demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `sklearn-mrmr` demo and comparison\n",
    "\n",
    "This notebook demonstrates how to use `sklearn-mrmr` with `sklearn`'s pipelines for cross-validation and hyperparameter optimization. Both a classification and a regression example are provided.\n",
    "\n",
    "**The best $k$ features is a tunable parameter!**\n",
    "\n",
    "</br></br>\n",
    "\n",
    "#### Table of contents\n",
    "\n",
    "* Classification with logistic regression\n",
    "    - Data\n",
    "    - Pipeline without feature selection\n",
    "    - Pipeline with MRMR feature selection\n",
    "    - Examine results\n",
    "* Regression example\n",
    "    - Data\n",
    "    - Pipeline without feature selection\n",
    "    - Pipeline with MRMR feature selection\n",
    "    - Examine results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "sys.path.append(os.path.abspath('sklearn-mrmr'))\n",
    "from mrmr import MRMRFeatureSelector\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification with logistic regression\n",
    "\n",
    "### Data\n",
    "\n",
    "This dataset concerns the manufacturing of semiconductors, and is availabe from Kaggle: https://www.kaggle.com/datasets/paresh2047/uci-semcom/."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <th>7</th>\n",
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       "      <th>577</th>\n",
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       "      <th>588</th>\n",
       "      <th>589</th>\n",
       "      <th>Pass/Fail</th>\n",
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       "      <td>14.9509</td>\n",
       "      <td>0.5005</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>2.3630</td>\n",
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       "      <td>1463.6606</td>\n",
       "      <td>0.8294</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>0.1247</td>\n",
       "      <td>1.4966</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>...</td>\n",
       "      <td>10.9003</td>\n",
       "      <td>0.5019</td>\n",
       "      <td>0.0223</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>4.4447</td>\n",
       "      <td>0.0096</td>\n",
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       "      <td>0.0060</td>\n",
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       "      <td>2559.94</td>\n",
       "      <td>2186.4111</td>\n",
       "      <td>1698.0172</td>\n",
       "      <td>1.5102</td>\n",
       "      <td>100.0</td>\n",
       "      <td>95.4878</td>\n",
       "      <td>0.1241</td>\n",
       "      <td>1.4436</td>\n",
       "      <td>0.0041</td>\n",
       "      <td>...</td>\n",
       "      <td>9.2721</td>\n",
       "      <td>0.4958</td>\n",
       "      <td>0.0157</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>3.1745</td>\n",
       "      <td>0.0584</td>\n",
       "      <td>0.0484</td>\n",
       "      <td>0.0148</td>\n",
       "      <td>82.8602</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2988.72</td>\n",
       "      <td>2479.90</td>\n",
       "      <td>2199.0333</td>\n",
       "      <td>909.7926</td>\n",
       "      <td>1.3204</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>0.1217</td>\n",
       "      <td>1.4882</td>\n",
       "      <td>-0.0124</td>\n",
       "      <td>...</td>\n",
       "      <td>8.5831</td>\n",
       "      <td>0.4990</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>0.0025</td>\n",
       "      <td>2.0544</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>73.8432</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>2233.3667</td>\n",
       "      <td>1326.5200</td>\n",
       "      <td>1.5334</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.3967</td>\n",
       "      <td>0.1235</td>\n",
       "      <td>1.5031</td>\n",
       "      <td>-0.0031</td>\n",
       "      <td>...</td>\n",
       "      <td>10.9698</td>\n",
       "      <td>0.4800</td>\n",
       "      <td>0.4766</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>99.3032</td>\n",
       "      <td>0.0202</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>73.8432</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 531 columns</p>\n",
       "</div>"
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      "text/plain": [
       "         0        1          2          3       4      5         6       7  \\\n",
       "0  3030.93  2564.00  2187.7333  1411.1265  1.3602  100.0   97.6133  0.1242   \n",
       "1  3095.78  2465.14  2230.4222  1463.6606  0.8294  100.0  102.3433  0.1247   \n",
       "2  2932.61  2559.94  2186.4111  1698.0172  1.5102  100.0   95.4878  0.1241   \n",
       "3  2988.72  2479.90  2199.0333   909.7926  1.3204  100.0  104.2367  0.1217   \n",
       "4  3032.24  2502.87  2233.3667  1326.5200  1.5334  100.0  100.3967  0.1235   \n",
       "\n",
       "        8       9  ...      577     582     583     584      585     586  \\\n",
       "0  1.5005  0.0162  ...  14.9509  0.5005  0.0118  0.0035   2.3630     NaN   \n",
       "1  1.4966 -0.0005  ...  10.9003  0.5019  0.0223  0.0055   4.4447  0.0096   \n",
       "2  1.4436  0.0041  ...   9.2721  0.4958  0.0157  0.0039   3.1745  0.0584   \n",
       "3  1.4882 -0.0124  ...   8.5831  0.4990  0.0103  0.0025   2.0544  0.0202   \n",
       "4  1.5031 -0.0031  ...  10.9698  0.4800  0.4766  0.1045  99.3032  0.0202   \n",
       "\n",
       "      587     588       589  Pass/Fail  \n",
       "0     NaN     NaN       NaN          1  \n",
       "1  0.0201  0.0060  208.2045          1  \n",
       "2  0.0484  0.0148   82.8602          0  \n",
       "3  0.0149  0.0044   73.8432          1  \n",
       "4  0.0149  0.0044   73.8432          1  \n",
       "\n",
       "[5 rows x 531 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('~/Downloads/uci-secom.csv')\n",
    "\n",
    "# Relabel target column, drop time column\n",
    "df = df.replace(to_replace=[-1, 1], value=[1, 0]) \\\n",
    "       .drop(['Time'], axis=1)\n",
    "\n",
    "# Eliminate columns that have >50 missing values (~10% of total sample)\n",
    "# leaving about 500 columns left\n",
    "df = df.loc[:, df.isnull().sum() < 50]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Pass/Fail']\n",
    "X = df.drop('Pass/Fail', axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1804)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline without feature selection\n",
    "\n",
    "This logistic regression uses all available features. K-nearest neighbors is chosen as an imputation strategy, and inputs are standardized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
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       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
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       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
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       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
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       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;imputer&#x27;, KNNImputer()),\n",
       "                                       (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;regressor&#x27;,\n",
       "                                        LogisticRegression(random_state=42))]),\n",
       "             param_grid={&#x27;regressor__max_iter&#x27;: [500]}, scoring=&#x27;f1_weighted&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;imputer&#x27;, KNNImputer()),\n",
       "                                       (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                                       (&#x27;regressor&#x27;,\n",
       "                                        LogisticRegression(random_state=42))]),\n",
       "             param_grid={&#x27;regressor__max_iter&#x27;: [500]}, scoring=&#x27;f1_weighted&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;imputer&#x27;, KNNImputer()), (&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;regressor&#x27;,\n",
       "                 LogisticRegression(max_iter=500, random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNNImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.impute.KNNImputer.html\">?<span>Documentation for KNNImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNNImputer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=500, random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('imputer', KNNImputer()),\n",
       "                                       ('scaler', StandardScaler()),\n",
       "                                       ('regressor',\n",
       "                                        LogisticRegression(random_state=42))]),\n",
       "             param_grid={'regressor__max_iter': [500]}, scoring='f1_weighted')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "basic_pipeline = Pipeline(steps=[\n",
    "    ('imputer', KNNImputer(n_neighbors=5)),\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('regressor', LogisticRegression(random_state=42))\n",
    "])\n",
    "\n",
    "param_grid_basic = {\n",
    "    'regressor__max_iter': [500]\n",
    "}\n",
    "\n",
    "grid_search_basic = GridSearchCV(estimator=basic_pipeline, param_grid=param_grid_basic, cv=5,\n",
    "                                 scoring='f1_weighted')\n",
    "\n",
    "grid_search_basic.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline with MRMR feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "mrmr_pipeline = Pipeline(steps=[\n",
    "    ('imputer', KNNImputer(n_neighbors=5)),\n",
    "    ('selector', MRMRFeatureSelector(method='ftest')),\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('regressor', LogisticRegression(random_state=42))\n",
    "])\n",
    "\n",
    "param_grid_mrmr = {\n",
    "    'selector__n_features_to_select': [10, 25, 50, 75, 100, 200, 300, 400, 500],\n",
    "    'regressor__max_iter': [500]\n",
    "}\n",
    "\n",
    "grid_search_mrmr = GridSearchCV(estimator=mrmr_pipeline, param_grid=param_grid_mrmr, cv=5,\n",
    "                                scoring='f1_weighted')\n",
    "\n",
    "grid_search_mrmr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_regressor__max_iter</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>pipeline</th>\n",
       "      <th>param_selector__n_features_to_select</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.172945</td>\n",
       "      <td>0.033130</td>\n",
       "      <td>0.050107</td>\n",
       "      <td>0.007713</td>\n",
       "      <td>500</td>\n",
       "      <td>{'regressor__max_iter': 500}</td>\n",
       "      <td>0.898995</td>\n",
       "      <td>0.890915</td>\n",
       "      <td>0.874754</td>\n",
       "      <td>0.887756</td>\n",
       "      <td>0.893427</td>\n",
       "      <td>0.889169</td>\n",
       "      <td>0.008093</td>\n",
       "      <td>1</td>\n",
       "      <td>basic</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.167527</td>\n",
       "      <td>0.020558</td>\n",
       "      <td>0.040363</td>\n",
       "      <td>0.005536</td>\n",
       "      <td>500</td>\n",
       "      <td>{'regressor__max_iter': 500, 'selector__n_feat...</td>\n",
       "      <td>0.897600</td>\n",
       "      <td>0.899593</td>\n",
       "      <td>0.899593</td>\n",
       "      <td>0.906218</td>\n",
       "      <td>0.899197</td>\n",
       "      <td>0.900440</td>\n",
       "      <td>0.002981</td>\n",
       "      <td>2</td>\n",
       "      <td>mrmr</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.272066</td>\n",
       "      <td>0.024242</td>\n",
       "      <td>0.052256</td>\n",
       "      <td>0.004164</td>\n",
       "      <td>500</td>\n",
       "      <td>{'regressor__max_iter': 500, 'selector__n_feat...</td>\n",
       "      <td>0.897600</td>\n",
       "      <td>0.899593</td>\n",
       "      <td>0.899593</td>\n",
       "      <td>0.906218</td>\n",
       "      <td>0.899197</td>\n",
       "      <td>0.900440</td>\n",
       "      <td>0.002981</td>\n",
       "      <td>2</td>\n",
       "      <td>mrmr</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.398431</td>\n",
       "      <td>0.010198</td>\n",
       "      <td>0.043667</td>\n",
       "      <td>0.009770</td>\n",
       "      <td>500</td>\n",
       "      <td>{'regressor__max_iter': 500, 'selector__n_feat...</td>\n",
       "      <td>0.897600</td>\n",
       "      <td>0.899593</td>\n",
       "      <td>0.899593</td>\n",
       "      <td>0.906218</td>\n",
       "      <td>0.899197</td>\n",
       "      <td>0.900440</td>\n",
       "      <td>0.002981</td>\n",
       "      <td>2</td>\n",
       "      <td>mrmr</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.495872</td>\n",
       "      <td>0.054194</td>\n",
       "      <td>0.041356</td>\n",
       "      <td>0.004664</td>\n",
       "      <td>500</td>\n",
       "      <td>{'regressor__max_iter': 500, 'selector__n_feat...</td>\n",
       "      <td>0.897600</td>\n",
       "      <td>0.899593</td>\n",
       "      <td>0.899593</td>\n",
       "      <td>0.906218</td>\n",
       "      <td>0.899197</td>\n",
       "      <td>0.900440</td>\n",
       "      <td>0.002981</td>\n",
       "      <td>2</td>\n",
       "      <td>mrmr</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.172945      0.033130         0.050107        0.007713   \n",
       "0       0.167527      0.020558         0.040363        0.005536   \n",
       "1       0.272066      0.024242         0.052256        0.004164   \n",
       "2       0.398431      0.010198         0.043667        0.009770   \n",
       "3       0.495872      0.054194         0.041356        0.004664   \n",
       "\n",
       "   param_regressor__max_iter  \\\n",
       "0                        500   \n",
       "0                        500   \n",
       "1                        500   \n",
       "2                        500   \n",
       "3                        500   \n",
       "\n",
       "                                              params  split0_test_score  \\\n",
       "0                       {'regressor__max_iter': 500}           0.898995   \n",
       "0  {'regressor__max_iter': 500, 'selector__n_feat...           0.897600   \n",
       "1  {'regressor__max_iter': 500, 'selector__n_feat...           0.897600   \n",
       "2  {'regressor__max_iter': 500, 'selector__n_feat...           0.897600   \n",
       "3  {'regressor__max_iter': 500, 'selector__n_feat...           0.897600   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.890915           0.874754           0.887756           0.893427   \n",
       "0           0.899593           0.899593           0.906218           0.899197   \n",
       "1           0.899593           0.899593           0.906218           0.899197   \n",
       "2           0.899593           0.899593           0.906218           0.899197   \n",
       "3           0.899593           0.899593           0.906218           0.899197   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score pipeline  \\\n",
       "0         0.889169        0.008093                1    basic   \n",
       "0         0.900440        0.002981                2     mrmr   \n",
       "1         0.900440        0.002981                2     mrmr   \n",
       "2         0.900440        0.002981                2     mrmr   \n",
       "3         0.900440        0.002981                2     mrmr   \n",
       "\n",
       "   param_selector__n_features_to_select  \n",
       "0                                   530  \n",
       "0                                    10  \n",
       "1                                    25  \n",
       "2                                    50  \n",
       "3                                    75  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assemble CV\n",
    "cv_basic = pd.DataFrame(grid_search_basic.cv_results_)\n",
    "cv_basic['pipeline'] = 'basic'\n",
    "cv_basic['param_selector__n_features_to_select'] = 530\n",
    "\n",
    "cv_mrmr = pd.DataFrame(grid_search_mrmr.cv_results_)\n",
    "cv_mrmr['pipeline'] = 'mrmr'\n",
    "\n",
    "cv = pd.concat([cv_basic, cv_mrmr])\n",
    "\n",
    "cv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dff = cv[['pipeline', 'param_selector__n_features_to_select', 'mean_test_score', 'std_test_score']]\n",
    "\n",
    "basic_mean = dff.query('pipeline == \"basic\"')['mean_test_score'].values[0]\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.lineplot(data=dff, x='param_selector__n_features_to_select', y='mean_test_score',\n",
    "             hue='pipeline', markers=True, dashes=False)\n",
    "plt.axhline(y=basic_mean, color='blue', linestyle='-', linewidth=2, label='basic')\n",
    "\n",
    "plt.xlabel('Features selected')\n",
    "plt.ylabel('Mean of 5-fold F1-score')\n",
    "plt.title('Performance Comparison of MRMR v. no feature selection')\n",
    "plt.legend(title='Pipeline')\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basic_std = dff.query('pipeline == \"basic\"')['std_test_score'].values[0]\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.lineplot(data=dff, x='param_selector__n_features_to_select', y='std_test_score',\n",
    "             hue='pipeline', markers=True, dashes=False)\n",
    "plt.axhline(y=basic_std, color='blue', linestyle='-', linewidth=2, label='basic')\n",
    "\n",
    "plt.xlabel('Features selected')\n",
    "plt.ylabel('Standard deviation of 5-fold F1-score')\n",
    "plt.title('Performance Comparison of MRMR v. no feature selection')\n",
    "plt.legend(title='Pipeline')\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plots above show that MRMR did improve the overall performance of the model, although only slightly. However, the standard deviation of the test results show a distinct benefit to using MRMR. The pipeline without feature selection has almost 4 times the standard deviation as the MRMR pipeline with 100 features.\n",
    "\n",
    "MRMR selects 200 features as optimal. You can retrieve the index of these selected features using the `selected_freatures_` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['103', '5', '13', '42', '49', '52', '69', '97', '141', '149',\n",
       "       ...\n",
       "       '467', '115', '416', '33', '62', '213', '211', '443', '349', '146'],\n",
       "      dtype='object', length=200)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features_ix = grid_search_mrmr.best_estimator_.named_steps['selector'].selected_features_\n",
    "\n",
    "X_train.columns[selected_features_ix]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression example\n",
    "\n",
    "This is the \"Community and Crime\" dataset from UCI: https://archive.ics.uci.edu/dataset/183/communities+and+crime/. The dataset consists of 127  features \"picked if there was any plausible connection to crime.\" The task is to predict violent crimes per population unit.\n",
    "\n",
    "### Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = [\n",
    "    'state', 'county', 'community', 'communityname', 'fold', 'population',\n",
    "    'householdsize', 'racepctblack', 'racePctWhite', 'racePctAsian', \n",
    "    'racePctHisp', 'agePct12t21', 'agePct12t29', 'agePct16t24', 'agePct65up', \n",
    "    'numbUrban', 'pctUrban', 'medIncome', 'pctWWage', 'pctWFarmSelf', \n",
    "    'pctWInvInc', 'pctWSocSec', 'pctWPubAsst', 'pctWRetire', 'medFamInc', \n",
    "    'perCapInc', 'whitePerCap', 'blackPerCap', 'indianPerCap', 'AsianPerCap', \n",
    "    'OtherPerCap', 'HispPerCap', 'NumUnderPov', 'PctPopUnderPov', \n",
    "    'PctLess9thGrade', 'PctNotHSGrad', 'PctBSorMore', 'PctUnemployed', \n",
    "    'PctEmploy', 'PctEmplManu', 'PctEmplProfServ', 'PctOccupManu', \n",
    "    'PctOccupMgmtProf', 'MalePctDivorce', 'MalePctNevMarr', 'FemalePctDiv', \n",
    "    'TotalPctDiv', 'PersPerFam', 'PctFam2Par', 'PctKids2Par', \n",
    "    'PctYoungKids2Par', 'PctTeen2Par', 'PctWorkMomYoungKids', 'PctWorkMom', \n",
    "    'NumIlleg', 'PctIlleg', 'NumImmig', 'PctImmigRecent', 'PctImmigRec5', \n",
    "    'PctImmigRec8', 'PctImmigRec10', 'PctRecentImmig', 'PctRecImmig5', \n",
    "    'PctRecImmig8', 'PctRecImmig10', 'PctSpeakEnglOnly', 'PctNotSpeakEnglWell', \n",
    "    'PctLargHouseFam', 'PctLargHouseOccup', 'PersPerOccupHous', \n",
    "    'PersPerOwnOccHous', 'PersPerRentOccHous', 'PctPersOwnOccup', \n",
    "    'PctPersDenseHous', 'PctHousLess3BR', 'MedNumBR', 'HousVacant', \n",
    "    'PctHousOccup', 'PctHousOwnOcc', 'PctVacantBoarded', 'PctVacMore6Mos', \n",
    "    'MedYrHousBuilt', 'PctHousNoPhone', 'PctWOFullPlumb', 'OwnOccLowQuart', \n",
    "    'OwnOccMedVal', 'OwnOccHiQuart', 'RentLowQ', 'RentMedian', 'RentHighQ', \n",
    "    'MedRent', 'MedRentPctHousInc', 'MedOwnCostPctInc', 'MedOwnCostPctIncNoMtg', \n",
    "    'NumInShelters', 'NumStreet', 'PctForeignBorn', 'PctBornSameState', \n",
    "    'PctSameHouse85', 'PctSameCity85', 'PctSameState85', 'LemasSwornFT', \n",
    "    'LemasSwFTPerPop', 'LemasSwFTFieldOps', 'LemasSwFTFieldPerPop', \n",
    "    'LemasTotalReq', 'LemasTotReqPerPop', 'PolicReqPerOffic', 'PolicPerPop', \n",
    "    'RacialMatchCommPol', 'PctPolicWhite', 'PctPolicBlack', 'PctPolicHisp', \n",
    "    'PctPolicAsian', 'PctPolicMinor', 'OfficAssgnDrugUnits', 'NumKindsDrugsSeiz', \n",
    "    'PolicAveOTWorked', 'LandArea', 'PopDens', 'PctUsePubTrans', 'PolicCars', \n",
    "    'PolicOperBudg', 'LemasPctPolicOnPatr', 'LemasGangUnitDeploy', \n",
    "    'LemasPctOfficDrugUn', 'PolicBudgPerPop', 'ViolentCrimesPerPop'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>state</th>\n",
       "      <th>communityname</th>\n",
       "      <th>fold</th>\n",
       "      <th>population</th>\n",
       "      <th>householdsize</th>\n",
       "      <th>racepctblack</th>\n",
       "      <th>racePctWhite</th>\n",
       "      <th>racePctAsian</th>\n",
       "      <th>racePctHisp</th>\n",
       "      <th>agePct12t21</th>\n",
       "      <th>...</th>\n",
       "      <th>PctForeignBorn</th>\n",
       "      <th>PctBornSameState</th>\n",
       "      <th>PctSameHouse85</th>\n",
       "      <th>PctSameCity85</th>\n",
       "      <th>PctSameState85</th>\n",
       "      <th>LandArea</th>\n",
       "      <th>PopDens</th>\n",
       "      <th>PctUsePubTrans</th>\n",
       "      <th>LemasPctOfficDrugUn</th>\n",
       "      <th>ViolentCrimesPerPop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>Lakewoodcity</td>\n",
       "      <td>1</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.34</td>\n",
       "      <td>...</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>Tukwilacity</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.26</td>\n",
       "      <td>...</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>Aberdeentown</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.39</td>\n",
       "      <td>...</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34</td>\n",
       "      <td>Willingborotownship</td>\n",
       "      <td>1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.51</td>\n",
       "      <td>...</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>42</td>\n",
       "      <td>Bethlehemtownship</td>\n",
       "      <td>1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.38</td>\n",
       "      <td>...</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   state        communityname  fold  population  householdsize  racepctblack  \\\n",
       "0      8         Lakewoodcity     1        0.19           0.33          0.02   \n",
       "1     53          Tukwilacity     1        0.00           0.16          0.12   \n",
       "2     24         Aberdeentown     1        0.00           0.42          0.49   \n",
       "3     34  Willingborotownship     1        0.04           0.77          1.00   \n",
       "4     42    Bethlehemtownship     1        0.01           0.55          0.02   \n",
       "\n",
       "   racePctWhite  racePctAsian  racePctHisp  agePct12t21  ...  PctForeignBorn  \\\n",
       "0          0.90          0.12         0.17         0.34  ...            0.12   \n",
       "1          0.74          0.45         0.07         0.26  ...            0.21   \n",
       "2          0.56          0.17         0.04         0.39  ...            0.14   \n",
       "3          0.08          0.12         0.10         0.51  ...            0.19   \n",
       "4          0.95          0.09         0.05         0.38  ...            0.11   \n",
       "\n",
       "   PctBornSameState  PctSameHouse85  PctSameCity85  PctSameState85  LandArea  \\\n",
       "0              0.42            0.50           0.51            0.64      0.12   \n",
       "1              0.50            0.34           0.60            0.52      0.02   \n",
       "2              0.49            0.54           0.67            0.56      0.01   \n",
       "3              0.30            0.73           0.64            0.65      0.02   \n",
       "4              0.72            0.64           0.61            0.53      0.04   \n",
       "\n",
       "   PopDens  PctUsePubTrans  LemasPctOfficDrugUn  ViolentCrimesPerPop  \n",
       "0     0.26            0.20                 0.32                 0.20  \n",
       "1     0.12            0.45                 0.00                 0.67  \n",
       "2     0.21            0.02                 0.00                 0.43  \n",
       "3     0.39            0.28                 0.00                 0.12  \n",
       "4     0.09            0.02                 0.00                 0.03  \n",
       "\n",
       "[5 rows x 103 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('~/Downloads/communities/communities.data', header=None, names=column_names)\n",
    "\n",
    "df = df.replace('?', np.NaN)\n",
    "\n",
    "# Retain columns with no missing values\n",
    "df = df.loc[:, df.isna().mean() == 0]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['ViolentCrimesPerPop']\n",
    "X = df.drop(['ViolentCrimesPerPop', 'state', 'communityname', 'fold'], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1804)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline without feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;regressor&#x27;, LinearRegression())]),\n",
       "             param_grid={}, scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[(&#x27;regressor&#x27;, LinearRegression())]),\n",
       "             param_grid={}, scoring=&#x27;neg_root_mean_squared_error&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">best_estimator_: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;regressor&#x27;, LinearRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('regressor', LinearRegression())]),\n",
       "             param_grid={}, scoring='neg_root_mean_squared_error')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "basic_pipeline = Pipeline(steps=[\n",
    "    ('regressor', LinearRegression())\n",
    "])\n",
    "\n",
    "param_grid_basic = {}\n",
    "\n",
    "grid_search_basic = GridSearchCV(estimator=basic_pipeline, param_grid=param_grid_basic, cv=5,\n",
    "                                 scoring='neg_root_mean_squared_error')\n",
    "\n",
    "grid_search_basic.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline with MRMR feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture output\n",
    "\n",
    "mrmr_pipeline = Pipeline(steps=[\n",
    "    ('selector', MRMRFeatureSelector(method='ftest')),\n",
    "    ('regressor', LinearRegression())\n",
    "])\n",
    "\n",
    "param_grid_mrmr = {\n",
    "    'selector__n_features_to_select': [10, 20, 30, 40, 50, 60, 70, 80, 90]\n",
    "}\n",
    "\n",
    "grid_search_mrmr = GridSearchCV(estimator=mrmr_pipeline, param_grid=param_grid_mrmr, cv=5,\n",
    "                                scoring='neg_root_mean_squared_error')\n",
    "\n",
    "grid_search_mrmr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>pipeline</th>\n",
       "      <th>param_selector__n_features_to_select</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.244931</td>\n",
       "      <td>0.047636</td>\n",
       "      <td>0.023073</td>\n",
       "      <td>0.008619</td>\n",
       "      <td>{}</td>\n",
       "      <td>-0.127676</td>\n",
       "      <td>-0.126915</td>\n",
       "      <td>-0.148637</td>\n",
       "      <td>-0.151775</td>\n",
       "      <td>-0.142950</td>\n",
       "      <td>0.139590</td>\n",
       "      <td>0.010433</td>\n",
       "      <td>1</td>\n",
       "      <td>basic</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.145766</td>\n",
       "      <td>0.066894</td>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>{'selector__n_features_to_select': 10}</td>\n",
       "      <td>-0.134673</td>\n",
       "      <td>-0.129705</td>\n",
       "      <td>-0.154335</td>\n",
       "      <td>-0.153288</td>\n",
       "      <td>-0.151855</td>\n",
       "      <td>0.144771</td>\n",
       "      <td>0.010422</td>\n",
       "      <td>9</td>\n",
       "      <td>mrmr</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.267478</td>\n",
       "      <td>0.033790</td>\n",
       "      <td>0.001649</td>\n",
       "      <td>0.002308</td>\n",
       "      <td>{'selector__n_features_to_select': 20}</td>\n",
       "      <td>-0.131147</td>\n",
       "      <td>-0.127776</td>\n",
       "      <td>-0.152110</td>\n",
       "      <td>-0.153017</td>\n",
       "      <td>-0.145892</td>\n",
       "      <td>0.141988</td>\n",
       "      <td>0.010572</td>\n",
       "      <td>8</td>\n",
       "      <td>mrmr</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.278733</td>\n",
       "      <td>0.013724</td>\n",
       "      <td>0.034461</td>\n",
       "      <td>0.007264</td>\n",
       "      <td>{'selector__n_features_to_select': 30}</td>\n",
       "      <td>-0.126851</td>\n",
       "      <td>-0.125252</td>\n",
       "      <td>-0.147379</td>\n",
       "      <td>-0.151705</td>\n",
       "      <td>-0.141980</td>\n",
       "      <td>0.138633</td>\n",
       "      <td>0.010737</td>\n",
       "      <td>1</td>\n",
       "      <td>mrmr</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.393360</td>\n",
       "      <td>0.117260</td>\n",
       "      <td>0.039005</td>\n",
       "      <td>0.014569</td>\n",
       "      <td>{'selector__n_features_to_select': 40}</td>\n",
       "      <td>-0.126717</td>\n",
       "      <td>-0.125997</td>\n",
       "      <td>-0.146867</td>\n",
       "      <td>-0.150903</td>\n",
       "      <td>-0.143193</td>\n",
       "      <td>0.138735</td>\n",
       "      <td>0.010400</td>\n",
       "      <td>2</td>\n",
       "      <td>mrmr</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.244931      0.047636         0.023073        0.008619   \n",
       "0       0.145766      0.066894         0.000469        0.000020   \n",
       "1       0.267478      0.033790         0.001649        0.002308   \n",
       "2       0.278733      0.013724         0.034461        0.007264   \n",
       "3       0.393360      0.117260         0.039005        0.014569   \n",
       "\n",
       "                                   params  split0_test_score  \\\n",
       "0                                      {}          -0.127676   \n",
       "0  {'selector__n_features_to_select': 10}          -0.134673   \n",
       "1  {'selector__n_features_to_select': 20}          -0.131147   \n",
       "2  {'selector__n_features_to_select': 30}          -0.126851   \n",
       "3  {'selector__n_features_to_select': 40}          -0.126717   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0          -0.126915          -0.148637          -0.151775          -0.142950   \n",
       "0          -0.129705          -0.154335          -0.153288          -0.151855   \n",
       "1          -0.127776          -0.152110          -0.153017          -0.145892   \n",
       "2          -0.125252          -0.147379          -0.151705          -0.141980   \n",
       "3          -0.125997          -0.146867          -0.150903          -0.143193   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score pipeline  \\\n",
       "0         0.139590        0.010433                1    basic   \n",
       "0         0.144771        0.010422                9     mrmr   \n",
       "1         0.141988        0.010572                8     mrmr   \n",
       "2         0.138633        0.010737                1     mrmr   \n",
       "3         0.138735        0.010400                2     mrmr   \n",
       "\n",
       "   param_selector__n_features_to_select  \n",
       "0                                    99  \n",
       "0                                    10  \n",
       "1                                    20  \n",
       "2                                    30  \n",
       "3                                    40  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assemble CV\n",
    "cv_basic = pd.DataFrame(grid_search_basic.cv_results_)\n",
    "cv_basic['pipeline'] = 'basic'\n",
    "cv_basic['param_selector__n_features_to_select'] = 99\n",
    "\n",
    "cv_mrmr = pd.DataFrame(grid_search_mrmr.cv_results_)\n",
    "cv_mrmr['pipeline'] = 'mrmr'\n",
    "\n",
    "cv = pd.concat([cv_basic, cv_mrmr])\n",
    "\n",
    "cv['mean_test_score'] *= -1.0\n",
    "\n",
    "cv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "dff = cv[['pipeline', 'param_selector__n_features_to_select', 'mean_test_score', 'std_test_score']]\n",
    "\n",
    "basic_mean = dff.query('pipeline == \"basic\"')['mean_test_score'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VAEJDQ11dqAACAwOpWrUqhw4dcs1bsGABderUSfNbXJvNBsC8efOoXr061apVS7Ffk48u/HO/Xi86OhogXUe9wNzPQUFBKc7fc3d357nnniMmJoZ169alWP7hhx92HSUCc58BPPbYY7i5uaWYHx8fn2oUu9KlS6d47X5+fvTq1Yvt27dz8uRJwGwndrv5JyEpKYlz587h6+tL1apV2bZtW6rX0Lt371ue35ececWKFam6lF2/LyD9bS497/eNtuNwOHjuuedSbccwDJYtW3bTn0+PRx99lIULFxIfH8/8+fNxOBw3PXIAKX/Xu3btSqFChVi8eHGKbmdWbSfZI488wtatW1N0UZw7dy6enp488MADt/z5tDz11FMpHjdr1oxz5865fpcy2i7SIzIykv379/Poo49y7tw51+/4lStXaNmyJevXr3d157y+bSckJHDu3DkqVapEkSJF0vx9yAoDBgzA4XC4HkdERHDx4kV69OiR4jPJ4XDQuHHjm34mgXm+265du9i/f3+az58/f57Vq1fTrVs3Ll++7Fr/uXPnaNu2Lfv377/hiJjp3ZdOp5NvvvmGTp06uc45vF7y529GZPTz85FHHklxdDb58+NWnxki2UXdDkVyoSlTplClShXc3NwoWbIkVatWdf1jvH//fgzDuOGQ0//sRlGmTJl0n7B85MgR7HY7lSpVSjE/KCiIIkWKpDrvo3z58jdcV9myZVM8Tv5HPDg4ONV8p9PJpUuXXN0qN2zYwKhRo9i4cWOqf9ovXbqUohD553bA7Ep1/XkoBw8epEuXLjfMCuZ+3bNnD4GBgWk+n3wCf1r8/PwAuHz58k23kezIkSNUrlzZ9Z4mS+5m88/9nJF9CaQ6B6dSpUqp/smpUqUKYHY/CwoKwul08v777/Phhx9y+PBhkpKSXMsmvy/Xu9l7f/0yw4YNY8KECXz++ec0a9aM+++/n8cee8yVNaNtLj3vd1qOHDlC6dKlUxXIN9rnmdG9e3eGDx/OsmXL+Pzzz+nYseMtC/Lk3/VLly4xffp01q9fj6enZ67YTrKHH36YYcOGMXfuXF599VUMw2DevHm0a9fO1fYz6p/vY/I/xxcuXMDPzy/D7SI9kouQ5PPB0nLp0iWKFi3K1atXGTt2LDNmzODYsWOu8wKTl8kO//ydSs6b/AXQP91q37/55ps88MADVKlShZo1a3Lffffx+OOPU7t2bcDswmsYBq+//jqvv/56mus4ffo0ZcqUSTU/vfsyPj6e6OhoatasedOsGXG7n5/XtzURK6j4EsmFGjVqlOa3hABOpxObzcayZctSfEuazNfXN8XjzIw+mN5vI2+27rSy3Wx+8j83Bw8epGXLllSrVo0JEyYQHByMh4cHS5cuZeLEiSkGGkjP+tLL6XRSq1YtJkyYkObz/yx0rletWjUAduzYkaFtpldm92VGvPPOO7z++uv07duXt956i2LFimG32xk6dGiqfQ7pb1fvvfceTzzxBIsWLWLlypU899xzjB07lp9//jnFUZf0trmsfM1ZrVSpUoSHh/Pee++xYcOGG448eL3rf9c7d+7M3XffzaOPPsq+fftS/S7n9HaSlS5dmmbNmvHVV1/x6quv8vPPPxMVFZXi/KyMSu/7mJkjIzeS3I7ffffdG15UPnlfPPvss8yYMYOhQ4fStGlT/P39sdlsdO/ePc3fh4y4/ouN6/3zdyp5O7NnzyYoKCjV8tcf9U5L8+bNOXjwoOt379NPP2XixIlMnTqV/v37u9Y/fPjwG/Zg+Gfx+89st9qX/xwAyAq5+TNDCiYVXyJ5TMWKFTEMg/Lly7uOXmSVcuXK4XQ62b9/f4qTnU+dOsXFixcpV65clm4vLUuWLCEuLo7Fixen+MbyVl1sbqZixYrs3Lnzlsv8+uuvtGzZMsP/8FWpUoWqVauyaNEi3n///Vv+M1uuXDl+++03nE5nim9v9+7d63o+KyV/w3396/r9998BXCfsz58/n3vuuYf//e9/KX724sWLFC9e/La2X6tWLWrVqsVrr73GTz/9xF133cXUqVMZM2ZMjrW5cuXK8f3333P58uUUR4myep8/+uij9O/fnyJFitC+ffsM/azD4WDs2LHcc889TJ48mVdeecXy7SR75JFHeOaZZ9i3bx9z587Fx8eHTp06ZWi7GZEd7SJ50A0/P79bXvtr/vz59O7dm/fee88179q1a1y8eDHFcjf7rChatGiq5ePj4zlx4kSG8pYoUSLT1yorVqwYffr0oU+fPsTExNC8eXNGjx5N//79qVChAmD2lsjo+tO7LwMDA/Hz87vl529GPnNz+vNTJKvpnC+RPOahhx7C4XDwxhtvpPrmzjAMzp07l+l1J/8TN2nSpBTzk48GdejQIdPrTq/kbyn/2c1nxowZmV5nly5d+PXXX9McIS15O926dePYsWNMmzYt1TJXr151jT52I2+88Qbnzp2jf//+Kc5fS7Zy5Uq+/fZbwNzPJ0+eZO7cua7nExMT+eCDD/D19aVFixYZen23cvz48RSvPTo6mv/7v/+jbt26rm/UHQ5HqvY0b968G57zkR7R0dGp9kWtWrWw2+2u4cJzqs21b9+epKQkJk+enGL+xIkTsdlstGvXLku207VrV0aNGsWHH36YqesThYeH06hRIyZNmnTDCybn5HaSdenSBYfDwZdffsm8efPo2LFjiutQRUVFuf75zQrZ0S7CwsKoWLEi48ePJyYmJtXzZ86ccU2n9fvwwQcfpDpqlbwP/llkgVmgrF+/PsW8Tz755IZHvv6pbdu2+Pn58c4776R5jcDr86bln38LfH19qVSpkut3r0SJEoSHh/Pxxx+nWRDebP3p3Zd2u53OnTuzZMkStmzZkmq55H18s/34Tzn9+SmS1XTkSySPqVixImPGjGHEiBH88ccfdO7cmcKFC3P48GG+/vprBg4cyPDhwzO17jp16tC7d28++eQTLl68SIsWLdi0aROzZs2ic+fO3HPPPVn8alJr06YNHh4edOrUiSeffJKYmBimTZtGiRIl0v2N8T+9+OKLzJ8/n4cffpi+ffsSFhbG+fPnWbx4MVOnTqVOnTo8/vjjfPXVVzz11FOsWbOGu+66i6SkJPbu3ctXX33lup7ZjTzyyCPs2LGDt99+m+3bt9OjRw/KlSvHuXPnWL58OatWreKLL74AYODAgXz88cc88cQTbN26lZCQEObPn8+GDRuYNGlSugfuSK8qVarQr18/Nm/eTMmSJZk+fTqnTp1KUdB27NiRN998kz59+nDnnXeyY8cOPv/8c9e345mxevVqBg8ezMMPP0yVKlVITExk9uzZOBwO1zl4OdXmOnXqxD333MO//vUv/vjjD+rUqcPKlStZtGgRQ4cOdX2Tf7v8/f3TvLZTRrz44os8/PDDzJw5M9XAFDm9nWQlSpTgnnvuYcKECVy+fJlHHnkkxfO9evVi3bp1WdaVKzvahd1u59NPP6Vdu3bUqFGDPn36UKZMGY4dO8aaNWvw8/NjyZIlgPn7MHv2bPz9/QkNDWXjxo18//33qc5/rFu3Lg6Hg3//+99cunQJT09P1zUK+/fvz1NPPUWXLl1o3bo1v/76KytWrEj3kWQ/Pz8++ugjHn/8cerXr0/37t0JDAwkKiqK7777jrvuuivVlwnXCw0NJTw8nLCwMIoVK8aWLVuYP38+gwcPdi0zZcoU7r77bmrVqsWAAQOoUKECp06dYuPGjfz555+prvOXmX35zjvvsHLlSlq0aOG6jMeJEyeYN28eP/74I0WKFLnpfvynnP78FMlyOTm0oojcXFrDI9/IggULjLvvvtsoVKiQUahQIaNatWrGoEGDjH379rmWudmQ2WkNNW8YhpGQkGC88cYbRvny5Q13d3cjODjYGDFiRIqhiA3DHC45rWGM0xru+mavLXno6TNnzrjmLV682Khdu7bh5eVlhISEGP/+97+N6dOnG4Bx+PDhW2b459DEhmEY586dMwYPHmyUKVPG8PDwMO644w6jd+/extmzZ13LxMfHG//+97+NGjVqGJ6enkbRokWNsLAw44033jAuXbqUeiemYdWqVcYDDzxglChRwnBzczMCAwONTp06GYsWLUqx3KlTp4w+ffoYxYsXNzw8PIxatWoZM2bMSLFM8lDJ7777bor5GdnHyftoxYoVRu3atQ1PT0+jWrVqqX722rVrxgsvvGCUKlXK8Pb2Nu666y5j48aNqfbljbZ9/XPJw0UfOnTI6Nu3r1GxYkXDy8vLKFasmHHPPfcY33//fYqfu902l9b7nZbLly8bzz//vFG6dGnD3d3dqFy5svHuu++mGCo7eX2ZGWr+Rm42BHxav+tJSUlGxYoVjYoVK7qGHs+p7dzMtGnTDMAoXLiwcfXq1RTPJV/W4lbS+n2/Puf1v9/pbRdpudnr3r59u/HQQw8ZAQEBhqenp1GuXDmjW7duxqpVq1zLXLhwwfX76evra7Rt29bYu3dvqmHik/dLhQoVDIfDkaL9JyUlGS+//LJRvHhxw8fHx2jbtq1x4MCBGw41f6PP/TVr1hht27Y1/P39DS8vL6NixYrGE088YWzZsuWm+2DMmDFGo0aNjCJFihje3t5GtWrVjLfffjvFJUAMwzAOHjxo9OrVywgKCjLc3d2NMmXKGB07djTmz5+fIsP1ry0j+9IwDOPIkSNGr169XJc4qFChgjFo0CAjLi7ulvsxrd/v2/n8NIwbXwZAJCfYDENnHIqI5FchISHUrFnT1eVRRERErKNzvkRERERERHKAii8REREREZEcoOJLREREREQkB+icLxERERERkRygI18iIiIiIiI5QMWXiIiIiIhIDtBFljPJ6XRy/PhxChcujM1mszqOiIiIiIhYxDAMLl++TOnSpbHbb3x8S8VXJh0/fpzg4GCrY4iIiIiISC5x9OhR7rjjjhs+nyuKrylTpvDuu+9y8uRJ6tSpwwcffECjRo3SXHbXrl2MHDmSrVu3cuTIESZOnMjQoUNvuO5x48YxYsQIhgwZwqRJk1zzw8PDWbduXYpln3zySaZOnZquzIULFwbMHezn55eun5HcJyEhgZUrV9KmTRvc3d2tjiMFiNqeWEHtTqygdidWycm2Fx0dTXBwsKtGuBHLi6+5c+cybNgwpk6dSuPGjZk0aRJt27Zl3759lChRItXysbGxVKhQgYcffpjnn3/+puvevHkzH3/8MbVr107z+QEDBvDmm2+6Hvv4+KQ7d3JXQz8/PxVfeVhCQgI+Pj74+fnpD4LkKLU9sYLanVhB7U6sYkXbu9XpSJYPuDFhwgQGDBhAnz59CA0NZerUqfj4+DB9+vQ0l2/YsCHvvvsu3bt3x9PT84brjYmJoWfPnkybNo2iRYumuYyPjw9BQUGum4ooERERERHJLpYe+YqPj2fr1q2MGDHCNc9ut9OqVSs2btx4W+seNGgQHTp0oFWrVowZMybNZT7//HM+++wzgoKC6NSpE6+//voNj37FxcURFxfnehwdHQ2YFXVCQsJtZRXrJL93eg8lp6ntiRXU7sQKandilZxse+ndhqXF19mzZ0lKSqJkyZIp5pcsWZK9e/dmer1z5sxh27ZtbN68+YbLPProo5QrV47SpUvz22+/8fLLL7Nv3z4WLlyY5vJjx47ljTfeSDV/5cqVGequKLlTRESE1RGkgFLbEyuo3YkV1O7EKjnR9mJjY9O1nOXnfGW1o0ePMmTIECIiIvDy8rrhcgMHDnRN16pVi1KlStGyZUsOHjxIxYoVUy0/YsQIhg0b5nqcfFJdmzZt1F0xD0tISCAiIoLWrVurH7rkKLU9sYLanVhB7S7nJSUlkZiYiGEYVkexVGJiIj/99BN33nknbm6ZL3tsNhsOhwOHw3HDc7qSe8XdiqXFV/HixXE4HJw6dSrF/FOnThEUFJSpdW7dupXTp09Tv35917ykpCTWr1/P5MmTiYuLw+FwpPq5xo0bA3DgwIE0iy9PT880zzFzd3fXB0k+oPdRrKK2J1ZQuxMrqN3ljJiYGP78888CX3iBee2toKAgTpw4kSXX5fXx8aFUqVJ4eHikei69bdvS4svDw4OwsDBWrVpF586dAfPixatWrWLw4MGZWmfLli3ZsWNHinl9+vShWrVqvPzyy2kWXgCRkZEAlCpVKlPbFRERERGxUlJSEn/++Sc+Pj4EBgZmScGRlzmdTmJiYvD19b3phY9vxTAM4uPjOXPmDIcPH6Zy5cqZXp/l3Q6HDRtG7969adCgAY0aNWLSpElcuXKFPn36ANCrVy/KlCnD2LFjAXOQjt27d7umjx07RmRkJL6+vlSqVInChQtTs2bNFNsoVKgQAQEBrvkHDx7kiy++oH379gQEBPDbb7/x/PPP07x58xsOSy8iIiIikpslJCRgGAaBgYF4e3tbHcdyTqeT+Ph4vLy8bqv4AvD29sbd3Z0jR4641pkZlhdfjzzyCGfOnGHkyJGcPHmSunXrsnz5ctcgHFFRUSl21vHjx6lXr57r8fjx4xk/fjwtWrRg7dq16dqmh4cH33//vavQCw4OpkuXLrz22mtZ+tpERERERHJaQT/ilV1ut4CDXFB8AQwePPiG3Qz/WVCFhIRkuA/rP9cRHBzMunXrMrQOERERERGR22H5RZZFREREREQKAhVfIiIiIiKSbk888YRrsLysMnPmTIoUKeJ6PHr0aOrWrZul28gNVHyJiIiIiEgKTzzxBDabDZvNhoeHB5UqVeLNN98kMTGR999/n5kzZ2br9ocPH86qVauydRtWyBXnfImIiIiISO5y3333MWPGDOLi4li6dCmDBg3C3d2dESNGZPu2fX198fX1zfbt5DQd+RIRERERkVQ8PT0JCgqiXLlyPP3007Rq1YrFixen6nYYHh7uGkDP39+f4sWL8/rrr6cYJC8uLo7hw4dTpkwZChUqROPGjW86Uvk/ux0mb3P8+PGUKlWKgIAABg0aREJCwg230bRpU3788ces3CW3TcWXiIiIiIjckre3N/Hx8Wk+N2vWLNzc3Ni0aRPvv/8+EyZM4NNPP3U9P3jwYDZu3MicOXP47bffePjhh7nvvvvYv39/ure/Zs0aDh48yJo1a5g1axYzZ85M0f3xn9vo2rUrXbt2zdA2spuKLxERERERuSHDMPj+++9ZsWIF9957b5rLBAcHM3HiRKpWrUrPnj159tlnmThxImBet3fGjBnMmzePZs2aUbFiRYYPH87dd9/NjBkz0p2jaNGiTJ48mWrVqtGxY0c6dOjgOi8srW288MILNGnSJNvPT8sInfOVH5zdDwdWQZOnrE4iIiIiIvnEt99+i6+vLwkJCTidTh599FFGjx7NoEGDUi3bpEmTFBd3btq0Ke+99x5JSUns2LGDpKQkqlSpkuJn4uLiCAgISHeeGjVq4HA4XI9LlSrFjh07AG66jRIlSqR7G9lNxVded+UcfNgEnIlQoQWUqG51IhERERHJB+655x4++ugjPDw8KF26NG5umSsdYmJicDgcbN26NUXxBGRoUA13d/cUj202G06n84bbcDqdxMTEEBQUlKnc2UHFV15XKACqtoM9S2Dzp9DhPasTiYiIiEg+UKhQISpVqpSuZX/55ZcUj3/++WcqV66Mw+GgXr16JCUlcfr0aZo1a5YdUdPchtPpJDo6Gj8/v2zZZmbonK/8oGF/8/7XORB32dosIiIiIlLgREVFMWzYMPbt28eXX37JBx98wJAhQwCoUqUKPXv2pFevXixcuJDDhw+zadMmxo4dy3fffZcl27/RNiZMmJBl28gKOvKVH5RvAQGV4dx++G3u38WYiIiIiEgO6NWrF1evXqVRo0Y4HA6GDBnCwIEDXc/PmDGDMWPG8MILL3Ds2DGKFy9OkyZN6NixY5ZlSGsbYWFhdOnSJcu2cbtsxvUD8Eu6RUdH4+/vz6VLl3LHocyfp8LylyGwOjyzEa474VFuLCEhgaVLl9K+fftU/YhFspPanlhB7U6soHaXc65du8bhw4cpX748Xl5eObbd8PBw6taty6RJk3Jsm+lxfbdDu/32O/zdbP+mtzZQt8P8om4PcPeBM3vgyE9WpxERERERkX9Q8ZVfePlD7W7m9OZp1mYREREREZFUdM5XftKwP2ydaY58ePkkFM49w2qKiIiISP60du1aqyPkGTrylZ8E1YLgv675tXWW1WlEREREROQ6Kr7ym+SRDrfOgKQEa7OIiIiIiIiLiq/8JvR+KBQIl0/AvmVWpxERERERkb+o+Mpv3Dyhfi9zWgNviIiIiIjkGiq+8qOwPmCzw+H1cGaf1WlERERERAQVX/lTkWCo0s6c3vw/a7OIiIiIiAig4iv/atjPvP/1S4iLsTaLiIiIiMgNhIeHM3To0GxZd1RUFA6Hg8jIyGxZf0ap+MqvKtwDxSpCXDTs+MrqNCIiIiIiOa5MmTIcO3aMmjVrWh0FUPGVf9ntfx/92vw/MAxr84iIiIiI5DCHw0FQUBBubm5WRwFUfOVvdR8FN284tROO/mJ1GhERERHJQYZhEBufaMnNyOAX/4mJiQwePBh/f3+KFy/O66+/7lrH7NmzadCgAYULFyYoKIhHH32U06dPu372woUL9OzZk8DAQLy9valcuTIzZswA0u52uGvXLjp27Iifnx+FCxemWbNmHDx48PZ3eDrkjhJQsod3UajVFbbPhk3ToGwTqxOJiIiISA65mpBE6MgVlmx795tt8fFIf6kxa9Ys+vXrx6ZNm9iyZQsDBw6kbNmyDBgwgISEBN566y2qVq3K6dOnGTZsGE888QRLly4F4PXXX2f37t0sW7aM4sWLc+DAAa5evZrmdo4dO0bz5s0JDw9n9erV+Pn5sWHDBhITE7Pkdd+Kiq/8rmF/s/javQhixoJvCasTiYiIiIikEBwczMSJE7HZbFStWpUdO3YwceJEBgwYQN++fV3LVahQgf/+9780bNiQmJgYfH19iYqKol69ejRo0ACAkJAQAJxOZ6rtTJkyBX9/f+bMmYO7uzsAVapUyf4X+BcVX/ld6bpwR0P4czNsmwXNX7Q6kYiIiIjkAG93B7vfbGvZtjOiSZMm2Gw21+OmTZvy3nvvkZSURGRkJKNHj+bXX3/lwoULrqIqKiqK0NBQnn76abp06cK2bdto06YNnTt35s4770xzO5GRkTRr1sxVeOU0nfNVEDTsb95vmQlJOXNIVURERESsZbPZ8PFws+R2fSF1O65du0bbtm3x8/Pj888/Z/PmzXz99dcAxMfHA9CuXTuOHDnC888/z/Hjx2nZsiXDhw9Pc33e3t5ZkiuzVHwVBKGdwScAov+E35dbnUZEREREJIVffkk5ONzPP/9M5cqV2bt3L+fOnWPcuHE0a9aMatWqpRhsI1lgYCC9e/fms88+Y9KkSXzyySdpbqd27dr88MMPJCQkZMvruBUVXwWBuxfUe9yc3vyptVlERERERP4hKiqKYcOGsW/fPr788ks++OADhgwZQtmyZfHw8OCDDz7g0KFDLF68mLfeeivFz44cOZJFixZx4MABdu3axbfffkv16tXT3M7gwYOJjo6me/fubNmyhf379zN79mz27duXEy9TxVeB0aAvYINDa+DsAavTiIiIiIi49OrVi6tXr9KoUSMGDRrEkCFDGDhwIIGBgcycOZN58+YRGhrKuHHjGD9+fIqf9fDwYMSIEdSuXZvmzZvjcDiYM2dOmtsJCAhg9erVxMTE0KJFC8LCwpg2bVqOnQOmATcKiqLloEpbs9vhlv/BfWOtTiQiIiIiwtq1a13TH330Uarne/ToQY8ePVLMu/46Yq+99hqvvfZaqp9zOp2ULVuWpKQk7Pa/jznVrl2bFSusGYJfR74KkoYDzPvtn0P8FWuziIiIiIgUMCq+CpKK90LREIi7BDvmW51GRERERKRAUfFVkNjt0KCfOb15Glx3uFZERERERLKXiq+Cpt5j4OYFJ3eYF14WEREREZEcoeKroPEpBjW7mNMadl5EREREJMeo+CqIGvY373d9DTFnrM0iIiIiIlJAqPgqiMrUh9L1ISkets+2Oo2IiIiISIGg4qugavTXsPNbZoAzydosIiIiIiIFgIqvgqrGg+BdFC5Fwf6VVqcREREREcn3VHwVVO7e5siHoIE3RERERERygIqvgqxBX8AGB76HcwetTiMiIiIikq+p+CrIilWASq3M6S3Trc0iIiIiIpIJhmGQmJiY69aVFhVfBV3ywBvbP4P4WGuziIiIiEjWMQyIv2LNzTDSHTM8PJxnn32WoUOHUrRoUUqWLMm0adO4cuUKffr0oXDhwlSqVIlly5YBsHbtWmw2G8uWLSMsLAxPT09+/PHHVOspVaoUs2bNuuF6brau7OKWbWuWvKFSKyhSFi5Gwa6Ff58HJiIiIiJ5W0IsvFPamm2/ehw8CqV78VmzZvHSSy+xadMm5s6dy9NPP83XX3/Ngw8+yKuvvsrEiRN5/PHHiYqKcv3MK6+8wvjx46lQoQJFixZNtZ45c+bwwgsvsHz5ch566KFU6/Hx8bnpurKDjnwVdHYHNOhnTmvgDRERERGxQJ06dXjttdeoXLkyI0aMwMvLi+LFizNgwAAqV67MyJEjOXfuHL/99pvrZ958801at25NxYoVKVasWKr1vPLKK+laz43WlR105Eug3uOw5h04vh3+3Ap3hFmdSERERERul7uPeQTKqm1nQO3atV3TDoeDgIAAatWq5ZpXsmRJAE6fPo2fnx8ADRo0uOV6ihYtesP1XC+tdWUHFV8ChQLM6379Nsc8+qXiS0RERCTvs9ky1PXPSu7u7ike22y2FPNsNhsATqfTNa9QodSvLTPrudG6soO6HYopeeCNnQvgyjlrs4iIiIiI5EMqvsRUJgxK1YGkOIj8zOo0IiIiIiL5joovMdls0PCvo1+b/wfOJGvziIiIiIjkMzrnS/5WswusfA0uHoEDq6BKG6sTiYiIiEg+t3bt2lTz/vjjj1TzjOuuHWakcR2xtNbz22+/uQboSOtnw8PD01xXdtGRL/mbh8/f1/nSsPMiIiIiIllKxZek1KCveb9/JVz4w9IoIiIiIiL5iYovSSmgIlS8FzBgy3Sr04iIiIiI5Bu5oviaMmUKISEheHl50bhxYzZt2nTDZXft2kWXLl0ICQnBZrMxadKkm6573Lhx2Gw2hg4dmubzhmHQrl07bDYb33zzTeZfRH6SPPDGttmQcM3aLCIiIiIi+YTlxdfcuXMZNmwYo0aNYtu2bdSpU4e2bdumuup0stjYWCpUqMC4ceMICgq66bo3b97Mxx9/nOJK1/80adIk18XW5C9V2oJ/MFw9D7u+tjqNiIiIiEi+YPlohxMmTGDAgAH06dMHgKlTp/Ldd98xffp0XnnllVTLN2zYkIYNGwKk+XyymJgYevbsybRp0xgzZkyay0RGRvLee++xZcsWSpUqddOccXFxxMXFuR5HR0cDkJCQQEJCws1fZB5kr9cbx9oxODd9QlKNrlbHyTbJ711+fA8ld1PbEyuo3YkV1O5yTkJCAoZh4HQ6cTqdVsexXPIohsn75HY5nU4MwyAhIQGHw5HiufS2b0uLr/j4eLZu3cqIESNc8+x2O61atWLjxo23te5BgwbRoUMHWrVqlWbxFRsby6OPPsqUKVNueQQNYOzYsbzxxhup5q9cuRIfH5/bypobeSQE0cbmhuP4Nn6YP5mLPhWsjpStIiIirI4gBZTanlhB7U6soHaX/dzc3AgKCiImJob4+Hir4+Qaly9fzpL1xMfHc/XqVdavX09iYmKK52JjY9O1DkuLr7Nnz5KUlETJkiVTzC9ZsiR79+7N9HrnzJnDtm3b2Lx58w2Xef7557nzzjt54IEH0rXOESNGMGzYMNfj6OhogoODadOmTaprB+QXNtbCzvk089pPUvvBVsfJFgkJCURERNC6dWvc3d2tjiMFiNqeWEHtTqygdpdzrl27xtGjR/H19cXLy8vqOJYzDIPLly9TuHDhLDnN6Nq1a3h7e9O8efNU+ze5V9ytWN7tMKsdPXqUIUOGEBERccNGt3jxYlavXs327dvTvV5PT088PT1TzXd3d8+/HySNBsLO+dh3LcDedgz4FLM6UbbJ1++j5Gpqe2IFtTuxgtpd9ktKSsJms2G327HbLR/awXLJXQ2T98ntstvt2Gy2NNtyetu2pe9K8eLFcTgcnDp1KsX8U6dOpasrYFq2bt3K6dOnqV+/Pm5ubri5ubFu3Tr++9//4ubmRlJSEqtXr+bgwYMUKVLEtQxAly5dCA8Pv92XlX8EN4KStSDxGkR+YXUaEREREcmHwsPDbzgy+e2KiorC4XAQGRmZLevPKEuLLw8PD8LCwli1apVrntPpZNWqVTRt2jRT62zZsiU7duwgMjLSdWvQoAE9e/YkMjISh8PBK6+8wm+//ZZiGYCJEycyY8aMrHhp+YPNBo36m9ObPwWduCkiIiIieUiZMmU4duwYNWvWtDoKkAu6HQ4bNozevXvToEEDGjVqxKRJk7hy5Ypr9MNevXpRpkwZxo4dC5gnuu3evds1fezYMSIjI/H19aVSpUoULlw41c4tVKgQAQEBrvlBQUFpHlkrW7Ys5cuXz86Xm/fUehhWjoQLh+HQaqjUyupEIiIiIiLp4nA4CAoKyjXdMC1P8cgjjzB+/HhGjhxJ3bp1iYyMZPny5a5BOKKiojhx4oRr+ePHj1OvXj3q1avHiRMnGD9+PPXq1aN///5WvYT8zaMQ1H3UnN70qbVZRERERCRfSkxMZPDgwfj7+1O8eHFef/1111Dxs2fPpkGDBhQuXJigoCAeffTRFNcEvnDhAj179iQwMBBvb28qV67s6s2WVrfDXbt20bFjR/z8/ChcuDDNmjXj4MGDOfI6LT/yBTB48GAGD057NL21a9emeBwSEuJ6I9Lrn+tIS0bXWaA07Ae/fAS/L4cLR6BoOasTiYiIiEg6NGgAJ0/m/HaDgmDLlvQvP2vWLPr168emTZvYsmULAwcOpGzZsgwYMICEhATeeustqlatyunTpxk2bBhPPPEES5cuBeD1119n9+7dLFu2jOLFi3PgwAGuXr2a5naOHTtG8+bNCQ8PZ/Xq1fj5+bFhw4ZUQ8dnl1xRfEkuV7wyVAiHQ2th60xoNcriQCIiIiKSHidPwrFjVqe4teDgYCZOnIjNZqNq1ars2LGDiRMnMmDAAPr27etarkKFCvz3v/+lYcOGxMTE4OvrS1RUFPXq1aNBgwaAebAGSPPCylOmTMHf3585c+a4RiisUqVK9r/Av6j4kvRp2N8svrb9H4S/Am6ph90XERERkdwlkwOI5/h2mzRpkuJaXE2bNuW9994jKSmJyMhIRo8eza+//sqFCxdcRVVUVBShoaE8/fTTdOnShW3bttGmTRs6d+7MnXfemeZ2IiMjadasmWWXPVDxJelTpR34lYHoY7B7EdTuZnUiEREREbmFjHT9y42uXbtG27Ztadu2LZ9//jmBgYFERUXRtm1b4uPjAWjXrh1Hjhxh6dKlRERE0LJlSwYNGsR//vOfVOvz9vbO6ZeQguUDbkge4XCDMHMESjZNszaLiIiIiOQrv/zyS4rHP//8M5UrV2bv3r2cO3eOcePG0axZM6pVq5ZisI1kgYGB9O7dm88++4xJkybxySefpLmd2rVr88MPP5CQkJAtr+NWVHxJ+tXvBXZ3+HMTnPjV6jQiIiIikk9ERUUxbNgw9u3bx5dffskHH3zAkCFDKFu2LB4eHnzwwQccOnSIxYsX89Zbb6X42ZEjR7Jo0SIOHDjArl27+Pbbb6levXqa2xk8eDDR0dF0796dLVu2sH//fmbPns2+ffty4mWq+JIMKFwSQu83pzdr2HkRERERyRq9evXi6tWrNGrUiEGDBjFkyBAGDhxIYGAgM2fOZN68eYSGhjJu3DjGjx+f4mc9PDwYMWIEtWvXpnnz5jgcDubMmZPmdgICAli9ejUxMTG0aNGCsLAwpk2blmPngOmcL8mYhv1h5wL4bR60fgu8i1idSERERETysOsvC/XRRx+ler5Hjx706NEjxbzrLxP12muv8dprr6X6OafTSdmyZUlKSkpxkeXatWuzYsWKLEiecTryJRlTtimUqAGJVyHyC6vTiIiIiIjkGSq+JGNsNvOiy2B2PUzj+gkiIiIiIpKaii/JuNrdwKMwnD8Ih9dZnUZEREREJE9Q8SUZ51kY6v7V71YDb4iIiIiIpIuKL8mcBn91Pdy3FC79aW0WEREREXG5fjAKyTpZsV9VfEnmlKgGIc3AcMKWGVanERERESnwHA4HAPHx8RYnyZ9iY2MBbmtYeg01L5nXsD/88QNsmwUtXgY3D6sTiYiIiBRYbm5u+Pj4cObMGdzd3VMMr14QOZ1O4uPjuXbt2m3tC8MwiI2N5fTp0xQpUsRV5GaGii/JvGodoHApuHwC9iyGWl2tTiQiIiJSYNlsNkqVKsXhw4c5cuSI1XEsZxgGV69exdvbG5vNdtvrK1KkCEFBQbe1DhVfknkOdwh7AtaONQfeUPElIiIiYikPDw8qV66srodAQkIC69evp3nz5rfVVRDMroa3c8QrmYovuT31e8P6dyFqI5zcCUE1rU4kIiIiUqDZ7Xa8vLysjmE5h8NBYmIiXl5et118ZZWC3RFUbp9fKajW0ZzWsPMiIiIiIjek4ktuX8P+5v1vX8G1S9ZmERERERHJpVR8ye0LuRsCq0HCFfh1jtVpRERERERyJRVfcvtstr+Pfm3+FHRhPxERERGRVFR8Sdao/Qh4+MLZ3+HweqvTiIiIiIjkOiq+JGt4+ZkFGGjgDRERERGRNKj4kqyT3PVw73cQfdzaLCIiIiIiuYyKL8k6JUOh3F1gJMHWmVanERERERHJVVR8SdZq2M+83zoTkhIsjSIiIiIikpuo+JKsVa0T+JaEmFOwZ4nVaUREREREcg0VX5K13Dygfm9zevP/rM0iIiIiIpKLqPiSrBf2BNgccORHOLXb6jQiIiIiIrmCii/Jev5loFp7c3qLjn6JiIiIiICKL8kuycPO/zoH4i5bm0VEREREJBdQ8SXZo3wLCKgM8TFmASYiIiIiUsCp+JLsYbP9ffRr8//AMKzNIyIiIiJiMRVfkn3q9gB3HzizB45ssDqNiIiIiIilVHxJ9vHyh9rdzOnNn1qbRURERETEYiq+JHsldz3cswQun7Q2i4iIiIiIhVR8SfYKqgXBTcCZCFtnWZ1GRERERMQyKr4k+yUf/do6A5ISrM0iIiIiImIRFV+S/ULvh0KBcPkE7FtqdRoREREREUuo+JLs5+YJ9XuZ0xp4Q0REREQKKBVfkjPC+oDNDofXw5l9VqcREREREclxKr4kZxQJhirtzOnN/7M2i4iIiIiIBVR8Sc5p9NfAG79+CXEx1mYREREREclhKr4k55QPh2IVIS4adnxldRoRERERkRyl4ktyjt0ODfuZ05s+BcOwNo+IiIiISA5S8SU5q+6j4OYNp3dB1M9WpxERERERyTEqviRneReFWl3NaQ07LyIiIiIFiIovyXkN/xp4Y/ciiDltbRYRERERkRyi4ktyXum6cEdDcCbAtllWpxERERERyREqvsQaDQeY91tmQFKitVlERERERHKAii+xRugD4BMA0cfg9+VWpxERERERyXYqvsQa7l5Q73FzevM0a7OIiIiIiOQAFV9inQZ9ARscWgtn91udRkREREQkW6n4EusULQdV2prTW6Zbm0VEREREJJup+BJrJQ+8sf1ziL9ibRYRERERkWyk4kusVfFeKFoe4i7BjvlWpxERERERyTYqvsRadjs07GdOb54GhmFtHhERERGRbJIriq8pU6YQEhKCl5cXjRs3ZtOmTTdcdteuXXTp0oWQkBBsNhuTJk266brHjRuHzWZj6NChKeY/+eSTVKxYEW9vbwIDA3nggQfYu3dvFrwaybC6PcHNC07ugD83W51GRERERCRbWF58zZ07l2HDhjFq1Ci2bdtGnTp1aNu2LadPn05z+djYWCpUqMC4ceMICgq66bo3b97Mxx9/TO3atVM9FxYWxowZM9izZw8rVqzAMAzatGlDUlJSlrwuyQCfYlCzizm9ScPOi4iIiEj+ZHnxNWHCBAYMGECfPn0IDQ1l6tSp+Pj4MH162qPfNWzYkHfffZfu3bvj6el5w/XGxMTQs2dPpk2bRtGiRVM9P3DgQJo3b05ISAj169dnzJgxHD16lD/++COrXppkRMP+5v3ubyDmjKVRRERERESyg5uVG4+Pj2fr1q2MGDHCNc9ut9OqVSs2btx4W+seNGgQHTp0oFWrVowZM+amy165coUZM2ZQvnx5goOD01wmLi6OuLg41+Po6GgAEhISSEhIuK2sApSohaNUPewntpO0dRbOO4fkyGaT3zu9h5LT1PbECmp3YgW1O7FKTra99G7D0uLr7NmzJCUlUbJkyRTzS5YseVvnX82ZM4dt27axefPNzx/68MMPeemll7hy5QpVq1YlIiICDw+PNJcdO3Ysb7zxRqr5K1euxMfHJ9NZ5W/B7g2oz3biNnxExIWKYMu5A7MRERE5ti2R66ntiRXU7sQKandilZxoe7GxselaztLiKzscPXqUIUOGEBERgZeX102X7dmzJ61bt+bEiROMHz+ebt26sWHDhjR/bsSIEQwbNsz1ODo6muDgYNq0aYOfn1+Wv44CKeEejA/m43P1LB0qu2FUuS/7N5mQQEREBK1bt8bd3T3btyeSTG1PrKB2J1ZQuxOr5GTbS+4VdyuWFl/FixfH4XBw6tSpFPNPnTp1y8E0bmTr1q2cPn2a+vXru+YlJSWxfv16Jk+eTFxcHA6HAwB/f3/8/f2pXLkyTZo0oWjRonz99df06NEj1Xo9PT3TPMfM3d1dHyRZxd0d6j0OP/0Xt20zoEanHNy03kexhtqeWEHtTqygdidWyYm2l971WzrghoeHB2FhYaxatco1z+l0smrVKpo2bZqpdbZs2ZIdO3YQGRnpujVo0ICePXsSGRnpKrz+yTAMDMNIcV6XWKBBH8AGB1fBuYNWpxERERERyTKWdzscNmwYvXv3pkGDBjRq1IhJkyZx5coV+vTpA0CvXr0oU6YMY8eOBcxBOnbv3u2aPnbsGJGRkfj6+lKpUiUKFy5MzZo1U2yjUKFCBAQEuOYfOnSIuXPn0qZNGwIDA/nzzz8ZN24c3t7etG/fPgdfvaRSrAJUagUHImDLdGj7ttWJRERERESyhOXF1yOPPMKZM2cYOXIkJ0+epG7duixfvtw1CEdUVBR2+98H6I4fP069evVcj8ePH8/48eNp0aIFa9euTdc2vby8+OGHH5g0aRIXLlygZMmSNG/enJ9++okSJUpk6euTTGg0wCy+tn8G9/wLPDSgiYiIiIjkfZYXXwCDBw9m8ODBaT73z4IqJCQEwzAytP5/rqN06dIsXbo0Q+uQHFSpFRQpCxejYNdCqPeY1YlERERERG6b5RdZFknF7oAG/czpTdMgg8W2iIiIiEhupOJLcqd6j4PDE05EwrFtVqcREREREbltKr4kdyoUADUfMqc3T7M2i4iIiIhIFlDxJblXw/7m/c6FcOWctVlERERERG6Tii/JvcqEQak6kBQH22dbnUZERERE5Lao+JLcy2aDhgPM6S3TwZlkbR4RERERkdug4ktyt5pdwKsIXDwCB1ZZnUZEREREJNNUfEnu5uHz93W+NPCGiIiIiORhKr4k92vQ17zfHwHnD1ubRUREREQkk1R8Se4XUBEqtgQM89wvEREREZE8SMWX5A3Jw85vnw0JV63NIiIiIiKSCSq+JG+o0hb8g+HqBdj1tdVpREREREQyLN3F13/+8x+uXv37iMOGDRuIi4tzPb58+TLPPPNM1qYTSWZ3QIM+5vTmT63NIiIiIiKSCekuvkaMGMHly5ddj9u1a8exY8dcj2NjY/n444+zNp3I9er1AocHHNsKx7ZZnUZEREREJEPSXXwZhnHTxyLZzjcQQjub05v/Z2kUEREREZGM0jlfkrckD7yxcz7Enrc2i4iIiIhIBqj4krwluBEE1YLEaxD5udVpRERERETSzS0jC3/66af4+voCkJiYyMyZMylevDhAivPBRLKNzWYe/VoyxOx62GQQ2PUdgoiIiIjkfukuvsqWLcu0adNcj4OCgpg9e3aqZUSyXa2HYeVIuHAYDq6Gyq2sTiQiIiIickvpLr7++OOPbIwhkgEehaDuo/DLR+aw8yq+RERERCQPUH8tyZsa9jPvf18OF45Ym0VEREREJB3SXXxt3LiRb7/9NsW8//u//6N8+fKUKFGCgQMHprjoski2Kl4ZKoQDBmydYXUaEREREZFbSnfx9eabb7Jr1y7X4x07dtCvXz9atWrFK6+8wpIlSxg7dmy2hBRJU/Kw89v+DxJV+IuIiIhI7pbu4isyMpKWLVu6Hs+ZM4fGjRszbdo0hg0bxn//+1+++uqrbAkpkqYq7cCvDMSeg13fWJ1GREREROSm0l18XbhwgZIlS7oer1u3jnbt2rkeN2zYkKNHj2ZtOpGbcbhBWB9zevOn1mYREREREbmFdBdfJUuW5PDhwwDEx8ezbds2mjRp4nr+8uXLuLu7Z31CkZup3wvs7vDnJjjxq9VpRERERERuKN3FV/v27XnllVf44YcfGDFiBD4+PjRr1sz1/G+//UbFihWzJaTIDRUuCaH3m9M6+iUiIiIiuVi6i6+33noLNzc3WrRowbRp05g2bRoeHh6u56dPn06bNm2yJaTITSUPvPHbPLh6wdosIiIiIiI3kO6LLBcvXpz169dz6dIlfH19cTgcKZ6fN28evr6+WR5Q5JbKNoUSNeD0Loj8Epo+Y3UiEREREZFUMnyRZX9//1SFF0CxYsVSHAkTyTE2298XXd78KTid1uYREREREUlDuo989e3bN13LTZ8+PdNhRDKt9iMQMQrOH4TDa6HivVYnEhERERFJId3F18yZMylXrhz16tXDMIzszCSScZ6+ULcHbPoENv9PxZeIiIiI5DrpLr6efvppvvzySw4fPkyfPn147LHHKFasWHZmE8mYBv3M4mvfUrh4FIoEW51IRERERMQl3ed8TZkyhRMnTvDSSy+xZMkSgoOD6datGytWrNCRMMkdSlSDkGZgOGHrTKvTiIiIiIikkKEBNzw9PenRowcRERHs3r2bGjVq8MwzzxASEkJMTEx2ZRRJv+Rh57fNgsQ4a7OIiIiIiFwnw6Mdun7Qbsdms2EYBklJSVmZSSTzqnWAwqXgyhnYs8TqNCIiIiIiLhkqvuLi4vjyyy9p3bo1VapUYceOHUyePJmoqChd40tyB4c7hD1hTm/+1NIoIiIiIiLXS/eAG8888wxz5swhODiYvn378uWXX1K8ePHszCaSOfV7w/p3IWojnNwJQTWtTiQiIiIikv7ia+rUqZQtW5YKFSqwbt061q1bl+ZyCxcuzLJwIpniVwqqdYTd35hHvzpNsjqRiIiIiEj6i69evXphs9myM4tI1mnY3yy+fvsKWr8BXv5WJxIRERGRAi5DF1kWyTNC7obAanBmL/w6Bxo/aXUiERERESngMj3aYVrmz5+flasTyTyb7e9h5zd/CroWnYiIiIhYLEPFV2JiIjt37uT3339PMX/RokXUqVOHnj17Zmk4kdtS+xHw8IWzv8Ph9VanEREREZECLt3F186dO6lUqRJ16tShevXqPPTQQ5w6dYoWLVrQt29f2rVrx8GDB7Mzq0jGePmZBRho2HkRERERsVy6i6+XX36ZSpUqsWjRIrp3784333xDeHg4nTp14s8//2TcuHHccccd2ZlVJOOSux7u/Q4uHbM2i4iIiIgUaOkuvjZv3sz48ePp2LEjH374IQCvvvoqw4cPx9vbO9sCityWkqFQ7i4wkmDbLKvTiIiIiEgBlu7i6+zZs5QuXRoAf39/ChUqRJMmTbItmEiWadjPvN86ExLjLY0iIiIiIgVXuoeat9lsXL58GS8vLwzDwGazcfXqVaKjo1Ms5+fnl+UhRW5LtU7gWxJiTsHeb6HmQ1YnEhEREZECKN3Fl2EYVKlSJcXjevXqpXhss9lISkrK2oQit8vNA+r3hvX/MQfeUPElIiIiIhZId/G1Zs2a7Mwhkr3CnoAf3oMjG+DUbvNcMBERERGRHJTu4qtFixbZmUMke/mXgWrtYc8S8+hXxwlWJxIRERGRAiZDF1kWydMaDjDvf5sL16JvvqyIiIiISBZT8SUFR/nmEFAZ4mPMAkxEREREJAep+JKCw2b7+6LLmz8Fw7A2j4iIiIgUKCq+pGCp2wPcfeDMXnPwDRERERGRHKLiSwoWL3+o3c2c3jTN2iwiIiIiUqCka7TDhx5K/3WRFi5cmOkwIjmiYX/YOtO84PLlE1anEREREZECIl1Hvvz9/V03Pz8/Vq1axZYtW1zPb926lVWrVuHv75+pEFOmTCEkJAQvLy8aN27Mpk2bbrjsrl276NKlCyEhIdhsNiZNmnTTdY8bNw6bzcbQoUNd886fP8+zzz5L1apV8fb2pmzZsjz33HNcunQpU/kljwmqBcFNwJmIfftsq9OIiIiISAGRriNfM2bMcE2//PLLdOvWjalTp+JwOABISkrimWeewc/PL8MB5s6dy7Bhw5g6dSqNGzdm0qRJtG3bln379lGiRIlUy8fGxlKhQgUefvhhnn/++Zuue/PmzXz88cfUrl07xfzjx49z/Phxxo8fT2hoKEeOHOGpp57i+PHjzJ8/P8OvQfKgRgPg6M/Yt/8ftkrvWJ1GRERERAqADJ/zNX36dIYPH+4qvAAcDgfDhg1j+vTpGQ4wYcIEBgwYQJ8+fQgNDWXq1Kn4+PjccF0NGzbk3XffpXv37nh6et5wvTExMfTs2ZNp06ZRtGjRFM/VrFmTBQsW0KlTJypWrMi9997L22+/zZIlS0hMTMzwa5A8qHonKBSILeYkQZe2W51GRERERAqAdB35ul5iYiJ79+6latWqKebv3bsXp9OZoXXFx8ezdetWRowY4Zpnt9tp1aoVGzduzGi0FAYNGkSHDh1o1aoVY8aMueXyly5dws/PDze3tHdJXFwccXFxrsfR0eZFehMSEkhISLitrGIFO/Y6j+H4aSLlz3xPQsKrVgeSAib5c0OfH5KT1O7ECmp3YpWcbHvp3UaGi68+ffrQr18/Dh48SKNGjQD45ZdfGDduHH369MnQus6ePUtSUhIlS5ZMMb9kyZLs3bs3o9Fc5syZw7Zt29i8eXO6c7z11lsMHDjwhsuMHTuWN954I9X8lStX4uPjk+msYh3v+GBaYyMwZg+rlswkxquM1ZGkAIqIiLA6ghRAandiBbU7sUpOtL3Y2Nh0LZfh4mv8+PEEBQXx3nvvceKEOVJcqVKlePHFF3nhhRcyurosd/ToUYYMGUJERAReXl63XD46OpoOHToQGhrK6NGjb7jciBEjGDZsWIqfCw4Opk2bNpk6101yh6Rry3E7uJLm/ieg5QCr40gBkpCQQEREBK1bt8bd3d3qOFJAqN2JFdTuxCo52faSe8XdSoaLL7vdzksvvcRLL73k2khmi4/ixYvjcDg4depUivmnTp0iKCgoU+vcunUrp0+fpn79+q55SUlJrF+/nsmTJxMXF+c6X+3y5cvcd999FC5cmK+//vqmb4qnp2ea55i5u7vrgyQPS6z9MBxcidvexdjavgk2m9WRpIDRZ4hYQe1OrKB2J1bJibaX3vXf1kWW/fz8buuoj4eHB2FhYaxatco1z+l0smrVKpo2bZqpdbZs2ZIdO3YQGRnpujVo0ICePXsSGRnpKryio6Np06YNHh4eLF68OF1HyST/MSq1IdHuge3iH3Bsm9VxRERERCQfS9eRr3r16mFL5xGBbdsy9g/ssGHD6N27Nw0aNKBRo0ZMmjSJK1euuM4f69WrF2XKlGHs2LGAOUjH7t27XdPHjh0jMjISX19fKlWqROHChalZs2aKbRQqVIiAgADX/OTCKzY2ls8++4zo6GjXUbzAwMAUIzlKPudRiJN+9bnj4s+wcwHcEWZ1IhERERHJp9JVfHXu3DnbAjzyyCOcOXOGkSNHcvLkSerWrcvy5ctdg3BERUVht/99gO748ePUq1fP9Xj8+PGMHz+eFi1asHbt2nRtc9u2bfzyyy8AVKpUKcVzhw8fJiQk5PZelOQpx4o2NouvXQuhzRiw39YBYRERERGRNKWr+Bo1alS2hhg8eDCDBw9O87l/FlQhISEYhpGh9f9zHeHh4Rleh+Rfp/1qY3j6Ybt8AqI2QshdVkcSERERkXwowwNuJNu6dSt79uwBoEaNGimORonkJU67O0bVjth++wJ2zlfxJSIiIiLZIsPF1+nTp+nevTtr166lSJEiAFy8eJF77rmHOXPmEBgYmNUZRbKds8aD2H/7AnYvgnb/AYdGYxIRERGRrJXhk1ueffZZLl++zK5duzh//jznz59n586dREdH89xzz2VHRpFsZ4Q0A5/iEHsODq+zOo6IiIiI5EMZLr6WL1/Ohx9+SPXq1V3zQkNDmTJlCsuWLcvScCI5xu4GNTqb0zsWWBpFRERERPKnDBdfTqczzYuIubu743Q6sySUiCVqdjHv934LCdeszSIiIiIi+U6Gi697772XIUOGcPz4cde8Y8eO8fzzz9OyZcssDSeSo4KbgF8ZiIuGA99bnUZERERE8pkMF1+TJ08mOjqakJAQKlasSMWKFSlfvjzR0dF88MEH2ZFRJGfY7VDjQXN6p7oeioiIiEjWyvBoh8HBwWzbto3vv/+evXv3AlC9enVatWqV5eFEclzNLrBxMuxbBnEx4OlrdSIRERERySfSVXwVK1aM33//neLFi9O3b1/ef/99WrduTevWrbM7n0jOKl0PipaHC4fh9+VQq6vViUREREQkn0hXt8P4+Hiio6MBmDVrFteuaTACyadstr8H3lDXQxERERHJQuk68tW0aVM6d+5MWFgYhmHw3HPP4e3tneay06dPz9KAIjmuVlf4YTzsj4CrF8C7qNWJRERERCQfSNeRr88++4z27dsTExODzWbj0qVLXLhwIc2bSJ5XojqUCAVnAuz51uo0IiIiIpJPpOvIV8mSJRk3bhwA5cuXZ/bs2QQEBGRrMBFL1XwIVu82ux7Wf9zqNCIiIiKSD2R4qPnDhw+7Cq8///xTF1aW/KnGQ+b94XUQc8baLCIiIiKSL2S4+LpeaGgof/zxRxZFEclFAipC6fpgOGH3N1anEREREZF84LaKL8MwsiqHSO6jUQ9FREREJAvdVvElkq/VeBCwQdRGuPSn1WlEREREJI+7reLr1VdfpVixYlmVRSR38S8D5e40p3d9bW0WEREREcnzbqv4GjFiBEWKFMmiKCK5UM2/Bt7YMd/aHCIiIiKS52W6+Dp+/DijRo2iZ8+eDB8+nL1792ZlLpHcofoDYHPAiUg4d9DqNCIiIiKSh6W7+PLx8eHMGXPI7d27dxMaGsoXX3xBQkIC3333HWFhYfz222/ZFlTEEr6BUKGFOb1zobVZRERERCRPS3fxde3aNdfohq+++irNmzdnz549fPXVV+zatYv777+ff/3rX9kWVMQyNbua9xr1UERERERuQ6a6HW7bto0XX3wRNzc3cyV2Oy+99BJbt27N0nAiuUK1DuDwgDN74NQuq9OIiIiISB6V7uLLZrNhs9nMH7Lb8ff3T/F8kSJFuHDhQtamE8kNvItApdbmtI5+iYiIiEgmpbv4MgyDKlWqUKxYMY4fP57q/K4DBw4QFBSU5QFFcoXkUQ93LgBdXFxEREREMsEtvQvOmDEjxeNKlSqlePzzzz/z4IMPZk0qkdymajtw94ELf8CxbXBHmNWJRERERCSPSXfx1bt375s+//rrr992GJFcy6OQWYDtXGDeVHyJiIiISAbd1kWWRQqUml3M+10Lwem0NouIiIiI5DkqvkTSq1Ir8PSHyycgaqPVaUREREQkj1HxJZJebp5QvZM5vXO+tVlEREREJM9R8SWSEcmjHu5eBEkJ1mYRERERkTwlXcVXsWLFOHv2LAB9+/bl8uXL2RpKJNcq3wJ8ikPsOTi8zuo0IiIiIpKHpKv4io+PJzo6GoBZs2Zx7dq1bA0lkms53KBGZ3N650JLo4iIiIhI3pKuoeabNm1K586dCQsLwzAMnnvuOby9vdNcdvr06VkaUCTXqdkFNn8Ke5ZAhwng7mV1IhERERHJA9J15Ouzzz6jffv2xMTEYLPZuHTpEhcuXEjzJpLvBTeBwqUhLhoOfG91GhERERHJI9J15KtkyZKMGzcOgPLlyzN79mwCAgKyNZhIrmW3mwNvbJxsXnC5ekerE4mIiIhIHpDh0Q4PHz6swksk+YLLvy+H+CvWZhERERGRPCFTQ82vW7eOTp06UalSJSpVqsT999/PDz/8kNXZRHKv0vWgaHlIiIV9y6xOIyIiIiJ5QIaLr88++4xWrVrh4+PDc8895xp8o2XLlnzxxRfZkVEk97HZ/j76tXOBtVlEREREJE9I1zlf13v77bf5z3/+w/PPP++a99xzzzFhwgTeeustHn300SwNKJJr1ewCP4yH/RFw9QJ4F7U6kYiIiIjkYhk+8nXo0CE6deqUav7999/P4cOHsySUSJ5QMhRKhIIzAfZ8a3UaEREREcnlMlx8BQcHs2rVqlTzv//+e4KDg7MklEieUfMh815dD0VERETkFjLc7fCFF17gueeeIzIykjvvvBOADRs2MHPmTN5///0sDyiSq9V4CFaPgcPrIOYM+AZanUhEREREcqkMF19PP/00QUFBvPfee3z11VcAVK9enblz5/LAAw9keUCRXC2gojny4fHtsPsbaDTA6kQiIiIikktluPgCePDBB3nwwQezOotI3lSzq1l87Vyg4ktEREREbihT1/kSkevU+OuLiKiNcOlPa7OIiIiISK6l4kvkdvmXgbLm+Y/s+traLCIiN2M4sRmJVqcQESmwVHyJZIVauuCyiORiSQmwcQpu71Wk/W/P4PhuKET9AoZhdTIRkQJFxZdIVqj+ANgc5rlf5w5anUZE5G+H18PUZrDiVWxxl3FzXsMe+RlMbwOTG8KPk+DySatTiogUCCq+RLKCbyBUaGFO71xobRYREYBLx2BeH5jVCc7sAZ8AEjtM4sfKr+Ks3QPcfeDcfvh+FEwIhS+6mxeMT0qwOrmISL6V4dEOk5KSmDlzJqtWreL06dM4nc4Uz69evTrLwonkKTW7wMHVZtfDFi9anUZECqrEePh5Cqx7FxKugM0ODfrBPa9iuBfm3PGlJLUfhr3Du+Z5qts/g6O/wO/LzJtPcajTHeo9BiWqW/1qRETylQwXX0OGDGHmzJl06NCBmjVrYrPZsiOXSN5TrSN8+7z5DfOpXVCyhtWJRKSgObAKlr0E5w6Yj4MbQ/t3oVQd83HCdUe1PAtD/V7m7czvEPk5/PolxJyCjZPNW5kwswir2QW8/HP+9YiI5DMZLr7mzJnDV199Rfv27bMjj0je5V0EKrWGfd+ZR79UfIlITrkYBStehT1LzMeFSkDrN80jWOn5kjSwCrR+A+59HQ58D9tnw+/L4dhW87Z8BFS/3yzEQpqBXWctiIhkRoaLLw8PDypVqpQdWUTyvpoP/V183ft6+v7pERHJrIRr8NMH8MN7kHjVHPin8ZMQ/krmjlQ53KDqfeYt5gzs+Aq2zTaP6O/4yrwVKQt1H4O6PcxpERFJtwx/dfXCCy/w/vvvY2h4WpHUqrYzT2K/8Acc32Z1GhHJz35fAR82gTVjzMKr3N3w1I9w39is6SLoGwhNB8EzG2HAamjQFzz9zKNsa9+BSbXh/x6AHfMh4ertb09EpADI8JGvH3/8kTVr1rBs2TJq1KiBu7t7iucXLtRIb1KAeRQyC7CdC2DHAvN8CRGRrHT+kNkN8Pfl5uPCpaDNGPO8rOw42m6zmZ9lZcKgzduw91tzkI7D6+DQWvPm5Q+1Hja7JZaqq6P+IiI3kOHiq0iRIjz44IPZkUUkf6jZxSy+di00/yHSuREikhXiY+HHibDhfUiKA7ubeWSq+Yvm4Bk5wcMHanczbxf+gMgvzYE6Lh2FzZ+atxI1zCKsdjcoVDxncomI5BEZLr5mzJiRHTlE8o9KrcDTHy6fgKiNEHKX1YlEJC8zDPNo0/JX4VKUOa9COLR71xwowypFQ+CeEdDiZfMo2PbPzAE/Tu+CFSMgYqTZE6De41DxXvN8MhGRAs7yr+SnTJlCSEgIXl5eNG7cmE2bNt1w2V27dtGlSxdCQkKw2WxMmjTppuseN24cNpuNoUOHppj/ySefEB4ejp+fHzabjYsXL97+CxFJ5uYJ1TuZ0zsXWJtFRPK2swfgsy4w9zGz8PK7A7r9Hzz+jbWF1/Xsdqh4D3T9HwzfBx3eg9L1wJkAexbDFw/DxBrw/Wjz9YiIFGCZKr7mz59Pt27daNKkCfXr109xy4i5c+cybNgwRo0axbZt26hTpw5t27bl9OnTaS4fGxtLhQoVGDduHEFBQTdd9+bNm/n444+pXbt2muu57777ePXVVzOUVyTdaj5k3u/+BpISbrqoiEgqcTFmsfJhEzi4Chwe0Gw4DN4EoQ/k3nOqvItCw/4wcC08tQGaPAPexSDmpNllcnIYTL/PPEoWF2N1WhGRHJfh4uu///0vffr0oWTJkmzfvp1GjRoREBDAoUOHaNeuXYbWNWHCBAYMGECfPn0IDQ1l6tSp+Pj4MH369DSXb9iwIe+++y7du3fH09PzhuuNiYmhZ8+eTJs2jaJFi6Z6fujQobzyyis0adIkQ3lF0q18C/AJgNhzZnccEZH0MAzYuRCmNDKLFWcCVG4Dz/wMLV83B/XJK4JqmiMvvrDPPFpXuS3Y7GZ37EWDYHwV+GYQHNlovm4RkQIgwx2wP/zwQz755BN69OjBzJkzeemll6hQoQIjR47k/Pnz6V5PfHw8W7duZcSIEa55drudVq1asXHjxozGSmHQoEF06NCBVq1aMWbMmNtaV7K4uDji4uJcj6OjowFISEggIUFHNvKq5PcuO95De7X7cWybgfO3+SSVa5Hl65e8LTvbnuRRZ/biWPEK9iM/AmAUKUdS67cxKrc1j3RlQVuxpt3ZoHJ783b5BPYdX2H/9XNs5w9B5GcQ+RlGsQo46/TEWaubOXqj5Cv6vBOr5GTbS+82Mlx8RUVFceeddwLg7e3N5cuXAXj88cdp0qQJkydPTtd6zp49S1JSEiVLlkwxv2TJkuzduzejsVzmzJnDtm3b2Lx5c6bXkZaxY8fyxhtvpJq/cuVKfHx8snRbkvMiIiKyfJ3FYkrTDEja+Q3Lba1w2t1v+TNS8GRH25O8xS3pKlVPfE2FMxHYSSLJ5s7vJTtxoGR7nAeccGBZlm/T2nZXGcqOoljA75Q99wNlLv6C2/lDONa8hX3NGE751SYqoDkn/eph2DVIR36izzuxSk60vdjY2HQtl+FPtaCgIM6fP0+5cuUoW7YsP//8M3Xq1OHw4cOWX3j56NGjDBkyhIiICLy8vLJ03SNGjGDYsGGux9HR0QQHB9OmTRv8/PyydFuScxISEoiIiKB169aprll324z7MD6YjvvlE7Sr7I5RtX3Wrl/ytGxte5I3GAa2nfNwrBqN7Yp5rrOzSnucrd+iUpFyVMqGTeaudtcBeB4jPobEPYux//oF9qM/ExT9K0HRv2L4BOCs2RVnnZ5QItTirHI7cle7k4IkJ9tecq+4W8lw8XXvvfeyePFi6tWrR58+fXj++eeZP38+W7Zs4aGHHkr3eooXL47D4eDUqVMp5p86deqWg2ncyNatWzl9+nSKgT+SkpJYv349kydPJi4uDofDkal1e3p6pnmembu7uz5I8oFsex9rdoGNk3Hb8w3UfCDr1y95nj5DCqiTO2Dpi+b5TwDFKkK7/2Cv3CpHhiHOVe3OvSg06G3ezh4wrxsW+QW2mJM4Nn2MY9PH5uiJdXtCra7moB6SJ+WqdicFSk60vfSuP8PF1yeffILT6QTMc6sCAgL46aefuP/++3nyySfTvR4PDw/CwsJYtWoVnTt3BsDpdLJq1SoGDx6c0VgAtGzZkh07dqSY16dPH6pVq8bLL7+c6cJLJNNqPgQbJ8PvyyH+St46WV5Est7Vi7DmbfNixIYT3H2g+XBoOti8TEVBV7wStBoF9/wLDq6G7bNh3zI4vt28rfiXeSmPeo+ZAxvpIvYiksdkuPiy2+3Yr/uw6969O927d8/UxocNG0bv3r1p0KABjRo1YtKkSVy5coU+ffoA0KtXL8qUKcPYsWMBc5CO3bt3u6aPHTtGZGQkvr6+VKpUicKFC1OzZs0U2yhUqBABAQEp5p88eZKTJ09y4IB5vZEdO3ZQuHBhypYtS7FixTL1WkTSVLo+FC0PFw6b/0DU6mp1IhGxgtNpHtH5fjTEnjXnhXaGtm+D/x1WJsudHG5QpY15u3IWfvvKHJ7+9C7YOd+8+QdD3UfNW9EQqxOLiKRLpr4y+uGHH3jsscdo2rQpx44dA2D27Nn8+OOPGVrPI488wvjx4xk5ciR169YlMjKS5cuXuwbhiIqK4sSJE67ljx8/Tr169ahXrx4nTpxg/Pjx1KtXj/79+2dou1OnTqVevXoMGDAAgObNm1OvXj0WL16cofWI3JLNZnY9BF1wWaSgOr4dpreBxYPNwqt4Vei1CLrNUuGVHoWKQ9Nn4OkN5vXDGvYHL3+4dBTW/RverwOzOpkFWnz6TngXEbFKho98LViwgMcff5yePXuyfft21/Drly5d4p133mHp0qUZWt/gwYNv2M1w7dq1KR6HhIRkeFCPf64DYPTo0YwePTpD6xHJtJpd4IfxsD8Crl7Q+QoiBUXseVj1JmydCRjg4Qvhr0CjJ8HNw+p0eY/NZp77VboetBkDe78zj4YdWguH15s3Tz/zM7fe41Cmfu69GLWIFFgZPvI1ZswYpk6dyrRp01KcWHbXXXexbdu2LA0nki+UDIXA6ubFUvd+Z3UaEcluziTYMh0+qA9bZwAG1HoYBm+BO59V4ZUV3L3Nbty9voGhv0H4q1CkLMRFm/v803vhw6bw02SIOWN1WhERlwwXX/v27aN58+ap5vv7+3Px4sWsyCSS/9T6q+vhjvnW5hCR7HV0M0y7F7593jzSXaIGPLEUunwKfrp4cLYoUhbCX4bnfoVei6FWN3DzgjN7YOW/YEI1mNPTPO82KdHqtCJSwGXqOl8HDhwgJCQkxfwff/yRChUqZFUukfylxkOwegwcXmd+C+sbaHUiEclKMWdg1WizGxyY3d/u+Zd5fpJDFwrOEXY7VGhh3q6+a55nG/k5HNsKe781b74loU53qPsYBFaxOrGIFEAZPvI1YMAAhgwZwi+//ILNZuP48eN8/vnnDB8+nKeffjo7MorkfQEVzfMUDCfs/sbqNCKSVZIS4ZdPYHLY34VX3Z7w7FZo8pQKL6t4F4GG/WDAanh6ozmUv09xiDkFG96HKQ3h09awdRZcS9+FUUVEskKG/yq88sorOJ1OWrZsSWxsLM2bN8fT05Phw4fz7LPPZkdGkfyhZldz1LOdC6HRAKvTiMjtOvKTeaHkUzvNx0G1ocN7ENzI2lySUslQc0j/lqNg/wrY/jnsXwl/bjJvy18xh/2v9xiUu1ODdIhItspw8WWz2fjXv/7Fiy++yIEDB4iJiSE0NBRfX9/syCeSf9R40Dz/IOonuPSnhpgWyasun4SIkfDbXPOxVxFoORLCngC7w8pkcjNuHuYFmqt3Mt/DX+eYRyvP7YdfvzBvRctDvZ5Q51HwL2N1YhHJhzLdH8LDw4PQ0NCszCKSv/mXgbJ3msXXrq/NUc9EJO9ISoBfPoa14yD+MmCDsN5w70goFGB1OsmIwkFw91C4awgc3QSRn5m9Ei4cNs/PXfMOVLzXPBpWtT24eVqdWETyiXQXX3379k3XctOnT890GJF8r+ZDZvG1c4GKL5G85PB6s4vhmb3m4zJh0P5d817yLpsNyjY2b/eNg92LzKNhRzbAge/Nm3dRcwTFeo9BqdpWJxaRPC7dxdfMmTMpV64c9erVy/CFjkXkL6GdYdnL5rlf5w6aA3GISO516ZjZXXjX1+ZjnwBoNdocLc+e4TGrJDfzKAR1HzVv5w6aIyVGfgmXj8Omj83bHY2g84dQvLLVaUUkj0p38fX000/z5ZdfcvjwYfr06cNjjz1GsWLFsjObSP7jG2gOg3xwtdnFpcWLVicSkbQkxsHGKbD+XUiIBZsdGvSDe14FH/3ty/cCKprn8d3zLzi4BrbPhr3fmQN0fNwCOoyHOj00OIeIZFi6v7abMmUKJ06c4KWXXmLJkiUEBwfTrVs3VqxYoSNhIhlR868LLu9cYG0OEUnbge/hozth1Rtm4RXcBAauM//hVuFVsNgdULkVdJsFQ3+D8s0h4Qp88zR8/STEXbY6oYjkMRnqM+Hp6UmPHj2IiIhg9+7d1KhRg2eeeYaQkBBiYmKyK6NI/lKtIzg84MweOLXb6jQikuzCEZjTEz7rAucOQKES8ODH0He5zvUR8CsNj38D974GNoc52uXHzeF4pNXJRCQPyXSHdbvdjs1mwzAMkpKSsjKTSP7mXQQqtTand863NIqIAAnXYN1/YEoj2Put+Y91k2fg2S1Qp7u6lsnf7A5o/iL0WQp+d8D5Q/BpK/j5I1AvIBFJhwwVX3FxcXz55Ze0bt2aKlWqsGPHDiZPnkxUVJSu8yWSETUfMu93LtAfbBEr7VsOHzaGNW9D4jUodzc89SPcNxa8/K1OJ7lV2Sbw1A9mTwZngnmh5i+7w5VzVicTkVwu3QNuPPPMM8yZM4fg4GD69u3Ll19+SfHixbMzm0j+VbUduPvAhT/g+DYNVy2S084fgmWvwP4V5uPCpaDNGPOcTB3pkvTwKQaPfAabP4UV/4Lfl8PUu6HLNAi52+p0IpJLpbv4mjp1KmXLlqVChQqsW7eOdevWpbncwoULsyycSL7lUQiq3Ae7FpqjHqr4EskZ8bHw40TY8D4kxYHdHZo+A81fAk/14JAMstmg0QDzSNi8PnBuP8zqZLanFi+Z3RRFRK6T7uKrV69e2PRtoEjWqdX17+Kr9Vu6ZpBIdjIM2LMEVrwKl46a8yrcA+3+A4FVrM0meV9QLXhynXkh7sjPYd04+OMHeGga+JexOp2I5CIZusiyiGShSq3A09+8gGfURgi5y+pEIvnT2f2w7CXz+noA/sHQ9h2o3kldDCXreBQyL8BcIRy+fR6ObDC7IXb+CKreZ3U6Eckl9FW7iFXcPKF6R3Na1/wSyXpxMRAxCj5sahZeDg9zpLpBmyD0fhVekj1qd4Mn10OpOnD1PHz5iHl+YWKc1clEJBdQ8SVipeQLLu/+BpISLY0ikm8YhvmFxuSGsGGSORpd5TbwzM/mNZo8fKxOKPldQEXoFwFNBpmPf/kI/tcazh20NpeIWE7Fl4iVyrcAnwCIPQeH11qdRiTvO73HHPBgfl+zS2+RctBjDjz6lfkPsUhOcfOE+96BHnPBuxic+NW8KPOvc61OJiIWUvElYiWHG4R2Nqd3aqRQkUy7Fg3LX4WP7jIHOnDzgvBXYdAv5qUd1MVQrFL1Pnh6g3kNufgY+HogfP202S1WRAocFV8iVkvuerhnic4JEMkow4Bf58AHYfDzFDCSzAvfDtoE4S+Du7fVCUXArzT0Xmx+IWCzw69fwCct4MRvVicTkRym4kvEamWbQuHSEBcN+yOsTiOSd5zcATPawddPwpXTUKwi9FwA3T+HouWsTieSkt1hfiHQ+1vzM//cAfi0FfzyifklgogUCCq+RKxmt0PNh8xpjXoocmtXL5jXU/q4uXmZBncfaDkSntkIlVtZnU7k5kLuMrshVmlnXuh72YswpyfEnrc6mYjkABVfIrlBcvH1+3KIv2JtFpHc7PxhmNIYNn0ChhNqPAiDN0OzF8wBDkTyAp9i0ONLuO/f5iUQ9n0HU5vBkY1WJxORbKbiSyQ3KF0fipaHhFjYt8zqNCK5k2HA4mch5hQEVIJei+DhmeB/h9XJRDLOZoMmT5lD0herCNF/wsz2sO5dcCZZnU5EsomKL5HcwGb7e+ANdT0USdu2WX+NZOgNPedDhXCrE4ncvtJ14cl1ULu7eTR3zRj4vwcg+oTVyUQkG6j4Esktkouv/RHmOS0i8rdLx2Dl6+Z0y9ehWHlr84hkJc/C8NDH0HkquBcyv2SYepcGYRLJh1R8ieQWJUMhsDo4E2Dvd1anEck9DAO+G2aOCFqmATR+yupEItmjbg/zKFhQLYg9B593hRX/gsR4q5OJSBZR8SWSm6jroUhqOxeYg9HY3eGByeaQ3SL5VfHK0O97aPSk+XjjZJjeBs4fsjaXiGQJFV8iuUnyqIeH1kHMGWuziOQGV87CspfM6RYvQYnq1uYRyQnuXtD+P9D9C/AqAse3w9TmsGO+1clE5Dap+BLJTQIqQul6YCTB7m+sTiNivWUvm92vStSAu4ZanUYkZ1XrYF4TrGxTiL8MC/rBosG6JIlIHqbiSyS3cXU9XGhtDhGr7VsGO+eDzW52N3TzsDqRSM7zvwN6fwstXgZssH02fHIPnNpldTIRyQQVXyK5TY2/uh5G/QSX/rQ2i4hVrl2Cb583p+98FsrUtzaPiJUcbnDPq9B7MRQuBWf3mQXY5v+ZA9KISJ6h4kskt/EvA2XvNKd3fW1tFhGrrHwdLp8wLz4bPsLqNCK5Q/nm8NSPULkNJMWZo4B+1UuXJxHJQ1R8ieRGyQNvaNRDKYgOrTMvqAxw/wfg7m1tHpHcpFBx6DEX2r5jjgC6Z7E5GMfRTVYnE5F0UPElkhuFdgabwxzh6txBq9OI5Jz4K7DkOXO6QT8IucvaPCK5kd0OTQdBv5VQtDxcioLp98EP74HTaXU6EbkJFV8iuZFvIFRoYU5r4A0pSFa/DRf+AL87oNVoq9OI5G5l6sOT66FmV3OU3FVvwmcPwuVTVicTkRtQ8SWSW+mCy1LQHN0MP39oTneaBF5+lsYRyRO8/KDLp/DAFHD3gUNrYepdcGCV1clEJA0qvkRyq2odzf78Z/bAqd1WpxHJXolxsHgwYEDt7lC5tdWJRPIOmw3qPQYD10LJmnDlDHz2EESMgqQEq9OJyHVUfInkVt5F/v4HVEe/JL/74T04sxcKBcJ9Y61OI5I3BVaF/t9Dw/7m4w2TzHPBLvxhZSoRuY6KL5HczNX1cL6u5SL518mdZvEF0P5d8ClmbR6RvMzdGzq8B91mg5c/HNsCU5vp0iUiuYSKL5HcrGo7sw//hT/g+Dar04hkvaREWDQInIlmV9vQzlYnEskfQu83rwkW3BjiomHeE7BkKCRctTqZSIGm4kskN/MoBFXuM6c16qHkRz9PgROR5jf0Hd4zz10RkaxRpCw8sRSavQDYYOsM+OQeOL3H6mQiBZaKL5HcrlZX837nQl2/RfKXswdgzTvmdNt3oHCQtXlE8iOHG7QcCY9/Db4lzUGcPrkHts5Ud3YRC6j4EsntKrUCT3+4fByiNlqdRiRrOJ3mxZQTr0GFe6BuT6sTieRvFe+BpzZAxZaQeBWWDIH5feDaJauTiRQoKr5Ecjs3T6je0ZzWqIeSX2ydDkc2gHsh6PS+uhuK5ATfQOg5H1q/CXY3cxCOqc3gzy1WJxMpMFR8ieQFNR8y73d/Yw5QIJKXXTxqXn8IoNUoKFrO2jwiBYndDncNgb4rzHPCLh6B6W1hw/vq2i6SA1R8ieQF5cPBJwBiz8HhtRaHEbkNhgHfPg/xMeYobA0HWJ1IpGC6o4E5GmKNB83RRiNGwuddIeaM1clE8jUVXyJ5gcPt7yG4Neqh5GW/zYUDEeDwgPsnm9/Ci4g1vPyh6wzo9F9w84aDq2DqXXBwjdXJRPIt/dUTySuSL7i8ZwkkxlmbRSQzYk7D8lfM6RYvQ2AVa/OIiHm+ZVhvGLgGAqtDzCmY/SCselPd3EWygYovkbyibFMoXNq8WOaB761OI5JxS1+EqxcgqJZ5zomI5B4lqsOA1RDWBzDgh/dgZnu4GGV1MpF8RcWXSF5ht/898MaO+dZmEcmoPUvMAWNsDnhgCjjcrU4kIv/k4QOdJsHDM8HTD47+AlPvht2LrU4mkm+o+BLJS5KLr9+XQ/wVa7OIpNfVC/DdC+b0XUOgVB1r84jIzdV4EJ76Aco0MK8D9tXj5u9wwjWrk4nkeSq+RPKS0vWhaAgkxMK+ZVanEUmfFa+Z55EEVDbP9RKR3K9oCPRdDncNNR9v/hQ+bQln9lmZSiTPU/ElkpfYbH8PvKFRDyUvOLgaIj8DbPDAZHD3sjqRiKSXwx1avwGPLYRCgXBqJ3wSDts/My8bISIZliuKrylTphASEoKXlxeNGzdm06ZNN1x2165ddOnShZCQEGw2G5MmTbrpuseNG4fNZmPo0KEp5l+7do1BgwYREBCAr68vXbp04dSpU1nwakSyWc2u5v2BCLh60dIoIjcVFwOL/xpYo9FAKNvE2jwikjmVWsJTG6BCuNnzYtEgWDgArkVbnUwkz7G8+Jo7dy7Dhg1j1KhRbNu2jTp16tC2bVtOnz6d5vKxsbFUqFCBcePGERQUdNN1b968mY8//pjatWuneu75559nyZIlzJs3j3Xr1nH8+HEeeuihLHlNItmqZKg5HHBSPOz91uo0Ije2+i24FAX+ZaHlSKvTiMjtKFwSHvsaWo4yB87ZMQ8+bg7HtlmdTCRPsbz4mjBhAgMGDKBPnz6EhoYydepUfHx8mD59eprLN2zYkHfffZfu3bvj6el5w/XGxMTQs2dPpk2bRtGiRVM8d+nSJf73v/8xYcIE7r33XsLCwpgxYwY//fQTP//8c5a+PpFs4ep6uMDaHCI3EvUz/PKxOd1pEnj6WhpHRLKA3Q7NhpnngvmXhQuH4X9t4KfJ4HRanS5vSrgGF4/Csa2wbzls+z9YPx6WvQLz+8LMjjClMUR+YXVSySJuVm48Pj6erVu3MmLECNc8u91Oq1at2Lhx422te9CgQXTo0IFWrVoxZsyYFM9t3bqVhIQEWrVq5ZpXrVo1ypYty8aNG2nSJHXXmLi4OOLi/r6wbXS0eag9ISGBhISE28oq1kl+7/Lce1jtftzXjME4tI7Ei8fNvviSp+TZtpceiddwWzQIGwbO2o+SVK455MfXmQfl63YnOSeoHvRbjWPp89j3LoGV/8J5cA1JnSZDoeKpFi9w7S4+Bq6cwXblDMScwXblNMSe/Xvelb/mXTmDLe5yulaZdO4QzoKy/7JQTra99G7D0uLr7NmzJCUlUbJkyRTzS5Ysyd69ezO93jlz5rBt2zY2b96c5vMnT57Ew8ODIkWKpNruyZMn0/yZsWPH8sYbb6Sav3LlSnx8fDKdVXKHiIgIqyNkWHOf8hSNPczu+WP5I7DVrX9AcqW82PZupfrxeVQ5d4Brbv6sNpqRsHSp1ZHkH/JjuxMLeHUlJDiAmn9+juPg98RNacrWck9xrnD1NBfPs+3OMHBPisUz8RKeCdF4Jkb/NX3puum/57s54zO0eqfNQZybH3Fu/sS5/3Xv5pdi+vL50lzTZ2mm5UTbi42NTddylhZf2eHo0aMMGTKEiIgIvLyyblStESNGMGzYMNfj6OhogoODadOmDX5+flm2HclZCQkJRERE0Lp1a9zd89ZFX+3FDsOqUdSy/U5o+wlWx5EMystt76ZO/oZbpPkPgtsD/6V1tQ4WB5Lr5dt2JxbqgPN0X+xf98f77O/cdWAczruH4Wz2ItjNfzNzZbtzJsHV83Dl7F9Ho05fd1TKvHdNx57FlpSxgspw84ZCgRiFAs3eKddN/31fwnzOyx83mw03oFD2vNoCKyfbXnKvuFuxtPgqXrw4Docj1SiDp06duuVgGjeydetWTp8+Tf369V3zkpKSWL9+PZMnTyYuLo6goCDi4+O5ePFiiqNfN9uup6dnmueYubu7554PEsm0PPk+1u4Kq0ZhP/oz9tjT4F/G6kSSCXmy7d1IUgJ8NwSMJAh9ALdana1OJDeQr9qdWK9MHRi4Fpa9jG37bBw/vocjaiN0mQb+d7gWy/Z2l5QAV87CldMQc+av+9OuQso1HfNXN0Ajg+epefqZxZJvCVdB5Zr2LQGFSoCvOd/m4Qs2G7bseaWSQTnxmZfe9VtafHl4eBAWFsaqVavo3LkzAE6nk1WrVjF48OBMrbNly5bs2LEjxbw+ffpQrVo1Xn75ZRwOB2FhYbi7u7Nq1Sq6dDEHLti3bx9RUVE0bdr0tl6TSI7xvwPK3glRP8GuhXDns1YnkoLup//CyR3gXRTaj7c6jYjkJI9C5rX8KoTDkqHm36apd8MDH0LF1plfb8K19BVTV07D1QsZX793sXQVUxQKBHfvzL8Okb9Y3u1w2LBh9O7dmwYNGtCoUSMmTZrElStX6NOnDwC9evWiTJkyjB07FjAH6di9e7dr+tixY0RGRuLr60ulSpUoXLgwNWvWTLGNQoUKERAQ4Jrv7+9Pv379GDZsGMWKFcPPz49nn32Wpk2bpjnYhkiuVfMh8w/czgUqvsRaZ36Htf82p+8bZ/7jIiIFT62uUKY+zO8Hx7fBnB7YGwzA7mxsPm8Y5oAUNyuikgutK2chLoPXErM5zEE/CpUw729YTP31vENHgCVnWV58PfLII5w5c4aRI0dy8uRJ6taty/Lly12DcERFRWG3/z0i/vHjx6lXr57r8fjx4xk/fjwtWrRg7dq16d7uxIkTsdvtdOnShbi4ONq2bcuHH36YZa9LJEeEdoZlL8Hx7XDuIARUtDqRFEROJyx+FpLioFIrqP2I1YlExErFKkDfFbD6TfjpAxxbptHKfQFuf7xpFlmJVzO2PofHP4qp5CKqxF+Pi/897V3MHBJfJJeyvPgCGDx48A27Gf6zoAoJCcEwjAytP62izMvLiylTpjBlypQMrUskV/ENhPIt4NAas+th8xetTiQF0eZpcPRn8PCFjpPAprMcRAo8Nw9oMwbKt8D4+km8Y8/BpfN/P+9eKOVRqBsVU38NSKHPFckvckXxJSK3oVZXs/jasUDFl+S8C0fg+78uw9FqNBQJtjSOiOQylVuT+ORGtiz6mIbNWuHmX8osqjw0rp8UTDouK5LXVesIdnc4swdO7bY6jRQkhgFLhkDCFXPwlwb9rE4kIrmRTzFO+9fBuKMhFCuvwksKNBVfInmddxGo/NdIUjsXWBpFCpjIL8yjrm5ecP8HOs9CRETkFvSXUiQ/qGleMoGd882jESLZ7fJJWDHCnA4fAcUrWZtHREQkD1DxJZIfVG0H7j5w4Q9zaF+R7GQY8N0LcO0SlKoLTTN3XUYREZGCRsWXSH7gUQiq3GdO71xobRbJ/3Yvgr3fgt3NvKiqQ2M3iYiIpIeKL5H8wtX1cKF53SWR7BB7HpYON6fvHgZBtazNIyIikoeo+BLJLyq3Bk9/uHwcojZanUbyqxWvmhdJDawGzYdbnUZERCRPUfElkl+4eUL1jua0Rj2U7LA/An79ErDB/ZPNNiciIiLppuJLJD+p+ZB5v/sbSEq0NIrkM3GXYclQc7rJMxDc0NI4IiIieZGKL5H8pHwL8AmA2HNweJ3VaSQ/+X40RP8JRUPg3n9ZnUZERCRPUvElkp843CG0szmtroeSVf7YAJs/Nac7/dccXVNEREQyTMWXSH6TPOrhniWQGGdtFsn7Eq7C4mfN6fq9oEILa/OIiIjkYSq+RPKbsk2hcCmIi4YD31udRvK6tWPh/EGzTbV+y+o0IiIieZqKL5H8xm6HGn8NvKGuh3I7jm2Dnz4wpztMAO8ilsYRERHJ61R8ieRHtf7qerhvGcRfsTaL5E2J8WZ3Q8NpdmWt1t7qRCIiInmeii+R/Kh0fXNUuoRYswATyagNk+DUTvAuBu3+Y3UaERGRfEHFl0h+ZLP9PfDGzoXWZpG85/ReWPdXwdXuP1CouLV5RERE8gkVXyL5VXLxdSACrl60NIrkIc4kWDQInAlQ5T6o1dXqRCIiIvmGii+R/KpkDQisDknxsPdbq9NIXvHLx3BsC3j6mYNs2GxWJxIREck3bIZhGFaHyIuio6Px9/enVKlL2O1+VseRTDO4du0aXl5eQD78JzPusjnkvJsn+KjrWO6SC9ueMxGunAbDMEc2dNfFlPOfXNjupABQuxOr5FzbczqjOXHCn0uXLuHnd+PawC1bUxQAJ05YnUBujw3wtjpENir81w24YGkQSSU3tj03oLQ5ednSIJJtcmO7k/xP7U6skvvanoqv21SqlHlZJcmrCsC3cVfOmF0PvfzBw9fqNOKSy9pewhXz3ECbDQqVALv+PORPuazdSQGhdidWyckjX+k7KKO/rrdp7164yZFFyeUSEhJZunQl7du3x93d3eo42eOnObDyNSh7J/TVsPO5Ra5qe9HHYUpjs4tqm7fhzsHW5pFsk6vanRQYandilZxse9HR4O9/6+V0zEYkv6vxoHkf9RNcOmZtFsl9DAO+HWYWXmXCoMnTVicSERHJt1R8ieR3/ndA2abm9K6vrc0iuc/OBfD7MrC7w/2Twe6wOpGIiEi+peJLpCBwXXB5vrU5JHe5cg6WvWRONx8OJUOtzSMiIpLPqfgSKQhCO4PNDse3w7mDVqeR3GL5yxB7DkrUgLuHWZ1GREQk31PxJVIQ+AZC+Rbm9K6F1maR3GHfctgxzyzKH/gA3DysTiQiIpLvqfgSKSiSux7uWGBtDrHetUvw7fPmdNNB5kAbIiIiku1UfIkUFNU7mYMqnNkDp3ZbnUasFDESLh+HYhUg/FWr04iIiBQYKr5ECgrvIlC5tTm9U0e/CqzD62HrTHP6/g/Aw8fSOCIiIgWJii+RgsQ16uEC8/pOUrDEx8Li58zpBn0h5G5r84iIiBQwKr5ECpKq7cDdBy4chuPbrE4jOW3N2+Z771cGWr1hdRoREZECR8WXSEHiUQiq3GdO79SohwXKn1vh5w/N6Y6TwMvP0jgiIiIFkYovkYLG1fVwITid1maRnJEYB4sGgeGE2o9AlTZWJxIRESmQVHyJFDSVWoGnnzna3dGfrU4jOeGHCeYolz7Foe1Yq9OIiIgUWCq+RAoady9z2HmAHfOtzSLZ79Qu+GG8Od3+XSgUYG0eERGRAkzFl0hBVPMh8373N5CUaGkUyUZJiWZ3Q2ciVO0ANR60OpGIiEiBpuJLpCAq3wJ8AiD2HBxeZ3UayS4/fwjHt4OnP3R4D2w2qxOJiIgUaCq+RAoihzuEPmBOa9TD/OncQXNoeYC2b4NfKWvziIiIiIovkQKrZlfzfs8SczQ8yT+cTvNiyonXoEI41HvM6kQiIiKCii+RgqtsUyhcCuIuwYHvrU4jWWnrDDjyo3lB7U7vq7uhiIhILqHiS6Sgstuhxl8Db+xcYG0WyTqX/oSIUeZ0y5FQNMTSOCIiIvI3FV8iBVnyBZf3LYP4K9ZmkdtnGPDt8xB/Ge5oBI0GWp1IRERErqPiS6QgK1PfPDKSEGsWYJK37ZgH+1eCwwMemAx2h9WJRERE5DoqvkQKMpvt76NfGvUwb4s5A8teNqdbvASBVa3NIyIiIqmo+BIp6JKLrwMRcPWipVHkNix7Ea6eh5K14K6hVqcRERGRNKj4EinoStaAwOqQFA97v7U6jWTGnm9h19dgc5jdDR3uVicSERGRNKj4EpHruh5q1MM85+pF+O4Fc/qu56B0XSvTiIiIyE2o+BIRqPnXkPOH1pnnDknesfI1iDkJAZWgxctWpxEREZGbUPElIhBQEUrVBSMJ9iyyOo2k18E1sH22OX3/ZHD3tjaPiIiI3JSKLxEx1epq3u9Q18M8IS4GljxnTjccAOWaWptHREREbknFl4iYajxo3kf9BJeOWZtFbm31GLgYBf7B0GqU1WlEREQkHVR8iYjJ/w4o+9fRk11fW5tFbi7qF/hlqjndaRJ4FrY0joiIiKSPii8R+Ztr1MP/b+/eo2u68/+Pv87JPSeSCLkOEUqRUg1aQqdWCWJKKUbbX2ak6qerrSjy+07LGNTUrdX6Gb1QnWk601Ud1VZpp5iI0jIoWqYuTV0rP0LaItfK7Xx+f6ROhYTQ5GyS52OtLPvyyT7vnbxX5JW99+e8a20dqF7pOWlViiQjdfpfUusEqysCAAA1RPgC8LPYIZLNLp34UvrhkNXVoCqfzpO+/0ZyhEn9Z1ldDQAAuAqELwA/CwiVWvaqWN77vrW14FLZ/5U2L6hYvud5yT/E0nIAAMDVsTx8vfzyy4qJiZGvr6+6deumzz//vNqxe/fu1bBhwxQTEyObzaYFCxZcMmbRokW69dZbFRgYqMDAQMXHx2v16tWVxhw6dEj33XefQkNDFRgYqBEjRujUqVO1fWrAjcl16yHh67pSXlZxu6GzTGp/rxQ72OqKAADAVbI0fC1btkypqamaPn26vvjiC3Xq1En9+/dXTk5OleOLiorUqlUrzZ07VxEREVWOadasmebOnaudO3dqx44d6t27twYPHqy9e/dKkgoLC9WvXz/ZbDatX79emzdvVklJiQYNGiSn01ln5wrcMNoPlOxeUs4+6dQ+q6vBef9ZKGXvlnyDpd88b3U1AADgGlgavubPn68xY8Zo1KhRio2N1eLFi+Xv76/XX3+9yvG333675s2bpwceeEA+Pj5Vjhk0aJB+85vfqE2bNrr55ps1a9YsBQQEaOvWrZKkzZs36+jRo3rjjTfUsWNHdezYUX//+9+1Y8cOrV+/vs7OFbhh+DWW2vStWN7De35dF74/IG2YW7GcOEdqFG5tPQAA4Jp4WvXCJSUl2rlzpyZPnuzaZrfblZCQoC1bttTKa5SXl2v58uUqLCxUfHzFFNrFxcWy2WyVwpuvr6/sdrs2bdqkhISqZw4rLi5WcXGxaz0vL0+SVFpaqtLS0lqpF+53/nvH97AyW/vB8sz8WGbPeyr79VOSzWZ1SfVOjXvPOOWxMkX28mI5W/VWeexwiX7FNeJnHqxA38Eq7uy9mr6GZeHr+++/V3l5ucLDK/8FNzw8XF9//fUvOvZXX32l+Ph4nTt3TgEBAVqxYoViY2MlSd27d5fD4dBTTz2l2bNnyxijSZMmqby8XNnZ2dUec86cOZoxY8Yl2//973/L39//F9UL66Wnp1tdwnXFo9ymRJu3PM8c0X/ee1ln/VtZXVK9daXea/ndOt36/7aqzO6r9X4D9eNFz7AC14KfebACfQeruKP3ioqKajTOsvBVl9q2batdu3YpNzdX7777rpKTk7Vx40bFxsYqNDRUy5cv12OPPaaFCxfKbrfrwQcfVOfOnWW3V38X5uTJk5Wamupaz8vLU/PmzdWvXz8FBga647RQB0pLS5Wenq6+ffvKy8vL6nKuK/ayj6V9H+jO4FNyJqRYXU69U6Pey82S55LHJEm2vjN0d9eRbqwQ9RE/82AF+g5WcWfvnb8r7kosC19NmzaVh4fHJbMMnjp1qtrJNGrK29tbrVu3liR16dJF27dv11/+8he9+uqrkqR+/frp0KFD+v777+Xp6ang4GBFRESoVavq/7rv4+NT5XNmXl5e/CCpB/g+VqHjb6V9H8hj30p59J8tXeaPE7h21faeMdLq/yOVFErR8fLo9og8+B6glvAzD1ag72AVd/ReTY9v2f/k3t7e6tKlizIyMlzbnE6nMjIyXM9n1Ran01npea3zmjZtquDgYK1fv145OTm69957a/V1gRta6wTJJ1DKPyFlbbW6moZn99vSofWSh49074uEXwAA6gFLbztMTU1VcnKyunbtqjvuuEMLFixQYWGhRo0aJUkaOXKkfvWrX2nOnDmSKibp2Ldvn2v5+PHj2rVrlwICAlxXuiZPnqwBAwYoOjpa+fn5Wrp0qTZs2KC1a9e6XjctLU3t27dXaGiotmzZovHjx2vixIlq27atm78CwHXMy1dqN1DavVT66l2pRQ+rK2o48k9Ja36ajOjuyVLTNtbWAwAAaoWl4ev+++/Xd999p2nTpunkyZO67bbbtGbNGtckHMeOHav0HNaJEycUFxfnWn/++ef1/PPPq1evXtqwYYMkKScnRyNHjlR2draCgoJ06623au3aterbt6/r8zIzMzV58mSdPn1aMTExmjJliiZOnOiekwZuJB2HVYSvfR9IA56TPOrlY6LXn4//Rzp3VorsJMWPs7oaAABQSyz/TSolJUUpKVU/zH8+UJ0XExMjY8xlj/e3v/3tiq85d+5czZ07t8Y1Ag1Wy16SfxOp6AfpyEapdR+rK6r/9q2U9q+S7J7S4JcJvAAA1CM8RACgeh5eUuzgiuU971tbS0NQdFr61/9ULPecIEV0tLQcAABQuwhfAC6vw7CKf/d/KJVdOnENatHaKVJhjtT0ZqnXk1ZXAwAAahnhC8DlRfeQGkVKxbnSwXVWV1N/HVhX8XydbBW3G3pe+tYWAADgxkb4AnB5drt0y9CK5T3vWVtLfVWcL300oWK526NS8zssLQcAANQNwheAKzt/62Hm6oo3/UXtWjdDys2SgqOlPlOtrgYAANQRwheAK/tVZ6lxjFRaVBHAUGtsx7ZI21+rWBm0UPJ2WFsQAACoM4QvAFdms/189YtZD2uN3Vkij39NqFiJ+710092W1gMAAOoW4QtAzZwPXwfTpR/PWlpKfdEue4Vspw9JARFSv5lWlwMAAOoY4QtAzYTFSqHtpPIS6et/WV3NjS97l27K+ekWzoHzJb9gS8sBAAB1j/AFoGZsNqnD8IrlPe9aW8uNqrhAOn1Eyvpcnh9NkF1OOWOHSO3usboyAADgBp5WFwDgBtJhqPTJTOnwRqngOykg1OqKrOV0SufOSgU5UuF3FW+QXPDdz8uF3/+076fl0iLXp9okFXsEyN5vDn8FAwCggSB8Aai5JjdJkbdJ2buk/Sul2/+31RXVvvLSiqBU+FOgKviu+uWi7yVn2dUd39NPCgiVs1GUtvv0VjdHAw+wAAA0IIQvAFenw7CK8PXVezdO+CoprCZIVXG16sczV3983yDJESYFhEmOppcuO0IrrhI6wiqmkrfZVF5aqh8+/rj2zxUAAFy3CF8Ark6HoVL6VOnYf6Tc41LQr9xfgzEVIen8FaqCnKqXz4es0qt8Y2ibx0/BKfTnj4CwqpcdoZKnd92cJwAAqFcIXwCuTlAzKTpeOrZF2rtC6pFSO8ctL6u4jc/1/NR3lZcvXr/q2/18f7oK1bT6IHV+2S9EsvMkFgAAqF2ELwBXr8OwivC1573Lh6+SoirC04UTUVyw/cfTV1+HT9BPt/PV4OqUT6OKGRsBAAAsQvgCcPViB0urn5ROfCFt+r+VQ9aFoaqk4OqOa7NL/k1+embqfHC68GrVRVeuPH3q5vwAAADqAOELwNULCJNa9pIOfyKte/ryYz28LwhTF00+4Vr+ad0/RLJ7uOUUAAAA3I3wBeDa9J5aEZS8/H4OTxcGqfOz/fkEcrsfAACACF8ArlWzLtLv3rO6CgAAgBsG03kBAAAAgBsQvgAAAADADQhfAAAAAOAGhC8AAAAAcAPCFwAAAAC4AeELAAAAANyA8AUAAAAAbkD4AgAAAAA3IHwBAAAAgBsQvgAAAADADQhfAAAAAOAGhC8AAAAAcAPCFwAAAAC4AeELAAAAANyA8AUAAAAAbkD4AgAAAAA3IHwBAAAAgBsQvgAAAADADTytLuBGZYyRJOXl5VlcCX6J0tJSFRUVKS8vT15eXlaXgwaE3oMV6DtYgb6DVdzZe+czwfmMUB3C1zXKz8+XJDVv3tziSgAAAABcD/Lz8xUUFFTtfpu5UjxDlZxOp06cOKFGjRrJZrNZXQ6uUV5enpo3b66srCwFBgZaXQ4aEHoPVqDvYAX6DlZxZ+8ZY5Sfn6+oqCjZ7dU/2cWVr2tkt9vVrFkzq8tALQkMDOQ/BFiC3oMV6DtYgb6DVdzVe5e74nUeE24AAAAAgBsQvgAAAADADQhfaNB8fHw0ffp0+fj4WF0KGhh6D1ag72AF+g5WuR57jwk3AAAAAMANuPIFAAAAAG5A+AIAAAAANyB8AQAAAIAbEL4AAAAAwA0IX2gQ5syZo9tvv12NGjVSWFiYhgwZoszMzEpjzp07p7Fjx6pJkyYKCAjQsGHDdOrUKYsqRn00d+5c2Ww2TZgwwbWNvkNdOH78uH73u9+pSZMm8vPzU8eOHbVjxw7XfmOMpk2bpsjISPn5+SkhIUEHDhywsGLc6MrLyzV16lS1bNlSfn5+uummm/TMM8/ownnd6DvUhk8//VSDBg1SVFSUbDabPvjgg0r7a9Jnp0+fVlJSkgIDAxUcHKzRo0eroKDALfUTvtAgbNy4UWPHjtXWrVuVnp6u0tJS9evXT4WFha4xEydO1Icffqjly5dr48aNOnHihIYOHWph1ahPtm/frldffVW33nprpe30HWrbmTNn1LNnT3l5eWn16tXat2+fXnjhBTVu3Ng15rnnntPChQu1ePFibdu2TQ6HQ/3799e5c+csrBw3smeffVaLFi3SSy+9pP379+vZZ5/Vc889pxdffNE1hr5DbSgsLFSnTp308ssvV7m/Jn2WlJSkvXv3Kj09XR999JE+/fRTPfLII+45AQM0QDk5OUaS2bhxozHGmLNnzxovLy+zfPly15j9+/cbSWbLli1WlYl6Ij8/37Rp08akp6ebXr16mfHjxxtj6DvUjaeeesrceeed1e53Op0mIiLCzJs3z7Xt7NmzxsfHx7z99tvuKBH10D333GMefvjhStuGDh1qkpKSjDH0HeqGJLNixQrXek36bN++fUaS2b59u2vM6tWrjc1mM8ePH6/zmrnyhQYpNzdXkhQSEiJJ2rlzp0pLS5WQkOAa065dO0VHR2vLli2W1Ij6Y+zYsbrnnnsq9ZdE36FurFq1Sl27dtVvf/tbhYWFKS4uTq+99ppr/5EjR3Ty5MlKfRcUFKRu3brRd7hmPXr0UEZGhr755htJ0u7du7Vp0yYNGDBAEn0H96hJn23ZskXBwcHq2rWra0xCQoLsdru2bdtW5zV61vkrANcZp9OpCRMmqGfPnurQoYMk6eTJk/L29lZwcHClseHh4Tp58qQFVaK++Oc//6kvvvhC27dvv2QffYe6cPjwYS1atEipqan64x//qO3bt+uJJ56Qt7e3kpOTXb0VHh5e6fPoO/wSkyZNUl5entq1aycPDw+Vl5dr1qxZSkpKkiT6Dm5Rkz47efKkwsLCKu339PRUSEiIW3qR8IUGZ+zYsdqzZ482bdpkdSmo57KysjR+/Hilp6fL19fX6nLQQDidTnXt2lWzZ8+WJMXFxWnPnj1avHixkpOTLa4O9dU777yjt956S0uXLtUtt9yiXbt2acKECYqKiqLvgAtw2yEalJSUFH300Uf65JNP1KxZM9f2iIgIlZSU6OzZs5XGnzp1ShEREW6uEvXFzp07lZOTo86dO8vT01Oenp7auHGjFi5cKE9PT4WHh9N3qHWRkZGKjY2ttK19+/Y6duyYJLl66+JZNek7/BJ/+MMfNGnSJD3wwAPq2LGjfv/732vixImaM2eOJPoO7lGTPouIiFBOTk6l/WVlZTp9+rRbepHwhQbBGKOUlBStWLFC69evV8uWLSvt79Kli7y8vJSRkeHalpmZqWPHjik+Pt7d5aKe6NOnj7766ivt2rXL9dG1a1clJSW5luk71LaePXte8lYa33zzjVq0aCFJatmypSIiIir1XV5enrZt20bf4ZoVFRXJbq/8a6WHh4ecTqck+g7uUZM+i4+P19mzZ7Vz507XmPXr18vpdKpbt251XiO3HaJBGDt2rJYuXaqVK1eqUaNGrnt6g4KC5Ofnp6CgII0ePVqpqakKCQlRYGCgxo0bp/j4eHXv3t3i6nGjatSokeu5wvMcDoeaNGni2k7fobZNnDhRPXr00OzZszVixAh9/vnnWrJkiZYsWSJJrveamzlzptq0aaOWLVtq6tSpioqK0pAhQ6wtHjesQYMGadasWYqOjtYtt9yiL7/8UvPnz9fDDz8sib5D7SkoKNDBgwdd60eOHNGuXbsUEhKi6OjoK/ZZ+/btlZiYqDFjxmjx4sUqLS1VSkqKHnjgAUVFRdX9CdT5fIrAdUBSlR9paWmuMT/++KN5/PHHTePGjY2/v7+57777THZ2tnVFo166cKp5Y+g71I0PP/zQdOjQwfj4+Jh27dqZJUuWVNrvdDrN1KlTTXh4uPHx8TF9+vQxmZmZFlWL+iAvL8+MHz/eREdHG19fX9OqVSszZcoUU1xc7BpD36E2fPLJJ1X+TpecnGyMqVmf/fDDD+bBBx80AQEBJjAw0IwaNcrk5+e7pX6bMRe89TgAAAAAoE7wzBcAAAAAuAHhCwAAAADcgPAFAAAAAG5A+AIAAAAANyB8AQAAAIAbEL4AAAAAwA0IXwAAAADgBoQvAAAAAHADwhcAANcRm82mDz74wOoyqnQ91wYANwLCFwCgVj300EOy2WyXfBw8eLBWjv/GG28oODi4Vo7VEBCYAOD64Wl1AQCA+icxMVFpaWmVtoWGhlpUTfVKS0vl5eVldRkAgAaCK18AgFrn4+OjiIiISh8eHh6SpJUrV6pz587y9fVVq1atNGPGDJWVlbk+d/78+erYsaMcDoeaN2+uxx9/XAUFBZKkDRs2aNSoUcrNzXVdUXv66aclVX2FJzg4WG+88YYk6ejRo7LZbFq2bJl69eolX19fvfXWW5Kkv/71r2rfvr18fX3Vrl07vfLKK65jlJSUKCUlRZGRkfL19VWLFi00Z86cas99w4YNuuOOO+RwOBQcHKyePXvq22+/de2/0vlfLCsrSyNGjFBwcLBCQkI0ePBgHT16tNKY119/Xbfccot8fHwUGRmplJQUSVJMTIwk6b777pPNZnOt16SOAwcO6K677pKvr69iY2OVnp5ebY0AgJrhyhcAwG0+++wzjRw5UgsXLtSvf/1rHTp0SI888ogkafr06ZIku92uhQsXqmXLljp8+LAef/xxPfnkk3rllVfUo0cPLViwQNOmTVNmZqYkKSAg4KpqmDRpkl544QXFxcW5Ati0adP00ksvKS4uTl9++aXGjBkjh8Oh5ORkLVy4UKtWrdI777yj6OhoZWVlKSsrq8pjl5WVaciQIRozZozefvttlZSU6PPPP5fNZqvx+V+otLRU/fv3V3x8vD777DN5enpq5syZSkxM1H//+195e3tr0aJFSk1N1dy5czVgwADl5uZq8+bNkqTt27crLCxMaWlpSkxMdAXgK9XhdDo1dOhQhYeHa9u2bcrNzdWECROu6usMAKiCAQCgFiUnJxsPDw/jcDhcH8OHDzfGGNOnTx8ze/bsSuPffPNNExkZWe3xli9fbpo0aeJaT0tLM0FBQZeMk2RWrFhRaVtQUJBJS0szxhhz5MgRI8ksWLCg0pibbrrJLF26tNK2Z555xsTHxxtjjBk3bpzp3bu3cTqdlz1vY4z54YcfjCSzYcOGKvfX5PwvPI8333zTtG3bttJrFxcXGz8/P7N27VpjjDFRUVFmypQp1dZU1dflSnWsXbvWeHp6muPHj7v2r169uspjAQBqjitfAIBad/fdd2vRokWudYfDIUnavXu3Nm/erFmzZrn2lZeX69y5cyoqKpK/v7/WrVunOXPm6Ouvv1ZeXp7Kysoq7f+lunbt6louLCzUoUOHNHr0aI0ZM8a1vaysTEFBQZIqJhDp27ev2rZtq8TERA0cOFD9+vWr8tghISF66KGH1L9/f/Xt21cJCQkaMWKEIiMja3z+F9q9e7cOHjyoRo0aVdp+7tw5HTp0SDk5OTpx4oT69OlzVV+DK9Wxf/9+NW/eXFFRUa798fHxV/UaAIBLEb4AALXO4XCodevWl2wvKCjQjBkzNHTo0Ev2+fr66ujRoxo4cKAee+wxzZo1SyEhIdq0aZNGjx6tkpKSy4Yvm80mY0ylbaWlpVXWdmE9kvTaa6+pW7dulcadv0Wvc+fOOnLkiFavXq1169ZpxIgRSkhI0LvvvltlHWlpaXriiSe0Zs0aLVu2TH/605+Unp6u7t27X/H8L1ZQUKAuXbq4nk27UGhoqOz2a3t0+2rrAADUDsIXAMBtOnfurMzMzCqDmSTt3LlTTqdTL7zwgitYvPPOO5XGeHt7q7y8/JLPDQ0NVXZ2tmv9wIEDKioqumw94eHhioqK0uHDh5WUlFTtuMDAQN1///26//77NXz4cCUmJur06dMKCQmpcnxcXJzi4uI0efJkxcfHa+nSperevfsVz/9inTt31rJlyxQWFqbAwMAqx8TExCgjI0N33313lfu9vLwu+XpdqY727dsrKytL2dnZrqt2W7durVHNAIDqEb4AAG4zbdo0DRw4UNHR0Ro+fLjsdrt2796tPXv2aObMmWrdurVKS0v14osvatCgQdq8ebMWL15c6RgxMTEqKChQRkaGOnXqJH9/f/n7+6t379566aWXFB8fr/Lycj311FM1mkZ+xowZeuKJJxQUFKTExEQVFxdrx44dOnPmjFJTUzV//nxFRkYqLi5Odrtdy5cvV0RERJXvNXbkyBEtWbJE9957r6KiopSZmakDBw5o5MiRNTr/iyUlJWnevHkaPHiw/vznP6tZs2b69ttv9f777+vJJ59Us2bN9PTTT+vRRx9VWFiYBgwYoPz8fG3evFnjxo1zfb0yMjLUs2dP+fj4qHHjxlesIyEhQTfffLOSk5M1b9485eXlacqUKdfwHQcAVGL1Q2cAgPolOTnZDB48uNr9a9asMT169DB+fn4mMDDQ3HHHHWbJkiWu/fPnzzeRkZHGz8/P9O/f3/zjH/8wksyZM2dcYx599FHTpEkTI8lMnz7dGGPM8ePHTb9+/YzD4TBt2rQxH3/8cZUTbnz55ZeX1PTWW2+Z2267zXh7e5vGjRubu+66y7z//vvGGGOWLFlibrvtNuNwOExgYKDp06eP+eKLL6o8t5MnT5ohQ4aYyMhI4+3tbVq0aGGmTZtmysvLa3z+umhSi+zsbDNy5EjTtGlT4+PjY1q1amXGjBljcnNzXWMWL15s2rZta7y8vExkZKQZN26ca9+qVatM69atjaenp2nRokWN68jMzDR33nmn8fb2NjfffLNZs2YNE24AwC9kM+aiG+QBAAAAALWON1kGAAAAADcgfAEAAACAGxC+AAAAAMANCF8AAAAA4AaELwAAAABwA8IXAAAAALgB4QsAAAAA3IDwBQAAAABuQPgCAAAAADcgfAEAAACAGxC+AAAAAMAN/j8fWKeAGXCy+wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.lineplot(data=dff, x='param_selector__n_features_to_select', y='mean_test_score',\n",
    "             hue='pipeline', markers=True, dashes=False)\n",
    "plt.axhline(y=basic_mean, color='blue', linestyle='-', linewidth=2, label='basic')\n",
    "\n",
    "plt.xlabel('Features selected')\n",
    "plt.ylabel('Mean of 5-fold RMSE')\n",
    "plt.title('Performance Comparison of MRMR v. no feature selection')\n",
    "plt.legend(title='Pipeline')\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basic_std = dff.query('pipeline == \"basic\"')['std_test_score'].values[0]\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.lineplot(data=dff, x='param_selector__n_features_to_select', y='std_test_score',\n",
    "             hue='pipeline', markers=True, dashes=False)\n",
    "plt.axhline(y=basic_std, color='blue', linestyle='-', linewidth=2, label='basic')\n",
    "\n",
    "plt.xlabel('Features selected')\n",
    "plt.ylabel('Standard deviation of 5-fold RMSE')\n",
    "plt.title('Performance Comparison of MRMR v. no feature selection')\n",
    "plt.legend(title='Pipeline')\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimal $k = 30$. Selected feature names can be retrieved from the selector:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PctIlleg', 'PctKids2Par', 'PctFam2Par', 'racePctWhite',\n",
       "       'PctYoungKids2Par', 'PctTeen2Par', 'racepctblack', 'pctWInvInc',\n",
       "       'pctWPubAsst', 'FemalePctDiv', 'TotalPctDiv', 'PctPersOwnOccup',\n",
       "       'MalePctDivorce', 'PctPopUnderPov', 'PctUnemployed', 'PctHousNoPhone',\n",
       "       'PctVacantBoarded', 'PctNotHSGrad', 'PctHousOwnOcc', 'NumIlleg',\n",
       "       'PctHousLess3BR', 'PctPersDenseHous', 'NumUnderPov', 'medFamInc',\n",
       "       'HousVacant', 'medIncome', 'PctLess9thGrade', 'PctLargHouseFam',\n",
       "       'population', 'PctWOFullPlumb'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features_ix = grid_search_mrmr.best_estimator_.named_steps['selector'].selected_features_\n",
    "\n",
    "X_train.columns[selected_features_ix]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can access the corresponding \"importance\" scores via the `scores_` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1991.286985941416,\n",
       " 1916.8950474985186,\n",
       " 1586.9173107581364,\n",
       " 1465.5826417829855,\n",
       " 1255.4472161009132,\n",
       " 1236.8411408033085,\n",
       " 1118.9885509001165,\n",
       " 788.5107576582648,\n",
       " 763.0663001535506,\n",
       " 720.2144335205188,\n",
       " 700.4106389894638,\n",
       " 630.6490089800511,\n",
       " 596.3959736716047,\n",
       " 589.3114868036836,\n",
       " 523.2291672858141,\n",
       " 495.8794086552691,\n",
       " 495.24363323445357,\n",
       " 482.92042173574885,\n",
       " 473.93769858420393,\n",
       " 471.8608188667643,\n",
       " 464.9924609945325,\n",
       " 428.93598330028624,\n",
       " 397.68230023614205,\n",
       " 358.0486373888497,\n",
       " 348.8498522132101,\n",
       " 326.5432389823072,\n",
       " 321.875337898018,\n",
       " 298.41997566157374,\n",
       " 258.4008576156917,\n",
       " 257.3031562019562]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search_mrmr.best_estimator_.named_steps['selector'].scores_"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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