{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7ac2d9ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, median_absolute_error, max_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "06359815",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>AcidCharge</th>\n",
       "      <th>alpha</th>\n",
       "      <th>beta</th>\n",
       "      <th>pi</th>\n",
       "      <th>pKa_water</th>\n",
       "      <th>TotalCharge</th>\n",
       "      <th>pKa_Final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Group1_1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.537295</td>\n",
       "      <td>1</td>\n",
       "      <td>5.861019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Group1_2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2.537295</td>\n",
       "      <td>1</td>\n",
       "      <td>6.045974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Group1_3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.289717</td>\n",
       "      <td>2</td>\n",
       "      <td>-6.496620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Group1_4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.55</td>\n",
       "      <td>2.289717</td>\n",
       "      <td>2</td>\n",
       "      <td>-4.585847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Group1_5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.12</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.639279</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.490751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1085</th>\n",
       "      <td>Group2_490</td>\n",
       "      <td>0</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.96</td>\n",
       "      <td>7.561958</td>\n",
       "      <td>0</td>\n",
       "      <td>9.770847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</th>\n",
       "      <td>Group2_491</td>\n",
       "      <td>0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.60</td>\n",
       "      <td>7.561958</td>\n",
       "      <td>0</td>\n",
       "      <td>10.726398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>Group2_492</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.58</td>\n",
       "      <td>7.561958</td>\n",
       "      <td>0</td>\n",
       "      <td>23.211866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1088</th>\n",
       "      <td>Group2_493</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.55</td>\n",
       "      <td>7.561958</td>\n",
       "      <td>0</td>\n",
       "      <td>34.114489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>Group2_494</td>\n",
       "      <td>0</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.09</td>\n",
       "      <td>7.561958</td>\n",
       "      <td>0</td>\n",
       "      <td>7.561958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1090 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name  AcidCharge  alpha  beta    pi  pKa_water  TotalCharge  \\\n",
       "0       Group1_1           1   0.00  0.00  0.00   2.537295            1   \n",
       "1       Group1_2           1   0.00  0.11  0.55   2.537295            1   \n",
       "2       Group1_3           1   0.00  0.00  0.00   2.289717            2   \n",
       "3       Group1_4           1   0.00  0.11  0.55   2.289717            2   \n",
       "4       Group1_5           1   1.12  0.45  0.60   0.639279            2   \n",
       "...          ...         ...    ...   ...   ...        ...          ...   \n",
       "1085  Group2_490           0   0.71  0.48  0.96   7.561958            0   \n",
       "1086  Group2_491           0   0.98  0.66  0.60   7.561958            0   \n",
       "1087  Group2_492           0   0.00  0.55  0.58   7.561958            0   \n",
       "1088  Group2_493           0   0.00  0.11  0.55   7.561958            0   \n",
       "1089  Group2_494           0   1.17  0.47  1.09   7.561958            0   \n",
       "\n",
       "      pKa_Final  \n",
       "0      5.861019  \n",
       "1      6.045974  \n",
       "2     -6.496620  \n",
       "3     -4.585847  \n",
       "4     -0.490751  \n",
       "...         ...  \n",
       "1085   9.770847  \n",
       "1086  10.726398  \n",
       "1087  23.211866  \n",
       "1088  34.114489  \n",
       "1089   7.561958  \n",
       "\n",
       "[1090 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"Data/pKa_Data.csv\")\n",
    "data\n",
    "# see the solvatochromic ref for details of \\alpha, \\beta and \\pi. I do not know the excat solution used here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e8f9427d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2482820be50>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.columns\n",
    "# plt.plot(data['AcidCharge'])\n",
    "plt.plot(data['TotalCharge'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "066ee1ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select columns to include into data set for ML model (inlcuding target)\n",
    "data = data[['Name', 'AcidCharge', 'alpha', 'beta', 'pi', 'pKa_water', 'TotalCharge','pKa_Final']]\n",
    "#data = data[['Name', 'AcidCharge', 'pKa_water', 'TotalCharge','pKa_Final']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "573d09ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate into test set and training set; test_size gives percentage of data in test set\n",
    "train_data, test_data = train_test_split(data, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a7e3b92f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split training set into training varaibles \"X\" and target variable \"Y\"\n",
    "X = train_data.drop(columns=['Name','pKa_Final'])# .to_numpy()\n",
    "Y = train_data['pKa_Final']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ff664744",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split test data set equivalent to training data in Test_X and Test_Y\n",
    "Test_X = test_data.drop(columns=['Name','pKa_Final'])# .to_numpy()\n",
    "Test_Y = test_data['pKa_Final']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d933884",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize training set \"X\" in order to minize weight of large values and avoid skewing\n",
    "#Define scaler\n",
    "scaler = MinMaxScaler()\n",
    "# compute min and max values of training set for use in test set and training set\n",
    "#scaler.fit(X)\n",
    "# Apply scaler to test and training set \"X\"\n",
    "#X = scaler.transform(X)\n",
    "#Test_X = scaler.transform(Test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "af219f51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Gradient boost ML\n",
    "GB_Model = GradientBoostingRegressor()\n",
    "# Different parameters are tested and results compared\n",
    "GB_Parameters = {'learning_rate': np.arange(0.05, 0.3, 0.05), 'loss': ['lad', 'huber'],\n",
    "                 'n_estimators': np.arange(120, 160, 20)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8448ad8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Random Forest ML\n",
    "RF_Model = RandomForestRegressor()\n",
    "# Different parameters are tested and results compared\n",
    "RF_Parameters = {\"n_estimators\": range(120, 160, 20), 'criterion': ['mae'],  \"min_samples_split\": [2,3,4]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "05436f59",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define Kernel Ridge ML\n",
    "KR_Model = KernelRidge()\n",
    "# Different parameters are tested and results compared\n",
    "KR_Parameters = {'alpha': np.arange(0.1, 1.0, 0.1), 'kernel': ['linear', 'polynomial', 'laplacian'],\n",
    "                 'degree': [2,3]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f7d6185e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=GradientBoostingRegressor(), n_jobs=6,\n",
       "             param_grid={&#x27;learning_rate&#x27;: array([0.05, 0.1 , 0.15, 0.2 , 0.25]),\n",
       "                         &#x27;loss&#x27;: [&#x27;lad&#x27;, &#x27;huber&#x27;],\n",
       "                         &#x27;n_estimators&#x27;: array([120, 140])},\n",
       "             scoring=&#x27;neg_mean_absolute_error&#x27;, verbose=2)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=GradientBoostingRegressor(), n_jobs=6,\n",
       "             param_grid={&#x27;learning_rate&#x27;: array([0.05, 0.1 , 0.15, 0.2 , 0.25]),\n",
       "                         &#x27;loss&#x27;: [&#x27;lad&#x27;, &#x27;huber&#x27;],\n",
       "                         &#x27;n_estimators&#x27;: array([120, 140])},\n",
       "             scoring=&#x27;neg_mean_absolute_error&#x27;, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=GradientBoostingRegressor(), n_jobs=6,\n",
       "             param_grid={'learning_rate': array([0.05, 0.1 , 0.15, 0.2 , 0.25]),\n",
       "                         'loss': ['lad', 'huber'],\n",
       "                         'n_estimators': array([120, 140])},\n",
       "             scoring='neg_mean_absolute_error', verbose=2)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute Gradient boost ML\n",
    "GB_Solver = GridSearchCV(GB_Model, GB_Parameters, n_jobs=6, cv=5, verbose=2,\n",
    "                         scoring='neg_mean_absolute_error')\n",
    "GB_Solver.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "70b068d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:407: FutureWarning: Criterion 'mae' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='absolute_error'` which is equivalent.\n",
      "  warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=6,\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;mae&#x27;], &#x27;min_samples_split&#x27;: [2, 3, 4],\n",
       "                         &#x27;n_estimators&#x27;: range(120, 160, 20)},\n",
       "             scoring=&#x27;neg_mean_absolute_error&#x27;, verbose=2)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=6,\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;mae&#x27;], &#x27;min_samples_split&#x27;: [2, 3, 4],\n",
       "                         &#x27;n_estimators&#x27;: range(120, 160, 20)},\n",
       "             scoring=&#x27;neg_mean_absolute_error&#x27;, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=6,\n",
       "             param_grid={'criterion': ['mae'], 'min_samples_split': [2, 3, 4],\n",
       "                         'n_estimators': range(120, 160, 20)},\n",
       "             scoring='neg_mean_absolute_error', verbose=2)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute Random Forest ML\n",
    "RF_Solver = GridSearchCV(RF_Model, RF_Parameters, cv=5, verbose=2,  n_jobs=6,  scoring='neg_mean_absolute_error')\n",
    "RF_Solver.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "62012c4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 54 candidates, totalling 270 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=KernelRidge(), n_jobs=6,\n",
       "             param_grid={&#x27;alpha&#x27;: array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]),\n",
       "                         &#x27;degree&#x27;: [2, 3],\n",
       "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;polynomial&#x27;, &#x27;laplacian&#x27;]},\n",
       "             scoring=&#x27;neg_mean_absolute_error&#x27;, verbose=2)</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=KernelRidge(), n_jobs=6,\n",
       "             param_grid={&#x27;alpha&#x27;: array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]),\n",
       "                         &#x27;degree&#x27;: [2, 3],\n",
       "                         &#x27;kernel&#x27;: [&#x27;linear&#x27;, &#x27;polynomial&#x27;, &#x27;laplacian&#x27;]},\n",
       "             scoring=&#x27;neg_mean_absolute_error&#x27;, verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KernelRidge</label><div class=\"sk-toggleable__content\"><pre>KernelRidge()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KernelRidge</label><div class=\"sk-toggleable__content\"><pre>KernelRidge()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KernelRidge(), n_jobs=6,\n",
       "             param_grid={'alpha': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]),\n",
       "                         'degree': [2, 3],\n",
       "                         'kernel': ['linear', 'polynomial', 'laplacian']},\n",
       "             scoring='neg_mean_absolute_error', verbose=2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute Kernel ridge ML\n",
    "KR_Solver = GridSearchCV(KR_Model, KR_Parameters, cv=5, verbose=2,  n_jobs=6,  scoring='neg_mean_absolute_error')\n",
    "KR_Solver.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "76d9f9ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient boosting \n",
      " Best parameters:\n",
      "{'learning_rate': 0.25, 'loss': 'huber', 'n_estimators': 140}\n",
      "Random Forest \n",
      " Best parameters:\n",
      "{'criterion': 'mae', 'min_samples_split': 2, 'n_estimators': 120}\n",
      "Kernel ridege \n",
      " Best parameters:\n",
      "{'alpha': 0.1, 'degree': 2, 'kernel': 'laplacian'}\n"
     ]
    }
   ],
   "source": [
    "# Evaluate best parameters for the respective models and rerun calculation\n",
    "print('Gradient boosting \\n Best parameters:')\n",
    "GB_BestFit = GB_Solver.best_params_\n",
    "GB_FinalModel = GB_Solver.best_estimator_\n",
    "print(GB_BestFit)\n",
    "\n",
    "print('Random Forest \\n Best parameters:')\n",
    "RF_BestFit = RF_Solver.best_params_\n",
    "RF_FinalModel = RF_Solver.best_estimator_\n",
    "print(RF_BestFit)\n",
    "\n",
    "print('Kernel ridege \\n Best parameters:')\n",
    "KR_BestFit = KR_Solver.best_params_\n",
    "KR_FinalModel = KR_Solver.best_estimator_\n",
    "print(KR_BestFit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5da0dc74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most accurate fits for Gradient boosting are:\n",
      "\n",
      "{'learning_rate': 0.25, 'loss': 'huber', 'n_estimators': 140}\n",
      "{'learning_rate': 0.25, 'loss': 'huber', 'n_estimators': 120}\n",
      "{'learning_rate': 0.2, 'loss': 'huber', 'n_estimators': 140}\n",
      "{'learning_rate': 0.2, 'loss': 'huber', 'n_estimators': 120}\n",
      "{'learning_rate': 0.15000000000000002, 'loss': 'huber', 'n_estimators': 140}\n",
      "{'learning_rate': 0.15000000000000002, 'loss': 'huber', 'n_estimators': 120}\n",
      "{'learning_rate': 0.1, 'loss': 'huber', 'n_estimators': 140}\n",
      "{'learning_rate': 0.1, 'loss': 'huber', 'n_estimators': 120}\n",
      "{'learning_rate': 0.25, 'loss': 'lad', 'n_estimators': 140}\n",
      "{'learning_rate': 0.25, 'loss': 'lad', 'n_estimators': 120}\n"
     ]
    }
   ],
   "source": [
    "# Print best results for Gradient boosting\n",
    "print('The most accurate fits for Gradient boosting are:\\n')\n",
    "for param in pd.DataFrame(GB_Solver.cv_results_).sort_values(\"rank_test_score\")[\"params\"][0:10]:\n",
    "    print(param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2732fa8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most accurate fits for Random Forest are:\n",
      "\n",
      "{'criterion': 'mae', 'min_samples_split': 2, 'n_estimators': 120}\n",
      "{'criterion': 'mae', 'min_samples_split': 2, 'n_estimators': 140}\n",
      "{'criterion': 'mae', 'min_samples_split': 3, 'n_estimators': 120}\n",
      "{'criterion': 'mae', 'min_samples_split': 3, 'n_estimators': 140}\n",
      "{'criterion': 'mae', 'min_samples_split': 4, 'n_estimators': 120}\n",
      "{'criterion': 'mae', 'min_samples_split': 4, 'n_estimators': 140}\n"
     ]
    }
   ],
   "source": [
    "# Print best results for Random Forest\n",
    "print('The most accurate fits for Random Forest are:\\n')\n",
    "for param in pd.DataFrame(RF_Solver.cv_results_).sort_values(\"rank_test_score\")[\"params\"][0:10]:\n",
    "    print(param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "91433ac4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most accurate fits for Kernel Ridge are:\n",
      "\n",
      "{'alpha': 0.1, 'degree': 2, 'kernel': 'laplacian'}\n",
      "{'alpha': 0.1, 'degree': 3, 'kernel': 'laplacian'}\n",
      "{'alpha': 0.2, 'degree': 3, 'kernel': 'laplacian'}\n",
      "{'alpha': 0.2, 'degree': 2, 'kernel': 'laplacian'}\n",
      "{'alpha': 0.30000000000000004, 'degree': 2, 'kernel': 'laplacian'}\n",
      "{'alpha': 0.30000000000000004, 'degree': 3, 'kernel': 'laplacian'}\n",
      "{'alpha': 0.1, 'degree': 3, 'kernel': 'polynomial'}\n",
      "{'alpha': 0.4, 'degree': 3, 'kernel': 'laplacian'}\n",
      "{'alpha': 0.4, 'degree': 2, 'kernel': 'laplacian'}\n",
      "{'alpha': 0.2, 'degree': 3, 'kernel': 'polynomial'}\n"
     ]
    }
   ],
   "source": [
    "# Print best results for Kernel Ridge\n",
    "print('The most accurate fits for Kernel Ridge are:\\n')\n",
    "for param in pd.DataFrame(KR_Solver.cv_results_).sort_values(\"rank_test_score\")[\"params\"][0:10]:\n",
    "    print(param)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0b4a167d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most accurate fits for GRADIENT BOOSTING are [accuracy is given as negative mean absolute error]:\n",
      "-0.9105666832433481 {'learning_rate': 0.25, 'loss': 'huber', 'n_estimators': 140}\n",
      "-0.9519880288724654 {'learning_rate': 0.25, 'loss': 'huber', 'n_estimators': 120}\n",
      "-0.9893179753222734 {'learning_rate': 0.2, 'loss': 'huber', 'n_estimators': 140}\n",
      "-1.0092968962226885 {'learning_rate': 0.2, 'loss': 'huber', 'n_estimators': 120}\n",
      "-1.0120973792872932 {'learning_rate': 0.15000000000000002, 'loss': 'huber', 'n_estimators': 140}\n",
      "-1.0387207037680333 {'learning_rate': 0.15000000000000002, 'loss': 'huber', 'n_estimators': 120}\n",
      "-1.132987058173752 {'learning_rate': 0.1, 'loss': 'huber', 'n_estimators': 140}\n",
      "-1.1993123776280286 {'learning_rate': 0.1, 'loss': 'huber', 'n_estimators': 120}\n",
      "-1.204950268120836 {'learning_rate': 0.25, 'loss': 'lad', 'n_estimators': 140}\n",
      "-1.2276960232326737 {'learning_rate': 0.25, 'loss': 'lad', 'n_estimators': 120}\n",
      "-1.2787391285338268 {'learning_rate': 0.2, 'loss': 'lad', 'n_estimators': 140}\n",
      "-1.2842168840909147 {'learning_rate': 0.15000000000000002, 'loss': 'lad', 'n_estimators': 140}\n",
      "-1.2951393093442647 {'learning_rate': 0.2, 'loss': 'lad', 'n_estimators': 120}\n",
      "-1.3618054626521725 {'learning_rate': 0.1, 'loss': 'lad', 'n_estimators': 140}\n",
      "-1.372019095354605 {'learning_rate': 0.15000000000000002, 'loss': 'lad', 'n_estimators': 120}\n",
      "-1.4541531976302653 {'learning_rate': 0.05, 'loss': 'huber', 'n_estimators': 140}\n",
      "-1.4952082035043976 {'learning_rate': 0.1, 'loss': 'lad', 'n_estimators': 120}\n",
      "-1.5615446611138537 {'learning_rate': 0.05, 'loss': 'huber', 'n_estimators': 120}\n",
      "-1.8762844080351369 {'learning_rate': 0.05, 'loss': 'lad', 'n_estimators': 140}\n",
      "-2.200481828433032 {'learning_rate': 0.05, 'loss': 'lad', 'n_estimators': 120}\n"
     ]
    }
   ],
   "source": [
    "# Print ranking of Gradient boosting fits:\n",
    "print('The most accurate fits for GRADIENT BOOSTING are [accuracy is given as negative mean absolute error]:')\n",
    "GB_Result = pd.DataFrame(GB_Solver.cv_results_)\n",
    "GB_Result = GB_Result.sort_values(\"rank_test_score\")\n",
    "for i, row in GB_Result.iterrows():\n",
    "    print(row[\"mean_test_score\"], row[\"params\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0529d248",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most accurate fits for RANDOM FOREST are [accuracy is given as negative mean absolute error]:\n",
      "-1.1290057553565709 {'criterion': 'mae', 'min_samples_split': 2, 'n_estimators': 120}\n",
      "-1.1425065242202392 {'criterion': 'mae', 'min_samples_split': 2, 'n_estimators': 140}\n",
      "-1.157743843418016 {'criterion': 'mae', 'min_samples_split': 3, 'n_estimators': 120}\n",
      "-1.164473507673526 {'criterion': 'mae', 'min_samples_split': 3, 'n_estimators': 140}\n",
      "-1.165117716261349 {'criterion': 'mae', 'min_samples_split': 4, 'n_estimators': 120}\n",
      "-1.1798431844925155 {'criterion': 'mae', 'min_samples_split': 4, 'n_estimators': 140}\n"
     ]
    }
   ],
   "source": [
    "# Print ranking of Random Forest fits:\n",
    "print('The most accurate fits for RANDOM FOREST are [accuracy is given as negative mean absolute error]:')\n",
    "RF_Result = pd.DataFrame(RF_Solver.cv_results_)\n",
    "RF_Result = RF_Result.sort_values(\"rank_test_score\")\n",
    "for i, row in RF_Result.iterrows():\n",
    "    print(row[\"mean_test_score\"], row[\"params\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0d8414d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most accurate fits for KERNEL RIDGE are [accuracy is given as negative mean absolute error]:\n",
      "-1.0830534128051328 {'alpha': 0.1, 'degree': 2, 'kernel': 'laplacian'}\n",
      "-1.0830534128051328 {'alpha': 0.1, 'degree': 3, 'kernel': 'laplacian'}\n",
      "-1.2675213420248643 {'alpha': 0.2, 'degree': 3, 'kernel': 'laplacian'}\n",
      "-1.2675213420248643 {'alpha': 0.2, 'degree': 2, 'kernel': 'laplacian'}\n",
      "-1.4008394688331693 {'alpha': 0.30000000000000004, 'degree': 2, 'kernel': 'laplacian'}\n",
      "-1.4008394688331693 {'alpha': 0.30000000000000004, 'degree': 3, 'kernel': 'laplacian'}\n",
      "-1.4983776609668753 {'alpha': 0.1, 'degree': 3, 'kernel': 'polynomial'}\n",
      "-1.504918413987433 {'alpha': 0.4, 'degree': 3, 'kernel': 'laplacian'}\n",
      "-1.504918413987433 {'alpha': 0.4, 'degree': 2, 'kernel': 'laplacian'}\n",
      "-1.5709272418309086 {'alpha': 0.2, 'degree': 3, 'kernel': 'polynomial'}\n",
      "-1.5940019246943944 {'alpha': 0.5, 'degree': 2, 'kernel': 'laplacian'}\n",
      "-1.5940019246943944 {'alpha': 0.5, 'degree': 3, 'kernel': 'laplacian'}\n",
      "-1.6058088591908075 {'alpha': 0.30000000000000004, 'degree': 3, 'kernel': 'polynomial'}\n",
      "-1.6303319612645857 {'alpha': 0.4, 'degree': 3, 'kernel': 'polynomial'}\n",
      "-1.649702739291015 {'alpha': 0.5, 'degree': 3, 'kernel': 'polynomial'}\n",
      "-1.6658664336167899 {'alpha': 0.6, 'degree': 3, 'kernel': 'polynomial'}\n",
      "-1.670626782756673 {'alpha': 0.6, 'degree': 2, 'kernel': 'laplacian'}\n",
      "-1.670626782756673 {'alpha': 0.6, 'degree': 3, 'kernel': 'laplacian'}\n",
      "-1.6792257848179077 {'alpha': 0.7000000000000001, 'degree': 3, 'kernel': 'polynomial'}\n",
      "-1.690525873106904 {'alpha': 0.8, 'degree': 3, 'kernel': 'polynomial'}\n",
      "-1.7004716160709148 {'alpha': 0.9, 'degree': 3, 'kernel': 'polynomial'}\n",
      "-1.7399109668059065 {'alpha': 0.7000000000000001, 'degree': 3, 'kernel': 'laplacian'}\n",
      "-1.7399109668059065 {'alpha': 0.7000000000000001, 'degree': 2, 'kernel': 'laplacian'}\n",
      "-1.7586812335411797 {'alpha': 0.1, 'degree': 2, 'kernel': 'polynomial'}\n",
      "-1.785427206854654 {'alpha': 0.2, 'degree': 2, 'kernel': 'polynomial'}\n",
      "-1.8035281543283443 {'alpha': 0.8, 'degree': 3, 'kernel': 'laplacian'}\n",
      "-1.8035281543283443 {'alpha': 0.8, 'degree': 2, 'kernel': 'laplacian'}\n",
      "-1.819262855960364 {'alpha': 0.30000000000000004, 'degree': 2, 'kernel': 'polynomial'}\n",
      "-1.8538749342868819 {'alpha': 0.4, 'degree': 2, 'kernel': 'polynomial'}\n",
      "-1.8619653121465543 {'alpha': 0.9, 'degree': 2, 'kernel': 'laplacian'}\n",
      "-1.8619653121465543 {'alpha': 0.9, 'degree': 3, 'kernel': 'laplacian'}\n",
      "-1.88855985937709 {'alpha': 0.5, 'degree': 2, 'kernel': 'polynomial'}\n",
      "-1.9236708971706218 {'alpha': 0.6, 'degree': 2, 'kernel': 'polynomial'}\n",
      "-1.9574770178535421 {'alpha': 0.7000000000000001, 'degree': 2, 'kernel': 'polynomial'}\n",
      "-1.9900406044644978 {'alpha': 0.8, 'degree': 2, 'kernel': 'polynomial'}\n",
      "-2.0218477810125024 {'alpha': 0.9, 'degree': 2, 'kernel': 'polynomial'}\n",
      "-4.430253738508337 {'alpha': 0.9, 'degree': 3, 'kernel': 'linear'}\n",
      "-4.430253738508337 {'alpha': 0.9, 'degree': 2, 'kernel': 'linear'}\n",
      "-4.430470880313314 {'alpha': 0.8, 'degree': 3, 'kernel': 'linear'}\n",
      "-4.430470880313314 {'alpha': 0.8, 'degree': 2, 'kernel': 'linear'}\n",
      "-4.430693859785249 {'alpha': 0.7000000000000001, 'degree': 2, 'kernel': 'linear'}\n",
      "-4.430693859785249 {'alpha': 0.7000000000000001, 'degree': 3, 'kernel': 'linear'}\n",
      "-4.430916982356932 {'alpha': 0.6, 'degree': 2, 'kernel': 'linear'}\n",
      "-4.430916982356932 {'alpha': 0.6, 'degree': 3, 'kernel': 'linear'}\n",
      "-4.431140243349213 {'alpha': 0.5, 'degree': 2, 'kernel': 'linear'}\n",
      "-4.431140243349213 {'alpha': 0.5, 'degree': 3, 'kernel': 'linear'}\n",
      "-4.431363637962961 {'alpha': 0.4, 'degree': 2, 'kernel': 'linear'}\n",
      "-4.431363637962961 {'alpha': 0.4, 'degree': 3, 'kernel': 'linear'}\n",
      "-4.431587161240922 {'alpha': 0.30000000000000004, 'degree': 3, 'kernel': 'linear'}\n",
      "-4.431587161240922 {'alpha': 0.30000000000000004, 'degree': 2, 'kernel': 'linear'}\n",
      "-4.431810808109495 {'alpha': 0.2, 'degree': 3, 'kernel': 'linear'}\n",
      "-4.431810808109495 {'alpha': 0.2, 'degree': 2, 'kernel': 'linear'}\n",
      "-4.432034573339864 {'alpha': 0.1, 'degree': 3, 'kernel': 'linear'}\n",
      "-4.432034573339864 {'alpha': 0.1, 'degree': 2, 'kernel': 'linear'}\n"
     ]
    }
   ],
   "source": [
    "# Print ranking of Kernel Ridge fits:\n",
    "print('The most accurate fits for KERNEL RIDGE are [accuracy is given as negative mean absolute error]:')\n",
    "KR_Result = pd.DataFrame(KR_Solver.cv_results_)\n",
    "KR_Result = KR_Result.sort_values(\"rank_test_score\")\n",
    "for i, row in KR_Result.iterrows():\n",
    "    print(row[\"mean_test_score\"], row[\"params\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60cce39d",
   "metadata": {},
   "source": [
    "# Plot Results for Training Set - Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "da009f67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== SHAP FOR TRAIN DATA ====\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Criterion 'mae' was deprecated in v1.0 and will be removed in version 1.2. Use `criterion='absolute_error'` which is equivalent.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== SHAP DONE ====\n"
     ]
    }
   ],
   "source": [
    "import shap\n",
    "print('===== SHAP FOR TRAIN DATA ====')\n",
    "#smodel=GB_FinalModel.fit(X,Y)      # rather slow\n",
    "smodel=RF_FinalModel.fit(X,Y)      # rather slow\n",
    "explainer = shap.Explainer(smodel)\n",
    "shap_values = explainer(X)\n",
    "print('===== SHAP DONE ====')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f95fdfc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Names  ['AcidCharge', 'alpha', 'beta', 'pi', 'pKa_water', 'TotalCharge']\n",
      "\n",
      " shap base_values  [13.29259884]\n"
     ]
    },
    {
     "data": {
      "image/png": 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mhjbqAvTN5XtqwoSS3HRZVU0wyUkgxYkjapNcEaYmxSTsMbFMjStkoU2FI6b55a6fWPf6KoZM7Ufusd0x3M2nb0Q2BFlz+zzWPfBz7fAyNh5sQrUBOsRzbyM4MCuirP3L1/R64zCWXfA51e+tRleGIWqDx4FzaC65U/tR9XUpNfNLSeqTQsqgNDx90kmf1A3VTMv+yos+ofTJReiInZDSYwEeZpOdEkVVhzFSXXg8AVI3LIy3dKMI0QX7syI23w9JIoxJmBhe3JQTf+fi9bdJQeMCgnXHMcLh2v9p4mNTmA2WLZRto/tcRmivEUQ+X4ehI7hTI5hHDCDsy8V65UfMskpc1MTr5PVg/O+P0CMTff3L6HcWxC/KDumPcUAvVDSKfm8evDcfTBOV6oNhPeCMMVDhh737wjdL4LVvITMZ+hdAp1T0t0sJvreOaBlgKJxHD8T7/Bkop4ldGSL4wgLsT5fh+HU5bu3HmLgH/OUY9IpNVB/3H4KL/Ng4cI3pSvrMkzFzkpr9LPDV4vjx9+oFBwxsfpvtYdvwzg/w63oYNwQG7V59AiQnPdFOT3d58803mTZtGg8//DDz5s3jzTffpLS0lMLCQqZMmcJhhx1Wt23DdJfy8nIuvfRSlixZwrXXXssxxxyTUO7GjRuZMWMG3333HevXryccDtOlSxeOOOIIzjjjDMztyH2LRqMcfPDBjBs3jltuuaVu/R133MHMmTM59thjue666+rWT5s2jXfffZePPvoIj8fT4ro8+uijPP74402OP2nSpLrjRiIRZsyYwTvvvENRUREul4thw4Zx4YUX0r9//7p95syZw9SpU7n55psJBoO89NJLFBUVcfbZZ3PhhRe2+Nw7gpitGTTdYkmjgRbOH6J4bPy23+c5xTajZtgJQ7HV3ukGwGnAK0cpJvXaelklNZrRz1msqGxZvXukgqVhdXX9uhQnbLrExLWFTnZLyjT7P2+xMdiyY4zvBrOONzGU4udNmjH/tSirvQPdORm+OMWke1rTYxXXaPZtcC4pLvjwBJOR+TvuC/KLtZrxL1kE4r/2+JwQqB2yWgEPjzOYMlhx4H8tvi2Or3cY8OJkg2P6bPlCJmppxr1k8WnRb6+jxwFvHWtwSGFt4KQ1h75k8eHqxO3yfLDsfJPz3rV5flH9JyndoalIeK80m3N1+2bCl2c4yPI1fU1f+MXmlNetBp/JRl/DusGqhrP0NDsqWaMVMQu2lQ4YsyAUq2+JBrJCEUodDjBU4jG1xheziSmIGAb7VfpJqm0NrzYMfvS5qWlmPHG3bTOuvBq0JlUnnmnEMNAa7NqhFNNCYQoqKhOGG4saii8L8smtCdDVH6gL9dCa5EAQVyxGxDTjQzBqjTcYwnY03+6UvrGClMqahHUaiLqcdFlRVbfOVlCZ5SLaIPhWOt5i74ja8Stw08BXFSGvVyp7fXY4juTE0WOq52zkp0PexqqO1h5HY6BxYxHAReM30kUMT4YJLgdWSaBJ3S0UEUyc2DhIvGBJ3j+Pvh8eieGqr+/aW75l/bTv685SNfp8ZFKGh/qx4xUWnfgFA02YNEIkTuhkEiSJYjQKA6suiFJobJwoLBRNL6TiGr8fFps/2PELgiw8VNTVMIqHADl42YiTzVelTmxvKioYQLO5ldzGwN/ouPEx8+vp2uM1L0QGYTIT1nmO64P59+Mp3fcJ7A2bPy8aDwGSWYc5YQCxD9ZgWPHXL4yPKrLwjO9OxrtTmh7kz8/AXa/VL19yODx4/hbrtE22DZNvh7fnxpeVgn9eAFMntL7MdqZGXZ6wnKTva5N6tBe7rCX9wQcfJBgMcvzxxwPx4P2GG24gFApx9NFHJ2xbXFzMJZdcwtq1a7n99tsZN25ck/J+/fVXZs+ezSGHHELnzp2JRqN8+eWXPPTQQ6xdu5brr7++xXVzOp3sueeefPfddwnrv/vuOwzDaLL++++/Z/DgwXg8nu2qyyGHHEIsFmP69Okcc8wxDBs2DICCggIgnnt/6aWXMn/+fCZOnMiJJ56I3+/ntdde49xzz+Xxxx9n4MDEK/Hnn3+eyspKjjnmGDIzM8nNzW3xeXcUH63WTQJ0gMfnay4frhmYvfXA8k+z7cYhUMJy1IaT/qcpvVjjcWy5rAfm2i0O0AEa/PbXqY7CvXNsrtm7+QuC27+xWxygA7y3Ct5fqZnQQ/F/X9l1ATrEW17v+s7m4XFNj9X4XKojcPOXNm8ft+M6dl3/WX2ADvUBOsRf/+s+szFRdQE6xBuC//yJvdUg/fWleocE6BCPU6/7zObr0+LH+2i1bhKgQ7yF/v7vEwN0gIpo4y3rPz9LyuCRH2yu3y/xNdVac8WHVqPPZMPLRhJjDdXMui3RetsBOsRf6EbpMAGHCaZKWG9ozX4VfrKiFjawzuUg2arfN8W22SMQ5uuUpnfvwobBz0keRlcFEuquAG80So3LXbe60uvBG42SU9MgSNWa4RtKcWqNsiw8kShaKVzRaH0HUdvGjsVIKasinJ7YQdWwLByRGFopwh5XkyDdchhEPA4sQ2Ha8de+KstN1NPg/dI6PmkSEHMaoDUOG4JJDvzzylj/9FK6XjwgodxVN89tEKBDPBnOJlZ3CbL533iwrtDEyiMYRJq8hgBmbUa32Uwg7P+8mPKXl5N1Sp+6dRsf/aXBa930Q2NjQoMgXWMSJQk3fmyczWzvwiYFhR+LJMBZW/coivB2tnvWf84VGjflCXV0EsLLJlx1ATrx4wSr0PgalBJp5sKgcU0U1L72zYnRdLSe2Os/E0zt1CBAj5cTxUWILHzvLsZs8Bp5CBDDSfCjVU0PsGYT3PNG4rqH34E/Top3sm2N93+sD9Ah/vd+3bMw5RBwN33vRMe3y4L0iooK/vvf/5KcnAzA8ccfz8knn8x9993HhAkT6lI0LMvivPPOo7Kykn/84x/ss88+zZY3fPhwXnvttYS8y1NPPZUbb7yR119/nQsvvJDs7JZP8Txy5Ei++eYbVq1aRbdu3di4cSOrVq1i4sSJvP322xQXF5OXl0dRURHr1q3jyCOP3O669OnTh8rKSqZPn86QIUOYOHFiQh3++9//8v333/PAAw+w77771q0//vjjOemkk7jvvvt47LHHEvYpKSnh5ZdfJj09vcXnuiuUlZWRlJSE2x1v+fD7/WitSUmJ/5BGIhGqq6vJysqq22f9+vXk5+dvcbk5izZFGJi99WOUhbadzxiIwrwN0N0oJjc3t+69bHgeyypafv5bs7gkgNbJWzjG9t/YWloBQ9avZ3llTpPnNte58fuxeFOM+tvQtfUqTVy3rfejuHjLr1X82PWtys2pCMO3q2ug0Y/l8gYXD80dY3FpMjtyioclZTZaa5RSCcdurLhpI2dtsLrl92zz+9nwPCIWrPdvcZdG5bdwuwRbf923JGKoJoG7rSC1Ng3FAAoiMSJKYTXYLM228Vo2wWZa0/1buKNpq6bbVrtdpIQjOC0LU2swTZy1LfDaNHFYIZyW1aCM+Iyh7miMaLI33spYe4FiRmP4/IG6V8FWiqq0JFIqa1C15xU1DbBtyjp5yCoJog1FxNOovkrFA6EGy5r6XPjgsvrbZJv/PkLL6q/OdW2Lebyt2UHTqy+72eC7oW19I4R/rf/QlpWVJaS/OAkRpWFeu8bd5GIgnngD4CBIhMTvSwcB4vcDPFAXoCrAhUahCTX7aWv+U5h4NkYzWzXujBo/WqzRnlt/zVrCIIpFYs6/EQtjLdnUZFuNUXsB0/Rz6yJExIx/hyV8X63cEP9MJhSkCSxYga82SN/u38HlJU1PpNxPeH0p7u55wM77rd1VJN0l0S4bIuX444+vC9ABkpOTOe644/D7/cyZM6duvW3blJWV4fP5ttoq7PF46n64o9EolZWVVFRUMHr0aGzb5pdfftnivs0ZMWIEQF2r+XfffYdSigsuuADTNPn2228B6uq6efsdWZd33nmHwsJCBg4cSEVFRd0jFoux99578+OPPxIKJX6BTZw4sd0F6ACZmZl1wRrE3+/NXxoALpcr4UsDaPIlsXn5kEJFn/Smx0hywiHd6zv7bOkYpw/Y9h+9qaBnGuTl5SVcbDU8jwndt//Lo/FvPsAlo5K3eIzDemzfn6QCxndX5OfnN1u/zesavx+TejdtdTmiV+I1+5bej8229lrBts9lj2w4q5nh+cZ3qy+zuWMc0Uzdf4uJPes7WY4rVM1+Kaa64PLhClfj93Mb2YITal+DhufhdigOKtyRP0SN6hBrwYWew2hSd6uZ3ZrLrjEaHS+sFAdV1tA11LQlOD8SbfqTqzXOWNM0hJDTQVFmGiuzM6jwehJz3QG/z0vI6cQyDCIOBzVeDxjx90sphaF13Tm5g4mBo6E1EbeDkMdB0O0g7HGCoTBjFhG3QXWmk0CaY5vvp7I0jrAVT30Bsg7rUn+utX8fGRMKmu5H88G22sbzGojgwFHbHt+ctMPrc5IzMzPJvXLPuuUCFuMmfnVpEiGfVc1cFCiqKCBECpoYLirYHASbBHFTWlvD5tr04ok8jWunUZTTlUhtEBwP5hPPUqOI4Wq0H1g014Gz/kIivtRcXbavgSPeit+wH0QMd79k3Ef2b7KtiRW/WOma0eQ5Cweph8dHw0n4vhrRC7Iafb+l+vCNG1a3uD2/g0A8B73xnbIh3eoCdPhtv7XtwebPSv1n5vdtlwXp3bt3b7KuR48eABQV1d+7djqd3HnnnVRXVzN16lSWL1/ebHmxWIx///vfHHvssey7776MHTuWcePGcdNNNwFQVdVMrsFWDBgwgOTk5IQgvU+fPhQUFNC/f/+E9T6fj8GDB+/wuqxYsYLVq1czbty4Jo833ngDy7KoqKhI2KewcPfqNNIch6GYfbLJsb1h8yAMXVPiucvpnm3/EV89yuDUAQqjdtOBWfEOng3dvK+xzY6TZw9WXLGXwuOo/clq5q9nc/9Tjwk3jVZ8fopBflL9ujvGKPbK2/JxrhqhOGuQwmE0/eMcnQ/D6geAIMkJTx5m0CcjXt51exucNkBhKnCZcMEQxaXDmz/WWQ3OxVDxjqN/PWDHfh3ceaDB5F7xm9leBxzREzrVNpoP7QT/nWRyQIHi7gMNNo9AN6YAHp+w9XoM7aT457j6fVJccFABeLeSqVOQDH0z6j8/BvH38LDuivsOrj9ej3TFE4cpGvYHLEyBb0836JFu8NwRBp1rPzsDs+C2/RQptTFFqgtG5MXL9TnhmtEGJw1s/lymH2Gyd+3vosOAEXmahqnNitrP6NbytJo8UZtG4Ih3KN38ed8rHzI2NxjWBqEOBYP9QbLD0XiHzWgMLLtJkNopHMXR6JhVDVrMbWBxkoclXjdOIMWya4c61PQIhhlUE8JSCr9pElEKTyRKfmUV+dXVOBq0ipu2jWNzq7lSbEr2UepJHKVDGwYht4san5eQx402DFzR+nwqRXwYRSMWw2ymsymGga1U/QsDoBSWw6AmyUXEZSRMkNTw9dr8/9SKCI6YJi2m6XnbMLLGd6GxbrftRdZx3cFQKLM+DG98cUNtR83Nss7uh+Gr/YAmO7HTPIRx1IbBjVuSwUh20vWB/Uka2Slhfd4Ve9Lt3weRnBbGxmQQX7AHnzKET8lhFVYzgU+UZMrpQSn9cFJNKitJYSVJrEPhZEtTtCtitefhw8KHXdsxtpSehMmglJ6U0JtqcomQglUb0Gsghrs2lWZzR1qI4CNMat06AO3zoFwuFCEUQSAGpoHdozPa60IDNk5iuGsDu1puE7ChTz5cNhH26w/5tUG2oTCJksJqvGzAywZS+oLxyuUkXTEa77nD4i03tclA7rQY3j/ti/Hu1dCpPgDWgGdMDu5nzmny2uB1w8t/hl61AXT3TjDzKkj+DQM79OkMT14MnWrvdgzrAc9f2fryRLu3y9JdtjQcWHPP7b///tx1111cffXVTJ06lX/961/06tUrYZt7772XF198kUMPPZRzzjmHjIwMHA4HixYt4sEHH2R7+8OapsmwYcOYM2cOWmvmzJnD2LFjgXgqzP/+97+69UOHDsXRoIPSjqxLz549+dOf/rTF5zMyEq/kN+fF7+46JytePtqB1ppNQcjajqH1DKV49giTx8drYjZ1I558sNJmUblmTIHBkJxtl6WU4t6DTW7bPz7ahtcB/kg89/uLdZqhnRR75ysqQhqPg7r89nUXGZQGNSkutthhdDO3Q/HU4SYPjY1/Zn4phR82aEZ3VnV1LAtqQjFNpySFo0HA4XUqZhxh8q9Dtz0kpNHoXNK2MgpMa2V4FG8cY1IZ1riMeP1itqYiBNkNOlP+aaTBpcMV/ghkeltWj4uGGpy3h6IqAlm1+9ha8/Zym6UVcFwfg/KQ5uv1moIUxWE9FIaqP36yK56P3txF3tmDTU4fqFlbrcn0KlIavI7H9TU4ureiLAQ5tedwzT46YbkipHGbbHVkl+7piq/PdlIa0KS445+LiKV5aaFNdQRO6G+Q5VN8tNLiX3NtSmpgYI7iiJ6KZRWQ5NTMWGCzqAxykxQTeymO62cwMFtha0hxK0IxnXCOb/5q8dmiKP/5KkSlw0FRiodO/jA9MlzccqyP0d0dvDg/xgsLYgQi8Zb1YLEiVkldoF5uGnyZmkSqrfHZNpucDkKmQbatSbNtOsUssgG3ZdMnFKbG4ahLo/GFIxRUVLH5YqLfxk1Uud1YSlGR5EU3GiXGMgwqXE5SalvjTcuq68S5mVYqIbBWQMw0m021iDkdaEOB3ej7WKn4cI0eJ9g2Ru1Y6sq26bQuiO1QxJwG7pCFQ8Nenx9O6oD0LY7s4khxMXDmOGJVETAVK/7yHev+uQhTx8NjCwNVm0CxuVun4Tbp+o/96P7YgVgNhl4sfX4JJXf+iHIb5N80AgVE19Xg2ycXT9/0hA6jDeWcO4Ccyflw9oMwaz0uM4o9sBvWuYfTqbyG4IIarBqNva6C6Pz69A5FrDY5R6Ow0GcfDAEbXpzb4BXd/D5ZKEJonMRDiXh7v4GfTJbjJ48oKZhYaNyEcONTYThxHwJVXqL48A3wYgTK4JciVH467ssmopSPyjtn41y5BteY7rj+egxUBlCzfkDlpsOoXpBb+zsYikAoil62CTVnFXpUd4wBeRCKQqoXSqshOzUxjauiBjxOuPVF1JtzcPXOgyuPrBt5RQHp/z6K1PsOiw+xaCpURn1KnrH8TvTrP0A4ijFhEGrz5E3NOXAQ/PowbKqKt6rviPldzjoYThsTP4/s1N9eXjsjreeJdlmQvmLFCg488MAm6wC6dGnaGrHffvtxzz33cNVVV9UF6r179657ftasWQwfPpy//e1vCfutWbOm1XUcOXIkn332GR9++CHr169n5MiRdeufeuopPvzwQ0pLS+vWt6YuW7tYKSwsZNOmTYwcObLDTta0symlyGnl7Ny+RkHTuO4G47r/tnIyvfHH4AZBfnOBX1YLg8/NkmsDw1H5MKrRiCvxQHbL5aVsx3jtjV+TnaHhBYDDUGQ38/65TEXmdjYwOU1FVoN9DJU4Qk/XVMWQxEbGhON7tvLt5zAU3ZoZFQfANBI/g42XW3J3Z7OGI7+4TMVpgxODrkO6mxzSvflA7Pxhza6u43GohHOc3Mdkch+Taw9x8dnCCFnJDob0SCbFXf9dc+E+Li7cp2G6gY9fi5I4+bYyIgp+djnQQJnLQdnmTbQmzbLqAiGDeL53kctJYTiKL2aRGQxR4fYQcph4YvERPlwOyE/WrI2a6Mbfi1rjteMt++u8bvqWx0d+iTUcchEIOx14Iw07QcaDcbQm6nTiiMVAQcTtwjINYi4HZqhJb1884Ri2UkScBpZpYGlNSlkENDjDNu5wvPN5j78OJ31oVpP9m+NIjb+OfR7al/SxXfjluA9R2sYkVju1j8JNFAwouGffutlHHVn155d1Sl+yTunbouM10Skd3r4RAmFwOzBMsy4De/M7rLUm+MyPhF7+BUPZ+A7uglE4CaqCcNQIVEZKPEnlbyXow+6AX1ej6sKGKJvbmjd3BWVIV3RRGZRV4kCjG6STGIQxpx2LceMxNE1yq+cCXK+ekbgy2QvnNR1AAo8LPC6MvZJgr26J6wFymumPlF57a/P20+OPLTCS3ZDcdMx1leRGndp8X7lmKdV8PX4Lh7lbBuhxEqQ3tMuC9JkzZybkpfv9fl5++WVSUlIS8rsbGj16NPfccw9/+tOf6gL1Pn3ivdgNw2jSQh0MBnnuuedaXcfNwfdjjz2Gw+GoG31lzz33xOVy1XXabBykb09dfL74r3lzKTATJ07k/vvv55lnnml2TPjS0tImuWVCCLE9MpIMjhzR8jtwvTo7MTOdBKtt8rSmNGYRse34FO9KsW9nxbR9k/nzzABVwc3fg5q+VdV0DYRQBkw6vwtmtpf8TvnYpSHsmKbnsFQczvgFwj/uWct3P4YTWjzdkShebHKr/HWrHbWzgtpGbWqKYRBxOjAtG60UMUe8TiGfB08gRMjnwXI6MGpz4ANpKZixShwNcuKdUQt3dPNoMZqg1wClCCc5savjw1RaWpN9VFd6XLtHq17znGO6sefsiZQ89SuGxyTvvL44k0yC88tI2rsTrsKtha2/0VYm91FK4TtrKL6zhm61CNUzF7XkH/D+PDj+7vjEQYCNA7156EOvE/XG5ag5K9FnPIY7WIOBhaVcGC6N+/IDMW48ZsedlxC/A7ssSE9PT+ess87iyCOPRGvNm2++SXFxMTfccMNWJ9/ZZ599uPfee7nyyivrAvW+ffsyduxYXnnlFa699lpGjRpFaWkpb775Jmlprb9i7d27NxkZGSxfvpyhQ4fWBdQej4fBgwczd+5c0tLS6Ns3sWVje+rSo0cPfD4fM2fOxOv1kpSURJcuXRg8eDCnnHIK33zzDQ899BBz585l5MiRJCUlUVxczHfffYfL5eLRRx9t9fkJIcT2MgzFPWekcOmTVRDS+CybasNEJxucN8rJrePdOEzF6z3dPP9FkOqgZtJebnr60ti4Lkz3AT58yQ1+aro1DRqv+FMXvvjKz/MvlFFVZZHlhfHHd2b8EZncc+4CgsX145KaWmNYFsrWOMMRkqpqMC2LsNeNPy0F0GAoQsleDMvGjFlYDrOufS6QloQ7EMYZjpLsD+OKNAjYYzYhW6MNheVQlOW6cWsYduswep/byhbtWulj8kgfk5ewztOvaUfEdu3QoVD0OHz2C3TLQZX44YVvICsZdeHBqG7Z0C0HDh6A+nIp7n55qD552yxWiM0k3SXRLgvSL730UubNm8eLL75IWVkZXbt25bbbbkuYzGhL9t57b/7xj39wxRVXcNFFF/Hwww9z5ZVXkpSUxPvvv88nn3xCbm4uxxxzDAMHDuQPf/hDq+qolGLEiBG8//77TVrLR40axdy5cxk+fHiTVJTtqYvH4+G2227jX//6F3fddRfRaJRJkyYxePBgHA4H9913HzNnzuTtt9+uC8hzcnIYNGgQkyZNatV5CSHEb3HAABdf3JbF/JVRumabFGQ1TcHpmm3y56MSe2TndGn5FO37jU5mv9HJTdZfeHdfHr34Z4KV9Z1EjViMjLIq3DWhupx0dyiCMxylJiMZXZsSE2tmCEitFDG3E3coijuSOMpMXXigNd6QhcttcOzKE1p8Dr8LKV6YuBcAahCoQ5rOoqkyk2HS0F1cMbE7kCA90S6bcfSRRx7ZYlqLEEIIsSUhf4x5H2yiujTKukXVrP9mIykVflyNhn3UQHluRl0HPaV1fESX2gmJ0BozZmHGLHwV1aRXhTEa/ALGDEXI7cATieGwNCl9Ujnss8T5LIQQO0+F+kvCcrr+exvVpH3YZS3pQgghRGt4kh3sc3Q8beIfJ8zBcjqbdjaFeE66YdTltmsVHwvdMlTdEI0KMGMWzpiN3+vEF4phWhpXyMJwmTjseMdS5VDsce2QXXWKQgi2d7T73d9uHaRblkV5eTPzyTeSlpaG0ylT6gohRHtnRWxQiqqMVLLCmzAaDKkY9bqbzppqGjijsfrRZyyL1NJqTK3xVUVJqq5Po9nzyf0IR21CG8N0ObwLKb131xE0hGifJN0l0W4dpJeUlHDkkUducztJxRFCiI6h7+gMfvpgE9phUpaXRWppJc5IFMPSCQH7ZptTXrRDkebQuFdVxicI8pj4iutncM6e0Jm8Y7qhDAkShGg78vfX0E7PSW9L4XCYefPmbXO7AQMGkJoqLSZCCNHeBauivPa3pSz/rgK0xhEKk1ZehTcYJeYwKc3PTJgYyVMdQOeE8R5TzDnnTMGwDGKBGO5MN4Hl1Wx6fx2+HilkHZq/1XkshBA7X5m6NmE5U/9tC1v+PuzWLelut5u99967rashhBBiB/GmOjnlbwOIBC2Kltbw3v8txK6sxlbgiFlkFpcRSPFhGwaOSBRHLEa43FmXBWN6TExPfNQXX88UCi/s14ZnI4RoSNJdEsm0lkIIITocl9ek5x6pHHpRd7TDQbR2NlRn1CKtrJqU8moc0Xgn0JjardujhNht6NrZeDc/fu/km0sIIUSH1XXvLBweAztqEPI6MSyNNqhLebGVIpxrbnUqeiGEaI+kJV0IIUSH5fI5GP/3YfH+ZkphOwy0YRB1OKhKS2FTl06k7ru2rasphGgBaUlPJC3pQgghOrQeB+Yy+Yl9mfPPxVSvC5AzJJNwVjKuVBejT+rEC6/+2NZVFEK0gATmiSRIF0II0eF12TubLntnN1kfrc1LF0J0BBKkNyTpLkIIIYQQQrQz0pIuhBBCCCHa3G47cU8rSZAuhBBCCCHanOSkJ5IgXQghhBBCtDkJ0hNJkC6EEEIIIdoBCdIbkiBdCCGEEEK0OWlJTyRBuhBCiHbJP6eEiheW4cjykHVWP5z5SW1dJSHETiQdRxNJkC6EEKJd0Ws2UTbiHtSGCmJksYF8Su76gf5zTkCHLXTMxjs4q62rKYQQO5UE6UIIIdoN/eViovvdQhZhADIpwUsNa8v6sHjMq8SKagAw0l10nz6W9KN7tGV1hRA7kKS7JJLJjIQQQrQbpYc9jqs2QN8shzUorLoAHcCuiLD8mFnUfFuyq6sohNhpVKPH75sE6UIIIdoFO2IRrDabrFfYqC1kq5Y+sXBnV0sIsYtoVMLj906CdCGEEO2CUhAgCYvEQL2CTthbyM6MbgrtiqoJIXYB3ejxeyc56UIIIdoFrUGh+ZXhdGYZLoJUkc06em1xH7sysgtrWK98Y4RZL25kzdIgKekODj4yiwHDUtqkLkLsLqT1PJEE6UIIIdqFwHcbiOIhTApLGd6ifayq8LY32kHWrQzx8/fVpGY4ePelDZRtiAGwfnWYJfNrSMt0cPU9vUhOdVBVEeHbjypxugyG7pdKWoZzl9VTiI5KgvREEqQLIYRoF3TUJoxvu/Zx9UnbSbWpZ8U0j92+isU/1mx1u8qyGLdd/CtJKSalJVEq3U6cls0b/ynmyr/3okt3z06vqxBi9yFBuhBCiHbBWZAMGJhE8FFNiGSiuLe6jyN3+4L61vjo9Y3bDNA3CwVsSqOKtwYVsj7Vi9KaARsrybiviBvu672TaypERyct6Q1JkC6EEGLXWroeZi+AgV1h3/51q62KMJkUU8hCDGw0inX0ooTuWyzK2z9jp1f3w1c3bdf2G5I8bEyKX1xopfilUzofLwvR9SeLs/ZoOnqNECJOOosmkiBdCCHEdrPe+RH/X96kfEmMqpQCkvbrQqcr9iRlTOe6bUrumceG++djV4bIiBTRObIY3SUHc8161tGbUvKBWaR3tuny8tH4kpx0ZREGNhDvRNqZpZSTSwRvs/WoeHMFORcM2mnnqbUmFNy+0KFHRQ1jlxfzbp/612JtqpeL37FIX1zG/gekkZXr2tFVFaLDk5z0RLvFEIwjRozglltu+U1lvPnmm4wYMYI5c+bsmEoJIcRuyjrqTszDp5E2fy7dQ/PpufELKl9bzq8Hvsaayz4DoOz5X1l71ZdE1/ixqmJsCuWxwh6MWrOBYnqwgW5YuLBwU7rOS9no+1DjbsbEAqCGVFbTn7X0xUVgi3Wpfmv1Tj3XuZ9VtGq/PqXVmJZdt7zJ68YMxfjv65VMu3I5b38bJGJpaqpjrFsZwrakDVEIGSc9UYtb0keMGNHiQt944w06d+681W2qq6t57rnn2Guvvbar7O31/fff8/LLL/Pjjz9SXl6OaZp07dqVffbZh+OOO44uXbrstGMLIcTupub57/G88V3d8ubQ0kclAdLZ+OBPpE7sSvlLS5vuSxqLGdFkHHSAIvpTtqGcPvyIn3SWMZT6/NS2C2A/f6+8VfvZSqFVfZBRmuxh1JpNOC2bMsNk+j/X8UokRnI4PkJMwOeg5rCuXDjGw5iu9e1nNRHNfXNtPlyl8TkV5w9RHNV76+1ra6s135dohnZSFKZKoCM6DgnME7U4SL/11lsTllesWMH06dM5+OCDOfjggxOey8jYdo5gdXU1jz/+OLB9FwAtpbXmzjvv5KWXXiIvL4/x48fTrVs3otEoS5cu5c033+T555/nq6++2uHHFkKIDuOXNfDxAujfBfIz4LH34/niNSE4ahT83yngjedXV767mtJTX6NnbWu3hclShlFDekKRay79HHefdBpLpoJ8lrOk2eEVFQEyWc2A2s6iKuG5LXIZbPznAlw9UkidUIgyduyPvL/CatV+v2amYDeqy7oUL6PWlmI2uOZYnu5DaXinb2ci602ee8Gmnx3k+OXrUAb8EHPxdp/OtQG/5q3lmhNL1jN5TDKLM5IpD2nKQvB9CeQmwR7Zisd+tIlphdKaw/0VTBvrZOjoVEyH4u7vbB773sJpay4bpjh7mMn8b6pBwx6jUvAmSc68EO1Fi4P0iRMnJizPmTOH6dOn07t37ybPtQdPPPEEL730EuPGjePWW2/F5UrM/7v88st58MEH26h2EAgE8Pl2/qgEQog2NHsBvPMD5KRCSTm89BWs3gRZKXDlkfC/OfDtr/GpNg0FkRj0yoOzD4ZN1fH9562INyRnJsOD58GpY+rLL9oE//kkvl+yG+56AzZUQqoXQhFwO+HC8dAzD1ZugNw0eHp2PDDv3wUsG34p2nL973kD/vUuBMJoBSFdQBc2oYmHzZvIpxOrSGMuETysoxcV5BJZWkVkaVVCUQqbXFaRRBU5rGEDPZocziBCOZ3YrhEewjZrLv4UgOSDOtPngyNR5o7L5Kwobd1kSd0rahiwoZKFneqHiFyf6uXrgmz2XbOp7gy7VId4u29nIo764Hix4eZLw0uvMj/dY34m/LqOX7LTWJ2ZTE5NCDaE+PapCgJOBz93SmVOlyx84SiRYITPMpPjnyfinVbfSUon47EV+J8ux2dokqvCHB6zCThNZn/j4GMFuf4wBrWzPOZ5WbxvF77YaKKBUXkwtJPi5V816/zgNiEUg7xk+MOeij+PMnCaiqqw5j+/aIqqNUf2NhjdWRGMap5bpFlSphnXTXFo98T3pSaimbFQs6JSc3gPA1PBW8ttqiPgNDRDOxmc3F/hdmz78xCIap5dqFlWoRnfXXFI4fZ/BkIxzRUf28z4ReNzwvBOigMKFGcMVHRtcEfinRU2H6zSVIYh1aU5uNDAUPDJGs3ALMUpAxQuU1qEW0OSvhIprXWrXpM5c+YwdepUzj//fC688MK69RUVFTz++OPMnj2b0tJS0tPT2W+//bjooovIzs4G4vnf06ZNa1Lm8OHDeeyxx7Btm+nTp/P111+zevVqKisrycrKYv/99+eiiy4iPT09Yb8RI0YwadKkurz08vJyJk2aREZGBjNnzsTj2fbYtJvr9K9//Yuff/6ZV199lQ0bNpCfn88555zDpEmTErZ/7733mDVrFkuWLKGsrAyfz8fQoUOZOnUqffr0Sdh28uTJ5Ofnc+WVV/LQQw/x008/kZaWxhtvvAHA7Nmzefzxx1mxYgUpKSmMGzeOY489lpNOOqnJ66u15uWXX+a1115jxYoVmKbJgAEDOP/883dq2pAQYjvd9Rr8+ZkdX+5fT4XrjofFa2Gfa6CiZUMD7gibg/PNakghieoGzysWsg8hkurWZbMGkxiZFOOlpm67RYwkSGrdnp1YRQFLWclAyth6uuTWdHvqELLOqh8xJhqNMn36dACmTJmC09nySYXWrAhyz1XLW10XDbwwuJCSlMROr2NWlDCsuKJu+bERvQg6G7WZ2RoMxQErNzB0fTkGsCbVS2YgTFLMTti00mkyt3MGi3LSiDQuBzhq4Rryq4K4bY0N/NwpjaI0H5nBCHuuL8djJZZX7nHyzLCeLTrHw3vAi5NNRs6wWFRWv/6RQxVP/6z5al39umn7Gty0bzx4Dsc0+zxnMW/D1ss/qKvioxMNlNpy0Bu1NPs9b/Fdcf262w8wuHbv7QvUr5ptcc+cpiFRmhu+PMVkYLbi+s8sbv9m62HTIYWKD07Yep1F835Vdycs99FXtVFN2ocdOrqL3+/nvPPOY9WqVUyaNIlBgwaxbNkyXnnlFb7++mueeeYZsrKyGDZsGFdeeSX33ntvQrpMZmYmEP9SnTFjBuPGjeOggw7C4/Hw888/8/rrrzNv3jxmzJix1S/azz//nHA4zBFHHNGiAL2hhx56iEgkwrHHHovT6eTll1/mlltuoaCggKFDh9Zt99JLL5Gens7xxx9PRkYGRUVFvPrqq5x77rnMmDGDwsLChHJLSkr4wx/+wNixYznkkEMIBOIdoT744AOuvfZa8vPzOffcc/F4PLz33nvMnz+/2frddNNNvPvuu4wdO5bJkycTjUaZNWsWF198MXfeeScHHnjgdp2vEGIniEThtpk7p+y/vgxXTIZ739ilATo0bd/2NQjQ489r0imhmPoAz4ufHNY22a4/37GOHpTQk878Sh7xDqDJVPymIL3suV8TgvTf4tsPK37T/goYXFLRJEhflpWSEKTnVwdZnpmSuLOh6FlWzfD19TnxXauCzR7no155rM5ITuioWkdrMgMR3HY8sPywVy6/dEqve/rXrBRO/XFlwigSGaEoaaEIlZ5tj0AzawX89Ws7IUAHuPZTTXmjyWD//q3Nn0YoklyKV37V2wzQAWav0Xy8RnNI4ZYD3jeX6YQAHeBv39hcsZfC04JW+M1e+bX54LsyDPfMsbn3YKPZIL6xj1ZrPlmjOWgrdRbNk5z0RDt0dJdnnnmGlStXcuWVV3LzzTdz/PHH85e//IVbbrmFkpISHnnkEQAKCgo46KCDAOrSZSZOnMg+++wDgMvlYtasWVx//fWcdtppHHfccdx0001cc801LFu2jNmzZ2+1HkuXxjss9evXb7vPIRqN8swzz3DWWWdx6qmn8q9//Qun08mLL76YsN0DDzzAXXfdxZQpUzj66KO55JJL+Pe//000GuW5555rUu7atWu5/PLLuf766zn22GM5/fTTicVi3HvvvaSmpvL0009z7rnnctppp/Hvf/8b02yaF/jRRx8xa9Ys/vKXv/C3v/2Nk046idNPP52nn36avn37cs8999DKGyM7XFlZGeFw/Te03++nurr+Bz0SiVBaWpqwz/r167e6XFxcnHB+cgw5Rns9RvHy1VC15RFJfpNAGPwhQiuLt71tG8hjJUlU1C2vpyc1dS3mcRqoIhNd+xOUTf3rl8V60mn9uZlpribvR15eXsJyS9/zpNTf/hNpNv5K1hpNvFX888JsbGDMyo1kBMMJ2wAUVrTsM1RVG0xbzaX5KIVZW17AYbIwJ3GG1lKfm5XpSU1280ViLTo2wNLSaJN1FeGm2wViUBWJ/70Ub8f15YpNoa3+DTZXVnUE/A0ylVrynud4ttz/YH0NLF69gXALuyisr+k431eNl9uSjO6SaIcG6bNnzyYtLY0TTjghYf1hhx1G165d+fjjj1tUjlKqrgXcsiyqq6upqKhg5MiRACxYsGCr+9fUxP9ik5KafvFsywknnJDQSt+pUycKCwtZs2ZNwnZeb7xlRGuN3++noqKCjIwMunXr1mz90tLSmqTMLFq0iA0bNtSl5mzmdDo59dRTm5Qxa9YsvF4vBx10EBUVFXUPv9/PAQccwLp161i9eucOR9ZSmZmZuN31MwUmJyeTklLfUuRyucjKykrYJz8/f6vLeXl5CbcP5RhyjPZ6jLz+veCgwewUe/WCnDQ8px60c8rfiiiJdzAr6NRkGwObAhbXLcdws5hRLGQkP7MPy9mDpQxlGcPYUDtJ0foGkxUpND1ZQFaj1veWyr5gYJP3o7g4Mehv6Xs+YkxiQNsaOTUhDFvTf2Ml53y/jEu/XsJe68qY1bczc/Mz+KBXHu6YxRnzVnL44rVkBMJ1OeUV3qZ3jIOOxJ/tRdkpVHi33OKdVx0kKWqhActIHHFms2ij4D7oMChJbn5c+sbS3HDFKBeN++se1iOev97QPvmQn6zIz89nci+FowURiM8BR/X3bvVvcFIvhbNRWWMKINtXX6mWvOfT9ndusU7H9lGM6pPLsKYf+SaSnDChu+ow31eNl9uSbvT4vduh6S5r166lb9++OByJxSql6NmzJ5988gl+v5/k5ORtlvX+++8zY8YMFi9eTCyWeEVfVVW1hb3iNgfnm4P17dHckIxpaWlNvuQXLVrEI488wvfff08wmHgLsrkyunTpgmEk/vWvXRv/EerWrVuT7bt3795k3cqVKwkGg0yYMGGL9S8rK2u2PCHELvbs5XDRozDrB8hMgqgFVcF4DoRtg9MBoaYtkACYRrw1NSMZShuklHTLgdf+Ev//WQfDmk1w/1sQjsY7gv5SBOEImGb8GEO6wdmHwMOzYFkJTBgKY/eAV76GzxZu1+loIISXcnKxMaikEzWko/mJTEoStvXib7SnIkg84A3T9Pt/I4XkswIHMSK4qSaTUvKabLc1Zp6XzjeOJHVc1+3ab2uy8zwkpyn8la0PF6KmQXZNiPFLi+vaBXuW1zBuWTFlHicDN1bjsWze6NuZFVkNUl605pecNAZuqCInEG8pjSnFoqwUMoNRCqoChBwG7/dKfJ1M2yY1FKXS46JrZQ2HLC/BUvB1lyz2C9dQUBmgKK1+0AJ31GJdkpuC6gC+iMVGn5v3e+fVjUzjMeNBZ2UYuqXB0Bz4thjW+WFoJ7j/EJN9uyhmTIQbv7Apqoajeiv+Oc7gu2LNVbNtFpfDod0Ujxxa/xvYO0Px0mSDaz6zWV4BY7vFWw0/WBX/+IctGJwN/zjYSAi2m1OYqnjlKIM/f2KztCIeIDc8Vksd1sNg3pmK5xbafL5W88MGcJlw6TCDc/eI1+HlI02mfhAfEtNpQMSOd641VPx1GZQVr3OmV1qBW0NazxPtshlHtycN48MPP+Taa69l0KBBXHXVVeTm5uJyubBtm0svvXSbZfXu3RuAxYsXc8ghh2xXPRsH0ps1PGZxcTHnn38+ycnJnHvuuXTv3h2Px4NSinvuuadJ0A40mxu/tfNo7jmtNWlpadx+++1b3K9Xr15bfE4IsQt1zoTXr93y86EIvP5tPHA/elS89fS1byDJE1+uHfaQ75fBV4theE/Yt1Gu9Q0nxB/bckmjEbgunwzTP4QH3oKKAIzsDdcfBzM+hec/ix/7huNgeC/45Gfok4+663W8H/7CWvqQy2p8VFFDKuXkNgnS/UYGqZO7U/X6ShxEiOFm6xQLGI2LCCGS2Z7RXYxkB3tWnb/TOuntf1g277ywcZvbNe5UC1DlcvDS4EJGrC1r8lz38hpyHQa+2k6gI9eW4rUsVqYnk2RbGIamUjn47x7d6FnuxxOzWJ6RTMxQ9C7zUzgyjdtOSKbmxSD/DfjQSmGiGb9+E31Xx/PYK5NdVA3PptfoVP4zwEH/LMUNAc3U920+WmnjC0Xp6a9h3ACTv/ytL26PgWVr3l6hWVMFE3squqe17HU9ZYDBKQMSfz8P66E4rMeWg+Wj+xgc3WfH3NCf1MtgUq/fXtagbMVfD9jyMJQ90hXvHi/DVIpdY4cG6V26dGH16tXEYrEmrekrVqwgPT29rhV9a1+os2bNwu128+ijjyYEtytXrmxRPfbff3/cbjdvv/0255xzTsKtoB3h448/JhgM8o9//KPJiCqVlZVNhnvckoKCAqD581q1alWTdYWFhaxatYpBgwa16G6EEKId87jgpP0T1513aNPt9uoVf+xoU8bGHw3t2QPuOitx3R61d+YmDMPx7a+kHPkS3pJqVrAHYFBJDhsoIIe1KDQhfFQeNr4uKN12gB5n4ybUwm0bUk5zp46i8cMXlS3azoYmUzRFTIPRqzYQM5oGdVHTYGFOKrk1YarcTgqqAhyzcRND+0QYe3Q2rjQnj823+fs3mqVGCgaaHJ9iYg944JAMkt3xgPS5i13cXqmZv1EzMk+R5cxj6YIUUtIddO3VNGUl26eYeZRZW1snkDgUsGkoJveS1kzRNqQlPdEOzUk/6KCDqKys5OWXX05Y/+6777JmzZqESY8253Q37OBQV6na1mzbru+prrXmiSeeaFE9MjIyOOuss1i/fj233nor0WjTW8qBQIA777yzReVtqX6NW7tfffXVJh00tqZ///7k5OTw1ltvUV5e34N/S51PJ06ciNaahx56qNmW9u05thBCbLdRfUi7+WA8hBjCZ+SwGi/VFNGfn9ifX9iHxVkH0+XV4/D039Kkdjs209TM2rGNMI1VV7SsA2VzbavZwQjDiisYvbbRd7OCU87qxH9uzmXiYemcNdBg6nmduPWJfhx/fmcyclwkuRRXjDBZe5HJj2eZbLjYQfEfHDx5uKMuQN+se5riyN4G+ckKl9tg4F4pzQboQrR3kpOeaIe2pJ955pl8+OGH3H333SxevJiBAwfWDcGYm5vL1KlT67ZNT0+noKCA9957j4KCAjIyMsjMzGTkyJGMHTuWjz76iKlTp3LEEUcQi8X45JNPCIVCLa7Leeedx6ZNm3jllVeYP38+48ePp2vXrsRiMZYuXcoHH3xAIBDgz3/+83af53777ceDDz7ITTfdxIknnkhKSgo//vgjX375JQUFBVhWy7p/OxwOrrjiCq6//nrOOussjj76aNxuN++9915dEN6whWjcuHFMnjyZmTNnsmTJEg444ADS09PZsGED8+fPp6ioiNdff327z0cIIVoq+aLRxGYfiOPFT+jKEgCKCvYmNmEkSXvnkXV6XwyXSacr9qTi5eWElzZoiVZgZnuxNiZ+l6tkB4X/PJBVZ3643fVx903/LaezTV6fQcDfzNCGLeRoEGkUpXg5a5yPPfdJo7B3PIg+96TMre5vGoohOa0+vBAdirSkJ9qhQXpycjJPPPEEjz32GJ988glvv/123agmU6dObdLD+NZbb+Xee+/lwQcfJBwOM3z4cEaOHMmECRMIBAI899xz3H///aSkpDBmzBguueQSxo4du4WjJzIMg+uuu45DDz2UV155hXfeeYeysjIcDgddu3bl6KOP5vjjj2/VeRYUFPDAAw/w8MMPM336dAzDYM899+TRRx/lzjvv3K7hjMaPH4/T6eTxxx/n8ccfJzU1lfHjxzNhwgTOPvvsJqk6N998MyNGjODVV1/lqaeeIhqNkpWVRf/+/bn44otbdT5CCLE9HC/8Ea48HOavggMGUNC/oMk2zlwfAxacTOX/VqJDFmmTu2OmxlMBS59ZRPHff0AHYmRdOIj8a4ajtabonPexYtt3gzdW3vLGm9YYfWgm/3u2BQN6b8OGJDeuJMXk07evQ6wQvycSpCdq9YyjYuf64IMPuOaaa/jrX/+61dFchBBid7G24A5K1qYSz/BuWbDuHdWJAd9sucHlt8w4CmDbmpvOW4y/soUDZDdS7nayLCsZG8XJaSEuu61Hq8oR4vdgvnogYXmIvqyNatI+7NCcdLH9otFok/SYaDTKs88+i8PhaNIxVQghdlf5Vw7GQxXb89PkSG9ZR/3WMgzFrY/3IyO7ZTeelQlWbWOgBlAwuKSS0RvKOPykFgyyLcTvmExmlGiXDcEomrd27Vouu+wyJkyYQOfOnSktLeW9995j+fLlTJkypUmKkBBC7K6Miw7F86dvCJOEbrYrZlN6F/yOG6bixn/15Zfvq1mzPIS/KsoX71Q0sx2ceXkB3fv5+PazSkytsTWYpmLPfVLJyt25FxRCiLazaNEipk2bxuzZsyktLeXrr79m+PDhTJs2jTFjxiQMntJSEqS3sfT0dAYPHsysWbPqRnjp2bMn119/Pcccc0wb104IIXahH1ZQwFIqaPkMiEn77ZrZEg1DMXhkKoNHpgJQVWbx07fx0ckcTsVBk7I4YGImaZnxdJrxR2fvknoJsTvpqK3n8+bN44ADDiAlJYWDDjqIF198se45v9/PI488IkF6R5Senr7VyYmEEOJ349H3cBHGRyWB2hlKt8UqCezkSjXv3L8UUr4pSllJhK69vbjckj0qxG/VUTtJXnPNNQwZMoT3338fl8vFCy+8UPfcqFGjmgxN3lISpAshhGgf7PhPdA8WsIoB+MnESYgoTWds3qz6w7W7qnZNZGQ7ycjevo6oQogt66gt6V988QUzZszA5/M16WeYm5tLcXFxq8qVS38hhBDtw3nj0CjcBOnLXIbxAalsfZI2Z65vq88LIToS1ejRMWittzjbfHl5eZPhtFtKgnQhhBDtw4GDiDz+RyrJopoMVjGIUrpseXsFedcN33X1E0LsVB11dJchQ4bw6quvNvvcO++8w1577dWqciXdRQghRLvhPm8MxT9A6T8XNHnOSHHS48XxbHrkZ4xkJzl/GEzyvrum46gQYufrqDnpf/zjHzn11FNJSkrijDPOAGD16tV89NFHPPnkk8ycObNV5UqQLoQQol3p9vAYss7qx4YH5hP4spjIaj/eIVkU3Lc/KWM6k3ZYt7auohBC1DnppJNYtmwZt9xyCw88EJ+Q6bjjjsPhcDBt2jQmT57cqnIlSBdCCNHuJI/KJXnGoW1dDSHELtSRUlwau+666zjzzDN59913KSkpITs7mwkTJtCtW+sbFSRIF0IIIYQQba6jprtsVlBQwLnnnrvDypMgXQghhBBCtDm7g7akr169epvbFBYWbne5EqQLIYQQQog211HTXbp3745SW6974/HTW0KCdCGEEEII0eY6arrLk08+2SRI37RpE2+88QZFRUXccMMNrSpXgnQhhBBCCCFa6eyzz252/Z/+9CdOOOEE1qxZ06pyZTIjIYQQQgjR5jrqZEZbc/bZZ/Pvf/+7VftKS7oQQgixBSsqNM8u1JgGnD5A0TV19wgchGiPdpfAvKFYLEZFRUWr9pUgXQghhGjG28tsJr9q1Y048dev4NvTTQZm736BhBDtQUfNSW9ONBpl/vz53Hzzzey5556tKkOCdCGEEKIZx78aww4Dtg0G1NgG0760eeFIs62rJsRuqaO2pBuGscXRXTIyMnj33XdbVa4E6UIIIUQjq6tsgkEdb9pTgA2Ebb5ZByBBuhA7Q0cN0m+66aYmQbrH46F79+5MnDiRlJSUVpUrQboQQoj2JRSBjdXQNavNqvDcz7VjGnsMMGp/fGMaHdudbsgL0b501L+uW265ZaeUK6O7CCGEaDfsu99GZ1wIhZehu18B87c9k9/OMHcD4FT1ATqAQ7G6BsISqAshdgFpSRdCCNEu2F8vQ139bN0Nb7VqA/aY2zAqHtul9agMan4tscBsJq3FUMxYYHHuUPn5FGJH60jpLrfeemuLt1VKceONN273MeRbRgghRLsQvftD3I3WGZU12M98gXHmfrukDmsqbEY+5KfE6Wn+XrPWfLrcliBdiJ2gI92j2p4Ul9YG6ZLuIoQQol2IlUWwMagkh40UUkEeEVxEHv5ql9Xhzo+DlDg8YBrQ7GgNipw06TgqxM7QkSYzsm27xQ/Lslp1DGkKEEII0S5Ybh+bKEBjkkQ5DqKESCVWDZ5dVIfp821I2nr7VdiO/7u6SuM2ITepfQcTQnQU7T0w39UkSBdCCNEuRPwWNg5yWI1JDAAPNQQ2Onf6sUtqNAMej1Lj28blgK3xOhV9Ho2ytFSDgs6pcNleBhcNM0l1S5AhRGvZbV2BdkaCdCGEEO1C7Pv1eKipC9A3825aD9VBSPHutGMPeNKiPGawzYY8Q/HE9zZlIeLDpWtYV6a55gOLu7/V/GEvg4k9DfbOl2BdiO2ljY77d/Ppp5/ywAMPsHDhQoLBYMJzSimWLVu23WVKTnorrVu3jhEjRvDoo4+2uoxbbrmFESNG7MBaCSFEG1u9EQ64DlwnQLcL4IMf654qn72edf9eQnBlNQCh1X7WPbGE0v8swn//1+jglruNxf48E176Jj6G+g72j+9ilIdb3mWtzFLgUuAwwGnEx1J3GWyKKG79SrPPsxZ3fN26HFQhRMfz+eefM3bsWCorK1m4cCH9+/enS5curF69GofDwZgxY1pVrrSkCyGE+E0iry0gfNO76ECU5KIfMcLh+BOrN6EPnYadnorfTmV5VXcUJutQxDJSUBUBUnSAdAJsDr1DJNVN8tmQ45F34JF30AWZ6Ff+hLUhiDmiACN3+2byi9mav32j+c/PNh4HTNtPccPnNHPELVA07VCqVJPdr/3M5tHvYlQGoVeW4ur9TI7ta+DowC2FQuxsuoP+edx8881MmTKFf/3rXzidTm677TaGDx/O/PnzOeywwzj22GNbVa4E6UIIIbbsq8VwxytQ5ocLx8PpB8bXz5oL//cikU02wV81BmE8bEIRTthdAWZFFWlUMYx1lNGDCEmo8g0obGJ4COPESxATixgONlFABsU46tJeFJsHZ1NFZWwcdR8r6Y2bCPlXDSXnzoObTMkN8YD8x41QZbvZaKfy8Wp4a5XFA3Prtzn2VRuCUUhybfu10HoLI740QylWhuLpM3PKFSf9T5PmivH9mSa90uUmthDN6ajpLgsWLOCqq66q+x7aPJrLkCFDuPHGG7n11luZPHnydpcrQboQQojm/bAc9r8O7NpUkM8XwvXPgtOEZSUA2KQDqTgIAMZW26MNbFJZza8MJ0QSNlCNj14spROrMdDEcFBDRoMAHVSD0ZNjmGwiGz9J+Emi7O7lGPevIO2hA5neqzvfrIfqCERtWFUFEVsBpwKKO1+laaBtKPDuxI6pDY5VGVGMfMZi+QWKdE/HDEaE2Jl0B71+DQQCJCcnYxgGbrebTZs21T3Xv39/fvnll1aVK0F6IzU1NTz99NN88803FBUVEQgEyM3NZezYsZx//vl4PFvu+b9u3TqOPPJIzj//fLp168ZTTz3F6tWrycjIYPLkyZx33nk4HE1f8qqqKh544AE++eQTampq6N+/P1deeSWDBw+u28a2baZPn87XX3/N6tWrqaysJCsri/3335+LLrqI9PT0nfFyCCF+zy5/sj5A32z1poTFMGk4qcFNBQY2GkWUZEDjwp+wrY3iB/ajhvoUFTchurGqbtlBjCTKm62OBn5gL6pJbbDOxIpC6YWf8eS0fPy+xtMhQUIuSnMt4Y1b72wdb123NXgc8YuS1mjmWOVhOOC/Fj+caUrqixCNaLNj/k0UFhZSUhJvuBg4cCBvvfUWhx9+OACffPIJWVlZrSpXgvRGNm7cyOuvv864ceM4/PDDMQyDuXPn8swzz7B48WIeeuihbZbx2Wef8fzzz3PCCSeQlZXFp59+yr///W/WrVvX7DSyl156KZmZmZx//vlUVFTw7LPP8sc//pE33niDpKQkAKLRKDNmzGDcuHEcdNBBeDwefv75Z15//XXmzZvHjBkzcDp3/jBlQojfkTlLt7mJgYWXTXUJKX66YOMmHj5HMKnv6LmJ3IQAHSCMmxgmDuo7Wjoaje6yWTmZCQF6Q6s7pW4hQN9Olg2lwfqLk5oopHvigbql4x1GfwulWLAJ3lmhmdSrYwYkQuwsdge9cD3ooIOYPXs2xx9/POeffz5/+MMfWLhwIW63m/fee48//elPrSq3g95Y2Hm6dOnCW2+9xdVXX83JJ5/MiSeeyB133ME555zD119/zYIFC7ZZxpIlS3jkkUe4+OKLOfnkk3n44Yc56KCDePvtt5k3b16T7QcMGMA//vEPTjzxRC644AJuvPFGKisreeedd+q2cblczJo1i+uvv57TTjuN4447jptuuolrrrmGZcuWMXv27B34Kvx2ZWVlhMP1ual+v5/q6uq65UgkQmlpacI+69ev3+pycXExWte36skx5BhyjJ17DB1uPlhuyEGgrp06ShIKmyTWksJqYrgJkVqXrGI12y6ksElsqY7ioJScuv0iOLFQxLbSrhR076BGimCs6d2DqnB8BiNNPF3mt9KakspQu3zP5RhyjLakjcRHRzFt2jQuu+wyAKZOncrdd99NRUUFGzZs4IYbbuC2225rVblK6x3xjbN7isViBAIBbNtm2bJlXHjhhVx11VWcfPLJCaktF154IVCf7jJ69GgefPDBhLLmz5/POeecw+mnn87ll18OxIdg/N///scrr7xCYWFh3bYVFRWMGzcuYduGLMsiEAhgWRY1NTUcddRRnHbaaVxxxRU77bUQQvwOpZ8OlYGtbhIgEx9lAIRJwYU/IYc8ShI2MQIkY2GygBGNgnLNYObTiY1APCXmZwazkVzchHATppoU+rOQHDbwFfsRpWknT1tpjpp2MgHvb+wAWhWGQDRxnQKyffVpMS3tPLqFY6e5YdWFDtJk4iMhEryR9mzC8pGVp7VRTdoHSXdpxksvvcTLL7/M8uXLse3E+a8aXqFuSffu3Zus69mzJwBFRUVNnuvSpUvC8ub88srKyoT177//PjNmzGDx4sXEYoktXFVVVduslxBCbJcph8B9/0tc5zAhVp+a4sJPFC9OgjipSQjQARzUsJiBlNCdAfxMIRvZSBphnCQRIg0/5XRG4yOESSpBNtIJgDAewsT7AQXx4sBiGHNZRm+qSQEUFgYOQ1NxwV706upiWUW806hBfBjzkKWxNICKB+caoFGQ3jBod5tNg3SPA0wDYjZsT87s5jawBscyFXx0kgToQjSno47u8tBDD3HaaaeRkZGxQ8uVIL2RGTNmcN9997HPPvtw8sknk52djdPpZOPGjdxyyy1NgvbmNDcU2NaeM83mOyU1vMnx4Ycfcu211zJo0CCuuuoqcnNzcblc2LbNpZdeitwQEULscHecAcEIPPUxuB1w6RFw26nw38/gyqdgQyVqj0JqfrRx6ADe2tbwxhy46cVaHGgyWY+PYG0ob7C5U2cUN0FcpBMihRqqSU4oI5P4LXsvAQpcxYSiVdheH+nn7UHK/ZMAOKKZY0ejUe58YiYLYgUcP24f9sw12XuGTVm4mY0hfp6pbvBH4mkvXgek1Oa6x3S8Nb0lccTmC4IGQYcBvHGMwfDcjhmICLGzddRx0i+77DKuvvpqjjzySM455xzGjx+/1ViwpSRIb+Ttt9+mc+fOPPDAAxhGfULUl19+2eIyVqxY0WTd8uXLgaat5i01a9Ys3G43jz76aMIIMytXrmxVeUIIsU1uJzwyNf5o6OQD4g/ABFJWlBGZ8T2R0krcD7+AatDSHiGFHKqIud2Eww1amUyFI8eNXexHYRPGhZt4C3Z31rGcAmrwYWLRg2WkE7+zWHXkwWS9fs52nUaOUc3BroUc2WsfnE6D9X9QvLBIs2CTZmgnOPWtRjv4nPHgvOGPrKXBrv23uQmNNtM6HtybRt0mg7LgmD6K0wYa9MvsoFGIELtAR21JX7hwIU8++STPPvssM2fOJD8/n7POOouzzz6bPn36tLpcCdIbMU0TpVRCy3QsFuOpp55qcRnffPMNixYton///kC8RfyZZ54B4j2AW2PzBUPDlnytNU888USryhNCiB3F6JGJ58ZD4wvXjYPLnoBvf4XeebgfPB9nRhoqy0fkg+UEnv4RIy+ZpEtG4eiZgfaHsaqjpAZilPW+EwuFixj9WUkMk42kARp7cFe4+Vgyjh/1m+vrMhVnDIoHA7bWnP+uRU2MeIAdjsVHcjEURGrzZmziregASseb+7YUS1iadJ/iqYmKg7saGAqSf+uIMEL8Ttgd9E+lX79+/P3vf+dvf/sb77zzDk899RT33nsvd9xxB/vuuy/nnHMOU6ZM2e5yJUhvZOzYsTz00ENcdtllHHzwwdTU1PDuu+82O775lvTp04epU6dywgknkJ2dzSeffMK3337LxIkTGTp0aKvr9dFHHzF16lSOOOIIYrEYn3zyCaFQqFXlCSHETtEpHf6bONzY5nuS7gm9cU/onfCcSnbjSHbjAKpTciivtsijFIXGwoGFCycxjH9Phb0T990RDKV4fILi1Ldq89KratNcMj3x/HuI3y4wVXx4RgsyXJrysAaHSmhRT3XBd+c46JvZgYalEKId6agt6ZsZhsHEiROZOHEiFRUVPPfcc9xxxx2cf/75EqTvCGeccQZaa15//XXuuecesrKyOPTQQznyyCM54YQTWlTGmDFj6iYzWrVqFZmZmZx33nmcd955ra7XhAkTCAQCPPfcc9x///2kpKQwZswYLrnkEsaOHdvqcoUQor3wXDgKx90fEsFDBBMDTSZ+PKnmTgnQNztlgMm36y3um6uhYcfXhgGDCRgGRGz2KjA4vq/ib1/bbAhqHKbi0G7wj4NNClM7dpAhRFvqqDnpjVVVVfHiiy/yn//8h6KiInw+X6vKkSEYd6DmhmUUQgjRMv53l1N+2LMUkU0QD6BJo4bczlEK1l7ZqjKj0SjTp08HYMqUKVud9G3+Rs2e9/khbEFuUvN55zGbG/c3uXX/Vs5CKoTYopfy/puwfELxyW1Uk9b58MMPmT59Oq+++irBYJC9996bc845h5NPPpmUlJRtF9CItKQLIYRoF3SnVErIqA3QARSVJJMa2jVDzA7JUXTPNFhZRrxBfQuteiM6SduWEDuD3gEjorSFm2++maeffpo1a9aQm5vLJZdcwpQpU+r6JraWJM4JIYRoJxSBZiYq8qdn7bIavHqKB8NQW55Z1LL4YMm2Z2IVQmw/WyU+Ooo77riDvfbai9dff501a9bw97///TcH6CAt6UIIIdqJlGFZqGQ3+BPno0g/c+Auq8PQzia//NHHhBdjrGpuslVLs6lml1VHiN+VjtpxdO3atWRnZ+/wcqUlfQfq3Lkzc+bMkXx0IYRopUGvH4rhrv9pShqcTtfLB+/SOvTLNjh9jy38PLodnDVM2reE2Bm0Snx0FDsjQAdpSRdCCNGOZB7Smf03nkrZO0WYKU4yD+2MMnd9e9JBBYq/fm036TxqmIoJfbfc+VQI0XodNSd9Z5EgXQghRLviSHHS6YQebVqHbJ9qdnSXfhkSRAghdg1JdxFCCCEaGZyj8JpNO48+cZgE6ULsLB214+jOIkG6EEII0YjDULx8lElS7f1mA7hub8XoLjI+uhA7i1Yq4fF7J+kuQgghRDMO72mw6RLFvA3QMw06JUnQIMTO1JE6i25JMBikrKyM3NxcHI7fFmZLS7oQQgixBR6HYp/OSgJ0IXYBW6mER0fy8ccfM3r0aFJSUujWrRvz588H4OKLL+aVV15pVZkSpAshhBBCiDbXUYdg/Oijjxg/fjyhUIirrroK266f6yE7O5unnnqqVeVKkC6EEEIIIdpcR81Jv+mmm5g4cSI//PADt912W8Jze+65J/PmzWtVuZKTLoQQQgghRCv98MMPvPTSSwCoRhcXOTk5bNiwoVXlSpAuhBBCCCHaXEdqPW/I4XAQjUabfW7Dhg2kpKS0qlxJdxFCCCGEEG2uo+akjxw5kv/85z/NPjdz5kxGjx7dqnKlJV0IIYQQQrQ5bXSgyLyBa665hgkTJnDMMcdw5plnopTim2++4cknn2TmzJl8/PHHrSpXgnQhhBC7NRWC/m+UEvv33Rg9MzGumoAa0b2tqyWEaKSjpruMGzeOp59+mssvv5zXX38diA+9mJ6ezlNPPcX+++/fqnKV1rrpvMdCCCHEbmD9p+vYcOTTJIWDLPcVYmFQaJUw4KuzMQbkt3X1hBANPDLojYTlqT8f2UY1aTnLsli2bBmdOnXC7Xbz5ZdfUlJSQnZ2Nvvttx9JSUmtLlta0oUQQux2dFWQwKWvEnxhCclY/JA2qO65hY4emH/+jP5vntiGNRRCNNEBW9K11gwcOJA333yTww8/nLFjx+6wsiVIF0IIsVuxy2oIFtyIEYyQD8xN7ddkmzU/Bum/66smhNjNOBwO8vLyEiYw2lFkdBchhBC7Ff8BD2MEI6x15/BpxlCK3Vko2yIpWsOelb8wouInfFWV6JjV1lUVQjSgDZXw6ChOPvlknnnmmR1errSkCyGE6HBiGwPENoVw989ImDxE14RRv6xlkzONeWnxFvTUmhCpgTBKa4rc3SiwihlYuYbKB74m/cr92uoUhBCNdNSOo0OHDuWFF17gkEMO4dhjjyU/P7/JpEbHHnvsdpcrHUeFEEJ0KEWXfMyGfy7Epf0kJUXIefEkkif2AcBasJ6aPe5gjTcXnxVisbsHmdXhhP1th03v2FpipiI7NA2Hw2yL0xBCNPLw0FkJyxfPO7yNarJ9DGPriSlKKSxr++/cSUu6EEKIDqPqfytY//BCurGQTqyFGrCPmId+6UrU8fuiq0OAQWGwBI1JQbSUAMmJhVjxH1TD0nw3N8DoUa2bDVAIsWN1pBSXhlo7Dvq2SJAuhBCiXbNDMWo+XUvFu6vxP7+AVErjAXotAxv77IdQR+8NaytRaHTtz5tXhwmQTEl6Mr5QhJRQhKhpUqF8OGyL4LIKkCBdiHaho6a7HHjggTulXAnShRBCtFuVryyl6Jz3yahcSzoVKEcy6Wxssp1RE4LicowRXbEx2fxTvzHNxx0TxlKclYph2ez/83IO+Hk5yWEvm9zJ7Lt6IdC1rhy9cB16cQlqv96oHAnehdilOmaMvtNIkC6EEKJdsqojrDnjPVyBajaY2fyU2ZV0YwO5JcVNtrVRGFkpaMMkohy4dQwbxeP7H0xxZgqGbWObBp8O6U2mP4hdBKUpHqp+KcdfGePV78MccOEj9Fu6EADtcmDOOA/jhBG7+rSFEB3MIYccstXnlVJ8+OGH212uBOlCCCHaBXt1GbE73kOHYzjO3ZdgBRAIE8aLaUHnjWG0kUIFnUnCiZdSFBAf/UAT+qmYkC+FZUmdGehfTRQn2HDBrK9IDYZZnZPOO3sNoDzdReGSDZiONDa+s5L+mS9zrobvsoYxvffh9C9fTd+KItIueBb3UUNRLvmpFGJX6KjpLrZtNxnNZdOmTSxevJhOnTrRt2/fVpUro7vsBi644ALWr1/Pm2++2dZVEUKI7aYjMaJ/nEnskc/rgm4b2ORLozqQ22T7bEpxEkMRI4n1uKhhbsperPT2ABSGbbFH5XK8dpgvswYnTAiyNiuVVLuKYxZ/i5MakmpTZ1Z7Cvgic7+6GQ8N26KwvJS8cwbR4+GDd/ZLIIQA7t/7/YTlP35zaBvVZMdYsmQJRx11FI888kir8taleUAIIcROpW0bbI1ymAn/tz5eQvSrFYT++S3utSV16aiKCG5qyAwYVNM0SFfEZ/bTOAiQT4gomxx5bE5otQ2TEm8maVF/kxn7upRWkVtazXpy6c38uvULUwYmTEluGyYrszpR8lIRv/z0NsFNYVJ6p2DHbGpW1pAxKJ3htwwlY1BGy14DrYlZ4HRsvaVwU0Bz24cRvvk5RE4wxuSRbqYcnoTD7JgtjEJsj47akr4lffv25eqrr+bPf/4z33zzzXbvL0H6buDhhx9GbogIIXaamAVL1kHXbEjxsrokRorPICMlHgLr4qr4JEI9srAXb8QIBCBmoQuz0X99G/3UF9g1QSp8nakJePASJNkdoCrsJYaDVKqxMHASwCSIhUGIdDQOXERqW9YNLBx4COGgfvpthcZA0c+/hu8z+tWtL3OmkBcqa3Iqhm3jsiyqSSNEEk6CAIQNV7OnHnS5CS73x8ucW1ue1pgfLmTVuz+gzx/IhgMGUPrResrmlhIY1Ik5/bqzphp65Ds4dU/4YL2DD98qI2AZDOjp4PjoBmLr/Dh6p/NZ1wJs0+TM/dys+6mM07/xkF4WwROzWep2cOfsCFrDUYckUR2B3hkNJm7SmvIlVURrYmT2S6MiqqhYGyQ/wyC5sw+AqKX5tRzykzXr/YpuqZDkaj4QCldECJaGSOuZ0uTWfWmlRSCs6dpp+8IGO2ZTucJPcr4XZ7Jzu/YVvz+7W5AO0L17dxYsWNCqfSVI3w04nfLFJ4TYOb747wL2OP9OUv1+bKUoTUnn57w+PL/nOOwBBTz2xj9xz/uVGG7CZAI2DkIYWDgJoIhgGQqFA0+gAgMfEdwY4Sg+ZbNB5+PEJpX1uCknSAY15AHxFvMurMHEQgMhvFg4oLZ9XNWG7gApsUBCvQuDJWRHKkiPVFPhqh+lJa0mVNdiHyUJi3JMLLoFV/NLysDmXwStE1rZ99/4A96oASi+f3El//sllbDtpGuNi3XLLb43LFCKlSURPv5Bk1lVhicaIyMUpvt3a1gRCKFsjeudteS7lvC/EQNI+7+lmLam85gR2LUTo6SGY9S4TC5Z6OLCxfHzTHLCCDPIhB9+Je/zleiYxlKKl0fvSXlKMgowLIuReg01ozrzXIWbIAbxJCKFAnK8MKYAjitZi/OlRURrYjh8Dr6PeFmS14kMh80fL+vMHvuks+illfz9/RgLHcnx/d0G1f1S8SSZHNRVcc0og8oI/P1bmxWVcFh3xR/3UjgMRfG3G3n7D99CeQjLadJpYleSasIoUzHgtF503q9Twss8b4Pmnjk2GwNwQj/FuXs0P0HM7NU2D/2gidhwzmDF0X22PpGM6Dh2xyD95ZdfpnPnzq3aV3LS26k333yTadOm8fDDDzNv3jzefPNNSktLKSwsZMqUKRx22GF120pOuhBiZ3hlYZQxI84lO+CvWxcP9SDgcPNZ9z2YsHQOAH66YBDDywYUiT8rMVxU0AMSkk9svFTXlqkwCeOmjHJ61Y1x7qYGB7GEssK40JiAwsCqC7jLnCnMSe9HaiBMeiCAW0fIpIIU/BS701ln5uGMaNyxeLAbM2FF5zRWZ6fSv3IZeZXVFLkL0bYDy0ycgdQbjBD01re0F/g30L9yPfMKunLTpKMZ++MSBq2OjzgTNQzeGjmQVblZzb+oWjNk5Vr2WbISZWvcURu/14nDtlnUJZdPB/dpsssPhelEGs2KevBPyzn9858AmN+tM1/375nwfFTB/K4ZxBxbDmBP+uInxs9fXrcccjh4af/hBN0uvHaM2/aoYebMTXw1oFfCfkGHwfzCeJrPoCzYGIANwfrnL9xT8c9DDJ4c+RZGZeJsr5spAw575gDyR8cD9aXlmj2ftgg0eLtvP8Dg2r0T6/9ZkebgFyysBh+x/04yOKm/BOq7g3v3/Shh+covtz5qSntxzjnnNFkXDoeZP38+v/zyC3feeSd/+tOftrtcaUlv5x588EGCwSDHH388EA/eb7jhBkKhEEcffXTbVk4IsVt79f0NHNsgQIf6YYx9sTDjln0PxFNRbJz4KGkSoAOESYcm2eGKeKCtiYf+LsJk1QbgcSZNp9E20ERw4iRceyxFtelhuTeflECY9Jr4jKNhPKwnD5O1dA6Xk+SsIRRLppwMwMBhQY+1VaAN3h0wkjm9OvPHN7+mV2kZlsOkwufB7/GQFAiTVRlgdW2QbtgWRUk5FPuyWJmTTsGmiroAHcBp2xz801Ke6pSZ0Ppef9qK+T0K6LNuA1n+ABpw2PH0naRQ04DWUhBrZsrxg39eWff/9RlpTZ7XhtpqgI7WjFm4KmGVJxaj9/oN/NS9gKDh4M33K1mWn9NkV0/MRtkabSh+Lm1a9PQFmmnd/VsM0AG0DQufW14XpD/1s50QoAM8/IPdJEh/5Ec7IUAHeHieLUH6bqKjtqR/9NFHTVLEPB4P3bt359prr+XUU09tVbnyqW7nKioqePrppzn77LM5++yzefrpp8nLy+O+++4jGAxuu4A2UlZWRjhc/wXt9/uprq6uW45EIpSWJn67r1+/fqvLxcXFCbn3cgw5hhxj5x6jKCmp2QCxMYWNIoLRqNV7sy3drjWaPOPERX0d7WZ+ouKt7jGiuPCynojDzzeZAynzpJEUjjTZvop4qkvQ8LE6qRMNf/YctianPMBey9ezJD+LwopKUqJR0oMhupdW0Gv9JvLKa3Bqi26BdeQFN2IbJihFzDAp2FDNgDVNx2xPC4RwR5t/LTYrTU1G2RoF2LU/7gWlFXQprUjYbm2GD7uZqdJVg/c6wx9o8nzUNDDsLd8oV4Bq5umG67RW+Jp5TTfvvyW2rfHkehv0HNjShvF/iouLiTWz8ebqN/zsWs1sZ9m7799gWxyjLWmlEh4dxcqVK1mxYkXCY+HChcyaNavVATpIkN7uHX/88SQnJ9ctJycnc9xxx+H3+5kzZ04b1mzrMjMzcbvddcvJycmkpNTnhbpcLrKyEm8H5+fnb3U5Ly8v4UpVjiHHkGPs3GNcsG8qT444KGHd5vAgZDr5rNsQIB6weajAovn+MR4qoVHIprYQwiWxATflgEW5mZYQxsfb2yN4CODDTwwvyz3dsVW89d1u5kfdQGOjWOHrjCPa9HlXzCLqMDh0/jIya0KJz2kL0OTpYkaVL8Dv8DXZ3xeKNllX5XUTdm7lRrXW5JZX4rRsQk4Hc3t2xVbxfPHDv/+ZLhs3UZTh5efOqaxP9zZbxGcDutX9f8iqtaT7a+qWI6bCsDWFpTXxfPrmqqAUn/cvTFgXMc26lnOvUzPxwGT2WbQcZSe+V+VeZ92FQ58MyHAnPM1pAw3SUh1Ee29l5BsFfU/qDsQ/u2cNMnAnZvRwwZD4MRp+ds8foppcIFy4p7Hb/g22xTHaUkcN0p955pkmF0OblZWV8cwzz7SqXEl3aee6d+/eZF2PHj0AKCoq2sW1EUL8npwywOCFp//Ag3d2Z/L775Hq0KR6FIu9nfjvXuNxjOjBnks/IO3FT9FrA4QsH17KMWo7ekYdLpyxCGvTkpiTl8PopaW4rHiKisbEQmE2CMMNgmg0G11JLEgdgNsOM7qsBIs0DGK4iOA3PbjsGG4dxEkNo/zf0T+4mB9ShlHty8RdWdMgiLOxHRbfJ/el2pkErhDeSGILt9/r4r0hvei/dlOT8ze0JsMoId9eB4DHDuMnKWGbztXl/Ni9M3usWoehay8kYlFO+fQ7fuxWgCcaoSQjjXWZaWjDQFk2ey1bhRmz+KZ3V+b16krU6WRhQS5dy8r4tns+iwsSg6qJ3aGoBipDkOKCjKoA+3pDpPVMxr8+iCOmSa2uYn7XbFxa09uMMK6fi583atzrqtmY5KLC58bnUngc4HHAHtkwzpWBr6yYWNAic1A6K/oV0NnvpGtXJ1Mmp9C7Sy45OU5S31nJ+6l5bExKwp/pJpblY4ATDuqquGG0QUUIbv/GZnml5rDuBn8ZFX8HznhqH/578ucYRfGWXc+ADDLSHBhOgwFn9KRgTF7dOQ7IUnx8ksld39lsDGhO7Gdw8bCmQdrYbgb/OxYenFvfcfS0gdLeuLvQzdw16gimTJnCV1991eSCCGDFihVMmTKFM888c7vLlSC9nWuc49TS54QQYkc4qb8BT04CJtWtGwT8X93SKXDXKThDUcyf1kPXNCjaiOqajSsjicC5j1L43McUVn6ONh2ESSGKj4rCzhRvMugXWInGiUEEkwDzvcP4NaUPrmiUrKoa/j3oaI5b+g2uWISv0wZR7UjCsC2OKP0fpo63YqdaVexb+QVvZ01iQ3oySaEwWimqvW5WOzLralrtdeOMWXjDMRSaFbnpPHjEKL7u15Xsqu+xSby9XOVxo6JJVJKGQ4WJYSSM9OLOcLL//Xtz0vBs7KjNqtdXsfjZZTj8Nv7hBXQe1IX9eioy5q6lYv0qInvkknRwFwprPHTusifzLA+BHy10TYyp+zgZ1bWQq14NsmSprmtF7OLTPDXRQY6v4fd9KjA04X2atMmiJmTTpyDxbkZVWLOoDAZmQXKToRd7wGU9Etb8odEWA8/oxcAzetG0W1y9zskw4wizyfrkfB/nfTKeskWVOHwmqYXJzexdb3RnxStHNS2nsYk9DSb23OZmogPqSK3nDW1tDJZQKIRpbvtz3RwJ0tu5FStWNJmlasWKFQB06dKlLaokhBBNKI8Tc2Rt+kRefUdG338ugUfPj2/jc+MNhvFqSPW5iQUi+E//N773vkLVRKmhE52DIbJDv1CisghpH2+PHMTIqp+IVOdT7Yi3YqfY1bh1YqdEp46RE93AOncBSbqacnczqRZKkWZUEHW7mHrWUUz5bB43znqbv5qH8mW/Qo77ZhFZNQGclkW1x02Vx02p8tDrwn44snyMP25PqgMOit5dhyvNRbejC3E3yPXof14/+p/Xr+lxJ2U3WhHvhLofsF8XBw1/iu8+xst5pTbP/6LJT1GcMkCR5t524NI52wSaBgKpbsWoNs5myOzftGOrEM3pSEH66tWrWblyZd3yDz/8QCiUmDIXDAZ57LHHKCwspDUkSG/nZs6cmZCX7vf7efnll0lJSWHEiBFtXDshhGgBX4OkZW/9/x0+F+mv/AH4A9ELniDy+C8YREnz+cl7/US+vGABAbeTr7r1pefi+o7yIcODXTcyTL2Bf+hFzlc1BL6toqy5IB1waou80EYefnwWt580mkGzV/LEy9M56pxLKMpKQQEhpwNfOIorGqXnU+PwHdu9bv90IH1Ay2YZba3+WQbTDtiphxBC/EbTp09n2rRpKKVQSvGHPzS+D1Xfwn7//fe36hgSpLdz6enpnHXWWRx55JForXnzzTcpLi7mhhtuwOttvkOREEJ0NM7HziXz/ypg9UYY2gOcDg5YNph+p8/jtUF7c90v7xFyxAP8sOHhV19f+gUW1+0fwkv67cfg+/v7BD6ZQ7kzlbxwOU47Rok7k3XeeGv2Ok822cFqnLbmry98yNfjBpA9r5Sf7rqFeXldSQkpFiZ15bUhfRnex8v4BgG6EGLn6kgt6SeeeCKDBw9Ga82JJ57I7bffTp8+ifMcuN1uBg8e3Gz/wpaQIL2du/TSS5k3bx4vvvgiZWVldO3aldtuuy1hMiMhhNgt5KbHHw3cc1k2hzyn2JiaRJo/HB8CEShy9YSok0CSwm+k0KN8LZ1jNiojCZe2GFb5K5vHAcmJVOLUMVb58ohpB+XE0y9M22bKn3phjDgQXptL5+d/Ye5qD+W+TM7dz8de04btyrMX4nevIwXpAwYMYMCAAUC8VX3SpEnNdhz9LSRIb+dM0+TCCy/kwgsv3OI2jz322C6skRBC7DqZowp4ozDEnQUjmXzXR+RUVuC2o8Rw8FXnPbDNeFdPp46QuryapFOHE7vwBRoP1FcYKGGVN5eu4bX0Yxl+kvCTRvjZeaSO7wPn7EeXc/ZDevoI0XY6UpDe0FlnnbVTypUgXQghRLtWmOfhoau6wVVTqJ4+lw+umU95ckrCNsW+XEZ08WH4HFSZyaRaiR24FPFRWVYmFdAvuIJkavASIlyZeHtaCNF2OmqQDvHx0J977jkWLlzYZLJJpRRPPPHEdpcpQboQQogOI/nsYWTfMZfyRhN6po7rjSPJifXrRja50kgJhhLa0ou88Ul6ooaTUlcGuZFSTCw8kyVIF6K90B00Rl+9ejUjR44kEAgQCATIzs6mrKwMy7LIyMggLa11IxzJDABCCCE6DKUUI14/mqy0+pFdcoekc8DD+8WfT3LhsWOs8eSwwZVOuSOJRcmFLPd1rtvebcWHb9SGwnnUoF17AkKILeqoM45ec801DBo0iJKSErTWzJo1i5qaGh588EE8Hg9vvfVWq8qVlvR2avLkyUyePLmtqyGEEO2Ot382R809nrLFlRgORXqv1Lrn9PerAZuNniyqjCSSIhEqfL6651NCIUwr3j5lXDUBlZ3SuHghhNguX331FXfeeScejweID73ocrm4+OKLKSkp4eqrr+Z///vfdpcrQboQQogOKbNfM7eQk93khzdR7kql0kymk7+apEiEkNOJOxolORIhggNrUCHuvx+/6ysthNiijtR63lBJSQn5+fkYhoFpmlRVVdU9d+CBB/LAAw+0qlxJdxFCCLHbMA7sjXtkFzoHSjCw8BAmORIhu6aGlEgEF1Eqnak4jh3S1lUVQjRiK5Xw6Chyc3MpKysDoHv37syZM6fuuZUrV+JwtK5NXFrShRBC7DaUYeD+4BI6//Mzcn9Yy/JZBqnVVURw4SSKlyAVhgt1yt5tXVUhRCOajhOYN7TPPvvwww8/cOSRR3Lsscdy6623Eg6Hcblc3HXXXRxyyCGtKleCdCGEELsVlerFec14nEDO7FUUH/4i2eFqIoaD1Z5sBuztxBiQ39bVFEI00lHTXa666ipWrlwJwE033cTChQu5+eab0VozZswY7r///laVq7TWetubCSGEEB1PNBrlpdsepfsrQbrYLvIOL8R18+GoFE9bV00I0cgNR8xNWL7treFtVJPfrqqqCqUUKSmt75wuLelCCCF2a/4uLhZc6mLklCk4nc62ro4QYgs6akt6c1JTU7e90TZIx1EhhBBCCCF+g0WLFnHKKaeQn5+Py+Vi7tz4XYFp06bx8ccft6pMCdKFEEIIIUSb0yrx0VHMmzePkSNH8sknn3DQQQdhWVbdc36/n0ceeaRV5UqQLoQQQggh2lxHHYLxmmuuYciQISxdupT//Oc/NOzuOWrUKL777rtWlSs56UIIIYQQos111Jz0L774ghkzZuDz+RJa0SE+hnpxcXGrypUgXQghhBBCtLmOGqRrrXG5XM0+V15ejtvtblW5ku4ihBBCCCHaXEdNdxkyZAivvvpqs8+988477LXXXq0qV1rShRBC7Db876xg04VvwepS0nw1JN02DpLaulZCiJboSJ1FG/rjH//IqaeeSlJSEmeccQYAq1ev5qOPPuLJJ59k5syZrSpXgnQhhBC7Bf/Ha1h++JvksYxsijACmuiVRfSYMpAV+3Rq6+oJIXZTJ510EsuWLeOWW27hgQceAOC4447D4XAwbdo0Jk+e3KpyJUgXQgixW1h37VekspFOrKlb5yTEfi8slCBdiA5A00Gb0oHrrruOM844g/fee4+SkhKys7OZMGEC3bp1a3WZEqQLIYTYLYSWV5PNpibrvYFQG9RGCLG9OlIe+p///Gcuu+wyCgoK6tZ16dKFc889d4cdQzqOCiGE2C0YXVMx0U3Wa8w2qI0QYntppRIe7dk999zDunXr6pYty8LpdNbNNLojSJAuhBBit+DaM4caUgniq1unAbvBshCi/epIQXrDCYu2tu63kHQXIYQQHZ7/P3PxT59LDYWU0BUnIVz4SSJMFiVtXT0hRAvY7Tsu3+UkSBdCCNGhRUtDLD/zPUx8KGwAbNxUksQGTAyq27iGQgix/SRIF0II0aFUfrWB0v8sxuWyyL12BD+dNRuLVNzEAHAQwUUETRJhXHgks1OIDqG9p7g0tnjxYhyOeChtWRYAixYtanbb4cOHb3f5Su/oBBohhBBiJ1n/+GKWXPAFDqLEcOAlhN9rkhzUeIjSifVksQEFxDD5ydmfftEFvLPHQQw4ahCDbhqG4ZSg/f/bu/M4m+r/geOvc+/s+84wjGXskspSdrKMLEUoSqZQWdKufvLNUCkhFEVfu9KXr76WEULZImXJLmOZwTBjzL5v935+f0xzue4dhhnm4v18PO6D+zmfez6fc869c9/3c97nc4SwRcOeOWH2/JtldcqpJzem0+nQrvlRoZQqtqwoiL8ZMpIuhBDirnFp5M805hQu5JCHHdEEY5/tQCZueJKKH/GmunYYqG08zc8VOuB+3MjFo0eJn32C1sd74+DnVI5bIYSw5m4aSV+wYMFtb0OCdCGEEDZNFRhRBiOG7AJC8iJxJA8ABwoI4Qx/0RjQcCLL4rWuhhx0ekVSgCPOmQZckgqInnSI2pOb3dmNEELc0N104eigQYNuextyzu8WRURE0KRJE/bu3VveXRFCiHuSUoq4kb9w0ukLzjp9RkzI16YAvYgOhcc/F4Zm4Wqxjky9E7k6R+y1ArI97Mlys2P3wrPkJ2Vet+34DCORl2/+9LQQ4tYZNc3scb+TkXQbs3XrVk6cOMErr7xS3l0RQog7yrD+EPn/O0iGwQ3HIc0o2B9H/qwdVCUOPQaMiRoKLG4cnoMjAOl4kIInnqSiAfmanr3eDVCahlEVvirHTU9AXDbfNviZAd8+iHePmiRnKbZHFbAlVuM/h/OJv5yP+ic+15z11Aq0p39DPW88osPLSQIHIW6Xuynd5U6QIN3GbN26lbVr10qQLoS466kCA5xPIl/ZkX0ylazdsbh0rI5DfBIZ66PI3xGNQ2YaKdmOxCW4Yk8+AcTjSgYXFhzDDiNViEVHARqFp35zcMbpqtH0y/iaRtDz0XGWGlTlOOc8K3PKrToGTYd3ThLZemcMOnt0BoUG1IjP4NDA37i4qTLhi5JIcXemQfRlxm8+wsc9mxNTwRPsdag8I5FxBUxKMHBgejT/VzGdGj0r4/6gD8bMApyCXDFkFWDIKMCpsgtpuYq0PAhyLww2jEqx7rSR/bGKp+vqCHCGPIMiOllRz1+Hj2vhCe3zaYqMfMWJRMWhBGhXGUK8NNycNFJzoYpH4fpSchTpuYoCA1Ty0NBpEJOqqOKpoWlwLkVR2UPDwc4y2MkzKC6kQxUPsNMVLs8tUFxMU1T10tDrJEASwpZIkC6EEOKmGJfswjhrCxiMaM2qow7FQEYOBHhA1GXIyIUCI3kp+VzKD8SDZDxIwRUwhhcG2x6Ahg4NhTsaGlUowAFHjCRQmQLsKSAfDaNp5FwDnMkmCyc0dKTiyTmq/LPMSAF6MrDnOA/hlRpP3cyTpCsfMpUHviqHGnbHOehaG9CjU4rMdA3nlv/lS0PhJGcK+N8jNYkJ9gEXe9P2+sem8sWCLfinZpECbJt3mt01K/LY6Vh21K7EukbV0RsVXq468hT02fY3/qmZHHigMss7NuRysgGMinER1+xIPeDlBDqtsHGdBkqBQUF2QeGOcrYDnQ57rbCP+ZkF/xwEhS7fgM7FngK9DgdlRFOKXJ0eBxSfttbxVsvCbdgVXcCIdfkczrLDgIaDDuz1QE4+Ocn5GIzgZAefdXXkoWoOfLzbSGymoleIxltNNCb+ofjpjKKah0Z4Cx1NAyWYF7fH3ZSTfidIkF5KBoOBOXPmEBERQWJiIlWrVuXFF18kNDTUrN6xY8eYP38+f/31F1lZWQQGBtKtWzcGDRpkmmOzR48exMbGAtCkSRPTa2fPnk2TJk04cuQIK1as4NChQ1y6dAm9Xk9ISAgDBw6kffv2d26jhRD3LeOPezG+MM/0XO2NLrauA1CJaPRcmelXBxjRMOAMFKavJOJLPq4Y0UjAn6KEFmdy0P1zcyJTe4AT+WiAE/EEEM8xu1okGX3RG4vq6LikVcJovHLZVa7mREaBBw/knOSIfT3s80GvFM5XpZ1rwP7qFQoD46v0+SMS/9QrF6W65+ZjZ1RsbBDM6QqenKzoDQ56quTmMGvGehwMhR2puPUE8xuEgEMxX7UOdqDXXWkcQNPATgOjgmxDYcDu4Ui+0iDfUNg37coofVHebt5Vl5jlAW/vVPSqbwCl8fjcLHI8nU2vyzNCXoGCxHzTa3IK4I2IXOz8oECnB+BIgmLZCUVkcmGdY4mK7TEGTgzWU8lNoilR9pRFMtv9TYL0Uvrqq6/Izs6mT58+QOEFpWPHjiUnJ4ennnoKgN9++413332XKlWq8Pzzz+Ph4cHhw4eZM2cOkZGRTJo0CYC3336b77//nr/++osJEyaY2qhevTpQmApz7tw5unTpQkBAAKmpqaxdu5Z3332Xjz/+2OKHgRBClDW1cNdN1b86QC9iwJ6iqDQRP3JxRo8RPaDDSB4OgCKQ81bXqV3z/wpaPKlGX/N+Wvmyz8KZarnniK7pSeCFTNxy8y3qOKBMwWyRyokZFvUqJ2ewvGkteu8/xS/1q4KDjg5/RJsCdIBzvh5kFxegAxQYi19mr4NcA+QYwNVYGMzb6cz7dr38XZ3GV3uMVLA3koPOsm6e9YtiCzIKwENvel4UoBfJyIcVkYpRD0swJcqeXCxqTmZ3KaWUlBQWLVpEWFgYYWFhLFq0iIoVKzJ9+nSys7PJzc1lwoQJNGzYkKVLl/Liiy/y9NNPEx4ezuuvv84vv/ximiGmXbt2VK5cGYAnnnjC9PD1LfzyGTx4MPPnz+eVV16hV69ehIWF8d1331G1alXmzZtXbB/LQ1JSErm5uabnGRkZpKdfuTV3Xl4eiYmJZq8pOotQ3PO4uDiuvveWtCFtSBvl0IZT6cd2ir6G87EjF2ezZXoUGkacycIByyDaeNXXlgE9+TjgkZ9LBd0lMP0gMKLDMghNdHUnyd4d79xcPu7e5J+a5gZuO1o4in2V/bUqWqxrX7UAHAsMnPd2/6czilx7vVmdgLQs7AuuM0OM/joBieGqPlwnlr+eSq55YMgtXNe19y20K+br3yIv3fJHlp0qMP3/rnrvShslaqM8yewu5uSOo7coIiKC8ePHM3LkSMLCwsyWLViwgFmzZjFt2jSUUrz11luMHTuWdu3amdVLSUmhT58+vPjii4wYMQKA8PBw1q5de8OpHXNycsjJyQHgm2++4ccff2Tr1q24ubmV2TYKIcS11I5IDB2mwPWCz6sY0KG/KspUQD5OgJ487LmMZQCcix16CqiFtdtrZ6Fw/ycMdzBbEqWrTJLRlwpcIpBY9jo0Ireg8KZFuS56MrzscTbkcNS/Ah92eYyF3/6MfYER3+wrQY4CPnqmBbsaB5tyxPVGxaTZm6kbk4imFL/VqswXoQ/xwq6/Wdq8NplOhf3w0wx8M+tn3LOvXNg6+tlWHKrsZ33neDqAo5UfPfkGSMm9kqfu+8+Nl3IMFqk4xfGyV8SNsCMtR9Hwi0zisTPLswcgKcdsNN/ZDpwqOJGcfyWAb1UZfrtw5SWV3eBImF5muRG3xYBBUWbPly6qXk49sQ2S7lJK1apVsygrSk+JiYkhL6/wj/XHH3/Mxx9/bHUd1/7KLU5SUhLffPMN27ZtIykpyWJ5RkaGBOlCiNtKa10b/fbRGL/dXnjhaNvaqN1nIDMPgrxRkXFwKR08nDD4epJ0KAuXv8/gbEj/ZxRcoSePApywJx878igwC7aNVCaKNDxJwRMvUq9aVjj2nosj2XjjcM1ouZ9KJg9HqnMWgDxPSHIoDHDVPyPE2XonfniwNgDfNa9NlexsUnV2tDwVR669ngWt6hHt78mzmw/z86Mh5Nrb0XF/FEoHL7zdjQeOxXLB153eMRfoq0ulaXI060OCsLfX0TM7kVPVfPFNyMQrJ5eDT9Sh/hMVOR9tIDlXh4PRQH7eP2PTOtCMRpx1Ck0HmVnGKwFzdgGowvjd3UNDb2egip8dSakaUSkFKAc9GopmfkaaVLZj10XFY5U1XOzhl2hF4wCNz9rZ4Win4e+m8cdIV6b/lsfhhHz8vPToHHQcvlwYkHsb8klIMdCggp7wTo7o7DRm7DdyMQN61dLoX1djwZGiC0fhzSYyDaW4fYySk25GgvRS0q5zOkbTNNNpqZEjR1KvXj2r9fz9/W/YjtFoZMSIEURHR/Pss89Sv3593Nzc0Ol0REREsGHDBozGWzwnKoQQN0F7LAT9YyFXCga3sVrPDgi8pkzFp6EOx2AfUgHjsTgCTyaQ+O8T5J5MpSDPSK7ScY5gHNztyczwIl4F4E4GOdgTwkEUEE0N/EiFa1NalA4vUkxPvfPTSXDyNqvilGmgUlImvpnZrF1YC8dAF776OZ2PIqpy2ckBdIWjyDuqVmTYj3uokZDGkeoBfDKwNe93cqbjSE8eCSpKa3kAgKGmtQfAR1f+zve9wX68ebf2lV3NR8f0nk4lXueMDuZpOy89oPHSA7fUtBCiFCRIL6WoqCjatm1rUQZQuXJlCgoKc/ecnJxo3rz5DddXXNB/6tQpTp48ydChQy3mUF+1atUt9FwIIe48LcAD7fH6AOiCfbHrCpVHXfkbaoxJRnN2QPN1JW3xEU4O2koSfoAiiEhycSWQaOKpiSPx6LgyfWIi3ugxkIQfDuRQNzWKS04+pNkXnmF0zM/DJd1A8+hY3vxvcxwDXQB4rYs7wzq6cTZFkZBhYPzmPPZfcGNx9WbUC7KjSz17YhrrcHWQy7iEuJ3kZkbmJEgvpRUrVtCnTx9TmklGRgY//vgj7u7uNGnSBE3T8PHxYcmSJYSGhuLl5WX2+pycHAwGA66uhTfjcHYuvIgqLS0NDw8PUz3dP6M7115CcOrUKbZu3Xqbtk4IIe4sXdCVkW+PFxpSv7oXibMPkW/Q0DoOJ2/oUvxIIoF8zlMFD9LQUKThgRE77IE0CtfhqtLoHPc7Zx0rc1ELICfPFaVBn/7++NXzMGvXTq9R01ejpq+OdYOvyd0WQtwRMk+6OQnSS8nLy4tBgwbRs2dPlFJEREQQFxfH2LFjTQH3+PHjeeedd3j66afp2bMnVatWJT09nejoaLZs2cLkyZNN86I3bNiQ5cuXM2nSJFq0aIGdnR1NmzalevXq1KhRg8WLF5OTk0NwcDDnzp3jf//7HzVr1uTvv61dYCWEEHc359ZBBLUOMj0/8MYxHDL/IJDTnOFBEim8KNORLByveW0mHqThTmauC0adI07kkerpSKWOle/gFgghSkpmdDEnQXopvfbaaxw4cIDly5eTlJRElSpVLOYsf+yxx1i0aBGLFi1iw4YNJCcn4+HhQVBQEM899xy1atUy1e3SpQvHjx9n48aNbNq0CaPRaLqZ0YwZM5g+fTpr164lOzubmjVrEh4eTmRkpATpQoj7QsDQhpycnskD7KEeu0nDByN2xFATZytzFRZgj4tdGhc9PchwciSoR1UqNL/xdUBCiDtPLhw1J1MwCiGEuGsoo+Joj59JXHcBN1JxqupK1XOH2M/DVCDZ7OZJRjRcScJdl8rKHo/S+f1OVHrUcspHIYRt6DnY/AZma+ZVKaee2AYZSRdCCHHX0HQaDX8KxZCRjyowYuflSMJD0+GARiIeeJKJAwXkYUcueqoTj9GokfdENv6P+N5w/UKI8iPpLubkUnUhhBB3Hb2bPXZehVnoPn++hrOWQwF2JOJJLL6k4UQDDqNDYUB/g7UJIYTtkSBdCCHEXU1nr6fR+UE46fNwIYtKxNCYg9hhxICeFDzLu4tCiBIwauaP+52kuwghhLjrOVV2xbOOIxzLxAHIxo1MdJynMm66tPLunhCiBOTCUXMSpAshhLgnuHjrOY8HjjiRgA+5OGBEh5M+s7y7JoQoAYPkpJuRdBchhBD3BL82ftiTRw6OZOOEER325OPumFLeXRNClICku5iTIF0IIcQ9wWlMB6pxDkdysCMfJ3LwJ57UBnIHUSHuBgY0s8f9TtJdhBBC3BN0bk54Dn6A+vP+pAA9dhSgc9az4ulaN36xEELYGAnShRBC3DMc5z6PfdijGGZvRwvxR73SiuyI/5Z3t4QQJWCQwXMzEqQLIYS4p+hahaBrFQJAfn5+OfdGCFFScjMjcxKkCyGEEEKIciezu5iTIF0IIYQQQpS7gvLugI2RIF0IIYQQQpQ7GUk3J0G6EEIIIYQodwUSo5uRedKFEEIIIYSwMTKSLoQQ4t6VmI5mMKL0MiYlhK0rkBsYmZEgXQghxL1n6XYY+g32Wbm8aK9jx4D68GJ5d0oIcT35EqObkaEFIYQQ95a/Y+C56ZCVC4BdvpF2i45A5MXy7ZcQ4rryNc3scb+TIF0IIcS95dU5FkUaoPsiAoNRkZmn7nyfhBA3lH/N434n6S5CCCHuLbtOWC3+6azG09MNFBihugfs6K+jsruMVQlhK7Jk9NyM/HUSQghxb8m3fkuUqSEtKTAW/j8qDeovMN7BTgkhbiRbM3/c7yRIF0IIce84es5qsQJ2Vq9nVpaWB+k5hjvQKSGEuHkSpAshhLh3bD1itVgBBr3eonzBEclPF8JW5KGZPe53kpMuhBDi3lGjgtViYzG5rn/E3s7OCCFuisTlZmQkXQghxL2j9+dWi+2U4tHoSIvy1advd4eEECWmaeaP+5wE6UIIIe4NY5ZATvETt9VOsJwnPbMADEZJeRFC2B4J0oUQQtwbpqwpdpECfq3ZwOoyl+kGvj0gF5AKUe5kJN2MzQXpERERNGnShL1795ao/ssvv0yPHj1K1ebevXtp0qQJERERpVqPEEKIcmI0Qn7xgXaeTk+Mt7/1ZUZ4ZbPibKoE6kKUK+2ax32uzC4czcjIoEuXLuTm5jJu3LhSB85l4cSJEyxbtox9+/aRkJAAQKVKlWjSpAlPP/00ISEh5dxDIYQQpXbkLDR597pVDLobj0mN/EUR0busOiWEEKVTZkH6hg0byMvLIygoiNWrV99ykP7EE0/QuXNn7O3tS9WfhQsXMmvWLDw9PQkNDaV69epomkZ0dDS//vorP/74IxEREVSoYH0mACGEEDZOKej5Cazdf8OqzgX5aEqhrnMKfWdM4b87zhvZEK2o4ApDHtDhYi9DekLcGfJZu1qZBemrV6/moYceonPnznz22WdER0dTrVq1m16PXq9Hb2Uu25uxbt06Zs6cycMPP8zUqVNxd3c3Wz5q1CgWLlyIUuVzsVBWVhYuLi7l0rYQQtyVUjLhUDTUrwJ+HlBgAK/nITO3RC/XgAppycR5+hRbJzkPtCnmdyt9/VcD/WrDyw/qaBOkYa8veRCRZ1DsiYPKblDNU4IPIW5IPiZmyiRIP3nyJMePH2fcuHG0a9eOL774gjVr1jBq1Cizevn5+SxdupSff/6Zs2fPYmdnR9WqVenevTvPPPMMUJiTPn78eGbPnk2TJk1Mr42Pj2f69Ons2rULg8FAgwYNeOONNyz6kp+fz1dffYWzszOfffaZRYAOYGdnx5AhQ6xuy6pVq/j++++JiYnB19eXvn37MmjQILM6u3fvZvXq1Rw7doyEhATs7e1p0KABL730Eo888ohZ3ZdffpnY2Fi++eYbvvzyS/bu3UtaWpop5/7AgQPMnDmT48eP4+TkRMuWLXnzzTfp1KkT3bt3Jzw83Gx9GzduZNmyZZw8eRKDwUBISAgDBw6kY8eO1g+OEELcaX9EwjuL4PBZaFMfRnSFUfPgZCzY62FQe2gWAp+vKgy+n2sDnw0Ex2LOoE6PgLcXFeadl8LQ3b/wUZe+N/265ZGwPLKwbRc7yPonjvd2gMcqaxy4rEjLBaOCYA8Y3lhjRaRiW8yVdVT1AD8nOJMKnatpfNlBRwVXiUiEMCMfCTNlEqSvWrUKZ2dnHn/8cVxcXGjTpg0//fQTw4cPx86usIn8/HxGjhzJvn37eOyxx3jiiSewt7fn1KlTbNmyxRSkW5Oens7QoUOJjY3lySefpE6dOhw9epRhw4bh6elpVvfgwYNcvnyZrl274uNT/IiJNStWrCA5OZknn3wSNzc31q9fz1dffUWFChUIDQ011YuIiCA9PZ0ePXrg5+dHfHw8q1evZvjw4cyePZuHHnrIbL1ZWVm88sorPPjggwwfPpykpCRTX4cPH46zszMDBw7Ey8uLHTt2WPy4KfL1118zf/58WrRowauvvopOp2Pr1q28//77jB49mn79+t3U9gohRJlLy4LQjwqDb4CIvbBuPxj+CbDzCuDfmwofRaavBTs9TB5kub7UTHhrYWFqSynNeaxTqdeRddVAe3IerIsy79fxJHjtV8u+nksrfAAsP6FIzDayuV/pzhoLce+RKP1qpQ7S8/Ly2LBhAx06dDClcHTv3p3Nmzezc+dO2rZtC8DSpUvZt28fL730EsOHDzdbh/EGoyOLFy/mwoULvP/++/Tp0weAPn36UKNGDWbMmEFgYKCp7qlTpwCoU6fOTW/LpUuX+O9//2safX/yySfp3r07y5YtMwvSx44di7Ozs9lrn376afr168eCBQssgvTU1FT69evHK6+8YlY+bdo0jEYj8+bNM6UGPfPMM4wePZrjx4+b1T1+/Djz588nLCyMkSNHmsqfffZZ3n77bWbNmkW3bt1wdXW96e0WQogys+nglQC9iKEEI+DLd1oP0n/aVyYB+p9B1Yn38Cr1esrKL+cUCVkKPxcJSoQwkY+DmVJPwbhlyxZSU1PNLhR97LHH8PPzY/Xq1aayDRs24ObmxuDBgy07cYOr7rdt24anpydPPfWUWfkzzzxjEZRmZhZ+Obi5ud3sptCjRw+z9BgnJyceeOABzp07Z1bv6gA9KyuLlJQU9Ho9DRs25OjRo1bX/dxzz5k9T0xM5MiRI7Ru3dosd1/TNIv0GijcfwDdunUjJSXF7NGmTRsyMzM5fPjwTW/z7ZKUlERu7pVc0YyMDNLT003P8/LySExMNHtNbGzsdZ/HxcWZXUcgbUgb0obttZHlcotjP77uVttICfGjLK4e6vPCO2WwlrLjYqcw5GSYnt/Nx1zauLfaKF8yB+PVSj2Svnr1ary9vQkICOD8+fOm8ubNm7NhwwYSEhLw8/Pj3LlzhISE4OjoeNNtxMTEUKdOHVPqTBEHBwcqV65s9oYsCtozMjK4WZUrV7Yo8/T0JDU11aI/s2bNYvfu3WZtQ2GQfS1vb2+LHw0XLxbe+c7axbXWyqKiogDo27f4fMprP4jl6dpUo2u338HBAV9fX7Oyq8+IWHtesWJFaUPakDZsvY3QJoV56NuPXVlQ2QcuJJnVxdMFUrMK/6/TwQd9rLbh1ay+5fpu0v7AYM4XM0f67eBqD5nF3/gUgLeb6Kjgc2VQ6K4+5tLGPdWGsB2lCtIvXrzInj17UErRu7f1yWXXrl1LWFhYaZoBrAe/1hTNfX7ixImbbqMks8pkZmYyZMgQcnJy6N+/PyEhIbi6uqJpGgsXLmTPnj0Wr3FycrIou9WZZWbMmGHxY6VIzZo1b2mdQghRZjQNNvwLFm29cuFor+Yw4ydYthMCPOCjAVDRC+b9Upga82wraFar+HVumQBzN8OPuyEjG2IS4XIqZN8gEv6HXpXuglMoHNOr4QntqsD2C4XPu1aD7jV1bD6nuJAOOk1Rx0fHSw3htwsQcdpIZDJU84QX6uuITIaTyYou1TV61LS5ewkKUf5k8NxMqYL0iIgIlFKMGTMGDw8Pi+Vz585lzZo1hIWFERwczNmzZ8nNzb3p0fSgoCDOnj1LQUGBWYCal5fHhQsXzNp+8MEH8ff3Z9u2bSQnJ+Pt7X3rG2jFnj17SEhI4MMPP6Rnz55my7755psSr6do1D46OtpimbWyqlWrsmvXLipUqCA3YRJC2DZnR3i1i3nZu08VPq72YQkvdtfp4OXOhY+rfbQcPvzPDV/+YNx5fDLTSHK1/J661oz2sDEK9lwqTIV/rgF83kZ/3akXO1azLOtTB/rUMQ/Eu96wdSHucxKkm7nln/JGo5GIiAhq1KhB79696dixo8UjNDSUc+fOceDAAUJDQ8nIyGDevHkW67rRqHLbtm1JTU1l1apVZuXLli0z5aAXsbe3Z8SIEWRlZfF///d/VtNe8vPzmTt3LnFxcTe93UWj7df2effu3Rw5cqTE6/H19aVBgwbs2LHDLChXSrF48WKL+l27Fv55nzVrFgUFBRbLi2aMEUKI+8a/+sHFueBnOdXutUb/uuqGdTRg1CN2rO1jx6URdsSPtGNae7ubmhtdCFEakpN+tVseSf/jjz+Ii4tj6NChxdZ5/PHHmTlzJqtXr2bMmDHs2LGD+fPnc/z4cZo3b46joyNnzpzh7NmzfP3118Wu54UXXmDjxo18/vnnREZGUrt2bY4ePcrWrVsJCgrCYDCY1e/evTuXLl1i9uzZ9OrVy3THUcB0x9H4+HiLkfCSaNy4Mb6+vkyfPp3Y2FgCAgKIjIxk3bp1hISEmGaXKYk333yTYcOGMXjwYPr164eXlxfbt2835blfneLToEEDXnnlFebMmcOAAQPo1KkT/v7+JCQkcPz4cXbu3Mnu3btvenuEEOKuFugDMXPh242Fc7EXoyQ56Y9VKsuOCSFumsTlZm55JL1o5pbHH3+82DpVqlShVq1abN68mby8PGbOnMmwYcO4dOkSX3/9NbNmzeLo0aO0b9/+um25u7vz73//m44dO7Jx40ZmzJhhukFQQECA1dcMHjyYJUuW0KpVK7Zv386UKVOYOnUqv//+O23btuU///lPsa+9UV9mzpxJw4YNWbZsGdOnT+fMmTPMmDGDunXr3tS6GjduzKxZs6hWrRqLFy/m22+/JSAggM8++wzAIi1o6NChTJ8+nYCAAH744QcmTZrEypUryc/P5513bGvmAiGEuGMc7eG1buBk/WZIWXb2LGx6/e8ZBx3s6C/zlgtRrjTN/HGf09StXsEobptjx47xwgsvMHLkyDK56FYIIe4LH/0HPlxuUZzs6ITPR4uK/dIfUA9mPa7Hy0mCAiHKk/aBeQqz+uT+vveLXF5ejpRSZvObFpUtXLgQgEcffbQceiWEEHepMX2tni6/7O593VG5xV0lQBdC2J5Sz5Mubl1eXh49evSga9euVK1alfT0dLZv386hQ4cIDQ296fQZIYS4r+n1EDEGuk80K66VEEuNy7Gc8becD9pJB3qdBOhC2AT5KJqRIL0c2dnZ0bJlS7Zt20ZCQgJGo5GgoCBGjhzJ888/X97dE0KIu0+XhyyKNGDHN+Oo/OG3Fst8LW9jIYQoNxKlX02C9HKk1+sZN25ceXdDCCHuHTv/tlpcKT2FBy5Ec7hyNbPy+r5WqwshyoPE6GYkJ10IIcS9w7H4sacqKQkWZZPby9egEDZDpkk3I3+dhBBC3Dua1Sp20aPx0WbPu1aHBwPka1AI2yFR+tUk3UUIIcS9Q6cDLxdIybJY9H+BCfQO0/FzlKJVZY1mlSRAF0LYLvkLJYQQ4t7yeneLIgWoL16kgZ+Ot5rqJUAXwhbJQLoZ+SslhBDi3hL+LFTzNz1VwP7QGuDsUH59EkKImyTpLkIIIe49Z2bD2r0YDkbxoyGGlEA3GpV3n4QQ1yej52ZkJF0IIcS9R9OgR1OM7/UiJdCtvHsjhCgJTTN/3OckSBdCCCGEEMLGSLqLEEIIIYQofzJ4bkZG0oUQQgghhLAxMpIuhBBCCCFsgAylX02CdCGEEEIIUf4kRjcj6S5CCCGEEELYGBlJF0IIIYQQ5U9G0s3ISLoQQgghhBA2RkbShRBCCCFE+ZMbGJmRkXQhhBBCCCFsjIykCyGEEEKI8icD6WZkJF0IIYQQQggbIyPpQgghhBCi/MlIuhkJ0oUQQgghhA2QKP1qEqQLIYQQQojyJzG6GclJF0IIIYQQwsZIkC6EEEIIIYSNkXQXIYQQQghR/iTdxYyMpAshhBBCiLtGeHg4bm5u5d2N205G0oUQQgghRPmTkXQzMpIuhBBCCCGEjZEgXQghhBBClD9NM3/coiNHjhAaGoqbmxseHh48+eSTnDp1yrR88ODBtGnTxvQ8OTkZnU7Hww8/bCrLzs7G0dGR77777pb7UVoSpAshhBBCiHvC+fPnad26NZcuXWLRokXMnTuXyMhIWrduzeXLlwFo06YNf/75Jzk5OQDs2LEDR0dHDh48SEpKCgC///47eXl5ZsH8nSY56aLMKaVIT08v724IIQT5+flkZ2cDkJaWhr29fTn3SAjb5+7ujlaKkexbVgZNTps2jby8PDZu3Ii/vz8AzZs3p1atWsyaNYvw8HDatGlDbm4uu3fvpl27dmzfvp2ePXuydetWfvvtN7p378727dsJDg6matWqpe/ULZIgXZS59PR0PD09y7sbQghh5o033ijvLghxV0hNTcXDw+OOt6veKX1YumPHDjp06GAK0AGCg4Np0aIFO3bsAKB69epUqVKFbdu2mYL0sLAwjEYj27ZtMwXp5TmKDhKki9vA3d2d1NTU8u7GfSUjI4Nu3brx008/3RfTUtk6OR62RY6H7ZFjYluuPR7u7u7l3aVblpycTOPGjS3KK1asyIkTJ0zP27Rpw/bt28nIyOCvv/5i/vz5GAwGvvvuO/Lz89m9ezdfffXVHey5JQnSRZnTNK1cfoHfz3Q6HXq9Hg8PD/nCswFyPGyLHA/bI8fEttxLx8PHx4dLly5ZlMfFxeHj42N63qZNG9544w22bt2Kp6cnDRo0wGAw8NZbb7Flyxays7PLfSRdLhwVQgghhBD3hFatWvHLL7+QmJhoKjt//jy7du2idevWprI2bdqQnZ3NlClTaN26NZqm0ahRI9zd3Zk4cSIVK1akVq1a5bEJJjKSLoQQQggh7ioGg4EVK1ZYlL/++ussWLCAzp0788EHH2AwGBg3bhw+Pj6MGDHCVK9u3boEBASwbds2vvjiC6AwE6BVq1ZERETQr1+/O7YtxZEgXYh7gIODA0OHDsXBwaG8uyKQ42Fr5HjYHjkmtuVuPB45OTn07dvXonzBggVs376dd955h4EDB6LT6Wjfvj1Tp041u5gUCkfTV6xYYZbW0rZtWyIiIso91QVAU0qp8u6EEEIIIYQQ4grJSRdCCCGEEMLGSJAuhBBCCCGEjZEgXQghhBBCCBsjF44KcZcquunCzp07OXPmDAaDgZCQEIYOHUqzZs0s6i9ZsoTly5eTmJhISEgIo0aNokmTJuXQ83vX7t27iYiI4MiRI1y4cIG+ffvy3nvvWa0rx+POOHv2LFOmTOGvv/7C2dmZLl26MHLkSJycnMq7a/e88+fPs2TJEo4cOcLp06cJDg5m+fLlFvV+++03vv76a6KjowkICOC5556zekGgKJ3Nmzezfv16/v77b1JTUwkKCqJPnz707t0bne7KmK0cD9shI+lC3KVyc3NZsGABtWvXZty4cUycOBF/f39GjBhhuvVxkSVLljBr1iz69evHjBkzCAoK4vXXX+fUqVPl1Pt7065du4iMjOThhx++7h375HjcGenp6QwbNozMzEw+//xzXn/9ddavX88nn3xS3l27L5w+fZqdO3cSFBRE9erVrdY5dOgQb7/9NnXr1uXLL7+ke/fuTJ48mVWrVt3Zzt4HvvvuOxwcHBg1ahTTp0+nXbt2TJ482eyumnI8bIvM7iLEXcpgMJCZmWl2d1elFAMHDsTV1ZU5c+YAkJeXR+fOnenVqxevv/666bXPPvssISEhfPrpp+XS/3uR0Wg0jUj16NGDVq1aWYyky/G4cxYuXMjcuXNZu3YtXl5eAGzYsIGxY8fy3//+t9jAUZSNqz8P4eHhHDt2zGIkfdSoUaSmprJo0SJT2SeffMKOHTtYt26d2QivKJ3k5GS8vb3Nyr744gt+/PFHtmzZYgrg5XjYDtnbQtylim7hfDVN06hduzaXL182lR06dIiMjAy6dOli9tpOnTqxa9cu5Hd62SnJF5gcjztn165dNGvWzBSgA3To0AEHBwd27txZfh27T9zo85CXl8eePXvo3LmzWXloaCgJCQmcOHHidnbvvnNtgA5Qp04dcnNzSUtLk+NhgyRIF+IeYjQaOXTokNkIYVRUFADVqlUzq1ujRg0yMzOJj4+/k12878nxuHOioqIsRssdHBwICgoyHQdRfmJiYsjPz7c4RjVq1ACQY3QHHDhwAE9PT7y9veV42CAJ0oW4hyxbtoyzZ8/y3HPPmcrS0tJwcHCwuFCuKGc6LS3tjvbxfifH485JS0uzem2Au7u77GcbUHQMrj1G8lm4M44dO8aaNWvo378/er1ejocNktldhLAhGRkZJCQk3LBepUqVLG7fvG/fPr788kuef/55Hn74YbNlmqZZrKMorcLaMlGoNMfjeuR4lC9JKbIt8p6/8xISEhg9ejQNGjQgLCzMbJkcD9shQboQNmTLli2MHz/+hvW+//576tSpY3p+8uRJ3n77bdq1a8eoUaPM6np4eJCbm0tubi6Ojo6m8oyMDMBy1ERccavH43rkeNw5Hh4epKenW5RnZGTIRaM2oOiammtHaIuO2bXX3IiykZGRwahRo3BycuKLL77Azq4wFJTjYXskSBfChvTo0YMePXrc1GtiYmIYOXIkdevWZcKECRajIEXBSFRUFHXr1jWVnzlzBldXVwICAkrf8XvUrRyPG5HjcedUr17dIo82Ly+PmJgYevbsWU69EkWCgoKwt7cnKiqKFi1amMrPnDkDID+kboPc3FzeeustkpKSWLBggdlF1XI8bI/kpAtxF0tISGDEiBH4+voyZcoU7O3tLeo0atQINzc3Nm3aZCozGAxs3ryZFi1ayKnNO0yOx53TokUL9uzZQ0pKiqlsy5Yt5OXl0bJly/LrmAAKL+Jt2rQpmzdvNiv/+eef8fPzK/HZKVEyBQUFvP/++0RGRvLVV18RGBhotlyOh+2RkXQh7lI5OTmMGjWK5ORk3nzzTYsRwwceeAAo/MM7ePBgZs2ahbe3N3Xr1mXVqlVcuHCBiRMnlkfX71mxsbEcPXoUKDw+Fy5cMH3hdezYEZDjcSc9/fTTLF++nLfffpshQ4aQlJTEtGnT6Nq1q4wK3gE5OTn89ttvQOFnIzMz0/R5eOSRR/D29mbIkCEMHTqUjz/+mNDQUA4ePMiqVasYM2aMzMldxiZNmsSOHTsYNWoUOTk5HD582LSsevXquLm5yfGwMXIzIyHuUhcvXrzuKfu9e/ea/q+UMt2GPikpSW5Df5tEREQUm8Mux6N8nD17lsmTJ3PgwAGcnJzo0qULr732msXsOqLsXe9v1OzZs03v96Lb0EdFRZluQ9+vX7872dX7Qo8ePYiNjbW6TI6HbZIgXQghhBBCCBsj5y6EEEIIIYSwMRKkCyGEEEIIYWMkSBdCCCGEEMLGSJAuhBBCCCGEjZEgXQghhBBCCBsjQboQQgghhBA2RoJ0IYQQQgghbIwE6UIIIYQQQtgYCdKFEHe18PBwNE0jOjq6vLtCfHw8np6efPvtt6ay6OhoNE0jPDy8/DombEa1atVo167dLb++Xbt2VKtWrcz6c68YOXIk9erVo6CgoLy7IkSZkSBdCBsUHx/P6NGjadiwIe7u7nh6elKrVi2effZZ/ve//5nVbdeu3XVvcT5lyhQ0TWPr1q1Wl6empuLi4oKmaSxcuLDY9VSrVg1N00wPBwcHqlWrxpAhQzh//vytbOY951//+hc+Pj68+OKL5d2VOyY8PJxVq1aVdzfEHXTgwAHCw8Pv+A/jrVu3Eh4eTkpKisWyMWPGEB0dzezZs+9on4S4nSRIF8LGnD9/nkaNGjFr1ixatGjBZ599xsSJE+nevTv79+9n/vz5Zdre0qVLycnJoWbNmsybN++6dQMDA1myZAlLlixhxowZNG/enPnz59O8eXMSEhLKtF93mwsXLjB//nxGjBiBvb29qTw4OJjs7GzGjh1bjr27fcaPHy9B+n3mwIEDjB8/vlyC9PHjx1sN0itVqsQzzzzDxIkTZTRd3DPsyrsDQghzkydP5tKlS6xZs4YePXqYLZs2bRoxMTFl2t68efNo06YNzzzzDMOHD+fEiRPUqVPHal0PDw+ef/550/Nhw4YREBDAzJkzmT9/PqNHjy7Tvt1Nvv32W5RSPPfcc2blmqZd90yHEKJsDBw4kEWLFrFq1Sr69OlT3t0RotRkJF0IGxMZGQlA+/btrS4PCgoqs7YOHTrEvn37CAsLo3///jg6Ot70SH2XLl0AOH36dLF11q9fj6ZpfPHFF1aXt27dGl9fX/Ly8gD4888/CQsLo3bt2ri4uODu7k7Lli1ZuXJlifoUFhaGpmlWl2maRlhYmEX5smXLaNWqFe7u7ri4uNC8eXNWrFhRovYAli9fTuPGjQkMDDQrt5aTfnVZ0eucnZ0JCQlhwYIFAJw7d44+ffrg4+ODu7s7AwYMIDU11ep2Xr58mRdeeAFfX19cXFzo0KED+/bts+jj119/TefOnalcuTIODg4EBgby/PPPFzsiumXLFrp164avry9OTk7UqFGDwYMHk5CQwNatW037eNGiRaY0qJLkSycmJjJq1CiqVq2Kg4MDlSpVYsiQIcTGxprVK2pj4cKFzJ07l/r16+Po6EhwcDCff/75DduBstvXAEeOHOHpp5/Gz88PR0dH6tSpw4QJE8jNzbWoe/z4cbp164abmxteXl48+eSTnDlzpth+bt68mc6dO+Pl5YWTkxONGjUqk9SNBQsW0KRJE9PnqH379mzcuNGiXnGfi4ULF5qly4WFhZnSudq3b2867kXv76JrRI4ePcqoUaOoWLEiTk5ONGvWjE2bNpmt+3rXa1x7rUm7du0YP348ANWrVze1e3WKXrt27XB1dWXZsmU3t5OEsFEyki6EjalRowYA//73v3njjTeKDTavVVy6SVZWVrGvmTt3Lq6urvTp0wc3Nzd69uzJ4sWL+eSTT7CzK9mfh5MnTwLg5+dXbJ3OnTsTGBjI4sWLeeutt8yWRUVFsXPnToYNG4aDgwMAK1euJDIykv79+xMUFERiYiKLFi2id+/efP/99wwYMKBEfSupsWPH8sknnxAaGspHH32EXq9n5cqV9O3bl5kzZzJixIjrvj4+Pp6///6b4cOH31S7a9euZc6cOQwbNgwfHx/mz5/PSy+9hL29PWPHjuXxxx9n4sSJ7Nmzh/nz5+Pk5GT1R1RoaCg+Pj6Eh4cTFxfHzJkzadu2Lbt27aJRo0amelOnTqVFixZ06tQJLy8vjhw5wty5c/n11185fPgwvr6+prpF/apSpQrDhw+natWqnDt3joiICGJiYqhXrx5Llixh4MCBtG7dmpdffhkANze3625zWloarVq14sSJEwwaNIhmzZpx5MgR5syZw8aNG9mzZw8VKlQwe80333xDfHw8Q4YMwdPTk++++4733nuPoKCgEr8XSruv9+/fT5s2bdDpdIwYMYKgoCB+/vlnxo0bx++//85PP/2ETlc47hUVFUWrVq3Iyspi+PDh1KhRg19++YX27dtb/Tx+++23vPrqqzz66KN88MEHuLm5sWnTJoYNG8bp06eZPHlyibbxWmPGjOHTTz/lkUce4aOPPiInJ4d58+YRGhrKkiVLLM76lMQrr7yCo6Mj3377LWPGjKFevXoAZu8zgBdeeAG9Xs97771Heno6c+bMoWvXrqxbt47OnTvfdLsffPABPj4+rFy5kmnTppn+3rRo0cJUR6/X07RpU7Zt24ZSqsR/O4WwWUoIYVNOnz6tPDw8FKCqVKmiBgwYoKZNm6b27t1rtX7btm0VcMPHli1bzF6Xk5OjfHx81AsvvGAq++mnnxSgVq9ebdFOcHCwCgkJUZcvX1aXL19WZ86cUfPnz1eenp5Kr9ergwcPXne73nnnHQVY1AsPD1eA+uOPP0xlGRkZFq/PzMxUtWvXVvXq1TMrHzdunAJUVFSUqWzQoEGquD9vgBo0aJDp+d69exWg3n//fYu6Tz75pHJ3d1dpaWnX3bZff/1VAWrq1KkWy6KiohSgxo0bZ1Hm6uqqzp07Zyq/fPmycnJyUpqmqenTp5utp1evXsrOzk6lp6dbbGevXr2U0Wg02yZN01THjh3N1mFtv27evFkBatKkSaay8+fPKwcHB1W/fn2Vmppq8RqDwWD6/7X780Y++OADBVhs33fffacANXToUFPZli1bFKACAwNVcnKyqTwzM1P5+fmpRx999IbtldW+btmypdLpdGrfvn1mdYcOHaoA9f3335vK+vfvrwC1fv16s7ojRoxQgGrbtq2p7OLFi8rR0VE9++yzFn0fNWqU0ul06tSpU6aytm3bquDg4Btu94kTJ5Smaap58+YqJyfHVJ6QkKAqVqyovL29zd4PxR3HBQsWWPz9sFZWpOjz2KxZM5Wbm2sqP3/+vHJ1dVW1atUyvVetfTauXc/Vn2trZdcaPHiwAlRcXFyxdYS4W0i6ixA2pkaNGhw8eJDhw4djNBpZunQpb775Jk2aNKFRo0ZW0xjs7e3ZtGmT1UfRCOe1Vq5cSVJSktkp7i5duhAYGFjsBaSnTp3C398ff39/atSowUsvvYS3tzc//vijxUjatQYNGgTA4sWLzcq/++476tatS7NmzUxlrq6upv9nZWWRmJhIVlYWHTp04Pjx46SlpV23rZuxdOlSoHDkLyEhwezRs2dP0tPT+f3336+7jsuXLwPg4+NzU20/9dRTVKlSxfTcz8+P2rVro9PpePXVV83qtm7dmoKCAqupKaNHjzYbNXzkkUfo1KkTv/76q9m+KtqvRqOR1NRUEhISePDBB/H09OSPP/4w1fvvf/9LXl4e//rXv/Dw8LBor2jE+FasXLkSHx8fi7MOAwYMICQkxGpK04svvoiXl5fpuYuLC48++qjpLE5JlGZfX758mZ07d9KtWzcefvhhs7r/+te/AEyzLhmNRiIiInjwwQcJDQ01qztmzBiLfq1YsYLc3FxefPFFi/dfjx49MBqN/PLLLyXeziKrV69GKcXo0aNxdHQ0lfv6+jJ8+HCSk5PZsmXLTa+3pN58803TmTEoTNN77rnnOHnyJEePHr1t7RadDYqPj79tbQhxp0i6ixA2qFq1asyaNYtZs2YRGxvL77//zqJFi1izZg3du3fn6NGjZgGhTqejY8eOVtd14MABq+Xz5s3D39+foKAgTp06ZSrv1KkTS5cuJS4ujooVK5q9pkqVKqYUgKKc5pCQkBKdVm7YsCEPPfQQS5cuZdKkSej1enbu3MmpU6f49NNPzerGx8czduxYVq9ebfXLNiUlxWrweCuOHz8OQP369Yutc+nSpeuuo2j7lVI31Xb16tUtyry9vQkMDDQLrIrKoTCf+1pFKQdXq1+/Phs3biQqKooHH3wQgF9//ZUJEybwxx9/kJOTY1Y/OTnZ9P+i4LfodWXpzJkzNG7c2GwGHCjchw0aNGD16tWkpaWZHd+iFLCr+fr6Wt0XxSnNvi7KJW/QoIHFOqpUqYKnp6epTnx8PBkZGVaPSaVKlfD09DQrK3r/FV3bYc2N3n/WXK/PDzzwgFmd26G49yQUXr/SsGHD29Ju0WdQUl3EvUCCdCFsXGBgIL1796Z3794MGDCAH374gXXr1pnNsnKzoqOj+eWXX1BKUbt2bat1Fi1axHvvvWdW5uLiUuyPgZIYNGgQb7zxBps2bSI0NJTFixej0+nMtsVoNNKpUyf+/vtvRo0aRdOmTfH09ESv17NgwQKWLl2K0Wi8bjvFfUFbm5qt6Et93bp1FoFjEWuBztX8/f0B80C3JPR6/U2VQ8l/CFwbrPz555907tyZkJAQPvvsM6pXr46zszOapvHss8+a7dOb/bFRVopr93r7o6RKs69vZX+UNEgsWveCBQuKvSjc2o+Ukq73Zpdd61anM7S2/de+J6+3j2613aSkJODKZ1KIu5kE6ULcRR577DF++OEHLly4UKr1LFiwAKUUc+bMsZqiMWHCBObPn28RpJfWgAEDePfdd1m8eDHt27dn+fLldOjQwSw4OXz4MIcOHeLDDz80zeZQZO7cuSVqp2ibkpKSzLbP2shh7dq12bBhA0FBQaYRxpvVoEEDNE0zOyNxJx0/fpxHH33Uokyn05lmW/nhhx8wGAysX7/ebFQ5MzPT4sdF0RScBw4csDoiWho1atQgMjKS/Px8ix9Fx44dw8/Pr8zOkpSVmjVrAlhN04iJiSE1NdVUJyAgADc3N44dO2ZR9+LFixazxhT9SPb19S3VD+Dr9fnaKVWLtqOoDhR+ZooC3KtZ+8yU5AfIsWPHLFLgis4aFP3ouPpzWlbtFqXkBQQE3LCuELZOctKFsDFbtmwhOzvborwo1xWun5pxI0ajkYULF1K/fn1efvll+vTpY/F47rnniIyM5Lfffrvldqzx9/ena9eurFq1iu+//56UlBRTrnqRopHNa0f7jhw5UuIpGIsCn82bN5uVT5061aJu0Sj+mDFjrI7elSS31d/fn/r16/Pnn3+WqH9l7fPPPzfbX/v372fz5s106NDBFPAWt18nTpxocWaiT58+ODg48PHHH1vN/796HW5ubjd1BqFXr14kJSUxZ84cs/L//Oc/nDp1it69e5d4XXeKv78/LVu2ZN26dRbpY5988gmAqd86nY6ePXty8OBBNmzYYFZ34sSJFuvu27cvjo6OhIeHW535JTU11eoUjzfy1FNPoWkaU6ZMMU1tCoUB8ddff423tzft2rUzldeuXZvff//drA/JycmmaSqvVjSDz/WO+7Rp08zajYmJYenSpdSuXdt0Zsrd3Z2KFSvy66+/mr2nzpw5Y/UGWTdq12AwsHfvXtq0aSPpLuKeICPpQtiYqVOnsnPnTrp3784jjzyCp6cncXFx/Pjjj+zbt4/27dvTrVu3W17/pk2bOHfuHB9++GGxdZ5++mnef/995s2bR6tWrW65LWsGDRrEmjVrePPNN3Fzc7MIyurVq0eDBg34/PPPycrKok6dOkRGRjJnzhwaNmzI/v37b9hG//79GTNmDC+//DJ///03vr6+rF+/3uo0lU2bNmX8+PGMGzeOxo0b069fPypVqkRsbCz79u1j3bp1ZsFGcfr27ctHH31EbGysxVzpt9vZs2fp0qULPXv2JDY2lpkzZ+Ls7Gz2o6RXr15MmzaNJ554gpdffhkHBwc2bdrEoUOHLKbPDAoKYvr06YwYMYIHHniAF154geDgYC5cuMDq1auZP38+jRs3BqB58+Zs3ryZyZMnU6VKFVxdXS1uwnW10aNHs2LFCkaNGsVff/1F06ZNTVMwBgUFMWHChNuyj0rryy+/pE2bNrRt25YRI0ZQuXJlNm7cyJo1a+jSpQvPPPOMqe7HH3/Mhg0b6NWrFyNGjDBNwbh3716r+/qbb75hyJAh1KtXz7SvL1++zOHDh1m1ahXHjh0r0fzzV6tVqxbvv/8+n376KS1btqR///6mKRjj4uJYvHix2QXaI0eO5Pnnn6dDhw4MHDiQlJQU/v3vfxMcHExcXJzZups0aYJOp+PTTz8lOTkZFxcXGjZsaJZnXlBQQOvWrenfvz/p6enMnj2b7OxsvvrqK7MAeuTIkYwdO5auXbvy1FNPcfHiRWbPnk3Dhg3Zs2ePWbvNmzcH4P/+7/9M93Vo3ry56czQ1q1byczMpF+/fje1r4SwWXd0LhkhxA39/vvv6q233lJNmjRRAQEBys7OTnl6eqpHH31UTZ061Ww6NaUKp2RzdHQsdn2TJ082my6tb9++ClCHDh26bj8aNWqkXF1dTdMPBgcHqzp16pRu45RSubm5ysfHRwEqLCzMap3o6GjVp08f5efnp5ydnVXTpk3V//73v5ualm337t2qRYsWytHRUfn6+qqhQ4eq5OTkYqeaW7t2rercubPy9vZWDg4OKigoSIWGhqqvv/66RNt14cIFZWdnp6ZMmWJWfr0pGK1NPVfcFHvWpr0rmoIxPj5ePf/888rHx0c5Ozur9u3bW52yc+XKlerhhx9WLi4uytfXVz3zzDPq7NmzKjg42GxawCI///yz6tixo/Lw8FCOjo6qevXqasiQISohIcFU5++//1YdOnRQbm5uCijR9IAJCQlq5MiRKigoSNnb26uKFSuqwYMHqwsXLpjVK5qCccGCBRbruN40m1crq32tlFKHDx9WvXr1Uj4+Psre3l7VqlVLhYeHW3wmlVLq2LFj6oknnlCurq7Kw8ND9ezZU50+fbrYff3bb7+pp556Svn7+yt7e3sVGBio2rVrp6ZMmaKys7Nv2OfizJs3Tz388MPKyclJubq6qrZt26oNGzZYrfv555+rqlWrKgcHB1W3bl01b968YvfFvHnzVO3atZWdnZ3Z/i36PB45ckSNHDlSVahQQTk6OqqmTZuqjRs3WrSZn5+v3n33XVWxYkXl6OioHnroIbVmzZpiP9effPKJqlq1qtLr9RbvjUGDBqmKFSuqvLy8Eu8fIWyZplQ5XSEkhBD3mFdffZWNGzdy4sSJYi9CLUthYWEsWrSo3C70FOJa4eHhjB8/nqioqJse/S+N2NhYatasyaRJk3jttdfuWLtC3E6Sky6EEGVkwoQJJCYmWs3jFULcPhMnTiQ4OJhhw4aVd1eEKDOSky6EEGUkICDAYvYOIcTt99VXX5V3F4QoczKSLoQQQgghhI2RnHQhhBBCCCFsjIykCyGEEEIIYWMkSBdCCCGEEMLGSJAuhBBCCCGEjZEgXQghhBBCCBsjQboQQgghhBA2RoJ0IYQQQgghbIwE6UIIIYQQQtgYCdKFEEIIIYSwMf8Pfb1k6dmJJTIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x390 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = X.columns\n",
    "#print('Features ', features[:])\n",
    "featureNames=[]\n",
    "for i in range(0,len(features)): # ignore the first and last feature \n",
    "    featureNames.append(features[i])\n",
    "nFeatures=len(featureNames)\n",
    "print('Feature Names ', featureNames)\n",
    "\n",
    "print('\\n shap base_values ',shap_values.base_values[0])\n",
    "base=shap_values.base_values[0]\n",
    "shap.plots.beeswarm(shap_values,max_display=15) # ,feature_names=featureNames)\n",
    "shap.plots.bar(shap_values) # ,feature_names=featureNames) # all shap values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e618af68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " =========    SHAP 2 ANALYSIS DONE ========\n"
     ]
    }
   ],
   "source": [
    "shap2_values = shap.TreeExplainer(smodel).shap_values(X)  # this is slow  X for train data, X2 for all \n",
    "print(' =========    SHAP 2 ANALYSIS DONE ========')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8892a567",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " =========    SHAP DEPENDENCE  =============\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "F1='pKa_water'  # 'AcidCharge', 'alpha', 'beta', 'pi', \n",
    "F2='TotalCharge'\n",
    "F3='alpha'\n",
    "F4='AcidCharge'\n",
    "F4='pi'\n",
    "F5='beta'\n",
    "print(' =========    SHAP DEPENDENCE  =============')\n",
    "shap.dependence_plot(F1, shap2_values, X)\n",
    "shap.dependence_plot(F2, shap2_values, X)\n",
    "shap.dependence_plot(F3, shap2_values, X)\n",
    "shap.dependence_plot(F4, shap2_values, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3e4ef366",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9940542453742085\n",
      "Mean absolute error: 0.5260489459256751\n",
      "Median absolute error: 0.325639751443612\n",
      "Maximum error: 5.938363063631289\n",
      "General accuracy between predicted and DFT data:\n",
      "\n",
      "y = 1.013269577259333X + -0.16957302528293638\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gradient boosting\n",
    "# Read in data\n",
    "GB_Train_pKaML = GB_FinalModel.predict(X)\n",
    "# Plot data\n",
    "plt.plot(GB_Train_pKaML, Y, 's')\n",
    "plt.plot(Y, Y)\n",
    "plt.xlabel('pKa(ML - Gradient Boosting)')\n",
    "plt.ylabel('pKa(DFT)')\n",
    "GB_r2_score = r2_score(GB_Train_pKaML, Y)\n",
    "GB_MAE_score = mean_absolute_error(GB_Train_pKaML, Y)\n",
    "GB_MedianMAE_score = median_absolute_error(GB_Train_pKaML, Y)\n",
    "GB_MaxError_score = max_error(GB_Train_pKaML, Y)\n",
    "print('R^2:', GB_r2_score)\n",
    "print('Mean absolute error:', GB_MAE_score)\n",
    "print('Median absolute error:', GB_MedianMAE_score)\n",
    "print('Maximum error:', GB_MaxError_score)\n",
    "\n",
    "#determine slope and intercept\n",
    "from scipy.stats import linregress\n",
    "GB_SlopeIntercept = linregress(GB_Train_pKaML, Y)\n",
    "print('General accuracy between predicted and DFT data:\\n')\n",
    "print('y = {}X + {}'.format(GB_SlopeIntercept.slope, GB_SlopeIntercept.intercept))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b7060d8",
   "metadata": {},
   "source": [
    "# Plot Results for Training Set - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "384a956c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9961837167762144\n",
      "Mean absolute error: 0.38084802058450407\n",
      "Median absolute error: 0.171947129699217\n",
      "Maximum error: 4.198055254206843\n",
      "General accuracy between predicted and DFT data:\n",
      "\n",
      "y = 1.016758786557315X + -0.21829975676420688\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random forest\n",
    "# Read in data\n",
    "RF_Train_pKaML = RF_FinalModel.predict(X)\n",
    "# Plot data\n",
    "plt.plot(RF_Train_pKaML, Y, 's')\n",
    "plt.plot(Y, Y)\n",
    "plt.xlabel('pKa(ML - Random Forest)')\n",
    "plt.ylabel('pKa(DFT)')\n",
    "RF_r2_score = r2_score(RF_Train_pKaML, Y)\n",
    "RF_MAE_score = mean_absolute_error(RF_Train_pKaML, Y)\n",
    "RF_MedianMAE_score = median_absolute_error(RF_Train_pKaML, Y)\n",
    "RF_MaxError_score = max_error(RF_Train_pKaML, Y)\n",
    "print('R^2:', RF_r2_score)\n",
    "print('Mean absolute error:', RF_MAE_score)\n",
    "print('Median absolute error:', RF_MedianMAE_score)\n",
    "print('Maximum error:', RF_MaxError_score)\n",
    "\n",
    "#determine slope and intercept\n",
    "from scipy.stats import linregress\n",
    "RF_SlopeIntercept = linregress(RF_Train_pKaML, Y)\n",
    "print('General accuracy between predicted and DFT data:\\n')\n",
    "print('y = {}X + {}'.format(RF_SlopeIntercept.slope, RF_SlopeIntercept.intercept))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c14a6af",
   "metadata": {},
   "source": [
    "# Plot Results for Training Set - Kernel Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "28cd2f46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9916005365290946\n",
      "Mean absolute error: 0.6554186469402092\n",
      "Median absolute error: 0.41159915115055146\n",
      "Maximum error: 8.650690696244233\n",
      "General accuracy between predicted and DFT data:\n",
      "\n",
      "y = 1.0203093172620468X + -0.2630093956438255\n"
     ]
    },
    {
     "data": {
      "image/png": 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R2YS5qzCbs3AhAChhwFn1MHjIjGJtnH48fjS1tVnP5H4YfIiIqNQsWYXZnIULG8mu4Gf1m5JaC+0K3Ab3UKTS4a0uIiIqNXNXYS5uRCjXVOUmSejZb2yMUO0Ghh6yCY74EBFRuaBCDv7SDJHURupfw3ZT62Lfp1bKUclLZc/WyIUw+BARUZk5evk27uoMBepNZZfwvXqWpNZMG4cMFNyjK7/Y/pFoXbsyd1InszH4EBFRmXnnxzMFam8r12O48lfxOMHYAiNyphQ4rzBhARUZesgiDD5EROQQauhxXjNUUhumfwO7TM0d0xC5BQYfIiIqtbQsXckn5dNC9he2qudIak20q5GFCjbsiqggBh8iIrJa7to9p1MyzH7PXOUaDFTuEI9/ND6KcTkTJOd4KGTIMQo265Mol9M+zh4TEwOZTIZJkyaJNUEQMGfOHFSvXh2enp6IiorC6dOnHdckEZELy12756ml+7Eo4a8Sz/eEFlc0AyShZ6B+RoHQE9s/EptGtoVaWfxfUXyai6zhlCM+R48eRVxcHJo2bSqpL1y4EIsXL8a6detQr149vP/+++jevTvOnz8Pb29vB3VLROSazFm7J9ej8jPYpHpfUmusXYN7KDgxOSygIiKCfcXFDouSuxI0kSWcLvjcvXsXAwcOxKpVq/D++3n/EQmCgNjYWMycORO9e/cGAHz++ecIDAzEhg0bMGrUqEKvp9PpoNPl3ZvOzMy07w9ARORmFnksRx/FPvH4a2MnTMkZXeL7gv08GWzI5pzuVtfYsWPRs2dPdOvWTVK/fPkyUlNTER0dLdbUajU6d+6MgwcPFnm9mJgY+Pr6ir9CQkLs1jsRkTvxQjauaAZIQs/zullmhR4ie3Gq4LNp0yYkJiYiJiamwGupqakAgMDAQEk9MDBQfK0wM2bMQEZGhvgrOTnZtk0TEbmhjvKTOK15WVJroF2LI0LDYt/HeTtkb05zqys5ORkTJ07E9u3bodFoijxPJpNJjgVBKFDLT61WQ61W26xPIiJ3t8wjFj0VR8TjLwzdMMswvNBzlXIgpndTNKz2YB8uztshe3Oa4JOYmIi0tDS0bNlSrBmNRuzduxeffPIJzp8/D+DByE+1atXEc9LS0gqMAhERke354B5OakZIar11c/CHUA/Ag5Azs2dDVKqgho+nBwK81Qw6VOacJvg89thjOHXqlKQ2bNgwNGjQANOmTUOdOnUQFBSEhIQENG/+YNVPvV6PPXv2YMGCBY5omYjIpeSu2ZPrYtpd8fdd5X/gM9WHkvPra9dBh7zbVgYT8M4PZwE8WKdn08i2DD1U5pwm+Hh7eyMiIkJS8/LyQpUqVcT6pEmTMG/ePISHhyM8PBzz5s1DhQoVMGDAAEe0TETkMnLX7Cns8fU1Hh/gMcVx8Xi14Qm8bxhU7PVyjAL6rzyIPVO7MvxQmXKa4GOOqVOnIjs7G2PGjEF6ejoeeeQRbN++nWv4EJHbeXh05mHm3GLKf42LaXcLhB4/ZOGERrpUyO5OW+BfKQLYfKLEHg0m4HxqJoMPlSmnDj67d++WHMtkMsyZMwdz5sxxSD9EROVBcaMzudRKOXZOiSoydJR0jR7yI1ipipXUwrXrsa3+o/C3oNfMbIMFZxOVnlM9zk5ERCUzZ0VlncFU7IhQcdeI95grCT2fGJ5BqHYDcpz739LkJvinlIiIzFIFGUjUvCqpPaGLwVmhloM6IrIcR3yIiKhEveQHJaFHJ3ggTLueoYecDkd8iIioGAK2qWajufyiWFmU0xdLjb0d2BOR9Rh8iIgIAJD4TzqSb98HACTfvo8ApOOIZqzknG66hbgo1Cj2OpW8VFDKHzy1RVTeMPgQERES/0lHn+V5Gzr3VezBEc1K8ThdqIiWuhUwFTNDwkMhg85gQrCfJ2J6N8UbX58s8XN9PD1K1ziRhTjHh4iIxJEeQMAvqmn40CMv9MzLeRHNdXHFhh7gwaKEA1YdRsqdbLQL84dKUfz5KoUc9YO4zhqVLY74EBG5mEpeKqiV8mIfafdQyJCWpUNSSoa4G3o13MIhzXjJeV10i3BZqFbYJQqV+5h8RLAvdr0RVepFFIlsjcGHiMjFBPt5YueUB6EjLUuLUV8kIscoSM7JMQoYvu4ogAeLGa6NSMIhzfvi69eFymin+xhCKW4MBPt5MthQucPgQ0TkgnJDR1IKCoSe/GQw4Tf5awg9959Ym5MzGOuMj5dFm0RljsGHiMjF5O6xdfDiLRxPTi/yvBqyNOxXT5LUOuhi8a8QYOcOiRyHwYeIyIWYs08XAAxV/Io5HuvF43TPmmiRPq9Ut7aInAGDDxGREylp1/W0LF2xoUcGEw6pxyNIljcSNCPnZVTv+CqEhL9s2itRecTgQ0TkJMwZzVHIin5/bdl17FK/Lqm11S7FdVTBB74aW7VJVK5xTJOIyEmYs+t6UfOYRyl+kISeM6ZaCNXG4zqqAAAaVvPBB32blrpHtVIuPh5PVB5xxIeIyIXJYcJx9Uj4yu6Ltdf1o/GNqVOBc9uF+Ze4/o9KIceKQS0Q4F34CBHX5qHyjsGHiMhFhcn+xf/UUyW1NtplSEOlAufmbjWRu/5PURhsyNkx+BARlRMlT1zWmn2tCYqtmOzxtXicaApHH/0cAIVPAlIrH8x84KKD5OoYfIiIygFzJi57FDdz+f8pYcAZ9TCoZEaxNl4/Dj+Y2hX7votpd8Xfc1SHXBmDDxFROWDOxOXiVmAGgIayf/CLeoak1lK7HLfgW+LnT9p8Qvy9WinHzilRDD/kkvhUFxGRC3hDuUkSeg4YGyNUu8Gs0POw3I1GiVwRR3yIiMoBS+bv5OcBAy5oBktqo/Sv4TdTa1u0ReRyGHyIiBws5U42Rn2RaPH7msj+xg/qtyS1SO1K3IG3rVojcjm81UVE5GDp9/Qlzt952FvKLyShZ4exOUK1Gxh6iErAER8iIieihh7nNUMltWH6N7DL1NwxDRE5GQYfIiI7KmltHku2d2gh+wtb1XMktabaVciEV4nvVcpliBvcEpnZBskTXETuhsGHiMhOzFmbR6WQY0xUnRKv9b5yDV5S7hCPfzK2wdicSWb3Eje4Jbo2CERSSobZ7yFyRQw+RER2Ys7aPHqjCbE7Lhb5uie0OKsZLqm9pJ+B/aYmFvWSu7dWJS9ViftxcaNRcmUMPkREdnL2eulGVx6RncVm9XuSWmPtGtyDZQsLqhQyMchwPy5ydww+RER2kHInGzO2Jln9/g89VqCvYq94/I2xI17PedXs9yvlMsx8siFq+VdA/SAfSZDhflzkzhh8iIjsIP2eHgaTZY+oA4AXsnFa87Kk9oL+LRw2NSrxvbH9IxEWUBEAR22IisLgQ0RkB2lZOovf015+CvGqGEmtofYzZENj1vvDAioiItjyLSqI3AmDDxGRjT1YifmYRe/5xONjPKU4LB7HGx7DTMPLxbyDiKzB4ENEVEoPr9VzMe2u2Ssxe+M+TmlekdR66+bgD6GeTXskogcYfIiISsGctXqK0kV+HGtVH0hq9bXroAMfJSeyF6uDT05ODlJTU3H//n1UrVoVlStXtmVfRETlXsqdbBy9fNuq0LPa4wN0UxwXjz8zPI53DYOLeUfxuPYOkXksCj53795FfHw8Nm7ciCNHjkCny5u8V6NGDURHR2PkyJFo3bq1zRslIipPrB3p8UMWTmhGSWq9dO/hpFDXrPe/3r0eQipXAAD4eColCxPyKS6ikpkdfJYsWYK5c+ciNDQUvXr1wvTp0xEcHAxPT0/cvn0bSUlJ2LdvH7p3745HH30US5cuRXh4uD17JyJyGHNWZX5YD/lRrFQtkdTqaT+HHh5mX6NLgwA+uUVUCmYHn4MHD2LXrl1o0qTwZdLbtGmD4cOHY8WKFVizZg327NnD4ENELsvSVZm/9JiLDorT4vGnhl5YaHjB1m0RUQnMDj5fffWVWeep1WqMGTPG6oaIiMq7X5NSMe3rU2adWwUZSNRIV1x+UjcPZ4RQO3RGRCWRW3KyQqFAWlqavXohIirXUu5k46tjyRj9ZSLMucn1lPyQJPToBCXCtOutDj1KuYwTmIlKyaLJzYJg+fLrRETOKv/6PGlZWoz6ItHM9XkEfKOag5byC2JlcU5ffGzsXap+Zj7ZkBOYiUqJ6/gQERXC2qe2qiIdRzVjJbXuuoW4INQodU+1/L1KfQ0id2dx8Pntt9/g61v8EwW9evWyuiEiovLAmqe2esv3YrFqhXicIVRAC91KGKGwSU8B3mqbXIfInVkcfIYMGVLs6zKZDEaj0eqGiIicj4CfVW+ikfwfsTI/5wWsMPIfgUTljcXBJzU1FQEBAfbohYjIofLP6bmYdtes91TDLRzSjJfUuugW4bJQzaa9cWVmItuwKPjIZDJ79UFEZDcPbyL6sNxAYemcnhcUOzHfY7V4/J/gh7a6T2Cy7IFZzH66EZrW8INaWfT7uDIzkW3wqS4icmnmTFJWK+VY/lJLC0KPgJ2q11FHnipW3skZhLXGJ6zqcd7PZ7H7jS4MNkRlwKLgM2TIEHh68j9MInIe5kxS1hlMOJ1i3krMNWRp2K+eJKl11C1BshBobYvIMQpIv6dn8CEqAxaNx166dEkycfn7779Hdna2zZsiIiprixL+KvGcIYrfJKHnsikQtbVflir0EFHZsmjE58CBA9Dr8+6Tv/TSSzhx4gTq1Klj88aIiMoLGUw4oJ6A6rLbYm1mznDEG7s5sCsiskap5vhwzg8RubpQ2XXsVr8uqbXTfoxr8Dfr/Uo5YOFyQERkR1y5mYioCCMVP+BNj43i8VlTTTyhjwFQ9BOukx4LR7dGebe+0rJ0GL7uqD3bJCILlGrlZpPJhB07diApKUlyDlduJqLyIi1LZ/F75DDhD/Uo+MnuibUpOaPwtbFzse9TyoF+rUMkk5STzJw0TURlo9QrN48aNUpyzJWbiai8SLmTjVFfHLPoPXVlKdihfkNSa6NdhjRUKvG9cYNb8cksonLOoqe6TCZTib8YeoiovEi/pzdzN/UHxim2SULPcVMYQrXxZoUeAAjw1hSoVfJSFbswIcBVmYnKEuf4EJFLyV2lOS1Li9MpmWa9RwkDTqtfhlqWI9Ym6Mfhe1M7sz+3qPAS7OeJnVOiSlw5miNFRGXDquCzc+dObN26FVeuXIFMJkPt2rXRt29fdOrUydb9iWJiYrB161acO3cOnp6eaNeuHRYsWID69euL5wiCgHfeeQdxcXFIT0/HI488gmXLlqFx48Z264uIyoeUO9k4n5qJUV8kWjTK00B2Fb+qp0tqrbTLcRO+Zl/jg75N0C6sapHhJdjPk8GGqJywbEMZAKNHj0a3bt2wceNG3Lp1Czdu3EB8fDy6dOmC8ePHl3wBK+3Zswdjx47F4cOHkZCQAIPBgOjoaNy7lzf5cOHChVi8eDE++eQTHD16FEFBQejevTuysrLs1hcROV7uthTD1x2zKPS8rtwiCT2HjI0Qqt1gYehpin6tajLYEDkJi0Z8tm3bhrVr1+Kzzz7DkCFDxE1LTSYT1q1bh1dffRXdu3e3y1Ndv/76q+R47dq1CAgIQGJiIjp16gRBEBAbG4uZM2eid+/eAIDPP/8cgYGB2LBhQ4FJ2ETkvPJuZ+mQmZ2DM9cyLNpc1AMGXNAMltRG6yfhV1Mbi3tpWM3H4vcQkeNYFHzWrl2LyZMnY+jQoZK6XC7H8OHDcf78eaxZs6ZMHmfPyHjwiGjlypUBAJcvX0Zqaiqio6PFc9RqNTp37oyDBw8WGXx0Oh10urzHXTMzzZsTQESOYc6mo8WJkP2NH9VvSWqR2pW4A2+Lr+WhkHFSMpGTsehW1x9//IHnnnuuyNf79OmDxMTEUjdVEkEQMHnyZHTo0AEREREAgNTUB7skBwZK98wJDAwUXytMTEwMfH19xV8hISH2a5yISs2cTUeL8qYyXhJ6dhibI1S7warQo5QDm0a25S0uIidj0YjPzZs3ERwcXOTrwcHBuHXrVqmbKsm4ceNw8uRJ7N+/v8BrubffcgmCUKCW34wZMzB58mTxODMzk+GHyMWoocd5zVBJbbh+CnaaWlh1PQ+FDJtGtkXLWuY95k5E5YdFwUev10OlKnpYV6lUSjYxtYfx48fj+++/x969e1GjRg2xHhQUBODByE+1atXEelpaWoFRoPzUajXUarX9GiYih2ouu4Bt6tmSWlPtKmTCy+JrxfaPRFhART5+TuTELH6cfdasWahQoUKhr92/f7/UDRVFEASMHz8e27Ztw+7du1G7dm3J67Vr10ZQUBASEhLQvHlzAA+C2p49e7BgwQK79UVEZSN3QvP/zvxn9nveVa7FYGWCePyTsQ3G5kyyuoewgIqICDb/iS8iKn8sCj6dOnXC+fPnSzzHHsaOHYsNGzbgu+++g7e3tzhvx9fXF56enpDJZJg0aRLmzZuH8PBwhIeHY968eahQoQIGDBhgl56IqGxYOqFZAx3OaYZJaoP007HP1NQe7RGRE7Eo+OzevdtObZRs+fLlAICoqChJfe3ateJTZlOnTkV2djbGjBkjLmC4fft2eHtbPnGRiMqHlDvZ2J6UanboaSM7iy3q9yS1CO1q3EXhI9Xm4rYSRK5BJgiC+at9uYHMzEz4+voiIyMDPj5cn4PIkVLuZKPLB7ugN3NRwg+UK9BPuVc8/sbYAa/njLHqs1/vXg9dGgSIx5zXQ1S+mfv3t9kjPvPnz8f48ePh5VXyhMDff/8dN2/eRM+ePc29PBG5udw5PACQlqVFZrYBybfvmxV6KkCLM5rhktoL+rdw2NTI6n5CKlfgfB4iF2R28Dlz5gxq1aqFfv36oVevXmjVqhWqVq0KADAYDDhz5gz279+PL7/8EtevX8f69evt1jQRuZbSLErYXn4K8aoYSa2h9jNko+BO6Zbw8eQezkSuyOz/stevX4+TJ09i2bJlGDhwIDIyMqBQKKBWq8WnuZo3b46RI0diyJAhfESciMyScicbRy/ftir0fOyxFL0Uh8TjDYaueNPwik36CvAuXXAiovLJqjk+giDg5MmTuHLlCrKzs+Hv74/IyEj4+/vbo8cyxTk+RGXH2pEeb9zHKY004PTRzUaiUN9mvf04vgNvdRE5EZvP8clPJpOhWbNmaNasmdUNEhFZs/1ElPw41qk+kNTqa9dBB9s9ccUnuIhcV6lvYmdnZyMnJ0dS40gJERUndyLzxbS7Fr0vzmMRohV5+wF+Zngc7xoGF/OOknkoZFg5qBUCvPNuz/MJLiLXZVXwuX//PqZOnYotW7YUujeX0WgsdWNE5Jqsub3li7v4UzNSUuulew8nhboWffbr3eshpHIF+HgqxTk8DDlE7sWq4PPGG29g165d+PTTTzF48GAsW7YMKSkpWLlyJebPn2/rHonIhVh6eytafhRxqiWSWj3t59DDw+LP7tIggPN2iNycVcHnhx9+wPr16xEVFYXhw4ejY8eOCAsLQ61atRAfH4+BAwfauk8ichFnr2eYfe56jxh0UpwSj5cbnsYCw4v2aIuI3IRVwef27dviJqE+Pj64ffs2AKBDhw549dVXbdcdEbmUxH/S8cbXp0o8rzIy8YdmtKT2pG4ezgihduqMiNyF3Jo31alTB1euXAEANGrUCFu2bAHwYCTIz8/PVr0RkQtJ/Ccd078+UeJ5PeWHJaEnR1AgTLueoYeIbMKqEZ9hw4bhzz//ROfOnTFjxgz07NkTS5cuhcFgwOLFi23dIxE5uV+TUjH6y8QSzhLwteodtJL/JVZiDb0Ra+hrkx74iDoRATbapPTq1as4duwY6tati8aNG0OpdN6l3rmAIZHtpNzJxsGLN/HG1yeLPa8q0nFUM1ZS665biAtCDYs/M7Z/JMICKhao8+ktItdmlwUMN23ahBdeeKFAvWbNmqhZsyZycnLQp08ffPfdd5Z3TEQuJfGfdDy/8hCMpuL/bfWcfB+WqJaLx5mCJ5rr4mCEwqrPDQuoyCe3iKhIFgWfoUOHolKlSujRo0eB14xGI/r164djx47ZrDkiKj/y755emNzbSOn39Dh7PbPEUR5AwI+qmYiQXxErC3JewHJjL6t75O0sIiqJRcFnwYIF6NOnDxISEtC2bVuxbjQa0bdvXxw+fBi7d++2dY9E5GDmLDqoAGACYM698yDcwmHNeEmtq+5D/C1Ut7g3pVyGuMEtEeCt4e0sIiqRRcFn4sSJuH37Nnr27Im9e/ciIiICRqMRzz//PA4ePIhdu3ahQYMG9uqViBzEnEUHzV2vvb9iFxZ4rBKP0wQ/PKr7BCYLHzKVAZjxREP0bFaNYYeIzGbxLOR33nkHt2/fRnR0NHbv3o2ZM2di79692LlzJxo1amSPHonIJQjYoZqCuvLrYuXdnEH4zPiEVVdbM7QVujYItFVzROQmrHr8aunSpbhz5w6aNWuGihUrYseOHWjSpImteyMiF1FDdgP71RMltU66JbgqWBdcVAo56gfxqUsispxFwWfy5Mni7/38/CAIAiIjI7Fu3TrJeVzLh4hyLfJYjj6KfeLxP6YAROkXQ7Dg1lbu5qIA4OPpgfpB3ry9RURWsSj4HD9+XHLctm1bGAwGSV0mk9mmMyIqN9KytBa/Rw4T/ta8JKm9lTMMXxq7W3wtbi5KRLZiUfDZtWtXgVru+ocMPESuKfGfdIxcb9kyFc1kF/Gd+m1J7XHdfJwTalr8+Uo5+Ig6EdmMVXt1AcCaNWsQEREBjUYDjUaDiIgIrF692pa9EZEDpdzJxs5z/6H/ykMo4YEuiU89YguEnlBtvFWhRyEDNo9qx9taRGQzVk1unjVrFpYsWYLx48eL6/kcOnQIr732Gq5cuYL333/fpk0SUdkyZ92ehxV2a2utoQfeMQyx+PPlAKY/2QA9m1Zn6CEim7Jqry5/f38sXboUL774oqS+ceNGjB8/Hjdv3rRZg2WNe3WRu0u5k43tp6/jnR/Omv2eurIU7FC/IalZsyChUg7EDW7NyctEZDG77NWVy2g0olWrVgXqLVu2hMFgsOaSRFQOJP6Tjn7LD8KCO1sYq/gWb3hskdRCtfF4sMSgeRRyGeb3jkC7sKoMPERkV1YFn5deegnLly8v8Nh6XFwcBg4caJPGiKhspdzJxvMrzA89ShiQpH4ZGlmOWJuoH4PvTB3M/sxJj4WjaYgfR3iIqMxYFXyAB5Obt2/fjkcffRQAcPjwYSQnJ2Pw4MGS9X64pg+RczifmgWjmTe+G8iu4lf1dEmtlXY5bsKyR867NQrkY+pEVKasCj5JSUlo0aIFAODSpUsAgKpVq6Jq1apISkoSz+Mj7kTOIeVOttmPrE9WbsEE5bfi8SFjI7yY85adOiMisi2rgk9h6/kQkXNJuZON86lZyMzOwZlrGTCYih/u8YABFzSDJbVX9RPxi+kRe7ZJRGRTVt/qIiLnlfhP+v+vz2Peva0I2d/4US0d1WmuXYF08MlHInIuVi9gSETOKeVONl6IMz/0zFDGS0LPbmMzhGo3MPQQkVPiiA+RG0m5k42jl28jx4xZzGrocV4zVFJ7Rf86/mdqaafuiIjsj8GHyE1Yshpzc9kFbFPPltSaauOQiYr2ao+IqEzwVheRG8gd6TEn9LyjXCsJPb8YWyNUu8HmoUetlHPzUSIqcxzxIXJRKXeykX5Pj7QsHUZ8frTENXo00OGcZpikNlg/DXtNzazu4fXu9dA42AcB3poCr1XyUnHRQiIqcww+RC7I0k1GW8vO4Sv1u5JahHY17qJCqfro0iCACxQSUbnCW11ELujgxZtmh54FyjhJ6Nlq7IBQ7YZShx4iovKIIz5ELubXpFS88fXJEs+rAC3OaIZLai/qZ+KQqbFN+uAcHiIqjxh8iFxIyp1sjNvwR4nntZMnYYNqnqTWSPsZ7qPgXBxLxPaPRFjAg0nQnMNDROURgw+RC/np5LUSFyb8yOMTPKM4KB5vNHTBDMOIUn+2WilH69qVGXaIqFxj8CFycrlPb529nol5P58r8jxv3McpzSuSWl/d2zgmNCjV53soZFg5qCXqB/kw9BBRucfgQ+TEzH16K0p+AutUCyW1+tp10MH8OTgTHwtD90ZBBeq8pUVEzoTBh8hJmbsoYZzHIkQrEsXjtYYeeMcwxKLPUsiATvX4aDoROT8GHyInZM5Ijy/u4k/NSEntGd27+FMIs/jzjAIwYNVh7JwSxdEdInJqDD5ETiJ3Lg8AXEy7W2zo6S4/hlWqxZJaPe3n0MPD6s/XGUxIv6dn8CEip8bgQ+QELFmJeb1HDDopTonHKwxPY77hRXu2R0TkNBh8iJxA+j19iaGnMjLxh2a0pNZTNw+nhVA7dkZE5FwYfIjKuZQ72Th65Xax5/SUH8Yy1cfisVGQoYHuc+TwP3EiIgn+ryJROfZrUirGbfijmEUJBWxRvYs28vNi5SPDc1hi6FfitTvUrYK+rUIAPBhReufHM7ZomYioXGPwISqnEv9Jx+gvE4t8vSru4KhmjKQWrVuAv4QQs64//cmG4uPpSSkZ1jdKROREuDs7UTl18t87Rb72rHy/JPTcFTSoq/3C7NBDROSuOOJDVA6l3MnG3J8Ku/Uk4AfVTDSRXxErC3P641PjM6X6vEpeKqiV8mInUHO3dSJyBQw+ROXEw+v0PJxBgnALhzXjJbWuug/xt1Dd4s96OMQE+3li55Qo8fMLw60piMgVMPgQlQMlrdPzvGIXFnqsEo9vCD54RPcpTGbcrZ70WBi6PbTHVmEhJtjPk8GGiFwegw9ROVD0Oj0C/qd6A2Hya2LlvZyBWGPsafa1uzUK4h5bRET/j8GHyIFyb28Vtk5PMG7ggGaipNZJtwRXhUCzr895OUREUi4ZfD799FN88MEHuH79Oho3bozY2Fh07NjR0W2RG8k/X6cwuWGkqNtbgxTb8Z7HOvE42VQVnfRLIFjwIGZs/0i0rl2Zt6+IiPJxueCzefNmTJo0CZ9++inat2+PlStX4oknnsCZM2dQs2ZNR7dHbsCcfbWUcmBsl7AC58hgwj71JNSQ3RRrb+UMw5fG7hb3ERZQkaGHiOghLreOz+LFi/Hyyy/jlVdeQcOGDREbG4uQkBAsX77c0a2RmzBnXy2DCfhox0VJrZYsFZc1L0lCT3vtR1aFHiIiKpxLjfjo9XokJiZi+vTpknp0dDQOHjxY6Ht0Oh10Op14nJmZadceybU8fEsrLUuH01asgvyy4ifM8ogXj/8yBSNavxCAzBZtEhHR/3Op4HPz5k0YjUYEBkonfwYGBiI1NbXQ98TExOCdd94pi/bIxZhzS6skcphwVP0qqsiyxNrUnBHYYuxSqt44qZmIqHAuFXxyyWTSfyULglCglmvGjBmYPHmyeJyZmYmQEC77TyUz55ZWcerKUrBD/Yak9oj2E/yHylZfM7Z/JMICKnKxQSKiIrhU8PH394dCoSgwupOWllZgFCiXWq2GWq0ui/aIRGMU32Kqxxbx+E9THTyjfw+lvbUVFlCRa/YQERXDpYKPSqVCy5YtkZCQgOeee06sJyQk4JlnSreXERFQcFsJSylgxCn1K6ggy5tXNkk/Bt+aOpT8XhlgFIp+nbe3iIhK5lLBBwAmT56MQYMGoVWrVmjbti3i4uJw9epVjB492tGtkZMr7Zye+rKr+E0tnXjfWvspbsDPrPfP79MUDav5FPk6b28REZXM5YJP//79cevWLbz77ru4fv06IiIi8PPPP6NWrVqObo2cXGnm9ExWbsEE5bfi8e+mBuivnwVzb215KGRoF+bPYENEVEouF3wAYMyYMRgzZoyj2yCCBwy4oBksqY3RT8DPpkfNvsbspxshunEQQw8RkQ24ZPAhKq3CtpywdE5PY9ll/KSeKak1165AOoq+XVWY1qHcdoKIyFYYfIgeYov1eaYrN2C08kfxeK+xCQbnzLBFe0REVAoMPkQPKc1cHjX0OK8ZKqmN0E9GgqmVVdfzUMj4pBYRkQ0x+BDZSKTsIr5Vvy2pNdPGIQMVrb7mykEteZuLiMiGXG6TUiJHmKNcJwk9vxlbIVS7oVShR62Uo36QZfOBiIioeBzxISoFDXQ4pxkmqQ3RT8MeUzOLruOhkGHloJYI8NaINa7LQ0Rkeww+RFZqJTuHr9XvSmpNtKuRhQoWXyvHKCDAW8PtJoiI7Iy3uoisMF8ZJwk93xrbIVS7warQQ0REZYcjPkQWqAAtzmiGS2oD9G/ioCnCQR0REZElGHyIHlLJSwW1Ul7gkfa28tPYqJorqTXSfob70ICIiJwDgw/RQ4L9PLFzShTS7+mx61waFiX8hSUey/Cc4oB4ziZDFKYbRjqwSyIisgaDD1Ehgv08EezniSsp13FFM0DyWj/d2zgqNHBQZ0REVBoMPkRFufA/PPVzH0mpgXYttFA7qCEiIiotPtVFVJhNA4H4vNCzzhCNUO0Gq0KPh0IGZQn/pamVcm5NQURUBjjiQ5RfdjqwIFRSelb3Lk4IYRZfKrZ/JMICKoqB5uHd3vPjYoVERGWDwYco17mfgU0vSkop467gbOxhwIpNS8MCKkoWJGSwISJyPAYfIgD4ojdwaUfecbsJQPR7CAbEJ7xyXUy7i0mbT5R5i0REVHoMPuTe7t0CPqgjrY3cA1SPFA9zn/AiIiLnx+BDLiPlTrZl82hObwO+Gio96a0bgJKTjImIXBWDDzm13LCTlqXFqC8SkWMUijxXrZRj55QoBPtqgHU9gX/yFiREpzeArm+Z9ZlFrez88GfxKS0iovKHwYecxsMjOuaEnfx0BhOybvwLxLaSvvDqQSCwsdl95F/ZuSh8SouIqHxi8CGnkHInG10/3F3sKEtJnpHvR4P4fKswe1QApicDCsv/M+C8HyIi58TgQ04h/Z6+FKFHwHeqWWgm/zuv1PWtB7e3iIjIrTD4kEsLxG38rhknLY49ClSt55iGiIjIoRh8yGX1U+zGBx5x4vFNwQf7nzmAML0vkJIBgHNxiIjcDYMPuSABCaqpCJeniJX3cgZijbEnsOWU5EzxSS+GHyIit8DgQy4lGDdwQDNRUuusW4x/hKBCz9cZTEi/p2fwISJyE9ydnVzGS4oESej5V/BHbe2XRYYeIiJyPxzxIYczZ8Xl4shgwl7VawiR3xBrs3KG4gtjtM16JCIi18DgQw5lzvo8aqUcH73QvNDXasr+w171a5La+RcOoL93CFpyM1EiInoIgw85lDnr8+gMJmhzjAXqLyt+xiyPL8Xji6bq0I48hIgafrZuk4iIXASDDzlUWpbWrPPy3wqTw4Tf1WNQVZYp1qbljMBmYxf8KJPZvEciInIdDD7kUJnZBrPOu3TjLgCgjuwadqqnSF57VLsUqahi896IiMj18Kkucgpf/n4VYxTfSULPSVNthGrjiww9ubuoF4e7qBMRuReO+FCZKezpreTb90t8nwJG/KkegYqyvNtik/Rj8K2pQ7Hv4y7qRET0MAYfKhPW7q5eT5aM7eppklpr7ae4AT+z3s9d1ImIKD/e6qIyYc3u6q8pv5aEniOm+gjVxpsdeoiIiB7GER8qd5Qw4Lx6CBQyQayN1U/AT6ZHi30f5+sQEVFJGHyoXGksu4Kf1G9Kai20K3AbPgCA2P6RCAuoWOh7OV+HiIhKwuBD5cZ05UaMVv4gHu81NsHgnBmSc8ICKiIi2LesWyMiIhfB4EOlYs4+WyWNwqiQg780QyS1kfrXsN3U2iY9EhER5WLwIauZu8/WzilRRb7eTHYR36nflta0cchAwdtZHgoZ5/AQEVGp8Kkuspq5+2wVNSI0W/m5JPRsN7ZEqHZDoaEHAFYOask5PEREVCoc8aEykbuKss5gghp6nNcMlbw+VD8Vu02RxV4jwFtjvwaJiMgtMPhQmVn+UgvcPrMXfU++Iqk30a5GFio4qCsiInInvNVFdpeWpUPXD3fjvy9HSULP98a2CNVuMCv0qBRco4eIiEqPIz5kd/eyMnBe+YKkNlA/AwdMTcy+xopBLTi/h4iISo3Bh+yqrfw0nv5pgKTWSPsZ7sOy+Tqc30NERLbAW11kN0s8lmGjaq54vMXQGaHaDRaHHiIiIlvhiA9ZTWcwwUMhQ45RkNQr4j6SNNIJzM/rZuGI0LAs2yMiIiqAwYesknInGwNWHS4QejrJ/8R61QJJrYF2LbRQl2V7REREhWLwIasUtnjhpx6xeFJxRDxeb+iOtw3Dyro1IiKiIjH4UKn54C5OakZKas/p3sFxIdxBHRERERWOwYdK5TF5ItaoFklq9bXroIPt1txRK7mGDxER2QaDD1ltrccCdFH8KR7HGXpinmFgie+L7R+JsIC8/bjSsnQAhCIfWTdnh3ciIiJzMPiQ5e7dQsSqOoAir/SU7n0kCXXMentYQEVEBPvaqTkiIqKiMfhQsVLuZEt2V/f5+yfU3PGq5Jxw7Xrk8I8SERE5AadYwPDKlSt4+eWXUbt2bXh6eqJu3bqYPXs29Hq95LyrV6/i6aefhpeXF/z9/TFhwoQC55D5Uu5ko+uHu/HU0v14aul+3F3ZQxJ6lhqeRah2g8Wh5+GnwYiIiMqKU/wz/dy5czCZTFi5ciXCwsKQlJSEESNG4N69e/jwww8BAEajET179kTVqlWxf/9+3Lp1C0OGDIEgCFi6dKmDfwLnlPvIuj8ycEwjHeV5XDcf54SaVl1XrXSKvE1ERC7IKYLP448/jscff1w8rlOnDs6fP4/ly5eLwWf79u04c+YMkpOTUb16dQDAokWLMHToUMydOxc+Pj4O6d3Z9ZIfwMeqZeJxtqBCE91qGPL90cmdrHwx7S4mbT7hgC6JiIjM4xTBpzAZGRmoXLmyeHzo0CFERESIoQcAevToAZ1Oh8TERHTp0qXQ6+h0Ouh0OvE4MzPTfk07kZT0+wj+qic+Vp0Sax/m9MMnxucKnMvJykRE5CycMvhcunQJS5cuxaJFeevHpKamIjAwUHJepUqVoFKpkJqaWuS1YmJi8M4779itV2d0/d+/Eby6uaT2mO4DXBKCHdQRERGRbTh0ssWcOXMgk8mK/XXs2DHJe65du4bHH38c/fr1wyuvSDfClMlkBT5DEIRC67lmzJiBjIwM8VdycrJtfjhndfxLVMsXem4LFVFH+yVDDxERuQSHjviMGzcOL7zwQrHnhIaGir+/du0aunTpgrZt2yIuLk5yXlBQEH7//XdJLT09HTk5OQVGgvJTq9VQq7mBJgQBWN4OSDsjlubmDMAq41MObIqIiMi2HBp8/P394e/vb9a5KSkp6NKlC1q2bIm1a9dCLpcOVrVt2xZz587F9evXUa1aNQAPJjyr1Wq0bNnS5r27lDvJQGyEpBSlW4QrQjUHNURERGQfTvFc8bVr1xAVFYWQkBB8+OGHuHHjBlJTUyVzd6Kjo9GoUSMMGjQIx48fx44dOzBlyhSMGDGCT3QV5+hqaejxDUHSK1fMDj3599Gq5KUq8VF17rtFRESO5BSTm7dv346LFy/i4sWLqFGjhuQ1QRAAAAqFAj/99BPGjBmD9u3bw9PTEwMGDBAfd6eHmEzA0uZA+pW82hMfAI+MBFIyzLpEbP9ItK5dWdxHK9jPEzunRElWen4Y990iIiJHkgm5yYEAPHic3dfXFxkZGS41UpR/6wlV5hXU29xJ8nrqsCMIqlUfAJCUkoGnlu4v8Zo/ju/Ax9iJiKhcMPfvb6cY8aHSyd16QmcwYbjiF7zt8YX42iVTNTym/xAecZewclAlBHirkZaldWC3RERE9sPg4wbS7+mRYzDgqHosqsrybmNNz3kFm4xdAQA5RgHD1x0FAKgUcqgUMuiNRQ8Gcq4OERE5IwYfN6C6cwl/a16S1Npql+I6qhR6vt5owmdDWyPAu+jH/DlXh4iInBGDj6vbtxj1duStTH3aVAs99fMAFL2oIwAEeKs5f4eIiFwOg4+rMhqAhbUBXd7eY5P1o7HV1KmYNxEREbk2Bh9X9N8ZYHlbSam1dhluoJKDGiIiIiofnGIBQ7LArhhp6Al5FEmv/MPQQ0REBI74uA5jDjA3CDAZ8mp9PwMi+pi9ICEREZGrY/BxBddPAis7SmtvXAK8HuyDlruVhM5gckBzRERE5QeDj7NLmA0ciM07rt0ZGPL9g5Wa8430LH+pJTKzc5B+T4+5P5+FwcQ1eoiIyP0w+Dgrgw54P0Ba6x8PNHxKslJzUTwUMqwc1KrQtXq4Rg8REbkqBh9nlJIIrOoqrU29DFSoDODBSs0l3dbKMQpcq4eIiNwOn+pyNr9Ml4aeek8AczLE0ENERERF44iPs8jJfvDUVn4DtgD1ejimHyIiIifE4OMMrv4OfBYtrU2/Cmh4m4qIiMgSDD7l3Q8TgcR1eceNewP91jqsHSIiImfG4FNe6e8B86pLa4O+Bep2cUg7REREroDBpzy6vBf4/Glpbca/gNrbMf0QERG5CAaf8mbrKODkprzjZgOA55ZbdAlzVmrmIoVEROSOGHzKC20mMD9EWhv6MxDa3uJLBft5YueUKKTf0xd5DhcpJCIid8TgUx5c3AF82Vtae/M6oKpg9SWD/TwZbIiIiB7C4ONAKXeyUfG7YfC9/ItYu9VwEK53mAvcyEElr2yGFyIiIhti8HGQa6mpCF5RX1LrrZuDP47XA47vB/BgHs7OKVEFwk/KnWzexiIiIrICg48jnP8V1Tf2l5Tqa9dBB+lkY53BhPR7ekmIMWcD0qICExERkbvjXl1lLb4fkC/0rDY8gVDthgKhpyjmbECaG5iIiIhIiiM+ZeX+bWBhbUnpKd37SBLqOKghIiIi98MRn7LyxbOSw9PDLzL0EBERlTEGn7IS/v+7qHd8HZiTAUHBxQOJiIjKGm91lZWuMx/8IiIiIofhiA8RERG5DQYfB8ndT6s43E+LiIjItniry0Gs3U+LG5ASERFZj8HHgazZT4sbkBIREVmPwccJcQNSIiIi63CODxEREbkNBh8iIiJyGww+RERE5DYYfIiIiMhtMPgQERGR22DwISIiIrfB4ENERERug8GHiIiI3AaDDxEREbkNrtz8EEEQAACZmZkO7oSIiIjMlfv3du7f40Vh8HlIVlYWACAkJMTBnRAREZGlsrKy4OvrW+TrMqGkaORmTCYTrl27Bm9vb8hkMke343CZmZkICQlBcnIyfHx8HN2OS+N3Xbb4fZcdftdly12/b0EQkJWVherVq0MuL3omD0d8HiKXy1GjRg1Ht1Hu+Pj4uNV/QI7E77ps8fsuO/yuy5Y7ft/FjfTk4uRmIiIichsMPkREROQ2GHyoWGq1GrNnz4ZarXZ0Ky6P33XZ4vdddvhdly1+38Xj5GYiIiJyGxzxISIiIrfB4ENERERug8GHiIiI3AaDDxEREbkNBh8q4MqVK3j55ZdRu3ZteHp6om7dupg9ezb0er3kvKtXr+Lpp5+Gl5cX/P39MWHChALnkPk+/fRT1K5dGxqNBi1btsS+ffsc3ZLTi4mJQevWreHt7Y2AgAA8++yzOH/+vOQcQRAwZ84cVK9eHZ6enoiKisLp06cd1LHriImJgUwmw6RJk8Qav2vbSklJwUsvvYQqVaqgQoUKiIyMRGJiovg6v+/CMfhQAefOnYPJZMLKlStx+vRpLFmyBCtWrMCbb74pnmM0GtGzZ0/cu3cP+/fvx6ZNm/DNN9/g9ddfd2Dnzmvz5s2YNGkSZs6ciePHj6Njx4544okncPXqVUe35tT27NmDsWPH4vDhw0hISIDBYEB0dDTu3bsnnrNw4UIsXrwYn3zyCY4ePYqgoCB0795d3LePLHf06FHExcWhadOmkjq/a9tJT09H+/bt4eHhgV9++QVnzpzBokWL4OfnJ57D77sIApEZFi5cKNSuXVs8/vnnnwW5XC6kpKSItY0bNwpqtVrIyMhwRItOrU2bNsLo0aMltQYNGgjTp093UEeuKS0tTQAg7NmzRxAEQTCZTEJQUJAwf/588RytViv4+voKK1ascFSbTi0rK0sIDw8XEhIShM6dOwsTJ04UBIHfta1NmzZN6NChQ5Gv8/suGkd8yCwZGRmoXLmyeHzo0CFERESgevXqYq1Hjx7Q6XSSoVYqmV6vR2JiIqKjoyX16OhoHDx40EFduaaMjAwAEP8sX758GampqZLvXq1Wo3PnzvzurTR27Fj07NkT3bp1k9T5XdvW999/j1atWqFfv34ICAhA8+bNsWrVKvF1ft9FY/ChEl26dAlLly7F6NGjxVpqaioCAwMl51WqVAkqlQqpqall3aJTu3nzJoxGY4HvMzAwkN+lDQmCgMmTJ6NDhw6IiIgAAPH75XdvG5s2bUJiYiJiYmIKvMbv2rb+/vtvLF++HOHh4fjtt98wevRoTJgwAevXrwfA77s4DD5uZM6cOZDJZMX+OnbsmOQ9165dw+OPP45+/frhlVdekbwmk8kKfIYgCIXWqWQPf2/8Lm1r3LhxOHnyJDZu3FjgNX73pZecnIyJEyciPj4eGo2myPP4XduGyWRCixYtMG/ePDRv3hyjRo3CiBEjsHz5csl5/L4LUjq6ASo748aNwwsvvFDsOaGhoeLvr127hi5duqBt27aIi4uTnBcUFITff/9dUktPT0dOTk6Bf2FQ8fz9/aFQKAr8KywtLY3fpY2MHz8e33//Pfbu3YsaNWqI9aCgIAAP/nVcrVo1sc7v3nKJiYlIS0tDy5YtxZrRaMTevXvxySefiE/T8bu2jWrVqqFRo0aSWsOGDfHNN98A4J/t4nDEx434+/ujQYMGxf7K/ZdaSkoKoqKi0KJFC6xduxZyufSPStu2bZGUlITr16+Lte3bt0OtVkv+h49KplKp0LJlSyQkJEjqCQkJaNeunYO6cg2CIGDcuHHYunUrdu7cidq1a0ter127NoKCgiTfvV6vx549e/jdW+ixxx7DqVOncOLECfFXq1atMHDgQJw4cQJ16tThd21D7du3L7A0w19//YVatWoB4J/tYjlwYjWVUykpKUJYWJjQtWtX4d9//xWuX78u/splMBiEiIgI4bHHHhP++OMP4X//+59Qo0YNYdy4cQ7s3Hlt2rRJ8PDwENasWSOcOXNGmDRpkuDl5SVcuXLF0a05tVdffVXw9fUVdu/eLflzfP/+ffGc+fPnC76+vsLWrVuFU6dOCS+++KJQrVo1ITMz04Gdu4b8T3UJAr9rWzpy5IigVCqFuXPnChcuXBDi4+OFChUqCF9++aV4Dr/vwjH4UAFr164VABT6K79//vlH6Nmzp+Dp6SlUrlxZGDdunKDVah3UtfNbtmyZUKtWLUGlUgktWrQQH7km6xX153jt2rXiOSaTSZg9e7YQFBQkqNVqoVOnTsKpU6cc17QLeTj48Lu2rR9++EGIiIgQ1Gq10KBBAyEuLk7yOr/vwskEQRAcM9ZEREREVLY4x4eIiIjcBoMPERERuQ0GHyIiInIbDD5ERETkNhh8iIiIyG0w+BAREZHbYPAhIiIit8HgQ0RERG6DwYeISjRo0CDMmzfP0W04ldDQUMTGxpbJdWQyGb799ttSf1Z+ffv2xeLFi216TaLygMGHyEVFRUVh0qRJktpHH30EtVqNDRs2mH2dkydP4qeffsL48eMl15bJZJg/f36B85988knIZDLMmTOn2F5Ka926dfDz85PUzp49ixo1aqB3797Q6XQ2/TxbmzNnDmQyGWQyGeRyOapXr46BAwciOTlZct7Ro0cxcuTIMu/v7bffxty5c5GZmVnmn01kTww+RG5i9uzZmDFjBrZt24YBAwaY/b5PPvkE/fr1g7e3t6QeEhKCtWvXSmrXrl3Dzp07Ua1aNZv0bImjR4+iY8eO6NGjB7766iuo1WqLr6HX6+3QWdEaN26M69ev499//8XmzZtx6tQpPP/885JzqlatigoVKpRpXwDQtGlThIaGIj4+vsw/m8ieGHyInFBUVBTGjRuHcePGwc/PD1WqVMFbb72FwrbeEwQB48ePx0cffYTt27fjySefFF9bvHgxmjRpAi8vL4SEhGDMmDG4e/eu+LrJZMJXX32FXr16FbjuU089hVu3buHAgQNibd26dYiOjkZAQICNf+Li7dy5E127dsWwYcOwZs0aKBQKAEBKSgr69++PSpUqoUqVKnjmmWdw5coV8X1Dhw7Fs88+i5iYGFSvXh316tXDlStXIJPJsHXrVnTp0gUVKlRAs2bNcOjQIclnHjx4EJ06dYKnpydCQkIwYcIE3Lt3z6K+lUolgoKCUL16dXTs2BEjRozA4cOHJaMsD9/qunDhAjp16gSNRoNGjRohISGhwHUPHjyIyMhIaDQatGrVCt9++y1kMhlOnDghnnPmzBk8+eSTqFixIgIDAzFo0CDcvHlTcp1evXph48aNFv1MROUdgw+Rk/r888+hVCrx+++/4+OPP8aSJUuwevVqyTkGgwGDBg3CV199hT179qBDhw6S1+VyOT7++GMkJSXh888/x86dOzF16lTx9ZMnT+LOnTto1apVgc9XqVQYOHCgZNRn3bp1GD58uI1/0uJt27YNPXv2xJtvvokPPvhArN+/fx9dunRBxYoVsXfvXuzfvx8VK1bE448/LhnZ2bFjB86ePYuEhAT8+OOPYn3mzJmYMmUKTpw4gXr16uHFF1+EwWAAAJw6dQo9evRA7969cfLkSWzevBn79+/HuHHjrP45UlNTsXXrVigUCjG4PcxkMqF3795QKBQ4fPgwVqxYgWnTpknOycrKwtNPP40mTZrgjz/+wHvvvVfgnOvXr6Nz586IjIzEsWPH8Ouvv+K///4rMNrUpk0bHDlypNzfNiSyiGM3hycia3Tu3Flo2LChYDKZxNq0adOEhg0bSs5RqVSCSqUSzp49a9Z1t2zZIlSpUkU83rZtm6BQKCSfk3vtiRMnCn/++afg7e0t3L17V9izZ48QEBAg6PV6oVmzZsLs2bMLnG9La9euFRQKhaBQKIRZs2YVeH3NmjVC/fr1Jb3rdDrB09NT+O233wRBEIQhQ4YIgYGBgk6nE8+5fPmyAEBYvXq1WDt9+rQAQPweBw0aJIwcOVLyefv27RPkcrmQnZ0tCIIg1KpVS1iyZEmR/c+ePVuQy+WCl5eX4OnpKQAQAAgTJkyQnJf/Or/99pugUCiE5ORk8fVffvlFACBs27ZNEARBWL58uVClShWxD0EQhFWrVgkAhOPHjwuCIAizZs0SoqOjJZ+TnJwsABDOnz8v1v78808BgHDlypUifw4iZ8MRHyIn9eijj0Imk4nHbdu2xYULF2A0GsVahw4dULFiRbz11lviaEV+u3btQvfu3REcHAxvb28MHjwYt27dEm/ZZGdnQ61WSz4nv6ZNmyI8PBxff/01PvvsMwwaNAgeHh6l+rni4+NRsWJF8de+ffuKPNfT0xPdu3fHqlWrcPbsWclriYmJuHjxIry9vcVrVa5cGVqtFpcuXRLPa9KkCVQqVaE/W67cOUtpaWnitdetWyfps0ePHjCZTLh8+bLZP2v9+vVx4sQJHD16FHPnzkVkZCTmzp1b5Plnz55FzZo1UaNGDbHWtm1byTnnz59H06ZNodFoxFqbNm0k5yQmJmLXrl2S/hs0aAAAku/G09MTwIPRMyJXoXR0A0RkP02aNMGiRYvQrVs3PP/889i8ebMYTP755x88+eSTGD16NN577z1UrlwZ+/fvx8svv4ycnBwAgL+/P+7fvw+9Xl9oOACA4cOHY9myZThz5gyOHDlS6p579eqFRx55RDwODg4u8lyFQoFvv/0Wffr0QZcuXbBz5040atQIwIPbQi1btix0cm7VqlXF33t5eRV67fwBLjf4mUwm8f+OGjUKEyZMKPC+mjVrFvfjSahUKoSFhQF4MNH5woULePXVV/HFF18Uer5QyByuh0OpIAiF1vIzmUx4+umnsWDBggLXyz8x/fbt2wCk3xeRs2PwIXJShw8fLnAcHh5eYH5IZGQkdu7ciW7duqFfv3746quv4OHhgWPHjsFgMGDRokWQyx8M/m7ZsqXAe4EHE2Fzf/+wAQMGYMqUKWjWrJkYOkrD29u7wBNkxVGr1di6dSv69u2LLl26YMeOHYiIiECLFi2wefNmBAQEwMfHp9R95deiRQucPn1aDC22MmvWLNSrVw+vvfYaWrRoUeD1Ro0a4erVq7h27RqqV68OAAUmXTdo0ADx8fHQ6XTik23Hjh0r0P8333yD0NBQKJVF/zWQlJSEGjVqwN/fv7Q/GlG5wVtdRE4qOTkZkydPxvnz57Fx40YsXboUEydOLPTcpk2bYteuXTh06BD69u0LvV6PunXrwmAwYOnSpfj777/xxRdfYMWKFZL3Va1aFS1atMD+/fuL7KNSpUq4fv06duzYUWy/N27cwIkTJyS/UlNTLf/BC6FSqfDNN9+gXbt26Nq1K06dOoWBAwfC398fzzzzDPbt24fLly9jz549mDhxIv79999Sfd60adNw6NAhjB07FidOnMCFCxfw/fffS9Y6skadOnXwzDPP4O233y709W7duqF+/foYPHgw/vzzT+zbtw8zZ86UnDNgwACYTCaMHDkSZ8+exW+//YYPP/wQQN7o0NixY3H79m28+OKLOHLkCP7++29s374dw4cPl9wq3bdvH6Kjo0v1MxGVNww+RE5q8ODByM7ORps2bTB27FiMHz++2IXuGjdujF27duHIkSPo06cPGjVqhMWLF2PBggWIiIhAfHw8YmJiCrxv5MiRJa7l4ufnV+Qto1wbNmxA8+bNJb8eDlql4eHhgS1btqBTp07o2rUrLl68iL1796JmzZro3bs3GjZsiOHDhyM7O7vUI0BNmzbFnj17cOHCBXTs2BHNmzfHrFmzbLJ+0euvv46ffvoJv//+e4HX5HI5tm3bBp1OhzZt2uCVV14pMCfIx8cHP/zwA06cOIHIyEjMnDlTDFK5836qV6+OAwcOwGg0okePHoiIiMDEiRPh6+srjv5ptVps27YNI0aMKPXPRFSeyITCbhoTUbkWFRWFyMhIm2yJUBKtVov69etj06ZNBSbSknOIj4/HsGHDkJGRIU5YLsmyZcvw3XffYfv27XbujqhscY4PERVLo9Fg/fr1BRa3o/Jr/fr1qFOnDoKDg/Hnn39i2rRpeP75580OPcCDEbSlS5fasUsix2DwIaISde7c2dEtkAVSU1Px9ttvIzU1FdWqVUO/fv2KfUy+MI7YH4yoLPBWFxEREbkNTm4mIiIit8HgQ0RERG6DwYeIiIjcBoMPERERuQ0GHyIiInIbDD5ERETkNhh8iIiIyG0w+BAREZHb+D8OJWpJ6Y4IZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Kernel ridge\n",
    "# Read in data\n",
    "KR_Train_pKaML = KR_FinalModel.predict(X)\n",
    "# Plot data\n",
    "plt.plot(KR_Train_pKaML, Y, 's')\n",
    "plt.plot(Y, Y)\n",
    "plt.xlabel('pKa(ML - Kernel Ridge)')\n",
    "plt.ylabel('pKa(DFT)')\n",
    "KR_r2_score = r2_score(KR_Train_pKaML, Y)\n",
    "KR_MAE_score = mean_absolute_error(KR_Train_pKaML, Y)\n",
    "KR_MedianMAE_score = median_absolute_error(KR_Train_pKaML, Y)\n",
    "KR_MaxError_score = max_error(KR_Train_pKaML, Y)\n",
    "print('R^2:', KR_r2_score)\n",
    "print('Mean absolute error:', KR_MAE_score)\n",
    "print('Median absolute error:', KR_MedianMAE_score)\n",
    "print('Maximum error:', KR_MaxError_score)\n",
    "\n",
    "#determine slope and intercept\n",
    "from scipy.stats import linregress\n",
    "KR_SlopeIntercept = linregress(KR_Train_pKaML, Y)\n",
    "print('General accuracy between predicted and DFT data:\\n')\n",
    "print('y = {}X + {}'.format(KR_SlopeIntercept.slope, KR_SlopeIntercept.intercept))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1f687e1",
   "metadata": {},
   "source": [
    "# Plot Results for Test Set - Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "39b96aab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9856418348360776\n",
      "Mean absolute error: 0.7656356373472082\n",
      "Median absolute error: 0.482586852235535\n",
      "Maximum error: 7.271589589645828\n",
      "General accuracy between predicted and DFT data:\n",
      "\n",
      "y = 1.0238258003001728X + -0.2890126904712904\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gradient boosting\n",
    "# Read in data\n",
    "GB_Test_pKaML = GB_FinalModel.predict(Test_X)\n",
    "# Plot data\n",
    "plt.plot(GB_Test_pKaML, Test_Y, 's')\n",
    "plt.plot(Y, Y)\n",
    "plt.xlabel('pKa(ML - Gradient Boosting)')\n",
    "plt.ylabel('pKa(DFT)')\n",
    "GB_r2_score = r2_score(GB_Test_pKaML, Test_Y)\n",
    "GB_MAE_score = mean_absolute_error(GB_Test_pKaML, Test_Y)\n",
    "GB_MedianMAE_score = median_absolute_error(GB_Test_pKaML, Test_Y)\n",
    "GB_MaxError_score = max_error(GB_Test_pKaML, Test_Y)\n",
    "print('R^2:', GB_r2_score)\n",
    "print('Mean absolute error:', GB_MAE_score)\n",
    "print('Median absolute error:', GB_MedianMAE_score)\n",
    "print('Maximum error:', GB_MaxError_score)\n",
    "\n",
    "#determine slope and intercept\n",
    "from scipy.stats import linregress\n",
    "GB_SlopeIntercept = linregress(GB_Test_pKaML, Test_Y)\n",
    "print('General accuracy between predicted and DFT data:\\n')\n",
    "print('y = {}X + {}'.format(GB_SlopeIntercept.slope, GB_SlopeIntercept.intercept))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c081926f",
   "metadata": {},
   "source": [
    "# Plot Results for Test Set - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d1a42ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.970444348681977\n",
      "Mean absolute error: 0.943736905738508\n",
      "Median absolute error: 0.39176822194106187\n",
      "Maximum error: 13.157284122151765\n",
      "General accuracy between predicted and DFT data:\n",
      "\n",
      "y = 1.0257342651744146X + -0.2593648538416957\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest\n",
    "# Read in data\n",
    "RF_Test_pKaML = RF_FinalModel.predict(Test_X)\n",
    "# Plot data\n",
    "plt.plot(RF_Test_pKaML, Test_Y, 's')\n",
    "plt.plot(Y, Y)\n",
    "plt.xlabel('pKa(ML - Random Forest)')\n",
    "plt.ylabel('pKa(DFT)')\n",
    "RF_r2_score = r2_score(RF_Test_pKaML, Test_Y)\n",
    "RF_MAE_score = mean_absolute_error(RF_Test_pKaML, Test_Y)\n",
    "RF_MedianMAE_score = median_absolute_error(RF_Test_pKaML, Test_Y)\n",
    "RF_MaxError_score = max_error(RF_Test_pKaML, Test_Y)\n",
    "print('R^2:', RF_r2_score)\n",
    "print('Mean absolute error:', RF_MAE_score)\n",
    "print('Median absolute error:', RF_MedianMAE_score)\n",
    "print('Maximum error:', RF_MaxError_score)\n",
    "\n",
    "#determine slope and intercept\n",
    "from scipy.stats import linregress\n",
    "RF_SlopeIntercept = linregress(RF_Test_pKaML, Test_Y)\n",
    "print('General accuracy between predicted and DFT data:\\n')\n",
    "print('y = {}X + {}'.format(RF_SlopeIntercept.slope, RF_SlopeIntercept.intercept))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6310d1cf",
   "metadata": {},
   "source": [
    "# Plot Results for Test Set - Kernel Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cf75aaf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9745992385569698\n",
      "Mean absolute error: 0.9038987997980082\n",
      "Median absolute error: 0.509696600442826\n",
      "Maximum error: 16.472932868011753\n",
      "General accuracy between predicted and DFT data:\n",
      "\n",
      "y = 1.0303752342308357X + -0.2493634757837082\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Kernel Ridge\n",
    "# Read in data\n",
    "KR_Test_pKaML = KR_FinalModel.predict(Test_X)\n",
    "# Plot data\n",
    "plt.plot(KR_Test_pKaML, Test_Y, 's')\n",
    "plt.plot(Y, Y)\n",
    "plt.xlabel('pKa(ML - Kernel Ridge)')\n",
    "plt.ylabel('pKa(DFT)')\n",
    "KR_r2_score = r2_score(KR_Test_pKaML, Test_Y)\n",
    "KR_MAE_score = mean_absolute_error(KR_Test_pKaML, Test_Y)\n",
    "KR_MedianMAE_score = median_absolute_error(KR_Test_pKaML, Test_Y)\n",
    "KR_MaxError_score = max_error(KR_Test_pKaML, Test_Y)\n",
    "print('R^2:', KR_r2_score)\n",
    "print('Mean absolute error:', KR_MAE_score)\n",
    "print('Median absolute error:', KR_MedianMAE_score)\n",
    "print('Maximum error:', KR_MaxError_score)\n",
    "\n",
    "#determine slope and intercept\n",
    "from scipy.stats import linregress\n",
    "KR_SlopeIntercept = linregress(KR_Test_pKaML, Test_Y)\n",
    "print('General accuracy between predicted and DFT data:\\n')\n",
    "print('y = {}X + {}'.format(KR_SlopeIntercept.slope, KR_SlopeIntercept.intercept))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76049ce1",
   "metadata": {},
   "source": [
    "# Feature importance analysis - Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bc2571ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 6 artists>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GB_feature_importance = GB_FinalModel.feature_importances_\n",
    "data_used = train_data.drop(columns= ['pKa_Final', 'Name'])\n",
    "plt.bar(data_used.columns, GB_feature_importance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e5de0c2",
   "metadata": {},
   "source": [
    "# Feature importance analysis - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4016cc15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 6 artists>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RF_feature_importance = RF_FinalModel.feature_importances_\n",
    "data_used = train_data.drop(columns= ['pKa_Final', 'Name'])\n",
    "plt.bar(data_used.columns, RF_feature_importance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a13ad42",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f9cd57e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}