example-german-naive.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.ga_attack import GA_Counterfactual, GAdvExample, alibi_ord_to_ohe, alibi_ohe_to_ord\n",
    "from src.utils import plot_graph, create_onehot_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.linear_model import LogisticRegression, SGDClassifier\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "from sklearn.datasets import load_iris, load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(555)\n",
    "\n",
    "import matplotlib.pyplot\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>duration_in_month</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>installment_as_income_perc</th>\n",
       "      <th>present_res_since</th>\n",
       "      <th>age</th>\n",
       "      <th>credits_this_bank</th>\n",
       "      <th>people_under_maintenance</th>\n",
       "      <th>account_check_status_0 &lt;= ... &lt; 200 DM</th>\n",
       "      <th>account_check_status_&lt; 0 DM</th>\n",
       "      <th>account_check_status_&gt;= 200 DM / salary assignments for at least 1 year</th>\n",
       "      <th>account_check_status_no checking account</th>\n",
       "      <th>credit_history_all credits at this bank paid back duly</th>\n",
       "      <th>credit_history_critical account/ other credits existing (not at this bank)</th>\n",
       "      <th>credit_history_delay in paying off in the past</th>\n",
       "      <th>credit_history_existing credits paid back duly till now</th>\n",
       "      <th>credit_history_no credits taken/ all credits paid back duly</th>\n",
       "      <th>purpose_(vacation - does not exist?)</th>\n",
       "      <th>purpose_business</th>\n",
       "      <th>purpose_car (new)</th>\n",
       "      <th>purpose_car (used)</th>\n",
       "      <th>purpose_domestic appliances</th>\n",
       "      <th>purpose_education</th>\n",
       "      <th>purpose_furniture/equipment</th>\n",
       "      <th>purpose_radio/television</th>\n",
       "      <th>purpose_repairs</th>\n",
       "      <th>purpose_retraining</th>\n",
       "      <th>savings_.. &gt;= 1000 DM</th>\n",
       "      <th>savings_... &lt; 100 DM</th>\n",
       "      <th>savings_100 &lt;= ... &lt; 500 DM</th>\n",
       "      <th>savings_500 &lt;= ... &lt; 1000 DM</th>\n",
       "      <th>savings_unknown/ no savings account</th>\n",
       "      <th>present_emp_since_.. &gt;= 7 years</th>\n",
       "      <th>present_emp_since_... &lt; 1 year</th>\n",
       "      <th>present_emp_since_1 &lt;= ... &lt; 4 years</th>\n",
       "      <th>present_emp_since_4 &lt;= ... &lt; 7 years</th>\n",
       "      <th>present_emp_since_unemployed</th>\n",
       "      <th>personal_status_sex_female : divorced/separated/married</th>\n",
       "      <th>personal_status_sex_male : divorced/separated</th>\n",
       "      <th>personal_status_sex_male : married/widowed</th>\n",
       "      <th>personal_status_sex_male : single</th>\n",
       "      <th>other_debtors_co-applicant</th>\n",
       "      <th>other_debtors_guarantor</th>\n",
       "      <th>other_debtors_none</th>\n",
       "      <th>property_if not A121 : building society savings agreement/ life insurance</th>\n",
       "      <th>property_if not A121/A122 : car or other, not in attribute 6</th>\n",
       "      <th>property_real estate</th>\n",
       "      <th>property_unknown / no property</th>\n",
       "      <th>other_installment_plans_bank</th>\n",
       "      <th>other_installment_plans_none</th>\n",
       "      <th>other_installment_plans_stores</th>\n",
       "      <th>housing_for free</th>\n",
       "      <th>housing_own</th>\n",
       "      <th>housing_rent</th>\n",
       "      <th>job_management/ self-employed/ highly qualified employee/ officer</th>\n",
       "      <th>job_skilled employee / official</th>\n",
       "      <th>job_unemployed/ unskilled - non-resident</th>\n",
       "      <th>job_unskilled - resident</th>\n",
       "      <th>telephone_none</th>\n",
       "      <th>telephone_yes, registered under the customers name</th>\n",
       "      <th>foreign_worker_no</th>\n",
       "      <th>foreign_worker_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>1169</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>5951</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>2096</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>7882</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>4870</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_in_month  credit_amount  installment_as_income_perc  present_res_since  age  credits_this_bank  people_under_maintenance  account_check_status_0 <= ... < 200 DM  account_check_status_< 0 DM  account_check_status_>= 200 DM / salary assignments for at least 1 year  account_check_status_no checking account  credit_history_all credits at this bank paid back duly  credit_history_critical account/ other credits existing (not at this bank)  credit_history_delay in paying off in the past  credit_history_existing credits paid back duly till now  credit_history_no credits taken/ all credits paid back duly  purpose_(vacation - does not exist?)  purpose_business  purpose_car (new)  purpose_car (used)  purpose_domestic appliances  purpose_education  purpose_furniture/equipment  purpose_radio/television  purpose_repairs  purpose_retraining  savings_.. >= 1000 DM   savings_... < 100 DM  savings_100 <= ... < 500 DM  savings_500 <= ... < 1000 DM   savings_unknown/ no savings account  \\\n",
       "0                  6           1169                           4                  4   67                  2                         1                                       0                            1                                                  0                                                               0                                                  0                                                       1                                                                        0                                                  0                                                        0                                               0                 0                  0                   0                            1                  0                            0                         0                0                   0                       0                     0                            0                              0                                    1   \n",
       "1                 48           5951                           2                  2   22                  1                         1                                       1                            0                                                  0                                                               0                                                  0                                                       0                                                                        0                                                  1                                                        0                                               0                 0                  0                   0                            1                  0                            0                         0                0                   0                       0                     1                            0                              0                                    0   \n",
       "2                 12           2096                           2                  3   49                  1                         2                                       0                            0                                                  0                                                               1                                                  0                                                       1                                                                        0                                                  0                                                        0                                               1                 0                  0                   0                            0                  0                            0                         0                0                   0                       0                     1                            0                              0                                    0   \n",
       "3                 42           7882                           2                  4   45                  1                         2                                       0                            1                                                  0                                                               0                                                  0                                                       0                                                                        0                                                  1                                                        0                                               0                 0                  0                   0                            0                  0                            0                         1                0                   0                       0                     1                            0                              0                                    0   \n",
       "4                 24           4870                           3                  4   53                  2                         2                                       0                            1                                                  0                                                               0                                                  0                                                       0                                                                        1                                                  0                                                        0                                               0                 0                  1                   0                            0                  0                            0                         0                0                   0                       0                     1                            0                              0                                    0   \n",
       "\n",
       "   present_emp_since_.. >= 7 years  present_emp_since_... < 1 year   present_emp_since_1 <= ... < 4 years  present_emp_since_4 <= ... < 7 years  present_emp_since_unemployed  personal_status_sex_female : divorced/separated/married  personal_status_sex_male : divorced/separated  personal_status_sex_male : married/widowed  personal_status_sex_male : single  other_debtors_co-applicant  other_debtors_guarantor  other_debtors_none  property_if not A121 : building society savings agreement/ life insurance  property_if not A121/A122 : car or other, not in attribute 6  property_real estate  property_unknown / no property  other_installment_plans_bank  other_installment_plans_none  other_installment_plans_stores  housing_for free  housing_own  housing_rent  job_management/ self-employed/ highly qualified employee/ officer  job_skilled employee / official  job_unemployed/ unskilled - non-resident  job_unskilled - resident  telephone_none  telephone_yes, registered under the customers name   \\\n",
       "0                                1                                0                                     0                                     0                             0                                                  0                                                    0                                           0                                  1                           0                        0                   1                                                  0                                                                          0                                1                               0                             0                             1                               0                 0            1             0                                                  0                                                1                                         0                         0               0                                                  1     \n",
       "1                                0                                0                                     1                                     0                             0                                                  1                                                    0                                           0                                  0                           0                        0                   1                                                  0                                                                          0                                1                               0                             0                             1                               0                 0            1             0                                                  0                                                1                                         0                         0               1                                                  0     \n",
       "2                                0                                0                                     0                                     1                             0                                                  0                                                    0                                           0                                  1                           0                        0                   1                                                  0                                                                          0                                1                               0                             0                             1                               0                 0            1             0                                                  0                                                0                                         0                         1               1                                                  0     \n",
       "3                                0                                0                                     0                                     1                             0                                                  0                                                    0                                           0                                  1                           0                        1                   0                                                  1                                                                          0                                0                               0                             0                             1                               0                 1            0             0                                                  0                                                1                                         0                         0               1                                                  0     \n",
       "4                                0                                0                                     1                                     0                             0                                                  0                                                    0                                           0                                  1                           0                        0                   1                                                  0                                                                          0                                0                               1                             0                             1                               0                 1            0             0                                                  0                                                1                                         0                         0               1                                                  0     \n",
       "\n",
       "   foreign_worker_no  foreign_worker_yes  \n",
       "0                  0                   1  \n",
       "1                  0                   1  \n",
       "2                  0                   1  \n",
       "3                  0                   1  \n",
       "4                  0                   1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./data/german_credit.csv')\n",
    "y = data['default']\n",
    "X_df = data.drop('default', axis=1)\n",
    "\n",
    "cats = [X_df.dtypes.index[i] for i in range(X_df.shape[1]) if X_df.dtypes[i]=='object']\n",
    "X_df = pd.get_dummies(X_df, columns=cats)\n",
    "\n",
    "X = X_df.values\n",
    "y = y.values\n",
    "\n",
    "X_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.775"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.4)\n",
    "clf.fit(X_train,y_train)\n",
    "clf.score(X_test,y_test)\n",
    "\n",
    "# logistic = SGDClassifier(loss='log', penalty='l1', early_stopping=True,\n",
    "#                          max_iter=10000, tol=1e-5, random_state=0)\n",
    "# sca = MinMaxScaler()\n",
    "# clf = Pipeline(steps=[('sca', sca), ('logistic', logistic)])\n",
    "# clf.fit(X,y)\n",
    "# clf.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = list(X_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['account_check_status',\n",
       " 'credit_history',\n",
       " 'purpose',\n",
       " 'savings',\n",
       " 'present_emp_since',\n",
       " 'personal_status_sex',\n",
       " 'other_debtors',\n",
       " 'property',\n",
       " 'other_installment_plans',\n",
       " 'housing',\n",
       " 'job',\n",
       " 'telephone',\n",
       " 'foreign_worker']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ord_names, one_hot = create_onehot_map(feature_names, sep=\"_\", min_dist=1, data=None)\n",
    "ord_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((600, 20), (600, 61))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_, ord_enc = alibi_ohe_to_ord(X_train, one_hot)\n",
    "data_.shape, X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4., 12., 18., 24., 72.])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.quantile(data_[:,0], np.linspace(0,1,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([], dtype=int64),)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(clf.predict(X_train)!=y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  9,  16,  42,  51,  64,  71,  72,  74,  76, 108, 173, 209, 210,\n",
       "        232, 236, 243, 245, 271, 292, 306, 315, 319, 323, 327, 329, 343,\n",
       "        350, 357, 358, 361, 378, 380]),)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(clf.predict_proba(X_test)[:,1]>0.6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 10/10 [00:26<00:00,  2.69s/it]\n"
     ]
    }
   ],
   "source": [
    "idx_test = 76\n",
    "ga = GAdvExample(feature_names=feature_names,\n",
    "                sol_per_pop=35, num_parents_mating=15, cat_vars_ohe=one_hot,\n",
    "                 num_generations=200, n_runs=10, black_list=None,\n",
    "                 verbose=False, beta=.97)\n",
    "\n",
    "x_all, x_changes, x_sucess = ga.attack(clf, x=X_test[idx_test,:],x_train=X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_f2249_row1_col8, #T_f2249_row1_col9, #T_f2249_row1_col61, #T_f2249_row1_col62, #T_f2249_row2_col26, #T_f2249_row2_col27, #T_f2249_row2_col61, #T_f2249_row2_col62, #T_f2249_row3_col41, #T_f2249_row3_col42, #T_f2249_row3_col61, #T_f2249_row3_col62 {\n",
       "  background-color: darkorange;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_f2249_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >duration_in_month</th>\n",
       "      <th class=\"col_heading level0 col1\" >credit_amount</th>\n",
       "      <th class=\"col_heading level0 col2\" >installment_as_income_perc</th>\n",
       "      <th class=\"col_heading level0 col3\" >present_res_since</th>\n",
       "      <th class=\"col_heading level0 col4\" >age</th>\n",
       "      <th class=\"col_heading level0 col5\" >credits_this_bank</th>\n",
       "      <th class=\"col_heading level0 col6\" >people_under_maintenance</th>\n",
       "      <th class=\"col_heading level0 col7\" >account_check_status_0 <= ... < 200 DM</th>\n",
       "      <th class=\"col_heading level0 col8\" >account_check_status_< 0 DM</th>\n",
       "      <th class=\"col_heading level0 col9\" >account_check_status_>= 200 DM / salary assignments for at least 1 year</th>\n",
       "      <th class=\"col_heading level0 col10\" >account_check_status_no checking account</th>\n",
       "      <th class=\"col_heading level0 col11\" >credit_history_all credits at this bank paid back duly</th>\n",
       "      <th class=\"col_heading level0 col12\" >credit_history_critical account/ other credits existing (not at this bank)</th>\n",
       "      <th class=\"col_heading level0 col13\" >credit_history_delay in paying off in the past</th>\n",
       "      <th class=\"col_heading level0 col14\" >credit_history_existing credits paid back duly till now</th>\n",
       "      <th class=\"col_heading level0 col15\" >credit_history_no credits taken/ all credits paid back duly</th>\n",
       "      <th class=\"col_heading level0 col16\" >purpose_(vacation - does not exist?)</th>\n",
       "      <th class=\"col_heading level0 col17\" >purpose_business</th>\n",
       "      <th class=\"col_heading level0 col18\" >purpose_car (new)</th>\n",
       "      <th class=\"col_heading level0 col19\" >purpose_car (used)</th>\n",
       "      <th class=\"col_heading level0 col20\" >purpose_domestic appliances</th>\n",
       "      <th class=\"col_heading level0 col21\" >purpose_education</th>\n",
       "      <th class=\"col_heading level0 col22\" >purpose_furniture/equipment</th>\n",
       "      <th class=\"col_heading level0 col23\" >purpose_radio/television</th>\n",
       "      <th class=\"col_heading level0 col24\" >purpose_repairs</th>\n",
       "      <th class=\"col_heading level0 col25\" >purpose_retraining</th>\n",
       "      <th class=\"col_heading level0 col26\" >savings_.. >= 1000 DM </th>\n",
       "      <th class=\"col_heading level0 col27\" >savings_... < 100 DM</th>\n",
       "      <th class=\"col_heading level0 col28\" >savings_100 <= ... < 500 DM</th>\n",
       "      <th class=\"col_heading level0 col29\" >savings_500 <= ... < 1000 DM </th>\n",
       "      <th class=\"col_heading level0 col30\" >savings_unknown/ no savings account</th>\n",
       "      <th class=\"col_heading level0 col31\" >present_emp_since_.. >= 7 years</th>\n",
       "      <th class=\"col_heading level0 col32\" >present_emp_since_... < 1 year </th>\n",
       "      <th class=\"col_heading level0 col33\" >present_emp_since_1 <= ... < 4 years</th>\n",
       "      <th class=\"col_heading level0 col34\" >present_emp_since_4 <= ... < 7 years</th>\n",
       "      <th class=\"col_heading level0 col35\" >present_emp_since_unemployed</th>\n",
       "      <th class=\"col_heading level0 col36\" >personal_status_sex_female : divorced/separated/married</th>\n",
       "      <th class=\"col_heading level0 col37\" >personal_status_sex_male : divorced/separated</th>\n",
       "      <th class=\"col_heading level0 col38\" >personal_status_sex_male : married/widowed</th>\n",
       "      <th class=\"col_heading level0 col39\" >personal_status_sex_male : single</th>\n",
       "      <th class=\"col_heading level0 col40\" >other_debtors_co-applicant</th>\n",
       "      <th class=\"col_heading level0 col41\" >other_debtors_guarantor</th>\n",
       "      <th class=\"col_heading level0 col42\" >other_debtors_none</th>\n",
       "      <th class=\"col_heading level0 col43\" >property_if not A121 : building society savings agreement/ life insurance</th>\n",
       "      <th class=\"col_heading level0 col44\" >property_if not A121/A122 : car or other, not in attribute 6</th>\n",
       "      <th class=\"col_heading level0 col45\" >property_real estate</th>\n",
       "      <th class=\"col_heading level0 col46\" >property_unknown / no property</th>\n",
       "      <th class=\"col_heading level0 col47\" >other_installment_plans_bank</th>\n",
       "      <th class=\"col_heading level0 col48\" >other_installment_plans_none</th>\n",
       "      <th class=\"col_heading level0 col49\" >other_installment_plans_stores</th>\n",
       "      <th class=\"col_heading level0 col50\" >housing_for free</th>\n",
       "      <th class=\"col_heading level0 col51\" >housing_own</th>\n",
       "      <th class=\"col_heading level0 col52\" >housing_rent</th>\n",
       "      <th class=\"col_heading level0 col53\" >job_management/ self-employed/ highly qualified employee/ officer</th>\n",
       "      <th class=\"col_heading level0 col54\" >job_skilled employee / official</th>\n",
       "      <th class=\"col_heading level0 col55\" >job_unemployed/ unskilled - non-resident</th>\n",
       "      <th class=\"col_heading level0 col56\" >job_unskilled - resident</th>\n",
       "      <th class=\"col_heading level0 col57\" >telephone_none</th>\n",
       "      <th class=\"col_heading level0 col58\" >telephone_yes, registered under the customers name </th>\n",
       "      <th class=\"col_heading level0 col59\" >foreign_worker_no</th>\n",
       "      <th class=\"col_heading level0 col60\" >foreign_worker_yes</th>\n",
       "      <th class=\"col_heading level0 col61\" >P0</th>\n",
       "      <th class=\"col_heading level0 col62\" >P1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f2249_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_f2249_row0_col0\" class=\"data row0 col0\" >12.000000</td>\n",
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       "      <td id=\"T_f2249_row0_col2\" class=\"data row0 col2\" >4.000000</td>\n",
       "      <td id=\"T_f2249_row0_col3\" class=\"data row0 col3\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row0_col4\" class=\"data row0 col4\" >22.000000</td>\n",
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       "      <td id=\"T_f2249_row0_col6\" class=\"data row0 col6\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row0_col7\" class=\"data row0 col7\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col8\" class=\"data row0 col8\" >1.000000</td>\n",
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       "      <td id=\"T_f2249_row0_col18\" class=\"data row0 col18\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col19\" class=\"data row0 col19\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col20\" class=\"data row0 col20\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col21\" class=\"data row0 col21\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col22\" class=\"data row0 col22\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col23\" class=\"data row0 col23\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row0_col24\" class=\"data row0 col24\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col25\" class=\"data row0 col25\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col26\" class=\"data row0 col26\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col27\" class=\"data row0 col27\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row0_col28\" class=\"data row0 col28\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col29\" class=\"data row0 col29\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col30\" class=\"data row0 col30\" >0.000000</td>\n",
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       "      <td id=\"T_f2249_row0_col46\" class=\"data row0 col46\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col47\" class=\"data row0 col47\" >0.000000</td>\n",
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       "      <td id=\"T_f2249_row0_col50\" class=\"data row0 col50\" >0.000000</td>\n",
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       "      <td id=\"T_f2249_row0_col52\" class=\"data row0 col52\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row0_col53\" class=\"data row0 col53\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col54\" class=\"data row0 col54\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row0_col55\" class=\"data row0 col55\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col56\" class=\"data row0 col56\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col57\" class=\"data row0 col57\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row0_col58\" class=\"data row0 col58\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col59\" class=\"data row0 col59\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row0_col60\" class=\"data row0 col60\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row0_col61\" class=\"data row0 col61\" >0.370000</td>\n",
       "      <td id=\"T_f2249_row0_col62\" class=\"data row0 col62\" >0.630000</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_f2249_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_f2249_row1_col0\" class=\"data row1 col0\" >12.000000</td>\n",
       "      <td id=\"T_f2249_row1_col1\" class=\"data row1 col1\" >1858.000000</td>\n",
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       "      <td id=\"T_f2249_row1_col3\" class=\"data row1 col3\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col4\" class=\"data row1 col4\" >22.000000</td>\n",
       "      <td id=\"T_f2249_row1_col5\" class=\"data row1 col5\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col6\" class=\"data row1 col6\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col7\" class=\"data row1 col7\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col8\" class=\"data row1 col8\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col9\" class=\"data row1 col9\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col10\" class=\"data row1 col10\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col11\" class=\"data row1 col11\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col12\" class=\"data row1 col12\" >0.000000</td>\n",
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       "      <td id=\"T_f2249_row1_col21\" class=\"data row1 col21\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col22\" class=\"data row1 col22\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col23\" class=\"data row1 col23\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col24\" class=\"data row1 col24\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col25\" class=\"data row1 col25\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col26\" class=\"data row1 col26\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col27\" class=\"data row1 col27\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col28\" class=\"data row1 col28\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col29\" class=\"data row1 col29\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col30\" class=\"data row1 col30\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col31\" class=\"data row1 col31\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col32\" class=\"data row1 col32\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col33\" class=\"data row1 col33\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col34\" class=\"data row1 col34\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col35\" class=\"data row1 col35\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col36\" class=\"data row1 col36\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col37\" class=\"data row1 col37\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col38\" class=\"data row1 col38\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col39\" class=\"data row1 col39\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col40\" class=\"data row1 col40\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col41\" class=\"data row1 col41\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col42\" class=\"data row1 col42\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col43\" class=\"data row1 col43\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col44\" class=\"data row1 col44\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col45\" class=\"data row1 col45\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col46\" class=\"data row1 col46\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col47\" class=\"data row1 col47\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col48\" class=\"data row1 col48\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col49\" class=\"data row1 col49\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col50\" class=\"data row1 col50\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col51\" class=\"data row1 col51\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col52\" class=\"data row1 col52\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col53\" class=\"data row1 col53\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col54\" class=\"data row1 col54\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col55\" class=\"data row1 col55\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col56\" class=\"data row1 col56\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col57\" class=\"data row1 col57\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col58\" class=\"data row1 col58\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col59\" class=\"data row1 col59\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row1_col60\" class=\"data row1 col60\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row1_col61\" class=\"data row1 col61\" >0.580000</td>\n",
       "      <td id=\"T_f2249_row1_col62\" class=\"data row1 col62\" >0.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f2249_level0_row2\" class=\"row_heading level0 row2\" >4</th>\n",
       "      <td id=\"T_f2249_row2_col0\" class=\"data row2 col0\" >12.000000</td>\n",
       "      <td id=\"T_f2249_row2_col1\" class=\"data row2 col1\" >1858.000000</td>\n",
       "      <td id=\"T_f2249_row2_col2\" class=\"data row2 col2\" >4.000000</td>\n",
       "      <td id=\"T_f2249_row2_col3\" class=\"data row2 col3\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col4\" class=\"data row2 col4\" >22.000000</td>\n",
       "      <td id=\"T_f2249_row2_col5\" class=\"data row2 col5\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col6\" class=\"data row2 col6\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col7\" class=\"data row2 col7\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col8\" class=\"data row2 col8\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col9\" class=\"data row2 col9\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col10\" class=\"data row2 col10\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col11\" class=\"data row2 col11\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col12\" class=\"data row2 col12\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col13\" class=\"data row2 col13\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col14\" class=\"data row2 col14\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col15\" class=\"data row2 col15\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col16\" class=\"data row2 col16\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col17\" class=\"data row2 col17\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col18\" class=\"data row2 col18\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col19\" class=\"data row2 col19\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col20\" class=\"data row2 col20\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col21\" class=\"data row2 col21\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col22\" class=\"data row2 col22\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col23\" class=\"data row2 col23\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col24\" class=\"data row2 col24\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col25\" class=\"data row2 col25\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col26\" class=\"data row2 col26\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col27\" class=\"data row2 col27\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col28\" class=\"data row2 col28\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col29\" class=\"data row2 col29\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col30\" class=\"data row2 col30\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col31\" class=\"data row2 col31\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col32\" class=\"data row2 col32\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col33\" class=\"data row2 col33\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col34\" class=\"data row2 col34\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col35\" class=\"data row2 col35\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col36\" class=\"data row2 col36\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col37\" class=\"data row2 col37\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col38\" class=\"data row2 col38\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col39\" class=\"data row2 col39\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col40\" class=\"data row2 col40\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col41\" class=\"data row2 col41\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col42\" class=\"data row2 col42\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col43\" class=\"data row2 col43\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col44\" class=\"data row2 col44\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col45\" class=\"data row2 col45\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col46\" class=\"data row2 col46\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col47\" class=\"data row2 col47\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col48\" class=\"data row2 col48\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col49\" class=\"data row2 col49\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col50\" class=\"data row2 col50\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col51\" class=\"data row2 col51\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col52\" class=\"data row2 col52\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col53\" class=\"data row2 col53\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col54\" class=\"data row2 col54\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col55\" class=\"data row2 col55\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col56\" class=\"data row2 col56\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col57\" class=\"data row2 col57\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col58\" class=\"data row2 col58\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col59\" class=\"data row2 col59\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row2_col60\" class=\"data row2 col60\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row2_col61\" class=\"data row2 col61\" >0.540000</td>\n",
       "      <td id=\"T_f2249_row2_col62\" class=\"data row2 col62\" >0.460000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f2249_level0_row3\" class=\"row_heading level0 row3\" >6</th>\n",
       "      <td id=\"T_f2249_row3_col0\" class=\"data row3 col0\" >12.000000</td>\n",
       "      <td id=\"T_f2249_row3_col1\" class=\"data row3 col1\" >1858.000000</td>\n",
       "      <td id=\"T_f2249_row3_col2\" class=\"data row3 col2\" >4.000000</td>\n",
       "      <td id=\"T_f2249_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col4\" class=\"data row3 col4\" >22.000000</td>\n",
       "      <td id=\"T_f2249_row3_col5\" class=\"data row3 col5\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col6\" class=\"data row3 col6\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col7\" class=\"data row3 col7\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col8\" class=\"data row3 col8\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col9\" class=\"data row3 col9\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col10\" class=\"data row3 col10\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col11\" class=\"data row3 col11\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col12\" class=\"data row3 col12\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col13\" class=\"data row3 col13\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col14\" class=\"data row3 col14\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col15\" class=\"data row3 col15\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col16\" class=\"data row3 col16\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col17\" class=\"data row3 col17\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col18\" class=\"data row3 col18\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col19\" class=\"data row3 col19\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col20\" class=\"data row3 col20\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col21\" class=\"data row3 col21\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col22\" class=\"data row3 col22\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col23\" class=\"data row3 col23\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col24\" class=\"data row3 col24\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col25\" class=\"data row3 col25\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col26\" class=\"data row3 col26\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col27\" class=\"data row3 col27\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col28\" class=\"data row3 col28\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col29\" class=\"data row3 col29\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col30\" class=\"data row3 col30\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col31\" class=\"data row3 col31\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col32\" class=\"data row3 col32\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col33\" class=\"data row3 col33\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col34\" class=\"data row3 col34\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col35\" class=\"data row3 col35\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col36\" class=\"data row3 col36\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col37\" class=\"data row3 col37\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col38\" class=\"data row3 col38\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col39\" class=\"data row3 col39\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col40\" class=\"data row3 col40\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col41\" class=\"data row3 col41\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col42\" class=\"data row3 col42\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col43\" class=\"data row3 col43\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col44\" class=\"data row3 col44\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col45\" class=\"data row3 col45\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col46\" class=\"data row3 col46\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col47\" class=\"data row3 col47\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col48\" class=\"data row3 col48\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col49\" class=\"data row3 col49\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col50\" class=\"data row3 col50\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col51\" class=\"data row3 col51\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col52\" class=\"data row3 col52\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col53\" class=\"data row3 col53\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col54\" class=\"data row3 col54\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col55\" class=\"data row3 col55\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col56\" class=\"data row3 col56\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col57\" class=\"data row3 col57\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col58\" class=\"data row3 col58\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col59\" class=\"data row3 col59\" >0.000000</td>\n",
       "      <td id=\"T_f2249_row3_col60\" class=\"data row3 col60\" >1.000000</td>\n",
       "      <td id=\"T_f2249_row3_col61\" class=\"data row3 col61\" >0.530000</td>\n",
       "      <td id=\"T_f2249_row3_col62\" class=\"data row3 col62\" >0.470000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f7196f00ca0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ga.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No edges exist!\n"
     ]
    }
   ],
   "source": [
    "plot_graph(x_changes, 0, figsize=(7,7), save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['duration_in_month',\n",
       " 'credit_amount',\n",
       " 'installment_as_income_perc',\n",
       " 'present_res_since',\n",
       " 'age',\n",
       " 'credits_this_bank',\n",
       " 'people_under_maintenance',\n",
       " 'account_check_status',\n",
       " 'credit_history',\n",
       " 'purpose',\n",
       " 'savings',\n",
       " 'present_emp_since',\n",
       " 'personal_status_sex',\n",
       " 'other_debtors',\n",
       " 'property',\n",
       " 'other_installment_plans',\n",
       " 'housing',\n",
       " 'job',\n",
       " 'telephone',\n",
       " 'foreign_worker']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_ord = list(x_changes.columns)\n",
    "feat_ord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# blist = [i for i in range(len(feat_ord)) if (\"age\" not in feat_ord[i]) and (\"foreign_worker\" not in feat_ord[i]) and (\"personal_status_sex\" not in feat_ord[i]) ]\n",
    "# blist = [i for i in range(len(feat_ord)) if (\"account_check_status\" in feat_ord[i]) or (\"purpose\" in feat_ord[i]) or (\"credit_history\" in feat_ord[i]) ]\n",
    "blist = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 1/1 [00:03<00:00,  3.05s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 1/1 [00:03<00:00,  3.28s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 1/1 [00:04<00:00,  4.36s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 1/1 [00:01<00:00,  1.50s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████| 1/1 [00:02<00:00,  2.77s/it]"
     ]
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     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ga = GAdvExample(feature_names=feature_names,\n",
    "                sol_per_pop=35, num_parents_mating=15, cat_vars_ohe=one_hot,\n",
    "                 num_generations=200, n_runs=1, black_list=blist,\n",
    "                 verbose=False, beta=0.95)\n",
    "\n",
    "results = []\n",
    "\n",
    "for xi in X_test:\n",
    "    x_all, x_changes, x_sucess = ga.attack(clf, x=xi,x_train=X_train)\n",
    "    if len(x_sucess)>0:\n",
    "        results.append((xi,x_changes, x_sucess))\n",
    "    print(len(x_sucess))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "400"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "adv = []\n",
    "data = []\n",
    "for result in results:\n",
    "    for xi in result[2]:\n",
    "        adv.append(xi)\n",
    "        data.append(result[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pickle\n",
    "# pickle.dump((clf, results), open(\"distribution_change_res.pkl\",\"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "adv = []\n",
    "data = []\n",
    "for result in results:\n",
    "    for xi in result[2]:\n",
    "        adv.append(alibi_ord_to_ohe(xi.reshape(1,-1), ord_enc)[0][0])\n",
    "        data.append(result[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "predprob_data = clf.predict_proba(data)[:,1]\n",
    "predprob_adv = clf.predict_proba(adv)[:,1]\n",
    "pred = clf.predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Counts')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "_ = plt.hist(predprob_data[pred==1], label=\"Original Prediction\")\n",
    "_ = plt.hist(predprob_adv[pred==1], label=\"Counterfactual Prediction\")\n",
    "plt.legend(loc='best', fontsize=13)\n",
    "fig.gca().set_xlabel(\"Prediction Probability\", fontsize=13)\n",
    "fig.gca().set_ylabel(\"Counts\", fontsize=13)\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "_ = plt.hist(predprob_data[pred==0], label=\"Original Prediction\")\n",
    "_ = plt.hist(predprob_adv[pred==0], label=\"Counterfactual Prediction\")\n",
    "plt.legend(loc='best', fontsize=13)\n",
    "fig.gca().set_xlabel(\"Prediction Probability\", fontsize=13)\n",
    "fig.gca().set_ylabel(\"Counts\", fontsize=13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "savefig() got unexpected keyword argument \"type\" which is no longer supported as of 3.3 and will become an error two minor releases later\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7,7))\n",
    "\n",
    "\n",
    "plt.scatter(predprob_data[pred==1], predprob_adv[pred==1], c=\"b\", vmin=0, vmax=1, label = \"Original Pred = 1, Counterfactual Pred = 0\")\n",
    "plt.scatter(predprob_data[pred==0], predprob_adv[pred==0], c=\"r\", vmin=0, vmax=1, label = \"Original Pred = 0, Counterfactual Pred = 1\")\n",
    "\n",
    "plt.hlines(0.5, 0, 1, linestyles=\"--\")\n",
    "plt.vlines(0.5, 0, 1, linestyles=\"--\")\n",
    "\n",
    "plt.legend(loc='best', fontsize=13)\n",
    "fig.gca().set_xlabel(\"Original Probability\", fontsize=15)\n",
    "fig.gca().set_ylabel(\"Counterfactual Probability\", fontsize=15)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"scatter_outcomes.png\", type=\"png\", dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>duration_in_month</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>installment_as_income_perc</th>\n",
       "      <th>present_res_since</th>\n",
       "      <th>age</th>\n",
       "      <th>credits_this_bank</th>\n",
       "      <th>people_under_maintenance</th>\n",
       "      <th>account_check_status</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>purpose</th>\n",
       "      <th>savings</th>\n",
       "      <th>present_emp_since</th>\n",
       "      <th>personal_status_sex</th>\n",
       "      <th>other_debtors</th>\n",
       "      <th>property</th>\n",
       "      <th>other_installment_plans</th>\n",
       "      <th>housing</th>\n",
       "      <th>job</th>\n",
       "      <th>telephone</th>\n",
       "      <th>foreign_worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_in_month  credit_amount  installment_as_income_perc  present_res_since  age  credits_this_bank  people_under_maintenance  account_check_status  credit_history  purpose  savings  present_emp_since  personal_status_sex  other_debtors  property  other_installment_plans  housing  job  telephone  foreign_worker\n",
       "0                  0              0                           0                  0    0                  0                         0                     0               0        3        0                  0                    0              0         0                       -1        0    0          0               0\n",
       "0                  0              0                           0                  0    0                  0                         0                     2               0        0        2                  0                    0              0         0                        0        0    0          0               0\n",
       "0                  0              0                           0                  0    0                  0                         0                     0              -1        0        0                 -1                   -1              0         0                       -1        0    0          0               0\n",
       "0                  0              0                           0                  0    0                  0                         0                     2               0        0        0                  0                    0              0         0                        0        0    0          0               0\n",
       "0                  0              0                           0                  0    0                  0                         0                     0               0        1        0                  1                    0              0         0                        1        0    0          0               0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_change_all = results[0][1]\n",
    "for result in results[1:]:\n",
    "    x_change_all = pd.concat([x_change_all, result[1]])\n",
    "    \n",
    "x_change_all.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "ax= plt.subplot(1,2,1)\n",
    "\n",
    "ind = clf.predict(adv)==0\n",
    "df1 = (x_change_all[ind]).sum(0).to_frame().sort_values(by=0,ascending=False)\n",
    "ax = df1[df1[0]!=0].plot.bar(legend=False, fontsize=13, ax = ax)\n",
    "ax.set_ylabel(\"Counts\", fontsize=14)\n",
    "ax.set_title(\"Pred. changed from Default to Not Default\")\n",
    "ax = plt.subplot(1,2,2)\n",
    "ind = clf.predict(adv)==1\n",
    "df2 = (x_change_all[ind]).sum(0).to_frame().sort_values(by=0,ascending=False)\n",
    "df2[df2[0]!=0].plot.bar(legend=False, fontsize=13, ax=ax)\n",
    "ax.set_title(\"Pred. changed from Not Default to Default\")\n",
    "\n",
    "plt.tight_layout()\n",
    "# plt.savefig(\"sensitive_germancredit.png\", type=\"png\", dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R7d27V7FYzOlIAJCxWCoCZsG2bXV1dcntdmvZsmVOxwGAjENxAeYgGo2qt7dXZWVlqq6udjoOAGQMloqAOcjLy1MgEJDH41EoFFIkEnE6EgBkBGZcgBQYGBjQ+Pi42tralJXF3wMAMF/4yn8gBRobG5VMJtXV1aXc3Fz5/X6nIwFAWmLGBUix8fFx9ff3q7KyUpWVlU7HAYC0wpw2kGKFhYUKBoOybVuWZWliYsLpSACQNphxAeZZb2+vpqen1drayuMDAOA0sccFmGdLly5VIpFQe3u7ioqK1NDQ4HQkADAWS0XAAnC73QoEAvJ6vbIsa2bWDwAwOxQXYAGVlJQoGAxqampKlmVpamrK6UgAYBT2uAAOCofDsm1bLS0t7H8BgFNAcQEcNj09rXA4rNLSUtXW1jodBwAWNZaKAIfl5OQoGAwqPz9foVBIY2NjTkcCgEWL4gIsEj6fT4FAQEeOHJFlWYrH405HAoBFh9uhgUWmvr5etm2rq6tLHo9Hzc3NTkcCgEWDPS7AIjY5Oam+vj6VlZWpurra6TgA4DiWioBFLD8/X4FAQG63W5ZlKRKJOB0JABzFjAtgkIGBAU1MTKi1tVVZWfzdASDzsMcFMEhjY6OSyaQ6OzuVn5+vpqYmpyMBwIJixgUw1Pj4uPr7+1VVVaWKigqn4wDAgmCuGTBUYWGhgsGgEomEQqGQJicnnY4EAPOOGRcgTfT09Cgej2v58uU8PgBA2mKPC5AmmpubFY/HFQqFVFJSovr6eqcjAUDKsVQEpBGPx6NgMKiSkhKFQiGNjIw4HQkAUoriAqShkpISBQIBRaNRWZal6elppyMBQEqwxwVIc7ZtKxwOy+VyqaWlxek4AHBaKC5AhpienlY4HJbP59OSJUucjgMAc8JSEZAhcnJyFAwGlZeXJ8uy9M477zgdCQBmjeICZBifz6dgMKixsTFZlqVEIuF0JAA4ZdwODWSohoYG2batrq4uZWdna+nSpU5HAoAPxR4XAJqcnFRvb68qKipUVVXldBwAOCGWigAoPz9fwWBQLpdLlmVpfHzc6UgAcFzMuAD4gP7+fk1OTqq1tVVZWfx9A2DxYI8LgA9oampSMplUZ2enCgoK1NjY6HQkAJDEjIvDSYDFLxKJaN++faqqqlJ5ebnTcQBkOOaAAZxUUVGRAoHAzAMco9Go05EAZDBmXADMSnd3t+LxuFpbW+VyuZyOAyDDUFwAzFo8HldnZ6e8Xq/q6uqcjgMgg7BUBGDWPB6PgsGgioqKFAqFNDo66nQkABmC4gJgzrxerwKBgCYmJmRZlmKxmNORAKQ5looApMTRxwe43W4tW7bM6TgA0hTFBUBKTU1NqaenRz6fTzU1NU7HAZBmWCoCkFK5ubkKBALKycmRZVk6cuSI05EApBFmXADMq3379ikSiai1tVVut9vpOAAMx1f+A5hXDQ0Nsm1bnZ2dys3Nld/vdzoSAIMx4wJgwUxMTKivr0+VlZWqrKx0Og4AA7HHBcCCKSgoUDAYlCSFQiFNTEw4nAiAaZhxAeCYvr4+RaNRtbW18fgAAKeEPS4AHOP3+5VIJNTe3q6ioiI1NDQ4HQnAIsdSEQBHud1uBQIBlZaWKhQKzcyIAsDxUFwALArFxcUKBAKanp5WKBTS1NSU05EALELscQGwKHV3dyuRSGj58uXsfwEwg+ICYNGKxWIKh8Pyer2qra11Og6ARYClIgCLVnZ2tgKBgAoKChQKhTQ2NuZ0JAAOo7gAWPRKS0sVCAQUiURkWZbi8bjTkQA4hKUiAEaxbVtdXV3yeDxqbm52Og6ABUZxAWCkaDSqnp4elZWVqaamxuk4ABYIS0UAjJSXl6dgMKjs7GxZlqVIJOJ0JAALgBkXAGlhYGBA4+PjamtrU1YWf5MB6Yqv/AeQFhobG5VMJtXZ2an8/Hw1NTU5HQnAPGDGBUDaGR8fV39/v6qqqlRRUeF0HAApxHwqgLRTWFioYDCoZDKpUCikyclJpyMBSBFmXACkvd7eXk1NTamtrY3HBwCGY48LgLS3dOlSJRIJhUIhFRcXq6GhwelIAOaIpSIAGcHtdisYDMrr9SoUCmlkZMTpSADmgOICIKOUlJQoEAgoGo3KsixNT087HQnALLDHBUBGC4fDsm1bLS0t7H8BDEBxAZDxpqen1d3drdLSUi1ZssTpOABOgqUiABkvJydHgUBAeXl5sixLY2NjTkcCcAIUFwD4E5/Pp2AwqCNHjsiyLMXjcacjAXgfbocGgPepr6+Xbdvq6upSdna2li5d6nQkAH/CHhcAOInJyUn19vaqvLxc1dXVTscBMh5LRQBwEvn5+QoGg3K73bIsS+Pj405HAjIaMy4AMAv9/f2amJhQW1ubsrL42w9YaOxxAYBZaGpqUjKZVEdHhwoKCtTU1OR0JCCjMOMCAHMUiUQ0MDCg6upqlZeXOx0HyAjMcwLAHBUVFSkYDCoej8uyLE1OTjodCUh7zLgAQIr09PQoFouptbWVxwcA84Q9LgCQIs3NzYrH42pvb1dxcbHq6+udjgSkHZaKACCFPB6PAoGAiouLZVmWRkZGnI4EpBWKCwDMA6/Xq2AwqMnJSVmWpenpaacjAWmBPS4AMM9s21Z3d7ckqaWlxeE0gNkoLgCwQKamptTT0yOfz6eamhqn4wBGYqkIABZIbm6uAoGAcnJyZFmW3nnnHacjAcahuADAAisrK1MwGNTY2JhCoZASiYTTkQBjcDs0ADikoaFBtm2rs7NTubm58vv9TkcCFj32uADAIjAxMaG+vj5VVFSoqqrK6TjAosVSEQAsAgUFBQoGg3K5XAqFQhofH3c6ErAoMeMCAItQf3+/Jicn1draqqws/sYEjmKPCwAsQk1NTUomk+ro6FBhYaEaGxudjgQsCtR4AFiksrKyFAgE5PP5ZFmWhoeHnY4EOI7iAgCLXHFxsYLBoGKxmCzLUjQadToS4Bj2uACAYbq7u5VIJLR8+XK5XC6n4wALiuICAAaKx+Pq6upSSUmJ6urqnI4DLBiWigDAQB6PR4FAQIWFhbIsS6Ojo05HAhYExQUADFZaWqpgMKjx8XFZlqVYLOZ0JGBesVQEAGnCtm2Fw2FlZWVp2bJlTscB5gXFBQDSzNTUlLq7u1VWVqaamhqn4wApxVIRAKSZ3NxcBYNB5eTkKBQK6ciRI05HAlKG4gIAaaqsrEyBQECjo6MKhUJKJBJORwJOG1/5DwBprqGhQbZtq6OjQ3l5efL7/U5HAuaMPS4AkEEmJibU19enyspKVVZWOh0HmDWWigAggxQUFCgYDEqSQqGQJiYmHE4EzA4zLgCQwXp7ezU1NaW2tjYeHwAjsMcFADLY0qVLlUgk1N7erqKiIjU0NDgdCTgplooAIMO53W4FAgGVlpbKsqyZWWlgMaK4AAAkScXFxQoGg5qampJlWZqamnI6EvAB7HEBABxXd3e3ksmkWlpa2P+CRYPiAgA4oVgspnA4LK/Xq9raWqfjACwVAQBOLDs7W4FAQAUFBbIsS2NjY05HQoajuAAAPlRpaamCwaAikYgsy1I8Hnc6EjIUS0UAgFmxbVtdXV1yu91atmyZ03GQYSguAIA5iUaj6unpUXl5uaqrq52OgwzBUhEAYE7y8vIUDAbl8XgUCoUUiUScjoQMQHEBAJyW8vJyBQIBHT58WO3t7Uomk05HMsI111yjO++8M6Vj9vX1yeVypfUeJIoLACAlGhsb1draqs7OTvX19TkdZ175/X69+OKLTseYd5/61Kf02GOPnfL53/3ud3XFFVfMYyKKCwAghbKysrRixQpVVlYqFArp0KFDTkdCmqG4AABSrrCwUIFAQMlkUqFQSJOTk6f0uu9973tatmzZzOMH/v3f/33m2KOPPqpAIDBz7Pe//70kad++fbrkkktUWVmp8vJyfe1rX5MkJZNJbdq0SU1NTaqqqtJVV1018z00L730kurr64+59ntnUb773e/qy1/+sq666ioVFxdr5cqVeuONNyRJV155pQYGBvT5z39eRUVFevDBB0/6nn77299qzZo1Ki0tVUNDgzZv3jxzbGRkRBdddJGKi4u1evVqdXd3zxxrb2/XX/zFX6isrExtbW3atm3bzLHJyUl985vfVFNTk7xer84///zjfsa//OUv5ff7tWfPnhPmi0ajuuKKK1ReXq7S0lKde+65+t///V/dcccdevnll/W1r31NRUVFM5/r17/+dTU0NKikpETnnHOOXn75ZUnS9u3bdd999+nnP/+5ioqK9JGPfOQDn+vRz/borMyJrn1SdgYaHh62h4eHnY4BABmjp6fHbm9vt5PJ5EnP27Ztmz04OGgnEgl769atdkFBgT00NGRv27bNrq2ttV977TU7mUzaXV1ddl9fnx2Px+0zzzzTvuWWW+xIJGJPTk7aL7/8sm3btv0v//Iv9rJly+zu7m77yJEj9l//9V/bV1xxhW3btr1z5067rq7umGs3NTXZ//Ef/2Hbtm3ffffddm5urv3cc8/Z8Xjcvv322+3Vq1cf99yT6e/vt4uKiux//dd/taenp+1Dhw7Z//3f/23btm1fffXVts/ns1999VU7FovZGzZssNevX2/btm1HIhG7vr7efvzxx+1YLGa/+eabdnl5ub1nzx7btm37q1/9qr127Vp7//79djwet3/3u9/Z0WjU7u3ttSXZsVjMfvzxx+1ly5bZXV1dJ834yCOP2J/73Ofs8fFxOx6P22+88YY9NjZm27Ztr1271n700UePOf9nP/uZfejQITsWi9kPP/ywXV1dbU9OTs58bhs3bjzh5/r+c0527RNhxgUAMO+WLl2qlpYWtbe3a//+/Sc879JLL1Vtba2ysrK0fv16LV++XK+99poee+wx3XrrrTr33HPlcrnU0tKipqYmvfbaaxoaGtJDDz2kwsJC5eXl6fzzz5ckbdmyRd/4xjfU3NysoqIi3X///dq6despb1w9//zz9Zd/+Zdyu9268sor9Yc//GHW73vLli268MILdfnllys7O1vl5eU666yzZo5fcsklOu+88+TxeLRx40bt3r1bkvTss8/K7/fr2muvlcfj0dlnn60vfvGLeuqpp5RMJvX444/rhz/8oerq6uR2u7VmzRrl5ubOjPuDH/xADz30kF566SW1tLScNGN2draGh4cVDofldrt1zjnnqKSk5ITnH50h8Xg8+uY3v6mpqSl1dHTM+rOZy7UllooAAAvE7XYrEAiopKRElmVpZGTkA+c88cQTOuuss1RaWqrS0lLt2bNHhw4d0r59+477ZXf79u1TU1OTPB7PB44NDQ2pqalp5uempibF4/EPX4r4k5qampl/FxQUKBqNzvpunRPlPtE1jt5S3t/fr1dffXXmcygtLdWWLVt04MABHTp0SNFo9KTjPvTQQ7rppps+sBx2PFdeeaXWrVunyy67TLW1tbr11lsVi8VOeP73v/99BQIBeb1elZaWamxsbM57mWZ7bYniAgBYYCUlJQoGg4pGo9q7d6+mp6clvfvL+oYbbtA//uM/anh4WKOjo1q1apVs21ZDQ8Mx+z+Oamho0MDAwHELRW1trfr7+2d+HhgYkMfjUXV1tQoLCzUxMTFzLJFI6O233z7l93CqT8s+Ue5Ted3atWs1Ojo6818kEtGPf/xjVVRUKC8v76TjvvDCC9q0aZN++ctffui1srOzdffdd8uyLL3yyit69tln9cQTT0j64Pt8+eWX9cADD2jbtm0aGRnR6OiovF6v7D99l+3xPpf3f9YHDhw4pWufCMUFAOCIJUuWKBgMamBgQF1dXYpEInK5XKqsrJQk/eQnP5nZVHr99dfr4Ycf1ptvvinbthUOh9Xf36/zzjtPS5Ys0e23367x8XFFo1H97ne/kyRdfvnl+vu//3v19vYqEono29/+ttavXy+Px6PW1lZFo1E999xzisVi2rRpk6ampk45e3V1tXp6ej70vI0bN+rFF1/Utm3bFI/HNTw8PLMcdDKf+9zn1NnZqZ/97GeKxWKKxWJ6/fXXFQqFlJWVpeuuu07f+MY3NDQ0pEQioV27dh2Tf+XKldq+fbtuuukmPfPMMye91s6dO/XWW28pkUiopKRE2dnZcrvdx32fR44ckcfjUWVlpeLxuO655x698847x3wufX19x3yXz1lnnaWtW7cqFovpjTfe0FNPPXVK1z4RigsAwDHv3a/icrn0la98RR//+MdVXV2tt956S5/4xCckvbv35Y477tCGDRtUXFysv/qrv9Lhw4fldrv161//WuFwWI2Njaqvr9fPf/5zSdJ1112nK6+8Up/85Ce1dOlS5eXl6R/+4R8kSV6vV//0T/+k66+/XnV1dSosLDylZZWjvvWtb2nTpk0qLS3Vww8/fMLzGhsb9Zvf/Ebf//73VVZWprPOOuuU9soUFxfrhRde0NatW1VbW6uamhrddtttM+Xk4Ycf1hlnnKFzzz1XZWVluu222z7wxX8f+chH9Oyzz+qGG27Q888/f8JrHThwQF/60pdUUlKiQCCgtWvXztz18/Wvf11PPfWUfD6fbr75Zq1bt06f/exn1draqqamJuXl5amhoWFmrEsvvVTSu19KePbZZ0uS7r33XnV3d8vn8+nuu+/Whg0bTunaJ8KzigAAi8bIyIj++Mc/qr6+/kM3aSIzMeMCAFg0fD6fgsGg3nnnHYVCobT+6nrMDcUFALDo1NfXa8WKFeru7j6lvSRO2rJli4qKij7w38qVK52ONsOEjKeKpSIAwKI2OTmp3t5eVVRUqKqqyuk4cBgzLgCARS0/P1/BYFBZWVkKhUIaHx93OhIcxIwLAMAo/f39mpiYUFtbm7Ky+Ps703zwqwYBAFjEmpqalEwm1dnZqYKCAjU2NjodCQuIGRcAgLEikYj27dunqqoqlZeXOx0HC4A5NgCAsYqKihQIBBSPx2VZlqLRqNORMM+YcQEApI2enh7FYjG1trae8vOEYBb2uAAA0kZzc7Pi8bja29tVUlKiuro6pyMhxZhxAQCkpbGxMQ0ODmrJkiXy+Xyzeu2ewVFtfqVPg2NRjYxPK55IyuPOkq8wR3XePF2zxq9VdaXzExwnRXEBAKS1oaEhjY6Oavny5crOzj7pua/2DOvBHe0KHxzXWDR2wvO8edlqqSrSrevatLqZTcELieICAEh7tm2ru7tbktTS0vKB44mkrbue3qMdew9oeHz6lMctL8zRupU1uvcLq+TOYk/NQqC4AAAyxtTUlLq7u+Xz+bRkyRJJ75aWG598Qzs73lY8OftfiZ4sly5YUaVHNp5DeVkA3A4NAMgYubm5CgaDys3NlWVZOnLkiO56es+cS4skxZO2drYf1Hee2ZPitDgeigsAIOOUlZUpGAzqpb379dwf9s+5tBwVT9ravueAXu87nKKEOBGKCwAgY23+7xGNTSVTMtbw+LS+93x7SsbCiVFcAAAZ6a39owofjKR0zPDBiPYMjqZ0TByL4gIAyEg/3dV30lue52IsGtPmXf0pHRPHorgAADLS4Nj8PNdocHRyXsbFuyguAICMNDKL72tZDOPiXRQXAEBGiidSsyl3ocbFuyguAICM5HHPz6/A+RoX7+LTBQBkJF9hjlHj4l0UFwBARqrz5s3PuKX58zIu3kVxAQBkpKs/7pc37+RPi54tb162rl3jT+mYOBbFBQCQkc6oL1VLVWFKx2ypKtTKWm9Kx8SxKC4AgIx167oVKk/RnpTywhzd/tlASsbCiVFcAAAZa3VzudatrJEny3Va43iyXPrMqhqd6y9LUTKcCMUFAJDR7v3CKl2womrO5cWT5dIFK6p0z8WrUpwMx0NxAQBkNHeWS49sPEfrP9ow62Wj8sIcrT+3QY9sPEfu05y1walx2bZtOx1ioR0+fFiSVFbGlB4A4P+81jusB7Z3KHwwctIHMHrzs9VSVaTbP7OC5aEFRnEBAOB99gyOavOufg2OTmpkfFrxRFIed5Z8hTmqK83XtWv83D3kEIoLAAAwBntcAACAMSguAADAGBQXAABgDIoLAAAwBsUFAAAYg+ICAACMQXEBAADGoLgAAABjUFwAAIAxKC4AAMAYFBcAAGAMigsAADAGxQUAABiD4gIAAIxBcQEAAMaguAAAAGNQXAAAgDEoLgAAwBgUFwAAYAyKCwAAMAbFBQAAGIPiAgAAjEFxAQAAxqC4AAAAY1BcAACAMSguAADAGBQXAABgDIoLAAAwBsUFAAAYg+ICAACMQXEBAADGoLgAAABjUFwAAIAxKC4AAMAYFBcAAGAMigsAADAGxQUAABiD4gIAAIxBcQEAAMaguAAAAGNQXAAAgDEoLgAAwBgUFwAAYAyKCwAAMAbFBQAAGIPiAgAAjEFxAQAAxqC4AAAAY1BcAACAMSguAADAGBQXAABgDIoLAAAwBsUFAAAYg+ICAACMQXEBAADGoLgAAABjUFwAAIAxKC4AAMAYFBcAAGAMigsAADAGxQUAABiD4gIAAIxBcQEAAMaguAAAAGNQXAAAgDEoLgAAwBgUFwAAYAyKCwAAMAbFBQAAGIPiAgAAjEFxAQAAxqC4AAAAY1BcAACAMSguAADAGBQXAABgDIoLAAAwBsUFAAAYg+ICAACMQXEBAADGoLgAAABjUFwAAIAxKC4AAMAYFBcAAGAMigsAADAGxQUAABiD4gIAAIxBcQEAAMaguAAAAGNQXAAAgDEoLgAAwBgUFwAAYAyKCwAAMAbFBQAAGIPiAgAAjEFxAQAAxqC4AAAAY1BcAACAMSguAADAGBQXAABgDIoLAAAwBsUFAAAYg+ICAACMQXEBAADGoLgAAABjUFwAAIAxKC4AAMAYFBcAAGAMigsAADAGxQUAABiD4gIAAIxBcQEAAMaguAAAAGNQXAAAgDEoLgAAwBgUFwAAYAyKCwAAMAbFBQAAGIPiAgAAjEFxAQAAxqC4AAAAY1BcAACAMSguAADAGBQXAABgDIoLAAAwBsUFAAAYg+ICAACMQXEBAADGoLgAAABjUFwAAIAxKC4AAMAYFBcAAGAMigsAADAGxQUAABiD4gIAAIxBcQEAAMaguAAAAGNQXAAAgDEoLgAAwBgUFwAAYAyKCwAAMAbFBQAAGIPiAgAAjEFxAQAAxqC4AAAAY1BcAACAMSguAADAGBQXAABgDIoLAAAwBsUFAAAYg+ICAACMQXEBAADGoLgAAABjUFwAAIAxKC4AAMAYFBcAAGAMigsAADAGxQUAABiD4gIAAIxBcQEAAMaguAAAAGNQXAAAgDEoLgAAwBgUFwAAYAyKCwAAMAbFBQAAGIPiAgAAjEFxAQAAxqC4AAAAY1BcAACAMSguAADAGBQXAABgDIoLAAAwBsUFAAAYg+ICAACMQXEBAADGoLgAAABjUFwAAIAxKC4AAMAYFBcAAGAMigsAADAGxQUAABiD4gIAAIxBcQEAAMaguAAAAGNQXAAAgDEoLovUSy+9pPr6eqdjAACwqFBcTlE8Hk/LawEAYJKMLy5+v1/333+/gsGgfD6frr32WkWj0ZkZjwceeEA1NTW69tprNTU1pVtuuUW1tbWqra3VLbfcoqmpKUn/N0Ny3333qaKiQn6/X1u2bJm5ztTUlP7u7/5OjY2Nqq6u1o033qjJycljXnv0Wpdffrk++9nPamhoSEVFRSoqKtLQ0JAKCgo0PDw8M+abb76pyspKxWKxhf3QAABwSMYXF0nasmWLduzYoe7ubnV2dmrTpk2SpAMHDujw4cPq7+/XP//zP+v//b//p//6r//S7t279Yc//EGvvfbazLlHzz906JAGBwf105/+VF/5ylfU0dEhSbrtttvU2dmp3bt3KxwOa3BwUPfcc88xrz16rSeeeELPP/+8amtrFYlEFIlEVFtbq0996lPatm3bzGuefPJJXXbZZcrOzl6gTwoAAIfZGWh4eNgeHh62bdu2m5qa7B//+Mczx5577jm7ubnZ3rlzp52dnW1PTk7OHGtubrafe+65mZ+3b99uNzU12bZt2zt37rTdbrcdiURmjl966aX2PffcYyeTSbugoMAOh8Mzx1555RXb7/fPvPb919q5c6ddV1d3TO6tW7faa9assW3btuPxuF1dXW2/+uqrp/txAABgDI/TxWkxaGhomPl3U1OThoaGJEmVlZXKy8ubOTY0NKSmpqbjnitJPp9PhYWFHzj+9ttva2JiQuecc87MMdu2lUgkZn5+/7WO5wtf+IJuvPFG9fT0qLOzU16vV+edd94c3jEAAGaiuEjat2/fzL8HBgZUW1srSXK5XMecV1tbq/7+fq1cufID50rSyMiIxsfHZ8rLwMCAVq1apYqKCuXn52vv3r2qq6s7bob3X+v9P0tSXl6evvzlL2vLli1qb2/XlVdeOYd3CwCAudjjIulHP/qR9u/fr8OHD+u+++7T+vXrj3ve5Zdfrk2bNuntt9/WoUOHdM899+iKK6445py7775b09PTevnll/Xss8/q0ksvVVZWlm644Qb97d/+rQ4ePChJGhwc1I4dO06Yqbq6WsPDwxobGzvm/1911VXavHmznnnmmQ9cGwCAdEdxkbRhwwZ9+tOfVnNzs5qbm3XnnXce97w777xTH/3oR3XmmWfqjDPO0Nlnn33MuTU1NfL5fKqtrdXGjRv1yCOPaMWKFZKkBx54QC0tLfrYxz6mkpISXXjhhTMbd49nxYoVuvzyy9Xc3KzS0tKZJalPfOITysrK0tlnny2/35+6DwEAAAO4bNu2nQ6x0A4fPixJKisrk9/v12OPPaYLL7zwtMZ86aWXdMUVV2j//v2piHhSf/Znf6YNGzbo+uuvn/drAQCwmLDHxTCvv/66fv/73+vpp592OgoAAAuOpSKDXH311brwwgv1gx/8QMXFxU7HAQBgwWX8UhEAADAHMy4AAMAYFBcAAGAMigsAADAGxQUAABiD4gIAAIxBcQEAAMaguAAAAGNkzDfn7hkc1eZX+jQ4FtXbYxNKJG3l5mTLV5ijOm+erlnj16q6UqdjAgCAk0j7L6B7tWdYD+5oV/jguMaisROe583LVktVkW5d16bVzeULmBAAAJyqtC0uiaStu57eox17D2h4fPqUX1demKN1K2t07xdWyZ3lmseEAABgttKyuCSStm588g3t7Hhb8eTs354ny6ULVlTpkY3nUF4AAFhE0nJz7l1P75lzaZGkeNLWzvaD+s4ze1KcDAAAnI60Ky6v9gxrx94Dcy4tR8WTtrbvOaDX+w6nKBkAADhdaVdcHtzRMas9LSczPD6t7z3fnpKxAADA6Uur4vLW/lGFD0ZSOmb4YER7BkdTOiYAAJibtCouP93Vd9JbnudiLBrT5l39KR0TAADMTVoVl8Gx6PyMOzo5L+MCAIDZSaviMpKivS0LNS4AAJidtCou8UTSqHEBAMDspFVx8bjn5+3M17gAAGB20uo3sq8wx6hxAQDA7KRVcanz5s3PuKX58zIuAACYnbQqLld/3C9vXnZKx/TmZevaNf6UjgkAAOYmrYrLGfWlaqkqTOmYLVWFWlnrTemYAABgbtKquEjSretWqDxFe1LKC3N0+2cDKRkLAACcvrQrLquby7VuZY08Wa7TGseT5dJnVtXoXH9ZipIBAIDTlXbFRZLu/cIqXbCias7lxZPl0gUrqnTPxatSnAwAAJwOl23bttMh5kMiaes7T+/R9r0HZvW06PLCHH1mVY3uuXiV3Kc5awMAAFIrbYvLUa/1DuuB7R0KH4yc9AGM3vxstVQV6fbPrGB5CACARSrti8tRewZHtXlXvwZHJzUyPq14IimPO0u+whzVlebr2jV+7h4CAGCRy5jiAgAAzJeWm3MBAEB6orgAAABjUFwAAIAxKC4AAMAYFBcAAGAMigsAADAGxQUAABiD4gIAAIxBcQEAAMaguAAAAGNQXAAAgDEoLgAAwBgUFwAAYAyKCwAAMAbFBQAAGIPiAgAAjEFxAQAAxqC4AAAAY1BcAACAMSguAADAGBQXAABgDIoLAAAwBsUFAAAYg+ICAACMQXEBAADGoLgAAABjUFwAAIAxKC4AAMAYFBcAAGAMigsAADAGxQUAABiD4gIAAIxBcQEAAMaguAAAAGNQXAAAgDEoLgAAwBgUFwAAYAyKCwAAMAbFBQAAGIPiAgAAjEFxAQAAxqC4AAAAY/x/L3ccA1QacGEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind = clf.predict(adv)==0\n",
    "\n",
    "plot_graph(x_change_all[ind], 0, figsize=(8,8), save=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SHAP Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer = shap.TreeExplainer(clf)\n",
    "shap_values = explainer.shap_values(pd.DataFrame(X_test, columns=feature_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>duration_in_month</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>installment_as_income_perc</th>\n",
       "      <th>present_res_since</th>\n",
       "      <th>age</th>\n",
       "      <th>credits_this_bank</th>\n",
       "      <th>people_under_maintenance</th>\n",
       "      <th>account_check_status_0 &lt;= ... &lt; 200 DM</th>\n",
       "      <th>account_check_status_&lt; 0 DM</th>\n",
       "      <th>account_check_status_&gt;= 200 DM / salary assignments for at least 1 year</th>\n",
       "      <th>account_check_status_no checking account</th>\n",
       "      <th>credit_history_all credits at this bank paid back duly</th>\n",
       "      <th>credit_history_critical account/ other credits existing (not at this bank)</th>\n",
       "      <th>credit_history_delay in paying off in the past</th>\n",
       "      <th>credit_history_existing credits paid back duly till now</th>\n",
       "      <th>credit_history_no credits taken/ all credits paid back duly</th>\n",
       "      <th>purpose_(vacation - does not exist?)</th>\n",
       "      <th>purpose_business</th>\n",
       "      <th>purpose_car (new)</th>\n",
       "      <th>purpose_car (used)</th>\n",
       "      <th>purpose_domestic appliances</th>\n",
       "      <th>purpose_education</th>\n",
       "      <th>purpose_furniture/equipment</th>\n",
       "      <th>purpose_radio/television</th>\n",
       "      <th>purpose_repairs</th>\n",
       "      <th>purpose_retraining</th>\n",
       "      <th>savings_.. &gt;= 1000 DM</th>\n",
       "      <th>savings_... &lt; 100 DM</th>\n",
       "      <th>savings_100 &lt;= ... &lt; 500 DM</th>\n",
       "      <th>savings_500 &lt;= ... &lt; 1000 DM</th>\n",
       "      <th>savings_unknown/ no savings account</th>\n",
       "      <th>present_emp_since_.. &gt;= 7 years</th>\n",
       "      <th>present_emp_since_... &lt; 1 year</th>\n",
       "      <th>present_emp_since_1 &lt;= ... &lt; 4 years</th>\n",
       "      <th>present_emp_since_4 &lt;= ... &lt; 7 years</th>\n",
       "      <th>present_emp_since_unemployed</th>\n",
       "      <th>personal_status_sex_female : divorced/separated/married</th>\n",
       "      <th>personal_status_sex_male : divorced/separated</th>\n",
       "      <th>personal_status_sex_male : married/widowed</th>\n",
       "      <th>personal_status_sex_male : single</th>\n",
       "      <th>other_debtors_co-applicant</th>\n",
       "      <th>other_debtors_guarantor</th>\n",
       "      <th>other_debtors_none</th>\n",
       "      <th>property_if not A121 : building society savings agreement/ life insurance</th>\n",
       "      <th>property_if not A121/A122 : car or other, not in attribute 6</th>\n",
       "      <th>property_real estate</th>\n",
       "      <th>property_unknown / no property</th>\n",
       "      <th>other_installment_plans_bank</th>\n",
       "      <th>other_installment_plans_none</th>\n",
       "      <th>other_installment_plans_stores</th>\n",
       "      <th>housing_for free</th>\n",
       "      <th>housing_own</th>\n",
       "      <th>housing_rent</th>\n",
       "      <th>job_management/ self-employed/ highly qualified employee/ officer</th>\n",
       "      <th>job_skilled employee / official</th>\n",
       "      <th>job_unemployed/ unskilled - non-resident</th>\n",
       "      <th>job_unskilled - resident</th>\n",
       "      <th>telephone_none</th>\n",
       "      <th>telephone_yes, registered under the customers name</th>\n",
       "      <th>foreign_worker_no</th>\n",
       "      <th>foreign_worker_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.228625</td>\n",
       "      <td>8.178123</td>\n",
       "      <td>4.098975</td>\n",
       "      <td>2.140119</td>\n",
       "      <td>5.157096</td>\n",
       "      <td>1.370633</td>\n",
       "      <td>1.190621</td>\n",
       "      <td>5.931064</td>\n",
       "      <td>19.933499</td>\n",
       "      <td>1.005349</td>\n",
       "      <td>18.504337</td>\n",
       "      <td>2.657122</td>\n",
       "      <td>11.395156</td>\n",
       "      <td>0.630917</td>\n",
       "      <td>1.792658</td>\n",
       "      <td>1.82789</td>\n",
       "      <td>2.642617</td>\n",
       "      <td>1.840977</td>\n",
       "      <td>4.761337</td>\n",
       "      <td>2.074912</td>\n",
       "      <td>4.5581</td>\n",
       "      <td>0.37743</td>\n",
       "      <td>0.11571</td>\n",
       "      <td>1.888181</td>\n",
       "      <td>0.151703</td>\n",
       "      <td>0.099133</td>\n",
       "      <td>0.802638</td>\n",
       "      <td>6.428612</td>\n",
       "      <td>1.718809</td>\n",
       "      <td>0.751202</td>\n",
       "      <td>4.111296</td>\n",
       "      <td>1.872205</td>\n",
       "      <td>2.998317</td>\n",
       "      <td>2.52468</td>\n",
       "      <td>2.715324</td>\n",
       "      <td>0.799875</td>\n",
       "      <td>4.221522</td>\n",
       "      <td>0.509152</td>\n",
       "      <td>0.742043</td>\n",
       "      <td>4.089321</td>\n",
       "      <td>0.711412</td>\n",
       "      <td>0.96471</td>\n",
       "      <td>0.669205</td>\n",
       "      <td>2.064798</td>\n",
       "      <td>2.036309</td>\n",
       "      <td>6.779368</td>\n",
       "      <td>5.53104</td>\n",
       "      <td>1.853671</td>\n",
       "      <td>6.093097</td>\n",
       "      <td>2.293244</td>\n",
       "      <td>1.342508</td>\n",
       "      <td>7.819567</td>\n",
       "      <td>3.863635</td>\n",
       "      <td>1.003496</td>\n",
       "      <td>1.6092</td>\n",
       "      <td>0.124181</td>\n",
       "      <td>0.743713</td>\n",
       "      <td>1.248319</td>\n",
       "      <td>1.283912</td>\n",
       "      <td>0.630858</td>\n",
       "      <td>0.698077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_in_month  credit_amount  installment_as_income_perc  present_res_since       age  credits_this_bank  people_under_maintenance  account_check_status_0 <= ... < 200 DM  account_check_status_< 0 DM  account_check_status_>= 200 DM / salary assignments for at least 1 year  account_check_status_no checking account  credit_history_all credits at this bank paid back duly  credit_history_critical account/ other credits existing (not at this bank)  credit_history_delay in paying off in the past  credit_history_existing credits paid back duly till now  credit_history_no credits taken/ all credits paid back duly  purpose_(vacation - does not exist?)  purpose_business  purpose_car (new)  purpose_car (used)  purpose_domestic appliances  purpose_education  purpose_furniture/equipment  purpose_radio/television  purpose_repairs  purpose_retraining  savings_.. >= 1000 DM   savings_... < 100 DM  savings_100 <= ... < 500 DM  savings_500 <= ... < 1000 DM   savings_unknown/ no savings account  \\\n",
       "0          13.228625       8.178123                    4.098975           2.140119  5.157096           1.370633                  1.190621                                5.931064                    19.933499                                           1.005349                                                       18.504337                                           2.657122                                               11.395156                                                                 0.630917                                           1.792658                                                  1.82789                                        2.642617          1.840977           4.761337            2.074912                       4.5581            0.37743                      0.11571                  1.888181         0.151703            0.099133                0.802638              6.428612                     1.718809                       0.751202                             4.111296   \n",
       "\n",
       "   present_emp_since_.. >= 7 years  present_emp_since_... < 1 year   present_emp_since_1 <= ... < 4 years  present_emp_since_4 <= ... < 7 years  present_emp_since_unemployed  personal_status_sex_female : divorced/separated/married  personal_status_sex_male : divorced/separated  personal_status_sex_male : married/widowed  personal_status_sex_male : single  other_debtors_co-applicant  other_debtors_guarantor  other_debtors_none  property_if not A121 : building society savings agreement/ life insurance  property_if not A121/A122 : car or other, not in attribute 6  property_real estate  property_unknown / no property  other_installment_plans_bank  other_installment_plans_none  other_installment_plans_stores  housing_for free  housing_own  housing_rent  job_management/ self-employed/ highly qualified employee/ officer  job_skilled employee / official  job_unemployed/ unskilled - non-resident  job_unskilled - resident  telephone_none  telephone_yes, registered under the customers name   \\\n",
       "0                         1.872205                         2.998317                               2.52468                              2.715324                      0.799875                                           4.221522                                             0.509152                                    0.742043                           4.089321                    0.711412                  0.96471            0.669205                                           2.064798                                                                   2.036309                         6.779368                         5.53104                      1.853671                      6.093097                        2.293244          1.342508     7.819567      3.863635                                           1.003496                                           1.6092                                  0.124181                  0.743713        1.248319                                           1.283912     \n",
       "\n",
       "   foreign_worker_no  foreign_worker_yes  \n",
       "0           0.630858            0.698077  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shap = pd.DataFrame([np.abs(shap_values[0]).sum(0)], columns = feature_names)\n",
    "df_shap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>duration_in_month</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>installment_as_income_perc</th>\n",
       "      <th>present_res_since</th>\n",
       "      <th>age</th>\n",
       "      <th>credits_this_bank</th>\n",
       "      <th>people_under_maintenance</th>\n",
       "      <th>account_check_status</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>purpose</th>\n",
       "      <th>savings</th>\n",
       "      <th>present_emp_since</th>\n",
       "      <th>personal_status_sex</th>\n",
       "      <th>other_debtors</th>\n",
       "      <th>property</th>\n",
       "      <th>other_installment_plans</th>\n",
       "      <th>housing</th>\n",
       "      <th>job</th>\n",
       "      <th>telephone</th>\n",
       "      <th>foreign_worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.228625</td>\n",
       "      <td>8.178123</td>\n",
       "      <td>4.098975</td>\n",
       "      <td>2.140119</td>\n",
       "      <td>5.157096</td>\n",
       "      <td>1.370633</td>\n",
       "      <td>1.190621</td>\n",
       "      <td>45.374249</td>\n",
       "      <td>18.303743</td>\n",
       "      <td>18.5101</td>\n",
       "      <td>13.812556</td>\n",
       "      <td>10.910401</td>\n",
       "      <td>9.562037</td>\n",
       "      <td>2.345327</td>\n",
       "      <td>16.411515</td>\n",
       "      <td>10.240013</td>\n",
       "      <td>13.02571</td>\n",
       "      <td>3.480589</td>\n",
       "      <td>2.532231</td>\n",
       "      <td>1.328935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_in_month  credit_amount  installment_as_income_perc  present_res_since       age  credits_this_bank  people_under_maintenance  account_check_status  credit_history  purpose    savings  present_emp_since  personal_status_sex  other_debtors   property  other_installment_plans   housing       job  telephone  foreign_worker\n",
       "0          13.228625       8.178123                    4.098975           2.140119  5.157096           1.370633                  1.190621             45.374249       18.303743  18.5101  13.812556          10.910401             9.562037       2.345327  16.411515                10.240013  13.02571  3.480589   2.532231        1.328935"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shap_sum = []\n",
    "for i in range(list(one_hot.keys())[0]):\n",
    "    shap_sum.append(df_shap.iloc[0,i])\n",
    "    \n",
    "for i,v in one_hot.items():\n",
    "    shap_sum.append(np.sum(df_shap.iloc[0,i:i+v]))\n",
    "\n",
    "df_shap_sum = pd.DataFrame([shap_sum], columns=x_change_all.columns)\n",
    "df_shap_sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IiIgEKXOCPHr0aCnjICIiIiIyCmVOkDt27ChhGERERERExuGVG4UAQGZmJgoKCnSu8aAeEREREb0Oypwg5+TkYMuWLfj111/1kmMA+P777w0aGBERERGRHMpcxSI0NBT37t3DzJkzYWVlhU8//RTDhw+Hk5MTpk6dKmWMRERERETClHkG+dq1a/j0009Rr149mJubw9vbG23btoW9vT1OnDiB1q1bSxknEREREZEQZZ5B/vPPP+Hi4gIAsLW1xZMnTwAAderUYSc9IiIiInptlDlBrlKlCpKTkwEA1apVw9mzZ1FUVISLFy+iYsWKkgVIRERERCRSmRNkX19f3Lt3DwDQp08fhIeHY8iQIdi+fTt69+4tWYBERERERCKVeQ/ye++9p/m6QYMG+PbbbxEXF4eqVavCw8NDkuCIiIiIiET7S3WQgeK6x69a+3jHjh24cuUK0tLSYGNjg6ZNm2LYsGE6WzTOnDmD/fv3IyMjAx4eHhg7diy8vb3/aphERERERK/klRLkP/74A9HR0cjOzoZKpdJ5rCytqM3NzTF58mTUqFEDOTk5WLNmDdauXYtZs2YBAG7duoVNmzZhxowZqF+/Po4ePYrAwECsWrUKtra2rxIqEREREdFfUuYE+fDhw9i5cyeqVKkCBwcHmJmZvfJgQ4YM0XxtZ2eHd999F6tWrdJcCw8PR8uWLdG4cWMAQK9evXD8+HFcvHiRra6JiIiISIgyJ8jHjh3DqFGj8O677xps8OjoaHh6emq+v3fvnk4ibGZmhpo1a2oOB2oLDw9HeHg4AGDJkiUv3O6RbIBYpW6lbWlpKXu7bsbAGIwpBrnHZwyMwZjGZwyMwdhikHt8qWN4pVbTTZs2NdjA58+fx8mTJ7FgwQLNNaVSqbeVwtbWFs+ePdP7//39/eHv76/5XqFQGCy20kj9852dnSUfgzEwhn9SDHKPzxgYgzGNzxgYg7HFIPf4horB3d291OtlLvP29ttv49q1a38rCLVff/0VGzZswKxZs3QO4NnY2CAnJ0fnuTk5OShfvrxBxiUiIiIiepkXziAfOXJE87WzszP27t2L27dvw8PDA5aWuv+rdhm4F4mIiEBoaChmz56NunXr6jzm6emJu3fvar4vKipCQkICWrZsWaafTURERET0d70wQT527JjO9zY2Nrh9+zZu376tc93MzKxMCfLRo0exf/9+zJs3D7Vr19Z73N/fH4sXL4avry/q1auHo0ePIj8/nwkyEREREQnzwgQ5ODjYoINt3boVFhYWWLhwoc717du3AwDq1q2LsWPHYsOGDZo6yHPmzGGJNyIiIiIS5i83CiksLER+fj5sbGzK/P/s3bv3pc/x9fWFr6/vXw2LiIiIiOhveWmCHB0djSdPnqBt27aaaz/88AP27duHwsJCNGzYEP/6179QoUIFSQP9pyv8qNcLHy9LKTqLjYcNEwwRERERPddLE+QffvhBp7zbnTt3sHv3bnTq1AnVq1fH4cOHcfDgQQwfPlzSQOnv+7tJOhN0IiIiMgUvLfN2//591K9fX/P9r7/+ijp16uDjjz/Ge++9h1GjRuHSpUuSBklEREREJMpLE+ScnBzY2dlpvr99+zaaNGmi+b5WrVpIT0+XJDgiIiIiItFemiDb29sjObl48T0/Px93795FnTp1NI8rlUqUK1dOugiJiIiIiAR6aYLctGlT7NixAzdu3MDOnTthY2ODevXqaR6/d+8eqlSpImmQRERERESivDRBHjhwIKysrBAQEICIiAiMHz9ep4teREQEGjVqJGmQRERERESivLSKhZ2dHRYuXIicnBzY2NjA3Fw3p542bdor1UImIiIiIjJmZW4U8rxudhUrVjRYMEREREREcnthghwTE/OXfqirqyucnZ3/0v9LRERERCSnFybIwcHBr/wDzczM0L17d3Tv3v0vB0WvLzYrISIiImNn8ASZiIiIiOif7KVVLIiIiIiITMkLE+Rnz5690g971ecTERERERmbFybIU6ZMwYEDB5CWlvbc56hUKly9ehUBAQH4+eefDR4gEREREZFIL9yD/NVXX2H37t2YNGkSPD094e3tDQcHB5QrVw5//vknHjx4gD/++ANWVlbo27cvOnfuLCpuIiIiIiJJvDBBrlq1KqZNmwaFQoFff/0Vv//+O+7cuYO8vDxUqlQJNWvWhL+/P5o2barXQISIiIiI6J+oTI1CnJ2d0bNnT/Ts2VPqeIiIiIiIZMVpXyIiIiIiLWVuNb127dpSr5uZmaFcuXKoUqUK2rZtC0dHR4MFR0REREQkWpkT5OzsbNy6dQtmZmbw8PAAANy/fx8A4O3tjYsXL2Lv3r346quv4OXlJUmwRERERERSK3OCXLduXdjY2GDChAmwtrYGAOTm5mLDhg3w9PTEnDlzsGbNGoSGhuKLL76QLGAiIiIiIimVOUE+evQovvjiC01yDADW1tZ4//338dVXX6F3797o3bs3AgICJAmUyFAKP+r1wseTX/L/W2w8bLhgiIiIyOiU+ZCeUqlEZmam3vXMzEwolUoAQPny5VFYWGiw4IiIiIiIRCvzDHLLli2xbt06DBs2DLVq1YKZmRnu3LmDHTt2oGXLlgCAO3fuwN3dXbJgiYiIiIikVuYE+aOPPsK2bduwevVqzSyxhYUFOnXqhOHDhwMAqlevjvHjx0sTKdFrRO5tHi8bX0QMRERExqrMCbK1tTXGjRuHESNGIDk5GUVFRahSpQpsbGw0z2H1CiIiIiL6pyvzHuSjR48iOzsbNjY28PT0hJeXl05yTERERET0OijzDPKPP/6I7du3o2HDhujQoQNatGihU9GCiIiIiOh18Eqd9G7evImoqChs3rwZ3333HVq0aIH27dujUaNGMDdn12oiIiIi+ucrc4JsZmaGBg0aoEGDBhg7diwuX76MqKgoLF++HBUqVMCGDRukjJOIiIiISIi/NO1raWmJOnXqoE6dOnBxcSm1PjIRERER0T9RmWeQASAnJwcXLlxAVFQUYmJi4Obmhnbt2qF9+/ZSxUdEREREJFSZE+QVK1bg6tWrKF++PNq0aYPBgwejdu3aUsZGRERERCRcmRNkS0tLTJs2DU2aNNE7kHf9+nU0atTI4MEREREREYlW5gT5008/1fk+PT0dEREROHXqFBQKBb7//nuDB0dEr6+/200QYDc/IiKSxivtQVapVLh06RJOnjyJ69evw8PDA127dkWbNm3K9P+fPXsWP//8M+7du4fc3Fzs2bNH5/EzZ85g//79yMjIgIeHB8aOHQtvb+9XCZGIiIiI6G8pU4L86NEjnDx5EpGRkbC2tka7du1w/fp1TJ48GdWrVy/zYBUqVEDXrl2Rl5eH7777TuexW7duYdOmTZgxYwbq16+Po0ePIjAwEKtWrYKtre2r/amIiIiIiP6ilybIX3zxBRITE9GqVStMnToV9evXBwAcOnTolQdr0qQJAODmzZt6j4WHh6Nly5Zo3LgxAKBXr144fvw4Ll68iI4dO77yWEREL/N3t3lwiwcR0evppQlybGws3nnnHXTu3BkeHh6SBXLv3j2dRNjMzAw1a9bEvXv3Sn1+eHg4wsPDAQBLliyBs7Pzc392WfYyvsyLfn5ZvA4x/N3xGYPxxPA6vB6NIQZDvBZextLSUsg4jMG4x2cMjMHYYpB7fKljeGmCvGTJEoSHh+PLL7+Ei4sLOnTogHbt2hk8EKVSqbeVwtbWFs+ePSv1+f7+/vD399d8r1AoDB6TNql//j8hBrnHZwyMwdhiEDG+s7Oz7H9OxiD/+IyBMRhbDHKPb6gY3N3dS73+0gTZy8sLY8eOxYgRI3D+/HmcOnUKO3fuhEqlwpUrV2Bvb4+KFSv+reAAwMbGBjk5OTrXcnJy4Obm9rd/NhGRseI2DyIi41PmKhZWVlbo0KEDOnTogKSkJJw8eRI//fQT9uzZgwYNGmDu3Ll/KxBPT0/cvXtX831RURESEhLQsmXLv/VziYiIiIhehfnLn6KvSpUqGDp0KNatW4epU6fC0rJsebZKpUJeXh4KCgoAAHl5ecjLy0NRURH8/f1x4cIFREdHo6CgAD/++CPy8/OZIBMRERGRUK9UB7kkc3NztGjRAi1atCjT8yMjI7F27VrN98OGDQMArFmzBnXr1sXYsWOxYcMGTR3kOXPmsMQbEREREQn1txLkV9WxY8cXlmzz9fWFr6+vuICIiIiIiEr4S1ssiIiIiIheV0yQiYiIiIi0CN1iQURExoel5oiIdHEGmYiIiIhICxNkIiIiIiItTJCJiIiIiLQwQSYiIiIi0sIEmYiIiIhICxNkIiIiIiItTJCJiIiIiLQwQSYiIiIi0sIEmYiIiIhICxNkIiIiIiItTJCJiIiIiLQwQSYiIiIi0sIEmYiIiIhIi6XcARARkWkr/KjXS5+T/JLHLTYeNkwwRETgDDIRERERkQ4myEREREREWpggExERERFp4R5kIiIyeS/bB/2yPdAA90ETvU44g0xEREREpIUJMhERERGRFm6xICIiMgJ/d5sHt3gQGQ5nkImIiIiItDBBJiIiIiLSwgSZiIiIiEgLE2QiIiIiIi08pEdEREQAeFCQSI0JMhERERkNY0jSjSEGkhe3WBARERERaWGCTERERESkhQkyEREREZEW7kEmIiIiMiIv2wMNSL8P+u/uwzZEDHJigkxERERERkfOw5LcYkFEREREpMXoZpBVKhV27tyJ06dPIz8/H40aNcK4ceNgZ2cnd2hEREREZAKMbgb5hx9+wKVLl/D1119j/fr1AIA1a9bIHBURERERmQqjS5DDw8PRu3dvuLm5wdbWFsOGDcO1a9eQkpIid2hEREREZALMioqKiuQOQi0nJwcffvghgoKC4OXlpbk+cuRITJ48GW+99ZbmWnh4OMLDwwEAS5YsER0qEREREb2mjGoGOScnBwBga2urc71ChQqax9T8/f2xZMkSgyXHn332mUF+zj91fMbAGIwtBrnHZwyMwZjGZwyMwdhikHt8qWMwqgS5fPnyAKCXDP/55596STMRERERkRSMKkGuUKECnJ2dER8fr7mWnJyMZ8+ewcPDQ8bIiIiIiMhUGFWCDBRvnTh06BBSUlKQk5ODnTt3onHjxnB1dZV8XDnJPT5jYAzGFoPc4zMGxmBM4zMGxmBsMcg9vtQxGNUhPaC4DvKOHTtw5swZ1kEmIiIiIuGMLkEmIiIiIpKT0W2xICIiIiKSExNkIiIiIiItTJCJiIiMWGZmJuLi4pCZmSl3KGSkCgoKJP35RUVFyMrKgintyjXpBDknJwd5eXkAig8Hnj59GpGRkSbzAli/fj3u3Lkjdxiyu3XrFlJTUwEAWVlZCA4Oxvr165GdnS1zZETySUlJKfW6+ndFhJycHERFReHQoUMAihPF9PR0YePLLTs7G4sXL8b48eMxd+5cjB8/HosXL0ZWVpbQOFQqFW7fvo1z584BAHJzczWfnSTWvn379K6pVCp8++23ko5bVFSEiRMnQqVSSTqOMTHpBDkwMBD3798HUPyi2717N/bs2YPdu3cLjSM9PR2xsbFCxwSK7zgXLlyImTNn4vjx43oNWkQ5ePCgrDMjmzZtQmFhIQBg586dSE9PR2ZmJjZv3iw8FoVCgUuXLiEqKkrnP1Mj1+/E8ePHkZCQAACIj4/HxIkTMXnyZMTFxQmN43nJh8jfk5kzZ5Z6fdasWULGj4+Px+TJk/HDDz/gwIEDAIB79+5hy5YtQsY3Bhs2bIC1tTVWrVqF3bt3Y9WqVbCxscGGDRuExZCUlITp06cjMDAQ69atAwD89ttvWL9+vbAYAODChQt4+PChJqYFCxYgICAASUlJQuMoKipCRkaG0DG1nTt3DpGRkTrXgoOD8eeff0o6rrm5Odzc3CQfp6ySk5Nx8OBBbNq0CQDw6NEjJCYmGnQMk06QHz58CG9vbwBAVFQU5s+fj6+++gq//PKLkPGzsrIQEBCACRMmICAgAEDxi1/9Dy61SZMmYcOGDejSpQtOnz6N8ePHY82aNbh165aQ8dV+++03TJw4EUFBQbhy5YrwGfy0tDRUqVIFRUVFuHz5MqZMmYJ//etfuHnzptA4wsPDMXnyZKxfvx67d+/W/Ldnzx4h4585c6bU/86dO4fY2FghMwdy/04cOXIEjo6OAIA9e/agbdu26NixI0JDQ4WMr/bZZ59pkgG169evC0tOAZT6e1hQUAAzMzMh42/duhXDhg3D8uXLYWFhAQB488038ccffwgZHyh+Ddy+fVvn2u3bt7F3714h48fExGDSpEmoUqUKzM3NUaVKFUyYMAG///67kPEBICQkBG3btsWWLVtgaWkJAPDx8RH+ObF7925Nt92dO3fCyckJbm5uCAkJETK+UqnEunXrMGzYMEyZMgUAcPHixVJndKX02WefYdeuXbhx4waA4gmex48fY/bs2ZKP/d5772HVqlW4desW0tLSkJ6ervlPpOvXr2PmzJn4448/NPladnY2tm/fbtBxLA360/5hVCoVzM3NkZqaioKCAtSoUQOAfqtrqYSEhMDFxQWbNm3S/MI1aNBA6Ay2ra0tunbtiq5duyIhIQHr1q3Dl19+CXd3d3Tr1g3+/v4wN5f2PmrhwoV49OgRTp06hfXr18PCwgIdO3aEn58fXFxcJB0bKL4zzsvLw4MHD2Bvb4/KlStDpVIJX0I8cOAApk6dipYtWwodV3t8hUIBMzMz2NnZITs7G0VFRXBwcEB6ejpcXV0xe/ZsuLu7SxaD3L8TT548gZ2dHfLz83H79m3MmDEDlpaWOHLkiJDx1dq2bYt58+Zh9OjR6NChA/bu3Ytjx45h9OjRko8dEBAAMzMz5OfnY9GiRTqPKRQK1KxZU/IYACAxMREdO3bUuWZjY4Pc3Fwh4wNAREQE+vTpo3PNw8MD33zzDQYOHCj5+HZ2dsjLy4ONjY3mWn5+vtC+AHfu3MGsWbN0PgcqVKggfCYxMzMTjo6OUKlUiI6Oxtq1a1GuXDl8/PHHQsYPDQ3V3MCrb95r166N3bt3Y8CAAUJiAAA3NzdMmzYNy5cvR5MmTRAfH48FCxbovEakol65UCfn2r7//nvJx1fbuXMnpk2bhiZNmmDUqFEAAG9vb9y9e9eg45h0guzh4aFJCho1agQAyMjIEPJCA4CbN28iODgYVlZWmmvqxESkjIwMREREICIiAgUFBejXrx9cXV1x7NgxXL9+HTNmzJA8Bnd3dwwbNgxDhgzB5cuXsX//foSFhaFhw4bo0aMHmjRpItnYPj4++Oabb/D06VO0aNECAPD48WPY29tLNmZplEqlbMkxUNyRKC0tDUOHDoWVlRXy8vKwa9cuODo6omvXrtiyZQtCQkIwb948yWKQ+3fCxsYG6enpuH//Pjw9PWFlZYWCggLh++769++PunXrYvXq1di3bx9sbGywePFiSW9O1OrWrQugePbyzTff1Fw3MzODvb092rRpI3kMQPG/u0Kh0LlJTkpK0szwi5Cbm6vzWgQAa2trKJVKycbUno3r3bs3vvnmGwwYMAAuLi5ISUnBgQMH0Lt3b8nGL6l8+fL4888/dZLy9PR04e+P5cqVw59//onExERUrVoVtra2KCwslPxwmtrly5excuVK2Nraaq45OjoK2W5RcobW2dkZffr0waFDhzB79mzk5eUhPT1d8t+NNWvWSPrzyyo5OVkvJ1C/VxuSSSfIo0ePxubNm2FpaYmJEycCKJ66VyfLUrO0tNT74H369CkqVqwoZPz/+7//w8mTJxEdHY2GDRti5MiRaNasmWamoFWrVhg3bpyQWIDi5dvz58/j5MmTePToEdq3bw9XV1esX78eb731FsaOHSvJuOPHj8fhw4dhaWmp+eBJSkrCu+++K8l4z9O6dWtcuXIFzZo1Ezqu2k8//YQ1a9agXLlyAIrfcIYMGYLJkyejV69e+PDDDzF58mRJY5D7d6Jjx46YN28e8vPzMXjwYADFM2hVqlQRMr429e9hbm4unJ2dUaFCBSHjqmfDqlWrhrZt2woZszS+vr749ttvMXz4cADFe5JDQ0PRuXNnYTFUrVoVv/32G5o2baq5dv36dUlfDxMmTNC79tVXX+l8HxMTAz8/P8li0NayZUusW7dO8/775MkTbN26Vfhr46233sJXX30FpVKpeQ3cu3cPTk5OwmIoebOkVCqFTKiV9ppQmzNnjuZrqWdxRazoloWTkxPu378PDw8PzbWEhAS4uroadByTTpC9vLw0SyVqvr6+8PX1FTJ+48aNERoaqrNsunfvXmEJ0ubNm+Hn54exY8fC2dlZ7/Hy5ctrkgQpJSQk4NSpU4iKioKjoyM6d+6MmTNnau7Uu3TpgilTpkiWIFeoUEHvz9m8eXNJxipJ+7BNfn4+Vq5cCR8fHzg4OOg8b/z48ZLHolKpkJmZqfMmmJmZqTnAaG1tLflsjdy/E4MHD4aPjw8sLS1Rv359AMUzV+okTZSDBw/i8OHDGDVqFNq0aYMtW7Zg1qxZmDJlCnx8fITEoE6Anj17hmfPnuk8JmIWt0+fPlAqlQgMDIRSqcTChQvRrVs3dOvWTfKxtWP49ttv0aVLF7i7u+Px48c4ceKEpMv6xjJLp/bBBx9g3bp1mkmksWPHol27dujbt6/QOMaMGYPTp0/D0tISHTp0AFC8HbJfv35Cxn/zzTcRFhams53i2LFjQn4fjeU1oVKpEBYWhjNnziArKwvbtm3DtWvXkJKSgq5duwqLo1u3blixYgX69esHlUqF8+fPY9++fQZfWTHpVtMlD19o015alMrTp0+xbNkyxMfHIz8/HzY2NvDy8sLMmTMlny0qLCzEoUOH8N577+ndFYs2bNgwtGnTBp07d9Ys75YUGhqKESNGSDL+/v37S71uaWkJFxcXNG3aVGdZzZDWrl1bpuepP5yktHXrVvz222/o3bs3nJ2doVAocPjwYTRq1Agffvghrl27hu+//x6BgYGSxSDn74QxmTp1KqZNm6Y5FwEAkZGR2LJlC7Zu3SokhtjYWAQHB5daJUDkfkOg+ACOyH232q5evYrjx48jNTUVLi4ueOedd2Rb5ZFTdna25u9A9L9FYWEhgoKCMH36dNk+rxQKhWYmPzU1Fe7u7igsLMQXX3whdNuPnHbt2oXo6Gj06dMH69atw9atW5GcnIwVK1YgKChIaCzh4eE4duwYUlJS4Orqiu7duxt8dcmkE+RBgwY99zGRHwDx8fFISUmBs7MzatWqJeyU+Icffijsw/ZFnreEXlRUJOTvIiAgADExMXB0dISTkxPS0tKQkZGBN954AykpKcjNzcXcuXNRu3ZtyWORU2FhIcLCwhAZGanZz9ahQwf06dMHlpaW+PPPP6FSqVCpUiXJY5Hrd0KpVOLYsWOIi4vT22c6f/58ITGo4yht6fbBgweoXr26kBhmzJiBhg0bonPnzrC2ttZ5zFiWWqVUWFiIkJAQjBgxQrakrKioCEeOHMGpU6egUCjg7OwMPz8/9OjRQ/LD08bmo48+0hzilkt+fj4uX76MlJQUuLi4oHnz5sJfG0VFRTh79izi4+P1VnakXmn85JNPsGjRIjg4OGDUqFEICQlBUVERRo8eLayaiEgmvcWiZBKcnp6OPXv2CFteV/P29oa3tzdSUlKQlpZW6nYHKfj4+CAmJkazlCyXTz75BNu2bdO7LuqXzsPDA40bN0avXr0013788UdkZGRgwYIF+P777xEaGqq3D9DQpk6dipUrV+pdnz59OlasWCHp2ABgYWGB/v37o3///qU+LmIG9+nTp7C0tNT8TgDFyWJhYaGQ8detW4eEhAS0aNFCLykUycbGBsnJyTh79izS09MxduxYPHr0SGgJxJSUFIwYMULYzUlJz5vAUK/stGvXTnPzJgULCwucO3cOY8aMkeTnl0VYWBgiIiLQu3dvuLm5ITk5GYcOHUJeXp6wrQWZmZnYu3dvqTeNq1atEhIDAHTo0AHHjx9Hjx49hI1ZUrly5dC6dWvZxgeAjRs34vz582jQoIGwggJqSqUSlStX1rlWUFCgObciysWLF1GlShWdPcj3799HUlKSQQ+6m3SCXJKjoyNGjRqF2bNno1WrVpKPt3btWvj5+aFu3bqIiorC6tWrYWZmhkmTJqFdu3aSj+/i4oKgoCC0bt0aLi4uOh+E77//vuTjq5X2oS8yEYiMjMTGjRt1rnXv3h3jxo3DiBEj0LdvX/z888+Sx5GWllbqddE1JuXacwoAQUFBGD58ON544w3NtcTEROzYsQMLFy6UfPzr169j1apVsi3na8exfPlyzU3s2LFjkZ2djYMHD2Lu3LlCYnjjjTfw6NEjVKtWTch4JY0cORInT55Ejx494OLigtTUVBw7dgwdOnSAjY0NDh8+DKVSiWHDhkkWw1tvvYULFy7IlhRFRETgs88+0/wbNGzYEPXr18fXX38tLEEODg6GUqmEn5+frDeNd+/exdGjR3H8+HG4urrqfF6JWN1ZvHgxevfujQYNGmiu3bhxA4cOHZK0sk9J58+fx9dffy3LwWFvb29ERETobGWIiooSvrq6Y8cOLFiwQOdaxYoVsXPnTibIUsrPzxfWxvPatWuag2c//fQTpk+fDltbW4SEhAhJkO/du4eaNWsiOTkZycnJOo+JSJDVB9QKCgr0OkOlpKQIKWkFFJ9MTkxMhKenp+bagwcPNDNTUi9lHjx4EEDxkq76a7WkpCRhp7SNYc9pYmIiatWqpXOtVq1amo6XUqtYsaLwWZnSiKrz+SINGjRAUFAQunTpolfSS8T70+nTpzF79mydk+kNGjTA8uXLERQUhDp16iAoKEjSBLmgoACrV6/GiRMn9CYRRBycffr0Kdzc3HSuubq6Cq1BHBsbiw0bNsj+e1GvXj3Uq1dPtvHj4+P1xq9Xr16pq35Ssra2FrbKXNLw4cOxcOFCnDt3Dnl5eViyZAni4+Px5ZdfCo0jKytLb9JGipJ7Jp0gl0xGcnNzcenSJWFl3tQ1Np8+fYqkpCS0aNECZmZmUCgUQsYX/aIuSV0doaioSPM1UFxvtXbt2sLKOXXp0gWLFy+Gv7+/Zqbq5MmTeOeddwAUl8PTPixlaNHR0QCK/z7UXwP/qzv7ohI/hvTdd9+hWbNmpe45FUVdf1n7w1ipVEq2jF7S4MGDsWXLFgwbNkxYabnSiKrz+SLh4eEAik/qazMzMxOSICcnJ+t9CDo4OGhu5j09PSWvj21paYm3334bAITXwgaAmjVr4vDhwzoTFj/++CO8vLyExeDk5CT0dfc8IptxlMbMzAyFhYU6e6ALCwuFd37t1asX9u/fj0GDBgnf/uTh4YGVK1fizJkzcHd3h4uLCz7++GPhNbEdHByQmJio87mcmJho8DhMOkHWTkaA4juz1q1bC9vj5OjoiJiYGDx48AD16tWDmZkZcnJyhB5CKCoqwp07dzR7n0UeiJo4cSKKiorg5uaGvn37ynbo5P3334eTkxMiIyPx66+/wtHREUOGDNGU+2vbtq2kNT/VNypbtmwR0inteeTecwoUV4/ZtWsXPvzwQ5ibm6OoqAh79+4VUlUGAFavXg2VSoWIiAi916PIDpei6ny+SHBwsLCxSlOzZk3s2LEDQ4cORbly5ZCfn49du3ZpksPk5GTJD4yKqB7zIiNHjsSiRYsQHh6uuXnPz88XemC0T58+CA4OxoABA/QSENHVGxQKBaKiopCWlgYnJye0a9dO2Gyqt7c3jh07plNK7Pjx48I6S6odO3YMqampOH78uN5+YKn3hJ86dQp+fn7o2bOnzvXNmzcL3avfoUMHTY30KlWqICkpCTt27DB4iV6TrmIht7Nnz2LNmjWwtLTEnDlzUL9+fVy4cAE///wzvvjiC8nHVygUWLp0KR4+fIjKlSsjKysL1apVw+zZs4W96ahUKgwfPhyhoaGynk6m4moeo0ePlm3PKVBcPumrr75CXl4e3NzckJKSAktLS3zxxRdCksOYmJjnPibyMGt4eDh+/PFH9OvXD5s3b8aECRM0dT7VNWBfd48fP8bSpUuRmpqqeX9ydnbWtDu/ffs2FAqFZoZXKjk5Obhy5QrS0tLQu3dvZGZmQqVSCUsOtcd3cnJCs2bNJCs7WRpjqfZ069YtLF68GJ6enpr3hoSEBMydO1fI1ot79+5hwYIFqFq1KqpWrYqkpCQ8evQIX375pdAZ/dOnTz/3sZKt2Q1twoQJmDBhgs4q+65du3D58mUhB8nVCgsLsXv3bvznP/9Bbm4ubGxs0KVLFwwePNigeYRJJ8iLFy8udXN9YGCgTncaKeXl5QH4X4eerKwsFBUVCVmyWL58OSpWrIgPP/wQNjY2UCqVCA0NRWZmJmbNmiX5+GrTpk3DggULZD0YlZeXh6SkJL3DaaJmLgH5T4uHhYXh9OnTsu05VcvLy8OlS5c0bYabNWsm6+EguWjX+XRxcUGPHj2EdpGbMmXKc1cTRFUvUKlUiI2N1ZQdrFOnjtCVpvj4eCxevBgODg5ISUlBaGgofvvtN5w4cQIzZsyQZMzDhw9rKuqU3AaozdLSElWqVMFbb70l6d9Jamrqcx8TWe5v/vz58PPz0+kgePr0aZw4cQKLFy8WEkNmZiYiIyM1tXfbt2+v19TpdRYXF4egoCDMmzcPHh4eCAsLw6lTpxAQECB8m4WalDXSTXqLRWxsbKnX//jjD2ExlCtXTrPFwcnJCbVr1xa2xH3r1i2sXbtWk5zb2Nhg5MiR+OSTT4SMr/bee+9h1apVGDBggN5BGBGzNJcuXUJwcDBycnL0HhM5QyL3aXG595yqWVlZydri+OHDh7h586be/tbnlb+Tir+/P/z9/YWOqa3kQd309HTNEqsIRUVFOHXqFG7cuIHs7GydvZ6izk9s3boVw4YNQ6dOnTSHJd98880yN/j5K27evKlJkEtuA9SmUqmQlJSEX3/9FZ9++qlk8RhLzeuHDx/qzZB26NCh1BKhUrG3t9cpByqXJ0+eIC4uTu/3QuouwLVq1cJHH32EpUuXomPHjjh16hS++uor2ZJjAJJOrJlkghwVFQWgeJpe/bXa48ePhXXsknuLQ7ly5ZCTk6NT6DwnJ0fYgSg1dQWLGzdu6D0mIkENDQ1F//794e/vL+tMpdynxeXecwrIWwQfKH5vWLt2LTw9PXHv3j14enoiISFBeK1w9fuQnZ0dcnNzcejQIVhYWKBnz57CGhOUtlz71ltvYfv27ULG3717NyIiItChQwdcvnwZXbt2RWRkpNCbtcTERL2/BxsbG+Tm5ko2pvbq5ctuBJ49eyb5IV5jaVZSuXJl3L17V6fKTXx8vNCVx9jYWMTFxem9N4ksi3r9+nWsWLEClpaWyMnJga2tLXJycuDq6ip5ggwUvwekpqYiLCwMCxculOUG6tGjRwgJCSn138KQZ0VMMkFW/wXm5+fr/GWam5vD3t5e2EGprVu3olatWggICNDZ4rBlyxYhWxxatmyJZcuWYfDgwXB1dUVKSgq+//57ITWgtcndZz4rK0vW4vNqxnJaXE5yFsEHireZTJo0CW3btsWoUaMQGBiIU6dO4dGjR0Lj+Pe//40JEybAzs4OO3fuRExMDCwtLZGWloZx48YJjUVb9erVn7vyZmhnz57FvHnz4OXlhZMnT2L48OFo1aoVDh8+LGR8oHh2Sr3VRy0pKcloWguXL19erx6soRlDsxKguDZ9YGAg/P39NXuQw8PDha3s7N27F2FhYfDy8tJ7bxKZIO/atQv9+/dHz549MWrUKGzevBn79++X7P1y8ODBpV5XqVSYNm2a5nuRh5jXrl0LR0dHTJo0SdLPCZNMkNUzZUuXLsXs2bNli0PuLQ5Dhw7F1q1bsWTJEuTn58PS0hK+vr4YMmSIkPHV5F7Ca9SoEWJjY1GnTh1Z45DjtHhQUJDmZiwgIOC523tEnZqXswg+ULyq06ZNG51rvr6+mDBhgqT1dktKSkrSlDC6cOECAgICUL58ecn2vZamZIOa3NxcRERECDvA+/TpU83hJwsLC6hUKtSpUwc3b94UMj5Q/G+vPi0PFM9YhoaGCt0L/jJSHxAzhmYlANC1a1fY2trizJkzuHDhApydnTFy5EhhKwonTpxAQECA8KYYJT1+/FgzoaPeXtG7d29MmTIF7733nsHH+/zzzw3+M/+uxMRELFiwQPLVbpNMkNXkTI4B+bc4WFlZYdy4cfjoo480G93lKPGlUqkQFhaGM2fOICsrC9u2bcO1a9eQkpKCrl27Sj6+uqNg27Zt9RJTkTMD6hu3K1eu6D0m1VYT7Y51devWlWSMVyFnEXwAmuXKChUqoHLlynjw4AEqVaok6ZL686j3mFpbW2sqeJRcTpRSaUv3Li4uwm7gHR0dNUv6rq6uuHr1KipVqiS02k2fPn2gVCoRGBgIpVKJhQsXolu3bujWrZuwGORmDM1KgOJksF27dnoJcVFRkbDPLW9vbyHjvIi1tTXy8/NhbW2NSpUqQaFQoEKFCpL9e6i3lxUWFiIkJAQjRowQts3redzd3ZGdnS35So5JJ8hFRUU4efKkbIdA5N7ioK7iYWZmplNPUWQVDwDYs2cPoqOjMXToUKxbtw4AULVqVezatUtIgnz37l3UqFEDiYmJSExM1HlMZIIsx1aTvn37ar6WuxA/IG8RfKB4duzixYvo1KkT2rRpg4CAAFhaWuo17ZBa7dq1sXnzZmRmZqJp06YAiutUi2xeUvL1aGNjI3ndYW1dunRBfHw8nJ2d0aNHDyxbtgxFRUUYOHCgsBjMzc0xZMgQDBkyRNLT8sbMGJqVAMCHH35Y6oG80aNHIyQkRPLx/fz8cOrUKVkPzgJAnTp18H//939o164dmjZtiqVLl6JcuXKSr4BaWFjg3LlzQusdP0+nTp2wYsUK9OrVS29Sy5CVp0w6QZb7EMjztjgMHTpUyPjGUMUDKN5ruGjRIjg4OGD9+vUAimcoXlReyJDk7iioJvdWk9IOhllaWuK9994TNmMgZxF8QLcxxMCBA+Hu7o5nz55JXl+0pHHjxmH37t2oWLGiZo/lnTt3hB5Qk/v12L17d83X7dq1Q7169aBUKoXX6S4qKsIff/yB9PR04ZWGjEHJZiUKhQJ5eXlCm5UAKLVjncgqtXfu3MGPP/6IY8eO6ZV2E/l3MWXKFE1Xx2HDhuHIkSN49uyZJNsrSnrrrbdw4cIFtG7dWvKxXmTz5s0AgG+++UbvMUOutpp0gqx9COTUqVPCD4HItcXBWKp4qCmVSr1kqKCgAOXKlRMah9zkPi3+vINhCoVC2MEwkTP2L2NmZob27dvLMraLiwumTJmic61kR8dNmzZh7NixksUg9+uxJCcnJ+FjJiUlISgoCMnJybCzs0N2djbc3Nwwa9Ys2fbJi+bp6YlVq1bJ1qxEXeWooKBA87VaSkoK3N3dhcRRt25do9iGpj1ZYWVlJfQ9s6CgAKtXr8aJEyf0SrKKqDKkJqr8qkknyNqHQMzNzWU5BCLH7ISxVPFQ8/b2RkREhM7Bl6ioKEkPQ8yaNQtBQUEAjKMhAiD/aXFjOBgmeqYWeHEzBm3GlLwDwC+//CJpgiz369EYfPfdd2jQoAGWLFkCKysr5ObmYufOndiwYYPRrDyJYGtrK3T1QlthYSGA4s9K9ddA8Q1s7dq1hR2YNIYtaGrnzp1DRESEpoFOx44dJe8oCRQ3p1GPo57Ffp2ZdIIs9yEQuWYnjKWKh9rw4cOxcOFCnDt3Dnl5eViyZAni4+Ml/QDSLvZuLImPMZwWl+Ng2B9//KE5LHj79u3nPk+qroYvasagzVheJ2pSLy8bw+tRbnFxcZgzZ45mNcva2hrDhg3DRx99JHNk4shdm1y97al69erCm3RkZmZq9riWrOqiTWTZvx9++AE//fQTOnfujDZt2iAlJQVbt25Famoq+vTpI+nY2lvQ5CTq/JhJJ8jPOwQi6k5R7tmJkslxSkoKzM3NhVcR8PDwwMqVK3HmzBm4u7vDxcUFH3/8saTdebRnQ+SYtSyN3KfF5ToYFhAQgNDQUADAF1988dznSbWs9k+dCZR6pUnu16MxcHNzQ0ZGhuZmEYDe9687uWuTq/Xq1Uv4iuunn36qORj4ooYsIjuu/vzzz5gzZ45ORY1WrVohKChI8gQZKN4SeeXKFc3kYrNmzYS/LkSdHzPpBFnuQyByz06sXbsWfn5+qFu3LqKiorB69WqYmZlh0qRJwpfT7Ozs0LNnT6FjqgUEBKBTp05o2bKlrOVr5D4tLtfBMHVyDIj9oHmRzMxMzX5LOduoyknu16Mx6NixI5YuXYqePXvC2dkZqamp+Omnn+Dn56ez2iHV6oYxkLs2uZocK64rVqzQfC13Qyu1vLw8eHp66lzz8PBAXl6e5GMnJiZi0aJFMDc3h4uLC1JTU7Ft2zbMmzcPHh4eko+vJur8mEknyBs2bNBZIlIfAtm4caOQJFXu2Ylr165p9jD+9NNPmD59OmxtbRESEiI8QZZrTxVQ/OG2Z88ebN68GW3btkWnTp1kKQYv92nxshwMEy0vLw/m5ubCaoNnZ2dj9erVuH79uuZao0aNMGnSJL2DpK87uV+PxkA9e6guP1nyupqx3NhJQe7a5GpyrLhq/7nlruqi1qFDB/z00086201++uknIW2mt27dCn9/f/Tv3x9mZmYoKirCgQMHsHXr1heu/hmaqPNjJp0gnzt3rtQ9VL/++quQBFnu2Ync3FxYWVnh6dOnSEpKQosWLWBmZgaFQiHJeM8j554qoLic18CBA3Hjxg2cPn0aCxcuhKurKzp16iSkdI6a3KfFS1Y00SbqhmnPnj146623ULt2bVy/fh1BQUEwMzPDjBkz0LhxY8nH37BhA6ytrbFq1SpNbXL1h7CI9u+vQuo9yHK/Ho3B65z4lpXctcnV5F5xPX78OOrWrQsvLy/Ex8dj+fLlsLCwwL/+9S/UqlVL0rG1u5wWFRXh+PHj+Pnnn+Hs7AyFQoGMjAzUq1dP0hgAICEhAXPmzNHEYmZmhj59+uDo0aOSj61N1Pkxk0yQ1cmnSqVCbGyszgfN48ePYW1tLSQOuWcnHB0dERMTgwcPHqBevXowMzNDTk6O0E5VgPx7qtQaNGiABg0aYOTIkVizZg22b98uNEEG5D0trl3RBCieTS0sLISjo6OwmM6cOaOZGTlw4ACGDh2K8uXLY8+ePUIS5JiYGKxbt06zp65KlSqYMGGCsO5xz3Pz5k1YWFjolJkqrQaoocn5evynGDlyZKkNLP7JSlb2SUlJka02uZrcK65HjhzRrKbt2bMHbdu2Rfny5REaGoqFCxdKOnbJ8nJylZuztbVFamoqqlatqrmWmpqK8uXLC41DVBMhk0yQtZcCSvYZd3BwwAcffCAkDrlnJ/r376/pFKbunBcdHS18j6Gce6q0JSQk4PTp04iKikK5cuV0usyJoFKp8OuvvyIuLk6W0+Lq6iZqhYWF2L17t9ADSTk5ObC1tYVSqURCQgK++OILWFhYCEtA7OzskJeXp3PoJD8/X3gHtUWLFuH9999H/fr1cfToUezatQvm5uYYOHCg5qZN6mVvuV+P/xQim1WIYiwVW7RXUl+04irCkydPYGdnh/z8fNy+fRszZsyApaUljhw5IvnYr1o44IcffpBkcsnX1xdLlixB7969NStshw8fFn7QXdT5MZNMkNWJ6cyZM7Fs2TJZYigsLMSsWbMQGBgo28Gwt99+Gy1atADwv+LjdevWFX7gRM49VUBx97bTp0/j4cOHeOuttzB58mQ0atRI+HLid999h8uXL6N+/frCVjFexMLCAh988AEmT54spOU3AFSqVAkPHz5EYmIi3njjDVhYWAi9Uerduze++eYbDBgwAC4uLkhJScGBAwfQu3dvnTJPUpd1SkhI0LSOPXnyJObPnw9bW1ssW7ZM2KqGsb0ejdXr2FXPWCr7lLavtbQVV+2ESSo2NjZIT0/H/fv34enpCSsrKxQUFBhlPeCwsDBJEuT3338fFhYWOHTokGbbVceOHYWX3ytJqiZCJpkgq5VMjkUeCLKwsEBOTo7sb64lk3M5DiLdv3//uXuqFi1apHmeVIeDIiMj0blzZ7z99tvCuwhqu3DhApYtW2YUB2LU0tPToVQqhY3XvXt3fPbZZwCAyZMnAyje9iCqsoy6U9dXX32lcz0mJkbne6lXfwoKCmBpaYnMzExkZWVpllSzs7MlHVebMb4eSR6xsbE4ffq0ziFq9Q2clOReZdXWsWNHzJs3D/n5+Rg8eDCA4io/clf3KI1Uqxrm5ubo27ev8NXVkjIzM7F3717ExcXpfT4ZctuPSSfIch8I6tatG3bv3o2hQ4cK2/drjB3k5G7hGRgYKNvY2ipVqiR8KV9byTauubm5iI6ORuvWrYXF0L17dzRt2hQWFhaarR2urq46ra7VMxdSMJZSTm5ubjh9+jSSk5PRoEEDAMVLvCLbr8v9eiTjcPr0aWzatAmtWrWCl5cXUlNTERAQgDFjxsgy01xUVITMzEw4ODgIHXfw4MHw8fGBpaUl6tevDwAoV64chg8fLjSOspBq4m3Lli1o1KgRfHx8hO871hYcHAylUgk/Pz9JV7dMOkGW+0BQeHg4UlNTceLECdjb28Pc3FzzmFQJqjF2kDOGFp4PHz7EzZs39Wbo1PWARRg0aBC2bt2KIUOGSN6cozTabVwBoGLFihg6dCjat28vNA7tAyAA4O7urvP9tGnTJNuTXJZSTtOnT9epjyqFoUOHIjg4GOXKldNUz7hy5Yrkp+W1yf16/Kd4Hfcgazt48CBmzZqFRo0aaa7duHEDGzZsEJogK5VKhISEICoqCubm5ti+fTsuXryIe/fuCfsM0f47AKD3+/g6HtjUplKpsH37dqSkpMDb2xsNGzZEo0aNUKdOHWGlOIHiFY0NGzZI3qDEpBNkuQ8EyZGgGmMHOQBQKBSIiorSzA62a9dO2NJuVFQU1q5dC09PT9y7dw+enp5ISEjQzBKIUqNGDezZswdjxozRuVkC9CtMSMFY2oi+jNwJSWpqquRjNGrUSG9G/+233xZWGxyQ//X4TyGivJacsrKyNKsYavXr1xe63QcobiiUlZWFgIAABAQEACju/rl7926jmGQB5H9vkpq6b4JCocD169cRHR2NZcuWobCwUKfhk9ScnJxQUFAg+TgmnSDLfSDIGBLUvLw8JCUl6Z1SF3lQ79atW1i8eDE8PT3h5uaGhIQEHDhwAHPnzhXy4RMWFoZJkyahbdu2GDVqFAIDA3Hq1Ck8evRI8rG1rV69GnXq1MHo0aNlOxRlDG1EX0buffsixtc+EFiS1AcE1Yzh9WgMkpOTcfbsWaSnp2Ps2LF49OgRCgsLUaNGDQDQVAB6XbVo0QLnzp3TmVw5f/685oC3KJcvX8bKlSt16nA7OjoiIyNDaBwvIvd7k5qUiXpubi4SExNx//593L9/HxYWFnoz61Lr06cPgoODMWDAAL1Op4Z8fzTpBFnuA0FyN2a4dOkSgoODkZOTo/eYyMMRO3bswKhRo3TK9Zw+fRo7duzA4sWLJR9foVCgTZs2Otd8fX0xYcIEDBs2TPLx1ZKTk7F06VK92TpRjKWNKAETJkx47mOifjflfj0ag+vXr2P58uXw8fFBTEwMxo4di+zsbBw8eBBz586VOzwhVCoV1q5dixMnTmjeF/744w+0adNGZ5VDROm/kofKlUql0d3Ai5aSkgJzc3OdFVepXpsLFy5EQkIC3njjDTRs2BCTJ0+WpfW8uiTplStX9B4z5PujySfIch4IkrsxQ2hoKPr37w9/f39ZZ4gePnyoN5veoUMHYVtdbG1tkZOTgwoVKqBy5cp48OABKlWqhNzcXCHjq9WqVQtJSUl6e25FMZY2oqR/WDA9PR0HDx4UusVC7tejMdi5cyemTZuGJk2aYNSoUQAAb29v3L17V+bIxLG0tNT5PHJzc4ObmxsA/XMLUnrzzTcRFhams53i2LFj8PHxERaDMVi7di38/PxQt25dREVFYfXq1TAzM8OkSZM0/05SHXpPSkqCtbU1HB0d4eTkJGw1qyRRh6lNOkEG5D0QJHdjhqysLPTo0UPIWC9SuXJl3L17V+fAQ3x8vLAT9A0bNsTFixfRqVMntGnTRtM8RcRBzZJxLF26FP7+/nontEXcMBlLG9GXed33+QH6hwVdXFzwySefYOHChejQoYOQGOR+PRqD5ORkNGnSROeauv6tqTCWswkjRozAV199hV9++QVKpRLTp09HYWGhUd28i3hvunbtmmYv8E8//YTp06fD1tYWISEhkv9erlu3Do8ePUJ0dDTOnTuHzZs3w9nZGY0bN8aQIUMkHVtbWQ5TG4LJJ8gvI/LDWHRjhkaNGiE2NlZIPcsX6d69OwIDA+Hv7w83NzekpKQgPDxcWAUJ7Q+AgQMHolq1anj27JmwRiVqJ0+eBAAcP35c57qZmZmQhMRY2oi+jOiqGiXJVRfYysoKCoVC2Hhyvx6NgZOTE+7fv6+zxSghIUFod0ljkJOTgytXriAtLQ29e/dGZmYmVCqV0BlEZ2dnrFixApcvX0ZKSgpcXFzQvHlz2RptlUbEmZnc3FxYWVnh6dOnSEpKQosWLWBmZibsvcHd3R0uLi6oWrUqXF1dcerUKRw6dEhogqxSqRAWFoYzZ84gKysL27Ztw7Vr15CSkmLQ3IkJ8kuI3nQvdWOGgwcPar52cXFBUFAQ2rZtq7fRXWSFja5du8LW1hZnzpzBhQsX4OzsjJEjRwr7ENauhx0dHY3169fDzMwMrq6uQmeRS64olEbKLT/G0kb0ZU0JPvroI8nGnjp1KlauXKl3Xbu0m9Ql3gD98wlKpRJnz55F7dq1JR9brSyvx9ddt27dsGLFCvTr1w8qlQrnz5/Hvn370Lt3b7lDEyY+Ph6LFy+Gg4MDUlJS0Lt3b9y7dw8nTpzAjBkzhMZSrlw5oXXZtd26dQtOTk5wcXFBVlYWduzYAQsLCwwZMkSz2iniwKajoyNiYmLw4MED1KtXD2ZmZsjJyRHSS+GHH35AdHQ0bt++jYoVK6JBgwYYNWqU8EN6e/bsQXR0NIYOHarprFi1alXs2rWLCfLrQo7GDNHR0Trf16hRA4mJiUhMTNS5LipBLiwsREhICEaMGCHbrJTc9bBfhZRbfoyhjajcTQnS0tJKvf6iqhJSKHk+wcbGBrVq1cIHH3wgNA5T5+/vDwA4dOgQVCoV9u3bh+7duwvb5mIMtm7dimHDhqFTp06afdhvvvkm1q5dK/nYZR1DxDaQTZs2aW4Idu7cifT0dJQrVw6bN2/G1KlTJR9frX///pptgOqEPDo6Wshhudu3b6N58+YYNWoUqlevLvl4z3P27FksWrQIDg4OWL9+PYDi82OGLsHJBFlGcjRm+PLLL1/p+bdu3ZK0y52FhQXOnTuHMWPGSDbGy8hdD/tVSLnlR91G9N1339Up+5eVlSVsKVWupgTqlZXCwkKdVRag+GCKVLP2z2MMs7d5eXk4cOAAbty4gezsbJ3XnrF0HBTB399fkyibosTERL3fPRsbGyGHmLUrqOTn5+PcuXOoXbu2ZoXrzp07wg6upqWloUqVKigqKsLly5fxzTffwNraGpMmTRIyvtrbb7+tKbGn3l5St25dIaVZZ8+e/dLnBAYGSj6TrlQqUblyZZ1rBQUFBu80ygT5JaRMSCZOnAilUonLly9rZuyaNWsmrO10WQQGBkqeKL711lu4cOGCbEtnctfDfhVSbvmJjY1FcHAwkpKS9B4TVVpMrqYE6pWVwsJCnVUWMzMz2Nvbv7DsmhQ2bNhQatmsjRs3SrrFRNvWrVtx+/ZtdOnSBTt37sTQoUPx888/m8z+Y23Pnj3TqxUv1wl+0ezs7KBQKHQORiUlJQn583/88cear9esWYMJEybozN5HRkbi+vXrkscBFCfreXl5ePDgAezt7VG5cmWoVCpZPitK7rsumSzK6datW5KP4e3tjYiICHTu3FlzLSoqyuBb0Ew6Qc7Lyyt1g7/2Pk8pZ3Pj4uKwZMkSWFlZwcnJCQqFAiEhIZgzZ47QlrIvIuKQYkFBAVavXq2ps6mdBIqorSl3PWxj8d1336FZs2bo3LmzbGX/5GpKoF5Z2bJlC0aPHi3pWGVx7ty5Ul/7v/76q7AE+fLly/jqq6/g5uaGPXv24N1330WDBg2wZcsW9OvXT0gMcrt16xbWrVsn602j3Hx9ffHtt99i+PDhAIr3JIeGhuokJyJcunRJbytFu3btsGXLFiHj+/j44JtvvsHTp08170ePHz/WO78jhVmzZiEoKAgAMGXKlOdOlKxatUryWIzB8OHDsXDhQpw7dw55eXlYsmQJ4uPjX3mF/GVMOkH+5ptvMHv2bJ0XW1ZWFhYtWqQ5qCPlh9GmTZvQo0cP9OnTR3Pt0KFD2LRpEwIDAyUb91WIOKRoaWmpWSZTqVSSj1dSWephm4KUlBSMGDFC1m5QcjclkDs5vn37NoDiv4fY2FidG9THjx8LvXFRKpWaereWlpYoKChA9erVERcXJywGuW3YsAGtW7dG+/btTbabYJ8+faBUKhEYGAilUomFCxeiW7du6Natm9A4KlWqhJiYGJ0VppiYGFSsWFHI+OPHj8fhw4dhaWmpOZeRlJSEd999V/Kxtc+BiDxAb6w8PDywcuVKnDlzRlNV4+OPPzb4zYpJJ8jm5ubYsmWLZv/rn3/+icWLF6Np06ZCxn/48CF69uypc+29997DgQMHhIxvLJ631URkebGX1cM2FlLO6L/xxht49OiRrDPncjclyMzMxN69exEXF6dXTUbE7Ix2TdfPP/9c5zEHBwehh/RcXV3x4MEDVK9eHdWqVcOpU6dQoUIFnVa/r7v09HR88MEHRtNCWA7m5uYYMmQIhgwZguzsbGH16Uvq27cvlixZgtatW2v2IF+4cEFzcFBqFSpUwODBg3WuNW/eXMjY2u+JoqsKGaOYmBjUr19fL3/6/fffDVpqz6QT5E8//RQLFizA4cOH8c477+Drr79GrVq1MGLECCHje3l5ITExUef06f3792Vp3Sinf8JWE2Nh6C0/2uXEGjRogKCgIHTp0kXvTlzUvlO5mxIEBwdDqVTCz89PlhlD9bL9zJkzsWzZMuHja+vbty8UCgWqV6+Ofv36YdmyZSgoKNA0KTAFjRo1QlxcnNDyesZMruQYAPz8/FC1alVERkYiLi4ODg4OmDt3rpDaw2qRkZGIjIxEZmYmli9fjpiYGDx58gStWrUSFgNQvD00KSlJb1+8iIN6xmDp0qWlno0KCgpCSEiIwcYx6QTZ2toas2fPxueff47IyEh4eHgI2fOq1qhRIyxZsgR+fn6a5WT1xnPtxEXOQzEi9iD/E7aaiKJQKJCQkKA3e6l+DRh6y0/JcmJAcftWbXI0hpDrUFRsbCw2bNgAGxsbycd6EbmTYwBo27at5utGjRohJCQEBQUFOn83Ule5kdv48eMRGBiIWrVqyVorXrQX7XPVJnrPa7169V6YEG/atEmyG7gjR47g559/xjvvvIP9+/cDKL5h2Llzp9AE+dKlSwgODkZOTo7eY8awL15EzlDaGM+ePdOpemIIJpcglyzADxR/EJw+fRqNGzfWPC4iIYiIiICFhQXOnDmjuWZubo6IiAjN91ImJ+plipK0lyn69u0rydjauNWkWHh4ODZv3owKFSrozF5K+RowhnJi2uQ+FOXk5GQ0bYSvX7+O6OhovRJrcs2yW1pawtJS9yNDRJUbOf3www9ISEhAUVGR3orC65wg/1P/bL/88otkCfJ//vMfzJkzB+7u7prPJnd391Lfq6QUGhqK/v37w9/f3yj3xYeGhkr2s9U3bnl5efj00091HsvKyjJ4wxKTS5BLmzEDit/89+7dC0DcjJncyUlZlilEJMjcalLswIEDmDp1Klq2bCl3KLKR+1BUnz59EBwcjAEDBujNGIos63X06FHs2rULzZo1w+XLl9G8eXNcvXpV+FLuy4iYLZLTiRMnEBgYqNNq2hT8U/e5Svl6fPr0qd7ZFDle/1lZWejRo4fwcQHg0aNHCAkJQVxcnN4K3/NyK0NS37ht3LhRJzcxNzeHvb29XonQv8vkEmS5k1JjImqZ4mX+CVtNRFAqlSadHAPyH4pSvz9cuXJF7zGRy5fHjx/HnDlz4OPjg1GjRmHatGm4cuUKLly4ICyGsnjdD6/Z2tqaXLnH0iQnJ+Ps2bPIyMjAmDFj8OjRIxQWFqJGjRpyh6ZDytdj9erVNTerateuXRM+kdOoUSPExsaiTp06QscFijsbOjo6YtKkSbJsQ1PfuLm7uwv585tcgqzt8ePHKF++vM5MUWZmJpRKJapUqSJfYBITvUzxMnJvNTEWrVu3xpUrV9CsWTO5Q5GN3IeijKVDXGZmJnx8fHSuNW3alDf4gr333nsICwtD//795Q5FNtevX8fy5cvh4+ODmJgYjBkzBtnZ2Th48CDmzp0rd3jCDB48GEuWLEGbNm1QUFCALVu24OzZs5oa+lLS7u7p4uKCoKAgtG3bVvi++MTERCxYsEBvq5VoderUEXJQ0aQT5H//+9+YMGGCzossOzsb69ate60Ph4lepngZfugXy8/Px8qVK+Hj4wMHBwedx0QeHpWT3IeitLuFyaly5crIzMyEvb09nJyc8Mcff6BSpUqy1Ak3ZT///DNSU1Nx5MgRvW5lptKUYefOnZg2bRqaNGmiKanm7e2Nu3fvyhyZWPXq1cOiRYtw4sQJ+Pj4QKVSYcGCBUJm0bW7ewJAjRo1kJiYiMTERJ3rUr9Huru7Izs7W/YukqIOKpp0gvz48WO9vWU1atTA48ePZYpIDNHLFFQ25ubmaNOmDQAxNX+NkdyHolQqFcLCwnDmzBlkZWVh27ZtuHbtGlJSUtC1a1fJx1dr27Ytbty4gXbt2sHPzw8LFy6EhYWFpJ09/4rXfQ/yP/WwmiElJyejSZMmOtesrKyM5jCrNqlej4WFhQgKCsL06dNlaSZk6A5xf1WnTp2wYsUK9OrVS28CQ2SJOVEHFU06Qba1tdUrfP7kyROjPBlqKOpZKQBwdnZGenp6qc+T+w7RFMldA9gYyH0oas+ePYiOjsbQoUOxbt06AMVNZHbt2iU0QdZuSNC9e3fUqlULz54900tUpGQsVW7k9E89rGZITk5OuH//vs7vZEJCgqbrqAiFhYUYPXo0Nm7cCCsrq+c+75tvvpFkfAsLC8THx8PCwkKSn/8qFi9ejHnz5uldDwwMxJw5cyQde/PmzQBK/3sWeUZD1EFFk06QGzVqhE2bNmHixImwsbGBUqnEli1b0LhxY7lDk8ynn36qqVwxYcKE5z7PGOopkumR+1DU2bNnsWjRIjg4OGD9+vUAijvKpaamCo3j4MGDOrOX6tmZsLAwYUmpsVS5kdudO3cQERGh6fLZqVMnk2oc0q1bN6xYsQL9+vWDSqXC+fPnsW/fPp32x1KzsLCAnZ3dS1fWnJ2dJYuhQ4cOOH78uGwVJNRiY2NLvf7HH39IPrax5AWiDiqadII8ZMgQBAUFYfTo0ahcuTKysrLg7e2NWbNmyR2aZFasWKH52lgOJJmyUaNGaZKNkm1MtYkooWMM5D4UpVQq9faaFhQUoFy5ckLjOHToUKnL+4cPHxaWlBpLlRs5Xbx4EatWrULLli3h5eWFlJQULFiwAFOmTDGZijP+/v4Ail+TKpUK+/btQ/fu3eHr6ys0jgEDBmDjxo0YNmyYLCucd+/exdGjR3H8+HG4urrqVMyYP3++5OOrKzoVFhbq9XN4/PgxKlSoIHkMxkLUQUWTTpDt7OwQEBCAuLg4pKamwsXFBbVq1XqtSxdp32Eby4EkUzZ79mzN159//rmMkRgHuQ9FeXt7a0oMqkVFRQmbMVRveVKpVMjIyNBJUpOSkoQk6sZW5UZO+/fvx/Tp03Uqy1y9ehW7du0ymQR56tSpWLlypSZRVps+fbrOhIvU1q1bB5VKhbNnz+rdpImYQHhZFz+pqf+M+fn5On9e9eF6EXujFy5cWOr1cuXKwdnZGe3atSt1W5ah3b17V8hBRbOi1/2URRkUFRUhMzNTr3LA60jdIvNlTLmskTGTspWqMTh9+vRzHxOxH/T+/ftYuHAhvLy8cOvWLTRs2BDx8fH48ssvhWz9GDRo0HMfMzc3x6BBg3RasktB/W+wceNGndbm2lVuTGUWedSoUdi8ebPOn1elUmH06NHYunWrfIEJNGLEiFK7o2mvfokQExPz3MdEJGVl8cMPP0j++7l06VKdiRWRtm3bhlOnTqFFixZwdnaGQqHA//3f/8HX1xdPnjzBxYsX8dFHH702e/dNegZZqVQiJCQEUVFRMDc3x/bt23Hx4kXcu3cPAwYMkDs8Sfz+++8638fExMDe3l7ToCMzM9No3mxIn5StVI2B3G+sHh4eWLlyJc6cOQN3d3e4uLjg448/1lvGk8qaNWtQVFSEWbNmYdmyZZrrZmZmsLOze+EBJUNhlZv/cXZ2RnR0tM65lBs3bki619VYqGvvFhYW6tThBYpXM5ycnITG80/4XAoLC5M8QS5Lcjxy5EhJWsAnJSVh5syZOqVgb968iSNHjmD27Nn4v//7P+zZs0f293FDMekEOTQ0FFlZWQgICEBAQAAAoHbt2ti9e/drmyBrL+Nv27YNPj4+6Nu3r2ZbSVhYGJ48eSJXePQSprDgc+vWLZw5cwYZGRn47LPPEB8fD6VSKewD0s7ODj179hQyVknqbU9yzU6yyo2ufv36ISgoCK1bt4abmxtSUlJw4cIFfPLJJ3KHJjl17d3CwkKdOrxmZmawt7d/4SFvqURGRmpKMC5fvhwxMTF48uSJ0bRgN5b3Z6niiImJwcyZM3Wu1atXD0FBQQCA5s2bS3a2adasWZpx1NvASmPIrXgmnSBfvnwZK1euhK2treaao6MjMjIyZIxKnMjISHz33Xc6L7RevXph3LhxGDFihIyR0fO8zvvjgeL9vps3b0b79u11Vjv27t2LBQsWCIkhNjYWcXFxeh2aRNbEPX78OOrWrQsvLy/Ex8dj+fLlsLCwwL/+9S/UqlVLsnFZ5UZX69at4ejoiNOnTyMuLg5OTk74/PPPTWJmXV17d8uWLbLU/i3pyJEj+Pnnn/HOO+9otgra2dlh586dRpMgG8v7s1RxODk54fz582jbtq3m2sWLFzU3zM+ePZOsy5521RRR78UmnSAD0FuyVCqVsvQYl4OVlRUSExN1esnfv39f+Il9IrWDBw9i/vz5qFWrFn755RcAxdseSh7EkMqePXvw448/wtPTU5ZGJWpHjhzRfAjt2bMHbdu2Rfny5REaGvrcgzKGwCo3+urUqWMSCfHzGENyDAD/+c9/MGfOHLi7u+PAgQMAircBJSUlyRyZ6Rg2bBhWrFiB//znP5o9yH/88QemTZsGoHj1T6rtFe3atdN8LWoLh0knyG+++SbCwsJ0tlMcO3YMPj4+MkYlTteuXbF48WJ06dJFswc5PDwc7777rtyhkYnKyMjQmyE1NzcX1mL5xIkT+Prrr+Hp6SlkvOd58uQJ7OzskJ+fj9u3b2PGjBmwtLTEkSNHJB2XVW507dmzB02bNtXpEnb79m389ttvGDhwoIyRmZ6nT5/C3d1d55qxbGkwFc2aNcOKFStw7tw5pKeno1GjRpgwYQLc3NwAFG+xaN68uZBYVCoVHj9+jOzsbJ3XgSG34pl0gjxixAh89dVX+OWXX6BUKjF9+nQUFhbiiy++kDs0Ifr27QsnJydERkbi119/haOjI4YOHSq8viWV3ev+geDm5obbt2/rJCSxsbF6H4xSsbKyQvXq1YWM9SI2NjZIT0/H/fv34enpqWntK/WNAqvc6IqIiNA7dOXh4YFvvvmGCbJg1atXx+XLl3USsGvXrumsgMrNWN6fpYyjSpUqsrdgj4+Px4oVK6BQKHSum5ubG7Tkn0knyM7OzlixYgUuX76MlJQUuLi4oHnz5kJOihuLDh06oEOHDnKHQWUkVStVY/H+++8jKCgI3bt3R2FhIX788UccPXoU48aNEzL+e++9hwMHDsie/HTs2BHz5s1Dfn6+poHMnTt3UKVKFUnHZZUbXbm5uXqfB9bW1lAqlTJFZLoGDx6MJUuWoE2bNigoKMCWLVtw9uxZfPbZZ0LGL9ndUk27u+XcuXOFxPIyhqzX/Ouvv6JNmzYAoNegRJv2Fgipbdu2DS1atMCgQYMwceJErFu3Djt27EDdunUNOg7rIJu45ORknD17FhkZGRgzZgwePXqEwsJC1KhRQ+7QTE5mZib27t2LuLg4vQ9gEU0yjMX169dx9OhRpKamwtnZGT169BDWnCI9PR0LFy5EVlaWLI1KtF2/fh2WlpaahFR9cFC7xJKUtm3bhkqVKpVa5cZUDvHOmTMHAwcORNOmTTXXrl27ht27d2Pp0qUyRmaaEhMTceLECaSkpMDZ2RnvvPOOsM+q55VOE10P+tatW3BycoKLiwuysrKwY8cOWFhYYMiQIbCzszP4eNoNYZ5XvcXMzEzomYVRo0bhu+++Q7ly5fDhhx9i69atUCqVmDlzJlavXm2wcUx6BrmoqAhnz55FfHy83on18ePHyxSVONevX8fy5cvh4+ODmJgYjBkzBtnZ2Th48KDR3AmbkuDgYCiVSvj5+ekdEDMljRo1emFCLGUx/pUrV8LJyQk9evSQfSWp5N9Byb3ZUtU6VWOVG6BPnz749ttv0aVLF7i7u+Px48c4ceIEPv74Y7lDM0k1atQQfmjQGLpbatu0aRNmzJgBANi5cyfS09NRrlw5bN68GVOnTjX4eNoHd4ODgw3+8/8KCwsLzde2trbIysqCra0tMjMzDTqOSSfIGzduxPnz59GgQQOTqVyhbefOnZg2bRqaNGmCUaNGAShutXv37l2ZIzNNsbGx2LBhg0m+Fl+FlMX4ExISsHnzZtmT47KQevGPVW6AVq1awcrKCsePH8eVK1fg4uKCKVOm6LSeJnEUCgUSEhL0VtikXN7XLndY8sZI3d1SpLS0NFSpUgVFRUW4fPkyvvnmG1hbW2PSpEnCYpC7+7C3tzeuX7+O5s2bo379+lizZg2srKwMfrjapBPk8+fP4+uvv5Z8X5+xSk5ORpMmTXSuqQ8DkXhOTk78uy8DKRPD6tWr488///xHJMhS11xllZtiTZs21dliQfIIDw/H5s2bUaFCBZ0VNjMzM0kTZGPobqnN3NwceXl5ePDgAezt7VG5cmWoVCrk5eVJPraxdB/++OOPNQeWR44ciZ07d0KpVGLixIkGHcekE2Rra2uTaBn6PE5OTrh//z48PDw01xISEuDq6ipjVKarT58+CA4OxoABA/RaG5tK57KykDIx9PX1xYoVK9CzZ0+9fwPtyhqmgFVuiuXl5SEpKUlvG56pvR7kduDAAUydOhUtW7YUOq7c3S1L8vHxwTfffIOnT5+iRYsWAIDHjx/rvV9JwVi6D2t/HlaqVEmyLU8mnSD36tUL+/fvx6BBg4ymA45I3bp1w4oVK9CvXz+oVCqcP38e+/btQ+/eveUOzSSp93dduXJF7zFT6VwmN/Vhm9KqhZjiv4GpV7m5dOkSgoODkZOTo/eYKb4e5KRUKoUnxyUZQ5fN8ePH4/Dhw7C0tNR8ViclJQlZ2TGm7sNpaWm4e/eupNttTC5BLtnDOyUlBcePH5f9xLoc/P39AQCHDh2CSqXCvn370L17d5P+QJQTO5fJ75+U9IgoQGTqVW5CQ0PRv39/+Pv7m/TBWWPQunVrXLlyRbb933v37kVYWBi8vLz0zomITJDv3bunKf2o1rx5c70SjVIxhu7DP//8M7Zu3YqKFSvqxGPo7TYmlyDLXeDaWBQWFuLIkSPo1q2bJlEmebFzWdmwMmUxQ9Y6LQ2r3ABZWVno0aOH3GGYrA0bNmi+zs/Px8qVK+Hj46N3OExE1akTJ04gICAAtWvXlnysF1m6dGmp1WuCgoIkLzdnLN2HDx48iNmzZ+udoTI0k0uQRfXwNnYWFhYICwvjdgojUlRUhCNHjuDUqVNQKBRwdnaGn58fevToAXNzc7nDE0KOYvzaH8IvIvWHsLqc1Muo99/NmTNHynBY5QbFpfZiY2NRp04duUMxSYWFhZqvzc3NNQ0rtK+L5O3tLcu42kqbIHj27JmQzwhj6T6sUqmE1MY3uQRZ2549e9C0aVOdwxa3b9/Gb7/9JnsnLRHUM0Om1BnLmIWFhSEiIgK9e/eGm5sbkpOTcejQIeTl5aFfv35yhyfEoUOHSk2QDx8+rEmQDd0tSa4P25K0y0m9iKhtIKxyU7yqExQUhLZt2+odguJqpPQMXZXg7/Dz88OpU6dkW3FVbw/Ny8vDp59+qvNYVlaWkITRWLoPd+rUCadPn4afn5+k45h0ghwREaFXT9XDwwPffPONSSTI6jf/1q1bw8XFRWdvNt/8xYuIiMBnn32GatWqAQAaNmyI+vXr4+uvv37tE2Q5i/Eby4ewse1BZ5Ub4O7du6hRowYSExORmJio8xjfI8WaOnUqVq5cqXddu9OblO7cuYMff/wRx44d09viMX/+fMnHV7/eNm7cqJksAIpn1u3t7SXvsFlYWIjRo0dj48aNaN26taRjvUzv3r0xd+5c/PTTT5L+W5h0gpybm6t352Ntba13KvJ1de/ePdSsWRPJyclITk7WeYxv/uI9ffoUbm5uOtdcXV3x559/yhSROMZWjF8OxrYHnVVugC+//FLuEOi/0tLSSr1e1q1Jf1fdunUNvnr1KtTbQ93d3WXZ8mNhYQE7OzujWHFbvXo1LCws0LRpU0kPz5p0gly1alX89ttvOkXgr1+/bjKNQwYNGgR7e3udP29SUhKysrJkjMp01axZE4cPH9a5Ofnxxx91Opm9ruQsxl+yss3ziK5s8/DhQ9y8eRPZ2dk61/v37y9kfFa5IWNw8OBBAMUzmOqv1ZKSkuDk5CQkDpF1fkvKzMzUbPFxdnZ+7k2B1PXyBwwYgI0bN2LYsGGy1ua/efMm1q1bh4oVK0o6jkknyH369MG3336LLl26wN3dHY8fP8aJEyckKzptbDZu3IiZM2fqXf/uu++ELFmRrpEjR2LRokUIDw+Hi4sLFAoF8vLyhCzfyU3OYvzGuFoSFRWFtWvXwtPTE/fu3YOnpycSEhKEnRcw5So3s2bNQlBQEIAX3zyZQilQYxAdHQ2g+DWp/hoovnm2t7cv8959Q5Cr7OGnn36qqVzxoj+v1OcT1q1bB5VKhbNnz+odCty9e7ekY2tzcXERcijRrMjEayZdvXoVx48fR2pqKlxcXPDOO+/IVmdRtJEjR5ZaLmbEiBEIDQ2VISLKycnBlStXkJaWBicnJzRr1kynKLspMIZi/HKbPn06+vXrh7Zt22LUqFEICQnBqVOn8OjRIwwbNkxIDB9++KHRdA8TKSoqSlNL9fTp0899HisiibVlyxaMHj0amZmZmvdHEd3j1EqWPdy2bRtu3bolpOyhuqoRAKSmpj73eVJv04qJiXnuYyIP+585cwYXLlxA//79Je06a9IzyADQtGlTnS0WJf3www96B/leF3Z2djq/eEDxL1+lSpVkjMq02draGrTQ+T+NMRTjV6lUePz4MbKzs3UOC4r8AFAoFJqSVmq+vr6YMGGCsATZVKvcaP/+lSUJfp0/I4zJwIEDsWTJEly9elVzrWnTppg0aZLkS+2AvGUPtT+j5TyrYCzvBWvXrgVQ3NmvJEPOopt8gvwyYWFhr+2bX4sWLbB69WqMGzcOVatWxePHj7Fp0ybZ23maksOHD6NXr14AoLe/TpupzJ7KXYw/Pj4eK1asgEKh0Llubm4udAnR1tYWOTk5qFChAipXrowHDx6gUqVKyM3NFRYDq9yUzev8GWFM1KuaK1euRJUqVZCUlITt27dj27Zt+OSTTyQfX86yh/v37y/T80ScTzh//jxOnTqlmcX38/MTXtVCVMUfJsgv8TrvQBk4cCDWrVuHadOmaa61bt3aJCoGGIubN29qEmTt/XUlmVJCImcx/m3btqFFixYYNGgQJk6ciHXr1mHHjh3CT683bNgQFy9eRKdOndCmTRsEBATA0tJS8s5R2ljlpmxe588IY/Lbb79h5cqVmi1n7u7u+OSTT3Q+v6QkZ9nDkm2kY2JiYG9vDxcXF6SmpiIzM1PI7G54eDh27doFf39/tG7dGklJSdi4cSOys7PRtWtXycdXK8ssuiHK/zFBfomynG7/p7KxscHUqVORnZ2NlJQUuLq6ws7OTu6wTIp2NzSWlJK/GP/9+/cxf/58lCtXDkVFRbCxscGwYcMwc+ZMoVtftGszDxw4EO7u7nj27JnQfa+sclM2r/NnhLET2WFUzrKHn3/+uebrbdu2wcfHB3379tW89sLCwvDkyRPJ4/jpp58wZ84cvPHGG5prLVu2RHBwsNAEuSxetFe7rEyjfy29kJ2dHWrXrs3kWGbPa3m8ceNGwZHI586dO9iyZQumT5+ORYsW6fwngoWFheZrW1tbZGVlwcLCApmZmULGL42ZmRnat2+Prl27Cu1Y9bzX3XfffScsBiK1hg0bYs2aNUhOToZKpUJycjKCg4OFdJADisse9uzZU1P2cO/evbKUPYyMjETv3r11bsx69eqFM2fOSD52RkYGatWqpXPN29tb1vfH5zHEjStnkImMxLlz5zB+/Hi967/++is++ugjGSIST+5i/N7e3rh+/TqaN2+O+vXrY82aNbCysoKnp6fQOIqKinD27FnEx8frVfMo7TUiBYVCoVcTvkqVKgaZmSF6VR9++CFWrVqFKVOmaK41btxYaFlWf39/2cseWllZITExUac+/v379yXtNqpWrVo1/PLLL/D19dVci4qKQtWqVSUfWw5MkF+C+8tIardv3wZQXD0hNjZW5zX3+PFjSTsFGRs5i/EDxV38VCoVgOIyiLt27cKzZ8+Et6PeuHEjzp8/jwYNGuhV8xCFVW7Khp8RYlSsWBHz5s1DRkaG5oBYyTbDUnr8+DEqVKgAOzs75Obm4tChQ7CwsEDPnj2Frux07doVixcvRpcuXTR7kMPDw/Huu+9KPvbQoUPx9ddf4+TJk3B1dUVqairi4+N1tgq+Tky6DvLBgwdLPWwSFham6XV+69YtWWe06PX3okORDg4O+OCDD0yq5qpcxfgBaPbil6Suky7K6NGj8fXXX8va1TM0NBRxcXF6VW68vLwwcuRI2eKSU0pKCszNzXVuGvgZYRrmzJmDCRMmwMPDA1u2bEFMTAwsLS3h7e2NcePGCY0lMjISkZGRSEtLg6OjIzp06KAzq2tIc+bMQWBgIABg37598PX1RVRUFNLT0+Hk5IS3335byEHFV/W8Pg+vwqRnkA8dOlRqgnz48GFNgsw3PpKaum7jzJkzddosm6KSxfjHjBmD7OxsIcX4geJ/g9LeVGfNmoWQkBDJx1eztrbWScLkwCo3xfVW/fz8ULduXURFRWH16tUwMzPDpEmTNIc2+RlhGpKSkjQ36RcuXEBAQADKly+PGTNmCI+lQ4cOwvY+P378GEVFRTAzM8ORI0cwYMAAWavYqFQqxMfHw8vLC5aWz09hDfH+aZIJsrqPuUqlQkZGhs4SWVJSkpC9PEQlmXpyDMhbjB8ofbm8oKBAeKWCXr16Yf/+/Rg0aJBsVRJY5Qa4du0axo4dC6D4BP/06dNha2uLkJAQk27oY6pUKhWSkpJgbW2tmTUteUZABJGrbG+88Qa+/PJLVKtWDXl5ec89TC7qbIS5uTkWLlz40m6/f7fEG2CiCbJ2L/OSG/zNzc1NaoaEjMv169cRHR2t18VN9B5YuchVjD8gIABmZmbIz8/Xq5ihUChQs2ZNSccv6dixY0hNTcXx48dRuXJlncdWrVolNBY7OzuTS4zVcnNzYWVlhadPnyIpKQktWrSAmZmZXiMZev3Vrl0bmzdvRmZmpqb7bkpKipAuftpEr7J9+umnOHHihKYWemFhocHHeFXVq1dHamqq5Fs7TDJBXrNmDYqKijBr1iydWTszMzPY2dkJ3XBPpHb06FHs2rULzZo1w+XLl9G8eXNcvXoVrVq1kjs0YeQqxq9eJo+JicGbb76puW5mZgZ7e3u9ts9SYyMO4+Do6IiYmBg8ePAA9erVg5mZGXJycnTKAZJpGDduHHbv3o2KFStqOtbduXNH+EqC6FW2ihUraracZmVlGcVkTfv27bFs2TL06tVLr8un9vv332WSCbL6sM3WrVvlDYRIy/HjxzFnzhz4+Phg1KhRmDZtGq5cuYILFy7IHZowchXjV1fPqFatGtq2bSvpWGVhSocyjVn//v01XQzVJ/Wjo6N1SmyRaXBxcdEpMQcAbdu2Ff5+IWfL69mzZ0s+Rlmoz4mU1nJafabHEEwyQdYWGxuLuLg4vX1EnMEh0TIzM+Hj46NzrWnTpggODpYpIvHUNUa1i/H36NFD2IGUmjVrIjs7W/ZSTkDx7FRERISmpFWnTp1Qu3ZtoTGYurfffhstWrQAAM2/f926dQ06S0X/HLdu3cKZM2eQkZGBzz77DPHx8VAqlULaPKvJ2fLaWBgyCX4Rk06Q9+7di7CwMHh5eenVGmWCTKJVrlwZmZmZsLe3h5OTE/744w9UqlRJU5fXVMhZjP/f//43JkyYADs7O+zcuVNTyiktLU1oKaeLFy9i1apVaNmyJby8vJCSkoIFCxZgypQpaNmypbA4CHo3RiX3hJNpiIqKwubNm9G+fXv8/vvvmut79+7FggULhMUhZ8trY1NUVITMzEzJ6mGbdIJ84sQJBAQEcFaGjELbtm1x48YNtGvXDn5+fli4cCEsLCzQvn17uUMTRu5i/MZSymn//v2YPn06mjVrprl29epV7Nq1iwmyQNnZ2di6davm4Kw2UbNYZBwOHjyI+fPno1atWvjll18AAB4eHkhMTBQaR8lVtn379snS8lpOSqUSISEhiIqKgrm5ObZv346LFy/i3r17Bm02ZdIJMlC8uZ3IGAwePFjzdffu3VGrVi08e/YMjRs3ljEqsYxhBtcYSjmlpqbq7TNs3Lix8AoWpm7Lli3IzMzEhAkTsGrVKnz66af44YcfhB/aJPllZGSgVq1aOtfMzc2FrvAVFhbiyJEj6Natm+wtr+UUGhqKrKwsBAQEICAgAEBxlZHdu3cbNEE2N9hP+gfy8/PDqVOn5A6DCIWFhZg+fTry8vI019588000adJEtjq4cig5gztr1izMmzcPly9fFjK+upTTzp07ZS3l5OzsjOjoaJ1rN27ckL15iKm5efMmpk6dimbNmsHMzAzNmjXDlClTEBkZKXdoJJibmxtu376tcy02Nhbu7u7CYrCwsEBYWJjJV9q6fPkypkyZojPB6ejoiIyMDIOOY9IzyHfu3MGPP/6IY8eO6e1hmT9/vkxRkSmysLBATk6OSSXDzyPnDK6xlHLq168fgoKC0Lp1a7i5uSElJQUXLlzAJ598IjQOU1dQUKCpAW1lZQWlUglnZ2c8evRI5shItPfffx9BQUHo3r07CgsL8eOPP+Lo0aPC20yr6x+LPBhojEreJCiVSr2zZH+XSSfIdevWZZtQMhrdunXD7t27MXToUJOtsyp3MX5jKeXUunVrODo64vTp04iLi4OTkxM+//xz1KlTR2gcps7d3R13796Ft7c3PD09ERYWBltbWx7UM0EtW7aEjY0Njh49ChcXF9y4cQMTJkxAo0aNhMbh4uKiuXkuWQPYVIoLvPnmmwgLC9PZTnHs2DG9KlB/l1lRab1ViUi4KVOmIDU1FZaWlrC3t4e5+f92QJnK3tPU1FTs3r0blpaWGD58OCpVqoRz584hISEBQ4YMERKDQqFAQkIClEqlznW2FjY9N27cQLly5fDmm28iPj4eq1atwrNnzzBu3Di89dZbcodHghQWFiIkJAQjRoyQfXvDwoULn/vYl19+KTAS+SgUCnz11VcAij8z3N3dUVhYiC+++AKOjo4GG8ekE+SS+4m0sc4liXb69OnnPsbGEWKEh4dj8+bNqFChAqytrTXXzczMSi1KL5ULFy6gevXqqFatGpKTk7Fu3TpYWFjgo48+QpUqVYTFQS8XFRXFmycTMHr0aGzevFn2bXC3bt2Cvb29zvtAUlISsrKyTCpvyc/Px+XLl5GSkgIXFxc0b97c4DcvJp0gDxo06LmPsYQPkTzkLMY/YcIEjBo1SvZSav/61780syErVqyAlZUVrK2tkZaWpunoRsZh5MiRms5e9Ppau3YtmjVrhtatW8sax/Tp0zFz5ky9BHnZsmVYsWKFjJG9fkx6D3LJJDg9PR179uxB8+bNZYqITFlUVNRzHzOVGSq5i/ErlUrZk2OguKuio6MjVCoVoqOjsXbtWpQrVw4ff/yx3KFRCSY8x2RSCgoKsHr1apw4cUJv7+/48eOFxaFQKPRWkapUqYLU1FRhMchh7dq1ZXrexIkTDTamSSfIJTk6OmLUqFGYPXs2WrVqJXc4ZGJ2796t8312djYKCwvh6OhoMgmy3MX4W7dujStXrug06JBDuXLl8OeffyIxMRFVq1aFra0tCgsLUVBQIGtcpE/uJXcSw9LSEm+//TYAyNrd1M7ODgqFQqfkY2pqKipVqiRbTCJon8nJz8/HuXPnULt2bbi6uiIlJQV37tzR/PsYChPkEvLz85GVlSV3GGSCgoODdb4vLCzE7t27NaXOTIHcxfjz8/OxcuVK+Pj46JV+FDlL9NZbb+Grr76CUqlE586dAQD37t2Dk5OTsBiI6H8mTpwIpVKJy5cvIy0tDU5OTmjWrBnKly8vNI4WLVpg9erVGDduHKpWrYrHjx9j06ZNRrHyJSXt1bM1a9ZgwoQJOt0DIyMjcf36dYOOadIJ8sGDB3W+z83NxaVLl4SXbSEqjYWFBT744ANMnjwZXbt2lTscIdTF+LUPm4gsxm9ubq7pklZYWChkzNKMGTMGp0+fhqWlpeZDICcnB/369ZMtJiJTFhcXhyVLlsDKygpOTk5QKBQICQnBnDlz9G7qpTRw4ECsW7cO06ZN01xr3br1C89UvW4uXbqkt5WiXbt22LJli0HHMekEuWSnKmtra7Ru3Ro9evSQKSIiXenp6Xrlxl5nchfjN+T+tb+qsLAQy5Ytw/Tp03VOZTdo0EDGqOh5uAfZNGzatAk9evRAnz59NNcOHTqETZs2ITAwUFgcNjY2mDp1KrKzs5GSkgJXV1dNMxtTUalSJcTExOi8J8bExBi8Xr5JJ8imUjOQ/hk2bNig831ubi6io6NlPzUtkrEU45eThYUF4uPjTbZZjDGJjIzUWcZV++WXX9C+fXsAEN5JjeTx8OFD9OzZU+fae++9hwMHDsgSj52dncklxmp9+/bFkiVL0Lp1a80e5AsXLmDUqFEGHcekE2Sg+O7/zp07SEtLg7OzM2rVqsVDFySLkkv6FStWxNChQzUfxK877WL8IhPiUaNGISQkBAAwePDg5z6v5CFKKXXo0AHHjx/napbMNm/eXGqCvGXLFs3vpakcoDV1Xl5eSExMhJeXl+ba/fv3db4nMfz8/FC1alVERkYiLi4ODg4OmDt3LurVq2fQcUw6QVYoFFi6dCkePnyIypUrIysrC9WqVcPs2bN1TogSSUWhUGi+HjhwYKnPycjIMInXo4WFBc6dO4cxY8YIHXf27Nmarz///HOhYz/P3bt3cfToURw/fhyurq46N+3z58+XMTLTUtr2iZSUFM7um6BGjRphyZIl8PPzg4uLC1JTUxEREYHOnTvrlOjkDZMY9erVM3hCXJJJNwpZvnw5KlasiA8//BA2NjZQKpUIDQ1FZmYmZs2aJXd4ZALKerDCVBrXGEsx/hfZtGkTxo4dK+kY+/bte+5jAwYMkHRs+t9Kgkql0ikvpb72zjvvYPTo0XKERjL55JNPXvoc0R03TZlCoUBCQoLeGR1D3qCYdII8duxYrF27VucgTG5uLj755BNs2rRJxsjIVKSnp2u+vnbtGk6fPo3+/ftr9lUdOHAAvr6+8PPzkzFKcf7973/jwoULqFu3rqzF+F+EndNefzExMSgqKkJgYCDmzp2ruW5mZgZ7e3tUrVpVxuiITFt4eDg2b96MChUqwNraWnPd0DcoJr3Foly5csjJydFJkHNycmBpadJ/LSSQo6Oj5uvDhw9j4cKFqFy5MoDi7kgeHh5YsGCBySTIxlKM/0VEzSkkJyfj7NmzyMjIwJgxY/Do0SMUFhaiRo0aQsY3Zeq25qtXr9arh01E8jpw4ACmTp0qee1nk84EW7ZsiWXLlmHw4MGaGbvvv/+eXfRIFpmZmTp3w0Bx6cHMzEx5ApKBsRTjfxERh3ivX7+O5cuXw8fHBzExMRgzZgyys7Nx8OBBnRlNkpaDg4OQpVwiKjulUimkMYpJJ8hDhw7F1q1bsWTJEuTn58PS0hK+vr4YMmSI3KGRCapXrx7WrFmDESNGwNnZGQqFAjt27EDdunXlDk0YYynGL7edO3di2rRpaNKkiaZ0kbe3N+7evStzZKblRUu5TJCJ5NG6dWtcuXIFzZo1k3Qck06QraysMG7cOHz00UfIzs6GnZ0dS7yRbMaPH49///vfmDx5suaaj48PpkyZImNUYhlLMX65JScno0mTJjrXrKysUFBQIE9AJkrUUi4RlV1+fj5WrlwJHx8fvS1QhjyrYtIJ8u3bt2Fvbw83NzfNvs/k5GRkZmbqtLolEsHe3h5ffPEF0tPTkZ6eDkdHR509yqbA2Irxl0bEHmQnJyfcv38fHh4emmsJCQlwdXWVfGz6H1FLuURUdubm5mjTpg0A/f4BhmTSCfJ3332HmTNn6lwrKirCd999hxUrVsgUFZk6U0yM1eQsxl9YWIjRo0dj48aNOgd3S/rmm28kj6Vbt25YsWIF+vXrB5VKhfPnz2Pfvn3o3bu35GPT/4hayiWisps4caKQcUw6QVYoFKhSpYrOtSpVqug0byAiceQsxm9hYQE7O7uXzkiIaNri7+8PoHh7iUqlwr59+9C9e/dSu7qRdEQt5RJR2WmXRy3JkJNLJp0g29nZQaFQ6HzgpaamomLFijJGRWS6IiIiYGFhgTNnzmiumZubIyIiQvO9lAekBgwYgI0bN2LYsGGyz+K3a9cONjY2SE9P11TzILFELeUSUdlNmDDhuY8ZsqmWSTcKCQ0NRVxcHMaNG4eqVavi8ePH2LRpE7y8vDBy5Ei5wyMiwQYPHqypv1yyg9ru3buFxREXF4fAwEBYW1trqnnk5eWZXDUPIqKSUlNTdb5PT0/HwYMH8fbbbxt0lc2kE2SlUol169bh/PnzmmutW7fGhAkTYGNjI2NkRCSHmJiY5z6mbh4hwpw5c9CqVSu9ah7nz583qWoexiAnJwdXrlxBWloaevfujczMTKhUKtlXGIjof7Kzs7Fw4UKDnh8z6S0WNjY2mDp1KrKzs5GSkgJXV1fY2dnJHRYRyURkEvwi/4RqHqYgPj4eixcvhoODA1JSUtC7d2/cu3cPJ06cwIwZM+QOj4j+y8rKyuDnx0w6QX78+DHKly8Pe3t7TWKcmZkJpVKpd3iPiExDZGQkzpw5g6ysLCxfvhwxMTF48uSJ0A6bclbzoP/ZunUrhg0bhk6dOmkatrz55ptYu3atzJERmS7tA9tA8W6As2fPonbt2gYdx6QT5H//+9+YMGEC7O3tNdeys7Oxbt06LmMSmaAjR47g559/xjvvvIP9+/cDKD7Mu3PnTqEJspzVPOh/EhMT0bFjR51rNjY2yM3NlScgItI7D2JjY4NatWrhgw8+MOg4Jp0gP378WKcQPwDUqFEDjx8/likiIpLTf/7zH8yZMwfu7u6a7Qzu7u5ISkoSGofc1TyomLrSkYuLi+ZaUlIS9x8TySg4OPilz0lLS4OTk9PfGsekE2RbW1tNi2m1J0+ewNraWsaoiEguT58+hbu7u841Oc4xl+UDgKTn6+uLb7/9FsOHDwdQvCc5NDQUnTt3ljkyInqRadOmYdu2bX/rZ5i//Cmvr0aNGmHTpk1QKpUAivexbNmyBY0bN5Y5MiKSQ/Xq1XH58mWda9euXePeXxPVp08f+Pj4IDAwEDk5OVi4cCHq1q2Lbt26yR0aEb2AISY2TLrMW3Z2NoKCghAfH4/KlSsjMzMT3t7emD17NqtZEJmg33//HUuWLEGbNm1w9uxZdOrUCWfPnsVnn32GN954Q+7wSEYlVxuJyHiNHDnyb88gm3SCDBTfZcTFxSE1NRUuLi4GPwVJRP8siYmJOHHiBFJSUuDs7Ix33nkHNWrUkDssIiIqI0MkyCa9B7moqAgnT57EjRs3kJ2drTMl/+WXX8oYGRHJpUaNGhg9erTcYZARePToEUJCQhAXF4dnz57pPCaysyIRiWfSCfLu3bsRERGBDh064PLly+jatSsiIyN5MpzIhCkUCiQkJGjOJqjxfcH0rF69Gu7u7pg8eTIPbxP9gxhic4RJJ8hnz57FvHnz4OXlhZMnT2L48OFo1aoVDh8+LHdoRCSD8PBwbN68GRUqVNBJiFhSzTQ9evQIixcvhrm5SZ9nJ/rHad++/d/+GSadID99+lRzOt3CwgIqlQp16tTBzZs35Q2MiGRx4MABTJ06FS1btpQ7FDICtWrVQlJSkl7pPyKS161bt3DmzBlkZGTgs88+Q3x8PJRKJerXrw8A+Oijj/72GCadIDs6OkKhUMDZ2Rmurq64evUqKlWqBAsLC7lDIyIZKJVKJsekMXHiRKxfvx6NGzeGg4ODzmNcUSCSR1RUFLZs2YJ27drh999/11zfu3cvFixYYLBxTDpB7tKlC+Lj4+Hs7IwePXpg2bJlKCoqwsCBA+UOjYhk0Lp1a1y5cgXNmjWTOxQyAufPn8eNGzdw7949WFlZaa5zyw2RfA4ePIh58+ahVq1a+OWXXwAAHh4eSExMNOg4Jp0gd+/eXfN1u3btUK9ePSiVSlSrVk3GqIhIpA0bNmi+zs/Px8qVK+Hj46M3Yzh+/HjRoZHMwsLC8Nlnn6FJkyZyh0JE/5WRkYFatWrpXDM3N4dKpTLoOCadIJf0d/t2E9E/T2FhoeZrc3NztGnTRu86mSYzMzN2ViUyMm5ubrh9+zbefPNNzbXY2FiDnxUw+UYhREREpdm5cyfc3d3RqVMnuUMhov+6ePEiNmzYgO7du+PQoUMYMGAAjh49inHjxqFp06YGG4cJMhHRf02dOhUrV67Uuz59+nSsWLFChohITgEBAfj9999RtWpVvS038+fPlykqIrp+/TqOHj2K1NRUzTmyRo0aGXQMbrEgIvqvtLS0Uq+np6cLjoSMQd26dVG3bl25wyCiEho1amTwhLgkJshEZPIOHjwIoHjfsfprtaSkJJ5PMFEDBgyQOwQiQtknKRwdHQ02JhNkIjJ50dHRAIoTZPXXQPEhLXt7e0yYMEGu0EgwOT6IiejFyvoe/P333xtsTO5BJiL6ry1btmD06NHIzMxEWloanJycYG9vL3dYJNCgQYPK9DxDfhAT0YulpqaW6XkuLi4GG5MJMhHRfz19+hRr1qzB1atXNdeaNm2KSZMmoWLFijJGRqLI8UFMRMaHCTIR0X+tXbsW2dnZGDFiBKpUqYKkpCRs374dFStWxCeffCJ3eEREBODcuXOIiIhAeno6HB0d0bFjR7z99tsGHYN7kImI/uu3337DypUrYWtrCwBwd3fHJ598gmnTpskcGYkSFRVVpuex1TSRPH744Qf89NNP6Ny5M9q0aYOUlBRs3boVqamp6NOnj8HGYYJMRPQC5ubmcodAAu3evfulzzEzM2OCTCSTn3/+GXPmzIG3t7fmWqtWrRAUFMQEmYhICg0bNsSaNWswcuRIuLi4IDU1FaGhoZLX2yTjERwcLHcIRPQCeXl58PT01Lnm4eGBvLw8g47DPchERP/19OlTrFq1CtevX9dca9y4MSZPnoxKlSrJGBkREQHAtm3b4ODggF69emmuHT58GJmZmRgxYoTBxmGCTERUQkZGhqbMW8kWw2Q6ioqKcPLkSdy4cQPZ2dnQ/rj88ssvZYyMyHQFBAQgJiYGjo6OcHZ2hkKhQEZGBurVqwczMzPN8/5uO3husSAiKsHBwYGJMWH37t2IiIhAhw4dcPnyZXTt2hWRkZHcf0wkI1Et4JkgExERleLs2bOYN28evLy8cOrUKQwfPhytWrXC4cOH5Q6NyGSJagHPBJmIiKgUT58+hZeXF4DiaiYqlQp16tTBzZs35Q2MyMQpFApERUVptsK1a9cOzs7OBh2D9YuIiIhK4ejoCIVCAQBwdXXF1atXERsbCwsLC5kjIzJdt27dwtSpU3Hp0iXk5OTg8uXLmDp1Kn7//XeDjsMZZCIiolJ06dIF8fHxcHZ2Ro8ePbBs2TIUFRUJW+IlIn07duzAqFGj4Ofnp7l2+vRp7NixA4sXLzbYOKxiQUREVAZpaWlQKpWoVq2a3KEQmaxRo0Zh8+bNOk2cVCoVxowZg5CQEIONwy0WREREpdiwYYPO905OTqhWrRo2btwoU0REVLlyZdy9e1fnWnx8POzs7Aw6DhNkIiKiUpw7d67U67/++qvgSIhIrXv37ggMDMSePXsQERGB77//HkuXLkX37t0NOg73IBMREWm5ffs2gOJl29jYWJ0GIY8fP4a1tbVcoRGZvK5du8LW1hZnzpzBhQsX4OzsjJEjRxq8Pjn3IBMREWkZNGjQcx9zcHDABx98gI4dO4oLiIiEY4JMRERUipkzZ2LZsmVyh0FEJSQnJ+Ps2bPIyMjAmDFj8OjRIxQWFqJGjRoGG4N7kImIiEpRMjnOy8tDQUGBTNEQEQBcv34dM2fOxB9//IHIyEgAQHZ2NrZv327QcZggExERlWLPnj24c+cOgOIP5dGjR2PUqFH47bffZI6MyHTt3LkT06ZNw+zZszWl3ry9vfUqW/xdTJCJiIhKcebMGbi7uwMADhw4gKFDh2LMmDHYs2ePzJERma7k5GQ0adJE55qVlZXBV3eYIBMREZUiJycHtra2UCqVSEhIQNeuXdGxY0ckJSXJHRqRyXJycsL9+/d1riUkJMDV1dWg47DMGxERUSkqVaqEhw8fIjExEW+88QYsLCyQl5cnd1hEJq1bt25YsWIF+vXrB5VKhfPnz2Pfvn3o3bu3QcdhgkxERFSK7t2747PPPgMATJ48GQAQExPDVtNEMvL39wcAHDp0CCqVCvv27UP37t3RoUMHg47DMm9ERETP8fjxY1hYWGiWbx89eoSCggJ4eHjIHBmR6VIqlbh06RLS09Ph5OSEZs2aoXz58gYdgwkyEREREf0jxMXFITAwENbW1nBycoJCoUBeXh7mzJmDWrVqGWwcJshERESlyMzMxN69exEXFwelUqnz2KpVq2SKisi0zZkzB61atUKfPn001w4dOoTz588jMDDQYONwDzIREVEpgoODoVQq4efnB2tra7nDISIADx8+RM+ePXWuvffeezhw4IBBx2GCTEREVIrY2Fhs2LABNjY2codCRP/l5eWFxMREeHl5aa7dv39f53tDYIJMRERUCicnJ7aWJjIyjRo1wpIlS+Dn5wcXFxekpqYiIiICnTt3RlRUlOZ57dq1+1vjcA8yERFRKSIjI/Hrr79iwIABsLe313nM0dFRnqCITNwnn3zy0ueYmZlhzZo1f2scJshERESlGDRo0HMf+/777wVGQkSiMUEmIiIqRWpq6nMfc3FxERgJEYnGPchERET/dfjwYfTq1QsA8Msvvzz3ee+//76okIhIBkyQiYiI/uvmzZuaBDk6Ovq5z2OCTPR64xYLIiIiIiIt5nIHQERERERkTJggExERERFpYYJMRERERKSFCTIRERERkZb/B9G9Yx/PnqAcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_shap_sum.T.sort_values(by=0, ascending=False).plot.bar(legend=False, figsize=(10,6),fontsize=13)\n",
    "ax.set_ylabel(\"Avg(|Shap Values|)\", fontsize=14)\n",
    "plt.tight_layout()\n",
    "# plt.savefig(\"shap_value_importance.png\", type=\"png\", dpi=600)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}