8_random_forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. From bagging to Random forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A downside of bagging is that dominant features can cause individual trees to have a similar structure **known as tree correlation**.\n",
    "Remember the feature importance results discussed earlier for the MTPL data?\n",
    "* ```bm``` is a very dominant variable\n",
    "* ```ageph``` was rather important\n",
    "* ```power``` also, but to a lesser degree."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem?\n",
    "* bagging gets its predictive performance from **variance reduction**\n",
    "* however, this reduction when tree correlation\n",
    "* dominant features therefore **hurt** the preditive performance of a bagged ensemble!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn import tree\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>nclaims</th>\n",
       "      <th>amount</th>\n",
       "      <th>avg</th>\n",
       "      <th>exp</th>\n",
       "      <th>coverage</th>\n",
       "      <th>fuel</th>\n",
       "      <th>use</th>\n",
       "      <th>fleet</th>\n",
       "      <th>sex</th>\n",
       "      <th>ageph</th>\n",
       "      <th>bm</th>\n",
       "      <th>agec</th>\n",
       "      <th>power</th>\n",
       "      <th>pc</th>\n",
       "      <th>town</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1618.001036</td>\n",
       "      <td>1618.001036</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>TPL</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>private</td>\n",
       "      <td>N</td>\n",
       "      <td>male</td>\n",
       "      <td>50</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>77</td>\n",
       "      <td>1000</td>\n",
       "      <td>BRUSSEL</td>\n",
       "      <td>4.355223</td>\n",
       "      <td>50.845386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>private</td>\n",
       "      <td>N</td>\n",
       "      <td>female</td>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>1000</td>\n",
       "      <td>BRUSSEL</td>\n",
       "      <td>4.355223</td>\n",
       "      <td>50.845386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>TPL</td>\n",
       "      <td>diesel</td>\n",
       "      <td>private</td>\n",
       "      <td>N</td>\n",
       "      <td>male</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>70</td>\n",
       "      <td>1000</td>\n",
       "      <td>BRUSSEL</td>\n",
       "      <td>4.355223</td>\n",
       "      <td>50.845386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>TPL</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>private</td>\n",
       "      <td>N</td>\n",
       "      <td>male</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>57</td>\n",
       "      <td>1000</td>\n",
       "      <td>BRUSSEL</td>\n",
       "      <td>4.355223</td>\n",
       "      <td>50.845386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>155.974606</td>\n",
       "      <td>155.974606</td>\n",
       "      <td>0.046575</td>\n",
       "      <td>TPL</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>private</td>\n",
       "      <td>N</td>\n",
       "      <td>female</td>\n",
       "      <td>28</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>70</td>\n",
       "      <td>1000</td>\n",
       "      <td>BRUSSEL</td>\n",
       "      <td>4.355223</td>\n",
       "      <td>50.845386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   nclaims       amount          avg       exp coverage      fuel      use  \\\n",
       "0        1  1618.001036  1618.001036  1.000000      TPL  gasoline  private   \n",
       "1        0     0.000000          NaN  1.000000       PO  gasoline  private   \n",
       "2        0     0.000000          NaN  1.000000      TPL    diesel  private   \n",
       "3        0     0.000000          NaN  1.000000      TPL  gasoline  private   \n",
       "4        1   155.974606   155.974606  0.046575      TPL  gasoline  private   \n",
       "\n",
       "  fleet     sex  ageph  bm  agec  power    pc     town      long        lat  \n",
       "0     N    male     50   5    12     77  1000  BRUSSEL  4.355223  50.845386  \n",
       "1     N  female     64   5     3     66  1000  BRUSSEL  4.355223  50.845386  \n",
       "2     N    male     60   0    10     70  1000  BRUSSEL  4.355223  50.845386  \n",
       "3     N    male     77   0    15     57  1000  BRUSSEL  4.355223  50.845386  \n",
       "4     N  female     28   9     7     70  1000  BRUSSEL  4.355223  50.845386  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'.\\data\\PC_data.txt', sep = '\\s+')\n",
    "df.columns = map(str.lower, df.columns)\n",
    "df = df.drop('id', axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting type(str) columns to categorical\n",
    "df['coverage'] = df['coverage'].astype('category').cat.codes\n",
    "df['fleet'] = df['fleet'].astype('category').cat.codes\n",
    "df['sex'] = df['sex'].astype('category').cat.codes\n",
    "df['town'] = df['town'].astype('category').cat.codes\n",
    "df['fuel'] = df['fuel'].astype('category').cat.codes\n",
    "df['use'] = df['use'].astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_names = ['ageph','agec','bm','power','coverage','fuel','sex','fleet','use']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_1 = df.sample(df.shape[0], replace = True)\n",
    "sample_2 = df.sample(df.shape[0], replace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_1 = sample_1.exp\n",
    "y_1 = np.array(sample_1['nclaims']/sample_1['exp']).ravel()\n",
    "x_1 = np.array(sample_1[['ageph','agec','bm','power','coverage','fuel','sex','fleet','use']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_2 = sample_2.exp\n",
    "y_2 = np.array(sample_2['nclaims']/sample_2['exp']).ravel()\n",
    "x_2 = np.array(sample_2[['ageph','agec','bm','power','coverage','fuel','sex','fleet','use']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "rgf_dt_1 = tree.DecisionTreeRegressor(criterion=\"mse\", max_depth = 3,\n",
    "                                      min_samples_split = 2000,\n",
    "                                      min_samples_leaf = 1000,\n",
    "                                      ccp_alpha = 0, splitter=\"best\").fit(x_1,y_1,exp_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,12))\n",
    "_ = tree.plot_tree(rgf_d_1, \n",
    "                   feature_names = col_names,\n",
    "                   filled = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "rgf_dt_2 = tree.DecisionTreeRegressor(criterion=\"mse\", max_depth = 3,\n",
    "                                      min_samples_split = 2000,\n",
    "                                      min_samples_leaf = 1000,\n",
    "                                      ccp_alpha = 0, splitter=\"best\").fit(x_2,y_2,exp_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iOUunjMUYQ5UmrTl/IfiO1xEZGcnQMd+wbMpYIiMtNZq1izaiNSIiguovvHrTdpO/GEy6NKmj/h0eHhFVjAYlT8a5Cxfumv+P7bvImTULKZIli+lNFhGJ91SsikiiUeap4gz4dBQhly5z6XIomdKnY9e+P+k5YCihoaHs/esgJ06fibp8tIlXuFZOOX32PHv+PMCzL7YD4OSZc4SHh+Pj43o59fb2Zvm0r+6axcfHG2stxhguBIeQMijFXbf57KtJDOnXk+2799/LzRYRiddUrIpIouHl5cVj+XLz4fAvqVHJ1Q0dM2kGXdu+RKXSJanQ6GWuX84vKHlyjp08BcCWHbsBSJs6JQVyP8Li70bj4+NDWFhYVKEKMe+sFitYgHW/baJk8SLs2LOf3Dmy3TX/gcNHaPH625y7cIHTZ89T4ZmnqFym5P3dGSIi8YSKVRFJVBrVrEKNl9qz76dFADxboQzd3/uYx/I+ip+vb7TLpgxKTt5Hc1GlSWseL1wAcBW83dq1oPoLr+Ll5UWeR3Iw8oM+UdvEtLPao31LXu7ahytXQunY8nl8fX3ZsmMPG7Zsp/ULDRkzcTrjp83l7LnznDpzjrEfv8uaWd8A8MPPv7Psh59UqIpIoqChACISb2goQOzSUAARiQ+0GoCIiIiIeCwVqyIiIiLisVSsioiIiIjH0jGrIhJvBAYEHA+9ejWD0zkSigB//xNXQkMzOp1DROROVKyKSKJgjEkFLAV+BzpZayMdjnRfjDFewAigBFDdWnvO4UgiInFKhwGISIJnjEkLrAR+AjrG10IVwJ29I/AzsNIYk8bhSCIicUrFqogkaMaY9MAqYBnQzSaAj5Pct6ErsBxY7b6NIiIJkopVEUmwjDGZgDXAHKB3QihU/+W+Lb2AubgK1kzOJhIRiRuaYCUiCZIxJiuujuq31tqBTueJC+6Cta8xJgxYY4ypaK094nQuEZHYpGJVRBIcY0wOXIXqaGvtx07niWvW2gHGmKvAj+6C9aDTmUREYouKVRFJUIwxj+AqVD+11n7mdJ6HxVo72BhzDVeHtZK19i+nM4mIxAYVqyKSYBhj8gIrgA+staOdzvOwWWuHuTus/xas+5zOJCLyoFSsikiCYIx5DNe349+x1o5zOo9TrLWj3B3W1caYKtbaXU5nEhF5ECpWRSTeM8YUAZYAb1hrJzmdx2nW2q/dBetKY0w1a+02pzOJiNwvFasiEq8ZY4oDi4DXrbXTnM7jKay1E92rBCw3xjxrrf3D6UwiIvdDxaqIxFvGmCeBBUB7a+1sp/N4GmvtVHfBusQYU8tau8HpTCIi90rFqojES8aYp3EtiN/KWvu9w3E8lrV2lvuQgIXGmHrW2vVOZxIRuReaYCUi8Y4xphyuQvUlFap3Z61dADQH5hpjyjqdR0TkXqhYFZF4xRhTGZgJNLXWLnU6T3xhrV0CPA/MMsZUcjqPiEhMqVgVkXjDGFMdmAI0tNaucjpPfGOtXQk0Aqa670sREY+nYlVE4gVjTG3gW6COtfZHp/PEV9baH4C6wLfu+1RExKOpWBURj2eMaQh8BdS01v7sdJ74zv0lq5rAV8aYBk7nERG5E60GICIezRjzPDAUqGat3exwnATDWrvBfSjAYmOMn7V2qtOZRERuRcWqiHgsY0wL4EOgirV2u9N5Ehpr7R/GmCrAUnfB+q3TmUREbqRiVUQ8kjGmNfAuUEnz7eOOtXabe4WF5cYYX2vt105nEhG5nopVEfE4xpgOQC+ggrV2n9N5Ejpr7U5jTAVghbvDOsrpTCIi/1KxKiIexRjTBXgdKGet/dvhOImGtXavMaY8sNJdsH7mcCQREUDFqoh4EGPMm0AbXIXqIafzJDbW2r+uK1j9rbWDHY4kIqJiVUQ8gzHmHaAZrkL1iNN5Eitr7UH3ONtV7g7rQKcziUjipmJVRBxljDFAf6ABrkL1uMOREj1r7RF3h3WFMcYP6Gettc6mEpHEyuj1R0Sc4i5UBwHVgMrW2lMOR5LrGGPSAyuAxUAvFawi4gQVqyLiCHeh+ilQBqhqrT3jcCS5BWNMGmAZsBboqoJVRB42Fasi8tAZY7yAkcDjQHVr7XlnE8mdGGNSAkuBjUAna22ks4lEJDFRsSoiD5UxxhsYC+QDalhrLzocSWLAGJMC1+EAu4C2KlhF5GHxcjqAiCQexhgfYDzwCK6OqgrVeML9u6oG5AbGu//oEBGJcypWReShMMb4ApOAjEBNa22Iw5HkHrl/ZzWAzMAk9+9URCRO6TAAEYlz7uWPpgL+QENrbajDkeQBGGMCgNnAFeB5a+01hyOJSAKmzqqIxCljjD8wC9frTQMVqvGf+3dYH9da3TPdv2MRkTihYlVE4owxJhCYB4QCja21Vx2OJLHE/btsDFwD5rp/1yIisU7FqojECWNMUuB74Ayuj4rDHI4kscz98X9T4BywwP07FxGJVSpWRSTWGWOS41rm6BDQ3Fob7nAkiSPu3+1LwD/AIvfvXkQk1qhYFZFYZYwJwrWA/C7gFWtthMORJI65f8etgD3AUvdjQEQkVqhYFZFYY4xJhWuW/EagnRaOTzzcv+t2wCZgufuxICLywFSsikisMMakBVYBPwKdNUM+8XEXrK8B64CV7seEiMgDUbEqIg/MGJMBWI3rONUeKlQTL/fvvjuuQ0FWGWPSOxxJROI5Fasi8kCMMZmBNcBM4G0VquJ+DLyFa3DAGmNMJocjiUg85uN0ABGJv4wx2XB99D/OWvuh03nEc7gL1neNMdeAH4wxFa21/zidS0TiHxWrInJfjDE5gZXAF9baIQ7HEQ9lrf3ghoL1oNOZRCR+UbEqIvfMGPMorkJ1iLV2uNN5xLNZaz+5rmCtZK390+lMIhJ/qFgVkXtijMmHa3mqgdbaMU7nkfjBWvu5MeYqrmNYK1tr9zidSUTiBxWrIhJjxpiCwDKgj7V2vNN5JH6x1o5xd1hXGWOqWGt3Op1JRDyfilURiRFjTFFgCa6lqb5zOo/ET9ba8caYMFzrsFaz1m51OpOIeDYVqyJyV8aY4sAi4DVr7Qyn80j8Zq2d5O6wLjPG1LDWbnI6k4h4LhWrInJHxpingPnAq9bauQ7HkQTCWjvd3WFdbIypba39zelMIuKZVKyKyG0ZY0rjWtj9ZWvtQqfzSMJirZ3jLli/N8bUt9b+5HQmEfE8mmAlIrdkjCmPq1BtpkJV4oq19nugGTDHGFPO6Twi4nlUrIrITYwxlYHpQBNr7TKn80jC5n6MNQVmuB97IiJRVKyKSDTGmBrAZKChtXa103kkcbDWrgIaApONMc86nUdEPIeKVRGJYoypC3wD1LHWrnU4jiQy7sdcXWCCMaaO03lExDPoC1YiAoAxpjEwHHjWWrvR6TySOFlrf3Z39xcaY/ystTOdziQizlKxKiIYY14AhgDVrLVbnM4jiZu19ndjTDVgiTHG11o7xelMIuIcFasiiZwxpgXwAVDFWrvd6TwiANbaze4vWy1zd1gnOJ1JRJyhYlUkETPGtAH6ApWstbudziNyPWvtdmNMRWCFu8P6ldOZROThU7EqkkgZYzoCbwAVrLX7nc4jcivW2t3GmAq4ClZ/a+1IpzOJyMOlYlUkETLGdAVeA8pZaw84HEfkjqy1+9xDKla6Dwn41OFIIvIQqVgVSWSMMb2AV3AVqoedziMSE9bav90F6yp3wTrI4Ugi8pCoWBVJJIwxBngHeAFXoXrU4Ugi98Rae8g9kvXfDusApzOJSNxTsSqSCLgL1YG4FlwvZ6094XAkkftirT1y3SEB/sA71lrrbCoRiUtGz3GRhM1dqA4GquBanuqUw5FEHpgxJh2wHFgGvKmCVSThUrEqkoC5C9VhwDNAVWvtWWcTicQeY0xqXMXqOqCrClaRhEnFqkgCZYzxAr4AiuIaoXre2UQisc8YkxJYAvwBdLTWRjqbSERim4pVkQTIGOMNfAnkAWpYa4MdjiQSZ4wxKYCFwF6grbU2wuFIIhKLVKyKJDDGGB/gGyAzUNtae8nZRCJxzxiTDJgPHAFettaGOxxJRGKJl9MBRCT2GGN8ge+AdEAtFaqSWFhrQ4BaQAZgkvu5ICIJgIpVkQTCGOMHTAOSAnWttZcdjiTyULkf83WA5MA093NCROI5FasiCYAxJgCYDRiggbU21OFIIo5wP/Yb4Hp/m+V+bohIPKZiVSSeM8YkAeYBl4DnrLXXHI4k4ihr7VWgMXAFmGeMCXQ4kog8ABWrIvGYMSYp8D1wEnjRWhvmcCQRj+B+LrwAnAK+dz9XRCQeUrEqEk8ZY5IDi4EDQEt9+1kkOvdzogVwCFjsfs6ISDyjYlUkHnIvhL4M2Am01rqSIrfmfm68AuwClhpjghyOJCL3SMWqSDzjHjG5AtgAtNfEHpE7cz9H2gEbgRXu55CIxBMqVkXiEWNMOmAVsBp4XbPQRWLG/VzpDPwArDTGpHU4kojEkIpVkXjCGJMBV5G6EHhDharIvXE/Z3riOtZ7tfs5JSIeTsWqSDxgjMkMrAGmA31UqIrcH/dz521gJrDG/dwSEQ/m43QAEbkzY0w2XB/9f22t/cjpPCLxnbtgfc8Ycw34wRhT0Vp72OlcInJrKlZFPJgxJhewEhhurf3U6TwiCYm19kNjzFX+K1gPOJ1JRG6mYlXEQxljcuMqVAdba0c6nUckIbLWDr2hw/qn05lEJDoVqyIeyBiTH1gO9LfWful0HpGEzFo7wl2wrjHGVLbW7nE6k4j8R8WqiIcxxhTCteD/W9babxyOI5IoWGvHGmPCgFXGmKrW2h1OZxIRFxWrIh7EGFMUWAJ0t9ZOdjqPSGJirR3v7rCuMMZUt9ZucTqTiKhYFfEYxpgSwCKgo7V2ptN5RBIja+137oJ1qTGmprV2o9OZRBI7FasiHsAYUxKYB7S11s5zOo9IYmatneE+JGCRMaaOtfZXpzOJJGYqVkUcZowpDcwGWlprFzmdR0TAWjvXXbAuMMY0sNauczqTSGKlCVYiDjLGVADmAC+qUBXxLNbahUAzYLYxpryzaUQSLxWrIg4xxlQFpgGNrbXLnc4jIjez1i4DmgDTjTFVnM4jkhipWBVxgDGmJjAJqG+tXeNwHBG5A2vtaqAB8J0xpobTeUQSGxWrIg+ZMaYeMA6oba39yeE4IhID7mNW6wDfGGPqOp1HJDHRF6xEHiJjTGNgOFBDS+KIxC/W2l+MMc8CC40xftbaGU5nEkkMVKyKPCTGmBeBT4BqWmxcJH6y1m40xlQDlhhjfDW8QyTuqVgVeQiMMS2B94HKGuMoEr9Za7cYYyoDy9wF6wSnM4kkZCpWReKYMaYt8A5Q0Vq7x+k8IvLgrLU7jDEVcY1m9bPWful0JpGESsWqSBwyxnQCegIVrLX7nc4jIrHHWrvHvVbySnfBOtLpTCIJkYpVkThijOkOdATKWWsPOBxHROKAtXa/MaYcsMoY42+tHep0JpGERsWqSBwwxrwFtMRVqB52OI6IxCFr7YEbCtYPnc4kkpCoWBWJRcYYA/TDNfGmvLX2qMORROQhsNYevq5g9QP6W2ut07lEEgIVqyKxxF2ovg/UxlWonnA4kog8RNbao+6CdSXgZ4zpo4JV5MEZPY9EHpy7UP0EqAhUsdaedjiSiDjEGJMOWA6sAHqqYBV5MCpWRR6QMcYL+AwoiWvB/7MORxIRhxljUgNLgZ+B11Wwitw/FasiD8BdqI4CCgPPWmsvOBxJRDyEMSYIWAJsATpYayMdjiQSL6lYFblPxhhv4CvgUaCmtTbY4Ugi4mGMMcmBRcA+oI21NsLhSCLxjopVkftgjPEBJgAZgTrW2ksORxIRD2WMSQosAI4CLa214Q5HEolXvJwOIBLfGGN8gclAGqCWClURuRP3a0QtIB0w2f0aIiIxpGJV5B4YY/yBGUAgUM9ae8XhSCISD1hrLwN1gSTAdPdarCISAypWRe7CGONvjFlmjAkAZgORQENrbajD0UQkHnG/ZjQALDDbGBPgfm3xdziaiEdTsSpyd9VxdVLnA8FAE2vtNWcjiUh85H7taAJcAubh6rRWczSUiIdTsSpydy8B6YErwCYgrbNxRCSeSwdsBK66f27mbBwRz6ZxqyJ34F4nsT4QiqsDchDXG4yIyP0KBbICj+P6omZuY0yQ1mkWuTUtXSVyB+5i9WtgMLBBU2hEJLa4xzQ/AbwBvKJiVeTWVKyKiIiIiMfSMasiIiIi4rF0zKpEExgYcDw09GoGp3MkJAEB/ieuXAnN6HQOEXk4Av18j4eGhet19D4E+PqcuHItTK+XEo0OA5BojDE2/OwRp2MkKD6ps2CtNU7nEJGHwxhjz8542+kY8VLqxu/r9VJuosMARERERMRjqVgVEREREY+lYlXi1Jp16+n93geOZmjbuQcZ8xRmzPhvbzrvwKHDZMpbhIq1G1GxdiMuXLzoQEIREVi34yDvTVrlaIbOo74nT6tPGb9sY9Rpe4+cpmbfb6n29jf8vOvQTds80XkUtftNpHa/ify04+DDjCuJhL5gJfHOpUuXSZo0SYwv/95bPXmm5BOEXr31Wv4VyjzD5K9HxVY8ERGPcSn0GkkD/GJ8+bealqNk/mxcDQuPOm3glDV80bEOKZMF0Pzjmcx7N/rArZTJAljw3kuxllnkRuqsSpzbuXsPtZu8xDNVa3Pg0GEAipSqQLM2HSn2TCXmfr+YOk2b879yVTl4+J/bXs+P63+hRbvXeLVLz3vaf6aMd/5S7tqff6Vcjfq899GQe7peEZHYtvufUzT5YCpV3/qGQyfPA1Cq6xjaDJvLM93G8v2vu2n64TTK9fyKw6duP0Ng/c5DtPt8Hl1GL7qn/WdMlfym006dv0SODCkJShqAn683V66GRTv/4uWr1Oo7kfbD53PxUug97U8kJtRZlTh36fJlVsybwY/rf2HI8FEM//gDTp4+zdjPPmHL9h106NaLjT8uY/qc+UyfM5+enTtEbRscHMKXEyaxaPlKnixRnD49u5Ln0UcAWLhsBUOGj462r+JFC/PJwH4xzpYpQ3p2b1hHYGAArV/rxpIVq6leuULs3HARkXt0OTSMee82Y/3OQwyf/wsft67O6QuX+ax9TbYfOEG3sYv58ePWzFm/kznrd9K5bqmobYOvXGXCij9YvnE/JfJkoWfjMjyaKTUAyzbuY/j8X6Ltq+gjmRjYovJdM0Vet2pQiiQBnL8USqC/b9Rpiwc0J3XyJExatZlP56ynX7OKD3o3iESjYlXiXNFCBQEoUbQIHw0dDkCuHDlIkiSQTBkzkD9vbry8vMiYIT1bd+yKtu3R48f55rtp1KlZjdbNXyRn9mxR59WsWpmaVe/+Qnsn/v7++Pu7fq5fqwbbdu5SsSoijimU0/VJUNFHMjJ09k8A5MiQkiT+vmRMlYy8WdLg5WXIkCoZOw6ejLbt8bMhfLdqCzWfyEvzyo+TPX3KqPOqlshD1RJ57iuTl/lvJamLl0NJmTQg2vmpk7sOy6pTsgCth825r32I3ImKVYlzW3fsBOCPbdt5NFcOAK577cNc948b1/3Nlyc3W9avYvXan3hn4CAuXLxIyxea0KBOzVjprAYHh5A8eTLAdThAqSdK3NNtExGJTf8WoNv+PkGujKmAmL9e5smShvVD27J2+0EGTlnDxctXeaFCUeqUzP9AndV0QUk5dPI8KZMGcDUsIlpX9VpYBBaLv68Pv+w6xCPuzCKxScWqxLnAgABqNm7G+QsXmPTlyHve3hhDxbKlqVi2NKfPnGXx8pVAzDur7374CbPnLyQyMpKDh4/wQd/eDBo2gucb1WfHrj28++HHBAQEkD9PburWrH7P+UREYkuAnw+N35/ChUtX+fL1eve8vTGGsoVzUrZwTs5cvMzyP/YDMe+sfjjtB+b/sptIazl86iJ9X6zAW03L0W74fCIiI+n7guuTpxV//ElYeAQl8mSmyQfTSBLgS6CfLyM71r7nzCJ3owlWEo0mWMU+TbASSVw0wer+aYKV3IpWAxARERERj6ViVUREREQ8lopVSRB2791P+ZoNKF2tDmt//vWm8281xapVxy6UrFyTirUb8fnorwCYMHkaBZ4oTcXajXilY9eHll9ExAl3m07VaOAUavb9lqpvfcOuw6cA6DhiAZV7jaN2v4mMXvgbAJNXb4maZNVx5IKHehsk4dMXrCRBeGfgIMZ/MYxUKYNo1Lw1K+bNiHb+7aZYffPFZ+TPmzvaaV06tOXVl5vHeWYREafdbTrV5Defw8/Xm7XbDzB20QY+fbUGAF+8Voe8WdJGu2yHWk/yclWtqCKxT8WqxKo169Yz6NMR+Pn5cubsOTq1bcW4SVPw9vJm4YxJbNu5iw7deuHn50eFMs/Q981ufP3tZL6dOgNrLUM/eI//PV70nvd74tQpcuXIDoC/nz9XrlwhMDAw6vxbTbEyGF7p1JWgFCn4eEBfChbIB8DIseOZPGMOXdq3oX7tGvd5T4iIxNy6HQf5dM56/Hy8OBt8hbbPPsGkVZvx8vJixltN2XnoJN3GLsbPx5syhXPwZuOyfLvyD6au2Ya1lg9ersLjj2a+5/3+O50KiJpOdf3SVH6+3gCEXgunSC7366iBTiO/J0USfwa0qEyBbOkAGLv4d2as3UH7Wk9S+6n8D3aHiFxHxarEOh8fb+ZNmcBHnw5n/a8bWDZnGu27vsHvf2xh3c+/0r1TOxrUqUlkZCSnz5xl3qIlrFk4m3Pnz9Oy/evMn/rfR/VHjh7jpVdfi3b93t5eLJ87PdppkZGRUT8HpUjOufMXohWrtzJ4wDukSZ2anbv30qnnW6xaMJO6NavzUtPGhIRcolqD5ylXuhSpU2ndQBGJez5ehim9mvDpnJ/4dfdh5vR9ka5jFvHHn0f5efdhOtUpSZ2S+YmMtJy5eJlFv+1lYf+XOB8SSvsR85nau0nUdR09c5FXP58X7fq9vbyY2+/FaKfdbTrVxUuhNPlwGsfPhTCxZyMABjSvROrkSdh9+BQ9v1rCgvdeouaT+WhargghoddoMGAypR/LQarkd34NFokpFasS6woWcP1FnSlDelIGpQAgY4YMnDt/gZYvNmHAoE+Zt2gJTRvWJ03qVGzbsYtKdRoDEBEREe26smTOxKoFM++6Ty+v/w6/vnAxmFQpg+66TZrUrjGEj+XPS3h4OAApg1zbpUiRnKef+h/7/zrAkyVUrIpI3CuQPT0AGVImIyiJa0pUhlTJOH8plBfLF2XQjLUs+m0PDcsUJHWyQHYcPEmddycBEBEZfRnKzGlSsOC9l+66z7tNp0qRNIDFA1uwaf9RBk5Zw9TeTaImVuXPlo7wCFejIMi9XYok/jyVLyt/HT9LieRZ7uduELmJilWJdddPWLlx2kpgQACfftSfsLAwSlWuyeJZU3i8aGFmTxoHQFhYWLTrimlnNX26tBw4dJhUKYMIvRp6164qwMWLwaRIkZzjJ07edFp4eDgbN2/lzS6dYn7DRUQewPWLi14/tcpa17CAj1pVJSw8gsq9xzOrz/MUfSQjk95w/aEfFh79D/2YdlbvNJ0qwl2Ient7EZQ0gAA/V8lw8fJVUiTx58S5kKjL/ntaeEQkm/86Rpf6T9/3/SByIxWr8lBNmTmXb6fO4MqVK7z4XEPSpU1DnWerUb5mA7y9vSj7dCn69eoedfmYdlb7v/UGLdp1JiIigvf79gZgyYrVhIWFUfvZqrecYtW83WucPXceay2D3usDwLBRX7J05WoiIy0vN2tC+nRp77RbEZGHYua6HUz9YStXrobxXNnCpA1KyrNP5KVm32/x9vLi6cey0+u5slGXj2ln9U7TqZ7Kn5XmH8/Cy8tggMGtXRP+2n0+j3MhV7DAey9VAmDUwl9Z+cdfRFpLs4pFSReUNNbvA0m8NMFKotEEq9inCVYiiYsmWN0/TbCSW9E6qyIiIiLisVSsioiIiIjHUrEqIiIiIh5Lxao4pmLtRoSGhsbZ9d9qxGqLdq9RpnpdylSvy+Zt2wH4fPRXVKzdiIq1G5EmZwG2bN8RdflZ874na4HHo13vkaPHSJrpEXbv3Q/AzLkLKFm5JiUr12T2/IVxdntERK5Xu99EQq+Fx9n1x3TU6uiFv1G730Rq95tIzhafsP3ACS5fDaPJB1Op3W8iz380jStXw257nSJ3o9UAJMG61YjVfr168EjOHOzZt59e777PnO/G07ldazq3a821a9coWakGRQsVBFxLbc2c9z1Zs0SfCjN05BhKPlE86t9DRoxh5bwZeHkZKtZuTIM6NR/ODRQRiUMxHbXaruaTtKv5JNfCIqjUexyFcmZgwa+7KfpIJt5qWo4hs9ax+Pd9NHjmsdtep8idqFiVO9qyfcdN41FfeKU9x0+ewhiY/s1Y0qROTZFSFShS6DG279zNu717MG7SFI4eO86sSePIkS0rxZ6pROGCBdi9dx/vvf0GNapUitrHqdNnaNO5O8EhIeTL/ShfDB3E8tU/0vf9wQQGBvB8w3q0adnsDilv7VYjVh/JmQMAf39/vL29o5234oe1VChbOurfc79fTI2qlRj19YSo044eO861a9fIkS1r1Gn58z5KyKVLeHl5RQ1BEBEB2H7gxE1jUl/5dA4nz4dgjOGb7g1InTwJpbqOoVCODOw8dJLeTcoyadUWjp0NZtIbjcmWLohnuo2lYI707D1ymreblqdK8dxR+zh94RKdRy0kJPQauTOnZmjbGqze8hfvT/2BQD8fGpYuSMsqxe+Q8tbuZdQqwA/b/qZsoZwA5MqQio37jgJw4dJV0qZIcvvrFLkLFatyRyvXrI02HhXgq+FDSZIkkPGTpjJ5xhxee/UVTp4+zdjPPokqbjf+uIzpc+Yzfc58enbuwOEjR/lx8VwAaj7XLFqxOmjYCHp07kDpkk/S6933Wfvzr3y/ZBkjPvmAEsWKRBulCrBw2QqGDB8d7bTiRQvzycB+Mb5dvfoN5PX2baKdNmf+Ilq+2BRwdVW/mzGbqeNGRytWh4wYTed2rflw6OdRpzWsW4unKj5LpLWMHjY4xhlEJOFbs+3vaGNSAYZ3qEUSf18mrdrMjLU7eLXGE5y+cJnP2teMKm5//Lg1c9bvZM76nXSuW4ojZy6yeGALAJ77YGq0YnXY3J/pXLcUJQtk491Jq/h51yGW/L6PT1pXp9ijmaL2+69lG/cxfP4v0U4r+kgmBraoHO20mI5a/df8X3bzYsWiADySKTVb/z5Oqa5jSJ08kHebVbztdYrcjYpVuaMbx6NWrViON/sNYNvO3QQHB1OtsusFKFeOHCRJEkimjBnInzc3Xl5eZMyQnq07dgHwSM7spEiRHIg+GhVg99799BnwEV5eXly6dIniRQrRrVM7Bn06nOFjvqJD65d5ssR/x43WrFqZmlWjv6jei8GfjaRYkUKUKfVU1GkRERH8/scWxn7+CQDzFy2lasVy+Pj89xQ5dvwEFy5cJM+jj0S7vj79P2LzTyvx9vKmav2m0QpxEUncbhyTWrHII/SbuJKdh04SfPkqlR93FZ05MqQkib8vGVMlI2+WNHh5GTKkSsaOg64JezkzpCRFEn8g+ohUgL1HTjNgylG8jOFS6DWK5MpIpzol+XTOT4xZtIHW1UtQIs9/o0+rlshD1RJ57po9pqNWwTXt6o8/j/F5e9dhUFPXbKVMoRx0rf8MIxf8ysRVm2lR+fFbXqfI3ahYlTu6cTxq+nRpuXAxmDULZ/PVhO/488BBIPpowBtHrAL8deAQwcGu0Xw3dkrz5n6EJg3q8tT/XB9ThYeHExYWxhdDB3Hs+Ale7tCFJbOnRF3+QTqrcxYsYseu3UwYPTza6WvWrafM009FZd+xew9r1q1n7vdL2LPvT97sN5AqFcqyZ/+f1Gj0Itt37ebPvw/yw6I5+Pv7kSxpUowxXLt2FWtttPtARBKvG8ekpkuRlIuXr7Kwf3MmrPiDA8fPAXd/DT1w4jzBV1zH30feMMwnd+Y0NHjmMf7nLkjDIyIJC49gaNsaHD8XTIcRC5j9zgtRl49JZ/VeRq0CrNt5kKcLZIvKbrGkSuYae506eSCnLly67XWK3I0eKXJHN45HzZf7UQ4cOkyNRi+SLUtmUqdOFaPryZo5E+26vsHe/X/y3ttvRDuvd7fOvNqlJxeDg/Hy8mLU0EFMnTWXFWvWEhISQo/XOkS7fEw7q7casdqxR29yZs9GxdqNyJ0rZ1Qndc6CRTSsWytq27e6v85b3V8H4OkqtaLGsVYu7xpn2KpjF954vRMAHVq3pEz1egC0ffklFaoiEuXGMam5M6fh0MnzNBo4hSxpU5DaXdDdTeY0Keg6ZhH7j57h7ablo53Xrf7TdBmziODLV/HyMgxtW4NZP+1gzZa/CQm9ymt1S0W7fEw6qxcuh8Z41CrAgl92U7dUgah/NypdiFafzmHWuh14e3nxVZd6t71OkbvRuFWJJq7GrT5dpRbrl38f69cbH2jcqkjiEhfjVqu8NZ7lH7wcq9fpiTRuVW5F66yKiIiIiMdSsSoPRWLtqoqIxIbE0FUVuR0Vq/JQTZg8LdpEqbhw/MRJqtVvSpnqdZkxZ/5N57/74Sdke6w4vd/7IOq0+YuWUvCpsjxd5b/jVi9eDKbeCy2pVKcRfQZ+FKeZRURuZ/LqLYxftjFO93HiXAj1+0+mep8JzFm/M9p5kZGWBgMmU+Odb6n77iT+OXUBgHcnraLa299Q5a3xrN7yFwCHTp6nXM+vyPTCR3E6XUsSFxWrkuAM/mwkvbt3ZtWCmQwfO47w8OgvmO1aNefbMdFXAyhd6kk2/bg82mlfTviOBrVrsHL+TE6dOsP2nbvjPLuIiBM+m/cz3Rs+w4J3mzF20YZoy1IZA8NercGiAc3pXK8UIxb8CkDLKo+z9P2WzHirKR9O+xGAdEFJmf9us6iVCURig4pViRWdevRm6w7XX+OTZ8xm5Jfj2bxtO5XqNOLpKrXo+370xfIPHDrMC6+0v+nn75cup1yN+pSuVodFy1feV5ZNW7ZR7plS+Pr6Uvix/Oz/60C08zNmSH/TN/ZTp0qFv79/tNP+OnCAooVdo1eLFi7IT7/+dl95RERupceXi9lx8AQAM9Zu58vFG9j293HqvDuJKm+N5/2pa6Jd/tDJ87zy6Zybfl66cR813vmWam9/w/JN++8ry5a/jvHMY9nx9fHmsezp+ev42ajzjDFkT58SAH9fH7y9XK+fOTO4VoPx8/XBy31aoL8vQUkD7iuDyO1o6SqJFY3q1WbG3AUUKfgYcxYs5vPBA0kZlIIV82ZgjKFi7Uacv3DhjtcRGRnJJ5+PYuX8GURGRlKtwfPRFtiPiIigSr2bF5CeNn4M6dKmifp3eHh4VDGaIkUKzp47d1+3qUC+vKz+8SeKFHyM1T/+RInHi9zX9YiI3Eq9px9j7vpdFMyRgQW/7mbwK9UIShLAvH4vYoyhdr+JXLgUesfriIy0fD7vZ+a/2yzq4/rrp1tFRERSr/93N203vlsD0gYljfp3eETkf6+bSfw5F3LzfsMjIhk8Yy3DXq0R7fQPp/3AK9VK3NNtF7kXKlYlVpR9uiTvfTSEkJBLXLp8mUwZM7Bz91569HmPK6Gh7N3/JydOnoq6/K0WvT595ix79u2nan3XyNNTp04THh4eNUXK29ubVQtm3jWLj49P1ML8Fy9eJFXKlPd1m1556Xk69XiLag2akiNrVjKkS3f3jUREYujpAtn5aNqPhFy5xuXQMDKmSs7uw6foM2EFodfC2X/0DCfPX4q6fPTXTdf/zwRfZt+RM9R3F6SnLl4iPCISH2/XB6fe3l7RRqLejo+313+vm5evkvIW3dFe45bSvFIxHsmUOuq02T/tJOTKNRqXKXRf94FITKhYlVjh5eVFoQL5eH/IZ9Ss5lqwf8z4b+nW6VUqly9L2Wfrcf2SvkEpknP0uOvjr81btwOQNk1qCuTLy7I5U/Hx8SEsLCzauNOYdlaLFynE2p9/pdQTJdi2cze5H8l5X7cpMDCQr0d+irWWVzp1pVql8vd1PSIit+LlZSiQPR1DZq2jWglXN3T8sk10qlOS8kVy8WyfCVj+e+FMkcSf4+eCAdh64DgAaZInIV/WtMzp+yI+3l6EhUdEFaoQ885qkVwZ+XnXYZ7Im4Wdh07ySMboA19GLfyNoKQBNLquKN2w9wiTV29hSq/nYuHeELk9FasSaxrVq031Bs/z1xbXwffPVqlEt979eCx/Xvz8fKNdNmVQEPnz5KZi7UYUL1oYcBW8PTq3p0q9Jnh5GfLlfpQvhg6K2iamndWer3ekRfvOXL58hdfavoKvry+bt21nw8bNtGnZjNHjJjBu4hTOnD3HqVOn+WrEUNb/uoF3P/qE3Xv3U7V+E6aNH8NfBw7R853+eHkZWjV7nsyZMsbivSUiAvVKFaDBgMlsGeWaiFeleG56j19G/mzp8PPxjnbZoKQB5Mmchtr9JlL0kUyAq+DtXLcU9d77Di8vQ+7MqRna9r+P6WPaWX29XinaD5/P5athtK3xBL4+3mz7+zgb9x+lUelC9Ju4kifzZqV2v4mUzJ+Nt58vT+/xy7gWFk6DAZNJGuDH1N5NuBR6jWaDZ7D94EmafDCVno3LULpgjli8xyQx0gQriSauJlglZppgJZK4xMUEq8RCE6zkVrQagIiIiIh4LBWrIiIiIuKxVKyKiIiIiMfSMasSTWBgwPHQ0KsZnM6RkAQE+J+4ciVU384SSSQC/XyPh4aF63X0PgT4+py4ci1Mr5cSjYpVcZwx5h0gr7X27l9Zvf99dAXKWmvrx9U+REQeJmPMXGCNtXZYHO5jErDbWjswrvYhcjcqVsVRxphUwD6gpLX2/uYExmw/ge791LPW/h5X+xEReRiMMU8Ac4Dc1to7j7l6sP3kAdbjaijc3zhAkQekY1bFad2BuXFZqAJYa68AHwD943I/IiIPSX/g/bgsVAGstfuA+UC3uNyPyJ2osyqOMcakBfYAxa21Bx/C/vyBvcDz1tr1cb0/EZG4YIx5BvgOV7fz2kPYX05gI5DPWns6rvcnciN1VsVJbwDTHkahCmCtvYqrGzHgYexPRCSODAD6P4xCFcBaewCYDvR8GPsTuZE6q+IIY0xGYAdQxFr70EZmGWN8gV1AG2vt6oe1XxGR2GCMqQiMAQpYa8Mf4n6zAluAgtba4w9rvyKgYlUcYoz5DIi01nZ1YN/NgHZAGasngIjEE8YYA6wDvrDWfufA/ocBWGu7POx9S+KmYlUeOmNMNmAz8Ji19oQD+/cGtgFdrbVLH/b+RUTuhzGmOjAE1ydSEQ7s/99PxIpaa/952PuXxEvFqjx0xpjRwHlrbS8HMzwH9ACeUndVRDydu6v6GzDYWjvDwRyDgBTW2vZOZZDER1+wkofKGJMLaAR87HCUmYA/UNvhHCIiMVEH8AVmOZzjY+A592u5yEOhYlUetr7ASGvtGSdDWGsj3Vn6G2P0PBARj+V+jeoP9HW/djnGvXTVSOAdJ3NI4qI3aXlojDF5gVrAp05ncZsPhAENnQ4iInIHjYCrwAKng7gNBWq7p1uJxDkdsyoPjTHmO2CntfZ9p7P8y/2FhaFAYSe+sCAiciee+oVQY0wfIL+1tpnTWSThU2dVHgpjTCGgMvC501lusBQ4BzR1OoiIyC08D5wFljkd5AafAVWMMQWdDiIJnzqr8lAYY2YCv1hrP3E6y42MMRWAsTzkRbZFRO7kuiEmra21axyOcxNjTE/gSWttY6ezSMKmzqrEOWPM48DTwBdOZ7kV9ySrw0Bzp7OIiFynOXDQEwtVt5HAM8aYYk4HkYRNnVWJc8aYBcAya+1wp7PcjjHmGeA7IO/DmrctInI7xhh/YA/wgrV2vdN5bscY0xmobK2t43QWSbjUWZU4ZYx5CigKfOl0ljux1v6E6+O2V5zOIiKC67VopycXqm5jgceNMU86HUQSLnVWJU4ZY5YBM621Y53OcjfGmCeAOUAea+0Vp/OISOJkjAkE9gH1rLW/O53nbowxrwINrLXVnM4iCZM6qxJnjDFlgdzAeKezxIS1dgPwO/Cq01lEJFFrB2yID4Wq23ggrzGmjNNBJGFSZ1XihHuO9RpgnLV2gsNxYswYUwTXcla5rbWXnM4jIomLMSYpsB+oZq3d6nSemDLGtARaAhWsCguJZeqsSlypBGTE9aWleMP95vAj0NHpLCKSKHUCfohPharbJCATUNHpIJLwqLMqsc7dVV0PfG6tneJ0nntljCkA/ICru3rR6TwikjgYY1Lg6qqWtdbudjrPvTLGvICr2H5G3VWJTeqsSlyoASQDpjkd5H5Ya3fhOhTgdaeziEii0gVYEh8LVbdpQArgWaeDSMKizqrEKndX9XfgfWvtbKfz3C9jTG7gZ1zrrp5zOo+IJGzGmNTAXuApa+2fTue5X8aYhkBv4Al1VyW2qLMqsa0eYHAtARVvWWv3A/OA7k5nEZFEoTswJz4Xqm5zAG+grtNBJOFQZ1VijTHGC9gC9LLWLnQ6z4MyxuQANgH5rLWnnc4jIgmTMSYdsBt43Fp7yOk8D8oYUwv4EChqrY10Oo/Ef+qsSmx6DrgELHI6SGyw1h4EpgJvOJ1FRBK0N4ApCaFQdVuI672gsdNBJGFQZ1VihTHGB9gBdLTWrnA6T2wxxmQBtgIFrbXHnc4jIgmLMSYTsB0obK096nSe2GKMqQIMBwpZa8OdziPxmzqrElteBI4DK50OEpustUeACbi+MCAiEtt6A98kpELVbQVwEnjB6SAS/6mzKg/MGOML7AFaWmt/dDpPbDPGpAd2AcWstYedziMiCYMxJjvwB1DAWnvS6TyxzRhTDhgH5LfWhjmdR+IvdVYlNrwM7E+IhSqA+01kLPC201lEJEF5GxiTEAtVAGvtD8BfuMawitw3dVblgRhjAnCtDdjYWvur03niijEmDa7u8RPW2r+dziMi8Zsx5hHgN1xrOZ91Ok9cMcaUBKYDeay1V53OI/GTOqvyoNoAWxNyoQpgrT0DjAT6Op1FRBKEvsCIhFyoAlhrfwG24XqvELkv6qzKfTPGJME1x7qWtXaT03nimjEmJbAP19zrvQ7HEZF4yhiTD1iLq9t4wek8cc0YUwKYD+S21l5xOo/EP+qsyoNoD/ycGApVAGvteeBToJ/DUUQkfusHfJoYClUAa+1G4Fdc7xki90ydVbkvxpjkuLqqlay1253O87AYY5Lhut2VE9PtFpHYYYwphGtZp9zW2hCn8zwsxpjCwHIS2e2W2KHOqtyv14CVia1gc7/Ifgy863AUEYmf3gMGJ7aCzVq7DViN671D5J6osyr37LpjN0tba/c4HOehcx+ruw/Xsbp/OJ1HROIHY0xxYAGJ9NhN97G663Dd/kRxCITEDnVW5X50Bb5PjIUqgLX2MvAh0N/pLCISr/QHPkiMhSqA+z1jIa73EJEYU2dV7onWG3Uxxvjj6q4m6PVlRSR2uNcbnYZrXdVEu97odevL5nMvCShyV+qsyr3qCcxMzIUqgPvNZiAwwOksIhIvDAAGJuZCFcBa+xcwC+jhdBaJP9RZlRgzxmQAdgLFrLWHnc7jNGOML7AbaGmtXet0HhHxTMaYssB4IL+1NszpPE4zxmQDNgMFEuqoWYldKlYlxowxQwEfa21np7N4CmNMC6AVUN7qySQiNzDGGOAH4Ctr7bdO5/EUxpjhwDVrbXens4jnU7EqMWKMyYJrZF5Ba+0xp/N4CmOMD7Ad6GStXeF0HhHxLMaYKsDnQCFrbYTTeTyFMSYTsAPX/XLU6Tzi2VSsSowYY0YCl621PZ3O4mmMMU2BLkApdVdF5F/ururPuKZVTXM6j6cxxnwCBFprOzqdRTybilW5K2NMDmATruOtTjmdx9MYY7yALUAva+1Cp/OIiGcwxtQCPsB1nH+k03k8jTEmHa7j/otbaw86nUc8l1YDkJh4BxilQvXW3G9CfYH+7k6KiCRy7teC/kBfFaq35n5PGQ30cTqLeDYVq3JHxpjcQD1giMNRPN1c9//rOZhBRDxHfSASmOd0EA83BKjvfq8RuSUdBiB3ZIyZCOy11mo90bswxtQABgFF1UkRSbyMMd64Dg3qaa1d7HQeT2eM6YtrBGtzp7OIZ1JnVW7LGPMYUA34zOks8cRiIAR4zukgIuKo54CLwBKng8QTw4DqxpgCTgcRz6TOqtyWMWY6sNFaO8jpLPGFMaYyMBLXEl/hTucRkYfLvZzdTqC9tXal03niC2NML+Bxa20Tp7OI51FnVW7JGFMUKAOMcDpLPLMSOAa86HQQEXFEM+AIsMrpIPHMcKCcMaaI00HE86izKrdkjJkLrLHWDnM4SrxjjCkDTADyabSiSOJhjPHDtRRTc2vtOqfzxDfGmK5AWWttfaeziGdRZ1VuYox5AvgfriVF5B5Za9cC+4CXnc4iIg/Vy7i+kKpC9f6MBp4wxvzP6SDiWdRZlZsYYxYD8621o5zOEl8ZY54EZgJ5rbWhTucRkbhljAnA9UdqQ2vtb07nia+MMR2AWtbaGk5nEc+hzqpEY4x5BigAfO10lvjM/Wa1BWjjdBYReSjaAn+oUH1gXwMFjTFPOx1EPIc6qxKNMWYVMMlaO87pLPGdMeZxYCGu9QMvO51HROKGMSYJsB+oYa3d7HCceM8Y8wrwgrW2ktNZxDOosypRjDEVgWzAt05nSQistX8A64EOTmcRkTjVEfhJhWqs+RbIYYyp4HQQ8QzqrAoQNcd6HfCFtfY7p/MkFMaYgriWsMltrQ12Oo+IxC5jTHJcXdWK1todTudJKIwxzYB2QBmrQiXRU2dV/lUNSAlMdThHguJ+81oBvOZ0FhGJE52B5SpUY90UIDVQ1ekg4jx1VuXfrupvwGBr7Qyn8yQ0xpi8wE9AHmvteYfjiEgsMcakxLUCwNPW2n0Ox0lwjDHPAT2Ap9RdTdzUWRWAOoAvMMvpIAmRtXYvsADo6nQWEYlV3XAt86dCNW7MBPyB2k4HEWeps5rIGWO8gD+Ad6y1853Ok1AZY3IBG3BNtTrjdB4ReTDGmLTAHqCEtfaAw3ESLGNMXeA9oLi1NtLpPOIMdValEXAVV+dP4oi19m9cXYKeTmcRkVjRE5iuQjXOzQfCgIZOBxHnqLOaiBljvIFtQFdr7VKn8yR0xpisuAYFPGatPeF0HhG5P8aYjMAOoKi19h+n8yR0xpjqwFCgsLU2wuk88vCps5q4PQ+cBZY5HSQxcL+pTQJ6OZ1FRB5IL2CiCtWHZilwDmjqdBBxhjqriZQxxhfYBbS21q5xOE6icV1Hpoi19ojTeUTk3lz3CUlBa+1xp/MkFu4BAWOBAtbacKfzyMOlzmri1Rw4qEL14XK/uX0NvO10FhG5L28DX6lQfbistauBw7jeuySRUWc1ETLG+AN7geetteudzpPYXPct4uLW2oNO5xGRmDHG5AQ24lrV47TDcRIdY8wzwHdAXmvtNafzyMOjzmri9AqwQ4WqM9xvcl8A7zidRUTuyTvASBWqzrDW/oTr8LVXnM4iD5c6q4mMMSYQ18SVetba353Ok1gZY1Lh6m6XstbudzqPiNyZMSYPsB5NonOUMeYJYA6u38MVp/PIw6HOauLTDtigQtVZ1tpzwOdAP6eziEiM9AM+U6HqLGvtBuB34FWns8jDo85qImKMSQbsB6paa7c6nSexM8akwPX7KG+t3el0HhG5NWNMQWAVkNtaG+x0nsTOGFME13JWua21l5zOI3FPndXEpROwRoWqZ7DWXgQ+Ad51OIqI3Nm7wCcqVD2D+z3sR1zvaZIIqLOaSFzXxStrrd3tdB5xMcYkxXUM8bPW2i1O5xGR6IwxxYBFuLp4lx2OI27GmALAD7h+LxedziNxS53VxKMLsESFqmdxf4Q1COjvdBYRuaX+wEcqVD2LtXYXrkMBXnc6i8Q9dVYTAWNMalzfPH/KWvun03kkOmNMAK7uagP3lwdExAMYY54EZuH65nmo03kkOmNMbuAXXL+fc07nkbijzmri0B2Yo0LVM7nfBN8HBjidRUSiGQAMVKHqmdzL/s3F9R4nCZg6qwmcMSYdsBt43Fp7yOk8cmvGGD9cU62auRe+FhEHGWNKAxNxTavStCQPZYzJAWwC8ltrTzmdR+KGOqsJ35vAFBWqns39ZtgfdVdFHGeMMcBA4D0Vqp7NPbJ6KvCG01kk7qizmoAZYzIBO4BC1tqjTueROzPG+AA7gXbW2lVO5xFJrIwxlXCNRC5orQ13Oo/cmTEmC7AN1+/rmNN5JPapWE3AjDHDgWvWWh3PE08YY14EOgClrZ6cIg+du6v6EzDCWjvZ6TwSM8aYoYCPtbaz01kk9qlYTaCMMdmBP4AC1tqTTueRmDHGeANbgR7W2sVO5xFJbIwxNYDBQFFrbYTTeSRmjDHpgV1AMWvtYafzSOzSMasJ19vAGBWq8Yv7zbEf0N/d4RGRh8T9nOsP9FOhGr+43+vG4nrvkwRGxWoCZIx5BGiIa5SnxD+zAV+gjtNBRBKZuoA3MMfpIHJfPgEaud8DJQFRsZow9cV1vNVZp4PIvbPWRgLvAAOMMXqOijwE7ufaAOAd93NQ4hlr7RlgJK7XT0lA9EaYwBhj8gM1gU+dziIP5HvgCtDI6SAiiURj4BKw0Okg8kA+BWoZY/I5HURij75glcAYY6YAW621HzqdRR6MMaYq8Bmupcd0/JxIHHEvG7cdeM1au9zpPPJgjDFv4XrdfMHpLBI71FlNQIwxhYEKwHCns0isWA6cBvSCKxK3XgBOAiucDiKx4nOgojGmkNNBJHaos5qAGGNmA+ustUOdziKxwxhTHvga1yjBMGfTiCQ8xhhfXCOpW1lrf3A6j8QOY0x34GlrbUOns8iDU2c1gTDGlACeAkY5nUVij7V2DfA30MLhKCIJVUvgLxWqCc4ooJQxprjTQeTBqbOaQBhjFgKLrLUjnc4iscsYUwrX7Ou81tqrTucRSSiMMf7APuA5a+0vTueR2GWM6QRUt9bWcjqLPBh1VhMAdzFTCPjK6SwS+6y1P+P68kdrp7OIJDBtcH0hVYVqwvQlUMQYU9LpIPJg1FlNAIwxy4Fp1loVqwmU+zCP+UBua+0Vp/OIxHfGmCS4uqq1rbWbnM4jccMY0wZobK2t6nQWuX/qrMZzxphyQC5ggtNZJO5YazcCvwHtnc4ikkC0B35VoZrgfQM8aowp63QQuX/qrMZj7jnWPwBfWWu/dTqPxC330mTLcXVXQ5zOIxJfGWOSAfuBKtbabU7nkbhljGkBvAKUsyp64iV1VuO3KkA64Dung0jcc7+prgE6ORxFJL57DVitQjXR+A5ID1R2OojcH3VW4yl3V/UXYKi1dprTeeThcI/TXYuru3rB6Twi8Y0xJghXV7W0tXaP03nk4TDGNAW6AKXUXY1/1FmNv2oCgcAMp4PIw2Ot3Q0swvWiKyL3riuwUIVqojMdSArUcDqI3Dt1VuMhY4wXsBF4z1o71+E48pAZYx4FfsW17upZp/OIxBfGmDTAXuAJa+1fTueRh8sYUx94Byih7mr8os5q/FQfiADmOR1EHj5r7Z/AbKCH01lE4pkewEwVqonWXMDieg+VeESd1XjGGOMNbAV6WGsXO51HnGGMyQ78ARSw1p50Oo+IpzPGpAd2AcWstYedziPOMMbUAAYDRa21EU7nkZhRZzX+aQJcAJY4HUScY609BEwB3nQ6i0g80QuYrEI10VsMBAPPOR1EYk6d1XjEGOMD7ATaWWtXOZ1HnGWMyYxrDGsha+1Rp/OIeCpjTBZgG1DQWnvM6TziLGNMJeALXI+HcKfzyN2psxq/vAQcUaEqAO4CdTzwltNZRDzcW8A4Faritgo4BjRzOojEjDqr8YQxxg/YA7xkrV3ndB7xDNcdh/e4+9AAEbmOMSYHsAnIb6095XQe8QzGmDK4xpTnt9ZeczqP3Jk6q/FHK2CPClW5nvvLVWOAPk5nEfFQfYDRKlTletbatcA+4GWns8jdqbPq4Ywxr+H6628H0NBa+5vDkcTDGGNS41o78imgFvCFtTbM2VQizjHG+ALtcQ3Q+AWtSSy3YIx5EpgFFARaWGuHOxxJbkPFqoczxhwFRgH/A5paa684HEk8jDEmEHgDeASojmtJluPOphJxjjEmE66l3ZbhGq36sV475Ubu185pwAZcX1zO4nAkuQ0dBuD5vIBOwFFgszHGOJxHPM83uDoDNQGDntciXu7/ngUK4XqOiERxv5duxvXe2hG9bno0/XI8XzLAD8gDlNeIOLmFl4FzuArVVIC3s3FEHOeN67lggLNAS0fTiMdxv5eWx/Xe6o/rvVY8lIpVz2dwdQWqadkVuRVr7WVr7atAF1yPF/1BI4ldJK7nQldrbTsdAiC34n5PrYrreyGqhzyYjln1cMYYo26qxJQeLyIuei7IvdDjxbOpWBURERERj6W2t4iIiIh4LB+nA8SmwMDA46GhoRmczhEfBQQEnLhy5UpGp3MkZIEB/sdDr17T4/M+BPj7nbgSelWPzzgS6O93PPRamB6b9yHAz/fElavX9NiMQwG+3sevhkfq8Xkf/H28ToSGRcT7x2eCOgzAGGMjLl9wOka85J0kCGutlsWKQ8YYG7r/F6djxEsBuUvq8RmHjDH2woqRTseIl4Iqd9RjM44ZY+yxwdWcjhEvZXpjaYJ4fOowABERERHxWCpWRURERMRjqVh9QC+3bc/uPXvj7PrX/LiWnHkLUrFaTWrUbRh1+mcjvqBspWo0aPIily5ditE2kvi0fqM/e/48EGfX/8MvG8ldpi5VXmhPnVZdAIiMjKRlt35Uer4d1Zp15OSZs3fdRhKf9oO/Ze+huJsKPHvNRip2Gkzl1z5h2LTlUaf3/mImNbp9Sov+X3E59Fq0bdZu3kvB5/tQs9swGvbWYRESdz5YvJe6X/xK1c9+Zv4W1/Pg6PlQnhu7gfqjfmP8+kM3bfP6tG08O/xnGoz+jS/XHXzYkR2VoL5gFV9cunSJpEmTxvjyzzdpxIcD3ov69+nTZ1i0dBk/rlzKhEmT+fqbb+ncsf0dtxGJqUuXr5A0SWCML9+0dlUGvtEx6t9bdu7Fy8uLlVNGM2XeEr6d+T09Xm1+x21EYuLSlaskDfSP0WX/VyAnKz7vgTGG6l2G0rx6KQ4cP8OFS1dYNLQr01f+xqQl62lbr3y07RpV/B/vtakX++ElXouMtHh53f7Qz2vhkRgDvt4x6wH2qJIbPx8vQkLDaTBmA3WKZmTEmr/oUulRnn40NS+N20TdohlJndQv2nafNylMnvSJb9hWouqsHj16jMrP1qJspWq0atsBgLCwMBo0eZFqtevRul1Her/TD4Cvxk+gXOXqlK1Ujd83bgLgqdLl6dC5KyXLVOCr8ROirnfU2K+oVL0W7V/rctt9h4SE8NX4CVSpUZt53y+6p9wzZs2hXOXqjBr7FQC//b6R8mVKA1C9SmV+/vW3u24jnu/oiVNUa9aRCk3a0ubNAQCEhYXzXPs3qdmiM6/2ep8+g13dnnHT5lGx6atUaNKWjdt2AfBM/Zd5re8gSjdoxbhp86Kud8x3s6j6Ygc6vTPotvsOuXSZcdPmUf2lTixY8eM95Z65eCUVm77K2O9mAZA5Y7qoF/ULF0NImzrVXbcRz3bs9Hlq9fiMaq8PocPHEwEIC4/gxX5jqffmcDp+Mol+X84FYMKin6jeZSjVXh/Cpj2u7k/5DoPoOmwKFToOZsKin6Ku96v5P1Kr+zC6DJty232HXAllwqKfqN3zMxat3xrjzNkzpMHLywtjDL6+Pnh5e/H30dMUfjQrAIUfzcrP2/+6abs5P2yiepehfDX/3p4H4pz1f56l+fhNNBu3kVojfuHwWdfAstE/HqD2yF+p88Wv7DoWzJ7jIfSZ53q9LDdkHT/sPc2BM5fpOWsH1lrenL2TRmM20GzcRs5eusbhs1eoP+o32k7azLhbdDoB/jx1iQEL99B47AYuhobHOLOfj6v8Cg2PpFDm5AAcOHMl6uc86ZOy+Z/oXxg3BrpO387zX/3OnuMh93YnxXOJqrOaNm0aliyYi4+PDy1av8rWbdvZvXcvxYsVpU/vNxj0yVDOX7jA6dNnmLdgIWuWL+bcuXO0aN2OBbOnc+bsWXp2e52sWbJQpmJVWr70IgBPl3qKz4YMpmK1mly4cIGgoKCofW7fsZMRo8Zw8NBhGjesz9wZU6O6qp279WT7jp3RMvZ+oztVKlWM+vf/ij/Ozs2/Y62lbuOmlCtTmvMXLpAieQoAgoJScPbcuWjXcattHiuQP07uU4k9aVOlZOE3n+Hj40OrHu+xbfc+9vx5kGIF8/FWp1Z8PPpbLlwM5vTZ8yxY8SMrp4zm3IWLvNKzP3O+HMKZ8xfo1uYlsmZMT4UmbWnesCYApUoUYWjf7lR5oT0XgkMISv7fX+U79v7JF9/O4NCR4zSsUYlZYz6O6qp2fe8Ttu/5M1rGNzu0pHLpp6L+XaJwAbYunYa1loav9qTMk8XJ92gOIiMtRao2wcsYfpozPtp13GqbAnlyxdXdKrEgTVAy5g7qhI+3N69+NIHtfx1h76HjFM2TjTeaPcvQKcu4EHKZMxdCWPjTVhZ/2pVzwZdpN+hbpr/fnrMXL/F6kypkSZeKqq8P4cVqJQF4quCjDO70HDW7DeNCyBWCkv3X0d/591HGzF3D4RNnqV+uOFP7t4vqqvYcPp2dfx+NlrH7i9WoWKLATdmX/LKNXJnSkjJZEvLnyMis1b/Trn55fvhjD+eDL0e77OP5svP7+L5Ya2n6zhhKF81D/hyZYvnelLhw+VoEM199gp//OssXP/xN18qPsmLXKeZ3eJK/T1+m/8I9jG/xOPtOXuLc5WtkTRnIhgPnOXHxKk/mTMXyXafIGOTPoAaPsXL3Kb75+TCNi2fmVPBVZrT9Hz43dExn/3GUOX8cJ00yX5o+kZV3auYD4NiFUDpOif5HlbeXYUbbJ27K3GX6NlbtPs27tV3b5k2flHV/nqVKgXT8euAchbOkiHb5vjXzkTqpH3tOhNB7zk5mt3syNu9Cj5aoitUzZ87SvnNXLly4wMFDh3mhSWP+/vsARQoXAqBY0SKs+XEtf/19gG3bd1Cpei0AIiIiAEgZFESunDkByJ49G6dPnwGgSCHX9lmyZOb8DcXq6h9+5PeNm+jSuRMN69UhMPC/F+PPh35818zJkv1XWNSsXo3tO3eSMiiIgwddHYsLFy6SKmXKu26jYtXznTl/gdfeGcT5iyEcOnqM5+tU4+/DRymSPzcAxR7Lyw+/bOTvw0fYtns/VV90fToQERkJQMrkyciVLTMA2TNn5PQ511/lhfO5ts+SMT3nLwZHK1bX/Pw7G7fupHOr56lfvQKBAQFR533ar8ddMydLmiTq52crPM2OvX9y6OgxAgP82bpsGnOXrmbQqG/o3739HbdRserZzl68RNdhU7hw6QqHT5ylcaUnOHDsDIUeyQJAkdxZWbt5LweOnWbHX0eo1f0z4L/HZlCyJOTMlBaAbBlSc+aCqyv07/aZ06XkwqXL0YrVHzfvYdOeQ3RqVJE6ZYoR6P/fx6Efv/ZcjHLvO3yCETNXMX2g6/FX8JEsPFnwEWp1/4zi+XOQPlXyaJdPFvjf479ayYLs/PuoitV4oqC7I1k0awo+X/UXh89e4bFMyTHG8Ei6pJy7HIYxhiR+3qzec5rnSmRm+e5THLsYyusVH2HB1hMs2HKctfvOEB5pKZbV9T7+WObkNxWqABN+PkzmoACal8xG8ewpo07PFBQQ4yJy2HOFuXAljFojf6V+sUy8VvER3py9k29/OUz2VIGkSx79EIB/DwnIlyEZ4ZEJZ9nRmEhUxeqU6TOoVaM6rV9uQbOXW2Mt5MqVk63btlOnVg02b3H9NZQrZw4eL1aUOdMnA65DBQDOX7jAwUOHyJI5M4cOHSZt2jQAGPPfcSw3rlv7Wod2tHzpRb6bOp06DZ8jb548vN6pA3nz5I5RZ/XixYukSOH662rd+p95s3tXsmXNyudfjKL3Gz1YumIlpZ6K/sS41Tbi+abNX0qNiqVp1aQuLbq5uju5smVm6+791Kpcls07XV/ky5k1M48XzMeM0YMB16ECAOeDQzh45BhZMqTj0NHjpE3lerG90+OzY4smNG9Yi6nzl1K/TQ/y5srOay83JU+u7DHqrF4MvkSK5K5PCtb/vpUer77EidNnSRXkevylThnEhYvRP6661Tbi2Was2kD1UoVpUeMZWn8wHqwlZ6Y0bP/rCDWeLsLW/f8AkCNjGormycbk/q8CrkMFAC6EXObQiTNkTpuSwyfOkibI9QfTdQ9Nblzyu139CrxYrRTTV27guT6jyJM1Ax0aViB31gwx6qyevRBCx08mMe7tViQJ+O9Nv0uTKnRpUoVx36+lRP4c0a7j4qUrpEjqKph/3vYnXZ+v+gD3mjxMO48FA7DtSDA50yQhW+pAdhy9iLWWv09fJmWgLwAlsgcx6ocDTHy5OKv3nubv05fJnjoJj6ZLSoPHM9GpwiMAhEVEcvzCVbzMrY9TndfhKfadDGHSr/8wcNFeqj2WnpZPZ+fspWsx6qxeDY/E38eLQF9vkvl7Y4whTVI/vnqpGGERkbT7bgslcqSMtk1waDjJA3w4GXw1Nu6yeCVRFasVypWlZZt2LFy8NOq0erVr0fSll6laqy6ZMmYkR7ZspEuXljq1alC+yrN4e3tTtvQz9OvTmzSpU/Ph4CFs3rKV1q1a4uMTs7svefLktGvzCu3avMLGTX9w7Phx8ubJHaPO6vRZc/hq3Df4+PhQqUJ5ij9eDIBqlStRtlI10qRJw7dfjwGga89eDHq//223Ec9WvtT/eKVnfxav/u+YvjpVytHs9T7UaPEaGdOlJXuWjKRLk4palctS6fl2eHt5UebJx3nn9TakSRnE4FET2LJzL62a1I354zNZUtq80IA2LzRg0/bdHDt5mjy5sseoszpz0QrGTZuHj7c3FZ55gscL5Sc8PJwJMxZQ+fn2RNpIxnz4NgA9Bn7KB290uuU24tnKFstHu0HfsvSX7VGn1XqmKC8PHEfdN4aTMXUKsmVITdqUyanxdBGe7ToUby8vnimSh94tapI6RVKGTF7K1v3/0LLmM/h4e8dov8mTBPBK7TK8UrsMf+w9xPEzF8mdNUOMOqvDpq/g2OnzvPqR6/sFn3d/gVyZ0lK7x+d4eRuK5clOyxrPANDri5n0b1OPOT9s4puFP+Hj7UX54vkplif7fdxb4oQAH29e+HojF6+E8cULRUif3J9K+dNRe+SveBnDh/Vdf8g8lSsVE3/9h4xBARTKnIIr11zd/2qPpWPtvjM0HP0bxhhaPZOdwplT3GmX5EmfjPdq5+dqeCSLtp3g8rXwGHdWu83YzrELoYRHWDqVdxXIK3adYtQPf2OMoXPFRwj09eZk8FUm/XKYblVy02nKVs5dCQML79TM+4D3WPyiCVa4Oqe+vr58OPgTMmXKFHUs6o2eLleJ9T+sfNCYHkkTrOLe/U6wCgsLx9fXh0FffEOmDGlp3rDWLS9XtuEr/Djr6weN6ZE0wSpu3e8Eq7DwCHx9vPlk8hIypQnixWqlbnm5Sp0+ZuWIng8a0yNpglXcu9sEq/V/nmX1ntO8XSNxFXAxkVAmWCWqzurt1GrQmNAroaRIkYJpk75xOo5INPVad+PK1asEJU/Gd5+/73QckSiN3/6C0KthpEgayDfvvOJ0HBFJoNRZFUCd1Yfhfjuros5qXLvfzqqos/ow3K2zKreXUDqriWqdVU+ye89eyld5ltIVqrD2p/U3nd+mfScyZH+E0V/+97Hu233fo1zl6jzxdFmmz5oNwOy58yldoQqlylbkzbffeWj5JWHb8+cBKj3fjvKN27Buw+Zo5506c44qL7Sn0vPtqPtKVy4Eh9x2m7lLV1O+cRvKNGzFW4OGP+RbIQnR3kPHebbrUKp0/oT12/ZHO+/0+WBqdhvGs12H0uitkVwIca232eHjidTsNoya3YaRvd5/x2KPnLmK2j0/o3qXoZw4q0aHPLh9J0OoN+o3ao/8lV/+PnfT+c8MXkuD0b/RYPRvrP/z7G23mfb7kajLdpm+7aHeBk+kzqpDGj3fjI8/HEiqlClp9HwzViz+Ptr5x44dZ+mKlYSGhtKujevjtWvXruHn50dwcDAVq9Vkw/ofo04DqFitJt998zWZMmW85zzqrMa9+NRZbdqxFx/26kyqoOQ06dCLpZP+67qFXLrM5dBQ0qdJzZdT5nDlSiidWz1/y22uXQvDz8/1LdwqL7Tn22EDyJQ+7T3nUWc1bsWnzmqzd79k4Kv1SZk8Cc3e/ZLvP3k96ryQK6FcCQ0jXarkjPt+LVdCw+jY6L/VVX7f9TdfzV/L6Debs2nPQeb8sIkBbes/UB51VuNefOqsvvLtH/SrlY+gQF9e+XYzM1+NvgpAzRG/sLBTybtuM+33I4SGRdKiVLYHypNQOquJ/pjVNT+u5aNPhuLn68fZs2fp2P5Vxk/4Fi9vbxbNncW27Tvo0Lkrfn5+VChXlr5v9+Kr8ROY+N0UrLUMHfwh/ytR/J73e/Lkqag1W/38/Lly5Uq0NVhvVXD+W5SGhl6laJHC0U6LiIggU6aMpEqV8p6ziOf64ZeNfDz6W/x8fTh7/iLtmzfmmxnz8fbyZv64T9m+509ee2cQvr6+lC9Vgj6dWzNu2jwmzVmEtZZP+nSlROGbF0q/mxOnz0at2erv58uV0NCoNViTJU0StVaqv58v165du+s2ERERZMqQjlRByW/clcRTazfvZeiUZfj5enP24iVerVeebxevx9vLi1kfdmDH30fpOmwqfr7elC2Wl17NazJh0U9MWfYr1lo+7NCI4vly3H1HNzh17mLUmq3+vj5cuXotag3WZIEBUWul+vv6cC0s+kSheWs3U6dMMQAW/7yNy6HXqNXjM4o8mpX32zWItsybxG/r/zzL56v/ws/bi3OXw2j1dHambPgHLy/D5FYl2HU8mDdn78TX24vSuVPTvUpuvvv1H6ZvPIIF+tfOT7FsQXfdz41OhVwje2rX66OfjxdXwiII9P1v9YuLoeHUH/UbWVMF8n7d/KQI9L3lNgDj1h9k9h9HaVsmJzULZ3jwOyUeS/TFKoCPtw/zZ03jo4+HsP7nX1i2cD7tOr3O7xv/YN369XTr8hoN69UlMjLyttOt/nXkyFFeatUm2vV7e3uxfNGCaKdFuhfLBtcUqnPnzkcrVm+nVdsOLFm2nE8GfRB12ucjRzH8i9FUq1KZgOsWdZeEwcfHm9lfDmHwqAn8vHEri78dQcc+H7Fx2y5+2rCZLq1foH71iq7H522mW/3ryPGTtOzWL9r1e3t7s2TiiGin2esWnA5KnoxzF4KjDQwA13qpX06ezfxxw+64zYhvpjFywnSqli1JgH/M5rpL/ODj7cW0ge0ZMnkpv+z4k/kfd+b1Tyfzx95DrN+2n9caV6Ju2ceJjIy87XSrfx09fZ42H3wT7fq9vA0LPn492mmR130amCJpIOeDL0cbGACu9VK/XrCWWR92inb66o27ebulazWNk+cukjTAn+8/eZ1eX8xk6a/bqV6ycGzcLeIhfLwM375cnM9X/cWGg+eY3vYJes7awZZ/LvDr3+dpVy4ntQpnJDLScubSNZbsOMnc9k9y/koYnaduZ2Kr/xpRMZ1Mdf2H1SkCfLhwOYzAoP+K1XntnyR1Uj+mbPiH4av/5u0aeW+5TfWC6WlcPDOXrkXQ5MvfefrRVKRKEv1xnpioWAUKFXR1njJmzBg1fSpTxoycO3+Oli+9SP8PBjFvwUKef64xaVKnvuV0q39lyZKZVUsX3nWfXl7/HS584cLFGHdEx439gvPnz/NMhSo8/1wj13psHdvTsV1bGjR5kd83brqvTq94roJ5XWvwZUyfhpQpXIupZ0yXhvMXgmneqBbvD/+a+ct/pGmdqqROGXTL6Vb/ypIxPcsnj7rrPo3Xfx2mC8EhN3VEIyMjaf1Gf95/o1PUAIDbbdOpZRPav9SI59q/ycZtu+6r0yueqUAuVyc9Y5oUUdOnMqYO4lzwZV6sVopBExexcP0WGld8gtQpkt5yutW/MqdNycKhXe66z+sXab946QopkyeJdn5kZCTtB0+kf5v6pLruvK37D5M3ewYC3IelBCVLwjOFXdPdyhfPx64Dx1SsJjD5M7peLzOk8Cco0FXuZEjuz/kr4TR5IjNDlv/Jkh0nqV8sE6mT+LLzeDANx2wAIOKGCVExXT/1+ub8xdBwgpL4Rjv/3ylUtQpnpN13W267zb/d2OQBPjyRMyUHTl8mVXYVq4na9R/9RJ/2A4GBgQz7ZBBhYWGULFOBJQvm3nK61b9i2llNly4tBw4eJFXKlFy9ejVGXdWrV6/i7+9PkiRJSJ4sGcaYqNO8vb1JnjxZjK5H4hfD7R6flsAAf4a8042wsHBKN2jF9998dsvpVv+KaWc1fZpUHPjnKKmCUhB69dpNXdU3PviMquVKUfap4nfc5urVa/j7++Ht7U2yZEkIVGc1Qbn+Q3NzwziqQH9fBnVsTFh4BBU6DmbuoE63nG71r5h2VtOmSs7B42dImSwJV6+F3dRVfWv0bCo/8Rili+aJdvr86w4BAChZ8BG2/vkP1UoWYsu+w+TNfu/H+otnM9EfoVE/WSwBvt4MrFuAsIhIagz/hSmt/0fhLCn4psXjgGuC1fVi2llNm8yPw2evEBTow9UbDgG4Fh6JBfx9vPj173PkSpvkttv8O60qPCKSLf9c5LUKiXsktYrVu5g8bQYTv5vC5cuXafZC09tOt/pXTDurA/q9Q4tXXiUiIoL3+7uKhyXLVnDt2jXq1KrBuwM+YNbceURGRnLo8GE+6P8urdt34siRo4SFhfGGe4TqiFFjWLh4KeHh4ZQvW4aCj6lrlZhMnb+MSXMWceVKKC/Uq37b6Vb/imln9d2u7WjV4z0iIyLp38P1Ue2yH37mWlgYuXNm48vJc3iyWEGmzV9Kg+oVad+88S23+WLiDBatWkd4RATlSpbgMXeXWBK+Gat+Z8qyX7h8NYymVZ687XSrf8W0s/rOy7V59aMJRERG0u+VugCs+G0H18IjeDRLesYtWMsTBXIxc9Xv1C1bjLb1ygOwfMNOujb9b3xqtacKsejnbdTqPoz0qVPQ/YX48QUeiR1z/jjG9I1HuXItgobFM5M2mR/VH0tPvVG/4W2g1COp6VE1d9TlY9pZfbNaHjpN3Uqkhd7VXX8wrdpzirBwS/HsQbw4bhNJ/LwJ9PXisyaFb7vN2LUHWL3nNJEWmj6RhbTJEvcf+loNQACtBvAwxKfVADyNVgOIW/FpNQBPo9UA4l58Wg3A0ySU1QC0zqqIiIiIeCwVqyIiIiLisVSsioiIiIjHUrF6nypWq0loaGicXf+bb79D6QpVKFupGrv37AVgxuw5lCxTgZJlKjBr7jzAtcZqqbIVKVW2IpOmTIt2HTPnzCVLrrxR/77VCFdJeKq80J7Qq1fj7Pprv9yFSs+3o1yj1uzc+xcAq3/+nXKNWlOx6av8ssk1GnDEN9Mo07AVZRq2YvLcxVHbvzVoOBWbvkqbNwcQERHBheAQqrzQniovtOfJ2i/xXPs34yy7OKtmt2GEXgu7+wXvw+1Grbb96Buqvj6Eqq8PYev+wwAcOXWOOj0/p0a3T/ly3g8ArNuyj0qdPqba60N4c+SMO16nJEwNRv9GaFjE3S94n7rP2E7B91Yx4efDUad1mrqVOiN/pc7IX9l+9CIAe06ERJ32/uK9UZf9af8ZGo/dQL1Rv7Fy96nbXmdCpNUAPNDZs2f59bffWbd6OT/9/AufjxzFF59/ytBhw1m55Hu8vLyoWK0mDevVpXbNZ+ncsT3Xrl3jqTIVaPZ8E8C1rNHM2XPJljVL1PX279uHZ54uFadFtiR8s8Z8jJ+fL2t+3sjIb6czcmAv3vt0DPPHD8PLGJp27M3CCZ9Ts1IZOrVswrVrYTzT4GVeqPcsW3ft4+SZc6yaOoaBn3/FolU/UbtK2agVCj4Z8y3p0qR2+BZKfBTg78s377wSNWp10pKf6dioIr2b1yJX5rTsO3yCvmPnMGVAOz6duow3mj1L6aJ5eO7tUTQsX4JHsqRj0dAu+Pv50ur9cWzZd5hHs6a75XWK3I83quXhyVypCA37b1msnlVykyNNEvafvMTARXv4pmVxJv5ymDeq5aZ07jQ0+fJ3TgVfJVmAD1/+dIjJr5TA19vrjteZECXoYnXL1m03jUp9vvnLnDhxEmMM07/7ljRpUlO4xFMUKVyI7Tt28m6ftxg3YSJHjx5l9vTJ5MienaJPlKJwoYLs3r2H/v3eoUb1/5Y/OXXqNK3bdyIkJIS8eXIzavgwlq9cRd/3BhIYGEjT5xrR9pWX7yl3smTJSJMmNeHh4Zw/f4F0aV2jBfPly0tIyCW8vLwISukaXvDvyFZfX198vP9bz23OvAXUqF6N0WO/ijrtViNcxRlbd+27aUzqS6/34fipMxhjmDLiQ9KkCuLx6s9TuEAeduz5k75d2vDNjAUcPX6K6aMHkSNLJkrUeJFC+R9lz/4DvNutHdXLPx21j1NnztGu9/sEX7pM3kdyMGLAm6xY9yvvfTqGAH9/mtSpRuum9e45u597UfXQq1cp9pirc28jLUHJXQtwn78YTOjVq1GjV319faIemz9v3ELl0k8BULVsSeYv+4HaVcpGXff3K9Yya+wn936HSqzZ9uc/N41JfXngOE6evYgx8G3f1qQOSsZTrwyg0CNZ2fn3Ud5qWZOJi3/m6OnzTO7fluwZ0lCq9fsUfCQzew6d4J2Xa1H1qUJR+zh9PphOQ74j5HIoubNlYFiX51m1cRcDxy8g0M+PRhX/x8u1St9T7tuNWs2V2fX66efrg5f7Tf7vo6cp/GhWAPJmz8CmPQep/GTBqOvy9/XB29vrruNb5eHbcfTiTWNS2323hZPBVzHAly8VI3VSP8oNWUfBTMnZdTyEnlVzM+W3Ixy/GMq4Fo+TLVUgFYb+RIGMydl3MoRe1fJQqUC6qH2cDrlG9xnbCbkazqPpkjK4YUF+2HuaQUv3E+DrRf1imXipZLZ7zp4hxc3LT+VI4x5d7eOFl3uwSt4MybgYGk5EpCXSWgJ9vdl48Dz+3l68NH4Tyfx9GNTgMdIk9bvldSZECbpYXbl6TbRRqQBfjx5JkiRJGDdhIpOnTee1Du04eeoUX34xnC3bttP+tS5s+mUd02bOZvrM2fTs1oXD/xxh7cqlANSs1yhasTpoyKf07PY6pZ8uRa8+fVn703oWLFzMiGFDKFH88WhjVQEWLl7KkGGfRzut+OPF+OSj96P+7efnR4F8+ShQtATh4RFR+25Uvx5Pli5PZGQkY0ZGv44vRn9J/bq1AVdX9bup05g2aUK0YlU8x6qffos2JhVgzEd9SBIYwDczFjB1/hI6tmjCqTPnGP3BW2zdtY9O73zEbwsmMmPhCmYuXEH3ti/xz7ETrJ42FoA6r3SJVqx+MuZburVtxjP/K8bbg0ewbsNmFq5cy2fvvUHxQvlvemwuXv0TQ7+cFO204oXyM+it6IuyXwgOoV7rbhw7eZrpXwwCwNvHm3+OncTLy7Dnr4OcuxBMpvSuF9HRE2dSt1p5AM5fDCFbZtcfTUHJk3H2wsWo6z189ARJkwSSJtW9z+OW2LNm0+5oY1IBRvZoRpIAPyYuXs/0VRtoV78Cp86HMLz7i2z/6x+6fDqFdWN6M3vNJmav2USXJlU4cuocSz/rBkCjt76IVqx+OnUZrz9XmVKFc9P3y7ms37afxeu3MaRzUx7Pm/2mx+bSX7bz+fQV0U4rljcb77dreFP+241a7Tt2Dh0burqi+XNk5MfNe6hesjA/b/uTYnmyR11uy77DnDofTKFHstz1OuXhW7vvbLQxqQBDGxciiZ83Uzb8w+w/jtG6dA5Oh1zjk0aF2HksmDdm7WBFl6eZt+U487ccp2P5XBw9H8r8Dq4/nF/8emO0YnXE6r/oUD4XT+VKxcBFe/jl73Ms23mKD+sXoGjWoKj9/mvFrlN88cPf0U4rkiUF79bOH+PbNWDRHtqWyQlAmdypeWn8HwxcuJdaRTKQLMCHU8HX+Of8Fea2f5IVu04xYvXf9KuV737uwngpQRerN45KrVq5Im+89Q7bd+zk4sVgqlerDMAjOXOSJEkSMmXMQP58efHy8iJTxgxs277dfX4OUqRwjZS8fkwqwK7de/h94ya8vLwICQnh8WJF6d7lNT76eCiffzGajq+25cknSkRdvuaz1aj57J3Xi9u1ew+bNm9hz7Y/2LxlK73e6cek8V/xdr/32LJhPd7e3lStWTeqaF7703pWrF7DzCkTAZi3YCFVK1fCxydB/3rjtRvHpFYu/RS9PxrO9r1/EhxyiaplSwGQK3tmkgQGkDF9GvI/mhMvLy8ypkvDtt37Xedny0yK5EmBmx+bu/88wO9DduFlDJcuX6FYwXx0af0iH4/+lhHfTKP9S414ouh/3aRnKzzDsxWeuWv2oOTJWD1tLL9v3Um/oaOZ8+UQPunTldZv9CdNyiCKFMhDutQpAVi3YTOr1m9g6sgPAUiZIhkXQy4BN49xnbt0NfWqV7jPe1Riy41jUiuWKMA7Y+ew8+8jBF8OpfITrsdMzkxpSBLgR4bUQeTNnhEvLy8ypE7B9r+OAJAjU1pSJHVN1Lt+RCrAnkPH2bTnIF7Gi5DQqxTNnY3XnqvE0CnLGD17NW3rlaNE/pxRl69WshDVShbibm43avXTqcsokjsbT7vHq3Z7vhpdhk1h3IJ15MiUhnSpXI/DU+eC6fXFDCb0bX3X6xRn3DgmtXzetAxYuIddx0MICQ2nQn5XJz1H6iQk8fMmQ3J/8qRPhpeXIUMKf3YeDwYge+pAkge43iNveHiy7+QlNi/Zh5eBS1cjKJw5Be3K5WT4qr/4at0hWj2djcezp4y6fOUC6ah8XbF7r0as/otCmVNQMlcqAD5asp9hzxWiRPYgXpm4mT9PXSJFoA9P5UyFr7cXZfOkYeKv/9z3/uKjBF3N3DgqNX26tFy4eJE1yxfz5bhv+Otv119Ctx+36vrr6a8DBwkOdj3Ab/yLP1/ePDRp1JCnnvwfAOHh4YSFhTFq+DCOHTtOy7btWLpgbtTlY9JZtdaSMmUQXl5epEmTmvPnXYMO/P38SBY1ZvUa1lr++vtv+vTrz/xZ0/B2f9S6c9cu1vy4lrnzv2f33n28+fY7DHp/wAPdlxK7bhyTmi5Nai4Eh7Byymi+njqXvw+53vBvN2oV92Pz78NHCXYXfzc+NvPmykHjWpV5spjrTT48PJyw8HBGDHiTYydP07pnfxZO+O+xGJPOakSE68sH3t7epEyRLGp8avFC+VkycQTHT52h90fD8fHx4c+D/9B3yCjmfDkk6rFZsngRRn47naZ1qrF87a889fh/s9jnL/+BSZ8PvI97U2LTjWNS06ZMzsVLV1j8aTe+WfgTfx91fbHjTmOAAQ4eO03wZdfx8ZE3DJ/JkzUDDSuU4H8FXCMkwyMiCAuPYFiX5zl+5gLtBn/L3EGvRV0+pp3VW41anb92M7sOHGVsr5ZRp6UJSsbEfm0IC4/g5YHjePKxXIReC6PVB+P4qENj0qdKccfrFOfcOCY1bTI/LoaGM7f9k0z69TAHz7i+BHf9y2W0YtT9UDx09gohoa7DOm6cjfRouqTUK5aR4u6CNDwikrBIy+CGBTlx8Sqdp21jWpv/RV3+QTqrC7edYPeJEEY0LXJdREvKQF+MMaQI8CE4NJzHswUxfv0hALYeuUiO1IlrtHqCLlZvHJWaL28eDh48xLN1GpAtaxbSxPCLHFmzZObVTq+zd+8++vd7J9p5vXt2p23HzgQHX8TLy4tRw4cxdfosVqxaTXBwMD26Rf8INSad1ccK5CdVypSUq1ydsLAwhgx2daU6tGtLmYqubmrb1i9jjOHtfv05dfo09Z97AYD5s6bx1ps9eevNngA8Xa5SVKF6qxGu4owbx6TmzZWdg0eOUfvlLmTNlJ40KWP2UXiWjOnp0Ocj9v11kHe7tYt23hvtW9Dh7Q+5GHIJLy/DiAG9mP79Mlau20DIpUt0a/tStMvHpLN6/mIITTr0wsvLYIzhs3d7APDRF+NZ9dMGkiUJ5FP3af2GjOL02fM0etX1WJzz5RCKPpaX1EEpqNj0VXJly8Kb7VsARB2rmyFtmhjdbok7N45JzZMtPYeOn6FBrxFkSZeK1CmSxuh6MqdLyeufTmbf4ZO883KtaOd1f6EanYdO5uLlULyMYVjX55m16ndWb9pN8OWrvN6kcrTLx6Szuufg8VuOWu322VRyZExDzW7DyJUlHSO6v8jSX7YzfMYKMIbuz1cj0N+Pr+b/yO4Dx3hr1CwA+rxci9Qpkt12fKs448YxqbnTJeXwuSs8/9XvZE4ZQOokfjG6nkxBAfSctYP9py7Rq1r0P0Q6V3yEHrN2EBIajpeBwQ0LMmfzMX7ce4aQq+F0LJ8r2uVj2ln9eNl+vt92nEgL/5y/wtvP5qXXnJ1kTx1Ig9G/kStNEoY0LkTH8rnoOmM7XsZVOBfNmgJjDBXzpaXeqN/w8TJ81qTQba8zIdK41Rh4ulwl1v+wMtav15No3Grci4txq2UbvsKPsxL+UmQatxq34mLcaqVOH7NyRM9YvU5PpHGrcS8uxq3WHPELCzuVjNXr9EQatyoiIiIiEsdUrMZAQu+qSvyVGLqqEj8lhq6qxF+JoauakKhYvYtvJn4X5xOfjh8/QdVadSlTsSrTZ82+6fx3B3xA1kfy0fudfnfdZuqMmVR+thblqzzL5i1b4zS3OOvbWd/z5eSbHy+x6fipM9Ro8Rrln2vDzIUrbjq//7AvyfV0LfoMvvkj5Pptukc7ffr3y6nWrCOVnm/Hlp17b7q8JCzfLf2ZrxesjdN9nDh7gbpvDKfq60OYvWbjTed/MOF78jV5i35fzo06bcdfR6jeZSjVuwxlwbrNAKzdvJeCz/ehZrdhNOwdu4dDiGeb9vuROJ/+dDL4Kk2+/J06I39l/pbj0c6LjLQ0+fJ36n7xK43GbOCfc64viL2/eC8NRv/mOpb17eWcu3wtTjN6ugT9Bav4YvDQYbz1Rg+eKVWSStVr0aBunWjLTrVr8wrlypZm2YqVd9zm5MlTLFqyjOWLFkT/5rjIfRoyZiJvtm/J0yWKUrVZB+pVKx/tsdn2xQaUfao4K9ZGPxZ30/bdhF23gPrRE6dYvPonlkwcocemxJphU5fT44VqlCz0KLW6D6NOmWLRhqO8UrsMpYvmZeWGnVGn9R83n9FvNidr+lTUe2M4z5ZyrUjRqOL/eK9NvYd9EyQRGLH6b16v+AhP5ExJwzEbqFEoPT7uARXGwCcNC5ItdSCr95xm9I8HGFi3QNQXpU4GX6X9d1tIFcMvjiVUibaz2vH1bmzd5lpH9bup0xk5aiybt2ylUvVaPF2uEn3fi76EzoGDB3m++cs3/fz9oiWUq1yd0hWqsGjJsvvKsumPzZQrUxpfX18KFXyM/X/+Fe38jBkz3PQGf6ttlixfga+PL9Vq1aVV2w5cuaI51vFR576D2bZ7HwBT5i1h1Lcz2LJzL1Vf7EDZhq/w7qdjol3+wD9Heen1Pjf9vGjVOio2fZXyjduwZM36+8qyacduyj5VHF9fHwrle5Q/D0Zf2y9jujQ3rVEIMOKbabRr9t+yQst+/AVfXx9qtHiNNm8O4IpG/sZb3T6bGrWW6vSVvzF27hq27j9Mre7DqNTpYwaOXxDt8gePn+HlgeNu+nnJL9uo3mUoVTp/wrJft99Xls37DlG6aB58fbx5LFdm/jpyKtr5GVIHcePD88yFS+TMlBYfb29SByWL2mbOD5uo3mUoX83/8b6yiGfpNWcnO4+5lpycteko4346yPaj/2/vLgObutoAjv9v6khbKMXdXYZ7cffBhAkbGzKcMYbb2JCNMRjuNhhuAzZchg139xeoAC11v++Hm6ZNW6jQNmnz/L4ASW5ycjj35Mm5J8/jS5cF/9Fmzimm/XPH6PFPXgXR549Lcf6+77onHeadpt3c0xy4YTy+EuvyU19qF9VypJbJnZUHLwMN9ymKQgF9Gio7ax1WOuMRu/uKB63K50rW62YkFruy2rVLJzZu3krFCuXZun0Hv//6C87OTuzfo61KNm7RBh8fn7c+R2RkJL/MnMWBv/8iMjKSFm07GlW3ioiIoFnr9nGOW79mJa6uOQz/Dg8PNwSjTk6OvPL2TrD98R3j6enJa9/X/PPXdmbPnc+yFavp17dXYrpDmJEurZuwafcBKpQuwfa9h5k5fhjOjln4Z81cFEWh2cd98fH1e+tzREZGMmPxGvaumUtkpErrzwcaVbeKiIig5adxq/Gs/f0nXF2yRT8uPCJ6nGUxrjj1Jheu3qRw/rxkzRKd4sjzxSt8/fzZvfJ35qxYz4oNO+n7WdcEn0uYn04N32Pr4fOUL5qPHccu8cuAbjhlcWDnL4NQFIU2Q3/Dxz/wrc8RGRnJrPX7+GvGICIjVTp+/7tRhauIiEjafzc7znErx/Ukh3N0IYnwiEjD+HTM7IC339tfFyBPDicu3nlMsXw5uXDrET7+gVQpVZCzy8ehqiofjl1IvUolKF0oT2K7RJih9hVzs+OSO2XzZGX3VQ9+6lgWRwdrNvWujqIodF7wH6+Dwt76HJGRKnOPPGRz7+pEqvDB4rNGla4iIlW6LjoT57hFn1QmRxZbo8cZxqm9NT6BcV83PCKSGfvu8sv75Yxu333Vg9+6VYjzeEtjscFqg3p1mTh5Cv7+/gQEBJInT26u37jJt9+PIjg4mFu37+DhGf0tKr6k1y9evOTm7ds0b9MBAE8vL8LDww2XSa2srDj4z64E22JtbY2qaoP59Wtfsjk7J+sYJycnGjdsiKIoNGnkxryFi5PQI8Jc1K9RhR9mLcE/IJCAwGDy5MzBjTsPGP7TLIKCQ7jz4BGeL14ZHm88NrU/X3j7cPveI1p9piVW93r1Ks7Y3Ld2foJtsbK2ih5n/v5kc8ya4DGzlq1jxtihXL1113Cbs2MW3GpV074I1q3OgjWbEtUXwvzUrVicKat24R8UTGBwCLldnLj5SMtPGhwaxp0nHnh5R3+ZMq5loQ3Ql68DuP3Egw7f/Q6Al48f4RERhkv4VlY6dv06OMG2WFvpDOPTNyAI5ywJV5ia9HUnvpuzHiudjjKF8+Lq7EgWB3vD/S1qleP6g2cSrKZztYpk4+e9dwkICScwNIJcjnbc8vBnws6bBIdFcs8rAC+/GPtAY45TfeWAV4Gh3PUMoNviswC8CAglPCLScAnfSqewpU+NBNtipVOix2lwOE4ONnEeM2bHTbrXzE+RHNFf8l8GhBISHkleZ/s4j7c0Fhus6nQ6ypUtw4/TfqZtq5YALFi8lG8HD6Bp40Y0aNKCmDlonRwdef5c2xh94aL2w6UcOVwoW7o0e3dtx9ramrCwMKP9fIldWa1SuRLHjp+gds0aXL12neLFiibY/viOCQ8P5/d5CwA4f/ESRYsUTnrHCJPT6XSUK1mUqXOX01qfpH/R2s0M/upjmtStQaMPehmPzaxZeOb5AoBL128BkCObM6WLF2bPqt/1YzM8zthMzMpqlXKl+PfMRWpVqcDVW/coVqhAgu1/+L9n9Bg6Hu/Xvrzw9sGtTjXqVKvE3JUbALhw7RZFCuRL4FmEudLpg7yf1/xNS32i/qU7jjGga1MaVS1Ni0EzjManY2YH3F/6AHD5rraNxMUpM6UL5mH7zwOwtrIiLDzCaK9pYldWK5UoyIkrd6lRtijXHzyjaL6EE7MXyZuDTT/1wz8omN5TV1Ekbw58A4IMpWFPXrnHkI+aJ/AswtzpdAqlc2fhtwP3aaZfDV118gl9GxahQQkX2s87bQhKQVvxdPcNAeDKU+3LVvZMtpTMlZkNX1fD2kpHWIxAFRK/slohX1ZOPfCmWiFnbrj7USSH8ZeqRcce4mhvTecqeY1u/+eaJy3L5XzHnsgYLDZYBW0rQIu2HXlwU9sv1bplC4Z8N4KyZUpja2v8zcfZ2ZlSJUvQuEUb3qtSGdAm7WFDBtGsdXt0Oh0lSxRn/u+/GY5J7Mrq8KGD+fyrXgQGBtH/m97Y2Nhw8dJl/jt7jl49v2D+oiUsW7GKl6+88fR8wdKFc+M9pkL5cuTI4ULjFm3IlCkTf6xYkmJ9JdJWl9ZNaNNjILePbAOglVtdhv0wk7IlimJrE2tsOmalVNFCNPu4L++V18r76XQ6vu31KS0/HYBOp1CyaCHm/PC94ZjErqx+2+tTvhw2gaCgEL75vBs2NtZcun6bM5ev89WHHVn0x2aWb9jBS5/XeL7yZtHUMRzeoK3oHzl1jn1HT9G0Xk0AXLI50+zjvmTO5MDKmZNSopuEiXRq+B4dv/+dq39o1fFa1CzHiHkbKV0oDzY2xh8rzlkyUaJAbtoM/Y3KJbUvOzqdjkEfNKX9d7PRKQrFC+Tit8EfGY5J7Mrq4A+a0WvqSoJCQundyQ0baysu333CuZuP+KJtPZbsOMqqPSfw9g3ghY8fc7/7lFV7TrBh/39YWVkxuXcnQNuvumLXcaytdLi9V5rKJQqmTEcJk2pXMTcfLjnLmZENAWhSJgdjd9ygVK4s2FoZ/2THycGG4q6Z6bzgPyrm08rt6nQK/RoWoeuis4ZqUtO7RF+mT+zKan+3ogz48zJBYZH0rFsQGysdV5/5cuHxazpXycMPu25TrZAznRf8R43C2RjRUquoteuKB1M6lUmp7kjXpIKVAKSCVVpIjQpWlkIqWKWu1KhgZSmkglXqS40KVpZCKlgJIYQQQgiRyiRYFUIIIYQQZkuCVSGEEEIIYbYkWBVCCCGEEGYrQ/3AysHBwT04OFhKPSSDvb29R1BQUG5TtyMjc7C3cw8OCZXxmQz2drYeQcEhMj5TiYOdrXtwaJiMzWSwt7XxCAoJlbGZiuxtrNxDwiNlfCaDnbXOIzgsIt2PzwwVrJqaoiiFgDNALjWRHas/5j8gd2KPESI5FEXZAyxSVXVrEo75G1igquq2VGuYsHiKonQCeqmq2ioJx3QGvlJVtXXqtUxYOkWruuIBVFNV9XEij9Hpj6ma2GPE28k2gJTVCDiUlKBTVdVHgD9QNtVaJSyeoii2QF3gSBIPPQQ0TvkWCWGkMdpYS4ojQD1FUeKWAxIi5ZQDfJMSdKqqGok2nhulWqssjASrKasRcDAZxx1EAgKRuqoDd1VVfZXgI43J2BRpoTFJnDtVVX0J3EMb20KkliSPTb2DSLCaYiRYTSH6SwXJWR0A+QYmUl9yv0hdAPIpiiL7xUSq0I+tvGhjLakkIBCprRHJ/1xvrI8NxDuSYDXlFAcU4E4yjj0EuCmKYpXgI4VInmR9kVJVNRw4BrildIOE0GsEHFVVNSIZx8o2FZFq9J/JDUlesHobsAKKpWijLJQEqymnEXAwOT+SUlX1OeAOVErxVgmLpyiKPVADLehMDtkKIFJTci+zAhwFaurHuBAprTLwXFVV96QeqI8FZOU/hUiwmnKSuwUgimwFEKmlNnBFVVXfZB4vE65ITcndooJ+TF8FaqVoi4TQJHcLQBRZ+U8hEqymAP2elGRPuHqyeiVSy7usXIEWDGRTFKVACrVHCAAURSkIOAHX3uFpZO4UqeVd586DQCPZt/ruJFhNGWUBf30aquQ6jKRhEanjnVb99WlYDiOrqyLlNQIO68dYcsnqlUhx+s/ieiQ93Z+BqqoPgUCgTAo1y2JJsJoy3nULQFQalgdAtRRpkRCAoihZ0PZCn3jHp5LVK5Ea3nXlCuA4UFk/1oVIKdWBe/rP5nchX6ZSgASrKSMlJlyQvYEi5dUDzqqqGviOzyOXs0SKSqHtU+jH9jm0ohdCpJR3Hpt68kU/BUiw+o7eMbVFbDKoRUp751V/vduADVA0BZ5LCNBS+liRvHR/scnqlUhpKbUIdQhoqC/BKpJJOu/dVQLc9emn3lVUGha7FHguISCFJtwYaVgkIBAppTHJTPcXDxmbIsWkQLo/A1VVnwGeSGrKdyLB6rtLqZWrqDQs15E0LCIFKIqSDSgFnE6hp5RtKiIlpdRlVtDGeGlFUZxT6PmEZasFXHuHdH+xycr/O5Jg9d2l1KWCKLJCIFJKA+CkqqqhKfR8Uj5QpIh3LE8dh6qqIcAptDEvxLuSz3UzI8HqO9CntqiLltYnpcigFiklRSdcVVUfAEFIGhbx7soCAfrUPilF5k6RUlI6WD2MpKZ8JxKsvptqwIMUSG0R03GgiqIomVPwOYVlSsnLrFFkK4BICTI2hVnSf/ZW5t3T/RmoqvoCeAhUTanntDQSrL6blP72FZWG5TyShkW8A0VRcgIF0cZSSpLVK5ESUnzuREtfVUhRFNcUfl5hWeoB51Ig3V9sMne+AwlW301qrA6ADGrx7tyAY6qqhqfw8x4C3CQNi0gufbo/N1Jov2oU/Vj/V//cQiRXanyRAln5fyfygZNM+tQWtUiB1BbxkF8OineVKhOuPg2LF5KGRSRfJcAjhdL9xSZf9MW7SrEf/sVyFKglqSmTR4LV5ItKbfE6FZ77FFBGURSnVHhuYRlSa9UfZIVAvBsZm8Is6VOflSbl0v0Z6GOFG0hqymSRYDX5Um3ClTQs4l0oipIfcAGupNJLyMq/eBeptXIFcBlwVRQlXyo9v8jYotL9haTS88uXqWSSYDX5UnPCBQkIRPI1Ag6rqhqZSs9/GKivKIp1Kj2/yKD0qXvqkbLp/gz0Y/4wEhCI5JHPdTMlwWoy6FNbVEFLM5Va5BuYSK7UvMyKqqpeSBoWkTxV0dL9vUjF15C5UyRXqs6daD8AfE9RlEyp+BoZkgSryVMXOK+qakAqvsZZoIiiKDlS8TVEBqOvDNSE1F0dAFkhEMmT2itXIGNTJIM+5VkhtBRoqUIfM1xAUlMmmQSryZPqE66kYRHJVASwBW6m8uvIr65FcqRWWqCYbgAOiqIUSeXXERmLG/BvKqT7i02+TCWDBKvJkxYTLsjlLJF0jYCDqqqqqfw6koZFJIl+rNREGzupRj/2Ze4USZXaWwCiyBf9ZJBgNYn06aTKoP1aP7XJoBZJlSZfpFRV9UFbwaqZ2q8lMoxawPVUSvcXm8ydIqnSahHqFFBWUlMmjQSrSdcAOJWKqS1iugTkVBQlbxq8lkjn9PtV02JPYBS5nCWSIs3Hpv6cEOKt9KnOcqClPktVqqoGo+VxrZ/ar5WRSLCadGk24erTsBxBLmeJxCkNhAAP0uj1ZPVKJEVarVwB3AfCgFJp9HoifWsEHEnFdH+xyRf9JJJgNenScsIF2XslEi+t9qtGkTQsIlHSKN2fgexbFUmUVvtVo8gX/SSSYDUJ9GmkCqOllUorMqhFYqXlZdaoNCwXkTQsImF1gQupnO4vNlm9EomV1otQUakpXdLwNdM1CVaTxo20SW0R0w0gk6IohdPwNUU6oyiKDm18plmwqidfpkRipHUwANq50Eh/bggRL32KM3tSP92fgaqqYWhXGdzS6jXTOzmJkybNJ1z95axDyOUs8XYVgJeqqv4vjV9XLrWKxEjry6yoqvoEeAWUT8vXFelOI+BQGm6fiiJf9JNAgtWkSfMJV08GtUhImm4BiOEUUF7SsIg30Y+NsqRNur/YZCuASIgpVv1BvugniQSriaRPH5ULLZ1UWpM0LCIhJplwJQ2LSIQGwOk0SvcXm3zRF29kgnR/MV0CciuKkscEr53uSLCaeI2Aw2mY2iKme0AEUNIEry3MnKIo1mjB4mETNUECAvE2plq5Au2caKA/R4SIrRRairP7af3CqqpGIKkpE02C1cQz1RYAScMiEvIe8FhVVU8Tvb6MTfE2ppw7PYAnaGmzhIgtrdP9xSZzZyJJsJp4prpUEEX2Xok3MfXYPAsUkzQsIjZ9ur8iwDkTNkPmTvEmpp47ZWwmkgSriaBPG5UZuG7CZkgaFvEmprzMGpWG5V8kDYuIyw0t3V+YCdsg21REHPrP0kaYNli9BmRRFKWQCduQLkjgkzimvlSAqqqPAR+gnKnaIMyPoii2QG20vU+mJJezRHxMtgUghiNAbf25IkSU8sArfYozk5DUlIknwWrimPpSQRS5ZCBiqwncUlXVx8TtkLEp4mPyuVNVVW/gDlDDlO0QZsfkY1NP5s5EkGA1ATFSW5h6dQDkcpaIy1zG5kUkDYuIwcTp/mKTuVPEZi5z50EkNWWCJFhNWEm0tFH3TN0QtG9gDRRFsTJ1Q4TZMIfLrDHTsLiZuCnCfLihpfuLMHVDkG0qIgYzSPcX011ABUqYuiHmTILVhJmqFFsc+jQsz5A0LAJQFCUTUA3tx03mQC5niZjM5TIraOdIdUVRHEzdEGEWqgBP9Z+pJiX7VhNHgtWEmculgihyOUtEqQNcUlXV39QN0ZOxKWIym7lTVVU/4DLaOSOE2YxNPZk7EyDB6luYSWqL2GRQiyjmNuFGpWEpbOqGCNMyk3R/scncKaKY29wpqSkTIB3zduWA1/q0UebiCFBH0rAIzOyLVIzLWW4mboowPbPZPhWD7FsVMdP9HTV1W6KoqvoI8AXKmrot5kqC1bczt29fqKr6Cm1DdnVTt0WYjqIojkAF4ISp2xKL7FsVYIZzJ3ASqKgoSlZTN0SYVA3gjv6z1JzI3PkWEqy+nTlOuCCXs4T2S9b/VFUNNnVDYpE0LBbOzNL9GaiqGgScQTt3hOUyu7GpJ5/rbyHB6hvo00M1wIwus8Ygl7OEWaSsikdUGpbipm6IMJkSmE+6v9hk7hTmOndKasq3kGD1zaoAz8whtUU8jgE1JA2LRTOntEAGMfatygqB5WqM+e1XjSJj04LpPzOrYz7p/gxUVXUH3IHKJm6KWZJg9c3M9VJBVBqWK2ibxIWFURQlO9rK5RlTt+UN5HKWZTPbuRP4DyihP4eE5akDXNZ/hpojmTvfQILVNzPXSwVR5HKW5WoIHFdVNdTUDXmDqDQssm/VwuhT77hhhqv+APpz5gTaFi9heeRzPZ2SYDUe+tQWddHSRJkruZxlucxyC0AUfRoWP7TUb8KylAN8zSzdX2wyd1ous5470WKOeoqi2Ji6IeZGgtX4VQfummFqi5hOAJUURcli6oaINGfOl1mjyOUsyyRjU5glfcqyiphfuj8DVVVfov0wUVJTxiLBavzM/VIBqqoGAmeBeqZui0g7iqLkAvICF0zdlgTI5SzLZPZzJ9q5k09/LgnLUQ84o09hZs5k7oyHBKvxM/dLBVHkcpblaQQcVVU1wtQNScBhoKGkYbEc+v/rhmj/92ZLVdVwtIwqbiZuikhb8rmejkmwGos+tUUNtMnM3MnlLMuTHi6zoqrqcyQNi6WJSvfnbuqGJILMnZYnXcydaGVgayqKYm/qhpgTCVbjqg1cUVXV19QNSYTTQClFUbKZuiEizaSXCRckILA0MjaFWdKnKiuBlrrMrOljj6tIakojEqzGlR72XAGShsXSKIpSEHAErpm6LYkke68sS7qZO9GCAWdFUQqYuiEiTTQATphxur/YZO6MRYLVuNLLvpYosr/FcjQCDquqGmnqhiSSpGGxEOkk3Z+B/hw6jAQElkI+19M5CVZj0KeBqoQZp7aIh1zOshzp6TKrpGGxLOkh3V9sMndajnQ1dwLHgcqSmjKaBKvG6gFn9Wmh0ovzQAFFUXKauiEi9eirQaWny6xR5HKWZUi3Y1MqrWVs+hRl+TD/dH8G+hjkHNrVCoEEq7Glt0sFkobFchQDrIA7pm5IEsnlLMuQ7uZO4DZgAxQ1dUNEqnIDjuk/K9MTmTtjkGDVWHq7VBBFLmdlfI2Bg6qqqqZuSBJJGpYMTv9/m17S/RnozyWZOzM++VzPACRY1dOnfyqFlg4qvZFLrRlferzMGjMNSy1Tt0WkmvSU7i82mTszvnQ5d6LFIqUVRXE2dUPMgQSr0RoAJ9NRaouYrgAuiqLkN3VDRMrT76lLj5dZo8jlrIwt3Y9N2beaMelTk2VD+8KcrqiqGgKcQlJTAhKsxpReLxVIGpaMrywQoKrqQ1M3JJnkclbGlp7nzgdAEFDG1G0RqSK9pfuLTeZOPQlWo6XXSwVR5HJWxpXex2ZUGpbMpm6ISFnpNN1fbDJ3Zlzpfe6UsaknwSqgT/tUEC0NVHp1EGgil7MypHS7cgVGaVjqmbotIsWlx3R/scnqVQYUY/tUup070ebNQoqiuJq6IaYmwarGjfSZ2iKmW4AtUMTUDREpR1EUK7TxmV73BEaRfasZU3rerxrlEOCmKIp8HmYsRdFSk902dUOSSx+T/IukppRgVS+9f/uSNCwZVyXAQ1XV56ZuyDuSsZkxZYS58xnghXauiYwjvab7i03mTiRYjZLe97VEkf0tGU9GGZuShiWDSefp/mKTuTPjyShzp4xNJFhFn+7JBS39U3onaVgynoxwmVXSsGRM6TndX2yyTSUDyQDp/mK6DLgqipLP1A0xJYsPVkn/qS1iegCEAKVN3RDx7hRFsUH7ActhEzclpcjlrIwl3W8BiOEwUF9RFGtTN0SkiDJAkD41WbomqSk1EqxmnEsFMfetWvSgzkCqAg9UVX1h6oakEBmbGUtGmju9gIdo55xI/zLM2NSz+LnTooNV/aWCJmSMSwVR5HJWxpFRLmNFOQcUljQs6V8GSfcXm8ydGUdGmzstfmxadLCKlubJFrhp6oakIEnDknFkpMusUWlYjiFpWDICN9J/ur/YZJtKBqD/7HMjYwWrNwAHRVEsNjWlpQc0jcgYqS0MVFX9H/ASqGDqtojkUxTFDqgJHDV1W1KYxV/OyiAy2mVW0M61WvpzT6RfFQEvVVWfmrohKUW2+EmwmtEuFUSx+EsGGUAt4Iaqqq9N3ZAUJmMzY8hwc6eqqj5oV9lqmrgp4t1kuLGpZ9Fzp8UGqxmkFNubyOWs9C+jjs1LaGlY8pq6ISJ59Cl0cqCl1MloZO5M/zLq3HkQC05NabHBKloy6xC0dE8ZzWEkDUt6lxEvs0oaloyhEXAog6T7i82iL7Wmd/rPvIyU7i+m+0AYUNLUDTEFSw5WM0optjhUVfUEHgPvmbotIukURcmM9n933NRtSSWyepW+ZdSVK9DqsFdVFCWTqRsikqUq8EifiixDsfSS6pYerGbEfS1RLHp/SzpXF7igqmqAqRuSSmRsplMZNN2fgf6cu4h2Dor0Rz7XMyiLDFYzaGqL2Cz2G1gGkJFXrkDSsKRnGTHdX2wyd6ZfGX3uPAQ0ssTUlBb3hvUqAC/1aZ4yqiNAbUVRbE3dEJFkGXK/ahRJw5KuZbh0f/GQsZkO6VOO1SLjpfszUFX1CfAKKG/qtqQ1Sw1WM/qlgqg0LLeQNCzpiqIoTkBZ4JSp25LKLPZyVjqX4edOtHOvvP5cFOlHTeCm/rMvI7PIudOSg9UMu3IVg1zOSn8aAKdVVQ0xdUNSmUWnYUmPMni6PwNVVYOB00B9U7dFJEmGH5t6Fvm5bnHBqj61RX0yZmqL2ORyVvqTobcAxGDRaVjSqYyc7i82mTvTH0uZOw9jgakpLS5YRUsJ9ESf3imj+xeoJmlY0hVLuMwatW/VIi9npWON0fKrZuT9qlFkbKYj+s+4qmTcdH8Gqqp6AE+BKqZuS1qyxGDVUi4VoKqqP1rFoDqmbotImKIoOdB+bX3W1G1JIxZ5OSsds5i5EzgDFFMUxcXUDRGJUhe4qP/MswQWN3daYrBqKZcKosjlrPSjIfCvqqphpm5IGjkEuFliGpb0xkLS/Rnoz8F/0c5JYf7kcz2Ds6gPCX0apzpoaZ0shcV9A0vHLGnlKioNizcWmIYlHbKEdH+xydyZfljU3IkWw9SxpNSUFhWsoqW2uGUBqS1iOglUUBQlq6kbIhJkEftVY5G9gemDjE1hlhRFcUT7wpvR0/0ZqKrqDdwBapi6LWnF0oJVS7tUEJWG5T8kDYtZUxQlD5ALbY+xJbG4y1nplMXNnWhlV3MripLb1A0Rb1UfLd1fsKkbksYsau60tGDV0i4VRJHLWeavEXBYVdUIUzckjR0GGlhaGpb0RP9/0wDLSPdnoD8Xj2BBAUE6JZ/rFsBiglV9aotqaJvmLY1czjJ/lniZ1WLTsKQzlpTuLzaZO82fRc6daLFMdUVRHEzdkLRgMcEq2g+rLllQaouYzgDFFUXJbuqGiDey1NUBsLAVgnRIxqYwS/rUYsXQPuMsiqqqfsBlLCQ1pSUFq5a45woAVVVD0ZIlSxoWM6QoSmEgM3DdxE0xFYvae5UOWezcCVwDsiiKUsjUDRHxsrR0f7FZzNxpScGqpV4qiCKXs8xXIyynMlB8jgB1LSkNS3oRI93fUVO3xRT05+RhLCQgSIfkc91CPtctIljVp22qgJbGyVLJ5SzzZcmXWaPSsNzGgtKwpCNR6f68Td0QE5K503xZ9NwJnAAqWkJqSosIVtFSW/ynqmqQqRtiQheAvIqi5DJ1Q0Q0RVEULPsyaxSLuZyVzsjY1I9N/bkqzIQ+pVhutBRjFkkf05wB6pm6LanNUoJVS79UEJWG5SgSEJibEoAK3DN1Q0zMYi5npTMWP3cCdwEFKG7qhggjjYCjFpjuLzaLmDstKVi19NUBkMtZ5qgxcNCC96tGOYYFpWFJD/T/F5aa7s9Af27K3Gl+5HNdYxFjM8MHq/p0TcWxwNQW8ZBLreZHLrNilIaltqnbIgyi0v35mbohZkDmTvMjc6fmP6CEoijZTN2Q1JThg1W01BbH9embLN01wElRlIKmbogARVF06DMBmLotZsIiVgjSEVm5inYIaCz7Vs2DPpVYVrTPNIumj21OkMFTU1pCsCp7rvRUVY1E0rCYk3KAr6qqj03dEDNhEXuv0hGZO/VUVX0E+KGds8L0ospTW/r2qSgZfu60hGBVLhUYk8tZ5kPGpjGLScNi7iTdX7xk7jQfMncay/BjM0MHq/o0TfnQ0jYJzUHkcpa5kMusMVhSGpZ0QNL9xSXbVMyA/rNL5k5jF4D8GTk1ZYYOVpHUFvG5A1ih1VMWJqIoihXaHqPDJm6Kucnwl7PSCdkCENdhoKH+3BWmUxwtldhdUzfEXKiqGo6WUcXNxE1JNRk9WJVvX7FIGhazUQV4pqqqu6kbYmZkbJoHmTtjUVX1OeAOVDZxUyydpPuLX4aeOzNksKooSj1FUfIi+1reJKoiSxVFUSTRdRpSFMVJUZTmyNh8k6g0LLkURWlv6sZYGkVR2usrA0m6v/hFzZ3NFUVxMnVjLImiKMUVRamCzJ1vEjU28yqKkuG2UmXIYBXoAgwAnIHSiqJk6I3HSaEoyhfAS7RvYKOBWqZtkcXJAyxA6/+LiqKMMXF7zIaiKIWBwWg/6ukDDDNleyzUMKA32o/dBuv/TwSgP1cvop27C9HOZZF2agGj0Pr/paIoPUzbHPOhKEpjoBSQDS326WLaFqW8jBqs/gc0A64DvwP/M21zzIo72kQbAtQFTpm2ORbnNpAd7UdEA4Fw0zbHrLwEPgXCgJbAadM2xyL9h9b3YcAnaP8nQhMB9Ef78Zkz2rks0s5ptHkzCFgEeJi2OWblCVqscx0t9slwc2dGDVZPo6VdqQJ0VVX1jonbYzZUVd0DTEL7BpYVqUmfpvS5bm8DtmiBwTTTtsh86CsltQaqA1XJgBNuOnAare+rA22kepWRqcBZtHP3tv5cFmnnLuCI9mV/ov6zTAD6GKcr8B5a7JPh5s6MGqw+ACKBsaqqHjN1Y8yNqqrzgX2Ah2xSN4n/oX0T/kb635iqqk+ANmgZK2TPZNr7D63vW+v/L4Se/lzti3buPjVxcyyOvv/dgb2qqi4wdXvMjT7WGYMW+zw0bWtSnpJRPysVRbGSlFVvps9Vp5M+Snv6MqvIysybyflrOtL3byfnr+nI2ExYRu2jDBusCiGEEEKI9C+jbgMQQgghhBAZgHViH+jgYO8eHBySYUt5pSZ7ezuPoKDg3FH/dnBwcA8ODpa+TAZ7e3uPoKCg3DFvc7C3cw8OCZX+TAZ7O1uPoOCQ3PHdJ+M0ceIbk/FxsLVxDw4Ll/5MgL2NtUdQaFj8Y9LO1j04NEz6MBnsbW08gkJC39CvNu7BoTI2E2Jva+0RFBL/2LS30bmHhKvSh8lgZ614BIdFvnUOTfQ2AEVR1LCXst8+OWxcCqCqqhL1b0VR5Hc1yaQoilFf6m9TA69JjujkyFSucZz+jKIoihoR4J3WTUp3rDJne2MfxqQoivpq46i0aFK6lr3rT28dk34n/kjrJmUIWet0f2u/+vwliUkS4tz2+7f24dOJtdO6SRlCvvEnE5xDZRuAEEIIIYQwWxKsCiGEEEIIs5Uhg9Wbt+/SqG0X6rfsyL8n4+bG7TXoO/KUrMTC5asNt33Zbwi1m7alSfuuzF64FIDTZy9QuW4TCpevnmZtNzc3b96kQYMG1KlTh2PH4qas/eqrr3B1dWXBgui0dwsWLKBw4cJ8+OGHRo+dOXMmTZo0oX79+ri7u6d6283RrfuPafbZIBp178/xc5fj3N933C8UrNeJxet3GG47fOo8bh/3p+mngzh98RoAc1dvpkWPIbToMYQ8tdpx+aZl1na4ees2bs1bU69xc44dP2F0n5fXCxq3bItb89a06dSV169fA/BZz97Ub9KC+k1acPHSFQBOnzlLxWq1KViiXJq/B3Nx++kL2oxbTYvRKzl543Gc+9+f/Cdtxq2m+aiV3Hji9cZjdp6+SYvRK2k6cgXjV1vu9pxbD5/Rou8kmvSawPGLN43u8/L2pVW/ybToO4ku307ntX8gAL0nL8Ct51ha9ZvMvPV/A/DU8yXtBv5Ey29+YNHmvWn+PszF7SeetPp+Ac2HzeXE1Qdx7u8ybimtvl9As2/ncuOR9vnyzW8baDNiIW1GLKTQBxMAOHvrMbW/+ZWyn/+Yls03K3e9gui87Crtl1zh9CPfOPfXm32B95df4/3l1zj5UJs3p+x/TKelV2m54DI7rr4AYPf1l7RfcoW2i64wee+jVG1zon9glZ6M+3E6y+bOJJuzE10/68W+7euN7p84chh1a1YnOCTE6Pbl836jdMnihn+XKVmc43t30ryTcdBlSUaPHs3KlSvJli0bnTt35uBB4w+fH374gXr16hEcHGy4rUuXLjRr1ozRo0cbbjt79izPnz/nwIEDadZ2czRh1lIW/zQCZ8esfDRoHHuW/2p0/7gBX1CnSnmCQ0MNt036fTnbF05Fp9Px8eDx7Fz8M/0+7UK/T7sQGhpG/Q/6UrF0sbR+K2ZhzMTJLF80n2zOzrz/0afs3xMd5Ds42PPnquXkzOnKwqXLWb7qDwYP+IYJY0ZStEhhbt2+w/djxrNtw1rKlCrJySP7adamo+nejIlNXneEef3a4ZzFns9+3sz2Cd2N7l/7fVdsbaw4dvUhi3afYWbv1vEe0+K9ErSrWRqAduPX4O7tT+5sWUzxlkxq0qINLBzbB+esmek+8jd2zYmeDx3sbFn1wwBcszuxbNsBVv91hP4ftgJg4di+lCqc1/DYX1fv5PsvOlGvShneH/YzXZrWxsUpa5q/H1P7YfU/zB/SDecsDnz602p2/tTL6P51Yz/H1saao5fvsXDncX7r34V5g7sBWoC6ZNdJAEoWyMn+X/vTYfSiNH8P5mLagcf81qk4TvbWfLX+Fht7GH9Jd3awZtMXxrd965YfW2sd/iERvL/8Gu3L56BpyWy0LusCwPvLr+HhF0qurLap0uZUDVaP/HuSab/NwdbWlpevvOn3dQ+Wr1mPlZWOvzas5sr1m/T7diS2tjY0ql+XscOHsHT1Olb/uRFVhRk/jqdalUpJfl0PrxcUKVQQADs7W4KCgnBwcDDcnyd33B/sKYrCVwO+xckxK9N/GEu50qVwdDSfCeHw4cNMmTJF68uXLxkwYABLly7FysqKPXv2cOXKFfr06YOtrS2NGzdm/PjxLFmyhJUrV6KqKr/99hvVqlVL8ut6eHhQpEgRAOzs7OL2ZZ48cY5xdXUlICDA6LYdO3YQEBBA48aNqVy5MjNmzECrS2AaR/+7yC+L12JjY82r1770/bgTK7fswUqnY9vCqVy9/YCBk2Zia2NNw5pVGP3N5yzftIs/tu9FVVWmj+hH1fKlkvy6ni+9KZxf6zNbW1uCgkNwsLcz3J/H1SXOMZGqilNW7cPe+7U/wSGh2NtpE8LBU+dwq/VecrogRR0++i9Tf5mJra0Nr169ol+fXixfuQadlRW7t23kytVrfDPoW+1cb9iAcaO+Z8mKVaz+Yx2qqvLr9ClUe69Kkl/X09OTIoULAWAb61zPkiULWbJo/WZna0doiPYFoGiRwtptdnZYWVkB4Ojo+K5dkGL+vfaImVtPYGttxSu/IHq1qsaag5fQ6RQ2jvqQ6489Gbrob2ytddSvUJjvu9Zn1YGL/Hn4MqoKP33RjCrF4p6XCfHyCaBQLmcAbG2sCAoJw8HOxnC/rY3WV8Gh4VQskjvBYyIiIsmdLQvOme3foTfe3bHz15mxaof+XPenz/vNWfXXYax0Orb8Opxr954waPoy7VyvWpaRPbuwYsch1u45hqqqTBv0Ke+VKZrk1/V89ZrCeXMCYGdrTVBIKA768zZLJnuyZNL6xdbGmpDQMAAUFPr+uBCnLJn4aUB3yhTNz4OnHlQooY3xUoXzcf7GfZrVSvrnYko5dvkeMzcewsbaGm+/AHq1q8vqvWew0ilsmvgl1x66M3TuVmxsrGhQsRgjPm7Gqn/+Y92Bc6jAlK/bUaVE/iS/rpePP4VzZwfALt7xqYUzwSFhVCyaz+jYHcev0r5uBQAcM5l2PMZ04sFr5hx7io2VDu+gML6smYd15z2xUmDNJ2W44RnIiJ33sbVSqFvEiaGNCrD2nAcbL3qhAhNbFqZSvqR/EfQKCKNgNq0f7Kx0BIVF4KA/vwH8giPosuwq+ZztmNy6CI721thaaxfiQ8IjKZc7E4DhtohIlVxZbXCyT72QMtVXVq2trdm2djnTZs7hxOmz/LN1HX2HjuDshUscP/UfQ/v1pnP71kRGRvLi5St27P6HQ39txtvHhy++GcL2dSsMz/X02XM+6zPQ6PmtdFbs3fan0W2RkdGFRZwcs+Lt89oowIrP9Eljccmejes3bzNg+GgO7Nj47m8+hVlbW7Nz506mTJnC8ePH2b9/P7179+bs2bMcO3aMYcOG0aVLF60vX7xg27ZtHD16FG9vbz777DP++usvw3M9ffqU7t2NV06srKzirHwa9aWTE97e3gn2ZXw8PDzInDkzBw8eZPDgwezatYu2bdsm+XlSkpW1FZvn/cTPi9dy8sJVdi39hf4TfuXc1VucOH+FwT260bF5A60/vV+z8+Bx9q36De/Xfnw1aipb5v1keK6nHl58+f1Pxs+v07F72Qyj2yJjFL1xypoZb18/o2A1PtZWVvzP3QudTuH2g8d4+/oZgtpt+47xeadW79oVKcLa2oodm/5k6s+/cuLUafbu2kafAYM5e/4C/x4/ydBB/enSsb1+fL5k+85dHN67G29vHz7/ug87N0dfAXn67Bmffmm8cmKls2Lf7u1GtxmPT0e8fXzijE9fX18WLlnG7m2bjG7/fsx4Bvfvm1JvP0VZ63SsG9GNmVtPcPrm/9g67mOGLNzNhXvPOXnzCf3b16R9rdJERqq89A1k93+32TXpU3z8g+k7Zyd/juxmeK5nL/3oPdu436x0CtvGG5//kTEylDhmssMnINgoGPANCOaDKRtw9/Zn9Xdd3nrMgl3/sWjPWZpULoq9rekv4FlZWbHx52H8smo7p67cZufsUQyctpTzNx9w4uJNBn3chg6Namhj08ePv46e5Z95Y7VgbNJ8Nv3yneG5nnm9oueEecbPr9Px1+/GmR4iI2P0TeZM+PgG4OBqvOrkGxDI0q0H2PLr9wD8OOBjXJyycvPB/xjyywr2zB1DqcL5OHLuGq3qVuHEpZtULlk4hXsn6aytrFg/vge/bjjE6esP2f7j1wyes5kLd55y4toD+nduQIe6FYiMjOTl6wB2nbrG7ml98PEPos+v61k//gvDcz178ZqvfzH+DLfSKeyItXIa81x3zGSPj3+Q0fh8HRDEBxNX8PylL2tGf2p07KGLdxj1SbOU7IIUY6VTWNm9NL8ffcqZx76s/7wsw3fc49Izf04/8qNP3by0KetCZKTKq4Aw/r75ii1flsMnKJxBW++yqnsZw3M99w1hwOa7Rs+vU2BDrJXTmOdtVnsrXgcZB6tbe5YjeyYb/jzvyZxjTxnVTPuyNGTrXQ7d9WF8i0KGxy45+Zxlp5/jVtwZe5vU21ma6rNIudLa6lPuXDlxctJWL/LkyonP69d8/nE3fpj+G9t3/8NH73ckezZnrly7QdMO2kQbEWFcMSxf3jyJCiJ1uugOe+3rRzZnpwSPccmeDYCypUsSHm6elcrKly8PaKuZzs7Ohr97e3vzxRdfMHHiRLZt28bHH3+Mi4sLly9fplGjRkA8fZkvH4cPH07wNY368vVrsmXLlqy2Ozs706BBAwCaNm3KtWvXTB6sliuurRjnzpEdp6yZtb+7ZsfH159PO7bkp/mr2HnwX7q1aUJ2J0eu3rpPyy+GAtqqUUz5crnyz4qZCb6mTonRn34BZEvE6v30Ef3oNWoq2Z0dqVi6OK7ZnPVtiOD81VvMnzQsUe83tZUvq02auXPnxkl/zuXJnRtvbx96fNqdST9NY/vOXXzU7X1cXLJz5eo1mrRqB8R3rufl4N9/kRDj8elLNv15ESUyMpIevb5h6uSJZMsWfd/0Gb9RpWIF6tetk5y3murKFHQFIJdzFpwyaV9mcmXLgk9AMN3dKjJt4zF2/3ebLvXLkT2LA9ceedJ+gpbSKSLSeGzmdcnKzomfJPiauhhXOnwDQ+KsiDpmtmfP5M84f/cZk9cd4c+R3d54TJ82Nfi6ZTU+/XkzF+49T9ZKb0oqW1Rbycvt4oxzlsyGv/v4BvBJ24ZMXbqFv46epWvzOmR3ysq1e09o3V/b0xinP12zs2fumARfU6eL0TcBgTg7Zja6PzIykt4/LOSHfh+RTX9f1OX90kXyE64/J779rAODpy9l2dYDFM6Tk5zZE/48S21lCmlXJ3Nlz4pTFu3/PFc2R3z8A/mkWTWmrt3PrlPX6NqwMtmzZubaw+e0Halddo+M3Z85nNg1tXeCrxnzXPcNDMY5i/GXUqfMDvw9vS/nbz9h8up/DAHx5fvPKJnfFXtbG8xR6ZzaKmXOrDY42msBY66strwOjuCDKq7MPPw//r7xik4Vc5DNwZobHoF0XXEd0FY0Y8rjaBfn8n18Yp63fsERODlYGd2fPZPWV23KZuebTXcMt8/sVJzXQeG0X3KVjhVyaFeka+fhi5q56fnnLS499U/WSm9ipHqwGvNSb8y/q6qKg709M6dMJCwsjNrN2rFn0x9UqViezWu0HziFhYUZPVdiV1Zz5nDh4eMnZHN2IjgkJFErgb6+fjg6ZsXdwzPJ7zGtvLUvHRyYNWsWYWFh1KhRg7179/Lee++xbds2IJ6+TOTKas6cOXn48CHZsmUjODg4WauqAHXr1uXixYu0adOG8+fPU6ZMmYQPSmUxdyHEHZt2/DKyP2Fh4dT/8Bt2Lp5O5bIl2PD7DwCEhYUbPVdiV1ZdXZx59NQdZ8cshISEJriqCvBeuZLsXjYDd69XjJ6xAGtrbWI5euYSdatWMOl2ipjePj7t+e2XqYSFhVGrQRP+3rGFKpUrsXW9FmDFPdcTt7Lq6urKw0ePyebsREg85/q334+mZfOmNKxf13Dblu07uXr9BquWLny3N5yKFN7cl/a21kz9sjlh4RE0HbmCzWM+pFLR3KwZ/j4AYbG+bCd2ZdXVKROPPX1wzmxPSFi40apV1JczKysdTpntDaul8R0TEhaOnY01VlY6sjjYmsXKqvHYjL5dRcXBzpbpQz4jLDwct57j2PbbCCqVLMyf07QvpmHhxud6YldWXbM58ui5F85ZMxMcGmbYAhBl5Ow/aFarEvWqRM+FvgGBOGbOhMdLH8NtOZyzsuanwYSFh9Nj3BxqVCiRrD5ISUb9GWOsqoC9rQ3TercnLDyCxkPnsPWHnlQslo+1Yz4H4hmfiVxZzeGUmUcer3DO4kBw6NvGp4NRYLrj+BXa1yn/bm84FRl/DkX/XVVV7G10TGpdhLCISNosusK6z8pSIU9mln2k7QkPi7VoktiV1RyZbXjiHYyTgzXB4ZFGq6qh4ZGogJ21jtOP/SicXfsyEhIeiZ21DgcbHVnsdCiKYrjNSqeQxdYqfa+svs26zdtZ/edGAgOD6N6tM645XGjXqjmN2nbBysqKBnVqMe77oYbHJ3ZldeKo7+jRdxAREZH8OFa7vPLPgUOEhobRrlVzJk6dwZYdu4mMjOTxk6f8OG4En/cdyCvv16iqytSJ2kb4ew8e8s3Qkdy8fZcWnT7it6mTKFPK9BNFfNauXcvKlSsJDAzk008/xdXVlQ4dOtCgQQOsrKxo2LAhEyZMMDw+sSurkydP5tNPPyUiIoIpU6YA8PfffxMaGkr79u0ZP348mzZtIjIykkePHjFlyhS2bNnCr7/+yt27d2ndujW7d++mTZs27Nixg0aNGpE7d25GjTLv5Ojrdx3gj+3/EBgcwsftmuGa3Zm2jevQ7LNBWOmsqFe9ImP69TA8PrErq+MHfEnPEVOIiIxg0uCvAdh77D9Cw8Jo27guP8xZwba9R4hUVZ4882DSkK+ZtnANh06eJ3Mme2aOif6ytm3vUTq3aJji7z01rN2widV/rCMwMIhPPvoAV9cctG/TGrfmrbVzvV5dxo8eYXh8YldWfxg3ms+/6k1ERAQ/ThwPwN979xMaFkqJYsVYuHQ5tWpU488Nm+jSsT39+vSi36BvKVK4EI1btqVY0aIsnjebe/cf0HfAEG7cukXzNh2ZNWMaZUonfU9yWtj07zX+PHKFoJAwujUoTw6nzLSqXoI241ZjpVOoU7YgI7o1MDw+sSuroz5sSJ/fdxARqTLuYzcA9l+4R1h4BDVL5+eznzej0ykoKEz/qsUbj1m05yx7z90lPCKSeuULUaaAa4r3QUrasPcEa3cfJSgklA9b1sM1myNt6lelRd9JWFnpqFe5DKO+6mJ4fGJXVsf16srXk+YTERHJxL4fALDv1CVCw8IpViA3S7cdoHq54mzcd4KOjWrQ+/3mfDVxPt6+/qiqyuT+HwPw9/ELzF63CwWFYZ93iBP0mpuNRy6w7sB5gkJC+aBRFXI4ZaF1zbK0+n4BVjqFuuWLMrJ79CX5xK6sjvm0BX1mrCciMpJxn2tbn/afu0VoeAS1yhTik59Wo1MUFEXhl74dDMftP3eLIV0bGf794PlLBs/Zwu0nnnQYvZjpfdpTqoB5Fp7advkFGy95ERQWSZdKrrhktqFF6ex0XnYVnaJQu7Aj3zYqYHh8YldWhzcuwMAtd4lQVUY21X7fc+iON2ERKlXyZ+HTNTfJZKsFpjM7aj86H7b9Hs99QwmLUOlXT9sTvPy0O/tvexMeqVKnsCOl9KvEqUEqWKUBqWCVcqSCVcqSClbvTipYpSypYJU6pILVu5MKVqlDKlgJIYQQQoh0TYJVIYQQQghhtswuWG3SvqtRgvnU8PTZc7LkLc7N29EbkVVVpUr9ZoaqVivXbqBsjQY0ad+Vnv21fbOBgUHUadYO54KljI6NelyT9l05evxkqrY9Kdzc3FK/L58+xd7enps3tQoto0aNon79+lStWpUNGzYAMGvWLNzc3HBzc8PZ2ZlLly5x5MgRatWqRb169Rg0aJDh+Z48eUL79u1xc3Pjp59+ivc1TaVFjyEEh4Qm/MBkat/re5p9Ngi3j/tz/a5WoWXx+h2UbvYRnw37wfC4/y7foFqHLyneODo90aOn7jT8qB/NPhtExz4jCAgMAqBi688Mla6OnbmUam1/V41btk3Vsfr96HHUa9ycBk1bcvPWbSD+6lez5y6gccu2NG7Zlux5C3Hp8hWOHDtOHbdmNGjaksHDRrztZcxCu/FrCA4NT/iByTR+9UFajF5JqzGruP1Uq2QTEhbOkIW76TDhD774dYvhsfP++o+OE/+g9dhVeHj7ExgSxgc/rafd+DV8NHUDQSFhb3oZk2rVb3Kqnuv9piymcOs+LN263+h2VVWp9ekIw+3z1v9No6/G0eircfz5978APHruRZHWfWnVbzKt+k02VL8C7cdfOdx6cOvhs1Rr+7tqM2IhwaGp8//+4rU/bUYspNX3C+g6fhmvA7R5cNLKv2k1fD4NB81m6zFtHoyvklVgcChNhs4h3/tjuf3EfH9s/f7yawSHRSb8wGQatv0eFaadYdWZ6EqT8VW9Wn/B01DtashWLSZ64h1MxelnDBWwfIO1uWjH1Re0X3KFD1dex9Mv6eeW6X+maQIz5y2iVnXjROpbd+4hd07jHwIM6vs1vb+IztdmZ2fLtrXLGTHBuExbNmdns8zLmhZmzJhB7drR+3QmTJiAra0tfn5+uLm50a1bNwYNGsSgQYMIDQ2levXqVKpUiadPn3LkyBHs7Oz46KOPuHDhAlWqVGHEiBEsXLgw3mIDGd2mOZOxtbXhyOkLzFuzlTkThtKxWQOa1K7KhNnLDI8rXbQgR9bNpXXP6JRV+XK5cnjtHBRF4Yc5K9h16ATd2jQhm1PWRP3wKyN79cqb02fO8u/BvRw/eYrZ8xYwb9av8Va/GtivDwP79SE0NJSa9RtTqWIFnj57xqF//sLOzo6PP+/JhYuXqVK5oqnflkl4+wVx9s5T/vnxc07dfMLCXWeY0asVi/acpXX1kjR7L7oC4IV7z/Hw9jfKOrDz9E0qFc3NqA8bMmPzcfacvUPnumVN8VZMauzX71O7YklDMYAoOw6fIVeM1FSt67/HNx+0JDQsnIY9x/Jhy3oANKxalhU/DIjzvLPX7qZG+eJxbrcU9rY2rBjRHVfnLCzfc4o1+87Sr2N9RnzcFFsba/wCQ2g7ciGd6leKt5KVnY01f47rwbjlu034Lkzvu8YFqF4wKyHh0QHxm6pe9aqdh8+q5zY6vm4RJ+Z3LWn4d3iEyrLT7mz+ohxnn/gx999nTGxVOEltSlaweunq9TiVp7p/9Q3unl4oisL65QtxyZ6NSnWaULF8Ga5cv8mEEd+y/I/1PH3uzubVSyhUID+V6zWlQrky3Lx9l0mjvqNVs8aG1/B68ZJeg4bh5x9AyeLFmDdjCvsPHWXcTz/j4GDPh1068vXn3d/Syvg9e+5OSEgoBfNHV7hQVZX1W7bTtVN7oxQ68xavYN2mrQzq8zWd2rXCysqKnK454jynr68fjdu9T8H8+Zk1bRJOSaiGc+nSpTiVpz788EPc3d1RFIVNmzbh4uJCuXLlqFSpEleuXGHSpEksXbqUp0+fsm3bNgoVKkSFChWoWLEiN27cYPLkybRu3Tq6L7286NmzJ35+fpQqVYoFCxawb98+xowZg4ODAx9//DG9evV6Syvf0JfPnhESEkKhQtEJgm1ttV+qBgcHU7lyZaPH79+/nyZNmgBaNoIoUZWEwsLCePToEYMGDeLly5dMnz6dqlWrJqlNl2/ei1N56rNhP+Dx4hUKCn/8Nh4XZyeqtv+CCqWKce3OA8b078HKLXt45vGCDb9PomDe3FTv2JPyJYty8/4jxg/8kpYNahlew+uVD33H/ox/QBAlihTg9/FDOHDiLBNnL8PB3o5urZvQs1vSc8ja6tOtBIWEUKmM9oHjmt2ZwCDjFUfHLJnjHBuVzipKyaLaLzx9/QNo/vlgCubNxYxRAwyVsJLj0uUrcapQffT5l3h4eKIoChvWrMTFJTsVqtWmYvlyXL12nQljR7Fs5WqePXvOlvVrKFSwIJWq16FC+XLcvHWbSeNG07plc8NreHm94KtvBuDv70/JEsWZP3sm+w4cYtykH7Xzvtv79PqyR5LanSVLZlxcXAgPD8fn9Wtcc2jn8NuqX+0/eJjGblqmhXx5o0tfamP13S9IXX3oEacKVc+ZW/H0CUBRYMW3ncmeNRO1hyyifKGcXH/sxcgPGrDm4CWev/JjzfD3KeDqRN2hiylXKCe3n75g9IcNjYLFF68DGDh/N/7BIRTP68KvvVpx6NIDfvzzCA621nSpV44ezZJWNSyzvS0uWR0Ij4jENyAEF0ft17+HLz/A0yeA2dtP8XGjinzkVpE9Z24TGBJKhwl/UL5wLiZ/3oQiubJx7o626vc6IJgcjsn/9fCVO4/iVJ7qMfZ3PF69RlFg9Y+DcHHKSvXuw6lQvCDX7j1h9Nfvs2rnYZ57ebNu6hAK5nGl5iffU754QW49fMbYXl1pUaey4TW8vH3pN2Ux/oFBlCiYh1nDe3Lwvyv8sGgj9na2dG1Why87Nn5jG98kd464+apVVWXjvhN0blqbcH26rKgKWDbWVljHGHfHL92ked9JuFUtZ8hU8NzLm9CwMArkjvsZlVRX7j+LU4Xqy2l/4OHtj6LAqpGfkN0xM7W+mUH5Inm5/tCdkd2bsWbfGZ699OWPMZ9RMGc26vSbSbnCubn1P0/GfNKC5tVLG17jxWt/+s/ahH9QCCXyuTKzf2cOXbjN5NV7sbe1oatbZXq0rJmkdmdxsCOLg5YO0NbGmpCwCMPfAULCwqhQVFsIia+SlZWVDlfnlMkTes09IE71qb4bb+Plr8UYiz4oSfZMNjSac5GyuTNx0yOQYY0LsO68J+6+oSz7qDT5ne1oMvciZXJl5s6LIIY3LkCTktFj52VAGN9uv4d/SATFcjgwrV1Rjt7zYfqBJ9jb6OhYIQefVEt6poP4SqbGV/UKtIwAWy+/4OvaeQylV08/8qXT0qvUK+rEt40KcP9lECVdHbCx0lGrkCO/Hv5fktuUrGD14JFjRpWnABbPnkGmTA4s/+NP1m7cyoDeX+L54gULf/uZS9eu883QEZw78g8btu5g49adDBvYl/89fc7R3VsBaPvBZ0bB6vRZc/l2QF/q1arByAk/8e/J0+z8ex+///wjVStXjJNYePfeA8yYs8DotvcqVeDnH8YZ3fbr3IUM7PMVU36dbbht+66/admsETpFR1So2qFNCz798H38/QNo2eVjGtarRfY3JMQ/vHsLLtmzsfyPP5k2cy4/jR+Z6L7cv3+/UeUpgGXLlpEpUyaWLVvGH3/8wcCBA/H09GTJkiVcunSJ3r17c/HiRdavX8/69esZPnw4T5484fjx4wC0atXKKFidOnUqw4cPp169enz//fccO3aMHTt2MG/ePKpWrRqnL3ft2sXPP/9sdFvVqlWZMcM4Z+gvv/zC4MGD+fFH45XmL774gj179vDrr8Z17zdv3syXX35pdNuFCxfw9PSkYsWKPH/+nEuXLrFhwwYiIyPp3r07R44cSXRfAhw6dc6o8hTAgh++I5ODPSu37GH9Xwf45pPOeL3yYd6kYVy5dY8BE2dyavMiNu05xKY9hxna80P+5+7JgTXaGOnYe4RRsDpjyTqGfvkhdapWYMyMRRw/d5ldh07w29jBvFeuZJz+3HPkFL8tX290W5WyJZk63Lh60ms/fzr3HcVzr5f8OWtSkt43wLEzl/h++jzsbW0Z8JmWc3P/6lm4ODuxcsseflm8lh+GJv1LSZQDh44YVaECWDp/jjZWV65h7fqNDPimN55eXiyeN5tLV67Sd8AQzp86xvpNW9iwaSvfDR3Ek6dPOXbgbwDadOpqFKxOm/Eb3w0ZSL06tRkxZjzHjp9g5+49zPntF6pWqRx3rP79DzN++93otvcqV+aXqZMN/7a1taVMqZKUqVyd8PBww2u/rfrVlu07+eIz4y/DFy5extPLi4oV3j1n4+ErD42qUAH8/k1bMtnZsObgJTYeu0bv1tV58TqAWX3b6IPbPRz9+Su2nrjO1hPXGdihNk9f+rJn8mcAdPtpvVGw+tu2kwzsUItaZQowYc1BTt54zN9nb/PLVy2oXCyPUYUlgL3n7vL7jlNGt1UqmpvJnzc1/NvWxoqS+XJQY9ACwiMiDa/9xPM1X7WoytiP3Og46Q+av1ccr9cBZLKzZfuE7oxcvo+95+9Sv3xhLj/woPaQRWTP6sCET5Ie6EU5dOaqUeUpgHmje5HJ3o5Vfx1mwz/H6dutJV7evswZ+TVX7jxm0PSlnFj5E5v3n2LzgVMM+aQdTz1fsW+hlvas89DpRsHqr6t3MLh7W+pUKsXYues4fvEmu/89z6/DvqBK6SJxxuPfxy8wa+0uo9sqlyrMlIEJpw7befQszetURqcoxN7EsWjzPto3rA5oxQwurp+Bg50tfX9axL5Tl2hWqxKz1u7imw9a8fPKbUnryHgcvnjXqAoVwJxBXclkb8vqvWfYcPgifdrXxcsngNkDunD1wXMGz93Cv7MHseXYZbYevcSg99343wsf/v5Zm+e6jl9mFKzO3HiYQV0aUrtcEcYv382Jqw/YffoGM77pSOXi+eP07T9nbjB781Gj2yoXz8ePX8VdGPANDGbZ7lNsnhT9efPNbxvYf/ZWvI9PDcfuvTaqPgXwa4diONha8ed5T7ZefkHPWnl4ERDGL+2Lcc1DK626t09Fdlx7yY6rL/imXj6e+Yayrac253yy5qZRsDrn2FO+qZuXGoUc+XHvI04/8mXvTW9+aluEinmzxDnH99/2ZsFx4y0iFfJkZnzLwgm+n/iqXrUsnZ2ulVwJCI3go1U3qF3YkZxZbfl3YBXsrXUM3X6PQ3e8yWJnTRY7bUFFUZQ4xQwSI1nBauzKU80aNeD78ZO5euMmvn7+tGyi5TQrUqggmTI5kCdXTsqULI5OpyNPrlxcuabtbyxSuCCO+go+Mat9ANy8fZezF6ah0+nw9w+gSqXyDO3fm2m/zeX3hcv45qvPqVE1elWgdfMmtG7e5K3tfu7ugc9rX0oUK2K4TVVVVq3byPoVC1m7cavhdmcn7VKMo2NW6tSsxt37D6lRNf5gNar6VZd2bej+db9E9WGU2JWnmjdvznfffceVK1fw9fWlVSstn1zRokXJlCkTefLkoUyZMlpf5snD5cuXDfdH1TePWekD4MaNG5w5c0bfl/689957DBs2jClTpjBr1iz69+9PjRo1DI9v06YNbdq0eWu7nz9/jo+PDyVKxM07u3z5cnx8fKhduzYfffSRNjgjIjhz5gxLliwxPM7T05NBgwaxcaO2hcLZ2ZnSpUuTV7+SFXuySozYlaea1qnGqF8Wcu3OfXz9A2lRX3ufhfPnIZODPbldXShVtCA6nY7cri5cvX3fcH/UCmbs/rx5/xHnrt7U+jMwiMplSzD4i278vHgd89ZspvdHHaleMTrRd6uGtWjVsBYJccqahQNrZnP2yk0mzF5qVM41MepXr8SJjQuZuexPVmzexeAvPsBFX0mqU/MGRvtekyN2FarmTRszfPQ4rl67jq+vHy2ba0FN0cKFtbGaOzelS5fSxmru3Fy5es1w/xvH6q1bnJ1wPvq8r1yJbwf1Z+ovM5k9dwH9+nxNjWrRq+1tWragTcsWb233jZu3OH/xErcun+PipSuMGDOBNcsXv7H6VUREBGfPnWfxvOgvtJ6eXgwZPoL1q1cku/9iil2FqnHFIoxffYDrj73wCwyhaZViABTKlY1MdjbkzpaFkvlyoNMp5MqWhWuPtP10hXM546ivcBVrCuX205f8sO4wOkUhIDiUikVy0799LWZuPcHC3Wf4qmU1qpaIXjVuXrU4zau+/RLyrf+94NIDd87O7suVhx5MXHOIRYM64JjZngYVCmNrY0XVEvl44OGNU2Z76pTRVvjdKhbm5pMXPH3hS/3yhRjSqQ5zd55m9cGLfN40aau7UWJXnmpSoyJj5qzl2r0n+AUG0axWJQCK5M1JJns7cudwplThvNq5nsOZq/cea32YNyeOmbUV3tjj8fajZ0xcsB6dTkdAUDCVSxVm4EetmbF6B/M3hNLr/eZUK1vM8PiWdavQsm7S34+qqvyx6yhrfhrE+n+OG913/OJNDp25yh8/DQbAztYGO7SrMO0bVufq3cdUKF6Q1/6BFC+QO/ZTJ0vsKlSNq5Rk3PLdXHvojl9gMM2qajmIC+fOTiZ7W3Jlz0qpAjnR6XTkypaVaw+eG+6PWsGMWTUJ4PYTTybd+Z9+fIZQqVg+BnRuwMwNh5gfcpxebetQtVR0DtEW1cvQonrCxWQiIyPp++sGJn7RGucs0Sv38wZ3w8c/iObD5vJ+w8qpXkwldvWphsWc+WHvI256BuIXEkHj4s4AFMpmj4OtFbmy2FA8hwM6nULOLDbccA8AoGA2e7Laa6Fa7HP87osgph54rPVhaATl82amT928/H7sKUtOPeeLGrmpkj+6SmLTktloWjL+OCYh8VW9iiomkNXemmoFs/LwVbDR67Uuk50bHoE0LZkN/xBtJVZV1ThjITGSFazGrjzlmsOF175+HPprM0tWreX+g0fA2yvaADx4+Bg/P3+AON8AShYvSrdOHahZTTvxw8PDCQsLY96MKTx39+DLfkPYs3mt4fGJWVm9fvM2t+/ep03XT7h6/Sb3Hz5i55+rePLsOR0//oKnz91RI1Vq16hG4QL5cXTMSnh4OOcuXmb4oPiD0NDQUFRVxc7Ojn9P/UexIoWT1pexKk/lzJmT169fc/ToURYvXsy9e/cS1Zf379/Hz89P35fGQV6pUqX48MMPqVmzplFfLliwgOfPn/P555+zd+9ew+MTs7J67do1bt26RcuWLbly5Qr37t3j2LFjhISEYGdnR6ZMmciaNauhrYcPH6ZBgwaGfwcHB/PRRx8xa9YscuXKZegLJycn/Pz8UFWV5OSijV15yjV7Nnz9A9i3ahbLNv7FgyfPEtWfD//3HL+AwHj7s2ThArzfujE19AFpeHgEYeHh/D5+CM+9XtJr1FR2Lo7uv8SsrEaVG7WyssLZMUuSE3+HhIZip9+C4ZQ1C6Fh4YSGhqGiYmdry/FzVyhWMF8Cz/J2satQ5czpyuvXvhzeu5vFy1dy/772o7A3VguKGqsPH755rJYowQddO1OzejUgeqzOnz2T58/d6dHrG/7ZGf0DnsSsrKqqirOTEzqdDheXbPi8fg28ufrV4aP/Ur9eHaOx2r3HV8ycPpVcuXImvwNjiF2FytUxE76BIeya9Ckr91/gobsP8LbqNtqfDz188AsKASD2YkXxvNnpXLcs1Upo/+/hEZGEhUfwa69WuHv7882cnWwZ+5Hh8YlZWVVVFafM9uh0CtmzOvA6QNuiUrNUfq489KBW6QJcf+xJ/3Y1Dbc1r1qcS/fdKZk/By99A8mmL8+ZPasDXq8DSa7Yladcsznh6x/EP/PHsXz7QR481f9AJmYfxqy2FNWHzzzx0/8QJ/Z4LFEwD12a1qZ6OS2IDw+PICwiglnDe+L+wpvePyxg+6zoK2nJXVn1Dwzmqecrun03g2der4hUVWpWKEkmezsmLtzAxp+HGbaf+AUEkTWzNlZPXLpFjfIluPHgKXceP6PTkGlcu/eEB0892Tt/3Nte8q1iV6Fydc6Cb0Awe6b1YeXfp3ng/gqINT5jHB81FB+6v8IvMGp8Gg/Q4vld6dKgEtVKaV9owiMiCAuPZGb/zri/8qXvzA1s/eErw+MTu7I6aslfNKtWinoVihpui6qqlsnOhiwOdmlS9S929akcmW3wC4lgy5fl+eOsB4+89du73nSO6/987B1sCPRin+NFXRzoUMGF9/QBYniESlhkJNPaFcXDL5TBW++y7rPoPeHvsrIaX9Urv+BwstpbEx6hcvmZP/3r5cM/JMKwinr6kS9VC2SliIs9t72CCIuI5NwTf8rmTvr2n2QFq7ErT5UqXoxHT/5Hm66fkD9fXlwSWT8+f97c9B36Pbfu3mfSqO+M7hsxZAB9Bg/H188fnU7HvF+nsH7zdvYfOYaffwDDBvQxenxiVlabuNWniVt9AL7sN4Thg/qRNWsWzhzaA2gZAIJDQqhYrgw/TJ/JPwcOa7XFu39g2Kva5ZOenDl/kTv3HtC/1xe41atDuw8/J3MmBzI5OLB0zq9vfP34xK48VapUKR4+fEjLli0pUKAALi4uiXqe/Pnz06tXL27dusXkyZON7hs1ahRff/01vr6+6HQ6Fi5cyLp169i3bx9+fn4MHz7c6PGJWVlt2rQpTZtqH2I9evRgxAjtV9I9e/bkf//7H2FhYYbbQNsC0LVrV8O/ly9fzrVr1xgyZAigVcqqV68eEydOpHXr1oSFhcV5H4kRu/JUycIFePTUnfa9vid/bldcnBO3nzhfLlf6j5/B7YdPGD/QeOvCd72602/8DPz8A9DpdPw+fggbdh/k4Mlz+AUEMvTLD40en5iVVR8/fz4aNB6dokNRYOYYLUPCtn1H+X3lJu49fkrHPiPYtmAq9x8/ZcDEmdy6/5g2PYcxY9QAnnu9ZMr8VegUHdmcsrJkygi8ff3o1HckmR3scbC3Z9GP3yehJ+OKXYWqVIniPHr8mFYd3qdA/nyGKwwJyZ83L737D+b2nbtMGjfa6L6R3w2lV/9B+Pn6odPpmP/7TP7cuJn9Bw/h5+fPsCHG5ZYTs7JatkxpsmVzpmGzVoSFhTNjmrZtJb7qVwBbtu/g/U4dDf9evvoPrt24ybcjtIT+k8aNpl6dd0v+HbsKVfG8Ljz2fM37k/8kXw5HsmdJXFnjvC5ZGbJwD3efvWT0h8bVzIZ2qsPghXvwCwxBp1P4tVcrNh+/xuFLD/APDmVAB+MxmZiV1dIFXHHObE/rsasIi4jkR30gO6hjLfrN/QvfwBDa1ChJrmxZaP5ecfacvUP7CWvI6ZyFoZ3rEhAcypczt7L53+tY6RSWDO6Y+E6LJXblqRIF8/DI3YtOQ6aRL2d2sjtlTfhJ0CpSDZy2lDuPnzO2V1ej+779rAMDpi7BLyAInU5h1vCebNp3goNnruIfGMzg7saBUmJXVn9csolth/4jMlLlsfsLJvb9kH9XaONyza4jhISGUb54QT4fO5sX3r58+L32ubLx52GcvHyLyYs3YW9nS8mCeWjXoCo6nY5G1bVLxb0nL2DoJ+0T9d7fJHYVquL5XHns6U2XcUvJl8OJ7I5x983HJ5+LE4PnbObOUy/GfGJ8nn7brREDf9+sjU9F4bf+ndl05CKHLt7BPyiEQZ2Nx3NiVlZvPfFg+Z7TVCtVkI2HL9KhbgV6tavDgFmbePriNeEREQzWV7N6UyWrjyev5PztJ9x76kXvdnXp0rBy4jsuhtjVp4q5OPDEJ4Tuq6+T19GObJkSF37lcbRl+I573HsZzPDGBYzuG9ggH9/tuIdfSAQ6RWFau6Jsu/KCY/d88A+N5Ju6eY0en9iV1V8OPmHX9ZdEqvDUJ4SRzQrFW/Vq8cnnHLrrg6rCB1VykiOLDQfvePPzwSfYW+sonsOBlqWzo9MpfFEjN12WX8PBxorZnZP+I0CTVrCq27w9x/fuSNHnNEdpUcGqVq1anDp1KuEHpnNpVcGq4Uf9OLJuboo+pzkyRQWrOm7NOHF4X4o/r6mYsoJVs1Er2PdTjxR9TlNL6wpWjb4ex6HFSd8jnt6YooJV02/nsH9G/xR/XlMxRQWrtouv8NfXFVL8ec2JVLASQgghhBDpmkmDVUtYVU0rlrCqmpYsYVXVVDLSqqqpZbRVVVOwhFVVU8lIq6qmktFXVRNLVlaFEEIIIYTZMotgdeXaDYYyp6nF3cOTlp0/pkGrTmzctjPO/ROnzqBg2aqMmjjFcNvICT9Rv2VH6jZvz/5DR+McY05WrFjBggULEn7gO3B3d6dZs2bUrVvXUEo1pmPHjlGnTh3q169vKL965coV6tevT/369dm6VUsNdvLkSWrVqkXdunX55JNPkpWiKjWt3vo3i9en7qq/u9cr2n71HY27D2Dz34fj3P/DnBUUdevK2F+jq6tMnruCGp2+okWPIYybudhw+8bdB2n1xVCafTaISzfuxnkuU1uxei0LlixL+IHvwN3dg+ZtO1G/SQs2bN4a5/4Jk6eQv1gZRo6d8NZjfH196dD1I5q0aseYCe+W6iu1rD10meV7z6fqa3h4+9Np0lpajlnF1hPXje6LjFTp/MM6Wo9dRYcJf/A/Ly27woQ1WhnWZqNWcOiSlhnisacPDb9bSp6Pp6VqCdjkWrPrSJySpynN46UP7QdNoWnvCWw5EPcK2I9LNlGifT/GzfvTcNv4+X8ayqnmbPQFr3z98Q0IpNvwGbTuP5mJC+LOv+bgj/1nWbY7da/yeXj70XHMYlp8N89QOjVKZGQkncYuodXw+bQbtYgnnt5vPGbL0Uu0GbGQNiMWUvKTH9h16lqqtjs51l/wNCp5mho8/UL5cOV1Oiy5yo6rL4zui4xU+WjVdTotvUrXFdd46qNldjj58DVtF12h7eIrLD/93OiYBcef0XbxlRRvp1kEq2nh59nzGDl0AAd2bGDOouWGCiFRen/xKasWGqfA+bpHd479vY2/NqxiwlTjhPiWaNq0aYwePZrDhw8ze/bsOH04btw49uzZw6pVqxg9WvuV96hRo1i1ahWHDh0yHFOtWjVOnTrF8ePHsbKy4vTp06Z4Oyb169J1DO/VnX9WzGTemi2Eh0cY3f/1B+1ZPi3uj3EmD+3FPytmMmnI1wA883zB30dPsXvZDPatmmWofGVppv86i1HDv+Xg338xZ/7COGOzz1dfsnrZogSPWbxsJZ07tOfAnp14er3g6jXjQM1SzNp+km+71GXnhO4s2n2W8IjoL5SKAr/1bsXuHz5jYMfazNmpnb89mlXhnx8/Z+OoD5iyXvty7+qUmR0TuhvSZ1mimWt28t3nHdgzdwzzN/4T51z/qlNTlo7/xui2iX0/ZM/cMayY1J9q5YqR3TELy7cfooNbdXbPGYOX92uu30vZHzynF79tOsy33Rrz15TeLNx5gvCI6P5U9FkF9kzvy+AuDZmz9dgbj+ncoBK7pvZm19Te5HFxwq1y3JzhlmDuv88Y2CAfm74oy7LT7oRHRP/4W1FgeruibO1Znn718rHwhJb2auGJ58x9vwQ7epZn3XlPQ1rCoNAIrnsEpEo7UzVY7T9sFJev3QBg7catzF28gotXrtG0QzfqNm/P+J+Mc3k+fPyE7l99E+fvu/7Zj1ubztRv2ZE9+5L3q+/zl67QoG4tbGxsKF+mFHfvPzS6P3eunHFyrxXVl2K0s7WLkyw6rXzzzTeGxP9//PEHc+bM4eLFizRq1IhatWoxduxYo8c/fPiQDz/8MM7f//rrL+rXr0+dOnXYvTt5dY/PnTtHw4YNsbGxoUKFCty9G72KFxQUhI2NDU5OThQpUoSXL18C8OLFC4oUKYK1tTU5cuTg7t272NjYGI7LlCmTUbnW1DRo0m9cuaXlrf3zr/3M/2Mrl27cpWWPoTT8qB8TZxuv/j166m5IpB/z77sPn6Tpp4No1L0/fx9N3irCheu3qV+9EjY21pQrWYR7j58a3Z/bNXu8uQDHz1pCyx5DOX5OGxP7/j2DtbU1bb/6jl6jpxEUHJKs9iRHv8HfcvnKVQD++HMDcxcs4uKlKzRp1Y46bs0YN8m4stnDR4/56PMv4/z9rz1/07BZK+o1bs7uv/eSHOcvXqRh/bra+V2uLHfv3Te6P3fuXHH6M75j7j14QOWKWgqgyhXL8+/JtNsLPmzx34Zk/xuPXWXxnrNceeBB+wlraDZqBT/+aVzN7bGnDz1nbo3z93/O3aH12FW0GL2SfeeTt9J+6b47dcsWxMbairIFXbmvz6sJWkBQMKczAHY2Vljp58bCubSUOLY21oYiLw52NjhljlvWMrUN+Xk5V+9qSf/X/3OchZv2cvn2Q1r3n0yjr8fxw6KNRo9/9NyLHmN/j/P3PcfP07zvJJr0msA/Jy4mqy0Xbj6gXpUy2FhbU65YAe79z8Po/lwuzm/M+7nj8BnaNdByDj946kHFEtpcWbFkIU5evpWs9iTHt/O2clWf6H/DoQss2nmCy/ef0XbkQpp+O4fJq/8xevwjj1d8Oe2POH//+78btBo+n+bD5rL3zM1kteXi3afUq1BUG5uFcnP/2UvDfYqiUChXdkAbh1b6cfi2Y2498aBATmcy2yctt/W7GPnXfa7rk/5vuezF8tPPufo8gPeXX6Pt4itMP/DY6PFPvIPpu/F2nL/vu+VNp6VXab/kCgduJy9zy5Xn/tQu7IiNlY7SOTPx8FV0eW9FUSigL7Fqa6UYzutSrg74hUQQEqHlW40av6vOevDxe0kv75oYycqzmlhdO7Zj07adVCxXhm1/7WHWtB9wdnJk37b1KIpCk/ZdDQm63yQyMpJffp/P/u1aCc6WXT42KssaERFB804fxjnuz2ULcM0RnaM0PDzC0KFOjo688vFJ9PuYMPUX+nz5WaIfn5K6devGhg0bqFixIlu2bGHOnDk4Oztz8OBBFEXBzc0NnwTeS2RkJNOnT+fQoUNERkbSrFkzo3KsERERNGkSN0ftxo0bcXV1Nfw7PDw8ug+dnHj1KvoDzNvb21CVKOo1AfLly8f58+cpUaIEZ86cwdtbO6HWr1/PpEmTKFmyJDlyvHst68To0tKNzX8fpkKpYmzfd4xfxwzEOWsW9iyfgaIotOgxBB9f/7c+R2RkJDOX/cnfy38lMjKStl9/Z1SKNSIigtY9h8U5bs2v43HN7mz4d3hEjPGYJTPer30TbP833Tszpl8Pnnu9pFOfkZzctBDPl974+gXw15Kfmbt6Mys276Zv906J7JF307VzJzZu2UbFCuXZuuMvfp8xHWdnJ/bv3oGiKDRu2RYfn0Sc3zNnc2DPTiIjI2nRrpNR+dWIiAiatekQ57j1q1fg6ho9bozGpqMjr7wTnrjjO6Zs6dIcOnKMihXKc/DIMapVqZyYrkgRHeuUYduJ65QrlJOdp28xvWcLnDLZsX18dxRFod34NYYk/G8SGakye/spdkz4RH+5fq1RCdaIiEg6Tlob57jlQzuRwyk6d2Z4RKShbxwz2ePtH/d1wyMimb7xGL/1bm10+5T1R+nZ4r0kvfeU1rlJTbYcOEX54gXZceQMM4Z+jlPWzOz6fTSKotCq32R8/N6+AhQZGclva/5i9++jiVRV2g+aYlSKNSIikrYD41aZWzV5IK7ZoudCo77MnAlvv7fPMTHtOHKGBWN6A1C6cD6OnLtO+eIFOXLuOlVKF0ng6JTTsV5Ftv17mfJF8rDz5FV+7tMBp8wO7PypF4qi0GbEQnz8g976HJGRkczafISdU3oRGanScewSo/KrERGRtB+9OM5xK0d2J4dTlhiPi4jRn/Z4+8ctLBEeEcG0dfuZPaBLgsds//cK7euk7Y+Y2pVzYee1l5TNnZk9118xuU0RHO2t2NijLIqi8P7ya7wOevu2mchIlfnHn7KxRzkiVe1yfcxSrBGRKh+sjHtlaGG3krhkjl4wCo9Uo/vG3grveF43PELl18P/4+f2WqGFlmWy0/PPW6iqysAG+QEICovg/P/86V0nb5zjU0KqBqv169Rk4rQZ+PsHEBAYSJ7cubh+8zbfjZ1EUHAwt+/ex8Mzeo9EfJWEXrx8xa0792jRWau04uX1kvDwcKyttaZbWVlxYIfxt+T4WFtboaraf8prX1+y6cupJmTD1h34+Qfwcde0CQBia9CgAePHj8ff35+AgADy5MnD9evXGTp0KEFBQdy6dQsPj+hv6vH24YsX3Lx505DE39PTM04fHj58OMG2WFtbR/fh69dki1H8IVu2bPj6RgdcUSvR06dPp3///lhZWVG+fHlDtaoPPviADz74gH79+rFjxw7ef//9ZPZQ4tWrVpHJc1fgHxBEQFAweVxduHH3Id9Pn09wSAi3HzzB82XMFaToYw196f2a2w+e0OYrLSD1euVDeHgE1tZaxQ4rKyv+WTEzwbZYW8UYj/4BODsmnMA8u76gQR5XFwrnz43XKx+csmamYc0qKIpCo9pVWbhuW2K74501qFeHiT9O0Y/NQPLkyc31Gzf5dsQYgoODuHX7Lh6enobHx9ufL15y89YdmrftCICn14s4Y/Pg338l2BajsekbXTo1qcf07PEp/QYPo0XbThQsWICcOVOmYlVi1ClTkKnrj+IfFEpgcBi5s2Xh5hMvxqw8QHBoGHefvcLTJzrAMj7XtT9f+gVy5+lLOukDUi/fQMIjIrHWVz+ystKxc2LCdeqtrXSGvvENDMY5ntXREcv28lmTKhTNk91w25bj1/EPCqFr/fLJ6oOUUrdyaX5cshn/wGACg0LInSMbNx/8j5Gz/yAoJJQ7j5/j+Sr6i5RR9SV9Z7708eP2o+e0G6T9jsHL2zfWua5jz9wxCbbFqC8DAnHOmriE+i98/AgJCydfTm3R5fP2jRjy83LaD5pCgdw5yJk9cZ9hKaFu+SJMWbsP/6AQAoNDyZ3dkZuPPRi95C+CQsO4+z8vvHyig/B4x6ZvIHf+50mH0Uv078+f8IgIrK2i+3PX1N4JtsUqxtzpGxCMczwFNL5fuIPPW9SgaN4cCR7zz5mbbJ38VZznSE21Cjky49ATAkIiCAyLIFdWW257BjLxn4cEh0Vy72UwXgFhhscb9af+z1eB4dx9EcSHq7SA9EVAOOERKtZW2mOtdAqbviiXYFusdUp03wRH4OxgFecxY/c8oHvVnBRx0fpt4j+P2PxFOXJmseHDVdfpUN6FPy948dF7qTdfpmqwqtPpKFe6FD/NmE1rfd3whctXM7Rfb5q41adh685GJTWdHLPyzF0LvC5e1jY753DJTplSJfhnyzqsra0JCwszfJBB4ldWq1SswL8nT1OrelWu3rhF8aKFE2z/qTPnWbl2A9vWLk/W+08JOp2O8uXLM3nyZNq21aqlzJ8/n2HDhtG0aVPq1atn3IdOTjx7pu0ruXDhAgA5cuSgbNmy7N+//419mJiV1ffee49jx45Ru3Ztrly5QvHi0Ss2Dg4OhIaG8vr1a7y9vQ2Vt4oWLcru3bvx9/fn008/pWjRooaSrADOzs6GMpepTafTUbZ4EaYtXG2oKLV4/Q4Gf9GNxrWr0uSTgcSs1eCYJQvP9V+mLt64A0CObE6ULlaIXUt+wdrairCwcMOHFyR+ZbVymRIcP3eZmpXKce32g0SVQvX1D8AxS2YCAoN4+D93cmRzos57FZi3Ris/evH6HYrkz5PkfkkunU5HubJl+HHaL7Rtpa2GLliyjG8H9adpYzcaNG0Z6/x24vlz7ccCFy5p2xhy5HChbJlS7P1r2xvHZmJWVqtUqsSx4yeoXbMGV69dp3ixonGOiS2+Y2xsbFi2cC6qqvJl7360TKAqXkrS6RTKFHRlxubjtNBXklq+9zz929fErWIRWo1ZhUp0fzpmssPdWwsQLj/U+tUlayZK5c/B1nEfY22lIyw8whCoQuJXVisWyc3JG0+oXjIf1x97UTS3cdWb+bv+wymzPe/Xj/4wPHP7KWsPXWbdCOMqUKag0+koWzQ/01dsM1SUWrL1AAM/bkOj6uVp1mdirHM9E89faKvxl25r5cJdnLNSukg+ds4aqZ3r4bHP9cStrFYuVYTjF29Ss0IJrt17QrH8ibtMuuvYOdrUr2r4t4OdLQvG9EZVVfr8uJBmNSslvkPekU6no2yh3Pyy/qChitTS3acY0LkBbpVL0HL4fKNz3TGTPc9faSWVL9/XPo9cHDNRqkAutv/4FdZWVvqxadyfiVlZrVQsLyeuPaBG6UJcf+RO0TzGV+bmbT+GU2YHurpVSfCYB89fksMpM46Z0narik6nUCpnJmYd/Z+hotSqMx70rpOXBsWc6bj0KjFOdbLaWeHhFwrA1efaF9bsmawp6ZqJPz8ri7WVQlhEpCFQhcSvrJbPk5nTj/yoWiALNz0DKZzduC8Wn3yOo701nSpGxwI6RVuFtbXWYaVTCI1QufciiMN3vVl88hn3XgSx4Pgz+tRNuVXWVA1WAbp2bEvLLt25d/EkAK2bN2boqAmULV0S2xh7FwGcnZwoVaI4Tdp35b1K2rK8Tqdj2IC+NO/0ITqdjpLFizFvRvQv9hO7svrdwL706DuYwKAg+vf6AhsbGy5eucaZ8xf5+vPuLFi2imVr/uTVK288X7xgye8zGDpqPCGhobTs8jFZMmdm+7oVKdcxSdCtWzeaNWvGo0faJNqmTRsGDRpEuXLlsLU13mfj7OxM6dKlcXNzo2pVbaLT6XQMHz6cJk2aoNPpKFWqlFHmgMSurH7//fd8+umnBAYGMnDgQK0PL17kv//+o1evXkycOJFWrVphZWXFwoULAVi6dClr1qzB2tqaX375BYBNmzaxaJH2Y5cSJUrQqlWrd+6jxOrS0o22X33Hzf3rAGjZoBbfTZlDmeKFsbUxPh2cHbNQskhBWvQYQpWyJQGtL4d8+QGte36LTtFRokgBfh8/xHBMYldWh371EV+NmEJgcDDfdO+MjY01l27c5eyVm/Ts1pZFf25nxebdvPLxxfOVDwsnD2fULwu5duc+kZEqo775TPsiU7IoObI50aLHEDI52LNi+ugEXzslde3ciRbtOvHghhZ8tm7RnCHDR1K2TGlsbWKPTSdKlSxJ45Ztea9yZUB/fg8eSLM2HbTzu0Rx5s+O7r/ErqwOHzqIz7/qQ2BQIP379tbG5qUr/HfuHL2+7MH8xUtZtmI1L1+9wtPrBUsXzIn3mHMXLvLdyDHoFB1ffP4JefOkXfAP0LF2GTr/sI5L87X8lM3eK87I5fsoXSAHttbGKx5Ome0pkdeFduPXUKlobkD7EBzYoRYdJ/6BTqdQPK8Lv/aKPr8Su7I6qGNt+v6+g8CQMHq1roaNtRVXHnhw7u4z3q9XjvGrD1KjZD7ajV9DrdL5Gf2RGyOX7yM0LJzOP6wjs70tf47sRkBwKJ9M38TVR5588NN6vutaj3rl0maPeucmtWg/aArXt8wCoEWdygz/bRVliuTH1jrWuZ41MyUL5aVVv8lULlUY0Mbm4O5taTvwJ3Q6hRIF8zBreE/DMYldWR3ySVu+njSfwOBQ+nZtgY21NZdvP+Ts9ft82bExS7bsZ+XOQ7x67Y+Xty/zR/cCYMfh/5jxbQ/D81y4+YBRv/+BTlH4rJ0beVwTV944pXSsV5FOY5dwZZlWRrtF9dKMWLSD0gVzYRNrbDpncaBkflfajFhI5eLaF3GdTsegLg1pP3oxOkWhRD5XZvbvbDgmsSurg993o/eM9QSFhNK7XV1srK24fP8Z528/4f2GlRm3bDc1yhSizYiF1C5XmDGftoj3GIAdx6/Qro5prgK0K+/CR6tucHqItmWmSUlnxu95SMmcmbCxUowe6+RgTfEcDry//BoV8mhfKnU6hb518/LBymsoikKxHA5Maxf9JT2xK6v96uVj4Ja7BIVF0LNmHmysdFx9HsDFp/50qpCDyXsfUa1AFt5ffo3qBbPyfZOCDKifj49X3UBRoH5RJ1wy2zA1xmu3XXwlRQNVMHG5VUuRFuVWLUValVu1FKYot5rRmLLcakaU1uVWLYUpyq1mNKYot2oJpNyqEEIIIYRI1yRYFUIIIYQQZkuCVSGEEEIIYbYSvWfVwcHePTg4JHWyvWZw9vZ2HkFBwbmj/u3g4OAeHBwsfZkM9vb2HkFBQblj3uZgb+ceHBIq/ZkM9na2HkHBIbnju0/GaeLENybj42Br4x4cFi79mQB7G2uPoNCw+Mekna17cGiY9GEy2NvaeASFhL6hX23cg0NlbCbE3tbaIygk/rFpb6NzDwlXpQ+Twc5a8QgOi3zrHJroYFUIIYQQQoi0JtsAhBBCCCGE2ZJgVQghhBBCmC0JVoUQQgghhNmSYFUIIYQQQpgtCVaFEEIIIYTZkmBVCCGEEEKYLQlWhRBCCCGE2ZJgVQghhBBCmC0JVoUQQgghhNmSYFUIIYQQQpgtCVaFEEIIIYTZkmBVCCGEEEKYLQlWhRBCCCGE2ZJgVQghhBBCmC0JVoUQQgghhNmSYFUIIYQQQpgtCVaFEEIIIYTZkmBVCCGEEEKYLQlWhRBCCCGE2ZJgVQghhBBCmC0JVoUQQgghhNmSYFUIIYQQQpgtCVaFEEIIIYTZkmBVCCGEEEKYLQlWhRBCCCGE2ZJgVQghhBBCmC0JVoUQQgghhNmSYFUIIYQQQpgtCVaFEEIIIYTZkmBVCCGEEEKYLQlWhRBCCCGE2ZJgVQghhBBCmC0JVoUQQgghhNmSYFUIIYQQQpgtCVaFEEIIIYTZkmBVCCGEEEKYLQlWhRBCCCGE2ZJgVQghhBBCmC0JVoUQQgghhNmSYFUIIYQQQpgtCVaFEEIIIYTZkmBVCCGEEEKYLQlWhRBCCCGE2ZJgVQghhBBCmC0JVoUQQgghhNmSYFUIIYQQQpgtCVaFEEIIIYTZkmBVCCGEEEKYLQlWhRBCCCGE2ZJgVQghhBBCmC0JVoUQQgghhNmSYFUIIYQQQpgtCVaFEEIIIYTZkmBVCCGEEEKYLQlWhRBCCCGE2ZJgVQghhBBCmC0JVoUQQgghhNmSYFUIIYQQQpgtCVaFEEIIIYTZkmBVCCGEEEKYLQlWhRBCCCGE2ZJgVQghhBBCmC0JVoUQQgghhNmSYFUIIYQQQpgtCVaFEEIIIYTZkmBVCCGEEEKYLQlWhRBCCCGE2ZJgVQghhBBCmC0JVoUQQgghhNn6PzxptIi+B3U7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,12))\n",
    "_ = tree.plot_tree(rgf_dt_2, \n",
    "                   feature_names = col_names,\n",
    "                   filled = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8.1 Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random forest** is a modification on bagging to get an ensemble of de-correlated trees.Process is very similar to bagging, with one small trick:\n",
    "* before each split, select a subset of features at random as candidate features for splitting\n",
    "* this essentially decorrelates the trees in the ensemble, improving predictive performance\n",
    "* the number of candidates is typically considered a tuning parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note:\n",
    "* **Bagging** introduces randomness in the rows of the data\n",
    "* **Random** forest introduces randomness in the rows and columns of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ```RandomForestRegressor``` is trained using bootstrap aggregation, where each new tree is fit from a bootstrap sample of the training observations. \n",
    "\n",
    "The **out-of-bag (OOB) error** is the average error for each  calculated using predictions from the trees that do not contain in their respective bootstrap sample. This allows the ```RandomForestRegressor``` to be fit and validated whilst being trained 1.\n",
    "\n",
    "The example below demonstrates how the OOB error can be measured at the addition of each new tree during training. The resulting plot allows a practitioner to approximate a suitable value of n_estimators at which the error stabilizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = df['exp']\n",
    "y = np.array(df['nclaims']/df['exp'])\n",
    "x = np.array(df[['ageph','agec','bm','power','coverage','fuel','sex','fleet','use']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd_forest = RandomForestRegressor(n_estimators=100, criterion='poisson', oob_score=True).fit(x,y,sample_weight = exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.36188414, 0.04825756, 0.05799063, ..., 0.07913702, 0.08660048,\n",
       "       0.61960145])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_forest.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.033311810233468186"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_forest.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-80-f2670df5d3e6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmin_estimators\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_estimators\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mrnd_forest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mrnd_forest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msample_weight\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[1;31m# record the OOB error for each 'n_estimators=i' setting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    391\u001b[0m                     \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    392\u001b[0m                     n_samples_bootstrap=n_samples_bootstrap)\n\u001b[1;32m--> 393\u001b[1;33m                 for i, t in enumerate(trees))\n\u001b[0m\u001b[0;32m    394\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    395\u001b[0m             \u001b[1;31m# Collect newly grown trees\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1043\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1044\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1045\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1046\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    857\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    858\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 859\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    860\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    861\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    775\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 777\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    778\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 263\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    265\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 263\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    265\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[1;34m(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)\u001b[0m\n\u001b[0;32m    167\u001b[0m                                                         indices=indices)\n\u001b[0;32m    168\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 169\u001b[1;33m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    170\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m   1249\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1250\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1251\u001b[1;33m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[0;32m   1252\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1253\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    387\u001b[0m                                            min_impurity_split)\n\u001b[0;32m    388\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 389\u001b[1;33m         \u001b[0mbuilder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    390\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    391\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# range of 'n_estimators' values to explore\n",
    "min_estimators = 1\n",
    "max_estimators = 100\n",
    "error_rate = []\n",
    "\n",
    "for i in range(min_estimators, max_estimators + 1):\n",
    "    rnd_forest.set_params(n_estimators=i)\n",
    "    rnd_forest.fit(x,y,sample_weight = exp)\n",
    "\n",
    "    # record the OOB error for each 'n_estimators=i' setting\n",
    "    oob_error = 1 - rnd_forest.oob_score_\n",
    "    error_rate.append((i, oob_error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'n_estimators': [3,4,5],\n",
    "              'max_depth': [2, 6, 8],\n",
    "              'min_samples_leaf': [0.001, 0.01]*df.shape[0],\n",
    "              'min_samples_split': [0.001, 0.01],\n",
    "              }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n",
      "C:\\Users\\Lorenzo\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:833: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
      "  warn(\"Some inputs do not have OOB scores. \"\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-95-19b3376c2194>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mgrid_rnd_forest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrnd_forest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mgrid_rnd_forest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[0;32m    839\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    840\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 841\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    842\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    843\u001b[0m             \u001b[1;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[1;34m(self, evaluate_candidates)\u001b[0m\n\u001b[0;32m   1286\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1287\u001b[0m         \u001b[1;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1288\u001b[1;33m         \u001b[0mevaluate_candidates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1289\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[1;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[0;32m    807\u001b[0m                                    (split_idx, (train, test)) in product(\n\u001b[0;32m    808\u001b[0m                                    \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcandidate_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 809\u001b[1;33m                                    enumerate(cv.split(X, y, groups))))\n\u001b[0m\u001b[0;32m    810\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    811\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1043\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1044\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1045\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1046\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    857\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    858\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 859\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    860\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    861\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    775\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 777\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    778\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 263\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    265\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 263\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    265\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[1;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[0;32m    591\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    592\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 593\u001b[1;33m             \u001b[0mestimator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    594\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    595\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    391\u001b[0m                     \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    392\u001b[0m                     n_samples_bootstrap=n_samples_bootstrap)\n\u001b[1;32m--> 393\u001b[1;33m                 for i, t in enumerate(trees))\n\u001b[0m\u001b[0;32m    394\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    395\u001b[0m             \u001b[1;31m# Collect newly grown trees\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1043\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1044\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1045\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1046\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    857\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    858\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 859\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    860\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    861\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    775\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 777\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    778\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    779\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 263\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    265\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    261\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    262\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 263\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    264\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    265\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\utils\\fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 222\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[1;34m(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)\u001b[0m\n\u001b[0;32m    167\u001b[0m                                                         indices=indices)\n\u001b[0;32m    168\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 169\u001b[1;33m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    170\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    171\u001b[0m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m   1249\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1250\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1251\u001b[1;33m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[0;32m   1252\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1253\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\dev_env\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    387\u001b[0m                                            min_impurity_split)\n\u001b[0;32m    388\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 389\u001b[1;33m         \u001b[0mbuilder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    390\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    391\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "grid_rnd_forest = GridSearchCV(rnd_forest, parameters, cv=5)\n",
    "grid_rnd_forest.fit(x,y,sample_weight=exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'GridSearchCV' object has no attribute 'best_score_'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-96-c92266a2b319>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"R-Squared::{}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrid_rnd_forest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Best Hyperparameters::\\n{}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrid_rnd_forest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_score_'"
     ]
    }
   ],
   "source": [
    "print(\"R-Squared::{}\".format(grid_rnd_forest.best_score_))\n",
    "print(\"Best Hyperparameters::\\n{}\".format(grid_rnd_forest.best_params_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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