{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7436cd2f",
   "metadata": {},
   "source": [
    "# Shapash - Counterfactual Business Scenarios\n",
    "\n",
    "This notebook shows how to compare business action scenarios for an at-risk customer and identify the best lever using local explanations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ccb36a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from category_encoders import one_hot\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from shapash import SmartExplainer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7c0abd",
   "metadata": {},
   "source": [
    "## 1. Build a synthetic default target\n",
    "\n",
    "We generate a synthetic consumer credit portfolio to illustrate default-risk scoring and counterfactual business scenarios.\n",
    "- `1`: borrower likely to default\n",
    "- `0`: borrower unlikely to default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "510753e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-1.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=300, n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
       "            n_estimators\n",
       "            <span class=\"param-doc-description\">n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">300</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('criterion',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
       "            criterion\n",
       "            <span class=\"param-doc-description\">criterion: {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"<br><br>The function to measure the quality of a split. Supported criteria are<br>\"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
       "            max_depth\n",
       "            <span class=\"param-doc-description\">max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_split',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
       "            min_samples_split\n",
       "            <span class=\"param-doc-description\">min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
       "            min_samples_leaf\n",
       "            <span class=\"param-doc-description\">min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
       "            min_weight_fraction_leaf\n",
       "            <span class=\"param-doc-description\">min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_features',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
       "            max_features\n",
       "            <span class=\"param-doc-description\">max_features: {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If \"sqrt\", then `max_features=sqrt(n_features)`.<br>- If \"log2\", then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
       "            max_leaf_nodes\n",
       "            <span class=\"param-doc-description\">max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
       "            min_impurity_decrease\n",
       "            <span class=\"param-doc-description\">min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('bootstrap',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
       "            bootstrap\n",
       "            <span class=\"param-doc-description\">bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('oob_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
       "            oob_score\n",
       "            <span class=\"param-doc-description\">oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br><n_jobs>` for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\">random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features < n_features``).<br>See :term:`Glossary <random_state>` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary <warm_start>` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\">class_weight: {\"balanced\", \"balanced_subsample\"}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The \"balanced_subsample\" mode is the same as \"balanced\" except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
       "            ccp_alpha\n",
       "            <span class=\"param-doc-description\">ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_samples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
       "            max_samples\n",
       "            <span class=\"param-doc-description\">max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus,<br>  `max_samples` should be in the interval `(0.0, 1.0]`.<br><br>.. versionadded:: 0.22</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
       "            monotonic_cst\n",
       "            <span class=\"param-doc-description\">monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes > 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ > 1`),<br>  - classifications trained on data with missing values.<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide <monotonic_cst_gbdt>`.<br><br>.. versionadded:: 1.4</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=300, n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = np.random.default_rng(42)\n",
    "n_samples = 1800\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"Age\": rng.integers(21, 71, n_samples),\n",
    "        \"Income\": rng.normal(55000, 18000, n_samples).clip(18000, 160000).round(0),\n",
    "        \"LoanAmount\": rng.normal(25000, 12000, n_samples).clip(2000, 90000).round(0),\n",
    "        \"CreditScore\": rng.normal(680, 55, n_samples).clip(480, 840).round(0),\n",
    "        \"EmploymentYears\": rng.integers(0, 31, n_samples),\n",
    "        \"NumLatePayments\": rng.poisson(0.9, n_samples).clip(0, 8),\n",
    "        \"DTI\": rng.normal(0.32, 0.12, n_samples).clip(0.05, 0.8).round(3),\n",
    "        \"HomeOwner\": rng.choice([\"Yes\", \"No\"], n_samples, p=[0.62, 0.38]),\n",
    "        \"MaritalStatus\": rng.choice([\"Single\", \"Married\", \"Divorced\"], n_samples, p=[0.36, 0.52, 0.12]),\n",
    "        \"LoanPurpose\": rng.choice(\n",
    "            [\"Debt consolidation\", \"Car purchase\", \"Home improvement\", \"Medical\", \"Education\"],\n",
    "            n_samples,\n",
    "            p=[0.34, 0.19, 0.21, 0.12, 0.14],\n",
    "        ),\n",
    "    }\n",
    ")\n",
    "\n",
    "homeowner_flag = (df[\"HomeOwner\"] == \"Yes\").astype(float)\n",
    "purpose_risk = df[\"LoanPurpose\"].map(\n",
    "    {\n",
    "        \"Debt consolidation\": 0.40,\n",
    "        \"Car purchase\": 0.10,\n",
    "        \"Home improvement\": 0.08,\n",
    "        \"Medical\": 0.22,\n",
    "        \"Education\": 0.06,\n",
    "    }\n",
    ")\n",
    "\n",
    "logit = (\n",
    "    -4.0\n",
    "    + 3.0 * (df[\"LoanAmount\"] / df[\"Income\"]).astype(float)\n",
    "    + 3.6 * df[\"DTI\"].astype(float)\n",
    "    + 0.018 * (700 - df[\"CreditScore\"]).astype(float)\n",
    "    + 0.65 * df[\"NumLatePayments\"].astype(float)\n",
    "    - 0.10 * df[\"EmploymentYears\"].astype(float)\n",
    "    - 0.45 * homeowner_flag\n",
    "    + purpose_risk.astype(float)\n",
    "    + rng.normal(0, 0.5, n_samples)\n",
    " )\n",
    "\n",
    "default_probability = 1 / (1 + np.exp(-logit))\n",
    "df[\"Default\"] = rng.binomial(1, default_probability)\n",
    "\n",
    "features = [\n",
    "    \"Age\",\n",
    "    \"Income\",\n",
    "    \"LoanAmount\",\n",
    "    \"CreditScore\",\n",
    "    \"EmploymentYears\",\n",
    "    \"NumLatePayments\",\n",
    "    \"DTI\",\n",
    "    \"HomeOwner\",\n",
    "    \"MaritalStatus\",\n",
    "    \"LoanPurpose\",\n",
    "]\n",
    "target_name = \"Default\"\n",
    "\n",
    "X_raw = df[features]\n",
    "y = df[[target_name]]\n",
    "\n",
    "X_train_raw, X_test_raw, y_train, y_test = train_test_split(\n",
    "    X_raw, y, test_size=0.25, random_state=42, stratify=y\n",
    ")\n",
    "\n",
    "encoder = one_hot.OneHotEncoder(cols=[\"HomeOwner\", \"MaritalStatus\", \"LoanPurpose\"])\n",
    "X_train = encoder.fit_transform(X_train_raw)\n",
    "X_test = encoder.transform(X_test_raw)\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators=300, random_state=42, n_jobs=-1)\n",
    "clf.fit(X_train, y_train.iloc[:, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "72b3fd3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "accuracy",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "precision",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "recall",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "f1",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "17bb92b4-2336-4c8f-b442-ec6c025b8e67",
       "rows": [
        [
         "test",
         "0.8333333333333334",
         "0.734375",
         "0.44761904761904764",
         "0.5562130177514792"
        ]
       ],
       "shape": {
        "columns": 4,
        "rows": 1
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      },
      "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>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.734375</td>\n",
       "      <td>0.447619</td>\n",
       "      <td>0.556213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      accuracy  precision    recall        f1\n",
       "test  0.833333   0.734375  0.447619  0.556213"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cls_metrics(y_true, y_pred):\n",
    "    return {\n",
    "        \"accuracy\": accuracy_score(y_true, y_pred),\n",
    "        \"precision\": precision_score(y_true, y_pred, zero_division=0),\n",
    "        \"recall\": recall_score(y_true, y_pred, zero_division=0),\n",
    "        \"f1\": f1_score(y_true, y_pred, zero_division=0),\n",
    "    }\n",
    "\n",
    "pred_test = clf.predict(X_test)\n",
    "metrics = pd.DataFrame([cls_metrics(y_test.iloc[:, 0], pred_test)], index=[\"test\"])\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9674715",
   "metadata": {},
   "source": [
    "## 2. Explain baseline model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8dbdc870",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Shap explainer type - <shap.explainers._tree.TreeExplainer object at 0x108dde000>\n"
     ]
    },
    {
     "data": {
      "image/png": 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ntk4grVOnjjtEKXh1R6X/AfIiBCAAAQiUSgAhZHJAAAIQSAOBygihuuXv8pPc6YLx2G+Og3KnpYnKAukESJXYaw/0NZ3eqWyjv9IhmDELnv4YzGLpm3cJhUrsaaE6nESi6u/iS1QIg/sj450iKqlShuuTTz5x7cbL7MWGLRNCGG+ZZPBeRr9nUXs4ddqmSrx9cevWrXNfV1wkv6pDmcfyhDBYr67Q2G677VwbwZM6H3/8cWvWrNl6uILyqWXJ5557bqWFUCfEammtSry2/A8M/Lg0Jn8dRrxTRJWF9kuMg9nLNPwzpQkIQAACkSSAEEYy7AwaAhBIN4HKCmHwm2NlVHRqpQ5XUfbw5ZdfLlmipyygvhmXRGgpoO600341ne7pM1jKuOkuOB1q4kswwxUUGWV0dFG7Mo+SN92fqKITQ3VlQpMmTUynQWqPmmTNl0SFUM8HZVbf+EuuqlevbrqCQUssdX2GSt++fU1325VXMiGE6pOumPCH/+gaC2XblB3U/kFx9Fdc6A5CHS6joqWVWk6qrJ2kWnXoygv/mZZfqpQnhNojevDBB7tndbWEYi/x0ty48MIL3df1AwBlC9UPnTyrz/yBPvo8uK+1MhlCzSv98MFfmaETbBs3bmzKQmrP5pgxY1w//F5SHXLTvXt39zUdlqQL6LVHUvsLlRkUPy1lVRETzTUKBCAAAQikjgBCmDq21AwBCECghEBlhVAV6KJz7bnzRfu7JHz6BtyX4KErwesL9Ln2jUkgfVZQ2UZ/2IxO0fQZRe03C947p3d1p97WW2/thCx4OIlk07cvGfWXqldECFesWOH2r/l+qT2NLfh3CagOMalXr165sylTQqiOiamyt9pLp6J+6yoGXR3hiyRHUuRZiaGEyF/QruckdT4bq7+XJ4TKpCorHCyKleabRNqLldrafvvtXcbVx03iqM+1r9TLd2WEUG0Hs8t+/D5rrL9L+BRHn+GOfV5zSP3y/dU7GofGQIEABCAAgdQSQAhTy5faIQABCDgCXgh15P6oUaMqTKW0U0N1CqMObJFI+aLlh5IRfzm9/7qkQNkXfZOtjJ+++fbXIvhndJiJlht6adC9eNprpr2KWpLq9wr655Xl6t+/v+lgEZWgEB533HFOdoKnWMYOXBfUS0YksUHB1XPKkund8q6b8HUuWrTIZapUrrzySuvZs6f7szKi/tCUeHsgfWy0t81f1u7r9Mshg6d8Bu8hVHZr/Pjx64mM9tIpxvEyW1qyK47+TkPfjg4MUkw0P4JF2VgtBZXMKabxirLIyhx7mRJ37euTWKt/us4iWNQ/jVVy6O9d1PzSHlR/Gmppp5r6azjifa76lAUNCr2Po0Q3Vuq/+OILd9ekP/jG91EyrassunTpUuF/J7wAAQhAAAIVJ4AQVpwZb0AAAhDIGAEtp9R9cjVr1rQtt9yyzJM3tRRRh7LoHWVg9A1/IkVCqQyiBHKTTTZZ7xVlpHzmR9+4++WQidRb1jNaLrh48WKT1OlkSS05zNbDRIJC6Pc2KuunpZCJchZjjVUSJ5lPxliXLl3qll1KoP3Jp/q7vq55oJNclZGsUaNGVcNV5vtaKqrTTzVHtXexvLFpniqzqh86aE5rmXJ5BwildABUDgEIQCBiBBDCiAWc4UIAAhCAQNUIxBPCqtXI2xCAAAQgAIHMEUAIM8eeliEAAQhAIIQEEMIQBo0uQwACEIBAqQQQQiYHBCAAAQhAoAIEEMIKwOJRCEAAAhDIegIIYdaHiA5CAAIQgEA2EdAhKDrsRWXSpElubx4FAhCAAAQgEFYCCGFYI0e/IQABCEAAAhCAAAQgAAEIVJEAQlhFgLwOAQhAAAIQgAAEIAABCEAgrAQQwrBGjn5DAAIQgAAEIAABCEAAAhCoIgGEsIoAeR0CEIBAPAK6nLuoqGi9jwoKCtzdfo0aNUr5XXC5FhXdf7hgwQI3LN2nGHunouddt25ddxeg2PsL0nVBfOx9imXxCbbVtGlT23jjjauMU3dB6t5BlTZt2pTcE5hIxatXr7avvvrKFi5c6O7p072FyeiTb1v3Bv7000/ur61bt07a3ZK6i1B3DPp7JRMZq56ZP3++6Z7GeHFOtA6egwAEIACBxAkghImz4kkIQAACCRFYs2aN7bHHHmU+O3r0aDv00EMTqo+HzF1a36NHD4firLPOsoEDB66HZZ999rHCwkLr1q2bXXPNNU6+evfu7Z4ZP368HXjggQljlBwddthh7vkJEybY/vvvn/C7pT34wAMPuH6ovP766+7S9kSKhOrUU081HWTjS7L65OubOXOm/fvf/3Z/nTVrlhMxXWg/e/Zs+/jjj+38889PpKsbPHPwwQfb77//bkcffbSNHDky4Tp23nln9+zgwYPd2CkQgAAEIJBaAghhavlSOwQgEEECXghbtWpl5513XgmBFStW2DvvvGPPPvuskxedVLnXXntFkFDFhxxVIfz000/txBNPdMA6duxoW2+9tQ0aNMi22GKLikMs5Y14QihB1Nf3228/mzhxYqXa8kJ4zDHHVEgqEcJK4eYlCEAAApUmgBBWGh0vQgACEIhPwAvh7rvvbjfffPMGD/lvwI888ki78MILN/hcS/i0tDQ/Pz9uA1rS+M8//1idOnXKDIGkU/WUVdSW6snLyyv3ubKWKaoeXb+gZbGllfLGVVYHKiqEYvTHH3+4KtXvWrVqrVe9Mm/VqlWLuzwyXoawrOdL67f6oF9qu7wModjE4/vCCy+UZNcefvhh0w8ZgkX16wcNiqHGU5kSTwhPPvlk++ijjyoshCtXriy5hmPZsmVu6afmRexcVZ+VhYw39xDCykSRdyAAAQhUngBCWHl2vAkBCEAgLoHyhFB725Q10TLHyZMnuzq05+3++++3e+65xy2zU+nZs6cNHz7c7cFSkahcdtll9sEHH7i/169f34466ijTN++SAQnDrbfeauecc47dcsstrh7tN9Mzffv2Lemr5Obuu++2//znPy5TqaLlmMpmNmjQwP1dGSI9t+OOO9r06dPtf//7n2vvjDPOsCOOOKKkLtUzderUknqUxbrgggtcu4mO691333ViPGDAAPcrXqmoEC5ZssQxVtHyXLFW+eKLL+zqq692SzAly2KscYmdRGTcuHGOs18yOnToUNNF9G+99ZZ7/4ADDrCLL764JCbx+qq2x44da/PmzXMf6wcD22yzjd13333u737JqGRu2rRp9uCDDzq+6k/Xrl1dHCSH119/veuXj5H6qT1+ivEnn3zi2vjyyy9LuqBYXXHFFaZ9jypaOiuh3G677UrmmcSzT58+7nMtBe3evbvLBAaXjF566aUl4/Xz7LrrrnNzIbboPfFRNlBsNTfbtWtnd911l1vy+eOPP1q/fv3cnzXem266ab0xbb755nbsscfaCSecUFJ1rBC+/PLLblwqO+20k4tRWT94iDuB+CIEIAABCJRKACFkckAAAhBIMoHShFAZkV9++cXGjBnjvuGWbPhvhLV8dMaMGe6bacmIBOH22293GSGJooTvuOOOcwKgb661ZPC5555z9UimlG2UnEkiVLTMcPvtt7eHHnrIPRPcRyepfOqpp9zeOAmOBFXv6jAWyYkyWmeffbYTFxXtdWzevLnde++9rl8SyX/961/2yiuvuDF07tzZicxvv/1mt912mxMbyZD6nMi4fD2SzVNOOSVuNIJCuOeeezrJChZdEK9S1h7CX3/91Xr16lVqtCWzd9xxx3pCGO9h8ZBkxiuSHonod999V2o7XgglOU888cQGz2kOiPGVV15pTz755Hqfaz4oVpJy/4OD4AOKoX9H82LOnDkuVqpPRYfb+D2RkjnJYawQan7F9l+Z7ljmqi84T3w/NB9uvPFGJ4nBPYSSW0mqiubT8uXL3XxS0bJULU9VCQqh6vJLZjX2O++80+rVq5fkf7FUBwEIQCDaBBDCaMef0UMAAikgEDxURlkdX/w3v/r7IYccYhdddJE7XMSLir5JfuSRR0qWiiqjpMyMxEGHomi/YXBPl7I9ksvddtvNZQC9ECrrJZlQUV8kMPr9+eefd/InYYndGyaRk4AqOyXx9N/oT5kyxSRgKi+++KLLWOqXsjrKVCk7qKxm+/bt3TMSHC01PP30092SQAlYWeMqS9CCoQkKYVkhK0sIr732WifXKpdffrkddNBBrr/+sJd4Qqi+K4unjJ0OOFFmTlkt7QONV4JLPCXpOgBHEj9s2LCSTJ+EUJkzn8HUoSvK6r799tvuORX1VQL2zDPPuIyYioS8cePG9u2335aIq+KmHw5IwPQDBR8nnbZaWSGUyA8ZMsT1W31QxlDzON5BOEEhlLhJ5sRK2cRYIVSmUKIpIdW8VX3K1io7rnaUrVTxQqistbKDypBKdCWDOqGXAgEIQAACySWAECaXJ7VBAAIQcPKlU0b1TbQkTvuo9E28X94nierUqVMJKX3TKxHQskZ/kqY+VDZR3+j7QzmUTdRSR2X+9E21RE1LCP3+Py+E+ibbf3OtenyWTkKozN1VV13llhD6ZZR6RvWqfi9U/ht9ZRf93jRdI6DMlD/98bXXXnMio4yg2lN/JKd+L1yi40pkygSFUEKmcQeLz2aWJYR+X5ziosyZuCmjJ5aSjnhCeO6555YsY9VyR8mhynvvvRe325JqSZrK448/bs2aNSsRPC+j6qtO85SUqmiJbYsWLdyfR4wY4foi4ZZ466TPUaNGuc+UyQseJqM9khLUzz77zMm+z+opcyxxqqwQ6pTRRPcQ+nkSzAp7MLFCqLH5ZbR6RvNl7733ti5duqw3Li+EQcDBDGIi84VnIAABCEAgcQIIYeKseBICEIBAQgRKWzKq5Yja2yfx0BK8GjVquPq0TNNngeI14Pcaxu5N07PKnGgZXocOHUoyhMoqKZPkizJ4WlIpYXzzzTdd28Gsnp6TtO66664lUqRv9D/88EO3LNQXf5WDsn+nnXaa+7KWJ/r9iv45yYxE6tFHH01oXIlAregewnjXTnhBiT3sR8tUtfctnhCKrSRTJSh7pQmhMl+PPfaYez74jERSQqkiIdS+TNVXWlH2UnMinhDqXkLtH4y33FT1+asj4gmhstSqW6W0JaOVEUItc1UGM1hihfDnn392e1D1e2wJ/hAjnhAqNvpBSnmHHyUyl3gGAhCAAATWJ4AQMiMgAAEIJJlAWYfKaEnmSy+95Pb8+RNGla3SnyVhxx9/fNze+CydlmHqJEfJhjJwki6/hNFnCIOZKVXmM4TKzmjZpzJTyjxqf5YvOrVUGRtlALWUNVEhVH/0TbqyUzpcRHsWtRxSWUgd/JHouMoLQTKE0Ge9gtkssVSWSiWeEAbv/JOQaJlsrOwF+x6UxmBGT4L86quvukclhIqRX6oq3rpwPlg222wzt580nhAG94pqSaoynLq4XvWoaD7pfS+E2nun/XsqutzeHyqTiBAGDz6KFyOfIfTsgs/Eu4dQGVnJtzLP+uXvV9QPNsREJ+sGhVDLl71oSoK1V5UCAQhAAALJJYAQJpcntUEAAhAoWTIa79oJHcWvQ2O0LFByoayclpNKELVfzWeX/Df22j+o/VySNX1zfPjhh693t6FfRqrlm8o06lAZvw9Qdfg9hNWrV3fZPH+vnfYw+iWLes4fLKITNLUfMREhlNDoOgW/RFH1SAolKcoiKhNV3rj8Prrypk0yhFD7IZWZU9FpppILCaw/hCUZQjh37tySO/f80lot6zzppJNKhighlAj5A3T0u3gpq6nndNeguOjAn3hC6EVPS1/1uSTKHxSkRnyGWHNB8qjis4b6QYBOAFUpSwiVAdYPHcRE81T7/MraQxhvrgeF8Mwzz3TzUieRau5pmaxKMCb6gcUmm2xSIoTasykuAwcOLNm7qX8fsVeIlDd3+BwCEIAABMomgBAyQyAAAQgkmUB51054+Qqe6ukvAtdhHBIyHRijJYb6JlyZE+3L81kmfZOsJaLaO6b9av6b8VQNcN0AACAASURBVGDmSPvPtH9NkigBCWa6fJZS11EouyQh1SEm6o9kQVdPJCKEXi4lDTowRJlCCaIOldEBILoiIJFx6TAVLbXU4SqS3nglGUKoOvr3719ysmVsO8kQwthTRpWN9NdG+Pb8KaNeuvR1ZXkVcz3rr8HQdSPxhFBy57OBut5DbQZPBVVGTdnF4Omh6ofmUPBk0rKE0Eun73PwlNogN58hLE8IR44c6Q7C0fJiFWUtJX+aKyp+z6T+HHvtRFCoJZaDBg1K8r9YqoMABCAQbQIIYbTjz+ghAIEUEChPCLXMUt9IK5umLIgySVq6qL19/q46dUuy5q+P0N91qIsyPMGDObSkT1cgSCK8EOpgGmXtvGiojeA9hBIPHSoTzEYqAylBkJioSD7ff//9uHsIg9dD6DoDZdn8vjD1Q8Kpk05VEhmXv3YiuDcxNizaP+kPygke9OKfEwfJVFmHyuhZ3TGoA0reeecdd5CJMnFayqk+aOmosmrxLqbXu5JcxUiltD2E+kzXbyjT6u+LlJxpmaaWPKoo7to/qnsetU8weLWE4iBR1MFBKjosxmfTfCZWewgl+DrpVGOW7Gk++X2ono/2hUr09UMBFcVGB7voXe0l1LzRCbTxLqZX33VgkJdZ/2xsXPwPKfxVE8HPfYbQy57EVUtqtdRZp936ooyhfoAhQVSJdzF9UFB9tjMF/3SpEgIQgEAkCSCEkQw7g4YABLKVgL5p1jUUOtijtKVxK1asMGW7mjRpUnIwjcbjhVD7xfSZJEpZv9KKxFVtSYz8ATeV5SKJUjaztGsBEhlXZdtO9D1dCfHDDz+4A3ckXsrAqUgKJSh+/2Si9ZX3nE4B1bgVy7KKlmPqRNmyYh7vfYmhMn6KtZaNllYknkuXLnXPVeRQFtWv+aEfEiR7maaWTks2VXdV5155ceBzCEAAAhAomwBCyAyBAAQgkCMEgkKoJXmU9Qn4ex31VS3N3Xfffd1VIDqERcXvn4QbBCAAAQhAIEoEEMIoRZuxQgACOU0AISw7vMqsanmuP9ky+LQ/PMWf5prTE4XBQQACEIAABAIEEEKmAwQgAIEcIaBlm998843tsssuVqdOnRwZVXKHof2bOshk/vz5tnz5cneAji6F1wE4FVlOmdxeURsEIAABCEAgcwQQwsyxp2UIQAACEIAABCAAAQhAAAIZJYAQZhQ/jUMAAhCAAAQgAAEIQAACEMgcAYQwc+xpGQIQgAAEIAABCEAAAhCAQEYJIIQZxU/jEIAABCAAAQhAAAIQgAAEMkcAIcwce1qGAAQgAAEIQAACEIAABCCQUQIIYUbx0zgEIAABCEAAAhCAAAQgAIHMEUAIM8eeliEAAQhAAAIQgAAEIAABCGSUAEKYUfw0DgEIQAACEIAABCAAAQhAIHMEEMLMsadlCEAAAhCAAAQgAAEIQAACGSWAEGYUP41DAAIQgAAEIAABCEAAAhDIHAGEMHPsaRkCEIAABCAAAQhAAAIQgEBGCSCEGcVP4xCAAAQgAAEIQAACEIAABDJHACHMHHtahgAEIAABCEAAAhCAAAQgkFECCGFG8dM4BCAAAQhAAAIQgAAEIACBzBFACDPHnpYhAAEIQAACEIAABCAAAQhklABCmFH8NA4BCEAAAhCAAAQgAAEIQCBzBBDCzLGnZQhAAAIQgAAEIAABCEAAAhklgBBmFD+NQwACEIAABCAAAQhAAAIQyBwBhDBz7GkZAhCAAAQgAAEIQAACEIBARgkghBnFT+MQgAAEIAABCEAAAhCAAAQyRwAhzBz7yLf8n//8x4qLi23AgAGRZwGA8BEoLCy0goICq1mzZvg6T48jT2DFihVWvXp1q1GjRuRZACB8BJYvX261a9e2atWqha/z9DjyBP766y+rU6eO+x4iWwpCmC2RiGA/EMIIBj2HhowQ5lAwIzgUhDCCQc+hISOEORTMCA4FIYxg0Bly6QQQQmZHmAkghGGOHn1HCJkDYSaAEIY5evQdIWQOQCBAQEL481czrO22LeESUQJaMhzWUlRUZGZ5VlCQH9Yh0O8IE8jPK7a1RWb5+XkRpsDQw0pgzZq1brkd8zesEYxuv/9escb2PeQKa9a0GUtGozsNGHmQgISwnQ22Nk0LARNVAuH1wahGLKnjzsNFksqzIpUJPf/8KkKMZyGQOwRC/LPY0Adh/s8bmbV/0Tp17IQQhj6aDCApBCSEHfIHW1uEMCk8qQQCEIAABCAAAQhAIHsJfPXTRlbUFiHM3gjRs7QTQAjTjpwGIQABCEAAAhCAAAQyRAAhzBB4ms1eAghh9saGnkEAAhCAAAQgAAEIJJcAQphcntSWAwQQwhwIIkOAAAQgAAEIQAACEEiIAEKYECYeihIBhDBK0WasEIAABCAAAQhAINoEEMJox5/RxyGAEDItIAABCEAAAhCAAASiQgAhjEqkGWfCBBDChFHxIAQgAAEIQAACEIBAyAkghCEPYFW7v3btWluyZIltuummVqtWrapWV/L+/Pnz7a+//rJddtnF1q1bZ6tWrbKNNtooafWnsiKEMJV0qRsCEIAABCAAAQhAIJsIIITZFI009uWHH36wa665xr755puSVlu0aGHnnHOO7bDDDlXuyU033WRfffWV3XDDDfb666/b6NGj7cEHH7T69evbyJEj7ZRTTrE2bdpUuZ1UVIAQpoIqdUIAAhCAAAQgAAEIZCMBhDAbo5LiPv3xxx929NFHOyEbMWKEbbnllvb999/btGnTrF27dnbSSSdVuQdBIVy+fLlJQNu3b28FBQXWtWtXGzNmjO2+++5VbicVFSCEqaBKnRCAAAQgAAEIQAAC2UgAIczGqKS4T5MnT7bZs2fbI488YrVr116vNS3t1NLRE044wQYOHGivvPKKLViwwC677DL39bFjx5qWgyqb2LdvX+vRo4d7f8WKFTZ+/Hh78803nfTVqFHDmjdv7jKEykJeeumlJtGaOHGizZo1y+rUqeN+9erVy4499tgNRnzvvfe6/qnezTbbzE499VQ74IADbOXKlSbZfPXVV2316tXWtm1bGzVqlDVs2NCmTp1qTz/9tPt6x44d7cILL3RLYb/++mvX/+HDh7s+LFu2zGbMmGFvv/22XX/99W7JbKdOnax///4uO4oQpngCUj0EIAABCEAAAhCAQNYQQAizJhTp68jpp5/uZO3iiy8utVFl8VS6dOliTZo0cTI2bNgwJ2DHH3+8yyhKLO+66y5r2rSpE6733nvPBgwYYNtuu63dfvvtVq1aNSeEn376qXv3ueeec3I4ZMgQO+6441zGUNlJ9SVYPvjgA7es9LzzzrNtttnG3n//fdNeR7V7ySWX2EcffeRkVe89+uijdswxx9gXX3zhMpz6uuq89dZbrVGjRq79jz/+2MmghLZbt27u9+7du9ugQYNcpnTfffd1fZszZ449+eSTds8991iH/MHWtmlh+oJCSxCAAAQgAAEIQAACEMgAAYQwA9Az3WSfPn1cZk5Zt9KKhPDf//63kyWVN954w2X5rrrqKtt4443d1/Tngw8+2ElVz549TaLZr18/91lwyWhQCBNZMur3HKr9Pffc04mlirKDhx56qA0ePNiOOOKI9bqujKbkUWKqogzotddeaw899JD9+OOPTggfe+yxkr7feOONNnfuXLd0VUXCqWckkO+++y5CmOlJSvsQgAAEIAABCEAAAmkhgBCmBXN2NSJxa9asmcu2lSWE48aNc0spVR5++GG77bbbXLYwWPbee++SbJuycpKyqgrhmjVr7Oqrr3bLQlW0jHPo0KGmr6vv6sfWW2+9Xj8kpMr4eVFctGiRnXjiiTZlyhS3hFSyJ0nMy8tz72k5qTKRjRs3Xq8eSfJ3332HEGbXlKU3EIAABCAAAQhAAAIpIoAQpghsNld73XXX2QsvvOCyZ9rHFyyFhYXueghlCINCOG/ePLd/8JlnninJ2Pn3JGoSsssvv9xl9BIRwiuuuMI6d+5cJiYdfqPloTfffLO1atXKSdyRRx5p559/vlv6GSzKUu68885uqamKzzJqL+Kvv/66gRBOmDDBHXQjYYwt7CHM5tlL3yAAAQhAAAIQgAAEkkkAIUwmzZDUtXTpUneQS8uWLZ1AKesnOZo+fXrJKaOxQuhPJtUSUmXrVHQoi2RQcqbrKoqKipys6f5BXWnh9/DFLhnVlRM69OW0005zh8bo4Jdgeemll1wdBx10kDugRplMiauurlDWT1m+Cy64wEniU0895eqaOXOm2wOoJaBbbLGFW86qw2Luu+8+++STTzYQQi0L1WE0Z511lls+++eff7rDbnbddVd3MA57CEMymekmBCAAAQhAAAIQgECVCCCEVcIX3pd1UqgyfgsXLiwZxFZbbeXEafvtt3cZQp0aKtnyRYe7SLh0jYRKfn6+u7ZCz+ozCZYuodfXN998c3fnYOyhMhI8idukSZOcTPbu3dvJZLAou6dso+pS0fJQ7V9U/7QfUHsLtSRURaKoNho0aOAOyfnss8/c1+vWrev637p165JDZYJLRvWMlsHqZFLfjt7RKag6WRUhDO/cpucQgAAEIAABCEAAAokTQAgTZ5WTT2p/3eLFi12WLnb5aGkDVjZNMicJ83vy9Ky+9ttvv7mMo6SwrCIJ0/UPsXX4d4qLi02ZTC1f1a/YIilV3/V+sKhvOnwmdm9gaX1RO8ok6pqMevXqucdYMpqTU51BQQACEIAABCAAAQjEIYAQMi0gEEMAIWRKQAACEIAABCAAAQhEhQBCGJVIM86ECSCECaPiQQhAAAIQgAAEIACBkBNACEMeQLqffAIIYfKZUiMEIAABCEAAAhCAQHYSQAizMy70KoMEEMIMwqdpCEAAAhCAAAQgAIG0EkAI04qbxsJAACEMQ5ToIwQgAAEIQAACEIBAMggghMmgSB05RQAhzKlwMhgIQAACEIAABCAAgTIIIIRMDwjEEEAImRIQgAAEIAABCEAAAlEhgBBGJdKMM2ECCGHCqHgQAhCAAAQgAAEIQCDkBBDCkAeQ7iefgIRwo8VDrVnDf5JfOTVCAAIQgECpBPIL1tm6onwIQQACEIBAGgn8tKSmtegy23bacScrKChIY8tlN5VXXFxcnDW9oSORIiAhXLxkifXu3TtS42awuUFg1apVlp+fbzVq1MiNATGKSBH4R/O3oMCqV68eqXEz2NwgsGLFCqtVq1ZWfUOdG2QZRToIbLH55lanTp2smr8IYToiTxtxCUgI9fOIAQMGQAgCoSNQWFjo/mNes2bN0PWdDkNA31BLBvmBBnMhjASWL19utWvXtmrVqoWx+/Q54gT++usvhDDic4DhBwgghEyHMBNACMMcPfqOEDIHwkwAIQxz9Og7QsgcgABCyBzIEQIIYY4EMqLDQAgjGvgcGTZCmCOBjOgwEMKIBp5hxydAhpCZEWYCCGGYo0ffEULmQJgJIIRhjh59RwiZAxAgQ8gcyBECCGGOBDKiw0AIIxr4HBk2QpgjgYzoMBDCiAaeYZeeIVy8ZDGnjEZ4goT5kGNOGY3wxA3h0PPzC2zduqKSnq9cudIdyMEpoyEMJl02/UBOB3pl07H9hAUCnkCzps3coUelFYSQuQKBmAzhRkuGWfNGq+ASVQJcehPVyLtx5+VFevhpHXx+XrGtKwZ4WqHTGASyhAAXzKUvEH+uKLDipldY10PORQjTh52WwkxAewg75A+2tk0LwzwM+g4BCEAAAhCAAAQgAAFb+ld1+67GRNuv+1kIIfMBAokQQAgTocQzEIAABCAAAQhAAAJhIIAQhiFK9DGrCCCEWRUOOgMBCEAAAhCAAAQgUAUCCGEV4PFqNAkghNGMO6OGAAQgAAEIQAACuUgAIczFqDKmlBJACFOKl8ohAAEIQAACEIAABNJIACFMI2yayg0CCGFuxJFRQAACEIAABCAAAQgYh8owCSBQUQIIYUWJ8TwEIAABCEAAAhCAQLYSIEOYrZFJoF/ffvut/f7777bHHnsk8HQ4Hvnjjz/s9ddft549e8btsC7V1oWumbyUGCEMx1yilxCAAAQgAAEIQAAC5RNACMtnVKEnFi5caCeffPIG72y77bZ28803V6iu8h6ePn26vfrqq3bHHXeU92hWfC42EydOtAkTJjipi1c+/vhjGz58uM2ePdvy4tz+fOKJJ9q//vUvu+CCC0odUyLtVAUIQlgVerwLAQhAAAIQgAAEIJBNBBDCJEfDC+Fll11mTZo0Kam9du3a1rhx46S2FjYh/Pzzz+3cc8+1mTNnlprhK08IlRXdaKONymSZSDtVCQRCWBV6vAsBCEAAAhCAAAQgkE0EEMIkR8ML4Z133mnNmzffoPYnnnjCXnnlFdt4443tjTfesE022cRlu/TnZ5991urWrWunnHKKde3a1b17wgkn2K677mqvvfaaLVu2zHbeeWe79NJLnRTFCuE333xj11xzjf3www+26aabunq6d+9ub731lstO3nLLLe49lRdffNHuu+8+u/XWW23cuHEuYyfZUh2tWrWy888/36ZOnWofffSRtW7d2s4++2xr166de/fnn3+2sWPH2vz5861FixbWt29f69GjR0l/99prL3vppZdcfzWOIUOGmIS4f//+tmTJEmvYsKHl5+e7Nnbaaaf1GHkhPPbYY02sVI455hj3S0V93Wabbaxfv372448/ur8vWLDA9X+77bazMWPGmLKIse3ssMMObjxPP/20rV692jp27GgXXnih4/T111+bBF6ZScme+t2lSxfHQvX5ctNNN5mWrG6++ebWIX+wtW1amOTZQ3UQgAAEIAABCEAAAhBILwGEMMm8vRDut99+TvZ8kRz26dPHpk2bZv/973+dqO29995Oyr744guTsBx++OH27rvvuuWSzzzzjFsyKaGSgAwYMMDtF7znnnvs9NNPdxIWFEKJypFHHmktW7Z0Evn22287oZo8ebITqMMOO8zOOOMM1wcVyaIEb8SIEXbmmWc6+VEbksHx48fbihUr3D6+zp07O0mSqEoC16xZY0cffbS1bdvWjj/+ePv+++9dG3fddZc1bdrU9VeSddJJJzkJlLBddNFFJh6PPPKIE9DLL7/cCZzq0LPB4oVQfZP0ieeMGTPsoYcecs9KLiV+Gos4qJ5zzjnH/vzzT1e/ZHnWrFkbtKOvif3AgQNtyy23dJ83atTIbrjhBvNt1qpVy7p162b6fffdd3eCqOW4kt6///7bxeeSSy5xIooQJvkfDtVBAAIQgAAEIAABCGSEAEKYZOxeCNu3b++ygL5oD6FkRFLy5ptv2u233+4+0h5ACdKcOXPc33WoisROn0vOJFiSqk6dOrnPlbGSnEjOgkLo65E8+aWqqkfZxZEjR7p9ex988IHde++9Tv4GDx5sPospIVSm7rTTTnNtTJo0yWXdlBFTmTt3rntfSz2VyZR0XXXVVSXj058PPvhgJ4ix/VXbygjq90SWcsZbMioxlbgecMAB6wmhMoH169e3UaNG2RZbbFHCOl47kmSJsTKBKpLua6+91ommBE/y99hjj60XM2U0d9ttNxs2bJh77u6773aSLYYIYZL/4VAdBCAAAQhAAAIQgEBGCCCEScZe3pLRWCFURlBC44VQmb7evXvblClTXAYvVrCUUXzwwQft8ccfX08IH374YSebkjZfJG7Lly93gqdM3qmnnmrXX3+9WzapZaVe+GKFUNmzTz75pORzL4Hqo9q57bbb1tsfqfaU7VT98QR27dq1TsQqK4TKhg4aNMh69eq1nhBqKawEWczq1KnjlqQqexmvHUml6jjiiCMcnkWLFrmlpeKsJaTxDrJ59NFH3VglgXpW0qvMJ3sIk/yPhuogAAEIQAACEIAABDJGACFMMvpUC6GWLEpmJH/BDKGyeNo/6JdWaliSGGUm9Y6Kllgqo6ZM4cUXX2z77ruv+3qsEEqClKnzwhgUwnnz5rnspJa0VqtWbQN6ZQmhlsZqeedTTz3llmXGK/EyhKUJod5ft26dy3gq4ydJVmZVghjbjkRR+y+VqVTR1RajR4922b5ff/01rhAWFha6ZaL77LOP2xMpERc/hDDJ/2ioDgIQgAAEIAABCEAgYwQQwiSj90Io2QieMupPxqxMhlD7/Q455BB3GI2yfRI9HboSFEIdhKIMmYRMSz+1h9Dv39t///3dKJ9//nn3NWXTlP3SwS4VFUItaZVcSSaHDh3q3ldb2luo/XdlCeHKlSvt0EMPtSuvvNJ23HFHKy4uLjnkxoehIkKoZazK+GnfpLKC6o/2M2699dYbtKO9gMpwKqOo5aVa5qqDZ5RxVTa0tKsuJNmSbe2lvOKKK1w3EcIk/6OhOghAAAIQgAAEIACBjBFACJOMvrx7CHX4ivYQKgunErtkVMsXtTQyuGRUl7BLuFR0OItOx1R2TvsFJYn+HkKdHCqBUdZMRRKpax58UR1aOqm9fpJKX8rLEKq/ElwdzKLy/vvvO7HSclQViaX2+EkG9UuH0ugUTxU9V1RU5N5X0SEuyhCqaO/knnvuuV4EShNCSbH6rtNOO3To4PZA6s9ffvmle18ZRy219fsgY9uRgCor+tlnn7nndUiO+qkTVMu66kLZVGUVJeI6zEYFIUzyPxqqgwAEIAABCEAAAhDIGAGEMGPoE2vYZ9yU9ZL0lLbU0tcmGdSS0gYNGrhTPoNFYvfvf/97vWWlifUi/lM62VOSqbbiXSJfWt3KFEp869WrV5Xm3btaHqp+BA+V8ZXGa0fP6uuJ3gmpZbPiJgn0BSGsctioAAIQgAAEIAABCEAgSwgghFkSiNK6EbsEsyrd1ZUNumrBn7RZlbqi8K7EUdd0aD+isrYIYRSizhghAAEIQAACEIBAtAgghFkebx2UohM8dXVDVYoyh6pL9+tttdVWVakqMu/q3kdd56GltzVq1EAIIxN5BgoBCEAAAhCAAASiQwAhjE6sGWmSCLBkNEkgqQYCEIAABCAAAQhAIOMEEMKMh4AOhI0AQhi2iNFfCEAAAhCAAAQgAIHSCCCEzA0IVJAAQlhBYDwOAQhAAAIQgAAEIJC1BBDCrA0NHctWAghhtkaGfkEAAhCAAAQgAAEIVJQAQlhRYjwfeQIIYeSnAAAgAAEIQAACEIBAzhBACHMmlAwkXQQQwnSRph0IQAACEIAABCAAgVQTQAhTTZj6c46AhLBt8RnWpmlhzo2NAUEAAhDILgLFZpaXXV2iNxCAAARyjMCyv6rbj7UmWJceZ5c6sr/++svq1KljBQUFWTP6vOLiYv1fggKBtBOQEC76aoa1ad0i7W3TIASqSqCoqMgsL88K8vOrWhXvQyDlBGpUL7DVa4pK2llbVGT5+fmWn4ckphw+DSSdwJq1a90308zfpKOlwioSWL1mnf1rl/62fcduCGEVWfJ6RAhICPXziAEDBkRkxAwzlwgUFha6b0hq1qyZS8NiLBEhsGLFCqtevbrVqFEjIiNmmLlEYPny5Va7dm2rVq1aLg2LsUSEABnCiASaYSZGACFMjBNPZScBhDA740KvEiOAECbGiaeykwBCmJ1xoVeJEUAIE+PEUxEhgBBGJNA5OkyEMEcDG5FhIYQRCXSODhMhzNHARmRYCGFEAs0wEyOAECbGiaeykwBCmJ1xoVeJEUAIE+PEU9lJACHMzrjQq8QIIISJceKpiBBACCMS6BwdJkKYo4GNyLAQwogEOkeHiRDmaGAjMiyEMCKBZpiJEUAIE+PEU9lJACHMzrjQq8QIIISJceKp7CSAEGZnXOhVYgQQwsQ48VRECEgIf/5qhrXdtmVERpy+YXKbTOpZu2snLM8KCrh2IvW0K9sCd++VRm7t2v//2ol8rp2o7OzivcwRWLPm/792opz5m5fia1VWrS6wg48aZ5vW2zRzMGg5dAQQwtCFjA6nkoCEsJ0N5mL6VEDmdtGkU03x9xVJ7y8VQgAC4SHAjdApilWKf97x6c/NrcneL1qrlq1SNACqzUUCCGEuRpUxVZqAhLBD/mBr27Sw0nXwIgQgAAEIQAACEMgEgY9/am6N90IIM8E+zG0ihGGOHn1POgGEMOlIqRACEIAABCAAgTQRQAjTBDrHmkEIcyygDKdqBBDCqvHjbQhAAAIQgAAEMkcAIcwc+zC3jBCGOXr0PekEEMKkI6VCCEAAAhCAAATSRAAhTBPoHGsGIcyxgDKcqhFACKvGj7chAAEIQAACEMgcAYQwc+zD3DJCGObo0fekE0AIk46UCiEAAQhAAAIQSBMBhDBNoHOsGYQwxwLKcKpGACGsGj/ehgAEIAABCEAgcwQQwsyxD3PLCGGYo0ffk04AIUw6UiqEAAQgAAEIQCBNBBDCNIHOsWYQwhwLqB9O7969bdCgQdanT58cHWFyhjV16lTbaqutrGfPnq5ChDA5XKkFAhCAAAQgAIH0E0AI0888F1pECHMhinHGgBAmFthzzjnH2rZta0OGDEEIE0PGUxCAAAQgAAEIZCkBhDBLA5Pl3UIIszxAle1eaUK4bt06U1bs6aefttWrV1vHjh3twgsvtE033dQ1NXLkSPv0009tzZo17mvKMvbo0cN9dsIJJ9hee+1lL730ki1btsy6du3qRKp27dpxu1lWXddcc40VFBTYt99+a9988421atXKzj//fNe3jz76yFq3bm1nn322tWvXztU9d+5cu+WWW+yPP/5wGb2LLrrI2rRp4/p50kkn2ZgxY1wdKjfeeKPVrVvXTjzxRPv666/t0ksvtW7dutkTTzzhPj/mmGPcr5kzZ9r111/v+lG/fn33vuSwQ/5ga9u0sLLoeQ8CEIAABCAAAQhkhABCmBHsoW8UIQx9COMPoDQhfOCBB2zatGk2cOBA23LLLe3WW2+1Ro0a2Q033OAq0pLJbbfd1ho0aGDz5s2zhx56yP2SHEoA9bsETBI4btw4J2b77bdf3E6UVdeZZ57pRHDAgAFOxMaPH28rzoQ8pgAAIABJREFUVqxwSzc7d+7s+iGpGzt2rJM6Pa/P9tlnH7v33nvtq6++socfftiqVatmvXr1cv1v376968eoUaOc4ElIP/74Yxs+fLgTy379+tnChQttxowZbkyrVq2yCy64wJo0aeKW1m688cb2/vvvI4Q5+m+CYUEAAhCAAARynQBCmOsRTs34EMLUcM14raUJobJ822yzjV122WWuj7Nnz7Zrr722RPqUQfz888/tyy+/tMWLF9ujjz5qEydOtO23394JoSSwU6dO7l0JV8OGDd3v8UpZdUnwdtppJzvttNPcq5MmTbIFCxbYTTfd5P6ujOCECRNcFm/y5Mn24osv2uOPP+4+W7p0qcvwSfz23nvvhIRQ48zLy3PvSyxHjBhhBxxwgLFkNONTlQ5AAAIQgAAEIJAkAghhkkBGrBqEMEcDXpoQSoa0DPSII45wI1+0aJFbWjllyhRr2rSpnXXWWfbLL7/Yjjvu6DKIEjKJmf4eK4Raprl27doSuQyi/Pvvv8usK1YIlan85JNPSoTwjTfecEs958yZ45a0qihb6IvGJyk88sgjKyyEffv2dQyUWUQIc/QfAMOCAAQgAAEIRJAAQhjBoCdhyAhhEiBmYxWlCeHRRx9tO++8c0lW7/XXX7fRo0e7ZZjau6elm1qKWa9ePTcsSWBlhFAiV1ZdsUJ42223ueWdPkMYFELV895775mWu6pINg8//HA777zz7KCDDnIZv+uuu8522GEH93m8JaPBDGGsEGovokRYhVNGs3E20ycIQAACEIAABBIhgBAmQolnYgkghDk6JySE+iVh8qVWrVpO9iRryu5tscUWdtVVV9mSJUvsvvvuM0mY5FDZwsaNG9szzzxj06dPr5QQetEsra6KCKGvSwKoJaLq05NPPukkdvPNN3d7GpXBVNZP4qglsF26dFlvD2FpQqjM5AcffOAOl9EeRmVEOVQmR/9RMCwIQAACEIBAjhNACHM8wCkaHkKYIrCZrlYyqENTgkWHt2iv3sUXX2yfffaZ+0gHtygDp1M9tedP0uU/00Es2kvos2/KFupZnUyqIqksKipyEhlbyqurPCF88803Xb2zZs1yVStz6PcQ5ufnuz2A6o/K888/7/Y56sRRjad69eq26667umf8oTKxQnjKKae4zOJ3333nlqTq1FQdLiOBRggzPXtpHwIQgAAEIACByhBACCtDjXcQwojOgT///NNWrlzpMoGxRRlDSddmm21WZTrJrEuCq4NuJG66KiJYtJdRbcUbTyKD0EE1m2yyid1///0IYSLAeAYCEIAABCAAgawjgBBmXUhC0SGEMBRhopPpIsAewnSRph0IQAACEIAABJJNACFMNtFo1IcQRiPOjDJBAghhgqB4DAIQgAAEIACBrCOAEGZdSELRIYQwFGGik+kigBCmizTtQAACEIAABCCQbAIIYbKJRqM+hDAacWaUCRJACBMExWMQgAAEIAABCGQdAYQw60ISig4hhKEIE51MFwGEMF2kaQcCEIAABCAAgWQTQAiTTTQa9SGE0Ygzo0yQAEKYICgegwAEIAABCEAg6wgghFkXklB0CCEMRZjoZLoIIITpIk07EIAABCAAAQgkmwBCmGyi0agPIYxGnBllggQQwgRB8RgEIAABCEAAAllHACHMupCEokMIYSjCRCfTRUBCuNHiodas4T/papJ2IACBKBHIM7PiKA2YsUIAAukksPB/m9vOfeZZyxYt0tksbYWcAEIY8gDS/eQSkBAuXrLEevfundyKqQ0CaSCwatUqy8/Ptxo1aqShNZqoDIG8PLNihDAuupUrV1q1atWsevXqlUHLOxDIKIEVK1ZYrVq1rKCgIKP90L+hVi1bZrQPNB4+Aghh+GJGj1NIQEJYXFxsAwYMSGErVA2B1BAoLCx034zUrFkzNQ1QKwRSSEDfUEsG+YFGCiFTdcoILF++3GrXru1+qEGBQNgIIIRhixj9TSkBhDCleKk8xQQQwhQDpvqUEkAIU4qXylNMACFMMWCqTykBhDCleKk8bAQQwrBFjP4GCSCEzIcwE0AIwxw9+o4QMgfCTAAhDHP06HvSCSCESUdKhWkkgBCmETZNJZ0AQph0pFSYRgIIYRph01TSCSCESUdKhWEmgBCGOXr0HSFkDoSZAEIY5ujRd4SQORBmAghhmKNH35NOwJ0yungxp4wmnSwVpoMAp4ymgzJtpIoAp4ymimxq6q1eowZXGwTQIoSpmWfUmh4CCGF6ONNKSAj8v3sIh1nzRqtC0mO6CQEIQAACEEg/ge+WNbPOR8yxZk2bpr/xLGwRIczCoNClhAkghAmj4sEoEJAQdsgfbG2bFkZhuIwRAhCAAAQgUCkC7y9sYy32nWPNmzev1Pu59hJCmGsRjdZ4EMJoxZvRlkMAIWSKQAACEIAABMongBCuzwghLH/O8ET2EkAIszc29CwDBBDCDECnSQhAAAIQCB0BhBAhDN2kpcOlEkAImRwQCBBACJkOEIAABCAAgfIJIIQIYfmzhCfCQgAhDEuk6GdaCCCEacFMIxCAAAQgEHICCCFCGPIpTPcDBBBCpgMEyBAyByAAAQhAAAIVIoAQIoQVmjA8nNUEEMKsDg+dSzcBMoTpJk57EIAABCAQRgIIIUIYxnlLn+MTQAiZGUkh8M4779imm25q22677Qb1rVu3znRh9kYbbZSUtlJZCUKYSrrUDQEIQAACuUIAIUQIc2UuMw4zhDCEs0CCdcghh9iaNWtMAtO4ceOMj2LIkCG23Xbb2RlnnLFBX15//XUbPXq0Pfjgg1a/fv2M97WsDiCEWR0eOgcBCEAAAllCACFECLNkKtKNJBBACJMAMd1VvPXWW3bJJZdY9erV7cgjj7SBAwemuwsbtFeWEOpunh9++MHat29vBQUFGe8rQpjVIaBzEIAABCAQAgIIIUIYgmlKFxMkgBAmCCqbHrv00ktt9erV1qxZM3v55ZftgQceKOneNddc46Tr+++/t2+++cZ23HFHO+uss6x58+b29ddfm97t1q2bPfHEE+6dY445xv1SUT0LFy60ESNGuL8vWrTIzj//fLv11lutbt26NnLkSPv0009dZlLLQwcNGmQ9evRwz5YlhOqH2lX2LT8/30444QTba6+97KWXXrJly5ZZ165d3fu1a9d2db377rt244032i+//OLakfT269fPjUfjk1zq66eccop1797dvePH/e2337rnWrVq5fo+depU++ijj6x169Z29tlnW7t27dzzb7/9tl1//fW2ZMkS69Spk/Xv39922GEH18cO+YOtbdPCbAo5fYEABCAAAQhkFQGEECHMqglJZ6pEACGsEr70v7xy5Urr06ePXXTRRU4ITz/9dLvpppusTZs2rjNnnnmmE6Ljjz/e7eebMmWKdezY0Unexx9/bMOHD3dSJMGS/M2YMcMeeughJ1iq56uvvrIbbrjB1SWpPPXUU0s+lyypzgYNGti8efPc1/27ZQmhJHLYsGH23HPPOVmVAKq9k046yUnguHHj3Hj222+/kjZ3331369u3r/v7hx9+6D6XGLZs2dIJpYROUjt58mTr0KFDybgHDBjgZHD8+PG2YsUK69mzp3Xu3NmJnqR27Nix9uOPPzqZPfroo23fffd1/ZozZ449+eSTds899yCE6Z/WtAgBCEAAAiEjgBAihCGbsnS3DAIIYcimx+zZs+26666zp59+2i0ZVWZrt912c8LlhXCnnXay0047zf1dwvbwww+77J8XQtWRl5fnPpcwSRYPOOCAcoVQexc///xz+/LLL23x4sX26KOP2sSJE2377bcvM0MYTwglgcrMqSjz2LBhQ/e7ZFSC9swzz5T0Uc+8+uqrdvnllzuBbdKkiXtPgrjrrru69yTCwXFPmjTJFixY4MakMnfuXJswYYLNnDnTZR/19zFjxrjP1q5d60RZbSs7SYYwZP8o6C4EIAABCKSdAEKIEKZ90tFgyggghClDm5qKzznnHCc6kkAVSZ5O8JQgajlmrBg9++yzdtttt9njjz8eVwiVhVO2rFevXmUKYbVq1dzSUy3j1DLULbfc0smVJEt/r2iGMCiEEjNJ2WWXXebkTkUZvmCR1E6bNs216YuWoWp/ouQvdtxa5vrJJ5+UCOEbb7zhlq0qE3jhhRfaBx98sMFhPMqGfvfddwhhaqYutUIAAhCAQA4RQAgRwhyazpEfCkIYoinwv//9z4466ijbZ599bLPNNnM9l0gpmyap0jLLqgjhLbfc4iTq5ptvdnUHl4zqWglJmsSsXr167nMt/Uy2EKoNtaXMZrAoo6d9gn6Jqj478cQT3RJWHbATO25JsGTZZwiDQqg+ax+iltPGFvYQhugfBF2FAAQgAIGMEUAIEcKMTT4aTjoBhDDpSFNXoWTorrvuKskG+pZOPvlkd2iMMmxVEUKdXqosmg5iUbbxjjvusNdee81JmJaK6uoISZSuuZCETp8+PelC6K+o0JgOO+wwl5HUctHevXu75bGSUC2H1R5Cv/dw//33r5AQalnoqFGjXMZTmdE///zTZs2a5Zafvvnmm2QIUzeFqRkCEIAABHKEAEKIEObIVGYYxj2EoZoEOoRFd/35U0B95yVskrennnrKhg4dut5eOu3H0/LJspaM6rRO7SXU6aF6f/78+a5qtaX9f8oK6kCW8847zz777DP3mQ6m0V5C7WfU6Zw6wVOHuwwePHgDpvH2ECoTqMNuVJTdLCoqcsKpIum97777SurRnYvnnnuuvfjiiy5LqL2MKv7r+nN5GUKJnuqX+KloTBJfX5fGp/2Qr7zyCkIYqn8VdBYCEIAABDJBACFECDMx72gzNQTIEKaGa6hrXbp0qW200UYl10AEB6NrGpQ99EtWUzVQiZoOrtFF9jVq1ChpRl/XdRg66dRfU1HZPhQXF7trJ1S/XwbLktHK0uQ9CEAAAhCIEgGEECGM0nzP9bEihLkeYcZXIQIIYYVw8TAEIAABCESUAEKIEEZ06ufksBHCnAwrg6osAYSwsuR4DwIQgAAEokQAIUQIozTfc32sCGGuR5jxVYgAQlghXDwMAQhAAAIRJYAQIoQRnfo5OWyEMCfDyqAqSwAhrCw53oMABCAAgSgRQAgRwijN91wfK0KY6xFmfBUigBBWCBcPQwACEIBARAkghAhhRKd+Tg4bIczJsDKoyhJACCtLjvcgAAEIQCBKBBBChDBK8z3Xx4oQ5nqEGV+FCCCEFcLFwxCAAAQgEFECCCFCGNGpn5PDRghzMqwMqrIEEMLKkuM9CEAAAhCIEgGEECGM0nzP9bEihLkeYcZXIQISwrbFZ1ibpoUVeo+HIQABCEAAAlEi8OFP29rWXeZY8+bNozTsUse6fPlyq127tlWrVg0eEAgdAYQwdCGjw6kkICFc9NUMa9O6RSqboW4IpIRAUVGRWV6eFeTnp6R+KoVAKgmsLSqy/Px8y8/LS2Uz1J0kAmutlvU4YqzVrVs3STWGuxqEMNzxi3rvEcKozwDGvx4BCWFxcbENGDAAMhAIHYHCwkIrKCiwmjVrhq7vdBgCK1assOrVq1uNGjWAAYHQEUAIQxcyOhwggBAyHSAQIIAQMh3CTAAhDHP06DtCyBwIMwGEMMzRo+8IIXMAAgghcyBHCCCEORLIiA4DIYxo4HNk2AhhjgQyosNACCMaeIYdnwAZQmZGmAkghGGOHn1HCJkDYSaAEIY5evQdIWQOQIAMIXMgRwgghDkSyIgOAyGMaOBzZNgIYY4EMqLDQAgjGniGTYaQOZB7BBDC3ItplEaEEEYp2rk3VoQw92IapREhhFGKNmMtl4CWjP785Qxru23Lcp/lAQhkG4Gitbp2wtxJoxQIhI3A2rVr/9+1E1ybEorQ1dq0gx3cZ1go+pqOTiKE6aBMG6kigBCmiiz1hpKAhLCdnWFttuJi+lAGkE5DAAIQgEBaCMz69gQ7ctB0y+PeSMcbIUzLtKORFBFACFMElmrDSUBC2CF/sLVtihCGM4L0GgIQgAAE0kFg5oIB1u9khNCzRgjTMetoI1UEEMJUkaXeUBJACEMZNjoNAQhAAAJpJoAQrg8cIUzzBKS5pBJACJOKk8rCTgAhDHsE6T8EIAABCKSDAEKIEKZjntFGeggghOnhTCshIYAQhiRQdBMCEIAABDJKACFECDM6AWk8qQQQwqTipLKwE0AIwx5B+g8BCEAAAukggBAihOmYZ7SRHgIIYXo400pICCCEIQkU3YQABCAAgYwSQAgRwoxOQBpPKgGEMKk4qSzsBBDCsEeQ/kMAAhCAQDoIIIQIYTrmGW2khwBCmB7OtBISAghhSAJFNyEAAQhAIKMEEEKEMKMTkMaTSgAhTCrO1Fa2cOFCO/nkk+3OO++05s2bJ9yY3ps4caJNmDDBCgoKEn4vlx4cOXKknXLKKdamTZsyh4UQ5lLUGQsEIAABCKSKAEKIEKZqblFv+gkghOlnXukWKyuEn3/+uZ177rk2c+ZMq169eqXbD/OLXbt2tTFjxtjuu++OEIY5kPQdAhCAAASyggBCiBBmxUSkE0khgBAmBWN6KilLCN9++2279tprTQFV2XHHHe3iiy+2evXqWf/+/W3JkiXWsGFDy8/Pt/PPP999fv/999tDDz1kq1atsgMPPNCOO+44a9y48QaD+frrr+3SSy+1fffd10nl6tWrrW/fvnb66ae7Z2fMmGEPP/ywq0fC2bNnTxsyZIj9+eef7vcLLrjAdthhB/fsb7/9Zuedd55dc8019sEHH9grr7xiG2+8sb3xxhu2ySabuGf152effdbq1q3rsnqSOZWVK1fa5MmT3Tt16tSxQw891Pr162e1a9e2J554wn1ddbz22mu25ZZb2uDBg22PPfZwmdFZs2a5d/SrV69erv/6+uuvv25FRUXWokULGzVqlL366qvWIX+wtW1amJ6g0goEIAABCEAghAQQQoQwhNOWLpdCACEM0dQoSwg/+eQTk7i1b9/eiZOES9mwESNG2COPPGK33nqrXX755W7JaNu2bZ083XjjjXbOOedYy5Yt7bbbbnMypSxabPn4449t+PDh1rFjRydTb731ls2ZM8fuuOMOJ1KSrWrVqlmzZs3sxx9/tHHjxjkZ7dKli5155pm26aab2tVXX+2qveWWW+zll1+2//73vzZt2jT3e/fu3W3vvfe2++67z7744gsnj4cffri9++67Nnv2bHvmmWcsLy/PRo8ebQsWLLCzzjrL/X38+PF24okn2mGHHVZSl+Rxn332cUIpHqp//vz5TkwlvOIjWXzuuefs8ccft6uuusr1fe7cuU4e9SxCGKJ/FHQVAhCAAAQyQgAhRAgzMvFoNCUEEMKUYE1NpeUtGf3999/t/fffd1m4efPmuWzYTTfdZPGWjEqkttpqKzv++ONdZ/XM1KlTnXxJkILFC6HkTCKmogybBOuII45wf5eoffrppy4T+eijj9pRRx1lJ510kr344otOupSJVCZQWb3TTjvN+vTp4yTuzTfftNtvv93VoeycpFWyqfLHH3/YkUce6T5X5lLvqt+dOnVyn6sdjfmGG27YoK5vv/3WZTDVroQ0dsmo3pEEjh071gmyHxd7CFMzd6kVAhCAAARyiwBCiBDm1oyO9mgQwhDFvywh1FLOSZMmOclr3bq1SYhq1KjhMoPxhFDLOvW5lpQGy3XXXeeWlpYnhMOGDbP69eu7paTKRkqu2rVr57KEyj5K+AYNGuSWY+rPEkT1TdlDLe+sVavWBhKnjKCWbXoh1BLU3r1725QpU6xmzZpOJNU39duXzTbbzI07Vi4lihJWCZ5kMlYIFy1a5Pr+ww8/uGW0+lzZTGVTyRCG6B8FXYUABCAAgYwQQAgRwoxMPBpNCQGEMCVYU1NpWUKoTJqWaGpppIqWc0qwJIRahqmloU899ZQTMRXtK+zRo4fLuJVX4mUIJWp6/5hjjnG/rrzySrfkUkV793bddVcnhCrK8GmJZqNGjZw0SiZVYiWuLCHUMk/tF5T8bbfddht0OREhvOKKK6xz587rvSsx1P5LLWU99dRT3XJbhLC8GcHnEIAABCAQdQIIIUIY9X8DuTR+hDBE0fRCqL10TZo0Kem5MnVDhw61Vq1aub1+2senPXs6lEVCKMnRcktJmw6TKS4utrvvvtuefPJJt2RSgvXTTz/Zgw8+6PYcxhYvhFp+KqlTFu2BBx5wmTv1Q8tGzzjjDOvWrZuTUGUMlRH0Qrh48WI79thjSyRQWcSKCqFEUgfMrF271o1N/fjyyy/tvffec0tTyxNCvas9kMoyrlixwsmx9hPqa3///bdbOqsxLF26FCEM0b8JugoBCEAAApkhgBAihJmZebSaCgIIYSqopqhOL4Sx1Stz1qFDBydKa9ascUsgtUxSewhvvvlm97j2zEmCVLRPb5dddjEtD9VST18kahKr0oRQJ4iqfhXtz1O7Ktp7KJlU0X49PSMB1Z2Jvuh5LfWURPpy1113uT2EOtBGJTZDqNNMdYiN3pEQ/vrrr6Ysnw6L8UWfS4Zj64pdMqplqMouqm/Kbm600UZOalXEa+edd3bCrINtyBCmaAJTLQQgAAEI5AwBhHD9UC5fvtydeh57DkPOBJyB5DQBhDCHwqv9ej///LM7RTPefYPKFEqygvsG9Y4yePqa/kMWr/gMoZZ96qAXZSQlUcGiLJsmUzBz6T/X15VFvOyyy2yvvfaqMnHtLVQ/tJ+wIv/hXbdunS1btswaNGjgDpFRtlGH4CjbqNNXVThUpsrhoQIIQAACEIgAAYQQIYzANI/MEBHCyIS68gONt4ewIrVNnz7dXfGgU0FjRbIi9aTjWYQwHZRpAwIQgAAEwk4AIUQIwz6H6f//EUAImQ3lEtC+updeesldNVGZone1lFT7F7O9IITZHiH6BwEIQAAC2UAAIUQIs2Ee0ofkEEAIk8ORWnKEAEKYI4FkGBCAAAQgkFICCCFCmNIJRuVpJYAQphU3jWU7AYQw2yNE/yAAAQhAIBsIIIQIYTbMQ/qQHAIIYXI4UkuOEEAIcySQDAMCEIAABFJKACFECFM6wag8rQQQwrTiprFsJ4AQZnuE6B8EIAABCGQDAYQQIcyGeUgfkkMAIUwOR2rJEQIIYY4EkmFAAAIQgEBKCSCECGFKJxiVp5UAQphW3DSW7QQQwmyPEP2DAAQgAIFsIIAQIoTZMA/pQ3IIIITJ4UgtOUJAQrjR4qHWrOE/OTIihgEBCEAAAhBIPoEvlvexAWf+x/Ly8pJfeQhrXL58udWuXduqVasWwt7T5agTQAijPgMY/3oEJISLlyyx3r17QwYCoSOwatUqy8/Ptxo1aoSu73QYAitXrnTfTFevXh0YISCwWf361qBBgxD0ND1dRAjTw5lWUkMAIUwNV2oNKQEJYXFxsQ0YMCCkI6DbUSZQWFhoBQUFVrNmzShjYOwhJbBixQong/xAI6QBjHi3EcKIT4CQDx8hDHkA6X5yCSCEyeVJbeklgBCmlzetJZcAQphcntSWXgIIYXp501pyCSCEyeVJbSEngBCGPIAR7z5CGPEJEPLhI4QhD2DEu48QRnwChHz4CGHIA0j3k0sAIUwuT2pLLwGEML28aS25BBDC5PKktvQSQAjTy5vWkksAIUwuT2oLOQGEMOQBjHj3EcKIT4CQDx8hDHkAI959hDDiEyDkw0cIQx5Aup9cAu6U0cWLOWU0uVipLU0EOGV0fdANGza0+vXrp4k+zVSVAEJYVYK8n0kCCGEm6dN2VQkghFUlyPs5ReD/3UM4zJo3WpVT42IwEIgagXXriu3rwqPt+DOmRW3ooR0vQhja0NFxM0MImQZhJoAQhjl69D3pBCSEHfIHW9umhUmvmwohAIH0ESgqMpvzw6l2xMDb09coLVWJAEJYJXy8nGECCGGGA0DzVSKAEFYJHy/nGgGEMNciyniiSgAhDF/kEcLwxYwe/x8BhJDZEGYCCGGYo0ffk04AIUw6UiqEQEYIIIQZwV6lRhHCKuHj5QwTQAgzHACarxIBhLBK+Hg51wgghLkWUcYTVQIIYfgijxCGL2b0mAwhcyA3CCCEuRFHRpEkAghhkkBSDQQyTAAhzHAAKtE8QlgJaLySNQTIEGZNKOhIJQgghJWAxiu5SwAhzN3YMrJoEUAIwxdvhDB8MaPHZAiZA7lBACHMjTgyiiQRQAiTBJJqIJBhAghhhgNQieYRwkpA45WsIUCGMGtCQUcqQQAhrAS0RF75448/7PXXX7eePXsm8njKnyksLLSaNWtaQUFBytvKxgbWrFljRUVFVqtWrTK7hxBmY/ToEwQqTgAhrDizTL+BEGY6ArRfFQIIYVXo8W6mCSCEpUTg8ccft5tuuqnkU4nEnnvuaQMHDrTGjRuXG7ePP/7Yhg8fbrNnz7a8vLwNnp86dapttdVW5Qrjiy++aKrrnHPOKbfN0h5YuXKlHXrooXbhhRfagQceWOl6wvLiwoULbeLEiTZhwoQSAb7hhhvstddeswceeAAhDEsg6ScEqkAAIawCvAy9ihBmCDzNJoUAQpgUjFSSIQIIYRlCePvtt9sdd9xh+p/Ud999ZzNmzHB/lsw1atSozJCVJ4QSvLZt29qQIUPKFZQXXnjBpk+fXukpsm7dOvv888+tefPmtskmm1S6nrC8qLGee+65NnPmTKtevbrr9m+//Wb6j3Xr1q3L5c3F9GGJNP2EQOkEEMLwzQ6EMHwxo8f/RwAhZDaEmQBCWI4QSip8+fvvv+3UU0+1Zs2a2fjx492X3377bbv++uttyZIl1qlTJ+vfv7/tsMMOLqunDGGfPn3sueees9WrV9sRRxxhp512mhMVvaPlm/Xr17dWrVrZmDFjNuiJJFTCqOWOm2++ufv8zjvvtH/++cfGjRtn7733ntWoUcN69OhhgwcPLnM56IknnmgjR460Dh062DXXXOOe/f777+2bb76xHXdnLXpuAAAgAElEQVTc0c466ywnjCrvvvuu3XjjjfbLL7/YpptuakceeaT169fPPat3f/jhB/f1U045xbp37+7e8XV+++237jmN6fzzz3fy/NFHHzkRO/vss61du3bu+Z9//tnGjh1r8+fPtxYtWljfvn3dOOIV1a2+aRnuO++84/oiHtdee61pAqtoDBdffLHVq1fPxUDxaNiwoeXn57t+KGv4/vvv2+jRo93zc+fOtVtuucXVqUztRRddZG3atDGWjIb5P2f0HQL/RwAhDN9sQAjDFzN6jBAyB3KDAEJYASHUo/fee6/7Jan78ccfbdCgQXb00Ufbvvvu68Rvzpw59uSTT9onn3zihLBjx47Wq1cve+utt9xn06ZNc1mrCy64wJo0aeKEceONN3aiFlu0709LHyVUI0aMcB/vuuuubumnZPGMM86wZcuWOek6/vjj7YQTTih1Vnbt2tWuvvpq9/6ZZ57ppE3vbLvttjZlyhTXT7UhSZT07r777k7S9PcPP/zQCZPEsGXLlq4difATTzxhkydPdn33dQ4YMMDJoIRZ/3PXHsrOnTs70apbt66TQAmumClDqj6oDdVz1113WdOmTTcYg+r++uuvnWhvv/32tt1227n9kPpa+/btTUtiJY3qs8bwyCOP2K233mqXX365E1+1oyXAr776qsv46j3Vqb7ts88+Lp5fffWVPfzww+5dMoS58R83RhFtAghh+OKPEIYvZvQYIWQO5AYBhLCCQvjyyy/blVdeaY8++qhbQqpMk8/urV271kmg9qtJemL3EEqwJED6PXbJ6NKlS50Q+SL56datmxOp4JJR/Q9TEqklkYcccoh7XDIkadP+uKefftq+/PLLknpOP/10J2KxQrjTTju5bKXKQw895GRI76vvEttnnnlmvb2PkikJlsYskVWRIEowlXmUYAXrnDRpki1YsKBkH6Y4aU+fRPqNN96wSy+91K666ionwyr688EHH+z4xBbVLXEdNmzYeh/9/vvvLuun5aDz5s2zOnXquPbiLRnVklsvhJJP7c2UJKqI/THHHGOjRo1ymUuEMDf+48Yook0AIQxf/BHC8MWMHiOEzIHcIIAQVlAI//vf/9p9991nTz31lMvUffDBBxscMqMMm/bqxQqhhEZLRCVDsUIouZFE+SLRkjjGCqEygxI57W9UJk5FGUmJ0KxZs0z903JVX5SJ1PLOsoTw2Weftdtuu80JkuROxS+J9fVIGJXdDC6h1Ti0Zl79jhVCZeiUJfUH83gJVJZUdak9L5a+jb333ttlJ+MJYVA29bn6oXa13FPLUbVUVctn1W55Qqi4qShb6Uvv3r2dFGq/JUKYG/9xYxTRJoAQhi/+CGH4YkaPEULmQG4QQAgrIIRawqklottss43LCirjpf10WnIZW+IdKqPMnsRM+wIlhNqzpr17ZZV77rnHLTVVZk5Fe96UmVM264ADDnBfU1ZPGS9lLUsriQqhRFD79JQ1DBZl+JSJ1NclmCral6jM3SWXXLKBEEr4xCCeECqbJxlTFrJatWrl/kuKlU29IAZdunQpOZRHS0G191FC+MUXXzi+knZ/zUQwQ6gxav+lP3FUe0MPP/xwO++889zeQ4Sw3JDwAASyngBCmPUh2qCDCGH4YkaPEULmQG4QQAjLEULtz9P/pLTPTdk6/VmZMkmRBERiJqnTPsE///zTZemU3dO+NmUIdQiMDkDRfjtJi/YEah+cxEXZRR0uozobNGgQtydqQ4elaJ+bDkhRhvGkk06y2rVru0yj9hBKyJRd8/sM41WUqBDq7kQdvHLyySfbYYcd5g6W0VJLZdB0WIvqUYZSewh1sI32Fu6///4VEkJJrd93OXToUNdd1adltlomG1viCaFkVBlSMdZeTu2P1NJYcfXXbGhprw6bKS4utgcffLBkyagfowRQ3CSLyrKKseKHEObGf9wYRbQJIIThiz9CGL6Y0WOEkDmQGwQQwjKE0Ge3JGLan6ZDS3QPoT/xU69q+aOkUUsNVSQlkj6BlazoXf/Zcccd52RORUs/tXRRQqelkz4DGNsd7UuUcOoQGBWJy6+//uqWdkqsVJRpVMZNbZdWJHLK8O2yyy4byJv2DEqk/J467WXUslhftFdRexaVhVQdfjz+63ouVtpiM4RvvvmmE00Jl4r2/inLqiWnKuIkoVU/ExFCSaokUBKpd3U3pGJ08803u9eVNVWGUEV7H3WQzCuvvOKkXEWx9eMNts0po7nxHzZGAQGEMHxzACEMX8zo8f8R4NoJZkOYCSCESYieMlBaaqg9bLr2IFgkLBI4XYGgrF5s0YEm2m/o78srrTvKPqr+YB06TEWnbfolnEkYSkkVkr7Fixe7jKTa9UVfX7RokctoxhtPRfugcYmR6svLy6vQ60VFRe4QmC233DIuP2UKdd1HbEx8I6tWrXJjlJDrNFIVhLBCIeBhCGQtAYQwa0NTascQwvDFjB4jhMyB3CCAEOZGHBlFkggghEkCSTUQyDABhDDDAahE8whhJaDxStYQIEOYNaGgI5UggBBWAhqv5C4BhDB3Y8vIokUAIQxfvBHC8MWMHpMhZA7kBgGEMDfiyCiSRAAhTBJIqoFAhgkghBkOQCWaRwgrAY1XsoYAGcKsCQUdqQQBhLAS0HgldwkghLkbW0YWLQIIYfjijRCGL2b0mAwhcyA3CCCEuRFHRpEkAghhkkBSDQQyTAAhzHAAKtE8QlgJaLySNQTIEGZNKOhIJQgghJWAxiu5SwAhzN3YMrJoEUAIwxdvhDB8MaPHZAiZA7lBACHMjTgyiiQRQAiTBJJqIJBhAghhhgNQieYRwkpA45WsIUCGMGtCQUcqQQAhrAQ0XsldAghh7saWkUWLAEIYvngjhOGLGT0mQ8gcyA0CCGFuxJFRJImAhLBt8RnWpmlhkmqkGghAIBMEJIRzfxxkRw6amonmabMSBBDCSkDjlawhQIYwa0JBRypBACGsBDReyV0CEsJFX82wNq1b5O4gGVnOEiiSBeXlWUF+fs6OMdGBFZtZvUad7MBeQxJ9hecyTAAhzHAAaL5KBBDCKuHj5QwTQAgzHACazy4CEsLi4mIbMGBAdnWM3kAgAQKFhYVWUFBgNWvWTOBpHoFAdhFACLMrHvSmYgQQworx4unsIoAQZlc86E2GCSCEGQ4AzVeJAEJYJXy8nGECCGGGA0DzVSKAEFYJHy9nmABCmOEA0Hx2EUAIsyse9KZiBBDCivHi6ewigBBmVzzoTcUIIIQV48XT2UUAIcyueNCbDBNACDMcAJqvEgGEsEr4eDnDBBDCDAeA5qtEACGsEj5ezjABhDDDAaD57CKAEGZXPOhNxQgghBXjxdPZRQAhzK540JuKEUAI/7/2zgNcquL8/y9dWuhKLwEBg0GBIIoGS5COolIFEUWkShH0B4ogEaUEUXpvBkQUUVApoqgBaT+QYIwSAREEgoAI+RFA2v0/30nO/s9d9t69p8zOnrPfeR4f9N5z3pn5vId1P/vOzDrjxauTiwCFMLnywdEYJkAhNJwAdu+JAIXQEz7ebJgAhdBwAti9JwIUQk/4eLNhAhRCwwlg98lFAEJ4aNcCqXZtxeQaGEejncDFS5eldJW75dbbO2jvS1cHFEJdZBk3EQQohImgzD50EaAQ6iLLuIkgQCFMBGX2ERgCEMLq0lOqluEX0wcmaT4N9PS5bLLz9BBp1nakTxETH4ZCmHjm7NE/AhRC/1gyUuIJUAgTz5w9+keAQugfS0YKAQEIYY3sPaRaWQphCNLpaAqnz2WX7f8aIs3aUAgdgePFJOATAQqhTyAZxggBCqER7OzUJwIUQp9AMkw4CFAIw5FHN7OgELqhxntIwD8CFEL/WDJS4glQCBPPnD36R4BC6B9LRgoBAQphCJLocgoUQpfgeBsJ+ESAQugTSIYxQoBCaAQ7O/WJAIXQJ5AMEw4CFMJw5NHNLCiEbqjxHhLwjwCF0D+WjJR4AhTCxDNnj/4RoBD6x5KRQkCAQhiCJLqcAoXQJTjeRgI+EaAQ+gSSYYwQoBAawc5OfSJAIfQJJMOEgwCFMBx5dDMLCqEbaryHBPwjQCH0jyUjJZ4AhTDxzNmjfwQohP6xZKQQEKAQhiCJLqdAIXQJjreRgE8EKIQ+gWQYIwQohEaws1OfCFAIfQLJMOEgQCEMRx7dzIJC6IYa7yEB/whQCP1jyUiJJ0AhTDxz9ugfAQqhfyyNR/rhhx/k0UcflTlz5kj58uWNjyeIA6AQBjFr/oyZQugPR0YhAbcEKIRuyfG+ZCBAIUyGLHAMbglQCN2SS8L7KITek0Ih9M4wqBEohEHNHMcdFgIUwrBkMjXnQSFMzbyHZdYUwrBkUkQyE8JTp07JmDFjZPv27ZI7d25p0qSJ9OjRQ3LkyCE///yz9O3bV44ePapolClTRp5++mmpXr267N69W4YNGyaNGjWS5cuXq9+3b99e/ROrWdc3aNBAVq5cKefPn5f7779funfvri4fPny41KtXT5o1a6b+e926dbJ69WoZO3as6uv555+XgQMHCsTsxIkTMnToUNV/RvEym9eBAwfUnPfs2aPmef3118vIkSPV/Ldu3SoTJkyQ48ePS+3ataVDhw5Ss2ZN1W+N7D2kWtkzIXoyOJWsEKAQZoUSryEBfQQohPrYMrJ+AhRC/YzZgz4CFEJ9bBMeOTMhhODt27dPevbsqURr1qxZ0qlTJ3nooYcEUrVs2TIlRpClefPmKblcvHixfPnll0rQIIetW7dWP1+wYIG89dZbUrhw4SvmaF1fq1Ytad68uWzZskXWrl0rs2fPlgoVKkiXLl2kYcOGqm809LtkyRL1j3XvVVddpQQUf0Ie0X9G8TKbFyQUIgjZxRzffvttJZeYf9euXaVdu3ZKNCGkGOOKFStk4cKFFMKEP7nJ0SGFMDnywFGkLgEKYermPgwzpxCGIYupOwcKYYhyn5EQ4n+yrVq1kn79+kmLFi3UjEeNGiV//etflYihnT17Vnbu3Kmk8e9//3tE5CxJ+/DDDyVbtmzqWlT3Bg0aJHfddVeGQmi/HhXCjh07ygMPPJAlIXznnXekQIECKnas/q14qHJmNq+HH35YihQpIkOGDJFrrrkmMtbJkyeryiSqhWgXL15U0jlx4kTZtm0bhTBEfyecTIVC6IQWryUB/wlQCP1nyoiJI0AhTBxr9uQ/AQqh/0yNRcxICCF5jz/+uMycOVMqVaqkxodq2JQpU2TNmjVqqSZkERU5LKvEMk8sLUXVLCMhQ4UNFcDoFuv6AQMGKDFDdS4rFUK7TGYWD9XNzOaF6iSk79y5c5I/f361LBRVwcGDB8uOHTukZMmS6YbfrVs3JcRcMmrsETbaMYXQKH52TgJCIeRDEGQCFMIgZ49jpxCG6BnISAhPnjwpbdq0UZUyq6qHatinn36qlmyiWggpxLLO7Nmzq8rhU0895ZsQtmzZUu1Z7N27tzz22GNSv359dRoqWqwlo/GE0IqHqmNm80L8y5cvy969ewUx3333XSXFWDq6f/9+mTRp0hXZ5x7CEP2FcDgVCqFDYLycBHwmQCH0GSjDJZQAhTChuNmZzwQohD4DNRnOEkIc3FK6dOnIUFCd69+/v+TNmzeyhw6Htdx2221q6SfEaOPGjeqQFSyfnDp1aqZLRrFkM16FENXHEiVKKPnCslT0gX2I+Dmqj+PHj5eDBw/KuHHj1HJV+x7CWEKYUTwsC81oXoiNZaoVK1aUr7/+WjF49dVXVX+Q4z59+qgqJ/YXolJat25d2bx5MyuEJh9ig31TCA3CZ9ckIMIKIZ+CQBOgEAY6fSk/eAphiB4BSwijp4TDYHBICw5gQbUQrWrVqjJ69GgpWLCgHD58WO2hw4mbaJUrV1ZVtcyWjKLSZ50Uau/PWuKZK1cuuXDhgvoVDnfBGNCwJBNLNnGwC6qRWMIKIbMfYBNLCDOLl9G8nnjiCdm1a5fqF8thUVnEElO0pUuXqoN1UEFEAwdI6vr16ymEIfo74WQqFEIntHgtCfhPgBVC/5kyYuIIUAgTx5o9+U+AQug/06SO+OOPP0qePHlinhB66NAhtdcvX758rudgCSFO7oR8Ih7EL7odOXJEVRBxCmhmLavxMpoX9g9COO2Hylj9paWlKQnGyaqFChVSP+aSUdepD/yNFMLAp5ATCDgBCmHAE5jiw6cQpvgDEPDpUwgDnsBkG36sQ2C8jNHvePHGQiGMRyi8v6cQhje3nFkwCFAIg5EnjjI2AQohn4wgE6AQBjl7STj2n376ST777DP1ZfR+NL/jxRsThTAeofD+nkIY3txyZsEgQCEMRp44Sgohn4HwEaAQhi+nnJEHAhRCD/ACfiuFMOAJ5PADT4BCGPgUpvQEWCFM6fQHfvIUwsCnkBPwkwCF0E+awYpFIQxWvjja8BGgEIYvp6k0IwphKmU7fHOlEIYvp5yRBwIUQg/wAn4rhTDgCeTwA0+AQhj4FKb0BCiEKZ3+wE+eQhj4FHICfhKgEPpJM1ixKITByhdHGz4CFMLw5TSVZkQhTKVsh2+uFMLw5ZQz8kCAQugBXsBvpRAGPIEcfuAJUAgDn8KUngCFMKXTH/jJUwgDn0JOwE8CFEI/aQYrFoUwWPniaMNHgEIYvpym0owohKmU7fDNlUIYvpxyRh4IQAjzHesv5Yr/4iEKbw0igbPns8vPeXtJq46jgzh8NeYzZ85Ijhw5JE+ePIGdAweeugQohKmb+zDMnEIYhiym7hwohKmbe848BgEI4bHjx6Vly5bkk4IESl5zjRQsWDCwM6cQBjZ1HLiIUAj5GASZAIUwyNnj2CmEfAZIwEYAQpiWliadO3cmFxIIHAEKYeBSxgHbCFAI+TgEmQCFMMjZ49gphHwGSIBCyGcgJAQohCFJZIpOg0KYookPybQphCFJZIpOg0KYoonntGMTYIWQT0aQCVAIg5w9jp1CyGcgyAQohEHOHsdOIeQzQAKsEPIZCAkBCmFIEpmi06AQpmjiQzJtCmFIEpmi06AQpmjiOW1WCPkMhI8AhTB8OU2lGVEIUynb4ZsrhTB8OU2lGVEIUynbnGtcAuqU0WPHeMpoXFL6LsibL5+ULVNGXwchjkwhDHFyU2BqFMIUSHKIp0ghDHFyU2BqFMIUSDKnmHUC//kewgFSvsS5rN/EK30lsPfnG6RhxxVSvFgxX+OmQjAKYSpkObxzpBCGN7epMDMKYSpkObxzpBCGN7ecmQsCEMIa2XtItbJnXNzNW/wgsGl/fbmx2XIpXry4H+FSKgaFMKXSHbrJUghDl9KUmhCFMKXSHbrJUghDl1JOyAsBCqEXev7cSyF0z5FC6J4d7zRPgEJoPgccgXsCFEL37HineQIUQvM54AiSiACF0HwyKITuc0AhdM+Od5onQCE0nwOOwD0BCqF7drzTPAEKofkccARJRIBCaD4ZFEL3OaAQumfHO80ToBCazwFH4J4AhdA9O95pngCF0HwOOIIkIkAhNJ8MCqH7HFAI3bPjneYJUAjN54AjcE+AQuieHe80T4BCaD4HHEESEaAQmk8GhdB9DiiE7tnxTvMEKITmc8ARuCdAIXTPjneaJ0AhNJ8DjiCJCFAIzSeDQug+BxRC9+x4p3kCFELzOeAI3BOgELpnxzvNE6AQms8BR5BEBCiE5pNBIXSfAwqhe3a80zwBCqH5HHAE7glQCN2z453mCVAIfc5By5YtpWvXrtKqVatI5Pfff1+mTp0qK1eu9Lm38Ib79NNP5csvv5S+fftmaZJOr88oKIUwS7i1XkQhdI+XQuieHe80T4BCaD4HHIF7AhRC9+x4p3kCFEKfcxBLCN977z2ZNm0ahdABa4jZxx9/LPPnz8/SXU6vpxBmCauRiyiE7rFTCN2z453mCVAIzeeAI3BPgELonh3vNE+AQuhzDrIihHv37pVRo0bJ/v37pXDhwvLYY49J48aN1Ujw8xw5csh3330nuK5SpUry1FNPyaxZs2Tnzp1SpUoVeeKJJ6R69erq+kOHDsno0aPl22+/lQoVKsj9998vTZo0Ub/LqB9cO2LECJk5c6bkz59fPvjgA1m3bp28/PLL6r5x48ZJqVKlpGPHjpHxfP/99yreDTfcIH369JHy5cura7du3SoTJkyQ48ePS+3ataVDhw5Ss2ZN9buHHnpIHnnkEVm/fr3s2bNHnn/+ealcuXI64hs3bpQpU6ao+zEWcGjUqJH07t1bLly4IFdffbW6fs6cObJkyRJZunSpnDt3TnLlyiXNmjVT12Fssa4Hy3r16qnr0DDH1atXy9ixY1UMzBP9X7p0SbEbMmSIbNiwQWpk7yHVyp7x+clguKwSoBBmldSV11EI3bPjneYJUAjN54AjcE+AQuieHe80T4BC6HMOIISQpWrVqkUi7969W8kUloxCRNq0aSMVK1ZUwgShWr58ubz66qtSo0YN6dWrl7q2c+fOSgYhL/ifJKTmlltuEVTCChYsqCQQwtSuXTvVV6dOnZQYIc68efOkePHiGfYDKcM4R44cqYSpe/fuSkBxX9myZVVfAwcOlD/84Q+R8SD+tddeK5MmTZJatWrJoEGD5MCBA2p5LMbQoEEDJVtr166VFStWSLZs2eTuu+9WDO644w4pXbq0kj38aTWwwDiaN28uLVq0kH379smWLVvkySeflPHjxysBRj9odevWVbFz5swp5cqVU32PGTNGnn32WbnppptiXv/oo49Kw4YNFRu0ZcuWKanEP5Dhd999V1588UUVE7J48803K7GmEPr8l8JhOAqhQ2C2yymE7tnxTvMEKITmc8ARuCdAIXTPjneaJ0Ah9DkHEJyiRYtKmTJlIpFRxTt27JgSQlSgUJ1bsGBBRI4giBCep59+WgnYjTfeKI8//ri6/5VXXlHVNVTR0CAuqGwh1qZNm2TYsGFKagoUKKB+j39v2rSpEs7M+kH8OnXqSPv27aV169by61//Wm677TZVnYNAvfXWW6p6GT0e/BxVOkjV5MmT1XgglmgXL15UIjlx4kS57rrrlBA+99xzShZjNbx4oqKJfyDAqBBaLaMloGDx1VdfqYoiBK9t27bSpUsXJcrRS0zx84yEEGPE2CHWEGoILBr3EPr8F8JFOAqhC2j/vYVC6J4d7zRPgEJoPgccgXsCFEL37HineQIUQp9zEG/JKGRq7ty56fYTQurwQgL5ixaw6dOny9/+9reIEFoSiGoZYs2YMSNd1Q3TgdgVKVIk036wBHX79u2qQvfRRx8peYPkQcxmz54tixcvVmSix7Nq1SrVJ6prgwcPlh07dkjJkiXTUezWrZsaA2KiioelpBm1hQsXKjlGQ/UQSz9R8YsleFgCConDcllUCbEUFYf3oErpVAgPHz6sZBrLdrNnz67Girm+/fbbrBD6/HfCaTgKoVNi//96CqF7drzTPAEKofkccATuCVAI3bPjneYJUAh9zkE8IYTQQGysChy6f/jhh9VyzKFDh14hYJAvnLZpVQjtQvjJJ5+oChf2AGLZo73F68dajolKHvYc/v73v1eVOlQNsdzUWqqZmRCiUgmhwjLSWC0rQoj7sHT0m2++Ecgh/sSprK+//rpaImrJ4k8//aSqmS+88IJa2onWo0cPVVmFEOJe+/X4PfZm1q9fX7B0FM2+ZNQaL8QQy3Zx6A9E9uzZsxRCn/9OOA1HIXRKjELonhjvTCYCFMJkygbH4pQAhdApMV6fTAQohD5nI54QnjhxQh28AlnCsk3ICKpozzzzjNx5552OhPDkyZOR/Xv9+/dXM0E87C383e9+l2k/uMY6bAWihH2JOKxm165dSkxvv/12FS8zIdy2bZs6iAWHzGAf4KlTp2TNmjVK0iC48YQQMob9k9iDiOWpqEq+9tprSgghrNgfuGjRIlXBw0E7WNras2dPtawVfUOssWQUQoj/tl+PCim+6gNVUOxHPHjwoFpqC+FDJRQVRcgw9kOePn1aLZNFbIgn9xD6/JfCYTgKoUNgtstZIXTPjneaJ0AhNJ8DjsA9AQqhe3a80zwBCqHPOcjK9xDiO/MgM5cvX1a940CVfv36xRSw6Arh5s2bZfjw4Uq80L744gu1hw8vRGiQJ1T3IGOZ9YNrIYBoVoUPcoZ9gZYgxhJCHByDZaxYMoqGZatYfmrNBWIJAcMeRowBh+JAumK1o0ePqiqfNXZUJnHADQ6hwX5EiCYO2EHDQTWoAr755pvqvyGQkNp77rlHVQBjXX/kyBG1rBUSDi44pAfSCvHEsliIocUMlVFUH1GZpBD6/JfCYTgKoUNgtssphO7Z8U7zBCiE5nPAEbgnQCF0z453midAITSUAwgUKmTFihWTvHnzeh4FRAeChHjWASkI6nc/sQaalpamDnnJnTu3FCpUyPFc8BBC6HAYT3TDvBDXYoRqHq63n1Zqvyf6evwOYliiRAlVZbQ39Ilx23/HQ2Ucp8/3GyiE7pFSCN2z453mCVAIzeeAI3BPgELonh3vNE+AQmg+BxxBEhGgEJpPBoXQfQ4ohO7Z8U7zBCiE5nPAEbgnQCF0z453midAITSfA44giQhQCM0ng0LoPgcUQvfseKd5AhRC8zngCNwToBC6Z8c7zROgEJrPAUeQRAQohOaTQSF0nwMKoXt2vNM8AQqh+RxwBO4JUAjds+Od5glQCM3ngCNIIgIUQvPJoBC6zwGF0D073mmeAIXQfA44AvcEKITu2fFO8wQohOZzwBEkEQEKoflkUAjd54BC6J4d7zRPgEJoPgccgXsCFEL37HineQIUQvM54AiSiACF0HwyKITuc0AhdM+Od5onQCE0nwOOwD0BCqF7drzTPAEKofkccARJRIBCaD4ZFEL3OaAQumfHO80ToBCazwFH4J4AhdA9O95pngCF0HwOOIIkIkAhNJ8MCqH7HFAI3bPjneYJUAjN54AjcE+AQuieHe80T4BCaD4HHEESEYAQVkvrKVXLngUbLlsAACAASURBVEmiUaXWULYcuFnqtFghxYsXT62J+zBbCqEPEBnCGAEKoTH07NgHAhRCHyAyhDECFEJj6NlxMhKAEB7+xwKpWqVCMg4vJcaULXcxaXzfCMmbN29KzNfPSVII/aTJWIkmQCFMNHH25ycBCqGfNBkr0QQohIkmzv6SmgCEMC0tTTp37pzU4+TgSCAWAQohn4sgE6AQBjl7HDuFkM9AkAlQCIOcPY7ddwIUQt+RMmACCVAIEwibXflOgELoO1IGTCABCmECYbMr3wlQCH1HyoBBJkAhDHL2OHYKIZ+BIBOgEAY5exw7hZDPQJAJUAiDnD2O3XcCFELfkTJgAglQCBMIm135ToBC6DtSBkwgAQphAmGzK98JUAh9R8qAQSZAIQxy9jh2CiGfgSAToBAGOXscO4WQz0CQCVAIg5w9jt13AhRC35EyYAIJUAgTCJtd+U6AQug7UgZMIAEKYQJhsyvfCVAIfUfKgEEmACE8tGuBVLu2YpCnEcixn/73BWnWYbwUK1oskONPhkFTCJMhCxyDWwIUQrfkeF8yEKAQJkMWOAa3BCiEbsnxvlASgBBWl55StQy/mD7RCf72UF4pUHebXFf9ukR3HZr+KIShSWVKToRCmJJpD82kKYShSWVKToRCmJJp56QzIgAhrJG9h1QrSyFM9FPyj4P5JG8dCqEX7hRCL/R4r2kCFELTGWD/XghQCL3Q472mCVAITWeA/ScVAQqhuXRQCL2zpxB6Z8gI5ghQCM2xZ8/eCVAIvTNkBHMEKITm2LPnJCRAITSXFAqhd/YUQu8MGcEcAQqhOfbs2TsBCqF3hoxgjgCF0Bx79pyEBCiE5pJCIfTOnkLonSEjmCNAITTHnj17J0Ah9M6QEcwRoBCaY8+ek5AAhdBcUiiE3tlTCL0zZARzBCiE5tizZ+8EKITeGTKCOQIUQnPs2XMSEqAQmksKhdA7ewqhd4aMYI4AhdAce/bsnQCF0DtDRjBHgEJojj17TkICFEJzSaEQemdPIfTOkBHMEaAQmmPPnr0ToBB6Z8gI5ghQCM2xZ89JSIBCaC4pFELv7CmE3hkygjkCFEJz7NmzdwIUQu8MGcEcAQqhOfbs2QcCgwcPlu3bt8uAAQOkWbNmniNSCD0jdB2AQugaXeRGCqF3hoxgjgCF0Bx79uydAIXQO0NGMEeAQmiOPXv2SOD06dNy3333ScGCBaVkyZIydepUjxFFKISeEboOQCF0jY5C6B0dIyQBAQphEiSBQ3BNgELoGh1vTAICFMIkSAKH4I7AihUrZPr06fLcc8/JsGHD5I033pBixYqpYJcvX5a5c+fKsmXL5MKFC1KpUiU5f/68zJ8/X/1+69atMmHCBDl+/LjUrl1bOnToIDVr1qQQukuFL3dRCL1jZIXQO0NGMEeAQmiOPXv2ToBC6J0hI5gjQCE0x549eyTQo0cPufbaa9Vy0RYtWkinTp3kwQcfVFEhgtOmTZO2bdtK/fr1ZdWqVbJmzRpZu3atHDhwQLp27Srt2rWTBg0ayOrVq9XPIZgLFy6UGtl7SLWyZzyOjrc7JUAhdErsyusphN4ZMoI5AhRCc+zZs3cCFELvDBnBHAEKoTn27NkDgaNHj0rHjh1Vle83v/mNjBs3Tnbs2CGLFi1SUbt06SKlS5eWl156Sf33li1bZOjQoUr8Jk+eLOvWrZORI0eq3128eFEGDhwoEydOlG3btlEIPeTFy60UQi/0/nMvhdA7Q0YwR4BCaI49e/ZOgELonSEjmCNAITTHnj17IIC9fq+99prceuutKsrhw4dl3759MnPmTLU8FAfMPPLII9KmTZsrhBAH0UAese/Q3rp166ZisELoITEebqUQeoD331sphN4ZMoI5AhRCc+zZs3cCFELvDBnBHAEKoTn27NkDAez5K1KkiKoOWg1LQps0aSK9e/eWQYMGKeHDn2j2CiGqifv375dJkyZdMQIeKuMhKR5vpRB6BMgKoXeAjGCUAIXQKH527pEAhdAjQN5ulACF0Ch+du6GwN69ewX7B61qoBVjypQpaknoO++8I8uXL1d7CFEhLFGihDpwBgfI4PdYFjpkyBDp06ePNG/eXE6dOqX2F9atW1c2b97MCqGbpPhwD4XQO0RWCL0zZARzBCiE5tizZ+8EKITeGTKCOQIUQnPs2bNLAhC/DRs2yOLFi9NFsETx5ZdflqpVq6o9gRC8woULS+XKleUvf/mLEj+0pUuXyqxZs9RppGj46orx48fL+vXrKYQu8+L1NgqhV4LcQ+idICOYJEAhNEmffXslQCH0SpD3myRAITRJn31rIwDRy5Ytm/oHbcaMGbJp06bI107gZ2lpaapqmDt3bilUqJC6jktGtaUkbmAKYVxEcS9ghTAuIl6QxAQohEmcHA4tLgEKYVxEvCCJCVAIkzg5HJp7Ajg0Bt9NWLFiRfnll1/UYTEjRoxQX0GRWaMQumfu9U4KoVeCrBB6J8gIJglQCE3SZ99eCVAIvRLk/SYJUAhN0mff2gjgjcXnn38u+HqKX/3qV+rL58uWLRu3PwphXETaLqAQekfLCqF3hoxgjgCF0Bx79uydAIXQO0NGMEeAQmiOPXtOQgIUQnNJoRB6Z08h9M6QEcwRoBCaY8+evROgEHpnyAjmCFAIzbFnz0lIgEJoLikUQu/sKYTeGTKCOQIUQnPs2bN3AhRC7wwZwRwBCqE59uw5CQlQCM0lhULonT2F0DtDRjBHgEJojj179k6AQuidISOYI0AhNMeePSchAQqhuaRQCL2zpxB6Z8gI5ghQCM2xZ8/eCVAIvTNkBHMEKITm2LPnJCRAITSXFAqhd/YUQu8MGcEcAQqhOfbs2TsBCqF3hoxgjgCF0Bx79pyEBCiE5pJCIfTOnkLonSEjmCNAITTHnj17J0Ah9M6QEcwRoBCaY8+ek5AAhDDfsf5SrvgvSTi6cA/p4PE8UqPFRqlWtVq4J6pxdhRCjXAZWjsBCqF2xOxAIwEKoUa4DK2dAIVQO2J2ECQCEMJjx49Ly5YtgzTs0Iz12ipVQjMXExOhEJqgzj79IkAh9Isk45ggQCE0QZ19+kWAQugXScYJBQEIYVpamnTu3DkU8+EkUosAhTC18h222VIIw5bR1JoPhTC18h222VIIw5ZRzscTAQqhJ3y82TABCqHhBLB7TwQohJ7w8WbDBCiEhhPA7j0RoBB6wsebw0aAQhi2jKbWfCiEqZXvsM2WQhi2jKbWfCiEqZXvsM2WQhi2jHI+nghQCD3h482GCVAIDSeA3XsiQCH0hI83GyZAITScAHbviQCF0BM+3hw2AhTCsGU0teZDIUytfIdtthTCsGU0teZDIUytfIdtthTCsGWU8/FEQJ0yeuwYTxl1QLFUqVJSoEABB3fwUl0EKIS6yDJuIghQCBNBmX3oIkAh1EWWcRNBgEKYCMrsIzAE/vM9hAOkfIlzgRmzyYGe+SW7/Jy/r7TqMNLkMNj3fwlQCPkoBJkAhTDI2ePYKYR8BoJMgEIY5Oxx7L4TgBDWyN5DqpU943vsMAY8fTaHfPHvodL0gefDOL3AzYlCGLiUccA2AhRCPg5BJkAhDHL2OHYKIZ8BErARoBA6exwohM546b6aQqibMOPrJEAh1EmXsXUToBDqJsz4OglQCHXSZezAEaAQOksZhdAZL91XUwh1E2Z8nQQohDrpMrZuAhRC3YQZXycBCqFOuowdOAIUQmcpoxA646X7agqhbsKMr5MAhVAnXcbWTYBCqJsw4+skQCHUSZexA0eAQugsZRRCZ7x0X00h1E2Y8XUSoBDqpMvYuglQCHUTZnydBCiEOukyduAIUAidpYxC6IyX7qsphLoJM75OAhRCnXQZWzcBCqFuwoyvkwCFUCddxg4cAQqhs5RRCJ3x0n01hVA3YcbXSYBCqJMuY+smQCHUTZjxdRKgEOqkG5LYFy5ckEuXLslVV13leEYnT56UjRs3StOmTSVbtmyO70/0DRRCZ8QphM546b6aQqibMOPrJEAh1EmXsXUToBDqJsz4OglQCHXSTXDsli1byrlz52TJkiVStGhR1Tv++95775XLly/LmjVrJHv27I5HNXHiRPn8889V3B9++EHGjx8v48aNkxw5csSN9dVXX8mAAQNk9erVWbreCvjpp5/Kl19+KX379o3bBy5wen1GQSmEWcIduYhC6IyX7qsphLoJM75OAhRCnXQZWzcBCqFuwoyvkwCFUCfdBMe2hLBVq1bSu3dv1ftbb70lM2fOVP/uVgh//PFHwQtdlSpV5Ouvv5Z+/frJypUrJVeuXHFn6FYIIWYff/yxzJ8/P24fuMDp9RTCLGGNexGFMC6ihF5AIUwobnbmMwEKoc9AGS6hBCiECcXNznwmQCH0GajJcBDCevXqyfr16+Xtt9+WfPnyyf333y9169ZVFTRLCBcsWCBLly5V1UNIXbNmzZRAYknn8uXLZefOnVKzZk1ZtWqVVK5cWa677jr54osvZPjw4dKhQwc5fvy4FC9eXFUbn3rqKTl//rz86U9/EjxMaDfccIM8++yzUqhQIYknhFhOOmXKFBUzf/780rhxY2nUqJEaD5aqXn311SrmnDlzVIUy1ri///77mNePGjVK8cD80NatW6cqlWPHjlVzR5UT/WM5bIUKFWTIkCGyYcMGqZG9h1Qre8ZkKgPTN4UwuVJFIUyufHA0zghQCJ3x4tXJRYBCmFz54GicEaAQOuOV1FdDCLE8c9asWUqsypUrJ9OnT1c/g8xZQog/c+bMqX5/4MABGTNmjBK4O+64Q+bOnSuLFy+WkiVLSoMGDaRUqVJK1iBKs2fPVqKJmCNGjFBLQKtVq6aWke7evVuJ49mzZ8USsUGDBmUqhJAyjLl58+bSokUL2bdvn2zZskWefPJJtSwVYooYaJDatWvXxhz3TTfdFPP6Rx99VBo2bCidOnVSMZYtW6akEv+gavruu+/Kiy++qGJCFm+++Wb59ttvKYQOnnIKoQNYCbiUQpgAyOxCGwEKoTa0DJwAAhTCBEBmF9oIUAi1oU18YMgVZAoVuwkTJqgK3YMPPqiqecOGDUu3ZHTPnj1K1iB7EKW2bdtKly5dlBB+8skngiqitd8QyzYtIcxoyejRo0dVFRHLS3E/qn2o/GVWIcSLJyqY+Kdz587qHqtltAQ0o3HHuh7zyUgIsS8SEjh69GgltdaBN9xD6Oy5pRA646X7agqhbsKMr5MAhVAnXcbWTYBCqJsw4+skQCHUSTfBsS0hRGXvgQceUEshIXvbtm1LJ4So4EGGqlevrqqEWGKKfYddu3ZVQrh58+bIvkNMIZ4QYj/hK6+8ImXKlFH7DL/77jvJnTu3qiTGWzK6cOFCJZ9opUuXVks/UfGLJXiZjdupEB4+fFgx2b9/vxLfu+++W3r16qUqoFwymvUHl0KYdVaJuJJCmAjK7EMXAQqhLrKMmwgCFMJEUGYfughQCHWRNRDXEsI777xTVfQghLfffrts2rQpIoQ///yztG/fXl544QW1RBKtR48eaklmVoTwm2++USd/vvfee5GvoWjTpo1abmodZIOlpZDQrAgh+sfSUcSFHOLP999/X15//XW1RNSSxZ9++inTceNe+/WI+9hjj0n9+vUFS0fR7EtGrfRADLdu3SrTpk2Tbt26qSWvFMKsP7wUwqyzSsSVFMJEUGYfughQCHWRZdxEEKAQJoIy+9BFgEKoi6yBuHYhtHdvF8LTp0+r6mHPnj3V4S0QN1TesGQ0K0IIYbrnnnuUUOLwmLS0NBWrUqVKMnDgQLUn8aWXXpKCBQvGFULIGA6xadeunRQuXFjtXXzttdeUEGL/IPY1Llq0SFXwsF+xdevWGY4b87BfX6RIEZk6daps375d7S88ePCgOkQG48ceQlQUseexVq1aAibYZ4h5QDwphFl/eCmEWWeViCsphImgzD50EaAQ6iLLuIkgQCFMBGX2oYsAhVAXWQNxIYSQMlTrMhJCyBUOnXnzzTfVJRAxnOYJyUMlbd68eWrJ6IwZMyIhUKXDslJU/tCw/w4VQjQcLoPvOIQEIg7i40Aa7AeEkGW2ZBT7DlGdxIsoGvY6du/eXY3/4sWL0qdPH9m7d6/63YoVK1QFMaNxx7r+yJEjMnjwYDlx4oQaF6T11KlTSjwxF4ghGn5Xp04dJbmoTFIIs/7wUgizzioRV1IIE0GZfegiQCHURZZxE0GAQpgIyuxDFwEKoS6ySR4XVTEkH/v23DRU2nB4DQ6uQcPy1EOHDqlTSbPy/YT2PjEOCF3RokWvGAoEDvsR8+bNq34Xb9zR1+MeiGGJEiVUldHe0CcO1bH/jofKOHsaKITOeOm+mkKomzDj6yRAIdRJl7F1E6AQ6ibM+DoJUAh10mXswBGgEDpLGYXQGS/dV1MIdRNmfJ0EKIQ66TK2bgIUQt2EGV8nAQqhTrqMHTgCFEJnKaMQOuOl+2oKoW7CjK+TAIVQJ13G1k2AQqibMOPrJEAh1EmXsQNHgELoLGUUQme8dF9NIdRNmPF1EqAQ6qTL2LoJUAh1E2Z8nQQohDrpMnbgCFAInaWMQuiMl+6rKYS6CTO+TgIUQp10GVs3AQqhbsKMr5MAhVAnXcYOHAEKobOUUQid8dJ9NYVQN2HG10mAQqiTLmPrJkAh1E2Y8XUSoBDqpMvYgSNAIXSWMgqhM166r6YQ6ibM+DoJUAh10mVs3QQohLoJM75OAhRCnXQZO3AEKITOUkYhdMZL99UUQt2EGV8nAQqhTrqMrZsAhVA3YcbXSYBCqJMuYweOAIXQWcoohM546b6aQqibMOPrJEAh1EmXsXUToBDqJsz4OglQCHXSZezAEYAQVkvrKVXLngnc2E0M+N9nc8jOs89Is9YjTHTPPqMIUAj5SASZAIUwyNnj2CmEfAaCTIBCGOTscey+E4AQHv7HAqlapYLvscMY8OKlNClfrYnUu61tGKcXuDlRCAOXMg7YRoBCyMchyAQohEHOHsdOIeQzQAI2AhDCtLQ06dy5M7mQQOAIUAgDlzIOmELIZyAkBCiEIUlkik6DQpiiiee0YxOgEPLJCDIBCmGQs8exs0LIZyDIBCiEQc4ex04h5DNAAqwQ8hkICQEKYUgSmaLToBCmaOJDMm0KYUgSmaLToBCmaOI5bRIgARIgARIgARIgARIgARJIRgLZ0rCJi40ESIAESIAESIAESIAESIAESCDlCFAIUy7lnDAJkAAJkAAJkAAJkAAJkAAJ/IcAhZBPgjEC2ANw4cIFKVq0qLExsGMSiCbg9Ll0ej2Jk0AiCRw7dkwKFCggefPmzVK3ly9fVtdlz549S9fzIhLwk4DT11Mscrt06ZLkzJnTz2EwFgn4QsDJ6+/Zs2flxIkTUqpUKSOvvxRCX1LOIE4I4DCDwYMHy65du9RtJUuWlPHjx0uJEiWchOG1JOArAafPZbzrR40aJevWrUs3xtKlS8uCBQt8HTeDkUAsAt9//708+eSTgjfYaPXq1ZPhw4dLrly5MgSGN9e4Bu2Pf/wjwZJAwgjEez3NaCDLly+X2bNny3vvvZfukjZt2sjJkyfT/ezee++VPn36JGxO7Ch1CTh9/X3iiSci74nxGn3XXXfJoEGDEgqQQphQ3OwMBGbOnCkrV66UGTNmSL58+dQLdLly5WTkyJEERALGCDh9LuNdDyE8ePBguhd1VGnwAQgbCegm0L17d/X6+tJLL8k///lP6dmzp/Tq1UvwpjhWW7VqlUyaNEmt2rjlllsohLoTxPjpCMR7PY3GtX//funXr59AJK+66qqYQog31U2aNIncWrhwYSlSpAjJk4B2Ak5ffydOnKie1fLly8vGjRsF7x9efvllqVmzpvaxWh1QCBOGmh1ZBDp06CB33nmnPP744+pHeCOCCuGHH34o2bJlIygSMELA6XMZ73q8oJ86dUpGjx5tZD7sNHUJoDKCCsmYMWOkdu3aCgQ+cPvxxx+V9MVq+BoVHIWO12K8wWaFMHWfHxMzj/d6Gj2mixcvyvHjx+Wjjz6SJUuWxBTCdu3aSevWrU1Mh32mMAE3r7/RuFq2bCnNmzeXHj16JIwkhTBhqNmRRaBx48YyYMCAyCd3X331lfrvpUuXSqFChQiKBIwQcPpcxrseQohP+q6//nr1qTQ+BKlbt66RubHT1CKwZ88eVRFctGiRXH311WryWKq8evVqWbx4caYwRowYofZkUQhT65kxPdt4r6cZjQ+rjaZNmxZTCPHBRoUKFdSqDLzBxr+zkYBuAl5efzG2ffv2qYLJM888o943JKpRCBNFmv0oAtij0qhRo3QPuvWXB29YsMeKjQQSTcDpc5mV67G3BfsI8uTJI19//bV88803ak9X06ZNEz099pdiBHbs2CFPP/10ug/Z3njjDSWI0XutotFQCFPsYUmC6Wbl9dSpEKISbh2MtGHDBnVYB7apVKxYMQlmzCGEmYCX19/Tp09L165d1SFgc+fOTejhMhTCMD+VSTo3fBKIN8b4E40VwiRNVIoNy+lz6fR6HKSEpSTTp09PMbKcbqIJWB+yvf7665HDulghTHQW2J8TAk5fT63YGVUI7X1jX+z9998vrVq1Um+22UhAJwG3r784ZbR///7qwwvsqU30flcKoc6ngrFjEojeK4AX9FdeeYV7CPm8GCXg9Ll0ej2e8Z07d8r8+fONzpOdh5+AtYdl7NixUqtWLTVhLAHFEegZ7SG0qLBCGP7nIxln6PT11IkQ4tqOHTtK/fr1pXfv3sk4fY4pRATcvP5i/zZk8JdffpHJkycnXAaBn0IYoocwKFOxThPDnyiL85TRoGQu3OOM91zOmzdPPvvss4jQxbseJ4ThREfsW8FXrOAIaXxCjb1dbCSgmwD2oBQsWFAdJnPkyBF1OAGePTyDaAMHDpRrrrlGLS1Fw/cPYu/gCy+8IDiwA2KYI0eOhC5Z0s2E8ZOXQLzX0+jnFctM8Zx+8MEHMmfOHFm2bJl6VvHM4gRSfOUPlucXK1ZMHVyHD0Jw4i73cSfvMxCmkWX2+ouvAsLvH3roIWnWrJngQK+HH35Yvf7iGcX3xqLheU7kNioKYZiewIDMBWuk8SZk9+7dasQ49ADVE+vwg4BMg8MMGYF4zyWqLR9//LGsWbNGzTze9fhE+ujRoxFKeCMybNgwdYIjGwnoJoCDCXBYF47lR8PzB8mzvocQJzDizQZee9GwxxBvrO0NR6fzlEbdmWL8rLyeRj+veP+Ar1Gxtzp16qhTnSGE+KD53LlzkV/j9bhLly6ETQIJIZDZ669VQcTziOcSH9hBDqMbXquxgi5RjUKYKNLs5woC+EuBtf38Qno+HMlEwOlzmdn1+CQQ+wHwYQeq4WwkkGgCeLOBT5ytT50T3T/7IwEnBJy+/mYUGxVEvPbiAxF88JEzZ04nw+C1JOALgSC9/lIIfUk5g5AACZAACZAACZAACZAACZBA8AhQCIOXM46YBEiABEiABEiABEiABEiABHwhQCH0BSODkAAJkAAJkAAJkAAJkAAJkEDwCFAIg5czjpgESIAESIAESIAESIAESIAEfCFAIfQFI4OQAAmQAAmQAAmQAAmQAAmQQPAIUAiDlzOOmARIgARIgARIgARIgARIgAR8IUAh9AUjg5AACZAACZAACZAACZAACZBA8AhQCIOXM46YBEiABEiABEiABEiABEiABHwhQCH0BSODkAAJkAAJkAAJkAAJkAAJkEDwCFAIg5czjpgESIAESIAESIAESIAESIAEfCFAIfQFI4OQAAmQAAmQAAmQAAmQAAmQQPAIUAiDlzOOmARIgARIgARIgARIgARIgAR8IUAh9AUjg5AACZAACZAACZAACZAACZBA8AhQCIOXM46YBEiABEiABEiABEiABEiABHwhQCH0BSODkAAJkAAJkAAJkAAJkAAJkEDwCFAIg5czjpgESIAESIAESIAESIAESIAEfCFAIfQFI4OQAAmQAAmQAAmQAAmQAAmQQPAIUAiDlzOOmARIgARIgARIgARIgARIgAR8IUAh9AUjg5AACZAACZAACXghcPz4cTl37pxcffXVkjt3bi+heC8JkAAJkIADAhRCB7B4KQmQAAmQgHMCv//97+XMmTPpbty0aVPkTX/79u1l9+7d6X7/4YcfSrFixZx3lmR33HfffXLgwAE1qh49eki3bt0SNkL097//+7+qvwcffFAGDhzoqu+dO3fKjBkzZNiwYVKyZElXMTK6ac+ePTJhwgT561//mu4ZueGGGxSvm266ydf+MgrmdI5z586VKVOmqHCVKlWSpUuXykcffST/8z//E+li8+bNkitXLl/Gf+nSJRX/vffek8mTJ0di9urVS7Zs2eI5x74MkkFIgAQCS4BCGNjUceAkQAIkEAwCderUuWKgeEONN/0nT56UP/zhD1f8fs2aNVK8ePFgTDCTUdqFsHv37vL4448nbE7oa/v27a5l4cSJEzJ8+HDZuHGjivH+++9LqVKlfBs/cvzMM89kGu+VV16RBg0a+NZndCC3c4wlhGvXrpXBgwf7LoRfffWVPPfcc+qDBVRPV61aFenDL+nXBpiBSYAEAkGAQhiINHGQJEACJBBcArGEsE+fPvLII4/IZ599Jk8++SSFUEN6vQrh3//+d+ncuXNkZH4K4alTp6RFixbpqoLXXnut/OpXv4pILDouUqSIfPDBB5InTx4NhETcznHfvn2C6iZagQIF5JZbbhFdQrhw4UKBGKNFCyGEH1KLVq5cOalevboWTgxKAiQQbgIUwnDnl7MjARIgAeMEYglhvXr1ZOrUqTJ+/HhZtGhRpkL4448/yvTp09WyQlRJ8uXLJ7feequg+oY4Vjt9+rRMmzZN1q9fL4cOHVI/hlCUL19eHnroIbnzzjvVz+bNmyfr1q1T/96mTRu5ePGivP3227Jr1y75zW9+o6o8NWrUyJQbqmbz58+X7777H/e64wAADEdJREFUTn7++edIX02bNlUSVaJECfUze4WwZcuWgjFiiR8kolmzZmpZpLWs8J///KdamomKEIQD8yxTpoy6Dks+c+bMGRkTxo9lin/729/Uz6677jq5/fbbpV27dpHrYgkh2MycOVPdA8maPXu2+vfnn39e9u7dGxkzxo/cWMtd8QsIW9myZWXcuHHqOuz3W7BggfzlL39R7NCQD1R8H3jggUz52SUHF3bs2DHywcDq1avl2WefVfcjH6iOVa1aVf03lhZj/GB09OhR9fsbb7xRLcWFTDrJL1hkNkeMwZp/hw4dZPHixfL9998rxnim3nrrLdUf/v3FF1+8QgiffvppeeONN1SMWrVqyYABAyLPFeaA/tF+97vfSb9+/eTs2bPpKshYfoql1UuWLIk8YxYTPMuPPvqojBkzRrFAa9y4sXTq1En9+4ULF+T111+XTz75RD0j+Htw/fXXq7xgCbfVBg0aJPj7hYbxQmo//fRT9ffnrrvuUktgw1Cpz/Rh5C9JgASEQsiHgARIgARIQCsBuxD+9re/jUgM3uxCnvAmH7Jh30doLRn99ttvBW/GM2qvvvpq5A3uY489Jjt27MjwWmv54UsvvaQEEA3SFb2/MV5VauvWrdKzZ88M+0GV5rXXXpMcOXKkE8JYN9x7771qbx6ktEmTJune+Nuv79q1q2C/GNrLL7+s3uzHamCNPWY4lCWWEK5YsUJGjBgRudVaUgohs6QOcgXxw3LR6GZVqMAM99iF0X4tZKV3794ZMho5cqS88847kd9//PHHUrhwYfXfaWlpSv6rVaum8mO16D169uAYFyQLVbKs5hfyk9kcW7durcQ8ulnzit5DGF0hjDV5zBkCiX5RcUWDyENM8WEB/t1q+MAEy0OxbzC64YOGoUOHqg8UoveJ/vLLL4K/C19//XVM/qjMo0KPds8990Q+PIn1d+Huu++W0aNHZ5hH/oIESCAcBCiE4cgjZ0ECJEACSUvALoQQmzlz5qixotKECgUaKhuoGlnNEkK7qEAa8QYYFanly5dHrv38888FJ1RCHC25g9TcfPPNqgppSY/15tYuDBlBw1hQdYvVUJV588031a/w5v6pp55S/dtFC1WdKlWqXCGEEBdUtuwNc8H9YIOGa1D9gyRBGq655hopXbq0mvu2bdvUn1aDfKKCs2HDhsjPsAQX3LwIISqk0dUzHJ6C+eLnEydOVNVBNIgE8vjvf/9byarVZs2aJbVr147J0C7vuN+qlmWUD+w1RYXVyi+kHcs0V65cGbkFlS98QJDV/IJ5ZnOMJYQYK6QOYp0VIQQvuzRbHwBkVQhx2A2eNasKjcmiKnrHHXeo5yWWEEKMUWm2GiTz8OHD6T5w+fOf/6zi2IXQymX0ByR45rJly5a0ry8cGAmQgHcCFELvDBmBBEiABEggEwJ2IUT1yqpOQDCsCgyqd1hSZzUIIRqWwVntiSeekLp166o9U/3794/83Hpzix9ASrCM8//+7/+UPOGNu/VmGvKE5anRwoC+9u/fn265HoQHy1IzaqjoQSh++OEHOXjwoFqCah2+gnus++1LRjFfLCFE1RNLWK0GicHvIAv2huWXkGD8Wb9+ffUre3UQcoI9mNmzZ1fVQ+u0SRzWg0NPvAghRCOz/XX2eUG0rflg6aPF2xLTWAxRQYTsWM2S9ox4Yxmj/ZRULNf89a9/reZpiRnuxYcDkDyrAoyfZZbfzOZoF0KwxrOEKibkKCuHyuCDg4YNG6pn1RJe5BPPQFaFELnPbA9hLCG0j7tRo0YyatQoQdXQeobAxDrgyC6E+PuB5dQQfSzRthqeMSxxZiMBEggvAQpheHPLmZEACZBAUhCwCyEOCMG+vegqBKph9tNG8SYe+5ggDvEallxCpvCmG3IQ/RUW1v2xhND6WfRyPftS1Oj+Ua1CBQbVu+h5WNfGEsK2bduqPVmQSfveRyz9g2DZK4/RfVr3Qoot8cQ+Mms/n70qhOoZllc6EUK74KG6mpkQ4isQsvJ1ENj7+MILL8RMH/bnYa+g1bDXzdoDiFy0atVKVQAxR1R6UTHF11NYzapaYbmkvWIKEcRyWksI4+U3q0IYPZesCKH11SnYazpp0iQ1dKsaGksIjx07ppYNWw3VbadCCAnHnkSr4XnDs4Nmr8pa87EL4cMPPyx9+/ZVUo0/Y+Um3t9F/p4ESCCYBCiEwcwbR00CJEACgSFgF0LsiUIFB/utrGZ9j5v9Oggh9uChwmI1/DuW4EU3yAm+DsFeYcMbabwR/vLLLyNLG2MJ4W233aZEAwd64N+tlpkQ4gAWa18X3uCjOoYlfPa9jrGEEIKDCikqivax4iAcS7CwBxL/oHr2xRdfpBNO9Am5sGTH4oYx2wULSwFRNc2KECIPONQG47daRkKIChkOuUGzV6HQH6QtumEfYnTV07oGyyAhwFaDjGC8ODgHsmVf8ojnBQfY2L+iAuMuWrSoLFu2TB3oYjVUs8DeYhQvv9FCmNEcsdcV1U+rZUUIra9WwVJixEWzqrd2IbTyGC1iboQQVVS75FnfP4l9mfj6DusDjC5dugg+XLBfiwo9lm5jb69VxceY7bIemBcdDpQESMARAQqhI1y8mARIgARIwCmBaCHEHkAsY7Oa9aY1WgixN84uHnjziv16WBIKEcBJmPjyelSR8KbVLhhYPgmJQFXEqhj6IYTRb6zxphpvrlEttB9QEksIMV/M9R//+Ee6r1aA/OIkSFQ4USWCCP/pT38SVOLsy1YheVhii4qo1XDACZYxYsmt9WbfkpdYQhj9NR8QFCwntA6UQVxLCMGtffv2kb6wzLBy5cqqkmtfuoplkBBoiCUqlfnz51f7GnGyZkb7MPG1EzjF0t4g1zj51L5fDvspIcI4gRVVQ6uhkgaJhTRZe/Ss5Zj2JcHxhDCzOdqfvejvkMyKEGI82NeIa61mVXqjT9fFhx1YNmufuyWE0fIMAcYHIMhdrCWjdgEFU/ydwZJofJhgNesDDwqh01czXk8C4SRAIQxnXjkrEiABEkgaAtFCCBmwiwaWPULqYglhvJMbIQZ//OMflSTaY2LZpP3NNWCguogDQZwIQyyIWIaHJZlWi3VQzNixY5U42Zdixopl7fHC1xnYv6oB40ezf6WFta8SEmrtF4yOifsgy1h+GUsIsQwXEpBZs4TwX//6V+SrOqzrUSFEtQu8UdXLaMksql74Sgvr5NBY/WGZML7qILOGEy6xRxENhxFBkjJqEB6cYuskv5nN0asQxhon9rDigwn7V2tkNB9LCPFBgX3PLK7Hs4KKaSwhxN5W/F2Ifv6tfiDJEELshaQQJs3LJAdCAkYJUAiN4mfnJEACJBB+AnbRwxthVPVw8qElE9byP/t11v4r0EH1DxUw67sFLWLYi4jTLa3v54OAYKmgdYonKjRYxglhtBpEDks0rSWF1smUqCbaq3GZLRnFF5Jjmam1lw9VGFSvjhw5Evl+Qyy9wxI8uxDiv9GvVdECA1R78ubNq4aHEzOx18x+CiligwvECSeNouHAHFSd8NUW9oZ9Ydj7ZX0HYixZsPrB/daBPjiR9Pz585Hv1YNIoiKGhv149pND8TPrABhILL4jMPrrDbA0FtVSaxyZPeGIBdGLFlwsQ0VuUQWzGiqmEHoceGKXHVyDa3EPml0Is5LfjOZoF0KwhChbLVaF0P61GBBTVEitHOFDA4zR2ieLfaRYFov5YC7IM5b94mfW82EtJca12GdqX2Zt7R+1HyZkVdoxRsRAlRp/d6yGPlA9xhLnq666Sv3YLoTWIUDIhfUVJ7gGB/oULFgw/C9UnCEJpDABCmEKJ59TJwESIIEgEUDlAweO4I0tKmHWF7rb54AlnRBHLFm03vTqmiOqS5BafC2Ek2P5sSwUyyrt37FnjfHy5cuCuDi4Bic7Zval4Lj2p59+UktLIRw4bdRJwzJMSJv9C+9j3Q8BRT8YD/btRfeD8eLkV3z3IfJiCa6TsUB6sLcSYylZsmTcMaHKjH7BHv16bfHm6DY+RBvssMQzVrNOq83KM2Q9/8iBdQBPvHEhPj6owLOG+9hIgARIIBYBCiGfCxIgARIgARIgARIgARIgARJIUQIUwhRNPKdNAiRAAiRAAiRAAiRAAiRAAhRCPgMkQAIkQAIkQAIkQAIkQAIkkKIEKIQpmnhOmwRIgARIgARIgARIgARIgAT+H0MYh7aSczPuAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_dict = {\n",
    "    \"Age\": \"Age\",\n",
    "    \"Income\": \"Annual income\",\n",
    "    \"LoanAmount\": \"Loan amount\",\n",
    "    \"CreditScore\": \"Credit score\",\n",
    "    \"EmploymentYears\": \"Employment history\",\n",
    "    \"NumLatePayments\": \"Late payments\",\n",
    "    \"DTI\": \"Debt-to-income ratio\",\n",
    "    \"HomeOwner\": \"Homeowner status\",\n",
    "    \"MaritalStatus\": \"Marital status\",\n",
    "    \"LoanPurpose\": \"Loan purpose\",\n",
    "}\n",
    "\n",
    "xpl = SmartExplainer(\n",
    "    model=clf,\n",
    "    preprocessing=encoder,\n",
    "    features_dict=feature_dict,\n",
    "    label_dict={0: \"Low default risk\", 1: \"High default risk\"},\n",
    "    title_story=\"Default risk scenario analysis\",\n",
    ")\n",
    "\n",
    "y_pred_test_df = pd.DataFrame(clf.predict(X_test), index=X_test.index, columns=[target_name])\n",
    "\n",
    "xpl.compile(\n",
    "    x=X_test,\n",
    "    y_pred=y_pred_test_df,\n",
    "    y_target=y_test,\n",
    "    additional_data=df.loc[X_test.index, [\n",
    "        \"Age\",\n",
    "        \"Income\",\n",
    "        \"LoanAmount\",\n",
    "        \"CreditScore\",\n",
    "        \"EmploymentYears\",\n",
    "        \"NumLatePayments\",\n",
    "        \"DTI\",\n",
    "        \"HomeOwner\",\n",
    "        \"MaritalStatus\",\n",
    "        \"LoanPurpose\",\n",
    "    ]],\n",
    ")\n",
    "\n",
    "xpl.plot.features_importance()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25338a16",
   "metadata": {},
   "source": [
    "## 3. Pick one high-risk borrower"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2cb7ebb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "1705",
         "rawType": "object",
         "type": "unknown"
        }
       ],
       "ref": "fd140271-a8d5-40d9-86c6-283fb20e07a4",
       "rows": [
        [
         "Age",
         "63"
        ],
        [
         "Income",
         "58292.0"
        ],
        [
         "LoanAmount",
         "46258.0"
        ],
        [
         "CreditScore",
         "614.0"
        ],
        [
         "EmploymentYears",
         "2"
        ],
        [
         "NumLatePayments",
         "1"
        ],
        [
         "DTI",
         "0.248"
        ],
        [
         "HomeOwner",
         "Yes"
        ],
        [
         "MaritalStatus",
         "Single"
        ],
        [
         "LoanPurpose",
         "Car purchase"
        ],
        [
         "default_probability",
         "0.8433333333333334"
        ]
       ],
       "shape": {
        "columns": 1,
        "rows": 11
       }
      },
      "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>1705</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>58292.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoanAmount</th>\n",
       "      <td>46258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CreditScore</th>\n",
       "      <td>614.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmploymentYears</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumLatePayments</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTI</th>\n",
       "      <td>0.248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HomeOwner</th>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaritalStatus</th>\n",
       "      <td>Single</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoanPurpose</th>\n",
       "      <td>Car purchase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default_probability</th>\n",
       "      <td>0.843333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             1705\n",
       "Age                            63\n",
       "Income                    58292.0\n",
       "LoanAmount                46258.0\n",
       "CreditScore                 614.0\n",
       "EmploymentYears                 2\n",
       "NumLatePayments                 1\n",
       "DTI                         0.248\n",
       "HomeOwner                     Yes\n",
       "MaritalStatus              Single\n",
       "LoanPurpose          Car purchase\n",
       "default_probability      0.843333"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proba_test = pd.Series(clf.predict_proba(X_test)[:, 1], index=X_test_raw.index)\n",
    "customer_idx = proba_test.sort_values(ascending=False).index[0]\n",
    "\n",
    "base_customer = X_test_raw.loc[[customer_idx]].copy()\n",
    "base_risk = float(clf.predict_proba(encoder.transform(base_customer))[:, 1][0])\n",
    "\n",
    "base_customer.assign(default_probability=base_risk).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8177748b",
   "metadata": {},
   "source": [
    "## 4. Define business scenarios (counterfactuals)\n",
    "\n",
    "Simplified action rules for the demo:\n",
    "- `loan_reduction`: reduce `LoanAmount` by 20%\n",
    "- `credit_repair`: improve `CreditScore` and reduce recent late payments\n",
    "- `income_growth`: increase `Income` and lower `DTI`\n",
    "- `full_restructure`: combine all three actions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "736ae88c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "index",
         "rawType": "int64",
         "type": "integer"
        },
        {
         "name": "scenario",
         "rawType": "object",
         "type": "string"
        },
        {
         "name": "baseline_risk",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "scenario_risk",
         "rawType": "float64",
         "type": "float"
        },
        {
         "name": "risk_delta",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "ref": "fa84f578-acc1-4d45-9936-6d1ecb2d6104",
       "rows": [
        [
         "3",
         "full_restructure",
         "0.8433333333333334",
         "0.43666666666666665",
         "-0.40666666666666673"
        ],
        [
         "0",
         "loan_reduction",
         "0.8433333333333334",
         "0.7",
         "-0.14333333333333342"
        ],
        [
         "2",
         "income_growth",
         "0.8433333333333334",
         "0.7133333333333334",
         "-0.13"
        ],
        [
         "1",
         "credit_repair",
         "0.8433333333333334",
         "0.74",
         "-0.10333333333333339"
        ]
       ],
       "shape": {
        "columns": 4,
        "rows": 4
       }
      },
      "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>scenario</th>\n",
       "      <th>baseline_risk</th>\n",
       "      <th>scenario_risk</th>\n",
       "      <th>risk_delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>full_restructure</td>\n",
       "      <td>0.843333</td>\n",
       "      <td>0.436667</td>\n",
       "      <td>-0.406667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>loan_reduction</td>\n",
       "      <td>0.843333</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>-0.143333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>income_growth</td>\n",
       "      <td>0.843333</td>\n",
       "      <td>0.713333</td>\n",
       "      <td>-0.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>credit_repair</td>\n",
       "      <td>0.843333</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>-0.103333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           scenario  baseline_risk  scenario_risk  risk_delta\n",
       "3  full_restructure       0.843333       0.436667   -0.406667\n",
       "0    loan_reduction       0.843333       0.700000   -0.143333\n",
       "2     income_growth       0.843333       0.713333   -0.130000\n",
       "1     credit_repair       0.843333       0.740000   -0.103333"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def apply_scenario(base_row, scenario_name):\n",
    "    row = base_row.copy()\n",
    "    if scenario_name == \"loan_reduction\":\n",
    "        row.loc[:, \"LoanAmount\"] = np.maximum(1000.0, row[\"LoanAmount\"].fillna(25000).iloc[0] * 0.8)\n",
    "        row.loc[:, \"DTI\"] = np.maximum(0.05, row[\"DTI\"].fillna(0.3).iloc[0] * 0.9)\n",
    "    elif scenario_name == \"credit_repair\":\n",
    "        row.loc[:, \"CreditScore\"] = min(850.0, row[\"CreditScore\"].fillna(680).iloc[0] + 40)\n",
    "        row.loc[:, \"NumLatePayments\"] = max(0, int(row[\"NumLatePayments\"].fillna(0).iloc[0]) - 1)\n",
    "    elif scenario_name == \"income_growth\":\n",
    "        row.loc[:, \"Income\"] = row[\"Income\"].fillna(55000).iloc[0] * 1.2\n",
    "        row.loc[:, \"DTI\"] = np.maximum(0.05, row[\"DTI\"].fillna(0.3).iloc[0] * 0.85)\n",
    "    elif scenario_name == \"full_restructure\":\n",
    "        row.loc[:, \"LoanAmount\"] = np.maximum(1000.0, row[\"LoanAmount\"].fillna(25000).iloc[0] * 0.8)\n",
    "        row.loc[:, \"CreditScore\"] = min(850.0, row[\"CreditScore\"].fillna(680).iloc[0] + 40)\n",
    "        row.loc[:, \"NumLatePayments\"] = max(0, int(row[\"NumLatePayments\"].fillna(0).iloc[0]) - 1)\n",
    "        row.loc[:, \"Income\"] = row[\"Income\"].fillna(55000).iloc[0] * 1.2\n",
    "        row.loc[:, \"DTI\"] = np.maximum(0.05, row[\"DTI\"].fillna(0.3).iloc[0] * 0.8)\n",
    "    else:\n",
    "        raise ValueError(f\"Unknown scenario: {scenario_name}\")\n",
    "    return row\n",
    "\n",
    "scenario_names = [\"loan_reduction\", \"credit_repair\", \"income_growth\", \"full_restructure\"]\n",
    "scenario_rows = []\n",
    "\n",
    "for name in scenario_names:\n",
    "    row = apply_scenario(base_customer, name)\n",
    "    row.index = [name]\n",
    "    scenario_rows.append(row)\n",
    "\n",
    "scenario_df = pd.concat(scenario_rows, axis=0)\n",
    "scenario_proba = clf.predict_proba(encoder.transform(scenario_df))[:, 1]\n",
    "\n",
    "result = pd.DataFrame(\n",
    "    {\n",
    "        \"scenario\": scenario_names,\n",
    "        \"baseline_risk\": [base_risk] * len(scenario_names),\n",
    "        \"scenario_risk\": scenario_proba,\n",
    "        \"risk_delta\": scenario_proba - base_risk,\n",
    "    }\n",
    ")\n",
    "result.sort_values(\"risk_delta\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32251674",
   "metadata": {},
   "source": [
    "## 5. Explain baseline vs best scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0ea51f2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Shap explainer type - <shap.explainers._tree.TreeExplainer object at 0x11ea85dc0>\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_scenario_name = result.sort_values(\"risk_delta\").iloc[0][\"scenario\"]\n",
    "best_row = scenario_df.loc[[best_scenario_name]].copy()\n",
    "\n",
    "compare_rows = pd.concat([base_customer, best_row], axis=0)\n",
    "compare_rows.index = [\"baseline\", \"best_scenario\"]\n",
    "\n",
    "compare_encoded = encoder.transform(compare_rows)\n",
    "compare_pred_df = pd.DataFrame(\n",
    "    clf.predict(compare_encoded),\n",
    "    index=compare_rows.index,\n",
    "    columns=[target_name],\n",
    ")\n",
    "\n",
    "xpl_compare = SmartExplainer(\n",
    "    model=clf,\n",
    "    preprocessing=encoder,\n",
    "    features_dict=feature_dict,\n",
    "    label_dict={0: \"Low default risk\", 1: \"High default risk\"},\n",
    "    title_story=\"Counterfactual comparison\",\n",
    ")\n",
    "xpl_compare.compile(x=compare_encoded, y_pred=compare_pred_df, additional_data=compare_rows)\n",
    "\n",
    "xpl_compare.plot.local_plot(index=\"baseline\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f837ea58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpl_compare.plot.local_plot(index=\"best_scenario\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51dc3f21",
   "metadata": {},
   "source": [
    "## 6. Decision checklist\n",
    "\n",
    "- Rank scenarios by impact and business cost.\n",
    "- Exclude non-actionable or sensitive variables.\n",
    "- Verify the plausibility of the proposed counterfactuals.\n",
    "- Monitor the effects actually observed after deployment."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "survptf_312",
   "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": "3.12.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}