUS_MAX\",\n",
    "    \"MODE\",\n",
    "    \"COEFFICIENT_OF_VARIATION\",\n",
    "    \"AVERAGE_DISPERSION\",\n",
    "    \"PERCENT_DEVIATION\",\n",
    "    \"ROOT_MEAN_SQUARE\",\n",
    "    \"PERCENT_BELOW_MEAN\",\n",
    "    \"PERCENT_ABOVE_MEAN\",\n",
    "    \"PEARSON_SK1_SKEWNESS\",\n",
    "    \"PEARSON_SK2_SKEWNESS\",\n",
    "    \"FISHER_MI_3_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_g1_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_G1_SKEWNESS\",\n",
    "    \"GALTON_SKEWNESS\",\n",
    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"DIRECTIONS\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"PERIODICITY\",\n",
    "    \"VAL\",\n",
    "    \"TIME\",\n",
    "    \"MIN_POWER\",\n",
    "    \"MAX_POWER\",\n",
    "    \"MIN_POWER_FREQ\",\n",
    "    \"MAX_POWER_FREQ\",\n",
    "    \"POWER_MEAN\",\n",
    "    \"POWER_STD\",\n",
    "    \"POWER_MODE\",\n",
    "    \"SPECTRAL_ENERGY\",\n",
    "    \"SPECTRAL_ENTROPY\",\n",
    "    \"SPECTRAL_KURTOSIS\",\n",
    "    \"SPECTRAL_SKEWNESS\",\n",
    "    \"SPECTRAL_ROLLOFF\",\n",
    "    \"SPECTRAL_CENTROID\",\n",
    "    \"SPECTRAL_SPREAD\",\n",
    "    \"SPECTRAL_SLOPE\",\n",
    "    \"SPECTRAL_CREST\",\n",
    "    \"SPECTRAL_FLUX\",\n",
    "    \"SPECTRAL_BANDWIDTH\",\n",
    "    \"PERIODICITY_SCDF\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "df['LABEL'] = df.LABEL.astype('category')\n",
    "df[\"LABEL_code\"] = df[\"LABEL\"].cat.codes\n",
    "\n",
    "bool_features = [\"TRANSIENTS\", \"BENFORD_LAW_PRESENTED\", \"SIG_SPACES\", \"PERIODICITY\",\"PERIODICITY_SCDF\"]\n",
    "\n",
    "\n",
    "for F in features:\n",
    "    if F in bool_features:\n",
    "        continue\n",
    "    if F in distribution_features:\n",
    "        df.replace([np.inf], 0.5, inplace=True)\n",
    "        df.replace([-np.inf], 0.5, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0.5\n",
    "    elif F in [\"DIRECTIONS\",\"NORMAL_DISTRIBUTION\"]: \n",
    "        df.replace([np.inf], 0.5, inplace=True)\n",
    "        df.replace([-np.inf], 0.5, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0.5\n",
    "    elif F in frequency_features: \n",
    "        df.replace([np.inf], -1, inplace=True)\n",
    "        df.replace([-np.inf], -1, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = -1\n",
    "    else:\n",
    "        df.replace([np.inf], 0, inplace=True)\n",
    "        df.replace([-np.inf], 0, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0\n",
    "\n",
    "\n",
    "for F in bool_features:\n",
    "    df.loc[df[F].isnull(), F] = False\n",
    "    df[F] = df[F].astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>DST_IP</th>\n",
       "      <th>SRC_IP</th>\n",
       "      <th>PACKETS</th>\n",
       "      <th>PACKETS_REV</th>\n",
       "      <th>BYTES</th>\n",
       "      <th>BYTES_REV</th>\n",
       "      <th>TIME_FIRST</th>\n",
       "      <th>TIME_LAST</th>\n",
       "      <th>DST_PORT</th>\n",
       "      <th>SRC_PORT</th>\n",
       "      <th>MEAN</th>\n",
       "      <th>MEDIAN</th>\n",
       "      <th>STDEV</th>\n",
       "      <th>VAR</th>\n",
       "      <th>BURSTINESS</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q3</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>MIN_MINUS_MAX</th>\n",
       "      <th>MODE</th>\n",
       "      <th>COEFFICIENT_OF_VARIATION</th>\n",
       "      <th>AVERAGE_DISPERSION</th>\n",
       "      <th>PERCENT_DEVIATION</th>\n",
       "      <th>ROOT_MEAN_SQUARE</th>\n",
       "      <th>PERCENT_BELOW_MEAN</th>\n",
       "      <th>PERCENT_ABOVE_MEAN</th>\n",
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       "      <th>PEARSON_SK2_SKEWNESS</th>\n",
       "      <th>FISHER_MI_3_SKEWNESS</th>\n",
       "      <th>FISHER_PEARSON_g1_SKEWNESS</th>\n",
       "      <th>FISHER_PEARSON_G1_SKEWNESS</th>\n",
       "      <th>GALTON_SKEWNESS</th>\n",
       "      <th>KURTOSIS</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>SCALED_ENTROPY</th>\n",
       "      <th>HURST_EXPONENT</th>\n",
       "      <th>BENFORD_LAW_PRESENTED</th>\n",
       "      <th>P_BENFORD</th>\n",
       "      <th>NORMAL_DISTRIBUTION</th>\n",
       "      <th>CNT_DISTRIBUTION</th>\n",
       "      <th>TIME_DISTRIBUTION</th>\n",
       "      <th>AREA_VALUES_DISTRIBUTION</th>\n",
       "      <th>MEAN_SCALED_TIME</th>\n",
       "      <th>MEDIAN_SCALED_TIME</th>\n",
       "      <th>Q1_SCALED_TIME</th>\n",
       "      <th>Q3_SCALED_TIME</th>\n",
       "      <th>DURATION</th>\n",
       "      <th>MEAN_DIFFTIMES</th>\n",
       "      <th>MEDIAN_DIFFTIMES</th>\n",
       "      <th>MIN_DIFFTIMES</th>\n",
       "      <th>MAX_DIFFTIMES</th>\n",
       "      <th>MEAN_SCALED_DIFFTIMES</th>\n",
       "      <th>SIG_SPACES</th>\n",
       "      <th>SWITCHING_METRIC</th>\n",
       "      <th>TRANSIENTS</th>\n",
       "      <th>CNT_ZEROS</th>\n",
       "      <th>CNT_NZ_DISTRIBUTION</th>\n",
       "      <th>BIGGEST_CNT_1_SEC</th>\n",
       "      <th>DIRECTIONS</th>\n",
       "      <th>PERIODICITY</th>\n",
       "      <th>VAL</th>\n",
       "      <th>TIME</th>\n",
       "      <th>MIN_POWER</th>\n",
       "      <th>MAX_POWER</th>\n",
       "      <th>MIN_POWER_FREQ</th>\n",
       "      <th>MAX_POWER_FREQ</th>\n",
       "      <th>POWER_MEAN</th>\n",
       "      <th>POWER_STD</th>\n",
       "      <th>POWER_MODE</th>\n",
       "      <th>SPECTRAL_ENERGY</th>\n",
       "      <th>SPECTRAL_ENTROPY</th>\n",
       "      <th>SPECTRAL_KURTOSIS</th>\n",
       "      <th>SPECTRAL_SKEWNESS</th>\n",
       "      <th>SPECTRAL_ROLLOFF</th>\n",
       "      <th>SPECTRAL_CENTROID</th>\n",
       "      <th>SPECTRAL_SPREAD</th>\n",
       "      <th>SPECTRAL_SLOPE</th>\n",
       "      <th>SPECTRAL_CREST</th>\n",
       "      <th>SPECTRAL_FLUX</th>\n",
       "      <th>SPECTRAL_BANDWIDTH</th>\n",
       "      <th>PERIODICITY_SCDF</th>\n",
       "      <th>LABEL</th>\n",
       "      <th>LABEL_code</th>\n",
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       "  <tbody>\n",
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       "      <td>False</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>39118CC8C45401711E837CFE8D9F982596F7BCEA55A320...</td>\n",
       "      <td>6231B05C8CCDF8C359D7FBF547D458F4C4675C2298AB84...</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>836</td>\n",
       "      <td>6633</td>\n",
       "      <td>1.601465e+09</td>\n",
       "      <td>1.601465e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>12AFEE6C4C1D38B06C4A1288C0B1DEDF0B8C4FC30C5C8E...</td>\n",
       "      <td>887</td>\n",
       "      <td>907.0</td>\n",
       "      <td>677.663633</td>\n",
       "      <td>459228</td>\n",
       "      <td>-0.133790</td>\n",
       "      <td>280.00</td>\n",
       "      <td>1520.00</td>\n",
       "      <td>167</td>\n",
       "      <td>1520</td>\n",
       "      <td>1353</td>\n",
       "      <td>1520</td>\n",
       "      <td>76.399508</td>\n",
       "      <td>632</td>\n",
       "      <td>71.251409</td>\n",
       "      <td>1090.328219</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.934092</td>\n",
       "      <td>2.588305</td>\n",
       "      <td>-0.497530</td>\n",
       "      <td>4.016682e-10</td>\n",
       "      <td>5.009683e-10</td>\n",
       "      <td>0.020968</td>\n",
       "      <td>0.776339</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.653813</td>\n",
       "      <td>False</td>\n",
       "      <td>0.727060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7097</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>39118CC8C45401711E837CFE8D9F982596F7BCEA55A320...</td>\n",
       "      <td>1E5857055DE1CE379453DA517FB660FBFDF0277608ED6A...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1970</td>\n",
       "      <td>1360</td>\n",
       "      <td>1.601465e+09</td>\n",
       "      <td>1.601465e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>F2A6B0CFC97E27F834CC5C925018EB7ACACB33C01075CE...</td>\n",
       "      <td>832</td>\n",
       "      <td>825.5</td>\n",
       "      <td>645.708138</td>\n",
       "      <td>416939</td>\n",
       "      <td>-0.126068</td>\n",
       "      <td>293.00</td>\n",
       "      <td>1365.00</td>\n",
       "      <td>239</td>\n",
       "      <td>1440</td>\n",
       "      <td>1201</td>\n",
       "      <td>239</td>\n",
       "      <td>77.609151</td>\n",
       "      <td>557</td>\n",
       "      <td>66.947115</td>\n",
       "      <td>1002.876114</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.918372</td>\n",
       "      <td>2.587082</td>\n",
       "      <td>-0.957040</td>\n",
       "      <td>1.857211e-09</td>\n",
       "      <td>3.216784e-09</td>\n",
       "      <td>-0.005597</td>\n",
       "      <td>0.576090</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.410224</td>\n",
       "      <td>False</td>\n",
       "      <td>0.647940</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.446429</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3330</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006865</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>39118CC8C45401711E837CFE8D9F982596F7BCEA55A320...</td>\n",
       "      <td>5E0C7366AF8F76A60B5653B5649154FB77AD59EC2F9404...</td>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "      <td>3807</td>\n",
       "      <td>25086</td>\n",
       "      <td>1.601465e+09</td>\n",
       "      <td>1.601465e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>B798731A3011B144DD29B8C473768A4E1AD8A69AE5EA4B...</td>\n",
       "      <td>1201</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>493.806642</td>\n",
       "      <td>243845</td>\n",
       "      <td>-0.417271</td>\n",
       "      <td>970.25</td>\n",
       "      <td>1520.00</td>\n",
       "      <td>218</td>\n",
       "      <td>1520</td>\n",
       "      <td>1302</td>\n",
       "      <td>1520</td>\n",
       "      <td>41.116290</td>\n",
       "      <td>410</td>\n",
       "      <td>34.138218</td>\n",
       "      <td>1294.830668</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>-0.646002</td>\n",
       "      <td>4.218250</td>\n",
       "      <td>-1.374769</td>\n",
       "      <td>1.730167e-09</td>\n",
       "      <td>1.847714e-09</td>\n",
       "      <td>0.160528</td>\n",
       "      <td>2.380351</td>\n",
       "      <td>2.364005</td>\n",
       "      <td>0.515600</td>\n",
       "      <td>0.644854</td>\n",
       "      <td>False</td>\n",
       "      <td>0.676030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>1.130435</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28829</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.241238e-16</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.158249</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923375</th>\n",
       "      <td>923375</td>\n",
       "      <td>2B4079E7083A44B9EB26A3D62B508BDEE0A1695B303E2C...</td>\n",
       "      <td>6D1F3979A2F28D1020EEE08BA72CEBDAFF30B425094A1F...</td>\n",
       "      <td>68</td>\n",
       "      <td>139</td>\n",
       "      <td>16409</td>\n",
       "      <td>144272</td>\n",
       "      <td>1.601636e+09</td>\n",
       "      <td>1.601636e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>681FFB41E89A2E2969E8C21D2FFAB8162BE146D4387DA7...</td>\n",
       "      <td>1162</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>443.187319</td>\n",
       "      <td>196416</td>\n",
       "      <td>-0.447806</td>\n",
       "      <td>696.25</td>\n",
       "      <td>1468.00</td>\n",
       "      <td>403</td>\n",
       "      <td>1468</td>\n",
       "      <td>1065</td>\n",
       "      <td>1468</td>\n",
       "      <td>38.140045</td>\n",
       "      <td>409</td>\n",
       "      <td>35.197935</td>\n",
       "      <td>1243.615049</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>-0.690453</td>\n",
       "      <td>4.553379</td>\n",
       "      <td>-0.827026</td>\n",
       "      <td>9.305143e-09</td>\n",
       "      <td>9.447449e-09</td>\n",
       "      <td>-0.206997</td>\n",
       "      <td>1.697841</td>\n",
       "      <td>1.746734</td>\n",
       "      <td>0.262910</td>\n",
       "      <td>0.530712</td>\n",
       "      <td>False</td>\n",
       "      <td>0.631030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.780303</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>False</td>\n",
       "      <td>0.989899</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116281</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.079699e-06</td>\n",
       "      <td>0.001137</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.928877</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>0.000311</td>\n",
       "      <td>0.00099</td>\n",
       "      <td>3.064153</td>\n",
       "      <td>32.005922</td>\n",
       "      <td>9.422625e+03</td>\n",
       "      <td>-2061.982151</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.498886</td>\n",
       "      <td>0.327262</td>\n",
       "      <td>-0.1827</td>\n",
       "      <td>1.855315</td>\n",
       "      <td>0.005522</td>\n",
       "      <td>-0.070924</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923376</th>\n",
       "      <td>923376</td>\n",
       "      <td>2B4079E7083A44B9EB26A3D62B508BDEE0A1695B303E2C...</td>\n",
       "      <td>6D1F3979A2F28D1020EEE08BA72CEBDAFF30B425094A1F...</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>2128</td>\n",
       "      <td>16552</td>\n",
       "      <td>1.601636e+09</td>\n",
       "      <td>1.601636e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>DC5CB4DCDDAB40D31E08EB83A76E7DCDB5B32540F5F5A6...</td>\n",
       "      <td>891</td>\n",
       "      <td>1111.0</td>\n",
       "      <td>610.040982</td>\n",
       "      <td>372151</td>\n",
       "      <td>-0.187176</td>\n",
       "      <td>353.00</td>\n",
       "      <td>1468.00</td>\n",
       "      <td>66</td>\n",
       "      <td>1468</td>\n",
       "      <td>1402</td>\n",
       "      <td>1468</td>\n",
       "      <td>68.467001</td>\n",
       "      <td>576</td>\n",
       "      <td>64.646465</td>\n",
       "      <td>1071.844438</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.945838</td>\n",
       "      <td>2.560484</td>\n",
       "      <td>-0.358878</td>\n",
       "      <td>3.523814e-09</td>\n",
       "      <td>3.816210e-09</td>\n",
       "      <td>0.034978</td>\n",
       "      <td>1.076184</td>\n",
       "      <td>2.660964</td>\n",
       "      <td>0.615689</td>\n",
       "      <td>0.676477</td>\n",
       "      <td>False</td>\n",
       "      <td>0.746787</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>False</td>\n",
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       "      <td>True</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>17836</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-8.428328e-17</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.284601</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923377</th>\n",
       "      <td>923377</td>\n",
       "      <td>2B4079E7083A44B9EB26A3D62B508BDEE0A1695B303E2C...</td>\n",
       "      <td>6D1F3979A2F28D1020EEE08BA72CEBDAFF30B425094A1F...</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>2489</td>\n",
       "      <td>16500</td>\n",
       "      <td>1.601636e+09</td>\n",
       "      <td>1.601636e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>192AA6A3A74999CE1FE77403D84361927CB061810B06F3...</td>\n",
       "      <td>909</td>\n",
       "      <td>1110.0</td>\n",
       "      <td>592.678665</td>\n",
       "      <td>351269</td>\n",
       "      <td>-0.210645</td>\n",
       "      <td>401.75</td>\n",
       "      <td>1468.00</td>\n",
       "      <td>66</td>\n",
       "      <td>1468</td>\n",
       "      <td>1402</td>\n",
       "      <td>1468</td>\n",
       "      <td>65.201173</td>\n",
       "      <td>558</td>\n",
       "      <td>61.386139</td>\n",
       "      <td>1077.743917</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.943176</td>\n",
       "      <td>2.728291</td>\n",
       "      <td>-0.389719</td>\n",
       "      <td>4.082828e-09</td>\n",
       "      <td>4.421609e-09</td>\n",
       "      <td>0.048535</td>\n",
       "      <td>1.121531</td>\n",
       "      <td>2.660964</td>\n",
       "      <td>0.615689</td>\n",
       "      <td>0.669291</td>\n",
       "      <td>False</td>\n",
       "      <td>0.746787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>False</td>\n",
       "      <td>1.263158</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18197</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.381004e+00</td>\n",
       "      <td>2.105539</td>\n",
       "      <td>0.509492</td>\n",
       "      <td>0.654717</td>\n",
       "      <td>0.196791</td>\n",
       "      <td>0.045178</td>\n",
       "      <td>0.20358</td>\n",
       "      <td>983.954386</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>4.725432e+06</td>\n",
       "      <td>26161.139938</td>\n",
       "      <td>0.654717</td>\n",
       "      <td>0.487634</td>\n",
       "      <td>0.278471</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>10.699371</td>\n",
       "      <td>8.491211</td>\n",
       "      <td>0.145225</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923378</th>\n",
       "      <td>923378</td>\n",
       "      <td>2B4079E7083A44B9EB26A3D62B508BDEE0A1695B303E2C...</td>\n",
       "      <td>6D1F3979A2F28D1020EEE08BA72CEBDAFF30B425094A1F...</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>2497</td>\n",
       "      <td>16500</td>\n",
       "      <td>1.601636e+09</td>\n",
       "      <td>1.601636e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>5C875E8A7D5E64C51FE4087F9A8FCCD0E11166E0148DD7...</td>\n",
       "      <td>910</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>592.568983</td>\n",
       "      <td>351138</td>\n",
       "      <td>-0.211259</td>\n",
       "      <td>401.75</td>\n",
       "      <td>1468.00</td>\n",
       "      <td>66</td>\n",
       "      <td>1468</td>\n",
       "      <td>1402</td>\n",
       "      <td>1468</td>\n",
       "      <td>65.117471</td>\n",
       "      <td>557</td>\n",
       "      <td>61.208791</td>\n",
       "      <td>1078.024466</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.941663</td>\n",
       "      <td>2.727109</td>\n",
       "      <td>-0.401924</td>\n",
       "      <td>1.201499e-09</td>\n",
       "      <td>1.301196e-09</td>\n",
       "      <td>0.046659</td>\n",
       "      <td>1.123477</td>\n",
       "      <td>2.660964</td>\n",
       "      <td>0.615689</td>\n",
       "      <td>0.669937</td>\n",
       "      <td>False</td>\n",
       "      <td>0.746787</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>1.263158</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18205</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.139697e-16</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.897091</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923379</th>\n",
       "      <td>923379</td>\n",
       "      <td>2B4079E7083A44B9EB26A3D62B508BDEE0A1695B303E2C...</td>\n",
       "      <td>6D1F3979A2F28D1020EEE08BA72CEBDAFF30B425094A1F...</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>2542</td>\n",
       "      <td>16500</td>\n",
       "      <td>1.601636e+09</td>\n",
       "      <td>1.601636e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>C656716B13F91BB097C9CCEDCCCA7B8EEEE207BDBF43C5...</td>\n",
       "      <td>909</td>\n",
       "      <td>1110.5</td>\n",
       "      <td>592.664323</td>\n",
       "      <td>351252</td>\n",
       "      <td>-0.210657</td>\n",
       "      <td>401.75</td>\n",
       "      <td>1468.00</td>\n",
       "      <td>66</td>\n",
       "      <td>1468</td>\n",
       "      <td>1402</td>\n",
       "      <td>1468</td>\n",
       "      <td>65.199596</td>\n",
       "      <td>558</td>\n",
       "      <td>61.386139</td>\n",
       "      <td>1077.778827</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.943198</td>\n",
       "      <td>2.727514</td>\n",
       "      <td>-0.389116</td>\n",
       "      <td>4.323308e-09</td>\n",
       "      <td>4.682044e-09</td>\n",
       "      <td>0.048535</td>\n",
       "      <td>1.121633</td>\n",
       "      <td>2.660964</td>\n",
       "      <td>0.615689</td>\n",
       "      <td>0.669372</td>\n",
       "      <td>False</td>\n",
       "      <td>0.746787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.900000</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>1.263158</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18198</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.257642e-16</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.708755</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>923380 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0                                             DST_IP  \\\n",
       "0                0  39118CC8C45401711E837CFE8D9F982596F7BCEA55A320...   \n",
       "1                1  39118CC8C45401711E837CFE8D9F982596F7BCEA55A320...   \n",
       "2                2  39118CC8C45401711E837CFE8D9F982596F7BCEA55A320...   \n",
       "3                3  39118CC8C45401711E837CFE8D9F982596F7BCEA55A320...   \n",
       "4                4  39118CC8C45401711E837CFE8D9F982596F7BCEA55A320...   \n",
       "...            ...                                                ...   \n",
       "923375      923375  2B4079E7083A44B9EB26A3D62B508BDEE0A1695B303E2C...   \n",
       "923376      923376  2B4079E7083A44B9EB26A3D62B508BDEE0A1695B303E2C...   \n",
       "923377      923377  2B4079E7083A44B9EB26A3D62B508BDEE0A1695B303E2C...   \n",
       "923378      923378  2B4079E7083A44B9EB26A3D62B508BDEE0A1695B303E2C...   \n",
       "923379      923379  2B4079E7083A44B9EB26A3D62B508BDEE0A1695B303E2C...   \n",
       "\n",
       "                                                   SRC_IP  PACKETS  \\\n",
       "0       28598F178AE82C013D63BCB803ECF5076F0ADEAE3CF59A...        3   \n",
       "1       632A7854237232035C4AA219F02B6BFAD2FEDA784DA0A1...        3   \n",
       "2       6231B05C8CCDF8C359D7FBF547D458F4C4675C2298AB84...       10   \n",
       "3       1E5857055DE1CE379453DA517FB660FBFDF0277608ED6A...        4   \n",
       "4       5E0C7366AF8F76A60B5653B5649154FB77AD59EC2F9404...       11   \n",
       "...                                                   ...      ...   \n",
       "923375  6D1F3979A2F28D1020EEE08BA72CEBDAFF30B425094A1F...       68   \n",
       "923376  6D1F3979A2F28D1020EEE08BA72CEBDAFF30B425094A1F...       16   \n",
       "923377  6D1F3979A2F28D1020EEE08BA72CEBDAFF30B425094A1F...       16   \n",
       "923378  6D1F3979A2F28D1020EEE08BA72CEBDAFF30B425094A1F...       16   \n",
       "923379  6D1F3979A2F28D1020EEE08BA72CEBDAFF30B425094A1F...       17   \n",
       "\n",
       "        PACKETS_REV  BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  \\\n",
       "0                 6    120       6128  1.601465e+09  1.601465e+09       443   \n",
       "1                 4    120       2651  1.601465e+09  1.601465e+09       443   \n",
       "2                11    836       6633  1.601465e+09  1.601465e+09       443   \n",
       "3                 2   1970       1360  1.601465e+09  1.601465e+09       443   \n",
       "4                26   3807      25086  1.601465e+09  1.601465e+09       443   \n",
       "...             ...    ...        ...           ...           ...       ...   \n",
       "923375          139  16409     144272  1.601636e+09  1.601636e+09       443   \n",
       "923376           23   2128      16552  1.601636e+09  1.601636e+09       443   \n",
       "923377           22   2489      16500  1.601636e+09  1.601636e+09       443   \n",
       "923378           22   2497      16500  1.601636e+09  1.601636e+09       443   \n",
       "923379           22   2542      16500  1.601636e+09  1.601636e+09       443   \n",
       "\n",
       "                                                 SRC_PORT  MEAN  MEDIAN  \\\n",
       "0       6816FA59C4FC4163C63824257C377845076EAE36F27020...  1041  1234.5   \n",
       "1       7B49C765CFBB935351B2A92A239DC45D3E2C6392237CDB...  1305  1305.5   \n",
       "2       12AFEE6C4C1D38B06C4A1288C0B1DEDF0B8C4FC30C5C8E...   887   907.0   \n",
       "3       F2A6B0CFC97E27F834CC5C925018EB7ACACB33C01075CE...   832   825.5   \n",
       "4       B798731A3011B144DD29B8C473768A4E1AD8A69AE5EA4B...  1201  1520.0   \n",
       "...                                                   ...   ...     ...   \n",
       "923375  681FFB41E89A2E2969E8C21D2FFAB8162BE146D4387DA7...  1162  1468.0   \n",
       "923376  DC5CB4DCDDAB40D31E08EB83A76E7DCDB5B32540F5F5A6...   891  1111.0   \n",
       "923377  192AA6A3A74999CE1FE77403D84361927CB061810B06F3...   909  1110.0   \n",
       "923378  5C875E8A7D5E64C51FE4087F9A8FCCD0E11166E0148DD7...   910  1114.0   \n",
       "923379  C656716B13F91BB097C9CCEDCCCA7B8EEEE207BDBF43C5...   909  1110.5   \n",
       "\n",
       "             STDEV     VAR  BURSTINESS       Q1       Q3   MIN   MAX  \\\n",
       "0       591.677277  350082   -0.275206   721.00  1520.00    94  1520   \n",
       "1       303.347985   92021   -0.622783  1198.25  1412.75  1091  1520   \n",
       "2       677.663633  459228   -0.133790   280.00  1520.00   167  1520   \n",
       "3       645.708138  416939   -0.126068   293.00  1365.00   239  1440   \n",
       "4       493.806642  243845   -0.417271   970.25  1520.00   218  1520   \n",
       "...            ...     ...         ...      ...      ...   ...   ...   \n",
       "923375  443.187319  196416   -0.447806   696.25  1468.00   403  1468   \n",
       "923376  610.040982  372151   -0.187176   353.00  1468.00    66  1468   \n",
       "923377  592.678665  351269   -0.210645   401.75  1468.00    66  1468   \n",
       "923378  592.568983  351138   -0.211259   401.75  1468.00    66  1468   \n",
       "923379  592.664323  351252   -0.210657   401.75  1468.00    66  1468   \n",
       "\n",
       "        MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  \\\n",
       "0                1426  1520                 56.837395                 478   \n",
       "1                 429  1091                 23.245056                 214   \n",
       "2                1353  1520                 76.399508                 632   \n",
       "3                1201   239                 77.609151                 557   \n",
       "4                1302  1520                 41.116290                 410   \n",
       "...               ...   ...                       ...                 ...   \n",
       "923375           1065  1468                 38.140045                 409   \n",
       "923376           1402  1468                 68.467001                 576   \n",
       "923377           1402  1468                 65.201173                 558   \n",
       "923378           1402  1468                 65.117471                 557   \n",
       "923379           1402  1468                 65.199596                 558   \n",
       "\n",
       "        PERCENT_DEVIATION  ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  \\\n",
       "0               45.917387       1173.077292            0.500000   \n",
       "1               16.398467       1323.004346            0.500000   \n",
       "2               71.251409       1090.328219            0.500000   \n",
       "3               66.947115       1002.876114            0.500000   \n",
       "4               34.138218       1294.830668            0.291667   \n",
       "...                   ...               ...                 ...   \n",
       "923375          35.197935       1243.615049            0.330000   \n",
       "923376          64.646465       1071.844438            0.500000   \n",
       "923377          61.386139       1077.743917            0.500000   \n",
       "923378          61.208791       1078.024466            0.500000   \n",
       "923379          61.386139       1077.778827            0.500000   \n",
       "\n",
       "        PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  \\\n",
       "0                 0.500000             -0.809563              3.191774   \n",
       "1                 0.500000              0.705460              8.602332   \n",
       "2                 0.500000             -0.934092              2.588305   \n",
       "3                 0.500000              0.918372              2.587082   \n",
       "4                 0.708333             -0.646002              4.218250   \n",
       "...                    ...                   ...                   ...   \n",
       "923375            0.670000             -0.690453              4.553379   \n",
       "923376            0.500000             -0.945838              2.560484   \n",
       "923377            0.500000             -0.943176              2.728291   \n",
       "923378            0.500000             -0.941663              2.727109   \n",
       "923379            0.500000             -0.943198              2.727514   \n",
       "\n",
       "        FISHER_MI_3_SKEWNESS  FISHER_PEARSON_g1_SKEWNESS  \\\n",
       "0                  -1.343120                1.609252e-09   \n",
       "1                  -6.358928                1.791211e-08   \n",
       "2                  -0.497530                4.016682e-10   \n",
       "3                  -0.957040                1.857211e-09   \n",
       "4                  -1.374769                1.730167e-09   \n",
       "...                      ...                         ...   \n",
       "923375             -0.827026                9.305143e-09   \n",
       "923376             -0.358878                3.523814e-09   \n",
       "923377             -0.389719                4.082828e-09   \n",
       "923378             -0.401924                1.201499e-09   \n",
       "923379             -0.389116                4.323308e-09   \n",
       "\n",
       "        FISHER_PEARSON_G1_SKEWNESS  GALTON_SKEWNESS  KURTOSIS   ENTROPY  \\\n",
       "0                     2.203559e-09         0.198999  1.342036  1.792481   \n",
       "1                     0.000000e+00         0.004662  0.250011  1.000000   \n",
       "2                     5.009683e-10         0.020968  0.776339  2.000000   \n",
       "3                     3.216784e-09        -0.005597  0.576090  2.000000   \n",
       "4                     1.847714e-09         0.160528  2.380351  2.364005   \n",
       "...                            ...              ...       ...       ...   \n",
       "923375                9.447449e-09        -0.206997  1.697841  1.746734   \n",
       "923376                3.816210e-09         0.034978  1.076184  2.660964   \n",
       "923377                4.421609e-09         0.048535  1.121531  2.660964   \n",
       "923378                1.301196e-09         0.046659  1.123477  2.660964   \n",
       "923379                4.682044e-09         0.048535  1.121633  2.660964   \n",
       "\n",
       "        SCALED_ENTROPY  HURST_EXPONENT  BENFORD_LAW_PRESENTED  P_BENFORD  \\\n",
       "0             0.693426        0.483551                  False   0.689545   \n",
       "1             1.000000        0.563329                  False   0.477121   \n",
       "2             0.666667        0.653813                  False   0.727060   \n",
       "3             1.000000        0.410224                  False   0.647940   \n",
       "4             0.515600        0.644854                  False   0.676030   \n",
       "...                ...             ...                    ...        ...   \n",
       "923375        0.262910        0.530712                  False   0.631030   \n",
       "923376        0.615689        0.676477                  False   0.746787   \n",
       "923377        0.615689        0.669291                  False   0.746787   \n",
       "923378        0.615689        0.669937                  False   0.746787   \n",
       "923379        0.615689        0.669372                  False   0.746787   \n",
       "\n",
       "        NORMAL_DISTRIBUTION  CNT_DISTRIBUTION  TIME_DISTRIBUTION  \\\n",
       "0                       0.0               0.0               0.00   \n",
       "1                       0.0               0.0               0.00   \n",
       "2                       0.0               0.0               0.00   \n",
       "3                       0.0               0.0               0.00   \n",
       "4                       0.0               0.0               0.00   \n",
       "...                     ...               ...                ...   \n",
       "923375                  0.0               0.0               0.57   \n",
       "923376                  0.0               0.0               0.00   \n",
       "923377                  0.0               0.0               0.60   \n",
       "923378                  0.0               0.0               0.00   \n",
       "923379                  0.0               0.0               0.00   \n",
       "\n",
       "        AREA_VALUES_DISTRIBUTION  MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  \\\n",
       "0                       0.625000              0.00                 0.0   \n",
       "1                       0.660714              0.00                 0.0   \n",
       "2                       0.625000              0.00                 0.0   \n",
       "3                       0.446429              0.00                 0.0   \n",
       "4                       0.900000              0.00                 0.0   \n",
       "...                          ...               ...                 ...   \n",
       "923375                  0.780303              1.71                 2.0   \n",
       "923376                  0.900000              0.00                 0.0   \n",
       "923377                  0.900000              0.60                 1.0   \n",
       "923378                  0.900000              0.00                 0.0   \n",
       "923379                  0.900000              0.00                 0.0   \n",
       "\n",
       "        Q1_SCALED_TIME  Q3_SCALED_TIME  DURATION  MEAN_DIFFTIMES  \\\n",
       "0                  0.0             0.0       0.0        0.000000   \n",
       "1                  0.0             0.0       0.0        0.000000   \n",
       "2                  0.0             0.0       0.0        0.000000   \n",
       "3                  0.0             0.0       0.0        0.000000   \n",
       "4                  0.0             0.0       0.0        0.000000   \n",
       "...                ...             ...       ...             ...   \n",
       "923375             1.0             2.0       3.0        0.030303   \n",
       "923376             0.0             0.0       0.0        0.000000   \n",
       "923377             0.0             1.0       1.0        0.052632   \n",
       "923378             0.0             0.0       0.0        0.000000   \n",
       "923379             0.0             0.0       0.0        0.000000   \n",
       "\n",
       "        MEDIAN_DIFFTIMES  MIN_DIFFTIMES  MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  \\\n",
       "0                    0.0            0.0            0.0               0.000000   \n",
       "1                    0.0            0.0            0.0               0.000000   \n",
       "2                    0.0            0.0            0.0               0.000000   \n",
       "3                    0.0            0.0            0.0               0.000000   \n",
       "4                    0.0            0.0            0.0               0.000000   \n",
       "...                  ...            ...            ...                    ...   \n",
       "923375               0.0            0.0            1.0               0.030303   \n",
       "923376               0.0            0.0            0.0               0.000000   \n",
       "923377               0.0            0.0            1.0               0.052632   \n",
       "923378               0.0            0.0            0.0               0.000000   \n",
       "923379               0.0            0.0            0.0               0.000000   \n",
       "\n",
       "        SIG_SPACES  SWITCHING_METRIC  TRANSIENTS  CNT_ZEROS  \\\n",
       "0            False          1.600000        True        0.0   \n",
       "1            False          2.000000       False        0.0   \n",
       "2            False          1.142857        True        0.0   \n",
       "3            False          2.000000        True        0.0   \n",
       "4            False          1.130435        True        0.0   \n",
       "...            ...               ...         ...        ...   \n",
       "923375       False          0.989899       False        0.0   \n",
       "923376       False          1.263158        True        0.0   \n",
       "923377       False          1.263158        True        0.0   \n",
       "923378       False          1.263158        True        0.0   \n",
       "923379       False          1.263158        True        0.0   \n",
       "\n",
       "        CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  DIRECTIONS  PERIODICITY  VAL  \\\n",
       "0                       0.0               6248    0.000000        False    0   \n",
       "1                       0.0               2611    0.000000        False    0   \n",
       "2                       0.0               7097    0.250000        False    0   \n",
       "3                       0.0               3330    0.750000        False    0   \n",
       "4                       0.0              28829    0.208333        False    0   \n",
       "...                     ...                ...         ...          ...  ...   \n",
       "923375                  0.0             116281    0.160000        False    0   \n",
       "923376                  0.0              17836    0.300000        False    0   \n",
       "923377                  0.0              18197    0.300000        False    0   \n",
       "923378                  0.0              18205    0.300000        False    0   \n",
       "923379                  0.0              18198    0.300000        False    0   \n",
       "\n",
       "        TIME     MIN_POWER  MAX_POWER  MIN_POWER_FREQ  MAX_POWER_FREQ  \\\n",
       "0        0.0 -1.544352e-15  -1.000000        0.589753       -1.000000   \n",
       "1        0.0 -0.000000e+00  -1.000000        0.006667       -1.000000   \n",
       "2        0.0 -0.000000e+00  -1.000000        0.006667       -1.000000   \n",
       "3        0.0 -0.000000e+00  -1.000000        0.006865       -1.000000   \n",
       "4        0.0  1.241238e-16  -1.000000        0.158249       -1.000000   \n",
       "...      ...           ...        ...             ...             ...   \n",
       "923375   0.0  7.079699e-06   0.001137        0.999801        0.928877   \n",
       "923376   0.0 -8.428328e-17  -1.000000        0.284601       -1.000000   \n",
       "923377   0.0 -1.381004e+00   2.105539        0.509492        0.654717   \n",
       "923378   0.0 -1.139697e-16  -1.000000        0.897091       -1.000000   \n",
       "923379   0.0 -5.257642e-16  -1.000000        0.708755       -1.000000   \n",
       "\n",
       "        POWER_MEAN  POWER_STD  POWER_MODE  SPECTRAL_ENERGY  SPECTRAL_ENTROPY  \\\n",
       "0        -1.000000  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "1        -1.000000  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "2        -1.000000  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "3        -1.000000  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "4        -1.000000  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "...            ...        ...         ...              ...               ...   \n",
       "923375    0.000613   0.000311     0.00099         3.064153         32.005922   \n",
       "923376   -1.000000  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "923377    0.196791   0.045178     0.20358       983.954386         -1.000000   \n",
       "923378   -1.000000  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "923379   -1.000000  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "\n",
       "        SPECTRAL_KURTOSIS  SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  \\\n",
       "0           -1.000000e+00          -1.000000          0.006667   \n",
       "1           -1.000000e+00          -1.000000          0.006667   \n",
       "2           -1.000000e+00          -1.000000          0.006667   \n",
       "3           -1.000000e+00          -1.000000          0.006667   \n",
       "4           -1.000000e+00          -1.000000          0.006667   \n",
       "...                   ...                ...               ...   \n",
       "923375       9.422625e+03       -2061.982151          0.006667   \n",
       "923376      -1.000000e+00          -1.000000          0.006667   \n",
       "923377       4.725432e+06       26161.139938          0.654717   \n",
       "923378      -1.000000e+00          -1.000000          0.006667   \n",
       "923379      -1.000000e+00          -1.000000          0.006667   \n",
       "\n",
       "        SPECTRAL_CENTROID  SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  \\\n",
       "0               -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "1               -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "2               -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "3               -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "4               -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "...                   ...              ...             ...             ...   \n",
       "923375           0.498886         0.327262         -0.1827        1.855315   \n",
       "923376          -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "923377           0.487634         0.278471         -1.0000       10.699371   \n",
       "923378          -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "923379          -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "\n",
       "        SPECTRAL_FLUX  SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  LABEL_code  \n",
       "0           -1.000000            0.000000             False  False           0  \n",
       "1           -1.000000            0.000000             False  False           0  \n",
       "2           -1.000000            0.000000             False  False           0  \n",
       "3           -1.000000            0.000000             False  False           0  \n",
       "4           -1.000000            0.000000             False  False           0  \n",
       "...               ...                 ...               ...    ...         ...  \n",
       "923375       0.005522           -0.070924              True   True           1  \n",
       "923376      -1.000000            0.000000             False   True           1  \n",
       "923377       8.491211            0.145225              True   True           1  \n",
       "923378      -1.000000            0.000000             False   True           1  \n",
       "923379      -1.000000            0.000000             False   True           1  \n",
       "\n",
       "[923380 rows x 85 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tor: 39530\tclear: 883850\n"
     ]
    }
   ],
   "source": [
    "print(f\"tor: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"LABEL_code\"] + features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_dataset(df):\n",
    "    assert isinstance(df, pd.DataFrame), \"df needs to be a pd.DataFrame\"\n",
    "    df.dropna(inplace=True)\n",
    "    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)\n",
    "    return df[indices_to_keep].astype(np.float64)\n",
    "\n",
    "df = clean_dataset(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tor: 39530\tclear: 883850\n"
     ]
    }
   ],
   "source": [
    "print(f\"tor: {len(df[df['LABEL_code'] == 1])}\\tclear: {len(df[df['LABEL_code'] == 0])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = df.sample(n = 50000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LABEL_code</th>\n",
       "      <th>PACKETS</th>\n",
       "      <th>PACKETS_REV</th>\n",
       "      <th>BYTES</th>\n",
       "      <th>BYTES_REV</th>\n",
       "      <th>MEAN</th>\n",
       "      <th>MEDIAN</th>\n",
       "      <th>STDEV</th>\n",
       "      <th>VAR</th>\n",
       "      <th>BURSTINESS</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q3</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>MIN_MINUS_MAX</th>\n",
       "      <th>MODE</th>\n",
       "      <th>COEFFICIENT_OF_VARIATION</th>\n",
       "      <th>AVERAGE_DISPERSION</th>\n",
       "      <th>PERCENT_DEVIATION</th>\n",
       "      <th>ROOT_MEAN_SQUARE</th>\n",
       "      <th>PERCENT_BELOW_MEAN</th>\n",
       "      <th>PERCENT_ABOVE_MEAN</th>\n",
       "      <th>PEARSON_SK1_SKEWNESS</th>\n",
       "      <th>PEARSON_SK2_SKEWNESS</th>\n",
       "      <th>FISHER_MI_3_SKEWNESS</th>\n",
       "      <th>FISHER_PEARSON_g1_SKEWNESS</th>\n",
       "      <th>FISHER_PEARSON_G1_SKEWNESS</th>\n",
       "      <th>GALTON_SKEWNESS</th>\n",
       "      <th>KURTOSIS</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>SCALED_ENTROPY</th>\n",
       "      <th>HURST_EXPONENT</th>\n",
       "      <th>BENFORD_LAW_PRESENTED</th>\n",
       "      <th>P_BENFORD</th>\n",
       "      <th>NORMAL_DISTRIBUTION</th>\n",
       "      <th>CNT_DISTRIBUTION</th>\n",
       "      <th>TIME_DISTRIBUTION</th>\n",
       "      <th>AREA_VALUES_DISTRIBUTION</th>\n",
       "      <th>MEAN_SCALED_TIME</th>\n",
       "      <th>MEDIAN_SCALED_TIME</th>\n",
       "      <th>Q1_SCALED_TIME</th>\n",
       "      <th>Q3_SCALED_TIME</th>\n",
       "      <th>DURATION</th>\n",
       "      <th>MEAN_DIFFTIMES</th>\n",
       "      <th>MEDIAN_DIFFTIMES</th>\n",
       "      <th>MIN_DIFFTIMES</th>\n",
       "      <th>MAX_DIFFTIMES</th>\n",
       "      <th>MEAN_SCALED_DIFFTIMES</th>\n",
       "      <th>SIG_SPACES</th>\n",
       "      <th>SWITCHING_METRIC</th>\n",
       "      <th>DIRECTIONS</th>\n",
       "      <th>TRANSIENTS</th>\n",
       "      <th>CNT_ZEROS</th>\n",
       "      <th>CNT_NZ_DISTRIBUTION</th>\n",
       "      <th>BIGGEST_CNT_1_SEC</th>\n",
       "      <th>PERIODICITY</th>\n",
       "      <th>VAL</th>\n",
       "      <th>TIME</th>\n",
       "      <th>MIN_POWER</th>\n",
       "      <th>MAX_POWER</th>\n",
       "      <th>MIN_POWER_FREQ</th>\n",
       "      <th>MAX_POWER_FREQ</th>\n",
       "      <th>POWER_MEAN</th>\n",
       "      <th>POWER_STD</th>\n",
       "      <th>POWER_MODE</th>\n",
       "      <th>SPECTRAL_ENERGY</th>\n",
       "      <th>SPECTRAL_ENTROPY</th>\n",
       "      <th>SPECTRAL_KURTOSIS</th>\n",
       "      <th>SPECTRAL_SKEWNESS</th>\n",
       "      <th>SPECTRAL_ROLLOFF</th>\n",
       "      <th>SPECTRAL_CENTROID</th>\n",
       "      <th>SPECTRAL_SPREAD</th>\n",
       "      <th>SPECTRAL_SLOPE</th>\n",
       "      <th>SPECTRAL_CREST</th>\n",
       "      <th>SPECTRAL_FLUX</th>\n",
       "      <th>SPECTRAL_BANDWIDTH</th>\n",
       "      <th>PERIODICITY_SCDF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>6128.0</td>\n",
       "      <td>1041.0</td>\n",
       "      <td>1234.5</td>\n",
       "      <td>591.677277</td>\n",
       "      <td>350082.0</td>\n",
       "      <td>-0.275206</td>\n",
       "      <td>721.00</td>\n",
       "      <td>1520.00</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>1426.0</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>56.837395</td>\n",
       "      <td>478.0</td>\n",
       "      <td>45.917387</td>\n",
       "      <td>1173.077292</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.809563</td>\n",
       "      <td>3.191774</td>\n",
       "      <td>-1.343120</td>\n",
       "      <td>1.609252e-09</td>\n",
       "      <td>2.203559e-09</td>\n",
       "      <td>0.198999</td>\n",
       "      <td>1.342036</td>\n",
       "      <td>1.792481</td>\n",
       "      <td>0.693426</td>\n",
       "      <td>0.483551</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.689545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6248.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.544352e-15</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.589753</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2651.0</td>\n",
       "      <td>1305.0</td>\n",
       "      <td>1305.5</td>\n",
       "      <td>303.347985</td>\n",
       "      <td>92021.0</td>\n",
       "      <td>-0.622783</td>\n",
       "      <td>1198.25</td>\n",
       "      <td>1412.75</td>\n",
       "      <td>1091.0</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>1091.0</td>\n",
       "      <td>23.245056</td>\n",
       "      <td>214.0</td>\n",
       "      <td>16.398467</td>\n",
       "      <td>1323.004346</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.705460</td>\n",
       "      <td>8.602332</td>\n",
       "      <td>-6.358928</td>\n",
       "      <td>1.791211e-08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.004662</td>\n",
       "      <td>0.250011</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.563329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.660714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2611.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>6633.0</td>\n",
       "      <td>887.0</td>\n",
       "      <td>907.0</td>\n",
       "      <td>677.663633</td>\n",
       "      <td>459228.0</td>\n",
       "      <td>-0.133790</td>\n",
       "      <td>280.00</td>\n",
       "      <td>1520.00</td>\n",
       "      <td>167.0</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>1353.0</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>76.399508</td>\n",
       "      <td>632.0</td>\n",
       "      <td>71.251409</td>\n",
       "      <td>1090.328219</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.934092</td>\n",
       "      <td>2.588305</td>\n",
       "      <td>-0.497530</td>\n",
       "      <td>4.016682e-10</td>\n",
       "      <td>5.009683e-10</td>\n",
       "      <td>0.020968</td>\n",
       "      <td>0.776339</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.653813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.727060</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7097.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3807.0</td>\n",
       "      <td>25086.0</td>\n",
       "      <td>1201.0</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>493.806642</td>\n",
       "      <td>243845.0</td>\n",
       "      <td>-0.417271</td>\n",
       "      <td>970.25</td>\n",
       "      <td>1520.00</td>\n",
       "      <td>218.0</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>1302.0</td>\n",
       "      <td>1520.0</td>\n",
       "      <td>41.116290</td>\n",
       "      <td>410.0</td>\n",
       "      <td>34.138218</td>\n",
       "      <td>1294.830668</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>-0.646002</td>\n",
       "      <td>4.218250</td>\n",
       "      <td>-1.374769</td>\n",
       "      <td>1.730167e-09</td>\n",
       "      <td>1.847714e-09</td>\n",
       "      <td>0.160528</td>\n",
       "      <td>2.380351</td>\n",
       "      <td>2.364005</td>\n",
       "      <td>0.515600</td>\n",
       "      <td>0.644854</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.676030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.130435</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28829.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.241238e-16</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.158249</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2591.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>495.0</td>\n",
       "      <td>264.0</td>\n",
       "      <td>528.906419</td>\n",
       "      <td>279743.0</td>\n",
       "      <td>0.033115</td>\n",
       "      <td>246.00</td>\n",
       "      <td>311.00</td>\n",
       "      <td>218.0</td>\n",
       "      <td>1440.0</td>\n",
       "      <td>1222.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>106.849782</td>\n",
       "      <td>377.0</td>\n",
       "      <td>76.161616</td>\n",
       "      <td>685.281986</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.523722</td>\n",
       "      <td>2.308537</td>\n",
       "      <td>0.508511</td>\n",
       "      <td>5.406964e-09</td>\n",
       "      <td>8.060226e-09</td>\n",
       "      <td>-6.661538</td>\n",
       "      <td>2.073255</td>\n",
       "      <td>2.321928</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.469820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.677121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.446429</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2479.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.268362e-17</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.356121</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923374</th>\n",
       "      <td>1.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>56972.0</td>\n",
       "      <td>493405.0</td>\n",
       "      <td>1118.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>480.880443</td>\n",
       "      <td>231246.0</td>\n",
       "      <td>-0.398479</td>\n",
       "      <td>535.00</td>\n",
       "      <td>1468.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>43.012562</td>\n",
       "      <td>447.0</td>\n",
       "      <td>39.982111</td>\n",
       "      <td>1216.175111</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>-0.727832</td>\n",
       "      <td>3.921973</td>\n",
       "      <td>-0.792271</td>\n",
       "      <td>8.992670e-10</td>\n",
       "      <td>9.130198e-10</td>\n",
       "      <td>-0.249732</td>\n",
       "      <td>1.719770</td>\n",
       "      <td>2.130827</td>\n",
       "      <td>0.320721</td>\n",
       "      <td>0.552064</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.626030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>3.24</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.050505</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111810.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.191150e+00</td>\n",
       "      <td>4.864823</td>\n",
       "      <td>0.955101</td>\n",
       "      <td>0.954903</td>\n",
       "      <td>0.033575</td>\n",
       "      <td>0.113403</td>\n",
       "      <td>0.01311</td>\n",
       "      <td>167.873978</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>7.912844e+06</td>\n",
       "      <td>-20176.874736</td>\n",
       "      <td>0.954903</td>\n",
       "      <td>0.480599</td>\n",
       "      <td>0.322661</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>144.895087</td>\n",
       "      <td>22.454121</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923375</th>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>16409.0</td>\n",
       "      <td>144272.0</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>443.187319</td>\n",
       "      <td>196416.0</td>\n",
       "      <td>-0.447806</td>\n",
       "      <td>696.25</td>\n",
       "      <td>1468.00</td>\n",
       "      <td>403.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>38.140045</td>\n",
       "      <td>409.0</td>\n",
       "      <td>35.197935</td>\n",
       "      <td>1243.615049</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.670000</td>\n",
       "      <td>-0.690453</td>\n",
       "      <td>4.553379</td>\n",
       "      <td>-0.827026</td>\n",
       "      <td>9.305143e-09</td>\n",
       "      <td>9.447449e-09</td>\n",
       "      <td>-0.206997</td>\n",
       "      <td>1.697841</td>\n",
       "      <td>1.746734</td>\n",
       "      <td>0.262910</td>\n",
       "      <td>0.530712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.631030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.780303</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.030303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.989899</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116281.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.079699e-06</td>\n",
       "      <td>0.001137</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.928877</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>0.000311</td>\n",
       "      <td>0.00099</td>\n",
       "      <td>3.064153</td>\n",
       "      <td>32.005922</td>\n",
       "      <td>9.422625e+03</td>\n",
       "      <td>-2061.982151</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.498886</td>\n",
       "      <td>0.327262</td>\n",
       "      <td>-0.1827</td>\n",
       "      <td>1.855315</td>\n",
       "      <td>0.005522</td>\n",
       "      <td>-0.070924</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923376</th>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2128.0</td>\n",
       "      <td>16552.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>1111.0</td>\n",
       "      <td>610.040982</td>\n",
       "      <td>372151.0</td>\n",
       "      <td>-0.187176</td>\n",
       "      <td>353.00</td>\n",
       "      <td>1468.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>68.467001</td>\n",
       "      <td>576.0</td>\n",
       "      <td>64.646465</td>\n",
       "      <td>1071.844438</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.945838</td>\n",
       "      <td>2.560484</td>\n",
       "      <td>-0.358878</td>\n",
       "      <td>3.523814e-09</td>\n",
       "      <td>3.816210e-09</td>\n",
       "      <td>0.034978</td>\n",
       "      <td>1.076184</td>\n",
       "      <td>2.660964</td>\n",
       "      <td>0.615689</td>\n",
       "      <td>0.676477</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.746787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.263158</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17836.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.428328e-17</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.284601</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923378</th>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2497.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>592.568983</td>\n",
       "      <td>351138.0</td>\n",
       "      <td>-0.211259</td>\n",
       "      <td>401.75</td>\n",
       "      <td>1468.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>65.117471</td>\n",
       "      <td>557.0</td>\n",
       "      <td>61.208791</td>\n",
       "      <td>1078.024466</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.941663</td>\n",
       "      <td>2.727109</td>\n",
       "      <td>-0.401924</td>\n",
       "      <td>1.201499e-09</td>\n",
       "      <td>1.301196e-09</td>\n",
       "      <td>0.046659</td>\n",
       "      <td>1.123477</td>\n",
       "      <td>2.660964</td>\n",
       "      <td>0.615689</td>\n",
       "      <td>0.669937</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.746787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.263158</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18205.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.139697e-16</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.897091</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923379</th>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2542.0</td>\n",
       "      <td>16500.0</td>\n",
       "      <td>909.0</td>\n",
       "      <td>1110.5</td>\n",
       "      <td>592.664323</td>\n",
       "      <td>351252.0</td>\n",
       "      <td>-0.210657</td>\n",
       "      <td>401.75</td>\n",
       "      <td>1468.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>1402.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>65.199596</td>\n",
       "      <td>558.0</td>\n",
       "      <td>61.386139</td>\n",
       "      <td>1077.778827</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.943198</td>\n",
       "      <td>2.727514</td>\n",
       "      <td>-0.389116</td>\n",
       "      <td>4.323308e-09</td>\n",
       "      <td>4.682044e-09</td>\n",
       "      <td>0.048535</td>\n",
       "      <td>1.121633</td>\n",
       "      <td>2.660964</td>\n",
       "      <td>0.615689</td>\n",
       "      <td>0.669372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.746787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.263158</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18198.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.257642e-16</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.708755</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>873380 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        LABEL_code  PACKETS  PACKETS_REV    BYTES  BYTES_REV    MEAN  MEDIAN  \\\n",
       "0              0.0      3.0          6.0    120.0     6128.0  1041.0  1234.5   \n",
       "1              0.0      3.0          4.0    120.0     2651.0  1305.0  1305.5   \n",
       "2              0.0     10.0         11.0    836.0     6633.0   887.0   907.0   \n",
       "4              0.0     11.0         26.0   3807.0    25086.0  1201.0  1520.0   \n",
       "5              0.0     10.0          0.0   2591.0        0.0   495.0   264.0   \n",
       "...            ...      ...          ...      ...        ...     ...     ...   \n",
       "923374         1.0    217.0        474.0  56972.0   493405.0  1118.0  1468.0   \n",
       "923375         1.0     68.0        139.0  16409.0   144272.0  1162.0  1468.0   \n",
       "923376         1.0     16.0         23.0   2128.0    16552.0   891.0  1111.0   \n",
       "923378         1.0     16.0         22.0   2497.0    16500.0   910.0  1114.0   \n",
       "923379         1.0     17.0         22.0   2542.0    16500.0   909.0  1110.5   \n",
       "\n",
       "             STDEV       VAR  BURSTINESS       Q1       Q3     MIN     MAX  \\\n",
       "0       591.677277  350082.0   -0.275206   721.00  1520.00    94.0  1520.0   \n",
       "1       303.347985   92021.0   -0.622783  1198.25  1412.75  1091.0  1520.0   \n",
       "2       677.663633  459228.0   -0.133790   280.00  1520.00   167.0  1520.0   \n",
       "4       493.806642  243845.0   -0.417271   970.25  1520.00   218.0  1520.0   \n",
       "5       528.906419  279743.0    0.033115   246.00   311.00   218.0  1440.0   \n",
       "...            ...       ...         ...      ...      ...     ...     ...   \n",
       "923374  480.880443  231246.0   -0.398479   535.00  1468.00    66.0  1468.0   \n",
       "923375  443.187319  196416.0   -0.447806   696.25  1468.00   403.0  1468.0   \n",
       "923376  610.040982  372151.0   -0.187176   353.00  1468.00    66.0  1468.0   \n",
       "923378  592.568983  351138.0   -0.211259   401.75  1468.00    66.0  1468.0   \n",
       "923379  592.664323  351252.0   -0.210657   401.75  1468.00    66.0  1468.0   \n",
       "\n",
       "        MIN_MINUS_MAX    MODE  COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  \\\n",
       "0              1426.0  1520.0                 56.837395               478.0   \n",
       "1               429.0  1091.0                 23.245056               214.0   \n",
       "2              1353.0  1520.0                 76.399508               632.0   \n",
       "4              1302.0  1520.0                 41.116290               410.0   \n",
       "5              1222.0   218.0                106.849782               377.0   \n",
       "...               ...     ...                       ...                 ...   \n",
       "923374         1402.0  1468.0                 43.012562               447.0   \n",
       "923375         1065.0  1468.0                 38.140045               409.0   \n",
       "923376         1402.0  1468.0                 68.467001               576.0   \n",
       "923378         1402.0  1468.0                 65.117471               557.0   \n",
       "923379         1402.0  1468.0                 65.199596               558.0   \n",
       "\n",
       "        PERCENT_DEVIATION  ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  \\\n",
       "0               45.917387       1173.077292            0.500000   \n",
       "1               16.398467       1323.004346            0.500000   \n",
       "2               71.251409       1090.328219            0.500000   \n",
       "4               34.138218       1294.830668            0.291667   \n",
       "5               76.161616        685.281986            0.800000   \n",
       "...                   ...               ...                 ...   \n",
       "923374          39.982111       1216.175111            0.360000   \n",
       "923375          35.197935       1243.615049            0.330000   \n",
       "923376          64.646465       1071.844438            0.500000   \n",
       "923378          61.208791       1078.024466            0.500000   \n",
       "923379          61.386139       1077.778827            0.500000   \n",
       "\n",
       "        PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  \\\n",
       "0                 0.500000             -0.809563              3.191774   \n",
       "1                 0.500000              0.705460              8.602332   \n",
       "2                 0.500000             -0.934092              2.588305   \n",
       "4                 0.708333             -0.646002              4.218250   \n",
       "5                 0.200000              0.523722              2.308537   \n",
       "...                    ...                   ...                   ...   \n",
       "923374            0.640000             -0.727832              3.921973   \n",
       "923375            0.670000             -0.690453              4.553379   \n",
       "923376            0.500000             -0.945838              2.560484   \n",
       "923378            0.500000             -0.941663              2.727109   \n",
       "923379            0.500000             -0.943198              2.727514   \n",
       "\n",
       "        FISHER_MI_3_SKEWNESS  FISHER_PEARSON_g1_SKEWNESS  \\\n",
       "0                  -1.343120                1.609252e-09   \n",
       "1                  -6.358928                1.791211e-08   \n",
       "2                  -0.497530                4.016682e-10   \n",
       "4                  -1.374769                1.730167e-09   \n",
       "5                   0.508511                5.406964e-09   \n",
       "...                      ...                         ...   \n",
       "923374             -0.792271                8.992670e-10   \n",
       "923375             -0.827026                9.305143e-09   \n",
       "923376             -0.358878                3.523814e-09   \n",
       "923378             -0.401924                1.201499e-09   \n",
       "923379             -0.389116                4.323308e-09   \n",
       "\n",
       "        FISHER_PEARSON_G1_SKEWNESS  GALTON_SKEWNESS  KURTOSIS   ENTROPY  \\\n",
       "0                     2.203559e-09         0.198999  1.342036  1.792481   \n",
       "1                     0.000000e+00         0.004662  0.250011  1.000000   \n",
       "2                     5.009683e-10         0.020968  0.776339  2.000000   \n",
       "4                     1.847714e-09         0.160528  2.380351  2.364005   \n",
       "5                     8.060226e-09        -6.661538  2.073255  2.321928   \n",
       "...                            ...              ...       ...       ...   \n",
       "923374                9.130198e-10        -0.249732  1.719770  2.130827   \n",
       "923375                9.447449e-09        -0.206997  1.697841  1.746734   \n",
       "923376                3.816210e-09         0.034978  1.076184  2.660964   \n",
       "923378                1.301196e-09         0.046659  1.123477  2.660964   \n",
       "923379                4.682044e-09         0.048535  1.121633  2.660964   \n",
       "\n",
       "        SCALED_ENTROPY  HURST_EXPONENT  BENFORD_LAW_PRESENTED  P_BENFORD  \\\n",
       "0             0.693426        0.483551                    0.0   0.689545   \n",
       "1             1.000000        0.563329                    0.0   0.477121   \n",
       "2             0.666667        0.653813                    0.0   0.727060   \n",
       "4             0.515600        0.644854                    0.0   0.676030   \n",
       "5             1.000000        0.469820                    0.0   0.677121   \n",
       "...                ...             ...                    ...        ...   \n",
       "923374        0.320721        0.552064                    0.0   0.626030   \n",
       "923375        0.262910        0.530712                    0.0   0.631030   \n",
       "923376        0.615689        0.676477                    0.0   0.746787   \n",
       "923378        0.615689        0.669937                    0.0   0.746787   \n",
       "923379        0.615689        0.669372                    0.0   0.746787   \n",
       "\n",
       "        NORMAL_DISTRIBUTION  CNT_DISTRIBUTION  TIME_DISTRIBUTION  \\\n",
       "0                       0.0               0.0               0.00   \n",
       "1                       0.0               0.0               0.00   \n",
       "2                       0.0               0.0               0.00   \n",
       "4                       0.0               0.0               0.00   \n",
       "5                       0.0               0.0               0.00   \n",
       "...                     ...               ...                ...   \n",
       "923374                  0.0               0.0               0.54   \n",
       "923375                  0.0               0.0               0.57   \n",
       "923376                  0.0               0.0               0.00   \n",
       "923378                  0.0               0.0               0.00   \n",
       "923379                  0.0               0.0               0.00   \n",
       "\n",
       "        AREA_VALUES_DISTRIBUTION  MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  \\\n",
       "0                       0.625000              0.00                 0.0   \n",
       "1                       0.660714              0.00                 0.0   \n",
       "2                       0.625000              0.00                 0.0   \n",
       "4                       0.900000              0.00                 0.0   \n",
       "5                       0.446429              0.00                 0.0   \n",
       "...                          ...               ...                 ...   \n",
       "923374                  0.888889              3.24                 3.0   \n",
       "923375                  0.780303              1.71                 2.0   \n",
       "923376                  0.900000              0.00                 0.0   \n",
       "923378                  0.900000              0.00                 0.0   \n",
       "923379                  0.900000              0.00                 0.0   \n",
       "\n",
       "        Q1_SCALED_TIME  Q3_SCALED_TIME  DURATION  MEAN_DIFFTIMES  \\\n",
       "0                  0.0             0.0       0.0        0.000000   \n",
       "1                  0.0             0.0       0.0        0.000000   \n",
       "2                  0.0             0.0       0.0        0.000000   \n",
       "4                  0.0             0.0       0.0        0.000000   \n",
       "5                  0.0             0.0       0.0        0.000000   \n",
       "...                ...             ...       ...             ...   \n",
       "923374             2.0             5.0       6.0        0.060606   \n",
       "923375             1.0             2.0       3.0        0.030303   \n",
       "923376             0.0             0.0       0.0        0.000000   \n",
       "923378             0.0             0.0       0.0        0.000000   \n",
       "923379             0.0             0.0       0.0        0.000000   \n",
       "\n",
       "        MEDIAN_DIFFTIMES  MIN_DIFFTIMES  MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  \\\n",
       "0                    0.0            0.0            0.0               0.000000   \n",
       "1                    0.0            0.0            0.0               0.000000   \n",
       "2                    0.0            0.0            0.0               0.000000   \n",
       "4                    0.0            0.0            0.0               0.000000   \n",
       "5                    0.0            0.0            0.0               0.000000   \n",
       "...                  ...            ...            ...                    ...   \n",
       "923374               0.0            0.0            1.0               0.060606   \n",
       "923375               0.0            0.0            1.0               0.030303   \n",
       "923376               0.0            0.0            0.0               0.000000   \n",
       "923378               0.0            0.0            0.0               0.000000   \n",
       "923379               0.0            0.0            0.0               0.000000   \n",
       "\n",
       "        SIG_SPACES  SWITCHING_METRIC  DIRECTIONS  TRANSIENTS  CNT_ZEROS  \\\n",
       "0              0.0          1.600000    0.000000         1.0        0.0   \n",
       "1              0.0          2.000000    0.000000         0.0        0.0   \n",
       "2              0.0          1.142857    0.250000         1.0        0.0   \n",
       "4              0.0          1.130435    0.208333         1.0        0.0   \n",
       "5              0.0          2.000000    1.000000         1.0        0.0   \n",
       "...            ...               ...         ...         ...        ...   \n",
       "923374         0.0          1.050505    0.190000         0.0        0.0   \n",
       "923375         0.0          0.989899    0.160000         0.0        0.0   \n",
       "923376         0.0          1.263158    0.300000         1.0        0.0   \n",
       "923378         0.0          1.263158    0.300000         1.0        0.0   \n",
       "923379         0.0          1.263158    0.300000         1.0        0.0   \n",
       "\n",
       "        CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  PERIODICITY  VAL  TIME  \\\n",
       "0                       0.0             6248.0          0.0  0.0   0.0   \n",
       "1                       0.0             2611.0          0.0  0.0   0.0   \n",
       "2                       0.0             7097.0          0.0  0.0   0.0   \n",
       "4                       0.0            28829.0          0.0  0.0   0.0   \n",
       "5                       0.0             2479.0          0.0  0.0   0.0   \n",
       "...                     ...                ...          ...  ...   ...   \n",
       "923374                  0.0           111810.0          0.0  0.0   0.0   \n",
       "923375                  0.0           116281.0          0.0  0.0   0.0   \n",
       "923376                  0.0            17836.0          0.0  0.0   0.0   \n",
       "923378                  0.0            18205.0          0.0  0.0   0.0   \n",
       "923379                  0.0            18198.0          0.0  0.0   0.0   \n",
       "\n",
       "           MIN_POWER  MAX_POWER  MIN_POWER_FREQ  MAX_POWER_FREQ  POWER_MEAN  \\\n",
       "0      -1.544352e-15  -1.000000        0.589753       -1.000000   -1.000000   \n",
       "1      -0.000000e+00  -1.000000        0.006667       -1.000000   -1.000000   \n",
       "2      -0.000000e+00  -1.000000        0.006667       -1.000000   -1.000000   \n",
       "4       1.241238e-16  -1.000000        0.158249       -1.000000   -1.000000   \n",
       "5      -1.268362e-17  -1.000000        0.356121       -1.000000   -1.000000   \n",
       "...              ...        ...             ...             ...         ...   \n",
       "923374 -5.191150e+00   4.864823        0.955101        0.954903    0.033575   \n",
       "923375  7.079699e-06   0.001137        0.999801        0.928877    0.000613   \n",
       "923376 -8.428328e-17  -1.000000        0.284601       -1.000000   -1.000000   \n",
       "923378 -1.139697e-16  -1.000000        0.897091       -1.000000   -1.000000   \n",
       "923379 -5.257642e-16  -1.000000        0.708755       -1.000000   -1.000000   \n",
       "\n",
       "        POWER_STD  POWER_MODE  SPECTRAL_ENERGY  SPECTRAL_ENTROPY  \\\n",
       "0       -1.000000     0.00000        -1.000000         -1.000000   \n",
       "1       -1.000000     0.00000        -1.000000         -1.000000   \n",
       "2       -1.000000     0.00000        -1.000000         -1.000000   \n",
       "4       -1.000000     0.00000        -1.000000         -1.000000   \n",
       "5       -1.000000     0.00000        -1.000000         -1.000000   \n",
       "...           ...         ...              ...               ...   \n",
       "923374   0.113403     0.01311       167.873978         -1.000000   \n",
       "923375   0.000311     0.00099         3.064153         32.005922   \n",
       "923376  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "923378  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "923379  -1.000000     0.00000        -1.000000         -1.000000   \n",
       "\n",
       "        SPECTRAL_KURTOSIS  SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  \\\n",
       "0           -1.000000e+00          -1.000000          0.006667   \n",
       "1           -1.000000e+00          -1.000000          0.006667   \n",
       "2           -1.000000e+00          -1.000000          0.006667   \n",
       "4           -1.000000e+00          -1.000000          0.006667   \n",
       "5           -1.000000e+00          -1.000000          0.006667   \n",
       "...                   ...                ...               ...   \n",
       "923374       7.912844e+06      -20176.874736          0.954903   \n",
       "923375       9.422625e+03       -2061.982151          0.006667   \n",
       "923376      -1.000000e+00          -1.000000          0.006667   \n",
       "923378      -1.000000e+00          -1.000000          0.006667   \n",
       "923379      -1.000000e+00          -1.000000          0.006667   \n",
       "\n",
       "        SPECTRAL_CENTROID  SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  \\\n",
       "0               -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "1               -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "2               -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "4               -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "5               -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "...                   ...              ...             ...             ...   \n",
       "923374           0.480599         0.322661         -1.0000      144.895087   \n",
       "923375           0.498886         0.327262         -0.1827        1.855315   \n",
       "923376          -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "923378          -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "923379          -1.000000        -1.000000         -1.0000       -1.000000   \n",
       "\n",
       "        SPECTRAL_FLUX  SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  \n",
       "0           -1.000000            0.000000               0.0  \n",
       "1           -1.000000            0.000000               0.0  \n",
       "2           -1.000000            0.000000               0.0  \n",
       "4           -1.000000            0.000000               0.0  \n",
       "5           -1.000000            0.000000               0.0  \n",
       "...               ...                 ...               ...  \n",
       "923374      22.454121           -0.000199               1.0  \n",
       "923375       0.005522           -0.070924               1.0  \n",
       "923376      -1.000000            0.000000               0.0  \n",
       "923378      -1.000000            0.000000               0.0  \n",
       "923379      -1.000000            0.000000               0.0  \n",
       "\n",
       "[873380 rows x 77 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_confusion_matrix(y_test, y_pred):\n",
    "    # Get and reshape confusion matrix data\n",
    "    matrix = confusion_matrix(y_test, y_pred)\n",
    "    percentage_matrix = matrix.astype('float') / matrix.sum(axis=1)[:, np.newaxis]\n",
    "    return matrix, percentage_matrix\n",
    "\n",
    "def plot_confusion_matrix(matrix, percentage_matrix, model=\"\"):\n",
    "    # Build the plot\n",
    "    fig, ax = plt.subplots(1, 2,figsize=(15,5))\n",
    "    sns.set(font_scale=1.4)\n",
    "    sns.heatmap(matrix, annot=True, annot_kws={'size':10}, fmt='g',\n",
    "                cmap=plt.cm.Greens, linewidths=0.2, ax=ax[0])\n",
    "    sns.heatmap(percentage_matrix, annot=True, annot_kws={'size':10}, fmt='g',\n",
    "                cmap=plt.cm.Greens, linewidths=0.2, ax=ax[1])\n",
    "    # Add labels to the plot\n",
    "    plt.tick_params(axis='both', which='minor', labelsize=5)\n",
    "    # tick_marks = np.arange(len(classifications_array)) + 0.5\n",
    "    # plt.xticks(tick_marks, classifications_array, rotation=85)\n",
    "    # plt.yticks(tick_marks, classifications_array, rotation=0)\n",
    "    ax[0].set_xlabel('Predicted label')\n",
    "    ax[0].set_ylabel('True label')\n",
    "    ax[0].set_title(f'Absolute Confusion Matrix for {model}')\n",
    "    ax[1].set_xlabel('Predicted label')\n",
    "    ax[1].set_ylabel('True label')\n",
    "    ax[1].set_title(f'Relative Confusion Matrix for {model}')\n",
    "    # pyplot.savefig(\"ddos_timeseries_plugin_classification.eps\", format=\"eps\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import STATUS_OK, Trials, fmin, hp, tpe\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "def tunne_together_XGBoost_classification(df_tunne, _features, verbose=True):\n",
    "    X=df_tunne[_features]  # Features\n",
    "    y=df_tunne['LABEL_code']  # Labels\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0, stratify=y)\n",
    "    \n",
    "    def objective(space):\n",
    "        clf = XGBClassifier(\n",
    "                        n_estimators = int(space['n_estimators']), max_depth = int(space['max_depth']), gamma = space['gamma'],\n",
    "                        reg_alpha = int(space['reg_alpha']),min_child_weight=int(space['min_child_weight']),\n",
    "                        colsample_bytree=int(space['colsample_bytree']), \n",
    "                        # subsample=int(space['subsample']),\n",
    "                        # eta=space['eta'],\n",
    "                            )\n",
    "\n",
    "        evaluation = [( X_train, y_train), ( X_test, y_test)]\n",
    "\n",
    "        clf.fit(X_train, y_train,\n",
    "                eval_set=evaluation, eval_metric=\"auc\",\n",
    "                early_stopping_rounds=10,verbose=False)\n",
    "\n",
    "        pred = clf.predict(X_test)\n",
    "        # accuracy = mean_absolute_error(y_test, pred)\n",
    "        # accuracy = accuracy_score(y_test, pred)\n",
    "        accuracy = f1_score(y_test, pred)\n",
    "        if verbose is True:\n",
    "            print (\"SCORE:\", accuracy)\n",
    "        return {'loss': -accuracy, 'status': STATUS_OK }\n",
    "    \n",
    "    space={'max_depth': hp.quniform(\"max_depth\", 5, 30, 2),\n",
    "        'gamma': hp.uniform ('gamma', 1,9),\n",
    "        'reg_alpha' : hp.quniform('reg_alpha', 0,180,1),\n",
    "        'reg_lambda' : hp.uniform('reg_lambda', 0,1),\n",
    "        'colsample_bytree' : hp.uniform('colsample_bytree', 0, 1),\n",
    "        'min_child_weight' : hp.quniform('min_child_weight', 0, 20, 1),\n",
    "        'n_estimators': hp.quniform('n_estimators', 80, 400, 20),\n",
    "        # 'subsample': hp.quniform('subsample', 3, 20, 1),\n",
    "        # 'eta': hp.quniform('eta', 0.005, 0.3, 0.005),\n",
    "        'seed': 0\n",
    "    }\n",
    "    \n",
    "    trials = Trials()\n",
    "    \n",
    "    best_hyperparams = fmin(fn = objective,\n",
    "                        space = space,\n",
    "                        algo = tpe.suggest,\n",
    "                        max_evals = 100,\n",
    "                        trials = trials)\n",
    "        \n",
    "    print(\"The best hyperparameters are : \",\"\\n\")\n",
    "    print(best_hyperparams)\n",
    "    return best_hyperparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [04:56<00:00,  2.97s/trial, best loss: -0.9830781627719581]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.8329982404315408, 'gamma': 8.10918924485917, 'max_depth': 14.0, 'min_child_weight': 0.0, 'n_estimators': 280.0, 'reg_alpha': 2.0, 'reg_lambda': 0.744097257505658}\n"
     ]
    }
   ],
   "source": [
    "best_hyperparams = tunne_together_XGBoost_classification(df_features.sample(100000), features, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def XGBoost_classification_tunned(best_hyperparams):\n",
    "      X=df_features[features]  # Features\n",
    "      y=df_features['LABEL_code']  # Labels\n",
    "      # #Split on train and test\n",
    "      X_train_a, X_test_a, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test\n",
    "      X_train = X_train_a[features]\n",
    "      X_test = X_test_a[features] \n",
    "      model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), \n",
    "            gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "            # subsample=int(best_hyperparams['subsample']),\n",
    "            # eta=best_hyperparams['eta'],\n",
    "      )\n",
    "      model.fit(X_train, y_train)\n",
    "      y_pred = model.predict(X_test)\n",
    "      \n",
    "      accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "      precision = precision_score(y_test, y_pred)  * 100\n",
    "      recall = recall_score(y_test, y_pred)  * 100\n",
    "      F1 = f1_score(y_test, y_pred)  * 100\n",
    "      print(\"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1))\n",
    "      matrix, percentage_matrix = get_confusion_matrix(y_test, y_pred)\n",
    "      plot_confusion_matrix(matrix, percentage_matrix, model=\"XGBoost\")\n",
    "      return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.97 & 99.98 & 99.43 & 99.71 \\\\\n"
     ]
    },
    {
     "data": {
      "image/png": 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sWVIAELa2tuLmzZtK2yi62VlaWoqvvvpKeHp6irJlywoAwtTUVFy+fFmp/I4dO6Qv5vLly4uuXbuKZs2aCT09PQFA9O7dW+nLSxsJrtevX4tevXoJJycnAUBUr15d9OrVS+zatUvaZvXq1VIdqlWrJry8vETt2rUFAKGvry82btyYqXP+5s0bKQhSF0RmVnbPU+PGjUWJEiVEoUKFRMuWLYWHh4cwNTWVzkdcXJwQIjUI7dWrl7C3txcARMOGDUWvXr3ErFmzhBDZS3AtWrRIOl+urq6ia9euom7dutJntXz5cqV9qPsM3717JyVljI2NRatWrYSXl5coUaKEACBsbGxUzqvix9/b21u6fj09PUWVKlWk91i5cmWmz33aeim6mJ04cUKl3KdPn0ThwoVFtWrVpB/F9AmuZ8+eidKlSwsAwt7eXnh4eIjOnTuL8uXLS++Tton0mDFjpPe0t7dX+kwUn3G5cuWEvr6+qFWrlujYsaMoX768SEhIUBtYPn78WFhaWgoAYuHChdLyqKgo6e967ty5GZ4Pxd9Po0aNBABRpEgR0atXL9GrVy/phiW716umY8mslStXCiC1q+H+/fulY9WU1EwbvKT/LsuqzCS4Nm/eLACoDe4WLlwo5HK59J3UrVs3Ub9+fal+6oLvrG5z9uxZYWJiIgW7Xl5ewt3dXRgYGEgJRoWMrj0iyn+ykuDaunWrACAMDAxUEjuKG365XC7q1q0rvLy8pDjQzMxMJbmvKT5Q3FgrYoBu3bqJJk2aSL8D5cuXl7p6K2IQRf27dOmi9JuSPsY4duyYACBKlSql9oHT+vXrBQDRunVradnHjx9F69atpeNwdXUVXbp0keKecuXKicePH2fqXP/2228CSO36lF2JiYmia9euKnFSuXLlBABhYmIi9u7dq7SN4lx369ZNABBlypQRnTt3VoqtJk+eLJUfM2aM6NKli7RO8Vt96tQpIUTWE1zx8fGiXr16AoAoWrSo8PDwEJ06dRLFixcXAISdnZ148uSJVF5TguvkyZPCwsJCAKkPDb28vETLli2luKFly5ZK3VEV13alSpVE1apVhZGRkXB1dRWdO3cWVlZWUjyo7sG7OmnrdffuXelcqqN4ULZw4UKNCa6NGzdK17Xit7Vly5ZSDGJiYiKuX78uhEh9+NerVy+p+12jRo2UPhPFZ+zs7CyMjIxE+/btRcOGDaWhDdR1L1PE2+bm5krnf+fOndJydcnptD53H5CT61XTsWRGYmKiaNKkiQAgmjRpIn755Rfpb//Nmzdqt/n+++8FAFGnTp1Mv486mf1OdXNzEwDEH3/8obQ8O/cw2dlm9OjR0mfQqlUr0bVrV1G1alXpPuTPP/8UQnz+2qOChQkuIcS5c+cEAOHo6Kh0Y9mzZ08BqM86KwIKuVwuduzYIS2Pj4+XApGaNWtKy8+ePSsFLWkDpuTkZDF06FABQHTq1ElafufOHenGys/PTylIuXbtmvSD+fPPP0vLtZHgUtAUlJ07d07o6ekJU1NTcfDgQaV1f/31lzA2NhYGBgbSj1VGFOdEX18/22NL5eQ8ARDNmjUTL1++lNY9evRI2NjYCABi9erVSu/l7u4uANVsflYTXPHx8aJQoUJCT09PBAcHK5Xdtm2bACCsra2VntKp+wz79u0r3bynfTKYkJAgxowZIyVY3r59K61T/Pjr6emp9C8fO3asACCKFy+u9jjUSVsvRSvIIUOGqJTbtWuXAFJbD2lKcCn+3jp06KCUuElJSRFz586VApG050VTgJj2M/7mm2+k5Yq/b03bKc6/iYmJFPB4eXkJIPVpWGbHY9myZYsAVJ/u5fR6VXcsWdG9e3dpXwDE+PHjNZZVnAt7e/ssv096mhJciYmJIiIiQmzdulUULlxYFCpUSOVvIigoSACpibn0AVJQUJAwNzcXAMS2bdtytI3i5i59cvn27dtSIJ62xRzH4CIqOD53M/bx40fx8OFDsWjRImFmZqaSDBHivxviokWLKj35F0KINWvWCJlMJqysrJRiCnXxwZEjRwSQ2lo1fev9kJAQ6fsmbWwphOZYLn2MkZKSIj2kPHnypMqxNm/eXAAQ/v7+0rLhw4cLIPXBX9pEyKdPn8SgQYMEANGgQQOVfamjaK3dv3//TJVXZ8aMGVJcnvYBS0pKiliwYIEAUh8Mp01MKM41ADFv3jyl38jFixcLIHWcqNjYWGn5s2fPpG3Sy2rsvH37dinREB8fLy2Pj4+Xxm5K2xpa3W9IZGSkNPbVpEmTlGLj0NBQ6WFk2nGX0l7blStXVnoo//r1a+khoaZWWOmlr5ciQaiu9XqHDh2Enp6eCA8PV5vgioqKEoUKFRIAxO+//6607du3b6WHTsOGDVNap2kcJMVnLJPJxJkzZ6Tlis9a3XYpKSnSGKGtWrUSQgjx9OlT6Txv3749U+dFCM33ATm5XjUdS2Y9e/ZMum9RXOOXLl3SWL5Vq1Zqv9+yKqPv1E+fPom7d+9K3ytubm4qLQ+zcw+T1W3CwsKETCYT1tbWKn/Hs2fPlvaVFsfg0g1McAkh+vfvLwCI77//Xmm5otuVpaWlysDKioBC3Q/4hw8fpEH5FJlfRWDUtGlTlZvkly9fiiVLloi///5bWqYIEPr27au2zoGBgVLdFD+kuZHg6tixowAgfvnlF7X1mjZtmgAg+vTpo3Z9WopzkpMb6JycJwBqu4YOHjxYABAjR45UWq6tBNerV68EAFGoUCGlL24FPz8/sXPnTvHx40dpWfrPUPGlraenp/bJU0pKitTCZN68edLytC240nv+/Ln0Ppqe/KSXtl6vX78WBgYGwtraWqVlUefOnYWenp548eKFxgTX2LFjRdu2bdV2l0vbZSTtzUNmElwPHz5U2V9GyQlFgrpFixZi7dq1AoAoVqyYePXqVabOiRCaE1w5vV7VHUtWREdHS8mdokWLZphYnjNnjgA0P+V79+6d9MRb3SstxXWX0cvU1FQpyFNQJJ5mzJihth6KlmmVKlXK0TaKVhhpv4cVdu3aJTZv3qzU/J0JLqKCI6Pu4+q+i77//nuVG01FS39NrV4VXQOnT58uLVMXH+zevVt07dpVqaVwWoqHKnPmzFFantkElxBC/PTTTwKAGDRokFLZJ0+eSDd8ii5ZERERwsDAQBgZGalt5ZOQkCDKlCkjAIigoCC1dU5L0UUquzfQ8fHx0u/UsWPH1Jbp0aOHSmJEca7r1aunUj4xMVFqoZu2t4Q2E1zz588XAFR+/4RIfbi1fPlypSSRut8QxU23pi7vt2/fFnK5XOjp6Um/R2mv7fQPnoUQ4ueffxaA+tbR6qSvl6JFXvqYWBHzffXVV0IIoTbBdfPmTdGrVy+NyU4/Pz8BpD5ATOtzCa6mTZuq3Z+m7V6+fClsbW0FALF+/XqpXFYHxFd3H5DT61XTsWSFolUmADFu3LgMy1asWFEAEEuXLlW7/sCBAxrjulWrVknlMvud2rhxY6lHjEJ27mGys83FixcFkDrhU9qksxCpDzUWLVok9uzZo7ScCS7d8D8/yHxMTAx27twJPT09DBw4UGlds2bN4OzsjJiYGGzevFnt9v369VNZZmJigrZt2wIAjh8/DgBo3LgxjIyM8M8//6BBgwZYsGABgoODIYSAvb09fH190bJlS2kfx44dAwD06tVL7fu2adMGRYoUQUxMDP7999+sH3g2pKSkSPVq3bq12jKKQSaPHj362f0ZGBgAABISErJdp5ycp8KFC6NSpUoq2xQvXhwA8P79+2zXKyN2dnZwcXFBXFwcqlevjqlTp+LEiRPSeRg5ciS6desGY2Njjfs4ceIEhBCoV68eypUrp7JeJpOhT58+AFIHCk+vUaNGKsvs7e2lQSazc+y2trZo06YNoqKiEBgYKC2PiorCwYMH0bJlSxQrVkzj9gsXLsShQ4fg7OwsLYuLi8Ply5exadMmqW7x8fGZrpOlpSWcnJyydBzLly9H6dKlcfz4cQwdOhRyuRzbtm2DnZ1dlvajTk6u1+wcS3pr1qzBu3fvAAARERFYsmSJxrJCCADQOC39p0+fsG3bNo0vdVxcXNCrVy/p5ePjg/bt28PJyQkfPnxA69atsXr1aql8cnIyTp48CUDzOevVqxfkcjlCQkLw8uXLbG0DAO7u7gCALl26YOjQodizZw+io6MBAJ6enujTp4/03UBEBVfnzp2l7yBPT08ULlwYAGBlZYX169cjIiICM2fOVJogJCIiAsHBwQCAVq1aqd1vZuOfLl26ICAgAGPHjpWWJScn4+HDh/jjjz/w6NEjAFn7rUuvf//+kMvl+OOPP5T2s2XLFggh0LNnT2lCnxMnTiAxMRGVK1dGiRIlVPZlYGCANm3aZOrYFOWB7Md2ly5dwrt371CsWDG0aNFCbZm+ffsCyHx8o6+vjyJFigD4crFdixYtIJPJsG3bNrRq1QrLli3DvXv3AAAVK1bEsGHD0LBhwwz3oYgRevbsqXZ9pUqVUKdOHaXfubTUHXtOY9oePXpAT08PO3fuRHJysrTc398fiYmJ0mehTpUqVbB161Zs2LBBafmLFy9w+PBh/PPPPwCyfq3XrFkzS+Xt7e2lyW+GDBmCkydPokqVKvDz88vSftTJ6fWa1WNJLz4+HsuWLZP+vW7dOoSGhmos/7nY7vbt2xrjurNnz6rdJu13aq9evdCtWze0aNEClpaWOHPmDOrVq4f79+9L5bNzD5OdbapWrYoiRYrg0aNHqFmzJmbNmoULFy4gOTkZxsbGGDNmDDp37qzxXFHB9T+f4NqyZQs+fPgAIyMj9O/fHy1btlR6xcXFAQCWLl2qdntNN5ylSpUCADx//hxA6g/Mzp07YWtri4sXL2LixImoXr067O3t0a9fP5UvPcV2ZcuW1Vh3xXsryn5pkZGR0vmoXr06ZDKZyksxU87Lly+RmJiY4f4UP7pv3779bFlNcnKeFEFteoqZCjV9+WtDQEAAXFxc8PTpU8yePRstWrRA4cKF0b59e2zatOmz5yOn14e1tbXKMrlcLgX02T12RUIh7WyKO3fuREJCgvTDk5EHDx5g4sSJaNy4Mezs7GBubo66devC19dX+lFW/G9mqDvOz7GwsJBmFExKSsKQIUM0Bi1ZlZPPLTvHktapU6cwZcoUyOVyDB48GAAwefJkXLhwQW15RTJSkQBKz9bWFiK1FbD0evbsWYZ18PT0xNatW6XX9u3b8eeff+Lhw4fYt28fkpKS8M033+DQoUMAUr9zFIGvpu/aQoUKSTcuz58/z9Y2ADBnzhx06dIFcXFxWLVqFTw9PWFjY4OGDRti3rx5iIyMzPDYiKhgWLRokfQdtGvXLoSFhaF79+6Ijo7Gd999hydPnqhs8/TpU+n/W1lZqY1/evTooVJWkw8fPmDVqlXw8PCAk5MTjI2NUa5cOXTr1g3Xrl0DkLXfuvRKliyJ1q1bIzo6Gn/99Ze0fMuWLQCAr7/+WlqmON6rV6+qPS6ZTCbNLJmZY1PEdq9evcpW3b9EfAN8+diuTp06WLVqFczMzHD06FGMHDkSFStWhKOjI4YNG4ZLly59dh85OXa5XA5LS0uV8jk97qJFi6Jly5Z49eqVUoJz69atsLCwQKdOnTLcPiUlBX/99Rd8fHxQpUoVmJqaokSJEmjbtq00s15Wr3UbG5ssH0enTp3Qp08fJCUlAUhNBGlj5tGcXq/ZOZa0fH198e+//6J69epo0KABYmJi4O3trTHB/LnYbsKECSqx3U8//ZRhHdJ+p27duhU7d+7EsWPH8OLFCwwcOBC3b99Gq1atpFkos3POsrONiYkJ9u7di9KlS+P27duYPn06GjRoABsbG3h7e2P37t05+p6l/Es/ryuQ11atWgUgNdhQl1lXCAkJwd9//63y5E7Tl6PiD8bQ0FBa1rFjRzx9+hSHDh1CYGAgTp48iXv37mHz5s3YvHkzBg8eLLVeyMwfnOLHKqOWPp+T9mlMVspqahmRVmJiovQkT52qVavC1NQUHz58wLlz59CsWbMM95eSkoIpU6agdu3aaN26NaysrHJ0ntJPifulqDvHFStWxPXr1/HPP//gwIEDOHHiBK5cuYIDBw7gwIED8PPzw/Hjx2Fubq52nzm9Pr7UsXfq1Anm5ubYv38/3r9/DzMzM2zZsgWFChVCly5dMtx2+fLl8PX1RUpKCooVK4aGDRuiYsWKqF69Opo3b45SpUplOUBL+wQ+K9LeEOzduxc//vijVlpw5eRzy+6xAKmBTPfu3ZGUlITJkydjzpw5eP78OQ4ePIju3bvj6tWrKgnftMnqe/fuoUKFCtl+/8zo2LEjBg0ahGXLlsHPzw9fffVVpgOPtOcsO9sAgJmZGXbv3o2QkBDs378fQUFBOHv2LM6fP4/z58/jl19+wfHjx+Hi4pKNoyOi/MrU1BRbtmxBWFgYzpw5g9atW+PKlSsoWrSoVEbxO25mZvbZJ/5mZmYZrr9//z7c3NwQFhYGY2Nj1K5dG61atULlypXRsGFDrFixAhs3bszpYeHrr79GYGAgtmzZAi8vL1y4cAF3795FjRo1lFqNKI7Nycnpsy2M6tSp89n3rVevHlasWKGxtUd6Fy9exJEjR9CsWTM0a9Ys38Y3aWmKnQcPHgxvb2/89ddfOHz4ME6ePImnT59i5cqVWLlyJWbNmoWpU6dq3G9+jWl79+6Nw4cPY/v27WjTpg3u37+PCxcu4Ouvv84wSfTp0ye0bdsWJ0+ehEwmg4uLC7y8vODs7Iw6dergyZMn+Oabb7Jcn+zEQ7GxsVKLMQBYvXo16tevn+X9pJfT6zUnsd3GjRuxZs0aGBsbY9OmTTAzM0OtWrVw+fJlTJw4EYsXL1bZpkGDBjh58iROnDiR7ffNLFNTUyxduhR79+7FkydPsH//fnh7e2frnGX3PDds2BD37t1DUFAQDh06hBMnTuDGjRsICAhAQEAA2rZti7/++gt6enrZOUTKp/6nE1xnz57FzZs3YWlpifDwcI2Jop49e+L333/HkiVLVBJcz58/V3vjq2ge6ujoqLTcxMQEnp6e8PT0lLbfsGEDZs6ciTVr1mDo0KGoVasWSpQogYcPH+Lhw4cas9UPHz4EAKUgTB2ZTAYhhPTUIq23b99muG1atra2MDIyQnx8PPz8/DS2gMosU1NTtG3bFrt374a/v/9nE1xBQUGYP38+AODWrVuwsrLS6nnKLsWPk7rzC2g+xzKZTAroAODdu3f4888/pacxa9aswbhx49Ruq+hGoDg2dR48eADgyx23Oorre9OmTdi3bx8aNmyIs2fPol+/fjA1NdW43dOnTzF69GikpKTAz88PI0aMUArWYmNjv2iLurSOHj2K3377DRYWFnB1dcWff/6JQYMGYd++fTned15cr0lJSejevTvCw8PRpEkT6Uncxo0bUb16dTx58gQDBgzA3r17lbZzdnZG7dq18e+//2LVqlX49ddftVKfjFStWhXAf9+fNjY2MDY2xqdPn/Do0SO1SbaYmBi8fv0aQOo5K1y4cJa3SatSpUqoVKkSJk2ahKSkJJw6dQoTJkzA1atXMXXqVOzfv1+rx0xEec/AwADbtm2Di4sLXrx4gX79+il1tVf85urr62PLli05SiaMHDkSYWFhcHd3R0BAgEosNXfu3GzvO61OnTrBxsYGhw4dQkxMDLZu3QpAufUW8N+xVa1aVSqTEx07doS+vj4ePXqEixcvol69ehmWX7x4MbZv3466devi4sWL+Sa+yW7sbGlpKXXVAlLr6ufnhyVLluD777/H4MGDpRbE6ZUoUQJ37tzBw4cPpW7z6eVFbNelSxeYmZlhz549WLVqlXSdfK5l/qJFi3Dy5Ek4ODjg0KFDqFKlitL6jIZJ0LYRI0bg8ePHaNOmDf7991+sX78eHh4e0v1YduXV9Xrt2jUMHz4cAPDbb7+hevXqAIAVK1agd+/eWLJkCZo3b67ycLlXr16YN28ezp07h+DgYFSrVk1rdVJH0To1MjJSiu2yc85ycp4NDAzQtm1bafigyMhI7NixA+PHj0dgYCD27duX4+uA8pf/6S6KitZS3t7eGbaCGjRoEADg4MGD0tgICoquNGnFxsbi4MGDACCNq7V48WKUKVMGs2fPVipbokQJTJs2TWoVoGgq7ubmBkC5q1dagYGBiIyMhI2NDWrUqJHhcRYqVAiA+uaoZ86cUbuNuuBNX18fTZo0AQCVm2GFzZs3o1KlSirjmWkyadIkyOVyrF69WmNXKSB17IBJkyYBSB2nqHLlygC0e56yK6Pze/36dZVxD06dOoUqVarAw8NDabm5uTl69uwpBQzqukkouLq6Qi6X4+LFi2q/7IUQ+P333wFAY5D0pSiCOn9/f+lzyWiMBgA4d+4ckpKSYGtri5EjR6pcf2n/ztImurT9xDIyMhL9+vWDEAILFy7Epk2bUKxYMezfv1/qopETeXG9fvvtt/jnn39ga2sLf39/qbuCnZ0dtm3bBrlcjn379mHRokUq2/7666/Q09PDb7/9pnSzp0lmumBk5O7duwD+6+Ktr6+Ppk2bAtB8zn7//XcIIeDi4gI7O7tsbRMTE4N69eqhePHiUhN6xfu7ublhxowZAJT/JnOrBSgR5Y5SpUpJifzDhw8rtaJycHBA2bJlERMTo7Hlw88//wwXF5cMW+gAqTEAAIwdO1YluRUXF4fTp08DyHlXOkNDQ/Tu3RsJCQnYu3cvdu7cCSMjI5UW+Ip44sSJE9K4g+l17NgRdevWlbqUZcTa2hpDhw4FkJrMS/udmt6JEyekfSoe6NWpUwfm5uZ4+fKlxnOtGOfxS8Y3WY2dJ02ahJIlS6qMQVmuXDksXrwYlpaWSEpKynBYEUWMoIjf0gsJCcHVq1ehr68PV1fXTB9LTpmZmaFTp0549+4dDh48iO3bt6NUqVKfrYPiWu/evbtKcgv4L7ZLf61r+/fV398fW7duhY2NDTZt2iSNvTV48GC8ePEiR/vOi+s1OjoaXl5e+PjxI3x8fJRawfXq1Qv9+/cHkJrMTj8el4uLi3Rf26NHD8TExGT4XomJiVK36exISkqS7lMUsV127mGys83OnTtRoUIF6ftIwcbGBsOHD5fGFmRsp4O+3Pj1+dvbt2+lGVVOnz6dYdmUlBRRtmxZAUCMHTtWCPHfrDWmpqZK0zC/f/9edOrUSQAQrVu3lpYrZkeztbVVmkJWCCHOnz8vjIyMhL6+vnjy5IkQQohbt24JQ0NDIZPJVKatDw4OFiVLlhQAxMyZM6XlmmZEbNasmQAgvLy8lGYFCgwMFMbGxmq3GTZsmNrZONJOb/3XX38prbt8+bI0S8n8+fMzPKdpffvttwKAMDMzE2vXrlWZ6SI0NFSa1trKykppCmRtnicFxexD/fr1U1quaRZFxewl1tbW4tGjR9LyiIgIaQpkpJnhKDo6WhgbGwuZTKYyPfGrV6+kKZ3TrlPsI+1sPorZ/mrVqqU081FiYqIYP368ACDs7OxERESEtO5zs4Po6ekJQP006uqoq1dycrIoVqyYMDQ0FOXKlRMODg5K1526WRSDgoJUzpPCiRMnpFlJAYhbt25J63bs2CGdg7Q+9xlrmgGvS5cuAoA0K5AQQuzfv1/6W79z506mzoumWRS/xPWakYCAAAGkTkMdGBiotoxi5lMDAwNx4cIFlfULFy6UPq9JkyaJFy9eqJS5ffu26Nevn5DL5QJInaY5LcV1l/a40jt+/Lj0nbxjxw5pueI7x8jISGW2m+PHjwtLS0uVv8vsbNOgQQMBQPj6+irNLpmQkCC6desmAIghQ4ZIyzVde0SU/2Q0pX16LVq0kH7T0/5+rlmzRgAQjo6OSrPhCSHEoUOHpO+vnTt3SsvVzaLo4OAgAIjBgwcrzar9+vVr8dVXX0n1nDhxotJ7KPYfHBystFzdLIoK169fl+oMqJ9BWQjl2YPT/57/+OOPAoAwNDRUO6OgOu/evRNOTk4CSJ2JN+3MhQp79uwRhQsXFgBEx44dlc7F1KlTBQBRunRppdmuU1JSpFn9TExMlNZ9bkZrRRx//PhxaVlGsyhmNXZWzM5brlw5ldko9+zZI8Ww79+/F0Koj0NevXolrKysBADx3XffiaSkJGndkydPhIuLi0p8qmlmagVN8YgmmuKjgwcPCgDSjMPfffed0np1syj26dNHABDVqlWTjluI1N9VxWcMQNStW1dpX4q/gyVLligt/9xnrC7GffLkiXRO08YWnp6eAoBo2bKlyuz2mmi6D/gS16smKSkpokOHDgKAqFixonj37p1Kmbi4OOHs7CydW3UzCNarV08AEA4ODiIgIEClTEJCgtizZ480eywA8e2330rrM/OdmpiYKEaNGiXdj6Sta3buYbK6TUhIiAAgjI2Nlf7uhRDi/v370j1r2hm8NV17VLD8zya4Fi9enKWbRsU0u1ZWViIuLk4KKBo3bizkcrlo2rSp8PLyEkWKFBEAhLOzs8qN4Ndffy39CDVp0kR07dpV2h5QnRJ669atwsDAQAAQFSpUEN26dROurq5SEsLHx0fpx0/TjfDBgwel93BychJeXl6idu3a0g+Rum0U58fExER07txZrF69Wlo3b948IZPJBABRpUoV4enpKRo1aiQt8/b2VqpXZih+GAEIGxsb4e7uLry9vUX9+vWl4y1durS4evWqyrbaOk8KWU1wvX//XpQrV05KgrRv31589dVXwsTERJQtW1ZKzqUNPjdt2iQdr4uLi/Dy8hKtW7cWZmZmAoBo166dUp3VJZJiYmKkm3ITExPRpk0b4eXlJSVJbG1tlZKvQuROgksIIcaOHSutSz9VuLpgLCkpSUoG6unpiRYtWggvLy8pmLO2thb29vYqwem1a9ek93F3dxdjxowRQmQvwaW4ebGyspKm31bo27ev9KOakJDw2fOSUUCp7etVkzt37khTV0+dOlVjuaSkJNGkSRPpb+zt27cqZfbv3y+df7lcLmrWrCk6d+4sOnToIE05rQgiRowYobIPxXXn4uKiMu20t7e3qFmzprSPAQMGqLx/2u+cGjVqCG9vb+naV3cjmJ1tgoODhYWFhQAgSpYsKTp06CA6dOggihcvLgCIMmXKKH2na7r2iCj/yUqC6/79+1IyKX1CaPjw4QJIfWhQq1Yt4eXlJWrVqiXtO/1DQXU3sUuXLpXKV6xYUXh5eQk3NzdhZGSklEBIH4MovicVcZziYV9GCS4hhBTvAdD4oCNtPGFqaiqaNm0qunTpIkqXLi0ACH19fbFr164Mz1t64eHhSg/5nJ2dRadOnUTnzp2lOgMQPXr0EB8+fFDaNiEhQXpYrK+vL5o3by66desmPQA0MzMTAQEBnz3XaWU1wZXV2DkxMVG0bt1aAKkPV9zc3ETXrl2lbeRyudi6datUXlMi6ejRo9Jvt4ODgxQfKpJqbm5uIi4uTiqfWwmuxMRE6R4HgMoDP3UJrsuXL0vXdZEiRUSnTp2Eh4eHlFhQXOulSpVS2pcihrSyshKenp5i7969QoisJ7iSk5OlRGW3bt2UykZEREj1WLBgQabOjab7gC9xvWoya9YsKe5Pn+xO69q1a9K5Hz16tMr6jx8/irFjx0rXuKWlpXB1dRXdunUT7u7uUvJZERumv29I+53auXNnpbiuZ8+eomPHjqJo0aJSbJj+uyc79zDZ2eaHH36Q6lm3bl3RtWtX0aJFCykO/+abb5TKa7r2qGD5n01wValSRQAQ33//fabKP3/+XLoBXb58ufTjHBYWJqZNmyYcHR2FoaGhcHJyElOmTBExMTEq+0hKShLLly8XjRo1EpaWlkJfX18ULVpUdOrUSfz9999q3zc4OFj07dtXlCxZUhgYGIiiRYuKdu3aqbRKECLjG+ETJ06INm3aCEtLS2FiYiJq164tNmzYID5+/Kh2m48fP4qvv/5aFC5cWBgZGYk+ffoorT9z5ozo1q2bKFasmDAwMBDFihUTLVq0EL///nuWk1sKV65cEUOHDhVVq1YVhQoVEnp6esLa2lq4urqKJUuWqARAX+I8CZH1BJcQqT+UI0eOFI6OjsLAwEA4ODiIUaNGicjISNG9e3e1wWdgYKDw8PAQRYoUEXp6esLKyko0bdpUrFq1SuUcakokxcfHCz8/P9GgQQNRqFAhYWJiIpydncXkyZPFy5cvVeqZWwmuf//9V1qXtsWVEJqDsdjYWDF9+nRRqVIlYWJiIkxNTUWlSpXEuHHjxIsXL8TIkSPV3kD8+uuvwsHBQRgYGAgnJyeRkpKS5QTXvXv3pOTipk2bVMq/fftWSnSkfYKlyecCSm1er+rExcVJgaOrq+tn/yafPn0qrK2tpUBF0z63b98uPD09hbOzszA3NxfGxsbCwcFBtG/fXixatEi8fv1a7baK607dy8TERJQpU0Z069ZN7N+/X2MdT58+Lby8vETRokWFgYGBKF68uOjatas4ceKE1rYJCQkR/fr1E6VLlxaGhobC1NRUuLi4iOnTp6tN/Km79ogo/8lKgkuI1AS5onz676W//vpLtG/fXtjZ2Um/9+3atRMHDx5U2Y+mm9i9e/eKpk2bCltbW+m7qXPnziIoKEhcvnxZABDFihVTalVx5coVUa9ePWFkZCQsLS2lVvSfS3AtX75cSpakbYmUXnx8vFi6dKlo1KiRsLCwEMbGxqJ8+fKif//+4saNG589Z+okJiaKLVu2iI4dO4qSJUsKQ0NDYWhoKEqVKiV8fHxEUFCQxm1TUlLE5s2bRfPmzYWVlZUwMjISZcuWFSNHjhT3799XKa/tBJcQWY+dP3z4IObMmSNq1aolChUqJAwMDESJEiWEj4+PuHTpklJZTYkkIVKv1+HDhwsnJydhaGgobGxshKurq9iwYYPKZ5hbCS4hhPD19ZUSBempS3AJkdqK0NPTU5QsWVLo6+sLa2tr0axZM7F27Vrx8eNHKZGSNlnz5s0b4enpKczNzYWJiYnUWiyrCa7Zs2dLyTV18YmiJbahoaG4du3aZ89NRvcB2r5e1Tl69KiUkFqzZs1nyy9btky6vtXFl0II8fDhQzF79mzRpEkT4ejoKH2/VK5cWQwaNEjs3btX7fdG2u/U9C+5XC4sLS1FzZo1xbhx48TDhw/VvndW72Gyu8327duFm5ubsLa2Fnp6esLGxka0bt1aqbWtgqZrjwoWmRCcH5OIiIiIiIiIiAqu/+lB5omIiIiIiIiIqODTz+sKEBERkW6Ki4vDmjVrcOTIEYSFhUFfXx/ly5dHt27d0K1bN6Wyvr6+OHLkiNr96Onp4fbt20rLXr16haVLl+L06dN4/fo1ihcvjo4dO2Lw4MEwNDRU2cfjx4/h5+eHS5cuISYmBo6OjujevTt69uwJuVz1ed/NmzexdOlS3LhxA+/fv0f58uXRt29fdOjQQW0dz507hxUrVuDu3btITExElSpVMGTIEGlWTyIiIiL6sthFkYiIiLQuNjYWPXv2xP379+Hs7Ix69erh06dPCAoKQmRkJDw9PTFnzhypvLu7O2JjY9G3b1+VfclkMowcOVL6d0REBHx8fPDy5Uu0bt0aDg4OOHfuHG7evIkGDRpg7dq1MDAwkMrfu3cPvXv3xsePH+Hh4QFra2scO3YMoaGh6NSpE+bPn6/0fufOncOQIUNgaGgIDw8PmJiYIDAwEOHh4Rg2bBjGjBmjVH7fvn2YNGkSbGxs8NVXXyE5ORkHDx5EdHQ0Zs2apZLMIyIiIiLtY4KLiIiItG7OnDnYuHEjvL298cMPP0itpGJjY+Hj44MHDx5g9erVcHV1RWxsLOrWrYtGjRphw4YNn9332LFjcfDgQcybNw+dO3cGAKSkpGDSpEnYv38/pk2bhj59+kjlu3fvjuvXr2Pz5s2oV68eACAhIQGDBg3ChQsXsGLFCri5uUnL27Rpg7dv32LXrl0oW7asUr0fPXqEPXv2wNnZGQDw9u1buLm5wczMDHv27IGdnR0A4MWLF/D29sb79+9x5MgRaTkRERERfRkcg4uIiIi07sCBA5DJZJg4caJSF0ALCwsMHjwYAHD06FEAQEhICACgUqVKn91veHg4AgMDUb58eSm5BQByuRyTJ0+Gnp4etm3bJi2/cuUKrl27hmbNmknJLQAwNDTEpEmTAECp/OHDh/HixQt06dJFSm4p6j169GikpKRg+/bt0vIdO3bgw4cP6N+/v1ISq3jx4hg4cCA+fPiAPXv2fPa4iIiIiChnmOAiIiIirUpOTsaQIUMwevRoWFhYqKxXjJH1/v17AJDG18pMguv8+fNISUlBw4YNVdbZ2NjA2dkZoaGhCA8PB5Da3RAAGjVqpFK+SpUqsLKywsWLF5GcnKxUvnHjxirlFftQlMls+bNnz372uIiIiIgoZ5jgIiIiIq3S09ND3759MWzYMLXrAwMDAQAVK1YE8F+C6+XLl+jbty/q1auHmjVrom/fvjhz5ozStqGhoQCA0qVLq923g4MDAODRo0dK5cuUKaOxfEJCAsLCwj67/0KFCsHa2hphYWFISEj4bHlHR0eluhARERHRl8NZFImIiHSIrFVJre7PLaVihuuDgoKytL+jR4/i8OHDMDU1RZcuXQD810VxyZIlcHNzQ7du3fD48WMcP34cFy9exLRp09C7d28AqWNeAYCVlZXa/VtaWgJIHTMrbXnF8pyWt7CwQFRUFOLi4mBtbY23b9/C0NAQJiYmKmVNTExgYGCAd+/eZXBGiIiIiDJPm7Ge+DtMa/vKD3Q2waXtAJ/of5HiC+9T8oc8rglRwWasZ5p7byaT5d57ZdGZM2cwfvx4AMDMmTNRpEgRpKSkoFChQihVqhSWLFkiDd4OAMHBwejTpw9mz56Nhg0bomzZskhKSgIApVkS01J0f4yPjweAz5ZXLM9secX+FS24kpKS1Ca30pb/9OmTxvWUPYzziHKOcR6RduRqnAfk61gvr+lsgouIiIhyLqsttDTZt28fpk6disTEREyYMEEaIF4ul8Pf31/tNtWqVUO/fv2watUq7N+/H2PHjoWxsTEAIDExUe02isSTmZkZAHy2vGJ5Zssr9m9qaiqV11RWUV5RloiIiIi+HI7BRUREpEvkWn7lkBACv/76K7799lukpKTghx9+kGZRzIxq1aoBAJ4+fQpAtUthejExMQAAc3PzL1I+NjYWMpkMhQoVksonJCSobaX18eNHJCYmqh1on4iIiChb8lGcl9/o4CERERH9D5PJtPvKgYSEBIwaNQqrV6+GqakpVqxYgR49eiiViYmJwZUrV3Dnzh21+/j48SOA/1pWlS1bFsB/Ca/0nj17BgAoV66cUnnFcnXlzczMUKxYsc+Wj4uLQ1RUFMqWLQu5XP7Z8oo6KupCRERElGP5JM7Lj5jgIiIiIq1LSkrCiBEjcOTIEdjb2+P333+Hq6urSrng4GD4+Pjg22+/VbufixcvAgCqV68OAKhbty7kcjnOnTunUjYyMhJ37tyBk5MTbGxsAAD169cHAJw9e1al/M2bNxEdHY1atWpBT0/vs+UVMzrWqVNHWpbV8kRERET0ZTDBRUREpEtkWn5lk5+fH06dOgV7e3v4+/srDR6fVv369WFnZ4e7d+8iICBAad3Jkyexa9cu2NnZoX379gCAokWLwtXVFSEhIdi1a5dUNiUlBXPnzkVycjL69OkjLa9RowYqVKiAY8eOKSWhEhISMH/+fABQKu/m5gZbW1sEBATg7t270vLY2FgsWbIEMplMmtERADp27AhjY2OsXbsW4eHh0vKXL19i/fr1MDU1RdeuXbN07oiIiIg0ygdxXn4lE0KIvK7El8DZdYhyjrPrEGlHbs6uI/MopdX9iQNPsrzNq1ev4O7ujoSEBLRo0QJVqlRRW87JyQkeHh44e/Yshg4divj4eDRt2hTly5fHo0ePcPLkSZiYmGDNmjVKraCePXuG7t27IyoqCu7u7ihTpgzOnTuHmzdvokmTJli5cqXSLIjBwcHo168fEhMT0bZtWxQtWhTHjh3Do0eP4OnpiTlz5ijV6+jRoxg1ahSMjIzg4eEBMzMzBAYGIjw8HL6+vhg5cqRS+a1bt+Knn36ClZUVPDw8AAAHDhxATEwMZs+eDU9PzyyfQ8oY4zyinGOcR6QduT2LojZjvezEefkZE1xEpBEDHyLt+F9LcO3bt09jl8O03N3dsXz5cgDA/fv3sXLlSpw/fx7R0dEoXLgwmjRpguHDh8PR0VFl27CwMCxZsgSnT59GXFwcSpQogY4dO2LAgAHSeF1p3b17F35+frh48SISEhLg6OiI7t27o0ePHlL3xLQuX76M5cuX4/r16xBCoGzZsujXr5/Ukiy9o0ePYt26dQgJCYGhoSEqVqyIIUOGoGnTpp89D5R1jPOIco5xHpF2MMGVfzDBRUQaMfAh0o5cTXB10HKC60/dCnxINzDOI8o5xnlE2pHrCS4txnq6Fufp53UFiIiISIt0cEYcIiIiIvp/jPU04iDzRERERERERERUoLEFFxERkS7hQz0iIiIi3cVYTyMmuIiIiHSJnFEPERERkc5irKcRuygSEREREREREVGBxhZcREREuoQP9YiIiIh0F2M9jZjgIiIi0iWcWYeIiIhIdzHW04hdFImIiIiIiIiIqEBjCy4iIiJdwod6RERERLqLsZ5GTHARERHpEs6sQ0RERKS7GOtpxC6KRERERERERERUoLEFFxERkS7hQz0iIiIi3cVYTyMmuIiIiHQJZ9YhIiIi0l2M9TRiF0UiIiIiIiIiIirQ2IKLiIhIl3DgUSIiIiLdxVhPIya4iIiIdAljHiIiIiLdxVhPI3ZRJCIiIiIiIiKiAo0tuIiIiHQJBx4lIiIi0l2M9TRigouIiEiXMOYhIiIi0l2M9TRiF0UiIiIiIiIiIirQ2IKLiIhIl3BmHSIiIiLdxVhPIya4iIiIdAljHiIiIiLdxVhPI3ZRJCIiIiIiIiKiAo0tuIiIiHQJZ9YhIiIi0l2M9TRigouIiEiXsG02ERERke5irKcRTw0RERERERERERVobMFFRESkS9hsnYiIiEh3MdbTiAkuIiIiXcKYh4iIiEh3MdbTiF0UiYiIiIiIiIioQGMLLiIiIl3CZutEREREuouxnkZMcBEREekSts0mIiIi0l2M9TTiqSEiIiIiIiIiogKNLbiIiIh0CZutExEREekuxnoaMcFFRESkSxjzEBEREekuxnoasYsiEREREREREREVaGzBRUREpEvkfKxHREREpLMY62nEBBcREZEu4bgMRERERLqLsZ5G7KJIREREREREREQFGltwERER6RI+1CMiIiLSXYz1NGKCi4iISIfI2GydiIiISGcx1tOMXRSJiIiIiIiIiKhAYwsuIiIiHcKnekRERES6i7GeZkxwERER6RDGPERERES6i7GeZuyiSEREREREREREBRpbcBEREekQOR/rEREREeksxnqaMcFFRESkQzguAxEREZHuYqynGbsoEhERERERERFRgcYWXERERDqET/WIiIiIdBdjPc2Y4CIiItIhDHqIiIiIdBdjPc3YRZGIiIiIiIiIiAo0tuAiIiLSIXyoR0RERKS7GOtpxgQXERGRDmGzdSIiIiLdxVhPM3ZRJCIiIiIiIiKiAo0tuIiIiHQIn+oRERER6S7GepoxwUVERKRDZGDQQ0RERKSrGOtpxi6KRERERERERERUoLEFFxERkQ5hs3UiIiIi3cVYTzMmuIiIiHQIYx4iIiIi3cVYTzN2USQiIiIiIiIiogKNLbiIiIh0iJyP9YiIiIh0FmM9zZjgIiIi0iEcl4GIiIhId+W3WO/QoUPYuHEjHjx4AD09PdSsWRMjRoxAtWrVMrX9p0+fsGrVKhw8eBDPnz+HiYkJatSogaFDh6J27dpZqgsTXERERPRFxMXFYc2aNThy5AjCwsKgr6+P8uXLo1u3bujWrZtS2fj4eGzatAl79+7F8+fPYW5ujubNm2PUqFEoUqSIyr5jY2OxevVqHDlyBOHh4bCxsUGbNm0wYsQImJubq5R/9eoVli5ditOnT+P169coXrw4OnbsiMGDB8PQ0FCl/OPHj+Hn54dLly4hJiYGjo6O6N69O3r27Am5XHWEh5s3b2Lp0qW4ceMG3r9/j/Lly6Nv377o0KFDDs4gERERUf61YsUK/PbbbyhZsiS8vb0RGxuLAwcO4PTp01i5ciWaNm2a4fZJSUn4+uuv8e+//8LJyQm9evVCZGQkAgMDcebMGSxevBitWrXKdH1kQgiR04PKj2StSuZ1FYgKPPF3GADgU/KHPK4JUcFmrGeaa+9lM72RVvcX+dPZbG0XGxuLnj174v79+3B2dka9evXw6dMnBAUFITIyEp6enpgzZw6A1OBm2LBhOHXqFGrVqoU6derg4cOHCAoKQpEiRRAQEAB7e3tp33Fxcejbty9u3bqFpk2bolKlSggODsb58+dRvnx5+Pv7o1ChQlL5iIgI+Pj44OXLl2jdujUcHBxw7tw53Lx5Ew0aNMDatWthYGAglb937x569+6Njx8/wsPDA9bW1jh27BhCQ0PRqVMnzJ8/X+lYz507hyFDhsDQ0BAeHh4wMTFBYGAgwsPDMWzYMIwZMyZb55A0Y5xHlHOM84i0IzfjPEC7sV524zwAePDgATp06IBy5cphx44dMDVNPQ8hISHw8fGBhYUFjhw5AmNjY4372L17N6ZMmYJ69eph/fr1Ujx24cIF9O/fHzY2Njh58iT09PQyVSe24CIiItIh+aXV+rJly3D//n14e3vjhx9+kFo9TZw4ET4+Pti9ezfatm0LV1dXBAQE4NSpU/Dy8sLs2bOlfezcuRPTp0/Hzz//DD8/P2n5ypUrcevWLfj6+mLkyJHS8kWLFmHlypVYunQpJk+eLC2fO3cunj9/jnnz5qFz584AgJSUFEyaNAn79++Hv78/+vTpI5WfPn06YmNjsXnzZtSrVw8AMGbMGAwaNAj79u1D27Zt4ebmBgBISEjAd999Bz09PezcuRNly5YFAIwYMQI+Pj5YtWoV2rZtC2dnZy2fYSIiIvpflF9ivU2bNiElJQXDhw+XklsAUKlSJXTt2hVbtmxBUFAQPDw8NO7j+vXrAAAvLy+lh43169dH+fLlcffuXTx9+hRlypTJVJ04iyIRERFp3YEDByCTyTBx4kSlLn0WFhYYPHgwAODo0aMAgI0bN0Iul2PcuHFK+/D29kaFChVw9OhRREREAEhNKG3fvh2WlpYYMmSIUvkRI0agcOHC+OOPP5CQkAAACA8PR2BgIMqXLy8ltwBALpdj8uTJ0NPTw7Zt26TlV65cwbVr19CsWTMpuQUAhoaGmDRpEgAolT98+DBevHiBLl26SMktxXGOHj0aKSkp2L59e9ZPIBEREVE+du7cOQBA48aNVdY1apTayuzs2YxbiFlZWQEAwsLClJYnJiYiKioKcrlcKpMZTHARERHpEJlMptVXdiQnJ2PIkCEYPXo0LCwsVNYrxrx6//49Xr58icePH6NChQqwtbVVKdu4cWOkpKTg/PnzAIDg4GC8f/8ederUURk7y9DQEHXr1sW7d+8QHBwMADh//jxSUlLQsGFDlX3b2NjA2dkZoaGhCA8PB/BfsKYIzNKqUqUKrKyscPHiRSQnJyuVzyi4U5QhIiIiyqm8jvOA1ARUWFgYrK2t1cZ6jo6OAIBHjx5luB8vLy+YmZlh3bp1OHDgAOLi4vD8+XNMmjQJr1+/hre3NwoXLpzperGLIhERkQ7JDzPr6OnpoW/fvhrXBwYGAgAqVqyI0NBQAEDp0qXVlnVwcADwX4CU2fKhoaGoU6dOpsrfunULjx49gr29vVReU1N4BwcH3LhxA2FhYShVqlSG+y9UqBCsra0RFhaGhIQEtYPZExEREWWFNmM9d3f3DNcHBQWpXR4dHQ0hBCwtLdWuVyS93r17l+H+HR0d4e/vj8mTJ6u05Pf19cXw4cMz3D49JriIiIhIo+wGPpocPXoUhw8fhqmpKbp06YJLly4BgMYASbFcESC9ffsWADQ2V1eUj42NzVH5z9Uns+UtLCwQFRWFuLg4WFtbqy1DREREVJAkJSUBgNK4WWkpHurFx8dnuJ/Y2FgsWrQIt2/fRvXq1VGzZk1ERUUhKCgI69atg62tLXr06JHpejHBRUREpEPyQwsuTc6cOYPx48cDAGbOnIkiRYogMTERADS2bkofICnKZzagymoA9rnyiuVZ3b9iTDAiIiKinNBmrJfVB5UKRkZGAP6Ly9JTxD1pB59X59tvv8Xx48cxcuRI+Pr6SstfvHiBXr16YebMmShdujQaNGiQqXoxwUVERKRDtJ3gym7gk96+ffswdepUJCYmYsKECdKA74qpozUlgNIHSIrymQ2oMlvezMwsU+UVyzNbPrMBHhEREVFm5IeHmebm5tDT09PYBVHR0l3d+FwKEREROH78OIoXL44RI0YorStevDjGjRuHCRMmYMeOHZlOcHGQeSIiIvpihBD49ddf8e233yIlJQU//PCDNIsioNoFMb2YmBgA/wVI6bsIaqu8ubn5FykfGxsLmUyGQoUKqV1PREREVNAYGBjAwcEBkZGReP/+vcr6p0+fAgDKlSuncR8vX74EADg5OSnNuK1QsWJFAKmtuTKLCS4iIiIdIpNp95UTCQkJGDVqFFavXg1TU1OsWLFCZRyFsmXLAvgvEErv2bNnAP4LkHKrvGK5uvJmZmYoVqzYZ8vHxcUhKioKZcuWVRu4EREREWVVfonz6tevDyGE2tmiz5w5AwCoW7euxu3t7OwApE4kJIRQWa+YyKdIkSKZrhOjLSIiIh2izamjc9IEPikpCSNGjMCRI0dgb2+P33//Ha6urirlihQpgjJlyuDOnTuIiopSWX/mzBnI5XLUrl0bAFClShWYm5vj8uXLKt0CExIScPHiRZiZmaFy5coAUgMruVyuNviKjIzEnTt34OTkBBsbGwCpwRoAnD17VqX8zZs3ER0djVq1akFPT++z5RXBXZ06dTScJSIiIqKsyQ9xHgB069YNMpkMixcvVmqJf+fOHezatQv29vZo2bKlxu1LlCiBmjVr4sWLF1i5cqXSuqioKCxatAgA0KlTp0zXiQkuIiIi0jo/Pz+cOnUK9vb28Pf3h7Ozs8ay3t7eSEpKwvz585We4O3cuRP37t1DmzZtpKd3hoaG6NSpEyIjI7FixQql/SxbtgzR0dHw8fGBvn7qMKNFixaFq6srQkJCsGvXLqlsSkoK5s6di+TkZPTp00daXqNGDVSoUAHHjh1TSlolJCRg/vz5AKBU3s3NDba2tggICMDdu3el5bGxsViyZAlkMhl69+6dpXNHRERElN+5uLhgwIABuHfvHjp06IC5c+di2rRp6NGjB5KSkjB79mxpsp3Y2Fj4+fnBz89PaR9z586FnZ0dfvvtN3Tv3h2//PILpkyZgq+++gqhoaHo1atXhkmy9GRCXVswHSBrVTKvq0BU4Im/wwAAn5I/5HFNiAo2Y73cG2C8xM/Ntbq/51NPZHmbV69ewd3dHQkJCWjRogWqVKmitpyTkxM8PDyQmJiIPn364OrVq3BxcUGDBg0QGhqKo0ePolixYvD394e9vb20XUxMDLy9vfH48WM0bNgQLi4uCA4Oxvnz51GpUiVs3bpVacyrZ8+eoXv37oiKioK7uzvKlCmDc+fO4ebNm2jSpAlWrlypNAticHAw+vXrh8TERLRt2xZFixbFsWPH8OjRI3h6emLOnDlKx3H06FGMGjUKRkZG8PDwgJmZGQIDAxEeHg5fX1+MHDkyy+eQMsY4jyjnGOcRaUduxnmAdmO97MR56QUEBGD79u14+PAhzMzM4OLigpEjR6JatWpSmbCwMLi7uwOA0gNBAHjz5g1WrVqF48ePIzw8HIaGhqhcuTJ69uyJdu3aZakuTHARkUYMfIi0IzcDH4fZLbS6v2ffHc/yNvv27cO333772XLu7u5Yvnw5AODDhw9YtWoVDhw4gPDwcNjZ2aFx48bw9fVF0aJFVbaNiorC0qVLERQUhMjISNjb26NVq1YYOnSoNPB7WmFhYViyZAlOnz6NuLg4lChRAh07dsSAAQOkmRDTunv3Lvz8/HDx4kUkJCTA0dER3bt3R48ePaTuiWldvnwZy5cvx/Xr1yGEQNmyZdGvXz+0b98+M6eMsohxHlHOMc4j0o7cTnBpM9bLTpyXnzHBRUQaMfAh0o7/tQQX0ZfGOI8o5xjnEWkHE1z5h35eV4CIiIi0J6cz4hARERFR/sVYTzMmuIiIiHRITmfEISIiIqL8i7GeZpxFkYiIiIiIiIiICjQmuOizitvYI2D6KkTuuomw3y/j129mwMjACADw2/AfIP4OU3qN6NRf2rZHi054sOk03v95H7tnroWNRWEAgGu1hirbKV4OdsUBAE2q1sPlZQcRt/8erq48DPeaTZTqNbPPODzbfglRu2/Cf+py2Fpa584JIconIiJeYfyYCWjawBUtm7fGL/MWID4+Pq+rRXlMpuX/iEh3GBkYYe24BXi75xZe+P+LcV2HaCzbqnYzXFt5BO/238Xf835HhZJOSuvHd/0Gjzafxds9t7B+wq8wM/5vDBozY1OsHjsfrwKu49n2S/i2+3ClbZ2KlcKRudsRu+8OglcfRbt6bll672+7D8ejzWcRszcER+f7o5Jj+eyeEiKN9u3Zj+qVa6q8alSppbb8n/v/Qsd2ndGobhOM8R2HN6/fAACeP3+hdj/VK9fEv5f/VdpHUlISvLt0x4qlK5WW7/QPQLvW7dGobhMMGzICYc/CcnRs58+eh2fHrqhfqyEGDRiisj//7TvQxu0rNKrbBBPGTERMdEyO3o+0i3GeZkxw0Wf9MWMVTI1N0HScJ3r8PBwdGrbCT/0nAgAqO5bH5LVzYO9dU3qtD/QHANStWAPrxi3AD1sWocGojihsbomNExcBAM7evqy0jb13TZwKPo89pwPx7PUL2FnZ4M+fNsD/xH64DGmJnSf/xL4f1qOEbTEAwBCPXhjYtgd6zfVF03FeKG5TFGvH/ZI3J4goDwghMGHMBHz6+AkbtqzH/AVzcer4KSxbsjyvq0Z5TCaTafVFRLrjlyHTUKdCNbhN7I7hflMxs/dYeDX1UClXuVQFHJi1CfvOHUHt4V/hyv2bOPbLTimJNcSjF77vOw7fbZiHxmO6oIStPbZ/t1Tafs24+XCt1gCdvx8In9kjMKx9H4z1GgwgNcn297zt+JjwCQ1GdcT8nSuwY9oK1K1YI1Pv/U373pjQ9Rv4LpuOOiPaITT8GQ7N3gITI9XZUIlyos1XrRF08m/pdTjoEBwdHdCrT0+VsmdOn8XMqd/Dp1cPbNuxBaamphj+zUikpKTA3r6o0n6CTv6NrzzaoopLFVSrXk1pP5s2bMbdu/dU9v3br4sx6btvsT1gG0xMjDF21LhsH9fLFy8xxnccOnXpiG07tqJw4cIY4zsOirnnAg8dxqIFv2Hi5PHYtH0jXr4Mx+xZc7L9fqR9jPM0Y4KLMlTRoSwaVq6NAb+Mw+0n93D65kXM2LQAPd06AwAqOZbHlQc3EPH2tfT6GP8JADCyU3/sPPUnthzdhRuhIegzbxTa1XNDaXsHJCYlKm3TokYjuJRxxuBFqYmzxlXqIik5GQsCViI0/Cnm/L4UnxLj0aBS6hOTdvXcsOPkfpwKPo9bj+9i/s4VKi28iHTZ49DHCL5+Az/+/APKlS+LWnVqYbjvMBw8cCivq0ZERPmQqbEJBn3lg9HLZ+Lqg5vYeyYQ83euwMg0Le8VhnXoi7O3/8XMTQtwL+wRJq39GTHvY9HLvQsAwLfTAPz6x2r4H9+H20/uod/8sWhfvyUqlHSCjUVh+LTojG9+m4yzty7j9M2LmLR2NiZ0+wYA0L5BS9haWKPP3NG4/eQeth7dhS1Hd2Gs16BMvXf/1t5Y8McqHLgQhPvPQzFs8RTYWBRG4yp1c+dE0v8MY2Nj2NrZSq8Dfx6AEMDocaNUyv6+zR/t2n8Fn149UMapDGb8MA3hL8Nx7ux56OnpKe0nLCwMR48E4ec5P8HAwEDax9MnT/H71t/hVFa5xeLpU6fRsHEDuDZvhtKlS2HYiKG4d/c+3r59m63j2r1rD6pUrYx+A/qiXPmy+PHn7/Hi+QtcvpTammzD2o0YMLA/WrZuifLly2HcxDG4f+8BkpOTs/V+RLkpXyS4IiMj8eLFC0RHR+d1VSid8KjXaDOlF15Fv1FabmlmDnPTQihpVwz3wh6p3bZBpVo4FXxB+nfY65d4+uq5lKRS0NfTx6z+E/Hzdj9ExqZ+UUfGvoWtpTW6NPkKANCpURuYm5jhRmiItN6jvjuK29jD2NAYPi064+qDm1o7bqL8zsbWFstXL4ONrY3S8rh3cXlUI8ov2IKL8hvGeflDdafKMNDXx9nbl6Vlp29eQn3nmip/607FHHEh5KrSshuhd9Cwcu3/X18KF+78tz486hVex0SiYeXacCpWCgBw4c4VaX1waAiK29ijVNGScCrmiDvPHiL2w7v/1j8KQcNKin1n/N4TVv2EbUF7pHVCCMggg6WZRdZPClEmxUTHYMO6jRg9zheGhoYq658/C4NLNRfp38bGxnBwdEDwtWCVsosXLoFnN0+UcSqjtPyn73/G0BFDYW1dWGm5pZUl/r18BaGPQpGUlIQ/9/2F4iWKw8Ii9ZqPjX2H7yZNQ6O6TdDStRXmzJqLT58+aTyW4Os3UKvOf/djJiYmqFTZGcHXriMuLg53Qu7AvdV/3YZr16mN3fv/gJ6e3mfOEuUWxnma5VmCa/fu3XBzc4OZmRmKFCkCBwcH2NjYoFChQmjRogX27duXV1WjNGLex+LI5ZPSv2UyGUZ26o+gq6dRybE8UlJSMLXnKDzbfgnXVh5B31ZdpbLFrIvgRWSE0v4i3r5Byf/vZqjg7doBVoUssWz/JmnZPzcuYOm+jfhj+iokBj7G3h/WYchvk6Rk2o9bf0NScjKe+1/Gu/130LRqPfjMHvklTgFRvmRhYY7GTRpJ/05JSYH/9h2o36BeHtaK8gOZTLsvouxgnJf/FLMuijcxUUhMSpSWRUS/homRsTRGqrT87WuUsLVXWuZgVxy2Ftb/rbf5b72psQmsza1ga2GNiLevAUAaVkKxLQDYWqauL2ZdRHnfRYpLY6l+7r3P3LqE529eSusGtfOBvp4eTt+8mIWzQZQ1O/0DYGdnh1ZtWqldb21rg1cRr6R/p6Sk4NWrV3ibLrF/9co1BF+/gYGDv1Zavnf3PiQkxMOrm6fKvnv28kGZMqXRub0n6tVsgF0Bu7F46SIp4fT99O8RF/cOm7ZtwCK/Rbh18zbmzJqr8VjevH4DOzs75frb2CAi4hXCnj0HAERFvUW/Xv3R0rUVpk2ZjtjYd+p2RXmEcZ5meZLgWrhwIQYMGAB3d3ccPHgQt27dwoMHD3Dr1i389ddfcHNzQ79+/eDn5/fZfcXHxyM2NlbpxUGWv5z5g6eiVjkXTN0wH84OZSGEwJ1nD9Bual+sPfQ7Vo+Zh86N2wIATI1MEJ+YoLR9fGI8jAyUn3oM8eiFtYd+x6eE/540FDIxg1MxR3y/ZSHqjWyPWdsWY8nwH1HRoSwAoHRRB3z49BHtp/WD6/iuCHvzEusnLPjCR0+Ufy1a8BtCbt/ByDFM9BJR3mKclz+ZGquJyxJS/62YPEhhx4k/0a2ZBzzqu0NProe+rbqibsXqMPz/7lQ7Tv6JKT4j4OxYDkYGRlj4zUwAgKGBAZ6+eo5zt//F4uE/oLC5FYoWtsP3fVLHCzLUN8ChS8dhaWaO7/uOh4G+AWpXqIaBbXvAUN8gU++dVj3nmvh1yAz8ErBSSqwRaZsQArt37YFP7x4ay7Rp2xo7/QNw/dp1JCYmYu3qdYiKjEJSYqJSuV0Bu+DW0g1Fi/6X5I2MjMKSRX6Y9v00tS1qXr16jfiEBMyZPxubtm1E7bq18N2kaYiPj8ezp89wPOgEfp47C+UrlIdLtaqY+eN07N/7J969U5+U+vTpk0orNENDAyQkJODDhw8AgDmz5mLAwP74ZdEvePjgIaZOnpbp80WUl/Tz4k1//fVXbN68GZ06dVJZ5+zsjObNm8PFxQW+vr7w9fXNcF9z5szBDz/8oLRs5syZWq0vpZo76DuM8RyE7rOG49bju7j1+C7+PH8Ub99FAwBuhIagQkknDOvQB3vPBOJTgmoyy8jACB/iP0r/trOyQdOq9TDST/lL81vvYZBBhp+2/gYAuPrgJuo718ToLgMxfMl32DzpN0xcPQsHLgQBALxnDcWTrRdQz7kmLt5RbtZOpOsW/boY27Zsx/xf56J8+XJ5XR3KY7rY3JwKFsZ5+dOnhE+qcdn/3+Smjc0A4PDlE/hh6yLsmrEa+nr6OH79LDb/vQuWZuYAgJ+2/QanYo64teYYEpMSserANlx7eAuxH1K7yfeZNxp/zFiFN38EI+Z9LKasn4tGVeog9kMcXkdHosfsEdg4YSGm9RyF0PBn8Nu7AWM8B2XqvRUaVKqFQ7O34NCl45ixiQ856cu5dfM2XkW8Qtuv2mos49XNEw/uP8CAPgMBAC1bu6NJ08YwMzOTyiQlJeHEsROYNXeW0rbz58xHpy4dNcZws374GS1buaNd+9ShW+bOn4PW7m1x/NgJmJiYICUlBa2at1HaJiUlBc+ePsOugN048OdBafmeP3fB0NAQCQnKye6EhESYm5tDXz+1VdjXg/qjuVtzAMDMH2eiu1cPvHr1CkWKKLe+pLzBWE+zPElwffjwAaVLl86wTMmSJRET8/npSKdMmYJx45RnkTAyMsIP7dfmpIqUzpIRP2FYhz7oPXcUdp/+70tSkdxSCHl6H241UrtNPY8Mh721cvNXe2s7vIz6r/lumzrNERr+FDcf31EqV7tCNVx/dFtp2dUHt1C1TEXYWdnAsUgJpfVhr1/iTUwUShUpwQQX/U+ZM2suAnb8gZ/nzULL1i3zujqUDzDoobzGOC9/ev4mHLaW1tCT6yE5JXWwaPvCRfDh00dEx6l+FrO3+2FBwCpYmpnjdXQkdkxbgccRzwAAHz59RPdZw2Bhag4BgXcf4hCx8xoeh6euf/jiMWoObQM7KxvEvH+HssVKITk5GU9fpXZ/OnTxGIp614C9dRFEvH2Noe37SPv+3HsDgGu1hvhr1kYc+fcUfGaPkGZ/I/oSzpw+g1q1a8HCUvM4b3p6evhu+hSMnTAGCfEJsLSyRM/uvdGgUQOpTPC1YCQmJaFhmmUAEHjwMIyNjfH7ttSZ6OPj43Ht6nUcOfw39vy5CyG3QzD4m4FSeVMzUziWcsTLFy9RqlQpmJsXwvad21TqVKRoEQz3HY5+A/pKy+yK2KFI0SKIfBOpVDbyzRs4O1eAra0tAKB0mf/GBytdJnVcvfCXEUxw5ROM9TTLky6Knp6e6N+/P/755x8kJSUprUtJScHZs2fx9ddfw8vL67P7MjIygoWFhdLLyMjos9tR5s3oPRZD2/dGj59HYMeJ/dLyH/pNwN/zflcqW6NsFdx59hAAcD7kCppU/W88oJJ2xeBgVxznQ/4bdLS+c02cuXUZ6b2IjEDlUuWVljk7lkVo+FNExUbjU8InVC5VQVpnY1EYNhaFERr+LP2uiHTWymWr8MfOXZi3YA6+aqf5qSIRUW5inJc/XXt4C4lJSUqT/TSpWheX7l1XSRD1aNEJi4Z9j4TEBLyOjoSxoTFaVG+E49fOAgDmDZqKvq26IvbDO7z7EIc6FarD0swcZ2//C5lMhsNzt6FqaWe8jo5EQmICPOq748qDG3j3IQ7OjuVwdL4/ZDIZwqNeQQgBj/ruOH7tXKbeu0rpitj/43ocunQc3j8NRVKy8jVGpG03gm+iRq3qGZbZsmkr1q1ZDxMTE1haWeL169e4G3IXderVUdpP5cqVVL7D/jy0DwF7dmDnbn/s3O2PylUqo1v3rli2MrUbt52dHR4+/G9Sr4SEBLwIe44SJUqgdJlSePcuDjKZDI6lHOFYyhHx8fFYuOA3JCQkwsbGWlruWMoR+vr6qFbdBVev/Ncg4OPHj7gTchcu1auhWPFisCtih3t370nrQx+FQiaToXhx5XGUifKjPGnBtXz5ckyYMAFt2rRBUlISbG1tYWRkhPj4eLx58wYGBgbo27cvFi5cmBfVozScHctheu/RmPP7Upy+eRFFC//XIuvPc39jSo8RGN/1G+w5E4jWtZuhbysvtJjgDQBY8edmnFgQgHO3/8Wlu9exePgP+OvCUenpHgBULV0RgZdOqLzv2kO/4/Si3RjjOQj7zh5Bx4at0LZOc9Qc1hbJKcnYcHgnFgyZhjcxUYh6F40FQ6bj/J0ruHzv+hc/J0T5waOHj7B65Rp8PXgAataqiTev/5vp1NbONg9rRnmNT/UorzHOy58+xn/Cpr8DsHL0HAxYMB4lbO0xods3GLBgPACgaGE7xLx/h08Jn3Av7BE2TPgVp25cwI3QO5g/aCqevX6BQ5eOAwBeRIZjZp+xuP3kPlJECrZOXoIVf22RWvZ/+PQRcwdNwZgV36NKqQqY2Wcs+swbDQB4HB6Gyo7l8UPf8Vh/eAd6u3uiSdW6GLZ4CgB89r1XjZmLZ69fYNzKH6SB6QFIdSfStof3H8CjQzulZcnJyXgb9RaWlpYwMDRAiRIlMGPaTLhUc4G1tTV++v4nNG3WRKnb4YMHD+BU1kll/46lHJX+bWxsBEtLSxQvkTo5g2e3Lli7ah1KlS6FUqUcsXb1OpiamcG1RTMYGRmhcZNGmPLtVEyeOgl6enL8MOMnWFpawMLCXOW9AKCzZ2dsWr8Z69ash2tzV6xasRolShZH3Xp1IJPJ0Kdvbyz3W4ESJUvA2rowZv0wGy3cWzC+zEcY62kmE3nYpvfDhw+4fv06Xr58iQ8fPsDY2BglSpRAjRo1YGJikqN9y1qV1FIt/7dN6j4CcwdNUbtO1qokOjZsjR/7TUCFkk54HPEMUzfMx57Th6Qy/Vp3w4/9JsDa3ApH/j2FwQsnIipNt8bb647jt91rsfqAarPaDg1b4cd+E1CueGncffYQk9bORtDV0wBSx/KaNWAiejTvBBMjY/z97yn4LpuONzFR2j0B/+PE32EAgE/JH/K4JpTeujXrsWSR+gGar99mN938xljPNNfeq+Ii7bbmuzs2UKv7o/8djPPyHxMjY6wYNQdeTdsh5v07/LJzBRbvWQcg9Te//y9jselIAACgfxtvzOg1BjYWhRF09QyG+32H8P8fZkIul+PXb2agt7snUlJSsCVoFyatmS11fSxa2A6rx85Di+qN8Cr6DWZtX4KNh3dK9ahfqRaWjvwJlRzK4+bjuxizYqZSC39N7120sB3Cd6r/jUtbd8ocxnmZU69mAyzyW6g0e/Xz5y/QrpUH1m5cg7r/30pr3ep1+H2bPz7Fx6OFWwtMnvqt0hhcw4eMQEXnihg9blSG7zew3yDUqVsHw0YOBZCaTNu4fhN2BexGTHQMqteohu+mT0FJh9Tvwbdv32Luz/Pwz8nT0NPTQ+OmjTB56iRYWVlpfI/Tp05j/twFiAiPQPUa1TDjxxkoWbIEgNRB9desWgv/bTvw4cMHNHdzxdQZ38HcXH3CjHI3zgO0G+vpWpyXpwmuL4mBD1HOMfAh0g4muIi0i3EeUc4xziPSDia48o886aJIREREXwabrRMRERHpLsZ6mjHBRUREpEMY9BARERHpLsZ6muXJLIpERERERERERETawhZcREREOoRP9YiIiIh0F2M9zZjgIiIi0iGMeYiIiIh0F2M9zdhFkYiIiIiIiIiICjS24CIiItIhbLZOREREpLsY62nGFlxERERERERERFSgsQUXERGRDuFTPSIiIiLdxVhPMya4iIiIdAiDHiIiIiLdxVhPM3ZRJCIiIiIiIiKiAo0tuIiIiHQIH+oRERER6S7GepoxwUVERKRD2GydiIiISHcx1tOMXRSJiIiIiIiIiKhAYwsuIiIiXcKnekRERES6i7GeRkxwERER6RA2WyciIiLSXYz1NGMXRSIiIiIiIiIiKtDYgouIiEiH8KEeERERke5irKcZE1xEREQ6hM3WiYiIiHQXYz3N2EWRiIiIiIiIiIgKNLbgIiIi0iF8qkdERESkuxjracYEFxERkQ5h0ENERESkuxjracYuikREREREREREVKCxBRcREZEO4UM9IiIiIt3FWE8zJriIiIh0CJutExEREekuxnqasYsiEREREREREREVaGzBRUREpEP4VI+IiIhIdzHW04wJLiIiIh3CoIeIiIhIdzHW04xdFImIiIiIiIiIqEBjCy4iIiIdwqd6RERERLqLsZ5mTHARERHpEMY8RERERLqLsZ5m7KJIREREREREREQFGltwERER6RA2WyciIiLSXYz1NGOCi4iISIcw6CEiIiLSXYz1NGMXRSIiIiIiIiIiKtDYgouIiEiH8KkeERERke5irKcZE1xEREQ6hDEPERERke5irKcZuygSEREREREREVGBxhZcREREOoTN1omIiIh0F2M9zZjgIiIi0iUMeoiIiIh0F2M9jdhFkYiIiIiIiIiICjS24CIiItIhbLZOREREpLsY62nGBBcREZEOkTPmISIiItJZjPU0YxdFIiIiIiIiIiIq0NiCi4iISIew2ToRERGR7mKspxkTXERERDpEzqCHiIiISGflt1jv0KFD2LhxIx48eAA9PT3UrFkTI0aMQLVq1TK9j6NHj2LTpk24ffs2AKB06dLo3bs3OnXqBLk88x0P2UWRiIiIcsWiRYtQsWJFxMbGqqzbsmULKlasqPF1+fJlpfLx8fFYvXo12rVrh+rVq6NJkyaYNm0aXr16pfa9Y2NjsWDBArRu3RrVqlVDixYtMHfuXLx7905t+VevXmHGjBlwc3ODi4sL2rRpg2XLliEhIUFt+cePH2P8+PFo1qwZqlevjg4dOmDr1q1ISUnJ4lkiIiIiKhhWrFiBMWPG4M2bN/D29karVq1w4cIF+Pj44J9//snUPhYtWoQRI0YgNDQUnTp1QseOHREREYHJkyfjl19+yVJ92IKLiIhIh+TXZut79+7F6tWrNa5XPLHr168fzM3NVdYXL15c+v9JSUkYOXIkTp06hVq1asHd3R0PHz5EQEAATp48iYCAANjb20vl4+Li0L9/f9y6dQtNmzZFmzZtEBwcjA0bNuD06dPw9/dHoUKFpPIRERHw8fHBy5cv0bp1azg4OODcuXNYsmQJLl68iLVr18LAwEAqf+/ePfTu3RsfP36Eh4cHrK2tcezYMfz0008IDg7G/Pnzc3TuiIiIiBTyS6z34MEDLFmyBBUqVMCOHTtgamoKAOjduzd8fHwwdepUHDlyBMbGxhr3cf78eaxcuRKVK1fGhg0bYGVlBQAYM2YMPD09sX79evTo0QOlSpXKVJ2Y4CIiItIh+a1pdlJSEpYsWYLVq1dDCKGxXEhICIyMjDBp0iTo6elluM+AgACcOnUKXl5emD17trR8586dmD59On7++Wf4+flJy1euXIlbt27B19cXI0eOlJYvWrQIK1euxNKlSzF58mRp+dy5c/H8+XPMmzcPnTt3BgCkpKRg0qRJ2L9/P/z9/dGnTx+p/PTp0xEbG4vNmzejXr16AFIDs0GDBmHfvn1o27Yt3NzcMnfCiIiIiDKQX2K9TZs2ISUlBcOHD5eSWwBQqVIldO3aFVu2bEFQUBA8PDw07mPdunUAgDlz5kjJLQCwtLTE+PHjcfbsWcTExGS6Tvnl3BAREZGOOXfuHDp06IBVq1bBxcUFhQsXVlsuISEBDx48QIUKFT6b3AKAjRs3Qi6XY9y4cUrLvb29UaFCBRw9ehQRERHSvrdv3w5LS0sMGTJEqfyIESNQuHBh/PHHH1LXw/DwcAQGBqJ8+fJScgsA5HI5Jk+eDD09PWzbtk1afuXKFVy7dg3NmjWTklsAYGhoiEmTJgGAUnkiIiIiXXDu3DkAQOPGjVXWNWrUCABw9uxZjdvHx8fj7NmzKFeuHJydnVXWt2vXDrNmzcrSWF5McBEREekQuUym1VdO7Nu3D69evcL48eOxfft2pad7ad2/fx+JiYmoVKnSZ/f58uVLPH78GBUqVICtra3K+saNGyMlJQXnz58HAAQHB+P9+/eoU6cODA0NlcoaGhqibt26ePfuHYKDgwGkNpVPSUlBw4YNVfZtY2MDZ2dnhIaGIjw8HMB/wZ0ikEurSpUqsLKywsWLF5GcnPzZYyMiIiL6nPwQ5yUmJiIsLAzW1tawsLBQWe/o6AgAePTokcZ93L9/H0lJSahYsSKeP3+OyZMno3HjxqhWrRq6dOmCffv2Zble7KJIRESkQ7Q9LoO7u3uG64OCgjSu69q1KyZPnqzU5FwdxfhbMpkM48aNw+XLlxEdHY3SpUuje/fu8PHxkWbQCQ0NBZA6u446Dg4OAP4LqDJbPjQ0FHXq1MlU+Vu3buHRo0ewt7eXypcpU0Zj+Rs3biAsLCzT40cQERERaaLNWC+7cV50dDSEELC0tFS7XpH00jSZDwCptf3r16/RpUsXWFlZoW3btoiLi8PRo0fx7bff4tGjRxg7dmxmDgUAE1xERET0hdSpUydT5UJCQgAAO3bsQL169dC+fXu8efMGJ0+exI8//ohLly5h0aJFkMlkePv2LQBoDKgUyxUBlaK8piSborxiZsfslv9cfdTNHElERERUECUlJQGA0qQ7aSlazcfHx2vcx/v37wEAFy9eRPPmzbFkyRIYGRkBAJ49e4Zu3bph5cqVcHNzQ/Xq1TNVLya4iIiIdEhOuxWml1ELLW2RyWQoXrw4Ro8erTTu1Zs3b9C/f38cOnQIjRo1gre3NxITEwFApbuhQvqASlE+swFYVgO2z5VXLM8owCMiIiLKLG3GetmN8xSJKEWclZ5ibFNNw1MAUBp39YcffpD2CaS2gB88eDDmz5+P/fv3ZzrBxTG4iIiIdIhMJtPqKzdMnz4dx48fV0puAYCtra00u+GePXsAQJpqWhE4pZc+oFKUz2wAltnyZmZmmSqvWK4oT0RERJQT+SHOMzc3h56ensYuiIqW6+rG51IoVKgQAKBIkSKwt7dXWV+1alUAwJMnTzJdLya4iIiIKN9SPLF7+vQpANUuiOkpppJWBFSf6yKY3fLm5ubZKk9ERERU0BkYGMDBwQGRkZFSV8O0FHFbuXLlNO7DyckJgOaHlopW8iYmJpmuFxNcREREOkSu5deXlpiYiODgYFy6dEnt+g8fPgD4ryl82bJlAfwXOKX37NkzAP8FVLlVXrFcXXkzMzMUK1ZM7XoiIiKirMgvcV79+vUhhJBmlE7rzJkzAIC6detq3N7BwQElS5ZEdHQ0bt68qbL++vXrAJCpWbYVmOAiIiLSIdqcOlrb43mpk5iYiB49eqBv376IiopSWX/x4kUAQI0aNQCkNmMvU6YM7ty5o7b8mTNnIJfLUbt2bQBAlSpVYG5ujsuXL6t0I0xISMDFixdhZmaGypUrA0gNxORyudpgLTIyEnfu3IGTkxNsbGwApAZ3AHD27FmV8jdv3kR0dDRq1aqlNM4EERERUXbllzivW7dukMlkWLx4sVLL+jt37mDXrl2wt7dHy5YtM9xHnz59AACzZs1SagkWGhqKDRs2wNjYWGUIi4wwwUVERER5xtTUFC1btkRKSgrmzp2LlJQUad3Tp0+xYMECyOVy9O/fX1ru7e2NpKQkzJ8/H0IIafnOnTtx7949tGnTBkWKFAGQOih8p06dEBkZiRUrVii997JlyxAdHQ0fHx/o66fOu1O0aFG4uroiJCQEu3btksoq6pecnCwFY0Bq4q1ChQo4duyYUpIrISEB8+fPBwCl8kRERES6wMXFBQMGDMC9e/fQoUMHzJ07F9OmTUOPHj2QlJSE2bNnS5PzxMbGws/PD35+fkr76Nu3L1q2bImrV6/Cw8MDP//8M6ZMmYKuXbsiLi4O06ZNQ/HixTNdJ5lIGxnqEFmrknldBaICT/wdBgD4lPwhj2tCVLAZ62meQUbbuh74Rqv7+8Njldb25ebmhufPn+PSpUtKg46Gh4ejZ8+eeP78OZydndGwYUO8efMGQUFB+PDhA6ZMmaKU4EpMTESfPn1w9epVuLi4oEGDBggNDcXRo0dRrFgx+Pv7Kw1WGhMTA29vbzx+/BgNGzaEi4sLgoODcf78eVSqVAlbt26VBjoFUrsVdu/eHVFRUXB3d0eZMmVw7tw53Lx5E02aNMHKlSuVZk0MDg5Gv379kJiYiLZt26Jo0aI4duwYHj16BE9PT8yZM0dr55BSMc4jyjnGeUTakZtxHqDdWE8bcV5AQAC2b9+Ohw8fwszMDC4uLhg5ciSqVasmlQkLC4O7uzsA4O7du0rbJycnIyAgAAEBAXj48CEMDAzg4uKCwYMHo2HDhlmqCxNcRKQRAx8i7cjNwMf74FCt7m9nu5Va25emBBcAREdHY+XKlTh69CjCw8NhamqKatWqYeDAgWqDmw8fPmDVqlU4cOAAwsPDYWdnh8aNG8PX1xdFixZVKR8VFYWlS5ciKCgIkZGRsLe3R6tWrTB06FBpoPi0wsLCsGTJEpw+fRpxcXEoUaIEOnbsiAEDBkgzJ6Z19+5d+Pn54eLFi0hISICjoyO6d++OHj16sHviF8A4jyjnGOcRaUduJ7i0GetpM87LD5jgIiKNGPgQaQcTXETaxTiPKOcY5xFpBxNc+Yd+Zgr9+OOPmd7hjBkzsl0ZIiIiypkvPyw86RrGeURERAUHYz3NMpXgOn78eKZ2JpPJGPgQERHlodyY+ZB0C+M8IiKigoOxnmZaTXARERERUcHCOI+IiIh0gTw7Gz169AgTJ05E586d8fLlS6xfvx5nzpzRdt2IiIgoi+QymVZf9L+HcR4REVH+xThPsywnuE6dOoVq1aohNDQUgYGB+PjxI+7cuYMWLVpg9+7dX6KORERElEkymUyrL/rfwjiPiIgof2Ocp1mWE1zffvst5s6diz/++AMGBgYAgPnz52P+/Pkcl4GIiIioAGOcR0RERAVVlhNcN27cQLt27VSWd+zYEQ8fPtRKpYiIiCh72EWRcoJxHhERUf7GOE+zLCe4SpcujUuXLqksP3DgAEqXLq2NOhEREVE2ybT8ov8tjPOIiIjyN8Z5mmVqFsW0Zs2ahf79++Py5ctISkrC5s2bERoaCn9/f2zZsuVL1JGIiIiIcgHjPCIiIiqostyCq0uXLjh16hQiIiJQtWpV7Nu3D/Hx8Th16hS8vb2/RB2JiIgok9hFkXKCcR4REVH+xjhPsyy34AKA6tWrY/PmzdquCxEREeWQLgYrlLsY5xEREeVfjPU0y3ILLgDYvHkzGjVqhMKFC6No0aJwdXXFvn37tF03IiIiIspljPOIiIioIMpygmv69OkYM2YM2rZtiy1btmDNmjVo0qQJ+vTpg99+++0LVJGIiIgySyaTafVF/1sY5xEREeVvjPM0y3IXxVWrVmHz5s1o3769tKxjx46oUaMGRo8ejTFjxmizfkRERJQFbLZOOcE4j4iIKH9jrKdZlltwCSHg6OiosrxChQr4+PGjVipFRERERLmPcR4REREVVFlOcM2cORPffPMN7ty5Iy179uwZxowZg6lTp2q1ckRERJQ1Mi2/6H8L4zwiIqL8jXGeZpnqoiiXy5X6ZwohUKVKFZiZmUEul+Pdu3eQyWS4desWJkyY8MUqS0RERBljs3XKKsZ5REREBQdjPc0yleA6fvz4l64HEREREeUBxnlERESkCzKV4HJ1dc3Uzl6+fJmjyhAREVHO8KkeZRXjPCIiooKDsZ5mWZ5F8e7du5g0aRJu3bqF5ORkAKlN2ePj4/Hq1SskJSVpvZJERESUObo45TPlHsZ5RERE+RtjPc2yPMj84MGD8fr1a0ycOBHh4eEYP348unXrhpiYGKxbt+5L1JGIiIiIcgHjPCIiIiqostyC69KlSzh37hxq1KiBzZs3o1KlShgxYgQqVKiAdevWoV+/fl+inkRERJQJWX5yRZQG4zwiIqL8jbGeZlk+NwYGBrCysgIAODs74+rVqwCAVq1aITg4WKuVIyIioqyRyWRafdH/FsZ5RERE+RvjPM2ynOBq1KgRfvnlF3z8+BF16tTB/v37IYTA5cuXYWxs/CXqSERERES5gHEeERERFVRZ7qK4cOFCdOzYEU5OThg6dCgWL14Ma2trxMXFYcaMGV+ijkRERJRJnFmHcoJxHhERUf7GWE+zLCe4KleujPv37+Pjx48wNTXF5cuXceLECdjY2KBBgwZfoo5ERESUSQx6KCcY5xEREeVvjPU0y1SC6+nTpxmud3Fxkco5OjrmvFZERERElCsY5xEREZEuyFSCq3Tp0koDkAkhVAYkUyxLTk7Wbg2zSfwdltdVINIZxnqmeV0FIsokXRwwlL4sxnlE/9sY5xEVLIz1NMtUgis0NPRL14OIiIi0QA4GPZQ1jPOIiIgKDsZ6mmUqwVWqVKkvXQ+t+5T8Ia+rQFTgKZ7oxSa+zeOaEBVsFgaF87oKRBoVxDjvY/L7vK4CUYFnomcGAJC1dsjjmhAVbOLIs7yuAv2/LA8yT0RERPkXm60TERER6S7GepoxwUVERKRDOLMOERERke5irKeZPK8rQERERERERERElBPZSnAlJyfjwIEDWLRoEaKjo3HhwgXExMRou25ERESURTIt/0f/exjnERER5V+M8zTLchfFZ8+eoU2bNoiMjERUVBQ6deqE+fPn4+zZszhy5AhcXFy+RD2JiIgoEzguA+UE4zwiIqL8jbGeZlluwTVy5Eg0adIEL168gLGxMQDA398frVq1wqhRo7ReQSIiIiLKHYzziIiIqKDKcguuU6dO4cKFC9DT05OWGRgYYPr06ahVq5ZWK0dERERZw4FHKScY5xEREeVvjPU0y3ILLlNTU0RERKgsv3v3LiwsLLRSKSIiIsoeGeRafdH/FsZ5RERE+RvjPM2yfERDhw7FN998gwMHDkAIgXv37mHDhg0YPHgwBg4c+CXqSERERES5gHEeERERFVRZ7qI4ffp0WFlZYdiwYfjw4QPatWuHIkWKYNy4cZgwYcKXqCMRERFlEputU04wziMiIsrfGOtpluUEFwD4+vrC19cX79+/R1JSEiwtLbVdLyIiIsoGzqxDOcU4j4iIKP9irKdZlhNcmzdvznB93759s10ZIiIiIso7jPOIiIiooMpygmvmzJlK/05KSsKrV6+gr6+P+vXrM/AhIiLKQzLwqR5lH+M8IiKi/I2xnmZZTnCFhoaqLIuLi8M333wDFxcXrVSKiIiIsofjMlBOMM4jIiLK3xjraaaVeSELFSqE77//HgsXLtTG7oiIiIgon2CcR0RERAVBtgaZV+f69etITk7W1u6IiIgoGzjwKH0JjPOIiIjyB8Z6mmU5wdWiRQuVE/ru3Ttcv34d48aN01rFiIiIKOvk2mmcTf+jGOcRERHlb4z1NMtygqt58+ZK/5bJZDA0NMTcuXPh7u6urXoRERERUS5jnEdEREQFVZYTXJGRkRg9ejTKli37JepDREREOcBm65QTjPOIiIjyN8Z6mmW5bdvWrVuhp6f3JepCREREOSSTybT6ov8tjPOIiIjyN8Z5mmW5Bde4ceMwfPhwjB07FqVKlYKxsbHSekdHR61VjoiIiIhyD+M8IiIiKqiynOCaMWMGACAwMBDAf83jhBCQyWScYYeIiCgPyaF7T+Mo9zDOIyIiyt8Y62mWqQTXqVOn0KhRI+jr6yM0NPRL14mIiIiySRebm9OXxTiPiIio4GCsp1mmElwtWrTAy5cvUaRIEZQqVepL14mIiIiIcgnjPCIiItIFmUpwCSG+dD2IiIhIC+R8qkdZxDiPiIio4GCsp1mmx+BiMzgiIqL8T8ZxGSgbGOcREREVDIz1NMt0gqtOnTqZmjb60aNHOaoQEREREeUuxnlERERU0GU6wTV+/HhYWlp+yboQERFRDsll8ryuAhVAjPOIiIgKBsZ6mmUqwSWTydCjRw8UKVLkS9eHiIiIcoBdzSirGOcREREVHIz1NMtU6o+DjxIRERHpJsZ5REREpAsyleDq168fTExMvnRdiIiIKIdkWv5PmxYtWoSKFSsiNjZW7fpDhw6he/fuqF27NurVq4dvvvkGwcHBasumpKTA398fnTt3Rs2aNdGgQQOMGTMGoaGhasvHx8dj9erVaNeuHapXr44mTZpg2rRpePXqldrysbGxWLBgAVq3bo1q1aqhRYsWmDt3Lt69e6e2/KtXrzBjxgy4ubnBxcUFbdq0wbJly5CQkJCJM5O3GOcREREVHPktzstK/JYZFy9eRKVKlTB8+PAsb5upBNeGDRtgbm6e5Z0TERFR7pLLZFp9acvevXuxevVqjetXrFiBMWPG4M2bN/D29karVq1w4cIF+Pj44J9//lEpP2PGDMycORPJycno2bMnGjdujL///hteXl64c+eOUtmkpCSMHDkSv/76KywtLdG3b19Uq1YNAQEB8PLyQnh4uFL5uLg49O/fH2vWrIGjoyP69esHR0dHbNiwAT4+PoiLi1MqHxERgR49eiAgIAAuLi7o168fChUqhCVLlmDw4MFITEzMwZn78hjnERERFRz5Kc7Lavz2Oe/evcOkSZOQkpKSrfpkepB5IiIioqxKSkrCkiVLsHr1ao1d4R48eIAlS5agQoUK2LFjB0xNTQEAvXv3ho+PD6ZOnYojR47A2NgYAHDq1CkEBASgSZMmWLVqFfT1U8OZzp07Y/Dgwfjuu++we/duaf8BAQE4deoUvLy8MHv2bGn5zp07MX36dPz888/w8/OTlq9cuRK3bt2Cr68vRo4cKS1ftGgRVq5ciaVLl2Ly5MnS8rlz5+L58+eYN28eOnfuDCC1hdmkSZOwf/9++Pv7o0+fPjk8k0RERET5R1bjt8z48ccf8eLFi2zXicPvExER6ZD81EXx3Llz6NChA1atWgUXFxcULlxYbblNmzYhJSUFw4cPl4IjAKhUqRK6du2KiIgIBAUFScs3btwIABg9erSU3AKApk2bonnz5rh16xauXbumVF4ul2PcuHFK7+vt7Y0KFSrg6NGjiIiIAAAkJCRg+/btsLS0xJAhQ5TKjxgxAoULF8Yff/whdT0MDw9HYGAgypcvLyW3AEAul2Py5MnQ09PDtm3bMn/SiIiIiDKQX+K8rMZvn3Pw4EHs378fLVu2zHadmOAiIiLSIfmpi+K+ffvw6tUrjB8/Htu3b1cKftI6d+4cAKBx48Yq6xo1agQAOHv2LIDUFmGXLl2CpaUlXFxcVMor9qEo//LlSzx+/BgVKlSAra2t2vIpKSk4f/48ACA4OBjv379HnTp1YGhoqFTW0NAQdevWxbt376SxJc6fP4+UlBQ0bNhQZd82NjZwdnZGaGioSjdIIiIiouzIL3FeVuK3z4mIiMAPP/yA+vXr56jVOxNcRERE9EV07doVQUFBGDJkCAwMDNSWSUxMRFhYGKytrWFhYaGy3tHREQDw6NEjAMDz58+RkJAAR0dHtdNkpy+vGHS+dOnSat/fwcEhW+UV5bK6fyIiIqKCLqvxW0aEEJgyZQqSkpIwd+5ctfFdZnEMLiIiIh0ik2n32ZW7u3uG6zNqel6nTp3P7j86OhpCCFhaWqpdrwiaFLMXvn37FgC0Vl6xPH15KyurDMsrZoLMankiIiKinNBmrJfdOC+r8VtGtmzZgjNnzmDOnDkoXrw4nj179tltNGGCi4iISIdoa8rn3JKUlAQAGlt4KboJxsfHZ6u8YgbD9N0NP1f+S9WHiIiIKCfyQ6ynrfjnwYMHWLBgAVq2bAlPT88c14sJLiIiItIoK4ODZoeRkRGA/xJL6SkGc1eM35XV8oqZexTLM1s+q/v/XHkzMzO164mIiIjySnbjvKzGY+okJiZi4sSJMDMzw48//piteqTHBBcREZEOyemAobnN3Nwcenp6GpuwK7r2KZq6K7oCZrZ8+i6I6cXExKgtr6lLYXbLm5ubq11PRERElBX5IdbLavymzpIlS3D79m0sW7YMNjY2WqkXE1xEREQ6JCcDc+YFAwMDODg44MmTJ3j//r1KS6enT58CAMqVKwcAKFGiBIyNjaXl6aUvX7ZsWaXl6SnGecit8kREREQ5kR9ivazGb+ocOHAAADBixAi164OCglCxYkXUq1cPW7ZsyVS9mOAiIiKiPFW/fn08fvwY586dQ8uWLZXWnTlzBgBQt25dAIBcLkedOnVw+vRp3LlzB87OzhmWL1KkCMqUKYM7d+4gKioK1tbWKuXlcjlq164NAKhSpQrMzc1x+fJlJCYmKo0tkZCQgIsXL8LMzAyVK1eW3kcul0tTZacVGRmJO3fuwMnJSWtPJomIiIjyg6zEb+r07dtXbQuw58+fY8+ePShTpgw8PDxQokSJTNdJu1MtERERUZ6SQ6bVV27o1q0bZDIZFi9erBTo3LlzB7t27YK9vb1S4OTt7Q0AmDdvntLYWv/88w9OnDiBatWqoXr16krlk5KSMH/+fAghpOU7d+7EvXv30KZNGxQpUgRA6qConTp1QmRkJFasWKFUz2XLliE6Oho+Pj7Q1099Rli0aFG4uroiJCQEu3btksqmpKRg7ty5SE5ORp8+fbRxmoiIiIjyTZyX1fgtvf79+8PX11fl1aVLFwCAk5MTfH19szT4PFtwERER6ZD80Gw9q1xcXDBgwACsX78eHTp0QNu2bREXF4e//voLSUlJmD17ttIsiG3atEGbNm1w+PBhdOrUCW5uboiIiMChQ4dQqFAh/PTTT0r779OnD44cOYI9e/bgwYMHaNCgAUJDQ3H06FEUK1YMkydPVio/atQonD59GsuWLcOVK1fg4uKC4OBgnD9/HpUqVcKwYcOUyk+dOhXBwcGYOnUqjh07hjJlyuDcuXO4efMmmjRpgm7dun25k0dERET/U/JLrJeV+C02NhabNm0CAPj6+n6xOslE2keZOuRT8oe8rgJRgWeslzrrRWzi2zyuCVHBZmFQONfea+Pd1VrdX/+KQ7S2Lzc3Nzx//hyXLl1SO+hoQEAAtm/fjocPH8LMzAwuLi4YOXIkqlWrplI2KSkJGzduxO7du/Hs2TNYWlqiTp068PX1lcbFSuvDhw9YtWoVDhw4gPDwcNjZ2aFx48bw9fVF0aJFVcpHRUVh6dKlCAoKQmRkJOzt7dGqVSsMHTpUGlg+rbCwMCxZsgSnT59GXFwcSpQogY4dO2LAgAHSTIukPR+T3+d1FYgKPBO91DFzZK0d8rgmRAWbOPIsV99Pm7GeNuK8zMRvYWFhcHd3BwDcvXs3w/1duHABffv2hbu7O5YvX56lujDBRUQaMcFFpB25meDadG+tVvfXr8Igre6PSBuY4CLKOSa4iLQjtxNc2oz1dC3OYxdFIiIiHZJb42YRERERUe5jrKcZB5knIiIiIiIiIqICjS24iIiIdEh+GXiUiIiIiLSPsZ5mTHARERHpEBmbrRMRERHpLMZ6mrGLIhERERERERERFWhswUVERKRD2GydiIiISHcx1tOMCS4iIiIdwpl1iIiIiHQXYz3N2EWRiIiIiIiIiIgKNLbgov9r787jY7r6OI5/J5FFIvYQSyytJbRIU2qtKkqrSqtqK0ItD0Vra1EldqWqitb61E5RVVpVimofW2OPfd9JSBAiss/zR2pqJGMdGXP7eec1r1dzzrn3nplXI7/87u+cCwAwEJOJe1cAAABGRaxnGwkuAAAMhCfrAAAAGBexnm2k/gAAAAAAAODUqOACAMBAeLIOAACAcRHr2UaCCwAAA6FsHQAAwLiI9WxjiSIAAAAAAACcGhVcAAAYCGXrAAAAxkWsZxsJLgAADMSFsnUAAADDItazjSWKAAAAAAAAcGpUcAEAYCCUrQMAABgXsZ5tJLgAADAQE8XZAAAAhkWsZxufDAAAAAAAAJwaFVwAABgIZesAAADGRaxnGwkuAAAMxMSTdQAAAAyLWM82ligCAAAAAADAqVHBBQCAgbhQtg4AAGBYxHq2keACAMBAKFsHAAAwLmI921iiCAAAAAAAAKdGBRcAAAbCk3UAAACMi1jPNhJcAAAYiInibAAAAMMi1rONTwYAAAAAAABOjQouAAAMhLJ1AAAA4yLWs40EFwAABuLCk3UAAAAMi1jPNpYoAgAAAAAAwKlRwQUAgIFQtg4AAGBcxHq2keACAMBATJStAwAAGBaxnm0sUQQAAAAAAIBTo4ILAAADoWwdAADAuIj1bCPBBQCAgZgozgYAADAsYj3b+GQAAAAAAADg1KjgAgDAQFwoWwcAADAsYj3bSHABAGAgPFkHAADAuIj1bGOJIgAAAAAAAJwaFVwAABgIT9YBAAAwLmI926jgwmOXkJCgEUNHqlql6nr5xVoa/+UEmc1mqzHnzp1XpeeraGvoNgfNEnC8hIQENX2zhbaHbrdqP3P6jKo9/1K6x6z8+Vf9p03nNO2LFnyv+rUbqkbFmurb8xNFR0db+q5ejdbH3fvqpRdqqmHdt/TLTyvt+0bgUCY7fwFwHvHx8Rr06WBVq1hdtavX0ewZc2yOPbj/oFo2ba1KQVXUoklL7d+336p/5YpfVb9uA1UKqqIe3XrpypUrVscGlg6yerV4511J0oBPQtL0BZYOUoe2He/r3NeuXdfgAUNU88XaerlqTQ34JETXrl239F+9elW9PuytKuWrqd4r9bVi+QpLX7vgDuleO6T/oIf+TIH0eLh5aHrPz3Xlh706v2Cber7d0ebYV56vrl2TVun6soP67bP5KlHwKav+Xo3/o+OzN+rKD3v1ba8v5O3ple55JnYdpt8/X2TVVjHgOW38cqmuLzuog/9dr3avNrPqb1mrkQ7+d72il+7XDyHTlDeHr9V7GP/+EEUs2qmIRTs1+cOR8vLM/KAfBRyAOM82Elx47EaNGK3Nm7Zo0tRv9NnoEfrh+x/0/aIlVmOGDx6umzdvOmiGgOPFx8er/0cDdPzocav28AsR6tGlt+Lj49Mcsy10u0YM/ixN++qVv2n8FxPUs8+Hmj53msIvhGv0sDGW/sH9hygmJkb/nTdN73Vsq+EhI7Vvzz77vykAQIb68vNx2r9vv6bNmKJPBvbVlG+m6rdVa9KMuxl7U107faCg55/T/MVzVS6wnLp1+lA3Y1NjsT1hezV4wBD95/2Omr1gpq5du6aBn4RYjj927LhKBpTUmj9WW17fTPtakvRxv95W7bMXzJS7u7uav9v8vs49fPBwHTp0WBMnj9c3077WiWMnNGTgUEv/wE9CFHM9RrMXzFSHTu01eOBQ7QnbK0ka+9UYq2t/OWGs3Nzc1KR5E/t/2PhX+7xDf5UvXlY1P26m9yd+qpCW3fX2i/XSjCtduIRWDJ2pZZtX6/ku9bTj6F6tG73QksTq+Pq7GtSqhz75drSq9mikArn9NL/fxDTnqVz6eXWu38qqLW8OX60cPlvrw7bouc6vKmTOWE3oMlT1XqgpSarz/Eua0fsLTVg2Qy988IZibt7QyuGzLdU/Ia2666WylVSvf7Be/7SNXnz2BY1o28feHxWQoViiiMcq+mq0fvxhmab8d5LKlH1WktSqTSvtCdurd5o2liSt+OkX3YiNdeQ0AYc6fuyEPv14oHRHZeP6tX9oxODPlDt3rjTHTPtmumZOny3/wv5p+mZ/O0fB77VSzVdSA5wPenXTqGGfKzk5WRfOXdCGPzZq2aoflL9AfhUr/rT27N6jxd8t0TNlnnk8bxAZirJ14N/pZuxNLV3yoyZOmaBSpUupVOlSOnb0uL6bv1Cv1K1tNXbVylXy8PRQj4+6y2Qy6eN+vbXhzw1aveo3NXyrgRbOX6hXXn1FbzSsL0ka/tlQvVb7dZ07e04FChbQieMnVPTposrtmzvNPHx8fOTj42P5fsAnA/VK3dqqWftlSbrruXPmzKk1q9dq5txvVfqZ0pKkj/r10nut2is+Pl4XIy7qz/X/04rfflaBAvlVrHgxhe0K06LvFqtM2WeVLXs2y3WTk5M1YdxEtWkXrGeeLW3fDxv/al6emdX+teZ6rX8r7Ty6VzuP7tXowiXUtUEbLfnfL1ZjO9dvpU37tytk9heSpD7TR6h+xVp6t9Zbmrpinro1bKsvvp+q79YvkyQFf95D5+ZvVYmCT+nw2dSbnm6Z3DS1+yhtPmBd4f9mlboKv3JJ/WeMkiQdPX9SL5errBY139QvoevUrWEbzVu3VF8vnyVJ6jiur87OD9UrQdW1evsfqlehpqb+Mk/bj4RJkib9PEf/eb3l4/vgYDfEerZRwYXHaueOncqSJYvKVyhvaWvX4T0NGT5IUmqZ+ZdfjNOAkE8dNEPA8XZs3aHyLzyvb+dNt2rf8OdGderaUb369khzzF+bQzV+yjjVrF3Dqj0m5oYOHTisl29rDyr/nBb+OF+urq7au2ef8vrlVf4C+S395YLKac/uvXZ9T3AcFzt/AXAOhw4dVlJSkgIDy1naAoMCtTdsr1JSUqzGhoXt0XNBgZY/kkwmkwKDAhW2K/UP3bDde/T8889Zxvvl85NfPj+F7d4jSTp+7LgKFy50zzn9tfkv7di2U926d/3n2nc5t8nFpPHfjFPJUiWtzpOcnKzY2FjtCdsrPz8/Fbjtd9jt877d8h9/0rXoa2rbrs095wk8iHJPlZZbpkzatP+fhNOGvaGqGPBcmsTDU/kK6a+DO63a9pw4pMqlnk/t97PuD798UZeioyz9ktS36fsKO35Av+34n9V5ft22Xm3H9Eozv2xePrdde5elPS4hTkfPn1Tl0kGSpKjrV9T4xdeVPUs2Zc+STY2qvqadR4kHnQFxnm3Ge0d4opw9e075C+TTT8t+UsPX31K9OvU1ZdJUS6A1ZtQXatDwDRUr/rSDZwo4TuNmb6tnn+7yzOxp1f7p4E/UqMlb6R4zfc5UPV8hKE37ubPnJElXrlxVu5Yd9NrL9TXokyG6/vf+JZGXIuWbx/qOe85cOXUx4qI93goAwEEiL0Uqe/bscnN3s7TlypVT8fHxuno1Os1Y3zy+Vm25cuVUxN+/C9Lvz6WI8AhJ0onjJ3To4GE1bthEr9asp6EhwxQTE5NmTt9On6kGb74hv3x+97h26rk9PT1V9cWqcnd3t/TNn7NAJUoWV44cOdL9HZYrVy5FRERYtZnNZs2YPlPvtm4hL+/09zMCHla+nHkUGX1ZiUmJlraIK5HK7OGpXFlzWI2NuBKpArn9rNr8ffMp99/jIq5esur38sysnD7ZlTtbTklSSf+n9f4brdVj8uA08zgVcdYqOeabPZea1Wigtbs2/nPtXP+c22QyqUAuP+XOmnruj6YNV1E/f0V9H6ao78OU0ye73p/Q/6E+E+BJ4fQJrvj4eF27ds3qld5eNXCM2NhYnT51Rt8vWqIhwwep50c9tGDuAs2ZNVdbNm3Rzh271LFzB0dPEzCMm38v9x097HO1btdKn30xXMePHtfAfoMkSXFx8XK77Q8HSXJ3d1diQuKdp4KTMplMdn0BjkScd//i4m7K/bbkliRLoigxIcF67M04ubmlHXtrXFxcnFWSKbXfTYmJiUpMTNSZ02eVmJiowcMHKWTYQO3auVuf9hlgNf7smbPa+tdWNXvXetPru537Tt/N+06rf/1N3Xt3v/uxd/wO2xa6TRcjLqpR4/RvEgGPwssjs+ITrX+mbn3v4Wb9/+fCP37SOy++rtcr1pKri6tav9JYFUqWk/vf4xau/0n9mnVRgH8xebh5aOx/BkqS3DOl/nxO7T5KIXPG6uLVyLvOydPdU0sGTFX45UuasmKu5dqd67dSpVJByuSaSZ8076a8OXLL/e+f/WL5i+j0xfOq+XFT1f2kpTzd/7k+nmzEebY5/R5cI0eO1ODB1hntkJAQ9R3wsYNmhNu5uroqJiZGI0ePsCyJCr8QroXzFyrFbNYnA/rJ09PzHmcBcL9cXVP/WQ9u31ovvVxdktR/yCdq2bi1Ll28JI/b/oC5JSEhQZ6eHhk+VzweRnwiDv69bMV5fQZ85KAZPbnc3T2UcEeiJ+Hvf+/vjLXcPTzSJJQSEhIslcTuHu6WY//pT5Snp6fc3Ny0ftM6eXh4WJJkQ0cOVot3WurixUvK83d11prVa1UyoISeLmb9xLi7nft2Cxcs0qgRn6t3316qUrXy3+/x/o79bfUaVX2xitWeXIC9xCXEp0lk3fo+Nj7Oqn3VtvUaPHeclgyYokyumfT77k2avWaJsnmnLiMcOv8rPZWvkPZNW6vEpERN+WWedh3br2ux19Xx9Xfl6uKiqSvm3XU+3p5eWjb4vypRsKiq9Wikm3/PYdrK+SpTNED/G5v6cK/v//eLfgn9XdduxMjHK4v+2/Nz1erTTKF/L2N874ve+vOL7zVw9hcKv0xl/5OMWM82hyW4/vzzz/seW716dZt9/fr1U8+ePa3aPDw8ZFbyQ88N9uPrm1seHh5W+/0UKVJYp0+fkST1+rC31fgu/+mqNxrW14BB7MkFPIzcvqkb0hcpWtjSVrhI6j4pEeER8s3rq6jIKKtjoiIvK1c6GwUDwMN63HFeipIeem5GlSdvHl29elVJSUnKlCk1xI+KjJKnp6d8svrcMdZXkXf8LoiMjFLu3Km/C/LkyZOmPyoy0rKpfJYsWaz6ij5VVJJ0MeKiJcG1acMmvVzr5bTzvMe5JWnWt7P15Zhx6tG7u95t1cLqPaadd2Saze43bdisTl06prk2YA/nosKVO1tOubq4Kjkl9W9Ov5y+io27qasx0WnGj1gwQWO+n6Js3j66dDVKC/t/o5PhZyVJsXE31XT4+8rq5SOzzLoeG6OIRTt1MuKser3dUeWLl9X1ZQclpVZ1ubq46vqygyrdvqbOXDovH68sWjl8torlL6KaHzfT0fMnLddNSUlR14mf6qNpw+Tp7qkr16/qr/E/6bcdfyrAv5iyZPbW7mMHLON3HtsrV1dX+fvmJ8EFp+WwBFeXLl20f/9+Sanr5G0xmUxKTradrPLw8JCHR9rKg7hknsr3JChbrqzi4+N18uQpFSmS+gf38eMnlL9Afk2ZPslq7BuvNVTIkIGqVKWSI6YKGIJfPj/55vHV4UNH9ezfTy49efykTCaT/PLnU/YcOXThfLgiwi8qr18eSdLuHbstTzmF8zNiuTmcz+OO824mk+C6U8mAEsqUKZP27N6j5/7exH3njl165tnScnGx3pWkbNky+nb6TJnNZplMJpnNZu3esUvt/tMutb9cGe3asUsN32ogKbX6Pjw8QmXLldGxo8fVqllrLf5xoQoULCBJOnTwsDJlyqRChVKf7Gs2m7Vv7361//t8Vte+y7ml1M3hvxwzTr379lLL1u+mOfbC+QuKCI9QXr+8kqRdO3apzN/HStKVK1d09sxZBT4X+EifJ2DLrmP7lJiUpEqlgrRx31ZJUrVnX9DWw7vT/HvXrEZDVQwIVI/Jg3XpapQ83T31crkqajMmNXE/qv0n2nfqsGb/9r0kqXyJcsrm5aNN+7dr59EPldnjn+rED95sq4oBz+ndzz7Q+agImUwm/TBwqp7yK6SXer+jQ2eOWV27e6P28nBz16iF3+hmfJz8cubRc8We0Xtje1sScaULF7dsLB/gX0ySdCL89GP41GBPxHq2OWwPrm3btqlhw4YqW7asYmNjlZKSku7rbkEPnnxFihbRiy+9qIGfDNShg4e0ccMmfTt9hlq2fleFCheyekmpd+Zy5crp4FkDzstkMql5q2aaMnGq/tr0lw4fPKLPho7WSzWrK3fuXCroX0CVqlZSSL9BOnLoiJYtWa5Vv6zWO83fdvTUYScmO38BD4M4L+NlzpxZbzSsr2GDR2jvnn1at+Z3zZ4xRy1aNZeUurl7XFzq0qXadWvr+vXrGj1yjI4dPa7RI8fo5s041X21jiTpnWaN9fPyFVq65EcdPnRYn/YbqOo1XlSBggVU9Kki8i/kryEDh+rokaPauX2nhgwcqrcav6Ws2bJKks6fv6AbN27oqaefSjPPu507+mq0Phs2Sm+8+YZefa2uIi9FWl7Jyckq6F9QVapVVv++A3T40GEtXfKjVq74VU2bN7Gc/+iRY/Lw8LAk3wB7uxkfp1m/LdbkD0eqfIlyalilrno37qivlv5XkpQ3h6883VMTU4fPHVen+i31VtVXVSx/Ec3vN0FnLp3Xyq2/S5LOR0UopGV3lS9RTkHFy2hun6806ec5unL9qs5HhevY+ZOW1+XrV3UzPk7Hzp9Uckqy2r3aTC+Xq6L2X36sqzHXlDeHr/Lm8FUOn+ySpBPhZ9SnSWfVKFdZpQuX0PcDJmtF6DrtO3lI5yLDtXLr75rafZSCipfR88XLamr3UVrw+zJFRl92yOeK+0ecZ5vDElweHh5asGCBJOnTT1mOZmQjRw+XfyF/tWn5nj7tN0DNWjRVi5bNHT0twLBatmmhJi0aa2C/wWrfqqMK+hdUyLB/Nv8dPGKgvLy91LZFe307baYGDO2vZ8o848AZAzAa4jzH6NWnp0o9U0od2nTUyGGfqVPX/6jWK7UkSbVfqqNVK1dLSl1iOP6br7Rz+061eOdd7dm9RxMnj1dmr8ySpHKB5fTpoP6a8vVUBbdoq6xZfTR4+CBJkouLi776+kt5Z/FW21bt1L1rT1Ws9II+6tvLMo/Lfy8jvJXwut3dzr150xbFxsbqpx9/Uu2X6li9wv9+guOwkUPl7e2lVs2CNX3KfzVoWIhVFfLlqCj5+PhQ4YDHqueUIdp+JEy/f75QX3cdppDZY7V046+SpPCFO9T0pTckSTuO7FHn8f31xX8GaPvXv0iSXh/QxlLpNWHZDC3f/JtWDp+tlcNn6+e/1qr31GH3NYe3q9WTq6urVgybpfCFOyyvHwZOlSQt27RKoxdP1ry+E7Rh7BIdPntCrUZ9aDm+xchuCjt+QL8Mm6Wfh87QtsO71eFL9rGGczOZ71Y3ngEOHDigP/74Q506dbLreVmiCDw6T9fUR2tfS7zi4JkAzi2rW457D7KTbZc22vV85X2r2vV8+Hd5XHHezeQbdj0f8G+U2dVbkmSq4+/gmQDOzbz6TIZez56xntHiPIc/RbFUqVIqVaqUo6cBAIAxULWAJwhxHgAAdkasZ5PDligCAAAAAAAA9uDwCi4AAGA/RtwwFAAAAKmI9WwjwQUAgIGwsTIAAIBxEevZxhJFAAAAAAAAODUquAAAMBDK1gEAAIyLWM82KrgAAAAAAADg1KjgAgDAQJ60u3pz5szRsGHDbPbPmzdP5cuXlyTFx8dr1qxZ+vHHH3Xu3Dn5+PioRo0a+uCDD5QnT540x167dk1Tp07V6tWrFR4erly5cqlu3brq0qWLfHx80oy/ePGiJk6cqA0bNujSpUvKnz+/GjRooA4dOsjd3T3N+JMnT2rChAnaunWroqOjVahQITVt2lQtWrSQiwv3CAEAQMZ70mK9lStXaubMmTp69KhcXV313HPPqUuXLipbtux9HR8ZGanJkyfr999/V0REhDw8PFS6dGkFBwerdu3aDzQXElwAABjIk7bx6P79+yVJwcHB6Sad8ufPL0lKSkpS165d9eeffyooKEi1atXSsWPHtHjxYv3xxx9avHix/Pz8LMfFxMSoTZs22rdvn1588UXVrVtXYWFhmjFjhjZs2KDvvvtOWbJksYyPiIhQ8+bNdeHCBdWpU0f+/v7avHmzxo8fr9DQUE2fPl1ubm6W8YcPH1bLli118+ZNvf7668qZM6fWrVunoUOHKiwsTKNHj35cHxkAAIBNT1KsN2nSJI0bN04FCxZUkyZNdO3aNa1YsUIbNmzQ5MmT9eKLL971+HPnzqlZs2a6ePGigoKC9Morryg6OlqrVq1Sly5d1K1bN3Xt2vW+52Mym83mR31TT6K45FhHTwFwep6uXpKka4lXHDwTwLlldcuRYdfafXmrXc9XLmeFRzr+zTff1PHjx7Vz5065urraHLdgwQINGjRIb7/9tkaMGGFpX7RokQYMGKA6depowoQJlvYxY8Zo2rRpaQKfL7/8UpMnT1bbtm3Vt29fS3uPHj30yy+/aNSoUXrzzTclSSkpKerTp4+WL1+uTz/9VK1atbKMb9q0qXbv3q3Zs2frhRdekCQlJCSoffv2+uuvvzRp0iTVrFnzkT4bPLybyTccPQXA6WV29ZYkmer4O3gmgHMzrz6TodezZ6z3KHHe0aNH9cYbb6hYsWJauHChvLxS/3Y8cOCAmjdvrqxZs2r16tXy9PS0eY6uXbvqt99+0wcffKAuXbpY2i9cuKDGjRvr8uXLWrZsmUqUKHFfc6K+HgAAAzHZ+etRJCQk6OjRoypRosRdk1uSNHPmTLm4uKhnz55W7U2aNFGJEiW0Zs0aRUREWM47f/58ZcuWTR07drQa36VLF+XIkUPff/+9EhISJEnh4eH69ddfVbx4cUtyS5JcXFzUt29fubq6at68eZb2HTt2aNeuXapevboluSVJ7u7u6tOnjyRZjQcAAMgoT0qcN2vWLKWkpOj999+3JLckqVSpUmrcuLEiIiK0du1am8fHxsbq999/V/bs2dWpUyervnz58ql58+ZKSUnRunXr7ntOJLgAADCQJynBdeTIESUmJqpUqVJ3HXfhwgWdPHlSJUqUUO7cudP0V61aVSkpKdqyZYskKSwsTDdu3FD58uXT7J3l7u6uChUq6Pr16woLC5MkbdmyRSkpKapcuXKac+fKlUsBAQE6ceKEwsPDJUmbN2+WJFWpUiXN+GeeeUbZs2dXaGiokpOT7+NTAAAAsJ8nJc67FS9VrVo1Td+tGGrTpk02j09OTtZHH32kDz74IN0boR4eHpKkGzfuv2qbBBcAAHgsbu2/ZTKZ1LNnT1WvXl1ly5ZVgwYNNG/ePKWkpEiSTpw4IUkqUqRIuufx909dPnP8+PEHGn9r3MOev2jRojbHJyQk6OzZs+n2AwAAGFliYqLOnj2rnDlzKmvWrGn6CxUqJOmf2Co9Pj4+atOmjd599900fWazWatWrZIklSxZ8r7nxSbzAAAYiL03Hq1Vq9Zd++9Wen7gwAFJ0sKFC/XCCy+ofv36ioyM1B9//KEhQ4Zo69at+vLLL3XlSuo+f9myZUv3PLfar1+/LkmW8dmzZ7/r+GvXrj3S+HvN59Z4AACAjGLPWO9h47yrV6/KbDbbjJVuJb1uxW4Pas6cOdqzZ4/8/Pwe6EmKJLgAADCQRy03tyeTyaT8+fPrww8/tNr7KjIyUm3atNHKlStVpUoVyzLDO5cb3nKrPT4+XlLqXUNJVk89vNv4pKQku46/1X5rPAAAQEZ5EmK9B42tHsTSpUs1cuRIubq6atSoUXfdpP5OJLgAAIBNd6vQupcBAwZowIABadpz586tvn37ql27dlq6dKmCg4MlybIp/J1utd/awPRWoHMr0WWv8d7e3vc1/lb7rfEAAADO6GHjvFv7Y91vLHa/pk6dqrFjx8rFxUWjRo1SpUqVHuh4ElwAABiIvZcoPi7lypWTJJ0+fTrNEsQ7RUdHS/qn3P1eSwQfdryPj89DjQcAAMgoT0Ks5+PjI1dXV5ux260YKr39udKTkJCgAQMG6Mcff5Snp6fGjh17z+WT6SHBBQCAgTwJZetS6h29AwcOKD4+XhUqVEjTHxsbKyn1DuDTTz8tKTXZlZ4zZ85IkooVKyZJGTb+Vnt64729vZUvX750+wEAAB6XJyHWc3Nzk7+/v06dOqUbN26kqWq/FXPdiq3uJjo6Wp07d9b27duVO3duTZo0SWXLln2oefEURQAAYHeJiYlq1qyZWrdurcuXL6fpDw0NlSQFBgYqT548Klq0qA4ePJju2I0bN8rFxUXPP/+8JOmZZ56Rj4+Ptm3blqY0PiEhQaGhofL29lbp0qUlSRUqVJCLi4vlcda3i4qK0sGDB/XUU08pV65ckqSKFStKSv/R1nv37tXVq1cVFBSU7iOtAQAA/g0qVqwos9mcbny1ceNGSUr3JuftYmJi1LZtW23fvl0lSpTQ4sWLHzq5JZHgAgDAUEx2/npYXl5eql27tlJSUvTZZ58pJSXF0nf69GmNGTNGLi4uatOmjSSpSZMmSkpK0ujRo2U2my1jFy1apMOHD6tu3brKkyePpNSNSxs2bKioqChNmjTJ6rpff/21rl69qubNmytTptRC9bx58+qll17SgQMHtGTJEsvYW3NLTk5Wq1atLO2BgYEqUaKE1q1bZ5XkSkhI0OjRoyXJajwAAEBGeRLiPEl65513ZDKZ9NVXX1ktVTx48KCWLFlyX09ADAkJ0b59+xQQEKB58+Ypf/78jzQnk/n2KNJA4pJjHT0FwOl5uqZuCngt8YqDZwI4t6xuOTLsWoei99j1fCWzlXnoY8PDw9WiRQudO3dOAQEBqly5siIjI7V27VrFxsaqX79+lgRXYmKiWrVqpZ07d6pMmTKqVKmSTpw4oTVr1ihfvnz67rvv5OfnZzl3dHS0mjRpopMnT6py5coqU6aMwsLCtGXLFpUqVUpz585VlixZLOPPnDmjpk2b6vLly6pVq5aKFi2qzZs3a+/evapWrZomT55s9SSgsLAwBQcHKzExUa+++qry5s2rdevW6fjx42rUqJFGjhz50J8LHt3N5BuOngLg9DK7pi4pMtXxd/BMAOdmXp3+lgaPiz1jvUeJ8yRp1KhR+vbbb5UvXz69+uqriomJ0c8//6ykpCRNmTJFVatWlZS6J9esWbMkSd26dZMk7du3T40aNZIkNWzYUP7+6f9bVK5cOVWvXv2+5kOCC4BNJLgA+/i3Jrgk6erVq5o8ebLWrFmj8PBweXl5qWzZsmrXrp0qV65sNTY2NlZTpkzRihUrFB4eLl9fX1WtWlXdunVT3rx505z78uXLmjhxotauXauoqCj5+fnplVdeUadOnSwbxd/u7NmzGj9+vDZs2KCYmBgVKFBADRo0UNu2bdN9BPWhQ4c0YcIEhYaGKiEhQYUKFVLTpk3VrFkzlic6GAku4NGR4ALs49+c4JKkxYsXa/78+Tp27Ji8vb1VpkwZde3a1Wqp4dmzZy2bxh86dEiS9M033+irr7665/lbt26t/v3739dcSHABsIkEF2AfGZngOhy9167nK5HtWbueD7AHElzAoyPBBdhHRie47BnrGS3O4ymKAAAYyJPwZB0AAAA8HsR6trHJPAAAAAAAAJwaFVwAABiIycRdPQAAAKMi1rONBBcAAIZC0AMAAGBcxHq2sEQRAAAAAAAATo0KLgAADISydQAAAOMi1rONBBcAAAbCk3UAAACMi1jPNpYoAgAAAAAAwKlRwQUAgIFwVw8AAMC4iPVsI8EFAICBsC8DAACAcRHr2cYSRQAAAAAAADg1KrgAADAQytYBAACMi1jPNhJcAAAYCEEPAACAcRHr2cYSRQAAAAAAADg1KrgAADAQNh4FAAAwLmI920hwAQBgIJStAwAAGBexnm0sUQQAAAAAAIBTo4ILAAADoWwdAADAuIj1bCPBBQCAgVC2DgAAYFzEeraxRBEAAAAAAABOjQouAAAMhbt6AAAAxkWsZwsJLgAADISQBwAAwLiI9WxjiSIAAAAAAACcGhVcAAAYCE/WAQAAMC5iPdtIcAEAYCgEPQAAAMZFrGcLSxQBAAAAAADg1KjgAgDAQLinBwAAYFzEeraR4AIAwFAIewAAAIyLWM8WligCAAAAAADAqVHBBQCAgfBkHQAAAOMi1rONCi4AAAAAAAA4NRJcAAAAAAAAcGosUQQAwEBMbDwKAABgWMR6tpHgAgDAQAh6AAAAjItYzzaWKAIAAAAAAMCpkeACAAAAAACAU2OJIgAABsKjowEAAIyLWM82KrgAAAAAAADg1EhwAQAAAAAAwKmxRBEAAAPhyToAAADGRaxnGxVcAAAAAAAAcGpUcAEAYCjc1QMAADAuYj1bSHABAGAghDwAAADGRaxnG0sUAQAAAAAA4NSo4AIAwEBMJu7rAQAAGBWxnm0kuAAAMBSCHgAAAOMi1rOFJYoAAAAAAABwalRwAQBgINzTAwAAMC5iPdtIcAEAYCiEPQAAAMZFrGcLSxQBAAAAAADg1KjgAgDAQHiyDgAAgHER69lGBRcAAAAAAACcGgkuAAAAAAAAODWWKAIAYCAmNh4FAAAwLGI920hwAQBgKAQ9AAAAxkWsZwtLFAEAAAAAAODUqOACAMBAuKcHAABgXMR6tpHgAgDAQHh0NAAAgHER69nGEkUAAAAAAAA4NSq4AAAwFO7qAQAAGBexni0kuAAAMBBCHgAAAOMi1rONJYoAAAAAAABwalRwAQBgKNzXAwAAMC5iPVtIcAEAYCA8WQcAAMC4iPVsY4kiAAAAAAAAnBoJLgAAAAAAADg1ligCAGAgJvZlAAAAMCxiPdtMZrPZ7OhJAAAA+4hLjrXr+Txdvex6PgAAADw8e8Z6RovzWKIIh4iPj9egQYMUHx/v6KkATo2fJdzJ09XLri8AeFD8bgLsg58lpIc4zzYquOAQ165dU7Zs2RQdHa2sWbM6ejqA0+JnCQDwpOF3E2Af/CwBD4YKLgAAAAAAADg1ElwAAAAAAABwaiS4AAAAAAAA4NRIcMEhPDw8FBISIg8PD0dPBXBq/CwBAJ40/G4C7IOfJeDBsMk8AAAAAAAAnBoVXAAAAAAAAHBqJLgAAAAAAADg1EhwAQAAAAAAwKmR4EKGi4uLU7t27ZQ9e3bly5dPX3zxhaOnBDi1+Ph4Pfvss1q/fr2jpwIA+JcjzgPsizgPuH+ZHD0B/Pt89NFH2rZtm9atW6dTp04pODhYhQsXVuPGjR09NcDpxMXFqUWLFtq3b5+jpwIAAHEeYEfEecCDIcGFDHXjxg1Nnz5dK1euVFBQkIKCgrRv3z5NnDiRwAd4QPv371eLFi3Ew3ABAE8C4jzAfojzgAfHEkVkqN27dysxMVFVqlSxtFWrVk1//fWXUlJSHDgzwPn88ccfevnll7V582ZHTwUAAOI8wI6I84AHRwUXMtSFCxeUO3duubu7W9ry5s2ruLg4RUVFydfX14GzA5xL586dHT0FAAAsiPMA+yH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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = XGBoost_classification_tunned(best_hyperparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}99.97 & 99.95 & 99.39 & 99.67 \\\\\n",
      "\\hline\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_validation=df_validation[features]  # Features\n",
    "y_validation=df_validation['LABEL_code']  # Labels\n",
    "\n",
    "\n",
    "y_pred = best_model.predict(X_validation)\n",
    "accuracy = metrics.accuracy_score(y_validation, y_pred)  * 100\n",
    "precision = precision_score(y_validation, y_pred)  * 100\n",
    "recall = recall_score(y_validation, y_pred)  * 100\n",
    "F1 = f1_score(y_validation, y_pred)  * 100\n",
    "                \n",
    "tmp_str = \"\"\n",
    "tmp_str += \"\\multirow{-6}{*}{\\\\rotatebox{90}{\\centering \\\\textbf{Our approach}}} & \\cellcolor{Gray} \\\\textbf{All} & \\\\rowcolor{LigthGray}\" \n",
    "tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "tmp_str += \"\\n\"\n",
    "tmp_str += \"\\hline\"\n",
    "tmp_str += \"\\n\"\n",
    "print(tmp_str)\n",
    "\n",
    "matrix, percentage_matrix = get_confusion_matrix(y_validation, y_pred)\n",
    "plot_confusion_matrix(matrix, percentage_matrix, model=\"XGBoost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"HTTPS_Brute_Force\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerow([problem, \"-\", accuracy, precision, recall, F1, 0, 0, 0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importance"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(best_model.feature_importances_, index = X_validation.columns).sort_values(ascending = True)\n",
    "\n",
    "f_sf = feat_importances[statistics_features].sum()\n",
    "f_tf = feat_importances[time_features].sum()\n",
    "f_df = feat_importances[distribution_features].sum()\n",
    "f_ff = feat_importances[frequency_features].sum()\n",
    "f_bf = feat_importances[behavior_features].sum()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect=\"equal\"))\n",
    "data = [f_sf, f_tf, f_df, f_ff, f_bf]\n",
    "ingredients = [\"Statistical-based\", \"Time-based\", \"Distribution-based\",  \"Frequency-based\",  \"Behavior-based\"]\n",
    "def func(pct, allvals):\n",
    "    return \"{:.1f}%\".format(pct)\n",
    "wedges, texts, autotexts = ax.pie(data, autopct=lambda pct: func(pct, data),\n",
    "                                  textprops=dict(color=\"w\"))\n",
    "ax.legend(wedges, ingredients,\n",
    "          loc=\"center\",\n",
    "          bbox_to_anchor=(0.5, 0, 0, -0.05),\n",
    "          prop={'size': 12},\n",
    "          ncol=2)\n",
    "plt.setp(autotexts, size=12, weight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5229471, 0.10387763, 0.057390332, 0.15032996, 0.101740375]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.bar(feat_importances.keys(), feat_importances.values)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PACKETS': 0.0,\n",
       " 'GALTON_SKEWNESS': 0.0,\n",
       " 'BENFORD_LAW_PRESENTED': 0.0,\n",
       " 'NORMAL_DISTRIBUTION': 0.0,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.0,\n",
       " 'SPECTRAL_BANDWIDTH': 0.0,\n",
       " 'SIG_SPACES': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.0,\n",
       " 'PERIODICITY': 0.0,\n",
       " 'VAL': 0.0,\n",
       " 'TIME': 0.0,\n",
       " 'MAX_POWER': 0.0,\n",
       " 'MAX_POWER_FREQ': 0.0,\n",
       " 'SPECTRAL_ENTROPY': 0.0,\n",
       " 'SPECTRAL_SKEWNESS': 0.0,\n",
       " 'SPECTRAL_SLOPE': 0.0,\n",
       " 'SPECTRAL_CREST': 0.0,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.0,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.0,\n",
       " 'PERIODICITY_SCDF': 0.0,\n",
       " 'PERCENT_BELOW_MEAN': 0.0,\n",
       " 'MEAN': 0.0,\n",
       " 'SPECTRAL_CENTROID': 0.002270329277962446,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.0026438329368829727,\n",
       " 'MEAN_SCALED_TIME': 0.002808178775012493,\n",
       " 'ROOT_MEAN_SQUARE': 0.002887215232476592,\n",
       " 'SPECTRAL_FLUX': 0.0029458715580403805,\n",
       " 'Q3_SCALED_TIME': 0.0030398506205528975,\n",
       " 'BURSTINESS': 0.003087590215727687,\n",
       " 'P_BENFORD': 0.003205921035259962,\n",
       " 'Q1_SCALED_TIME': 0.0032257880084216595,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.0033141670282930136,\n",
       " 'SPECTRAL_KURTOSIS': 0.0035727033391594887,\n",
       " 'BYTES_REV': 0.004044903442263603,\n",
       " 'PERCENT_ABOVE_MEAN': 0.004374073352664709,\n",
       " 'POWER_MEAN': 0.004426089581102133,\n",
       " 'MAX_DIFFTIMES': 0.004436027724295855,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.004720539785921574,\n",
       " 'TRANSIENTS': 0.004855575039982796,\n",
       " 'BIGGEST_CNT_1_SEC': 0.004900412168353796,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.005136122461408377,\n",
       " 'Q1': 0.005274446215480566,\n",
       " 'MODE': 0.0061560701578855515,\n",
       " 'STDEV': 0.006225562188774347,\n",
       " 'PERCENT_DEVIATION': 0.006317284423857927,\n",
       " 'MIN_POWER_FREQ': 0.0071703423745930195,\n",
       " 'CNT_DISTRIBUTION': 0.007561664097011089,\n",
       " 'MEDIAN_DIFFTIMES': 0.007787048816680908,\n",
       " 'TIME_DISTRIBUTION': 0.008926471695303917,\n",
       " 'DURATION': 0.009012185968458652,\n",
       " 'MIN_DIFFTIMES': 0.00915460754185915,\n",
       " 'KURTOSIS': 0.011343968100845814,\n",
       " 'BYTES': 0.012641369365155697,\n",
       " 'POWER_STD': 0.013515910133719444,\n",
       " 'SPECTRAL_ENERGY': 0.013553353026509285,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.014520744793117046,\n",
       " 'MIN': 0.016398625448346138,\n",
       " 'SPECTRAL_ROLLOFF': 0.01680542156100273,\n",
       " 'SPECTRAL_SPREAD': 0.01770181767642498,\n",
       " 'AVERAGE_DISPERSION': 0.020839840173721313,\n",
       " 'DIRECTIONS': 0.021683167666196823,\n",
       " 'MEAN_DIFFTIMES': 0.02195887267589569,\n",
       " 'MIN_POWER': 0.02600313536822796,\n",
       " 'SCALED_ENTROPY': 0.027019105851650238,\n",
       " 'MIN_MINUS_MAX': 0.031215468421578407,\n",
       " 'MEDIAN_SCALED_TIME': 0.037318941205739975,\n",
       " 'HURST_EXPONENT': 0.03769627586007118,\n",
       " 'POWER_MODE': 0.042364999651908875,\n",
       " 'PACKETS_REV': 0.04702821373939514,\n",
       " 'MAX': 0.047437772154808044,\n",
       " 'VAR': 0.05991500988602638,\n",
       " 'SWITCHING_METRIC': 0.07030121982097626,\n",
       " 'MEDIAN': 0.07479926198720932,\n",
       " 'ENTROPY': 0.07493501156568527,\n",
       " 'Q3': 0.09952153265476227}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature corelation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_val(selected_features, best_hyperparams=None, cycles=10):\n",
    "    if best_hyperparams is None:\n",
    "        best_hyperparams = tunne_together_XGBoost_classification(df_features.sample(100000), selected_features, verbose=False)\n",
    "    \n",
    "    X=df_features[selected_features]  # Features\n",
    "    y=df_features['LABEL_code']  # Labels\n",
    "\n",
    "    best = 0\n",
    "    best_model = None\n",
    "    for i in range(0, cycles):    \n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test   \n",
    "        model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "        )\n",
    "        model.fit(X_train, y_train)                \n",
    "        y_pred = model.predict(X_test)\n",
    "        accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "        precision = precision_score(y_test, y_pred)  * 100\n",
    "        recall = recall_score(y_test, y_pred)  * 100\n",
    "        F1 = f1_score(y_test, y_pred)  * 100\n",
    "        if F1 > best:   \n",
    "            best = F1\n",
    "            best_model = model\n",
    "            \n",
    "    \n",
    "    X_validation=df_validation[selected_features]  # Features\n",
    "    y_validation=df_validation['LABEL_code']  # Labels\n",
    "\n",
    "    y_pred = best_model.predict(X_validation)\n",
    "    accuracy = metrics.accuracy_score(y_validation, y_pred)  * 100\n",
    "    precision = precision_score(y_validation, y_pred)  * 100\n",
    "    recall = recall_score(y_validation, y_pred)  * 100\n",
    "    F1 = f1_score(y_validation, y_pred)  * 100\n",
    "    \n",
    "    return accuracy,precision,recall,F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [03:54<00:00,  2.34s/trial, best loss: -0.9723002810116419]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.15339674691900862, 'gamma': 6.025987865470435, 'max_depth': 10.0, 'min_child_weight': 2.0, 'n_estimators': 400.0, 'reg_alpha': 0.0, 'reg_lambda': 0.3042168610697139}\n",
      "100%|██████████| 100/100 [02:48<00:00,  1.69s/trial, best loss: -0.9937791601866253]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.021947585670282294, 'gamma': 1.4410254342402191, 'max_depth': 28.0, 'min_child_weight': 0.0, 'n_estimators': 280.0, 'reg_alpha': 0.0, 'reg_lambda': 0.7298384376129919}\n",
      "100%|██████████| 100/100 [02:35<00:00,  1.55s/trial, best loss: -0.98272398553636] \n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.42792204467597333, 'gamma': 4.180700428371697, 'max_depth': 26.0, 'min_child_weight': 5.0, 'n_estimators': 260.0, 'reg_alpha': 6.0, 'reg_lambda': 0.9018022927855124}\n",
      "100%|██████████| 100/100 [02:57<00:00,  1.78s/trial, best loss: -0.9693836978131213]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.4944849690313147, 'gamma': 3.062977314412632, 'max_depth': 28.0, 'min_child_weight': 9.0, 'n_estimators': 260.0, 'reg_alpha': 0.0, 'reg_lambda': 0.34933916093037043}\n",
      "100%|██████████| 100/100 [00:54<00:00,  1.85trial/s, best loss: -0.9682603455202893]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.630633140914624, 'gamma': 7.9230207404215465, 'max_depth': 10.0, 'min_child_weight': 0.0, 'n_estimators': 300.0, 'reg_alpha': 2.0, 'reg_lambda': 0.5592106024097131}\n",
      "100%|██████████| 100/100 [02:18<00:00,  1.39s/trial, best loss: -0.990215264187867]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.9768253207416444, 'gamma': 1.9758260289907201, 'max_depth': 8.0, 'min_child_weight': 1.0, 'n_estimators': 100.0, 'reg_alpha': 0.0, 'reg_lambda': 0.4491365698647187}\n",
      "100%|██████████| 100/100 [02:50<00:00,  1.70s/trial, best loss: -0.9871144084342054]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.18782839704840265, 'gamma': 3.6180267076725943, 'max_depth': 28.0, 'min_child_weight': 5.0, 'n_estimators': 100.0, 'reg_alpha': 1.0, 'reg_lambda': 0.569105117891947}\n",
      "100%|██████████| 100/100 [01:26<00:00,  1.15trial/s, best loss: -0.9809069212410502]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7122154852447691, 'gamma': 5.598553288793127, 'max_depth': 22.0, 'min_child_weight': 13.0, 'n_estimators': 220.0, 'reg_alpha': 2.0, 'reg_lambda': 0.15684549384954183}\n",
      "100%|██████████| 100/100 [01:34<00:00,  1.06trial/s, best loss: -0.9818611987381702]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7249659502119605, 'gamma': 2.034345196528194, 'max_depth': 22.0, 'min_child_weight': 0.0, 'n_estimators': 80.0, 'reg_alpha': 11.0, 'reg_lambda': 0.002979552965622523}\n",
      "100%|██████████| 100/100 [02:09<00:00,  1.29s/trial, best loss: -0.9875486381322957]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.37097397263741294, 'gamma': 2.787681239554221, 'max_depth': 28.0, 'min_child_weight': 4.0, 'n_estimators': 320.0, 'reg_alpha': 0.0, 'reg_lambda': 0.12612411688917274}\n"
     ]
    }
   ],
   "source": [
    "import FEATURE_REDUCTED as FR\n",
    "REPORTS = []\n",
    "\n",
    "report = perform_val(FR.without_flow_header(FR.botnet_best_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.doh_best_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.dos_best_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.dns_malware_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.tor_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.vpn_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.all_best_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.combo_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.combo_2_features))\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.without_flow_header(FR.crypto_features))\n",
    "REPORTS.append(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"HTTPS_Brute_Force\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    for report, f_set in zip(REPORTS, FR.FEATURE_SETS):\n",
    "        writer.writerow([problem, f_set, report[0], report[1], report[2], report[3], 0, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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