ids_unsw_binary.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intrusion detection in IDS using Network Traffic Classification based on Single Flow Time Series Analysis \n",
    "\n",
    "Problem: binary detection of Intrusion in IDS system\n",
    "\n",
    "Raw data from: UNSW-NB15 [1]\n",
    "\n",
    "-----------------------------------------------------------------------\n",
    "[1] Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015."
   ]
  },
  {
   "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",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_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",
    "\n",
    "# ML algorithms\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\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 xgboost import XGBClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "import catboost as cb\n",
    "\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 imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../cnsm_final_datasets/ids_unsw_nb_15_binary.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>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>...</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>149.171.126.18</td>\n",
       "      <td>175.45.176.1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>179</td>\n",
       "      <td>...</td>\n",
       "      <td>-316303.733903</td>\n",
       "      <td>0.025341</td>\n",
       "      <td>0.465500</td>\n",
       "      <td>0.329833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-142.570453</td>\n",
       "      <td>382.427060</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>0.028264</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>175.45.176.1</td>\n",
       "      <td>149.171.126.18</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>26581</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>149.171.126.12</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>746</td>\n",
       "      <td>0</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>445</td>\n",
       "      <td>...</td>\n",
       "      <td>6629.691442</td>\n",
       "      <td>0.857953</td>\n",
       "      <td>0.531418</td>\n",
       "      <td>0.291302</td>\n",
       "      <td>0.079684</td>\n",
       "      <td>1.335840</td>\n",
       "      <td>0.163986</td>\n",
       "      <td>0.993135</td>\n",
       "      <td>0.257254</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>149.171.126.12</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>370</td>\n",
       "      <td>0</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>1540</td>\n",
       "      <td>...</td>\n",
       "      <td>4637.314944</td>\n",
       "      <td>0.568099</td>\n",
       "      <td>0.520225</td>\n",
       "      <td>0.288753</td>\n",
       "      <td>0.051809</td>\n",
       "      <td>1.165001</td>\n",
       "      <td>0.163854</td>\n",
       "      <td>0.993135</td>\n",
       "      <td>0.361351</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>149.171.126.12</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>588</td>\n",
       "      <td>0</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>445</td>\n",
       "      <td>...</td>\n",
       "      <td>2903.826062</td>\n",
       "      <td>0.679153</td>\n",
       "      <td>0.541261</td>\n",
       "      <td>0.288383</td>\n",
       "      <td>0.116073</td>\n",
       "      <td>1.248354</td>\n",
       "      <td>0.371784</td>\n",
       "      <td>0.911681</td>\n",
       "      <td>0.080400</td>\n",
       "      <td>True</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979454</th>\n",
       "      <td>2979454</td>\n",
       "      <td>149.171.126.15</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2360</td>\n",
       "      <td>584</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>2049</td>\n",
       "      <td>...</td>\n",
       "      <td>-320173.252379</td>\n",
       "      <td>0.014216</td>\n",
       "      <td>0.425575</td>\n",
       "      <td>0.384493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-316.101688</td>\n",
       "      <td>196.224287</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>0.054011</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979455</th>\n",
       "      <td>2979455</td>\n",
       "      <td>149.171.126.11</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>5600</td>\n",
       "      <td>0</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>137</td>\n",
       "      <td>...</td>\n",
       "      <td>103147.938616</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.009826</td>\n",
       "      <td>0.007384</td>\n",
       "      <td>-2.461098</td>\n",
       "      <td>428.683046</td>\n",
       "      <td>457032.737889</td>\n",
       "      <td>-0.599179</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979456</th>\n",
       "      <td>2979456</td>\n",
       "      <td>149.171.126.11</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>5600</td>\n",
       "      <td>0</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>137</td>\n",
       "      <td>...</td>\n",
       "      <td>103147.938616</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.009826</td>\n",
       "      <td>0.007384</td>\n",
       "      <td>-2.461098</td>\n",
       "      <td>428.683046</td>\n",
       "      <td>457032.737889</td>\n",
       "      <td>-0.599179</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979457</th>\n",
       "      <td>2979457</td>\n",
       "      <td>149.171.126.11</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>5600</td>\n",
       "      <td>0</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>137</td>\n",
       "      <td>...</td>\n",
       "      <td>103147.938616</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.009826</td>\n",
       "      <td>0.007384</td>\n",
       "      <td>-2.461098</td>\n",
       "      <td>428.683046</td>\n",
       "      <td>457032.737889</td>\n",
       "      <td>-0.599179</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979458</th>\n",
       "      <td>2979458</td>\n",
       "      <td>149.171.126.11</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>5600</td>\n",
       "      <td>0</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>137</td>\n",
       "      <td>...</td>\n",
       "      <td>103147.938616</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.009826</td>\n",
       "      <td>0.007384</td>\n",
       "      <td>-2.461098</td>\n",
       "      <td>428.683046</td>\n",
       "      <td>457032.737889</td>\n",
       "      <td>-0.599179</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2979459 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0          DST_IP          SRC_IP  PACKETS  PACKETS_REV  \\\n",
       "0                 0  149.171.126.18    175.45.176.1       10            0   \n",
       "1                 1    175.45.176.1  149.171.126.18        6            0   \n",
       "2                 2  149.171.126.12    175.45.176.2       12            0   \n",
       "3                 3    175.45.176.2  149.171.126.12        8            0   \n",
       "4                 4  149.171.126.12    175.45.176.2       12            0   \n",
       "...             ...             ...             ...      ...          ...   \n",
       "2979454     2979454  149.171.126.15    175.45.176.2        4            4   \n",
       "2979455     2979455  149.171.126.11    175.45.176.2      112            0   \n",
       "2979456     2979456  149.171.126.11    175.45.176.2      112            0   \n",
       "2979457     2979457  149.171.126.11    175.45.176.2      112            0   \n",
       "2979458     2979458  149.171.126.11    175.45.176.2      112            0   \n",
       "\n",
       "         BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  ...  \\\n",
       "0           40          0  1.424220e+09  1.424220e+09       179  ...   \n",
       "1            0          0  1.424220e+09  1.424220e+09     26581  ...   \n",
       "2          746          0  1.424220e+09  1.424220e+09       445  ...   \n",
       "3          370          0  1.424220e+09  1.424220e+09      1540  ...   \n",
       "4          588          0  1.424220e+09  1.424220e+09       445  ...   \n",
       "...        ...        ...           ...           ...       ...  ...   \n",
       "2979454   2360        584  1.421934e+09  1.421934e+09      2049  ...   \n",
       "2979455   5600          0  1.421934e+09  1.421934e+09       137  ...   \n",
       "2979456   5600          0  1.421934e+09  1.421934e+09       137  ...   \n",
       "2979457   5600          0  1.421934e+09  1.421934e+09       137  ...   \n",
       "2979458   5600          0  1.421934e+09  1.421934e+09       137  ...   \n",
       "\n",
       "         SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0           -316303.733903          0.025341           0.465500   \n",
       "1                      NaN          0.006667                NaN   \n",
       "2              6629.691442          0.857953           0.531418   \n",
       "3              4637.314944          0.568099           0.520225   \n",
       "4              2903.826062          0.679153           0.541261   \n",
       "...                    ...               ...                ...   \n",
       "2979454     -320173.252379          0.014216           0.425575   \n",
       "2979455      103147.938616          0.006667           0.009826   \n",
       "2979456      103147.938616          0.006667           0.009826   \n",
       "2979457      103147.938616          0.006667           0.009826   \n",
       "2979458      103147.938616          0.006667           0.009826   \n",
       "\n",
       "         SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0               0.329833             NaN     -142.570453     382.427060   \n",
       "1                    NaN             NaN             NaN            NaN   \n",
       "2               0.291302        0.079684        1.335840       0.163986   \n",
       "3               0.288753        0.051809        1.165001       0.163854   \n",
       "4               0.288383        0.116073        1.248354       0.371784   \n",
       "...                  ...             ...             ...            ...   \n",
       "2979454         0.384493             NaN     -316.101688     196.224287   \n",
       "2979455         0.007384       -2.461098      428.683046  457032.737889   \n",
       "2979456         0.007384       -2.461098      428.683046  457032.737889   \n",
       "2979457         0.007384       -2.461098      428.683046  457032.737889   \n",
       "2979458         0.007384       -2.461098      428.683046  457032.737889   \n",
       "\n",
       "         SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0                 -0.000199          0.028264  False  \n",
       "1                  0.000000               NaN  False  \n",
       "2                  0.993135          0.257254   True  \n",
       "3                  0.993135          0.361351   True  \n",
       "4                  0.911681          0.080400   True  \n",
       "...                     ...               ...    ...  \n",
       "2979454           -0.000199          0.054011  False  \n",
       "2979455           -0.599179          0.000002   True  \n",
       "2979456           -0.599179          0.000002   True  \n",
       "2979457           -0.599179          0.000002   True  \n",
       "2979458           -0.599179          0.000002   True  \n",
       "\n",
       "[2979459 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",
    "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": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>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",
       "      <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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>149.171.126.18</td>\n",
       "      <td>175.45.176.1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>179</td>\n",
       "      <td>26581</td>\n",
       "      <td>64</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8.426150</td>\n",
       "      <td>71</td>\n",
       "      <td>-0.767317</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "      <td>13.165859</td>\n",
       "      <td>6</td>\n",
       "      <td>9.375000</td>\n",
       "      <td>64.498062</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.474713</td>\n",
       "      <td>15.665518</td>\n",
       "      <td>-0.962798</td>\n",
       "      <td>1.855764e-19</td>\n",
       "      <td>2.200665e-19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.640746</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>0.217322</td>\n",
       "      <td>0.520925</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.581329</td>\n",
       "      <td>0.812500</td>\n",
       "      <td>0.105863</td>\n",
       "      <td>0.110606</td>\n",
       "      <td>0.085446</td>\n",
       "      <td>0.151154</td>\n",
       "      <td>0.182105</td>\n",
       "      <td>0.020234</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>0.085438</td>\n",
       "      <td>0.236825</td>\n",
       "      <td>False</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>640</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-163.305301</td>\n",
       "      <td>28.052888</td>\n",
       "      <td>0.025540</td>\n",
       "      <td>0.025341</td>\n",
       "      <td>-0.196765</td>\n",
       "      <td>2.391191</td>\n",
       "      <td>-0.13590</td>\n",
       "      <td>-9.838254e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.167659e+07</td>\n",
       "      <td>-316303.733903</td>\n",
       "      <td>0.025341</td>\n",
       "      <td>0.465500</td>\n",
       "      <td>0.329833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-142.570453</td>\n",
       "      <td>382.427060</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>175.45.176.1</td>\n",
       "      <td>149.171.126.18</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>26581</td>\n",
       "      <td>179</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.536769</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.084505</td>\n",
       "      <td>0.096086</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.157423</td>\n",
       "      <td>0.157432</td>\n",
       "      <td>0.031486</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>5.960464e-06</td>\n",
       "      <td>0.096073</td>\n",
       "      <td>0.327734</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>360</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>60</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>149.171.126.12</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>746</td>\n",
       "      <td>0</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>445</td>\n",
       "      <td>1540</td>\n",
       "      <td>122</td>\n",
       "      <td>60.0</td>\n",
       "      <td>96.555683</td>\n",
       "      <td>9323</td>\n",
       "      <td>-0.116420</td>\n",
       "      <td>60.0</td>\n",
       "      <td>197.00</td>\n",
       "      <td>60</td>\n",
       "      <td>296</td>\n",
       "      <td>236</td>\n",
       "      <td>60</td>\n",
       "      <td>79.144003</td>\n",
       "      <td>82</td>\n",
       "      <td>67.213115</td>\n",
       "      <td>153.201936</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.642117</td>\n",
       "      <td>3.169156</td>\n",
       "      <td>0.569383</td>\n",
       "      <td>1.851464e-07</td>\n",
       "      <td>2.127170e-07</td>\n",
       "      <td>0.094891</td>\n",
       "      <td>1.931664</td>\n",
       "      <td>1.251629</td>\n",
       "      <td>0.349133</td>\n",
       "      <td>0.638009</td>\n",
       "      <td>False</td>\n",
       "      <td>0.592635</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.593576</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.427693</td>\n",
       "      <td>0.551941</td>\n",
       "      <td>0.267584</td>\n",
       "      <td>0.666187</td>\n",
       "      <td>0.720536</td>\n",
       "      <td>0.065503</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>0.267580</td>\n",
       "      <td>0.244799</td>\n",
       "      <td>False</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1466</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.330470</td>\n",
       "      <td>0.494456</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.370146</td>\n",
       "      <td>0.040775</td>\n",
       "      <td>0.33047</td>\n",
       "      <td>1.850730e+03</td>\n",
       "      <td>2.638120e+03</td>\n",
       "      <td>1.884500e+04</td>\n",
       "      <td>6629.691442</td>\n",
       "      <td>0.857953</td>\n",
       "      <td>0.531418</td>\n",
       "      <td>0.291302</td>\n",
       "      <td>0.079684</td>\n",
       "      <td>1.335840</td>\n",
       "      <td>0.163986</td>\n",
       "      <td>0.993135</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>149.171.126.12</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>370</td>\n",
       "      <td>0</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>1540</td>\n",
       "      <td>445</td>\n",
       "      <td>106</td>\n",
       "      <td>60.0</td>\n",
       "      <td>85.632938</td>\n",
       "      <td>7334</td>\n",
       "      <td>-0.106282</td>\n",
       "      <td>60.0</td>\n",
       "      <td>106.25</td>\n",
       "      <td>60</td>\n",
       "      <td>245</td>\n",
       "      <td>185</td>\n",
       "      <td>60</td>\n",
       "      <td>80.785790</td>\n",
       "      <td>69</td>\n",
       "      <td>65.094340</td>\n",
       "      <td>133.064834</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.537176</td>\n",
       "      <td>3.012859</td>\n",
       "      <td>0.502625</td>\n",
       "      <td>3.981232e-07</td>\n",
       "      <td>4.965469e-07</td>\n",
       "      <td>-0.989189</td>\n",
       "      <td>1.797994</td>\n",
       "      <td>0.811278</td>\n",
       "      <td>0.270426</td>\n",
       "      <td>0.463169</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.560795</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.385295</td>\n",
       "      <td>0.528398</td>\n",
       "      <td>0.325725</td>\n",
       "      <td>0.687046</td>\n",
       "      <td>0.687051</td>\n",
       "      <td>0.098150</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>2.145767e-06</td>\n",
       "      <td>0.325723</td>\n",
       "      <td>0.301330</td>\n",
       "      <td>False</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>850</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.605359</td>\n",
       "      <td>0.769212</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.660268</td>\n",
       "      <td>0.041418</td>\n",
       "      <td>0.60536</td>\n",
       "      <td>3.301338e+03</td>\n",
       "      <td>1.967891e+03</td>\n",
       "      <td>1.400714e+04</td>\n",
       "      <td>4637.314944</td>\n",
       "      <td>0.568099</td>\n",
       "      <td>0.520225</td>\n",
       "      <td>0.288753</td>\n",
       "      <td>0.051809</td>\n",
       "      <td>1.165001</td>\n",
       "      <td>0.163854</td>\n",
       "      <td>0.993135</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>149.171.126.12</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>588</td>\n",
       "      <td>0</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>1.424220e+09</td>\n",
       "      <td>445</td>\n",
       "      <td>1793</td>\n",
       "      <td>109</td>\n",
       "      <td>60.0</td>\n",
       "      <td>72.622311</td>\n",
       "      <td>5274</td>\n",
       "      <td>-0.200293</td>\n",
       "      <td>60.0</td>\n",
       "      <td>197.00</td>\n",
       "      <td>60</td>\n",
       "      <td>217</td>\n",
       "      <td>157</td>\n",
       "      <td>60</td>\n",
       "      <td>66.625973</td>\n",
       "      <td>65</td>\n",
       "      <td>59.633028</td>\n",
       "      <td>129.291660</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.674724</td>\n",
       "      <td>3.676556</td>\n",
       "      <td>0.265408</td>\n",
       "      <td>4.637886e-21</td>\n",
       "      <td>5.328525e-21</td>\n",
       "      <td>0.284672</td>\n",
       "      <td>1.312703</td>\n",
       "      <td>1.251629</td>\n",
       "      <td>0.349133</td>\n",
       "      <td>0.668388</td>\n",
       "      <td>False</td>\n",
       "      <td>0.592635</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.522170</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.625811</td>\n",
       "      <td>0.908832</td>\n",
       "      <td>0.186909</td>\n",
       "      <td>1.074223</td>\n",
       "      <td>1.198481</td>\n",
       "      <td>0.108953</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>0.522408</td>\n",
       "      <td>0.208559</td>\n",
       "      <td>False</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1308</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.659842</td>\n",
       "      <td>0.968874</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.918348</td>\n",
       "      <td>0.776121</td>\n",
       "      <td>0.107515</td>\n",
       "      <td>0.65984</td>\n",
       "      <td>3.880606e+03</td>\n",
       "      <td>1.366362e+03</td>\n",
       "      <td>9.122741e+03</td>\n",
       "      <td>2903.826062</td>\n",
       "      <td>0.679153</td>\n",
       "      <td>0.541261</td>\n",
       "      <td>0.288383</td>\n",
       "      <td>0.116073</td>\n",
       "      <td>1.248354</td>\n",
       "      <td>0.371784</td>\n",
       "      <td>0.911681</td>\n",
       "      <td>True</td>\n",
       "      <td>True</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979454</th>\n",
       "      <td>2979454</td>\n",
       "      <td>149.171.126.15</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2360</td>\n",
       "      <td>584</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>2049</td>\n",
       "      <td>761</td>\n",
       "      <td>428</td>\n",
       "      <td>256.0</td>\n",
       "      <td>404.804891</td>\n",
       "      <td>163867</td>\n",
       "      <td>-0.027852</td>\n",
       "      <td>199.0</td>\n",
       "      <td>485.00</td>\n",
       "      <td>124</td>\n",
       "      <td>1076</td>\n",
       "      <td>952</td>\n",
       "      <td>124</td>\n",
       "      <td>94.580582</td>\n",
       "      <td>324</td>\n",
       "      <td>75.700935</td>\n",
       "      <td>571.461285</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.750979</td>\n",
       "      <td>2.539495</td>\n",
       "      <td>0.480782</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-0.601399</td>\n",
       "      <td>1.740777</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.662870</td>\n",
       "      <td>False</td>\n",
       "      <td>0.647940</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.485169</td>\n",
       "      <td>0.446429</td>\n",
       "      <td>0.075086</td>\n",
       "      <td>0.112217</td>\n",
       "      <td>0.033360</td>\n",
       "      <td>0.154753</td>\n",
       "      <td>0.154762</td>\n",
       "      <td>0.022109</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>3.814697e-06</td>\n",
       "      <td>0.078853</td>\n",
       "      <td>0.280380</td>\n",
       "      <td>False</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3424</td>\n",
       "      <td>0.5</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-84.683695</td>\n",
       "      <td>13.705768</td>\n",
       "      <td>0.014415</td>\n",
       "      <td>0.014216</td>\n",
       "      <td>-0.043359</td>\n",
       "      <td>1.236042</td>\n",
       "      <td>-0.00964</td>\n",
       "      <td>-2.167937e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.200887e+07</td>\n",
       "      <td>-320173.252379</td>\n",
       "      <td>0.014216</td>\n",
       "      <td>0.425575</td>\n",
       "      <td>0.384493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-316.101688</td>\n",
       "      <td>196.224287</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979455</th>\n",
       "      <td>2979455</td>\n",
       "      <td>149.171.126.11</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>5600</td>\n",
       "      <td>0</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>137</td>\n",
       "      <td>137</td>\n",
       "      <td>110</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>110.00</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.427715</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>2.028738</td>\n",
       "      <td>1.753884</td>\n",
       "      <td>1.046604</td>\n",
       "      <td>2.873820</td>\n",
       "      <td>4.743202</td>\n",
       "      <td>0.042732</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>0.453518</td>\n",
       "      <td>0.094222</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12320</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>110</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.004268</td>\n",
       "      <td>457032.318944</td>\n",
       "      <td>0.605845</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>1066.131081</td>\n",
       "      <td>14852.657994</td>\n",
       "      <td>0.00837</td>\n",
       "      <td>5.330655e+06</td>\n",
       "      <td>-9.047426e+07</td>\n",
       "      <td>2.457780e+06</td>\n",
       "      <td>103147.938616</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.009826</td>\n",
       "      <td>0.007384</td>\n",
       "      <td>-2.461098</td>\n",
       "      <td>428.683046</td>\n",
       "      <td>457032.737889</td>\n",
       "      <td>-0.599179</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979456</th>\n",
       "      <td>2979456</td>\n",
       "      <td>149.171.126.11</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>5600</td>\n",
       "      <td>0</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>137</td>\n",
       "      <td>137</td>\n",
       "      <td>110</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>110.00</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.427715</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>2.028738</td>\n",
       "      <td>1.753884</td>\n",
       "      <td>1.046604</td>\n",
       "      <td>2.873820</td>\n",
       "      <td>4.743202</td>\n",
       "      <td>0.042732</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>0.453518</td>\n",
       "      <td>0.094222</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12320</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>110</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.004268</td>\n",
       "      <td>457032.318944</td>\n",
       "      <td>0.605845</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>1066.131081</td>\n",
       "      <td>14852.657994</td>\n",
       "      <td>0.00837</td>\n",
       "      <td>5.330655e+06</td>\n",
       "      <td>-9.047426e+07</td>\n",
       "      <td>2.457780e+06</td>\n",
       "      <td>103147.938616</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.009826</td>\n",
       "      <td>0.007384</td>\n",
       "      <td>-2.461098</td>\n",
       "      <td>428.683046</td>\n",
       "      <td>457032.737889</td>\n",
       "      <td>-0.599179</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979457</th>\n",
       "      <td>2979457</td>\n",
       "      <td>149.171.126.11</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>5600</td>\n",
       "      <td>0</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>137</td>\n",
       "      <td>137</td>\n",
       "      <td>110</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>110.00</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.427715</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>2.028738</td>\n",
       "      <td>1.753884</td>\n",
       "      <td>1.046604</td>\n",
       "      <td>2.873820</td>\n",
       "      <td>4.743202</td>\n",
       "      <td>0.042732</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>0.453518</td>\n",
       "      <td>0.094222</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12320</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>110</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.004268</td>\n",
       "      <td>457032.318944</td>\n",
       "      <td>0.605845</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>1066.131081</td>\n",
       "      <td>14852.657994</td>\n",
       "      <td>0.00837</td>\n",
       "      <td>5.330655e+06</td>\n",
       "      <td>-9.047426e+07</td>\n",
       "      <td>2.457780e+06</td>\n",
       "      <td>103147.938616</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.009826</td>\n",
       "      <td>0.007384</td>\n",
       "      <td>-2.461098</td>\n",
       "      <td>428.683046</td>\n",
       "      <td>457032.737889</td>\n",
       "      <td>-0.599179</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2979458</th>\n",
       "      <td>2979458</td>\n",
       "      <td>149.171.126.11</td>\n",
       "      <td>175.45.176.2</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>5600</td>\n",
       "      <td>0</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>1.421934e+09</td>\n",
       "      <td>137</td>\n",
       "      <td>137</td>\n",
       "      <td>110</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>110.00</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.301030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.427715</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>2.028738</td>\n",
       "      <td>1.753884</td>\n",
       "      <td>1.046604</td>\n",
       "      <td>2.873820</td>\n",
       "      <td>4.743202</td>\n",
       "      <td>0.042732</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>9.536743e-07</td>\n",
       "      <td>0.453518</td>\n",
       "      <td>0.094222</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12320</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>110</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.004268</td>\n",
       "      <td>457032.318944</td>\n",
       "      <td>0.605845</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>1066.131081</td>\n",
       "      <td>14852.657994</td>\n",
       "      <td>0.00837</td>\n",
       "      <td>5.330655e+06</td>\n",
       "      <td>-9.047426e+07</td>\n",
       "      <td>2.457780e+06</td>\n",
       "      <td>103147.938616</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.009826</td>\n",
       "      <td>0.007384</td>\n",
       "      <td>-2.461098</td>\n",
       "      <td>428.683046</td>\n",
       "      <td>457032.737889</td>\n",
       "      <td>-0.599179</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2979459 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0          DST_IP          SRC_IP  PACKETS  PACKETS_REV  \\\n",
       "0                 0  149.171.126.18    175.45.176.1       10            0   \n",
       "1                 1    175.45.176.1  149.171.126.18        6            0   \n",
       "2                 2  149.171.126.12    175.45.176.2       12            0   \n",
       "3                 3    175.45.176.2  149.171.126.12        8            0   \n",
       "4                 4  149.171.126.12    175.45.176.2       12            0   \n",
       "...             ...             ...             ...      ...          ...   \n",
       "2979454     2979454  149.171.126.15    175.45.176.2        4            4   \n",
       "2979455     2979455  149.171.126.11    175.45.176.2      112            0   \n",
       "2979456     2979456  149.171.126.11    175.45.176.2      112            0   \n",
       "2979457     2979457  149.171.126.11    175.45.176.2      112            0   \n",
       "2979458     2979458  149.171.126.11    175.45.176.2      112            0   \n",
       "\n",
       "         BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  SRC_PORT  \\\n",
       "0           40          0  1.424220e+09  1.424220e+09       179     26581   \n",
       "1            0          0  1.424220e+09  1.424220e+09     26581       179   \n",
       "2          746          0  1.424220e+09  1.424220e+09       445      1540   \n",
       "3          370          0  1.424220e+09  1.424220e+09      1540       445   \n",
       "4          588          0  1.424220e+09  1.424220e+09       445      1793   \n",
       "...        ...        ...           ...           ...       ...       ...   \n",
       "2979454   2360        584  1.421934e+09  1.421934e+09      2049       761   \n",
       "2979455   5600          0  1.421934e+09  1.421934e+09       137       137   \n",
       "2979456   5600          0  1.421934e+09  1.421934e+09       137       137   \n",
       "2979457   5600          0  1.421934e+09  1.421934e+09       137       137   \n",
       "2979458   5600          0  1.421934e+09  1.421934e+09       137       137   \n",
       "\n",
       "         MEAN  MEDIAN       STDEV     VAR  BURSTINESS     Q1      Q3  MIN  \\\n",
       "0          64    60.0    8.426150      71   -0.767317   60.0   60.00   60   \n",
       "1          60    60.0    0.000000       0   -1.000000   60.0   60.00   60   \n",
       "2         122    60.0   96.555683    9323   -0.116420   60.0  197.00   60   \n",
       "3         106    60.0   85.632938    7334   -0.106282   60.0  106.25   60   \n",
       "4         109    60.0   72.622311    5274   -0.200293   60.0  197.00   60   \n",
       "...       ...     ...         ...     ...         ...    ...     ...  ...   \n",
       "2979454   428   256.0  404.804891  163867   -0.027852  199.0  485.00  124   \n",
       "2979455   110   110.0    0.000000       0   -1.000000  110.0  110.00  110   \n",
       "2979456   110   110.0    0.000000       0   -1.000000  110.0  110.00  110   \n",
       "2979457   110   110.0    0.000000       0   -1.000000  110.0  110.00  110   \n",
       "2979458   110   110.0    0.000000       0   -1.000000  110.0  110.00  110   \n",
       "\n",
       "          MAX  MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  \\\n",
       "0          80             20    60                 13.165859   \n",
       "1          60              0    60                  0.000000   \n",
       "2         296            236    60                 79.144003   \n",
       "3         245            185    60                 80.785790   \n",
       "4         217            157    60                 66.625973   \n",
       "...       ...            ...   ...                       ...   \n",
       "2979454  1076            952   124                 94.580582   \n",
       "2979455   110              0   110                  0.000000   \n",
       "2979456   110              0   110                  0.000000   \n",
       "2979457   110              0   110                  0.000000   \n",
       "2979458   110              0   110                  0.000000   \n",
       "\n",
       "         AVERAGE_DISPERSION  PERCENT_DEVIATION  ROOT_MEAN_SQUARE  \\\n",
       "0                         6           9.375000         64.498062   \n",
       "1                         0           0.000000         60.000000   \n",
       "2                        82          67.213115        153.201936   \n",
       "3                        69          65.094340        133.064834   \n",
       "4                        65          59.633028        129.291660   \n",
       "...                     ...                ...               ...   \n",
       "2979454                 324          75.700935        571.461285   \n",
       "2979455                   0           0.000000        110.000000   \n",
       "2979456                   0           0.000000        110.000000   \n",
       "2979457                   0           0.000000        110.000000   \n",
       "2979458                   0           0.000000        110.000000   \n",
       "\n",
       "         PERCENT_BELOW_MEAN  PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  \\\n",
       "0                  0.800000            0.200000              0.474713   \n",
       "1                  0.000000            0.000000              0.000000   \n",
       "2                  0.666667            0.333333              0.642117   \n",
       "3                  0.750000            0.250000              0.537176   \n",
       "4                  0.666667            0.333333              0.674724   \n",
       "...                     ...                 ...                   ...   \n",
       "2979454            0.750000            0.250000              0.750979   \n",
       "2979455            0.000000            0.000000              0.000000   \n",
       "2979456            0.000000            0.000000              0.000000   \n",
       "2979457            0.000000            0.000000              0.000000   \n",
       "2979458            0.000000            0.000000              0.000000   \n",
       "\n",
       "         PEARSON_SK2_SKEWNESS  FISHER_MI_3_SKEWNESS  \\\n",
       "0                   15.665518             -0.962798   \n",
       "1                    0.000000              0.000000   \n",
       "2                    3.169156              0.569383   \n",
       "3                    3.012859              0.502625   \n",
       "4                    3.676556              0.265408   \n",
       "...                       ...                   ...   \n",
       "2979454              2.539495              0.480782   \n",
       "2979455              0.000000              0.000000   \n",
       "2979456              0.000000              0.000000   \n",
       "2979457              0.000000              0.000000   \n",
       "2979458              0.000000              0.000000   \n",
       "\n",
       "         FISHER_PEARSON_g1_SKEWNESS  FISHER_PEARSON_G1_SKEWNESS  \\\n",
       "0                      1.855764e-19                2.200665e-19   \n",
       "1                      0.000000e+00                0.000000e+00   \n",
       "2                      1.851464e-07                2.127170e-07   \n",
       "3                      3.981232e-07                4.965469e-07   \n",
       "4                      4.637886e-21                5.328525e-21   \n",
       "...                             ...                         ...   \n",
       "2979454                0.000000e+00                0.000000e+00   \n",
       "2979455                0.000000e+00                0.000000e+00   \n",
       "2979456                0.000000e+00                0.000000e+00   \n",
       "2979457                0.000000e+00                0.000000e+00   \n",
       "2979458                0.000000e+00                0.000000e+00   \n",
       "\n",
       "         GALTON_SKEWNESS  KURTOSIS   ENTROPY  SCALED_ENTROPY  HURST_EXPONENT  \\\n",
       "0               0.000000  2.640746  0.721928        0.217322        0.520925   \n",
       "1               0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "2               0.094891  1.931664  1.251629        0.349133        0.638009   \n",
       "3              -0.989189  1.797994  0.811278        0.270426        0.463169   \n",
       "4               0.284672  1.312703  1.251629        0.349133        0.668388   \n",
       "...                  ...       ...       ...             ...             ...   \n",
       "2979454        -0.601399  1.740777  2.000000        0.666667        0.662870   \n",
       "2979455         0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "2979456         0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "2979457         0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "2979458         0.000000  0.000000  0.000000        0.000000        1.000000   \n",
       "\n",
       "         BENFORD_LAW_PRESENTED  P_BENFORD  NORMAL_DISTRIBUTION  \\\n",
       "0                        False   0.477121                  0.0   \n",
       "1                        False   0.301030                  0.0   \n",
       "2                        False   0.592635                  0.0   \n",
       "3                        False   0.477121                  0.0   \n",
       "4                        False   0.592635                  0.0   \n",
       "...                        ...        ...                  ...   \n",
       "2979454                  False   0.647940                  0.0   \n",
       "2979455                  False   0.301030                  0.0   \n",
       "2979456                  False   0.301030                  0.0   \n",
       "2979457                  False   0.301030                  0.0   \n",
       "2979458                  False   0.301030                  0.0   \n",
       "\n",
       "         CNT_DISTRIBUTION  TIME_DISTRIBUTION  AREA_VALUES_DISTRIBUTION  \\\n",
       "0                     0.0           0.581329                  0.812500   \n",
       "1                     0.0           0.536769                  0.875000   \n",
       "2                     0.0           0.593576                  0.750000   \n",
       "3                     0.0           0.560795                  0.791667   \n",
       "4                     0.0           0.522170                  0.750000   \n",
       "...                   ...                ...                       ...   \n",
       "2979454               0.0           0.485169                  0.446429   \n",
       "2979455               0.0           0.427715                  0.875000   \n",
       "2979456               0.0           0.427715                  0.875000   \n",
       "2979457               0.0           0.427715                  0.875000   \n",
       "2979458               0.0           0.427715                  0.875000   \n",
       "\n",
       "         MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  Q1_SCALED_TIME  Q3_SCALED_TIME  \\\n",
       "0                0.105863            0.110606        0.085446        0.151154   \n",
       "1                0.084505            0.096086        0.000007        0.157423   \n",
       "2                0.427693            0.551941        0.267584        0.666187   \n",
       "3                0.385295            0.528398        0.325725        0.687046   \n",
       "4                0.625811            0.908832        0.186909        1.074223   \n",
       "...                   ...                 ...             ...             ...   \n",
       "2979454          0.075086            0.112217        0.033360        0.154753   \n",
       "2979455          2.028738            1.753884        1.046604        2.873820   \n",
       "2979456          2.028738            1.753884        1.046604        2.873820   \n",
       "2979457          2.028738            1.753884        1.046604        2.873820   \n",
       "2979458          2.028738            1.753884        1.046604        2.873820   \n",
       "\n",
       "         DURATION  MEAN_DIFFTIMES  MEDIAN_DIFFTIMES  MIN_DIFFTIMES  \\\n",
       "0        0.182105        0.020234          0.000009   9.536743e-07   \n",
       "1        0.157432        0.031486          0.000009   5.960464e-06   \n",
       "2        0.720536        0.065503          0.000011   9.536743e-07   \n",
       "3        0.687051        0.098150          0.000007   2.145767e-06   \n",
       "4        1.198481        0.108953          0.000009   9.536743e-07   \n",
       "...           ...             ...               ...            ...   \n",
       "2979454  0.154762        0.022109          0.000009   3.814697e-06   \n",
       "2979455  4.743202        0.042732          0.000025   9.536743e-07   \n",
       "2979456  4.743202        0.042732          0.000025   9.536743e-07   \n",
       "2979457  4.743202        0.042732          0.000025   9.536743e-07   \n",
       "2979458  4.743202        0.042732          0.000025   9.536743e-07   \n",
       "\n",
       "         MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  \\\n",
       "0             0.085438               0.236825       False          0.444444   \n",
       "1             0.096073               0.327734       False          0.000000   \n",
       "2             0.267580               0.244799       False          0.545455   \n",
       "3             0.325723               0.301330       False          0.571429   \n",
       "4             0.522408               0.208559       False          0.545455   \n",
       "...                ...                    ...         ...               ...   \n",
       "2979454       0.078853               0.280380       False          0.857143   \n",
       "2979455       0.453518               0.094222       False          0.000000   \n",
       "2979456       0.453518               0.094222       False          0.000000   \n",
       "2979457       0.453518               0.094222       False          0.000000   \n",
       "2979458       0.453518               0.094222       False          0.000000   \n",
       "\n",
       "         TRANSIENTS  CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  \\\n",
       "0             False        0.0                  0.0                640   \n",
       "1             False        0.0                  0.0                360   \n",
       "2             False        0.0                  0.0               1466   \n",
       "3             False        0.0                  0.0                850   \n",
       "4              True        0.0                  0.0               1308   \n",
       "...             ...        ...                  ...                ...   \n",
       "2979454        True        0.0                  0.0               3424   \n",
       "2979455       False        0.0                  0.0              12320   \n",
       "2979456       False        0.0                  0.0              12320   \n",
       "2979457       False        0.0                  0.0              12320   \n",
       "2979458       False        0.0                  0.0              12320   \n",
       "\n",
       "         DIRECTIONS  PERIODICITY  VAL      TIME   MIN_POWER      MAX_POWER  \\\n",
       "0               1.0        False    0  0.000000 -163.305301      28.052888   \n",
       "1               1.0         True   60  0.000007         NaN            NaN   \n",
       "2               1.0        False    0  0.000000    0.330470       0.494456   \n",
       "3               1.0        False    0  0.000000    0.605359       0.769212   \n",
       "4               1.0        False    0  0.000000    0.659842       0.968874   \n",
       "...             ...          ...  ...       ...         ...            ...   \n",
       "2979454         0.5        False    0  0.000000  -84.683695      13.705768   \n",
       "2979455         1.0         True  110  0.000002    0.004268  457032.318944   \n",
       "2979456         1.0         True  110  0.000002    0.004268  457032.318944   \n",
       "2979457         1.0         True  110  0.000002    0.004268  457032.318944   \n",
       "2979458         1.0         True  110  0.000002    0.004268  457032.318944   \n",
       "\n",
       "         MIN_POWER_FREQ  MAX_POWER_FREQ   POWER_MEAN     POWER_STD  \\\n",
       "0              0.025540        0.025341    -0.196765      2.391191   \n",
       "1                   NaN             NaN          NaN           NaN   \n",
       "2              0.006667        0.999801     0.370146      0.040775   \n",
       "3              0.006667        0.999801     0.660268      0.041418   \n",
       "4              0.006667        0.918348     0.776121      0.107515   \n",
       "...                 ...             ...          ...           ...   \n",
       "2979454        0.014415        0.014216    -0.043359      1.236042   \n",
       "2979455        0.605845        0.006667  1066.131081  14852.657994   \n",
       "2979456        0.605845        0.006667  1066.131081  14852.657994   \n",
       "2979457        0.605845        0.006667  1066.131081  14852.657994   \n",
       "2979458        0.605845        0.006667  1066.131081  14852.657994   \n",
       "\n",
       "         POWER_MODE  SPECTRAL_ENERGY  SPECTRAL_ENTROPY  SPECTRAL_KURTOSIS  \\\n",
       "0          -0.13590    -9.838254e+02               NaN       2.167659e+07   \n",
       "1           0.00000              NaN               NaN                NaN   \n",
       "2           0.33047     1.850730e+03      2.638120e+03       1.884500e+04   \n",
       "3           0.60536     3.301338e+03      1.967891e+03       1.400714e+04   \n",
       "4           0.65984     3.880606e+03      1.366362e+03       9.122741e+03   \n",
       "...             ...              ...               ...                ...   \n",
       "2979454    -0.00964    -2.167937e+02               NaN       2.200887e+07   \n",
       "2979455     0.00837     5.330655e+06     -9.047426e+07       2.457780e+06   \n",
       "2979456     0.00837     5.330655e+06     -9.047426e+07       2.457780e+06   \n",
       "2979457     0.00837     5.330655e+06     -9.047426e+07       2.457780e+06   \n",
       "2979458     0.00837     5.330655e+06     -9.047426e+07       2.457780e+06   \n",
       "\n",
       "         SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0           -316303.733903          0.025341           0.465500   \n",
       "1                      NaN          0.006667                NaN   \n",
       "2              6629.691442          0.857953           0.531418   \n",
       "3              4637.314944          0.568099           0.520225   \n",
       "4              2903.826062          0.679153           0.541261   \n",
       "...                    ...               ...                ...   \n",
       "2979454     -320173.252379          0.014216           0.425575   \n",
       "2979455      103147.938616          0.006667           0.009826   \n",
       "2979456      103147.938616          0.006667           0.009826   \n",
       "2979457      103147.938616          0.006667           0.009826   \n",
       "2979458      103147.938616          0.006667           0.009826   \n",
       "\n",
       "         SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0               0.329833             NaN     -142.570453     382.427060   \n",
       "1                    NaN             NaN             NaN            NaN   \n",
       "2               0.291302        0.079684        1.335840       0.163986   \n",
       "3               0.288753        0.051809        1.165001       0.163854   \n",
       "4               0.288383        0.116073        1.248354       0.371784   \n",
       "...                  ...             ...             ...            ...   \n",
       "2979454         0.384493             NaN     -316.101688     196.224287   \n",
       "2979455         0.007384       -2.461098      428.683046  457032.737889   \n",
       "2979456         0.007384       -2.461098      428.683046  457032.737889   \n",
       "2979457         0.007384       -2.461098      428.683046  457032.737889   \n",
       "2979458         0.007384       -2.461098      428.683046  457032.737889   \n",
       "\n",
       "         SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0                 -0.000199              True  False  \n",
       "1                  0.000000             False  False  \n",
       "2                  0.993135              True   True  \n",
       "3                  0.993135              True   True  \n",
       "4                  0.911681              True   True  \n",
       "...                     ...               ...    ...  \n",
       "2979454           -0.000199              True  False  \n",
       "2979455           -0.599179              True   True  \n",
       "2979456           -0.599179              True   True  \n",
       "2979457           -0.599179              True   True  \n",
       "2979458           -0.599179              True   True  \n",
       "\n",
       "[2979459 rows x 84 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": [
      "botnet: 98764\tclear: 2880695\n"
     ]
    }
   ],
   "source": [
    "print(f\"botnet: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"LABEL\"] + 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": [
    {
     "data": {
      "text/plain": [
       "(864799, 77)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "botnet: 73944\tclear: 790855\n"
     ]
    }
   ],
   "source": [
    "print(f\"botnet: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")\n",
    "# print(f\"botnet: {len(df[df['LABEL_code'] == 1])}\\tclear: {len(df[df['LABEL_code'] == 0])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[features]\n",
    "y = df['LABEL']\n",
    "sm = SMOTE(sampling_strategy=\"not majority\")\n",
    "X, y = sm.fit_resample(X, y)\n",
    "X['LABEL'] = y\n",
    "df = X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = df.sample(n=250000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "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']  # Labels\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, stratify=y)\n",
    "    \n",
    "    def objective(space):\n",
    "        clf = XGBClassifier(\n",
    "                        n_estimators = int(space['n_estimators']), max_depth = int(space['max_depth']), \n",
    "                        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\", 20, 100, 20),\n",
    "        'gamma': hp.uniform ('gamma', 1,9),\n",
    "        'reg_alpha' : hp.quniform('reg_alpha', 0,90,1),\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', 100, 600, 100),\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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [1:50:20<00:00, 66.21s/trial, best loss: -0.9829568890135453]  \n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.2586930934740388, 'gamma': 4.097207938758439, 'max_depth': 40.0, 'min_child_weight': 6.0, 'n_estimators': 400.0, 'reg_alpha': 2.0, 'reg_lambda': 0.6790421773925466}\n"
     ]
    }
   ],
   "source": [
    "best_hyperparams = tunne_together_XGBoost_classification(df_features.sample(200000), features, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def while_XGBoost_classification_tunned(best_hyperparams, cycles=100):\n",
    "    X=df_features[features]  # Features\n",
    "    y=df_features['LABEL']  # Labels\n",
    "    best_results = (0,\"\",None,None, None)\n",
    "    i = 0\n",
    "    print(f\"\\r{i}\", end=\"\")\n",
    "    for i in range(cycles):\n",
    "        print(f\"\\r{i}\", end=\"\", flush=True)\n",
    "        #Split on train and test\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",
    "            \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",
    "                # 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",
    "        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 best_results[0] < F1:\n",
    "            matrix, percentage_matrix = get_confusion_matrix(y_test, y_pred)\n",
    "            best_results = (F1,  \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1), matrix, percentage_matrix, model)\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(best_results[0])\n",
    "    print(best_results[1])\n",
    "    plot_confusion_matrix(best_results[2], best_results[3], model=\"XGBoost\")\n",
    "    return best_results[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "\n",
      "\n",
      "98.5082968037783\n",
      "98.50 & 98.14 & 98.88 & 98.51 \\\\\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = while_XGBoost_classification_tunned(best_hyperparams, cycles=10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98.50 & 98.16 & 98.86 & 98.51 \\\\\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']  # 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",
    "print(\"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1))\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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"IDS_UNSW-binary\"\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeEAAAITCAYAAAAw+KSBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAACkDUlEQVR4nOzdd3gU5drH8e/MlvTeG53Qe2+iIFJFRbChR8XeeznH8x7rsR8bVgTFgmBFVMCuCNK7dEhCQkJ6T3Y3ye7M+0cgGgmQQJJJdu/PdXEpuzO7v12yufd55imKrus6QgghhGh2qtEBhBBCCE8lRVgIIYQwiBRhIYQQwiBShIUQQgiDSBEWQgghDCJFWAghhDCIFGEhhBDCIFKEhRBCCINIERZCCCEMIkVYCCGEMIgUYSGEEMIgUoSFEEIIg0gRFkIIIQwiRVgIIYQwiBRhIYQQwiBShIUQQgiDSBEWQgghDCJFWAghhDCIFGEhhBDCIFKEhRBCCINIERZCCCEMIkVYCCGEMIgUYSGEEMIgUoSFEEIIg0gRFkIIIQwiRVgIIYQwiBRhIYQQwiBShIUQQgiDSBEWQgghDCJFWAghhDCIFGEhhBDCIFKEhRBCCINIERZCCCEMIkVYCCGEMIgUYSGEEMIgUoSFEEIIg0gRFkIIIQwiRVgIIYQwiBRhIYQQwiBShIUQQgiDSBEWQgghDCJFWAghhDCIFGEhhBDCIFKEhRBCCINIERZCCCEMIkVYCCGEMIgUYSGEEMIgUoSFEEIIg0gRFkIIIQwiRVgIIYQwiBRhIYQQwiBShIUQQgiDSBEWQgghDCJFWAghhDCIFGEhhBDCIGajAwjhyZwuDXRQVQVVVU54bGWVi0qni4pKF1VODVVRas4zHfnvX29TlT9vr4vLpaHpoCjUHC+EaF5ShIVoIi6XBoDJVLvDqdxeRWGpg9wiO/lFDgpKqv8UljgodzhxVDhxVDpxVFYXXEelk4oqF7p+ajksZpUAXyv+vhYCfa34+1oJ9LPg72slwNdKgK+FAF8roYHeRIb6EhLghXKkIOu6jsulo6hgUqXjTIjGpuj6qX60hRBQ3Zo92gIFqKhykZFTxsHMEg5ll5KVX05+8Z/FtsqpGZz4xMwmhYhgX6LCfIkO9SUqzI+oUF/iIvyJCvXFz8cC/FmgTSalpmgLIRpGirAQ9aTrOi5Nx3ykZVtqq+RQdimpmSUcyikjPbuUQzml5BU5DE7atHy8zMRH+tM2JpB2MYF0jAuifWxQTXH++5cSIcTxSREW4jicLg2TWt3Kq6hysT+tkF0pBexNK2RfaiFFZRVGR2xRwoK86RQfXP0nIZjENiEE+lmB6vfSbJLubCH+ToqwEEf8tVBk5pWxM7mAvakF7EktJC27FE2Tj0pDhQd706NDOL06htE3MZKoUF9AirIQR0kRFh7rr4UgNauETbuz2X4gjz2phZTbqwxO557Cgrzp2VGKshBHSREWHkPXddA1FNWErrnQdZ0XFm1j275c6Vo2yNGi3LtTOIO7RxMc4CUFWXgUKcLCremaCxS1+rpuVgq2feuxHdiEKSCM6BkP8NAbv7P9QJ7RMQXV85U7xQcztGcMI/rEEhfhj6bpKAoy+lq4LSnCwu3o+pEpQDrYk7dSvmcNtqTNuMqKao5RrN60u/t9Vv2RxbMfbDQmqDihuAh/hvSMZkTvWBLbhKDrOpquy3xl4VakCAu38Neu5orDByj941fKdv2OZis57jkxlz9GRUgHrnj0++YLKk5JSIAXg3tEM7x3LH06h6OgSAtZuAVZMUu0arrLhWIy4SzOpXT7z5TtWIWzMLNe59oObCL0rK74eJmxVzibOKk4HYWlFXy3NpXv1qYS5G9ldL94xg9tS5voQFwu7ZhVyYRoLaQlLFqdo4XXZS+jbMdvlO34jYrD+xv8ONbItsRf9wLvfL2Dxb8mNUFS0dQ6xAUxdmACYwa1wd/HIgVZtDpShEWrUN3drIPmonzvOkp3rMCevA0012k9bts73yEpX+Oel39rpKTCCGaTyqDuUYwb3IYBXaOqb1SQTSlEiyfd0aJF0zUNRVVxlRdTvP4bSrf8gOYoa7THtx3YSLtuoxrt8YQxnC6NNX9ksuaPTIIDvDhrQDxTRnYgMsQXl6bJYC7RYklLWLRIusuJYjJTkZVM0dollO9ec9qt3rr4dRtO1LR7uPulFew/VNTojy+MoyowuEcMF5zZke7tw2T+sWiRpAiLFqV6Xq9C+d71FK/7ior0vU36fKq3P23vfpefN6bz0qItTfpcwjidE4I5b3RHRvWJQ9d1uW4sWgwpwsJwR+f16lWVlGz5gZINy3AW5zTb88de/TRlvnFc/fgPzfacwhjhwd5MHtGBScPb4eNlRkeuGwtjSREWhjl6vddZnEfRuq8o3fYzeqW92XMEj5xByMgZTPvXMpwtfK9f0Ti8rSbGDmrDtLM6ERnii6brUoyFIaQIi2ZX3fJVcBbnUvjrR5Tt+h1044qfV2xn4q5+mtc+28a3aw4alkM0P1VVOGtAApdP6EpYkLe0jEWzkyIsmpWua2i2Ugp+W0Tp1p+aZLBVgykqbe+ez57Ddh587Xej0wgDmE0K4wa35bLxXQjy9wJkNS7RPGSKkmgWuqahVzkoXPUZJRuXozsrjY70J13DnrSFjh0HG51EGMTp0lm+5iA/bUhjwrB2XDyuC/4+FlRVCrFoWlKERZPSNRe6y0nxuq8pXrsErcJmdKQ62ZK2ENljJAlR/hzKbrx5yKJ1qXRqfLUyme/XpTJlZAdmjO2Mt9UsxVg0GemOFk1Cd7kAnZJN31G0+nNc5cVGRzohk38wbe+Yx9LfU3jzi+1GxxEthK+3mfPO6MiFYzpjVhWZ2iQanbSERaPSXS5QFcp2/Ebhbx/jLMk1OlK9uMqKqMhJZUCXSKOjiBbE5nCy8Pu9fLc2lVnn9mB0/3hZgUs0KmkJi0ah6zqKouBI30vusjepyk0zOlKDhZw5k6ChU7ngwaVoMlNJ1KF7+1BuvrAPbWMCa37mhTgd8nVOnDZdc6FV2Mj95nUOv/dQqyzAAPbkLagmM6P6xBkdRbRQu1IKuP2FX3nj823YKpy45NuaOE1ShMUp049MLyrbsZJDb9xK6bafgNbbseJI34dW6WDsoDZGRxEtmKbpLFt9kOuf/JHv16Wi6zoulxRjcWrkmrA4Jbqu4SzKIXfZmzhSdxgdp3FoTuwp2+mS0MfoJKIVKCmv5PXPtvPdmlRuurA3XdqGShe1aDBpCYsG0V3VU44Kf/uYQ3PudJ8CfIQtaTM+PlbCg72NjiJaiaSMYu59ZSWvfrqVyiqXtIpFg0gRFvVydJMFx6FdHHrrTopWfQYup8GpGp89eSuKojBlZAejo4hW5ru1qdz07M/sSikAqgcrCnEyMjpanJSuudAcNvK+n0v5zlVGx2lyCTe9SlaVPzc/+7PRUUQrpCgwYWg7rjmvp8wtFiclPx3iuI5+P7MlbebQm7d5RAEGsO3fSGyYdEeLU6PrsHzNQW559md2H5RWsTgxKcKiTrrmAs1F3rdzyP7kaTR7qdGRmo0teSsms4VB3aOMjiJasewCG/9643fe/GI7VU5NrhWLOkkRFsfQNY2qgizS591LyabvjI7T7Bxpu9CdVZwzpK3RUUQrp+uw9PcUbnnuZ/amFUqLWBxDirCocXTwVcnm78mYdy9VuYcMTmQM3VmJPW0X3duFGB1FuImsfBsPvraK95ftRtN0WeRD1JAiLICjq17Zyfr0afK/e7tlbTVoANuBTQT4Wgn0sxodRbgJXYfPft7PP19fRXFZpXRPC0CKsKB60Ijj0B7S59yJbd8Go+O0CPbkrSiqyqTh7YyOItzMrpQCbn3uZzbvzTE6imgBpAh7MF1zoWsuCn5ZQOaCR3CVFhgdqcWoys/AWZLPsF6xRkcRbqjUVsVj89bxztc7pHvaw0kR9lC6y4WrvJjD7z1E8ZrFoMsvgb+zHdhIm0hfo2MIN7b41yQeeuN3ymxV0j3toaQIeyBd06jMTiFj3n1UHN5vdJwWy5a0FbPFQs8OYUZHEW5sR3I+tz3/C3vTCtFk9LTHkSLsgcp2reLw+//GVV5kdJQWzX7wD3TNxYRh7YyOItxcYWkF/3r9d77+LdnoKKKZSRH2EEenHxX8soDcJS+ju6oMTtTy6ZV2HOn76N1JWsKi6bk0nblf7eD1z7ahaTqaJq1iTyBF2APoWvXOR1mfPUvR6i+MjtOq2A5sItjfirfVZHQU4SGWrznII3PXUOmUHZk8gRRhN6drLly2Eg7P/xe2veuMjtPq2JO3oKgmxg9tZ3QU4UG27M3lnpd/o6isQgqxm5Mi7MZ0TaMyK4WMefdSmZ1idJxWqTL7IC5bCaP6ylQl0bzSskq588UVpBwuwSVd025LirAbK9+9msMf/B+usiKjo7Rqtv2baB/tb3QM4YGKSit48LVVrNuRKetOuykpwm7m6Ae1YMVCcr580eOXn2wMtuQtWL2sdIgNMjqK8EAVVS6efn8Dn/1cPZ1QirF7kSLsRo5+OHOXvUnRqs8MTuM+7Cnb0HWdySPbGx1FeChdh/eX7Wb2J1uP/F0KsbuQIuwmqqcg6eR+PZvSLT8YHcetaPYyKrOS6ZcYYXQU4eG+X5fK8ws2oevIFCY3IUXYDei6BrpOzhcvUPbHCqPjuCXbgU2EBXphNstHRhjrty0ZPPXeBnRd5hK7A/mN0srpmgaaRvZnz1K+Z43RcdyWLWkLqsnEmAHxRkcRgrU7Mnls3jpcmiaFuJWTItyKVRdgF1kfP4lt/0aj47i1isMH0CpsnDkgwegoQgCweW8O/5mzhiqXJlOYWjEpwq1U9SpYVWQufAx7yjaj47g/XcOWtJXOcYFGJxGixo6kfB5643cqq1yyHWIrJUW4FdI1F3pVJZkfPowjbZfRcTyGLWkzXl4WYiP8jI4iRI29qYX887VV2CtkmcvWSIpwK6NrLrRKB4c//D/ZhrCZ2ZO3oigKU0Z0MDqKELUkZRTzwOyVlDucUohbGSnCrYiuudAcNg6//xCVWbIMZXNzlRVSmXuIgd0ijY4ixDHSskt56I3fqahyyTXiVkSKcCtR0wW94GGqcg8ZHcdj2fZvJCrEG1U+OaIFOphZwsNvr8HlklHTrYX8KmkFdL16GlLmosepzEk1Oo5HsyVvRTWZGdFbNnQQLdOeg4U88e666nnEsrJWiydFuIXTdR10nazPnqUifa/RcTye49AetKoKxg5qY3QUIY5ry95cnl+wCQVZ4rKlkyLcCuR89Qr2pM1GxxAAmhN7yna6tgk2OokQJ7Rq22Fe+2wbiqIYHUWcgBThFi7/+3mU71xldAzxF7akLfj6WAgN9DY6ihAn9N3aVOZ/s9PoGOIEpAi3ULquU7R6MSUblxsdRfxN9VQllSmyq5JoBT7/5UDNNoii5ZEi3ALpmkb5rt8p+GWB0VFEHZxF2VQVZjOkR7TRUYSol/eW7uKHdakyYroFkiLcwuiaC0f6HnK+ng3IB6alsh3YSFy4j9ExhKi31z/fxp7UAlnMo4WRItyC6JqLqoIssj95ClxOo+OIE7AlbcVktjCgqyzcIVoHp0vnv++up6C0QgpxCyJFuIU4uhpW5sLH0CpsRscRJ+FI24nucjJ+aFujowhRbyXllTz69hqcLk3mELcQUoRbDIXsz5/FVZJndBBRD3pVBY60XfRoH2p0FCEaJDWrlGc/rJ5DLIwnRbiFKPjpfdkRqZWxHdhMgK8Vf2+z0VGEaJD1O7N4b6n8vmkJpAgbTNdclO1eQ/H6r42OIhrIlrwFRVWZJLsqiVbo818O8MumQzJi2mBShA2ku1xUFWaT+82rRkcRp6AqLx1naQHDe8cYHUWIUzL7k60kpRfJQC0DSRE2iK5p6K4qsj99Gr3SYXQccYpsBzbRJtLP6BhCnJIqp8Zj76yj1FaJS5NCbAQpwgZRVJWcr16hKj/D6CjiNNiStmCxWuguA7REK1VUWsEz729EkaFahpAibICjS1La9q4zOoo4TfaDf6BrGhOHtTM6ihCnbEdyPot+2Cs7LhlAinAz0zUXjrRdFPz6kdFRRCPQK2xUZOyjd6dwo6MIcVo+/mEvu1JkRa3mJkW4GemaC5ethOwvngddftDdhe3AJkICrHhb5eMkWi9Nh+c+3Ii90iUjppuR/NZoZtmfPoNmKzE6hmhE1VOVTIwbLKtnidYtv9jBCws2oapyfbi5SBFuJrquU7jqUyoOy5Zi7qYy6yAueymj+sUbHUWI07ZhdzZfrjggreFmIkW4GeguF5U5qRT9/oXRUUST0LHt30yHGH+jgwjRKN5bupuDmSVyfbgZSBFuJjlLXgLNZXQM0UTsyVvw8rLSLibQ6ChCnDanS+Pp9zZQJRs9NDkpwk1M13UKf1tIVe4ho6OIJmRL2Yau60wZ2d7oKEI0isz8ct784g9URa4PNyUpwk1Id7mozEqmaM0So6OIJqbZSqjMTqFfYoTRUYRoND9tSGPrvlzplm5CUoSblE7OkpdlOpKHsB3YRHiQF2b5VAk3MvuTLTg1XRbyaCLy66KJ6LpOwS8fyrKUHsSWtAXVZObMAW2MjiJEo8kptDP/m50o0i3dJKQINwHd5aIiYx/F65caHUU0o4qM/WiVds4aKFOVhHtZ+nsKe1NlNa2mIEW4kem6DrpGzlezpRva0+gatqStdI6TEdLCveg6vLRoC9Ih3fikCDcyRVHI/+k9nIWZRkcRBrAnbcHb20p0qK/RUYRoVOk5ZXz03R65NtzIpAg3It3lwpG+l5KN3xodRRjElrwFRVGYMqqD0VGEaHRf/HKA1KxS6ZZuRFKEG5Oqkvft2yCdNh7LVVpAZV46A7tFGR1FiEbn0nReXLhZBmk1IrPRAdyFrrko3fYLldkpRkcRBrPt30TMoMlGx2jRTKrClJHtGTe4LVGhvhSVVbBpTw4fLN9Nub0KgInD2jH1jI5EhfqQU2hn6aoUvl6VXO/nUBR4+paRdG8fxkff7WHh93sBiAzx4faL+5HYJoScAhtzlvzB9v15NecN7RnNQ1cP4bF5a9mwK7txX7gbSM4o5uuVyUwZ1R6TKu240yXvYCPRnVUUrpA9gkV1l7RqNjO8V4zRUVqsG6f15trzetE2JhCnSyMyxJfJI9rzzC0jMZsUZoztzM3T+xAf6U+VUyMuwp/rL+jFZeO71Ps5Jg1vT/f2YcfcftOFfejTOQJVgbYxgdx/+UCsRyZ3KwpcPqEbew4WSAE+gYU/7MVR4ZLrw41AinAjqF6a8mNc5cVGRxEtgOPQbjRnJWcPkfnCdQnytzJuSPW2j7M/2cKl/7ecB15biabptI0JZFTfeC4am1hz/8UPLWPeVzsAmDE2keAAr5M+R0SwD/+Y1L3O+7q2CyUtq5QZ/1rKpt3ZBPl7ERdZvfnGmf0TaBsTyAfLdzfGS3Vb5fYq3pf3qFFIET5NuqbhLMqheMMyo6OIlsLlxH7wD7q1CTE6SYsU4GtlzfbD7EzO59dN6QDsOVhISXklAMEBXnh7VV8pW7GlerGbL1ckYa9wYjapDOh68uvtt0zvg6+3mcqqYzdN0TQdi1klwNeKl9UEVF/rNJsUZo7vwrb9uWw/kHfMeaK2b9ccJCO3DJdseXhapAifJkVVyf9+HmhOo6OIFsR+YDN+vhZC6tFq8zTpOWU888FGHnxtFZXO6lG28ZH+NS1cp/PPkbfWv6wBenREbnzkibeMPGtAPAO6RbFlXw57UwuPuX/7gVxiwv1Y8NhEenYMJ7fIzuHcciYMa0dUmB/vL5MWXn1oms6cxX9gUmWQ1umQInwadJcLW8o2bAc2GR1FtDC25K0oisrkEbKr0sn4epu59/IBAOQU2Fi+JgWbo3pw1oyxiXhbTUwa0R5/XysA/j6W4z5WoJ+Va8/rSUWlk9c+3VbnMW9+sZ2fNx4iPaeULXtz+O+76zCZFC4am8i6nZnsS6su3FJcTm7Lvlw27s6WKUunQUZHnw5FIf/7d4xOIVogZ2EWVcU5DO0Zw4ff7jE6TosV4Gvh0euG0TEuGJdLY/anW3G6dBZ8u4frzu/FBWd24oIzOwFQ5XRhMZtOOBjohgt6EejnxfxvdpJdYKvzmOKySl5cuLnWbRednUiQvxcfLt9DbLgf984cQMf4YPJLHLzx2TY27JZBWscz76sdvHrfWUbHaLWkJXyKdM1FycblVOWlGx1FtFC2/ZuID/cxOkaLFehn5b83jaBzmxBcms7LH29l675cAL5amczrn23jwKEi0rJKmbtkB0np1QMfS21VdT7eoO5RnNEvnuSMYhavSKp3Dn8fCxec2YmV2zI4mFnCDdN6Exfpz/8+2oTm0rjrsv54WUyn/4LdVHpOGd+sSpFrw6dIWsKnQNd1tEoHhSs/MTqKaMHsSVsIGjiRvokRNcVFVPO2mnjs+mG0jw3C6dJ44aPNrNxae8ex37ak88P6VJyu6l/uR1vEaVkldT7miN6xAHSIC2LJc1Nr3XfZ+K6MHdSGa//7wzHnTR/bGR+riY+O9FgktgkhNauU37ZkMLh7NKP7xxMT7sfBzLqfV8DC7/dy9qA2+HqbZSGPBpKW8CkqXLEQzVFmdAzRgtlTd6K7nEwY2s7oKC3OTRf2oWN8MAAvLTy2AL/xwBgW/Xcy553REYDR/eMJC/KmosrFxj05dT5mqa2SvCJ7rT9VzurR0TZHFYUljmPOCQ30ZsqI9vy08RCH88qB6i/ZoQFemE0KYUHeNbeJ4yu3V7HgO7nscioUXX66GkTXdVy2YtJm3wAuGREtTixm5qNUhnXk8ke+NzpKixEf6c9r941BVRU0Tafgb8Xx05/24edjqZnnW2avqhmMNe+rHXx5pKt5RO9Yrj2vJ5VOFzc89VOdz/XkTSPo1Sm81opZf3XL9D6MHZTADU/9RG6RHYD/XDOEQd2jqaxyYbWYKCqt4Jonvq8ZyS3qZjGrvPPvcQT6e6FKa7jepDv6FBT9/oUUYFEvtgObCG3THX9vM2UO+ZkBGNIjGvXIyGNVVQgPrn3d3Nfbwmc/78dsUjl7UBuCA7xIOVzMlyuS+HnjoZrjvL1MhAf71DkXuD5iwvw4e3Ablq8+WFOAoXr0tNViIrFNCGlZJbz15R9SgOuhyqmx8Pu93Ditt9FRWhVpCTeAruto9jLSZl+P7qw0Oo5oBSwRCSRc/xLvLd3JZz8fMDqOEE3KbFKZ99A4ggO8ar5oiROTa8INolO0ZrEUYFFvVbmHcJYVMfzIoCEh3JnTpfHR93ukADeAFOEG0CrslGz+zugYopWxHdhIuyg/o2MI0Sx+2pBGfrEdTTpZ60WKcD3pmkbx2iXolceOsBTiROxJW7BYrXSRtaSFB3C6dD75cR/SFq4fKcL1pDsrKdm43OgYohWyp2xH1zQmjWhndBQhmsUP69MoKa+UqV31IEW4HnRNo3j912gVdS+DJ8SJaBU2Kg7vp0+ncKOjCNEsqpwan/60HynBJydFuB50VxXF678xOoZoxWwHNhEa4FVrVyAh3Nm3aw9is9e9xKj4k/xGOImja0RrdlkdS5w6W/JWFJOJswe3MTqKEM2iotLF0t9TcGkyx/pEpAifjKZRvO4ro1OIVq4yMxmXvYzR/eONjiJEs1m+5iCKDNE6ISnCJ6C7XJRu/xlXebHRUUSrp2NL2kLH2ACjgwjRbPKLHazefhin7Dd8XFKET0AxmSjZLGv+isZhT9qCl5eVNlFSiIXn+HpVMmaTlJrjkXfmOHRNoyIzicrsg0ZHEW7CnrIVgCkj2xsbRIhmtCulgIOZJWiy33CdpAgfj6JQsulbo1MIN+IqL6Yi+yD9u0QaHUWIZrXktyRkY6W6SRE+Dr3KQdmu342OIdyM7cAmIoK9kJlKwpP8tjmdctlFrE7yq6AOustF6bZf0KsqjI4i3Iw9aQuqycwZ/ROMjiJEs6l0aixfLdOV6iJFuA6KyUTJFhmQJRqfI2MfWqWDswbIVCXhWZavlulKdZEi/De6puHI2EdV7qGTHyxEQ2ku7MnbSEwIMjqJEM0qt8jOup1ZuGS6Ui1ShP9GUVUZkCWalC1pMz7eViJDfIyOIkSz+mlDGiaZrlSLvBt/o1XYKN+9xugYwo3ZkreiKAqTZaqS8DCb9mRTLutJ1yJF+C+qB2T9jO6sNDqKcGOukjwq8w8zuHu00VGEaFZOl86KzemygtZfSBH+C1khSzQX2/6NxIRKd7TwPL9sPiQraP2FvBNH6LpORWYSVfkZRkcRHsCevAWT2cyQntIaFp5lz8FCcgtlb/ajpAgfpeuyOIdoNo603WjOKs6RrQ2FB/ppwyGZM3yEFOEjFFWlfM9ao2MID6G7qnCk7qBbuxCjowjR7H7ZdAiTKuUHpAgD1V3RlblpOIuyjY4iPIjtwGb8fawE+1uNjiJEszqcV86BQ0WyqQNShKvpmnRFi2ZnT96CoqpMGi5TlYTn+WljGrKAlhRhABTVJF3RotlVFWTiLM5jWK8Yo6MI0exWbs0AaQhLEdZ1naqCTKry0o2OIjxQ+YGNxEf4Gh1DiGZXXFbJvrRCdN2zK7HHF2F0jbLdq41OITyUPWkLZouF3p3CjY4iRLNbtzMLT78s7PFFuLorWpapFMawp+5Ad7mYMKyd0VGEaHbrd2VhUj37wrDHF2FnSR6VWSlGxxAeSq904EjfQ6+OoUZHEaLZpWWVkl9sNzqGoTy6COsul4yKFoazHdhEkJ8Vb6vZ6ChCNLs1f2R69FrSHl2EFZOMihbGsydvRVFNTBje1ugoQjS7jbuzPXotac995YDLVkJFxn6jYwgPV5mTiqu8mFF94oyOIkSz234gj8oql9ExDOOxRVjXXNhT/kAmqomWwHZgI+2i/YyOIUSzq3JqbN2X47FrSXtsEUZRsaftMDqFEADYkrZitVrpFB9kdBQhmt26ndmoimeOkvbYIqwoCo7UnUbHEAIAe8p2dF1j8ghZwlJ4no27s1GkCHsWl71U9g4WLYbmKKMiM5m+iRFGRxGi2RWUOMjKLzc6hiE8sgjrmgv7QemKFi2L7cBGwgK8MJs98mMpPNz2A3keOVXJMz/tioIjVYqwaFnsSVtRTCbOHtTG6ChCNLtdKQUeuXqWRxZhRVGxp8n1YNGyVGQm4XKUc2Z/maokPM+ulHyPvC7skUXY5SinKld2TRItjK5hT9pCx9hAo5MI0ewy88opLa80Okaz87girGsuHAdlfrBomWxJW/DyshAf6W90FCGa3R/JeR43X9jjijCKgl2mJokWyp6yFUVRmDxSpioJz7MzKR8Fz+qS9rgirCgqDrkeLFooV1kRFTmpDOwSZXQUIZrdrpQCVA8bnOVxRVirsFGZk2Z0DCGOy7Z/E5EhXqge9+kUni75cDEVHraOtEd9zHVdw5GxD7keLFoye/IWVJOZUX1llLTwLJqmsze1AE33nN/RHlWE0TQqMpONTiHECTnS96FVOhg7UOYLC8+zN7UQTZMi7JYUk5nKnINGxxDixDQn9pTtdEmQzRyE50nNKvWo/YU955UeUZl90OgIQpyULWkzPj5WwoK8jY4iRLNKzSwxOkKz8qgirDurqCrINDqGECdlT66eqnTuyA5GRxGiWaXnlHrUXGGPKsKVuWmge84/rmi9nMW5VBVkMrhHtNFRhGhWTpdOVp7N6BjNxmOKsO5yUpElg7JE62E7sInYMOmOFp4nKaPIY1rDHlOEUVSq8mS9aNF62JK2YDJbGNRdFu4QniU1s9RjZpJ6TBFWVJVKKcKiFXGk7UJ3VnHOkLZGRxGiWR3MKsHkISOkPeNVHiFFWLQmurMSe9ouurcLMTqKEM3Kk0ZIe0wR1qoqcJXkGR1DiAaxHdhEgK+VQD+r0VGEaDY5hTaPWb7SY4pwVf5hoyMI0WD25K0oqsqk4e2MjiJEs9F1SM8uNTpGszAbHaA56C4XlTmpRsdoXKqJkJHT8e95Bib/EJzFeZTtWEHRmiWgOQGImn4/fl2GHHNq3vfvULJh6XEf2q/rUIKHT8MSFotWYceeuoOCXxbU9CQoJgthE67Dr8sQ9KoKitYuqfV4Jv9gEm5+nZJN31Hw03uN/MI9S1V+Bs6SfIb1imXRD/uMjiNEs8nMK6d9bJDb76rkIS1hHWdxjtEhGlX4hOsIGXUR5uBI9KpKrOFxhJ55GRGTb6o5xhpZPaDHWVaIsyS/5o9e6Tju4/p07EfUhffhFdMR3eXC5BtIQM8ziLnk36BU/7gEDppEYN+xKGYLpoAQws+ZhVdsp5rHCBl5EWguilZ/0USv3rPYDmykTYSv0TGEaFa5RXaPWEPaI1rCqCacpQVGp2g0itmKX+JgADI/fARH2k4CB04ifPw1+PccRd7yt0BRMQdHAnDojVtPWHj/KqDXmQAUb/qW/G/fxhQYTsKNr2CNSMAakUBlTire8V2rH/e1m/HvfSZhY67AK64LFYcPYA6OIqDvGIpWf4lm94zupKZmS9pKYP/x9OwYxo6kfKPjCNEscovsbt8KBg8pwoqi4CorNDpGo9GdlaS+NAvFyxe9wlZdcAPDAXDZStCdVXjFdUZRVJxlhfUuwFC9yUX1k1R/A1Wo/hDomgvX0aJ6ZNUx1ScA1cuv1m2hoy9Fq3BQtO6r032Z4gj7wT/QNRcTh7WTIiw8Rm6hFGG34k4t4aP0ChvWqHbE/uO/qFZvnCX5ZC9+AdBruqIVk4WEm17FFBhGZU4qBT9/iCN1x3Efs2TLD/gmDiJo4ET8e4xCtXqDrpP/w3xcR95D+8E/8Os6lIQbXqrOobmwp+7EGtkWvx4jKPhlQfWXA9Eo9Eo7jvS99OrY3ugoQjSb3CLP+B3iIdeEwVXmfkUYwBIcXV0oa/5e3QV9tAibfPwx+YegKCresZ2JufT/8E7odtzHsydvpWjVZzXnKiYzKAomvz+31SvZ/D2Fqz6lMicVx+H95Hz1ClW5aYSceRmu8mJKNiyrPlDxmB+vJmc7sJlgfyveVnlPhWfIK7IbHaFZeERLWNc0XOXuOfnbdnA7Kc/NxC9xCJHn3U7E1NuozEunImMfpRZvHBl7Kd36E6q3LzEzH8Erqj3Boy4i66NH63y8wAHjCTnjYsr3rSf369ewhMYQfclDhIycTlVeOmU7V4KuUbhiEYUrFtWc5xXfFb/OA8n7bi4AkdPuwS9xELrTSdHaL2sKuzg19uQtKGMuZ/yQ9ixZmWR0HCGaXHFZJVVODYvZvb94uverO0Kzl7rt7kl6hQ290kHZjhVU5qSiKCp+XYZQtuM3cr95ldItP4CuodnLKN32CwBe0cfZHk9RCRl9GQCFqz5Dc5RRcXg/ZTtXAeBbx3Sno0LPmklVUQ4lm38gaPBk/LsNp+CXj3Ck7yZ09KV4xSU27gv3MJXZB3HZShjVL9boKEI0m4IS928Ne0QRdrfrweagSMLOmUXktHvqvF8xWfBp1xv/3mdhDv5z8X9FNQGgVZTXeZ7JLxCTj3/1X/S/TA04spuJavGq8zyfjv3xadOdolWfgubEK7a64BZvWEb53vUAeMV2rv8LFHWy7d9E+2h/o2MI0WyyC6QIt3q6ruN0s+UqtYpyAgdMwL/bcAIHjAeqC+HR68D2tB2Ejr2CyHNvJezsq1BMFlSfAAL6jKm+P2V7nY/rKi/B5agu0MFDpoKiYgoIxa/bUAAqMuvuBg096zIq8zMo3f5r9Q1Heh3MQRE1o7bdtSeiOdmSt2L1stIhNujkBwvhBnIKbDhd7v27w/2vCWuumlG97kJzlFP0+xeEjJpB+ITrCT1zJqp39VSh8v0bsR/YTKFqJmr6ffh1GUzbu+ejmEwoJgvOsiIKV34CgFdcIlHT7gUgfd69aLYSilZ+Sti4q/DvOQrfxIEoZiuKasJZVkjxxmXHZPHrPhKvqPZkf/G/mkLrSN+LX9ehxF//Aqq5es1jR4as9nS67Cnb0HWdySPbM/uTrUbHEaLJFZVV1OqUc0fuX4SpXjHK3RT+9jHO0nyCBk7EHBKNsziX0j9WULjyUwBs+9aT9fFThIyYhjWiDZrmwrF/E/k/f1DzpUQxWTAHhlX//5GRzMXrv8ZZkkvQkKlYIxLQKx3YDv5B/k/vo9n+NrhNUQkdfQkV2SmU715dc3PJxuVYwuPx6zIYp72U4rVfUXmcVrSoP81eSmVWMv0SY4yOIkSzsDmqUNx8qrCi6+79PUPXdfKWvUHp1p+MjiLEaQs542KChl/Ihf9ahtPp3t10Qkwa0Z4bzu/l1ot2uP01YUVR3LIlLDyTLWkLqsnEmAHxRkcRosnZ7FVuXYDBA4owgObwjJVXhPurOHwArcLGmQMSjI4iRJOzVTiNjtDkPKII664qoyMI0Th0DVvSVjrHBRidRIgmZ7O7/+9uzyjCzkqjIwjRaGxJm/HyshIb7md0FCGaVLlDirBb0J3u/w8pPIc9eSuKojBlpGzoINybzSHd0W5BirBwJ66yQipzDzGwW9TJDxaiFSuX7mj3INeEhbux7d9IVLA3qkd8goWnssvALPcg14SFu7Elb0U1mxnRO87oKEI0GZemU1nlMjpGk/KQIiwtYeFeHIf2oFVVMHaQTFUS7k3T3Ho9Kfcvwrqmgebe36SEB9Kc2FO207VNsNFJhGhSmnsv6ugJRdj9rykIz2RL2oKvj4XQQG+jo3i0G6f15uv/ncfNF/au9zleFhNv/XMsX//vvFq9Ge1jA/nfHWfw6ZOTef72M2gfG1jrvKmjOrDkuam0iwn8+0O6LTevwe5fhHFKERbuqXqqkipTlQw0qm8cE4a2bfB5Myd0JTb82L2h7505kMQ2ISgKdGkbwr0zB9Tc5201MWNsIiu3ZXAws+SYc92Vu7eE3X4DB1d5MakvzTI6hhBNou0976NYvHG6NHSqNyzRdWr/v3t/xA2hAFarGYvZhHJkm5/KKicVlSf/0q+qCr7e1prz7BVVOJ3Vl8wC/LxxOl3YK6rw9bFiUlVKyx0AWC0mrBYz5fZKj/o39fW2YDKZjI7RZNx+K0PdJS1h4Z5Czrwc1eoDigJODVUBk8WEqjbsF1ZVZQX28lLsZSXYy0uodDjQ8Zxf8qcitl0XrH6hVFYcKZBe3tiK88hOTznJmQrtuvQCrGiahqqqFGWnUlKQi6KqBPQeQqWjnOyUvbRJ7IXJy5vMpG2oikr77v0oLsgl+5BnbQvasedAQIpw69XAX0hCtAbm4CiChk6lIj8fc1AwWRmlzH+tek9nb18z4ZEBhIb7ERLqS2CIDwGBXvj5W/H2teJlNWG2KJhMKqrJhMXqhcXqRWBIOAAupxOHvQx7WQnlJcXYyoqxlZXUFGl7Welf/l6K5oEDH8dddD0OWxnrfljM5H/cTlyHrqQn7ebXL98/4XkDx5xLl75D2bl+BQmdexAYEs7Odb+yZ/PvAASFRhIV355OvQYBkH0ome8XvsmISReDrvPl289QVlzQ5K+vJWmb2Auz2WJ0jCbj9kVYNVuNjiBEo4u5+mlUk4mUOfPwjo+j/ZVX0Kt/HH9szsBhc5J+sJD0g/XYwlOFkFBfwiP9CQnzIzjUh4Agb/wDvPD1CyE0OpIoa3XBPlq0/67SYcdeXoqtrJjykqLqAn20ZV1WcuTv1f+tdNib4N1ofj9++naDu4RDImIYeOa5lJcW8/vyj7mk82PHHPPdwjcYMeliQiNjKczJZNWyRfgFhtBr6Bh2rP+1pgCrqsmDvvy491aGbl+EFTf+BiU8U/jkWzB5+VG0dRsF6zfA+g3ETJrI+PN7sG9XNhUNWW9Xg8I8G4V59dvu09vbTHiUP6HhfgSH+hIU4oN/oDd+/lZ8/GMJDGuDxaKgmlRMpj+vl9Y8ncuFw1aGrayE8tKiP4v0X4p1TUu7vATN1TILTYOvySoKY6Zdjdli4cdPPzrul5GSglyWf/hqrdvOmnY1uq6z8ZdviErowNnTryUkMobCnEx+/Gwu2YeST/VltAp//xlyNx5ShBWQa1zCDXi37Yl/7zNRdJ3kOfNqbt/z5DP0fv4ZzprYhW8X72yy53c4nKSnFpGeWnTyg1UICfElLMKf0HBfgkJ9CAzywT/ACx+/YEKiIohKUDCZFEwmU92t7Ao79vIybKXF2EqL/izYR4r1X4t4RQveN7z30DHEtOvMwT3bOPDH+nqfFxweTbf+I9iy8lvsZSVMu/5BTGYL3y16kzPP/wdnz7iWBS/8qwmTG6+unwt34vZFGECxWNGrKoyOIcTpMVuJnPEgig6Hv/oGe0ZGzV3lyckUbtzIoOGD2Lr+EFkZLWAKiwaF+TYK8+tXHK3eZsIj/QgL9z/SyvYmIMgbP38vfPxiCAyLx2xWMZlP0Mq2lx+5ll2Eray4ulVdXlKrWFd3nZegNeOgzerBRdCuax9uferdWvedPeNaug0YyeK3nznmvCHjLqCqsoLNK5Zh9fYhJCKGfVvXsn/bOnoPO5uYtp2wevlQWeEe3fx1MZndu0y596s7QrF4SREWrV7Mpf/GZPHCWVbGoY8/Peb+fc+/yKAP5jNlRm/mvryq1XX+VDqcHE4r5nBa8ckPViAo2IfwKL8j17J9CQz2xj/AG1+/QIKjwoiIVzGbFVSTqc4pLlUVjupW9pFr2UevW//ZNf5nS7vCYTutVSPs5aXHDKjy9Q9CNZlw2Mqwl5cec05EbFs69RzIup++pMJhw+JVvSiLf1AoqsmEX0AQ4N5T0FS14aP9WxuPKMKqxQvN6BBCnIaAfuPwTuiOoiikvPMeLvuxLR+tspKD775Hp5uup9/gNmxZl2ZA0maiQ3GhneJCO5B30sOtVjNhkX6ERvgRGuZLYLAPAYHe+AV44eMbTUBoXHUr26RgMptQlNrrGGmaiwqbDVt5CbbSIspLi48MPCvF90gx9PELwD8oFHt5Cb2GjqHvyPEU5+ew+O1n+Paj14/J9I/7nyMwJJxVSxfVjI7+q2Hjp2O3lbFt1fdA9ZeG/Kx0Ytsncv3Db2C2WMjPSqeq0nEKb2DrYLK4/5gejyjCisXL6AhCnDI1IIyQc64BTaM0OYXcX1cc99jsb78j9rxzGXduN/bsyMReLpuXAFRWOslMLyYzvR6tbCAoxIewCD9Cw/1qrmUHBHrh4xdAUHgo4bHHtrI79hxY0+3scjkxmcx4+/oz8fLb/jK968/Wdk0Lr46BR3Htu9AmsSerli6kqvLPXrwfPnmbsy64ktCoOLIPJfPL4vmn98a0cFar+y/J6vYrZgGkv3M/lZmeNcFduI/4m1/DGhKNrutsv/cByg6c+GfZJz6Ovq+8xLaN6Xz9yfZmSum5zFaV8HB/wiKru8aDgo9cyw7wwsfXipeXCbNFrRmApqh/b2VrVNjLsZeX/uVa9tFiXXpMN7nLg3aFCw6P5vJ7njI6RpPyiJawapaWsGidws65proAu1zk/PzrSQswgD09g/zff6ffGaPYsv5Q/eYLi1PmrNTIOlxC1uH6DYYLCPImPLJ6xHita9n+/gSGh9TZyv6rqsqKP6d5lRQeKc6lx7S0beWlVNjKWvU1Y6uX+7eEPaIIKxZZsEO0Pl5xXfAfMB5d19Eqq0j94MN6n7vv5VcJHjSIKdN78dYLK9HdfE/W1qS02EFpsYOU/Sc/1mxRq7vFI/wJDfMlKNjnL63sSCITYrCcoJWtaxoVDhu2spIj07yKjzta3F5WgrOqsole9amxSBF2D3JNWLQ6qpnIS/6FSTWh6zqpCz6iqrgB046cTlLenEPnO29j0Ih2rF95sjWNRUvkrNLIPlxK9uFjR0/XxT/Qi/BIf8LC/QgK9SUwyPvIYip+BISGEBatYjIrR6Z5Hfvr31lVib28DHtZMeWlRdUt7LLiOlvajmZoZXv5+DXp47cEHlGEVQ+4uC/cS/TF/8Ts5Vc99zUri6xl3zb4MXJ/XUHctPMYM6kLu7YepqxUpum5u7KSCspKKjh4IP+kx5rM1a3ssHA/QsL9CAqpHjFevZhKBBGBMVis1a1s1WRC/XsrW9epsNuqr2WXFmErLaq12llNS/tIAf/rALP68vH1R9d1t141y+2LsK65MPkFGx1DiHrz73Um3u17U1JRRpB3AMlvzUU/xeUbdz3xFP3feI1xU7uzeMGWRk4qWjOXUyMns5SczPq1sv0CrEc2BvElOMyXoCAf/AO98PXzwT8kkLCo9tWt7CNrjP+9cDqdVTj+Oi+7piu8uFbxtpeVYLeVoWsa3n7+6JqG4sarZrl9EUbXMQeGGZ1CiHpRfYMInXQDWaW5RPmGkb92HcXbTn2Ec2VOLrk//Uyv8ePYsi6tXi0kIepSXlpJeWk+qUkn/xlSzSphYb6ERfoTEla9xnhgzepn4UTERWO2/DkArc5WtsOGoiitemBZfbh/EVZVzAHhRqcQol5irngMTVHQ0FB0nZR57578pJNIev1NwkaOYPL0Xrzx3Ao0l3v/UhPG05waudll5GaX1et4Xz9r9YjxiCMbgwRXX8uOTQhCcfMVs9STH9K6KYqKOTjC6BhCnFTIWZfjFR7PipS1xPlHkf7ZF1Tk5DbKYx945VVCw/0YNrpDozyeEI3JVl5JWkoBW9cf4tdv97Jk0TYWzFlHdmYpquq+14PBA4owIC1h0eJZo9oTOHQq69O3Mji2N5WFhWR88WWjPX7B2nWUJSUz+pxEgkJ8Gu1xhWhKgUHuP6jWI4qw6hsAbt6lIVozlajL/kNpZTlZZbkE+QaR8vY7aJWNO2dz9xNPoigw4fwejfq4QjQVvwD3n17qEUVYURRM/iFGxxCiTlEz7sfiG8icDR8yqeNoiv/YQf6atY3+PFWFRWQtXUaXntF07hbZ6I8vRGMym1W8vDxg2JLRAZqLjJAWLZFft+H4dB7A13t+4OyOozCZzCS9NbfJnu/gO/OpLCll0oW9MJs95uMvWqHAYM+4bOIxn0JzgBRh0bKo3v6EnXsr6cVZrEnbRN+o7mR+swz7oUNN+rz7/vciAUHejBjbqUmfR4jTERru/qtlgYcUYV1zSUtYtDgxlz+Krpp4ac1c7hh2DS67nbRFHzf58xZv3Ubp7t2MHNvJY37RidYnLMIPzQPWPPeIIoyuS0tYtCjBoy7CK6od72/9jG4RnYgKiCB1/vu4ym3N8vx7nnwGNI2J03o2y/MJ0VChEX4esfGIZxRhRcUkLWHRQlgiEggacSFbMnfyQ/JK/tF7GmVJyWT/+HOzZXCWlZHxxWI6domga6/oZnteIeorPNIf1eTec4TBQ4qwoqpYQmKMjiEEANEzH8HmdPD6uve4edA/8LJ6k/zW29DMy/Md+mgRFQWFTJrWE4tVpvCJliUs0t+tN244yiOKMIAlPA4Uj3m5ooWKvOBuLH7BvLpuPlaTlZHxA8j55VdK9+4zJM/eZ5/H19+L0eckGvL8QtTFZFYJCHT/OcLgQUVYNVsxB0cZHUN4MJ/OA/HtNoxv9//Klsyd3DfyRnSnk4PvfWBYptLdeyjeto2ho9sTEeVvWA4h/io0zNcjWsHgQUUYwCuqrdERhKeyehNx/l1kleXywbYvGBTXl7bBcaQt/JiqwiJDo+15+jk0p4vJ03sbmkOIo0IjPOcLoccUYd3lxBrZzugYwkPFznwUzBZeXD2XKlcVNw+ciSMrm8xvlhkdDc3h4NBHC2nTIZReA+KMjiPEkelJmtExmoXHFGEUFWtUO6NTCA8UNPQ8vGM7sWDbYlKL0rmiz4X4efuT/Nbb6E6n0fEAOLx4CY6cXMaf1wMvb/dfKlC0bKERfs09TtEwHlOEFVXFK1q2cRPNyxwSRdCZl7Ejey/L9v1MgNWPSZ1GU7B+A0Vbthodr5Y9Tz2Dt7eZsyZ2NTqK8HAxcUGYTJ5RnjzjVR5hDgxD8fI1OobwIDGXP06Fq5LZ695FR+eeETegoJAy712jox2jPDmFwg0bGDS8LTHxQUbHER5KNSlExgQYHaPZeFQRBrBGtDE6gvAQEVNuxRIYxuvr36fQXkzX8E50C+9IxuIlOLKyjY5Xpz3PvYCrspLJ03uBZwxOFS1MVEygx7SCwcOKsK5reMl1YdEMfNr3wa/XaH5KXsWGjG0A3Dn0GqqKi0n/9HOD052A08nBd+cTmxBM/yHyhVU0v9iEIHRPuSCMhxVhNA1rpExTEk3MbCVi+n3k2QqYv+UzAM7reg6hfsGkzHsXraLC4IAnlv3t99gOZ3L2ud3w9bMaHUd4mNiEYI/YuOEojyrCismMV4wMzhJNK+ayh1HMXry4Zi4VzgqsqpmLuk+mZNdu8lb+bnS8etn9xFNYLCbGTpZBWqJ5xbcLke5od2aJaINc7BJNJXDARLzju/DJjq9JKkgF4I5h12A2mUl6622D09WfIyODglWr6DekDfHtQoyOIzyExWoizIMW6gAPLMKq2YolIt7oGMINmQPDCR53Jfvykvlyz3cAxAfGMCCmF1nffoftYKrBCRtm3yuvUWVzMGVGbxRVvriKphcdF4jqYT9rHleEdV3Dp63soSoaX/Q/HqdKc/Hy2ndqBpbcN+IGNIeDtI8WGZzuFDidpLw1h4gofwaNaGd0GuEBPO16MHhgEUbX8WnXy+gUws2ETbgOa1AkczYuIM9WAMDodkOJCYwi9f0PcZaWGZzw1OT+ugJbWhpjJnXB30N2tRHGiU0IbvYtPY3mcUVYUU1HWsKe1eUhmo5XfDf8+41j5cH1/J62EQAVlWv6XkR5ahpZ3/9ocMLTs+uJpzCpCudM7W50FOHm2rQPRfWgQVnggUUYQPX2wyo7KonGoJqJuvifFDpKmLt5Yc3N1w+6DG8vH5LfnAOtfCH6ypxccn/6iZ794mjfOczoOMJNBYX4EBTiY3SMZueRRVjX5LqwaBzRlz6E6uXDy2vmYq9yABDqE8yZbYaQu2IlJbt2G5ywcSS9/hZVZeVMnt4b1SS9SKLxdUgM96hFOo7yyCIMOt7tZO9UcXr8+4zBp20vPt+5nL15yTW33z/yRnSXi4Pz3zcwXeM7MPs1QsJ8GTZa5tqLxtchMQLdwwZlgYcW4errwj1A8ciXLxqB6hdM6ITrSC5M4/Ndf+4J3D+2J+2DEzj08adUFhQYmLDxFaxdR9mBJEafk+iR3YaiCSnQsUuEx10PBg8twgCq1Ruv6PZGxxCtVOwVT+ACXlo9F03/85rvLQP/QUVuHoeXfG1cuCa0+79PoSgw4fweRkcRbiQmLghvH4vRMQzhsUVY11x4y3VhcQpCx16JNSyGuZsWkl2eV3P7Zb3PI8AngOQ5c9GdTgMTNp2qwiKyvllKl57RdO4WaXQc4SY6JIZ73Pzgozy2CIOCT3u5LiwaxhrTiYDBk1l7aDMrDq6tud3X4suUTmMp3LyFwo2bDEzY9A6++x6VJSVMurAXZrMH/woRjaZDlwiPnTTqsZ8gRVXxTugGqsnoKKK1UM1EXfp/lFSU8dbGBbXuumfEdZhUleS35xkUrnnt+99LBAR5M3JsJ6OjiFbObFZp0y7UY5dGNRsdwEiqxQuv2E5UpO81OopoBaKm34/Z249Xfn2J8kpbze2JYR3oGZFIxuIlOA5nGpiw+RRv3Ubp7t2MGNuV7ZsyKMgrNzpSvUyc1pNBI9qxcXUqyz7/o85jpl3ej5794ti64RBfLdp23MdSFBg5tjP9hiTgH+hFfk45q34+wM4th2uOCQrxYerFfYhrE0xRgY3vluwkZX9+zf1dekZx8dWDWDRvPft25TTeC21F2nQIxeTBPSqe+8oB3eXEL3GI0TFEK+DXYyQ+nfrx1Z7v2Zmzr9Z9dw67hqqSUtI//cygdMbY8+QzoGlMurB1jK3o0TeWAUPbnPCYzt0i6dkvrl6PN+nCXpw1sQtBwT64nDpRsYFceHl/+g5OqHVM+87hKIpCZEwg0y7v/2cXvgJnTehC+sFCjy3AUD01yeVq3QvanA6PLsKKyYx/j5FGxxAtnOrtT9jkW0grOsyiHbVHPU9OHEO4XygH35mPy+4wKKExnGVlZHz+BR0SI+jWO8boOMfl629lwgU9mDaz3wmnwFi9zEyaXr915YNDfeg/pLqgfzhnHc889C3rfksB4Owp3WoWNEloF0JuVilP/Ws5B3bn4OfvRVhk9VZ9vfvHERkTyM/L95zOy2v1uvWO9qj9g//Oc1/5EebAMLxiOhodQ7RgMVc8jq4qvLRmLi7NVXO7WTVzaY+plO7bR+6K3wxMaJxDCz+moqCQiRf0wOrVMsdXTJnem8Ej21NYYDtht/nZU7oSFOyDs8p13GOOiokPRlEVyssqSNlfPUJ+5U/7AfD1s9KmXSgAmqZjMqv4+FqwWE01t6kmhdHju5CyP4+DB/LrfhIPEBkTQEiYn9ExDOXxRVh3ufDrNtzoGKKFChl9CV6RbXh3yyccLs2udd9tQ6/GYrGS9MYcg9K1DHufeR5ffy/OGJdodJQ6VVY6WfdbCm+/uJKSorp7K9p0CGXA0LbkZJWy+4+skz7m0UL919HhLuefXapHW7sHD+QRGu7HfY+Np23HMIqL7BTkljNgWFtCwnz5eZlnt4K794lB8+CuaJAijGIySZe0qJM1oi2Bwy9gY8Z2fkxaVeu+mIAohsT2Ifv7HyhPTjEoYctQumcPxdu2MXR0ByKiA4yOc4wlC7fy3ZKdVDjqnrttMqtMmVE9XXHpp9vrdX0y63AJmkvDy9vCkFHtsVhNtUaKe/tUj3ld9sUOtm1MJy+njKS9uXzy7kZUk8KosZ3YuzOLjLQiAI/byP6onv3iPHZU9FEePTr6KHNgONboDlRmJZ/8YOEhVKIuf5jyKgdvbPjgmHvvG3EDWkUFqR8urONcz7Pn6ecY9MF8Jl/Yi/mvrTY6Ti0n2xPgzPGJhEf6s3H1QQ4dLKTfSQZvAZQWO1j/+0GGntGB8ef3YPz5PdA1HZdTqzXS11ZWyZKFW2udO+rsTvj5e/HL8r2Ehvtxwcx+xMYHUVLiYNlnf7B/t2cM0oqMCSA03LO7okFawkB1l7S/dEmLv4icdg8W3yBmr32X0oqyWveNbDOYuMBo0j78CGdJiUEJWxbN4eDQgo9o0yGUXgPqN7q4JYiOC2To6A6UFDv4aWnDuoZ/+GoXPy/bQ3ZmCVkZxXy5cCs2WyUAdltVned4+1gYdmZHdm47TE5mKROn9SA80o8vPtqC5tI5/7K+mC2e8WtZuqKrSUuY6i5pvx4jKfjlQ6OjiBbAN3EIvl0Hs3Tfz2zL2nXM/df1vxh7egaZy78zIF3LdfjLr4iZPInx5/Vg387s43b/tiRdelSPzA0M8uaB/06odV/fQQn0HZTAY/d8U+e5ug6b1qay6ucDoFdfHz7vkj4A5GSV1nnOyLGdsFpN/Ppt9TS3uDYh5GSVsnPLYRK7R9Grfxxh4X5kZ9Z9vjuRruhqnvGVqx4sQRFYZUMHj6dafQk//w4Ol2Tz0bbFx9x/7YBL8fHyJfmtt0GTb/F/t+epp/H2NnPWpK5GR6mXCkcVJUX2Wn8qK6q/PFRWOCkpstd5nsmsct/j53DfY+Pp3qd6etbQ0R1QTSolxQ4yUguPOScg0JtBI9qxbWN6zShtXdfxD/BGNSkEBnkfua0pXmnLEhld3RWtKFKEpSV8hK658Os6nMoszx5k4+lirngUTCZeXDOXKq12Sy7YO5Cx7YaT9/saiv/YYVDClq08+SCFGzYwaPhgtq0/RGZ6sdGRTmjtbyms/a32Z37qJX3oOyiBXdsza1bM6tY7hvHndcfp1Hj1qV9wOTV2bctkwLC2XDizP1OmO/H2saBrOt99ubPOQnrGOZ1RVFjx/Z+LvRw6WEhi9yge/O8EzBYT5aUVrWb1sdNxtCvaE7cu/Dt5B45QVBkl7emChk/DK7oDH2z9gkPFh4+5/74RN6JoOinvzG/+cK3InudewFVRyZQZvXGXho7Vy0RgsE9NaxXguyU7WbcyhbLSCkxmlYzUQha9s4Hd249dujQkzJe+gxPYtCat1jSp5V/sIGV/Hpqmk5NVyhcLNuN0un8Pi3RF/0nRdU/o/Ki/9Ln3UpktrWFPYw6LI/a6F9iVu5//rpiNTu2PRZ/o7vxr1C2kLfyY9E88a3nKUxE1fhydbr6Rbz7dzua1aUbHES1IdFwg1999htExWgxpCf+F7nLh32u00TGEAWJmPoLDVcGr6+YfU4ABbht8JZUFBRz+8isD0rU+2d/9gO3wYc4+txu+flaj44gWpP/Qth69VvTfSRH+C8VkIqDPGBSTxegoohlFTL0dS0Aor617nyLHsVOOZvSYTKBPIMlz5qFVVhqQsHXa/cTTWCwmzp7SOgZpiaZnsZroMzDeo9eK/jt5J/7G5O2HbxfZWclT+HTsh1+PUfxwYCWbDm8/9n6zNxd0OYeibdspWLfegIStlyMjg/yVK+k7uA0J7UKMjiNagJ79Yj1mHnR9ybvxN7rmInDAeKNjiOZg9iJi2j3klOfx/ta6r/PeNfxaTKqJ5Dlzmzmce9g/+3WqbA4mz+gtA3EEA4e384gpWA0hRfhvFNWET5vuWEJb7tZsonHEznwExWzlxTVzqXAd283cIaQNfSK7cvjrpdjTMwxI6AacTpLffJOIKH8Gj2hndBphoOi4QGLigzx2nezjkSJcB11zEdB3nNExRBMKHDwFr7jOLPxjCSmFh+o85p5h1+Est3Ho40+bOZ17yVuxEltqGmdN6oJ/oJfRcYRB+g9tIwOy6iBFuA6KaiKg39koZhnV6Y7MwVEEj7mc3bn7+XrPj3UeM77TaCICwjk4/z1cNlszJ3Q/u/77FCZVYfzU7kZHEQawWE30lgFZdZJ35DhUL1/8uo8wOoZoAjFXPEaV5mL22rqnI6mqyuW9zqfsQBI5P//a/AHdUGVOLjk//USPfnG07xxmdBzRzHr0jcViMRkdo0WSInw8uk7Q4HONTiEaWfikG7EEhvPG+g/Itx+7vi/ArYOvwsvqTdKbczxjId9mkvz6W1SVlTN5em9pEXmYQSNkQNbxyCfhOBRVxSuqLV7xXYyOIhqJd5se+PUZw68pa1ibvrnOY6L8Ixge14/sn36mbP+BZk7o/g68MpuQMF+GjpbNUjxFbEKwDMg6ASnCJ6C7XAQNmmx0DNEYzFYiL3qQAnsR72z++LiH3TfiBnSnk9T3ZFvLplCwbgNlB5IYfU4iQSE+RscRzWDE2I4yIOsEpAifgGIy4dd1KCZ/WWigtYu55N+oVm9eWjMPh7OizmOGJvQjISiWtAULqSpu2bv/tGa7nngKRYEJ5/cwOopoYmGRfnTtGS2XH05A3pl6CBo8xegI4jQE9BuHd5vufLpjKfvzj785x439Z+I4nEnm0uXNmM7zOIuKyPpmKV16RtO5W6TRcUQTGn5mRzRNLgafiBThk1BUE4GDJqH6BhodRZwC1T+UkHOu4UD+QRbv/va4x13V9yJ8vf1IeuttdJerGRN6poPvvkdlcQmTpveSZQzdVECQt0xLqgd5d+pBUU0ED5lqdAxxCmL/8ThONF5aOw9Nr/u6VKBXAOM7jiR/7XqKtx27frRoGvv+9yIBgd6MHNvJ6CiiCQwb3QEZinVyUoTroaY17BNgdBTRAGHnXIM1JJq3N35Ebnn+cY+7d8T1KCikzHu3GdOJ4m3bKd29mxFjOhEa7md0HNGIfHwtDBjeFlVawScl71A9KSYzQdIabjW84rrgP2A8v6duYGXq8Xc/6hHRmS5hHcj4fDEVOTnNmFAA7HnyadA0Jl3Y0+goohENGtFOuqHrSd6lelJUE0GDJ6P6+BsdRZyMaibqkn9R7Cjl7U0LT3jo7UNnUVlURPpnXzRTOPFXzrJyMj7/gg6JEXTrLZumuAOL1cTQ0R1kXnA9SRFuAGkNtw7RFz2IycuPl9fOw1ZlP+5x07pNJMQ3mJS330GrPHYXJdE8Di38mIqCQiZe0AOrlyxt2Nr1G5yAl7fZ6BithhThBlBUE0GDpDXckvn3Go13hz4s3v0tu3OPv+KV1WTlwm4TKN6xk/zVa5oxoajL3qefw9ffi9HnJBodRZwGi9XEqHGdjY7RqkgRbiDFbJU1pVso1TeI0Ek3klqUzqc7vjnhsXcNuxazyUzynLnNlE6cSOnevRRv3cqQMzoQES0DIFurIaPa4+trRVGkK7q+pAg3kKKqBA2eguotreGWJuaKx9AUhZdWz8N1nOlIAG2D4ugX3Z3MZcuxpaY1Y0JxInueeQ7N6WTK9F5GRxGnwNfPysizO6HIteAGkSJ8ChSzRVbRamJh46+lw0OfEz7x+nodH3LWTLzC47FXOXh58qOMbje05r62wfE8efYDvH/hS/z37Pt5YNTNuOx20j6qXkM65tzJDF/8Kb5t2zbJaxH1ozkqOPThAhLah9J7QJzRcUQDjTq7M2azXNNvKCnCp0BRTQQNOVdaw03Er/sIAvufU+/jrVHtCBx6HpmlOQR5H9uVecfQWXQKa4eCQuew9oT7hZI6/wNc5eWo3t7ET7+QvFWrsaWmNubLEKfg8JKvceTkMv68Hnj7WIyOI+opONSXgSPayojoUyBF+BQpZgshoy8xOoZbUX0DCTvnGiLPvxNFre83apWoyx7GVmUn0u/YzeJ9LT7EB8WwNXMnVy2+G02r7qbOW7MWgLjzzsUS4E/awkWN9TLEadrz1NN4eZs5a6JsI9pajJkk/1anSorwKVJUE4EDxmONlC7MxhIx6UaCBk3CWZRDVWFWvc6Jmn4fFt9AbFUOFEWhylVV6/6jS1VaTVZuHXwVqlr9I687nZgDAog971xyfv4Vx+HMxn0x4pSVJx+kYP16Bg5vS0x8kNFxxEnExAfRs1+cLM5xiuRdOx2aTtiE64xO4Ta0SgfF65eSPu8+nCV5Jz3er9twfBIHsicviSj/cH5JXk2ho6TWMQ5nBQcKDtI9sjMj2g4EoHTffjSHg/jp01CtVtIWfdIkr0ecur3Pv4iropIpM3ojA21btrPP7Sb7BZ8GKcKnQTGZ8Enohl/3EUZHcQu5X79K/g/voFfYTnqs6u1H2Lm3kl2WR4eQNhTZi/lgW92rXr28eh7llTZ0TaNw8xb2/e9FrGGhRE8cT9Z331OZV13wFZMMKmkxnE4OvvMuMfFB9BvSxug04jg6domgfadwaQWfBnnnTpOua4SNuxrF4m10lNbvBNOK/i7m8sfQVRMOZwVWk4V3t3x63NWx2gTH4WvxIfX9D9n16BM4srJpc+nFoOukf/I5/omd6ffaKwz/4hP6vfYK/omy2EBLkP3dD9gyDnP2ud3w9bMaHUf8jaIqjJvaXfYLPk1ShE+ToqiYfIMIHjHN6CgeI3jkDLyi2rExYxvtQxLYfPgP1hzadNzjbxp4OY7sbA5/vRQAn7hYIsecReY3y6gqLibxzttQrVb2Pv8i1pBgEu+8rbleijiJ3f99GovFxNlTuhodRfzNoOFtiYjylxHRp0kW+GwEiqoSPPQ8Srf9jLOeA4rEqbFEJBA0cjpbMncSeGQ6Uv/YXnxy8Ru1jrtlyJWc2X4YSQWp+Hv7s/Otl9CdTgDaXHYpLruD9C++xOTni09cHLkrVpK3chUxUyYS2LUrJl9fXLaTd4uLpuXIyCB/5Ur6njmaLesOcehgodGRBOAf4MWYSfLFqDFIS7jRKISfM8voEG4v+rJHsDsreH3de5RUlJJvK6z1x6W5ACirKMdWZWdypzMp2LiJos1bAPDr2IGw4UPJWPIVrvJyONKVZg0PQzGbsYaEVD9RA7rGRdPa/8prVNns1YO0pNXVIoyb2h2zWZXlKRuBtIQbiWIy4dtpAD4d+2NP2mx0HLcUd81zWPyDOVyUQXFFKS+uPnbd51enPEGkXxjvbf2MM9sPQ0Eh5e13au5ve8VMqkpKOfxV9drSLrud8tQ0gnp0Z+jCD1CtVsoPpuKyO5rtdYmTcLlIfvMtEu+6g8Ej27HutxSjE3m0dh3D6NVfVjRrLNISbkS65iJ8/LVgku82jc2n0wCsUe0BMNdjIY+YgEi6h3ciY/ESHFnVlwgCe/YgpF9fMj5fjOb4s8juf/EVSvftR9c0SvftZ/9LrzTNixCnLG/FSmypqYyZ2JWAQBkEaRSTWWXKRb1rFr0Rp0/RdV2GtjUiXdcp+GUBxWsWGx3FfVi9aXPHPHIqirnvu/8esyBHXd6Y8iQBlbDpxltrFVzRelkjwun/5uvs2ZHN5x9Ib5MRRo9P5IyzO8tlgUYkLeFGpigKoWdcjCVMumsaS+zMR8Fs4cXVc+tVgKd2HUeYXwgp8+ZLAXYjlbl55Pz4Ez36xtK+c7jRcTxORJQ/I8fKLkmNTYpwU1BUIs+/E+q9/rE4nqCh5+Ed24mPti8mtSj9pMdbVTMXdZtMyZ695K1c1QwJRXNKfuMtqsrKmTy9lywQ0ZwUOPfiPkancEvyU9wEFJMJa1R7gofL3OHTYQ6JIujMy9iRvZele3+u1zm3D7sGi9lC8ptzmjidMMqBV2YTEubLsDM7GB3FYwwc1pb4tiHyxacJyDvaRBRFIWTUDKzR8oviVMVc8QQVrkpmr3sXnZMPXYgPiGZgTC+yvvue8pSDTR9QGKJg3QbKDhzgjHGdCQrxMTqO2wsN92Pc1O7I8KGmIQOzmpCuuagqzCZj7j3ozkqj47QqEVNuJaDPWTy36k02ZGyr1zkvTXiESEsAm66/CWdpWRMnPH0dbriWmEkTyfr2O5LeqG65j1jy+XGPL/5jBzv+/fBJHzds+DC6PnAvtvQMttxye83tisVCxxuuI2zYEFyOCjK+XELmkVXEACwhwQx463Wyln/HwXffO41X1vTMQUEMfOdtkvbmseidDUbHcVuqqnDNHSOJjAmQVnATkXe1CSmqCUtINCGjLzU6Sqvi074Pfr1G81PyqnoX4NHthhATGEnq+x+2igIcPmoE0ePPOeb2irz8Y/7oruoFSCry80/6uD4JCXS4oe6dvWKnTCJq3FgUiwVraAgdrp2Ff+dONfcnXHwRustF+md1b4TRkjiLi8n65hsSe0SR2D3S6Dhua/Q5iUTHBUoBbkIyobWJKapK0JBzse3fiCNtp9FxWj6zlYgL7yPPVsD8LZ/V6xQVlVl9L8aWdois735o4oCnxxIUSPxFM4iZNAFFPfYX28Zrrq/198Ae3en5xKM4srJJnjPvuI+rmM1Ejx9H2ytmYvKpu4s2oGv1MoObrr+ZyDFn0u7KKwjo2oWy/Qfwjo4i6uwxZHzxJc7S0tN4hc3n4LvvE3HWWUy8sBfJ+3/BWSVzVxtTQvuQ6tHQsipWk5KvN81B14g87w4Uq1y/OpmYS/+DYvHixTVzqXBW1Oucawdeio+XD8lvvg0tfBGBjjffSOyUSTiyc7BnnnidccVsptMtN6KoKklvzqleZvM4oieMp8P11wJQsmdPncfoR94bc0AAZj+/6tuO7AN7dD3tjC+/avBrMtK+/71IQKA3I8d2OvnBot68vM1ceHn/eozEEKdLinAzUFQTJv9gwsbJ2tInEjhgIt4JXflkx9ckFaTW65xQn2DOajuU3JW/U7JrVxMnPH0uh4PDXy9l29331exjfDwxkybgExdXvfb1lq0nfmBdI3/tOrbd8wBFW+ruwi/+4w8A+r/6EvHTp6G7XJTs3Ilvu7aEjxpBxuIvW92mFcXbtlO6axcjxnQiNNzP6DhuY+K0nvgHessOSc1AuqObiaKaCOw7Btu+ddj2bzQ6TotjDgwneNyV7MtL5ss939X7vPtG3oiiaRx8d37ThWtE+19+tX6tdVUlZsokADK++PKkh2cu/47MpctPeEzWt99jDQ4mdMhgtMpKDn/9DbbUNLr9+59UFReT+c2ymudu6T0Kf7XnqWcYOP8dJk/vxQdvrjU6TqvXvW8MvQfEGx3DY0gRbka6phEx5VbS374TV1mR0XFalOh/PE6V5uLlte/UeypEv+gedAhOIG3BQirzC5o4YSOpZ3ELHTwI76goylPTKNlZjxZ+fR5X00j7aBFpHy2quSmgW1dCBw0keU71Zhhd7r+H0MGD0J1O0r/4kvRP6ndd3kjOsnIyPvuc9pdeTPc+Mezalml0pFYrMNibc2f0Qdd0WRmrmUh3dDNSVBXVy5eoC+8HVb7/HBU24TqsQZHM2biAPFv9i+mtg6+kIi+fjCVfN2E6Y4QNHQxAwdp1Tfo8ba+YiSM7h6zvfiDm3MmEjxhO6ocfUbJrN21nXkpAl8Qmff7GcmjRJ1TkFzDhgp5YvWSlulOhqAoXXNYPs0WVAtyMpAg3M8VkwiuuM2HjrjY6SovgFd8N/37jWHlwPb+n1b+b/tJe5xHgE0DynLnoVSdfT7q1Ce7TG4DCTU23UUHIgP4E9ejOoY8/RXc6CUisLriZ3ywjf+16APw7d26y529se595Hl8/K6PPaR1fHFqasyd3pU2HUJmO1Mzk3TaAoqgEDZyAf++zjI5iLNVM1MX/pNBRwtzNC+t9mq/Fh3M7j6Vwy1YKN7jf9XWvqCisoaHoLleTrvzV5vLLsGdkkPPLr9U36NVd2l4REXhFhNe6rTUo3buX4q1bGXJGByKjA4yO06r06BfLsDM7ynQkA0gRNoiu60RMuhGvmI5GRzFM9CUPoXr58PKaudir6r/b0d3Dr8OkqqS8ffx5s62ZV1goAI6cHLTKY1daC+iSyMB5cxg4bw7mwMBTeo7wUSPx79C++vrwkevJJXv2AtDvlRdIuGg6AKV7953S4xtlzzPPoVU5mTyjl9FRWo2omADOu7iPLEtpECnCBlEUBRSFqIv+iep7ar9IWzP/PmPwadeLz3cuZ29ecr3P6xTSjl6RXchY8jX2jMNNmNA4luAggOMumqFYLHiFh+EVHlbngh8npaq0uewSypJTyFu1uubmzKXLyf7hJ1wVFVTk55My713KDiSd0mswiuao4NCCBSS0C6X3QBnhezI+vhYuuXYwqqpIK9ggsna0wXTNhSN9L5kLHgHNZXScZqH6BRN/6xuklmTy0I/PojWgy/P1yU8Q5DKz6YZbcNntTZhStGb957wBAcHMfvIXHHb3GzPQGBRV4fLrh9C2QyiqXAc2jLzzBlNUE94JXQkbc4XRUZpN7BWP4wJeWj23QQV4YuezCPcPI+Xd96QAixPa8+TTeHmZOWtSF6OjtFhjJnahXacwKcAGk3e/BVCU6vWl/XueYXSUJhc69kqsYbHM3bSQ7PITrxj1V2bVzMye51G6bz+5v/7WhAmFO7AdTKVg3XoGDmtLTHyQ0XFanO59YhgxRtaFbgmkCLcQuq4TMflmrFHtjY7SZKwxnQgYPJm1hzaz4mDDVja6dciVWCxWkt96G+QKiqiHvf97EVdFJVNm9EZqzZ8iowM479K+MhCrhZAi3EIoigKqSrS7DtRSTERd+m9KKsp4a+OCBp0a4x/B0Li+ZP/wU6sbKCQM5HRy8J13iYkPov/QNkanaRECAr2Zef0QTDIQq8WQItyCHN3oIeayh91ux6WoGQ9g9vbnlbXvUF7ZsE0C7ht5I3plFakfNKx4C5H93Q/YMg5z9pRu+PpbjY5jKC9vM5ffOARff6tcB25B5F+ihVFUE9aIBKIv/heKyWJ0nEbh12MkPp368dWe79mZ07B5pyPaDCQuMIbUDz/CWVLSRAmFO9v9xFOYLSbOntLN6CiGMZlVLr12MGHhfrIiVgsj/xotkKKa8I7vSuQFd4PSuv+JVG9/wibfQlrRYRbtaPgaz9f1vxR7RgZZy+u/s5IQf+U4fJj831bSd1ACCe1DjI7T7BQFps3sR3zbEGkBt0DyL9JCKaqKb+IgwifeYHSU0xJzxePoqsJLa+biauA86Gv6X4yvly/Jb76N7vKMOdSiaex/5VWqbHamTO/tcZsTjL+gJ117RcvewC2UFOEWTFEUAvudTciZlxkd5ZSEjL4Er8g2vLvlEw6XZjfo3GDvQM5uP5K81Wsp/mNHEyUUHkPTSH7jTcKj/Bk8sp3RaZrNyLGdGDyinQzCasGkCLcCISMuJGjwFKNjNIglog2Bwy9gU8Z2fkxa1eDz7x1xA4qmc/Cd+Y0fTnikvN9WYUtNZczErgQEehsdp8n1GRTPmEldjY4hTkKKcCsRNu5q/HuONjpGPalEz3yE8ioHr2/4oMFn94rsQufQdqR/9jkVublNkE94ql1PPIWqwjnndTc6SpPq3C2Scy+STRlaAynCrYSu60Sceys+nfobHeWkIqfdjcUviNlr36W0oqzB598+9GoqCwrJWLykCdIJT1aZm0fODz/So28s7TuHGx2nSXRIDGfGVQNRQLqhWwEpwq1E9a5LEH3h/XjFt9z1cH0Th+DbdQhL9/3MtqxdDT5/eo/JBPkEkfz2vDq38RPidCW/OYfKsnImT+/ldtN1OnaJ4NJrjuyKJAOxWgX3+gl0c4qigmoi5tL/4BXX8gqxavUl/Pw7OFySzUfbFjf4fG+zN9O6nEPR9h0UrF3XBAmFqHbgpdmEhPky7MwORkdpNJ26RnDJrEEoqiIjoVsRKcKtjKKqKGYLMZc/gk+73kbHqSXmikfBZOLFNXOp0pwNPv+uYddgMplJnvN2E6QT4k+FGzZQtv8AZ5yTSHBo61+drnO3SC6eNQhVCnCrI0W4FVJUE4pqJvqSh/BNHGR0HACChk/DK7oDH2z9gkPFhxt8frvgePpGdSPzm6XYD6U3QUIhatv1xFMo6Ey4oKfRUU5LYvdILrp6IKoiXdCtkRThVkpRVVBVoi68H7/uIw3NYg6LI/iMi9metZtv9/96So9x74jrcdpspC36pHHDCXEczuJisr7+hsTuUSR2jzQ6zilJ7BHFjKsGyjXgVkyKcCumKCooCpHn30lAv3GG5YiZ+Qh2VwWvrpuPTsOnRIzrOIpI/wgOvvs+rvKGbe4gxOk4OP99KotLmHhhL8yW1vXrsGuvaC66akB1C1hGQbdareunThzj6IcvYtKNBA05t9mfP2Lq7VgCQnlt3fsUORq+wYKqqvyj9zTKkpLJ+fmXJkgoxInte/4FAgK9GTW2s9FR6q173xim/2MACtICbu2kCLuBo4U47OyrCBl1UbM9r0/Hfvj1GMUPB1ay6fD2U3qMWwb9Ay+rN0lvzgFZWEAYoHj7H5Tu2sXwMR0Ji/AzOs5JDT2jPdOvGICiIAXYDUgRdjMhZ1xM6Ngrm/6JzF5ETLuHnPI83t/62Sk9RKRfGCPiB5D90y+U7dvfyAGFqL89Tz2DrmlMurCX0VGOS1Fg/Hk9OOe8Hkf+LgXYHUgRdkPBQ6cSPunGJt0GMXbmIyhmKy+umUuF69QW1bhv5I3oTiep73/YyOmEaBhnWTmHP/2M9p3D6d4nxug4xzCbVaZfOYDBo9oZHUU0MinCbiqg79lEzXgAxdr4cyADB0/BK64zC/9YQkrhoVN6jCFx/WgTFEfaR4uoKipq3IBCnIJDH39KRX4BEy7oidXLZHScGj5+Fv5x8zC69IiW1q8bkiLsphRFwbdjP+JmPYM5OKrRHtccFEHwmMvZnXuAr/f+eMqPc+PAmTgys8hcurzRsglxuvY88xy+fhZGn5NodBQAgkN9ufaOkcTGB8kiHG5KirAbU1QTluBo4q55Du+2jbMgQfQVT1CluZi99t1T3qHlyr7T8fP2I/mtt9GdDV9ZS4imUrZ3H8VbtzHkjA5ERgcYmiU2IYhr7xxJYLAPqputcS3+JP+ybk4xmVCtPsRc9jCBA8af1mOFT7oRa1A4b6z/gHx74Sk9RqDVnwkdzyB//QaKtm47rTxCNIU9zzyHVuVk8oxeYFDjM7FHFFfeMhxvb7PbbTIhapN/XQ+gqCqKqhI+4XrCJ94AqrnBj+Hdpgd+fcbwa8oa1qZvPuUs94y8AQWFlLnvnvJjCNGUNEcFhxYsIKFdKH0GxjfrcysKnDk+kUtmDcJsUqUF7AHkX9jDBPQ7m5iZD6P6NKCrzWQh8qIHKbAX8c7mj0/5ubtHdKZrWAcyPl9MRXb2KT+OEE3t8JKvsWfncM7U7nj7WJrlOX18LVx23RBGjateNETmAHsGKcIeRlFUvOO7EH/t81gj29brnJhL/w/V6s1La+bhcFac8nPfMXQWVUVFpH/e8G0OhWhue558Gi8vM2dNavptQ6PjArn+njNo3ylMRkB7GCnCHkhRTZj8Q4i96il8Ewef8NiAvuPwbtOdT3csZX9+yik/5/ndxhPiG0zy3HfRKk69kAvRXGwHUylYt56Bw9oSmxDUZM/TZ1A819w+koAAL+l+9kDyL+6hFNWEYrYQPeMBQkZfWufCHqp/KCHjr+FA/kEW7/72lJ/LarIyo9skinfuIv/31acTW4hmtfd/L+KqqGTy9N40dgPVZFaZMqMX513SF9WkSAH2UPKv7sGUI4U3eMSFxF75JOagiFr3x/7jcZxovLR2HpqunfLz3DFsFmaTmeS35p5WXiGandPJwbnvEBMfRP+h9bt8Ux+Bwd7Mum0EfQe3AWQJSk8mRVigKApeMR2Iv+5F/LoOAyB03CysIdG8vfEjcsvzT/mx2wTFMiC6J5nLvsWWmtpYkYVoNtk//IgtPYOzp3TF19962o+X2COKG+8dTWRMgCzAIVD0U11xQbgdXddQFJWy3Wvx6TKIdelbeGnNvNN6zJcnPkqE6semG27GWVbWSEmFaF7eMTH0fe0VdmzOYMmiU5vfbvUyM+H87vQd3AZd02X0swCkJSz+4mj3tF+XwZhUEz8mrTqtxzur/XBiAiM5+P4HUoBFq+bIzCT/t9/oMyiBNu1DG3x+mw6h3HT/aHoPTABk+pH4k7SERZ1cmgtQ+GTH13y557sGL1GpojL/gudxHc5m6133gXbq15SFaBFUlcEL3qeo1Mlbz/+Gpp38M2Eyq4yZ2IWhozug60j3sziGtIRFnUyqCZOqckmvqTwx9j5iAhq2CcQNg2bibfUh6Y05UoCFe9A0kt94k/BIfwaPbHfSw6NiA7n+7lEMOaMDiqJIARZ1kiIsTkhRFDqEtOH58f/m3C7jUOuxR3G4bwij2wwmZ8VvlO7Z2wwphWgeeb+tovxgKmdN7EpAoHedxygKjBjTkWvvHElYuJ8UX3FC0h0t6k3XdVIKD/Ha+vc4VHz4uMc9fc4/aesbxeYbb6Gy4NQ2ehCipbJGhNP/zdfZsyObzz+ovY56eKQ/Uy/pQ1ybYJl2JOpFWsKi3hRFoW1wHM+e8xAX95yKpY6NIAbE9qJ9cAKHFn0iBVi4pcrcPHJ++IEefWPpkBgOgNmsctaELtxw7xnExAdJARb1Ji1hcUo0XSO3PJ83N3zIzpx9Nbe/c95zmIvK2XzL7bJXsHBrgxa8T3kFfLt4JxOn9aze91e6nkUDSREWp8ylaZhUldVpG3lv62dM6jyG87qdw67H/kvhplPf7lCI1iDy7LF0uvUmFEVB0zRUVToWRcNJERanzaW5cGouzCgUb/uD3Y/91+hIQjQZxWwm9rxzaXPJRWAyoZpMRkcSrVjDd3cX4m+qpzOZ0DUN3/h4QgcPomD9BqNjCdHoQgb0p8MN1+IVGSnXfUWjkJawaFS6pqGoKkXbd5Aydx621DSjIwlx2nwSEmh35eWEDhqI7nKhSOtXNBIpwqJJaC4XiqKQ9d0PHFq4iKriEqMjCdFg3tFRJFx6CRGjR6FrmnQ9i0YnRVg0Kd3lQne5OPzNMg5/uUSKsWgVrOFhJFw8g6izx6JrOqpZiq9oGlKERbOQYixaA0tQEPHTpxE9aQKKoki3s2hyUoRFs5JiLFois78/cRecR+zUKSgmkxRf0WykCAtDSDEWLYHJx4fYqVOIu+B8VKtFiq9odlKEhaGkGAsjWEKCiZk4gZjJkzD5+qDIQhvCIFKERYtwtBhnffcDmcuW4zicaXQk4Yb8O3UkZuoUIkaOAJCWrzCcFGHRohydg1m0bTuZ3yyjYOMm2Y9YnB5VJWzoEOLOn0pAl0Q0pxPVLOsUiZZBirBokY4W44r8AjKXLiP7h59wlkhXtag/s78/UePGEjt1CtbQUFlkQ7RIUoRFi6brOug6uqaRt2o1mUuXUbZvv9GxRAvmEx9HzJTJRI0dg2I2gaLIEpOixZIiLFoNzelCNZsoTznI4a+XkrdyFVplpdGxRAtg8vMlbNgwIsecSVCP7jU/K0K0dFKERaujaxooCi67g7xVq8hbtZriP3bItWMPo5jNhAzoT+RZowkZNLC6q1nTpMtZtCpShEWrdnSQTVVpKXkrV5G3cjUlu3eD/Fi7J0UhsFs3IkaPIvyMUZh9feRar2jVpAgLt3G0IFcWFZH320pyV/4u14/dhE9CAhGjRxE55iy8wkJlhLNwG1KEhVs6ek2wIi+P3BUryVv1O+XJKUbHEvWlqvh36kjIgP6EDRuKX9s20uIVbkmKsHB7NdOdcvMo3LSJoq3bKdr+B67ycqOjib+wBAUR3L8vIf37EzKgH2Y/P3SXq3p0s6xoJdyUFGHhUY52Y+qaRnnKQQo3b6F423ZKdu9BdzqNjudZVJWAxM6EDOhP6KCB+LVvByBdzcKjSBEWHkvX9ZrRtFplFcU7d1K0ZStF27ZjS02TwV2NTVHwiY0hoGtXQgb0I7hfP8y+PmguF4qqylxe4ZGkCAtxhH5kipOiqlSVllK8/Q/K9h+gLCmZ8uQUnGVlBidsXSxBQfgndiagcycCunbBv3Pn6tHMRxZfUeX6rhBShIU4Ht3lAv5c5L8iL4/SfQcoT0qiLCmZsqRkWUrzCNVqxb9TR/w7dyKgSyIBXbvgFRYGVHcvK6oq13WFqIMUYSEa4O+FubKgkNL9+yk7kER5ykEcWdlU5OSgVVQYGbPJmAMC8ImNwTsmpvq/0dH4tW+HT3wciqpW9ybouoxiFqKepAgLcZrqKjxVpaVUZOdgP5yJIzubiuwcHDk5VGTnUJGX16IHgZkDAvCOicYnNrb6vzEx+CTE4x0TjdnHp+Y4zelEURQpuEKcBinCQjQh3eVCP1Kgjw480jWNquJiHJlZVBYV47KV4yy34So/8l+bDaetHFe5DWfNbdX/rdfSnKqKYjKhmk0oZjNmP3/MgQFYAgIwBwRgDvDHEhiIOaD6NktQIJbgoOr7/PxQLZaah5JCK0TTkiIshIF0TatpSR+dD3uia6eay1V9rA7wl/9CTfE92SjjmucEuVYrhMGkCAshhBAGka/AQgghhEGkCAshhBAGkSIshBBCGESKsBBCCGEQKcJCCCGEQaQICyGEEAaRIiyEEEIYRIqwEEIIYRApwkIIIYRBpAgLIYQQBpEiLIQQQhhEirAQQghhECnCQgghhEGkCAshhBAGkSIshBBCGESKsBBCCGEQKcJCCCGEQaQICyGEEAYxGx2gPlwuF1VVVUbHEEII0cpYLBZMJpPRMY6rRRdhXdfJzMykqKgIXTc6jRBCiNZGUSA4OJiYmBgURTE6zjFadBHOzMyksLCIgIBgvLy8gJb3BgohhGipdCoqKigsLAIgNjbW2Dh1aLFF2OVyUVRUXYADAoKMjiOEEKIVslq9ASgqKiIqKqrFdU232IFZVVVV6DpHWsBCCCHEqfHy8kLXaZFji1psEf6TdEELIYQ4HS23jrSCIiyEEEK4pxZ7TfhkFEVBVY39dqNpOnozDNvWdf2URvWd6nmN/Rii9fCkz5UQLUGrLMKKohAQ4I3JZGxD3uXSKC11nPIvjKSkA7z77lw2b95ESUkxQUHB9O3bj6uuuobOnRMB2LZtK/Pnz+PFF2c36LF/+20Fv/zyIw8//DgAmzZt5JZbrue11+YwYMDAk55fWVnJ66+/Qteu3ZkwYRIAjz32MJs3b+TLL5c28JUe39tvv8m8eXNYu3bzcY9paHaj3HTTdQC88cbbBic5NYqiEBjghWrwwBXN5aKktOKUPlePPfYwy5Z9fdz7n3zyWcaMOft04nmMpvi8N7bDhw8zbdoU/v3vR5gyZarRcU5JqyzCqqpgMqk8v2AT6dmlhmSIjwrg3pkDUFUFl6vhvyySk5O47rqr6NGjF/fccz8hIaHk5GTz6aeLuPbaK3nttbfo2bM3S5YsJiUlucGPv3Dhh7X+3rVrV+bOnU/79h3qdX5eXh6LFn3Ev//9SM1ts2Zdy8UXX9rgLKJ1UFUF1WRi3wsvYTuUbkgG34R4Eu++85Q/VwBhYeE888zzdd6XkND2dOIJ0ehaZRE+Kj27lKSMYqNjnJKFCz8kMDCIF1+cjdn85z/D6NFncdFFF/DOO3N54YVXGu35/Pz86dmz92k9Rnx8QiOlES2Z7VA65ckpRsc4ZRaL5bR/1oVoLq26CLdm+fn5wLHXvnx8fLjrrnux2+21utaGDu1f0+Vy+PBh3n77DTZsWEdhYRGBgQEMHTqcO++8h6CgYG666Tq2bNlUc95rr80BqNWl63A4eOWVF1m5cgVFRYXExsYxder5zJz5j5ouHoAnnniEuXPf4ssvlx7TPaXrOosWLeDLL78gKyuTiIgILrhgOpdddkXNdeQlSxazePFnHDyYgq7rtGnTlquuuoaxY8c1+D1LSUlmzpzX2b17F5GRkVx00WVcdNElNfcXFRXy9ttv8vvvK8nLy8PX15d+/QZwxx331EzST08/xEsv/Y/t27dRUVFB586dmTXrOoYPH1nzOElJB3j99VfYsmULAIMGDeb22+8iLi6+5pisrExefPF5Nm7cgJeXF5dffmWDX48wxk03XUdkZCSVlZWsWfM7vXr1YfbsN6ioqGDOnDf44YfvKCwsqPlZHTdufM25mqYxf/48lixZTFFREQMHDmLSpCk89NADfPHFN8TGxtbZjVtXt2lxcTFvvDGbFSt+pby8jM6dE7nxxlsYNGhIzXlDh/bn3nsfZO/ePaxY8TNOp5Nhw0Zw9933ExYWVnPc8uXfsHDhR6SmHiQ4OJjx4ydy3XU3cuhQGpddNoMHH3yI88+/sOb47OwsLrhgCv/5z2M1l5uO58svP+fdd+dRVFRIr169ue22u+jSpWvN/Vu2bGL+/Hns2rUTu91OREQkkyZN4ZprrkdVqy8Zfv/9t7z//rscOpSGj48PQ4YM49Zb7yQiIqLmcZYsWcyiRQtITz9EaGgYU6ZMZdas62rN6/3ll5+YN28Ohw6l0bZte6655rp6/7u3VFKEDTJixChWr17FtddeybnnnseAAYNo1649iqLUXLPq06cvRUWF7N27h2eeeZ64uAQcDjs333wdISEh3HvvgwQEBLB9+zbmzp2Dt7c3DzzwEPff/08efvjfANx//4O0b9+BPXv21Hr+l156nnXr1nL77XcRGhrGmjW/M3v2SwQFBXHOORN5+unnefDBe7n66ms588wxdb6GV199iUWLPuLSSy9n8OCh7N69k9deewWn08mVV87is88+5oUXnuPaa2/kttvuoqSkmA8+eI///OchevXqTWRkVIPes5deep6LL76MWbOuY+XKFbzwwrPousbFF1+GruvcffcdlJQUc8stdxAaGsaBA/t5663XefbZ//LSS6+haRr33HMHERERPPLI45hMZj755CPuu+8uFi36nISENqSlpXLddVfTrl07/vOfR3G5XLz77lyuv34WH3ywiNDQUOx2OzfeeC1ms5l//vPfqKrKnDlvkJ5+iF69+pzCT4NobE6n85jbTCZTzZfDH3/8gQkTJvLccy/WDAR74IF72L59G9dddwPt23dgxYpf+L//+ydVVVVMmlT9pXT27Jf45JNFXHXVLHr27MX333/LM8/8t8H5KioquPXWG8jPz+fGG28hIiKCr79ewp133sbLL7/KwIGDa459881XGT36LB5//CkyMjJ4+eX/oaoqjz/+FACfffYxzz//DFOnXsDNN99KRkYGs2e/SElJMQ8++G969OjJ8uVLaxXhZcu+wcfH97if7aNyc3OYN28ON910G35+fsybN4ebb76eBQs+Jjo6hv3793HrrTcxduw4nnjiaXRd57vvljNv3hzatWvPuHHj2bZtK48++n/MmnUdffv2Jycnm1dffZn//OefvPHGXADee+8d3nzzNWbMuIQ777yHffv2MnfuW+TkZPPQQw8DsHLlCv71r/sZP34it956B/v27eWRR/7d4Pe+pZEibJALL5xBfn4eCxa8z/PPPwNUr286ZMgwLr74Mrp370F8fALBwSG1utf27dtLVFQU//nPYzUtswEDBrFz5w42b65u/bZv3wE/Pz+A43bLbdmymcGDh9R8yx8wYCC+vr6EhIRitVpJTKz+phsXF1/rW+9RpaWlLFq0kBkzLubWW+8AYPDgIeTn57Nly2auvHIWGRkZzJz5D2bNurbmvJiYWK66aibbtm2t1cKoj/PPn8Ztt90JwJAhw8jNzWX+/HeYMeMS8vPz8fHx5vbb76Jv3341ryk9/RBLlnwBQGFhAampB2u1fHv06MHcuXNqJvEf/TIze/Yb+Pn5AzBw4GAuvPBcFix4j9tuu4ulS78iOzuLjz76tOYae48ePZk+/bwGvR7RNLKyMhk5cvAxt99882384x9XA2CxmHnggYewWq0ArFu3lrVrV/P440/V/FwOHTocu93O66+/wjnnTMBms/Hpp4u45JJLue66GwEYNmwEt912Exs2rGtQxuXLl7J//z7mzn2Pnj171TzWzTdfx2uvvcK77/45pqNjx0783/89WvP3Xbt28PPPPwLVLfN5895m9Oiz+Ne//q/mGIfDzvfff4vTWcW5557HM888yeHDGcTGxtU8/7hx5+Dt7X3CnC6Xi2eeeYHu3XsA0LNnL6ZNm8onnyzi9tvv4sCBfQwePIRHHnm8ptU7ePBQVq5cwebNG48U4S14eXlzxRVX1bzfQUHB7N69E13XKS8v45135nL++Rdy9933AdWf76CgYJ588jEuvfRyOnToyDvvvE337j145JEnav59QOH11xvvsp0RpAgb6Prrb+KSS2aydu1qNmxYz+bNG/nuu+V8//233HnnvXUOgkpM7MJbb72DpmmkpaWRnp5GSkoyBw+m4HK56v3c/fsPZPHiz8jJyWH48BGMGDGKWbPq37WzY8d2XC7nMd+kj36IAO64426gumCnpqZw6NAhNm/eCFSPvv47XdePeQ1/vV5+9tnn1LrvzDPPYsWKX0hNPUj79h147bU56LrO4cOHOXQojdTUg2zfvrXmuUJDw2jfvgNPPfU469atYciQYQwbNoI777yn5jE3blxP//4D8PLyrmlN+fn50adPP9avr/5Fu3XrFuLi4msNcouKiq75ZSqMFR4eznPPvXTM7ZGRkTX/365d+5qCANX/7oqiMGLEqFqt6FGjRvPtt8tITk4iLy8Pp9PJqFFn1nrcCRMmNbgIb9y4nrCwcLp27Vbr+UaOPIPZs1+ipKSEwMBA4Ngv0pGRUdjtdgDS0lIpLCzgzDPPqnXMzJn/YObMfwAwbtx4XnrpfyxfvpRrrrme7du3kZaWyv/93yNAdaH962UxRVFquoDj4uJrCjBUD3rr1asXW7dWz2aYOHEKEydOoaKigrS0VA4dOsT+/XtxuVw1n7t+/frz5puvcdllMxgz5myGDx/BkCFDGT58BAB//LGdigoHo0aNPua9AFi/fi2xsXHs2bOb66+/qdbrPPvscVKExekJDAzknHMmcM45EwDYu3cPjzzyb1577WUmTJhY5zkfffQh7733DsXFRYSGhtGtWzd8fHwoLa3/SPG77rqXyMhIvv12Gf/737P873/P0qtXb+6//18106NOpLi4ekBcSEjocY9JTz/E00//l40b12OxWGjbtl3NY9c1/WTp0q954olHat32xRff1Px/aGh4rfuOPndJSQkA3367jDfeeJXs7CwCA4NITOxS65u+oii88sobvPvuXH799WeWLfsGs9nM6NFn8cADDxEYGEhxcTE//vg9P/74/TH5QkJCap4vODj4mPvDwsKPXOsXRjKbLXTr1v2Ex/j4+Nb6e3FxMbquM2bMyDqPz8vLrfk5O/pzcFRERGRdp5xQcXEx+fl5dbbYAfLz82qK8N9bq4qi1Hx+6vM59PPzZ8yYcTVFeNmyr2nbtl3NpZNbb72xZgwJQL9+A2qm2YWGhh3zeCEhoWRlZQHgcDj43/+e5dtvl+J0OomNjaNXr96Yzeaane969erDCy+8wsKFH7Jw4Ye8//67hIaGcdVV13DRRZfUvIa7776tzvy5ubmUlpai6/oxn7uwsPA6z2lNpAgbICcnh1mzLuf6629m6tTza93XpUtXbrzxFh544B7S04+dJvLdd8t55ZUXuPXWO5gyZSrBwdW/EB566AF27txR7wxWq5Wrr76Wq6++lqysTFat+o133pnLf/7zLxYu/Oyk5wcEBABQWFhI27btam7PysokIyOdXr36cM89d2A2m3n33Q/p3DkRs9lMSkoyy5fXPe9w1KgzanXDAURERJCZeRiAkpLaI+GPFryQkFC2bt3CY4/9h4suuoTLLvtHTatn9uyX2LZta63Hu//+f3LffQ+yf/8+fv75Rz74YD7BwcHcd98/CQjwZ9CgIVx22RXH5DvaOggODiY9/dAx9x/9ZSJan4AAf3x9fWsGMf5dfHwCO3f+AUBBQX6tn/ni4qJaxypKdTfxX9nttr89XwAJCW147LEn63y+mJj67fbz18/hXxUXF7Fnzx569+6Dj48P5547lWXLvmbXrp388svPNa1kgAcffAib7c98vr5/fkH5+2cOqj93R4v+iy8+xy+//Mh///sMgwYNwcfHB4CJE8fWOmfo0OEMHToch8POxo0b+PjjhbzwwrP07Nmr5jU8+uh/adPm2ClkoaGhBAYGoqoqBQUFf3udrf8zJ8tWGiAsLAyTycxnn31CRUXFMfenpqbi5eVFQkKbYxYk2bZtKwEBAVx++ZU1Bdhms7Ft25ZarcsTLWTicDiYMeN8Fiz4AIDo6BimT7+Yc84ZT1ZW5knPh+proGazmVWrfqt1+0cffci///1PysrKSE09yNSp59OtW/eabuXVq38HQNe1Yx4zKCiYbt261/pjsVhq7l+9elWt43/44TuioqJJSEjgjz+2oWka1157Q00BdrlcNd2Emqbxxx/bmDjxbHbt2omiKCQmduHGG2+hY8dONa+7X78BpKQk07lzYk2Grl27sXDhh6xY8TNQfY348OEMdu/eVZOlqKiw5pe0aH369RuAzWZD1/VaP39JSfuZO/ctXC4nvXr1xtvbm59++rHWuX//DPj5+VNcXFTrs/3XL4LVz1c9QCkkJKTW861bt4YPP3wPs7l+C6a0bduO4ODgYzIsW/YNd999e81Yh379BpCQ0IbZs1+irKyUiRMn13qMv2b46xeMtLTUWl84s7Oz+OOPbTWL5mzbtpUBAwZxxhln1hTgPXt2UVhYWPMZf+WVF7n66svRdR1vbx9GjjyD22+/C6j+0t6zZy8sFgu5uTm1cphMJl5/fTaHD2fg5eVFr169+eWXn2r9nvv7626NpCVsAJPJxP33/5P777+Hq6++nOnTL6Zdu/Y4HA7Wr1/Dp59+wg033ExgYCD+/gEUFhawevXvJCYm0r17D7744lNefvkFRo48g7y8XBYseJ/8/HwCAgJrnsPfP4AdO7azceP6mkFWR3l7e9O1azfmzZuDxWKhU6fOpKUdZOnSrznrrLOPnF89KGnjxvW0a9f+mOudwcEhXHzxZSxcuACr1Uq/fv3ZuXMHn3/+KbfffiehoaHExMTy6acfExERRWBgAGvWrObjjz8CwG53NPh9++SThfj6+pKY2JUffviOtWtX8/DDj6MoCt279wTg+eef4dxzz6O4uITPP/+Y/fv3HXk+O4mJXfH29ubRR/+Pa6+9gdDQMDZsWMe+fXu56KLq6++zZl3Hddddxb333sG0aTOwWq0sXvw5v/32K08++SwAEydO5uOPP+KBB+7hpptuwc/Pn3ffnYvLdewXC9E6DB8+kn79+nP//Xdx9dXX0a5de3bt2sHbb7/J0KHDa77wXnPN9bzxxqt4e3szaNBg1qxZfcylixEjRvHJJwt58snHmDr1fA4cOMDChR/UmmozZcp5fPbZJ9x++81ceeUsoqOjWb9+LR988B4zZlyM2WyhPkwmE9deeyPPP/80ISGhjBo1mrS0g7z99lvMmHFRTZc2wLnnnsfrr89m2LARtaYGnYjV6sV9993FjTfegsulMWfO6wQFBXHxxZcB0L17T3766Qe++OIz2rVrz/79+5g/fy6KotRctx44cDALF37I448/zPjxk3A6q/jww/cIDAxiwIBBBAUFcfnlVzJnzhuUl5fTv/8AcnNzmTPndUCpuYR10023csstN/Lgg/dy/vnTSEtLZf78efV6HS1Zqy7C8VEBrfa5R4wYxbx577FgwfvMn189B89isdKlS1eeeOIpzjqrujtnypSprF69ivvvv4vrr7+JK664iszMDL7++iu++OJTIiIiGD58FNOmzeDpp58gJSWZ9u07MGPGxezZs4u77rqNf//7EcLDa3/oHnzw37z11ut89NH7Nd1LU6eez3XXVQ988PPz59JLL+fLLz9n9epVLFv2wzGv4dZb7yA0NJTFiz/jww/fIyYmlnvvvZ8LLpgOwLPPvsALLzzHE088jMVipX379jz//Eu8+OLzbNu2pdYc3/r417/+w4cfvsdbb71ObGxcrZGsAwYM5N57H2Thwg/5+ecfCQ0NpX//QTz99PM88MA9bNu2heHDR/Lyy6/z+uuv8MILz1FWVkpCQhsefPChmrmbnTsn8uab83jzzdd45JH/A3Q6dOjIM8+8wBlnjAaqF4N47bW3ePHF53nhhedQFIXzzptGbGzcMd1lrZFvQvzJD3Kz51ZVlRdemM1bb73Oe++9Q2FhARERkVx66Uxmzbq+5rgrrrgKX18/PvroAz75ZCG9e/flqqtmMW/en0uVDhkylNtuu4tPP13IL7/8RJcu3Xj66f9x/fVX1xzj4+PDG2/M5Y03ZvPqqy9TXl5GTEwsN998W52XQk5k+vSL8PHxYcGC91my5AsiI6O44oorueKKq2odN3z4SF5/fXaDlnfs0qUrY8aM5dlnn6KsrIyBAwdx11331lwXv+OOu3E6nbz11utUVVUemf1wLSkpSaxa9Rsul4vhw0fw6KP/ZcGC9/nnP+8FFPr06cvrr88hKKh6r/gbbriZsLBwPv/8Ez788D0CAgIZNGgwN910K/7+1b9r+/btz4svzubNN1/lwQfvJSYmloceeph7772zQe9XS6PoLXSldIfDQVJSMuHh0VittfcUdpe1o4VoSdxh7WgjfPPNVzzxxCM1i3W0VO+/P5+FCz/kq6+W17rM4wkqKyvIy8uiY8cOJ52W1dxaZUtY13VKSx2y24sQjUjXdUpKK+Rz5WaWLv2a5OQDfPbZp8yada3HFeCWrlUWYTg6p1Q+qEI0JvlcuZ/9+/exePHnnHnmWcyc2bCubtH0WmV3tBBCCFFfLbk7WqYoCSGEEAaRIiyEEEIYRIqwEEIIYRApwkIIIYRBpAgLIYQQBpEiLIQQQhik1c4TVhRFFhUQopHJ50qI5tUqi7CiKAQFeqGoxi6vp2suiksavrzeY489zLJlX5/wmOjoGLKyMg1dCu/88yfTv/9A/vOfRw15/vpoLUsGtgbVy1Z6oxq8HKzm0ig5xeVgb7rpulp740L1ZiRdunTlmmtuoH//AfV+rLfffpN58+awdu3mBudoqMcee5jNmzfy5Zd1b/N5uprztZyOoUP7c80113PddTcaHaXZtMoirKoKimoi58uXqMw/ds/d5mANiyfy/DtRVaXBKwzNmnUt06ZdWPP3d96Zy969e3jmmedrbqusrMJqtRAe3vo3rRatg6oqqCaVLxZsIS+71JAM4VEBTJvZ75Q+V0clJnbl/vsfBKrXdy8qKmTx4s+4885bmD9/AR06dGzMyI1i1qxrufjiS42OIQzQKovwUZX56VRmpRgdo8Hi4xOIj0+o+XtwcAgWi4WePXsbmEqIann/3959h0Vx7Q0c/+7SQbpYsIKCvQECCpZoYru2NxFN1GvBiohdArFForF3ioANRJGoMcau0cQGahQkNxqvRo1iC0VgUSmy7PvHXiauoKIxUjyf59lHd2d25pzDnvnNnHNmzp9ZPLirKO1kvDEjI6MidcnZ2ZVu3Tqzb9/3+PhMLqWUvdizxwPh/VKug3BF9nwza0DAHB4+TKVjx05s3hxBamoK9vYNmTXrS27fvkVISCB37tyhXr36+PnNwN6+gbStixfjCQ0N5vLly+jp6eLu3h4fn8nSdGQvk5+fz7Jlizl4cB8qlYr27TsW+e7u3bvYtWsHf/xxE5VKRe3adRg2bASdO38EQEFBAWFhIRw6dIDU1BQqV7bio4+6Mnr0WGne1NzcXMLCQjhy5BDp6Q+lbRROVVi4nU2bNrB797dkZGTg4uJKy5YOb6vIhQpMX18fPT1d4K/+7hMnfmLDhnBu3LhOpUrGfPhhF7y8xkuT0xc6ffokISGB3L59i6pVqzF8+Eh69OgpLb927Srr1oWSmJhAVtYjLCzM6dixM97eE9DX12fChHEoFJls2rRFY7u+vlO4d+8uUVExRZqjlUolu3btZNeuHdy5k4SZmTldunRj5Mgx6OmpH+MbEDCH5OQH1KpVh0OHDlClShW2bPlGY97i5x0//iOBgav4888H1K9vj5eXN61bu5Q4LwBnz54hLCyYGzeuo62tTcuWDnh7T6BuXZvXKtv4+AsEB6/m2rVrVK1alWnT/Er656xQRBAuR3755RdSUlKYOHEKubm5LF78NVOm+AAyRo/2Ql/fgEWL5jNnzgyio3cAkJBwAR8fL5ycnJk/fyEKhYKwsGC8vUezYcPmVz5H9ejRwzRp0oxZswJIT39IcPAabt68wbp1EWhpabFjRwzLly9h5Mix+PhMRqHIZPPmCGbPnkGzZs2pUqUqmzdv4ttvtzNhwmSsrWty6dJ/WLs2CB0dbUaN8kKlUvH551P55ZdERo0ag42NLceP/8isWf48ffpUOuAFBq4iJiYaT8+RNGminkw8OHj1P13sQrmjIj8/X/p/ZmYm27ZtJS8vj169+gBw6NAB5syZQdeu3RkzZhz3799n7dpAbty4zpo1IchkfwXrhQvnM2bMOKysrNi8eRNffTUHOzt77OzsSU1NYezYkTRt2pRZs+aio6NLXNxpoqOjsLKyYsiQ4XTv3oO5c2eTlHSbWrVqA5CVlUVc3GnGjvUuNgcLF87nwIG9DBkynBYtWnH16hXWrw/j6tUrrFwZJKUvISEBXV09Fi1aSnZ2zksDMMD8+QGMHj2W6tWtiY7ewuTJPoSHb6JRo8Ylysvdu3fw9Z1Cr169GTfOB4VCwdq1QUyZMoEdO3Yjl8tLVLZXrvzGxInjcHRszYIFi7l37x6zZ/v/zb97+SSCcDny5Mlj5s9fJJ1xxsdfYNeuHQQGrsXJyRmApKTbrFmzgqysLIyNjQkODqROnbosW7ZKqqBNmzbjs8/6sXfvbvr1G/DSfZqamrFqVZB0Bmtubo6v7xTi4k7j7t6eu3fvMmjQEDw9R0rfUU/sPYjExIt89FFXEhIu0LBhY3r2VB8AHRwc0dfXlybrPnfuLGfOxPLVVwukK19X17ZkZ2cTHLyaLl26kZ2dzTffRDNw4GBGjBgtrZOSksKZM7Fvq4iFCiAhIR53d+cin3t5jaduXRtUKhVBQatxdW3L3LnzpeW1atXCx8eL2NhTuLm1kz7/4otZtGnjBkCNGjXp168P8fEXsLOz5/r137G3t+frr5dgZGQEgLOzC+fOnSE+/jxDhgynY8fOLF68gCNHDuHpOQqAn346ilKppEuX7kXSefPmDfbs+Y5x43wYMmQ4AC4urlSubMXcubOIiztN27buACiV+fj5zaBKlaolKhs/vxl06vQhAE5OznzySS8iIzeyYMGSEuXl8uVL5ObmMHToCKysrACoUqUqJ078RHZ2NoaGhiUq28jIjZibW7B06QqpNczU1JRZs96/QCyCcDliYmKi0eRjYWEBQJMmzaTPTE1NAfWZto6ONpcu/YdBg4agUv11dWBtXYO6dW04d+4s/foNeOaqQU0ulyOXq0fIurm5azQhubu3R0tL+38HuvZMnDhF2t+tWzdJSkoiPv48AHl5eQA4OLQmOHg1Y8Z40q5dB9zc2uHh8am0zfPnzyGTyXBza6eRlnbtOnDw4H5u3LhOWloq+fn5uLu310jrhx9+JIKwoKFBg4b4+c0ECuceVxAXd5q1a4PIzs6mW7ceJCf/ydChnhq/t1atHDEyqsS5c2c0gvCzXR7W1jUAePRIPXDNxaUNLi5tyM9/ys2bN0hKSuL69Wukp6djamoGgIGBAR06fKARhA8fPoSTk7MUyJ4VH68e3f1sV0zh+3nzvuTChfNSEDY1NdMIwAUFBRQUFEjvZTKZdPKtra1Nx46dpGV6enq0aePGqVMnSpyXJk2aoaenh6fnYDp1+pA2bdxwcHCiSZOmAPzxx80Sle3Fiwm4u7eXAjDABx90fuWVfEUkgnA5YmhoVOznz/dhFVIosigoKGDz5k1s3rypyPLCvqXnrxqevUXAwsJSY5lcLsfMzIysLPVB6M6dJBYunM/58+fQ0dGhTp262NnZA0i3mAwePARDQwP27NlNUNBqAgNXYWtbj6lTfXF0bE1mZiYqlYpOndyLzUdqaoq0PzMzM41llpZi9LigydDQiEaNGmt85uLShidPstm8OQJnZ1cAlixZwJIlC4p8PzU1VeP9s/Wr8OS0MNAVFBQQEhLIzp3f8OTJE6pWrUbjxk3Q09PTuMWqe/d/cfDgfq5du4qFhSXx8eeZOXNOselXKDKBor9tbW1tTE3NpBOA59MGMG/eXI3bH6tVqy71M5ubm0vpL2RuboFCkVXivFhbWxMcHE5k5Ea+//47YmKiMTY25pNP+jNmzDgyMzNLVLYKRWaRulyYv/eNCMIVmJGRETKZjE8/HUSXLt2KLC/sD964MUrj88qV/zo7Vyg0R8kqlUoyMjIwNzenoKCAqVMnoq2tzcaNUdjZ2aOtrc3Nmzc4cOCv+x3lcjn9+g2gX78BPHz4kNjYU2zatB4/v2ns3/8DxsaV/teMFVZsPmrWrMXly78C8PBhGnXq1JWWFVZ6QXiVRo0a8f33u6T3Pj6TcHBwKrKesbFxibcZGbmR6Ogo/Pxm0LFjJ6mLxdPz3xrrOTk5U7lyZY4ePYKlpSW6urp07Ni52G2amKhbs9LSUqle/a973/Pzn5KZmVEkeD1r5MgxeHj81cWko/PXlWZWVhYqlUqjvzstLRULC/PXykuTJk1ZtGgZT58+JTExgV27drJp03rs7OyxsbEFXl22ZmZmPHyYprGssNXifSMeW1mBGRkZ0aBBQ27d+oNGjRpLLxsbW8LD13LhgrrZ+NlljRo11mgiO3fujEaz0rFjP6BU5uPo2JqMjAxu3fqD3r370qhRY7S11ed0sbGnAVCp1FcLo0YNY/nyJYC6Cb1nz954eAwgKyuLx48f0aqVI0+ePEGlUmmk4/r1a6xbF4pSmU+zZi3Q09Pn6NEfNPJY2JQmCK9y+fIltLS0qFOnLubmFty7d7fI7z4oaDVXr/63xNtMTLyIrW09evbsIwWt5ORkrl//Xfr9A2hpadGlS3dOnTrBsWM/0KHDBy9swSp8oMiRI4c0Pj9y5BBKpZIWLVq9MD3W1tYaeapf305alpOTw4ULP0vvnzx5QmzsKSlYliQv27ZtoW/fHuTl5aGjo4OTkzP+/uqm/wcP7pe4bJ2cnImNPU1OTraUnjNnYnn69OnLirtCElfCFZyX13imTJnA7Nkz6NatO0qlkq1bN3Pp0q8ag6leJDU1FX//6Xh4fEpS0m1CQtbg7OxK69bOyGQyqle3Zvv2GKysqmJiYkxcXCwxMVsByM7OAdT9QVu2bMbCwoJmzVqQkpLM1q1RtGrliJmZOW3butOqlQO+vpMZPnwUdevacPnyr4SHr8XVtS1mZuozdU/PkYSGBmNgYICTU2tiY0+JICwU8fjxY3799RfpfV7eU06ePM6ePbvp2/djLC0tGTvWm0WL5iOXa+Hu3p5Hj7LYsCGclJRkGjZsVOJ9NW7chI0b1xEZuZGmTZtz504SEREbyMvLIzs7W2Pd7t3/xdatm5HL5axYseaF27SxsaVHj16EhYWQk5NDy5YOXLv2X9atC8XR0QlX17avXyior4rnzfsSLy8fjIyMiIzcRG5urtRPXZK8ODq2JihoNZ9/PhUPjwFoaWnx7bc70NXV/d94Ea0Sla2n52hOnPiJiRO9GTx4KBkZ6YSGBksn8u+Tcp1jXcua7+W+X4eLSxtWrgxk/fow/P190dHRpmHDRqxeHVKih4N88okHjx49ws9vKtraOnTt2p3x4ydJTVqLFy9n+fIlzJs3Bx0dXWxsbFi6dCUrViwlMTGB/v0/ZfRoL3R0dNi7dzcbNoRjZFSJdu064O3tA6ibq5cvX0NoaDARERtIT3+IlVUVPvtsEJ6eo6W0DB3qiYGBITExW4mJ2UqzZs2ZMGEKixd//c8U3nuqctWSN8eWxX1fvXqFkSOHSe/19PSoUaMmY8d6M3jwEAD69Pk/jIyMiIqKYPfubzEwMKR58xbMnTtfGnxVEkOHepKZmUFMTDTr14dTrVo1unXrgVwuJyJig3SXAoCdnT316tUnMzNDupvhRWbMmE2tWrXYu/d7IiM3YmVVhf79P8PTc1SRft2SMjMzZ9y4CYSEBJKWlkqTJk0JDg6XundKkhc7O3uWLl3J+vXhzJr1BUplPo0aNWbVqiBpOyUp29q1axMcHM7q1SuYOdMfCwsLfHwms2rV8jfKW3kmU5XRJ6Xn5ORw/foNKleuhq6unsay8v7saEEoiyrCs6MFoTh5ebmkpj6gXj3bVz4b4V0rl1fCKpWKTEWumO1FEN4ilUqFIitH1CtBeIfKZRAG9QHjTR/wLghC8US9EoR3S4yOFgRBEIRSIoKwIAiCIJQSEYQFQRAEoZSIICwIgiAIpUQEYUEQBEEoJSIIC4IgCEIpEUFYEARBEEqJCMIVVFl42EFZSIMglCeizrx/ym0QVk9WLS/V17NTgr0uL69RuLo6SK82bRzp1MmdYcMGERMTrTFzUd++/yIgoPi5R4tz4sRxAgJmv3K9gIA59O37rzfez4tkZWUxd+4sLl6Mlz7z8hqFl9eov73tknJ1dSA8fO0729+bUE9Q4fDqFd+h96lelUR+fj4BAXPo1MmdTp3aacxC9Hc9X/9KWm+FiqVcPjFLJpNhbKKHVik/O1pZoCTrbzw72t6+Ib6+fuptKQtQKDKJi4tl1aplJCYmMG/eQuRyOYsWLcPIyKjE242Ojnr1SqhnJRow4LM3SvvLXL36Xw4c2EfPnn2kz3x9/d/6foS3S/3saD3kWqVbrwqUShRZ/3y9KokzZ2LZv38Pnp6jaN3amQYNSj7D0usqab0VKpZyGYTlchlaci1Wn9nAXcWDUklDDZNqTHD1RC6XvfFj/oyMjIrMZNSuXQfq1KnLihVLOHz4IN269aBBg4ZvI8lF1KxZ6x/ZbnEKJ/sWyi65XIZcS4vDMaGkJ98vlTSYV6lOlwFj3km9KonMzEwAevbs/VqzKwlCSZXLIFzoruIBN9OTSjsZb52HxwCioiLYtWsH3br1oG/ff+Hg4MTs2XMBOHz4IJGRG0lKuo2BgQEuLm0YP34SVlZWeHmNIiHhAqBukg0KCgPA23s0n3/+BRERG1AosliwYAmHDh0gPv483323T9p3fn4+y5Yt5uDBfahUKtq374iPz2TMzdVz+hY2KYeEhEvfuXDhPN7eozX2Vfhvq1aOhISEF/lebm4uW7ZEcujQAe7fv0eVKlXp3fv/GDx4iHSV4uU1ipo1a1GrVm127vyG9PSHNGjQiEmTptKkSdNXluPjx4+ZM2cGJ078hL6+Ph9+2BVvbx/09dWTqSuVSrZs2czBg/u4e/cOMpkMOzt7xo71xtGxNaCezWv16hWcPHmcjIx0rK1r0Lt3XwYNGiLtJzMzk5CQNRw//hOPHz+SttG6tYu0Tm5uLiEhgRw+fJDs7Cd07vwR5uYWJfg1vHvpyfdJuXertJPx1j1frwB2797Ftm1buHMnCQsLS3r27I2n5yi0tLQICJjD/v17APj4417SbzkjI53w8LWcPn2S1NRUDA0NadXKkYkTp2JtbQ1QpM4C7N37PfPmfcm33+6V1itUXL11dHR6F8UilLJyHYQrKrlcTuvWzhw+fLBIH1Zi4kXmzp2Fp+coWrZ0IDn5TwIDVzF7tj8hIevw9fVnzpyZAPj6+mFjY8uVK1cAWL8+jClTfMnNzaF58xYcOnSgyL6PHj1MkybNmDUrgPT0hwQHr+HmzRusWxeBVgmaKRs2bMi0aX4sXbqQadP8ij2QqFQqpk2bxKVL/2HEiNHUr29PfPzPhIYGcfduEv7+s6R1f/zxB+rWtWHqVF8KCgpYs2Yl/v7T2bVr7yvTs337Ntq2dWf+/MXcunWTtWuDSU7+k0WLlgEQHLyanTu3M27cBOrXtyMlJZkNG8Lx9/fl++/3o69vwMqVSzl79gwTJkzGwsKSuLjTrFmzElNTU3r27ENubi7jx48hLS2NsWO9sbKyYs+e3Uya5MOqVYHSvLFffjmTs2fjGDPGm1q1avPddzs5eHD/K8tTeHuer1dbtkSydm0QHh6fMmnSVK5e/S/r1oWSnPwnM2bMwdNzJFWrVmXjxnUsXLiUOnXqolKpmDJlIgpFJt7eE7GwsOT3368RGhrM4sXzWbky6I3SVly9Fd4PIgiXURYWluTn50vNYYUSExPQ09Pn3/8ehq6uLgCmpmb89tslVCoVNja2Uv/x801yH3/cn06dPnzpfk1NzVi1KggDA/XVorm5Ob6+U4iLO427e/tXptvIqJJ0ALGxsS32YBIXF8vPP5/lq68W8NFHXQFwcXFFT8+AsLBgBgwYiK1tPUB9Zb5qVRBGRpUAePLkCQEBs7l27b80bNj4pWmpW9eGRYuWIZfLadvWDblczooVS7l+/Xfq1atPSkoqXl7j6d//U+k7urq6+PtP5/ffr9G0aXMSEuJxdnaR0uno6IShoaF0FXvgwD6uXbvKunURNG3aDIA2bdwYN24UQUGr2bgxihs3rvPjj0fx9f2Cjz/uB4CraxsGDerPzZs3XlmmwttTWK/UJ1zr6Nv3E6ZMmQ6Ai0sbTE3N+PrrAD77bDC2tvWoUaMmoO5ntra2JiUlBQMDfSZMmEzLlq0A9W/izp0kdu/+9o3T9bJ6K1Rs5XZ0dEVXOCjl+ZGirVo5kJOTzcCBHgQHr+HixXhcXFwZMWL0K0eV2tvbv3K/bm7uUgAGcHdvj5aWNgkJ8S/51uuJjz+PlpZ2kROCwibC+PgL0me2tvWkAAxQpUoVALKzswF1kH729exgns6dP9IYgNOxYycAKS8BAfMZMGAg6enpXLyYwN69u6Wr07y8pwA4ODixe/cuJk/2Yfv2bdy7dxdPz1G4ubUD4Pz5c1haVqZhw0ZSGpRKJe7u7fntt8soFAouXkwA0DiJkcvldOrU+Y3LUHgzhb+PhIR4cnNzaNeug8bvp/BvdO7cmWK/b2VlRVBQGC1atOTevXucPXuGb77Zxi+/XCQvL++d5UOoOMSVcBmVkpKMnp4+pqamGp83a9aC5ctXEx0dRXR0FJGRG7GwsGTYsBEaV3TFMTAwfOV+LSwsNd7L5XLMzMzIysp6/Uy8gEKhwMzMrEhzsqWlet+PHv21Lz09fY11ZDJ1UC0oUHHv3j0+/rinxvKZM7+kZ8/eQNG8FF69ZmUpAPjtt8ssWbKAy5cvoa+vj61tPapWrQb8dbCePHkaVapU4eDB/SxbtphlyxbTrFlzfH2/wM7OnszMTNLSUnF3dy42r2lpqSgU6tYMMzOz5/Jr9aIiEv4hhfWq8ORsyhSfF6yX8sJtHDy4n5CQQP788wEmJqbY2zdAX1//hesLwsuIIFwG5efnEx9/nubNWxTb7+nq2hZX17bk5GRz/vzPxMREs3z5Ypo2bUbjxk3+1r4VCoXGe6VSSUZGhjQwSyaToVRq9lNnZz95rX2YmJiQkZGBUqnUyF9aWipQNFi9iJWVFRs3at7W8eyAl+fzUrh9c3MLHj9+xKRJ46lfvz7R0TuoU6cucrmc2NhT/PjjUek7urq6DB8+kuHDR/LgwX1OnTrBhg3rmD37C6Kjd2BsbEytWrUJCPi62DRWr24t5efhwzSqVasuLcvMzChRPoW349l6ZWxsDMDcufOpXbtOkXUtLIofNHfxYgIBAbPp3/9TBg4cIrXMrFmzksTEi9J6MpmMggKlxndft54I7wfRHF0GfffdTlJTU6X+w2etXr2C4cMHo1Kp0Nc3wN29PRMmTAbgwQP1bSVaWm/+Zz137ozGYLBjx35AqcyXRgsbGRmRnJys8Z1nDz4l2b+DgyNKZT7Hjv2g8XlhU3CLFq1KlFYdHR0aNWqs8TI1NZOWx8Wd0lj/yJHDyGQyHByc+OOPP8jMzGDAgIHY2NhKV0axsacBUKkKyMnJwcOjL1u2bAagWrXq9Os3gC5dukpl3aqVenCcubm5RjrOno0jKioCbW0tqeyOHtXM76lTJ0qUT+HteLZeNW3aDB0dHVJSkjX+blpaWgQHr+HevbvFbuM//0mkoKCAkSPHSAFYqVTy889nASgoKABKVk+e93fqrVB+iSvhUvT48WN+/fUXQN28mpGRwdmzsXz33bd069aDDz4o2mfo5ORMdHQUX301h65de5Cf/5SoqAhMTEylg32lSsb8+usvnD9/Dnv717vHODU1FX//6Xh4fEpS0m1CQtbg7OxK69bq5lY3t3acPHmclSuX0a5dBxITEzhwYK/GNipVUl9lxMaexMTEBDs7zb7oNm3ccHR0YsGCr0hOTsbOzp6EhAtERm6iR49eb21k6OXLl5k/P4CuXbtx+fIlwsPX0qtXX2rXrs2jR1kYGVVi06b1aGlpoa2tzbFjP7Bnz25A3eesr69Pw4aNWL8+DB0dHerXt+P27T/Yt28PH3yg7s/u2bMPO3Z8w4QJ4xg61JNq1apx7twZNm+OwMNjANraOtSqVZu+fT8mNDQIpTIfe/sGHDiwj99///2t5FPQVNJ6NXjwUMLCQnj8+DEODo6kpKQQFhYMyIr8Zgs1bqy+NW7p0kX06tWHzEwFO3fGcO3aVUD9uzEyMsLNrR2RkRuJiNhA06bNOHny+CuftvV8vTUxMXlLJSKUZeU6CNcwqVau93316hVGjhwGqJuvDA2NqFevPtOn+9Onz/8V+522bd2YO3c+W7ZE4u8/DZDRokVLgoPDpP5jD48BXLlymcmTfZg580sqVy553+Mnn3jw6NEj/Pymoq2tQ9eu3Rk/fpI06KtXrz7cvXuHffv2sGvXTlq1cuDrr5cwevRwaRu2tvXo0qUbO3Z8Q1xcLFu3btfYh0wmY9myVYSFrWXbti3S/bfjxo3ns88Gv0YJvtyIEaP57bdLTJ06iUqVKjF48FBGjFDfw1ypkjGLFy8nMHAlX3zxOYaGhjRo0ICQkHVMnuzDxYsJtGvXAT+/mYSGBrN1ayRpaWmYm1vQu3dfRo3yAsDAwICQkHWEhKwhMHAVjx8/onp1a8aN82HgwH9LaZk+3R9LS0u2b49BoVDg6tqGYcM8CQ0Nfmv5fVvMq1R/9UpleN8lrVdjxozD0rIyO3d+Q1RUBMbGJrRu7YyX13jpRPJ5jo5OTJvmR3R0FMeO/YCFhQUODq1ZuHApn38+lcTEBNq2dWfYsBFkZKSzZUsk+fn5tG3rzhdfzGb69MkvTPfz9bZr1+5/uyyEsk+mKqNPDM/JyeH69RtUrlwNXV09jWUV5bGVglCWVJTHVgrC8/LycklNfUC9erZlbhBdubwSVqlUZClykcvf/EHvb0NBgUocKIQKQ6VSocgS9UoQ3qVyGYRBfcB402fLCoJQPFGvBOHdEsPxBEEQBKGUiCAsCIIgCKVEBGFBEARBKCUiCAuCIAhCKSkHQVgMEhEEQRD+jrIbR8psENbR0UEmU0+GLgiCIAhvKjc3F5lMHVfKmjJ7i5KWlhZmZmakp2cAoKenB5Tu/YuCIAhCeaIiNzeXrKwMzM2LztxWFpTZJ2aB+p7F+/fvk5GRQdlNpSAIglBWyWTqmdmqV6/+yjnXS0OZDsKFlEolT58+Le1kCIIgCOWMjo5OmbwCLlQugrAgCIIgVERldmCWIAiCIFR0IggLgiAIQikRQVgQBEEQSokIwoIgCIJQSkQQFgRBEIRSIoKwIAiCIJQSEYQFQRAEoZT8P4cp81vd+BTmAAAAAElFTkSuQmCC",
      "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",
    "f_df = feat_importances[default_faetures].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, f_df]\n",
    "ingredients = [\"Statistical-based\", \"Time-based\", \"Distribution-based\",  \"Frequency-based\",  \"Behavior-based\",  \"Default\"]\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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.19595072, 0.2390195, 0.009192086, 0.114366814, 0.09969402, 0.009192086]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PERIODICITY_SCDF            0.000000\n",
       "PERIODICITY                 0.000000\n",
       "CNT_ZEROS                   0.000000\n",
       "AREA_VALUES_DISTRIBUTION    0.000000\n",
       "PEARSON_SK1_SKEWNESS        0.000343\n",
       "                              ...   \n",
       "POWER_MODE                  0.052095\n",
       "TRANSIENTS                  0.066955\n",
       "MIN_DIFFTIMES               0.069143\n",
       "MEAN_DIFFTIMES              0.116936\n",
       "CNT_DISTRIBUTION            0.315298\n",
       "Length: 76, dtype: float32"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PERIODICITY_SCDF', 'PERIODICITY', 'CNT_ZEROS',\n",
       "       'AREA_VALUES_DISTRIBUTION', 'PEARSON_SK1_SKEWNESS', 'MEAN',\n",
       "       'MAX_POWER_FREQ', 'SPECTRAL_CREST', 'COEFFICIENT_OF_VARIATION',\n",
       "       'Q3_SCALED_TIME', 'PEARSON_SK2_SKEWNESS', 'SPECTRAL_SKEWNESS',\n",
       "       'SPECTRAL_SLOPE', 'SPECTRAL_CENTROID', 'ROOT_MEAN_SQUARE', 'POWER_MEAN',\n",
       "       'MAX_POWER', 'SPECTRAL_FLUX', 'PERCENT_BELOW_MEAN', 'MEAN_SCALED_TIME',\n",
       "       'BURSTINESS', 'GALTON_SKEWNESS', 'SPECTRAL_BANDWIDTH',\n",
       "       'SPECTRAL_ENTROPY', 'FISHER_PEARSON_g1_SKEWNESS', 'NORMAL_DISTRIBUTION',\n",
       "       'SPECTRAL_ENERGY', 'TIME', 'PACKETS', 'MAX_DIFFTIMES', 'Q1_SCALED_TIME',\n",
       "       'BYTES', 'MEAN_SCALED_DIFFTIMES', 'TIME_DISTRIBUTION',\n",
       "       'BIGGEST_CNT_1_SEC', 'FISHER_PEARSON_G1_SKEWNESS', 'DURATION', 'STDEV',\n",
       "       'Q1', 'DIRECTIONS', 'BENFORD_LAW_PRESENTED', 'POWER_STD', 'SIG_SPACES',\n",
       "       'CNT_NZ_DISTRIBUTION', 'P_BENFORD', 'BYTES_REV', 'MIN', 'MODE',\n",
       "       'PERCENT_ABOVE_MEAN', 'SPECTRAL_KURTOSIS', 'PERCENT_DEVIATION',\n",
       "       'SPECTRAL_ROLLOFF', 'KURTOSIS', 'VAL', 'MAX', 'SPECTRAL_SPREAD',\n",
       "       'SCALED_ENTROPY', 'PACKETS_REV', 'MIN_MINUS_MAX', 'AVERAGE_DISPERSION',\n",
       "       'FISHER_MI_3_SKEWNESS', 'MIN_POWER_FREQ', 'MEDIAN_SCALED_TIME',\n",
       "       'HURST_EXPONENT', 'SWITCHING_METRIC', 'VAR', 'MEDIAN_DIFFTIMES',\n",
       "       'ENTROPY', 'MIN_POWER', 'Q3', 'MEDIAN', 'POWER_MODE', 'TRANSIENTS',\n",
       "       'MIN_DIFFTIMES', 'MEAN_DIFFTIMES', 'CNT_DISTRIBUTION'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PERIODICITY_SCDF': 0.0,\n",
       " 'PERIODICITY': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.0,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.0003426095936447382,\n",
       " 'MEAN': 0.000350017158780247,\n",
       " 'MAX_POWER_FREQ': 0.00040926257497631013,\n",
       " 'SPECTRAL_CREST': 0.0004587827133946121,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.00046714115887880325,\n",
       " 'Q3_SCALED_TIME': 0.0004898212500847876,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.0005061243427917361,\n",
       " 'SPECTRAL_SKEWNESS': 0.0005208553629927337,\n",
       " 'SPECTRAL_SLOPE': 0.0005210680537857115,\n",
       " 'SPECTRAL_CENTROID': 0.0005447583389468491,\n",
       " 'ROOT_MEAN_SQUARE': 0.0005785102257505059,\n",
       " 'POWER_MEAN': 0.0006212952430360019,\n",
       " 'MAX_POWER': 0.0006316439830698073,\n",
       " 'SPECTRAL_FLUX': 0.0006324296118691564,\n",
       " 'PERCENT_BELOW_MEAN': 0.000657297729048878,\n",
       " 'MEAN_SCALED_TIME': 0.0006650415016338229,\n",
       " 'BURSTINESS': 0.0007016162271611392,\n",
       " 'GALTON_SKEWNESS': 0.0007173424819484353,\n",
       " 'SPECTRAL_BANDWIDTH': 0.0007352747488766909,\n",
       " 'SPECTRAL_ENTROPY': 0.0007369748782366514,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.0007489206036552787,\n",
       " 'NORMAL_DISTRIBUTION': 0.0007559791556559503,\n",
       " 'SPECTRAL_ENERGY': 0.0007583526312373579,\n",
       " 'TIME': 0.0007751499651931226,\n",
       " 'PACKETS': 0.0008602779707871377,\n",
       " 'MAX_DIFFTIMES': 0.0009188628173433244,\n",
       " 'Q1_SCALED_TIME': 0.0010629843454807997,\n",
       " 'BYTES': 0.0010787430219352245,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.0010850689141079783,\n",
       " 'TIME_DISTRIBUTION': 0.0011402617674320936,\n",
       " 'BIGGEST_CNT_1_SEC': 0.0012355151120573282,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.0012909946963191032,\n",
       " 'DURATION': 0.0013134559849277139,\n",
       " 'STDEV': 0.0013382908655330539,\n",
       " 'Q1': 0.0013857110170647502,\n",
       " 'DIRECTIONS': 0.001449749106541276,\n",
       " 'BENFORD_LAW_PRESENTED': 0.001486055669374764,\n",
       " 'POWER_STD': 0.0015251368749886751,\n",
       " 'SIG_SPACES': 0.0015938596334308386,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.0015964473132044077,\n",
       " 'P_BENFORD': 0.0017749045509845018,\n",
       " 'BYTES_REV': 0.0017770493868738413,\n",
       " 'MIN': 0.0019167951541021466,\n",
       " 'MODE': 0.0019845236092805862,\n",
       " 'PERCENT_ABOVE_MEAN': 0.002302903914824128,\n",
       " 'SPECTRAL_KURTOSIS': 0.0025962619110941887,\n",
       " 'PERCENT_DEVIATION': 0.0026658358983695507,\n",
       " 'SPECTRAL_ROLLOFF': 0.0027787024155259132,\n",
       " 'KURTOSIS': 0.0028989510610699654,\n",
       " 'VAL': 0.0031001383904367685,\n",
       " 'MAX': 0.003857272444292903,\n",
       " 'SPECTRAL_SPREAD': 0.004747753031551838,\n",
       " 'SCALED_ENTROPY': 0.005368053913116455,\n",
       " 'PACKETS_REV': 0.005476015154272318,\n",
       " 'MIN_MINUS_MAX': 0.005520065315067768,\n",
       " 'AVERAGE_DISPERSION': 0.006745875347405672,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.00726374750956893,\n",
       " 'MIN_POWER_FREQ': 0.00984955858439207,\n",
       " 'MEDIAN_SCALED_TIME': 0.018997792154550552,\n",
       " 'HURST_EXPONENT': 0.021321497857570648,\n",
       " 'SWITCHING_METRIC': 0.02298860065639019,\n",
       " 'VAR': 0.024392442777752876,\n",
       " 'MEDIAN_DIFFTIMES': 0.02840791456401348,\n",
       " 'ENTROPY': 0.03148627653717995,\n",
       " 'MIN_POWER': 0.03420407325029373,\n",
       " 'Q3': 0.04041729494929314,\n",
       " 'MEDIAN': 0.05004610866308212,\n",
       " 'POWER_MODE': 0.052094630897045135,\n",
       " 'TRANSIENTS': 0.06695455312728882,\n",
       " 'MIN_DIFFTIMES': 0.06914284825325012,\n",
       " 'MEAN_DIFFTIMES': 0.11693570017814636,\n",
       " 'CNT_DISTRIBUTION': 0.31529828906059265}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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(200000), selected_features, verbose=False)\n",
    "    \n",
    "    X=df_features[selected_features]  # Features\n",
    "    y=df_features['LABEL']  # 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']  # 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": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [16:33<00:00,  9.94s/trial, best loss: -0.978735981346776]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7693204063786697, 'gamma': 2.280755463613442, 'max_depth': 60.0, 'min_child_weight': 9.0, 'n_estimators': 300.0, 'reg_alpha': 2.0, 'reg_lambda': 0.45547605414451126}\n",
      "100%|██████████| 100/100 [08:02<00:00,  4.82s/trial, best loss: -0.9804729340034605]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.3652871242968445, 'gamma': 6.2289550302145855, 'max_depth': 60.0, 'min_child_weight': 3.0, 'n_estimators': 300.0, 'reg_alpha': 0.0, 'reg_lambda': 0.37120129508198746}\n",
      "100%|██████████| 100/100 [12:40<00:00,  7.61s/trial, best loss: -0.9787966179458608]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.13043740318518973, 'gamma': 2.004233301799533, 'max_depth': 60.0, 'min_child_weight': 10.0, 'n_estimators': 300.0, 'reg_alpha': 7.0, 'reg_lambda': 0.14653056041090268}\n",
      "100%|██████████| 100/100 [02:35<00:00,  1.56s/trial, best loss: -0.9753368762236957]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.015469462896479796, 'gamma': 3.0017783139694973, 'max_depth': 20.0, 'min_child_weight': 7.0, 'n_estimators': 100.0, 'reg_alpha': 1.0, 'reg_lambda': 0.8393691759816206}\n",
      "100%|██████████| 100/100 [10:35<00:00,  6.36s/trial, best loss: -0.9783443252288613]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6716805505370962, 'gamma': 3.752089837054889, 'max_depth': 60.0, 'min_child_weight': 6.0, 'n_estimators': 300.0, 'reg_alpha': 3.0, 'reg_lambda': 0.8170748567258945}\n",
      "100%|██████████| 100/100 [10:13<00:00,  6.13s/trial, best loss: -0.977450755465242]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.8901667688198353, 'gamma': 7.383191576753324, 'max_depth': 40.0, 'min_child_weight': 0.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.10382372403975954}\n",
      "100%|██████████| 100/100 [08:29<00:00,  5.10s/trial, best loss: -0.9801725841512418]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.9644324334181992, 'gamma': 2.3840078316015916, 'max_depth': 80.0, 'min_child_weight': 10.0, 'n_estimators': 600.0, 'reg_alpha': 2.0, 'reg_lambda': 0.44920010387432835}\n",
      "100%|██████████| 100/100 [07:36<00:00,  4.57s/trial, best loss: -0.9805488590205079]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.9725109991685458, 'gamma': 2.7329711973601687, 'max_depth': 80.0, 'min_child_weight': 2.0, 'n_estimators': 400.0, 'reg_alpha': 0.0, 'reg_lambda': 0.4793629044669958}\n",
      "100%|██████████| 100/100 [05:18<00:00,  3.18s/trial, best loss: -0.9804296257248287]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.10796319050173508, 'gamma': 4.628693694476686, 'max_depth': 40.0, 'min_child_weight': 12.0, 'n_estimators': 300.0, 'reg_alpha': 0.0, 'reg_lambda': 0.6618230678764654}\n",
      "100%|██████████| 100/100 [02:10<00:00,  1.30s/trial, best loss: -0.9791283910157735]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6216486030634815, 'gamma': 4.705415187501833, 'max_depth': 100.0, 'min_child_weight': 3.0, 'n_estimators': 200.0, 'reg_alpha': 1.0, 'reg_lambda': 0.8684092580049951}\n"
     ]
    }
   ],
   "source": [
    "import FEATURE_REDUCTED as FR\n",
    "REPORTS = []\n",
    "\n",
    "report = perform_val(FR.botnet_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.doh_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.dos_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.dns_malware_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.tor_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.vpn_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.all_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.combo_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.combo_2_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.crypto_features)\n",
    "REPORTS.append(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"IDS_UNSW-binary\"\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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}