final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network Intrusion Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to remove\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import experiments\n",
    "\n",
    "import numpy as np\n",
    "from modAL.models import ActiveLearner\n",
    "from modAL.uncertainty import entropy_sampling, uncertainty_sampling\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.6 s, sys: 2.61 s, total: 19.2 s\n",
      "Wall time: 7.19 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ni = experiments.NetworkIntrusionDetection()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(494021, 42)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ni.df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 338 ms, sys: 56.8 ms, total: 395 ms\n",
      "Wall time: 394 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>attacks</th>\n",
       "      <th>prevalence</th>\n",
       "      <th>prevalence (overall)</th>\n",
       "      <th>records</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>smurf.</th>\n",
       "      <td>280790</td>\n",
       "      <td>0.742697</td>\n",
       "      <td>0.568377</td>\n",
       "      <td>378068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neptune.</th>\n",
       "      <td>107201</td>\n",
       "      <td>0.524264</td>\n",
       "      <td>0.216997</td>\n",
       "      <td>204479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>back.</th>\n",
       "      <td>2203</td>\n",
       "      <td>0.022145</td>\n",
       "      <td>0.004459</td>\n",
       "      <td>99481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>satan.</th>\n",
       "      <td>1589</td>\n",
       "      <td>0.016072</td>\n",
       "      <td>0.003216</td>\n",
       "      <td>98867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ipsweep.</th>\n",
       "      <td>1247</td>\n",
       "      <td>0.012657</td>\n",
       "      <td>0.002524</td>\n",
       "      <td>98525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>portsweep.</th>\n",
       "      <td>1040</td>\n",
       "      <td>0.010578</td>\n",
       "      <td>0.002105</td>\n",
       "      <td>98318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>warezclient.</th>\n",
       "      <td>1020</td>\n",
       "      <td>0.010377</td>\n",
       "      <td>0.002065</td>\n",
       "      <td>98298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>teardrop.</th>\n",
       "      <td>979</td>\n",
       "      <td>0.009964</td>\n",
       "      <td>0.001982</td>\n",
       "      <td>98257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pod.</th>\n",
       "      <td>264</td>\n",
       "      <td>0.002707</td>\n",
       "      <td>0.000534</td>\n",
       "      <td>97542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nmap.</th>\n",
       "      <td>231</td>\n",
       "      <td>0.002369</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>97509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              attacks  prevalence  prevalence (overall)  records\n",
       "label                                                           \n",
       "smurf.         280790    0.742697              0.568377   378068\n",
       "neptune.       107201    0.524264              0.216997   204479\n",
       "back.            2203    0.022145              0.004459    99481\n",
       "satan.           1589    0.016072              0.003216    98867\n",
       "ipsweep.         1247    0.012657              0.002524    98525\n",
       "portsweep.       1040    0.010578              0.002105    98318\n",
       "warezclient.     1020    0.010377              0.002065    98298\n",
       "teardrop.         979    0.009964              0.001982    98257\n",
       "pod.              264    0.002707              0.000534    97542\n",
       "nmap.             231    0.002369              0.000468    97509"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "ni.report_labels()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline and Oracle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 22 s, sys: 6.97 s, total: 29 s\n",
      "Wall time: 19.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_baseline_oracle = ni.report_baseline_oracle()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>baseline_unsupervised_f1</th>\n",
       "      <th>oracle_f1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>smurf.</th>\n",
       "      <td>0.38</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neptune.</th>\n",
       "      <td>0.49</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>back.</th>\n",
       "      <td>0.09</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>satan.</th>\n",
       "      <td>0.91</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ipsweep.</th>\n",
       "      <td>0.07</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>portsweep.</th>\n",
       "      <td>0.53</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>warezclient.</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>teardrop.</th>\n",
       "      <td>0.30</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pod.</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nmap.</th>\n",
       "      <td>0.51</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              baseline_unsupervised_f1  oracle_f1\n",
       "label                                            \n",
       "smurf.                            0.38       1.00\n",
       "neptune.                          0.49       1.00\n",
       "back.                             0.09       1.00\n",
       "satan.                            0.91       1.00\n",
       "ipsweep.                          0.07       1.00\n",
       "portsweep.                        0.53       1.00\n",
       "warezclient.                      0.01       0.99\n",
       "teardrop.                         0.30       1.00\n",
       "pod.                              0.00       0.98\n",
       "nmap.                             0.51       1.00"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_baseline_oracle[['baseline_unsupervised_f1', 'oracle_f1']].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "baseline_random_FN                 1313.50\n",
       "baseline_random_FP                 1314.50\n",
       "baseline_random_f1                    0.14\n",
       "baseline_random_precision             0.14\n",
       "baseline_random_recall                0.14\n",
       "baseline_unsupervised_FN           2528.40\n",
       "baseline_unsupervised_FP           1584.00\n",
       "baseline_unsupervised_f1              0.33\n",
       "baseline_unsupervised_precision       0.34\n",
       "baseline_unsupervised_recall          0.32\n",
       "oracle_FN                             0.50\n",
       "oracle_FP                             0.00\n",
       "oracle_average precision              1.00\n",
       "oracle_f1                             1.00\n",
       "oracle_precision                      1.00\n",
       "oracle_recall                         0.99\n",
       "oracle_roc auc                        1.00\n",
       "oracle_train time (s)                 0.24\n",
       "prevalence                            0.14\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_baseline_oracle.mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "baseline_random_FN                 2594.14\n",
       "baseline_random_FP                 2596.90\n",
       "baseline_random_f1                    0.27\n",
       "baseline_random_precision             0.27\n",
       "baseline_random_recall                0.27\n",
       "baseline_unsupervised_FN           6104.01\n",
       "baseline_unsupervised_FP           3337.23\n",
       "baseline_unsupervised_f1              0.29\n",
       "baseline_unsupervised_precision       0.30\n",
       "baseline_unsupervised_recall          0.30\n",
       "oracle_FN                             0.71\n",
       "oracle_FP                             0.00\n",
       "oracle_average precision              0.00\n",
       "oracle_f1                             0.01\n",
       "oracle_precision                      0.00\n",
       "oracle_recall                         0.01\n",
       "oracle_roc auc                        0.00\n",
       "oracle_train time (s)                 0.14\n",
       "prevalence                            0.27\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_baseline_oracle.std().round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Active Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_active_learning = ni.report_active_learning()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">initial_FN</th>\n",
       "      <th colspan=\"2\" halign=\"left\">initial_FP</th>\n",
       "      <th colspan=\"2\" halign=\"left\">initial_average precision</th>\n",
       "      <th colspan=\"2\" halign=\"left\">initial_f1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">initial_precision</th>\n",
       "      <th colspan=\"2\" halign=\"left\">initial_recall</th>\n",
       "      <th colspan=\"2\" halign=\"left\">initial_roc auc</th>\n",
       "      <th colspan=\"2\" halign=\"left\">query time (s)</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_100_FN</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_100_FP</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_100_average precision</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_100_f1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_100_precision</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_100_recall</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_100_roc auc</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_10_FN</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_10_FP</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_10_average precision</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_10_f1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_10_precision</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_10_recall</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_10_roc auc</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_1_FN</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_1_FP</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_1_average precision</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_1_f1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_1_precision</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_1_recall</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_1_roc auc</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_25_FN</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_25_FP</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_25_average precision</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_25_f1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_25_precision</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_25_recall</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_25_roc auc</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_50_FN</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_50_FP</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_50_average precision</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_50_f1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_50_precision</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_50_recall</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sample_50_roc auc</th>\n",
       "      <th colspan=\"2\" halign=\"left\">train time (s)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
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       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>learner</th>\n",
       "      <th>sampling strategy</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">LogisticRegression</th>\n",
       "      <th>entropy_sampling</th>\n",
       "      <td>20.2</td>\n",
       "      <td>29.86</td>\n",
       "      <td>14.8</td>\n",
       "      <td>17.18</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.91</td>\n",
       "      <td>15.4</td>\n",
       "      <td>31.90</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.02</td>\n",
       "      <td>14.5</td>\n",
       "      <td>27.91</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.20</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "      <td>18.5</td>\n",
       "      <td>27.85</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8.53</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "      <td>13.1</td>\n",
       "      <td>28.44</td>\n",
       "      <td>3.7</td>\n",
       "      <td>5.93</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "      <td>13.9</td>\n",
       "      <td>32.00</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.81</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_sampling</th>\n",
       "      <td>20.2</td>\n",
       "      <td>29.86</td>\n",
       "      <td>14.8</td>\n",
       "      <td>17.18</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.7</td>\n",
       "      <td>30.27</td>\n",
       "      <td>9.7</td>\n",
       "      <td>8.81</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "      <td>19.1</td>\n",
       "      <td>27.73</td>\n",
       "      <td>9.2</td>\n",
       "      <td>8.46</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "      <td>20.0</td>\n",
       "      <td>29.82</td>\n",
       "      <td>14.8</td>\n",
       "      <td>19.86</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.04</td>\n",
       "      <td>19.0</td>\n",
       "      <td>26.36</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.62</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "      <td>19.7</td>\n",
       "      <td>29.89</td>\n",
       "      <td>9.1</td>\n",
       "      <td>7.67</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RandomForestClassifier</th>\n",
       "      <th>entropy_sampling</th>\n",
       "      <td>8.0</td>\n",
       "      <td>6.18</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.42</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.03</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5.20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>random_sampling</th>\n",
       "      <td>8.0</td>\n",
       "      <td>6.18</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>5.89</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "      <td>8.2</td>\n",
       "      <td>6.23</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.03</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.18</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.03</td>\n",
       "      <td>11.3</td>\n",
       "      <td>8.33</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "      <td>10.2</td>\n",
       "      <td>8.05</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         initial_FN        initial_FP         \\\n",
       "                                               mean    std       mean    std   \n",
       "learner                sampling strategy                                       \n",
       "LogisticRegression     entropy_sampling        20.2  29.86       14.8  17.18   \n",
       "                       random_sampling         20.2  29.86       14.8  17.18   \n",
       "RandomForestClassifier entropy_sampling         8.0   6.18        0.2   0.63   \n",
       "                       random_sampling          8.0   6.18        0.2   0.63   \n",
       "\n",
       "                                         initial_average precision        \\\n",
       "                                                              mean   std   \n",
       "learner                sampling strategy                                   \n",
       "LogisticRegression     entropy_sampling                       0.72  0.36   \n",
       "                       random_sampling                        0.72  0.36   \n",
       "RandomForestClassifier entropy_sampling                       0.97  0.05   \n",
       "                       random_sampling                        0.97  0.05   \n",
       "\n",
       "                                         initial_f1       initial_precision  \\\n",
       "                                               mean   std              mean   \n",
       "learner                sampling strategy                                      \n",
       "LogisticRegression     entropy_sampling        0.76  0.32              0.76   \n",
       "                       random_sampling         0.76  0.32              0.76   \n",
       "RandomForestClassifier entropy_sampling        0.94  0.07              1.00   \n",
       "                       random_sampling         0.94  0.07              1.00   \n",
       "\n",
       "                                               initial_recall        \\\n",
       "                                           std           mean   std   \n",
       "learner                sampling strategy                              \n",
       "LogisticRegression     entropy_sampling   0.33           0.76  0.31   \n",
       "                       random_sampling    0.33           0.76  0.31   \n",
       "RandomForestClassifier entropy_sampling   0.01           0.90  0.12   \n",
       "                       random_sampling    0.01           0.90  0.12   \n",
       "\n",
       "                                         initial_roc auc       query time (s)  \\\n",
       "                                                    mean   std           mean   \n",
       "learner                sampling strategy                                        \n",
       "LogisticRegression     entropy_sampling             0.97  0.04           0.00   \n",
       "                       random_sampling              0.97  0.04           0.00   \n",
       "RandomForestClassifier entropy_sampling             0.99  0.03           0.11   \n",
       "                       random_sampling              0.99  0.03           0.11   \n",
       "\n",
       "                                              sample_100_FN         \\\n",
       "                                          std          mean    std   \n",
       "learner                sampling strategy                             \n",
       "LogisticRegression     entropy_sampling   0.0           5.0   5.91   \n",
       "                       random_sampling    0.0          23.7  30.27   \n",
       "RandomForestClassifier entropy_sampling   0.0           1.2   1.81   \n",
       "                       random_sampling    0.0           9.5   5.89   \n",
       "\n",
       "                                         sample_100_FP         \\\n",
       "                                                  mean    std   \n",
       "learner                sampling strategy                        \n",
       "LogisticRegression     entropy_sampling           15.4  31.90   \n",
       "                       random_sampling             9.7   8.81   \n",
       "RandomForestClassifier entropy_sampling            0.0   0.00   \n",
       "                       random_sampling             0.3   0.67   \n",
       "\n",
       "                                         sample_100_average precision        \\\n",
       "                                                                 mean   std   \n",
       "learner                sampling strategy                                      \n",
       "LogisticRegression     entropy_sampling                          0.86  0.25   \n",
       "                       random_sampling                           0.75  0.29   \n",
       "RandomForestClassifier entropy_sampling                          0.98  0.05   \n",
       "                       random_sampling                           0.97  0.06   \n",
       "\n",
       "                                         sample_100_f1        \\\n",
       "                                                  mean   std   \n",
       "learner                sampling strategy                       \n",
       "LogisticRegression     entropy_sampling           0.88  0.20   \n",
       "                       random_sampling            0.75  0.31   \n",
       "RandomForestClassifier entropy_sampling           0.99  0.03   \n",
       "                       random_sampling            0.90  0.16   \n",
       "\n",
       "                                         sample_100_precision        \\\n",
       "                                                         mean   std   \n",
       "learner                sampling strategy                              \n",
       "LogisticRegression     entropy_sampling                  0.87  0.25   \n",
       "                       random_sampling                   0.75  0.33   \n",
       "RandomForestClassifier entropy_sampling                  1.00  0.00   \n",
       "                       random_sampling                   1.00  0.01   \n",
       "\n",
       "                                         sample_100_recall        \\\n",
       "                                                      mean   std   \n",
       "learner                sampling strategy                           \n",
       "LogisticRegression     entropy_sampling               0.91  0.13   \n",
       "                       random_sampling                0.75  0.30   \n",
       "RandomForestClassifier entropy_sampling               0.97  0.05   \n",
       "                       random_sampling                0.84  0.22   \n",
       "\n",
       "                                         sample_100_roc auc        \\\n",
       "                                                       mean   std   \n",
       "learner                sampling strategy                            \n",
       "LogisticRegression     entropy_sampling                0.99  0.02   \n",
       "                       random_sampling                 0.98  0.03   \n",
       "RandomForestClassifier entropy_sampling                0.99  0.03   \n",
       "                       random_sampling                 0.98  0.03   \n",
       "\n",
       "                                         sample_10_FN        sample_10_FP  \\\n",
       "                                                 mean    std         mean   \n",
       "learner                sampling strategy                                    \n",
       "LogisticRegression     entropy_sampling          14.5  27.91          6.6   \n",
       "                       random_sampling           19.1  27.73          9.2   \n",
       "RandomForestClassifier entropy_sampling           2.1   2.42          0.1   \n",
       "                       random_sampling            8.2   6.23          0.2   \n",
       "\n",
       "                                               sample_10_average precision  \\\n",
       "                                           std                        mean   \n",
       "learner                sampling strategy                                     \n",
       "LogisticRegression     entropy_sampling   7.20                        0.81   \n",
       "                       random_sampling    8.46                        0.73   \n",
       "RandomForestClassifier entropy_sampling   0.32                        0.97   \n",
       "                       random_sampling    0.63                        0.97   \n",
       "\n",
       "                                               sample_10_f1        \\\n",
       "                                           std         mean   std   \n",
       "learner                sampling strategy                            \n",
       "LogisticRegression     entropy_sampling   0.24         0.83  0.26   \n",
       "                       random_sampling    0.32         0.77  0.29   \n",
       "RandomForestClassifier entropy_sampling   0.05         0.98  0.03   \n",
       "                       random_sampling    0.05         0.94  0.07   \n",
       "\n",
       "                                         sample_10_precision        \\\n",
       "                                                        mean   std   \n",
       "learner                sampling strategy                             \n",
       "LogisticRegression     entropy_sampling                 0.93  0.10   \n",
       "                       random_sampling                  0.86  0.21   \n",
       "RandomForestClassifier entropy_sampling                 1.00  0.00   \n",
       "                       random_sampling                  1.00  0.01   \n",
       "\n",
       "                                         sample_10_recall        \\\n",
       "                                                     mean   std   \n",
       "learner                sampling strategy                          \n",
       "LogisticRegression     entropy_sampling              0.81  0.29   \n",
       "                       random_sampling               0.77  0.30   \n",
       "RandomForestClassifier entropy_sampling              0.97  0.05   \n",
       "                       random_sampling               0.90  0.12   \n",
       "\n",
       "                                         sample_10_roc auc       sample_1_FN  \\\n",
       "                                                      mean   std        mean   \n",
       "learner                sampling strategy                                       \n",
       "LogisticRegression     entropy_sampling               0.98  0.03        18.5   \n",
       "                       random_sampling                0.98  0.03        20.0   \n",
       "RandomForestClassifier entropy_sampling               0.99  0.03         6.8   \n",
       "                       random_sampling                0.99  0.03         8.0   \n",
       "\n",
       "                                                sample_1_FP         \\\n",
       "                                            std        mean    std   \n",
       "learner                sampling strategy                             \n",
       "LogisticRegression     entropy_sampling   27.85         7.7   8.53   \n",
       "                       random_sampling    29.82        14.8  19.86   \n",
       "RandomForestClassifier entropy_sampling    5.20         0.2   0.63   \n",
       "                       random_sampling     6.18         0.2   0.63   \n",
       "\n",
       "                                         sample_1_average precision        \\\n",
       "                                                               mean   std   \n",
       "learner                sampling strategy                                    \n",
       "LogisticRegression     entropy_sampling                        0.77  0.27   \n",
       "                       random_sampling                         0.71  0.35   \n",
       "RandomForestClassifier entropy_sampling                        0.97  0.05   \n",
       "                       random_sampling                         0.97  0.05   \n",
       "\n",
       "                                         sample_1_f1       sample_1_precision  \\\n",
       "                                                mean   std               mean   \n",
       "learner                sampling strategy                                        \n",
       "LogisticRegression     entropy_sampling         0.78  0.29               0.89   \n",
       "                       random_sampling          0.76  0.32               0.76   \n",
       "RandomForestClassifier entropy_sampling         0.96  0.05               1.00   \n",
       "                       random_sampling          0.94  0.07               1.00   \n",
       "\n",
       "                                               sample_1_recall        \\\n",
       "                                           std            mean   std   \n",
       "learner                sampling strategy                               \n",
       "LogisticRegression     entropy_sampling   0.17            0.78  0.30   \n",
       "                       random_sampling    0.34            0.77  0.31   \n",
       "RandomForestClassifier entropy_sampling   0.01            0.92  0.09   \n",
       "                       random_sampling    0.01            0.90  0.12   \n",
       "\n",
       "                                         sample_1_roc auc       sample_25_FN  \\\n",
       "                                                     mean   std         mean   \n",
       "learner                sampling strategy                                       \n",
       "LogisticRegression     entropy_sampling              0.98  0.03         13.1   \n",
       "                       random_sampling               0.97  0.04         19.0   \n",
       "RandomForestClassifier entropy_sampling              0.99  0.03          1.8   \n",
       "                       random_sampling               0.99  0.03         11.3   \n",
       "\n",
       "                                                sample_25_FP        \\\n",
       "                                            std         mean   std   \n",
       "learner                sampling strategy                             \n",
       "LogisticRegression     entropy_sampling   28.44          3.7  5.93   \n",
       "                       random_sampling    26.36          9.0  6.62   \n",
       "RandomForestClassifier entropy_sampling    2.25          0.3  0.67   \n",
       "                       random_sampling     8.33          0.5  1.08   \n",
       "\n",
       "                                         sample_25_average precision        \\\n",
       "                                                                mean   std   \n",
       "learner                sampling strategy                                     \n",
       "LogisticRegression     entropy_sampling                         0.85  0.20   \n",
       "                       random_sampling                          0.75  0.29   \n",
       "RandomForestClassifier entropy_sampling                         0.97  0.06   \n",
       "                       random_sampling                          0.96  0.06   \n",
       "\n",
       "                                         sample_25_f1        \\\n",
       "                                                 mean   std   \n",
       "learner                sampling strategy                      \n",
       "LogisticRegression     entropy_sampling          0.86  0.25   \n",
       "                       random_sampling           0.78  0.28   \n",
       "RandomForestClassifier entropy_sampling          0.98  0.04   \n",
       "                       random_sampling           0.88  0.16   \n",
       "\n",
       "                                         sample_25_precision        \\\n",
       "                                                        mean   std   \n",
       "learner                sampling strategy                             \n",
       "LogisticRegression     entropy_sampling                 0.96  0.06   \n",
       "                       random_sampling                  0.84  0.21   \n",
       "RandomForestClassifier entropy_sampling                 0.99  0.02   \n",
       "                       random_sampling                  0.99  0.01   \n",
       "\n",
       "                                         sample_25_recall        \\\n",
       "                                                     mean   std   \n",
       "learner                sampling strategy                          \n",
       "LogisticRegression     entropy_sampling              0.84  0.28   \n",
       "                       random_sampling               0.78  0.29   \n",
       "RandomForestClassifier entropy_sampling              0.97  0.05   \n",
       "                       random_sampling               0.82  0.22   \n",
       "\n",
       "                                         sample_25_roc auc       sample_50_FN  \\\n",
       "                                                      mean   std         mean   \n",
       "learner                sampling strategy                                        \n",
       "LogisticRegression     entropy_sampling               0.98  0.03         13.9   \n",
       "                       random_sampling                0.98  0.03         19.7   \n",
       "RandomForestClassifier entropy_sampling               0.99  0.03          1.4   \n",
       "                       random_sampling                0.98  0.03         10.2   \n",
       "\n",
       "                                                sample_50_FP        \\\n",
       "                                            std         mean   std   \n",
       "learner                sampling strategy                             \n",
       "LogisticRegression     entropy_sampling   32.00          2.8  5.81   \n",
       "                       random_sampling    29.89          9.1  7.67   \n",
       "RandomForestClassifier entropy_sampling    1.71          0.1  0.32   \n",
       "                       random_sampling     8.05          0.1  0.32   \n",
       "\n",
       "                                         sample_50_average precision        \\\n",
       "                                                                mean   std   \n",
       "learner                sampling strategy                                     \n",
       "LogisticRegression     entropy_sampling                         0.82  0.29   \n",
       "                       random_sampling                          0.74  0.30   \n",
       "RandomForestClassifier entropy_sampling                         0.98  0.05   \n",
       "                       random_sampling                          0.96  0.06   \n",
       "\n",
       "                                         sample_50_f1        \\\n",
       "                                                 mean   std   \n",
       "learner                sampling strategy                      \n",
       "LogisticRegression     entropy_sampling          0.85  0.31   \n",
       "                       random_sampling           0.76  0.32   \n",
       "RandomForestClassifier entropy_sampling          0.98  0.03   \n",
       "                       random_sampling           0.89  0.16   \n",
       "\n",
       "                                         sample_50_precision        \\\n",
       "                                                        mean   std   \n",
       "learner                sampling strategy                             \n",
       "LogisticRegression     entropy_sampling                 0.88  0.31   \n",
       "                       random_sampling                  0.75  0.33   \n",
       "RandomForestClassifier entropy_sampling                 1.00  0.00   \n",
       "                       random_sampling                  1.00  0.00   \n",
       "\n",
       "                                         sample_50_recall        \\\n",
       "                                                     mean   std   \n",
       "learner                sampling strategy                          \n",
       "LogisticRegression     entropy_sampling              0.83  0.31   \n",
       "                       random_sampling               0.77  0.31   \n",
       "RandomForestClassifier entropy_sampling              0.97  0.05   \n",
       "                       random_sampling               0.84  0.23   \n",
       "\n",
       "                                         sample_50_roc auc        \\\n",
       "                                                      mean   std   \n",
       "learner                sampling strategy                           \n",
       "LogisticRegression     entropy_sampling               0.98  0.03   \n",
       "                       random_sampling                0.98  0.03   \n",
       "RandomForestClassifier entropy_sampling               0.99  0.03   \n",
       "                       random_sampling                0.98  0.03   \n",
       "\n",
       "                                         train time (s)        \n",
       "                                                   mean   std  \n",
       "learner                sampling strategy                       \n",
       "LogisticRegression     entropy_sampling            0.03  0.00  \n",
       "                       random_sampling             0.03  0.01  \n",
       "RandomForestClassifier entropy_sampling            0.11  0.00  \n",
       "                       random_sampling             0.11  0.00  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ni.report_active_learning_across_labels()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('RandomForestClassifier', 'random_sampling'): {'0.04±0.04'},\n",
       " ('RandomForestClassifier', 'entropy_sampling'): {'0.18±0.07'},\n",
       " ('LogisticRegression', 'random_sampling'): {'0.04±0.04'},\n",
       " ('LogisticRegression', 'entropy_sampling'): {'0.04±0.04'}}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ni.report_active_learning_query_time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ni.plot_active_learning_time_series_overlapping(\n",
    "    attribute='f1', label='smurf',\n",
    "    learner1='RandomForestClassifier', sampling1='entropy',\n",
    "    learner2='RandomForestClassifier', sampling2='random',\n",
    "    title='Random forest for smurf attack',\n",
    "    ylim=[0.9980, 1.0001], ylabel='F1',\n",
    "    legend=['Entropy sampling', 'Random sampling']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ni.plot_active_learning_time_series_overlapping(\n",
    "    attribute='f1', label='smurf',\n",
    "    learner1='LogisticRegression', sampling1='entropy',\n",
    "    learner2='LogisticRegression', sampling2='random',\n",
    "    title='Logistic regression for smurf attack',\n",
    "    ylim=[0.9980, 1.0001], ylabel='F1',\n",
    "    legend=['Entropy sampling', 'Random sampling']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ni.plot_active_learning_time_series_overlapping(\n",
    "    attribute='f1', label='nmap',\n",
    "    learner1='RandomForestClassifier', sampling1='entropy',\n",
    "    learner2='RandomForestClassifier', sampling2='random',\n",
    "    title='Random forest for nmap attack',\n",
    "    ylim=[0, 1], ylabel='F1',\n",
    "    legend=['Entropy sampling', 'Random sampling']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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udtw+bQnb9h5iS24eD3++mh8f3YOs3DxGdm9Z6+diGIYRiolCFdi5P58Pl2ZxyVHdaJZc9tKN6lFxa/6MwR1Zk72Pe95bwfBurejfsXlg279nr2PGqmz+MHEQyzbn8uSMtezcr8NdbeSRYRh1gXU0V4Fp6ZvILyrmx0f3qHYeiQnCw5cMJzW5Ebe8upDsvYfIPVjAvHU5PPC/VZw5uCM/ProH9543iL7tUnlt/kZSkhIY2NkmrRmGEX3MUqgkxcWOV+dvZEzP1qVa99WhffMUHrlkBFf+5xuO+tPngfVdWzXhrxcORURo2rgRT1wxkvMen83Qri1JSjT9Ngwj+pgoVJLZa3awIecAvzz1iFrJ77h+bXn52rF8v3UvoG8kOnVgB9KaJAXSHNGhOa9efzTNw7iqDMMwooHVNpXk5XkbaNOsMWcM7lhreR7bty3H9i3/nULWl2AYRl1iPolKkJV7kM+/28bFR3Wz2EOGYcQ1JgqVYFp6JsUOLh/TveLEhmEY9RgThQooLna8kb6JY/u2oVtrewWmYRjxjYlCBcxZm0PmroNlJpoZhmHEIyYKFTA1fRNpTZI4bWCH6B7o36fArL9H9xj1hWdOhnlPxboURkNm5t/hhfMqTheH2Oijcti1P59Plm/l8rHdSUmKYgfzrgzI/BZadI7eMeoLxcWwZSG07RfrkhgNmY1zIGtxrEsRE8xSKId3Fm8mv6iYS47qFt0Drf9Kvw/uju5x6gOH9oArhgM5sS6J0ZDZvQkO7YUovjzrcMVEIQLOOaZ+u4lhXdM4slOUQ0ys9yKr5pkocHCXfh/YGdtyGA0X5yB3kzZO8vfFujR1jolCBJZm5vL91r1cHG0rwTlYP1OX/QqxIeNfg4MmCkaM2J8NhXm6nLcntmWJASYKEXhrYSbJjRI4d1iU/fw7VsO+bZCcBgdzo3us+oAvBuY+MmLF7qBX6x4yUTCA/MJi3luyhdMGdaRFSlLFO9QE33XU/0w4lAvFRdE93uGO36+SlwtFhbEti9Ew2b2hZNksBQNg+qrt7D5QwAUju0T/YOtnQlp36Dxcf+c1cGsh2IVmfSxGLMgNshQa4P/RRCEMby/MpG1qMsdXEKyuxhQX68ijXuOhiRf4rqH3KwR3MJsLyYgFuzeWLJv7yNi1P58vv9/O+cM70yja7zDYulRbw71PgBTvdZsNfVhqsCjaCCQjFuzeBM3a67JZCsYHS7dQUOS4YGTX6B/M70/oeXyJpZDXwC2FYFGwEUhGLMjdBB0G6bJZCg2PwqJiFmzYRe4BfRfyWws3M6Bj87p5/eX6WdD2CGjRCZqYpQCoKDRto8vmPjLqGufUfdSuP0hig+xobvBhLt5amMn/e2sZAL3aNmP9jv3cfdaRdXPwzG9h0Pm6HHAfNXRLYSe07qOCYO4jo645uEsnrLXsDiktGqSl0OBFYcaqbDq0SOYn43qyaONuWqQ04kd1MeqoMF/7E1p4biqzFJSDu6DjEMhKNveRUff4I4/SukFyC7MUGhpFxY45a3M4fVAHbp7Qt24P7rtGmnmukkbJkNTUhmEe3AVNWkPT1uY+Muoef+RRy25qKVhHc8NixZZccg8WVPie5KhwYId++/5z0M7mhuw+Ki72+hRaqzAcqMVrseAFmP7n2svPiE/82cwte6hLtwG6j6IqCiJyhoisEpE1InJXhDQXi8hKEVkhIq9GszyhzF6jFfMxfWIhCl4ruGnQsVNaNmz3kR8htUkrFYbach8teR3e/zl886/ayc+IX3ZvhKRm+gya+6h2EZFE4AngVCAT+FZE3nPOrQxK0w/4NXCsc26XiLSPVnnC8fWaHQzo2Jx2zZPr8rDKfs9SaBYkCk1aNWz3kW8l+aKw/fua5/nDZ/DuzZCYrNe24CAkNal5vkZ8krtJXUciDbajOZqWwhhgjXNunXMuH3gdmBiS5nrgCefcLgDn3PYolqcUeQVFfJuxi+Ni4TqCIEsh2H3UsmG7j3zLoInvPqphn0LmAnjjJ9B+IJx6v67bu7VmeRqHN5sXQm5m9fffvVFHHkGDtRSiKQpdgKAgImR664I5AjhCRL4WkXkicka4jETkpyKSLiLp2dnZtVK49Ixd5BcWc2y/GInC/h2AlExag/h1H+3eVLlAf6GWwsFdNXvJyeyHoHEq/Pitkje5mSjEN1N/DO//ovr7796oI4+gxFIoLq6dstUToikKEmZd6D+8EdAPOBG4DPi3iLQss5NzU5xzo51zo9u1a1crhZu9ZgdJicKYnq1rJb8qcyBHK76EoNd8xqOlsG87PDoCVvy34rS+IDZppRaUK6rZ6I89m3V4a2r7kled7s2qfn7G4U1RAezZAutmVG+Oy6G96mJs6YlCcgvANbgX7URTFDKB4DfUdAW2hEnzrnOuwDm3HliFikTU+XrNDkZ0b0Wz5BiNyj2wo7TrCFQUCg9CQV5syhQNdqyG4gLIWVtxWv+P7I8+gpq5kPZug+addLl5R2+diUK9ZU+WVvyR2LsVcFBcCN9/WPX8AyOPPPdRSpp+R6tfIfi9DYcR0RSFb4F+ItJLRBoDlwLvhaR5B5gAICJtUXfSuiiWCdCgd8u35MauPwFgf07pkUcQFP8ojlxIuzL0e9+2itP6VlJKSxWG4HVVpbhIj+mLQUpLaJRiolBfKSqAJ8bAvKcipwm+t5WxTEPx5yik+aLghbqJxlyFjfPgkcGwdVnt511DoiYKzrlC4BbgE+A74A3n3AoRuV9EzvOSfQLkiMhKYDpwp3Mu6jOW5q7LwTliMz/B50BOycQ1n3iMlFpVUUhuAYmNguIfVXNY6v4d6n7yRUFEl61PoX6yb5u22LcsjJxmj+eI6Htq9VxI/mzmUu4jotPZvHmBfmevqv28a0hUfSfOuY+Aj0LW/T5o2QG/9D51xtrt6iMcVBdB7yJxYAc0HVt6XTxbCpWpjA/uLAn34V+L6rqP/Faj7z7yl00UdFiuK4bGzWJdksqzx7uf5VWi/j0/+iZY8xl8/wGM/Enlj7F7gw5d9sNmR9N9lO0Nt94T6lGPPQ1yRnPWnjxaNU0iJSmx4sTRoLhYWzFl3EdxGBQvYClUYrSxH+ICgtxH1bQU/Mq/lCh0NPcRwAe/hIcH6fDN+oJ/33b8EPk1rXs2a6Xe5yRo1bPqLqRdGWolJHjVYjQtBV/cTBQOD7bl5tExLYYTmPJ2q2ujWYgoxKP7aOd6/d63teLhpQd3lVgIyWkauri67qOApdCxZJ1ZCnoP1nyu1/qF82DD3FiXqHL49624AHatD59mT5bebxEY9CNYN7Nqz8/WZSXvUYCSPoVDtdyn4FyQpbC5dvOuBRqkKGTl5tGxRQxmMfuEC3EB8fdKzkN71U3WrD0U5Vd8XsGikJCgy9V2H20FRIej+jTvqMMLD+0Nv0/+AcheXb3jxZJdG0pmyFeYdj3s3w7j79Tr8dKPYO2X0S1fbbA3qEWdHWGm+94saOFNhRp4vja8vnu/bLo9WSXuKJ+Du9RS6DSsZF1FlsKhfbDqY/juA/1kplfqVNi3raTz2kTh8GDbnhhbCv4fuGnIHAnfhxkvfQq7Nuh3d6/vpCIX0oGdpa9JTeIf7c1SSywxqWSd70oKrRB85j0J/xoPhYeqd8xY8eJEePOayqXd+I1+D/oRXP0xtOkD067WfobDmb1bS1yLkURhzxZ9YRVo5d6yB6z+pGy6N6+BaVeVXuePAuo0vGRdUhNISArfp7AnC549FV67FKZeoZ9nT63cbGq//GndzX10OHCosIic/fl0SkuJXSECYbNDLIWERBWGeLEU/P6Ebkfr975yXDfFxSqGwTO8m7SuvvsoeDiqjy8KkfoVslfpPJHDdPx4WHZv1Nb/+pklrrry2DRPXXPtjoTUdnDGX/W6V2cIZ12yNwva9NXZxuE6m53TNP49FoFuY2HLotLpigp0BFNmurb0fbYs1u9gS8GPfxQ6JHXHD/DsaXrtL3oBbvgKLn9DO+9X/6/ic/HL3/ckFbvy5l7EgAYnCtv3aCuwY4tYioJvKYQZEluboS62fw8rQ6eG1CEBUaiEpRAcIdWnaZua9SkEdzJDkChEEKfdnmWzO6N6x4wFwX0CiysRZHjjN9DtqJLO1J7H6Sth0/9T9WPvy4b5z9QsFEll8fsL2vUPLwoHd0FhXsnMdYDOI9TtFHy/s7/XdK4IMueXrM9aoi+8Cm2ohcY/2rZCBaHwIEz+UN+c2Gko9DsNWvWCVZURhe/1f95pOOAqN1y7DmlwopCVq7OFO8bSUtgf5l0KPrUZKXX2Q/DfG+rmTxuOXRlq+bTrr7/L6+QNDobn07RVzUYflbEUOnjbIlgK/uQl3+1VH9g4R1v+vSfA4lfKjzF1cBdkf1diuYG2hkdfo6+GzVpatWPPfRw+ugOyFlev7FVh71YV9XYDvFnyIefpu2GCGwKdR+j3lqDyBVsOwYKataS0leATGik1/T/qXrzmE+gc5GoSgf5n6nvX8/eXfy7Zq/Q/kda1dNkPExqcKGzdcxiIwoEcDdSWFKYMtRn/KGcNFByI3RvMdq3X1lNyc2jUpPwWUXAwPB/ffVRVUSsqVKsk1FJIbg6Nm4cXp4K8ErHwxaE+sGGO9tmMuko7LddNj5x207f63T1kfsywS/X+VNVaWOVNQcqYXbX9qkr+fh0B5FsKhXll75F/74IthY5DQBJKC8GWRdr67zRMrx3owIOcNeFFIdRSyFmjZWjTp2zaI86AokOwtpx7AGoptOtfUtbgfojCfPjiD5Ubwh0lGp4o5GqHWsxFIZyVALXrPvLjDcWqktuVoePFRbSVXlVRaNpG/2QFB6p23P3bAVfWUoDIcxVyg/oRdtcTS2FftraaexwD/c9SEV34UuT0m+bpMN8uo0qvb9IKBl8Iy6ZFHpkVyo41emyIvij4It6is1oKUNaF5Le2g0UhOVVdY8GWzJbFWvn3PF6to8JDsHU54Eq3/H1S0kpbCjnrwgsC6H1IToPVH0c+l/079P/fbkBJWYMthY1z4KsH4fN7I+cRZRqcKGTl5tGscSLNYxUID/TBiCQKtfVKzgM7S9xQuTHoOC0uUjFq1VN/p1YQYuJAOFGoZlC8cLOZfSKFuvBdRo2b1x/30UbP/dHjWH3H99BLNBDc/gjXa+M36v8ON5N59DU6XHfp1Mode5UXcK7vKdrirkxo9OoSPOek7RG6HDoCyU+TGtIQ6DxCrQPntBW+bblW/t3HaYNj80J1HUEE91FaiaVQkKf/pdYRRCExCfqeDKs/jRxu2y93u/7aAExqVloUtq3Q78WvlizXMQ1OFLbtyaNDWgoi4SJ71xEHdpTt0PJp0tKb3FYFl8nOdWUDa+WsKVmOhaWwN0vnJgREoX3lLIXgIamBSKlV7FcIzGYOZyl0Kj3m3cfvXO5xTN1bCkUF8NrlGiStKmyYo24ffxjliCt0cteyaeGPsXlB6f6EYLqMhI5D4bN74LFR+nn9isjP4fcfafphl2lL2q9Yo0Hw7PQmLfU7nKXQrB00alx6fecR+tztzYLtK/WZ7DxCRQG0ZZ61BFI7hH9ekoP6FHatB1xkSwG0X2H/9sgxmgKiMEAt6BadS89V2LZSG0YpLeCL+yMfJ4o0OFHIys2L7XBUCB/iwqdJKw39W9kY7vn7dZz61B+XXh8cqjoWQyz9kUe+KDTvWDlR8Gd1Q4k1VdXO5kitRr8ce8PMrt61ARIbQ7cxapkcqsMY+luXact7+VtV22/D19B1dElF2HEIdBkNX/+z7ISrrKU6YqbbmPB5icAZf1G/eKdhWkl+/4G2rEPZlw2bvoEBZ+voJYiuCynU8mvXP7ylEM4yDO5s9t1InUdoMMp2A1RYI3UyQ9CLdopK/lORLAVQy0kSdVJbOLJXaX+iP8mujCgsV5E/7jYd3prxdeRjRYkGJwrbcvPoEMvhqOC5jyK83KeqoS6+/JM3Vj2jtNtp51rtZGt7RGwsBV8UWvfS79T2Ot470iSpgztLIqT6NK2upbBNz71ZmBcytegcfna1/8Ytv7x1aS34ETO3VGEUT16uViA9ji29/sy/aQUZ6pPe5Fkh3SNYCqAV/KRnYdJ/4KLnAVGLIJTV/wOc9mM07wht+kHGV5Uve1XZk6VuluRDvRDgAAAgAElEQVTm+rvdAK1cg4V9z5bS/Qk+HQaXdDZvWaTuoFbePe5xjFpn2d9HFgV/VvOhvfqfAmjTO3JZm7bWaxxpvoLfyex7Klp0KXEfFRXq9g6DYOyN0LwzfH5PnY8erLYoiMiA2ixIXVBU7Ni291BsLYX8/dpii+g+qkKk1MwF8M1T+uBD6SGFOWv1ZSGt+8SmT2Hnem0xtfCG3fmt9kijKoJDXPhU232UpaE1EsP0G0V62c7uDdCqB7Ts6f2uQyH1wyNsXVZ53/ym+Tqvo8cxpdd3Ha1RQtOfLRldsycLFr+mM2jDVZzhSG0PXY8q6TsIZtVHKqAdh+jvnsfp8M5Igepqyt6gmEaglWrB/tKjdvZsCW8pNG6qE/V8Ueg0vCSfHseqRe6KSs9kDiYQ/2iP/qeatin7nIYy4GwV7K/+UbZCz15V0lkOkNbFm8BWqG7gwjz9Pyc1gQm/0c7wL/8Q3T6bEGpiKXxaa6WoI3L2HaKo2MU2xEWkuEc+lY2UWpgP792qle0l3oiTrUGisHOtCkLLbrFzH7XsVlIx+5VxJBdSWFHwY0FVo08hnH8YIs9q3rVBwyL4b92qy87mzQvUdVV4sGRET0Vs+BoSGmnFHcpJv9XzeO9W7fT81/H6PJzx56qVa8BZ6lrJDXJv5B/QIZf9zyypXHsdD/l7YWuU+hX8OQo+oSOQCvL0GWkR+gp4j84jYHO6+ut9dxKU9CtAxZZC3h6ttMtzHfmMvlZHc31xv84T8t+keGCnPv/+vB1QkXZF2g/hu+o6DNTv4ZfD8B+ruLx8gbrt6oByRUFEHo3weQwo8y7lw53AxLVYuo/Km7gGlXcfzX0Mtq+Acx6C1r3V1PQtBedKhs617K5jvOs68qo/HNXHD0wXaQTSgZ1lRSGxkZr74UYfLXo58mzt0EokmIClEFSOQ3u1UmnVQy24pKaR3Ue5m+GTu2svNMHBXZDzgwZwg8p32G6YoxVc46ZltzVuBuc8ooMNXr1ILa7rp8OR51atbP3P1u9VQS6ktV+qePU/q2RdD69fYX2UXEh7g2IaQZAofOdt9+coRLjnnYfrdS4uKC0KaV20IdCkdclEslBCLYXyOpl9klLgwmdhwm91NNeUE2HqlfDm1aXLDyVClrtZO8IlEdp6opGQCOc/Aec9rm6up4+rk6i2FVkKVwPLgQUhn3QgP7pFq338iWuHZdwjn8pGSl34ks5i7X+m/u40tMRS2J+tLbfWfdTMh7p3IZURhWpYCqB/EN8N4nNoH3x0J/zv1+H9rb67IRypYdxHvlXQsru2flv2iGwpLHheZ/JWNiJmRfjvNPAnkFWmX2HvVnUr9D4xcpq+J2sk1NHXwPVfQvtqeHvb9tNnyBeFwkPa+k3rVrovo3kHvU/R6Gx2rqzl17S19pWt+G9JzCOI3BAIFoLQuQjjblF3W6TRiH6Qyr1bVZwqYymA5nfCnXDxSzoQYMcP2tfVbWxp6y4wV2GzDkFt26/spNaRV8J1n+u8i8oOQKkBFQ3W/xZY7pybE7pBRO6NSomiyFbPUojY0eyctgba9o1eIQLuo0jzFDxLobw+hZ3rdHjc0T8rWddxKPzwqZr3/iiJNn1KjrN7Y4kPuFR5dup49wFnV+08ysMPme136IGKoCSULwrhOt+HXAQf36kTjDp6fSffva8T2goOaOXcLehPVpivx45UQSSlqPgEWwp+/4Hfn9CqR2RLwQ8zvWUh9BgXPk1V2LwQEO0L6DikcpbCsmnanzD0kvLTnfTbmpVNRF1I855W98mcx2DHKrjirbJDP3sep63irx/19k3QVnZKmo62yd+n1uqhPSVC3igZRlwZ3trxydutfvbQ+znmpxpiI/Pb8BPXgukwSF1tyc1V8IMZ+9Pyr0GyJwr+fSmvkzkcA8/TTyR8S2HPFnUfhXMHgj4bP5tXOupvlKhIFCYBeeE2OOd6hVt/OLN1Tx5JiUKbZo3DJ1j6Bvz3p3Ddl9B1VPg0NcV3H0WyFBqn6gNcnqXgT6Pve3LJuk7DtKLYtiJolESfEp9opH6FmX+Db57W1mToTNfqsn6Wfrc/smRdQqKOBgonCnm5er6pHcpuG3whfPJrWPo6dPyjrlv6unZg79+urcVgUfDzj2QpgFYwweGzfQFo5VUYLXvoUEDnSrcgD+wsGX8eGn2zumxO11ZvSpq2Yhe/qhOfEsox4pe8rveqbb/aKUN59D9bxWDOYxpLa+gl0O+UsukGnKVhMj77XdXyz98Hx98eeXukOSfDLtMO2HlPQueRXppIDYEmmqZZ28gWQSR895F/39vUcoOxSSu1ELO/08bJyKsip60DQYCKRSHVOVfNiGSHH1u94agJCREejCWv6ffSqdEThQM7NEa7X1mHIlJxqIu1X3oji4JaLZ2G6vfWJToqI6GRjjZJSNSHLpz7qLioJGTy3Cd1OGJFFBdrRdb1qMh/sLlP6LH7nFx6fWoHNaFD2fgN4MKPoW/WRiNQLnsTTrlPK4l1M+GEX2kfysp34LQ/llSiAVGIUEFA2VAXuzbokEffqmrZXd1vodbL+lkqvC261I4oOKeWzhGn6+9Ow2D+FBX1SBX+1uXaojzz7zU/fmXoNkavy6wH9Pv0v4RP1/cUuDurZJSMK1LrIm+3uvuSU/W5TmmhfnPQuTXfTIFxt5a2PLZ/p0KZkBgU6C7ECkhO1Qp07hM6ryepaYmrJxyXT1Xrpar4/9MtnqXQuoqWQkX4E9jWfKG//ZGEMaSiq/SOvyAiVZxZc/iRlXswcifz3m0akz6hEax4O3rD6/wQF+W1WFr1gJXvhp/hWlSglVOfk0rnkdZN/3RZS9V91KqndtSKaCUXzh2y4WtvNMQAFYfKvCBkyav6MpFIE602L9R8j76x7JDQ1A7h36mwcU7kkTSgrdO9WXp/lk0DnK4b9CP1xWZ+W5I24F8OY3X4NO+krTK/AvOHo/rX07cYQq/Z2i/UnTDyKu3EDY2zX1V2b9RGgm+h+cMiy3MhLX1dr9XgC2t27MqSkKgT2gDO+JuKdCSSmmhlnZyqFXRLb9hqj3H63bKbrvfTHHOLPg8r3i7JY/Wn8OTRMPMB/V3e7PQxnuvnu/e1Yi3vP9W0dYlrtiokpejIsEO5+vz6cyVqk+AJbMGvA40RFYlC8FWuZYmse7btORQ5EN6K/2or8MS7tKN2/czoFOLAzsiuI58LnlGz8oVzYWlIyILNC9Qv2+ek0utFtKWZtaTs0LlIw1KXv6Ut5Iu9Ia3f/Kv8chUXl/iMp/85vHDOe1LjB424suy25h3Cz1PYMFcrxHAxeUArpeQ0WDLVs+KOUtdY/zP1Re3BL4gJDokQiX6namX8nTd6yR+O6uMvB3c2O6duu97j1f8PNQ/tsNnrrPZFoV1/PZ9IVkhxkVpM/U4rv3KubcbfAWc/BEMm1W6+fU7WBsncx/X65u2BD27TbfOnaP9YuHdt+7TsVuKvL+9+1xTfWqhsJ3NV8fsVktMij4KqQyoSBRdhud7hnCvfUlj+pppu427Vh6CqIQcqy4FyZjP7tOmjow26HgVvX6cmss+aL9QM7jW+7H6dhuqwtp0hkRzTupV1HxUVqDXS/0xodwQMnAgLXig/Suaaz7WjcdAF6uJY+nrp7bmbtYIedVWJLzaYVE8UgifiFBysuNM2KUVfZrL8TT0/v4M1pYVW8CvfKQlAtjdL3ROR5oEAHHme+ob9yUW7N5bMT4DwlkLOGr2GfU4qGc3ijxwKZc8WePuGkldfRiJzATRKKWkdJibpciSxWT9Tz6+iDubapnVvOOraqvvjK0IExt2sk/YyvoIv7tMW86n36xDhJa/q+TZppVZIOPzBFpWdlFcdfLdUVTuZK0uaJwodBtb+Na4GFYnCMBHZIyJ7gaHe8h4R2SsiEd5mfXiy52AheQXF4S2FnevVBTFkklZAR56nY+Cj8d7a/TvKr7B8mraGK9/Rsnz625IYKGu/1Pg24YZvdhymIRwKDpT2fbbsrqOegl/+sW6m+sx9N8S4W9REXvRK5DLNfUx9uz/6l1aMM/6mo3185k9Ra2vsDeH3T+2ovubgeQebF2iZQ8M1hDLsUvUdJySVdp0M+pFWHJnzVehy1mqrsryO2oREjS2zdZkOLsjfWyIEoJVASsvSloI/6qjPSXpvWvYI36IvLoK3rlPBfO4MHcJZGGH09uZ0tZCCOxA7D1cXYLihtkumamvSd+fEA0Mu1v/DR3fCt//W4aHH/Fw7huc+qQ2N8qyAbmP02R1yUfTK6DdwaruT2ccXtMPAdQQViIJzLtE518I519w518hb9n9H6Ck9PMnaoxV8p3CzmZe/qd9+ZTNkklYUP3iTtneug89+XzrIXHU4tE9dUxW5j3waNYbzn9T+gbeu0+NvWVjWdeTjdzZD6QfYbwUHu5CWv6UVjD+CqesojaA574nwoZe3LNa+jKNv1HKd9FvI3QiLXtQKbP1XsOA5tTiCW93B+H7+4BFI/mQc/5Wdkeh2tMbYOfKc0pbWEadra3va1fCXbmo1VKYzcOglakF9erf+Dh2qGNoPs/ZLdR/4cy/8kMyhzPq79qmc9SAMu1ytkWdPKT3aCXTMf9aSEleUT6dhKs67Qt63vOhl7U/xGy7xQlIKjLleY/607K7PlQgcc6tao2u/KH8kGcDpf1KLMVrUlfuoPohCPLE18BrO5NIbnFM/bfdxJZVZr/EaO2fpG+pnf+pYjTz51LEw76nIsdLLY/1X8NQx6p7peXzl90tuDpOeUzF54VxtiUcShTZ9dRQGlHYfBUTBG49fkKcRMI88V8eK+5z4/7TyenJs2dnCcx/XvoJRk/V3n5P1mk3/Czw2El44BxA4/o7I5+IPOQ0egbRxDrQfVLFLLSFBXWoTnyy9Prm5Fzyso7qtJj0HF79Yfl6grfNjfq7XFUpbCv5v31IozNf7F3zdu4xU0QiOy5QxW4f4DrtMK7rzn4BLXtYX0kybXHoW9Ke/0/H3/U4rfVy/s3nVx9pn45wKzbs362S1U2MTTjmqHHWdntv5T5f0Kx15no5gK8qPbn9BZQhYClEShS6joOuYsqP1YkQM3zRTt+RtWsz9jZ6jU0rICJcNc7SVcvY/StYlJKrV8M1TWnn2PQVO/I3+4f93Fyx/O/xbmiKxP1t97a17w9UfV33SU+fhcNof9NjJLSLPJ0hI1H4R/yXkPoFZzZ4o/PCpdlYPvqD0/n1Oghtmwjs3wRtXQr/TtXJ0Ts957I0l/lUROPkeFYN2/eGEu7TTL5LvF4JEwWs1FxVqYLdhl1buOkQaPXLqfZXbP5SRV+pQy/3ZYSyFHrD6E3VrHNipAdiCRSEQknmhPh/7c+Ct63XC3lkPlqQ78lw47xC8da36zE/7o17L+f+Co2+G3ieUPm77I3V02ie/gRl/1aGZm9Nh6KUw8fE6G6tepzRrCz95t/S6xEYw7mf6zMdaFPwJbMGTMWuT1PZw3WfRybsaNBhRaJmdzqmJn5Pw8gkqAH1O0hbY149oZTUopIIcfbV2fo29EUb8WCvBy6fqXIbpfwn/IpNISIJWACf9tvzZm+Ux9kb1NTdrEz76p8+Qi9RiCPapp3bQYXW7N2lQrf/9Wiu9XieU3b/DILjuC5j9iPYRZM7X9Wld1d8bTI9x8JussrNbI9Gii5bl60dUQHLW6uSl7rUwM7g6JDVRYVv5TtmO8d4n6kgn/z63G1C6c98PoLZlEfQ6UePaHMiBaz/V4ZbBDJmks8bnPKYW6My/acswnJg1SoZbF2qn8tovdVjy+F9pxMzDoBOyThnxY21M9aygvyna9Dpen9Pq/nfrGeLqOFZ3TRk9erRLT69m3JnMBRo5cvsKbY0dyFFT//Q/V+y+qO88OkKtiAM7teV57aeRI0NGkw1z1A3W91T9s3/6W/jld9EdPRItHhulYtG6N8x5FCY+oRVZOAoPwX9OVxFp2gZumHVYDD80Gg4issA5N7qidA3GUgC0M/WGmdpSXfWxzgfoe3j48aJOWjed5IPT0UOxEATQ+P+n/0XjGWXM1o7b+igIoC6k797XvoGjrossCKAWwEUvaN/A+DtNEIzDlgbT0RwgMUn/lNd/2XAEAbzOZqduqMr68KPFmOvVQsvfW/FQ1MOZziNUELodHTn8QzCtesDkD8r2IxjGYUTDshQaMkMu0tbqaX+MdUnUN37OwxquIdzM5/rCkefqPIvT/1z5fhXDOMxpWH0KhmEYDZTK9ik0PPeRYRiGERETBcMwDCNAVEVBRM4QkVUiskZE7ion3SQRcSJSoWljGIZhRI+oiYKIJAJPAGcCA4HLRGRgmHTNgZ8DFYSUNAzDMKJNNC2FMcAa59w651w+8DowMUy6PwAPEOG1n4ZhGEbdEU1R6AIEB/HP9NYFEJERQDfn3AflZSQiPxWRdBFJz87Orv2SGoZhGEB0RSFcoJbA+FcRSQAeBsp5a7e3k3NTnHOjnXOj27VrV4tFNAzDMIKJpihkAt2CfncFtgT9bg4MBmaISAZwNPCedTYbhmHEjmiKwrdAPxHpJSKNgUuBQJB+51yuc66tc66nc64nMA84zzlnM9MMwzBiRNREwTlXCNwCfAJ8B7zhnFshIveLyHnROq5hGIZRfaIa+8g59xHwUci630dIe2I0y2IYhmFUjM1oNgzDMAKYKBiGYRgBTBQMwzCMACYKhmEYRgATBcMwDCOAiYJhGIYRwETBMAzDCGCiYBiGYQQwUTAMwzACmCgYhmEYAUwUDMMwjAAmCoZhGEYAEwXDMAwjgImCYRiGEcBEwTAMwwhgomAYhmEEMFEwDMMwApgoGIZhGAFMFAzDMIwAJgqGYRhGABMFwzAMI4CJgmEYhhHARMEwDMMIYKJgGIZhBDBRMAzDMAKYKBiGYRgBTBQMwzCMACYKhmEYRgATBcMwDCOAiYJhGIYRwETBMAzDCGCiYBiGYQQwUTAMwzACmCgYhmEYAaIqCiJyhoisEpE1InJXmO2/FJGVIrJURL4QkR7RLI9hGIZRPlETBRFJBJ4AzgQGApeJyMCQZIuA0c65ocCbwAPRKo9hGIZRMdG0FMYAa5xz65xz+cDrwMTgBM656c65A97PeUDXKJbHMAzDqIBoikIXYFPQ70xvXSSuBT4Ot0FEfioi6SKSnp2dXYtFNAzDMIKJpihImHUubEKRHwOjgb+H2+6cm+KcG+2cG92uXbtaLKJhGIYRTKMo5p0JdAv63RXYEppIRE4B7gZOcM4dimJ5DMMwjAqIpqXwLdBPRHqJSGPgUuC94AQiMgL4F3Cec257FMtiGIZhVIKoiYJzrhC4BfgE+A54wzm3QkTuF5HzvGR/B1KBaSKyWETei5CdYRiGUQdE032Ec+4j4KOQdb8PWj4lmsc3DMMwqkZURaGuKCgoIDMzk7y8vFgXxagjUlJS6Nq1K0lJSbEuimHEFXEhCpmZmTRv3pyePXsiEm7QkxFPOOfIyckhMzOTXr16xbo4hhFXxEXso7y8PNq0aWOC0EAQEdq0aWOWoWFEgbgQBcAEoYFh99swokPciIJhGIZRc0wUaonExESGDx8e+Pz1r38tN/2MGTOYM2dOHZUuNvTs2ZMdO3YAcMwxx8S4NIZhVIa46Gg+HGjSpAmLFy+udPoZM2aQmpoatrIsLCykUaP4ujXxLoCGES/EV80D3Pf+ClZu2VOreQ7s3IJ7zh1UrX179uzJVVddxfvvv09BQQHTpk0jJSWFp59+msTERF5++WUee+wxnn32WVq3bs2iRYsYOXIkd999N9dccw3r1q2jadOmTJkyhaFDh3Lvvfeydu1aNm/ezKZNm/jVr37F9ddfz5VXXsmkSZOYOFED0V5xxRVccsklnHfeeYGyZGVlcckll7Bnzx4KCwt56qmnOP7447npppv49ttvOXjwIJMmTeK+++4LlP3yyy9n+vTpFBQUMGXKFH7961+zZs0a7rzzTm688UZmzJjB73//e9q0acOqVasYP348Tz75JAkJpY3Q1NRU9u3bx4wZM7j33ntp27Yty5cvZ9SoUbz88suICB999BG//OUvadu2LSNHjmTdunV88MEH1bxrhmFUB3Mf1RIHDx4s5T6aOnVqYFvbtm1ZuHAhN910Ew8++CA9e/bkxhtv5LbbbmPx4sUcf/zxAKxevZrPP/+cf/zjH9xzzz2MGDGCpUuX8uc//5mf/OQngfyWLl3Khx9+yNy5c7n//vvZsmUL1113Hc899xwAubm5zJkzh7POOqtUGV999VVOP/10Fi9ezJIlSxg+fDgAf/rTn0hPT2fp0qXMnDmTpUuXBvbp1q0bc+fO5fjjj2fy5Mm8+eabzJs3j9//PjAHkfnz5/OPf/yDZcuWsXbtWt5+++1yr9WiRYt45JFHWLlyJevWrePrr78mLy+PG264gY8//pjZs2dj0XANIzbEnaVQ3RZ9TSnPfXTBBRcAMGrUqHIrzIsuuojExEQAZs+ezVtvvQXASSedRE5ODrm5uQBMnDiRJk2a0KRJEyZMmMD8+fM5//zzufnmm9m+fTtvv/02F154YRkX1FFHHcU111xDQUEB559/fkAU3njjDaZMmUJhYSFZWVmsXLmSoUOHAgQsjSFDhrBv3z6aN29O8+bNSUlJYffu3QCMGTOG3r17A3DZZZcxe/ZsJk2aFPE8x4wZQ9eu+uqM4cOHk5GRQWpqKr179w7MO7jsssuYMmVKxDwMw4gOZinUAcnJyYB2RhcWFkZM16xZs8Cyc2WjjPvDMEOHY/q/r7zySl555RWee+45rr766jL7jx8/nlmzZtGlSxeuvPJKXnzxRdavX8+DDz7IF198wdKlSzn77LNLjf/3y56QkBBY9n/75xKpPJEIzse/JuHO1zCMusdEIUY0b96cvXv3Rtw+fvx4XnnlFUA7pdu2bUuLFi0AePfdd8nLyyMnJ4cZM2Zw1FFHATB58mQeeeQRAAYNKmsxbdiwgfbt23P99ddz7bXXsnDhQvbs2UOzZs1IS0tj27ZtfPxx2Pcclcv8+fNZv349xcXFTJ06leOOO67KeQwYMIB169aRkZEBUMr9ZhhG3RF37qNY4fcp+JxxxhnlDks999xzmTRpEu+++y6PPfZYme333nsvV199NUOHDqVp06a88MILgW1jxozh7LPPZuPGjfzud7+jc+fOAHTo0IEjjzyS888/P+wxZ8yYwd///neSkpJITU3lxRdfpFevXowYMYJBgwbRu3dvjj322Cqf+7hx47jrrrtYtmwZ48eP50c/+lGV82jSpAlPPvkkZ5xxBm3btmXMmDFVzsMwjJoj9c1sHz16tEtPTy+17rvvvuPII4+MUYnqlnvvvZfU1FTuuOOOMtsOHDjAkCFDWLhwIWlpaXVSnhkzZvDggw/Wyiihffv2kZqainOOm2++mX79+nHbbbdFTN+Q7rth1BQRWeCcG11ROnMfxQmff/45AwYM4NZbb60zQahtnnnmGYYPH86gQYPIzc3lhhtuiHWRDKPBYZaCUW+x+24YlccsBcMwDKPKmCgYhmEYAUwUDMMwjAAmCoZhGEYAE4Vawg+dPXjwYM4999xACIiakpGRweDBg2slr1gxY8YMzjnnHADee++9CsOKG4YRO0wUagk/9tHy5ctp3bo1TzzxRKyLdFhy3nnncdddd8W6GIZhRCD+ZjR/fBdsXVa7eXYcAmdWvnU7bty4QKTRffv2MXHiRHbt2kVBQQF//OMfmThxIhkZGZx55pkcd9xxzJkzhy5duvDuu+/SpEkTFixYwDXXXEPTpk1LhYzIy8vjpptuIj09nUaNGvHQQw8xYcIEnn/+ed555x2KiopYvnw5t99+O/n5+bz00kskJyfz0Ucf0bp161JlnDZtGvfddx+JiYmkpaUxa9YsMjIyuPLKK9m/fz8Ajz/+OMcccwwzZszgnnvuoUOHDixevJgLLriAIUOG8M9//pODBw/yzjvv0KdPHyZPnkxKSgorVqxg27ZtPPTQQwELwef5558nPT2dxx9/nMmTJ9OiRQvS09PZunUrDzzwAJMmTaK4uJhbbrmFmTNn0qtXL4qLi7nmmmvKDbJnGEbtYJZCLVNUVMQXX3wRiC6akpLCf//7XxYuXMj06dO5/fbbA8HffvjhB26++WZWrFhBy5YtA1FRr776ah599FHmzp1bKm/f+li2bBmvvfYaV111VSB43fLly3n11VeZP38+d999N02bNmXRokWMGzeOF198sUw577//fj755BOWLFnCe++9B0D79u357LPPWLhwIVOnTuXnP/95IP2SJUv45z//ybJly3jppZdYvXo18+fP57rrrisVpiMjI4OZM2fy4YcfcuONN5YKrheOrKwsZs+ezQcffBCwIN5++20yMjJYtmwZ//73v8tcB8Mwokf8WQpVaNHXJn7so4yMDEaNGsWpp54KaLTT3/zmN8yaNYuEhAQ2b97Mtm3bAOjVq1cgXtKoUaPIyMggNzeX3bt3c8IJJwAa+dQPUjd79mxuvfVWQAPI9ejRg9WrVwMwYcKEQFjrtLQ0zj33XEBDXge/H8Hn2GOPZfLkyVx88cWB0N4FBQXccsstLF68mMTExEDeoGG3O3XqBECfPn047bTTAvlPnz49kO7iiy8mISGBfv360bt3b77//vtyr9v5559PQkICAwcODFyX2bNnc9FFF5GQkEDHjh2ZMGFC5W6CYRg1xiyFWsLvU9iwYQP5+fmBVv0rr7xCdnY2CxYsYPHixXTo0CHQeo4UQjpS6OnyZp+HhrUODnkdLlz3008/zR//+Ec2bdrE8OHDycnJ4eGHH6ZDhw4sWbKE9PR08vPzq5x/TcJo++dX32bZG0Y8YaJQy6SlpfHoo4/y4IMPUlBQQG5uLu3btycpKYnp06ezYcOGcvdv2bIlaWlpzJ49GyAQPhtKh9NevXo1GzdupH///tUq59q1axk7diz3338/bdu2ZdOmTZUYXW0AAAtUSURBVOTm5tKpUycSEhJ46aWXKCoqqnK+06ZNo7i4mLVr17Ju3bpqle+4447jrbfeori4mG3btjFjxowq52EYRvWIP/fRYcCIESMYNmwYr7/+OldccQXnnnsuo0ePZvjw4QwYMKDC/Z977rlAR/Ppp58eWP+zn/2MG2+8kSFDhtCoUSOef/75Ui3tqnDnnXfyww8/4Jzj5JNPZtiwYfzsZz/jwgsvZNq0aUyYMKHUS38qS//+/TnhhBPYtm0bTz/9NCkpKVXO48ILL+SLL75g8ODBHHHEEYwdO7beBvkzjPqGBcQzao3Jkydzzjnn1MooIT+Mdk5ODmPGjOHrr7+mY8eOpdLYfTeMylPZgHhmKRiHJeeccw67d+8mPz+f3/3ud2UEwTCM6GCiYNQazz//fK3lZf0IhhEb4qajub65wYyaYffbMKJDXIhCSkoKOTk5VlE0EJxz5OTkVKsT2zCM8okL91HXrl3JzMwkOzs71kUx6oiUlBS6du0a62IYRtwRF6KQlJREr169Yl0MwzCMek9U3UcicoaIrBKRNSJSJjSmiCSLyFRv+zci0jOa5TEMwzDKJ2qiICKJwBPAmcBA4DIRGRiS7Fpgl3OuL/Aw8LdolccwDMOomGhaCmOANc65dc65fOB1YGJImonAC97ym8DJUlGwHMMwDCNqRLNPoQuwKeh3JjA2UhrnXKGI5AJtgB3BiUTkp8BPvZ/7RGRVNcvUNjTvBoCdc8PAzrlhUJNz7lGZRNEUhXAt/tAxo5VJg3NuCjClxgUSSa/MNO94ws65YWDn3DCoi3OOpvsoE+gW9LsrsCVSGhFpBKQBO6NYJsMwDKMcoikK3wL9RKSXiDQGLgXeC0nzHnCVtzwJ+NLZDDTDMIyYETX3kddHcAvwCZAI/Mc5t0JE7gfSnXPvAc8CL4nIGtRCuDRa5fGosQuqHmLn3DCwc24YRP2c613obMMwDCN6xEXsI8MwDKN2MFEwDMMwAjQYUago5EY8ICLdRGS6iHwnIitE5Bfe+tYi8pmI/OB9t4p1WWsTEUkUkUUi8oH3u5cXNuUHL4xK41iXsTYRkZYi8qaIfO/d63EN4B7f5j3Ty0XkNRFJibf7LCL/EZHtIrI8aF3Y+yrKo159tlRERtZWORqEKFQy5EY8UAjc7pw7EjgauNk7z7uAL5xz/YAvvN/xxC+A74J+/w142DvfXWg4lXjin8D/nHMDgGHoucftPRaRLsDPgdHOucHowJVLib/7/DxwRsi6SPf1TKCf9/kp8FRtFaJBiAKVC7lR73HOZTnnFnrLe9HKogulw4m8AJwfmxLWPiLSFTgb+Lf3W4CT0LApEH/n2wIYj47cwzmX75zbTRzfY49GQBNvPlNTIIs4u8/OuVmUnacV6b5OBF50yjygpYh0qo1yNBRRCBdyo0uMylIneBFnRwDfAB2cc1mgwgG0j13Jap1HgF8Bxd7vNsBu51yh9zve7nVvIBt4znOZ/VtEmhHH99g5txl4ENiIikEusID4vs8+ke5r1Oq0hiIKlQqnES+ISCrwFvB/zrk9sS5PtBCRc4DtzrkFwavDJI2ne90IGAk85ZwbAewnjlxF4fD86BOBXkBnoBnqPgklnu5zRUTtOW8oolCZkBtxgYgkoYLwinPubW/1Nt+09L63x6p8tcyxwHkikoG6BE9CLYeWnpsB4u9eZwKZzrlvvN9voiIRr/cY4BRgvXMu2zlXALwNHEN832efSPc1anVaQxGFyoTcqPd4/vRnge+ccw8FbQoOJ3IV8G5dly0aOOd+7Zzr6pzrid7TL51zVwDT0bApEEfnC+Cc2wpsEpH+3qqTgZXE6T322AgcLSJNvWfcP+e4vc9BRLqv7wE/8UYhHQ3k+m6mmtJgZjSLyFloK9IPufGnGBep1hGR44CvgGWU+Nh/g/YrvAF0R/9gFznn4irwoIicCNzhnDtHRHqjlkNrYBHwY+fcoViWrzYRkeFox3pjYB1wNdrAi9t7LCL3AZegI+wWAdehPvS4uc8i8hpwIhoeextwD/AOYe6rJ46Po6OVDgBXO+fSa6UcDUUUDMMwjIppKO4jwzAMoxKYKBiGYRgBTBQMwzCMACYKhmEYRgATBcMwDCOAiYJRJUTEicg/gn7fISL31lLez4vIpIpT1vg4F3nRRadH+1hBx+wsIm9WnLLCfP5PRJrWRpmqceyewRE8q7jf5dEok1H7mCgYVeUQcIGItI11QYLxIuFWlmuBnznnJkSrPMGISCPn3BbnXG0I3v+hAeHqEz0BE4V6gomCUVUK0ffE3ha6IbSlLyL7vO8TRWSmiLwhIqtF5K8icoWIzBeRZSLSJyibU0TkKy/dOd7+iSLydxH51osdf0NQvtNF5FV0wl5oeS7z8l8uIn/z1v0eOA54WkT+HpJeRORxEVkpIh+KyEf++YhIhi+EIjJaRGZ4y81E4+B/6wWom+itnywi00TkfeDT4FZ2OefTSURmichir8zHh5Tv52jsn+m+lRPuHMNch997x1ouIlO8iU+IyAwR+Zt3H1b7x/PK+pWILPQ+x4TJ8ytvEp3/+2sRGSoiJ3jlX+xdj+bAX4HjvXVlnhvjMMM5Zx/7VPoD7ANaABlAGnAHcK+37XlgUnBa7/tEYDfQCUgGNgP3edt+ATwStP//0MZKPzS+SwoaL/63XppkIB0NjnYiGhCuV5hydkZngLZDg8h9CZzvbZuBxuYP3ecC4DN01ntnr8yTvG0ZQFtveTQww1v+MzqTFqAlsBoN2DbZK39rb1tPYLm3HOl8bgfu9tYnAs3DlDG4HBHPMWSf1kHLLwHnBl2Hf3jLZwGfe8tNgRRvuR+QHuYcrgq6b0cEpXkfONZbTvXKdSLwQayfXftU7mOWglFlnEZefRF98Ull+dbp+x4OAWuBT731y9DKxucN51yxc+4HNITDAOA0NM7LYjRkRxu0sgKY75xbH+Z4R6EVd7bT8MqvoO8hKI/xwGvOuSLn3Ba0kq2I04C7vLLNQEWsu7ftMxc+1ESk8/kWuNrroxni9J0Y5VHZc5wg+oayZWjQwEFB2/ygiQsouQ9JwDNe+mnoi6lCmQacIxqA8RpU0AG+Bh7yrJqWriS0tVFPaFRxEsMIyyPAQuC5oHWFeC5Jz0UR/HrE4Jg0xUG/iyn9HIbGXXFomOBbnXOfBG8QjXe0P0L5woUWrgyR4r4Ezg2t+IOPc6FzblVI2cZWULYy5+PtNx59adBLIvJ359yL5ZS1wnMUkRTgSdQy2uQJTnD5/ftQRMl9uA2NvTMMPee80HydcwdE5DM0pPXFqPWEc+6vIvIhannME5FTKiqjcXhhloJRLbwW8BuUfgViBjDKW56ItjirykUikuD1M/QGVgGfADd5rVJE5AjRF8uUxzfACSLS1uuEvgyYWcE+s4BLPZ9/JyC4IzqDknO7MGj9J8CtQX76ERWeYYTzEZEe6PshnkGj3YZ77+5eoHkVztEXgB2i79moTGd3GpDlnCsGrkRdWeH4N/AoagXu9M6lj3NumXPub6hbbEBImY3DHBMFoyb8A43o6PMMWknNB8prKZfHKrRi+xi40TmXh1Y+K4GFXmftv6jAynUaRvjXaHjlJcBC51xFoZX/C/yAurSeonQFex/wTxH5Cm1V+/wBFb+lXtn+UIlzjHQ+JwKLRWQRKjz/DLPvFOBjEZlemXN0+qrOZ7xzegd1UVXEk8BVIjIP7S8Iex+dvtxoD6Wtxf/zOrSXAAfR+7gUKBSRJdbRfPhjUVINIwIi8jzaQVrj+QXxiIh0RvtRBnhWhREHmKVgGEaVEZGfoO6ru00Q4guzFAzDMIwAZikYhmEYAUwUDMMwjAAmCoZhGEYAEwXDMAwjgImCYRiG8f8HBwB8xD138XogWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ni.plot_active_learning_time_series_overlapping(\n",
    "    attribute='f1', label='nmap',\n",
    "    learner1='LogisticRegression', sampling1='entropy',\n",
    "    learner2='LogisticRegression', sampling2='random',\n",
    "    title='Logistic regression for nmap attack',\n",
    "    ylim=[0, 1], ylabel='F1',\n",
    "    legend=['Entropy sampling', 'Random sampling']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ensemble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EE08wfvx4fvazn3H//fcDMGPGDKZMmcLq1asZNGgQJ5xwQo04f/Ob39CxY0emTZvG+++/z7Bhw/jMZz5T48z/6aef5re//S1PPfUUEcFBBx3E4Ycfzk033cTDDz/c4FnxO++8w9y5cxk+fHg27Q9/+ANPPPEEAN/85jc599xzG/nlF/bwww9zyimnNFjuoosu4tJLLwXgi1/8Ig8++CAnnXQSAOvXr2fq1KlMmDCBn/70pzz66KPceOON7LTTTsyYMYMZM2YwePDgRsVzxBFHUFFRQZs2bXjqqae2fMMKKKukMHPRStq2qqB35/alDsVsq9TXfHTaaacBcOCBB3LfffcByXMVvvOd73D22Wdz2mmn0b17dyZOnMjEiRMZNGgQkHSdMXfuXHr27Env3r3p378/APvvvz9HHnkkkujfvz/V1dXZuk4++WTatm1L27ZtOeKII5g6dWqNZDVx4kRmzJjBvffeC8DKlSuZO3dujaTwxBNPcOqpp9KuXbss/scffzyLqy6PP/44AwYMYM6cOVxyySXsscdH3dY0VfPREUccwZtvvsnuu+/OFVdc0WD5SZMm8fOf/5w1a9bw9ttvs//++2dJIX+/5L7Dxx57LOvddsCAAQwYMKBRcRWz+aisksKLi9+lb9edqWjhi8zWRBo4oy+FNm3aAMnF6PXr1wNwySWXcMIJJzBhwgQOPvhgHn30USKCUaNG8dWvfrXG56urq7NlALRo0SIbb9GiRbZMYJMbNmqPRwS/+tWvOOaYY+qMN9fEsrly1xRefvllDj30UE499dQaCakpTJo0iXbt2nHOOedw6aWXcs0119RZdt26dXz9619n+vTp9OjRg9GjR7Nu3bpsfqH9Apt+Z6VWNtcUNm4Md29hZeuVV16hf//+/PCHP6SqqoqXXnqJY445httuuy3rXG/RokUsXbp0s5b7wAMPsG7dOpYvX87kyZMZMmRIjfnHHHMMN954Y9at98svv8zq1atrlBk+fDj3338/a9asYfXq1fz5z3/msMMOa3QMe++9N6NGjeKqq66qt9z111+/RbWHtm3bcu2113L77bfz9ttvA3DkkUduco0hlwA6d+7MqlWrstpRfYYPH87vf/97IOnIcMaMGZsdX1Mrm5pC9fLVrP5gg7u3sGah9jWFY489tt7bUq+99lomTZpERUUFffv25bjjjqNNmzbMnj2bQw45BID27dvzu9/9joqKikbHMXToUE444QTmz5/PT37yE7p27Vqjeen888+nurqawYMHExF06dIlux6RM3jwYM455xyGDh2afaahpqPaLrzwQsaMGcNrr71WZ5mXXnopuzuptnHjxtWIa8qUKTXm77nnnpx11lmMHTuWH/3oR8ybN49dd921RplOnTrxla98hf79+9OrV69NEmQhX/va1zj33HMZMGAAAwcOzL6DUtKWVt1KpaqqKgrdx9uQvzy/mG/c9Sz/d/Gh/jWzbZXZs2ez3377lToM20wnnngi9913H61bt96q5cycOZPbbrut3qakUiv0Nyrp6YioauizZVNTWPbe+7Rv05I+u3codShmVgIPPvhgkyynX79+23VC2FplkxT+/dDefPlTvXyR2cysHmVzoRlwQrAms6M1u1r52Nq/zbJKCmZNobKykuXLlzsx2HYnIli+fDmVlZVbvIyyaT4yayrdu3dn4cKFLFu2rNShmG2isrKS7t27b/HnnRTMNlOrVq0KdtBm1hy4+cjMzDJFTQqSjpU0R9I8SZcUmN9T0iRJz0qaIen4YsZjZmb1K1pSkFQBjAWOA/oCZ0nqW6vYj4F7ImIQcCZwQ7HiMTOzhhWzpjAUmBcRr0bEB8DdwMm1ygSQ64yoI7C4iPGYmVkDipkUugEL8sYXptPyjQa+IGkhMAH4RqEFSbpA0nRJ033Hh5lZ8RQzKRT6pVjtG7vPAsZFRHfgeOAOSZvEFBE3R0RVRFR16dKlCKGamRkUNyksBHrkjXdn0+ah84B7ACLiX0AlUJwnR5iZWYOKmRSmAX0k9ZbUmuRC8vhaZeYDRwJI2o8kKbh9yMysRIqWFCJiPXAR8Agwm+Quo1mSLpc0Mi32XeArkp4H7gLOCfcdYGZWMkX9RXNETCC5gJw/7dK84ReBwk+9MDOzbc6/aDYzs4yTgpmZZZwUzMws46RgZmYZJwUzM8s4KZiZWcZJwczMMk4KZmaWcVIwM7OMk4KZmWWcFMzMLOOkYGZmGScFMzPLOCmYmVnGScHMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzjpGBmZhknBTMzyzgpmJlZxknBzMwyTgpmZpZxUjAzs4yTgpmZZZwUzMws46RgZmYZJwUzM8s4KZiZWcZJwczMMk4KZmaWcVIwM7OMk4KZmWWKmhQkHStpjqR5ki6po8znJL0oaZakO4sZj5mZ1a9lsRYsqQIYCxwNLASmSRofES/mlekDjAKGRcQ7knYvVjxmZtawYtYUhgLzIuLViPgAuBs4uVaZrwBjI+IdgIhYWsR4zMysAcVMCt2ABXnjC9Np+fYG9pb0T0lTJB1baEGSLpA0XdL0ZcuWFSlcMzMrZlJQgWlRa7wl0AcYAZwF3Cqp0yYfirg5IqoioqpLly5NHqiZmSWKmRQWAj3yxrsDiwuUeSAiPoyI14A5JEnCzMxKoJhJYRrQR1JvSa2BM4HxtcrcDxwBIKkzSXPSq0WMyczM6lG0pBAR64GLgEeA2cA9ETFL0uWSRqbFHgGWS3oRmAR8PyKWFysmMzOrnyJqN/Nv36qqqmL69OmlDsPMbIci6emIqGqonH/RbGZmGScFMzPLOCmYmVnGScHMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzjpGBmZhknBTMzyzgpmJlZxknBzMwyTgpmZpbZ4qQgad+mDMTMzEpva2oKE5ssCjMz2y60rG+mpOvqmgVs8ixlMzPbsdWbFIBzge8C7xeYd1bTh2NmZqXUUFKYBsyMiCdrz5A0uigRmZlZyTSUFE4H1hWaERG9mz4cMzMrpYYuNLePiDXbJBIzMyu5hpLC/bkBSX8qcixmZlZiDSUF5Q1/vJiBmJlZ6TWUFKKOYTMza4YautB8gKR3SWoMbdNh0vGIiJ2LGp2ZmW1T9SaFiKjYVoGYmVnpuUM8MzPLOCmYmVnGScHMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzjpGBmZhknBTMzyzgpmJlZpqhJQdKxkuZImifpknrKnS4pJFUVMx4zM6tf0ZKCpApgLHAc0Bc4S1LfAuU6ABcDTxUrFjMza5xi1hSGAvMi4tWI+AC4Gzi5QLn/BH5OHY/9NDOzbaeYSaEbsCBvfGE6LSNpENAjIh6sb0GSLpA0XdL0ZcuWNX2kZmYGFDcpqMC07EE9kloAvwC+29CCIuLmiKiKiKouXbo0YYhmZpavmElhIdAjb7w7sDhvvAPQD5gsqRo4GBjvi81mZqVTzKQwDegjqbek1sCZwPjczIhYGRGdI6JXRPQCpgAjI2J6EWMyM7N6NPQ4zi0WEeslXQQ8AlQAt0XELEmXA9MjYnz9S9hG3l8Fcx6C/qeD8lq8PlwL025N3s3Mtgd9joaug4q6iqIlBYCImABMqDXt0jrKjihmLHV64R548Nuw5wDoss9H0+c9ChN/XJKQzMwK2mm3HTsp7BDefu2j9/ykkJv+/VehbadtH5eZ2SYK3b/TtJwUVrxe8z1/epuO0G63bR+TmVmJuO+jd16v+Z4/fZee2z4eM7MSclKor6awS69tHo6ZWSmVd1JYtxLWvpMMv1P90fQIWDEfOu1VkrDMzEqlvJNCrsmo/R7JcKQ/uF71Jqxf55qCmZWd8k4KuSaj3sPhg/fyag3pdNcUzKzMlHdSeCcvKcBHTUi5ZLGLk4KZlZfyTgorXoc2O8OeB3w0Dnk1Bd99ZGblpbyTwjuvJ01EuRpBLhmsqIb2H4NWbUsWmplZKZR3UljxepIQKjtCZaeaNQVfTzCzMlS+SaH2bae77JVXU3jd1xPMrCyVb1JYvQw+XPPRwX+XXkky2PAhrFzkmoKZlaXyTQq1bzvttFdSc1gxH2KDf6NgZmWpfJNC7dtOd9kLNnwAC6fVnG5mVkbKNynkfpOQu+20U6/k/dV/pONOCmZWfso3Kax4Hdp1gdbtkvFczeC1x0AVsHO30sVmZlYi5ZsUat922rFH8v7uQujYHSr8qAkzKz9lnBSqa143aFUJHfZMhn09wczKVHkmhQ3rYeXCTa8b5N+JZGZWhsozKby7KL3ttNbBP/9OJDOzMlSeSWFFHV1jZzWFXts0HDOz7UV5JoXc7aib1BR61Xw3Mysz5XmLzaJnoE3HTWsKfUcmj+jsdmBp4jIzK7HyTArzp0CPodCioub0Nh3gkK+XJiYzs+1A+TUfrXkbls2GngeXOhIzs+1O+SWFBVOT956HlDYOM7PtUPklhfn/ghatoNvgUkdiZrbdKcOkMAW6DvKjNs3MCiivpPDhOlj8jK8nmJnVobySwuJnk2cm+HqCmVlB5ZUU5v8ree9xUGnjMDPbTpVZUpgCnfeBdruVOhIzs+1S+SSFjRthwRRfTzAzq0dRk4KkYyXNkTRP0iUF5n9H0ouSZkj6m6TidU+67KWkCwtfTzAzq1PRkoKkCmAscBzQFzhLUt9axZ4FqiJiAHAv8PNixZNdT3BNwcysTsWsKQwF5kXEqxHxAXA3cHJ+gYiYFBFr0tEpQPeiRdO5Dww53z2gmpnVo5gd4nUDFuSNLwTqu+3nPOChQjMkXQBcANCzZ88ti6b38ORlZmZ1KmZNQQWmRcGC0heAKuDqQvMj4uaIqIqIqi5dujRhiGZmlq+YNYWFQI+88e7A4tqFJB0F/Ag4PCLeL2I8ZmbWgGLWFKYBfST1ltQaOBMYn19A0iDg18DIiFhaxFjMzKwRipYUImI9cBHwCDAbuCciZkm6XNLItNjVQHvgj5KekzS+jsWZmdk2UNQnr0XEBGBCrWmX5g0fVcz1m5nZ5imfXzSbmVmDnBTMzCzjpGBmZhknBTMzyzgpmJlZxknBzMwyTgpmZpZxUjAzs4yTgpmZZZwUzMws46RgZmYZJwUzM8s4KZiZWcZJwczMMk4KZmaWcVIwM7OMk4KZmWWcFMzMLOOkYGZmGScFMzPLOCmYmVnGScHMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzjpGBmZhknBTMzyzgpmJlZxknBzMwyTgpmZpZxUjAzs4yTgpmZZZwUzMws46RgZmaZoiYFScdKmiNpnqRLCsxvI+kP6fynJPUqZjxmZla/oiUFSRXAWOA4oC9wlqS+tYqdB7wTEZ8EfgFcVax4zMysYcWsKQwF5kXEqxHxAXA3cHKtMicD/5sO3wscKUlFjMnMzOrRsojL7gYsyBtfCBxUV5mIWC9pJbAb8FZ+IUkXABeko6skzdnCmDrXXnYZ8DaXB29zediabd6rMYWKmRQKnfHHFpQhIm4Gbt7qgKTpEVG1tcvZkXiby4O3uTxsi20uZvPRQqBH3nh3YHFdZSS1BDoCbxcxJjMzq0cxk8I0oI+k3pJaA2cC42uVGQ98OR0+Hfh7RGxSUzAzs22jaM1H6TWCi4BHgArgtoiYJelyYHpEjAd+A9whaR5JDeHMYsWT2uomqB2Qt7k8eJvLQ9G3WT4xNzOzHP+i2czMMk4KZmaWKZuk0FCXG82BpB6SJkmaLWmWpG+m03eV9FdJc9P3XUoda1OSVCHpWUkPpuO9025T5qbdqLQudYxNSVInSfdKeind14eUwT7+dvo3PVPSXZIqm9t+lnSbpKWSZuZNK7hflbguPZ7NkDS4qeIoi6TQyC43moP1wHcjYj/gYOD/pdt5CfC3iOgD/C0db06+CczOG78K+EW6ve+QdKfSnPwSeDgi9gUOINn2ZruPJXUDLgaqIqIfyY0rZ9L89vM44Nha0+rar8cBfdLXBcCNTRVEWSQFGtflxg4vIt6IiGfS4fdIDhbdqNmdyP8Cp5QmwqYnqTtwAnBrOi7g0yTdpkDz296dgeEkd+4RER9ExAqa8T5OtQTapr9n2gl4g2a2nyPiMTb9nVZd+/Vk4PZITAE6SdqzKeIol6RQqMuNbiWKZZtIe5z0u29+AAAHJElEQVQdBDwFfCwi3oAkcQC7ly6yJnct8ANgYzq+G7AiItan481tX38cWAb8Nm0yu1VSO5rxPo6IRcAYYD5JMlgJPE3z3s85de3Xoh3TyiUpNKo7jeZCUnvgT8C3IuLdUsdTLJJOBJZGxNP5kwsUbU77uiUwGLgxIgYBq2lGTUWFpO3oJwO9ga5AO5Lmk9qa035uSNH+zsslKTSmy41mQVIrkoTw+4i4L538Zq5qmb4vLVV8TWwYMFJSNUmT4KdJag6d0mYGaH77eiGwMCKeSsfvJUkSzXUfAxwFvBYRyyLiQ+A+4FM07/2cU9d+LdoxrVySQmO63Njhpe3pvwFmR8Q1ebPyuxP5MvDAto6tGCJiVER0j4heJPv07xFxNjCJpNsUaEbbCxARS4AFkvZJJx0JvEgz3cep+cDBknZK/8Zz29xs93OeuvbreOBL6V1IBwMrc81MW6tsftEs6XiSs8hclxv/VeKQmpykQ4HHgRf4qI39P0iuK9wD9CT5B/tsRDSrjgcljQC+FxEnSvo4Sc1hV+BZ4AsR8X4p42tKkgaSXFhvDbwKnEtygtds97GknwJnkNxh9yxwPkkberPZz5LuAkaQdI/9JnAZcD8F9muaHK8nuVtpDXBuRExvkjjKJSmYmVnDyqX5yMzMGsFJwczMMk4KZmaWcVIwM7OMk4KZmWWcFGyzSApJ/5M3/j1Jo5to2eMknd5wya1ez2fT3kUnFXtdeevsKunehks2uJxvSdqpKWLagnX3yu/BczM/9/lixGRNz0nBNtf7wGmSOpc6kHxpT7iNdR7w9Yg4oljx5JPUMiIWR0RTJLxvkXQItyPpBTgp7CCcFGxzrSd5Tuy3a8+ofaYvaVX6PkLSPyTdI+llSVdKOlvSVEkvSPpE3mKOkvR4Wu7E9PMVkq6WNC3tO/6recudJOlOkh/s1Y7nrHT5MyVdlU67FDgUuEnS1bXKS9L1kl6U9H+SJuS2R1J1LhFKqpI0OR1up6Qf/GlpB3Unp9PPkfRHSX8BJuafZdezPXtKekzSc2nMh9WK72KSvn8m5Wo5hbaxwPdwabqumZJuTn/4hKTJkq5K98PLufWlsT4u6Zn09akCy3w8/RFdbvyfkgZIOjyN/7n0++gAXAkclk7b5O/GtjMR4ZdfjX4Bq4CdgWqgI/A9YHQ6bxxwen7Z9H0EsALYE2gDLAJ+ms77JnBt3ucfJjlZ6UPSv0slSX/xP07LtAGmk3SONoKkQ7jeBeLsSvIL0C4kncj9HTglnTeZpG/+2p85Dfgrya/eu6Yxn57OqwY6p8NVwOR0+Gckv6QF6AS8TNJh2zlp/Lum83oBM9Phurbnu8CP0ukVQIcCMebHUec21vrMrnnDdwAn5X0P/5MOHw88mg7vBFSmw32A6QW24ct5+23vvDJ/AYalw+3TuEYAD5b6b9evxr1cU7DNFknPq7eTPPiksaZF8ryH94FXgInp9BdIDjY590TExoiYS9KFw77AZ0j6eXmOpMuO3UgOVgBTI+K1AusbQnLgXhZJ98q/J3kOQX2GA3dFxIaIWExykG3IZ4BL0tgmkySxnum8v0bhribq2p5pwLnpNZr+kTwToz6N3cYjlDyh7AWSTgP3z5uX6zTxaT7aD62AW9LyfyR5MFVtfwROVNIB47+TJHSAfwLXpLWaTvFR19a2g2jZcBGzgq4FngF+mzdtPWmTZNpEkf94xPw+aTbmjW+k5t9h7X5XgqSb4G9ExCP5M5T0d7S6jvgKdS3cGHX1+5JtG8mBP389/xYRc2rFdlADsW2yPennhpM8NOgOSVdHxO31xNrgNkqqBG4gqRktSBNOfvy5/bCBj/bDt0n63jmAZJvX1V5uRKyR9FeSLq0/R1J7IiKulPR/JDWPKZKOaihG2764pmBbJD0Dvoeaj0CsBg5Mh08mOePcXJ+V1CK9zvBxYA7wCPC19KwUSXsrebBMfZ4CDpfUOb0IfRbwjwY+8xhwZtrmvyeQfyG6mo+27d/ypj8CfCOvnX5Qg1tYx/ZI2ovk+RC3kPR2W+i5u+8BHTZjG3MJ4C0lz9lozMXujsAbEbER+CJJU1YhtwLXkdQC30635RMR8UJEXEXSLLZvrZhtO+ekYFvjf0h6dMy5heQgNRWo70y5PnNIDmwPARdGxDqSg8+LwDPpxdpf00AtN5JuhEeRdK/8PPBMRDTUtfKfgbkkTVo3UvMA+1Pgl5IeJzmrzvlPkuQ3I43tPxuxjXVtzwjgOUnPkiSeXxb47M3AQ5ImNWYbI3lU5y3pNt1P0kTVkBuAL0uaQnK9oOB+jOThRu9Ss7b4rfSC9vPAWpL9OANYL+l5X2je/rmXVLM6SBpHcoF0q39f0BxJ6kpyHWXftFZhzYBrCma22SR9iaT56kdOCM2LawpmZpZxTcHMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzz/wGd/82P/nEUMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Mk0KVBdOgQ4+k6cTMrJFyUqjy/rTkckwzs0bMSQGg4iP4ZL47mc2s0XNSgOQsAdzJbGaNnpMCfHHlkc8UzKyRc1KAJCm0bJtchmlm1og5KcAXw1tIpY7EzKyknBQiYMF0X3lkZkaRk4KkwZJmSpotaWgNZb4haZqkNyTdU8x48lpSnoz77/4EM7PiDXMhqQy4EfgKUA5MkPR4REzLKbMjcDGwX0R8JKljseJh6sPwyv+tuXxZeiMYX3lkZlbUsY/2BGZHxJsAku4DjgWm5ZQ5C7gxIj4CiIgFRYvm88rkVpvVlTWHnY+EbfoVbddmZpuKYiaFbYF5OfPlQPVB9ncCkPQiUAZcHhHPVN+QpLOBswG6du26btH0/UbyMDOzGhWzTyHfpTxRbb4psCMwCDgFuFVSuzWeFHFzRAyIiAEdOnSo90DNzCxRzKRQDnTJme8MzM9T5rGIWBkRbwEzSZKEmZmVQDGTwgRgR0ndJTUHTgYer1bmUeAgAEntSZqT3ixiTGZmVouiJYWIqATOA54FpgMPRMQbkq6UdExa7FlgkaRpwGjgRxGxqFgxmZlZ7RRRvZl/4zZgwICYOHFiqcMwM9ukSJoUEQPqKudfNJuZWcZJwczMMk4KZmaWcVIwM7OMk4KZmWWcFMzMLOOkYGZmGScFMzPLOCmYmVnGScHMzDJOCmZmlnFSMDOzjJOCmZll1jkpSOpRn4GYmVnprc+Zwqh6i8LMzDYKTWtbKen6mlYBa9xL2czMNm21JgVgCPADYHmedafUfzhmZlZKdSWFCcDUiHip+gpJlxclIjMzK5m6ksKJwLJ8KyKie/2HY2ZmpVRXR/PmEbF0g0RiZmYlV1dSeLRqQtJfihyLmZmVWF1JQTnT2xczEDMzK726kkLUMG1mZg1QXR3Nu0r6mOSMoVU6TTofEbFFUaMzM7MNqtakEBFlGyoQMzMrPQ+IZ2ZmGScFMzPLOCmYmVnGScHMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzjpGBmZhknBTMzyxQ1KUgaLGmmpNmShtZS7kRJIWlAMeMxM7PaFS0pSCoDbgQOB3oBp0jqladcG+B84OVixWJmZoUp5pnCnsDsiHgzIlYA9wHH5in3C+A31HDbTzMz23CKmRS2BeblzJenyzKS+gNdIuLJ2jYk6WxJEyVNXLhwYf1HamZmQHGTgvIsy27UI6kJ8DvgB3VtKCJujogBETGgQ4cO9RiimZnlKmZSKAe65Mx3BubnzLcBdgHGSJoL7A087s5mM7PSKWZSmADsKKm7pObAycDjVSsjYklEtI+IbhHRDRgHHBMRE4sYk5mZ1aJoSSEiKoHzgGeB6cADEfGGpCslHVOs/ZqZ2bqr6x7N6yUingKeqrbs0hrKDipmLGZmVjf/otnMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzjpGBmZhknBTMzyzgpmJlZxknBzMwyTgpmZpZxUjAzs4yTgpmZZZwUzMws46RgZmYZJwUzM8s4KZiZWcZJwczMMk4KZmaWcVIwM7OMk4KZmWWcFMzMLOOkYGZmGScFMzPLOCmYmVnGScHMzDJOCmZmlnFSMDOzjJOCmZllnBTMzCzjpGBmZhknBTMzyzgpmJlZxknBzMwyTgpmZpYpalKQNFjSTEmzJQ3Ns/77kqZJmiLpOUnbFTMeMzOrXdGSgqQy4EbgcKAXcIqkXtWKvQoMiIi+wEPAb4oVj5mZ1a2YZwp7ArMj4s2IWAHcBxybWyAiRkfE0nR2HNC5iPGYmVkdipkUtgXm5cyXp8tqcgbwdL4Vks6WNFHSxIULF9ZjiGZmlquYSUF5lkXegtI3gQHAtfnWR8TNETEgIgZ06NChHkM0M7NcTYu47XKgS858Z2B+9UKSDgUuAQ6MiOVFjMfMzOpQzDOFCcCOkrpLag6cDDyeW0BSf+BPwDERsaCIsZiZWQGKlhQiohI4D3gWmA48EBFvSLpS0jFpsWuBzYEHJU2W9HgNmzMzsw2gmM1HRMRTwFPVll2aM31oMfdvZmZrp6hJYUNZuXIl5eXlLFu2rNShmK2hZcuWdO7cmWbNmpU6FLM6NYikUF5eTps2bejWrRtSvouezEojIli0aBHl5eV079691OGY1alBjH20bNkyttpqKycE2+hIYquttvJZrG0yGkRSAJwQbKPl96ZtShpMUjAzs/XXIPoUNgZlZWX06dOHyspKunfvzl133UW7du2YO3cuPXv2ZOedd87Kjh8/nubNm5cwWjOz/HymUE9atWrF5MmTmTp1KltuuSU33nhjtm6HHXZg8uTJ2cMJwcw2Vg3uTOGKJ95g2vyP63WbvTptwWVH9y64/D777MOUKVPqNQYzsw3BZwr1bNWqVTz33HMcc8wx2bI5c+bQr18/+vXrx7nnnlvC6MzMatfgzhTW5ht9faqoqKBfv37MnTuX3Xffna985SvZuqrmIzOzjZ3PFOpJVZ/C22+/zYoVK1brUzAz21Q4KdSztm3bcv311zNs2DBWrlxZ6nDMzNaKk0IR9O/fn1133ZX77ruv1KGYma2VBtenUCqffvrpavNPPPFENj116tQNHY6Z2TrxmYKZmWWcFMzMLOOkYGZmGScFMzPLOCmYmVnGScHMzDJOCvWkrKwsG9+oX79+XH311Rs8hssvv5xhw4atsXzu3Lnssssu9bKP66+/np49e3LqqaeutnzMmDG0bduW/v3706NHD374wx9m60aOHEmHDh2y1+Zb3/pWrfsYOXIk55133hrLu3XrRp8+fejbty8HHnggb7/9dr3UqTaDBg1i4sSJtS6viquqfi+99FLR4zIrFv9OoZ5UDXPR0P3xj3/k6aefznu/4YEDB/Lkk09SUVFB//79Of7449lvv/0AOOmkk7jhhhvWe/+jR4+mffv2XHbZZVx11VXccsst673N+lAVl9mmruElhaeHwnuv1+82t+4Dh6/bN/9u3bpx2mmn8cQTT7By5UoefPBBevTowT//+U8uuOACILld49ixY2nTpg3XXnstDzzwAMuXL+f444/niiuuYO7cuQwePJj999+fcePGseuuuzJkyBAuu+wyFixYwN13382ee+4JwGuvvcbBBx/MvHnz+PGPf8xZZ521WjyrVq1i6NChjBkzhuXLl3Puuefyne98Z424r7vuOm6//XYAzjzzTC688ELOOecc3nzzTY455hi+/e1vc9FFF+Wtc6tWrejXrx/vvvvuOr1mhdhnn324/vrr6yx35ZVX8sQTT1BRUcG+++7Ln/70JyQxaNAg9tprL0aPHs3ixYu57bbbGDhwIBUVFQwZMoRp06bRs2dPKioqilYHs42Rm4/qSdUoqVWP+++/P1vXvn17XnnlFb773e9mzTvDhg3jxhtvZPLkyTz//PO0atWKUaNGMWvWLMaPH8/kyZOZNGkSY8eOBWD27NlccMEFTJkyhRkzZnDPPffwwgsvMGzYMH71q19l+5oyZQp//etf+de//sWVV17J/PnzV4vztttuo23btkyYMIEJEyZwyy238NZbb61WZtKkSdxxxx28/PLLjBs3jltuuYVXX32Vm266iU6dOjF69OgaEwLARx99xKxZszjggAOyZffff3/22txxxx3r/kKnnnnmGY477rg6y5133nlMmDCBqVOnUlFRwZNPPpmtq6ysZPz48QwfPpwrrrgCgBEjRrDZZpsxZcoULrnkEiZNmlRQPAcddBD9+vVjr732WrcKmW0kGt6Zwjp+o19ftTUfnXDCCQDsvvvuPPzwwwDst99+fP/73+fUU0/lhBNOoHPnzowaNYpRo0bRv39/IBk6Y9asWXTt2pXu3bvTp08fAHr37s0hhxyCJPr06cPcuXOzfR177LG0atWKVq1acdBBBzF+/Hj69euXrR81ahRTpkzhoYceAmDJkiXMmjVrteagF154geOPP57WrVtn8T///PNZXDV5/vnn6du3LzNnzmTo0KFsvfXW2br6aj466KCDeP/99+nYsSNXXXVVneVHjx7Nb37zG5YuXcqHH35I7969Ofroo4HVj0vVazh27FjOP/98APr27Uvfvn0LisvNR9ZQ+ExhA2jRogWQdEZXVlYCMHToUG699VYqKirYe++9mTFjBhHBxRdfnN22c/bs2ZxxxhmrbQOgSZMm2XyTJk2ybULSFJWr+nxE8Ic//CHbx1tvvcVXv/rVNcqsi4EDBzJlyhRef/11RowYUZQ+ltGjR/P222/Tu3dvLr300lrLLlu2jO9973s89NBDvP7665x11lksW7YsW5/vuMCar5lZY+KkUCJz5syhT58+/OQnP2HAgAHMmDGDww47jNtvvz0bXO/dd99lwYIFa7Xdxx57jGXLlrFo0SLGjBnDHnvssdr6ww47jBEjRmTDev/73//ms88+W63MAQccwKOPPsrSpUv57LPPeOSRRxg4cGDBMey0005cfPHFXHPNNbWWu+GGG9bp7KFVq1YMHz6cO++8kw8//BCAQw45ZI0+jKoE0L59ez799NPs7Kg2BxxwAHfffTeQDGTo26paY9Pwmo9KpKpPocrgwYNrvSx1+PDhjB49mrKyMnr16sXhhx9OixYtmD59Ovvssw8Am2++OX/+858pKysrOI4999yTI488knfeeYef//zndOrUabXmpTPPPJO5c+ey2267ERF06NCBRx99dLVt7Lbbbpx++ulZ5/WZZ55ZZ9NRdeeccw7Dhg1bo78i14wZM7Krk6obOXLkanGNGzdutfXbbLMNp5xyCjfeeCOXXHIJs2fPZsstt1ytTLt27TjrrLPo06cP3bp1WyNB5vPd736XIUOG0LdvX/r165e9BmaNhda1qaBUBgwYENWvG58+fTo9e/YsUUS2ro466igefvhhmjdvvl7bmTp1KrfffjvXXXddPUVW//wetVKTNCkiBtRVzmcKVjK5VwKtj1122WWjTghmmxL3KZiZWabBJIVNrRnMGg+/N21T0iCSQsuWLVm0aJH/+WyjExEsWrSIli1bljoUs4I0iD6Fzp07U15ezsKFC0sditkaWrZsSefOnUsdhllBGkRSaNasWd4B2szMbO0UtflI0mBJMyXNljQ0z/oWku5P178sqVsx4zEzs9oVLSlIKgNuBA4HegGnSOpVrdgZwEcR8WXgd0DtP4E1M7OiKuaZwp7A7Ih4MyJWAPcBx1Yrcyzwf+n0Q8Ah8sAzZmYlU8w+hW2BeTnz5UD1cYWzMhFRKWkJsBXwQW4hSWcDZ6ezn0qauY4xta++7UbAdW4cXOfGYX3qvF0hhYqZFPJ9469+zWghZYiIm4Gb1zsgaWIhP/NuSFznxsF1bhw2RJ2L2XxUDnTJme8MzK+pjKSmQFvgwyLGZGZmtShmUpgA7Cipu6TmwMnA49XKPA6clk6fCPwj/As0M7OSKVrzUdpHcB7wLFAG3B4Rb0i6EpgYEY8DtwF3SZpNcoZwcrHiSa13E9QmyHVuHFznxqHodd7khs42M7PiaRBjH5mZWf1wUjAzs0yjSQp1DbnREEjqImm0pOmS3pB0Qbp8S0l/kzQr/ftfpY61Pkkqk/SqpCfT+e7psCmz0mFU1u/WbhsZSe0kPSRpRnqs92kEx/ii9D09VdK9klo2tOMs6XZJCyRNzVmW97gqcX36eTZF0m71FUejSAoFDrnREFQCP4iInsDewLlpPYcCz0XEjsBz6XxDcgEwPWf+GuB3aX0/IhlOpSH5PfBMRPQAdiWpe4M9xpK2Bc4HBkTELiQXrpxMwzvOI4HB1ZbVdFwPB3ZMH2cDI+oriEaRFChsyI1NXkT8JyJeSac/Ifmw2JbVhxP5P+C40kRY/yR1Bo4Ebk3nBRxMMmwKNLz6bgEcQHLlHhGxIiIW04CPcaop0Cr9PdNmwH9oYMc5Isay5u+0ajquxwJ3RmIc0E7SNvURR2NJCvmG3Ni2RLFsEOmIs/2Bl4EvRcR/IEkcQMfSRVbvhgM/Bj5P57cCFkdEZTrf0I719sBC4I60yexWSa1pwMc4It4FhgHvkCSDJcAkGvZxrlLTcS3aZ1pjSQoFDafRUEjaHPgLcGFEfFzqeIpF0lHAgoiYlLs4T9GGdKybArsBIyKiP/AZDaipKJ+0Hf1YoDvQCWhN0nxSXUMheQ0pAAAGlklEQVQ6znUp2vu8sSSFQobcaBAkNSNJCHdHxMPp4verTi3TvwtKFV892w84RtJckibBg0nOHNqlzQzQ8I51OVAeES+n8w+RJImGeowBDgXeioiFEbESeBjYl4Z9nKvUdFyL9pnWWJJCIUNubPLS9vTbgOkRcV3OqtzhRE4DHtvQsRVDRFwcEZ0johvJMf1HRJwKjCYZNgUaUH0BIuI9YJ6kndNFhwDTaKDHOPUOsLekzdL3eFWdG+xxzlHTcX0c+FZ6FdLewJKqZqb11Wh+0SzpCJJvkVVDbvyyxCHVO0n7A88Dr/NFG/tPSfoVHgC6kvyDfT0iGtTAg5IGAT+MiKMkbU9y5rAl8CrwzYhYXsr46pOkfiQd682BN4EhJF/wGuwxlnQFcBLJFXavAmeStKE3mOMs6V5gEMnw2O8DlwGPkue4psnxBpKrlZYCQyJiYr3E0ViSgpmZ1a2xNB+ZmVkBnBTMzCzjpGBmZhknBTMzyzgpmJlZxknB1oqkkPTbnPkfSrq8nrY9UtKJdZdc7/18PR1ddHSx95Wzz06SHqq7ZJ3buVDSZvUR0zrsu1vuCJ5r+bz/LkZMVv+cFGxtLQdOkNS+1IHkSkfCLdQZwPci4qBixZNLUtOImB8R9ZHwLiQZEG5T0g1wUthEOCnY2qokuU/sRdVXVP+mL+nT9O8gSf+U9ICkf0u6WtKpksZLel3SDjmbOVTS82m5o9Lnl0m6VtKEdOz47+Rsd7Ske0h+sFc9nlPS7U+VdE267FJgf+AmSddWKy9JN0iaJumvkp6qqo+kuVWJUNIASWPS6dZKxsGfkA5Qd2y6/HRJD0p6AhiV+y27lvpsI2mspMlpzAOrxXc+ydg/o6vOcvLVMc/rcGm6r6mSbk5/+ISkMZKuSY/Dv6v2l8b6vKRX0se+ebb5fPojuqr5FyX1lXRgGv/k9PVoA1wNDEyXrfG+sY1MRPjhR8EP4FNgC2Au0Bb4IXB5um4kcGJu2fTvIGAxsA3QAngXuCJddwEwPOf5z5B8WdmRZHyXliTjxf8sLdMCmEgyONogkgHhuueJsxPJL0A7kAwi9w/guHTdGJKx+as/5wTgbyS/eu+Uxnxium4u0D6dHgCMSad/RfJLWoB2wL9JBmw7PY1/y3RdN2BqOl1TfX4AXJIuLwPa5IkxN44a61jtOVvmTN8FHJ3zOvw2nT4C+Hs6vRnQMp3eEZiYpw6n5Ry3nXLKPAHsl05vnsY1CHiy1O9dPwp7+EzB1lokI6/eSXLjk0JNiOR+D8uBOcCodPnrJB82VR6IiM8jYhbJEA49gK+SjPMymWTIjq1IPqwAxkfEW3n2twfJB/fCSIZXvpvkPgS1OQC4NyJWRcR8kg/ZunwVGJrGNoYkiXVN1/0t8g81UVN9JgBD0j6aPpHcE6M2hdbxICV3KHudZNDA3jnrqgZNnMQXx6EZcEta/kGSG1NV9yBwlJIBGL9NktABXgSuS89q2sUXQ1vbJqJp3UXM8hoOvALckbOskrRJMm2iyL09Yu6YNJ/nzH/O6u/D6uOuBMkwwf8bEc/mrlAy3tFnNcSXb2jhQtQ07ktWN5IP/tz9fC0iZlaLba86YlujPunzDiC5adBdkq6NiDtribXOOkpqCfyR5MxoXppwcuOvOg6r+OI4XEQy9s6uJHVeVn27EbFU0t9IhrT+BsnZExFxtaS/kpx5jJN0aF0x2sbFZwq2TtJvwA+w+i0Q5wK7p9PHknzjXFtfl9Qk7WfYHpgJPAt8N/1WiqSdlNxYpjYvAwdKap92Qp8C/LOO54wFTk7b/LcBcjui5/JF3b6Ws/xZ4H9z2un711nDGuojaTuS+0PcQjLabb777n4CtFmLOlYlgA+U3GejkM7utsB/IuJz4H9ImrLyuRW4nuQs8MO0LjtExOsRcQ1Js1iPajHbRs5JwdbHb0lGdKxyC8mH1Higtm/KtZlJ8sH2NHBORCwj+fCZBrySdtb+iTrOciMZRvhikuGVXwNeiYi6hlZ+BJhF0qQ1gtU/YK8Afi/peZJv1VV+QZL8pqSx/aKAOtZUn0HAZEmvkiSe3+d57s3A05JGF1LHSG7VeUtap0dJmqjq8kfgNEnjSPoL8h7HSG5u9DGrny1emHZovwZUkBzHKUClpNfc0bzx8yipZjWQNJKkg3S9f1/QEEnqRNKP0iM9q7AGwGcKZrbWJH2LpPnqEieEhsVnCmZmlvGZgpmZZZwUzMws46RgZmYZJwUzM8s4KZiZWeb/AZ7+eyxomWhbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for label in ni.features:\n",
    "    try:\n",
    "        ni.plot_active_learning_time_series_overlapping(\n",
    "            attribute='f1', label=label.replace('.', ''),\n",
    "            learner1='RandomForestClassifier', sampling1='entropy',\n",
    "            learner2='VotingClassifier', sampling2='entropy',\n",
    "            title=f'RF vs. ensemble for {label} attack',\n",
    "            ylim=[0, 1], ylabel='F1',\n",
    "            legend=['RF', 'Ensemble of RF, LR, and IF']\n",
    "        )\n",
    "        plt.show()\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Error analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncertainty sampling drawbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = 'nmap.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_predictions(estimator):\n",
    "    predictions = estimator.predict(active_learning_data.x_dev)\n",
    "    print(ni._get_metrics(active_learning_data.y_dev,\n",
    "                    predictions, None))\n",
    "    return predictions\n",
    "\n",
    "def get_feature_importance(estimator, n=10):\n",
    "    df = pd.DataFrame({'feature': ni.splits[label]['x_train'].columns,\n",
    "             'importance': estimator.feature_importances_}).sort_values(by='importance', ascending=False)[:n][::-1]\n",
    "    df.set_index('feature').plot(kind='barh', legend=False)\n",
    "    plt.title(f'Top {n} features by importance')\n",
    "    plt.xlabel('Gini importance')\n",
    "    \n",
    "def get_gets_wrong(predictions):\n",
    "    return pd.DataFrame(active_learning_data.x_dev[predictions != active_learning_data.y_dev],\n",
    "                 columns=ni.splits['nmap.']['x_train'].columns)\n",
    "    \n",
    "def get_right_wrong_comp(predictions, n=10):\n",
    "    # doesn't get right\n",
    "    gets_wrong = get_gets_wrong(predictions)\n",
    "    gets_right = pd.DataFrame(active_learning_data.x_dev[(predictions == active_learning_data.y_dev) & (active_learning_data.y_dev)],\n",
    "             columns=ni.splits['nmap.']['x_train'].columns)\n",
    "    z = (gets_wrong.mean() - gets_right.mean()) / gets_right.std()\n",
    "    z = z.abs()\n",
    "    return z[pd.notnull(z) & np.isfinite(z)].sort_values(ascending=False)[:n]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "active_learning_data = ni._active_learning_data_split(label)\n",
    "active_learner = ActiveLearner(ni.active_learning_rf, uncertainty_sampling,\n",
    "                              active_learning_data.x_train_start.values,\n",
    "                              active_learning_data.y_train_start.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'precision': 1.0, 'recall': 0.6956521739130435, 'f1': 0.8205128205128205, 'FP': 0, 'FN': 7}\n"
     ]
    }
   ],
   "source": [
    "predictions = get_predictions(active_learner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1. , 0. ],\n",
       "       [0.6, 0.4],\n",
       "       [1. , 0. ],\n",
       "       [0.6, 0.4],\n",
       "       [1. , 0. ],\n",
       "       [0.6, 0.4],\n",
       "       [1. , 0. ]])"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices = np.array(range(len(active_learning_data.y_dev)))[predictions != active_learning_data.y_dev]\n",
    "active_learner.predict_proba(active_learning_data.x_dev[indices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_feature_importance(active_learner.estimator, n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "src_bytes                      28.821429\n",
       "srv_diff_host_rate              0.750000\n",
       "service_eco_i                   0.750000\n",
       "service_private                 0.750000\n",
       "protocol_type_icmp              0.750000\n",
       "dst_host_srv_diff_host_rate     0.749792\n",
       "dst_host_count                  0.617108\n",
       "dst_host_serror_rate            0.532857\n",
       "dst_host_diff_srv_rate          0.491316\n",
       "srv_count                       0.455465\n",
       "dtype: float64"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_right_wrong_comp(predictions, n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1488"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = active_learner.query(active_learning_data.x_train_pool.values)[0][0]\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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>count</th>\n",
       "      <th>diff_srv_rate</th>\n",
       "      <th>dst_bytes</th>\n",
       "      <th>dst_host_count</th>\n",
       "      <th>dst_host_diff_srv_rate</th>\n",
       "      <th>dst_host_rerror_rate</th>\n",
       "      <th>dst_host_same_src_port_rate</th>\n",
       "      <th>dst_host_same_srv_rate</th>\n",
       "      <th>dst_host_serror_rate</th>\n",
       "      <th>dst_host_srv_count</th>\n",
       "      <th>dst_host_srv_diff_host_rate</th>\n",
       "      <th>dst_host_srv_rerror_rate</th>\n",
       "      <th>dst_host_srv_serror_rate</th>\n",
       "      <th>duration</th>\n",
       "      <th>hot</th>\n",
       "      <th>is_guest_login</th>\n",
       "      <th>is_host_login</th>\n",
       "      <th>land</th>\n",
       "      <th>logged_in</th>\n",
       "      <th>num_access_files</th>\n",
       "      <th>num_compromised</th>\n",
       "      <th>num_failed_logins</th>\n",
       "      <th>num_file_creations</th>\n",
       "      <th>num_outbound_cmds</th>\n",
       "      <th>num_root</th>\n",
       "      <th>num_shells</th>\n",
       "      <th>rerror_rate</th>\n",
       "      <th>root_shell</th>\n",
       "      <th>same_srv_rate</th>\n",
       "      <th>serror_rate</th>\n",
       "      <th>src_bytes</th>\n",
       "      <th>srv_count</th>\n",
       "      <th>srv_diff_host_rate</th>\n",
       "      <th>srv_rerror_rate</th>\n",
       "      <th>srv_serror_rate</th>\n",
       "      <th>su_attempted</th>\n",
       "      <th>urgent</th>\n",
       "      <th>wrong_fragment</th>\n",
       "      <th>flag_OTH</th>\n",
       "      <th>flag_REJ</th>\n",
       "      <th>flag_RSTO</th>\n",
       "      <th>flag_RSTR</th>\n",
       "      <th>flag_S0</th>\n",
       "      <th>flag_S1</th>\n",
       "      <th>flag_S2</th>\n",
       "      <th>flag_S3</th>\n",
       "      <th>flag_SF</th>\n",
       "      <th>flag_SH</th>\n",
       "      <th>protocol_type_icmp</th>\n",
       "      <th>protocol_type_tcp</th>\n",
       "      <th>protocol_type_udp</th>\n",
       "      <th>service_IRC</th>\n",
       "      <th>service_X11</th>\n",
       "      <th>service_auth</th>\n",
       "      <th>service_ctf</th>\n",
       "      <th>service_domain</th>\n",
       "      <th>service_domain_u</th>\n",
       "      <th>service_eco_i</th>\n",
       "      <th>service_ecr_i</th>\n",
       "      <th>service_finger</th>\n",
       "      <th>service_ftp</th>\n",
       "      <th>service_ftp_data</th>\n",
       "      <th>service_http</th>\n",
       "      <th>service_nntp</th>\n",
       "      <th>service_ntp_u</th>\n",
       "      <th>service_other</th>\n",
       "      <th>service_pop_3</th>\n",
       "      <th>service_private</th>\n",
       "      <th>service_red_i</th>\n",
       "      <th>service_shell</th>\n",
       "      <th>service_smtp</th>\n",
       "      <th>service_ssh</th>\n",
       "      <th>service_telnet</th>\n",
       "      <th>service_tftp_u</th>\n",
       "      <th>service_tim_i</th>\n",
       "      <th>service_time</th>\n",
       "      <th>service_urh_i</th>\n",
       "      <th>service_urp_i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count  diff_srv_rate  dst_bytes  dst_host_count  dst_host_diff_srv_rate  \\\n",
       "0    1.0            0.0        0.0           234.0                    0.96   \n",
       "\n",
       "   dst_host_rerror_rate  dst_host_same_src_port_rate  dst_host_same_srv_rate  \\\n",
       "0                   0.0                         0.97                     0.0   \n",
       "\n",
       "   dst_host_serror_rate  dst_host_srv_count  dst_host_srv_diff_host_rate  \\\n",
       "0                  0.97                 1.0                          0.0   \n",
       "\n",
       "   dst_host_srv_rerror_rate  dst_host_srv_serror_rate  duration  hot  \\\n",
       "0                       0.0                       1.0       0.0  0.0   \n",
       "\n",
       "   is_guest_login  is_host_login  land  logged_in  num_access_files  \\\n",
       "0             0.0            0.0   0.0        0.0               0.0   \n",
       "\n",
       "   num_compromised  num_failed_logins  num_file_creations  num_outbound_cmds  \\\n",
       "0              0.0                0.0                 0.0                0.0   \n",
       "\n",
       "   num_root  num_shells  rerror_rate  root_shell  same_srv_rate  serror_rate  \\\n",
       "0       0.0         0.0          0.0         0.0            1.0          1.0   \n",
       "\n",
       "   src_bytes  srv_count  srv_diff_host_rate  srv_rerror_rate  srv_serror_rate  \\\n",
       "0        0.0        1.0                 0.0              0.0              1.0   \n",
       "\n",
       "   su_attempted  urgent  wrong_fragment  flag_OTH  flag_REJ  flag_RSTO  \\\n",
       "0           0.0     0.0             0.0       0.0       0.0        0.0   \n",
       "\n",
       "   flag_RSTR  flag_S0  flag_S1  flag_S2  flag_S3  flag_SF  flag_SH  \\\n",
       "0        0.0      0.0      0.0      0.0      0.0      0.0      1.0   \n",
       "\n",
       "   protocol_type_icmp  protocol_type_tcp  protocol_type_udp  service_IRC  \\\n",
       "0                 0.0                1.0                0.0          0.0   \n",
       "\n",
       "   service_X11  service_auth  service_ctf  service_domain  service_domain_u  \\\n",
       "0          0.0           0.0          0.0             0.0               0.0   \n",
       "\n",
       "   service_eco_i  service_ecr_i  service_finger  service_ftp  \\\n",
       "0            0.0            0.0             0.0          0.0   \n",
       "\n",
       "   service_ftp_data  service_http  service_nntp  service_ntp_u  service_other  \\\n",
       "0               0.0           0.0           0.0            0.0            0.0   \n",
       "\n",
       "   service_pop_3  service_private  service_red_i  service_shell  service_smtp  \\\n",
       "0            0.0              1.0            0.0            0.0           0.0   \n",
       "\n",
       "   service_ssh  service_telnet  service_tftp_u  service_tim_i  service_time  \\\n",
       "0          0.0             0.0             0.0            0.0           0.0   \n",
       "\n",
       "   service_urh_i  service_urp_i  \n",
       "0            0.0            0.0  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "active_learning_data.x_train_pool.values[index].reshape(1, -1),\n",
    "    columns=ni.splits['nmap.']['x_dev'].columns\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_learning_data.y_train_pool[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_instance, y_instance = ni._get_active_learning_instance(active_learning_data.x_train_pool,\n",
    "                                                          active_learning_data.y_train_pool,\n",
    "                                                          [index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "active_learner.teach(x_instance, y_instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'precision': 1.0, 'recall': 0.7391304347826086, 'f1': 0.85, 'FP': 0, 'FN': 6}\n"
     ]
    }
   ],
   "source": [
    "predictions = get_predictions(active_learner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_feature_importance(active_learner.estimator, n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9210"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = active_learner.query(active_learning_data.x_train_pool.values)[0][0]\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "active_learning_data.y_train_pool[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_instance, y_instance = ni._get_active_learning_instance(active_learning_data.x_train_pool,\n",
    "                                                          active_learning_data.y_train_pool,\n",
    "                                                          [index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "active_learner.teach(x_instance, y_instance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'precision': 1.0, 'recall': 0.7391304347826086, 'f1': 0.85, 'FP': 0, 'FN': 6}\n"
     ]
    }
   ],
   "source": [
    "predictions = get_predictions(active_learner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "Name: src_bytes, dtype: float64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "active_learning_data.x_train_pool.values[index].reshape(1, -1),\n",
    "    columns=ni.splits['nmap.']['x_dev'].columns\n",
    ").src_bytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vL4ecBfzc9s8kHQl8B9i/pyfF9nRgOsCa4zbPbR8jIvpJkv7AWgCcIel04DLgaWBr4KqSvEcAj9TUn1G3PZkq6X8E+F5d21sAj9i+FcD2s/C3RL5N22wAMJoqsb8q6QN7l5/byuORpe62wMW2nyhtP1X27wx8qGyfC3yjyzMQERGDJkl/ANm+R9Ik4H3AacBVwELbO3dwyPM12zOB08ooexLwu7q6AtobJQv4rO1Z3QhRwGm2f/CKQum4Dtqul1F6RMQQlmv6A0jShsBS2+cBZwA7AWMl7Vz2ryFpq/aOtb0EuAX4NnCZ7ZV1Ve4CNpS0Q2lrlKTVgVnAMZLWKOVvlbRuByHOAo6UNLLU3UjSG4BrgA9LWq+Ut03v30w16wBwKHBjD05HREQMsIz0B9YEYJqkl4HlwDHACuA75fr+6sC3gIUdHD8DuAjYo36H7ZfKmoAzJa1NdT3/PcCPgPHAXFXXEB6ng+vutq+U9Dbg9+VywxLgMNsLJZ0KXC9pJdX0/xHAccBPJB1f2v1Ej85GOyZsNJrWfONWRES/kJ0Z2Rg6Wlpa3NraOthhREQMK5Lm2G7pql6m9yMiIppEpvebkKQJVKvtay2zvVMD2j4ROLiu+CLbp/a17YiI6Jsk/SZkewEwsZ/aPpXquwYiImKIyfR+REREk0jSj4iIaBJJ+hEREU0iST8iIqJJJOlHREQ0iST9iIiIJpGP7MWQsmDRYsZPuXyww4iIIerBfE13n2SkHxER0SSS9KMhJI2R9JnBjiMiIjqWpL+KKbfT7fBxd4/rRZ0xQJJ+RMQQlmv6Q1S55/0vgDcCI4B/B+4D/gsYCTwBHGH7EUnXUd3b/l3AzPLd+k8B21HdUvdU4CfAm4GlwFG250s6GdiQ6ta7TwCHtBPHEcC+wFrAupI+APwKeB2wBvBl278CpgKbSZoHXGX7+HLL3Q8DawKX2P5qI89RRET0TJL+0LUP8LDtfQEkjQZ+C3zQ9uOSJlN9x/2Rpf4Y27uXuucAbwXeY3ulpDOB22zvL2kv4Of8/bv3JwG72H6hk1h2Brax/VQZ7R9g+1lJ6wN/kDQTmAJsbXtiiWFvYHNgR0BUb0Z2sz27EScnIiJ6Lkl/6FoAnCHpdOAy4Glga+AqSVCN/h+pqT+j7viLbK8s27sABwLY/p2k9cqbCICZXSR8qEbuT5VtAV+XtBvwMrARsEE7x+xdfm4rj0dSvQl4VdKXdBRwFMCI147tIpSIiOitJP0hyvY9kiYB7wNOA64CFtreuYNDnu/ksdrrooPjumr7UGAsMMn2ckkPUk391xNwmu0fdNW47enAdIA1x23uLqpHREQvZSHfECVpQ2Cp7fOAM4CdgLGSdi7715C0VTebm02VrJG0B/CE7Wd7Gdpo4LGS8PcENinlzwGjaurNAo6UNLL0u5GkN/Syz4iIaICM9IeuCcA0SS8Dy4FjgBXAd8rU/OrAt4CF3WjrZOCnkuZTLeQ7vA9xnQ/8WlIrMA+4C8D2k5JuknQH8NuykO9twO/L5YglwGHAY33oOyIi+kB2ZlNj6Fhz3OYed/i3BjuMiBii8o187ZM0x3ZLV/Uy0o8hZcJGo2nNP+qIiH6RpB8ASHovcHpd8QO2DxiMeCIiovGS9AMA27OoFt9FRMQqKqv3IyIimkSSfkRERJNI0o+IiGgSSfoRERFNIkk/IiKiSSTpR0RENIkk/YiIiCaRpB8REdEk8uU8MaQsWLSY8VMuH+wwIppevuN+1ZSRfkRERJNI0o+IiGgSSfqrOEnHSfpfSYskndXgtk+UtFDSfEnzJO1Uyq+T1FJTb7ykOxrZd0RE9Fyu6a/6PgP8E7A70OW9lrtL0s7A+4HtbS+TtD7wmka1HxERjZeR/ipM0tnAm4GZwOtqyveT9EdJt0m6WtIGpXyspKskzZX0A0kPlWTennHAE7aXAdh+wvbDvYzzKEmtklpXLl3cmyYiIqIbkvRXYbaPBh4G9gSertl1I/AO29sBFwInlPKvAr+zvT1wCfCmTpq/EthY0j2Svidp97r955cp/3nAb7qIc7rtFtstI9YZ3e3nFxERPZPp/eb0RmCGpHFUU/IPlPJdgAMAbF8h6ekOjsf2EkmTgF2p3lTMkDTF9jmlyqG2W6G6pg9c1g/PIyIieiAj/eZ0JnCW7QnAp4G1Srl60ojtlbavs/1V4FjgwMaGGRERjZSk35xGA4vK9uE15TcCHwaQtDc16wDqSdpC0uY1RROBhxocZ0RENFCm95vTycBFkhYBfwA2LeWnABdImgxcDzwCPNdBGyOBMyWNAVYA9wFH9WfQERHRN7I92DHEECFpTWCl7RXlI3nftz1xIGNoaWlxa2vrQHYZETHsSZpju8uPZWekH7XeBPxC0mrAS8CnBjmeiIhooCT9+Bvb9wLb1ZZJWg+4pp3q77b95IAEFhERDZGkH50qiX1Ap/gjIqJ/ZPV+REREk0jSj4iIaBJJ+hEREU0iST8iIqJJJOlHREQ0iST9iIiIJpGkHxER0STyOf0YUhYsWsz4KZcPdhjRBB6cuu9ghxAx4Loc6UvaQNKPJf22PH67pE/2f2gRERHRSN2Z3j8HmAVsWB7fA3y+vwKKiIiI/tGdpL++7V8ALwPYXgGs7NeoouEkHSfpfyUtknRWg9s+UdJCSfMlzZO0Uym/TtLdpWyepIMa2W9ERPRMd67pP19uumIASe8AFvdrVNEfPgP8E7A70OXtF7ur3IL3/cD2tpdJWh94TU2VQ23nXrkREUNAd0b6/w+YCWwm6Sbg58Bn+zWqaChJZwNvpvo7vq6mfD9Jf5R0m6SrJW1QysdKukrSXEk/kPRQSebtGQc8YXsZgO0nbD/cw/iOktQqqXXl0ryfjIjoL50m/XJf9bWoRofvBD4NbGV7/gDEFg1i+2jgYWBP4OmaXTcC77C9HXAhcEIp/yrwO9vbA5cAb+qk+SuBjSXdI+l7knav239+zfT+eh3EN912i+2WEeuM7vkTjIiIbul0et/2y5L+0/bOwMIBiikGzhuBGZLGUU3JP1DKdwEOALB9haSnOzge20skTQJ2pXpTMUPSFNvnlCqZ3o+IGCK6M71/paQDJanfo4mBdiZwlu0JVLM4a5XyHv2tba+0fZ3trwLHAgc2NsyIiGiE7l7TvwhYJulZSc9Jeraf44qBMRpYVLYPrym/EfgwgKS9qVkHUE/SFpI2rymaCDzU4DgjIqIBuly9b3vUQAQSg+Jk4CJJi4A/AJuW8lOACyRNBq4HHgGe66CNkcCZksYAK4D7gKP6M+iIiOgd2e68grRbe+W2Z/dLRDHoJK0JrLS9onwk7/u2Jw5E3y0tLW5tzRKAiIiekDTHdpcfx+7O5/SPr9leC9gRmAPs1cvYYuh7E/CL8umNl4BPDXI8ERHRAN2Z3t+v9rGkjYFv9FtEMehs3wtsV1tWPm53TTvV3237yQEJLCIi+qQ3d9n7C7B1owOJoa0k9gGZ4o+IiP7RZdKXdCblK3ipVvtPBG7vz6AiIiKi8boz0q9dVbUCuMD2Tf0UT0RERPST7iT9Mba/XVsg6XP1ZRERETG0defLeQ5vp+yIBscRERER/azDkb6kjwKHAJtKmlmzaxSQ1doRERHDTGfT+zdTfRPb+sB/1pQ/B+QuexEREcNMh0nf9kNU36G+88CFExEREf2lOx/ZewfV3djeRnX71RHA87Zf28+xRRNasGgx46dcPthhxDDz4NR9BzuEiGGhOwv5zgI+CtwLrA38M9WbgIiIiBhGuvWNfLbvkzTC9krgp5Ju7ue4IiIiosG6M9JfKuk1wDxJ35D0BWDdfo6rX0g6WdIXO9i3h6R3dnH8OZIOakAcX+prG0ONpDGSPjPYcURERMe6k/Q/VuodCzwPbAwc2J9BDZI9gE6TfgP1W9KXtHpnj7t7XC/qjAGS9CMihrAuk35ZxS9gnO1TbP8/2/f1f2iNIelESXdLuhrYopQdJ+lOSfMlXShpPHA08AVJ8yTt2kmTu0m6WdL9baN+VaZJukPSAkmTS/k4SbNLm3dI2lXSVGDtUnZ+BzGvK+lySbeX49ramyTpeklzJM2SNK6UXyfp65KuBz5XZiT+S9K1wOmSXi/p0vJ8/yBpm3LcyZKmS7oS+HkHsRwh6SJJvwaulDRS0jWS5pbn+sFSdSqwWXle08qxx0u6tfR7Sid/o6MktUpqXbl0cSenPiIi+qI7o7v9gDOoVu5vKmki8DXbH+jv4PpK0iTgI1S3iV0dmAvMAaYAm9peJmmM7WcknQ0ssX1GF82OA3YBtgRmAhcDH6K6EdG2VN9rcKuk2VRfbjTL9qmSRgDr2L5B0rG2O7tj3T7Aw7b3Lc9jtKQ1qBZQftD24+WNwKnAkeWYMbZ3L/XPAd4KvMf2ynLTpNts7y9pL6oE39b/JGAX2y90Es/OwDa2nyqj/QNsPytpfeAP5cubpgBbtz0vSXsDmwM7Ur1pnClpN9uz6xu3PR2YDrDmuM1dvz8iIhqjO1O/J1P9x30dgO15ZWQ8HOwKXGJ7KUDNNwvOB86XdClwaQ/bvNT2y8CdkjYoZbtQ3YhoJfBoGXHvANwK/KQk7Ettz+tmHwuAMySdDlxW3ihsTXVL46skQfXRyUdqjplR18ZFJZ62+A4EsP07SetJGl32zewi4QNcZfupsi3g65J2A14GNgI2aOeYvcvPbeXxSKo3Aa9K+hERMTC6k/RX2F5cEs1w1N7IcV9gN+ADwFckbdWD9pbVbKvu9ys7tmeX5LgvcK6kabbbnUavO+6eMkvxPuC0Mv1+CbDQdkdflvR8J4/bi8/t1OtIbZ1DgbHAJNvLJT0IrNXOMQJOs/2DbrQfEREDoDsL+e6QdAgwQtLmZap4uHxkbzZwgKS1JY0C9qN6zhvbvhY4gWoB2kiqrxce1Yd+JksaIWks1RuKWyRtAjxm+4fAj4HtS/3lZfTfLkkbAkttn0d1aWV74G5grKSdS501evBmZTZVskbSHsATtp/t6ZMsRlM9p+WS9gQ2KeX1528WcKSkkaXfjSS9oZd9RkREA3R2w51zbX8M+BOwFdUI9wKq/8z/fWDC6xvbcyXNAOZRfaXwDVQj3PPK9LaAb5Zr+r8GLi4L0z5r+4YedHUJ1XXv20v7J9j+q6TDgeMlLQeWAB8v9acD8yXNtX1oO+1NAKZJehlYDhxj+6WycPA7JfbVgW8BC7sR38lU368wH1hK+3dO7K7zgV9LaqU6r3cB2H5S0k2S7gB+a/t4SW8Dfl9miZYAhwGPddb4hI1G05pvV4uI6Bey2183JelO4J+oFqvtWb+/5hpvRMO0tLS4tbV1sMOIiBhWJM2x3dJVvc6u6Z8NXAG8Gaj9X1hUo9k39ynCiIiIGFCd3WXvO1RTyd+3fcwAxjToJJ0IHFxXfJHtUxvcz3rANe3serftJxvZVzdieS9wel3xA7YPGMg4IiKi/3Q4vR8xGDK9HxHRc92d3u/O6v2IiIhYBSTpR0RENIkk/YiIiCaRpB8REdEkkvQjIiKaRJJ+REREk+jODXciBsyCRYsZP+XywQ4juuHBfF1yxLCTkX5ERESTSNKPiIhoEkn6gaQ9JF3Wg/pjJH2mP2OKiIjGS9JfRUka0Y/NjwGS9CMihpkk/WFK0rqSLpd0u6Q7JE2W9KCkkyTdCBws6S2Sri515krarJMmXyvpEkl3Sjpb0mqSPinpmzV9fkrSfwFTgc0kzZM0rew7XtKtkuZLOqWjGPvznEREROeyen/42gd42Pa+AJJGU90l70Xbu5SyPwJTbV8iaS06f5O3I/B24CGqWyp/CLgQmC/pBNvLgU8AnwaeA7a2PbH0szeweWlDwExJuwFj24nxVSQdBRwFMOK1Y3t5OiIioisZ6Q9fC4D3SDpd0q62F5fyGQCSRgEb2b4EwPaLtpd20t4ttu+3vRK4ANjF9vPA74D3S9oSWMP2gnaO3bv83AbMBbakehPQUYyvYHu67RbbLSPWafd9QURENEBG+sOU7XskTQLeB5wm6cqy6/nyWz1tsoPHPwK+BNwF/LSDYwWcZvsHr9pRF6Ptr/UwroiIaJCM9IcpSRsCS22fB5wBbF+73/azwF8k7V/qrylpnU6a3FHSppJWAyYDN5Z2/ghsDBwwLtnjAAAV00lEQVRCNQMA1fT+qJpjZwFHShpZ+tpI0hu6ijEiIgZWRvrD1wRgmqSXgeXAMcDFdXU+BvxA0tdKnYOB+zto7/dUC/QmALOBS2r2/QKYaPtpANtPSrpJ0h3Ab20fL+ltwO8lASwBDgPe0k6MERExSGTXz+pGvFL5DP83bV/T3321tLS4tbW1v7uJiFilSJpju6Wrepnejw6VL+G5B3hhIBJ+RET0r0zvNxFJE4Bz64qX2d6pvfq2nwHe2u+BRUTEgEjSbyLl43YTBzuOiIgYHJnej4iIaBJJ+hEREU0iST8iIqJJJOlHREQ0iST9iIiIJpGkHxER0SSS9CMiIppEPqcfQ8qCRYsZP+XywQ4juuHBqfsOdggR0UMZ6UdERDSJJP2IiIgmkaTfTySdLOmLHezbQ9I7uzj+HEkHNSCOL/W1jQbE8HlJ6wx2HBERzS5Jf3DsAXSa9Bto0JM+8HkgST8iYpAl6TeQpBMl3S3pamCLUnacpDslzZd0oaTxwNHAFyTNk7RrJ03uJulmSfe3jfpVmSbpDkkLJE0u5eMkzS5t3iFpV0lTgbVL2fmdxP3xEt/tks4tZZtIuqaUXyPpTaX8FTMQkpaU33tIuk7SxZLuknR+ifU4YEPgWknXdtD/UZJaJbWuXLq4m2c7IiJ6Kqv3G0TSJOAjwHZU53UuMAeYAmxqe5mkMbafkXQ2sMT2GV00Ow7YBdgSmAlcDHyI6k552wLrA7dKmg0cAsyyfaqkEcA6tm+QdKztDu+sJ2kr4ETgXbafkPT6suss4Oe2fybpSOA7wP5dxLsdsBXwMHBTafM7kv4fsKftJ9o7yPZ0YDrAmuM2dxd9REREL2Wk3zi7ApfYXmr7WaokDTAfOF/SYcCKHrZ5qe2Xbd8JbFDKdgEusL3S9qPA9cAOwK3AJySdDEyw/Vw3+9gLuLgtIdt+qpTvDPx32T639NuVW2z/xfbLwDxgfDdjiIiIAZCk31jtjVL3Bb4LTALmSOrJ7Mqymm3V/X5lx/ZsYDdgEXCupI93sw/Rftyv6qL8XkF53UgS8JoO4l1JZpIiIoaUJP3GmQ0cIGltSaOA/ajO78a2rwVOAMYAI4HngFF96GeypBGSxlIl+lskbQI8ZvuHwI+B7Uv95ZLW6KS9a4APS1oPoGZ6/2aqyxUAhwI3lu0Hqd7AAHwQ6KztNn15vhER0SBJ+g1iey4wg2pa+5fADVSj4/MkLQBuA75p+xng11RvELpayNeeS6guGdwO/A44wfZfqT4RME/SbcCBwLdL/enA/I4W8tleCJwKXC/pduC/yq7jqC4XzAc+BnyulP8Q2F3SLcBOwPPdiHk68NuOFvJFRMTAkJ11UzF0tLS0uLW1dbDDiIgYViTNsd3SVb2M9CMiIppEFloNMkknAgfXFV9k+9QG97Me1fX7eu+2/WQj+4qIiKEpSX+QleTe0ATfQT9PUn2+PyIimlSm9yMiIppEkn5ERESTSNKPiIhoEkn6ERERTSJJPyIiokkk6UdERDSJJP2IiIgmkc/px5CyYNFixk+5fLDDiOLBqfsOdggR0UAZ6UdERDSJJP2IiIgmkaQ/REn6mqT3DEK/P5L09l4e+6VGxxMREY2TpD+IJHW4psL2SbavHuB4Rtj+Z9t39rKJJP2IiCEsSb8BJK0r6XJJt0u6Q9JkSZMkXS9pjqRZksaVutdJ+rqk64ETJT0oabWybx1Jf5a0hqRzJB1UyneQdHNp/xZJoySNkDRN0q2S5kv6dCfx7SFptqRLJN0p6eyaPpeUWYU/AjuX+FokHSPpGzVtHCHpzLJ9aXleCyUdVcqmAmtLmifp/FJ2WIl3nqQfSBrRQXxHSWqV1Lpy6eIG/EUiIqI9SfqNsQ/wsO1tbW8NXAGcCRxkexLwE155J70xtne3fQpwO7B7Kd8PmGV7eVtFSa8BZgCfs70t8B7gBeCTwGLbOwA7AJ+StGknMe4I/H/ABGAz4EOlfF3gDts72b6xpv7FNXUAJpc4AI4sz6sFOE7SeranAC/Ynmj7UElvK8e8y/ZEYCVwaHuB2Z5uu8V2y4h1RnfyFCIioi/ykb3GWACcIel04DLgaWBr4CpJACOAR2rqz6jbngxcC3wE+F5d21sAj9i+FcD2swCS9ga2aZsNAEYDmwMPdBDjLbbvL8deAOxCldhXAr+sr2z7cUn3S3oHcG+J46ay+zhJB5TtjUu/T9Y18W5gEnBrOQdrA491EFtERAyAJP0GsH2PpEnA+4DTgKuAhbZ37uCQ52u2ZwKnSXo9VZL8XV1dAW6nDQGftT2ru2F28PhF2ys7OGYG8GHgLuAS25a0B9Vsw862l0q6Dlirg/h+ZvvfuhlfRET0s0zvN4CkDYGlts8DzgB2AsZK2rnsX0PSVu0da3sJcAvwbeCydhLwXcCGknYobY0qCwBnAcdIWqOUv1XSup2EuaOkTcu1/MnAjZ3UbfM/wP7AR/n77MRo4OmS8LcE3lFTf3lbPMA1wEGS3lDie72kTbrRZ0RE9JOM9BtjAjBN0svAcuAYYAXwHUmjqc7zt4CFHRw/A7gI2KN+h+2XJE0GzpS0NtX1/PcAPwLGA3NVzZ8/TpWgO/J7YGqJdTZwSVdPyvbTku4E3m77llJ8BXC0pPnA3cAfag6ZDsyXNLdc1/8ycGV5o7Ec+Bfgoa76jYiI/iG7vZnjWJWUKfkv2n7/YMfSlZaWFre2tg52GBERw4qkObZbuqqX6f2IiIgmken9VYikCcC5dcXLbO8EXDfwEUVExFCSpL8Ksb0AmDjYcURExNCU6f2IiIgmkaQfERHRJJL0IyIimkSSfkRERJNI0o+IiGgSSfoRERFNIh/ZiyFlwaLFjJ9y+WCHMSQ9OHXfwQ4hIoa5jPQjIiKaRJJ+REREk0jSj4iIaBIDlvQlnSzpix3s20PSO7s4/hxJBzUgji/1tY0+9r+HpMvK9gckTSnbYyX9UdJtknaVdLCk/5V0bQftHCHprAbEc4SkDfvaTmlrf0lvb0RbERHReENlpL8H0GnSb6A+J31JIxoRiO2ZtqeWh+8G7rK9ne0bgE8Cn7G9ZyP66sQRQLeTfhfPfX8gST8iYojq16Qv6URJd0u6GtiilB0n6U5J8yVdKGk8cDTwBUnzJO3aSZO7SbpZ0v1to35Vpkm6Q9ICSZNL+ThJs0ubd5TR81Rg7VJ2fgcxryvpckm3l+Pa2ntQ0kmSbgROkHRLzTHjJc3v5DzsI+mucuyHasqPkHSWpInAN4D3ldi+CuwCnC1pWifnY0NJV0i6V9I3atr9aDkXd0g6vZSNKLMlbefpC+UctgDnl37X7iD+2ud+sKRPSbq1nKNfSlqnzNR8AJhW2tqs/FwhaY6kGyRt2UH7R0lqldS6cuniTp5uRET0Rb99ZE/SJOAjwHaln7nAHGAKsKntZZLG2H5G0tnAEttndNHsOKpkuCUwE7iYKolOBLYF1gdulTQbOASYZfvUMjpdx/YNko613dmd6PYBHra9b3keo2v2vWh7l1I+WdKbbd8PTAZ+0cF5WAv4IbAXcB8wo76O7XmSTgJabB9bjtsT+KLt1k5inUh1fpcBd0s6E1gJnA5MAp4GrpS0P/BnYCPbW5f22879sd3op/65r2f7h2X7P4BP2j5T0kzgMtsXl33XAEfbvlfSTsD3ynmof/7TgekAa47b3F3EERERvdSfI/1dgUtsL7X9LFWSBphPNbI8DFjRwzYvtf2y7TuBDUrZLsAFtlfafhS4HtgBuBX4hKSTgQm2n+tmHwuA90g6XdKutmuHnrUJ+xfAh8v2ZNpJ5sWWwAO277Vt4LxuxtEd19hebPtF4E5gE6rnfp3tx22vAM4HdgPuB94s6UxJ+wDP9rCv2ue3dRm5LwAOBbaqryxpJNUlm4skzQN+QPWmLSIiBkl/X9Nvb9S2L/BdqpHoHEk9mW1YVrOtut+v7NieTZXsFgHnSvp4dzqwfU+JbQFwWhmBt3m+ZnsG8GFJb60O872dNdudvnuh9nyspJpR6eh8PE01G3Id8C/Aj3rYV+1zPwc41vYE4BRgrXbqrwY8Y3tizc/bethnREQ0UH8m/dnAAZLWljQK2K/0t7Hta4ETgDHASOA5YFQf+plcrlmPpUr0t0jaBHisTEP/GNi+1F8uaY2OGlO1kn2p7fOAM2qOewXbf6JKtF+h41E+wF3AppI2K48/2v2n1it/BHaXtH65rPFR4HpJ6wOr2f4lVcxtz6s3534U8Eg5j4fWlP+trTK784Ckg+Fvay+27e2TioiIvuu3a/q250qaAcwDHgJuoBrxnleukwv4Zrmu/GvgYkkfBD5bVq931yXAzsDtpf0TbP9V0uHA8ZKWA0uAtpH+dGC+pLm2D22nvQlUi9FeBpYDx3TS9wxgGrBpRxVsvyjpKOBySU8ANwJb9+D59YjtRyT9G3At1Tn+je1flYT7U0ltb/T+rfw+h2rB4AvAzrZf6EY3X6F6c/EQ1YxI25uGC4EfSjoOOIjqDcH3JX0ZWKPsv72zhidsNJrWfN1sRES/UHWZOWJoaGlpcWtrV2sKIyKilqQ5tlu6qjdUPqcfERER/WzI3WVP0onAwXXFF9k+tcH9rAdc086ud9t+spdtXsKrp/r/1fas3rRX2nwv1Ufwaj1g+4DettlJXw2PPyIiho5M78eQkun9iIiey/R+REREvEJG+jGkSHoOuHuw4+ih9YEnBjuIXhiOcSfmgTMc4x6OMUNj4t7E9tiuKg25a/rR9O7uzhTVUCKpdbjFDMMz7sQ8cIZj3MMxZhjYuDO9HxER0SSS9CMiIppEkn4MNdMHO4BeGI4xw/CMOzEPnOEY93CMGQYw7izki4iIaBIZ6UdERDSJJP2IiIgmkaQfA0LSPpLulnSfpCnt7F9T0oyy/4+Sxtfs+7dSfnf5WuIhH7ekf5Q0R9KC8nuvoR5zzf43SVoi6YsDFXPpty+vkW0k/V7SwnLO1xrKMUtaQ9LPSqz/W+6MOSC6EfNukuZKWiHpoLp9h0u6t/wcPlAxl757FbekiTWvjfmSJg/1mGv2v1bSIklnNSwo2/nJT7/+ACOAPwFvBl5DdXvdt9fV+Qxwdtn+CDCjbL+91F+T6r4AfwJGDIO4twM2LNtbA4uGesw1+38JXAR8cZi8RlYH5gPblsfrDcRrpI8xHwJcWLbXAR4Exg+RmMcD2wA/Bw6qKX89cH/5/bqy/boh9ProKO63ApuX7Q2BR4AxQznmmv3fBv4bOKtRcWWkHwNhR+A+2/fbfgm4EPhgXZ0PAj8r2xcD75akUn6h7WW2HwDuK+0N6bht32b74VK+EFhL0ppDOWYASftT/We+cABirdWXuPcG5tu+HcD2k7ZXDvGYDawraXVgbeAl4NmhELPtB23PB16uO/a9wFW2n7L9NHAVsM8AxAx9iNv2PbbvLdsPA48BXX5z3WDGDCBpErABcGUjg0rSj4GwEfDnmsd/KWXt1rG9AlhMNWLrzrH9pS9x1zoQuM32sn6Ks914im7HLGld4F+BUwYgznp9OddvBSxpVpkqPWEA4n1FPEVPYr4YeJ5q1Pl/wBm2n+rvgOnbv6eh/m+xS5J2pBp1/6lBcXWm1zFLWg34T+D4RgeVr+GNgaB2yuo/K9pRne4c21/6Ene1U9qK6tbIezcwrs70JeZTgG/aXlIG/gOpL3GvDuwC7AAsBa5Rdcex9m6d3Uh9iXlHYCXVdPPrgBskXW37/saG+Cp9+fc01P8tdt6ANA44Fzjc9qtG1v2gLzF/BviN7T83+t9iRvoxEP4CbFzz+I3Awx3VKVOeo4Gnunlsf+lL3Eh6I3AJ8HHbAzGyeEU8RU9i3gn4hqQHgc8DX5J0bH8HXB9T0dPXyPW2n7C9FPgNsH2/R9y3mA8BrrC93PZjwE3AQHz3el/+PQ31f4sdkvRa4HLgy7b/0ODYOtKXmHcGji3/Fs8APi5pakOi6u/FDPnJD9VI7H6qhXhtC1q2qqvzL7xywdMvyvZWvHIh3/0M3EK+vsQ9ptQ/cLic67o6JzOwC/n6cq5fB8ylWhC3OnA1sO8Qj/lfgZ9SjQbXBe4EthkKMdfUPYdXL+R7oJzv15Xt1w+V10cncb8GuAb4/EC9nvsac92+I2jgQr4BOwH5ae4f4H3APVTX0k4sZV8DPlC216JaMX4fcAvw5ppjTyzH3Q3803CIG/gy1TXbeTU/bxjKMde1cTIDmPQb8Bo5jGrx4R3AN4Z6zMDIUr6QKuEfP4Ri3oFqlPo88CSwsObYI8tzuQ/4xBB7fbQbd3ltLK/7tzhxKMdc18YRNDDp52t4IyIimkSu6UdERDSJJP2IiIgmkaQfERHRJJL0IyIimkSSfkRERJNI0o+IIUPSBpL+W9L95e6Ev5d0QNnXIuk73Wjj5p6U9xdJ4yUdMpB9RnQlH9mLiCGh3IjmZuBnts8uZZtQfab5zEENrofKt+/tQvVdB+8f7Hgi2mSkHxFDxV7AS20JH8D2Q20JX9Ieki4r2ydL+omk68qswHFtx0ha0l7jbeWlnesl/ULSPZKmSjpU0i3l/vablXrnSDpb0g2l3vtL+VqSflrq3iZpz1J+hKSLJP2a6s5oU4FdJc2T9IUy8r+h3BRorqR31sRznaSLJd0l6fyaux7uIOlmSbeX+EZJGiFpmqRby/3hP93oP0SsunLDnYgYKrai+jrd7toS2BMYBdwt6fu2l3fz2G2Bt1F9D/79wI9s7yjpc8Bnqe49ANX9zncHNgOulfQWqq/WxfYESVsCV0p6a6m/M9XX6T4laQ9qRvqS1gH+0faLkjYHLuDv37e/XXn+D1N9D/+7JN0CzAAm2761fH/8C8AngcW2dyi3a75J0pWubj0d0akk/YgYkiR9l2qK/CXbO7RT5XJXtyteJukxqnuP/6Wbzd9q+5HSz5/4+z3LF1C9kWjzC1d3ZLtX0v1UbzR2Ac4EsH2XpIeobu8L5X7zHfS5BnCWpIlUd9h7a82+W2z/pcQzj+rNxmLgEdu3lr6eLfv3BraRdFA5djSwOdV34Ud0Kkk/IoaKhcCBbQ9s/4uk9YHWDuovq9leSc/+P6s99uWaxy/XtVO/6Kmj2z23eb6TfV8AHqWaZVgNeLGDeNqei9rpn1L+WduzOukrol25ph8RQ8XvgLUkHVNTts5gBVMcLGm1cp3/zVQ3fZoNHApQpvXfVMrrPUd16aHNaKqR+8vAx4ARXfR9F7ChpB1KX6PKAsFZwDGS1miLQdK6vX2C0Vwy0o+IIcG2Je0PfFPSCcDjVCPnfx3EsO4Grqe6dHB0uR7/PeBsSQuAFcARtpeVtXe15gMrJN1OdevU7wG/lHQwcC2dzwpg+yVJk4EzJa1NdT3/PcCPqKb/55YFf48D+zfiycaqLx/Zi4hoh6RzgMtsXzzYsUQ0Sqb3IyIimkRG+hEREU0iI/2IiIgmkaQfERHRJJL0IyIimkSSfkRERJNI0o+IiGgS/z+kDSvvD2gwWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_feature_importance(active_learner.estimator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "src_bytes                      34.302328\n",
       "dst_host_same_src_port_rate    13.638587\n",
       "dst_host_serror_rate            0.783118\n",
       "dst_host_count                  0.731372\n",
       "dst_host_diff_srv_rate          0.730442\n",
       "srv_diff_host_rate              0.716498\n",
       "service_eco_i                   0.716498\n",
       "protocol_type_icmp              0.716498\n",
       "service_private                 0.716498\n",
       "dst_host_srv_diff_host_rate     0.716306\n",
       "dtype: float64"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_right_wrong_comp(predictions, n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'FP': 0, 'FN': 0}\n"
     ]
    }
   ],
   "source": [
    "clf = ni.active_learning_rf\n",
    "clf.fit(ni.splits[label]['x_train'], ni.splits[label]['y_train'])\n",
    "predictions = get_predictions(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_feature_importance(clf, n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_right_wrong_comp(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised sampling strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_learn_anomalies = ni.report_learn_anomalies()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "initial_f1       0.94\n",
       "sample_10_f1     0.94\n",
       "sample_50_f1     0.95\n",
       "sample_100_f1    0.93\n",
       "dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['label', 'initial_f1', 'sample_10_f1', 'sample_50_f1', 'sample_100_f1']\n",
    "q = df_learn_anomalies[cols]\n",
    "q.mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "initial_f1       0.07\n",
       "sample_10_f1     0.05\n",
       "sample_50_f1     0.09\n",
       "sample_100_f1    0.09\n",
       "dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q.std().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "initial_f1       0.94\n",
       "sample_10_f1     0.98\n",
       "sample_50_f1     0.98\n",
       "sample_100_f1    0.99\n",
       "dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qq = df_active_learning[(df_active_learning.learner == 'RandomForestClassifier')\n",
    "                   & (df_active_learning['sampling strategy'] == 'entropy_sampling')][cols]\n",
    "qq.mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "initial_f1       0.07\n",
       "sample_10_f1     0.03\n",
       "sample_50_f1     0.03\n",
       "sample_100_f1    0.03\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qq.std().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nvS15NKmRsudJTcFul555fh3n/f5vXDFjEavXbeSNe44qrvxbW8SeLxvC9AnD2W3UYFpKV6OH770LZ7x5T35+1yKWP7+uw/lMGz+Mt+4/mr6t22fVhJk1vx73RvO2rj66Y/4yPvXz2Tzx3Br+ftoYPvyGl7P7zkO22fzNzLpCT6g+2q69sHYD/3XTQ3zvjw8zcadB/O9HXs0rxg7r7rDMzBrKSaHCc6vXcekdC/nRbY+wbNVaTjhoHP92zJQtfuJvZtaT+ExXcvODT/Lxy+/h2dXrecMeozjtsN05aOKI7g7LzGybcVLIfj/3CT7ys7uZvMtgvvL2/VxVZGa9Uu9JChHUenPnt3Me5/TL72bKrjtyyQdfydCB9b8+ambWrHpNUrjjl99m1/sv5J6WfbinZV8eaNmddUon/9XPPc2ZwxfywZGP0vei0yA6+5siTajvQBh3MEx8HYw5EPr4xTmzmlYthYW3w4L/gyfup4NfHN16h/0bvOJdDZ1Fr0kKA0aMZuWg8bzl+dt527obNx3Zl9Se65KxMHY69B3QHSFuH15YDnN/BXdf0t2RmPUcI3aDsQdBa4NrGQbv0tjy6UVJYf/D3gWHvQs2rIfH701ZPXJDYX0HpA06fGLNKqZeZeMGeGIOPH5f6jaz6nYYDONfBTuO7u5IukyvSQqF1j4w5oD0Z9W1tMKu+6c/M+tV3J6CmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzQ0KQg6UhJD0qaJ+msKuPHS7pZ0j2S7pV0dCPjMTOz+hqWFCS1AhcARwFTgBMlTamY7HPAlRExDTgB+G6j4jEzs4418k7hYGBeRMyPiLXAFcBxFdMEsGPuHgosaWA8ZmbWgUYmhTHA4lJ/Wx5WdjZwkqQ24Hrg9GoFSTpV0kxJM5cuXdqIWM3MjMYmBVUZFhX9JwIXR8RY4GjgUkkviSkiLoyI6RExfdSoUQ0I1czMoLFJoQ0YV+ofy0urh04BrgSIiD8D/YGRDYzJzMzqaGRSmAFMljRJUj/Sg+RrKqZZBBwOIGlvUlJw/ZCZWTdpWFKIiPXAacANwAOkbxndL+kcScfmyc4APiTpL8DlwMkRUVnFZGZm20ifRhYeEdeTHiCXh32+1D0XeE0jYzAzs87zG81mZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7NCQ5OCpCMlPShpnqSzakzzLklzJd0v6bJGxmNmZvX1aVTBklqBC4A3AW3ADEnXRMTc0jSTgc8Ar4mI5ZJ2blQ8ZmbWsUbeKRwMzIuI+RGxFrgCOK5img8BF0TEcoCIeLKB8ZiZWQcamRTGAItL/W15WNkewB6Sbpd0h6QjqxUk6VRJMyXNXLp0aYPCNTOzRiYFVRkWFf19gMnAocCJwA8lDXvJhyIujIjpETF91KhRXR6omZkljUwKbcC4Uv9YYEmVaX4VEesi4hHgQVKSMDOzbtDIpDADmCxpkqR+wAnANRXTXA28EUDSSFJ10vwGxmRmZnU0LClExHrgNOAG4AHgyoi4X9I5ko7Nk90ALJM0F7gZ+JeIWNaomMzMrD5FVFbzb9+mT58eM2fO7O4wzMx6FEmzImJ6R9P5jWYzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzwhYnBUl7dWUgZmbW/bbmTuHGLovCzMy2C33qjZR0fq1RwEt+S9nMzHq2ukkB+ABwBrCmyrgTuz4cMzPrTh0lhRnAnIj4U+UISWc3JCIzM+s2HSWF44HV1UZExKSuD8fMzLpTRw+aB0fE89skEjMz63YdJYWr2zsk/bLBsZiZWTfrKCmo1P3yRgZiZmbdr6OkEDW6zcysCXX0oHl/Sc+S7hgG5G5yf0TEjg2NzszMtqm6SSEiWrdVIGZm1v3cIJ6ZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzQ0KQg6UhJD0qaJ+msOtMdLykkTW9kPGZmVl/DkoKkVuAC4ChgCnCipClVphsCfAy4s1GxmJlZ5zTyTuFgYF5EzI+ItcAVwHFVpvsS8J/U+NlPMzPbdhqZFMYAi0v9bXlYQdI0YFxEXFevIEmnSpopaebSpUu7PlIzMwMamxRUZVjxQz2SWoDzgDM6KigiLoyI6RExfdSoUV0YopmZlTUyKbQB40r9Y4Elpf4hwL7ALZIWAIcA1/hhs5lZ92lkUpgBTJY0SVI/4ATgmvaREfFMRIyMiIkRMRG4Azg2ImY2MCYzM6ujYUkhItYDpwE3AA8AV0bE/ZLOkXRso+ZrZmZbrqPfaN4qEXE9cH3FsM/XmPbQRsZiZmYd8xvNZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzQkOTgqQjJT0oaZ6ks6qM/5SkuZLulfQHSRMaGY+ZmdXXsKQgqRW4ADgKmAKcKGlKxWT3ANMj4hXAVcB/NioeMzPrWCPvFA4G5kXE/IhYC1wBHFeeICJujojnc+8dwNgGxmNmZh1oZFIYAywu9bflYbWcAvym2ghJp0qaKWnm0qVLuzBEMzMra2RSUJVhUXVC6SRgOvC1auMj4sKImB4R00eNGtWFIZqZWVmfBpbdBowr9Y8FllROJOkI4F+BN0TEmgbGY2ZmHWjkncIMYLKkSZL6AScA15QnkDQN+AFwbEQ82cBYzMysExqWFCJiPXAacAPwAHBlRNwv6RxJx+bJvgYMBn4habaka2oUZ2Zm20Ajq4+IiOuB6yuGfb7UfUQj529mZpunoUlhW1m3bh1tbW2sXr26u0PZIv3792fs2LH07du3u0Mxs16uKZJCW1sbQ4YMYeLEiUjVvvS0/YoIli1bRltbG5MmTerucMysl2uKto9Wr17NTjvt1OMSAoAkdtpppx57l2NmzaUpkgLQIxNCu54cu5k1l6ZJCmZmtvWa4pnC9qC1tZX99tuP9evXM2nSJC699FKGDRvGggUL2Hvvvdlzzz2Lae+66y769evXjdGamVXnO4UuMmDAAGbPns2cOXMYMWIEF1xwQTFut912Y/bs2cWfE4KZba+a7k7hi9fez9wlz3ZpmVNG78gX3rpPp6d/1atexb333tulMZiZbQu+U+hiGzZs4A9/+APHHntsMezhhx9m6tSpTJ06lY9+9KPdGJ2ZWX1Nd6ewOVf0XemFF15g6tSpLFiwgAMPPJA3velNxbj26iMzs+2d7xS6SPszhYULF7J27dpNnimYmfUUTgpdbOjQoZx//vl8/etfZ926dd0djpnZZnFSaIBp06ax//77c8UVV3R3KGZmm6Xpnil0l5UrV27Sf+211xbdc+bM2dbhmJltEd8pmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4K+kdpHBgwezcuVKN5VtZj2ak0IDuK0jM+upmi8p/OYsePy+ri3zZfvBUed2bZlmZtshP1NoADeVbWY9VfPdKWwHV/SuPjKznsp3CmZmVnBSMDOzgpOCmZkVnBS6SHvT2RMnTnRT2WbWYzkpmJlZwUnBzMwKTZMUIqK7Q9hiPTl2M2suTZEU+vfvz7Jly3rkyTUiWLZsGf379+/uUMzMmuPltbFjx9LW1sbSpUu7O5Qt0r9/f8aOHdvdYZiZNUdS6Nu3L5MmTeruMMzMeryGVh9JOlLSg5LmSTqryvgdJP08j79T0sRGxmNmZvU1LClIagUuAI4CpgAnSppSMdkpwPKI2B04D/hqo+IxM7OONfJO4WBgXkTMj4i1wBXAcRXTHAf8JHdfBRwuSQ2MyczM6mjkM4UxwOJSfxvwylrTRMR6Sc8AOwFPlSeSdCpwau5dKenBLYxpZGXZvYCXuXfwMvcOW7PMEzozUSOTQrUr/srvjHZmGiLiQuDCrQ5ImhkR07e2nJ7Ey9w7eJl7h22xzI2sPmoDxpX6xwJLak0jqQ8wFHi6gTGZmVkdjUwKM4DJkiZJ6gecAFxTMc01wPtz9/HATdET30AzM2sSDas+ys8ITgNuAFqBH0fE/ZLOAWZGxDXAj4BLJc0j3SGc0Kh4sq2uguqBvMy9g5e5d2j4MssX5mZm1q4p2j4yM7Ou4aRgZmaFXpMUOmpyoxlIGifpZkkPSLpf0sfz8BGSfifpofx/eHfH2pUktUq6R9J1uX9SbjblodyMSr/ujrErSRom6SpJf83b+lW9YBt/Mu/TcyRdLql/s21nST+W9KSkOaVhVberkvPz+exeSQd0VRy9Iil0ssmNZrAeOCMi9gYOAT6al/Ms4A8RMRn4Q+5vJh8HHij1fxU4Ly/vclJzKs3k28BvI2IvYH/SsjftNpY0BvgYMD0i9iV9ceUEmm87XwwcWTGs1nY9Cpic/04FvtdVQfSKpEDnmtzo8SLisYi4O3c/RzpZjGHT5kR+AryteyLsepLGAn8H/DD3CziM1GwKNN/y7gi8nvTNPSJibUSsoIm3cdYHGJDfZxoIPEaTbeeIuJWXvqdVa7seB1wSyR3AMEm7dkUcvSUpVGtyY0w3xbJN5BZnpwF3ArtExGOQEgewc/dF1uW+BXwa2Jj7dwJWRMT63N9s2/rlwFLgv3OV2Q8lDaKJt3FEPAp8HVhESgbPALNo7u3crtZ2bdg5rbckhU41p9EsJA0Gfgl8IiKe7e54GkXSMcCTETGrPLjKpM20rfsABwDfi4hpwCqaqKqomlyPfhwwCRgNDCJVn1Rqpu3ckYbt570lKXSmyY2mIKkvKSH8LCL+Jw9+ov3WMv9/srvi62KvAY6VtIBUJXgY6c5hWK5mgObb1m1AW0TcmfuvIiWJZt3GAEcAj0TE0ohYB/wP8Gqaezu3q7VdG3ZO6y1JoTNNbvR4uT79R8ADEfHN0qhycyLvB361rWNrhIj4TESMjYiJpG16U0S8B7iZ1GwKNNHyAkTE48BiSXvmQYcDc2nSbZwtAg6RNDDv4+3L3LTbuaTWdr0GeF/+FtIhwDPt1Uxbq9e80SzpaNJVZHuTG1/u5pC6nKTXArcB9/FiHftnSc8VrgTGkw6wd0ZEUzU8KOlQ4MyIOEbSy0l3DiOAe4CTImJNd8bXlSRNJT1Y7wfMBz5AusBr2m0s6YvAu0nfsLsH+EdSHXrTbGdJlwOHkprHfgL4AnA1VbZrTo7fIX1b6XngAxExs0vi6C1JwczMOtZbqo/MzKwTnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBNoukkPSNUv+Zks7uorIvlnR8x1Nu9XzemVsXvbnR8yrNc7SkqzqessNyPiFpYFfEtAXznlhuwXMzP/cPjYjJup6Tgm2uNcDbJY3s7kDKcku4nXUK8JGIeGOj4imT1CcilkREVyS8T5AahOtJJgJOCj2Ek4JtrvWk34n9ZOWIyit9SSvz/0Ml/VHSlZL+JulcSe+RdJek+yTtVirmCEm35emOyZ9vlfQ1STNy2/H/VCr3ZkmXkV7Yq4znxFz+HElfzcM+D7wW+L6kr1VML0nfkTRX0q8lXd++PJIWtCdCSdMl3ZK7Bym1gz8jN1B3XB7BzI8nAAAEK0lEQVR+sqRfSLoWuLF8lV1neXaVdKuk2Tnm11XE9zFS2z83t9/lVFvGKuvh83lecyRdmF98QtItkr6at8Pf2ueXY71N0t3579VVyrwtv0TX3n+7pFdIekOOf3ZeH0OAc4HX5WEv2W9sOxMR/vNfp/+AlcCOwAJgKHAmcHYedzFwfHna/P9QYAWwK7AD8CjwxTzu48C3Sp//LeliZTKpfZf+pPbiP5en2QGYSWoc7VBSg3CTqsQ5mvQG6ChSI3I3AW/L424htc1f+Zm3A78jvfU+Osd8fB63ABiZu6cDt+Tu/yC9SQswDPgbqcG2k3P8I/K4icCc3F1rec4A/jUPbwWGVImxHEfNZaz4zIhS96XAW0vr4Ru5+2jg97l7INA/d08GZlZZhveXttsepWmuBV6TuwfnuA4Fruvufdd/nfvznYJttkgtr15C+uGTzpoR6fce1gAPAzfm4feRTjbtroyIjRHxEKkJh72AN5PaeZlNarJjJ9LJCuCuiHikyvwOIp24l0ZqXvlnpN8hqOf1wOURsSEilpBOsh15M3BWju0WUhIbn8f9Lqo3NVFreWYAH8jPaPaL9JsY9XR2Gd+o9Atl95EaDdynNK690cRZvLgd+gIX5el/Qfphqkq/AI5RaoDxg6SEDnA78M18VzMsXmza2nqIPh1PYlbVt4C7gf8uDVtPrpLMVRTln0cst0mzsdS/kU33w8p2V4LUTPDpEXFDeYRSe0erasRXrWnhzqjV7kuxbKQTf3k+74iIBytie2UHsb1kefLnXk/60aBLJX0tIi6pE2uHyyipP/Bd0p3R4pxwyvG3b4cNvLgdPklqe2d/0jKvriw3Ip6X9DtSk9bvIt09ERHnSvo16c7jDklHdBSjbV98p2BbJF8BX8mmP4G4ADgwdx9HuuLcXO+U1JKfM7wceBC4AfjnfFWKpD2UflimnjuBN0gamR9Cnwj8sYPP3AqckOv8dwXKD6IX8OKyvaM0/Abg9FI9/bQOl7DG8kiaQPp9iItIrd1W+93d54Ahm7GM7QngKaXf2ejMw+6hwGMRsRF4L6kqq5ofAueT7gKfzsuyW0TcFxFfJVWL7VURs23nnBRsa3yD1KJju4tIJ6m7gHpXyvU8SDqx/Qb4cESsJp185gJ354e1P6CDu9xIzQh/htS88l+AuyOio6aV/xd4iFSl9T02PcF+Efi2pNtIV9XtvkRKfvfm2L7UiWWstTyHArMl3UNKPN+u8tkLgd9Iurkzyxjppzovyst0NamKqiPfBd4v6Q7S84Kq2zHSjxs9y6Z3i5/ID7T/ArxA2o73Ausl/cUPmrd/biXVrAZJF5MekG71+wXNSNJo0nOUvfJdhTUB3ymY2WaT9D5S9dW/OiE0F98pmJlZwXcKZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmhf8PiPcTGN5bK08AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aLv3bS/Tq0pHP7NSvSeUY0C4zwxXw2uIPOevOqQzashM//9LwjRLqO++v5XvjpvP1O6Yw9uz9WLOunq/9cTLr1jt/PH0funfacHdeXw/XT5zDjx+cyX/nLuMXxw2ne3W4I6tbX8/5Y1/gxZoV/PxLw9lxq41bQele3YEd+nUt+zoX2ZyU9XsKZjYYeDjHg+YvEJrwbXjQfJ27F/oWYsnuFL5733TGv7iQv3/7QHYbEL7PtGbdeia++i6168NV6tR5y7lr0nz2GNiD67+6V8GTfN36eo743dPUu/PYdw6iqn3xj2xm1KzgzRxVTiMG9WTb3l2KLqu5Vq6tY/SYSbz09vtNLqOdwYE79OGEvQfy+V23orpD/qvsRe+v5rgbn2V9vfPXcw9g4JbZ1/WEl9/hW3c/z8HD+rJ81TpeWfQBf/7GJxk5uFfW6evrnT/8Zy6/emw2W3buwJf2HMCJIwfxx2feZOzkBVx57K6ctv/gJi+nSEthZs+7+8iC05Xx7aOxhBYB+xBez7yc0AgW7n5zbJbgekJ766uAM9y94Nm+FEnhoRcXcv7YaVxw6FC+d/iwvNP+c+YifvDADOrWO/26b1zP3r6dcdDQvpw4ciAvL/yAix+YwU0n78Wo4S37mfl7H9Vy/9QFrM1RjVPIyrV1/GPGIt5esZquW1TRu2v+etAVq9axvt6575v7sWv/HnmnvXvSfH784EzM4KaT9+aI3Qo37TTtreXc9NQbPPnqu9TVh33+3EO25+IjSvd8SmRzVvGkUC7NTQrPz1/O6FsmMXxAD8aetV9R1UJvLVvFTf9+g9W1G7e08OGaOp5+fSm16+upamfsOqAHD557gKocCFfpk+Yu4x8vLeKjtfl/6KqdGSfvtw17b5v9ij/TvZPfonunDhzZyOS7bOVaHpy+kLV16/nWwdtrO0mboaSQxfxlH/GlG5+lW3UVfzv3wCY/xc+0YlUt419cyOOzFnPR53ZkxKCeJSlXRKRUik0KLfk3mhvlgwUvc8ef/8Zh9XVcdOAwei2ZDF1K08RMz84dOW3/wa2rbrpuLbz+GKxfV+lIpDXp0AmGfh7aqYWdzVWbSQrTHh/L5Wt+H3oeiwPP+T/YanjFYtqsPf8neLSYn28QaaTjb4PhJ1Q6CsmhzSSFA0+8kFfmHsPOW3eHutVw+yiYchsc/dtKh7Z5evVh6D0UvtKYZvNF8nG4+3h46X4lhc1Ym0kKVd36svMeqS/z7XY8zBgHh18J1QV+5fKdmTDuVFi9PPRv0S1c7Qwq+AZtcdbXwX2nwIJJhafd9kA49nrotGVp5p3N6hUw/xk44Hzop7dzpIR2Ow4m3QSr3oPOxb1UIJtWm0kKG9nn6zD9bphxH+x7Vu7p6uvh4e/Amvdh+Ilh2CsPw/gL4JynoX3+5imKMnkMvPYoDP8ydMrzkLpuDUwfC7d8Fk4aW74T9px/QX0d7Hhk4WlFGmP4ifDs72HW32HkGZWORrJou0lhwN7Qf0+Ycivs8w3I9WritDuhZgp88WYYEX9HfrvPwL2jYdKNcOCFzYvjg4Uw8SrY4TA4bkzuOBqMOBnuOxVuPRT2Py88uINwBzFon/yfLdbsR6FL37COREppq92hzzB46QElhc1U234FYOSZsORVmJ/j1yo/WgqPXx5OuHukfqlvpyNh2Ch46mp4v6Z5MUz4UXjDZ9QvCycEgG32g7Ofgn47w7+vhn9dHv7+dDSsyN5MRqOsXwevPw7DPg/t1NaPlJhZuFuY/0zzjx0pi7adFHY7Hqp7wNTbso9//HKoXQlf+PXGJ+xR14A7PFr0b6Bv7I0n4eW/wqcvgt7bF/+5HgPgzMfhh4vC3/kvhPgm/LDpsTSY/wysfV9VR1I+ux0POMzM+zPKUiFtt/oIoGPnUB0z6UZ47bGNx9d+GKqH+u288bgtt4WDfwBPXAk/H0D2Rl8LqFsNvbZrWhWUWYgfQkL59EXw5E9hzhOww6GNL6/B7Eehqhq2O6TpZYjk03v7UDX50v1w4AWVjkYytO2kAPCp74aHxeuzNMPQuRfsd27uz+5/PrSrgg8XN23eZrDnqdChcc1zZ3XA+TD9Hnj0YvjWs1DVhN9DcIfZj4RnJh03XQN80gbtdgJMuBTuPTkcQ1KcvU5r3kVfEbQ1uvYLr6U2RVXH5j9oLpWqLcJziT8fHxLDoCb8sumqZeG5xKe/X/r4RNJ2/3KoOl36eqUjaVnW5P7tj1JRUmhNhh4WrsCevyP8NUVVNQw7opRRiWysSx/4xr8qHYVkoaTQ2hx/Kxx6GTl+xK6wLbrrS0UibZiSQmtjFh6Ci4g0Qdt+JVVERDagpCAiIgklBRERSSgpiIhIQklBREQSSgoiIpJQUhARkYSSgoiIJJQUREQkoaQgIiIJJQUREUkoKYiISEJJQUREEkoKIiKSUFIQEZGEkoKIiCSUFEREJKGkICIiCSUFERFJKCmIiEiirEnBzI4ws9lmNsfMLskyfhszm2hm08xshpkdWc54REQkv7IlBTNrD9wAjAJ2AUab2S4Zk/0YGOfuewInATeWKx4RESmsnHcK+wJz3H2uu9cC9wLHZkzjQPfY3QNYWMZ4RESkgHImhQHAglR/TRyWdgVwipnVAI8A52cryMzONrOpZjZ1yZIl5YhVREQob1KwLMM8o380cIe7DwSOBO4ys41icvcx7j7S3Uf27du3DKGKiAiUNynUAINS/QPZuHroTGAcgLv/F6gG+pQxJhERyaOcSWEKMNTMhphZR8KD5PEZ07wFHApgZjsTkoLqh0REKqRsScHd64DzgAnAK4S3jF42syvN7Jg42UXAWWb2IjAWON3dM6uYRERkE6kqZ+Hu/gjhAXJ62GWp7lnAgeWMQUREiqdvNIuISEJJQUREEkoKIiKSUFIQEZGEkoKIiCSUFEREJKGkICIiCSUFERFJKCmIiEhCSUFERBJKCiIiklBSEBGRhJKCiIgklBRERCShpCAiIgklBRERSSgpiIhIQklBREQSSgoiIpJQUhARkYSSgoiIJJQUREQkoaQgIiIJJQUREUkoKYiISEJJQUREEkoKIiKSUFIQEZGEkoKIiCSUFEREJKGkICIiCSUFERFJKCmIiEhCSUFERBJlTQpmdoSZzTazOWZ2SY5pvmxms8zsZTO7p5zxiIhIflXlKtjM2gM3AIcDNcAUMxvv7rNS0wwFLgUOdPflZtavXPGIiEhh5bxT2BeY4+5z3b0WuBc4NmOas4Ab3H05gLu/W8Z4RESkgHImhQHAglR/TRyWNgwYZmbPmNkkMzsiW0FmdraZTTWzqUuWLClTuCIiUs6kYFmGeUZ/FTAUOAQYDdxqZj03+pD7GHcf6e4j+/btW/JARUQkKGdSqAEGpfoHAguzTPN3d1/n7m8CswlJQkREKqCcSWEKMNTMhphZR+AkYHzGNA8CnwEwsz6E6qS5ZYxJRETyKFtScPc64DxgAvAKMM7dXzazK83smDjZBGCZmc0CJgI/cPdl5YpJRETyM/fMav7N28iRI33q1KmVDkNEpEUxs+fdfWSh6fSNZhERSSgpiIhIQklBREQSSgoiIpJQUhARkYSSgoiIJJQUREQkoaQgIiIJJQUREUkoKYiISEJJQUREEkoKIiKSUFIQEZFEk5OCme1UykBERKTymnOn8FjJohARkc1CVb6RZnZdrlHARr+lLCIiLVvepACcAVwErM0ybnTpwxERkUoqlBSmADPd/dnMEWZ2RVkiEhGRiimUFE4A1mQb4e5DSh+OiIhUUqEHzV3dfdUmiURERCquUFJ4sKHDzP5S5lhERKTCCiUFS3VvV85ARESk8golBc/RLSIirVChB817mNkHhDuGTrGb2O/u3r2s0YmIyCaVNym4e/tNFYiIiFSeGsQTEZGEkoKIiCSUFEREJKGkICIiCSUFERFJKCmIiEhCSUFERBJKCiIiklBSEBGRhJKCiIgkypoUzOwIM5ttZnPM7JI8051gZm5mI8sZj4iI5Fe2pGBm7YEbgFHALsBoM9sly3TdgAuA58oVi4iIFKecdwr7AnPcfa671wL3Asdmme6nwC/J8bOfIiKy6ZQzKQwAFqT6a+KwhJntCQxy94fzFWRmZ5vZVDObumTJktJHKiIiQHmTgmUZlvxQj5m1A34DXFSoIHcf4+4j3X1k3759SxiiiIiklTMp1ACDUv0DgYWp/m7AbsBTZjYP2A8Yr4fNIiKVU86kMAUYamZDzKwjcBIwvmGku7/v7n3cfbC7DwYmAce4+9QyxiQiInmULSm4ex1wHjABeAUY5+4vm9mVZnZMueYrIiJNV+g3mpvF3R8BHskYdlmOaQ8pZywiIlKYvtEsIiIJJQUREUkoKYiISEJJQUREEkoKIiKSUFIQEZGEkoKIiCSUFEREJKGkICIiCSUFERFJKCmIiEhCSUFERBJKCiIiklBSEBGRhJKCiIgklBRERCShpCAiIgklBRERSSgpiIhIQklBREQSSgoiIpJQUhARkYSSgoiIJJQUREQkoaQgIiIJJQUREUkoKYiISEJJQUREEkoKIiKSUFIQEZGEkoKIiCSUFEREJKGkICIiCSUFERFJlDUpmNkRZjbbzOaY2SVZxn/PzGaZ2Qwze8LMti1nPCIikl/ZkoKZtQduAEYBuwCjzWyXjMmmASPdfXfgAeCX5YpHREQKK+edwr7AHHef6+61wL3AsekJ3H2iu6+KvZOAgWWMR0RECihnUhgALEj118RhuZwJPJpthJmdbWZTzWzqkiVLShiiiIiklTMpWJZhnnVCs1OAkcC12ca7+xh3H+nuI/v27VvCEEVEJK2qjGXXAINS/QOBhZkTmdlhwI+Ag919bRnjERGRAsp5pzAFGGpmQ8ysI3ASMD49gZntCfwBOMbd3y1jLCIiUoSyJQV3rwPOAyYArwDj3P1lM7vSzI6Jk10LdAXuN7PpZjY+R3EiIrIJlLP6CHd/BHgkY9hlqe7Dyjl/ERFpnLImhU1l3bp11NTUsGbNmkqH0iTV1dUMHDiQDh06VDoUEWnjWkVSqKmpoVu3bgwePBizbC89bb7cnWXLllFTU8OQIUMqHY6ItHGtou2jNWvW0Lt37xaXEADMjN69e7fYuxwRaV1aRVIAWmRCaNCSYxeR1qXVJAUREWm+VvFMYXPQvn17hg8fTl1dHUOGDOGuu+6iZ8+ezJs3j5133pkdd9wxmXby5Ml07NixgtGKiGSnO4US6dSpE9OnT2fmzJn06tWLG264IRm3/fbbM3369ORPCUFENlet7k7hJw+9zKyFH5S0zF36d+fyo3ctevr999+fGTNmlDQGEZFNQXcKJbZ+/XqeeOIJjjnmmGTYG2+8wYgRIxgxYgTf/va3KxidiEh+re5OoTFX9KW0evVqRowYwbx589h77705/PDDk3EN1UciIps73SmUSMMzhfnz51NbW7vBMwURkZZCSaHEevTowXXXXcevfvUr1q1bV+lwREQaRUmhDPbcc0/22GMP7r333kqHIiLSKK3umUKlrFy5coP+hx56KOmeOXPmpg5HRKRJdKcgIiIJJQUREUkoKYiISEJJQUREEkoKIiKSUFIQEZGEXkktka5du7Jy5Uo1lS0iLZqSQhmorSMRaalaX1J49BJ456XSlrnVcBh1dWnLFBHZDOmZQhmoqWwRaala353CZnBFr+ojEWmpdKcgIiIJJQUREUkoKYiISEJJoUQams4ePHiwmsoWkRZLSUFERBJKCiIikmg1ScHdKx1Ck7Xk2EWkdWkVSaG6upply5a1yJOru7Ns2TKqq6srHYqISOv48trAgQOpqalhyZIllQ6lSaqrqxk4cGClwxARaR1JoUOHDgwZMqTSYYiItHhlrT4ysyPMbLaZzTGzS7KM38LM7ovjnzOzweWMR0RE8itbUjCz9sANwChgF2C0me2SMdmZwHJ33wH4DXBNueIREZHCynmnsC8wx93nunstcC9wbMY0xwJ/it0PAIeamZUxJhERyaOczxQGAAtS/TXAJ3NN4+51ZvY+0BtYmp7IzM4Gzo69K81sdhNj6pNZdhugZW4btMxtQ3OWedtiJipnUsh2xZ/5zmgx0+DuY4AxzQ7IbKq7j2xuOS2Jlrlt0DK3DZtimctZfVTYu566AAAI50lEQVQDDEr1DwQW5prGzKqAHsB7ZYxJRETyKGdSmAIMNbMhZtYROAkYnzHNeOBrsfsE4Elvid9AExFpJcpWfRSfEZwHTADaA7e7+8tmdiUw1d3HA7cBd5nZHMIdwknliidqdhVUC6Rlbhu0zG1D2ZfZdGEuIiINWkXbRyIiUhpKCiIikmgzSaFQkxutgZkNMrOJZvaKmb1sZhfG4b3M7HEzez3+37LSsZaSmbU3s2lm9nDsHxKbTXk9NqPSsdIxlpKZ9TSzB8zs1bit928D2/i7cZ+eaWZjzay6tW1nM7vdzN41s5mpYVm3qwXXxfPZDDPbq1RxtImkUGSTG61BHXCRu+8M7Ad8Oy7nJcAT7j4UeCL2tyYXAq+k+q8BfhOXdzmhOZXW5HfAP919J2APwrK32m1sZgOAC4CR7r4b4cWVk2h92/kO4IiMYbm26yhgaPw7G7ipVEG0iaRAcU1utHjuvsjdX4jdHxJOFgPYsDmRPwFfrEyEpWdmA4EvALfGfgM+S2g2BVrf8nYHDiK8uYe717r7ClrxNo6qgE7x+0ydgUW0su3s7v9h4+9p5dquxwJ3ejAJ6GlmW5cijraSFLI1uTGgQrFsErHF2T2B54BPuPsiCIkD6Fe5yErut8DFQH3s7w2scPe62N/atvV2wBLgj7HK7FYz60Ir3sbu/jbwK+AtQjJ4H3ie1r2dG+TarmU7p7WVpFBUcxqthZl1Bf4CfMfdP6h0POViZkcB77r78+nBWSZtTdu6CtgLuMnd9wQ+ohVVFWUT69GPBYYA/YEuhOqTTK1pOxdStv28rSSFYprcaBXMrAMhIfzZ3f8aBy9uuLWM/9+tVHwldiBwjJnNI1QJfpZw59AzVjNA69vWNUCNuz8X+x8gJInWuo0BDgPedPcl7r4O+CtwAK17OzfItV3Ldk5rK0mhmCY3WrxYn34b8Iq7/29qVLo5ka8Bf9/UsZWDu1/q7gPdfTBhmz7p7icDEwnNpkArWl4Ad38HWGBmO8ZBhwKzaKXbOHoL2M/MOsd9vGGZW+12Tsm1XccDp8W3kPYD3m+oZmquNvONZjM7knAV2dDkxlUVDqnkzOxTwNPAS3xcx/5DwnOFccA2hAPsRHdvVQ0PmtkhwPfd/Sgz245w59ALmAac4u5rKxlfKZnZCMKD9Y7AXOAMwgVeq93GZvYT4CuEN+ymAd8g1KG3mu1sZmOBQwjNYy8GLgceJMt2jcnxesLbSquAM9x9akniaCtJQURECmsr1UciIlIEJQUREUkoKYiISEJJQUREEkoKIiKSUFKQRjEzN7Nfp/q/b2ZXlKjsO8zshMJTNns+J8bWRSeWe16pefY3swcKT1mwnO+YWedSxNSEeQ9Ot+DZyM99tRwxSekpKUhjrQWOM7M+lQ4kLbaEW6wzgXPd/TPliifNzKrcfaG7lyLhfYfQIFxLMhhQUmghlBSkseoIvxP73cwRmVf6ZrYy/j/EzP5tZuPM7DUzu9rMTjazyWb2kpltnyrmMDN7Ok53VPx8ezO71symxLbjv5kqd6KZ3UP4wl5mPKNj+TPN7Jo47DLgU8DNZnZtxvRmZteb2Swz+4eZPdKwPGY2ryERmtlIM3sqdnex0A7+lNhA3bFx+Olmdr+ZPQQ8lr7KzrM8W5vZf8xseoz50xnxXUBo+2diw11OtmXMsh4ui/OaaWZj4hefMLOnzOyauB1ea5hfjPVpM3sh/h2Qpcyn45foGvqfMbPdzezgGP/0uD66AVcDn47DNtpvZDPj7vrTX9F/wEqgOzAP6AF8H7gijrsDOCE9bfx/CLAC2BrYAngb+EkcdyHw29Tn/0m4WBlKaN+lmtBe/I/jNFsAUwmNox1CaBBuSJY4+xO+AdqX0Ijck8AX47inCG3zZ37mOOBxwrfe+8eYT4jj5gF9YvdI4KnY/XPCN2kBegKvERpsOz3G3yuOGwzMjN25luci4EdxeHugW5YY03HkXMaMz/RKdd8FHJ1aD7+O3UcC/4rdnYHq2D0UmJplGb6W2m7DUtM8BBwYu7vGuA4BHq70vqu/4v50pyCN5qHl1TsJP3xSrCkefu9hLfAG8Fgc/hLhZNNgnLvXu/vrhCYcdgI+R2jnZTqhyY7ehJMVwGR3fzPL/PYhnLiXeGhe+c+E3yHI5yBgrLuvd/eFhJNsIZ8DLomxPUVIYtvEcY979qYmci3PFOCM+IxmuIffxMin2GX8jIVfKHuJ0GjgrqlxDY0mPs/H26EDcEuc/n7CD1Nluh84ykIDjF8nJHSAZ4D/jXc1Pf3jpq2lhagqPIlIVr8FXgD+mBpWR6ySjFUU6Z9HTLdJU5/qr2fD/TCz3RUnNBN8vrtPSI+w0N7RRzniy9a0cDFytfuSLBvhxJ+ez/HuPjsjtk8WiG2j5YmfO4jwo0F3mdm17n5nnlgLLqOZVQM3Eu6MFsSEk46/YTus5+Pt8F1C2zt7EJZ5TWa57r7KzB4nNGn9ZcLdE+5+tZn9g3DnMcnMDisUo2xedKcgTRKvgMex4U8gzgP2jt3HEq44G+tEM2sXnzNsB8wGJgDfilelmNkwCz8sk89zwMFm1ic+hB4N/LvAZ/4DnBTr/LcG0g+i5/Hxsh2fGj4BOD9VT79nwSXMsTxmti3h9yFuIbR2m+13dz8EujViGRsSwFILv7NRzMPuHsAid68HTiVUZWVzK3Ad4S7wvbgs27v7S+5+DaFabKeMmGUzp6QgzfFrQouODW4hnKQmA/mulPOZTTixPQqc4+5rCCefWcAL8WHtHyhwl+uhGeFLCc0rvwi84O6Fmlb+G/A6oUrrJjY8wf4E+J2ZPU24qm7wU0LymxFj+2kRy5hreQ4BppvZNELi+V2Wz44BHjWzicUso4ef6rwlLtODhCqqQm4EvmZmkwjPC7JuRw8/bvQBG94tfic+0H4RWE3YjjOAOjN7UQ+aN39qJVUkBzO7g/CAtNnfL2iNzKw/4TnKTvGuQloB3SmISKOZ2WmE6qsfKSG0LrpTEBGRhO4UREQkoaQgIiIJJQUREUkoKYiISEJJQUREEv8fK54HD+aRArkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+FpY/1egozKxBnBSAvz+wiF0GLmDQlrs0OpTGGz4OljkpmLWqlk8Ky1atYd78+WwRi1v7eUKHYWOdFMxaWEs+aJ41/2m++vt7WPP88yxbtZYp0Z4mTHBSYPjY9Ewhwk1pZi2oJWsKX7viXu59bAkjNhvEhFGb8Y5tl6YJTgqp+WjtSlj1XKMjMbMGaLmawi0PLeYfcxfxhbfuwgdft30aOeOXsHgkjJ7U2OD6gtzVBcufgs1GNjYWM9vkWq6mcO51cxgzfDDHvGrbF0YuvBcm7OTmEii+1eyPpZq1ppZKCvc+toQ/3fMEJ7xmCiM2K1WSFt7rpqMOTgpmLa2lksK51z3AiCEDed9rtnth5LKnYOnjTgodhuXmo2WLGxuHmTVEyzxTWPDPWxl4x3TO2GlLxtz/9AsTFj+U/jspJK4pmLW0lkkKc67/Ff8z+FyYS/orGzAItnp5I8Lqe4aNAeRvNZu1qJZJCvu962T++fBR7LjlqHUnbjYaRozb9EH1RQMGpsTgmoJZS6o0KUg6CDiL1P/o+RFxZs30bYEfA2PyPKdExIwqYhkwfAt23HmLKopuPu4Uz6xlVfagWdJA4BzgYGAacIyk2h/t/QIwPSL2Ao4GvltVPLYe3P+RWcuq8tNHrwLmRMTciFgFXAIcXjNPAJvn16OBBRXGYz3l/o/MWlaVSWEiML803J7HlZ0GHCepHZgBnFSvIEknSpopaebChQuriNXKho/zg2azFlVlUqj39eCoGT4GuDAiJgGHABdJWiemiDgvItoiom3ChAkVhGovMnyLFzrFM7OWUmVSaAe2KQ1PYt3moQ8A0wEi4h/AUGB8hTFZTwwfB2tWwOpljY7EzDaxKpPCzcBUSVMkDSE9SL68Zp6HgTcCSNqFlBTcPtRoxRfY3IRk1moqSwoRsQb4GHAlcA/pU0Z3STpd0mF5tpOBD0m6Hfg5cHyE2ywarujqwh9LNWs1lX5PIX/nYEbNuFNLr+8GXltlDLYB3NWFWctqqQ7xrIc6ksJyd4pn1mqcFGxdw918ZNaqWqbvI1sPQ3OneH7Q3P/M+xsMHg4TX9HoSLq2dg3c9H1YsaT3y97lbfDS3Xq/3BbhpGDrGjjIneL1R6uXwy+OS0n9pFthQB9uCLj713Dl56spe86f4ENXV1N2C3BSsPqGjXVS6G/u/k16DrR8MTx4HexwQKMj6tzMH8EWk+Gk23o3ed1wLvzhFHh0trvD30B9+FbCGspdXfQ/My+AsTukfTfzgkZH07mF98FD18PeJ/R+bWaPo2HQULilD69/H+ekYPW5++z+5fG7YP4N0PZ+2PNYuPf3sOTRRkdV38wLYMDgFGdvG7YF7Pp2mD0dVj7b++W3ACcFq2/4WP9Oc38y8wIYuBns+W7Y+3iItXDbTxsd1bpWLYPbL4Zph8HIivoxa3s/rFoKd1xaTflNzknB6hvuZwr9xqrnYPYvYNf/k/bbuB1g+/3hlgvh+bUNDq7GXf8LK55JF+6qTGqDl+yenlu4g4T15qRg9Q0bC2uWpzs769vu/CWsXAJtJ7wwru39sKQ9fRKnL7nlAhi/I2xXYUcGErQdD4/dAY/cWt1ympQ/fWT1jchV+69unU4y67viedhyGmzz6hfG7XQIjHwJXPxOWLc3+saJ5+EtZ1R/TO3+TrjqVDj/gL61/hvrrd+stpaFk4J1ZpdD4dnHYO3KRkdiPbHTIS++0A4cDO/4ITz458bFVM+goemZR9WGbg5HXQDtN1e/rE3ppXtUvgj1t05J29raYubMmY0Ow8ysX5F0S0S0dTdfE9WrzMxsYzkpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzQqVJQdJBku6TNEfSKZ3M805Jd0u6S9LFVcZjZmZdG1RVwZIGAucAbwLagZslXR4Rd5fmmQp8DnhtRCyWtGVV8ZiZWfeqrCm8CpgTEXMjYhVwCXB4zTwfAs6JiMUAEfFEhfGYmVk3qkwKE4H5peH2PK5sR2BHSddLukHSQfUKknSipJmSZi5cuLCicM3MrMqkoDrjomZ4EDAV2B84Bjhf0ph13hRxXkS0RUTbhAkTej1QMzNLqkwK7cA2peFJwII68/wmIlZHxIPAfaQkYWZmDVBlUrgZmCppiqQhwNHA5TXz/Br4FwBJ40nNSXMrjMnMzLpQWVKIiDXAx4ArgXuA6RFxl6TTJR2WZ7sSWCTpbuBa4DMRsaiqmMzMrGuKqG3m79va2tpi5syZjQ7DzKxfkXRLRLR1N5+/0WxmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZW2OCkIGnn3gzEzMwab2NqClf1WhRmZtYnDOpqoqSzO5sErPNbymZm1r91mRSAE4CTgZV1ph3T++GYmVkjdZcUbgbujIi/106QdFolEZmZWcN0lxSOBFbUmxARU3o/HDMza6TuHjSPjIhlmyQSMzNruO6Swq87Xkj6ZcWxmJlZg3WXFFR6vX2VgZiZWeN1lxSik9dmZtaEunvQvIekJaQaw7D8mjwcEbF5pdGZmdkm1WVSiIiBmyoQMzNrPHeIZ2ZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzK1SaFCQdJOk+SXMkndLFfEdKCkltVcZjZmZdqywpSBoInAMcDEwDjpE0rc58o4CPAzdWFYuZmfVMlTWFVwFzImJuRKwCLgEOrzPfl4H/ppOf/TQzs02nyqQwEZhfGm7P4wqS9gK2iYjfdVWQpBMlzZQ0c+HChb0fqZmZAdUmBdUZV/xQj6QBwLeAk7srKCLOi4i2iGibMGFCL4ZoZmZlVSaFdmCb0vAkYEFpeBSwG3CdpHnAPsDlfthsZtY4VSaFm4GpkqZIGgIcDVzeMTEinomI8RExOSImAzcAh0XEzApjMjOzLlSWFCJiDfAx4ErgHmB6RNwl6XRJh1W1XDMz23Dd/UbzRomIGcCMmnGndjLv/lXGYmZm3fM3ms3MrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzgpGBmZoVKk4KkgyTdJ2mOpFPqTP93SXdLmi3paknbVRmPmZl1rbKkIGkgcA5wMDANOEbStJrZbgPaIuLlwGXAf1cVj5mZda/KmsKrgDkRMTciVgGXAIeXZ4iIayNiWR68AZhUYTxmZtaNKpPCRGB+abg9j+vMB4Ar6k2QdKKkmZJmLly4sBdDNDOzsiqTguqMi7ozSscBbcDX602PiPMioi0i2iZMmNCLIZqZWdmgCstuB7YpDU8CFtTOJOlA4D+BN0TEygrjMTOzblRZU7gZmCppiqQhwNHA5eUZJO0FfB84LCKeqDAWMzPrgcqSQkSsAT4GXAncA0yPiLsknS7psDzb14GRwKWSZkm6vJPizMxsE6iy+YiImAHMqBl3aun1gVUu38zM1k+lSWFTWb16Ne3t7axYsaLRoWyQoUOHMmnSJAYPHtzoUMysxTVFUmhvb2fUqFFMnjwZqd6HnvquiGDRokW0t7czZcqURodjZi2uKfo+WrFiBePGjet3CQFAEuPGjeu3tRwzay5NkRSAfpkQOvTn2M2suTRNUjAzs43XFM8U+oKBAwey++67s2bNGqZMmcJFF13EmDFjmDdvHrvssgs77bRTMe9NN93EkCFDGhitmVl9rin0kmHDhjFr1izuvPNOxo4dyznnnFNM22GHHZg1a1bx54RgZn1V09UUvvTbu7h7wZJeLXPa1pvzxbft2uP59913X2bPnt2rMZiZbQquKfSytWvXcvXVV3PYYYcV4x544AH23HNP9txzTz760Y82MDozs641XU1hfe7oe9Py5cvZc889mTdvHnvvvTdvetObimkdzUdmZn2dawq9pOOZwkMPPcSqVate9EzBzKy/cFLoZaNHj+bss8/mG9/4BqtXr250OGZm68VJoQJ77bUXe+yxB5dcckmjQzEzWy9N90yhUZYuXfqi4d/+9rfF6zvvvHNTh2NmtkFcUzAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcEfSe0lI0eOZOnSpe4q28z6NSeFCrivIzPrr5ovKVxxCjx2R++W+dLd4eAze7dMM7M+yM8UKuCuss2sv2q+mkIfuKN385GZ9VeuKZiZWcFJwczMCk4KZmZWcFLoJR1dZ0+ePNldZZtZv+WkYGZmBScFMzMrNE1SiIhGh7DB+nPsZtZcmiIpDB06lEWLFvXLi2tEsGjRIoYOHdroUMzMmuPLa5MmTaK9vZ2FCxc2OpQNMnToUCZNmtToMMzMmiMpDB48mClTpjQ6DDOzfq/S5iNJB0m6T9IcSafUmb6ZpF/k6TdKmlxlPGZm1rXKkoKkgcA5wMHANOAYSdNqZvsAsDgiXgZ8C/haVfGYmVn3qqwpvAqYExFzI2IVcAlweM08hwM/zq8vA94oSRXGZGZmXajymcJEYH5puB14dWfzRMQaSc8A44AnyzNJOhE4MQ8ulXTfBsY0vrbsFuB1bg1e59awMeu8XU9mqjIp1Lvjr/3MaE/mISLOA87b6ICkmRHRtrHl9Cde59bgdW4Nm2Kdq2w+age2KQ1PAhZ0No+kQcBo4KkKYzIzsy5UmRRuBqZKmiJpCHA0cHnNPJcD78uvjwSuif74DTQzsyZRWfNRfkbwMeBKYCDwo4i4S9LpwMyIuBz4IXCRpDmkGsLRVcWTbXQTVD/kdW4NXufWUPk6yzfmZmbWoSn6PjIzs97hpGBmZoWWSQrddbnRDCRtI+laSfdIukvSJ/L4sZL+KOn+/H+LRsfamyQNlHSbpN/l4Sm525T7czcqQxodY2+SNEbSZZLuzft63xbYx5/Kx/Sdkn4uaWiz7WdJP5L0hKQ7S+Pq7lclZ+fr2WxJr+itOFoiKfSwy41msAY4OSJ2AfYBPprX8xTg6oiYClydh5vJJ4B7SsNfA76V13cxqTuVZnIW8IeI2BnYg7TuTbuPJU0EPg60RcRupA+uHE3z7ecLgYNqxnW2Xw8Gpua/E4FzeyuIlkgK9KzLjX4vIh6NiFvz62dJF4uJvLg7kR8DRzQmwt4naRLwVuD8PCzgAFK3KdB867s58HrSJ/eIiFUR8TRNvI+zQcCw/H2m4cCjNNl+joi/sO73tDrbr4cDP4nkBmCMpK16I45WSQr1utyY2KBYNonc4+xewI3ASyLiUUiJA9iycZH1um8DnwWez8PjgKcjYk0ebrZ9vT2wELggN5mdL2kETbyPI+IR4BvAw6Rk8AxwC829nzt0tl8ru6a1SlLoUXcazULSSOCXwCcjYkmj46mKpEOBJyLilvLoOrM2074eBLwCODci9gKeo4maiurJ7eiHA1OArYERpOaTWs20n7tT2XHeKkmhJ11uNAVJg0kJ4WcR8as8+vGOqmX+/0Sj4utlrwUOkzSP1CR4AKnmMCY3M0Dz7et2oD0ibszDl5GSRLPuY4ADgQcjYmFErAZ+BbyG5t7PHTrbr5Vd01olKfSky41+L7en/xC4JyL+pzSp3J3I+4DfbOrYqhARn4uISRExmbRPr4mIY4FrSd2mQBOtL0BEPAbMl7RTHvVG4G6adB9nDwP7SBqej/GOdW7a/VzS2X69HHhv/hTSPsAzHc1MG6tlvtEs6RDSXWRHlxtfaXBIvU7SfsBfgTt4oY3986TnCtOBbUkn2FER0VQdD0raH/h0RBwqaXtSzWEscBtwXESsbGR8vUnSnqQH60OAucAJpBu8pt3Hkr4EvIv0CbvbgA+S2tCbZj9L+jmwP6l77MdXPbAiAAAFWklEQVSBLwK/ps5+zcnxO6RPKy0DToiImb0SR6skBTMz616rNB+ZmVkPOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCrRdJIembpeFPSzqtl8q+UNKR3c+50cs5Kvcuem3Vyyotc2tJl3U/Z7flfFLS8N6IaQOWPbncg+d6vu/dVcRkvc9JwdbXSuDtksY3OpCy3BNuT30A+LeI+Jeq4imTNCgiFkREbyS8T5I6hOtPJgNOCv2Ek4KtrzWk34n9VO2E2jt9SUvz//0l/VnSdEn/lHSmpGMl3STpDkk7lIo5UNJf83yH5vcPlPR1STfnvuM/XCr3WkkXk76wVxvPMbn8OyV9LY87FdgP+J6kr9fML0nfkXS3pN9LmtGxPpLmdSRCSW2SrsuvRyj1g39z7qDu8Dz+eEmXSvotcFX5LruL9dlK0l8kzcoxv64mvo+T+v65tqOWU28d62yHU/Oy7pR0Xv7iE5Kuk/S1vB/+2bG8HOtfJd2a/15Tp8y/5i/RdQxfL+nlkt6Q45+Vt8co4EzgdXncOseN9TER4T//9fgPWApsDswDRgOfBk7L0y4EjizPm//vDzwNbAVsBjwCfClP+wTw7dL7/0C6WZlK6t9lKKm/+C/keTYDZpI6R9uf1CHclDpxbk36BugEUidy1wBH5GnXkfrmr33P24E/kr71vnWO+cg8bR4wPr9uA67Lr79K+iYtwBjgn6QO247P8Y/N0yYDd+bXna3PycB/5vEDgVF1YizH0ek61rxnbOn1RcDbStvhm/n1IcCf8uvhwND8eiows846vK+033YszfNb4LX59cgc1/7A7xp97PqvZ3+uKdh6i9Tz6k9IP3zSUzdH+r2HlcADwFV5/B2ki02H6RHxfETcT+rCYWfgzaR+XmaRuuwYR7pYAdwUEQ/WWd4rSRfuhZG6V/4Z6XcIuvJ64OcRsTYiFpAust15M3BKju06UhLbNk/7Y9TvaqKz9bkZOCE/o9k90m9idKWn6/gvSr9Qdgep08BdS9M6Ok28hRf2w2DgB3n+S0k/TFXrUuBQpQ4Y309K6ADXA/+TazVj4oWura2fGNT9LGZ1fRu4FbigNG4NuUkyN1GUfx6x3CfN86Xh53nxcVjb70qQugk+KSKuLE9Q6u/ouU7iq9e1cE901u9LsW6kC395Oe+IiPtqYnt1N7Gtsz75fa8n/WjQRZK+HhE/6SLWbtdR0lDgu6Sa0fyccMrxd+yHtbywHz5F6ntnD9I6r6gtNyKWSfojqUvrd5JqT0TEmZJ+T6p53CDpwO5itL7FNQXbIPkOeDov/gnEecDe+fXhpDvO9XWUpAH5OcP2wH3AlcC/5rtSJO2o9MMyXbkReIOk8fkh9DHAn7t5z1+Ao3Ob/1ZA+UH0PF5Yt3eUxl8JnFRqp9+r2zXsZH0kbUf6fYgfkHq7rfe7u88Co9ZjHTsSwJNKv7PRk4fdo4FHI+J54D2kpqx6zgfOJtUCn8rrskNE3BERXyM1i+1cE7P1cU4KtjG+SerRscMPSBepm4Cu7pS7ch/pwnYF8JGIWEG6+NwN3Jof1n6fbmq5kboR/hype+XbgVsjoruulf8XuJ/UpHUuL77Afgk4S9JfSXfVHb5MSn6zc2xf7sE6drY++wOzJN1GSjxn1XnvecAVkq7tyTpG+qnOH+R1+jWpiao73wXeJ+kG0vOCuvsx0o8bLeHFtcVP5gfatwPLSftxNrBG0u1+0Nz3uZdUs05IupD0gHSjv1/QjCRtTXqOsnOuVVgTcE3BzNabpPeSmq/+0wmhubimYGZmBdcUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCv8f2iEMMjig9WQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for label in ni.features:\n",
    "    ni.plot_active_learning_time_series_overlapping(\n",
    "        attribute='f1', label=label.replace('.', ''),\n",
    "        learner1='RandomForestClassifier', sampling1='entropy',\n",
    "        learner2='RandomForestClassifier', sampling2='iforest',\n",
    "        title=f'Entropy vs. unsupervised sampling for {label} attack',\n",
    "        ylim=[0, 1], ylabel='F1',\n",
    "        legend=['RF', 'IF']\n",
    "    )\n",
    "    plt.show()"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}