From 585c8b552cb9c211164e9dd4706e60ccb8cf7feb Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Wed, 29 Sep 2021 11:36:52 +0200
Subject: [PATCH 01/16] qq plot modifs

---
 cobra/evaluation/evaluator.py | 20 +++++++-------------
 1 file changed, 7 insertions(+), 13 deletions(-)

diff --git a/cobra/evaluation/evaluator.py b/cobra/evaluation/evaluator.py
index 3e458e4..cbf836c 100644
--- a/cobra/evaluation/evaluator.py
+++ b/cobra/evaluation/evaluator.py
@@ -640,7 +640,7 @@ def _compute_qq_residuals(y_true: np.ndarray,
 
         df["z_res"] = df["res"].apply(lambda x: (x-m)/s)
         df["rank"] = df.index+1
-        df["percentile"] = df["rank"].apply(lambda x: x/n)
+        df["percentile"] = df["rank"].apply(lambda x: x/(n+1))  # divide by n+1 to avoid inf
         df["q_theoretical"] = norm.ppf(df["percentile"])
 
         return pd.Series({
@@ -708,21 +708,15 @@ def plot_qq(self, path: str=None, dim: tuple=(12, 8)):
             x = self.qq["quantiles"]
             y = self.qq["residuals"]
 
-            ax.plot(x, x, "r--", label="Q-Q of a perfect model",
-                    color="darkorange",
-                    linewidth=3)
-            ax.plot(x, y, "o--", label="Q-Q of the current model",
-                    color="cornflowerblue",
-                    linewidth=3)
+            ax.plot(x, x, "r--", label="perfect model", color="darkorange", linewidth=3)
+            ax.plot(x, y, "o--", label="current model", color="cornflowerblue", linewidth=3)
 
             ax.set_xlabel("Theoretical quantiles", fontsize=15)
-            ax.set_xticks(range(int(np.floor(min(x))),
-                                int(np.ceil(max(x[x<float('inf')])))+1,
-                                1))
+            ax.set_xticks(range(int(np.floor(min(x))), int(np.ceil(max(x[x < float("inf")])))+1, 1))
+
             ax.set_ylabel("Standardized residuals", fontsize=15)
-            ax.set_yticks(range(int(np.floor(min(y))),
-                                int(np.ceil(max(y))) + 1,
-                                1))
+            ax.set_yticks(range(int(np.floor(min(y))), int(np.ceil(max(y[x < float("inf")])))+1, 1))
+
             ax.legend(loc="best")
             ax.set_title("Q-Q Plot", fontsize=20)
 

From 3c8478582cacc94fc6217454f3b58420fb23643e Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Wed, 29 Sep 2021 11:39:35 +0200
Subject: [PATCH 02/16] separate tutorials folder

---
 .../Cobra_Tutorial_LinearRegression.ipynb     | 1991 ---------------
 .../Cobra_Tutorial_LogisticRegression.ipynb   | 2155 -----------------
 2 files changed, 4146 deletions(-)
 delete mode 100644 docs/tutorials/Cobra_Tutorial_LinearRegression.ipynb
 delete mode 100644 docs/tutorials/Cobra_Tutorial_LogisticRegression.ipynb

diff --git a/docs/tutorials/Cobra_Tutorial_LinearRegression.ipynb b/docs/tutorials/Cobra_Tutorial_LinearRegression.ipynb
deleted file mode 100644
index 4060734..0000000
--- a/docs/tutorials/Cobra_Tutorial_LinearRegression.ipynb
+++ /dev/null
@@ -1,1991 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial for Linear Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Cobra requires the usual Python packages for data science:\n",
-    "- numpy (>=1.19.4)\n",
-    "- pandas (>=1.1.5)\n",
-    "- scipy (>=1.5.4)\n",
-    "- scikit-learn (>=0.23.1)\n",
-    "- matplotlib (>=3.3.3)\n",
-    "- seaborn (>=0.11.0)\n",
-    "\n",
-    "These packages, along with their versions are listed in requirements.txt and can be installed using pip.\n",
-    "\n",
-    "\n",
-    "Note: if you want to install cobra with e.g. pip, you don't have to install all of these requirements as these are automatically installed with cobra itself."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install -r requirements.txt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The easiest way to install cobra is using pip:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install -U pythonpredictions-cobra"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*****"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This section we will walk you through all the required steps to build a predictive linear regression model using **Cobra**. All classes and functions used here are well-documented. In case you want more information on a class or function, run the next cell:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(function_or_class_you_want_info_from)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Building a good model involves three steps\n",
-    "\n",
-    "1. **Preprocessing**: properly prepare the predictors (a synonym for “feature” or variable that we use throughout this tutorial) for modelling.\n",
-    "\n",
-    "2. **Feature Selection**: automatically select a subset of predictors which contribute most to the target variable or output in which you are interested.\n",
-    "\n",
-    "3. **Model Evaluation**: once a model has been build, a detailed evaluation can be performed by computing all sorts of evaluation metrics.\n",
-    "\n",
-    "\n",
-    "\n",
-    "Let's dive in!!!\n",
-    "***"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Miles Per Gallon Prediction\n",
-    "- GOAL : Predict the distance, measured in miles, that a car can travel per gallon of fuel\n",
-    "- BASETABLE : seaborn dataset MPG"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "import the necessary libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "from pandas.api.types import is_datetime64_any_dtype\n",
-    "\n",
-    "pd.set_option('display.max_columns', 50)\n",
-    "pd.set_option(\"display.max_rows\", 50)\n",
-    "from cobra.preprocessing import PreProcessor\n",
-    "from cobra.evaluation import generate_pig_tables, plot_incidence\n",
-    "from cobra.evaluation import evaluator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "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>mpg</th>\n",
-       "      <th>cylinders</th>\n",
-       "      <th>displacement</th>\n",
-       "      <th>horsepower</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>acceleration</th>\n",
-       "      <th>model_year</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>18.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>307.0</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>3504</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>chevrolet chevelle malibu</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>15.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>165.0</td>\n",
-       "      <td>3693</td>\n",
-       "      <td>11.5</td>\n",
-       "      <td>70</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>buick skylark 320</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>18.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>318.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>3436</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>plymouth satellite</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>16.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>304.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>3433</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>amc rebel sst</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>17.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>302.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>3449</td>\n",
-       "      <td>10.5</td>\n",
-       "      <td>70</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>ford torino</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
-       "0  18.0          8         307.0       130.0    3504          12.0   \n",
-       "1  15.0          8         350.0       165.0    3693          11.5   \n",
-       "2  18.0          8         318.0       150.0    3436          11.0   \n",
-       "3  16.0          8         304.0       150.0    3433          12.0   \n",
-       "4  17.0          8         302.0       140.0    3449          10.5   \n",
-       "\n",
-       "   model_year origin                       name  \n",
-       "0          70    usa  chevrolet chevelle malibu  \n",
-       "1          70    usa          buick skylark 320  \n",
-       "2          70    usa         plymouth satellite  \n",
-       "3          70    usa              amc rebel sst  \n",
-       "4          70    usa                ford torino  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "df=sns.load_dataset('mpg')\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the example below, we assume the data for model building is available in a pandas DataFrame. This DataFrame should contain a an ID column, a target column (e.g. “**mpg**”) and a number of candidate predictors (features) to build a model with.\n",
-    "\n",
-    "***\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "mpg             float64\n",
-       "cylinders         int64\n",
-       "displacement    float64\n",
-       "horsepower      float64\n",
-       "weight            int64\n",
-       "acceleration    float64\n",
-       "model_year        int64\n",
-       "origin           object\n",
-       "name             object\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.dtypes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "it is required to set all category vars to object dtype\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.loc[:, df.dtypes == 'category'] = (df.select_dtypes(['category']).apply(lambda x: x.astype('object')))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data preprocessing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### The first part focusses on preparing the predictors for modelling by:\n",
-    "\n",
-    "1. Defining the ID column, the target, discrete and contineous variables\n",
-    "\n",
-    "2. Splitting the dataset into training, selection and validation datasets.\n",
-    "\n",
-    "3. Binning continuous variables into discrete intervals\n",
-    "\n",
-    "4. Replacing missing values of both categorical and continuous variables (which are now binned) with an additional “Missing” bin/category\n",
-    "\n",
-    "5. Regrouping categories in new category “other”\n",
-    "\n",
-    "6. Replacing bins/categories with their corresponding average of the target values\n",
-    "\n",
-    "*Disclaimer*: Cobra's Preprocesser is valid only if the original data does not contain extreme irregularities, such as outliers or very skewed distributions. This should always be checked beforehand by its user."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this toy dataset, the index will serve as ID,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"id\"] = df.index + 1\n",
-    "id_col = \"id\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "and MPG is the target,\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    398.000000\n",
-       "mean      23.514573\n",
-       "std        7.815984\n",
-       "min        9.000000\n",
-       "25%       17.500000\n",
-       "50%       23.000000\n",
-       "75%       29.000000\n",
-       "max       46.600000\n",
-       "Name: mpg, dtype: float64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "target_col = \"mpg\"\n",
-    "df[target_col].describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finding out which variables are categorical (\"discrete\") and which are continous:\n",
-    "\n",
-    "\n",
-    " => discrete are definitely those that contain strings:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['origin', 'name']\n",
-      "\n",
-      "origin\n",
-      "['usa', 'japan', 'europe'] \n",
-      "\n",
-      "name\n",
-      "['chevrolet chevelle malibu', 'buick skylark 320', 'plymouth satellite', 'amc rebel sst', 'ford torino', 'ford galaxie 500', 'chevrolet impala', 'plymouth fury iii', 'pontiac catalina', 'amc ambassador dpl', 'dodge challenger se', \"plymouth 'cuda 340\", 'chevrolet monte carlo', 'buick estate wagon (sw)', 'toyota corona mark ii', 'plymouth duster', 'amc hornet', 'ford maverick', 'datsun pl510', 'volkswagen 1131 deluxe sedan', 'peugeot 504', 'audi 100 ls', 'saab 99e', 'bmw 2002', 'amc gremlin', 'ford f250', 'chevy c20', 'dodge d200', 'hi 1200d', 'chevrolet vega 2300', 'toyota corona', 'ford pinto', 'plymouth satellite custom', 'ford torino 500', 'amc matador', 'pontiac catalina brougham', 'dodge monaco (sw)', 'ford country squire (sw)', 'pontiac safari (sw)', 'amc hornet sportabout (sw)', 'chevrolet vega (sw)', 'pontiac firebird', 'ford mustang', 'mercury capri 2000', 'opel 1900', 'peugeot 304', 'fiat 124b', 'toyota corolla 1200', 'datsun 1200', 'volkswagen model 111', 'plymouth cricket', 'toyota corona hardtop', 'dodge colt hardtop', 'volkswagen type 3', 'chevrolet vega', 'ford pinto runabout', 'amc ambassador sst', 'mercury marquis', 'buick lesabre custom', 'oldsmobile delta 88 royale', 'chrysler newport royal', 'mazda rx2 coupe', 'amc matador (sw)', 'chevrolet chevelle concours (sw)', 'ford gran torino (sw)', 'plymouth satellite custom (sw)', 'volvo 145e (sw)', 'volkswagen 411 (sw)', 'peugeot 504 (sw)', 'renault 12 (sw)', 'ford pinto (sw)', 'datsun 510 (sw)', 'toyouta corona mark ii (sw)', 'dodge colt (sw)', 'toyota corolla 1600 (sw)', 'buick century 350', 'chevrolet malibu', 'ford gran torino', 'dodge coronet custom', 'mercury marquis brougham', 'chevrolet caprice classic', 'ford ltd', 'plymouth fury gran sedan', 'chrysler new yorker brougham', 'buick electra 225 custom', 'amc ambassador brougham', 'plymouth valiant', 'chevrolet nova custom', 'volkswagen super beetle', 'ford country', 'plymouth custom suburb', 'oldsmobile vista cruiser', 'toyota carina', 'datsun 610', 'maxda rx3', 'mercury capri v6', 'fiat 124 sport coupe', 'chevrolet monte carlo s', 'pontiac grand prix', 'fiat 128', 'opel manta', 'audi 100ls', 'volvo 144ea', 'dodge dart custom', 'saab 99le', 'toyota mark ii', 'oldsmobile omega', 'chevrolet nova', 'datsun b210', 'chevrolet chevelle malibu classic', 'plymouth satellite sebring', 'buick century luxus (sw)', 'dodge coronet custom (sw)', 'audi fox', 'volkswagen dasher', 'datsun 710', 'dodge colt', 'fiat 124 tc', 'honda civic', 'subaru', 'fiat x1.9', 'plymouth valiant custom', 'mercury monarch', 'chevrolet bel air', 'plymouth grand fury', 'buick century', 'chevroelt chevelle malibu', 'plymouth fury', 'buick skyhawk', 'chevrolet monza 2+2', 'ford mustang ii', 'toyota corolla', 'pontiac astro', 'volkswagen rabbit', 'amc pacer', 'volvo 244dl', 'honda civic cvcc', 'fiat 131', 'capri ii', 'renault 12tl', 'dodge coronet brougham', 'chevrolet chevette', 'chevrolet woody', 'vw rabbit', 'dodge aspen se', 'ford granada ghia', 'pontiac ventura sj', 'amc pacer d/l', 'datsun b-210', 'volvo 245', 'plymouth volare premier v8', 'mercedes-benz 280s', 'cadillac seville', 'chevy c10', 'ford f108', 'dodge d100', 'honda accord cvcc', 'buick opel isuzu deluxe', 'renault 5 gtl', 'plymouth arrow gs', 'datsun f-10 hatchback', 'oldsmobile cutlass supreme', 'dodge monaco brougham', 'mercury cougar brougham', 'chevrolet concours', 'buick skylark', 'plymouth volare custom', 'ford granada', 'pontiac grand prix lj', 'chevrolet monte carlo landau', 'chrysler cordoba', 'ford thunderbird', 'volkswagen rabbit custom', 'pontiac sunbird coupe', 'toyota corolla liftback', 'ford mustang ii 2+2', 'dodge colt m/m', 'subaru dl', 'datsun 810', 'bmw 320i', 'mazda rx-4', 'volkswagen rabbit custom diesel', 'ford fiesta', 'mazda glc deluxe', 'datsun b210 gx', 'oldsmobile cutlass salon brougham', 'dodge diplomat', 'mercury monarch ghia', 'pontiac phoenix lj', 'ford fairmont (auto)', 'ford fairmont (man)', 'plymouth volare', 'amc concord', 'buick century special', 'mercury zephyr', 'dodge aspen', 'amc concord d/l', 'buick regal sport coupe (turbo)', 'ford futura', 'dodge magnum xe', 'datsun 510', 'dodge omni', 'toyota celica gt liftback', 'plymouth sapporo', 'oldsmobile starfire sx', 'datsun 200-sx', 'audi 5000', 'volvo 264gl', 'saab 99gle', 'peugeot 604sl', 'volkswagen scirocco', 'honda accord lx', 'pontiac lemans v6', 'mercury zephyr 6', 'ford fairmont 4', 'amc concord dl 6', 'dodge aspen 6', 'ford ltd landau', 'mercury grand marquis', 'dodge st. regis', 'chevrolet malibu classic (sw)', 'chrysler lebaron town @ country (sw)', 'vw rabbit custom', 'maxda glc deluxe', 'dodge colt hatchback custom', 'amc spirit dl', 'mercedes benz 300d', 'cadillac eldorado', 'plymouth horizon', 'plymouth horizon tc3', 'datsun 210', 'fiat strada custom', 'buick skylark limited', 'chevrolet citation', 'oldsmobile omega brougham', 'pontiac phoenix', 'toyota corolla tercel', 'datsun 310', 'ford fairmont', 'audi 4000', 'toyota corona liftback', 'mazda 626', 'datsun 510 hatchback', 'mazda glc', 'vw rabbit c (diesel)', 'vw dasher (diesel)', 'audi 5000s (diesel)', 'mercedes-benz 240d', 'honda civic 1500 gl', 'renault lecar deluxe', 'vokswagen rabbit', 'datsun 280-zx', 'mazda rx-7 gs', 'triumph tr7 coupe', 'ford mustang cobra', 'honda accord', 'plymouth reliant', 'dodge aries wagon (sw)', 'toyota starlet', 'plymouth champ', 'honda civic 1300', 'datsun 210 mpg', 'toyota tercel', 'mazda glc 4', 'plymouth horizon 4', 'ford escort 4w', 'ford escort 2h', 'volkswagen jetta', 'renault 18i', 'honda prelude', 'datsun 200sx', 'peugeot 505s turbo diesel', 'volvo diesel', 'toyota cressida', 'datsun 810 maxima', 'oldsmobile cutlass ls', 'ford granada gl', 'chrysler lebaron salon', 'chevrolet cavalier', 'chevrolet cavalier wagon', 'chevrolet cavalier 2-door', 'pontiac j2000 se hatchback', 'dodge aries se', 'ford fairmont futura', 'amc concord dl', 'volkswagen rabbit l', 'mazda glc custom l', 'mazda glc custom', 'plymouth horizon miser', 'mercury lynx l', 'nissan stanza xe', 'honda civic (auto)', 'datsun 310 gx', 'buick century limited', 'oldsmobile cutlass ciera (diesel)', 'chrysler lebaron medallion', 'ford granada l', 'toyota celica gt', 'dodge charger 2.2', 'chevrolet camaro', 'ford mustang gl', 'vw pickup', 'dodge rampage', 'ford ranger', 'chevy s-10'] \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "col_dtypes = df.dtypes\n",
-    "discrete_vars = [col for col in col_dtypes[col_dtypes==object].index.tolist() if col not in [id_col, target_col]] \n",
-    "print(discrete_vars)\n",
-    "print()\n",
-    "for col in discrete_vars:\n",
-    "    print(col)\n",
-    "    print(df[col].unique().tolist() ,\"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we also check for numerical columns that only contain a few different values, thus to be interpreted as discrete, categorical variables\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cylinders\n",
-      "[8, 4, 6, 3, 5] \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for col in df.columns:\n",
-    "    if col not in discrete_vars and col not in [id_col, target_col]: # if we didn't mark it as discrete already because it was string typed, or also excluding it if it is the target:\n",
-    "        val_counts = df[col].value_counts()\n",
-    "        if len(val_counts) > 1 and len(val_counts) <= 10: # The column contains less than 10 different values. \n",
-    "            print(col)\n",
-    "            print(df[col].unique().tolist() ,\"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By taking a look at the printed variables, it is clear that we have to include those in the list of discrete variables. This can be done as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['origin', 'name', 'cylinders']"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "discrete_vars.extend([\"cylinders\"])\n",
-    "discrete_vars"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The remaining variables can be labelled continous predictors, without including the target variable.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['acceleration', 'displacement', 'weight', 'model_year', 'horsepower']"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "continuous_vars = list(set(df.columns)\n",
-    "                       - set(discrete_vars) \n",
-    "                       - set([id_col, target_col]))\n",
-    "continuous_vars                "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we can prepare **Cobra's Preprocessor**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The target encoder's additive smoothing weight is set to 0. This disables smoothing and may make the encoding prone to overfitting.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# using all Cobra's default parameters for preprocessing for now:\n",
-    "preprocessor = PreProcessor.from_params(\n",
-    "    model_type=\"regression\"\n",
-    ")\n",
-    "\n",
-    "# These are the options though:\n",
-    "# --> help(PreProcessor.from_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "split data into train-selection-validation set:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from cobra.preprocessing import PreProcessor\n",
-    "basetable = preprocessor.train_selection_validation_split(\n",
-    "                data=df,\n",
-    "                train_prop=0.6,\n",
-    "                selection_prop=0.2,\n",
-    "                validation_prop=0.2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And fit the preprocessor pipeline:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d03dde738f36499bbce9c22a9399e6e8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Computing discretization bins...:   0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b0a50374b4ab414fbbcc421bea7d42eb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Discretizing columns...:   0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5b059186f33645e490fdbae82a827f76",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Fitting category regrouping...:   0%|          | 0/3 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2cb8fe6dfb444fe5ad4bd322b90be832",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Fitting target encoding...:   0%|          | 0/8 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Fit the preprocessor pipeline:\n",
-    "preprocessor.fit(basetable[basetable[\"split\"] == \"train\"],\n",
-    "                 continuous_vars=continuous_vars,\n",
-    "                 discrete_vars=discrete_vars,\n",
-    "                 target_column_name=target_col)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This pipeline can now be performed on the basetable!!\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5916bf4864994cdfbd85308022610f3c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Discretizing columns...:   0%|          | 0/5 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "76a527fd75d94b418469db865ab15a35",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Applying target encoding...:   0%|          | 0/8 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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>mpg</th>\n",
-       "      <th>cylinders</th>\n",
-       "      <th>displacement</th>\n",
-       "      <th>horsepower</th>\n",
-       "      <th>weight</th>\n",
-       "      <th>acceleration</th>\n",
-       "      <th>model_year</th>\n",
-       "      <th>origin</th>\n",
-       "      <th>name</th>\n",
-       "      <th>id</th>\n",
-       "      <th>split</th>\n",
-       "      <th>acceleration_bin</th>\n",
-       "      <th>displacement_bin</th>\n",
-       "      <th>weight_bin</th>\n",
-       "      <th>model_year_bin</th>\n",
-       "      <th>horsepower_bin</th>\n",
-       "      <th>origin_processed</th>\n",
-       "      <th>name_processed</th>\n",
-       "      <th>cylinders_processed</th>\n",
-       "      <th>origin_enc</th>\n",
-       "      <th>name_enc</th>\n",
-       "      <th>cylinders_enc</th>\n",
-       "      <th>acceleration_enc</th>\n",
-       "      <th>displacement_enc</th>\n",
-       "      <th>weight_enc</th>\n",
-       "      <th>model_year_enc</th>\n",
-       "      <th>horsepower_enc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>18.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>307.0</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>3504</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>chevrolet chevelle malibu</td>\n",
-       "      <td>1</td>\n",
-       "      <td>train</td>\n",
-       "      <td>8.0 - 12.2</td>\n",
-       "      <td>304.0 - 350.0</td>\n",
-       "      <td>3402.0 - 3760.0</td>\n",
-       "      <td>70.0 - 71.0</td>\n",
-       "      <td>110.0 - 138.0</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>chevrolet chevelle malibu</td>\n",
-       "      <td>8</td>\n",
-       "      <td>20.119178</td>\n",
-       "      <td>17.50</td>\n",
-       "      <td>15.146552</td>\n",
-       "      <td>15.672</td>\n",
-       "      <td>15.858333</td>\n",
-       "      <td>18.108333</td>\n",
-       "      <td>20.529412</td>\n",
-       "      <td>19.230000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>15.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>350.0</td>\n",
-       "      <td>165.0</td>\n",
-       "      <td>3693</td>\n",
-       "      <td>11.5</td>\n",
-       "      <td>70</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>buick skylark 320</td>\n",
-       "      <td>2</td>\n",
-       "      <td>train</td>\n",
-       "      <td>8.0 - 12.2</td>\n",
-       "      <td>304.0 - 350.0</td>\n",
-       "      <td>3402.0 - 3760.0</td>\n",
-       "      <td>70.0 - 71.0</td>\n",
-       "      <td>159.0 - 220.0</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>buick skylark 320</td>\n",
-       "      <td>8</td>\n",
-       "      <td>20.119178</td>\n",
-       "      <td>15.00</td>\n",
-       "      <td>15.146552</td>\n",
-       "      <td>15.672</td>\n",
-       "      <td>15.858333</td>\n",
-       "      <td>18.108333</td>\n",
-       "      <td>20.529412</td>\n",
-       "      <td>13.687500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>18.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>318.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>3436</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>plymouth satellite</td>\n",
-       "      <td>3</td>\n",
-       "      <td>selection</td>\n",
-       "      <td>8.0 - 12.2</td>\n",
-       "      <td>304.0 - 350.0</td>\n",
-       "      <td>3402.0 - 3760.0</td>\n",
-       "      <td>70.0 - 71.0</td>\n",
-       "      <td>138.0 - 159.0</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>plymouth satellite</td>\n",
-       "      <td>8</td>\n",
-       "      <td>20.119178</td>\n",
-       "      <td>23.69</td>\n",
-       "      <td>15.146552</td>\n",
-       "      <td>15.672</td>\n",
-       "      <td>15.858333</td>\n",
-       "      <td>18.108333</td>\n",
-       "      <td>20.529412</td>\n",
-       "      <td>15.695833</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>16.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>304.0</td>\n",
-       "      <td>150.0</td>\n",
-       "      <td>3433</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>amc rebel sst</td>\n",
-       "      <td>4</td>\n",
-       "      <td>validation</td>\n",
-       "      <td>8.0 - 12.2</td>\n",
-       "      <td>250.0 - 304.0</td>\n",
-       "      <td>3402.0 - 3760.0</td>\n",
-       "      <td>70.0 - 71.0</td>\n",
-       "      <td>138.0 - 159.0</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>amc rebel sst</td>\n",
-       "      <td>8</td>\n",
-       "      <td>20.119178</td>\n",
-       "      <td>23.69</td>\n",
-       "      <td>15.146552</td>\n",
-       "      <td>15.672</td>\n",
-       "      <td>15.882353</td>\n",
-       "      <td>18.108333</td>\n",
-       "      <td>20.529412</td>\n",
-       "      <td>15.695833</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>17.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>302.0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>3449</td>\n",
-       "      <td>10.5</td>\n",
-       "      <td>70</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>ford torino</td>\n",
-       "      <td>5</td>\n",
-       "      <td>validation</td>\n",
-       "      <td>8.0 - 12.2</td>\n",
-       "      <td>250.0 - 304.0</td>\n",
-       "      <td>3402.0 - 3760.0</td>\n",
-       "      <td>70.0 - 71.0</td>\n",
-       "      <td>138.0 - 159.0</td>\n",
-       "      <td>usa</td>\n",
-       "      <td>ford torino</td>\n",
-       "      <td>8</td>\n",
-       "      <td>20.119178</td>\n",
-       "      <td>23.69</td>\n",
-       "      <td>15.146552</td>\n",
-       "      <td>15.672</td>\n",
-       "      <td>15.882353</td>\n",
-       "      <td>18.108333</td>\n",
-       "      <td>20.529412</td>\n",
-       "      <td>15.695833</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
-       "0  18.0          8         307.0       130.0    3504          12.0   \n",
-       "1  15.0          8         350.0       165.0    3693          11.5   \n",
-       "2  18.0          8         318.0       150.0    3436          11.0   \n",
-       "3  16.0          8         304.0       150.0    3433          12.0   \n",
-       "4  17.0          8         302.0       140.0    3449          10.5   \n",
-       "\n",
-       "   model_year origin                       name  id       split  \\\n",
-       "0          70    usa  chevrolet chevelle malibu   1       train   \n",
-       "1          70    usa          buick skylark 320   2       train   \n",
-       "2          70    usa         plymouth satellite   3   selection   \n",
-       "3          70    usa              amc rebel sst   4  validation   \n",
-       "4          70    usa                ford torino   5  validation   \n",
-       "\n",
-       "  acceleration_bin displacement_bin       weight_bin model_year_bin  \\\n",
-       "0       8.0 - 12.2    304.0 - 350.0  3402.0 - 3760.0    70.0 - 71.0   \n",
-       "1       8.0 - 12.2    304.0 - 350.0  3402.0 - 3760.0    70.0 - 71.0   \n",
-       "2       8.0 - 12.2    304.0 - 350.0  3402.0 - 3760.0    70.0 - 71.0   \n",
-       "3       8.0 - 12.2    250.0 - 304.0  3402.0 - 3760.0    70.0 - 71.0   \n",
-       "4       8.0 - 12.2    250.0 - 304.0  3402.0 - 3760.0    70.0 - 71.0   \n",
-       "\n",
-       "  horsepower_bin origin_processed             name_processed  \\\n",
-       "0  110.0 - 138.0              usa  chevrolet chevelle malibu   \n",
-       "1  159.0 - 220.0              usa          buick skylark 320   \n",
-       "2  138.0 - 159.0              usa         plymouth satellite   \n",
-       "3  138.0 - 159.0              usa              amc rebel sst   \n",
-       "4  138.0 - 159.0              usa                ford torino   \n",
-       "\n",
-       "  cylinders_processed  origin_enc  name_enc  cylinders_enc  acceleration_enc  \\\n",
-       "0                   8   20.119178     17.50      15.146552            15.672   \n",
-       "1                   8   20.119178     15.00      15.146552            15.672   \n",
-       "2                   8   20.119178     23.69      15.146552            15.672   \n",
-       "3                   8   20.119178     23.69      15.146552            15.672   \n",
-       "4                   8   20.119178     23.69      15.146552            15.672   \n",
-       "\n",
-       "   displacement_enc  weight_enc  model_year_enc  horsepower_enc  \n",
-       "0         15.858333   18.108333       20.529412       19.230000  \n",
-       "1         15.858333   18.108333       20.529412       13.687500  \n",
-       "2         15.858333   18.108333       20.529412       15.695833  \n",
-       "3         15.882353   18.108333       20.529412       15.695833  \n",
-       "4         15.882353   18.108333       20.529412       15.695833  "
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "basetable = preprocessor.transform(basetable,\n",
-    "                                   continuous_vars=continuous_vars,\n",
-    "                                   discrete_vars=discrete_vars)\n",
-    "\n",
-    "\n",
-    "basetable.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Predictor Insights Graphs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we can the create the so-called Predictor Insights Graphs (PIGs in short). These are graphs that represents the insights of the relationship between a single predictor and the target. More specifically, the predictor is binned into groups, and we represent group size in bars and group target mean in a colored line. Moreover, we have the option to force order of predictor values. First, we compute the output needed to plot the PIG."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "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>variable</th>\n",
-       "      <th>label</th>\n",
-       "      <th>pop_size</th>\n",
-       "      <th>global_avg_target</th>\n",
-       "      <th>avg_target</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>acceleration</td>\n",
-       "      <td>8.0 - 12.2</td>\n",
-       "      <td>0.151899</td>\n",
-       "      <td>23.262025</td>\n",
-       "      <td>16.983333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>acceleration</td>\n",
-       "      <td>12.2 - 13.5</td>\n",
-       "      <td>0.139241</td>\n",
-       "      <td>23.262025</td>\n",
-       "      <td>18.972727</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>acceleration</td>\n",
-       "      <td>13.5 - 14.2</td>\n",
-       "      <td>0.063291</td>\n",
-       "      <td>23.262025</td>\n",
-       "      <td>21.200000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>acceleration</td>\n",
-       "      <td>14.2 - 14.8</td>\n",
-       "      <td>0.063291</td>\n",
-       "      <td>23.262025</td>\n",
-       "      <td>24.440000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>acceleration</td>\n",
-       "      <td>14.8 - 15.5</td>\n",
-       "      <td>0.139241</td>\n",
-       "      <td>23.262025</td>\n",
-       "      <td>24.772727</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>weight</td>\n",
-       "      <td>2742.0 - 3007.0</td>\n",
-       "      <td>0.101266</td>\n",
-       "      <td>23.262025</td>\n",
-       "      <td>23.625000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>weight</td>\n",
-       "      <td>3007.0 - 3402.0</td>\n",
-       "      <td>0.177215</td>\n",
-       "      <td>23.262025</td>\n",
-       "      <td>21.407143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>weight</td>\n",
-       "      <td>3402.0 - 3760.0</td>\n",
-       "      <td>0.088608</td>\n",
-       "      <td>23.262025</td>\n",
-       "      <td>16.971429</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>weight</td>\n",
-       "      <td>3760.0 - 4225.0</td>\n",
-       "      <td>0.088608</td>\n",
-       "      <td>23.262025</td>\n",
-       "      <td>16.242857</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>weight</td>\n",
-       "      <td>4225.0 - 5140.0</td>\n",
-       "      <td>0.113924</td>\n",
-       "      <td>23.262025</td>\n",
-       "      <td>12.777778</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>132 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        variable            label  pop_size  global_avg_target  avg_target\n",
-       "0   acceleration       8.0 - 12.2  0.151899          23.262025   16.983333\n",
-       "1   acceleration      12.2 - 13.5  0.139241          23.262025   18.972727\n",
-       "2   acceleration      13.5 - 14.2  0.063291          23.262025   21.200000\n",
-       "3   acceleration      14.2 - 14.8  0.063291          23.262025   24.440000\n",
-       "4   acceleration      14.8 - 15.5  0.139241          23.262025   24.772727\n",
-       "..           ...              ...       ...                ...         ...\n",
-       "5         weight  2742.0 - 3007.0  0.101266          23.262025   23.625000\n",
-       "6         weight  3007.0 - 3402.0  0.177215          23.262025   21.407143\n",
-       "7         weight  3402.0 - 3760.0  0.088608          23.262025   16.971429\n",
-       "8         weight  3760.0 - 4225.0  0.088608          23.262025   16.242857\n",
-       "9         weight  4225.0 - 5140.0  0.113924          23.262025   12.777778\n",
-       "\n",
-       "[132 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from cobra.evaluation import generate_pig_tables\n",
-    "predictor_list = [col for col in basetable.columns\n",
-    "                  if col.endswith(\"_bin\") or col.endswith(\"_processed\")]\n",
-    "pig_tables = generate_pig_tables(basetable[basetable[\"split\"] == \"selection\"],\n",
-    "                                 id_column_name=id_col,\n",
-    "                                 target_column_name=target_col,\n",
-    "                                 preprocessed_predictors=predictor_list)\n",
-    "pig_tables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then, we can plot a PIG graph for each of the predictors in the basetable. For instance, for the variable acceleration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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SkpJ4++23HclFWVkZzz33HNnZ2YwbN47GjRvX+tiJEydisVj48MMPHRvfwfHXed26dURERFSZ6Hy6cdZFXd9Pffv2pXXr1mzatInPPvusymM++OADkpOTHfuS1KXu6NGjCQ4O5ueff+brr7+uUnfZsmX8+9//ZteuXXTs2LHWa6mPNiqczvu6YthSYWHhKYc6nc77QUSkIWgolMg55PLLL2fu3LkkJSUxceLEWsflV+jYsSNPPPEEr7zyChMnTqRdu3ZERESQkJDAvn378PLy4p133nF8aLn55ptZvnw5r732GnPmzCE8PJyUlBS2b99Oo0aNCA4OJjMzk6ysLCIjI+v12iqGbCxcuJB77rmHIUOGcN111zFq1Chmz57NmDFj6NWrF25ubmzZsoXMzEzatGnDnj17nJ6U7OXlxSuvvMKf//xnnnjiCb766isiIiLYunUr6enphIeHc+jQoSrDy6ZMmcJNN93EzJkzWbJkCR07dsRqtbJ27VpKS0urLT9a23WcSkREBJ988gm//fYb0dHRbN26lYMHDxIXF+eYsF+bDh06MHnyZF588UVuvfVWunbtSmhoKDt37mTfvn00adKEt99+u8qqUxVxPv/88/z44488+uijtU4gP111fT9VJMG33norr776KjNmzCAqKorExET27NlD06ZNeemllwDqVNfb25u3336bu+++mxdeeIHPP/+ctm3bkpWVxebNmwF48skniY+Pr/Va6qONCiEhIXV+XwcGBhIYGMjhw4e54YYb6NixI3//+99rbP903g8iIg3hou2xSCyB+w5AwCYwbzD+ve+AUX6xsdvtfPPNNy5bEaS0tLTK7sb1JSUlhdjY2Cq7857rBg8e7Nghee/evbXWGzp0KNOnTwfg1ltv5YsvvmDIkCEcOnSIxYsXY7PZuPLKK5k1a5ZjiAfAoEGD+Ne//kX37t05cOAAK1asoKysjBtuuIEffviBkSNHAtW/ga4PcXFxPProowQFBbFixQo2btwIwEsvvcSkSZMICwtj9erVbNmyhebNm/PMM88wc+ZMGjVqxObNmx29DafSt29fvv32W4YMGcL+/ftZtGgRYWFh/Oc//6F9+/YA+Pv7O+o3bdqU6dOnM2nSJIKCgli9ejXbtm0jPj6eKVOm8Pbbb1eZWF3bdTgT10cffYTFYmHRokVYLBbuvfdevv7665P2VlS46aab+OqrrxgyZAh79+5l4cKF2O12br31VmbNmlVtGdG7776bwYMHk5+fz4oVK9i3b59TcdbF6byf2rVrx4wZM7jmmmvIz89n4cKFHDlyhAkTJvDdd99V2WOhLnV79OjBrFmzuOaaaygtLWXJkiUcOnSIgQMH8t///pfbbrvtlNdTH21UqOv72mQy8c9//pPWrVuzbds2R69Gber6fhARaQgm++mud3ce+/UIXJ0EZTaovIq4O+Buhu+i4PLqK1lesNauXctNN93E9u3bTzpR+GyZOXMmb731Vq1jm09XSkoKw4YNY968efX+Te3Z9uSTT1JeXs7rr79e4/mcnBx8fHxq3HvhYpaVlcWRI0eIiIio8WczduxYdu/ezYYNGxwJ3Nk2Y8YMJk+ezNVXX33WJ4iLiIi40kXXY5FYYiQVRSckFWAcF9mM8xdTz4Wrc0tXP//5qEmTJkoqarBjxw5GjRrFHXfcQWlpaZVz06dPZ9euXVxyySUNllSIiIhcTC66xOKNdKOn4mTKbPBWzStDnraKYTm//fYbQ4cOpWvXrrzyyivs2rWLCRMm0KVLF+655x6Kioocj5k2bRrDhg2ja9eu3HDDDY6VfcDYIOkvf/kLPXv2pEOHDowfP96xy2rFc82dO5dLL72U7t27c88999Q4lCQlJYWbb74ZgPbt27NmzRrKysp49dVXGThwIO3bt2fIkCH873//czxm6NCh/POf/6R///6MGjWK8vJytm3bxrXXXkunTp24/vrreeedd7jpppscj1m/fj1XX301nTp1YvTo0Y6JtWvWrGHy5Mmkp6cTGxvrWCaxwjfffMOgQYOqJB+zZ8/mkksuwWq1nvTncKLY2NgqqwLNmDGDgQMHOo53797NzTffTKdOnbj00kv57LPPak16nnzySZ5//nnuvfdeOnXqxLhx46pMni4tLeXFF1+kT58+9O7dmwcffNAxnrri9Xn//ffp2bMnkydPrvE5CgsLmTRpEh07duSKK66o8vpXHgp100038f7773PHHXfQuXNnxowZU+sqURe6vn370r59e9avX8+gQYO45557eOCBBxg1ahRPPfUUwcHBPPvss64OU0RE5IJ0wSQWb6SD/yYwbTj57cPM6j0VJyoD3s88dVv+m4znrYtPPvmEDz74gOeee47//Oc//OUvf+Hxxx/nk08+Yd26dXz//feAMSb5nXfeYfLkycycOZOBAwdyyy23OHZc/etf/0p5eTnffvsts2bNIiwsrNoHpo8//pjXX3+djz76iC1btvDvf/+7WjzNmjXjvffeA4xlFrt27conn3zCwoULeffdd5kzZw5XXnklU6ZMqbIO/48//sinn37KG2+8wdGjR7nzzjuJj49n5syZXHHFFfzrX/9y1M3MzOSuu+5izJgx/PTTT9x///1MmTKFhQsX0rVrV/72t78RHBzM8uXLq+2YfNlll5GVlVXlQ/WcOXMYOXIkFovFqZ+DM4qLi7nzzjvp0qULP/74I0899RSff/45X331Va2PmT59OtHR0cycOZPevXtz1113OZKHN998k82bN/Pxxx/z5ZdfYrfbufvuu6skKuvXr+f777/nrrvuqrH9hQsX0qZNG2bNmkX//v259dZbOXLkSI11//WvfzF69GhmzJhB69at+fvf/97ge1KcC9zd3fniiy947LHHCAsLY+PGjSxduhSr1crtt9/Ojz/+WO+T0kVERMRwwawK9UY6FDTw3OMCm/G8j4aeum6Fe++9l7i4OOLi4njppZcYPXo0ffv2BYw1x5OSkgD49NNPueuuuxg+fLjjcStXrmT69Oncf//9DBkyhBEjRjg+iP/pT3/izjvvrPLBddKkSXTu3BmAMWPGsHXr1mrxWCwWGjUyJpQEBQXh5uZGTEwML774Il26dAHgnnvu4f3332fv3r2OyZJjxoxxLGE6bdo0vLy8ePrpp3FzcyM6OpqNGzeSmZkJwNdff03v3r255ZZbAGO1mqSkJD7//HOGDh2Kv78/ZrPZsUFVZU2aNKFv377MnTuXzp07U1RUxNKlSx1JkjM/B2f89NNPNGrUiEceeQSAVq1a8dBDD/H+++9X6XmpLCoqisceewwwejB+++03fv75Z6677jq++uor/u///o927doB8M9//pPevXuzYcMGx1KUN998My1atKg1pvbt2/PQQw8BRiK5YMECfvrppyorFFUYOHAgEyZMAOC+++5j3LhxjlWQLjZ+fn78+c9/5s9//rOrQwFgwoQJjtdGRETkQnbBJBaPhsJzhxo2ufAz1y2pAGjevLnjvqenZ5UPfl5eXo5x4YmJibz55pu88847jvOlpaWOD6U33HADs2fPZuPGjezdu5dt27YBVPmWuvKHVj8/P8rLy52Kcfjw4axYsYJXXnmFpKQkduzYAVTdzCkiIsJxf9euXcTHx1eZ+N2lSxfmz58PGOv3L1u2jK5duzrOl5eX06RJE6fiGT16NB988AF//etfWbx4MYGBgY7Ns5z5OTgjKSmJPXv2VInRZrNRWlpKaWmpY7nWyirXNZvNtGvXjqSkJJKTkykrK+NPf/pTlfolJSXs3bvX8RpW/hnWpEOHDlXaj4+PJzExsca6lb+F9/PzA3D69RYRERGpDxdUYuHMh/z7DsCnpxgO5Q7cFQxTa/8y+bSduOpSbfsUWK1WnnjiCfr371+l3MfHB5vNxu23386RI0cYNWoUQ4cOpaysjEmTJlWpe+JOuM5+i//WW28xbdo0rrrqKsaNG8ezzz7L0KFDq9Sp2HwLjF6PE9uufFxeXs7o0aO57777qtQ51R4NFS699FKeffZZdu7cyZw5c7j88ssxmUxO/xxqUzn5KC8vp1evXjz//PPV6tW2UtaJ5VarFZPJ5Gj3yy+/rLKsKRg9MBXDmSr/DGty4s/HZrNVe00r1FSuSfEiIiLSkC6YxMJZj4bC59knn8DtboaH69gTUd9at25NWlpalWVSn332WXr16kXbtm1Zt24dy5YtIyQkBMCxU+zpfJis2IW4wrfffsvTTz/NFVdcAcCePXtO2nbbtm1ZsGABVqvVsd7/9u3bq1zL+vXrq1zL119/TUZGBg8//HC15z+Rn58fAwcOZM6cOSxbtowvv/zSEVddfg7u7u4UFBQ4jivviNu6dWvmz59PRESEI2GYM2cOy5cvZ8qUKTXG9ccffzjuW61Wdu7cSf/+/YmMjMRisZCbm+vodcjPz+fxxx/noYcecvQonEpCQoLjfnl5OTt27Kgy2VxERETkXHLBTN52VrSnsU+Fj9nomajMHaP8uyijnivddtttfPnll8ycOZMDBw4wdepUvv/+e6KioggICMBsNjN79mwOHjzInDlzHBOwT1xi0xkVS2/u2LGDkpISAgMDWbRoEcnJyWzYsMGxK3BtbY8ePZqioiJeeuklkpKSmD59OrNnz3acv/HGG/njjz9444032LdvH3PmzOG1115zzNfw8fEhPz+fvXv31jp8Z/To0Xz++ecEBwc7PqzX9efQsWNH/vOf/7Bv3z4WLVrEjBkzHOfGjh1LaWkpTz31FImJiaxYsYIXXnjBMf+kJhs2bODTTz8lKSmJl156iaKiIkaPHo2fnx/XXHMN//jHP1i1ahWJiYk88cQTJCQk0KpVq1rbO9GmTZt4//33SUxMZMqUKZSVlTFmzBinHy8iIiLSkC66xAKMze+2tDOGOwWYjR9CgNk43tLu3Ngcb9SoUTz66KNMnTqV0aNHM3/+fN5//33i4+MJCwtzrCo1evRoPv74Y5566inc3d2rfIvurJiYGPr378+NN97I0qVLeemll0hISGD06NE88cQTjBw5ki5dujjmWpzI19eXjz76iA0bNjB27FhmzJjBmDFjHPMSIiIi+Pjjj1m5ciVXXHEFr776Kg888AA33ngjAH369CEqKoqxY8eyc+fOGp9jyJAhjp9Lhbr+HJ5++mlKSkq44oor+Pjjj3nwwQcd5/z8/Pj00085ePAgV155JU888QRXXnklDz/8cK0/t8GDB7N+/XrGjx/P9u3b+e9//+tIRCZPnkz//v15+OGHufrqqykpKeHf//53nfaeuPLKK9m0aRPjx49ny5YtfPzxx9p/QURERM5ZF+XO21K/kpOTSU9Pp0ePHo6y559/nqNHj/LKK6+4MLKz51Q7Y4uIiIhcbC7KHgupXwUFBdx6663MmTOHgwcPMm/ePH744QdGjhzp6tBEREREpIFcdJO3pf7Fx8fz7LPP8uabb5Kamkp4eDiTJ09m8ODBrg5NRERERBqIhkKJiIiIiMgZ01AoERERERE5Y0osRERERETkjCmxEBERERGRM6bEQkREREREzpgSCxEREREROWNKLERERERE5IwpsThHrFmzhtjYWMrLy09Zd8aMGQwcOPC0n2vo0KFMnz79tB/vKqtXryYhIcFlzz9nzhwyMzPrvd3z9fUQERERqUyJhZw3brnlFrKyslzy3AcPHuTBBx+kqKjIJc8vIiIicq5TYiHiBO0jKSIiInJyF1RiYYm+6rRuPcc+XmubPcc+Xuvj6io5OZlbb72Vzp07M2bMGP79738zdOjQGuumpaXx4IMP0qtXL3r37s0LL7xASUlJlTpvvfUW3bp1o3///lWG0pSVlfHqq68ycOBA2rdvz5AhQ/jf//7nVIxDhw5l2rRpXHXVVXTq1Ik77riDgwcPMmnSJDp37sz48eNJTEx01F+/fj1XX301nTp1YvTo0cyaNcvpOIYOHcpXX33F9ddfT+fOnbn66qvZsmVLrXEB3Hbbbbz33nsAfP/991x++eV06NCB3r178+yzzzqGkj355JM88cQTjB8/nt69e7Nr1y5yc3OZNGkSXbt2ZdiwYXzzzTfExsZW+Znfd999dOnShcGDB/P6669TWloKwLBhwwAYMWIEM2bMqBJbYmIisbGx7Nu3z1GWkZFBfHw8u3fvrtPrcdNNN/HWW285jlNSUoiNjWX//v0A5Ofn88QTT9C9e3cuueQSnn76aQoKCmpsS0RERKQhXVCJxbmsvLycu+++G19fX77//nvuuusupk6dWmPd0tJSbrnlFoqKivjiiy945513WLp0Ka+88oqjTnp6Ojt37mTatGk8+uijPP/886xcuRKATz75hIULF/Luu+8yZ84crrzySqZMmUJ6erpTsb777rs8/PDDfP3112zbto0rr7ySAQMGMH36dMxmM2+//TYAmZmZ3HXXXYwZM4affvqJ+++/nylTprBw4UKn45g6dSp33nkn06ZNw9PTk3/84x81xvTdd98B8Pbbb3P77bezfv16nn/+eR5++GHmzp3L888/z4wZM5g3b57jMT/++CP3338/n3zyCW3btuWRRx4hKyuL//3vfzzzzDO8//77jrp2u53777+fRo0a8f333/P666+zePFi3nzzTQBH4jZt2jRGjRpVJbbo6Gji4uKqPPe8efNo06YNbdu2PePXo7K//e1v5Obm8vXXX/Pxxx+zd+9eJk+eXOd2REREROqbEosGsnr1ag4dOsTLL79MmzZtGDNmDBMnTqyx7rJly0hLS+O1114jLi6OPn368MwzzzBt2jTy8/MBcHd35+WXX6Zt27ZceeWVjBkzhm+//RaAmJgYXnzxRbp06UJkZCT33HMPVquVvXv3OhXruHHj6N+/Px07dqRXr17ExMRw3XXXERMTw9ixY0lKSgLg66+/pnfv3txyyy20bNmSUaNGceutt/L55587Hcf48eMZPnw4cXFx3H777Wzbtq3GmJo0aQJAo0aN8PX1xcvLixdffJERI0YQERHByJEjadeuHXv27HE8Jj4+nksvvZROnTqxf/9+Vq5cycsvv0x8fDyDBg1i0qRJVV6flJQUpkyZQnR0ND169OCZZ57hq6++ory83PH8jRs3xsvLq1p8o0ePrpJYzJkzx5GAnOnrUeHAgQPMnz+ff/7zn8TFxdGhQwdeffVV5s2bR2pqap3aEhEREalvbq4O4GKxa9cuWrRoQUBAgKOsS5cu/PLLL9XqJiYm0qJFCwIDAx1l3bp1w2q1OobbNG/e3PFhF6Bdu3aOxGL48OGsWLGCV155haSkJHbs2AGAzWZzKtbIyEjHfU9PT8LDw6scVwwPSkpKYtmyZXTt2tVxvvKHcGfiqPxcfn5+2Gw2rFYrFovlpDF26NABLy8v3n33Xfbs2cOuXbvYv38/ffr0cdRp3ry54/6uXbvw8/OjdevWjrIuXbo47icmJpKXl0ePHj0cZXa7nbKyMg4dOoTZfPIcfPTo0bz11lukpqbi5ubGhg0beOmll5z+OTgjMTERu93OkCFDqp3bt28fzZo1q1N7IiIiIvXpgkosrInf13ub6358rV7asVgs1SYA1zYh2NPTs1qZ1WoFjn8YPfGDrs1mw93dHTDmXlTMkxg3bhzPPvtsrXM5auLmVvVtUduH6vLyckaPHs19991XY31n4vDw8KjWrjMTpZctW8Z9993H+PHjGTBgAPfffz/PP/98rW27ubmdtN3y8nJatmzJxx9/XO1cWFgYGRkZJ40nIiKCzp07M2/ePNzc3GjXrh0tWrQAzuz1qHjdK+77+PhUmcdSITg42Kn2RERERM6WCyqxOJe1bduW5ORk8vPz8ff3B2D79u011o2OjubAgQMcPnzY0WuxefNmLBYLLVq0IDExkZSUFAoLC/H19QVgy5YtREdHA/Dtt9/y9NNPc8UVVwA4hgfV98pGrVu3Zv369bRs2dJR9vXXX5ORkcHDDz98VuOYPn06V155JS+88AJgJAYHDhygZ8+eNdaPjo6msLCQffv20apVK4Aqw65at25NWloagYGBNGrUCDAmpn/xxRe89tprmEymU8Y0atQoFi5ciMlkYvTo0Y7yuvwcPDw8KCwsdBwnJydXibGoqAir1UpUVBQA+/fv5+WXX+aFF17Ax8fnlDGKiIiInC2aY9FA+vbtS3h4OH//+99JTExk7ty5fPHFFzXW7devH61ateKvf/0rO3fuZM2aNUyZMoVRo0bRuHFjwFhx6cknnyQhIYFvv/2WuXPncuuttwIQGBjIokWLSE5OZsOGDfz1r38FcAxhqi833ngjf/zxB2+88Qb79u1jzpw5vPbaa4SGhp6VOHx8fNi9ezf5+fkEBgayadMmdu7cye7du3nyySfJzMyste3WrVvTv39/nnrqKXbu3MnKlSt59913Hef79+9PZGQkjz32GDt37mTTpk089dRTmM1mPD09HR/ad+7cWeWDf2WXX345mzZtYv369Vx++eWO8rr8HDp06MBPP/3Eli1b2LZtG++9954jqYmOjmbAgAH89a9/5ffff2fnzp088cQTZGdnExISclo/UxEREZH6osSigZjNZt577z2ysrIYN24c77//PldddZVj+NKJdd9//31MJhPXXXcdDz30EEOGDOHFF1901ImPjyc8PJzrrruOjz/+mJdeeomOHTsC8NJLL5GQkMDo0aN54oknGDlyJF26dHGM7a8vERERfPzxx6xcuZIrrriCV199lQceeIAbb7zxrMRx66238sYbbzB16lQmTZpESEgI119/Pbfddhvu7u786U9/OmnbL7/8Mr6+vlx77bU888wzTJgwwfHzt1gsfPDBB1gsFq6//nruueceevTowZQpUwBj0vaECRN49NFHHStUnSg4OJiuXbvSsWPHKvMd6vJzuO222+jWrRsTJ07k4Ycf5u67764yFO2f//wnLVu25Pbbb2fixImEhITwwQcfnNbPU0RERKQ+meza+atBZGdns2PHDgYMGOAo+/TTT1myZAlffvmlCyO7OBw9epSVK1cycOBARzLx66+/8tprrzmWxxURERGR06ceiwZ077338vXXX3Pw4EFWrlzJ559/zsiRI10d1kXB09OTv/3tb0ydOpXk5GQ2bdrE+++/z2WXXebq0EREREQuCOqxaEALFizgnXfeYd++fTRt2pTrr7+eu+66y6mJwXLm1q9fzz//+U/H0rNjx47l4YcfrnFlKhERERGpGyUWIiIiIiJyxjQUSkREREREzpgSCxEREREROWNKLERERERE5IwpsRARERERkTOmxEJERERERM6YEgsRERERETljSiwaSEpKCrGxsezfv7/G8zNmzGDgwIH1/rxPPvkkjz32WL23KyIiIiJSmfaxaCApKSkMGzaMefPm0bJly2rni4uLKSoqokmTJvX6vPn5+QD4+/vXa7siIiIiIpW5uTqAM2W321m7bj27EnaTl5ffoM8dEOBPbExbevXscca7Z3t5eeHl5VVPkR2nhEJEREREGsJ5PxRq7br1rFu/scGTCoC8vHzWrd/I2nXrnX7MvHnzGDRoEN26dWPKlCmUl5cDVYdCrVmzhoEDBzJt2jQGDhxI7969efzxxykuLq6xzdTUVO688066detGr169mDx5MoWFhUDVoVBDhw4lNja22q3CtGnTGDZsGF27duWGG25gy5Ytp/VzEREREZGLz3mfWOxK2O3qEOoUw/Tp03nzzTf56KOPWLBgAe+9916N9bKzs5k9ezb/+te/ePHFF5k3bx4zZsyose4LL7yAm5sb33//PZ999hmbNm3io48+qlbvu+++Y/ny5Sxfvpz58+cTERHB7bffDsDChQt55513mDx5MjNnzmTgwIHccsstZGRkOH1tIiIiInLxOu8Ti/PNk08+Sffu3enVqxcPPvgg3377bY31ysvL+dvf/kZcXBzDhw9nwIABbN26tca6Bw8exN/fn4iICDp06MDUqVMZP358tXpNmjQhODiY4OBg3n77bUJCQnj00UcB+PTTT7nrrrsYPnw4rVq14t5776VDhw5Mnz693q5dRERERC5c531iERvT1tUh1CmGjh07Ou63a9eOw4cPk5OTU2PdFi1aOO77+fk5hk2d6C9/+Qtz5syhT58+/OUvf2Hnzp20bt261hi++OILVq5cydtvv42bmzHNJjExkTfffJOuXbs6bhs3bmTfvn1OX5uIiIiIXLzO+8nbvXr2AHD55G1nmc3Hc7mKBbnc3d1rrHtieW0LeA0fPpwlS5awYMECli5dyuTJk1m+fDmvvPJKtbobN27ktdde44MPPiAsLMxRbrVaeeKJJ+jfv3+V+j4+Ps5dmIiIiIhc1M77xMJkMtG7V0969+rp6lCckpCQQN++fQHYsmULwcHBZ7xy01tvvcVll13Gtddey7XXXssPP/zA008/XS2xyM7O5sEHH+SOO+5gwIABVc61bt2atLS0KkvhPvvss/Tq1YvRo0efUXwiIiIicuE774dCnW+mTJnC5s2bWblyJe+++65j8vSZSEpK4oUXXmDHjh0kJSUxb9482rdvX6WO1WrloYceolWrVtx0001kZmY6bqWlpdx22218+eWXzJw5kwMHDjB16lS+//57oqKizjg+EREREbnwnfc9FuebiRMncv/991NaWso111zDrbfeesZtPvfcc7zwwgvceuutlJaW0qdPH954440qdVJTU1m7di0A/fr1q3Luiy++YNSoUWRnZzN16lQyMjKIiori/fffJz4+/ozjExEREZELn3beFhERERGRM6ahUCIiIiIicsaUWIiIiIiIyBlTYiEiIiIiImdMiYWIiIiIiJwxJRYiIiIiInLGlFiIiIiIiMgZU2IhIiIiIiJnTImFiIiIiIicMSUWIiIiIiJyxpRYiIiIiIjIGVNiISIiIiIiZ0yJhYiIiIiInDElFiIiIiIicsaUWIiIiIiIyBlTYiEiIiIiImdMiYWIiIiIiJyxBk8sdhbD8ATw2wQtt8JracfP7S+BEQnguwnit8OvRxo6OhEREREROR0NmliU2eHy3dDCAzbHw/uR8I9U+Dob7HYYlwhBbrAuDm4JgqsSYW9JQ0YoIiIiIiKnw60hn+xgKfTyhfdbgLcZ2njB8ABYUgDN3GFXMSyLBX8LtPOGBXnw7yyYEtGQUYqIiIiISF01aI9FK0+YFmUkFXY7rCiApfkwzB9WF0JXHyOpqNDfD1YVNmSEIiIiIiJyOlw2ebv5Vui/C/r6wdWNIbUMwt2r1gl1h5RS18QnIiIiIiLOa9ChUJX9EA2HyuDeA/BwMhTZwPOENMfTBCV218QnIiIiIiLOc1li0cPX+LfIBrfsg9uD4Ii1ap0SO/hoQVwRERERkXNeg35sP1gKPx6uWtbOC0rtxuTttLKq59LKjHIRERERETm3NWhi8UcxTEiEjEoJxIYiCHYzJmpvPgqFlXotlhdAH9+GjFBERERERE5HgyYWg/yNZWRv3Qd/HIWfD8OTB+HvYca5lh7Gue1H4dU0Y6WoPzdtyAhFREREROR0NGhi4W6Cn9uAmwl674S7D8BDIfCXELCYjAndGeXQ/Q/4IhtmRhtL1F4MEkvgvgMQsAnMG4x/7ztglIuIiIiInOtMdrtd6y652K9H4OokKLNB5Wkm7oC7Gb6LgssbuSo6EREREZFTU2LhYokl0GmHsTpWbXzMsKUdRF8kvTciIiIicv7RYq4u9ka60VNxMmU2eCu9YeIRERERETkd6rFwsYBNkH+KxAKM+Sl/CzN6LaKO3cLcwGQ6+zGKiIiIiJyKEgsXM2+A030BvE3Hk4yKW7QnRHkYk9691R8lIiIiIg1EiYWLOdtjcTrC3Sv1cHhU7e0IUW+HiIjIRWHqBx+7OoQzNum+u10dgjjBzdUBXOwmBsGnmVVXgzqRBRjgB338IKnEmPCdVAK51pM8CDhUZtyWFVQ/52s+nnBU6e3whFYe4KneDhERERGpAyUWLvZoKHyeffIJ3J5m+LRV9VWhcsthb+nxRKNy0nGgFE6WdxTaYOtR43YiExDhXrWHo/Iwq6b12NuRWGJMYP8qGwps4Gc2kq1HQ7UKloiIiMj5RImFi0V7GvtUnGofi5o+ZDd2M27dfKqfK7ND8glJR0XikVgCeSdJZOxASplxW1JDb4efuWoPR0WvR7SnsXu6h5O9HTXt35FvM3pwPs/W/h0iIiIi5xMlFueAyxsZ+1S8lQ5fVvrm/qYgePg0v7l3rzSx+0R2uzGMqnIPR1Lp8ePkUjjZtI8CG2w5atxOZAaae1Sf01Fxv4nF6O1ILDGSipr27yjDSDauTtL+HSIiIiLnCyUW54hoT5jawridbSYTNHEzbj18q58vtRlDqZJq6fE42WRzG8ZjD5TC4hp6OwKO9XbkWaHYyf07GuJnIiIiIiJnRomFVONhhjZexu1EdjtkW6vP6ajo9UguPfnyuXk22FxDT0dNyjB6cJRYiIiIiJz7lFhInZhMxuTtpm7Qq4bejhIb7C89IfGoNMyqsI5L6xacpaV4RURERKR+KbGQeuVphhgv43Yiux0yy40kY2gCHHViBxU/LXsrIiIicl7QxzZpMCYThLgb+3Hc2tRY9epk3DEmsIuIiIjIuU+JhbjEo6HGUron4242VsUSERERkXOfEgtxiYr9O3zMNfdcmIBprbXUrIiIiMj5QomFuEzF/h13BRvL0FbezNsOJJfV9kgREREROdcosRCXqti/40hXsHWHKeHHzz11ELLLXRebiIiIiDhPiYWcUx4NNXbtBsixwtOHXBuPiIiIiDhHiYWcU7zM8Fbk8eOPM2FzkeviERERERHnKLGQc86YRnBZgHHfBjyQbOyBISIiIiLnLiUWcs4xmeCdyOO7Ny4vgG9zXRqSiIiIiJyCEgs5J8V6wUOV9rB4LAUKrK6LR0REREROTomFnLOebgZhx7otDpXBS2mujUdEREREaqfEQs5ZARZ4tfnx4zfSYU+x6+IRERERkdopsZBz2sQm0MfXuF9qh4dTXBuPiIiIiNRMiYWc08wmeC/y+K7cPx+B2UdcGpKIiIiI1ECJhZzzevjCHU2PHz+UDCU218UjIiIiItUpsZDzwkvh0Mhi3N9dAm9nuDYeEREREalKiYWcF4Ld4YXw48f/SIVDpa6LR0RERESqUmIh5437gqG9l3G/0AZPHHRtPCIiIiJynBILOW+4meC9FsePv8qBFQWui0dERESkJlarlf99+38kJ1dfztJqtfK/b/6PNWvXn7SN3XsS+fKrb/joX//ml9lzKCo66jiXmprGF199w6effc72HX9UedzyFavYuTOhfi6kjpRYyHlliD9c0/j48QMHwGp3XTwiIiIilZWXlzN33gJycnJrPL9+w0Zycms+VyE9PYMFvy2iR49uXD1hPKWlZSz4baHj/JJly2nfLo7LRgxj6bIVHD1qJB2FRUWkpBwkJqZN/V1QHSixkPPOaxHgfWz92U1H4dMs18YjIiIiApCTk8t338/iSF5ejeezsrLZvmMnjQMDT9rOlq3biI5qTXxcLE2bBjF82BAOJKdw+Iix5n5u7mGioloT2bw5np6e5OXlA7Bhwya6dumE2eyaj/hKLOS809ITngw7fvz3g5BT7rp4RERERAAOpaYSGdmcqyeMr3bOZrPx26LF9OvbGy8vr5O2k5aeQXh4M8exv78f/v5+pKWlO44zM7PIy8+npKQEXz9fCgoLOXQolbZtXdNbAeDmsmcWOQOPh8F/smFfKWRb4ZlDMLXFqR8nIiIicrZ0aN+u1nObNm/B28uLuNgYtm//o9Z6AEVFRfj6+lYp8/H2oaCgEIC+fXozf8FCrFYrPbp3xc/XlyVLl9G1a2eX9VaAEgs5T3mb4c3mMCHJOP4wE+5qCp18XBuXiIiIyIlyDx9m0+bfufbqCU7VLy8vx2KxVCmzWMxYrVYAoqNa0/L2W7BarXh6epKfX0BqajoDB/Rn1eo17ErYQ7OwUIYOGYS7u3u9X09tNBRKzlvjA+FSf+O+DXggGeyayC0iIiLnELvdzsJFS+jRvSsBAf5OPcZisTiSiApWqw139+N9Am5ubnh6egLGhPBu3bqQlp5Owu5Ebrz+Gmw2G1u2bq+/C3GCeizkvGUywTuR0GkHlANLC+D/cuG6Jq6OTERERMSQX1BAamoamZlZrFmzDoByq5X0jAzSMzIYe8Woao/x9fWlqKioSllRURE+PtWHZuTl55OekcHgQQPY/PtWwkJD8PDwILJ5cw4kJ5+di6qFEgs5r8V7w19C4M0M4/ixFLiiEfhaTv44Z0394OP6acjFJt13t6tDOC/o9Ra5sF0Iv+P6/T7/+Pn6MvFP11cpmztvAc3CwujWtUuNjwkLDSE1NY327eIByM8vIL+ggLDQ0Gp116/fSPduXTGZTJhMRg8JgM1uo6EHcmgolJz3ngmH0GMpckoZvJzm2nhEREREKpjNZgIbNapyc7MYw5j8/IwJ2larlcKiImw2GwAdOrQjYfcetu/4g6zsbBYsXETLFpEEBjaq0vaRI3lkZGbSJjoKgJDgYA4eSiUrK5ukpL2EhoQ07LU26LOJnAWNLPBKxPHj19IhscR18YiIiIjURWpaOv/575eOVZ+ahYUxZPBA1q/fyPffz8LTw4Phw4ZUe9y6DRvo0b0bJpOxwVd4eDNi2kYzY9aPuLm706lThwa9Dg2FkgvCzUHGylBri6DUDo8kww+uW8ZZRERELnInG7Z21YRxVY6bR4RXqx8fF0t8XOxJn2P40OrJxoD+lzCg/yV1iLT+qMdCLghmE7xXaR+LH4/AnCOui0dERETkYqPEQi4YvXzh9qDjxw8mQ6nNdfGIiIiIXEyUWMgF5aUICDj2rk4ogXczXBuPiIiIyMVCiYVcUELd4fnw48fPp0JqmeviEREREblYKLGQC879IdDOy7hfYIMnU1wbj4iIiMjFQImFXHDcj+3IXeGLHFhV4Lp4RERERC4GSizkgjQ8ACYEHj9+IBmsDb39pIiIiMhFpMETi8QSGLMHGm+G5lvg0WQoPrZyz7ajMGgX+G2C2G3wdXZDRycXkjeag5exXwwbiuA/ej+JiIiInDUNmliU2oykwtMEK2Ph69Yw6zD8/SCUHDvX2Rt+bwdPhMGt+2BNYUNGKBeSVp7G+6jC5IOQW+66eEREREQuZA2aWKwtgj0l8N9WEO8Ng/zhHxHwdQ7sKIZ9pfBCOER7wu1NoZMPLM5vyAjlQvPXMGjhYdzPKofnUl0bj4iIiMiFqkETi1hPmN0G/CzHy0xAiR2aWIz7/84Gm92YbLuzGLr5NGSEcqHxMcObzY8fv59hDLkTERERkfrVoIlFsLsxqbaCzQ5TM2CAH7T0hBfDjeEqHhuh3y54NBQuDai9PRFnTAiEof7GfSvwl2SwayK3iIiISL1y6apQj6TApiJ4OQLK7bC7BO4IgjVx8K8W8FY6zMh1ZYRyITCZ4N1IqOgoW5QP3x92ZUQiIiIiFx6XJBZ2OzyYbAxL+SYK2nvDl9mwogDebwHdfeHPwfBIKDxzyBURyoWmvTdMCjl+/GgKFNlcF4+IiIjIhabBEwubHW7fDx9mwrQoGBdolK8rgnbeYDYdr9vdB5JKGjpCuVA91wyC3Yz7B0rh1TTXxiMiIiJyIWnwxOLRFPhfDsyIggmNj5eHu8PWEybV/lFsrBAlUh8C3YxhdxVeTYO9SlxFRERE6kWDJharC+DtDHg+HHr4QlrZ8dtNTYx/H042NtGbkQuvpMHDoQ0ZoVzobguCHsdWGiuxG4muiIiIiJy5Bk0svjts/Dv5IDTbUvUW4QG/xcD6IuiyA548CC9FGPtZiNQXswneizx+PPMwzM9zWTgiIiIiFwy3hnyy15sbt9r09oVlsQ0Xj1yc+vjBLUHwebZx/Jdk2NIO3E0nf5yIiIiI1M6ly82KuMorEeB/7N2/sxjey3BtPCIiIiLnOyUWclEKc4dnw48fP3fImOMjIiIiIqdHiYVctB4Ihjgv436+Df520LXxiIiIiJzPlFjIRcvDDO9Umsj9n2xYU+i6eERERETOZ0os5KI2IgDGNTp+/MABYxNHEREREakbJRZy0XszEjyPrQi1rgj+m+3aeERERETOR0os5KIX5QmPV9qI8cmDcLjcdfGIiIiInI+UWIgAk5tBpLtxP7Mcnk91bTwiIiIi5xslFiKAj7nq5o3vZcCOo66LR0REROR8o8RC5JhrGsNgP+O+FWNHbs3jFhEREXGOEguRY0wmeLcFWI4d/5YPvzdu7dKYRERERM4XSixEKunoDfcFHz+e2bIvpSZL7Q8QEREREUCJhUg1z4dD0LFcIsfTnwXhXVwaj4iIiMj5QImFyAkau8FLEcePF4R3IcfDz3UBiYiIiJwHlFiI1OCOptDNx7hfZnZjZsu+rg1IRERE5BynxEKkBhYTvBd5/Hhzkyh2BUTU/gARERGRi5wSC5Fa9PODnpkJjuPvWvbDatKvjIiIiEhN9ClJ5CTGJa/B01oKQJpPE5aFtHNxRCIiIiLnJiUWIifRqKyIkQc3Oo5nN+9BvpuXCyMSEREROTcpsRA5hcFpWwk5ehiAo26e/BTZy7UBiYiIiJyDlFiInIKb3caE/Ssdx6uD49jvG3ySR4iIiIhcfJRYiDih/ZFkOuTuA8BuMvFdq0uwuTYkERERkXOKEgsRJ03Yvwo3mxWAfX6hrGsa4+KIRERERM4dSixEnBRcksfQ1N8dxz9E9uaoxcOFEYmIiIicO9xcHYDI+WTEoU2sDY7hsIcf+R4+zInoxpUHVrs6LBERETmHWK1Wpk3/ngGX9CMysjkAySkprFq1ltzcXHz9fOnWpQvt2sXV2sbuPYmsXr2WwqIiIptHMGTwIHx8vAFITU1j/m+LKC0tpW+fXrRvF+943PIVq2gaFERcXMOPrFCPhUgdeNrKGb//eCKxOLQDaV6BrgtIREREzinl5eXMnbeAnJxcR9nhw0f4+Zc5REW14rrrrqZnj+4sWbacvfv21dhGenoGC35bRI8e3bh6wnhKS8tY8NtCx/kly5bTvl0cl40YxtJlKzh69CgAhUVFpKQcJCamzVm9xtoosRCpo245ibTJOwSAzWzh+5b9sLs4JhEREXG9nJxcvvt+Fkfy8qqU796TSNOmQfTo3o3ARo2IjWlLbGxbEhL21NjOlq3biI5qTXxcLE2bBjF82BAOJKdw+MgRAHJzDxMV1ZrI5s3x9PQkLy8fgA0bNtG1SyfMZtd8xFdiIVJHJuCq/Ssw2Y11oXYGRrK1cUvXBiUiIiIudyg1lcjI5lw9YXyV8jZtohg0oH+VMhMmyq3WGttJS88gPLyZ49jf3w9/fz/S0tIdx5mZWeTl51NSUoKvny8FhYUcOpRK27au6a0AzbEQOS3Ni3Lon/EHy0LbAzCjRT/iDqfgYa/5D4SIiIhc+Dq0b1djeePAwCrHRUVF7N6TSM8e3WusX1RUhK+vb5UyH28fCgoKAejbpzfzFyzEarXSo3tX/Hx9WbJ0GV27dnZZbwUosRA5baNT1rGhSTRF7l5kewWwsFknRh7a5OqwRERE5BxWVlbG7Dnz8PXxoUP7+BrrlJeXY7FYqpRZLGasx3o4oqNa0/L2W7BarXh6epKfX0BqajoDB/Rn1eo17ErYQ7OwUIYOGYS7u/tZv6YKGgolcpp8y0sYk7LWcTwvvCs5Hn4ujEhERETOZSUlJfz482zy8vK5YvTIWj/0WywWRxJRwWq14e5+vE/Azc0NT09PANZv2Ei3bl1IS08nYXciN15/DTabjS1bt5+9i6mBEguRM9AvYyfNC7MAKLO4M6tFHxdHJCIiIueio0ePMuuHn8nLy+PKcWNo1KhRrXV9fX0pKiqqUlZUVISPj0+1unn5+aRnZNC2TTRpaRmEhYbg4eFBZPPmpKen1/t1nIwSC5EzYMbO1ftWOI43BUWTEBDuwohERETkXGO1Wvl59hyOFhczYfxYGjcOPGn9sNAQUlPTHMf5+QXkFxQQFhpare769Rvp3q0rJpMJkwnsdmOtSpvd1uCrViqxEDlD0QVp9Mja7Tj+rmU/rJhcGJGIiIicSzb/vpXMzCyGDR2Em7s7hUVFFBYVUVxcDBiJR2FRETabseJkhw7tSNi9h+07/iArO5sFCxfRskUkgYFVezmOHMkjIzOTNtFRAIQEB3PwUCpZWdkkJe0lNCSkQa9Tk7dF6sG4A6vZ0rgVpRZ3Un2CWB7ajkHpDTuuUURERM5NexITsdls/PDjL1XKm4WFcdWEcaSmpTPrh5+4eeKNBAT40ywsjCGDB7J27XqKi4uJjGzOkMEDq7W7bsMGenTvhslkfKEZHt6MmLbRzJj1I+HhzejUqUODXF8FJRYi9SCwrIjLDm7kpxa9AfileU+6ZSfiX17s4shERETEFSbdd7fj/nXXXHXSus0jwqvUB4iPiyU+Lvakjxs+dEi1sgH9L2FA/0vqEGn90VAokXoyJG0LwcXGjphH3Tz5pXlPF0ckIiIi0nCUWIjUE3e7jQn7VzqOV4bEc8CnqQsjEhEREWk4SixE6lGHwwdol7sfALvJxHetLmnwFRlEREREXEGJhUg9u2r/Siw2Y1Obvf5hrGva1sURiYiIiJx9SixE6llISR5D0rY4jn+I7E2xueadNUVEREQuFEosRM6CkQc30qi0EIA8D1/mRHRzcUQiIiIiZ5cSC5GzwNNWzrgDqx3Hi8M6ku7V6CSPEBERETm/KbEQOUt6ZO8hKj8VAKvZwvct+2kit4iIiFywlFiInCUm4Op9KzDZjXTij8AWbAts6dqgRERERM4SJRYiZ1FkUTb9Mv5wHM9o2Zcyk8WFEYmIiIicHUosRM6yK1LW4VNeDECWVyMWNuvk4ohERERE6p8SC5GzzK+8mNEp6x3H88K7kuvh68KIREREROqfEguRBnBJ+g7CC7MBKLW4Myuyj4sjEhEREalfSixEGoAFO1fvX+E43ti0DXv8m7kwIhEREZH65dbQT5hYAg8lw/IC8DXDdY3hxQjwMsOhUrjvAMzPhyALPBkG94U0dIQiZ0fb/FS6Ze1hY9M2AHzSdgRWk5kSizte1jJ6ZO9maOoWgkvyXBypiIiISN01aI9FqQ3G7AFPE6yMha9bw6zD8PeDYLPDmEQ4aof1cfBqc3g4BebrM5ZcQMYnr8bNWg5AkbsXJW4eYDJR7ObByuA4Xul4NdsbRbo4ShEREZG6a9DEYm0R7CmB/7aCeG8Y5A//iICvc2BOHuwuhm9aG+duaAK3BcGqgoaMUOTsKjdZsJtMNZ6zmS2UWtz5rO2lZHoGNHBkIiIiImemQROLWE+Y3Qb8Ki3jbwJK7LAwH4b4Q5NKg7M+agnPhDdkhCJn18JmnU65+3a5ycyisI4NEo+IiIhIfWnQxCLYHYZX+iLWZoepGTDAz5h70cIDnjoIkVug/Xb4d1ZDRidy9q0PaovNfPIN8mxmC2uDY06ZgIiIiIicSxp88nZlj6TApiJYFw8PJsOX+XBVIMyKho1FcP8BCHKD8YGujFKk/hRb3J2qV2LxYHK3m2ldkE7r/HSiCtJpUZCBh916liMUEREROT0uSSzsdngoBT7IgO+iob03uJkg0AL/agkWE3T3hd+PwoeZSizkwuFlLaPYzcOpuoXu3mxr3IptjVsBYLZZiSzKciQarfPTCCwrOovRioiIiDivwRMLmx3u2G9M2J4WBeMCjfJwd7DajaSiQqwX/Jbf0BGKnD09snezMjju5MOh7HYsNitWS9VfT5vZwn6/UPb7hbL4WFnjkvxjiUYarfPTiTiag8VuO2vxi4iIiNSmwROLR1PgfzkwIwquCDxe3tcXZh+BMju4H0sudhyFVs59uStyXhiauoW1TWMopfbEwsNWzhNbv8NmMrPXP5S9fqHs9Q8jzbtxtbq5nv7kevo79sbwsJbRsjCD1vnpjmFUvtaSs3Y9IiIiIhUaNLFYXQBvZ8DLEdDDF9LKjp+7vgm8kAp37oO/N4O1hfCfbJge1ZARipxdwSV53L57Pp+1vZRyk7lKz4XZZsXNbuP23fMJObZJXljxYfpm7gKg0OLJPr8Q9vqHkeQXyn6/EEpPmLNRanFnd0AEuwMiHGWhR3PZtA8u8YN+fhDjCeaaV7wVEREROW0Nmlh8d9j4d/JB41ZZWTdYEGNM2O60A5q5w/stYExgQ0Yocva1P5LMk1u/Y1FYR9Y1jaHE4o6ntYyeWQkMSdta687bvtYS2h9Jpv2RZACsmDjkE0RSpV6NHE//ao9L927MZ9nwWbZx3MQCff2gn6+RaPT0Ad+TL1QlIiIickoNmli83ty41SbWy0guRC50wSV5XLt/BdfuX3HabViwE1mURWRRFoPStwNw2N2Hvf6hJPmFsdc/lGSfptXmc+RY4Zcjxs1oB7r4HE80+vlBpDvUso+fiIiISI1cutysiNSvwLIiuubspWvOXgBKTRYO+AXT7NJxrCyAlYWQVV71MVZgQ5Fxey/TKItwP5ZkHEs2uniDR4PueiMiIiLnGyUWIhcwD7uVNvlpTAozju122FNiJBgrC4zb9mKqbcZ3sAym5xo3AC8T9PI9nmz09YOm+ushIiIileijgchFxGSCtl7G7ZYgo+xwOawpPJ5srC6EghNWrC22w9IC41YhxrNqr0a816knhSeWwBvp8FW28Rx+ZpgYBI+GQrRn/V6riIiINCwlFiIXuUA3uKyRcQNjP5ltR2FFwfFkY29p9ccllBi3/x6bFN7IYiwbXZFs9PIF/0rTO349AlcnQZkNKhaEy7fBp5nweTZ8FwWXNzqrlyoiIiJnkRILEanCYoLOPsbtvmNlqWWwqlKisaEISk8YP3XECnPyjBuAGejkbSQa0R7w1CE4euKYK4wko8xmJB1b2qnnQkRE5HylxEJETqmZO0xobNwAim2wsQjHhPAVBZBxwqRwG7D5qHFzRpkN3kqHqS3qNXQRERFpIEosRKTOvMzHl6YFY1L43tLjicbKAthytPqk8JMpA77MVmIhIiJyvlJiISJnzGSCKE/jNvHYpPA867FJ4QXwXKpz7eTbjCRFe2iIiIicf7QyvYicFQEWuDQAng0Hfyf/0tiBDjuMIVHZ5aesLiIiIucQJRYictZNDAJ3J+vuKIZHUiB8C9yQBAvzwFaXMVUiIiLiEkosROSsezQU3E/x18YN8K40BKrUDt/mwrDdELMdXk41VqcSERGRc5MSCxE566I9jX0qfMzVey7cMcp/bAPpneGTltDLp2qdxBL42yGI3AJXJsIvR4z9NkREROTcocRCRBrE5Y2MfSruCoYAs/HHJ8BsHG9pZ5z3t8CdTWFNPPweD5OCIbDSJntWYNZhuGIPtNoKzx6C/SUuuiARERGpQomFiDSYaE9jOdkjXcHa3fh3aouaN8Xr5APvtYBDneDLVjDQr+r5lDJ4IRVab4ORu+H7XChTL4aIiIjLKLEQkXOat9mY/L0kFna2h8dCIbjSQtl2YG6esXN38y3wRArsLnZZuCIiIhctpxOLvSVw5z5ovRW8Nxq77j6SDJ9mncXoREQqifWC15pDSkeYHgUjAqDylhcZ5fDPdGOy9+Bd8HW2sUu4iIiInH1OJRabi6DrH7C8AMY0MlZrAeObwrv3G7vliog0FA8zXN0Y5raFpA7wdDOIOGFW+JICmLjPWLb2wWTYetQloYqIiFw0nNp5++Fk6OsLv7QBGzA10yh/K9IY0/xGOtwUdBajFBGpRStPeCEcnmkGc47AJ1nHVo06dj7XCu9mGLfevvDnpnBdY/CznLRZERGR02a1Wpk2/XsGXNKPyMjmAOTl57No0VJS09Lw9/Oj/yV9admyRa1t7N6TyOrVayksKiKyeQRDBg/Cx8cbgNTUNOb/tojS0lL69ulF+3bxjsctX7GKpkFBxMXFnN2LrIFTicWaQvguGswmsJ8wOfLqxvAf9Vhc8KZ+8LGrQ6gXk+6729UhyFniZoIrAo3boVL4b7YxVHNv6fE6awqN20PJcEMTI8no4QMmU22tioiI1E15eTnz5v9GTk6uo8xutzN79lwaNw7k2qsnsHffPn6dO58br7+GgICAam2kp2ew4LdFDB40gOCmTVm2fCULflvI2DGjAViybDnt28UREhLMz7/MIap1K7y9vSksKiIl5SD9+vZusOutzKmhUAEWSKtlY6oDpcaSkSIi54pwD/hbM9jTAea3hWsbg3ul5KHAZvRs9NppDPN8PwMOl7suXhERuTDk5OTy3fezOJKXV6X84MFD5B4+zJDBA2nSpDHdu3UlLCyUHX/srLGdLVu3ER3Vmvi4WJo2DWL4sCEcSE7h8JEjAOTmHiYqqjWRzZvj6elJXl4+ABs2bKJrl06Yza75cO7Us17TGCYfhEX5xrwKMCZMJhTD84dgfOBZi09E5LSZTTA8AKZFGcvWvtEc4ryq1vn9KExKhmZb4IuoIezxD0Or1oqIyOk4lJpKZGRzrp4wvkp5Wno6wU2b4uHh4ShrFhZGWlp6je2kpWcQHt7Mcezv74e/v5+jvr+/H5mZWeTl51NSUoKvny8FhYUcOpRK27Zt6v/CnOTUUKhXm8OOYhiWAL7HUpFRuyGzHHr4GudFRM5lTd3gkVB4OARWFMInmTA9F44eyyKK7bAuOIZ1wTGEHM2lX8ZOemUl4F+utWtFRMQ5Hdq3q7G8sKgIX1+fKmU+Pt4UFBbWWL+oqAhfX9+q9b19KCgw6vft05v5CxZitVrp0b0rfr6+LFm6jK5dO7ustwKcTCx8zPBbDMw9YvRa5FihkQX6+xmrRJk1PllEzhMmk/G3q78fvBMJ/8sxhkVtrrRqVIZ3Y2a17MtPkb3olLuPfhl/EJN3UBv/iIjIaSkvK8diqbpqiMViwWq11ly/vKb6Zkf96KjWtLz9FqxWK56enuTnF5Cams7AAf1ZtXoNuxL20CwslKFDBuHu7l7TU5wVTiUWFS5rZNxERC4EgW5wXwjcGwwbiuChJTvY0LQNxRajq9pqtrApKJpNQdE0Kc6jb+ZO+mTuIrCsyMWRi4jI+cTNzUJpaWmVMqvViptbzR/Fa0o6rFYb7u7H67u5uTkev37DRrp160JaejoJuxO58fpr+G3hYrZs3U73bl3q92JOwqnE4vZ9p67zWaszC0RExFVMJmNY5/X7lnHlgVVsDIpmZXA8+/xDHXVyvAL4JbIXs5v3oP3hA/TL2Em7wwewaEaGiIicgq+vL1lZOVXKioqK8PXxqbV+UVFRtfo+NdTPy88nPSODwYMGsPn3rYSFhuDh4UFk8+YcSE6uv4twglOJxaYavpwrsBm7cTdxg8H+9R2WiIhreNrK6Zu5i76Zuzjk3ZhVwfGsDW5LkZsx69tuMrOtcSu2NW5Fo9JC+mTuok/mTpqW5Ls4chEROVeFhYayYcMmysrKHEOTUlPTCA0NraV+CKmpaY79KfLzC8gvKCCshvrr12+ke7eumEwmTCZjaVsAm91Wp6++bDYbR4uL8fbyOu15Gs4lFjXPQ+FgKYzZA5cqsRCRC1D40VyuOrCSsclr+L1Ja1aGxLE7IMJx/oiHL3MjujE3ohtxR5Lpm7GTTrn7cLPbXBi1iIica8LDm+Hv78+ChYvp1bM7+/YdIC09g6FDBgPGsKjikhLHh/oOHdoxc9ZPNGsWRmhoCMuWr6Rli0gCA6vOSThyJI+MzEyGDB4IQEhwMBs2biYrK5ukpL00b37qFZbS0tJZvXYdqalp2Gw2rrn6SjZv3kJAgD99eveq03XWaY7FiSI84NlwY2fuPwefSUsiIucud7uVHtl76JG9hwzPAFaFxLGmaSz5Hse7pHc2imRno0j8yo7SKyuBvhk7CSs+DECmZwALm3VifVBbii3ueFnL6JG9m6GpWwguyavlWUVE5EJhNpsZNeoyFi5awv9Nn0GjgABGjRxBQIDx7XxqWjqzfviJmyfeSECAP83CwhgyeCBr166nuLiYyMjmjuShsnUbNtCjezdMx3Z6DQ9vRkzbaGbM+pHw8GZ06tThpHGlpBzkx59nEx7ejD69e7Jy1RoAgoKasHrNOjw9vejapZPT13lGiQWA1V775nkiIheakJI8xiWv5YqU9WwNbMmqkDj+aBSJ/dgf9QJ3bxY268zCZp2JykulZWE6y0PaYzWZsZmNFT6K3TxYGRzH2qYx3L57Pu2PNOwYWBEROfsm3Xd3lePARo2YMH5sjXWbR4RXqx8fF0t8XOxJn2P40CHVygb0v4QB/S9xKsaVq9bQtk00lw4fis1mY8XK1QB079aV8vJytu/4o/4Tixm51ctswKEyeD0N+vo5/XwiIhcEi91Gl9y9dMndS46HH6uDY1kdHEuu5/GxoUkBzUgKaFbj421mC6VY+KztpTy59Tv1XIiISIPLzsmhT++eNZ6LiIhg46bf69SeU4nF1Um1n+vtCx+2qNNziohcUJqUFjDq4AZGHtzIH42aszIknm2NW2IznXryW7nJzMKwjly3f0UDRCoiInKcj7c3Obm5tGgRWe1cbm4uPt7edWrPqcRibw3Ds0wmCDAb68CLiAiYsdP+SDLtjyST5+7Ns11upNx88j+SNrOF5aHt2ecfRtjRXJodzTH+LcolqCQfs5azFRGRsyQuLoY1a9fj4eFBy5ZGT4HdZic5OYW16zaccijWiZxKC1p61j1QEZGLWUDZUcpNllNXBDCZSPFtSopv0yrF7rZyQo/mEnb0MM2O5tCsKJewo0o4RESkfvTs0Z38ggIWLlrimAD+3YxZAERFtaZ3rx51aq/WxGLsHucbMQE/tKnT84qIXPC8rGUUu3mc9uPLzG6k+AaT4lt12T13axmhxYdpdjSXsKJc49+juQSV5HF6K4+LiMjFyGw2M3zoELp37crBQ4coLi7Gw8OD8GbNaNo0qM7t1ZpY5FmNhEFERE5Pj+zdrAyOc6wGVROzzUqvzAT6ZCWQ6tOYNO/GpHo3Ic07kDwP3xofU2ZxrzXhCCs+zJq90N4b2nsZ/7byALP+oIuIyAkOHjpEaEgIjRsH0rhxYJVzBQWF7PhjJ716dne6vVoTi8V1G1IlIiInGJq6hbVNYyil9sTCzW5jROpmgkvyiC5Iq3Ku0M2TVG8j2ahIOFK9G1fZP6OyMos7yb7BfJVTtdzbBPGVEo12SjhERASYOesngoKacPnIEQQ2qrr5XkFhAevWb6ifxKImRTYoseEY2Ws/VraqAK5tUpeWREQufMEledy+ez6ftb2U8kr7WIDRU+Fmt3H77vm1LjXrW15Cm/w02uTXnnBUJBtpJ0k4jtphY5Fxq8zHDPFexxONisSjpRIOEZGLRllZGf83fQbDhgwiOjrqjNpyKrHYdhRu21f9P6XKlFiIiFTX/kgyT279jkVhHVnXNIYSizue1jJ6ZiUwJG3rae1fUVvCUeDmRZp3Y2JHjmXHUdheDNuPQkZ5ze0U2WBDkXGrrCLhOLGHo64JR2IJvJEOX2VDgQ38zDAxCB4NhWgtCiIick64dNhQEvbsYc68BXTp3JF+ffs4JnLXlVOJxSPJkFoGbzSHn46AhwnGBsKvR+CXI/BbzGk9t4jIRSG4JI9r96/g2rO8V4VfeTFt8lO5t+rUC7LKqZJobD8KO4rrnnD4ntjDcayXo0UNCcevR4w9kMpsUHasLN8Gn2bC59nwXRRcXrXXXUREXMBsNjNoQH9CgoNZvGQZ6RmZjBwxHJMTezGdyKnEYnUhTG0BNweBnwX+mwX3Bhu3G5Pg3QwY7H/qdkREpOE1dYOB/satsswyI8GoSDS2H0s+MmtJOAptsL7IuFVWkXBUJBqN3eAvB4whWCcqw0g2rk6CLe3UcyEicq6Ij4ulSePG/DpnHtOmf0+vnnVbahacTCzK7ND62IqJcV7w+9Hj524Jgtv31/l5RUTExYLdYZA7DDpJwrG9UuJR14TjZMps8Fa68aWViIicG0JDQ7j2mgn8Onc+ixYvrfOQKKcSi7bHkokB/kZiUWiDP44aq4xYMZamFRGRC8PJEo7tNfRwZNWScJxMGfBlthILERFX6tWzO75+VZc29/Hx4cpxY1i2fCX79h+oU3tOJRZ3BMHjKcbkuyfD4BI/uGM/3NUU3s6Azt51ek4RETkPBbvDYPfqQ18zTujh+DDTufYKbPUfo4iIOK+24U5ms5lBA/szqI7tOZVYPBhqDIdKOTYD75OWMGaPMQSqhQf8p1Udn1VERC4YIe7GrSLh+CrbmKh9Kn7aJlxEpMF98+10Rlw6jKCgJnzz7fSTVzbBDddd43TbTu9j8VjY8ftxXpDQ3hhvG+Lu9HOJiMhFYGKQsfpT2SnqXRnYENGIiEhlwSFNcXc3UoDg4KZQj/sWOZVYtN4KNzQxbh2PDXsymZRUiIhIdY+GGkvKlp2i1+K3fEgqgSitDCUi0mCGDx1y/P6wISepWXdOdURf1Ri+yYEuO6D9dngp1dj4SERE5ETRnsY+FT5mOPH7J0ul+yllMGCXsRiIiIi4Rm7uYbJzcgAoLStjydJl/PzLr/yxc1ed23IqsXi9OeztCCti4bIA+DgLYrZBn53GHhZpp+rvFhGRi8rljYx9Ku4KhgCz8Z9NgBnuCYZPW4LXsa73Q2UwMAE21WGpWhERqR+JSXv5Ztp0dh5LIn5buJgdf+yirLycRYuXsmXrtjq1V6epc3384M1I2H8syejkDY8mQ4utdXpOERG5CER7GsvJHukK1u7Gv1NbwB1N4de2xydvZ5XDkARYWeDaeEVELjbrN2ykbZto+vbpTWFhIUlJe+nZoztXjhtD71492LJ1e53aq/OaHGll8H4G/O0Q/DfbWH7wvuC6tiIiIhezwf6wIAYCj42NOmKFS3fDgjzXxiUicjHJzT1MfHwcZrPZsWdFm+goAMJCQ8nPz69Te04lFqllMDUDBu2C5lvgqUPQygNmt4GUjvB2ZB2vQkRELnq9fWFJDIQcW0akyAaj98CPh10alojIRcPDw4OS4mIA9u3bT4C/P4GBjQDIPXwYH++6bVbnVGLRfAv8NcX44z89CtI7wWetYHgAmOu4RFViibEHRuPNRruPJkPxCSuHlNqMSeLPHapb2yIicn7p5ANLY6H5sVnepXaYkGgsGCIiImdXq5YtWLlqDYuXLGPf/gPExrYF4Pfft7Jq9VqiolrVqT2nlpv9rBVMCAR/y6lqnlypzUgq2nnByljIKIfb9xnn3qjU6zElzdjFVURELnyxXrAsFobvNr58sgJ/2guFNrizqaujExG5cA3o348VK1dzKDWV+PhYunfrCsD2P3YSHdWavn1616k9pxKLW4LqHmhN1hbBnhJYGwd+FogH/hEBjyQfTyy2FMGnWcYmfCLiGlM/+NjVIdSLSffd7eoQxEmtPI3k4tIE2F4MduDP+yHfCg+Hujo6EZELk7u7O4MHDahWfsN1V2My1X3nvDpP3j4TsZ7GvAy/Sj0fJqDEbty32uH2/fBqBASdYe+IiIicX5q5w+JY6O5zvOyRFHjhENjtrotLRORiczpJBTRwYhHsbszLqGCzG5PCB/gZx6+nQ1M3uKmeekhEROT80tQNfouB/n7Hy55Nhb8eVHIhInKua9DE4kSPpBibIr0cAQnF8FoafNTClRGJiIirNbLAnDYwotIXUa+nw30HjC+kRETk3ORUYvFFNmSX13wurQxeT6vbk9rt8GCysR/GN1HGZO479sPfmxnjbEVE5OLma4Efo2F84PGyj7Lgln1QruRCROSc5FRicds+SCqp+dzaQmNfC2fZjs2j+DATpkXBuEA4UArLC4x2/DYZt1WF8FIaXL7b+bZFROTC4WmG/4uCPzU5XvZVDlybBCW22h8nIiKuUeuqUMMSYF2hcd8ODEmoOQspslWdaHcqj6bA/3JgRhRcEWiURXjA7vZV612/F/r5whNhzrctIiIXFncTfNEK/MzwcZZRNvMwjE2EmdHg49IBvSIi5ze73c72HX+wf/8BysrLsZ8wmc0EjB83xun2ak0s3ouE6blGUvFCKtzQ5PgGRhUsJgi0wPVNamyimtUF8HaGMaeih68xjKpCmxOWl/UyQRM3I+kQEZGLl9kEH7YwVhR8I90om5cHl+2Gn9sYczJERKTulq9Yxe9bthIc3BQ/X9/TXg2qQq2JRTtvePbYLt4mjE2KzvRD/neHjX8nHzRulZV1A7czuxYREblAmUzwWgQEmI1VosAYQjssAea2hSCndmUSEZHKdiXsplfP7vTq2aNe2nPqT/Gz4ca/ywvgtzyjp2FyM9h2FLr6GGuPO+P15sbNGcvjnKsnIiIXB5MJngkHf4uxqiDAhiIYtAvmxzj/f5GIiBisVivh4c3qrT2nRqcetcHYPTBwF7yVAf/KgqxyeCsduuyAP47WWzwiIiIn9XAo/KuF0ZsOxk7dA3bB/loWGRERkZq1atmCpKR99daeUz0WT6TAmkJYFgu9fcFjo1H+VWtj1abJB2FWm3qLSURE5KT+HGzMubhpL1iBxBLov8vYXC/G65QPFxERoEWLSJavWEVeXh6hoaG4uVVNDUwm6NK5k9PtOZVYfJsL/4yAS/zAWmmyeKg7PN0M/rzf6ecTERGpFzc0AV8zXJMEpXZIKTN6Lua3hU51WK1QRORi9dvCxQDs23+AffsPVDtvMpnqP7EotEFILWNXvc1QrM2KRETEBcYGwi9tYFyisfx5RjkMSoA5bY0edhERqd2k++6u1/acmmPRxxfeyai622nF2NbPsqGX/niLiIiLDA8weikqlp09bIXhCbA437VxiYicL4qLi9m3/wAJu/eQnJxCaWnpabXjVI/FPyOMb4DitsOIACOp+CATdhbD+kJYGHNazy0iIlIv+vkZ/xeNSIBsKxTYjDmA30fDqEaujk5E5Ny1Zu06Nm76HavV6igzm8107dKJvn1616ktpxKL7r6wNg7+kQozc42N8WYdhv5+sCIOumksq4iIuFg3H1gaC8N3Q2qZMUx3fCL8rzVc3djV0YmInHt+/30rGzZupmuXTrRt0wYfH2+Kio6SsHsPmzZvwcfHl86dOjjdntNbCrXzhm+iTitmERGRBtHOG5bHGhvn7SuFMjtclwSftYJbglwdnYjIuWXLtu1069qZPr17Ocp8fHxo2jQIs9nMtm3b6z+xWHqScapmE/iZIcoTAixOP6+IiMhZEeVpLI8+PAF2lYANuHUfFFjh/hBXRyciF7Li4hKWLlvO/gPJuLlZiI2JoU/vnpjN1ac1Z2Vls2jJUrKzc2jcOJDBAwcQGmr8kSosKmLO3PlkZWXTtm00QwYNxGQyZjjv3bePpKR9DBs6+IzjLSwsJCI8vMZzEeHN2LT59zq151RiMTjh+GTtygtAmSrdNwM3BcG/WoJb5RMiIiINrLmHMSxqxG74/dgmrpOSId8GT4a5NjYRuXAtWbqMwqIiJowfy9Hio8ybvxAvL0+6de1SpV5ZWRk//jybtm2iGTZkMNt37ODn2b9y059uwMPDg40bN+Pt5cU1V13JT7/8yr59+2nduhUA69dvYsSlw+ol3kYBARxKTSUysnm1c4dSU/HzrdsKTU6tCjUrGrzMcH8wLImBne2Nb4MeCQUPE7wVCe+3gB8PwwupdXp+ERGRsyLEHRbFGCsbVph8EP52EOxaJl1EzoL9B5Lp0qkjQUFNaB4RQUzbNqQcPFSt3u49iVjMZvpf0pcmTRrT/5J+eHh4sHtPIgC5hw/TokUkTZo0Jiw0hNzDhwFITNpLUFATGjUKqJd4O3XqwIaNm1m9Zi2ZWVkUFhaSmZXFqtVr2bBxM+3bx9epPad6LF5Kg4dDYErE8bIYjA3z/M3wbQ6sjDN6M/6ZBi/U3KMiIiLSoBq7GUvRjkuEhceG9b6cZgyLejvSGM4rIlJfvLw82bV7D5GRzSkpLeXAgWSiolpVq5eWnk5YszDH8CaTyUSzsDDS0tJp3y4efz8/srKyKS8vJycnl9atW2G329mwYRMjLxteb/G2bxfPkSN5bNq8hQ0bNzvKTSYTnTt1qNbTcipOJRZbiuAftSQLff2MP9IA7bzgUFmdnl9EROSs8rMYm+hdkwQ/HzHK3ss0hkV92tJY6VBEpD4MGtif+QsW8a9P/4Pdbqd5RDi9evaoVq+osIjAwMAqZT4+3mRmZQPQtUtnZv34M9t3/EFERDjRUa1JTEyiaXAQAQH101tRoV/f3nTr2pm09AxKSkrw8vQkJDQEby+vOrflVGIR7QnTcuDSGq5jei608jTuJ5fVvkO3iIiIq3iZYUY03LQXpuUaZf/NhkIbfNUKPJwaGCwicnJHjuQR3LQpvXp2p7S0lCXLVrBi5SoG9L+kSr3y8nIslqqrHlksFsdeEoGBjbh54g0UF5fg4+Nt9FZs3Mzll49gT2ISq1avwcvLi0uHDSUwsG6b9RQXF+Pp6YnJZKK4uNhRHhYaUq0egFcdEgynEotnwuH6JNhbCuMCIdgNMsuNORWL8+HL1rD9KDyZAuMDnX5uqYUl+qrTely39lGs+/G1Gs/1HPs4G7cnnVa71sTvayz/dt52Vm09eFptPjaxD5Gh1TPVlVtSmDZ/x2m1ed2l7ejXqfrko+T0PF7/ajUAD74xr05t9u0YwfUj2td4rq5tVWge4s/jN/Wt8dxrX64iJeP0tgt+59ERNZZ/O287D75xeu8pV71OdXWy1+l0f59c8TrV9+/Tv76Zx71PfXxabX445W7uuqF6rBu2JtJr/F9Pq807rxvOxy/dW+O5hvy7VzmH+P7Y7US1/d27+28f8um0BXUPFFg765907xhdrVyvU/3//3Qmr9O5/nev4v+es/E6ncnfvdr+tl1Mjhw5wrLlK7nlphvx8/MDYKibhR9+/IXu3bri43N84zeLxa3KhnQAVqsVd7fjH83NZjM+Pt6AMScjJCQYH29vFi1eypXjx3Dw4CGWLl/B2CtG1SnOf//nC66eMJ7Q0BA+/exzx3Cs2tx/711Ot+1UYnFNY/BvA1NS4ZFkY+k+dxP09YV5bWFogJFkXNYIXqn+eyMiIiIickHLyMzCw8PdkVQAhAQHY7fbyc8vqJJY+Pr5UFRUVOXxhUVHq9SpYLfb2bhxM6NHXUZObi4Ws5mmQUFYzGbWrltf5ziHDhlEwLHJ3/WxZG1lTiUWM3JhgB8sj4MSG+RaIcSt6qS3sYHGTURERETkYuPr60NJSSn5+QX4+xvJRU6uMfYyIMC/St2w0FDWb9iI3W7HZDJht9tJS0urcbJ0QsJuwsJC8ff3p7i4BPuxzR9sNvtprXAXHxfruB8Q4E9wcDAe7tXnMpSUlJCcnFKntp0aVXr3AVhaYNz3NEOYu1bSEBERERGpEBYaStOmQfy2aDFZWdmkpaWzaPFSYmPa4u3tTWFREeXl5QC0iY6irKyMpctWkJOTy/IVqygtLaNtm6rDJW02Gxs3/U6P7l0BY+6F1Wojae8+diXsdmyod7pm/fAzuTm5NZ7LyMhk/m+L6tSeyW4/da7TYTs8EAJ3B9epbbmATP3g9Mb/nmsm3Xd3nerrus9vum5xxgcZcH/y8eMYT1gQA5EerotJzo4L4Xf8dH6/L9brdoWCwkKWL19JysFDmM1m2kRH0a9vb9zc3Jj6wccMGzrY0WOQnp7B4iXLyMnNJSioCYMHDiAkpOqH7T927iIjI5NBA/s7ynYl7Gb5ipX4ePtw2YjhNGnSuE4x/jpnHocPG0vkZefk0KhRAG6W6oOYCgoL8fH25k83Xud0204NhfpTE3gwGWYfgXiv6is/mYCHQ51+ThERkXPGfSHGkrS37TPmECaUwIBd8FuMsSqiiIiz/Hx9GXnZpTWeOzE5Cg0N4bprTz7RPj4utsrQJYDYmLbExrQ97Ri7dO7E9j/+AIzEIjAwEG/vqis/mU1mIj086NC+XZ3adiqx+PuxDQN/OmLcTqTEQkREzmc3B4GvGW7YC2V22F9qJBfz20J7b1dHJyJSf5o1C6NZszDHcc/u3ettJ2+nEgtb93p5LhERkXPWVY3hRzNcmQjFdkgtg0G7YG5b6O7r6uhEROrf8KFDTno+PT2jTvM4nEosTiWtzJjQLSIicj4b2chIJK7YY+zMnW2FoQnwS1vo73fqx4uInE8KCgpZtmIlhw4dwmq1UXnqtdVqxW631/8+FnlW+EcqLMmHEjtUPKXdDkU2OFAKZerVEBGRC8BAf2N+xWW7jeXV82wwIgF+aAOX1s9oARGRc8LS5StISTlIfFwMh1LTcHNzo1lYKAeSU8jOzql1vkhtnEos/pIM/8uBUQGwoxh8zBDrBcsLIL0MPmhxWtciIiLiMqdaKece7yZMjRtNvocPR+0wapeVW/csoHPuvoYJ0Enny2o5InLuOXjwEP369KZDh3Zs3badxMQk+vXtQ98+dn76eTZJSXuJjmrtdHtO7WMx+wi8GA6z2sC9wRDhDtOiIKE9dPeBLUdP+3pERETOSeFHc3hoxw80LskHoNxs4bO2l7IuqI2LIxMRqR/l5eWO5WobNw4kMysbAJPJRMcO7Tl0KLVO7TmVWByxQu9jE9c6eMP6YzuQ+1rg0VAj8RAREbnQhJTk8dCOHwkuNv6js5nMfBk9lOUh8S6OTETkzPn7+3EkLw+AxoGBlJSUkHfs2M3NjaPFxXVqz6nEopm7MUEbjI2DssqN1TIAgiudExERudA0KS3gwR0/0qzI+CbPbjIxrfVAfgvr5OLIRETOTNs20axYuZo/du7C19eXoKAmLF+xipSDh1i/cRONAwPr1J5TicW4QHjyIMzPg5ae0NoTXjgE+0rgw0yjTERE5ELVqKyIB//4iRYFGY6yWS378ktED+wneZyIyLmsZ4/utG0TzYEDyQAMHjiAg4dSmfXDT2RmZtGvX+86tefU5O0p4ZBYAm+kGytivNkcrk2Cf2UZmcmXzs/pEBEROS/5lpcwaefPfBxzOYkBzQCY07w7JRZ3rjywCpOL4xMRqSuz2cyggf0dx82ahXHLTTeSm3uYxo0D8fDwqFN7TiUW/hb4uQ2U2IzjsYGwrR1sLIKuPtDW66QPFxERuSB4W8u4b9dsPm07gj8CIwFY1KwTxRZ3rt+7DLP6L0TkPOfh4VGnTfEqcyqxuH0fPN3MGAJVoY2XcdtVDOP2GOt7i4iIXOg8bOX8OWEOn7cZxu9NogBYFRJPqdmNm5IWY7HbXByhiEjtPv7kszrVv/vPtztdt9bEYlPR8Y3wPs+GIf7GRkEn+vmwMfdCRETkYuFut3Hb7gV8HTWYdcExAGxo2pZSsxu37fkNd3sN/2GKiJwDunTuhOksjd2sNbF4I93YFK/ieW/dV71OReJxc1C9xyUiInJOs2BnYtIiPG1lLA9tD8DWJq15L340YUW5bApqQ7HFHS9rGT2ydzM0dQvBJfomTkRcq3evHmet7VoTi/dbwJ+bGsnD0ATjuN0JcyksJgi0QHvNsRARkYuQGbh233K8rGUsCO8CwF7/Zuz1C6PiK8FiNw9WBsextmkMt++eT/sjya4LWESkkp07E05ZJy4uxun2ak0sGllgkL9xf1GMscO2n8XpdkVERC4KJmBs8hqsmFgU3vlYYdVxBjazhVKMnbuf3Pqdei5E5JywYOGiGstNJhMWiwV3d7f6SSwqq0gwREREpDoTUGZxw2S3YTfVvkVUucnMorCOXLt/RcMFJyJSiz/fcWu1srKycg6lprJq9VpGDB9ap/ac2iBPRERETm59UNuTJhVg9Fysa+r8t38iImeTp6dntZufny8xbdvQs3s3li6v25cgSixERETqQbHF3al6JU7WExFxJX9/P3Jycuv0GKeGQomIiMjJeVnLKHY79S61bjYtRSsi54bi4uJqZXa7ncLCItZv2ESjRgF1ak+JhYiISD3okb2blcFx2MwnX+mkzOLGZ22Gc+2+5fiVV/9PXUSkoXz62eeYatnUwmKxMHLE8Dq151RiUWiFKWkw+wgU2sBmr3reBCR2rNPzioiIXFCGpm5hbdMYSjn1EoqbgqLZE9CM6/cupVPu/gaITkSkumFDB1crM2HCw8OdiIhwPD0969SeU4nF/cnwTQ5c0Qiae2hihoiIyImCS/K4ffd8Pmt7KeUmc5WeC7PNisVuIzo/lZ2BLQDId/fhk5iR9MrcxVX7V+JjLXVV6CJykYqPi63X9pxKLGbmwhvNYVJIvT63iIjIBaX9kWSe3Podi8I6sq5pDCUWdzytZfTMSmBI2laCS/LYFtiCb1oPJM/DF4C1wbEkNIrgxqQlxB9JcfEViMjFJicnl02bf+dQaholJSV4e3vRPCKCrl07E+Bftz0nnEos3EwQp921RURETim4JI9r96+oda+KDocP8Lct0/mu1SWsb9oWgMMefnwQN5pL0ncw/sBqvGxlDRmyiFykUg4e5Keff8Xby4sWLSLx9vbi6NFikvbuZVfCbiZcOZamQUFOt+dUYnFdE/gsC4bXbWK4iIiI1MDXWsItiQvpnLOXaa0HUODuDcCK0Hb80ag5E5MW0zY/1cVRisiFbuXKNTSPiGDU5SOwWI4P37Rarfw8ew7LV6xi/NgrnG7PqcSitQe8lAOddkAvH/A5YZKFyQTvRDr9nCIiIgJ0yd1LdH4q01oP4PcmUQDkeAXwbruxDE7dypiUtXjYyl0cpYhcqHJyc7m8d48qSQUYK0J16dSROXPn16k9pxKLDzIh0AL5Vvgtv/p5E0osRERETod/eTF37J7PhqA2TG91CUVuxtjjxc06sj0wkpuSFtO6IN3FUYrIhahx40AyMrJo2aJFtXO5hw/jH3AW5ljs1VKyIiIiZ40J6JG9h7Z5h/hf64HsaNwSgEzvQN5qN5Zhqb8zKmU97nabawMVkQtK3z69mTf/N8rLy2nbJhpfXx+Ki4vZu+8A69Zv4JJ+fcjIzHTUDwkOPml79bJB3tpC6OVbHy2JiIhcvBqVFXFPwhxWB8cyo2U/ii0e2E1mFoR3ZXtgSyYmLqJFUZarwxSRC8SPP/0CwIaNm9i4abOj3G43Nq1bsnS549hkMnH/vXedtD2nEouUUng4GZYUQIkNKvbHswPFNrAB1u7OXUBiCTyUDMsLwNcM1zWGFyPAywy/5cGTB+GPYohwh7+GwR1NnWtXRETkQmAC+mbuIvbIQb6OGkRCo+YApPo04Y0OV3LZwY1cdmgTFvVeiMgZunLcmHptz6nE4sFkY27FrUFGQuBthn6+MC8Pth6F76Kce7JSG4zZA+28YGUsZJTD7fuMc/cEwxV74Olm8E1jWFMId+yHEDcYE3h6FyciInK+alJawP07f2F5SDt+aNGHUos7NpOZX5v3YGvjltyUuJjwozmuDlNEzmMREeGO+2VlZZSWleHl6VltMreznEosFuXDKxHGh/8PMuD7w/Bqc3jZDqP2wMzDcGXjU7eztgj2lMDaOPCzQDzwjwh4JBkau0EXH/hbM6NuGy+jh+TrHCUWIiJycTIDAzN2EH8kha+iB5Pkb/wnmeIbzGsdJjAqZR332sFicm2cInL+OpCcwurVa8nMOj7MMiQ4mF69utc4qftkzKeuAkU2aH9sg7x4b9hUdOzBJrgv2EgAnBHrCbPbGElFBRNQYodrG8PUE1aWMmEMtRIREbmYBZfk8eCOnxi/fxVux5afLTdb+LFFH/rvgoRiFwcoIuelA8kp/PzLr5jNZvpf0pcRw4dySb8+YIKff5nDgeSUOrXnVI9FSw9IKoUBGDtwH7bC3hJo7WnsaZHl5BLbwe4w3P34sc0OUzNggB/EnLCzd3oZfJsDz4YjIiJy0TNjZ1jaFtofPsCX0UM44BcCwOpC6LzDGFnwQIjxpZ+IiDPWrFlHVOtWjLzs0irlXTp3Ys7c+axbt4EWkc2dbs+pHovrm8CjyfB5NjRzh47e8EgKLM6Hl9KMnojT8UiK0fvxckTV8kIrTEiEcA9j+JWIiIgYwooP88j2WVyRvBaLzQpAsR0eSoGhCcYXfyIizsjOySE+Pq7Gc/HxcWRl1W0VOqcSi6ebGcnFnCPG8YctYGm+8QdsQyG85nwiA4DdbkwIfz8DvomC9t7Hzx2xwsg9kFQCP7epvsu3iIjIxc6CncsObeKx7TPoVOn/0CUF0HEHfJxp/F8rInIyPj7eFBTUPKehoKAAN3f3Gs/VxqmP7W4mmNrCSAIA+vkZm+atjoPkTjAswPkntNnh9v3wYSZMi4JxgcfPZZXDkF1GUrE4FqJPsydERETkYtC8KId1cfBUGFRMXyy0wT0HjC/pkktdGp6InOOio1qzes1akk+YS3EgOYXVa9YRHdW6Tu3VaYO8A6XGClGpZcbSsybAo45jOR9Ngf/lwIwouCLweHmpzVhuNqscliqpEBERcYqH2VhhcWwg3LLP2AsKjCXhO+6AdyLh5iZg0twLETlBr549SEvL4IeffsHDwwMfH2+Kio5SVlZGSEgw/fr2rlN7TiUWtmNDlz7KBCtGQjEiAP5+EPaXwsIYiPA4dTurC+DtDGNORQ9fSCs7fu7zbGNY1Zy2xsZ5Fec8TNCkXvYHFxGRmkz94GNXh1AvJt13t6tDcKmevrAxHp4+BG+kG5vYHrHCrftgRi583BLCjo1quBBe84v99RapD+7u7ky4ciz79u/n0KE0SkpK8PLypFmzMFq1bImpjt9IOPWR/flU+E82/LeVkVCEbjHKX28OVybC5IPwhRM9Jd8dNv6dfNC4VdbdB8qB4burll/iC8trnlMiIiIilXiZjXmP4wKNhCLx2ETuH4/Aiu3wQQu4tokrIxSRc43JZKJ1q1YEBwdTUlyCt7cXPj4+p9WWU4nFZ1lGL8OfgsBaaTJYJ5/jG9w54/Xmxk1ERETOnv5+8Hs8PHEQ3s80yrKtcN1eY5Pbrm5e+JVr8wsRgR07drJ+4yby8/MdZY0DA+ndu2ed51g4NXk7uxxivWo+F+wGedY6PaeIiIicZb4WY+GVBW2hRaXhyv+XCy91vIatgS1dF5yInBO2bN3GwsVLCAluyvBhQxgz+nKGDx1MYONA5sydT2JiUp3ac6rHopOPMQdiRA2rP808bOxrISIiIueeYQGwtZ0xuuDf2UZZvocP/4odSa/MXVy1fyU+Vi0fJVIfrFYrK1evYdeu3djtdtq2iWZA/35YLJZqdbOyslm0ZCnZ2Tk0bhzI4IEDCA01Nr4sLCpiztz5ZGVl07ZtNEMGDXTMd9i7bx9JSfsYNnTwGce7+fctdOncif6X9K1SHhsbw9JlK1i7bgPR0VFOt+dUj8U/wuH/cmDQLmNCmAn44TBM3Av/yoRntDu2iIjIOSvAAp+2MvaHalZpWfq1wbG83Oka/mikccoi9WHlqtUkJe1l9OWXccWokew/cIB16zdUq1dWVsaPP88mLDSUa6+eQHizMH6e/SulpUaSv3HjZry9vLjmqitJTj7Ivn37HY9dv34TPbp3q5d4i4qO1rqzdqtWLTmSl1en9pxKLC4NMFZrKrXD3w4aK038IxW2H4WZ0TC6UZ2eU0RERFxgdCPY1g56ZB1fKeWwhx8fxI3m21YDKDbXbTMsETmupKSErdt2MGTwIJo1C6NZszB69exBRmb13at370nEYjbT/5K+NGnSmP6X9MPDw4PdexIByD18mBYtImnSpDFhoSHkHj4MQGLSXoKCmtCoUR02kTuJyMjm7ErYXeO5vfv2ERHerE7tOb2Q67AA43bUBrnlxrcfftV7dUREROQc1sQNbklcSOecvUxrPYACd2M884rQdvzRqDkTkxbTNj/VxVGKnH8Opabh5uZGZPMIR1l8XCzxcbHV6qalpxPWLMwxvMlkMtEsLIy0tHTat4vH38+PrKxsysvLycnJpXXrVtjtdjZs2MTIy4bXW8wtW0SyavVavpsxi7Zt2uDr401xcQl79+/nwIFkunXtzKbNW47FCF06dzppe04nFsU2SCiGw7VM1B7o7/xFiIiIiGt1yd1LdH4q01oP4PcmxhjqHK8A3m03lsGpWxmTshYPW7mLoxQ5f+QdySPA35+E3XvYsGETZWVlREdH0bdPr2pzLIoKiwgMDKxS5uPjTWaWMRGqa5fOzPrxZ7bv+IOIiHCio1qTmJhE0+AgAgLqp7cCYPGSZQCkpaWTlpZe7fyGjZsd900mU/0kFnOOGPMpcq3GMKgTmQBrd2daEhERkXOFf3kxd+yez/qgNkxv1Z+jbp4ALG7WkR2BkUxMWkTrggwXRylyfigtKyMvP58tW7cxePAAykrLWLx0GXa7jQH9L6lSt7y8vFqyYbFYsFqNb/ADAxtx88QbKC4uwcfH2+it2LiZyy8fwZ7EJFatXoOXlxeXDhtKYODpz0mo740mnZpj8WAyRHnC/LawKb76bWN8vcYkIiIiDcQE9Mzew9+3/B/tco9PEM3wDuStduP4IbIXZSanPi6IXNTMZhOlpaWMGD6U8GbNaNmyBZf068u27X9gt1f9at5icXMkERWsVivubm6V2jPj42MMVdy9J5GQkGB8vL1ZtHgpl48cQUzbNixdvuLsX1gdONVjkVIGb0fC0PrreREREZFzSKOyIu5JmMPq4FhmtOxHscUDu8nMgvCubA9swU2Ji4ksqj4JVUQMvj6+mM1mGjU63oMQGNgIq9XK0aNHq+xm7evnQ1FRUZXHFxYdrXHHa7vdzsaNmxk96jJycnOxmM00DQrCYjazdt36s3dBp8GpryCG+MPvR892KCIiIuJKJqBv5i4mb5lOzJGDjvJUnyBebz+e2RHdsar3QqRGYWGh2Gw2srKzHWW5Obm4u7vj5VV1p+mw0FDS0tMdPRl2u520tDTCwkKrtZuQsJuwsFD8/f0xYcJ+bGKCzWbHXtMcBRdyqsfiXy1g+G5ILIHuPuBTw9+Um4PqOzQRERFxhSalBdy/82eWh7bnh8jelFrcsZkt/Nq8B9sat2Ri4iLCj+a6OkyRc0pgYCNat27FbwsXM2TQQMrLy1m5ei3t28VjNpspLCrC08MDNzc32kRHsWr1GpYuW0HHDu3ZvuMPSkvLaNsmukqbNpuNjZt+Z8wVlzuew2q1kbR3H//f3n2HR1G1bQC/dze9kUIqnTR6DSi9I03pIoqogIKgr4WPF0QERRRRBITwUkNoQakCUlSUDqFXCYRUEiAJpEPKZsv5/og7JCSBQMpmd+/fdXHpzpxNnidTdp+ZM+ckJiZJE+pVFaUqLHZn5I8IdSMXCCpmvQwsLIiIiIyJHEDnpGtomB6Pjd5dEW2fP559vK0rfmgyFP1un0WPhCuQFzusC5Fp6tWjG44dP4mdu/dAJpOhQQM/tHuxLQAgeO0G9OjeFQ0b+MPCwgID+vXF4SPHEHb9BlxcnPFy/76wsLAo9PPCb0bAy8sTdnZ2AABzc3N06dwRhw4fgY21DV7qXX5Dz5aHUhUWXycAgx2Bb2oA7pw7h4iIyGS4KjPxUdhvOOTRFHtqtYFabga1XIHdtV/EFad6eDP6ENxyM/QdJlGVYGFhgR7du6JH965F1j0+ApO7uxtGvDr0iT+vuHkw/P184e/nW+ZYK0KpCosMDTDJDfCzenpbIiIiMi5yCPRIvILG6XHY4N0NcXb53S9i7d3xXZOheCX+DBqlx+GQZzOcc/FFrsIcVhoVAlIi0D3hClyVmXrOgIgqQ6mewOrjAPzJcwIREZFJ88hNx6fXdqJ//BkotPlDZaoU5thetwPmNB+Bk64NkGtmAchkyDWzwEnXBviu6TBcq1ZLz5ETUWUo1R2L/tWAT28DV3KANjaAfeH5PCAD8EnRh9iJiIjIyCgg0OfuRTRJj8OG+t1w1zb/IUshk0PICrfVyhXIgwJrfHth2tVtvHNBZORKVViM/Xe+nH0Z+f8ex8KCiIjItNTMTsGUazuwoNFAxNu6AjJZiW3VMjkOeTTFq7eq1mReRFS+SlVYaFtXdBhERERkaMyEFvetHJ9YVAD5dy7OVvdjYUFk5DjLDRERET23XEXphotUlrIdERkuFhZERET03Kw0qlK1syxlOyIyXCwsiIiI6LkFpERA/u8IUSUSAgHJNysnICLSGxYWRERE9Ny6J1yBmdA+uZFMBhln6CYyeiwsiIiI6Lm5KjMxJuIALDSqoncuxKNi4phHUxx3a1jJ0RFRZWJhQURERGXSOCMe065uQ4d712GlzoNMCFip89AxKQw+mXeldlvqdsRlp7r6C5SIKlSphpslIiIiehJXZSZevXWiyJCySrkZFjd8GXF2bhAyOdb59MAH1/eg/sMkPUVKRBWFdyyIiIiowlhq1ZgQvh+uufkz7KrkZljh3wcJ1o76DYyIyh0LCyIiIqpQ9upcvH9jH+xV2QCAbDMrLPPvh3RzGz1HRkTliYUFERERVThXZSYm3NgPi3/ns0iztMeyBv2QrbDQc2REVF5YWBAREVGlqJ2djHERf0qjR921ccFqv95Qyfh1hMgY8EgmIiKiStMw4zbeiDkivY5wqIEN3t3xlJkwiMgAsLAgIiKiStU2OQKvxJ2SXl908cavtdtzCj0iA8fCgoiIiCpdz4TL6JJ4VXp92LMp/vZsrseIiKisWFgQERFRpZMBGHIrFC1SoqRlu2q/iLMuvvoLiojKhIUFERER6YUcAqOjDhWanXtj/S644VBDj1ER0fNiYUFERER6Yy40ePfmH/DMTgEAaOUKrPbrjXib6nqOjIieFQsLIiIi0isbTR7eD98PR+VDAIBSYYFl/n2RbGmv58iI6FmwsCAiIiK9c8rLwsTwfbBWKwEADyxs8D//fnhgZqXnyIiotFhYEBERUZXgmZOG8eG/w0yrBgDct3bEcv++UMrN9BwZEZUGCwsiIiKqMrwfJuKtyIOQifxZLeLs3LDGpyc0nJ2bqMrjUUpERERVSou0GAyLPS69DnOqg1/qduIEekRVHAsLIiIiqnI63wtD7zsXpNen3Bpgb80APUZERE/DwoKIiIiqpAG3z+KF++HS6z9qtMZxt4Z6jIiInoRPQxEREVGVJAMwMuYoHphbI8yxNgBgS92OsFfl6DcwIioW71gQERFRlaUQWoyJOIDaD+8BAIRMjnU+PXDioZ4DI6IiWFgQERFRlWapVWNC+H645mYAAFRyM7wcCYTxxgVRlcLCgoiIiKo8e3Uu3r+xD/aqbABAmgboEwHcydNzYEQkYWFBREREBsFVmYkJ4fthoVEBAOJVQN9IIF2t58CICAALCyIiIjIgtbOSMS7iT2n0mas5wOAoQKnVa1hEBBYWREREZGAaZtxGUN1Hrw8/BEbHAlrOoEekVywsiIiIyOCMdgHm1nj0eksa8OltQLC4INKbSi8sopTAy5GA0yWg5hVgcjyQ++/ty1tKoPdNwPYi0PAasD+jsqMjIiIiQzHVHfjA9dHrn+4B85P0Fw+RqavUwiJPm19UWMqAk/5ASD1gZzrw+Z38KwwDowAXM+BsA+AtF2BoFBCjrMwIiYiIyFDIZMCiWsAwx0fL/nsH2Jiit5CITFqlFhZnsoFIJbC2LtDQGuhiD3xdAwhJBQ49AMJzgZV1gEbWwDQPoL0dEJRcmRESERGRIVHIgA31gM52j5a9EwscyNRbSEQmq1ILC39LYJ8PYKd4tEwGQCmAU1lASxvAvsC6jnZAaFZlRkhERESGxkoO7PIGmljlv1YDGBIFXMjWa1hEJqdSCwtXc6Cnw6PXWgEE3gM62QEJKsDLvHB7d3PgNie+ISIioqdwNAP2+wI1//0u8VAL9IsAotmlmqjSmD29ScX59DZwMRs42xBYkARYPlbmWMry72YQERERPU1NC+B3X6BjOJCuAZLU+bNzn/DPv7hJVJkOHjqC9IwMDBn0SrHrk5NTcOjIUaSkpMLJyRFdO3eCu7sbACArOxu//3EAyckp8PX1RrcunSGTyQAAMbGxiI6ORY/uXSsrlVLTy3CzQgAfxQNL7wE/1wcaW+ffxnx8chulAGw4IC4RERGVUmNr4Def/IuTABChBAZEAlka/cZFpiX+9m2EXb9R4nqVSoXde/bBw90drw4bAi9PD+zZtx95eflddS5cuARrKysMHzoY8fF3EBt7S3rvuXMXEdC6VYXn8Dwq/Wu7VgBjbgHL7gOb6wMDHfOX1zAHElWF2yaqAE9eYSAiIqJn0NEO2FQv/zlOIH/wmFejAY2MVyup4qlUKhw6fAyeHh4ltomIjIJCLkfHDu3g7OyEjh3aw8LCAhGRUQCAtPR01K5dC87OTvBwd0NaejoAICo6Bi4uzqhWzaHEn61PlX6ETb4NbEoFdtQHhjg9Wv6iLXApp/AVheMP85cTERERPYshTkBgrUev92UCv9TtBPawpop26vQZ1PDyRI0aniW2SUxKgoenh9S9SSaTwdPDA4mJ+ROx2NvZITk5BWq1GqmpabCzs4MQAufPX0RA65aVksfzqNTC4tRDYNE94CsvIMA2/46E7l8Xe6COBfB2LHAtB5iXmD9S1LvVKzNCIiIiMhYT3YDpBS4an3JrgL01A/QXEBm9hMREREZFo0P7dk9sl52VDVsbm0LLbGys8TArfzjUli2aI/ZWHFasWgNrG2t416+HqKhoVHd1gYND1bxbAVTyw9vb0vP/+9md/H8FqVrlDxU39hbQ+jrgbQn86g3UtazMCImIiMiYzPEC7qqAtf9OmvdHjdZwzMtCx3vX9RsYGR2NRoODh46gU4f2sLJ68hdYtVoNhUJRaJlCoYBGk991x9GxGkaPGoncXCVsbKzz71ZcuIS+fXsjMioaoadOw8rKCr16dIejY7UKy+lZVWphMb9m/r+S+FgBR/wrLx4iIiIybjJZ/uS7SSpg/7+T5m2p2xH2qhw0T4vVa2xkXM6cPQ/HatXg4+P91LYKhZlUROhoNBqYmz36ai6Xy2FjYw0g/5kMNzdX2Fhb49Dhoxg86GXcuXMXR4+fwCsD+pVvImXAp5iIiIjIqJnLgC31gdoP7wEAhEyOdT49EG3nrufIyJjcjIhEXPxtrFgZhBUrg3Dh4mUkJCRixcqgIm1t7WyQnV14Bses7BzYPNY9CgCEELhw4RICWrdEaloaFHI5qru4oHatmkhKSqqwfJ6HXuexICIiIqoMdgpgQvh+LGw8CPetqkElN8MK/z74OGwXPHPS9R0eGYHBg16GVvto7oTLl6/g3r1k9OrVvUhbD3d3nDt/AUIIyGQyCCGQmJiIVi1bFGl782YEPDzcYW9vj9xcJcS/QxBotQKiio1GwDsWREREZBLs1bl4/8Y+2KvyrxRnm1lhmX8/pJsXvUpM9Kwc7O3hWK2a9M/S0hIKMwUcq+U/A5GVnQ21Wg0A8PGuD5VKhaPHTiA1NQ3HT4QiL08F38e6UWm1Wly4eFkaCcrRsRo0Gi2iY2IRfjNCmlCvqmBhQURERCbDVZmJCeH7YaHJnzwrzdIeyxr0Q7bCQs+RkbELXrtBmqfCwsICA/r1RWJiEjZv3Y6ExES83L8vLCwK74fhNyPg5eUJOzs7AIC5uTm6dO6IQ4eP4NatOHTq0L7S83gSdoV6RoH/W6HvEMrsg4nj9R0CERGR3tTOSsa4iD+x3K8PtHIF7tq4YLVfb7x/Yx/MhfbpP4CoFF58oW2h149//3J3d8OIV4c+8Wc0bOCPhg0Kj2zk7+cLfz/f8gmynPGOBREREZmchhm38XrMEel1hEMNbPDuDpYVRM+PhQURERGZpBeSI/By3Gnp9UUXb/xauz1n5yZ6TiwsiIiIyGT1SriEzon/SK8PezbF357N9RgRkeFiYUFEREQmSwZg6K2TaJESJS3bVftFnHWpmn3YiaoyFhZERERk0uQQGB11CD6Zd6VlG+t3wQ2HGnqMisjwsLAgIiIik2cuNHj35h/wzE4FAGjlCqz26414m+p6jozIcLCwICIiIgJgo8nD++H74Kh8CABQKiywzL8vki3t9RwZkWFgYUFERET0L6e8LEwM3wdrtRIA8MDCBv/z74cHZlZ6joyo6mNhQURERFSAZ04a3rv5O8y0agDAfWtHrPDvA6Wc8woTPQkLCyIiIqLH+DxIxFuRByET+bNa3LJzxxqfntDI+NWJqCQ8OoiIiIiK0SItBsNunZBehznVwS91O3ECPaISsLAgIiIiKkHnpGvodeei9PqUWwPsrRmgx4iIqi4WFkRERERP8PLtM2h7P1x6/UeN1jju1lCPERFVTSwsiIiIiJ5ABuD1mKNolB4nLdtStyMuO9XVW0xEVRELCyIiIqKnUAgtxkQcQO2H9wAAQibHOp8eiLZz13NkRFUHx00jIiIiKgVLrRoTwvdjYeNBuG9VDSq5GZb590Wj9DiEOdZBrsIcVhoVAlIi0D3hClyVmfoOmahS8Y4FERERUSnZq3Px/o19sFdlAwByzSxxwcUHuWYWgEyGXDMLnHRtgO+aDsO1arX0HC1R5WJhQURERPQMXJWZGBFzFPh3jgvIZIXWa+UK5CnMsca3F+5bOughQiL9YGFBRERE9IxuVKsF2VNmtFDL5Djk0bSSIiLSPxYWRERERM/onIsvxFNm4dbKFThb3a+SIiLSPxYWRERERM8oV2FeqnbKUrYjMgYsLIiIiIiekZVGVap2lqVsR2QMWFgQERERPaOAlAjItZonNxICAckRlRMQURXAwoKIiIjoGXVPuAIzoX1yI5kMKVb20DzlWQwiY8E9nYiIiOgZuSozMSbiACw0qqJ3LgoUHNcda2OtTw8WF2QSuJcTERERPYfGGfGYdnUbOty7Dit1HmRCwEqdh45JYXjhXrjU7pJzfRYXZBLM9B0AERERkaFyVWbi1Vsn8OqtE4WWCwDWGiUOezYDoCsugLcj/4biaV2oiAwUS2ciIiKiciYDMCQuFF0TrkjLLjnXRzDvXJAR455NREREVAF0xUW3AsXFZRYXZMS4VxMRERFVEBmAwcUUF2t8erK4IKPDPZqIiIioAj0qLi5Ly64418Man55Qs7ggI8K9mYiIiKiC5RcXp4oUF8EsLsiIcE8mIiIiqgS64qI7iwsyUtyLiYiIiCqJDMAgFhdkpLgHExEREVUiFhdkrLj3EhEREVUyXXHR4y4f6CbjwT2XiIiISA9kAAbGFy4urrK4IAPGvZaIiIhITx4VF5ekZSwuyFBxjyUiIiLSo/zi4nTR4sK3F4sLMijcW4mIiIj0TFdc9CxYXDjVZXFBBoV7KhEREVEVIAPwCosLMmDcS4mIiIiqiJKKiyAWF2QAuIcSERERVSGPiouL0rJ//i0ulFr9xUX0NGb6DoCIiIiICssvLs4AAP7yagkgv7gYHg1srQ9Y8tJwlZSRkYFjx08iITERZmbm8PXxxosvtIGZWdGv3MnJKTh05ChSUlLh5OSIrp07wd3dDQCQlZ2N3/84gOTkFPj6eqNbl86QyWQAgJjYWERHx6JH966VmVqpcLckIiIiqoJ0xUWvAncufssAhkWDdy6qII1Ggz37fodCocDQwYPQu2d3RMfE4tTps0XaqlQq7N6zDx7u7nh12BB4eXpgz779yMvLAwBcuHAJ1lZWGD50MOLj7yA29pb03nPnLiKgdatKy+tZsLAgIiIiqqJkAF5+rLjYw+KiSkq6dw8ZGZno0aMbnJ2dUKOGF15oG4CbNyOKtI2IjIJCLkfHDu3g7OyEjh3aw8LCAhGRUQCAtPR01K5dC87OTvBwd0NaejoAICo6Bi4uzqhWzaEyUys1vRUWSi3Q5BrwV+ajZf/kAF3CAbuLgP8/QEiKvqIjIiIiqhqk4uJO4eJiKIuLKsXJ0REv9+8LC3NzaZlMJoNGqynSNjEpCR6eHlL3JplMBk8PDyQmJgEA7O3skJycArVajdTUNNjZ2UEIgfPnLyKgdcvKSeg56KWwyNUCI2OAa7mPlim1wMuRQHNr4HIjYKoH8HYscDpLHxESERERVR0yAC/fPoPPPB4t28viokqxtrZGrVo1pddCCFy5+g88PT2LtM3OyoatjU2hZTY21niYlf/Ft2WL5oi9FYcVq9bA2sYa3vXrISoqGtVdXeDgUDXvVgB6eHg7LAd4PQYQjy/PBWLzgNlegKMZ4G0JLL0PHH4AvGBb2VESERERVS0yAN945f//3MT8/+qKi+18oLvKOX7iJJKTUzB82OAi69RqNRQKRaFlCoUCGk3+3Q1Hx2oYPWokcnOVsLGxzr9bceES+vbtjcioaISeOg0rKyv06tEdjo7VKiWf0qj0XfDYQ6C3AxDaoPByZ0X+AROUAmgFEPoQuJELtLIp9scQERERmRyZLL+4mM47F1WWEAJHj53A1X/C0LtXD7g4Oxdpo1CYSUWEjkajgXmB0aPkcjlsbKwB5D+T4ebmChtraxw6fBR9+/SGn68Pjh4/UbHJPKNKLyzGuwLf1wRsHvvNdSzzD5TP7gAWF4D24cBkd6BX1b3bQ0RERFTpZDJgTjHFxZAoFhf6JoTA34cO459rYXipVw/Ur1e32Ha2djbIzs4utCwrOwc2NkWvqAshcOHCJQS0bonUtDQo5HJUd3FB7Vo1kZSUVBFpPLcqc9NMLYAIJTDWBTjdAFhZG1iYBOxI03dkRERERFWLrrj4vEBxsS8zv7jIZXGhN8dPhOLmzUj07dMb3t71S2zn4e6OxKQkCJH/cIAQAomJifDwcC/S9ubNCHh4uMPe3h4yyCD+faBAqxUQjz9boGdVprDYkAKceAgsrQ20tgXedQU+dQdm3tV3ZERERERVj0wGfF1McTGUxYVeJCYm4fKVq3ihbQDc3FyRlZ0t/QPyJ71Tq9UAAB/v+lCpVDh67ARSU9Nw/EQo8vJU8PXxLvQztVotLly8LI0E5ehYDRqNFtExsQi/GSFNqFdVVJmZt89mA42sAbns0bLWNsAPifqLiYiIiKgq0xUXMgBz/v3OpLtzscMbsKoyl5CNX2RUNAAg9NQZhJ46U2jdxAnvInjtBvTo3hUNG/jDwsICA/r1xeEjxxB2/QZcXJzzh6q1sCj0vvCbEfDy8oSdnR0AwNzcHF06d8Shw0dgY22Dl3r3rJzkSqnKFBZe5sCfmYWXXc/NHx2KiIiIiIonk+WPqgk8Ki72FyguqHJ07NAOHTu0K3H9BxPHF3rt7u6GEa8OfeLPbNjAHw0b+Bda5u/nC38/3+cPtAJVmTr2TWcgUQV8Eg9EKfOfrfguEfikaFczIiIiIipAV1x8UWDKBF1xoZIpSn4jUTmqMoVFHUvgbz/gXDbQIgyYdgf4tgYwprq+IyMiIiKq+mQy4CvPosXFKr/eLC6oUui1K5RoXfj1C7bAMf/i2xIRERHRk+mKCwD4OiH/v9cda2OVX2+8e/NPmAtNyW8mKqMqc8eCiIiIiMpOV1zMLHDnQldc8M4FVSQWFkRERERGRiYDviy2uHiJxQVVGBYWREREREZIJgO+8gL63j4nLbvuWIvFBVUYFhZERERERqzfnfMsLqhSsLAgIiIiMnIsLqgysLAgIiIiMgHFFRcr/V5CHosLKicsLIiIiIhMxOPFxY1/71ywuKDywMKCiIiIyIQUW1z4s7igsmNhQURERGRiihQX1VhcUNmxsCAiIiIyQf3unEe/22el1ywuqKxYWBARERGZqL53LhQpLlb692FxQc+FhQURERGRCet75wL6xz8qLsKr1cwvLuRmeoyKDBELCyIiIiIT1+duMcWF30ssLuiZsLAgIiIiIhYXVGYsLIiIiIgIgK64OCO9Dq9WEytYXFApsbAgIiIiIkmfuxcLFRc3WVxQKbGwICIiIqJC+ty9iAFFigs+0E1PxsKCiIiIiIp4qUhxUYPFBT0RCwsiIiIiKhaLC3oW3CuIiIiIqEQv3b0IANhTqy2A/OJiuV8fDLt1AsfcG+Ociy9yFeaw0qgQkBKB7glX4KrM1GfIpCcsLIiIiIjoiV66exEyCPxW6wUAQES1Gviu6TDIhIBWnj9Ld66ZBU66NsCZ6n4YE3EAjTPi9Rky6QG7QhERERHRU/W+ewkvx5+WXguZXCoqdLRyBfIU5ljj2wv3LR0qO0TSMxYWRERERFQqve9eQp0HSU9tp5bJccijaSVERFUJCwsiIiIiKrUka6enttHKFThb3a8SoqGqhIUFEREREZVarsK8VO2UpWxHxoOFBRERERGVmpVGVap2lqVsR8aDhQURERERlVpASgTkWs0T28i1GrRJvllJEVFVwcKCiIiIiEqte8IVmAntE9uYCS26JV6tpIioqmBhQURERESl5qrMxJiIA7DQqIrcuZBrNbDQqDAm4gAnyTNBnCCPiIiIiJ5J44x4TLu6DYc8muJsdT8oFeaw1KjQJvkmuiVeZVFholhYEBEREdEzc1Vm4tVbJ/DqrRP6DoWqCHaFIiIiIiKiMmNhQUREREREZcbCgoiIiIiIyoyFBRERERERlRkLCyIiIiIiKjMWFkREREREVGYsLIiIiIiIqMxYWBARERERUZlxgjwiIiIionKg0Whw5NhxREVFQyFXoEWLZmjVskWxbZOTU3DoyFGkpKTCyckRXTt3gru7GwAgKzsbv/9xAMnJKfD19Ua3Lp0hk8kAADGxsYiOjkWP7l0rKavS4x0LIiIiIqJycOLkKSQmJmHgywPQtWtnnD13ATcjIou0U6lU2L1nHzzc3fHqsCHw8vTAnn37kZeXBwC4cOESrK2sMHzoYMTH30Fs7C3pvefOXURA61aVltOzYGFBRERERFRGKpUK18Kuo1OH9nBzc0X9enXRqmULXL16rUjbiMgoKORydOzQDs7OTujYoT0sLCwQERkFAEhLT0ft2rXg7OwED3c3pKWnAwCiomPg4uKMatUcKjO1UmNhQURERERURsnJKdBoNPD09JCWeXp6IOnePWi12kJtE5OS4OHpIXVvkslk8PTwQGJiEgDA3s4OyckpUKvVSE1Ng52dHYQQOH/+IgJat6y8pJ6RwT1jkZubi3/++Udvv//undt6+93l5dy5c8/8HmPIG3j23Jm3YWPepcO8DZupntOZd+mZat7lqUmTJrCysnpim6zsbFhZWcHM7NHXaxtra2i1WuTk5MDW1lZanp2VDUdHx0Lvt7Gxxv3kFABAyxbNsXP3HlwLu44aNbzgXb8eoqKiUd3VBQ4OVfNuBQDIhBBC30E8i3PnzuGNN97QdxhEREREZCJCQkIQEBDwxDY3wm8i9NRpvPPWm9KyjIxMbAj5GaNHvQ4HB3tp+c5dv8Hd3R3tXmwrLTt95izu3E3AkEGvAAC0Wi1yc5WwsbGGEAJbtu5A3769ce/efYSeOg0rKyv06tEdjo7Vyjnb52dwdyyaNGmCkJAQfYdBRERERCaiSZMmT21jplBAoync5Umj0eSvMyv8lVuhMJPWFWxrXqCdXC6HjY01gPxnMtzcXGFjbY1Dh49i8KCXcefOXRw9fgKvDOj3XDlVBIMrLKysrJ5aMRIRERERVSZbW1solUpoNBooFAoAQHZONhQKBaysLAu3tbNBdnZ2oWVZ2TmwsbEp8nOFELhw4RL693sJqWlpUMjlqO7iAoVcjjNn9dtF7HF8eJuIyAAZWC9WInpGPMYNT/XqLpDL5Uj49wFsAEhISISra3XI5YW/cnu4uyMxKUnazkIIJCYmwsPDvcjPvXkzAh4e7rC3t4cMMgjkv0erFahquwkLCyNU8GRkSicmU80bANL/HYbO1PI2ZbqRREyFqR7fppo3YNq5A6Z3jBsDc3NzNPD3w5Gjx5CUdA8xMbG4eOkKmjfN70aVlZ0NtVoNAPDxrg+VSoWjx04gNTUNx0+EIi9PBV8f70I/U6vV4sLFy9JIUI6O1aDRaBEdE4vwmxHShHpVBQsLI5Sbmyv9v0wmM5kTclZWFlQqFYD8vB/vu2is5s+fjzfeeAMqlcqkPoh029rULFmyBCtXrtR3GJXOlM9runOZKeUNmO453VSPcWPRsUM7uLm5Yeeu33DoyDG0CWgFX18fAEDw2g3SPBUWFhYY0K8vEhOTsHnrdiQkJuLl/n1hYWFR6OeF34yAl5cn7OzsAOQXL106d8Shw0dw61YcOnVoX7kJPoXBjQpFTxYUFITTp09DCIHmzZtj4sSJRW6/GaOgoCCcPHkSSqUSNWvWxJw5c2BmZgatVmvU+c+dOxfr1q2Ds7Mztm/fDk9PT32HVCm2bdsGjUaDV155BdbW1voOp9J888032L59O7Zu3Qpvb++nv8FImOp5bfny5Th27BjMzMzg4+ODGTNmmMzFA1M9p5vaMX706FHk5OQgNzcXAwcO1Hc4VA6M9+g0QStWrEBwcDC6dOkCLy8vHD58GL///ru03lhryKVLl2LdunXo1asXOnbsiNjYWLz11ltG/wE0d+5c7Ny5Exs2bICDgwPOnz+v75AqTVxcHH744Qf8+eefha5kG7NvvvkG+/btQ0hISLFfOIz1+DbV81pwcDDWrVuHQYMGoUmTJqhRo0ahosJY8wZM95xuasf4/PnzMXPmTAQFBWH27NkYO3YsMjIyABhfrqbE4EaFoqKEEMjOzkZoaCg+/vhjDBs2DBqNBqNGjUJeXh4ePnwIa2traYQCY5KWloYjR45g6tSp6N+/PwCgRYsWmD59Oo4dO4YuXbroOcKKMWvWLOzbtw/r1q1Do0aNUK9ePURF5d9eLTgahbHR5ebh4YGHDx9i9uzZUCqVGDRoUJHbx8YkNDQUGzZswJIlS9CwYUMA+cf9vX9nc/Xw8JC6yRjLFW1TPa8JIaDVanH+/Hm8/fbbGD58uLQuIyMDWVlZ8PT0NLrtrWOq53RTO8b/+usv/P7771i1ahVq1KgBlUqF/v3748cff8Ts2bONIkdTZbylvwmRyWQwMzODEEK6eqtQKJCcnIzAwEC8+uqrGD58OK5duwbAuK4E5E91n4rq1atLy1q3bg2NRoMbN27oMbKKc/PmTURHR0tFBQC0bNkS27Ztw4MHD4zui1ZButzi4+MxcOBAfPjhh5g1axZ27dqFvLw8PUdXcZydndGvXz9cvnxZyvOjjz7Ce++9h9GjR+P1119HcnIyZDIZtFrtU36aYTDV85pMJoNCoYBarcaDBw8AAHl5eZg0aRJGjRqFMWPGYPTo0UhNTTWq7a1jiud0wPSO8aSkJHh4eKBOnTqwtLREtWrV8N577+HChQtIT083muPZFLGwMBKWlpaoX78+lixZgk8++QS9evWCu7s7vvrqK8yaNQtubm4YN24c7t27Z1QPALq6usLOzg579uwBkP8BbG5ujtq1a0vjQxc8CRtD3nXr1sWyZcvQqFEj6WHG7t27w9nZGWfOnAEAo/jgeRJ7e3t4eXnh7bffxqRJkzBr1izs3LnTaIsLf39/dOrUCZcvX8bBgwfx3//+F3l5efjggw8wdepUKBQKjBw5EiqVyqi6ipjqeQ0Aatasib///htAftcgtVqNKVOm4P/+7/+g1WoxYsQIKJVKo9regGme0wHTO8Y1Gg2Sk5Oh1WqlC0aenp6Ij49HVlYW71gYMMPfO0k6sc6aNQtDhw6Fg4MDsrKyMHHiRHTo0AEvvPACli9fjnr16mHRokUAjGMYO92Hy48//ih1kzA3NwcAqasEAOkkrNFoDD5vIQQsLCyk0SF0J2QfHx+4uLhg06ZNAPJzNpYP3OK0a9cOgwcPBgB88MEH+PDDD/Hll18aZXGh246DBw+Gv78/vv76a2RmZuLTTz9Fr1690LNnTwQGBsLOzg7BwcF6jrb8mOp5TZf3Rx99BHNzc0yePBn37t3DqFGj0LlzZ/Ts2RNLliyBvb09AgMD9Rxt+TLFczpgmsd4nz594Orqivj4eCl/V1dXmJmZmeyIf8aCz1gYAd0wfAqFAv/973/x4MEDKJVKuLnlj22sVCphaWmJhg0bSrfWDVXB/qW6D5fiHnJTqVSFrmp9++23uH79OjZs2FA5gZazjIwMVKtWrdgPUd0DjdOmTcOECRPwyy+/4LXXXjOKD9yStGyZP563Wq2GmZkZ3n//fQDAl19+CblcjldeecXgn7nQbVdd1we5XI4ZM2bg+vXrCAsLKzQCmIODA1xdXZGamqrHiMuXKZ3XCtIdt7a2thg/fjxCQkJw9epVvPbaawDy93lHR0fUrl1bmr/GWJjSOb0gUzvGNRoN3NzcsHz5clhaWkr7vK77Y8HtvG/fPjRo0AD169fXV7j0jHjHwkgU7Fdvb2+P9PR0zJgxA0B+dwIg/4qPjY0NtFqtwV7NftqXZd3EM0D+3wEAFi1ahK1bt2Ly5MkVGltFWbduHdauXQug+C5Oug/jGjVqoFevXggNDUVERERlhqg3BT+E3n//fXz88ceYMWMG9u7dq+fInt+aNWuQkpJSqLuDXC6Xur0tWLAA69evl/Zv3XpbW1tp6F1DPb4fZyrnteLI5XJ06dIF/fv3h729PX788UcA+fu8XC6HpaUlLCwsjC7vxxnjOb0gXX6mcowLIaTj2tbWFmZmj65v6wYn0N2l+umnn/Dpp59Kr8kwcB4LAxMSEoK4uDikpKRg/Pjx8PX1LbRed0U/LCwMU6ZMgUKhQKdOnZCamopDhw5h48aN8PHx0VP0z2/Xrl1ITU3F7du3MWzYMNSsWbPQSVdHl/+oUaPQrFkz2NjYYMWKFfj555/RpEkTPUReNt9//z3WrFmDOnXqYO/evYVOwsW5du0aZsyYgaZNm2Ly5MmoVq1aJUVavp62nz+u4DCUwcHB6Ny5s0GOAf/dd99h7dq1+P3331G3bt0ntg0LC0NgYCC6dOmC69evY8+ePdi8ebNB5h0cHIywsDDMmzev2P7jxnpee1Leupyzs7Px999/46effoJCoUCrVq2Ql5eHgwcPYsuWLU89Nqqqp21zHWM7p+/evRupqanIzMzE+PHjYWlpWeIwusZwjN+6dQuZmZmoU6cObGxspAEZirtIePToUUycOBFHjhzB1q1bsWLFCmzYsMEgt7Mp4x0LA7Jo0SIEBgZCqVQiPj4ev/zyi7ROVx/qDlY/Pz8sWrQIvr6+iI6OBgCD/fBduHAh5s+fj4iICERGRmLSpElYuXIlbt26VaStLv9atWphzZo1CAoKMtgPoG+++QZbt27FrFmz4OTkhKysrKe+p3Hjxpg4cSIOHjxY6EqfISnNfv44uVwu3bl45513DOqDV+fbb7/F9u3bsXPnTtStW7dQrsXl/eDBA9y/fx87d+5EYmJiiWPfGwKNRoPffvsNP/30U7G5GuN5DXhy3rqH0W1sbNC/f39s2bIF3bt3h0KhgJOTE7Zu3WqwRQXw9G2uY0zn9O+//x7ff/89Tp06hX379uG1116DWq0u8Zk4Qz/GFyxYgA8++ADvvPMO3nvvPaxatQo5OTkl9jyoXr06qlevjqlTp2Lp0qVYv369QW5nkyfIIKSnp4uRI0eKQ4cOFVmn0Wie+LqkZYYgJiZGvPLKKyI0NFRaFhwcLDp27CimTp0qoqKiCrXXarVCCCH++OMPMWDAAHHz5s1Kjbe8fPPNN6Jt27YiMjJSKJVK0bJlS7F79+4nvkeXuxBCPHz4sKJDrBDPsp8XzLe49YZk9erVwt/fX9y7d6/IuqflnZWVJZRKZYXGV9EOHz4sWrRoIfz9/cW0adOKbWNM5zWd583bGJQmdyGM55x+8eJF0bt3bxEeHi7UarU4d+6cGDBggLh//36RtsZwjG/atEm0b99enDp1SkRHR4tFixaJESNGiMOHDwshit+vHzx4IBo3biwaNmworl+/XtkhUznhHQsDoVQqER4eLj2kqNFo8Nlnn2HChAkYPnw49u3bh3v37gF41Of+ypUr0vsNdXg6tVqN+Pj4Qs8WvP3222jVqhUOHjyIkJAQxMfHS+tkMhkiIiLQu3dvrF271iCv6O3fvx/r16/HunXr4O3tDYVCgY4dOyIsLAwApH64j9OtBwAbG5tKibW8Pct+rrvqpZvHwFD3cQDSg4m6SQ7VajVmzpyJ999/H8OGDcPu3buRlJQE4FHeV69eBZC/rQ39QfVatWqhXbt2WL9+PQ4ePIjPPvtMWpeTkwPAuM5rOs+St257A4bbv76g0uQOGMc5Hcg/l6nVaigUCigUCjg6OuLWrVuYPXs2hgwZgp07dxrNMS6EQEREBEaMGIEXXngB9erVw8SJE6FSqbBr1y4AxR+7dnZ2+Prrr7F37140aNCgssOmcmLYZ2Ujp/vwEELA1dUVHTt2RExMDHJzczFp0iQkJCSgbdu28PPzw08//YRt27ZJw/Ht27cPX375JdLS0vSZwnMRQkj/rKysULduXcTFxRUagq5Ro0YICAjArVu3EBoaKi3ft28fpkyZgoyMDLi4uOgj/DLr27cvjhw5ggYNGkgPugUEBGDbtm1ISkoqdgK8ffv24YsvvpC2tyGNCFXW/bxg3oZEt49rtVp069YNH3zwAebOnYt//vkHkydPRmxsLJo0aQI/Pz8sW7YM27dvl75w7du3D7NmzTLYvB9Xt25d3L17FzKZDIsWLcIff/yBr7/+GvPmzcPBgwelCwuGfF4rzrPkXXB7G9LxDZR9mxvqOV13jAP5X6T9/PykZwxWrVoFb29vNGrUCD4+Pli2bBl2794NpVIJwLCPcZlMhszMTJw9e1ZaZm5ujm7duiE2NhZA0YFIFi9ejJMnT2Lw4MGoV69eZYZL5a3S75FQqSUmJgohHt0yXL58uejWrZs4e/asmD59uoiLi5Parl69WnTo0EFERkYKIYS4fv16ofWGRJe3zldffSU6duwofv/9dxEdHS1iY2NFhw4dxOHDh8XKlStFhw4dRGZmphDCuPIuKCUlRYwcOVIEBQUJjUZT5Fa5MeRtqvu5Wq0WQggRGxsrpk2bJt555x0xfvx4kZGRIbUNCgoSHTp0EPHx8UII48hbt711+X/44Yfi559/FkIIERYWJpo2bSoaNGggYmNjpfcact6xsbHiypUrIiMjQ6hUKiFE/t/A2PMWwnS3+ePn9LNnz0r/f+TIEZGamiq9XrFihejQoYNISEgQQhh23kII8fPPP4sZM2aIO3fuSMvWrVsnunTpIpRKpXQMCCFEdna2mDJlirh69ao+QqVyxjsWVdS1a9cwYMAAhIeHS7cMx48fD39/f7z99ts4fvx4oYp/7NixsLW1lWZqbdCgAWrVqqWX2MuiYN46M2fORLdu3bB48WK8+eabGDp0KLp3744uXbrg3XffhZWVFS5fvgzAePJ+/GqOs7Mz/P39sXfvXmi1Wmnccx1jyNtU93PdHag6deqge/fuiIiIwMOHD2FmZiZ1exszZgysrKxw5MgRAMaRt+6Be13+AQEBUne+DRs2wMnJCVZWVoUmBDPUvEt6iFUul6N169ZGmzdgutu8uM+ygIAA6f87d+4MJycn6Rh/7733YG1tjWPHjgEwvLy3bt0qdeUEgEGDBmHcuHFwd3eXluXm5sLc3BwWFhbSCIehoaFQKBSYO3cuH9Q2EiwsqihbW1s8ePAAn3zySaG+80uWLMFLL72EpKQkXLp0Cbm5uQDy+2RXr15dmjzKUJWU9+zZszF37lzMnz8fQUFBmD17NgAgNTUVNjY20kzUhurxvAuOciT+vZU+depUpKWlSbkbw+za3M8L592rVy9MmDABX331FWxsbKQvYFlZWXB2di70IW2IitvPdaOXeXh4ICYmBtOnT8eZM2ewceNGrF27Fr/88gvmzJmj58if388//4zt27djxowZ2Lp1K9q1a4cjR47g1KlTAAA3NzejzFvHFLc5UPwx/vgFI5VKVegYr1atGlxdXSs91vKwe/dufPLJJ1JXJysrK9SpUwcKhUL6nFKpVIWeEVy4cCE+//xzpKWlFdvFlwwTC4sqSneCcXBwwIcffig9xGVmZoZvv/0WvXr1wrx58xAYGIiQkBD8+OOPiIyMlGYkNlQl5Q0AzZo1w4svvggPDw/s2rULR48exebNm5GVlVVohlJDVFzeuuJCd3fCysoKn332Ga5fv441a9YAMLy+1o/jfl50P3/jjTfg7e2Nq1ev4s8//8SpU6cQHByM27dvG/wDjcXlrbty2b59e8TFxeHixYtYsWIFatWqhebNm2PTpk14/fXX9Rz58xFPeIh19+7dAPKvXBtb3gWZ2jbXedI5HcifDG79+vXYuHEj/vzzT6xatQp37941uKGTdYVCvXr1cPPmTUyaNEkaClpH9zllbm4u3ZlesGAB1qxZg59++sngL5hQYSwsqqjz58/D0tISU6ZMQdu2bfHxxx9LXz4sLS2xZMkSjB49GnFxcdi8eTNiYmKwdu1a1KlTR8+Rl82T8tadwC5fvoz9+/fj22+/xZ9//onFixcb/ImppLx1H0S6k3Hr1q3RuHFjnDlzRho5yZBxPy95P9+xYwdmzpyJWbNm4dChQ1i9ejVq1qypz7DL7El5KxQKfPLJJ1i5cmWhsfpbtWoljZplaJ70EKtuHh65XG50eRdkattc50nndCEEqlWrhsTERGzevBkLFy7E+fPnDfIY191pyMjIwJAhQ1C3bl2MGTMGMTExRdrqPscWLlyI4OBg/Pzzz2jatGmlxksV78nT+JLe2NnZoVmzZmjatCns7OygVqvx8ccfY9GiRdKBOGHCBKjVaqhUKsjlclhaWuo56rIrTd69e/dGQEAANBoNzM3N4ejoqN+gy8HT8tZoNJDL5XB2dsbYsWNhZWVV7Mzjhob7ecl5z5o1C6NGjYKVlRVsbGzg5OSk56jLrqS8Fy5ciGbNmmHAgAFPnV3e0AQEBODatWu4e/cuvLy8AAAODg5ITU1Fbm4urK2tMXDgQOmqrkajMapuIaa4zYHSHeO6bkBAfsFpqF16c3NzYWZmhjZt2mDgwIEYO3Ysxo4di6CgINSrV0+aabtRo0a4ffs2fvnlF4Od5JBKQZ9PjtOTFRxR4ubNm+L//u//RPfu3cWVK1eEEKLY0YGMwdPy1mq1Jpm3Wq02ybxNdT8XouhEWcagNNvbmOTk5IjY2FhpJCQh8kcA6tmzZ6F2J0+eFFlZWZUdXqUwtW2uY0qfZdu3b5dGscrJyRFjxowR3bp1E9HR0VKbBw8eiHfeeUeEh4frK0yqBDIhDPzpTyMk/q3uARTqBhMREYGVK1fiypUrmDdvHlq0aKHHKMsf82beAPNm3i30GGX5KphzwddLly7F9u3bcfDgQQD5XUP27NmDTZs2GXy3zoJMcZsDpps38Chf3TxEMTEx0p2LguvJeLGwMDCRkZH48ccfkZCQgC1btsDc3NzgH+AtDebNvJm38TK1vFeuXIktW7bgr7/+woIFCxAcHIxNmzaZVH9zU9vmOqaUd25uLv7zn//g7Nmz2Llzp8E/G0elw8LCAEVFRcHOzs6ormyVBvNm3qaAeRt/3qtXr8Yvv/yC/v37Y82aNSbb39yUtnlBppR3Tk4Opk6dismTJ7OwMBEsLAzI47fVTQXzNi3M27SYYt6hoaF45513UK1aNQQFBZlcUWGK2xww3bzJtLCjWxWhmzAoJycHeXl5xbYxxhMS82bej2PexoN5F59306ZN0b59e2zYsMHoigpuc9PIWzcsNq9N0+NYWFQBGo0GZmZmuH37NkaMGIHw8HB9h1Tuijv5mELexWHezNsUMO/i89ZoNLCzs8Pq1avh5+enpygrBre5aeStGxI5KSkJW7duRW5urr5DoiqEhUUVoFAocOfOHYwcORKNGzc2qitYuklyirtSY8x5P+kqjjHn/STGnDe3d1HMu/i8dXNUGNPVax1uc+PPW6vVSvkOHDgQCQkJsLKy0ndYVIXwGYtKoOtXWdIwa3l5eZg1axbMzc3x1VdfGc0HzooVK3D27FmsXr262PXGmrcQAnl5eSVO5Gasec+ZMwcBAQHo06dPseuNNW9T3d4ajQZKpRI2NjbFrmfexpU3YLrHuKlu85K+s6SmpqJnz54YMGCAUeVL5YOFRQU7dOgQzp8/j08//RRyubzEAzU6Ohr169fXQ4QVY968eQgODoZCocDu3bvh7e1dbDtjyzsoKAiXLl1CYmIiXn31VQwfPrzYdsaW97x587B+/Xps374dDRo0KLGdseVtqtt71apVuHjxImJjY9GvXz8MHjwYNWrUKNKOeRsPUz3GTW2bX7lyBXl5eahVqxZcXFykWdELPngeFxeHkydPYsSIESwqqAh2hapgly9fxo4dOxAcHAyNRiMVFzq6us4YTkg63377LbZt24bly5ejUaNGyMjIAFC4u4gx5r106VIEBwejcePGaNOmDb744gscPXq0UBvdA2/GlPe3336LHTt2YNu2bSV+4dDt88aUt6lu78DAQKxbtw5t2rTB4MGDsXHjRixfvrxQG2M8vk01b8B0j3FT2+bz58/HxIkTMX36dAwaNAg//vgjLl68CCC/654u19q1a+O1115jUUHFq5D5vEmo1WohhBA7duwQTZs2FaNGjRKBgYHSco1Go8/wKswXX3whXnzxRXHjxg0hhBCjRo0SM2bM0HNUFS8zM1OMGzdO/PXXX9KysWPHioMHD4qUlJRCbbVabWWHV2H2798v/P39RWxsbKHlDx48EHfv3i20zJjyNtXtnZiYKEaMGCGOHDkiLQsNDRX+/v7i+PHjeoysYplq3kKY7jFuatv87NmzokOHDuLMmTPi4cOHYs+ePeL9998Xb731ljh06JDUzpi2MVUM3rGoILoH9K5fvw5fX1+0bNkSp06dwooVK4q9c2HohBCIiYnB3r17ERQUBH9/fwBAmzZtEBUVVaidMbK0tERiYiKuXLkiLQsLC8OCBQvQp08fTJ48Gb///jsA43pos3v37vDy8kJQUJC0bOLEiRgzZgz69OmDmTNn4ty5cwCMK29T3d6WlpZIS0tDWloagPzjuXnz5vD19UVERIS0zNiYat6AaR/jprTNc3Nz4ebmhlatWsHW1hb9+/fHuHHj4OXlhWXLlkl3Y41pG1PFMNN3AMZOJpOhQ4cOmDRpEhYvXozQ0FAAwPjx46FQKEp85sLQyGQy1KtXD0ePHoWtra2U18CBA7F27Vrs3r0br7zyitGelMzMzNCvXz8cOHAAMTExuHnzJurVq4fx48dDCIGQkBBs27YN1atXR0BAgL7DLRcajQYWFhaYN28evv32W6xdu1YaZvHtt9+GXC7HqlWrkJGRAU9Pz2L7JRsqU9zeQP4XKUtLS6SmpiIvLw8WFhawtraGg4MDbt++DeDRqDHGxFTzNuVj3NS2uYuLC6Kjo3HhwgW0adMGANCqVStYWlpi48aNCAkJgYuLCxo3bqznSKmqY2FRwSZNmoSkpCRYWlri/fffx4oVK4yuuBAFHuqytbUFAOmOTI0aNdCnTx+cPHkSPXv2hLW1tVEWF3K5HP3795fu3OTk5GDq1Klo1qwZAMDb2xsffvghzpw5YzRfNHUfqA0bNkTfvn1x+PBh5Obm4ocffkCtWrUA5Of91ltv4eTJkyU+2GyITHF7A4CTkxNmz54NlUoFMzMzCCGg1WqRl5cnTRCm2y+Sk5NRvXp1fYZbbkw1b1M+xk1pmwshUL9+ffTt2xcbNmyAk5MTfHx8AACNGzfG4MGD8b///Q/Hjh1D48aNDf47C1Us7hkVzMHBAb6+vlCr1bCzs8P48ePRokULhIaGYtWqVVK3KENW8KGuguRyOczMzNC1a1ccOHAAYWFh0rC7xqh27dqYOHEivvvuOzRp0gSpqakA8k/aNWvWROvWrfHPP//oOcryZ2dnhz59+iA7Oxvp6enSkIwajQa+vr4ICAgwyrxNcXsLIdCiRQu0adMGcrkcMpkMCoVCGjlGZ9GiRVi8eLHRTJxlqnnrmOIxbkrbXCaTwdLSEr1790ZGRga2bNmCW7duSevbtm2Lnj17YuPGjcjKyjL47yxUsbh3VBIzMzNotVqpuGjVqhX279+PtWvX6ju05xYYGIg5c+YAeHK/y969e2PAgAH4/PPPce/ePaN7vqQgrVYLIQQUCgWOHj2KlJQU6W+TmZkpXeUzRDt27MDly5eLXVe7dm3Mnj0bK1euhIuLi/Q3AB713TVUDx48KHGdMW/v4vIu6TgXQsDCwgIAsHDhQixfvhzDhw83iomztFqtSeb9OGM+xh9nSttcCCFdGOzWrRtefvllXLp0CRs2bMCNGzekdi1btoSrqyuUSqW+QiUDwa5QZbR582ZERETA2toa7dq1Q/v27Utsq/tCbWdnh3HjxsHc3LzEiYaqOt08FU/r5qHrJjV69GgkJydjzpw5+Oqrr+Dk5FRJkZavp21vuVwOuVyO4cOH44MPPkBycjIcHR1hYWGBI0eOICQkRE+Rl82cOXOwfft27N27t9j1QghpGMpLly7hjz/+gLOzM9LS0nDhwgVMmzatMsMtN5s3b8aNGzfw7rvvwsvLq8h6Y93eT8u7II1GA7VaDa1Wi/Xr12PNmjXYvn27QfbF3rdvH5KSkqBSqdCkSRO0b98ecrkcGo2mSF96Y8obAPbv34/ExERYWFigZ8+ecHd3L7TeWI/xHTt24ObNm5DJZGjVqhV69eoFuVxeqIuvjjFs86ioKFhZWUGhUMDDw0PKUZfvsGHDIJPJsHfvXvz444947bXXUL9+fezZswd5eXlF7tgQPY57SBn89NNP2LRpE/r374/Dhw/DxcXliYUF8Ki4sLe3x6RJkwzyeYO5c+di9+7d+PLLLxEcHIw7d+6U+NCeLj9vb2907NgRx44dk/qnGprSbm8hBDp16oRVq1Zh165d0t9n48aNUr9VQ/LNN99g7969CAkJKfZL5uMfwA8ePMDx48dha2sLR0dHbNy40WDHeI+MjMTWrVvh4OCAoUOHonbt2kXaGNv2BkqXNwDpqrWPjw82btwIOzs7hISEGNQXLZ2FCxfi119/RdeuXREeHo6///4bu3fvxnfffQeFQgG1Wl1osjBjyRsAfvjhB/z6668ICAjA4cOHceXKFcybN09ab6zH+IIFC7Bjxw707t0b4eHhuHLlCuLi4jB27Ngin83GsM0XLlyIQ4cOIT09Hb6+vhg9ejS6dOkCAFI3ZblcjqFDh8LLywuHDx/Gp59+itq1a+Phw4dYunQpHBwc9JwFVXkVMISt0dNqtSIlJUUMHDhQHDhwoNj1Orp5K4zF7NmzRUBAgAgLCxP3798XXbp0EZcvXxZClDy+dcHl6enplRJneXrW7a3VaqVlSqVSCCFEXl5e5QRbzjZv3iz8/f3FpUuXCi1XqVTF7ue6ZZmZmUKtVovs7OzKC7Yc6fL4448/ROPGjcWoUaPE/PnzRVxcXKE2Bf8JYfjb+1nyLmjPnj2iTZs20vw1hiYuLk706tVLHD58WAghRFZWlvj1119Fhw4dxMcffyy1e/x8buh5CyHE1atXRY8ePaRj/OzZs6JFixaFtrkQxneM37hxQ/Tu3VucOXNGCJGfz5w5c8TIkSNFWlpaie8z1G2+detW0alTJ3Hp0iVx4MAB8cEHH4gvv/yySLvH9/H4+HgRFxdXZH4eopLwjsVz0FX2mZmZsLe3BwDk5eVh3rx5iIqKgouLC5o2bYq3334bCoVCuo2emJgIDw8PPUf//Pbu3YuDBw9i/fr1aNiwIYD8OxHr16/H/PnzS7z7kpSUJOVdrVq1Sou3vJTH9jY3N9dnCs8tKysLLVq0kPrgqtVqfP/994iKioKdnR2aNWuGsWPHFso7ISEBnp6eAABra2t9hv/cdPty8+bN0apVK7Ro0QJHjx6FWq3G22+/DXd39yL7uzFs7+fJOyEhAf3790eHDh3g6Oioh6jLLjs7GxkZGdIzMTY2Nujduzd+//137N+/HxqNBosXLy40ip8x5A0AaWlp0Gq18Pb2BgA4OzvDxcUF8+bNg1wuR7NmzTBu3LgiuRv6MZ6ZmQmlUglfX18AgL29PYYOHYoNGzYgMjKy2G6+hrzN4+Pj0b59ezRv3hwAEB0djbCwMJw7dw5CCGmIWYVCId2hUqvVqFmzpj7DJgPEh7efU/Xq1WFvb48DBw4AAP7v//4PUVFRaNu2LSwsLPDrr79i1qxZAPIP1F27dmHYsGHIycnRZ9hl0rx5c/z8889o2LCh1J2pQ4cOuH37tjQihnhsdKjdu3cbfN6AaW5vAHjnnXfg5uaGefPmQalU4uOPP8b169fRtGlT2NraYufOnZg5cyaAR3kPHz7c4PMG8vtTm5mZ4eHDh3jttdfw1ltv4fTp09i6dSs+//xz/PLLL1JbY9nPgWfLu+D2NrQvWgXVrFkT1tbW0qSGQH5x8eKLL2LMmDFITEzEwoULAeR3ZzWWvAGgbt26qFGjBuLi4gDkd3XVarVo0KABrKyssGvXLnz99dcAiuZuyOrUqQOVSoXjx48DAFQqFdzc3FCtWjWoVKoi7Xfu3GmQ21z3maxSqZCamoq4uDgolUrs2rULV65cweeff4733nsPn332mbRNZTIZgoKCcOLECX2GToZKr/dLDExKSkqhLgC//vqrGDVqlNi4caP46KOPRGRkpBBCiJycHLF161YxdOhQcf78eSGEEAkJCSI2NlYvcZdVSkpKiV26kpKSREBAgFi5cmWx6xMTEw06b1Pd3gXz1mq1ok+fPqJfv37iiy++EElJSUKI/G4/27ZtE6+++qq4fv26EMK48taZPn262LJlixAivztB27ZtRevWrcXJkyelNszb8BTMW6lUioULF4o33nhDrFu3TmRnZ4sbN26I9u3bix07doiNGzeKcePGiYyMDCGEYectRNHcr169Kq0LCwuTur3k5OSIzZs3ixEjRojo6GghhGHnXjDvBw8eiA0bNoirV68KjUYjhBAiNzdXtGrVSuzdu7fIe9PS0kRMTExlhluuIiIiROvWrUW3bt1E+/btxauvviqSk5NFcnKyuHDhgmjRooX44YcfpPZvvPGGdF4neha8Y1FKBw8exMcff4yIiAjpCkCbNm3g5uaG3377DTdu3JBuo1tZWaFr165ITExEREQEAMDDwwN16tTRW/zPS5d3VFRUkXVarRZubm549913ceLEiWLbuLu7G3Teprq9dXkA+VevFixYgHv37iEmJgaOjo7SkIudO3dGbGwsIiMjARhX3rohka2trXH27FkAwD///AOFQgF3d3ecOXMGsbGxAJi3oXn8+LawsMDQoUPRvHlzrF27Fr169cKwYcPQs2dPDB48GF27dsWZM2dw/fp1AIabN1B87k2aNAGQv+39/f3h7OwM4NG5LTIyEjdv3gRguLkXzFs3OuPw4cPRqFEjaV4G3fDRBa1cuRIrV66Eo6Mj6tatq4fIy4ePjw+2bt2Kjz76CE2bNkWnTp3g4uICJycntGzZEt988w0OHDiAhIQEAMDGjRulUcCIngULi1KqX78+zpw5g4ULFyIyMhJCCNSoUQPvvfcebG1tER8fj02bNkntq1evDj8/P4Ptf6pTMO+CXz4ASCfjtm3bIiUlBceOHQNQtDuUIeL2Lry9a9eujenTp+OLL76AhYWF1M/e0dERfn5+0rMnhqq4vHX796BBg2Bubo5PPvkEx48fx6+//oq3334bu3fvxp49e4rtNmEomPdC6Qt2rVq1MGnSJISEhGDOnDnYsmULvvrqKwCAra0tGjdubFBdYEry+LlNRwghDZ9csJuTk5MTGjRoADs7O32EW24K5q27CGZpaSnt7xqNBpmZmVCr1dLIR4sWLcKSJUvQuXNnvcVdnurVq4eBAwciICBAmo9Cdy63sLCAra0tR32iMmNhUUoKhQIuLi44ffo0pk6diujoaOnqzvTp09GzZ0/8+uuvmDlzJvbu3Ys5c+bgypUraNmypb5DL5PH8y54V0JXQLRo0QIjR47ETz/9hNDQUIMcQvdx3N6Ft7etrS0GDhwIPz8/REREICQkBJGRkVi8eDEiIyMNdlhVnSft51ZWVti+fTuuXLmCZcuWwd3dHcOHD8eECRMwcOBAg31QG2Dep0+fxrRp0xAdHQ0g/7kKT09PdO3aFQ4ODtiwYQPCw8MRHByMxMREg51/p6CStrnuvP3w4UPs2rULS5Yswblz57BgwQJERUWhXr16+gy7zJ60r+vWm5ubQ6FQQKFQYOnSpQgODsbPP/9sdFfufX19peGxZTIZNBoNbty4AVtbW6O4MEj6xcKilK5evQovLy8cP34cKpWq0JdNb29vTJkyBaNGjcI///yDDRs2ICoqChs3bjTomXeB4vMu+EGk6zbx+uuvo3fv3pg6dSry8vIM/uTE7V10e8vlcuTm5mLXrl1YuHAhPvroIxw+fBhBQUElzmNiKErKW6vVwsfHB8uWLcPKlSvh6+sLjUYDABg+fLjRbm9TzVtHo9EgISEBP//8M/773//i77//RmBgoFHMLv203LVaLe7du4fNmzdjzpw5OHv2LNasWfPUiRKruqflDeTfnfHy8sLUqVOxYsUKhISESN3EjEn79u0xYcIETJs2DYMGDcJbb72FzZs34/PPPzf4O1NUBejjwQ5DdP78eWnM5+zsbNG3b18xZMgQERkZWeTBx7y8PJGbm6uPMMvdk/LWKfgwXGJiol7iLG/c3iVvb912jomJeeJ474akNHkbI+b95Lxzc3NFYmKi9NC2MShN7iqVSiiVSpGamioePHigr1DLVWnyViqVYtKkSaJt27YGN0/Fs1Kr1eLs2bNi6dKlYsuWLUXmLSF6XjIhDPzSciV5+PAhcnJy4OrqCiB/HoNBgwbB2toa33//vTQGuLFh3sy7YN7169c3iq5uj+P2Zt6mkDdgurmXNu/w8HBYW1uXONs8ET0ZC4vnoFKpYG5uLp2Y7O3t8fXXX8PPz0/foVUo5s28mbfxYt6mlTdgurkXl/fs2bPh6+srPcxNRM+HhcVzUqvVMDMzQ15eHrp374769etj9erVsLCw0HdoFYp5M2/mbbyYt2nlDZhu7qaaN1FFY2FRBgVPTImJiSZz65R5M29TwLyZt6kw1dxNNW+iisTCoox0JyZTw7xNC/M2Lczb9Jhq7qaaN1FFYWFBRERERERlxqeUiIiIiIiozFhYEBERERFRmbGwICIiIiKiMmNhQUREREREZcbCgoiIiIiIyoyFBRGRCeKAgEREVN5YWBAR6dnt27fh7++P33//vVJ+X2BgIDZt2iS9fvPNNzF+/PgK/73+/v4ICgoqcX1l/x2IiKh8cVYYIiITs2TJEvz3v/+VXs+aNQtyuf6vM7m5uWHz5s2oW7euvkMhIqLnwMKCiMjE+fj46DsEAICFhQVatGih7zCIiOg56f8SFRGRHj18+BBz5sxBt27d0KRJE7z44ouYOnUqMjMzpTYajQbLly9Hz5490bx5cwwcOBB//fVXqdcDwPr169G7d280adIE/fv3x759+54Y161btzBx4kS0bNkSAQEBmDJlClJTU6X106ZNw8SJEzF58mS0atUKn3zyCQAgOjoa//nPf/Diiy+iSZMm6N69O5YuXSo9U+Hv7w8A+P7779G9e3cARbtCpaamYsaMGejcuTOaN2+O0aNH4+rVq9L6HTt24IUXXkBoaCgGDhyIJk2aoF+/fvj777+f+vdOS0vDxIkT0axZM3Tv3h1r166V1j3eFWratGn4z3/+g3Xr1qFbt25o1qwZ3nzzTURFRUnvuX//Pj766CO88MILaN68OV5//XWcOXPmqXEQEVH5Y2FBRCZt8uTJOHjwICZPnoygoCCMGTMGe/bswf/+9z+pzdy5cxEYGIghQ4Zg+fLlaN68Of7zn//g3LlzpVofGBiIefPmoV+/fli+fDnat2+PTz/9FPv37y82puTkZLz++uu4e/cuvv/+e3z11Ve4dOkSxo4di7y8PKndkSNHoFQqsXTpUowYMQJZWVkYPXo00tPTMW/ePKxYsQIvvPACFi9ejEOHDgEANm/eDCC/mAgMDCzyu7OysjBy5EicPHkSkydPxsKFCyGEwKhRoxAeHl6o3fTp0/HGG29gxYoVcHJywieffIL09PQn/r3XrFkDOzs7LF26FC+99BLmzp2LrVu3ltj+5MmT2LlzJz7//HP88MMPuHXrFqZNmyat/+yzzxAXF4e5c+fif//7H6ytrTF+/PinxkFEROWPXaGIyGQplUqoVCp8+eWX6Ny5MwDghRdewMWLF6Wr3unp6di0aRMmTZqEiRMnAgDatWuHmJgYnDt3Dj4+Pk9c7+fnh5UrV2LcuHH4+OOPAQAdO3ZEVlYWfvzxR/Tt27dIXOvWrYNSqcSaNWvg7OwMAGjWrBleeukl7Nu3D4MGDQIAqNVqzJ49W2rzzz//oHbt2li0aJG0rF27dvjrr79w9uxZdO/eXepq5OnpiUaNGhX53Tt27EBcXBx+++03qYtUx44d0adPHwQGBmLJkiUAAJVKhSlTpqBfv34AABcXFwwcOBCnT5/GSy+9VOLfvGXLlvj+++8BAJ06dUJCQgJWrFiB4cOHF9s+KysLK1asgJubGwAgKSkJ33zzDdLS0uDk5IRz587h/fffl+6++Pr6Ijg4GDk5OXB0dCwxDiIiKn8sLIjIZFlaWmLNmjUA8rvhxMbGIiIiAlFRUbC0tAQAXL58GRqNRvriqrNhwwYA+XcNnrT+6NGjUCqV6Nq1K9RqtbS+c+fO2L59O+Lj4yGTyQq99/Tp02jRogUcHByk93h6esLb2xuhoaFSYeHs7CwVEADQpEkTbNq0CSqVCpGRkYiNjUVYWBjUanWhOx1PcvbsWfj4+BR67sLCwgI9e/bErl27CrUt+DyEh4cHACAnJ+eJP79Xr16FXnfr1g379+9HWlpase29vLykouLx3+Pk5ISWLVti8eLFCA8PR5cuXdClSxdMnTr16YkSEVG5Y2FBRCbt77//xty5cxEfHw8nJyc0adIEVlZW0Gq1AICMjAwAKPQFvqCnrdd1yXnttdeKXX///v1CX5x177l8+TIaN25cpL2rq6v0/y4uLkXWL1++HKtXr8aDBw9Qo0YNtGzZEmZmZqWetyIzMxPVq1cvsrx69erIysoqtMzKykr6f92oUrq/W0ke/9m6v9u9e/dga2tbpL21tXWh14//noULF2Lp0qXYv38/9u7dC3NzcwwZMgQzZsyAhYXFE2MhIqLyxcKCiExWbGwsPvroIwwePBgbN26UroZ/9NFH0gPC9vb2APIfOnZ3d5fee/36dQghSr1+6dKlhdbr1KtXr8jzAHZ2dujcuTP+85//FGlf3JdvnZ07d2LRokWYNWsWBgwYIP3udu3aPfVvoVOtWjVER0cXWX7//v1y6VqkK8R0UlJSAACOjo5QqVTP/PMcHR3x+eef4/PPP8f169exe/duBAcHo2bNmnjvvffKHC8REZUeH94mIpMVFhYGlUqF9957TyoqsrOzcf78eekKf7NmzWBmZiY9/Kwzc+ZMBAUFPXV98+bNYW5ujpSUFDRt2lT6FxERgaVLlxYbV+vWrREdHQ1/f3+pvZ+fHwIDA3H+/PkS87l48SI8PDwwcuRIqai4du0aUlNTC92xeNKcFa1bt0ZkZGShkZfy8vLw119/oVWrViW+r7SOHTtW6PUff/yBunXrFlt0PU1qaiq6du2KAwcOAAAaNmyIqVOnwsvLCwkJCWWOlYiIng3vWBCRyWrYsCEUCgV++OEHjBw5EmlpaVizZg2Sk5OlbjQuLi547bXXsGzZMpiZmaFJkybYv38/rl+/jpkzZz51vbOzM95880189913yMjIQLNmzXDjxg0sXLgQPXr0gJ2dXZE7Fu+88w527dqFcePGYfTo0TA3N8eaNWtw6dIl6QHw4jRt2hS//PILAgMD0bZtW0RFRWHp0qWQyWTIzc2V2jk4OOD8+fMICAhA8+bNC/2MIUOGYN26dXj33Xfx8ccfw97eHmvXrkVycjImTJhQ5r95aGgo5s2bh86dO+PAgQM4ePAgFi5c+Fw/y9nZGXXq1MGcOXOQlZUFT09PHD58GHfu3CnyLAcREVU8FhZEZLLq1auHefPmITAwEO+99x5cXV3RuXNnDB06FLNnz0ZSUhLc3d0xffp0ODk5ISQkBGlpafD19cWqVavQtGlTAHjq+ilTpsDZ2RlbtmzB4sWL4ebmhrfeegsffPBBsXF5eXlh06ZN+OGHHzBlyhTIZDI0btwYwcHBaNiwYYn5DBkyBDExMfjll1+wevVq1KhRA2PHjkVUVFShOx0ffPABFi1ahHPnzuHkyZOFfoadnR1CQkIwb948zJ49GxqNBi1atEBISEixo0g9q08++QTHjh3Dxo0b4enpifnz50sjSz2PBQsW4Pvvv8f8+fORnp6OevXq4ccff0T79u3LHCsRET0bmSjtE31EREREREQl4DMWRERERERUZiwsiIiIiIiozFhYEBERERFRmbGwICIiIiKiMmNhQUREREREZcbCgoiIiIiIyoyFBRERERERlRkLCyIiIiIiKjMWFkREREREVGb/D8NnJaqwFzDeAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_incidence(pig_tables,\n",
-    "               variable='acceleration',\n",
-    "               model_type=\"regression\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By using the following function, we can plot the PIG graph for each of the predictors in the basetable in one step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "acceleration\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "cylinders\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "displacement\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "horsepower\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "model_year\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "name\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "origin\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "weight\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting PIGs now for ALL predictors:\n",
-    "for predictor in list(pig_tables.variable.unique()):\n",
-    "    print(predictor)\n",
-    "    try:\n",
-    "        if predictor + \"_bin\" in basetable.columns:\n",
-    "            column_order = list(basetable[predictor + \"_bin\"].unique().sort_values())\n",
-    "        else:\n",
-    "            column_order = None \n",
-    "        plot_incidence(pig_tables,\n",
-    "                       variable=predictor,\n",
-    "                       model_type=\"regression\",\n",
-    "                       column_order=column_order)\n",
-    "    except ValueError as ve:\n",
-    "        print(f\"Can't plot PIG for {predictor}. Error was: {ve}\")\n",
-    "    except TypeError as ve:\n",
-    "        print(f\"Can't plot PIG for {predictor}. Error was: {ve}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Feature selection"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once the predictors are properly prepared, we can start building a predictive model, which boils down to selecting the right predictors from the dataset to train a model on.\n",
-    "As a dataset typically contains many predictors, **we first perform a univariate preselection** to rule out any predictor with little to no predictive power. Later, using the list of preselected features, we build a multiple regression model using **forward feature selection** to choose the right set of predictors."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In previous steps, these were the predictors, as preprocessed so far:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['acceleration_bin',\n",
-       " 'cylinders_processed',\n",
-       " 'displacement_bin',\n",
-       " 'horsepower_bin',\n",
-       " 'model_year_bin',\n",
-       " 'name_processed',\n",
-       " 'origin_processed',\n",
-       " 'weight_bin']"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preprocessed_predictors = [\n",
-    "    col for col in basetable.columns\n",
-    "    if col.endswith(\"_bin\") or col.endswith(\"_processed\")\n",
-    "]\n",
-    "sorted(preprocessed_predictors)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But for feature selection, we use the target encoded version of each of these."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['acceleration_enc',\n",
-       " 'cylinders_enc',\n",
-       " 'displacement_enc',\n",
-       " 'horsepower_enc',\n",
-       " 'model_year_enc',\n",
-       " 'name_enc',\n",
-       " 'origin_enc',\n",
-       " 'weight_enc']"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preprocessed_predictors = [col for col in basetable.columns.tolist()\n",
-    "                           if '_enc' in col]\n",
-    "sorted(preprocessed_predictors)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A univariate selection on the preprocessed predictors can be conducted. The thresholds for retaining a feature can be changed by the user, for instance both at 10 for now.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from cobra.model_building import univariate_selection\n",
-    "\n",
-    "\n",
-    "df_rmse = univariate_selection.compute_univariate_preselection(\n",
-    "    target_enc_train_data=basetable[basetable[\"split\"] == \"train\"],\n",
-    "    target_enc_selection_data=basetable[basetable[\"split\"] == \"selection\"],\n",
-    "    predictors=preprocessed_predictors,\n",
-    "    target_column=target_col,\n",
-    "    model_type=\"regression\",\n",
-    "    preselect_rmse_threshold = 10, #RMSE maximum for selection\n",
-    "    preselect_overtrain_threshold = 10) #difference between RMSE on train and selection set\n",
-    "\n",
-    "from cobra.evaluation import plot_univariate_predictor_quality\n",
-    "plot_univariate_predictor_quality(df_rmse)\n",
-    "\n",
-    "#As the square root of a variance, RMSE can be interpreted as the standard deviation of \n",
-    "# the unexplained variance, and has the useful property of being in the same units as the response variable. \n",
-    "#Lower values of RMSE indicate better fit."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we compute correlations between the preprocessed predictors and plot it using a correlation matrix:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                origin      name  cylinders  acceleration  displacement  \\\n",
-      "origin        1.000000  0.589247   0.615303      0.322869      0.717992   \n",
-      "name          0.589247  1.000000   0.804929      0.494469      0.845581   \n",
-      "cylinders     0.615303  0.804929   1.000000      0.550769      0.908731   \n",
-      "acceleration  0.322869  0.494469   0.550769      1.000000      0.550468   \n",
-      "displacement  0.717992  0.845581   0.908731      0.550468      1.000000   \n",
-      "weight        0.640605  0.848314   0.880700      0.469703      0.936249   \n",
-      "model_year    0.161989  0.587830   0.308751      0.251305      0.297790   \n",
-      "horsepower    0.517359  0.832709   0.802506      0.668878      0.865908   \n",
-      "\n",
-      "                weight  model_year  horsepower  \n",
-      "origin        0.640605    0.161989    0.517359  \n",
-      "name          0.848314    0.587830    0.832709  \n",
-      "cylinders     0.880700    0.308751    0.802506  \n",
-      "acceleration  0.469703    0.251305    0.668878  \n",
-      "displacement  0.936249    0.297790    0.865908  \n",
-      "weight        1.000000    0.266933    0.859803  \n",
-      "model_year    0.266933    1.000000    0.345704  \n",
-      "horsepower    0.859803    0.345704    1.000000  \n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from cobra.evaluation import plot_correlation_matrix\n",
-    "\n",
-    "# compute correlations between preprocessed predictors:\n",
-    "df_corr = (univariate_selection\n",
-    "           .compute_correlations(basetable[basetable[\"split\"] == \"train\"],\n",
-    "                                 preprocessed_predictors))\n",
-    "print(df_corr)\n",
-    "\n",
-    "# plot correlation matrix\n",
-    "plot_correlation_matrix(df_corr)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get a list of the selected predictors after the univariate selection, run the following call:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['weight_enc',\n",
-       " 'horsepower_enc',\n",
-       " 'displacement_enc',\n",
-       " 'cylinders_enc',\n",
-       " 'model_year_enc',\n",
-       " 'origin_enc',\n",
-       " 'acceleration_enc',\n",
-       " 'name_enc']"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preselected_predictors = (univariate_selection\n",
-    "                          .get_preselected_predictors(df_rmse))\n",
-    "preselected_predictors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After an initial preselection on the predictors, we can start building the model itself using forward feature selection to choose the right set of predictors. Since we use target encoding on all our predictors, we will only consider models with positive coefficients (no sign flip should occur) as this makes the model more interpretable."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Modeling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cb1dc6cdf2204eb084b2bacaa8756a2c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sequentially adding best predictor...:   0%|          | 0/8 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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>predictors</th>\n",
-       "      <th>last_added_predictor</th>\n",
-       "      <th>train_performance</th>\n",
-       "      <th>selection_performance</th>\n",
-       "      <th>validation_performance</th>\n",
-       "      <th>model_type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>[name_enc]</td>\n",
-       "      <td>name_enc</td>\n",
-       "      <td>1.033698</td>\n",
-       "      <td>7.400305</td>\n",
-       "      <td>7.353274</td>\n",
-       "      <td>regression</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>[name_enc, horsepower_enc]</td>\n",
-       "      <td>horsepower_enc</td>\n",
-       "      <td>1.028891</td>\n",
-       "      <td>7.274417</td>\n",
-       "      <td>7.220308</td>\n",
-       "      <td>regression</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>[horsepower_enc, name_enc, model_year_enc]</td>\n",
-       "      <td>model_year_enc</td>\n",
-       "      <td>1.019866</td>\n",
-       "      <td>7.106559</td>\n",
-       "      <td>7.056552</td>\n",
-       "      <td>regression</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>[horsepower_enc, model_year_enc, name_enc, wei...</td>\n",
-       "      <td>weight_enc</td>\n",
-       "      <td>1.016552</td>\n",
-       "      <td>6.978043</td>\n",
-       "      <td>6.908673</td>\n",
-       "      <td>regression</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>[weight_enc, horsepower_enc, model_year_enc, n...</td>\n",
-       "      <td>cylinders_enc</td>\n",
-       "      <td>1.014342</td>\n",
-       "      <td>6.998041</td>\n",
-       "      <td>6.920083</td>\n",
-       "      <td>regression</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>[weight_enc, horsepower_enc, cylinders_enc, mo...</td>\n",
-       "      <td>acceleration_enc</td>\n",
-       "      <td>1.013128</td>\n",
-       "      <td>6.984950</td>\n",
-       "      <td>6.902685</td>\n",
-       "      <td>regression</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                          predictors last_added_predictor  \\\n",
-       "0                                         [name_enc]             name_enc   \n",
-       "1                         [name_enc, horsepower_enc]       horsepower_enc   \n",
-       "2         [horsepower_enc, name_enc, model_year_enc]       model_year_enc   \n",
-       "3  [horsepower_enc, model_year_enc, name_enc, wei...           weight_enc   \n",
-       "4  [weight_enc, horsepower_enc, model_year_enc, n...        cylinders_enc   \n",
-       "5  [weight_enc, horsepower_enc, cylinders_enc, mo...     acceleration_enc   \n",
-       "\n",
-       "   train_performance  selection_performance  validation_performance  \\\n",
-       "0           1.033698               7.400305                7.353274   \n",
-       "1           1.028891               7.274417                7.220308   \n",
-       "2           1.019866               7.106559                7.056552   \n",
-       "3           1.016552               6.978043                6.908673   \n",
-       "4           1.014342               6.998041                6.920083   \n",
-       "5           1.013128               6.984950                6.902685   \n",
-       "\n",
-       "   model_type  \n",
-       "0  regression  \n",
-       "1  regression  \n",
-       "2  regression  \n",
-       "3  regression  \n",
-       "4  regression  \n",
-       "5  regression  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from cobra.model_building import ForwardFeatureSelection\n",
-    "\n",
-    "forward_selection = ForwardFeatureSelection(model_type=\"regression\",\n",
-    "                                            #model_name=\"linear-regression\",\n",
-    "                                            max_predictors=30,\n",
-    "                                            pos_only=True)\n",
-    "\n",
-    "# fit the forward feature selection on the train data\n",
-    "# has optional parameters to force and/or exclude certain predictors (see docs)\n",
-    "forward_selection.fit(basetable[basetable[\"split\"] == \"train\"],\n",
-    "                      target_column_name = target_col,\n",
-    "                      predictors = preselected_predictors)\n",
-    "                      #forced_predictors: list = [],\n",
-    "                      #excluded_predictors: list = [])\n",
-    "\n",
-    "# compute model performance\n",
-    "performances = (forward_selection\n",
-    "                .compute_model_performances(basetable, target_column_name = target_col))\n",
-    "performances"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As can be seen, we have completed 7 steps till no further improvement can be observed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from cobra.evaluation import plot_performance_curves\n",
-    "\n",
-    "# plot performance curves\n",
-    "plot_performance_curves(performances)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Based on the performance curves (RMSE per model with a particular number of predictors in case of linear regression), a final model can then be chosen and the variables importance can be plotted:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# look at the number of steps that were completed before stopping, fill that in between the brackets\n",
-    "model = forward_selection.get_model_from_step(5)\n",
-    "final_predictors = model.predictors\n",
-    "final_predictors\n",
-    "from cobra.evaluation import plot_variable_importance\n",
-    "variable_importance = model.compute_variable_importance(\n",
-    "    basetable[basetable[\"split\"] == \"selection\"])\n",
-    "plot_variable_importance(variable_importance)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "Finally, if wanted, we can again export the model to a dictionary to store it as JSON"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_dict = model.serialize()\n",
-    "\n",
-    "model_path = os.path.join(\"output\", \"model.json\")\n",
-    "with open(model_path, \"w\") as file:\n",
-    "    json.dump(model_dict, file)\n",
-    "\n",
-    "# To reload the model again from a JSON file, run the following snippet:\n",
-    "# from cobra.model_building import LinearRegressionModel\n",
-    "# with open(model_path, \"r\") as file:\n",
-    "#     model_dict = json.load(file)\n",
-    "# model = LinearRegressionModel()\n",
-    "# model.deserialize(model_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have build and selected a final model, it is time to evaluate its predictions on the test set against various evaluation metrics. The used evaluation metrics are:\n",
-    "1. Coefficient of Determination: R2\n",
-    "2. Mean Absolute Error: MAE\n",
-    "3. Mean Squared Error: MSE\n",
-    "4. Root Mean Squared Error: RMSE\n",
-    "\n",
-    "Furthermore, plotting makes the evaluation of a linear regression model a lot easier. We will use a **prediction plot**, which presents predictions from the model against actual values and a **Q-Q plot** from the standardized prediction residuals."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from cobra.evaluation import RegressionEvaluator\n",
-    "# get numpy array of True target labels and predicted scores:\n",
-    "y_true = basetable[basetable[\"split\"] == \"selection\"][target_col].values\n",
-    "y_pred = model.score_model(basetable[basetable[\"split\"] == \"selection\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "evaluator = RegressionEvaluator()\n",
-    "evaluator.fit(y_true, y_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "R2       0.212337\n",
-       "MAE      5.693276\n",
-       "MSE     48.789531\n",
-       "RMSE     6.984950\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluator.scalar_metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-1.80914284 -1.69602694 -1.54625806 -1.52796195 -1.52796195 -1.43096001\n",
-      " -1.4166163  -1.39415103 -1.23121612 -1.11939584 -1.11529326 -1.11529326\n",
-      " -1.00932902 -0.86988885 -0.86622091 -0.85797337 -0.81040141 -0.7666015\n",
-      " -0.75564988 -0.75534556 -0.75353778 -0.73048975 -0.67149665 -0.55635089\n",
-      " -0.52644138 -0.51947589 -0.51008658 -0.44480889 -0.43233562 -0.42600252\n",
-      " -0.41192963 -0.3883056  -0.38066679 -0.27453724 -0.21954411 -0.21870163\n",
-      " -0.21520367 -0.18527119 -0.10706373 -0.09507113 -0.08611862 -0.06744586\n",
-      " -0.0570345  -0.00456674  0.0447204   0.21727495  0.25711972  0.28618844\n",
-      "  0.29316605  0.30227995  0.30716803  0.3191798   0.35465539  0.36659538\n",
-      "  0.4470963   0.49493455  0.55680189  0.58678743  0.61650618  0.64239132\n",
-      "  0.65855833  0.70672433  0.70834831  0.74037924  1.00426728  1.0070275\n",
-      "  1.11123842  1.11201745  1.25885088  1.26591737  1.45293858  1.48038975\n",
-      "  1.57310488  1.61822906  1.64258739  1.64723939  1.67383875  2.00773111\n",
-      "  3.14192066]\n"
-     ]
-    }
-   ],
-   "source": [
-    "residuals = evaluator.qq['residuals']\n",
-    "print(residuals)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "evaluator.plot_predictions()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "evaluator.plot_qq()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/tutorials/Cobra_Tutorial_LogisticRegression.ipynb b/docs/tutorials/Cobra_Tutorial_LogisticRegression.ipynb
deleted file mode 100644
index f874ceb..0000000
--- a/docs/tutorials/Cobra_Tutorial_LogisticRegression.ipynb
+++ /dev/null
@@ -1,2155 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial for Logistic Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Cobra** requires the usual Python packages for data science:\n",
-    "- numpy (>=1.19.4)\n",
-    "- pandas (>=1.1.5)\n",
-    "- scipy (>=1.5.4)\n",
-    "- scikit-learn (>=0.23.1)\n",
-    "- matplotlib (>=3.3.3)\n",
-    "- seaborn (>=0.11.0)\n",
-    "\n",
-    "These packages, along with their versions are listed in requirements.txt and can be installed using pip.\n",
-    "\n",
-    "\n",
-    "Note: if you want to install cobra with e.g. pip, you don't have to install all of these requirements as these are automatically installed with cobra itself."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install -r requirements.txt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The easiest way to install cobra is using pip:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*****"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This section we will walk you through all the required steps to build a predictive logistic regression model using **Cobra**. All classes and functions used here are well-documented. In case you want more information on a class or function, run the next cell:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "help(function_or_class_you_want_info_from)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Building a good model involves three steps\n",
-    "\n",
-    "1. **Preprocessing**: properly prepare the predictors (a synonym for “feature” or variable that we use throughout this tutorial) for modelling.\n",
-    "\n",
-    "2. **Feature Selection**: automatically select a subset of predictors which contribute most to the target variable or output in which you are interested.\n",
-    "\n",
-    "3. **Model Evaluation**: once a model has been build, a detailed evaluation can be performed by computing all sorts of evaluation metrics.\n",
-    "\n",
-    "\n",
-    "\n",
-    "Let's dive in!!!\n",
-    "***"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Survival Prediction using Titanic data\n",
-    "- GOAL : Predict if individuals survives in titanic sinking\n",
-    "- BASETABLE : seaborn dataset Titanic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "import the necessary libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "from pandas.api.types import is_datetime64_any_dtype\n",
-    "\n",
-    "pd.set_option('display.max_columns', 50)\n",
-    "pd.set_option(\"display.max_rows\", 50)\n",
-    "from cobra.preprocessing import PreProcessor\n",
-    "from cobra.evaluation import generate_pig_tables, plot_incidence\n",
-    "from cobra.evaluation import evaluator"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "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>survived</th>\n",
-       "      <th>pclass</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>age</th>\n",
-       "      <th>sibsp</th>\n",
-       "      <th>parch</th>\n",
-       "      <th>fare</th>\n",
-       "      <th>embarked</th>\n",
-       "      <th>class</th>\n",
-       "      <th>who</th>\n",
-       "      <th>adult_male</th>\n",
-       "      <th>deck</th>\n",
-       "      <th>embark_town</th>\n",
-       "      <th>alive</th>\n",
-       "      <th>alone</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>male</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7.2500</td>\n",
-       "      <td>S</td>\n",
-       "      <td>Third</td>\n",
-       "      <td>man</td>\n",
-       "      <td>True</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Southampton</td>\n",
-       "      <td>no</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>female</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.2833</td>\n",
-       "      <td>C</td>\n",
-       "      <td>First</td>\n",
-       "      <td>woman</td>\n",
-       "      <td>False</td>\n",
-       "      <td>C</td>\n",
-       "      <td>Cherbourg</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>female</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7.9250</td>\n",
-       "      <td>S</td>\n",
-       "      <td>Third</td>\n",
-       "      <td>woman</td>\n",
-       "      <td>False</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Southampton</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>female</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>53.1000</td>\n",
-       "      <td>S</td>\n",
-       "      <td>First</td>\n",
-       "      <td>woman</td>\n",
-       "      <td>False</td>\n",
-       "      <td>C</td>\n",
-       "      <td>Southampton</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>male</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>S</td>\n",
-       "      <td>Third</td>\n",
-       "      <td>man</td>\n",
-       "      <td>True</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Southampton</td>\n",
-       "      <td>no</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
-       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
-       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
-       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
-       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
-       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
-       "\n",
-       "     who  adult_male deck  embark_town alive  alone  \n",
-       "0    man        True  NaN  Southampton    no  False  \n",
-       "1  woman       False    C    Cherbourg   yes  False  \n",
-       "2  woman       False  NaN  Southampton   yes   True  \n",
-       "3  woman       False    C  Southampton   yes  False  \n",
-       "4    man        True  NaN  Southampton    no   True  "
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import seaborn as sns\n",
-    "df=sns.load_dataset('titanic')\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the example below, we assume the data for model building is available in a pandas DataFrame. This DataFrame should contain a an ID column, a target column (e.g. “**survived**”) and a number of candidate predictors (features) to build a model with.\n",
-    "\n",
-    "***\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "survived          int64\n",
-       "pclass            int64\n",
-       "sex              object\n",
-       "age             float64\n",
-       "sibsp             int64\n",
-       "parch             int64\n",
-       "fare            float64\n",
-       "embarked         object\n",
-       "class          category\n",
-       "who              object\n",
-       "adult_male         bool\n",
-       "deck           category\n",
-       "embark_town      object\n",
-       "alive            object\n",
-       "alone              bool\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.dtypes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "it is required to set all category vars to object dtype\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.loc[:, df.dtypes == 'category'] = (df.select_dtypes(['category']).apply(lambda x: x.astype('object')))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data preprocessing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### The first part focusses on preparing the predictors for modelling by:\n",
-    "\n",
-    "1. Defining the ID column, the target, discrete and contineous variables\n",
-    "\n",
-    "2. Splitting the dataset into training, selection and validation datasets.\n",
-    "\n",
-    "3. Binning continuous variables into discrete intervals\n",
-    "\n",
-    "4. Replacing missing values of both categorical and continuous variables (which are now binned) with an additional “Missing” bin/category\n",
-    "\n",
-    "5. Regrouping categories in new category “other”\n",
-    "\n",
-    "6. Replacing bins/categories with their corresponding incidence rate per category/bin.\n",
-    "\n",
-    "*Disclaimer*: Cobra's Preprocesser is valid only if the original data does not contain extreme irregularities, such as outliers or very skewed distributions. This should always be checked beforehand by its user."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this toy dataset, the index will serve as ID,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[\"id\"] = df.index + 1\n",
-    "id_col = \"id\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "and survived is the target,\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "target_col = \"survived\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we remove the columns 'who' and 'adult_male' since they are duplicate of 'sex', and also 'alive', which seems to be a duplicate of 'survived'\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.drop(['who','adult_male','alive'], axis=1, inplace=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finding out which variables are categorical (\"discrete\") and which are continous:\n",
-    "\n",
-    "\n",
-    " => discrete are definitely those that contain strings:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['sex', 'embarked', 'class', 'deck', 'embark_town']\n",
-      "\n",
-      "sex\n",
-      "['male', 'female'] \n",
-      "\n",
-      "embarked\n",
-      "['S', 'C', 'Q', nan] \n",
-      "\n",
-      "class\n",
-      "['Third', 'First', 'Second'] \n",
-      "\n",
-      "deck\n",
-      "[nan, 'C', 'E', 'G', 'D', 'A', 'B', 'F'] \n",
-      "\n",
-      "embark_town\n",
-      "['Southampton', 'Cherbourg', 'Queenstown', nan] \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "col_dtypes = df.dtypes\n",
-    "discrete_vars = [col for col in col_dtypes[col_dtypes==object].index.tolist() if col not in [id_col, target_col]] \n",
-    "print(discrete_vars)\n",
-    "print()\n",
-    "for col in discrete_vars:\n",
-    "    print(col)\n",
-    "    print(df[col].unique().tolist() ,\"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we also check for numerical columns that only contain a few different values, thus to be interpreted as discrete, categorical variables\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pclass\n",
-      "[3, 1, 2] \n",
-      "\n",
-      "sibsp\n",
-      "[1, 0, 3, 4, 2, 5, 8] \n",
-      "\n",
-      "parch\n",
-      "[0, 1, 2, 5, 3, 4, 6] \n",
-      "\n",
-      "alone\n",
-      "[False, True] \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for col in df.columns:\n",
-    "    if col not in discrete_vars and col not in [id_col, target_col]: # if we didn't mark it as discrete already because it was string typed, or also excluding it if it is the target:\n",
-    "        val_counts = df[col].value_counts()\n",
-    "        if len(val_counts) > 1 and len(val_counts) <= 10: # The column contains less than 10 different values. \n",
-    "            print(col)\n",
-    "            print(df[col].unique().tolist() ,\"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By taking a look at the printed variables, it is clear that we have to include those in the list of discrete variables. This can be done as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['sex',\n",
-       " 'embarked',\n",
-       " 'class',\n",
-       " 'deck',\n",
-       " 'embark_town',\n",
-       " 'pclass',\n",
-       " 'sibsp',\n",
-       " 'parch',\n",
-       " 'class',\n",
-       " 'deck',\n",
-       " 'alone']"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "discrete_vars.extend([\"pclass\",\"sibsp\",\"parch\",\"class\",\"deck\",\"alone\"])\n",
-    "discrete_vars"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The remaining variables can be labelled continous predictors, without including the target variable.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['fare', 'age']"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "continuous_vars = list(set(df.columns)\n",
-    "                       - set(discrete_vars) \n",
-    "                       - set([id_col, target_col]))\n",
-    "continuous_vars                       "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we can prepare **Cobra's Preprocessor**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The target encoder's additive smoothing weight is set to 0. This disables smoothing and may make the encoding prone to overfitting.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# using all Cobra's default parameters for preprocessing for now:\n",
-    "preprocessor = PreProcessor.from_params(\n",
-    "    model_type=\"classification\")\n",
-    "\n",
-    "# These are the options though:\n",
-    "# --> help(PreProcessor.from_params)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "split data into train-selection-validation set:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from cobra.preprocessing import PreProcessor\n",
-    "basetable = preprocessor.train_selection_validation_split(\n",
-    "                data=df,\n",
-    "                train_prop=0.6,\n",
-    "                selection_prop=0.2,\n",
-    "                validation_prop=0.2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And fit the preprocessor pipeline:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a22c82ef58b34bd087fe1fe54268aa07",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Computing discretization bins...:   0%|          | 0/2 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9faa45a97cd944b0b129990bd7077cb4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Discretizing columns...:   0%|          | 0/2 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "50cb5554d6ce44ba8a64594afffd5f1e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Fitting category regrouping...:   0%|          | 0/11 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1cdd7a702c01427cb7c3909fd8bf3083",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Fitting target encoding...:   0%|          | 0/13 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "preprocessor.fit(basetable[basetable[\"split\"] == \"train\"],\n",
-    "                 continuous_vars=continuous_vars,\n",
-    "                 discrete_vars=discrete_vars,\n",
-    "                 target_column_name=target_col)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This pipeline can now be performed on the basetable!!\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d2f5d77137b746efa248826b014256f8",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Discretizing columns...:   0%|          | 0/2 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "22c41ed6071b4a2b851e81203e7b78e2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Applying target encoding...:   0%|          | 0/13 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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>survived</th>\n",
-       "      <th>pclass</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>age</th>\n",
-       "      <th>sibsp</th>\n",
-       "      <th>parch</th>\n",
-       "      <th>fare</th>\n",
-       "      <th>embarked</th>\n",
-       "      <th>class</th>\n",
-       "      <th>deck</th>\n",
-       "      <th>embark_town</th>\n",
-       "      <th>alone</th>\n",
-       "      <th>id</th>\n",
-       "      <th>split</th>\n",
-       "      <th>fare_bin</th>\n",
-       "      <th>age_bin</th>\n",
-       "      <th>sex_processed</th>\n",
-       "      <th>embarked_processed</th>\n",
-       "      <th>class_processed</th>\n",
-       "      <th>deck_processed</th>\n",
-       "      <th>embark_town_processed</th>\n",
-       "      <th>pclass_processed</th>\n",
-       "      <th>sibsp_processed</th>\n",
-       "      <th>parch_processed</th>\n",
-       "      <th>alone_processed</th>\n",
-       "      <th>sex_enc</th>\n",
-       "      <th>embarked_enc</th>\n",
-       "      <th>class_enc</th>\n",
-       "      <th>deck_enc</th>\n",
-       "      <th>embark_town_enc</th>\n",
-       "      <th>pclass_enc</th>\n",
-       "      <th>sibsp_enc</th>\n",
-       "      <th>parch_enc</th>\n",
-       "      <th>alone_enc</th>\n",
-       "      <th>fare_enc</th>\n",
-       "      <th>age_enc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>male</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7.2500</td>\n",
-       "      <td>S</td>\n",
-       "      <td>Third</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Southampton</td>\n",
-       "      <td>False</td>\n",
-       "      <td>1</td>\n",
-       "      <td>train</td>\n",
-       "      <td>0.0 - 7.7</td>\n",
-       "      <td>19.0 - 22.0</td>\n",
-       "      <td>male</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>Third</td>\n",
-       "      <td>Missing</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.190202</td>\n",
-       "      <td>0.378987</td>\n",
-       "      <td>0.237113</td>\n",
-       "      <td>0.289216</td>\n",
-       "      <td>0.378987</td>\n",
-       "      <td>0.237113</td>\n",
-       "      <td>0.523438</td>\n",
-       "      <td>0.345679</td>\n",
-       "      <td>0.493023</td>\n",
-       "      <td>0.160714</td>\n",
-       "      <td>0.255814</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>female</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71.2833</td>\n",
-       "      <td>C</td>\n",
-       "      <td>First</td>\n",
-       "      <td>C</td>\n",
-       "      <td>Cherbourg</td>\n",
-       "      <td>False</td>\n",
-       "      <td>2</td>\n",
-       "      <td>train</td>\n",
-       "      <td>46.9 - 78.1</td>\n",
-       "      <td>36.0 - 41.0</td>\n",
-       "      <td>female</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>First</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.734043</td>\n",
-       "      <td>0.378987</td>\n",
-       "      <td>0.637681</td>\n",
-       "      <td>0.597403</td>\n",
-       "      <td>0.378987</td>\n",
-       "      <td>0.637681</td>\n",
-       "      <td>0.523438</td>\n",
-       "      <td>0.345679</td>\n",
-       "      <td>0.493023</td>\n",
-       "      <td>0.615385</td>\n",
-       "      <td>0.303030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>female</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>7.9250</td>\n",
-       "      <td>S</td>\n",
-       "      <td>Third</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Southampton</td>\n",
-       "      <td>True</td>\n",
-       "      <td>3</td>\n",
-       "      <td>selection</td>\n",
-       "      <td>7.9 - 8.1</td>\n",
-       "      <td>25.0 - 28.0</td>\n",
-       "      <td>female</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>Third</td>\n",
-       "      <td>Missing</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.734043</td>\n",
-       "      <td>0.378987</td>\n",
-       "      <td>0.237113</td>\n",
-       "      <td>0.289216</td>\n",
-       "      <td>0.378987</td>\n",
-       "      <td>0.237113</td>\n",
-       "      <td>0.346995</td>\n",
-       "      <td>0.345679</td>\n",
-       "      <td>0.306250</td>\n",
-       "      <td>0.161290</td>\n",
-       "      <td>0.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>female</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>53.1000</td>\n",
-       "      <td>S</td>\n",
-       "      <td>First</td>\n",
-       "      <td>C</td>\n",
-       "      <td>Southampton</td>\n",
-       "      <td>False</td>\n",
-       "      <td>4</td>\n",
-       "      <td>selection</td>\n",
-       "      <td>46.9 - 78.1</td>\n",
-       "      <td>31.0 - 36.0</td>\n",
-       "      <td>female</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>First</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.734043</td>\n",
-       "      <td>0.378987</td>\n",
-       "      <td>0.637681</td>\n",
-       "      <td>0.597403</td>\n",
-       "      <td>0.378987</td>\n",
-       "      <td>0.637681</td>\n",
-       "      <td>0.523438</td>\n",
-       "      <td>0.345679</td>\n",
-       "      <td>0.493023</td>\n",
-       "      <td>0.615385</td>\n",
-       "      <td>0.480000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>male</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>S</td>\n",
-       "      <td>Third</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Southampton</td>\n",
-       "      <td>True</td>\n",
-       "      <td>5</td>\n",
-       "      <td>train</td>\n",
-       "      <td>7.9 - 8.1</td>\n",
-       "      <td>31.0 - 36.0</td>\n",
-       "      <td>male</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>Third</td>\n",
-       "      <td>Missing</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.190202</td>\n",
-       "      <td>0.378987</td>\n",
-       "      <td>0.237113</td>\n",
-       "      <td>0.289216</td>\n",
-       "      <td>0.378987</td>\n",
-       "      <td>0.237113</td>\n",
-       "      <td>0.346995</td>\n",
-       "      <td>0.345679</td>\n",
-       "      <td>0.306250</td>\n",
-       "      <td>0.161290</td>\n",
-       "      <td>0.480000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class deck  \\\n",
-       "0         0       3    male  22.0      1      0   7.2500        S  Third  NaN   \n",
-       "1         1       1  female  38.0      1      0  71.2833        C  First    C   \n",
-       "2         1       3  female  26.0      0      0   7.9250        S  Third  NaN   \n",
-       "3         1       1  female  35.0      1      0  53.1000        S  First    C   \n",
-       "4         0       3    male  35.0      0      0   8.0500        S  Third  NaN   \n",
-       "\n",
-       "   embark_town  alone  id      split     fare_bin      age_bin sex_processed  \\\n",
-       "0  Southampton  False   1      train    0.0 - 7.7  19.0 - 22.0          male   \n",
-       "1    Cherbourg  False   2      train  46.9 - 78.1  36.0 - 41.0        female   \n",
-       "2  Southampton   True   3  selection    7.9 - 8.1  25.0 - 28.0        female   \n",
-       "3  Southampton  False   4  selection  46.9 - 78.1  31.0 - 36.0        female   \n",
-       "4  Southampton   True   5      train    7.9 - 8.1  31.0 - 36.0          male   \n",
-       "\n",
-       "  embarked_processed class_processed deck_processed embark_town_processed  \\\n",
-       "0              Other           Third        Missing                 Other   \n",
-       "1              Other           First          Other                 Other   \n",
-       "2              Other           Third        Missing                 Other   \n",
-       "3              Other           First          Other                 Other   \n",
-       "4              Other           Third        Missing                 Other   \n",
-       "\n",
-       "  pclass_processed  sibsp_processed  parch_processed alone_processed  \\\n",
-       "0                3                1                0           False   \n",
-       "1                1                1                0           False   \n",
-       "2                3                0                0            True   \n",
-       "3                1                1                0           False   \n",
-       "4                3                0                0            True   \n",
-       "\n",
-       "    sex_enc  embarked_enc  class_enc  deck_enc  embark_town_enc  pclass_enc  \\\n",
-       "0  0.190202      0.378987   0.237113  0.289216         0.378987    0.237113   \n",
-       "1  0.734043      0.378987   0.637681  0.597403         0.378987    0.637681   \n",
-       "2  0.734043      0.378987   0.237113  0.289216         0.378987    0.237113   \n",
-       "3  0.734043      0.378987   0.637681  0.597403         0.378987    0.637681   \n",
-       "4  0.190202      0.378987   0.237113  0.289216         0.378987    0.237113   \n",
-       "\n",
-       "   sibsp_enc  parch_enc  alone_enc  fare_enc   age_enc  \n",
-       "0   0.523438   0.345679   0.493023  0.160714  0.255814  \n",
-       "1   0.523438   0.345679   0.493023  0.615385  0.303030  \n",
-       "2   0.346995   0.345679   0.306250  0.161290  0.500000  \n",
-       "3   0.523438   0.345679   0.493023  0.615385  0.480000  \n",
-       "4   0.346995   0.345679   0.306250  0.161290  0.480000  "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "basetable = preprocessor.transform(basetable,\n",
-    "                                   continuous_vars=continuous_vars,\n",
-    "                                   discrete_vars=discrete_vars)\n",
-    "basetable.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Predictor Insights Graphs"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we can the create the so-called Predictor Insights Graphs (PIGs in short). These are graphs that represents the insights of the relationship between a single predictor and the target. More specifically, the predictor is binned into groups, and we represent group size in bars and group (target) incidence in a colored line. Moreover, we have the option to force order of predictor values. First, we compute the output needed to plot the PIG."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "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>variable</th>\n",
-       "      <th>label</th>\n",
-       "      <th>pop_size</th>\n",
-       "      <th>global_avg_target</th>\n",
-       "      <th>avg_target</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>age</td>\n",
-       "      <td>1.0 - 14.0</td>\n",
-       "      <td>0.101124</td>\n",
-       "      <td>0.455056</td>\n",
-       "      <td>0.722222</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>age</td>\n",
-       "      <td>14.0 - 19.0</td>\n",
-       "      <td>0.073034</td>\n",
-       "      <td>0.455056</td>\n",
-       "      <td>0.692308</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>age</td>\n",
-       "      <td>19.0 - 22.0</td>\n",
-       "      <td>0.067416</td>\n",
-       "      <td>0.455056</td>\n",
-       "      <td>0.250000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>age</td>\n",
-       "      <td>22.0 - 25.0</td>\n",
-       "      <td>0.061798</td>\n",
-       "      <td>0.455056</td>\n",
-       "      <td>0.363636</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>age</td>\n",
-       "      <td>25.0 - 28.0</td>\n",
-       "      <td>0.078652</td>\n",
-       "      <td>0.455056</td>\n",
-       "      <td>0.357143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>sibsp</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.044944</td>\n",
-       "      <td>0.455056</td>\n",
-       "      <td>0.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>sibsp</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.016854</td>\n",
-       "      <td>0.455056</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>sibsp</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.005618</td>\n",
-       "      <td>0.455056</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>sibsp</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.005618</td>\n",
-       "      <td>0.455056</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>sibsp</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.016854</td>\n",
-       "      <td>0.455056</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>52 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   variable        label  pop_size  global_avg_target  avg_target\n",
-       "0       age   1.0 - 14.0  0.101124           0.455056    0.722222\n",
-       "1       age  14.0 - 19.0  0.073034           0.455056    0.692308\n",
-       "2       age  19.0 - 22.0  0.067416           0.455056    0.250000\n",
-       "3       age  22.0 - 25.0  0.061798           0.455056    0.363636\n",
-       "4       age  25.0 - 28.0  0.078652           0.455056    0.357143\n",
-       "..      ...          ...       ...                ...         ...\n",
-       "2     sibsp            2  0.044944           0.455056    0.500000\n",
-       "3     sibsp            3  0.016854           0.455056    0.000000\n",
-       "4     sibsp            4  0.005618           0.455056    1.000000\n",
-       "5     sibsp            5  0.005618           0.455056    0.000000\n",
-       "6     sibsp            8  0.016854           0.455056    0.000000\n",
-       "\n",
-       "[52 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from cobra.evaluation import generate_pig_tables\n",
-    "predictor_list = [col for col in basetable.columns\n",
-    "                  if col.endswith(\"_bin\") or col.endswith(\"_processed\")]\n",
-    "pig_tables = generate_pig_tables(basetable[basetable[\"split\"] == \"selection\"],\n",
-    "                                 id_column_name=id_col,\n",
-    "                                 target_column_name=target_col,\n",
-    "                                 preprocessed_predictors=predictor_list)\n",
-    "pig_tables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then, we can plot a PIG graph for each of the predictors in the basetable. For instance, for the variable age."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_incidence(pig_tables,\n",
-    "               variable='age',\n",
-    "               model_type=\"classification\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By using the following function, we can plot the PIG graph for each of the predictors in the basetable in one step."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "age\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "alone\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "class\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "deck\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "embark_town\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "embarked\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "fare\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "parch\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "pclass\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sex\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sibsp\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plotting PIGs now for ALL predictors:\n",
-    "for predictor in list(pig_tables.variable.unique()):\n",
-    "    print(predictor)\n",
-    "    try:\n",
-    "        if predictor + \"_bin\" in basetable.columns:\n",
-    "            column_order = list(basetable[predictor + \"_bin\"].unique().sort_values())\n",
-    "        else:\n",
-    "            column_order = None \n",
-    "        plot_incidence(pig_tables,\n",
-    "                       variable=predictor,\n",
-    "                       model_type=\"classification\",\n",
-    "                       column_order=column_order)\n",
-    "    except ValueError as ve:\n",
-    "        print(f\"Can't plot PIG for {predictor}. Error was: {ve}\")\n",
-    "    except TypeError as ve:\n",
-    "        print(f\"Can't plot PIG for {predictor}. Error was: {ve}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Feature selection"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once the predictors are properly prepared, we can start building a predictive model, which boils down to selecting the right predictors from the dataset to train a model on.\n",
-    "As a dataset typically contains many predictors, **we first perform a univariate preselection** to rule out any predictor with little to no predictive power. Later, using the list of preselected features, we build a logistic regression model using **forward feature selection** to choose the right set of predictors."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In previous steps, these were the predictors, as preprocessed so far:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['age_bin',\n",
-       " 'alone_processed',\n",
-       " 'class_processed',\n",
-       " 'deck_processed',\n",
-       " 'embark_town_processed',\n",
-       " 'embarked_processed',\n",
-       " 'fare_bin',\n",
-       " 'parch_processed',\n",
-       " 'pclass_processed',\n",
-       " 'sex_processed',\n",
-       " 'sibsp_processed']"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preprocessed_predictors = [\n",
-    "    col for col in basetable.columns\n",
-    "    if col.endswith(\"_bin\") or col.endswith(\"_processed\")]\n",
-    "sorted(preprocessed_predictors)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But for feature selection, we use the target encoded version of each of these."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preprocessed_predictors = [col for col in basetable.columns.tolist()\n",
-    "                           if '_enc' in col]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A univariate selection on the preprocessed predictors can be conducted. The thresholds for retaining a feature are now on default but can be changed by the user.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from cobra.model_building import univariate_selection\n",
-    "\n",
-    "df_auc = univariate_selection.compute_univariate_preselection(\n",
-    "    target_enc_train_data=basetable[basetable[\"split\"] == \"train\"],\n",
-    "    target_enc_selection_data=basetable[basetable[\"split\"] == \"selection\"],\n",
-    "    predictors=preprocessed_predictors,\n",
-    "    target_column=target_col,\n",
-    "    preselect_auc_threshold=0.53,  # if auc_selection <= 0.53 exclude predictor\n",
-    "    preselect_overtrain_threshold=0.05  # if (auc_train - auc_selection) >= 0.05 --> overfitting!\n",
-    "    )\n",
-    "from cobra.evaluation import plot_univariate_predictor_quality\n",
-    "plot_univariate_predictor_quality(df_auc)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we compute correlations between the preprocessed predictors and plot it using a correlation matrix:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from cobra.evaluation import plot_correlation_matrix\n",
-    "df_corr = (univariate_selection\n",
-    "           .compute_correlations(basetable[basetable[\"split\"] == \"train\"],\n",
-    "                                 preprocessed_predictors))\n",
-    "plot_correlation_matrix(df_corr)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get a list of the selected predictors after the univariate selection, run the following call:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['sex_enc',\n",
-       " 'class_enc',\n",
-       " 'pclass_enc',\n",
-       " 'alone_enc',\n",
-       " 'sibsp_enc',\n",
-       " 'age_enc',\n",
-       " 'parch_enc']"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "preselected_predictors = (univariate_selection\n",
-    "                          .get_preselected_predictors(df_auc))\n",
-    "preselected_predictors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "After an initial preselection on the predictors, we can start building the model itself using forward feature selection to choose the right set of predictors. Since we use target encoding on all our predictors, we will only consider models with positive coefficients (no sign flip should occur) as this makes the model more interpretable."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Modeling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7d566bd7936d401395af0b9d2d06e705",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Sequentially adding best predictor...:   0%|          | 0/7 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "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>predictors</th>\n",
-       "      <th>last_added_predictor</th>\n",
-       "      <th>train_performance</th>\n",
-       "      <th>selection_performance</th>\n",
-       "      <th>validation_performance</th>\n",
-       "      <th>model_type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>[sex_enc]</td>\n",
-       "      <td>sex_enc</td>\n",
-       "      <td>0.762707</td>\n",
-       "      <td>0.743668</td>\n",
-       "      <td>0.811077</td>\n",
-       "      <td>classification</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>[sex_enc, class_enc]</td>\n",
-       "      <td>class_enc</td>\n",
-       "      <td>0.826232</td>\n",
-       "      <td>0.811569</td>\n",
-       "      <td>0.889300</td>\n",
-       "      <td>classification</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>[class_enc, sex_enc, age_enc]</td>\n",
-       "      <td>age_enc</td>\n",
-       "      <td>0.849683</td>\n",
-       "      <td>0.831551</td>\n",
-       "      <td>0.888285</td>\n",
-       "      <td>classification</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>[age_enc, class_enc, sex_enc, sibsp_enc]</td>\n",
-       "      <td>sibsp_enc</td>\n",
-       "      <td>0.857332</td>\n",
-       "      <td>0.833333</td>\n",
-       "      <td>0.898724</td>\n",
-       "      <td>classification</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>[age_enc, class_enc, sex_enc, sibsp_enc, parch...</td>\n",
-       "      <td>parch_enc</td>\n",
-       "      <td>0.857702</td>\n",
-       "      <td>0.832506</td>\n",
-       "      <td>0.900899</td>\n",
-       "      <td>classification</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>[age_enc, class_enc, parch_enc, sex_enc, sibsp...</td>\n",
-       "      <td>pclass_enc</td>\n",
-       "      <td>0.857702</td>\n",
-       "      <td>0.832506</td>\n",
-       "      <td>0.900899</td>\n",
-       "      <td>classification</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                          predictors last_added_predictor  \\\n",
-       "0                                          [sex_enc]              sex_enc   \n",
-       "1                               [sex_enc, class_enc]            class_enc   \n",
-       "2                      [class_enc, sex_enc, age_enc]              age_enc   \n",
-       "3           [age_enc, class_enc, sex_enc, sibsp_enc]            sibsp_enc   \n",
-       "4  [age_enc, class_enc, sex_enc, sibsp_enc, parch...            parch_enc   \n",
-       "5  [age_enc, class_enc, parch_enc, sex_enc, sibsp...           pclass_enc   \n",
-       "\n",
-       "   train_performance  selection_performance  validation_performance  \\\n",
-       "0           0.762707               0.743668                0.811077   \n",
-       "1           0.826232               0.811569                0.889300   \n",
-       "2           0.849683               0.831551                0.888285   \n",
-       "3           0.857332               0.833333                0.898724   \n",
-       "4           0.857702               0.832506                0.900899   \n",
-       "5           0.857702               0.832506                0.900899   \n",
-       "\n",
-       "       model_type  \n",
-       "0  classification  \n",
-       "1  classification  \n",
-       "2  classification  \n",
-       "3  classification  \n",
-       "4  classification  \n",
-       "5  classification  "
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from cobra.model_building import ForwardFeatureSelection\n",
-    "\n",
-    "forward_selection = ForwardFeatureSelection(model_type=\"classification\",\n",
-    "                                            max_predictors=30,\n",
-    "                                            pos_only=True)\n",
-    "\n",
-    "# fit the forward feature selection on the train data\n",
-    "# has optional parameters to force and/or exclude certain predictors (see docs)\n",
-    "forward_selection.fit(basetable[basetable[\"split\"] == \"train\"],\n",
-    "                      target_column_name = target_col,\n",
-    "                      predictors = preselected_predictors)\n",
-    "                      #forced_predictors: list = [],\n",
-    "                      #excluded_predictors: list = [])\n",
-    "\n",
-    "# compute model performance\n",
-    "performances = (forward_selection\n",
-    "                .compute_model_performances(basetable, target_column_name = target_col))\n",
-    "performances"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As can be seen, we have completed 6 steps till no further improvement can be observed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from cobra.evaluation import plot_performance_curves\n",
-    "\n",
-    "# plot performance curves\n",
-    "plot_performance_curves(performances)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Based on the performance curves (AUC per model with a particular number of predictors in case of logistic regression), a final model can then be chosen and the variables importance can be plotted:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['age_enc', 'class_enc', 'parch_enc', 'sex_enc', 'sibsp_enc', 'pclass_enc']\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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XSZLf/va3mTFjRpLk/vvvzwsvvJAZM2Zk8uTJWbduXaqqyl133ZVRo0Zlzpw5GTBgQG6//fYsWLAgQ4cOzezZszNixIh89atfLTU+AAD8yYrtfI8dOzaf/exnc+qpp2bTpk0544wzsmLFigwZMiTXX399TjnllDQ2NuY973lPli5dmgMPPDBTpkxJp06d0rFjx1x22WWpqirnn39+rrvuutTX1+fCCy8sNT4AAPzJ6qqqqmo9RFt7/bKTY8+elU7dvUAToL2bfna/Wo8A8Ka81SAAABQivgEAoBDxDQAAhYhvAAAoRHwDAEAh4hsAAAoR3wAAUIj4BgCAQsQ3AAAUIr4BAKAQ8Q0AAIWIbwAAKER8AwBAIeIbAAAKEd8AAFCI+AYAgELENwAAFCK+AQCgEPENAACFiG8AAChEfAMAQCHiGwAAChHfAABQiPgGAIBCGmo9QElTxu6VPn361HoMAAB2Una+AQCgEPENAACFiG8AAChEfAMAQCHiGwAAChHfAABQiPgGAIBCxDcAABQivgEAoBDxDQAAhYhvAAAoRHwDAEAh4hsAAAoR3wAAUEhDrQcoadptz6dT9/W1HgOANjb97H61HgHgTdn5BgCAQsQ3AAAUIr4BAKAQ8Q0AAIWIbwAAKER8AwBAIeIbAAAKEd8AAFCI+AYAgELENwAAFCK+AQCgEPENAACFiG8AAChEfAMAQCHiGwAAChHfAABQiPgGAIBCxDcAABQivgEAoBDxDQAAhYhvAAAoRHwDAEAh4hsAAAoR3wAAUIj4BgCAQmoa38ccc0zWr19fyxEAAKAYO98AAFBIQ1s86bx587JgwYKsWbMmK1asyLnnnpvGxsbMnDkzSbL//vvn0ksvbfn+J598MtOmTcvmzZuzatWqfP7zn88hhxySKVOm5Pnnn8/69etz5pln5oQTTsi1116bBx98MJs3b87IkSNz+umnt8UpAADAW65N4jtJ1q5dm1mzZmX58uUZNWpU6urq8p3vfCc9e/bMzJkz8+KLL7Z879NPP50LLrgg++67b+66667MmzcvAwcOzEMPPZRvf/vbSZIHHnggSXLnnXfm61//enr37p158+a94bhz587N3Llzt7itubm5rU4TAABarc3i+7DDDkt9fX169eqVzp07p7m5OT179kySfPKTn9zie//sz/4s119/fXbddde8+uqr6dKlS7p06ZKLLrooF110UdasWZMPf/jDSZIZM2ZkxowZWbZsWY444og3HLepqSlNTU1b3LZkyZIMHz68jc4UAABap83ie+HChUmSZcuWZcOGDUmSlStXpkePHrniiitaYjpJrrzyylx99dXp169fvvzlL+c///M/s3Tp0ixcuDBf+cpXsn79+hx55JH50Ic+lO9///uZMWNGqqrKyJEjM3LkyOy5555tdRoAAPCWabP4XrZsWSZOnJjVq1fnkksuSVVV+eu//uvU19dn//33z4EHHtjyvR/+8IfziU98Ij179swee+yRFStW5J3vfGdefvnlfPSjH02nTp3y8Y9/PI2NjenevXs+8pGPpHv37hk2bFje/e53t9UpAADAW6quqqrqrX7SefPm5dlnn8155533Vj/1f8vrl50ce/asdOreu9bjANDGpp/dr9YjALwpbzUIAACFtMllJyeddFJbPC0AALyt2fkGAIBCxDcAABQivgEAoBDxDQAAhYhvAAAoRHwDAEAh4hsAAAoR3wAAUIj4BgCAQsQ3AAAUIr4BAKCQVsX36tWr23oOAABo91oV3+ecc05bzwEAAO1eQ2u+qXv37pk9e3b22Wef1Nf/odcPP/zwNh0MAADam1bF9zve8Y4sWrQoixYtarlNfAMAwPZpVXx/4QtfyJNPPpmnn346++yzT/bbb7+2ngsAANqdVsX3nDlzMn/+/AwZMiS33HJLjj/++Jx55pltPRsAALQrrYrv+fPn5xvf+EYaGhqyYcOGjB07VnwDAMB2atW7nVRVlYaGP3R6x44d07FjxzYdCgAA2qNW7Xwfcsgh+dSnPpWhQ4fmkUceycEHH9zWcwEAQLvTqvieMmVKfvjDH+aZZ57JSSedlKOOOqqNxwIAgPZnq5edbNq0Kc3NzfnkJz+ZYcOGZcKECXnve9+b0047rdR8AADQbmx15/vb3/52brjhhixbtiwjRoxIktTX12fo0KFFhnurTRm7V/r06VPrMQAA2EltNb5POeWUnHLKKfnWt76VMWPGJEmam5vT2NhYZDgAAGhPWvVuJ5s2bcpVV12VJJk0aVLuvPPOtpwJAADapVbF92233ZbPfOYzSZIbb7wxt956a5sOBQAA7VGr4ru+vj677LJLkj+8z3ddXV2bDgUAAO1Rq95qcPjw4Rk/fnyGDBmShQsX5phjjmnruQAAoN1pVXx/4hOfyNFHH53Fixfnox/9aAYNGtTWcwEAQLuz1ctO7rjjjiTJNddck+9973tZtGhR7r777syYMaPIcAAA0J5sded7jz32SJL07du3yDAAANCebTW+d9111/zsZz/zwTQAAPAW2Gp8v/6Wgs8//3w2bNiQAw88ME888UQ6d+6cOXPmFBkQAADai63G9+vXdp9zzjm5/vrr09DQkE2bNuWcc84pMhwAALQnrXqf75dffrnlnzdt2pTly5e32UAAANBeteqtBseMGZORI0dm4MCBefrpp/M3f/M3bT0XAAC0O3VVVVWt+cY1a9bk2WefTZ8+fbL77ru39VxvqSVLlmT48OE59uxZ6dS9d63HAShq+tn9aj0CAP9Xq3a+n3rqqVxyySVZvXp1PvShD2XAgAE5+uij23o2AABoV1p1zfcVV1yRL3zhC+nRo0fGjBmT6667rq3nAgCAdqdV8Z0ke++9d+rq6rL77runc+fObTkTAAC0S62K7+7du+e2227La6+9lu9+97vp1q1bW88FAADtTqvi+6qrrsqSJUvyjne8I48//niuvPLKtp4LAADanVa94PKSSy7JNddc09azAABAu9aqne/m5uYsWrQo69evT3Nzc5qbm9t6LgAAaHdatfP9m9/8JpMmTcry5cvTs2fP1NfXZ8GCBW09GwAAtCut2vn+m7/5m9TX16dv377p0KFDLr300raeCwAA2p1W7Xxff/31ueOOO9KzZ88sW7YskyZNyuGHH97WswEAQLvSqp3vHj16pGfPnkmSXr16pUuXLm06FAAAtEet2vnu0qVLzjzzzBx22GFZuHBh1q1blxkzZiRJJk+e3KYDAgBAe9Gq+B4+fHjLP/fu3bvNhgEAgPasVfE9atSotp4DAADavVZd8w0AAPzpxDcAABQivgEAoBDxDQAAhYhvAAAoRHwDAEAh4hsAAAoR3wAAUIj4BgCAQsQ3AAAUIr4BAKCQmsX3r3/968ycOTNJMmzYsFqNAQAAxTTU6sD77bdf9ttvv1odHgAAiisW34sXL86FF16YhoaGdOjQIaNHj84PfvCDXHvttWlubs6nP/3pvPDCC9l3330zderU/PznP88Xv/jFNDQ0pFu3brn66qtzzz33ZMGCBVmzZk1WrFiRc889Nx/84AdLnQIAAPxJisX3T37ykxxwwAGZMmVKHn744TzzzDMt961bty7nnXde9txzz/zt3/5t7rvvvjz88MM59thjc+aZZ+a+++7LqlWrkiRr167NrFmzsnz58px88skZPnx4Ghr+32nMnTs3c+fO3eLYzc3NZU4SAAC2olh8jxkzJjfffHPOOuusdO3adYvrvN/97ndnzz33TJIcfPDBWbx4cSZNmpQbbrghEydOTO/evTNkyJAkyWGHHZb6+vr06tUr3bp1y/Lly/Nnf/ZnLc/V1NSUpqamLY69ZMmSDB8+vMBZAgDAH1fsBZcLFizI0KFDM3v27IwYMSI333xzy30vvvhili5dmiT5+c9/ngEDBuSuu+7KqFGjMmfOnAwYMCC33357kmThwoVJkmXLlmXNmjXp2bNnqVMAAIA/SbGd78GDB+f888/Pddddl/r6+kyYMCG//OUvkyQ9evTIFVdckZdeeikHH3xwjjzyyDz22GOZMmVKOnXqlI4dO+ayyy7Lz372syxbtiwTJ07M6tWrc8kll6RDhw6lTgEAAP4kdVVVVbUeorXmzZuXZ599Nuedd952Pe71y06OPXtWOnXv3UbTAeyYpp/dr9YjAPB/+ZAdAAAopGbv8/3fcdJJJ9V6BAAA+G+z8w0AAIWIbwAAKER8AwBAIeIbAAAKEd8AAFCI+AYAgELENwAAFCK+AQCgEPENAACFiG8AAChEfAMAQCHiGwAAChHfAABQiPgGAIBCxDcAABQivgEAoBDxDQAAhYhvAAAoRHwDAEAh4hsAAAoR3wAAUIj4BgCAQhpqPUBJU8bulT59+tR6DAAAdlJ2vgEAoBDxDQAAhYhvAAAoRHwDAEAh4hsAAAoR3wAAUIj4BgCAQsQ3AAAUIr4BAKAQ8Q0AAIWIbwAAKER8AwBAIeIbAAAKEd8AAFBIQ60HKGnabc+nU/f1tR4DKGD62f1qPQIAvIGdbwAAKER8AwBAIeIbAAAKEd8AAFCI+AYAgELENwAAFCK+AQCgEPENAACFiG8AAChEfAMAQCHiGwAAChHfAABQiPgGAIBCxDcAABQivgEAoBDxDQAAhYhvAAAoRHwDAEAh4hsAAAoR3wAAUIj4BgCAQsQ3AAAUIr4BAKAQ8Q0AAIWIbwAAKER8AwBAIeIbAAAKaaj1AGvWrMnnPve5rF69OitWrMjJJ5+cwYMH59JLL03nzp3Ts2fP7LLLLpk2bVrmzJmT+fPnp66uLieccEJOO+20Wo8PAACtVvP4fu655zJy5Mgcd9xxeemllzJhwoR07tw506dPz4ABA3LttdfmpZdeytNPP52777473/zmN1NXV5fTTz89hx9+ePr27bvF882dOzdz587d4rbm5uaSpwQAAG+q5vHdq1evzJ49O/fcc0+6dOmSjRs3ZunSpRkwYECSZOjQobn77rvz5JNP5ne/+11OP/30JMkrr7yS559//g3x3dTUlKampi1uW7JkSYYPH17kfAAA4I+peXzfcsstOeiggzJ+/Pg8+OCD+bd/+7fsscceefrpp9O/f/889thjSZK+ffumf//++epXv5q6urp87Wtfy8CBA2s8PQAAtF7N4/voo4/O1KlTc9ddd6VHjx7p0KFDLr744nz2s59Np06d0rFjx/Tu3TuDBg3K+973vowbNy7Nzc0ZMmRIevfuXevxAQCg1Woe3+9973vz/e9/f4vbvvGNb+SGG27I7rvvnmuvvTYdO3ZMkpx11lk566yzajEmAAD8yWoe32+mZ8+e+fjHP55OnTqla9eumTZtWq1HAgCAP9kOGd8jRozIiBEjaj0GAAC8pXzIDgAAFCK+AQCgEPENAACFiG8AAChEfAMAQCHiGwAAChHfAABQiPgGAIBCxDcAABQivgEAoBDxDQAAhYhvAAAoRHwDAEAh4hsAAAoR3wAAUIj4BgCAQsQ3AAAUIr4BAKAQ8Q0AAIWIbwAAKER8AwBAIeIbAAAKaaj1ACVNGbtX+vTpU+sxAADYSdn5BgCAQsQ3AAAUIr4BAKAQ8Q0AAIWIbwAAKER8AwBAIeIbAAAKEd8AAFCI+AYAgELENwAAFCK+AQCgEPENAACFiG8AACikodYDlDTttufTqfv6Wo8B7d70s/vVegQA2CHZ+QYAgELENwAAFCK+AQCgEPENAACFiG8AAChEfAMAQCHiGwAAChHfAABQiPgGAIBCxDcAABQivgEAoBDxDQAAhYhvAAAoRHwDAEAh4hsAAAoR3wAAUIj4BgCAQsQ3AAAUIr4BAKAQ8Q0AAIWIbwAAKER8AwBAIeIbAAAKEd8AAFCI+AYAgELeVvF9zDHHZP369bUeAwAA/lveVvENAABvZw1tfYB58+ZlwYIFWbNmTVasWJFzzz03VVXlG9/4Rsv3fOlLX8pTTz2Vq6++Oh07dswpp5yS7t27Z+bMmUmS/fffP5deemmSZOrUqVmyZEmSZObMmenevXtbnwIAALwl2jy+k2Tt2rWZNWtWli9fnpNPPjmjR4/OTTfdlN122y0XX3xxfvzjH6d3795Zv3597rjjjmzcuDHHHXdc7rjjjvTs2TMzZ87Miy++mCQZPXp0Dj300EyZMiUPPPBATjjhhC2ONXfu3MydO3eL25qbm0ucJgAAbFWR+D7ssMNSX1+fXr16pVu3bqmrq8sFF1yQzp0759lnn81BBx2UJNlnn32SJCtWrEi3bt3Ss2fPJMknP/nJlucaPHhwkqRXr15Zt27dG47V1NSUpqamLW5bsmRJhg8f3hanBgAArVYkvhcuXJgkWbZsWVavXp1bb701//Zv/5YkOeOMM1JVVZKkvv4Pl6D37Nkzq1atysqVK9OjR49cccUV+fCHP5wkqaurKzEyAAC85YrE97JlyzJx4sSsXr06l1xySebNm5dRo0alU6dO6datW5YuXZo+ffq0fH99fX0uueSS/PVf/3Xq6+uz//7758ADDywxKgAAtJm66vVt5zYyb968PPvssznvvPPa8jBb9fplJ8eePSuduveu2Ryws5h+dr9ajwAAOyRvNQgAAIW0+WUnJ510UlsfAgAA3hbsfAMAQCHiGwAAChHfAABQiPgGAIBCxDcAABQivgEAoBDxDQAAhYhvAAAoRHwDAEAh4hsAAAoR3wAAUIj4BgCAQsQ3AAAUIr4BAKAQ8Q0AAIWIbwAAKER8AwBAIeIbAAAKEd8AAFCI+AYAgELENwAAFCK+AQCgkIZaD1DSlLF7pU+fPrUeAwCAnZSdbwAAKER8AwBAIeIbAAAKEd8AAFCI+AYAgEJ2inc72bRpU5LkxRdfrPEkAADsDPbYY480NLwxtXeK+H755ZeTJB/72MdqPAkAADuDBQsWvOlbXO8U8T148OD8j//xP3LTTTelQ4cOtR7nbWPSpEm54YYbaj3G24b12j7Wa/tYr+1nzbaP9do+1mv77Izrtccee7zp7TtFfO+6667p3Llz9t5771qP8rbS2NjoQ4m2g/XaPtZr+1iv7WfNto/12j7Wa/tYr//HCy4BAKAQ8Q0AAIWIbwAAKGSnie+mpqZaj/C2Y822j/XaPtZr+1iv7WfNto/12j7Wa/tYr/+nrqqqqtZDAADAzmCn2fkGAIBaE98AAFCI+AYAgELa3YfsbN68OVOnTs1//Md/pLGxMVdcccUWH65z33335Stf+UoaGhoyevTonHLKKTWctva2tV5J8tprr+WMM87IlVdemX79+tVo0h3DttZr/vz5mT17djp06JCBAwdm6tSpqa/feX/H3dZ6/cu//Etuuumm1NXVpampKSeffHINp90xtObPZJJcdNFF6d69e84777waTLnj2NZ6zZo1K9/61rey++67J0kuvfTS9O3bt1bj1ty21uuXv/xlpk2blqqq8s53vjP/8A//kF122aWGE9fW1tbr5ZdfzuTJk1u+99e//nU+85nPZNy4cbUad4ewrZ+xf/7nf86sWbNSX1+f0aNHZ/z48TWctkaqduZf/uVfqgsuuKCqqqp69NFHq0mTJrXc19zcXH3gAx+oVq5cWa1fv7466aSTqqVLl9Zq1B3C1tarqqrql7/8ZTVq1Kjq/e9/f/X000/XYsQdytbW67XXXquGDx9erV27tqqqqvr0pz9d3XvvvTWZc0extfXauHFjdeyxx1arVq2qNm7cWB133HHV73//+1qNusPY1p/JqqqqW2+9tTrllFOqf/iHfyg93g5nW+v1mc98pvrVr35Vi9F2SFtbr82bN1cf/vCHq9/85jdVVVXV7bffXj3zzDM1mXNH0Zo/j1VVVT//+c+rCRMmVBs3biw53g5pW2s2bNiwasWKFdX69etbmmxn0+625B555JEcccQRSZKDDjoojz/+eMt9zzzzTPbaa6907949jY2NGTp0aB5++OFajbpD2Np6JUlzc3O+8pWv7NQ7Rf/V1tarsbExt912W3bbbbckycaNG3fqHaNk6+vVoUOH3H333enatWtWrlyZJOncuXMtxtyhbOvP5KOPPprHHnvM23b9X9tar4ULF+amm27KuHHjcuONN9ZixB3K1tZr8eLF6dGjR2bPnp1TTz01K1eu3On/7t/Wz1eSVFWVyy+/PFOnTk2HDh1Kj7jD2daa7bvvvlm9enWam5tTVVXq6upqMWZNtbv4XrNmTbp06dLydYcOHbJx48aW+7p27dpyX+fOnbNmzZriM+5ItrZeSTJ06NC8613vqsVoO6StrVd9fX169eqVJJkzZ07Wrl2bYcOG1WTOHcW2fr4aGhpyzz335CMf+UgOPfTQNDS0uyvhttvW1mzp0qWZOXNmLr744lqNt8PZ1s/YyJEjM3Xq1MyePTuPPPJIfvCDH9RizB3G1tZrxYoVefTRRzN+/PjMmjUrDz74YH7605/WatQdwrZ+vpI/XM46YMCAnf4Xlddta80GDBiQ0aNHZ+TIkTnqqKPSrVu3WoxZU+0uvrt06ZJXX3215evNmze3/Af9/7/v1Vdf3SLGd0ZbWy/eaFvrtXnz5nzxi1/MAw88kOuuu26n/I3+v2rNz9dxxx2X+++/Pxs2bMidd95ZeMIdz9bW7Pvf/35WrFiRc845JzfddFPmz5+fefPm1WrUHcLW1quqqkycODG77757Ghsbc+SRR+aJJ56o1ag7hK2tV48ePbL33nunf//+6dixY4444og33endmbTm77B//ud/3ulfP/ZfbW3NFi1alB/+8IdZsGBB7rvvvixfvjzf+973ajVqzbS7+D7kkENy//33J0l+8YtfZODAgS339evXL88991xWrlyZ5ubmPPzwwzn44INrNeoOYWvrxRtta70uvvjirF+/Ptdff33L5Sc7s62t15o1a3Lqqaemubk59fX12W233XbqF6e+bmtrdtppp2XevHmZM2dOzjnnnJx44ok56aSTajXqDmFbP2MnnnhiXn311VRVlYceeiiDBw+u1ag7hK2t13ve8568+uqree6555IkDz/8cAYMGFCTOXcUrflv5MKFC3PIIYeUHm2HtbU169q1a3bdddfssssu6dChQ3bfffesWrWqVqPWTLv7hMvXX2X75JNPpqqqXHXVVXniiSeydu3aNDU1tbzbSVVVGT16dD72sY/VeuSa2tZ6vW7ChAmZOnWqdzvZynoNHjw4o0ePzqGHHtqy433aaafl2GOPrfHUtbOtn6+5c+fmW9/6VhoaGrLvvvvmoosu2umvmWztn8l58+bl2Wef9W4n21ivO++8M3PmzEljY2Pe97735VOf+lStR66pba3XT3/601xzzTWpqioHH3xwPv/5z9d65Jra1notX748Z5xxRv7pn/6p1qPuMLa1Zrfeemu+/e1vp2PHjtlrr71y+eWXp7GxsdZjF9Xu4hsAAHZU/h8vAAAUIr4BAKAQ8Q0AAIWIbwAAKER8AwBAIeIb4G3s/vvvz9y5c9v0GF//+tfb9PkBdibeahCArRo2bFgeeOCBWo8B0C74HHGAt7F58+blRz/6UZYsWZJ3vetdWbJkSUaOHJmnnnoqTzzxRI466qhMnjw5EyZMyD777JPFixenqqpce+21eec735lp06blkUceSZKceOKJmThxYqZMmZKVK1dm5cqVOfLII/PKK69k6tSpOe+88/K5z30uq1evzooVK3LyySdn/PjxmTBhQgYNGpSnnnoqa9asyZe+9KXsueeeuf7663Pvvfdm06ZNGTduXMaOHZs5c+Zk/vz5qaurywknnJDTTjutxisIUJb4BmgHfvvb3+aWW27JunXrMnz48Nx///3ZbbfdcvTRR2fy5MlJ/vCxz5dddlm+8Y1v5MYbb8ywYcOyZMmS3H777dm4cWPGjx+f9773vUmS9773vTn99NOT/OGyk6lTp2bhwoUZOXJkjjvuuLz00kuZMGFCxo8fnyQZMmRIPve5z+Xaa6/Nd7/73Rx++OG5//77c8cdd6S5uTnXXHNNnnrqqdx999355je/mbq6upx++uk5/PDD07dv35qsGUAtiG+AduA973lPunbtmsbGxvTq1Ss9evRIktTV1bV8z+thfcghh+S+++7LHnvskUMPPTR1dXXp2LFj/vzP/zzPPPNMkmSfffZ5wzF69eqV2bNn55577kmXLl2ycePGlvv233//JMkee+yRZcuWZfHixRkyZEg6dOiQ3XbbLZ///Odz991353e/+11L1L/yyit5/vnnxTewU/GCS4B24L9G9h/z+OOPJ0l+/vOfp3///unXr1/LJScbNmzIo48+mr333vsNz/f6S4NuueWWHHTQQbn66qszYsSIbO0lQ3379s0TTzyRzZs3Z8OGDTnjjDPSt2/f9O/fP//7f//vzJkzJyeddFIGDhz43z5ngLcjO98AO4nvfOc7+drXvpbddtst06dPzzve8Y78+7//e5qamrJhw4aMGDEiBxxwwBse169fv5x33nkZM2ZMpk6dmrvuuis9evRIhw4d0tzc/KbH2m+//XLEEUdk3Lhx2bx5c8aNG5dBgwblfe97X8aNG5fm5uYMGTIkvXv3buvTBtiheLcTgJ3AhAkTMnXq1PTr16/WowDs1Fx2AgAAhdj5BgCAQux8AwBAIeIbAAAKEd8AAFCI+AYAgELENwAAFPJ/AGLVEfutiOHAAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# look at the number of steps that were completed before stopping, fill that in between the brackets\n",
-    "model = forward_selection.get_model_from_step(5)\n",
-    "\n",
-    "# Note that chosen model the following variables:\n",
-    "final_predictors = model.predictors\n",
-    "print(final_predictors)\n",
-    "from cobra.evaluation import plot_variable_importance\n",
-    "\n",
-    "variable_importance = model.compute_variable_importance(\n",
-    "    basetable[basetable[\"split\"] == \"selection\"]\n",
-    ")\n",
-    "plot_variable_importance(variable_importance)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Note**: variable importance is based on correlation of the predictor with the model scores (and not the true labels!)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "\n",
-    "Finally, if wanted, we can again export the model to a dictionary to store it as JSON"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_dict = model.serialize()\n",
-    "\n",
-    "model_path = os.path.join(\"output\", \"model.json\")\n",
-    "with open(model_path, \"w\") as file:\n",
-    "    json.dump(model_dict, file)\n",
-    "\n",
-    "# To reload the model again from a JSON file, run the following snippet:\n",
-    "# from cobra.model_building import LogisticRegressionModel\n",
-    "# with open(path, \"r\") as file:\n",
-    "#     model_dict = json.load(file)\n",
-    "# model = LogisticRegressionModel()\n",
-    "# model.deserialize(model_dict)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Evaluation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have build and selected a final model, it is time to evaluate its predictions on the test set against various evaluation metrics. The used evaluation metrics are:\n",
-    "1. Accuracy\n",
-    "2. AUC: Area Under Curve\n",
-    "3. Precision\n",
-    "4. Recall\n",
-    "5. F1\n",
-    "6. Matthews Correlation Coefficient\n",
-    "7. Lift\n",
-    "\n",
-    "Furthermore, we can evaluate the classification performance using a confusion matrix.\n",
-    "\n",
-    "\n",
-    "Also plotting makes the evaluation of a logistic regression model a lot easier. We will first use a **Receiver Operating Characteristic (ROC) curve**, which is a plot of the false positive rate (x-axis) versus the true positive rate (y-axis). Next, the **Cumulative Gains curve**, **Cumulative Lift curve** and **Cumulative Response curve** can be called."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from cobra.evaluation import ClassificationEvaluator\n",
-    "\n",
-    "# get numpy array of True target labels and predicted scores:\n",
-    "y_true = basetable[basetable[\"split\"] == \"selection\"][target_col].values\n",
-    "y_pred = model.score_model(basetable[basetable[\"split\"] == \"selection\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "evaluator = ClassificationEvaluator()\n",
-    "evaluator.fit(y_true, y_pred)  # Automatically find the best cut-off probability"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "accuracy             0.775281\n",
-       "AUC                  0.832506\n",
-       "precision            0.753086\n",
-       "recall               0.753086\n",
-       "F1                   0.753086\n",
-       "matthews_corrcoef    0.546901\n",
-       "lift at 0.05         2.200000\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluator.scalar_metrics"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "evaluator.plot_confusion_matrix()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "evaluator.plot_roc_curve()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "evaluator.plot_cumulative_gains()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "evaluator.plot_lift_curve()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "evaluator.plot_cumulative_response_curve()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From 2be37c688a00d0b495489bd55a6956ca4ef81172 Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Wed, 29 Sep 2021 12:47:14 +0200
Subject: [PATCH 03/16] reset y-axis performance plots regression

---
 cobra/evaluation/plotting_utils.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/cobra/evaluation/plotting_utils.py b/cobra/evaluation/plotting_utils.py
index 245dda2..2fef624 100644
--- a/cobra/evaluation/plotting_utils.py
+++ b/cobra/evaluation/plotting_utils.py
@@ -134,7 +134,10 @@ def plot_performance_curves(model_performance: pd.DataFrame,
         if model_type == "classification":
             ax.set_yticks(np.arange(0.5, max_metric + 0.02, 0.05))
         elif model_type == "regression":
-            ax.set_yticks(np.arange(0, max_metric+(0.1*max_metric)))
+            # In regression, the scale of the y-axis can largely vary depending
+            # on the dataset, it is easier to just set the y-axis bounds,
+            # but not the tick distance.
+            ax.set_ylim(0, max_metric*1.1)
 
         # Make pretty
         ax.legend(loc='lower right')

From c0734186f4a809853bd008bb10bde30d36f0c7c0 Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Wed, 29 Sep 2021 13:21:52 +0200
Subject: [PATCH 04/16] fix README

---
 README.rst | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/README.rst b/README.rst
index 52282d4..bc65a2b 100644
--- a/README.rst
+++ b/README.rst
@@ -9,7 +9,7 @@
 
 ------------------------------------------------------------------------------------------------------------------------------------ 
 
-.. image:: C:/Users/hendrik.dewinter/PycharmProjects/cobra/material/logo.png
+.. image:: material/logo.png
     :width: 300
 
 **Cobra** is a Python package to build predictive models using linear or logistic regression with a focus on performance and interpretation. It consists of several modules for data preprocessing, feature selection and model evaluation. The underlying methodology was developed at Python Predictions in the course of hundreds of business-related prediction challenges. It has been tweaked, tested and optimized over the years based on feedback from clients, our team, and academic researchers.
@@ -43,7 +43,7 @@ This package requires the usual Python packages for data science:
 - numpy (>=1.19.4)
 - pandas (>=1.1.5)
 - scipy (>=1.5.4)
-- scikit-learn (>=0.23.1)
+- scikit-learn (>=0.24.1)
 - matplotlib (>=3.3.3)
 - seaborn (>=0.11.0)
 
@@ -54,19 +54,19 @@ These packages, along with their versions are listed in ``requirements.txt`` and
   pip install -r requirements.txt
 
 
-**Note**: if you want to install cobra with e.g. pip, you don't have to install all of these requirements as these are automatically installed with cobra itself.
+**Note**: if you want to install Cobra with e.g. pip, you don't have to install all of these requirements as these are automatically installed with cobra itself.
 
 Installation
 ------------
 
-The easiest way to install cobra is using ``pip``:    ::
+The easiest way to install Cobra is using ``pip``:    ::
 
   pip install -U pythonpredictions-cobra
 
-Contributing to cobra
+Contributing to Cobra
 =====================
 
-We'd love you to contribute to the development of cobra! There are many ways in which you can contribute, the most common of which is to contribute to the source code or documentation of the project. However, there are many other ways you can contribute (report issues, improve code coverage by adding unit tests, ...).
+We'd love you to contribute to the development of Cobra! There are many ways in which you can contribute, the most common of which is to contribute to the source code or documentation of the project. However, there are many other ways you can contribute (report issues, improve code coverage by adding unit tests, ...).
 We use GitHub issue to track all bugs and feature requests. Feel free to open an issue in case you found a bug or in case you wish to see a new feature added.
 
 For more details, check our `wiki <https://github.com/PythonPredictions/cobra/wiki/Contributing-guidelines-&-workflows>`_.
@@ -81,11 +81,11 @@ HTML documentation of the `individual modules <https://pythonpredictions.github.
 
 **Logistic Regression**
 
-- A step-by-step tutorial `<https://pythonpredictions.github.io/cobra/docs/Cobra_Tutorial_LogisticRegression.ipynb>`_
+- A step-by-step tutorial `<https://pythonpredictions.github.io/cobra/tutorials/tutorial_Cobra_logistic_regression.ipynb>`_
 
 **Linear Regression**
 
-- A step-by-step tutorial `<https://pythonpredictions.github.io/cobra/docs/Cobra_Tutorial_LinearRegression.ipynb>`_
+- A step-by-step tutorial `<https://pythonpredictions.github.io/cobra/tutorials/tutorial_Cobra_linear_regression.ipynb>`_
 
 Outreach
 -------------

From 9e12942d25dc3a48008bda6d36dbf0b9bc528eca Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Wed, 29 Sep 2021 13:37:20 +0200
Subject: [PATCH 05/16] add cleaned unrun tutorials under new folder

---
 .gitignore                                    |   6 +-
 .../tutorial_Cobra_linear_regression.ipynb    | 792 +++++++++++++++++
 .../tutorial_Cobra_logistic_regression.ipynb  | 821 ++++++++++++++++++
 3 files changed, 1616 insertions(+), 3 deletions(-)
 create mode 100644 tutorials/tutorial_Cobra_linear_regression.ipynb
 create mode 100644 tutorials/tutorial_Cobra_logistic_regression.ipynb

diff --git a/.gitignore b/.gitignore
index 4ec86e3..6aa9052 100644
--- a/.gitignore
+++ b/.gitignore
@@ -53,15 +53,15 @@ junit/
 *.mo
 *.pot
 
-# Django stuff:
+# Django stuff
 *.log
 local_settings.py
 
-# Flask stuff:
+# Flask stuff
 instance/
 .webassets-cache
 
-# Scrapy stuff:
+# Scrapy stuff
 .scrapy
 
 # Sphinx documentation
diff --git a/tutorials/tutorial_Cobra_linear_regression.ipynb b/tutorials/tutorial_Cobra_linear_regression.ipynb
new file mode 100644
index 0000000..219c7de
--- /dev/null
+++ b/tutorials/tutorial_Cobra_linear_regression.ipynb
@@ -0,0 +1,792 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cobra's approach to linear regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cobra requires the usual Python packages for data science:\n",
+    "\n",
+    "- numpy (>=1.19.4)\n",
+    "- pandas (>=1.1.5)\n",
+    "- scipy (>=1.5.4)\n",
+    "- scikit-learn (>=0.24.1)\n",
+    "- matplotlib (>=3.3.3)\n",
+    "- seaborn (>=0.11.0)\n",
+    "\n",
+    "These packages, along with their versions are listed in requirements.txt and can be installed using pip.\n",
+    "\n",
+    "If you want to install Cobra with e.g. pip, you don't have to install all of these requirements as these are automatically installed with Cobra itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pip install -r requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The easiest way to install Cobra is using pip:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pip install -U pythonpredictions-cobra"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*****"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This section we will walk you through all the required steps to build a predictive linear regression model using **Cobra**. All classes and functions used here are well-documented. In case you want more information on a class or function, run `help(function_or_class_you_want_info_from)`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Building a good model involves three steps:\n",
+    "\n",
+    "1. **Preprocessing**: properly prepare the predictors (a synonym for “feature” or variable that we use throughout this tutorial) for modelling.\n",
+    "\n",
+    "2. **Feature Selection**: automatically select a subset of predictors which contribute most to the target variable or output in which you are interested.\n",
+    "\n",
+    "3. **Model Evaluation**: once a model has been build, a detailed evaluation can be performed by computing all sorts of evaluation metrics.\n",
+    "\n",
+    "Let's dive in!\n",
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Miles Per Gallon Prediction\n",
+    "\n",
+    "- BASETABLE: seaborn dataset MPG.\n",
+    "- GOAL: Predict the distance (measured in miles) that a car can travel per gallon of fuel."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import the necessary libraries first."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "from pandas.api.types import is_datetime64_any_dtype\n",
+    "\n",
+    "pd.set_option(\"display.max_columns\", 50)\n",
+    "pd.set_option(\"display.max_rows\", 50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cobra.preprocessing import PreProcessor\n",
+    "\n",
+    "from cobra.model_building import univariate_selection\n",
+    "from cobra.model_building import ForwardFeatureSelection\n",
+    "# from cobra.model_building import LinearRegressionModel\n",
+    "\n",
+    "from cobra.evaluation import RegressionEvaluator\n",
+    "from cobra.evaluation import generate_pig_tables\n",
+    "from cobra.evaluation import plot_univariate_predictor_quality\n",
+    "from cobra.evaluation import plot_correlation_matrix\n",
+    "from cobra.evaluation import plot_performance_curves\n",
+    "from cobra.evaluation import plot_variable_importance\n",
+    "from cobra.evaluation import plot_incidence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = sns.load_dataset(\"mpg\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the example below, we assume the data for model building is available in a pandas DataFrame. This DataFrame should contain a an ID column, a target column (e.g. “**mpg**”) and a number of candidate predictors (features) to build a model with.\n",
+    "\n",
+    "***\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is required to set all category vars to object dtype."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[:, df.dtypes==\"category\"] = (df.select_dtypes([\"category\"]).apply(lambda x: x.astype(\"object\")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### The first part focusses on preparing the predictors for modelling by:\n",
+    "\n",
+    "1. Defining the ID column, the target, discrete and contineous variables\n",
+    "\n",
+    "2. Splitting the dataset into training, selection and validation datasets.\n",
+    "\n",
+    "3. Binning continuous variables into discrete intervals\n",
+    "\n",
+    "4. Replacing missing values of both categorical and continuous variables (which are now binned) with an additional “Missing” bin/category\n",
+    "\n",
+    "5. Regrouping categories in new category “other”\n",
+    "\n",
+    "6. Replacing bins/categories with their corresponding average of the target values\n",
+    "\n",
+    "*Note to user*: as any good data scientist knows, you still need to deal in your data with irregularities, such as outliers or very skewed distributions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this toy dataset, the index will serve as ID."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"id\"] = df.index + 1\n",
+    "id_col = \"id\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The target is the \"MPG\" column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_col = \"mpg\"\n",
+    "df[target_col].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finding out which variables are categorical (discrete) and which are continuous.\n",
+    "\n",
+    "Discrete variables are definitely those that contain strings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col_dtypes = df.dtypes\n",
+    "discrete_vars = [col for col in col_dtypes[col_dtypes==object].index.tolist() if col not in [id_col, target_col]] \n",
+    "print(discrete_vars)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we also check for numerical columns that only contain a few different values, thus to be interpreted as discrete, categorical variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in df.columns:\n",
+    "    if col not in discrete_vars and col not in [id_col, target_col]: # if not already discrete because a string, or the target\n",
+    "        if len(val_counts) > 1 and len(val_counts) <= 10: # the column contains less than 10 different values\n",
+    "            print(col)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By taking a look at the printed variables, it is clear that we have to include those in the list of discrete variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discrete_vars.extend([\"cylinders\"])\n",
+    "discrete_vars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The remaining variables can be labeled continous predictors, without including the target variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "continuous_vars = list(set(df.columns)\n",
+    "                       - set(discrete_vars) \n",
+    "                       - set([id_col, target_col]))\n",
+    "continuous_vars                "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can prepare **Cobra's PreProcessor** object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using all Cobra's default parameters for preprocessing for now\n",
+    "preprocessor = PreProcessor.from_params(\n",
+    "    model_type=\"regression\"\n",
+    ")\n",
+    "\n",
+    "# These are all available options: help(PreProcessor.from_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Split the data into train-selection-validation sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "basetable = preprocessor.train_selection_validation_split(\n",
+    "                data=df,\n",
+    "                train_prop=0.6,\n",
+    "                selection_prop=0.2,\n",
+    "                validation_prop=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fit the preprocessor pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fit the preprocessor pipeline:\n",
+    "preprocessor.fit(basetable[basetable[\"split\"]==\"train\"],\n",
+    "                 continuous_vars=continuous_vars,\n",
+    "                 discrete_vars=discrete_vars,\n",
+    "                 target_column_name=target_col)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This pipeline can now be performed on the basetable!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "basetable = preprocessor.transform(basetable,\n",
+    "                                   continuous_vars=continuous_vars,\n",
+    "                                   discrete_vars=discrete_vars)\n",
+    "basetable.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predictor Insights Graphs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we can the create the so-called Predictor Insights Graphs (PIGs in short). These are graphs that represents the insights of the relationship between a single predictor and the target. More specifically, the predictor is binned into groups, and we represent group size in bars and group target mean in a colored line. Moreover, we have the option to force order of predictor values. First, we compute the output needed to plot the PIG."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictor_list = [col for col in basetable.columns if col.endswith(\"_bin\") or col.endswith(\"_processed\")]\n",
+    "pig_tables = generate_pig_tables(basetable[basetable[\"split\"]==\"selection\"],\n",
+    "                                 id_column_name=id_col,\n",
+    "                                 target_column_name=target_col,\n",
+    "                                 preprocessed_predictors=predictor_list)\n",
+    "pig_tables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we can plot a PIG graph for each of the predictors in the basetable. For instance, for the variable \"acceleration\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "plot_incidence(pig_tables, variable=\"acceleration\", model_type=\"regression\",\n",
+    "               column_order = list(basetable[\"acceleration_bin\"].unique().sort_values()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature selection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the predictors are properly prepared, we can start building a predictive model, which boils down to selecting the right predictors from the dataset to train a model on.\n",
+    "\n",
+    "As a dataset typically contains many predictors, **we first perform a univariate preselection** to rule out any predictor with little to no predictive power. \n",
+    "\n",
+    "Later, using the list of preselected features, we build a multiple regression model using **forward feature selection** to choose the right set of predictors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In previous steps, these were the predictors, as preprocessed so far:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preprocessed_predictors = [\n",
+    "    col for col in basetable.columns\n",
+    "    if col.endswith(\"_bin\") or col.endswith(\"_processed\")\n",
+    "]\n",
+    "sorted(preprocessed_predictors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But for feature selection, we use the target encoded version of each of these."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preprocessed_predictors = [col for col in basetable.columns.tolist() if \"_enc\" in col]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A univariate selection on the preprocessed predictors can be conducted. The thresholds for retaining a feature can be changed by the user, for instance both at 10 for now.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_rmse = univariate_selection.compute_univariate_preselection(\n",
+    "    target_enc_train_data=basetable[basetable[\"split\"]==\"train\"],\n",
+    "    target_enc_selection_data=basetable[basetable[\"split\"]==\"selection\"],\n",
+    "    predictors=preprocessed_predictors,\n",
+    "    target_column=target_col,\n",
+    "    model_type=\"regression\",\n",
+    "    preselect_rmse_threshold = 10, # max. RMSE for selection\n",
+    "    preselect_overtrain_threshold = 10) # difference between RMSE on train and selection set\n",
+    "\n",
+    "plot_univariate_predictor_quality(df_rmse)\n",
+    "\n",
+    "# As the square root of a variance, RMSE can be interpreted as the standard deviation of \n",
+    "# the unexplained variance, and has the useful property of being in the same units as the response variable. \n",
+    "# Lower values of RMSE indicate better fit."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we compute correlations between the preprocessed predictors and plot it using a correlation matrix:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute correlations between preprocessed predictors:\n",
+    "df_corr = (univariate_selection\n",
+    "           .compute_correlations(basetable[basetable[\"split\"]==\"train\"],\n",
+    "                                 preprocessed_predictors))\n",
+    "print(df_corr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot correlation matrix\n",
+    "plot_correlation_matrix(df_corr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get a list of the selected predictors after the univariate selection, run the following call:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preselected_predictors = univariate_selection.get_preselected_predictors(df_rmse)\n",
+    "preselected_predictors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After an initial preselection on the predictors, we can start building the model itself using forward feature selection to choose the right set of predictors. Since we use target encoding on all our predictors, we will only consider models with positive coefficients (no sign flip should occur) as this makes the model more interpretable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modeling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "forward_selection = ForwardFeatureSelection(model_type=\"regression\",\n",
+    "                                            # model_name=\"linear-regression\",\n",
+    "                                            max_predictors=30,\n",
+    "                                            pos_only=True)\n",
+    "\n",
+    "# fit the forward feature selection on the train data\n",
+    "# has optional parameters to force and/or exclude certain predictors (see docs)\n",
+    "forward_selection.fit(basetable[basetable[\"split\"]==\"train\"],\n",
+    "                      target_column_name=target_col,\n",
+    "                      predictors=preselected_predictors)\n",
+    "\n",
+    "# compute model performance\n",
+    "performances = (forward_selection\n",
+    "                .compute_model_performances(basetable, target_column_name = target_col))\n",
+    "performances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As can be seen, we have completed 7 steps till no further improvement can be observed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot performance curves\n",
+    "plot_performance_curves(performances)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Based on the performance curves (RMSE per model with a particular number of predictors in case of linear regression), a final model can then be chosen and the variables importance can be plotted:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# look at the number of steps that were completed before stopping, fill that in between the brackets\n",
+    "model = forward_selection.get_model_from_step(5)\n",
+    "final_predictors = model.predictors\n",
+    "final_predictors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "variable_importance = model.compute_variable_importance(\n",
+    "    basetable[basetable[\"split\"]==\"selection\"])\n",
+    "plot_variable_importance(variable_importance)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: variable importance is based on correlation of the predictor with the model scores (and not the true labels!)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, if wanted, we can convert the model to a dictionary to store it as JSON."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_dict = model.serialize()\n",
+    "\n",
+    "model_path = os.path.join(\"output\", \"model.json\")\n",
+    "with open(model_path, \"w\") as file:\n",
+    "    json.dump(model_dict, file)\n",
+    "\n",
+    "# To reload the model again from a JSON file, run the following snippet:\n",
+    "# with open(model_path, \"r\") as file:\n",
+    "#     model_dict = json.load(file)\n",
+    "# model = LinearRegressionModel()\n",
+    "# model.deserialize(model_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have build and selected a final model, it is time to evaluate its predictions on the test set against various evaluation metrics. The used evaluation metrics are:\n",
+    "\n",
+    "1. Coefficient of Determination (R2)\n",
+    "2. Mean Absolute Error (MAE)\n",
+    "3. Mean Squared Error (MSE)\n",
+    "4. Root Mean Squared Error (RMSE)\n",
+    "\n",
+    "Furthermore, plotting makes the evaluation of a linear regression model a lot easier. We will use a **prediction plot**, which presents predictions from the model against actual values and a **Q-Q plot** from the standardized prediction residuals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get numpy array of True target labels and predicted scores\n",
+    "y_true = basetable[basetable[\"split\"]==\"selection\"][target_col].values\n",
+    "y_pred = model.score_model(basetable[basetable[\"split\"]==\"selection\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluator = RegressionEvaluator()\n",
+    "evaluator.fit(y_true, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluator.scalar_metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluator.plot_predictions()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluator.qq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluator.plot_qq()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/tutorial_Cobra_logistic_regression.ipynb b/tutorials/tutorial_Cobra_logistic_regression.ipynb
new file mode 100644
index 0000000..8e85a97
--- /dev/null
+++ b/tutorials/tutorial_Cobra_logistic_regression.ipynb
@@ -0,0 +1,821 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cobra's approach to logistic regression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Cobra** requires the usual Python packages for data science:\n",
+    "\n",
+    "- numpy (>=1.19.4)\n",
+    "- pandas (>=1.1.5)\n",
+    "- scipy (>=1.5.4)\n",
+    "- scikit-learn (>=0.24.1)\n",
+    "- matplotlib (>=3.3.3)\n",
+    "- seaborn (>=0.11.0)\n",
+    "\n",
+    "These packages, along with their versions are listed in requirements.txt and can be installed using pip.\n",
+    "\n",
+    "If you want to install Cobra with e.g. pip, you don't have to install all of these requirements as these are automatically installed with Cobra itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pip install -r requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The easiest way to install cobra is using pip:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pip install -U pythonpredictions-cobra"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*****"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This section we will walk you through all the required steps to build a predictive logistic regression model using **Cobra**. All classes and functions used here are well-documented. In case you want more information on a class or function, run `help(function_or_class_you_want_info_from)`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Building a good model involves three steps:\n",
+    "\n",
+    "1. **Preprocessing**: properly prepare the predictors (a synonym for “feature” or variable that we use throughout this tutorial) for modelling.\n",
+    "\n",
+    "2. **Feature Selection**: automatically select a subset of predictors which contribute most to the target variable or output in which you are interested.\n",
+    "\n",
+    "3. **Model Evaluation**: once a model has been build, a detailed evaluation can be performed by computing all sorts of evaluation metrics.\n",
+    "\n",
+    "Let's dive in!\n",
+    "***"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Survival Prediction using Titanic data\n",
+    "\n",
+    "- BASETABLE: seaborn dataset Titanic.\n",
+    "- GOAL: Predict if individual survives in Titanic sinking."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import the necessary libraries first."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "from pandas.api.types import is_datetime64_any_dtype\n",
+    "\n",
+    "pd.set_option(\"display.max_columns\", 50)\n",
+    "pd.set_option(\"display.max_rows\", 50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cobra.preprocessing import PreProcessor\n",
+    "\n",
+    "from cobra.model_building import univariate_selection\n",
+    "from cobra.model_building import ForwardFeatureSelection\n",
+    "# from cobra.model_building import LogisticRegressionModel\n",
+    "\n",
+    "from cobra.evaluation import ClassificationEvaluator\n",
+    "from cobra.evaluation import generate_pig_tables\n",
+    "from cobra.evaluation import plot_univariate_predictor_quality\n",
+    "from cobra.evaluation import plot_correlation_matrix\n",
+    "from cobra.evaluation import plot_performance_curves\n",
+    "from cobra.evaluation import plot_variable_importance\n",
+    "from cobra.evaluation import plot_incidence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = sns.load_dataset(\"titanic\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the example below, we assume the data for model building is available in a pandas DataFrame. This DataFrame should contain a an ID column, a target column (e.g. “**survived**”) and a number of candidate predictors (features) to build a model with.\n",
+    "\n",
+    "***\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is required to set all category vars to object dtype."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[:, df.dtypes==\"category\"] = (df.select_dtypes([\"category\"]).apply(lambda x: x.astype(\"object\")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### The first part focusses on preparing the predictors for modelling by:\n",
+    "\n",
+    "1. Defining the ID column, the target, discrete and contineous variables\n",
+    "\n",
+    "2. Splitting the dataset into training, selection and validation datasets.\n",
+    "\n",
+    "3. Binning continuous variables into discrete intervals\n",
+    "\n",
+    "4. Replacing missing values of both categorical and continuous variables (which are now binned) with an additional “Missing” bin/category\n",
+    "\n",
+    "5. Regrouping categories in new category “other”\n",
+    "\n",
+    "6. Replacing bins/categories with their corresponding incidence rate per category/bin.\n",
+    "\n",
+    "*Note to user*: as any good data scientist knows, you still need to deal in your data with irregularities, such as outliers or very skewed distributions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this toy dataset, the index will serve as ID."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"id\"] = df.index + 1\n",
+    "id_col = \"id\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The target is the \"survived\" column."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_col = \"survived\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we remove the columns \"who\" and \"adult_male\" since they are duplicate of \"sex\", and also \"alive\", which seems to be a duplicate of \"survived\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop([\"who\", \"adult_male\", \"alive\"], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finding out which variables are categorical (discrete) and which are continuous.\n",
+    "\n",
+    "Discrete variables are definitely those that contain strings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "col_dtypes = df.dtypes\n",
+    "discrete_vars = [col for col in col_dtypes[col_dtypes==object].index.tolist() if col not in [id_col, target_col]] \n",
+    "print(discrete_vars)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we also check for numerical columns that only contain a few different values, thus to be interpreted as discrete, categorical variables\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for col in df.columns:\n",
+    "    if col not in discrete_vars and col not in [id_col, target_col]: # if not already discrete because a string, or the target\n",
+    "        if len(val_counts) > 1 and len(val_counts) <= 10: # the column contains less than 10 different values\n",
+    "            print(col)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By taking a look at the printed variables, it is clear that we have to include those in the list of discrete variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discrete_vars.extend([\"pclass\", \"sibsp\", \"parch\", \"class\", \"deck\", \"alone\"])\n",
+    "discrete_vars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The remaining variables can be labelled continous predictors, without including the target variable.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "continuous_vars = list(set(df.columns)\n",
+    "                       - set(discrete_vars) \n",
+    "                       - set([id_col, target_col]))\n",
+    "continuous_vars                   "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can prepare **Cobra's PreProcessor** object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# using all Cobra's default parameters for preprocessing for now:\n",
+    "preprocessor = PreProcessor.from_params(\n",
+    "    model_type=\"classification\"\n",
+    ")\n",
+    "\n",
+    "# These are all available options: help(PreProcessor.from_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Split data into train-selection-validation sets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "basetable = preprocessor.train_selection_validation_split(\n",
+    "                data=df,\n",
+    "                train_prop=0.6,\n",
+    "                selection_prop=0.2,\n",
+    "                validation_prop=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fit the preprocessor pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preprocessor.fit(basetable[basetable[\"split\"]==\"train\"],\n",
+    "                 continuous_vars=continuous_vars,\n",
+    "                 discrete_vars=discrete_vars,\n",
+    "                 target_column_name=target_col)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This pipeline can now be performed on the basetable!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "basetable = preprocessor.transform(basetable,\n",
+    "                                   continuous_vars=continuous_vars,\n",
+    "                                   discrete_vars=discrete_vars)\n",
+    "basetable.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predictor Insights Graphs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we can the create the so-called Predictor Insights Graphs (PIGs in short). These are graphs that represents the insights of the relationship between a single predictor and the target. More specifically, the predictor is binned into groups, and we represent group size in bars and group (target) incidence in a colored line. Moreover, we have the option to force order of predictor values. First, we compute the output needed to plot the PIG."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictor_list = [col for col in basetable.columns\n",
+    "                  if col.endswith(\"_bin\") or col.endswith(\"_processed\")]\n",
+    "pig_tables = generate_pig_tables(basetable[basetable[\"split\"]==\"selection\"],\n",
+    "                                 id_column_name=id_col,\n",
+    "                                 target_column_name=target_col,\n",
+    "                                 preprocessed_predictors=predictor_list)\n",
+    "pig_tables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we can plot a PIG graph for each of the predictors in the basetable. For instance, for the variable age."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "plot_incidence(pig_tables, variable=\"age\", model_type=\"classification\",\n",
+    "               column_order = list(basetable[\"age_bin\"].unique().sort_values())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature selection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once the predictors are properly prepared, we can start building a predictive model, which boils down to selecting the right predictors from the dataset to train a model on.\n",
+    "\n",
+    "As a dataset typically contains many predictors, **we first perform a univariate preselection** to rule out any predictor with little to no predictive power. \n",
+    "\n",
+    "Later, using the list of preselected features, we build a logistic regression model using **forward feature selection** to choose the right set of predictors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In previous steps, these were the predictors, as preprocessed so far:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preprocessed_predictors = [\n",
+    "    col for col in basetable.columns\n",
+    "    if col.endswith(\"_bin\") or col.endswith(\"_processed\")]\n",
+    "sorted(preprocessed_predictors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But for feature selection, we use the target encoded version of each of these."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preprocessed_predictors = [col for col in basetable.columns.tolist() if \"_enc\" in col]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A univariate selection on the preprocessed predictors can be conducted. The thresholds for retaining a feature are now on default but can be changed by the user.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_auc = univariate_selection.compute_univariate_preselection(\n",
+    "    target_enc_train_data=basetable[basetable[\"split\"]==\"train\"],\n",
+    "    target_enc_selection_data=basetable[basetable[\"split\"]==\"selection\"],\n",
+    "    predictors=preprocessed_predictors,\n",
+    "    target_column=target_col,\n",
+    "    preselect_auc_threshold=0.53,  # if auc_selection <= 0.53 exclude predictor\n",
+    "    preselect_overtrain_threshold=0.05)  # if (auc_train - auc_selection) >= 0.05 --> overfitting!\n",
+    "\n",
+    "plot_univariate_predictor_quality(df_auc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we compute correlations between the preprocessed predictors and plot it using a correlation matrix:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_corr = (univariate_selection\n",
+    "           .compute_correlations(basetable[basetable[\"split\"]==\"train\"],\n",
+    "                                 preprocessed_predictors))\n",
+    "plot_correlation_matrix(df_corr)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get a list of the selected predictors after the univariate selection, run the following call:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preselected_predictors = univariate_selection.get_preselected_predictors(df_auc)\n",
+    "preselected_predictors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "After an initial preselection on the predictors, we can start building the model itself using forward feature selection to choose the right set of predictors. Since we use target encoding on all our predictors, we will only consider models with positive coefficients (no sign flip should occur) as this makes the model more interpretable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modeling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "forward_selection = ForwardFeatureSelection(model_type=\"classification\",\n",
+    "                                            max_predictors=30,\n",
+    "                                            pos_only=True)\n",
+    "\n",
+    "# fit the forward feature selection on the train data\n",
+    "# has optional parameters to force and/or exclude certain predictors (see docs)\n",
+    "forward_selection.fit(basetable[basetable[\"split\"]==\"train\"],\n",
+    "                      target_column_name = target_col,\n",
+    "                      predictors = preselected_predictors)\n",
+    "                      #forced_predictors: list = [],\n",
+    "                      #excluded_predictors: list = [])\n",
+    "\n",
+    "# compute model performance\n",
+    "performances = (forward_selection\n",
+    "                .compute_model_performances(basetable, target_column_name = target_col))\n",
+    "performances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As can be seen, we have completed 6 steps till no further improvement can be observed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot performance curves\n",
+    "plot_performance_curves(performances)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Based on the performance curves (AUC per model with a particular number of predictors in case of logistic regression), a final model can then be chosen and the variables importance can be plotted:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# look at the number of steps that were completed before stopping, fill that in between the brackets\n",
+    "model = forward_selection.get_model_from_step(5)\n",
+    "\n",
+    "# Note that chosen model the following variables:\n",
+    "final_predictors = model.predictors\n",
+    "final_predictors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "variable_importance = model.compute_variable_importance(\n",
+    "    basetable[basetable[\"split\"]==\"selection\"]\n",
+    ")\n",
+    "plot_variable_importance(variable_importance)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: variable importance is based on correlation of the predictor with the model scores (and not the true labels!)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, if wanted, we can convert the model to a dictionary to store it as JSON."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_dict = model.serialize()\n",
+    "\n",
+    "model_path = os.path.join(\"output\", \"model.json\")\n",
+    "with open(model_path, \"w\") as file:\n",
+    "    json.dump(model_dict, file)\n",
+    "\n",
+    "# To reload the model again from a JSON file, run the following snippet:\n",
+    "# with open(path, \"r\") as file:\n",
+    "#     model_dict = json.load(file)\n",
+    "# model = LogisticRegressionModel()\n",
+    "# model.deserialize(model_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have built and selected a final model, it is time to evaluate its predictions on the test set against various evaluation metrics. The used evaluation metrics are:\n",
+    "\n",
+    "1. Accuracy\n",
+    "2. Area Under Curve (AUC)\n",
+    "3. Precision\n",
+    "4. Recall\n",
+    "5. F1\n",
+    "6. Matthews correlation coefficient\n",
+    "7. Lift\n",
+    "\n",
+    "Furthermore, we can evaluate the classification performance using a confusion matrix.\n",
+    "\n",
+    "Plotting makes the evaluation of a logistic regression model a lot easier. We will first use a **Receiver Operating Characteristic (ROC) curve**, which is a plot of the false positive rate (x-axis) versus the true positive rate (y-axis). Next, we display the **Cumulative Gains curve**, **Cumulative Lift curve** and **Cumulative Response curve**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get numpy array of True target labels and predicted scores:\n",
+    "y_true = basetable[basetable[\"split\"]==\"selection\"][target_col].values\n",
+    "y_pred = model.score_model(basetable[basetable[\"split\"]==\"selection\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluator = ClassificationEvaluator()\n",
+    "evaluator.fit(y_true, y_pred)  # Automatically find the best cut-off probability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluator.scalar_metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluator.plot_confusion_matrix()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluator.plot_roc_curve()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluator.plot_cumulative_gains()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluator.plot_lift_curve()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "evaluator.plot_cumulative_response_curve()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 819cab879993c5da3654bbe779d02d0acf4ce1ad Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Wed, 29 Sep 2021 15:50:32 +0200
Subject: [PATCH 06/16] a round of cleaning

---
 README.rst                                   | 24 ++++++++------------
 cobra/evaluation/evaluator.py                |  8 +++----
 cobra/model_building/forward_selection.py    |  6 ++---
 cobra/model_building/models.py               |  6 ++---
 cobra/model_building/univariate_selection.py |  1 +
 cobra/utils.py                               | 12 +---------
 6 files changed, 19 insertions(+), 38 deletions(-)

diff --git a/README.rst b/README.rst
index bc65a2b..ce9f72a 100644
--- a/README.rst
+++ b/README.rst
@@ -54,7 +54,7 @@ These packages, along with their versions are listed in ``requirements.txt`` and
   pip install -r requirements.txt
 
 
-**Note**: if you want to install Cobra with e.g. pip, you don't have to install all of these requirements as these are automatically installed with cobra itself.
+**Note**: if you want to install Cobra with e.g. pip, you don't have to install all of these requirements as these are automatically installed with Cobra itself.
 
 Installation
 ------------
@@ -71,23 +71,17 @@ We use GitHub issue to track all bugs and feature requests. Feel free to open an
 
 For more details, check our `wiki <https://github.com/PythonPredictions/cobra/wiki/Contributing-guidelines-&-workflows>`_.
 
-Help and Support
-================
-
-Documentation
--------------
-
-HTML documentation of the `individual modules <https://pythonpredictions.github.io/cobra.io/docstring/modules.html>`_
+Documentation and Extra Material
+=====================
 
-**Logistic Regression**
+- A `blog post <https://github.com/PythonPredictions/Cobra-DS-meetup-Leuven>`_ on the overall methodology.
 
-- A step-by-step tutorial `<https://pythonpredictions.github.io/cobra/tutorials/tutorial_Cobra_logistic_regression.ipynb>`_
+- A `research article <https://doi.org/10.1016/j.dss.2016.11.007>`_ by Geert Verstraeten (co-founder Python Predictions) discussing the preprocessing approach we use in Cobra.
 
-**Linear Regression**
+- HTML documentation of the `individual modules <https://pythonpredictions.github.io/cobra.io/docstring/modules.html>`_.
 
-- A step-by-step tutorial `<https://pythonpredictions.github.io/cobra/tutorials/tutorial_Cobra_linear_regression.ipynb>`_
+- A step-by-step `tutorial <https://pythonpredictions.github.io/cobra/tutorials/tutorial_Cobra_logistic_regression.ipynb>`_ for **logistic regression**.
 
-Outreach
--------------
+- A step-by-step `tutorial <https://pythonpredictions.github.io/cobra/tutorials/tutorial_Cobra_linear_regression.ipynb>`_ for **linear regression**.
 
-- Check out the Data Science Leuven Meetup `talk <https://www.youtube.com/watch?v=w7ceZZqMEaA&feature=youtu.be>`_ by one of the core developers (second presentation)
+- Check out the Data Science Leuven Meetup `talk <https://www.youtube.com/watch?v=w7ceZZqMEaA&feature=youtu.be>`_ by one of the core developers (second presentation). His `slides <https://github.com/PythonPredictions/Cobra-DS-meetup-Leuven/blob/main/DS_Leuven_meetup_20210209_cobra.pdf>`_ and `related material <https://github.com/PythonPredictions/Cobra-DS-meetup-Leuven>`_ are also available.
diff --git a/cobra/evaluation/evaluator.py b/cobra/evaluation/evaluator.py
index cbf836c..3d9cae6 100644
--- a/cobra/evaluation/evaluator.py
+++ b/cobra/evaluation/evaluator.py
@@ -26,8 +26,8 @@
 
 
 class ClassificationEvaluator():
-
-    """Summary
+    """Evaluator class encapsulating classification model metrics
+    and plotting functionality
 
     Attributes
     ----------
@@ -540,8 +540,8 @@ def _compute_lift(y_true: np.ndarray, y_pred: np.ndarray,
 
 
 class RegressionEvaluator():
-
-    """Summary
+    """Evaluator class encapsulating regression model metrics
+    and plotting functionality
 
     Attributes
     ----------
diff --git a/cobra/model_building/forward_selection.py b/cobra/model_building/forward_selection.py
index ec21fb3..9da6055 100644
--- a/cobra/model_building/forward_selection.py
+++ b/cobra/model_building/forward_selection.py
@@ -3,7 +3,6 @@
 import pandas as pd
 from tqdm.auto import tqdm
 
-# from cobra.model_building import LogisticRegressionModel as MLModel
 from cobra.model_building import LogisticRegressionModel, LinearRegressionModel
 
 log = logging.getLogger(__name__)
@@ -282,11 +281,11 @@ def _find_next_best_model(self, train_data: pd.DataFrame,
             # Check if the model is better than the current best model
             # and if it is, replace the current best.
             if self.MLModel == LogisticRegressionModel \
-                    and performance > best_performance:  # AUC metric is used.
+                    and performance > best_performance:  # AUC metric is used
                 best_performance = performance
                 best_model = model
             elif self.MLModel == LinearRegressionModel \
-                    and performance < best_performance:  # RMSE metric is used.
+                    and performance < best_performance:  # RMSE metric is used
                 best_performance = performance
                 best_model = model
 
@@ -310,7 +309,6 @@ def _train_model(self, train_data: pd.DataFrame, target_column_name: str,
         self.MLModel
             trained model
         """
-        # model = MLModel()
         model = self.MLModel()
 
         model.fit(train_data[predictors], train_data[target_column_name])
diff --git a/cobra/model_building/models.py b/cobra/model_building/models.py
index 11beda8..8fdd0b1 100644
--- a/cobra/model_building/models.py
+++ b/cobra/model_building/models.py
@@ -10,10 +10,9 @@
 
 
 class LogisticRegressionModel:
-
     """Wrapper around the LogisticRegression class, with additional methods
     implemented such as evaluation (using AUC), getting a list of coefficients,
-    a dictionary of coefficients per predictor, ... for convenience.
+    a dictionary of coefficients per predictor, ... for convenience
 
     Attributes
     ----------
@@ -229,10 +228,9 @@ def _is_valid_dict(self, model_dict: dict) -> bool:
 
 
 class LinearRegressionModel:
-
     """Wrapper around the LinearRegression class, with additional methods
     implemented such as evaluation (using RMSE), getting a list of coefficients,
-    a dictionary of coefficients per predictor, ... for convenience.
+    a dictionary of coefficients per predictor, ... for convenience
 
     Attributes
     ----------
diff --git a/cobra/model_building/univariate_selection.py b/cobra/model_building/univariate_selection.py
index b7d86d5..4de4b63 100644
--- a/cobra/model_building/univariate_selection.py
+++ b/cobra/model_building/univariate_selection.py
@@ -1,6 +1,7 @@
 """
 Module to perform univariate preselection and compute correlation amongst
 predictors
+
 Authors:
 - Geert Verstraeten (methodology)
 - Matthias Roels (current implementation)
diff --git a/cobra/utils.py b/cobra/utils.py
index f138fd7..f394caf 100644
--- a/cobra/utils.py
+++ b/cobra/utils.py
@@ -1,16 +1,6 @@
 def clean_predictor_name(predictor_name: str) -> str:
     """Strip the redundant suffix (e.g. "_enc" or "_bin") off from the end
-    of the predictor name to return a clean version of the predictor.
-
-    Parameters
-    ----------
-    predictor_name : str
-        Description
-
-    Returns
-    -------
-    str
-        Description
+    of the predictor name to return a clean version of the predictor
     """
     return (predictor_name.replace("_enc", "")
                           .replace("_bin", "")

From 9d591a45a33acd3aa448d01800349dac1c31bfe4 Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Wed, 29 Sep 2021 17:00:37 +0200
Subject: [PATCH 07/16] bunch of documentation cleaning

---
 README.rst                                    | 21 +++---
 cobra/evaluation/evaluator.py                 |  4 +-
 cobra/evaluation/pigs_tables.py               |  2 +-
 cobra/evaluation/plotting_utils.py            | 14 ++--
 cobra/model_building/forward_selection.py     |  2 +-
 cobra/model_building/models.py                | 31 ++++-----
 cobra/model_building/univariate_selection.py  | 11 +--
 .../categorical_data_processor.py             | 25 +++----
 cobra/preprocessing/kbins_discretizer.py      | 17 ++---
 cobra/preprocessing/preprocessor.py           | 67 +++++++++----------
 cobra/preprocessing/target_encoder.py         | 58 +++++++---------
 .../tutorial_Cobra_linear_regression.ipynb    | 11 ++-
 .../tutorial_Cobra_logistic_regression.ipynb  | 15 ++---
 13 files changed, 118 insertions(+), 160 deletions(-)

diff --git a/README.rst b/README.rst
index ce9f72a..a6533de 100644
--- a/README.rst
+++ b/README.rst
@@ -14,7 +14,7 @@
 
 **Cobra** is a Python package to build predictive models using linear or logistic regression with a focus on performance and interpretation. It consists of several modules for data preprocessing, feature selection and model evaluation. The underlying methodology was developed at Python Predictions in the course of hundreds of business-related prediction challenges. It has been tweaked, tested and optimized over the years based on feedback from clients, our team, and academic researchers.
 
-Main Features
+Main features
 =============
 
 - Prepare a given pandas DataFrame for predictive modelling:
@@ -63,18 +63,11 @@ The easiest way to install Cobra is using ``pip``:    ::
 
   pip install -U pythonpredictions-cobra
 
-Contributing to Cobra
-=====================
-
-We'd love you to contribute to the development of Cobra! There are many ways in which you can contribute, the most common of which is to contribute to the source code or documentation of the project. However, there are many other ways you can contribute (report issues, improve code coverage by adding unit tests, ...).
-We use GitHub issue to track all bugs and feature requests. Feel free to open an issue in case you found a bug or in case you wish to see a new feature added.
-
-For more details, check our `wiki <https://github.com/PythonPredictions/cobra/wiki/Contributing-guidelines-&-workflows>`_.
 
-Documentation and Extra Material
+Documentation and extra material
 =====================
 
-- A `blog post <https://github.com/PythonPredictions/Cobra-DS-meetup-Leuven>`_ on the overall methodology.
+- A `blog post <https://www.pythonpredictions.com/news/the-little-trick-we-apply-to-obtain-explainability-by-design/>`_ on the overall methodology.
 
 - A `research article <https://doi.org/10.1016/j.dss.2016.11.007>`_ by Geert Verstraeten (co-founder Python Predictions) discussing the preprocessing approach we use in Cobra.
 
@@ -85,3 +78,11 @@ Documentation and Extra Material
 - A step-by-step `tutorial <https://pythonpredictions.github.io/cobra/tutorials/tutorial_Cobra_linear_regression.ipynb>`_ for **linear regression**.
 
 - Check out the Data Science Leuven Meetup `talk <https://www.youtube.com/watch?v=w7ceZZqMEaA&feature=youtu.be>`_ by one of the core developers (second presentation). His `slides <https://github.com/PythonPredictions/Cobra-DS-meetup-Leuven/blob/main/DS_Leuven_meetup_20210209_cobra.pdf>`_ and `related material <https://github.com/PythonPredictions/Cobra-DS-meetup-Leuven>`_ are also available.
+
+Contributing to Cobra
+=====================
+
+We'd love you to contribute to the development of Cobra! There are many ways in which you can contribute, the most common of which is to contribute to the source code or documentation of the project. However, there are many other ways you can contribute (report issues, improve code coverage by adding unit tests, ...).
+We use GitHub issue to track all bugs and feature requests. Feel free to open an issue in case you found a bug or in case you wish to see a new feature added.
+
+For more details, check our `wiki <https://github.com/PythonPredictions/cobra/wiki/Contributing-guidelines-&-workflows>`_.
diff --git a/cobra/evaluation/evaluator.py b/cobra/evaluation/evaluator.py
index 3d9cae6..817aae0 100644
--- a/cobra/evaluation/evaluator.py
+++ b/cobra/evaluation/evaluator.py
@@ -1,3 +1,4 @@
+
 import numpy as np
 import pandas as pd
 
@@ -24,10 +25,9 @@
 from sklearn.metrics import mean_squared_error
 from sklearn.metrics import r2_score
 
-
 class ClassificationEvaluator():
     """Evaluator class encapsulating classification model metrics
-    and plotting functionality
+    and plotting functionality.
 
     Attributes
     ----------
diff --git a/cobra/evaluation/pigs_tables.py b/cobra/evaluation/pigs_tables.py
index 4cf8aa2..4657c09 100644
--- a/cobra/evaluation/pigs_tables.py
+++ b/cobra/evaluation/pigs_tables.py
@@ -1,3 +1,4 @@
+
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
@@ -6,7 +7,6 @@
 
 import cobra.utils as utils
 
-
 def generate_pig_tables(basetable: pd.DataFrame,
                         id_column_name: str,
                         target_column_name: str,
diff --git a/cobra/evaluation/plotting_utils.py b/cobra/evaluation/plotting_utils.py
index 2fef624..63928fa 100644
--- a/cobra/evaluation/plotting_utils.py
+++ b/cobra/evaluation/plotting_utils.py
@@ -5,11 +5,10 @@
 import matplotlib.pyplot as plt
 import seaborn as sns
 
-
 def plot_univariate_predictor_quality(df_metric: pd.DataFrame,
                                       dim: tuple=(12, 8),
                                       path: str=None):
-    """Plot univariate quality of the predictors
+    """Plot univariate quality of the predictors.
 
     Parameters
     ----------
@@ -56,11 +55,10 @@ def plot_univariate_predictor_quality(df_metric: pd.DataFrame,
 
         plt.show()
 
-
 def plot_correlation_matrix(df_corr: pd.DataFrame,
                             dim: tuple=(12, 8),
                             path: str=None):
-    """Plot correlation matrix amongst the predictors
+    """Plot correlation matrix amongst the predictors.
 
     Parameters
     ----------
@@ -80,7 +78,6 @@ def plot_correlation_matrix(df_corr: pd.DataFrame,
 
     plt.show()
 
-
 def plot_performance_curves(model_performance: pd.DataFrame,
                             dim: tuple=(12, 8),
                             path: str=None,
@@ -88,7 +85,7 @@ def plot_performance_curves(model_performance: pd.DataFrame,
                                           "selection": "#ff9500",
                                           "validation": "#8064a2"}):
     """Plot performance curves generated by the forward feature selection
-    for the train-selection-validation sets
+    for the train-selection-validation sets.
 
     Parameters
     ----------
@@ -150,17 +147,16 @@ def plot_performance_curves(model_performance: pd.DataFrame,
 
         plt.show()
 
-
 def plot_variable_importance(df_variable_importance: pd.DataFrame,
                              title: str=None,
                              dim: tuple=(12, 8),
                              path: str=None):
-    """Plot variable importance of a given model
+    """Plot variable importance of a given model.
 
     Parameters
     ----------
     df_variable_importance : pd.DataFrame
-        DataFrame containing columns predictor and importance
+        DataFrame containing columns predictor and importance.
     title : str, optional
         Title of the plot.
     dim : tuple, optional
diff --git a/cobra/model_building/forward_selection.py b/cobra/model_building/forward_selection.py
index 9da6055..66508b3 100644
--- a/cobra/model_building/forward_selection.py
+++ b/cobra/model_building/forward_selection.py
@@ -1,3 +1,4 @@
+
 import logging
 
 import pandas as pd
@@ -7,7 +8,6 @@
 
 log = logging.getLogger(__name__)
 
-
 class ForwardFeatureSelection:
     """Perform forward feature selection for a given dataset using a given
     algorithm.
diff --git a/cobra/model_building/models.py b/cobra/model_building/models.py
index 8fdd0b1..03f80a2 100644
--- a/cobra/model_building/models.py
+++ b/cobra/model_building/models.py
@@ -1,3 +1,4 @@
+
 # third party imports
 import numpy as np
 import pandas as pd
@@ -5,14 +6,14 @@
 from sklearn.metrics import roc_auc_score, mean_squared_error
 from numpy import sqrt
 from sklearn.linear_model import LogisticRegression, LinearRegression
+
 # custom imports
 import cobra.utils as utils
 
-
 class LogisticRegressionModel:
     """Wrapper around the LogisticRegression class, with additional methods
     implemented such as evaluation (using AUC), getting a list of coefficients,
-    a dictionary of coefficients per predictor, ... for convenience
+    a dictionary of coefficients per predictor, ... for convenience.
 
     Attributes
     ----------
@@ -31,12 +32,12 @@ def __init__(self):
         self._eval_metrics_by_split = {}
 
     def serialize(self) -> dict:
-        """Serialize model as JSON
+        """Serialize model as JSON.
 
         Returns
         -------
         dict
-            dictionary containing the serialized JSON
+            Dictionary containing the serialized JSON.
         """
         serialized_model = {
             "meta": "logistic-regression",
@@ -56,7 +57,7 @@ def serialize(self) -> dict:
         return serialized_model
 
     def deserialize(self, model_dict: dict):
-        """Deserialize a model previously stored as JSON
+        """Deserialize a model previously stored as JSON.
 
         Parameters
         ----------
@@ -66,7 +67,7 @@ def deserialize(self, model_dict: dict):
         Raises
         ------
         ValueError
-            In case JSON file is no valid serialized model
+            In case JSON file is no valid serialized model.
         """
 
         if not self._is_valid_dict(model_dict):
@@ -92,27 +93,27 @@ def get_coef(self) -> np.array:
         return self.logit.coef_[0]
 
     def get_intercept(self) -> float:
-        """Returns the intercept of the model
+        """Returns the intercept of the model.
 
         Returns
         -------
         float
-            intercept of the model
+            Intercept of the model.
         """
         return self.logit.intercept_[0]
 
     def get_coef_by_predictor(self) -> dict:
-        """Returns a dictionary mapping predictor (key) to coefficient (value)
+        """Returns a dictionary mapping predictor (key) to coefficient (value).
 
         Returns
         -------
         dict
-            map ``{predictor: coefficient}``
+            A map ``{predictor: coefficient}``.
         """
         return dict(zip(self.predictors, self.logit.coef_[0]))
 
     def fit(self, X_train: pd.DataFrame, y_train: pd.Series):
-        """Fit the model
+        """Fit the model.
 
         Parameters
         ----------
@@ -126,7 +127,7 @@ def fit(self, X_train: pd.DataFrame, y_train: pd.Series):
         self._is_fitted = True
 
     def score_model(self, X: pd.DataFrame) -> np.ndarray:
-        """Score a model on a (new) dataset
+        """Score a model on a (new) dataset.
 
         Parameters
         ----------
@@ -136,7 +137,7 @@ def score_model(self, X: pd.DataFrame) -> np.ndarray:
         Returns
         -------
         np.ndarray
-            score of the model for each observation
+            Score of the model for each observation.
         """
         # We select predictor columns (self.predictors) here to
         # ensure we have the proper predictors and the proper order!!!
@@ -161,7 +162,7 @@ def evaluate(self, X: pd.DataFrame, y: pd.Series,
         Returns
         -------
         float
-            the performance score of the model (AUC)
+            The performance score of the model (AUC).
         """
 
         if (split is None) or (split not in self._eval_metrics_by_split):
@@ -230,7 +231,7 @@ def _is_valid_dict(self, model_dict: dict) -> bool:
 class LinearRegressionModel:
     """Wrapper around the LinearRegression class, with additional methods
     implemented such as evaluation (using RMSE), getting a list of coefficients,
-    a dictionary of coefficients per predictor, ... for convenience
+    a dictionary of coefficients per predictor, ... for convenience.
 
     Attributes
     ----------
diff --git a/cobra/model_building/univariate_selection.py b/cobra/model_building/univariate_selection.py
index 4de4b63..432f18d 100644
--- a/cobra/model_building/univariate_selection.py
+++ b/cobra/model_building/univariate_selection.py
@@ -1,19 +1,10 @@
-"""
-Module to perform univariate preselection and compute correlation amongst
-predictors
-
-Authors:
-- Geert Verstraeten (methodology)
-- Matthias Roels (current implementation)
-- Jan Benisek (initial implementation)
-"""
+
 import pandas as pd
 from sklearn.metrics import roc_auc_score, mean_squared_error
 from numpy import sqrt
 
 import cobra.utils as utils
 
-
 def compute_univariate_preselection(target_enc_train_data: pd.DataFrame,
                                     target_enc_selection_data: pd.DataFrame,
                                     predictors: list,
diff --git a/cobra/preprocessing/categorical_data_processor.py b/cobra/preprocessing/categorical_data_processor.py
index 0e41dc8..4c6dbe3 100644
--- a/cobra/preprocessing/categorical_data_processor.py
+++ b/cobra/preprocessing/categorical_data_processor.py
@@ -1,18 +1,3 @@
-"""
-This class implements the Python Prediction's way of dealing with
-categorical data preprocessing. There are three steps involved here:
-
-- An optional regrouping of the different categories based on category size
-  and significance of the category w.r.t. the target
-- Missing value replacement with the additional category ``Missing``
-- Change of dtype to ``category`` (could potentially lead to memory
-  optimization)
-
-Authors:
-- Geert Verstraeten (methodology)
-- Jan Benisek (implementation)
-- Matthias Roels (implementation)
-"""
 
 # standard lib imports
 import re
@@ -29,11 +14,19 @@
 
 log = logging.getLogger(__name__)
 
-
 class CategoricalDataProcessor(BaseEstimator):
     """Regroups the categories of categorical variables based on significance
     with target variable.
 
+    This class implements the Python Prediction's way of dealing with
+    categorical data preprocessing. There are three steps involved:
+
+        - An optional regrouping of the different categories based on category size
+          and significance of the category w.r.t. the target;
+        - Missing value replacement with the additional category ``Missing``;
+        - Change of dtype to ``category`` (could potentially lead to memory
+          optimization).
+
     Attributes
     ----------
     category_size_threshold : int
diff --git a/cobra/preprocessing/kbins_discretizer.py b/cobra/preprocessing/kbins_discretizer.py
index 6255d28..809bae5 100644
--- a/cobra/preprocessing/kbins_discretizer.py
+++ b/cobra/preprocessing/kbins_discretizer.py
@@ -1,14 +1,3 @@
-"""
-This module is a rework of
-https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_discretization.py
-However, it is purely written in pandas instead of numpy because it is more intuitive.
-
-Also includes some custom modifications to align it with the Python Predictions methodology.
-
-Authors:
-- Geert Verstraeten (methodology)
-- Matthias Roels (implementation)
-"""
 
 # standard lib imports
 from copy import deepcopy
@@ -26,7 +15,6 @@
 
 log = logging.getLogger(__name__)
 
-
 class KBinsDiscretizer(BaseEstimator):
     """Bin continuous data into intervals of predefined size. It provides a
     way to partition continuous data into discrete values, i.e. transform
@@ -34,6 +22,11 @@ class KBinsDiscretizer(BaseEstimator):
     expressive as it introduces nonlinearity to the model, while maintaining
     the interpretability of the model afterwards.
 
+    This module is a rework of
+    https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_discretization.py,
+    though it is purely written in pandas instead of numpy because it is more intuitive. It also includes
+    some custom modifications to align it with the Python Predictions methodology.
+
     Attributes
     ----------
     auto_adapt_bins : bool
diff --git a/cobra/preprocessing/preprocessor.py b/cobra/preprocessing/preprocessor.py
index 3cc9882..7081a86 100644
--- a/cobra/preprocessing/preprocessor.py
+++ b/cobra/preprocessing/preprocessor.py
@@ -1,26 +1,17 @@
-"""
-Here we make use of the classes for discretization, preprocessing of
-categorical variables, and incidence replacement. All of which will be
-employed to create a preprocessing pipeline, which can be stored as a
-JSON file so that it can easily be re-used for scoring.
-
-Authors:
-- Geert Verstraeten (methodology)
-- Matthias Roels (implementation)
-"""
 
 # std lib imports
 import inspect
-from datetime import datetime
 import time
 import math
 import logging
 from random import shuffle
+from datetime import datetime
 
 # third party imports
 import pandas as pd
 from sklearn.base import BaseEstimator
 from sklearn.exceptions import NotFittedError
+
 # custom imports
 from cobra.preprocessing import CategoricalDataProcessor
 from cobra.preprocessing import KBinsDiscretizer
@@ -28,14 +19,14 @@
 
 log = logging.getLogger(__name__)
 
-
 class PreProcessor(BaseEstimator):
     """This class implements a so-called facade pattern to define a
     higher-level interface to work with the CategoricalDataProcessor,
     KBinsDiscretizer and TargetEncoder classes, so that their fit and transform
-    methods are called in the correct order. Additionally, it provides features
-    such as (de)serialization to/from JSON so that preprocessing pipelines can
-    be stored and reloaded.
+    methods are called in the correct order.
+
+    Additionally, it provides methods such as (de)serialization to/from JSON
+    so that preprocessing pipelines can be stored and reloaded, example for scoring.
 
     Attributes
     ----------
@@ -213,18 +204,18 @@ def from_pipeline(cls, pipeline: dict):
 
     def fit(self, train_data: pd.DataFrame, continuous_vars: list,
             discrete_vars: list, target_column_name: str):
-        """Fit the data to the preprocessing pipeline
+        """Fit the data to the preprocessing pipeline.
 
         Parameters
         ----------
         train_data : pd.DataFrame
-            Data to be preprocessed
+            Data to be preprocessed.
         continuous_vars : list
-            list of continuous variables
+            List of continuous variables.
         discrete_vars : list
-            list of discrete variables
+            List of discrete variables.
         target_column_name : str
-            Column name of the target
+            Column name of the target.
         """
 
         # get list of all variables
@@ -273,26 +264,26 @@ def fit(self, train_data: pd.DataFrame, continuous_vars: list,
 
     def transform(self, data: pd.DataFrame, continuous_vars: list,
                   discrete_vars: list) -> pd.DataFrame:
-        """Transform the data by applying the preprocessing pipeline
+        """Transform the data by applying the preprocessing pipeline.
 
         Parameters
         ----------
         data : pd.DataFrame
-            Data to be preprocessed
+            Data to be preprocessed.
         continuous_vars : list
-            list of continuous variables
+            List of continuous variables.
         discrete_vars : list
-            list of discrete variables
+            List of discrete variables.
 
         Returns
         -------
         pd.DataFrame
-            Transformed (preprocessed) data
+            Transformed (preprocessed) data.
 
         Raises
         ------
         NotFittedError
-            In case PreProcessor was not fitted first
+            In case PreProcessor was not fitted first.
         """
 
         start = time.time()
@@ -325,23 +316,23 @@ def transform(self, data: pd.DataFrame, continuous_vars: list,
     def fit_transform(self, train_data: pd.DataFrame, continuous_vars: list,
                       discrete_vars: list,
                       target_column_name: str) -> pd.DataFrame:
-        """Fit preprocessing pipeline and transform the data
+        """Fit preprocessing pipeline and transform the data.
 
         Parameters
         ----------
         train_data : pd.DataFrame
             Data to be preprocessed
         continuous_vars : list
-            list of continuous variables
+            List of continuous variables.
         discrete_vars : list
-            list of discrete variables
+            List of discrete variables.
         target_column_name : str
-            Column name of the target
+            Column name of the target.
 
         Returns
         -------
         pd.DataFrame
-            Transformed (preprocessed) data
+            Transformed (preprocessed) data.
         """
 
         self.fit(train_data, continuous_vars, discrete_vars,
@@ -357,21 +348,25 @@ def train_selection_validation_split(data: pd.DataFrame,
         """Adds `split` column with train/selection/validation values
         to the dataset.
 
+        Train set = data on which the model is trained and on which the encoding is based.
+        Selection set = data used for univariate and forward feature selection. Often called the validation set.
+        Validation set = data that generates the final performance metrics. Often called the test set.
+
         Parameters
         ----------
         data : pd.DataFrame
-            Input dataset to split into train-selection and validation sets
+            Input dataset to split into train-selection and validation sets.
         train_prop : float, optional
-            Percentage data to put in train set
+            Percentage data to put in train set.
         selection_prop : float, optional
-            Percentage data to put in selection set
+            Percentage data to put in selection set.
         validation_prop : float, optional
-            Percentage data to put in validation set
+            Percentage data to put in validation set.
 
         Returns
         -------
         pd.DataFrame
-            DataFrame with additional split column
+            DataFrame with additional split column.
         """
         if not math.isclose(train_prop + selection_prop + validation_prop, 1.0):
             raise ValueError("The sum of train_prop, selection_prop and "
diff --git a/cobra/preprocessing/target_encoder.py b/cobra/preprocessing/target_encoder.py
index 64cdfcd..e714a76 100644
--- a/cobra/preprocessing/target_encoder.py
+++ b/cobra/preprocessing/target_encoder.py
@@ -1,12 +1,4 @@
-"""
-Incidence Replacement Module. The implementation is inspired by
-https://github.com/scikit-learn-contrib/category_encoders.
 
-Authors:
-
-- Geert Verstraeten (methodology)
-- Matthias Roels (implementation)
-"""
 import logging
 
 import pandas as pd
@@ -16,9 +8,7 @@
 
 log = logging.getLogger(__name__)
 
-
 class TargetEncoder(BaseEstimator):
-
     """Target encoding for categorical features, inspired by
     http://contrib.scikit-learn.org/category_encoders/targetencoder.html.
 
@@ -44,19 +34,19 @@ class TargetEncoder(BaseEstimator):
     - with a binary classification target, a value of a categorical variable is
     replaced with:
 
-    [count(variable=value) * P(target=1|variable=value) + weight * P(target=1)]
-    / [count(variable=value) + weight]
+        [count(variable=value) * P(target=1|variable=value) + weight * P(target=1)]
+        / [count(variable=value) + weight]
 
     - with a regression target, a value of a categorical variable is replaced
     with:
 
-    [count(variable=value) * E(target|variable=value) + weight * E(target)]
-    / [count(variable=value) + weight]
+        [count(variable=value) * E(target|variable=value) + weight * E(target)]
+        / [count(variable=value) + weight]
 
     Attributes
     ----------
     imputation_strategy : str
-        in case there is a particular column which contains new categories,
+        In case there is a particular column which contains new categories,
         the encoding will lead to NULL values which should be imputed.
         Valid strategies then are to replace the NULL values with the global
         mean of the train set or the min (resp. max) incidence of the
@@ -103,7 +93,7 @@ def attributes_to_dict(self) -> dict:
         -------
         dict
             Contains the attributes of TargetEncoder instance with the names
-            as keys
+            as keys.
         """
         params = self.get_params()
 
@@ -159,12 +149,12 @@ def fit(self, data: pd.DataFrame, column_names: list,
         Parameters
         ----------
         data : pd.DataFrame
-            data used to compute the mapping to encode the categorical
+            Data used to compute the mapping to encode the categorical
             variables with.
         column_names : list
-            Columns of data to be encoded
+            Columns of data to be encoded.
         target_column : str
-            Column name of the target
+            Column name of the target.
         """
         # compute global mean (target incidence in case of binary target)
         y = data[target_column]
@@ -186,10 +176,10 @@ def _fit_column(self, X: pd.Series, y: pd.Series) -> pd.Series:
         Parameters
         ----------
         X : pd.Series
-            data used to compute the encoding mapping for an individual
+            Data used to compute the encoding mapping for an individual
             categorical variable.
         y : pd.Series
-            series containing the targets for each observation (value) of
+            Series containing the targets for each observation (value) of
             this categorical variable.
 
         Returns
@@ -218,14 +208,14 @@ def transform(self, data: pd.DataFrame,
         Parameters
         ----------
         data : pd.DataFrame
-            the data to encode.
+            Data to encode.
         column_names : list
-            the name of the categorical columns in the data to be encoded.
+            Name of the categorical columns in the data to be encoded.
 
         Returns
         -------
         pd.DataFrame
-            the resulting transformed data.
+            The resulting transformed data.
 
         Raises
         ------
@@ -261,14 +251,14 @@ def _transform_column(self, data: pd.DataFrame,
         Parameters
         ----------
         data : pd.DataFrame
-            the data to encode.
+            Data to encode.
         column_name : str
-            the name of the column in the data to be encoded.
+            Name of the column in the data to be encoded.
 
         Returns
         -------
         pd.DataFrame
-            the resulting transformed data.
+            Resulting transformed data.
         """
         new_column = TargetEncoder._clean_column_name(column_name)
 
@@ -298,21 +288,21 @@ def _transform_column(self, data: pd.DataFrame,
     def fit_transform(self, data: pd.DataFrame,
                       column_names: list,
                       target_column: str) -> pd.DataFrame:
-        """Fit the encoder and transform the data
+        """Fit the encoder and transform the data.
 
         Parameters
         ----------
         data : pd.DataFrame
-            Data to be encoded
+            Data to be encoded.
         column_names : list
-            Columns of data to be encoded
+            Columns of data to be encoded.
         target_column : str
-            Column name of the target
+            Column name of the target.
 
         Returns
         -------
         pd.DataFrame
-            data with additional columns, holding the target-encoded variables.
+            Data with additional columns, holding the target-encoded variables.
         """
         self.fit(data, column_names, target_column)
         return self.transform(data, column_names)
@@ -326,12 +316,12 @@ def _clean_column_name(column_name: str) -> str:
         Parameters
         ----------
         column_name : str
-            column name to be cleaned
+            Column name to be cleaned.
 
         Returns
         -------
         str
-            cleaned column name
+            Cleaned column name.
         """
         if "_bin" in column_name:
             return column_name.replace("_bin", "") + "_enc"
diff --git a/tutorials/tutorial_Cobra_linear_regression.ipynb b/tutorials/tutorial_Cobra_linear_regression.ipynb
index 219c7de..7b03c65 100644
--- a/tutorials/tutorial_Cobra_linear_regression.ipynb
+++ b/tutorials/tutorial_Cobra_linear_regression.ipynb
@@ -274,7 +274,7 @@
    "outputs": [],
    "source": [
     "for col in df.columns:\n",
-    "    if col not in discrete_vars and col not in [id_col, target_col]: # if not already discrete because a string, or the target\n",
+    "    if col not in discrete_vars and col not in [id_col, target_col]: # omit discrete because a string and target\n",
     "        if len(val_counts) > 1 and len(val_counts) <= 10: # the column contains less than 10 different values\n",
     "            print(col)"
    ]
@@ -349,11 +349,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "basetable = preprocessor.train_selection_validation_split(\n",
-    "                data=df,\n",
-    "                train_prop=0.6,\n",
-    "                selection_prop=0.2,\n",
-    "                validation_prop=0.2)"
+    "basetable = preprocessor.train_selection_validation_split(data=df,\n",
+    "                                                          train_prop=0.6,\n",
+    "                                                          selection_prop=0.2,\n",
+    "                                                          validation_prop=0.2)"
    ]
   },
   {
diff --git a/tutorials/tutorial_Cobra_logistic_regression.ipynb b/tutorials/tutorial_Cobra_logistic_regression.ipynb
index 8e85a97..c3ee859 100644
--- a/tutorials/tutorial_Cobra_logistic_regression.ipynb
+++ b/tutorials/tutorial_Cobra_logistic_regression.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Cobra** requires the usual Python packages for data science:\n",
+    "Cobra requires the usual Python packages for data science:\n",
     "\n",
     "- numpy (>=1.19.4)\n",
     "- pandas (>=1.1.5)\n",
@@ -38,7 +38,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The easiest way to install cobra is using pip:"
+    "The easiest way to install Cobra is using pip:"
    ]
   },
   {
@@ -289,7 +289,7 @@
    "outputs": [],
    "source": [
     "for col in df.columns:\n",
-    "    if col not in discrete_vars and col not in [id_col, target_col]: # if not already discrete because a string, or the target\n",
+    "    if col not in discrete_vars and col not in [id_col, target_col]: # omit discrete because a string and target\n",
     "        if len(val_counts) > 1 and len(val_counts) <= 10: # the column contains less than 10 different values\n",
     "            print(col)"
    ]
@@ -364,11 +364,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "basetable = preprocessor.train_selection_validation_split(\n",
-    "                data=df,\n",
-    "                train_prop=0.6,\n",
-    "                selection_prop=0.2,\n",
-    "                validation_prop=0.2)"
+    "basetable = preprocessor.train_selection_validation_split(data=df,\n",
+    "                                                          train_prop=0.6,\n",
+    "                                                          selection_prop=0.2,\n",
+    "                                                          validation_prop=0.2)"
    ]
   },
   {

From de4f90194f7a063f0465133218d140d9a764aca7 Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Wed, 29 Sep 2021 17:08:43 +0200
Subject: [PATCH 08/16] some more documentation cleaning

---
 cobra/model_building/models.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/cobra/model_building/models.py b/cobra/model_building/models.py
index 03f80a2..f27acfc 100644
--- a/cobra/model_building/models.py
+++ b/cobra/model_building/models.py
@@ -137,10 +137,10 @@ def score_model(self, X: pd.DataFrame) -> np.ndarray:
         Returns
         -------
         np.ndarray
-            Score of the model for each observation.
+            Score (i.e. predicted probabilities) of the model for each observation.
         """
         # We select predictor columns (self.predictors) here to
-        # ensure we have the proper predictors and the proper order!!!
+        # ensure we have the proper predictors and the proper order
         return self.logit.predict_proba(X[self.predictors])[:, 1]
 
     def evaluate(self, X: pd.DataFrame, y: pd.Series,
@@ -180,7 +180,7 @@ def evaluate(self, X: pd.DataFrame, y: pd.Series,
 
     def compute_variable_importance(self, data: pd.DataFrame) -> pd.DataFrame:
         """Compute the importance of each predictor in the model and return
-        it as a DataFrame
+        it as a DataFrame.
 
         Parameters
         ----------
@@ -190,7 +190,7 @@ def compute_variable_importance(self, data: pd.DataFrame) -> pd.DataFrame:
         Returns
         -------
         pd.DataFrame
-            DataFrame containing columns predictor and importance
+            DataFrame containing columns predictor and importance.
         """
 
         y_pred = self.score_model(data)
@@ -340,7 +340,7 @@ def fit(self, X_train: pd.DataFrame, y_train: pd.Series):
         self._is_fitted = True
 
     def score_model(self, X: pd.DataFrame) -> np.ndarray:
-        """Score a model on a (new) dataset
+        """Score a model on a (new) dataset.
 
         Parameters
         ----------
@@ -350,10 +350,10 @@ def score_model(self, X: pd.DataFrame) -> np.ndarray:
         Returns
         -------
         np.ndarray
-            score of the model for each observation
+            Score of the model for each observation.
         """
         # We select predictor columns (self.predictors) here to
-        # ensure we have the proper predictors and the proper order!!!
+        # ensure we have the proper predictors and the proper order
         return self.linear.predict(X[self.predictors])
 
     def evaluate(self, X: pd.DataFrame, y: pd.Series,
@@ -375,7 +375,7 @@ def evaluate(self, X: pd.DataFrame, y: pd.Series,
         Returns
         -------
         float
-            the performance score of the model (RMSE)
+            The performance score of the model (RMSE).
         """
 
         if (split is None) or (split not in self._eval_metrics_by_split):
@@ -393,7 +393,7 @@ def evaluate(self, X: pd.DataFrame, y: pd.Series,
 
     def compute_variable_importance(self, data: pd.DataFrame) -> pd.DataFrame:
         """Compute the importance of each predictor in the model and return
-        it as a DataFrame
+        it as a DataFrame.
 
         Parameters
         ----------
@@ -403,7 +403,7 @@ def compute_variable_importance(self, data: pd.DataFrame) -> pd.DataFrame:
         Returns
         -------
         pd.DataFrame
-            DataFrame containing columns predictor and importance
+            DataFrame containing columns predictor and importance.
         """
 
         y_pred = self.score_model(data)

From 7d283352eed629e8a782c1b530e2b59ac92b672d Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Wed, 29 Sep 2021 17:32:53 +0200
Subject: [PATCH 09/16] further docs cleaning, incl. regrouping explanation

---
 cobra/evaluation/plotting_utils.py                |  1 +
 cobra/preprocessing/categorical_data_processor.py | 13 +++++++++++--
 cobra/preprocessing/kbins_discretizer.py          |  3 ++-
 cobra/preprocessing/preprocessor.py               |  3 +++
 cobra/preprocessing/target_encoder.py             |  4 ++--
 5 files changed, 19 insertions(+), 5 deletions(-)

diff --git a/cobra/evaluation/plotting_utils.py b/cobra/evaluation/plotting_utils.py
index 63928fa..cc76779 100644
--- a/cobra/evaluation/plotting_utils.py
+++ b/cobra/evaluation/plotting_utils.py
@@ -1,3 +1,4 @@
+
 # third party imports
 import numpy as np
 import pandas as pd
diff --git a/cobra/preprocessing/categorical_data_processor.py b/cobra/preprocessing/categorical_data_processor.py
index 4c6dbe3..75ec543 100644
--- a/cobra/preprocessing/categorical_data_processor.py
+++ b/cobra/preprocessing/categorical_data_processor.py
@@ -22,11 +22,20 @@ class CategoricalDataProcessor(BaseEstimator):
     categorical data preprocessing. There are three steps involved:
 
         - An optional regrouping of the different categories based on category size
-          and significance of the category w.r.t. the target;
-        - Missing value replacement with the additional category ``Missing``;
+          and significance of the category w.r.t. the target.
+            - For a given categorical variable, all categories below the (weighted)
+              category size threshold are put into a rest category (by default ``Other``)
+            - The remaining categories are subject to a statistical test, if there is
+              sufficient dependence with the target variable compared to all other categories,
+              the category is kept as-is, otherwise it is also put into the rest category
+            - Beware: one can force categories to be kept, and if no single category passes
+              the statistical test, the categorical variable is left unprocessed altogether
+        - Missing value replacement with the additional category ``Missing``.
         - Change of dtype to ``category`` (could potentially lead to memory
           optimization).
 
+    See the README of the GitHub repository for more methodological background information.
+
     Attributes
     ----------
     category_size_threshold : int
diff --git a/cobra/preprocessing/kbins_discretizer.py b/cobra/preprocessing/kbins_discretizer.py
index 809bae5..e9805cc 100644
--- a/cobra/preprocessing/kbins_discretizer.py
+++ b/cobra/preprocessing/kbins_discretizer.py
@@ -25,7 +25,8 @@ class KBinsDiscretizer(BaseEstimator):
     This module is a rework of
     https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/preprocessing/_discretization.py,
     though it is purely written in pandas instead of numpy because it is more intuitive. It also includes
-    some custom modifications to align it with the Python Predictions methodology.
+    some custom modifications to align it with the Python Predictions methodology. See the README of the
+    GitHub repository for more background information.
 
     Attributes
     ----------
diff --git a/cobra/preprocessing/preprocessor.py b/cobra/preprocessing/preprocessor.py
index 7081a86..5f02dd0 100644
--- a/cobra/preprocessing/preprocessor.py
+++ b/cobra/preprocessing/preprocessor.py
@@ -28,6 +28,9 @@ class PreProcessor(BaseEstimator):
     Additionally, it provides methods such as (de)serialization to/from JSON
     so that preprocessing pipelines can be stored and reloaded, example for scoring.
 
+    We refer to the README of the GitHub repository for more background information
+    on the preprocessing methodology.
+
     Attributes
     ----------
     categorical_data_processor : CategoricalDataProcessor
diff --git a/cobra/preprocessing/target_encoder.py b/cobra/preprocessing/target_encoder.py
index e714a76..0b0c78c 100644
--- a/cobra/preprocessing/target_encoder.py
+++ b/cobra/preprocessing/target_encoder.py
@@ -18,8 +18,8 @@ class TargetEncoder(BaseEstimator):
 
     Note that, when applying this target encoding, values of the categorical
     feature that have not been seen during fit will be imputed according to the
-    configured imputation strategy: replacement with the mean, minimum or
-    maximum value of the categorical variable.
+    configured imputation strategy (replacement with the mean, minimum or
+    maximum value of the categorical variable).
 
     The main problem with Target encoding is overfitting; the fact that we are
     encoding the feature based on target classes may lead to data leakage,

From 183535c4d769ee99723db8d846ad7a6600d65b77 Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Wed, 29 Sep 2021 17:55:42 +0200
Subject: [PATCH 10/16] additional explanations in docs

---
 cobra/model_building/forward_selection.py | 34 +++++++++++++----------
 cobra/preprocessing/kbins_discretizer.py  |  4 ++-
 cobra/preprocessing/target_encoder.py     |  7 ++---
 3 files changed, 26 insertions(+), 19 deletions(-)

diff --git a/cobra/model_building/forward_selection.py b/cobra/model_building/forward_selection.py
index 66508b3..0d75545 100644
--- a/cobra/model_building/forward_selection.py
+++ b/cobra/model_building/forward_selection.py
@@ -12,6 +12,12 @@ class ForwardFeatureSelection:
     """Perform forward feature selection for a given dataset using a given
     algorithm.
 
+    Predictors are sequentially added to the model, starting with the one that
+    has the highest univariate predictive power, and then proceeding with those that
+    jointly lead to the best fit, optimizing for selection AUC or RMSE. Interaction
+    effects are not explicitly modeled, yet they are implicitly present given the
+    feature selection and the underlying feature correlation structure.
+
     Attributes
     ----------
     model_type : str
@@ -29,9 +35,9 @@ class ForwardFeatureSelection:
     """
 
     def __init__(self,
-                 model_type: str = "classification",
-                 max_predictors: int = 50,
-                 pos_only: bool = True):
+                 model_type: str="classification",
+                 max_predictors: int=50,
+                 pos_only: bool=True):
 
         self.model_type = model_type
         if model_type == "classification":
@@ -45,7 +51,7 @@ def __init__(self,
         self._fitted_models = []
 
     def get_model_from_step(self, step: int):
-        """Get fitted model from a particular step
+        """Get fitted model from a particular step.
 
         Parameters
         ----------
@@ -55,12 +61,12 @@ def get_model_from_step(self, step: int):
         Returns
         -------
         self.MLModel
-            Fitted model from the given step
+            Fitted model from the given step.
 
         Raises
         ------
         ValueError
-            in case step is larger than the number of available models
+            In case step is larger than the number of available models.
         """
         if len(self._fitted_models) < step:
             raise ValueError(f"No model available for step {step}")
@@ -90,8 +96,8 @@ def compute_model_performances(self, data: pd.DataFrame,
         Returns
         -------
         DatFrame
-            contains for each model the performance for train, selection and
-            validation sets as well as the set of predictors used in this model
+            Contains for each model the performance for train, selection and
+            validation sets as well as the set of predictors used in this model.
         """
         results = []
         predictor_set = set([])
@@ -127,7 +133,7 @@ def compute_model_performances(self, data: pd.DataFrame,
     def fit(self, train_data: pd.DataFrame, target_column_name: str,
             predictors: list, forced_predictors: list = [],
             excluded_predictors: list = []):
-        """Fit the forward feature selection estimator
+        """Fit the forward feature selection estimator.
 
         Parameters
         ----------
@@ -145,8 +151,8 @@ def fit(self, train_data: pd.DataFrame, target_column_name: str,
         Raises
         ------
         ValueError
-            in case the number of forced predictors is larger than the maximum
-            number of allowed predictors in the model
+            In case the number of forced predictors is larger than the maximum
+            number of allowed predictors in the model.
         """
         # remove excluded predictors from predictor lists
         filtered_predictors = [var for var in predictors
@@ -194,7 +200,7 @@ def _forward_selection(self, train_data: pd.DataFrame,
         -------
         list
             List of fitted models where the index of the list indicates the
-            number of predictors minus one (as indices start from 0)
+            number of predictors minus one (as indices start from 0).
         """
         fitted_models = []
         current_predictors = []
@@ -252,7 +258,7 @@ def _find_next_best_model(self, train_data: pd.DataFrame,
         Returns
         -------
         self.MLModel
-            Best performing model
+            Best performing model.
         """
         # placeholders
         best_model = None
@@ -307,7 +313,7 @@ def _train_model(self, train_data: pd.DataFrame, target_column_name: str,
         Returns
         -------
         self.MLModel
-            trained model
+            Trained model.
         """
         model = self.MLModel()
 
diff --git a/cobra/preprocessing/kbins_discretizer.py b/cobra/preprocessing/kbins_discretizer.py
index e9805cc..c30d7de 100644
--- a/cobra/preprocessing/kbins_discretizer.py
+++ b/cobra/preprocessing/kbins_discretizer.py
@@ -42,7 +42,9 @@ class KBinsDiscretizer(BaseEstimator):
         Format string to display the bin labels
         e.g. ``min - max``, ``(min, max]``, ...
     n_bins : int
-        Number of bins to produce. Raises ValueError if ``n_bins < 2``.
+        Number of bins to produce. Raises ValueError if ``n_bins < 2``. A warning
+        is issued when a variable can only produce a lower number of bins than
+        asked for.
     starting_precision : int
         Initial precision for the bin edges to start from,
         can also be negative. Given a list of bin edges, the class will
diff --git a/cobra/preprocessing/target_encoder.py b/cobra/preprocessing/target_encoder.py
index 0b0c78c..85c8df1 100644
--- a/cobra/preprocessing/target_encoder.py
+++ b/cobra/preprocessing/target_encoder.py
@@ -23,10 +23,9 @@ class TargetEncoder(BaseEstimator):
 
     The main problem with Target encoding is overfitting; the fact that we are
     encoding the feature based on target classes may lead to data leakage,
-    rendering the feature biased.
-    This can be solved using some type of regularization. A popular way to
-    handle this is to use cross-validation and compute the means in each
-    out-of-fold. However, the approach implemented here makes use of
+    rendering the feature biased. This can be solved using some type of regularization.
+    A popular way to handle this is to use cross-validation and compute the means
+    in each out-of-fold. However, the approach implemented here makes use of
     additive smoothing (https://en.wikipedia.org/wiki/Additive_smoothing).
 
     In summary:

From 834c869a9024088aee1279c8d691bc214e85165a Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Wed, 29 Sep 2021 22:26:52 +0200
Subject: [PATCH 11/16] fix raise ValueError optimal step

---
 cobra/model_building/forward_selection.py     |    5 +-
 .../tutorial_Cobra_linear_regression.ipynb    | 1263 +++++++++++++++--
 .../tutorial_Cobra_logistic_regression.ipynb  |   39 +-
 3 files changed, 1187 insertions(+), 120 deletions(-)

diff --git a/cobra/model_building/forward_selection.py b/cobra/model_building/forward_selection.py
index 0d75545..3f7cf49 100644
--- a/cobra/model_building/forward_selection.py
+++ b/cobra/model_building/forward_selection.py
@@ -68,8 +68,9 @@ def get_model_from_step(self, step: int):
         ValueError
             In case step is larger than the number of available models.
         """
-        if len(self._fitted_models) < step:
-            raise ValueError(f"No model available for step {step}")
+        if len(self._fitted_models) <= step:
+            raise ValueError(f"No model available for step {step}. "
+                             "The first step starts from index 0.")
 
         return self._fitted_models[step]
 
diff --git a/tutorials/tutorial_Cobra_linear_regression.ipynb b/tutorials/tutorial_Cobra_linear_regression.ipynb
index 7b03c65..7c9b76a 100644
--- a/tutorials/tutorial_Cobra_linear_regression.ipynb
+++ b/tutorials/tutorial_Cobra_linear_regression.ipynb
@@ -1,5 +1,17 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0, \"C:/Users/samuel.borms/Desktop/code/cobra\")\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -27,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,11 +111,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
     "import json\n",
+    "import os\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
@@ -115,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,9 +149,127 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>chevrolet chevelle malibu</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>buick skylark 320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>plymouth satellite</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>amc rebel sst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>ford torino</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+       "0  18.0          8         307.0       130.0    3504          12.0   \n",
+       "1  15.0          8         350.0       165.0    3693          11.5   \n",
+       "2  18.0          8         318.0       150.0    3436          11.0   \n",
+       "3  16.0          8         304.0       150.0    3433          12.0   \n",
+       "4  17.0          8         302.0       140.0    3449          10.5   \n",
+       "\n",
+       "   model_year origin                       name  \n",
+       "0          70    usa  chevrolet chevelle malibu  \n",
+       "1          70    usa          buick skylark 320  \n",
+       "2          70    usa         plymouth satellite  \n",
+       "3          70    usa              amc rebel sst  \n",
+       "4          70    usa                ford torino  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df = sns.load_dataset(\"mpg\")\n",
     "df.head()"
@@ -155,9 +286,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "mpg             float64\n",
+       "cylinders         int64\n",
+       "displacement    float64\n",
+       "horsepower      float64\n",
+       "weight            int64\n",
+       "acceleration    float64\n",
+       "model_year        int64\n",
+       "origin           object\n",
+       "name             object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.dtypes"
    ]
@@ -171,7 +322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,7 +366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,9 +383,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    398.000000\n",
+       "mean      23.514573\n",
+       "std        7.815984\n",
+       "min        9.000000\n",
+       "25%       17.500000\n",
+       "50%       23.000000\n",
+       "75%       29.000000\n",
+       "max       46.600000\n",
+       "Name: mpg, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "target_col = \"mpg\"\n",
     "df[target_col].describe()"
@@ -251,9 +421,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['origin', 'name']\n"
+     ]
+    }
+   ],
    "source": [
     "col_dtypes = df.dtypes\n",
     "discrete_vars = [col for col in col_dtypes[col_dtypes==object].index.tolist() if col not in [id_col, target_col]] \n",
@@ -269,13 +447,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "cylinders\n"
+     ]
+    }
+   ],
    "source": [
     "for col in df.columns:\n",
     "    if col not in discrete_vars and col not in [id_col, target_col]: # omit discrete because a string and target\n",
-    "        if len(val_counts) > 1 and len(val_counts) <= 10: # the column contains less than 10 different values\n",
+    "        val_counts = df[col].nunique()\n",
+    "        if val_counts > 1 and val_counts <= 10: # the column contains less than 10 different values\n",
     "            print(col)"
    ]
   },
@@ -288,9 +475,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['origin', 'name', 'cylinders']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "discrete_vars.extend([\"cylinders\"])\n",
     "discrete_vars"
@@ -305,9 +503,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['weight', 'acceleration', 'displacement', 'model_year', 'horsepower']"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "continuous_vars = list(set(df.columns)\n",
     "                       - set(discrete_vars) \n",
@@ -324,9 +533,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The target encoder's additive smoothing weight is set to 0. This disables smoothing and may make the encoding prone to overfitting.\n"
+     ]
+    }
+   ],
    "source": [
     "# using all Cobra's default parameters for preprocessing for now\n",
     "preprocessor = PreProcessor.from_params(\n",
@@ -345,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -364,9 +581,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f3d88d2b07d46ea927b5fba28687798",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing discretization bins...:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7790ba7745ac4bafb4bb0b514a3e9d27",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Discretizing columns...:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98a4f591d7ce4353ba0fdfeaa29d10aa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fitting category regrouping...:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0076f4df02ce4517bbfb589d2d7e09cf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fitting target encoding...:   0%|          | 0/8 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Fit the preprocessor pipeline:\n",
     "preprocessor.fit(basetable[basetable[\"split\"]==\"train\"],\n",
@@ -384,9 +658,291 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ceb52400760a486a895bc210ebd5f870",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Discretizing columns...:   0%|          | 0/5 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "38f4134ac4104ec0bf747c05e046f61f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Applying target encoding...:   0%|          | 0/8 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model_year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>name</th>\n",
+       "      <th>id</th>\n",
+       "      <th>split</th>\n",
+       "      <th>weight_bin</th>\n",
+       "      <th>acceleration_bin</th>\n",
+       "      <th>displacement_bin</th>\n",
+       "      <th>model_year_bin</th>\n",
+       "      <th>horsepower_bin</th>\n",
+       "      <th>origin_processed</th>\n",
+       "      <th>name_processed</th>\n",
+       "      <th>cylinders_processed</th>\n",
+       "      <th>origin_enc</th>\n",
+       "      <th>name_enc</th>\n",
+       "      <th>cylinders_enc</th>\n",
+       "      <th>weight_enc</th>\n",
+       "      <th>acceleration_enc</th>\n",
+       "      <th>displacement_enc</th>\n",
+       "      <th>model_year_enc</th>\n",
+       "      <th>horsepower_enc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>chevrolet chevelle malibu</td>\n",
+       "      <td>1</td>\n",
+       "      <td>train</td>\n",
+       "      <td>3441.0 - 3832.0</td>\n",
+       "      <td>8.0 - 12.0</td>\n",
+       "      <td>250.0 - 307.0</td>\n",
+       "      <td>70.0 - 71.0</td>\n",
+       "      <td>110.0 - 140.0</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>chevrolet chevelle malibu</td>\n",
+       "      <td>8</td>\n",
+       "      <td>20.242038</td>\n",
+       "      <td>17.500000</td>\n",
+       "      <td>14.973438</td>\n",
+       "      <td>17.720833</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>16.089474</td>\n",
+       "      <td>19.777778</td>\n",
+       "      <td>18.252174</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>buick skylark 320</td>\n",
+       "      <td>2</td>\n",
+       "      <td>train</td>\n",
+       "      <td>3441.0 - 3832.0</td>\n",
+       "      <td>8.0 - 12.0</td>\n",
+       "      <td>307.0 - 350.0</td>\n",
+       "      <td>70.0 - 71.0</td>\n",
+       "      <td>155.0 - 230.0</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>buick skylark 320</td>\n",
+       "      <td>8</td>\n",
+       "      <td>20.242038</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>14.973438</td>\n",
+       "      <td>17.720833</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>15.300000</td>\n",
+       "      <td>19.777778</td>\n",
+       "      <td>13.443478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>plymouth satellite</td>\n",
+       "      <td>3</td>\n",
+       "      <td>train</td>\n",
+       "      <td>3200.0 - 3441.0</td>\n",
+       "      <td>8.0 - 12.0</td>\n",
+       "      <td>307.0 - 350.0</td>\n",
+       "      <td>70.0 - 71.0</td>\n",
+       "      <td>140.0 - 155.0</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>plymouth satellite</td>\n",
+       "      <td>8</td>\n",
+       "      <td>20.242038</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>14.973438</td>\n",
+       "      <td>19.091667</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>15.300000</td>\n",
+       "      <td>19.777778</td>\n",
+       "      <td>14.430435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>amc rebel sst</td>\n",
+       "      <td>4</td>\n",
+       "      <td>selection</td>\n",
+       "      <td>3200.0 - 3441.0</td>\n",
+       "      <td>8.0 - 12.0</td>\n",
+       "      <td>250.0 - 307.0</td>\n",
+       "      <td>70.0 - 71.0</td>\n",
+       "      <td>140.0 - 155.0</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>amc rebel sst</td>\n",
+       "      <td>8</td>\n",
+       "      <td>20.242038</td>\n",
+       "      <td>23.416667</td>\n",
+       "      <td>14.973438</td>\n",
+       "      <td>19.091667</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>16.089474</td>\n",
+       "      <td>19.777778</td>\n",
+       "      <td>14.430435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>ford torino</td>\n",
+       "      <td>5</td>\n",
+       "      <td>selection</td>\n",
+       "      <td>3441.0 - 3832.0</td>\n",
+       "      <td>8.0 - 12.0</td>\n",
+       "      <td>250.0 - 307.0</td>\n",
+       "      <td>70.0 - 71.0</td>\n",
+       "      <td>110.0 - 140.0</td>\n",
+       "      <td>usa</td>\n",
+       "      <td>ford torino</td>\n",
+       "      <td>8</td>\n",
+       "      <td>20.242038</td>\n",
+       "      <td>23.416667</td>\n",
+       "      <td>14.973438</td>\n",
+       "      <td>17.720833</td>\n",
+       "      <td>15.18</td>\n",
+       "      <td>16.089474</td>\n",
+       "      <td>19.777778</td>\n",
+       "      <td>18.252174</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+       "0  18.0          8         307.0       130.0    3504          12.0   \n",
+       "1  15.0          8         350.0       165.0    3693          11.5   \n",
+       "2  18.0          8         318.0       150.0    3436          11.0   \n",
+       "3  16.0          8         304.0       150.0    3433          12.0   \n",
+       "4  17.0          8         302.0       140.0    3449          10.5   \n",
+       "\n",
+       "   model_year origin                       name  id      split  \\\n",
+       "0          70    usa  chevrolet chevelle malibu   1      train   \n",
+       "1          70    usa          buick skylark 320   2      train   \n",
+       "2          70    usa         plymouth satellite   3      train   \n",
+       "3          70    usa              amc rebel sst   4  selection   \n",
+       "4          70    usa                ford torino   5  selection   \n",
+       "\n",
+       "        weight_bin acceleration_bin displacement_bin model_year_bin  \\\n",
+       "0  3441.0 - 3832.0       8.0 - 12.0    250.0 - 307.0    70.0 - 71.0   \n",
+       "1  3441.0 - 3832.0       8.0 - 12.0    307.0 - 350.0    70.0 - 71.0   \n",
+       "2  3200.0 - 3441.0       8.0 - 12.0    307.0 - 350.0    70.0 - 71.0   \n",
+       "3  3200.0 - 3441.0       8.0 - 12.0    250.0 - 307.0    70.0 - 71.0   \n",
+       "4  3441.0 - 3832.0       8.0 - 12.0    250.0 - 307.0    70.0 - 71.0   \n",
+       "\n",
+       "  horsepower_bin origin_processed             name_processed  \\\n",
+       "0  110.0 - 140.0              usa  chevrolet chevelle malibu   \n",
+       "1  155.0 - 230.0              usa          buick skylark 320   \n",
+       "2  140.0 - 155.0              usa         plymouth satellite   \n",
+       "3  140.0 - 155.0              usa              amc rebel sst   \n",
+       "4  110.0 - 140.0              usa                ford torino   \n",
+       "\n",
+       "  cylinders_processed  origin_enc   name_enc  cylinders_enc  weight_enc  \\\n",
+       "0                   8   20.242038  17.500000      14.973438   17.720833   \n",
+       "1                   8   20.242038  15.000000      14.973438   17.720833   \n",
+       "2                   8   20.242038  18.000000      14.973438   19.091667   \n",
+       "3                   8   20.242038  23.416667      14.973438   19.091667   \n",
+       "4                   8   20.242038  23.416667      14.973438   17.720833   \n",
+       "\n",
+       "   acceleration_enc  displacement_enc  model_year_enc  horsepower_enc  \n",
+       "0             15.18         16.089474       19.777778       18.252174  \n",
+       "1             15.18         15.300000       19.777778       13.443478  \n",
+       "2             15.18         15.300000       19.777778       14.430435  \n",
+       "3             15.18         16.089474       19.777778       14.430435  \n",
+       "4             15.18         16.089474       19.777778       18.252174  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "basetable = preprocessor.transform(basetable,\n",
     "                                   continuous_vars=continuous_vars,\n",
@@ -410,9 +966,153 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 24,
+   "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>variable</th>\n",
+       "      <th>label</th>\n",
+       "      <th>pop_size</th>\n",
+       "      <th>global_avg_target</th>\n",
+       "      <th>avg_target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>acceleration</td>\n",
+       "      <td>8.0 - 12.0</td>\n",
+       "      <td>0.088608</td>\n",
+       "      <td>23.591139</td>\n",
+       "      <td>16.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>acceleration</td>\n",
+       "      <td>12.0 - 13.5</td>\n",
+       "      <td>0.177215</td>\n",
+       "      <td>23.591139</td>\n",
+       "      <td>19.371429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>acceleration</td>\n",
+       "      <td>13.5 - 14.2</td>\n",
+       "      <td>0.075949</td>\n",
+       "      <td>23.591139</td>\n",
+       "      <td>18.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>acceleration</td>\n",
+       "      <td>14.2 - 14.9</td>\n",
+       "      <td>0.088608</td>\n",
+       "      <td>23.591139</td>\n",
+       "      <td>21.957143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>acceleration</td>\n",
+       "      <td>14.9 - 15.5</td>\n",
+       "      <td>0.151899</td>\n",
+       "      <td>23.591139</td>\n",
+       "      <td>24.975000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>weight</td>\n",
+       "      <td>2880.0 - 3200.0</td>\n",
+       "      <td>0.139241</td>\n",
+       "      <td>23.591139</td>\n",
+       "      <td>21.136364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>weight</td>\n",
+       "      <td>3200.0 - 3441.0</td>\n",
+       "      <td>0.050633</td>\n",
+       "      <td>23.591139</td>\n",
+       "      <td>18.825000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>weight</td>\n",
+       "      <td>3441.0 - 3832.0</td>\n",
+       "      <td>0.075949</td>\n",
+       "      <td>23.591139</td>\n",
+       "      <td>17.066667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>weight</td>\n",
+       "      <td>3832.0 - 4294.0</td>\n",
+       "      <td>0.088608</td>\n",
+       "      <td>23.591139</td>\n",
+       "      <td>15.285714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>weight</td>\n",
+       "      <td>4294.0 - 5140.0</td>\n",
+       "      <td>0.088608</td>\n",
+       "      <td>23.591139</td>\n",
+       "      <td>14.571429</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>128 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        variable            label  pop_size  global_avg_target  avg_target\n",
+       "0   acceleration       8.0 - 12.0  0.088608          23.591139   16.400000\n",
+       "1   acceleration      12.0 - 13.5  0.177215          23.591139   19.371429\n",
+       "2   acceleration      13.5 - 14.2  0.075949          23.591139   18.250000\n",
+       "3   acceleration      14.2 - 14.9  0.088608          23.591139   21.957143\n",
+       "4   acceleration      14.9 - 15.5  0.151899          23.591139   24.975000\n",
+       "..           ...              ...       ...                ...         ...\n",
+       "5         weight  2880.0 - 3200.0  0.139241          23.591139   21.136364\n",
+       "6         weight  3200.0 - 3441.0  0.050633          23.591139   18.825000\n",
+       "7         weight  3441.0 - 3832.0  0.075949          23.591139   17.066667\n",
+       "8         weight  3832.0 - 4294.0  0.088608          23.591139   15.285714\n",
+       "9         weight  4294.0 - 5140.0  0.088608          23.591139   14.571429\n",
+       "\n",
+       "[128 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "predictor_list = [col for col in basetable.columns if col.endswith(\"_bin\") or col.endswith(\"_processed\")]\n",
     "pig_tables = generate_pig_tables(basetable[basetable[\"split\"]==\"selection\"],\n",
@@ -431,14 +1131,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
-   "source": [
-    "plot_incidence(pig_tables, variable=\"acceleration\", model_type=\"regression\",\n",
-    "               column_order = list(basetable[\"acceleration_bin\"].unique().sort_values()))"
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "<class 'numpy.ndarray'>",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-26-b30b6990e4f3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mcol_order\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbasetable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"acceleration_bin\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplot_incidence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpig_tables\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvariable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"acceleration\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"regression\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn_order\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcol_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32mC:/Users/samuel.borms/Desktop/code/cobra\\cobra\\evaluation\\pigs_tables.py\u001b[0m in \u001b[0;36mplot_incidence\u001b[1;34m(pig_tables, variable, model_type, column_order, dim)\u001b[0m\n\u001b[0;32m    151\u001b[0m         \u001b[1;31m# Left axis - average target\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m         \u001b[1;31m# -----------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 153\u001b[1;33m         ax.plot(df_plot['label'], df_plot['avg_target'],\n\u001b[0m\u001b[0;32m    154\u001b[0m                 \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"#00ccff\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\".\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m                 \u001b[0mmarkersize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1645\u001b[0m         \"\"\"\n\u001b[0;32m   1646\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1647\u001b[1;33m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1648\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1649\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    214\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 216\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    329\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    330\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 331\u001b[1;33m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    332\u001b[0m             \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    333\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\__init__.py\u001b[0m in \u001b[0;36m_check_1d\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1347\u001b[0m                     message='Support for multi-dimensional indexing')\n\u001b[0;32m   1348\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1349\u001b[1;33m                 \u001b[0mndim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1350\u001b[0m                 \u001b[1;31m# we have definitely hit a pandas index or series object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1351\u001b[0m                 \u001b[1;31m# cast to a numpy array.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    875\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 877\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    878\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_with\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m_get_with\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    890\u001b[0m             )\n\u001b[0;32m    891\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 892\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_values_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    893\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m_get_values_tuple\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    920\u001b[0m         \u001b[1;31m# mpl hackaround\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    921\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many_none\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 922\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    923\u001b[0m             \u001b[0mdeprecate_ndim_indexing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    924\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m_get_values\u001b[1;34m(self, indexer)\u001b[0m\n\u001b[0;32m    935\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    936\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 937\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_slice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    938\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    939\u001b[0m             \u001b[1;31m# mpl compat if we look up e.g. ser[:, np.newaxis];\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mget_slice\u001b[1;34m(self, slobj, axis)\u001b[0m\n\u001b[0;32m   1607\u001b[0m         \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mblk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1608\u001b[0m         \u001b[0mblock\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mblk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_block_same_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mslice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1609\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mslobj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1610\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1611\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, block, axis, do_integrity_check, fastpath)\u001b[0m\n\u001b[0;32m   1551\u001b[0m     ):\n\u001b[0;32m   1552\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBlock\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1553\u001b[1;33m         \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1554\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1555\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mfastpath\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mno_default\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAssertionError\u001b[0m: <class 'numpy.ndarray'>"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "col_order = list(basetable[\"acceleration_bin\"].unique().sort_values())\n",
+    "plot_incidence(pig_tables, variable=\"acceleration\", model_type=\"regression\", column_order=col_order)"
    ]
   },
   {
@@ -468,9 +1201,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['acceleration_bin',\n",
+       " 'cylinders_processed',\n",
+       " 'displacement_bin',\n",
+       " 'horsepower_bin',\n",
+       " 'model_year_bin',\n",
+       " 'name_processed',\n",
+       " 'origin_processed',\n",
+       " 'weight_bin']"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "preprocessed_predictors = [\n",
     "    col for col in basetable.columns\n",
@@ -488,7 +1239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -504,9 +1255,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "df_rmse = univariate_selection.compute_univariate_preselection(\n",
     "    target_enc_train_data=basetable[basetable[\"split\"]==\"train\"],\n",
@@ -533,9 +1295,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                origin      name  cylinders    weight  acceleration  \\\n",
+      "origin        1.000000  0.565844   0.562649  0.597816      0.280317   \n",
+      "name          0.565844  1.000000   0.809445  0.858332      0.522110   \n",
+      "cylinders     0.562649  0.809445   1.000000  0.874555      0.586350   \n",
+      "weight        0.597816  0.858332   0.874555  1.000000      0.495881   \n",
+      "acceleration  0.280317  0.522110   0.586350  0.495881      1.000000   \n",
+      "displacement  0.667828  0.849869   0.909164  0.935436      0.555109   \n",
+      "model_year    0.184259  0.601193   0.384962  0.326719      0.318294   \n",
+      "horsepower    0.502376  0.851491   0.825762  0.876739      0.653886   \n",
+      "\n",
+      "              displacement  model_year  horsepower  \n",
+      "origin            0.667828    0.184259    0.502376  \n",
+      "name              0.849869    0.601193    0.851491  \n",
+      "cylinders         0.909164    0.384962    0.825762  \n",
+      "weight            0.935436    0.326719    0.876739  \n",
+      "acceleration      0.555109    0.318294    0.653886  \n",
+      "displacement      1.000000    0.352080    0.885181  \n",
+      "model_year        0.352080    1.000000    0.419041  \n",
+      "horsepower        0.885181    0.419041    1.000000  \n"
+     ]
+    }
+   ],
    "source": [
     "# compute correlations between preprocessed predictors:\n",
     "df_corr = (univariate_selection\n",
@@ -546,9 +1334,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# plot correlation matrix\n",
     "plot_correlation_matrix(df_corr)"
@@ -563,9 +1364,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['horsepower_enc',\n",
+       " 'weight_enc',\n",
+       " 'cylinders_enc',\n",
+       " 'displacement_enc',\n",
+       " 'origin_enc',\n",
+       " 'model_year_enc',\n",
+       " 'acceleration_enc',\n",
+       " 'name_enc']"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "preselected_predictors = univariate_selection.get_preselected_predictors(df_rmse)\n",
     "preselected_predictors"
@@ -587,9 +1406,130 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3cc9a3567fd4e78a919aaef296ee20f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sequentially adding best predictor...:   0%|          | 0/8 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>predictors</th>\n",
+       "      <th>last_added_predictor</th>\n",
+       "      <th>train_performance</th>\n",
+       "      <th>selection_performance</th>\n",
+       "      <th>validation_performance</th>\n",
+       "      <th>model_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[name_enc]</td>\n",
+       "      <td>name_enc</td>\n",
+       "      <td>0.975224</td>\n",
+       "      <td>7.369621</td>\n",
+       "      <td>6.879241</td>\n",
+       "      <td>regression</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[name_enc, model_year_enc]</td>\n",
+       "      <td>model_year_enc</td>\n",
+       "      <td>0.948429</td>\n",
+       "      <td>7.218273</td>\n",
+       "      <td>6.713415</td>\n",
+       "      <td>regression</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[name_enc, model_year_enc, weight_enc]</td>\n",
+       "      <td>weight_enc</td>\n",
+       "      <td>0.914063</td>\n",
+       "      <td>6.775732</td>\n",
+       "      <td>6.259350</td>\n",
+       "      <td>regression</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[name_enc, model_year_enc, weight_enc, horsepo...</td>\n",
+       "      <td>horsepower_enc</td>\n",
+       "      <td>0.911907</td>\n",
+       "      <td>6.725785</td>\n",
+       "      <td>6.217832</td>\n",
+       "      <td>regression</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[horsepower_enc, name_enc, model_year_enc, wei...</td>\n",
+       "      <td>acceleration_enc</td>\n",
+       "      <td>0.909398</td>\n",
+       "      <td>6.719533</td>\n",
+       "      <td>6.203912</td>\n",
+       "      <td>regression</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          predictors last_added_predictor  \\\n",
+       "0                                         [name_enc]             name_enc   \n",
+       "1                         [name_enc, model_year_enc]       model_year_enc   \n",
+       "2             [name_enc, model_year_enc, weight_enc]           weight_enc   \n",
+       "3  [name_enc, model_year_enc, weight_enc, horsepo...       horsepower_enc   \n",
+       "4  [horsepower_enc, name_enc, model_year_enc, wei...     acceleration_enc   \n",
+       "\n",
+       "   train_performance  selection_performance  validation_performance  \\\n",
+       "0           0.975224               7.369621                6.879241   \n",
+       "1           0.948429               7.218273                6.713415   \n",
+       "2           0.914063               6.775732                6.259350   \n",
+       "3           0.911907               6.725785                6.217832   \n",
+       "4           0.909398               6.719533                6.203912   \n",
+       "\n",
+       "   model_type  \n",
+       "0  regression  \n",
+       "1  regression  \n",
+       "2  regression  \n",
+       "3  regression  \n",
+       "4  regression  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "forward_selection = ForwardFeatureSelection(model_type=\"regression\",\n",
     "                                            # model_name=\"linear-regression\",\n",
@@ -617,9 +1557,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# plot performance curves\n",
     "plot_performance_curves(performances)"
@@ -634,21 +1585,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# look at the number of steps that were completed before stopping, fill that in between the brackets\n",
-    "model = forward_selection.get_model_from_step(5)\n",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['name_enc', 'model_year_enc', 'weight_enc']"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# pick the optimal step based on visual inspection in the plot above (try to find a knee point)\n",
+    "model = forward_selection.get_model_from_step(2)\n",
+    "\n",
     "final_predictors = model.predictors\n",
     "final_predictors"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "variable_importance = model.compute_variable_importance(\n",
     "    basetable[basetable[\"split\"]==\"selection\"])\n",
@@ -659,7 +1633,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: variable importance is based on correlation of the predictor with the model scores (and not the true labels!)."
+    "**Note**: variable importance is based on correlation of the predictor with the model predictions (and not the true values!)."
    ]
   },
   {
@@ -671,21 +1645,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'meta': 'linear-regression',\n",
+       " 'predictors': ['name_enc', 'model_year_enc', 'weight_enc'],\n",
+       " '_eval_metrics_by_split': {'train': 0.9140634547353548,\n",
+       "  'selection': 6.775732306406747,\n",
+       "  'validation': 6.259350037707075},\n",
+       " 'params': {'copy_X': True,\n",
+       "  'fit_intercept': True,\n",
+       "  'n_jobs': None,\n",
+       "  'normalize': False},\n",
+       " 'coef_': [0.9051503874363699, 0.0913109248403244, 0.0814910021531995],\n",
+       " 'intercept_': -1.8253833629000091}"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_dict = model.serialize()\n",
+    "model_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_dict = model.serialize()\n",
-    "\n",
-    "model_path = os.path.join(\"output\", \"model.json\")\n",
-    "with open(model_path, \"w\") as file:\n",
-    "    json.dump(model_dict, file)\n",
+    "if False:\n",
+    "    ## To save the model as a JSON file, run the following snippet\n",
+    "    model_path = os.path.join(\"output\", \"model.json\")\n",
+    "    with open(model_path, \"w\") as file:\n",
+    "        json.dump(model_dict, file)\n",
     "\n",
-    "# To reload the model again from a JSON file, run the following snippet:\n",
-    "# with open(model_path, \"r\") as file:\n",
-    "#     model_dict = json.load(file)\n",
-    "# model = LinearRegressionModel()\n",
-    "# model.deserialize(model_dict)"
+    "    ## To reload the model again from a JSON file, run the following snippet\n",
+    "    with open(model_path, \"r\") as file:\n",
+    "        model_dict = json.load(file)\n",
+    "    model = LinearRegressionModel()\n",
+    "    model.deserialize(model_dict)"
    ]
   },
   {
@@ -711,18 +1716,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# get numpy array of True target labels and predicted scores\n",
+    "# get numpy array of True target labels and predicted values\n",
     "y_true = basetable[basetable[\"split\"]==\"selection\"][target_col].values\n",
     "y_pred = model.score_model(basetable[basetable[\"split\"]==\"selection\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -732,36 +1737,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "R2       0.196095\n",
+       "MAE      5.440320\n",
+       "MSE     45.910548\n",
+       "RMSE     6.775732\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "evaluator.scalar_metrics"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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h5+AsrHkmIiIicoSTZrlwSGJKsz5mQEAAvvjiC7vXDRs2zOZyly5dsHbtWpttK1assP5/wYIFAIDIyEgsX77c7j6rprqbMmUKpkyZ4nC7XYEjz0RERESOqG+WC/JZDM9EREREjnDSLBfk2RieiYiIqtysX+22oYf76lfJezhhlgvyfAzPREREQHX9qjYPAsTq+lUGaKqLE2a5IM/H8ExERASwfpUazwmzXJDn42wbREREAOtXyTHNPMsFeT6OPBMREQGsXyVyoszMTDz11FMApIVTUlNT67xtaWkp0tKqy6WuXLmCefPmOb2NcjE8ExERAaxfJXKA2Wxu9H1GjhyJOXPm1Hl9aWkp/vvf/1ovt2nTBh999JFD7XMGlm0QEREB1YfeMxZD1OZDUMdKwZmH5MlL5F42IqfAhIQYP8S39W/y/i5cuIAnn3wSvXr1QnZ2Nrp06YJ33nkH48aNw9SpU7F792489thjCA8Px9KlS2EwGNCxY0e8/fbbCAkJwc6dO/HWW28hIiICPXr0sO539erVOH78OF577TVcu3YNf/7zn61Lfr/++uv4+uuvkZ+fj0mTJmHw4MFISUnB008/jXXr1kGv1+P111/H8ePHYTQa8frrr2PQoEFYvXo1tm/fjoqKCpw/fx6jRo3Cyy+/DLPZjMWLF+P48eMQBAHTpk3D7Nmzm9QvDM9ERERVbtavntRokJiY6O7WEFmt2a/D2oOVsm6rEICXp6htAvRXv5QjI1tvvTyhfxAm3qWyd3cbZ8+exZIlS9CvXz+88sor+PbbbwEAgYGB+O9//4uioiI8//zz+PLLL6FSqZCamoovv/wSv//97/Hqq69i2bJl6NSpE1544QW7+/9//+//YcCAAfjkk09gNpuh0+mwYMECnD59Gj/++CMAKcRXqSrnWLt2LbZs2YJFixbhp59+AgBoNBqkp6cjICAAY8aMwcyZM3H9+nVcuXIF69atAyCNajcVyzaIiIiIfIhFBHIKTM2yr3bt2qFfv34AgIkTJyIrKwsAMHbsWADA0aNH8dtvv+HRRx/FpEmTkJ6ejosXL+LMmTPo0KEDOnfuDEEQMHHiRLv737dvH2bMmAEAUCqVUKvV9bYnKyvLuq8OHTqgffv2OHv2LAAgOTkZarUagYGBiI+PR0FBATp27Ijz58/jL3/5C3bu3InQ0NAm9wlHnomIiIh8iEIAEmKaJ+IJgmD3cnBwMABAFEXcfffdeP/9921up9Foat23OYiiWOd1AQEB1v8rlUqYzWaEh4fjxx9/xK5du/Dtt99i48aNePvtt5vUBoZnIiIiIg838S5Vg2UW9dU8z7o3BLPuDWn04168eBGHDx9Gnz59sH79evTr1w8ajcZ6fe/evfHmm28iLy8PnTp1QkVFBS5fvoy4uDhcuHAB+fn5iI2Nxfr16+3uPzk5Gd9++y1mz54Ns9mMiooKhISEoLy83O7tBwwYgLVr1yI5ORkFBQW4dOkS4uLikJ2dbff2RUVFCAgIwP3334/Y2FgsWrSo0X1wK5ZtEBEREfmA+Lb+GNsvuFlOFrTuMz4eP/zwAyZMmICSkhI8+uijNtdHRkbi7bffxosvvogJEybgoYcewpkzZxAYGIg333wTc+bMwaOPPor27dvb3f/ixYuRmZmJCRMmYOrUqTh9+jQiIiLQt29fjB8/Hu+8847N7WfMmAGLxYIJEybg73//O95++22bEedbFRYWYubMmZg0aRIWLVqEF198scl9Ioj1jX97kKysLGvNTXPT8MQQh7DfHMN+cwz7zTHsN8ew3xzDfnOMp/bbhQsXrLNceCJn9lt9uZMjz0REREREMjE8ExEREVEtHTp08NhRZ3dieCYiIiIikonhmYiIiIhIJoZnIiIiIiKZGJ6JiIiIiGRieCYiIiIikonhmYiIiIhIJoZnIiIiIiKZGJ6JiIiIiGRieCYiIiIikonhmYiIiIhIJoZnIiIiIiKZGJ6JiIiIiGRieCYiIiIikonhmYiIiIhIJoZnIiIiorpo0oDUzsB7CumnJs3dLSI383N3A4iIiIg8kiYN2DwHMOmky9o86TIAJKa4r13kVhx5JiIiIrInY3F1cK5i0knbqcVieCYiIiKyR5vfuO3UIjA8ExEREdmjjm3cdmoRGJ6JiIiI7BmyBPBT2W7zU0nbqcVieCYiIiKyJzEFGJ0KqDsBEKSfo1N5smALx9k2iIiIiOqSmMKwTDY48kxEREREJBPDMxERERGRTG4Jz2azGZMnT8ZTTz0FACguLsbjjz+O0aNH4/HHH0dJSYk7mkVEREREVC+3hOevvvoK8fHx1supqalITk7G5s2bkZycjNTUVHc0i4iIiIioXi4Pz5cvX8Yvv/yC6dOnW7dt27YNkydPBgBMnjwZW7dudXWziIiIiIga5PLZNt566y0sXLgQ5eXl1m3Xr19H69atAQCtW7dGUVGR3ftqNBqntKmystJp+/Zl7DfHsN8cw35zDPvNMew3x7DfHMN+c4y7+s2l4fnnn39GZGQkkpKSkJmZ2ej7JyYmOqFVUih31r59GfvNMew3x7DfHMN+cwz7zTHsN8ew3xzjzH7Lysqq8zqXhudDhw5h+/bt2LlzJ/R6PcrKyvDSSy8hKioKhYWFaN26NQoLCxEZGenKZhERERERyeLSmucFCxZg586d2L59O95//30MGjQIf//73zFixAikp6cDANLT0zFy5EhXNouIiIiISBaPmOd5zpw52L17N0aPHo3du3djzpw57m4SEREREVEtbluee+DAgRg4cCAAICIiAsuWLXNXU4iIiIiIZPGIkWciIiIiIm/A8ExEREREJBPDMxERERGRTAzPREREREQyMTwTEREREcnE8ExEREREJBPDMxERERGRTAzPREREREQyMTwTEREREcnE8ExEREREJBPDMxERERGRTAzPREREREQyMTwTEREREcnE8ExEREREJBPDMxERERGRTAzPREREREQyMTwTERERUeNo0oDUzsB7CumnJq1x13sxP3c3gIiIiIi8iCYN2DwHMOmky9o86TIAJKY0fL2X48gzEREREcmXsbg6GFcx6aTtcq73cgzPRERERCSfNr/+7Q1d7+UYnomIiIhIPnVs/dsbut7LMTwTERERkXxDlgB+Ktttfippu5zrvRzDMxERERHJl5gCjE4F1J0ACNLP0anVJwM2dL2X42wbRERERNQ4iSn1h+GGrvdiHHkmIiIiIpKJ4ZmIiIiISCaGZyIiIiIimRieiYiIiIhkYngmIiIiIpKJ4ZmIiIiISCaGZyIiIiIimRieiYiIiIhkYngmIiIiIpKJ4ZmIiIiISCaGZyIiIiIimRieiYiIiIhkYngmIiIiIpKJ4ZmIiIiISCaGZyIiIiIimRieiYiIiIhkYngmIiIiIpKJ4ZmIiBpPkwakdgbeU0g/NWnubhEReRMvfg/xc3cDiIjIy2jSgM1zAJNOuqzNky4DQGKK+9pFRN7By99DOPJMRESNk7G4+kOvikknbSciaoiXv4cwPBMRUeNo8xu3nYioJi9/D2F4JiKixlHHNm47EVFNXv4ewvBMRESNM2QJ4Key3eankrYTETXEy99DGJ6JiKhxElOA0amAuhMAQfo5OtUrTvQhIg/g5e8hnG2DiIgaLzHFaz7oiMgDefF7CEeeiYiIiIhkYngmIiIiIpKJ4ZmIiIiISCaGZyIiIiIimRieiYiIiIhkculsG3q9HikpKTAYDDCbzbj//vsxb948LF26FCtWrEBkZCQA4MUXX8SwYcNc2TQiIiIi19KkARmL0U2bD2TESvMce+kMFC2JS8NzQEAAli1bhpCQEBiNRsyYMQNDhw4FAMyePRtPPPGEK5tDRERE5B6aNGDzHMCkgwAA2jzpMsAA7eFcWrYhCAJCQkIAACaTCSaTCYIguLIJRERERO6XsRgw6Wy3mXTSdvJogiiKoisf0Gw2Y+rUqcjPz8eMGTOwcOFCLF26FD/88ANCQkKQlJSERYsWITw83OZ+WVlZUKlUdey1aSorKxEUFOSUffsy9ptj2G+OYb85hv3mGPabY9hv8nXb0AMCakcwEQJOjj3hhhZ5H2e+3nQ6Hfr162f3OpeH5yqlpaV49tln8eqrryIyMhIREREQBAEffvghCgsL8fbbb9vcPisrq84n0VQajQaJiYlO2bcvY785hv3mGPabY9hvjmG/OYb91gipnaVSjVupOwFzzrm6NV7Jma+3+nKn22bbCAsLw8CBA5GRkYHo6GgolUooFAo8+OCDOHbsmLuaRUREROR8Q5YAfrccUfdTSdvJo7k0PBcVFaG0tBSANNS+Z88exMXFobCw0HqbrVu3omvXrq5sFhEREZFrJaYAo1MBdSeIEKQR59GpPFnQC7h0to3CwkIsWrQIZrMZoihizJgxGD58OBYuXIiTJ08CAGJiYvDmm2+6sllERERErpeYAiSm4CTLXbyKS8Nzt27dkJ6eXmv7u+++68pmEBERERE5hCsMEhERERHJxPBMRERERCQTwzMRERERkUwMz0REREREMjE8ExERERHJxPBMRERERCQTwzMRERERkUwMz0RERESeSJMGpHYG3lNIPzVp7m4RwcWLpBARERGRDJo0YPMcwKSTLmvzpMsAl/B2M448ExEREXmajMXVwbmKSSdtJ7dieCYiIiLyNNr8xm0nl2F4JiIiIvI06tjGbSeXYXgmIiIi8jRDlgB+KtttfippO7kVwzMRERGRp0lMAUanAupOAATp5+hUnizoATjbBhEREZEnSkxhWPZAHHkmIiIiIpKJ4ZmIiIiISCaGZyIiIiIimRieiYiIiIhkYngmIiKSS5MGpHYG3lNIPzVp7m4REbkYZ9sgIiKSQ5MGbJ5TvWSyNk+6DHBGBKIWhCPPREREcmQsrg7OVUw6aTsRtRgMz0RERHJo8xu3nYh8EsMzERGRHOrYxm0nIp/E8ExERCTHkCWAn8p2m59K2k5ELQbDMxERkRyJKcDoVEDdCYAg/RydypMFiVoYzrZBREQkV2IKwzJRC8eRZyIiIiIimRieiYiIiIhkYngmIu/GFd+IiMiFWPNMRN6LK74REZGLceSZiLwXV3wj8g48QkQ+hCPPROS9uOIbkefjESLyMRx5JiLvxRXfiDwfjxCRj2F4JiLvxRXfiDwfjxA5D8th3ILhmYi8F1d8I/J8PELkHFXlMNo8AGJ1OQwDtNOx5pmIvBtXfCPybEOW2NY8AzxC1BzqK4fhe6JTceSZiIiInIdHiJyD5TBuw5FnIiIici5PPkKkSZNGa7X5UinJkCWe29aa1LE3SzbsbCen4sgzERERtUzeXDfME6bdhuGZiIiIWiZvnkaP5TBuw7INImqYtx7WJCKqj7fXDXtyOYwP48gzEdXPmw9rEhHVh9PokQMYnomoft58WJOIqD6sGyYHMDwTUf28/bAmEVFdWDdMDmDNMxHVj9MhEZEvY90wNRJHnomofjysSUREZMXwTET142FNIiIiK5ZtEFHDeFiTiIgIAEeeiYiIiIhkY3gmIiIiIpKJ4ZmIiIiISCaGZyIiIiIimRieiYiIiIhkculsG3q9HikpKTAYDDCbzbj//vsxb948FBcXY/78+SgoKEBMTAz+8Y9/IDw83JVNIyIiIiJqkEtHngMCArBs2TKsWbMG6enpyMjIwJEjR5Camork5GRs3rwZycnJSE1NdWWziIiIiIhkcWl4FgQBISEhAACTyQSTyQRBELBt2zZMnjwZADB58mRs3brVlc0iIiIiX6VJA1I7A+8ppJ+aNHe3iLycIIqi6MoHNJvNmDp1KvLz8zFjxgwsXLgQ/fv3x8GDB623GTBgAA4cOGBzv6ysLKhUqlt31ywqKysRFBTklH37MvabY9hvjmG/OYb95hj2m2M8rd/CCtah3bHXoLBUWrdZFEG41PNNlMaMd2PLbHlav3kLZ/abTqdDv3797F7n8hUGlUolfvzxR5SWluLZZ5/FqVOnZN83MTHRKW3SaDRO27cvY785hv3mGPabY9hvjmG/Ocbj+i3jAaBGcAYAhaUSMWc+QcyohW5qVG0e129ewpn9lpWVVed1bpttIywsDAMHDkRGRgaioqJQWFgIACgsLERkZKS7mnY7iEYAACAASURBVEVERES+QpvfuO1EMrg0PBcVFaG0tBSANNS+Z88exMXFYcSIEUhPTwcApKenY+TIka5sFhEREfkidWzjthPJ4NKyjcLCQixatAhmsxmiKGLMmDEYPnw4evfujRdeeAErV65Eu3bt8OGHH7qyWUREROSLhiwBNs8BTLrqbX4qaTuRg1wanrt162YdYa4pIiICy5Ytc2VTiIiIyNclpkg/MxZLpRrqWCk4V20ncoDLTxgkIiIicpnEFIZlalZcnpuIiIiISCaGZyIiIiIimRieiXyFr66idfN5ddvQw7eeFxEReSXWPBP5Ak2a7Rnl2jzpMuDdtX41npcA+M7zIiIir9WokecdO3bgk08+wauvvoqLFy8CAA4cOIArV644pXFEJFPGYtupmADpcsZi97Snufjq8yIiIq8la+T52rVreOaZZ3DixAnExMTgwoULeOSRR9C+fXusXr0aAQEBeOONN5zdViKqi6+uouWrz4uIbGnSOJ0ceQ1ZI89/+ctfoNPpsHHjRmzcuBGiKFqvS05Oxr59+5zWQCKSwVdX0fLV50VE1arKs7R5AMTq8iye30AeSlZ4zsjIwAsvvIBOnTpBEASb69q2bcuyDSJ3G7JEWjWrJl9YRctXnxcRVWN5FnkZ2TXPCoX9m964cQOBgYHN1iAickBiCjA6FVB3AiBIP0enev9hzxrPS/Sl50VE1VieRV5GVs1z//798c033+Dee++1bqsagV61ahUGDRrklMYRUSP46ipaN5/XSY0GiYmJ7m4NETU3dezNkg0724k8kKzw/NJLL2HGjBkYP3487rvvPgiCgBUrVuD06dM4ffo0li9f7ux2EhERkS8assR2qk2A5Vnk0WSVbdxxxx1YvXo1kpKSsHr1aiiVSmzZsgVt27bFihUr0KVLF2e3k4iIiHyRr5adkc+SvUhKbGws3n33XWe2hYiIiFoiXy07I5/E5bmJiIiIiGSSNfI8bdq0WlPU3WrlypXN0iAiIiIiIk8lKzx37dq1VnguLi7GkSNHEBQUxNk2iKjFyr1sRE6BCQkxfohv6+/u5hC1TFyh0D72i1PICs9//etf7W4vLy/HM888g759+zZro4iIvMGv5wz49KcymM2Anx+wYKKaAZrI1apWKKyaraNqhUKgZQdF9ovTNKnmOSQkBP/zP/+DTz/9tLnaQ0TkNXZp9DCZARGA0QTkFJjc3SSilocrFNrHfnGaJp8wWFpaitLS0uZoCxGRVwkJqi5nUwhAQozsCYyIqLlwhUL72C9OI+udfseOHbW2GY1G5Obm4j//+Q8GDhzY7A0jIvJ0Nc8FGXlnIEs2iNyBKxTax35xGlnh+amnnoIgCBBF0fbOfn4YOXIkXnvtNac0jojIkxWXWaz/D/IXUKqzIEzFGUCJXIorFNrHfnEaWeF527ZttbYFBgYiKiqqwSnsiIh81Y3y6vC89mAlIkKVGNI90I0tImqBqk5+46wSttgvTiMrPMfExDi7HUREXqe4RngGgGtas5taQtTCcYVC+9gvTlFneP7tt98ataPbb7+9yY0hIvIWRpOIskrbUrZrpZY6bk1ERL6izvA8fvx4WSUZoihCEARoNJpmbRgRkSe7UV47KF9neCYi8nl1huevvvrKle0gIvIqt5ZsACzbICJqCeoMz3fddZcr20FE5FXsjTyX6EQYTCIC/HgiNRGRr2r0jP4WiwV6vb7W9uDg4GZpEBGRVxCB6DAFisstMNUYcL5eakG7SKX72kVERE4lKzyLoojPP/8c33//PS5cuGD3Nqx59iGaNE5tQ9SAgXcEYuAdgRBFEX/7QYvfLktLc1/Tmhmeicg71Pd57+4s4O7Hr4es8PzVV1/h888/x5NPPokPPvgAzzzzDJRKJdavXw+j0YinnnrK2e0kV9Gk2U6qrs2TLgMe86Il8iSCIKBthLI6PPOkQSLyBvV93gPuzQIenkVkLYX1/fff4/nnn8eTTz4JABg1ahSee+45rF+/HnFxccjLs7P8I3mnjMW2qxEB0uWMxe5pD5EXiFZXv5UyPBM5gSYNSO0MvKeQfmrS3N0i71ff5727s4C7H78BssLzhQsXkJiYCKVSCT8/P5SWlkp3VigwY8YMpKenO7WR5ELa/MZtJyJEhykgCECUWoEAf3e3hsjHVI1CavMAiNWjkAzQTVPf5727s4C7H78Bsso2WrVqBZ1O+gbQvn17aDQaJCcnAwBKSkpQWVnpvBaSa6ljb75B2dlORAAAiyhix3E9WoUoEBGqQN/4APSLD4CfkrNsEDW7+kYhPeAQvtdq6PPenVnAw7NIneHZaDTC318aQunbty+OHTuGYcOGYfz48Vi6dClKSkrg7++PtLQ0a5AmHzBkiW2dEQD4qaTtRAQA0FaI+DZD+hsJDRLwwf9EuLlFRD7Mw0chvVZDn/fuzAIenkXqDM/33HMPRo8ejbFjx+LZZ5/F1atXAQBPP/00SktLsXr1auj1egwePBivvvqqyxpMTlb1Ld5Dz3Al8gQ3yqrrmluFyKp+IyJHefgopNeS83nvrizg4VmkzvA8btw4bN68GStXrkRUVBQeeOABqFQq3HnnnfjTn/6EP/3pT65sJ7lSYorHvECJPFHN1QUZnomczMNHIb1afZ/37s4C7n78etT5rv/aa69h586d+Ne//oVhw4ZhzZo1ePjhhzFy5Eh88MEHOH36tCvbSUTkMWquLhgRKr2NXteacSzPgJ+PVeLCNZO7mkbkexJTgNGpgLoTAEH6OTrVY4MV+b56TxhUKBQYPHgwBg8ejDfeeAMZGRnYsGEDvvnmG6SmpiI+Ph7jx4/HuHHj0LFjR1e1mYjIreyVbfx0uBI/H5dWX52eHIwO0Y1ewJWI6uLBo5DU8sh+d/fz88Pw4cMxfPhwGAwG/PLLL1i3bh0++ugjfPTRR8jOznZmO4mIPEbNso2IEGmGjeiwGnM9aznXMxGRr3KoWC87OxsHDhzAoUOHYLFY0K5du+ZuFxGRx7JX8xytrl6SmwulEBH5Ltkjz9nZ2Vi/fj02btyIS5cuISoqCmPGjMG4cePQp08fZ7aRqGXQpHnsmcUN8uS2O6FtNcs2qmqebUaeS81N2j8REXmuesNzbm4u1q9fjw0bNiAvLw9qtRqjRo3C+PHjMWjQICgUPMucqFlUraBVdTZ51QpagOeE0Lp4ctud0DZRFG3Dc0jt8Hxda4EoihAELppCRORr6gzPEyZMwG+//YagoCDce++9eOmllzB06FAEBAS4sn1ELYM3r6DlyW13QtsqDCL0NyfT8FcCqkApIKsCFVAFCtDpRRjNQIlORKsQhmciIl9TZ3iOiYnBnDlzMHLkSKhUKle2iajl8eYVtDy57U5om9kCDOwagOJyCxQK2IwuR6sVyNdLJRvXSs2cA5qIyAfVGZ7/+c9/urIdRC2bN6+g5cltd0Lb1MEKPHlfqN3rosMUyL92MzxrLbid51ITEfkcDosQeYIhS6QVs2rylhW0PLntLm5bdBhn3CAi8nUMz0SewJtX0PLktru4bZxxg4jI93EJLCJP4c0raHly213YtvYRSsS1USI6TInb2/m75DE9kidPXUhE1EQMz0REjbD5SAVKK0REhCjQNy7AOs8zACTE+OOVaeFubJ0H8OSpC4mImgHDMxFRI2SeNiD/qlSS0aWNn014Jnj21IVERM2A7/pERI1Qc4EUTkVnhydPXUhE1Az4zk9EJJPJLEJbIQIABAEIV3ERlFrqmgbQE6YuJCJqBgzPREQyFZdXjzqHqwQoFbXDc06BET9k6vD5ljIcPWdwZfM8gydPXegNNGlAamfgPYX0U5Pm7hYR0S1cGp4vXbqEmTNn4oEHHsC4ceOwbNkyAMDSpUsxZMgQTJo0CZMmTcKOHTtc2SwicjYfCQQ1w3OrEIXd56W5YMSGrErsP23AmSsm5zXGU/vUk6cu9HRVJ1tq8wCI1SdbesrvlogAuPiEQaVSiUWLFqFHjx4oKyvDtGnTcPfddwMAZs+ejSeeeMKVzSEiV/Ch2Rdu1AzPlgK7zyu6ZwcAPQE4caEUT+9TT5660JPxZEvyMrmXjcgpMCEhxg/xbVvO9JwuHXlu3bo1evToAQAIDQ1FXFwcrly54somEJGr1RcIvEzNkwUjrv9i93lFn1pqvXjdWeHZh/qUauDJluRFci8b8d4aLdL3V+C9NVrkXja6u0ku47ap6i5cuACNRoNevXrh0KFDSEtLQ3p6OpKSkrBo0SKEh9eeK1Wj0TilLZWVlU7bty9jvzmmpfVbN20+7J1WJ2rzcbIR/eAJ/XbmfCiAEABAK8Mpu7eJ0h0AgqT/X7qhd0qbG9OnntBv3sgd/RYf1BYBlZdqbTcEtUWul/wO+XpzjDf22/48FUymUIgQYDKJWLO7EGO6l7q0De7qN7eE5/LycsybNw9//OMfERoaikcffRRz586FIAj48MMP8de//hVvv/12rfslJiY6pT0ajcZp+/Zl7DfHtLh+y4i9WcNpS1DHNqofPKHfMs6XAZBOAowINAJ2SpojQ/ygsAAWEdAZlIi7vRsC/Zt5Vo5G9Kkn9Js3ck+/vWtbjgMAfioEjHjXa36HfL05xpv6bdPhCrQJV+Ke3gIy87QwmgERAn67Hox2se1cOoWnM/stKyurzutcPtuG0WjEvHnzMGHCBIwePRoAEB0dDaVSCYVCgQcffBDHjh1zdbOIyFl8aPYFmzme7xxv93kph76JSHX1W+t1rRNKN3yoT6kGnmxJt9AbRXy6SYs/pRVj69FKdzcHW49WYtXeCvzzpzJc01owb7waIYHS4IDBBHy3S9fAHnyDS8OzKIpYvHgx4uLi8Pjjj1u3FxYWWv+/detWdO3a1ZXNIiJn8qFAMCwpEA/0DUJyQgBa9xxd5/OKrhGer5Wam78hPtSndIvEFGDOOWCBRfrpI79Tk1nEx+u1WPxNMXZp3B8CGyv3shEbsipcWterN4p494dSHDpjxJUSC5bv1uGj9aUor3TSuRQNyMiuxPLdUji2iMAejR4J7f3w1P2h1ttk5RpaxBSdLi3byMrKwo8//og77rgDkyZNAgC8+OKLWLduHU6ePAkAiImJwZtvvunKZhGRs/nI7AuD7gi03VDH84oOUwIFUk3HNWeMPNfz2ESe6Ps95TiaJwXPZT/rUFxuwfj+qgbu5X6Xb5hRVmnB+2u1MJkBPyWwYKLaJTNLVBpFXC+zff84lmfCn78rwYwhIegbH+D0NlTJPKXH179UjyrHt/XDMw+oIQgCEjv4Y3C3AOw5KYXmtB06JLT3R1CA7y4i5dLw3L9/f+Tk5NTaPmzYMFc2g4jIqWxHnt0zSkTkKUxmEXtzbEdsj+UZMb6/mxokU1auAf/aWob4tn4wmQFRBMxmaSGk4AAF2kcqnfr44SoFfjc8BJ9sLLPZXqIT8elPZegb548ZQ0MQrnJuEcGhMwb8e1s5xJuXO92mxLxxoQiqcS7Hg4NVOJZnhLZCxI1yC9IzdXhkSIhT2+VObpttg4jIV0WHOblsw0O01DleqXEO/mZAhUG0XhYATE/27FHnrUcrsWK3DiKAkwUmKBWACECplM5jeHNFCWbdG4LB3QIb2lWT9O4SgBcnqnH2igkCgG3HKlGik/ry0BkjThaU4KG7VRicEABBqA6zl4vNuFJsRonOgnNXTLhaakGUWoGI0NpBOzhAgMkMm7/jrFwDLlw34WqJBft/M0C8+euLiVTihQlqqAJt9xMapMDDd6vwxdZyAMD2Y3rcdUcg4trUHzMPndHjwjUzesT6e9V7CMMzEVEzi73ND/f1CkJ0mAIdo507OuUuVXO8mkw3D2VPcs2hbPIuoijipyPVNc63t/XDuP5B6NreM18rFouIFXt02Par3rqtdbgCUwYGo7DEAoso4sf90vP5cns5rmstGN8/yCa4OspgEpF31YSu7Wz7JrGDPxI7SNuGJQVi5Z4KZGik9un0Iv6zvRzZ+UaMuDPQ+je45UgldmbrIYdCkL4Y1CxJOXLWgH2nbGuXI0IFzJ+oRmiQ/ZHuu7oGYG+OASfOGyEC+OrncvzpwTD4KWv3zamLRqzYrUPeVWlwYX1WJZ59IBR3dnZdKUpTMDwTEcmQeVqPHcf1iAhVoE+XAPS/ve43+XYRSjx0t2ePrDVVToERxptT9RnN0mF4XwnP+3IqceS3UARE2D6nn49XotIg4rrWjPJKESPuDKoVdMhW9nkTLlyXAlKAH/Ds2NBa4csiivhulw73dAtE7G3uiyUGk4h/bS3DoTPVJSbxbf3w7AOhUAdLbS4qsyAr12h9TmsOVOC61ozHhoXYDYlyGc3Axxu0OHXRhGfGhKJXHSFSFajArOEhGNA1AF//Uo6rpRYIAA7kGnD4nMEafsNU8ttiuTmqLJWkmOr8Ox54e0C9JSKCICBlmAqvf1cCgwkoKDJj85FKjO0XDED6IpVz0YR1ByqQc9F2nk+LCPzfpjIMTwrE/X2CXTrdnSMYnr2VJk1aTUybD6hjpSmqePJQi3axyIy1B3VQBQgY3C3Q9g2Qr5e6yeybi0VmnL4kveG3DlegP2SMkPhwv0eF2Y6oHz5jwAN9g5t/TmsXEkURy3fpsO2YHoAKv17S2pwctulQJYpqnMB16IwRL0/hiHt9fjpSYf3/PYmBdkct1+yvwM/H9MjI1qN7R3/Et/FD21a1j9hcLjbDbBGl29zsc71RxIl8o/X6i0VmtI9U1rp/1XXDegTajHqbzCIqDCJ+u2zCfzPKcaOsurykb5w/nhgVigC/6td0ZKgCL08Jwz83aZF9QXo/2H3SgIIiM5Ji/dG9gz/i2/oBAqCoMRqde9mIkwVG3NHu5vU15Fw04av9USiplPb36aYyvPFIONrY6YMqiR388eeHw/GPtVrkXjbVqMeWwm+HKD8kxfpDEEScyDfBIkojzIO7BSAytHq/RWVm7MsxwCJKJSkJMVLb+sQFwE8J7DkpXeevBHrHNfyed1uYEpPuCsb3e6Tf+/qsCgztEYiQQAH/WKdF9nk7k+PfZLYAW3+VXgfvzGqFkDpGuD0Bw7M30qTZTqSvzZMuAz7zwUyNI4oi/rGu1PrGvzfHUH0YvZleLz5Z39qIvikur7E0t5xRER//Ox3YNRBXis1Ye0A6hH3xhgX/t0mL58eqmzQC5y5Gs4i0HeXYfbLqULXQ4EicRQR+Pec7I+7NLf+qCZqbAVMQgFG9gmrdplRnwfZjUnmBySz156/n6p4OTgCw8XCl9UtNeaUFn/5UVuftb3XojMGmxOjiDTP+sqL2qnijegXiwcEqmwBcJThAwPPj1Ph6R7l1holzhWacKzRj3UHp72HQHQF4YpQ0hVtViZN0pKauafqq49j4/sH1Bucqgf4Cpg8OxntrtDCbbcNvv/gA9Ls5G0dD7933JNa+vm9cAPrGBdi9riEj7wxC5ikDjGZgZM9A7DyhR0KM9IWoKjwrBCA5IQA9Ovrj6DkjzhWacKVEeo/tExdgDc6e+rnD8OyNMhbbrkAFSJczFvvEhzI1Xv5Vs82IibHmh34zvF6s9a1mwE/hQ/Wtjeib4poLpMgJzy3g73TiABXUwQp8u1N6ntnnTfj3tnI8OSoECoX3BOgSnQWfbixD7pWao2IilArBGkYA4N6kQBQUmZFZoxb0crHvnhDaVFtqLOrRPz4At4XVDoRhKgVemRqGd34oRblerHX9rUQ0XF5QH7PF9r4GY+3H7NnJHw/fXf9MEX5KAbOHhyAqVIG1B+uftzqnwASTzJdJYgc/jO8fLO/GAOLb+mPBRHW9ATO+bf0n49V3fUP3tUepEPDsA6G4VmrBP9ZXT/H3+/tCsfukHnd1lebLr3o9DOgaCFEU8WueEeuzKqxlHjafOy6cIlAOhmdvpM1v3HYHeOq3PbLviJ1J6SPVN8NLM7xeqt78RVEK5kfOes5om05vwc4TehQUmdG7iz/6xgXIP3mnEX1zo7z6Q7aVnTPWb5WpG4CMgA9xTYjFKNNnGGX+rP7H9BLnr5lwssCE3l38cVuYEsOTglBWIWLNAekw7YHfDFAFCkgZqmqWk6icLe+qCZ9sKMONGkcWenbyQ6jiBob1aW/zOn+gr/Sh3jGqAiv3Ss/36DkjrpaYcVu4b54Y2hRTB6kQrlJgZ7Ye9/euPepcpV2kEk/eF4Kl68tgEaVR6q5tlTaH7csrLTh92QwBtiOs/n4C+nTxt14v2rl/zetq3heQjh4EBwAVN99C/RTAuH51t7UmQRAw8S4VLKJ0wltdEmL84KeE9RwB6b7ST9H6tiJ9WZt0l/zgXMWRgOtskWol9p0y2Ezxd6nIjHd/F4FgO/M/C4KAXp0DbGq9a37uNOULkzMwPHsjdax0CNje9mZw67e9+ePVCA4Q0CGaLxdPdeRs7cOcWblGDLojqFleL3e09wNqDNBk5Rowrl+wSyfBN1uAgusmtI9U2oSyw2cMWLVPCjL7ThkQ6F+Ozrf5oWO0Eh2j/RAbrUTbCKX9UoJG9E1jyzZKghKQYxkCALiiiAeqRp6a6e/UXfblGLD5aCVW7AYmDAjGxAHBGN8/CGWV1Yffd5zQQx0sYNJdnn3S5IHf9PjP9nIYboYaQZDmqx11ZyBOniyo84N6dO8gHDlnxG+XTDBbpJPGqg7RezOTWcSxPAMuFpnRrUPTA1lEqALTB6swYUDDtfBJsQF4eUr9I6j2BnXUwQrMfUBd5/X13RcA7mjvj4+ejGzSgNHkgSr07ORf5/3jb6zAAnE5cozxSAjKRfzQh22OPuVeNmLXkYu4p3d7jwmHzaHqS0PNkhJ7wbkx9/cUntMSkm/IEttaSgDwU0nbm0hbYYHmgtHm296mw5U4lm/E6F5BmHhXsM3JE+R+V0vN1jO/lQopZAKAtkKEwSQioBleL7e388eDd6vw/c15T6+WWvDPn8rw3NhQp9e3Wiwilm7Q4kR+a4goxbu/a4VWIdWPWXTLClx6o3QCjnQ2txTmlAJwW7gCkwcGo198jXlZZfZNpVG0zlPrpwRCgxp+ztHd7wWOS/+/JsTWuW9vIooiDp+tPsrRpbU02ioIAh6+R4WyShH7T0vXrztYiZBAhd06V3c7fcmIHzNtz/gPDhAwZ3QIkmIbPilKEARMGxSMd37QAgAyTxlwf2+T1w0wlOos2PprJS7fMN+cF9hinXlBebASL0xQo1tM08Oc3JNIm1Je0ND1Td13Q+q8/81zH+JNOsQDQBmAzduk624G6Pi2/jB00vlUcAbklZQ48/7O5LmnMlLdElOA0amAuhMAQfo5OrXJdZSiKOKLLWXYrTFAqZAK+pUKIPuCEaII/HSkEm+uKMFvl+o+mYNc72iNUefEDv4Y2y8Iz40Nxf9OUUtfdJrp9XJfryDMGl5dB3jivBHf7tRBFBuuU2yKHSf0OJ5vggjpA3j/adu5S7t39EdD5bVmEbhcbME/fypHZs37y+ybmvXOESEKWeUI0d3vtf7/mtCp2f5O3amgyIyrN1dMDPQHunWo/jBTCAIeHxGCpNjqbd/v0eHQGXlzzbpK7mUj/p6utQnObVop8MdpYbKCc5Xb2/mjV2fpuYoAfsisqP8OMhSVWfD1L+V46T838G56KU5fbP732tzLRmzIqkDuZWnfGw9V4vBZIy7dqA7OgPQl/OMNWvx8rBJGs3P/xn1afec+tADxbf0xtl+ww8G3qfd3Fu/6mkzVElOa/UM485TBOvWOQgBG9wpEl7Z++OW43nrG9JViC/72gxYj7gxE91bN+vDkoOgwBRI7+OHURakOdVgPOyN9TXy9mMwi/JQC7kkMxHVt9RnlGRo9osIUGNev8XV6cp26JUCcK7Sd6ii+rb/1cO8d7f0QEarA+Wtm5F8z4/w1E04VmKCrsbrZ9VtX/JPRNzXrYeXOP1pzie7r/ndA/P1Zr6gBrk/N8qCesQHwv+Wog59SwNP3h+LtVaUoKJJqTL/YWo4FExUe8+F39JzRJiS2DpeC860rpskxeWAwfj0nLQhx8YYZ5ZUWh6bXKq+0YOOhSmw/VgnjzZdnic6Ed3/UYv4EtXWBjKa6tSTvxQlqqAIF6Oo4UU9vBL7N0GHT4UqM6x+EIYmBDb6GTWZg9T4dhvUIRJSadeCuOEeJXI/hmQBI5RrLd1d/Ox7RMxDTBkujjH26BCAjW4/v9+hQaZRGWbb9qscuv2g8Hqy3PQxOLte7SwB6dwlAeaUFSifMcFBpFDHv8xtQBwtoHa7Ey1PUuFZqsa4+lZ5ZgSi1AoPucM7roOZysgpBmgbpVrceMo1SK9G7i/T/3MtG/P1HKTD4K4EEBw5D32jsTBuQFjOoCiZGM1CiE23KTbxRzZKNPnH2+zHQX0DvLv64WGRu8swIztCrsz+2HpVCqkIAZg8PcSg4A0CHKD+M7BWI28KUGNo90KESpk2HK7Ahq9Jm+eoqogj8e1sZ/vRgeL2LU8h16wlYpy6aMGVgMIL8BbSLUKJNKyXyrpqw5WglfrtkRPnNgwZFZRYcPmPE0O5BDdYGa64EY0tOJX46XImRdwb5/GJBDXLyOUrkHgzPBEA6vFpWKb15R4YqMGlg9RueIAgY2iMISbH++HqHDsdvTkivNynx2eZy/O8UzxlVasnqGvEyW0QUl1scHgUqLrNABFBaISLA3wJBEPC74SEoLrfgZIE0Cvyf7eWICFE4FEwbUjPYDOwa4FDd3EuT1MgpMCIhxjZkm8wi1h2swOjeQfUGKJuTBWXMtFElOkyB/JvLz14rNXv8qln1ua41W5+LUgGb8oxb9ezkj81HKz3yRJ/4tv5YMKn6SMXtTVwhsKEpzRpSWGKxCc5tWyluLgMtXS4uF/HWylLMGx+KmMim9WNOgdF64q9CAbsBOCHGHwkx/jCYROw4ocfGQxXQVoiYOCC41sj1tEEq3N0t0HrisEUUcfC86ub/gfBGrHLns5x4jhK5j+e8o5HbZJ83JW12qwAAIABJREFUYm9O9YhSylAVguyc4BGpVmLeuFD886fq5UtFETiUa2B49kAWUcShXCN+3K+DIAh4/eEwh+betVey4KcU8MyYULzzgxYXi8wwW4ClG7S4JzEQA25vfMCtT81DyjFRjn0BqOtknrUHpVG/nSf06N3FH3cnBtq93cA7AhATpcSNMgs6NKIN0erq8HzivLFRQc1sEbEzW4/DZwzoFuNvE3QOnTFg9T4dDCYRoiiNxo/p47zSGcC2ZKNbjH+9XzbsneijrbDg9CUT+spYpczZ3D21V83R2wn9g7EvR48otQKTB6rQN84fZ66YsPFQJY7eXCykqMyCY3nGJofnEp1onTRn5jBVvX0Q4Cfgvl5BGNo9EMfyjOjSxg8bsiqsI9cmM/DdLh1W7tGhWwep/luhAG7opDYG+QNDuvOopLUkzEdXGm2pGJ69lMksYn1WBa5rLRjWw/4Hvhx6o4hvdpRbLw+4PQB3dq77w00QBIzuHYTDZ43W+Slvne2AXKPqRL26ahB1ehHLfi5D5c3Ms++UAYO7Nf7DrK4p2lSBCswbJ9W3luhE6I3A9l/12Jmtb9bJ7HX66sdXBTbfSFZhiRkbD0m129pKERkaA/adMthdACZKrXRo5L7mYfx1ByuhVAiyFkA4fUk6GbNqFpWTBSabRQIqDSKuFFf3y6q9Fejazrlno9cs2ejdpeHHqRlQcy+bkLq5DMXlFiycrG7yaG9jiaKIi0VmxEQ5/yNPFEWUVYpQB1f/rVR9kd10uAITBwTjn5vLbBZ+WDg5DLG3Ka1lV/Ft/fHcWH8cyzPgs81l6B8fUO88yXKVVVa/Zrp1kPclJtBfQP/bpdvWnDpMEKQTcU0W4Hi+0XpEssrQHvUfzWlRnHCOErkXX9leanWmDusOVmJvjgHvpmutZ0431vqDFdaz51WB0nRTDYlv648HB1cHgBPnTdDbWaWJnOvEeSMWp5Vg+e5ynL1iqnV9aJAC9/Wu/j2tOVDh0FnzxfWcLBelVmLeODXatlJAEGxX/2ouNVcda84P49bhSjw1OhQBNfKUtDJj881wcOtA/8Wi+vuluNyCL7aU4W8/aK3BGbBdJKAumgvOmwWnrNKC0zVmp+jdRf7osSiKWLVXh6IyqRThs81lKNW59gv30XNGvL68FF9ud+5jn71iwvtrtHhrVSlMZhGiKOLIWQP+sqIUn20uQ95VMzYcqqi18EOXNn52z1fo2SkAi6eH47FhIdYvybfOliFXVaivog5u/BfRqiMKk+4Kxvh+QfUehbm9HcfmyHcxPHupmtOTmS3A3pzGTwd1/poJPx2pXhVpenKw7JNSRt0ZhNahRtyTGIiXJ6tlz+NJzefoWSOullqw9ai+1vRtVe7rFWSdk/i6VlqJr7FsyzZq/55jb/PD7BEh8FPenN6wmWtcK2zCc/O+zvrFB+DRIbZfGC1i8z3GvUmBUN78kxIAjOhpf/TQZBbx0+EK/OnbYmSerh7h9RNMUMIIhWiEUqxEgnE7AGnk9/f32dbaxjpxjuFjedUzVMS1UTaqdlsQBDwxKgQhN393xeUivthaBovFNV+49UYR3+2S6k33nDRgw6GmTylnj8Ek4qP1WpwsMOFaqQX/zdBhycpSfLKxzOaL0KUiM/wU8v9W2tVY4Keq5jh9fwXeW9O4QZMKg2idAz7IH7VmSpGrauqw8QNU+PPD4fjrzPD/3969x0dVn/kD/5wzl8xMMklIQggECIJcwlWhIqh4AcELWlCr1dJubd2ytbbsqm1XZd22btHubm1tdbdt6vZXu6Vu62qhK2oVvKEi3lBQAoIglxACuU9mMrdzzu+Pk8yck8vkzMyZzJnJ5/168SJzSebkZObMM895vs+Dm5Z4MLpY/5xobOXYcspf/GiYo+Q+yZP3D0dw4wWK4dXesqzgdy/7Y2+I08bZcUGt8VP6giDgC59pxayZYwx/D5lHURTdSO7BMoFup4Ar5rvwxBtqwLDl3W6cX1swYE37YPr2OB5IJpvZ+zMYPAPABbUu7DkSidXxv/yh2iWgdxKWoqh1omIKbeamVDnwndVefHQsguoyW79yhT1HwnjhgyCONUu6rCAAfKayEdc3rEKbPAr7xQswXX4NU3buBUrq4Kldg4VTC3oyuur3VRRnri3YOWc6MapIxK5D4ZTqzsu9NtxyaSF+vqULAFB/PIqn3+nGZweZQKgo6iK59oCM5fNcWHFW6vXcz76nlrcB6nCbqw2UzaTCade/1l7dG+pzO7BsjgsrznahqV1K6bXy4dFIbMRzJJpcFxNfd/z5VeQ2L29W7rVh6Rwbakbb8OBmn9rW0i5YapEokdn47M5RgT5tjf7usuQmvcmKuvjq6GkJggB8SXNa0KgMdEXLe3/d1Y039o7CuGO+WPZu5ngH5tSowa/REbFHTkto96vPgcICIeEp0ktmu7D1gxDa/DJ83QqeeM2PL11ifJSwrmwjQaeJTC3CWjjViTPGyDh5ugMlnsw0F//yJYX45GQHOgIKOgIKNu8M4MYlama3rUvGPRs7UFooYkKFDbf1jAI2arD9IskK/vBqAM0+/SfhsaNsuGmJB7XPzAeiR1AGYIr8jnqjDHXhUU/95KgiG1q71GhKrWfNTABttwmYUe1Ia9rcnBonrlzgwjPvqme7nn4niClV6kLI7rC+RvhQUxRHmtU+0U+80Y3dR8K4ZZk3qU4nANDULuGvu+Jn165d5EmpD7NRF8924bn3gvBpPgjZROCSOQW44mw3invO7BVVpdahaFKl/nWeTICqrXf2GpiQmazeLib5OGaaqC8GzzkoEJJ1HQgevLk0dlA26sjpKN46EIaiqAs//KHMvfGS6vV9Qfzvjm4ATpzoiJ9uLbALmFPj7NcGKtGiu/c1i7fm1DgS9nd22AUsmubAs7vUTNir9WGcVxsx/OZmdEDIyXYJ9cciONAYxZKZBaYNdrhivpoprK8/itLCcab8zL48BSJuON+DX7+gLp598cMQFk8vQE2lHW1+GZKslr0Umhh07D0W6Rc4z6lx4BuX93wQNjBcQTsmvG/m2opWnePGoZNR7GuIQgHwyLM+iIJaPvPVZfEPdPsbotAOrtzfIOHex9uxeqEHl8wpMNTPXFEU/GG7H9GeXXzGGBvOr81spw+nXcCsiY5YD3RALZ26brE5vY77tgfsG0wnoss8Z+gDRL6OmSbqizXPQ/jkZARb3gmkvCAvE1o0b7hVpaLhwFk7RjnWLB/qwpV0F3ipE7K6h6XO7dl3u/GvT3Xizf3Boe9sIW8fDCe8XTvAIBJVx1IPRts2zMjiLYddH2wYXVwmywo6A/HnTaLgeftHIfxhewBvHwz3W3mfC84504mZ49VgRFGA/37FD1lWYhl+YPCylVTMqVHrrXtP+DhswMoFrvgZpMGGKGiu1wfP1u96I4oCvra8CIU9FWJRCQhH1Q+DkqYGenq1HY4+n+VDEeCPr6t1xIcGWCDb165DEew9pt5PALDmwsKUSm+SdfHsAjh66v8ddmOdSYyyiULKH5j0ZRs8bUiUDmaeE+jNBEaigPBWEDPG21E73oHx5TaML7ejtFDIyrhdbfA8UJ1ji0+CTRR0gU6LT8JvtvnxhQs9qC6z61oOpbvA69WPgvjj6wGEo0Bzp4wvXZze0IDBRCUFv34h3mP64MkoTrRKuGaRJzfGHmve5wQBuGS2E+VeGyaPUff99Go77CJi43l3HQqjZaHUr0Xa6U4JDT0fUuw2YFaCYRW9Zk5w4C9vxz9sjC01dpahs1uJ1cUXuYSEi4ymjrPj+Q/Urw805l7wLAgCvnBhIb7/xw5EJbVcpdkn66YLmhk8A+oCwprRtoFLdQwMV9BmEP0ZyDxLsoL3Pglj1sTEfZ2TUewRMX+KE9v3xj9MChDQ2iVjdM/xTDvIxO0U8NKHQTS2qX+HY80SfvRkJ+ad4UCFV8T8yU5MHRffb7KioK1Lxu9fjbfgvGh2AWpGD8/bnXbbza7/BwCvW0RXUH39+wKy4UXeurINE2ueiUYiBs8J7G+IItqT4FCgLnKpPx7PeBQWCCj3ihhTKmLZXNewnapq7tQEz179QfDDo2E8+oIf48psuHOVFzZRwMHGCP7zuS74uhX8xzNduOdzxaYu8BpTakO4Z7e8+XEI1y5ym15X6OuW8avnu/plyJ/dFUJ7QMGaCwst3/GjQ5PBvWmJB5fM1ndemFLlwK2XF6HuebU3czCitvX67upiXT27ttNKbbXD0OK/KVUOVJfZYkG3x+Dfp7RQxMN/OwptfnnA8cFa2rrro6clhCKK5f8mfY0pteGqBW74umWsOtcDt1NAmz9+BiATEwIHrRU3MFyhyC2gwK5Ol7Qlu2n1G4cc3HCwMYq6F/ywicCiaU7cvNR4rXwi588owI794diH92+tLIoFzr20+2XJzAK88EEQT7/TjXBUPR73nn155aOQrjd3MKzgrv/u0P2seZOGt4wgk0NYit0CGtvUrzu7jX9g6tKVbeTW65LIahg8JzC92g6biFjNXF/+kAJ/SMLRZgnvH44MOFwhE6rLbbhoVgGaO2XUaGreWnwSHnmmC5IMHGiM4s87uzGmxIaNr/pjLYpau2R8cjKKeZOcph3gp42zY3y5DcdbJISjwGv1IVxm4rSzUx0Sfvp/Pt2HBq0d+8M4clrC1y8rwthR1qzbjkoKTrbFS1oWnjlwqcWcGif+/iov/n2TD7ICHG6S8L9vxBevAejTZcP432/+ZAeqRomo8NpQmkSNvMspYKxz6P1a5BIxrswWmzh4qCmadt3zyXYJf3mrG26nAFvEjdratH6cISv7dGNIdTS3KYYYrnDZWSlOFqzfqM9q+46ol3sfs8euQ+pzTZL7l/6kIz4y3diHd7tNwBXz3fjMmU48/moAezRlQVE5cdcJAeqHudmDVMHkGm3W2NdtvFRn6dwCzJrogC8oY8IwDIshymd8BSUwpcqBb6/24qOjEZQWipAVoKFFwvEWCcdborHJbYBau5dM26B01I53DBiUlHttuPocNzbtVFslaVeZA2q24dbLizBtnLnbKAgCls114bGXehZb7Qnh0nkuQ4t6jCjxiLrs6oW1TpQUiTjcFMWHR9VM9IlWCQ9u7sQDXyw19U3eLKc65NiHsKICKWFm/syxDly32B1rebVtTwhnjnXgM2c64e8zrCLRNMi+BmsLZqapY+040ZPdPtCYfvDc6pNjteITStOfsJaKRENisi3lcqXt6/XlIIB6WdPJQ1EU7NKc5Tg7icEoRqTy4X10sQ3fWlmEZ98LYtPObigA7KK+7EwQBHicQKDnM6bdbm7f8WzzeuJ/c18SmedUp2QSUX/5c0TJkMEO8Iqi4N83deJAoxooiKI1DtBXzHfhk8aoLjMDAKOLRdzxWW/GesGeO9WJJ3cE0BVU0Nol4/3DESyYYs6bbYFDwDevLMKDm3244XyPboHca/Uh/OFVPyIS8PkLPJYMnAGgoSUe8FYUDr3Yafk8Fw42RmPBy2MvdWF8RQnCUQVjSm1obJNwRmVywyqGw9Sx9thCx4ONEQDpnYHQdpUpcGRnQVwma56zxkAnj2PNElp7fnenXT3DZAWCIODKBW5Mr7YPmLl2OwX87G/LDLd9zDXFmsxzZxKZZyIyjzWOhjlIEARUl9txoFGCwwasOGv4ap4TEQUBX720EPf8vkNXo7poujOjQxQcdgEXziqI9XDdtjs4aPAclRTs2B/CoaYoJlTYMa5Mv12SpK5UP3wq/sZX7rXhvptK+vWyvqC2ADWjbdhzJIJzzlSX8Kfzpnm4KYrndqkZ3+XzXP2GWqRqbJkNKxe40NAqoVj0DXl/QRBw89JCHH+iE6c7ZQQjwC+f68Ld1xXjvptKcKqj/1CNTGhqlyCKasbVyESyqZq6509ORtWBCSlOMgPUtoy9CuzD34qtIyDHxtcDiftc5xTvRLVUY6Dre7y4J37mKiqp7S2tcIzrNVTmOpN1x9mkHaudTOaZiMzD4DkN1y324PPne9IKDjKhyCXiixd58F9b/VAU9bTlrAmZfxO5ZLYLf90VjNVcHz0dxcTR2kVkUbzwQRC7DocRiiXGB27fZhPVQS7afseD7ecJFXZM6BlNrO2VbBPU9mNrLjK2mFCWFfznsz609yzs++DTCL6z2pw69vHldozvqTOsrz9u6Hs8BSK+flkRHniqE1EJaGiV8Ph2P25eWoTKEhsqS9LerCFtfNUfWyR7+9VezBzieVTmtaHcK6LFJyMcVbOXZ4xJ/TCjnS7osg9/lu2vu/SjnE+0WiuAlGUFnzRF4Q8qCIQUnDfD4JRQA508DjbGz5D0trO00u8+UukyzwHjrwlZViByshWRKRg8pyGZEcdmOdEq4ckdAZQXizij0o7F0wd+s1w4tQDlXnFYT1uWFopYMMWJtw6oAfHW3UHd4AN/SNEND0ikd4GjlGQtubZXclQBdnwcxoGTUXxhiSc2xW8woiigptKO9k8jsW3YfcT4MJFMmDjajpuWePDfL6tBzuv7wugO+1I+07Hl3W40tUto8cn4+6u8cA5R5qItWSg22Bt26lg7Wnzq3/lAYySt4LlbFzwPf5atsM84cKsFkAqAf/+zL9YF8dxpTmNrDQx08ghHNVPy0mxnSeapKrVhycwCFLuF2AfyoUSiCm77dRsKCwSUeER87/PFudHek8ii8uQc5MjR2CZh95EIXtoTwjtDDN2YUuXAlQvcw/pmv2xufFHXwcYo9hyJb+PUsXZ4+gQjAoCJFTZMH2eP/ZtYYYMgqLcl+6bd279a+yjNnTJ+vqULv3jOF6vhBKD7utfSOfpFaUdOpTc8Rqd+I1A3CTOemQXUTVIvG7CktgCLpqmBvwB1+MODf/GlNLjntfoQduwP4+MTUbQM0r1Ey+hobi1t6caBxjSH72hrnrOQeZ4x3gGHPT7wwmoBpE0U4Na8prQ14kM+32rXAGs/Be6U1f81gXNUUnStFdetHJ5OQsOiZ7/gQTGp16FVjC2z4W8uLsTqcz34zCBde/ryBRUoijpUpSsoM3AmSpO13gloSM2d8XZn5cXW++wzeYwdY0eJaGyT0dwp4xd/7dKVXfzNxYWo8IqISAo+PjF4VjzVuuXe/tX7GiIIhRW8sjccCyjeOxTBR8fasWqhB4GQjOfeC+LvLivSLUCcOcGBzy1294zRBj46FsWHR8OYPTHNxY+a1mACMGhrsIEIgoAvXlSIdr+M/SfUscXJZuR7lXvFWMu/Zp+EsWWD18EHw0qso4zd1j8LO5ja8Q5cOd+FqePssQEwqdLW7Rc4hj/zbGY/9Ewpcgmx53hXtwKvG2k93wD1A2fvcJyyItG0UetZZ7BFX77p0iwszNRobqKRhK+iNEiygobWKOqPR/D+YWPlCOnSTRf0WvPP1zssQ0E8yOu1YIoTNZV2nDk2cVY8naz5lCoHVi7w4NrFhfjhF0pw3ox44BuKAH96PYCn3wkiKgOPbw8gGNEHZZed7cbCqfHv+f0r/e+TjDf2hfDDlybit8KPsEe8NH5Db2swAwocAlaf64a9Z+xvqqfRtc+ZZl/iTG6bX99lwmi2anSJDdcs8mD2RGfaU+n8mgWD2ah5BrJzBicZunHNvfsrUSs6A5o64h/Sx5Ra8ziTkjT3S67iaG4ic+XRUXH4hSIKvv8/nfjJX3x49IWuYXlM/XRBa/bsPH9GARxpBnlm8bpFfGVpEb69youxo/o/3QscgD/YPyj7/PmeWKa1xSdj085Av/sY9enpKI5gFl63r8FRca7+xsFahg2gNwu6aqE7ls1PlrbjymBDZ3pZob+xrlVdFmqec4E2kxibImegFV0ibqeA+ZPVqZTVCc5O5Jw090uu0o3mZuaZKG0s20iD2ynA3jOBMBTFsIwj1mYLKyxYtgH0BHlJTA8bDtOrHfjnG0rwyDM+fHRMzYQLUBdYDTQ4oNgj4vMXePCbbT2DX3aHsHBqQUplCA0t8SxetbxXf6OmNZgR6bbf0j5ntCVAA9EuFrRC8JytzLPVFWozz73tCw20oktk2jiH6cOULCHN/WIVf93VjcOnovB1K7jxAk+s29BgmHkmMpc1o68cIQgCij3D17BeURS0+OIBj1WDZ8Cap7rtNgFXn+OOZcXtdmBG9eDbt2iaEzMnxEtQfveSH1Epueynoij64FnRBM99WoMNh2TKNtr7lG2kIhRR8PGJ5Bc29lq5wI1rF7lx2dkueJwMngeizSTGMoxLNqjPL60sPN8sJ0/2y/6GKN79JKIu/B3idQz0zTwzeCZKFzPPafK6BbT2VGz4uhWMLs7cY/m6FYR7yofdTiHtetKRKJmsuCAI+NJFhfj+/3QgFFUXzfm6FYwqMv7m0xFQYh0jXLYoygtFKF0ChAFagw2HlMs2khwOoigKfvIXHz4+EYWsAD/6UklKo4G1fYvr61m2MRBt5tnfm3nWtKJTfEez9nyzHAMt+nKBfkT30MGzNvPsdfN9gyhdDJ7TpB6I1MxiMg3rU5ELJRu5IJnSh4piGz53ngfhqIJlc13GeuhqHNeM5a6ucEG87jDq6+tRW1ub1M8xS7FHgN2mTowLhBQEQvKgH8LadDXPyf3egiBAEBDr2HCgMZpS8ExD056G12YYUbsGqF2DfVl8vllSz37JZV7diO6hP1RqnxdFzDwTpY0RWJqKk8wApKNF26bOop028tHFs11YcZY76cAZ6FPvXJ794FEUBF3pRqJTvg6bEOvLnUrZxlTNaPMDJ0zsl006RQXaso30s/NN7RJ++2IXnn2vG3uPpV5yQ5lT7E7ufUf7vChi5pkobcw8pynZDEA6mHm2hmR6UDe0Wit4BtRs+sn2nl7PnTImVAx8v7Ur1OmQoYgCWwpPt6njzBuWQoMrLRJRXWZDkUsw5Tl2vEXC6/vU1ptzahxDjmSn4ad93/EZeN/RBtiseSZKH4PnNOkyABku2zh7shOlheqQiylV/NNlwycn1el+0Z4a6DtXJW4Zp8s8W6Tl13kznJg5wYEKr2joeZRqB5kzKu2wieqY88Y2CV1BOakBDYebovjDdj88BQImj7Fjmjelzch7k8fY8f0bS0z7eSfbNT2eS/gh3YqKdcHz0O87d19XAl+3DF+3gspSaxyHiHIZI7A0FQ9j5rmq1IYqHviy6tn3goj0JFGjQ0z5k2QFJ9rigch4i2SezzmzYOg7maDAIaBmtA2HmtR9cLAxqpvmOJSOgIxPT6nfaxMFBs/DpEkbPPN4Y0naBYOdgaHfd9xOAW6nDZXmfcYiGtGYVkiTN8kMQF6o3wjUTcKMZ2YBdZPUyyOERzOiWhASD4A53SEj2hOHlBYKKByBwwnSqXv2a3o8Gx0NTuk71R4/jjF4tiZ90maEvO8QWQgzz2kq8QgYVSjC6xFGRla4fiPw/FogGoAAqAMHnl+r3pbjK9iNWDbXhR371XpQhx2oGT34S6iyVMT9XyxBQ4uEUBrjvbOhqV3Cp6eiKC0UMabUlvKQlDPH2vHX99WvDzQmt/isWxM8exg8DxvdaG6WbVhSUZ/BOLKsQExhQTMRpYbBc5rGV9jxb18uzfjjKIoCQbDAwXH7eiDaZ1R1NKBePwKC54kVNpR7RbT4ZIQiatnGrIkDl22IgoDRxTaMLrbuhypZUSAA/Z5bHx2L4PHt6t/5wpkF+NLFhSn9/DPHxg8xR5ulpKZw+kPxjBqD58TePxzG6U4ZXUEZl8x2pfxhxx+UY50ZnPbk+3vT8LD3dMIJhBQoinqWxjvI5MBgWIEvKMPrFlFg7/9aJ6LkMXjOEZ+cjOKRZ7pQXixi5ngHrlvsGfqbMsF3NLnr84wgCDh7sgNbPwgBAN47FB40eLayXzznw/EWCa0+Gf/25dJ+b7ztJo3mLnKJGFdmw4lWCZIMHGqKona8sf2lzzwziEvkmfe6cbintnzOREfKf7OmjvjffXSxDSIDLcu65ly1fWaxR0j4gXTv8Qh+8Zw6yWv+ZAduvZyLB4jSxXekHNHcKcMfUnD0tITTmn7Pw847Mbnr89D8yfFFb7sOhyHLuVWSAQBN7TJOdciIygNPGtQPSEnvMDF1rB1et4D5kx1w2o0HY36WbRhmVq9n/WJBvj1Y2cWzXVgyswDzJjkTvq66NDXRbidfR0Rm4NExR+h6PGdzUtuSDYC9T9bb7lGvHyGmjLHHWhT6uhUcPNl/IVxEUnC8JYqoZM3AWtsnvNnX/8OYdjR3MuPIB3L9eR48eHMpVpzlwv6GCD45aaz2OaANnvmmn5B+yqBZwbN1y43IOF+Qo7mJzMayDRN8cjKCxjYZnQEZ8yc7UTXK/DedZu10wWwOSOmta96+HorvKATvRDVwHgH1zr1EUcBZZzjx6l61dGPXoTCmjdOXIhxvlnD/k52wieqgiduusNapUl3wPEDmud3EzHOBQ4j3x5Z6+mN/NnF/bAAIhPWZZzmU1mbktULNAjJ/MPXuC03stJF3tJlnjuYmMgeDZxNs/SCEdz5ROzCUe8XMBM+6zHOWswe1a4DaNdhXX4/a2trsbkuWzJ8cD57fOxTBDefrF3Qe7xmOIsmA3YKr4LVnL4Yq20hlNHdf+xuiiEqAogDSEP2xewX6tKrzpb0V+Us7fMaXRub54tkFmFRpQ1OHjJrRDJ7zQRczz0SmY/BsAm3DeiOjUlPRYpWyDQKg9nfuXe3e2iXj6GkJNZXxl1NDa7yUwypjubUSlW10hxWEeiorHDZz6o2nV9tht6mBs82WuD92r4Cm24abwXNCRbrMc+rHoOnVDkyvzr0FsCPR7k/D+Mvb3fB1K5h3hgNfWDJwRxztexIzz0TmYPBsgkw3rJdkBa2a4DmrZRsEQG0VNbfGgQONUcyf7OwXYGrHcltlsqCWNnjWfjAD+nfaMKO1VUQCzp/hxLFmCefNKBgy6wwAX11WhK6gjEBIQbFbxKm0tyJ/aTPPXWmUbVDuiEjAkdPqcaa9rRtiAAAgAElEQVSta/C/uU/zfPA2PQ88/021O9IILLkjMguDZxMUaxbrdAbMf+Nq65LR29ChxCMk1bGAMmfNRYWD9k1taI0Hz9VlFgyeNWcvWjplyIoSa0umK9kwqc/v2wfCeHWvWtq0cKqx7zHa0o76D80gC6vfqPbFTzOA1baX9CUY0d2lzTy/+W0geqTnm0bWgCsiMzGFaQL9iG7z37i0mcHybNc7U4zLIQwYOHcG5NjzoMBuzTMFLqcQC7iiMtDhjz9vzVws2KtE83M6MvABc6TTBc8c12xdvRNafUcAKPEAtn5j0j+q2GPsjKf2TIQ32qC/sXfAFRElhZlnE+gyABl449Iu6Kqw8LQ6Uh3XlGyMK7fuoIlyr4iuoLqtzT4plmX2ugXMnuhAu182beR8iWZdQIefwZ3ZCjVlG9r+2Mn485sBvHcojDGlNiyd48LMCcz8m87ECa26M56DvO+EIgrCPcsv7EoQBejqf6cRMuCKyEzDGjw3Njbiu9/9LpqbmyGKIm644QZ8+ctfRnt7O26//XY0NDSguroaDz30EEpKSoZz09KizwCYn3nWLuhi5tm6AiEZngIRx1viiwXHl1n382lFsS1WM9nSKWPqWPX6OTVOzKlxJvjO5JVoXyMJTjFTaopcAuZNcqDIJejWYCSjoVXCyXYZJ9tlLJxq7t+fepg4odXtFGAT1Y4+oYgaKPedNNgdVlBYIMAfUlAkdGDAj/EjaMAVkVmG9Z3dZrPhrrvuwqxZs9DV1YXrrrsO559/Pp566iksXrwYa9euRV1dHerq6vCd73xnODctLcW6sg0ZiqKYssiq18oFbiyeXoDmTtm0GlQyz/a9Ibx9MISPT0Txwy+U6OudLbhYsNfKBS6sOMuFCq/Ybzy32bTBs5GyjY+ORvDo1i54CgTMmjh4JwFS2W0Cvnller3ET3VwQErGeSf2lGwMcH2SBEGA1y2gvafkqisoo8Ch/7uVFop46JZRkGQFwQ+3Ai959JnvETbgisgswxqJVVZWYtasWQCAoqIiTJ48GU1NTdi2bRtWr14NAFi9ejW2bt06nJuVtgKH2tILAMJRINR/4Fxa7DYBlSU2zJzgwNgM9JCm9Ow6FEb98SgkGdh1OKLrtGHl4HlChR2Tx9hR7DGno0YiurINA8FzV0hGV1DBqQ6ZmephIMvqvu41psS6z9ucZvKEVl2npwSvE5sooHDuDcCKOsBbA0BQ/19Rpy8Xqd8I1E0CHhTV/1OoxSYaCbJ2Tvn48eOor6/HvHnz0NLSgsrKSgBqgN3a2jrg99TX12dkW4LBYNo/2+2oQERS33B27TmAUnf/kcf5xoz9lg+q3C7sgVpm9PqH7XDaFLgcDgQjIgLNn6C+U/+mNhL3myQDwBgA6oLKj/bWI9HsmEMNbgDFAIBwdzvq64+NyP1mBiP7rb3bBkmuAAAUOiUc/mTfcGyapWXm+TYfxbO+j9H7fwpH8CQiriqcnn47OjEfSOGxBLkUQAEAYO/HRxBsDQ/5+FjyrP6qnsctbngaY/f8M0Q5qF7vOwL5ub9FY8MJdFZfZXib+DpNDfdbarK137ISPPv9fqxbtw733HMPioqKDH9fpqbZ1ZswKW9ekx/+oIxit4jp085E2QgorzBjv+WD8ZNkbP24HYoCnOhw4t9vLkWxW0Bnt4IST1m/+1t5v0mygj+9HkBpoYhRRSIWTSsw7WcX7WxDV1CBAgETJk3XrRXo61CgG0A3AKB6TBlqa8dber9ZmZH99uHRMNCzmKy6ooD7GRl8ndbWApeqZYlOANU9/1Ix7kQXjvQEzKUV41Fbm8brdfsVQG/g3EOUg6g+9B+ovtR4GSVfp6nhfktNJvfbu+++O+htwx48RyIRrFu3DldffTVWrFgBACgvL8epU6dQWVmJU6dOoaysf8BhdX9zcWZqMkMRBW1dMsq9Ihzs72xJXreIaWPt2H8iCgXA+4fDuGiWS1eqYFWKoqArqKDFJ2NChQ2dAQUv7lHHjnvdgqnBc7En3t2jPSAnDJ61o7nNmHA4Euw6FMa+hgi6ggrOn1GQVLeMpnaWbOQi7xADuo63ROHrVuB1CSgvtsHtTPBaMnExI1G+G9bgWVEUrF+/HpMnT8ZXvvKV2PVLly7Fpk2bsHbtWmzatAnLli0bzs2ytE9ORvHT/1MHE591hgO3XZHeoiDKjLMnO7H/hFrs/t4hNXjOBfc+3hELnO7/YoluoMIok3o89yrxCDjRU5E11DAhBs/J238iEvvgUzPallzwrFssmP9nzfLFBTMKMHuiA8VuYcDF5C/uDmF7vfqcWHOhBxfPTnBcMnExI1G+G9aj5LvvvovNmzfjzTffxKpVq7Bq1Sq88sorWLt2LV5//XWsWLECr7/+OtauXTucm2VpLZo2dS4HgwirOvuMeKCyvyEKf46MSPZq+gM3d8q66YJmDUjpNbfGiQtnFuDqz7hQ7k2c3QyE4tvB4NkY3YjuJFtmNrXHjzOVzDznjLFlNtSOd6C63A5PQf/Xq24091AtDE1ezEiUz4Y18/yZz3wG+/fvH/C2xx57bDg3JWfoB6QwI2RVZV4bzqi04fApCZIM/PfLfnxtRRFsiVbFWUC5V8TBk+rXzZ0yIlI86DI7eL50nvFsvDbzXDhAUED96aYMJjkoRVe2wTZ1eUM3mts1xLGot+uGCaPDifKddSc45JiT7RLe3B+Cr1tBZYmIy852m/JzmzWjuSuGyNZRdo0rU4NnAHj3UAQXN0Yxo9raU9q0H8iafRIUTcI8mz3FA+H4m37COk2K0WeejZ/5iEQVtPYcZwQAo0v4YSVfaDPPRUZ6udeuYbBMZACDZ5O0+mRseVddqTxtnN204LmFmeecUdinvODQyVwInuMfyJo7ZV37OLMzz8nQZZ6HypgRAP1+SmZEt90G/PjmUjR1SGjvkuGwcX+npX7jsGdvo5KCQEhB8ZHHdY/tw5tQe3roS7RSloXfjciKGDybpFjTWcGXRNZnKBzNnTvmT3Fi254QJFkNSKZXW//lpf1A1tIpw6HZ5NLC7AVRXDCYPK9LewwyHjwLgoBij5Cw+wkZVL8ReH5tfIqf74h6GchIkBmVFHz7t+3whxSIgoxfhP8OYtQPAJB8xxFwq4GzABM+hA7z70ZkZdZ/d88R+hHd5kxEC0cVdPRMjRKF7J5Gp6FNqXLgO6u92N8QxfRqO6ZUWTvrDAAVmg9kzU2NcMvtgDgVgPndNvxBGZt2dqMjIEMUga9fNnjnmPWfK0YgpGbTPCzbMKRIcwzKlQWrg8rVDOf29frx14B6efv6jGy/3SZA7nm7kRURgagTRVCDZz/iLV89BUL66y+G+XcjsjIGzyYpcgkQACgAuoIKopICe5qnP1s09c6jikTLLz4jNYDOhaC516giEaIgQ1ZEdGA0uoTS2G2lDU8C5Z837bEEAXj5I7VtltOutq4cbCw4Oz4kr2/ZRqL9a2m5nOHMQq9kr1tAd88aAZ8wGkVKW8/XZbr7pI19oIlimMo0iSgKugUZXcH0s8/NnfGSDdY7UybYRAGjlMbYZUlQA3+nEoBnxz+a+lhupwBHT0wcjgKhiKk/fsRz2AQU9Hxuk2TEAqqhNLZKutaAWZcow2l1g/VEzmCvZO1Zz06hIvZ1l+brIjPqnbPwuxFZFSMyEw017SlZLbpOG/xTUWZUSIdiX0+RduLC6P/DudITELrMzSiptbXx53H7EINSKHm6jhsGP8A/+JdO/P1/teOO/9eGdr8F/ia5nOHMQq9kbVbZZ4sP+vahPPa1oU4bQ2EfaKIYlm2YqNgt4ETP175A+plnSVYPjL5uRdcVgchMFfYWHJQjKFOO4fLowzhLfla9wVtj+mOVesTYh8LOgIyqAXoKRyQFkgQUOJCbZQdZdMnsAkQktYysb/eXgXSH4+sqAiEFxWYEWenK5Ul3WeiVrE3a+Gq/BRx+C/AdhcNdjImeDnRhFMrMWC/DPtBEMQyeTWR25nnZXBeWzXUhFFFii0KIzHbjBR78zYtnQox2xa/MUEZJ25WmY5As5+5PI/jlX7tgE4FzznTilkuLTN+OfJVsi8xTusmCIkQrrKtYskFf8wzkVoZzmHsla19TnSULgLWfAgDm9fwzFftAEwFg2YapMtWursAhcFAEZYxrzo0QV/yyJ9MsqP+vqMvIm2SJpoNHxyBnZ3rb1EkyIPIIlVFNHfHg2TKTBWvXqM+/YXg+5gNd0saEM55ENDRmnk2kzzzzIEY5ZJgySiUebfA88AdM7eI1tqnLLO1Ybkt1OGGG0zBdzbOJSZuk9bQXnOE7CmxnSQflNwbPJjqzyo4V81zwegRMqeKupdzyxBsBNLVLGFUoYukcF8aWmR9MlWjLNgYNnrUDUph6zqSmdm3mmfs6F2VixkDSNO0FBSC32gsSpYARnommVzsw3aRxzB0BGS/uDsLXLWPOJCfOPsNpys8lGsiJVgnPvx+MXT53mhOA+cFzsZHMc5jTBVNVfzyCFz4Iwh+UMXOCA6sWehLeX1e2YaXMMxmmPeOpbU/4Wn0IiqJmpmeMd8DlyOBriQNUaIRh8GxRO/aH8Mx7ajDz2r4w/vEab04N36Dc8p/P+XSXMzXNslQbPPsT1zwDDJ6T1dUtY88RtYH2UH9DRVF0ZRuWqXmmpFSWiLj/iyUodoso0ATIm98KoL3nNfajL5XA5cjg3zeX2wsSpYDBs0W9ezAc+1pRgP0NUQbPlDFlRaIukNLWJpupoljENee6UVIoonyQ3uUMnlOnHdHdNcQp/K6gEstUFjj0JTWUO+w2AaP7tDJVFEVXwmHKkJREcrm9IFEKWORmQW1dMo42x0+n2kVgejU/51DmOO36wCnd0fKDKXSJuHKBG+fPKMCMQUqctAsGjfQqpjjtiO6hhqRo651LPSJ7aueC+o1A3STgQVH9v37jgHcLRtRuNQDgtEOXkc4IDlChEYbBs8n++2U/fvp/nfjBHztSHnm7dXcw1te5TD6GO3EzprT9ycSthOGDsOVke7sTPX62ty0NNgsdCUbUgkGTnzP6CYOJjz+HT8WD51OdMj45yXnplta7KM93BIASX5Q3wHNG23Uj41lnQNdeUGF7QRoBmM40Wf3xCE539kxQ61bgKUju+/1BGa/s6ULvn+amyF04M/Qc8Pw29Q5mHIw0K6MB5M7K6Gxvd6LHB3Jzn/boW0LxyclI1sqERkzZRgaez0V9Ms+KMnj2ORJVIAhqWZgAloZZXoJFeaEzv4COgIzOgIzKEpvurIN3uKZG9rQX3Fdfj9ra2uF5TKIsyfO0zvBLt+fmyx+FEJLUwHmsvA9z5b+qN/SuXDZDopXRVpbt7U70+NnetjRpg9TeQCpbRky3jQw8Z5x2Ac6elIgkA6EEyeTp1XbYbYAoADYbS8MsL8GivN+97Mf6jR341z/78OHRiO69R9uNg4jMwaOlydQDlXo6NNlpT+Gogm274+3CLo/+HCI0P8Oslcu5ujI629udyuNbfZ/2qB3vwDPvBSFJmQ+kXvggiDf3h9ARkHHNuR6cX6s/PfPI10ahO6wgEFL61WLnlQw9n4tcIlq71ODJl6B0Y0qVA3d+1ov9DVFMr7Yz62x1CRblFfdJ2iiavJj2bAQRmYPBs8m0fWyTzTy/sS8UWyFdJh/HQulJ/R3MWrmcqyujs73dQz1+Lu7THsMZSHUG4gti2/z9XyM2UUCRS0CRK2ObYA0Zej4XuQS0dqlfD9VxY0qVg0FzrliyQV/mA8QW5RV39x2Uoql5Hq6yDaIRhOdzTKYt20hmRLckK/irZkjFcuXXsENz6tzMlcu5ujI629ud6PGzvW0mmFLlwJUL3BkPpnQjugcInkeMDD1ntB03/IMsWt5zJIwt73Rj77FIygubaZhpFuWhz6I8bWlGZ7cMX3AY29QRjUDMPJtMNyp1kAlqAwmGFUwZY0erLwy3U8CSc88H3vg/9RSud6L6hmrWwrPen7N9fWZ+fqZke7uNPH6u7dMsKCkcekT3iJCh57Ou40a3gpIB7vP2wTB27Fd7yX/+fA8unZfvaf480bMory/9WhsFoiAPeBsRmYPBs8mKdZln44FBoUvE3y4vwupzJTS2SSiouRGYfWMmNlE1yEHY8rK93YkeP9vbliNKEozo7grKaO+S4S4QUOQSM9+fNtsy8Jy5aFYB5k5yoMglYEKFHScGqAw53BQ/qzWpkpMFc11xn8zzvEkF6A4r6Aoq/QaoEFH6GDybzOvpW3uWnIpiGyp4sKM8pg+e9a+RPUci+M02PwBg4VQnvra8aFi3LR9M7zN85kSf2wMhGSd7pknaRGDiaL4N5Dpd5jmg4OLZLlw8m2cTiDKFxVAm86aYeSYaKfpmnrW9iEdMj+cs+lQzHKW63JbfHU1GCG3Ns69bTtjfm4jSx+DZZLqaZwOZ5xafxAU7NKK4nAIKepKdUQnoDjN4Hk6HT8VLNs6oZNY5HxQ4NK8pWf+aIiLz8chpskKXgJuWeFDsFg0t1Pif1wLY3xDFRbMKsHyeS9fqjihfFXvE2CTODn98Eqf2g6THyeA5XQNlILX1zgye84f2NZXKdFsiMo5HTpOJgoClc4zVmjW2Snj/sDoC7LldQSya7mTwTCNCSaEmeO6WMRZqnb9+uiBfC6k42S7hP5/tgj8oY1SRiOtmx29TFEW/WHAM11fki2KPiO6wAlEAfrvNj+nj7ajwilgyk7XPRGZj8JyO+o1ptZl6Yoc/9vW8SQ5Ul/HPQSNDiUfTrk7T61lbtlHIso2UOGxAY5ta1yyK+pKwti451n/e5QDGljJ4zhffvcYLURBwvDmKH/ypE580RTGuzMbgmSgDGK2lqn6jftqT74h6GTAUQL9/OIw9R+IZoLk1nPJFI8cVZ7tx8WwXSjwiyoriGWZt8Oxm8JwSXZ/noAJt5cZhzWLBmko7RJH7OF+Igvq31A9I4d+XKBN4XjRV29frx6QC6uXt63VXDbbqedvuoO5yV5ALPGjkqKm0Y0a1A2NH2XS9nJl5Tp/TDth7EspRSV1A1ov1zvnP180BKUSZxqNnqnxHB71+2+4gXtwThK9bwcoFLlx2tlt3F0lWcLwl/iZmE4Hp1fxTELHbRvoEQUCRS0C7X92X3ZF4jmTRNCdKCkUcbopiRjXPduWjLo7mJso4Rmyp8k5USzUGuD4cVXCqo2fVc6B/RvmDTyPo6kk8F9iBdSu9mFLFNzIiXbcNBs8pK3KJaPerJRra4Hl8hR3jK3jYz0f+oIwjpyXdWU1mnokygx9LU7VkA2D36K+ze4AlG/qNSu3rlQ/jB7dlc12YxgwQjVBRSYmN6JYVBYUuEW6nAFFQ+0FTarS1rtrgmfLX0WYJP/0/XyxxAzDzTJQpTEGkqndR4ADdNryfhmN36zso5VSHhL3H1ZINAcCFs9iMk0aerqCMf9rYAX9IQWGBgIduGQVREPCjL5UCUEubehdAUfK0QVPKwXOa3YRoeBUPkGUuYuaZKCMYPKejds2AbybeBJnn1/aGYl/PqXGg3MtWUTTyeJxCrKezP6QgIilw2OJv9DZ2gUiLNmgKRtSvI1EFDqOjuNPsJkTDT/u+E7uOmWeijGDwnAHFmh62vj7B88rPuDG6RMTLH4VwEbPONEKJooBit4COnjUBnQGZHyRNNFDZxgNPdSIYVjCp0o7PnefRtQjsJ1E3IQbPllTkEiAA0J7rZOaZKDMYPGeANgPg61agKAqEnlPQBQ4BS2a6cEEtA2ca2Uo8IjoC6qK2zoCCcm+WNyiP9C3bCEUUNLRIkBWguTOML17kSfDdSNhNiKxJFAUUuQVdqSBrnokyg8FzBjjtAlwOIBgBJFltv1XYp1m9wHpOGuHUUfRq8NwRkHG6U8KxZgmFBQLKvCJGFzMTnar5k52oGW1DkUvEyeOncLQ5CrknpqoaJQ49+jxBNyGyLq9bhK9bfU1NqLCx2wZRhvBjaYaogYGqs5sDUIj60o3oDsjYeyyCXzzXhR9v9uGZd4MJvpOGMqpIxJljHagaZUOBXdEPRxljIGeSoJsQWZc2WL5ukUe3joCIzMPgOZH6jUDdJOBBUf2/fqPhb9WXbsh4rT6E/Q2RQScOEo00JZoPmB2v/hSBrXfFLrPHs7kON8XHchuaLFi7BlhRB3hrAAjq/yvqcqfeOY1jdy4r7vO+Q0SZwbKNwaS52lybATjVIeN/XvMjFAHGjrLhzlVeXeBANBKVFGqC54gTHpTELns6PgCwOAtblZ8On0phLPcg3YQsbwR3CtG2qxtoxgARmYMR3GASrTY3YNVCN+65rhg/+lIJgmEFoUj8toH6cRKNNLrMs1CFgKAJno88lY1NyjuhiIImnx0tPjWQstuA6vI8ryVP89idy7RnPI82SwnuSUTpYOZ5MGmuNh9fru5aRVHwWn28t/NFswq4WJAI+prnTqESdiX+OvEED2djk/KGoij4+/9qR3dYAVAeu76mwg57vtfBjuBOIZ3d8YD57QNhXDI7gilVnGBLZDZmngcz2KryJFebf3IyioZW9YDmtAOLpzvT3TKivKBdVBuCB92azHOhi6+TdAiCAOcAqZFJY/I86wyYduzORYWa1nQKgP0N0cHvTEQpY/A8GJNWm7/yUTybdu7UgqFbRBGNEOVeEfcvehn/EZmKH4QugF8ojd3mOesLWdyy/DBQj19DnTZy3QjuFDJrggMOOyAKaonO9OoR8PcmygK+sgbTu7Bk+3r1dJ93onrwTWLBia9bxpsfh2OXOVGQKM4mChg9/1rA3Q1sX4/usKbmefoVWdyy/FDk6l+eYXixYKbVb0zr2Jrw+004dueqKVUO3PlZL/Y3RDG92s6SDaIMsciR1KLSWG3e1C7hn/7QEbs8qdKGGqu8cRFZSc/rLPCbNiCotnJkq7r0aUczr7nQg8oSG0YXW+DMV7rdMIx8f652CjHBlCoHg2aiDLPAkTQ/ne7Ur3SeOZ4HM6LBKIqCQCjeA53Bc/oKNSViigLMnOCwxmLldLthjOBuGkRkDUyFZsjR0/qFGrYRsE6HKFmhiIJ2v4wWn4wyrwiXQ0BUUvK/I8Qw0GaefUELDWdKtxvGCO6mQUTWwOA5Q6ZXO2C3BRGV1IUbsyYw80zU16adAWzdrS6q/dxiNy47253lLcof2gWD/qCFBmZ4J6qlFgNdPxzfT0SUJpZtZMiUKge+vcqLa85149urvKxBIxqAbspgwELZ0TzgcsQzz33PhGVVut0wRnA3DSKyBmaeM4gLN4gSK3Zrg2cLZUfzQLNm3cXBkxI+OWmRgRnpdsMYwd00iMgahjV4vvvuu/Hyyy+jvLwcTz/9NADg4Ycfxp/+9CeUlZUBAO644w5cdNFFw7lZNNzSbVOVrZ+dbXn4u5VqM89+CwbPObzP+64N3N8QtUbwDKTfDWMEd9Mgouwb1rKNa6+9Fo8++mi/62+++WZs3rwZmzdvZuCc73rbTPmOAFDibabqN1r7Z2dbnv5uxZoR3ftPRPFafQhHTlmkxCDH9/mcGgccNgBQ4ODADCIi0wxr8HzOOeegpKRk6DtS/spkm6l8bmGVp79biUd/CHrsJT927A8Ncu9hluP7fEqVA3eu8uKCyV24k+suiIhMY4lUxMaNG7Fp0ybMnj0bd91116ABdn19fUYePxgMZuxn57NU9tsM31EM1IRM8R3FvjT/Bpn82Way2n7LJkUBRKESshL/7fy+FtTX9287Ntyv03zZ53PHBBFuC6C+Ldtbklv4vpAa7rfUcL+lJlv7LevB80033YRvfOMbEAQBP/vZz/CjH/0IDzzwwID3ra2tzcg21NfXZ+xn57OU9tv2gdtMCd6J6f8NMvmzTWS5/ZZlJW+3o01T71xTXYnaWle/+w376zRP9jmPb6nhfksN91tquN9Sk8n99u677w56W9Zb1VVUVMBms0EURVx//fXYs2dPtjeJMimTbabyuYVVHv9uJYX6/K7bKtMF83ifExFR6rIePJ86dSr29datWzF16tQsbg1lXO0aYEUd4K0BIKj/r6gzZ+V8Jn92tuXx79a37rnQKsFzHu9zIiJK3bCWbdxxxx1466230NbWhgsvvBDf+ta38NZbb2Hfvn0AgOrqatx3333DuUmUDZlsM5XPLazy9HfTtqsDAI9Vgmcgb/c5ERGlbliD55/85Cf9rrv++uuHcxOIyGLWXOjBxyeiaGxTh3p4nBYKnomIiPrIetkGEY1sgiAgEIovGLRU5pmIiKgPBs9ElHWBkBL72uPiYYmIiKyL71JElFWRqIKIWrEBAUBB1htoEhERDY7Bc6bUbwTqJgEPiur/fUf6DnV7ph+fyCIONEZjw0gUAIeaLDKem4iIaADM8WRC/Ubg+bXx0b6+I+plQF25P9TtmX58Igv59FQUiuby/oYoR0kTEZFlMfOcCdvXxwPXXtGAer2R2zP9+EQWMr3aDocdEAXAYVcvExERWRXfpTLBdzTx9UPdnunHJ7KQKVUO3PlZL/Y3RDG92s6sMxERWRqD50zwTlRLJQa63sjtmX58IouZUuVg0ExERDmBZRuZsGQDYPfor7N71OuN3J7pxyciIiKilDB4zoTaNcCKOsBbA0BQ/19RF1+sN9TtmX58IiIiIkoJyzYypXZN4mB1qNsz/fhERERElDRmnomIiIiIDGLwTERERERkEINnIiIiIiKDGDwTERERERnE4JmIiIiIyCAGz2S++o1A3STgQVH9v37j8H5/OrL52ERERGR5bFVH5qrfCDy/FogG1Mu+I+plwFjrvHS/Px3ZfGwiIiLKCcw8k7m2r48Hn72iAfX64fj+dGTzsYmIiCgnMHgmc/mOJne92d+fjmw+NhEREeUEBs9kLu/E5K43+/vTkc3HJiIiopzA4JnMtWQDYPfor7N71OuH4/vTkc3HJiIiopzA4JnMVbsGWFEHeGsACOr/K+qML7hL9/vTkc3HJiIiopzAbhtkvto16UzuL8IAAAuqSURBVAWc6X5/OrL52ERERGR5zDwTERERERnE4JmIiIiIyCAGz0REREREBjF4JiIiIiIyiMEzEREREZFBDJ5pZKnfCNRNAh4U1f/rN2Z7i4iIiCiHsFUdjRz1G4Hn1wLRgHrZd0S9DLA9HRERERnCzDONHNvXxwPnXtGAej0RERGRAQyeaeTwHU3ueiIiIqI+GDzTyOGdmNz1RERERH0weKaRY8kGwO7RX2f3qNcTERERGcDgmUaO2jXAijrAWwNAUP9fUcfFgkRERGQYu23QyFK7hsEyERERpYyZZyIiIiIigxg8ExEREREZxOCZiIiIiMggBs9ERERERAYxeCYiIiIiMojBMxERERGRQQyeiYiIiIgMYvBMRERERGQQg2ciIiIiIoMYPBMRERERGcTgmYiIiIjIIAbPREREREQGMXgmIiIiIjKIwTMRERERkUGCoihKtjfCiHfffTfbm0BEREREI8SCBQsGvD5ngmciIiIiomxj2QYRERERkUEMnomIiIiIDGLwTERERERkkD3bG5Btr776KjZs2ABZlnH99ddj7dq12d4kS7r77rvx8ssvo7y8HE8//TQAoL29HbfffjsaGhpQXV2Nhx56CCUlJVneUmtpbGzEd7/7XTQ3N0MURdxwww348pe/zH03hFAohDVr1iAcDkOSJFx22WVYt24d95sBkiThuuuuw5gxY/CrX/2K+8ygpUuXorCwEKIowmaz4amnnuK+M6CzsxP/9E//hI8//hiCIOD+++/HGWecwf2WwKFDh3D77bfHLh87dgzr1q3D6tWrud+G8Nvf/hZPPPEEBEHAtGnT8MADD6C7u3vY99uIzjxLkoT77rsPjz76KLZs2YKnn34aBw8ezPZmWdK1116LRx99VHddXV0dFi9ejOeffx6LFy9GXV1dlrbOumw2G+666y48++yz+OMf/4g//OEPOHjwIPfdEJxOJx577DH85S9/waZNm7B9+3a8//773G8G/O53v8OUKVNil7nPjHvsscewefNmPPXUUwC474zYsGEDlixZgueeew6bN2/GlClTuN+GMHnyZGzevDn2XHO73Vi+fDn32xCamprwu9/9Dk8++SSefvppSJKELVu2ZGW/jejgeffu3aipqcGECRPgdDqxcuVKbNu2LdubZUnnnHNOv09y27Ztw+rVqwEAq1evxtatW7OxaZZWWVmJWbNmAQCKioowefJkNDU1cd8NQRAEFBYWAgCi0Sii0SgEQeB+G8LJkyfx8ssv43Of+1zsOu6z1HHfJdbV1YW333479nxzOp0oLi7mfkvCjh07MGHCBFRXV3O/GSBJEoLBIKLRKILBICorK7Oy30Z08NzU1ISqqqrY5TFjxqCpqSmLW5RbWlpaUFlZCUANEltbW7O8RdZ2/Phx1NfXY968edx3BkiShFWrVuG8887Deeedx/1mwP3334/vfOc7EMX4oZ37zLhbbrkF1157Lf74xz8C4L4byrFjx1BWVoa7774bq1evxvr16xEIBLjfkrBlyxZcddVVAPh8G8qYMWPw1a9+FZdccgkuuOACFBUV4YILLsjKfhvRwfNALa4FQcjCllC+8/v9WLduHe655x4UFRVle3Nygs1mw+bNm/HKK69g9+7d+Pjjj7O9SZb20ksvoaysDLNnz872puSkxx9/HH/+85/x61//Ghs3bsTbb7+d7U2yvGg0ir179+Kmm27Cpk2b4Ha7WWqQhHA4jBdffBGXX355tjclJ3R0dGDbtm3Ytm0btm/fju7ubmzevDkr2zKig+eqqiqcPHkydrmpqSn26YWGVl5ejlOnTgEATp06hbKysixvkTVFIhGsW7cOV199NVasWAGA+y4ZxcXFOPfcc7F9+3butwTee+89vPjii1i6dCnuuOMOvPnmm/j2t7/NfWbQmDFjAKivzeXLl2P37t3cd0OoqqpCVVUV5s2bBwC4/PLLsXfvXu43g1599VXMmjULFRUVAPi+MJQ33ngD48ePR1lZGRwOB1asWIFdu3ZlZb+N6OB5zpw5+PTTT3Hs2DGEw2Fs2bIFS5cuzfZm5YylS5di06ZNAIBNmzZh2bJlWd4i61EUBevXr8fkyZPxla98JXY9911ira2t6OzsBAAEg0G88cYbmDx5MvdbAnfeeSdeffVVvPjii/jJT36CRYsW4cc//jH3mQGBQABdXV2xr19//XVMnTqV+24Io0ePRlVVFQ4dOgRArd+dMmUK95tBW7ZswcqVK2OXud8SGzduHD744AN0d3dDUZSsPt9G/HjuV155Bffff3+svdOtt96a7U2ypDvuuANvvfUW2traUF5ejm9961u49NJL8Q//8A9obGzE2LFj8bOf/QylpaXZ3lRLeeedd7BmzRpMmzYtVod6xx13YO7cudx3Cezbtw933XUXJEmCoii4/PLL8c1vfhNtbW3cbwbs3LkTv/nNb/CrX/2K+8yAY8eO4bbbbgOg1tpfddVVuPXWW7nvDKivr8f69esRiUQwYcIEPPDAA5BlmfttCN3d3bj44ouxdetWeL1eAODzzYCf//zneOaZZ2C321FbW4sNGzbA7/cP+34b8cEzEREREZFRI7psg4iIiIgoGQyeiYiIiIgMYvBMRERERGQQg2ciIiIiIoMYPBMRERERGcTgmYjIYh5++GGce+65af+cjz/+GNOnT8fOnTtN2CoiIgIYPBMRERERGcbgmYiIiIjIIAbPREQWtnPnzljpxbp163D22Wdj2bJl2LhxY7/7bty4ERdddBHOOussfP3rX8fp06f73UeWZdTV1WH58uWYPXs2LrvsMvz5z3+O3f7ss89ixowZ2LFjR+y648ePY/78+fjpT3+amV+SiCiHMHgmIsoB9957L2bMmIFHHnkECxcuxH333Yfdu3fHbt+6dSvuu+8+XHzxxXj44Ycxbdo03HPPPf1+zr/8y7/gF7/4BW644QbU1dXh0ksvxT333IOXXnoJAHDFFVfgyiuvxD333IOuri4oioJ77rkH48ePj42wJiIayezZ3gAiIhraypUr8Y1vfAMAsHDhQrz00kt4/vnnMXfuXADAL3/5SyxZsgQ/+MEPAABLlixBa2srnnjiidjPOHLkCB5//HE88MADuOaaawAA5513Hk6fPo1HHnkEl1xyCQDgn//5n3HVVVfh/vvvx4wZM/Dee+/hf//3f+F0OofzVyYisiRmnomIcsD5558f+9rhcGDSpEk4efIkAECSJNTX12PZsmW671m+fLnu8o4dOyCKIpYvX45oNBr7t3jxYuzbtw+SJAEASktL8cMf/hBPPvkk/u3f/g233XYbZsyYkeHfkIgoNzDzTESUA4qLi3WXHQ4HwuEwAKC1tRXRaBTl5eW6+/S93NbWBkmSsGDBggEf4/Tp06iqqgIALFq0CBUVFWhvb8cNN9xg1q9BRJTzGDwTEeW4srIy2O12tLS06K7ve7mkpAR2ux2PP/44BEEY8Of0+vGPfwxJklBRUYH7778fDz74YGY2nogoxzB4JiLKcTabDTNmzMC2bdtw0003xa5/4YUXdPdbtGgRJEmCz+fTlYH0tXPnTvz+97/HQw89hKKiItxyyy1YsWIFLrvssoz9DkREuYLBMxFRHvj617+Ob37zm/je976H5cuX4+2338b27dt195k8eTJuvPFG3HHHHbjlllswZ84chEIhHDhwAJ9++ik2bNgAv9+Pu+++G1deeSUuv/xyAMDnP/95fP/738c555yjy04TEY1EXDBIRJQHli9fjnvvvRcvvfQSbrvtNuzduxcbNmzod7/vfe97uPXWW7F582Z87Wtfw1133YVXXnkF55xzDgDgX//1XxEOh3HvvffGvucf//Ef4fF48L3vfW/Yfh8iIqsSFEVRsr0RRERERES5gJlnIiIiIiKDGDwTERERERnE4JmIiIiIyCAGz0REREREBjF4JiIiIiIyiMEzEREREZFBDJ6JiIiIiAxi8ExEREREZND/B8TrUmUDCUoHAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "evaluator.plot_predictions()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "quantiles    [-2.241402727604945, -1.9599639845400545, -1.7...\n",
+       "residuals    [-1.5978545483912392, -1.5063985699598552, -1....\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "evaluator.qq"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "evaluator.plot_qq()"
    ]
diff --git a/tutorials/tutorial_Cobra_logistic_regression.ipynb b/tutorials/tutorial_Cobra_logistic_regression.ipynb
index c3ee859..c5cb763 100644
--- a/tutorials/tutorial_Cobra_logistic_regression.ipynb
+++ b/tutorials/tutorial_Cobra_logistic_regression.ipynb
@@ -104,6 +104,7 @@
    "outputs": [],
    "source": [
     "import json\n",
+    "import os\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "import seaborn as sns\n",
@@ -290,7 +291,8 @@
    "source": [
     "for col in df.columns:\n",
     "    if col not in discrete_vars and col not in [id_col, target_col]: # omit discrete because a string and target\n",
-    "        if len(val_counts) > 1 and len(val_counts) <= 10: # the column contains less than 10 different values\n",
+    "        val_counts = df[col].nunique()\n",
+    "        if val_counts > 1 and val_counts <= 10: # the column contains less than 10 different values\n",
     "            print(col)"
    ]
   },
@@ -452,8 +454,8 @@
    },
    "outputs": [],
    "source": [
-    "plot_incidence(pig_tables, variable=\"age\", model_type=\"classification\",\n",
-    "               column_order = list(basetable[\"age_bin\"].unique().sort_values())))"
+    "col_order = list(basetable[\"age_bin\"].unique().sort_values()))\n",
+    "plot_incidence(pig_tables, variable=\"age\", model_type=\"classification\", column_order=col_order)"
    ]
   },
   {
@@ -639,10 +641,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# look at the number of steps that were completed before stopping, fill that in between the brackets\n",
+    "# pick the optimal step based on visual inspection in the plot above (try to find a knee point)\n",
     "model = forward_selection.get_model_from_step(5)\n",
     "\n",
-    "# Note that chosen model the following variables:\n",
     "final_predictors = model.predictors\n",
     "final_predictors"
    ]
@@ -680,16 +681,26 @@
    "outputs": [],
    "source": [
     "model_dict = model.serialize()\n",
+    "model_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if False:\n",
+    "    ## To save the model as a JSON file, run the following snippet\n",
+    "    model_path = os.path.join(\"output\", \"model.json\")\n",
+    "    with open(model_path, \"w\") as file:\n",
+    "        json.dump(model_dict, file)\n",
     "\n",
-    "model_path = os.path.join(\"output\", \"model.json\")\n",
-    "with open(model_path, \"w\") as file:\n",
-    "    json.dump(model_dict, file)\n",
-    "\n",
-    "# To reload the model again from a JSON file, run the following snippet:\n",
-    "# with open(path, \"r\") as file:\n",
-    "#     model_dict = json.load(file)\n",
-    "# model = LogisticRegressionModel()\n",
-    "# model.deserialize(model_dict)"
+    "    ## To reload the model again from a JSON file, run the following snippet\n",
+    "    with open(model_path, \"r\") as file:\n",
+    "        model_dict = json.load(file)\n",
+    "    model = LogisticRegressionModel()\n",
+    "    model.deserialize(model_dict)"
    ]
   },
   {

From 3e310d76c7de1abaf270821759d307ddfebc05de Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Thu, 30 Sep 2021 11:09:34 +0200
Subject: [PATCH 12/16] various docs changes and small fixes

---
 README.rst                                    |   15 +-
 cobra/evaluation/evaluator.py                 |   76 +-
 cobra/evaluation/pigs_tables.py               |   22 +-
 cobra/model_building/forward_selection.py     |    4 +-
 cobra/preprocessing/preprocessor.py           |   32 +-
 cobra/preprocessing/target_encoder.py         |    3 +-
 requirements.txt                              |    4 +-
 .../tutorial_Cobra_linear_regression.ipynb    |  638 +++---
 .../tutorial_Cobra_logistic_regression.ipynb  | 1746 +++++++++++++++--
 9 files changed, 2040 insertions(+), 500 deletions(-)

diff --git a/README.rst b/README.rst
index a6533de..3a7d9d9 100644
--- a/README.rst
+++ b/README.rst
@@ -23,7 +23,7 @@ Main features
    - create bins from continuous variables
    - regroup categorical variables based on statistical significance
    - replace missing values
-   - add columns with average of target values (linear regression) or incidence rate per category/bin (logistic regression)
+   - replace categories/bins with average of target values (linear regression) or incidence rate (logistic regression)
  
 - Perform univariate feature selection based on RMSE (linear regression) or AUC (logistic regression)
 - Compute correlation matrix of predictors
@@ -38,18 +38,7 @@ These instructions will get you a copy of the project up and running on your loc
 Requirements
 ------------
 
-This package requires the usual Python packages for data science:
-
-- numpy (>=1.19.4)
-- pandas (>=1.1.5)
-- scipy (>=1.5.4)
-- scikit-learn (>=0.24.1)
-- matplotlib (>=3.3.3)
-- seaborn (>=0.11.0)
-
-
-These packages, along with their versions are listed in ``requirements.txt`` and can be installed using ``pip``:    ::
-
+This package requires only the usual Python packages for data science, being numpy, pandas, scipy, scikit-learn, matplotlib, seaborn, and tqdm. These packages, along with their versions are listed in ``requirements.txt`` and can be installed using ``pip``:    ::
 
   pip install -r requirements.txt
 
diff --git a/cobra/evaluation/evaluator.py b/cobra/evaluation/evaluator.py
index 817aae0..e2015c0 100644
--- a/cobra/evaluation/evaluator.py
+++ b/cobra/evaluation/evaluator.py
@@ -77,7 +77,7 @@ def __init__(self,
 
     def fit(self, y_true: np.ndarray, y_pred: np.ndarray):
         """Fit the evaluator by computing the relevant evaluation metrics on
-        the inputs
+        the inputs.
 
         Parameters
         ----------
@@ -120,7 +120,7 @@ def _compute_scalar_metrics(y_true: np.ndarray,
                                 y_pred_b: np.ndarray,
                                 lift_at: float) -> pd.Series:
         """Convenient function to compute various scalar performance measures
-        and return them in a pd.Series
+        and return them in a pd.Series.
 
         Parameters
         ----------
@@ -136,7 +136,14 @@ def _compute_scalar_metrics(y_true: np.ndarray,
         Returns
         -------
         pd.Series
-            contains various performance measures of the model
+            Contains various performance measures of the model, being:
+                Accuracy
+                AUC
+                Precision
+                Recall
+                F1
+                Matthews correlation coefficient
+                Lift at given percentage
         """
         return pd.Series({
             "accuracy": accuracy_score(y_true, y_pred_b),
@@ -152,7 +159,7 @@ def _compute_scalar_metrics(y_true: np.ndarray,
         })
 
     def plot_roc_curve(self, path: str=None, dim: tuple=(12, 8)):
-        """Plot ROC curves of the model
+        """Plot ROC curve of the model.
 
         Parameters
         ----------
@@ -193,7 +200,7 @@ def plot_roc_curve(self, path: str=None, dim: tuple=(12, 8)):
 
     def plot_confusion_matrix(self, path: str=None, dim: tuple=(12, 8),
                               labels: list=["0", "1"]):
-        """Plot the confusion matrix
+        """Plot the confusion matrix.
 
         Parameters
         ----------
@@ -224,7 +231,7 @@ def plot_confusion_matrix(self, path: str=None, dim: tuple=(12, 8),
         plt.show()
 
     def plot_cumulative_response_curve(self, path: str=None, dim: tuple=(12, 8)):
-        """Plot cumulative response curve
+        """Plot cumulative response curve.
 
         Parameters
         ----------
@@ -275,7 +282,7 @@ def plot_cumulative_response_curve(self, path: str=None, dim: tuple=(12, 8)):
             plt.show()
 
     def plot_lift_curve(self, path: str=None, dim: tuple=(12, 8)):
-        """Plot lift per decile
+        """Plot lift per decile.
 
         Parameters
         ----------
@@ -324,7 +331,7 @@ def plot_lift_curve(self, path: str=None, dim: tuple=(12, 8)):
             plt.show()
 
     def plot_cumulative_gains(self, path: str=None, dim: tuple=(12, 8)):
-        """Plot lift per decile
+        """Plot cumulative gains per decile.
 
         Parameters
         ----------
@@ -369,7 +376,7 @@ def plot_cumulative_gains(self, path: str=None, dim: tuple=(12, 8)):
     def _find_optimal_cutoff(y_true: np.ndarray,
                              y_pred: np.ndarray) -> float:
         """Find the optimal probability cut off point for a
-        classification model. Wrapper around _compute_optimal_cutoff
+        classification model. Wrapper around _compute_optimal_cutoff.
 
         Parameters
         ----------
@@ -381,7 +388,7 @@ def _find_optimal_cutoff(y_true: np.ndarray,
         Returns
         -------
         float
-            optimal cut off probability for the model
+            Optimal cut-off probability for the model.
         """
         return ClassificationEvaluator._compute_optimal_cutoff(roc_curve(y_true=y_true,
                                                                          y_score=y_pred))
@@ -390,7 +397,10 @@ def _find_optimal_cutoff(y_true: np.ndarray,
     def _compute_optimal_cutoff(fpr: np.ndarray, tpr: np.ndarray,
                                 thresholds: np.ndarray) -> float:
         """Find the optimal probability cut-off point for a
-        classification model
+        classification model.
+
+        The optimal cut-off would be where TPR is high and FPR is low, hence
+        TPR - (1-FPR) should be zero or close to zero for the optimal cut-off.
 
         Parameters
         ----------
@@ -404,11 +414,8 @@ def _compute_optimal_cutoff(fpr: np.ndarray, tpr: np.ndarray,
         Returns
         -------
         float
-            optimal probability cut-off point
+            Optimal probability cut-off point.
         """
-
-        # The optimal cut off would be where tpr is high and fpr is low, hence
-        # tpr - (1-fpr) should be zero or close to zero for the optimal cut off
         temp = np.absolute(tpr - (1-fpr))
 
         # index for optimal value is the one for which temp is minimal
@@ -420,7 +427,7 @@ def _compute_optimal_cutoff(fpr: np.ndarray, tpr: np.ndarray,
     def _compute_cumulative_gains(y_true: np.ndarray,
                                   y_pred: np.ndarray) -> tuple:
         """Compute cumulative gains of the model, returns percentages and
-        gains cumulative gains curves
+        gains cumulative gains curves.
 
         Code from (https://github.com/reiinakano/scikit-plot/blob/
                    2dd3e6a76df77edcbd724c4db25575f70abb57cb/
@@ -436,7 +443,7 @@ def _compute_cumulative_gains(y_true: np.ndarray,
         Returns
         -------
         tuple
-            x-labels, gains
+            With x-labels, and gains.
         """
 
         # make y_true a boolean vector
@@ -461,7 +468,7 @@ def _compute_lift_per_bin(y_true: np.ndarray,
                               y_pred: np.ndarray,
                               n_bins: int = 10) -> tuple:
         """Compute lift of the model for a given number of bins, returns x-labels,
-        lifts and the target incidence to create cumulative response curves
+        lifts and the target incidence to create cumulative response curves.
 
         Parameters
         ----------
@@ -476,7 +483,7 @@ def _compute_lift_per_bin(y_true: np.ndarray,
         Returns
         -------
         tuple
-            x-labels, lifts per decile and target incidence
+            Includes x-labels, lifts per decile, and target incidence.
         """
 
         lifts = [ClassificationEvaluator._compute_lift(y_true=y_true,
@@ -491,10 +498,7 @@ def _compute_lift_per_bin(y_true: np.ndarray,
     @staticmethod
     def _compute_lift(y_true: np.ndarray, y_pred: np.ndarray,
                       lift_at: float=0.05) -> float:
-        """Calculates lift given two arrays on specified level
-           %timeit
-           50.3 µs ± 1.94 µs per loop (mean ± std. dev. of 7 runs,
-                                        10000 loops each)
+        """Calculates lift given two arrays on specified level.
 
         Parameters
         ----------
@@ -508,7 +512,7 @@ def _compute_lift(y_true: np.ndarray, y_pred: np.ndarray,
         Returns
         -------
         float
-            lift of the model
+            Lift of the model.
         """
 
         # Make sure it is numpy array
@@ -541,7 +545,7 @@ def _compute_lift(y_true: np.ndarray, y_pred: np.ndarray,
 
 class RegressionEvaluator():
     """Evaluator class encapsulating regression model metrics
-    and plotting functionality
+    and plotting functionality.
 
     Attributes
     ----------
@@ -566,7 +570,7 @@ def __init__(self):
 
     def fit(self, y_true: np.ndarray, y_pred: np.ndarray):
         """Fit the evaluator by computing the relevant evaluation metrics on
-        the inputs
+        the inputs.
 
         Parameters
         ----------
@@ -588,7 +592,7 @@ def fit(self, y_true: np.ndarray, y_pred: np.ndarray):
     def _compute_scalar_metrics(y_true: np.ndarray,
                                 y_pred: np.ndarray) -> pd.Series:
         """Convenient function to compute various scalar performance measures
-        and return them in a pd.Series
+        and return them in a pd.Series.
 
         Parameters
         ----------
@@ -600,7 +604,7 @@ def _compute_scalar_metrics(y_true: np.ndarray,
         Returns
         -------
         pd.Series
-            contains various performance measures of the model, being:
+            Contains various performance measures of the model, being:
                 R-squared (coefficient of determination, usually denoted as R-squared)
                 Mean absolute error (expected value of the absolute error loss)
                 Mean squared error (expected value of the quadratic error)
@@ -617,7 +621,7 @@ def _compute_scalar_metrics(y_true: np.ndarray,
     def _compute_qq_residuals(y_true: np.ndarray,
                               y_pred: np.ndarray) -> pd.Series:
         """Convenience function to compute various scalar performance measures
-        and return them in a pd.Series
+        and return them in a pd.Series.
 
         Parameters
         ----------
@@ -629,7 +633,7 @@ def _compute_qq_residuals(y_true: np.ndarray,
         Returns
         -------
         pd.Series
-            theoretical quantiles and associated actual residuals
+            Theoretical quantiles and associated actual residuals.
         """
         ## also possible directly via statsmodels.api.qqplot()
 
@@ -649,7 +653,7 @@ def _compute_qq_residuals(y_true: np.ndarray,
         })
 
     def plot_predictions(self, path: str=None, dim: tuple=(12, 8)):
-        """Plot predictions from the model against actual values
+        """Plot predictions from the model against actual values.
 
         Parameters
         ----------
@@ -671,8 +675,8 @@ def plot_predictions(self, path: str=None, dim: tuple=(12, 8)):
 
             x = np.arange(1, len(y_true)+1)
 
-            ax.plot(x, y_true, "o", label="actuals", color="darkorange", linewidth=3,)
-            ax.plot(x, y_pred, "r--.", label="predictions", color="cornflowerblue", linewidth=3)
+            ax.plot(x, y_true, ls="--", label="actuals", color="darkorange", linewidth=3)
+            ax.plot(x, y_pred, label="predictions", color="cornflowerblue", linewidth=3)
 
             ax.set_xlabel("Index", fontsize=15)
             ax.set_ylabel("Value", fontsize=15)
@@ -685,7 +689,7 @@ def plot_predictions(self, path: str=None, dim: tuple=(12, 8)):
         plt.show()
 
     def plot_qq(self, path: str=None, dim: tuple=(12, 8)):
-        """Display a Q-Q plot from the standardized prediction residuals
+        """Display a Q-Q plot from the standardized prediction residuals.
 
         Parameters
         ----------
@@ -708,8 +712,8 @@ def plot_qq(self, path: str=None, dim: tuple=(12, 8)):
             x = self.qq["quantiles"]
             y = self.qq["residuals"]
 
-            ax.plot(x, x, "r--", label="perfect model", color="darkorange", linewidth=3)
-            ax.plot(x, y, "o--", label="current model", color="cornflowerblue", linewidth=3)
+            ax.plot(x, x, ls="--", label="perfect model", color="darkorange", linewidth=3)
+            ax.plot(x, y, label="current model", color="cornflowerblue", linewidth=3)
 
             ax.set_xlabel("Theoretical quantiles", fontsize=15)
             ax.set_xticks(range(int(np.floor(min(x))), int(np.ceil(max(x[x < float("inf")])))+1, 1))
diff --git a/cobra/evaluation/pigs_tables.py b/cobra/evaluation/pigs_tables.py
index 4657c09..5239434 100644
--- a/cobra/evaluation/pigs_tables.py
+++ b/cobra/evaluation/pigs_tables.py
@@ -100,11 +100,11 @@ def plot_incidence(pig_tables: pd.DataFrame,
                    dim: tuple = (12, 8)):
     """Plots a Predictor Insights Graph (PIG), a graph in which the mean
     target value is plotted for a number of bins constructed from a predictor
-    variable. When the target actually is a binary classification target,
-    the plotted mean target value is actually the target incidence rate.
+    variable. When the target is a binary classification target,
+    the plotted mean target value is a true incidence rate.
 
     Bins are ordered in descending order of mean target value
-    unless specified otherwise with `column_order` list.
+    unless specified otherwise with the `column_order` list.
 
     Parameters
     ----------
@@ -161,10 +161,10 @@ def plot_incidence(pig_tables: pd.DataFrame,
                 label='average incidence rate' if model_type == "classification" else "global mean target value",
                 zorder=10)
 
-        # dummy line to have label on second axis from first
+        # Dummy line to have label on second axis from first
         ax.plot(np.nan, "#939598", linewidth=6, label='bin size')
 
-        # set labels & ticks
+        # Set labels & ticks
         ax.set_ylabel('incidence' if model_type == "classification" else "mean target value",
                       fontsize=16)
         ax.set_xlabel('{} bins' ''.format(variable), fontsize=16)
@@ -173,12 +173,12 @@ def plot_incidence(pig_tables: pd.DataFrame,
 
         if model_type == "classification":
             # Mean target values are between 0 and 1 (target incidence rate),
-            # so format them as percentages:
+            # so format them as percentages
             ax.set_yticks(np.arange(0, max(df_plot['avg_target'])+0.05, 0.05))
             ax.yaxis.set_major_formatter(
                 FuncFormatter(lambda y, _: '{:.1%}'.format(y)))
 
-        # removes ticks but keeps the labels
+        # Remove ticks but keep the labels
         ax.tick_params(axis='both', which='both', length=0)
         ax.tick_params(axis='y', colors="#00ccff")
         ax.yaxis.label.set_color('#00ccff')
@@ -191,7 +191,7 @@ def plot_incidence(pig_tables: pd.DataFrame,
         ax2.bar(df_plot['label'], df_plot['pop_size'],
                 align='center', color="#939598", zorder=1)
 
-        # set labels & ticks
+        # Set labels & ticks
         ax2.set_ylabel('population size', fontsize=16)
         ax2.set_xlabel('{} bins' ''.format(variable), fontsize=16)
         ax2.xaxis.set_tick_params(rotation=45, labelsize=14)
@@ -209,17 +209,17 @@ def plot_incidence(pig_tables: pd.DataFrame,
 
         ax2.grid(False)
 
-        # title & legend
+        # Title & legend
         if model_type == "classification":
             title = "Incidence plot - " + variable
         else:
             title = "Mean target plot - " + variable
         fig.suptitle(title, fontsize=22, y=1.02)
         ax.legend(frameon=False, bbox_to_anchor=(0., 1.01, 1., .102),
-                  loc=3, ncol=1, mode="expand", borderaxespad=0.,
+                  loc=3, ncol=3, mode="expand", borderaxespad=0.,
                   prop={"size": 14})
 
-        # Sets order ot layers
+        # Set order of layers
         ax.set_zorder(1)
         ax.patch.set_visible(False)
 
diff --git a/cobra/model_building/forward_selection.py b/cobra/model_building/forward_selection.py
index 3f7cf49..9357913 100644
--- a/cobra/model_building/forward_selection.py
+++ b/cobra/model_building/forward_selection.py
@@ -132,8 +132,8 @@ def compute_model_performances(self, data: pd.DataFrame,
         return df
 
     def fit(self, train_data: pd.DataFrame, target_column_name: str,
-            predictors: list, forced_predictors: list = [],
-            excluded_predictors: list = []):
+            predictors: list, forced_predictors: list=[],
+            excluded_predictors: list=[]):
         """Fit the forward feature selection estimator.
 
         Parameters
diff --git a/cobra/preprocessing/preprocessor.py b/cobra/preprocessing/preprocessor.py
index 5f02dd0..0c63cfe 100644
--- a/cobra/preprocessing/preprocessor.py
+++ b/cobra/preprocessing/preprocessor.py
@@ -145,8 +145,8 @@ def from_params(cls,
         Returns
         -------
         PreProcessor
-            class encapsulating CategoricalDataProcessor,
-            KBinsDiscretizer, and TargetEncoder instances
+            Class encapsulating CategoricalDataProcessor,
+            KBinsDiscretizer, and TargetEncoder instances.
         """       
         categorical_data_processor = CategoricalDataProcessor(model_type,
                                                               regroup,
@@ -174,21 +174,23 @@ def from_pipeline(cls, pipeline: dict):
         Parameters
         ----------
         pipeline : dict
-            The (fitted) pipeline as a dictionary
+            The (fitted) pipeline as a dictionary.
 
         Returns
         -------
         PreProcessor
-            Instance of PreProcessor instantiated from a stored pipeline
+            Instance of PreProcessor instantiated from a stored pipeline.
 
         Raises
         ------
         ValueError
-            Description
+            If the loaded pipeline does not have all required parameters
+            and no others.
         """
 
         if not PreProcessor._is_valid_pipeline(pipeline):
-            raise ValueError("Invalid pipeline")  ### TODO: specify error
+            raise ValueError("Invalid pipeline, as it does not "
+                             "contain all and only the required parameters.")
 
         categorical_data_processor = CategoricalDataProcessor()
         categorical_data_processor.set_attributes_from_dict(
@@ -406,7 +408,7 @@ def serialize_pipeline(self) -> dict:
         Returns
         -------
         dict
-            Return the pipeline as a dictionary
+            Return the pipeline as a dictionary.
         """
         pipeline = {
             "metadata": {
@@ -434,7 +436,7 @@ def _is_valid_pipeline(pipeline: dict) -> bool:
         Parameters
         ----------
         pipeline : dict
-            Loaded pipeline from json file
+            Loaded pipeline from JSON file.
         """
         keys = inspect.getfullargspec(PreProcessor.from_params).args
         valid_keys = set([key for key in keys
@@ -455,30 +457,30 @@ def _is_valid_pipeline(pipeline: dict) -> bool:
 
     @staticmethod
     def _get_variable_list(continuous_vars: list, discrete_vars: list) -> list:
-        """merge lists of continuous_vars and discrete_vars and add suffix
-        "_bin" resp. "_processed" to the predictors
+        """Merge lists of continuous_vars and discrete_vars and add suffix
+        "_bin" resp. "_processed" to the predictors.
 
         Parameters
         ----------
         continuous_vars : list
-            list of continuous variables
+            List of continuous variables.
         discrete_vars : list
-            list of discrete variables
+            List of discrete variables.
 
         Returns
         -------
         list
-            Merged list of predictors with proper suffixes added
+            Merged list of predictors with proper suffixes added.
 
         Raises
         ------
         ValueError
-            in case both lists are empty
+            In case both lists are empty.
         """
         var_list = ([col + "_processed" for col in discrete_vars]
                     + [col + "_bin" for col in continuous_vars])
 
         if not var_list:
-            raise ValueError("Variable var_list is None or empty list")
+            raise ValueError("Variable var_list is None or empty list.")
 
         return var_list
diff --git a/cobra/preprocessing/target_encoder.py b/cobra/preprocessing/target_encoder.py
index 85c8df1..8b694fb 100644
--- a/cobra/preprocessing/target_encoder.py
+++ b/cobra/preprocessing/target_encoder.py
@@ -76,7 +76,8 @@ def __init__(self, weight: float=0.0,
         if weight == 0:
             log.warning("The target encoder's additive smoothing weight is "
                         "set to 0. This disables smoothing and may make the "
-                        "encoding prone to overfitting.")
+                        "encoding prone to overfitting. Increase the weight "
+                        "if needed.")
 
         self.weight = weight
         self.imputation_strategy = imputation_strategy
diff --git a/requirements.txt b/requirements.txt
index 9670f11..6ea2694 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,7 @@
 numpy>=1.19.4
 pandas>=1.1.5
 scipy>=1.5.4
-scikit-learn>=0.24
-matplotlib>=3.3.3
+scikit-learn>=0.24.1
+matplotlib>=3.4.3
 seaborn>=0.11.0
 tqdm>=4.59.0
\ No newline at end of file
diff --git a/tutorials/tutorial_Cobra_linear_regression.ipynb b/tutorials/tutorial_Cobra_linear_regression.ipynb
index 7c9b76a..559740e 100644
--- a/tutorials/tutorial_Cobra_linear_regression.ipynb
+++ b/tutorials/tutorial_Cobra_linear_regression.ipynb
@@ -23,18 +23,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Cobra requires the usual Python packages for data science:\n",
-    "\n",
-    "- numpy (>=1.19.4)\n",
-    "- pandas (>=1.1.5)\n",
-    "- scipy (>=1.5.4)\n",
-    "- scikit-learn (>=0.24.1)\n",
-    "- matplotlib (>=3.3.3)\n",
-    "- seaborn (>=0.11.0)\n",
-    "\n",
-    "These packages, along with their versions are listed in requirements.txt and can be installed using pip.\n",
-    "\n",
-    "If you want to install Cobra with e.g. pip, you don't have to install all of these requirements as these are automatically installed with Cobra itself."
+    "Cobra requires the usual Python packages for data science, such as numpy, pandas and scikit-learn. These packages, along with their versions are listed in requirements.txt and can be installed using pip."
    ]
   },
   {
@@ -50,7 +39,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The easiest way to install Cobra is using pip:"
+    "If you want to install Cobra with e.g. pip, you don't have to install all of these requirements as these are automatically installed with Cobra itself. Hence, the easiest way to install Cobra is using pip:"
    ]
   },
   {
@@ -84,9 +73,9 @@
     "\n",
     "1. **Preprocessing**: properly prepare the predictors (a synonym for “feature” or variable that we use throughout this tutorial) for modelling.\n",
     "\n",
-    "2. **Feature Selection**: automatically select a subset of predictors which contribute most to the target variable or output in which you are interested.\n",
+    "2. **Feature selection**: automatically select a subset of predictors which contribute most to the target variable or output in which you are interested.\n",
     "\n",
-    "3. **Model Evaluation**: once a model has been build, a detailed evaluation can be performed by computing all sorts of evaluation metrics.\n",
+    "3. **Model evaluation**: once a model has been build, a detailed evaluation can be performed by computing all sorts of evaluation metrics.\n",
     "\n",
     "Let's dive in!\n",
     "***"
@@ -96,7 +85,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Miles Per Gallon Prediction\n",
+    "## Miles per gallon prediction\n",
     "\n",
     "- BASETABLE: seaborn dataset MPG.\n",
     "- GOAL: Predict the distance (measured in miles) that a car can travel per gallon of fuel."
@@ -111,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -447,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -475,7 +464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -484,7 +473,7 @@
        "['origin', 'name', 'cylinders']"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -503,16 +492,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['weight', 'acceleration', 'displacement', 'model_year', 'horsepower']"
+       "['acceleration', 'model_year', 'horsepower', 'weight', 'displacement']"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -533,14 +522,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "The target encoder's additive smoothing weight is set to 0. This disables smoothing and may make the encoding prone to overfitting.\n"
+      "The target encoder's additive smoothing weight is set to 0. This disables smoothing and may make the encoding prone to overfitting. Increase the weight if needed.\n"
      ]
     }
    ],
@@ -562,7 +551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -581,13 +570,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2f3d88d2b07d46ea927b5fba28687798",
+       "model_id": "5aef80effec54a22948feced697ba49a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -601,7 +590,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7790ba7745ac4bafb4bb0b514a3e9d27",
+       "model_id": "9f6fb759ecd74d8cad24d2431c05e4df",
        "version_major": 2,
        "version_minor": 0
       },
@@ -615,7 +604,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "98a4f591d7ce4353ba0fdfeaa29d10aa",
+       "model_id": "f617606da88d41b2b3bbd46fb36cdc19",
        "version_major": 2,
        "version_minor": 0
       },
@@ -629,7 +618,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0076f4df02ce4517bbfb589d2d7e09cf",
+       "model_id": "1c5fe94f8bcc403a9ed1f5593e8b4d4a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -658,13 +647,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ceb52400760a486a895bc210ebd5f870",
+       "model_id": "4fe4568e415249f89b52c31a4edad67f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -678,7 +667,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "38f4134ac4104ec0bf747c05e046f61f",
+       "model_id": "144f05ee81044c5087d9d88c5e535bbd",
        "version_major": 2,
        "version_minor": 0
       },
@@ -721,22 +710,22 @@
        "      <th>name</th>\n",
        "      <th>id</th>\n",
        "      <th>split</th>\n",
-       "      <th>weight_bin</th>\n",
        "      <th>acceleration_bin</th>\n",
-       "      <th>displacement_bin</th>\n",
        "      <th>model_year_bin</th>\n",
        "      <th>horsepower_bin</th>\n",
+       "      <th>weight_bin</th>\n",
+       "      <th>displacement_bin</th>\n",
        "      <th>origin_processed</th>\n",
        "      <th>name_processed</th>\n",
        "      <th>cylinders_processed</th>\n",
        "      <th>origin_enc</th>\n",
        "      <th>name_enc</th>\n",
        "      <th>cylinders_enc</th>\n",
-       "      <th>weight_enc</th>\n",
        "      <th>acceleration_enc</th>\n",
-       "      <th>displacement_enc</th>\n",
        "      <th>model_year_enc</th>\n",
        "      <th>horsepower_enc</th>\n",
+       "      <th>weight_enc</th>\n",
+       "      <th>displacement_enc</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -753,22 +742,22 @@
        "      <td>chevrolet chevelle malibu</td>\n",
        "      <td>1</td>\n",
        "      <td>train</td>\n",
-       "      <td>3441.0 - 3832.0</td>\n",
-       "      <td>8.0 - 12.0</td>\n",
-       "      <td>250.0 - 307.0</td>\n",
+       "      <td>9.0 - 12.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>110.0 - 140.0</td>\n",
+       "      <td>110.0 - 139.0</td>\n",
+       "      <td>3416.0 - 3683.0</td>\n",
+       "      <td>304.0 - 350.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>chevrolet chevelle malibu</td>\n",
        "      <td>8</td>\n",
-       "      <td>20.242038</td>\n",
-       "      <td>17.500000</td>\n",
-       "      <td>14.973438</td>\n",
-       "      <td>17.720833</td>\n",
-       "      <td>15.18</td>\n",
-       "      <td>16.089474</td>\n",
-       "      <td>19.777778</td>\n",
-       "      <td>18.252174</td>\n",
+       "      <td>20.407383</td>\n",
+       "      <td>17.50000</td>\n",
+       "      <td>15.595</td>\n",
+       "      <td>16.446154</td>\n",
+       "      <td>19.9375</td>\n",
+       "      <td>20.043478</td>\n",
+       "      <td>17.683333</td>\n",
+       "      <td>16.137500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -782,23 +771,23 @@
        "      <td>usa</td>\n",
        "      <td>buick skylark 320</td>\n",
        "      <td>2</td>\n",
-       "      <td>train</td>\n",
-       "      <td>3441.0 - 3832.0</td>\n",
-       "      <td>8.0 - 12.0</td>\n",
-       "      <td>307.0 - 350.0</td>\n",
+       "      <td>validation</td>\n",
+       "      <td>9.0 - 12.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>155.0 - 230.0</td>\n",
+       "      <td>153.0 - 230.0</td>\n",
+       "      <td>3683.0 - 4210.0</td>\n",
+       "      <td>304.0 - 350.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>buick skylark 320</td>\n",
        "      <td>8</td>\n",
-       "      <td>20.242038</td>\n",
-       "      <td>15.000000</td>\n",
-       "      <td>14.973438</td>\n",
-       "      <td>17.720833</td>\n",
-       "      <td>15.18</td>\n",
-       "      <td>15.300000</td>\n",
-       "      <td>19.777778</td>\n",
-       "      <td>13.443478</td>\n",
+       "      <td>20.407383</td>\n",
+       "      <td>23.83125</td>\n",
+       "      <td>15.595</td>\n",
+       "      <td>16.446154</td>\n",
+       "      <td>19.9375</td>\n",
+       "      <td>13.777273</td>\n",
+       "      <td>16.387500</td>\n",
+       "      <td>16.137500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -813,22 +802,22 @@
        "      <td>plymouth satellite</td>\n",
        "      <td>3</td>\n",
        "      <td>train</td>\n",
-       "      <td>3200.0 - 3441.0</td>\n",
-       "      <td>8.0 - 12.0</td>\n",
-       "      <td>307.0 - 350.0</td>\n",
+       "      <td>9.0 - 12.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>140.0 - 155.0</td>\n",
+       "      <td>139.0 - 153.0</td>\n",
+       "      <td>3416.0 - 3683.0</td>\n",
+       "      <td>304.0 - 350.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>plymouth satellite</td>\n",
        "      <td>8</td>\n",
-       "      <td>20.242038</td>\n",
-       "      <td>18.000000</td>\n",
-       "      <td>14.973438</td>\n",
-       "      <td>19.091667</td>\n",
-       "      <td>15.18</td>\n",
-       "      <td>15.300000</td>\n",
-       "      <td>19.777778</td>\n",
-       "      <td>14.430435</td>\n",
+       "      <td>20.407383</td>\n",
+       "      <td>18.00000</td>\n",
+       "      <td>15.595</td>\n",
+       "      <td>16.446154</td>\n",
+       "      <td>19.9375</td>\n",
+       "      <td>15.462500</td>\n",
+       "      <td>17.683333</td>\n",
+       "      <td>16.137500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -842,23 +831,23 @@
        "      <td>usa</td>\n",
        "      <td>amc rebel sst</td>\n",
        "      <td>4</td>\n",
-       "      <td>selection</td>\n",
-       "      <td>3200.0 - 3441.0</td>\n",
-       "      <td>8.0 - 12.0</td>\n",
-       "      <td>250.0 - 307.0</td>\n",
+       "      <td>train</td>\n",
+       "      <td>9.0 - 12.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>140.0 - 155.0</td>\n",
+       "      <td>139.0 - 153.0</td>\n",
+       "      <td>3416.0 - 3683.0</td>\n",
+       "      <td>250.0 - 304.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>amc rebel sst</td>\n",
        "      <td>8</td>\n",
-       "      <td>20.242038</td>\n",
-       "      <td>23.416667</td>\n",
-       "      <td>14.973438</td>\n",
-       "      <td>19.091667</td>\n",
-       "      <td>15.18</td>\n",
-       "      <td>16.089474</td>\n",
-       "      <td>19.777778</td>\n",
-       "      <td>14.430435</td>\n",
+       "      <td>20.407383</td>\n",
+       "      <td>16.00000</td>\n",
+       "      <td>15.595</td>\n",
+       "      <td>16.446154</td>\n",
+       "      <td>19.9375</td>\n",
+       "      <td>15.462500</td>\n",
+       "      <td>17.683333</td>\n",
+       "      <td>17.194444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -872,23 +861,23 @@
        "      <td>usa</td>\n",
        "      <td>ford torino</td>\n",
        "      <td>5</td>\n",
-       "      <td>selection</td>\n",
-       "      <td>3441.0 - 3832.0</td>\n",
-       "      <td>8.0 - 12.0</td>\n",
-       "      <td>250.0 - 307.0</td>\n",
+       "      <td>validation</td>\n",
+       "      <td>9.0 - 12.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>110.0 - 140.0</td>\n",
+       "      <td>139.0 - 153.0</td>\n",
+       "      <td>3416.0 - 3683.0</td>\n",
+       "      <td>250.0 - 304.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>ford torino</td>\n",
        "      <td>8</td>\n",
-       "      <td>20.242038</td>\n",
-       "      <td>23.416667</td>\n",
-       "      <td>14.973438</td>\n",
-       "      <td>17.720833</td>\n",
-       "      <td>15.18</td>\n",
-       "      <td>16.089474</td>\n",
-       "      <td>19.777778</td>\n",
-       "      <td>18.252174</td>\n",
+       "      <td>20.407383</td>\n",
+       "      <td>23.83125</td>\n",
+       "      <td>15.595</td>\n",
+       "      <td>16.446154</td>\n",
+       "      <td>19.9375</td>\n",
+       "      <td>15.462500</td>\n",
+       "      <td>17.683333</td>\n",
+       "      <td>17.194444</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -902,43 +891,43 @@
        "3  16.0          8         304.0       150.0    3433          12.0   \n",
        "4  17.0          8         302.0       140.0    3449          10.5   \n",
        "\n",
-       "   model_year origin                       name  id      split  \\\n",
-       "0          70    usa  chevrolet chevelle malibu   1      train   \n",
-       "1          70    usa          buick skylark 320   2      train   \n",
-       "2          70    usa         plymouth satellite   3      train   \n",
-       "3          70    usa              amc rebel sst   4  selection   \n",
-       "4          70    usa                ford torino   5  selection   \n",
+       "   model_year origin                       name  id       split  \\\n",
+       "0          70    usa  chevrolet chevelle malibu   1       train   \n",
+       "1          70    usa          buick skylark 320   2  validation   \n",
+       "2          70    usa         plymouth satellite   3       train   \n",
+       "3          70    usa              amc rebel sst   4       train   \n",
+       "4          70    usa                ford torino   5  validation   \n",
        "\n",
-       "        weight_bin acceleration_bin displacement_bin model_year_bin  \\\n",
-       "0  3441.0 - 3832.0       8.0 - 12.0    250.0 - 307.0    70.0 - 71.0   \n",
-       "1  3441.0 - 3832.0       8.0 - 12.0    307.0 - 350.0    70.0 - 71.0   \n",
-       "2  3200.0 - 3441.0       8.0 - 12.0    307.0 - 350.0    70.0 - 71.0   \n",
-       "3  3200.0 - 3441.0       8.0 - 12.0    250.0 - 307.0    70.0 - 71.0   \n",
-       "4  3441.0 - 3832.0       8.0 - 12.0    250.0 - 307.0    70.0 - 71.0   \n",
+       "  acceleration_bin model_year_bin horsepower_bin       weight_bin  \\\n",
+       "0       9.0 - 12.0    70.0 - 71.0  110.0 - 139.0  3416.0 - 3683.0   \n",
+       "1       9.0 - 12.0    70.0 - 71.0  153.0 - 230.0  3683.0 - 4210.0   \n",
+       "2       9.0 - 12.0    70.0 - 71.0  139.0 - 153.0  3416.0 - 3683.0   \n",
+       "3       9.0 - 12.0    70.0 - 71.0  139.0 - 153.0  3416.0 - 3683.0   \n",
+       "4       9.0 - 12.0    70.0 - 71.0  139.0 - 153.0  3416.0 - 3683.0   \n",
        "\n",
-       "  horsepower_bin origin_processed             name_processed  \\\n",
-       "0  110.0 - 140.0              usa  chevrolet chevelle malibu   \n",
-       "1  155.0 - 230.0              usa          buick skylark 320   \n",
-       "2  140.0 - 155.0              usa         plymouth satellite   \n",
-       "3  140.0 - 155.0              usa              amc rebel sst   \n",
-       "4  110.0 - 140.0              usa                ford torino   \n",
+       "  displacement_bin origin_processed             name_processed  \\\n",
+       "0    304.0 - 350.0              usa  chevrolet chevelle malibu   \n",
+       "1    304.0 - 350.0              usa          buick skylark 320   \n",
+       "2    304.0 - 350.0              usa         plymouth satellite   \n",
+       "3    250.0 - 304.0              usa              amc rebel sst   \n",
+       "4    250.0 - 304.0              usa                ford torino   \n",
        "\n",
-       "  cylinders_processed  origin_enc   name_enc  cylinders_enc  weight_enc  \\\n",
-       "0                   8   20.242038  17.500000      14.973438   17.720833   \n",
-       "1                   8   20.242038  15.000000      14.973438   17.720833   \n",
-       "2                   8   20.242038  18.000000      14.973438   19.091667   \n",
-       "3                   8   20.242038  23.416667      14.973438   19.091667   \n",
-       "4                   8   20.242038  23.416667      14.973438   17.720833   \n",
+       "  cylinders_processed  origin_enc  name_enc  cylinders_enc  acceleration_enc  \\\n",
+       "0                   8   20.407383  17.50000         15.595         16.446154   \n",
+       "1                   8   20.407383  23.83125         15.595         16.446154   \n",
+       "2                   8   20.407383  18.00000         15.595         16.446154   \n",
+       "3                   8   20.407383  16.00000         15.595         16.446154   \n",
+       "4                   8   20.407383  23.83125         15.595         16.446154   \n",
        "\n",
-       "   acceleration_enc  displacement_enc  model_year_enc  horsepower_enc  \n",
-       "0             15.18         16.089474       19.777778       18.252174  \n",
-       "1             15.18         15.300000       19.777778       13.443478  \n",
-       "2             15.18         15.300000       19.777778       14.430435  \n",
-       "3             15.18         16.089474       19.777778       14.430435  \n",
-       "4             15.18         16.089474       19.777778       18.252174  "
+       "   model_year_enc  horsepower_enc  weight_enc  displacement_enc  \n",
+       "0         19.9375       20.043478   17.683333         16.137500  \n",
+       "1         19.9375       13.777273   16.387500         16.137500  \n",
+       "2         19.9375       15.462500   17.683333         16.137500  \n",
+       "3         19.9375       15.462500   17.683333         17.194444  \n",
+       "4         19.9375       15.462500   17.683333         17.194444  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -966,7 +955,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1001,42 +990,42 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>acceleration</td>\n",
-       "      <td>8.0 - 12.0</td>\n",
+       "      <td>9.0 - 12.0</td>\n",
        "      <td>0.088608</td>\n",
-       "      <td>23.591139</td>\n",
-       "      <td>16.400000</td>\n",
+       "      <td>22.744304</td>\n",
+       "      <td>13.714286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>acceleration</td>\n",
-       "      <td>12.0 - 13.5</td>\n",
-       "      <td>0.177215</td>\n",
-       "      <td>23.591139</td>\n",
-       "      <td>19.371429</td>\n",
+       "      <td>12.0 - 13.4</td>\n",
+       "      <td>0.113924</td>\n",
+       "      <td>22.744304</td>\n",
+       "      <td>16.944444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>acceleration</td>\n",
-       "      <td>13.5 - 14.2</td>\n",
-       "      <td>0.075949</td>\n",
-       "      <td>23.591139</td>\n",
-       "      <td>18.250000</td>\n",
+       "      <td>13.4 - 14.4</td>\n",
+       "      <td>0.202532</td>\n",
+       "      <td>22.744304</td>\n",
+       "      <td>20.550000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>acceleration</td>\n",
-       "      <td>14.2 - 14.9</td>\n",
-       "      <td>0.088608</td>\n",
-       "      <td>23.591139</td>\n",
-       "      <td>21.957143</td>\n",
+       "      <td>14.4 - 15.0</td>\n",
+       "      <td>0.151899</td>\n",
+       "      <td>22.744304</td>\n",
+       "      <td>24.825000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>acceleration</td>\n",
-       "      <td>14.9 - 15.5</td>\n",
-       "      <td>0.151899</td>\n",
-       "      <td>23.591139</td>\n",
-       "      <td>24.975000</td>\n",
+       "      <td>15.0 - 15.5</td>\n",
+       "      <td>0.088608</td>\n",
+       "      <td>22.744304</td>\n",
+       "      <td>27.214286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1049,42 +1038,42 @@
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>weight</td>\n",
-       "      <td>2880.0 - 3200.0</td>\n",
-       "      <td>0.139241</td>\n",
-       "      <td>23.591139</td>\n",
-       "      <td>21.136364</td>\n",
+       "      <td>2866.0 - 3064.0</td>\n",
+       "      <td>0.063291</td>\n",
+       "      <td>22.744304</td>\n",
+       "      <td>21.280000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>weight</td>\n",
-       "      <td>3200.0 - 3441.0</td>\n",
-       "      <td>0.050633</td>\n",
-       "      <td>23.591139</td>\n",
-       "      <td>18.825000</td>\n",
+       "      <td>3064.0 - 3416.0</td>\n",
+       "      <td>0.063291</td>\n",
+       "      <td>22.744304</td>\n",
+       "      <td>17.480000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>weight</td>\n",
-       "      <td>3441.0 - 3832.0</td>\n",
-       "      <td>0.075949</td>\n",
-       "      <td>23.591139</td>\n",
-       "      <td>17.066667</td>\n",
+       "      <td>3416.0 - 3683.0</td>\n",
+       "      <td>0.012658</td>\n",
+       "      <td>22.744304</td>\n",
+       "      <td>14.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>weight</td>\n",
-       "      <td>3832.0 - 4294.0</td>\n",
-       "      <td>0.088608</td>\n",
-       "      <td>23.591139</td>\n",
-       "      <td>15.285714</td>\n",
+       "      <td>3683.0 - 4210.0</td>\n",
+       "      <td>0.164557</td>\n",
+       "      <td>22.744304</td>\n",
+       "      <td>14.807692</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>weight</td>\n",
-       "      <td>4294.0 - 5140.0</td>\n",
-       "      <td>0.088608</td>\n",
-       "      <td>23.591139</td>\n",
-       "      <td>14.571429</td>\n",
+       "      <td>4210.0 - 5140.0</td>\n",
+       "      <td>0.151899</td>\n",
+       "      <td>22.744304</td>\n",
+       "      <td>13.041667</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1093,22 +1082,22 @@
       ],
       "text/plain": [
        "        variable            label  pop_size  global_avg_target  avg_target\n",
-       "0   acceleration       8.0 - 12.0  0.088608          23.591139   16.400000\n",
-       "1   acceleration      12.0 - 13.5  0.177215          23.591139   19.371429\n",
-       "2   acceleration      13.5 - 14.2  0.075949          23.591139   18.250000\n",
-       "3   acceleration      14.2 - 14.9  0.088608          23.591139   21.957143\n",
-       "4   acceleration      14.9 - 15.5  0.151899          23.591139   24.975000\n",
+       "0   acceleration       9.0 - 12.0  0.088608          22.744304   13.714286\n",
+       "1   acceleration      12.0 - 13.4  0.113924          22.744304   16.944444\n",
+       "2   acceleration      13.4 - 14.4  0.202532          22.744304   20.550000\n",
+       "3   acceleration      14.4 - 15.0  0.151899          22.744304   24.825000\n",
+       "4   acceleration      15.0 - 15.5  0.088608          22.744304   27.214286\n",
        "..           ...              ...       ...                ...         ...\n",
-       "5         weight  2880.0 - 3200.0  0.139241          23.591139   21.136364\n",
-       "6         weight  3200.0 - 3441.0  0.050633          23.591139   18.825000\n",
-       "7         weight  3441.0 - 3832.0  0.075949          23.591139   17.066667\n",
-       "8         weight  3832.0 - 4294.0  0.088608          23.591139   15.285714\n",
-       "9         weight  4294.0 - 5140.0  0.088608          23.591139   14.571429\n",
+       "5         weight  2866.0 - 3064.0  0.063291          22.744304   21.280000\n",
+       "6         weight  3064.0 - 3416.0  0.063291          22.744304   17.480000\n",
+       "7         weight  3416.0 - 3683.0  0.012658          22.744304   14.000000\n",
+       "8         weight  3683.0 - 4210.0  0.164557          22.744304   14.807692\n",
+       "9         weight  4210.0 - 5140.0  0.151899          22.744304   13.041667\n",
        "\n",
        "[128 rows x 5 columns]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1131,38 +1120,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 20,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
-    {
-     "ename": "AssertionError",
-     "evalue": "<class 'numpy.ndarray'>",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-26-b30b6990e4f3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mcol_order\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbasetable\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"acceleration_bin\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplot_incidence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpig_tables\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvariable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"acceleration\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel_type\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"regression\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumn_order\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcol_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32mC:/Users/samuel.borms/Desktop/code/cobra\\cobra\\evaluation\\pigs_tables.py\u001b[0m in \u001b[0;36mplot_incidence\u001b[1;34m(pig_tables, variable, model_type, column_order, dim)\u001b[0m\n\u001b[0;32m    151\u001b[0m         \u001b[1;31m# Left axis - average target\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m         \u001b[1;31m# -----------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 153\u001b[1;33m         ax.plot(df_plot['label'], df_plot['avg_target'],\n\u001b[0m\u001b[0;32m    154\u001b[0m                 \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"#00ccff\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\".\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m                 \u001b[0mmarkersize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1645\u001b[0m         \"\"\"\n\u001b[0;32m   1646\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1647\u001b[1;33m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1648\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1649\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    214\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 216\u001b[1;33m             \u001b[1;32myield\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_next_color\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    329\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    330\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 331\u001b[1;33m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    332\u001b[0m             \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    333\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\__init__.py\u001b[0m in \u001b[0;36m_check_1d\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m   1347\u001b[0m                     message='Support for multi-dimensional indexing')\n\u001b[0;32m   1348\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1349\u001b[1;33m                 \u001b[0mndim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1350\u001b[0m                 \u001b[1;31m# we have definitely hit a pandas index or series object\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1351\u001b[0m                 \u001b[1;31m# cast to a numpy array.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    875\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    876\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 877\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    878\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_with\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m_get_with\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    890\u001b[0m             )\n\u001b[0;32m    891\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 892\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_values_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    893\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    894\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m_get_values_tuple\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    920\u001b[0m         \u001b[1;31m# mpl hackaround\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    921\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many_none\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 922\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    923\u001b[0m             \u001b[0mdeprecate_ndim_indexing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    924\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m_get_values\u001b[1;34m(self, indexer)\u001b[0m\n\u001b[0;32m    935\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    936\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 937\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_slice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    938\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    939\u001b[0m             \u001b[1;31m# mpl compat if we look up e.g. ser[:, np.newaxis];\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mget_slice\u001b[1;34m(self, slobj, axis)\u001b[0m\n\u001b[0;32m   1607\u001b[0m         \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mblk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1608\u001b[0m         \u001b[0mblock\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mblk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_block_same_class\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mslice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1609\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mslobj\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1610\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1611\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, block, axis, do_integrity_check, fastpath)\u001b[0m\n\u001b[0;32m   1551\u001b[0m     ):\n\u001b[0;32m   1552\u001b[0m         \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mBlock\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1553\u001b[1;33m         \u001b[1;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1554\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1555\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mfastpath\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mno_default\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mAssertionError\u001b[0m: <class 'numpy.ndarray'>"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 864x576 with 1 Axes>"
+       "<Figure size 864x576 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -1201,7 +1168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1217,7 +1184,7 @@
        " 'weight_bin']"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1239,7 +1206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1255,12 +1222,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1290,42 +1257,42 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, we compute correlations between the preprocessed predictors and plot it using a correlation matrix:\n"
+    "Next, we compute correlations between the preprocessed predictors and plot it using a correlation matrix."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                origin      name  cylinders    weight  acceleration  \\\n",
-      "origin        1.000000  0.565844   0.562649  0.597816      0.280317   \n",
-      "name          0.565844  1.000000   0.809445  0.858332      0.522110   \n",
-      "cylinders     0.562649  0.809445   1.000000  0.874555      0.586350   \n",
-      "weight        0.597816  0.858332   0.874555  1.000000      0.495881   \n",
-      "acceleration  0.280317  0.522110   0.586350  0.495881      1.000000   \n",
-      "displacement  0.667828  0.849869   0.909164  0.935436      0.555109   \n",
-      "model_year    0.184259  0.601193   0.384962  0.326719      0.318294   \n",
-      "horsepower    0.502376  0.851491   0.825762  0.876739      0.653886   \n",
+      "                origin      name  cylinders  acceleration  model_year  \\\n",
+      "origin        1.000000  0.571729   0.596669      0.266787    0.162567   \n",
+      "name          0.571729  1.000000   0.787726      0.434689    0.609038   \n",
+      "cylinders     0.596669  0.787726   1.000000      0.521251    0.350427   \n",
+      "acceleration  0.266787  0.434689   0.521251      1.000000    0.322990   \n",
+      "model_year    0.162567  0.609038   0.350427      0.322990    1.000000   \n",
+      "horsepower    0.489740  0.823225   0.792839      0.633843    0.416519   \n",
+      "weight        0.625704  0.839408   0.871615      0.425933    0.278704   \n",
+      "displacement  0.702018  0.830467   0.908609      0.521136    0.311756   \n",
       "\n",
-      "              displacement  model_year  horsepower  \n",
-      "origin            0.667828    0.184259    0.502376  \n",
-      "name              0.849869    0.601193    0.851491  \n",
-      "cylinders         0.909164    0.384962    0.825762  \n",
-      "weight            0.935436    0.326719    0.876739  \n",
-      "acceleration      0.555109    0.318294    0.653886  \n",
-      "displacement      1.000000    0.352080    0.885181  \n",
-      "model_year        0.352080    1.000000    0.419041  \n",
-      "horsepower        0.885181    0.419041    1.000000  \n"
+      "              horsepower    weight  displacement  \n",
+      "origin          0.489740  0.625704      0.702018  \n",
+      "name            0.823225  0.839408      0.830467  \n",
+      "cylinders       0.792839  0.871615      0.908609  \n",
+      "acceleration    0.633843  0.425933      0.521136  \n",
+      "model_year      0.416519  0.278704      0.311756  \n",
+      "horsepower      1.000000  0.852931      0.854378  \n",
+      "weight          0.852931  1.000000      0.931375  \n",
+      "displacement    0.854378  0.931375      1.000000  \n"
      ]
     }
    ],
    "source": [
-    "# compute correlations between preprocessed predictors:\n",
+    "# compute correlations between preprocessed predictors\n",
     "df_corr = (univariate_selection\n",
     "           .compute_correlations(basetable[basetable[\"split\"]==\"train\"],\n",
     "                                 preprocessed_predictors))\n",
@@ -1334,12 +1301,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 2 Axes>"
       ]
@@ -1364,23 +1331,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['horsepower_enc',\n",
-       " 'weight_enc',\n",
-       " 'cylinders_enc',\n",
+       "['weight_enc',\n",
        " 'displacement_enc',\n",
-       " 'origin_enc',\n",
+       " 'cylinders_enc',\n",
+       " 'horsepower_enc',\n",
        " 'model_year_enc',\n",
+       " 'origin_enc',\n",
        " 'acceleration_enc',\n",
        " 'name_enc']"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1406,13 +1373,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e3cc9a3567fd4e78a919aaef296ee20f",
+       "model_id": "76c2f6130b3f4f5c85857b34f10ef555",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1457,45 +1424,72 @@
        "      <th>0</th>\n",
        "      <td>[name_enc]</td>\n",
        "      <td>name_enc</td>\n",
-       "      <td>0.975224</td>\n",
-       "      <td>7.369621</td>\n",
-       "      <td>6.879241</td>\n",
+       "      <td>0.951267</td>\n",
+       "      <td>7.224819</td>\n",
+       "      <td>7.199707</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>[name_enc, model_year_enc]</td>\n",
        "      <td>model_year_enc</td>\n",
-       "      <td>0.948429</td>\n",
-       "      <td>7.218273</td>\n",
-       "      <td>6.713415</td>\n",
+       "      <td>0.933275</td>\n",
+       "      <td>7.097814</td>\n",
+       "      <td>7.078123</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>[name_enc, model_year_enc, weight_enc]</td>\n",
-       "      <td>weight_enc</td>\n",
-       "      <td>0.914063</td>\n",
-       "      <td>6.775732</td>\n",
-       "      <td>6.259350</td>\n",
+       "      <td>[name_enc, model_year_enc, horsepower_enc]</td>\n",
+       "      <td>horsepower_enc</td>\n",
+       "      <td>0.916923</td>\n",
+       "      <td>6.838945</td>\n",
+       "      <td>6.806006</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>[name_enc, model_year_enc, weight_enc, horsepo...</td>\n",
-       "      <td>horsepower_enc</td>\n",
-       "      <td>0.911907</td>\n",
-       "      <td>6.725785</td>\n",
-       "      <td>6.217832</td>\n",
+       "      <td>[horsepower_enc, name_enc, model_year_enc, acc...</td>\n",
+       "      <td>acceleration_enc</td>\n",
+       "      <td>0.914016</td>\n",
+       "      <td>6.794003</td>\n",
+       "      <td>6.763116</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>[horsepower_enc, name_enc, model_year_enc, wei...</td>\n",
-       "      <td>acceleration_enc</td>\n",
-       "      <td>0.909398</td>\n",
-       "      <td>6.719533</td>\n",
-       "      <td>6.203912</td>\n",
+       "      <td>[horsepower_enc, name_enc, model_year_enc, acc...</td>\n",
+       "      <td>displacement_enc</td>\n",
+       "      <td>0.911310</td>\n",
+       "      <td>6.696004</td>\n",
+       "      <td>6.679070</td>\n",
+       "      <td>regression</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>[horsepower_enc, acceleration_enc, displacemen...</td>\n",
+       "      <td>cylinders_enc</td>\n",
+       "      <td>0.910083</td>\n",
+       "      <td>6.705958</td>\n",
+       "      <td>6.685935</td>\n",
+       "      <td>regression</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[horsepower_enc, cylinders_enc, acceleration_e...</td>\n",
+       "      <td>origin_enc</td>\n",
+       "      <td>0.909738</td>\n",
+       "      <td>6.696299</td>\n",
+       "      <td>6.679883</td>\n",
+       "      <td>regression</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>[horsepower_enc, cylinders_enc, acceleration_e...</td>\n",
+       "      <td>weight_enc</td>\n",
+       "      <td>0.909611</td>\n",
+       "      <td>6.681966</td>\n",
+       "      <td>6.666221</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1506,26 +1500,35 @@
        "                                          predictors last_added_predictor  \\\n",
        "0                                         [name_enc]             name_enc   \n",
        "1                         [name_enc, model_year_enc]       model_year_enc   \n",
-       "2             [name_enc, model_year_enc, weight_enc]           weight_enc   \n",
-       "3  [name_enc, model_year_enc, weight_enc, horsepo...       horsepower_enc   \n",
-       "4  [horsepower_enc, name_enc, model_year_enc, wei...     acceleration_enc   \n",
+       "2         [name_enc, model_year_enc, horsepower_enc]       horsepower_enc   \n",
+       "3  [horsepower_enc, name_enc, model_year_enc, acc...     acceleration_enc   \n",
+       "4  [horsepower_enc, name_enc, model_year_enc, acc...     displacement_enc   \n",
+       "5  [horsepower_enc, acceleration_enc, displacemen...        cylinders_enc   \n",
+       "6  [horsepower_enc, cylinders_enc, acceleration_e...           origin_enc   \n",
+       "7  [horsepower_enc, cylinders_enc, acceleration_e...           weight_enc   \n",
        "\n",
        "   train_performance  selection_performance  validation_performance  \\\n",
-       "0           0.975224               7.369621                6.879241   \n",
-       "1           0.948429               7.218273                6.713415   \n",
-       "2           0.914063               6.775732                6.259350   \n",
-       "3           0.911907               6.725785                6.217832   \n",
-       "4           0.909398               6.719533                6.203912   \n",
+       "0           0.951267               7.224819                7.199707   \n",
+       "1           0.933275               7.097814                7.078123   \n",
+       "2           0.916923               6.838945                6.806006   \n",
+       "3           0.914016               6.794003                6.763116   \n",
+       "4           0.911310               6.696004                6.679070   \n",
+       "5           0.910083               6.705958                6.685935   \n",
+       "6           0.909738               6.696299                6.679883   \n",
+       "7           0.909611               6.681966                6.666221   \n",
        "\n",
        "   model_type  \n",
        "0  regression  \n",
        "1  regression  \n",
        "2  regression  \n",
        "3  regression  \n",
-       "4  regression  "
+       "4  regression  \n",
+       "5  regression  \n",
+       "6  regression  \n",
+       "7  regression  "
       ]
      },
-     "execution_count": 35,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1544,7 +1547,7 @@
     "\n",
     "# compute model performance\n",
     "performances = (forward_selection\n",
-    "                .compute_model_performances(basetable, target_column_name = target_col))\n",
+    "                .compute_model_performances(basetable, target_column_name=target_col))\n",
     "performances"
    ]
   },
@@ -1557,12 +1560,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1585,16 +1588,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['name_enc', 'model_year_enc', 'weight_enc']"
+       "['name_enc', 'model_year_enc', 'horsepower_enc']"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1609,12 +1612,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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LxpYeA+AtmXlu35YeAYDtyBV+AAAomOAHAICCCX4AACiY4AcAgIIJfgAAKJjgBwCAggl+AAAomOAHAICCCX4AACiY4AcAgIIJfgAAKJjgBwCAggl+AAAomOAHAICCCX4AACiY4AcAgIIJfgAAKJjgBwCAggl+AAAomOAHAICCCX4AACiY4AcAgIIJfgAAKJjgBwCAggl+AAAomOAHAICCCX4AACiY4AcAgIIJfgAAKJjgBwCAggl+AAAomOAHAICCCX4AACiY4AcAgIIJfgAAKJjgBwCAggl+AAAomOAHAICCCX4AAChYo4L/hRdeaOo5AACAJtCo4B8/fnxTzwEAADSBNo35Q126dMlNN92U3r17p1Wrv79GGDJkSJMOBgAAvHWNCv5dd901ixcvzuLFixu2CX4AANjxNSr4v/rVr+a///u/8+STT6Z3797Zd999m3ouAABgO2hU8M+aNStz587NfvvtlxtuuCHHHXdczjnnnKaeDQAAeIsaFfxz587Nj370o7Rp0yabNm3K6NGjBT8AALwNNOpTeqqqSps2f39t0LZt27Rt27ZJhwIAALaPRl3hHzRoUD7zmc/kwAMPzKJFi3LAAQc09VwAAMB20KjgnzJlSu67777U1tbmlFNOydChQ5t4LAAAYHvY5i09W7ZsSV1dXf75n/85gwcPzrhx4/KBD3wgZ511VnPNBwAAvAXbvML/k5/8JNdcc02eeeaZHHvssUmSVq1a5cADD2yW4QAAgLdmm8F/+umn5/TTT8+Pf/zjjBw5MklSV1eXdu3aNctwAADAW9OoT+nZsmVLZsyYkSSZMGFCfvrTnzbpUAAAwPbRqOC/7bbb8rnPfS5Jcu211+bWW29t0qEAAIDto1HB36pVq+y0005J/v45/DU1NU061PZ03XXX5dFHH33Nx8eNG5fa2tpXbL/55pubciwAAGgWjfpYzmHDhmXs2LHZb7/98vjjj+fII49s6rm2m/Hjx7+p/b773e/mzDPP3M7TAABA82pU8H/yk5/MEUcckaVLl+bkk0/OgAEDmnqu1zRixIhcf/316dy5cw455JDcfPPNefe7350RI0bk5JNPzj333JOampocf/zxOeusszJlypQcf/zxef/735/zzz8/q1atyh577JEHH3wwDzzwQJLkO9/5Tp555pmsX78+V1xxRebOnZvnnnsu06ZNy7Rp01rsXAEA4K3aZvDPmTMnp512Wr7+9a833MazePHi3HPPPZk0aVKzDPhyw4YNy69//ev06NEjPXv2zIIFC9KuXbv06tUr9957b2655ZbU1NTk7LPPzpAhQxr2mz17dnr27Jlvf/vbqa2tzfDhwxseO/zww3PSSSflyiuvzL333ptPfOITufnmm1819mfPnp3Zs2dvta2urq7JzhcAAN6KbQZ/jx49kiR9+vRplmEa4+ijj84111yTPfbYIxMnTsysWbNSVVWOOeaYXHbZZTn77LOTJM8991xWrFjRsF9tbW3+3//7f0mSvn37Zrfddmt4bODAgUmSbt265Zlnntnm8UeNGpVRo0ZttW3lypUZNmzY9jg9AADYrrb5pt2dd945Dz74YHr27PmK/1pK//79s3Llyjz66KM5/PDDs27dusyfPz99+vRJv3798sMf/jCzZs3KKaeckv79+2+138MPP5wkWbFiRVavXr3N41RV1aTnAQAAzWGbV/hf+vjNFStWZNOmTXnve9+bJ554IrvssktmzZrVLAO+moMPPjgrV65Mq1atcvDBB+fJJ5/MgAED8sEPfjBjxoxJXV1d9ttvv3Tv3r1hn5EjR2bKlCk544wzsueeezZ86tBr6du3b84777xcfvnlTX06AADQZGqqRlzKHj9+fK6++uq0adMmW7Zsyfjx4/P973+/Oebbbn7/+99n3bp1GTJkSJYtW5aPf/zjmTdv3nZ57pdu6Tnq3B+kQ5fur78DwA5s5rl9W3oEALajRn1Kz9NPP93w/1u2bMmzzz7bZAM1lb322iuTJk3KVVddlc2bN2fq1KktPRIAADS5RgX/yJEjc8IJJ6R///558skn8+lPf7qp59ru3vGOd7TobUgAANASGhX8Z5xxRk466aT86U9/Ss+ePbf6hBsAAGDH1ajgX7JkSb785S/nhRdeyIknnph3vetdOeKII5p6NgAA4C3a5sdyvuSSSy7JV7/61XTt2jUjR47MlVde2dRzAQAA20Gjgj9J9t5779TU1GS33XbLLrvs0pQzAQAA20mjgr9Lly657bbbsn79+tx9993p3LlzU88FAABsB40K/hkzZmTlypXZdddd89hjj2X69OlNPRcAALAdNOpNu1/+8pfz9a9/valnAQAAtrNGXeGvq6vL4sWLs3HjxtTV1aWurq6p5wIAALaDRl3hX7ZsWSZMmJBnn302u+++e1q1apX58+c39WwAAMBb1Kgr/J/+9KfTqlWr9OnTJ61bt85FF13U1HMBAADbQaOu8F999dWZM2dOdt999zzzzDOZMGFChgwZ0tSzAQAAb1GjrvB37do1u+++e5KkW7du6dixY5MOBQAAbB+NusLfsWPHnHPOOTn44IPz+OOPZ8OGDbniiiuSJJMmTWrSAQEAgDevUcE/bNiwhv/v3r17kw0DAABsX40K/hEjRjT1HAAAQBNo1D38AADA25PgBwCAggl+AAAomOAHAICCCX4AACiY4AcAgIIJfgAAKJjgBwCAggl+AAAomOAHAICCCX4AACiY4AcAgIIJfgAAKJjgBwCAggl+AAAomOAHAICCCX4AACiY4AcAgIIJfgAAKJjgBwCAggl+AAAomOAHAICCCX4AACiY4AcAgIIJfgAAKJjgBwCAggl+AAAomOAHAICCCX4AACiY4AcAgIIJfgAAKJjgBwCAggl+AAAomOAHAICCCX4AACiY4AcAgIK1aekBSjJldK/07NmzpccAAIAGrvADAEDBBD8AABRM8AMAQMEEPwAAFEzwAwBAwQQ/AAAUTPADAEDBBD8AABRM8AMAQMEEPwAAFEzwAwBAwQQ/AAAUTPADAEDBBD8AABRM8AMAQMEEPwAAFEzwAwBAwQQ/AAAUTPADAEDBBD8AABRM8AMAQMEEPwAAFEzwAwBAwQQ/AAAUTPADAEDBBD8AABRM8AMAQMEEPwAAFKxNSw9QkktvW5EOXTa29BhAM5t5bt+WHgEAXpMr/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAXbIYN/48aNOfLII1/z8YULF2bixInNOBEAALw97ZDBDwAAbB9tmuJJ77jjjvzqV7/Khg0b8vTTT+ess87K/Pnzs2TJkpx//vlZt25dbrrpprRr1y7vfOc7c/HFF6euri7nnXdenn/++fTq1avhuf74xz/mkksuSZJ07do1M2bMeN3jf+5zn8uJJ56YoUOHpra2Npdddlm+853v5Mtf/nKWL1+e+vr6fPazn80hhxySe++9Nz/60Y8a9v3Wt76VJUuW5PLLL0/btm1z+umn5+STT97+3yQAAGgGTRL8SfLiiy/mhhtuyN13350bb7wxt99+exYuXJgbb7wxtbW1ufPOO9OxY8fMmDEjs2fPTpL0798/EydOzCOPPJKFCxcmSS688MLMmDEj/fr1y5w5c3L99dfn0EMP3eaxTzvttNx6660ZOnRofvzjH2fkyJGZM2dOdt1118yYMSOrV6/OmWeembvvvjvLli3Lddddl/bt22fq1Kl54IEH0r1792zcuDFz5sx5xXPPnj27Yd6X1NXVbafvGgAAbF9NFvz77rtvkqRTp07p27dvampq0qVLl6xfvz79+vVLx44dkyQHH3xwHnjggSTJYYcdliR53/velzZt/j5abW1tLrrooiTJpk2b0rt379c99iGHHJLp06fnb3/7WxYsWJBJkyZl+vTpWbRoUR599NEkyebNm7N69ersvvvumTx5cnbZZZf86U9/yv77758kr3mcUaNGZdSoUVttW7lyZYYNG/aGvj8AANAcmiz4a2pqXnN7bW1t1q1blw4dOuR3v/tdevfunZqamvzhD3/Ihz70oTzxxBPZvHlzkr+H92WXXZY999wzixYtytNPP92oY5944omZPn16Bg8enLZt26ZPnz7p0aNHJkyYkA0bNuS73/1u2rRpk29/+9u57777kiQf/ehHU1VVkqRVK29vAADg7a/Jgv+1tG7dOp/+9Kdz1llnpVWrVunVq1fOO++8tG7dOl/4whcyZsyY9OnTJ23btk2STJs2LZMnT86WLVuSJNOnT8+qVate9zinnHJKhg4dmp/97GdJktGjR+eCCy7ImWeembVr12bs2LHp2LFjBg0alBEjRqRDhw7p3LlzVq1alZ49ezbdNwAAAJpRTfXSJe3C/PWvf83555+fm266qcmP9dItPUed+4N06NK9yY8H7Fhmntu3pUcAgNfU7Ff4t6dp06altrb2FdtPO+20fO9738v06dNbYCoAANhxvO2D/7V8+MMfbr5BAABgB+WdqQAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQMMEPAAAFE/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQsDYtPUBJpozulZ49e7b0GAAA0MAVfgAAKJjgBwCAggl+AAAomOAHAICCCX4AACiY4AcAgIIJfgAAKJjgBwCAggl+AAAomOAHAICCCX4AACiY4AcAgIK1aekBSrBly5YkyVNPPdXCkwAA8I+gR48eadOmcSkv+LeDp59+OklyxhlntPAkAAD8I5g/f3569uzZqD8r+LeDgQMH5p3vfGeuu+66tG7duqXHYQcyYcKEXHPNNS09BjsQa4JXY13wctYEr+b/rosePXo0ej/Bvx3svPPO2WWXXbL33nu39CjsYNq1a9foV9/8Y7AmeDXWBS9nTfBq3uy68KZdAAAomOAHAICCCX4AAChY62nTpk1r6SFKMXDgwJYegR2QdcHLWRO8GuuCl7MmeDVvZl3UVFVVNcEsAADADsAtPQAAUDDBDwAABRP8AABQMMH/BtXX12fq1KkZNWpUxo0bl+XLl2/1+C9/+cuceuqpGTVqVG6//fYWmpLm9HprYu7cuTnttNMyevToTJ06NfX19S00Kc3p9dbFSy688MJcfvnlzTwdLeH11sSjjz6asWPHZsyYMfnMZz6TjRs3ttCkNKfXWxf/+q//mhEjRuTUU0/NLbfc0kJT0hIeeeSRjBs37hXb31RrVrwh//Zv/1ZNnjy5qqqqevjhh6sJEyY0PFZXV1d96EMfqtasWVNt3LixOuWUU6pVq1a11Kg0k22tifXr11fDhg2r1q1bV1VVVU2cOLGaN29ei8xJ89rWunjJrbfeWp1++unV1772teYejxawrTVRX19fffjDH66WLVtWVVVV3X777VVtbW2LzEnzer2fFYMHD65Wr15dbdy4saExKN91111XDR8+vDrttNO22v5mW9MV/jdo0aJFOeyww+k6TYgAAAa3SURBVJIk+++/fx577LGGx2pra9OrV6906dIl7dq1y4EHHpiHHnqopUalmWxrTbRr1y633XZb2rdvnyTZvHlzdtpppxaZk+a1rXWRJA8//HAeeeSRjBo1qiXGowVsa00sXbo0Xbt2zU033ZQzzzwza9asSZ8+fVpqVJrR6/2s2GefffLCCy+krq4uVVWlpqamJcakmfXq1StXXnnlK7a/2dYU/G/Q2rVr07Fjx4avW7dunc2bNzc81qlTp4bHdtlll6xdu7bZZ6R5bWtNtGrVKt26dUuSzJo1K+vWrcvgwYNbZE6a17bWxapVq3LVVVdl6tSpLTUeLWBba2L16tV5+OGHM3bs2PzgBz/Ib3/72/zmN79pqVFpRttaF0nyrne9K6eeempOOOGEDB06NJ07d26JMWlmxxxzTNq0afOK7W+2NQX/G9SxY8e8+OKLDV/X19c3/IW8/LEXX3xxq78UyrStNfHS15dddlkWLFiQK6+80tWZfxDbWhf33ntvVq9enfHjx+e6667L3Llzc8cdd7TUqDSTba2Jrl27Zu+9906/fv3Stm3bHHbYYa+40kuZtrUuFi9enPvuuy/z58/PL3/5yzz77LP5+c9/3lKjsgN4s60p+N+gQYMG5f7770+S/OEPf0j//v0bHuvbt2+WL1+eNWvWpK6uLg899FAOOOCAlhqVZrKtNZEkU6dOzcaNG3P11Vc33NpD+ba1Ls4666zccccdmTVrVsaPH5/hw4fnlFNOaalRaSbbWhN77bVXXnzxxYY3bD700EN517ve1SJz0ry2tS46deqUnXfeOTvttFNat26d3XbbLc8//3xLjcoO4M225it/V8A2HXXUUVmwYEFGjx6dqqoyY8aM3HXXXVm3bl1GjRqVKVOm5JxzzklVVTn11FPTvXv3lh6ZJratNTFw4MD8+Mc/zkEHHZSPfOQjSf4ee0cddVQLT01Te72fFfzjeb01MX369Hzuc59LVVU54IADMnTo0JYemWbweuti1KhRGTt2bNq2bZtevXplxIgRLT0yLeCttmZNVVVVM8wJAAC0ALf0AABAwQQ/AAAUTPADAEDBBD8AABRM8AMAQMEEPwCv6f7778/s2bOb9Bg333xzkz4/wD86H8sJQIsaPHhwFixY0NJjABTLP7wFwGu644478utf/zorV67MHnvskZUrV+aEE07IkiVL8sQTT2To0KGZNGlSxo0bl969e2fp0qWpqirf+MY38o53vCOXXnppFi1alCQZPnx4PvKRj2TKlClZs2ZN1qxZk8MPPzzPPfdcpk2blvPOOy9f+tKX8sILL2T16tU57bTTMnbs2IwbNy4DBgzIkiVLsnbt2nzrW9/KP/3TP+Xqq6/OvHnzsmXLlowZMyajR4/OrFmzMnfu3NTU1OT444/PWWed1cLfQYCWJ/gBeF1//vOfc8MNN2TDhg0ZNmxY7r///rRv3z5HHHFEJk2alCQZNGhQLr744vzoRz/Ktddem8GDB2flypW5/fbbs3nz5owdOzYf+MAHkiQf+MAHcvbZZyf5+y0906ZNy+OPP54TTjghRx99dP76179m3LhxGTt2bJJkv/32y5e+9KV84xvfyN13350hQ4bk/vvvz5w5c1JXV5evf/3rWbJkSe65557ccsstqampydlnn50hQ4akT58+LfI9A9hRCH4AXtdee+2VTp06pV27dunWrVu6du2aJKmpqWn4My/F/KBBg/LLX/4yPXr0yEEHHZSampq0bds273vf+1JbW5sk6d279yuO0a1bt9x00035xS9+kY4dO2bz5s0Nj7373e9OkvTo0SPPPPNMli5dmv322y+tW7dO+/btc8EFF+See+7J//7v/za8kHjuueeyYsUKwQ/8w/OmXQBe1/8N+9fy2GOPJUl+//vfp1+/funbt2/D7TybNm3Kww8/nL333vsVz/fSW8luuOGG7L///rn88stz7LHHZltvMevTp0+eeOKJ1NfXZ9OmTfnoRz+aPn36pF+/fvnhD3+YWbNm5ZRTTkn//v3f9DkDlMIVfgC2izvvvDM33nhj2rdvn5kzZ2bXXXfN7373u4waNSqbNm3Ksccem/e85z2v2K9v374577zzMnLkyEybNi133XVXunbtmtatW6euru5Vj7XvvvvmsMMOy5gxY1JfX58xY8ZkwIAB+eAHP5gxY8akrq4u++23X7p3797Upw2ww/MpPQC8ZePGjcu0adPSt2/flh4FgJdxSw8AABTMFX4AACiYK/wAAFAwwQ8AAAUT/AAAUDDBDwAABRP8AABQsP8PHGFNAJZeYPoAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1645,26 +1648,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'meta': 'linear-regression',\n",
-       " 'predictors': ['name_enc', 'model_year_enc', 'weight_enc'],\n",
-       " '_eval_metrics_by_split': {'train': 0.9140634547353548,\n",
-       "  'selection': 6.775732306406747,\n",
-       "  'validation': 6.259350037707075},\n",
+       " 'predictors': ['name_enc', 'model_year_enc', 'horsepower_enc'],\n",
+       " '_eval_metrics_by_split': {'train': 0.9169232178443707,\n",
+       "  'selection': 6.838944681329686,\n",
+       "  'validation': 6.806005504725775},\n",
        " 'params': {'copy_X': True,\n",
        "  'fit_intercept': True,\n",
        "  'n_jobs': None,\n",
        "  'normalize': False},\n",
-       " 'coef_': [0.9051503874363699, 0.0913109248403244, 0.0814910021531995],\n",
-       " 'intercept_': -1.8253833629000091}"
+       " 'coef_': [0.9450829379577408, 0.05658483169165589, 0.04851143443900967],\n",
+       " 'intercept_': -1.1958331574318315}"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1676,7 +1679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1716,7 +1719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1727,7 +1730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1737,20 +1740,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "R2       0.196095\n",
-       "MAE      5.440320\n",
-       "MSE     45.910548\n",
-       "RMSE     6.775732\n",
+       "R2       0.287247\n",
+       "MAE      5.335241\n",
+       "MSE     46.771164\n",
+       "RMSE     6.838945\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1761,12 +1764,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "            ax.plot(self.cumulative_gains[0]*100, self.cumulative_gains[1]*100,\n",
+    "                    color=\"cornflowerblue\", linewidth=3,\n",
+    "                    label=\"cumulative gains\")\n",
+    "            ax.plot(ax.get_xlim(), ax.get_ylim(), linewidth=3,\n",
+    "                    ls=\"--\", color=\"darkorange\", label=\"random selection\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1781,18 +1797,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "quantiles    [-2.241402727604945, -1.9599639845400545, -1.7...\n",
-       "residuals    [-1.5978545483912392, -1.5063985699598552, -1....\n",
+       "residuals    [-1.9964120088907422, -1.8508611130523214, -1....\n",
        "dtype: object"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1803,12 +1819,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:/Users/samuel.borms/Desktop/code/cobra\\cobra\\evaluation\\evaluator.py:711: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"r--\" (-> color='r'). The keyword argument will take precedence.\n",
+      "  ax.plot(x, x, \"r--\", label=\"perfect model\", color=\"darkorange\", linewidth=3)\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
diff --git a/tutorials/tutorial_Cobra_logistic_regression.ipynb b/tutorials/tutorial_Cobra_logistic_regression.ipynb
index c5cb763..9702819 100644
--- a/tutorials/tutorial_Cobra_logistic_regression.ipynb
+++ b/tutorials/tutorial_Cobra_logistic_regression.ipynb
@@ -1,5 +1,17 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0, \"C:/Users/samuel.borms/Desktop/code/cobra\")\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -11,23 +23,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Cobra requires the usual Python packages for data science:\n",
-    "\n",
-    "- numpy (>=1.19.4)\n",
-    "- pandas (>=1.1.5)\n",
-    "- scipy (>=1.5.4)\n",
-    "- scikit-learn (>=0.24.1)\n",
-    "- matplotlib (>=3.3.3)\n",
-    "- seaborn (>=0.11.0)\n",
-    "\n",
-    "These packages, along with their versions are listed in requirements.txt and can be installed using pip.\n",
-    "\n",
-    "If you want to install Cobra with e.g. pip, you don't have to install all of these requirements as these are automatically installed with Cobra itself."
+    "Cobra requires the usual Python packages for data science, such as numpy, pandas and scikit-learn. These packages, along with their versions are listed in requirements.txt and can be installed using pip."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,12 +39,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The easiest way to install Cobra is using pip:"
+    "If you want to install Cobra with e.g. pip, you don't have to install all of these requirements as these are automatically installed with Cobra itself. Hence, the easiest way to install Cobra is using pip:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,9 +73,9 @@
     "\n",
     "1. **Preprocessing**: properly prepare the predictors (a synonym for “feature” or variable that we use throughout this tutorial) for modelling.\n",
     "\n",
-    "2. **Feature Selection**: automatically select a subset of predictors which contribute most to the target variable or output in which you are interested.\n",
+    "2. **Feature selection**: automatically select a subset of predictors which contribute most to the target variable or output in which you are interested.\n",
     "\n",
-    "3. **Model Evaluation**: once a model has been build, a detailed evaluation can be performed by computing all sorts of evaluation metrics.\n",
+    "3. **Model evaluation**: once a model has been build, a detailed evaluation can be performed by computing all sorts of evaluation metrics.\n",
     "\n",
     "Let's dive in!\n",
     "***"
@@ -84,7 +85,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Survival Prediction using Titanic data\n",
+    "## Survival prediction using Titanic data\n",
     "\n",
     "- BASETABLE: seaborn dataset Titanic.\n",
     "- GOAL: Predict if individual survives in Titanic sinking."
@@ -99,7 +100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,9 +138,163 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 6,
+   "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>survived</th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>class</th>\n",
+       "      <th>who</th>\n",
+       "      <th>adult_male</th>\n",
+       "      <th>deck</th>\n",
+       "      <th>embark_town</th>\n",
+       "      <th>alive</th>\n",
+       "      <th>alone</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>man</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>no</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C</td>\n",
+       "      <td>First</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Cherbourg</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>S</td>\n",
+       "      <td>First</td>\n",
+       "      <td>woman</td>\n",
+       "      <td>False</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>man</td>\n",
+       "      <td>True</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>no</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
+       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
+       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
+       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
+       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
+       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
+       "\n",
+       "     who  adult_male deck  embark_town alive  alone  \n",
+       "0    man        True  NaN  Southampton    no  False  \n",
+       "1  woman       False    C    Cherbourg   yes  False  \n",
+       "2  woman       False  NaN  Southampton   yes   True  \n",
+       "3  woman       False    C  Southampton   yes  False  \n",
+       "4    man        True  NaN  Southampton    no   True  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df = sns.load_dataset(\"titanic\")\n",
     "df.head()"
@@ -156,9 +311,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "survived          int64\n",
+       "pclass            int64\n",
+       "sex              object\n",
+       "age             float64\n",
+       "sibsp             int64\n",
+       "parch             int64\n",
+       "fare            float64\n",
+       "embarked         object\n",
+       "class          category\n",
+       "who              object\n",
+       "adult_male         bool\n",
+       "deck           category\n",
+       "embark_town      object\n",
+       "alive            object\n",
+       "alone              bool\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.dtypes"
    ]
@@ -172,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -216,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -233,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -267,9 +448,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['sex', 'embarked', 'class', 'deck', 'embark_town']\n"
+     ]
+    }
+   ],
    "source": [
     "col_dtypes = df.dtypes\n",
     "discrete_vars = [col for col in col_dtypes[col_dtypes==object].index.tolist() if col not in [id_col, target_col]] \n",
@@ -285,9 +474,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pclass\n",
+      "sibsp\n",
+      "parch\n",
+      "alone\n"
+     ]
+    }
+   ],
    "source": [
     "for col in df.columns:\n",
     "    if col not in discrete_vars and col not in [id_col, target_col]: # omit discrete because a string and target\n",
@@ -305,9 +505,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['sex',\n",
+       " 'embarked',\n",
+       " 'class',\n",
+       " 'deck',\n",
+       " 'embark_town',\n",
+       " 'pclass',\n",
+       " 'sibsp',\n",
+       " 'parch',\n",
+       " 'class',\n",
+       " 'deck',\n",
+       " 'alone']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "discrete_vars.extend([\"pclass\", \"sibsp\", \"parch\", \"class\", \"deck\", \"alone\"])\n",
     "discrete_vars"
@@ -322,9 +543,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['age', 'fare']"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "continuous_vars = list(set(df.columns)\n",
     "                       - set(discrete_vars) \n",
@@ -341,9 +573,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The target encoder's additive smoothing weight is set to 0. This disables smoothing and may make the encoding prone to overfitting.\n"
+     ]
+    }
+   ],
    "source": [
     "# using all Cobra's default parameters for preprocessing for now:\n",
     "preprocessor = PreProcessor.from_params(\n",
@@ -362,7 +602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -381,9 +621,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "482f57ca0b0c47fa8c5b14eb784d8bf4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Computing discretization bins...:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7168a6788f6c4411a5e9e5218cc0feb3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Discretizing columns...:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eb11994a2cc440a8b442f357c89bc175",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fitting category regrouping...:   0%|          | 0/11 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9eaa854bb3c0430682051db9f5ed638c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fitting target encoding...:   0%|          | 0/13 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "preprocessor.fit(basetable[basetable[\"split\"]==\"train\"],\n",
     "                 continuous_vars=continuous_vars,\n",
@@ -400,9 +697,345 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64a8686766f6461ba5f60d915afedc7f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Discretizing columns...:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "febb5e8f6cf44e548f45d84f0d14cd7b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Applying target encoding...:   0%|          | 0/13 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>survived</th>\n",
+       "      <th>pclass</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>class</th>\n",
+       "      <th>deck</th>\n",
+       "      <th>embark_town</th>\n",
+       "      <th>alone</th>\n",
+       "      <th>id</th>\n",
+       "      <th>split</th>\n",
+       "      <th>age_bin</th>\n",
+       "      <th>fare_bin</th>\n",
+       "      <th>sex_processed</th>\n",
+       "      <th>embarked_processed</th>\n",
+       "      <th>class_processed</th>\n",
+       "      <th>deck_processed</th>\n",
+       "      <th>embark_town_processed</th>\n",
+       "      <th>pclass_processed</th>\n",
+       "      <th>sibsp_processed</th>\n",
+       "      <th>parch_processed</th>\n",
+       "      <th>alone_processed</th>\n",
+       "      <th>sex_enc</th>\n",
+       "      <th>embarked_enc</th>\n",
+       "      <th>class_enc</th>\n",
+       "      <th>deck_enc</th>\n",
+       "      <th>embark_town_enc</th>\n",
+       "      <th>pclass_enc</th>\n",
+       "      <th>sibsp_enc</th>\n",
+       "      <th>parch_enc</th>\n",
+       "      <th>alone_enc</th>\n",
+       "      <th>age_enc</th>\n",
+       "      <th>fare_enc</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>selection</td>\n",
+       "      <td>18.0 - 22.0</td>\n",
+       "      <td>0.0 - 7.5</td>\n",
+       "      <td>male</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Missing</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.194286</td>\n",
+       "      <td>0.318396</td>\n",
+       "      <td>0.291667</td>\n",
+       "      <td>0.279518</td>\n",
+       "      <td>0.318396</td>\n",
+       "      <td>0.291667</td>\n",
+       "      <td>0.523077</td>\n",
+       "      <td>0.326582</td>\n",
+       "      <td>0.497717</td>\n",
+       "      <td>0.351852</td>\n",
+       "      <td>0.132075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C</td>\n",
+       "      <td>First</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Cherbourg</td>\n",
+       "      <td>False</td>\n",
+       "      <td>2</td>\n",
+       "      <td>selection</td>\n",
+       "      <td>36.0 - 42.0</td>\n",
+       "      <td>39.4 - 73.5</td>\n",
+       "      <td>female</td>\n",
+       "      <td>C</td>\n",
+       "      <td>First</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Cherbourg</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.708108</td>\n",
+       "      <td>0.572727</td>\n",
+       "      <td>0.629921</td>\n",
+       "      <td>0.684783</td>\n",
+       "      <td>0.572727</td>\n",
+       "      <td>0.629921</td>\n",
+       "      <td>0.523077</td>\n",
+       "      <td>0.326582</td>\n",
+       "      <td>0.497717</td>\n",
+       "      <td>0.390244</td>\n",
+       "      <td>0.481481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>True</td>\n",
+       "      <td>3</td>\n",
+       "      <td>train</td>\n",
+       "      <td>25.0 - 29.0</td>\n",
+       "      <td>7.9 - 8.1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Missing</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.708108</td>\n",
+       "      <td>0.318396</td>\n",
+       "      <td>0.291667</td>\n",
+       "      <td>0.279518</td>\n",
+       "      <td>0.318396</td>\n",
+       "      <td>0.291667</td>\n",
+       "      <td>0.328729</td>\n",
+       "      <td>0.326582</td>\n",
+       "      <td>0.284810</td>\n",
+       "      <td>0.325000</td>\n",
+       "      <td>0.218750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>S</td>\n",
+       "      <td>First</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>False</td>\n",
+       "      <td>4</td>\n",
+       "      <td>train</td>\n",
+       "      <td>32.0 - 36.0</td>\n",
+       "      <td>39.4 - 73.5</td>\n",
+       "      <td>female</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>First</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.708108</td>\n",
+       "      <td>0.318396</td>\n",
+       "      <td>0.629921</td>\n",
+       "      <td>0.684783</td>\n",
+       "      <td>0.318396</td>\n",
+       "      <td>0.629921</td>\n",
+       "      <td>0.523077</td>\n",
+       "      <td>0.326582</td>\n",
+       "      <td>0.497717</td>\n",
+       "      <td>0.454545</td>\n",
+       "      <td>0.481481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>Third</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Southampton</td>\n",
+       "      <td>True</td>\n",
+       "      <td>5</td>\n",
+       "      <td>train</td>\n",
+       "      <td>32.0 - 36.0</td>\n",
+       "      <td>7.9 - 8.1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Missing</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.194286</td>\n",
+       "      <td>0.318396</td>\n",
+       "      <td>0.291667</td>\n",
+       "      <td>0.279518</td>\n",
+       "      <td>0.318396</td>\n",
+       "      <td>0.291667</td>\n",
+       "      <td>0.328729</td>\n",
+       "      <td>0.326582</td>\n",
+       "      <td>0.284810</td>\n",
+       "      <td>0.454545</td>\n",
+       "      <td>0.218750</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class deck  \\\n",
+       "0         0       3    male  22.0      1      0   7.2500        S  Third  NaN   \n",
+       "1         1       1  female  38.0      1      0  71.2833        C  First    C   \n",
+       "2         1       3  female  26.0      0      0   7.9250        S  Third  NaN   \n",
+       "3         1       1  female  35.0      1      0  53.1000        S  First    C   \n",
+       "4         0       3    male  35.0      0      0   8.0500        S  Third  NaN   \n",
+       "\n",
+       "   embark_town  alone  id      split      age_bin     fare_bin sex_processed  \\\n",
+       "0  Southampton  False   1  selection  18.0 - 22.0    0.0 - 7.5          male   \n",
+       "1    Cherbourg  False   2  selection  36.0 - 42.0  39.4 - 73.5        female   \n",
+       "2  Southampton   True   3      train  25.0 - 29.0    7.9 - 8.1        female   \n",
+       "3  Southampton  False   4      train  32.0 - 36.0  39.4 - 73.5        female   \n",
+       "4  Southampton   True   5      train  32.0 - 36.0    7.9 - 8.1          male   \n",
+       "\n",
+       "  embarked_processed class_processed deck_processed embark_town_processed  \\\n",
+       "0              Other           Other        Missing                 Other   \n",
+       "1                  C           First          Other             Cherbourg   \n",
+       "2              Other           Other        Missing                 Other   \n",
+       "3              Other           First          Other                 Other   \n",
+       "4              Other           Other        Missing                 Other   \n",
+       "\n",
+       "  pclass_processed  sibsp_processed  parch_processed alone_processed  \\\n",
+       "0            Other                1                0           False   \n",
+       "1                1                1                0           False   \n",
+       "2            Other                0                0            True   \n",
+       "3                1                1                0           False   \n",
+       "4            Other                0                0            True   \n",
+       "\n",
+       "    sex_enc  embarked_enc  class_enc  deck_enc  embark_town_enc  pclass_enc  \\\n",
+       "0  0.194286      0.318396   0.291667  0.279518         0.318396    0.291667   \n",
+       "1  0.708108      0.572727   0.629921  0.684783         0.572727    0.629921   \n",
+       "2  0.708108      0.318396   0.291667  0.279518         0.318396    0.291667   \n",
+       "3  0.708108      0.318396   0.629921  0.684783         0.318396    0.629921   \n",
+       "4  0.194286      0.318396   0.291667  0.279518         0.318396    0.291667   \n",
+       "\n",
+       "   sibsp_enc  parch_enc  alone_enc   age_enc  fare_enc  \n",
+       "0   0.523077   0.326582   0.497717  0.351852  0.132075  \n",
+       "1   0.523077   0.326582   0.497717  0.390244  0.481481  \n",
+       "2   0.328729   0.326582   0.284810  0.325000  0.218750  \n",
+       "3   0.523077   0.326582   0.497717  0.454545  0.481481  \n",
+       "4   0.328729   0.326582   0.284810  0.454545  0.218750  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "basetable = preprocessor.transform(basetable,\n",
     "                                   continuous_vars=continuous_vars,\n",
@@ -426,9 +1059,501 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 20,
+   "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>variable</th>\n",
+       "      <th>label</th>\n",
+       "      <th>pop_size</th>\n",
+       "      <th>global_avg_target</th>\n",
+       "      <th>avg_target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>age</td>\n",
+       "      <td>0.0 - 14.0</td>\n",
+       "      <td>0.084270</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.466667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>age</td>\n",
+       "      <td>14.0 - 18.0</td>\n",
+       "      <td>0.073034</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.461538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>age</td>\n",
+       "      <td>18.0 - 22.0</td>\n",
+       "      <td>0.101124</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.222222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>age</td>\n",
+       "      <td>22.0 - 25.0</td>\n",
+       "      <td>0.095506</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.411765</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>age</td>\n",
+       "      <td>25.0 - 29.0</td>\n",
+       "      <td>0.101124</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.277778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>age</td>\n",
+       "      <td>29.0 - 32.0</td>\n",
+       "      <td>0.061798</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>age</td>\n",
+       "      <td>32.0 - 36.0</td>\n",
+       "      <td>0.050562</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.666667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>age</td>\n",
+       "      <td>36.0 - 42.0</td>\n",
+       "      <td>0.061798</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>age</td>\n",
+       "      <td>42.0 - 50.0</td>\n",
+       "      <td>0.056180</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>age</td>\n",
+       "      <td>50.0 - 74.0</td>\n",
+       "      <td>0.078652</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.642857</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>age</td>\n",
+       "      <td>Missing</td>\n",
+       "      <td>0.235955</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.309524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>alone</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0.404494</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.555556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>alone</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0.595506</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.283019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>class</td>\n",
+       "      <td>First</td>\n",
+       "      <td>0.247191</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.659091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>class</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>0.752809</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.305970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>deck</td>\n",
+       "      <td>B</td>\n",
+       "      <td>0.067416</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>deck</td>\n",
+       "      <td>Missing</td>\n",
+       "      <td>0.752809</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.298507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>deck</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>0.179775</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.656250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>embark_town</td>\n",
+       "      <td>Cherbourg</td>\n",
+       "      <td>0.174157</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.612903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>embark_town</td>\n",
+       "      <td>Missing</td>\n",
+       "      <td>0.005618</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>embark_town</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>0.820225</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.342466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>embarked</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0.174157</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.612903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>embarked</td>\n",
+       "      <td>Missing</td>\n",
+       "      <td>0.005618</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>embarked</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>0.820225</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.342466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>fare</td>\n",
+       "      <td>0.0 - 7.5</td>\n",
+       "      <td>0.117978</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>fare</td>\n",
+       "      <td>7.5 - 7.9</td>\n",
+       "      <td>0.191011</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.205882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>fare</td>\n",
+       "      <td>7.9 - 8.1</td>\n",
+       "      <td>0.095506</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.235294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>fare</td>\n",
+       "      <td>8.1 - 11.5</td>\n",
+       "      <td>0.078652</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.357143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>fare</td>\n",
+       "      <td>11.5 - 14.5</td>\n",
+       "      <td>0.061798</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.636364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>fare</td>\n",
+       "      <td>14.5 - 22.4</td>\n",
+       "      <td>0.095506</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.588235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>fare</td>\n",
+       "      <td>22.4 - 26.6</td>\n",
+       "      <td>0.067416</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>fare</td>\n",
+       "      <td>26.6 - 39.4</td>\n",
+       "      <td>0.067416</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.416667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>fare</td>\n",
+       "      <td>39.4 - 73.5</td>\n",
+       "      <td>0.084270</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.466667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>fare</td>\n",
+       "      <td>73.5 - 512.3</td>\n",
+       "      <td>0.140449</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.760000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>parch</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.792135</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.354610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>parch</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.101124</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>parch</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.089888</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.562500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>parch</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.011236</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>parch</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.005618</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>pclass</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.247191</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.659091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>pclass</td>\n",
+       "      <td>Other</td>\n",
+       "      <td>0.752809</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.305970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sex</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0.382022</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.779412</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sex</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.617978</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.154545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sibsp</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.662921</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.338983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sibsp</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.241573</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.604651</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sibsp</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.039326</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.571429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>sibsp</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.028090</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>sibsp</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.016854</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>sibsp</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.005618</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>sibsp</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.005618</td>\n",
+       "      <td>0.393258</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       variable         label  pop_size  global_avg_target  avg_target\n",
+       "0           age    0.0 - 14.0  0.084270           0.393258    0.466667\n",
+       "1           age   14.0 - 18.0  0.073034           0.393258    0.461538\n",
+       "2           age   18.0 - 22.0  0.101124           0.393258    0.222222\n",
+       "3           age   22.0 - 25.0  0.095506           0.393258    0.411765\n",
+       "4           age   25.0 - 29.0  0.101124           0.393258    0.277778\n",
+       "5           age   29.0 - 32.0  0.061798           0.393258    0.363636\n",
+       "6           age   32.0 - 36.0  0.050562           0.393258    0.666667\n",
+       "7           age   36.0 - 42.0  0.061798           0.393258    0.363636\n",
+       "8           age   42.0 - 50.0  0.056180           0.393258    0.500000\n",
+       "9           age   50.0 - 74.0  0.078652           0.393258    0.642857\n",
+       "10          age       Missing  0.235955           0.393258    0.309524\n",
+       "0         alone         False  0.404494           0.393258    0.555556\n",
+       "1         alone          True  0.595506           0.393258    0.283019\n",
+       "0         class         First  0.247191           0.393258    0.659091\n",
+       "1         class         Other  0.752809           0.393258    0.305970\n",
+       "0          deck             B  0.067416           0.393258    0.750000\n",
+       "1          deck       Missing  0.752809           0.393258    0.298507\n",
+       "2          deck         Other  0.179775           0.393258    0.656250\n",
+       "0   embark_town     Cherbourg  0.174157           0.393258    0.612903\n",
+       "1   embark_town       Missing  0.005618           0.393258    1.000000\n",
+       "2   embark_town         Other  0.820225           0.393258    0.342466\n",
+       "0      embarked             C  0.174157           0.393258    0.612903\n",
+       "1      embarked       Missing  0.005618           0.393258    1.000000\n",
+       "2      embarked         Other  0.820225           0.393258    0.342466\n",
+       "0          fare     0.0 - 7.5  0.117978           0.393258    0.000000\n",
+       "1          fare     7.5 - 7.9  0.191011           0.393258    0.205882\n",
+       "2          fare     7.9 - 8.1  0.095506           0.393258    0.235294\n",
+       "3          fare    8.1 - 11.5  0.078652           0.393258    0.357143\n",
+       "4          fare   11.5 - 14.5  0.061798           0.393258    0.636364\n",
+       "5          fare   14.5 - 22.4  0.095506           0.393258    0.588235\n",
+       "6          fare   22.4 - 26.6  0.067416           0.393258    0.500000\n",
+       "7          fare   26.6 - 39.4  0.067416           0.393258    0.416667\n",
+       "8          fare   39.4 - 73.5  0.084270           0.393258    0.466667\n",
+       "9          fare  73.5 - 512.3  0.140449           0.393258    0.760000\n",
+       "0         parch             0  0.792135           0.393258    0.354610\n",
+       "1         parch             1  0.101124           0.393258    0.500000\n",
+       "2         parch             2  0.089888           0.393258    0.562500\n",
+       "3         parch             3  0.011236           0.393258    1.000000\n",
+       "4         parch             4  0.005618           0.393258    0.000000\n",
+       "0        pclass             1  0.247191           0.393258    0.659091\n",
+       "1        pclass         Other  0.752809           0.393258    0.305970\n",
+       "0           sex        female  0.382022           0.393258    0.779412\n",
+       "1           sex          male  0.617978           0.393258    0.154545\n",
+       "0         sibsp             0  0.662921           0.393258    0.338983\n",
+       "1         sibsp             1  0.241573           0.393258    0.604651\n",
+       "2         sibsp             2  0.039326           0.393258    0.571429\n",
+       "3         sibsp             3  0.028090           0.393258    0.000000\n",
+       "4         sibsp             4  0.016854           0.393258    0.000000\n",
+       "5         sibsp             5  0.005618           0.393258    0.000000\n",
+       "6         sibsp             8  0.005618           0.393258    0.000000"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "predictor_list = [col for col in basetable.columns\n",
     "                  if col.endswith(\"_bin\") or col.endswith(\"_processed\")]\n",
@@ -448,13 +1573,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
-   "source": [
-    "col_order = list(basetable[\"age_bin\"].unique().sort_values()))\n",
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "col_order = list(basetable[\"age_bin\"].unique().sort_values())\n",
     "plot_incidence(pig_tables, variable=\"age\", model_type=\"classification\", column_order=col_order)"
    ]
   },
@@ -485,9 +1621,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['age_bin',\n",
+       " 'alone_processed',\n",
+       " 'class_processed',\n",
+       " 'deck_processed',\n",
+       " 'embark_town_processed',\n",
+       " 'embarked_processed',\n",
+       " 'fare_bin',\n",
+       " 'parch_processed',\n",
+       " 'pclass_processed',\n",
+       " 'sex_processed',\n",
+       " 'sibsp_processed']"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "preprocessed_predictors = [\n",
     "    col for col in basetable.columns\n",
@@ -504,7 +1661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,9 +1677,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "df_auc = univariate_selection.compute_univariate_preselection(\n",
     "    target_enc_train_data=basetable[basetable[\"split\"]==\"train\"],\n",
@@ -539,14 +1707,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Next, we compute correlations between the preprocessed predictors and plot it using a correlation matrix:\n"
+    "Next, we compute correlations between the preprocessed predictors and plot it using a correlation matrix."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "df_corr = (univariate_selection\n",
     "           .compute_correlations(basetable[basetable[\"split\"]==\"train\"],\n",
@@ -563,9 +1744,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['sex_enc',\n",
+       " 'fare_enc',\n",
+       " 'deck_enc',\n",
+       " 'sibsp_enc',\n",
+       " 'class_enc',\n",
+       " 'pclass_enc',\n",
+       " 'alone_enc',\n",
+       " 'embarked_enc',\n",
+       " 'embark_town_enc',\n",
+       " 'parch_enc']"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "preselected_predictors = univariate_selection.get_preselected_predictors(df_auc)\n",
     "preselected_predictors"
@@ -587,11 +1788,180 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c9d9c04ce19a45668428e0ee0b47f05d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sequentially adding best predictor...:   0%|          | 0/10 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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>predictors</th>\n",
+       "      <th>last_added_predictor</th>\n",
+       "      <th>train_performance</th>\n",
+       "      <th>selection_performance</th>\n",
+       "      <th>validation_performance</th>\n",
+       "      <th>model_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[sex_enc]</td>\n",
+       "      <td>sex_enc</td>\n",
+       "      <td>0.748789</td>\n",
+       "      <td>0.809127</td>\n",
+       "      <td>0.778474</td>\n",
+       "      <td>classification</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[sex_enc, deck_enc]</td>\n",
+       "      <td>deck_enc</td>\n",
+       "      <td>0.812642</td>\n",
+       "      <td>0.852712</td>\n",
+       "      <td>0.818069</td>\n",
+       "      <td>classification</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[deck_enc, sex_enc, embarked_enc]</td>\n",
+       "      <td>embarked_enc</td>\n",
+       "      <td>0.828076</td>\n",
+       "      <td>0.853373</td>\n",
+       "      <td>0.796999</td>\n",
+       "      <td>classification</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[deck_enc, sex_enc, embarked_enc, sibsp_enc]</td>\n",
+       "      <td>sibsp_enc</td>\n",
+       "      <td>0.837775</td>\n",
+       "      <td>0.877050</td>\n",
+       "      <td>0.804566</td>\n",
+       "      <td>classification</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[deck_enc, sex_enc, sibsp_enc, embarked_enc, f...</td>\n",
+       "      <td>fare_enc</td>\n",
+       "      <td>0.844617</td>\n",
+       "      <td>0.871892</td>\n",
+       "      <td>0.807306</td>\n",
+       "      <td>classification</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>[deck_enc, fare_enc, sex_enc, sibsp_enc, embar...</td>\n",
+       "      <td>parch_enc</td>\n",
+       "      <td>0.846352</td>\n",
+       "      <td>0.872817</td>\n",
+       "      <td>0.805088</td>\n",
+       "      <td>classification</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[deck_enc, fare_enc, sex_enc, parch_enc, sibsp...</td>\n",
+       "      <td>class_enc</td>\n",
+       "      <td>0.846494</td>\n",
+       "      <td>0.873347</td>\n",
+       "      <td>0.806654</td>\n",
+       "      <td>classification</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>[deck_enc, fare_enc, sex_enc, parch_enc, sibsp...</td>\n",
+       "      <td>pclass_enc</td>\n",
+       "      <td>0.846494</td>\n",
+       "      <td>0.873347</td>\n",
+       "      <td>0.806654</td>\n",
+       "      <td>classification</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>[deck_enc, fare_enc, sex_enc, parch_enc, class...</td>\n",
+       "      <td>embark_town_enc</td>\n",
+       "      <td>0.846509</td>\n",
+       "      <td>0.873347</td>\n",
+       "      <td>0.806654</td>\n",
+       "      <td>classification</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          predictors last_added_predictor  \\\n",
+       "0                                          [sex_enc]              sex_enc   \n",
+       "1                                [sex_enc, deck_enc]             deck_enc   \n",
+       "2                  [deck_enc, sex_enc, embarked_enc]         embarked_enc   \n",
+       "3       [deck_enc, sex_enc, embarked_enc, sibsp_enc]            sibsp_enc   \n",
+       "4  [deck_enc, sex_enc, sibsp_enc, embarked_enc, f...             fare_enc   \n",
+       "5  [deck_enc, fare_enc, sex_enc, sibsp_enc, embar...            parch_enc   \n",
+       "6  [deck_enc, fare_enc, sex_enc, parch_enc, sibsp...            class_enc   \n",
+       "7  [deck_enc, fare_enc, sex_enc, parch_enc, sibsp...           pclass_enc   \n",
+       "8  [deck_enc, fare_enc, sex_enc, parch_enc, class...      embark_town_enc   \n",
+       "\n",
+       "   train_performance  selection_performance  validation_performance  \\\n",
+       "0           0.748789               0.809127                0.778474   \n",
+       "1           0.812642               0.852712                0.818069   \n",
+       "2           0.828076               0.853373                0.796999   \n",
+       "3           0.837775               0.877050                0.804566   \n",
+       "4           0.844617               0.871892                0.807306   \n",
+       "5           0.846352               0.872817                0.805088   \n",
+       "6           0.846494               0.873347                0.806654   \n",
+       "7           0.846494               0.873347                0.806654   \n",
+       "8           0.846509               0.873347                0.806654   \n",
+       "\n",
+       "       model_type  \n",
+       "0  classification  \n",
+       "1  classification  \n",
+       "2  classification  \n",
+       "3  classification  \n",
+       "4  classification  \n",
+       "5  classification  \n",
+       "6  classification  \n",
+       "7  classification  \n",
+       "8  classification  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "forward_selection = ForwardFeatureSelection(model_type=\"classification\",\n",
     "                                            max_predictors=30,\n",
@@ -600,14 +1970,12 @@
     "# fit the forward feature selection on the train data\n",
     "# has optional parameters to force and/or exclude certain predictors (see docs)\n",
     "forward_selection.fit(basetable[basetable[\"split\"]==\"train\"],\n",
-    "                      target_column_name = target_col,\n",
-    "                      predictors = preselected_predictors)\n",
-    "                      #forced_predictors: list = [],\n",
-    "                      #excluded_predictors: list = [])\n",
+    "                      target_column_name=target_col,\n",
+    "                      predictors=preselected_predictors)\n",
     "\n",
     "# compute model performance\n",
     "performances = (forward_selection\n",
-    "                .compute_model_performances(basetable, target_column_name = target_col))\n",
+    "                .compute_model_performances(basetable, target_column_name=target_col))\n",
     "performances"
    ]
   },
@@ -620,9 +1988,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# plot performance curves\n",
     "plot_performance_curves(performances)"
@@ -637,9 +2016,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['deck_enc', 'fare_enc', 'sex_enc', 'sibsp_enc', 'embarked_enc', 'parch_enc']"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# pick the optimal step based on visual inspection in the plot above (try to find a knee point)\n",
     "model = forward_selection.get_model_from_step(5)\n",
@@ -650,9 +2040,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "variable_importance = model.compute_variable_importance(\n",
     "    basetable[basetable[\"split\"]==\"selection\"]\n",
@@ -676,9 +2077,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'meta': 'logistic-regression',\n",
+       " 'predictors': ['deck_enc',\n",
+       "  'fare_enc',\n",
+       "  'sex_enc',\n",
+       "  'sibsp_enc',\n",
+       "  'embarked_enc',\n",
+       "  'parch_enc'],\n",
+       " '_eval_metrics_by_split': {'train': 0.8463522972002873,\n",
+       "  'selection': 0.8728174603174603,\n",
+       "  'validation': 0.8050880626223091},\n",
+       " 'params': {'C': 1000000000.0,\n",
+       "  'class_weight': None,\n",
+       "  'dual': False,\n",
+       "  'fit_intercept': True,\n",
+       "  'intercept_scaling': 1,\n",
+       "  'l1_ratio': None,\n",
+       "  'max_iter': 100,\n",
+       "  'multi_class': 'auto',\n",
+       "  'n_jobs': None,\n",
+       "  'penalty': 'l2',\n",
+       "  'random_state': 42,\n",
+       "  'solver': 'liblinear',\n",
+       "  'tol': 0.0001,\n",
+       "  'verbose': 0,\n",
+       "  'warm_start': False},\n",
+       " 'classes_': [0, 1],\n",
+       " 'coef_': [[3.805721985140643,\n",
+       "   0.6303750013369196,\n",
+       "   4.422528045527391,\n",
+       "   2.385099647746924,\n",
+       "   2.932569579182775,\n",
+       "   0.8389611142315032]],\n",
+       " 'intercept_': [-6.257622551740109],\n",
+       " 'n_iter_': [5]}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model_dict = model.serialize()\n",
     "model_dict"
@@ -686,7 +2131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -722,7 +2167,7 @@
     "4. Recall\n",
     "5. F1\n",
     "6. Matthews correlation coefficient\n",
-    "7. Lift\n",
+    "7. Lift at given percentage\n",
     "\n",
     "Furthermore, we can evaluate the classification performance using a confusion matrix.\n",
     "\n",
@@ -731,7 +2176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -742,7 +2187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -752,56 +2197,131 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "accuracy             0.825843\n",
+       "AUC                  0.872817\n",
+       "precision            0.760000\n",
+       "recall               0.814286\n",
+       "F1                   0.786207\n",
+       "matthews_corrcoef    0.640665\n",
+       "lift at 0.05         2.540000\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "evaluator.scalar_metrics"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "evaluator.plot_confusion_matrix()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "evaluator.plot_roc_curve()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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4JiIiIiLix7SmWkREREQkjxSqRURERETySKFaRERERCSPFKpFRERERPLI43b/KIgr9EVEREREctKoUaN8eR6PC9WQf29OfEdiYiI1a9Y0uwzxMDovJCc6LyQnOi8kJ/k5mavlHyIiIiIieaRQLSIiIiKSRwrVIiIiIiJ5pFAtIiIiIpJHCtUiIiIiInmkUC0iIiIikkcK1SIiIiIieaRQLSIiIiKSRwrVIiIiIiJ5pFAtIiIiIpJHCtUiIiIiInmkUC0iIiIikkcK1SIiIiIieaRQLSIiIiKSRwrVIiIiIiJ5pFAtIiIiIpJHVxWqN2/eTP/+/QHYt28fffv2pV+/fowcORK73Q7ApEmTuOuuu+jTpw9btmwBYPny5dx111088cQTzvuNHj2agwcPFsR7ERERERExRdCV7vD+++/z3XffER4eDsDYsWN58sknadasGSNGjGDRokWUKVOGdevWMWfOHI4cOcLjjz/Ol19+yWeffcZHH33EW2+9xbZt2wgICKBIkSKUK1euwN+YiIiIiD9JyzBI2J9JRpZhdile4fq970LJNvn2fFcM1XFxcbz99ts899xzACQkJNC0aVMA2rZty6pVq6hUqRKtW7fGYrFQpkwZbDYbp06dIjIyEqvVSnp6OuHh4UyaNIlRo0blW/EiIiIiAja7wYTvz7HnmM3sUrzGzZlnqFQy/57viqH65ptvdluuYRgGFosFgMjISJKSkkhOTqZo0aLO+1wYf+SRRxg7dizVq1dn//79NGzYkHnz5pGYmEj37t1p0KBBjq+ZmJiYx7clvsZqteq8kEvovJCc6LyQnPj6ebFhfwR7jkWZXYZXWRg0mEHk35LkK4bqiwUEuJZhp6SkEB0dTZEiRUhJSXEbj4qKonjx4kyYMAGbzcaTTz7JmDFjeOGFF3jzzTcZPHgw77//fo6vUbNmzVy8FfFliYmJOi/kEjovJCc6LyQnvnxeHD9rY82Ks87jmuWCKBqpvSicDIMqp2dxIqI+Z8NqOIctlpB8fZlrDtW1atVi7dq1NGvWjOXLl9O8eXPi4uL43//+x8CBAzl69Ch2u53ixYs7HzNr1iy6d+8OgN1ux2KxkJaWln/vQkRERMQPGYbBJ8tSyMhyHJcrEcgTt0cRFGgxtzBPcWo7LBwMB5ZAuXZw2xKwuP5sNm7Mv5e65lA9dOhQhg8fzvjx46lcuTI333wzgYGBNG7cmN69e2O32xkxYoTz/snJyaxbt46JEycCULJkSefuISIiIiKSe2t2ZPDnQUeitgAD2kcqUANkWWHtWFj/X7BlOMYOLoNtn0HNewrkJS2GYXjUJaIbN26kUaNGZpchHsaXP7aT3NN5ITnReSE58cXzIinNzojPz5JsdUS5jnVD6dM60uSqPMDeBbDoETjzl2vMEggN/w0tX4KQIs7h/Myd1zxTLSIiIiLmm70q1RmoixcJ4M5mESZXZLKUo7D0Kdj2ufv49c2g0xQoVb9AX16hWkRERMTLJOzPZM2ODOfxve0iCAv202UfdhtseQ9WPg/prgs2CY2BNv+Fuv8CS8FfuKlQLSIiIuJF0jMNPlnu2nWtSdUQ6lTI350svMqJrbDoUSDbiuYa/aD9OIgsXWhlaL8VERERES/y/fo0TpyzAxARaqFPaz9f9lGqPtR7yPF1sWpw1wK4/dNCDdSgmWoRERERr7H/eBYLNludx71aRhAd4UdzpIYBSQchurz7eOtXISoOGg2BoDBTSvOjvwURERER72WzG3y8NAX7+VUO1csG0aqGHy37OLsXvukGM+tD6nH328KKQbPnTQvUoFAtIiIi4hUWbbGy/7gNgKBA6N8uEovFDy5OtGXCutdgei3YPQ+sp2D5c2ZXdQkt/xARERHxcMfP2fh2nasbddfG4cQWDTSxokJycCUsfBhOJmQbtDhmpA17oezqcbUUqkVEREQ8mGEYfLos1dmKvGzxQG6qb94yh0KRdhKWD4U/PnQfL1nPsed0mebm1PUPFKpFREREPNjanRkkHMgEzrci7+DDrcgNA/6cAcuegbQTrvHgSGg5Gho+AQGeGV89syoRERERISnNzqyVqc7j+DqhVI714fj2Qz/Y/oX7WNU7ocNbl+744WF8+G9FREREpPDtOprJ6m0ZZNmNK9/5Co6csvlXK/Kqd7pCdVQcxL8NVbuZWtLVUqgWERERyScnk2xM+C6J9Kz8f+5+bSMIC/HRZR8XVL8bEmdC8VrQcqRj2YeXUKgWERERyQcXLigsiEDdrFoI9Sr60J7UyYdhyRCo+y+o0NE1brHAnd951K4eV0uhWkRERCQfrP8rg637XRcU3tUynMjQvIfD8FALdSsE5/l5PILdBpsmw6oXISMJjm+CAVsgKNR1Hy8M1KBQLSIiIpJnKVY7X2S7oLB97VBuqh9uYkUe6NhGWPCQ4/8XnN4Be36Eat3NqyufKFSLiIiI5NGc1akkpTkuKCwaaaF7cx+/oPBapJ+DVcNh0yRHw5YLilWHTu9CXAfzastHCtUiIiIi/8AwDH7eZGXn4Sxy2s/DMOCP88s+AO5pG0m4r19QeDUMA3bMhSX/hpQjrvHAUGj+H2j8rPuyDy+nUC0iIiLyD5YlpPPlr2lXviPQsHIw9Sv50AWFuXVuv6O9+J757uMVOkPHyVCsqjl1FSCFahEREZHLOJ1s58tfU698RyA63ELfNt6zBVyBMuxwYKnrOLI0tJ/o2DLP4puz+ArVIiIiIpfx+YoUrOdXdsQWDaBXy5zXSgdYoGKpIKLCvXPninwXUxFajIQVz0P9R6DVGAgranZVBUqhWkRERCQHv+3O4Pc9rrXS/dtFUr2sj2xtl59ST8ChlVDtTvfxRk85lnvENjSlrMKmX6dERERELpKabuez5SnO4zY1QxWoL2bYYetHMK06zLsbTm5zvz0w2G8CNWimWkREJN9t3ZfBT79bycjKaa8IMUNaWnHCE85e9f1TrAZnUx1/f9HhFnq20J7Tbk4kOC5EPLTSNbZoMPRa7LNrpq9EoVpERCQfnThnY8rPyWQUQKtqyYtgSLLl6pF920QSGaYP9wHITIU1L8OGN8Ce7SSPrgCNnvbbQA0K1SIiIvnGMAw+XZ6qQO1DmlYLoVEVLfsAYPePsOhROLfXNRYQ5AjTLYZDsH/vfKJQLSIikk/W/ZXhbAJiAf51UxFKRGmG0xPs2buHShUrXdNjQoLg+uKBWPx49hWApIOw5EnY+aX7eNnWjo6I19U2pSxPo1AtIiKSD5KtdmatdO1n3KFOKI2rqgmIp7CeyqJSrGJPrvzQ133tdFhxaPs/qH0fWPRL4wX6kxAREckHc1ankpTmuLCtWGQA3ZvlvJ+xiNdp+zqOz16AG++D+7dDnQcUqC+iX9lERMRnLdpiZekfVjJzd33aNTmZZHd+fU+7CMJC/HzJgHin9HMQUsQ9MJdpAa1fgTItoXw782rzcArVIiLik3YeyeSLlVfXXjo/NaoSTL2KWvYhXsYwYPtsWPoktH4Vat/vfnuz500py5to3l5ERHxOps1gxpKUK98xn5WMDqBPa//eAUG80Om/4Mtb4Ic+kHIUlj3r6JIo10Qz1SIi4nPm/2bl6BnHcozQYHj2jmgiwgp2OYYFKFYkgMAALfsQL5GVDhv+B2vGgC3dNR4YAmd3QcR15tXmhRSqRUTEp5xMCWT+xjTncfdmEVQopR93Im72L4GFg+H09myDFmjwGLQaA6HRppXmrfRdRkREfIbdMFiwPZqs89cMVooNpEPtUHOLEvEkqX/Dsmfgz5nu46UaQuepULqxOXX5AIVqERHJ0eKtVuZtSCMj0zC7lKtmABlZjosEAwNgQPtIArQcQ8Th4Er4thtYT7vGQqKg1StQ/xEICDSvNh+gUC0iIpfYdzyLL1amYnhPnr7EzQ3CKFdCP+ZEnErWgcBsn9zc0As6TIQiZUwryZdo9w8REXFjszt2zvDmQF2zXBBdGoWbXYaIZwmNgfYTIKYS9PgRus5WoM5H+hVeRETcLNxsZf8JR7eU4EB4sVc0JaK852PhHdu3Ubd2TbPLEDHXru/h0Cpo+1/38eq9oeqdEBRmSlm+TKFaREScjp+18d16184ZXZuEU7a4d/2oCPae/C+S/84dgCX/hr++dhxXvBniOrhut1gUqAuIln+IiAgAhmHwyfIUMrIcx+VKBNK5nn74ingFexZsGA/Ta7oCNcCq4ebV5Ge8a/pBREQKzNodGfx5wJGoLTh2zggK1M4ZIh7vyFpY8BAc3+w+XnsgtH3NnJr8kEK1iIiQlGZn1qpU53F83VAqxepHhIhHs56BlS/A5ik4NpQ8r8SN0GkKlGttVmV+Sd8xRUSE2atSSbY6figXLxLAnc0iTK5IRC7LMGDbF7B0CKQec40HhUOLkdBoiKPVuBQqhWoRET/354FM1uzIcB7f0zaCsGAt+xDxaImfuAfqSrdBx0mO7fLEFLpQUUTEj6VnGnyyLMV53KRqCHUraoZLxKNZLI4AHRTm2Ge661zoPk+B2mSaqRYR8WPfr0/j+Dk7ABGhFvq01rIPEY9zYBmUbgrB2RoaxVSCO7+H65s5Wo2L6TRTLSLip/Yfz2LBZqvzuFfLCKIj9GNBxGOkHIMf74XZ7WHdfy+9vUInBWoPou+eIiJ+yGY3mLE0Bfv5DQOqlwmiVQ0t+xDxCIYdNk+FaTUg8VPH2Pr/wqnt5tYl/0jLP0RE/NCiLVb2HXe0Ig8KhP7tI7FYdHGiiOn+3gwLH4Yja9zHq/aA0BhzapKrolAtIuJnjp+z8e06VyvyLo3DiS2q3t4ipspIhtWj4LeJYNhc40WrQMfJUPEmsyqTq6RQLSLiRwzD4NNlqc5W5GWLB3JzfbUiFzHVX9/C4sch6YBrLCAYmg6Dps+7X6AoHkuhWkTEyyWl2TmZZL+q+/51JIuEA5nA+VbkHdSKXMRUO76E7+9yHyvfHjq+CyVqmFKS5I5CtYiIF9t2KJM3v08i6+oytZsOdUKprFbkIuaq0g2uqwMntkJ4SWg/Dmre69iLWryKvpuKiHip9EyD6YtTchWoixcJoLtakYsUPluGewvxwGDoNAUSpkOb/0J4cdNKk7xRqBYR8VLfrUtzLvsIDYLSxa7uYsOIUAs9mkcQFqKZMJFCk3YKVj7v2Bbv7iXuM9FlWzr+E6+mUC0i4oX2/Z3Fgi2uxi1920bSqkaoiRWJSI4MAxI/gaVPQ9pxx1jCx1D7PlPLkvyn5i8iIl7mQuMW43zjlhplg2hZXY1bRDzOqe0wpyPMH+AK1AAHl5tXkxQYzVSLiJjgTIqdAyeycvXYbQez2H/CsY9tsBq3iHiezDRYNxbWv+ZYQ31BVHmIfxuq3mFebVJgFKpFRArZkVM2xn51jrQMI8/P1bVJOKVi1LhFxGPs/QUWPQJndrnGLIHQ8EloOQpCiphVmRQwhWoRkUJkNww+XpqSL4G6XIlAOtdT4xYRj2DY4cf+sO0z9/Hrmzt29yhVz5y6pNAoVIuIFKLlCensOupY9hEYADXLBefqeYqEWejWNFyNW0Q8hSUAQmNcx6FFHVvk1R3kuE18nkK1iEghOZ1s56s1ac7jWxqEcaf2ihbxHa1fhZ1fQYVO0G4cRMaaXZEUIoVqEZFC8vkK17KP2KIB3N4o3OSKRCRXMpJgzSvQ+GmIKOkaDysK9yVAeAnTShPzKFSLiOTSoZNZ7Dtuu6r7nkiy8/ueTOdx/3aRBAdp6YaIVzEMx0z0kn9D8iFIPQq3THe/jwK131KoFhHJhZ1HMhn3bRK2XLQIb1MzlOplc7eWWkRMcnYPLHoM9vzoGkv4GBo8DrGNzKtLPIZCtYjINcrMMpixJCVXgTo63ELPFlr2IeI1bBmwYTysGQ1ZrmsiiCgF7SdAqYbm1SYeRaFaROQa/fhbGkfPOBJ1WDA0qHx13QxDgiy0rx1KZJh2AhDxCgdXwMLBcDIh26AF6j3kuCgxrJhppYnnUagWEbkGh05lMf83q/O4R4sIOtTWXtEiPiXtJCx/Dv74yH28ZD3HntNlmptTl3g0hWoRkatkNwxmLkl1LvuoUjqIdjeGmluUiOS/A0vcA3VwJLR62bF+OkDRSXKmM0NEfFpSmp0NuzLIyMx7B8NjZ+3sOuZq3NK/fQQBFu3gIeJzqvWEirfA3p+ganfo8CZElze7KvFwCtUi4rMybQbjvk3i0Kmr2/buWtzaMIyyxfUtVMTrZaZB0gEofoNrzGKBju841lJX6WpebeJV9BNBRHzW/N+sBRKoSxcN4LaG2sFDxOvt+QkWPQoBgTBgCwRluz6iaGXHfyJXSaFaRHzSkVM25m90bX/VqEoIJYrkfdeN0GBoXStMjVtEvFnyYVgyBHbMdo2tew1ajjSvJvF6CtUi4nPshsGMpSlknb+gsFJsIP/qHElAgIKwiF+z22DTZFj1oqPV+AVhxSA6zry6xCfkKlQfPnyY5557DsMwiImJYdy4cYSHh7N48WLeeecdgoKC6NmzJ3fffTdHjx7lySefJCAggAkTJhAbG8u3335LUFAQt99+e36/HxERVvyZzl9HXRcUDmivQC3i78LOJsBn/eHYRvcbag2Adv9zNHMRyYNcherp06dz6623cs899zBhwgTmzp1Lnz59GDt2LHPnziU8PJy+ffsSHx/P/PnzefDBBzEMg/nz59OnTx8WL17MxIkT8/mtiIi/2H4okz/2Z3LiRBESTqe63WbgCNUX3Fw/jHIl9KGciN9KPwurhlPx93eAbG1Qi1WHTu9CXAfTShPfkqufNDVr1uTo0aMAJCcnU7p0aXbt2kVcXBwxMTEANGrUiPXr1xMREYHVasUwDMLDw5k+fToDBgzAom2oRCQX9hzLYvx3SdgNgEg4YL3sfUvFBNClsS4oFPFb9iz4pDGc+Qtn6ggMhWYvQpPnIEj7zEv+yVWoLl26NOPGjWPevHlkZGTw2GOPsWvXLqKiopz3iYyMJDk5mS5dujB27FgCAgIYNGgQkydPpnHjxowYMYI6derQq1evS54/MTEx9+9IfJLVatV5Idjs8OmG4tiN4Cve14JB20on+GvnkUKoTDyJvl9IdsVj7yD2zDgAkq9rydEbh5MZWQF27ja5MvE1uQrVr7/+OmPHjqVNmzYsXbqUoUOH8tRTT5GSkuK8T0pKClFRUURGRjJmzBgAXn75ZQYPHszo0aOZOnUqTzzxBLfffjsRERFuz1+zZs08vCXxRYmJiTovhPm/pXEixbGjR0gQNI1LIjb20nWQFhzdDqteX6KQKxRPoO8XfswwHHtMZ3fDWLBu4VCxmykb/xRV9Um5ZLNx48Yr3+kq5SpUR0dHO2elS5Uqxblz56hSpQr79u3jzJkzREREsGHDBgYOHOh8zI4dOwgNDSUuLo709HQsFgs2m42MjIxLQrWIyMX+Pmvj+/WuLfK6NQknLuwYNWtqeYeIAAeXw+In4PbPoUS2X6oCg+GuXziXmEhZBWopQLkK1cOHD2f06NHY7XYMw2DEiBEEBwczbNgwBg4ciGEY9OzZk9jYWOdjpk6dyogRIwC488476d27N7Vr16Zo0aL58kZExHcZhsHMpSlknu/jElcykE71wtix3dy6RMQDpJ6A5c9CwnTH8cLBcPeSS2esRQpYrkJ11apVmTFjxiXj8fHxxMfH5/iYcePGOb/u2bMnPXv2zM1Li4gfWr09g22HHFvkWSyOLfICtUWeiH8z7PDHdEegtp5yjR/bCKe2Q4kappUm/kn7TImIRzuXamfOKte2eZ3rhlGhpL51ifi1EwmOGelDK9zHq/WEDm9CVFlz6hK/pp9MIuLRZq1KJSXdAKBEVADdmmoNtYjfykyFNS/Dhjcc2+VdEF0ROk6CymoqJ+ZRqBYRj7V1XwbrdmY4j/u3iyQ0WMs+RPzSgWXw031wbq9rLCAIGj8DzYdDsDY9EHMpVIuIR7JmGny63LXso/kNIdwYd+X9qUXERwWGwrl9ruOyraHTFLjuRvNqEskmwOwCRERy8u26VE4mOVoKFwmzcHcrzUKJ+LUyzaHuvyCsONz0IfRepkAtHkUz1SJS6AzDYPHWdP48kImR4x3gjwOZzsNeLSOICtccgIjfOLIOUo5A1Tvcx9v8F1q9DBElzalL5B8oVItIoVu3M4MvVqZe+Y5ArXJBtKgeUsAViYhHSD8LK1+ETZMhrBiU2eYeoMOKmlaayJVo6kdEClWy1c6sVVcXqCNCLdzTLhKLmjiI+DbDgG1fwLQasOkdwHDsPb3yBbMrE7lqmqkWkUI1Z3UqSWmORR/FIgO4p13Oa6UtQMVSQURH6Hd/EZ92+i9Y9Cjs+8V9vOIt0PR5c2oSyQWFahEpNIkHM1m9zbVF3j3tIqhXUUs7RPxSVjqsfx3WvgK2dNd4kTKOBi7VeqrVuHgVhWoRKRQZWQYzl6Y4jxtXCVGgFvFX+5c4OiKe3u4aswRA/cccFyKGRptXm0guKVSLSKGYtz6N4+ccW+RFhFro00Zb5In4pSwr/HiPY3ePC2IbQ+cpENvIvLpE8kiLFUWkwB04kcXPm6zO47tahBOjtdIi/ikoDNpPcHwdEgXxb0O/NQrU4vU0Uy0iBcpuN5ixNAX7+Q2pbygTROuaoeYWJSKFJ+kgRJVzH6t+N5zdDTf+n2MNtYgP0FSRiBSoxVvT2fu3DYCgQOivLfJE/ENmCix7Dj6oBPsXu99msUCz5xWoxacoVItIgTmZZOObta49qW9vFE7pYoEmViQihWLX9zCtFmz4H9izHBclZqVf+XEiXkzLP0SkQBiGwafLUknPchyXLR7ILQ3CzC1KRArWuQOw5An46xv38cjrHc1cilxvSlkihUGhWkQKxPq/Mti6PxNwNHLp3z6CoEAt+xDxSfYs+O0tWD3CsezjgvDroN0bUGuA9pwWn6dQLSL5LsVq54uVrmUf7WuHUqV0sIkViUiBObwGFj4Mxze7j9d5ENr8F8JLmFOXSCFTqBaRfJe9FXnRSAvdm2tPahGftHmqY700hmusxI3QaQqUa21aWSJmUKgWkXyVeDCTVdlbkbeNJDxEH/uK+KSKNzv2nc5Kg6BwaDESGj0FgfpkSvyPQrWI5JuMLINPlrnWUzasHEz9SmpFLuKzYio6gvShFRA/yXEs4qcUqkUk38zbkMbfZx2tyMNDLPRtE2lyRSKSL7KssO41CAiE5v9xv63Js9DkOV2IKH5PoVpEciU902DqL8nsOJzpXE55Yfs8cLQiLxqprfBFvN6+hbDoETi9EwKCodpdUKKG63aL/p2LgJq/iEgufbM2la37MknPdITp7IG62vVBtK6lVuQiXi3lGPxwD8zt7AjUAPZM2PSOuXWJeCjNVIvINdtzLItFW3PujnZddAD3xUcSoI+CRbyTYYct78GKYZB+1jUeGgOtx0Ldf5lXm4gHU6gWkWuSZTOYsTQF4/ySj1rlghh8S5SjwwsQGgQWBWoR7/T3Zlj4EBxZ6z5eoy+0Hw+Rpc2pS8QLKFSLyDVZsNnKwZM2AEKC4N52kYRpyzwR75aZBqv+A7+9CYbNNV60KnScDBU7m1ebiJdQqBaRq/b3WRvfr09zHndrEk7JmEATKxKRfBEQBPsXugJ1YAg0GQbNnnfsQy0iV6QLFUXkqhiGYw/qzPM/c+OuC6RTPf2wFfEJgcGOLohYIC4eBmyBVi8pUItcA81Ui8hVWb09g8SDji0+LBYY0D6SwAAt+xDxOrZM2D4LavZz3w6vTAvouxqub6Y9p0VyQaFaRK7oXKqdOatSnced64ZRoZS+fYh4nUOrYeHDcGKrY3u82ve7316muTl1ifgALf8QkSuatSqVlHTHdh8logLo1jTc5IpE5JqknYJf/gVftHIEaoBlz0LqCXPrEvEhmmoSkX+0dV8G63ZmOI/vbRdBaLA+GhbxCoYBiZ/A0qch7bhrPCgCmg5z7D0tIvlCoVpELsuaafDpcteyj2bVQqgdF2JiRSJy1U5ug0WD4cBS9/Eq3SD+bYiOM6MqEZ+lUC0il/XtulROJtkBKBJmoXfrCJMrEpErykyDda/Cutcc66YviCrvCNNV7zCvNhEfplAtIjna+3cWi7a4WpH3ahlBVLguwxDxeOvGwpoxrmNLIDQaAi1GQkgR8+oS8XH6CSkil7i4FXnNckG0qK5lHyJeofHTrnbi1zeHezdCu/8pUIsUMM1Ui8glFm6xcuCEeytyi/atFfE8dhtkpbkH5tAYR2vxtONQ50H3vahFpMAoVIuIm7/P2vhunasVedcm4ZRSK3IRz3PsN8ee08VrwK0z3G+r1t2cmkT8mH59FRGni1uRl78ukM5qRS7iWTKSYMkQ+LQJHF0Pf86E/UvMrkrE7ylUi4jTr2pFLuK5DAN2fgXTasJvE8Fw7MxDYCic3mFqaSKi5R8icl5Smp05q117UnesG0pFtSIX8Qxn98Lix2D3D+7jcZ2g02QoVs2UskTERT8xRQSAWStTSba6WpHf0VR7UouYzpYJG8fDry85Lki8IKIUtJ8ANfo6PlYSEdMpVIsIf+zPYG22VuT3tI0gTK3IRcyVdgpmtYWTCdkGLVDvIWj9KoQVM600EbmUQrWIn0vPNPhkmWvZR9NqIdSpoD2pRUwXVgyiK7hCdcl60GkKlGlubl0ikiNdqCji575bl+ZsRR4ZaqF3Ky37EPEIFgt0nAThJaHdOLh3gwK1iAdTqBbxY/v+zmLBFqvzuFerCKIj9G1BpNCdTIT5/wdZVvfxmEowaB80fgoC9OGyiCfTv1ARP2Wzu7cir1E2iJZqRS5SuDJTYe0rsP5/YM90hOiWo9zvExxuSmkicm00JSXipxZutrL/fCvy4EDo316tyEUK1Z6f4OPasPZVR6AG2PAGWE+bW5eI5IpmqkX80PGzNr5br1bkIqZIPgxLnoQdc9zHy7R0XIioXT1EvJJCtYifudCKPMPROJFyJdSKXKRQ2G2waTKsetHRavyCsGLQ5nWo8wBY9AGyiLdSqBbxM2t2ZPDnRa3IgwK17EOkQB3bCAsecvw/uxv/D9r+DyJKmlOXiOQbhWoRP5KUZmf2qmytyOuEUilW3wZECtz+xe6Bulh16DwFyrc3rSQRyV/6nEnEj8xe5WpFXrxIAHc0057UIoWi4ZNwXR0IDIVWL8OAzQrUIj5GU1QifiJhfyZrdrhakd/bTq3IRQrEmd1g2KFYVddYYDDcOhOCI93HRcRnaKZaxA+kZxp8sjzFedykqlqRi+Q7W4Zje7yPb4RfBuLcBP6CUvUUqEV8mEK1iB/4fn0aJ845WpFHhFro01rLPkTy1YFlMKM+rHzR0RXx4HJI+NjsqkSkEGn5h4iPsdsN1v2VwdkUR4jOtMGCzdlakbdUK3KRfJN6HJY/e2mALtUArqttTk0iYgqFahEfM3t1Kou2pOd4W/WyQbSqoWUfInlm2OGPabD8ObCeco2HREGrMVD/EQjQj1gRf6J/8SI+ZNfRTBZfJlAHB0L/dmpFLpJnJ/6AhYPh0Er38RvugvYTIaqsKWWJiLkUqkV8RJbNYObSVC5cGlWhZCDVywQDEBAADSuHEFtUrchF8iT5MHzSyHFR4gXRFaHjO1D5NtPKEhHzKVSL+Iiff7dy6JQNgJAgePjmIlwXrRAtkq+KlIHaA2Hzu47lHY2fheb/gWBd/Cvi7xSqRXzA0TM25m1Mcx7f2SxCgVokP9gyHXtMZ9f6VUg9Bi1Hw3U3mlOXiHgcbQEg4uXshsHMpSlkOSapqVAykI51Qs0tSsTb2bNg4wT4sKpjh4/swopCty8VqEXEjUK1iJdblZjOjsNZAARYYECHSAICdDGiSK4dWQefNIGlT0HSflj2jNkViYgX0PIPES92NtXO3F9dyz461w8j7jr9sxbJFesZWPkCbJ4CZOuGeGwDZCRDSBGzKhMRL6CfviJe7IsVqaSmO374l4wOoGvjcJMrEvFChgHbZ8HSIZBy1DUeFA7NR0DjpyBQ+7uLyD9TqBbxUpv3ZrBhl2tbr3vbRRIarGUfItfk9F+w6BHYt8B9vNKtjm3yYiqZU5eIeB2FahEvZM0w+HRZqvO4RfUQapUP/odHiMglfp/kWC9ty9YwqUgZ6PAmVOsJapQkItdAoVrECxw6mcXShHQyshxLPf4+a+d0ih2AImEW7m6lPXJFrllEKVegtgRAg8cd2+SFRptbl4h4JYVqEQ+XYrUz4fskzqYaOd7eu3UERcK0kY/INbuhF1ScBmknoPMUiG1kdkUi4sUUqkU83Jdr0i4bqOtWCKZZNV1AJfKPDDts/QCKVoO4Dq5xiwVu/wxCoiFAzZJEJG8UqkU82PZDmaz407Xes0vjMK6LcvzwDwuxUKdCMBat+xS5vONbYMHDcORXKFYNBmyBoDDX7WHFzKtNRHyKQrWIh8rMMpi5LMV5XL9SMHc01dppkauSkQy/vuToimicbzd6eidsegcaP21ubSLik3K1EDM1NZXnnnuOfv360atXL7Zs2QLA4sWL6dmzJ71792b27NkAHD16lD59+tCvXz+OHTsGwLfffssPP/yQT29BxDf9+Fsax844LkYMC4Z+bSJNrkjEOxQ5thim14INb7gCdUAwNP8P1HvE3OJExGflaqb6ww8/pFq1arz++uts27aNbdu2UbNmTcaOHcvcuXMJDw+nb9++xMfHM3/+fB588EEMw2D+/Pn06dOHxYsXM3HixHx+KyK+49CpLOb/ZnUe92gRQbEiuhhR5B+d2w+Ln6D8rm/dx8u1g07vQoma5tQlIn4hV6F65cqV3HrrrQwcOJDIyEhGjhzJrl27iIuLIyYmBoBGjRqxfv16IiIisFqtGIZBeHg406dPZ8CAAVoHKnIZdsNg5pJUbI5JaqqUDqLdjaHmFiXiyWyZ8PtbsHokZLqWTBF+HbQbB7X6a89pESlwuQrVp0+f5ty5c3z44Yd88803vPbaa9x9991ERUU57xMZGUlycjJdunRh7NixBAQEMGjQICZPnkzjxo0ZMWIEderUoVevXpc8f2JiYu7fkfgkq9XqN+fFpoPh7Drm2Cc3wGLQKu4o27cdMrkqz+RP54VcXlDaYaqsHE6ALc05drr8XRyv/hS2gKKwbZt5xYnH0PcLKWi5CtVFixYlPj4egA4dOvDee+/xwAMPkJLimiFISUkhKiqKyMhIxowZA8DLL7/M4MGDGT16NFOnTuWJJ57g9ttvJyLC/eKrmjX1EZ24S0xM9Ivz4lSyndUrzziPb2sUTpvGN5hXkIfzl/NCrqQmZI2CFUPhutrsrTqMiq3uQft6SHb6fiE52bhxY749V64WaTZq1Ihly5YBsH79eqpWrUqVKlXYt28fZ86cISMjgw0bNtCgQQPnY3bs2EFoaChxcXGkp6djsViw2WxkZGTkzzsR8XKGYfD5ihSsmY7j0kUDuK1huLlFiXgaw3Bsk3exRkOg0xS49zfSijcs/LpExO/laqb6oYce4j//+Q+9e/cmKCiI1157jeDgYIYNG8bAgQMxDIOePXsSGxvrfMzUqVMZMWIEAHfeeSe9e/emdu3aFC1aNF/eiIi3+213Jpv2ZDqP+7ePJDhI60BFnE7tgEWPwMHlMGCz+4WHgcFQ7yHzahMRv5fr5R+TJk26ZDw+Pt65LORi48aNc37ds2dPevbsmZuXFvFJqel2Pl/hWj7VtlYoN5QJNrEiEQ+SZYV1/4V1Y8F2/tPNhYPh7iW6AFFEPIaav4h4gC9/dbUij4mw0LOFln2IALBvoWN2+vRO15glAGIbOgJ2kHbGERHPoFAtYrIdhzNZnq0Ved82kUSEak9q8XMpR2Hp07DtM/fx0k2g01SIbZDz40RETKJQLWKiTJvBzKWuZR/1KgbTsLKWfYgfM+yweSqsfB7Sz7rGQ2Og9Vio+y8ICDSvPhGRy1CoFjHRjxvTOJq9FXnbSDVGEv/2yyD44yP3sRp9of14iCxtTk0iIldBnzGLmOSSVuTNIyiuVuTi7+oMAs7/Ylm0KvT8BW7/TIFaRDyeZqpFcslmN/h8RSoJBzIxjGt/fGq64WxFXjk2UK3Ixf9c+IeT/dOZMs2h4RMQEgPNnoegMHNqExG5RgrVIrm0cLOVZQnpV77jFQQGwID2kQQEaNmH+JFz+2DR41D1TqjzgPttHSaaUZGISJ4oVIvkwvGzNr5bn5Yvz9WjeThlS+ifovgJWyZsnAC/vgRZqXB4FVTpChElza5MRCRP9JNc5BoZhsEny1LIyHIclysRyCO3FHEuA70WESEWIsO0jlr8xKFVsPBhOPGHa8x6CvbMhxsHmFeXiEg+UKgWuUZrdmTw50FHorZYHEs3SsZoiy+Ry0o7BSuGwtYP3MevqwOdp0KZFubUJSKSjxSqRa5BUpqd2atSnccd64RSKVb/jERyZBjw50xY9jSknXCNB0VAq9HQ4AkI1L7sIuIblAZErsHsVakkWx07FhQvEsAdzSJMrkjEQ507AD8NgANL3cer3AHxb0F0nBlViYgUGIVqkauUsD+TNTsynMf3tosgLFg7dojkKDQaTm1zHUeVh/i3oeod5tUkIlKAdIWUyFVIzzT4ZLmrnXiTqiHUqRBiYkUiHi40BtpPBEsgNH4G7vtTgVpEfJpmqkWuwnfr0zhxztGpJSLUQp/WWvYh4pR8BP76BuoPdh+vfjfENoJiVU0pS0SkMClUi1zBvuNZLNjsaid+d8sIoiP0IY8IdhtsngIrX4CMc1C8OsTFu263WBSoRcRvKBmIXMRuGGRkOf6zZhrMWJri7KZcvWwQLWto2YcIx36Dz1vA4sccgRpg4SNgzzK3LhERk2imWiSb/SeyeGteEmdTjUtuCwqE/u0isVh0caL4sfRzsHoE/P42GHbXeLEboNNkCNCPFRHxT/ruJ3Jels3gw4UpOQZqgK6Nw4ktqiYv4qcMA3Z+BUuegOTDrvHAUGj2AjQZCkGh5tUnImIyhWqR83763crhUzbA0XE86Hx+tligboUQbqofZl5xImY6uwcWPQZ7fnQfj+vkmJ0uVs2cukREPIhCtQhw9LSNHzakOY97tYqgcz2FaBEAlj3rHqgjYqH9BKjRx/Fbp4iI6EJFEbthMHNZClnnl4dWKBlIxzr6GFvEqd0bEBQOWKDeYLh/G9Tsq0AtIpKNZqrFL6RY7Vgzc14rvWlPJjsOO3YsCLDAgA6RBAQoLIifSj0BwZEQHO4ai6kInac6Lka8vplppYmIeDKFavF5P2xM45u1aVe+I9C5fhhx1+mfhfghw4CE6Y6lHvUfgVaj3W+v1d+UskREvIWWf4hP2/t3Ft+uu7pAXTI6gK6Nw698RxFfcyIBZrWDnx8A60lY9184uc3sqkREvIqm5MRn2ezujVvCQyyEh+S8rCMq3EL/dpGEBmvZh/iRzFRYMwY2/M+9aUuRMo5wLSIiV02hWnzWws1WDpxwbJEXHAj/6RVNqRjtMy0CwJ75sOhRx3Z5FwQEQaOnocVwx7pqERG5agrV4pOOn7Xx3XrXso+uTcIVqEUAkg7B0idhx1z38TKtoPMUuK62KWWJiHg7hWrxOcb5LfIyzn+aXa5EoPacFgE49hvMbg8ZSa6xsOLQ9nWofT9YdJmNiEhuKVSLz1mzI4PEg45EbbHAgPaRBAVqrbQI19WBmEpwfIvj+Mb/g7b/g4iS5tYlIuIDNC0hPiUpzc7sVanO4451QqkUq98dRQAIDIZOU6BELbh7CdwyXYFaRCSfKFSLT5m9KpVkq2O7jxJRAdzRLMLkikRMYBiwfTZ83xsMu/ttZVrA/22F8u1NKU1ExFdpCk98xh/7M1izI8N5fE/bCMK0RZ74mzO7HLt67P3ZcZxwi2O9dHZaOy0iku/0nVV8QnqmwSfLXMs+mlYLoU6FEBMrEilkWemOPac/ru0K1AAbx186Wy0iIvlOM9XiE75bn8bJJEdwiAi10LuVln2IHzmwFBYOhlPZuyBaoP6j0HqMZqZFRAqBQrV4vX3Hs1iw2eo8vrtlBNERChHiB1KPw/JnIeFj9/FSDR17TpduYk5dIiJ+SKFavJrNbjBjiasVeY2yQbSsoWUf4ge2fgTLnwHraddYSBS0GuOYoQ5QsyMRkcKkUC1ebdEWK/uztSLv3z4Si0UXJ4ofOLbRPVDf0AvaT4CosubVJCLixxSqxWsdP2fj23WuVuRdGqsVufiRNq/CX19BUDjET4LKt5ldkYiIX1OoFq9kGAafXNSK/Kb6akUuPmr3D4710RGlXGOhMdBjPhS7AYJ1Ya6IiNl0NZd4pbU7MvjzwPlW5KgVufiopIPwXU/4ugsse/bS20vVV6AWEfEQCtXidZLS7MzK1oo8vq5akYuPsWfBxgkwrSbs/Mox9ucM2L/E3LpEROSylETE68xZ7WpFXrxIAHc21Uyd+JAj62DBQ3B8k/t47QfgujqmlCQiIlemUC1e5c8Dmfy6/aJW5CFa9iE+wHoGVr4Am6cAhmu8RC3o9C6Ua2tWZSIichUUqsVrOFqRpziPm1QNoW5F7UktXs4wYNvnsPQpSD3mGg8Kg+YjoPHTEKjzXETE0ylUi9f4fn0ax8+5WpH3aa1lH+IDDiyFH+9xH6t0q2ObvKKVzahIRERyQRcqilf4OynIrRX5XS3Uilx8RPn2jhANUKQMdJ0D3X9QoBYR8TKaqRaPZ7MbLNgejf38MtPqZYJoXVMfh4uXSj8HodGuY4vFMSu9aRK0GOV+m4iIeA1N9YnHW7TFyrGkYACC1IpcvFXq3/Bjf/i0MWRZ3W8rWhnaj1egFhHxYgrV4tFyakUeW1StyMWLGHbY8h58VB0SP4HTO2Hdf82uSkRE8pmWf4jHMgyDz5anOluRly0eyM1qRS7e5PgWWPAwHPnVffzsHseuH/rERUTEZyhUi8datzODP/Znnj8yGNBBrcjFS2SmwOpRjq6Ihs01HlMZOk2GijebVpqIiBQMhWrxSMlW91bk9cumUTm2hIkViVylv76DxY9D0n7XWEAwNB0KTV+A4HDzahMRkQKjUC0eafaqVJLSHNt9FIsMoHXlZJMrErkKP94LiZ+6j5VrB52mQIka5tQkIiKFQhcqisdJPHhRK/J2EYQEGf/wCBEPUbKe6+vw6+CWj+HuJQrUIiJ+QKFaPEpGlsHMpa5W5I2qhFBPrcjFWzR8EkrWhToPwv3b4MYBuhhRRMRPaPmHeJSLW5H3baNW5OKBrKdh5YvQ4DEoUcs1HhgMfddo3bSIiB9SqBaPsf9EFr9syt6KPJwYtSIXT2IYjjXTy552NHM5mQB3L3WfjVagFhHxS0os4hHsdoMZS1KcrchvKBNE65qh5hYlkt2p7TC3E8zv7wjUAAeXw75fzK1LREQ8gmaqxSMs3prOvuOO/XyDAqF/O7UiFw+RZYW1Y2H9f8HmuoCWIuUg/i2ocJN5tYmIiMdQqBbTnUyy8c1a157UtzcKp3QxtSIXD7BvISwcDGf+co1ZAqHhv6HlKAiJMq00ERHxLArVYirDMPhkWSrp2VqR39JArcjFZClHYenTsO0z9/HSTaHzVChV35SyRETEcylUi6nW/+VqRW4B+rePUCtyMd+5fbDtc9dxaAy0+S/UGQQB+hRFREQupQsVxTQpVjtfrHQt+2hfO5QqpYNNrEjkvOubQb2HHV/X6OfYc7rewwrUIiJyWZqplkKz62gmCzank5Hl2OLjVJLd2Yq8aKSF7s21J7WYICMJTvwBZVq4j7d+Far1gAqdzKlLRES8ikK1FIpzqXbe+iGZ1PSc243f0zaS8BAt+5BCZBjw1zew+AnISoH7t0NESdftYUUVqEVE5Kpp+YcUilmrUi8bqJvfEEL9SmpFLoXo3D74pht81wOSDzo6JC57xuyqRETEi2mmWgrc1n0ZrNvp2t+3T+sIrot2/D4XHmKhSmmdhlJIbJmwcQL8+hJkudbzE1EKKmq/aRERyT2lGSlQ1kzHlnkXNL8hhI51tWWemODQKlj4sGP9dHZ1H4I2YyGsmDl1iYiIT1ColgL17dpUTiXbASgSZuHuVroYUQpZ2klYMQy2fuA+XrIudJpy6QWKIiIiuaBQLfkmPdPgi5WpHDrp6ORigLP1OECvlhFEhWsZvxQiw4A5HeH4ZtdYcCS0fMnRFTFA3wJFRCR/6CeK5Js5q1NZmZie4221ygXRorouRpRCZrFAsxdgXm/HcdU7ocObEB1nalkiIuJ7FKolX+w8ksmyhJwDdVS4hXvbRWKxaMs8KWC2TAi8qIHQDb2gzgKo3BWqdjOnLhER8XkK1ZJnmTaDmUtTnMe144Lp2iQccLQeL10sUHtQS8Hb+zMsehQ6TYUKHV3jFgvc9L55dYmIiF9QqJY8m/+blSOnHRcjhgZD/3YRFI9SO2cpJMmHYckQ2DHbcbxoMAzYAkHaZUZERApPnq4aW7duHe3atXMeL168mJ49e9K7d29mz3b8gDt69Ch9+vShX79+HDt2DIBvv/2WH374IS8vLR7iyCkb8zemOY+7N1OglkJit1Fs76cwraYrUAOkHr902zwREZECluuZ6iNHjjBt2jSyshw7PWRmZjJ27Fjmzp1LeHg4ffv2JT4+nvnz5/Pggw9iGAbz58+nT58+LF68mIkTJ+bXexCT2A2DGUtTyHJMUlMpNpAOtUPNLUr8w7HfYMFDlD62wX28Vn9o94ajmYuIiEghytVMdXp6OiNHjmTUqFHOsV27dhEXF0dMTAwhISE0atSI9evXExERgdVqxWq1Eh4ezvTp0xkwYIAuWvMBK/5M56+jjl+qAgNgQPtIAgL09yoFKP0cLP43fNoEsgfqYjdAr0Vw6wwFahERMUWuZqpHjx7NAw88QGxsrHMsOTmZqKgo53FkZCTJycl06dKFsWPHEhAQwKBBg5g8eTKNGzdmxIgR1KlTh169el3y/ImJibkpSwpRcnoAs9eW4MLvZY3KJ5P09zES/y6Y17NarTov/Fz4qY2U/f1pgtNdJ5k9IISTVR7iZOWBGCkhoHNE0PcLyZnOCylo1xyqjx07xoYNG9i/fz/vvPMOZ8+eZciQITz00EOkpLh2gEhJSSEqKorIyEjGjBkDwMsvv8zgwYMZPXo0U6dO5YknnuD2228nIsK9y17NmjXz+LakoL37UxIZtkwASsUEcN/N5QkOKrhZ6sTERJ0X/u5cBGxIdh1X6Mzuik9RtfEtlDSvKvFA+n4hOdF5ITnZuHFjvj3XNYfq2NhYfv75Z+dxq1atmDBhApmZmezbt48zZ84QERHBhg0bGDhwoPN+O3bsIDQ0lLi4ONLT07FYLNhsNjIyMi4J1eLZft+dwW+7M53HA9pHFmigFgEgugK0GAkbx0OHiVC9N5nbtpldlYiICJCPW+oFBwczbNgwBg4ciGEY9OzZ0215yNSpUxkxYgQAd955J71796Z27doULVo0v0qQQpCabuezFa5PJFrXDKV62eB/eIRILhxc4djBo/5g9/FGQ6DuvyCsqClliYiIXE6eQ/WqVaucX8fHxxMfH5/j/caNG+f8umfPnvTs2TOvLy0m+HptGmdSDMDRKfGuFuEmVyQ+JfUELH8OEqZBQDCU7wAlarhuDwyGwKKmlSciInI5edqnWvzLX0cyWfaHqxV539YRRIbpFJJ8YBjwxzSYVsMRqAHsmbD8GXPrEhERuUrqqChXJdNmMGNpKsb54zoVgmlcNcTUmsRHnEiAhYPh0Ar38Wo9oMOb5tQkIiJyjRSq5ar89JuVI6dtAIQGwT1tI7TXuORNZiqsGQMb/gf2LNd4dAWInwRVuphXm4iIyDVSqJYrOnLaxo/ZWpHf2SyCEmpFLnmx+0dY9Cic2+saCwiCRk9Di+EQHGlaaSIiIrmhUC3/yG4YzMzeirxUIPF11Ipc8sBug5UvuAfqMq2g07tQso5pZYmIiOSFrjKTf7Tyz3R2HlErcslHAYHQaQpggbDicNMH0Ge5ArWIiHg1zVTLZZ1JsTP3V9eyj5vqh1HuOp0yco1O/AElaoEl2+/wZZrDLdOh0q0QoX6IIiLi/TRTLZf1xcoU0jIc+32UigmgS2PtSS3XIP0sLHocPq4Lf0y/9PYbByhQi4iIz1Colhxt2pPBxl2uVuT3toskRK3I5WoYBmyb5dhzetMkwIDlz0LqcbMrExERKTD6LF8ukZZh8OlyVyvyVjVCqFlOrcjlKpzZ5djVY+/P7uOlm4AtPefHiIiI+ACFarnE12tS3VqR92oZYXJF4vGy0mHDG7B2DGRZXeOR10OHiXBDL9C+5iIi4sMUqsXNrqOZLM3WiryPWpHLlRxYBgsfhlPbsg1aoP6j0HoMhMaYVpqIiEhhUagWp6yLWpHXjgumiVqRyz/58xOY3999rFRD6DwVSjc2pyYRERETKFSL00+/Wzl8ytWK/N52akUuV1ClK0SWhpSjEBIFrcY4ZqgD1HFTRET8i0K1AHD0tI0fNqgVuVyBYbivjQ6NgfYTYedcx/+jyppVmYiIiKkUqsXRinyZqxV5hZJqRS4XyUyBX0c7tsW75SP322r0dvwnIiLixxSqhVWJ6ew47GhFHmCB/+ugVuSSza55sPgxOLfPcVzrXoiLN7cmERERD6NtHfzc2dRLW5GXVytyAUg6CN/2gG+6ugI1OC5OFBERETdKT37u8xWppKY79vsoGR1A1yZqRe737Fnw+9uwagRkJrvGw0pAuzfgxv8zrzYREREPpVDtxzbvzWDjrgzncf/2akXu946shQUPwfHN7uO1H4C2r0N4CXPqEhER8XAK1X7KmmHw6bJU53GL6mpF7tdsGbDkSdg8BZw7lQMlakGnKVCujVmViYiIeAWFaj/19dpUTqc4tvuICrdwdyu1IvdrAcHn102fD9RB4dB8BDR+CgLVAEhERORKFKr9gN0w2HMsy7l2+lyqwZKtrlbkvVtFUEStyP2bxQIdJ8H0G6F8B8fXMZXMrkpERMRrKFT7gdkrU1mULURnVzsumKbVNBPpV7LSYfNkqPswBGe7MDWmEgzYDEWrujd4ERERkStSqPZx2w9lXjZQhwTBPW3Vityv7FsEix6B0zvAehpajXa/vVg1c+oSERHxcgrVPiwzy9Ep8YLYogGUjHa0Hg8KhA61w7guWq3I/ULKMVj2NCR+6hpb91+oNQCKVTWvLhERER+hUO3DftiYxrEzjosRw0MsPN0tmmJFtHbarxh22PI+rBgG6Wdc4yHR0Gas1k2LiIjkE4VqH3XoZBY//W51HvdoHq5A7W/+3gwLH4Yja9zHq/eB9uOhyPXm1CUiIuKDFKp9kN0wmLE0BZtjkpoqpYNoe2OouUVJ4clIhtWj4LeJYNhc40WrQMd3oOLNZlUmIiLisxSqfdCyP9LZfcwRpgIDYED7SAJ0MaL/SPgYNo5zHQcEQ9Nh0PR5990+REREJN9oPYCPOZVs56s1rk6JtzYMo0xxXYzoV+r+C0rWdXxdvj0M2OLY5UOBWkREpMBoptqHGIbBZ8tTsGY6jksXDeC2RgpSPs2WCdZTEBnrGgsMhs7vwantUKu/9pwWEREpBArVPuS33Zls3pvpPO7fPpLgQAUqn3X4V1jwEITGQO9lYMn2wdP1zRz/iYiISKFQqPYRqel2Pl/h2pO6ba1QbigTbGJFUmCspx1b5G15zzWW8DHUvt+8mkRERPycQrWP+PLXNM6mGgDERFjo2ULLPnyOYTiatyx9CtKOu8aDwiErzby6RERERKHaF+w4nMnyP12tyPu2iSQiVNeg+pRT22HhYDiwxH28cheIfxtiKppSloiIiDgoVHu5TJvBzKWuZR/1KwXTsLKWffiMLCusHQvr/wu2DNd4kXIQ/xZUvVMXIoqIiHgAhWov9+PGNI6eb0UeFuyYpbYoZPmGzDSYWR9O73CNWQKh4b+h5SgIiTKrMhEREbmIQrUXO3Qqi/m/ZW9FHkFxtSL3HcHhENfJFaqvbwadpkCp+qaWJSIiIpdSqPZSdsNg5tJUVyvy2CDa1VYrcp/T5lXHOuqGTziaulj0S5OIiIgn0k9oL7U8IZ1dR7MARyvy/h0i1Ircm/29Cb68FVL/dh8PjYH/2wr1HlagFhER8WD6Ke2FTifb+fJXVyvyWxqEUba4PnTwShlJji3yPmkEe3+CZc9eep8AtZkXERHxdArVXuizFe6tyG9XK3LvYxiw8yuYVhM2TgDj/Dqe7bPg3H5zaxMREZFrpulNL/Pbrgw27bmoFXmQln14lbN7YfHjsHue+3hcPHR8F6LjTClLREREck+h2oukptv5LFsr8jZqRe5dbJmwcTz8+pJ7B8SIUtB+PNTopz2nRUREvJRCtRf5ao2rFXl0uIWezbXsw2sc/hV+GQQnE9zH6z4EbcZCWDFz6hIREZF8oVDtJXYeyWRZgnsr8sgwLYn3GsmH3AN1ybqOPafLtDCvJhEREck3SmVeINNmMGOJa9lHvYrBNKqiZR9epVpPqHQrBEdCuzfg3o0K1CIiIj5EM9VeYH62VuShwdCvTYRakXuyk4mQcc7RAfECi8UxMw26EFFERMQHaabawx05ZePHi1uRR2nfYo+UmQYr/wMz6sH8/pBldb89Ok6BWkRExEcpVHswu2EwY2mKsxV5pdhA2t+oVuQeae/P8HFtWPsK2DPh9E5YO9bsqkRERKSQaPmHB1uekM5f2VqRD2gfSUCAln14lOQjsHSIo2lLdte3gBt6mlOTiIiIFDqFag91OtnOV2tcexnf3CCMciX01+Ux7DbY/C6sfNGxfvqCsGLQ5jWoMxAs+iBIRETEXyileagvVqaQluHYk7pUTABd1Irccxz7DRY8BMc2uI/X6u/Y2SOilDl1iYiIiGkUqj3Q77sz+G23qxX5ALUi9xzp52B2e8hIco0VuwE6vetoMy4iIiJ+SZ9Pe5iLW5G3rhlK9bLak9pjhEZD8+GOrwNDoeVoGLBFgVpERMTPaabaw3y9Jo0zKa5W5He10LIPU2UkQUiU+1jDJ+HsHmg0BIpVM6UsERER8SyaqfYgfx3JZGm2VuR92kSoFblZbBmOLfHeKw8n/3S/LTAYOk1WoBYREREnJTYPkWkzmLE01Xlcp0IwjauEmFiRHzu4HGbUh5UvQPpZWDgYDMPsqkRERMSDafmHh/jpNytHTtsARyvye9qqFXmhSz0By5+DhGnu4+nnIO0ERJQ0py4RERHxeArVHuDIKRs/bnTtSd29WQQl1Iq88BgGJEyHZc+C9aRrPLgItHoZGjwGAfqnIiIiIpenpGAyu2Ewc1kKWRdakZcKpENttSIvNCcSHMs7Dq1wH6/WAzq8CVHlzKlLREREvIpCtclW/pnOziNqRW6KzVNg8eNgz3KNRVeA+ElQpYt5dYmIiIjXUag20ZkUO3N/dS37uKl+GOWu019JoSnVwNFuHBzLOxo9DS2GQ3CkuXWJiIiI11GCM9HnKy5qRd5Ye1IXquubQb2H4fgW6DwFrqttdkUiIiLipRSqTbJpj3sr8v7tIwlRK/KCYc+CTe9AWHGo1d/9tvbjITAELNpdUkRERHJPodoEaRkGny53tSJvVSOEGmpFXjCOrocFD8Pfv0FYMah4i/vWeEFh5tUmIiIiPkPTcyb4ek2qsxV5VLiFXi0jTK7IB6WfhUWPwafNHIEawHoaNowzty4RERHxSZqpLmS7jmay9I9srchbqxV5vjIM2D4blj4JKUdd40Fh0Hw4NH7GtNJERETEdylUF6Ks863ILzS8rhMXTJOqakWeb07/BYsehX2/uI9XvAU6vgNFK5tTl4iIiPg8hepC9NPvVg6fOt+KPAjuaadW5PkiKx02/A/WjAGb61MAIq93NHC54S7Qn7OIiIgUIIXqQnL0tI0fNrj2pL5Trcjzj80Kmya7ArUlAOo/Cq3GQGi0ubWJiIiIX9Bi3kJwcSvyiqUCia+jVuT5JjQGOkx0fB3bCPqthfi3FKhFRESk0GimuhCsTExnx2FHK+wAi1qR54lhhwNLIS7effyGXtAtGKp0gwB9AiAiIiKFSzPVBexMip25q13LPm6uH0Z5tSLPneNb4Ys2MKcj7FvofpvFAtW6K1CLiIiIKXIVqg8fPsx9991H//79uffee9m9ezcAixcvpmfPnvTu3ZvZs2cDcPToUfr06UO/fv04duwYAN9++y0//PBDPr0Fz/bFylRnK/KS0QF0aaJW5NcsM4VS296AmQ3g8GrH2KJHIMtqbl0iIiIi5+VqyvTNN9/k3nvvpVOnTqxYsYLx48czYcIExo4dy9y5cwkPD6dv377Ex8czf/58HnzwQQzDYP78+fTp04fFixczceLEfH4rnmfz3gw27spwHqsVeS7s+h4WPUaJpP2usYBguOFu82oSERERuUiuQvXQoUOJiooCwGazERoayq5du4iLiyMmJgaARo0asX79eiIiIrBarRiGQXh4ONOnT2fAgAH/uJVcYmJibsryKOlZFj5eVwJwLEe4sXQaJB3DB95aoQhKO0LpP18l6tgit/GU4k04WnsEGUWqwM49JlUnnsJqtfrE9wvJXzovJCc6L6Sg5SpUFy9eHIDdu3fz2muv8c4773Dq1Cln0AaIjIwkOTmZLl26MHbsWAICAhg0aBCTJ0+mcePGjBgxgjp16tCrV69Lnr9mzZq5fDue4/MVKSSnO7Z4iwq38OBt11MkrKzJVXkBexb89hasHgGZKc7hrJBiBMVPILLWAKpoz2k5LzEx0Se+X0j+0nkhOdF5ITnZuHFjvj1Xri9UXLNmDY8++iivv/46lStXpkiRIqSkuEJQSkoKUVFRREZGMmbMGEaPHs306dMZPHgwU6ZMYeTIkSxdupTU1NR8eSOeZNfRLJZsdTUh6d0qgiJqRX51lj0Dy552C9TUeZDdbX+AG/9PTVxERETEI+Uq6a1Zs4ZXXnmFDz74gDp16gBQpUoV9u3bx5kzZ8jIyGDDhg00aNDA+ZgdO3YQGhpKXFwc6enpWCwWbDYbGRkZl3sZr5RlM5i5NMXZirx2XDBNq6kV+VVr+CQEnb+Ys8SN0HsF3PQ+tpCiZlYlIiIi8o9ytfzj1VdfJTMzk2HDhgFQqVIlRo8ezbBhwxg4cCCGYdCzZ09iY2Odj5k6dSojRowA4M4776R3797Url2bokWL5v1deJCff7dy6Hwr8pAguKetWpFflmGAYYOAbKdhTEVoM9axs0ejIRCoX0hERETE8+UqVH/33Xc5jsfHxxMfH5/jbePGjXN+3bNnT3r27Jmbl/ZoR8/YmLfRvRX5ddHaNzlHp3fCwkegTAtoNdr9tob/NqcmERERkVxSF5J8YhiOZR9ZjklqKpQMpKNakV8qKx3W/RfWjQVbOhxcBjX6QYkaZlcmIiIikmsK1flkZWKGeyvyDmpFfol9ixxNW07vcI0ZNkewVqgWERERL6ZQnQ/OptqZ+6trF5PO9cOIUytyl5Rjjh09Ej91Hy/dBDpNhdgGOT9ORERExEso+eWDL1akkpruakXetbFakQNg2GHL+7BiGKSfcY2HRDsuRqz7EARozbmIiIh4P4XqPNq8N4MN2VqR39suktBgLfsg5Sh82x2OrHEfr94H2o+HItebU5eIiIhIAVCozgNrhsFny13LPlpUD6FW+WATK/Ig4ddBlmsnFIpWgY6ToeJN5tUkIiIiUkDU5i8PvlmbyqlkOwBFwiz0ahlhckUeJCAIOk2BoDBoPhwGbFWgFhEREZ+lmepc2n0si8XZW5G3jiAq3E9/Rzm3H7ZMhVYvgyXbn0GZ5jBoP0SUNK82ERERkUKgUJ0LWTaDGUtcrchvLB9MM39sRW7LhN/ehNUjISsVYqpAnQfc76NALSIiIn7AT6dW8+aXTRe1Im/nh63ID/8KnzaG5c86AjXAiqGQmWJuXSIiIiIm0Ez1NTp2xsb3G1wX4N3RNJyS/tSK3HrasUXelvfcx6+r7dhzOjjSnLpERERETKRQfQ0Mw2DmsotakdcNM7eowmIYjuYtS5+CtOOu8aAIaDkKGj4Jgdr5RERERPyTQvU1WL0tg+2HsrUibx9JoD+0Ij+1HRYOhgNL3Mcrd4WOb0N0BXPqEhEREfEQCtVX6Vyqndmrs7UirxdGXEk/+ePb9I57oC5SDuLfhqp3gL+tJRcRERHJgS5UvEpfrHS1Ir8uOoCuTfyoFXmrlyGyNFgCodFTcH8iVLtTgVpERETkPD+Zas2brfsyWP+XqxV5f19uRZ5yFCxBEHGdayw0Bm6Z4dger1R900oTERER8VSaqb4Ca6bBJ8tcyz6a3+CjrcjtNtg0GabVgKVDLr29YmcFahEREZHLUKi+gm8vakV+dysfbEV+7Hf4vCUsehTSz0LiJ7B/sdlViYiIiHgNLf/4B3uOZbEoWyvyu1v5WCvyjCRYNQJ+fwsMu2u8WDUI8MHZeBEREZEColB9GVk2gxlLUzDO9yKvVS6I5jf4SCtyw4C/vobFT0DyIdd4YAg0fQGaDoUgP9l/W0RERCQfKFRfxoLNVg6edLUiv7ddpG+0Ij+7FxY/Brt/cB+P6wgdJ0PxG0wpS0RERMSbKVTn4O+zNr5f72pF3q1JOCVjfKAV+emdMKMeZLneGxGloP0EqNFXW+SJiIiI5JIPLRDOH4Zh8MmyFDLPtyKPuy6QTvV8ZClE0apQvsP5AwvUexju3wY1+ylQi4iIiOSBQvVFVm/PIPGgoxW5xdtbkV9YEH6BxQIdJ8H1LaDvauj0LoQVM6c2ERERER+iUJ3NuVQ7c1Zla0VeN4wKpbxwhYxhwB/THdvkZaa53xZTCfqthjLNTSlNRERExBcpVGcza1UqKedbkZeICqBbUy9sRX4yEWa3h5/vhyNrYN1YsysSERER8XkK1edt3ZfBup2uVuT3tovwrlbkmWmw8j+OCxEPLneNb/8CbBmXf5yIiIiI5JkXrm3If9ZMg0+Xu5Z9NKsWQu04L9qTes9Pjm6IZ3e7xiyB0PhpaDHCsf+0iIiIiBQYhWrg23WpnExytSLv3dpLWpEnH4YlQ2DHbPfxMi2h0xQoWcecukRERET8jN+H6r1/Z7Foi6sVea+WXtKKfMv7sOwZyDjnGgsrBm1ehzoPgMUL3oOIiIiIj/DrUH1xK/Ka5YJoUd1Llkqkn3UP1LX6Q7s3HM1cRERERKRQ+XWoXrjFyoETXtqKvOG/IXEmZKU79puO63Dlx4iIiIhIgfDbUP33WRvfrXPt4dy1STilPLEVuWHAjrlQohZcd6NrPDAY7vgWIq+HoFDz6hMRERER/9xS7+JW5OWvC6SzJ7YiP7Mbvr4d5t0NCx8Gw+5+e0xFBWoRERERD+CXofpXT29FbsuAta/CxzfCnvmOsUMr4c9PzK1LRERERHLkd8s/ktLszFnt2pO6Y91QKnpSK/KDy2HBw3AqMdugBeo/AlW6mVaWiIiIiFyeB6XJwjFrZSrJVlcr8juaesie1KknYPmzkDDdfbxUA+g8FUo3MaUsEREREbkyvwrVf+zPYO1FrcjDzG5Fbtjhj+mOQG095RoPLgKtx0D9RyHAr/6aRERERLyO36S19EyDT5a5ln009ZRW5CcS4JcHAcM1Vq0ndHgTosqaVpaIiIiIXD2/uVDxu3VpzlbkkaEWerfykGUfJetAvcGOr6MrQvd50G2uArWIiIiIF/GLmep9f2exYIvVedyrVQTRESb9PpF8GIqUcR9r/QpExkLjZyDYQ8K+iIiIiFw1n5+pttkvbUXe0oxW5EkH4bue8HFtSP3b/bawotBihAK1iIiIiJfy+VC9cLOV/edbkQcHmtCK3J4FGyfCtJqw8yuwnoZlzxbe64uIiIhIgfPp5R/Hz9r4br2JrciPrHN0Qvz7d/dxSwDYbRDggW3RRUREROSa+WyovtCKPMPROJFyJQqxFbn1DKx8ETa/i9uuHsVrQqd3oXy7wqlDRERERAqFz4bqNTsy+POiVuRBgQW87MMwYPssWDoEUo66xoPCoPkIaPw0BHrANn4iIiIikq98ck11Upqd2auytSKvE0ql2EL4/eHn++GHvu6BuuIt8H8J0Ox5BWoRERHxWP3792fXrl2XvX39+vVs27YNgMcee6xQakpMTGTSpEmF8lp55ZOhevYqVyvy4kUCuKNZIe2qUbmL6+siZaDrHOjxIxStXDivLyIiIlJAvvzyS/7+27GDWWEF3Zo1axZagM8rn1v+kbA/kzU7TGpFXq2nI1jHVIZWL0NodOG8roiIiBSaXzal8d36NNIz8+85Q4OhW5NwbqofnuPtVquV559/nsOHD5OZmcnw4cPZs2cPu3fv5plnniE9PZ1bb72VxYsX079/f6pXr87OnTuJiIigcePGrFy5knPnzvHRRx+xaNGiHB93wdGjRxk1ahTp6ekcP36cJ598ktKlS7NixQoSEhKoWrUqvXr14vvvv+eee+7hxx9/xGKxMHr0aFq0aEFcXBxjxowBoGjRorz66qtERUU5n//UqVM888wzZGRkUKlSJdasWcOCBQv46aef+PTTT8nKysJisTBp0iR27tzJF198wYQJE7jpppto2LAhe/bsoUSJErz99tvs37+f559/nqCgIOx2O+PGjeP666/Pv7+Ya+BTM9XpmQafLE9xHjepGkKdCgWw5CL1b5j/f7Bvofu4xQJ3fAPxbypQi4iI+KhfNlnzNVADpGc6nvdyvvjiC8qWLcusWbMYP348mzdv/sfnq1u3Lh9//DEZGRmEhYUxbdo0qlatyvr1669Yy+7du7n//vuZNm0ao0eP5tNPP6V27dq0adOGZ599ljJlHE3sihcvTvXq1dmwYQMZGRmsXbuWDh06MHz4cEaOHMnMmTNp27YtH3zwgdvzT5kyhY4dO/LJJ59wyy23YLM5tj7eu3cv7733Hp9//jlVq1Zl5cqVbo87cOAA//73v5k1axanTp1i69atrF69mrp16zJt2jQef/xxkpKSrvj+CopPzVR/vz6NE+ccrcgjQi30aZ3Pyz4MO2z9EFYMdew3feRXGLDFcSHiBdomT0RExKfdVD+sQGaqb6p/+V3Kdu/eTdu2bQGoWLEi9913H1999ZXzdsMw3O5/4403AhAdHU3VqlWdX6enp7vd7+LHAZQsWZJ3332XuXPnYrFYyMrKumxdd999N19//TXHjx8nPj6eoKAgdu3axUsvvQRAZmYmFStWdHvMrl276N69OwCNGzd2jpcoUYKhQ4cSGRnJ7t27qV+/vtvjihUr5pyFvv7660lPT+euu+7i/fff58EHHyQqKoohQ4ZcttaC5jOhev/xLBZsdv2Gd3fLfG5FfnyrY8/pw6tdY6d3wl/fQo3e+fc6IiIi4tFuqn/5ZRoFpUqVKmzdupVOnTpx4MABJk6cSHx8PMePHwcgISHhqp8rNDT0Hx/35ptv0qtXL9q1a8eXX37J119/DYDFYrkkhLdo0YL//e9/HDt2jJEjRwJQqVIlXnvtNcqUKcPGjRudr3XBDTfcwO+//07NmjXZtGkTAElJSbz11lssXboUgPvvv/+S18qped+iRYto1KgRjz32GPPmzeODDz5g7NixV/1nkZ98IlTb7AYfL03Bfv7PvnrZIFrWyKdlH5kpsPol2DgeDJtrPKYydHwHKt2SP68jIiIichl9+vThhRde4N5778Vms/HCCy9QoUIFPv/8c/r27cuNN95IZGTkVT1XmzZt/vFxt9xyC6+//jrvvfcepUuX5vTp0wDUq1ePN954g3Llyjnva7FYuPnmm1m9ejVxcXEAjBo1iqFDhzrXRr/yyituzz9o0CCee+455s+fT6lSpQgKCqJIkSI0bNiQ3r17ExQURHR0NH///bfba+Wkdu3aDB06lHfffRe73c7zzz9/VX8GBcFi5DTvb6KNGzfSqFGja3rML5vSmLPa0TkxKBBG9Y4htmg+LMP46ztY/Dgk7XeNBQRDk+eg2YsQXLi/pfqzxMREatasaXYZ4mF0XkhOdF5ITnReeI5ly5ZRrFgx6taty+rVq5kyZQozZswwpZbc5M7L8fqZ6uPnbHy7Llsr8sbheQ/USYdg0aOw61v38XLtHB0RS+gfpYiIiEhulCtXjhdeeIHAwEDsdjsvvvii2SXlC68O1YZh8OmyVGcr8rLFA/9xkf/VP3EW7FvgOg4rAe3HQa0Bjh0+RERERCRXqlSpwqxZs8wuI9959ZZ6a3dmkHDAcemtBRjQIZ9akUdXgBaOxfbUHggPbIcb/0+BWkRERERy5LUz1UlpdmatdLUij68TSuXctCK3noaDK6BqN/fxRkOgXFso0zyPlYqIiIiIr/Pameo5q91bkd95ra3IDQMSP4NpNeD7u+BkovvtgcEK1CIiIiJyVbwyVP95IJNft7takd/TNoKwkGtYmnFqB8ztDD/e4+iOaM+EhYMdQVtERERE5Bp5XahOzzT4ZJmrFXnjKiHUrXiVe1JnWR17Ts+oA/sXucaLlIWGT+RzpSIiIiKe5fPPP+ftt98u9Nft378/u3btuqbHLFiwgGPHjnH8+HFGjRpVMIXlI68L1d9vSON49lbkba5y2ce+RTCjLvw6CmznZ7ktAY610/cnQrUeuhBRRERExEPMmDGD5ORkSpYs6RWh2qsuVNx/PIsFm1ytyO9qEUHMlVqRpxyDZU9D4qfu46WbQKepENugACoVERERn7Z6FPz60tXdt84guOk997Ff/gVb33cdtxgJLUdd9im++uorvvzyS+x2O0888QS7du3il19+IS0tjWLFijFp0iTmzZvHsmXLsFqt7N+/n0GDBtGjRw82bNjAq6++SnR0NIGBgdSvXx+Ajz76iB9++IGgoCAaN27Ms88+y9tvv82+ffs4ffo0Z86c4Z577uGXX35hz549vPbaa87HAuzZs4fnn3+eoKAg7HY748aN4/rrr2fcuHFs2LABu93Offfdx6233up8TFJSEi+++KKzS+N//vMfqlevzpw5c/j888+x2+3Ex8dTt25dEhMTGTp0KP/73/8YOnQos2fPZtWqVUycOJHQ0FCKFi3Kq6++SmJiIu+//z7BwcEcPHiQ2267jcGDB1/d300+8ppQbbcbzMjeirxMEK1rXsWyjx/6wIGlruOQaGgzFuo+BAH50HVRREREpBBER0c723Fv3LiR6dOnExAQwMCBA9m6dSsAycnJfPjhh+zdu5eHH36YHj168NJLL/HWW29RqVIlRo50bBm8fft25s+fzxdffEFQUBCPP/44S5YsASAsLIwPP/yQ9957j2XLljFlyhS+/PJLfvjhB7dQvXr1aurWrcuzzz7Lhg0bSEpKYseOHRw8eJDPP/+c9PR07r77blq1auV8zJQpU2jevDn9+vVj7969PP/880yaNIn333+f7777jtDQUMaNG0eTJk2oWbMmo0aNIjg4GHD0Jxk+fDiff/45sbGxfPzxx7z77ru0b9+ew4cP891335GRkUGbNm0Uqv/Joq3p7DtuAxytyO9tH4nlapZrtPkvfNYCMKB6H2g/HopcX7DFioiIiOSzSpUqARAQEEBwcDBPPfUUERERHD16lKwsRye8GjVqAHD99deTkeFY7nrixAnnYxs2bMj+/fvZvXs39erVcwbWxo0bs3PnTgBq1aoFQFRUFFWrVgUgJiaG9PR0t3ruuusu3n//fR588EGioqIYMmQIO3bsICEhgf79+wOQlZXFoUOHnI/ZsWMHa9asYf78+QCcPXuWAwcOUK1aNcLCHA38nnnmmRzf/+nTpylSpAixsbEANGnShPHjx9O+fXtuuOEGgoKCCAoKcj5PYfOKUH3inI1v1rr2pO7SKJzSObUiz0yBoHDHWukLrm8GrV52LPeoeFMhVCsiIiI+r+Wof1yucUU3vXfpkpArCAhw5Jtt27axcOFC5syZQ1paGj169MA4v4NZThOOsbGx7Nq1iypVqrB161ZiYmKoXLky06ZNIysri8DAQNavX8+dd97Jtm3brm7SEli0aBGNGjXiscceY968eXzwwQd06tSJZs2a8fLLL2O325k8eTLly5d3PqZy5cp069aNrl27cvLkSebMmUNcXBy7d+8mIyODkJAQnnjiCV588UUsFovzfQEUK1aM5ORk/v77b0qVKsW6deuoWLHiZd93YfP4UG0YBp8ud29FfnODHH4D2fkNLH7csSap7oPutzX3jZ7yIiIiIhUqVCA8PJw+ffoAULJkSf7+++/L3n/06NE899xzFClShMjISGJiYqhevTq33norffv2xW6306hRIzp16sS2bduuuo7atWszdOhQ55KU559/nlq1arFu3Tr69etHamoqnTp1okiRIs7HPPzww7z44ovMnj2b5ORkHnvsMYoXL86gQYO49957sVgsdOjQgdjYWBo0aMBzzz3Hyy+/DDiC85gxY3j88cexWCzExMQwduxY5wy72SyG4VmbM2/cuJFGjRo5j9fuTOeDBY4t9CzAsJ7R7p0Tz+2DxU/Aru8cx2HF4P7tEFGyEKuWgpaYmEjNmjXNLkM8jM4LyYnOC8mJzgvJycW5My88eqY62ereirxD9lbktkz4baLj6tss132wBMEphWoRERERKTweHarnrE4lKc0xkV4sMoDuF1qRH1oNCx+GE1vdH1D3X9B6LIQXL+RKRURERMSfeWyoTjyYyept2VqRt4sgzHYafhnmvq8jwHV1oNMUKNuykKsUEREREfHQUJ2RZTBzqasVeaMqIdQL/R2mdYW04647BkVAy5eg4b8hMNiESkVEREREPDRUz1vvakUeHmKhb5sICKwBAdnKrdIN4t+G6DiTqhQRERERcbhCj29z/Jy9FXnLcEcr8tAY6DARosrDHd/And8qUIuIiIiIR/DImeoaWUuobl9BQoXRtK4Z6rrhhl5QuQsER5hXnIiIiIjIRfJ1pnrTpk306tWLPn36MGnSJABSUlIYMGAAvXv3dm4ovmHDBt577/JdhIZk3MVtWW8ysOqvBGTvkGOxKFCLiIiIiMfJ11A9cuRIxo0bx+eff87mzZv5888/WbVqFfHx8YwcOZK5c+diGAYzZszg//7v/674fMU3jwDP6k0jIiIiInKJfFv+kZycTEZGBnFxjnXOrVu3ZvXq1dSoUYP09HSsVisRERF8//33dO7cmdDQ0Ms+18b2G1wHv/2WXyWKl9u4caPZJYgH0nkhOdF5ITnReSEFKV9Ddfbe7pGRkRw4cICWLVuybNkyZs2axeOPP87rr7/O448/zogRIyhfvjyDBg1ye578ahUpIiIiIlJY8m35R5EiRUhJce0tnZKSQnR0NAEBAbz44ou89tpr/PDDDwwYMIB3332XJ598kiNHjrBnz578KkFERERExBT5GqqDg4PZv38/hmGwcuVKGjdu7Lz95MmT7Nmzh8aNG5OWlkZgYCAWi4W0tLT8KkFERERExBT5uqXeSy+9xDPPPIPNZqN169bUq1fPedu7777L4MGDAejXrx8DBw6kTJky1KhRIz9LEBEREREpdBbDMHd7jU2bNvHKK68QGBhI69ateeyxx0hJSWHw4MGkp6fz0ksvUaNGDTZs2MBvv/3Gv/71LzPLlQJy+PBhXnjhBWw2G4ZhMHr0aCpXrszixYt55513CAoKomfPntx9990cPXqUJ598koCAACZMmEBsbCzffvstQUFB3H777Wa/FSkA69at49lnn2XZsmUAOi/8XGpqKqNGjeLgwYNkZmYyfPhw6tatq/PCzx0+fJjnnnsOwzCIiYlh3LhxhIeH67zwQ5s3b+aNN95g5syZ7Nu3j2HDhmGxWKhWrRojR44kICCASZMmsXTpUoKCgnjhhReoW7cuy5cv56233qJMmTJMnDiRgIAARo8ezQMPPEC5cuWu/MKGybp162bs27fPsNvtxoMPPmgkJCQYP//8szFt2jQjISHBePnllw273W48/vjjhtVqNbtcKSDPPfecsWDBAsMwDGP58uXGo48+amRkZBidOnUyzpw5Y6Snpxs9evQwjh8/bnz00UfGggULjF9++cWYNm2akZaWZjzxxBOG3W43+V1IQTh8+LDx8MMPGy1btjQMw9B5IcZbb71lvPfee4ZhGEZiYqLx9ddf67wQ45VXXjE++eQTwzAMY/z48caMGTN0Xvih9957z+jSpYvRq1cvwzAM46GHHjLWrFljGIZhDB8+3Pjll1+MP/74w+jfv79ht9uNQ4cOGT169HDe9+zZs8bLL79sJCQkGImJica4ceOu+rVNbVOefRs+i8Xi3IYvIiLimrfhE+82dOhQ2rVrB4DNZiM0NJRdu3YRFxdHTEwMISEhNGrUiPXr1xMREYHVasVqtRIeHs706dMZMGAAluyNgsQnpKenM3LkSEaNGuUc03khK1euJDg4mIEDBzJ58mTatGmj80KoWbMm586dAxz5IigoSOeFH4qLi+Ptt992HickJNC0aVMA2rZty+rVq9m4cSOtW7fGYrFQpkwZbDYbp06dIjIyEqvVSnp6OuHh4bz//vuX7FL3T0wP1Rdvw5eUlETLli05ceIEs2bN4u6772bhwoXUqFGDESNG8P7775tYsRSU4sWLExwczO7du3nttdd49NFHSU5OJioqynmfyMhIkpOT6dKlC2vWrGH9+vW0bNmSffv2YRgGI0aMYM6cOSa+C8lvFz52i42NdY7pvJDTp09z7tw5PvzwQ+Lj43nttdd0XgilS5fm008/5fbbb2f58uXccsstOi/80M0330xQkOuSQcMwnL8sXciZl8ufjzzyCGPHjqVs2bLs37+fhg0bMm/ePEaMGMHvv/9+xdc2NVRrGz7Jbs2aNTz66KO8/vrrVK5cOcfzIyoqisjISMaMGcPo0aOZPn06gwcPZsqUKYwcOZKlS5eSmppq4ruQ/HLs2DE2bNjAO++8Q//+/Tl79ixDhgzReSEULVqU+Ph4ADp06MAff/yh80J4/fXXGTt2LD/88AMvvvgiQ4cO1XkhBAS4ou6FnHm586JKlSpMmDCBQYMGMXfuXLp06cLKlSsZMWIEkydPvvJrFcg7uErahk8uWLNmDa+88goffPABderUAaBKlSrs27ePM2fOkJGRwYYNG2jQoIHzMTt27CA0NJS4uDjS09OxWCzYbDYyMjLMehuSj2JjY/n555+ZOXMmM2fOJCYmhgkTJui8EBo1auS8aHX9+vVUrVpV54UQHR3tnJUuVaoU586d03kh1KpVi7Vr1wKwfPlyGjduTMOGDVm5ciV2u53Dhw9jt9spXry48zGzZs2ie/fuANjt9qvOnvm6pV5uaBs+AXj11VfJzMxk2LBhAFSqVInRo0czbNgwBg4ciGEY9OzZ020ZwNSpUxkxYgQAd955J71796Z27doULVrUjLcghSQ4OFjnhZ976KGH+M9//kPv3r0JCgritdde03khDB8+nNGjR2O3251LOXReyNChQxk+fDjjx4+ncuXK3HzzzQQGBtK4cWN69+6N3W53ngPgWGK4bt06Jk6cCEDJkiXp27cv/fr1u+Jrmb6lnoiIiIiItzN1+YeIiIiIiC9QqBYRERERySOFahERERGRPFKoFhERERHJI4VqEREREZE8UqgWEREREckjhWoRERERkTz6f+8M6al+9ZCCAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "evaluator.plot_cumulative_gains()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WLVqUk046Kb169cpRRx2Vurq6LFy4MOuvv3722WefVh97yJAhOeOMM3LkkUemsbExw4YNS4cOy/4q1DWVSqWyzHsBAIB2zhvEAABAAcIaAAAKENYAAFCAH15cRiPGz2zrEYqY9IVle3kZAID2zhVrAAAoQFgDAEABwhoAAAqo2j3W8+fPz+mnn54XXnghTU1NOeGEEzJo0KDm7ddcc03+93//t/mtJM8555z06dOnWuMBAMAyqVpY33LLLenZs2cuuuiiNDQ05KCDDmoR1o8++mguvPDCbL755tUaCQAAiqlaWO+9997Za6+9kiSVSiUdO3Zssf2xxx7LVVddlZdeeimf+tSncvzxx1drNAAAWGZVC+tu3bolSRobG3PSSSfl5JNPbrF9v/32y7Bhw9K9e/eMHDkyd911V3bbbbfF9lNfX5/6+vrF1mtra1NbW7tcZgcAgP+kplKpVKp1sH/+85858cQTM2zYsBx66KHN65VKJY2NjenRo0eS5Prrr09DQ0NOPPHEao32vnkdawAAkiq+KsjLL7+cY445JqNHj24R1cmbV7E/85nPZM6cOalUKpk6dap7rQEAWKFU7VaQCRMmZPbs2Rk/fnzGjx+fJDnssMMyd+7c1NbWZtSoURk+fHi6dOmSHXfcMbvuumu1RgMAgGVW1VtBVkZuBQEAIPEGMQAAUISwBgCAAoQ1AAAUIKwBAKAAYQ0AAAUIawAAKEBYAwBAAcIaAAAKENYAAFCAsAYAgAKENQAAFCCsAQCgAGENAAAFCGsAAChAWAMAQAHCGgAAChDWAABQgLAGAIAChDUAABQgrAEAoABhDQAABQhrAAAoQFgDAEABwhoAAAro1NYDsGIaMX5mW49QxKQv9GrrEQCAlYQr1gAAUICwBgCAAoQ1AAAUIKwBAKAAYQ0AAAUIawAAKEBYAwBAAcIaAAAKENYAAFCAsAYAgAKENQAAFCCsAQCgAGENAAAFCGsAAChAWAMAQAHCGgAAChDWAABQgLAGAIAChDUAABQgrAEAoABhDQAABQhrAAAoQFgDAEABwhoAAAoQ1gAAUICwBgCAAoQ1AAAUIKwBAKAAYQ0AAAUIawAAKEBYAwBAAcIaAAAKENYAAFCAsAYAgAKENQAAFCCsAQCgAGENAAAFCGsAAChAWAMAQAHCGgAAChDWAABQQKe2HgBWJCPGz2zrEYqY9IVebT0CAKx0XLEGAIAChDUAABQgrAEAoABhDQAABQhrAAAoQFgDAEABVXu5vfnz5+f000/PCy+8kKamppxwwgkZNGhQ8/YpU6bkiiuuSKdOnXLIIYfk8MMPr9ZoAACwzKoW1rfcckt69uyZiy66KA0NDTnooIOaw3r+/Pm54IILctNNN6Vr164ZOnRodt9996y11lrVGg8AAJZJ1cJ67733zl577ZUkqVQq6dixY/O2p59+Or17987qq6+eJNlmm21y//33Z5999qnWeMBSeGMcAPjPqhbW3bp1S5I0NjbmpJNOysknn9y8rbGxMT169Gjx2MbGxiXup76+PvX19Yut19bWpra2tuzQAADQSlV9S/N//vOfOfHEEzNs2LDsv//+zevdu3fPnDlzmj+eM2dOi9B+OwENAMAHUdVeFeTll1/OMccck9GjR+fQQw9tsa1v37557rnn0tDQkKampvzpT3/K1ltvXa3RAABgmVXtivWECRMye/bsjB8/PuPHj0+SHHbYYZk7d25qa2szZsyYHHvssalUKjnkkEOyzjrrVGs0AABYZlUL6zPOOCNnnHHGu27ffffds/vuu1drHAAAKMobxAAAQAHCGgAAChDWAABQgLAGAIAChDUAABQgrAEAoABhDQAABQhrAAAoQFgDAEABwhoAAAoQ1gAAUICwBgCAAoQ1AAAUIKwBAKAAYQ0AAAUIawAAKEBYAwBAAcIaAAAKENYAAFCAsAYAgAKENQAAFCCsAQCgAGENAAAFCGsAAChAWAMAQAHCGgAAChDWAABQgLAGAIAChDUAABQgrAEAoABhDQAABQhrAAAoQFgDAEABwhoAAAoQ1gAAUICwBgCAAoQ1AAAUIKwBAKAAYQ0AAAUIawAAKEBYAwBAAcIaAAAK6NTWAwB8UI0YP7OtRyhi0hd6tfUIAO2CK9YAAFCAsAYAgAKENQAAFCCsAQCgAGENAAAFCGsAAChAWAMAQAHCGgAAChDWAABQgLAGAIAChDUAABQgrAEAoABhDQAABQhrAAAoQFgDAEABwhoAAAoQ1gAAUICwBgCAAoQ1AAAUIKwBAKAAYQ0AAAUIawAAKEBYAwBAAcIaAAAKENYAAFCAsAYAgAKENQAAFCCsAQCgAGENAAAFCGsAAChAWAMAQAFVD+tp06alrq5usfVrrrkm++23X+rq6lJXV5e//e1v1R4NAADet07VPNikSZNyyy23pGvXrotte/TRR3PhhRdm8803r+ZIACzBiPEz23qEZTbpC73aegSgnanqFevevXtn3LhxS9z22GOP5aqrrsrQoUMzceLEao4FAADLrKpXrPfaa6/MmDFjidv222+/DBs2LN27d8/IkSNz1113ZbfddlvscfX19amvr19svba2NrW1tcVnBgCA1qhqWL+bSqWSz372s+nRo0eSZNddd83jjz++xLAW0AAAfBB9IF4VpLGxMZ/5zGcyZ86cVCqVTJ061b3WAACsUNr0ivXPfvazvP7666mtrc2oUaMyfPjwdOnSJTvuuGN23XXXthwNAADek6qH9QYbbJAbb7wxSbL//vs3rx900EE56KCDqj0OAAAU8YG4FQQAAFZ0whoAAAoQ1gAAUICwBgCAAoQ1AAAUIKwBAKAAYQ0AAAUIawAAKEBYAwBAAcIaAAAKENYAAFCAsAYAgAKENQAAFCCsAQCgAGENAAAFCGsAAChAWAMAQAGtDuvhw4fn6aefXuK26dOn58ADDyw2FAAArGg6LW3jnXfemYULFyZJ7rvvvkyZMmWJcf2HP/whzz///PKZEAAAVgBLDet77703kydPTpLU1NTkW9/61rs+9nOf+1zZyQAAYAWy1LD+6le/mqOOOiqVSiV77LFHLr/88myyySYtHtOxY8d079493bt3X66DAgDAB9lSw3q33XbLFVdckf79++fggw/OZpttlvXWW69aswEAwApjqWH92muv5dlnn03//v3zk5/8JEOHDhXWAKy0Royf2dYjLLNJX+jV1iNAu7XUsN5uu+0yZsyYnH322alUKvnsZz+bmpqaJT62pqYmDzzwwHIZEgAAPuiWGtaXXHJJfvrTn6ahoSGXX3559ttvv6y77rrVmg0AAFYYSw3rHj165Mgjj0ySTJ06NUcffXT69u1blcEAAGBFstSwbmhoyGqrrZYOHTpk3LhxzWvvpmfPniVnAwCAFcZSw3rHHXdMfX19ttxyy+ywww7ven/1W5544omiwwEAwIpiqWE9duzYfPSjH23+9X8KawAAaK+WGtYHH3xw868HDx683IcBAIAV1VLD+uqrr271jmpqanLUUUct6zwAALBCWmpYX3jhha3ekbAGAKA9W2pYT58+vVpzAADACq1DWw8AAAArA2ENAAAFCGsAAChAWAMAQAHCGgAAChDWAABQgLAGAIAChDUAABQgrAEAoABhDQAABQhrAAAoQFgDAEABwhoAAAoQ1gAAUICwBgCAAoQ1AAAUIKwBAKCATm09AADQtkaMn9nWIxQx6Qu92noE2jlXrAEAoABhDQAABQhrAAAoQFgDAEABwhoAAAoQ1gAAUICwBgCAAryONQDQLnn9bkpzxRoAAAoQ1gAAUICwBgCAAoQ1AAAUIKwBAKAAYQ0AAAUIawAAKEBYAwBAAcIaAAAKENYAAFCAsAYAgAKENQAAFCCsAQCgAGENAAAFCGsAACig6mE9bdq01NXVLbY+ZcqUHHLIIamtrc2NN95Y7bEAAGCZ1FQqlUq1DjZp0qTccsst6dq1a4t4nj9/fvbdd9/cdNNN6dq1a4YOHZqJEydmrbXWqtZo79uI8TObf73//AtzwIJvtup5v+04PJO7XNpira5pVHZZeG2rnn9Lp6/mZ51PbbE2ct6w9F90e6uef23nS/K7Tp9t/njSF3olk7dJ/v1gq54/rsv1ebjj3i3WLpq7aXrmxVY9/9wP3ZnnO2zVYm3S3DVb9dwk+coqj2ZWzXrNH69e+WcufmPzVj9/RNdX3jzmF3q9ufDiA8l127bquQ1ZJ6O7Pt5ibcuFt+WLTUe06vnP1fTPeatMabE2cMH3M3z+Ka16/rQOe+XyD/2gxdp7/dzb5eTvt1z81eeSRya16vmlP/eS5Iw3ds+GlWmtev6yfu7lyD8l62zTcu1bNa17bsp97r2l96KHcua8Qa167rJ+7mXtAUndAy3XHr4q+fXxrXp6ic+9tvy6lz0nJlt+ruVaO/q6ly+/46/7dvR1L1uMSD59Vcs1X/da99wkOf6FpPtH/u/jxn8kE9dv/fOX4XMv3dZLPv+P1h+rjXWq5sF69+6dcePG5atf/WqL9aeffjq9e/fO6quvniTZZpttcv/992efffZZbB/19fWpr69fbL22tja1tbXLZ3AAgJXEiPEz07pvKd70le+/mlk1qzR/vHrl1Vz8Ho/3dr0XzcqZrXxuw5xFGf2O57+l+eLYB0hVw3qvvfbKjBkzFltvbGxMjx49mj/u1q1bGhsbl7gPAQ0AwAdRVW8FSZIZM2bklFNOaXEryPTp0/Otb30rkya9+f3T2LFjM2DAgOy9997vtpsPjHd+F7aieq/f9TnvFZvzbp32et7JynHuzrv1VobzTvwZb632et7V8IF4VZC+ffvmueeeS0NDQ5qamvKnP/0pW2+9dVuPBQAArVbVW0He6Wc/+1lef/311NbWZsyYMTn22GNTqVRyyCGHZJ111mnL0QAA4D2pelhvsMEGzbeB7L///s3ru+++e3bfffdqjwMAAEV8IG4FAQCAFZ2wBgCAAoQ1AAAUIKwBAKAAYQ0AAAUIawAAKEBYAwBAAcIaAAAKENYAAFCAsAYAgAKENQAAFCCsAQCgAGENAAAFCGsAAChAWAMAQAHCGgAAChDWAABQgLAGAIAChDUAABQgrAEAoABhDQAABQhrAAAoQFgDAEABwhoAAAoQ1gAAUICwBgCAAoQ1AAAUIKwBAKAAYQ0AAAUIawAAKEBYAwBAAcIaAAAKENYAAFCAsAYAgAKENQAAFCCsAQCgAGENAAAFCGsAAChAWAMAQAHCGgAAChDWAABQgLAGAIAChDUAABQgrAEAoABhDQAABQhrAAAoQFgDAEABwhoAAAoQ1gAAUICwBgCAAoQ1AAAUIKwBAKAAYQ0AAAUIawAAKEBYAwBAAcIaAAAKENYAAFCAsAYAgAKENQAAFCCsAQCgAGENAAAFCGsAAChAWAMAQAHCGgAAChDWAABQgLAGAIAChDUAABQgrAEAoABhDQAABQhrAAAoQFgDAEABwhoAAAoQ1gAAUECnah5s0aJFOfvss/Pkk0+mS5cuOe+887Lhhhs2bz/vvPPy4IMPplu3bkmS8ePHp0ePHtUcEQAA3peqhvUdd9yRpqam1NfX56GHHso3vvGNXHnllc3bH3vssfzP//xPevXqVc2xAABgmVX1VpAHHnggAwcOTJJstdVWefTRR5u3LVq0KM8991zOOuusDBkyJDfddFM1RwMAgGVS1SvWjY2N6d69e/PHHTt2zIIFC9KpU6e8/vrrOfLII3P00Udn4cKFGT58eDbffPP8v//3/1rso76+PvX19Yvtu7a2NrW1tcv9HAAAYEmqGtbdu3fPnDlzmj9etGhROnV6c4SuXbtm+PDh6dq1a5Jkhx12yPTp0xcLawENAMAHUVVvBRkwYEB++9vfJkkeeuihfPzjH2/e9uyzz2bo0KFZuHBh5s+fnwcffDCbbbZZNccDAID3rapXrPfcc8/cc889GTJkSCqVSsaOHZurr746vXv3zqBBg3LggQfm8MMPT+fOnXPggQdm4403ruZ4AADwvlU1rDt06JCvf/3rLdb69u3b/Ovjjjsuxx13XDVHAgCAIrxBDAAAFCCsAQCgAGENAAAFCGsAAChAWAMAQAHCGgAAChDWAABQgLAGAIAChDUAABQgrAEAoABhDQAABQhrAAAoQFgDAEABwhoAAAoQ1gAAUICwBgCAAoQ1AAAUIKwBAKAAYQ0AAAUIawAAKEBYAwBAAcIaAAAKENYAAFCAsAYAgAKENQAAFCCsAQCgAGENAAAFCGsAAChAWAMAQAHCGgAAChDWAABQgLAGAIAChDUAABQgrAEAoABhDQAABQhrAAAoQFgDAEABwhoAAAoQ1gAAUICwBgCAAoQ1AAAUIKwBAKAAYQ0AAAUIawAAKEBYAwBAAcIaAAAKENYAAFCAsAYAgAKENQAAFCCsAQCgAGENAAAFCGsAAChAWAMAQAHCGgAAChDWAABQgLAGAIAChDUAABQgrAEAoABhDQAABQhrAAAoQFgDAEABwhoAAAoQ1gAAUICwBgCAAoQ1AAAUIKwBAKAAYQ0AAAUIawAAKEBYAwBAAcIaAAAKENYAAFCAsAYAgAKENQAAFCCsAQCggKqG9aJFi3LWWWeltrY2dXV1ee6551psv/HGGzN48OAcfvjhueuuu6o5GgAALJNO1TzYHXfckaamptTX1+ehhx7KN77xjVx55ZVJkpdeeimTJ0/Oj370o8ybNy/Dhg3LTjvtlC5duvzH/S5YsCD/+te/lvf4S/TG7FltctzSZsx4/T093nmv2Jx367TX805WjnN33q23Mpx34s94a7XX8y5p3XXXTadOi2d0TaVSqVRriAsuuCBbbrll9ttvvyTJwIED87vf/S5Jcuedd+Y3v/lNvv71rydJTjzxxBx//PHZcsstW+yjvr4+9fX1Ldaampry17/+tQpnAABAe3fnnXdmgw02WGy9qlesGxsb07179+aPO3bsmAULFqRTp05pbGxMjx49mrd169YtjY2Ni+2jtrY2tbW1Ldba8op1NXz+85/PhAkT2nqMqnPe7Yvzbl/a63kn7ffcnXf7srKf97rrrrvE9aqGdffu3TNnzpzmjxctWtR8Gf2d2+bMmdMitJemU6dOS/yuYWXRpUuXlfr83o3zbl+cd/vSXs87ab/n7rzbl/Z63lX94cUBAwbkt7/9bZLkoYceysc//vHmbVtuuWUeeOCBzJs3L6+99lqefvrpFtsBAOCDrKpXrPfcc8/cc889GTJkSCqVSsaOHZurr746vXv3zqBBg1JXV5dhw4alUqlk1KhR+dCHPlTN8QAA4H2ralh36NCh+YcT39K3b9/mXx9++OE5/PDDqzkSAAAU4Q1iVgDv/GHN9sJ5ty/Ou31pr+edtN9zd97tS3s976q+3B4AAKysXLEGAIAChDUAABQgrAEAoICqvioI7920adNy8cUXZ/LkyW09SlXMnz8/p59+el544YU0NTXlhBNOyKBBg9p6rKpYuHBhzjjjjDzzzDOpqanJOeec065ey/2VV17J4MGD873vfa/FqwWtzA4++ODmd6PdYIMNcsEFF7TxRNUxceLETJkyJfPnz8/QoUNz2GGHtfVIy93NN9+cH//4x0mSefPm5Yknnsg999yT1VZbrY0nW77mz5+fMWPG5IUXXkiHDh1y7rnntos/301NTTnttNPy97//Pd27d89ZZ52VjTbaqK3HWq7e3ivPPfdcxowZk5qammy88cb52te+lg4d2se1XGH9ATZp0qTccsst6dq1a1uPUjW33HJLevbsmYsuuigNDQ056KCD2k1Y33XXXUmSG264IVOnTs2ll16aK6+8so2nqo758+fnrLPOyiqrrNLWo1TNvHnzUqlU2s03zW+ZOnVq/vznP+eHP/xh5s6dm+9973ttPVJVDB48OIMHD06SnHPOOTnkkENW+qhOkt/85jdZsGBBbrjhhtxzzz359re/nXHjxrX1WMvdjTfemFVXXTU33nhj/va3v+Xcc8/Nd7/73bYea7l5Z69ccMEFOfnkk7P99tvnrLPOyp133pk999yzjaesjvbx7cMKqnfv3u3iC9Db7b333vnSl76UJKlUKunYsWMbT1Q9e+yxR84999wkyT/+8Y928ZfuWy688MIMGTIka6+9dluPUjXTp0/P3Llzc8wxx2T48OF56KGH2nqkqvj973+fj3/84znxxBPz+c9/Pp/61KfaeqSqeuSRR/LUU0+1m5ci+9jHPpaFCxdm0aJFaWxsTKdO7eN63lNPPZVddtklSdKnT588/fTTbTzR8vXOXnnsscey3XbbJUl22WWX/OEPf2ir0aqufXyGr6D22muvzJgxo63HqKpu3bolSRobG3PSSSfl5JNPbtuBqqxTp0459dRT8+tf/zrf+c532nqcqrj55pvTq1evDBw4MFdddVVbj1M1q6yySo499tgcdthhefbZZzNixIjcdtttK314vPrqq/nHP/6RCRMmZMaMGTnhhBNy2223paampq1Hq4qJEyfmxBNPbOsxqmbVVVfNCy+8kH322SevvvpqJkyY0NYjVcUmm2ySu+66K3vssUemTZuWF198MQsXLlxpLxa9s1cqlUrzn+lu3brltddea6vRqs4Vaz5w/vnPf2b48OE58MADs//++7f1OFV34YUX5vbbb8+ZZ56Z119/va3HWe5+9KMf5Q9/+EPq6uryxBNP5NRTT81LL73U1mMtdx/72MdywAEHpKamJh/72MfSs2fPdnHePXv2zM4775wuXbqkT58++dCHPpSZM2e29VhVMXv27DzzzDPZYYcd2nqUqrnmmmuy88475/bbb89Pf/rTjBkzJvPmzWvrsZa7Qw45JN27d8+wYcPy61//OpttttlKG9VL8vb7qefMmdOu/gVWWPOB8vLLL+eYY47J6NGjc+ihh7b1OFX1k5/8JBMnTkySdO3aNTU1Ne3ihz2uv/76XHfddZk8eXI22WSTXHjhhfnwhz/c1mMtdzfddFO+8Y1vJElefPHFNDY2tovz3mabbfK73/0ulUolL774YubOnZuePXu29VhVcf/992fHHXds6zGqarXVVkuPHj2SJKuvvnoWLFiQhQsXtvFUy98jjzySHXfcMT/84Q+z995756Mf/Whbj1RVm266aaZOnZok+e1vf5ttt922jSeqnpX73xxZ4UyYMCGzZ8/O+PHjM378+CRv/lBEe/ihtk9/+tM57bTTcsQRR2TBggU5/fTT28V5t1eHHnpoTjvttAwdOjQ1NTUZO3bsSn8bSJLstttuuf/++3PooYemUqnkrLPOajdX8p555plssMEGbT1GVR111FE5/fTTM2zYsMyfPz+jRo3Kqquu2tZjLXcbbrhhLrvsskyYMCE9evTI+eef39YjVdWpp56aM888M5dcckn69OmTvfbaq61HqhpvaQ4AAAWs/P/ODAAAVSCsAQCgAGENAAAFCGsAAChAWAMAQAHCGmAFV1dXl+OPP77IvqZOnZp+/frlkUceKb5vgJWdsAag2WabbZb6+vr07du3rUcBWOGs/O9GAECrde/ePVtttVVbjwGwQnLFGmAFMmfOnJxxxhnZfvvts/322+eqq65qsX3BggW57LLL8qlPfSpbbLFFBg8enD/+8Y8tHtPQ0JD//u//zic/+ckMGDAgxxxzTJ588skki98K8k6t2T9AeyWsAVYgp5xySn79619n9OjROf/88/OLX/wif/7zn5u3n3nmmbn66qszfPjwXHHFFenTp09GjBiRBx98MMmbYXz00UfnN7/5TU455ZRcdtlleeONN3Lsscdm1qxZ//H4/2n/AO2ZW0EAVhDTp0/P3XffnUsvvTT77rtvkmTLLbfMoEGDkiRPP/10br755px33nk57LDDkiS77LJLXnrppXz729/Otddem7vvvjuPP/54rr/++my77bZJkk033TSHHXZYHn300XTq9O5/LbRm/wDtmSvWACuIt64K77LLLs1ra6+9dvM90ffdd1/z9gULFjT/t+uuu+bBBx9MU1NT/vznP6dHjx7NUZ0ka665ZqZMmZKddtppqcdvzf4B2jNXrAFWELNnz07nzp3TvXv3Fusf/vCHM2fOnDQ0NCRpGd5v9+qrr2bWrFlZc80139fxW7P/ddZZ533tG2BlIKwBVhA9e/bM/PnzM3v27Ky22mrN6w0NDencuXN69OiRmpqa3HDDDenYseNiz19jjTXSo0ePzJw5c7Ft9957bzbYYIOlHr81+wdoz9wKArCC2G677ZIkv/rVr5rXZs2alYceeihJss0226RSqaSxsTFbbLFF839//OMfc80116RTp07ZeuutM3v27BY/bDhr1qyMGDEi99xzz1KP35r9A7RnvgoCrCD69OmTAw44IGPHjs28efPykY98JBMnTsyCBQuSJJtsskn22muvjB49OiNHjkzfvn1z33335corr8xxxx2XDh06ZLfddsumm26aUaNGZdSoUVljjTUyadKkrL322tl3333z+OOPv+vxW7N/gPZMWAOsQM4///z06tUr48aNy/z583PooYdmnXXWyRtvvJEkufjii3PZZZflqquuyiuvvJL1118/X/7yl3PssccmSTp37pzvfve7+eY3v5mxY8dm0aJF2XbbbXPNNdekR48e//H4/2n/AO1ZTaVSqbT1EAAAsKLz73YAAFCAsAYAgAKENQAAFCCsAQCgAGENAAAFCGsAAChAWAMAQAHCGgAACvj/aWOoUEmWFCAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "evaluator.plot_lift_curve()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "evaluator.plot_cumulative_response_curve()"
    ]

From d4dcd8333f3f188c6277cfbb96db0e653f9a773d Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Thu, 30 Sep 2021 11:54:17 +0200
Subject: [PATCH 13/16] reorder readme

---
 README.rst                                    |   9 +-
 cobra/evaluation/evaluator.py                 |   2 +-
 cobra/evaluation/pigs_tables.py               |   4 +-
 cobra/preprocessing/preprocessor.py           |  34 +-
 requirements.txt                              |   2 +-
 .../tutorial_Cobra_linear_regression.ipynb    | 487 +++++-----
 .../tutorial_Cobra_logistic_regression.ipynb  | 842 +++++-------------
 7 files changed, 505 insertions(+), 875 deletions(-)

diff --git a/README.rst b/README.rst
index 3a7d9d9..ec5dce5 100644
--- a/README.rst
+++ b/README.rst
@@ -1,4 +1,6 @@
 
+.. image:: material/logo.png
+    :width: 300
 
 .. image:: https://img.shields.io/pypi/v/pythonpredictions-cobra.svg
     :target: https://pypi.org/project/pythonpredictions-cobra/
@@ -9,9 +11,6 @@
 
 ------------------------------------------------------------------------------------------------------------------------------------ 
 
-.. image:: material/logo.png
-    :width: 300
-
 **Cobra** is a Python package to build predictive models using linear or logistic regression with a focus on performance and interpretation. It consists of several modules for data preprocessing, feature selection and model evaluation. The underlying methodology was developed at Python Predictions in the course of hundreds of business-related prediction challenges. It has been tweaked, tested and optimized over the years based on feedback from clients, our team, and academic researchers.
 
 Main features
@@ -23,7 +22,7 @@ Main features
    - create bins from continuous variables
    - regroup categorical variables based on statistical significance
    - replace missing values
-   - replace categories/bins with average of target values (linear regression) or incidence rate (logistic regression)
+   - add columns where categories/bins are replaced with average of target values (linear regression) or with incidence rate (logistic regression)
  
 - Perform univariate feature selection based on RMSE (linear regression) or AUC (logistic regression)
 - Compute correlation matrix of predictors
@@ -38,7 +37,7 @@ These instructions will get you a copy of the project up and running on your loc
 Requirements
 ------------
 
-This package requires only the usual Python packages for data science, being numpy, pandas, scipy, scikit-learn, matplotlib, seaborn, and tqdm. These packages, along with their versions are listed in ``requirements.txt`` and can be installed using ``pip``:    ::
+This package requires only the usual Python libraries for data science, being numpy, pandas, scipy, scikit-learn, matplotlib, seaborn, and tqdm. These packages, along with their versions are listed in ``requirements.txt`` and can be installed using ``pip``:    ::
 
   pip install -r requirements.txt
 
diff --git a/cobra/evaluation/evaluator.py b/cobra/evaluation/evaluator.py
index e2015c0..dad8761 100644
--- a/cobra/evaluation/evaluator.py
+++ b/cobra/evaluation/evaluator.py
@@ -466,7 +466,7 @@ def _compute_cumulative_gains(y_true: np.ndarray,
     @staticmethod
     def _compute_lift_per_bin(y_true: np.ndarray,
                               y_pred: np.ndarray,
-                              n_bins: int = 10) -> tuple:
+                              n_bins: int=10) -> tuple:
         """Compute lift of the model for a given number of bins, returns x-labels,
         lifts and the target incidence to create cumulative response curves.
 
diff --git a/cobra/evaluation/pigs_tables.py b/cobra/evaluation/pigs_tables.py
index 5239434..d8327ee 100644
--- a/cobra/evaluation/pigs_tables.py
+++ b/cobra/evaluation/pigs_tables.py
@@ -96,8 +96,8 @@ def compute_pig_table(basetable: pd.DataFrame,
 def plot_incidence(pig_tables: pd.DataFrame,
                    variable: str,
                    model_type: str,
-                   column_order: list = None,
-                   dim: tuple = (12, 8)):
+                   column_order: list=None,
+                   dim: tuple=(12, 8)):
     """Plots a Predictor Insights Graph (PIG), a graph in which the mean
     target value is plotted for a number of bins constructed from a predictor
     variable. When the target is a binary classification target,
diff --git a/cobra/preprocessing/preprocessor.py b/cobra/preprocessing/preprocessor.py
index 0c63cfe..1afa175 100644
--- a/cobra/preprocessing/preprocessor.py
+++ b/cobra/preprocessing/preprocessor.py
@@ -64,23 +64,23 @@ def __init__(self,
 
     @classmethod
     def from_params(cls,
-                    model_type: str = "classification",
-                    n_bins: int = 10,
-                    strategy: str = "quantile",
-                    closed: str = "right",
-                    auto_adapt_bins: bool = False,
-                    starting_precision: int = 0,
-                    label_format: str = "{} - {}",
-                    change_endpoint_format: bool = False,
-                    regroup: bool = True,
-                    regroup_name: str = "Other",
-                    keep_missing: bool = True,
-                    category_size_threshold: int = 5,
-                    p_value_threshold: float = 0.001,
-                    scale_contingency_table: bool = True,
-                    forced_categories: dict = {},
-                    weight: float = 0.0,
-                    imputation_strategy: str = "mean"):
+                    model_type: str="classification",
+                    n_bins: int=10,
+                    strategy: str="quantile",
+                    closed: str="right",
+                    auto_adapt_bins: bool=False,
+                    starting_precision: int=0,
+                    label_format: str="{} - {}",
+                    change_endpoint_format: bool=False,
+                    regroup: bool=True,
+                    regroup_name: str="Other",
+                    keep_missing: bool=True,
+                    category_size_threshold: int=5,
+                    p_value_threshold: float=0.001,
+                    scale_contingency_table: bool=True,
+                    forced_categories: dict={},
+                    weight: float=0.0,
+                    imputation_strategy: str="mean"):
         """Constructor to instantiate PreProcessor from all the parameters
         that can be set in all its required (attribute) classes
         along with good default values.
diff --git a/requirements.txt b/requirements.txt
index 6ea2694..f2c226f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,4 +4,4 @@ scipy>=1.5.4
 scikit-learn>=0.24.1
 matplotlib>=3.4.3
 seaborn>=0.11.0
-tqdm>=4.59.0
\ No newline at end of file
+tqdm>=4.62.2
\ No newline at end of file
diff --git a/tutorials/tutorial_Cobra_linear_regression.ipynb b/tutorials/tutorial_Cobra_linear_regression.ipynb
index 559740e..579dfc5 100644
--- a/tutorials/tutorial_Cobra_linear_regression.ipynb
+++ b/tutorials/tutorial_Cobra_linear_regression.ipynb
@@ -498,7 +498,7 @@
     {
      "data": {
       "text/plain": [
-       "['acceleration', 'model_year', 'horsepower', 'weight', 'displacement']"
+       "['horsepower', 'model_year', 'weight', 'acceleration', 'displacement']"
       ]
      },
      "execution_count": 14,
@@ -522,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -576,7 +576,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5aef80effec54a22948feced697ba49a",
+       "model_id": "474fd3ddbe2841868966de83ef9f9d9f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -590,7 +590,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9f6fb759ecd74d8cad24d2431c05e4df",
+       "model_id": "4babe5bb228649ca8c60ae12fa198e5e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -604,7 +604,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f617606da88d41b2b3bbd46fb36cdc19",
+       "model_id": "ae7436e6f3114d05b2450f265ddb6348",
        "version_major": 2,
        "version_minor": 0
       },
@@ -618,7 +618,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1c5fe94f8bcc403a9ed1f5593e8b4d4a",
+       "model_id": "d279851728e840979cd8748daf153426",
        "version_major": 2,
        "version_minor": 0
       },
@@ -653,7 +653,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4fe4568e415249f89b52c31a4edad67f",
+       "model_id": "d283b89edf3542f08903f2b495ff2e2c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -667,7 +667,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "144f05ee81044c5087d9d88c5e535bbd",
+       "model_id": "860e523ca34e4a66b9191744bf1bf523",
        "version_major": 2,
        "version_minor": 0
       },
@@ -710,10 +710,10 @@
        "      <th>name</th>\n",
        "      <th>id</th>\n",
        "      <th>split</th>\n",
-       "      <th>acceleration_bin</th>\n",
-       "      <th>model_year_bin</th>\n",
        "      <th>horsepower_bin</th>\n",
+       "      <th>model_year_bin</th>\n",
        "      <th>weight_bin</th>\n",
+       "      <th>acceleration_bin</th>\n",
        "      <th>displacement_bin</th>\n",
        "      <th>origin_processed</th>\n",
        "      <th>name_processed</th>\n",
@@ -721,10 +721,10 @@
        "      <th>origin_enc</th>\n",
        "      <th>name_enc</th>\n",
        "      <th>cylinders_enc</th>\n",
-       "      <th>acceleration_enc</th>\n",
-       "      <th>model_year_enc</th>\n",
        "      <th>horsepower_enc</th>\n",
+       "      <th>model_year_enc</th>\n",
        "      <th>weight_enc</th>\n",
+       "      <th>acceleration_enc</th>\n",
        "      <th>displacement_enc</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -742,22 +742,22 @@
        "      <td>chevrolet chevelle malibu</td>\n",
        "      <td>1</td>\n",
        "      <td>train</td>\n",
-       "      <td>9.0 - 12.0</td>\n",
+       "      <td>112.0 - 145.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>110.0 - 139.0</td>\n",
-       "      <td>3416.0 - 3683.0</td>\n",
+       "      <td>3441.0 - 3842.0</td>\n",
+       "      <td>8.5 - 12.1</td>\n",
        "      <td>304.0 - 350.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>chevrolet chevelle malibu</td>\n",
        "      <td>8</td>\n",
-       "      <td>20.407383</td>\n",
-       "      <td>17.50000</td>\n",
-       "      <td>15.595</td>\n",
-       "      <td>16.446154</td>\n",
-       "      <td>19.9375</td>\n",
-       "      <td>20.043478</td>\n",
-       "      <td>17.683333</td>\n",
-       "      <td>16.137500</td>\n",
+       "      <td>19.983117</td>\n",
+       "      <td>17.500000</td>\n",
+       "      <td>14.964063</td>\n",
+       "      <td>17.348276</td>\n",
+       "      <td>17.727273</td>\n",
+       "      <td>17.433333</td>\n",
+       "      <td>15.352</td>\n",
+       "      <td>15.180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -772,22 +772,22 @@
        "      <td>buick skylark 320</td>\n",
        "      <td>2</td>\n",
        "      <td>validation</td>\n",
-       "      <td>9.0 - 12.0</td>\n",
+       "      <td>154.0 - 230.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>153.0 - 230.0</td>\n",
-       "      <td>3683.0 - 4210.0</td>\n",
+       "      <td>3441.0 - 3842.0</td>\n",
+       "      <td>8.5 - 12.1</td>\n",
        "      <td>304.0 - 350.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>buick skylark 320</td>\n",
        "      <td>8</td>\n",
-       "      <td>20.407383</td>\n",
-       "      <td>23.83125</td>\n",
-       "      <td>15.595</td>\n",
-       "      <td>16.446154</td>\n",
-       "      <td>19.9375</td>\n",
-       "      <td>13.777273</td>\n",
-       "      <td>16.387500</td>\n",
-       "      <td>16.137500</td>\n",
+       "      <td>19.983117</td>\n",
+       "      <td>23.353333</td>\n",
+       "      <td>14.964063</td>\n",
+       "      <td>13.837500</td>\n",
+       "      <td>17.727273</td>\n",
+       "      <td>17.433333</td>\n",
+       "      <td>15.352</td>\n",
+       "      <td>15.180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -801,23 +801,23 @@
        "      <td>usa</td>\n",
        "      <td>plymouth satellite</td>\n",
        "      <td>3</td>\n",
-       "      <td>train</td>\n",
-       "      <td>9.0 - 12.0</td>\n",
+       "      <td>validation</td>\n",
+       "      <td>145.0 - 154.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>139.0 - 153.0</td>\n",
-       "      <td>3416.0 - 3683.0</td>\n",
+       "      <td>3157.0 - 3441.0</td>\n",
+       "      <td>8.5 - 12.1</td>\n",
        "      <td>304.0 - 350.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>plymouth satellite</td>\n",
        "      <td>8</td>\n",
-       "      <td>20.407383</td>\n",
-       "      <td>18.00000</td>\n",
-       "      <td>15.595</td>\n",
-       "      <td>16.446154</td>\n",
-       "      <td>19.9375</td>\n",
-       "      <td>15.462500</td>\n",
-       "      <td>17.683333</td>\n",
-       "      <td>16.137500</td>\n",
+       "      <td>19.983117</td>\n",
+       "      <td>23.353333</td>\n",
+       "      <td>14.964063</td>\n",
+       "      <td>14.694444</td>\n",
+       "      <td>17.727273</td>\n",
+       "      <td>18.883333</td>\n",
+       "      <td>15.352</td>\n",
+       "      <td>15.180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -831,23 +831,23 @@
        "      <td>usa</td>\n",
        "      <td>amc rebel sst</td>\n",
        "      <td>4</td>\n",
-       "      <td>train</td>\n",
-       "      <td>9.0 - 12.0</td>\n",
+       "      <td>selection</td>\n",
+       "      <td>145.0 - 154.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>139.0 - 153.0</td>\n",
-       "      <td>3416.0 - 3683.0</td>\n",
+       "      <td>3157.0 - 3441.0</td>\n",
+       "      <td>8.5 - 12.1</td>\n",
        "      <td>250.0 - 304.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>amc rebel sst</td>\n",
        "      <td>8</td>\n",
-       "      <td>20.407383</td>\n",
-       "      <td>16.00000</td>\n",
-       "      <td>15.595</td>\n",
-       "      <td>16.446154</td>\n",
-       "      <td>19.9375</td>\n",
-       "      <td>15.462500</td>\n",
-       "      <td>17.683333</td>\n",
-       "      <td>17.194444</td>\n",
+       "      <td>19.983117</td>\n",
+       "      <td>23.353333</td>\n",
+       "      <td>14.964063</td>\n",
+       "      <td>14.694444</td>\n",
+       "      <td>17.727273</td>\n",
+       "      <td>18.883333</td>\n",
+       "      <td>15.352</td>\n",
+       "      <td>17.165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -861,23 +861,23 @@
        "      <td>usa</td>\n",
        "      <td>ford torino</td>\n",
        "      <td>5</td>\n",
-       "      <td>validation</td>\n",
-       "      <td>9.0 - 12.0</td>\n",
+       "      <td>train</td>\n",
+       "      <td>112.0 - 145.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>139.0 - 153.0</td>\n",
-       "      <td>3416.0 - 3683.0</td>\n",
+       "      <td>3441.0 - 3842.0</td>\n",
+       "      <td>8.5 - 12.1</td>\n",
        "      <td>250.0 - 304.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>ford torino</td>\n",
        "      <td>8</td>\n",
-       "      <td>20.407383</td>\n",
-       "      <td>23.83125</td>\n",
-       "      <td>15.595</td>\n",
-       "      <td>16.446154</td>\n",
-       "      <td>19.9375</td>\n",
-       "      <td>15.462500</td>\n",
-       "      <td>17.683333</td>\n",
-       "      <td>17.194444</td>\n",
+       "      <td>19.983117</td>\n",
+       "      <td>17.000000</td>\n",
+       "      <td>14.964063</td>\n",
+       "      <td>17.348276</td>\n",
+       "      <td>17.727273</td>\n",
+       "      <td>17.433333</td>\n",
+       "      <td>15.352</td>\n",
+       "      <td>17.165</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -894,16 +894,16 @@
        "   model_year origin                       name  id       split  \\\n",
        "0          70    usa  chevrolet chevelle malibu   1       train   \n",
        "1          70    usa          buick skylark 320   2  validation   \n",
-       "2          70    usa         plymouth satellite   3       train   \n",
-       "3          70    usa              amc rebel sst   4       train   \n",
-       "4          70    usa                ford torino   5  validation   \n",
+       "2          70    usa         plymouth satellite   3  validation   \n",
+       "3          70    usa              amc rebel sst   4   selection   \n",
+       "4          70    usa                ford torino   5       train   \n",
        "\n",
-       "  acceleration_bin model_year_bin horsepower_bin       weight_bin  \\\n",
-       "0       9.0 - 12.0    70.0 - 71.0  110.0 - 139.0  3416.0 - 3683.0   \n",
-       "1       9.0 - 12.0    70.0 - 71.0  153.0 - 230.0  3683.0 - 4210.0   \n",
-       "2       9.0 - 12.0    70.0 - 71.0  139.0 - 153.0  3416.0 - 3683.0   \n",
-       "3       9.0 - 12.0    70.0 - 71.0  139.0 - 153.0  3416.0 - 3683.0   \n",
-       "4       9.0 - 12.0    70.0 - 71.0  139.0 - 153.0  3416.0 - 3683.0   \n",
+       "  horsepower_bin model_year_bin       weight_bin acceleration_bin  \\\n",
+       "0  112.0 - 145.0    70.0 - 71.0  3441.0 - 3842.0       8.5 - 12.1   \n",
+       "1  154.0 - 230.0    70.0 - 71.0  3441.0 - 3842.0       8.5 - 12.1   \n",
+       "2  145.0 - 154.0    70.0 - 71.0  3157.0 - 3441.0       8.5 - 12.1   \n",
+       "3  145.0 - 154.0    70.0 - 71.0  3157.0 - 3441.0       8.5 - 12.1   \n",
+       "4  112.0 - 145.0    70.0 - 71.0  3441.0 - 3842.0       8.5 - 12.1   \n",
        "\n",
        "  displacement_bin origin_processed             name_processed  \\\n",
        "0    304.0 - 350.0              usa  chevrolet chevelle malibu   \n",
@@ -912,19 +912,19 @@
        "3    250.0 - 304.0              usa              amc rebel sst   \n",
        "4    250.0 - 304.0              usa                ford torino   \n",
        "\n",
-       "  cylinders_processed  origin_enc  name_enc  cylinders_enc  acceleration_enc  \\\n",
-       "0                   8   20.407383  17.50000         15.595         16.446154   \n",
-       "1                   8   20.407383  23.83125         15.595         16.446154   \n",
-       "2                   8   20.407383  18.00000         15.595         16.446154   \n",
-       "3                   8   20.407383  16.00000         15.595         16.446154   \n",
-       "4                   8   20.407383  23.83125         15.595         16.446154   \n",
+       "  cylinders_processed  origin_enc   name_enc  cylinders_enc  horsepower_enc  \\\n",
+       "0                   8   19.983117  17.500000      14.964063       17.348276   \n",
+       "1                   8   19.983117  23.353333      14.964063       13.837500   \n",
+       "2                   8   19.983117  23.353333      14.964063       14.694444   \n",
+       "3                   8   19.983117  23.353333      14.964063       14.694444   \n",
+       "4                   8   19.983117  17.000000      14.964063       17.348276   \n",
        "\n",
-       "   model_year_enc  horsepower_enc  weight_enc  displacement_enc  \n",
-       "0         19.9375       20.043478   17.683333         16.137500  \n",
-       "1         19.9375       13.777273   16.387500         16.137500  \n",
-       "2         19.9375       15.462500   17.683333         16.137500  \n",
-       "3         19.9375       15.462500   17.683333         17.194444  \n",
-       "4         19.9375       15.462500   17.683333         17.194444  "
+       "   model_year_enc  weight_enc  acceleration_enc  displacement_enc  \n",
+       "0       17.727273   17.433333            15.352            15.180  \n",
+       "1       17.727273   17.433333            15.352            15.180  \n",
+       "2       17.727273   18.883333            15.352            15.180  \n",
+       "3       17.727273   18.883333            15.352            17.165  \n",
+       "4       17.727273   17.433333            15.352            17.165  "
       ]
      },
      "execution_count": 18,
@@ -990,42 +990,42 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>acceleration</td>\n",
-       "      <td>9.0 - 12.0</td>\n",
-       "      <td>0.088608</td>\n",
-       "      <td>22.744304</td>\n",
-       "      <td>13.714286</td>\n",
+       "      <td>8.5 - 12.1</td>\n",
+       "      <td>0.113924</td>\n",
+       "      <td>23.778481</td>\n",
+       "      <td>17.644444</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>acceleration</td>\n",
-       "      <td>12.0 - 13.4</td>\n",
-       "      <td>0.113924</td>\n",
-       "      <td>22.744304</td>\n",
-       "      <td>16.944444</td>\n",
+       "      <td>12.1 - 13.4</td>\n",
+       "      <td>0.088608</td>\n",
+       "      <td>23.778481</td>\n",
+       "      <td>19.057143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>acceleration</td>\n",
-       "      <td>13.4 - 14.4</td>\n",
-       "      <td>0.202532</td>\n",
-       "      <td>22.744304</td>\n",
-       "      <td>20.550000</td>\n",
+       "      <td>13.4 - 14.2</td>\n",
+       "      <td>0.139241</td>\n",
+       "      <td>23.778481</td>\n",
+       "      <td>21.063636</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>acceleration</td>\n",
-       "      <td>14.4 - 15.0</td>\n",
-       "      <td>0.151899</td>\n",
-       "      <td>22.744304</td>\n",
-       "      <td>24.825000</td>\n",
+       "      <td>14.2 - 14.9</td>\n",
+       "      <td>0.113924</td>\n",
+       "      <td>23.778481</td>\n",
+       "      <td>27.755556</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>acceleration</td>\n",
-       "      <td>15.0 - 15.5</td>\n",
-       "      <td>0.088608</td>\n",
-       "      <td>22.744304</td>\n",
-       "      <td>27.214286</td>\n",
+       "      <td>14.9 - 15.5</td>\n",
+       "      <td>0.151899</td>\n",
+       "      <td>23.778481</td>\n",
+       "      <td>22.641667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1038,63 +1038,63 @@
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>weight</td>\n",
-       "      <td>2866.0 - 3064.0</td>\n",
+       "      <td>2882.0 - 3157.0</td>\n",
        "      <td>0.063291</td>\n",
-       "      <td>22.744304</td>\n",
-       "      <td>21.280000</td>\n",
+       "      <td>23.778481</td>\n",
+       "      <td>26.780000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>weight</td>\n",
-       "      <td>3064.0 - 3416.0</td>\n",
-       "      <td>0.063291</td>\n",
-       "      <td>22.744304</td>\n",
-       "      <td>17.480000</td>\n",
+       "      <td>3157.0 - 3441.0</td>\n",
+       "      <td>0.101266</td>\n",
+       "      <td>23.778481</td>\n",
+       "      <td>21.075000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>weight</td>\n",
-       "      <td>3416.0 - 3683.0</td>\n",
-       "      <td>0.012658</td>\n",
-       "      <td>22.744304</td>\n",
-       "      <td>14.000000</td>\n",
+       "      <td>3441.0 - 3842.0</td>\n",
+       "      <td>0.088608</td>\n",
+       "      <td>23.778481</td>\n",
+       "      <td>18.371429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>weight</td>\n",
-       "      <td>3683.0 - 4210.0</td>\n",
-       "      <td>0.164557</td>\n",
-       "      <td>22.744304</td>\n",
-       "      <td>14.807692</td>\n",
+       "      <td>3842.0 - 4280.0</td>\n",
+       "      <td>0.088608</td>\n",
+       "      <td>23.778481</td>\n",
+       "      <td>16.357143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>weight</td>\n",
-       "      <td>4210.0 - 5140.0</td>\n",
-       "      <td>0.151899</td>\n",
-       "      <td>22.744304</td>\n",
-       "      <td>13.041667</td>\n",
+       "      <td>4280.0 - 5140.0</td>\n",
+       "      <td>0.101266</td>\n",
+       "      <td>23.778481</td>\n",
+       "      <td>12.875000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>128 rows × 5 columns</p>\n",
+       "<p>133 rows × 5 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "        variable            label  pop_size  global_avg_target  avg_target\n",
-       "0   acceleration       9.0 - 12.0  0.088608          22.744304   13.714286\n",
-       "1   acceleration      12.0 - 13.4  0.113924          22.744304   16.944444\n",
-       "2   acceleration      13.4 - 14.4  0.202532          22.744304   20.550000\n",
-       "3   acceleration      14.4 - 15.0  0.151899          22.744304   24.825000\n",
-       "4   acceleration      15.0 - 15.5  0.088608          22.744304   27.214286\n",
+       "0   acceleration       8.5 - 12.1  0.113924          23.778481   17.644444\n",
+       "1   acceleration      12.1 - 13.4  0.088608          23.778481   19.057143\n",
+       "2   acceleration      13.4 - 14.2  0.139241          23.778481   21.063636\n",
+       "3   acceleration      14.2 - 14.9  0.113924          23.778481   27.755556\n",
+       "4   acceleration      14.9 - 15.5  0.151899          23.778481   22.641667\n",
        "..           ...              ...       ...                ...         ...\n",
-       "5         weight  2866.0 - 3064.0  0.063291          22.744304   21.280000\n",
-       "6         weight  3064.0 - 3416.0  0.063291          22.744304   17.480000\n",
-       "7         weight  3416.0 - 3683.0  0.012658          22.744304   14.000000\n",
-       "8         weight  3683.0 - 4210.0  0.164557          22.744304   14.807692\n",
-       "9         weight  4210.0 - 5140.0  0.151899          22.744304   13.041667\n",
+       "5         weight  2882.0 - 3157.0  0.063291          23.778481   26.780000\n",
+       "6         weight  3157.0 - 3441.0  0.101266          23.778481   21.075000\n",
+       "7         weight  3441.0 - 3842.0  0.088608          23.778481   18.371429\n",
+       "8         weight  3842.0 - 4280.0  0.088608          23.778481   16.357143\n",
+       "9         weight  4280.0 - 5140.0  0.101266          23.778481   12.875000\n",
        "\n",
-       "[128 rows x 5 columns]"
+       "[133 rows x 5 columns]"
       ]
      },
      "execution_count": 19,
@@ -1127,7 +1127,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 2 Axes>"
       ]
@@ -1227,7 +1227,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1269,25 +1269,25 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                origin      name  cylinders  acceleration  model_year  \\\n",
-      "origin        1.000000  0.571729   0.596669      0.266787    0.162567   \n",
-      "name          0.571729  1.000000   0.787726      0.434689    0.609038   \n",
-      "cylinders     0.596669  0.787726   1.000000      0.521251    0.350427   \n",
-      "acceleration  0.266787  0.434689   0.521251      1.000000    0.322990   \n",
-      "model_year    0.162567  0.609038   0.350427      0.322990    1.000000   \n",
-      "horsepower    0.489740  0.823225   0.792839      0.633843    0.416519   \n",
-      "weight        0.625704  0.839408   0.871615      0.425933    0.278704   \n",
-      "displacement  0.702018  0.830467   0.908609      0.521136    0.311756   \n",
+      "                origin      name  cylinders  horsepower  model_year    weight  \\\n",
+      "origin        1.000000  0.571069   0.586881    0.503373    0.220294  0.635906   \n",
+      "name          0.571069  1.000000   0.806237    0.833102    0.647684  0.856403   \n",
+      "cylinders     0.586881  0.806237   1.000000    0.811333    0.436942  0.873356   \n",
+      "horsepower    0.503373  0.833102   0.811333    1.000000    0.476911  0.865079   \n",
+      "model_year    0.220294  0.647684   0.436942    0.476911    1.000000  0.393047   \n",
+      "weight        0.635906  0.856403   0.873356    0.865079    0.393047  1.000000   \n",
+      "acceleration  0.253132  0.441351   0.520317    0.619921    0.331274  0.410300   \n",
+      "displacement  0.704440  0.837183   0.919231    0.851480    0.424150  0.929419   \n",
       "\n",
-      "              horsepower    weight  displacement  \n",
-      "origin          0.489740  0.625704      0.702018  \n",
-      "name            0.823225  0.839408      0.830467  \n",
-      "cylinders       0.792839  0.871615      0.908609  \n",
-      "acceleration    0.633843  0.425933      0.521136  \n",
-      "model_year      0.416519  0.278704      0.311756  \n",
-      "horsepower      1.000000  0.852931      0.854378  \n",
-      "weight          0.852931  1.000000      0.931375  \n",
-      "displacement    0.854378  0.931375      1.000000  \n"
+      "              acceleration  displacement  \n",
+      "origin            0.253132      0.704440  \n",
+      "name              0.441351      0.837183  \n",
+      "cylinders         0.520317      0.919231  \n",
+      "horsepower        0.619921      0.851480  \n",
+      "model_year        0.331274      0.424150  \n",
+      "weight            0.410300      0.929419  \n",
+      "acceleration      1.000000      0.495092  \n",
+      "displacement      0.495092      1.000000  \n"
      ]
     }
    ],
@@ -1306,7 +1306,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 2 Axes>"
       ]
@@ -1337,12 +1337,12 @@
     {
      "data": {
       "text/plain": [
-       "['weight_enc',\n",
+       "['horsepower_enc',\n",
        " 'displacement_enc',\n",
+       " 'weight_enc',\n",
        " 'cylinders_enc',\n",
-       " 'horsepower_enc',\n",
-       " 'model_year_enc',\n",
        " 'origin_enc',\n",
+       " 'model_year_enc',\n",
        " 'acceleration_enc',\n",
        " 'name_enc']"
       ]
@@ -1379,7 +1379,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "76c2f6130b3f4f5c85857b34f10ef555",
+       "model_id": "192a9f0ed75f429ea7ae516b75b2504e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1424,72 +1424,72 @@
        "      <th>0</th>\n",
        "      <td>[name_enc]</td>\n",
        "      <td>name_enc</td>\n",
-       "      <td>0.951267</td>\n",
-       "      <td>7.224819</td>\n",
-       "      <td>7.199707</td>\n",
+       "      <td>0.965551</td>\n",
+       "      <td>7.050967</td>\n",
+       "      <td>6.327197</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>[name_enc, model_year_enc]</td>\n",
        "      <td>model_year_enc</td>\n",
-       "      <td>0.933275</td>\n",
-       "      <td>7.097814</td>\n",
-       "      <td>7.078123</td>\n",
+       "      <td>0.950131</td>\n",
+       "      <td>6.932540</td>\n",
+       "      <td>6.223183</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>[name_enc, model_year_enc, horsepower_enc]</td>\n",
-       "      <td>horsepower_enc</td>\n",
-       "      <td>0.916923</td>\n",
-       "      <td>6.838945</td>\n",
-       "      <td>6.806006</td>\n",
+       "      <td>[model_year_enc, name_enc, weight_enc]</td>\n",
+       "      <td>weight_enc</td>\n",
+       "      <td>0.939471</td>\n",
+       "      <td>6.685434</td>\n",
+       "      <td>5.954216</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>[horsepower_enc, name_enc, model_year_enc, acc...</td>\n",
-       "      <td>acceleration_enc</td>\n",
-       "      <td>0.914016</td>\n",
-       "      <td>6.794003</td>\n",
-       "      <td>6.763116</td>\n",
+       "      <td>[weight_enc, model_year_enc, name_enc, horsepo...</td>\n",
+       "      <td>horsepower_enc</td>\n",
+       "      <td>0.938280</td>\n",
+       "      <td>6.656585</td>\n",
+       "      <td>5.929000</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>[horsepower_enc, name_enc, model_year_enc, acc...</td>\n",
-       "      <td>displacement_enc</td>\n",
-       "      <td>0.911310</td>\n",
-       "      <td>6.696004</td>\n",
-       "      <td>6.679070</td>\n",
+       "      <td>[weight_enc, horsepower_enc, name_enc, model_y...</td>\n",
+       "      <td>cylinders_enc</td>\n",
+       "      <td>0.937998</td>\n",
+       "      <td>6.652905</td>\n",
+       "      <td>5.923981</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>[horsepower_enc, acceleration_enc, displacemen...</td>\n",
-       "      <td>cylinders_enc</td>\n",
-       "      <td>0.910083</td>\n",
-       "      <td>6.705958</td>\n",
-       "      <td>6.685935</td>\n",
+       "      <td>[cylinders_enc, horsepower_enc, weight_enc, na...</td>\n",
+       "      <td>displacement_enc</td>\n",
+       "      <td>0.937729</td>\n",
+       "      <td>6.654564</td>\n",
+       "      <td>5.926207</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>[horsepower_enc, cylinders_enc, acceleration_e...</td>\n",
+       "      <td>[cylinders_enc, displacement_enc, horsepower_e...</td>\n",
        "      <td>origin_enc</td>\n",
-       "      <td>0.909738</td>\n",
-       "      <td>6.696299</td>\n",
-       "      <td>6.679883</td>\n",
+       "      <td>0.937513</td>\n",
+       "      <td>6.647764</td>\n",
+       "      <td>5.917903</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>[horsepower_enc, cylinders_enc, acceleration_e...</td>\n",
-       "      <td>weight_enc</td>\n",
-       "      <td>0.909611</td>\n",
-       "      <td>6.681966</td>\n",
-       "      <td>6.666221</td>\n",
+       "      <td>[cylinders_enc, displacement_enc, horsepower_e...</td>\n",
+       "      <td>acceleration_enc</td>\n",
+       "      <td>0.937498</td>\n",
+       "      <td>6.646871</td>\n",
+       "      <td>5.917192</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1500,22 +1500,22 @@
        "                                          predictors last_added_predictor  \\\n",
        "0                                         [name_enc]             name_enc   \n",
        "1                         [name_enc, model_year_enc]       model_year_enc   \n",
-       "2         [name_enc, model_year_enc, horsepower_enc]       horsepower_enc   \n",
-       "3  [horsepower_enc, name_enc, model_year_enc, acc...     acceleration_enc   \n",
-       "4  [horsepower_enc, name_enc, model_year_enc, acc...     displacement_enc   \n",
-       "5  [horsepower_enc, acceleration_enc, displacemen...        cylinders_enc   \n",
-       "6  [horsepower_enc, cylinders_enc, acceleration_e...           origin_enc   \n",
-       "7  [horsepower_enc, cylinders_enc, acceleration_e...           weight_enc   \n",
+       "2             [model_year_enc, name_enc, weight_enc]           weight_enc   \n",
+       "3  [weight_enc, model_year_enc, name_enc, horsepo...       horsepower_enc   \n",
+       "4  [weight_enc, horsepower_enc, name_enc, model_y...        cylinders_enc   \n",
+       "5  [cylinders_enc, horsepower_enc, weight_enc, na...     displacement_enc   \n",
+       "6  [cylinders_enc, displacement_enc, horsepower_e...           origin_enc   \n",
+       "7  [cylinders_enc, displacement_enc, horsepower_e...     acceleration_enc   \n",
        "\n",
        "   train_performance  selection_performance  validation_performance  \\\n",
-       "0           0.951267               7.224819                7.199707   \n",
-       "1           0.933275               7.097814                7.078123   \n",
-       "2           0.916923               6.838945                6.806006   \n",
-       "3           0.914016               6.794003                6.763116   \n",
-       "4           0.911310               6.696004                6.679070   \n",
-       "5           0.910083               6.705958                6.685935   \n",
-       "6           0.909738               6.696299                6.679883   \n",
-       "7           0.909611               6.681966                6.666221   \n",
+       "0           0.965551               7.050967                6.327197   \n",
+       "1           0.950131               6.932540                6.223183   \n",
+       "2           0.939471               6.685434                5.954216   \n",
+       "3           0.938280               6.656585                5.929000   \n",
+       "4           0.937998               6.652905                5.923981   \n",
+       "5           0.937729               6.654564                5.926207   \n",
+       "6           0.937513               6.647764                5.917903   \n",
+       "7           0.937498               6.646871                5.917192   \n",
        "\n",
        "   model_type  \n",
        "0  regression  \n",
@@ -1565,7 +1565,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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QvXv3RqNGjcR59+/fB5DS7eDs2bMa21G/Kabt6+fg4KBVOR4+fAgg5Y03LRsbGzg6OsLX1xfR0dEoUqSIWNbU3TZSK1u2rMbvpqamAFI+vGUymcY8IyMjREVFib9XqlQJlSpVQmxsLG7fvo0XL17g+fPnuHv3Ll68eAGlUpluf+XKlUs3TV3O5ORkjeeYuguHWupwfu/ePQAp/RBTB0sA4le86m1lJD4+Hi9evEDt2rVhZGSkMc/MzAzjxo3LdF1tZPQ3bd++Pfbt24cjR46Iwejw4cMwNDTEV199BSBllAJBEBAREYGVK1em24aBgQHevHmDoKAgFCtWDJ6enjh8+DCaNWuG6tWro0mTJmjWrFmuQ7y/vz8SEhJQo0YNjSClVrNmTRw/fhwPHz5E3bp1M32+lpaW8PDwwJUrVyAIAlQqFa5du4auXbtCLpfj6tWr4nM4f/48KlasKG7D0dERjo6OSExMxO3bt+Hv74+XL1/i/v37Yh/atMdX2v0HBAQgLi4OVapUSfccqlSpAgMDg1zVD5Cz13jx4sXRqVMnKBQK3L9/X3wuvr6++O+//wB8CJh5KW195PS4ykinTp2wY8cOLFq0CFu3bkWTJk3QpEkTNGzYUHzvAD7utRkbGwt/f3/Y2dnhl19+STf/3bt3AFLqt2LFinj48CFKliyZrswSiSTHQ1rGxMQgICAANWrUgLm5ebr5NWvWxPr169OVXZv3NSJdYBCmfDN37twMh0/LSHR0NABg3bp1mS6TtjUrbRDLjLrPakZv0gBgb28PX19fxMfHi2/ExsbGmW4vdWtWatqMIJCYmIglS5Zg165d4kUhxYoVQ+3atWFtba3RQpnVdtUtsYIgAIAYtjN7jmrqet65c2emy6St59S03U9uZVTvdevWRfHixcUgHB4ejkuXLqF58+awsLDQKNetW7dw69atTLcfERGBYsWKYf78+ahSpQr27duHq1ev4urVq1i0aBGqVKmCWbNmZdoXMjPqY0x9/KRlb28PAEhISMj2+TZt2hQ3btzAo0ePkJiYiJiYGNSpUwdyuRy///47rl27hrp16+Lhw4cYOHCguJ5KpcKvv/6KDRs2iH9DW1tbVK9eHaVKlYKfn594vKilfQ2p1zMzM0tXLplM9lF/95y+xnfu3InVq1cjODgYQEr/Yw8PDzg5OeH27dvpnkteSPv3yOlxlRFXV1fs3r0ba9euxb///ovdu3dj9+7dMDU1Rd++ffHDDz9AIpF81GtTffyFhIRg1apV2a4fFRWFokWLZrpcTqgvKMzpsa/N+xqRLjAI0yfJ1NQUMpkMt2/f/qhWp4yoP9TVH6hpqT/srKys8nS/GZk3bx62b9+Or776Cr1794aLi4u436+//jrDIKwNdctSbGxsuiGrEhISYGhoCKlUKi536tQplC5d+qP2k5G4uDhxGfWHWtqWu5xeFS6VStGmTRv88ccfePjwIW7fvg2FQqFxtzL1PocNG5bu6/SMGBgYYMCAARgwYABev36Nixcv4tixY7hw4QKGDBmC06dP5+g4VB9jQUFBGc7PyTHWpEkTLF26FJcuXUJycjKkUilq164NqVQKmUyGa9euQaFQQBAEja/r169fj2XLlqFOnToYNGgQ3NzcxG4P3333Hfz8/LLdt6WlJYAPoTU1QRA+6or+nLzGjx49iqlTp8LFxQVTp05F5cqVUaJECQDA1KlTM7zwMK28OP5yelxlxtXVFcuWLUNSUhJu3ryJc+fOYd++fVi7di2KFSuGXr16fdRrU71urVq1NC7OzWp5bV7D2sjLY58oP7CPMH2SXFxcoFQqMxzq6NatW1i0aFG6/pXaUrfuqYeGSi0mJga+vr4oW7ZsvowJe+jQIdja2mL58uWoW7eu+OGQkJAgDk+Vm9YQZ2dnAClDxKU1a9YseHh4ICAgQBzqK21/RQB4/vw55s+fj3/++SfT/RQpUgQlSpSAr69vupE2kpKS0LBhQwwYMAAAxLCTNnikHQJMG+q+wKdPn8axY8dgYWGhEQLVz0v99XJaK1aswLp165CUlISAgAAsWbIEZ86cAQCULFlSHO2hXr16CAoKwqtXr3JUvvLly8PIyAh3797N8I5b6r692nS9qFSpEuzs7HD58mXcuHEDLi4usLCwgLm5OSpXroxr167h/PnzsLS01OhjeejQIchkMvzyyy9o0qSJGIIFQRCHn8vu2CpTpgyKFCmCmzdvppv39OnTdK16OZGT1/ihQ4cAAIsXL0aLFi3EEAxA6+eSF8dfTo6rzBw4cAAzZ86EIAgwNDRE3bp1MW7cOLGrhfp96WNem0WKFEHJkiUz/RsdOHAAK1euFI9rZ2dnvH79OsMT744dO4pdjlJfA5AZc3NzODg44Pnz5xle45CTY58oPzAI0ydJfWHOnDlzNIbfiomJwbRp0/Dbb79l2H9WGy1atECRIkWwfft2sZ8ikDKW7OzZs5GQkKAxpqguGRkZITExUaPfsFKpFMsB5K5/XNu2bSGVSrF27VpxqC0g5UP/6NGjKF26NEqXLo327dtDJpNh2bJlGh+CCoUCM2fOxPr169ON7ZxW+/btER0djdWrV2tM37x5M+Li4lC/fn0AKeEQgBg4gZTWOW2GaEvL1dUVzs7OOHLkCK5evYqvvvpK48SldOnSqF27Ns6dO5duzNUDBw5g9erVOH/+PAwNDWFsbIzffvsNy5cv1wgwSUlJCAkJgaGhoVYXYaZmaGiINm3aIDg4GCtWrNCYd+7cORw9ehRly5bV+iKkJk2a4Nq1a7h58ybq1KkjTq9Tpw78/Pxw9uxZNGrUSOPCJCMjIyiVynRhZPXq1eIFm2nHtE7LwMAAbdu2xcuXL8WxsYGUusnNRYSp5eQ1ru6yoe7bqnbgwAGxv3Pq56IOvalfO3lx/OXkuMrMrVu3sHXr1nQX+6pDacmSJQHgo1+bnTp1QkREBBYtWqTRCv706VPMmDEDGzZsEE+827dvD0EQsGjRIo331aNHj+LFixfia1h9fGX3ntSpUyckJCRgzpw5Gn+X+/fvY+vWrbCwsEg3PjZRQWHXCPok1atXD15eXtiyZQvatGmDpk2bwtDQEKdOncKbN2/Qo0cPjYuMcsLc3Bxz5szBmDFj0KNHD7Rs2RK2tra4fPkyHj9+jFq1ammMS6pL7dq1w/r169GlSxe0aNECCoUCFy5cgL+/P2xsbBAWFoaIiAixX522nJycMGLECKxYsQIdOnRAs2bNIAgCjhw5gsTERMybNw9AygUq48aNw7x589C2bVs0b94clpaWOHfuHPz8/NCsWTOx9TUzQ4YMwdmzZ7F27Vpcu3YNHh4eePbsGc6ePQt3d3dx1IK2bdti+fLl+OOPPxAQEAAHBwdcvHgR0dHR4od/TrRv3x6LFi0S6zGtGTNmoHfv3hg9ejSaNGmCihUrwt/fH2fPnoWVlRWmTp0KALCzs8O3336LDRs2oG3btmjatCmkUinOnz8PPz8/DBs2LFd9YceNG4cbN27gt99+w7Vr11C9enUEBATgn3/+gZmZGRYuXKhVCxuQ0k9YPZJJ2iD8+++/IyYmRhwtInX93Lp1Cz179sTXX38NAwMDXLlyBffv34etrS1CQ0OzPckBgDFjxuDSpUuYN28eLly4ACcnJ1y6dAkRERFa98vPSE5e4+3bt8fhw4cxYsQItGnTBubm5rh79y6uXr2a4XNR98+dNGkSGjZsKI7PnRfHn7bHVWa+++47HD16FD/++COOHTuGsmXLIjAwECdOnICdnR369OkD4ONfm4MHD8aFCxewZcsW+Pj4oE6dOoiKisKxY8cQHx+PRYsWicd1165dceLECRw4cACPHj1C3bp1ERQUhBMnTsDBwUG8YM7a2hqGhoa4cuUK5s6di5YtW6a7kQaQMn7zhQsXcPDgQTx69Aj16tVDaGgoTp06BUEQsHTpUp1dV0CUU2wRpk/Wzz//jAULFqBEiRL4+++/sX//fhQtWhRz5szJ9sMmO61atcL27dvRsGFDnD9/Hrt37wYAjB8/Hhs3bsy3W+WOGTMGI0eOhFQqxfbt23Hq1CmUKlUKf/zxB4YOHQoA+Pfff3O17eHDh2Pp0qUoUaIE/vrrLxw8eBDu7u7YunUr3N3dxeX69++PdevWwdXVFSdOnMCuXbsgl8sxceJErFixQqOVMSNmZmbYvn07Bg4ciLdv32Lz5s24f/8++vTpg/Xr14t1WbRoUWzevBn169fHuXPnsGfPHjg5OWH79u3iRW450a5dO0ilUhQvXjzDEUDKly+Pffv24ZtvvsGjR4+wefNmPHr0CB06dMDevXs1vpodN24cpk2bBnNzc+zfvx+7d++GmZlZhkN2acvGxga7d+/GgAEDEBISgq1bt+Lu3bvo2LEj9u3bBw8PD6231bBhQxgYGIj9g9Vq1aoFuVwOqVSKxo0ba6zTq1cv/O9//4OVlRX27NmDgwcPwszMDEuWLMGMGTMAaHdsWVpaYseOHejRowcePXqEXbt2oWjRonnyOtH2Nf7FF19g6dKlKFOmDA4ePIj9+/cjMTERU6ZMwe+//57uuQwdOhQeHh64ePGi2Ec2r46/nBxXGXFwcMCOHTvg6emJe/fuYcOGDbh27Rrat2+P3bt3a1xk9zGvTWNjY2zevBkjR45EYmIitm/fjn///Rc1atTA5s2b0bZtW3FZdReaH374AQkJCdi2bRsuX76Mdu3aYfv27WJfcUNDQ0yZMgWWlpbYvn17hncABFJa8Ddu3IhRo0YhOTkZO3bswOXLl9GsWTPs2rVLY7hJooImEXg5JhERERHpIbYIExEREZFeYhAmIiIiIr3EIExEREREeolBmIiIiIj0EoMwEREREeklBmEiIiIi0ksMwkRERESklxiEiYiIiEgvMQgTERERkV5iECYiIiIivcQgTERERER6iUGYiIiIiPQSgzARERER6SUGYSIiIiLSSwzCRERERKSXGISJiIiISC8xCBMRERGRXmIQJiIiIiK9xCBMRERERHqJQZiIiIiI9BKDMBERERHpJQZhIiIiItJLDMJEREREpJcYhImIiIhILzEIExEREZFeYhAmIiIiIr3EIExEREREeolBmIiIiIj0EoMwEREREeklBmEiIiIi0ksMwkRERESklxiEiYiIiEgvMQgTERERkV5iECYiIiIivcQgTERERER6iUGYiIiIiPQSgzARERER6SUGYSIiIiLSSwzCRERERKSXGISJiIiISC8xCBMRERGRXpIXxE59fHwKYrdEREREpIdq1qyZ4fQCCcJA5gXSNV9fX7i5uRXIvj93rFvdYd3qDutWd1i3usO61R3WrW4UZL1m1QDLrhFEREREpJcYhImIiIhILzEIExEREZFeYhAmIiIiIr3EIExEREREeolBmIiIiIj0EoMwEREREeklBmEiIiIi0ksMwkRERESklxiEiYiIiEgvMQgTERERkV5iECYiIiIivcQgTERERER6iUGYiIiIiPQSgzARERER6SUGYSIiIiLSSwzCRERERKSXGISJiIiISC8xCBMRERGRXmIQJiIiIiK9xCBMRERERHqJQZiIiIiI9BKDMBERERHpJQZhIiIiItJLDMJEREREpJcYhImIiIhILzEIExEREZFeYhAmIiIiIr3EIExEREREeolBmIiIiIj0EoMwEREREeklBmEiIiIi0kvygi5Avoj0A24tBh5vhWtyDHDBHHDuA1TzBiydCrp0RERERFQAPv8g/OIowv4aiktvmuNO2CIkqYxhKE2A+7NLqH+7OWw6rAXKfl3QpSQiIiKifPZ5B+FIPzzZNRV7/P4HpUoG1funm6QywY13TXA7rCG6JUxFxSHObBkmIiIi0jOfdR/hsPOrsMdvMJJVRmIIVlNBjmSVEfb4DUbYwdHA091A4Bkg9C4Q9xZQJhdQqYmIiIgoP3zWLcKXLkVDqZJluYxSJcPFe/ZoG9kdEkmamUZWgHFRwNgOMNHip0ERpN8IEREREX2KPusgfOddnXQtwWmpIMeN0Ga4FdYYprIYmMqjYSJP+Wkqi/nwWB4KU9kLmMo/LGMkjdfMvVJDwMQuJTxr9dMWkH7WfwIiIiKiT9ZnncKSVMZaL6sS5IhRWCFGYaX1OlIoYCpPHZajYSKLEcOyqSwApvIHGsukC89G1lkE5TQtziZ2gNyMrc5EREREeeCzDsKGBkokJWvzFAUAOQ+XKuQuPKeE4vf/ZNEwkUen+v1hqmAdA1NZDIxkcR+yr8xI+64axnaAsY1uW505NB0REREVUp91EHavVQo3LgVm2T1CCgVqNiiFlr2bIz4mEXExiYiLff8zJvH9tIRUjz/MT05U5LhMKsgRq7BC7EeHZ3VYDoeJLEBskU4fniUprc7pgnIWXTbkptq1OnNoOiIiIirEPusgXL9tfdy+fgCqLAaAkBnIUa9NfRgYymFgI4eFjZnW21ckK98H5jRB+X1Yjo9O0AjO8TGJSMqn8CyB8kN/5lTdNUzkMTCVvYKp/OGHLhzvlzGWxUMiEQCZcfZ9nFVJeLJ/CYemIyIiokLrsw7CNvZF0O375tjzy1koFQqohA+jxUklKsjkcnT7/gvY2BfJ1fblBjJYWJvCwtpU63XU4Tk+NhFxaYKyZphOEB/nJjwLkCFWYYlYhaXW66jDs8n7VmeN/s+yRzCV+4jhOUlphN3PxkAhGKXbjgpyqFRy7PEbjKHnV8Gm7dIcl5+IiIhI1z7rIAwAFauWwtBp7XH55APcueyPxIRkGBkbwL2eI+q1rJTrEJxbuQ3P8anCctqg/CFYv2+djk1CUkLOx0HOTXjOilIlw+WLofCMqw5YuwHWlVL+2VQCLJwAmUGe7IeIiIgoNz77IAyktAx79q4Lz9514evrCzc3t4IuUo7IDWQoYmWKIla5D8+aQTlNNw51y3MuwnNWVJDj+rsvEXPNEvbGgbA3/hv2JmtgYxQEqUwGWDm/D8duHwKylXPKBYFEREREOqYXQVgf5SY8KxVKzeAc8z48x6pboJPE/tCvn7+DNiNtCJDCN6IOfFNNk0mSUdT4NeyNX6UEZJO/YW+8BpaGoZBIJYBlhQ+tx9Zu7wOyK2Cg/XMhIiIiyo5OgvC+ffuwf/9+AEBiYiJ8fX1x8eJFWFhY6GJ3lEdkcu3D89xhG7Qcmi49pWCAoPiyCIovqzHdUBoPO+NA2BkHwt7kFeyN/4K9yWqYyyMhkUgAi3LpA7K1G2DI44qIiIhyTidBuHPnzujcuTMAYPr06ejSpQtD8GdG26HpXN2LoWJNFwQHRiDkdQSCAyMQFR6X4fJJKhMExlVAYFwFjekmshjYm7yCnfGrlFZkk79gb7waJvL32zFzeB+KK30Ix9aVUsZQJiIiIsqETrtG3L17F0+fPsXUqVN1uRsqANoOTfdl98bpLkhMiEtC8PtQrA7IQYERiI9JzHA78UpzvIhxxYsYV43pRQzCYG8cmBKQ37cg2xmvgaEsKWUBk2JpAvL7fyZ2vDsfERERQSIIgqCrjY8YMQJ9+vRBvXr1NKb7+PjA1LRg+nsmJCTA2Fj7Wy9T5t74R+K/g08gKFXphqaTyKRo0K4iSjhqNwKFIAhIjFMgMjQeke9S/kW9f6xIVuWgVCpYG4bA3iQwVQvyKxQ1egOZVAkAUMgtkWTmhEQzJySaVhAfKwztP9mAzONWd1i3usO61R3Wre6wbnWjIOs1Li4ONWvWzHCezlqEo6Ki4O/vny4EqxXUyA2FcdSIT5WbG1CtVmWdDk0nCAIiw2I1Wo+DAyMQ8iYSSkVGAVmK8KRiCE8qhkeRNVJNVcDW+G1KC7LJ+4BsfA72Rnshlbw/FzS0SD/Mm3UloEgZQCLNYF/5h8et7rBudYd1qzusW91h3epGQdarj49PpvN0FoSvXbuG+vXr62rz9InQ9dB0EokEVrbmsLI1h7O7gzhdpVQhLCQaIa8jxZAc/DoCoUFREFTpv+RQQY6QBAeEJDgAEXXF6XJJ0vuL8963IIek9EG2MNjwoXFYbgpYu6YPyBblAaksT58vERER5R+dBWF/f384ODhkvyBRLkhlUhQtbomixS3hVqOMOF2RrERoUJRGOA4OjEDEu5gMt6MQDPEm3hFv4h01phtJ4z5coGcSCPvwlD7IZgZbPywkMwKsXDTHQbaulDL8m8xQJ8+biIiI8o7OgvB3332nq00TZUpuIEMxB2sUc7DWmJ6UkIyQN5HpAnJMZHyG20lUmSIg1hkBsc4a083kkbA3fvW+e0Ug7KNewT74LxjJdn1YSCoHLCumD8hWLoA8B/2jIv2AW4uBx1vhmhwDXDAHnPsA1bwBSyftt0NEREQZ4g01SC8YGhuglGNRlHIsqjE9LiZR7HeceiSLhLikDLcTq7CEf4wl/GMqa0y3NHiXqgX5FexjAlE09CAMpPs+LCSRpnSnSHs3PWtXwMBcc0cvjgLHuwLKZEBITrl1SXI08OB34NEm4Ku9QNmv86BmiIiI9BeDMOk1U3MjlHUuhrLOxcRpgiAgJjI+XetxyOsIJCcpM9xOZHJRRCYXxZOoauI0CVSwMQpK1YL8CvbxgbCNOATp8781N1Ck7IeAbFwUuD4DYXEWuBTcGnfCGiJJZQxDaQLcbS6ivv0x2BzvCnS/w5bh3GJrOxERgUGYKB2JRCLeYc+pcklxuqASEBEaky4gv3sbBZUy/QgWAqQITSyB0MQS8I2sLU6XSZJR1OiNZgty4itYRR2D5OVRAMCTSHfseT4SSpVMvGlJksoEN959gdthjdGt3CpUPN4dKN8hpa+y1Cjlp9xYu99lRoAs1eMCHhUjX7G1nYiI3mMQJtKSRCqBtV0RWNsVgUu10uJ0pUKFsOCodN0rwkKigQxG6VYKBghKKIOghDIa0w2kCbA3DoSFwTs8iqoJlZD+5amCHCqVHHuej8BQ48mweTclb56cVK4ZjNMG5bS/S7MI1R/7u1SHb0uRfsDxrgiLNWdrOxERMQgTfSyZXAq7klawK2mF1D2HkxMVePc2UjMgv45AVFjGt5hOVhkjMM4Jgcg+gCWrDLHpyUSUMH0JmSQZcokCMmkyZBKF+FguUaTMk6b8lEmUkEuSIZMqUn5KFJCr13m/TMq6iZBLYlOtq4BUkpObmnwkiSzvQnXaeY+24kmYM/b4D8+8td1xDSreXgo0WZV/z5mIiAoEgzCRjhgYyVGirC1KlLXVmJ4Ql5RygV6q1uPgwAjEZXKL6YxJEJVcFFGRRbNfNA9IoNII1SmBOYNALQbt9+H7fZDOLJxrTv+wHc1wngCZNDrduuKNUHIgLNEee/xnI1lllG6e2NruPwxDjX6GTdAVwMgq5Z+hFWBoqfm7+Njyw+8G5p/s3QmJiCg9BmGifGZsaojSFexRuoK9xvTYqJQL9DYvOQng0wpTAqRQCEZQKNMHyIIigTJdoBZbvTMJ52/iSyNZZZDldhUqOU4FdkNz4U8YSv1gJIuHoTQREm2Ct0T6IRSrg7NGaLbKJlBb6Fd/bcoZXuRJlOcYhIk+EWYWJnC0MIGhkRxJiRmPTpGagYEUnQc1hkKhgjJZmfJToczgdyWUChUUySk/lamX01j+/XIKJZTJKo3fM+rrXNAEyJCskiEZeRvOBcjgG1kHvpF1Uk1VwVCaCCNZPIykCe/DccpPjcfqeeLjtzCSPYeRVD0tHgbSpCwajSUpYTh1SM4sUGc0z9BCt32stcGwphsvjiLsr6G49KY57oQt+tC3/dkl1L/dHDYd1vIiz4/B41Y3CkG9MggTfWLc6znhxvnHUGXRLVcqBao1rADX6mUyXyiPCIIAQSWkC9RZ/66epm04/7CMUqn6BMO5FEkqEySpTBD9kVuSQJUqKKf8NHwfqMXHqUK2kTQEhrKXMJLGw0imGcLlkuT0odqgiHbdODIM1JYfd1dEhjXdiPTDk11Tscfvfxn0bW+C22EN0S1hKioOcf5kwkWhwuNWNwpJvTIIE31i6reqhNuX/KDKZMxiAJDJZajXslK+lEcikUAik8BQJkUeN77mmiAIUCkFjZZtbcL5XxsuQqHIPkFLJICNvQUSE5KRmJCM5ERF3pUdUiQqzZCoNAOSP25bUihgKNNsjU5pfU7Tai0NhZHsVYat1+ppcun75yg31a5FOm3YTowouLAmCICgApDmp8Zj9TxV+uUze5x6miCkWTf/thPmswd7/AZn3bfdbzCGHhkHm1rdU7rXSCQA3v+USN93uZFk/FO9TOrl0y2TZvmMls1s25Cm2U8W+8h223ncbYwnGbpRiOqVQZjoE2NjXwTdhjbFnrX/QqlUQaX8ENykMglkMim6DW0KG/siBVjKgiWRSCCTSyCTS5GT9ssXj4O1am2v2cQZnr3ritNUKhWSEhRITEhGUkIyEuOTkZj4/mfqaepl4pOQmKhAYvz7ean+KbI4wckpFeRIUJojQWme/cLZkEmSMw3JKT8jYCR7k2n3kHiFGXb7/QiFkE1Y29wSNpZKZBsYswiwgqBKmS2oIECS8hgSAJL3XxRIUvImJO+nA4Ig/TBPnPb+Z6p1BUGabhoEica6KetpTvtQhlTrCpIMpkFzXXGZzMt+OfgrKFRZf1wrVHKc8CmNui+nq5/J+8yYugRCSpZUz38/HUCqZT88K81tpJqewTbUfeg1pkvS7Bt4v1xm0zNaP6PyAJBI3z+WQiKRvM/I0g8/AUik2Qf5sEgZ9vhNzv4kY1vblPdciSxVaM/gMaSANNXjzJbT9nHa7UllH7ab+nGm28rhPnO07cz3GXZmgXYnbxfWwKbN4owP6nzCIEz0CapYtRSGTm2Hyycf4M5lfyQmJMPI2ADu9RxRr2UlvQ7BHyO3re1SqRTGpoYwNv2IbgPvqZQqMRR/CNDvQ3R8EpLeB2hxmdSP1YH6/TSlIu+GtVMKBohXGiBeqbtjK1lliDV3JkMuS34fLnMaEnkhYWYEyPAoshYeRdYq6KJ8glSZngyoBBmEbI6rZJUh1t4aCxN5bMrpiUSABKo0P1MeS9NNEyCRqDSmSSUqcXlxOfU0iRISKDSXe79NZDBN/JlqmuYy6uXST0tXVnEZlUbZpemmpdmmRrlSfl4ObpXtyZtSJcPl/yLh2SaP/sy5xCBM9ImysS8Cz9514dm7Lnx9feHm5lbQRSr0PoXWdqlMChMzI5iYfXw/E6VCKQbolKCs+ND6nLa1OlEzVKdtrU5dF7ojgRKGUCo//oSCSHvqbwPet3/n+FCXIFkwRnKycZ6WSt+pIMedd7XhWcDlYBAmIr3yObW2y+QymJrLYGr+caFaEAQoFar3gVqRQWt1+hbsDwE7JYiHvI4AkL/D/n34ShwAJOLX5uL0lF9S+rmnnof360gk75fB+2VSzVNvTwJI8GF7Eqkk631ltr3U89TbU5crw3kfyvDi8VuxG0fW9SGgTMXiAISU1nUBEFL+ex8A309PNf/9bxksk3JciD9Tz8f7bYvzU28z1Xz1dlPP19hedvtNvT1BI8Rqswx9+pJUBX9ywSBMRHqHre2aJBIJ5AYyyA1MYGaRu23MHbYBScnZf6QYyhX4YVGvLENiStjMIpDq2U1LDq8/gRuXAsULjjIihQI165eCZ/9W+ViyT1u6wJ1BIF80eguSFNoct0oMm91NHEUn5WfKNtT/VKqUbYvLqJcTkGaZNOumXU6Vajkhg+Xer/thmaz3q7GcCmn2mcFyKnV9abeu5jIp014+0e7kzdAgH+9amgkGYSIi+mjutUppFdY86pTKk24h+qR+2/q4ff0AVFmMMiIzkKNem/r5V6hC4ENrfeaBzL229setpY2ZLor5WdL25M29dql8LFVm5SAiIvpI9dvWh8wg67YVhrXcsbEvgm7fN4eBgRRSiWYLmlSigoGBFN2+b16ouvV8Knjc6kZhqlcGYSIi+mgMa7pVsWopDJ3WHjWbusLIOOU24UbGBqjZ1BVDp7VHxaoF37JWGPG41Y3CVK/sGkFERHlCHdY+hwsRP0Xs264bPG51o7DUK4MwERHlGYY1Kox43OpGYahXdo0gIiIiIr3EIExEREREeolBmIiIiIj0EoMwEREREeklBmEiIiIi0ksMwkRERESklxiEiYiIiEgvMQgTERERkV5iECYiIiIivcQgTERERER6iUGYiIiIiPQSgzARERER6SUGYSIiIiLSSwzCRERERKSXGISJiIiISC8xCBMRERGRXmIQJiIiIiK9xCBMRERERHqJQZiIiIiI9BKDMBERERHpJQZhIiIiItJLDMJEREREpJcYhImIiIhILzEIExEREZFeYhAmIiIiIr3EIExEREREeolBmIiIiIj0klxXG/7111/xzz//IDk5GT179kS3bt10tSsiIiIiohzTSRC+cuUKbt68iR07diA+Ph7r16/XxW6IiIiIiHJNJ0H4woULcHZ2xvDhwxETE4Px48frYjdERERERLkmEQRByOuN/vzzz3j9+jXWrl2LV69e4fvvv8exY8cgkUgAAD4+PjA1Nc3r3WolISEBxsbGBbLvzx3rVndYt7rDutUd1q3usG51h3WrGwVZr3FxcahZs2aG83TSImxlZYXy5cvD0NAQ5cuXh5GREcLCwmBraysu4+bmpotdZ8vX17fA9v25Y93qDutWd1i3usO61R3Wre6wbnWjIOvVx8cn03k6GTWiZs2aOH/+PARBQFBQEOLj42FlZaWLXRERERER5YpOWoSbNWuGa9euoWvXrhAEAVOmTIFMJtPFroiIiIiIckVnw6fxAjkiIiIi+pTxhhpEREREpJcYhImIiIhILzEIExEREZFeYhAmIiIiIr3EIExEREREeolBmIiIiIj0EoMwEREREeklBmEiIiIi0ksMwkRERESklxiEiYiIiEgvMQgTERERkV5iECYiIiIivcQgTERERER6iUGYiIiIiPQSgzARERER6SUGYSIiIiLSSwzCRERERKSXGISJiIiISC8xCBMRERGRXmIQJiIiIiK9xCBMRERERHqJQZiIiIiI9BKDMBERERHpJQZhIiIiItJLDMJEREREpJcYhImIiIhILzEIExEREZFeYhAmIiIiIr3EIExEREREeolBmIiIiIj0EoMwEREREeklBmEiIiIi0ksMwkRERESklxiEiYiIiEgvMQgTERERkV5iECYiIiIivcQgTERERER6iUGYiIiIiPQSgzARERER6SUGYSIiIiLSSwzCRERERKSXGISJiIiISC8xCBMRERGRXmIQJiIiIiK9xCBMRERERHqJQZiIiIiI9BKDMBERERHpJbmuNtypUyeYm5sDABwcHDB37lxd7YqIiIiIKMd0EoQTExMhCAK2bNmii80TEREREX00nXSNePjwIeLj4zFgwAD07dsXt27d0sVuiIiIiIhyTSIIgpDXG3306BFu376Nbt264fnz5xg0aBCOHTsGuTylAdrHxwempqZ5vVutJCQkwNjYuED2/blj3eoO61Z3WLe6w7rVHdat7rBudaMg6zUuLg41a9bMcJ5OukY4OjqibNmykEgkcHR0hJWVFUJCQlCiRAlxGTc3N13sOlu+vr4Ftu/PHetWd1i3usO61R3Wre6wbnWHdasbBVmvPj4+mc7TSdeIvXv3Yt68eQCAoKAgxMTEwM7OThe7IiIiIiLKFZ20CHft2hU//fQTevbsCYlEgjlz5ojdIoiIiIiIPgU6SaeGhoZYvHixLjZNRERERJQneEMNIiIiItJLDMJEREREpJcYhImIiIhILzEIExEREZFeYhAmIiIiIr3EIExEREREeolBmIiIiIj0EoMwEREREeklBmEiIiIi0ktaB+GIiAgdFoOIiIiIKH9le4vlq1evYsaMGVAqlWjdujVKliyJbt265UfZiIiIiIh0JtsW4eXLl2Pr1q0oWrQohg4dih07duRHuYiIiIiIdCrbICyVSmFlZQWJRAIjIyOYmZnlR7mIiIiIiHQq2yBcpkwZLF68GBEREVi3bh1KliyZH+UiIiIiItKpbIPw9OnTUbJkSdSsWROmpqaYOXNmfpSLiIiIiEinsg3C9+/fR3JyMqZOnYqbN2/i6dOn+VEuIiIiIiKdyjYIz5gxA1988QUA4IcffsDs2bN1XSYiIiIiIp3LNggbGBigTJkyAIDSpUtDKuU9OIiIiIio8Mt2HOGSJUtiyZIlqFatGu7cuQN7e/v8KBcRERERkU5l27w7d+5c2NjY4N9//4WtrS3mzp2bH+UiIiIiItKpbFuEDQ0NUaNGDVSuXBkAcPv2bdSuXVvnBSMiIiIi0qVsg/DIkSMRFhaGEiVKQBAESCQSBmEiIiIiKvSyDcLv3r3Dzp0786MsRERERET5Jts+wo6OjggKCsqPshARERER5ZtsW4Rv3LiBZs2awcbGRpx24cIFnRaKiIiIiEjXsg3Cx48fz49yEBERERHlq2yD8K1bt7Bv3z4kJycDAIKDg/HHH3/ovGBERERERLqUbR/hadOmoU6dOoiJiUHJkiVhZWWVD8UiIiIiItKtbIOwtbU12rZtC3Nzc4wcOZIXzhERERHRZyHbICyVSvHkyRPEx8fj2bNniIyMzI9yERERERHpVLZBeOLEiXjy5Am8vLzw448/okuXLvlRLiIiIiIincr2YrmKFSuiRIkSSExMxLp16yCRSPKjXEREREREOpVtEB4/fjx8fHxgYWEh3mJ5//79+VE2IiIiIiKdyTYI+/v74/Tp0/lRFiIiIiKifJNtH2F3d3c8e/YsP8pCRERERJRvsm0RNjc3R9euXWFqaipO4y2WiYiIiKiwyzYIX7lyBVevXoVcnu2iRERERESFRrZdI8qWLYvQ0ND8KAsRERERUb7Jtpn35s2baN68OaytrcVp7BpBRERERIVdtkF4+vTpqF+/fn6UhYiIiIgo32TbNWLVqlX5UQ4iIiIionyVbYuwRCLB8OHD4ejoCKk0JTePHTtW5wUjIiIiItKlbINwly5d8qMcRERERET5KtuuEe3atUNcXBzu3LmDqKgotGnTJj/KRURERESkU9kG4SlTpiAgIAANGzZEYGAgfv755/woFxERERGRTmXbNeLFixfYtm0bAKBFixbo0aOHzgtFRERERKRr2bYIJyYmIj4+HgCQkJAApVKp80IREREREelatkG4b9++6NChA4YPH44OHTqgX79+Wm04NDQUTZs2hZ+f38eWkYiIiIgoz2XaNWLr1q3o06cPypYti927dyMgIAAODg4ad5jLTHJyMqZMmQJjY+M8LSwRERERUV7JtEV4y5YtOHv2LKZMmYJ79+4hMjIS9+/f1+r2yvPnz0ePHj1gb2+fp4UlIiIiIsormbYIjxs3DidOnEBoaCgOHz6sMa9Ro0aZbnDfvn2wsbFB48aNsW7dukyX8/X1zUVxP15CQkKB7ftzx7rVHdat7rBudYd1qzusW91h3erGp1qvEkEQhKwWOHDgADp27Kj1Bnv37g2JRAKJRAJfX1+UK1cOv/zyC+zs7MRlfHx8ULNmzVwX+mP4+vrCzc2tQPb9uWPd6g7rVndYt7rDutUd1q3usG51oyDrNavcme3waXv37s1REFYPtQYAXl5emDZtmkYIJiIiIiL6FGQbhJOSktCxY0c4OjpCKk3pUrx48WKdF4yIiIiISJeyDcI//vhjrje+ZcuWXK9LRERERKRL2Y4jXKlSJVy8eBH79+9HREQEihUrlh/lIiIiIiLSqWyD8KRJk1C6dGm8ePECRYsWxeTJk/OjXEREREREOpVtEI6IiEDXrl0hl8tRo0YNqFSq/CgXEREREZFOZRuEAYi3SX779i1kMplOC0RERERElB+yDcI///wzJk2ahAcPHmDUqFGYOHFifpSLiIiIiEinsh01wtnZGb/88gtevnyJcuXKwcrKKh+KRURERESkW9kG4W3btmHz5s2oUKECnj59imHDhqFDhw75UTYiIiIiIp3JNgjv2bMHf//9N4yMjBAfH48+ffowCBMRERFRoZdtH2FbW1vxAjljY2N2jSAiIiKiz0K2LcKCIKBjx46oXr06Hjx4AIVCAW9vbwC81TIRERERFV7ZBuGhQ4eKj9u1a6fTwhARERER5Zdsg3CdOnXyoxxERERERPlKqxtqEBERERF9bhiEiYiIiEgvZdo1YuzYsZBIJBnO40VyRERERFTYZRqEe/TokZ/lICIiIiLKV5kGYfVFcjExMfjtt98QHByMZs2awcXFJd8KR0RERESkK9n2EZ40aRJKly6NFy9eoGjRopg8eXJ+lIuIiIiISKeyDcIRERHo2rUr5HI5atSoAZVKlR/lIiIiIiLSKa1GjfDz8wMAvH37VrzdMhERERFRYZZtEJ48eTImTZqEBw8eYNSoUZg4cWJ+lIuIiIiISKeyvbOci4sLdu3alR9lISIiIiLKN5kG4ebNm2uMIyyXy6FQKGBoaIijR4/mS+GIiIiIiHQl0yB87NgxCIKA6dOno0ePHnB3d8eDBw+wffv2/CwfEREREZFOZBqEDQ0NAQABAQFwd3cHAFSqVAn+/v75UzIiIiIiIh3Kto9wkSJFsGzZMri7u+PmzZuws7PLj3IREREREelUtqNGLFq0CBYWFjh79izs7OywYMGC/CgXEREREZFOZRuEjYyMYGRkBKlUCkEQ8qNMREREREQ6l20Q/t///oeAgAA0atQIgYGB+Pnnn/OjXEREREREOpVtH+EXL15g27ZtAIAWLVqgR48eOi8UEREREZGuZdsinJiYiPj4eABAQkIClEqlzgtFRERERKRr2bYI9+3bFx06dEDFihXx9OlTjBw5Mj/KRURERESkU9kG4fbt26NJkyYICAiAg4MDrK2t86NcREREREQ6lWkQ/umnnzJdae7cuTopDBERERFRfsk0CN+7dw8JCQlo3749qlevzqHTiIiIiOizkunFcgcPHsTq1auRmJiIdevW4datWyhTpgwaN26cn+UjIiIiItKJLPsIOzs748cffwQAXLt2DYsXL8bbt2+xe/fufCkcEREREZGuZHuxXExMDE6ePIlDhw4hPj4e7du3z49yERERERHpVKZB+MiRIzhy5Ahev36NVq1aYfr06XBwcMjPshERERER6UymQXjs2LEoX748XF1d8fjxYyxdulSct3jx4nwpHBERERGRrmQahDdv3pyf5SAiIiIiyleZBuE6derkZzmIiIiIiPJVpsOnERERERF9zhiEiYiIiEgvMQgTERERkV5iECYiIiIivZTtDTVyQ6lU4ueff4a/vz8kEgmmT58OZ2dnXeyKiIiIiChXdNIifObMGQDAzp078cMPP2iMQUxERERE9CnQSYtwixYt8MUXXwAAXr9+DQsLC13shoiIiIgo13QShAFALpdjwoQJOHnyJFasWKGr3RARERER5YpEEARBlzsICQnBN998g8OHD8PU1BQA4OPjIz7ObwkJCTA2Ni6QfX/uWLe6w7rVHdat7rBudYd1qzusW90oyHqNi4tDzZo1M5ynkxbhAwcOICgoCEOGDIGJiQkkEgmkUs3uyG5ubrrYdbZ8fX0LbN+fO9at7rBudYd1qzusW91h3eoO61Y3CrJefXx8Mp2nkyDcqlUr/PTTT+jduzcUCgUmTZrEsysiIiIi+qToJAibmppi+fLlutg0EREREVGe4A01iIiIiEgvMQgTERERkV5iECYiIiIivcQgTERERER6iUGYiIiIiPQSgzARERER6SUGYSIiIiLSSwzCRERERKSXGISJiIiISC8xCBMRERGRXmIQJiIiIiK9xCBMRERERHqJQZiIiIiI9BKDMBERERHpJQZhIiIiItJLDMJEREREpJcYhImIiIhILzEIExEREZFeYhAmIiIiIr3EIExEREREeolBmIiIiIj0EoMwEREREeklBmEiIiIi0ksMwkRERESklxiEiYiIiEgvMQgTERERkV5iECYiIiIivcQgTERERER6iUGYiIiIiPQSgzARERER6SUGYSIiIiLSSwzCRERERKSXGISJiIiISC8xCBMRERGRXmIQJiIiIiK9xCBMRERERHqJQZiIiIiI9BKDMBERERHpJQZhIiIiItJLDMJEREREpJcYhImIiIhILzEIExEREZFeYhAmIiIiIr3EIExEREREeolBmIiIiIj0EoMwEREREeklBmEiIiIi0kvyvN5gcnIyJk2ahMDAQCQlJeH777/Hl19+mde7ISIiIiL6KHkehP/++29YWVlh4cKFiIiIQMeOHRmEiYiIiOiTk+dBuHXr1vjqq68AAIIgQCaT5fUuiIiIiIg+Wp4HYTMzMwBATEwMRo0ahR9++CGvd0FERERE9NEkgiAIeb3RN2/eYPjw4ejVqxe6du2abr6Pjw9MTU3zerdaSUhIgLGxcYHs+3PHutUd1q3usG51h3WrO6xb3WHd6kZB1mtcXBxq1qyZ4bw8bxF+9+4dBgwYgClTpqB+/fqZLufm5pbXu9aKr69vge37c8e61R3Wre6wbnWHdas7rFvdYd3qRkHWq4+PT6bz8nz4tLVr1yIqKgpr1qyBl5cXvLy8kJCQkNe7ISIiIiL6KHneIvzzzz/j559/zuvNEhERERHlKd5Qg4iIiIj0EoMwEREREeklBmEiIiIi0ksMwkRERESklxiEiYiIiEgvMQgTERERkV5iECYiIiIivcQgTERERER6iUGYiIiIiPQSgzARERER6SUGYSIiIiLSSwzCRERERKSXGISJiIiISC8xCBMRERGRXmIQJiIiIiK9xCBMRERERHqJQZiIiIiI9BKDMBERERHpJQZhIiIiItJLDMJEREREpJcYhImIiIhILzEIExEREZFeYhAmIiIiIr3EIExEREREeolBmIiIiIj0EoMwEREREeklBmEiIiIi0ksMwkRERESklxiEiYiIiEgvMQgTERERkV5iECYiIiIivcQgTERERER6iUGYiIiIiPQSgzARERER6SUGYSIiIiLSSwzCRERERKSXGISJiIiISC8xCBMRERGRXmIQJiIiIiK9xCBMRERERHqJQZiIiIiI9BKDMBERERHpJQZhIiIiItJLDMJEREREpJcYhImIiIhILzEIExEREZFeYhAmIiIiIr2ksyB8+/ZteHl56WrzREREREQfRa6Ljf7222/4+++/YWJioovN55hfVDwW3wnA1idBiElWwvzyO/SpWAze7qXhZPFplJGIiIiI8pdOWoTLlCmDlStX6mLTOXb0ZSjc917D776vEZ2shAAgOlmJ331fw33vNRx9GVrQRSzU/KLiMezCY1hsOI/K54NgseE8hl14DL+o+IIuGhEREVGWJIIgCLrY8KtXrzB27Fjs3r073TwfHx+YmprqYrcaXsYr0PFGKBJUmS9jLAUO1LBFGROdNI5/1s6FJeIH3wgoVIAi1XQ5ALkUWOZmhSY2RgVVvELvZbwCGwPjcDA4AXFKAaYyCdrZG6NfKVMerx+Jdas7rFvdYd3qDutWNz6Veo2Li0PNmjUznFdgQTizAuWlYRce43ff10jO4hnKJECLUtYY7FYSMokEUgkgk0je/wNkUonm7xKJOC3dsqnmyVLPk0ogBdLNk0gkOq8DXfGLiof73muIU2R+lmEql+JO19rsfpILR1+Gouup+0hWqjSOXwMJYCCTYm+Lyvi6jG3BFbAQY93qDutWd1i3usO61Y1PqV6zyp2fdRC22HAe0clKne8ntyRQh+fUgTpNwE4zL2041yq4ZxHGP4T3TMqQwTyZFPjreSiuBkchi8Z2yCRAs5JW+M61JGQSQJqqXBk+lgJSpC9r6ueY3ePU5U69DQlQaE48eJKhO6xb3WHd6g7rVndYt7rxqdVrVrnzs27vj/mEQzAACAAUggCFUv3b50UpAKcCI3AqMKKgiwIA6cNypmH6Q/gXH0sl70N65stkG/QzCf2pp0slwNnXEUjI4s0DABIUKvQ8dR+eZWzFgK+O+eq8L/7+/lG66anOC8RlMttG2n2kW+7DxtLvP5NtZFLOrMqqbTkzK+vGR2+RqMy6bhOVKgw7/xgDXEtkUldpypKmDFmVN6syayyTyXYyXD/NdjJeP/W2c17uDJ9/mnIvuP0SSdnUbZJShZ+uPsPEamU0pmd0iprZaWtGJ7QZrp/BxIz3k8H2tF03w+UyLvnHlHGaz3Ot6nbadX9Mr+WY5XLZ7Ss38qKNIbN6y/F2criZadf9tarbGT7PMau29nWbnz7FJp7pWtRrslKFpXcCsKqRcz6VKmM6axHOyqfWIiyXSNC2rC2UKgEqCFAKgFIlQCmo/yHlp0rzd1UW8z5My2BZQYDq88u9RERERFqzMJAhsn9jne9Hb1uE+1Qslm0fYQMJMNitRL6fkQjvw/CHsP0hgKsgQKnKeF7acK7KYl5eBvfU85SCgGV3XyFJizRvIJGgo2NRjXKmPtlI/VxVGZRVleo5ZfhYhffb0JyuSnPSwfMOIiKiT8un8M39Zx2Evd1LY9Pjt0jO4mtmA5kUY9xL52OpUkjUX61/kl9qZE89BN2neJKRESGTgJzusUqACtD4qdV6qR6nC/1ZBP2MQv/Eq8+QqMw+uhtJJZjw/itm9dLq73eE91M+/J7mZ6ovgtKvm8020u5Do54z3k/6/We8j6zKqm05s9rPLr8QKLT4EkwukaCTY9EMtpt1vWZV3qzKrLF+JtvJeL/aP/eM52VR7gzLn3m5fd5Fa/VNl1QCeNiYpytTakKGUzXLkuX6GS6XfqL262a0nHbby9l+MtimAPhHJ2h1Mi8BUK6IsRZL5l3jQF58qZx3Zcn5Oq9iE7Wu21Jmn94oSJm9Vgra69gkrUpmbiDTeVmy81kHYScLE+xtUTnbqxbZAT7nPuWTjIxIJBLIC8nFco8j47U6yfjOtUSO+gMSYGGY/Ugyn9IJXGGizSg9KXVbknWbQ6xb3WHd6oa29epVsVj+FSoTOrvF8qfi6zK2uNO1Nga7lYSFQUr7q4WBDIPdSuJO19ocEiWX1CcZpnIpDNLkSwNJytWgPMnIHW/30jCQZf3S/JROMgoT1q3usG51h3WrO3lVt6lvLiVdd1bvby5VmI7Zzz4IAymhbVUjZ0T2b4z7jYshsn9jrGrkzJD2kXiSoRs8ydAd1q3usG51h3WrO3lRt7yDbXqF6Zj9rEeNyIivry/c3NwKZN+fO9Zt3vKLisfSOwHY8iQI0clKFDGQwatiMYxxL/1JvHkUZqxb3WHd6g7rVndyW7e6HC/3ypUr+OGHH1ChQgUAQGxsLBwcHLBo0SIEBwfjyy+/hLe3NwYPHiyuM3ToUMTGxmLLli148eIFZs+eDYVCgZiYGNSuXRve3t54/fo12rdvj8qVK2vsb+PGjZDJPvTZvXbtGooUKQJXV1etyjtixAisWrVKY9qncswWyA01ssIg/Hli3eoO61Z3WLe6w7rVHdat7uSkbnXZx/jKlSvYuXMnli5dKk7z9vZGy5YtUaVKFfTv3x8WFhb4888/AQDh4eHo1asXihYtii1btmD06NHo0qULmjRpAkEQMGLECHTs2BFubm6Z3vAstYkTJ8LT0xNNmjTJUbkzU5DHrN4On0ZERET0sRbfCcA0n+e5Gu4rWQBWP3iN1Q9ea0w3N5BhWs1y8Nayn2xSUhKCg4NhaWkJALC2toaVlRX8/Pzg5OSEo0ePonXr1rh+/ToAoGjRoti/fz/MzMzg7u6OZcuWQS6XIzAwMNt93bt3D+fPn8f9+/dRoUIF9O7dG+XLl4eTkxO6du2KefPmQalUIjw8HNOmTUONGjXQsGFDXLx4EV5eXnB1dcWTJ08QExOD5cuXo1SpUjmstfyjF32EiYiIiHJr8Z2APB/zNiZZicV3ArJc5vLly/Dy8oKnpyc6d+6Mli1bon79+uL8Nm3a4PDhwwCA06dPo0WLFuK8CRMmwMPDA0uWLEGDBg3w008/ITo6GgDw9OlTeHl5if/mzZunsd8qVaqgcePGGDduHEqWLIk3b95g0aJFmDRpEp4+fYoJEyZg06ZNGDRoEPbt25eu3O7u7ti4cSMaNmwolu9TxRZhIiIioix4u5fOdYtwZswNZNm2BterVw9Lly5FeHg4BgwYAAcHB435LVq0QO/evdG5c2fY2dnB2PjDONKXL19Gv3790K9fP8TGxmL+/PlYs2YN+vTpgwoVKmDLli1al9Xa2hrW1tYAAHt7e6xZswbGxsaIjY2Fubl5uuUrVaoEAChevDjevXun9X4KAoMwERERURa83UtnGFrzaxxia2trLFy4EH379sWBAwfE6WZmZnB0dMTChQvRrVs3jXUWLlwIY2Nj1KlTR1wuPDxc631KJBLxhilS6YcOBLNnz8aiRYvg5OSEFStWaNXV4lPGrhFEREREuZCf4+VWqFABXl5emDVrlsb0du3awcfHR6PLBAAsW7YMv/zyCzp37owePXrg/v374ggTabtGeHl5ISBAs5uGh4cHFi1aBD8/P43p7du3x+jRo9GrVy88f/4cwcHBH/3cChJHjaA8w7rVHdat7rBudYd1qzusW93Jad0efRma7R1sOa7+pztqBFuEiYiIiHIp7c2lpODNpQoT9hEmIiIi+gjqO9h+TD9gKhhsESYiIiIivcQgTERERER6iV0jiIiIiD5GpB9wazHweCuQHAMYmAPOfYBq3oClU0GXjrLAFmEiIiKi3HpxFNjlDjz4HUiOBiCk/Hzwe8r0F0cLuoSUBbYIExEREeVGpB9wvCugiEs/T0gGFMkp87vfyXHL8JUrV/DDDz+gQoUKAIDY2Fg4ODhg0aJFCA4Oxpdffglvb29xbGAAGDp0KGJjY7Flyxa8ePECs2fPhkKhQExMDGrXrg1vb2+8fv0a7du3R+XKlTX2t3HjRshkshxXwZUrV7Bz504sXbpU63UiIiJw/vx5tGvXDuvWrUO9evXg7u6e433nBQZhIiIioty4tRhQJme9jDIZuL0UaLIqx5tX32JZzdvbG//88w+qVKmCMmXK4Pjx42IQDg8Px4sXL1C0aFEAwJIlS9CnTx80adIEgiBgxIgROH36NNzc3HJ8i+W89ujRI/zzzz9o166dRpAvCAzCRERERFm5tRi4Ni2l/29OCcnAvdUp/1IzMAdqT0vpR6yFpKQkBAcHw9LSEkDKbZetrKzg5+cHJycnHD16FK1bt8b169cBAEWLFsX+/fthZmYGd3d3LFu2DHK5XOtbIi9duhRXrlyBQqFAq1atMHjwYDx69Ei8s52VlRXmzJmjsc7Ro0exceNGSKVS1KxZEz/++CPCwsIwYcIEBAUFwcTEBPPnz8fatWvx8OFD7Nq1Czdv3oSnpyfq16+Pn376Ca9evYJSqUT//v3h6ekJLy8vuLq64smTJ4iJicHy5ctRqlQprZ6DNthHmIiIiCgrtxbnLgRnJTkmZbtZuHz5Mry8vODp6YnOnTujZcuWGrdSbtOmDQ4fPgwAOH36NFq0aCHOmzBhAjw8PLBkyRI0aNAAP/30E6KjowGkv8XyvHnz0u374MGDWLRoEbZv3w4LCwsAwP/+9z9MnToVW7ZsQZMmTfD777+Ly0dERGDlypXYuHEjduzYgaCgIFy8eBFr1qxB8+bNMX/+fEyYMAF37tzB0KFDUa9ePXTv3l1cf9euXbCxscHOnTuxYcMGLFu2DGFhYQAAd3d3bNy4EQ0bNhSfb15hizARERFRVqp5575FODMG5tm2Bqu7RoSHh2PAgAFwcHDQmN+iRQv07t0bnTt3hp2dHYyNjcV5ly9fRr9+/dCvXz/ExsZi/vz5WLNmDfr06aNV14iFCxdi8eLFePfuHRo3bgwA8PPzw/Tp0wEAycnJKFeunLj8y5cvERYWJnZ1iI2NxcuXL+Hv74+uXbtCEATUqFEDNWrUwJUrV9Ltz8/PDw0aNAAAmJubw8nJCQEBAQCASpUqAQCKFy+Od+/eZVnunGIQJiIiIspKNe+MQ+u/w1JGhxCy6CcsMQAqD85VH2E1a2trLFy4EH379sWBAwfE6WZmZnB0dMTChQvRrVs3jXUWLlwIY2Nj1KlTR1wuPDxcq/0lJSXh2LFjWLJkCQDA09MTbdq0gaOjI+bPn4+SJUvCx8cHISEh4joODg4oUaIE1q9fDwMDA+zbtw9ubm7w9/fH3bt3UaVKFVy7dg1nz57FF198AZVKpbFPJycnXL9+HS1btkRMTAweP36cLvjrAoMwERERUW5U8wYebUoZHSIzMgPAY8xH76pChQrw8vLCrFmzMH78eHF6u3btMGXKFCxZsgTPnz8Xpy9btgyzZs3CvHnzYGhoCAcHB0ybNg0RERFi14jU5syZg9KlSwMADA0NYWlpiW+++QbGxsZo2LAhSpYsiWnTpmHChAlQKBSQSCSYPXs2goODAQA2Njbo168fvLy8oFQqUapUKXz99dcYOnQoJk2ahB07dsDMzAxz5syBoaEhHj9+jI0bN4r7/+abb/C///0PPXv2RGJiIkaMGAFbW9uPrrfsSARBEHS+lzR8fHxQs2bN/N4tAMDX1xdubm4Fsu/PHetWd1i3usO61R3Wre6wbnUnx3X74mjKEGnKZM2WYYlBSgj+ai9Q9uu8L2ghU5DHbFa5kxfLEREREeVW2a9TxgmuPBgwtAAgTflZeXDKdIbgTxq7RhARERF9DEunlD7AH9EPmAoGW4SJiIiISC8xCBMRERGRXmLXCCIiIqKPEBYcjUsnHuDOFX8kJSTD0NgA7nUdUb9VJdjYFyno4lEWGISJiIiIcunJ3UDsWfsvlEoVVMqUgbiSEpJx48IT3L7kh25Dm6Ji1by7JTDlLXaNICIiIsqFsOBo7Fn7L5KTlGIIVlMpBSQnKbFn7b8IC47O8bavXLmC+vXri7dB7ty5M0aNGoWkpCS8evUKLi4uWLduncY6Q4cOFccHfvHiBQYPHowBAwbgm2++wcKFC6FSqfDq1SvUqFFD4xbL6rF/c+PKlSsYMyZlnOQRI0akm79jxw6sXLky0/UjIiJw8OBBAMC6detw586dXJUjt9giTERERJQLl048gFKpynIZpVKFyycfwLN33RxvX32LZTVvb2/8888/qFKlCsqUKYPjx4+LtzQODw/HixcvULRoUQDAkiVL0KdPHzRp0gSCIGDEiBE4ffo03NzctLrFcm6sWpXzUTMePXqEf/75B+3atROfS35iECYiIiLKwn8nHuDfv28jKVGR43VVSgHXzj7GtbOPNaYbGsnRtL0HGrSqpNV2kpKSEBwcDEtLSwApt122srKCn58fnJyccPToUbRu3RrXr18HABQtWhT79++HmZkZ3N3dsWzZMsjlcgQGBma7r4cPH2L27NliWB4yZAhGjx6Nly9fYtu2beKd5dIG34YNG+LixYu4fv065syZAwsLC8hkMlSrVg0AsHjxYty7dw8RERFwdXXF3LlzsXbtWjx8+BC7du3CzZs34enpifr16+Onn37Cq1evoFQq0b9/f3h6esLLywuurq548uQJYmJisHz5cpQq9XHdTtg1goiIiCgLl048yFUIzkpSogKXTjzIcpnLly/Dy8sLnp6e6Ny5M1q2bIn69euL89u0aYPDhw8DAE6fPo0WLVqI8yZMmAAPDw8sWbIEDRo0wE8//YTo6JQuGupbLKv/zZs3T2O/rq6uSEpKQmBgIIKDgxEeHo5KlSrh+fPnWLduHXbs2IEKFSrgwoULGZZ7+vTpWLx4MTZu3AgHBwcAQFxcHCwsLLBhwwb8+eefuHXrFoKCgjB06FDUq1cP3bt3F9fftWsXbGxssHPnTmzYsAHLli1DWFgYAMDd3R0bN25Ew4YNxef+MdgiTERERJSF+q0q5bpFODOGRnLUz6Y1WN01Ijw8HAMGDBBDpVqLFi3Qu3dvdO7cGXZ2djA2NhbnXb58Gf369UO/fv0QGxuL+fPnY82aNejTp49WXSO6du2KAwcOwNDQEJ07dwYA2NraYsKECTAzM8OzZ8/Elt603r17B0dHRwBAjRo18PLlSxgaGiIsLAxjx46Fqakp4uLikJycnOH6fn5+aNCgAQDA3NwcTk5OCAgIAABUqpRSZ8WLF8e7d++yfA7aYBAmIiIiykKDVpUy7MJweOsV3LjwJN2FcqlJZRLUbFwxV32E1aytrbFw4UL07dsXBw4cEKebmZnB0dERCxcuRLdu3TTWWbhwIYyNjVGnTh1xufDwcK336enpiX79+kEqleKPP/5AdHQ0VqxYgbNnzwIA+vfvD0HI+HkXK1ZM7LJx9+5dWFpa4saNG3jz5o3Yunvy5EkIggCpVAqVSrOftZOTE65fv46WLVsiJiYGjx8/TncSkFcYhImIiIhyoX6rSrh9yQ+qLEZckMmkqNdSu37AWalQoQK8vLwwa9YsjB8/Xpzerl07TJkyBUuWLMHz58/F6cuWLcOsWbMwb948GBoawsHBAdOmTUNERITYNSK1OXPmoHTp0uLvZmZmcHV1hUKhgLm5OQRBQI0aNdC9e3fI5XJYWFggODg4w4A6Y8YMjB8/Hubm5jAzM4OlpSUqVqyIv//+G71794ZEIkHp0qURHByMMmXK4PHjx9i4caO4/jfffIP//e9/6NmzJxITEzFixAjY2tp+dB1mRCJkFud1yMfHBzVr1szv3QIAfH194ebmViD7/tyxbnWHdas7rFvdYd3qDutWd3JatxmNIwyktATLZFKOI/xeQR6zWeVOtggTERER5VLFqqUwdGo7XD75AHcu+yMpMRmGRgZwr+eIei15Z7lPHYMwERER0UewsS8Cz951P6ofMBUMDp9GRERERHqJQZiIiIiI9BKDMBERERHpJQZhIiIiItJLOrlYTqVSYdq0aXj06BEMDQ0xa9YslC1bVhe7IiIiIiLKFZ20CJ86dQpJSUnYtWsXvL29093DmoiIiIiooOkkCPv4+KBx48YAgGrVquHevXu62A0RERERUa7ppGtETEwMzM3Nxd9lMhkUCgXk8g+78/Hx0cWutVKQ+/7csW51h3WrO6xb3WHd6g7rVndYt7rxKdarToKwubk5YmNjxd9VKpVGCC6o2ysTEREREanppGtEjRo1cO7cOQDArVu34OzsrIvdEBERERHlmkQQBCGvN6oeNeLx48cQBAFz5syBk5NTXu+GiIiIiCjXdBKEiYiIiIg+dbyhBhERERHpJQZh0opSqSzoInzWWL9U2KhUKo2fAMAvGPNG6joFWK95JW29Ut4qrJ9jn2UQViqViImJgUKhKOiifBYEQYBMJkNkZCROnTqF58+fF3SRPisqlUqs3+vXr+Pt27cFXaTPRuoAwfeDvCWVSnHjxg1MmzYN+/btQ3x8PCQSSUEXq9BTqVSQSlM+mm/evImYmBjWax5R1+upU6ewYcMGhIWFFXCJPh9KpRIymQwqlQrnz59HZGRkQRdJa59dEH79+jW+/fZbrF69GhcvXhSn84w69yQSCZ4+fYpevXrBx8cHp0+fFuexXj+eVCrF48eP0adPH5w+fRonT54U57F+P446QOzbtw8LFizA33//XcAl+nz8999/WLBgAerWrYtKlSrBxMSkoIv0WVCHtRkzZmDZsmXYvHkzEhISCrhUn4+VK1diw4YNUCgUCA8P53tsHpHJZHj79i2GDh2KPXv24Nq1a4WmhVgn4wgXBEEQEB4ejgkTJqBDhw5o164djI2NcefOHbi7u/OMOofUrRKCIEChUODAgQPo168funXrBiAlWLRq1UrjximkPXX9KpVKKBQKbNiwAQMHDkTHjh0BpNRv27ZtYWhoWLAF/Qxs374dJ06cwIgRI2Bra5vu5j6kHXWLj9qLFy/QoEEDtGnTBgBw4MABxMXFoVevXgVVxM/G6dOnER4ejk2bNiEuLg5xcXF4+/YtypUrB0EQ+HmWA6lb2OPi4hAQEIAtW7ZAKpUiNjYWu3btQqdOnWBkZFTAJS180h6LGzduRI0aNTB06FAAwJ07dyCXy1GpUqWCKqJWPpsWYYlEAisrK1SsWBFffPEFjI2NkZiYiCNHjiA+Pr6gi1eopH7jSExMhIGBAWxsbPDo0SNxmWvXruHhw4cFVcRCLXX9AoCRkRFKlSqF6OhocZqPjw9evnxZEMUr9NK2Qrx+/RqdO3dGrVq14OjoiJUrV+K///4roNIVTuruOwBw4cIFCIKAIkWKQBAEvHjxAkBKMLa3ty/IYhZa6lZJdR/W8PBw8T3C1NQUO3fuxLVr1wCAITgHBEEQA29ERARMTU3h7++P3bt3A0ipyzNnziAiIqJgC1oIqVQq8ViMi4sDkNIqnPq+EadOndI4ef5UfRbNInv27IFKpUKbNm0QGhqKf//9F61bt8a5c+fw8OFDdpDPgdStPjNmzEBISAgaNGgAJycnREREYOnSpShdujSePn0KBweHAi5t4ZO6fmfPno3w8HC0a9cOdnZ2CAoKwu7duyGTyfDkyRNYW1sXcGkLn9T92e/fv4/69evD2toa4eHhePfuHYoWLYqgoCDY2toWdFELDXWYiImJwaRJk/D8+XPUrl0bzZs3R1RUFHbs2IGQkBDExsbi22+/LejiFirqFjWJRIILFy7g7NmzcHV1Rdu2bXHkyBFs3LgRPXr0wJ07d8SWd9KeRCLBgwcPMHHiRFSoUAHVqlXDvHnzMGzYMEgkEpw7dw7Ozs4oVqxYQRe1UEndmLN69Wo8f/4cbdu2RbNmzTBz5kwUK1YML168wNWrV+Hp6VnApc1eoR9HeNGiRXj58iUCAwPx1VdfoVatWti7dy8EQUBwcDAmTZrEm3nkUEJCAnbu3InQ0FBUqVIF9+/fh7W1NerVq4czZ84gMjISAwYM4JtHLsXHx2Pbtm0IDw+HjY0NQkND4ebmBhMTE9y8eRMREREYNWoU6zeX/Pz8MGXKFMTExMDT0xOOjo64cuUKpFIpAgICYG9vjxkzZhR0MQuVV69eYerUqejevTtatWqFyZMno0qVKnBzc4O5uTnu3LmDzp07A0j/jQdl78KFC9iwYQOaN2+Oq1evomLFiujatSumTJkCY2Nj1K5dG15eXgVdzELnzp072LRpEzp16oQSJUpg7ty56NmzJypUqIArV65AIpGI3f3Y5STnli9fjtDQUDRu3Bjjx4/H33//jXv37sHX1xePHz/Gjz/+iAoVKhR0MbMnFFIKhUKYN2+e0L9/f0EQBCEmJkbo16+fcPjwYSEuLk4ICQkR4uLiBEEQBKVSWZBF/eSpVCqN31euXCl06dJFePbsmSAIgnDq1Clh3rx5wv79+zWWUygU+VXEQi1t/c6bN09o27atOH3Dhg3C0qVLhatXr2osx/rNuUuXLgleXl7Cw4cPhejoaGHs2LHC4cOHhdu3bwu+vr7C0aNHxWXT/l3og7TvmeHh4UL16tWFDRs2CIIgCC9fvhRGjhwp/PHHHxrL8ZjVTur6vXLlijBs2DBh165dgiAIwosXL4QhQ4YIf/75pyAIghAbG5vhepRe6uMvOTlZmDt3rjBs2DDh5cuXgiAIwrVr14SOHTsK//33n8Z6rFftpH7PPHnypNCqVSsxJ/z5559Cy5YtheDgYEEQBCEpKUkQhMJRt4XytD0mJgYymQxNmzbF06dPceXKFZiZmWH06NFYvnw5Xrx4gaJFi8LExET8Wo8yJqQ6C378+DFevXqF7777DmXLlhVHL2jUqBHKly8PS0tLjfUKQ9+fgpa6fv39/RETE4Mff/wR5ubm2LhxIwCgU6dOSE5ORmJiosZ6rN/spe0PXKRIEVy9ehWRkZEwNzdHr169cODAAQQEBMDV1RWtW7cGwNafrKR+z9y8eTN27doFX19f7Nu3D7///jtu3bqF0qVLY8CAAXB3d9dYl8ds9pRKpXghcnBwMMqUKYOKFSsiICAA/v7+KFOmDPr374///vsPcXFx4mgc/CzLWup+7Pv378ezZ8/QuHFjVKhQARcvXkRoaChq1aqF4cOHo3jx4hrrsl6zJrzvOCCRSPDixQtERETAwcEB7dq1w6+//oq4uDh07twZjRs3xvbt2wEABgYGheaYLXRdIw4fPoy//voLlStXhouLC0xMTLB+/XrMmDEDZcuWxbNnz1C+fPmCLmahs2nTJhw/fhwODg6wsLBA69at8csvv6B169bo1q0bkpKSOILBR9iyZQuOHz+OYsWKwcHBAW3btsXIkSMxduxYtGrVCrGxsTAzMyvoYhYqqcPs0qVL4eDggKpVq+Lt27eYOXMm9uzZAxsbG5w5cwZOTk4oU6ZMAZe4cFm4cCFCQ0PRq1cvDBkyBH/88Qfevn2LMWPG4J9//mE/648QGBiIWbNmwdLSEi4uLihSpAjevHkDS0tLfPXVVyhWrBi7mORCTEwMhg8fjkqVKuHdu3dwcnKClZUVAgMDUbx4cXTr1k38HOPJcM4dPnwY27ZtQ6NGjfD69Wu0adMGN2/eRGRkJH766aeCLl6uFapX2aFDh7B//35Mnz4dCoUCW7duRcmSJVG/fn3MnDkTgiCgXLlyADj+ak74+vri5s2b2L59O2rVqoXLly9DEAQMHz4cJ06cQFRUFEPwR7h8+TIuX76MrVu3onr16jhy5AiSkpIwadIkrFy5EgkJCRqtPqQdiUSC+Ph4eHt7w8DAALa2tvjuu+/g4eGB7t27i31WmzVrhjJlyrBucyAuLg4xMTEYM2YMzp8/jwYNGuCff/5B8+bNMXHiRAaIj5CQkIClS5di4MCB6NmzJw4cOAC5XA43Nzc8fvwYgYGBACC2GpP2fHx88MUXX2DChAkICgpCSEgIatasCWdnZ5iZmWl8jvEYzpmAgADs27cPa9euRZEiRRAQEIBXr16hfv36iImJgb+/f7rRTwqLQtUifPz4cchkMrx+/Rq3bt1Chw4dAAD16tVDeHh4uq87SDtBQUGYN28epFIpbG1tMXDgQGzatAljx46FQqGAsbFxQRexUPPz88POnTuRmJgIIyMj9O3bFwcPHsSwYcMQExPDsZg/wvPnz7Fjxw4MHDgQy5Ytg1wuh7OzM/r06YONGzeiX79+BV3EQkcQBLx79w6LFy/GmzdvULduXQwbNgz/+9//0L9/f/EbN7ZY5o66e9TXX3+No0ePwtPTE7Gxsfj6668RExPD0Xg+wvHjx7FkyRIUL14cI0eORFxcHO7evSuOEkG59/jxY2zevBkuLi64fv06Bg0ahEePHqFly5aQyWSF+hvNQvUuFh4ejnnz5uHp06dYsmQJ/vvvP9y8eROGhoYoXrw4z55zSS6Xw97eHoGBgejevTs2btwojmNpbGzMev1IgiAgJCQEQUFBGDBgALZu3Yq3b99CEASYm5uzfj9CXFwc3rx5g1GjRqFp06YYM2YM7t+/j6ioKDEEs35zRiKRwM7ODpUqVYKBgQEcHR0xatQoFClSRKPbGUNw7pibm6Ny5cqYOXMmRo0aBRcXF+zfvx8hISFiCOYxmzvNmjWDh4cHlEolrKyssHr1apQvX14MwazX3HN2dsaTJ0+wY8cOLF++HBcvXsSJEydgZGRUqEMwUMhahAFgypQpMDExwbt372BpaYkpU6YUdJEKhez6Q92/fx+3bt3CvXv3UKJECYwaNUqr9ShFdvV05swZPHnyBPfv30f58uUxevTofCzd523RokV48+YNGjdujAMHDqBNmzbikEiUuYiICFhZWaWbnvpYPnPmDN69ewczMzNxPFC+J3y8sLAwrFq1CsnJyXj27Bn+97//wdXVtaCL9VmIiYnB1q1bERMTg6ZNm6J27doFXaRCJ+3dN9Xj3wcHB+Onn35C+fLlER0djXnz5hVgKfNOoQnC6j+EUqlEUFAQXr16hTp16gDgV3TZSV0/QUFBWY5PGxkZKY4OkfaWqpSx1PUbHR2NIkWKiPPShobU9c/6/Tjqeo+Pj8f169fh5+eH6tWrw8PDo6CL9skLDw/HoUOH0LhxY8hkMpQuXVpjfkbvqeo7STEEZy/16z6rE4fg4GBYWFjA2NiYn2NaSFtHWdVZ6tvY8302e+/evcvyBhjqeoyPj4dKpRJbgT+H+v3kX3XqTtcymQwKhQIymQwlS5YUz/L45pE9df2sWLECBw8eREJCgsb81ENQJSUlAeDwXTmhrt/ly5fj2LFjYh0CKV8zp65f9UVxn8ObR37I6KIL9bm7VCqFSqWCiYkJGjdujH79+sHDwwMKhSK/i1noWFtbIzo6Gu3bt8e6devSzU/dPnLixAnxfZYhOGuph5l6+/Yt4uPjM6wz9TFqb28vtrzxcyxzqV/zb968weXLl8XfU7+/pn6/ePz4Md9nc6Bo0aL466+/0KtXL+zatQuAZn2qj2MTExNxqM/PJSd8cq+8RYsWYc2aNdi2bRsSEhLEK2dVKpX4hnHy5Ens2LEDAN88spL6ID5w4AD27t2LAQMGiK0P6mXUB/LWrVsxd+7cTN+8SVPqsLB9+3acOHECXbp0gaGhocbVs+r63bJlC2bOnCme0FHmUn/wPX/+HFu2bMGDBw8ApLwhq+en/hscOnQIADS+0iNNqeurUaNGaNasGYyMjDROjlMfn4sWLcL+/fvTnTxTxtTvm3/99RcmT56MvXv3aswXBAGCIEAul+PNmzeYNWsWnj17VhBFLTSSk5PFer169SqGDh2KlStXYuDAgQAgflOsPlmLiorCiBEjcOPGDb7PaiH1iUSLFi3g5+eHyMjIdMuos9bJkyexZMkShIaGfjY54ZNJkQqFAiNHjoSxsTGqVauG48eP488//xTnq/8I69evx+nTp9G1a9eCKmqhoB7IOjo6GiqVChUqVEDVqlWxfv16ACn1mZycLNbr0qVLce3aNUyYMEFstaTMqb/ujI+PBwBYWVmhUqVKOHLkCICUD0SFQqFRvz4+Pvjxxx8Z1LSgfoN99OgRpk2bhhs3bmD37t04fPiwOF8d2KKiouDt7a3REk+aUrdU/vfff1i1ahVCQkKwfPlyREdHY8uWLXj16hUSExMhl8sRExODcePGITExEb/88gtMTU0L+Bl82lKHidu3b2Pnzp1YsmQJjIyMcOjQIfj7+2t0Lbl16xYmTZoEJycnODs7F2DJP22XLl3CnTt3AAA3btzA8uXLsWrVKmzbtg2RkZFYunQpgJTPM6lUiidPnsDb2xu1atVCr169CrLon7zY2FgkJSWJ3R3u3r0Ld3d37NmzB9u3b8f169fFzy/1CcXGjRuxc+dO9OvX77MaR/yTCcIqlQply5bFd999hz///BPVq1eHjY0NIiIixA/FuXPnIi4uDnPnzuW4ttmQSCTw8fHBt99+iwULFiA0NBT9+/fHy5cvceDAAQApd35JSkrChAkTEBUVhWXLlmXZf5g+kEgkuH79Ory9vbF582ZER0ejYcOGePLkCS5evAggpWUyMTERP/30k/imzfrNWuoWy3PnzmHp0qX47rvvsHTpUnh4eMDX1xdnz54FkFK/T58+hbe3N7y8vMRxgyk99Xvof//9h99++w3Jyck4duwYDh06hMmTJ+Pu3bsYPHgwHj16BD8/P4wZMwZubm6YPHlyAZf805f66+EDBw7g4sWLsLGxwd9//40LFy7Ax8cHe/fuFUPFwYMHsWzZMgwePBg9e/YsyKJ/8kxNTVGjRg34+fkhLi4OcXFxYjBet24dtm7digMHDkAikeDMmTOYMWMGunfvzmETsyAIApKSkjBu3DicOHECr169wpAhQ7BkyRLs3bsXZmZmmDFjBn744QdMmDABr169AgDMnz8fd+/exbJly1ChQoUCfhZ5q8CD8N27d3HhwgUYGhri33//Rdu2bdG5c2f06tULe/bsQWhoKOLj4zFw4EC4u7tjxIgRn01zvC5dv34dixcvxrRp0yAIAs6fPw+ZTIYvvvgCJ0+exOvXr5GYmIhVq1ahfPnymDp1Kus1G6lD2oMHDzBnzhyMHDkSb968wdWrV2FoaIiiRYvi5MmT4nE7c+ZMlC1bFtOmTWP9ZkOpVGrUUfny5REVFYWAgAAAQJMmTWBlZYWbN28CSBnRYN68eZg5cyaqVatWEEX+5KXuHnXy5Els2bIF/fr1w5gxY9C5c2dcvHgRt27dwuLFi7F27Vq4u7vj1q1b6NSpEwYMGFCAJS88JBIJEhISMGTIEISGhmLYsGHw8vKCtbU1VqxYgRIlSoh/h1OnTmH//v2YNm0a6tevX8Al/3Sp+1B7eHjgzJkz2LNnDyQSCYYPH45z587Bx8cHNjY22L59O2rXro2wsDBcu3YN48ePR4sWLQq49J82pVIJQ0ND9OzZE3v27MGCBQvQo0cPrFy5Evb29tiyZQvq1KmD6dOnw8XFBQ4ODli6dCliY2OxePFijYvBPxcFOmrEgQMHsHnzZigUCnTo0AFfffUVevTogXHjxmHHjh3o1q0bunTpAiDlriZpr2ymD9JeFHD69GlIJBLY2tpi+/btCA4ORunSpdG8eXO4ubmJLZPZjSJB6b158wZPnz7F06dP4eHhgeXLl6NTp05wcHBAiRIloFAoULZsWQDAy5cveWvfHEhKShJPymrVqgULCwts374dw4YNQ61atfD27VuYmprCwsICmzdvRteuXfm1fSbU7wmCICAhIQHv3r3DihUrULlyZXTt2hWGhoY4fPgwHj58CG9vb37LlgNpR4J48eIF+vfvj0GDBqFnz56Ij4/HkiVLEB4eDiClrzWQcnOd0qVLs64zkboLT1BQEE6ePAknJyfxfgFVq1bFmzdvcOjQIcydOxd2dnYAPlzkzXrNWurBBZKSkvDgwQPMnDkT3t7eaNCgAR4+fIi///4bpqamGD58uHiMf+45ocCC8NWrV3HmzBlMmDABSUlJaN26NWbPng1nZ2c8fvwYpqam8PDw4KgQWkhdR1evXoWNjQ3KlSuHQ4cO4datW5g2bRqWLl2KkJAQjBkzBnZ2dhwLNAdS1++FCxdw8eJFfPvtt/j666/h5uaG7du3Y9OmTbh79y7mzp0LAwMDHrdaCgkJgUqlQrFixRAVFYWffvoJVapUQatWrTBv3jx069YNCoUCf/zxB1avXs27R+bQq1evMGfOHFhaWqJWrVqQy+W4du0amjZtiqZNm4qtQ7yoSHupX9vnzp1DcnIyLC0tYWBggFmzZmHChAmoVasWgoODER4eDhcXlwIuceFz6dIlbNiwAU+ePEG7du1Qr149+Pj4wMzMDJUrVwYA1K1bt4BLWbikPm5/++03LF++HEePHoWPjw/++ecfTJkyBUWLFsXNmzdha2srNuDoQ1bI90/qpKQkTJ48GZs3b8bOnTtx4sQJGBoaYuHChejfvz/evn2L+vXrw8PDQ7zgi7ImlUoRFhaGgQMH4tKlS/D29sa+fftQtWpVXLhwAdOnT8ejR48wadIkhuBckEqliImJwcOHD9GwYUM8fPgQ/v7+mD17NqKiorBw4UJcvHgRU6ZMgYGBgbgOZS05ORmHDx/WaKGQyWTo27cvnJycMGLECKxatQqenp4YNWoUQ3AORUZGYv78+Rg4cCA6dOiA3377DTY2NqhduzYOHDiAwMBAmJiYiK3GpB318bp582Zs2rQJiYmJWLRoEUJDQ/H9999jxowZCAwMhL29vRiCMxoGkD64evUqLl26BAAIDAzE6tWrMWnSJPz555948+YN/P39UblyZYSEhMDa2loMwTxus5eUlITY2FhIpVIoFAqMHz8ecXFx6Nq1KwYNGgRPT09UqFABM2fORGJiIqpXr67xLaY+ZIV8/bRWXzgkk8mwatUqLFmyBMuXL8ejR49Qs2ZNbNiwQePsWR/+AHll165daNSoEUaPHg0LCwv8888/sLKywqhRo1CyZEmsXbsW5ubm4pXLpD1BELBo0SL88MMP+Pvvv+Ht7Y2HDx/C09MTixYtwldffYV169bBwsJC4+pxylxAQABevnyJfv36ITQ0FHv37sWbN29QrFgx3Lp1CwBgaWmJUqVKiXeIAvjBl5W0YUv9e0hICDZu3IjvvvsOz549E08sHB0dxWX5npC9OXPmYMGCBQCA+Ph4+Pr6YuHChYiJiUGRIkXw9OlTVKtWDZ07dxZHk1HjiXHmEhMTYWpqirp16+L169ewtLSESqWCkZERbGxs4OXlha1bt0IikWDw4MEao2zwuM1aXFwcNm7ciNjYWAAp17aEhYVh9OjRYj/1vn37YtSoUahevbre1me+dY2IjY2FmZkZtm3bJg7BYWtrK175eerUKRgYGEAikfBr5Ry4fv06/P39YW1tjTNnzuD58+eYMGECIiIiEBISIvaxBngTB22lbjF/9uwZbG1tERMTg5kzZyI4OBjlypVDUFAQtm3bprEe61d7r169wurVq1GhQgUIgoAHDx6gY8eOuHv3LiIiIhAZGYnAwEAMGDAAX375ZUEX95OX+pj9/fffYWdnBzc3N3HM9T/++AMAMHPmTMydO1e83oLfDmkvKioKHTt2xKRJk9CiRQuMGDECpqamMDc3x7hx4/Drr7+iffv2KF++PADWrTZSv2dev34d+/fvR4cOHfDw4UMEBgZi1KhROHjwIA4ePIiIiAhs3rwZtra2rNsciIyMREJCAgICAmBiYoK//voLnTt3hqurKwICAtChQwd4enpi1qxZAPTzuM2XAU13796NY8eOoVmzZmjWrBliY2Mxffp0rFixAoMHD0b16tU1OrkzBGcuo5OEd+/ewd7eHgEBAahVqxaSk5OxaNEiTJgwQVzmc7kDjK6lfmOOjY3F1q1bIZVK4eHhgQEDBkChUCA4OBhr167FnTt3UKVKlXRjLVLm1MevhYUFnj17huvXr2Pbtm1wcXHBmTNn0LJlSxQrVgwvXrxAhQoVeIGsFlIfszt27MC///6L2rVr4/r162jQoAE6d+6MTZs24eXLl5g8ebJGnerbB97HkMlkcHZ2xrhx4/DXX3+hWbNmWLduHZYvX46lS5ciODiYrew5JJPJEB4ejunTp8PDwwMVK1bE+fPnUblyZcTFxWHixImwsLDAtm3bMHbsWLx69Qq2tras22ykfk+wtLTEoUOH4Ovri9atW0OpVOLo0aM4e/Ysbt26hQULFmDr1q3ihd36WLc6bxH+5Zdf8OjRI7Rr1w5nzpxBuXLl0KFDByxbtgxyuRzTp0/X5e4/Ww8ePEDx4sURGxuLmTNn4pdffsHZs2cREBCAGzduYPDgwahSpUpBF7NQSX0mvGjRIlSsWBFBQUHw9PTEjz/+CKlUCnd3d0ycOBFxcXEcrSCH1PWr/nbo0qVL8PX1RUBAAKZOnYpdu3bh7t27GDJkiBjW+O1Q1tR1Ghoaij/++ANyuRyDBw+GRCLB9u3bERoaiiFDhiAmJga2trYwNTVlnebCw4cPMWXKFIwdOxZ37tzB8ePHsWPHDhw5cgTv3r1DQkICRowYAUA/W9RyInX9hIWFYfz48ahYsSImTJiAZ8+e4Z9//kF0dDQGDRoEf39/BAYG4tq1awgLC8OSJUtYt9lIXb979uyBkZERateuje3bt8PGxgZOTk4wMjLC+fPn0ahRI9jZ2WHatGlYtmzZZ3WTjJzQaYtwaGgobGxsMG3aNJw8eRJRUVG4desW4uPjMXr0aB7QuXThwgX89ddfSEpKwsSJE+Ho6IiDBw+iY8eOAIBevXqJt/llHWtPfae4sWPHolixYrC0tMSOHTtQsmRJ/Prrr9i5cyeuXr2KpKQkGBsbF3RxCx31TV6WLFmCIkWKYMKECahQoQLWr1+P2bNnw9XVFbVq1dJosWRgy5pEIkFAQAAWLFgAOzs7REVFYe/evejduzeaN2+OTZs24fbt2/jiiy8A8MRCW2m7OQUHB6NUqVKoV68e6tWrh7dv36J79+7Yv3+/xnqs36yl/kw6duwYYmNj8fz5c5iamiI0NBTly5dHeHg4jh8/Dn9/f5QrVw7//fcf7Ozs8L///a+AS//pS338jRs3Dubm5rCwsMCpU6fQtWtXnD17FpGRkejXrx+MjIxw4cIFXLhwAWPHjtXbEAzo6GK5N2/e4Pvvv8eiRYvw4MEDXLp0CXFxcVixYgWAlD+Wra0t7OzseDVtLjRq1AgzZsxA06ZNMW3aNAQGBuLRo0cAUuqWIVh7ab8Qef36Nezt7TFt2jR88cUXmDx5Mo4fPw5TU1MMHDgQf/zxBwwNDflhp6XUr+/r169jzZo1mDt3Luzt7TF//nwYGBigd+/eiIiIgEqlEk/meFFc5lLXaVBQENasWQMbGxtMmTIFHTp0QGhoKA4fPgwnJycMHz5cDMEATyy0oVKpxBB87NgxvHv3DlZWVihevDhOnz4NIGXorri4ODx//lw8VjnKUfbUn0kbN27Epk2bYGNjA3t7e5w4cQIbNmxAbGwsatasiSFDhqBq1aooUqQIhgwZgqFDhxZwyQsH9QhS69evR+nSpTF16lTExsaKNysaOHAgzMzMYGVlBRcXFzRq1AjLly9HnTp1CrroBSrPW4T9/f2xfPlyfPnllyhSpAjmz5+PkJAQ1K9fH8OHD4ejoyNGjx4tLs83jsy9ffsWxYsX1wi16scmJibo3LkzatWqhRs3bmDu3Lno1auX2JrGEJy91GfPr1+/hpGREZKTk3Hjxg0kJSXB0NAQsbGxMDQ0hFwuF+uUrT7aSV1Pr169QnBwMFq2bImIiAgYGhoiIiICEydOxM8//4x58+ZptMDx+M1Y6pbKly9fokSJEnB1dYW/vz9u3ryJBg0aICQkBPfv30ejRo3EQfB5Ypw99fEqlUqhVCoxa9Ys3Lx5E5UrV0bt2rVhb2+PU6dO4c8//4RSqcTmzZs1bjLA+tVObGwsLl++jOnTp8PZ2Rlly5ZF3759cfjwYTg4OKBHjx5i6ySP25yLiIjA/v37kZSUhBMnTmDUqFFo0KABpkyZgp9//hmDBg0CABgbG/OOnO/laRCOi4vDgQMHEBkZia5duwIAYmJiMHnyZJibm6Nly5YaLT48wDPn6+uLJUuWYOzYsXBzcxPfpNMG4jJlyqBMmTIIDAxEZGQkLy7KAXVI27BhA06cOIHExERMmjQJlSpVwsCBA9G+fXvs378fvXv31jhWGYK1o66nmTNnQi6XY/To0Th69Ch27NiBuXPnYtu2bTh27BiMjY15oaGW1PU0ZcoUREVFoVy5cnB0dIS9vT2uXLkCc3NztGnTBl988QVsbGzE9fhem7WIiAj8+uuvGDt2LGJjYzFlyhTUrVsXU6dOxZYtWxAUFISKFSuiXbt2uHPnDho1agRDQ0OeFOeCkZERypQpgzdv3sDZ2Rnly5dHy5Yt0apVK1StWlVjWR632jt8+DASExPRqlUrjB8/Hm/fvsWff/6JpKQkfP/99/jyyy/5npCJPH0Fm5qa4uuvv0bp0qWxdetWAECXLl0wcOBAdOvWjSE4B+zs7NC4cWNs3rwZ7969g1Qq1fhKNPVXx8+fP8fVq1dZp7mwadMm+Pj4YMeOHRg5ciQ2b96MESNGoE+fPkhKSsK4cePQpk2bgi5moZL62Ny9ezceP36MVq1awdTUFDVr1kRcXBxGjRqFa9euYdWqVShatGgBlrbwWb9+Pezt7bFs2TJcvHgRr169Qs2aNfHu3Tvcu3cPhoaGGh94lLVHjx4hOjoaI0eORFhYGKysrBASEoJnz54BADp27IikpCRcvnwZUqkUzZs3h6GhIZRKJUNwLsjlclSrVg3nz5/HmjVrMGbMGMTFxaF+/friWPeUc8+fP8eiRYuwYMECHDlyBAAwa9YsBAcHY9CgQejXrx8AdjvLiE5Gjbh06RLOnTuHcuXKoXv37nm9+c9a6q8+IyIisH37doSEhGDy5MmQy+VQqVQaQ6H9+uuvePfuHb7//nt++GkhdQtOTEwMVqxYgXv37mH79u0AgMWLF6NIkSIYPHiwuA5P3LSTup5ev34NuVyO0NBQHD16FJaWlmjTpg2srKxw8eJF3Lt3T+wixVa1zKWuU/VoG0uWLIG5uTlevXoFBwcH2Nvbw9XVVbzugrQXEhKCzZs3QyqVon///pg9ezZKliyJgQMH4ptvvsEPP/yA1q1b4/Xr1wgKCkL16tULusifjZs3b+L27dswMzNDt27dAPC9NjdWrlyJ9u3bIzk5GRcvXkSJEiWwatUqhIWFYe3atRqjR7F+M6aTIKxSqfDXX38hOjoavXv31vhKn9JTH5zqn/7+/jh16pR4AF+9ehVSqRQjR47UWG/u3Ll49+4dpk6dCgsLi4IoeqH0/PlznDx5ElWqVIGBgQEuX74MABgxYgS8vb1Rv359sWsP5dzBgwexd+9elCpVCs7OzjAyMkJwcDDs7OzQtm1bjWOVIVg7J06cwPHjx9G4cWPY29tj/Pjx+OabbzBq1Cj0798fHTp0EL9xY53mzI0bN3D9+nUYGRmhRYsWmD17Ntq2bYuKFSuiR48e2LRpE4eizAc8bnPnyJEj8PPzQ2RkJIKDgzF27FioVCqcOnUKvXv3hpmZWUEX8ZOnk6NOKpWiXbt26Nu3L2QyGUNwNqKiogCk9Nm5ePEifv75Z5iamuLUqVM4ffo0GjVqhNevX2PDhg3i8mPHjoVEIsHixYsZgnPg4sWLmDx5MkxNTXHy5EkcO3YM9evXx82bN9GxY0e4uroyBOfQwYMHsWvXLgApI8YcOXIEmzZtgrOzM06ePAkHBwc4OTnB398f0dHRGuvygy97586dw5o1a+Dl5YWyZcuievXq+PHHH/Ho0SMMHDgQbdq0EUMwwDrVlvor+H///RdmZmYIDQ3FjRs3MGbMGOzYsUP8xsjS0rKAS6ofeNzmjqenJ3r27AlnZ2fcvHkTU6ZMgb29PQYPHgwzMzN2NdGCzsYRlstTNs2m+KzduXMHI0aMwF9//QVra2vcu3cPkydPRmJiIg4dOoSvvvoKBgYGaNOmDaRSKeLi/t/e3cdUWb4BHP/i4R2cggeOvPiCCEIqqYiFaDUHNtFJTSLfcmpaZlpTLNIMDSRTQ2HGNMlWThdYiTlHgZgQDlOcKDsoMhEQeRNMhQP4cs7h94c75wf5M6GfJyyuz3/K82z3rj27n+u5znXfdwtr165lzJgxxp4f8XB/rDIUFRU9EF9ra2sWLVpESkoKw4cPB+S45M66d+8eVlZWHD16lMGDBzNs2DBu3LjBrl27KCkp4d133+X8+fPMnj2boKAgHBwcunvIT7w/Pnvl5eVMmTLFuMI7Pj6ekJAQNm/eTGtrq7HHWubazjHEyTAvWFhY4OnpiYODAwUFBTg6OrJ48WIOHTrEunXrAKlWiiebUqkkIiICe3t7LC0tsbe3N/5NnttHM3mEZGJ+OJ1Oh5+fH2FhYcbjkOvr64mLi2PPnj1s27YNgMrKSoKCghg/fjy2trYsX75ckuBOaL+Ypbi4GLi/jdfGjRs7xLesrIxRo0YxadIkUlNTuXHjhiTBnaDX67GwsCAoKIgXXniBlJQUGhsb8ff355tvvjHuI37q1Cm0Wq0kwZ1g2MO2paWFrKwsKisrCQgIIC8vz7hX+M2bN6msrMTOzq7DQkOZa/+coQvQzMyM6upqSktLgfuJglqtJjQ0FE9PT+MezIYk2HCNEE+60NBQgoODAVkU1xUmP2JZ/G+GqoRer2f37t2kpqYyefJkli1bxtSpU1m0aBEAR44cIS4uDjc3N6lK/AV3794lMjISJycnpk+fzsCBA3n55Zd566230Ol0HDlyhA0bNuDu7k5jYyNarVYWHXZBVVUV5ubmNDY2UlhYyKlTp1iwYAFpaWm0tLTQ0tLCZ599JklaF1y+fJnY2FiUSiV6vZ7Jkydz584dkpOTGTp0KDqdjoSEBJkLuqB9tTw9PZ2vvvoKNzc3PD09mTVrFmvWrGHLli00NTVx8+ZN4zZeUmUX4t9PEuFu0tbWhkajYcWKFcaK2urVq5kxYwZBQUH8+uuvXL16leXLl2NlZSVJcCe1r/rcvn2b7du3o1QqmTVrFrt27WL8+PEoFApKSkqorKw0xldeeF137NgxvvzySwIDA6mpqSE4OJjy8nKqq6tZvXo1LS0txp/o5Pl9uJycHNRqNW+//TYajYaPPvqI8PBwBgwYwNatW7GxseHNN9/ExsbGuFUaSEz/it27d5Obm0t4eDgBAQGsX78eHx8ffvnlFxITExk8eLDxWpkThOgZZBb9G7X/5jAzM0OhUODg4EBISAgeHh5s3bqVxMREioqKmDlzJqtWrcLKykr2q+wCMzMzzMzMKCwsJCsrizNnztDY2MiKFStwcnLi448/Rq/X8+qrr3aIr7zwHs2w6KKtrY2Ghgb27t1LQkIC7u7uVFRUUFVVhb+/P3fu3KGkpMSYBMvRs39u6NCh7N+/n/T0dOzt7Xn++edpbm4mMTGRkJAQNBoNMTEx9OnTx5gEy5zQdU1NTeTn57Nu3TqmTZuGSqXCycmJCRMm4OLiwvbt2ztcL3OCED2DyRbLiY7aV2+OHz+OlZUV5ubmKJVK1Go1/fr1o7W1lZEjR9Lc3NzhXulX7Zrc3FxiY2NJS0tj4sSJXLlyhYCAAAIDA8nKysLCwqLD9RLfR2u/gKupqQmdToezszOZmZnk5eXxwQcfcOHCBdzd3YmKiuqwZY8kFP+bIaZubm7Ex8cTFRWFh4cHU6ZMISUlhaVLl1JTU4O1tTVLlizB2traeK88s11nZWXFgAEDqK6uxsPDw/j//v7++Pn5ER8fL1V2IXogSYT/JobJdfPmzVy8eBEXFxf69u3L9evXOX/+PBkZGVRVVbFmzRo5/7uL/rjKvrq6GjMzM27duoWrqys2Njbs27ePHTt2sGDBAvz8/LpxtP9MCoWC27dvEx0djbm5OfPmzaO+vp7k5GQyMzPZv38/GRkZTJkyRfat7ATDori7d+9y9uxZfH19iY6OZtWqVezdu5fa2lpiYmLQaDQkJCQwYMAA+an+/2RpacnTTz9NTk4OJSUlqNVq47O6f/9+rl27JvEVogeSHmETa5+kFRUVkZycTEJCAjdv3iQ7O5urV68SHh5OTU0NTk5OuLu7A9Kf1lmGCk5bWxvJyckMHz4cnU5HXV0dBQUFvPfeezg4OFBRUYG9vT39+vUDJL5d0dbWxr1793jnnXcYMWIEL774Il5eXlRUVBAdHc2IESOoqqpiy5YtD1TbxcPV1dURGRmJq6srv/32G0lJSZw4cYKTJ0+yc+dOKioqGDRoEBYWFlKpfIxOnz5NYWEhdnZ2xpNPa2tr6d+/fzePTAjRHaQibGIKhYLa2lqOHTtGbW0t9fX1APTt2xelUkl+fj79+/c3TsKGF54kaZ3Tq1cvGhoaeP/993FzcwPunxQ1ffp0hgwZwqZNm/j0008ZNGgQ8N8EWOL759onXlqtFktLSxwdHVm2bBlw/+CMrKwskpKSsLS0xNLS8oH7REd/PII6Pj6eSZMmsXDhQrKysoiKiuLgwYOcOXOG9PR0wsLCANnT+nEbO3YsY8eONf67ra1NkmAhejB5Y5lA+yJ7fX09kZGRnD9/nuvXr3P9+nXi4uKor69n3759uLi4dLhXkoiu0el0nD17Fn9/f2JjY5k7dy5BQUFcvnyZoKAgAgMDO1wvCXDnGJ7DtLQ0YmNjKS4upry8nJiYGOB+IpydnY1GozFWgWVR3MO1X5B5584dGhsbsbCwoK6ujrt37xIcHMyoUaMoLy9n586dxiQYpB/YlOSXISGEVIQfs/YT65EjR6ipqSE0NJQ5c+aQn59Pc3MzR48exd7entGjR/PGG29084j/WU6ePIlerycwMJD09HTKysrIzc3F29sbAFtbW3Q6HRYWFvj6+uLr6wvIC6+z2ld0k5KSOHXqFBEREbi6uvLFF18QERHBJ598wunTp4mKiupQSZP4PpwhmU1MTOTWrVssXryYGTNmkJeXx9dff42HhwdFRUUdWkukum568swKISQRfswME2tqaiqHDx/mqaeeQq1WM27cOAICAqirq0Oj0TB37lxjv6q88DpHq9WiVqu5du0a+fn5FBQU8NJLL1FTU8O5c+dwd3fHzc2NzMxMli5d2uFeeeE9Wvt+6+bmZhQKBXFxcbi7u9Pc3Ex2djbx8fH069eP119/HZVK1d1DfqK1tLRw5coVvLy8UCgUxMTEoNVqmT17NufOncPT05MhQ4aQkZHB8ePH2bFjB66urg8cASyEEMJ0ZKY1gWPHjvHjjz+ydu1aVq9eTUhICPv27aO+vp5p06axadOmDou25IXXOebm5oSHh6NUKsnLy2PlypWEhYWxYcMGHBwcOHv2LNXV1Xz44YdMmDChu4f7j2J4DquqqliyZAnr169nx44dHD58GAA7Ozt++OEH9Ho9KpUKlUpl3FdYPKihoYE5c+ag1WpRKBRoNBq0Wi1jx44lJSWFnJwcDhw4gEqlIjg4GG9vb+rq6gD5aBNCiL+TZGAm4OXlhbe3N+np6bS2thIaGoq5uTkZGRkAODo6djgBTXRenz59mD59Oj4+PmRkZNDU1MTEiROZP38+vXv3ZtGiRQwfPlyStC4yMzPj4sWLrF+/nldeeYWQkBB69+7N559/zp49e5g3bx4jR45kxIgRxnvkA+7hlEol9vb2fPfdd2zcuJGamhpGjx6NpaUlzz77LOvWrUOtVuPg4MD48eNRqVRcunSpu4cthBA9jmyfZiLFxcX89NNPODs7M2fOHK5du4azs3N3D+tfo7i4mJ9//pm+ffsyf/787h7Ov8KJEycoKyvD39+fAwcOoNfrOXjwINu2bUOv1/Pcc88B0m/9Z9rHxtBKUlVVxcKFC4mIiKCyspJLly6RlpbG1KlTee211wD4/fffcXR07M6hCyFEjyQ9wibi4+NDQ0MDmZmZlJaW4unpCUg/8OPSPr4XL15k2LBhgMT3/+Ht7c3t27dJSkpi7dq1FBYWkpubi1KpxMfHB5Ak+FEMsdm5cyelpaXMnDmTb7/9lu+//x5HR0eeeeYZfH198fDwYNy4ccD9mEoSLIQQ3UMqwiak1+upq6t7YIs08XhIfB+/2tpa9u7dS2trq/Gkw4EDB3b3sP5Rbty4QWRkJNHR0QwePJgzZ86wcuVKtFota9asITQ01HitfFgIIUT3ktKZCfXq1UuSNBOS+D5+SqUSDw8PFAoFmzdvZuDAgci3ctfY2toyZMgQqqurARgzZgw+Pj6MGTPG2F5iIEmwEEJ0L6kICyEeSlpN/ppDhw5RWFiISqXiwoUL+Pn5GXvZJaZCCPHkkERYCCFM4PTp0xQUFGBjY8PcuXMBOS5ZCCGeNJIICyHE30AqwUII8eSRWVkIIUxMDs4RQognk8zMQghhYrIoTgghnkySCAshhBBCiB5JEmEhhBBCCNEjSSIshBBCCCF6JEmEhRBCCCFEjySJsBBCCCGE6JEkERZCCCGEED3SfwAMLH/nfwwyrAAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1594,7 +1594,7 @@
     {
      "data": {
       "text/plain": [
-       "['name_enc', 'model_year_enc', 'horsepower_enc']"
+       "['model_year_enc', 'name_enc', 'weight_enc']"
       ]
      },
      "execution_count": 29,
@@ -1617,7 +1617,7 @@
    "outputs": [
     {
      "data": {
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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1655,16 +1655,16 @@
      "data": {
       "text/plain": [
        "{'meta': 'linear-regression',\n",
-       " 'predictors': ['name_enc', 'model_year_enc', 'horsepower_enc'],\n",
-       " '_eval_metrics_by_split': {'train': 0.9169232178443707,\n",
-       "  'selection': 6.838944681329686,\n",
-       "  'validation': 6.806005504725775},\n",
+       " 'predictors': ['model_year_enc', 'name_enc', 'weight_enc'],\n",
+       " '_eval_metrics_by_split': {'train': 0.939470933609984,\n",
+       "  'selection': 6.685434152886161,\n",
+       "  'validation': 5.954216285392323},\n",
        " 'params': {'copy_X': True,\n",
        "  'fit_intercept': True,\n",
        "  'n_jobs': None,\n",
        "  'normalize': False},\n",
-       " 'coef_': [0.9450829379577408, 0.05658483169165589, 0.04851143443900967],\n",
-       " 'intercept_': -1.1958331574318315}"
+       " 'coef_': [0.05772933322408178, 0.9432043504937953, 0.04340259842095151],\n",
+       " 'intercept_': -1.0353999755487742}"
       ]
      },
      "execution_count": 31,
@@ -1746,10 +1746,10 @@
     {
      "data": {
       "text/plain": [
-       "R2       0.287247\n",
-       "MAE      5.335241\n",
-       "MSE     46.771164\n",
-       "RMSE     6.838945\n",
+       "R2       0.179502\n",
+       "MAE      5.191685\n",
+       "MSE     44.695030\n",
+       "RMSE     6.685434\n",
        "dtype: float64"
       ]
      },
@@ -1764,25 +1764,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "            ax.plot(self.cumulative_gains[0]*100, self.cumulative_gains[1]*100,\n",
-    "                    color=\"cornflowerblue\", linewidth=3,\n",
-    "                    label=\"cumulative gains\")\n",
-    "            ax.plot(ax.get_xlim(), ax.get_ylim(), linewidth=3,\n",
-    "                    ls=\"--\", color=\"darkorange\", label=\"random selection\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1804,7 +1791,7 @@
      "data": {
       "text/plain": [
        "quantiles    [-2.241402727604945, -1.9599639845400545, -1.7...\n",
-       "residuals    [-1.9964120088907422, -1.8508611130523214, -1....\n",
+       "residuals    [-2.0120779188541276, -1.9153531826632304, -1....\n",
        "dtype: object"
       ]
      },
@@ -1822,17 +1809,9 @@
    "execution_count": 38,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:/Users/samuel.borms/Desktop/code/cobra\\cobra\\evaluation\\evaluator.py:711: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"r--\" (-> color='r'). The keyword argument will take precedence.\n",
-      "  ax.plot(x, x, \"r--\", label=\"perfect model\", color=\"darkorange\", linewidth=3)\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
diff --git a/tutorials/tutorial_Cobra_logistic_regression.ipynb b/tutorials/tutorial_Cobra_logistic_regression.ipynb
index 9702819..9459681 100644
--- a/tutorials/tutorial_Cobra_logistic_regression.ipynb
+++ b/tutorials/tutorial_Cobra_logistic_regression.ipynb
@@ -580,7 +580,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "The target encoder's additive smoothing weight is set to 0. This disables smoothing and may make the encoding prone to overfitting.\n"
+      "The target encoder's additive smoothing weight is set to 0. This disables smoothing and may make the encoding prone to overfitting. Increase the weight if needed.\n"
      ]
     }
    ],
@@ -627,7 +627,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "482f57ca0b0c47fa8c5b14eb784d8bf4",
+       "model_id": "3aa357534b1e4d6da3177138bae7272f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -641,7 +641,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7168a6788f6c4411a5e9e5218cc0feb3",
+       "model_id": "3f06e97c0120468a98f24db7f6a279d8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -655,7 +655,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "eb11994a2cc440a8b442f357c89bc175",
+       "model_id": "e1311cecbf4047f98d1e5641337b7684",
        "version_major": 2,
        "version_minor": 0
       },
@@ -669,7 +669,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9eaa854bb3c0430682051db9f5ed638c",
+       "model_id": "9a0bacdd2ce643419a6009b44bd0504e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -703,7 +703,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "64a8686766f6461ba5f60d915afedc7f",
+       "model_id": "f8760d520ac54ca99f397d5a9a60cad9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -717,7 +717,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "febb5e8f6cf44e548f45d84f0d14cd7b",
+       "model_id": "77fc6fbf60d84ef78522457762c86cb4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -804,28 +804,28 @@
        "      <td>False</td>\n",
        "      <td>1</td>\n",
        "      <td>selection</td>\n",
-       "      <td>18.0 - 22.0</td>\n",
-       "      <td>0.0 - 7.5</td>\n",
+       "      <td>19.0 - 22.0</td>\n",
+       "      <td>0.0 - 7.7</td>\n",
        "      <td>male</td>\n",
        "      <td>Other</td>\n",
-       "      <td>Other</td>\n",
+       "      <td>Third</td>\n",
        "      <td>Missing</td>\n",
        "      <td>Other</td>\n",
-       "      <td>Other</td>\n",
+       "      <td>3</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.194286</td>\n",
-       "      <td>0.318396</td>\n",
-       "      <td>0.291667</td>\n",
-       "      <td>0.279518</td>\n",
-       "      <td>0.318396</td>\n",
-       "      <td>0.291667</td>\n",
-       "      <td>0.523077</td>\n",
-       "      <td>0.326582</td>\n",
-       "      <td>0.497717</td>\n",
-       "      <td>0.351852</td>\n",
-       "      <td>0.132075</td>\n",
+       "      <td>0.213068</td>\n",
+       "      <td>0.34023</td>\n",
+       "      <td>0.235690</td>\n",
+       "      <td>0.292162</td>\n",
+       "      <td>0.34023</td>\n",
+       "      <td>0.235690</td>\n",
+       "      <td>0.539062</td>\n",
+       "      <td>0.350617</td>\n",
+       "      <td>0.513761</td>\n",
+       "      <td>0.297297</td>\n",
+       "      <td>0.192308</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -842,9 +842,9 @@
        "      <td>Cherbourg</td>\n",
        "      <td>False</td>\n",
        "      <td>2</td>\n",
-       "      <td>selection</td>\n",
-       "      <td>36.0 - 42.0</td>\n",
-       "      <td>39.4 - 73.5</td>\n",
+       "      <td>train</td>\n",
+       "      <td>36.0 - 41.0</td>\n",
+       "      <td>39.0 - 73.5</td>\n",
        "      <td>female</td>\n",
        "      <td>C</td>\n",
        "      <td>First</td>\n",
@@ -854,17 +854,17 @@
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.708108</td>\n",
-       "      <td>0.572727</td>\n",
-       "      <td>0.629921</td>\n",
-       "      <td>0.684783</td>\n",
-       "      <td>0.572727</td>\n",
-       "      <td>0.629921</td>\n",
-       "      <td>0.523077</td>\n",
-       "      <td>0.326582</td>\n",
-       "      <td>0.497717</td>\n",
-       "      <td>0.390244</td>\n",
-       "      <td>0.481481</td>\n",
+       "      <td>0.715847</td>\n",
+       "      <td>0.58000</td>\n",
+       "      <td>0.658537</td>\n",
+       "      <td>0.618182</td>\n",
+       "      <td>0.58000</td>\n",
+       "      <td>0.658537</td>\n",
+       "      <td>0.539062</td>\n",
+       "      <td>0.350617</td>\n",
+       "      <td>0.513761</td>\n",
+       "      <td>0.393939</td>\n",
+       "      <td>0.545455</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -882,28 +882,28 @@
        "      <td>True</td>\n",
        "      <td>3</td>\n",
        "      <td>train</td>\n",
-       "      <td>25.0 - 29.0</td>\n",
+       "      <td>25.0 - 28.0</td>\n",
        "      <td>7.9 - 8.1</td>\n",
        "      <td>female</td>\n",
        "      <td>Other</td>\n",
-       "      <td>Other</td>\n",
+       "      <td>Third</td>\n",
        "      <td>Missing</td>\n",
        "      <td>Other</td>\n",
-       "      <td>Other</td>\n",
+       "      <td>3</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>True</td>\n",
-       "      <td>0.708108</td>\n",
-       "      <td>0.318396</td>\n",
-       "      <td>0.291667</td>\n",
-       "      <td>0.279518</td>\n",
-       "      <td>0.318396</td>\n",
-       "      <td>0.291667</td>\n",
-       "      <td>0.328729</td>\n",
-       "      <td>0.326582</td>\n",
-       "      <td>0.284810</td>\n",
-       "      <td>0.325000</td>\n",
-       "      <td>0.218750</td>\n",
+       "      <td>0.715847</td>\n",
+       "      <td>0.34023</td>\n",
+       "      <td>0.235690</td>\n",
+       "      <td>0.292162</td>\n",
+       "      <td>0.34023</td>\n",
+       "      <td>0.235690</td>\n",
+       "      <td>0.348189</td>\n",
+       "      <td>0.350617</td>\n",
+       "      <td>0.296530</td>\n",
+       "      <td>0.411765</td>\n",
+       "      <td>0.230769</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -920,9 +920,9 @@
        "      <td>Southampton</td>\n",
        "      <td>False</td>\n",
        "      <td>4</td>\n",
-       "      <td>train</td>\n",
+       "      <td>validation</td>\n",
        "      <td>32.0 - 36.0</td>\n",
-       "      <td>39.4 - 73.5</td>\n",
+       "      <td>39.0 - 73.5</td>\n",
        "      <td>female</td>\n",
        "      <td>Other</td>\n",
        "      <td>First</td>\n",
@@ -932,17 +932,17 @@
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.708108</td>\n",
-       "      <td>0.318396</td>\n",
-       "      <td>0.629921</td>\n",
-       "      <td>0.684783</td>\n",
-       "      <td>0.318396</td>\n",
-       "      <td>0.629921</td>\n",
-       "      <td>0.523077</td>\n",
-       "      <td>0.326582</td>\n",
-       "      <td>0.497717</td>\n",
-       "      <td>0.454545</td>\n",
-       "      <td>0.481481</td>\n",
+       "      <td>0.715847</td>\n",
+       "      <td>0.34023</td>\n",
+       "      <td>0.658537</td>\n",
+       "      <td>0.618182</td>\n",
+       "      <td>0.34023</td>\n",
+       "      <td>0.658537</td>\n",
+       "      <td>0.539062</td>\n",
+       "      <td>0.350617</td>\n",
+       "      <td>0.513761</td>\n",
+       "      <td>0.475000</td>\n",
+       "      <td>0.545455</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -964,24 +964,24 @@
        "      <td>7.9 - 8.1</td>\n",
        "      <td>male</td>\n",
        "      <td>Other</td>\n",
-       "      <td>Other</td>\n",
+       "      <td>Third</td>\n",
        "      <td>Missing</td>\n",
        "      <td>Other</td>\n",
-       "      <td>Other</td>\n",
+       "      <td>3</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>True</td>\n",
-       "      <td>0.194286</td>\n",
-       "      <td>0.318396</td>\n",
-       "      <td>0.291667</td>\n",
-       "      <td>0.279518</td>\n",
-       "      <td>0.318396</td>\n",
-       "      <td>0.291667</td>\n",
-       "      <td>0.328729</td>\n",
-       "      <td>0.326582</td>\n",
-       "      <td>0.284810</td>\n",
-       "      <td>0.454545</td>\n",
-       "      <td>0.218750</td>\n",
+       "      <td>0.213068</td>\n",
+       "      <td>0.34023</td>\n",
+       "      <td>0.235690</td>\n",
+       "      <td>0.292162</td>\n",
+       "      <td>0.34023</td>\n",
+       "      <td>0.235690</td>\n",
+       "      <td>0.348189</td>\n",
+       "      <td>0.350617</td>\n",
+       "      <td>0.296530</td>\n",
+       "      <td>0.475000</td>\n",
+       "      <td>0.230769</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -995,40 +995,40 @@
        "3         1       1  female  35.0      1      0  53.1000        S  First    C   \n",
        "4         0       3    male  35.0      0      0   8.0500        S  Third  NaN   \n",
        "\n",
-       "   embark_town  alone  id      split      age_bin     fare_bin sex_processed  \\\n",
-       "0  Southampton  False   1  selection  18.0 - 22.0    0.0 - 7.5          male   \n",
-       "1    Cherbourg  False   2  selection  36.0 - 42.0  39.4 - 73.5        female   \n",
-       "2  Southampton   True   3      train  25.0 - 29.0    7.9 - 8.1        female   \n",
-       "3  Southampton  False   4      train  32.0 - 36.0  39.4 - 73.5        female   \n",
-       "4  Southampton   True   5      train  32.0 - 36.0    7.9 - 8.1          male   \n",
+       "   embark_town  alone  id       split      age_bin     fare_bin sex_processed  \\\n",
+       "0  Southampton  False   1   selection  19.0 - 22.0    0.0 - 7.7          male   \n",
+       "1    Cherbourg  False   2       train  36.0 - 41.0  39.0 - 73.5        female   \n",
+       "2  Southampton   True   3       train  25.0 - 28.0    7.9 - 8.1        female   \n",
+       "3  Southampton  False   4  validation  32.0 - 36.0  39.0 - 73.5        female   \n",
+       "4  Southampton   True   5       train  32.0 - 36.0    7.9 - 8.1          male   \n",
        "\n",
        "  embarked_processed class_processed deck_processed embark_town_processed  \\\n",
-       "0              Other           Other        Missing                 Other   \n",
+       "0              Other           Third        Missing                 Other   \n",
        "1                  C           First          Other             Cherbourg   \n",
-       "2              Other           Other        Missing                 Other   \n",
+       "2              Other           Third        Missing                 Other   \n",
        "3              Other           First          Other                 Other   \n",
-       "4              Other           Other        Missing                 Other   \n",
+       "4              Other           Third        Missing                 Other   \n",
        "\n",
        "  pclass_processed  sibsp_processed  parch_processed alone_processed  \\\n",
-       "0            Other                1                0           False   \n",
+       "0                3                1                0           False   \n",
        "1                1                1                0           False   \n",
-       "2            Other                0                0            True   \n",
+       "2                3                0                0            True   \n",
        "3                1                1                0           False   \n",
-       "4            Other                0                0            True   \n",
+       "4                3                0                0            True   \n",
        "\n",
        "    sex_enc  embarked_enc  class_enc  deck_enc  embark_town_enc  pclass_enc  \\\n",
-       "0  0.194286      0.318396   0.291667  0.279518         0.318396    0.291667   \n",
-       "1  0.708108      0.572727   0.629921  0.684783         0.572727    0.629921   \n",
-       "2  0.708108      0.318396   0.291667  0.279518         0.318396    0.291667   \n",
-       "3  0.708108      0.318396   0.629921  0.684783         0.318396    0.629921   \n",
-       "4  0.194286      0.318396   0.291667  0.279518         0.318396    0.291667   \n",
+       "0  0.213068       0.34023   0.235690  0.292162          0.34023    0.235690   \n",
+       "1  0.715847       0.58000   0.658537  0.618182          0.58000    0.658537   \n",
+       "2  0.715847       0.34023   0.235690  0.292162          0.34023    0.235690   \n",
+       "3  0.715847       0.34023   0.658537  0.618182          0.34023    0.658537   \n",
+       "4  0.213068       0.34023   0.235690  0.292162          0.34023    0.235690   \n",
        "\n",
        "   sibsp_enc  parch_enc  alone_enc   age_enc  fare_enc  \n",
-       "0   0.523077   0.326582   0.497717  0.351852  0.132075  \n",
-       "1   0.523077   0.326582   0.497717  0.390244  0.481481  \n",
-       "2   0.328729   0.326582   0.284810  0.325000  0.218750  \n",
-       "3   0.523077   0.326582   0.497717  0.454545  0.481481  \n",
-       "4   0.328729   0.326582   0.284810  0.454545  0.218750  "
+       "0   0.539062   0.350617   0.513761  0.297297  0.192308  \n",
+       "1   0.539062   0.350617   0.513761  0.393939  0.545455  \n",
+       "2   0.348189   0.350617   0.296530  0.411765  0.230769  \n",
+       "3   0.539062   0.350617   0.513761  0.475000  0.545455  \n",
+       "4   0.348189   0.350617   0.296530  0.475000  0.230769  "
       ]
      },
      "execution_count": 19,
@@ -1094,393 +1094,81 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>age</td>\n",
-       "      <td>0.0 - 14.0</td>\n",
-       "      <td>0.084270</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.466667</td>\n",
+       "      <td>0.0 - 12.0</td>\n",
+       "      <td>0.067416</td>\n",
+       "      <td>0.348315</td>\n",
+       "      <td>0.583333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>age</td>\n",
-       "      <td>14.0 - 18.0</td>\n",
-       "      <td>0.073034</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.461538</td>\n",
+       "      <td>12.0 - 19.0</td>\n",
+       "      <td>0.123596</td>\n",
+       "      <td>0.348315</td>\n",
+       "      <td>0.409091</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>age</td>\n",
-       "      <td>18.0 - 22.0</td>\n",
-       "      <td>0.101124</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.222222</td>\n",
+       "      <td>19.0 - 22.0</td>\n",
+       "      <td>0.084270</td>\n",
+       "      <td>0.348315</td>\n",
+       "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>age</td>\n",
        "      <td>22.0 - 25.0</td>\n",
-       "      <td>0.095506</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.411765</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>age</td>\n",
-       "      <td>25.0 - 29.0</td>\n",
-       "      <td>0.101124</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.277778</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>age</td>\n",
-       "      <td>29.0 - 32.0</td>\n",
-       "      <td>0.061798</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.363636</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>age</td>\n",
-       "      <td>32.0 - 36.0</td>\n",
-       "      <td>0.050562</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.666667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>age</td>\n",
-       "      <td>36.0 - 42.0</td>\n",
-       "      <td>0.061798</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.363636</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>age</td>\n",
-       "      <td>42.0 - 50.0</td>\n",
-       "      <td>0.056180</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>age</td>\n",
-       "      <td>50.0 - 74.0</td>\n",
-       "      <td>0.078652</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.642857</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>age</td>\n",
-       "      <td>Missing</td>\n",
-       "      <td>0.235955</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.309524</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>alone</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.404494</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.555556</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>alone</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0.595506</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.283019</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>class</td>\n",
-       "      <td>First</td>\n",
-       "      <td>0.247191</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.659091</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>class</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>0.752809</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.305970</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>deck</td>\n",
-       "      <td>B</td>\n",
-       "      <td>0.067416</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.750000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>deck</td>\n",
-       "      <td>Missing</td>\n",
-       "      <td>0.752809</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.298507</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>deck</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>0.179775</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.656250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>embark_town</td>\n",
-       "      <td>Cherbourg</td>\n",
-       "      <td>0.174157</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.612903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>embark_town</td>\n",
-       "      <td>Missing</td>\n",
-       "      <td>0.005618</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>embark_town</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>0.820225</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.342466</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>embarked</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0.174157</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.612903</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>embarked</td>\n",
-       "      <td>Missing</td>\n",
-       "      <td>0.005618</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>embarked</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>0.820225</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.342466</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>fare</td>\n",
-       "      <td>0.0 - 7.5</td>\n",
-       "      <td>0.117978</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>fare</td>\n",
-       "      <td>7.5 - 7.9</td>\n",
-       "      <td>0.191011</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.205882</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>fare</td>\n",
-       "      <td>7.9 - 8.1</td>\n",
-       "      <td>0.095506</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.235294</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>fare</td>\n",
-       "      <td>8.1 - 11.5</td>\n",
-       "      <td>0.078652</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.357143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>fare</td>\n",
-       "      <td>11.5 - 14.5</td>\n",
-       "      <td>0.061798</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.636364</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>fare</td>\n",
-       "      <td>14.5 - 22.4</td>\n",
-       "      <td>0.095506</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.588235</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>fare</td>\n",
-       "      <td>22.4 - 26.6</td>\n",
-       "      <td>0.067416</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>fare</td>\n",
-       "      <td>26.6 - 39.4</td>\n",
        "      <td>0.067416</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.416667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>fare</td>\n",
-       "      <td>39.4 - 73.5</td>\n",
-       "      <td>0.084270</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.466667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>fare</td>\n",
-       "      <td>73.5 - 512.3</td>\n",
-       "      <td>0.140449</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.760000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>parch</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.792135</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.354610</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>parch</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.101124</td>\n",
-       "      <td>0.393258</td>\n",
+       "      <td>0.348315</td>\n",
        "      <td>0.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>parch</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.089888</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.562500</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>parch</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.011236</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>parch</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.005618</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>pclass</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.247191</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.659091</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>pclass</td>\n",
-       "      <td>Other</td>\n",
-       "      <td>0.752809</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.305970</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>sex</td>\n",
-       "      <td>female</td>\n",
-       "      <td>0.382022</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.779412</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>sex</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.617978</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.154545</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>sibsp</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.662921</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.338983</td>\n",
+       "      <td>age</td>\n",
+       "      <td>25.0 - 28.0</td>\n",
+       "      <td>0.073034</td>\n",
+       "      <td>0.348315</td>\n",
+       "      <td>0.230769</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>sibsp</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.241573</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.604651</td>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>sibsp</td>\n",
        "      <td>2</td>\n",
-       "      <td>0.039326</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.571429</td>\n",
+       "      <td>0.022472</td>\n",
+       "      <td>0.348315</td>\n",
+       "      <td>0.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>sibsp</td>\n",
        "      <td>3</td>\n",
        "      <td>0.028090</td>\n",
-       "      <td>0.393258</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.348315</td>\n",
+       "      <td>0.400000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>sibsp</td>\n",
        "      <td>4</td>\n",
-       "      <td>0.016854</td>\n",
-       "      <td>0.393258</td>\n",
+       "      <td>0.011236</td>\n",
+       "      <td>0.348315</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>sibsp</td>\n",
        "      <td>5</td>\n",
-       "      <td>0.005618</td>\n",
-       "      <td>0.393258</td>\n",
+       "      <td>0.011236</td>\n",
+       "      <td>0.348315</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1488,65 +1176,29 @@
        "      <td>sibsp</td>\n",
        "      <td>8</td>\n",
        "      <td>0.005618</td>\n",
-       "      <td>0.393258</td>\n",
+       "      <td>0.348315</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>51 rows × 5 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "       variable         label  pop_size  global_avg_target  avg_target\n",
-       "0           age    0.0 - 14.0  0.084270           0.393258    0.466667\n",
-       "1           age   14.0 - 18.0  0.073034           0.393258    0.461538\n",
-       "2           age   18.0 - 22.0  0.101124           0.393258    0.222222\n",
-       "3           age   22.0 - 25.0  0.095506           0.393258    0.411765\n",
-       "4           age   25.0 - 29.0  0.101124           0.393258    0.277778\n",
-       "5           age   29.0 - 32.0  0.061798           0.393258    0.363636\n",
-       "6           age   32.0 - 36.0  0.050562           0.393258    0.666667\n",
-       "7           age   36.0 - 42.0  0.061798           0.393258    0.363636\n",
-       "8           age   42.0 - 50.0  0.056180           0.393258    0.500000\n",
-       "9           age   50.0 - 74.0  0.078652           0.393258    0.642857\n",
-       "10          age       Missing  0.235955           0.393258    0.309524\n",
-       "0         alone         False  0.404494           0.393258    0.555556\n",
-       "1         alone          True  0.595506           0.393258    0.283019\n",
-       "0         class         First  0.247191           0.393258    0.659091\n",
-       "1         class         Other  0.752809           0.393258    0.305970\n",
-       "0          deck             B  0.067416           0.393258    0.750000\n",
-       "1          deck       Missing  0.752809           0.393258    0.298507\n",
-       "2          deck         Other  0.179775           0.393258    0.656250\n",
-       "0   embark_town     Cherbourg  0.174157           0.393258    0.612903\n",
-       "1   embark_town       Missing  0.005618           0.393258    1.000000\n",
-       "2   embark_town         Other  0.820225           0.393258    0.342466\n",
-       "0      embarked             C  0.174157           0.393258    0.612903\n",
-       "1      embarked       Missing  0.005618           0.393258    1.000000\n",
-       "2      embarked         Other  0.820225           0.393258    0.342466\n",
-       "0          fare     0.0 - 7.5  0.117978           0.393258    0.000000\n",
-       "1          fare     7.5 - 7.9  0.191011           0.393258    0.205882\n",
-       "2          fare     7.9 - 8.1  0.095506           0.393258    0.235294\n",
-       "3          fare    8.1 - 11.5  0.078652           0.393258    0.357143\n",
-       "4          fare   11.5 - 14.5  0.061798           0.393258    0.636364\n",
-       "5          fare   14.5 - 22.4  0.095506           0.393258    0.588235\n",
-       "6          fare   22.4 - 26.6  0.067416           0.393258    0.500000\n",
-       "7          fare   26.6 - 39.4  0.067416           0.393258    0.416667\n",
-       "8          fare   39.4 - 73.5  0.084270           0.393258    0.466667\n",
-       "9          fare  73.5 - 512.3  0.140449           0.393258    0.760000\n",
-       "0         parch             0  0.792135           0.393258    0.354610\n",
-       "1         parch             1  0.101124           0.393258    0.500000\n",
-       "2         parch             2  0.089888           0.393258    0.562500\n",
-       "3         parch             3  0.011236           0.393258    1.000000\n",
-       "4         parch             4  0.005618           0.393258    0.000000\n",
-       "0        pclass             1  0.247191           0.393258    0.659091\n",
-       "1        pclass         Other  0.752809           0.393258    0.305970\n",
-       "0           sex        female  0.382022           0.393258    0.779412\n",
-       "1           sex          male  0.617978           0.393258    0.154545\n",
-       "0         sibsp             0  0.662921           0.393258    0.338983\n",
-       "1         sibsp             1  0.241573           0.393258    0.604651\n",
-       "2         sibsp             2  0.039326           0.393258    0.571429\n",
-       "3         sibsp             3  0.028090           0.393258    0.000000\n",
-       "4         sibsp             4  0.016854           0.393258    0.000000\n",
-       "5         sibsp             5  0.005618           0.393258    0.000000\n",
-       "6         sibsp             8  0.005618           0.393258    0.000000"
+       "   variable        label  pop_size  global_avg_target  avg_target\n",
+       "0       age   0.0 - 12.0  0.067416           0.348315    0.583333\n",
+       "1       age  12.0 - 19.0  0.123596           0.348315    0.409091\n",
+       "2       age  19.0 - 22.0  0.084270           0.348315    0.000000\n",
+       "3       age  22.0 - 25.0  0.067416           0.348315    0.500000\n",
+       "4       age  25.0 - 28.0  0.073034           0.348315    0.230769\n",
+       "..      ...          ...       ...                ...         ...\n",
+       "2     sibsp            2  0.022472           0.348315    0.250000\n",
+       "3     sibsp            3  0.028090           0.348315    0.400000\n",
+       "4     sibsp            4  0.011236           0.348315    0.000000\n",
+       "5     sibsp            5  0.011236           0.348315    0.000000\n",
+       "6     sibsp            8  0.005618           0.348315    0.000000\n",
+       "\n",
+       "[51 rows x 5 columns]"
       ]
      },
      "execution_count": 20,
@@ -1580,7 +1232,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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9xqZNm+jSpQtZWVns2LEDLy8v4uLiSEtLIysri65duwLw9NNPc8011/DKK6/w448/Eh8fz+HDhzl69Cjt27cnNTXVJo1v3rx57N27l7/++osJEyaQlJSEp6cnW7ZsoaSkhP79+3PXXXc16fsSQgh3ID0uQohWZfTo0SxdupTJkydz6tQpfvvtN3Q6Hddeey2ff/55rfSgmiZOnMinn37K6NGjOXToEOvXr2fo0KF8+umn+Pv719o/Pj6eb775hjlz5uDj48PatWs5evQogwYN4tVXX7U7/0tjLVy4kGeeeYbExEQ2bdrEpk2biI+P59lnn7WZgd3Dw4OXX36ZhQsX0qNHD/bu3cuGDRsIDw/nlltu4csvv2zUbPTdunXj/fffJyIigjVr1lBSUsLs2bNZunRpnRXHrE2cOJFly5YxdepUsrKy+O233ygqKmLmzJksX768VtnqWbNmcfHFF1NdXc1ff/3F4cOHG/5LaqDu3bvz6aefMnLkSHJycli9ejXp6elMmzaN5cuXM3v2bMC2RyYxMZFly5YxZcoUsrOzWb16Nb6+vixevJgLLrgAsK185+fnx0cffcT8+fPp0KED27ZtY+vWrXTs2JH777+fDz74wO7/JSGEaO00BkmiFUII0QgbN27k2muvZfjw4Xz44YeObo5DFRYWkp6eTmxsrN2g95ZbbmH16tXmnhghhBBnT3pchBBCiLOUlZXFxRdfzGWXXVZr7M2ff/7JH3/8QXx8vAQtQgjRBGSMixBCCHGW4uPjGTt2LGvWrOH8889nwIAB+Pn5kZaWxt69ewkMDOTpp592dDOFEMItSOAihBBCnINXX33VXAlt9+7dlJSU0K5dOy6//HJuvPHGeicfFUII0XAyxkUIIYQQQgjh9GSMixBCCCGEEMLpSeAihBBCCCGEcHoSuAghhBBCCCGcngQuQgghhBBCCKcngYsQQgghhBDC6UngIoQQQgghhHB6ErgIIYQQQgghnJ4ELkIIIYQQQginJ4GLEEIIIYQQwulJ4CKEEEIIIYRwehK4CCGEEEIIIZyeBC5CCCGEEEIIpyeBixBCCCGEEMLpSeAihBBCCCGEcHoSuAghhBBCCCGcngQuQgghhBBCCKcngYsQQgghhBDC6UngIoQQQgghhHB6ErgIIYQQQgghnJ4ELkIIIYQQQginJ4GLEEIIIYQQwulJ4CKEEEIIIYRwehK4CCGEEEIIIZyeBC5CCCGEEEIIpyeBixBCCCGEEMLpSeAihBBCCCGEcHoSuAghhBBCCCGcngQuQgghhBBCCKcngYsQQgghhBDC6UngIoQQQgghhHB6ErgIIYQQQgghnJ4ELkIIIYQQQginJ4GLEEIIIYQQwulJ4CKEEEIIIYRwehK4CCGEEEIIIZyeBC5CCCGEEEIIpyeBSzM5WgG3pkDwdvDYqv69NUVtbw5paWkkJiZy4sSJczrOxo0bSUxMpLq62u7jixYtYvbs2ef0Gs4gJyeHH3/80dHNsGv58uWMHj26zscfeOAB/v3vf7dgi4Rwf7Nnz2bRokXnfJxx48axdOlSu4+dOHGCxMRE0tLSzvl1HMlgMPD555+j1+sd3RTRCp3peudM36FnS757nYOnoxvgjlYWwIxjUKWHKuO2Ij28mwUf5cCyLjAppGlfMzo6mrVr19K2bdtzOk7//v1Zu3Ytnp7u/V/jhRdeoKqqismTJzu6KY320EMPOboJQridxYsX4+Xldc7HWbZsGf7+/k3QIue1efNmHnvsMWbOnImHh9z/FM5l8uTJnH/++U1+XPnudQ7ufXXqAEcrVNBSaudGVBUqmJlxDHb1hHifpntdrVZLRETEOR/H29u7SY7j7AwGg6ObcNaCgoIc3QQh3E5oaGiTHOdcbx65Alc+f4q6GQwGNm3ewsFDhyksLGrR1w4ODiIxoRtDBg9Co9Gc07F8fX3x9fVtopZZyHevc5BbJU3sxUwVnNSnSg+LMpv2dWt2nSYmJvLNN98wbdo0+vTpwxVXXEFKSop5/3379nHNNdfQt29fLrjgApYtWwbUThU7cuQIV155JX379mXu3Lnk5+fbvO6WLVuYMWMGSUlJTJkyhW+++cb82AMPPMBTTz3FPffcQ79+/Rg9ejTLly83P15WVsYTTzzBsGHDGDx4MPfddx/FxcUAVFZWsmDBAoYNG8bQoUO58847yc7OtvveN27cyOjRo3niiScYOHAgixcvpqqqimeffZbRo0fTq1cvxo4dy//+9z9A3Vn9+uuvWbFiBePGjQOgqKiI+++/n4EDB3LeeefxyCOPmNtS0/Lly5k1axaLFi1iwIABjBkzhiVLltjs88UXX3DBBRfQv39/rrzySnbt2mV+bNy4cTz33HOMHDmSyZMn15uWN3DgQEaOHMmHH35o83s1dVcvXryYu+++2/zehw0bxltvvWX3eMI1aOMvO6ufwRffW+cxB198b53Pa6zt27dz1VVX0bdvX/r168f//d//kZmZiV6vZ9SoUXz55Zc2+0+ePJlPP/0UOPP54v777+cf//gHQ4cO5eDBgxw9epQbbriB/v3706dPH6688koOHz5sfs6ePXuYNWsWSUlJXHHFFbzyyis2qaz1vV5N1qli53Lusk4Vq6qq4oknnmDQoEGMGTOGv/76y+Y16zvvmM5rX3zxBaNHj6Zfv37MmzeP8vJy8/N/+OEHpkyZQt++fZkxYwbbt283P7Zq1SrzY9OnT+fPP/+s873bOyetWbOG6dOn06dPHwYOHMhdd91FcXExaWlpXHvttQD06tWLjRs3AvWf84Rr2LR5C5u3bGvxoAWgsLCIzVu2sWnzlgY/55dffmHMmDEMGDCAhx9+mIoKlYtvnSrWkM+RtfT0dG644QYGDBjAkCFDmD9/PiUlJYDtd++4ceNITEys9WMin4fmI4FLA72YCUHbQbO1/p//ZlnSw+pSBbyedeZjBW1Xr3u2XnvtNR588EG++uorCgoKeOmllwDIzc1lzpw5dOnSha+//pq7776bxx57jC1bbE8YlZWV3HTTTcTFxbF8+XLGjx9vk7udlZXFTTfdxLRp01ixYgW33XYbTz31FKtXrzbvs2TJEnr06MGKFSu48MILeeyxx8zBz3/+8x/Wr1/Pa6+9xieffMLhw4d55plnAHjppZfYsWMHb731Fp988gkGg4Gbb765zjt9mZmZFBcX8/XXXzN9+nTeeecdVq9ezauvvspPP/3E9OnTWbBgAZmZmVx//fVMmjSJCy+80BywPfjgg+Tl5fHZZ5/x1ltvcfz4cebPn1/n73bfvn3s2bOHJUuW8K9//YunnnqKP/74A4DVq1fzyiuvMH/+fL7++mtGjx7Nddddx+nTp83P/+6773j33Xd58cUX7ablZWZmcuDAAZYsWcK8efN44YUXWLdund22/Prrr2i1WpYvX84NN9zASy+9xJEjR+psuxBnq7i4mJtvvpkRI0bw/fff895775GWlsZ///tfPDw8mDRpEr/88ot5/0OHDpGcnMxFF13UoPPFd999x2233cY777xD165dufXWW4mJieHbb79lyZIl6PV6nnvuOUBd9N9www306NGDr7/+mqlTp/L222+bj9WQ16vP2Z67rC1evJjff/+d//73v7z88st88sknNo+f6bxjGov3zjvvsHjxYlatWmUOoNavX899993HVVddxXfffcfQoUO5+eabKS4u5sCBA9x7773ceOONrFixglmzZnH77bezf//+Ot+v9TkpPT2dO+64gyuuuIKVK1fyyiuvsGHDBj7//HOio6NZvHgxAH/++Sf9+/dv0DlPOL+Dhw6feScnasPSpUt56aWXePPNN1m7di1vvPGG3f3q+xzV9MQTT+Dp6clXX33F+++/z/bt23nzzTdr7bds2TLWrl3L2rVr+fXXX4mNjeX6668HGnYNIM6epIo10IuZUNzC4xCL9ep150We3fOvu+46hg8fDsCVV17JRx99BMDKlSsJCAjg0UcfRavV0qVLF/Lz82sNtFy3bh15eXk89thjBAQEEB8fz8aNG8nLywPgs88+Y+jQoVx33XUAdOzYkWPHjvHRRx+ZezISEhK48cYbAbjzzjv5+OOPOXz4MN27dzefSAYNGgTA448/zqZNmygrK+PTTz/lyy+/pGfPngA899xzDB06lK1bt5r3r+mGG26gQ4cO5tddsGAB/fr1A+Cf//wnr7/+OsePH2fYsGH4+vpSXV1N27ZtSUlJ4ddff2XDhg3mdJFnn32WcePGkZ6eTnR0tN3Xe/bZZwkPDychIYFNmzbxxRdfMGbMGN59911uuukmxo8fD8Att9zCunXrWLp0KbfddhsA06ZNo3v37nX+7by8vHj66adp27Yt3bp1Y9OmTSxZsoQRI0bU2jcoKIgHHngArVbLDTfcwDvvvMOePXvo2rVrnccX4myUlZVx8803c/3116PRaGjfvj0TJ0403+mfMmUKV199NUVFRQQFBfHTTz8xZMgQwsPDefnll894vujRowcTJkwAoLS0lJkzZ3LllVcSEBAAwPTp0809ij/++CO+vr488sgjeHp6Eh8fz7Zt28jKygIadn6qz9mcu6wZDAaWLl3Kvffey+DBgwF1x/af//wnwBnPOwDV1dU8+OCD5ru5o0aNYvfu3QB8/vnnTJo0iauvvhqAefPmYTAYKCws5L333uOyyy7jH//4BwAdOnRg165dfPLJJyxcuNDu+7U+JyUnJ/PQQw9x+eWXAxAXF8eIESM4cuQIWq2WkBA1SDMsLAxPT88GnfOEaGoPPPAAAwcOBNRn9JlnnuHuu++utV99n6OaTp48SWJiIrGxsXh7e/Paa6/ZTV2zTgm95557aNeuHfPmzQOQz0Mzk8ClgeZFwmOnWjZ4CfQ4+6AFMF/EAwQGBtqkf3Xv3h2tVmt+/JprrgEwd/ub9mvfvr35ogGgd+/e5nSHY8eO8ddff9G/f3/z46ZgwKR9+/Y2bTDtc/z4caqrq+nVq5f58aSkJJKSkjh06BBVVVXmL2STiooKjh8/XmfgEhsba14eP348f//9N8888wzHjh1j3759AHar4Bw9ehSDwcDYsWNrPZacnGw3cGnfvj3h4eE2vxdTOszRo0d56aWXeOWVV8yPV1ZWEhUVZbet9sTFxdn8Hnv27FkrHc36WNZ/y4CAAKqqztTvJ0TjRUREMH36dD788EP279/PkSNHOHjwIElJSQD07duXqKgoVq9ezSWXXMJPP/3E3LlzgYadL+Li4szL/v7+XHnllXz77bfs2bPH/Dk2XeQfPHiQHj162PRY9uvXj19//bXBr1efszl3WcvLyyM3N9fmBkXv3r3Ny2c675gGvdd1Hj969CgzZ840P+bh4cF9991nfuzQoUN89dVX5serqqpqtdGa9TmpU6dOeHt789///pfDhw9z+PBhjhw5wpQpU+w+tyHnPOH8EhO6sXnLNoe3oaH69OljXu7Zsyf5+fnk5uba3beuz1FNN910Ew888AC//fYbI0eOZOLEifUW8fn4449Zt24d33zzjflcJJ+H5iWBSwPNi2xYEHFriqoeVt9loxdwUwS81qGenZpAzQo5pjSrxlTOqZmaZX2RUF1dzZQpU7j11ltt9rGuMmPvtQwGA97e3nW+pk6nA+CTTz6pNRiuvosOHx9LtYNFixbxxRdfcNlll3HJJZfw6KOP1nmXVafT4e/vbzf/va5CBTXTu3Q6nfl963Q67r//fkaOHGmzj3WlIeu22lOzUo9er6/z79YUlZCE89Ad/erMOzXS5u+eb5LjZGZmctlll9GjRw9GjhzJrFmz+P3339m6dat5n8mTJ/PLL7/Qs2dPUlJSmDhxItCw84X1eaGkpIQZM2YQEhLC+PHjmTp1KseOHTOng2m12lrnJ+v1hrxefc7m3GWPdZuszxtnOu+Y7gifzXlcp9Pxf//3f1x66aU22+tru/U56cCBA1x55ZWMHTuWgQMHMmfOHHOPfV2vd6ZznnB+Qwarm4KOHpzfUNaf5TN9Lur6HNU0depURowYwapVq/jzzz+ZP38+a9eutZsKum3bNp5//nneeOMNm6BEPg/NSwKXJjYvUpU8rm+AvpcH3H0OPSnnqmPHjqxatQq9Xm/+4M+fP5+oqCiGDRtm3q9bt26kpKRQUFBgTg0w9VwAdO7cma1bt9KxY0fzts8++4zTp0/b7a61FhcXh1arZd++feZ0tnXr1vH444+zbNkytFoteXl55juURUVF3Hvvvdx11131pliZLFmyhEceeYSpU6cCmMd8mE5W1l2/nTt3prS0FJ1OR5cuXQA138LTTz/NE088Yfdkk5qaSnFxsflO7J49e8wD8zp37kxGRobN7+XRRx9lyJAhdd6xrCktLY2SkhJzb9euXbuIj49v0HOFaC6//vorAQEBvPPOO+ZtpjFoJlOmTOGKK64gISGB8847z3zuaOz5YtOmTWRkZPDdd9+ZLzrWrl1rfq1u3bqxatUqdDqducdx79695uefy/mpPvWdu1auXGner02bNoSHh7N7925z74z1GJMznXfOpGPHjjbnY4PBwJQpU3jggQfo3LkzqampNu/91VdfJTQ01Dywvj7ffvstAwYMMI+LNLXNdLyaqTNNcc4TjqfRaBg6ZDBDhwx2dFMa5NChQ+bP4K5du4iIiDjnyl+LFi3iwgsvZNasWcyaNYtvv/2WRx55pFbgkp2dzZ133sn//d//MWrUKJvH5PPQvGRwfhOL91HztPh7qJ4Va16o7cu6NG0p5Ma6+OKLKSkpYeHChRw/fpwVK1bw/fff1/rwjRgxgpiYGB588EGOHDnCsmXL+Pnnn82PX3XVVezbt48XX3yR5ORkfvrpJ55//nkiI88clQUGBnLppZeycOFCduzYwb59+3j++ecZNmwYQUFBzJw5kyeffJL169dz9OhR7r//fg4dOkSnTp0a9B5DQ0NZs2YNqampbNmyxZxCUVlZCag7H6dOnSIzM5P4+HhGjRrFfffdx86dOzlw4AD3338/OTk5tGvXzu7xy8rK+M9//sPRo0f58ssv+emnn8ypbXPnzuWTTz7h66+/JiUlhddee42vvvrKfHHSEFVVVTzwwAMcOnSIJUuW8PPPPzNnzpwGP1+I5hAaGsrp06f5+++/SU1N5e233+aXX34xf65AVTSMiYnhww8/tEmxaOz5IjQ0lLKyMn799VfS0tJYunQpn332mfm1pkyZQmlpKQsXLuTYsWMsXbrUZlLZczk/1ae+c5f1HWCNRsNVV13Fa6+9xt9//82uXbtsLn7O5rxj7dprr+XHH39k6dKlnDhxghdeeIGCggL69+/PnDlz+Omnn/jwww85ceIEn3/+OW+++abNhVR9QkNDOXToEDt37iQ5OZlnnnmG3bt3m1NQTTdz9u3bR0VFRZOc84RorKeeeoodO3awbt06Xn31VfPg+HNx7NgxnnjiCfbt28exY8f45ZdfbNJCQfWo3H333XTq1InZs2eTlZVl/qmsrJTPQzOTHpdmMClEzdOyKBM+yVHjYgI9YHaY6mlxZNACajD322+/zYIFC/jiiy+IiYlh4cKFDBgwwGaMi5eXF2+//TYPP/wwl156Kd27dzdfDIDKiX7rrbd44YUX+OCDD4iIiOCOO+7gqquualA75s+fz4IFC7jhhhvQarVMnDiR+++/H1CD7p577jnuvvtuKioqGDBgAO+9916Da7MvXLiQxx57jClTptCuXTtmzZqFl5cX+/btY+zYsVxyySX8/PPPXHzxxWzYsIHnnnuOBQsWmAcdjxgxgkceeaTO47dr147Y2FhmzJhBREQEzz33nHkA7uTJk8nJyeG1117j9OnTdOnShddff50ePXo0qO2gBinHxMRw+eWXExoaysKFC23yeYVwhEmTJrF582buuusuQOWYz58/n0WLFlFeXm7+fE6ZMoW33nqLCy64wPzcxp4v+vfvz+23386TTz5JRUUFCQkJPProo8yfP59Tp04RExPDm2++yeOPP84XX3xBnz59mDZtmrlyz7men+pT37nL2i233EJ5eTn33HMPWq2W2267zaY3pbHnHWsDBw7kySef5PXXX+fJJ5+kZ8+evPXWWwQFBdGvXz9eeOEFXnvtNV544QViY2NZuHAhY8aMadCxZ8+ezb59+5g7dy7e3t4MHjyY22+/nW+//RZQhQtGjhzJVVddxUsvvdQk5zwhGuuaa67htttuo7KykpkzZzbJzb3HHnuMJ554gjlz5lBZWcmwYcN48cUXbfZJT083F+OoWTDn448/ls9DM9MYZCYpIRpl+fLlvPzyy/XOiyCEaF6pqalkZmbaFOt4/PHHKSsrs5uPLoQQwvVJqpgQQgiXU1xcbE6JOnnyJL/88gvffvstF110kaObJoQQoplIqpgQQgiX06NHDx599FFeeukl0tPTiYmJYf78+Zx//vmObpoQQohmIqliQgghhBBCCKcnqWJCCCGEEEIIpyeBixBCCCGEEMLpSeAihBBCCCGEcHotGrh8mA2arfZ/UirhRAVMPAQB26HHXlhZUP/xvsyFrnvAfxtccgROV1ke+7sY4ndDxE54J8v2efNS1ez2QgghhBBCCNfQooPzy/RQoLOs6w0w9Qh08YGlXaD/fujhC49Ew3cF8MQp2NsLOtuZsHFzCYw+CP/tAAP84c5U8PGAn7qpx/vvg8vbwmB/9RqpSRDuCRlVcNFh2NoDtJqWed9CCCGEEEKIc9Oi5ZD9PNSPyWunIbUKfkuANUVwsBz+SoQgLfT0g1WF8F42PBVb+1iLT8NlbWBOuFr/uDN02A1HyqGrLxwoh0tDIcEXQrVwrEIFLk9nwL8jJWgRQgghhBDClThsjEuRDh5PhydioI0nbCiB/v4qaDEZGQjrS+w/f0MJjA60rLf3ho7elv07eMO2UpV+lqeDOC84WQl/FMGVbZvvfQkhhBBCCCGansMmoHwrC3w0cIOxxyS9CmK8bPeJ9IK0SvvPt7u/p2X/Z2Lh6uNQaYAHoyHGG25LgfuipLdFCCGEEEIIV+OQwMVggLey4Y524GUMIkr1aoyKNR8NVNQxAsfu/h6W/ae3gZwQqNBDqCekVqoB+4vbw4Mn4dMcGBEI73cCf6mtJoQQQgghhFNzyCX7tlI4WgGzrVK2fD1UkGGtwlB3UGF3f73t/n4eKmgBWJAOD0TBxhL4PFcN+q82qLEyQgghhBBCCOfmkMBlZSEMDVDpWyaxXqril7WMKoiukQ5ms391jf2r7e9/okIFLLPaqDEwQwPUWJrxwWqsjBBCCCGEEMK5OSRw2VACYwJttw0LgB1lUGJVLnltsdpuz7AA9bhJaqWaC8be/k+mw/wo8NCABjB11FQbVNqaEEIIIYQQwrk5JHDZUwa9/Gy3jQlSVcHmJMPeMng2QwU4NxoH71fqVQ+Mzhho3BIB/8tVk0vuLoPrkmFSMHTztT3usQqVmjazjVof5K8qi+0sha/zVe+LEEIIIYQQwrk5JHDJrIK2NcoCaDXwbTycroaB++HjHPg6HjoZJ59cVwLRu1TPCsDwQHinIzyVAcMPQIgWPupU+7WeTFdVxTTGIgCjglQ55NEHIcBDFQgQQgghhBBCODeNwSDJUkIIIYQQQgjnJoWAhRBCCCGEEE5PAhchhBBCCCGE05PARQghhBBCCOH0JHARQgghhBBCOD0JXIQQQgghhBBOTwIXIYQQQgghhNOTwEUIIYQQQgjh9CRwEUIIIYQQQjg9CVyEEEIIIYQQTk8CFyGEEKIeRyvg1hQI3g4eW9W/t6ao7UIIIVqOxmAwGBzdCCGEEMIZrSyAGcegSg9VVtu9AC8PWNYFJoU4qnVCCNG6SOAihBBC2HG0ApL2Qam+7n38PWBXT4j3abl2CSFEayWpYkIIIYQdL2aqnpb6VOlhUWbLtEcIIVo76XERQggh7AjeDkVnCFwAgj2goH/zt0cIIVo7T0c3QAghhHBGxQ0IWhqznxCtyWtvvOXoJjTa7bfe7OgmiDOQVDEhhBDCjsAGfkM2dD8hhBDnRk63QgghhB3XhKnqYfXxAmaHtURrhBBCSOAihBBC2DEvErw09e/j5QF3R7ZMe4QQorWTwEUIIYSwI94Hbm9n/zEPVCnkZV2kFLIQQrSUFg9cqgxwTyqE74CwHXDLCagwDmy8+QRottr+vFxPmckvc6HrHvDfBpccgdNWs4P9XQzxuyFiJ7yTZfu8eanwUU5TvzMhhBDuJrXSsqy12h7gATt7yOSTQgjRklo8cLk3DZbnw7ddYUVXWFkIT6arx/aWwfOxkJ5k+bkpwv5xNpfAdcnwcBRs6A6FOrg22fL47SlwYwQs6Qz/SoXsarU9owp+K4Jr2jbjmxRCCOHyqg3qO8pkXXcIMn5rFumhQiYTEEKIFtWigUt+Nfw3C97pCOcFwohAeCwatpaqx/eXw6AAiPKy/PjX0cLFp+GyNjAnHJL84ePO8HMhHClXjx8oh0tD4YJgCNXCsQq1/ekM+HckaM+QtyyEEKJ1+7sY8nVqOc4LBvvDhcGWx1cUOKZdQgjRWrVo4LK2WAUi44Ms2+aEw8puqickVweJvg071oYSGB1oWW/vDR29YX2JWu/gDdtK4UQF5OnUl87JSvijCK6U3hYhhBBnYB2YTA0BjQamhVo9nt/SLRJCiNatRQOXoxUquPg8D3rthY674d9pUKmHfWVqNsxHTkLsLui7Dz7MrvtY6VUQU6NOZaQnpBnzkZ+JheuTIX4P3BcFMd6wMEMtS2+LEEKIM/k+37JsClgmh4DpK2R9iSUNWQghRPPzbMkXK9LD8QqV5vVWRyjSwS0pKo+4m7EqS19/+Fc7+L0Ybk6BAC3MbFP7WKV68KkRdvl4WHKOp7eBnBA18D/UUw2w/LsYFreHB0/CpzkqVe39TnWnowkhhGidDpfDQWOKsZ8GxhozBcI9YXgArCsBA/BjAVwr87gIBztaAS9mqmubYr2aFPWaMFXSW6reCXfSopfsnhoo1MOnnWFkoKrG8kIcvJUF/4yAzL5wRzs1ZuVf7eCmcDUmxh5fD0s1MpMKvW0Q4uehghaABenwQBRsLIHPc2FvLxUwLT7dPO9VCCGE67JOE5sQrL5PTCRdTDiTlQWQtA/ezVI3iA2of9/NUttXylgs4UZaNHCJ8VJdPNbRf6IvlBtUd3vbGv0/PXzVuBR7Yr0go0YXfUY1RNuZ5vhEhQpYZrVRXftDAyBIC+OD1VgZIYQQwtr3Vhd71oEKwDSrEsg/F6p0ZyEc4WgFzDimslCqajxWhdo+45jaTwh30KKBy/AAqAZ2l1m27StT5SWfy4CpR2z3314G3esYrD8sQA32N0mthJRKtb2mJ9NhfhR4aFRusuk7ptoABilnKYQQwkp+NfxVZFmfUmOulp6+0MlbLRfp4c9ihHCIFzOh6gyBc5UeFtUzJ54QrqRFA5duvnBJCMxNhq0l6ovhgZNwY7i6o7WyAF49re4MvHYaPs6Be6PUcyv1qvKYzhho3BIB/8tVk0vuLlNzukwKVq9h7ViFqi5mGiczyF9VFttZCl/nq94XIYQQwuTnQnWTDdR3Rs2efI3Gttfle0nFEQ7yaU7tnpaaqoBPZNJt4SZafFj6J50hyQ/GHYJ/HIXpofB0LJwfBJ93hrezVMWxN7LU+khjyeN1JRC9yzKL8fBANR/MUxkw/ACEaOGjTrVf78l0eDBafdEAjApS5ZBHH1QzH9/RriXetRBCCFdhPb5lWoj9fWqOc5Hee+EIxQ1MU2zofkI4O43BIKdbIYQQAlQKcbudav4vgK09YIB/7f0q9BC+03JBuLcn9PRruXYKARC8XaUrnnE/Dyjo3/ztsfbaG2+17As2gdtvvdnRTRBnIIWAhRBCCKP1xZagJdYL+tcRjPh4wIXBlnVJFxOOcEUDJtT2AmZLyW7hJiRwEUIIIYys08SmhljSjO2xSReTwEU4gLYB+3h5wN2Rzd4UIVqEBC5CCCGEUc3ApT6TglWlSoB1xZBTXe/uQjSpXaXwbrZlvWYQo0HNbbesi0xCKdyHBC5CCCEEcKQcDpSrZT8NXBBc//7tvCwl+PXIRH+i5egMcMMJ2+p3N4fbTsId7AG7eqrJvoVwFxK4CCGEENiOUxkfDH4N+Ia0rjom6WKipbyeBZtL1bK3RlVsfb0j5PRVQTdAgb5hqWRCuBIJXIQQQggalyZm3i/UsvxTgZpzTIjmlFIJD560rD8UZZms29cDxgRZHvu1sGXbJkRzk8BFCCFEq1eggz+LLOtTGhi49PaFjt5quVAPa4ubvm1CmBgMcGsKlBgD5J6+8ECU7T4TrVIcf5HARbgZCVyEEEK0ej8XWMYLDPCHWO+GPU+jse2dkXQx0ZyW5sEPVv/H3ukI3jWu5CZYBS6ritR4GCHchQQuQgghWj3r8S3TGjmYueY4F5nWWTSHvGr4V6pl/ZYIGBFYe79evhDtpZbzdbCltGXaJ0RLkMBFCCFEq6YzwI/nELicHwQBxm/ToxVwsKLp2iaEyX0nIdPYLRjjBU/H2t9Po7FNF5NxLsKdSOAihBCiVVtfAjk6tRztBf39G/d8Hw/bC8UV+U3WNCEA+KPIds6W19pDSD0lwyZYDdCXcS7CnUjgIoQQolX7Pt+yPDUEPDR17lon616a72Wci2hC5Xq46YRlfXooTG9T/3PGWwXS64uhSNcsTROixUngIoQQolVbcQ5pYiaTQ9RM5QB/F0Nudb27C9FgC9LhkDH9MNgDFrc/83MivaCfn1quBn4vqnd3IVyGBC5CCCFarWMVsK9cLftq4ILg+vevS6QXDAlQyzpgpfS6iCawpwyeybCsPxPX8Ip3E6QssnBDErgIIYRotazTui4IBv9z+FaUdDHRlPQGlSJm6rwbEQA3hzf8+TKfi3BHErgIIYRotawH0p9tmpi9568shCopiyzOwX+zVOEIAC+NmrOlMeOvRgaqXkRQqWYnpNqdcAMSuAghhGiVCnXwh9VM91POMXDp4wftjfNnFOhgbXH9+wtRl7RKmH/Ssj4/Cnr6Ne4Yvh4w2qq62K8yzkW4AQlchBBCtEq/WPWK9PeDuAaOHaiLRgPTQi3r1tXKhGgogwFuT4EivVpP9FGBy9mQdDHhbiRwEUII0SrZpImFNs0xrdPFVsg4F3EWvs6Hb63+77zdUfWenA3rwOW3QjXZqhCurMUDlyoD3JMK4TsgbAfccgIqjHcVTlTAxEMQsB167D1zVZYvc6HrHvDfBpccgdNVlsf+Lob43RCxE97Jsn3evFT4KKcp35UQQghXojPAj1Z3oKeeY5qYyflBEGD8Zj1cAQfLm+a4onXIr4bbUy3rN4bbpns1Vm9fiPJUy7k62FZ6bu0TwtFaPHC5Nw2W58O3XWFFVzWA8cl01TV6yVEI84TN3eG6MLjsKByvYzDZ5hK4LhkejoIN3VWu8rXJlsdvT4EbI2BJZ/hXKmQby3JkVMFvRXBN22Z+o0IIIZzWxhLL90KUJwz0b5rj+nrYlqGV6mKiMeafhHTjTdgoT3gu9tyOp9FIWWThXlo0cMmvVlUy3ukI5wXCiEB4LBq2lsKaInVn6u2OagDaA1Hq8fey7R9r8Wm4rA3MCYckf/i4M/xcCEeMd7cOlMOloaq8ZahW1eoHeDoD/h0J2rOYGVkIIYR7sE7jmhLSuGpNZ2Lde2OdjiZEfdYWw5tW1zyLO0Co57kf1zpd7FcJXISLa9HAZW2xqpE/3qrbc044rOwGG0qgvz8EaS2PjQy0lAKsaUMJjA60rLf3ho7elv07eKsu0RMVkKeDOC84WQl/FMGV0tsihBCtmvXA+aYa32JiXZ1sbTHkVde9rxCgUuZvPGFZnxYCl4U2zbHHWwUu60qgSNc0xxXCEVo0cDlaoYKLz/Og117ouBv+nQaVetU1GuNlu3+klyoJaI/d/T0t+z8TC9cnQ/weuC8KYrxhYYZalt4WIYRovZIrYI+xd95HY3szrSlEecEQY+qZDvhJ7nKLM3gmQ2WKAAR6wOsdVJpXU4jygiRjKeUqg7qBK4SratHApUivxqwsPg1vdYQ3O8CyPLjvJJTqwadGa3w0UFFHBQy7+3tY9p/eBnL6QXZfeCIGUivVgP0r2sCDJ6HDLrjimDqOEEKI1sM6TWxcEARo6973bE0NtXq9/KY/vnAf+8vUjVWThbEqi6Qp2aSLSeAiXFiLBi6eGijUw6edVRrYpBB4IQ7eygJvjaW6mEmFQaWW2ePrYWd/ve3+fh6W/NAF6WrczMYS+DwX9vaCaoMKooQQQrQe1gPmmzpNzHxcq3SxlYXq+0aImvQGuCkFKo3/P4YGwK0RTf86MkBfuIsWDVxivMATiPexbEv0hXKD6srMqLLdP6MKomukg5nEekFGjbzhjGr7+5+oUAHLrDZqDMzQADWWZnywGisjhBCidSjSwe9Wd5ybqgxyTX391NhKgHyd6vEXoqZ3stU4KFDXR+90bJ509lGBKosFVEpaah1p+EI4uxYNXIYHQDWwu8yybV8ZBHnA8EDYUQYlVoPG1hbDsAD7xxoWYPmwg/oQplTa3//JdDXrrIcGNICpo6baoMowCyGEaB1+KbTc3e7r1/QpOSYaTY3qYlIWWdRwqhLuS7Os3xcFffya57X8PGwLGkl1MeGqWjRw6eYLl4TA3GTYWgJ/FcEDJ9UESxcEqYH7c5Jhbxk8m6F6Q24MV8+t1KseGNOsr7dEwP9y1eSSu8vUnC6TgtVrWDtWoaqLzWyj1gf5q4FpO0vV7LRD6wiMhBBCuB+bNLFm6m0xHz/U6nXzm/e1hOv5V6pKnwfo5gOPRDfv60m6mHAHLT4B5SedVXWLcYfgH0dheig8Hau6Rr+Nh9PVMHA/fJwDX8dDJ2Na2boSiN5l6d4cHqi6VJ/KgOEHIEQLH3Wq/XpPpsOD0ZbqHKOCVDnk0QfV7MZ3tGuJdy2EEMLRdAb4oQXGt5iMC7KMuzxYAYfLm/f1hOv4Nh++yresv9VRjd1tTtYD9FcVqvE1QrgajcEgyVJCCCHc3/piGHFQLUd6wqmkpp140p5LjsB3xmDpxTi4J7J5X084v0Id9NwLJ43jeq8Pg/c6Nf/r6g0QswsyjeODt3SHgc2YdfLaG28138Gbye233uzoJogzaPEeFyGEEMIRrNPEpoQ0f9ACNdLFZJyLQE3JYApa2nnC83Et87oeGtvJKCVdTLgiCVyEEEK0CitaME3MZIrVOJq/iiC/uu59hftbXwxvZFnWX2kPbT1b7vUnSuAiXJwELkIIIdzeiQpLRUtvDYwPapnXjfZSRWFAVdX8SS4WW61KPdx4Akz5+ZOC4fI2LdsG6wH6f5fYVnIVwhVI4CKEEMLtWadpjQuCQG3LvbZ19TJJF2u9nsuEvcYCDQEe8N8OlsJBLSXay1JyucoAf8j8QsLFSOAihBDC7VmniTXXpJN1mRpqWf6xQM0hJlqXg+WqyqnJUzHQ0afu/ZvTBKveRkkXE65GAhchhBBurVgHa4os6y0duPT3g1gvtZynU+McROuhN8DNJywTnw7yd+xUDNbjXGQiSuFqJHARQgjh1n4ttFw0Jvm1/J1ujcY2WFoh6WKtygc5lpQsLWoOOm0Lp4hZGxUEPsbX31cOaZWOa4sQjSWBixBCCLfmyDQxe68rgUvrkVEF/06zrM+LhH7+jmsPqElRRwZa1qXXRbgSCVyEEEK4Lb0BfrAug+ygwOWCYPAz3uU+UA5Hyh3TDtGy7kqFfGPlri7e8GiMY9tjIuliwlVJ4CKEEMJtbS6F08a5UyI8YUgzzhReHz8P28n/pLqY+/uhAL7Is6y/1VH1djgDm8ClSAX4QriCFpz2SAghhGhZK/Ity1NC1OzhjjI1xJImtqIA7op0XFtE8yrSwS0nLOvXtrUNXB0tyU8F8lnVkF0NO8pggINT2ETDFBQU8NfadaRnZODp6UW3rvEMGzoYT09PCouKWLPmT9IzMggKDGTkecPp2LFDncc6fOQoGzZsoqS0lPZxsYw9fwz+/qpednp6Br/+tobKykqGDxtCr549zM9b+/d6wsLa0qN7YrO/35qcJPYXQgghmt73TpAmZmI9zuXPIiiQyf/c1iOnILVKLYd7wovtHduemjw0tpNRSllk16DT6fj+x5/QarVcNv0fTBw/jmPHk9mwcTMGg4Eff/wZX18fZs24lO7dE1j5868UFtr/42ZmnmbVb2sYNGgAMy79B5WVVaz6bbX58T/+Wkuvnt25cOIF/PnX35SVqRl8S0pLSUs7SWJCtxZ5zzVJ4CKEEMItpVTCTvVdi3eNCzVHiPGGgca72tXAz5Iu5pY2lcCrpy3ri+JU8OJsZJyL68k8fZqCgkIuuGAsbdu2ITY2hqFDBnHo0GFOnjxFXn4+Y88fTdu2bRg4oD9RUZHs23/A7rF27d5DfJfO9OieSHh4GOMvGEtKahr5BerElJeXT5cunWkfF4ePjw+Fhaqm/Nat2+nfLwkPD8eEEBK4CCGEcEvf51uWzw+CIK3DmmIm1cXcW5UBbjwBpiEjE4Ph6rYObVKdxltNRLm2GEqkB9DptQkNZdqUSXh7eZm3aTQaKiorycjMJCI8HG9vb/Nj0VFRZGRk2j1WRuZpYmKizetBQYEEBQWa9w8KCiQrK5vCoiIqKioICAyguLiEU6fS6datazO9wzOTwEUIIYRbcqY0MRPrdvxYADoZFO1WXsyEXcZePj8N/LeDmsfHGcV6Qy9ftVxpgD9lYlSn5+fnR/v2ceZ1g8HArt17iIuLpaS0lIAA24FK/v5+FJeU2D1WaWkpAQG21Ur8/fwpLlb7Dx82lNVr/uCTTz9nQP++BAYEsHXbNvr37+uw3haQwflCCCHcUIkOVhdZ1h01f0tNA/whxgtOVUGuDtaX2M6pIVzXkXJ4/JRl/YkY6NLCk5021sRg2Gsszf1rIUxyks+JaJi1f68jOzuHmTOms2PHLrRa225lrVaLTme/K626utrO/h7m/eO7dKbj9deh0+nw8fGhqKiY9PRMRo8ayfoNGzl46AjRUZGMGzsGL6seoOYmPS5CCCHczqoiqDD2ZvT2hU5OcgGp0ajqZibWVc+E6zIY4J8pUG78P9ffzzWqxskAfddkMBj486+/2b1nHxMnXEBY27Z4etYOUnQ6HZ6e9vso7AU1Op0eLy/L/p6envj4qJPnlq3bGDCgHxmZmRw6fJSrrpiJXq9n1+69Tfzu6ieBixBCCLdjHRBMC3VUK+yzTheT+Vzcw0c58Juxh88DeKcjeDppipi10YGqcAWonpeTlY5tjzgzg8HAb2t+Z8/efVw44QK6dO4EQEBAAKWlZTb7lpaWEuBvv8612r+01v7+dvYvLCoi8/RpunWNJyPjNFGR7fD29qZ9XByZmfbH0DQXCVyEEEK4Fb3BNiBwljQxkwuCwdd4sbivHI5VOLY94tycroJ5aZb1uyNhoIMmOm2sAK1tquKqorr3Fc5h7d/rOXToCJMumkh8fBfz9qjISLKzs6mqqjJvS0/PIDLSftdfVGQ70tMzzOtFRcUUFRcTZWf/LVu2MXBAfzQaDRqNCp4A9AY9LT1MTwIXIYQQbmVrKWRWq+VwTxjqZBeR/h4qeDGRXhfXdneaGq8E0MkbHo+uf39nI+liriMjI5Odu3YzdMgg2rWLoKS01PwTExNNUFAQq1b/Tk5uLlu37SAj87R54kidTkdJaSl6vR6A3r17cujwEfbu2092Tg6rVq+hY4f2hIba3ukpKCgkKyubrsYgqV1EBCdPpZOdncOxY8eJbNeuRX8HLR64fJ4Lmq22P/84oh67+UTtx16upwfqy1zougf8t8ElR9RdD5O/iyF+N0TshHeybJ83L1V16wohhHA/1mWGp4SA1glTdqbJOBe38FMB/C/Xsv7fDqoXw5XUnM9FL5XunNaRo8cAWL9hEx98+InND8DkyRdSVlbGl0uXc/DgISZfNJHgYFX3Oj0jkw8+/MRcNSw6Koqx549my5ZtfPXVN/h4ezP+grG1XnPz1q0MHKh6WwBiYqJJ6BbP8m++w9PLi6Sk3i3x1s00BlN/Twt5+KTqGn+jg2WbrwZCPWHkAfhHKFwTZnksWKvuTtW0uQRGH1QniQH+cGcq+HjAT8aJPPvvg8vbwmB/mHoEUpPUnbeMKrjoMGzt4ZxfZkIIIc5N/32ww5jqvbQLzGjj2PbYk1YJ7XerZS8NZPdV33fCdZTooPc+SDaOC7mqLXzW2bFtOht6A0TugmxjL+X2HtDP/rCIRnntjbfO/SAt7PZbb3Z0E8QZtHiPy75ySPKDKC/LT6ixgMH+chgUYPuYvaAFYPFpuKwNzAmHJH/4uDP8XKjKEQIcKIdLQ1V3fKjWkkP8dAb8O1KCFiGEcEdplZagxUtjezfZmcR5q8pToCYtlBQd1/OfU5agpa0WFsXVv7+z8tDYTkYp/xeFM2v5wKUMEn1rb88w1rS395g9G0pUNQyT9t7Q0VvVxAfo4A3bSuFEBeTpIM5LVcv4owiudNJZbIUQQpwb6/Ei5wc6dy+GdbUzSRdzLVtL4OXTlvWX2kO7lpvKoslNlHEuwkW0aOBSqYejFeqLpeseNQblgTSo0KuAxhN45CTE7oK+++DD7LqPlV6lJvGyFump7rYBPBML1ydD/B64LwpivGFhhlqW3hYhhHBP1gHA1FBHtaJhrKud/VgIOhlb4BKqDXDjCdAb1y8Igmtd/Iao9QD9tcVQqq97XyEcyf6sNM3kcAVUAwEe8FUXFcTcmQpFeuhp7Gnp6w//age/F8PNKWqQ20w7+cmlejWmxZqPh2XCseltICdEBUWhnpBaqQbsL24PD56ET3NgRCC836nudDQhhBCuo0RnmUsDbAfAO6OB/hDlCRnVanzBxhL1vSSc28uZsN2YjuirgTc7qIlFXVmcN/TwVSn7FQb4qwgudPLPj2idWjRw6eWnBiCGGV+1rz8YgCuPQ0l/lcLV1vhYkj8cLof/ZtkPXHw9VFBirUJvG4T4eagfgAXp8ECU+mL4PBf29oK5yWqszP1RTf1OhRBCtLTfiiw3r3r5Qmcfx7bnTDw0qlfoXWN2wYoCCVyc3bEKNbbF5NEY6NrAFHdnNzFYBS6g0sUkcBHOqMX7GsJqhEo9fNXAxOxqS9Bi/Vhds7jGeqm7VNYyqiHaTo7piQoVsMxqo8bADA2AIC2MD1ZjZYQQQrg+6zLIzjbpZF2mSllkl2EwwC0pUGYMjpP8YJ79uf1ckk1ZZJmIUjipFg1cludB5E411sVke6mq+vVChipbbG17GXSv407GsACVh2mSWgkplWp7TU+mw/wodXdLgyUvtdqgTkRCCCFcm94AP1gFLtYD353Z+CDwMaYZ7S2H4xWObY+o22e5loHrGuCdjqpynbsYE2h5P7vL1FhiIZxNiwYuY4JUathNKXCoXH3J3HsS7o1UXzIrC+DV02rsy2un4eMcuNeYxlWpV5XHTIMXb4lQkz69k6U+YNclw6Rg6FYj0DlWoaqLmdLNBvmrymI7S+HrfOebUVkIIUTjbSu1XGiFae3fxHJGAVo1uNvEuiqacB7Z1XB3mmX9X+1giIv8H2uoAC2cZ/WefpXqYsIJtWjgEuYJP3dTqVsD9sNNJ+Cf4ao35Pwg+LwzvJ0FvfbCG1lqfaQx33ddCUTvUj0rAMMD1d2OpzJg+AEI0cJHnWq/5pPp8GC0ZeDcqCA1lmb0QVUk4I52LfLWhRBCNCPrC/7JIa5VPdK6+pmkizmneamWCRo7eMNTMY5tT3OxSReTwEU4IY3BIMlSQgghXNvA/arXBeDLLvaLujir1ErosFste2kgp68ahymcw6+FMPGwZf37rjDFRcZQNdaWEhh8QC1HesKpJJVmfzZee+OtpmtYC7n91psd3QRxBlIIWAghhEs7WWkJWjyxvWvsCtp7Q18/tVxlkAkAnUmpHv55wrJ+eRv3DVoA+vurVEuAzGqVii+EM5HARQghhEuzHpQ/JkilDrsa6zlnZJyL83j8FBwzpqiHauHl9o5tT3PTalTFVRNJFxPORgIXIYQQLs26DLKzTzpZF+sqaD8UWArRCMfZXgovZlrWX4iDKDtTLrgb6x5L6f0TzkYCFyGEEC6rVA+rrC6urAe6u5JB/mpMAUBWNWySOcYcSmeAG0+Azrg+JhCuD3Nok1rMBKvA5c9iKNPXva8QLU0CFyGEEC5rdSGUG3snevhCvI9j23O2PDS2YyckXcyxXj0NW43jpnw08HZHS3VSd9fe2zKHXoXBds48IRxNAhchhBAuyx3SxEys08VWSODiMMkV8PApy/oj0ZBQx2TY7krSxYSzksBFCCGESzIYbHsmprp44DI+CLytZi4/UeHY9rRGBgPcmqJSEAF6+6pJslubCVaTokrgIpyJBC5CCCFc0vYyOFWllttq1cTErixQC+OsLhglXazlfZEHK40X6hpUiph3K7xSOj9IzSkEsKsMMqoc2x4hTFrhx1EIIYQ7sJ5lfnIIeLrBGATrdDdJF2tZudVwZ6pl/dYI1w+Gz1agFkYEWNZXSa+LcBISuAghhHBJK9woTczEeoD+miIo0tW9r2ha/06D09VqOdYLFsY6tj2ONkHGuQgnJIGLEEIIl3Oq0lL1yRO4yE0Cl44+kOSnlisNcqe7pawuhA9yLOtvdIBgF5zItClNrDERpUHmFhJOQAIXIYQQLucHq96W0UEQ4kYXmZIu1rLK9HBzimX9slC4ONRRrXEeA/zV2DGAjGrYU+7Y9ggBErgIIYRwQe5UTawm6/fzQwHo5U53s3oqHY4YK7iFaGFxB8e2x1loNXCBpIsJJyOBixBCCJdSplepKyauPn9LTUMCIMJTLZ+uhs2ljm2PO9tVCs9lWNafjYVoL8e1x9nIfC7C2UjgIoQQwqWsLoIyYy9Eog90dbPJAT00toP0rauniaajM8BNKWAcj8/IQLgx3KFNcjrW87n8WQTlese1RQiQwEUIIYSL+T7fsmw927w7se5FkvlcmscbWbCxRC17a+DtDipoFBYdfSDBRy2XG2BtsWPbI4QELkIIIVyGwWB7Ie9uaWImE4LVxTTAzjJIqXRse9xNaiU8eNKy/mAU9PBzXHucmaSLCWcigYsQQgiXsaMM0oyzeLfRwgg3nSAwSKtmLzex7mUS58ZggFtToNiY9tTDFx6IcmybnFnNsshCOJIELkIIIVyGdW/LpBDwdOPUHkkXaxpHK1SgErwdPLZCwHbb3+fbHcFHrobqdH6QmisJ1I2DzCqHNke0ci3+Uf08FzRbbX/+cUQ9dqICJh5SJ5Uee2HlGU7UX+ZC1z3gvw0uOQKnrT5MfxdD/G6I2AnvZNk+b14qfJSDEEIIF2M9UN1d08RMrMsiry6CEp3j2uKqVhZA0j54NwuK9GDAUtgBYHKwGpQv6hakheFWvyOZFFU4UosHLnvLYHoopCdZfj7spLpuLzkKYZ6wuTtcFwaXHYXjFfaPs7kErkuGh6NgQ3co1MG1yZbHb0+BGyNgSWf4VypkG8uGZFTBb0VwTdvmfZ9CCCGaVnqVpTSwFrgwuN7dXV4nH+htrJhWYYBfixzbHldztAJmHINSPdTVSfB7kdpP1M8mXUz+HwoHavHAZV85JPlBlJflJ9QT1hTBwXLVZdvTT+WbjgiE97LtH2fxabisDcwJhyR/+Lgz/FwIR4wzux4oh0tD1eRJoVo4ZjwxPZ0B/45UEysJIYRwHT9a9cKPCoQ2nnXv6y6sq6bJOJfGeTETqs5QvrfKAIsyW6Y9rmxCjQH6BpkUVThIywcuZZBop+b+hhLo76+6JE1GBsL6EvvH2VACo626Ltt7Q0dvy/4dvGFbqUo/y9NBnBecrIQ/iuBK6W0RQgiXY5MmFuqoVrSsmuNc9HLB2GCf5tTd02JSBXwiqeNnNMhf3QQG1fO5t9yx7RGtV4sGLpV61SX7fYEamxK/Gx5Igwq9+iDE1JitNtIL0uooAWl3f0/L/s/EwvXJEL8H7ouCGG9YmKGWpbdFCCFcS7neNkVlqpuPbzEZEgDhxp6lzGrYUurY9riS4gZOltjQ/VozrQbGW1W5k+piwlFaNHA5XKFmqA3wgK+6wPNx8Fku3JOmclBrVvXw0ai8Xnvs7u9h2X96G8jpB9l94YkYVbP972K4oo2q3d5hF1xhzH0VQgjh3NYUWc7XCT6QYKfn3h1pNTBFqoudlcAGXuE0dL/Wrma6mBCO0KIf115+KpB4uyP09YdL28DL7eGdbPD1UD0v1ioM4F9HC+3ur7fd389DjZ8BWJCuxs1sLFGVzfb2gmqDGisjhBDCua2wnnQy1GHNcAjrdDHrdDlRv2vCwOsM+3gBs8NaojWuzzpw+aNI9YIK0dJa/D5DWI3BlD181eC4GC9V8ctaRhVE13HWifWCjOoa+1fb3/9EhQpYZrVRY2CGBqixNOOD1VgZIYQQzstgsB2Y3lrSxEwmBIOXMcV5R1ndKdTC1rxIVf64Pl4ecHdkizTH5XX2gW4+arnMAOuKHdse0Tq1aOCyPA8id6qxLibbS9WAr2EB6oRsXad+bbHabs+wAPW4SWolpFTa3//JdJgfBR4a0ACml682SGUMIYRwdrvKINV4YytUC+e1snk3grVwvtV7lnSxhsmphrqmvvFCZWgs6wLxPi3ZKtcm6WLC0Vo0cBkTpO5+3JQCh8rhhwK49yTcG6lmZu3oDXOS1Vwvz2ao3pAbw9VzK/WqB0ZnDDRuiYD/5arJJXeXqTldJgVDtxp5z8cqVHWxmW3U+iB/1cW5sxS+zle9L0IIIZyXdZrYRVa9D63J1FDLsqSLnVm5HuYmW3pcYr0g2ENd9AR7wE0RsKsnTGplvXfnaqIELsLBWjRwCfOEn7up1K0B++GmE/DPcNUbotXAt/FwuhoG7oePc+DreDUBF8C6EojepXpWQM3i+k5HeCoDhh+AEC181Kn2az6ZDg9Gg8b4RTcqSJVDHn1QFQm4o12LvHUhhBBn6ftWPL7FxHqcy29FttkJorbH09W8caC+6/9KhIL+oBuo/n2tg/S0nI2xQWryV4DtZZB1pnrTQjQxjcEgyVJCCCGcU2aVumllQF0wne4LbVvBxJP29N5rmT/j23i4ONShzXFam0tg2AFLWvjr7eFWuUnZZEYdtKTq/69z3XPjvfbGWy3XqCZy+603O7oJ4gykCKAQQgin9UOBJd3nvMDWG7SAbVGCFTLOxa4KvUo5NwUt5wfCPyMc2SL3M8FqPhdJFxMtTQIXIYQQTssmTayVj0ewTpP7oQD0ki9RS80Usfc6qcI8oulYj3P5tVCKHImWJYGLEEIIp1Sut72j21rHt5gMC4Aw4wCD9CpVeEZYbC5RhX1Mno2FLjKOpckNClDV/QBOVsH+cse2R7QuErgIIYRwSr8XQYkx56erDyS08otQrQYmS7qYXRXGKmKmFLExgar6qGh6nhoYJ+liwkEkcBFCCOGUaqaJaSTlx6bXSeZzsXgi3VK4wN8D3u8kKWLNqWa6mBAtRQIXIYQQTsdgsO1RaO1pYiYXBoOpPsG2UjhZ6dDmOIUtkiLW4qwDl9+LVY+XEC1BAhchhBBOZ085pBgvykO0MDKw/v1bi2CtmszZpLX3uphSxEzT2owOhFslRazZdfaxzINTqldz7QnREiRwEUII4XSsZ4e/KBi8JO3HzLq6WmsPXJ5MV0EuSIpYS5N0MeEIEri4saMVcGsKBG8Hj63q31tT1HYhhHBmK6QMcp2mhlqWVxWqO96t0dYSeMYqReyZWEsvgGh+1oGLDNAXLUUCFze1sgCS9sG7WVCkVxO4FenVetI+9bgQQjij01Ww0Zh64gFcJIGLjXgf6OGrlssNsLoVXjRWGieaNKWIjQqE2yRFrEWNDQJjVWS2lUJ2tUObI1oJCVzc0NEKmHFM3YWrqvFYFWr7jGPS8yKEcE4/FqibLQDnBUKYZ727t0rTWnlZZOsUMT8NvN9RUsRaWogWhgaoZQPwWysMoEXLk8DFDb2YCVVnSB2o1MMTp6BA59qzL0s6nBDux/pCfKr0ttg1tcY4l9Y0e/m2UnjaOkUsDrr6Oq49rZmki4mWJvex3NCnObV7WmqqBj7OVT8AQR6qWk2I1s6/HnVsr/Gvn6Zl51lYWaB6jqqsepZM6XAf5cCyLjBJLnqEcCkVetsLIBnfYt/wQGirhVwdnKqC7WUwwN/RrWp+9lLEbpcUMYeZEAyPpavlXwpVAC3zLYnmJIGLGyo+i4GaRXr1c/JMEU89tJx78BPsAd4N6Ae0ToerqQoVzMw4Brt6ymBNIVzJH8WWc1i8D3SXO+l2eWpgcgh8arz5tCK/dQQuT2XA7jK1LClijjckQH1vF+ohrQoOVshnVjQvCVzcUKCHCkLORAMEeJxdoGOPDsjTqZ9z4auxDWjsBT+/FZ15wqsqPSzKhNc6nFt7hBAtx7oM8tQQuXtbn6nWgUsBPBrj2PY0t22lsDDdsv50rKSIOZqnBi4Ihq/z1fovhRK4iOYlgYsbuiZMpUvV13niBdwUoS7qdQYo0qk7JgU6KNSd4V87+xXqVHWbplBugPJqOH2OFUqqgE9yJHARwlUYDLbzkkiaWP0uClFf4tXA1lI4VQkx3o5uVfOorDHR5MhAuKOdI1skTCbUCFz+JX8X0YwkcHFD8yLVGI/6Buh7ecDdkWpZq4FQTwg9x9etMKabnW3wY/r3HDtsbDRVb5IQovntLYfkSrUc7KHGL4i6hWhhdBCsLlLrPxTAjW463mNhBuySFDGnZD1A//ciFWQ2JOVbiLMhgYsbivdRA9NrDlwH1dPi5aEeb+qxHz4e6if8HP5XGQxq3Ip1YGMvuFmQAZUN6OEJlJOnEC7DOk3swhC5+GmIqSGWwGWFmwYuO0phgVWK2MJY6CbpSE4j3ge6eMOxSijRw/oSGBPk6FYJdyWBi5uaFKIGpi/KVOlSxXp1ET87TPW0OOuAdY0GArTqJ9qr7v0yqhuWDjc7rKlbKIRoLpIm1njTQuCeNLW8qhDK9ODnRgGfqYqYKXP4vABJEXNGE4LhrWy1/EuhBC6i+Tj09HbjCTj/oGX9wsOg2Wr7801+3c9ffBridkHQdpX7WmKVY7Q8D6J3QvtdtnfxAGYchT+KmvCNOKl4HzW+o6A/6Aaqf1/r4LxBS2PMi1Q9R/WxTocTQji3rCp1pxbUF5OUMm+Yrr6WwdBlBkvvi7t4OgN2GlPEfDXwfieV3iyci8znIlqKwwKX3wrh3WzbbXvLYElnSE+y/EwKtv/85Xnw8El4owOsSYDNJTDPeNdJZ4CbTsALcapLeW6yZXKuHaWQr5O7Aa7OlA7n76F6Vmry1zRPOpwQonmsLFSzbwMMDzi3lNPWxnoyypo36lzZjlJ4yipFbEEsJEiKmFMaF2S5oNxaCjnnWFxHiLo4JHAp0anA4rwAy7ZCnZpDZGgARHlZfnzqaOHLp1V38cWhMCgA3uwIH+ZAsQ6yqyFHBzPbwKw2ajnL+CF67BQ85uYlI1sLUzrcTRFqIK+1R6Pljq0QrsT6gntaqKNa4Zqs0+q+L7DcqHNlVQbbFLERAXCnpIg5rVBPNacLqBsQv0mvi2gmDglcHjoF5wepH5N9ZaobuEMDSjnqDKqHZbTV84cFQLUBtpeqO3X+Hqrm+9ZSNVdJmCdsLVEDv0dKpRq3YZ0O91ysZfsPctIUwmVU6uFnq8+sjG9pnBGB0Earlk9WwY4yx7anKTydLilirkbSxURLaPHAZX0xLM1TaVzW9pVDqBauOK7GpgzZDz8W2D9GvnHOkBirHCFPjQpO0qrUye3ZWBh1EM4/BC/FqW2PpktvizubHQbG727+LIajFQ5tjhCigf4stkya29kbekg6UKN4amx7mL+v47vTVewshSetUsSeioVE+T/h9KwDl1+LLKmfQjSlFg1cKvTwfyfg5fbQpkb+8v5yVfnq4hD4qRtMDoFpR2BjSe3jlBq/4Hxq3H3x0VhmU7+9HeT2g9y+KpVoU4nqeh4SADckq0H7t6WAXj5ZbiPKy/bL+8PsuvcVQjiPFdbVxEJVdUHRONPcZJxLzRSx4QFwl6SIuYQhARBkvKpMqYTTvtJ1KppeiwYuT6RDNx819qSmZ2IhtY+a9b2vv+oZmRwCb2XV3tfX+KVWUSPoqDCoFDGTIC0EGm/BP3oKHo9WvT17y+Fgb9hSotaF+5hrVf74oxyVViiEcF4Gg+2F9lS51jkrFwZbepw3l0J6fbXindgzGZZUNx8NfNBJUsRchZdGDdI3ORASV/fOQpylFg1c/per8pgDt6uf5zLhr2K1bJq93VoPX5WvW1OYpwpeMqweqzaoKhb25v5YV6z+HRaoUtXGBKoAZ2wQbLDToyNc19QQSzWi1Cr3Kw0qhLvZXw7HK9VykIc6P4vGa+MJo6x+d3WlWjuzXTVTxGIkRczVWKeLSeAimkOLBi6/J8CenrCjh/q5MRwG+avlGUfh1hTb/beXWurTW/PQwOAAWFts2ba+WOX59vevvf+jVpXENBowZpNRjeRguhtvD7i6rWX9fUkXE8KpWaeJXRisPsPi7FhXY3O1dDFTiliV8Ut5WIDMw+WKrAOXw8ExVGvkAy2aVov+j+rooybLMv200aoZfrv6qrLG72erXpnD5SrYWFsM/zLmtpbpbXtYbo2AFzPVfC5bSlTQc324JTXM5M8i8NaoMsugAqUfCtScMSsLLNuF+7BOF/s6H/KknrwQTssmTSzUUa1wD9Zpdr8WQbm+7n2dzbMZsF1SxFxevA90MlaHrdB6kxwo0adoWk4TCl8bpiqN/ecU9Nmnurl/7maZQPCLXIjeZdn/irbwcDTckgLjD6u5XF600yv5WI1KYle0hSQ/GHFADSSbZWe8jXBtff2hv59arjDAEhnHJIRTyq6G9cZ0XQ0wuY4Jh0XDJPhCgvE7s1QPa1wkVXZXqRoDa/JkjP1sC+H8NJqa6WKxde8sxFnQGAzuMFWVELYWn4Z/parlwf6wqYdj2yOEqO2THLg2WS2PCIC/uzu0OW7h32kqGwHglgh4o4Nj23MmVQYYdkDNuwYqRWxtovS2uLKv8mDGMbXcsTiTf+/9xqHtaYzbb73Z0U0QZ+A0PS5CNKWr2qoUQVAVdva6wYRsQrgb6/EtUk2saVj/Hr/PV1XbnNlzGZagxUcD73eUoMXVjQuyXFymBLSjROvj0PYI9yKBi3BLYZ5qTiCTD3Ic1xYhRG2Vevi5xvwt4tydF6gmcwZVWXGXE9+02V0Gj1uliD0RAz38HNce0TTaeKoCSgAGjYZDITLzt2g6ErgItzU33LL8SY6lWo0QwvH+KoZC4+DxTt7QS8Y0NAkvDUyyGmOwwknLItesIjY0AObJOG63YTvOpb3jGiLcjgQuwm1NDLbM63O6WlWRE0I4h+9rpIlpJD2oyVhXZ3PWssjP10gR+0BSxNzKhBoTUcp9QwGg1+spKS1Frz/7koeeZ95FCNfkqYFr28KzxoGqH+SosttCCMcyGGx7AiRNrGlNCgYtoAM2lUJmFUTamZzZUfaUqYqfJo9LipjbGRYIPrpKKrTe5PoEkeUTTLuKQkc3y63odDq+WPoVo84bQfv2qqzulq3b2bBxk81+fZN6M2rkeXaPkXbyJH/9tY6CwkLatYtg3NgxhIaoPPu8vDx++mUVxcXFJPXpw9Ahg8zP27lrDxUV5QwZPMjucWvKyMhkw6bNpKdnoNfrmTljOjt27CI4OIhhQ4c06n1Lj4twa9bpYt/nw+mqOncVQrSQA+VwtEItB3rAmMD69xeN08YTRlr9Tn9wot7m6hopYkP8JUXMHXlpIKHwlHn9QKikizWl6upqfv5lFbm5tvM95ObmktSnN3PnzDb/DB0y2O4xioqK+eHHn0lM7MasGZcS4O/Pjz/+jKnY8PoNm4iJieaSi6eyc9dusrNzzK+9d98++ib1aVBb09JOsvyb7wAYNtTSlrCwtmzdtoPtO3bV9VS7JHARbi3RF4YbBwlWA5/lOrQ5Qghs08QmBoOPfBM1OZvqYk4UuDyfAVuNKWLexokmPSVFzC0lFqSZlw+E2JloT5yV3Nw8ln31DQWFtXuw8vLyiYgIJ8Df3/zj7e1t9zj79u8nPCyMAf370bZtG8aNHUNRcTFpaSfNx+rcsSPtIiJo0yaUvPx8APbs3Ue3rl3x8WlYtbh16zfSrWs8/7h4Kn2T+pgDo4ED+jNoYH/27tvfqPcvXxfC7c0Nsyx/kOP85UGFcHc2aWJSBrlZWKff/VII5WefUt5k7KWI9ZQUMbfVwypwORwcg04jl5xN4eSpU8TGxjDj0n/YbDcYDOTl5xMa2rCTakbGaWJioszrXl5eRESEk5Gp8usDgwLJys6moqKCwsJCggIDqaqqYu++/fRN6t3g9ubk5pKY0M3uY7GxsRQVNW6mXPlfJNze5W3Bz3hHb3eZZUCoEKLl5VTD38VqWQNMlsClWST4QFfjDdESPfzeuGuDJldtgLnJUGm8cTTYH/4tKWJuLaK8gLYV6j9eudab5IB2Dm6Re+jTuxejRo7Ay8t24FphYRHV1dXs23eAjz7+jM/+9wXbtu+grnnmS0pLCQgIsNnm7+dHcXEJAEMGDWTb9p28+/5HdOjQgaioSHbv2UtiQrc6e3Hs8ffzIzcvz+5jeXl5+Ps17u6FBC7C7QVr4bI2lnWZ00UIx/mpAEw3/4cGQDsnGjTuTjQa294sR6eLvZAJWyRFrFXRIOliLSnPGBwEBAQwZcpFDBjQjy1bt7N9x067+1dXV6PVam22abVadDodANHRUVw/ZzbXz72WCReMpaqqiv37D5LUpze7du/ho08+47sVP1BaWv9kUd27J7Bx0xb27T9AWXk5AAa9gdTUNDZt3kq3bl0b9T4lcBGtwvVWg/T/l+scaRNCtEaSJtZyrNPFVhQ4Lk12bxk8ahmnzWPR0EtSxFqFHhK4tJhOnTryf9dfx7ChgwkPC6NH90QGDxrAnr377O7vaRWkmOh0Ojw9LQWHtVotfr5qkq1du/fQvXsClZWVbNi4mRmXTadN2zZs2ryl3nYNHjSQ+PjOrF7zBx9+9CkAy5Z/w3ff/0hMTLRNtbKGkHLIolUYE6gmuUuuhDwdfJcPs9o6ulUCVHWpFzPh0xwo1qsqU9eEqUpD8Q0b+ydcRJUBfrIaTzpVApdmNTIQQrRQoIOUSpUqm+Tfsm2omSI2yB/ujar3KcKNJBScRGMwYNBoOBEYQanWG39dpaOb5bZMQYZJmzZtKCmxnx8fEBBAaantY6VlZYSF1b44qqysZP+BQ8yaeSmpKam0aRNKgL8/HdrHsX7Dplr7W/Pw8GD8uLEM7N+fk6dOUV5ejre3NzHR0YSHh9X7XLvHa/QzhHBBHhqYU2OQvnC8lQWQtA/ezYIiPRhQ/76bpbbLpKHuZW2xuogG6OANfeSue7Py0sBFVjOYOyJd7MVM2CwpYq1WgK6C9iVZABg0HhwKjnVwi9zXzp27+XzJUptt2VnZdQ7Wj4pqx6n0DPN6VVUVWVnZREbWHny2c9ceevZIxNvLCzQa87gZvd7AmWYXPXnqFNXV1bRpE0rvXj0ZNHAASX16Ex4eRnFxCZs2b23U+5TARbQa11kFLr8Uwkm56eNQRytgxjEo1UPN6XWqUNtnHLPM9yFcn/Us7tNC1DgM0bys0/FWtHDgsq8M/mOVIvZoNPSWYLXVkXSxltGhQ3vyCwpYt34j+QUFHDx0mK3bdzCgfz/AMmu9KT2sR/fuZGaeZsuWbeTm5rF6zR8EBQXSPs42uKyoqODgoUP06d0LgIjwcHJz80hPz+DIkaNERtZfdOHrb1aw9KuvyS+ofQIqLilm8xYJXISwq5MPjAtSy3rgY+l1cagXM6HqDGONqvSwKLNl2iOal8Fge+EsaWIt46IQyxf9xpKWm4S3ZorYQH+4T1LEWqXuVoHL/pC4M92gF2epTZtQpk65iLS0kyz5YhkbN21mxPCh5lLExcUlfPDhJ6RnqC/V4OAgJl80kQOHDvPlsuWUlpUxZdKFaGrcUdq5azc9e/QwVzELDg5iyOCBfP/jTxQWFTFk8MAztq2qqoovly7n6LHj5/w+ZYyLaFXmhsFqY1nQD3LggSi56+son+bU7mmpqQr4JAde69ASLRLN6VAFHDH2ngV4wPlBjm1PaxHmCecFwl/FKqPjxwKYE37Gp52zlzJhkzFFzEsDH3aSFLHWqlNxJt66Kiq1XuT6BpPtE0xERe3JE0Xj3X7rzTbr7ePiaD/Tfq9WcHBQrf07duxAx471f8EOGVx78PyA/v3MPTkNMeGCcRw6coSffv6Vfn37MGL4sFoBUkNJj4toVS5tA8HG//WHK2BdiWPb05oVN7CyW0P3E87NOk1sYjD4yrdPi2npdLH9kiImrHga9CQUWv5DSLpY6+Ph4cGYUSMZN3YMu3bv5etvV1BaWormLCYlla8O0ar4e6gJKU3ez3ZcW1q7wAaefRq6n3Bu30uamMNY/75/KYSKZrwZUG2AOclQYcwHGiApYgLoXpBqXpbApfXq0T2RS/9xMQUFhXyx9Cuysxt/ESaXBKLVmWs1SP/LPCjR1b2vaD7XhJ05V9ULmN34aonCyeRVq4pioCalmyKBS4vq7mspLV6shz+Km++1FtlJEfOSFLFWL7HgpHn5UHAMurO40y7cQ2RkOy6feSnBwcGs+f3PRj/fof9zbjwB5x+0rO8sheEHwH8bDNwPm8+QxrP4NMTtgqDtahCg9QXo8jyI3gntd9mmKADMOAp/FDXVuxCuZlgAJFp9iS/Ld2hzWq15kZYZ1Ovi5QF3167MKFzMykIwnZ6HBECkl0Ob0+poNDXSxfKb53X2l8EjVili/4mWktdCiSzPp02FuvAq9/ThRECEg1skWsqQwQMJCAyw2ebv78/0S6bRp3cvAgMDG3U8hwUuvxXCu1Y9RCU6mHREXVRu7QGjAmHKESiq42748jx4+CS80QHWJKggZ56xcIXOADedgBfiYGGsCmpMMwbvKIV8HYyRgaGtlkYDc60Gp34g6WIOkV995sDl0WiZhNIdfJ9vWZY0Mcew/r1/X2D5TmwqOgPMPWGbIna/pIgJIw221cUkXaz1GDJ4EIEBAbW2e3h4MGb0SK6bfVWjjueQqmIlOhVYnGf1Pr7IU93JL8apyQIXxcEPBWr7DXYqoLx8Gu5oBxeHqvU3O8L4QypYKdFDjg5mtlFVVK5NhqxqaOcFj52Cx2Ja4E0Kpza7LTx4Ul04/1EMxyqgi1wgt6iFlnmv6OqjyrQW69XdlGrj9jez4JYICNI6ooWiKVQZVI+LyTQJXBxiVKAqTFKoh+RK2FvetAPmF2Wqcsugvss/6CgpYsJWYsFJ1rfrAajAZfLJxs3fIVzH50uWMnHCBYSFta01KWYtGrjy8pkNPrZDelweOqVKYVqXw9xQogIZD+OJTqNRJRzX28nF1RlUD8toq+cPC1CDAreXQrinGoS9rRS2lqrSm2GesLVETWo3snG9UsINxXjbzij9oczp0qL2lcHyfMv6V12goD/oBsKJPhBqDFSOV8K9aXYPIVzE38WqlxugvRckSeqQQ3h7wIXNlC52oBwetkoReyQakvyb7vjCPSQWnkRj7OpLDmxHqdbbwS0SzSWiXTheXqpvJCIinIh29fxENK4+e4v3uKwvhqV5sKenmoDOJL0KEn1t9430hB1ltY+Rr4NyA8RY5Ul7alRwklYFWg08GwujDqoA6LX2atuj6dLbIizmhsOPxjvBH+XAY9GWwFk0r2eselumhdhe5MR4w+L2MDtZrb+VrcpYTwxGuCCbamKhMm+SI00LUd+/oP4u86PP/Zg640STphSx/n5qfiwhagqsLqd9SRYpge0waDw4HBxD37xkRzdLNIPx48Zali8YW8+ejdeoHpdqg5o0bm4yTDoMh8vV+IBdpQ17foUe/u8EvNwe2tQImUr14FPjC83Hw37ZxlLjtlr7ayz7394OcvtBbl+4KQI2laiUhSEBcEOyGrR/WwroZQrXVmtaCLQ13tlPqbRMTCma1/EK+F+uZf1BOxc5V7eF6aGW9f9LVmNihOuxvrMvaWKONSnE8qW/vgSyzjQDbAO8fFplTIC6EypVxER9rKuLyTiX1iMvL5+cXPXFX15ezh9//sX3P6xk/4GDZ3hmbQ0OXHKqYdgBFbRsK1W14Iv0Kt1jxEFLbmt9nkiHbj5q7ElNvhrLHRuTCr1K+bK3L9jZ32C7f5AWAo0Xpo+egsej1d2mveVwsDdsKbHcfRKtj4+HukA2+UDSxVrEcxmWClPjgmCYndRNjQbe7KDSPkH1pN4lKWMu51A5HKpQy/4eMFaKojhUuCcMN44tNWDpcT5bB8tVkRwTSRETZ9JD5nNpdY4eO87nXyzlgDFIWbX6d/btP0hVdTVrfv+TXbv3NOp4DU4VuzsVCnRwpDfEeoP3NrV9WReYfEQNdP4tof5j/C9XpYQFblfrlQbVzRy4Ha5qCxk17v5kVEG0nbKZYZ4qeMmosgwurDao4Mre/uuM42SGBcL/UmBMoOVLdEOJ7YSE7kobf9lZPW9Ary5s/u55u48Nvvhetu09dlbH1R39yu72mx/8L+9+seqsjrnpm+cY2Ce+1va3P/+FWx5+q87nmWLdJcYfa/996mZuunJireds3X2UIf+476zaecPl43lr4S12H2sNfyftM89BF/V3su5tqevvZPr7fGL8sUf+Ts7zearJ9PcrBz6Wv5Pd7S39dzL9Ta43/tTU2M+T6XiPG3/skb9T03yerLnKeS+uXRD3zh4OQKfiTLx1VVRqvcj2DeHpzzaRkZHfqOO9Mq/2exbOa8vWbXTrGs/wYUMpKSnhxIkUhg4ZzKCB/dm6bTu7du8lqU/vBh+vwT0uKwpgQQx09FFl7Ux8PNR8DFsbkC72e4Ia27Kjh/q5MRwG+avlYQEqwDCVaDQY4O8Stb1WozUwOMAyoRmosTOeGuhv527Po1aVxDQaSwnWatRdJyFEyzHdnxgaoHpchBBCtA5eBj3dCi2VHMplgL7by8vLp2eP7nh4eJB8IgWArvFdAIiKjKSoqHF5+g0OXHQG8K1j72pDw2rCd/SBrr6WnzZa8PNQyzPaqFKod6SqikP3pKk5XK4w9oaU6W17ZG6NUIP7l+eplK9bU+D6cEtqmMmfReCtURdJoAKlHwpgbxmsLLBsF0K0rIeiZKC2EEK0NtbzuZRrZTZad+ft7U15eTkAycknCA4KIjRUDXjMy8/H369xpSYbHLiMC4LH0yHPaoCsBjXg/ZXT5z6hY7AWfuiqel0G7FclNH/sapm/4YtciN5l2f+KtvBwNNySAuMPw6AANQdMTY/VqCR2RVtVjnPEATVQf5ad8TZCiObVxw+myEBtIYRodawDlwoPCVzcXaeOHVi3fiO///EXySdSSEzsBsDOnbtZv2ETXbp0atTxNAZDw+bPPVoB5x1Q41KGB8BPhapCyYFyVe1nbXfo7nvm4wjhjGYcha/y1fK9kfCcjBlsUoU66LjbMp/H550tvakNdX2ypYBCmBb29oJI+c5zWlcft1SPeyJGDdwWzmFVIUw4rJY7e8PR3g3v/VyUqTIiQA2S3dwD+smAfGHHa2/YH7tjAP7T72ryfVRllnv2fkPn4ky7+7a022+92dFNcDtVVVX8tXYd6ekZRMdEMWbUSLRaLf9bspSoyHaMHnUenp4Nn52lwT0u8T6wqyfcHAG5OrWeWaXKW27vKUGLcG1zreY/+iRHpT+KpvNmliVo6VpHZcEzWdReTWAIkKODm080LEVVtLxqg0rFNZkqvWtOZXQgBBm//Y9Xwv7yhj3vcLkqxGPyULQELaLxNNj2uuyX6mJuzcvLi3Fjx3D1VZcz7vwxaLUqlerKy2cwbuyYRgUt0Mh5XNp5wX2RsL47HOoNqxLgtghoL2OrhIu7MNhSkS6jGn4qqH9/0XBlenjJ6mbaA1FqQtjGCtHCe50s698WwKe5de4uHGhdMeQZA9U4L+jXuBRm0cy8PdQ5z2RFA853pokmy403C5L87M/BJERDWAcuB0NiHdgS4Siasxzk2uDAJa8aJh+G4Qcs29YXQ8JemHXMMimkEK7IUwOzZU6XZvF+NmQax8bFedn+nhtrQjDcEmFZvyMV0irPrX2i6a2o0dsiRRicz7RQy7L1JKF1WXxaVfoEy0ST3o269SmERWLhSTTGLvPkwEjKpLqYaKAGn3b+nQa7y+B5qx69cUFqQP3GEnjoZN3PFcIVWKeLrSiAbJmp/ZxVGeA5q96We6PO/WLnuVjoYvyOK9DBDZIy5nS+tw5cQh3WDFGPScGWqQ3Wl9R/vquZIvZgtP2pB4RoqMDqcuJKswHQazw4FBxzhmcIoTT4EuKHAnipve1dGm8PNUD/mViZgV64vu6+lnmDqgzwmfS6nLPPciDF2CMS4Qk3hNe/f0MEauGDTpaLrp8L4d3scz+uaBpHylXRFgA/jczV46wivFShHVBzm62sI11Mb4DrT0CZ8eZAHz9VylyIc2WbLibjXETDNDhwKdOr2ertCfKwDLwVwpXNDbMsS7rYudEZ4OkMy/rd7cC/iVJLRgfBXe0s6/ekQXJF0xxbnBvrNLHxwWquLuGcrIsm1DXOZfFpy2TPWiRFTDQd68DlgAQuooEaPJR/RCA8m6HungVYTfJYqofnM+G8wOZonhAt6/K2cFequru4swy2l0pKxNlang+HjMFEiBZubVfv7o22IBZ+LICDFWry2rnJ8FsCeMh4CoeyThObJtXEnNq0UHjQOIn5zwVQqbcNSo6Uw/waKWID5Hwomkjnogy8dVVUar3I8g0h2yeI8IrGzaIunJ/BYGDvvv2cOJFCVXU1NWdh0QD/uGRag4/X4Psmz8aqMS4ddsOlR+GfJ+Cyo9BhF+wqg+elKIRwAyFauNSqVO/7koJ0VgwGWJBuWb89Qv1um5KfB3zU2XIS+70YXs9q2tcQjVOggz+trjtkklHn1ssXOhnHixXq4a9iy2P2UsQelhQx0YS8DHq6Flm+KA4GS6+LO1r793p+/+MviktK8PL0xMfb2+bH27txhRka3OOS5A+7e6nJp9aXwJ4ydSFydZhKAenk0+j3IoRTmhsGnxnL7P4vF16IAx9JjWiUlYWqxwpUetidTdzbYjI0AO6PsqSk3Z8GFwVDN5lXyiF+KgDTGO+B/hAjhYKcmkaj0sVeMwb8KwrgAmOZ5NeyLIGMFvigo6SIiabXvSCNfaEdADgQGsd5Wfsd3CLR1A4eOsyQwQMZMnhQkxyvUaehDt5qErgNxnlcNveAV9pL0CLcy9gg6Gi84MrVwXcyp0uj1OxtuSlcDQRuLo9Gq7vBoO4OX5esxteIlidpYq6nZllkg0GliD1gGX7A/CgYGNDSLROtgc0A/eBY9Eiur7vR6XTExEQ32fEaNV1lfjWsKYISvapCUtO1YXY2CuFiPDRwXRg8Ybz4/iD77GZ6b63+LIZ1xvkevDQwL7J5X8/HAz7qBEP2q7v960vUhJf3SlpLizhaAS9mwqc5UGT1xSAzqruGMYHgr4FSAxyrBO02dUfTVG+nty883HTXHELYiCrLI6SyhALvAMo8fUgJiKBTyWlHN0s0oU4dO3DsWDJxsU0zpqTBgcuKfLjiuKouZo8GCVyE+5hjFbj8XAgnKyFW0l4axLq3ZU4YxLXA762/PzwSDY8aX/vhUzA5BHrJjO3NamUBzDgGVXqoqvHYVcdhWRdVMl84r9VFUGHVQ2nAErQA3BguqbKi+WhQvS4bIxIBVV1MAhf30qFDe9b+vZ7CwkIiIyPx9LQNPTQa6Nc3qcHHa3Dg8sBJGOQPr3VQs19L5R7hzjr7wPmBasC3HvgkFx6QO/hntLkEfjUOzvZAjT9pKfOjVVrf1lKoNKaMre+uen1E0ztaoYKW0jpuZpXq1eO7ekK8pBM7JdPfsL7ZDOafgimh8jcUzcc6cNkfEsdFp7Y5uEWiKf22+ncAkk+kkHwipdbjGo2meQKXoxXwantLLrkQ7m5uuApcQKWL3R+p7gyIui20mrflirYte7HjpVEpYwP2q8Blayk8k6F6YkTTezFT9bTUp0qvCrq81qFl2iQaR/6GwhkkWo1zSQ5sR5nWCz9dzT5c4apuv/XmJj1egzuAe/jCicomfW0hnNploWpyVVDzkawvcWhznN7eMvgm37I+3wE9VL384MkYy/oTp2BHacu3ozX4NKd2elhNVcAnMpGr05K/oXAGQdXlxJWo0nZ6Dy1HgmLO8AzhisrLy0k+kcKhw0dITU2jsvLsgooG97i8GAf/d0KVQB4aYH8G7LaNGuovhHML0MKsNvCe8Uv7gxw1Eauw72mr3pZLQqC3g3pn50WqAGp9iRqsf20ybO4uefpNrfgMd+obu59oefI3FM6ie8FJ0gIiAJUu1if/hINbJJrSxk2b2bZ9JzqdJTHVw8ODfn2TGDF8aKOO1eBQ4/LjUKSDWcfq3kc3sFGvLYTTmxtuCVy+yIWX41RAI2wdrYDPcy3rDzowPUurgQ87Qb99qjzy7jJVaGGBTJLbpAI9bKuI1befcE7yNxTOontBGqti+gFwMEQmonQnO3fuZsvW7Qzo35duXbvi7+9HaWkZhw4fYcfOXQQGBpDUp3eDj9fgwOUF+X8kWqERAZDgo1LFivSwPB9mS/W8Wp7LsJRIHx8EQxw850OCLzwTB3emqvVnMuDiUNVbLJpGLz/YcIb0SS/k8+LMrgmDd7PqTxeTv6FoCV2KMvDSVVGl9eK0Xyg53oGEVRY7ulmiCezas5eBA/oxbOgQ8zZ/f3/Cw8Pw8PBg9+69zRO4XCcnLtEKaTSq12X+SbX+QY58idd0shI+tMqBf8hJBsPfHgFf51kqw113HLb3BD+5e3xODAZ45NSZgxYALw+4u5nn8RFnb14kfJRT/wB9+RuKluBl0NG1KIP9oe0BVRb5vKwDDm6VaAolJSXExtgftxQbE832HTsbdbxGfYUfr4AbkqHzbvDbBttK4Z5UeDe74cc4UA7jD0Hgdui4G563you/+QRottr+vJxZ97G+zIWue8B/G1xyBE5b3Tb6uxjid0PETngny/Z581LVyVqIhri2reWDsqZIfQ6ExYuZqooXwPAANaGdM/DQwPudLGkuByvg4ZMObZLL0xvgrjRYYHXe9kDdlbfmhRoHuayLlNF1ZvE+6m/k7yF/Q+F43QtSzcuSLuY+QoKDOZWebvexU+npBAY0LhWiwYHLjlLovx/WFsO0EMuFigEVcDSk6kiVASYdhg7esKMHvN4enkyHz4zP3VsGz8dCepLl56YI+8faXKLmaXg4CjZ0h0KdGoRrcnsK3BgBSzrDv1Ihu1ptz6iC34rgmrYNfeeitYvxhguDLesS9FpkV8NbVjcuHop2rpLRnX1UYRGTRafhryLHtceV6Qxwwwl41WpuuMnBap6WmyIg2EN9oQR7qPVdPWXySVcwKUT+hsI5dLcqi3wwJBY9TvRlIs5aUlJvtm7bwYaNm8jKzqakpISs7GzWb9jE1m076NWrR6OO1+BUsbtT1d3UH7qqtIvXjL0Yi9qrIObFzDOn0JysVLnvr3dQ6RpdfWF8MPxRDFeHwf5yeCoAomre+rFj8Wm4rA3MCVfrH3eGDrvhSLk67oFyuDRU5bqHauFYBYR7qspH/45UA3iFaKi54bCyUC1/mAP/iZZJWAFeybRMQNjXT13IOpsbw9XYpJ8L1Y2WOcmwsycESpGFBqvUwzXJsDTPsm1mG/i0E3h7qDk+ZJ4P1xXvI39D4XjRZXkEV5ZQ6B1AqacvKQHhdCrJOvMThVPr1bMHBQWFbN+xi63bdpi3azQa+ib1ZkD/fo06XoMDl40lsCxeXawZDLaPzWxjm+Nel04+8EUXtWwwwLoS+LNIBTIZVZCrg0TfhrVnQ4kKQEzae0NHb1UCtauv6tXZVgo+GsjTQZyXCpz+KIKXpAdSNNLFIdBWq/6PnqiE34tgnBNepLekAh0stvpOeTDKuXpbTDQaeLcj9N6n2nysEu4/qc474szK9HDZUUvgDnB9GLzdUW4ACSGajgbV67IpIhFQ41wkcHEPI4YPZUD/vmRknqaiogJfHx/aRbbDz7eBF/1WGpwqFqxVwYU9KZWqa7kx4nbDyIMwPBBmtIF9ZSqKeuQkxO6Cvvvgw3rGzqRXQUyNnplIT0gzzmfzTCxcnwzxe+C+KJXuszBDLcuXrWgsHw+4yiq98ANJF+O/WSoQAFV57bI2jm1PfeK84dX2lvU3smBVYd37C6VIp9J7rYOWf7WDdyRoEUI0A9t0MbnL7KrKy8sxGHs5ysvLKS8vByAqsh0dO7QnMrIdmhqPNVSDe1xmtlGVlTr7wCjj4FsNcKgcHj8F/wht1OvybTycqoJbUlQamqmnpa+/+mL8vRhuTlFzZsy0c0FUqq89oZyPB1QYe4Omt4GcEKjQQ6gnpFaqAfuL28ODJ9WMwSMC1eBde5NpClHT3HBLiuRXeSqtIqSVphuV6uElq8IZD7jADYHZbdXf7bsCtX59Muzu1Xr/hmeSW62Clk2llm0PR8ETMc7ZsyaEcH2JBZYKKscCIyn38MJXX1/BbuGM3vvgY2Zc+g8iI9vx7vsfoTnDl8Ztt9zU4GM3OHB5Ng72lcMFhyDAeKE/+TBkVcOgAPV4YwwyFhEo1atB9oX94Mq20NbYoiR/OFyu7uraC1x8PVRQYq1CbxuE+HlYSp8uSFcXVxtL1ER5e3vB3GQ1Vub+qMa1XbRO/f0gyQ92lamJDb/MVQUgWqP3stVnH1Ra5jUuUCJao4G3OsLfeyFHB6lVqirie50c3TLnk1EFEw7BHqsbYc/Gqh5rIYRoLsHVZcSWZHMyIBy9h5bDwTH0yT/h6GaJRho3dgzBISqf/oJx5zfpsRscuPh7wG8J8HOBKgmbq1N3KkcGqipjDRmofLIStpaqieBMevqqwf1FejV43loPX/iljnSOWC/IqLbdllEN0XYG9p+oUAHLGx3g5dNqErogrSoM8LOki4gG0mhgbhjcbezJ/iCndQYulXo14aTJvZHg5SJ34KO81Hng8uNq/f0cuLQNTJHKSWYplapk/WFj2W8N6nf2z1b4f10I0fK6F6RxMkBVXjoYEiuBiwvq0T3RvBwcHERERATeXrUv0CsqKkhNTau1vT6NTpK6METNSP12R3g+Di4JbXh1pf3lcOlR2/lWtpZChCc8nQ5Tj9juv70MutcxbmdYgCrNbJJaqb5wh9kpB/1kOsyPUu3UYJnhu9pQu9CAEPW5uq0l2l9fAvvLHNoch/g0F9KMn+F2nvB/4Y5tT2PNaguzrHpxb0hWaVFC9XKPPGAJWrTAx50kaBFCtBzrcS77ZZyLy/vm2+/Jy82z+9jp01n8+tuaRh2v3h6Xf6U06li8eoYqPWOCoKefKkf6YhwcrYAHTsJDUWpsy8uH1BwBU0JgZQF8nKN6eUDd5c3VqSBHq4FbImDMITgvAIYFwp2pMCkYutUIdI5VqOpi73RU64P84ZkM2FkKX+fD+KDGvUfRukV4wbRQ9X8HVDW9xqZJujKdQX1+TO6JdM2Z6F/voCoMZlarnto7UuGzzo5ulWPtKoWJh9XvBMBbo+bBmu7ERReEEO4nvigDL301VR6enPZrQ653IG0ri8/8ROE0Vv70C/n5akCpwWDgl1W/4amtHXIUl5QQHNS4C/F6A5cVBbbrp6rUJJKdvFXKRU61Cj58PNQcDmfipVHzwNyWAkMPqHStu9qpwfgaDXzeGZ5Ih/vSoIuPWh9pLASwrgTGHoLjvVVZ5eGBKhj5zynVjgnB8LadwOnJdHjQalK8UUFqLM3ogyqQuqNdQ35NQljMDbMELh/nwIJY8HSRVKlztSzPcjc+VKtuILiicE/Va3zJUbX+v1w175MzV0ZrThtL1ED8PGOVOD8NfNMVJrbykt9CiJbnZdARX5jOgVBVCvJgSCzDsw46uFWiMfr1TWLv/v0A5OTmEhoaip+fbc+Ch8aD9t7e9O7Vs1HH1hgMDUuW+iQHHj0FX8VDf3/L9oPG9K+5YfBvGbgpWoFqA8TtstyZ/r5r6xgjYTBAv/2qOAHAI9GqwpQrm5MMHxlLW4d7wt6e0K4BE+C6k9+LYNoRKDbm0AZ7wA/dLDeNhBDibLz2xltn/dzfopL4puNwAPrnHOX6I6uaqln1uv3Wm1vkdVqTVavXMHjgQEJCmuZOWIOTPB48qeZGsQ5aQJUxfjIGXsi0/zwh3I2nBmZbVdH6oJ75htzJDwWWoCXAA+50g97Kl+NUoQ+A7GpVnr01jXv7sUD1tJiCljAtrEmUoEUI4Vg287kEx6KnlaQ1uKHx48bWG7RkZp5u1PEaXFWsSF/3IPxSvUohE6K1mBtmCda/K1AXvTWr4rkTgwEWWI1tuTkcwtzg/YZ6wnsd4SJjYZDl+fB5nu1ko+5qaR5cdQxMdQmivWBVNzUOUQghHCmmLJegylKKvP0p9fIlLSCMDiWt5C6hmykuLuGvv9dx6tQpdDo91oleOp0Og8HQqHlcGtzjMiEI7k+DzSW2238vgvtPNn4CSiFcWU8/GGLsfawyqDES7mxNEWwwfva9NTAv0rHtaUoXhqhAzOS2FDhV6bj2tIT3s+EKq6ClkzesTZSgRQjhHDTUrC7W3nGNEefkz7V/k5qaRkK3roSEBBMeHkaf3j0JCQnGYDBw0YUTGnW8Bgcur3WAQC0MOwDhO6D7Hmi7Q01I2c0HXpb/U6KVmWt1sevu6WILrXpb5oZBjLfj2tIcno9TF+8A+Tq48YT7poy9ehr+74SlLHx3XxW0dPFxaLOEEMJG90JL4HJAyiK7rJMnTzFi2FBGjTyPnj26o/XwYMTwYVw+8zLax8Vy7NjxRh2vwYFLpBds7wFfx8MN4aoi1z8j4Kdu8HuiqhAmRGtyRRvwNaZP7iiDHaWObU9z2VgCvxWpZS3uOXt6kBY+6GRZ/7FQTTDqTgwGWJCuSseb9PODPxIg1s0CUSGE60ssOGlePh4YSYWHG+Qnt0LV1dW0batKdrZpE0pWtvpy1Wg09Ondi1On0ht1vEbNwOChUbPePxMHb3WEhbGqDLEQrVGop5p13cTdLnRNFlqdU65s67535s8Psi04cFcqnKhwXHuaksGg5sx6+JRl2/AAWJPQ+qqoCSFcQ0hVKTEl6otV56HlcLCLl7FspYKCAikoLAQgNDSUiooKCo3rnp6elJWXN+p49QYuSftgj7GKUJ+9ar2un777zuLdCOHi5lpVF/ssR02U6k52l6niAybz3bC3xdrCWJX6Cqogyf+dAL2Lp4zpDXBrCjxnVfnxgiD4pZsKvoUQwllJupjr69Y1nr/XbWD/gYMEBgQQFtaWtX+vJ+3kKbZs206b0NBGHa/ewGWgvyp7CjAwQK3X9TPAv74jCeGexgVBB2OaTY6u9qStru5pq96WS0Pdf/C2vwd81MlyYvytCN7McmSLzk21Aa5LhjetxmBdHKLmHgqU9F4hhJOzKYssgYtLGjxoIN26xpOSovKUzx89ipOn0vnm2xVkZWUzYsTQRh2v3vtt1jnfHxqXDQbLLPRlenU3L0C+AEUr5aGB68LgSeMF/gc57jP7+pFy+CLPsv6gm/e2mAwPhHsj4VljD8W9J9UM8l1963+es6nQw5XH4et8y7ar2qpzuZdMiSCEcAHxhel46qup9vAkw68Ned4BtKksOfMThdPw8PBgzOiR5vXo6Cium30VeXn5tGkTird34wZZNniMS6VepRsMO2DZ9ncxhO+E+9JA5+LpFEKcrTlW6WIrCyC9ynFtaUrPZloqT10YrHpdW4vHY6CXMVAp1cPcE651jivRwbQjtkHLTeHwcScJWoQQrsPboCO+yFLWUtLF3IO3tzeRke0aHbRAIyagnH9SzVWxwGps1EB/eKk9PHxSVeV5JLrRry+Ey+viA2MC4Y9idaH/SY7rV95KrYSPrIoNtJbeFhMfY8rY0AOgA9YWwyun4R4XmL+mQAdTDsPfVjcl50XC87GW3nIhhHAV3QvSzGliB4LjGJ510MEtEmfy1jvvN2r/m2+8vsH7Njhw+TIPFsXZzl3RxhNuiVAHWZghgYtoveaGq8AFVLrYvZGufZH4YqaaWBNgZCCMDnJsexxhYAA8HA2PG9MAHzwJk4KhhxOP88muhgsPwzar0tyPR6tzsyv/fxRCtF7dC1L5lmEAHAyJRU8jS+KKFtevb1Kzfec0OHDJ10FUHWUzO3hDppukxwhxNmaEwu0eUKyHA+Vq7pNhgY5u1dk5XQVvWw1Ib229LdYeiobv8mF7GVQYB7qv6w6eThgEnKqECYdhn1VlyZfi4G4X6CUSQoi6xJTmElRVSpGXPyVefqT5h9Oh1M1nfXZxQ4cMarZjNzhwGeAPb2XBRcG179y9nQ39paqYaMUCtDCrDbxvTK/6IMd1A5dXTkOZsbelv5/6zLdWXhr4qDMM3K96oDaXwnMZ8KCT9S4fr4Dxh+BYpVrXAG93VJMFCyGEK/MAOhadZk/bTgA83/tSfHVVDMo5zLj0XURUFDq0faJ+Bw4cOuM+3bsnNPh4DQ5cHo+BiYehx16YHKImLcuqgpWFcLRCzQkgRGs2N9wSuCzJhUXtVXldV5JfDa+dtqw/KClG9PGDJ2LUOD+Ax9JhSgj0dZKbNQfKVdBy0tjr7Ql80hmuaOvQZgkhRJPYG9Ke/aHtLRs0Gso9vVkX0Z1N4Qlcf/hXehWkOq6Bol6rVq+xu12j0aDVavHy8myewOX8IFibCE9nqEH6uToI0cKIAFU2eWgrqjgkhD3nBajJCw9XQKEevs6Dq8PO/Dxn8kaWajtAd181d4uAf0fCN/kqBbDKmDK2qTt4Ozgw3V6qbihlV6t1Hw0s7QLTQh3aLCGEaBJZPsG8320COo/a827oPbRUouX9bhN4YPcy6XlxUjf+35xa26qqqjmVns76DZuYOH5co47XqK/dIQHwdTxk9IXKAZDVF77tKkGLEKB6JqxLI3+QU/e+zqhEB4uselseiFLz1Ag1puWjTuBr/H3sLIOnMup9SrNbVwxjD1mClgAP+LGrBC1CCPexOjqJak39l6rVGg/WRPVpoRaJxvLx8an1ExgYQEK3rgweOIA/1/7dqOM1KnDRG9QdvrXF8GdR7R8hWrtrw2xnXU+ucGhzGuXdbMtFcEdvNVmhsEj0hadjLesL02Gzg+ZBW1WoBuIX6NR6qBZWdYNxrXg8khDC/WwJ64beTm+LNb2Hls3hDU81Es4jKCiQ3Ny8M+9opcGpYuuLYdYxOFUF9uZh0wC6gY16bSHcTpw3TAiGn4091h/lwKMx9T/HGVTo4flMy/p9kTJRoT3/aqcmdfyzWM3vcl0ybOsBvi2YMvZdPsw8BpXGE3GEpxpj2M9JxtwIIURTKdfWUc62hooG7idaXnl5ea1tBoOBkpJStmzdTkhI4+64NThwuTNV3dV7o4O6ODvb7+kD5XB7CmwogTBPuD0C7jWWWz1RATeeUBOndfBWpTwnhdR9rC9z4cFTxjKgwfBOR1U0AODvYrj2uMrXXxgDN0ZYnjcvFZL84ToXG38gXMPcMEvg8mGOmkPD2VOuPs6xDO6O9ITrpRqVXR4aNaYvaR+U6GF/OfznFDzXQpM5/y9XndeMHS3EecGqBNUbJIQQ7sZXV0W555lnV/fRyZwczurd9z9CU0eVH61Wy0UTxzfqeA0OXHaXwfL4+gOJM6kywKTDMDYI3uyggpirjkOMl0pLueQo9PCFzd3huwK47Cjs7QWdfWofa3OJutv53w6qVPOdqXBtMvxkrG52e4oKVgb7w9QjML0NhHtCRpVK4WmpCw3R+lwSqoL8/2/vvsObKtsHjn+T7kGhe7D33nsIIoiCKAKi8MOFIrgRRXAwRFFEQTa+KluWZTsQX3xZsvcebYFCKW2hm+40eX5/HJo2tGxoknp/riuXzTlPDs/tOUnOnWclGyEyR1uYsqMNL+CYq2BCgdaWDwKLtwXB3lRxgYnl4I0L2vOJcdo5b/uAp7/+8Qq8fiG/xbuqi9Y9rFIRn49CCFESNEsIZ4d/rZt2F9ObjDSPv/WUu8I6Oj3ycKFtOnQ4OztRtmwILi539iV224lLBWdINd663M1E52gD/GdWADc9VHOFzl7ajV2wE5zOgn9qQikHqOOm9eOeEw/jyhY+1vTL0NsbXr72y/DCylDhKERkacc9laXNiFTDVbuJPJutJS7jY7UZghxs/BdwYb9c9VoiPuvaIo7z4m07cQlN0qY0B/B2gNf9b15ewGA/WJUEG65qicTLkXCotraez4MwKQ6GXcx/XtcVNtTQPjeFEKKkeiTmCHv8apDDjT9cHZWJjrFHi7FW4k7UrlXzvh7vtn9XHRuidYnYfw+DUSu5wC9VtKRFKa0719ar0KmU1nWssbuWtORp5wk7b/Dv7UqH9gV+4SzvrA0ozitfwRkOZGjdz5KMWpeK6BzYchX6yaBj8YANKNANcUXSvSf9D4pJwfiY/OdDAizfg6JoOh3MqQRe1z5BI7Lz13m5n5SCMZcsk5am7rC5piQtQoiSzz87lVfCN+BsNKA3WX6R6k1GnI0GXgnfIFMh27jExCT+t3EzPy9exuy5C1i89Be2bN1G6tU7n9nrtltcJsVp3axanNKmBnW5rsVCB6Q0vv1/uNxRbaB/99LwjDe8F6V1GSso0Aku5hT9+hhDEeUd88t/XRb6n9MGsH4SDCHO8NYFGB4krS3iwWvqDvVc4ViWtgp9aJJtrmL+W4pWRwBPPbwTYN362JPyzjC1PAw4rz2ffgWeLnP/ZvZSCj64aDlF9UOe8Hs18JLkUgjxL1E3JYqPjq5gU1B99vrVINvBCRejgebxYXSMPSpJi427GB3Nb7//iZurKxUqlMfNzZXMzCzOnjvH6bBwevV8Cj/f2x90ftuJS/fSwD2Mb7ne2qpa4vLGBRgaBRkmcLmu/cdFB9lFTWHGDcrr88v39IaE0tpsSWUcISpHa+GZXh4+iYZFCdDGE+ZWsr/VzYXt0+m0Ae7vX/ulfF687SUuSmlT+uZ5wx98bvsTQYA2wcfKZPg9RXv+ynk4UufeEwuj0sazzI7P3/aYlzbOUD6vhBD/Nv7ZqTx7fjvPnr+zNT+E9e3YsZtyZcvSrWsXHBzyvxyNRiO/r1vPtu07efqp7rd9vNu+TbnfU7o2u7ZoZYZJG2T/im/+mgR5stWNv6Rd9VpSYlHeZFneTa89AL6M0RbU250OSxO1Qf8DIrWxMiOC7kdEQlh63geGX4RcYEe6NobLlmZ/+t9V2JOh/e2ig6GB1q2PPdLp4MeKUO84JBrhfI7WrevHind/TIOCF87BLwWmtu9VBpZULvxjjRBCCGHLEpOS6NqymUXSAtqMYo0a1Gf9Xxvu6Hg3TVy+i4P+PlqXre/iblZS6yp2qxuf6BzYnwFPlcnfVsdV684V7KTNXFZQrOHG/bjLOkFs7nXlc4sufz5bS1hmVYApl6Glh9aPv3OB9TaEuN/8naB7GViTrD2fn2C5gKG1fVVg5fdX/WTMxN0KdtImHOl3Tnv+Uzz0LHN3MzBmmbQ1WvJacABe9NHG0zhKF1chhBB2xtu7DJcvx1OxQoVC+5KSkynldWezF900cRl2URsgH+hkOTi0KLeTuJzMgl5n4FKD/PVW9mdoC6i184Rv4iDdmD8zz7Y0aOVR9LFaeWj787rfROXAhZyiy38RAx8HaWsw6IC8hppcpXWXEeJBGeCbn7gsTIBxIbYxxmpnGmy6NibOAfhQWlvuyXPesDIJViRrzweeh2N1wPsOut6lGbUp4TcWGKv4lj9MK2/76wAJIYQQRWndqiX/3fA/cnNzqV6tKh4e7mRlZXEu8gJ79+2nbZtWXL5yxVw+wP/mU5ve9GvV1LTov+9Wh1LaNMcvR8KkctoUrB9Fw6dB2r6Kztq+z0K0Xxx3pcOca10uckxaVwx/R+3G7w1/6BAGbT2glae2jktXL6h+XVecs9na7GI/XTtOM3f4OhYOZ2grYHe24Wlqhf3rWhoCHOFyrjam67+p97YW0v1SsLXleV9ZC+Re6XRai+7WtPxzPSRKm6b9diTlQrcI7TMvz0dB2uK5N1i3SwghhLB5v/72BwD7DxzkwMFD5u3qWsvBlq3bzM91Oh1vvTHopscr1qG4Tjr4o5o2u1fLU1p3rfcC4N0A7ct5bVV49Tw0Paktrra6av4N1Y506BgG5+pp21p7asnI6EuQkAuPesGPhVuh+CJGm1Us78v/oVLadMjtT2vJksyiJB4kJx284KvNygcwL8H6icvhjPyuSDq0G2Rx7/yd4IeK0POM9vznROjlrc00djOXDdAlHA4X6Cr7VQh8HPzAqiqEEEIUi549nryvx9MpJZ2lhHiQjmdCvRPa3846iGlg3dm7+p7NH/j9TBlYXtV6dSmJXjgHixK1vwMctYlA/G5wvqNyoHMYhGXnb5teHt6WH1SEEHZuxqwfrF2FO/b2m4OtXYXbZjQa+WX5Sh5q24by5csBkJWVxaYt/3DhQhSuri60aN7spgtAXoyO5p9/dpCSmkpAgD+PdOxAmdLar6tJSUms/+/fpKWl0aB+fVq2aGZ+3eEjx8jOzqJF82Y3OnSRDAYDOQYDri4uhQbr3y6Z/FSIB6yuGzR3h70Z2kQUSxKtd2MalqWtKZPnE/lV/76bVl4bp3LJoHUbe+M8hFYp3OUrIgs6h2szkYG2GvDcStoUy0IIIcSN5Obm8t8N/yMxMcli+98bN2MwGOjdqweXL19h85Z/KFO6NMHBhbtWXL2axh/r/qJ5syZUqliRvfv2s27dX/Tr2wedTsfOXXsICQmmdq2arFn7O1WrVMbPz5fc3FyOnzhB7549bru+F6IusmvXHq7E58/xH+DvT4vmTalYsYjuUjchk2sKUQwGFFjDZV6C9eoxIRbymli7ekFjd+vVpaTydswfmwfagP3HwsHrIOj3a//texZan8pPWpx08EsVSVqEEELcXGJiEitWriEl1XJa3JSUFCIjz9Px4fb4+fpSp3YtataoztFjx4s8zomTJ/Hz9aVJ40b4+HjzSMcOXE1L4+LFaACSkpKpXLEiAf7+eHuXISk5GYBjx09QvVo1XFxub3DshaiL/P7Hn+j1etq1bU2Xzo/Qtk0r0MHv69ZzIeoWs39dR1pchLjPimoez3BwxrHJC+TqHTmQAR/PW07ZzMRirVeisyfzG/YFvdY8W3PnWmZs0Ebp21Pz+IN2v7o3tK7cnp0BtQHYkKrMTS5XTfBLYv5zJ1Mur4b9l9hdUcy4y39Lzp8QQvw7RF+6RNmyIbRq2Zwffppr3h4bdxl3d3dzVy+A4OAg9u47UORxYmMvExKS3xLj5OSEv78fsXFxlC9fDs9SnlyJjycwMIDU1FRKeXpiMBg4fuIkfXr3vO367t69lyqVK/H4Y49abG/UsAHr/9rA3r37qXCtq9vtkBYXIYqBuzGHBomR5ue7/G/c5/RB2RjcANO1pKVa6iWqpsXe4hXiXrSPO5Y/3/r1/cTynivF/53ZTN2UqOKtnBBCCLtUv15dHmrXBicny8XXMjIy8PCw7Ebh7uZGWlpakcdJz8jAw8OjiPLa9JYtmjXlwMHDzJ67gAoVKhAUFMjRY8epWaM6zs7Ot13fhMREateuVeS+2rVrEV+g+9jtkBYXIYpJq/jTHPCrBsA+v+r0iNqNozLd4lX3R6qjGzuu/foP0OXSwWL5d//NtgfUQY/CxI3nM9YrxdlSQTRLPFOMNRNCCFHS5ObmFrk6vclkMk81fDvljUYjoLXWvPLyC+QYDLi5umIwGDh58jR9nunJkaPHOHjoMN5lytC50yO4u7vdsF7u7jdOntLS0nB0urPVr6XFRYhiUjMlmjLZ2ps3zcmN42XubEDavdgcXB+DXvudonzaFWql3FmfUnHn9vlWx6S7+UesSa9nr1+NYqqREEKIkqpg0pHHaDTi6OhYKGkBcLxJ+YLHdHPVFkg8cvQYtWrVICcnh1279/JM7554+3izZ+++m9arapXK7Nq9h6jrxrJciLrIrt17qVrlNhc8y6v3HZUWQtw1PYqW8WH8VbYJoHUXa5gU+cD/3QwHZ/4JrGt+/tilAzdpAxD3S5bD7f2KlH2b5YQQQogb8fDwICMj02JbekYG7u5Fz8Kjlc+w2JaRmYmvr0+hsjk5OZw8FcazfXoRdSEKb+8yeLi7U6F8OXbu2nPTerVo3ozY2Mus/e0PnJ2dcXd3IyMjE4PBQECAP21at7yjOCVxEaIYtbxy2py4nChTgVQnN7wMmbd41b3ZGliPLAetP2pQRiL1iyFZEuBqNJDleOt+wC5GQzHURgghREkWFBhIeno6qalX8fIqBUBMTCxBgUWvvxAUFED0pRjzc4PBwJUr8TRr2qRQ2cNHjlGndk2cnZxApzOvem8yqfypSm/AycmJXj2fIvL8eS5diiU7OxtXVxeCg4OoVLFika1BNyNdxYQoRv7ZqVRN1T4oTDo9ex5wN6FsvSObguqbn3e5dEje9MWkWUI4epPxpmX0JiPN48OKqUZCCCFKqtKlvahQvhx//28j8fEJnDh5irDwCOrX13pcmEwm0jMyzN3DateqRVzcZfbtO0BiYhIbN22hVClPypcra3Hc7OxsToeFUb+edhx/Pz8SE5OIiYklIuIMgTdIjArS6XRUrlSJhg3r07BBfRo1bEDlSpXuOGkBaXERoti1unKaM17ayo+7/WvSKebwA+u6tT2gNhlOWv9U36xUmiREPKB/SVzvkZgj7PGrQQ43Xh3YUZnoGHu0GGslhBCipOrc6RE2bt7C8pWrcXd355GH2xMcpE15nJaWzsJFS3i6x5OUKxuCl1cpuj3ehX+272TfgYMEBgbwRNfHCiUTh48cpU7t2uZZzLy8StGieVN+X7ceHx9vHu/S+Zb1OnHiFPsPHCT16lXzNu8yZWjZsrmMcRHC1jVOPMPySm3JcXAi1s2b8x4BVEq/fN//HYNOz/+CG5qfd445hMOt2nTFfeOfncor4RuYW/1RcnV681TUoLW0OCoTr4RvwD879SZHEUIIIYp2/Rpe7u5udO/2eJFlvbxKFSpfsWKFW65c36J5s0LbmjRuRJPGjW6rjkePHWfL1m1Uq1qFFi2a4ebqSmZmJmfORbL+rw083qUzVatWua1jgSQuQhQ7F1MujRPPsvvaWi67/Gs+kMRlt39NUp21Odq9ctJpeeX+dEm6Xws0FidrLdBYNyWKj46uYFNQffb61SDbwQkXo4Hm8WF0jD0qSYsQQogS7eChwzRq2IB2bVtbbK9ZswZb/9nOnr37JXERwta1unLKnLgc8K1Kr/M7cFY3Hw9xJ4zo+Du4kfl5p5jDON3H44vb55+dyrPnt/Ps+e3WrooQQghRrDIyMqlQvlyR+ypVqsiJk6fu6HgyTlcIK6h6NRb/rBQAMh1dOOJzZ308b+WAbzUSXL0AcDdk0fbyyft6fCGEEEKIWylfvhynw8KL3HcuMpKyIcF3dDxpcRHCCnRoUyP/Xr4FoHUXa3afBs6bgP+GNDI/7xh7FBdT7n05thBCCCHE7apYoTw7d+1hxao1VK9WDQ93N7Kysjl3/jwXLkTRpHFDDh46AoBOB40aNrjp8SRxEcJKWsSH8Ue55iidjjCvsiQ6e+KTk3bPxz3qXYlYd20BKVdjDu3jjt3zMYUQQggh7tTmLf8AEBsbR2xsXKH9+w8cMv+t0+kkcRHCVnnnpFMz5SKnypRH6XTs8avB45cO3NMxFfDfkMbm5+3ijuNuzLnHmgohhBBC3Ln7PTmOjHERwopaXTlt/nu3fw1M93i8U6XLccFTWwzKyZRLxxhZI0QIIYQQJYMkLkJYUYOkSNxyswGIdy3NmVJ3NkjtegVbW1pfPoVXbuY9HU8IIYQQwlYUe+JyJhuejADvQ1DuCHwQBVnXfmYefB50+y0fUwp3hzMLTYRqx8D9APSIgMuG/H3b06DqUfA/DD9dsXzdB1GwIOG+hybEHXNSRpoWGJS/69oUyXfjrGcgEV4hgLbAYaeYw/dcPyGEEEIIW1GsiUuOSUtaXHSwoyYsrgxrkuHTaG3/8Uz4tizENMh/DPIv+lh70+GlSBgZBLtqQaoRXozM3//2BXjNH5ZVhnejIP7apEqxBvjfVXje5wEGKsQdKNhd7JBPFbL0Tnd1nL8KtLa0iA+/LwP9hRBCCCFsRbEmLnsyICIb5leC2m7QoRR8URYWJ2r7T2ZBMw8Icsp/uN+ghtMvQ29veNkPGrjDwsrwVypEZGn7T2VBrzLQyQvKOMBZrTcO42NhWCA46B50tELcngrpVwjO0N4EOQ5OHPS9/RVk80S5+3LCuyIAOqV4NObQ/ayiEEIIIYTVFWviUtMF1lUDT4f8bTog2ai1hCQaoabr7R1rVzq098x/Xt4ZKjrDznTteQVnOJAB57MhyQjlnCA6B7ZchX7S2iJsSN6aLnl2+d15d7ENBVpbGiWeJeDa4pZCCCGEECVFsSYu/k7Q2Sv/uUnBjMvathOZ2tzMo6Kh7BFoeALmx9/4WDEGCLmuR02gI1y8NvPr12XhlUioegyGB0GIM3wVq/0trS3C1jSPD0evtMFeZ72CuezidYtX5It1LcMhn/xWmi73OKWyEEIIIYQtsuqsYu9fhIMZMKGs1k0MoKE7/FkNXvWDwRdgeVLRr80wgct1tXfRQ7bS/u7pDQmNIL4hfB4CUTnagP2+3vBJNFQ4An3PascRwtq8cjOpm3zB/Hz3HQzS3xDSCKXTsvG6Secpd63bmRBCCCFESWKVxEUpGBIFMy/D0ipQ1w3e9Ie4hvBOgDZm5d0AGOQH318p+hiuesi+LunINlmOiXHTQ5lrS2x+GQMfBcHudFiaCMfrQq7SxsoIYQsKdhfb418DE7duGkxw9mSfX3Xz88cuHXwgdRNCCCGEsLZiT1xMCl45ryUkv1SBHmW07Tod+Dhalq3tqo1LKUpZJ4jNtdwWmwvBRUzIdD5bS1ie9dbGwLT0gFIOWhe1Xen3HJIQ90W95At4GrR1V5KdPTlduuwtX/O/kEaYdNrbuHpqNJXTbjJ/uBBCCCGEHSv2xOWDi7AkEVZVgV7eBbZHQfcIy7IHM6HWDQbrt/KAbQVme43KgQs52vbrfREDHweBXqcNhM5rqMlVWuuPELbAQZloHh9ufn6rNV1SndzYWaBMl2hpbRFCCCFEyVWsicuuNJhyGcaGaNMexxryH0+WgT9TYNplbZHKGZdhYQJ8GKS9NseklTNeSzTe8NcSoJ+uwNFMbU2Xrl5Q/bpE52y2NrtYn2tJUjN3bWaxwxmwOllrfRHCVhRc0+WId2XSHVxuWHZjUANy9VozZYW0y9RMjX7g9RNCCCGEsJZiTVxWJGv//Tgago9YPtp5wtLK8OMVqHscZl3Rnre7NuXxjnStXNS1rmOtPeGnijAuFlqfgtIOsKBS4X/zixj4JFjrigbwUCltOuT2p8FDr42pEcJWhGQmUj5NG9iVq3dgv1+1IsulO7iwLbCO+fljlw7cxogYIYQQQgj75XjrIvfPxHLa40ae9dEeRXm4FKimltte8tUeNzOvUuFtU8prDyFsUav400R5+gPami7t444XKrMlqB7ZDs4ABGckUi/pfLHWUQghhBCiuBVr4iJubcasH6xdhTv29puDrV2FEqVpfASrK7QiV+9IlKc/l9x8CMnMn+I4S+/ElqB65uddLh207rzmQgghhBDFQO53hLAxHsZsGiRFmp9fP0h/e0BtMhy1wVx+WSk0TjhTnNUTQgghhLAKSVyEsEEFB+nv9auO8dqUxwadAxuDG5j3db50CAdkajwhhBBClHySuAhhg2qmRFMmW5vvO83JjWNlKgBa60uqszYVXpmcNFrEh1mtjkIIIYQQxUkSFyFskB5lkZTs9q+JUafn7+CG5m2PxBzBSZmKerkQQgghRIkjiYsQNqplge5iR8tU5L3mA0l09QLA3ZBFm8snrVU1IYQQQohiJ4mLEDbqimtpdHktKjpd/mJEQLaDExGlgq1UMyGEEEKI4ieJixA26IqLF3OrP4rSFf0WNeodmFv9Ua64eBVzzYQQQgghrEMSFyFs0MbgBuTeIGnJk6vTsymofjHVSAghhBDCuiRxEcIG7fOtjknvcNMyJr0De/1qFFONhBBCCCGsy9HaFRBCFJbl4HRb5bJvs5wQQgjbMWPWD9auwh17+83B1q6CENLiIoQtcjUabqucy22WE0IIIYSwd5K4CGGDmiWEozcZb1pGbzLSXBagFEIIIcS/hCQuQtigR2KO4HiLxSUdlYmOsUeLqUZCCCGEENYliYsQNsg/O5VXwjfgbDQUannRm4w4Gw28Er4B/+xUK9VQCCGEEKJ4yeB8IWxU3ZQoPjq6gk1B9dnrV4NsBydcjAaax4fRMfaoJC1CCCGE+FeRxEUIG+afncqz57fz7Pnt1q6KEEIIIYRVSVcxIYQQQgghhM2TxEUIIYQQQghh84o9cTmTDU9GgPchKHcEPoiCrGuTJ53Phi5h4HEQah+HP1NufqzQRKh2DNwPQI8IuFxgSYvtaVD1KPgfhp+uWL7ugyhYkHBfwxJCCCGEEEI8QMU6xiXHpCUtdVxhR024nAuvRGr7JpaDHmegtivsrQW/pkDvM3C8LlR2KXysvenwUiR8XwGauMOQKHgxEtZX1/a/fQFe84fm7tA9Anp6g58jxBrgf1fhm3LFFLSwIKsFCyGEEEKIu1GsicueDIjIhj21wNMBagNflIX3o+CJ0nA6C/6pCaUcoI4b/J0Kc+JhXNnCx5p+GXp7w8t+2vOFlaHCUYjIgmqucCoLepWBGq5QxgHOZmuJy/hYGBYIDrrijFwIIYQQQghxL4q1q1hNF1hXTUta8uiAZCPsSofG7lrSkqedJ+xML/pYu9KhvWf+8/LOUNE5v3wFZziQoXU/SzJCOSeIzoEtV6Gfz30PTQghhBBCCPEAFWuLi78TdHbKf25SMOMydPaCGAOEOFmWD3SCizlFH6vI8o755b8uC/3PQY6CT4IhxBneugDDg6S1RQghhBBCCHtj1XVc3r8IBzNgb234Lg5crmv/cdFBtir6tRmmIsrr88v39IaE0pBtgjKOEJWjDdifXh4+iYZFCdDGE+ZWAneZW00IIYQQQgibZpVbdqW0wfQzL8PSKlDXDVz1WpJRULa6cVJRZHmTZXk3vZa0AHwZAx8Fwe50WJqoDfrPVdpYGSGEEEIIIYRtK/bExaTglfPw/RX4pQr0KKNtL+ukzfhVUKwBgp0KHSK/fO515XOLLn8+W0tYnvXWxsC09NDG0nT20sbKCCGEEEIIIWxbsScuH1yEJYmwqgr08s7f3soDDmVCujF/27Y0bXtRWnlo+/NE5cCFnKLLfxEDHweBXqdNBpDXUJOrtNYfIYQQQgghhG0r1sRlVxpMuQxjQ6CZh9aikvfoUEqbFezlSDieCRNitdaQ165Nd5xj0soZryUab/hrCdBPV+BopramS1cvqO5q+W+ezdZmF+tzLUlq5q7NLHY4A1Yna60vQgghhBBCCNtWrInLimTtvx9HQ/ARy4cC1lbVFqVsehIWJsDqqlDp2uKTO9K1clHXZg1r7Qk/VYRxsdD6FJR2gAWVCv+bX8Ros4rprs0k9lApbTrk9qfBQw/vBDzgoIUQQgghhBD3rFhnFZtYTnvcSDVX2FKz6H0PlwLV1HLbS77a42bmVSq8bUp57SGEEEIIIYSwDzIRsBBCCCGEEMLmSeIihBBCCCGEsHmSuAghhBBCCCFsniQuQgghhBBCCJsniYsQQgghhBDC5kniIoQQQgghhLB5krgIIYQQQgghbJ4kLkIIIYQQQgibJ4mLEEIIIYQQwuY5WrsCQgghiteMWT9Yuwp37O03B1u7CjbDHs8fyDkUQtw7aXERQgghhBBC2DxJXIQQQgghhBA2TxIXIYQQQgghhM2TxEUIIYQQQghh8yRxEUIIIYQQQtg8SVyEEEIIIYQQNk8SFyGEEEIIIYTNk8RFCCGEEEIIYfNkAUohhBBC2BR7XGRTFtgU4sGzWotLtgnqHYe/U/O3jY8B3X7Lx3tRNz7GpqvQ4AS4H4CHT0NEVv6+U1nQ8AR4H4LPLlm+bvplGHvdNiGEEEIIIYTtskrikmWCfufgeJbl9uNZ8G4AxDTIf3wRUvQxonLgqQh43gf21YYgJ+hxBkxK2/9xNLT3hP/VgMlxcCQj/9/+4Qq8F/jg4hNCCCGEEELcX8WeuJzIhFan4Ex20fsau2lJSN6jlEPRx/kpHhq6wfAgqOMGcytpyczGq9r+U1nwZBlo4q7tP3UtSfr+CvT1gdI3OK4QQgghhBDC9hR74rIlDTqWgp21LLebFJzOhpqut3ecXWnQvlT+c3e9lqTsTNeeV3CGAxmQnKslSRWcIcOkJTzvBtyfWIQQQgghhBDFo9gTlzf8YXJ5LdEoKDInP7GoeBRqH4dvY/O7fl0vxgAhTpbbAp3gYo7295hg+CYWfA9DVy9o5QkzL2tdy7yktUUIIYQQQgi7YjOzip281pWrrBP8Xk1rLRlybWD+h0GFy2eYwEVnuc1FB9nXEp02nhDbAK6awNcR0o0wNwH21IIZl2FiHNRyhYWVIMCp0OGFEEIIIYSwKWHhEfx3w/8stlWuXIknuj5WqGx8fAKbtmwlISERb+8yPNz+IQIDtW5H6RkZrP9rA/HxCVSvXpWOHdqj02k31uciIzlz9hydH+n44AO6QzaTuDxRGuIbakkGQH03iM/VWkmKSlxc9flJSp5sBb4FWlOc9eB7rWVn+hV4yRdSjDDyEpyqq7XIjLkE31d8MDEJIYQQQghxvyQmJlKlciU6dHjIvM3RoXBXIoPBwK+/r6N6tap06vgwx0+c4Pd1f/JC/344Oztz4MAh3Fxd6dO7J7/98SeRkeepXLkSAPv2HaTLo52KKaI7Y1MLUPpel0bVdoVLhqLLlnWC2Ov2xRoguIjWk6tGmB8Pb/vDnnStpSXICbp4wa70+1N3IYQQQgghHqTExCR8fX3xcHc3P1xcXAqVC484g4NeT7u2rfHx8aZd2zY4OzsTHnEGgKTkZCpUKI+PjzdBgQEkJScDcObsOXx9fShd2qs4w7ptNpO4TI3T1mQp6GDGjQfrt/KEbWn5zzNMWvlWHkUc+zK84geeDqAjf9xMroIbDKERQgghhBDCpiQmJePtXeaW5WLj4ggKDjJ3/9LpdAQHBREbGwdAKU9P4uMTyM3NJTExCU9PT5RS7Nt/gGZNmzzIEO6JzXQVe7w0fBStrb/yqi/syYAJsfCfa924jAqu5IKPg9YF7BVfbfD+lzHQswx8EaPNHNaplOVxU4zwcwIcqK09b+wOxzJhRxr8kgQti0h0hBBC2C9ZdV0IURIZjUZSU1OJjDzP7t17UUC1qlVo2aIZDtd1F8tIz6BMmTIW29zd3bgSnwBA40YNWfPr7xw/cZKyZUOoWqUyZ86cxd/fDy+v626mbYjNJC41XbVB+R9Fa60vQU4woRz8n4+2PyoHKh+DTTXg4VJQyQVWVYWhUVry0soT1lYD/XUD9qfEwWt+4HHtfFZygbEh0D0C6rpBaJXijVMIIYQQQog7lZySgslkwtHJka6PdyElJYV/tu3AYMihQ/uHLMrm5uYWSmYcHBwwGo0AlClTmhef70dWVjbu7m4opdh/4BBdu3Yh4sxZdu7ajaurK492eoQyZUoXW4y3YtXERTW1fN7JC/beoEtdJZfC5buW1h43Myak8LYPg4oe8C+EEEIIIYQt8vXx4dVXXsLNVRtH4efniwL+u+F/PNSuLXp9/ggQBwdHc5KSx2g04uSYf+uv1+txd3cDtDExAQH+uLu5sWnzVno+/STR0ZfYum07T3Xv9uCDu002M8ZFCCGEEEIIcWN5SUseH29vTCYTmZmZFts9PN3JyMiw2JaekYm7u3uhYyqlOHDgEM2aNiYxKQkHvR4/X18qlC9HXFzc/Q/iHkjiIoQQQgghhI07c+Ysc+YttGhJuRIfj4uLc6GEJCgwkNi4OJTSpqFSShEbE0tQUGCh44aFhRMUFEipUqXQoUNdm7rKZFIoG5vFShIXIYQQQgghbFxI2RBAsWnzVpKSk4mMPM/2Hbto3KghOp2O9IwMcnNzAW3QvsFgYOs/20lMTGLb9p3kGAxUr1bV4pgmk4n9Bw/TrGljQBv7YjSaOHsuktNh4eYFK22FJC5CCCGEEELYODdXV57q/gRXr6YRGrqSTVu2Uq9uHZo20ZKOefN/Nq/T4uzsTPduXYmNjeOX5SuJiY3lySe64uzsbHHM02HhlA0JxtPTEwAnJyc6tG/Hps1bOH/+Ag+1bVO8Qd6CzcwqJoQQQgghhLgxf38/ej79ZJH7rp9WPTAwgOee7X3T49WuVZPatWpabKtZozo1a1S/t4o+INLiIoQQQgghhLB5krgIIYQQQgghbJ4kLkIIIYQQQgibJ4mLEEIIIYQQwuZJ4iKEEEIIIYSweZK4CCGEEEIIIWyeJC5CCCGEEEIImyeJixBCCCGEEMLmSeIihBBCCCGEsHmSuAghhBBCCCFsniQuQgghhBBCCJsniYsQQgghhBDC5kniIoQQQgghhLB5krgIIYQQQgghbJ7VEpdsE9Q7Dn+n5m9LzIU+Z8DrIFQ6CgsSbn6MTVehwQlwPwAPn4aIrPx9p7Kg4QnwPgSfXbJ83fTLMPa6bUIIIYQQQgjbZZXEJcsE/c7B8SzL7S9HQoIRtteC0cEw+DzsSCv6GFE58FQEPO8D+2pDkBP0OAMmpe3/OBrae8L/asDkODiSkf9v/3AF3gt8YOEJIYQQQggh7rNiT1xOZEKrU3Am23L7mWz4LQV+rAD13eAVPy0pmXWl6OP8FA8N3WB4ENRxg7mVtGRm41Vt/6kseLIMNHHX9p+6liR9fwX6+kBphwcVoRBCCCGEEOJ+K/bEZUsadCwFO2tZbt+dDsFOUM01f1s7T9h5gxaXXWnQvlT+c3e9lqTsTNeeV3CGAxmQnKslRRWcIcOkJTzvBtzfmIQQQgghhBAPlmNx/4Nv+Be9PcYAIU6W2wKd4KLhDsvnaH+PCYbuEfBptNZy08oTvo3V/vaS1hYhhBBCCCHsSrEnLjeSYQIXneU2Fx3kKFAKdLrbK599bYxLG0+IbQBXTeDrCOlGmJsAe2rBjMswMQ5qucLCShBwXQIkhBBCCCGEsC02Mx2ya4GkI0+2Ajdd4aQFwFVfdHn3AhE567WkBWD6FXjJF1KMMPIS7KoFdVxhjMwuJoQQQgghhM2zmcSlrDPEXtctLNagjXspsrzT7Ze/aoT58fC2P+xJ11pagpygixfsSr8/9RdCCCGEEEI8ODaTuLTygGgDRBaYbWxbmjY2pcjyntr+PBkmOJihHed6Uy9rs5R5OoCO/CmTcxWowsWFEEIIIYQQNsZmEpcqLvCYF7wYqa25Mi8eFidqrSQARqW1qOSYtOev+GozkX0Zo02x/GqkNnNYp1KWx00xws8J8Na14zR2h2OZ2vowvyRByyISHSGEEEIIIYRtsZnEBbSB8mUcoOUp+DwGZleE1tdaXKJyIPgI7LjWtauSC6yqqiUlzU5CXC6srQb668bDTImD1/zAwyH/dWNDtBnHInPgs5BiC08IIYQQQghxl6w6q5hqavk8wAl+rVZ02Uouhct3La09bmZMEYnJh0HaQwghhBBCCGEfbKrFRQghhBBCCCGKIomLEEIIIYQQwuZJ4iKEEEIIIYSweZK4CCGEEEIIIWyeJC5CCCGEEEIImyeJixBCCCGEEMLmSeIihBBCCCGEsHmSuAghhBBCCCFsniQuQgghhBBCCJsniYsQQgghhBDC5kniIoQQQgghhLB5krgIIYQQQgghbJ4kLkIIIYQQQgibJ4mLEEIIIYQQwuZJ4iKEEEIIIYSweZK4CCGEEEIIIWyeJC5CCCGEEEIImyeJixBCCCGEEMLmSeIihBBCCCGEsHk2l7gsTQTdfsvH0xFFlz2cAa1PgfsBaHoS9qbn74s1wEOnwesgDDoPSuXv+y0ZBkQ+yCiEEEIIIYQQ95PNJS7HM6FnGYhpkP+YX6lwuXQjdI2AVh6wvzY85AlPRMBVo7Z/Qiz4O8KuWrAhFX5LyX/tuBgYFVwc0QghhBBCCCHuB5tLXE5kQQM3CHLKf5RxLFzulyRw0sGkclDbDSaXg9IO2naAU1nwuBfUcdOSm1NZ2vbVSVDfDaq4FF9MQgghhBBCiHtje4lLJtR0vXW5XenQ1gP0Ou25TgdtPWFnmva8gjMczIAsExzP0p4rBV/GwkhpbRFCCCGEEMKu2FTikmOCM9nwewpUOwZVj8JHFyHbVLhsjAFCnC23BTrCRYP297BA+CMFPA5CgCP09oYVydDEHSpJa4sQQgghhBB2pYhOWNYTng25gIceVlbRkpghUXDVBDMrWJbNMIGLznKbiz4/yanuCufqQ0IuBDiBScH4GFhdFVYkwcfR4OcICytpZYUQQgghhBC2y6YSl7puEN8QfK/VqqE7KKDfOZhaHhwLJCquOshWlq/PNoF7gTYkB52WtACEJkELD+35oPOwpSZsugrvRsGf1R9oWEIIIYQQQoh7ZFNdxSA/aclT2xUMCq7kWm4v66xNeVxQrAGCnQof06Tg61j4JBhOZoGzThug38VLGysjhBBCCCGEsG02lbisSoLAw9pYlzwHM6CMAwRdl9C08oAdafnrsygF29O17ddbkghtPLUB+jog7/C5SmvREUIIIYQQQtg2m0pcOpTSEolBFyAsSxtc/2E0fBiozRoWa4DMa1nHM96QZoJ3orSZyN6/qK3h0tfH8phGpa3p8kmQ9ryGi9albG0yLE6ElkUkOkIIIYQQQgjbYlOJi68j/FUdzmdDk5PaWJTX/eDja0lH8BH4JVH728sB/qimtbo0OQnb02BdNSjlYHnMRYlaQlTu2gxkHg4wqwK8dl5LjKaUK774hBBCCCGEEHfHpgbnAzR2h001i96nmlo+b+4BB+rc/Hgv+WqPgvr7ag8hhBBCCCGEfbCpFhchhBBCCCGEKIokLkIIIYQQQgibJ4mLEEIIIYQQwuZJ4iKEEEIIIYSweZK4CCGEEEIIIWyeJC5CCCGEEEIImyeJixBCCCGEEMLmSeIihBBCCCGEsHmSuAghhBBCCCFsniQuQgghhBBCCJsniYsQQgghhBDC5kniIoQQQgghhLB5krgIIYQQQgghbJ4kLkIIIYQQQgibJ4mLEEIIIYQQwuZJ4iKEEEIIIYSweZK4CCGEEEIIIWyeJC5CCCGEEEIImyeJixBCCCGEEMLmOVq7AtfLNsE7UbA8CVx08H4gDA8quuzhDHj9gvbf2m7wnwrQ3EPbF2uAPme1fX194IcKoNNp+35LhlXJMK9SMQQkhBBCCCHEfWA0GtnyzzbOnDmLg96BRo0a0KRxoyLLxscnsGnLVhISEvH2LsPD7R8iMDAAgPSMDNb/tYH4+ASqV69Kxw7t0V27UT4XGcmZs+fo/EjH4grrttlci8uHF2FnGvxdHX6oCONiYFli4XLpRugaAa08YH9teMgTnoiAq0Zt/4RY8HeEXbVgQyr8lpL/2nExMCq4eOIRQgghhBDifti+YxexsZfp8WR3Hn64PXv3HSAsPKJQOYPBwK+/ryMoMJBnn+lFSHAQv6/7k5ycHAAOHDiEm6srfXr3JCoqmsjI8+bX7tt3kOZNmxZbTHfCphKXdCP8FA+Ty0NTD+hRRmttmXG5cNlfksBJB5PKaa0tk8tBaQdtO8CpLHjcC+q4acnNqSxt++okqO8GVVyKLSwhhBBCCCHuicFg4PiJkzzUtjUBAf5UqVyJJo0bcfTo8UJlwyPO4KDX065ta3x8vGnXtg3Ozs6ER5wBICk5mQoVyuPj401QYABJyckAnDl7Dl9fH0qX9irO0G6bTSUuhzMhW0E7z/xt7TxhbwYYlWXZXenQ1gP017p/6XTQ1lNrrQGo4AwHMyDLBMeztOdKwZexMFJaW4QQQgghhB2Jj0/AaDQSHJw/hiI4OIi4y5cxmUwWZWPj4ggKDjJ3/9LpdAQHBREbGwdAKU9P4uMTyM3NJTExCU9PT5RS7Nt/gGZNmxRfUHfIpsa4xBjAxwFcC6RTgY6Qo+ByLgQ7WZat6Wr5+kBHOJSp/T0sEDqFwY/x0LEU9PaGFcnQxB0q3aS1JSsri2PHjt23mO7UpeiLVvu379a+fftuu2xJjw9KfowSn+2Ra9SSxGebSnqMEp+lf0OM91O9evVwdXW9aZn0jAxcXV1xdMy/fXd3c8NkMpGZmYmHh4d5e0Z6BmXKlLF4vbu7G1fiEwBo3Kgha379neMnTlK2bAhVq1TmzJmz+Pv74eVV6v4Fdp/ZVOKSYQKX69qA8p5nm4ooqytcNq9cdVc4Vx8SciHACUwKxsfA6qqwIgk+jgY/R1hYSSub59ixY/Tv3/++xlXSrVwRau0qPFAlPT4o+TFKfPavpMdY0uODkh+jxGf/rBnj4sWLadas2U3L5Obm4uBgeaPs4OAAgNFoKqKsQ6GyRqM2GLxMmdK8+Hw/srKycXd3QynF/gOH6Nq1CxFnzrJz125cXV15tNMjlClT+l7Du29sKnFx1RdOUPKeu1+X0LjqtG5l15ctWM5BpyUtAKFJ0MJDez7oPGypCZuuwrtR8Gf1/NfUq1ePxYsX35+AhBBCCCGEuIV69erdsoyjg0OhBCUvESnYCgPg4OBo3lewrFOBcnq9Hnd3N0AbExMQ4I+7mxubNm+l59NPEh19ia3btvNU9253FdODYFOJS1knSDJCjgmcryUgsblay4rPdTUt66xNeVxQrMGyO1kek4KvY+HXanAyC5x12gB9Jx2MuWRZ1tXV9ZYZrxBCCCGEEMXJw8OD7OxsjEajuTUlIzMDBwcHXF0tx0F4eLqTkZFhsS09IxN3d/dCx1VKceDAIZ7o9hiJSUk46PX4+frioNezZ6/1us8VxaYG5zdy15KKHen527alQVN3cLyuW1grD9iRpg24B+2/29O17ddbkghtPLUB+jogL1fNVaAKFxdCCCGEEMKm+Pn5otfribk2wB4gJiYWf38/9HrLW/qgwEBi4+JQ126UlVLExsQSFBRY6LhhYeEEBQVSqlQpdOhQ1+6OTSZlvs+2FTaVuLjr4SVfePMC7EmHX5NhYiwM0dbKIdYAmdeyjme8Ie3aYpUnMuH9i9oaLn19LI9pVNqaLp9cm4ChhovWpWxtMixOhJZFJDri30kVeHcqW3un3gclPb5/g5J+Dkt6fEIIcS+cnJyoVbMGW7b+Q1zcZc6di+TgoSM0bFAf0Abv5+bmAlCtahUMBgNb/9lOYmIS27bvJMdgoHq1qhbHNJlM7D94mGZNGwPa2Bej0cTZc5GcDgs3L1hpK3TKxr4dMkzwxnlYmQxeDvBBoPYA0O2HeRXhZT/t+d50GHweTmRBAzf4voK2/ktBCxK0cjMq5G9bnABDL0KQE/xSWVsHRtyaUso8rV5JlJKSgpubG87OzkDJi7ekxwfaHPdOTvn9RU0mU6FfoexZRkaGRTO/xGd/Svo1ev3nSkn8nCnpMZb0+OydwWBg89ZtnD1zFidnZxo3akDjRg0BmDHrBzo98jC1a9UEIC7uMpu3/ENiUhK+vj483P4hAgL8LY538tRpLl++Qof27czbToeFs237Dtzd3HmsS2d8fLyLL8BbsLnERdiOffv2mb9QmzSx3Tm974cZM2awdetW9Ho93t7ejBo1ipCQEGtX674p6fEBzJs3jyNHjqDX66lfvz4vvPACDg4OJeZL9z//+Q+7d+/GwcGBihUrMmzYMNzcSs6vLiU9Pij51+jixYs5c+YMer2eli1b0qlTJ/R6vUV/fHtX0mMs6fEJ+1dyfuYR99WECRN4//33GT58OO+99x5Dhw7l0qVLJbL7RmhoKD///DMvvPACvXv3xmAw0Lt3b/744w/S0tKsXb17VtLjA/jpp5/4z3/+Q82aNXFwcOCvv/6iV69exMfHl4gbwgULFjBv3jweffRR6tWrx+nTp3niiSc4fPiwtat2X5T0+KDkX6OzZs1i2rRpuLi4EBYWxoIFC3jzzTfNU7JeP7uRPSrpMZa0+Eri/YoAlBDXOXDggOrYsaM6duyYioqKUuHh4apz587q//7v/9Tu3buV0Wi0dhXvq3Hjxqlx48ZZbBs7dqzq2LGjCg0NVenp6Vaq2f1RkuMzmUwqOztbDRw4UC1cuNC8/ezZs+r5559XHTt2VJGRkeay9sZkMimj0ajef/99NWPGDPP2rKws9c4776gWLVqo7du3W7GG96akx6fUv+MaTUtLU/369VOrVq1SSimVm5urtm7dqnr37q26d++ucnJylFLKbr87SnqMJTG+gu+l7du32/3niMgnLS6ikKtXr6KUIiQkhHLlylGtWjXWrFkDwNSpU0vUr6AAbm5unD17lszMTPO20aNH89hjj/H999+zY8cOwH5/vSnJ8el0OpydnXF0dCQ1NRXQ4qhcuTLff/89lStX5tVXX+Xq1avodDpMJtMtjmhbdDoder0eV1dXYmJiUEqhlMLFxYVp06bRvn17PvjgA06dOgUg8dmgf8M16uDggMlkMnclcnBwoG3btowfPx4nJyeee+45jEYjer3ebj9nSnKMJS0+k8lkbsXcsWMHM2fO5JdffuH48eNWrpm4HyRxEYUEBQXh4+NDeHg4oK2+6uHhwU8//YTRaGTSpEkkJycD9nmze71atWoRGxvL0aNHgfzFnEaMGEG7du0YNWoUKSkpdtudo6THB1CuXDl+/fVX8xdWbm4unp6eTJkyhYCAAAYOHGjXg6ArV67Mrl27iI6ORqfTkZOTA8C3335LkyZNePfdd8nNzZX4bFhJvkZdXV0JCAhgwYIF5m16vZ7q1avz1Vdf4eTkxAcffGDXY3lKeowlKb6899A333zDtGnTSElJYdOmTcydO5d9+2xrTRJx5+zvE1I8cOXKlcPV1ZW5c+eSlpaGo6Mjubm5uLu7M2fOHKKiopg0aRKAzX+A3Y5u3bpRpUoVPv74Y6Kioiz68n7++edUqVKF6dOnW7mWd68kx5eXOH/44Yc4Ozvz2muvAZiv2VKlSjFq1Cj0ej3/+9//rFnVezJw4ECCgoJ4++23MZlMODs7m2/ux48fT5kyZVi4cKGVa3n3SnJ8Jf0azWshGjZsGLm5uYwcOdJif7Vq1Rg4cCAJCQl2+4t3SY+xJMb3zz//sGrVKoYPH84vv/zC/PnzyczMZM2aNRw8eNDa1RP3QBIXYcFkMuHu7s6kSZM4ePAgn3/+OUaj0fwl6+HhwZdffsmBAwdKxGD9vBv46dOnExISwmuvvcb58+ctZk+pUaOG3Q5iL6nx5X3R6nQ6lFI4OzszZswY4uPjefvttwHtxhC0X/Ozs7M5ffq01ep7J65/T+Wdw0mTJmE0GnnuuecwGAzmaa3d3d3x9fUlNja22Ot6P5T0+EriNVpQ3q/bQUFBDBgwgPDwcL788kvzfkdHR1q3bs2FCxc4dOiQlWp5b0p6jCUxvri4OIKDg2nSpAkeHh40adKEQYMGcebMGebNm8exY8esXUVxlyRxEUD+zVLetIdBQUHMmTOHTZs2MWLECJKSksxfsi4uLhiNRpycnOy+xSWvXy/ADz/8QEBAAAMGDOCff/4hISEB0LrKGY1GcnNzbTpRMxgM5l+p85Sk+ADmzp1LQkKCRXeavGuwYcOGDB06lAsXLvDiiy+Sk5ODUgpXV1fKlStnvhG2dde/p/KSzMDAQCZPnkxOTg69evXi3LlzpKen4+joSKlSpVBKYTKZbP4cXj+Go6TFV5SSdo3myTsXeYlZ586defLJJzl8+DAjRowwl/P09KRmzZp2Ob11XmJdkmPMY6/xFTUuLDg4mKtXr5q7SAM0atSIvn378t///pclS5Zw5MiR4qymuE9kHZd/sb///htvb2+aNm0KWC4ylff3iRMnePXVV2nQoAE9evSgatWqrFu3jo0bN7Jw4UK8vW1nUaKbWbRoEefOnePSpUu8+eab1K9f/4Z9dT/88EPOnj1LamoqISEhHD16lGXLllGjRg0r1Pz2/PTTT5w8eZLw8HB69+5N27ZtqV69epFl7TE+gK+//pr58+ezfv16KlWqVGQZg8HAiRMnGDt2LCkpKdStWxfQug2sXLmSKlWqFGON78zatWtJTEzk4sWLPPPMM5QrV45SpUoBlu/NxMREPvzwQy5evIi3tzelS5dmz549LF++nGrVqlkzhJtauHAhERERnDt3jhdeeIGGDRsSGBhYqJy9xgfaGhgXLlwgISGBwYMHF/ketPdrNDY2lvj4eF566SXKlStn3pd3jaanp7Np0yZ++ukncnNzeeihh0hISODvv/9mzZo1VKxY0YoR3Np///tfUlNT0ev1dO3a1eJGPW8Mkj3HOG/ePE6cOMGECRMKjaeyx3NYcFxYWFgY3t7euLu7k52dzZtvvkm9evXo378/lStXBrTB+pMmTcLDw4O6desyYsQIuxi3I/JJ4vIvZDKZiI+Pp1u3brRq1YrXXnuNhg21VVeLSl6uXLnCF198wYULF0hNTcXR0ZEpU6ZQp04da4Zx2yZPnsyKFSt46qmnOHnyJEeOHGH9+vUEBARYxFtwga29e/dy7tw5cnNzadu2rU19UF9v2rRpLFu2jHfffZfTp09z5swZypUrx7Bhw/Dx8TGXs9f4AL766itWr17Nzz//TK1atYq8Tq//8vnpp5+Ij4/HYDDwf//3fzZ90zt58mRWrVrFQw89RHR0NFFRUTzxxBM888wz5nNzfXy//fYbly5dIicnhyeeeMKmb3jz3oP9+vUjLCyM8PBwnnjiCQYNGnTDllt7ig9gypQp/PLLLzz22GOcPHmSevXqMWrUKKBkXKOTJk3i119/pUGDBkRHR3Px4kWmTZtGq1atzDePefGZTCZSU1OZMWMGycnJKKUYNGgQNWvWtHYYN/XNN9+wdu1aQkJCyMrKIikpifHjx9O0aVPc3d0B7D7G2bNnM3HiRAYPHsx7771X6L1nr/F9++23rF27FoDHHnuMN998k9OnTzN27FiaNWtGq1atqFOnDl999RU1atSgVatWvPXWW6xevfqGP/IJG3V/ZlUW9mjw4MGqffv2atSoUWrfvn3m7QXnPzcYDEoppbKzs1VcXJyKiIhQCQkJxV7Xu5WQkKD+7//+T+3cudO8rVu3bmrv3r0qOzvboqzJZFK5ubnFXcV7cuXKFdWvXz+1ZcsW87bQ0FDVokULdezYsULl7S0+pZSaPXu2qlmzprp8+XKhfdevKZC3Lsj122zZuXPn1FNPPWVxjc6bN0+1a9dOjRgxQp05c8a83R6v0XPnzqmePXtafMbMnDlTtWjRQkVHRxcqb2/xKaVUcnKy6tevn9q0aVOhfSXhGt27d6/q0qWLOn36tHnbu+++qx577DHzd0SeomKxh3MaGRmpnnzySXXo0CGVlZWljEaj+vTTT1Xbtm3VsmXLVEpKirmsvcaolFKbN29WjRo1UjVr1lQfffRRkWWKWqvF1uIreN3t2rVLtW3bVm3ZskVNmzZNvfTSS+qDDz5QiYmJat++fer9999XzZo1U927d1e9e/dW2dnZKisrSz377LMWn6/CPsgYl3+xSpUq4erqSlhYGEuXLjVPE1iwBSJvXIuzszMBAQFUrVrV4ld8W2cwGIiIiCApKQmArKws4uPjmTFjBk8++SQTJkwwr0uTN5f9li1brFnlO2IwGDh9+rTFGi19+vShdOnSbNu2DbAc7G1v8QHmX9rPnDkDaGNyRo8ezRtvvMEzzzzDr7/+SlxcHJC/Log9DbzMzc0lKirKop/2yy+/TJMmTdi4cSOLFy8mKioKyL9G9+zZY63q3jGTyUR0dDS5ubnmbS+++CIuLi7mOK6/Ru0pPsA8sP7q1auA9tn58ccf8/rrr9OnTx/WrVvH5cuXAfu8Rg0GA3q93jzWCKBv375kZGRw/vx5i7J53x9571fALqZ4NhqNxMXFkZOTg4uLC3q9nnHjxtGrVy9++OEH/v77b/MYQnuMMe/9V758eVq3bs3ChQvZuHEjH3/8sblM3vdIXiy2GN/o0aPNY98AlixZwq+//kq/fv1o374977zzDn369CEhIYFx48ZRtmxZJk2axB9//MGUKVOYN28ezs7OzJgxg6SkJEqXLm3liMSdso0rURSrvC+eRo0a0bVrV0aPHs358+dZsWIFERERrF69mvT0dHO3omnTprF161ZrVvmOqAIDRgMDA+nevTtTp05l+vTp9OrVi8qVK/Pss8/So0cPIiIimDZtmvkD+tChQwwZMoTY2Fi7GAQcFBREgwYNiIyMJDs72zyQ1MXFhYyMDMBysPehQ4d455137CY+gI4dO/Lmm28yfvx4jh07xgcffEBkZCT16tWjRo0afP/996xcudL8pbtu3TpGjx5tTlZtve+yq6srFSpU4MKFCxgMBvP2OnXq0KxZM86fP8/OnTvN2//55x9GjBjBlStX7OIcenh4EBISQkxMjDk+Z2dn9Hq9eUHGgufon3/+Yfjw4XYTH4CPjw9t27bl3LlzZGVl8dZbbxETE0OLFi2oUaMGU6dOZcWKFebZ++ztGjUYDMTGxpKVlWWua9myZUlNTTX/aFBQaGgoX331lTleW44vr45lypShTp06nDt3DqPRaP4sff/99+nWrRsTJ060SDZDQ0MZP368zce4fv1687IGoP1geenSJXQ6HVOmTOGvv/7iiy++YMKECWzcuNH8A4otxnfy5Elyc3MtJrGIjIxk5cqV5h93AJ544gn69OlDUlISkyZN4vjx4wQEBJCRkcHw4cN59tlnWblyJVOmTMHX19caoYh7Ya2mHmF9J0+eVF27dlXZ2dlqz549ql+/fqpTp07q0UcfVUlJSUoppRITE9VHH32kjh49at3K3oHY2FilVH5z98GDB9WoUaPUSy+9pDp27KgiIiLMZXfs2KGee+459d///lcppVRKSorNd4XbvXu3+vPPP9X27dtVdna22rdvnzp8+LAyGo3mmHv06KG+/fZbi9flndP4+PjirvIdi4yMVEeOHFHJyckqNzdXXb58WX344YdqwIABavDgwRbdNubMmaPatm2roqKilFLadX3hwgVrVf225MV39epVpZRSEydOVG3atFHr169XZ8+eVZGRkapt27Zq8+bN6scff1Rt27ZVqampSimloqOjzde4rTp//rw6efKkuXvJihUrzNeoyWRSOTk5qmPHjmr27NkWr8vMzFSJiYkqJibGGtW+I5cvX7a4zn744QfVsWNHtXfvXvXJJ59Y7Js9e7Zq27at+bPHHq7R7du3q/Pnz5uff/HFF2r//v3m59HR0apRo0Zq8+bNhV67b98+i89ZWzVy5Eg1cuRIc9evr7/+Wj300EPq5MmTSinL7lHDhg1TXbp0MXcx3rNnj83HOHPmTFWzZk21e/dupZRSOTk5Siml3n77bbVs2TKllFInTpxQ9evXV7Vq1VKRkZHm1+7evdvm4jOZTOZztXLlSvNn4jfffKNq165dqKvmH3/8oXr27KmmT5+ulFLqwoULavXq1So0NNTm33/ixiRx+RcIDQ0t9AFkNBpVQkKC6tmzp3nswMCBA1X9+vXV4MGDLcZHXD8WxJYdO3ZMNWvWTJ06darQvsjISNW/f3914sQJi+3PP/+8+uSTT4qrivfkm2++UV26dFGPPvqo6tGjhxowYIBKTk4uVK5Hjx5q3Lhx5uczZ85UQ4cOtYtzOWnSJNW9e3fVtGlT9dxzz6lZs2Ypg8GgNm/erNq2bav69++v0tPTLW4qOnXqpBYtWmTFWt++gvH16dPHfPP+xRdfqK5du6q2bduqpk2bqlGjRplf06lTJ/XPP/9Yq8p3ZOLEiap79+6qWbNmqlevXuabwOvHQjz66KPqP//5j/n5Dz/8oL799lub60tflMmTJ6uePXuqVq1aqRdffNGcSA8ePFjVrVtXtW/f3uImUCmlunTpon744QdrVPeOGI1GdeHCBVWzZk315ZdfWowByLvxVUqpuLg41apVK4vEZd68eUUmMrZo/PjxqkmTJoXGAg4aNEg98sgj5vOXdz1GR0erLl26qAMHDhR7Xe/GuHHjVOPGjVWLFi0s3mdKKbVgwQI1evRopZRSH3/8sWrfvr1q1KiRGjNmjBVqensKjiuKjo5W3bt3V88884z5x5/PP/9c1a5du9Dn5I4dO+ziM0XcPukq9i/w66+/MnToUCIjI83b9Ho9Pj4+VKxYkX379vHxxx9z/vx5xo4dS1paGjNnzjSP/bCntQU8PDy4evUqQ4cO5cSJE4DWx95kMlG2bFl8fX3ZunWruS86QEBAgF3MKrJu3TrWrl3LlClTCA0N5ZNPPiEjI4Pp06eb17dQ17rWGAwG8zSeU6ZMYfr06QwcONDmz+XSpUtZuXIlI0eOZPny5bRp04YNGzawfft2OnTowODBgxk7dizu7u7mrozp6en4+PgUObWurbk+vnbt2vHbb7+xfft2Ro4cyVdffcXEiROZM2cOn3/+OaBND+zu7o6np6eVa39rv/zyCytWrGDkyJGsWrUKg8HAvHnzgPzFFo1GI1lZWWRnZ+Pi4gLA1KlT+e677+jevbvF4qi2aMGCBfzyyy+89dZb/Oc//+Hs2bPMmDEDgFmzZtGtWzfi4uI4dOgQWVlZgDa+wM/Pj4CAAGtW/bbodDrKly9PrVq1+O2331i7dq25G46Tk5O5K1FGRgZZWVk4OTkBWpfir7/+muDgYKvV/XZNmDCBVatWsXjxYvN01Hldw2bNmkX58uV5+eWXLdb5cHR0NM8MZ+vyZmFcvnw5ffv2NY9DyhvnEhQUxNmzZ/n000/Zs2cPixYtYv78+Sxbtoxx48ZZs+pFMplMFl3VQkJCGDduHK6urgwcOJC0tDRGjRpF3759ef3119m+fbu5bOvWrXFwcDCfX2H/HK1dAfHg5E1/W7lyZUJDQ3nrrbeYPn06VapUMU9f6efnx9ChQ6lWrRqzZs2iWrVq+Pv7M3fuXIKCgqwdwh0rXbo0/v7+eHl58c477zBlyhTq16+PyWTC0dGR6tWrs3btWq5cuUK5cuWIi4tj27ZtvPXWW9au+i3Fx8fTtGlTateuDUDz5s2pXbs2hw4dMg+cNJlMGAwGTCYTzs7OzJ8/n7lz57J8+XKbn75aKUV4eDjPPfccLVu2BOCNN95g69atrFy5kg4dOvDCCy8AcPToUWJiYvDy8mLfvn1cvHiRWrVqWbP6t3Sz+H755Rfatm1Lo0aNAG3V57Vr1+Lt7c3x48dJT0+3ixvCyMhIevfubY6vd+/eHD58mJUrVxIQEEDjxo3x9PTk6tWrGI1GXFxcWLhwIXPmzGHlypU2fw5NJhOnTp1i4MCBdOrUCYAnn3wSo9HI33//TdWqVfn4449xdHTkq6++Ijw8nODgYC5evEhERASNGze2cgS3lneD2KBBA44dO8aqVavIyMjg+eefp2LFiubPGqPRSE5ODg4ODsydO5fZs2ezcuVKm18P6tSpU8ybN48JEyaYr7e892ZcXBzNmjXj+++/Z/To0bzzzjv07duXoKAgIiIiyMrKomzZslaO4Oa+/fZbVq5cyeLFi6latSoVKlRg2bJlREdHm+vetm1bvvzyS+Lj4/nhhx8oX7485cuXZ8mSJZQpU8a6AVyn4Dotv/32GzExMej1egYOHMiQIUOYMmUKAwcOZPbs2YwePRq9Xs+rr75KaGgoDRo0MB/H1n8QEbdPEpcSLO+NmpqaSq9evUhJSeGVV15h3rx55sWY8n6NGTZsGNWqVUMpRbt27WjatKlNrpB7K/v378fFxYUPP/yQFStW8N5775mTF4C3334bDw8PDhw4wJ49e6hUqRILFiyw+TUiQGtZKDhIW6fT0a5dO9atW0dCQgI+Pj7o9XqcnZ1p1qwZM2bMwM3NjcWLF1OvXj0r1vz26HQ6UlJSCA8PN29zcnLi4YcfZuPGjYD2i6GjoyOrVq3izz//pHTp0nh6ejJ79myLxfBs0e3El/djw+HDh/nzzz+JjIzEzc2NadOm2UWL0tWrV7l06ZL5+dSpUwkMDGTv3r34+Pjg7e3Nd999h4+PD3Xr1uXLL7/EycmJRYsWmX/5tmV6vZ60tDROnjxp3rZ48WICAgIIDQ2lXLlyNG/enK+++ooFCxawb98+tm7dSkhICPPnz7f59ZIg/0axQoUKVKtWjdq1azN06FAcHR15/fXX2bdvH23atKF06dKUK1eOsWPHcunSJYvWC1tWq1Yt+vXrx3fffceTTz6JXq/nxRdf5OrVq4SFhVGrVi2eeOIJvv32W+bPn8++fftYs2YNvr6+zJo1y6bfhzk5OWRnZ7N06VJq1KiB0WikSZMmBAQEcPbsWcqWLYvRaMTJyYmhQ4fStGlTypcvb359kyZNrFj7ouUlLV9//TVr1qyhfv361KxZk9zcXJo2bcqwYcP49ttvzcnLyJEjKVu2rM3/UCfugRW7qYlikJmZqYYOHaqWL1+ujEajevnll1XHjh3V2bNnzWUKjnvI60dq6+sK3MjOnTvVO++8o7Kzs9WpU6fUsGHD1COPPKKOHDliUS47O1tlZGTYxZiPPBEREWrEiBHmMQNKKbVlyxZVv379QuthLFy4UNWuXVuFh4cXdzXvydKlS9XIkSMt4lmwYIHq0KGDys7Otuhjf+HCBXXhwgWVmJhojarelTuJ7+rVqyo2NtY8qYI9OH36tFq/fr1SSltj6Ouvv1bR0dEqLS1Nbd68Wb388stq7NixSiltTE/NmjVVWFiYNat8x7Zu3ao2bNiglFLq+PHj6rPPPlPnz59XKSkpatGiRapfv37m8VZ5nzNZWVnWrPJd2b17t+rbt69SSqm///5btW/fXj3++OOqT58+5nEFzz//vKpdu3aRYwptUcH1SV599VX1yiuvqLFjx6r3339fnTx5Ul2+fFlNnDhRPfvss+ZznJGRoVJSUlRaWpq1qn1brv/uLvgdPnDgQPX8888XWd4ebNq0SXXs2FGdO3dOKaWtz3bq1Cn122+/qYSEBLV37171/PPPqy5duqj09HTz664fVydKBklc/gWWL19unh0mMzNTvfLKK+rhhx8usQsvFZxxKSwsrFDykjezkb0xGo0qKSnJYqDhxo0bVYsWLSxmQlu7dq3Kycmx+dnRipKZmakiIyMtYvzhhx9U586dLcrt3LnT5m8kinK78e3YscN8c2hPCr6vTCaTysjIsNj/448/qmeffdb83NZnRytKwXOXnZ1tntkoz5gxY9Qrr7xS3NW6r0wmk4qIiFDdunUzn8OXX35Z1alTRw0fPtyceB8+fFhdvHjRmlW9a/v27VODBw9WDz30kNqxY4fFvjfffFO99dZbVqrZ/ZN3re7fv189/fTT6q+//rJyje7OX3/9pbp3766Sk5PV3r171VtvvaVat26t6tevr5566imVlpamdu/erT777DMZiP8vIIPzSzB1bRDhM888Q4UKFTCZTLi6ujJz5kyqVKnCwIEDOXfunJVref/kxRsYGGgeQFq9enUGDRpEkyZNGDZsmHk8iC3MSX+n9Ho9ZcqUwcHBwRxrVlaWeXE40AbIDh8+nOjoaLtaKBS08+fq6krFihUtYjQYDBYDKydPnsynn35qXl/AXtxJfCNHjiQ9Pd1aVb1ree8rpRQ6nQ43NzeLwcy1atXi6tWrJCYmAth0t5sbyeuCmzeOrFSpUhb7mzZtSnJysnlgvj3S6XRUrVqVihUrEhERwccff8ylS5f4+OOP2bt3L7NmzSIuLo4GDRrY/JiPG2nQoAG1atXCy8uLkJAQAPP3RqtWrUhKSrL7Ad1512rlypXx9/c3d0kFbHaSgYIL8eYpX748JpOJnj178vzzz+Pg4MCHH37I7t27uXTpEtu3b6dFixaMGTNGBuL/C8gYlxLs+pvzvJvbvOTl3XffpVevXqxZs8Yu+l7fSsF4C67yW716dQYPHsykSZP47LPPCA0NxcnJyS6Tlzx5dXdycjJ/SE+bNs08QLZSpUpWrN3duf58FIwx73x+9913zJs3jyVLltjdTW9Jj6+gvNiSk5M5fPgwrVu3BmDnzp2ULl0aV1dXa1bvvsg7Z8nJyURGRlK3bl2cnJw4fPgw3t7eNrPS+N3Iu6l1dHSkf//+lC1blpkzZ1KtWjV8fHyYMWOGXQ92Vkrh5OTE66+/zosvvoiPjw9paWm4u7sDcPr0aXx9fW325v5OKKXw9vbm3XffpW/fvtSvX5/+/fvb5PdfwYH4q1at4uLFi+Tm5vL+++8zfPhwwsPDqVOnDg0bNsTDw4P09HQqVqxY6PPEnq9NcWuSuPxLubq6MnXqVEaMGGHtqhSLatWqMWzYMDw9PW1+SuA74eTkhLu7O+PHj+fPP/9kyZIldjFA9k7kfZFNnjyZefPmsXTpUruYbOB2leT4zpw5w/Tp03nnnXdo2LAhERERzJ0713yDWBIcPHiQWbNmcenSJerXr8/+/fv5+eef7fpzJu+mtl+/fqSnp/PRRx+ZJ2/p1q0bHTp0wMPDw8q1vHs6nc7cA8HV1ZWwsDAmTJgAgL+/Pxs2bGDJkiXmKbztWV6s9erVY8yYMYwaNQp/f3+6dOli7apZyJuYBLTpqlevXk3z5s2pUqUKBoOBDh060KFDB65cucLvv/9OYGAgy5YtIzc3l7Zt21q59qI46VRJ+ElBiJvI67Zir25W/4SEBLp27YrRaOTnn38ukTOp7Ny5kwEDBlC6dGnmzJlTYm7q85SE+G50jebk5BAVFcXBgwfx8/OjWrVqNj/7253KzMzkwIEDHD9+HH9/f5o0aVIiWrBBO38Gg8GcpOSdZ3v/TL3e1atXmTVrFikpKXh5edGnTx+qVq1q7Wrdd0lJSSxdupSuXbuaZxa1tlmzZlmsMbZ9+3ZGjx7N7NmzqVy5MvHx8cTGxnLw4EE6depEeHg4c+bMISkpibJlyzJ9+nRzzwNpafl3kMSlBMp7A5e0L5ei5E2Pm5mZiYODg13/ylmUvHOZkpJCUlJSoS5gOTk5zJo1ix49etjMF9GdutU5TEtL49133+Wjjz6y+TUiivJviS8tLY3s7Gx8fX0B+//BoKAbncOCXVvsWUn/HIX8GHNyciziu/770l7P6e2eQ1u6wd+yZQt//PEHX331lbl1a+vWrXz11Vf89NNPxMTEMHfuXI4cOUJubi6lSpUiNDQUV1dXsrKy8PHxQafTmWMX/w729+4UN5X3oRQXF8fy5cvteoDorRiNRhwdHbl48SLPPfccp0+ftnaV7qu8cxkdHc2zzz7L8ePHLfYrpXB2dmbIkCF2m7Tc6hwajUbzOi32eFP/b4vv1KlT5n0lJWm52Tm0xxvc65X0z1HIjzEqKoq+ffsSFRVl3nf9Tbw9Xrd3cg5tJWm5cOECHTp04JtvvsHR0ZG///6brKwsKlWqhKurK88//zwvvvgibm5ufPTRR+zZsweDwcDGjRvx8PDA19fXnGhK0vLvYv+fusLMZDKZb3R79OhBTEyM3Q+CvVmDYF6s/fr1o27dunbZxeZm8uJ75plnaNGiBd26dbPYn/cFa+tftPdyDvO+ZG05Rokvmueee46GDRvSpk2bYqzZ/VPSP2dKenxwezE+//zzN+yuaOufpyXpHL777ru888477Nq1C4Dw8HC+++47xo4dS9myZfnqq68YNGgQP//8M+PHj+epp54iKyuLgIAAvL29LY5VEn48EHdGuorZqRs1ZycmJtK5c2e6d+/O2LFjbfZD+HbdrLtJTk4OY8aMwcnJyS5jzZvKuOB0qgXPq1KKBQsWcPbsWbuML09JPodQsuNTSmEwGCy6nRSMNzc3l6lTp5KSkmKX8eUpyecQSn58t5KZmcm7775LcHCw3cZYUs5hfHw8Q4cO5eDBg3Tv3p0nn3ySNm3asHr1akJDQ6lWrRqjRo3CxcWFS5cusXHjRsqUKcNvv/1GbGwsq1atsplWI2EdkrjYkSNHjpCTk0P58uXx9fU1N48W/EC7cOECO3bs4LnnnrPpD69bmT9/PsePHyc7O5uHH36Y7t27m2+eCsZ79uxZqlSpYs2q3pXp06ezd+9eLl68SNeuXWnXrp15ytiCyUtGRobdzsBU0s9hSY/vhx9+4ODBg8THx/P444/z6KOPmgedF4zvypUr+Pv7W7Oqd62kn8OSHh/A0qVLiYiIwMnJidatW9OhQ4ciyx05coQGDRoUc+3uXUk8h6GhoYwePZpmzZoRFBTEc889R/PmzVmzZg3Lli2jYsWKfPHFF4SHhzNt2jTi4uKoUKECkyZNkoH4QhIXezFx4kTWrFmDu7s7V69e5emnn6ZLly40btwYKFkDYb///nvmz59Pr169MBgM/PLLLzz55JP06tWLZs2aAbY1wPBOLV68mBkzZjBs2DBycnJYt24dzs7OtGnThldffRWw7/ig5J/Dkh7fnDlz+Omnnxg4cCAGg4Hly5dTr149HnvsMZ544gnAvuODkn8OS3p8oK1dtXjxYh555BEyMzNZv349L730Ei+88IK5O5g9x1jSzmHBH+VGjRpFfHw86enplCpVigEDBtCsWTNz8lK9enXGjBmDo6MjycnJlC5dWgbiC0ASF7uwb98+3nvvPSZPnkydOnXYvHkzf/zxBxkZGbz88ss8/PDDQMlIXnJychg2bBiPPPIITz/9NKD9Uvbll18SEBDAc889R7t27axbyXuUt15A3ho6Fy9eZPny5ezcuZP27dvz9ttvA/Z7Pkv6OSzp8QGMHDmSqlWrMmDAAEDrg/6f//yHxMREnnjiCZ555hlArlFbVdLjA0hJSWHIkCG88MILdOrUCdCmFv/ggw/o0KEDr776KtWqVbNyLe9eST2HebO6/fHHH4SFhdG2bVtmzpyJm5sbAwcOtEhefHx8mDJlSombxU/cG7kC7EDeoLQmTZrg4eHBE088wcCBAwkJCeH7779n69atgO0OKrwTer2eixcvWsyg1aBBAz7//HOuXr3KihUrOHLkiBVreO/i4uIsZl8qV64cL7zwAh07dmTbtm38/PPPgP2ez5J+Dkt6fKAl00ePHjU/r169Ou+99x4hISGsX7+eDRs2AHKN2qqSHh9oiyhfuXKFs2fPAloS3bp1a77//nv27t3LvHnzuHz5spVrefdKyjkcNGgQEyZMYOfOnQDmJKRVq1Zs2rSJ8+fP880335CZmcncuXPZv38/Tz/9ND169MDPz8+idUWSFgGSuNgFX19fzp49y4EDB8zbmjRpQv/+/alSpQqLFy8uNFWuPVJK4ejoyOOPP05sbCxnzpwx76tZsyYfffQRFy5cYOXKlVas5b3r168fV65cYc2aNeZtfn5+PPPMMzRu3JitW7dy4sQJ61XwHpT0c1jS48vTu3dvLl++bL7ZAChfvjyDBw/G3d2dP//8k4SEBCvW8O6V9HNY0uPLo9fradq0KVFRUaSmppqnxm3YsCETJ05kw4YNLF682NrVvCsl5RxGRESwdetW5s+fz6xZs3j//fdJSUkhMzMTX19fxowZw8qVK8nJyWHkyJFkZGQwf/58du7cSb9+/fj888/R6/WYTCZrhyJsiCQuNk4pRZUqVejatSs///wzERER5n1169alZ8+eZGdnm1td7PkNnvfrbdu2bQkLC2P58uWkpKSY99eqVYsxY8awfPlyNm7caK1q3rPKlSvTuHFjNmzYwLZt28zb/f39eeGFF4iJibG4YbQnJf0clvT48tSvXx9nZ2d+/fVXwsPDzdsrVKjAkCFD2L59u/kzx96U9HNY0uPL4+TkxBNPPMHatWtZtmwZgPkmt1GjRkycOJE5c+ZY/OBnL0rKOaxWrRo///yz+T7mwoULDBgwgNmzZ3P69GmaNm1Ko0aN2Lt3L9WrV+eDDz7g/Pnz7Nixw3wMpZS0tAgLcjXYOJ1Oh4uLC126dCElJYXQ0FDOnz9v3t+iRQs6d+7M4sWLSU9Pt/s3uFKKunXr8umnn7J48WJ+/PFHkpKSzPvz1ooICwuzYi3vjZ+fH/379ycrK4vQ0FCLG8CQkBBatWrFgQMHbjpvvy0r6eewpMcHUKlSJd566y0OHjzIwoULLRa0q1q1Kg899BCHDh2yXgXvUUk/hyU9PtBibNmyJZ9//jmTJ09m0aJFQP5Nf6tWrahduzYXLlywZjXvWkk5h82bN2f27NmsW7eOt99+m+7du3Pp0iVeeukl1q5di4eHB/PmzePKlSvUr1+fKVOmMHToUPPr7bU7qnhw7Psut4RTSplvXjt27MiTTz7JoUOH+Pnnny3GSDRu3Bh/f3+ys7OtVdW7UlRXE51Oh1KK9u3bM2XKFBYtWsSsWbMs4jWZTHbxYXZ94lGwNaxWrVoMHTqUjIwMli5dyvLlywFthpiUlBTzqsD2Ju/clJRzuGrVKg4fPmyxrSTFd728a1QpRdOmTfnss8/Yv38/8+bNY8uWLeZyWVlZeHp6Wqua96SkXaNFKenxFax7jx49GDVqFF9++SXff/+9+ebe2dkZJycnMjMzrVnVu1LSrtF27drx7bffMnz4cMqXL89HH33EiBEjmD9/PgkJCURERDB37lxycnKoUqUKer0eo9Fo7WoLGyWzitmQmJgYXFxccHZ2Nt8U5CUveS0pK1eu5I8//sDBwYG+fftSpUoV86/2S5cuxcvLy5oh3LZff/2V3377jeHDh1O9evVC+/NmK9q2bRsTJ07Ez88PpRQ+Pj5s2rSJ0NBQm56zftmyZRw/fhxnZ2dat25N586dLfbnxRcWFsaKFSvMN4UBAQGcPHmSxYsXU7NmTWtU/batWrWKsLAwdDodTZs2NceYF5u9n8Nx48aZ328hISGF9tt7fOvWrSMuLg6DwUC9evXMq97nTa+aF9/BgweZPXs2ly9fxmQyERgYyJ49e1i2bJnNz9pU0q/RX375hfDwcNzc3GjdurX5HOax9/hAu04vXryITqejVatW1K9fHyg8o926dev47LPPeOihh8xJy/r161mxYgUVKlSwVvVv6Y8//iAuLg53d3eaNWtmfk9d/z6053OYZ8uWLQwZMoSvvvqKbt26cebMGY4cOcLEiRNp2rQp06ZNs3YVhR2QxMVGTJo0ie3bt3P58mUaN25Mhw4dzFOOguU0gDt37mTz5s0sW7aMChUqkJaWxsyZM6lTp461qn/HZs+ezcSJE+nevTsDBgygbt26hcrkfWBHRERw8uRJNm/eTEBAAD179qRGjRpWqPXtmTZtGqGhoTz11FMcOXIEvV7PJ598Qq1atYDCN01Xr14lISGBdevW4efnR/PmzalcubKVo7i57777jlWrVtGlSxdOnz6NyWSiQ4cOvP766xbl7PUcfvnll/z+++/MmTOnyPfV9efQ3uKbPHkyq1ev5uGHHzZ3A6tcuTJff/01gHmthLz4YmNjuXjxIn/++SeBgYF06tSJqlWrWjOEWyrp1+jUqVNZsmQJTzzxBHv37qV37968/PLLhcrZa3yQv35Zy5YtOXr0KOXKleOpp54yTw+cJy/Gw4cPs23bNg4fPoy3tzcDBgwwf+7aosmTJ7Ns2TKaN2/OiRMnqFSpEg0bNmTIkCFA4eTFHs/h9bZs2cLbb7/NN998Q9euXQFtimQHBweLWIW4ISWsbs2aNapt27Zqx44dau3atWrWrFmqfv36auLEiRbljEajxfOoqCh14cIFlZCQUJzVvScmk0kppdThw4dVixYt1KBBg9T777+vjh8/blHGaDSayxb1elsVGxurnn76abV582allFIxMTGqffv2au/evRblbhSfPTh16pTq0qWL2rNnj1JKqdTUVDVu3Dj13HPPqcTERIuy9ngOQ0NDVc2aNdWhQ4csthsMBou65+bm2mV8Fy5cUI8++qj5Gk1PT1erV69Wbdu2Ve+995653I3iswcl+Ro1mUwqISFB9ejRQ23YsKHI/Xns9RpVSqkDBw6ozp07q4MHDyqllIqPj1evv/66Gjx4cKHvQpPJZI4pb19OTk6x1vdOxcbGqm7dupnfhwkJCeqnn35STz/9tProo4/M5ez5HN7Ili1bVKNGjdTq1atVdna2eXtubq4VayXshYxxsQGxsbHUq1eP1q1b89RTTzFw4EAmTZrEwoUL+fLLLy3KqmsNZLm5uZQrV47y5cvj4+NjjWrflbxfUqpUqUL58uWpXbs2ycnJ/PTTT+YZ03Q6HXq93lw2Nja20OttlU6nw2Aw4O7uDkBQUBAuLi5MmDCBF198kREjRmAwGCymeNy3bx/JyclWrPWdSU1NJTs729zFr1SpUjzzzDMcPnzYYtpOwC7PYVpaGo0aNbJ4r3311VcMHjyY9957jzlz5gCYfx0E+4ovIyODlJQUypcvD4C7uztdunShXr16/Pnnn7z77ruAZXxhYWEYDAar1flOleRrNG/a39TUVEqVKgVov1h/8cUXvPzyywwbNoz58+cD2jnM+5yxl/jyJCQkoNPpzC0mvr6+vPTSS2zbts1idk3Q4tHpdBYT1Dg5ORV7ne+EyWQiNzeXgIAAAHx8fOjbty+vvvoqx44dY9SoUYD9fs7cTPv27fn2229ZvXq1eV0X0GIV4lYkcbGivA8jR0dH0tPTzdsdHBx49NFHmTlzJr/88gvTp08HMN/Mz5kzh+3bt1ulzvdDbm4uzs7OeHt78+ijjzJw4ECuXr3KnDlzeOONN5g1a5a57K+//sozzzxjNwMsAwIC8PLyYvTo0cyYMYNu3brh4+NDnz59ePTRRzl06BD9+/cHtPO8Y8cOBgwYQG5urpVrfvsqVqxIVlaWeSpng8GAv78/Xl5eRd7crlmzxq7O4YABA/Dz82PChAlkZ2fz3nvvcfLkSerXr4+Hhwdr1qxh9OjRgPaeXLt2rV3FV758eVxcXFi/fr15m7u7O61ateKVV14hNjaWyZMnA1p8f//9N//3f/9HRkaGtap8xypWrEh2dnaJvUb9/Pzw8PAwLwQ6bNgwzpw5Q4sWLXB2dmb16tWMGTMG0D5n7O0aBShbtiw5OTnm2euMRiMBAQE4OjoWOe3/kiVLeOqpp8jNzbXpGRnzZgUNDg7G2dmZJUuWmPd5enrSqVMnBg4cyMmTJ/nxxx8B7X1ob9+Ft9K5c2dzgi3EHbFia4+4Jjw8XNWuXVv9/PPP5m15zd1//PGHatq0qfrjjz/M+/r3769OnjxZ7PW8WwkJCUU2a3/99ddq0qRJSimt6bh9+/aqcePGav369eYyly5dUpGRkcVW17txfXxXr15Vr7zyinrttddUixYt1KlTp8z7oqKiVKdOndSCBQvM22JjY4u1vncjLCxMRUREqIiICKWUUosWLVKHDx82X6dZWVmqSZMmFtdpnqSkJJs/h3nxnT592rytS5cuqlu3bmrUqFEqLi5OKaVUdna2WrFihXr22WfN78GYmBi7iS88PFwppdSUKVNU37591YIFC1RGRoY6deqUatOmjVq1apVatGiRGjhwoEpNTTW//uLFi9aq+m27dOmSunLlikpJSVFKadfokSNHSsw1mhdfXle31atXq/79+6tFixapIUOGmN+bmZmZavny5ap3795q//79Sin7uEaVUmrDhg3m+C5duqQmTZqkTp48aT6HSUlJqn79+mrHjh2FXnvixAnz/wNbNX78ePXqq6+a31vr169XvXr1svjuV0qp5ORkNXXqVDVo0CCVnJyslLKfc3in7LnLm7AOR2snTkJbpOnTTz/lu+++o3Tp0jz55JPmrkQdO3bk6aef5sCBAzz22GM4ODiY56u3Bxs3bmT+/PmMHDnSPIgwb6IBPz8/8+JgGzduxGg0UqtWLTZt2kRwcDANGjQgODjYmtW/paLi8/T0ZM6cOYSHhzN16lRzlxyj0UhQUBAVKlSwmI8/r6uArZo8eTIbN24kLS0NFxcXXnrpJfr3728xiNJkMhX6lTPv18JBgwZRpkyZ4q72bSsYn6urKz179mTQoEFMnTqV/v37c+7cOcqUKYNSCmdnZ9q3b88333xDREQEtWrVIigoyNoh3NT152/AgAG89dZbGAwG5s2bx48//khKSgq9evWiZ8+eREdH880333Dy5ElatGgBaL9+27IpU6awY8cOLl26ROvWrRk8eDD/93//ZzEjoz1fowXja9OmDa+++ipt27bln3/+4bfffiM5Odn8OePq6srDDz/MlClTCA8Pp0mTJjZ/jQKEh4czZswY2rRpw6effkpwcDCDBg3C3d3dfA6NRmOhc7ho0SJKlSpFjx49rFHt2zZ+/HhCQ0NZtmyZuYtfs2bNOHToEBs2bMDV1dU8IU/p0qV5+umnmT17NkeOHOGhhx6yi3N4N+y5y5uwDklcbETv3r2Jj49nwoQJmEwmevTogV6vx83NjdKlS9tt17AqVaqwZ88eJk+ezPvvv0/16tXNX0Jdu3bl8OHDDBo0iDNnzhAaGsrp06f5/vvvCQ0NpVatWhb9X21RUfEV3BcdHc13333HyJEjzf13vby8cHNzAwpP6WlrFi9ezOrVq5k+fToGg4GIiAjGjRtHmTJlzDPCGI1GUlNTyc3NNU/HPWXKFObMmWNen8ZWXR/fmTNn+OKLL6hQoQKPPfYYI0eOpG7duhbXYenSpalRo4b55sOWFXX+vvjiC4KDgxk2bBj9+/fn9OnTBAYGUrt2bQA8PDyoW7cupUuXtnLtb8/8+fNZvnw53333HRcuXGDFihXs3LmTatWqmd9bOTk5dnuNFhXf7t27efHFF3njjTcYP348hw4dYsmSJeZZxfz8/KhRo4b5c8YeVKxYEW9vb3bu3MmYMWMYM2YMPj4+5kRFKUVycjK5ubnm5QKmTJnC7NmzWbNmjRVrfmtff/01q1evZtWqVRYzRvr6+vL888/zn//8h9WrV5OWlmY+h0FBQdSpUwcXFxcr1VoI2ySJi41wdXXl1VdfRafTMWbMGBITE+nVqxdubm6kpaUREBBgnhrRnjg4OODr68vu3bsZMWIE3377rXkaVZPJxObNm/Hz8+PHH38kJCSEkJAQlFJ2kbTAzeNzcHCgf//+LFy4kI8//piWLVty/Phxdu/ezXvvvQfY/q9NYWFhdOvWjYYNGwLaqunr16/n0KFD5sTFwcEBJycn83SWM2fOZN68eSxdutSmpyKFouNbt24dBw4c4PHHHzf/gBAeHs6ePXto2bIla9euJSIiwubXMIGi4/vzzz/Ztm0b7du3Jzg4mODgYKKjo/n5559p0aIF69atIzY2Fm9vbyvX/vacPn2afv360bJlS1q2bMnBgwc5duwYu3fvxsHBgWbNmuHs7Gy+Pu3tGi0qvqNHj7Jz5078/f2ZPHkyo0ePZuXKlZw9e9Zc5siRI4wdO9ba1b8tJpMJZ2dnypcvT2BgIEopxo4dy6hRo/Dz88NgMODk5IROp8PBwQFnZ2fzObT19YQuXrzI5s2beeqpp8xJi9FoZPv27RgMBsqVK8fIkSOZOXMmK1eu5NChQzz00EMcO3aMc+fOmVvShBAaSVxsiKenJ6+//jrlypXjyy+/ZPny5Tg4OHD58mXmz59vFzfy1zt27BjBwcEsXLiQ5557jhEjRjBhwgSqVKlCuXLlWLRoER4eHlStWtXcheyRRx6xdrVv243iy0teOnbsiLOzM3PnzuXs2bN4eXkxf/58KlWqZN2K3waj0cjFixctbmC9vb2pVKkSu3btIicnx3xNent7ExISwogRI0hOTmbJkiXUq1fPWlW/LTeKr3Llyub4HB0dycnJYe3atSxdupSgoCD0ej1z5syx+e5TN4tv9+7d5vOnlCImJoalS5eyYsUKjEYjM2bMsPkujKANuj937pw5RqPRyD///IO7uzs7d+7k6tWrvPDCC7z//vv4+voSFBRkV9foreJLTU3l3Xff5YsvvmD9+vUsXryYsLAw3NzcWLRokd3c9Oa1wrdr146wsDBatGjB4sWLGT9+PJMmTeLYsWM0btyYwMBAAgICGDZsGOfOnWPZsmU2fw79/Pzo06cPhw4d4sCBAzRu3JhXXnmFpKQkUlJSSE5O5vXXX+ftt9+mbdu2TJs2jdWrV6PT6ViwYIHNd5cWorhJ4mJjnJ2d6dWrFy1atOD06dPk5ORQv359ypUrZ+2q3ZXAwEDq1KmDu7s7oaGh9O7dm+HDh5uTlwYNGpjL5n152ZMbxffNN99QpUoVfH19eeqpp3jqqacwGAyYTCa7afp3cHDgySef5Oeff+by5cv4+vri4OBAQECAecpqyJ/WM++GePny5dSsWdPKtb+1m8Wn1+vND2dnZ15//XVeeOEF0tLS8PX1tenxEHlu9/zpdDqaNWvGmjVrSEhIwMPDw9ydytY5OTkxZMgQwsLCADh69CjVq1c3tzQcO3aM4cOHm6fSrVq1Knv37rWba/RW8R09epThw4dTunRp+vTpQ58+fezucwbyu8x6enpy4cIFxo4di16vZ8WKFXTo0IHMzEx27NhBbm4uHh4eXLhwgZUrV9rFOXR1daV79+7s27eP5cuX8/fff+Pl5cVXX32FUoqTJ08ydOhQSpUqxfPPP8/ixYsxGo3k5uba1TkUorjo1PUj3YS4j9LS0sjIyDD/epuTk8PTTz+Nm5sb33zzjc2vvn0rJT2+3Nxc4uLiCAwMNN/IOboQcQAACgtJREFUz5o1iw0bNrBixQpz18XIyEg8PT3JyMigQoUKVq717btVfHlTkB85csQiybYXt3v+Dh06RKNGjaxb2bt0/TixrKwsXF1dzc9nzZrFsWPHmDVrFqdOncLd3d2urtFbxTdz5kyOHz/OxIkTcXNzs/nupzeTlpbGK6+8wqJFi3B2dqZ///6cPHmSxo0bM3XqVDw9PTl48CBeXl5299l65swZBgwYQE5ODqNHj6Zbt27mfT/++CO7d+9mypQpeHh42OWPeEIUF3l3iAfK09PTfFNvMBhwdnZmzZo1ZGZm8sknn5h/SbRXJT0+R0dHypYti6Ojo/nLNCMjg4yMDIxGIwDfffcdjz/+OE5OTnZ1Qwi3jk+n0/Hdd9/x7LPPkpiYaNPrQxTlds7f5MmT6du3r13GB1jMbAdY3NQDuLi4cOXKFXJycqhVq5bdXaO3is/V1ZX4+HhcXFzsOmnJS9AyMzM5evQo48ePJy4ujjfeeAOdTseQIUNITEykcePGdpe0gDa+7PPPP8fDw4P69etb7HNxcSElJQU3NzdJWoS4BXmHiGLj5ORkXnxyzZo1REdHM27cOHJycqxdtfuipMdXcHYfJycnnJ2dmTZtGj///DOhoaF2MwvVjdwqPh8fH7u/Mcz7b8H4Fi5cWCLiy7vhu3LlCqdOnTJvj4+Px9/fv8hFC+3JzeLz8/Ozq0Vsi6LT6fDw8OChhx7izTffZPPmzcybN4/XXnuNxx57DBcXlyIXD7UnDz/8MKtXr6Z8+fKkpKSYt8fGxpaIcyhEcZAxLqJYOTo6mm/uN27cSGxsrF1OOnAjJT0+gFKlSuHm5sb48eNZsmQJS5cutfkBsndC4rNfSinOnDnDa6+9RvXq1fHw8CA8PJz58+cXaqmwRzeLr6SMh+jatSu7du3i22+/NU8u8Mwzz9C1a1fzNMj2zMvLi+joaPr27UulSpVwc3Pj8OHDLFiwoERco0I8aDLGRVhFbm4ujo4lN28uyfHt3LmTAQMG4OzszNKlS6lbt661q3RfSXz2LScnh927d3PkyBG8vb1p27YtFStWtHa17puSHh9Adna2ORGz9bWu7kZGRgb//e9/OXDgAMHBwTz22GNUqVLF2tUSwi5I4iKEuCOpqamMGzeOwYMH22Vf81uR+IQQQgjbJImLEOKOFVzDpSSS+IQQQgjbI4mLEEIIIYQQwubJrGJCCCGEEEIImyeJixBCCCGEEMLmSeIihBBCCCGEsHmSuAghhBBCCCFsniQuQggh7quPPvqI7t2737TMCy+8wODBg4upRkIIIUqCkrlCnhBCCJs2ZswY9Hr57UwIIcTtk8RFCCFEsatWrZq1qyCEEMLOyM9dQghRAqWlpTFu3Dg6duxIvXr1aNWqFSNGjCA1NdVcJjs7m3HjxtG6dWuaNGnCp59+ynfffccjjzxicayFCxfSpUsX6tWrxxNPPMG6detuqw6zZ8+mTZs2NGnShA8++ICEhATzvoJdxXbv3k3NmjXZt28fffv2pX79+nTq1Inly5cXOt6jjz5K/fr16dy5MzNnzsRkMt3t/yIhhBB2RlpchBCiBPrggw8IDw/ngw8+wN/fn8OHDzN16lS8vb356KOPAPjkk0/YtGkTH3zwASEhIcydO5dff/0Vf39/83FmzJjB999/z2uvvUazZs3YsmUL77//Pjqdjq5du97w3z979iyhoaGMHj2arKwsvv32WwYPHkxoaOgNu4gNHTqUV155hSFDhrBkyRJGjhxJ48aNqVatGmvXrmXq1Kl89NFHVK9enYMHDzJ58mR8fX3p27fv/f2fJ4QQwiZJ4iKEECVMdnY2BoOBzz77jPbt2wPQsmVLDh48yJ49ewA4d+4cv//+O+PHj6dXr14AtGrVik6dOpmPk5qayo8//sjAgQN57733AGjXrh3p6elMmjTppokLwA8//EDlypUB8Pb2ZtCgQezatYs2bdoUWf7FF19kwIABANStW5cNGzawdetWqlWrxv79+ylbtiz/93//h06no0WLFjg6OhIQEHD3/6OEEELYFekqJoQQJYyLiwtz586lffv2XLx4kW3btjFv3jzOnDmDwWAAYO/evQB07tzZ/Do3Nzc6dOhgfn7o0CGys7N5+OGHyc3NNT/at29PVFQUUVFRN6xD9erVzUkLQPv27XFycmL//v03fE2jRo3Mf3t5eeHu7k5GRgYAzZo149y5c/Tu3Zsff/yRsLAwXn311ULd2oQQQpRc0uIihBAl0P/+9z/Gjx9PVFQU3t7e1KtXD1dXV/OYkKSkJJycnPDy8rJ4nZ+fn/nv5ORkgBt2xbpy5Qrly5cvcl/B4wDodDrKlCnD1atXb1hnV1dXi+d6vR6lFABPPfUURqORxYsX89133zFp0iRq1qzJl19+Sf369W94TCGEECWHJC5CCFHCREZGMmTIEHr27MmiRYsICgoCYMiQIZw5cwaAwMBADAYDqampFslLYmKi+e9SpUoBMHPmTAIDAwv9OwVbVK6XkpJi8dxkMpGUlISPj89dx9WzZ0969uxJQkICGzduZObMmQwfPpw///zzro8phBDCfkhXMSGEKGFOnDiBwWBg0KBB5qQlIyOD/fv3m1swGjdujF6vZ+PGjebX5eTk8M8//5ifN2zYECcnJxISEqhfv775ER4ezsyZM29ah1OnThEfH29+/r///Y/c3FxatGhxVzF9+umnvPvuuwD4+vrSp08fnnnmGWJiYu7qeEIIIeyPtLgIIUQJU7t2bRwcHPj222/p168fSUlJzJ07l/j4eJydnQGoWLEiTz75JOPGjSMjI4OyZcuycOFCrly5QkhICAA+Pj688MILfP3116SkpNCgQQNOnTrF5MmT6dSpE56enjesg16v5/XXX+edd97hypUrfPvtt7Rv354mTZrcVUzNmzdnxIgRfPfdd7Rp04bY2FiWLl3Ko48+elfHE0IIYX8kcRFCiBKmcuXKTJgwgRkzZjBo0CD8/f3p0KEDvXv35vPPPycuLo7AwEA+++wzXF1dmTJlCrm5uXTv3p3HH3+ciIgI87E+/PBDfHx8CA0NZdq0aQQEBPDSSy/x9ttv37QOzZs3p3HjxgwfPhyj0Ui3bt3M0zDfjaeffpq0tDQWL17M/PnzKVWqFI899hgffPDBXR9TCCGEfdGpvH4DQggh/jUSExPZvn07HTt2tGg56du3L35+fsyYMcOKtRNCCCEKkxYXIYT4F3J1dWXs2LGsX7+evn374ujoyJ9//smhQ4eYN2+etasnhBBCFCItLkII8S915MgRJk+ezLFjxzAYDNSsWZM33niDhx9+2NpVE0IIIQqRxEUIIYQQQghh82Q6ZCGEEEIIIYTNk8RFCCGEEEIIYfMkcRFCCCGEEELYPElchBBCCCGEEDZPEhchhBBCCCGEzZPERQghhBBCCGHz/h+sHZeDUfTT0wAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x576 with 2 Axes>"
       ]
@@ -1682,7 +1334,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1717,7 +1369,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 2 Axes>"
       ]
@@ -1751,15 +1403,15 @@
      "data": {
       "text/plain": [
        "['sex_enc',\n",
-       " 'fare_enc',\n",
-       " 'deck_enc',\n",
-       " 'sibsp_enc',\n",
        " 'class_enc',\n",
        " 'pclass_enc',\n",
+       " 'fare_enc',\n",
+       " 'age_enc',\n",
+       " 'sibsp_enc',\n",
        " 'alone_enc',\n",
+       " 'parch_enc',\n",
        " 'embarked_enc',\n",
-       " 'embark_town_enc',\n",
-       " 'parch_enc']"
+       " 'embark_town_enc']"
       ]
      },
      "execution_count": 26,
@@ -1796,7 +1448,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c9d9c04ce19a45668428e0ee0b47f05d",
+       "model_id": "2c4aa0d946be486caa61606f82c5eebc",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1841,81 +1493,81 @@
        "      <th>0</th>\n",
        "      <td>[sex_enc]</td>\n",
        "      <td>sex_enc</td>\n",
-       "      <td>0.748789</td>\n",
-       "      <td>0.809127</td>\n",
-       "      <td>0.778474</td>\n",
+       "      <td>0.738934</td>\n",
+       "      <td>0.821885</td>\n",
+       "      <td>0.800416</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>[sex_enc, deck_enc]</td>\n",
-       "      <td>deck_enc</td>\n",
-       "      <td>0.812642</td>\n",
-       "      <td>0.852712</td>\n",
-       "      <td>0.818069</td>\n",
+       "      <td>[sex_enc, class_enc]</td>\n",
+       "      <td>class_enc</td>\n",
+       "      <td>0.817718</td>\n",
+       "      <td>0.878615</td>\n",
+       "      <td>0.837188</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>[deck_enc, sex_enc, embarked_enc]</td>\n",
-       "      <td>embarked_enc</td>\n",
-       "      <td>0.828076</td>\n",
-       "      <td>0.853373</td>\n",
-       "      <td>0.796999</td>\n",
+       "      <td>[class_enc, sex_enc, age_enc]</td>\n",
+       "      <td>age_enc</td>\n",
+       "      <td>0.834236</td>\n",
+       "      <td>0.884594</td>\n",
+       "      <td>0.842840</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>[deck_enc, sex_enc, embarked_enc, sibsp_enc]</td>\n",
-       "      <td>sibsp_enc</td>\n",
-       "      <td>0.837775</td>\n",
-       "      <td>0.877050</td>\n",
-       "      <td>0.804566</td>\n",
+       "      <td>[class_enc, sex_enc, age_enc, embarked_enc]</td>\n",
+       "      <td>embarked_enc</td>\n",
+       "      <td>0.840116</td>\n",
+       "      <td>0.883343</td>\n",
+       "      <td>0.844790</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>[deck_enc, sex_enc, sibsp_enc, embarked_enc, f...</td>\n",
-       "      <td>fare_enc</td>\n",
-       "      <td>0.844617</td>\n",
-       "      <td>0.871892</td>\n",
-       "      <td>0.807306</td>\n",
+       "      <td>[class_enc, embarked_enc, sex_enc, age_enc, pa...</td>\n",
+       "      <td>parch_enc</td>\n",
+       "      <td>0.841274</td>\n",
+       "      <td>0.882856</td>\n",
+       "      <td>0.843425</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>[deck_enc, fare_enc, sex_enc, sibsp_enc, embar...</td>\n",
-       "      <td>parch_enc</td>\n",
-       "      <td>0.846352</td>\n",
-       "      <td>0.872817</td>\n",
-       "      <td>0.805088</td>\n",
+       "      <td>[parch_enc, class_enc, embarked_enc, sex_enc, ...</td>\n",
+       "      <td>sibsp_enc</td>\n",
+       "      <td>0.843148</td>\n",
+       "      <td>0.890990</td>\n",
+       "      <td>0.849402</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>[deck_enc, fare_enc, sex_enc, parch_enc, sibsp...</td>\n",
-       "      <td>class_enc</td>\n",
-       "      <td>0.846494</td>\n",
-       "      <td>0.873347</td>\n",
-       "      <td>0.806654</td>\n",
+       "      <td>[parch_enc, sibsp_enc, class_enc, embarked_enc...</td>\n",
+       "      <td>fare_enc</td>\n",
+       "      <td>0.843590</td>\n",
+       "      <td>0.890712</td>\n",
+       "      <td>0.848688</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>[deck_enc, fare_enc, sex_enc, parch_enc, sibsp...</td>\n",
+       "      <td>[parch_enc, sibsp_enc, fare_enc, class_enc, em...</td>\n",
        "      <td>pclass_enc</td>\n",
-       "      <td>0.846494</td>\n",
-       "      <td>0.873347</td>\n",
-       "      <td>0.806654</td>\n",
+       "      <td>0.843694</td>\n",
+       "      <td>0.890712</td>\n",
+       "      <td>0.848688</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>[deck_enc, fare_enc, sex_enc, parch_enc, class...</td>\n",
+       "      <td>[parch_enc, fare_enc, sibsp_enc, class_enc, pc...</td>\n",
        "      <td>embark_town_enc</td>\n",
-       "      <td>0.846509</td>\n",
-       "      <td>0.873347</td>\n",
-       "      <td>0.806654</td>\n",
+       "      <td>0.843694</td>\n",
+       "      <td>0.890712</td>\n",
+       "      <td>0.848688</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1925,25 +1577,25 @@
       "text/plain": [
        "                                          predictors last_added_predictor  \\\n",
        "0                                          [sex_enc]              sex_enc   \n",
-       "1                                [sex_enc, deck_enc]             deck_enc   \n",
-       "2                  [deck_enc, sex_enc, embarked_enc]         embarked_enc   \n",
-       "3       [deck_enc, sex_enc, embarked_enc, sibsp_enc]            sibsp_enc   \n",
-       "4  [deck_enc, sex_enc, sibsp_enc, embarked_enc, f...             fare_enc   \n",
-       "5  [deck_enc, fare_enc, sex_enc, sibsp_enc, embar...            parch_enc   \n",
-       "6  [deck_enc, fare_enc, sex_enc, parch_enc, sibsp...            class_enc   \n",
-       "7  [deck_enc, fare_enc, sex_enc, parch_enc, sibsp...           pclass_enc   \n",
-       "8  [deck_enc, fare_enc, sex_enc, parch_enc, class...      embark_town_enc   \n",
+       "1                               [sex_enc, class_enc]            class_enc   \n",
+       "2                      [class_enc, sex_enc, age_enc]              age_enc   \n",
+       "3        [class_enc, sex_enc, age_enc, embarked_enc]         embarked_enc   \n",
+       "4  [class_enc, embarked_enc, sex_enc, age_enc, pa...            parch_enc   \n",
+       "5  [parch_enc, class_enc, embarked_enc, sex_enc, ...            sibsp_enc   \n",
+       "6  [parch_enc, sibsp_enc, class_enc, embarked_enc...             fare_enc   \n",
+       "7  [parch_enc, sibsp_enc, fare_enc, class_enc, em...           pclass_enc   \n",
+       "8  [parch_enc, fare_enc, sibsp_enc, class_enc, pc...      embark_town_enc   \n",
        "\n",
        "   train_performance  selection_performance  validation_performance  \\\n",
-       "0           0.748789               0.809127                0.778474   \n",
-       "1           0.812642               0.852712                0.818069   \n",
-       "2           0.828076               0.853373                0.796999   \n",
-       "3           0.837775               0.877050                0.804566   \n",
-       "4           0.844617               0.871892                0.807306   \n",
-       "5           0.846352               0.872817                0.805088   \n",
-       "6           0.846494               0.873347                0.806654   \n",
-       "7           0.846494               0.873347                0.806654   \n",
-       "8           0.846509               0.873347                0.806654   \n",
+       "0           0.738934               0.821885                0.800416   \n",
+       "1           0.817718               0.878615                0.837188   \n",
+       "2           0.834236               0.884594                0.842840   \n",
+       "3           0.840116               0.883343                0.844790   \n",
+       "4           0.841274               0.882856                0.843425   \n",
+       "5           0.843148               0.890990                0.849402   \n",
+       "6           0.843590               0.890712                0.848688   \n",
+       "7           0.843694               0.890712                0.848688   \n",
+       "8           0.843694               0.890712                0.848688   \n",
        "\n",
        "       model_type  \n",
        "0  classification  \n",
@@ -1993,7 +1645,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -2022,7 +1674,7 @@
     {
      "data": {
       "text/plain": [
-       "['deck_enc', 'fare_enc', 'sex_enc', 'sibsp_enc', 'embarked_enc', 'parch_enc']"
+       "['parch_enc', 'class_enc', 'embarked_enc', 'sex_enc', 'age_enc', 'sibsp_enc']"
       ]
      },
      "execution_count": 29,
@@ -2045,7 +1697,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -2084,15 +1736,15 @@
      "data": {
       "text/plain": [
        "{'meta': 'logistic-regression',\n",
-       " 'predictors': ['deck_enc',\n",
-       "  'fare_enc',\n",
-       "  'sex_enc',\n",
-       "  'sibsp_enc',\n",
+       " 'predictors': ['parch_enc',\n",
+       "  'class_enc',\n",
        "  'embarked_enc',\n",
-       "  'parch_enc'],\n",
-       " '_eval_metrics_by_split': {'train': 0.8463522972002873,\n",
-       "  'selection': 0.8728174603174603,\n",
-       "  'validation': 0.8050880626223091},\n",
+       "  'sex_enc',\n",
+       "  'age_enc',\n",
+       "  'sibsp_enc'],\n",
+       " '_eval_metrics_by_split': {'train': 0.8431478147962346,\n",
+       "  'selection': 0.8909899888765294,\n",
+       "  'validation': 0.8494022869022869},\n",
        " 'params': {'C': 1000000000.0,\n",
        "  'class_weight': None,\n",
        "  'dual': False,\n",
@@ -2109,13 +1761,13 @@
        "  'verbose': 0,\n",
        "  'warm_start': False},\n",
        " 'classes_': [0, 1],\n",
-       " 'coef_': [[3.805721985140643,\n",
-       "   0.6303750013369196,\n",
-       "   4.422528045527391,\n",
-       "   2.385099647746924,\n",
-       "   2.932569579182775,\n",
-       "   0.8389611142315032]],\n",
-       " 'intercept_': [-6.257622551740109],\n",
+       " 'coef_': [[1.8355006899588193,\n",
+       "   4.425089647429058,\n",
+       "   2.6962961751414154,\n",
+       "   4.728968513306399,\n",
+       "   5.021971105587632,\n",
+       "   2.1483972871281782]],\n",
+       " 'intercept_': [-8.671610876045246],\n",
        " 'n_iter_': [5]}"
       ]
      },
@@ -2187,7 +1839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2197,23 +1849,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "accuracy             0.825843\n",
-       "AUC                  0.872817\n",
-       "precision            0.760000\n",
-       "recall               0.814286\n",
-       "F1                   0.786207\n",
-       "matthews_corrcoef    0.640665\n",
-       "lift at 0.05         2.540000\n",
+       "accuracy             0.814607\n",
+       "AUC                  0.890990\n",
+       "precision            0.710145\n",
+       "recall               0.790323\n",
+       "F1                   0.748092\n",
+       "matthews_corrcoef    0.604243\n",
+       "lift at 0.05         2.510000\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2229,7 +1881,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 2 Axes>"
       ]
@@ -2251,7 +1903,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -2271,7 +1923,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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4JiIiIiLix7SmWkREREQkjxSqRURERETySKFaRERERCSPFKpFRERERPLI43b/KIgr9EVEREREctKoUaN8eR6PC9WQf29OfEdiYiI1a9Y0uwzxMDovJCc6LyQnOi8kJ/k5mavlHyIiIiIieaRQLSIiIiKSRwrVIiIiIiJ5pFAtIiIiIpJHCtUiIiIiInmkUC0iIiIikkcK1SIiIiIieaRQLSIiIiKSRwrVIiIiIiJ5pFAtIiIiIpJHCtUiIiIiInmkUC0iIiIikkcK1SIiIiIieaRQLSIiIiKSRwrVIiIiIiJ5pFAtIiIiIpJHVxWqN2/eTP/+/QHYt28fffv2pV+/fowcORK73Q7ApEmTuOuuu+jTpw9btmwBYPny5dx111088cQTzvuNHj2agwcPFsR7ERERERExRdCV7vD+++/z3XffER4eDsDYsWN58sknadasGSNGjGDRokWUKVOGdevWMWfOHI4cOcLjjz/Ol19+yWeffcZHH33EW2+9xbZt2wgICKBIkSKUK1euwN+YiIiIiD9JyzBI2J9JRpZhdile4fq970LJNvn2fFcM1XFxcbz99ts899xzACQkJNC0aVMA2rZty6pVq6hUqRKtW7fGYrFQpkwZbDYbp06dIjIyEqvVSnp6OuHh4UyaNIlRo0blW/EiIiIiAja7wYTvz7HnmM3sUrzGzZlnqFQy/57viqH65ptvdluuYRgGFosFgMjISJKSkkhOTqZo0aLO+1wYf+SRRxg7dizVq1dn//79NGzYkHnz5pGYmEj37t1p0KBBjq+ZmJiYx7clvsZqteq8kEvovJCc6LyQnPj6ebFhfwR7jkWZXYZXWRg0mEHk35LkK4bqiwUEuJZhp6SkEB0dTZEiRUhJSXEbj4qKonjx4kyYMAGbzcaTTz7JmDFjeOGFF3jzzTcZPHgw77//fo6vUbNmzVy8FfFliYmJOi/kEjovJCc6LyQnvnxeHD9rY82Ks87jmuWCKBqpvSicDIMqp2dxIqI+Z8NqOIctlpB8fZlrDtW1atVi7dq1NGvWjOXLl9O8eXPi4uL43//+x8CBAzl69Ch2u53ixYs7HzNr1iy6d+8OgN1ux2KxkJaWln/vQkRERMQPGYbBJ8tSyMhyHJcrEcgTt0cRFGgxtzBPcWo7LBwMB5ZAuXZw2xKwuP5sNm7Mv5e65lA9dOhQhg8fzvjx46lcuTI333wzgYGBNG7cmN69e2O32xkxYoTz/snJyaxbt46JEycCULJkSefuISIiIiKSe2t2ZPDnQUeitgAD2kcqUANkWWHtWFj/X7BlOMYOLoNtn0HNewrkJS2GYXjUJaIbN26kUaNGZpchHsaXP7aT3NN5ITnReSE58cXzIinNzojPz5JsdUS5jnVD6dM60uSqPMDeBbDoETjzl2vMEggN/w0tX4KQIs7h/Myd1zxTLSIiIiLmm70q1RmoixcJ4M5mESZXZLKUo7D0Kdj2ufv49c2g0xQoVb9AX16hWkRERMTLJOzPZM2ODOfxve0iCAv202UfdhtseQ9WPg/prgs2CY2BNv+Fuv8CS8FfuKlQLSIiIuJF0jMNPlnu2nWtSdUQ6lTI350svMqJrbDoUSDbiuYa/aD9OIgsXWhlaL8VERERES/y/fo0TpyzAxARaqFPaz9f9lGqPtR7yPF1sWpw1wK4/dNCDdSgmWoRERERr7H/eBYLNludx71aRhAd4UdzpIYBSQchurz7eOtXISoOGg2BoDBTSvOjvwURERER72WzG3y8NAX7+VUO1csG0aqGHy37OLsXvukGM+tD6nH328KKQbPnTQvUoFAtIiIi4hUWbbGy/7gNgKBA6N8uEovFDy5OtGXCutdgei3YPQ+sp2D5c2ZXdQkt/xARERHxcMfP2fh2nasbddfG4cQWDTSxokJycCUsfBhOJmQbtDhmpA17oezqcbUUqkVEREQ8mGEYfLos1dmKvGzxQG6qb94yh0KRdhKWD4U/PnQfL1nPsed0mebm1PUPFKpFREREPNjanRkkHMgEzrci7+DDrcgNA/6cAcuegbQTrvHgSGg5Gho+AQGeGV89syoRERERISnNzqyVqc7j+DqhVI714fj2Qz/Y/oX7WNU7ocNbl+744WF8+G9FREREpPDtOprJ6m0ZZNmNK9/5Co6csvlXK/Kqd7pCdVQcxL8NVbuZWtLVUqgWERERyScnk2xM+C6J9Kz8f+5+bSMIC/HRZR8XVL8bEmdC8VrQcqRj2YeXUKgWERERyQcXLigsiEDdrFoI9Sr60J7UyYdhyRCo+y+o0NE1brHAnd951K4eV0uhWkRERCQfrP8rg637XRcU3tUynMjQvIfD8FALdSsE5/l5PILdBpsmw6oXISMJjm+CAVsgKNR1Hy8M1KBQLSIiIpJnKVY7X2S7oLB97VBuqh9uYkUe6NhGWPCQ4/8XnN4Be36Eat3NqyufKFSLiIiI5NGc1akkpTkuKCwaaaF7cx+/oPBapJ+DVcNh0yRHw5YLilWHTu9CXAfzastHCtUiIiIi/8AwDH7eZGXn4Sxy2s/DMOCP88s+AO5pG0m4r19QeDUMA3bMhSX/hpQjrvHAUGj+H2j8rPuyDy+nUC0iIiLyD5YlpPPlr2lXviPQsHIw9Sv50AWFuXVuv6O9+J757uMVOkPHyVCsqjl1FSCFahEREZHLOJ1s58tfU698RyA63ELfNt6zBVyBMuxwYKnrOLI0tJ/o2DLP4puz+ArVIiIiIpfx+YoUrOdXdsQWDaBXy5zXSgdYoGKpIKLCvXPninwXUxFajIQVz0P9R6DVGAgranZVBUqhWkRERCQHv+3O4Pc9rrXS/dtFUr2sj2xtl59ST8ChlVDtTvfxRk85lnvENjSlrMKmX6dERERELpKabuez5SnO4zY1QxWoL2bYYetHMK06zLsbTm5zvz0w2G8CNWimWkREJN9t3ZfBT79bycjKaa8IMUNaWnHCE85e9f1TrAZnUx1/f9HhFnq20J7Tbk4kOC5EPLTSNbZoMPRa7LNrpq9EoVpERCQfnThnY8rPyWQUQKtqyYtgSLLl6pF920QSGaYP9wHITIU1L8OGN8Ce7SSPrgCNnvbbQA0K1SIiIvnGMAw+XZ6qQO1DmlYLoVEVLfsAYPePsOhROLfXNRYQ5AjTLYZDsH/vfKJQLSIikk/W/ZXhbAJiAf51UxFKRGmG0xPs2buHShUrXdNjQoLg+uKBWPx49hWApIOw5EnY+aX7eNnWjo6I19U2pSxPo1AtIiKSD5KtdmatdO1n3KFOKI2rqgmIp7CeyqJSrGJPrvzQ133tdFhxaPs/qH0fWPRL4wX6kxAREckHc1ankpTmuLCtWGQA3ZvlvJ+xiNdp+zqOz16AG++D+7dDnQcUqC+iX9lERMRnLdpiZekfVjJzd33aNTmZZHd+fU+7CMJC/HzJgHin9HMQUsQ9MJdpAa1fgTItoXw782rzcArVIiLik3YeyeSLlVfXXjo/NaoSTL2KWvYhXsYwYPtsWPoktH4Vat/vfnuz500py5to3l5ERHxOps1gxpKUK98xn5WMDqBPa//eAUG80Om/4Mtb4Ic+kHIUlj3r6JIo10Qz1SIi4nPm/2bl6BnHcozQYHj2jmgiwgp2OYYFKFYkgMAALfsQL5GVDhv+B2vGgC3dNR4YAmd3QcR15tXmhRSqRUTEp5xMCWT+xjTncfdmEVQopR93Im72L4GFg+H09myDFmjwGLQaA6HRppXmrfRdRkREfIbdMFiwPZqs89cMVooNpEPtUHOLEvEkqX/Dsmfgz5nu46UaQuepULqxOXX5AIVqERHJ0eKtVuZtSCMj0zC7lKtmABlZjosEAwNgQPtIArQcQ8Th4Er4thtYT7vGQqKg1StQ/xEICDSvNh+gUC0iIpfYdzyLL1amYnhPnr7EzQ3CKFdCP+ZEnErWgcBsn9zc0As6TIQiZUwryZdo9w8REXFjszt2zvDmQF2zXBBdGoWbXYaIZwmNgfYTIKYS9PgRus5WoM5H+hVeRETcLNxsZf8JR7eU4EB4sVc0JaK852PhHdu3Ubd2TbPLEDHXru/h0Cpo+1/38eq9oeqdEBRmSlm+TKFaREScjp+18d16184ZXZuEU7a4d/2oCPae/C+S/84dgCX/hr++dhxXvBniOrhut1gUqAuIln+IiAgAhmHwyfIUMrIcx+VKBNK5nn74ingFexZsGA/Ta7oCNcCq4ebV5Ge8a/pBREQKzNodGfx5wJGoLTh2zggK1M4ZIh7vyFpY8BAc3+w+XnsgtH3NnJr8kEK1iIiQlGZn1qpU53F83VAqxepHhIhHs56BlS/A5ik4NpQ8r8SN0GkKlGttVmV+Sd8xRUSE2atSSbY6figXLxLAnc0iTK5IRC7LMGDbF7B0CKQec40HhUOLkdBoiKPVuBQqhWoRET/354FM1uzIcB7f0zaCsGAt+xDxaImfuAfqSrdBx0mO7fLEFLpQUUTEj6VnGnyyLMV53KRqCHUraoZLxKNZLI4AHRTm2Ge661zoPk+B2mSaqRYR8WPfr0/j+Dk7ABGhFvq01rIPEY9zYBmUbgrB2RoaxVSCO7+H65s5Wo2L6TRTLSLip/Yfz2LBZqvzuFfLCKIj9GNBxGOkHIMf74XZ7WHdfy+9vUInBWoPou+eIiJ+yGY3mLE0Bfv5DQOqlwmiVQ0t+xDxCIYdNk+FaTUg8VPH2Pr/wqnt5tYl/0jLP0RE/NCiLVb2HXe0Ig8KhP7tI7FYdHGiiOn+3gwLH4Yja9zHq/aA0BhzapKrolAtIuJnjp+z8e06VyvyLo3DiS2q3t4ipspIhtWj4LeJYNhc40WrQMfJUPEmsyqTq6RQLSLiRwzD4NNlqc5W5GWLB3JzfbUiFzHVX9/C4sch6YBrLCAYmg6Dps+7X6AoHkuhWkTEyyWl2TmZZL+q+/51JIuEA5nA+VbkHdSKXMRUO76E7+9yHyvfHjq+CyVqmFKS5I5CtYiIF9t2KJM3v08i6+oytZsOdUKprFbkIuaq0g2uqwMntkJ4SWg/Dmre69iLWryKvpuKiHip9EyD6YtTchWoixcJoLtakYsUPluGewvxwGDoNAUSpkOb/0J4cdNKk7xRqBYR8VLfrUtzLvsIDYLSxa7uYsOIUAs9mkcQFqKZMJFCk3YKVj7v2Bbv7iXuM9FlWzr+E6+mUC0i4oX2/Z3Fgi2uxi1920bSqkaoiRWJSI4MAxI/gaVPQ9pxx1jCx1D7PlPLkvyn5i8iIl7mQuMW43zjlhplg2hZXY1bRDzOqe0wpyPMH+AK1AAHl5tXkxQYzVSLiJjgTIqdAyeycvXYbQez2H/CsY9tsBq3iHiezDRYNxbWv+ZYQ31BVHmIfxuq3mFebVJgFKpFRArZkVM2xn51jrQMI8/P1bVJOKVi1LhFxGPs/QUWPQJndrnGLIHQ8EloOQpCiphVmRQwhWoRkUJkNww+XpqSL4G6XIlAOtdT4xYRj2DY4cf+sO0z9/Hrmzt29yhVz5y6pNAoVIuIFKLlCensOupY9hEYADXLBefqeYqEWejWNFyNW0Q8hSUAQmNcx6FFHVvk1R3kuE18nkK1iEghOZ1s56s1ac7jWxqEcaf2ihbxHa1fhZ1fQYVO0G4cRMaaXZEUIoVqEZFC8vkK17KP2KIB3N4o3OSKRCRXMpJgzSvQ+GmIKOkaDysK9yVAeAnTShPzKFSLiOTSoZNZ7Dtuu6r7nkiy8/ueTOdx/3aRBAdp6YaIVzEMx0z0kn9D8iFIPQq3THe/jwK131KoFhHJhZ1HMhn3bRK2XLQIb1MzlOplc7eWWkRMcnYPLHoM9vzoGkv4GBo8DrGNzKtLPIZCtYjINcrMMpixJCVXgTo63ELPFlr2IeI1bBmwYTysGQ1ZrmsiiCgF7SdAqYbm1SYeRaFaROQa/fhbGkfPOBJ1WDA0qHx13QxDgiy0rx1KZJh2AhDxCgdXwMLBcDIh26AF6j3kuCgxrJhppYnnUagWEbkGh05lMf83q/O4R4sIOtTWXtEiPiXtJCx/Dv74yH28ZD3HntNlmptTl3g0hWoRkatkNwxmLkl1LvuoUjqIdjeGmluUiOS/A0vcA3VwJLR62bF+OkDRSXKmM0NEfFpSmp0NuzLIyMx7B8NjZ+3sOuZq3NK/fQQBFu3gIeJzqvWEirfA3p+ganfo8CZElze7KvFwCtUi4rMybQbjvk3i0Kmr2/buWtzaMIyyxfUtVMTrZaZB0gEofoNrzGKBju841lJX6WpebeJV9BNBRHzW/N+sBRKoSxcN4LaG2sFDxOvt+QkWPQoBgTBgCwRluz6iaGXHfyJXSaFaRHzSkVM25m90bX/VqEoIJYrkfdeN0GBoXStMjVtEvFnyYVgyBHbMdo2tew1ajjSvJvF6CtUi4nPshsGMpSlknb+gsFJsIP/qHElAgIKwiF+z22DTZFj1oqPV+AVhxSA6zry6xCfkKlQfPnyY5557DsMwiImJYdy4cYSHh7N48WLeeecdgoKC6NmzJ3fffTdHjx7lySefJCAggAkTJhAbG8u3335LUFAQt99+e36/HxERVvyZzl9HXRcUDmivQC3i78LOJsBn/eHYRvcbag2Adv9zNHMRyYNcherp06dz6623cs899zBhwgTmzp1Lnz59GDt2LHPnziU8PJy+ffsSHx/P/PnzefDBBzEMg/nz59OnTx8WL17MxIkT8/mtiIi/2H4okz/2Z3LiRBESTqe63WbgCNUX3Fw/jHIl9KGciN9KPwurhlPx93eAbG1Qi1WHTu9CXAfTShPfkqufNDVr1uTo0aMAJCcnU7p0aXbt2kVcXBwxMTEANGrUiPXr1xMREYHVasUwDMLDw5k+fToDBgzAom2oRCQX9hzLYvx3SdgNgEg4YL3sfUvFBNClsS4oFPFb9iz4pDGc+Qtn6ggMhWYvQpPnIEj7zEv+yVWoLl26NOPGjWPevHlkZGTw2GOPsWvXLqKiopz3iYyMJDk5mS5dujB27FgCAgIYNGgQkydPpnHjxowYMYI6derQq1evS54/MTEx9+9IfJLVatV5Idjs8OmG4tiN4Cve14JB20on+GvnkUKoTDyJvl9IdsVj7yD2zDgAkq9rydEbh5MZWQF27ja5MvE1uQrVr7/+OmPHjqVNmzYsXbqUoUOH8tRTT5GSkuK8T0pKClFRUURGRjJmzBgAXn75ZQYPHszo0aOZOnUqTzzxBLfffjsRERFuz1+zZs08vCXxRYmJiTovhPm/pXEixbGjR0gQNI1LIjb20nWQFhzdDqteX6KQKxRPoO8XfswwHHtMZ3fDWLBu4VCxmykb/xRV9Um5ZLNx48Yr3+kq5SpUR0dHO2elS5Uqxblz56hSpQr79u3jzJkzREREsGHDBgYOHOh8zI4dOwgNDSUuLo709HQsFgs2m42MjIxLQrWIyMX+Pmvj+/WuLfK6NQknLuwYNWtqeYeIAAeXw+In4PbPoUS2X6oCg+GuXziXmEhZBWopQLkK1cOHD2f06NHY7XYMw2DEiBEEBwczbNgwBg4ciGEY9OzZk9jYWOdjpk6dyogRIwC488476d27N7Vr16Zo0aL58kZExHcZhsHMpSlknu/jElcykE71wtix3dy6RMQDpJ6A5c9CwnTH8cLBcPeSS2esRQpYrkJ11apVmTFjxiXj8fHxxMfH5/iYcePGOb/u2bMnPXv2zM1Li4gfWr09g22HHFvkWSyOLfICtUWeiH8z7PDHdEegtp5yjR/bCKe2Q4kappUm/kn7TImIRzuXamfOKte2eZ3rhlGhpL51ifi1EwmOGelDK9zHq/WEDm9CVFlz6hK/pp9MIuLRZq1KJSXdAKBEVADdmmoNtYjfykyFNS/Dhjcc2+VdEF0ROk6CymoqJ+ZRqBYRj7V1XwbrdmY4j/u3iyQ0WMs+RPzSgWXw031wbq9rLCAIGj8DzYdDsDY9EHMpVIuIR7JmGny63LXso/kNIdwYd+X9qUXERwWGwrl9ruOyraHTFLjuRvNqEskmwOwCRERy8u26VE4mOVoKFwmzcHcrzUKJ+LUyzaHuvyCsONz0IfRepkAtHkUz1SJS6AzDYPHWdP48kImR4x3gjwOZzsNeLSOICtccgIjfOLIOUo5A1Tvcx9v8F1q9DBElzalL5B8oVItIoVu3M4MvVqZe+Y5ArXJBtKgeUsAViYhHSD8LK1+ETZMhrBiU2eYeoMOKmlaayJVo6kdEClWy1c6sVVcXqCNCLdzTLhKLmjiI+DbDgG1fwLQasOkdwHDsPb3yBbMrE7lqmqkWkUI1Z3UqSWmORR/FIgO4p13Oa6UtQMVSQURH6Hd/EZ92+i9Y9Cjs+8V9vOIt0PR5c2oSyQWFahEpNIkHM1m9zbVF3j3tIqhXUUs7RPxSVjqsfx3WvgK2dNd4kTKOBi7VeqrVuHgVhWoRKRQZWQYzl6Y4jxtXCVGgFvFX+5c4OiKe3u4aswRA/cccFyKGRptXm0guKVSLSKGYtz6N4+ccW+RFhFro00Zb5In4pSwr/HiPY3ePC2IbQ+cpENvIvLpE8kiLFUWkwB04kcXPm6zO47tahBOjtdIi/ikoDNpPcHwdEgXxb0O/NQrU4vU0Uy0iBcpuN5ixNAX7+Q2pbygTROuaoeYWJSKFJ+kgRJVzH6t+N5zdDTf+n2MNtYgP0FSRiBSoxVvT2fu3DYCgQOivLfJE/ENmCix7Dj6oBPsXu99msUCz5xWoxacoVItIgTmZZOObta49qW9vFE7pYoEmViQihWLX9zCtFmz4H9izHBclZqVf+XEiXkzLP0SkQBiGwafLUknPchyXLR7ILQ3CzC1KRArWuQOw5An46xv38cjrHc1cilxvSlkihUGhWkQKxPq/Mti6PxNwNHLp3z6CoEAt+xDxSfYs+O0tWD3CsezjgvDroN0bUGuA9pwWn6dQLSL5LsVq54uVrmUf7WuHUqV0sIkViUiBObwGFj4Mxze7j9d5ENr8F8JLmFOXSCFTqBaRfJe9FXnRSAvdm2tPahGftHmqY700hmusxI3QaQqUa21aWSJmUKgWkXyVeDCTVdlbkbeNJDxEH/uK+KSKNzv2nc5Kg6BwaDESGj0FgfpkSvyPQrWI5JuMLINPlrnWUzasHEz9SmpFLuKzYio6gvShFRA/yXEs4qcUqkUk38zbkMbfZx2tyMNDLPRtE2lyRSKSL7KssO41CAiE5v9xv63Js9DkOV2IKH5PoVpEciU902DqL8nsOJzpXE55Yfs8cLQiLxqprfBFvN6+hbDoETi9EwKCodpdUKKG63aL/p2LgJq/iEgufbM2la37MknPdITp7IG62vVBtK6lVuQiXi3lGPxwD8zt7AjUAPZM2PSOuXWJeCjNVIvINdtzLItFW3PujnZddAD3xUcSoI+CRbyTYYct78GKYZB+1jUeGgOtx0Ldf5lXm4gHU6gWkWuSZTOYsTQF4/ySj1rlghh8S5SjwwsQGgQWBWoR7/T3Zlj4EBxZ6z5eoy+0Hw+Rpc2pS8QLKFSLyDVZsNnKwZM2AEKC4N52kYRpyzwR75aZBqv+A7+9CYbNNV60KnScDBU7m1ebiJdQqBaRq/b3WRvfr09zHndrEk7JmEATKxKRfBEQBPsXugJ1YAg0GQbNnnfsQy0iV6QLFUXkqhiGYw/qzPM/c+OuC6RTPf2wFfEJgcGOLohYIC4eBmyBVi8pUItcA81Ui8hVWb09g8SDji0+LBYY0D6SwAAt+xDxOrZM2D4LavZz3w6vTAvouxqub6Y9p0VyQaFaRK7oXKqdOatSnced64ZRoZS+fYh4nUOrYeHDcGKrY3u82ve7316muTl1ifgALf8QkSuatSqVlHTHdh8logLo1jTc5IpE5JqknYJf/gVftHIEaoBlz0LqCXPrEvEhmmoSkX+0dV8G63ZmOI/vbRdBaLA+GhbxCoYBiZ/A0qch7bhrPCgCmg5z7D0tIvlCoVpELsuaafDpcteyj2bVQqgdF2JiRSJy1U5ug0WD4cBS9/Eq3SD+bYiOM6MqEZ+lUC0il/XtulROJtkBKBJmoXfrCJMrEpErykyDda/Cutcc66YviCrvCNNV7zCvNhEfplAtIjna+3cWi7a4WpH3ahlBVLguwxDxeOvGwpoxrmNLIDQaAi1GQkgR8+oS8XH6CSkil7i4FXnNckG0qK5lHyJeofHTrnbi1zeHezdCu/8pUIsUMM1Ui8glFm6xcuCEeytyi/atFfE8dhtkpbkH5tAYR2vxtONQ50H3vahFpMAoVIuIm7/P2vhunasVedcm4ZRSK3IRz3PsN8ee08VrwK0z3G+r1t2cmkT8mH59FRGni1uRl78ukM5qRS7iWTKSYMkQ+LQJHF0Pf86E/UvMrkrE7ylUi4jTr2pFLuK5DAN2fgXTasJvE8Fw7MxDYCic3mFqaSKi5R8icl5Smp05q117UnesG0pFtSIX8Qxn98Lix2D3D+7jcZ2g02QoVs2UskTERT8xRQSAWStTSba6WpHf0VR7UouYzpYJG8fDry85Lki8IKIUtJ8ANfo6PlYSEdMpVIsIf+zPYG22VuT3tI0gTK3IRcyVdgpmtYWTCdkGLVDvIWj9KoQVM600EbmUQrWIn0vPNPhkmWvZR9NqIdSpoD2pRUwXVgyiK7hCdcl60GkKlGlubl0ikiNdqCji575bl+ZsRR4ZaqF3Ky37EPEIFgt0nAThJaHdOLh3gwK1iAdTqBbxY/v+zmLBFqvzuFerCKIj9G1BpNCdTIT5/wdZVvfxmEowaB80fgoC9OGyiCfTv1ARP2Wzu7cir1E2iJZqRS5SuDJTYe0rsP5/YM90hOiWo9zvExxuSmkicm00JSXipxZutrL/fCvy4EDo316tyEUK1Z6f4OPasPZVR6AG2PAGWE+bW5eI5IpmqkX80PGzNr5br1bkIqZIPgxLnoQdc9zHy7R0XIioXT1EvJJCtYifudCKPMPROJFyJdSKXKRQ2G2waTKsetHRavyCsGLQ5nWo8wBY9AGyiLdSqBbxM2t2ZPDnRa3IgwK17EOkQB3bCAsecvw/uxv/D9r+DyJKmlOXiOQbhWoRP5KUZmf2qmytyOuEUilW3wZECtz+xe6Bulh16DwFyrc3rSQRyV/6nEnEj8xe5WpFXrxIAHc0057UIoWi4ZNwXR0IDIVWL8OAzQrUIj5GU1QifiJhfyZrdrhakd/bTq3IRQrEmd1g2KFYVddYYDDcOhOCI93HRcRnaKZaxA+kZxp8sjzFedykqlqRi+Q7W4Zje7yPb4RfBuLcBP6CUvUUqEV8mEK1iB/4fn0aJ845WpFHhFro01rLPkTy1YFlMKM+rHzR0RXx4HJI+NjsqkSkEGn5h4iPsdsN1v2VwdkUR4jOtMGCzdlakbdUK3KRfJN6HJY/e2mALtUArqttTk0iYgqFahEfM3t1Kou2pOd4W/WyQbSqoWUfInlm2OGPabD8ObCeco2HREGrMVD/EQjQj1gRf6J/8SI+ZNfRTBZfJlAHB0L/dmpFLpJnJ/6AhYPh0Er38RvugvYTIaqsKWWJiLkUqkV8RJbNYObSVC5cGlWhZCDVywQDEBAADSuHEFtUrchF8iT5MHzSyHFR4gXRFaHjO1D5NtPKEhHzKVSL+Iiff7dy6JQNgJAgePjmIlwXrRAtkq+KlIHaA2Hzu47lHY2fheb/gWBd/Cvi7xSqRXzA0TM25m1Mcx7f2SxCgVokP9gyHXtMZ9f6VUg9Bi1Hw3U3mlOXiHgcbQEg4uXshsHMpSlkOSapqVAykI51Qs0tSsTb2bNg4wT4sKpjh4/swopCty8VqEXEjUK1iJdblZjOjsNZAARYYECHSAICdDGiSK4dWQefNIGlT0HSflj2jNkViYgX0PIPES92NtXO3F9dyz461w8j7jr9sxbJFesZWPkCbJ4CZOuGeGwDZCRDSBGzKhMRL6CfviJe7IsVqaSmO374l4wOoGvjcJMrEvFChgHbZ8HSIZBy1DUeFA7NR0DjpyBQ+7uLyD9TqBbxUpv3ZrBhl2tbr3vbRRIarGUfItfk9F+w6BHYt8B9vNKtjm3yYiqZU5eIeB2FahEvZM0w+HRZqvO4RfUQapUP/odHiMglfp/kWC9ty9YwqUgZ6PAmVOsJapQkItdAoVrECxw6mcXShHQyshxLPf4+a+d0ih2AImEW7m6lPXJFrllEKVegtgRAg8cd2+SFRptbl4h4JYVqEQ+XYrUz4fskzqYaOd7eu3UERcK0kY/INbuhF1ScBmknoPMUiG1kdkUi4sUUqkU83Jdr0i4bqOtWCKZZNV1AJfKPDDts/QCKVoO4Dq5xiwVu/wxCoiFAzZJEJG8UqkU82PZDmaz407Xes0vjMK6LcvzwDwuxUKdCMBat+xS5vONbYMHDcORXKFYNBmyBoDDX7WHFzKtNRHyKQrWIh8rMMpi5LMV5XL9SMHc01dppkauSkQy/vuToimicbzd6eidsegcaP21ubSLik3K1EDM1NZXnnnuOfv360atXL7Zs2QLA4sWL6dmzJ71792b27NkAHD16lD59+tCvXz+OHTsGwLfffssPP/yQT29BxDf9+Fsax844LkYMC4Z+bSJNrkjEOxQ5thim14INb7gCdUAwNP8P1HvE3OJExGflaqb6ww8/pFq1arz++uts27aNbdu2UbNmTcaOHcvcuXMJDw+nb9++xMfHM3/+fB588EEMw2D+/Pn06dOHxYsXM3HixHx+KyK+49CpLOb/ZnUe92gRQbEiuhhR5B+d2w+Ln6D8rm/dx8u1g07vQoma5tQlIn4hV6F65cqV3HrrrQwcOJDIyEhGjhzJrl27iIuLIyYmBoBGjRqxfv16IiIisFqtGIZBeHg406dPZ8CAAVoHKnIZdsNg5pJUbI5JaqqUDqLdjaHmFiXiyWyZ8PtbsHokZLqWTBF+HbQbB7X6a89pESlwuQrVp0+f5ty5c3z44Yd88803vPbaa9x9991ERUU57xMZGUlycjJdunRh7NixBAQEMGjQICZPnkzjxo0ZMWIEderUoVevXpc8f2JiYu7fkfgkq9XqN+fFpoPh7Drm2Cc3wGLQKu4o27cdMrkqz+RP54VcXlDaYaqsHE6ALc05drr8XRyv/hS2gKKwbZt5xYnH0PcLKWi5CtVFixYlPj4egA4dOvDee+/xwAMPkJLimiFISUkhKiqKyMhIxowZA8DLL7/M4MGDGT16NFOnTuWJJ57g9ttvJyLC/eKrmjX1EZ24S0xM9Ivz4lSyndUrzziPb2sUTpvGN5hXkIfzl/NCrqQmZI2CFUPhutrsrTqMiq3uQft6SHb6fiE52bhxY749V64WaTZq1Ihly5YBsH79eqpWrUqVKlXYt28fZ86cISMjgw0bNtCgQQPnY3bs2EFoaChxcXGkp6djsViw2WxkZGTkzzsR8XKGYfD5ihSsmY7j0kUDuK1huLlFiXgaw3Bsk3exRkOg0xS49zfSijcs/LpExO/laqb6oYce4j//+Q+9e/cmKCiI1157jeDgYIYNG8bAgQMxDIOePXsSGxvrfMzUqVMZMWIEAHfeeSe9e/emdu3aFC1aNF/eiIi3+213Jpv2ZDqP+7ePJDhI60BFnE7tgEWPwMHlMGCz+4WHgcFQ7yHzahMRv5fr5R+TJk26ZDw+Pt65LORi48aNc37ds2dPevbsmZuXFvFJqel2Pl/hWj7VtlYoN5QJNrEiEQ+SZYV1/4V1Y8F2/tPNhYPh7iW6AFFEPIaav4h4gC9/dbUij4mw0LOFln2IALBvoWN2+vRO15glAGIbOgJ2kHbGERHPoFAtYrIdhzNZnq0Ved82kUSEak9q8XMpR2Hp07DtM/fx0k2g01SIbZDz40RETKJQLWKiTJvBzKWuZR/1KgbTsLKWfYgfM+yweSqsfB7Sz7rGQ2Og9Vio+y8ICDSvPhGRy1CoFjHRjxvTOJq9FXnbSDVGEv/2yyD44yP3sRp9of14iCxtTk0iIldBnzGLmOSSVuTNIyiuVuTi7+oMAs7/Ylm0KvT8BW7/TIFaRDyeZqpFcslmN/h8RSoJBzIxjGt/fGq64WxFXjk2UK3Ixf9c+IeT/dOZMs2h4RMQEgPNnoegMHNqExG5RgrVIrm0cLOVZQnpV77jFQQGwID2kQQEaNmH+JFz+2DR41D1TqjzgPttHSaaUZGISJ4oVIvkwvGzNr5bn5Yvz9WjeThlS+ifovgJWyZsnAC/vgRZqXB4FVTpChElza5MRCRP9JNc5BoZhsEny1LIyHIclysRyCO3FHEuA70WESEWIsO0jlr8xKFVsPBhOPGHa8x6CvbMhxsHmFeXiEg+UKgWuUZrdmTw50FHorZYHEs3SsZoiy+Ry0o7BSuGwtYP3MevqwOdp0KZFubUJSKSjxSqRa5BUpqd2atSnccd64RSKVb/jERyZBjw50xY9jSknXCNB0VAq9HQ4AkI1L7sIuIblAZErsHsVakkWx07FhQvEsAdzSJMrkjEQ507AD8NgANL3cer3AHxb0F0nBlViYgUGIVqkauUsD+TNTsynMf3tosgLFg7dojkKDQaTm1zHUeVh/i3oeod5tUkIlKAdIWUyFVIzzT4ZLmrnXiTqiHUqRBiYkUiHi40BtpPBEsgNH4G7vtTgVpEfJpmqkWuwnfr0zhxztGpJSLUQp/WWvYh4pR8BP76BuoPdh+vfjfENoJiVU0pS0SkMClUi1zBvuNZLNjsaid+d8sIoiP0IY8IdhtsngIrX4CMc1C8OsTFu263WBSoRcRvKBmIXMRuGGRkOf6zZhrMWJri7KZcvWwQLWto2YcIx36Dz1vA4sccgRpg4SNgzzK3LhERk2imWiSb/SeyeGteEmdTjUtuCwqE/u0isVh0caL4sfRzsHoE/P42GHbXeLEboNNkCNCPFRHxT/ruJ3Jels3gw4UpOQZqgK6Nw4ktqiYv4qcMA3Z+BUuegOTDrvHAUGj2AjQZCkGh5tUnImIyhWqR83763crhUzbA0XE86Hx+tligboUQbqofZl5xImY6uwcWPQZ7fnQfj+vkmJ0uVs2cukREPIhCtQhw9LSNHzakOY97tYqgcz2FaBEAlj3rHqgjYqH9BKjRx/Fbp4iI6EJFEbthMHNZClnnl4dWKBlIxzr6GFvEqd0bEBQOWKDeYLh/G9Tsq0AtIpKNZqrFL6RY7Vgzc14rvWlPJjsOO3YsCLDAgA6RBAQoLIifSj0BwZEQHO4ai6kInac6Lka8vplppYmIeDKFavF5P2xM45u1aVe+I9C5fhhx1+mfhfghw4CE6Y6lHvUfgVaj3W+v1d+UskREvIWWf4hP2/t3Ft+uu7pAXTI6gK6Nw698RxFfcyIBZrWDnx8A60lY9184uc3sqkREvIqm5MRn2ezujVvCQyyEh+S8rCMq3EL/dpGEBmvZh/iRzFRYMwY2/M+9aUuRMo5wLSIiV02hWnzWws1WDpxwbJEXHAj/6RVNqRjtMy0CwJ75sOhRx3Z5FwQEQaOnocVwx7pqERG5agrV4pOOn7Xx3XrXso+uTcIVqEUAkg7B0idhx1z38TKtoPMUuK62KWWJiHg7hWrxOcb5LfIyzn+aXa5EoPacFgE49hvMbg8ZSa6xsOLQ9nWofT9YdJmNiEhuKVSLz1mzI4PEg45EbbHAgPaRBAVqrbQI19WBmEpwfIvj+Mb/g7b/g4iS5tYlIuIDNC0hPiUpzc7sVanO4451QqkUq98dRQAIDIZOU6BELbh7CdwyXYFaRCSfKFSLT5m9KpVkq2O7jxJRAdzRLMLkikRMYBiwfTZ83xsMu/ttZVrA/22F8u1NKU1ExFdpCk98xh/7M1izI8N5fE/bCMK0RZ74mzO7HLt67P3ZcZxwi2O9dHZaOy0iku/0nVV8QnqmwSfLXMs+mlYLoU6FEBMrEilkWemOPac/ru0K1AAbx186Wy0iIvlOM9XiE75bn8bJJEdwiAi10LuVln2IHzmwFBYOhlPZuyBaoP6j0HqMZqZFRAqBQrV4vX3Hs1iw2eo8vrtlBNERChHiB1KPw/JnIeFj9/FSDR17TpduYk5dIiJ+SKFavJrNbjBjiasVeY2yQbSsoWUf4ge2fgTLnwHraddYSBS0GuOYoQ5QsyMRkcKkUC1ebdEWK/uztSLv3z4Si0UXJ4ofOLbRPVDf0AvaT4CosubVJCLixxSqxWsdP2fj23WuVuRdGqsVufiRNq/CX19BUDjET4LKt5ldkYiIX1OoFq9kGAafXNSK/Kb6akUuPmr3D4710RGlXGOhMdBjPhS7AYJ1Ya6IiNl0NZd4pbU7MvjzwPlW5KgVufiopIPwXU/4ugsse/bS20vVV6AWEfEQCtXidZLS7MzK1oo8vq5akYuPsWfBxgkwrSbs/Mox9ucM2L/E3LpEROSylETE68xZ7WpFXrxIAHc21Uyd+JAj62DBQ3B8k/t47QfgujqmlCQiIlemUC1e5c8Dmfy6/aJW5CFa9iE+wHoGVr4Am6cAhmu8RC3o9C6Ua2tWZSIichUUqsVrOFqRpziPm1QNoW5F7UktXs4wYNvnsPQpSD3mGg8Kg+YjoPHTEKjzXETE0ylUi9f4fn0ax8+5WpH3aa1lH+IDDiyFH+9xH6t0q2ObvKKVzahIRERyQRcqilf4OynIrRX5XS3Uilx8RPn2jhANUKQMdJ0D3X9QoBYR8TKaqRaPZ7MbLNgejf38MtPqZYJoXVMfh4uXSj8HodGuY4vFMSu9aRK0GOV+m4iIeA1N9YnHW7TFyrGkYACC1IpcvFXq3/Bjf/i0MWRZ3W8rWhnaj1egFhHxYgrV4tFyakUeW1StyMWLGHbY8h58VB0SP4HTO2Hdf82uSkRE8pmWf4jHMgyDz5anOluRly0eyM1qRS7e5PgWWPAwHPnVffzsHseuH/rERUTEZyhUi8datzODP/Znnj8yGNBBrcjFS2SmwOpRjq6Ihs01HlMZOk2GijebVpqIiBQMhWrxSMlW91bk9cumUTm2hIkViVylv76DxY9D0n7XWEAwNB0KTV+A4HDzahMRkQKjUC0eafaqVJLSHNt9FIsMoHXlZJMrErkKP94LiZ+6j5VrB52mQIka5tQkIiKFQhcqisdJPHhRK/J2EYQEGf/wCBEPUbKe6+vw6+CWj+HuJQrUIiJ+QKFaPEpGlsHMpa5W5I2qhFBPrcjFWzR8EkrWhToPwv3b4MYBuhhRRMRPaPmHeJSLW5H3baNW5OKBrKdh5YvQ4DEoUcs1HhgMfddo3bSIiB9SqBaPsf9EFr9syt6KPJwYtSIXT2IYjjXTy552NHM5mQB3L3WfjVagFhHxS0os4hHsdoMZS1KcrchvKBNE65qh5hYlkt2p7TC3E8zv7wjUAAeXw75fzK1LREQ8gmaqxSMs3prOvuOO/XyDAqF/O7UiFw+RZYW1Y2H9f8HmuoCWIuUg/i2ocJN5tYmIiMdQqBbTnUyy8c1a157UtzcKp3QxtSIXD7BvISwcDGf+co1ZAqHhv6HlKAiJMq00ERHxLArVYirDMPhkWSrp2VqR39JArcjFZClHYenTsO0z9/HSTaHzVChV35SyRETEcylUi6nW/+VqRW4B+rePUCtyMd+5fbDtc9dxaAy0+S/UGQQB+hRFREQupQsVxTQpVjtfrHQt+2hfO5QqpYNNrEjkvOubQb2HHV/X6OfYc7rewwrUIiJyWZqplkKz62gmCzank5Hl2OLjVJLd2Yq8aKSF7s21J7WYICMJTvwBZVq4j7d+Far1gAqdzKlLRES8ikK1FIpzqXbe+iGZ1PSc243f0zaS8BAt+5BCZBjw1zew+AnISoH7t0NESdftYUUVqEVE5Kpp+YcUilmrUi8bqJvfEEL9SmpFLoXo3D74pht81wOSDzo6JC57xuyqRETEi2mmWgrc1n0ZrNvp2t+3T+sIrot2/D4XHmKhSmmdhlJIbJmwcQL8+hJkudbzE1EKKmq/aRERyT2lGSlQ1kzHlnkXNL8hhI51tWWemODQKlj4sGP9dHZ1H4I2YyGsmDl1iYiIT1ColgL17dpUTiXbASgSZuHuVroYUQpZ2klYMQy2fuA+XrIudJpy6QWKIiIiuaBQLfkmPdPgi5WpHDrp6ORigLP1OECvlhFEhWsZvxQiw4A5HeH4ZtdYcCS0fMnRFTFA3wJFRCR/6CeK5Js5q1NZmZie4221ygXRorouRpRCZrFAsxdgXm/HcdU7ocObEB1nalkiIuJ7FKolX+w8ksmyhJwDdVS4hXvbRWKxaMs8KWC2TAi8qIHQDb2gzgKo3BWqdjOnLhER8XkK1ZJnmTaDmUtTnMe144Lp2iQccLQeL10sUHtQS8Hb+zMsehQ6TYUKHV3jFgvc9L55dYmIiF9QqJY8m/+blSOnHRcjhgZD/3YRFI9SO2cpJMmHYckQ2DHbcbxoMAzYAkHaZUZERApPnq4aW7duHe3atXMeL168mJ49e9K7d29mz3b8gDt69Ch9+vShX79+HDt2DIBvv/2WH374IS8vLR7iyCkb8zemOY+7N1OglkJit1Fs76cwraYrUAOkHr902zwREZECluuZ6iNHjjBt2jSyshw7PWRmZjJ27Fjmzp1LeHg4ffv2JT4+nvnz5/Pggw9iGAbz58+nT58+LF68mIkTJ+bXexCT2A2DGUtTyHJMUlMpNpAOtUPNLUr8w7HfYMFDlD62wX28Vn9o94ajmYuIiEghytVMdXp6OiNHjmTUqFHOsV27dhEXF0dMTAwhISE0atSI9evXExERgdVqxWq1Eh4ezvTp0xkwYIAuWvMBK/5M56+jjl+qAgNgQPtIAgL09yoFKP0cLP43fNoEsgfqYjdAr0Vw6wwFahERMUWuZqpHjx7NAw88QGxsrHMsOTmZqKgo53FkZCTJycl06dKFsWPHEhAQwKBBg5g8eTKNGzdmxIgR1KlTh169el3y/ImJibkpSwpRcnoAs9eW4MLvZY3KJ5P09zES/y6Y17NarTov/Fz4qY2U/f1pgtNdJ5k9IISTVR7iZOWBGCkhoHNE0PcLyZnOCylo1xyqjx07xoYNG9i/fz/vvPMOZ8+eZciQITz00EOkpLh2gEhJSSEqKorIyEjGjBkDwMsvv8zgwYMZPXo0U6dO5YknnuD2228nIsK9y17NmjXz+LakoL37UxIZtkwASsUEcN/N5QkOKrhZ6sTERJ0X/u5cBGxIdh1X6Mzuik9RtfEtlDSvKvFA+n4hOdF5ITnZuHFjvj3XNYfq2NhYfv75Z+dxq1atmDBhApmZmezbt48zZ84QERHBhg0bGDhwoPN+O3bsIDQ0lLi4ONLT07FYLNhsNjIyMi4J1eLZft+dwW+7M53HA9pHFmigFgEgugK0GAkbx0OHiVC9N5nbtpldlYiICJCPW+oFBwczbNgwBg4ciGEY9OzZ0215yNSpUxkxYgQAd955J71796Z27doULVo0v0qQQpCabuezFa5PJFrXDKV62eB/eIRILhxc4djBo/5g9/FGQ6DuvyCsqClliYiIXE6eQ/WqVaucX8fHxxMfH5/j/caNG+f8umfPnvTs2TOvLy0m+HptGmdSDMDRKfGuFuEmVyQ+JfUELH8OEqZBQDCU7wAlarhuDwyGwKKmlSciInI5edqnWvzLX0cyWfaHqxV539YRRIbpFJJ8YBjwxzSYVsMRqAHsmbD8GXPrEhERuUrqqChXJdNmMGNpKsb54zoVgmlcNcTUmsRHnEiAhYPh0Ar38Wo9oMOb5tQkIiJyjRSq5ar89JuVI6dtAIQGwT1tI7TXuORNZiqsGQMb/gf2LNd4dAWInwRVuphXm4iIyDVSqJYrOnLaxo/ZWpHf2SyCEmpFLnmx+0dY9Cic2+saCwiCRk9Di+EQHGlaaSIiIrmhUC3/yG4YzMzeirxUIPF11Ipc8sBug5UvuAfqMq2g07tQso5pZYmIiOSFrjKTf7Tyz3R2HlErcslHAYHQaQpggbDicNMH0Ge5ArWIiHg1zVTLZZ1JsTP3V9eyj5vqh1HuOp0yco1O/AElaoEl2+/wZZrDLdOh0q0QoX6IIiLi/TRTLZf1xcoU0jIc+32UigmgS2PtSS3XIP0sLHocPq4Lf0y/9PYbByhQi4iIz1Colhxt2pPBxl2uVuT3toskRK3I5WoYBmyb5dhzetMkwIDlz0LqcbMrExERKTD6LF8ukZZh8OlyVyvyVjVCqFlOrcjlKpzZ5djVY+/P7uOlm4AtPefHiIiI+ACFarnE12tS3VqR92oZYXJF4vGy0mHDG7B2DGRZXeOR10OHiXBDL9C+5iIi4sMUqsXNrqOZLM3WiryPWpHLlRxYBgsfhlPbsg1aoP6j0HoMhMaYVpqIiEhhUagWp6yLWpHXjgumiVqRyz/58xOY3999rFRD6DwVSjc2pyYRERETKFSL00+/Wzl8ytWK/N52akUuV1ClK0SWhpSjEBIFrcY4ZqgD1HFTRET8i0K1AHD0tI0fNqgVuVyBYbivjQ6NgfYTYedcx/+jyppVmYiIiKkUqsXRinyZqxV5hZJqRS4XyUyBX0c7tsW75SP322r0dvwnIiLixxSqhVWJ6ew47GhFHmCB/+ugVuSSza55sPgxOLfPcVzrXoiLN7cmERERD6NtHfzc2dRLW5GXVytyAUg6CN/2gG+6ugI1OC5OFBERETdKT37u8xWppKY79vsoGR1A1yZqRe737Fnw+9uwagRkJrvGw0pAuzfgxv8zrzYREREPpVDtxzbvzWDjrgzncf/2akXu946shQUPwfHN7uO1H4C2r0N4CXPqEhER8XAK1X7KmmHw6bJU53GL6mpF7tdsGbDkSdg8BZw7lQMlakGnKVCujVmViYiIeAWFaj/19dpUTqc4tvuICrdwdyu1IvdrAcHn102fD9RB4dB8BDR+CgLVAEhERORKFKr9gN0w2HMsy7l2+lyqwZKtrlbkvVtFUEStyP2bxQIdJ8H0G6F8B8fXMZXMrkpERMRrKFT7gdkrU1mULURnVzsumKbVNBPpV7LSYfNkqPswBGe7MDWmEgzYDEWrujd4ERERkStSqPZx2w9lXjZQhwTBPW3Vityv7FsEix6B0zvAehpajXa/vVg1c+oSERHxcgrVPiwzy9Ep8YLYogGUjHa0Hg8KhA61w7guWq3I/ULKMVj2NCR+6hpb91+oNQCKVTWvLhERER+hUO3DftiYxrEzjosRw0MsPN0tmmJFtHbarxh22PI+rBgG6Wdc4yHR0Gas1k2LiIjkE4VqH3XoZBY//W51HvdoHq5A7W/+3gwLH4Yja9zHq/eB9uOhyPXm1CUiIuKDFKp9kN0wmLE0BZtjkpoqpYNoe2OouUVJ4clIhtWj4LeJYNhc40WrQMd3oOLNZlUmIiLisxSqfdCyP9LZfcwRpgIDYED7SAJ0MaL/SPgYNo5zHQcEQ9Nh0PR5990+REREJN9oPYCPOZVs56s1rk6JtzYMo0xxXYzoV+r+C0rWdXxdvj0M2OLY5UOBWkREpMBoptqHGIbBZ8tTsGY6jksXDeC2RgpSPs2WCdZTEBnrGgsMhs7vwantUKu/9pwWEREpBArVPuS33Zls3pvpPO7fPpLgQAUqn3X4V1jwEITGQO9lYMn2wdP1zRz/iYiISKFQqPYRqel2Pl/h2pO6ba1QbigTbGJFUmCspx1b5G15zzWW8DHUvt+8mkRERPycQrWP+PLXNM6mGgDERFjo2ULLPnyOYTiatyx9CtKOu8aDwiErzby6RERERKHaF+w4nMnyP12tyPu2iSQiVNeg+pRT22HhYDiwxH28cheIfxtiKppSloiIiDgoVHu5TJvBzKWuZR/1KwXTsLKWffiMLCusHQvr/wu2DNd4kXIQ/xZUvVMXIoqIiHgAhWov9+PGNI6eb0UeFuyYpbYoZPmGzDSYWR9O73CNWQKh4b+h5SgIiTKrMhEREbmIQrUXO3Qqi/m/ZW9FHkFxtSL3HcHhENfJFaqvbwadpkCp+qaWJSIiIpdSqPZSdsNg5tJUVyvy2CDa1VYrcp/T5lXHOuqGTziaulj0S5OIiIgn0k9oL7U8IZ1dR7MARyvy/h0i1Ircm/29Cb68FVL/dh8PjYH/2wr1HlagFhER8WD6Ke2FTifb+fJXVyvyWxqEUba4PnTwShlJji3yPmkEe3+CZc9eep8AtZkXERHxdArVXuizFe6tyG9XK3LvYxiw8yuYVhM2TgDj/Dqe7bPg3H5zaxMREZFrpulNL/Pbrgw27bmoFXmQln14lbN7YfHjsHue+3hcPHR8F6LjTClLREREck+h2oukptv5LFsr8jZqRe5dbJmwcTz8+pJ7B8SIUtB+PNTopz2nRUREvJRCtRf5ao2rFXl0uIWezbXsw2sc/hV+GQQnE9zH6z4EbcZCWDFz6hIREZF8oVDtJXYeyWRZgnsr8sgwLYn3GsmH3AN1ybqOPafLtDCvJhEREck3SmVeINNmMGOJa9lHvYrBNKqiZR9epVpPqHQrBEdCuzfg3o0K1CIiIj5EM9VeYH62VuShwdCvTYRakXuyk4mQcc7RAfECi8UxMw26EFFERMQHaabawx05ZePHi1uRR2nfYo+UmQYr/wMz6sH8/pBldb89Ok6BWkRExEcpVHswu2EwY2mKsxV5pdhA2t+oVuQeae/P8HFtWPsK2DPh9E5YO9bsqkRERKSQaPmHB1uekM5f2VqRD2gfSUCAln14lOQjsHSIo2lLdte3gBt6mlOTiIiIFDqFag91OtnOV2tcexnf3CCMciX01+Ux7DbY/C6sfNGxfvqCsGLQ5jWoMxAs+iBIRETEXyileagvVqaQluHYk7pUTABd1Irccxz7DRY8BMc2uI/X6u/Y2SOilDl1iYiIiGkUqj3Q77sz+G23qxX5ALUi9xzp52B2e8hIco0VuwE6vetoMy4iIiJ+SZ9Pe5iLW5G3rhlK9bLak9pjhEZD8+GOrwNDoeVoGLBFgVpERMTPaabaw3y9Jo0zKa5W5He10LIPU2UkQUiU+1jDJ+HsHmg0BIpVM6UsERER8SyaqfYgfx3JZGm2VuR92kSoFblZbBmOLfHeKw8n/3S/LTAYOk1WoBYREREnJTYPkWkzmLE01Xlcp0IwjauEmFiRHzu4HGbUh5UvQPpZWDgYDMPsqkRERMSDafmHh/jpNytHTtsARyvye9qqFXmhSz0By5+DhGnu4+nnIO0ERJQ0py4RERHxeArVHuDIKRs/bnTtSd29WQQl1Iq88BgGJEyHZc+C9aRrPLgItHoZGjwGAfqnIiIiIpenpGAyu2Ewc1kKWRdakZcKpENttSIvNCcSHMs7Dq1wH6/WAzq8CVHlzKlLREREvIpCtclW/pnOziNqRW6KzVNg8eNgz3KNRVeA+ElQpYt5dYmIiIjXUag20ZkUO3N/dS37uKl+GOWu019JoSnVwNFuHBzLOxo9DS2GQ3CkuXWJiIiI11GCM9HnKy5qRd5Ye1IXquubQb2H4fgW6DwFrqttdkUiIiLipRSqTbJpj3sr8v7tIwlRK/KCYc+CTe9AWHGo1d/9tvbjITAELNpdUkRERHJPodoEaRkGny53tSJvVSOEGmpFXjCOrocFD8Pfv0FYMah4i/vWeEFh5tUmIiIiPkPTcyb4ek2qsxV5VLiFXi0jTK7IB6WfhUWPwafNHIEawHoaNowzty4RERHxSZqpLmS7jmay9I9srchbqxV5vjIM2D4blj4JKUdd40Fh0Hw4NH7GtNJERETEdylUF6Ks863ILzS8rhMXTJOqakWeb07/BYsehX2/uI9XvAU6vgNFK5tTl4iIiPg8hepC9NPvVg6fOt+KPAjuaadW5PkiKx02/A/WjAGb61MAIq93NHC54S7Qn7OIiIgUIIXqQnL0tI0fNrj2pL5Trcjzj80Kmya7ArUlAOo/Cq3GQGi0ubWJiIiIX9Bi3kJwcSvyiqUCia+jVuT5JjQGOkx0fB3bCPqthfi3FKhFRESk0GimuhCsTExnx2FHK+wAi1qR54lhhwNLIS7effyGXtAtGKp0gwB9AiAiIiKFSzPVBexMip25q13LPm6uH0Z5tSLPneNb4Ys2MKcj7FvofpvFAtW6K1CLiIiIKXIVqg8fPsx9991H//79uffee9m9ezcAixcvpmfPnvTu3ZvZs2cDcPToUfr06UO/fv04duwYAN9++y0//PBDPr0Fz/bFylRnK/KS0QF0aaJW5NcsM4VS296AmQ3g8GrH2KJHIMtqbl0iIiIi5+VqyvTNN9/k3nvvpVOnTqxYsYLx48czYcIExo4dy9y5cwkPD6dv377Ex8czf/58HnzwQQzDYP78+fTp04fFixczceLEfH4rnmfz3gw27spwHqsVeS7s+h4WPUaJpP2usYBguOFu82oSERERuUiuQvXQoUOJiooCwGazERoayq5du4iLiyMmJgaARo0asX79eiIiIrBarRiGQXh4ONOnT2fAgAH/uJVcYmJibsryKOlZFj5eVwJwLEe4sXQaJB3DB95aoQhKO0LpP18l6tgit/GU4k04WnsEGUWqwM49JlUnnsJqtfrE9wvJXzovJCc6L6Sg5SpUFy9eHIDdu3fz2muv8c4773Dq1Cln0AaIjIwkOTmZLl26MHbsWAICAhg0aBCTJ0+mcePGjBgxgjp16tCrV69Lnr9mzZq5fDue4/MVKSSnO7Z4iwq38OBt11MkrKzJVXkBexb89hasHgGZKc7hrJBiBMVPILLWAKpoz2k5LzEx0Se+X0j+0nkhOdF5ITnZuHFjvj1Xri9UXLNmDY8++iivv/46lStXpkiRIqSkuEJQSkoKUVFRREZGMmbMGEaPHs306dMZPHgwU6ZMYeTIkSxdupTU1NR8eSOeZNfRLJZsdTUh6d0qgiJqRX51lj0Dy552C9TUeZDdbX+AG/9PTVxERETEI+Uq6a1Zs4ZXXnmFDz74gDp16gBQpUoV9u3bx5kzZ8jIyGDDhg00aNDA+ZgdO3YQGhpKXFwc6enpWCwWbDYbGRkZl3sZr5RlM5i5NMXZirx2XDBNq6kV+VVr+CQEnb+Ys8SN0HsF3PQ+tpCiZlYlIiIi8o9ytfzj1VdfJTMzk2HDhgFQqVIlRo8ezbBhwxg4cCCGYdCzZ09iY2Odj5k6dSojRowA4M4776R3797Url2bokWL5v1deJCff7dy6Hwr8pAguKetWpFflmGAYYOAbKdhTEVoM9axs0ejIRCoX0hERETE8+UqVH/33Xc5jsfHxxMfH5/jbePGjXN+3bNnT3r27Jmbl/ZoR8/YmLfRvRX5ddHaNzlHp3fCwkegTAtoNdr9tob/NqcmERERkVxSF5J8YhiOZR9ZjklqKpQMpKNakV8qKx3W/RfWjQVbOhxcBjX6QYkaZlcmIiIikmsK1flkZWKGeyvyDmpFfol9ixxNW07vcI0ZNkewVqgWERERL6ZQnQ/OptqZ+6trF5PO9cOIUytyl5Rjjh09Ej91Hy/dBDpNhdgGOT9ORERExEso+eWDL1akkpruakXetbFakQNg2GHL+7BiGKSfcY2HRDsuRqz7EARozbmIiIh4P4XqPNq8N4MN2VqR39suktBgLfsg5Sh82x2OrHEfr94H2o+HItebU5eIiIhIAVCozgNrhsFny13LPlpUD6FW+WATK/Ig4ddBlmsnFIpWgY6ToeJN5tUkIiIiUkDU5i8PvlmbyqlkOwBFwiz0ahlhckUeJCAIOk2BoDBoPhwGbFWgFhEREZ+lmepc2n0si8XZW5G3jiAq3E9/Rzm3H7ZMhVYvgyXbn0GZ5jBoP0SUNK82ERERkUKgUJ0LWTaDGUtcrchvLB9MM39sRW7LhN/ehNUjISsVYqpAnQfc76NALSIiIn7AT6dW8+aXTRe1Im/nh63ID/8KnzaG5c86AjXAiqGQmWJuXSIiIiIm0Ez1NTp2xsb3G1wX4N3RNJyS/tSK3HrasUXelvfcx6+r7dhzOjjSnLpERERETKRQfQ0Mw2DmsotakdcNM7eowmIYjuYtS5+CtOOu8aAIaDkKGj4Jgdr5RERERPyTQvU1WL0tg+2HsrUibx9JoD+0Ij+1HRYOhgNL3Mcrd4WOb0N0BXPqEhEREfEQCtVX6Vyqndmrs7UirxdGXEk/+ePb9I57oC5SDuLfhqp3gL+tJRcRERHJgS5UvEpfrHS1Ir8uOoCuTfyoFXmrlyGyNFgCodFTcH8iVLtTgVpERETkPD+Zas2brfsyWP+XqxV5f19uRZ5yFCxBEHGdayw0Bm6Z4dger1R900oTERER8VSaqb4Ca6bBJ8tcyz6a3+CjrcjtNtg0GabVgKVDLr29YmcFahEREZHLUKi+gm8vakV+dysfbEV+7Hf4vCUsehTSz0LiJ7B/sdlViYiIiHgNLf/4B3uOZbEoWyvyu1v5WCvyjCRYNQJ+fwsMu2u8WDUI8MHZeBEREZEColB9GVk2gxlLUzDO9yKvVS6I5jf4SCtyw4C/vobFT0DyIdd4YAg0fQGaDoUgP9l/W0RERCQfKFRfxoLNVg6edLUiv7ddpG+0Ij+7FxY/Brt/cB+P6wgdJ0PxG0wpS0RERMSbKVTn4O+zNr5f72pF3q1JOCVjfKAV+emdMKMeZLneGxGloP0EqNFXW+SJiIiI5JIPLRDOH4Zh8MmyFDLPtyKPuy6QTvV8ZClE0apQvsP5AwvUexju3wY1+ylQi4iIiOSBQvVFVm/PIPGgoxW5xdtbkV9YEH6BxQIdJ8H1LaDvauj0LoQVM6c2ERERER+iUJ3NuVQ7c1Zla0VeN4wKpbxwhYxhwB/THdvkZaa53xZTCfqthjLNTSlNRERExBcpVGcza1UqKedbkZeICqBbUy9sRX4yEWa3h5/vhyNrYN1YsysSERER8XkK1edt3ZfBup2uVuT3tovwrlbkmWmw8j+OCxEPLneNb/8CbBmXf5yIiIiI5JkXrm3If9ZMg0+Xu5Z9NKsWQu04L9qTes9Pjm6IZ3e7xiyB0PhpaDHCsf+0iIiIiBQYhWrg23WpnExytSLv3dpLWpEnH4YlQ2DHbPfxMi2h0xQoWcecukRERET8jN+H6r1/Z7Foi6sVea+WXtKKfMv7sOwZyDjnGgsrBm1ehzoPgMUL3oOIiIiIj/DrUH1xK/Ka5YJoUd1Llkqkn3UP1LX6Q7s3HM1cRERERKRQ+XWoXrjFyoETXtqKvOG/IXEmZKU79puO63Dlx4iIiIhIgfDbUP33WRvfrXPt4dy1STilPLEVuWHAjrlQohZcd6NrPDAY7vgWIq+HoFDz6hMRERER/9xS7+JW5OWvC6SzJ7YiP7Mbvr4d5t0NCx8Gw+5+e0xFBWoRERERD+CXofpXT29FbsuAta/CxzfCnvmOsUMr4c9PzK1LRERERHLkd8s/ktLszFnt2pO6Y91QKnpSK/KDy2HBw3AqMdugBeo/AlW6mVaWiIiIiFyeB6XJwjFrZSrJVlcr8juaesie1KknYPmzkDDdfbxUA+g8FUo3MaUsEREREbkyvwrVf+zPYO1FrcjDzG5Fbtjhj+mOQG095RoPLgKtx0D9RyHAr/6aRERERLyO36S19EyDT5a5ln009ZRW5CcS4JcHAcM1Vq0ndHgTosqaVpaIiIiIXD2/uVDxu3VpzlbkkaEWerfykGUfJetAvcGOr6MrQvd50G2uArWIiIiIF/GLmep9f2exYIvVedyrVQTRESb9PpF8GIqUcR9r/QpExkLjZyDYQ8K+iIiIiFw1n5+pttkvbUXe0oxW5EkH4bue8HFtSP3b/bawotBihAK1iIiIiJfy+VC9cLOV/edbkQcHmtCK3J4FGyfCtJqw8yuwnoZlzxbe64uIiIhIgfPp5R/Hz9r4br2JrciPrHN0Qvz7d/dxSwDYbRDggW3RRUREROSa+WyovtCKPMPROJFyJQqxFbn1DKx8ETa/i9uuHsVrQqd3oXy7wqlDRERERAqFz4bqNTsy+POiVuRBgQW87MMwYPssWDoEUo66xoPCoPkIaPw0BHrANn4iIiIikq98ck11Upqd2auytSKvE0ql2EL4/eHn++GHvu6BuuIt8H8J0Ox5BWoRERHxWP3792fXrl2XvX39+vVs27YNgMcee6xQakpMTGTSpEmF8lp55ZOhevYqVyvy4kUCuKNZIe2qUbmL6+siZaDrHOjxIxStXDivLyIiIlJAvvzyS/7+27GDWWEF3Zo1axZagM8rn1v+kbA/kzU7TGpFXq2nI1jHVIZWL0NodOG8roiIiBSaXzal8d36NNIz8+85Q4OhW5NwbqofnuPtVquV559/nsOHD5OZmcnw4cPZs2cPu3fv5plnniE9PZ1bb72VxYsX079/f6pXr87OnTuJiIigcePGrFy5knPnzvHRRx+xaNGiHB93wdGjRxk1ahTp6ekcP36cJ598ktKlS7NixQoSEhKoWrUqvXr14vvvv+eee+7hxx9/xGKxMHr0aFq0aEFcXBxjxowBoGjRorz66qtERUU5n//UqVM888wzZGRkUKlSJdasWcOCBQv46aef+PTTT8nKysJisTBp0iR27tzJF198wYQJE7jpppto2LAhe/bsoUSJErz99tvs37+f559/nqCgIOx2O+PGjeP666/Pv7+Ya+BTM9XpmQafLE9xHjepGkKdCgWw5CL1b5j/f7Bvofu4xQJ3fAPxbypQi4iI+KhfNlnzNVADpGc6nvdyvvjiC8qWLcusWbMYP348mzdv/sfnq1u3Lh9//DEZGRmEhYUxbdo0qlatyvr1669Yy+7du7n//vuZNm0ao0eP5tNPP6V27dq0adOGZ599ljJlHE3sihcvTvXq1dmwYQMZGRmsXbuWDh06MHz4cEaOHMnMmTNp27YtH3zwgdvzT5kyhY4dO/LJJ59wyy23YLM5tj7eu3cv7733Hp9//jlVq1Zl5cqVbo87cOAA//73v5k1axanTp1i69atrF69mrp16zJt2jQef/xxkpKSrvj+CopPzVR/vz6NE+ccrcgjQi30aZ3Pyz4MO2z9EFYMdew3feRXGLDFcSHiBdomT0RExKfdVD+sQGaqb6p/+V3Kdu/eTdu2bQGoWLEi9913H1999ZXzdsMw3O5/4403AhAdHU3VqlWdX6enp7vd7+LHAZQsWZJ3332XuXPnYrFYyMrKumxdd999N19//TXHjx8nPj6eoKAgdu3axUsvvQRAZmYmFStWdHvMrl276N69OwCNGzd2jpcoUYKhQ4cSGRnJ7t27qV+/vtvjihUr5pyFvv7660lPT+euu+7i/fff58EHHyQqKoohQ4ZcttaC5jOhev/xLBZsdv2Gd3fLfG5FfnyrY8/pw6tdY6d3wl/fQo3e+fc6IiIi4tFuqn/5ZRoFpUqVKmzdupVOnTpx4MABJk6cSHx8PMePHwcgISHhqp8rNDT0Hx/35ptv0qtXL9q1a8eXX37J119/DYDFYrkkhLdo0YL//e9/HDt2jJEjRwJQqVIlXnvtNcqUKcPGjRudr3XBDTfcwO+//07NmjXZtGkTAElJSbz11lssXboUgPvvv/+S18qped+iRYto1KgRjz32GPPmzeODDz5g7NixV/1nkZ98IlTb7AYfL03Bfv7PvnrZIFrWyKdlH5kpsPol2DgeDJtrPKYydHwHKt2SP68jIiIichl9+vThhRde4N5778Vms/HCCy9QoUIFPv/8c/r27cuNN95IZGTkVT1XmzZt/vFxt9xyC6+//jrvvfcepUuX5vTp0wDUq1ePN954g3Llyjnva7FYuPnmm1m9ejVxcXEAjBo1iqFDhzrXRr/yyituzz9o0CCee+455s+fT6lSpQgKCqJIkSI0bNiQ3r17ExQURHR0NH///bfba+Wkdu3aDB06lHfffRe73c7zzz9/VX8GBcFi5DTvb6KNGzfSqFGja3rML5vSmLPa0TkxKBBG9Y4htmg+LMP46ztY/Dgk7XeNBQRDk+eg2YsQXLi/pfqzxMREatasaXYZ4mF0XkhOdF5ITnReeI5ly5ZRrFgx6taty+rVq5kyZQozZswwpZbc5M7L8fqZ6uPnbHy7Llsr8sbheQ/USYdg0aOw61v38XLtHB0RS+gfpYiIiEhulCtXjhdeeIHAwEDsdjsvvvii2SXlC68O1YZh8OmyVGcr8rLFA/9xkf/VP3EW7FvgOg4rAe3HQa0Bjh0+RERERCRXqlSpwqxZs8wuI9959ZZ6a3dmkHDAcemtBRjQIZ9akUdXgBaOxfbUHggPbIcb/0+BWkRERERy5LUz1UlpdmatdLUij68TSuXctCK3noaDK6BqN/fxRkOgXFso0zyPlYqIiIiIr/Pameo5q91bkd95ra3IDQMSP4NpNeD7u+BkovvtgcEK1CIiIiJyVbwyVP95IJNft7takd/TNoKwkGtYmnFqB8ztDD/e4+iOaM+EhYMdQVtERERE5Bp5XahOzzT4ZJmrFXnjKiHUrXiVe1JnWR17Ts+oA/sXucaLlIWGT+RzpSIiIiKe5fPPP+ftt98u9Nft378/u3btuqbHLFiwgGPHjnH8+HFGjRpVMIXlI68L1d9vSON49lbkba5y2ce+RTCjLvw6CmznZ7ktAY610/cnQrUeuhBRRERExEPMmDGD5ORkSpYs6RWh2qsuVNx/PIsFm1ytyO9qEUHMlVqRpxyDZU9D4qfu46WbQKepENugACoVERERn7Z6FPz60tXdt84guOk997Ff/gVb33cdtxgJLUdd9im++uorvvzyS+x2O0888QS7du3il19+IS0tjWLFijFp0iTmzZvHsmXLsFqt7N+/n0GDBtGjRw82bNjAq6++SnR0NIGBgdSvXx+Ajz76iB9++IGgoCAaN27Ms88+y9tvv82+ffs4ffo0Z86c4Z577uGXX35hz549vPbaa87HAuzZs4fnn3+eoKAg7HY748aN4/rrr2fcuHFs2LABu93Offfdx6233up8TFJSEi+++KKzS+N//vMfqlevzpw5c/j888+x2+3Ex8dTt25dEhMTGTp0KP/73/8YOnQos2fPZtWqVUycOJHQ0FCKFi3Kq6++SmJiIu+//z7BwcEcPHiQ2267jcGDB1/d300+8ppQbbcbzMjeirxMEK1rXsWyjx/6wIGlruOQaGgzFuo+BAH50HVRREREpBBER0c723Fv3LiR6dOnExAQwMCBA9m6dSsAycnJfPjhh+zdu5eHH36YHj168NJLL/HWW29RqVIlRo50bBm8fft25s+fzxdffEFQUBCPP/44S5YsASAsLIwPP/yQ9957j2XLljFlyhS+/PJLfvjhB7dQvXr1aurWrcuzzz7Lhg0bSEpKYseOHRw8eJDPP/+c9PR07r77blq1auV8zJQpU2jevDn9+vVj7969PP/880yaNIn333+f7777jtDQUMaNG0eTJk2oWbMmo0aNIjg4GHD0Jxk+fDiff/45sbGxfPzxx7z77ru0b9+ew4cP891335GRkUGbNm0Uqv/Joq3p7DtuAxytyO9tH4nlapZrtPkvfNYCMKB6H2g/HopcX7DFioiIiOSzSpUqARAQEEBwcDBPPfUUERERHD16lKwsRye8GjVqAHD99deTkeFY7nrixAnnYxs2bMj+/fvZvXs39erVcwbWxo0bs3PnTgBq1aoFQFRUFFWrVgUgJiaG9PR0t3ruuusu3n//fR588EGioqIYMmQIO3bsICEhgf79+wOQlZXFoUOHnI/ZsWMHa9asYf78+QCcPXuWAwcOUK1aNcLCHA38nnnmmRzf/+nTpylSpAixsbEANGnShPHjx9O+fXtuuOEGgoKCCAoKcj5PYfOKUH3inI1v1rr2pO7SKJzSObUiz0yBoHDHWukLrm8GrV52LPeoeFMhVCsiIiI+r+Wof1yucUU3vXfpkpArCAhw5Jtt27axcOFC5syZQ1paGj169MA4v4NZThOOsbGx7Nq1iypVqrB161ZiYmKoXLky06ZNIysri8DAQNavX8+dd97Jtm3brm7SEli0aBGNGjXiscceY968eXzwwQd06tSJZs2a8fLLL2O325k8eTLly5d3PqZy5cp069aNrl27cvLkSebMmUNcXBy7d+8mIyODkJAQnnjiCV588UUsFovzfQEUK1aM5ORk/v77b0qVKsW6deuoWLHiZd93YfP4UG0YBp8ud29FfnODHH4D2fkNLH7csSap7oPutzX3jZ7yIiIiIhUqVCA8PJw+ffoAULJkSf7+++/L3n/06NE899xzFClShMjISGJiYqhevTq33norffv2xW6306hRIzp16sS2bduuuo7atWszdOhQ55KU559/nlq1arFu3Tr69etHamoqnTp1okiRIs7HPPzww7z44ovMnj2b5ORkHnvsMYoXL86gQYO49957sVgsdOjQgdjYWBo0aMBzzz3Hyy+/DDiC85gxY3j88cexWCzExMQwduxY5wy72SyG4VmbM2/cuJFGjRo5j9fuTOeDBY4t9CzAsJ7R7p0Tz+2DxU/Aru8cx2HF4P7tEFGyEKuWgpaYmEjNmjXNLkM8jM4LyYnOC8mJzgvJycW5My88eqY62ereirxD9lbktkz4baLj6tss132wBMEphWoRERERKTweHarnrE4lKc0xkV4sMoDuF1qRH1oNCx+GE1vdH1D3X9B6LIQXL+RKRURERMSfeWyoTjyYyept2VqRt4sgzHYafhnmvq8jwHV1oNMUKNuykKsUEREREfHQUJ2RZTBzqasVeaMqIdQL/R2mdYW04647BkVAy5eg4b8hMNiESkVEREREPDRUz1vvakUeHmKhb5sICKwBAdnKrdIN4t+G6DiTqhQRERERcbhCj29z/Jy9FXnLcEcr8tAY6DARosrDHd/And8qUIuIiIiIR/DImeoaWUuobl9BQoXRtK4Z6rrhhl5QuQsER5hXnIiIiIjIRfJ1pnrTpk306tWLPn36MGnSJABSUlIYMGAAvXv3dm4ovmHDBt577/JdhIZk3MVtWW8ysOqvBGTvkGOxKFCLiIiIiMfJ11A9cuRIxo0bx+eff87mzZv5888/WbVqFfHx8YwcOZK5c+diGAYzZszg//7v/674fMU3jwDP6k0jIiIiInKJfFv+kZycTEZGBnFxjnXOrVu3ZvXq1dSoUYP09HSsVisRERF8//33dO7cmdDQ0Ms+18b2G1wHv/2WXyWKl9u4caPZJYgH0nkhOdF5ITnReSEFKV9Ddfbe7pGRkRw4cICWLVuybNkyZs2axeOPP87rr7/O448/zogRIyhfvjyDBg1ye578ahUpIiIiIlJY8m35R5EiRUhJce0tnZKSQnR0NAEBAbz44ou89tpr/PDDDwwYMIB3332XJ598kiNHjrBnz578KkFERERExBT5GqqDg4PZv38/hmGwcuVKGjdu7Lz95MmT7Nmzh8aNG5OWlkZgYCAWi4W0tLT8KkFERERExBT5uqXeSy+9xDPPPIPNZqN169bUq1fPedu7777L4MGDAejXrx8DBw6kTJky1KhRIz9LEBEREREpdBbDMHd7jU2bNvHKK68QGBhI69ateeyxx0hJSWHw4MGkp6fz0ksvUaNGDTZs2MBvv/3Gv/71LzPLlQJy+PBhXnjhBWw2G4ZhMHr0aCpXrszixYt55513CAoKomfPntx9990cPXqUJ598koCAACZMmEBsbCzffvstQUFB3H777Wa/FSkA69at49lnn2XZsmUAOi/8XGpqKqNGjeLgwYNkZmYyfPhw6tatq/PCzx0+fJjnnnsOwzCIiYlh3LhxhIeH67zwQ5s3b+aNN95g5syZ7Nu3j2HDhmGxWKhWrRojR44kICCASZMmsXTpUoKCgnjhhReoW7cuy5cv56233qJMmTJMnDiRgIAARo8ezQMPPEC5cuWu/MKGybp162bs27fPsNvtxoMPPmgkJCQYP//8szFt2jQjISHBePnllw273W48/vjjhtVqNbtcKSDPPfecsWDBAsMwDGP58uXGo48+amRkZBidOnUyzpw5Y6Snpxs9evQwjh8/bnz00UfGggULjF9++cWYNm2akZaWZjzxxBOG3W43+V1IQTh8+LDx8MMPGy1btjQMw9B5IcZbb71lvPfee4ZhGEZiYqLx9ddf67wQ45VXXjE++eQTwzAMY/z48caMGTN0Xvih9957z+jSpYvRq1cvwzAM46GHHjLWrFljGIZhDB8+3Pjll1+MP/74w+jfv79ht9uNQ4cOGT169HDe9+zZs8bLL79sJCQkGImJica4ceOu+rVNbVOefRs+i8Xi3IYvIiLimrfhE+82dOhQ2rVrB4DNZiM0NJRdu3YRFxdHTEwMISEhNGrUiPXr1xMREYHVasVqtRIeHs706dMZMGAAluyNgsQnpKenM3LkSEaNGuUc03khK1euJDg4mIEDBzJ58mTatGmj80KoWbMm586dAxz5IigoSOeFH4qLi+Ptt992HickJNC0aVMA2rZty+rVq9m4cSOtW7fGYrFQpkwZbDYbp06dIjIyEqvVSnp6OuHh4bz//vuX7FL3T0wP1Rdvw5eUlETLli05ceIEs2bN4u6772bhwoXUqFGDESNG8P7775tYsRSU4sWLExwczO7du3nttdd49NFHSU5OJioqynmfyMhIkpOT6dKlC2vWrGH9+vW0bNmSffv2YRgGI0aMYM6cOSa+C8lvFz52i42NdY7pvJDTp09z7tw5PvzwQ+Lj43nttdd0XgilS5fm008/5fbbb2f58uXccsstOi/80M0330xQkOuSQcMwnL8sXciZl8ufjzzyCGPHjqVs2bLs37+fhg0bMm/ePEaMGMHvv/9+xdc2NVRrGz7Jbs2aNTz66KO8/vrrVK5cOcfzIyoqisjISMaMGcPo0aOZPn06gwcPZsqUKYwcOZKlS5eSmppq4ruQ/HLs2DE2bNjAO++8Q//+/Tl79ixDhgzReSEULVqU+Ph4ADp06MAff/yh80J4/fXXGTt2LD/88AMvvvgiQ4cO1XkhBAS4ou6FnHm586JKlSpMmDCBQYMGMXfuXLp06cLKlSsZMWIEkydPvvJrFcg7uErahk8uWLNmDa+88goffPABderUAaBKlSrs27ePM2fOkJGRwYYNG2jQoIHzMTt27CA0NJS4uDjS09OxWCzYbDYyMjLMehuSj2JjY/n555+ZOXMmM2fOJCYmhgkTJui8EBo1auS8aHX9+vVUrVpV54UQHR3tnJUuVaoU586d03kh1KpVi7Vr1wKwfPlyGjduTMOGDVm5ciV2u53Dhw9jt9spXry48zGzZs2ie/fuANjt9qvOnvm6pV5uaBs+AXj11VfJzMxk2LBhAFSqVInRo0czbNgwBg4ciGEY9OzZ020ZwNSpUxkxYgQAd955J71796Z27doULVrUjLcghSQ4OFjnhZ976KGH+M9//kPv3r0JCgritdde03khDB8+nNGjR2O3251LOXReyNChQxk+fDjjx4+ncuXK3HzzzQQGBtK4cWN69+6N3W53ngPgWGK4bt06Jk6cCEDJkiXp27cv/fr1u+Jrmb6lnoiIiIiItzN1+YeIiIiIiC9QqBYRERERySOFahERERGRPFKoFhERERHJI4VqEREREZE8UqgWEREREckjhWoRERERkTz6f+8M6al+9ZCCAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -2291,7 +1943,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -2313,7 +1965,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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SkqK77rpLly9ftm9/be3umjVrVKRIEXXs2PGms7EdOnTQkiVLdPjwYQ0dOjTL8wMHDtS3336rJUuW6IcfflBAQIDi4+O1du1aJScna/r06fYP/t2uBx98UKGhoVqyZInatWunpk2bysPDQ9HR0fb7UV+7g0r//v21Zs0ajRgxQmvXrlXlypV18uRJrV+/XqVLl1bPnj0zHdvLy0vPP/+81qxZo7Jly2rjxo06fvy4nn76afsMdsWKFTVq1Ci9+uqr6tixo1q1aqUiRYooJiZGx48fV7t27fT444/f9nVd+/KbdevWqWPHjnrwwQeVlpam6OhoXb58OdOXIE2cOFE9evTQc889p6CgIFWrVk1Hjx7Vxo0bVaJECU2YMOGOXlsAroOSDSBXcnNz05QpU9SuXTu9//77OnjwoDZt2iR3d3f5+/vrueeeU5cuXbLc2WLy5MkqU6aM1q5dqyVLlqhatWp688039fvvvxsv2ZI0d+5cvfHGG/r666916NAh1apVS4sWLdKaNWtuWrILFCig8PBwzZgxQ5s3b9b+/ft1zz33KCgoSEOGDNEbb7yh6Oho/f7776pUqZLKly9v/yKUZcuWydfX96Ylu06dOqpSpYp+++23TEtFrilcuLAWL16sd999V6tXr9by5cvl5eWl+vXra9CgQQoICPhHr8vYsWNVu3ZtrVixQqtWrZKbm5uqVq2qf//73/YZZ0mqUKGCVqxYofDwcO3cuVNfffWVvL291aFDBw0dOjTLBwMDAgLUsmVLzZs3TzExMfL19dXrr7+eZWa6V69eqlKliubPn6/169fLsiz5+vpq0KBB6ty58x1f1+uvv65atWrps88+U1RUlGw2m+677z6NHz9eLVq0sG/n4+OjlStXKjw8XJs2bdLWrVtVpkwZPfbYY1luLQggb7JZ1/vEDgAAuciJEyfUsmVLtWzZUuHh4c6OAwC3xJpsAAAAwDBKNgAAAGAYJRsAAAAwjDXZAAAAgGHMZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAMgxJ06c0JNPPunw9ufPn9dLL72UZXz69OlauXKlwWRmUbIBAACQa5UuXfq6JTu3c3d2AAAAADjJlpekrS87tm3tAdIj8zKPrR8o7Yv83+NGE6SHXnLocKGhoapevbp++eUXJSYmavbs2SpfvrzCw8MVHR2t9PR0devWTY0bN9bzzz+vDz74QOvWrdM777yjkiVLKjU1VT4+PpKkN954Q99//70yMjLUp08fPfroow4fv2vXrlqyZIk+//xz2Ww2tW3bVr169XLsNbkJZrIBAADgFHXq1NHChQv18MMP64svvtCBAwcUExOjDz/8UB9++KF+++03WZYlSUpNTdVrr72m9957T/Pnz1fhwoUlSZs2bdKJEye0YsUKLV68WBEREYqPj3f4+L/88otWr16t5cuXa9myZYqOjtavv/76j6+NmWwAAAA4Rc2aNSVJ99xzjy5cuKCjR4+qTp06cnNzk5ubm0aPHq0TJ05Iki5duqTixYvL29tbklSvXj1J0s8//6z9+/crNDRUkpSWlqaTJ086fPzVq1fr1KlT6tOnjyQpLi5Ox44ds8+S3ylKNgAAQH710EsOL++4rkfmZV1C8g/4+PhoxYoVysjIUHp6ugYOHKhx48ZJkkqVKqX4+HhdunRJJUuW1L59+3TPPffIx8dHgYGBmjRpkjIyMhQeHq6KFSs6fPxRo0bpX//6l959913ZbDYtXLhQ/v7+//haKNkAAADIFWrUqKEmTZqoW7duysjIULdu3eTh4SFJcnd31/jx4/XUU0+pePHicnf/s8a2aNFC3333nbp3764rV66oVatW8vT0dPj41atXV6NGjdStWzelpKSoTp06Klu27D++Fpt1baELAAAAACNy/IOPe/bssa+ZOXbsmLp166bu3btrwoQJysjIkCS99dZb6ty5s7p27aq9e/fmdEQAAADgH8nRkh0ZGamxY8fq6tWrkqQpU6YoLCxMy5cvl2VZ2rBhg/bv36/vvvtOH374oWbMmKGXX3bwtjIAAABALpGjJbtSpUqaM2eO/fH+/fsVEBAgSQoKCtKWLVv0ww8/qHHjxrLZbCpXrpzS09N16dKlnIwJAAAA/CM5+sHH4OBg+21YJMmyLNlsNklS0aJFlZCQoMTERJUoUcK+zbXxkiVLZjpWVFSUoqKispwjJCREISEh2XMBtzAg3PXfDEQ+XfLWGwEAAOCmnHp3kQIF/jeRnpSUpGLFisnT01NJSUmZxr28vLLs68wyDQAAANyMU7/xsWbNmtq+fbskKSYmRg0bNlT9+vX17bffKiMjQ6dOnVJGRkaWWWwAAAAgN3PqTPaoUaM0btw4zZgxQz4+PgoODpabm5saNmyokJAQZWRkaPz48c6MCAAAANw27pNtEGuyAQAAIDl5uQgAAACQF1GyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAY5u7sAHB9A8IvOTuCEZFPl3R2BAAAkEcwkw0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABjm7uwAgKsaEH7J2RGMiHy6pLMjAACQ5zCTDQAAABjm9Jns1NRUjR49WidPnlSBAgU0adIkubu7a/To0bLZbKpWrZomTJigAgV4PwAAAADX4PSSvWnTJqWlpen999/X5s2bNWvWLKWmpiosLEyBgYEaP368NmzYoNatWzs7KgAAAOAQp08PV61aVenp6crIyFBiYqLc3d21f/9+BQQESJKCgoK0ZcsWJ6cEAAAAHOf0mewiRYro5MmTevTRR3X58mVFRERox44dstlskqSiRYsqISEhy35RUVGKiorKMh4SEqKQkJBszw0AAADciNNL9sKFC9W4cWMNHz5cp0+fVu/evZWammp/PikpScWKFcuyH2UaAAAAuZXTl4sUK1ZMXl5ekqTixYsrLS1NNWvW1Pbt2yVJMTExatiwoTMjAgAAALfF6TPZffr00ZgxY9S9e3elpqZq2LBhqlWrlsaNG6cZM2bIx8dHwcHBzo4JAAAAOMzpJbto0aKaPXt2lvGlS5c6IQ2AW+FLeAAAuDWnLxcBAAAA8hpKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADD3J0dAABcwYDwS86OYETk0yWdHQEA8gVmsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMc3d0w7Nnz2rbtm06efKkEhIS5O3trXvvvVcPPfSQSpUqlZ0ZAQAAAJdyy5IdHR2tBQsWaNeuXbIsS8WLF1fhwoUVHx+v5ORkFShQQPfff7/69++vFi1a5ERmAAAAIFe7Yck+duyYxowZo+PHjys4OFjDhg1TzZo1VbRoUfs2CQkJ2rlzp7Zs2aIXX3xRlSpV0muvvaaqVavmSHgAAAAgN7phyR44cKCGDBmi9u3by83N7brbeHl5qWnTpmratKlGjBihTz/9VIMGDdL69euzLTAAAACQ292wZK9atUqFChVy+EAFCxZUly5d1KFDByPBAAAAAFd1w7uL3E7BNrEfAAAAkFc4fHcRSbIsS8uXL9f69et14cIFlSxZUq1atVKPHj3k7n5bhwIAAADyrNtqxrNmzdKGDRv0+OOPq3jx4jp37pwWLVqkI0eOaOLEidmVEQAAAHApNyzZJ06cUIUKFTKNrV+/Xm+99ZZ8fX3tY7Vq1dLIkSMp2QAAAMD/u+Ga7E6dOmnSpEm6cOGCfczX11eRkZH68ccfdfz4ce3cuVNLlixRtWrVciQsAAAA4ApuWLJXr14tm82mdu3aacaMGYqPj9fEiROVmJiokJAQtW7dWj179lSBAgU0bdq0nMwMAAAA5Go3XC5SqlQpjR07Vv369dPbb7+t4OBg9e7dW9OmTVPBggUVGxsrb2/vG95DGwAAAMivbjiTfU25cuX06quvavny5Tp06JBat26tFStWqHjx4sYK9ty5cxUSEqInnnhCH374oY4dO6Zu3bqpe/fumjBhgjIyMoycBwAAAMgJNy3Zv/32m9atW6f9+/eratWqmjlzpiIjI/Xtt98qODhYK1eulGVZ/yjA9u3btWvXLq1YsUJLlizRmTNnNGXKFIWFhWn58uWyLEsbNmz4R+cAAAAActINS/aHH36odu3aadKkSQoJCdErr7wiSapRo4bmzp2radOm6ZNPPlHbtm21du3aOw7w7bffys/PT88884wGDx6sZs2aaf/+/QoICJAkBQUFacuWLXd8fAAAACCn3XBN9ttvv62xY8eqW7du2rt3r0JCQjRkyBCVKlVKktSgQQMtWbJE33zzjWbPnq02bdrcUYDLly/r1KlTioiI0IkTJzRkyBBZliWbzSZJKlq0qBISErLsFxUVpaioqCzjISEhCgkJuaMsAAAAgAk3LNnJycny9vaWJHl7e8uyLP3xxx9ZtmvSpImaNGlyxwFKlCghHx8feXh4yMfHR4UKFdKZM2fszyclJalYsWJZ9qNMAwAAILe6Ycl+8sknNWrUKC1cuFC//fabGjdurPLlyxsP0KBBAy1evFh9+/bVuXPnlJycrEaNGmn79u0KDAxUTEyMHnzwQePnBQDc2oDwS86OYETk0yWdHQFAPnPDkj18+HAFBgbq4MGDKl++vB555JFsCdC8eXPt2LFDnTt3lmVZGj9+vCpUqKBx48ZpxowZ8vHxUXBwcLacGwAAAMgONyzZktS4cWM1btw420P85z//yTK2dOnSbD8vAAAAkB1ueHeR3r1768CBA7d1sL179yo0NPQfhwIAAABc2Q1nsvv27atnnnlGPj4+ateunVq1anXdDyCeO3dO3377rT7++GMdO3ZML7/8crYGBgAAAHK7G5bsZs2aqWHDhlq4cKHeeOMNjR07VuXKldO9996rwoULKyEhQWfPntWZM2dUrFgxhYaGKiIiQl5eXjmZHwAAAMh1brom29PTU0OHDtXAgQO1detWbd++XSdPnlRiYqLKly+vBg0aqFGjRgoICJCHh0dOZQYAAABytZuW7Gs8PDzUtGlTNW3aNLvzAAAAAC7vhh98BAAAAHBnKNkAAACAYZRsAAAAwDBKNgAAAGDYbZfstLQ0nT9/XmlpadmRBwAAAHB5Dpfs3bt3q0+fPqpXr56aNm2qQ4cOacSIEZo5c2Z25gMAAABcjkMle+vWrerZs6ckadiwYbIsS5Lk5+enyMhIvffee9mXEAAAAHAxDpXs6dOnq23btlq4cKF69eplL9kDBw7U4MGDFRUVla0hAQAAAFfiUMn+5Zdf1KFDB0mSzWbL9FxgYKBOnTplPhkAAADgohwq2aVKldLhw4ev+9yRI0dUqlQpo6EAAAAAV+bQ16o//vjjmj17try8vBQUFCRJSk9P15YtW/TWW2+pU6dO2RoSAAAAcCUOleyhQ4fqzJkzevHFF+3LRbp27SrLstS6dWs9++yz2RoSAAAAcCUOlWw3NzdNmTJF/fv3144dOxQbGysvLy81aNBA1atXz+6MAAAAgEtxqGRf4+vrK19fX0nSuXPndO7cOaWnp8vNzS1bwgEAAACuyKEPPsbFxWn48OFavHixJOnTTz9VixYt1KVLFz366KM6fvx4toYEAAAAXIlDJXvq1KnasmWLypUrp4yMDE2fPl3333+/Fi9eLG9vb02dOjW7cwIAAAAuw6GSvXHjRr3wwgtq1aqVvv/+e124cEH9+/fXAw88oMGDB2vbtm3ZnRMAAABwGQ6V7CtXrujee++V9GfhLlSokBo1aiRJ8vDwyL50AAAAgAtyqGT7+voqOjpaFy5c0OrVq9WoUSMVKlRI6enpWr58uapVq5bdOQEAAACX4dDdRZ599ln9+9//1uLFi1WwYEENGjRIkvTII4/o4sWLeuedd7I1JAAAAOBKHCrZTZs21eeff64ff/xRtWvXVsWKFSVJgwcP1gMPPKAqVapkZ0YAAADApTh8n+xKlSqpUqVKmca6dOliPBAAAM42IPySsyMYEfl0SWdHAPIth0p2RkaGPvjgA23atEnJycnKyMjI9LzNZtOiRYuyJSAAAADgahwq2a+99poWL16smjVrqmzZsipQwKHPSwIAAAD5kkMle9WqVRo6dKiGDh2a3XkAAAAAl+fQlHRKSooaNmyY3VkAAACAPMGhkt2sWTNFR0dndxYAAAAgT3BouUiTJk00ZcoUnThxQnXq1NFdd92V6XmbzaY+ffpkRz4AAADA5ThUsl944QVJf36l+saNG7M8T8kGAAAA/sehkn3w4MHszgEAAADkGQ5/GY0kxcbGas+ePUpMTJS3t7fq1KkjT0/P7MoGAAAAuCSHS/bs2bM1f/58paSk/G9nd3f17dtXw4cPz5ZwAAAAgCtyqGQvWrRIc+fOVf/+/dW2bVvdfffdunDhgr744gstWLBAZcuWVc+ePbM7KwAAAOASHCrZy5cv14ABAzRs2DD72N13363q1avLzc1Ny5Yto2QDAAAA/8+h+2SfOXNGgYGB130uICBAJ0+eNBoKAAAAcGUOlexKlSrp+++/v+5zO3bsUNmyZY2GAgAAAFyZQ8tFQkND9fLLLys9PV1t2rRRqVKldPHiRa1du1bz589XWFhYNscEAAAAXIdDJfvJJ5/U8ePHtWDBAs2bN0+SZFmW3N3dFRoaqv79+2drSAAAAMCVOHwLv+HDh+upp57Snj17FBcXp+LFi6tOnTry9vbOznwAAACAy7mtL6O5cuWK4uPjFR8fL3d3d6WlpWVXLgAAAMBlOVSyU1JS9OKLL+rzzz+XZVn2cTc3N3Xv3l1jxoyRzWbLtpAAAACAK3GoZE+dOlXR0dEaO3asWrVqJW9vb126dElffvmlZsyYIW9vbz399NPZnRUAAABwCQ6V7NWrV2vEiBHq0aOHfeyee+5RaGioLMvS/PnzKdkAAADA/3PoPtlpaWkqV67cdZ+rXLmyEhISjIYCAAAAXJlDJfvJJ5/U22+/rbi4uEzjV69e1cKFC9WlS5dsCQcAAAC4IoeWi1y5ckXHjh1T8+bN9dBDD6l06dKKjY3V9u3bFRsbq4IFC2rw4MGSJJvNpnfeeSdbQwMAAAC5mUMl+/Dhw6pevbokKS4uzj6j7evrK0lKTk7OpngAAACA63GoZC9ZsiS7cwAAAAB5hsNfRhMfH6/k5GSVLVtWKSkpWrx4sU6fPq3WrVvrwQcfzM6MAAAAgEtxqGTv2LFDgwYNUrdu3TRy5EiNGzdOq1atUrly5bRixQpNnz5dbdu2ze6sAAAgmw0Iv+TsCP9Y5NMlnR0BcOzuIrNmzVK9evXUt29fxcXF6YsvvlC/fv20YcMG9e7dW3Pnzs3unAAAAIDLcKhk79+/X/3799fdd9+tTZs2KT09Xe3bt5ckNW/eXEePHs3WkAAAAIArcahkFy5cWCkpKZKkTZs2qXTp0va7jZw+fVrFixfPvoQAAACAi3FoTXZgYKBmz56tQ4cOad26dfavV//yyy81a9YsNW7cOFtDAgAAAK7EoZnscePGqVixYnrrrbcUEBCgZ555RpI0efJkVa5cWSNHjszWkAAAAIArcWgm++6779bChQuzjK9cuVLe3t6mMwEAAAAuzeH7ZEvS999/r23btuncuXMaPHiwfvnlF9WoUUNlypTJrnwAAACAy3GoZP/xxx8KCwvTxo0b5enpqaSkJIWEhGjhwoU6dOiQlixZYv+KdQAAAFfD/cFhmkNrsqdPn669e/dq2bJl2rZtmyzLkiRNmzZNZcuW1RtvvJGtIQEAAABX4lDJ/uKLLzRixAg1aNBANpvNPn733Xfr6aef1g8//JBtAQEAAABX41DJTk5OVqlSpa77XKFChez30AYAAADgYMmuW7euFi9erPT0dPvYtRntjz/+WLVr186edAAAAIALcuiDjyNHjlRoaKgeffRRPfTQQ7LZbFq+fLl+/fVX/fjjj1q0aFF25wQAAABchkMz2bVq1dJHH32kWrVqKTo6Wm5uboqOjpa3t7dWrFihevXqZXdOAAAAwGU4NJO9Zs0aBQYGasaMGdmdBwAAAHB5Ds1kjxkzRt9//312ZwEAAADyBIdKdvny5RUXF5fdWQAAAIA8waHlIu3bt9fkyZO1adMmValSJcvt/Gw2m/r06ZMd+QAAAACX41DJnjlzpiQpOjr6us9TsgEAAID/cahkHzx4MLtzAAAAAHmGQ2uyc8LFixfVtGlTHTlyRMeOHVO3bt3UvXt3TZgwQRkZGc6OBwAAADgsV5Ts1NRUjR8/XoULF5YkTZkyRWFhYVq+fLksy9KGDRucnBAAAABwnEPLRbLb1KlT1bVrV82bN0+StH//fgUEBEiSgoKCtHnzZrVu3TrTPlFRUYqKispyrJCQEIWEhGR/aAAAAOAGnF6yV65cqZIlS6pJkyb2km1Zlmw2mySpaNGiSkhIyLIfZRoAAAC5ldNL9scffyybzaatW7fqp59+0qhRo3Tp0iX780lJSSpWrJgTE96Z9qlT1SHtdYe2jXHrpSUeMzONhaYMU1D6Yof2X+X+H31WcFSmsaFXu6tuxjqH9l9ccIa+ce+deXBJA+ncTof2r+OxTHvd2mQam5ZcUyV01qH9JxXaoN8L3J9pLDK51PU3vo4RhX9UnO1e++Pi1mlN/6OWw/sPuOti5oGzP0hLG95yv0hJsSqrkXcdyDReJ32t/p3Sw6FzH7PV1SuFv8o01iRtkXqlPu/Q/nsKBOutQsszjd3uz560KPPg+oHSvsgb7vPXZ7LjZ2/sHy1U2drj0P5z/snP3huSen4vlW3wt3HbdTe/3iti+mevUsZujbva0qF97/hn743//3eZ+lLoD5mf2ztP+nJQpqEb/SSY+NnL0d97b/ztceu5Up2Bmcf+8nvvxn8C/vSPfvaUg7/3/n7d1wy3Mj/+y++9W127S/zeu9F1S1LtAdIj8zKPrR+oyORbXfmfcvXvvZtd9zW38XvvugadlDzL/e9x4ilpbnnH97/Jz94tFb1XGnzK8XM5mdNL9rJly+z/OzQ0VC+99JKmTZum7du3KzAwUDExMXrwwQedmBAAACBvGhB+6ZZvrP5qxKLLirMVtj8ubl3W9Ns8319VyojTOAf3jU3K0Mi/7S9JkU+XvI0EOSdXfPDx70aNGqU5c+YoJCREqampCg4OdnYkAAAAwGE2y7KsW28GR/z93ZkrupN3g3nhuqXbv3au27Vx3Y7hul1bfv2dznU7Lr9ed07IlTPZAAAAgCujZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAw9ydHSA1NVVjxozRyZMnlZKSoiFDhuhf//qXRo8eLZvNpmrVqmnChAkqUID3AwAAAHANTi/Zq1atUokSJTRt2jTFxsbq8ccfV/Xq1RUWFqbAwECNHz9eGzZsUOvWrZ0dFQAAAHCI00t2mzZtFBwcLEmyLEtubm7av3+/AgICJElBQUHavHlzlpIdFRWlqKioLMcLCQlRSEhI9gcHAAAAbsDpJbto0aKSpMTERD377LMKCwvT1KlTZbPZ7M8nJCRk2Y8yDQAAgNwqVyx0Pn36tHr16qXHHntM7du3z7T+OikpScWKFXNiOgAAAOD2OL1kX7hwQf369dPIkSPVuXNnSVLNmjW1fft2SVJMTIwaNmzozIgAAADAbXF6yY6IiFB8fLzCw8MVGhqq0NBQhYWFac6cOQoJCVFqaqp9zTYAAADgCpy+Jnvs2LEaO3ZslvGlS5c6IQ0AAADwzzl9JhsAAADIayjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwd2cHuJGMjAy99NJLOnTokDw8PPTKK6+ocuXKzo4FAAAA3FKuLdnR0dFKSUlRVFSUdu/erddee03vvPPOTfdJS0vTmTNncihhVn/Exznt3KacOHHltvfJC9ct3f61c92ujet2DNft2vLr73Su23H59bpNuueee+TunrVS2yzLspyQ55amTJmiOnXqqF27dpKkJk2a6JtvvrE/HxUVpaioqEz7pKSk6JdffsnRnAAAAMi/NmzYoAoVKmQZz7Uz2YmJifL09LQ/dnNzU1pamv2dQkhIiEJCQjLt4+yZ7JwwePBgRUREODtGjuO68xeuO3/huvOX/HrdUv699rx+3ffcc891x3Ntyfb09FRSUpL9cUZGxnWn4v/K3d39uu8k8hIPD488f43Xw3XnL1x3/sJ15y/59bql/Hvt+fW6c+3dRerXr6+YmBhJ0u7du+Xn5+fkRAAAAIBjcu1MduvWrbV582Z17dpVlmVp8uTJzo4EAAAAOCTXluwCBQpo4sSJzo4BAAAA3LZcu1wE1/f3D3vmF1x3/sJ15y9cd/6SX69byr/Xnl+vO9fewg8AAABwVcxkAwAAAIZRsgEAAADDKNkAAACAYbn27iLIas+ePZo+fbqWLFni7Cg5IjU1VWPGjNHJkyeVkpKiIUOGqGXLls6Ole3S09M1duxYHT16VDabTS+//HK+uk/8xYsX9cQTT2jBggXy9fV1dpwc07FjR/u33FaoUEFTpkxxcqKcMXfuXH311VdKTU1Vt27d1KVLF2dHynYrV67UJ598Ikm6evWqfvrpJ23evFnFihVzcrLslZqaqtGjR+vkyZMqUKCAJk2alC/+jKekpOiFF17Q8ePH5enpqfHjx6tKlSrOjpWt/tpXjh07ptGjR8tms6latWqaMGGCChTIH3O8lGwXERkZqVWrVumuu+5ydpQcs2rVKpUoUULTpk1TbGysHn/88XxRsr/++mtJ0vvvv6/t27dr5syZeuedd5ycKmekpqZq/PjxKly4sLOj5KirV6/Ksqx88wb6mu3bt2vXrl1asWKFkpOTtWDBAmdHyhFPPPGEnnjiCUnSyy+/rE6dOuX5gi1JmzZtUlpamt5//31t3rxZs2bN0pw5c5wdK9t98MEHKlKkiD744AP9+uuvmjRpkubPn+/sWNnm731lypQpCgsLU2BgoMaPH68NGzaodevWTk6ZM/LHW4k8oFKlSvnil9FftWnTRs8995wkybIsubm5OTlRzmjVqpUmTZokSTp16lS++I/vNVOnTlXXrl1VpkwZZ0fJUQcPHlRycrL69eunXr16affu3c6OlCO+/fZb+fn56ZlnntHgwYPVrFkzZ0fKUfv27dPhw4fzze3NqlatqvT0dGVkZCgxMVHu7vljnu/w4cMKCgqSJPn4+OjIkSNOTpS9/t5X9u/fr4CAAElSUFCQtmzZ4qxoOS5//ITnAcHBwTpx4oSzY+SookWLSpISExP17LPPKiwszLmBcpC7u7tGjRqlL7/8Um+++aaz4+SIlStXqmTJkmrSpInmzZvn7Dg5qnDhwnrqqafUpUsX/fbbbxowYIDWrl2b50vI5cuXderUKUVEROjEiRMaMmSI1q5dK5vN5uxoOWLu3Ll65plnnB0jxxQpUkQnT57Uo48+qsuXLysiIsLZkXJEjRo19PXXX6tVq1bas2ePzp49q/T09Dw7cfT3vmJZlv3PdNGiRZWQkOCsaDmOmWzkaqdPn1avXr302GOPqX379s6Ok6OmTp2qdevWady4cbpy5Yqz42S7jz/+WFu2bFFoaKh++uknjRo1SufPn3d2rBxRtWpVdejQQTabTVWrVlWJEiXyxbWXKFFCjRs3loeHh3x8fFSoUCFdunTJ2bFyRHx8vI4ePaoHH3zQ2VFyzMKFC9W4cWOtW7dO//3vfzV69GhdvXrV2bGyXadOneTp6anu3bvryy+/1H333ZdnC/b1/HX9dVJSUr7621lKNnKtCxcuqF+/fho5cqQ6d+7s7Dg55tNPP9XcuXMlSXfddZdsNlu++JDIsmXLtHTpUi1ZskQ1atTQ1KlTVbp0aWfHyhEfffSRXnvtNUnS2bNnlZiYmC+uvUGDBvrmm29kWZbOnj2r5ORklShRwtmxcsSOHTvUqFEjZ8fIUcWKFZOXl5ckqXjx4kpLS1N6erqTU2W/ffv2qVGjRlqxYoXatGmjihUrOjtSjqpZs6a2b98uSYqJiVHDhg2dnCjn5O2/i4RLi4iIUHx8vMLDwxUeHi7pzw9U5PUPxT3yyCN64YUX1KNHD6WlpWnMmDF5/przu86dO+uFF15Qt27dZLPZNHny5Dy/VESSmjdvrh07dqhz586yLEvjx4/PNzN8R48eVYUKFZwdI0f16dNHY8aMUffu3ZWamqphw4apSJEizo6V7SpXrqzZs2crIiJCXl5eevXVV50dKUeNGjVK48aN04wZM+Tj46Pg4GBnR8oxfK06AAAAYFje/ztoAAAAIIdRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsA8pDQ0FANGjTIyLG2b98uf39/7du3z/ixASCvo2QDAK7rvvvuU1RUlHx9fZ0dBQBcTt7/tgMAwB3x9PTU/fff7+wYAOCSmMkGABeVlJSksWPHKjAwUIGBgZo3b16m59PS0jR79mw1a9ZMtWvX1hNPPKGtW7dm2iY2NlYvvviiHnroIdWvX1/9+vXToUOHJGVdLvJ3jhwfAPIrSjYAuKjnn39eX375pUaOHKlXX31VX3zxhXbt2mV/fty4cXrvvffUq1cvvf322/Lx8dGAAQO0c+dOSX+W5L59+2rTpk16/vnnNXv2bP3xxx966qmnFBcXd8vz3+r4AJCfsVwEAFzQwYMHtXHjRs2cOVNt27aVJNWpU0ctW7aUJB05ckQrV67UK6+8oi5dukiSgoKCdP78ec2aNUuLFy/Wxo0bdeDAAS1btkwNGzaUJNWsWVNdunTRjz/+KHf3G/8nwpHjA0B+xkw2ALiga7PFQUFB9rEyZcrY11B/99139ufT0tLs/zRt2lQ7d+5USkqKdu3aJS8vL3vBlqRSpUrpq6++0sMPP3zT8ztyfADIz5jJBgAXFB8fr4IFC8rT0zPTeOnSpZWUlKTY2FhJmUv4X12+fFlxcXEqVarUHZ3fkeOXLVv2jo4NAHkBJRsAXFCJEiWUmpqq+Ph4FStWzD4eGxurggULysvLSzabTe+//77c3Nyy7O/t7S0vLy9dunQpy3Pbtm1ThQoVbnp+R44PAPkZy0UAwAUFBARIktavX28fi4uL0+7duyVJDRo0kGVZSkxMVO3ate3/bN26VQsXLpS7u7vq1aun+Pj4TB9UjIuL04ABA7R58+abnt+R4wNAfsZvQQBwQT4+PurQoYMmT56sq1evqly5cpo7d67S0tIkSTVq1FBwcLBGjhypoUOHytfXV999953eeecd9e/fXwUKFFDz5s1Vs2ZNDRs2TMOGDZO3t7ciIyNVpkwZtW3bVgcOHLjh+R05PgDkZ5RsAHBRr776qkqWLKk5c+YoNTVVnTt3VtmyZfXHH39IkqZPn67Zs2dr3rx5unjxosqXL6/hw4frqaeekiQVLFhQ8+fP1+uvv67JkycrIyNDDRs21MKFC+Xl5XXL89/q+ACQn9ksy7KcHQIAAADIS/j7PAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBh/wcfEj94jq83mgAAAABJRU5ErkJggg==\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]

From 51f228532f5d76b37b1b779e0eba61550af8351c Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Thu, 30 Sep 2021 14:45:44 +0200
Subject: [PATCH 14/16] add __version__ string

---
 README.rst                                    |  3 ++-
 cobra/__init__.py                             |  1 +
 cobra/version.py                              |  1 +
 setup.py                                      |  5 ++++-
 .../tutorial_Cobra_linear_regression.ipynb    | 21 +++++++++++++++++++
 5 files changed, 29 insertions(+), 2 deletions(-)
 create mode 100644 cobra/version.py

diff --git a/README.rst b/README.rst
index ec5dce5..ddf8fb7 100644
--- a/README.rst
+++ b/README.rst
@@ -1,6 +1,7 @@
 
 .. image:: material/logo.png
-    :width: 300
+    :width: 350
+
 
 .. image:: https://img.shields.io/pypi/v/pythonpredictions-cobra.svg
     :target: https://pypi.org/project/pythonpredictions-cobra/
diff --git a/cobra/__init__.py b/cobra/__init__.py
index e69de29..7152555 100644
--- a/cobra/__init__.py
+++ b/cobra/__init__.py
@@ -0,0 +1 @@
+from .version import __version__
\ No newline at end of file
diff --git a/cobra/version.py b/cobra/version.py
new file mode 100644
index 0000000..ff1068c
--- /dev/null
+++ b/cobra/version.py
@@ -0,0 +1 @@
+__version__ = "1.1.0"
\ No newline at end of file
diff --git a/setup.py b/setup.py
index e6ce441..5f10de5 100644
--- a/setup.py
+++ b/setup.py
@@ -1,3 +1,6 @@
+
+exec(open("cobra/version.py").read())  # reads in __version__
+
 import pathlib
 from setuptools import setup, find_packages
 
@@ -9,7 +12,7 @@
 
 setup(
     name="pythonpredictions-cobra",
-    version="1.0.2",
+    version=__version__,
     description=("Package to build predictive models using "
                  "logistic regression"),
     long_description=README,
diff --git a/tutorials/tutorial_Cobra_linear_regression.ipynb b/tutorials/tutorial_Cobra_linear_regression.ipynb
index 579dfc5..f6b4f34 100644
--- a/tutorials/tutorial_Cobra_linear_regression.ipynb
+++ b/tutorials/tutorial_Cobra_linear_regression.ipynb
@@ -136,6 +136,27 @@
     "from cobra.evaluation import plot_incidence"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.1.0'"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import cobra\n",
+    "cobra.__version__"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,

From 35129a3874943b49678523a6b964da4ad45719f6 Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Thu, 30 Sep 2021 15:04:05 +0200
Subject: [PATCH 15/16] change link logo

---
 README.rst                                    |  4 +--
 setup.py                                      | 10 +++---
 .../tutorial_Cobra_linear_regression.ipynb    | 32 +++++++------------
 .../tutorial_Cobra_logistic_regression.ipynb  | 11 +++++++
 4 files changed, 29 insertions(+), 28 deletions(-)

diff --git a/README.rst b/README.rst
index ddf8fb7..4c64341 100644
--- a/README.rst
+++ b/README.rst
@@ -1,5 +1,5 @@
 
-.. image:: material/logo.png
+.. image:: https://github.com/PythonPredictions/cobra/blob/master/material/logo.png?raw=true
     :width: 350
 
 
@@ -12,7 +12,7 @@
 
 ------------------------------------------------------------------------------------------------------------------------------------ 
 
-**Cobra** is a Python package to build predictive models using linear or logistic regression with a focus on performance and interpretation. It consists of several modules for data preprocessing, feature selection and model evaluation. The underlying methodology was developed at Python Predictions in the course of hundreds of business-related prediction challenges. It has been tweaked, tested and optimized over the years based on feedback from clients, our team, and academic researchers.
+**Cobra** is a Python package to build predictive models using linear or logistic regression with a focus on performance and interpretation. It consists of several modules for data preprocessing, feature selection and model evaluation. The underlying methodology was developed at `Python Predictions <https://www.pythonpredictions.com>`_ in the course of hundreds of business-related prediction challenges. It has been tweaked, tested and optimized over the years based on feedback from clients, our team, and academic researchers.
 
 Main features
 =============
diff --git a/setup.py b/setup.py
index 5f10de5..e4ec6ef 100644
--- a/setup.py
+++ b/setup.py
@@ -13,11 +13,11 @@
 setup(
     name="pythonpredictions-cobra",
     version=__version__,
-    description=("Package to build predictive models using "
-                 "logistic regression"),
+    description=("A Python package to build predictive linear and logistic regression "
+                 "models focused on performance and interpretation"),
     long_description=README,
     long_description_content_type="text/x-rst",
-    packages=find_packages(include=['cobra', 'cobra.*']),
+    packages=find_packages(include=["cobra", "cobra.*"]),
     url="https://github.com/PythonPredictions/cobra",
     license="MIT",
     author="Python Predictions",
@@ -27,7 +27,7 @@
         "pandas>=1.1.5",
         "scipy>=1.5.4",
         "scikit-learn>=0.24.1",
-        "matplotlib>=3.3.3",
+        "matplotlib>=3.4.3",
         "seaborn>=0.11.0",
-        "tqdm>=4.59.0"]
+        "tqdm>=4.62.2"]
 )
diff --git a/tutorials/tutorial_Cobra_linear_regression.ipynb b/tutorials/tutorial_Cobra_linear_regression.ipynb
index f6b4f34..07cd57b 100644
--- a/tutorials/tutorial_Cobra_linear_regression.ipynb
+++ b/tutorials/tutorial_Cobra_linear_regression.ipynb
@@ -115,12 +115,23 @@
     "pd.set_option(\"display.max_rows\", 50)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cobra\n",
+    "cobra.__version__"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
+    "## specific function imports\n",
     "from cobra.preprocessing import PreProcessor\n",
     "\n",
     "from cobra.model_building import univariate_selection\n",
@@ -136,27 +147,6 @@
     "from cobra.evaluation import plot_incidence"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'1.1.0'"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import cobra\n",
-    "cobra.__version__"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 6,
diff --git a/tutorials/tutorial_Cobra_logistic_regression.ipynb b/tutorials/tutorial_Cobra_logistic_regression.ipynb
index 9459681..4f556d0 100644
--- a/tutorials/tutorial_Cobra_logistic_regression.ipynb
+++ b/tutorials/tutorial_Cobra_logistic_regression.ipynb
@@ -115,12 +115,23 @@
     "pd.set_option(\"display.max_rows\", 50)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cobra\n",
+    "cobra.__version__"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
+    "## specific function imports\n",
     "from cobra.preprocessing import PreProcessor\n",
     "\n",
     "from cobra.model_building import univariate_selection\n",

From a2bb0c191191fe4af97801871621fa27e9a53879 Mon Sep 17 00:00:00 2001
From: sborms <sam.borms@pythonpredictions.com>
Date: Thu, 30 Sep 2021 15:24:29 +0200
Subject: [PATCH 16/16] change link logo

---
 README.rst                                    |   2 +-
 .../tutorial_Cobra_linear_regression.ipynb    | 583 +++++++++---------
 .../tutorial_Cobra_logistic_regression.ipynb  | 542 ++++++++--------
 3 files changed, 557 insertions(+), 570 deletions(-)

diff --git a/README.rst b/README.rst
index 4c64341..a615245 100644
--- a/README.rst
+++ b/README.rst
@@ -1,5 +1,5 @@
 
-.. image:: https://github.com/PythonPredictions/cobra/blob/master/material/logo.png?raw=true
+.. image:: https://github.com/PythonPredictions/cobra/raw/master/material/logo.png
     :width: 350
 
 
diff --git a/tutorials/tutorial_Cobra_linear_regression.ipynb b/tutorials/tutorial_Cobra_linear_regression.ipynb
index 07cd57b..f9b3a14 100644
--- a/tutorials/tutorial_Cobra_linear_regression.ipynb
+++ b/tutorials/tutorial_Cobra_linear_regression.ipynb
@@ -117,9 +117,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.1.0'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import cobra\n",
     "cobra.__version__"
@@ -127,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -265,7 +276,7 @@
        "4          70    usa                ford torino  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -286,7 +297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -304,7 +315,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -322,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -366,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -383,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -400,7 +411,7 @@
        "Name: mpg, dtype: float64"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -421,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -447,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -475,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -484,7 +495,7 @@
        "['origin', 'name', 'cylinders']"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -503,16 +514,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['horsepower', 'model_year', 'weight', 'acceleration', 'displacement']"
+       "['acceleration', 'displacement', 'horsepower', 'weight', 'model_year']"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -533,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -562,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -581,13 +592,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "474fd3ddbe2841868966de83ef9f9d9f",
+       "model_id": "5ce2036a3c914b878e8dce0bee1d0224",
        "version_major": 2,
        "version_minor": 0
       },
@@ -601,7 +612,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4babe5bb228649ca8c60ae12fa198e5e",
+       "model_id": "af1c3b2f4b5e49848a4ed6053b22ec6f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -615,7 +626,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ae7436e6f3114d05b2450f265ddb6348",
+       "model_id": "9aeb1d82ef8c49d2882562455dbe176e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -629,7 +640,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d279851728e840979cd8748daf153426",
+       "model_id": "1d02e1b9f4de4771af56114f36145021",
        "version_major": 2,
        "version_minor": 0
       },
@@ -658,13 +669,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d283b89edf3542f08903f2b495ff2e2c",
+       "model_id": "2d97e40c618446c0b10e26d851ea4836",
        "version_major": 2,
        "version_minor": 0
       },
@@ -678,7 +689,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "860e523ca34e4a66b9191744bf1bf523",
+       "model_id": "bd8f55edef42402da3f8edf1b6f68a60",
        "version_major": 2,
        "version_minor": 0
       },
@@ -721,22 +732,22 @@
        "      <th>name</th>\n",
        "      <th>id</th>\n",
        "      <th>split</th>\n",
-       "      <th>horsepower_bin</th>\n",
-       "      <th>model_year_bin</th>\n",
-       "      <th>weight_bin</th>\n",
        "      <th>acceleration_bin</th>\n",
        "      <th>displacement_bin</th>\n",
+       "      <th>horsepower_bin</th>\n",
+       "      <th>weight_bin</th>\n",
+       "      <th>model_year_bin</th>\n",
        "      <th>origin_processed</th>\n",
        "      <th>name_processed</th>\n",
        "      <th>cylinders_processed</th>\n",
        "      <th>origin_enc</th>\n",
        "      <th>name_enc</th>\n",
        "      <th>cylinders_enc</th>\n",
-       "      <th>horsepower_enc</th>\n",
-       "      <th>model_year_enc</th>\n",
-       "      <th>weight_enc</th>\n",
        "      <th>acceleration_enc</th>\n",
        "      <th>displacement_enc</th>\n",
+       "      <th>horsepower_enc</th>\n",
+       "      <th>weight_enc</th>\n",
+       "      <th>model_year_enc</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -753,22 +764,22 @@
        "      <td>chevrolet chevelle malibu</td>\n",
        "      <td>1</td>\n",
        "      <td>train</td>\n",
-       "      <td>112.0 - 145.0</td>\n",
+       "      <td>8.0 - 12.5</td>\n",
+       "      <td>250.0 - 307.0</td>\n",
+       "      <td>110.0 - 145.0</td>\n",
+       "      <td>3426.0 - 3872.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>3441.0 - 3842.0</td>\n",
-       "      <td>8.5 - 12.1</td>\n",
-       "      <td>304.0 - 350.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>chevrolet chevelle malibu</td>\n",
        "      <td>8</td>\n",
-       "      <td>19.983117</td>\n",
+       "      <td>19.815436</td>\n",
        "      <td>17.500000</td>\n",
-       "      <td>14.964063</td>\n",
-       "      <td>17.348276</td>\n",
-       "      <td>17.727273</td>\n",
-       "      <td>17.433333</td>\n",
-       "      <td>15.352</td>\n",
-       "      <td>15.180</td>\n",
+       "      <td>14.770769</td>\n",
+       "      <td>15.078571</td>\n",
+       "      <td>16.660000</td>\n",
+       "      <td>18.188000</td>\n",
+       "      <td>17.225</td>\n",
+       "      <td>18.78125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -783,22 +794,22 @@
        "      <td>buick skylark 320</td>\n",
        "      <td>2</td>\n",
        "      <td>validation</td>\n",
-       "      <td>154.0 - 230.0</td>\n",
+       "      <td>8.0 - 12.5</td>\n",
+       "      <td>307.0 - 350.0</td>\n",
+       "      <td>145.0 - 165.0</td>\n",
+       "      <td>3426.0 - 3872.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>3441.0 - 3842.0</td>\n",
-       "      <td>8.5 - 12.1</td>\n",
-       "      <td>304.0 - 350.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>buick skylark 320</td>\n",
        "      <td>8</td>\n",
-       "      <td>19.983117</td>\n",
-       "      <td>23.353333</td>\n",
-       "      <td>14.964063</td>\n",
-       "      <td>13.837500</td>\n",
-       "      <td>17.727273</td>\n",
-       "      <td>17.433333</td>\n",
-       "      <td>15.352</td>\n",
-       "      <td>15.180</td>\n",
+       "      <td>19.815436</td>\n",
+       "      <td>23.570417</td>\n",
+       "      <td>14.770769</td>\n",
+       "      <td>15.078571</td>\n",
+       "      <td>14.673913</td>\n",
+       "      <td>14.243478</td>\n",
+       "      <td>17.225</td>\n",
+       "      <td>18.78125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -813,22 +824,22 @@
        "      <td>plymouth satellite</td>\n",
        "      <td>3</td>\n",
        "      <td>validation</td>\n",
-       "      <td>145.0 - 154.0</td>\n",
+       "      <td>8.0 - 12.5</td>\n",
+       "      <td>307.0 - 350.0</td>\n",
+       "      <td>145.0 - 165.0</td>\n",
+       "      <td>3426.0 - 3872.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>3157.0 - 3441.0</td>\n",
-       "      <td>8.5 - 12.1</td>\n",
-       "      <td>304.0 - 350.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>plymouth satellite</td>\n",
        "      <td>8</td>\n",
-       "      <td>19.983117</td>\n",
-       "      <td>23.353333</td>\n",
-       "      <td>14.964063</td>\n",
-       "      <td>14.694444</td>\n",
-       "      <td>17.727273</td>\n",
-       "      <td>18.883333</td>\n",
-       "      <td>15.352</td>\n",
-       "      <td>15.180</td>\n",
+       "      <td>19.815436</td>\n",
+       "      <td>23.570417</td>\n",
+       "      <td>14.770769</td>\n",
+       "      <td>15.078571</td>\n",
+       "      <td>14.673913</td>\n",
+       "      <td>14.243478</td>\n",
+       "      <td>17.225</td>\n",
+       "      <td>18.78125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -843,22 +854,22 @@
        "      <td>amc rebel sst</td>\n",
        "      <td>4</td>\n",
        "      <td>selection</td>\n",
-       "      <td>145.0 - 154.0</td>\n",
+       "      <td>8.0 - 12.5</td>\n",
+       "      <td>250.0 - 307.0</td>\n",
+       "      <td>145.0 - 165.0</td>\n",
+       "      <td>3426.0 - 3872.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>3157.0 - 3441.0</td>\n",
-       "      <td>8.5 - 12.1</td>\n",
-       "      <td>250.0 - 304.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>amc rebel sst</td>\n",
        "      <td>8</td>\n",
-       "      <td>19.983117</td>\n",
-       "      <td>23.353333</td>\n",
-       "      <td>14.964063</td>\n",
-       "      <td>14.694444</td>\n",
-       "      <td>17.727273</td>\n",
-       "      <td>18.883333</td>\n",
-       "      <td>15.352</td>\n",
-       "      <td>17.165</td>\n",
+       "      <td>19.815436</td>\n",
+       "      <td>23.570417</td>\n",
+       "      <td>14.770769</td>\n",
+       "      <td>15.078571</td>\n",
+       "      <td>16.660000</td>\n",
+       "      <td>14.243478</td>\n",
+       "      <td>17.225</td>\n",
+       "      <td>18.78125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -873,22 +884,22 @@
        "      <td>ford torino</td>\n",
        "      <td>5</td>\n",
        "      <td>train</td>\n",
-       "      <td>112.0 - 145.0</td>\n",
+       "      <td>8.0 - 12.5</td>\n",
+       "      <td>250.0 - 307.0</td>\n",
+       "      <td>110.0 - 145.0</td>\n",
+       "      <td>3426.0 - 3872.0</td>\n",
        "      <td>70.0 - 71.0</td>\n",
-       "      <td>3441.0 - 3842.0</td>\n",
-       "      <td>8.5 - 12.1</td>\n",
-       "      <td>250.0 - 304.0</td>\n",
        "      <td>usa</td>\n",
        "      <td>ford torino</td>\n",
        "      <td>8</td>\n",
-       "      <td>19.983117</td>\n",
+       "      <td>19.815436</td>\n",
        "      <td>17.000000</td>\n",
-       "      <td>14.964063</td>\n",
-       "      <td>17.348276</td>\n",
-       "      <td>17.727273</td>\n",
-       "      <td>17.433333</td>\n",
-       "      <td>15.352</td>\n",
-       "      <td>17.165</td>\n",
+       "      <td>14.770769</td>\n",
+       "      <td>15.078571</td>\n",
+       "      <td>16.660000</td>\n",
+       "      <td>18.188000</td>\n",
+       "      <td>17.225</td>\n",
+       "      <td>18.78125</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -909,36 +920,36 @@
        "3          70    usa              amc rebel sst   4   selection   \n",
        "4          70    usa                ford torino   5       train   \n",
        "\n",
-       "  horsepower_bin model_year_bin       weight_bin acceleration_bin  \\\n",
-       "0  112.0 - 145.0    70.0 - 71.0  3441.0 - 3842.0       8.5 - 12.1   \n",
-       "1  154.0 - 230.0    70.0 - 71.0  3441.0 - 3842.0       8.5 - 12.1   \n",
-       "2  145.0 - 154.0    70.0 - 71.0  3157.0 - 3441.0       8.5 - 12.1   \n",
-       "3  145.0 - 154.0    70.0 - 71.0  3157.0 - 3441.0       8.5 - 12.1   \n",
-       "4  112.0 - 145.0    70.0 - 71.0  3441.0 - 3842.0       8.5 - 12.1   \n",
+       "  acceleration_bin displacement_bin horsepower_bin       weight_bin  \\\n",
+       "0       8.0 - 12.5    250.0 - 307.0  110.0 - 145.0  3426.0 - 3872.0   \n",
+       "1       8.0 - 12.5    307.0 - 350.0  145.0 - 165.0  3426.0 - 3872.0   \n",
+       "2       8.0 - 12.5    307.0 - 350.0  145.0 - 165.0  3426.0 - 3872.0   \n",
+       "3       8.0 - 12.5    250.0 - 307.0  145.0 - 165.0  3426.0 - 3872.0   \n",
+       "4       8.0 - 12.5    250.0 - 307.0  110.0 - 145.0  3426.0 - 3872.0   \n",
        "\n",
-       "  displacement_bin origin_processed             name_processed  \\\n",
-       "0    304.0 - 350.0              usa  chevrolet chevelle malibu   \n",
-       "1    304.0 - 350.0              usa          buick skylark 320   \n",
-       "2    304.0 - 350.0              usa         plymouth satellite   \n",
-       "3    250.0 - 304.0              usa              amc rebel sst   \n",
-       "4    250.0 - 304.0              usa                ford torino   \n",
+       "  model_year_bin origin_processed             name_processed  \\\n",
+       "0    70.0 - 71.0              usa  chevrolet chevelle malibu   \n",
+       "1    70.0 - 71.0              usa          buick skylark 320   \n",
+       "2    70.0 - 71.0              usa         plymouth satellite   \n",
+       "3    70.0 - 71.0              usa              amc rebel sst   \n",
+       "4    70.0 - 71.0              usa                ford torino   \n",
        "\n",
-       "  cylinders_processed  origin_enc   name_enc  cylinders_enc  horsepower_enc  \\\n",
-       "0                   8   19.983117  17.500000      14.964063       17.348276   \n",
-       "1                   8   19.983117  23.353333      14.964063       13.837500   \n",
-       "2                   8   19.983117  23.353333      14.964063       14.694444   \n",
-       "3                   8   19.983117  23.353333      14.964063       14.694444   \n",
-       "4                   8   19.983117  17.000000      14.964063       17.348276   \n",
+       "  cylinders_processed  origin_enc   name_enc  cylinders_enc  acceleration_enc  \\\n",
+       "0                   8   19.815436  17.500000      14.770769         15.078571   \n",
+       "1                   8   19.815436  23.570417      14.770769         15.078571   \n",
+       "2                   8   19.815436  23.570417      14.770769         15.078571   \n",
+       "3                   8   19.815436  23.570417      14.770769         15.078571   \n",
+       "4                   8   19.815436  17.000000      14.770769         15.078571   \n",
        "\n",
-       "   model_year_enc  weight_enc  acceleration_enc  displacement_enc  \n",
-       "0       17.727273   17.433333            15.352            15.180  \n",
-       "1       17.727273   17.433333            15.352            15.180  \n",
-       "2       17.727273   18.883333            15.352            15.180  \n",
-       "3       17.727273   18.883333            15.352            17.165  \n",
-       "4       17.727273   17.433333            15.352            17.165  "
+       "   displacement_enc  horsepower_enc  weight_enc  model_year_enc  \n",
+       "0         16.660000       18.188000      17.225        18.78125  \n",
+       "1         14.673913       14.243478      17.225        18.78125  \n",
+       "2         14.673913       14.243478      17.225        18.78125  \n",
+       "3         16.660000       14.243478      17.225        18.78125  \n",
+       "4         16.660000       18.188000      17.225        18.78125  "
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -966,7 +977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1001,42 +1012,42 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>acceleration</td>\n",
-       "      <td>8.5 - 12.1</td>\n",
+       "      <td>8.0 - 12.5</td>\n",
        "      <td>0.113924</td>\n",
-       "      <td>23.778481</td>\n",
-       "      <td>17.644444</td>\n",
+       "      <td>24.746835</td>\n",
+       "      <td>18.522222</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>acceleration</td>\n",
-       "      <td>12.1 - 13.4</td>\n",
-       "      <td>0.088608</td>\n",
-       "      <td>23.778481</td>\n",
-       "      <td>19.057143</td>\n",
+       "      <td>12.5 - 13.4</td>\n",
+       "      <td>0.050633</td>\n",
+       "      <td>24.746835</td>\n",
+       "      <td>23.125000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>acceleration</td>\n",
        "      <td>13.4 - 14.2</td>\n",
        "      <td>0.139241</td>\n",
-       "      <td>23.778481</td>\n",
-       "      <td>21.063636</td>\n",
+       "      <td>24.746835</td>\n",
+       "      <td>21.972727</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>acceleration</td>\n",
        "      <td>14.2 - 14.9</td>\n",
-       "      <td>0.113924</td>\n",
-       "      <td>23.778481</td>\n",
-       "      <td>27.755556</td>\n",
+       "      <td>0.139241</td>\n",
+       "      <td>24.746835</td>\n",
+       "      <td>27.990909</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>acceleration</td>\n",
        "      <td>14.9 - 15.5</td>\n",
-       "      <td>0.151899</td>\n",
-       "      <td>23.778481</td>\n",
-       "      <td>22.641667</td>\n",
+       "      <td>0.177215</td>\n",
+       "      <td>24.746835</td>\n",
+       "      <td>23.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1049,66 +1060,66 @@
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>weight</td>\n",
-       "      <td>2882.0 - 3157.0</td>\n",
-       "      <td>0.063291</td>\n",
-       "      <td>23.778481</td>\n",
-       "      <td>26.780000</td>\n",
+       "      <td>2811.0 - 3110.0</td>\n",
+       "      <td>0.088608</td>\n",
+       "      <td>24.746835</td>\n",
+       "      <td>26.114286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>weight</td>\n",
-       "      <td>3157.0 - 3441.0</td>\n",
-       "      <td>0.101266</td>\n",
-       "      <td>23.778481</td>\n",
-       "      <td>21.075000</td>\n",
+       "      <td>3110.0 - 3426.0</td>\n",
+       "      <td>0.088608</td>\n",
+       "      <td>24.746835</td>\n",
+       "      <td>20.185714</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>weight</td>\n",
-       "      <td>3441.0 - 3842.0</td>\n",
-       "      <td>0.088608</td>\n",
-       "      <td>23.778481</td>\n",
-       "      <td>18.371429</td>\n",
+       "      <td>3426.0 - 3872.0</td>\n",
+       "      <td>0.113924</td>\n",
+       "      <td>24.746835</td>\n",
+       "      <td>17.400000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>weight</td>\n",
-       "      <td>3842.0 - 4280.0</td>\n",
-       "      <td>0.088608</td>\n",
-       "      <td>23.778481</td>\n",
-       "      <td>16.357143</td>\n",
+       "      <td>3872.0 - 4338.0</td>\n",
+       "      <td>0.101266</td>\n",
+       "      <td>24.746835</td>\n",
+       "      <td>15.562500</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>weight</td>\n",
-       "      <td>4280.0 - 5140.0</td>\n",
-       "      <td>0.101266</td>\n",
-       "      <td>23.778481</td>\n",
-       "      <td>12.875000</td>\n",
+       "      <td>4338.0 - 4997.0</td>\n",
+       "      <td>0.037975</td>\n",
+       "      <td>24.746835</td>\n",
+       "      <td>14.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>133 rows × 5 columns</p>\n",
+       "<p>135 rows × 5 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "        variable            label  pop_size  global_avg_target  avg_target\n",
-       "0   acceleration       8.5 - 12.1  0.113924          23.778481   17.644444\n",
-       "1   acceleration      12.1 - 13.4  0.088608          23.778481   19.057143\n",
-       "2   acceleration      13.4 - 14.2  0.139241          23.778481   21.063636\n",
-       "3   acceleration      14.2 - 14.9  0.113924          23.778481   27.755556\n",
-       "4   acceleration      14.9 - 15.5  0.151899          23.778481   22.641667\n",
+       "0   acceleration       8.0 - 12.5  0.113924          24.746835   18.522222\n",
+       "1   acceleration      12.5 - 13.4  0.050633          24.746835   23.125000\n",
+       "2   acceleration      13.4 - 14.2  0.139241          24.746835   21.972727\n",
+       "3   acceleration      14.2 - 14.9  0.139241          24.746835   27.990909\n",
+       "4   acceleration      14.9 - 15.5  0.177215          24.746835   23.000000\n",
        "..           ...              ...       ...                ...         ...\n",
-       "5         weight  2882.0 - 3157.0  0.063291          23.778481   26.780000\n",
-       "6         weight  3157.0 - 3441.0  0.101266          23.778481   21.075000\n",
-       "7         weight  3441.0 - 3842.0  0.088608          23.778481   18.371429\n",
-       "8         weight  3842.0 - 4280.0  0.088608          23.778481   16.357143\n",
-       "9         weight  4280.0 - 5140.0  0.101266          23.778481   12.875000\n",
+       "5         weight  2811.0 - 3110.0  0.088608          24.746835   26.114286\n",
+       "6         weight  3110.0 - 3426.0  0.088608          24.746835   20.185714\n",
+       "7         weight  3426.0 - 3872.0  0.113924          24.746835   17.400000\n",
+       "8         weight  3872.0 - 4338.0  0.101266          24.746835   15.562500\n",
+       "9         weight  4338.0 - 4997.0  0.037975          24.746835   14.000000\n",
        "\n",
-       "[133 rows x 5 columns]"
+       "[135 rows x 5 columns]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1131,14 +1142,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 2 Axes>"
       ]
@@ -1179,7 +1190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1195,7 +1206,7 @@
        " 'weight_bin']"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1217,7 +1228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1228,17 +1239,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A univariate selection on the preprocessed predictors can be conducted. The thresholds for retaining a feature can be changed by the user, for instance both at 10 for now.\n"
+    "A univariate selection on the preprocessed predictors is conducted. The thresholds for retaining a feature can be changed by the user, for instance both at 10 for now."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1254,8 +1265,8 @@
     "    predictors=preprocessed_predictors,\n",
     "    target_column=target_col,\n",
     "    model_type=\"regression\",\n",
-    "    preselect_rmse_threshold = 10, # max. RMSE for selection\n",
-    "    preselect_overtrain_threshold = 10) # difference between RMSE on train and selection set\n",
+    "    preselect_rmse_threshold=10, # max. RMSE for selection\n",
+    "    preselect_overtrain_threshold=10) # difference between RMSE on train and selection set\n",
     "\n",
     "plot_univariate_predictor_quality(df_rmse)\n",
     "\n",
@@ -1273,32 +1284,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                origin      name  cylinders  horsepower  model_year    weight  \\\n",
-      "origin        1.000000  0.571069   0.586881    0.503373    0.220294  0.635906   \n",
-      "name          0.571069  1.000000   0.806237    0.833102    0.647684  0.856403   \n",
-      "cylinders     0.586881  0.806237   1.000000    0.811333    0.436942  0.873356   \n",
-      "horsepower    0.503373  0.833102   0.811333    1.000000    0.476911  0.865079   \n",
-      "model_year    0.220294  0.647684   0.436942    0.476911    1.000000  0.393047   \n",
-      "weight        0.635906  0.856403   0.873356    0.865079    0.393047  1.000000   \n",
-      "acceleration  0.253132  0.441351   0.520317    0.619921    0.331274  0.410300   \n",
-      "displacement  0.704440  0.837183   0.919231    0.851480    0.424150  0.929419   \n",
+      "                origin      name  cylinders  acceleration  displacement  \\\n",
+      "origin        1.000000  0.605342   0.643212      0.279890      0.710813   \n",
+      "name          0.605342  1.000000   0.818558      0.497936      0.854124   \n",
+      "cylinders     0.643212  0.818558   1.000000      0.546624      0.915946   \n",
+      "acceleration  0.279890  0.497936   0.546624      1.000000      0.544408   \n",
+      "displacement  0.710813  0.854124   0.915946      0.544408      1.000000   \n",
+      "horsepower    0.536827  0.849631   0.804520      0.648119      0.880200   \n",
+      "weight        0.627180  0.855681   0.881700      0.478272      0.940863   \n",
+      "model_year    0.230794  0.643975   0.430153      0.371497      0.409101   \n",
       "\n",
-      "              acceleration  displacement  \n",
-      "origin            0.253132      0.704440  \n",
-      "name              0.441351      0.837183  \n",
-      "cylinders         0.520317      0.919231  \n",
-      "horsepower        0.619921      0.851480  \n",
-      "model_year        0.331274      0.424150  \n",
-      "weight            0.410300      0.929419  \n",
-      "acceleration      1.000000      0.495092  \n",
-      "displacement      0.495092      1.000000  \n"
+      "              horsepower    weight  model_year  \n",
+      "origin          0.536827  0.627180    0.230794  \n",
+      "name            0.849631  0.855681    0.643975  \n",
+      "cylinders       0.804520  0.881700    0.430153  \n",
+      "acceleration    0.648119  0.478272    0.371497  \n",
+      "displacement    0.880200  0.940863    0.409101  \n",
+      "horsepower      1.000000  0.870223    0.499351  \n",
+      "weight          0.870223  1.000000    0.373358  \n",
+      "model_year      0.499351  0.373358    1.000000  \n"
      ]
     }
    ],
@@ -1312,12 +1323,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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j99x32FMYGYftvNWwpzAyTv3CFcOewsi49rafDHsKI+PWm/5r2FPQBMOo8U2yGHg/8GzgRmBFklOq6qqBbkcBn6qqDyTZDjgNWDLVuAa+kiRJ6jWkL7ftAlxTVde2OZwA7A8MBr4FPLQtbwpM+6nJwFeSJEmjZkvghoH7NwK/PaHPm4EzkrwGeAiw93SDWuMrSZKkXslc3LI0ycqB29L7MbVDgOVVtRXwHOBfk0wZ25rxlSRJ0npVVcuAZVN0uQnYeuD+Vq1t0CuAfdt4FybZCNgC+H7foGZ8JUmS1CvJrN9mYAWwbZJtkjwAOBg4ZUKf7wDPanP8DWAj4OapBjXwlSRJ0kipqruBw4HTgW/QXb3hyiTHJHlu6/anwCuTXA4cDxxaVVNeZstSB0mSJPUa1l8srqrT6C5RNth29MDyVcBuazOmga8kSZJ6DelyZnPCUgdJkiQtCGZ8JUmS1GuMEr5mfCVJkrQwmPGVJElSr0WLxifla+ArSZKkXpY6SJIkSfOMGV9JkiT18nJmkiRJ0jxjxleSJEm9xijha+ArSZKkfpY6SJIkSfOMGV9JkiT1MuMrSZIkzTNmfCVJktRrjBK+8yPjm2R5kgPb8oeTbLeW298+NzOTJEnSfDHvMr5V9YdzOX66QpZU1b1zuR9JkqT5wBrfWZLkpUlWJbk8yclJrkuyYVv30MH7A9uck2Tntnx7kre17S9K8sjWvk2SC5NckeStE7Z/Q5IVbb9vaW1Lklyd5Djg68DWLcv89TbG69bH+ZAkSRo1yezfhmVogW+SJwFHAc+sqh2AVwDnAL/buhwMnFRVd00xzEOAi9r25wKvbO3/AHygqp4MfHdgn/sA2wK7ADsCOyXZs63eFvinqnoSsAWwZVVt38b4l3U8XEmSJA3ZMDO+zwQ+XVU/AKiqHwIfBl7e1r+c6QPOnwOntuWLgSVteTfg+Lb8rwP992m3S4FLgCfSBbwA366qi9rytcBjkvxjkn2BH0+28yRLk6xMsvKyL3xymqlKkiTNP0lm/TYsI/Xltqq6AFiSZC9gcVV9fZpN7qqqasv3cN+a5Zqkf4C3V9WO7fa4qvpIW/eTgXncCuxAl4F+FV1APtl8l1XVzlW18477vWCaqUqSJGmYhhn4ng0clOThAEke1tqPAz7BupUXXEBXKgHwooH204HDkmzc9rllkkdM3DjJFsCiqvoMXTnGU9ZhLpIkSfPWONX4Du2qDlV1ZZK3AV9Ocg9d+cGhwMeBt7KmVOH+OAL4RJI/B/5tYJ9nJPkN4MKWZr8deDFdtnjQlsC/JFn9weBN6zAXSZKkeWucruow1MuZVdVHgY9OaN4dOLGqfjTQ79CB5b0GljceWD4ROLEtXwfsOjDmUQP9/oHuy28TbT/Q53LM8kqSJI2VkbqOb5J/BPYDnjPsuUiSJGm8/nLbSAW+VfWaYc9BkiRJ42mkAl9JkiSNFmt8JUmStCCMUdw7WtfxlSRJkuaKGV9JkiT1GqdSBzO+kiRJWhDM+EqSJKnXGCV8zfhKkiRpYTDjK0mSpF7jVONr4CtJkqReYxT3WuogSZKkhcGMryRJknqNU6mDGV9JkiQtCGZ8JUmS1GucMr4GvpIkSeo1RnGvpQ6SJElaGMz4SpIkqdc4lTqY8ZUkSdKCYMZXkiRJvcYo4WvgK0mSpH6WOkiSJEnzjBnfWbL9Ix887CmMjON+duewpzASHrnnvsOewsj43rlfHPYURsaPDttp2FMYGYsXLx72FEbGyhtuH/YURsYmj3jEsKegCcYo4WvGV5IkSaMnyb5Jrk5yTZI3TrL+PUkua7dvJvnRdGOa8ZUkSVKvRUNI+SZZDLwfeDZwI7AiySlVddXqPlX1uoH+rwF+a7pxzfhKkiSpVzL7txnYBbimqq6tqp8DJwD7T9H/EOD46QY18JUkSdJ6lWRpkpUDt6UTumwJ3DBw/8bWNtlYjwa2Ac6ebr+WOkiSJKnXXFzOrKqWActmabiDgROr6p7pOprxlSRJ0qi5Cdh64P5WrW0yBzODMgcw4ytJkqQpLBrO5cxWANsm2YYu4D0YeOHETkmeCGwOXDiTQc34SpIkaaRU1d3A4cDpwDeAT1XVlUmOSfLcga4HAydUVc1kXDO+kiRJ6jWsP1lcVacBp01oO3rC/TevzZgGvpIkSerlX26TJEmS5hkzvpIkSeoVxifla8ZXkiRJC4IZX0mSJPUa0uXM5oSBryRJknoN66oOc8FSB0mSJC0IZnwlSZLUa4wSvmZ8JUmStDCY8ZUkSVKvRWOU8jXwlSRJUq8xinstdZAkSdLCYMZXkiRJvbyc2QRJliT5+myMJUmSJM2FoWd8k2xQVXcPex7TmS/zlCRJmk1jlPCd1RrfxUk+lOTKJGckeVCSHZNclGRVkpOTbA6Q5Jwk702yEjgiyUFJvp7k8iTntj6Lk7wzyYq2/R+19r2SnJvk80muTvLBJIvaukOSXNHGekdrOyjJu9vyEUmubcuPSXJBW94pyZeTXJzk9CSPmmyes3iuJEmStJ7NZsZ3W+CQqnplkk8Bzwf+DHhNVX05yTHAXwN/0vo/oKp2BkhyBfC/quqmJJu19a8AbquqpyZ5IHBBkjPaul2A7YBvA18E/iDJV4B3ADsBtwJnJDkAOK/NA2AP4JYkW7blc5NsCPwjsH9V3ZzkBcDbgMMmzlOSJGmhGafLmc1mxve6qrqsLV8MPBbYrKq+3No+Cuw50P+TA8sXAMuTvBJY3Nr2AV6a5DLgq8DD6YJrgK9V1bVVdQ9wPLA78FTgnKq6uZUkfBzYs6r+G9g4ySbA1sAn2jz2oAuKnwBsD5zZ9nUUsFXPPO8jydIkK5OsPPukj093fiRJkuadzMFtWGYz43vnwPI9wGbT9P/J6oWqelWS3wZ+F7g4yU505+U1VXX64EZJ9gJqwlgT70/0FeDlwNV0we5hwK7AnwK/DlxZVbtON8+JqmoZsAzgYxffON0cJEmSNERzeR3f24Bbk+zR7r8E+PJkHZM8tqq+WlVHAzfTZWZPB/64lSKQ5PFJHtI22SXJNq229wXA+cDXgKcn2SLJYuCQgf2dBxwJnAtcCjwDuLOqbqMLhn8lya5tPxsmedLsnQZJkqT5K8ms34Zlrq/q8DLgg0keDFxLl3WdzDuTbEuX5T0LuBxYBSwBLkl3hm4GDmj9VwDvAx4HfAk4uaruTfLGdj/A56vq31r/8+iC6XOr6p4kNwD/AVBVP09yIPD/kmxKd07eC1w5K2dAkiRJI2FWAt+qup6uTnb1/XcNrH7aJP33mnD/DyYbFviLdvuF9inhx1X1e5OMezxdze/E9v9koKSkqvaZsP4y7lt/POk8JUmSFppF4/PdtuFfx1eSJEmja5z+ctu8C3yr6hzgnCFPQ5IkSfPMvAt8JUmStP6MUcJ3Tq/qIEmSJI0MM76SJEnqZY2vJEmSFoRxuqqDpQ6SJElaEMz4SpIkqdc4lTqY8ZUkSdKCYMZXkiRJvcYn32vGV5IkSQuEGV9JkiT1WjRGNb4GvpIkSeo1RnGvpQ6SJElaGMz4SpIkqZeXM5MkSZLmGTO+kiRJ6jVGCV8DX0mSJPUbp6s6WOogSZKkBcGMryRJknqNUcLXjK8kSZIWBjO+kiRJ6jVOlzMz8J0lB+6w1bCnMDK+uPstw57CSDhsZx8Tq/3osJ2GPYWR8aJD3zbsKYyM80/+m2FPYWRs+uANhz2FkfGSHX5t2FPQBONUHjBOxyJJkiT1MvCVJElSrySzfpvhfvdNcnWSa5K8safP/05yVZIrk3xiujEtdZAkSdJISbIYeD/wbOBGYEWSU6rqqoE+2wJvAnarqluTPGK6cQ18JUmS1GvRcL7btgtwTVVdC5DkBGB/4KqBPq8E3l9VtwJU1fenG9RSB0mSJI2aLYEbBu7f2NoGPR54fJILklyUZN/pBjXjK0mSpF5zkfFNshRYOtC0rKqWreUwGwDbAnsBWwHnJnlyVf1oqg0kSZKkSc3FdXxbkDtVoHsTsPXA/a1a26Abga9W1V3AdUm+SRcIr+gb1FIHSZIkjZoVwLZJtknyAOBg4JQJfT5Ll+0lyRZ0pQ/XTjWoGV9JkiT1GsaX26rq7iSHA6cDi4Fjq+rKJMcAK6vqlLZunyRXAfcAb6iqKf+KloGvJEmSRk5VnQacNqHt6IHlAl7fbjNi4CtJkqRec1DiOzQGvpIkSeq1aIwiX7/cJkmSpAXBjK8kSZJ6jVOWdJyORZIkSeplxleSJEm9xqjE18BXkiRJ/fxymyRJkjTPmPGVJElSrzFK+JrxlSRJ0sJgxleSJEm9FpnxlSRJkuYXM76SJEnqNU5XdTDwlSRJUq8xintHr9QhyfVJtljXPpIkSdIgM74zlGRxVd0z7HlIkiStT365bYIkS5L8R5LlSb6Z5ONJ9k5yQZJvJdklycOSfDbJqiQXJfnNtu3Dk5yR5MokHwYyMO6Lk3wtyWVJ/jnJ4hnM5ZgkfzJw/21JjmjLb0iyos3hLQN9Ppvk4jaHpQPttyf5+ySXA7vOxrmSJEnScMxmqcPjgL8HnthuLwR2B44E/gJ4C3BpVf1mu39c2+6vgfOr6knAycCvAyT5DeAFwG5VtSNwD/CiGczjWOClbYxFwMHAx5LsA2wL7ALsCOyUZM+2zWFVtROwM/DaJA9v7Q8BvlpVO1TV+RN3lGRpkpVJVn7kQ8tmMDVJkqT5JXPwb1hms9Thuqq6AiDJlcBZVVVJrgCWAI8Gng9QVWe3TO9DgT2BP2jtn09yaxvvWcBOwIp0VdUPAr4/3SSq6voktyT5LeCRdMH2LS3w3Qe4tHXdmC4QPpcu2H1ea9+6td9CF2x/Zop9LQOWAdxxNzXd3CRJkuabcSp1mM3A986B5XsH7t/b9nPXWo4X4KNV9ab7MZcPA4cCv0qXAV493tur6p/vs5NkL2BvYNeq+mmSc4CN2uo7rOuVJEkaD+vzqg7n0UoVWrD5g6r6MV3G9YWtfT9g89b/LODAJI9o6x6W5NEz3NfJwL7AU4HTW9vpwGFJNm7jbdnG3hS4tQW9TwSeti4HKUmSNE4WZfZvw7I+r+rwZuDYJKuAnwIva+1vAY5v5RFfAb4DUFVXJTkKOKPV6t4FvBr49nQ7qqqfJ/kS8KPVGduqOqPVDV/YSiduB14MfBF4VZJvAFcDF83S8UqSJGmEzErgW1XXA9sP3D+0Z90Bk2x7C13t7WTjfhL45CTtS6aaTwuUnwYcNGG7fwD+YZJN9uvZ/8ZT7UeSJGncZYz+gsXYXcc3yXbAqcDJVfWtYc9HkiRpPvPLbSOgXXLsrElWPauqHrO+5yNJkqTRNm8D31YiseOw5yFJkjTOxqjSYb1e1UGSJEkamnmb8ZUkSdLcWzRGKV8zvpIkSVoQzPhKkiSpl1d1kCRJ0oIwRpUOljpIkiRpYTDjK0mSpF6LGJ+UrxlfSZIkLQhmfCVJktRrnGp8DXwlSZLUa5yu6mCpgyRJkhYEM76SJEnq5V9ukyRJkuYZM76SJEnqNUYJXwNfSZIk9bPUQZIkSZpnzPhKkiSp1xglfM34SpIkaWEw4ztL/vjEK4Y9hZFx+ln/MewpjIRTv+BjYrXFixcPewoj4/yT/2bYUxgZuz/vL4Y9hZHxpRPfOuwpjIxnv3b5sKcwMn72hdcNewrAeGVJx+lYJEmSpF5mfCVJktQrY1Tka8ZXkiRJvTIHtxntN9k3ydVJrknyxknWH5rk5iSXtdsfTjemGV9JkiSNlCSLgfcDzwZuBFYkOaWqrprQ9ZNVdfhMxzXwlSRJUq8h/QGLXYBrqupagCQnAPsDEwPftWKpgyRJkkbNlsANA/dvbG0TPT/JqiQnJtl6ukENfCVJktRrLmp8kyxNsnLgtvR+TO1zwJKq+k3gTOCj021gqYMkSZJ6zUWlQ1UtA5ZN0eUmYDCDu1VrGxzjloG7Hwb+brr9mvGVJEnSqFkBbJtkmyQPAA4GThnskORRA3efC3xjukHN+EqSJKnXMK7jW1V3JzkcOB1YDBxbVVcmOQZYWVWnAK9N8lzgbuCHwKHTjWvgK0mSpJFTVacBp01oO3pg+U3Am9ZmTANfSZIk9RqnulgDX0mSJPXyTxZLkiRJ84wZX0mSJPUan3yvGV9JkiQtEGZ8JUmS1MsaX0mSJGmeMeMrSZKkXuOUJTXwlSRJUi9LHSRJkqR5xoyvJEmSeo1PvnfMM75JPpxku2n6LE9y4CTtS5K8cO5mJ0mSpPVprAPfqvrDqrrqfm6+BDDwlSRJC1oy+7dhmReBb5I3JHltW35PkrPb8jOTfDzJPkkuTHJJkk8n2bitPyfJzm35FUm+meRrST6U5H0Du9gzyVeSXDuQ/f1bYI8klyV53Xo8XEmSpJGxiMz6bXjHMj+cB+zRlncGNk6yYWtbBRwF7F1VTwFWAq8f3DjJrwF/BTwN2A144oTxHwXsDvweXcAL8EbgvKrasareM+tHJEmSpPVqvgS+FwM7JXkocCdwIV0AvAfwM2A74IIklwEvAx49YftdgC9X1Q+r6i7g0xPWf7aq7m1lEY+c6aSSLE2yMsnKq8868f4clyRJ0kgbp1KHeXFVh6q6K8l1wKHAV+iyvM8AHgdcB5xZVYeswy7uHFie8Y+jqpYBywBefsIVtQ77lyRJ0hybLxlf6ModjgTObcuvAi4FLgJ2S/I4gCQPSfL4CduuAJ6eZPMkGwDPn8H+/gfYZLYmL0mSNB9lDv4Ny3wLfB8FXFhV3wPuoKvBvZkuE3x8klV0ZRD3qeGtqpuAvwG+BlwAXA/cNs3+VgH3JLncL7dJkqSFylKHIaiqs4ANB+4/fmD5bOCpk2yz18DdT1TVspbxPRn4bOtz6IRtNm7/3wU8c9YOQJIkSUM1bwLfWfDmJHsDGwFn0AJfSZIk9Rvm5cdm24IJfKvqyGHPQZIkScOzYAJfSZIkrb1h1uTONgNfSZIk9RqnwHc+XdVBkiRJut/M+EqSJKnXMK+7O9vM+EqSJGlBMOMrSZKkXovGJ+FrxleSJEkLgxlfSZIk9RqnGl8DX0mSJPXycmaSJEnSPGPGV5IkSb3GqdTBjK8kSZIWBDO+kiRJ6jVOlzMz8JUkSVIvSx0kSZKkecaMryRJknp5OTNJkiRpnjHjK0mSpF5jlPA18JUkSVK/RWNU62CpgyRJkhYEM76z5D37bzfsKYyMF+/4qGFPYSRce9tPhj2FkbHyhtuHPYWRsemDNxz2FEbGl05867CnMDKeceBRw57CyPjIR9447ClogvHJ95rxlSRJ0gJhxleSJEn9xijla8ZXkiRJC4IZX0mSJPXyTxZLkiRpQUhm/zaz/WbfJFcnuSZJ77cekzw/SSXZeboxDXwlSZI0UpIsBt4P7AdsBxyS5JcuoZVkE+AI4KszGdfAV5IkSb0yB7cZ2AW4pqquraqfAycA+0/S7/8C7wDumMmgBr6SJEkaNVsCNwzcv7G1/UKSpwBbV9XnZzqoga8kSZL6zUHKN8nSJCsHbkvXakrJIuDdwJ+uzXZe1UGSJEm95uKqDlW1DFg2RZebgK0H7m/V2lbbBNgeOCfdt+V+FTglyXOramXfoGZ8JUmSNGpWANsm2SbJA4CDgVNWr6yq26pqi6paUlVLgIuAKYNeMOMrSZKkKcz08mOzqaruTnI4cDqwGDi2qq5McgywsqpOmXqEyRn4SpIkaeRU1WnAaRPaju7pu9dMxjTwlSRJUq/x+bttBr6SJEmayhhFvn65TZIkSQuCGV9JkiT1movLmQ2LGV9JkiQtCGZ8JUmS1GsYlzObK2Z8JUmStCCY8ZUkSVKvMUr4GvhKkiRpCmMU+VrqIEmSpAVhJAPfJOck2XmWxjogyXYD949JsvdsjC1JkjTuMgf/hmUkA9+1lWTxFKsPAH4R+FbV0VX173M+KUmSJI2UdQp8k3w2ycVJrkyytLXtm+SSJJcnOau1bZzkX5JckWRVkue39n2SXNj6fzrJxpPsY9I+Sa5P8o4klwAHJXllkhVtv59J8uAkvwM8F3hnksuSPDbJ8iQHtjGeleTSNq9jkzxwYOy3tH1ekeSJ63KeJEmS5qtk9m/Dsq4Z38OqaidgZ+C1SR4JfAh4flXtABzU+v0VcFtVPbmqfhM4O8kWwFHA3lX1FGAl8PrBwWfQ55aqekpVnQCcVFVPbfv9BvCKqvoKcArwhqrasar+c2DsjYDlwAuq6sl0X/T744Gxf9D2+QHgyMkOPsnSJCuTrFz+kQ+t3ZmTJEmaBzIHt2FZ16s6vDbJ89ry1sBS4Nyqug6gqn7Y1u0NHLx6o6q6Ncnv0ZUgXJAu9H8AcOGE8Z82TZ9PDixvn+StwGbAxsDp08z9CcB1VfXNdv+jwKuB97b7J7X/Lwb+YLIBqmoZsAzgRz+7p6bZnyRJkobofge+SfaiC2h3raqfJjkHuAyYaVlAgDOr6pB16POTgeXlwAFVdXmSQ4G9ZjiPPne2/+/By75JkqSFysuZAbApcGsLep9Il53dCNgzyTYASR7W+p5Jl02ltW8OXATsluRxre0hSR4/YR8z6bPaJsB3k2wIvGig/X/auomuBpasHht4CfDlGRy3JEmS5qF1CXy/CGyQ5BvA39IFqTfTlTuclORy1pQivBXYPMnXW/szqupm4FDg+CSr6EoY7pMtnkmfAX8FfBW4APiPgfYTgDe0L7E9dmDsO4CXA59OcgVwL/DB+3MiJEmSxtU4Xc7sfv8Kv6ruBPbrWf2FCX1vB142yRhnA0+dpH2vGfRZMuH+B+i+iDax3wUMXM6MLpBeve4s4LemGruqVrLuZROSJEnz0jCvwjDbxuI6vpIkSdJ0/NKWJEmSeo1RwteMryRJkhYGM76SJEnqN0YpXzO+kiRJWhDM+EqSJKnXMC8/NtsMfCVJktTLy5lJkiRJ84wZX0mSJPUao4SvGV9JkiQtDGZ8JUmS1G+MUr4GvpIkSeo1Tld1sNRBkiRJC4IZX0mSJPXycmaSJEnSPGPGV5IkSb3GKOFr4CtJkqQpjFHka6mDJEmSFgQzvpIkSerl5cwkSZKkecaMryRJknp5OTNJkiRpnjHjO0sO+siKYU9hZFx5xX8Newoj4dabPA+rbfKIRwx7CiPjJTv82rCnMDKe/drlw57CyPjIR9447CmMjFe84m+HPYWR8cJL3zfsKQBjdVEHA19JkiRNYYwiX0sdJEmStCCY8ZUkSVIvL2cmSZIkzTNmfCVJktTLy5lJkiRpQcgc3Ga032TfJFcnuSbJL136JMmrklyR5LIk5yfZbroxDXwlSZI0UpIsBt4P7AdsBxwySWD7iap6clXtCPwd8O7pxrXUQZIkSb2GVOqwC3BNVV3bzSEnAPsDV63uUFU/Huj/EKCmG9TAV5IkSetVkqXA0oGmZVW1bOD+lsANA/dvBH57knFeDbweeADwzOn2a+ArSZKkKcx+yrcFucum7Tj9OO8H3p/khcBRwMum6m/gK0mSpF5DKnW4Cdh64P5Wra3PCcAHphvUL7dJkiRp1KwAtk2yTZIHAAcDpwx2SLLtwN3fBb413aBmfCVJktRrGAnfqro7yeHA6cBi4NiqujLJMcDKqjoFODzJ3sBdwK1MU+YABr6SJEkaQVV1GnDahLajB5aPWNsxDXwlSZLUy7/cJkmSJM0zZnwlSZLUK0Op8p0bBr6SJEnqNz5xr6UOkiRJWhjM+EqSJKnXGCV8zfhKkiRpYTDjK0mSpF7jdDmztQ58k7wZuB14KHBuVf37Wm6/F3BkVf3e2u57fUtyAPDNqrpq2HORJEkahnG6qsP9LnWoqqPXNuidhw4Athv2JCRJkrTuZhT4JvnLJN9Mcj7whNa2PMmBbflvk1yVZFWSdw2s/2CSlW3bX8rwJtklyYVJLk3ylSSrx16c5F1Jvt7GfE1r3ynJl5NcnOT0JI9q7eckeU/b1zeSPDXJSUm+leStA/t7cZKvJbksyT8nWdzab0/ytiSXJ7koySOT/A7wXOCdrf9j1+E8S5IkzU+Zg9uQTFvqkGQn4GBgx9b/EuDigfUPB54HPLGqKslmA5svAXYBHgt8KcnjJgz/H8AeVXV3kr2BvwGeDyxt2+7Y1j0syYbAPwL7V9XNSV4AvA04rI3186raOckRwL8BOwE/BP4zyXuARwAvAHarqruS/BPwIuA44CHARVX1l0n+DnhlVb01ySnAqVV14nTnSZIkSaNtJjW+ewAnV9VPAVowOOg24A7gI0lOBU4dWPepqroX+FaSa4EnTth2U+CjSbYFCtiwte8NfLCq7gaoqh8m2R7YHjgzXZX1YuC7A2OtntcVwJVV9d0232uBrYHd6YLhFW37BwHfb9v8fGDeFwPPnsF5IclSuiCdJx7852y12wEz2UySJGneGJ8K31m4qkPLyO4CPAs4EDgceObq1RO7T7j/f4EvVdXzkiwBzpliV6ELaHftWX9n+//egeXV9zdo23+0qt40ybZ3VdXqud3DDM9LVS0DlgE8+30XTTw2SZKkeW+cruowkxrfc4EDkjwoySbA7w+uTLIxsGlVnQa8DthhYPVBSRa1+tjHAFdPGHtT4Ka2fOhA+5nAHyXZoO3jYW3bX0mya2vbMMmTZjD/1c4CDkzyiNVjJnn0NNv8D7DJWuxDkiRJI2rawLeqLgE+CVwOfAFYMaHLJsCpSVYB5wOvH1j3HeBrbbtXVdUdE7b9O+DtSS7lvlnWD7dtVyW5HHhhVf2cLqP8jtZ2GfA7MznIdhxXAUcBZ7S5ngk8aprNTgDe0L5855fbJEnSgpM5+DcsM/2V/tvovkjWZ5ee9n+vqldNGOscWklDVV0IPH5g9VGt/W66APr1E7a9DNhzkvntNdn4k6z7JF0QP3H7jQeWTwRObMsX4OXMJEmSxoJ/uU2SJEm9xqnGd84C36o6dK7GliRJktbW/f7LbZIkSdJ8YqmDJEmSeo1TqYMZX0mSJC0IZnwlSZLUa5iXH5ttZnwlSZK0IJjxlSRJUq9xqvE18JUkSVKvMYp7LXWQJEnSwmDGV5IkSf3GKOVrxleSJEkLghlfSZIk9Rqny5kZ+EqSJKnXOF3VwVIHSZIkLQhmfCVJktRrjBK+ZnwlSZK0MJjxlSRJUr8xSvka+EqSJKnXOF3VwVIHSZIkLQhmfCVJktTLy5lJkiRJ80yqathz0CxJsrSqlg17HqPAc7GG52INz8Uanos1PBdreC7W8FyMJzO+42XpsCcwQjwXa3gu1vBcrOG5WMNzsYbnYg3PxRgy8JUkSdKCYOArSZKkBcHAd7xYi7SG52INz8Uanos1PBdreC7W8Fys4bkYQ365TZIkSQuCGV9JkiQtCAa+81iS05JsNk2fY5LsvZ6mpPUkyfIkB7blDyfZbi23v31uZrZ2kixJ8vVhz0MLz0yeN4PPswntS5K8cO5mN7eSnJNk51ka64DB8zhX7zlJ3pzkyPs7fpK9kpw62/OaCxPPqWaXge88lM6iqnpOVf1oqr5VdXRV/ft6mpqGoKr+sKqumqvxVz/e5mr8+yvJvPjLk8OYZ5Lrk2yxrn3G2To+b5YA8zbwXVtJFk+x+gDgF0HaXL/nLJD3tAMYOKeaXSP3ZqZOktcn+Xq7/UnLMFyd5Djg68DWg29cSf6qrT8/yfFJjmztg5nB65O8JcklSa5I8sThHeHMtWP/RpIPJbkyyRlJHpTklUlWJLk8yWeSPLj1X57kA0kuSnJt+6R/bBtj+cC4+yS5sJ2PTyfZeGgHuWZOL02yqh3TyUmuS7JhW/fQwfsD2/wie5Pk9iRva9tflOSRrX2bdqxXJHnrhO3f0M7jqiRvaW2TPd6Wt8fjFUleN4uHvXiSn+2Obf6r2nnYfOBY35tkJXBEkoPanC5Pcm7rszjJOweO6Y9a+15Jzk3y+XZsH1wd0Cc5pB3X15O8o7UdlOTdbfmIJNe25cckuaAt75Tky0kuTnJ6kkdNNs9ZPFfzWqYOoO7vmG9I8tq2/J4kZ7flZyb5eN/zfMLz5hVJvpnka+2x+L6BXeyZ5CvttWR19vdvgT2SXDbLz4WJx/bZ9ti6MsnS1rZvO5bLk5zV2jZO8i/tMbwqyfNb+7SvcVOcn+uTvCPJJcBBmeT1NsnvAM8F3tnOxWNz3/ecZyW5tM3r2CQPHBj7LZnmvSjJX7afy/nAE1rb4Ph/m+SqdszvGlj/wSQr27a/N8m4u7RjvrT9bFePvTjJu9rrwKokr2ntUz3P39P29Y0kT01yUpJvZeB1NsmL22PrsiT/vPp5kEleryc7p2v3qNG0qsrbiN2AnYArgIcAGwNXAr8F3As8baDf9cAWwFOBy4CNgE2AbwFHtj7LgQMH+r+mLf8f4MPDPtYZno8lwN3Aju3+p4AXAw8f6PPWgWNbDpwABNgf+DHwZLoPehcDO7bzdi7wkLbNnwNHD/k4nwR8E9ii3X8Y8C/AAe3+UuDvJ/m5ngPs3JYL+P22/HfAUW35FOClbfnVwO1teR+6by6nnZ9TgT3bOf/F4609Js8cmOtmc/yzXQU8vbUdA7x34Fj/aWD7K4AtB+fUztPq434gsBLYBtgLuAN4DLAYOBM4EPg14DvArwAbAGfTZVx+FVjRxjkRWAFsCbwMeDuwIfAV4FdanxcAx042z7U4F//RfrbfBD4O7A1cQPec3qU9Jj7bzs9FwG+2bR8OnEH3WvFh4NuseRy9GPga3WvEPwOLW/v1q/tMMpdjgD8ZuP824Ii2/IZ2LlYBbxno81m659eVwNKB9tuBvwcuB3afg+fN04BPt+Xz2rFuCPw13fN60ud5+xnt3H7+17dzu2Eb430Dz7NP0z03tgOuae17Aaeuh9eEh7X/H0T3AfSRwA3ANhPWv4P2HGn3N2eK17iBY5+qz/XAnw2MOdXr7YED65bTPa82anN9fGs/bvVjihm8F7HmffDBwEOBa4AjB8Z/OHA1a76kv9nA/r/YfmbbAje2ufziZ9bG26At7w18pi3/Md1zffW61Y+JqZ7n72jLRwD/BTyK7nXnxjbH3wA+B2zY+v0Ta16L+16v73NOvc3ubV78qnAB2h04uap+ApDkJGAP4NtVddEk/XcD/q2q7gDuSPK5KcY+qf1/MfAHszjnuXZdVV3Wli+mCxK2b5+qN6P7gHD6QP/PVVUluQL4XlVdAZDkyrbtVnRvZBckAXgAcOGcH8XUnkn3Bv4DgKr6YZIPA39GF1S8HHjlNGP8nC54he48Pbst7wY8vy3/K90bJXSB7z7Ape3+xnRvFt/hvo+3a4HHJPlH4PN0QdZsmfizfSzdm9iXW9tH6YKP1T45sHwBsDzJp1jz2N4H+M2B7NymdMf0c+BrVbU6c3s83XPtLuCcqrq5tX8c2LOqPtsyaZsAWwOfoPtQsEfb1xOA7YEz22NoMfDdnnnO1OOAg4DD6ILLF7Y5Phf4C7pA4tKqOiDJM+mCiR3pgrzzq+qYJL8LvKIdy2/QvVHvVlV3Jfkn4EVtu6kc247xvemy4gcDuyTZh+5c7kL3YemUJHtW1bnAYe0x+yBgRZLPVNUtdB/gv1pVf3o/zsdMXAzslOShwJ3AJXRB3R50H/ime57vAny5qn4IkOTTwOMH1n+2qu4Frkr7Dcp69Nokz2vLW9N9qDu3qq6D7jWirdub7mdEa7+1ZTqnO/anTdNn8DE81evtZJ5A99z+Zrv/UboP3e9t96d7L9qD7n3wpwBJTpmw/ja6D7IfSVe7O1i/+6n2M/tWut/UTMwobwp8NMm2dMHn6t+i7Q18sKruhl+8Bm/P1M/z1fO6Ariyqr7b5nst3c9sd7ogfkXb/kHA99s2fa/XmkMGvvPLT2ZhjDvb//cwv37+dw4s30P34rGcLht6eZJD6T7RT+x/74Rt76U77nvoMpiHzNF8Z0VVXZCu7GAvukzddF8Eu6uqVl+jcOLPeLJrFwZ4e1X9830akyUMPN7aG+kOwP8CXgX8b7rgbDZM/NluNk3/wXm9KslvA78LXJxkJ7pjek1V3eeNuZ3Diedguus5foXuA8fVdJnAw4BdgT8Ffp3ujW7X6ea5Fq6b8CHtrIEPcEuAR9M+wFTV2Uke3gK+PWnBQ1V9Psmtbbxn0f+m26uqrk9yS5LfossyXlpVt7TAd7IPSufyy0HatsAtdD/Tz9yPczEjLaC/DjiU7ue1CngG3YeI61j35/ng4zPrMM5aaY/XvYFdq+qnSc6hy9rPtEQtTH/s0/UZfAwvp//19v5Yp/eiqro7yS50j/EDgcPpkgcw/fP8/wJfqqrntde6c6bYVZj6eT7de02Aj1bVmybZdqrXa80Ra3xH03nAAelqqB4CPK+19bkA+P0kG6Wrz/qlmqYxtQnw3XQ1ry9ay20vAnZL8jiAJA9J8vhptplrZ9PV0j28zelhrf04umzjv6zD2BewJiM0eK5OBw7Lmrq+LZM8YuLG6WrJF1XVZ4CjgKesw1ymcxtwa5I92v2XAF+erGOSx1bVV6vqaOBmuoDrdOCPs6Y2+vHteQRd1nKblsV8AXA+3a/Gn55ki1Z7d8jA/s6j+/XquXTB3jOAO6vqNrpg+FeS7Nr2s2GSJ63jsU984xx8U70/b4qr33R3bLcnVNWbZ7jth+mCyZfTZYBXj/f2gfEeV1UfmRCk7UB3rjZq29xRVffcj7mvjcGf03l0H84uZWbP8xV0P//N030R8flM73/oXn/m0qbArS3ofSJddnYjuprjbeA+rxFn0mVTae2bM7NjX5vXwb7X275zcTWwZPXYTPE87nEu3fvgg9pvXX5/cGV7zdq0qk4DXgfsMLD6oCSLWn3sY9pcBm0K3NSWDx1oPxP4o/Y4WH1+1/V5fhZw4OrX1SQPS/LoabZZH4+vBcvAdwRV1SV0n66/BnyV7g3o1in6r6D7dcsq4At0v3K5bc4nOnx/RXd+LqCrjZyx9mvtQ4Hjk6yi+/XeUL/sV1VX0tVSfjnJ5cC726qP09XsHb8Owx8BvLplDrcc2OcZdEH1hW3diUz+grslcE6Sy4CPAZNlL2bTy+i+3LGK7lf5x/T0e2fal9Losn2X0z1frgIuae3/zJqgcQXwPuAbdNnAk9uvJt8IfKltf3FV/Vvrfx5dMH1uC95uoAuWqaqf02Wa3tF+XpcBvzMrR9/vPFrQ0YLNH1TVj+mChBe29v3oHi9w/950VzsZ2JfuOwSrs+d9H5QmC9LWp/PoaisvrKrv0f0K/LyZPM+r6ibgb+heby+gqz+d7vVzFXBPui8lzdWX274IbJDkG3RfpruI7sPdUuCk9phbXYrwVmDztC96As+Y4bGvzetg3+vtCcAb0n1R7BdfxGqldy8HPt1eW+4FPjjTg2/vg5+ke05+ge65O2gT4NQ27/OB1w+s+w7dz/MLwKvaXAb9HfD2JJdy3w+UH27brmrn8YXr+jyv7sohRwFntLmeSfdYncqk51Szw7/cNiaSbFxVt6e7ssG5dF8uuWTY89K6S1erun9VvWTYc5nPWqB4ZFWN5G9E2q9cT62q7dv95e3+iavX0ZU0HEuXxfop3fN8VfstwfF0H1C+QleOsFNV/SDJC+g+qCyiq2d+dVVdlOR6ui9F/mCKOX0Q+FFVvXGg7QjgD9vd2+m+PHcjXR36EroM2WbAm6vqnCS3V9XQr5gylYHXzw3oAv5jq+rkYc9La2/weTPsuWg0GfiOiSSfoPuSwkZ0v9p8+5CnpFmQ7stk+wHPGfiSiO6HUQ98R00rB7kEOKiqvjXs+cyldJfC2pvu9fMMuitY+OY4Dxn4ajoGvpKk+0j3V6NOpSsFmaurMUjSemfgK0kLVCuROGuSVc9qlyKTpLFi4CtJkqQFwas6SJIkaUEw8JUkSdKCYOArSZKkBcHAV5IkSQuCga8kSZIWhP8PXwXwcoT4kuIAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 2 Axes>"
       ]
@@ -1342,23 +1353,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['horsepower_enc',\n",
-       " 'displacement_enc',\n",
-       " 'weight_enc',\n",
+       "['weight_enc',\n",
+       " 'horsepower_enc',\n",
        " 'cylinders_enc',\n",
-       " 'origin_enc',\n",
+       " 'displacement_enc',\n",
        " 'model_year_enc',\n",
+       " 'origin_enc',\n",
        " 'acceleration_enc',\n",
        " 'name_enc']"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1384,13 +1395,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "192a9f0ed75f429ea7ae516b75b2504e",
+       "model_id": "022af5cc021143dea492e60f0108227f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1435,72 +1446,72 @@
        "      <th>0</th>\n",
        "      <td>[name_enc]</td>\n",
        "      <td>name_enc</td>\n",
-       "      <td>0.965551</td>\n",
-       "      <td>7.050967</td>\n",
-       "      <td>6.327197</td>\n",
+       "      <td>1.210750</td>\n",
+       "      <td>7.172305</td>\n",
+       "      <td>6.156961</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>[name_enc, model_year_enc]</td>\n",
        "      <td>model_year_enc</td>\n",
-       "      <td>0.950131</td>\n",
-       "      <td>6.932540</td>\n",
-       "      <td>6.223183</td>\n",
+       "      <td>1.190524</td>\n",
+       "      <td>7.002583</td>\n",
+       "      <td>6.033176</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>[model_year_enc, name_enc, weight_enc]</td>\n",
+       "      <td>[name_enc, model_year_enc, weight_enc]</td>\n",
        "      <td>weight_enc</td>\n",
-       "      <td>0.939471</td>\n",
-       "      <td>6.685434</td>\n",
-       "      <td>5.954216</td>\n",
+       "      <td>1.153409</td>\n",
+       "      <td>6.520763</td>\n",
+       "      <td>5.505019</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>[weight_enc, model_year_enc, name_enc, horsepo...</td>\n",
+       "      <td>[weight_enc, name_enc, model_year_enc, horsepo...</td>\n",
        "      <td>horsepower_enc</td>\n",
-       "      <td>0.938280</td>\n",
-       "      <td>6.656585</td>\n",
-       "      <td>5.929000</td>\n",
+       "      <td>1.148468</td>\n",
+       "      <td>6.479577</td>\n",
+       "      <td>5.472030</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>[weight_enc, horsepower_enc, name_enc, model_y...</td>\n",
-       "      <td>cylinders_enc</td>\n",
-       "      <td>0.937998</td>\n",
-       "      <td>6.652905</td>\n",
-       "      <td>5.923981</td>\n",
+       "      <td>[horsepower_enc, weight_enc, name_enc, model_y...</td>\n",
+       "      <td>acceleration_enc</td>\n",
+       "      <td>1.137532</td>\n",
+       "      <td>6.459645</td>\n",
+       "      <td>5.485417</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>[cylinders_enc, horsepower_enc, weight_enc, na...</td>\n",
+       "      <td>[horsepower_enc, acceleration_enc, weight_enc,...</td>\n",
        "      <td>displacement_enc</td>\n",
-       "      <td>0.937729</td>\n",
-       "      <td>6.654564</td>\n",
-       "      <td>5.926207</td>\n",
+       "      <td>1.135331</td>\n",
+       "      <td>6.434603</td>\n",
+       "      <td>5.460676</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>[cylinders_enc, displacement_enc, horsepower_e...</td>\n",
-       "      <td>origin_enc</td>\n",
-       "      <td>0.937513</td>\n",
-       "      <td>6.647764</td>\n",
-       "      <td>5.917903</td>\n",
+       "      <td>[displacement_enc, horsepower_enc, acceleratio...</td>\n",
+       "      <td>cylinders_enc</td>\n",
+       "      <td>1.134848</td>\n",
+       "      <td>6.442906</td>\n",
+       "      <td>5.463855</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>[cylinders_enc, displacement_enc, horsepower_e...</td>\n",
-       "      <td>acceleration_enc</td>\n",
-       "      <td>0.937498</td>\n",
-       "      <td>6.646871</td>\n",
-       "      <td>5.917192</td>\n",
+       "      <td>[displacement_enc, horsepower_enc, cylinders_e...</td>\n",
+       "      <td>origin_enc</td>\n",
+       "      <td>1.134675</td>\n",
+       "      <td>6.449028</td>\n",
+       "      <td>5.471073</td>\n",
        "      <td>regression</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1511,22 +1522,22 @@
        "                                          predictors last_added_predictor  \\\n",
        "0                                         [name_enc]             name_enc   \n",
        "1                         [name_enc, model_year_enc]       model_year_enc   \n",
-       "2             [model_year_enc, name_enc, weight_enc]           weight_enc   \n",
-       "3  [weight_enc, model_year_enc, name_enc, horsepo...       horsepower_enc   \n",
-       "4  [weight_enc, horsepower_enc, name_enc, model_y...        cylinders_enc   \n",
-       "5  [cylinders_enc, horsepower_enc, weight_enc, na...     displacement_enc   \n",
-       "6  [cylinders_enc, displacement_enc, horsepower_e...           origin_enc   \n",
-       "7  [cylinders_enc, displacement_enc, horsepower_e...     acceleration_enc   \n",
+       "2             [name_enc, model_year_enc, weight_enc]           weight_enc   \n",
+       "3  [weight_enc, name_enc, model_year_enc, horsepo...       horsepower_enc   \n",
+       "4  [horsepower_enc, weight_enc, name_enc, model_y...     acceleration_enc   \n",
+       "5  [horsepower_enc, acceleration_enc, weight_enc,...     displacement_enc   \n",
+       "6  [displacement_enc, horsepower_enc, acceleratio...        cylinders_enc   \n",
+       "7  [displacement_enc, horsepower_enc, cylinders_e...           origin_enc   \n",
        "\n",
        "   train_performance  selection_performance  validation_performance  \\\n",
-       "0           0.965551               7.050967                6.327197   \n",
-       "1           0.950131               6.932540                6.223183   \n",
-       "2           0.939471               6.685434                5.954216   \n",
-       "3           0.938280               6.656585                5.929000   \n",
-       "4           0.937998               6.652905                5.923981   \n",
-       "5           0.937729               6.654564                5.926207   \n",
-       "6           0.937513               6.647764                5.917903   \n",
-       "7           0.937498               6.646871                5.917192   \n",
+       "0           1.210750               7.172305                6.156961   \n",
+       "1           1.190524               7.002583                6.033176   \n",
+       "2           1.153409               6.520763                5.505019   \n",
+       "3           1.148468               6.479577                5.472030   \n",
+       "4           1.137532               6.459645                5.485417   \n",
+       "5           1.135331               6.434603                5.460676   \n",
+       "6           1.134848               6.442906                5.463855   \n",
+       "7           1.134675               6.449028                5.471073   \n",
        "\n",
        "   model_type  \n",
        "0  regression  \n",
@@ -1539,7 +1550,7 @@
        "7  regression  "
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1566,17 +1577,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As can be seen, we have completed 7 steps till no further improvement can be observed"
+    "As can be seen, model improvement gradually flattens when more variables are added."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1594,21 +1605,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Based on the performance curves (RMSE per model with a particular number of predictors in case of linear regression), a final model can then be chosen and the variables importance can be plotted:\n"
+    "Based on the performance curves (RMSE per model with a particular number of predictors in case of linear regression), a final model can then be chosen and the variable importance can be plotted."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['model_year_enc', 'name_enc', 'weight_enc']"
+       "['name_enc', 'model_year_enc', 'weight_enc']"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1623,12 +1634,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1659,26 +1670,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'meta': 'linear-regression',\n",
-       " 'predictors': ['model_year_enc', 'name_enc', 'weight_enc'],\n",
-       " '_eval_metrics_by_split': {'train': 0.939470933609984,\n",
-       "  'selection': 6.685434152886161,\n",
-       "  'validation': 5.954216285392323},\n",
+       " 'predictors': ['name_enc', 'model_year_enc', 'weight_enc'],\n",
+       " '_eval_metrics_by_split': {'train': 1.1534086552825373,\n",
+       "  'selection': 6.5207634572977815,\n",
+       "  'validation': 5.505019145326779},\n",
        " 'params': {'copy_X': True,\n",
        "  'fit_intercept': True,\n",
        "  'n_jobs': None,\n",
        "  'normalize': False},\n",
-       " 'coef_': [0.05772933322408178, 0.9432043504937953, 0.04340259842095151],\n",
-       " 'intercept_': -1.0353999755487742}"
+       " 'coef_': [0.8928990886890319, 0.0906241395141556, 0.09211683994952749],\n",
+       " 'intercept_': -1.7828679230545568}"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1690,7 +1701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1730,7 +1741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1741,7 +1752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1751,20 +1762,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "R2       0.179502\n",
-       "MAE      5.191685\n",
-       "MSE     44.695030\n",
-       "RMSE     6.685434\n",
+       "R2       0.258294\n",
+       "MAE      5.308761\n",
+       "MSE     42.520356\n",
+       "RMSE     6.520763\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1775,12 +1786,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1795,18 +1806,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "quantiles    [-2.241402727604945, -1.9599639845400545, -1.7...\n",
-       "residuals    [-2.0120779188541276, -1.9153531826632304, -1....\n",
+       "residuals    [-1.9577706611755745, -1.727671198202121, -1.7...\n",
        "dtype: object"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1817,12 +1828,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
diff --git a/tutorials/tutorial_Cobra_logistic_regression.ipynb b/tutorials/tutorial_Cobra_logistic_regression.ipynb
index 4f556d0..b0fc48e 100644
--- a/tutorials/tutorial_Cobra_logistic_regression.ipynb
+++ b/tutorials/tutorial_Cobra_logistic_regression.ipynb
@@ -117,9 +117,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.1.0'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import cobra\n",
     "cobra.__version__"
@@ -127,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -149,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -301,7 +312,7 @@
        "4    man        True  NaN  Southampton    no   True  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -322,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -346,7 +357,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -364,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -408,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -441,7 +452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -459,7 +470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -485,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -516,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -535,7 +546,7 @@
        " 'alone']"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -554,7 +565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -563,7 +574,7 @@
        "['age', 'fare']"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -584,7 +595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -613,7 +624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -632,13 +643,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3aa357534b1e4d6da3177138bae7272f",
+       "model_id": "46cc692e5282480993e906d8ad90135e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -652,7 +663,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3f06e97c0120468a98f24db7f6a279d8",
+       "model_id": "9f7a241f920842789e9ba45c368ed269",
        "version_major": 2,
        "version_minor": 0
       },
@@ -666,7 +677,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e1311cecbf4047f98d1e5641337b7684",
+       "model_id": "31e699dbfba8468fa9f44ca57b063854",
        "version_major": 2,
        "version_minor": 0
       },
@@ -680,7 +691,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9a0bacdd2ce643419a6009b44bd0504e",
+       "model_id": "f536123c13d84aaf90a6f1b52419c21a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -708,13 +719,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f8760d520ac54ca99f397d5a9a60cad9",
+       "model_id": "6e959071b70346949904368da93acaaa",
        "version_major": 2,
        "version_minor": 0
       },
@@ -728,7 +739,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "77fc6fbf60d84ef78522457762c86cb4",
+       "model_id": "989caf81c993416d8a4fd5ebeef32e9c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -826,17 +837,17 @@
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.213068</td>\n",
-       "      <td>0.34023</td>\n",
-       "      <td>0.235690</td>\n",
-       "      <td>0.292162</td>\n",
-       "      <td>0.34023</td>\n",
-       "      <td>0.235690</td>\n",
-       "      <td>0.539062</td>\n",
-       "      <td>0.350617</td>\n",
-       "      <td>0.513761</td>\n",
-       "      <td>0.297297</td>\n",
-       "      <td>0.192308</td>\n",
+       "      <td>0.196532</td>\n",
+       "      <td>0.343820</td>\n",
+       "      <td>0.251656</td>\n",
+       "      <td>0.307876</td>\n",
+       "      <td>0.343820</td>\n",
+       "      <td>0.251656</td>\n",
+       "      <td>0.551181</td>\n",
+       "      <td>0.345679</td>\n",
+       "      <td>0.521951</td>\n",
+       "      <td>0.282051</td>\n",
+       "      <td>0.137255</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -853,9 +864,9 @@
        "      <td>Cherbourg</td>\n",
        "      <td>False</td>\n",
        "      <td>2</td>\n",
-       "      <td>train</td>\n",
-       "      <td>36.0 - 41.0</td>\n",
-       "      <td>39.0 - 73.5</td>\n",
+       "      <td>validation</td>\n",
+       "      <td>35.0 - 40.0</td>\n",
+       "      <td>39.1 - 77.1</td>\n",
        "      <td>female</td>\n",
        "      <td>C</td>\n",
        "      <td>First</td>\n",
@@ -865,17 +876,17 @@
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.715847</td>\n",
-       "      <td>0.58000</td>\n",
-       "      <td>0.658537</td>\n",
-       "      <td>0.618182</td>\n",
-       "      <td>0.58000</td>\n",
-       "      <td>0.658537</td>\n",
-       "      <td>0.539062</td>\n",
-       "      <td>0.350617</td>\n",
-       "      <td>0.513761</td>\n",
-       "      <td>0.393939</td>\n",
-       "      <td>0.545455</td>\n",
+       "      <td>0.740741</td>\n",
+       "      <td>0.606742</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.651685</td>\n",
+       "      <td>0.606742</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.551181</td>\n",
+       "      <td>0.345679</td>\n",
+       "      <td>0.521951</td>\n",
+       "      <td>0.418605</td>\n",
+       "      <td>0.509434</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -904,17 +915,17 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>True</td>\n",
-       "      <td>0.715847</td>\n",
-       "      <td>0.34023</td>\n",
-       "      <td>0.235690</td>\n",
-       "      <td>0.292162</td>\n",
-       "      <td>0.34023</td>\n",
-       "      <td>0.235690</td>\n",
-       "      <td>0.348189</td>\n",
-       "      <td>0.350617</td>\n",
-       "      <td>0.296530</td>\n",
-       "      <td>0.411765</td>\n",
-       "      <td>0.230769</td>\n",
+       "      <td>0.740741</td>\n",
+       "      <td>0.343820</td>\n",
+       "      <td>0.251656</td>\n",
+       "      <td>0.307876</td>\n",
+       "      <td>0.343820</td>\n",
+       "      <td>0.251656</td>\n",
+       "      <td>0.349727</td>\n",
+       "      <td>0.345679</td>\n",
+       "      <td>0.306061</td>\n",
+       "      <td>0.404762</td>\n",
+       "      <td>0.243243</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -931,9 +942,9 @@
        "      <td>Southampton</td>\n",
        "      <td>False</td>\n",
        "      <td>4</td>\n",
-       "      <td>validation</td>\n",
-       "      <td>32.0 - 36.0</td>\n",
-       "      <td>39.0 - 73.5</td>\n",
+       "      <td>train</td>\n",
+       "      <td>31.0 - 35.0</td>\n",
+       "      <td>39.1 - 77.1</td>\n",
        "      <td>female</td>\n",
        "      <td>Other</td>\n",
        "      <td>First</td>\n",
@@ -943,17 +954,17 @@
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>False</td>\n",
-       "      <td>0.715847</td>\n",
-       "      <td>0.34023</td>\n",
-       "      <td>0.658537</td>\n",
-       "      <td>0.618182</td>\n",
-       "      <td>0.34023</td>\n",
-       "      <td>0.658537</td>\n",
-       "      <td>0.539062</td>\n",
-       "      <td>0.350617</td>\n",
-       "      <td>0.513761</td>\n",
-       "      <td>0.475000</td>\n",
-       "      <td>0.545455</td>\n",
+       "      <td>0.740741</td>\n",
+       "      <td>0.343820</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.651685</td>\n",
+       "      <td>0.343820</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.551181</td>\n",
+       "      <td>0.345679</td>\n",
+       "      <td>0.521951</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.509434</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -971,7 +982,7 @@
        "      <td>True</td>\n",
        "      <td>5</td>\n",
        "      <td>train</td>\n",
-       "      <td>32.0 - 36.0</td>\n",
+       "      <td>31.0 - 35.0</td>\n",
        "      <td>7.9 - 8.1</td>\n",
        "      <td>male</td>\n",
        "      <td>Other</td>\n",
@@ -982,17 +993,17 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>True</td>\n",
-       "      <td>0.213068</td>\n",
-       "      <td>0.34023</td>\n",
-       "      <td>0.235690</td>\n",
-       "      <td>0.292162</td>\n",
-       "      <td>0.34023</td>\n",
-       "      <td>0.235690</td>\n",
-       "      <td>0.348189</td>\n",
-       "      <td>0.350617</td>\n",
-       "      <td>0.296530</td>\n",
-       "      <td>0.475000</td>\n",
-       "      <td>0.230769</td>\n",
+       "      <td>0.196532</td>\n",
+       "      <td>0.343820</td>\n",
+       "      <td>0.251656</td>\n",
+       "      <td>0.307876</td>\n",
+       "      <td>0.343820</td>\n",
+       "      <td>0.251656</td>\n",
+       "      <td>0.349727</td>\n",
+       "      <td>0.345679</td>\n",
+       "      <td>0.306061</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.243243</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1008,10 +1019,10 @@
        "\n",
        "   embark_town  alone  id       split      age_bin     fare_bin sex_processed  \\\n",
        "0  Southampton  False   1   selection  19.0 - 22.0    0.0 - 7.7          male   \n",
-       "1    Cherbourg  False   2       train  36.0 - 41.0  39.0 - 73.5        female   \n",
+       "1    Cherbourg  False   2  validation  35.0 - 40.0  39.1 - 77.1        female   \n",
        "2  Southampton   True   3       train  25.0 - 28.0    7.9 - 8.1        female   \n",
-       "3  Southampton  False   4  validation  32.0 - 36.0  39.0 - 73.5        female   \n",
-       "4  Southampton   True   5       train  32.0 - 36.0    7.9 - 8.1          male   \n",
+       "3  Southampton  False   4       train  31.0 - 35.0  39.1 - 77.1        female   \n",
+       "4  Southampton   True   5       train  31.0 - 35.0    7.9 - 8.1          male   \n",
        "\n",
        "  embarked_processed class_processed deck_processed embark_town_processed  \\\n",
        "0              Other           Third        Missing                 Other   \n",
@@ -1028,21 +1039,21 @@
        "4                3                0                0            True   \n",
        "\n",
        "    sex_enc  embarked_enc  class_enc  deck_enc  embark_town_enc  pclass_enc  \\\n",
-       "0  0.213068       0.34023   0.235690  0.292162          0.34023    0.235690   \n",
-       "1  0.715847       0.58000   0.658537  0.618182          0.58000    0.658537   \n",
-       "2  0.715847       0.34023   0.235690  0.292162          0.34023    0.235690   \n",
-       "3  0.715847       0.34023   0.658537  0.618182          0.34023    0.658537   \n",
-       "4  0.213068       0.34023   0.235690  0.292162          0.34023    0.235690   \n",
+       "0  0.196532      0.343820   0.251656  0.307876         0.343820    0.251656   \n",
+       "1  0.740741      0.606742   0.666667  0.651685         0.606742    0.666667   \n",
+       "2  0.740741      0.343820   0.251656  0.307876         0.343820    0.251656   \n",
+       "3  0.740741      0.343820   0.666667  0.651685         0.343820    0.666667   \n",
+       "4  0.196532      0.343820   0.251656  0.307876         0.343820    0.251656   \n",
        "\n",
        "   sibsp_enc  parch_enc  alone_enc   age_enc  fare_enc  \n",
-       "0   0.539062   0.350617   0.513761  0.297297  0.192308  \n",
-       "1   0.539062   0.350617   0.513761  0.393939  0.545455  \n",
-       "2   0.348189   0.350617   0.296530  0.411765  0.230769  \n",
-       "3   0.539062   0.350617   0.513761  0.475000  0.545455  \n",
-       "4   0.348189   0.350617   0.296530  0.475000  0.230769  "
+       "0   0.551181   0.345679   0.521951  0.282051  0.137255  \n",
+       "1   0.551181   0.345679   0.521951  0.418605  0.509434  \n",
+       "2   0.349727   0.345679   0.306061  0.404762  0.243243  \n",
+       "3   0.551181   0.345679   0.521951  0.500000  0.509434  \n",
+       "4   0.349727   0.345679   0.306061  0.500000  0.243243  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1070,7 +1081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1105,42 +1116,42 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>age</td>\n",
-       "      <td>0.0 - 12.0</td>\n",
-       "      <td>0.067416</td>\n",
-       "      <td>0.348315</td>\n",
-       "      <td>0.583333</td>\n",
+       "      <td>0.0 - 13.0</td>\n",
+       "      <td>0.073034</td>\n",
+       "      <td>0.342697</td>\n",
+       "      <td>0.615385</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>age</td>\n",
-       "      <td>12.0 - 19.0</td>\n",
-       "      <td>0.123596</td>\n",
-       "      <td>0.348315</td>\n",
-       "      <td>0.409091</td>\n",
+       "      <td>13.0 - 19.0</td>\n",
+       "      <td>0.112360</td>\n",
+       "      <td>0.342697</td>\n",
+       "      <td>0.350000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>age</td>\n",
        "      <td>19.0 - 22.0</td>\n",
-       "      <td>0.084270</td>\n",
-       "      <td>0.348315</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.061798</td>\n",
+       "      <td>0.342697</td>\n",
+       "      <td>0.181818</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>age</td>\n",
        "      <td>22.0 - 25.0</td>\n",
-       "      <td>0.067416</td>\n",
-       "      <td>0.348315</td>\n",
+       "      <td>0.078652</td>\n",
+       "      <td>0.342697</td>\n",
        "      <td>0.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>age</td>\n",
        "      <td>25.0 - 28.0</td>\n",
-       "      <td>0.073034</td>\n",
-       "      <td>0.348315</td>\n",
-       "      <td>0.230769</td>\n",
+       "      <td>0.067416</td>\n",
+       "      <td>0.342697</td>\n",
+       "      <td>0.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1154,32 +1165,32 @@
        "      <th>2</th>\n",
        "      <td>sibsp</td>\n",
        "      <td>2</td>\n",
-       "      <td>0.022472</td>\n",
-       "      <td>0.348315</td>\n",
-       "      <td>0.250000</td>\n",
+       "      <td>0.033708</td>\n",
+       "      <td>0.342697</td>\n",
+       "      <td>0.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>sibsp</td>\n",
        "      <td>3</td>\n",
-       "      <td>0.028090</td>\n",
-       "      <td>0.348315</td>\n",
-       "      <td>0.400000</td>\n",
+       "      <td>0.016854</td>\n",
+       "      <td>0.342697</td>\n",
+       "      <td>0.666667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>sibsp</td>\n",
        "      <td>4</td>\n",
        "      <td>0.011236</td>\n",
-       "      <td>0.348315</td>\n",
+       "      <td>0.342697</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>sibsp</td>\n",
        "      <td>5</td>\n",
-       "      <td>0.011236</td>\n",
-       "      <td>0.348315</td>\n",
+       "      <td>0.016854</td>\n",
+       "      <td>0.342697</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1187,32 +1198,32 @@
        "      <td>sibsp</td>\n",
        "      <td>8</td>\n",
        "      <td>0.005618</td>\n",
-       "      <td>0.348315</td>\n",
+       "      <td>0.342697</td>\n",
        "      <td>0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>51 rows × 5 columns</p>\n",
+       "<p>53 rows × 5 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "   variable        label  pop_size  global_avg_target  avg_target\n",
-       "0       age   0.0 - 12.0  0.067416           0.348315    0.583333\n",
-       "1       age  12.0 - 19.0  0.123596           0.348315    0.409091\n",
-       "2       age  19.0 - 22.0  0.084270           0.348315    0.000000\n",
-       "3       age  22.0 - 25.0  0.067416           0.348315    0.500000\n",
-       "4       age  25.0 - 28.0  0.073034           0.348315    0.230769\n",
+       "0       age   0.0 - 13.0  0.073034           0.342697    0.615385\n",
+       "1       age  13.0 - 19.0  0.112360           0.342697    0.350000\n",
+       "2       age  19.0 - 22.0  0.061798           0.342697    0.181818\n",
+       "3       age  22.0 - 25.0  0.078652           0.342697    0.500000\n",
+       "4       age  25.0 - 28.0  0.067416           0.342697    0.333333\n",
        "..      ...          ...       ...                ...         ...\n",
-       "2     sibsp            2  0.022472           0.348315    0.250000\n",
-       "3     sibsp            3  0.028090           0.348315    0.400000\n",
-       "4     sibsp            4  0.011236           0.348315    0.000000\n",
-       "5     sibsp            5  0.011236           0.348315    0.000000\n",
-       "6     sibsp            8  0.005618           0.348315    0.000000\n",
+       "2     sibsp            2  0.033708           0.342697    0.500000\n",
+       "3     sibsp            3  0.016854           0.342697    0.666667\n",
+       "4     sibsp            4  0.011236           0.342697    0.000000\n",
+       "5     sibsp            5  0.016854           0.342697    0.000000\n",
+       "6     sibsp            8  0.005618           0.342697    0.000000\n",
        "\n",
-       "[51 rows x 5 columns]"
+       "[53 rows x 5 columns]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1236,14 +1247,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x576 with 2 Axes>"
       ]
@@ -1284,7 +1295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1303,7 +1314,7 @@
        " 'sibsp_processed']"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1324,7 +1335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1335,17 +1346,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A univariate selection on the preprocessed predictors can be conducted. The thresholds for retaining a feature are now on default but can be changed by the user.\n"
+    "A univariate selection on the preprocessed predictors is conducted. The thresholds for retaining a feature are now the default values but can be changed by the user."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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zvY+jIyAPMeMPAAAAmADFHwAAADABij8AAABgAhR/AAAAwAQo/gAAAIAJUPwBAAAAE6D4AwAAACZA8QcAAABMgOIPAAAAmADFHwAAADABij8AAABgAhR/AAAAwARMXfxtNptCQkIUHBysq1evOjoOAAAAkG+cHR3AkeLj45WSkqKYmBhHRwEAAADylamLf3h4uE6dOqVRo0YpISFBaWlpunjxogYPHqw2bdqoQ4cOuv/+++Xi4qKIiAiNHj1aiYmJkqQ333xTNWvWdPAZAAAAALljMQzDcHQIR4mLi9PQoUM1ePBgOTk5qVmzZtq7d6/mzJmjZcuWqXXr1po7d67q1Kmj6dOnq2LFiurZs6dOnTqlkSNHavXq1bccMyoqSlFRUdm2paen66efflJkZxeV97QU1OkBAADkiypjDzo6Au6AqWf8bypbtqzmz5+vdevWyWKxyGaz2W+rVq2aJOnYsWP69ttv9dlnn0nSX74nICgoSEFBQdm2xcXFyd/fP5/SAwAAALdH8Zc0e/Zsde/eXS1bttSHH36ojz76yH6b1frb+599fX3VqVMndezYUZcvX1Z0dLSj4gIAAAB/m6mv6nNTQECApk2bpl69emnnzp32dfy/FxYWps8++0whISF64YUX5Ofn54CkAAAAwJ0x9Rr/gnJzqQ9r/AEAQFHAGv97EzP+AAAAgAlQ/AEAAAAToPgDAAAAJkDxBwAAAEyA4g8AAACYAMUfAAAAMAGKPwAAAGACFH8AAADABCj+AAAAgAlQ/AEAAAAToPgDAAAAJuDs6ABmUmHQZlWqVMnRMQAAAGBCzPgDAAAAJkDxBwAAAEyA4g8AAACYAMUfAAAAMAGKPwAAAGACFH8AAADABLicZwHqODlGVvdSjo4BAABMZM/0Po6OgEKCGX8AAADABCj+AAAAgAlQ/AEAAAAToPgDAAAAJkDxBwAAAEyA4g8AAACYAMUfAAAAMAGKPwAAAGACFH8AAADABCj+AAAAgAlQ/AEAAAAToPgDAAAAJkDxBwAAAEzAtMU/JiZGb7/9tqNjAAAAAAXCtMUfAAAAMBNnRwcoKKmpqRo5cqR+/fVXZWRk6J///Kf9thkzZujQoUO6cuWKatWqpcmTJ2vPnj2aOnWqnJ2d5ebmptmzZ+vixYsaOXKknJ2dlZWVpRkzZui+++7L9jhRUVGKiorKti09PV2StNBjkcp7WvL/ZAEAAP6/MxHT8/R4VcYezNPjoeCYpvivWbNGFStW1KxZs3Tq1Cl99dVXSkpKUnJysry8vLRs2TJlZWWpffv2unDhgrZs2aJ27dopNDRU27Zt07Vr17Rz507Vr19fw4cPV2xsrJKSkm4p/kFBQQoKCsq2LS4uTv7+/gV5ugAAAEA2plnqc/LkSTVo0ECSdP/998vLy0uSVKxYMSUkJGjo0KEaO3asrl+/royMDIWFhSk+Pl6hoaHavHmznJ2d1a1bN3l5eemFF17QqlWr5OTk5MAzAgAAAHLPNMW/evXqOnjwt5emfvnlF82cOVOStH37dp07d04zZ87U0KFDlZqaKsMwtGHDBnXp0kWRkZHy8/PT2rVrtXXrVjVu3FgrVqxQQECA3nvvPUeeEgAAAJBrFsMwDEeHKAhpaWkaNWqULly4oMzMTLVp00aJiYkKDQ1VWFiYihcvLovFYn8vgLOzsyZOnCg3NzdZrVZFRETIMAyNGDFCLi4uysrK0siRI1W3bt3bPvbNpT6RnV1Y4w8AAO5prPG/d5mm+DsSxR8AABQVFP97l2mW+gAAAABmRvEHAAAATIDiDwAAAJgAxR8AAAAwAYo/AAAAYAIUfwAAAMAEKP4AAACACVD8AQAAABOg+AMAAAAmQPEHAAAATMDZ0QHMpMKgzapUqZKjYwAAAMCEmPEHAAAATIDiDwAAAJgAxR8AAAAwAYo/AAAAYAIUfwAAAMAEKP4AAACACVD8AQAAABPgOv4FqOPkGFndSzk6BgAAMJE90/s4OgIKCWb8AQAAABOg+AMAAAAmQPEHAAAATIDiDwAAAJgAxR8AAAAwAYo/AAAAYAIUfwAAAMAEKP4AAACACVD8AQAAABOg+AMAAAAmQPEHAAAATIDiDwAAAJiAqYt/69atlZaW5ugYAAAAQL4zdfEHAAAAzMLZ0QHyQ0xMjLZs2aKUlBQlJibqlVdekaurq+bOnSvDMFS3bl2NHz/evv+xY8c0ZcoUZWZmKjExUePGjVOjRo00cuRInT59WqmpqerTp4+eeeYZzZo1S7t27ZLNZlPbtm3Vv39/B54pAAAAkDtFsvhL0o0bN7Rs2TIlJCSoS5cuslgs+uijj+Tt7a3Fixfr/Pnz9n2PHz+uESNGqGbNmvrkk08UExOjBx54QLt379batWslSTt27JAkffLJJ3r//ff1j3/8QzExMbc8blRUlKKiorJtS09PlyQt9Fik8p6W/DplAACAW5yJmJ6nx6sy9mCeHg8Fp8gW/6ZNm8pqtcrHx0ceHh5KT0+Xt7e3JOnFF1/Mtu8//vEPzZs3T8WLF1dKSoo8PT3l6empUaNGacyYMUpOTlanTp0kSdOnT9eMGTN06dIlPf7447c8blBQkIKCgrJti4uLk7+/fz6dKQAAAHB7Rbb4Hz58WJJ06dIlZWRkSJKuXLmiUqVKacKECfYiL0kTJ07U22+/rerVq+udd97R2bNnFR8fr8OHD+vdd99VWlqaWrZsqY4dO2rz5s2aOXOmJOnpp59W+/btVbFixYI/QQAAAOBvKLLF/9KlSwoNDVVSUpLCw8NlGIZeeuklWa1W1alTRw8++KB9306dOum1116Tl5eXypcvr8TERJUtW1YXL15UcHCwrFarnnvuObm6uqpkyZIKDAxU8eLF1bx5c1WoUMGBZwkAAADkjsUwDMPRIfJaTEyMTp48qWHDhjk6iqT/LfWJ7OzCGn8AAHBPY43/vYvLeQIAAAAmUCSX+nTt2tXREQAAAIBChRl/AAAAwAQo/gAAAIAJUPwBAAAAE6D4AwAAACZA8QcAAABMgOIPAAAAmADFHwAAADABij8AAABgAhR/AAAAwASK5Cf3FlYVBm1WpUqVHB0DAAAAJsSMPwAAAGACFH8AAADABCj+AAAAgAlQ/AEAAAAToPgDAAAAJkDxBwAAAEyA4g8AAACYANfxL0AdJ8fI6l7K0TEAAEAe2zO9j6MjALfFjD8AAABgAhR/AAAAwAQo/gAAAIAJUPwBAAAAE6D4AwAAACZA8QcAAABMgOIPAAAAmADFHwAAADABij8AAABgAhR/AAAAwAQo/gAAAIAJUPwBAAAAEzB18U9LS1Pr1q1zvf+uXbs0ZMiQfEwEAAAA5A9TF38AAADALJwdHaCgpaSkaNiwYbp27ZqqVKkiSTp69KgmTJggSSpVqpQmTZokT09PvfXWWzpw4IAyMjL06quvqkSJEpKkGzdu6NVXX1WnTp3UqVOnbMePiopSVFRUtm3p6emSpIUei1Te05LfpwgAAArYmYjpeXq8KmMP5unxAEmyGIZhODpEQVqyZImuXbumIUOGaP/+/RoyZIh8fHw0adIk1ahRQ9HR0YqLi1O9evW0adMmzZo1S1evXtWyZcv06KOPaunSpUpNTVWfPn3k7++fq8eMi4uTv7+/Iju7UPwBAMBtUfyRH0w343/q1Cm1bNlSkvTQQw/J2dlZJ06c0Pjx4yVJGRkZuv/+++Xh4aEGDRpIkkqWLKnBgwdr165d+u6771SzZk37LD4AAABwLzDdGv/q1atr3759kqQffvhBNptN1apV09SpUxUZGanhw4frySeflK+vrw4e/O3ZdlJSkp5//nlJ0pNPPqm5c+fq3//+ty5cuOCo0wAAAAD+FtPN+Pfo0UP/+te/1KNHD/n6+srFxUXjxo3TiBEjZLPZZLFYNHHiRN1///365ptv1KNHD2VmZuqVV16xH8PHx0evvvqqRo0apffee08WC8t3AAAAULiZbo2/I7DGHwAA/B2s8Ud+MN1SHwAAAMCMKP4AAACACVD8AQAAABOg+AMAAAAmQPEHAAAATIDiDwAAAJgAxR8AAAAwAYo/AAAAYAIUfwAAAMAEKP4AAACACTg7OoCZVBi0WZUqVXJ0DAAAAJgQM/4AAACACVD8AQAAABOg+AMAAAAmQPEHAAAATIDiDwAAAJhArop/UlJSfucAAAAAkI9yVfz79++f3zkAAAAA5KNcXce/ZMmSWrFihapVqyar9bfnCi1atMjXYEVRx8kxsrqXcnQMAACKrD3T+zg6AlBo5ar4ly5dWkeOHNGRI0fs2yj+AAAAwL0jV8V/8uTJOnbsmI4fP65q1aqpdu3a+Z0LAAAAQB7KVfGPjIzUxo0bVb9+fS1dulTt2rXT888/n9/ZAAAAAOSRXBX/jRs3atWqVXJ2dlZGRoaCg4Mp/gAAAMA9JFdX9TEMQ87Ovz1HcHFxkYuLS76GAgAAAJC3cjXj36hRIw0aNEiNGzfWnj171LBhw/zOBQAAACAP5ar4v/HGG/rqq6904sQJde3aVU8++WQ+xwIAAACQl3Jc6pOZman09HQNHDhQzZs3V0hIiB555BH16cM1cgEAAIB7SY4z/h9++KEWLFigS5cuKSAgQJJktVrVuHHjAgkHAAAAIG/kWPwDAwMVGBiodevWqVu3bpKk9PR0ubq6Fkg4AAAAAHkjV1f1yczM1NSpUyVJYWFhWr9+fX5mAgAAAJDHclX816xZo9dff12StHDhQq1evTpfQwEAAADIW7kq/larNdt1/C0WS76GAgAAAJC3cnU5T39/f/Xs2VP169fX4cOH1bp16/zOBQAAACAP5ar4v/zyy2rVqpV+/vlnPfPMM6pVq1Z+5yowycnJGj16tJKSkhQfH6+ePXuqXr16Gj9+vDw8POTt7a1ixYppypQpioyM1MaNG2WxWPT0009zWVMAAADcM3Is/tHR0erevbtmzJhhX95z5MgRbdq0SUOHDi2QgPnt9OnTat++vdq2basLFy4oJCREHh4emjZtmvz8/DRr1ixduHBBx48f16ZNm/TBBx9Ikvr166cWLVrI19c32/GioqIUFRWVbVt6erokaaHHIpX3ZJkUAAB5qcrYg46OANwTciz+5cuXl6Rbym1R4uPjoxUrVuiLL76Qp6enbDab4uPj5efnJ0lq3LixNm3apGPHjunXX39V3759JUlXr17V6dOnbxmboKAgBQUFZdsWFxcnf3//AjkfAAAA4M/kWPyLFy+u3bt3q1KlSgWVp8AtXbpUDRo0UM+ePfXtt9/q66+/Vvny5XX8+HHVqFFD+/fvl/Tbk58aNWrovffek8Vi0fLly1WzZk0HpwcAAAByJ8fif/OynWfOnFFGRoYefPBB/fDDD/Lw8FBkZGSBBMxvrVq10oQJE7Rp0yaVKFFCTk5OGjt2rEaNGiV3d3e5uLioXLlyqlWrlh599FH16NFD6enpql+/vsqVK+fo+AAAAECuWAzDMG63U//+/TVv3jw5OzsrMzNT/fv315IlSwoin0OsWrVK7dq1U5kyZTRr1iy5uLho4MCBd3y8m0t9Iju7sMYfAIA8xhp/IHdydVWfixcv2r/OzMxUQkJCvgUqDLy9vfXcc8/J3d1dJUqU0JQpUxwdCQAAALgruSr+3bp1U/v27fXAAw/op59+0osvvpjfuRwqICBAAQEBjo4BAAAA5JlcFf9evXopICBAZ86cUdWqVVWmTJn8zgUAAAAgD+Wq+P/0008KDw/XtWvX1KlTJ/n5+alVq1b5nQ0AAABAHrHmZqcJEyZo8uTJKl26tLp166Y5c+bkdy4AAAAAeShXxV+SqlatKovFojJlysjDwyM/MwEAAADIY7kq/iVLltSaNWt048YNffrpp/Ly8srvXAAAAADyUK6K/6RJkxQXF6fSpUvr0KFDmjhxYn7nAgAAAJCHcvXm3vDwcM2YMSO/swAAAADIJ7ma8U9PT9eRI0eUlpam9PR0paen53cuAAAAAHkoVzP+p06dUlhYmBISEuTt7S2r1aqtW7fmdzYAAAAAeSRXxf/VV1/VlClT5Ovrq+TkZI0bNy6fYxVNFQZtVqVKlRwdAwAAACaUq+I/b948RUdHy9vbW5cuXVJYWJhatGiR39kAAAAA5JFcrfEvVaqUvL29JUk+Pj7y9PTM11AAAAAA8lauZvw9PDz0/PPPq2nTpjp8+LBSU1M1c+ZMSdLQoUPzNSAAAACAu5er4t+mTRv71+XKlcu3MAAAAADyR66Kf5cuXfI7BwAAAIB8lKs1/gAAAADubRR/AAAAwARytdQHeaPj5BhZ3Us5OgYAAJC0Z3ofR0cAChQz/gAAAIAJUPwBAAAAE6D4AwAAACZA8QcAAABMgOIPAAAAmADFHwAAADABij8AAABgAhR/AAAAwAQo/gAAAIAJUPwBAAAAE6D4AwAAACZA8QcAAABMwJTF/8cff9TcuXMlSc2bN3dwGgAAACD/OTs6gCPUrl1btWvXdnQMAAAAoMCYovj//PPPGjlypJydnZWVlaXAwEB9/fXXmjVrltLT0zVkyBCdO3dONWvW1Lhx47R3715NnTpVzs7OcnNz0+zZs/XFF19oy5YtSklJUWJiol555RX985//vOWxoqKiFBUVlW1benq6JGmhxyKV97QUyDkDAFBUVRl70NERgHuSKYr/zp07Vb9+fQ0fPlyxsbE6ceKE/bbU1FQNGzZMFStW1GuvvaZt27YpNjZW7dq1U2hoqLZt26Zr165Jkm7cuKFly5YpISFB3bt3l7+/v5ydsw9hUFCQgoKCsm2Li4uTv79//p8oAAAA8BdMsca/W7du8vLy0gsvvKBVq1bJycnJfluFChVUsWJFSVLDhg31888/KywsTPHx8QoNDdXmzZvt5b5p06ayWq3y8fGRl5eXEhISHHI+AAAAwN9liuK/detWNW7cWCtWrFBAQIAWL15sv+38+fOKj4+XJO3du1d+fn7asGGDunTposjISPn5+Wnt2rWSpMOHD0uSLl26pOTkZHl7exf8yQAAAAB3wBRLferVq6cRI0Zo/vz5ysrKUkhIiA4cOCBJKlWqlCZMmKALFy6oYcOGatmypfbv368333xTbm5uslqtioiI0O7du3Xp0iWFhoYqKSlJ4eHh2V45AAAAAAozi2EYhqND3AtiYmJ08uRJDRs27G/f9+Ya/8jOLry5FwCAu8Sbe4E7Y4qlPgAAAIDZmWKpT17o2rWroyMAAAAAd4wZfwAAAMAEKP4AAACACVD8AQAAABOg+AMAAAAmQPEHAAAATIDiDwAAAJgAxR8AAAAwAYo/AAAAYAJ8gFcBqjBosypVquToGAAAADAhZvwBAAAAE6D4AwAAACZA8QcAAABMgOIPAAAAmADFHwAAADABij8AAABgAhR/AAAAwAS4jn8B6jg5Rlb3Uo6OAQCAae2Z3sfREQCHYcYfAAAAMAGKPwAAAGACFH8AAADABCj+AAAAgAlQ/AEAAAAToPgDAAAAJkDxBwAAAEyA4g8AAACYAMUfAAAAMAGKPwAAAGACFH8AAADABCj+AAAAgAmYsviHhIToxIkTjo4BAAAAFBhTFn8AAADAbJwdHSC/JScna/To0UpKSlJ8fLx69uxpv+3atWsaPny4kpOTlZmZqddee02PPvqoOnbsqIcfflhHjx6VxWLRvHnzVKJECc2YMUOxsbHKyspS37591a5dOweeGQAAAJB7Rb74nz59Wu3bt1fbtm114cIFhYSEqFy5cpKk+fPn67HHHlNoaKguXLigHj16aOvWrUpJSVH79u01ZswYvf7669q+fbs8PT0VFxen1atXKy0tTYGBgWrevLm8vLyyPV5UVJSioqKybUtPT5ckLfRYpPKeloI5cQAA7mFVxh50dASgyCnyxd/Hx0crVqzQF198IU9PT9lsNvttJ06cUMeOHSVJ5cqVk6enpy5fvixJqlOnjiTpvvvuU1pamn799VcdPnxYISEhkiSbzaazZ8/eUvyDgoIUFBSUbVtcXJz8/f3z7RwBAACA2ynya/yXLl2qBg0a6O2331ZAQIAMw7DfVr16dcXGxkqSLly4oGvXrqlUqVKSJIsl+8y8r6+vmjVrpsjISK1YsULt2rVT5cqVC+w8AAAAgLtR5Gf8W7VqpQkTJmjTpk0qUaKEnJyc7EtvXnrpJY0aNUqff/65UlNTFRERIWfnPx+S1q1b67vvvlPPnj11/fp1tWnTRp6engV5KgAAAMAdsxi/nwJHvri51Ceyswtr/AEAyAXW+AN5r8gv9QEAAABA8QcAAABMgeIPAAAAmADFHwAAADABij8AAABgAhR/AAAAwAQo/gAAAIAJUPwBAAAAE6D4AwAAACZA8QcAAABMgOIPAAAAmICzowOYSYVBm1WpUiVHxwAAAIAJMeMPAAAAmADFHwAAADABij8AAABgAhR/AAAAwAQo/gAAAIAJUPwBAAAAE6D4AwAAACbAdfwLUMfJMbK6l3J0DAAACrU90/s4OgJQJDHjDwAAAJgAxR8AAAAwAYo/AAAAYAIUfwAAAMAEKP4AAACACVD8AQAAABOg+AMAAAAmQPEHAAAATIDiDwAAAJgAxR8AAAAwAYo/AAAAYAIUfwAAAMAEikTxnzNnjlavXn1H923evPnfvs+JEycUEhJyR48HAAAAOEKRKP4AAAAAcubs6AB/lJGRofDwcJ0+fVpZWVkaPHiwJkyYoCZNmujo0aPy9fWVt7e3YmNj5erqqkWLFkmStmzZos8++0ypqal68803Vb9+fa1cuVJffPGFbty4odKlS2vu3LnauHGjPvzwQ2VlZWnQoEH2x505c6aSkpI0duxYbd68WcuXL5fValXjxo01bNgwxcfHa9iwYTIMQ2XLlv3L/FFRUYqKisq2LT09XZK00GORynta8mHUAABwnCpjDzo6AoBcKHTFPzo6WqVLl9akSZOUmJio3r1768aNG+rQoYPCw8MVEBCgkSNHasiQIerdu7eOHz8uSapYsaIiIiL0008/6V//+pc+/PBDXblyxV7gn3/+eR08+NsfJi8vL82fP9/+mFOnTpXFYlF4eLiuXLmiOXPm6MMPP5Sbm5uGDx+uHTt2aOvWrerQoYMCAwO1adOmv1xaFBQUpKCgoGzb4uLi5O/vn08jBgAAANxeoSv+x44d0549e3TgwAFJks1mU2JiourWrSvpt9JevXp1+9dpaWmSpKZNm0qS/Pz8dPHiRVmtVrm4uGjo0KFyd3fX+fPnZbPZJEnVqlWzP96lS5d09OhRValSRZJ05swZJSQkqH///pKklJQUnTlzRqdOnVJgYKAkqVGjRnf8ngIAAADAEQpd8ff19VX58uUVFham1NRUzZ8/Xx9//LEslpyXyBw4cEAdO3bU0aNHVaFCBR05ckRbtmxRdHS0bty4oa5du8owDEmS1fq/tzb4+PhoyZIlCgkJ0fbt21WvXj3dd999Wrp0qVxcXBQTE6PatWvr5MmT+v7771WrVi37KwcAAADAvaLQFf/g4GC9+eab6t27t5KTk9WzZ89sRf2vxMXFqU+fPkpPT1dERISqVq0qNzc3BQcHS5LKli2r+Pj4P72vxWLRxIkT9cILL2jt2rXq27evQkJClJmZqYoVK6pdu3YaMGCAhg8frk2bNqlSpUp5es4AAABAfrMYN6fBkW9urvGP7OzCm3sBAEUOb+4F7g1czhMAAAAwAYo/AAAAYAIUfwAAAMAEKP4AAACACVD8AQAAABOg+AMAAAAmQPEHAAAATIDiDwAAAJgAxR8AAAAwAYo/AAAAYALOjg5gJhUGbValSpUcHQMAAAAmxIw/AAAAYAIUfwAAAMAEKP4AAACACVD8AQAAABOg+AMAAAAmQPEHAAAATIDiDwAAAJgA1/EvQB0nx8jqXsrRMQAAKNT2TO/j6AhAkcSMPwAAAGACFH8AAADABCj+AAAAgAlQ/AEAAAAToPgDAAAAJkDxBwAAAEyA4g8AAACYAMUfAAAAMAGKPwAAAGACFH8AAADABCj+AAAAgAlQ/AEAAAAToPgDAAAAJlBoiv+cOXO0evXqO7pv8+bNc7VfWlqaoqOj7+gxAAAAgHtZoSn+BeHixYsUfwAAAJiSc34cNCMjQ+Hh4Tp9+rSysrI0ePBgTZgwQU2aNNHRo0fl6+srb29vxcbGytXVVYsWLZIkbdmyRZ999plSU1P15ptvqn79+lq5cqW++OIL3bhxQ6VLl9bcuXO1ceNGffjhh8rKytKgQYPsjztz5kwlJSVp7Nixslgst+RasGCBjh8/rrlz56pPnz4aPny4kpOTlZmZqddee00pKSnauXOnxo4dq0WLFmnv3r1asGCBNmzYoF9//VWnTp2Sq6urzp49q/j4eE2ZMkV169bN9hhRUVGKiorKti09PV2StNBjkcp73poLAIB7WZWxBx0dAUAu5MuMf3R0tEqXLq1Vq1Zp3rx5ioiIUEpKijp06KAPPvhAsbGxatSokVatWqWMjAwdP35cklSxYkW9//77mjhxosLDw5WVlaUrV65o+fLlio6OVmZmpg4e/O2Pi5eXl1avXq1HH31UkjR16lTZbDaFh4f/aemXpLCwMNWoUUMDBw7U/Pnz9dhjj2nVqlWaPXu2Ro8erRYtWmj37t2SpN27dys+Pl42m03btm3TU089JUmqUKGClixZopCQkFsKviQFBQUpJiYm238LFizI8zEGAAAA/o58mfE/duyY9uzZowMHDkiSbDabEhMT7bPjXl5eql69uv3rtLQ0SVLTpk0lSX5+frp48aKsVqtcXFw0dOhQubu76/z587LZbJKkatWq2R/v0qVLOnr0qKpUqZLrjCdOnFDHjh0lSeXKlZOnp6eSk5NVrVo1HThwQM7OznrooYe0e/dunTt3zp63du3akqTy5ctr7969dzxGAAAAQEHKlxl/X19ftW/fXpGRkVq8eLECAgJUsmTJv5yJv+nmE4WjR4+qQoUKOnLkiLZs2aJ///vfGjNmjLKysmQYxm/Brf+L7uPjoyVLluj48ePavn37Xx7farUqKytLklS9enXFxsZKki5cuKBr166pVKlSatOmjaZPn65mzZqpRYsWmjVrlv1VBUm3PQcAAACgMMqX4h8cHKyTJ0+qd+/eCg4OVsWKFbMV9b8SFxenPn36KDw8XBEREapatarc3NwUHBysfv36qWzZsoqPj//T+1osFk2cOFFvvfWWEhMT/3Qfb29vZWRkaPr06XrppZf07bffqlevXnr55ZcVEREhZ2dntWrVSt9//71atGihZs2a6YcfflDbtm3vajwAAAAAR7MYN6fQkW/i4uLk7++vyM4uvLkXAFDk8OZe4N6QL2v8HW3cuHE6ceLELdsXL16s4sWLOyARAAAA4FhFtvgDAAAA+B9TfYAXAAAAYFYUfwAAAMAEKP4AAACACVD8AQAAABOg+AMAAAAmQPEHAAAATIDiDwAAAFMKCQnRiRMnFBMTo61bt0qSVq5c6eBU+YfiDwAAAFPr2rWr/P39JUnz5893cJr8UyQ/wKuwqjBosypVquToGAAAAEXazz//rJEjR8rZ2VlZWVkKDAzUxx9/LKvVqosXLyooKEi9evWy7z9nzhz5+PjoypUrunr1qsaNG1ckPxCW4g8AAIAiZefOnapfv76GDx+u2NhYnThxQhcuXND69euVlZWljh07KiAg4Jb7DRgwQCtXriySpV9iqQ8AAACKmG7dusnLy0svvPCCVq1aJScnJzVs2FCurq4qXry4/Pz8dObMGUfHLHAUfwAAABQpW7duVePGjbVixQoFBARo8eLF+vHHH5WZmakbN27o+PHjqlq16p/e1zCMAk5bcCj+AAAAKFLq1aund955R3369NGaNWsUEhIim82mF198Ub169dKAAQNUpkyZP71v9erVNWzYsAJOXDAsRlF+WlNIxMXFyd/fX1u3buXNvQAAAAVs165dWrNmjWbNmuXoKA7FjD8AAABgAlzVBwAAAEVas2bN1KxZM0fHcDiKfwHqODlGVvdSjo4BAMA9Yc/0Po6OABQpLPUBAAAATIDiDwAAAJgAS30AAABwxxoPfz9Pj8cSr/zDjD8AAADuSYsXL1aLFi2UlpYmSXrjjTe0ffv2bPs0b97c/vWWLVsUEhKikJAQde/eXZs3b77lmEePHtXu3btznWHIkCFKT0+/wzMoWMz4AwAA4J60YcMGPf300/r000/VtWvXHPfdu3evli9froULF8rDw0OJiYkKCgpSjRo1VKNGDft+X3zxhXx8fNS0adNcZbiXPhuA4g8AAIB7zq5du1SlShUFBwdr+PDhty3+0dHRCg0NlYeHhySpdOnSio6OlpeXl32fCxcu6KOPPpKLi4vq1q2rUaNG6f7775eLi4tGjBihcePGKS0tTRcvXtTgwYPVpk0btW7dWp999pnCw8Pl6uqqs2fPKj4+XlOmTFHdunXzdQz+Loo/AAAA7jnR0dHq3r27fH195erqqv379//pfhaLRZIUHx+vypUrZ7utZMmS2b4vV66cunTpIh8fH9WvX1/Xr1/Xyy+/rDp16mjnzp3q16+fmjVrpr1792rOnDlq06ZNtvtXqFBBERERWrt2raKiohQREZGHZ3z3KP4AAAC4p1y9elXbt29XQkKCIiMjlZycrJUrV8rd3f2W9fY2m03Sb6X83LlzqlWrlv22PXv2yMfHR1WrVv3Lx6pWrZokqWzZspo/f77WrVsni8ViP+7v1a5dW5JUvnx57d27967PM6/x5l4AAADcUzZs2KBnn31WS5cu1ZIlS7R27Vrt2LFDlStX1pdffmnfLzY21r5+v2vXrlqyZImuX78uSbp8+bJGjRqlGzduZDu2xWJRVlaW/Xur9be6PHv2bHXu3FnTp09Xs2bNZBjGLbluvrpQWDHjDwAAgDvmiMtvRkdHa9q0afbv3dzc1LZtW6Wmpsrd3V2dO3eWh4eHXFxc7MttGjZsqMDAQD333HNydnZWamqqhg4dmu0VAEmqV6+epk2bpurVq2fbHhAQoGnTpmnRokUqX768EhMT8/9E85jF+LOnK8hTcXFx8vf3l3ubV2R1L+XoOAAA3BO4njuQt1jqAwAAAJgAxf9vaN26tf0DIgAAAIB7CcUfAAAAMIEi/+bemJgYbdmyRSkpKUpMTNQrr7wiwzC0atUq2Ww2WSwWzZ07Vz/99JPefvttubi4KDAwUCVLltTcuXNlGIbq1q2r8ePHS5LGjRunuLg4SdLcuXNvuf4rAAAAUBgV+eIvSTdu3NCyZcuUkJCg7t2769lnn9WiRYvk5uamsWPH6v/+7/9Urlw5paWlKTo6WjabTW3btlV0dLS8vb21ePFinT9/XpL07LPPqkmTJnrjjTe0Y8cOPf3009keKyoqSlFRUdm23bye7EKPRSrvWbgv8wQAMJ8qYw86OgKAAmCK4t+0aVNZrVb5+PjIy8tLFotFI0aMkIeHh06ePKkGDRpI+t8HNCQmJsrLy0ve3t6SpBdffNF+rHr16kmSfHx8lJqaestjBQUFKSgoKNu2m1f1AQAAKGrORDyYp8fjiWj+McUa/8OHD0uSLl26pKSkJK1evVqzZs3ShAkTVKxYMfsHMNz8gAZvb29du3ZNV65ckSRNmDBBBw4ckFT4P5gBAADALBYvXqwWLVrYL77yxhtvaPv27dn2ad68uf3rLVu2KCQkRCEhIerevbs2b958x48dFxenwMDAv3Wfm6tLpN+Wo2/duvWOH/9OmGLG/9KlSwoNDVVSUpLCw8MVExOjoKAgOTs7y8vLS/Hx8apUqZJ9f6vVqvDwcL300kuyWq2qU6eOHnwwb5/NAgAA4O5s2LBBTz/9tD799FN17do1x3337t2r5cuXa+HChfLw8FBiYqKCgoJUo0YN+6f75reLFy8qOjpa3bt3v23e/GCK4t+0aVMNGzbM/n3Lli3/dL9mzZpl2+eP+23bts3+9e+PBwAAgIK1a9cuValSRcHBwRo+fPhti3R0dLRCQ0Pl4eEhSSpdurSio6Pl5eWVbb9Vq1Zp/fr1slqtevDBB/Xmm2/q3LlzGjNmjNLS0lSsWDG99dZb2e7z3XffadasWXJyclLlypUVERGhzMxMjRw5Ur/++qsyMjI0ZswYffjhhzp+/Lj9AjI+Pj7q0aOHpkyZoj179kiSOnTooNDQUL3xxhtydXXV2bNnFR8frylTpqhu3bp3NWamWOoDAACAouXmzLmvr69cXV21f//+P93v5jLt+Ph4Va5cOdttJUuWvGUZd0xMjMaMGaOoqCj5+vrKZrNp6tSpCgkJUWRkpJ5//nm9/fbb9v0Nw9CYMWM0d+5crVy5UuXKldNHH32kNWvWqGLFioqKitLMmTO1f/9+hYWFqUaNGho4cKD9/v/5z38UFxentWvX6oMPPtDGjRt19OhRSVKFChW0ZMkShYSE3HLxmDtR5Gf8HfEyCgAAAPLP1atXtX37diUkJCgyMlLJyclauXKl3N3d7VdTvMlms0n6rUSfO3dOtWrVst+2Z88e+fj4qGrVqvZtkydP1tKlSzVt2jQ1aNBAhmHo2LFjWrhwod577z0ZhiFn5/9V6ISEBMXHx2vw4MGSpNTUVD322GNKTEzUE088IUm6//771bdvX/sl4X/vxIkTatKkiSwWi1xcXPTQQw/pxIkTkqTatWtLksqXL6+9e/fe9bgx4w8AAIB7yoYNG/Tss89q6dKlWrJkidauXasdO3aocuXK+vLLL+37xcbG2tfvd+3aVUuWLNH169clSZcvX9aoUaN048aNbMdeu3atxo8fr5UrV+rHH3/U999/L19fXw0bNkyRkZEaP368AgIC7PuXLl1a5cuX17x58xQZGamwsDA98sgjql69ug4e/O0KRb/88otef/11Wa1WZWVlZXu86tWr25f5ZGRk6Pvvv7c/Ecnri8oU+Rl/AAAA5B9HXH4zOjpa06ZNs3/v5uamtm3bKjU1Ve7u7urcubM8PDzk4uKiiIgISVLDhg0VGBio5557Ts7OzkpNTdXQoUOzvQIgSTVr1lTPnj3l4eGhcuXK6aGHHtKIESM0btw4paWlKTU1VaNHj7bvb7VaNXr0aPXv31+GYcjDw0PTpk1To0aNNGrUKPXu3VuZmZkaNWqUvL29lZGRoenTp6t48eKSpFatWum7775TUFCQMjIyFBAQcNdr+f+Kxbh5LUvkm5vX8Y/s7MIHeAEACh2umw6YA0t9AAAAABOg+AMAAAAmQPEHAAAATIDiDwAAAJgAxR8AAAAwAS7nWYAqDNqsSpUqOToGAAAATIgZfwAAAMAEKP4AAACACbDUpwBkZmZKks6fP+/gJAAAADCD8uXLy9k5e9Wn+BeAixcvSpJ69erl4CQAAAAwg61bt97y3lKKfwGoV6+e7r//fi1atEhOTk6OjnNPCgsL04IFCxwd457E2N0dxu/uMH53jrG7O4zf3WH87lxhGbvy5cvfso3iXwCKFy8uDw8PVa1a1dFR7lmurq5cEekOMXZ3h/G7O4zfnWPs7g7jd3cYvztXmMeON/cCAAAAJkDxBwAAAEyA4g8AAACYAMW/gAQFBTk6wj2N8btzjN3dYfzuDuN35xi7u8P43R3G784V5rGzGIZhODoEAAAAgPzFjD8AAABgAhR/AAAAwAQo/gAAAIAJ8AFeeSwrK0vjxo3T0aNH5erqqgkTJmT74K61a9dqzZo1cnZ21oABA9SqVSsHpi1cbjd2kpSQkKAePXpow4YNKlasmIOSFk63G7/ly5fr008/lSS1bNlSAwcOdFTUQul247dq1SrFxMTIYrHoueee09NPP+3AtIVLbn53s7Ky1L9/f/n7+6tHjx4OSlo43W78JkyYoL1798rDw0OSNG/ePJUoUcJRcQud243f119/rXfffVeGYahu3boKDw+XxWJxYOLCI6ex+/HHHzVp0iT7vvv27dO7776rJ554wlFxC53b/ewtXbpUGzdulMViUVhYmJ566ikHpv3/DOSpzz//3BgxYoRhGIbx/fffG2FhYfbb4uPjjQ4dOhhpaWnGtWvX7F/jNzmNnWEYxvbt243OnTsbDRs2NFJTUx0RsVDLafzOnDljdOnSxbDZbEZWVpYRFBRk/Pjjj46KWijlNH6XL1822rdvb6SnpxtJSUnGE088YWRlZTkqaqFzu99dwzCMGTNmGN27dzc++OCDgo5X6N1u/IKDg43Lly87Ito9IafxS0pKMtq3b28fv0WLFjGWv5Ob313DMIxNmzYZQ4cOLcho94Scxu/q1atGy5YtjbS0NOPKlSvGk08+6aiY2bDUJ4/t2bNHjz/+uCSpQYMGOnTokP22AwcOqGHDhnJ1dVWJEiVUpUoVHTlyxFFRC52cxk6SrFarli1bplKlSjkgXeGX0/iVL19e7733npycnGSxWGSz2XjF5A9yGr8yZcpo/fr1cnFx0aVLl1SsWDFmDH/ndr+7mzdvlsVise+D7HIav6ysLJ0+fVpjx45VcHCw1q1b56iYhVZO4/f999/rgQce0NSpU9WzZ0/5+PioTJkyjopa6Nzud1eSrl+/rjlz5mj06NEFHa/Qy2n83NzcVKFCBd24cUM3btwoNP9msNQnjyUnJ8vT09P+vZOTk2w2m5ydnZWcnJzt5VkPDw8lJyc7ImahlNPYSVLz5s0dFe2ekNP4ubi4qEyZMjIMQ9OmTVOdOnVUrVo1B6YtfG738+fs7KyVK1dqzpw5CgkJcVTMQimnsTt27Jg2btyod955R++++64DUxZeOY3f9evX1bt3b/Xr10+ZmZnq06eP6tWrp1q1ajkwceGS0/glJiZq165dWr9+vdzd3dWrVy81aNCAv3//3+3+7knSunXrFBAQwBOmP3G78bvvvvvUvn17ZWZm6qWXXnJUzGyY8c9jnp6eSklJsX+flZVl/wH4420pKSms0/ydnMYOt3e78UtLS9OwYcOUkpKi8PBwR0Qs1HLz89e7d2/997//1e7du/Xtt98WdMRCK6exW79+vS5cuKDQ0FB99NFHWr58ubZv3+6oqIVSTuPn5uamPn36yM3NTZ6ennrkkUd4pfgPchq/UqVK6cEHH1TZsmXl4eGhJk2a6Mcff3RU1EInN3/3PvnkE3Xv3r2go90Tchq/7du3Kz4+Xlu3btVXX32lLVu26MCBA46Kakfxz2ONGjWy/6O2b98+PfDAA/bb6tevrz179igtLU1JSUk6ceJEttvNLqexw+3lNH6GYejll19WzZo1FRERIScnJ0fFLLRyGr+TJ09q4MCBMgxDLi4ucnV1ldXKn8+bchq7f/3rX4qOjlZkZKS6dOmivn378ubAP8hp/E6dOqUePXooMzNTGRkZ2rt3r+rWreuoqIVSTuNXt25dHTt2TAkJCbLZbNq/f79q1KjhqKiFzu3+3U1KSlJ6erruu+8+R8Qr9HIav5IlS6p48eJydXVVsWLFVKJECV27ds1RUe2YTs1jTz31lHbs2KHg4GAZhqFJkyZp2bJlqlKlivz9/RUSEqKePXvKMAwNGTKEdda/c7uxQ85yGr+srCx99913Sk9P13//+19J0tChQ9WwYUMHpy48bvfzV6tWLQUFBdnXqj/88MOOjlxo8Lt7d243fp07d1ZgYKBcXFzUuXNn+fn5OTpyoXK78Xv99df1wgsvSJICAgKYVPqd243dzz//rIoVKzo6ZqF1u/HbuXOnAgMDZbVa1ahRo0KxZNliGIbh6BAAAAAA8hevVQMAAAAmQPEHAAAATIDiDwAAAJgAxR8AAAAwAYo/AAAAYAIUfwCAwyxevFgtWrRQWlqaJOmNN9645QO+fn8JvC1btigkJEQhISHq3r27Nm/eXKB5AeBexnX8AQAOs2HDBj399NP69NNP1bVr1xz33bt3r5YvX66FCxfKw8NDiYmJCgoKUo0aNfhQJgDIBWb8AQAOsWvXLlWpUkXBwcFatWrVbfePjo5WaGioPDw8JEmlS5dWdHS0qlevnt9RAaBIYMYfAOAQ0dHR6t69u3x9feXq6qr9+/f/6X4Wi0WSFB8fr8qVK2e7rWTJkvmeEwCKCoo/AKDAXb16Vdu3b1dCQoIiIyOVnJyslStXyt3dXenp6dn2tdlskqQKFSro3LlzqlWrlv22PXv2yMfHR1WrVi3Q/ABwL2KpDwCgwG3YsEHPPvusli5dqiVLlmjt2rXasWOHKleurC+//NK+X2xsrH39fteuXbVkyRJdv35dknT58mWNGjVKN27ccMg5AMC9hhl/AECBi46O1rRp0+zfu7m5qW3btkpNTZW7u7s6d+4sDw8Pubi4KCIiQpLUsGFDBQYG6rnnnpOzs7NSU1M1dOjQbK8AAAD+msUwDMPRIQAAAADkL5b6AAAAACZA8QcAAABMgOIPAAAAmADFHwAAADABij8AAABgAhR/AAAAwAQo/gAAAIAJ/D9vhVWYPUulNAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1375,12 +1386,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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tFHyr6p4khwJnAKsAn6iqy5IcDSytqlOB/wt8LMnf07vQ7eCqqRvsTXwlSZI0UFt3bquq04DTJo0d0ff8cuBPZ3JMWx0kSZI0Fqz4SpIkaaC2Kr7zwYqvJEmSxoIVX0mSJA1kxVeSJEnqGCu+kiRJGmiUKr4mvpIkSRpsdPJeWx0kSZI0Hqz4SpIkaaBRanWw4itJkqSxMNIV3yRHAbdW1XvbjkWSJKmLRqniO9KJryRJkmZnlBLfkWp1SHJQkouTXJTkU5PWvSrJkmbdF5Ks3Yzvl+TSZvzsZuyJSc5LcmFzvC3bOB9JkiTNnZFJfJM8ETgceEZVbQccNmmTU6pq52bdFcAhzfgRwJ83489vxl4N/GtVbQ/sBFw74DUXJVmaZOnJJ35ibk9IkiRpGGQeHi0ZpVaHZwAnV9XvAKrqhkml+a2TvA3YAFgInNGMnwMcl+Qk4JRm7HvAm5NsRi9h/vGyXrCqFgOLAS697taa29ORJEnSXBqZiu80HAccWlXbAP8MrAlQVa+mVyneHDg/ycZV9Rl61d/bgdOSPKOdkCVJktqVZM4fbRmlxPcbwH5JNgZIstGk9esCv0yyGvCSicEkj6mqc6vqCOC3wOZJHg1cXVUfBL4MbLtSzkCSJGnIjFLiOzKtDlV1WZK3A99Kci9wAfDTvk3eApxLL7k9l14iDHBMc/FagDOBi4B/BF6W5G7gV8A7VspJSJIkad6MTOILUFXHA8cPWPdR4KPLGH/RMjZ/V/OQJEkaa05nJkmSJHXMSFV8JUmSNLes+EqSJEkdY8VXkiRJg41OwdfEV5IkSYPZ6iBJkiR1jBVfSZIkDWTFV5IkSeoYK76SJEkaaIQKvia+kiRJGsxWB0mSJKljrPhKkiRpoBEq+FrxlSRJ0niw4itJkqSBRqnH18R3jtx7b7UdwqzcR7fjB9hmv33bDmHWLjn5822HMCv3Pe/wtkOYtVF4Hz1knTXbDmFW7q3u/zzKKN3jtcN23WbTtkMYCSOU99rqIEmSpPFgxVeSJEkDLVgwOiVfK76SJEkaC1Z8JUmSNJA9vpIkSVLHWPGVJEnSQE5nJkmSpLEwQnmvrQ6SJEkaD1Z8JUmSNNAotTpY8ZUkSdJYsOIrSZKkgUap4mviK0mSpIFGKO+11UGSJEnjwYqvJEmSBhqlVgcrvpIkSRoLVnwlSZI00AgVfEe74pvkqCRvmOE+WyS5dL5ikiRJ6pIkc/5oy0gnvpIkSdKEkUt8k7w5yY+SfAd4XDP2mCSnJzk/ybeTPL4Zf0iSLya5qHk8ZdKxHp3kgiQ7t3AqkiRJrUvm/tGWkUp8k+wIHABsD/wFMJGwLgZeW1U7Am8APtKMfxD4VlVtB+wAXNZ3rMcBXwAOrqolA15vUZKlSZZ+/jOfnIczkiRJ0lwZtYvbngp8sapuA0hyKrAm8BTg5L6ekjWaf58BHARQVfcCNyfZEHgQ8GXgRVV1+aAXq6rF9JJqLvr5LTXnZyNJktSyUZrObNQS32VZANxUVdvPYJ+bgZ8DuwMDE19JkiR1x0i1OgBnAy9IslaSdYHnAbcBP0myH0B6tmu2PxN4TTO+SpL1m/G7gBcCByU5cKWegSRJ0hCxx3dIVdUPgM8BFwFfBSZ6c18CHJLkInp9vHs344cBT09yCXA+8IS+Y/0BeC7w90mev3LOQJIkabiM0nRmI9fqUFVvB96+jFV7LWPbX3N/Etxv62b9Tdx/gZwkSZI6bOQSX0mSJM2dEbq2bbRaHSRJkqRBrPhKkiRpIKczkyRJ0lgYobzXVgdJkiSNByu+kiRJGmiUWh2s+EqSJGksWPGVJEnSQCNU8DXxlSRJ0mC2OkiSJEkdY8VXkiRJA1nxlSRJkuZRkr2SXJnkqiRvGrDNi5NcnuSyJJ9Z3jGt+EqSJGmgNgq+SVYBjgX2BK4FliQ5taou79tmS+CfgD+tqhuTPHh5x7XiK0mSpGGzC3BVVV1dVXcBnwX2nrTNq4Bjq+pGgKr6zfIOasV3jqy9Rre/lAcc9La2Q5i1k054S9shzNp9zzu87RBmxffRcLjvvmo7hFlZfdXu12Ruz71thzBrGy9cve0QZu0vt9qk7RBGQks9vg8HrulbvhZ48qRtHguQ5BxgFeCoqjp9qoN2O1uTJEnSvJqPvDfJImBR39Diqlo8w8OsCmwJ7AFsBpydZJuqummqHSRJkqSVpklyp0p0rwM271verBnrdy1wblXdDfwkyY/oJcJLBh20+58nSZIkad4kmfPHNCwBtkzyqCSrAwcAp07a5kv0qr0k2YRe68PVUx3UxFeSJElDparuAQ4FzgCuAE6qqsuSHJ3k+c1mZwDXJ7kc+Cbwxqq6fqrj2uogSZKkgdq6f0VVnQacNmnsiL7nBby+eUyLia8kSZIGWuCd2yRJkqRuseIrSZKkgUao4GvFV5IkSePBiq8kSZIGaunObfPCxFeSJEkDLRidvNdWB0mSJI0HK76SJEkaaJRaHaz4SpIkaSxY8ZUkSdJAI1TwteIrSZKk8TCviW+Sg5N8eJbH+GmSTaax3QZJ/nY2ryVJkqQHyjz815ahrvgmWWUGm28AmPhKkiTNoQWZ+0dr5zKdjZK8NMl5SS5M8u9JVklya5JjklyW5H+S7JLkrCRXJ3l+3+6bN+M/TnJk3zG/lOT8Zv9FfeO3JnlfkouA3frG10ry1SSvGhDmu4DHNDEek55jklya5JIk+zfHOXYiviRfTPKJ5vkrkrw9yRZJrkjysSa2ryVZa8DXZVGSpUmWfvZTH5/Ol1KSJEktWe7FbUm2AvYH/rSq7k7yEeAlwDrAN6rqjUm+CLwN2BN4AnA8cGpziF2ArYHbgCVJvlJVS4FXVNUNTVK5JMkXqur65rjnVtX/bV4fYCHwWeCEqjphQKhvArauqu2b/fYBtge2AzZpXuNs4NvAU5v4Hg5s2uz/1OY1ALYE/qqqXpXkJGAf4MTJL1hVi4HFAD/+9e21vK+lJElS14zbdGbPBHaklzhe2Cw/GrgLOL3Z5hLgW1V1d/N8i779v15V11fV7cApwO7N+Ouaqu73gc3pJZsA9wJfmBTDl4FPTpH0LsvuwH9W1b1V9WvgW8DONIlvkicAlwO/TrIpveryd5t9f1JVFzbPz590PpIkSeqg6UxnFuD4qvqnBwwmb6iqiSrnfcCdAFV1X5L+406uhFaSPYBnAbtV1W1JzgLWbNbfUVX3TtrnHGCvJJ/pe80VUlXXJdkA2As4G9gIeDFwa1XdkmTjiXNp3Asss9VBkiRp1I1QwXdaFd8zgX2TPBggyUZJHjmD19iz2Wct4AX0ktj1gRubpPfxwK7LOcYRwI3AsVNscwuwbt/yt4H9m37kBwFPA85r1n0f+Dt6ie+3gTc0/0qSJKnPgmTOH62dy/I2qKrLgcOBryW5GPg69/fFTsd59FoXLga+0PT3ng6smuQKehelfX8axzkMWCvJewbEeT1wTnMx2zHAF5vXvAj4BvAPVfWrZvNvA6tW1VXAD+hVfU18JUmSRti07txWVZ8DPjdpeGHf+qMmbb+w+fc44LhlHO9O4DkDXmvhpOUt+hb/ejlxHjhp6I3NY/J2Hwc+3jy/m94FdRPrfkrvYryJ5fdO9ZqSJEmjbNxaHSRJkqTOm1bFd5g0F5+duYxVz2zaHSRJkjRHRmk6s84lvk1yu33bcUiSJI2DEcp7bXWQJEnSeOhcxVeSJEkrT5vTj801K76SJEkaC1Z8JUmSNNDo1Hut+EqSJGlMWPGVJEnSQE5nJkmSpLGwYHTyXlsdJEmSNB6s+EqSJGmgUWp1sOIrSZKksWDFd47cec+9bYcwKyed8Ja2Q5i1Fx/01rZDmLVt9tu37RBmxffRcPjCiUe0HcKsbLRw9bZDmLV7q9oOYdauv/WutkOYtbecdEnbIczKC7d9aNshAKN1y2ITX0mSJA1kq4MkSZLUMVZ8JUmSNJDTmUmSJEkdY8VXkiRJA41Sj6+JryRJkgYanbTXVgdJkiSNCSu+kiRJGmjBCLU6WPGVJEnSWLDiK0mSpIFGqOBr4itJkqTBRmlWB1sdJEmSNBas+EqSJGmgESr4WvGVJEnSeLDiK0mSpIGczkySJEnqmJFOfJPskeS/245DkiSpq5K5f7TFVgdJkiQN5HRmLUqyRZIfJvl0kiuSfD7J2kl2TvLdJBclOS/JupP22yXJ95Jc0Gz3uGb8ic32Fya5OMmWSdZJ8pXmWJcm2b+ds5UkSdJc6Vzi23gc8JGq2gr4PXAo8DngsKraDngWcPukfX4IPLWqngQcAbyjGX818K9VtT2wE3AtsBfwi6rarqq2Bk5fVhBJFiVZmmTpySd+Yk5PUJIkaRgsmIdHW7ra6nBNVZ3TPD8ReDPwy6paAlBVv4f/VZpfHzg+yZZAAas1498D3pxkM+CUqvpxkkuA9yV5N/DfVfXtZQVRVYuBxQCXXndrzeUJSpIkaW51teI7Ocn8/TT2eSvwzaaC+zxgTYCq+gzwfHoV4tOSPKOqfgTsAFwCvC3JEXMWuSRJUockmfNHW7qa+D4iyW7N8wOB7wObJtkZIMm6SSZXs9cHrmueHzwxmOTRwNVV9UHgy8C2SR4G3FZVJwLH0EuCJUmSxs6CzP2jtXNp76Vn5Urg/yS5AtgQ+BCwP/ChJBcBX6ep6PZ5D/DOJBfwwBaPFwOXJrkQ2Bo4AdgGOK8ZOxJ42/ydiiRJklaGrvb43lNVL500tgTYddLYWc2Dqvoe8Ni+dYc34+8C3jVpvzOahyRJ0lhrs0I717pa8ZUkSZJmpHMV36r6Kb2WBEmSJM2zUbqBRecSX0mSJK08tjpIkiRJHWPFV5IkSQONUKeDFV9JkiSNByu+kiRJGmjBCJV8rfhKkiRpLFjxlSRJ0kCjVCU18ZUkSdJAI9TpMFJJvCRJkjSQFV9JkiQN5MVtkiRJ0jxKsleSK5NcleRNU2y3T5JKstPyjmnFV5IkSQO1UfBNsgpwLLAncC2wJMmpVXX5pO3WBQ4Dzp3OcU1858jNt93ddgiz8tB11mw7hFn7wolHtB3CrD2k4/8f7ruv2g5h1kbhfbTPS49uO4RZ+d25H2o7hFn7+NJr2g5h1o549mPbDmHWjtz3iW2HMBIWtNPpsAtwVVVdDZDks8DewOWTtnsr8G7gjdM5qK0OkiRJWqmSLEqytO+xaNImDwf6/4K8thnrP8YOwOZV9ZXpvq4VX0mSJA00Hxe3VdViYPGK7p9kAfB+4OCZ7GfFV5IkScPmOmDzvuXNmrEJ6wJbA2cl+SmwK3Dq8i5ws+IrSZKkgVqazWwJsGWSR9FLeA8ADpxYWVU3A5tMLCc5C3hDVS2d6qAmvpIkSRqojYvbquqeJIcCZwCrAJ+oqsuSHA0srapTV+S4Jr6SJEkaOlV1GnDapLFlTr1TVXtM55gmvpIkSRooeOc2SZIkqVOs+EqSJGmglm5gMS+s+EqSJGksWPGVJEnSQKNU8TXxlSRJ0kBpaSLf+WCrgyRJksaCFV9JkiQNNEqtDlZ8JUmSNBZGNvFN8h9JntA8v7XteCRJkroomftHW0a21aGqXtl2DJIkSV23wIvbhkuSdZJ8JclFSS5Nsn+Ss5Ls1LfNvyS5LMmZSR7UjL0uyeVJLk7y2WbsqCSfSvK9JD9O8qq2zkuSJElzZyQSX2Av4BdVtV1VbQ2cPmn9OsDSqnoi8C3gyGb8TcCTqmpb4NV9228LPAPYDTgiycPmNXpJkqQhtSBz/2jtXNp76Tl1CbBnkncneWpV3Txp/X3A55rnJwK7N88vBj6d5KXAPX3bf7mqbq+q3wHfBHZZ1osmWZRkaZKlX/7scXN1LpIkSZoHI9HjW1U/SrID8BfA25Kcubxdmn//Enga8DzgzUm2mbR+8vaTX3cxsBjgnB/fuMxtJEmSumyEWnxHo+LbtCLcVlUnAscAO0zaZAGwb/P8QOA7SRYAm1fVN4F/BNYHFjbb7J1kzSQbA3sAS+b5FCRJkobSAjLnj7aMRMUX2AY4Jsl9wN3Aa4D39q3/A7BLksOB3wD7A6sAJyZZHwjwwaq6qbkt38X0Whw2Ad5aVb9YaWciSZKkeTESiW9VnQGcMWl4j771C1m23QeMX1xVB81BaJIkSZ1mq4MkSZLUMSNR8Z1LVXVU2zFIkiQNizanH5trVnwlSZI0Fqz4SpIkaaBRumWxia8kSZIGGqG811YHSZIkjQcrvpIkSRpolFodrPhKkiRpLFjxlSRJ0kAjVPA18ZUkSdJgo9QeMErnIkmSJA1kxVeSJEkDZYR6Haz4SpIkaSxY8ZUkSdJAo1PvNfGdM2utvkrbIczKRgtXbzuEWRuFc7i3qu0QZmX1Vbv/IdIovI9+d+6H2g5hVjZ58mvbDmHW9nvjorZDmLWTLrym7RBmbeM112g7hJHgPL6SJElSx1jxlSRJ0kCjU++14itJkqQxYcVXkiRJA41Qi68VX0mSJI0HK76SJEkaaJRuYGHiK0mSpIFGqT1glM5FkiRJGsiKryRJkgYapVYHK76SJEkaC1Z8JUmSNNDo1HtNfCVJkjQFWx0kSZKkjrHiK0mSpIFGqUo6SuciSZIkDWTFV5IkSQPZ4zvCkhyc5MNtxyFJkjQMMg+Ptoxt4pvEarckSdIY6XTim2SLJD9M8ukkVyT5fJK1kxyRZEmSS5MsTlOjT3JWkg8kWQoclmTnJN9NclGS85Ks2xz6YUlOT/LjJO9p7wwlSZLalcz9oy2dTnwbjwM+UlVbAb8H/hb4cFXtXFVbA2sBz+3bfvWq2gn4EPA54LCq2g54FnB7s832wP7ANsD+STZf1gsnWZRkaZKlp3zmk/NwapIkSZoro/Bx/zVVdU7z/ETgdcBPkvwDsDawEXAZ8F/NNp9r/n0c8MuqWgJQVb+HPzZwn1lVNzfLlwOPBK6Z/MJVtRhYDPCDn/2+5vzMJEmSWrZghO7dNgqJ7+SEs4CPADtV1TVJjgLW7Fv/h2kc886+5/cyGl8nSZKksTYKrQ6PSLJb8/xA4DvN898lWQjsO2C/K4FNk+wMkGRdL3iTJEl6oFHq8R2FRO9K4P8k+QRwOfBRYEPgUuBXwJJl7VRVdyXZH/hQkrXo9fc+a+WELEmS1A2x1WGo3FNVL500dnjzeICq2mPS8hJg10mbHdc8JrZ5LpIkSeq8UUh8JUmSNE9G6MZt3U58q+qnwNZtxyFJkqTh1+nEV5IkSfPL6cwkSZI0Fkap1WEUpjOTJEmSlsuKryRJkgay4itJkiR1jBVfSZIkDeQNLCRJkjQWFoxO3murgyRJksaDFV9JkiQNNEqtDlZ8JUmSNBas+EqSJGkgpzOTJEmS5lGSvZJcmeSqJG9axvrXJ7k8ycVJzkzyyOUd04rvHLn0tze3HcKsbJcN2g5h1u6tajuEWet6H9XtubftEGZtFN5HH196TdshzMp+b1zUdgizdvIxi9sOYdaef9yb2w5h1j655Nq2Q5iVv9z6wW2HALTzuynJKsCxwJ7AtcCSJKdW1eV9m10A7FRVtyV5DfAeYP+pjmvFV5IkSQMtyNw/pmEX4Kqqurqq7gI+C+zdv0FVfbOqbmsWvw9sttxzmdmpS5IkSbOTZFGSpX2PyR/1PBzo//jq2mZskEOAry7vdW11kCRJ0kDz0epQVYuBOekJSvJSYCfgz5a3rYmvJEmShs11wOZ9y5s1Yw+Q5FnAm4E/q6o7l3dQE19JkiQN1NJ0ZkuALZM8il7CewBwYP8GSZ4E/DuwV1X9ZjoHNfGVJEnSQG3kvVV1T5JDgTOAVYBPVNVlSY4GllbVqcAxwELg5PSy859X1fOnOq6JryRJkoZOVZ0GnDZp7Ii+58+a6TFNfCVJkjTQghG6dZvTmUmSJGksWPGVJEnSQKNT7zXxlSRJ0lRGKPO11UGSJEljwYqvJEmSBpqPO7e1xYqvJEmSxoIVX0mSJA00QrOZjW7FN8lPk2zSdhySJEldlnl4tGVkE19JkiSp30gkvkm+lOT8JJclWbSM9a9Pcmnz+LtmbIskVyT5WLPf15Ks1ax7TJLTm2N+O8njV/IpSZIkDYcRKvmOROILvKKqdgR2Al6XZOOJFUl2BP4aeDKwK/CqJE9qVm8JHFtVTwRuAvZpxhcDr22O+QbgIyvlLCRJkjRvRiXxfV2Si4DvA5vTS2gn7A58sar+UFW3AqcAT23W/aSqLmyenw9skWQh8BTg5CQXAv8ObLqsF02yKMnSJEu/ecqn5/qcJEmSWpd5+K8tnZ/VIckewLOA3arqtiRnAWtOc/c7+57fC6xF74+Bm6pq++XtXFWL6VWHOWHpNTXtoCVJkrTSjULFd33gxibpfTy9doZ+3wZekGTtJOsAL2zGlqmqfg/8JMl+AOnZbp5ilyRJGmrJ3D/aMgqJ7+nAqkmuAN5Fr93hj6rqB8BxwHnAucB/VNUFyznmS4BDmvaJy4C95zpoSZKkLhiha9u63+pQVXcCz1nGqi36tnk/8P5J+/0U2Lpv+b19z38C7DXHoUqSJKlFnU98JUmSNI+8c5skSZLULVZ8JUmSNFCb04/NNRNfSZIkDdTmLAxzzVYHSZIkjQUrvpIkSRpohAq+VnwlSZI0Hqz4SpIkabARKvma+EqSJGmgUZrVwVYHSZIkjQUrvpIkSRrI6cwkSZKkjrHiK0mSpIFGqOBrxVeSJEnjIVXVdgwj4Zob7uz0F3K9tVZrO4RZu/7Wu9oOYextvHD1tkOYtVF4Hz1swzXbDmFWTrrwmrZDmLW1V+v+B6ovO/jtbYcwa9/90jvbDmFWnvTIdYei2HrpdbfOeY6z9cMXtnJu3f/OlCRJ0rxxOjNJkiSpY6z4SpIkaSCnM5MkSZI6xoqvJEmSBhqhgq+JryRJkqYwQpmvrQ6SJEkaC1Z8JUmSNJDTmUmSJEkdY8VXkiRJA43SdGYmvpIkSRpohPJeWx0kSZI0Hqz4SpIkabARKvla8ZUkSdJYsOIrSZKkgZzOTJIkSeoYK76SJEkayOnMJEmSNBZGKO8dn1aHJF9Kcn6Sy5IsasYOSfKjJOcl+ViSDzfjD0ryhSRLmsefthu9JEmSZmtsEl/gFVW1I7AT8LokDwfeAuwK/Cnw+L5t/xX4l6raGdgH+I9lHTDJoiRLkyz99PHL3ESSJKnbMg+PloxTq8Prkryweb458DLgW1V1A0CSk4HHNuufBTwh9ze1rJdkYVXd2n/AqloMLAa45oY7a57jlyRJ0iyMReKbZA96yexuVXVbkrOAHwJbDdhlAbBrVd2xUgKUJEkaUk5n1j3rAzc2Se/j6bU3rAP8WZINk6xKr6VhwteA104sJNl+ZQYrSZI0LJK5f7RlXBLf04FVk1wBvAv4PnAd8A7gPOAc4KfAzc32rwN2SnJxksuBV6/0iCVJkjSnxqLVoaruBJ4zeTzJ0qpa3FR8vwh8qdn+d8D+KzVISZKkITQ6jQ7jU/Ed5KgkFwKXAj+hSXwlSZI0esai4jtIVb2h7RgkSZKG2giVfMc68ZUkSdLUnNVBkiRJ6hgrvpIkSRqozenH5poVX0mSJI0FK76SJEkaaIQKvlZ8JUmSNB6s+EqSJGmgUerxNfGVJEnSFEYn87XVQZIkSWPBiq8kSZIGGqVWByu+kiRJGgsmvpIkSRoo8/CY1usmeyW5MslVSd60jPVrJPlcs/7cJFss75i2OsyRH1x3Y9shzMp91XYEs/eWky5pO4RZ23WbTdsOYVb+cqtN2g5h1kbhfXTkvk9sO4RZ2XjNNdoOYdY+ueTatkOYte9+6Z1thzBrT3nBP7UdwqzcfsGH2w4BaKfVIckqwLHAnsC1wJIkp1bV5X2bHQLcWFV/kuQA4N3A/lMd14qvJEmShs0uwFVVdXVV3QV8Fth70jZ7A8c3zz8PPDOZOk038ZUkSdJAmYf/puHhwDV9y9c2Y8vcpqruAW4GNp7qoCa+kiRJWqmSLEqytO+xaGW8rj2+kiRJGmweenyrajGweIpNrgM271verBlb1jbXJlkVWB+4fqrXteIrSZKkgVqa1WEJsGWSRyVZHTgAOHXSNqcCL2+e7wt8o6qmvFzfiq8kSZKGSlXdk+RQ4AxgFeATVXVZkqOBpVV1KvBx4FNJrgJuoJccT8nEV5IkSQO1dee2qjoNOG3S2BF9z+8A9pvJMW11kCRJ0liw4itJkqSBpjn9WCdY8ZUkSdJYsOIrSZKkwUan4GviK0mSpMFGKO+11UGSJEnjwYqvJEmSBmprOrP5YMVXkiRJY2HkE98kr0tyRZJPtx2LJElS12Qe/mvLOLQ6/C3wrKq6dnkbJlm1qu5ZCTFJkiR1gq0OHZHk34BHA19N8o9JvpfkgiTfTfK4ZpuDk5ya5BvAmUnWSfKJJOc12+7d6klIkiRpTox0xbeqXp1kL+DpwF3A+6rqniTPAt4B7NNsugOwbVXdkOQdwDeq6hVJNgDOS/I/VfWHNs5BkiRJc2OkK76TrA+cnORS4F+AJ/at+3pV3dA8fzbwpiQXAmcBawKPWNYBkyxKsjTJ0jM+/6l5C1ySJEmzN9IV30neCnyzql6YZAt6Se2E/mpugH2q6srlHbCqFgOLAb58ya9q7kKVJEkaDvb4dtP6wHXN84On2O4M4LVJ739zkifNc1ySJElDa5RmdRinxPc9wDuTXMDUle63AqsBFye5rFmWJElSx418q0NVbdE8/R3w2L5VhzfrjwOO69v+duBvVk50kiRJw81WB0mSJKljRr7iK0mSpBU3QgVfK76SJEkaD1Z8JUmSNNgIlXxNfCVJkjRQm9OPzTVbHSRJkjQWrPhKkiRpIKczkyRJkjrGiq8kSZIGGqGCr4mvJEmSpjBCma+tDpIkSRoLVnwlSZI0kNOZSZIkSR1jxVeSJEkDjdJ0ZqmqtmPQNCRZVFWL245jNrp+Dl2PHzyHYdD1+MFzGBZdP4euxw+jcQ7jxlaH7ljUdgBzoOvn0PX4wXMYBl2PHzyHYdH1c+h6/DAa5zBWTHwlSZI0Fkx8JUmSNBZMfLtjFHqIun4OXY8fPIdh0PX4wXMYFl0/h67HD6NxDmPFi9skSZI0Fqz4SpIkaSyY+EqSJGksmPhKkiRpLJj4SpIkaSx4y+IhluSQqvp43/IqwOFV9c8thrVcSXaYan1V/WBlxTJbSR4DXFtVdybZA9gWOKGqbmozrplIsmNVnT9p7LlV9d9txTRTSdYA9gG2oO/nVlUd3VZMM5FkP+D0qrolyeHADsDbuvS9AJDkocAuQAFLqupXLYc0Y0keDjySB76Pzm4vovGUZO2quq3tOFZEkrWAR1TVlW3Hopmz4jvcnpnktCSbJnki8H1g3baDmob3NY9jgXPpTffyseb5sS3GtSK+ANyb5E/oncfmwGfaDWnGPpZk64mFJH8FvKXFeFbEl4G9gXuAP/Q9uuItTdK7O/As4OPAR1uOaUaSvBI4D3gRsC/w/SSvaDeqmUnybuAc4HDgjc3jDa0GNQNJHpvkzCSXNsvbNn9IdUaSpyS5HPhhs7xdko+0HNa0JXkecCFwerO8fZJTWw1KM+J0ZkMuyf70ksU/AAdW1TkthzRtSU4BjqyqS5rlrYGjqmrfdiObviQ/qKodkrwRuKOqPpTkgqp6UtuxTVeSRwOfBw4EngocBDy3qm5uNbAZSHJpVW29/C2H08R7Jsk7gUuq6jMdfB9dCTylqq5vljcGvltVj2s3sulrzmHbqrqz7VhWRJJv0UvW/33ivdO1740k59L7w+nULp5DkvOBZwBn9cV/SVVt025kmi4rvkMsyZbAYfSqjj8DXpZk7XajmpHHTSS9AFV1KbBVi/GsiLubCunLgYnWgNVajGfGqupq4ADgFHrtAs/uUtLb+G6SLv9iuS7JvwP7A6c1rRtd+/l7PXBL3/ItzViXXE3Hvn8nWbuqzps0dk8rkcxCVV0zaejeVgJZMXcv4+enFcQOscd3uP0XcGhV/U+SAK8HlgBPbDesabs4yX8AJzbLLwEubjGeFfHXwKuBt1fVT5I8CvhUyzFNS5JLeOAP5I2AVYBzk1BV27YT2QrZHTg4yU+AO4EA1aFzeDGwF/Deqropyab0KnddchW9986X6b2v9qb3Pf56gKp6f5vBTSXJh+jFfBtwYZIz6b2PAKiq17UV2wz9rrnuoACS7Av8st2QZuyaJE8BKslq9Io7V7Qc00xcluRAYJWmOPU64Lstx6QZsNVhiCVZr6p+P2nssVX1o7ZimokkawKvAZ7WDJ0NfLSq7mgvqhWXZENg86rqRPKe5JFTra+qn62sWGZr0Ll05RxG5CLJI6daP8wX3SZ5+VTrq+r4lRXLbDRtS4uBpwA3Aj8BXlpVP20zrplIsgnwr/R63QN8DThsooVm2DWfur4ZeHYzdAa9C1U7+XttHJn4DrEkDwHeATy8qvZK8gRgt/6ZHoZd169+TXIW8Hx6n46cD/wGOKeqXt9mXDORZFfgsqq6pVleD9iqqs5tN7LpS/JWen84fbequnRRGwBJLgR2ojcrxWn0LtZ7YlX9RYthrbAkC4CFk/8wH3ZJ1qHXq39vs7wKsEbXZhdozmPBxPe0Vo7m/fI/VfX0tmPRiutaj9m4OY7eX5ObNss/Av6urWBmKsnz6f7Vr+s3v9xfRK9C92R6lYou+Shwa9/yrXRsRgF6vZl/BSxNcl6S9yXZu+2gZuC+qrqH3vvoQ1X1Ru7/vu6EJJ9Jsl6TdF0KXN5c9NklZwJr9S2vBfxPS7HMWJI1mo/ZDwP+PskRSY5oO66ZSPLBZTze2oXv5+YPpvuSrN92LFpxJr7DbZOqOgm4D6D5xdmliwCOpDfn500AVXUh8KgW41kRqzb9mC/m/ovbuibV99FOVd1Hx/r7q+qTVfUK4On0esb34/7e8S6YuEjyIDp6kSTwhOaPwBcAX6X3vfyyViOauTWr6o9/BDbPu3TBcNen9QNYE9ge+HHz2BbYDDgkyQfaC2vabgUuSfLx/uS97aA0fZ365TeG/tBMGTRxIcOuQJeuxr+7qm7uXZf3R13rrTmaXtX9O1W1pOmx+3HLMc3U1Ulex/1V3r+lV0HtjOYiyScAvwa+TW86pC7d/KGzF0n2Wa25GOkFwIer6u4kXft+/kOSHSZuHJJkR+D2lmOaic2qaq+2g5ilbYE/7Ws3+Si97+ndgUum2nFInNI81FEmvsPt9cCpwGOSnAM8iN4v/K7o/NWvVXUycHLf8tX0pgTrklcDH6Q3aX/R+7h3UasRzdzG9GakuAm4Afhd8wlIJ1TV5fTe/xPLPwHe3V5EK+TfgZ8CFwFnNxccdqrHl16LwMlJfkHvwqqH0ptiriu+m2Sb/mkiO2hDYCH3F3HWATaqqnuTDP38yl25EFKDmfgOt8cAz6F3t7B9gCfTrf9nr6V39eud9O529jV6FdTOaGamOITeFHJrTow3H7t3QlX9ht48vp1VVS8ESLIV8OfAN5OsUlWbtRvZ9DR/+L2TXtW6/3306NaCmqGq+iC9P6Am/CxJZy7yaS5MeirweGDiphtXVtXd7UU1Y12f1g/gPfSmlDuLXvxPA97R9I4Pfb/1KHwvjztndRhiSS6uqm3Tu83pW4H3Akc0F1gNvSSHTJ6BIsm7qupNbcU0U0lOpndrzQPpJe0vAa6oqsNaDWwGkjyWXpvDQ6pq6yTbAs+vqre1HNq0JXkuvaTlacAG9G7f/e2q+kSbcU1Xku/Q63n/F+B59FofFlRVZy5MatqujqSXfBXwHeDorkxDBZDkvKrape04VlTXp/WbkORh9PrDr6BX/b22qs5uN6rpGYXv5XFn4jvE0vHbnCY5Dfh0VX26Wf4wsFZVHdJuZNPX9/9g4o+Q1eglXLu2Hdt0ZTRuc/phen2A366qX7Qdz0wlOb+qdkzfrU0nxtqObbqSfJ3elHL9N6TZo6o6M8tJkn+hd1Hh5+i7KGyi57cLkmxH749A6H0/XNRmPDOV5JX0Wk42ozfrz67A96rqGW3GNV2j8L087rr0sfk4mrjN6Z7Au9O925zuA5ya5D56d626qUtJb2PiY9CbkmwN/Ap4cIvxrIi1q+q8SRcZdqY/FqCqDm3mtd45yQ7AeU0LR1fc2cx9++MkhwLX0at0dcmmVfXWvuW3JelSfyz0ZhOAB7ZcFdCVpOsw4FXcf3HViUkWV9WHWgxrpg4Ddga+X1VPT/J4evPVd8UofC+PNRPf4dbJ25wm2ahv8ZXAl4BzgH9OslFV3dBKYCtmcXp3bHsLvQsNFwJd+0ir87c5TbIfvVafs+j1BX4oyRur6vOtBjZ9h9GbNut19NqWngFMeTexIfS1JAcAJzXL+9Kb8aQzRuDGA4cAT564iUuSdwPfA7qU+N5RVXckIckaVfXDJI9b/m7tSvKpqnoZvd9nXf9eHmu2OmjONRde9L+x+kuN5UUAK1eWfZvTl3SpLzDJRcCeE1XeJA+idwel7dqNbPQluYXe93PoXYE/MZf4KsCtVbVeW7GtiCR/yf++WLUTF90muQTYeeL2uM3Ft0smPnLvgiRfpNcX+3f0ksYbgdWG/S6GSS6nd/OirwJ78MDfa3SsoDPWTHw1L5qPgnarqnPajmVFJJnylsRV9f6VFcuKWsY5rEWvVeYP0I1zmNDfT9csLwAuGvZf+En+iynmrq6q56/EcMZekn+jV617OvAf9KrW53WlBav5nn458MVm6AXAcVX1gbZimo0kfwasD5xeVXe1Hc9UmrnQXwM8ml57Q7j/D0ILOh1i4qt506UL8SZLcmTzdOIHW7/qQoWo7xweR6+n7sv0zuV59H7Zv7St2GYqyTH0Jr7/z2Zof3oXfP5De1EtX/OLfaCq+tbKimVFJXl883H0Dsta37ELwyYuUp34dyHw1ap66nJ3HhLN/4fdm8VvV9UFbcYzbpJ8tKpe03YcWnEmvpo3Sd5Lr//slOroGy3J8cBhVXVTs7wh8L4uzeOb5GzgL6vqlmZ5XeArVfW0diObmSQv4oG/8L841fbDpJmj9Pbq3S56Yk7ZNarqtnYjW77m4qlFSb7ZN9x/C+xOXBgGkOTcqnpyku8DLwKuBy6rqj9pObQpTbpu4n/xY3Zp+ry4TfPpb+jdfe6eJHdw/0dCXeoJ3HYi6QWoqhuTdK2K/RCg/2PEu5qxzkjy7qr6R/puFdo31gVn0usPvLVZXoveDV2e0lpE01RVE3f5+yi9j6R/n+QtwA70Lu7pkv9OsgG9myic34z9R3vhTNv5PPDTp4k/PCY+bvdjdmmaTHw1b6pq3bZjmAMLkmxYVTfCHysvXfu+OQE4r7moBJq+wNaiWTF7ApOT3OcsY2xYrVlVE0kvVXVrkrXbDGgFHF5VJzU31HkGvVk2PkrvjpJd8V56fZpPpfdp1LfpncNQq6pHTTxvfgZtSd/FeZKmr2u/wNUxTWvAA35Id+UOPY33Ad9r7uAGsB/w9hbjmbGqenuSr3L/pPd/3ZW+wCSvAf4WeHSSi/tWrUtviryu+EOSHSb6YZPsBNzeckwzNTGbw18CH6uqryTpzN3/GscDt3D/rZcPpPeH4Ytbi2gGBtz84bvAM1sMS+oUe3w1b7p+h54JSZ7A/RPcf6OqLm8znnGSZH1gQ+CdQP+trm/p72vsr8oPoyQ7A58FJu46tymwf1WdP3iv4ZLkv+ldzb4nvTaH2+ldJNmZKeWSXF5VT1je2LCamM6M3s0ftp+4+UNVvajl0KTOsOKr+dT1O/QA0CS6JrstqKqbgZuBv1rOpmfSS8aG1SXAvwF/Dvye3s1QLms1opnr5A11JvlBkl2r6vsASZ4MLG05ppno5M0fpGFi4qv55A9prSyTp5wbNifQS3gn2mQOBD5Fr3WmE5oZKE7pW/4lHbsDILAj8N0kP2+WHwFc2VRSq6q2bS+0abm2uTjvS8DXk9wIdOZGNNIwMPHVfPKHtFaWYe/Z2nrSx+nfbO4EpZVrr7YDmI2qemHz9Khmern1gdNbDEnqHBNfzRt/SEt/1PWP2EdCl27TvTxduPmJNIxMfDWv+u4yVMA5w35bSnXWsLc6dP0jdkkaCc7qoHmT5Ah6PYwTfYEvAE6uqq5NgaSWJTmkqj4+aexdVfWm5vlGw3z3qiSPnGr9KFUiJWmYmfhq3iS5Etiuqu5oltcCLqwqL3DTjCQ5Dfh0VX26WT6W3k0hDmk3MklSl9jqoPn0C3o3rrijWV6D3jyg0kztA5ya5D56FyjdZNIrSZopE1/NuSQfotfTezNwWZKvN8t7Aue1GZu6pbk964RX0psh5Bzgn4e9vUGSNHxsddCcS/LyqdZX1fErKxZ1W5Kf8MCpyvovYquqevRKDkmS1GEmvpKGWpIFwG5VdU7bsUiSum1B2wFodCV5bpILktyQ5PdJbkny+7bjUrdU1X3Ah9uOQ5LUfSa+mk8fAF4ObFxV61XVulW1XssxqZvOTLJPkmGfr1eSNMRsddC8ae7W9symYietsCS3AOsA99CbJST0enz9Q0qSNG0mvpo3SXYG3gp8C7hzYryq3t9aUJIkaWw5nZnm09uBW+nN5bt6y7Go45JsCGxJ7/0EQFWd3V5EkqSuMfHVfHpYVW3ddhDqviSvBA4DNgMuBHYFvgc8o8WwJEkd48Vtmk+nJXl220FoJBwG7Az8rKqeDjwJuKnViCRJnWPiq/n0GuCrSW53OjPN0h1VdQdAkjWq6ofA41qOSZLUMbY6aD6tD7wEeFRVHZ3kEcCmLcekbro2yQb0bln89SQ3Aj9rNSJJUuc4q4PmTZKPAvcBz6iqrZqLk75WVTu3HJo6LMmf0fuj6vSquqvteCRJ3WHFV/PpyVW1Q5ILAKrqxiTO7qAVkmQHYHeggHNMeiVJM2WPr+bT3UlWoZeokORB9CrA0owkOQI4HtgY2AT4ZJLD241KktQ1tjpo3iR5CbA/sAO9pGVf4PCqOrnVwNQ5Sa4Etuu7wG0t4MKq8gI3SdK02eqgeVNVn05yPvBMereYfUFVXdFyWOqmX9C7ccUdzfIawHXthSNJ6iITX82rZtqpH7Ydh7opyYfotcrcDFyW5OvN8p7AeW3GJknqHlsdJA2tJC+fan1VHb+yYpEkdZ+JryRJksaCszpIGnpJnpvkgiQ3eBdASdKKsuIraegluQp4EXBJ+UNLkrSCrPhK6oJrgEtNeiVJs2HFV9LQS7Iz8FbgW8CdE+NV9f7WgpIkdY7TmUnqgrcDt9Kby9fbXkuSVoiJr6QueFhVbd12EJKkbrPHV1IXnJbk2W0HIUnqNnt8JQ29JLcAawN3AXfTuwV2VdV6rQYmSeoUWx0kdcH6wEuAR1XV0UkeAWzackySpI6x4itp6CX5KHAf8Iyq2irJhsDXqmrnlkOTJHWIFV9JXfDkqtohyQUAVXVjEmd3kCTNiBe3SeqCu5OsAhRAkgfRqwBLkjRtJr6SuuCDwBeBByd5O/Ad4B3thiRJ6hp7fCV1QpLHA8+kN6PDmVV1RcshSZI6xsRXkiRJY8FWB0mSJI0FE19JkiSNBRNfSZIkjQUTX0mSJI0FE19JkiSNhf8fobTu5p7Wp0EAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 2 Axes>"
       ]
@@ -1407,25 +1418,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "['sex_enc',\n",
+       " 'fare_enc',\n",
        " 'class_enc',\n",
        " 'pclass_enc',\n",
-       " 'fare_enc',\n",
+       " 'deck_enc',\n",
        " 'age_enc',\n",
        " 'sibsp_enc',\n",
        " 'alone_enc',\n",
-       " 'parch_enc',\n",
        " 'embarked_enc',\n",
-       " 'embark_town_enc']"
+       " 'embark_town_enc',\n",
+       " 'parch_enc']"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1451,7 +1463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "scrolled": false
    },
@@ -1459,12 +1471,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2c4aa0d946be486caa61606f82c5eebc",
+       "model_id": "02a6a2f0a7994ed5975b5fc5bd387b91",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Sequentially adding best predictor...:   0%|          | 0/10 [00:00<?, ?it/s]"
+       "Sequentially adding best predictor...:   0%|          | 0/11 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1504,81 +1516,54 @@
        "      <th>0</th>\n",
        "      <td>[sex_enc]</td>\n",
        "      <td>sex_enc</td>\n",
-       "      <td>0.738934</td>\n",
-       "      <td>0.821885</td>\n",
-       "      <td>0.800416</td>\n",
+       "      <td>0.761615</td>\n",
+       "      <td>0.800476</td>\n",
+       "      <td>0.752577</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>[sex_enc, class_enc]</td>\n",
-       "      <td>class_enc</td>\n",
-       "      <td>0.817718</td>\n",
-       "      <td>0.878615</td>\n",
-       "      <td>0.837188</td>\n",
+       "      <td>[sex_enc, fare_enc]</td>\n",
+       "      <td>fare_enc</td>\n",
+       "      <td>0.826159</td>\n",
+       "      <td>0.858624</td>\n",
+       "      <td>0.785649</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>[class_enc, sex_enc, age_enc]</td>\n",
-       "      <td>age_enc</td>\n",
-       "      <td>0.834236</td>\n",
-       "      <td>0.884594</td>\n",
-       "      <td>0.842840</td>\n",
+       "      <td>[fare_enc, sex_enc, class_enc]</td>\n",
+       "      <td>class_enc</td>\n",
+       "      <td>0.834634</td>\n",
+       "      <td>0.883635</td>\n",
+       "      <td>0.808219</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>[class_enc, sex_enc, age_enc, embarked_enc]</td>\n",
-       "      <td>embarked_enc</td>\n",
-       "      <td>0.840116</td>\n",
-       "      <td>0.883343</td>\n",
-       "      <td>0.844790</td>\n",
+       "      <td>[class_enc, fare_enc, sex_enc, age_enc]</td>\n",
+       "      <td>age_enc</td>\n",
+       "      <td>0.846029</td>\n",
+       "      <td>0.900939</td>\n",
+       "      <td>0.810176</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>[class_enc, embarked_enc, sex_enc, age_enc, pa...</td>\n",
+       "      <td>[class_enc, fare_enc, age_enc, sex_enc, parch_...</td>\n",
        "      <td>parch_enc</td>\n",
-       "      <td>0.841274</td>\n",
-       "      <td>0.882856</td>\n",
-       "      <td>0.843425</td>\n",
+       "      <td>0.846955</td>\n",
+       "      <td>0.899748</td>\n",
+       "      <td>0.814090</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>[parch_enc, class_enc, embarked_enc, sex_enc, ...</td>\n",
-       "      <td>sibsp_enc</td>\n",
-       "      <td>0.843148</td>\n",
-       "      <td>0.890990</td>\n",
-       "      <td>0.849402</td>\n",
-       "      <td>classification</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>[parch_enc, sibsp_enc, class_enc, embarked_enc...</td>\n",
-       "      <td>fare_enc</td>\n",
-       "      <td>0.843590</td>\n",
-       "      <td>0.890712</td>\n",
-       "      <td>0.848688</td>\n",
-       "      <td>classification</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>[parch_enc, sibsp_enc, fare_enc, class_enc, em...</td>\n",
+       "      <td>[fare_enc, parch_enc, age_enc, class_enc, sex_...</td>\n",
        "      <td>pclass_enc</td>\n",
-       "      <td>0.843694</td>\n",
-       "      <td>0.890712</td>\n",
-       "      <td>0.848688</td>\n",
-       "      <td>classification</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>[parch_enc, fare_enc, sibsp_enc, class_enc, pc...</td>\n",
-       "      <td>embark_town_enc</td>\n",
-       "      <td>0.843694</td>\n",
-       "      <td>0.890712</td>\n",
-       "      <td>0.848688</td>\n",
+       "      <td>0.846955</td>\n",
+       "      <td>0.899748</td>\n",
+       "      <td>0.814090</td>\n",
        "      <td>classification</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1588,25 +1573,19 @@
       "text/plain": [
        "                                          predictors last_added_predictor  \\\n",
        "0                                          [sex_enc]              sex_enc   \n",
-       "1                               [sex_enc, class_enc]            class_enc   \n",
-       "2                      [class_enc, sex_enc, age_enc]              age_enc   \n",
-       "3        [class_enc, sex_enc, age_enc, embarked_enc]         embarked_enc   \n",
-       "4  [class_enc, embarked_enc, sex_enc, age_enc, pa...            parch_enc   \n",
-       "5  [parch_enc, class_enc, embarked_enc, sex_enc, ...            sibsp_enc   \n",
-       "6  [parch_enc, sibsp_enc, class_enc, embarked_enc...             fare_enc   \n",
-       "7  [parch_enc, sibsp_enc, fare_enc, class_enc, em...           pclass_enc   \n",
-       "8  [parch_enc, fare_enc, sibsp_enc, class_enc, pc...      embark_town_enc   \n",
+       "1                                [sex_enc, fare_enc]             fare_enc   \n",
+       "2                     [fare_enc, sex_enc, class_enc]            class_enc   \n",
+       "3            [class_enc, fare_enc, sex_enc, age_enc]              age_enc   \n",
+       "4  [class_enc, fare_enc, age_enc, sex_enc, parch_...            parch_enc   \n",
+       "5  [fare_enc, parch_enc, age_enc, class_enc, sex_...           pclass_enc   \n",
        "\n",
        "   train_performance  selection_performance  validation_performance  \\\n",
-       "0           0.738934               0.821885                0.800416   \n",
-       "1           0.817718               0.878615                0.837188   \n",
-       "2           0.834236               0.884594                0.842840   \n",
-       "3           0.840116               0.883343                0.844790   \n",
-       "4           0.841274               0.882856                0.843425   \n",
-       "5           0.843148               0.890990                0.849402   \n",
-       "6           0.843590               0.890712                0.848688   \n",
-       "7           0.843694               0.890712                0.848688   \n",
-       "8           0.843694               0.890712                0.848688   \n",
+       "0           0.761615               0.800476                0.752577   \n",
+       "1           0.826159               0.858624                0.785649   \n",
+       "2           0.834634               0.883635                0.808219   \n",
+       "3           0.846029               0.900939                0.810176   \n",
+       "4           0.846955               0.899748                0.814090   \n",
+       "5           0.846955               0.899748                0.814090   \n",
        "\n",
        "       model_type  \n",
        "0  classification  \n",
@@ -1614,13 +1593,10 @@
        "2  classification  \n",
        "3  classification  \n",
        "4  classification  \n",
-       "5  classification  \n",
-       "6  classification  \n",
-       "7  classification  \n",
-       "8  classification  "
+       "5  classification  "
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1646,17 +1622,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As can be seen, we have completed 6 steps till no further improvement can be observed"
+    "As can be seen, model improvement gradually flattens when more variables are added."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1674,21 +1650,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Based on the performance curves (AUC per model with a particular number of predictors in case of logistic regression), a final model can then be chosen and the variables importance can be plotted:\n"
+    "Based on the performance curves (AUC per model with a particular number of predictors in case of logistic regression), a final model can then be chosen and the variable importance can be plotted."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "['parch_enc', 'class_enc', 'embarked_enc', 'sex_enc', 'age_enc', 'sibsp_enc']"
+       "['fare_enc', 'parch_enc', 'age_enc', 'class_enc', 'sex_enc', 'pclass_enc']"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1703,12 +1679,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1740,22 +1716,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'meta': 'logistic-regression',\n",
-       " 'predictors': ['parch_enc',\n",
+       " 'predictors': ['fare_enc',\n",
+       "  'parch_enc',\n",
+       "  'age_enc',\n",
        "  'class_enc',\n",
-       "  'embarked_enc',\n",
        "  'sex_enc',\n",
-       "  'age_enc',\n",
-       "  'sibsp_enc'],\n",
-       " '_eval_metrics_by_split': {'train': 0.8431478147962346,\n",
-       "  'selection': 0.8909899888765294,\n",
-       "  'validation': 0.8494022869022869},\n",
+       "  'pclass_enc'],\n",
+       " '_eval_metrics_by_split': {'train': 0.8469551282051282,\n",
+       "  'selection': 0.8997477931904161,\n",
+       "  'validation': 0.8140900195694718},\n",
        " 'params': {'C': 1000000000.0,\n",
        "  'class_weight': None,\n",
        "  'dual': False,\n",
@@ -1772,17 +1748,17 @@
        "  'verbose': 0,\n",
        "  'warm_start': False},\n",
        " 'classes_': [0, 1],\n",
-       " 'coef_': [[1.8355006899588193,\n",
-       "   4.425089647429058,\n",
-       "   2.6962961751414154,\n",
-       "   4.728968513306399,\n",
-       "   5.021971105587632,\n",
-       "   2.1483972871281782]],\n",
-       " 'intercept_': [-8.671610876045246],\n",
+       " 'coef_': [[0.17209928999041363,\n",
+       "   0.6121273655755664,\n",
+       "   4.439417790859315,\n",
+       "   2.256701549111471,\n",
+       "   4.621198868597589,\n",
+       "   2.256701549111471]],\n",
+       " 'intercept_': [-6.192793226125688],\n",
        " 'n_iter_': [5]}"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1794,7 +1770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1839,7 +1815,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1850,7 +1826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1860,23 +1836,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "accuracy             0.814607\n",
-       "AUC                  0.890990\n",
-       "precision            0.710145\n",
-       "recall               0.790323\n",
-       "F1                   0.748092\n",
-       "matthews_corrcoef    0.604243\n",
-       "lift at 0.05         2.510000\n",
+       "accuracy             0.808989\n",
+       "AUC                  0.899748\n",
+       "precision            0.701493\n",
+       "recall               0.770492\n",
+       "F1                   0.734375\n",
+       "matthews_corrcoef    0.587335\n",
+       "lift at 0.05         2.920000\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1887,12 +1863,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x576 with 2 Axes>"
       ]
@@ -1909,12 +1885,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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4vEsQQf7GzAAXC/bEz1uzz/Ivl47B6kfh8HLwCoAKHSCkUtrxPFiiQUVaREQkT1u0MZF9J+xT0SZgcKdAapTVQ04kl7BZYefH8OsYSLlsHzMnwsaXocunhkbLDirSIiIiedSmfcms3pXseH13M3/uqOhjYCKR65zfD6uGwvGo6wZN0HAEtM4fO7OpSIuIyG25tuVZRrs1GO1EnB+XPJNvfmIelJRq4+sNiY7XDSp507Wxn4GJRP6f1Qx/vAebXrKvh76mSA2InANlWxmXLZupSIuIyG1ZsyuZr39LvPmJhgiBPVeMDpHjShfxZHDHIO3LLMY7vRNWPAyn/0wbM3lCszHQ/EXwyl9/2FORFhGR27Ing8c+i/tcu7nQz0clWgxmtcDS/0D8vrSxkg0h8hMo2cCwWDlJRVpERG6L7bofVy/jReHA3HP3/cWLFwkJCTE6Ro7x9oR2df0ILayHnEgu4OFp3wd6USfw9IWWr0Djp8Az/978qiItIiLZ5q6GftQLyz03u0VHH6NWrbJGxxDJn8xJ6ZdqhHWE8HehcjcoWsOYXG6Ue6YNRERERCRviFkFc2vBgcXpjzV5qkCUaFCRFhERERFXJcXDzw/Dt53hUgysedw+VkBpaYeI5BsWq435UYku3/yWmloc7z8u5GyoAuBSYu7b9k5EcsCB72DNY3AlNm3MkgJn/4ZyrY3LZSAVaRHJN6KPp/LrnlvZM9gTklQCs5OXp3aOEMl3rsTC2idg/yLn8ep9oMMUCAw1JlcuoCItIvnG5au2m58kOaZcMU+qldb/VkTyDZsN9nwG6/7nvHwjsBR0/Aiq9TIuWy6h3/FEJF9qUMmb+1sF3PCcQwcPUqVqVTclyt88TFA0yAOTHggikj9cPgErB0PMCufxug9D+DvgV8SYXLmMirSI5Et+3iZKFLrx3rpn/a03PUdEpEDy8ITYLWmvC1WEzrMgrJNhkXIj7dohIiIiIs4CS0G79wETNBoFA/9Sic6AZqRFJE+y2WzEnLZw/JzFMXYoVo+qFhG5ZZZUOB5lf5jK9WoPgJKNoEQ9Y3LlASrSIpIn7Ticykc/JxgdQ0Qkb4vbbl8LfXoHPLARyjRPO2YyqUTfhJZ2iEietO/EjWefSxXR2mcRkUyZk+DX52F+Uzi9HbDZC7X5VrYQFc1Ii0ieVynUkzLXFedSRTxpV8fPwEQiIrnYid9gxWCI35c25uUHdQbZbzIUl6lIi0ie16yqL53uUHEWEbmhlMv2Wegd04Dr9t0v1xY6z4Yi1QyLllepSIuIiIjkdzErYOUwuHw0bcwnGNq+BfWHgUmrfbNCRVpEREQkP9vyJvz6nPNYpa7QaQYUKm9MpnxCRVqkALLZbJyMt7Djn1R2Hknh3CWr0ZFuWVKqHgcuIuKSKj1g43iwpIBfMegwGWo+aN+VQ26LirRIAWG12jgUZ2bHP6lsP5zCmTxYnjPjrd/JREQyV6w2NH8Rzv5tL9EBJY1OlG/ofz8i+ViK2Ub08VS2/5PCriOpXL6a/2ZxS4Z40LCSj9ExRESMZ7PB3/PsM893POJ87M5xmoHOASrSIvlMQpKVXTGp7IxJYffRVFLMGZ/n6w11K/jQsJI31Up74emZN3+DDfY34aH/OYhIQXfxsP1mwqOrwcsfwiKgcOW04/p9MkeoSIvkA2cvWdhxOJUdMSkcOGnGmsnEcyF/Ew0q+dCgkjc1y3rj7aXfWEVE8jSrxb6d3a9jwZxoHzNfhd8nwF3zDI1WEKhIi+RBNpuNY+f+vzwfTuHYWUum54YWti99aFDJm0qhXpq9FRHJL85F2x+scmpT2pjJAxr9D1pNMC5XAaIiLeJGyak2th1K4WpK1tcqn/n/2edzlzO/WbByqOf/zzz7UFqPyhYRyV8sqbD1LfussyUlbbx4Xeg8B0o3My5bAaMiLeImZouND5ddZv/JTBYt3wYvD6hZzpsGlby5o6IPhQO1sb6ISL4Utw1WPAxndqWNeXjbbya8cyx46uZrd1KRFnGTr39LzNYS7e9jol6YvTzXreCDv4+WbIiI5GtWCyx7COL3pY2VagaRc+yz0eJ2KtIibvDrnmTW7U52vG5U2ZuQLM4a+3iZqF3Om+plvPDKozttiIhIFnh4Qqfp8E0H+84crV6DRqPs42IIFWmRHHYo1syXUVccrxtX8eGRzoGYdNOfiIjcSGoieAc4j1VoD+0nQ+VuULiKMbnEQQspRXLQhStWZqy4jPn/7wssW9ST/3ZQiRYRkZv4Zzl8UgP2L0p/rNFIlehcQkVaJIeYLTZmrEjgwhX7Dh0BviYe7xKEr7dKtIiIZCLxLCzvB991g4TjsOZxuHre6FSSCS3tEMkGX/56hc37U7Be9yQUqw3HUwVNJnikcxAlQrSOTUREMmCzwb6FsPYJuHrmunErnN8LZVsal00ypSItcpti4y388lfyDc/p3dyf2uW93ZRIRETylISTsHo4HPrBebzWQ9DuAwgobkgsuTkVaZHblHiDh6uYgPA6vnRu4Oe+QCIikjfYbPDXHIh6BpIvpo0HlYOIGfYbCiVXU5EWyUYVSnjyzD2FHK89Pezb1YmIiDi5dAxWDIKja53H73gU2rwJvoUy/DLJXVSkRbKRpwk9GEVERG7O0wdO70h7XbgqdJ4N5cMNiyS3Trt2iIiIiLhbYCi0/wBMHtDkWRiwUyU6D9KMtIiIiEhOsqTYl3BUust5vFY/CG0CxWoZk0tum2akRURERHJK7Fb4ojEs7gonNzkfM5lUovM4FWkRERGR7JaaCOuegS+bw9ndgA1WDAbzjbdLlbxFSztEREREstOxdbByCFw4lDbmFQD1h4GHqld+oqspIiIikh2SL0LUaNg103m8QkeImAmFKxuTS3KMirSIiIjI7Tq0FFY/Cgkn0sZ8QyD8Paj7X/t6aMl3VKRFREREbsfm12HDOOexKvdAp48gqIwxmcQtVKRFbsHVFBvTll/mwCmzYyzzB4SLiEiBUPVe2PSKfZu7gJLQYSpUv0+z0AWAirTILdj+Twr7TpozPe7rrd80RUQKnGI1ocVLcH4ftHsP/IsZnUjcREVa5BYkp2Y+/1zI30TEHX5uTCMiIm5ls8KuWWCzQIPHnI81G6sZ6AJIRVoki8Lr+PJAmwDHa5MJPPSbqIhI/hR/AFYOhePrwcsfwjpDkappx/X7f4GkB7KIZJHJBJ4eJsc/KtEiIvmQ1Qxb34HP6ttLNID5qv0GQynwNCMtIiIikpEzu+xPI4z7I23M5AlNn4Xm443LJbmGirQIkJhsZfOBFC4lWgE4ezaQ/ZcT050Xc9ri7mgiIuJu5mT7jPOW1+0z0teUaACRcyC0kWHRJHdRkRYBvv4tkY17U64bCYKYJMPyiIiIQU7+DisHw7k9aWOePvZdOZo8C57exmWTXEdFWgQ4koWZ5kol9ctHRCRfsVrg50EQvy9trExL6DzHvsWdyL+oCYj8S7s6viRfOUfxEiUyPadsUU8aVNKshIhIvuLhCREzYGF78A6ENm/Yt7kzaW8GyZiKtMi/hNf15fLpK9SqVcHoKCIikpNSEuyF+fpdl8q3g47ToFJXCKloVDLJI/RHLBERESl4Dn4Pn1SH/d+kP9bgMZVocYlmpCVfMVsyf/LgjWTtq0REJM+5EgdrR8L+hfbXa0ZAhY56rLdkiYq05AtXkqx8sPSytqcTEZGM2WwQPR9+GQVJ59PGTR72pxaqSEsWqEhLvvDnP6nZVqL9vE1czpZ3EhGRXOHSUVj9KBz+yXm8zkAIfw/8ixqTS/I8FWnJF5JT0xZnmACPLKz+9/SAVrV8KV7IkzMnsi+biIgYxGaFnTMgagykJqSNFwqDiI+hYqRx2SRfUJGWfKdDPV/6tgk0OoaIiBjp0hFY3h9O/HrdoAkajoDWr4NPkGHRJP9QkRYREZH8x8vf+emERWrYH+9dtpVxmSTf0fZ3IiIikv8ElIQOk8HkCXeOgwE7VKIl27m9SEdFRREZGUlERAQzZ85Md/zkyZP079+fnj170qNHD9avX+/uiCIiIpKHmCwp8M+y9AdqPgj/3QutXwMvP/cHk3zPrUXaYrEwYcIEZs+ezbJly1i6dCkHDx50Omf69Ol06dKFJUuW8P777/PKK6+4M6KIiIjkJSc3Uem3e+G7HnDiN+djJhMUqWpMLikQ3Fqkd+3aRVhYGOXLl8fHx4du3bqxZs0ap3NMJhMJCfY7ay9fvkzJkiXdGVFERETygpQE+OVJ+KoVvgn/ADZYMRjMSUYnkwLErTcbxsXFUapUKcfr0NBQdu3a5XTOiBEjGDx4MF988QVXr15l7ty5mb5fdHR0jmXNTFJSkiGfKzcWF+cPFALgfHw80dFHb+v9dJ0LBl3n/E/XOH8KPLORUrtfwudq2l6lFs8AzpS5n/j9h+wPWZF8Jzf+es51u3YsW7aMXr168fDDD7N9+3ZGjx7N0qVL8chgY+BatWq5PV90dLQhnys3diIlCQ4mAlC0SBFq1Sp3W++n61ww6Drnf7rG+UxSPKx7Gv52nmRLKNGaoJ5fUKpQGKUy+VLJ+4z69bxt27ZMj7n1j2yhoaHExsY6XsfFxREaGup0zqJFi+jSpQsADRs2JDk5mfj4eHfGFBERkdzmwGKYV9u5RPsVhS6fcazJx/aHrIi4mVuLdL169YiJieHYsWOkpKSwbNkyOnTo4HRO6dKl2bRpEwCHDh0iOTmZokX16E4REZECa9ME+KE3XEmbjKP6/TBoD9Tub7+pUMQAbl3a4eXlxfjx4xkyZAgWi4XevXtTrVo1Jk+eTN26denYsSPPPfccL7zwAvPmzcNkMvHGG29g0i8QERGRgqvGf2Dz62BJhsBS0HE6VOtpdCoR96+RDg8PJzw83Gls1KhRjh9XrVqVBQsWuDuWiIiI5FZFa0CLl+HCQQh/G/yKGJ1IBMiFNxuKiIhIAWWzwo7pgA0ajnA+1myMlnBIrqMiLSIiIsY7v8++D/TJ3+xPIawYCUWqpR1XiZZcSBstioiIiHEsqbB5Enx2h71Eg/2hKlveMDaXiAs0Iy0iIiLGiNsOKx6GMzvSxjy8oNlYuHOcYbFEXKUiLSIiIu5lToJNr8DWt8FmSRsPbQyRn0CJ+sZlE7kFKtKSL8RdtNz8JBERMd7xDbByMMTvTxvz8oOWE6Dx/+wz0iJ5hH62Sp637VAK63YnO16HldRPaxGRXMlqgZVDnEt0uXDoPMv5xkKRPEI3G0qedvycmblrEhyv61bw5s5qPgYmEhGRTHl4QueZ9h/7BEOnGXD/WpVoybM0dSd51pUkKx/9lECy2f66RCEPhnQKxMNDWySJiOQKyZfshfn6revKtYWIj6FSVwguZ1w2kWygGWnJk6xWGzNXJXDmkhUAXy94vGsQgX76KS0ikivsXwSfVIN9X6c/Vn+YSrTkC5qRllzlSpKVVBfuG1y1I4k9x8yO1w93DKJsUf10FhExXMIpWPM4HPzO/nrtE1ChEwQUNzaXSA5Q85BcY8GGK6zdlYztFr+ua2M/GlXRumgREUPZbPD3PFj3FCRfSBv39IFLh1WkJV9SkZZcIdVs45e/br1E1wvz5p6m/jmSSUREXHTxMKwcBkdXO4/XGwrhb4NviDG5RHKYirTkClab/Z9rQgJufsNgxZJePNxRNxeKiBjGaoEdU+HX58GcmDYeUtm+pV2FDsZlE3EDFWnJdXy84J1BRYyOISIiN3LxMCx7EE79njZm8oBG/4NWE8A7wLhsIm6iIi0iIiK3zjsILhxMe128LnSeA6WbGZdJxM20V5iIiIjcuoAS0P5D8PCGFi9Dv20q0VLgaEZaREREbiz1KsSsgGo9ncdr9oUyzSGkkiGxRIymGWkRERHJ3PEo+PwO+KEXHP/V+ZjJpBItBZqKtIiIiKSXfAlWPwZfh0P8AfvYyiFgTjI2l0guoqUdIiIi4uyf5bDqEUg4njbmUwiaPAOevsblEsllVKRFRETELvEsrHsSouc7j1fuAZ2mQ3BZQ2KJ5FYq0iIiIgWdzQb7FsLaJ+DqmbRx/xLQYQrUuN++HlpEnKhIi4iIFHSbXrH/c71aD0G7DyCguCGRRPIC3WwoIiJS0NV8ELz87D8OKge9lkLXL1SiRW5CM9IiIiIFXdHq0PJVuPgPtHkDfAsZnUgkT1CRFhERKSisFtj+IWCCxk86H2v6jBGJRPI0FWkREZGC4OxuWDEYYrfYt7Cr1AWK1jA6lUiepjXSIiIi+ZklBTa+Ap83spdoAEsybH3b2Fwi+YBmpEVERPKrU1tg5WD7bPQ1Ht7Q/EVoNsa4XCL5hIq0iIhIfpOaCL+Nhz/fB5s1bbx0c4icA8VqG5dNJB9RkRYREclPjv4CK4fYd+C4xisA2rwODUaAh6dx2UTyGRVpERGR/MJqgTWPOZfoCp2g80wIqWRcLpF8SjcbioiI5BcentB5NmAC3xDoPAfuW6kSLZJDNCMtIiKSVyVdsBdmkyltrGwr+zroipEQVMawaCIFgWakRURE8hqbDaK/gk+qwd6v0h+v+1+VaBE30Iy0GMJqsxEXb8VqswGQYjY4kIhIXnH5OKweDv8stb9eOxLCIiCghLG5RAogFWlxO4vVxqsLL3HivMXoKCIieYfNCrtmQdSzkHI5bdw7AC4fVZEWMYCKtLhdzGnzDUt0SIBWHImIOIk/CKuGwrF1zuMNHoc2k8An2IhUIgWeirS4neW6ZwP4eEHxQml7mvr7mOjW2M+AVCIiuZDVDNs+gI0vgjkpbbxIdfvuHOXaGBZNRFSkxWBhJbwY3auQ0TFERHKfC4dgaV+I+yNtzOQJTUdDi/HgpUkHEaOpSIuIiORGvoXh0pG01yUa2Le1C21kVCIR+RctRhUREcmN/ItBx6ng6QutX4eHtqhEi+QympGWbHP5qpWftycRd+HGu3EkJNnclEhEJI9IvQKHf4Lq9zmPV+8DpVtAofLG5BKRG1KRlmyzelcSK3ck3fzE61z/MC4RkQLpyBr7jhwXD8P966B8eNoxk0klWiQX09IOyTZnL1lvftK/1A/zzoEkIiJ5QNIFWDEEFnWyl2iAlUOcd+cQkVxNM9KSIzrd4UuNMjcuyUWCPAgroZ+CIlIAHVgCax6DK6fSxvyKQPMX7GuiRSRPUIuRHFGxhBcNKvkYHUNEJHe5Egdrn4D93ziPV+ttv7EwsJQxuUQkS26pSNtsNmJjYzl16hQ1a9YkICAgp3KJiIjkHzYbRH8BvzwJSefTxgNCoeM0qN7bsGgiknUuF+n58+czffp0zp49i8lkYtGiRdSpU4cRI0bQpEkTBg0alIMx5XZdSrRy7vKtr2G+FQlJOfv+IiJ51sbx8PtrzmN1BkH4u+Bf1JBIInL7XCrSs2fPZvLkyQwdOpQ777yTgQMHOo41a9aMZcuWqUjnYjsOpzBjRYLTo7lFRMSNag+EP96x30hYKAwiZkLFzkanEpHb5FKR/vLLLxk5ciRDhw7FYnHeI7hSpUrExMTkRDbJJlsPpri9RBcO1IYwIiIORapCq4lwKcb+cBWfIKMTiUg2cKlInzlzhrp162Z4zMPDg+Tk5GwNJdnr+hJdopAHgX45t3mzCahV3pvqZXQfq4gUQFYz/PEueHhBk6edjzV5yphMIpJjXGo7YWFhbNmyhRYtWqQ7tnXrVqpUqZLtwSRn9GruT9Oq2lpJRCTbnd4BKwbD6T/tW9hV6gbFahqdSkRykEt//z5w4EBmzZrFRx995FjGce7cOb755hvmzZun9dEiIlJwmZNgwzj4oom9RANYkmHbe8bmEpEc59KMdJ8+fbh48SLTpk1jypQpAAwbNgx/f39GjBhBjx49cjSkiIhIrnTiN/vTCM/vTRvz9IWWr6Rf2iEi+Y7LC1mHDBlC37592bFjB/Hx8YSEhNCwYUOCg4NzMp/couNnzfy0PYnLV9MWRh8/Z7nBV4iIyC1LSYANz8P2qYAtbbxsa+g8G4rWMCyaiLiPS0V6yZIlhIeHU6RIEVq3bu107MKFC6xbt46ePXvmRD65RQs2JLLvpDnT4x6mnLvRUESkQIhZCauGwaUjaWPeQdD2TbjjUTBp1yKRgsKlX+1jx47l2LFjGR47fvw4Y8eOzdZQknXnEjLf587fx0TNstpNQ0Qky6xm+9MJry/RFe+CQbuhwWMq0SIFjEutymazZXrswoULBAVpP8zc6L8dAp32c64c6oWfj2akRUSyzMPLvnRjQWvwKwLtP4Ba/UB/2ydSIGVapFevXs2aNWscrz/66COKFCnidE5KSgp//PEH9erVy7mEkmXVSntRIsTT6BgiInnX1XPgV9S5KJdtCXfNg4qREBhqWDQRMV6mRfr8+fPs37/f8fro0aOcOXPG6Rxvb29atWrF8OHDcy6hiIiIu9lssOczWPc/aD8Zavd3Pl5ngDG5RCRXybRI33///dx///0A9O/fn5dfflkPXhERkfzvYgysegSOrLS//uVJCOus2WcRScelNdKff/55TucQERExls0K26fBhrGQeiVt3DcEEk6qSItIOi5v4ZCQkMCaNWuIiYkhOTk53fHRo0dnazARERG3ObfX/mCVk79dN2iCRqOg9WvgHWhYNBHJvVwq0kePHqVv374kJSVx9epVihYtysWLFzGbzYSEhBAUFKQiLSIieY8lFf54Gza9ApaUtPFitaHzHCjT3LhsIpLrubTh5euvv069evXYuHEjNpuNmTNnsnPnTt5++20CAgJ4//33czqniIhI9oo/APObwYZxaSXawwuavwj9/lSJFpGbcqlI//XXX/Tt2xcfHx8AUlNT8fT0pEePHvz3v//l9ddfz9GQIiIi2c6vGFw5mfY6tDH02watJoCXr3G5RCTPcKlIJycnExQUhIeHByEhIZw+fdpxrFq1auzduzfHAoqIiOQI/6LQcRp4+UHbt+DB36FEfaNTiUge4lKRrlixIidOnACgdu3aLFiwgOTkZFJTU1m0aBElS5bM0ZAiIiK3JeUy7F2Qfrxabxh8CJo+a1/WISJyC1z6XaNbt26OWedRo0YxePBgGjdujMlkwmq1MmnSJJc/MCoqiokTJ2K1WunTpw/Dhg1Ld87y5cuZOnUqJpOJmjVr8u6777r8/gXZqfMWLl6xOl57uPTHJBGRfO7wz/Z9oS8fhYBQqNA+7ZjJBEFljMsmInmaS0X6v//9r+PHDRo0YOnSpURFRZGcnEzz5s2pXr26Sx9msViYMGECc+fOJTQ0lPvuu48OHTpQtWpVxzkxMTHMnDmTr776ipCQEM6dO3eL31LBlJhsZepPl0m12F+XKuxB0SA1aREpwK6eo/TOsXDi+7SxVUNh4G77cg4RkduUpb/HKl26NP/5z38AsNlsLF++nK5du97063bt2kVYWBjly5cH7DPda9ascSrSCxcu5KGHHiIkJASAYsWKZSVigWK12pi96gqnL9pno328YFjnIEwmk8HJREQMYLPBgW9hzeMUTky7pwf/4tDyVfDUjYQikj1cKtLnz5+nSJEiTsUsKSmJRYsWMW/ePE6cOOFSkY6Li6NUqVKO16GhoezatcvpnJiYGAD69u2L1WplxIgRtG3bNsP3i46OdiV+tkpKSjLkc2/kt38C+etokON1RPULJJyJI/qMgaHyuNx4nSX76TrnP15JZwj9+1UKxa12Gr9YuhtxtcdioSjoBvl8R7+WC4bceJ0zLdJXr17l9ddf58cffyQ5OZnAwECGDx/O4MGDWbBgAR9++CHnz5+nVatWvPHGG9kWyGKxcOTIET7//HNiY2Pp168fP/74I4UKFUp3bq1atbLtc10VHR1tyOdmZtuhFDYfSXC8vquhHz1bVDYwUf6Q266z5Axd53zEZoO/58FvT0HyBcdwql8o3nfNIqRKD0IMCyc5Tb+WCwajrvO2bdsyPZZpkZ42bRrfffcdvXv3pmbNmpw8eZKPP/6YnTt3snLlSpo3b85TTz1F/fqubxUUGhpKbGys43VcXByhoaHpzrnjjjvw9vamfPnyVKxYkZiYmFv6nLwkNt7CmUuWLH1tUoqNT3+54nhdp7w3ve70z65oIiJ5x4ZxsOVfN77XH8Y/oYOpUaWZMZlEJN/LtEivWrWKxx9/nOHDhzvGmjZtyrBhw+jduzcTJ0685Q+rV68eMTExHDt2jNDQUJYtW5ZuR45OnTqxbNkyevfuzfnz54mJiXGsqc5vNu1L5pM1V25+ogtKFPJgaEQgHh5aFy0iBVC9wfDnB2C+CoWrQMQsqNAeay77a2ARyV8yLdInTpygadOmTmPNmtn/VN+rV6+sfZiXF+PHj2fIkCFYLBZ69+5NtWrVmDx5MnXr1qVjx460adOG3377ja5du+Lp6cno0aMpUqRIlj4vt9sZk5It7+PrBY93CSLQT7t0iEgBVbgKtJkEl47Zn0zoHWB0IhEpADIt0maz2fFI8Gu8vb0B8PfP+vKB8PBwwsPDncZGjRrl+LHJZGLs2LGMHTs2y5+RV9hsaT8uX9yTkIBbL8LentCxvh9li+lBAiJSAFhSYOtb4OEDzUY7H2s0KuOvERHJITdsX59//jklSpRwvLb9f/P77LPPnLalM5lMPPvsszkUsWDo1tifxlV8bn6iiEhBFfsHrBwMZ3aBpw9U6Q7FahudSkQKsEyLdJkyZTK8S7FMmTJs3brVaUxFWkREckzqVdj4Emx7F2z///RWSwpsnwKdphubTUQKtEyL9Nq1a92ZQ0REJL1j62HlELhwMG3Myx9aT4SGI43LJSJCFp9sKDe2eX8yv+xOJtVsu+F5Zy5Z3ZRIRCSPSb4Ev46BnTOcx8u3h86z7DcXiogYTEU6m6WYbXy+7grJ5lv7Ok9tuCEiYvfPclj1CCQcTxvzKQTh79q3uTNpm08RyR1UpLNZcqrtlkt00SAPapbzzplAIiJ5idUMUc86l+jKPexroYPLGpdLRCQDKtI5yN/HxNP3BN/wHBNQpqgnXp6aYRERwcMLOs+Br1qCf3HoMAVq3K9ZaBHJlVSkc5CnB4SV0H9iEZFMJZ4G/xLORblMc+j6BYR1hoDixmUTEbmJW16Za7PZiIuLw2y+xfULIiIi19hssGsWzKkGez5Lf7zWgyrRIpLruVyk169fT58+fahXrx7t27dn3759ALz44ot8//33ORZQRETymQuH4JuOsGoYpFyCdf+DK7FGpxIRuWUuFeklS5YwfPhwKleuzKuvvorVmrZtW1hYGIsWLcqxgCIikk9YLfDHe/BpPTj2S9q4f3G4EmdcLhGRLHKpSE+fPp3Bgwfz5ptvcvfddzsdq1atGocOHcqRcCIikk+c3W2/gXD902C+ah8zeUDT0dB/J5S8w9h8IiJZ4NKdcCdPnqRly5YZHvPx8SEhISFbQ4mISD5hSYHNk2DzRLCmpo2XqG/fnaNUE+OyiYjcJpdmpEuXLk10dHSGx3bv3k1YWFi2hhIRkXzg/H74ojFsejmtRHv6QKtX4aE/VKJFJM9zaUb6vvvuY+rUqRQrVoxOnToB9t07Nm3axOzZs3n88cdzNKSIiORBASXg6tm016WbQ+QcKFbbuEwiItnIpSI9dOhQTp06xXPPPYenpycAffv2xWq18p///IcBAwbkaEgREcmD/IpAx2mwvD+0eR0ajAAPT6NTiYhkG5eKtMlk4qWXXmLQoEH8/vvvxMfHExISQvPmzalUqVJOZxQRkdwu+SIc+gFq93cer3YvDGkFgaHG5BIRyUEuFenExEQCAgIICwvTemgREXF26EdY/SgknITAMhDW0fm4SrSI5FMu3WzYsmVLnnzySVatWkVKSkpOZxIRkbwg8QwsfQCW3G0v0QCrhoI5ydhcIiJu4lKRfvbZZzl79iwjR46kRYsWPPvss6xbt06PCRcRKYhsNoj+EubWgn0L0sYDSkLbt8DT17hsIiJu5NLSjoceeoiHHnqIuLg4fvrpJ37++WceffRRQkJC6NixI926daNVq1Y5nVVERIx26RisGQ7/LHMerz0A2r0H/sWMySUiYgCXivQ1oaGhDBo0iEGDBnHy5El++ukn5s2bx5IlS9izZ09OZRQREaPZrLBrFkQ9CymX08aDK0DEx1DpLuOyiYgY5JaK9DVHjhxh+fLlLF++nDNnzlC6dOnsziUiIrnJr8/D1jedxxqMsG9r5xNsTCYREYO5XKRPnDjB8uXL+emnn4iOjqZYsWLcddddvPzyyzRu3DgnM4qIiNHqD4PtU8CcCEVqQOfZUK610alERAzl8pMN//77b0JCQujcuTOjR4+mWbNmeHi4dK+iiIjkdYUrQ9s37btztBgPXn5GJxIRMZxLRbpatWqMHDmSVq1aOZ5sKCIi+ZA5GTZPtBflO593PtZwhDGZRERyKZeK9KRJk3I6h4iIGO3k77ByMJzbAx7eUOVuKF7X6FQiIrlWpkV6/fr1NG7cmKCgINavX3/TNwoPD8/WYCIi4iapV2DDC/DnZMBmH7Omwo7p0GmaodFERHKzTIv0I488wsKFC6lfvz6PPPLIDd/EZDIRHR2d7eFERCSHHVkNq4bBxcNpY96B0OYNaPCYcblERPKATIv0mjVrKFGihOPHIiKSjyRdgPVPw+5PnMcrRtr3hS4UZkgsEZG8JNMiXbZsWcePTSYTJUqUwNvbO915ZrOZ06dP50w6ERHJfgeWwJrH4MqptDG/ItDuffsTCk0mw6KJiOQlLu1f17Fjx0yXbuzdu5eOHTtmaygREckhVjNsfNG5RFe/DwbtgToDVaJFRG6BS0XaZrNleiw5ORkfH59sCyQiIjnIwws6zwGTBwSEwt3fQo9vILCU0clERPKcTJd27N27l7179zper1+/nn/++cfpnOTkZH766ScqVqyYYwFFROQ2JJyyl+TrZ5pLN4NuX0FYhH1Jh4iIZEmmRXr16tVMnToVsK+RnjYt4y2QypUrx4QJE3ImnYiIZI3NCjtnQNQY6PAh1P2v8/Ea9xuTS0QkH7nh9ncPP/wwNpuNxo0b8+mnn1KvXj2nc7y9vTO8AVFERAx0fh+sHAInNthfr3sKKt4FQaWNzSUiks9kWqSvL8nXL/EQEZFcymqGre/AppfBkpw2HlgKrp5VkRYRyWaZFumDBw9SoUIFfHx8OHjw4E3fqGrVqtkaTEREbsHpHbBiMJz+M23MwwuajoHmL4CXn2HRRETyq0yLdPfu3R1PNuzevTumTLZEstlserKhiIhRzEnw+6uw5U2wWdLGSzaCyDlQsoFh0URE8rtMi/Rnn31GlSpVHD8WEZFc5lw0/HAvnL9u+Z2XH7R4BZo8ZZ+RFhGRHJPp77LNmjXL8MciIpJLBJaG5Itpr8u2gc6zoWh14zKJiBQgLj2Q5dy5cxw7dszx2maz8fXXXzNx4kTWrl2bY+FEROQG/ApDx4/AO8j+7/+sU4kWEXEjl4r0c889x6effup4PXnyZF555RV+/fVXRowYweLFi3MsoIiIAFfPw+656cer9YQhh6HBcPvTCkVExG1c+l13z549NG/eHACr1cqCBQv43//+x88//8yjjz7qVLJFRCSb7f8W5tWGFQ9DzKr0xwOKuz+TiIi4VqQvX75M4cKFAdi9ezcXL17k7rvvBqB58+YcPXo0xwKKiBRYCafgh97w432QGGcfWzXUvlOHiIgYzqUiXapUKcde0uvXr6dy5cqEhoYC9pLt4+OTcwlFRAoamw12z7PPQh+4bulcYGlo94H2hBYRySVc2hupd+/evP3222zcuJH169fz1FNPOY7t3LnTsU2eiIjcposxsGoYHPnXEo56Q6Dt2/YbDEVEJFdwqUg/8sgjhIaG8tdff/HCCy9w3333OY5duHCBPn365FhAEZECwWaF7dNgw1hIvZI2HlIJImZBWEfjsomISIZc3q2/Z8+e9OzZM934hAkTsjNPnhN7wcLfR1Ox2eyvk1JtxgYSkbwpagz88c51AyZo/CS0ehW8A41KJSIiN+BykTabzaxcuZJt27Zx4cIFChcuTOPGjencuTNeXgXz6VmXEq28suAiZqvRSUQkz2vwOOycbp+NLlYbOs+BMs2NTiUiIjfgUgM+d+4cDz/8MPv27aNs2bIUL16cHTt2MH/+fGrWrMknn3xC0aJFczprrnPkjPmGJbpcMU/3hRGRvC2kon0N9JVYuPN58PI1OpGIiNyES0V60qRJXLhwgYULF1K/fn3H+K5duxg5ciSTJk3i7bffzrGQeUHRIA8aVvZ2vA709aBVLf2PUET+JfUq/D4BvAKgxYvOxxoMNyaTiIhkiUtFOioqihdffNGpRAPUr1+fp556itdeey1HwuUlZYp60re11jGKyA0c3wArB0P8fvDwgqr3QIn6N/86ERHJlVzaRzolJYXAwIxLYmBgIKmpqdkaSkQkX0m5DGtGwNdt7CUawGqGv2Ybm0tERG6LS0X6jjvuYNasWSQmJjqNJyYmMmvWLO64444cCScikucd/gnm1YEd09LGfIIh4mNo/4FhsURE5Pa5tLTjueeeY8CAAbRr145WrVpRrFgxzp8/z4YNG7DZbHz++ec5nVNEJG+5eg7W/Q/2/Ov3x8rdoNMMCC5nTC4REck2LhXpWrVqsWLFCj755BP++usv9u3bR4kSJejbty+DBg0qkDt2iIhkyGaD/Ytg7QhIPJ027l8c2n8INfuCyWRcPhERyTY3LdLx8fGcOHGCEiVK8Mwzz7gjk4hI3mWzwObXnEt0zQftyzgCShgWS0REsl+mRTohIYFx48axcuVKx1i9evV45513qFChglvCiYjkOR5e9oepfHknBJaGTtOhSg+jU4mISA7ItEhPmTKFqKgoRo4cSZ06dTh+/Dgff/wxzz//PF988YU7M4qI5F6Xj0NQWeflGqWaQI9voEJH8A0xLpuIiOSoTIv02rVrefLJJxk4cKBjrHr16vTv35/Lly8THBzsloAiIrmS1QLbp8CGcdDhQ6g32Pl4tXuNySUiIm6T6fZ3J0+epF69ek5j9evXx2azceLEiRwPJiKSa53bAwta23flMCfC+qch4aTRqURExM0ynZG2WCx4eTkf9vT0BMBqteZsKhGR3MiSAlvetN9MaElJGw+uAMkXIKiMYdFERMT9brhrx3vvvUdISNr6PpvNBsDbb79NoUKFHOMmk4kPPvggZxKKiOQGsX/YH+99ZlfamIc3NH8Bmj0Hnj7GZRMREUNkWqSbNm2KxWLh/Pnz6cbNZnO6cRGRfCk1ETa+DNveBdt1fxtX+k777hzF6xgWTUREjJVpkdbTCkWkwDu3B5bcAxcOpo15+UPridBwJHh4GpdNREQM59KTDUVECqTAMvabCa+p0AEiZkHhysZlEhGRXCPTXTtERAo8v8LQcbp9L+jOs+G+1SrRIiLioBlpERGAxDNw8DuoP8x5vOrdMOQw+BUxJpeIiORaKtIiUrDZbLDva1j7BFw9C4XCoGKk8zkq0SIikgG3L+2IiooiMjKSiIgIZs6cmel5K1asoEaNGvz1119uTCciBYlXUpz9ZsJlD9hLNMDKYWBOMjaYiIjkCbdUpG02G6dOneLPP/8kMTHx5l/wLxaLhQkTJjB79myWLVvG0qVLOXjwYLrzEhIS+Oyzz7jjjjtu+TNERG7KZoNds6gc1QP++TFtPKgcdJoOXn7GZRMRkTzD5SI9f/582rRpQ/v27XnooYc4fPgwACNGjGDevHkuvceuXbsICwujfPny+Pj40K1bN9asWZPuvMmTJzN06FB8fX1djSci4poLh+CbjrBqGJ7mhLTxO4bDoL+hclfjsomISJ7i0hrp2bNnO8rtnXfeycCBAx3HmjVrxrJlyxg0aNBN3ycuLo5SpUo5XoeGhrJr1y6nc/7++29iY2Np164dc+bMueH7RUdHuxI/WyUlJTk+99g5H8C+djIhIYHo6ONuzyM54/rrLPmEzULRw59RYv8UPKxpSzeSA8I4Vf9VrhZtAv+cAE4Yl1GynX4tFwy6zgVDbrzOLhXpL7/8kpEjRzJ06FAsFovTsUqVKhETE5MtYaxWK2+88QaTJk1y6fxatWply+e6wmqzsSsmlW3/xFK8eHEATidagRQAgoKCqFWrtNvySM6Kjo52688vcYN1T8Pe99Jemzw4W+m/FO8+hYre/sblkhylX8sFg65zwWDUdd62bVumx1wq0mfOnKFu3boZHvPw8CA5OdmlIKGhocTGxjpex8XFERoa6nh95coV9u/fz4ABAxyfO3z4cKZPn069evVc+oyc8vfRVKb9lAAEwRHdiCSS5zQaBbtmQmoClKgPnedwJj6Q4irRIiKSRS6tkQ4LC2PLli0ZHtu6dStVqlRx6cPq1atHTEwMx44dIyUlhWXLltGhQwfH8eDgYDZv3szatWtZu3YtDRo0yBUlGuDIGcsNj1cqqUcFi+RqhSpAu3eh1avw0B9QqonRiUREJI9zaUZ64MCBvPLKK3h7exMZad9f9dy5c3zzzTfMmzePV1991bUP8/Ji/PjxDBkyBIvFQu/evalWrRqTJ0+mbt26dOzYMevfiRtVL+NFrXLejtfFgj1oUsXHwEQi4pCaCL+9CN5B0OoV52P/ftiKiIjIbXCpSPfp04eLFy8ybdo0pkyZAsCwYcPw9/dnxIgR9OjRw+UPDA8PJzw83Gls1KhRGZ77+eefu/y+7lSttBfdm+ivg0VynaO/wMohcPEf8PCCavdCSW2jKSIiOcPlJxsOGTKEvn37sn37di5cuEBISAgNGzYkODg4J/OJiNxc8kVY/yz8NSttzGqGv+dByfcNiyUiIvnbLT0iPCgoiDZt2uRUFhGRW3foR1j9KCScTBvzLQzt3oc6AzP9MhERkdvlUpGeP3/+Tc956KGHbjuMiIjLEs/A2pGwb4HzeNVe0HEaBGk7ShERyVkuFekb3UxoMpkAFWkRcRObDfZ+ZS/RSefSxgNC7QW6em/jsomISIHiUpHeu3dvurFLly6xYcMGZs2axbvvvpvtwUREMmSzwNa3nEt0nYEQ/h74FzUul4iIFDgu7SOdkUKFCtG1a1f69u3LSy+9lJ2ZREQy5+EFkZ+AyROCK0Dvn+GueSrRIiLidrd0s2FGypUrx+7du7Mji4hIepeOQHB5MF335/7QRnD3YqjQHny0c5CIiBgjyzPSAKdPn+aTTz6hXLly2ZVHRMTOaoYtb8HcmvDXnPTHq96tEi0iIoZyaUa6efPmjpsKr0lNTeXKlSv4+vo6HtIiIpItTu+ElYMhbpv99fpnoFIXCNYf2kVEJPdwqUj369cv3ZiPjw+lSpWiTZs2FClSJNuDiUgBZE6Gza/BljfsM9LXFK4CKQnG5RIREcnATYt0amoqLVq0oFy5coSGhrojk4gURCc3wYrBcD46bczTF1q8DE2eBk9vw6KJiIhk5KZrpD09PRk4cCD//POPO/KISEGTegV+eRK+auVcosu2hgE74c7nVKJFRCRXuumMtIeHBxUrVuTs2bPuyCMiBcnZ3fBdD7gUkzbmHQRt3oAGw5136hAREcllXPq/1JNPPsm0adPYt29fTucRkYIkuILzWuiKkTBoNzR8XCVaRERyvUxnpLdu3Urt2rUJDAxk+vTpXLhwgZ49exIaGkqxYsXS7eKxaNGiHA8rIvmMbyGImAE/9Yd2H0Dt/vCv31tERERyq0yL9IABA/j666+pX78+1atXp3r16u7MJSL5zZU4OPAtNHjMebxyNxhyGHxDjMklIiKSRZkWaZvN5vjxpEmT3BJGRPIhmw32fA7rnoSkeChUESp3dT5HJVpERPIgLUIUkZxz6Qgs7gI/D7SXaIDVj9r3ixYREcnjbrhrx/r1613e9q5nz57ZkUdE8gObFXZMh1+fg9TrHqRSqCJ0ngVevoZFExERyS43LNLTpk1z6U1MJpOKtIjYnd8HK4fAiQ3XDZqg0Uho9Rr4BBkWTUREJDvdsEh/9tln1K1b111ZRCQvs6TCH+/CppfBct3SjaK1IHIOlGlhWDQREZGccMMi7efnR0BAgLuyiEheFjUa/vwg7bWHFzR7Du58QUs5REQkX9LNhiKSPRo/BT7B9h+HNoaH/oBWr6pEi4hIvnXTR4SLiGTIZnN+eEqh8tDufbh6Dpo8ZZ+RFhERyccy/T/d3r173ZlDRPKKlMvw6/PgV9g+43y9eoMNiSQiImIETRmJiOtiVsDKYXD5KJg8oVpvKNnA6FQiIiKG0BppEbm5q+fh50Hw7V32Eg1gs8CeLwyNJSIiYiTNSIvIje3/FtY8DolxaWN+xaDDZKj5oHG5REREDKYiLSIZSzgFa0fAgcXO4zX62kt0QEljcomIiOQSKtIi4sxmg78/hXX/g+QLaeNBZaDjdKh6t2HRREREchMVaRFxZrPA9g+dS3S9odD2LftOHSIiIgLoZkMR+TcPL+g8x74rR0hl6LMGOs9UiRYREfkXzUiLFHQXDkFIJTBd9+fq0IbQ8wcoHw7egcZlExERycU0Iy1SUFlS4feJMK827JqZ/njlrirRIiIiN6AiLVIQxW2D+U3gtxfAkgJRo+HSMaNTiYiI5Cla2iFSkKRehU2vwB/v2G8qvKZoTTBfNS6XiIhIHqQiLVJQHI+ClUMg/kDamJc/tHoNGo0CD0/jsomIiORBKtIi+V3yJfh1LOz8yHm8fHvoPAsKVzEml4iISB6nIi2Sn53ZBd91h8vXrX/2KQTh70C9IWAyGZdNREQkj1ORFsnPClV0fl25B3SaDsFlDYkjIiKSn2jXDpH8zLcQdJoB/sWh21fQ83uVaBERkWyiIi2SXySchD8npx+v3BWG/AM1+2oph4iISDbS0g6RvM5mg92fwPqnIfkihFSBKt2dz/EJNiabiIhIPqYZaZG87MI/sKiTfVu75Iv2sTWPgTnZ2FwiIiIFgGakRfIiqwW2T4EN48CcmDZeuAp0ng1evsZlExERKSBUpEXymrN/w8rBcGpz2pjJAxo/BS1fAe8A47KJiIgUICrSInmFJQW2vAG/vwbW1LTx4vUgcg6UampcNhERkQJIRVokr4ga7bwrh4c3NH8Rmo0BTx/jcomIiBRQutlQJK9o8oz9qYQApe+E/tuhxYsq0SIiIgbRjLRIbmWzOe/7HFwO2k+G5AvQ8Anw8DQsmoiIiKhIi+Q+yRchagz4FYE2k5yP1R1kSCQRERFJT0VaJDc5tBRWPwoJJ8DkCdXvg9DGRqcSERGRDGiNtEhukHgGlj0IS3rYSzSAzQL7FhqbS0RERDKlGWkRI9lssHcB/DISrp5NGw8oCR2m2mekRUREJFdSkRYxyuXjsHo4/LPUebx2f2j3PvgXMyaXiIiIuERFWsTdbFb4azasfxZSLqWNB5eHiI+hUhfjsomIiIjLVKRF3M1mhV0znUt0g8ftO3T4BBuXS0RERG6JbjYUcTcPL+g8x/7vItXgP1HQcapKtIiISB6jGWmRnHZ+PxSpCqbr/txa8g7otQzKtgFvf+OyiYiISJZpRlokp5iT4beX4NM6sGN6+uMVO6tEi4iI5GEq0iI54dRm+KIx/D4BrGb49Tm4dMToVCIiIpKNtLRDJDulXoHfXoRtHwC2tPHi9cCSalQqERERyQEq0iLZ5cgaWDUULh5OG/MOhNaToMFj4OFpXDYRERHJdirSIrcr6QJEPWvfG/p6YREQMRNCKhqRSkRERHKYirTI7Ti9A77rBgkn08Z8C9ufTFhnIJhMRiUTERGRHKYiLXI7ClcB03W/jKrdCx2nQWAp4zKJiIiIW2jXDpHb4RNsf6x3YCnosQju/lYlWkREpIBQkRZx1aVj8Me76ccr3QWDD0H13u7PJCIiIobR0g6Rm7FZYefH8OsYSLkMhatB1budz/EOMCabiIiIGEYz0iI3En8AFraHNY/ZSzTAmsftTy0UERGRAs3tRToqKorIyEgiIiKYOXNmuuNz586la9eu9OjRg4EDB3LixAl3RxSxP41wy1vwWX04HpU2XqQGdF8AXr7GZRMREZFcwa1F2mKxMGHCBGbPns2yZctYunQpBw8edDqnVq1afPvtt/z4449ERkby9ttvuzOiCJzeScWND9iXcpiT7GMmT7jzeRiwA8q2MjSeiIiI5A5uLdK7du0iLCyM8uXL4+PjQ7du3VizZo3TOc2bN8ff3x+ABg0aEBsb686IUpCZk+2P957fBP9Lf6eNl2wI/f6A1hPBy8+4fCIiIpKruPVmw7i4OEqVStsaLDQ0lF27dmV6/qJFi2jbtm2mx6Ojo7M1342cORMIBAFw9uxZoqOvuO2zxT1C97xO0ZgvHK+tHj6crfY45yoNgnPecM59P9/EPZKSktz6+4i4n65xwaDrXDDkxuuca3ft+P7779m9ezdffPFFpufUqlXLbXkOXbkKh68CULx4cWrVquC2zxY3Kf8mzPsRki+SWKQRAT2/pGTRGpQ0OpfkmOjoaLf+PiLup2tcMOg6FwxGXedt27ZlesytRTo0NNRpqUZcXByhoaHpztu4cSMzZszgiy++wMfHx50RpSCx2Zwf4R1UBjpMgeRLHPFtR62iNYzLJiIiIrmeW9dI16tXj5iYGI4dO0ZKSgrLli2jQ4cOTufs2bOH8ePHM336dIoVK+bOeFJQJMXDzw9D1Jj0x2r3h4aPg0k7Q4qIiMiNuXVG2svLi/HjxzNkyBAsFgu9e/emWrVqTJ48mbp169KxY0feeustEhMTGTVqFAClS5dmxowZ7owp+dmB7+x7Ql+JtZflGvdDqSZGpxIREZE8yO1rpMPDwwkPD3cau1aaAebNm+fmRFIgXImFtU/A/kVpYzYrHPxORVpERESyJNfebCiSLWw22PM5rHvSvqTjmsBS0HE6VOtpVDIRERHJ41SkJf+6dARWPQIxK5zH6w6G8LfBr4gxuURERCRfUJGW/MdmhR3T4dfnIDUhbbxQReg8C8I6GRZNRERE8g8Vacl/bFbY8+l1JdoEjUZB69fAO9DQaCIiIpJ/aI8vyX88vKDzHPu/i9aCB36D9u+rRIuIiEi20oy05H3n9kDRms57P5eoB71XQJlW4OVrXDYRERHJtzQjLXmXOQl+HQuf1oft09Ifr9BBJVpERERyjIq05E3HN8Bnd8CWN8BmgQ1j4WKM0alERESkANHSDslbUi7bZ6F3/GsGOrQxYDMkkoiIiBRMKtKSd8SsgJXD4PLRtDGfYGj7FtQf5rxGWkRERCSHqUhL7nf1PKz7H+z5zHm8UlfoNAMKlTcml4iIiBRoKtKSu8X9CYu7QOLptDG/YtBhMtR8EEwm47KJiIhIgaYiLblbkergFZD2ukZfe4kOKGlcJhERERG0a4fkdj5BEDETgsrAPd9D969UokVERCRXUJGW3OPiYdj8RvrxihEw+BBUvdv9mUREREQyoaUdYjyrxb6d3a9jwZxof0phtZ7O53j5GRJNREREJDOakRZjnYuGBW3gl1H2Eg2w9gmwpBibS0REROQmNCMtxrCkwta34PcJzqW5WB2InAOePsZlExEREXGBirS4X9w2WPEwnNmVNubhDXc+b/9HJVpERETyABVpcZ/Uq7DpFfjjHbBZ0sZLNYXOc6BEPeOyiYiIiNwiFWlxn1/HwPYpaa+9/KHVa9BoFHh4GpdLREREJAt0s6G4T7Ox4FvY/uPy7WHgX9DkKZVoERERyZM0Iy05x2YF03V/VgsqDR2nQeoVqDdEj/cWERGRPE1FWrJf4llY9yQElIJ27zgfq/WgIZFEREREspuKtGQfmw32LbTvA331jH02usb9ULqZ0clEREREsp3WSEv2SDgJ3/eEZX3tJRrsSzv+WWpoLBEREZGcohlpuT02G/w1B6KegeSLaeNBZaHTDKjS3bhsIiIiIjlIRVqy7sI/sGooHF3rPH7Ho9DmTfAtZEwuERERETdQkZZbZ7XA9g9hwzgwX00bL1wVOs+G8uHGZRMRERFxExVpyZq9C9JKtMkDGj8NLV8G7wBDY4mIiIi4i242lFvn4QmRc8DDG4rXgwc3Q/hbKtEiIiJSoGhGWm7uzC4oVsf5CYTF60Kf1VC6OXj6GJdNRERExCCakZbMpSbCumfg84awY2r64+XaqkSLiIhIgaUiLRk7+gt8Wg+2vWvfD/rX5+HiYaNTiYiIiOQaWtohzpIvQtRo2DXTebxMc/tNhSIiIiICqEjL9Q79CKsftT+l8BrfEAh/F+o+DCaTcdlEREREchkVaYHEM/DLKNj7lfN4lXug00cQVMaYXCIiIiK5mIp0QRf7B3x7FySdSxsLKAkdpkL1+zQLLSIiIpIJFemCrlgt+6O8rxXp2v2h3fvgX8zYXCIiIiK5nO4eK+i8AyFiFgRXgHuXQ5fPVKJFREREXKAiXZDEH4TfJ6YfD+sID++HSl3cn0lEREQkj9LSjoLAaoY/J8NvL4L5qn05R7V7nc/x8jUmm4iIiEgepRnp/O7MX/BVS1j/jL1EA6wdBZYUY3OJiIiI5HGakc6vzMmw+XXY8rp9RvqaEndA5Bw92ltERETkNqlI50enNsOKwXDu77QxTx9o8RI0eRY8vY3LJiIiIpJPqEjnJ6lX7Ougt30A2NLGy7SEznOgWE2jkomIiIjkOyrS+UnUc7Bjatpr70BoPQkaPg4mLYcXERERyU5qV/lJ83HgV8T+47DOMHA3NHpCJVpEREQkB2hGOi+zWsDDM+11YCnoON2+O0edgXq8t4iIiEgOUpHOixJPw9qREFQG2r3nfKzmf4zJJCIiIlLAqEjnJTYbRM+HX0ZB0nnABNXvhzLNjU4mIiIiUuBo8WxecekofNcNfur//yUawAYxKwyNJSIiIlJQaUY6t7NZYefHEDUaUhPSxguFQcRMqNjZuGwiIiIiBZiKdG52fj+sHAInfr1u0AQNn4DWE8EnyLBoIiIiIgWdinRuZDXDH+/CxpfAkpw2XrQmdJ4NZVsZl01EREREABXpXMoEB5eklWiTJzR7Dpq/AF5+hiYTERERETvdbJgbeXhC5Bzw9IGSjaDfH9D6NZVoERERkVxEM9K5QdyfUOIO54erFKsNfX6B0s3AQ5dJREREJLfRjLSRUhLglyfhiyaw/cP0x8u2VIkWERERyaVUpI0Sswo+rQd/TgZssGEcXPjH6FQiIiIi4iJNd7pbUjysexr+nus8Xi4cPLyNySQiIiIit0xF2p0OLIY1j8OV2LQxv6LQ/gOo1Q9MJsOiiYiIiMitUZF2hyuxsGYEHPjWebz6/dDhQwgMNSaXiIiIiGSZinROO7UFFt9lX9JxTWAp6DgdqvU0LJaIiIiI3B4V6ZxWvC74Fkkr0nUHQ/jb4FfE2FwiIiIiclu0a0dO8w6wP9Y7pDLctwoiZ6tEi4iIiOQDKtLZ6fw+2Phy+vEK7eG/eyGsk9sjiYiIiEjO0NKO7GBJhT/egU2vgCXZvpyj+n3O53hqazsRERGR/EQz0rcrbjvMbwYbnreXaIBf/geWFGNziYiIiEiO0ox0VpmT7DPQW98GmyVtPLQxRH4Cnj7GZRMRERGRHOf2GemoqCgiIyOJiIhg5syZ6Y6npKTw5JNPEhERQZ8+fTh+/Li7I95U0Ysb4bM7YMsbaSXayw/avgUP/g4l6hsbUERERERynFuLtMViYcKECcyePZtly5axdOlSDh486HTON998Q6FChVi1ahWDBg3inXfecWfEG/K1XeaBlNG0/SsC4venHSgXDgN2QdNnwUOT/CIiIiIFgVuL9K5duwgLC6N8+fL4+PjQrVs31qxZ43TO2rVr6dWrFwCRkZFs2rQJm83mzpiZujf1VTpY5qQN+ARDpxlw/1ooUs24YCIiIiLidm4t0nFxcZQqVcrxOjQ0lLi4uHTnlC5dGgAvLy+Cg4OJj4/HaD5esNT7GRL4/z2gK3eDQXvgjkfApHs2RURERAqaPL0OITo62m2fVRgPfIOKsMTrTZpXuEhqWFc4fhlwXwZxn6SkJLf+/BJj6Drnf7rGBYOuc8GQG6+zW4t0aGgosbGxjtdxcXGEhoamO+fUqVOUKlUKs9nM5cuXKVIk4ycB1qpVK0fz/lvTO2xER7emam33fq64X3R0tNt/fon76Trnf7rGBYOuc8Fg1HXetm1bpsfcuiahXr16xMTEcOzYMVJSUli2bBkdOnRwOqdDhw589913AKxYsYLmzZtjMpncGTNTJpOJXBJFREREDFKrVi3uueceunfvzqOPPsqlS5ccxw4cOMCAAQOIjIykc+fOTJs2zeler/Xr13PvvffStWtXevbsyRtvvGHEt3BDe/bs4fnnnzc6RqZc3eHt008/pXv37nTr1o158+Y5xt988002bdqULVncWqS9vLwYP348Q4YMoWvXrnTp0oVq1aoxefJkx02H9913HxcuXCAiIoK5c+fyzDPPuDOiiIiIyA35+fnx/fffs3TpUkJCQpg/fz5gX3owfPhwhg0bxooVK/j+++/Zvn07X375JQD79+/n1Vdf5e2332b58uV8++23VKhQIVuzmc3m236PGTNmMGDAALd+5q1wZYe3/fv388033/DNN9/w/fffs27dOo4cOQJAv379mDVrVrZkcfsa6fDwcMLDw53GRo0a5fixr68vH374obtjiYiISB6zcsdVfth6leTUUPjlfLa8p6833N3Un84N/F06v0GDBuzbtw+AH3/8kUaNGtG6dWsA/P39GT9+PP379+ehhx5i9uzZPProo1SpUgUAT09PHnzwwXTveeXKFV577TV2794NwIgRI4iMjKRhw4Zs374dgJ9//pl169bxxhtv8Nxzz+Hj40N0dDSNGjVi1apVLFmyhEKFCgHQuXNnvvzySzw8PHjppZc4efIkAM8//zyNGzd2+uyEhAT27dtHzZo1AfuOaxMnTiQ5ORk/Pz9ef/11KleuzOLFi1m5ciWJiYlYrVZmzpzJq6++yoEDBzCbzYwYMYJOnTpx/PhxRo8ezdWrVwF48cUXadSokesXJANr165lxIgRgH2HtwkTJmCz2ZxWMBw6dIj69evj72+/jk2bNmXlypUMHTqUsmXLcuHCBc6cOUOJEiVuK0uevtlQRERECq6VO5JITs3e90xOtb+vK0XaYrGwadMm7rvvPgAOHjxInTp1nM6pUKECiYmJJCQkcODAAR5++OGbvu9HH31EUFAQP/74IwAXL1686dfExcWxYMECPD09sVgsrFq1it69e7Nz507KlClD8eLFefrppxk4cCBNmjTh5MmTDB48mJ9++snpfXbv3k316tUdrytXrsz8+fPx8vJi48aNvP/++0yZMgWwLwH54YcfKFy4MO+99x7Nmzdn0qRJXLp0iT59+tCyZUuKFSvG3Llz8fX1JSYmhqeeeorFixeny//ggw9y5cqVdONjxoyhZcuW6b7XjHZ4K1q0qOOc6tWr88EHHxAfH4+fnx9RUVHUrVvXcbx27dr8+eefREZG3vS/7Y2oSIuIiEie1LmB3//PSGffe/p629/3RpKSkrjnnnuIi4ujSpUqtGrVKvsCAJs2beK9995zvA4JCbnp19x11114enoC0LVrV6ZNm0bv3r1ZtmwZXbt2BWDjxo1OD8JLSEjgypUrBAYGOsbOnDnjtMnD5cuXGTNmDEeOHMFkMpGamvYfu1WrVhQuXBiADRs2sHbtWj755BMAkpOTOXXqFCVLlmTChAns3bsXDw8PYmJiMsx/bflLdqlSpQpDhgxh8ODB+Pv7U7NmTTw80lY0FytWjNOnT9/256hIi4iISJ7UuYF9CYa7d3O4tkb66tWrDB48mPnz5zNgwACqVq3K1q1bnc49duwYAQEBBAUFUbVqVXbv3u1YNnE7kpOTnV5fW8IA0LBhQ44ePcr58+dZvXo1w4cPB8BqtbJw4UJ8fX1v+L2lpKQ4Xk+ePJk777yTadOmcfz4cae109d/JsCHH35I5cqVncamTJlC8eLF+f7777FardSvXz/Dz72VGWlXd3jr06cPffr0AeC9995z2inu2lKV26UniYiIiIhkgb+/Py+88AJz587FbDbTo0cPtm3bxsaNGwH7zPVrr73GkCFDABg8eDAff/wxhw8fBuzF9quvvkr3vi1btnTcwAhpSzuKFy/OoUOHsFqtrF69OtNcJpOJTp06MWnSJKpUqeIoma1bt+bzzz93nJfRnsyVK1d23JQH9hnpawX02q5qGWndujVffPGFY4eSPXv2OL6+RIkSeHh48P3332OxWDL8+i+//JLvv/8+3T//LtHg+g5v586dA+DkyZOsXLmSHj16OI7FxMRQrdrtP5VaRVpEREQki2rXrk2NGjVYunQpfn5+fPTRR0yfPp3IyEh69OhBvXr16NevHwA1a9bk+eef5+mnn6ZLly50796dY8eOpXvP4cOHc+nSJbp3787dd9/N5s2bAXj66ad55JFH6Nu3701vkuvatSs//PCDY1kHwLhx49i9ezc9evSga9euGZb4KlWqkJCQQEJCAgBDhgzhvffeo2fPnjfcneOxxx7DbDZz9913061bNyZPngzYZ5q/++477r77bv755x8CAgJu8l/05jLb4S0uLo6hQ4c6znviiSfo2rUrjz76KC+99JLj5svU1FSOHDnitGY6q0y26zc3zEO2bduW7k5Td9Cm7wWDrnPBoOuc/+kaFwy6ztlr3rx5BAYGOpZF5BbZdZ1XrVrF33//zZNPPunS+TfqnJqRFhERERGHBx54AG9vb6Nj5Biz2ezS7imu0M2GIiIiIuLg6+tLz549jY6RY7p06ZJt76UZaRERERGRLFCRFhERERHJAhVpEREREZEsUJEWEREREckCFWkRERERkSxQkRYRERERyYI8/UAWEREREZGcltkDWfJskRYRERERMZKWdoiIiIiIZIGKtIiIiIhIFqhIi4iIiIhkgYp0JqKiooiMjCQiIoKZM2emO56SksKTTz5JREQEffr04fjx4waklNtxs2s8d+5cunbtSo8ePRg4cCAnTpwwIKXcrptd52tWrFhBjRo1+Ouvv9yYTrKLK9d5+fLldO3alW7duvH000+7OaFkh5td55MnT9K/f3969uxJjx49WL9+vQEp5XaMHTuWFi1a0L179wyP22w2XnvtNSIiIujRowd///23mxOmDyT/YjabbR07drQdPXrUlpycbOvRo4ftwIEDTud88cUXthdffNFms9lsS5cutY0aNcqApJJVrlzjTZs22RITE202m802f/58XeM8yJXrbLPZbJcvX7Y9+OCDtj59+th27dplQFK5Ha5c58OHD9vuuece24ULF2w2m8129uxZI6LKbXDlOr/wwgu2+fPn22w2m+3AgQO29u3bGxFVbsOWLVtsu3fvtnXr1i3D4+vWrbMNHjzYZrVabdu3b7fdd999bk7oTDPSGdi1axdhYWGUL18eHx8funXrxpo1a5zOWbt2Lb169QIgMjKSTZs2YdMGKHmGK9e4efPm+Pv7A9CgQQNiY2ONiCq3wZXrDDB58mSGDh2Kr6+vASnldrlynRcuXMhDDz1ESEgIAMWKFTMiqtwGV66zyWQiISEBgMuXL1OyZEkjosptaNq0qePXaUbWrFlDz549MZlMNGjQgEuXLnH69Gk3JnSmIp2BuLg4SpUq5XgdGhpKXFxcunNKly4NgJeXF8HBwcTHx7s1p2SdK9f4eosWLaJt27buiCbZyJXr/PfffxMbG0u7du3cnE6yiyvXOSYmhsOHD9O3b1/uv/9+oqKi3B1TbpMr13nEiBH8+OOPtG3blmHDhvHCCy+4O6bksH//PChVqtQN//+d01SkRW7i+++/Z/fu3QwZMsToKJLNrFYrb7zxBmPGjDE6iuQwi8XCkSNH+Pzzz3n33Xd58cUXuXTpktGxJJstW7aMXr16ERUVxcyZMxk9ejRWq9XoWJKPqUhnIDQ01Omv8ePi4ggNDU13zqlTpwAwm81cvnyZIkWKuDWnZJ0r1xhg48aNzJgxg+nTp+Pj4+POiJINbnadr1y5wv79+xkwYAAdOnRgx44dDB8+XDcc5jGu/p7doUMHvL29KV++PBUrViQmJsbNSeV2uHKdFy1aRJcuXQBo2LAhycnJ+tvifObfPw9iY2Mz/P+3u6hIZ6BevXrExMRw7NgxUlJSWLZsGR06dHA6p0OHDnz33XeA/W7/5s2bYzKZjIgrWeDKNd6zZw/jx49n+vTpWk+ZR93sOgcHB7N582bWrl3L2rVradCgAdOnT6devXoGppZb5cqv506dOrFlyxYAzp8/T0xMDOXLlzcirmSRK9e5dOnSbNq0CYBDhw6RnJxM0aJFjYgrOaRDhw4sWbIEm83Gjh07CA4ONnQtvJdhn5yLeXl5MX78eIYMGYLFYqF3795Uq1aNyZMnU7duXTp27Mh9993Hs88+S0REBCEhIbz//vtGx5Zb4Mo1fuutt0hMTGTUqFGA/TfoGTNmGJxcboUr11nyPleuc5s2bfjtt9/o2rUrnp6ejB49Wn+LmMe4cp2fe+45XnjhBebNm4fJZOKNN97QJFce89RTT7Flyxbi4+Np27YtTzzxBGazGYAHHniA8PBw1q9fT0REBP7+/rz++uuG5jXZtNWEiIiIiMgt09IOEREREZEsUJEWEREREckCFWkRERERkSxQkRYRERERyQIVaRERERGRLFCRFpF8acqUKdSoUSPdP4MGDXLp648fP06NGjX45ZdfcjYo9n1Rr+WrW7cud911F9OmTSMlJSXbPmPx4sXUqFGDK1euAHDu3DmmTJnC8ePHnc7bvHkzNWrUYP/+/dn22Tdy/bWpX78+Xbp0YebMmY7trm7FrFmz2Lx5cw6kFBHJmPaRFpF8Kzg4mNmzZ6cby426d+9O//79SUlJYfPmzUybNo2EhIRse3x5u3bt+Prrr/H39wfsRXrq1Kk0a9aMcuXKOc6rU6cOX3/9NRUqVMiWz3XFww8/TGRkJElJSaxbt453330Xs9nMY489dkvvM3v2bPr168edd96ZQ0lFRJypSItIvuXp6UmDBg2MjuGSkiVLOrI2a9aM2NhYFixYwOjRo7PlgRJFixZ16QlvQUFBbv9vVrZsWcdnNm/enAMHDvD999/fcpEWEXE3Le0QkQLn9OnTjB07lo4dO1K/fn0iIyN5//33b7qUYs2aNdx77700aNCApk2b0qdPH8djpwGsViszZ84kIiKCunXrEhkZyXfffZeljHXq1CExMZH4+HgANm3aRJ8+fahXrx4tW7bk5ZdfdizTAEhNTeXNN9+kXbt21K1bl9atW/P44487vqfrl3YcP36cHj16ADBgwADH0gpIv7Sjf//+jBw5Ml2+a5917ZleycnJvPXWW4SHh1O3bl3uvvtu1q9fn6XvvWbNmpw6dcpp7J133qFHjx40bNiQtm3b8vTTT3PmzBnH8Q4dOnDhwgWmTp3q+H6uLfPIzusiInI9zUiLSL7277W2np6exMfHU7hwYcaOHUuhQoWIiYlhypQpxMfHM2HChAzf5+jRo4waNYr+/fvz7LPPkpKSwu7du7l48aLjnFdffZUlS5bw2GOPUadOHX777Teef/55ChcuTPv27W8p94kTJ/D29iYkJIQDBw4wdOhQWrZsyZQpUzh16hTvvvsux44dY86cOQB8/PHH/Pjjjzz99NOUK1eOM2fOEBUVhdVqTffeJUuW5J133uGZZ55h/Pjx1KlTJ9McXbp04a233iIxMZGAgAAAbDYbP//8M126dHHMlo8cOZJdu3bxxBNPUKFCBX766SeGDx/Ot99+S61atW7pez916pTTchOwL0V55JFHKFmyJOfPn2fu3LkMHDiQpUuX4uHhwdSpUxkwYACRkZH06dMHgKpVqwLZe11ERK6nIi0i+daFCxfSlcS5c+fSsmVLp7XHjRo1wt/fn+eff54XXngBHx+fdO+1Z88eAgMDnb4uPDzc8eMjR47w1VdfMWnSJHr16gVAy5YtOXPmDFOnTr1pYbPZbJjNZlJTU/n9999ZsGABHTp0wNPTk48++ogyZcowffp0PD09AQgJCeF///sf27dvp2HDhvz11190797d8dkAXbt2zfCzfHx8HDPQVatWveFSjrvuuovXXnuNX375hW7dugGwY8cOTp486Xj/TZs2sW7dOj7//HOaNWsGQOvWrYmJiWH69Ol8+OGHN/zerVYrZrPZsUZ65cqVvPnmm07nTJo0yfFji8XimJnetm0bTZs2pXbt2nh6elKqVCmn7+d2r4uIyI2oSItIvhUcHMzcuXOdxipVqoTNZuPTTz9l4cKFHD9+nOTkZMfxU6dOERYWlu69qlevzuXLlxkzZgw9evSgUaNGjhlasJdJDw8PIiIinGbBW7RowbJly7BYLI4SnJG5c+c6ZW3fvj3jx48HYNeuXURGRjp9fWRkJF5eXmzbto2GDRtSs2ZNFixYQLFixWjTpg01atTItrXVzZs3Z/ny5Y4ivXz5cipUqEC9evUA2LhxIyVKlKBRo0bpvvfFixff9DMmTpzIxIkTHa8HDRrk+Kxr1q9fz/Tp0zlw4AAJCQmO8ZiYGJo2bZrpe9/udRERuREVaRHJtzw9PR1l73rz5s3jrbfeYujQoTRt2pRChQrx119/MWHCBKdSfb3KlSvz0UcfMXPmTIYNG4aXlxcRERGMGzeOokWLEh8fj8VioXHjxhl+/ZkzZyhVqlSmWe+++24GDBiAj48PZcuWJSgoyOlrixcvnu57K1y4sGNpyWOPPYaHhwdfffUV77zzDqGhoQwePJiBAwfe9L/TzXTt2pVXXnmFhIQEAgIC+Pnnn7n33nsdx+Pj4zlz5kyGS0RcKamDBw+mS5cuJCQk8OmnnzJv3jxatmzpmPHftWsXjz32GJ06dWLo0KEUK1YMk8nE/fffn+n1uj7b7VwXEZEbUZEWkQLn559/JjIykv/973+OsUOHDt3069q1a0e7du24fPky69at4/XXX+fVV1/l/fffJyQkBC8vL7766qsMZ4JvtmNG8eLFMyz9ACVKlODcuXNOYxaLhQsXLhASEgKAr68vo0aNYtSoUcTExLBgwQJef/11KlWqRNu2bW/6vd1IREQEL7/8MqtXr6Zs2bKcPn2aLl26OI6HhIQQGhrKtGnTsvT+ZcqUcXzvTZo0oUePHrz11lu0bdsWk8nE6tWrKVKkCB988IHjv+2JEydceu/bvS4iIjeiIi0iBU5SUlK6ddA//vijy18fHBxMjx492Lp1K9u3bwfs27ZZLBYuX75Mq1atsjXvHXfcwerVq3nqqaccM7wrV67EbDZnONNasWJFxowZw/z58zl06FCGRdrb2xvgpjO6YC+jrVu35qeffqJMmTJUqVKFmjVrOo63aNGCuXPnEhAQQJUqVbL6bTpyjRo1iieffJK1a9fSsWNHkpKS8Pb2dirCGV0vb2/vdN9PTl4XEREVaREpcFq2bMnnn39O/fr1qVChAj/++CNHjhy54dcsWLCAHTt20KZNG0qWLElMTAw///wz99xzD2Bf+tG3b1+eeuopBg8eTL169UhOTubAgQPExMQ4rQG+VcOHD6dXr148/vjjPPDAA8TGxvLOO+/QunVrGjZsCMDjjz9OnTp1qF27Nr6+vqxYsQKLxUKTJk0yfM8yZcrg5+fHkiVLCA4OxsvLK9MZcbDv3jFu3DiCgoLo16+f07FWrVrRunVrHn74YYYOHUrVqlVJSEhg7969JCcn8/TTT9/S9xsZGUnlypWZM2cOHTt2pFWrVnz66adMnDiRDh068Oeff/LDDz+k+7rKlSuzfv162rRpQ0BAAJUqVcrR6yIioiItIgXO448/Tnx8PJMnTwbsSxdeeOEFHn300Uy/pkaNGqxdu5ZJkyZx8eJFSpQoQZ8+fRg1apTjnJdeeomKFSvyzTff8OGHHxIUFETVqlW57777bitvtWrVmDVrFu+99x4jRowgKCiIbt268eyzzzrOadiwIcuXL2fOnDlYrVaqVq3Khx9+mGk59vX15dVXX2XatGn079+f1NRU9u3bl2mGjh07Mn78eOLj49PtBmIymZg6dSozZszg008/5dSpU4SEhFCzZk369+9/y9+vh4cHjzzyCGPGjGHHjh2Eh4fzzDPP8MUXX/DNN9/QoEEDPv74YyIjI52+bvTo0UyYMIFHHnmEq1ev8tlnn3HnnXfm2HURETHZru2mLyIiIiIiLtOTDUVEREREskBFWkREREQkC1SkRURERESyQEVaRERERCQLVKRFRERERLJARVpEREREJAtUpEVEREREskBFWkREREQkC/4P7pvY4uSfqrMAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1929,12 +1905,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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IiYVrIiIiIiKFmNZUi4iIiIhcI4VqEREREZFrpFAtIiIiInKNFKpFRERERK6R3+3+kRtX6IuIiIiIZCUuLi5HnsfvQjXk3JuTgiMhIYE6depYXYb4GZ0XkhWdF5IVnReSlZyczNXyDxERERGRa6RQLSIiIiJyjRSqRURERESukUK1iIiIiMg1UqgWEREREblGCtUiIiIiItdIoVpERERE5BopVIuIiIiIXCOFahERERGRa6RQLSIiIiJyjRSqRURERESukUK1iIiIiMg1UqgWEREREblGCtUiIiIiItdIoVpERERE5BopVIuIiIiIXKMrCtXr169nwIABAOzZs4d+/frRv39/RowYgdvtBuDtt9+mV69e9O3blw0bNgCwZMkSevXqxWOPPea536hRo9i/f39uvBcREREREUsEXu4O77//Pt9//z1hYWEAjBkzhscff5yWLVsyfPhw5s+fT7ly5Vi1ahXTpk3j0KFDPProo8yYMYP//e9/fPTRR7z55pts3rwZu91OZGQkFSpUyPU3JiIiIpIXMlwGf+51kpJmWF2KXIWyu9+Fku1z7PkuG6pjY2N56623eOaZZwDYtGkTLVq0AOC6665j2bJlVKlShXbt2mGz2ShXrhwul4uTJ08SERGBw+EgLS2NsLAw3n77bUaOHJljxYuIiIhY7ZNFyfy2Jd3qMuQq3eQ8TZWSOfd8lw3VN910k89yDcMwsNlsAERERJCYmEhSUhJFihTx3Of8+EMPPcSYMWOoVasWe/fupWnTpsyaNYuEhAR69OhBkyZNsnzNhISEa3xbUtA4HA6dF3IBnReSFZ0XkpXcOi8Ongnity3Fcvx5JffNCxzCYHJuSfJlQ/Xf2e3eZdjJyclER0cTGRlJcnKyz3hUVBTFihVj/PjxuFwuHn/8cUaPHs1zzz3HG2+8wZAhQ3j//fezfI06depk461IQZaQkKDzQi6g80KyovNCspIb54VhGHw3MxHIAKBC8QAqlgjI0deQHGAYVDv1FcfDG3MmtLZn2GYLztGXuepQXbduXVauXEnLli1ZsmQJrVq1IjY2lv/+978MGjSIw4cP43a7KVbM+1vbV199RY8ePQBwu93YbDZSU1Nz7l2IiIiI5LHfdzrZcdgM1AF2eOjmSErGKFT7lZNbYN4Q2LcQKnSAWxbCuRUXAGvX5txLXXWoHjp0KC+88ALjxo2jatWq3HTTTQQEBNCsWTP69OmD2+1m+PDhnvsnJSWxatUqJkyYAEDJkiU9u4eIiIiI5EcZLoMZv6V4jjs1CFWg9icZDlg5Blb/B1zn1rvvXwyb/wd17sqVl7QZhuFXl6quXbuWuLg4q8sQP6OPcyUrOi8kKzovJCs5fV7M3+Dgy6VmqA4PsfHKXTFEhKr9h1/Y/QvMfwhOb/eO2QKg6T+hzYsQHOkZzsncedUz1SIiIiL+6NhZF4s3pZHmvHC+8NSpKH4/lpzFo7Jn9Tbvbh+3xoUqUPuD5MOw6EnY/IXveNmW0GUSlGqcqy+vUC0iIiL5XobL4I1ZiRw57b7IPcLhQFqOv26JaDsdG4Tm+PPKVXC7YMN7sPRZSDvjHQ+Jgfb/gYb/AFvu/9KjUC0iIiL53uJNaZcI1Lmnd5twggJsl7+j5J7jG2H+w0CmTyhq94frx0JEmTwrQ6FaRERE8rWUNDez1nh3FWtbO5hKJX0jzuHDhylTJmcDVvniAdQsF5SjzynZUKoxNHoA1k+CojWg80So1CXPy1CoFhERkXxtzu8OkhzmLGXxKDt3dYi4YPY4ITCVOnW0TCPfMwxI3A/RFX3H270CUbEQ9wQEWvP3rFX1IiIikm+dSHQxf4PDcxzfKkzLMQqqM7vh227waWNIOeZ7W2hRaPmsZYEaFKpFREQkH5u5IpUMl/l1lVIBNKues13yxA+4nLDqVZhaF3bOAsdJWPKM1VVdQMs/RERE8okMl8H05SlsP9fFT2DPMZfn615twrHZNEtdoOxfCvMehBObMg3azBlpw50nu3pcKYVqERGRfGLeBgfzN+b8tnAFQZMqQbposCBJPQFLhsKfH/qOl2xk7jldrpU1dV2CQrWIiEg+kJjqZs5ax+XvWAhFhNjo1Sbc6jIkJxgG/PUJLH4KUo97x4MioM0oaPoY2P0zvvpnVSIiIuJj1ppUUtPNHS7KFLFzX+fIyzyi8ChbNIDQYC37KBBm94ctX/qOVe8OHd+8cMcPP6NQLSIi4ucOnzbbb58X3zqcKqX1I1wKoOrdvaE6KhY6vQXVu1la0pXSv0gRERE/981vKbjONQusWS6QRpW1dlgKqFp3QsKnUKwutBlhLvvIJxSqRUTE7xiGcfk7XfSx1/Z4f7PtUAZ/7HJ6jntrhwspCJIOwsInoOE/oFJn77jNBt2/96tdPa6UQrWIiPiVZQlpfLk0GYfz8vfNWmlYdConS/IbLWoEU7mUfnRLPuZ2wbqJsOx5SE+EY+tg4AYIDPHeJx8GalDzFxER8SOnk93879drCdQFV2AA9GgZZnUZItl3ZC38ryUsfMwM1ACntsKuOdbWlUP0666IiPiN71alkq6+JhcIDTIvTiwRHWB1KSJXL+0sLHsB1r1tNmw5r2gt6PIuxHa0rrYcpFAtIiJ+Yf+JDJYleHe4eOL2KOpWvPoL8hISEqhTp05OliYi2WEYsHU6LPwnJB/yjgeEQKt/Q7OnfZd95HMK1SIi4hdm/JbK+csL68cGZStQi4ifOLvXbC++a67veKUboPNEKFrdmrpykUK1iIhYbtNeJ3/uNRdS22zQq7XWDovka4Yb9i3yHkeUgesnmFvmFdDdaxSqRUQkz6WkuTly2ru2cvpvKZ6v29UOoXxx/XgSyddiKkPrEfDrs9D4IWg7GkKLWF1VrtJ3LRERyVOHT7sYM+MsKWkX7iUdHAjdWmiWWiRfSTkOB5ZCje6+43FPmss9Sje1pKy8pi31REQkT01fnpJloAa4qXEoRSL0o0kkXzDcsPEjmFILZt0JJzb73h4QVGgCNWimWkRE8tCWA07W7z63dhqILRngWV5ZuVQgXeM0Sy2SLxzfZF6IeGCpd2z+EOi9oMCumb4chWoREckTbsNg2nLv2ulWtYK5r3OkhRWJyFVzpsCKl2DN6+DOtKl8dCWI+1ehDdSgUC0iInlk1bZ09hxzARAUAN21dlokf9k5B+Y/DGd3e8fsgWaYbv0CBEVYVpo/UKgWEZFcl55hMHNFquf4hkahFItSd0CRfCFxPyx8HLbN8B0v387siFiiviVl+RuFahGRAiTDZfD7znSSHFlfCGiV3UczOJlkbqEXFWbj5qaapRbJN2b38107HVoMrvsv1L8HbLqw+DyFahGRAuTzJSkszdTq2x/d3jyMsODCu+5SJN+57jX4oi1gQL17zEAdXsLqqvyOQrWISAGx51iG3wfqskXttK8TYnUZInIxaWchONJ3Brpca2j3MpRrAxU7WFebn1OoFhEpAAzDYNoy784asSUDqFbav77FhwbZuK5eCIEBmqUW8TuGAVu+hkWPQ7tXoP69vre3fNaSsvIT//qOKyIi2bJhj5MtB83trew2GNwlkjJFdSGgiFyBU9vNXT32/GweL34aqt6uJR5XSaFaRCSfc7kNpmfa/7lDvRAFahG5vIw0WPNfWDEaXJmWjgUEw5kdCtVXSaFaRCSf+/WvNA6fNnfWCA0yLwQUEbmkvQth3hA4tSXToA2aPAJtR0NItGWl5VcK1SIieWDLAScrtqaT4cr5re7+3Ov0fH1LXBhRYdriSkQuIuUoLH4K/vrUd7xUU7hhMpRpZk1dBYBCtYhILjuR6OKNWYk4Xbn7OsUi7XRuGJq7LyIi+df+pfBdN3Cc8o4FR0Hbl6HxQ2DXsrFroVAtIpLLvl2ZmuuBGqBXmzCCA7WzhohcRMkGEJBpS8uavaHjBIgsZ1lJBYlCtYhILtpzNIMVW9M9x71a587yjDJFA6jqZ1voiYifCYmB68fD0ueg8ztQpavVFRUo+g4sIpJLDMNgWqZdORpXCeKmJrqIUETywI4f4MAyuO4/vuO1+kD17hCopWI5TaFaRCSXZN47OsAO8a3DLa5IRAq8s/tg4T9h+0zzuPJNENvRe7vNpkCdS3SJuIhILvj73tHX1QuhTBFdBCQiucSdAWvGwdQ63kANsOwF62oqZDRTLSKSDc4Mg8+XJLPjcEbWt7vgRKK5d3RYsI3bm2nZh4jkkkMr4ZcH4Nh63/H6g+C6V62pqRBSqBYRyYaf1jlYtjn98ncEujYN1d7RIpLzHKfNiw7XTwIy7YFfvB50mQQV2llVWaGkUC0icpXOpLj58ffUK7pvbMkA7R0tIjnLMGDzl7DoCUg54h0PDIPWIyDuCbPVuOQphWoRkav0/apU0s6t+ihfLIAHboyELLaHttugZIwdu017R4tIDkv4zDdQV7kFOr8NMVWsq6mQU6gWEbkKB0+6+DUhzXPcq00YZYvpAkQRyUM2mxmgp9aF0GLQ8U2o0dMcF8soVIuIXIXpv6VgnFu6WLdCIPUqBllbkIgUfPsWQ5kWEJTpgueYKtD9Byjb0mw1LpbTlTMiIlcoYb+TjXucgLnao1ebcGyaGRKR3JJ8BObcDV9fD6v+c+HtlbooUPsRhWoRkSvgNnz3nW5dO5iKJfRhn4jkAsMN6yfDlNqQ8Lk5tvo/cHKLtXXJJekngojIFVi5NZ29x10ABAfCHS3UHVFEcsHR9TDvQTi0wne8ek8IibGmJrkiCtUiIpeRnmEwc4V3C70bGoVSLFIf9IlIDkpPguUj4fcJYLi840WqQeeJUPlGqyqTK6RQLSJyGfPWOziVbHZHjAqzcXMTdUcUkRy0/TtY8Cgk7vOO2YOgxTBo8azvBYritxSqRUQu4WyKm7mZGr3c0SKM0GBdnCgiOWTrDPihl+9Yxeuh87tQvLYlJUn26PNLEZFL+GFNKg5zww/KFrXTrk6ItQWJSMFSrRuUaGB+HVYSun4CvRcoUOdDmqkWEcnk4EkXp88t9UhNN1iyydvoJb51OAF2zVKLyDVwpfu2EA8Igi6TYNNUaP8fCCtmWWlybRSqRUTOWbLJwaeLU7K8rVb5QBpWUqMXEcmm1JOw9FlzW7w7F/p2PyzfxvxP8jWFahERIMnhZkamHT7+rrcavYhIdhgGJHwGi/4FqcfMsU0fQ/17LC1Lcp5CtYgIMHtNKilpZv/xqDAbFYoHABBgt9GyRjCVSurbpYhcpZNbYN4Q2LfQd3z/EoXqAkg/JUSk0Dt6xsXCP71rp+/uEEHTqsGXeISIyCU4U2HVGFj9qrmG+ryoitDpLah+h3W1Sa5RqBaRQu+bFSm4zGsTqV42kCZVtHZaRLJp988w/yE4vcM7ZguApo9Dm5EQHGlVZZLLFKpFpFDbfsjJ2h1Oz7HWTotIthhumDMANv/Pd7xsK3N3j1KNrKlL8oxCtYgUaGdT3Kzenk6a08jy9tXbvR/NNq8eTNXS+rYoItlgs0NIjPc4pIi5RV7DweZtUuDpp4eIFFgut8G47xM5cNJ12fsG2qFHK7UCFpFr0O4V2PYNVOoCHcZCRGmrK5I8pFAtIgXWr3+lXVGgBujcMJSS0QG5XJGIFAjpibDiZWj2Lwgv6R0PLQL3bIKw4paVJtZRqBaRAik13eD71d59pxtXCaJskaxDc7EoO+3rqv24iFyGYZgz0Qv/CUkHIOUw3DzV9z4K1IWWQrWIFEg//pFKYqq5jrpYpJ3BN0QSHKgLEEUkm87sgvmPwK453rFNH0OTR6F0nHV1id9QqBaRAudkkptf1jk8x91bhilQi0j2uNJhzThYMQoyMnVdDS8F14+HUk2tq038ikK1iBQ4365MwXluKXVsiQBa1lQjFxHJhv2/mh0RT2zKNGiDRg+YFyWGFrWsNPE/CtUi4ve2H3Ky8M800jN8t8VLTIxhwe5EnzHDgA27vftO92oTjl37TovI1Ug9AUuegT8/8h0v2cjcc7pcK2vqEr+mUC0ifi0x1c2bs5NITc9qn+lQOO7MYtzUsFIQdSqoO6KIXKV9C30DdVAEtH3JXD9tV3SSrGk3chHxa7PWpF4kUF9aSBD0ah2eCxWJSIFXIx4q32x+Xb0H3JMAcU8oUMsl6ewQEb91+LSLxZvSPMc9W4VROtO2ePv376dChQoXPM4GVCoZQLEo7TstIpfhTIXEfVCspnfMZoPO75hrqavdbl1tkq8oVIuI35q5IgWX2/y6ZrlAbm4Sii3T+uiwtDTqVNVFiCKSTbt+hPkPgz0ABm6AwFDvbUWqmv+JXCEt/xARv7TtkJPfd2a64LB1uE+gFhHJtqSD8EMf+KYrnNkJp7bBqletrkryOc1Ui4jfMQyDactSPMctagRTpbS+XYnINXK7YN1EWPa82Wr8vNCiEB1rXV1SIGTrp9TBgwd55plnMAyDmJgYxo4dS1hYGAsWLOCdd94hMDCQ+Ph47rzzTg4fPszjjz+O3W5n/PjxlC5dmu+++47AwEBuvfXWnH4/IlIArNmezq6j5kbTgQHQo2WYxRWJSH4XemYT/G8AHFnre0PdgdDhv2YzF5FrkK3lH1OnTqVr1658/vnn1KhRg+nTp+N0OhkzZgwfffQRn376KV999RXHjx9n7ty53H///dx7773MnTsXh8PBggULuOWWW3L6vYhIAeB0GcxY4e1a1rlhKCWidcGhiGRT2hlY8BiVl/XxDdRFa0HvBdD1YwVqyRHZmqmuU6cOhw8fBiApKYkyZcqwY8cOYmNjiYmJASAuLo7Vq1cTHh6Ow+HAMAzCwsKYOnUqAwcO1NpIEcnSwo0OTiSaVydGhtq4pWnoZR4hInIR7gz4rBmc3o4ndQSEQMvnofkzEBhiZXVSwGQrVJcpU4axY8cya9Ys0tPTeeSRR9ixYwdRUVGe+0RERJCUlMRtt93GmDFjsNvtDB48mIkTJ9KsWTOGDx9OgwYN6N279wXPn5CQkP13JAWSw+HQeVEIpDptfL+qBOc/RGte8Qx7dh6+6P11XkhWdF5IZsVK30Hp02MBSCrRhsP1XsAZUQm27bS4MiloshWqX3vtNcaMGUP79u1ZtGgRQ4cO5cknnyQ5Odlzn+TkZKKiooiIiGD06NEAvPTSSwwZMoRRo0YxefJkHnvsMW699VbCw30bNNSpU+ca3pIURAkJCTovCoGvliaTlmHuS10qxs6dnSoRGHDxT7V0XkhWdF4UYoZh7jGdWc0x4NjAgaI3Ub7Tk1TXJ+WSydq1ay9/pyuUrTXV0dHRnlnpUqVKcfbsWapVq8aePXs4ffo06enprFmzhiZNmnges3XrVkJCQoiNjSUtLQ2bzYbL5SI9PT1n3omI5GtHz7hY+Ke30Ut8q/BLBmoRER/7l8CnTeDE3z6lCAiCXj9zttwtFwZukRyUrZnqF154gVGjRuF2uzEMg+HDhxMUFMSwYcMYNGgQhmEQHx9P6dKlPY+ZPHkyw4cPB6B79+706dOH+vXrU6RIkRx5IyKSv32zItXT6KV6mUCaVA2ytiARyR9SjsOSp2HTVPN43hC4c6ECtOS5bIXq6tWr88knn1ww3qlTJzp16pTlY8aOHev5Oj4+nvj4+Oy8tIgUQDsOO1m7w/upVe+2avQiIpdhuOHPqWagdpz0jh9ZCye3QPHalpUmhZO6KYiIpcxGL94t9JpXD6aqGr2IyKUc32TOSB/41Xe8Rjx0fAOiyltTlxRq+sklIpb6faeTHUcyAAi0Q49WavQiIhfhTIEVL8Ga183t8s6Lrgyd34aqaion1lGoFhHLZLgMZvzmbUfeqUEoJdXoRUSysm8x/HgPnN3tHbMHQrOnoNULEBR+sUeK5AmFahGxzKI/0zh21rw6MTzExi1xavQiIhcREAJn93iPy7eDLpOgRD3rahLJJFtb6omIXKtkh5sf1njXUt/eLIyIUH1LEpGLKNcKGv4DQovBjR9Cn8UK1OJXNFMtInnCkW6w7ZATp8s8XrcrnZQ0A4CS0Xaur692wSJyzqFVkHwIqt/hO97+P9D2JQgvaU1dIpegUC0iuS7DZfDf786y95gry9t7qtGLiACknYGlz8O6iRBaFMpt9g3QoUUsK03kcvRZq4jkul8T0i4aqKuVDiSumhq9iBRqhgGbv4QptWHdO4Bh7j299DmrKxO5YpqpFpFclZLm5vtV3rXT1UoHEhVuzkpHhdq5rVmoGr2IFGantsP8h2HPz77jlW+GFs9aU5NINihUi0iu+vEPB0kOc+10sUg7T94RRXCgQrRIoZeRBqtfg5UvgyvNOx5ZzmzgUiNercYlX1GoFpFcczLRxbz1Ds9xj1ZhCtQiAnsXmh0RT23xjtns0PgR80LEkGjrahPJJoVqEck1365K9ez2UalkAC1qBFtbkIhYL8MBc+4yd/c4r3QzuGESlI6zri6Ra6RQLSLZ9ufedLYezADjwtucboMVW9I9x73bhGPXR7kiEhgK14+H2X0hOAravQKNhoBd3VQlf1OoFpFs2XzAyRuzkq7ovo0qB1GrvHb4ECmUEvdDVAXfsVp3wpmdUO//zDXUIgWAttQTkavmNgy+XpZyRfcNCoD41uG5XJGI+B1nMix+Bj6oAnsX+N5ms0HLZxWopUDRTLWIXLUVW9LZd9xcLB0cCLc0DcvyIn2bDepWDKJsUX2sK1Ko7PgB5j8CiXvN43lDYOAGCFTnVCm4FKpF5KqkOQ2+Xendd/rGxqHc2izMwopExG+c3QcLH4Pt3/qOR5Q1m7lElrWkLJG8oFAtIldl3gYHp5LdAESH2bipsQK1SKHnzoDf34Tlw81lH+eFlYAOr0PdgdpzWgo8hWoRuWJnU9zM/d07S31HizBCg/WDUqRQO7gC5j0Ix9b7jje4H9r/B8KKW1OXSB5TqBaRK/bD6lTSnObXZYsG0LaO1keKFGrrJ5vrpTPvq1m8HnSZBBXaWVaWiBUUqkXkihw65WLJX95Wwr3bhBFg1yy1SKFW+SZz3+mMVAgMg9YjIO5JCNAWmlL4KFSLyBWZ8VsK7nOTUbXLB1I/Vj80RQq9mMpmkD7wK3R62zwWKaQUqkXksrYccLJ+t7nuw4bZHdGmi45ECo8MB6x61ex62Orfvrc1fxqaP6MLEaXQU6gWkUtyGwbTlnsbvbSqFUxsSX3rECk09syD+Q/BqW1gD4IavaB4be/tNvWREwF1VBSRy1i1LZ09x8xGL0EB0L2FttATKRSSj8Dsu2D6DWagBnA7Yd071tYl4qc03SQiF5WeYTBzhXcLvRsahVIsSt0RRQo0ww0b3oNfh0HaGe94SAy0GwMN/2FdbSJ+TKFaRC5q/gYHJ5PMRi9RYTZubqpZapEC7eh6mPcAHFrpO167H1w/DiLKWFOXSD6gUC0iWUpMdTP3d4fnuFvzMMLU6EWkYHKmwrJ/w+9vgOHyjhepDp0nQuUbrKtNJJ9QqBaRLM1ak0pqurmHXpkidtqp0YtIwWUPhL3zvIE6IBiaD4OWz5r7UIvIZelCRRG5wOHTLhZv8jZ6iW8dTmCAZqlFCqyAILMLIjaI7QQDN0DbFxWoRa6CZqpF5ALf/JaCy1xKTc1ygTSqrEYvIgWGywlbvoI6/X23wyvXGvoth7Ittee0SDYoVIuIj60Hnfyxy+k5VqMXkQLkwHKY9yAc32huj1f/Xt/by7Wypi6RAkDLP0TE4++NXlrWCKZyKf3uLZLvpZ6En/8BX7Y1AzXA4qch5bi1dYkUIPppKSIea7ans/uoeaFSYAD0aKUt9ETyNcOAhM9g0b8g9Zh3PDAcWgwz954WkRyhUC0iADhdBt9kavTSuWEoxdXoRST/OrEZ5g+BfYt8x6t1g05vQXSsFVWJFFgK1SICwMKNDk4kmlcnRobauKWprvoXyZecqbDqFVj1qrlu+ryoimaYrn6HdbWJFGAK1SJCksPN7LXeRi+3NQsjPESXXIjkS6vGwIrR3mNbAMQ9Aa1HQHCkdXWJFHD6qSkizF6TSkqa2eilVIydDvXU6EUk32r2L2878bKt4O610OG/CtQiuUwz1SKF3NEzLhb+qUYvIvmS2wUZqb6BOSTGbC2eegwa3O+7F7WI5BqFapFC5kyKm837nZ7mLiu3pnm+rl42kCZV1OhFJF848ru553Sx2tD1E9/bavSwpiaRQkyhWqQQSUlzM3raGU4nG1nerkYvIvlAeiIsGw5/vAmGGw6vhnr3QmxHqysTKdT0mZBIITJnreOigbp59WCqltbv2SJ+yzBg2zcwpQ78PsEM1AABIXBqq6WliYhmqkUKjeNnXczf6N3ho1HlIMKCzVnpIhF2bolToxcRv3VmNyx4BHbO9h2P7QJdJkLRGpaUJSJeCtUihcTMlalkmM0SqVI6gIe7Rmqph4i/czlh7Tj47UXzgsTzwkvB9eOhdj/Qv2MRv6BQLVII7DqSwapt6Z5jrZ0WyQdST8JX18GJTZkGbdDoAWj3CoQWtaw0EbmQQrVIAWcYBtN/S/EcN60aRI2y2uFDxO+FFoXoSt5QXbIRdJkE5VpZW5eIZEmhWiSfMwyD5VvS2XM0I8vbU9IMth40bwuwQ89W4XlZnohkl80Gnd+Gz1tCi2HQ9DGw68e2iL/Sv06RfG755nSmLky+ovt2qBdC6SIBuVyRiFy1Ewmw6j9ww2QIDPWOx1SBwXsgSBcSi/g7hWqRfCzNaTBzZcrl7whEhdm4rZl+MIv4FWcKrHwZVv8X3E4zRLcZ6XsfBWqRfEGhWiQf+3mdgzMp5r7TMeG2i26LF2CHehWDiArT1vQifmPXjzD/ITizyzu25nVo+k9dhCiSDylUi+RTp5Pd/PSHd4ut7i3DaVcnxMKKROSKJB2EhY/D1mm+4+XamBciKlCL5EsK1SL51PerU0k7d21i+WIBtKkVbG1BInJpbhesmwjLnjdbjZ8XWhTavwYN7gObPk0Sya8UqkXyoQMnMliakOY57tUmDLtd+06L+K0ja+GXB8z/Z1bv/+C6/0J4SWvqEpEco1Atkkf+2JnOvA0O0jOMa36u08lujHNPU7diIPVjNUst4tf2LvAN1EVrwQ2ToOL1lpUkIjlLoVokD5xMdPH+L0k4XTn7vDagd2vtOy3i95o+Dn99Cqe2Qqt/Q7OnIVDXQIgUJArVInng21WpOR6oAW5oFEqFEvpnLOJXTu8Eww1Fq3vHAoKg66cQFOE7LiIFhn4ai+SyvccyWLEl3XN8X+cIyuRAA5awYBuli+iiJhG/4Uo3t8Rb8RKUaQF3LjK7Ip5XqpFlpYlI7lOoFslFhmEwbXkK51dRN6ocROta+shXpMDZtxjmDYGTCebx/iWw6WOof4+lZYlI3lGoFslFf+51svmAue+d3QbxrbT+WaRASTkGS542A3RmpZpAifrW1CQillCoFsklLrfBtOXe5izt64ZQtti1L/sQET9guOHPKbDkGXCc9I4HR0Hb0dD4IbDrR6xIYaJ/8SI55OgZF2/NTuTwafcFt4UEQbfmWbcQF5F85vif5lKPA0t9x2v2gusnQFR5S8oSEWspVIvkkC9+TckyUAN0bRJGdLguKhTJ95IOwmdx5kWJ50VXhs7vQNVbLCtLRKynn/IiOeCvfU7+3OvM8rZ6FYPo0ig0jysSkVwRWQ7qDzK/tgdCi2fhnk0K1CKimWqRa+V2mzt8nNeuTgj/1zHCwopEJMe4nOYe05m1ewVSjkCbUVCinjV1iYjf0Uy1yDX6bWs6+0+YnV2CA+GOFlo7LZLvuTNg7Xj4sLq5w0dmoUWg2wwFahHxoVAtcg3SnAbfrvTOUt/UOJQiEfpnJZKvHVoFnzWHRU9C4l5Y/JTVFYlIPqDlHyLX4Jf1Dk4nm61dYsJt3NhEs9Qi+ZbjNCx9DtZPAk/LJuDIGkhPguBIqyoTkXxAoVokm86muPnxD+8+1He0CCM0yHaJR4iIXzIM2PIVLHoCkg97xwPDoNVwaPYkBARbV5+I5AsK1SLZ9P3qVNLObfhRrlgAbWur/bhIvnNqO8x/CPb84jtepau5TV5MFWvqEpF8R6FaJBsOnnTx619pnuNercOw2zVLLZKv/PG2uV7a5f23TGQ56PgG1IgHm/5Ni8iVU6gWyYYZv6XgPrfksk6FQOrHBl36ASLif8JLeQO1zQ5NHjW3yQuJtrYuEcmXFKpFrtLmA0427DHXfdiAXm3CsWlGSyT/qdkbKk+B1ONwwyQoHWd1RSKSjylUi1wFt+Hb6KV17WBiS+ifkYhfM9yw8QMoUgNiO3rHbTa49X8QHA32AOvqE5ECQWlA5Cqs2prO3mOZG72EW1yRiFzSsQ3wy4Nw6DcoWgMGboDAUO/toUWtq01EChR1qRC5QukZBjNXerfQ69IolGKR+ick4pfSk2Dx0/BpUzNQA5zaBuvesbYuESmwspUIUlJSeOaZZ+jfvz+9e/dmw4YNACxYsID4+Hj69OnD119/DcDhw4fp27cv/fv358iRIwB89913zJ49O4fegkjemL/BwckkNwBRYTa6qtGLiF+KPLIAptaFNa+DYX6yhD0IWv0bGj1kbXEiUmBla/nHhx9+SI0aNXjttdfYvHkzmzdvpk6dOowZM4bp06cTFhZGv3796NSpE3PnzuX+++/HMAzmzp1L3759WbBgARMmTMjhtyKSexJT3cxZ652l7tY8jNBgXZwo4lfO7oUFj1Fxx3e+4xU6QJd3oXgda+oSkUIhW6F66dKldO3alUGDBhEREcGIESPYsWMHsbGxxMTEABAXF8fq1asJDw/H4XBgGAZhYWFMnTqVgQMHarcEyVd+WJOK41yjlzJF7LSvq0YvIn7D5YQ/3oTlI8CZ7B0PKwEdxkLdAdpzWkRyXbZC9alTpzh79iwffvgh3377La+++ip33nknUVFRnvtERESQlJTEbbfdxpgxY7Db7QwePJiJEyfSrFkzhg8fToMGDejdu/cFz5+QkJD9dyQFksPhsOy8OJkSwOI/i2NuoActK55g65ZDltQivqw8L8R/BKYepNrSF7C7vJ8mnarYi2O1nsRlLwKbN1tXnPgNfb+Q3JatUF2kSBE6deoEQMeOHXnvvfe47777SE72zhAkJycTFRVFREQEo0ePBuCll15iyJAhjBo1ismTJ/PYY49x6623Eh7uu4NCnTr6iE58JSQkWHZevDM3EbdhTlPXKhfIre2q6pMWP2HleSH+pA5kjIRfh0KJ+uyuPozKbe9C+3pIZvp+IVlZu3Ztjj1XtkJ1XFwcixcvpn79+qxevZrq1atTrVo19uzZw+nTpwkPD2fNmjUMGjTI85itW7cSEhJCbGwsaWlp2Gw2XC4X6enpF4RqkZx07KyLeesdpKYbV/3YDBes2+X0HPdWoxcRaxkGHN8IJRv6jsc9ASExUP8+Urdut6Y2ESnUshWqH3jgAf7973/Tp08fAgMDefXVVwkKCmLYsGEMGjQIwzCIj4+ndOnSnsdMnjyZ4cOHA9C9e3f69OlD/fr1KVKkSI68EZGsuNwG78xJ4sBJ1zU/V6uawVQqpa3dRSxzcivMfwj2L4GB630vPAwIgkYPWFebiBR62V7+8fbbb18w3qlTJ8+ykL8bO3as5+v4+Hji4+Oz89IiV2XZ5rQcCdThITa6t9QWeiKWyHDAqv/AqjHgSjfH5g2BOxfqAkQR8RuadpMCy+E0+C5Ts5a2tYOpWS7oqp/HZoMaZQMpHqU2xiJ5bs88c3b61DbvmM0OpZuaATtQO/GIiH9QqJYC6+c/Ujmbaq6jLhJho1/7CEKCNKslki8kH4ZF/4LN//MdL9McukyG0k2sqUtE5CIUqqVAOp3s5qd1Ds9x95bhCtQi+YHhhvWTYemzkHbGOx4SA+3GQMN/gF2fGomI/1GolgLpu1WppGeYX1coHkDrmsHWFiQiV+bnwfDnR75jtfvB9eMgoow1NYmIXAGFasmXfvojld+2pONyZ71N3pHTbs/XvduEY7drllokX2gwGP6cAhhQpDp0ngiVb7C6KhGRy1KolnxnywEn039LvfwdgfqxQdStePUXJ4pIHjDO/VKceQePcq2g6WMQHAMtn4XAUGtqExG5SgrVkq+4DYNpy1Ou6L4RITb6tFVjIRG/dHYPzH8UqneHBvf53tZxghUViYhcE4VqyVdWb0tnzzFz3+mgAHiyWxQRIfYs71s82k5woJZ9iPgVlxPWjoffXoSMFDi4DKrdDuElra5MROSaKFRLvuHMMJiZad/pLg1DqV5WSztE8o0Dy2Deg3D8T++Y4yTsmgv1BlpXl4hIDlColnxjwUYHJxLNCxAjQ23c3FRrLUXyhdST8OtQ2PiB73iJBnDDZCjX2pq6RERykEK15Aup6TZmr/XuO92teRjhF1n2ISJ+wjDgr09h8b8g9bh3PDAc2o6CJo9BgD5tEpGCQaFa8oUVeyJJTTd3CihdxE77umpNLOLXzu6DHwfCvkW+49XugE5vQnSsFVWJiOQahWrxe0dOu1h/IMxz3Kt1OIEBugBRxK+FRMPJzd7jqIrQ6S2ofod1NYmI5CJ9fi5+75sVKbgNM0TXLBdIo8r6uFjE74XEwPUTwBYAzZ6Ce/5SoBaRAk0z1eLXth9y8vtOp+e4V+twbDbNUov4laRDsP1baDzEd7zWnVA6DopWt6QsEZG8pFAtfsswDL7O1OilRY1gqpTWKSviN9wuWD8Jlj4H6WehWC2I7eS93WZToBaRQkPLP8RvrdmRzq4jZqOXAJtBj5Zhl3mEiOSZI7/DF61hwSNmoAaY9xC4M6ytS0TEIpr2E7/kdBl8s8Lb6KVJhRRKRBe3sCIRASDtLCwfDn+8BYbbO160JnSZCHb9WBGRwknf/cQvLdzo4PhZ8wd2RIiNFpWSLa5IpJAzDNj2DSx8DJIOescDQqDlc9B8KARqq0sRKbwUqsXvJDvcPo1ebm8eRmiQYWFFIoXcmV0w/xHYNcd3PLaLOTtdtIY1dYmI+BGtqRa/M3utg5Q0M0SXirHToZ5mv0Qstfhp30AdXhpu+R/0+lmBWkTkHIVq8SvHzrhYsNE7S92zlRq9iFiuw+sQGAbYoNEQuHcz1Oln7u4hIiKAln+In/lmRSquc9c+VSsTSNOqavQikqdSjkNQBARl2m0npjLcMNm8GLFsS8tKExHxZ5qpFr+x47CTNTvSPce926jRi0ieMQz4cwpMqQ2rxlx4e90BCtQiIpegUC1+wTAMpi33bqHXrFow1crogxSRPHF8E3zVAX66DxwnYNV/4MRmq6sSEclXlFrEL/yx08mOw2bTiAA79GylRi8iuc6ZAitGw5r/+jZtiSxnhmsREbliCtViuQyXwYwV3nbkHRuEUDImwMKKRAqBXXNh/sPmdnnn2QMh7l/Q+gVzXbWIiFwxhWqx3OJNaRw9Y16dGB5i47Y4zVKL5JrEA7Docdg63Xe8XFu4YRKUqG9JWSIi+Z1CtVgqJc3ND6u9a6lvjQslIlRL/UVyxZHf4evrIT3ROxZaDK57DerfCzb92xMRyS6FarHUnLUOks81eikRbadjg1CLKxIpwEo0gJgqcGyDeVzv/+C6/0J4SWvrEhEpADQtIZY5ftbF/A2ZG72EEaRGLyK5JyAIukyC4nXhzoVw81QFahGRHKJQLZaZuTKVjHONXqqUDqBZtWBrCxIpKAwDtnwNP/QBw+17W7nW8H8boeL1lpQmIlJQafmHWGLXkQxWbVOjF5Ecd3qHuavH7p/M4003m+ulM9PaaRGRHKfvrJLnDMNg+m/eLfSaVg2iRlm1Ixe5Jhlp5p7TH9f3BmqAteMunK0WEZEcp5lqyXPrdzvZejBzo5dwiysSyef2LYJ5Q+Bk5i6INmj8MLQbrZlpEZE8oFAteSrD5TtLfX29EEoXUaMXkWxJOQZLnoZNH/uOl2pq7jldprk1dYmIFEIK1ZKnfv0rjSOnzY+iw4Jt3NpMjV5EsmXjR7DkKXCc8o4FR0Hb0eYMtV2/rIqI5CWFaskzKWluvs/U6OWWuFCiwvSxtEi2HFnrG6hr9obrx0NUeetqEhEpxBSqJc/8+IeDJIfZ6KVYpJ3OavQikn3tX4Ht30BgGHR6G6reYnVFIiKFmkK15IkTiS7mrf9bo5dAbaEnckV2zjbXR4eX8o6FxEDPuVC0JgTpYl8REavps3fJE9+uTMXpMr+uVDKA5jXU6EXkshL3w/fxMPM2WPz0hbeXaqxALSLiJxSqJdftOZbBiq2+jV7savQicnHuDFg7HqbUgW3fmGN/fQJ7F1pbl4iIXJSWf0iuMgyDacu9W+g1rhJErfJq9CJyUYdWwS8PwLF1vuP174MSDSwpSURELk+hWnLVxj1OthwwG73YbRCvRi8iWXOchqXPwfpJgOEdL14XurwLFa6zqjIREbkCCtWSa1xu30Yv19ULoUxR7Z0r4sMwYPMXsOhJSDniHQ8MhVbDodm/IEDXIIiI+DuFask1SxPSOHTKbPQSGgS3q9GLyIX2LYI5d/mOVelqbpNXpKoVFYmISDboQkXJFY50g+9WeRu9dG0aRnS4TjeRC1S83gzRAJHl4PZp0GO2ArWISD6jmWrJFT/+kUpiqrkutGiEnS6N1OhFBIC0sxAS7T222cxZ6XVvQ+uRvreJiEi+oalDyXGnktz8kqnRS49WYQSr0YsUdilHYc4A+LwZZDh8bytSFa4fp0AtIpKPKVRLjvtuVQrp5oYfxJYIoGVNXWQlhZjhhg3vwUe1IOEzOLUNVv3H6qpERCSHafmH5Kh9xzNYvtnb6KWXGr1IYXZsA/zyIBz6zXf8zC5z1w/92xARKTAUquWanE1xs/NIBsa5bXXnbXB4dthtUCmIOhXU6EUKIWcyLB9pdkU0XN7xmKrQZSJUvsmy0kREJHcoVEu2nUlx8+JXZzwXJGZms0Gv1mr0IoXQ9u9hwaOQuNc7Zg+CFkOhxXMQpK0lRUQKIoVqybbvV6VmGagB2tcJoVwxNXqRQmbO3ZDwue9YhQ7QZRIUr21NTSIikicUqiVbDpzM4NeENM9xvYpBBJ07m0pE2+neQrPUUgiVbOQN1WEloMNYqDtAa6dFRAoBhWrJlhnLUz3rqOtWDOTx26OsLUjEHzR93Nzho0wLaP8fCCtudUUiIpJHFKrlqiXsd7JxrxMAG9Bba6elsHGcgqXPQ5NHoHhd73hAEPRboXXTIiKFkEK1XBW3YTBteYrnuE3tYCqU0GkkhYRhmMs7Fv/LbOZyYhPcuch3eYcCtYhIoaTmL3JVVmxJZ99xc4uw4EC4Q2unpbA4uQWmd4G5A8xADbB/Cez52dq6RETEL2iKUa5YmtPg25WpnuMbG4dSNFK/l0kBl+GAlWNg9X/A5W1sRGQF6PQmVLrRutpERMRvKFTLFZu3wcGpZDcA0WE2bmqsj7mlgNszD+YNgdPbvWO2AGj6T2gzEoJ1ga6IiJgUquWKnE1xM/d37yz1HS3CCA3WNmFSQCUfhkX/gs3/8x0v0wJumAylGltSloiI+C+FarkiP6xOJc3c8IOyRe20rRNibUEiuensHtj8hfc4JMbcIq/BYLCrqZGIiFxIC2Llsg6dcrHkL2+jl16twwmwa5ZaCrCyLaHRg+bXtfvDvZvNYwVqERG5CM1Uy2XN+C0F97lGL7XLB9KgUpC1BYnkpPREOP4nlGvtO97uFajREyp1saYuERHJVzRTLZe05YCT9bszNXppE45NLZelIDAM2DYTptSFmbdCyjHf20OLKFCLiMgVU6iWi/p7o5dWtYKJLakPN6QAOLsHvu0G3/eEpP1mh8TFT1ldlYiI5GNKSHJRq7als+eY2eglKAC6t9AWepLPuZywdjz89iJkeH9hJLwUVNZ+0yIikn0K1ZKl9AyDmSu8W+h1aRRKsShdpCX52IFlMO9Bc/10Zg0fgPZjILSoNXWJiEiBoFAtWZq/wcHJJLPRS1SYja5NNUst+VTqCfh1GGz8wHe8ZEPoMunCCxRFRESyQaFaLpCY6mbu7w7P8e3NwwhToxfJjwwDpnWGY+u9Y0ER0OZFsyuiXd8CRUQkZ+hCRbnArDWppKabe+iVKWKnvRq9SH5ls0HL57zH1bvDPX9Bs38pUIuISI7STxXxcfi0i8WbvI1e4luHExigWWrJJ1xOCPjbPuo1e0ODX6Dq7VC9mzV1iYhIgaeZavHxzW8puMyl1NQsF0ijymr0IvnE7p9gah3YM9933GaDG99XoBYRkVylUC0eWw86+WOX03Pcq7UavUg+kHQQfugDM26G0ztg/hDIcFz+cSIiIjnomkL1qlWr6NChg+d4wYIFxMfH06dPH77++msADh8+TN++fenfvz9HjhwB4LvvvmP27NnX8tKSw/7e6KVFjWCqlNbqIPFjbhdFd38OU+rA1q+94ynHLtw2T0REJJdlOzUdOnSIKVOmkJGRAYDT6WTMmDFMnz6dsLAw+vXrR6dOnZg7dy73338/hmEwd+5c+vbty4IFC5gwYUJOvQfJAWu2p7P7qNnoJTAAerTUFnrix478Dr88QJkja3zH6w6ADq+bzVxERETyULZmqtPS0hgxYgQjR470jO3YsYPY2FhiYmIIDg4mLi6O1atXEx4ejsPhwOFwEBYWxtSpUxk4cKCWFfgRp8vgm0yNXjo3DKVEtBq9iB9KOwsL/gmfN4fMgbpoTeg9H7p+okAtIiKWyNZM9ahRo7jvvvsoXbq0ZywpKYmoqCjPcUREBElJSdx2222MGTMGu93O4MGDmThxIs2aNWP48OE0aNCA3r17X/D8CQkJ2SlLrpBhwFmHHZdh/mKz+UgoJxIjAQgNclMtYi8JCYaVJV7A4XDovCjkwk6upfwf/yIo7ahnzG0P5kS1BzhRdRBGcjDoHBH0/UKypvNCcttVh+ojR46wZs0a9u7dyzvvvMOZM2d44okneOCBB0hOTvbcLzk5maioKCIiIhg9ejQAL730EkOGDGHUqFFMnjyZxx57jFtvvZXw8HCf16hTp841vi25GMMwmPRTEr/vdGZ5e/eWkTRpWCKPq7q8hIQEnReF3dlwWJPkPa50AzsrP0n1ZjdT0rqqxA/p+4VkReeFZGXt2rU59lxXHapLly7NTz/95Dlu27Yt48ePx+l0smfPHk6fPk14eDhr1qxh0KBBnvtt3bqVkJAQYmNjSUtLw2az4XK5SE9PvyBUS+75Y5fzooG6VIydDvXU6EX8VHQlaD0C1o6DjhOgVh+cmzdbXZWIiAiQg81fgoKCGDZsGIMGDcIwDOLj432Wh0yePJnhw4cD0L17d/r06UP9+vUpUqRITpUgl5HhMpjxm3eHj5hwG6Hn2o9Hhtjp006NXsRP7P/V3MGj8RDf8bgnoOE/ILSIJWWJiIhczDWH6mXLlnm+7tSpE506dcryfmPHjvV8HR8fT3x8/LW+tFylxZvSOHrG7OwSHmJjZN8YIkO1Vbn4kZTjsOQZ2DQF7EFQsSMUr+29PSAIAopYVp6IiMjFKFEVEilpbmat8e7wcWtcqAK1+A/DgD+nwJTaZqAGcDthyVPW1iUiInKF1N2jkJjzu4Mkh7mjR/EoOx0bhFpckcg5xzfBvCFw4Fff8Ro9oeMb1tQkIiJylRSqC4ETiS7mb/C2be7ZKowgrZ0WqzlTYMVoWPNfcGd4x6MrQae3odpt1tUmIiJylRSqC4GZK1LJMJslUqVUAM2rB1tbkMjOOTD/YTi72ztmD4S4f0HrFyAowrLSREREskOhuoDbfTSDldvSPce92oSrm6VYy+2Cpc/5BupybaHLu1CygWVliYiIXAtdqVaAGYbBtOXeLfSaVAmiZrkgCysSAewB0GUSYIPQYnDjB9B3iQK1iIjka5qpLsDW73ay9aC5VjXADj1bq8mOWOD4n1C8Ltgy/Q5frhXcPBWqdIVw9UMUEZH8TzPVBdTfG710qBdCmSIBFlYkhU7aGZj/KHzcEP6ceuHt9QYqUIuISIGhUF1ALU1I4/Bps9FLWLCN25qFWVyRFBqGAZu/MvecXvc2YMCSpyHlmNWViYiI5Bot/yiAUtMNvl/tbfTStWkoUWH6/UnywOkd5q4eu3/yHS/THFxp1tQkIiKSBxSqC6Aff08lMdVs9FIs0k7nhmr0IrksIw3WvA4rR0OGd090IspCxwlQszdo1xkRESnAFKoLmJOJLn5Z7w01PVqFERyoMCO5aN9imPcgnNycadAGjR+GdqMhJMay0kRERPKKQnUB8+2qVJznGr1UKhlAixpq9CK56K/PYO4A37FSTeGGyVCmmTU1iYiIWEChugDZeyyDFVu8jV56twnHro/cJTdVux0iykDyYQiOgrajzRlqu3aaERGRwkWhuoA43+jFOHfcqHIQtcqr0YvkMMPwXRsdEgPXT4Bt083/R5W3qjIRERFLKVQXEH/udbL5gNnoxW6D+FZq9CI5yJkMv40yt8W7+SPf22r3Mf8TEREpxBSqCwCX22Dacu8WetfVDaFsMX38LjlkxyxY8Aic3WMe170bYjtZW5OIiIif0ebFBcCyhDQOnTKvTgwNgtubq9GL5IDE/fBdT/j2dm+gBvPiRBEREfGhmep8zpFu8N0q7yz1zU3DiA7X70pyDdwZ8MdbsGw4OJO846HFocPrUO//rKtNRETETylU53M/rUvl7LlGL0Uj7HRRoxe5FodWwi8PwLH1vuP174PrXoOw4tbUJSIi4ucUqvOxU0lufl7nbfTSvWUYIUHaQk+ywZUOCx+H9ZPAs4cMULwudJkEFdpbVZmIiEi+oFCdj323KoV0c8MPKpYIoFVNNXqRbLIHnVs3fS5QB4ZBq+HQ7EkI0HklIiJyOVp8m0/tP57B8s1/a/Ri1yy1ZJPNBp3fNsN0lVvgnk3QcpgCtYiIyBXSTHU+Ne03b6OXBrFB1KmgRi9yhTLSYP1EaPggBGXaKSamCgxcD0Wq+zZ4ERERkctSqM6H/tybzl/7zHUfNhvEt9EWenKF9syH+Q/Bqa3gOAVtR/neXrSGNXWJiIjkc1r+kc+43QbTMzV6aV8nhPLF9LuRXEbyEZhzN0zvYgZqgFX/gVPbra1LRESkgFAay2eWbU7nwEmz0UtIIHRroVlquQTDDRveh1+HQdpp73hwNLQfYy75EBERkWumUJ2PpDkNvluV4jm+qUkYMWr0IhdzdD3MexAOrfAdr9UXrh8HkWWtqUtERKQAUqjOR35e5+BMinl5Yky4jRsbq9GLZCE9CZaPhN8ngOHyjhepBp3fgco3WVWZiIhIgaVQnU+cTnbz0x/etdTdW4ar0YtkbdPHsHas99geBC2GQYtnfXf7EBERkRyjtQP5xPerU0k71+ilfLEA2tTS/sFyEQ3/ASUbml9XvB4GbjB3+VCgFhERyTWaqc4HDpzIYGlCmudYjV7Ew+UEx0mIKO0dCwiCG96Dk1ug7gDtOS0iIpIHFKrzgem/pWKc6/RSt2Ig9WLV6EWAg7/BLw9ASAz0WQy2TB88lW1p/iciIiJ5QqHaz/21z8mfe50A2IDercOtLUis5zhlbpG34T3v2KaPof691tUkIiJSyClU+zG322Dacu8Wem1qB1OhhP7KCi3DgITPYdGTkHrMOx4YBhmpF3+ciIiI5DolND+2Yms6+0+YW6IFB8IdLTRLXWid3ALzhsC+hb7jVW+DTm9BTGVLyhIRERGTQrWfSnMazFzpnaW+sXEoRSO1WUuhk+GAlWNg9X/Ale4dj6wAnd6E6t11IaKIiIgfUKj2U/PWOzidbF6dGB1m46Ym2g6t0HGmwqeN4dRW75gtAJr+E9qMhOAoqyoTERGRv1Go9kNnU9zMzdTo5Y4WYYSq0UvhExQGsV28obpsS+gyCUo1trQsERERuZBCtR/6YXUqaeaGH5QtGkDbOiHWFiTWaf+KuY666WNmUxeblgCJiIj4I/2E9jOHTrpY8lfmRi9hBKjRS8F3dB3M6AopR33HQ2Lg/zZCowcVqEVERPyYfkr7mRkrUnCfa/RSp0Ig9dXopWBLTzS3yPssDnb/CIufvvA+9oC8r0tERESuikK1H9l8wMn63d5GL73ahGPTzg4Fk2HAtm9gSh1YOx4Mtzm+5Ss4u9fa2kREROSqaU21n3Abvo1eWtUKJlaNXgqmM7thwaOwc5bveGwn6PwuRMdaUpaIiIhkn1Kbn1i1NZ29x8xGL0EB0L2lGr0UOC4nrB0Hv73o2wExvBRcPw5q99ee0yIiIvmUQrUfSM8wmLnSG7JuaBRKMTV6KVgO/gY/D4YTm3zHGz4A7cdAaFFr6hIREZEcoVDtB+ZvcHAyyVxTGxVm4+amavRS4CQd8A3UJRuae06Xa21dTSIiIpJjNB1qscRUN3N/d3iOuzUPIyxYSwAKnBrxUKUrBEVAh9fh7rUK1CIiIgWIZqot9sOaVFLTzT30yhSx006NXvK/EwmQftbsgHiezWbOTIMuRBQRESmANFNtocOnXCzZ5G300qtNOIEBmqXOt5ypsPTf8EkjmDsAMhy+t0fHKlCLiIgUUArVFpqxIgXXue2Ja5ULpGElNXrJt3b/BB/Xh5Uvg9sJp7bByjFWVyUiIiJ5RMs/LLL1oJN1u5ye495q9JI/JR2CRU+YTVsyK9saasZbU5OIiIjkOYVqC/y90UvLGsFUKqW/inzF7YL178LS58310+eFFoX2r0KDQWDTB0EiIiKFhZKcBdZsT2f3UbPRS2AA9GilLfTylSO/wy8PwJE1vuN1B5g7e4SXsqYuERERsYxCdR5zZhh8s8Lb6KVLw1CKRwVYWJFclbSz8PX1kJ7oHStaE7q8a7YZFxERkUJJn0/nsQUbHZxINK9OjAy10bVpqMUVyVUJiYZWL5hfB4RAm1EwcIMCtYiISCGnmeo8lORwM3utd5u125uHER6i32v8WnoiBEf5jjV9HM7sgrgnoGgNS8oSERER/6JEl4dmZ2r0UirGznV11ejFb7nSzS3x3qsIJ/7yvS0gCLpMVKAWERERD4XqPHL0jIuFf3obvcS3VqMXv7V/CXzSGJY+B2lnYN4QMAyrqxIRERE/puUfeeSbTI1eqpcNpEkVNXrxOynHYckzsGmK73jaWUg9DuElralLRERE/J5CdR7YfsjJ2h1q9OK3DAM2TYXFT4PjhHc8KBLavgRNHgG7/qmIiIjIxSkp5DLjb41emlcPpmpp/bH7jeObzOUdB371Ha/REzq+AVEVrKlLRERE8hWlu1y2doeTnUfONXqxq9GLX1k/CRY8Cu4M71h0Jej0NlS7zbq6REREJN9RqM5FGS6Db1Z4Z6k7NQilZLQavfiNUk3MduNgLu+I+xe0fgGCIqytS0RERPIdhepctOjPNI6dNa9ODA+xcUucGr34lbItodGDcGwD3DAJStS3uiIRERHJpxSqc0myw80Pa7ztyG9vFkZEqHYwtIQ7A9a9A6HFoO4A39uuHwcBwWDT342IiIhkn0J1Lpmz1kFKmrm3ccloO9fXV6MXSxxeDb88CEd/h9CiUPlm363xAvXpgYiIiFw7Tc/lgmNnXCzY6G1HrkYvFkg7A/Mfgc9bmoEawHEK1oy1ti4REREpkDRTnQtmrkwl41yjl2qlA2laVY1e8oxhwJavYdHjkHzYOx4YCq1egGZPWVaaiIiIFFwK1Tls55EMVm9P9xz3ahOmRi955dR2mP8w7PnZd7zyzdD5HShS1Zq6REREpMBTqM5Bf2/0ElctiOplNUud6zLSYM1/YcVocKV5xyPKmg1cavYC/WIjIiIiuUihOgf9scvJ9kNmI5EAO/RsFW5xRYWEywHrJnoDtc0OjR+GtqMhJNra2kRERKRQ0IWKOSTDZTDjN+8sdcf6IZSKUaOXPBESAx0nmF+XjoP+K6HTmwrUIiIikmc0U51DFm9K4+gZb6OXW5upHXmuMNywbxHEdvIdr9kbugVBtW5g1y8zIiIikrc0U50DUtLczMrU6OXWuFAi1egl5x3bCF+2h2mdYc8839tsNqjRQ4FaRERELJGt5Hfw4EHuueceBgwYwN13383OnTsBWLBgAfHx8fTp04evv/4agMOHD9O3b1/69+/PkSNHAPjuu++YPXt2Dr0F68353UGSw2z0UjzKTscGaiiSo5zJlNr8OnzaBA4uN8fmPwQZjks/TkRERCSPZGv5xxtvvMHdd99Nly5d+PXXXxk3bhzjx49nzJgxTJ8+nbCwMPr160enTp2YO3cu999/P4ZhMHfuXPr27cuCBQuYMGFCDr8Va5xIdDF/gzfc9WwVRpAaveScHT/A/EconrjXO2YPgpp3WleTiIiIyN9kK1QPHTqUqKgoAFwuFyEhIezYsYPY2FhiYmIAiIuLY/Xq1YSHh+NwODAMg7CwMKZOncrAgQMvuXdzQkJCdsqyxJy/oslwmeuny0Q5iXTuJB+V77cCUw9R5q9XiDoy32c8uVhzDtcfTnpkNdi2y6LqxF84HI589f1C8obOC8mKzgvJbdkK1cWKFQNg586dvPrqq7zzzjucPHnSE7QBIiIiSEpK4rbbbmPMmDHY7XYGDx7MxIkTadasGcOHD6dBgwb07t37guevU6dONt9O3tp9NIPNR856jgd0LkbNcqUtrKgAcGfA72/C8uHgTPYMZwQXJbDTeCLqDqSa9pyWcxISEvLN9wvJOzovJCs6LyQra9euzbHnyvbVdCtWrODhhx/mtddeo2rVqkRGRpKc7A1BycnJREVFERERwejRoxk1ahRTp05lyJAhTJo0iREjRrBo0SJSUlIu8Sr+6++NXppUCaJmOTV6uWaLn4LF//IJ1DS4n53XzYZ6/6cmLiIiIuKXshWqV6xYwcsvv8wHH3xAgwYNAKhWrRp79uzh9OnTpKens2bNGpo0aeJ5zNatWwkJCSE2Npa0tDRsNhsul4v09PSLvYxfW7/bydaDmRq9tFajlxzR9HEIPLcdYfF60OdXuPF9XMFFrKxKRERE5JKytfzjlVdewel0MmzYMACqVKnCqFGjGDZsGIMGDcIwDOLj4yld2rsUYvLkyQwfPhyA7t2706dPH+rXr0+RIkWu/V3ksb83ermuXghlimgrt6tmGGC4wJ7pNIypDO3HmDt7xD0BAcGWlSciIiJypbIVqr///vssxzt16kSnTp2yvG3s2LGer+Pj44mPj8/OS/uFpQlpHD5tNnoJC7Zxuxq9XL1T22DeQ1CuNbQd5Xtb039aU5OIiIhINqmj4lVKTTf4frW30UvXpqFEhanRyxXLSINV/4FVY8CVBvsXQ+3+ULy21ZWJiIiIZJtC9VX68fdUElPNRi/FIu10bqhGL1dsz3yzacuprd4xw2UGa4VqERERyccUqq/CyUQXv6z3Nnrp0SqM4EDtRnFZyUfMHT0SPvcdL9McukyG0k2yfpyIiIhIPqFQfRW+XZWK02V+XalkAC1q6CK6SzLcsOF9+HUYpJ32jgdHmxcjNnwA7LrAU0RERPI/heortPdYBiu2eLf/690mHLv2TL645MPwXQ84tMJ3vFZfuH4cRJa1pi4RERGRXKBQfQXON3oxzh03qhxErfJq9HJJYSUgw3tBJ0WqQeeJUPlG62oSERERySXatuIK/LnXyeYDZqMXuw3iW6nRy2XZA6HLJAgMhVYvwMCNCtQiIiJSYGmm+jJcboNpy70zrtfVDaFsMa0D9nF2L2yYDG1fAlum39PKtYLBeyG8pHW1iYiIiOQBherLWJaQxqFT5tWJoUFwe3M1evFwOeH3N2D5CMhIgZhq0OA+3/soUIuIiEghoOUfl+BIN/hulXeW+uamYUSH648MgIO/wefNYMnTZqAG+HUoOJOtrUtERETEApqpvoSf1qVy9lyjlyIRNrqo0Qs4Tplb5G14z3e8RH1zz+mgCGvqEhEREbGQQvVFnEpy8/O6TI1eWoYTElSIt9AzDLN5y6InIfWYdzwwHNqMhKaPQ4B2RBEREZHCSaH6Ir5blUK6ueEHFUsE0KpmIW70cnILzBsC+xb6jle9HTq/BdGVrKlLRERExE8oVGdh//EMlm/+W6MXeyGepV73jm+gjqwAnd6C6neAGuCIiIiI6ELFrEz7zdvopUFsEHUqFPJlDW1fgogyYAuAuCfh3gSo0V2BWkREROQczVT/zZ970/lrn7nuw2aD+DaFbAu95MNgC4TwEt6xkBi4+RNze7xSjS0rTURERMRfaaY6E7fbYHqmRi/t6oRQvlgh+b3D7YJ1E2FKbVj0xIW3V75BgVpERETkIhSqM1m+JZ0DJ81GLyGB0K2wNHo58gd80QbmPwxpZyDhM9i7wOqqRERERPKNQjINe3lpToNvV6Z4jm9qEkaRiAL+O0d6IiwbDn+8CYbbO160BtgL+TpyERERkaugUH3Oz+scnEkxL0+MCbdxY+MC3OjFMGD7TFjwGCQd8I4HBEOL56DFUAgswO9fREREJIcpVAOnk9389Id3LXX3gtzo5cxuWPAI7JztOx7bGTpPhGI1LSlLREREJD9TqAa+X51K2rlGL+WLBdCmVgFt9HJqG3zSCDK8v0AQXgquHw+1+2mLPBEREZFsKuCLhi/vwMkMliakeY57tQkruI1eilSHih3PHdig0YNw72ao01+BWkREROQaFPpQPWN5Ksa5Ti91KwZSP7YAzVKff2Pn2WzQ+W0o2xr6LYcu70JoUWtqExERESlACnWo/mufk417nQDYgN6tw60tKKcYBvw51dwmz5nqe1tMFei/HMq1sqQ0ERERkYKo0IZqt9tg2nLvFnptagdToUQBWGJ+IgG+vh5+uhcOrYBVY6yuSERERKTAK7ShesXWdPafMBu9BAfCHS3y+Sy1MxWW/tu8EHH/Eu/4li/BlW5dXSIiIiKFQAGYmr16aU6DmZkavdzYOJSikfn494tdP5rdEM/s9I7ZAqDZv6D1cHP/aRERERHJNYUyVM9b7+B0snkRX3SYjZua5NN25EkHYeETsPVr3/FybaDLJCjZwJq6RERERAqZQheqz6a4mZup0csdLcIIzY+NXja8D4ufgvSz3rHQotD+NWhwH9jy8cy7iIiISD5T6EL1D6tTSTM3/KBs0QDa1gmxtqDsSjvjG6jrDoAOr5vNXEREREQkTxWqUH3opIslf3kbvfRuE0ZAfm300vSfkPApZKSZ+03Hdrz8Y0REREQkVxSqUD1jRQruc/1Q6lQIpH5skLUFXQnDgK3ToXhdKFHPOx4QBHd8BxFlITCfzraLiIiIFBCFZuHt5gNO1u/2Nnrp1SYcm7+35j69E2beCrPuhHkPguH2vT2msgK1iIiIiB8oFKHabfg2emlVK5hYf2704kqHla/Ax/Vg11xz7MBS+Osza+sSERERkSz5cbLMOau2prP3mNnoJSgAurfw4y309i+BXx6EkwmZBm3Q+CGo1s2yskRERETk4gp8qE7PMJi50ruF3g2NQikWFWBhRReRchyWPA2bpvqOl2oCN0yGMs0tKUtERERELq/Ah+r5GxycTDLXIkeF2bi5qZ/NUhtu+HOqGagdJ73jQZHQbjQ0fhjsBf6vSURERCRfK9BpLTHVzdzfHZ7jbs3DCAv2s4sTj2+Cn+8HDO9YjXjo+AZElbesLBERERG5cgX6QsUf1qSSmm6G1TJF7LTzx0YvJRtAoyHm19GVoccs6DZdgVpEREQkHymwM9WHT7lYssnb6CW+dTiBAX4wS510ECLL+Y61exkiSkOzpyAo3Jq6RERERCTbCuxM9YwVKbjObetcs1wgjSpb3OglcT98Hw8f14eUo763hRaB1sMVqEVERETyqQIZqrcedLJul9Nz3NvKRi/uDFg7AabUgW3fgOMULH7amlpEREREJFcUuOUff2/00rJGMJVLWfQ2D60yOyEe/cN33GYHtwvsfri1n4iIiIhctQIXqtdsT2f3UbPRS2AA9GhlwRZ6jtOw9HlY/y4+u3oUqwNd3oWKHfK+JhERERHJNQUqVDszDL5Z4W300qVhKMXzstGLYcCWr2DRE5B82DseGAqthkOzf0FAcN7VIyIiIiJ5okCtqV6w0cGJRPPqxMhQG12bhuZtAT/dC7P7+QbqyjfD/22Cls8qUIuIiIjfGjBgADt27Ljo7atXr2bz5s0APPLII3lSU0JCAm+//XaevNa1KjChOsnhZvZab6OX25qFER6Sx2+v6m3eryPLwe3ToOccKFI1b+sQERERyWEzZszg6FFzB7O8Crp16tTJswB/rQrM8o/ZmRq9lIqx06GeBY1easSbwTqmKrR9CUKi874GERERyVU/r0vl+9WppDkvf98rFRJkdn6+sXHW14I5HA6effZZDh48iNPp5IUXXmDXrl3s3LmTp556irS0NLp27cqCBQsYMGAAtWrVYtu2bYSHh9OsWTOWLl3K2bNn+eijj5g/f36Wjzvv8OHDjBw5krS0NI4dO8bjjz9OmTJl+PXXX9m0aRPVq1end+/e/PDDD9x1113MmTMHm83GqFGjaN26NbGxsYwePRqAIkWK8MorrxAVFeV5/pMnT/LUU0+Rnp5OlSpVWLFiBb/88gs//vgjn3/+ORkZGdhsNt5++222bdvGl19+yfjx47nxxhtp2rQpu3btonjx4rz11lvs3buXZ599lsDAQNxuN2PHjqVs2bI59xdzFQrETPXRMy4W/pmHjV5SjsLc/4M983zHbTa441vo9IYCtYiISAH18zpHjgZqgDSn+bwX8+WXX1K+fHm++uorxo0bx/r16y/5fA0bNuTjjz8mPT2d0NBQpkyZQvXq1Vm9evVla9m5cyf33nsvU6ZMYdSoUXz++efUr1+f9u3b8/TTT1OunNnErlixYtSqVYs1a9aQnp7OypUr6dixIy+88AIjRozg008/5brrruODDz7wef5JkybRuXNnPvvsM26++WZcLnODid27d/Pee+/xxRdfUL16dZYuXerzuH379vHPf/6Tr776ipMnT7Jx40aWL19Ow4YNmTJlCo8++iiJiYmXfX+5pUDMVH+TqdFL9bKBNKmSS41eDDds/BB+HWruN33oNxi4wbwQ8TxtkyciIlKg3dg4NFdmqm9sfPFrwXbu3Ml1110HQOXKlbnnnnv45ptvPLcbhuFz/3r16gEQHR1N9erVPV+npaX53O/vjwMoWbIk7777LtOnT8dms5GRkXHRuu68805mzpzJsWPH6NSpE4GBgezYsYMXX3wRAKfTSeXKlX0es2PHDnr06AFAs2bNPOPFixdn6NChREREsHPnTho3buzzuKJFi3pmocuWLUtaWhq9evXi/fff5/777ycqKoonnnjiorXmtnwfqrcfcrJ2Rx40ejm20dxz+uBy79ipbbD9O6jdJ+dfT0RERPzSjY0vvkwjt1SrVo2NGzfSpUsX9u3bx4QJE+jUqRPHjh0DYNOmTVf8XCEhIZd83BtvvEHv3r3p0KEDM2bMYObMmQDYbLYLQnjr1q3573//y5EjRxgxYgQAVapU4dVXX6VcuXKsXbvW81rn1axZkz/++IM6deqwbt06ABITE3nzzTdZtGgRAPfee+8Fr5VVvps/fz5xcXE88sgjzJo1iw8++IAxY8Zc8Z9FTsrXodr4W6OX5tWDqVo6h9+SMxmWvwhrx4Hh8o7HVIXO70CVm3P29URERET+pm/fvjz33HPcfffduFwunnvuOSpVqsQXX3xBv379qFevHhEREVf0XO3bt7/k426++WZee+013nvvPcqUKcOpU6cAaNSoEa+//joVKlTw3Ndms3HTTTexfPlyYmNjARg5ciRDhw71rI1++eWXfZ5/8ODBPPPMM8ydO5dSpUoRGBhIZGQkTZs2pU+fPgQGBhIdHc3Ro0d9Xisr9evXZ+jQobz77ru43W6effbZK/ozyA02I6t5fwutXbuWuLi4K7rvmu3pTP45CYBAO4zqH0PJ6BxcfrH9e1jwKCTu9Y7Zg6D5M9DyeQiyoLFMIZWQkECdOnWsLkP8jM4LyYrOC8mKzgv/sXjxYooWLUrDhg1Zvnw5kyZN4pNPPrGklqvJnZeTb2eqM1wG36zwzlJ3ahCac4E68QDMfxh2fOc7XqGD2RGxuP5RioiIiGRHhQoVeO655wgICMDtdvP8889bXVKOyLehetGfaRw7a16dGB5i45a4HGz0YmTAnl+8x6HF4fqxUHegucOHiIiIiGRLtWrV+Oqrr6wuI8flyy31kh1ufljjbUd+e7MwIkJz8K1EV4LW5mJ76g+C+7ZAvf9ToBYRERGRLOXLmeo5ax2kpJlLwUtG27m+/jU0enGcgv2/QvVuvuNxT0CF66Bcq2uoVEREREQKg3w3U33sjIsFG72bo/dslc1GL4YBCf+DKbXhh15wIsH39oAgBWoRERERuSL5LlTPXJlKxrlGL1VLBxBXLRuNXk5uhek3wJy7zO6IbifMG2IGbRERERGRq5SvQvXOIxms3p7uOb7qRi8ZDnPP6U8awN753vHI8tD0sRysVERERMT/fPHFF7z11lt5/roDBgxgx44dV/WYX375hSNHjnDs2DFGjhyZO4XloHwTqv/e6CWuWhDVy17FLPWe+fBJQ/htJLjOBXOb3Vw7fW8C1OipCxFFRERE/MQnn3xCUlISJUuWzBehOt9cqPjHLifbD5m95wPs5lrqK5J8BBb/CxI+9x0v0xy6TIbSTXK4UhERESnwlo+E3168svs2GAw3vuc79vM/YOP73uPWI6DNyIs+xTfffMOMGTNwu9089thj7Nixg59//pnU1FSKFi3K22+/zaxZs1i8eDEOh4O9e/cyePBgevbsyZo1a3jllVeIjo4mICCAxo0bA/DRRx8xe/ZsAgMDadasGU8//TRvvfUWe/bs4dSpU5w+fZq77rqLn3/+mV27dvHqq696Hguwa9cunn32WQIDA3G73YwdO5ayZcsyduxY1qxZg9vt5p577qFr166exyQmJvL88897ujT++9//platWkybNo0vvvgCt9tNp06daNiwIQkJCQwdOpT//ve/DB06lK+//pply5YxYcIEQkJCKFKkCK+88goJCQm8//77BAUFsX//fm655RaGDBlyZX83OShfhOoMl8GM37yz1B3rh1Aq5gobvczuC/sWeY+Do6H9GGj4ANhzsPuiiIiISC6Kjo72tONeu3YtU6dOxW63M2jQIDZu3AhAUlISH374Ibt37+bBBx+kZ8+evPjii7z55ptUqVKFESPMLYO3bNnC3Llz+fLLLwkMDOTRRx9l4cKFAISGhvLhhx/y3nvvsXjxYiZNmsSMGTOYPXu2T6hevnw5DRs25Omnn2bNmjUkJiaydetW9u/fzxdffEFaWhp33nknbdu29Txm0qRJtGrViv79+7N7926effZZ3n77bd5//32+//57QkJCGDt2LM2bN6dOnTqMHDmSoCBzZYJhGLzwwgt88cUXlC5dmo8//ph3332X66+/noMHD/L999+Tnp5O+/btFaovZvGmNI6eMa9ODAu2cWuzq2gP3v4/8L/WgAG1+sL14yCybO4UKiIiIpJLqlSpAoDdbicoKIgnn3yS8PBwDh8+TEaG+Wl+7dq1AShbtizp6eZy1+PHj3se27RpU/bu3cvOnTtp1KiRJ7A2a9aMbdu2AVC3bl0AoqKiqF69OgAxMTGkpaX51NOrVy/ef/997r//fqKionjiiSfYunUrmzZtYsCAAQBkZGRw4MABz2O2bt3KihUrmDt3LgBnzpxh37591KhRg9BQs5HfU089leX7P3XqFJGRkZQuXRqA5s2bM27cOK6//npq1qxJYGAggYGBnufJa34fqlPS3MzK1Ojl1rhQIi/W6MWZDIFh5lrp88q2hLYvmcs9Kt+Yy9WKiIhIodBm5CWXa1zWje9duCTkMux2M99s3ryZefPmMW3aNFJTU+nZsyfGuR3MstrAoXTp0uzYsYNq1aqxceNGYmJiqFq1KlOmTCEjI4OAgABWr15N9+7d2bx58xVvAjF//nzi4uJ45JFHmDVrFh988AFdunShZcuWvPTSS7jdbiZOnEjFihU9j6latSrdunXj9ttv58SJE0ybNo3Y2Fh27txJeno6wcHBPPbYYzz//PPYbDbP+wIoWrQoSUlJHD16lFKlSrFq1SoqV6580fed1/w+VM/93UGSw/wDLR5lp1ODi/z2se1bWPCouSap4f2+t7UqGD3lRURERCpVqkRYWBh9+/YFoGTJkhw9evSi9x81ahTPPPMMkZGRREREEBMTQ61atejatSv9+vXD7XYTFxdHly5d2Lx58xXXUb9+fYYOHepZkvLss89St25dVq1aRf/+/UlJSaFLly5ERkZ6HvPggw/y/PPP8/XXX5OUlMQjjzxCsWLFGDx4MHfffTc2m42OHTtSunRpmjRpwjPPPMNLL70EmMF59OjRPProo9hsNmJiYhgzZoxnht1qNsPwr82Z165dS1xcHAAnEl38+39nyHCZtw2+IYIWNf7WPfHsHljwGOz43jwOLQr3boHwknlYteS2hIQE6tSpY3UZ4md0XkhWdF5IVnReSFYy585r5dcz1TNXpHoCdZVSATSvHuy90eWE3yeYV99meC9ixBYIJxWqRURERCTv+G2o3n00g5XbvI1eemVu9HJgOcx7EI5v9H1Qw39AuzEQViwPKxURERGRws4vQ/XfG700rhJEzXJBkHoSfh3mu68jQIkG0GUSlG+Tx5WKiIiIiPhpqF6/28nWg95GL/Gtw+HQSph5O6Qe894xMBzavAhN/wkBV9FdUUREREQkB/llqM7c6OW6eiGUKRIAYbXBnqncat2g01sQHWtBhSIiIiIiXhfZ8Nlah097G73cfr7RS0gMdJwAURXhjm+h+3cK1CIiIiLiF/xyprquayG13L9iazWGqLBMub9mb6h6GwSFW1eciIiIiMjf5OhM9bp16+jduzd9+/bl7bffBiA5OZmBAwfSp08fz4bia9as4b33Lt5F6In0XtyS8QZdiiz1vcFmU6AWEREREb+To6F6xIgRjB07li+++IL169fz119/sWzZMjp16sSIESOYPn06hmHwySef8H//93+Xfb6g3/4N/tWbRkRERETkAjm2/CMpKYn09HRiY811zu3atWP58uXUrl2btLQ0HA4H4eHh/PDDD9xwww2EhIRc9LnWXr/Ge/D77zlVouRza9eutboE8UM6LyQrOi8kKzovJDflaKjO3Ns9IiKCffv20aZNGxYvXsxXX33Fo48+ymuvvcajjz7K8OHDqVixIoMHD/Z5npxqFSkiIiIikldybPlHZGQkycnJnuPk5GSio6Ox2+08//zzvPrqq8yePZuBAwfy7rvv8vjjj3Po0CF27dqVUyWIiIiIiFgiR0N1UFAQe/fuxTAMli5dSrNmzTy3nzhxgl27dtGsWTNSU1MJCAjAZrORmpqaUyWIiIiIiFgiR7fUe/HFF3nqqadwuVy0a9eORo0aeW579913GTJkCAD9+/dn0KBBlCtXjtq1a+dkCSIiIiIiec5mGNZur7Fu3TpefvllAgICaNeuHY888gjJyckMGTKEtLQ0XnzxRWrXrs2aNWv4/fff+cc//mFluZJLDh48yHPPPYfL5cIwDEaNGkXVqlVZsGAB77zzDoGBgcTHx3PnnXdy+PBhHn/8cex2O+PHj6d06dJ89913BAYGcuutt1r9ViQXrFq1iqeffprFixcD6Lwo5FJSUhg5ciT79+/H6XTywgsv0LBhQ50XhdzBgwd55plnMAyDmJgYxo4dS1hYmM6LQmj9+vW8/vrrfPrpp+zZs4dhw4Zhs9moUaMGI0aMwG638/bbb7No0SICAwN57rnnaNiwIUuWLOHNN9+kXLlyTJgwAbvdzqhRo7jvvvuoUKHC5V/YsFi3bt2MPXv2GG6327j//vuNTZs2GT/99JMxZcoUY9OmTcZLL71kuN1u49FHHzUcDofV5UoueeaZZ4xffvnFMAzDWLJkifHwww8b6enpRpcuXYzTp08baWlpRs+ePY1jx44ZH330kfHLL78YP//8szFlyhQjNTXVeOyxxwy3223xu5DccPDgQePBBx802rRpYxiGofNCjDfffNN47733DMMwjISEBGPmzJk6L8R4+eWXjc8++8wwDMMYN26c8cknn+i8KITee+8947bbbjN69+5tGIZhPPDAA8aKFSsMwzCMF154wfj555+NP//80xgwYIDhdruNAwcOGD179vTc98yZM8ZLL71kbNq0yUhISDDGjh17xa9taZvyzNvw2Ww2zzZ84eHhV70Nn+RvQ4cOpUOHDgC4XC5CQkLYsWMHsbGxxMTEEBwcTFxcHKtXryY8PByHw4HD4SAsLIypU6cycOBAbDabxe9CclpaWhojRoxg5MiRnjGdF7J06VKCgoIYNGgQEydOpH379jovhDp16nD27FnAzBeBgYE6Lwqh2NhY3nrrLc/xpk2baNGiBQDXXXcdy5cvZ+3atbRr1w6bzUa5cuVwuVycPHmSiIgIHA4HaWlphIWF8f7771+wS92lWB6q/74NX2JiIm3atOH48eN89dVX3HnnncybN4/atWszfPhw3n//fQsrltxSrFgxgoKC2LlzJ6+++ioPP/wwSUlJREVFee4TERFBUlISt912GytWrGD16tW0adOGPXv2YBgGw4cPZ9q0aRa+C8lp5z92K126tGdM54WcOnWKs2fP8uGHH9KpUydeffVVnRdCmTJl+Pzzz7n11ltZsmQJN998s86LQuimm24iMNB7yaBhGJ5fls7nzIvlz4ceeogxY8ZQvnx59u7dS9OmTZk1axbDhw/njz/+uOxrWxqqtQ2fZLZixQoefvhhXnvtNapWrZrl+REVFUVERASjR49m1KhRTJ06lSFDhjBp0iRGjBjBokWLSElJsfBdSE45cuQIa9as4Z133mHAgAGcOXOGJ554QueFUKRIETp16gRAx44d+fPPP3VeCK+99hpjxoxh9uzZPP/88wwdOlTnhWC3e6Pu+Zx5sfOiWrVqjB8/nsGDBzN9+nRuu+02li5dyvDhw5k4ceLlXytX3sEV0jZ8ct6KFSt4+eWX+eCDD2jQoAEA1apVY8+ePZw+fZr09HTWrFlDkyZNPI/ZunUrISEhxMbGkpaWhs1mw+VykZ6ebtXbkBxUunRpfvrpJz799FM+/fRTYmJiGD9+vM4LIS4uznPR6urVq6levbrOCyE6OtozK12qVCnOnj2r80KoW7cuK1euBGDJkiU0a9aMpk2bsnTpUtxuNwcPHsTtdlOsWDHPY7766it69OgBgNvtvuLsmaNb6mWHtuETgFdeeQWn08mwYcMAqFKlCqNGjWLYsGEMGjQIwzCIj4/3WQYwefJkhg8fDkD37t3p06cP9evXp0iRIla8BckjQUFBOi8KuQceeIB///vf9OnTh8DAQF599VWdF8ILL7zAqFGjcLvdnqUcOi9k6NChvPDCC4wbN46qVaty0003ERAQQLNmzejTpw9ut9tzDoC5xHDVqlVMmDABgJIlS9KvXz/69+9/2deyfEs9EREREZH8ztLlHyIiIiIiBYFCtYiIiIjINVKoFhERERG5RgrVIiIiIiLXSKFaREREROQaKVSLiIiIiFwjhWoRERERkWv0/yYiLidrzikRAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1949,12 +1925,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]
@@ -1969,14 +1945,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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rKSlJjzzyiK5evWpf/+a9tevWrVOePHnUtm3bO15dbdOmjSIjI3X06FENGjQow+t9+vTRN998o8jISH3//ffy9/dXfHy81q9fr8TERE2dOtX+Bry79eSTTyo4OFiRkZFq1aqVGjRoIA8PD0VHR9uf53zziSW9e/fWunXrNGzYMK1fv15ly5bV6dOntXHjRhUpUkRdu3ZNt29PT0+98sorWrdunYoVK6YtW7bo5MmTGjBggP2KdOnSpTVixAi9/fbbatu2rZo2bao8efIoJiZGJ0+eVKtWrfT888/f9Xnd/JCYDRs2qG3btnryySeVkpKi6OhoXb16Nd2HBY0bN05dunTR4MGDFRgYqAoVKuj48ePasmWLChYsqNdff93oewvgwUBMA3A5Nzc3TZw4Ua1atdKKFSt0+PBhbd26Ve7u7vLz89PgwYPVoUOHDE+SmDBhgooWLar169crMjJSFSpU0Lvvvqvffvvtvse0JM2dO1fTpk3TV199pSNHjqhq1apatGiR1q1bd8eYzpEjh8LCwjR9+nRt27ZNBw8eVPHixRUYGKj+/ftr2rRpio6O1m+//aYyZcqoZMmS9g8MWbp0qXx8fO4Y09WrV1e5cuX066+/prvF46bcuXNr8eLF+uCDD7R27VotW7ZMnp6eqlWrlvr27St/f/97+r6MHj1a1apV0/Lly7V69Wq5ubmpfPny+ve//22/gixJpUqV0vLlyxUWFqY9e/Zo8+bN8vLyUps2bTRo0KAMb9Dz9/dXkyZNNG/ePMXExMjHx0fvvPNOhivN3bp1U7ly5TR//nxt3LhRlmXJx8dHffv2Vfv27Y3P65133lHVqlX1+eefKyoqSjabTVWqVNHYsWPVuHFj+3re3t5atWqVwsLCtHXrVu3YsUNFixbVc889l+GRfQAePjbrVu+mAQDARU6dOqUmTZqoSZMmCgsLc/U4AHBH3DMNAAAAGCKmAQAAAEPENAAAAGCIe6YBAAAAQ1yZBgAAAAwR0wAAAIAhYhoAAAAwREwDAAAAhohpAAAAwBAxDQAAABgipgEAAABDxDQAAABgiJgGAAAADBHTAAAAgCFiGgAAAJni1KlTevHFFx1e/+LFi3rjjTcyLJ86dapWrVp1Hye7f4hpAAAAZAlFihS5ZUxnZe6uHgAAAABOsP0Nacebjq1bLUR6Zl76ZRv7SAci/u/ruq9LT73h0O6Cg4NVsWJF/fLLL0pISNCsWbNUsmRJhYWFKTo6WqmpqerUqZPq1aunV155RStXrtSGDRs0Z84cFSpUSMnJyfL29pYkTZs2Td99953S0tLUo0cPPfvssw7vv2PHjoqMjNQXX3whm82mli1bqlu3bo59T26DK9MAAADIdNWrV9fChQv19NNPa82aNTp06JBiYmL00Ucf6aOPPtKvv/4qy7IkScnJyZo0aZI+/PBDzZ8/X7lz55Ykbd26VadOndLy5cu1ePFihYeHKz4+3uH9//LLL1q7dq2WLVumpUuXKjo6Wv/73//u6by4Mg0AAIBMV7lyZUlS8eLFdenSJR0/flzVq1eXm5ub3NzcNHLkSJ06dUqSdOXKFRUoUEBeXl6SpJo1a0qSfv75Zx08eFDBwcGSpJSUFJ0+fdrh/a9du1ZnzpxRjx49JElxcXE6ceKE/aq3CWIaAAAgO3jqDYdvy7ilZ+ZlvPXjHnh7e2v58uVKS0tTamqq+vTpozFjxkiSChcurPj4eF25ckWFChXSgQMHVLx4cXl7eysgIEDjx49XWlqawsLCVLp0aYf3P2LECP3rX//SBx98IJvNpoULF8rPz++ezoOYBgAAgNNVqlRJ9evXV6dOnZSWlqZOnTrJw8NDkuTu7q6xY8fqpZdeUoECBeTu/meyNm7cWN9++606d+6sGzduqGnTpsqXL5/D+69YsaLq1q2rTp06KSkpSdWrV1exYsXu6Txs1s2bUwAAAADcFd6ACAAAABgipgEAAABDxDQAAABgiDcgwmEhYVdcPcJ9ETGgkKtHAAAADwmuTAMAAACGuDJtgCu0AAAAkLgyDQAAABgjpgEAAABDxDQAAABgiJgGAAAADBHTAAAAgCFiGgAAADBETAMAAACGiGkAAADAEDENAAAAGCKmAQAAAEPENAAAAGCImAYAAAAMEdMAAACAIWIaAAAAMERMAwAAAIaIaQAAAMAQMQ0AAAAYIqYBAAAAQ8Q0AAAAYIiYBgAAAAwR0wAAAIAhYhoAAAAwREwDAAAAhohpAAAAwBAxDQAAABgipgEAAABDxDQAAABgiJgGAAAADBHTAAAAgCFiGgAAADBETAMAAACGiGkAAADAEDENAAAAGCKmAQAAAEPENAAAAGCImAYAAAAMEdMAAACAIWIaAAAAMERMAwAAAIaIaQAAAMAQMQ0AAAAYIqYBAAAAQ8Q0AAAAYIiYBgAAAAwR0wAAAIAhYhoAAAAwREwDAAAAhohpAAAAwBAxDQAAABgipgEAAABDxDQAAABgiJgGAAAADBHTAAAAgCFiGgAAADBETAMAAACGiGkAAADAEDENAAAAGCKmAQAAAEPuzjxYcnKyRo4cqdOnTytHjhwaP3683N3dNXLkSNlsNlWoUEGvv/66cuSg8QEAAJD1OTWmt27dqpSUFK1YsULbtm3TzJkzlZycrNDQUAUEBGjs2LHatGmTmjVr5syxAAAAACNOjeny5csrNTVVaWlpSkhIkLu7u/bt2yd/f39JUmBgoLZt25YhpqOiohQVFZVhf0FBQQoKCnLK7AAAAMDfOTWm8+TJo9OnT+vZZ5/V1atXFR4ert27d8tms0mS8ubNq2vXrmXYjmgGAABAVuTUmF64cKHq1aunoUOH6uzZs+revbuSk5Ptr1+/fl358+d35kgAAACAMafGdP78+ZUzZ05JUoECBZSSkqLKlStr165dCggIUExMjJ588klnjgT8o5CwK64e4b6IGFDI1SMAAPDQcWpM9+jRQ6NGjVLnzp2VnJysIUOGqGrVqhozZoymT58ub29vNW/e3JkjAQAAAMacGtN58+bVrFmzMixfsmSJM8cAAAAA7gse6AwAAAAYIqYBAAAAQ8Q0AAAAYIiYBgAAAAwR0wAAAIAhYhoAAAAwREwDAAAAhohpAAAAwBAxDQAAABgipgEAAABDxDQAAABgiJgGAAAADBHTAAAAgCFiGgAAADBETAMAAACGiGkAAADAEDENAAAAGCKmAQAAAEPENAAAAGCImAYAAAAMEdMAAACAIWIaAAAAMERMAwAAAIaIaQAAAMAQMQ0AAAAYIqYBAAAAQ8Q0AAAAYIiYBgAAAAwR0wAAAIAhYhoAAAAwREwDAAAAhohpAAAAwBAxDQAAABgipgEAAABDxDQAAABgiJgGAAAADBHTAAAAgCF3Vw8AIOsKCbvi6hHuWcSAQq4eAQDwEOPKNAAAAGCImAYAAAAMEdMAAACAIWIaAAAAMERMAwAAAIaIaQAAAMAQMQ0AAAAYIqYBAAAAQ8Q0AAAAYIiYBgAAAAwR0wAAAIAhYhoAAAAwREwDAAAAhohpAAAAwBAxDQAAABgipgEAAABDxDQAAABgiJgGAAAADBHTAAAAgCFiGgAAADBETAMAAACGiGkAAADAEDENAAAAGCKmAQAAAEPENAAAAGCImAYAAAAMEdMAAACAIWIaAAAAMERMAwAAAIaIaQAAAMAQMQ0AAAAYIqYBAAAAQ8Q0AAAAYIiYBgAAAAwR0wAAAIAhYhoAAAAwREwDAAAAhohpAAAAwBAxDQAAABgipgEAAABDxDQAAABgyN3ZB5w7d642b96s5ORkderUSf7+/ho5cqRsNpsqVKig119/XTly0PgAAADI+pxarbt27dLevXu1fPlyRUZG6ty5c5o4caJCQ0O1bNkyWZalTZs2OXMkAAAAwJhTY/qbb76Rr6+vBg4cqH79+qlhw4Y6ePCg/P39JUmBgYHavn27M0cCAAAAjDl8m8f58+e1c+dOnT59WteuXZOXl5cee+wxPfXUUypcuLBD+7h69arOnDmj8PBwnTp1Sv3795dlWbLZbJKkvHnz6tq1axm2i4qKUlRUVIblQUFBCgoKcvQUAAAAgPvqH2M6OjpaCxYs0N69e2VZlgoUKKDcuXMrPj5eiYmJypEjhx5//HH17t1bjRs3vuO+ChYsKG9vb3l4eMjb21u5cuXSuXPn7K9fv35d+fPnz7Ad0QwAAICs6LYxfeLECY0aNUonT55U8+bNNWTIEFWuXFl58+a1r3Pt2jXt2bNH27dv12uvvaYyZcpo0qRJKl++/C33Wbt2bS1evFg9e/bUhQsXlJiYqLp162rXrl0KCAhQTEyMnnzyyft/lgBwF0LCrrh6hHsWMaCQq0cAgGzhtjHdp08f9e/fX61bt5abm9st1/H09FSDBg3UoEEDDRs2TJ999pn69u2rjRs33nL9Ro0aaffu3Wrfvr0sy9LYsWNVqlQpjRkzRtOnT5e3t7eaN29+f84MAAAAyGS3jenVq1crV65cDu8oZ86c6tChg9q0aXPH9f7zn/9kWLZkyRKHjwMAAABkFbd9msfdhPT92A4AAAB40NzVh7ZYlqVly5Zp48aNunTpkgoVKqSmTZuqS5cucnd3+ue/AAAAAC51VwU8c+ZMbdq0Sc8//7wKFCigCxcuaNGiRTp27JjGjRuXWTMCAAAAWdJtY/rUqVMqVapUumUbN27Ue++9Jx8fH/uyqlWravjw4cQ0AAAAsp3b3jPdrl07jR8/XpcuXbIv8/HxUUREhH788UedPHlSe/bsUWRkpCpUqOCUYQEAAICs5LYxvXbtWtlsNrVq1UrTp09XfHy8xo0bp4SEBAUFBalZs2bq2rWrcuTIoSlTpjhzZgAAACBLuO1tHoULF9bo0aPVq1cvvf/++2revLm6d++uKVOmKGfOnIqNjZWXl9dtn0ENAAAAPOxue2X6phIlSujtt9/WsmXLdOTIETVr1kzLly9XgQIFCGkAAABka3eM6V9//VUbNmzQwYMHVb58ec2YMUMRERH65ptv1Lx5c61atUqWZTlrVgAAACBLuW1Mf/TRR2rVqpXGjx+voKAgvfXWW5KkSpUqae7cuZoyZYo+/fRTtWzZUuvXr3fawAAAAEBWcduYfv/99zV69Gh98803WrZsmZYuXarLly/bX69du7YiIyM1atQoffDBB04ZFgAAAMhKbvsGxMTERHl5eUmSvLy8ZFmWfv/99wzr1a9fX/Xr18+8CQEAAIAs6rYx/eKLL2rEiBFauHChfv31V9WrV08lS5Z05mwAAABAlnbbmB46dKgCAgJ0+PBhlSxZUs8884wz5wIAAACyvNvGtCTVq1dP9erVc9YsAAAAwAPltm9A7N69uw4dOnRXO/vhhx8UHBx8z0MBAAAAD4LbXpnu2bOnBg4cKG9vb7Vq1UpNmzZV/vz5M6x34cIFffPNN/rkk0904sQJvfnmm5k6MAAAAJBV3DamGzZsqDp16mjhwoWaNm2aRo8erRIlSuixxx5T7ty5de3aNZ0/f17nzp1T/vz5FRwcrPDwcHl6ejpzfgAAAMBl7njPdL58+TRo0CD16dNHO3bs0K5du3T69GklJCSoZMmSql27turWrSt/f395eHg4a2YAQCYICbvi6hHui4gBhVw9AoBs5I4xfZOHh4caNGigBg0aZPY8AAAAwAPjtm9ABAAAAHBnxDQAAABgiJgGAAAADBHTAAAAgKG7jumUlBRdvHhRKSkpmTEPAAAA8MBwOKb37dunHj16qGbNmmrQoIGOHDmiYcOGacaMGZk5HwAAAJBlORTTO3bsUNeuXSVJQ4YMkWVZkiRfX19FREToww8/zLwJAQAAgCzKoZieOnWqWrZsqYULF6pbt272mO7Tp4/69eunqKioTB0SAAAAyIociulffvlFbdq0kSTZbLZ0rwUEBOjMmTP3fzIAAAAgi3MopgsXLqyjR4/e8rVjx46pcOHC93UoAAAA4EHg0MeJP//885o1a5Y8PT0VGBgoSUpNTdX27dv13nvvqV27dpk6JAAAAJAVORTTgwYN0rlz5/Taa6/Zb/Po2LGjLMtSs2bN9PLLL2fqkAAAAEBW5FBMu7m5aeLEierdu7d2796t2NhYeXp6qnbt2qpYsWJmzwgAAABkSQ7F9E0+Pj7y8fGRJF24cEEXLlxQamqq3NzcMmU4AAAAICtz6A2IcXFxGjp0qBYvXixJ+uyzz9S4cWN16NBBzz77rE6ePJmpQwIAAABZkUMxPXnyZG3fvl0lSpRQWlqapk6dqscff1yLFy+Wl5eXJk+enNlzAgAAAFmOQzG9ZcsWvfrqq2ratKm+++47Xbp0Sb1799YTTzyhfv36aefOnZk9JwAAAJDlOBTTN27c0GOPPSbpz7DOlSuX6tatK0ny8PDIvOkAAACALMyhmPbx8VF0dLQuXbqktWvXqm7dusqVK5dSU1O1bNkyVahQIbPnBAAAALIch57m8fLLL+vf//63Fi9erJw5c6pv376SpGeeeUaXL1/WnDlzMnVIAAAAICtyKKYbNGigL774Qj/++KOqVaum0qVLS5L69eunJ554QuXKlcvMGQEAAIAsyeHnTJcpU0ZlypRJt6xDhw73fSAAAADgQeFQTKelpWnlypXaunWrEhMTlZaWlu51m82mRYsWZcqAAAAAQFblUExPmjRJixcvVuXKlVWsWDHlyOHQ+xYBAACAh5pDMb169WoNGjRIgwYNyux5AAAAgAeGQ5eYk5KSVKdOncyeBQAAAHigOBTTDRs2VHR0dGbPAgAAADxQHLrNo379+po4caJOnTql6tWr65FHHkn3us1mU48ePTJjPgAAACDLciimX331VUl/fpT4li1bMrxOTAMAACA7ciimDx8+nNlzAADgEiFhV1w9wn0RMaCQq0cAsiWHP7RFkmJjY7V//34lJCTIy8tL1atXV758+TJrNgAAACBLczimZ82apfnz5yspKen/NnZ3V8+ePTV06NBMGQ4AAADIyhyK6UWLFmnu3Lnq3bu3WrZsqUcffVSXLl3SmjVrtGDBAhUrVkxdu3bN7FkBAACALMWhmF62bJlCQkI0ZMgQ+7JHH31UFStWlJubm5YuXUpMAwAAINtx6DnT586dU0BAwC1f8/f31+nTp+/rUAAAAMCDwKGYLlOmjL777rtbvrZ7924VK1bsvg4FAAAAPAgcus0jODhYb775plJTU9WiRQsVLlxYly9f1vr16zV//nyFhoZm8pgAAABA1uNQTL/44os6efKkFixYoHnz5kmSLMuSu7u7goOD1bt370wdEgAAAMiKHH403tChQ/XSSy9p//79iouLU4ECBVS9enV5eXll5nwAAABAlnVXH9py48YNxcfHKz4+Xu7u7kpJScmsuQAAAIAsz6GYTkpK0muvvaYvvvhClmXZl7u5ualz584aNWqUbDZbpg0JAAAAZEUOxfTkyZMVHR2t0aNHq2nTpvLy8tKVK1f05Zdfavr06fLy8tKAAQMye1YAAAAgS3EopteuXathw4apS5cu9mXFixdXcHCwLMvS/PnziWkAAABkOw49ZzolJUUlSpS45Wtly5bVtWvX7utQAAAAwIPAoZh+8cUX9f777ysuLi7d8j/++EMLFy5Uhw4dMmU4AAAAICtz6DaPGzdu6MSJE2rUqJGeeuopFSlSRLGxsdq1a5diY2OVM2dO9evXT5Jks9k0Z86cTB0aAAAAyAociumjR4+qYsWKkqS4uDj7FWofHx9JUmJiYiaNBwAAAGRdDsV0ZGRkZs8BAAAAPHAc/tCW+Ph4JSYmqlixYkpKStLixYt19uxZNWvWTE8++WRmzggAAABkSQ69AXH37t1q2LChFi9eLEkaM2aMpk2bpi1btqhXr15au3Ztpg4JAAAAZEUOXZmeOXOmatasqZ49eyouLk5r1qxRr169NHz4cE2ePFlz585Vy5YtM3tWAABwn4SEXXH1CPdFxIBCrh4B2ZxDV6YPHjyo3r1769FHH9XWrVuVmpqq1q1bS5IaNWqk48ePZ+qQAAAAQFbkUEznzp1bSUlJkqStW7eqSJEi9qd7nD17VgUKFMi8CQEAAIAsyqHbPAICAjRr1iwdOXJEGzZssH+s+JdffqmZM2eqXr16mTokAAAAkBU5dGV6zJgxyp8/v9577z35+/tr4MCBkqQJEyaobNmyGj58eKYOCQAAAGRFDl2ZfvTRR7Vw4cIMy1etWiUvL6/7PRMAAADwQHD4OdOS9N1332nnzp26cOGC+vXrp19++UWVKlVS0aJFM2s+AAAAIMtyKKZ///13hYaGasuWLcqXL5+uX7+uoKAgLVy4UEeOHFFkZKT9o8UBAACA7MKhe6anTp2qH374QUuXLtXOnTtlWZYkacqUKSpWrJimTZuWqUMCAAAAWZFDMb1mzRoNGzZMtWvXls1msy9/9NFHNWDAAH3//feZNiAAAACQVTkU04mJiSpcuPAtX8uVK5f9GdSOuHz5sho0aKBjx47pxIkT6tSpkzp37qzXX39daWlpDu8HAAAAcDWHYrpGjRpavHixUlNT7ctuXqH+5JNPVK1aNYcOlpycrLFjxyp37tySpIkTJyo0NFTLli2TZVnatGnT3c4PAAAAuIxDMT18+HDt2bNHzz77rMaPHy+bzaZly5apc+fO+uqrrzRkyBCHDjZ58mR17NjR/vSPgwcPyt/fX5IUGBio7du3G54GAAAA4HwOPc2jatWq+vjjj/X+++8rOjpabm5uio6OVu3atbV8+XJVqVLlH/exatUqFSpUSPXr19e8efMkSZZl2a9w582bV9euXbvltlFRUYqKisqwPCgoSEFBQY6cAgAAAHDfORTT69atU0BAgKZPn258oE8++UQ2m007duzQTz/9pBEjRujKlSv2169fv678+fPfcluiGQAAAFmRQ7d5jBo1St999909HWjp0qVasmSJIiMjValSJU2ePFmBgYHatWuXJCkmJkZ16tS5p2MAAAAAzuRQTJcsWVJxcXH3/eAjRozQ7NmzFRQUpOTkZDVv3vy+HwMAAADILA7d5tG6dWtNmDBBW7duVbly5TI8Js9ms6lHjx4OHzQyMtL+v5csWeLwdgAAAEBW4lBMz5gxQ5IUHR19y9fvNqYBAACAh4FDMX348OHMngMAAAB44Dh0zzQAAACAjIhpAAAAwJBDt3kAAAA8DELCrvzzSg+AiAGFXD0C/j+uTAMAAACGiGkAAADAEDENAAAAGCKmAQAAAEPENAAAAGCImAYAAAAMEdMAAACAIWIaAAAAMERMAwAAAIaIaQAAAMAQMQ0AAAAYslmWZbl6iAdNSNgV+/9unTxZbVLecWi7GLduivSYkW5ZcNIQBaYudmj71e7/0ec5R6RbNuiPzqqRtsGh7RfnnK6v3bvbv44YUEiKrC1d2OPQ9rM9luoHtxbplk1JrKyCOu/Q9uNzbdJvOR5PtywisbBD20rSsNw/Ks72mP3rAtZZTf29qsPbhzxy+c9jDij054Lz30tL6ji0bayKafgjh9Itq566Xv9O6uLQ9idsNfRW7s3pltVPWaRuya84tP3+HM31Xq5l6Zbd7c9eYOii9As39pEORDi0/f3+2ZOk0b83Vllrv0Pb3+vPnrp+JxWrnX7ZNJtj2+r+/ezdVCZtn8b80cShbe/1Z09Fa0nB36df9sM86cu+Dm1+P372XPnnnprNlar3Sb8sG/25p6F/+1d8NvpzT9VCpGfmpV/Gn3uObStJfU9L+Ur839cJZ6S5JR3f/h5+9pT3ManfGceP5WLurh4AAAAAmSsk7Ioc+zXiT8MWXVWcLbf96wLWVU29y+P9VZm0OI1xcNvY62ka/rftb7JfEMtCuM0DAAAAMMRtHgb+/tvWg+puf7vjvB9sJr/NPwznznk77mE4b4l/xh3FeT/YOO+sgyvTAAAAgCFiGgAAADBETAMAAACGiGkAAADAEDENAAAAGCKmAQAAAEPENAAAAGCImAYAAAAMEdMAAACAIWIaAAAAMERMAwAAAIaIaQAAAMAQMQ0AAAAYIqYBAAAAQ8Q0AAAAYIiYBgAAAAwR0wAAAIAhYhoAAAAwREwDAAAAhohpAAAAwBAxDQAAABgipgEAAABDxDQAAABgiJgGAAAADBHTAAAAgCFiGgAAADBETAMAAACGiGkAAADAEDENAAAAGCKmAQAAAEPENAAAAGCImAYAAAAMEdMAAACAIWIaAAAAMERMAwAAAIaIaQAAAMAQMQ0AAAAYIqYBAAAAQ8Q0AAAAYIiYBgAAAAwR0wAAAIAhYhoAAAAwREwDAAAAhohpAAAAwBAxDQAAABgipgEAAABDxDQAAABgiJgGAAAADBHTAAAAgCFiGgAAADBETAMAAACGiGkAAADAEDENAAAAGCKmAQAAAEPENAAAAGCImAYAAAAMEdMAAACAIWIaAAAAMERMAwAAAIaIaQAAAMCQu7MOlJycrFGjRun06dNKSkpS//799a9//UsjR46UzWZThQoV9PrrrytHDvoeAAAADwanxfTq1atVsGBBTZkyRbGxsXr++edVsWJFhYaGKiAgQGPHjtWmTZvUrFkzZ40EAAAA3BOnXQZu0aKFBg8eLEmyLEtubm46ePCg/P39JUmBgYHavn27s8YBAAAA7pnTrkznzZtXkpSQkKCXX35ZoaGhmjx5smw2m/31a9eu3XLbqKgoRUVFZVgeFBSkoKCgzBsaAAAAuAOnxbQknT17VgMHDlTnzp3VunVrTZkyxf7a9evXlT9//ltuRzQDAAAgK3LabR6XLl1Sr169NHz4cLVv316SVLlyZe3atUuSFBMTozp16jhrHAAAAOCeOS2mw8PDFR8fr7CwMAUHBys4OFihoaGaPXu2goKClJycrObNmztrHAAAAOCeOe02j9GjR2v06NEZli9ZssRZIwAAAAD3FQ91BgAAAAwR0wAAAIAhYhoAAAAwREwDAAAAhohpAAAAwBAxDQAAABgipgEAAABDxDQAAABgiJgGAAAADBHTAAAAgCFiGgAAADBETAMAAACGiGkAAADAEDENAAAAGCKmAQAAAEPENAAAAGCImAYAAAAMEdMAAACAIWIaAAAAMERMAwAAAIaIaQAAAMAQMQ0AAAAYIqYBAAAAQ8Q0AAAAYIiYBgAAAAwR0wAAAIAhYhoAAAAwREwDAAAAhohpAAAAwBAxDQAAABgipgEAAABDxDQAAABgiJgGAAAADBHTAAAAgCFiGgAAADBETAMAAACGiGkAAADAEDENAAAAGCKmAQAAAEPENAAAAGCImAYAAAAMEdMAAACAIWIaAAAAMERMAwAAAIaIaQAAAMAQMQ0AAAAYIqYBAAAAQ8Q0AAAAYIiYBgAAAAwR0wAAAIAhYhoAAAAwREwDAAAAhohpAAAAwBAxDQAAABgipgEAAABDxDQAAABgiJgGAAAADBHTAAAAgCFiGgAAADBETAMAAACGiGkAAADAEDENAAAAGCKmAQAAAEPENAAAAGCImAYAAAAMEdMAAACAIWIaAAAAMERMAwAAAIaIaQAAAMAQMQ0AAAAYIqYBAAAAQ8Q0AAAAYIiYBgAAAAwR0wAAAIAhYhoAAAAwREwDAAAAhohpAAAAwBAxDQAAABgipgEAAABDxDQAAABgyN3VA6SlpemNN97QkSNH5OHhobfeektly5Z19VgAAADAP3J5TEdHRyspKUlRUVHat2+fJk2apDlz5vzjdikpKTp37pwTJszo9/g4lxz3fjt16sZdrc95P9ju9rylh+PcOW/HPQznLfHPuKM47wcb5+18xYsXl7t7xnS2WZZluWAeu4kTJ6p69epq1aqVJKl+/fr6+uuv060TFRWlqKiodMuSkpL0yy+/OG1OAAAAZF+bNm1SqVKlMix3+ZXphIQE5cuXz/61m5ubUlJS0pV/UFCQgoKC0m3nyivTztCvXz+Fh4e7egyn47yzl+x63lL2PXfOO3vhvLOXh/28ixcvfsvlLo/pfPny6fr16/av09LSbnkJ/e/c3d1v+dvBw8LDw+OhPr/b4byzl+x63lL2PXfOO3vhvLOX7HreLn+aR61atRQTEyNJ2rdvn3x9fV08EQAAAOAYl1+ZbtasmbZt26aOHTvKsixNmDDB1SMBAAAADnF5TOfIkUPjxo1z9RgAAADAXXP5bR64tb+/4TK74Lyzl+x63lL2PXfOO3vhvLOX7HreLn80HgAAAPCg4so0AAAAYIiYBgAAAAwR0wAAAIAhlz/NAxnt379fU6dOVWRkpKtHcZrk5GSNGjVKp0+fVlJSkvr3768mTZq4eqxMl5qaqtGjR+v48eOy2Wx68803s9Wz1i9fvqwXXnhBCxYskI+Pj6vHcYq2bdvaP/W1VKlSmjhxoosnco65c+dq8+bNSk5OVqdOndShQwdXj+QUq1at0qeffipJ+uOPP/TTTz9p27Ztyp8/v4snyzzJyckaOXKkTp8+rRw5cmj8+PHZ5p/vpKQkvfrqqzp58qTy5cunsWPHqly5cq4eK1P9tVlOnDihkSNHymazqUKFCnr99deVI8fDf92WmM5iIiIitHr1aj3yyCOuHsWpVq9erYIFC2rKlCmKjY3V888/ny1i+quvvpIkrVixQrt27dKMGTM0Z84cF0/lHMnJyRo7dqxy587t6lGc5o8//pBlWdnqF2VJ2rVrl/bu3avly5crMTFRCxYscPVITvPCCy/ohRdekCS9+eabateu3UMd0pK0detWpaSkaMWKFdq2bZtmzpyp2bNnu3osp1i5cqXy5MmjlStX6n//+5/Gjx+v+fPnu3qsTPP3Zpk4caJCQ0MVEBCgsWPHatOmTWrWrJmLp8x8D/+vCw+YMmXKZJs/dP6qRYsWGjx4sCTJsiy5ubm5eCLnaNq0qcaPHy9JOnPmzEP/L9m/mjx5sjp27KiiRYu6ehSnOXz4sBITE9WrVy9169ZN+/btc/VITvHNN9/I19dXAwcOVL9+/dSwYUNXj+R0Bw4c0NGjR7PFo8PKly+v1NRUpaWlKSEhQe7u2ee63dGjRxUYGChJ8vb21rFjx1w8Ueb6e7McPHhQ/v7+kqTAwEBt377dVaM5Vfb5CX9ANG/eXKdOnXL1GE6XN29eSVJCQoJefvllhYaGunYgJ3J3d9eIESP05Zdf6t1333X1OE6xatUqFSpUSPXr19e8efNcPY7T5M6dWy+99JI6dOigX3/9VSEhIVq/fv1DHxtXr17VmTNnFB4erlOnTql///5av369bDabq0dzmrlz52rgwIGuHsMp8uTJo9OnT+vZZ5/V1atXFR4e7uqRnKZSpUr66quv1LRpU+3fv1/nz59XamrqQ3uB6O/NYlmW/Z/rvHnz6tq1a64azam4Mo0s4+zZs+rWrZuee+45tW7d2tXjONXkyZO1YcMGjRkzRjdu3HD1OJnuk08+0fbt2xUcHKyffvpJI0aM0MWLF109VqYrX7682rRpI5vNpvLly6tgwYLZ4rwLFiyoevXqycPDQ97e3sqVK5euXLni6rGcJj4+XsePH9eTTz7p6lGcYuHChapXr542bNig//73vxo5cqT++OMPV4/lFO3atVO+fPnUuXNnffnll6pSpcpDG9K38tf7o69fv55t/raVmEaWcOnSJfXq1UvDhw9X+/btXT2O03z22WeaO3euJOmRRx6RzWbLFm/WWLp0qZYsWaLIyEhVqlRJkydPVpEiRVw9Vqb7+OOPNWnSJEnS+fPnlZCQkC3Ou3bt2vr6669lWZbOnz+vxMREFSxY0NVjOc3u3btVt25dV4/hNPnz55enp6ckqUCBAkpJSVFqaqqLp3KOAwcOqG7dulq+fLlatGih0qVLu3okp6pcubJ27dolSYqJiVGdOnVcPJFzPNx/t4gHRnh4uOLj4xUWFqawsDBJf76x4WF/c9ozzzyjV199VV26dFFKSopGjRr10J9zdta+fXu9+uqr6tSpk2w2myZMmPDQ3+IhSY0aNdLu3bvVvn17WZalsWPHZqurdcePH1epUqVcPYbT9OjRQ6NGjVLnzp2VnJysIUOGKE+ePK4eyynKli2rWbNmKTw8XJ6ennr77bddPZJTjRgxQmPGjNH06dPl7e2t5s2bu3okp+DjxAEAAABDD//fJwMAAACZhJgGAAAADBHTAAAAgCFiGgAAADBETAMAAACGiGkAeAAFBwerb9++92Vfu3btkp+fnw4cOHDf9w0ADztiGgCyuSpVqigqKko+Pj6uHgUAHjgP/6cFAADuKF++fHr88cddPQYAPJC4Mg0AWdz169c1evRoBQQEKCAgQPPmzUv3ekpKimbNmqWGDRuqWrVqeuGFF7Rjx45068TGxuq1117TU089pVq1aqlXr146cuSIpIy3efydI/sHgOyKmAaALO6VV17Rl19+qeHDh+vtt9/WmjVrtHfvXvvrY8aM0Ycffqhu3brp/fffl7e3t0JCQrRnzx5Jf8Zwz549tXXrVr3yyiuaNWuWfv/9d7300kuKi4v7x+P/0/4BIDvjNg8AyMIOHz6sLVu2aMaMGWrZsqUkqXr16mrSpIkk6dixY1q1apXeeustdejQQZIUGBioixcvaubMmVq8eLG2bNmiQ4cOaenSpapTp44kqXLlyurQoYN+/PFHubvf/l8FjuwfALIzrkwDQBZ28+pvYGCgfVnRokXt9zh/++239tdTUlLs/2nQoIH27NmjpKQk7d27V56envaQlqTChQtr8+bNevrpp+94fEf2DwDZGVemASALi4+PV86cOZUvX750y4sUKaLr168rNjZWUvrY/qurV68qLi5OhQsXNjq+I/svVqyY0b4B4GFATANAFlawYEElJycrPj5e+fPnty+PjY1Vzpw55enpKZvNphUrVsjNzS3D9l5eXvL09NSVK1cyvLZz506VKlXqjsd3ZP8AkJ1xmwcAZGH+/v6SpI0bN9qXxcXFad++fZKk2rVry7IsJSQkqFq1avb/7NixQwsXLpS7u7tq1qyp+Pj4dG8YjIuLU0hIiLZt23bH4zuyfwDIzvhTEACyMG9vb7Vp00YTJkzQH3/8oRIlSmju3LlKSUmRJFWqVEnNmzfX8OHDNWjQIPn4+Ojbb7/VnDlz1Lt3b+XIkUONGjVS5cqVNWTIEA0ZMkReXl6KiIhQ0aJF1bJlSx06dOi2x3dk/wCQnRHTAJDFvf322ypUqJBmz56t5ORktW/fXsWKFdPvv/8uSZo6dapmzZqlefPm6fLlyypZsqSGDh2ql156SZKUM2dOzZ8/X++8844mTJigtLQ01alTRwsXLpSnp+c/Hv+f9g8A2ZnNsizL1UMAAAAADyL+fg4AAAAwREwDAAAAhohpAAAAwBAxDQAAABgipgEAAABDxDQAAABgiJgGAAAADBHTAAAAgKH/B/XZquy9Rp5NAAAAAElFTkSuQmCC\n",
+      "image/png": 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lRXfddZeuXLli3/762t01a9aoWLFi6ty58y1nYzt16qTIyEgdOXJEw4YNy/L6M888o2+//VaRkZH64Ycf5Ofnp/j4eK1du1bJycmaMWOG/ca/2/Xggw8qODhYkZGR6tChg5o3by43NzdFR0fbn0d9/QkqAwYM0Jo1azRy5EitXbtWVatW1alTp7R+/XqVLVtWvXr1ynRsDw8PPf/881qzZo3Kly+vjRs36sSJExoyZIh9Brty5coaPXq0XnvtNXXu3Flt2rRRsWLFFBMToxMnTqhDhw56/PHHb/u6rn/4zbp169S5c2c9+OCDSktLU3R0tK5cuZLpQ5AmTZqknj176rnnnlNAQIBq1KihY8eOaePGjSpVqpQmTpx4R99bAHkHJRtAruTi4qKpU6eqQ4cO+uCDD3To0CFt2rRJrq6u8vX11XPPPaeuXbtmebLFlClTVK5cOa1du1aRkZGqUaOG3nzzTf3+++/GS7YkzZ8/XzNnztQ333yjw4cPq06dOlq8eLHWrFlzy5JdqFAhhYWFadasWdqyZYsOHDige+65RwEBARo8eLBmzpyp6Oho/f7776pSpYoqVqxo/yCUZcuWydvb+5Ylu169eqpWrZp+++23TEtFritatKiWLFmid999V6tXr9by5cvl4eGhhg0bauDAgfLz8/tX35dx48apbt26WrFihVatWiUXFxdVr15d//3vf+0zzpJUqVIlrVixQmFhYdq1a5e+/vpreXp6qlOnTho2bFiWGwP9/PzUunVrLViwQDExMfL29tYbb7yRZWa6d+/eqlatmhYuXKj169fLsix5e3tr4MCB6tKlyx1f1xtvvKE6dero888/V1RUlGw2m+677z5NmDBBrVq1sm/n5eWllStXKiwsTJs2bdK2bdtUrlw5PfbYY1keLQggf7JZN7pjBwCAXOTkyZNq3bq1WrdurbCwMGfHAYB/xJpsAAAAwDBKNgAAAGAYJRsAAAAwjDXZAAAAgGHMZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAMgxJ0+e1FNPPeXw9hcuXNDLL7+cZXzGjBlauXKlwWRmUbIBAACQa5UtW/aGJTu3c3V2AAAAADjJ1pelba84tm3dEOmRBZnH1j8j7Y/439dNJkoPvezQ4YKDg1WzZk398ssvSkxM1Ny5c1WxYkWFhYUpOjpa6enp6t69u5o2barnn39eH374odatW6d33nlHpUuXVmpqqry8vCRJM2fO1Pfff6+MjAz17dtXjz76qMPH79atmyIjI/XFF1/IZrOpffv26t27t2Pfk1tgJhsAAABOUa9ePS1atEgPP/ywvvzySx08eFAxMTH66KOP9NFHH+m3336TZVmSpNTUVL3++ut6//33tXDhQhUtWlSStGnTJp08eVIrVqzQkiVLFB4ervj4eIeP/8svv2j16tVavny5li1bpujoaP3666//+tqYyQYAAIBT1K5dW5J0zz336OLFizp27Jjq1asnFxcXubi4aMyYMTp58qQk6fLlyypZsqQ8PT0lSQ0aNJAk/fzzzzpw4ICCg4MlSWlpaTp16pTDx1+9erVOnz6tvn37SpLi4uJ0/Phx+yz5naJkAwAAFFQPvezw8o4bemRB1iUk/4KXl5dWrFihjIwMpaen65lnntH48eMlSWXKlFF8fLwuX76s0qVLa//+/brnnnvk5eUlf39/TZ48WRkZGQoLC1PlypUdPv7o0aP1n//8R++++65sNpsWLVokX1/ff30tlGwAAADkCrVq1VKzZs3UvXt3ZWRkqHv37nJzc5Mkubq6asKECXr66adVsmRJubr+WWNbtWql7777Tj169NDVq1fVpk0bubu7O3z8mjVrqkmTJurevbtSUlJUr149lS9f/l9fi826vtAFAAAAgBHc+AgAAAAYluMle+/evfaF6cePH1f37t3Vo0cPTZw4URkZGZKkt956S126dFG3bt20b9++nI4IAAAA/Cs5WrIjIiI0btw4Xbt2TZI0depUhYaGavny5bIsSxs2bNCBAwf03Xff6aOPPtKsWbP0yisOPrsRAAAAyCVy9MbHKlWqaN68eXrhhRckSQcOHJCfn58kKSAgQFu2bFH16tXVtGlT2Ww2VahQQenp6fa7SP8qKipKUVFRWc4RFBSkoKCg7L+YGwgJu+yU85oUMaT0P28EAACAW8rRkh0YGGh/1qEkWZYlm80mSSpevLgSEhKUmJioUqVK2be5Pv73ku3MMg0AAADcilNvfCxU6H+nT0pKUokSJeTu7q6kpKRM4x4eHs6IBwAAANwRp5bs2rVra8eOHZKkmJgYNW7cWA0bNtS3336rjIwMnT59WhkZGVlmsQEAAIDczKkfRjN69GiNHz9es2bNkpeXlwIDA+Xi4qLGjRsrKChIGRkZmjBhgjMjAgAAALeND6MxiBsfAQAAIPFhNAAAAIBxlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwV2cHQN4XEnbZ2RGMiBhS2tkRAABAPsFMNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGuzg6QmpqqMWPG6NSpUypUqJAmT54sV1dXjRkzRjabTTVq1NDEiRNVqBDvBwAAAJA3OL1kb9q0SWlpafrggw+0ZcsWzZkzR6mpqQoNDZW/v78mTJigDRs2qG3bts6OCgAAADjE6dPD1atXV3p6ujIyMpSYmChXV1cdOHBAfn5+kqSAgABt3brVySkBAAAAxzl9JrtYsWI6deqUHn30UV25ckXh4eHauXOnbDabJKl48eJKSEjIsl9UVJSioqKyjAcFBSkoKCjbcwMAAAA34/SSvWjRIjVt2lQjRozQmTNn1KdPH6WmptpfT0pKUokSJbLsR5kGAABAbuX05SIlSpSQh4eHJKlkyZJKS0tT7dq1tWPHDklSTEyMGjdu7MyIAAAAwG1x+kx23759NXbsWPXo0UOpqakaPny46tSpo/Hjx2vWrFny8vJSYGCgs2MCAAAADnN6yS5evLjmzp2bZXzp0qVOSAMAAAD8e05fLgIAAADkN5RsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMc3V2ACCvCgm77OwIRkQMKe3sCAAA5DvMZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGCYq7MDAMhbQsIuOzuCERFDSjs7AgAgH2MmGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAY5urohufOndP27dt16tQpJSQkyNPTU/fee68eeughlSlTJjszAgAAAHnKP5bs6Ohovffee9q9e7csy1LJkiVVtGhRxcfHKzk5WYUKFdL999+vAQMGqFWrVjmRGQAAAMjVblqyjx8/rrFjx+rEiRMKDAzU8OHDVbt2bRUvXty+TUJCgnbt2qWtW7fqpZdeUpUqVfT666+revXqORIeAAAAyI1uWrKfeeYZDR48WB07dpSLi8sNt/Hw8FDz5s3VvHlzjRw5Up999pkGDhyo9evXZ1tgAAAAILe7acletWqVihQp4vCBChcurK5du6pTp05GggEAAAB51U2fLnI7BdvEfgAAAEB+4fDTRSTJsiwtX75c69ev18WLF1W6dGm1adNGPXv2lKvrbR0KAAAAyLduqxnPmTNHGzZs0OOPP66SJUvq/PnzWrx4sY4ePapJkyZlV0YAAAAgT7lpyT558qQqVaqUaWz9+vV666235O3tbR+rU6eORo0aRckGkK+FhF12dgQjIoaUdnYEACgQbrom+8knn9TkyZN18eJF+5i3t7ciIiL0448/6sSJE9q1a5ciIyNVo0aNHAkLAAAA5AU3LdmrV6+WzWZThw4dNGvWLMXHx2vSpElKTExUUFCQ2rZtq169eqlQoUKaPn16TmYGAAAAcrWbLhcpU6aMxo0bp/79++vtt99WYGCg+vTpo+nTp6tw4cKKjY2Vp6fnTZ+hDQAAABRUN53Jvq5ChQp67bXXtHz5ch0+fFht27bVihUrVLJkSWMFe/78+QoKCtITTzyhjz76SMePH1f37t3Vo0cPTZw4URkZGUbOAwAAAOSEW5bs3377TevWrdOBAwdUvXp1zZ49WxEREfr2228VGBiolStXyrKsfxVgx44d2r17t1asWKHIyEidPXtWU6dOVWhoqJYvXy7LsrRhw4Z/dQ4AAAAgJ920ZH/00Ufq0KGDJk+erKCgIL366quSpFq1amn+/PmaPn26Pv30U7Vv315r16694wDffvutfHx8NHToUA0aNEgtWrTQgQMH5OfnJ0kKCAjQ1q1b7/j4AAAAQE676Zrst99+W+PGjVP37t21b98+BQUFafDgwSpTpowkqVGjRoqMjNTmzZs1d+5ctWvX7o4CXLlyRadPn1Z4eLhOnjypwYMHy7Is2Ww2SVLx4sWVkJCQZb+oqChFRUVlGQ8KClJQUNAdZQEAAABMuGnJTk5OlqenpyTJ09NTlmXpjz/+yLJds2bN1KxZszsOUKpUKXl5ecnNzU1eXl4qUqSIzp49a389KSlJJUqUyLIfZRoAAAC51U2Xizz11FMaPXq0unXrpq5du6pp06aqWLGi8QCNGjXS5s2bZVmWzp07p+TkZDVp0kQ7duyQJMXExKhx48bGzwsAAABkl5vOZI8YMUL+/v46dOiQKlasqEceeSRbArRs2VI7d+5Uly5dZFmWJkyYoEqVKmn8+PGaNWuWvLy8FBgYmC3nBgAAALLDTUu2JDVt2lRNmzbN9hAvvPBClrGlS5dm+3kBAACA7HDT5SJ9+vTRwYMHb+tg+/btU3Bw8L8OBQAAAORlN53J7tevn4YOHSovLy916NBBbdq0ueENiOfPn9e3336rTz75RMePH9crr7ySrYEBAACA3O6mJbtFixZq3LixFi1apJkzZ2rcuHGqUKGC7r33XhUtWlQJCQk6d+6czp49qxIlSig4OFjh4eHy8PDIyfwAAABArnPLNdnu7u4aNmyYnnnmGW3btk07duzQqVOnlJiYqIoVK6pRo0Zq0qSJ/Pz85ObmllOZAQAAgFztliX7Ojc3NzVv3lzNmzfP7jwAAABAnnfTGx8BAAAA3BlKNgAAAGAYJRsAAAAwjJINAAAAGHbbJTstLU0XLlxQWlpaduQBAAAA8jyHS/aePXvUt29fNWjQQM2bN9fhw4c1cuRIzZ49OzvzAQAAAHmOQyV727Zt6tWrlyRp+PDhsixLkuTj46OIiAi9//772ZcQAAAAyGMcKtkzZsxQ+/bttWjRIvXu3dtesp955hkNGjRIUVFR2RoSAAAAyEscKtm//PKLOnXqJEmy2WyZXvP399fp06fNJwMAAADyKIdKdpkyZXTkyJEbvnb06FGVKVPGaCgAAAAgL3PoY9Uff/xxzZ07Vx4eHgoICJAkpaena+vWrXrrrbf05JNPZmtIAAAAIC9xqGQPGzZMZ8+e1UsvvWRfLtKtWzdZlqW2bdvq2WefzdaQAADnCAm77OwIRkQMKe3sCAAKGIdKtouLi6ZOnaoBAwZo586dio2NlYeHhxo1aqSaNWtmd0YAAAAgT3GoZF/n7e0tb29vSdL58+d1/vx5paeny8XFJVvCAQAAAHmRQzc+xsXFacSIEVqyZIkk6bPPPlOrVq3UtWtXPfroozpx4kS2hgQAAADyEodK9rRp07R161ZVqFBBGRkZmjFjhu6//34tWbJEnp6emjZtWnbnBAAAAPIMh0r2xo0b9eKLL6pNmzb6/vvvdfHiRQ0YMEAPPPCABg0apO3bt2d3TgAAACDPcKhkX716Vffee6+kPwt3kSJF1KRJE0mSm5tb9qUDAAAA8iCHSra3t7eio6N18eJFrV69Wk2aNFGRIkWUnp6u5cuXq0aNGtmdEwAAAMgzHHq6yLPPPqv//ve/WrJkiQoXLqyBAwdKkh555BFdunRJ77zzTraGBAAAAPISh0p28+bN9cUXX+jHH39U3bp1VblyZUnSoEGD9MADD6hatWrZmREAAADIUxx+TnaVKlVUpUqVTGNdu3Y1HggAAADI6xwq2RkZGfrwww+1adMmJScnKyMjI9PrNptNixcvzpaAAAAAQF7jUMl+/fXXtWTJEtWuXVvly5dXoUIO3S8JAAAAFEgOlexVq1Zp2LBhGjZsWHbnAQAAAPI8h6akU1JS1Lhx4+zOAgAAAOQLDpXsFi1aKDo6OruzAAAAAPmCQ8tFmjVrpqlTp+rkyZOqV6+e7rrrrkyv22w29e3bNzvyAQAAAHmOQyX7xRdflPTnR6pv3Lgxy+uUbAAAAOB/HCrZhw4dyu4cAAAAQL7h8IfRSFJsbKz27t2rxMREeXp6ql69enJ3d8+ubAAAAECe5HDJnjt3rhYuXKiUlJT/7ezqqn79+mnEiBHZEg4AAADIixwq2YsXL9b8+fM1YMAAtW/fXnfffbcuXryoL7/8Uu+9957Kly+vXr16ZXdWAAAAIE9wqGQvX75cISEhGj58uH3s7rvvVs2aNeXi4qJly5ZRsgEAAID/z6HnZJ89e1b+/v43fM3Pz0+nTp0yGgoAAADIyxwq2VWqVNH3339/w9d27typ8uXLGw0FAAAA5GUOLRcJDg7WK6+8ovT0dLVr105lypTRpUuXtHbtWi1cuFChoaHZHBMAAADIOxwq2U899ZROnDih9957TwsWLJAkWZYlV1dXBQcHa8CAAdkaEgAAAMhLHH6E34gRI/T0009r7969iouLU8mSJVWvXj15enpmZz4AAAAgz7mtD6O5evWq4uPjFR8fL1dXV6WlpWVXLgAAACDPcqhkp6Sk6KWXXtIXX3why7Ls4y4uLurRo4fGjh0rm82WbSEBAACAvMShkj1t2jRFR0dr3LhxatOmjTw9PXX58mV99dVXmjVrljw9PTVkyJDszgoAQI4ICbvs7AhGRAwp7ewIQIHlUMlevXq1Ro4cqZ49e9rH7rnnHgUHB8uyLC1cuJCSDQAAAPx/Dj0nOy0tTRUqVLjha1WrVlVCQoLRUAAAAEBe5lDJfuqpp/T2228rLi4u0/i1a9e0aNEide3aNVvCAQAAAHmRQ8tFrl69quPHj6tly5Z66KGHVLZsWcXGxmrHjh2KjY1V4cKFNWjQIEmSzWbTO++8k62hAQAAgNzMoZJ95MgR1axZU5IUFxdnn9H29vaWJCUnJ2dTPAAAACDvcahkR0ZGZncOAAAAIN9w+MNo4uPjlZycrPLlyyslJUVLlizRmTNn1LZtWz344IPZmREAAADIUxy68XHnzp1q0aKFlixZIkkaP368Zs6cqY0bN6p///5avXp1toYEAAAA8hKHSvacOXPUoEED9evXT3Fxcfryyy/Vv39/bdiwQX369NH8+fOzOycAAACQZzhUsg8cOKABAwbo7rvv1qZNm5Senq6OHTtKklq2bKljx45la0gAAAAgL3GoZBctWlQpKSmSpE2bNqls2bL2p42cOXNGJUuWzL6EAAAAQB7j0I2P/v7+mjt3rg4fPqx169bZP179q6++0pw5c9S0adNsDQkAAADkJQ7NZI8fP14lSpTQW2+9JT8/Pw0dOlSSNGXKFFWtWlWjRo3K1pAAAABAXuLQTPbdd9+tRYsWZRlfuXKlPD09TWcCAAAA8jSHn5MtSd9//722b9+u8+fPa9CgQfrll19Uq1YtlStXLrvyAQAAAHmOQyX7jz/+UGhoqDZu3Ch3d3clJSUpKChIixYt0uHDhxUZGWn/iHUAAACgoHNoTfaMGTO0b98+LVu2TNu3b5dlWZKk6dOnq3z58po5c2a2hgQAAADyEodK9pdffqmRI0eqUaNGstls9vG7775bQ4YM0Q8//JBtAQEAAIC8xqGSnZycrDJlytzwtSJFitifoQ0AAADAwZJdv359LVmyROnp6fax6zPan3zyierWrZs96QAAAIA8yKEbH0eNGqXg4GA9+uijeuihh2Sz2bR8+XL9+uuv+vHHH7V48eLszgkAAADkGQ7NZNepU0cff/yx6tSpo+joaLm4uCg6Olqenp5asWKFGjRokN05AQAAgDzDoZnsNWvWyN/fX7NmzcruPAAAwIlCwi47O8K/FjGktLMjAI7NZI8dO1bff/99dmcBAAAA8gWHSnbFihUVFxeX3VkAAACAfMGh5SIdO3bUlClTtGnTJlWrVi3L4/xsNpv69u2bHfkAAACAPMehkj179mxJUnR09A1fp2QDAAAA/+NQyT506FB25wAAAADyDYfWZOeES5cuqXnz5jp69KiOHz+u7t27q0ePHpo4caIyMjKcHQ8AAABwWK4o2ampqZowYYKKFi0qSZo6dapCQ0O1fPlyWZalDRs2ODkhAAAA4LhcUbKnTZumbt26qVy5cpKkAwcOyM/PT5IUEBCgrVu3OjMeAAAAcFscWpOdnVauXKnSpUurWbNmWrBggSTJsizZbDZJUvHixZWQkJBlv6ioKEVFRWUZDwoKUlBQUPaGBgAAAG7B6SX7k08+kc1m07Zt2/TTTz9p9OjRunz5f582lZSUpBIlSmTZjzINAACA3MrpJXvZsmX2fw8ODtbLL7+s6dOna8eOHfL391dMTIwefPBBJyYEAAAAbk+uWJP9d6NHj9a8efMUFBSk1NRUBQYGOjsSAAAA4DCnz2T/VWRkpP3fly5d6sQkAAAAwJ3LlTPZAAAAQF5GyQYAAAAMo2QDAAAAhuWqNdkAAADOEBJ2+Z83yuUihpR2dgT8BTPZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYZrMsy3J2iPwiJOyy/d87pk5Tp7Q3HNovxqW3It1mZxoLThmugPQlDu2/yvUFfV54dKaxYdd6qH7GOof2X1J4lja79pEkRQwp/edgZCPp/C6H9p/ntkz7XNplGpueXFuldM6h/ScX2aDfC92faSwiuYxD+0rSyKI/Ks52r/3rktYZzfijjsP7h9x16c9zXr/2cz9ISxs7tG+symvUXQczjdVLX6v/pvR0aP/jtvp6tejXmcaapS1W79TnHdp/b6FAvVVkeaax2/3ZCwhdnHlw/TPS/giH9jf5s3fduD9aqaq116H9/+3Pnnp9L5VvlHlsps2xfWXuZ++6Khl7NP5aa4f2/bc/eyrXUAr+IfPYvgXSVwMd2t3Ez54zf++p7Xyp3jOZxwrQ7z2N+Nsf/QXo957qhkiPLMg8xu89x/aVpIGnJPcK//s68bQ0v6Lj+/+Lnz0Vv1cadNrxczmZq7MDAAAAwDlCwi7LsbcXfxq5+IribEXtX5e0rmjGbZ7vr6pkxGm8g/vGJmVo1N/2l/4ySZbLsFwEAAAAMIzlIgb9/d1ZXnQn7wbzw3VLt3/tXHfexnU7huvO2wrq73Su23EF9bpzAjPZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMc3V2gNTUVI0dO1anTp1SSkqKBg8erP/85z8aM2aMbDabatSooYkTJ6pQId4PAAAAIG9wesletWqVSpUqpenTpys2NlaPP/64atasqdDQUPn7+2vChAnasGGD2rZt6+yoAAAAgEOcPj3crl07Pffcc5Iky7Lk4uKiAwcOyM/PT5IUEBCgrVu3OjMiAAAAcFucPpNdvHhxSVJiYqKeffZZhYaGatq0abLZbPbXExISsuwXFRWlqKioLONBQUEKCgrK3tAAAADALTi9ZEvSmTNnNHToUPXo0UMdO3bU9OnT7a8lJSWpRIkSWfahTAMAACC3cvpykYsXL6p///4aNWqUunTpIkmqXbu2duzYIUmKiYlR48aNnRkRAAAAuC1OL9nh4eGKj49XWFiYgoODFRwcrNDQUM2bN09BQUFKTU1VYGCgs2MCAAAADnP6cpFx48Zp3LhxWcaXLl3qhDQAAADAv+f0mWwAAAAgv6FkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDKNkAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAwyjZAAAAgGGUbAAAAMAwSjYAAABgGCUbAAAAMIySDQAAABhGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAAADAMEo2AAAAYBglGwAAADCMkg0AAAAYRskGAAAADKNkAwAAAIZRsgEAAADDXJ0d4GYyMjL08ssv6/Dhw3Jzc9Orr76qqlWrOjsWAAAA8I9ybcmOjo5WSkqKoqKitGfPHr3++ut65513brlPWlqazp49m0MJs/ojPs5p5zbl5Mmrt71Pfrhu6favnevO27hux3DdeVtB/Z3OdTuuoF63Sffcc49cXbNWaptlWZYT8vyjqVOnql69eurQoYMkqVmzZtq8ebP99aioKEVFRWXaJyUlRb/88kuO5gQAAEDBtWHDBlWqVCnLeK6dyU5MTJS7u7v9axcXF6WlpdnfKQQFBSkoKCjTPs6eyc4JgwYNUnh4uLNj5Diuu2DhugsWrrtgKajXLRXca8/v133PPffccDzXlmx3d3clJSXZv87IyLjhVPxfubq63vCdRH7i5uaW76/xRrjugoXrLli47oKloF63VHCvvaBed659ukjDhg0VExMjSdqzZ498fHycnAgAAABwTK6dyW7btq22bNmibt26ybIsTZkyxdmRAAAAAIfk2pJdqFAhTZo0ydkxAAAAgNuWa5eL4Mb+frNnQcF1Fyxcd8HCdRcsBfW6pYJ77QX1unPtI/wAAACAvIqZbAAAAMAwSjYAAABgGCUbAAAAMCzXPl0EWe3du1czZsxQZGSks6PkiNTUVI0dO1anTp1SSkqKBg8erNatWzs7VrZLT0/XuHHjdOzYMdlsNr3yyisF6jnxly5d0hNPPKH33ntP3t7ezo6TYzp37mz/lNtKlSpp6tSpTk6UM+bPn6+vv/5aqamp6t69u7p27ersSNlu5cqV+vTTTyVJ165d008//aQtW7aoRIkSTk6WvVJTUzVmzBidOnVKhQoV0uTJkwvEf+MpKSl68cUXdeLECbm7u2vChAmqVq2as2Nlq7/2lePHj2vMmDGy2WyqUaOGJk6cqEKFCsYcLyU7j4iIiNCqVat01113OTtKjlm1apVKlSql6dOnKzY2Vo8//niBKNnffPONJOmDDz7Qjh07NHv2bL3zzjtOTpUzUlNTNWHCBBUtWtTZUXLUtWvXZFlWgXkDfd2OHTu0e/durVixQsnJyXrvvfecHSlHPPHEE3riiSckSa+88oqefPLJfF+wJWnTpk1KS0vTBx98oC1btmjOnDmaN2+es2Nluw8//FDFihXThx9+qF9//VWTJ0/WwoULnR0r2/y9r0ydOlWhoaHy9/fXhAkTtGHDBrVt29bJKXNGwXgrkQ9UqVKlQPwy+qt27drpueeekyRZliUXFxcnJ8oZbdq00eTJkyVJp0+fLhB/+F43bdo0devWTeXKlXN2lBx16NAhJScnq3///urdu7f27Nnj7Eg54ttvv5WPj4+GDh2qQYMGqUWLFs6OlKP279+vI0eOFJjHm1WvXl3p6enKyMhQYmKiXF0LxjzfkSNHFBAQIEny8vLS0aNHnZwoe/29rxw4cEB+fn6SpICAAG3dutVZ0XJcwfgJzwcCAwN18uRJZ8fIUcWLF5ckJSYm6tlnn1VoaKhzA+UgV1dXjR49Wl999ZXefPNNZ8fJEStXrlTp0qXVrFkzLViwwNlxclTRokX19NNPq2vXrvrtt98UEhKitWvX5vsScuXKFZ0+fVrh4eE6efKkBg8erLVr18pmszk7Wo6YP3++hg4d6uwYOaZYsWI6deqUHn30UV25ckXh4eHOjpQjatWqpW+++UZt2rTR3r17de7cOaWnp+fbiaO/9xXLsuz/TRcvXlwJCQnOipbjmMlGrnbmzBn17t1bjz32mDp27OjsODlq2rRpWrduncaPH6+rV686O062++STT7R161YFBwfrp59+0ujRo3XhwgVnx8oR1atXV6dOnWSz2VS9enWVKlWqQFx7qVKl1LRpU7m5ucnLy0tFihTR5cuXnR0rR8THx+vYsWN68MEHnR0lxyxatEhNmzbVunXr9H//938aM2aMrl275uxY2e7JJ5+Uu7u7evTooa+++kr33Xdfvi3YN/LX9ddJSUkF6m9nKdnItS5evKj+/ftr1KhR6tKli7Pj5JjPPvtM8+fPlyTdddddstlsBeImkWXLlmnp0qWKjIxUrVq1NG3aNJUtW9bZsXLExx9/rNdff12SdO7cOSUmJhaIa2/UqJE2b94sy7J07tw5JScnq1SpUs6OlSN27typJk2aODtGjipRooQ8PDwkSSVLllRaWprS09OdnCr77d+/X02aNNGKFSvUrl07Va5c2dmRclTt2rW1Y8cOSVJMTIwaN27s5EQ5J3//XSTytPDwcMXHxyssLExhYWGS/ryhIr/fFPfII4/oxRdfVM+ePZWWlqaxY8fm+2su6Lp06aIXX3xR3bt3l81m05QpU/L9UhFJatmypXbu3KkuXbrIsixNmDChwMzwHTt2TJUqVXJ2jBzVt29fjR07Vj169FBqaqqGDx+uYsWKOTtWtqtatarmzp2r8PBweXh46LXXXnN2pBw1evRojR8/XrNmzZKXl5cCAwOdHSnH8LHqAAAAgGH5/++gAQAAgBxGyQYAAAAMo2QDAAAAhlGyAQAAAMMo2QAAAIBhlGwAyEeCg4M1cOBAI8fasWOHfH19tX//fuPHBoD8jpINALih++67T1FRUfL29nZ2FADIc/L/px0AAO6Iu7u77r//fmfHAIA8iZlsAMijkpKSNG7cOPn7+8vf318LFizI9HpaWprmzp2rFi1aqG7dunriiSe0bdu2TNvExsbqpZde0kMPPaSGDRuqf//+Onz4sKSsy0X+zpHjA0BBRckGgDzq+eef11dffaVRo0bptdde05dffqndu3fbXx8/frzef/999e7dW2+//ba8vLwUEhKiXbt2SfqzJPfr10+bNm3S888/r7lz5+qPP/7Q008/rbi4uH88/z8dHwAKMpaLAEAedOjQIW3cuFGzZ89W+/btJUn16tVT69atJUlHjx7VypUr9eqrr6pr166SpICAAF24cEFz5szRkiVLtHHjRh08eFDLli1T48aNJUm1a9dW165d9eOPP8rV9eZ/RDhyfAAoyJjJBoA86PpscUBAgH2sXLly9jXU3333nf31tLQ0+z/NmzfXrl27lJKSot27d8vDw8NesCWpTJky+vrrr/Xwww/f8vyOHB8ACjJmsgEgD4qPj1fhwoXl7u6eabxs2bJKSkpSbGyspMwl/K+uXLmiuLg4lSlT5o7O78jxy5cvf0fHBoD8gJINAHlQqVKllJqaqvj4eJUoUcI+Hhsbq8KFC8vDw0M2m00ffPCBXFxcsuzv6ekpDw8PXb58Octr27dvV6VKlW55fkeODwAFGctFACAP8vPzkyStX7/ePhYXF6c9e/ZIkho1aiTLspSYmKi6deva/9m2bZsWLVokV1dXNWjQQPHx8ZluVIyLi1NISIi2bNlyy/M7cnwAKMj4LQgAeZCXl5c6deqkKVOm6Nq1a6pQoYLmz5+vtLQ0SVKtWrUUGBioUaNGadiwYfL29tZ3332nd955RwMGDFChQoXUsmVL1a5dW8OHD9fw4cPl6empiIgIlStXTu3bt9fBgwdven5Hjg8ABRklGwDyqNdee02lS5fWvHnzlJqaqi5duqh8+fL6448/JEkzZszQ3LlztWDBAl26dEkVK1bUiBEj9PTTT0uSChcurIULF+qNN97QlClTlJGRocaNG2vRokXy8PD4x/P/0/EBoCCzWZZlOTsEAAAAkJ/w93kAAACAYZRsAAAAwDBKNgAAAGAYJRsAAAAwjJINAAAAGEbJBgAAAAyjZAMAAACGUbIBAAAAw/4fGzxB9jKoo2YAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 864x576 with 1 Axes>"
       ]