notebooks/week5_lecture.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "interested-concern",
   "metadata": {},
   "source": [
    "# Week 5 - An introduction to machine learning (Part I)\n",
    "### Motivation\n",
    "\n",
    "The (multivariate) linear regression techniques from Lecture 4 depend upon making some (often stringent) assumptions about statistical properties of the underlying mechanisms of data generation. For example, the error bars on standard linear regression coefficients are only valid when, amongst other things, our residual are normally distributed .\n",
    "\n",
    "In one view, machine learning is a toolkit of techniques that allow us to describe data and make predictions, while making _fewer assumptions about the statistical properties of our data_. Instead, we squeeze the most out of the data we've already got, to make assertions that will (hopefully!) generalise to data yet unseen.\n",
    "\n",
    "### Aims\n",
    "\n",
    "#### This week\n",
    "\n",
    "- Recreate the simple and multivariate regressions using the Python scikit-learn package.\n",
    "- Generate some intuition for the properties of regression with machine learning by using a wages dataset from the US Census Bureau's [1985 Current Population Survey (CPS)](https://www.census.gov/programs-surveys/cps/library/publications.1985.html)\n",
    "- (if we have time) Take a look at the [Titanic dataset] (a classic in the world of machine learning pedagogy 😃!) to introduce the idea of machine learning classifiers: we'll use scikit-learn to work out the survival chances of Titanic passengers.\n",
    "\n",
    "#### Next week\n",
    "- Continue the Titanic example.\n",
    "- Show how machine learning techniques are not restricted to working with tables of numbers, by automatically classifying the sentiment of movie reviews (so called 'natural language processing' or 'NLP').\n",
    "- Show how we can be more systematic about how we pick the 'best' model.\n",
    "- Introduce the homework with a hands-on with data from a [nature machine intelligence](https://www.nature.com/natmachintell/) paper, [\"An interpretable mortality prediction model for COVID-19 patients\"](https://www.nature.com/articles/s42256-020-0180-7).\n",
    "\n",
    "### Learning objectives\n",
    "\n",
    "- Acquire a basic familiarity with the language of machine learning (ML/data mining/AI/pattern recognition/statistics, unsupervised vs. supervised learning, classification and regression, train/test split, bias/variance tradeoff (generalisability), ...)\n",
    "- Implement an end-to-end machine learning classification model using the scikit-learn framework."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elementary-fountain",
   "metadata": {},
   "source": [
    "## 0. Setup\n",
    "\n",
    "You will need to install the [pandas](https://pandas.pydata.org/), [scikit-learn](https://scikit-learn.org/stable/), [matplotlib](https://matplotlib.org/), [plotnine](https://plotnine.readthedocs.io/en/stable/) and [xlrd](https://xlrd.readthedocs.io/en/latest/) packages to run this notebook, and you'll require Python >=3.6.\n",
    "\n",
    " To install these packages, launch the \"Anaconda Prompt (Anaconda3)\" program and run the following commands:\n",
    "\n",
    "`conda install -c anaconda pandas scikit-learn matplotlib xlrd`\n",
    "\n",
    "`conda install -c conda-forge plotnine`\n",
    "\n",
    "<img src=\"../img/AZ_conda_prompt.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "executed-ladder",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import plotnine as p9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chinese-castle",
   "metadata": {},
   "source": [
    "### Load the metabric dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "convenient-waterproof",
   "metadata": {},
   "outputs": [],
   "source": [
    "metabric = pd.read_csv(\"../data/metabric_clinical_and_expression_data.csv\").dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "referenced-quick",
   "metadata": {},
   "source": [
    "### Simple linear regression with sklearn\n",
    "\n",
    "If we have a continuous variable, and we want to model its relationship with another variable, we can use simple linear regression. In linear regression we call the variable of interest the response, and the other variable the predictor. The mathematical details of linear regression are beyond the scope of this course, but in the case of simple linear regression it basically amounts to fitting a line through the data that is closest to all of the points. For example, we may want to predict survival time based on tumour size, because survival time appears to differ across the range of tumour sizes:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "universal-destruction",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8781209000578)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "p9.ggplot(metabric, p9.aes(\"Tumour_size\", \"Survival_time\"))\n",
    "    + p9.geom_point()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "catholic-adoption",
   "metadata": {},
   "source": [
    "As with `statsmodels`, using `scikit-learn`, we can run simple linear regression using [sklearn.linear_model.LinearRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html).\n",
    "\n",
    "There are three steps to completing this analysis:\n",
    "1. **Instantiate** the model: create an object that holds the model specification.\n",
    "2. **Fit** the model: fit the specified model to the data using the `.fit` function.\n",
    "3. **Display** the results: use the `X` method of the fitted model to return a detailed breakdown of the model characteristics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "incorrect-signature",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.linear_model as linear\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "graphic-accounting",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the model\n",
    "model = linear.LinearRegression()\n",
    "\n",
    "# Define the predictor ('X') and the response ('y').\n",
    "X = metabric.loc[:, [\"Tumour_size\"]].values\n",
    "y = metabric[\"Survival_time\"]\n",
    "\n",
    "# Fit the model\n",
    "model.fit(X, y)\n",
    "\n",
    "# Get the predictions\n",
    "X = np.linspace(0, 150, 10).reshape(-1,1) # `model.predict` expects a 2-dimensional numpy.array\n",
    "y_hat = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "controversial-avenue",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8781209277175)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the results\n",
    "predictions = pd.DataFrame({'X':X.reshape(-1), 'y_hat': y_hat}) # plotnine works with pd.DataFrame\n",
    "(\n",
    "p9.ggplot(metabric, p9.aes(\"Tumour_size\", \"Survival_time\"))\n",
    "    + p9.geom_point()\n",
    "    + p9.geom_line(p9.aes(x='X', y='y_hat'), data=predictions, color='red') # Overlay the regression line on the scatter plot\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "difficult-bloom",
   "metadata": {},
   "source": [
    "### Multivariate linear regression with sklearn\n",
    "\n",
    "As with statsmodels, we can apply a similar procedure to run the multivariate regression too. This time, however, we need to a little more work with the categorical variables. We'll first do that work using `pandas`, then demonstrate how to use `sklearn` to achieve the same result that will generalise to more complex examples."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cooked-savannah",
   "metadata": {},
   "source": [
    "We cannot use `linear.LinearRegression` directly because, unlike `statsmodels`, `sklearn` doesn't automatically treat the `str` types in `Cancer_type` ande `ER_status` columns.\n",
    "\n",
    "First, we convert the `ER_status` to `0.0`s and `1.0`s (i.e. we 'binarize' the variable)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dependent-vegetation",
   "metadata": {},
   "outputs": [],
   "source": [
    "metabric['ER_status_binary'] = (metabric.ER_status == 'Positive').astype(np.float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "looking-techno",
   "metadata": {},
   "source": [
    "Next, let's 'one-hot-encode' the `Cancer_type`. This is a transformation of a single column of categorical features into an array of binary features, one-per-category, where each row has a single `1.0` ('one-hot') in the column corresponding to the matching category. This is made clearer by example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "younger-audio",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Breast Invasive Ductal Carcinoma',\n",
       " 'Breast Mixed Ductal and Lobular Carcinoma',\n",
       " 'Breast Invasive Lobular Carcinoma',\n",
       " 'Breast Invasive Mixed Mucinous Carcinoma',\n",
       " 'Breast']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_types = list(metabric.Cancer_type.unique())\n",
    "cancer_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "rubber-delaware",
   "metadata": {},
   "outputs": [],
   "source": [
    "for cancer_type in cancer_types:\n",
    "    column_name = 'Cancer_type_' + cancer_type\n",
    "    metabric[column_name] = (metabric.Cancer_type == cancer_type).astype(np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "sensitive-dallas",
   "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>ER_status_binary</th>\n",
       "      <th>Cancer_type_Breast Invasive Ductal Carcinoma</th>\n",
       "      <th>Cancer_type_Breast Mixed Ductal and Lobular Carcinoma</th>\n",
       "      <th>Cancer_type_Breast Invasive Lobular Carcinoma</th>\n",
       "      <th>Cancer_type_Breast Invasive Mixed Mucinous Carcinoma</th>\n",
       "      <th>Cancer_type_Breast</th>\n",
       "      <th>Tumour_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ER_status_binary  Cancer_type_Breast Invasive Ductal Carcinoma  \\\n",
       "1               1.0                                           1.0   \n",
       "4               1.0                                           0.0   \n",
       "5               1.0                                           1.0   \n",
       "8               1.0                                           1.0   \n",
       "9               1.0                                           0.0   \n",
       "\n",
       "   Cancer_type_Breast Mixed Ductal and Lobular Carcinoma  \\\n",
       "1                                                0.0       \n",
       "4                                                1.0       \n",
       "5                                                0.0       \n",
       "8                                                0.0       \n",
       "9                                                0.0       \n",
       "\n",
       "   Cancer_type_Breast Invasive Lobular Carcinoma  \\\n",
       "1                                            0.0   \n",
       "4                                            0.0   \n",
       "5                                            0.0   \n",
       "8                                            0.0   \n",
       "9                                            1.0   \n",
       "\n",
       "   Cancer_type_Breast Invasive Mixed Mucinous Carcinoma  Cancer_type_Breast  \\\n",
       "1                                                0.0                    0.0   \n",
       "4                                                0.0                    0.0   \n",
       "5                                                0.0                    0.0   \n",
       "8                                                0.0                    0.0   \n",
       "9                                                0.0                    0.0   \n",
       "\n",
       "   Tumour_size  \n",
       "1         10.0  \n",
       "4         40.0  \n",
       "5         31.0  \n",
       "8         16.0  \n",
       "9         28.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the predictors ('X') and the response ('y')\n",
    "X = metabric.loc[:, [\n",
    "    \"ER_status_binary\",\n",
    "    \"Cancer_type_Breast Invasive Ductal Carcinoma\",\n",
    "    \"Cancer_type_Breast Mixed Ductal and Lobular Carcinoma\",\n",
    "    \"Cancer_type_Breast Invasive Lobular Carcinoma\",\n",
    "    \"Cancer_type_Breast Invasive Mixed Mucinous Carcinoma\",\n",
    "    \"Cancer_type_Breast\",   \n",
    "    \"Tumour_size\"]]\n",
    "y = metabric[\"Survival_time\"]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "colonial-karma",
   "metadata": {},
   "source": [
    "Now we have a `pandas.DataFrame` that is an array of `float`s, we can use `linear.LinearRegression()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "executive-embassy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the model\n",
    "model = linear.LinearRegression()\n",
    "# Fit the pipeline\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "married-discovery",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(161.80168620862426,\n",
       " array([ 13.03538225, -17.57512295,  -7.66583289,  -7.4337667 ,\n",
       "         -2.07277446,  34.74749699,  -1.16952734]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the fit regression coefficients and intercept\n",
    "model.intercept_, model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "gross-monitoring",
   "metadata": {},
   "source": [
    "You might be thinking that's a lot of extra work compared with the `statsmodels` version. Luckily, `sklearn` has the concepts of `Transformer`s and `Pipeline`s that are built to handle this, and much more complex preprocessing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "technological-approval",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = metabric.loc[:, [\n",
    "    \"ER_status\",\n",
    "    \"Cancer_type\",\n",
    "    \"Tumour_size\"]]\n",
    "y = metabric[\"Survival_time\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "charming-evans",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "label_transformer = OneHotEncoder(drop='if_binary')\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('labels', label_transformer, ['ER_status', 'Cancer_type']),\n",
    "    ],\n",
    "    remainder='passthrough'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fitted-longitude",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  1., ...,  0.,  0., 10.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  1., 40.],\n",
       "       [ 1.,  0.,  1., ...,  0.,  0., 31.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  1., ...,  0.,  0., 20.],\n",
       "       [ 1.,  0.,  0., ...,  0.,  1., 20.],\n",
       "       [ 1.,  0.,  0., ...,  1.,  0., 22.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "stuffed-column",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('labels',\n",
       "                                                  OneHotEncoder(drop='if_binary'),\n",
       "                                                  ['ER_status',\n",
       "                                                   'Cancer_type'])])),\n",
       "                ('model', LinearRegression())])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate the model\n",
    "model = linear.LinearRegression()\n",
    "pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])\n",
    "# Fit the pipeline\n",
    "pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "compressed-philosophy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(161.8016862086244,\n",
       " array([ 13.03538225,  34.74749699, -17.57512295,  -7.4337667 ,\n",
       "         -2.07277446,  -7.66583289,  -1.16952734]))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the fit regression coefficients and intercept (sanity check - let's ensure we get what we had before!)\n",
    "pipeline['model'].intercept_, pipeline['model'].coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "steady-dance",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ktmx283/opt/anaconda3/envs/202101-data-science-python/lib/python3.8/site-packages/plotnine/layer.py:467: PlotnineWarning: geom_point : Removed 6 rows containing missing values.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8781209277106)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the results\n",
    "y_hat = pipeline.predict(X)\n",
    "predictions = pd.DataFrame({'y': y, 'y_hat': y_hat}) # plotnine works with pd.DataFrame\n",
    "\n",
    "(\n",
    "p9.ggplot(predictions, p9.aes('y', 'y_hat'))\n",
    "    + p9.geom_point()\n",
    "    + p9.scales.xlim(0, 300)\n",
    "    + p9.scales.ylim(0, 300)\n",
    ") # plotnine will emit a `PlotnineWarning` indicating that we have cropped some points from our plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "formal-exhaust",
   "metadata": {},
   "source": [
    "As with any regression model, it's instructive to plot a histogram of the residuals. (TL;DR; - it's not exactly very predictive!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "present-welcome",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8781209282759)>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "residuals = pd.DataFrame({'residuals': np.abs(y-y_hat)})\n",
    "(\n",
    "# the aes object contains only the variable that will be plotted on the x axis i.e. the one for which we want to generate a distribution\n",
    "p9.ggplot(residuals, p9.aes('residuals'))\n",
    "    # specify a histogram\n",
    "    + p9.geom_histogram(bins=20)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quantitative-specific",
   "metadata": {},
   "source": [
    "### Moving beyond linear models\n",
    "\n",
    "We can work on the US Census Bureau's [1985 Current Population Survey (CPS)](https://www.census.gov/programs-surveys/cps/library/publications.1985.html) to investigate how more powerful, non-linear models behave."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "advised-reality",
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib, os # Standard library imports\n",
    "from sklearn import svm, ensemble"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "israeli-introduction",
   "metadata": {},
   "source": [
    "First, download the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "silver-audit",
   "metadata": {},
   "outputs": [],
   "source": [
    "wages_path = '../data/wages.txt'\n",
    "if not os.path.exists(wages_path):\n",
    "    urllib.request.urlretrieve('http://lib.stat.cmu.edu/datasets/CPS_85_Wages', wages_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "auburn-excess",
   "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>EDUCATION</th>\n",
       "      <th>SOUTH</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EXPERIENCE</th>\n",
       "      <th>UNION</th>\n",
       "      <th>WAGE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>RACE</th>\n",
       "      <th>OCCUPATION</th>\n",
       "      <th>SECTOR</th>\n",
       "      <th>MARR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>5.10</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>4.95</td>\n",
       "      <td>57</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.67</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>7.50</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   EDUCATION  SOUTH  SEX  EXPERIENCE  UNION  WAGE  AGE  RACE  OCCUPATION  \\\n",
       "0          8      0    1          21      0  5.10   35     2           6   \n",
       "1          9      0    1          42      0  4.95   57     3           6   \n",
       "2         12      0    0           1      0  6.67   19     3           6   \n",
       "3         12      0    0           4      0  4.00   22     3           6   \n",
       "4         12      0    0          17      0  7.50   35     3           6   \n",
       "\n",
       "   SECTOR  MARR  \n",
       "0       1     1  \n",
       "1       1     1  \n",
       "2       1     0  \n",
       "3       0     0  \n",
       "4       0     1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = [\n",
    "    'EDUCATION',\n",
    "    'SOUTH',\n",
    "    'SEX',\n",
    "    'EXPERIENCE',\n",
    "    'UNION',\n",
    "    'WAGE',\n",
    "    'AGE',\n",
    "    'RACE',\n",
    "    'OCCUPATION',\n",
    "    'SECTOR',\n",
    "    'MARR',\n",
    "]\n",
    "\n",
    "wages = pd.read_csv('../data/wages.txt', skiprows=27, skipfooter=6, sep='\\t',\n",
    "       header=None, engine='python')\n",
    "wages.columns = names\n",
    "wages.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sorted-ordinance",
   "metadata": {},
   "source": [
    "Let's take a look"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "completed-haven",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8781209282672)>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    p9.ggplot(wages, p9.aes(\"EXPERIENCE\", \"EDUCATION\"))\n",
    "        + p9.geom_point(p9.aes(color='WAGE'), alpha=0.7)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "related-blair",
   "metadata": {},
   "source": [
    "Let's first fit a Support Vector Machine (SVM) on a single variable. Consider the effect of `EDUCATION` on wages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "jewish-throw",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GDh2K8PDwLt9Hd2LG7XZ7R8/Or+jbk69fZGRkl27X5Tovx44du+zOEBERGY1GhIWF+Ww1WLPZDLPZDJPJhLS0NKGJCwCEh4cjPDwcoaGhmDRpUrcTl+5SU0XfLo+8aAlHXrSD36QvjTHTEeOlc4yZjhgznVNbzPT4yMulXLg4yGAwIDg4+ErvloiIiOiiuryQ5dSpUxg5ciTef/9973UtLS2IjIxEVFSU98dqtaKoqEhIZ4mIiIi6nLz89a9/RWVlJe6///4O//aHP/wB7733Hv7xj39g2LBhyMjI6NFOEhEREUm6PG20evVq/OpXv4K/f8dfmT59erutUn/5y1/w5z//uWd6SERERHSeLo+8HD16tMNebp1Oh2uvvRaBgYHe6xISEnD06NGe6yERERHRebq1YPfCjUl+fn7Yt29fu+taW1tZPIqIiIiE6fLIS0JCAnbt2vWTt/vhhx+QkJBwJX0iIiIiuqQuJy/Tp0/Hm2++ifLy8kve5tSpU3jzzTcxY8aMHukcERERdU4NByXKrcvJyzPPPAO9Xo+UlBQsW7YMR48eRVNTE5qbm3H8+HH8/e9/x4033gij0Yjf/va3IvtMRETU66npoES5dXnNS3h4OHJycjBnzhw8+uij0Ol07f7d4/HghhtuwIoVK4SWJyYiIiLA4XAgLy/Pezk7OxvJyclITk5WsFfy6NaC3UGDBuH777/Hli1bsGnTJpSWlsLj8aB///64+eabMXbsWFH9JCIiovPY7XYkJiZ2OChRNLfbDYfDAbvdrtjZRpd1PMC4ceMwbty4i/5bTU0NVq1ahblz515Rx4iIiOjSLjyRWzqlWzRpjU1iYqJiyUuX17x0prGxEf/6178wa9YsWK1WPPLIIz1xt0RERPQTpFOeRScS0hobl8sFAHC5XIqtsbns5MXj8WD9+vV4+OGHYbFYcPfdd+P777/Hb37zmw61X4iIiJTgdruRm5sr64es0+nEqlWr4HQ6ZWlPruTF4XBg9erVyMzMRHFxMTIzM+FwOIS2eSndnjbatWsXVqxYgY8//hjl5eUIDg5Geno6Pv30U6xYsQLjx48X0U8iIqJucbvdqK2tRW5uLkwmE/r06dNhqqUnSaMSFRUVyMnJwZAhQ2Cz2WA2m4W0J7fExERYrVZUVFQgIyMDM2fOhM1mU6QvXU5eXnrpJSxfvhxHjhyBwWBAeno67r33XkyfPh319fX45JNPRPaTiKhXUMNiSF/hcDiQn58Pk8mErKwseDweobtxsrKykJOT472ckZGBtLQ03HXXXULak1thYSHy8vLgdDpx7tw5bNq0CWPGjFFkd1OXk5cXX3wROp0OkydPxvvvvw+LxeL9t4aGBiGdIyLqLdxuN9xuN1wuF/Ly8mC1WhEWFsYE5grY7Xb0798fmzdvRnp6unfkRZT09HSMHz/eOzIxb948xUYmRIiLi0NISAiKi4uxZs0aDB06FHFxcYr0pctrXp544gnExMTg66+/xjXXXIOHH34Y69ev5zlGREQ9QFpPkJ2dDaCtZodS6wlEkrMarNFohMlkAtC2E8dsNgtNXsxmMywWCyIjIwEAkZGRPjNlBAAbNmzAkiVL8M9//hMA8M9//hMbNmxQpC9dHnn5y1/+gtdeew05OTlYvnw5PvvsM3z44YeIjo7G9OnTodPpOhSuIyKirlGqZoec3G63txqs1Wr1bu0VmVAYjUakpKTIOoJlMpmQlpbmTZx8hTSyVFZWhldeeQXz5s3DoEGDFOlLt3Yb6XQ6TJw4Ee+99x7Ky8vx6aefIjU1FR999BE8Hg8efPBBvPTSSzh27Jio/hIR+SSj0Qiz2dxupMDXpowcDke7kaXVq1cLH11SInkxm8246667fGrUBfj3yFJMTAzCwsIQExOj2GPscvKyePFibNq0CfX19QDaAuKOO+7A6tWrUV5ejnfffRcJCQl46aWXFMvEiIi0Tq5tr0qw2+2YPHkyAGDy5MmYNWsW7Ha7wr2i7lLDyJLO08VFKwaDAa2trfD398fw4cMxZswYjB07FmPGjEF0dLT3dqWlpfj444/x5JNPCuv0T6moqFCs7UsJDg7GuXPnlO6G6uj1eoSFhaGqqgotLS1Kd0dVGDMdMV46p4WYkXs3FWOmc2qLGWm90E/pcvLicrmwY8cObNu2Ddu2bcOOHTtw7tw56HQ6JCYmYuzYsRg3bhxGjx6Na6655oo6f6WYvGgH31gujTHTEeOlc5cTM76+NZsx0zm1vc90NXnp8oJdk8mEiRMnYuLEiQDaKuzm5+dj27Zt2L59OzZu3IgPP/zQ23h5eflldJuIiOSkhnNqiLrrsg5mBNoW70rFftLT07F161asWrUKmZmZqhz5ICKi9qS6MkDb6LronT9EPaXbyUtLSwv27NmDbdu2YevWrdi2bRvKy8uRkJCA1NRULF26FKNHjxbRVyIi6kEOhwN5eXkA2nb/iKw+S9STupy8vPDCC9i2bRu+//57NDc3Y8SIERg9ejTuuecejB49GlarVWQ/iYioh9ntdlitVp+tK0O+q8vJy+LFi2EymfDQQw/hN7/5Da666iqR/SIiIsGMRiPCwsKQnJzMowhIU7pc50U6QTIzMxODBw9GXFwc7r77brz11lvYtWsXV3ETEfUAOcvnA75dV0Ypcr+GvVGXk5fHHnsMH330EY4ePYqSkhK8/vrr6N+/P5YvX44xY8YgNDQUEyZMwHPPPYfMzEyRfSYi8jlutxtOp9NbPr+qqooffhold/LSG5Oly9ptFBMTgzvvvBN33nknAKC+vh5bt27FW2+9hVdffRUA0Nzc3KX7ampqwt/+9jfk5eXh3LlziIyMxF133YWbb74ZAFBUVISlS5fi+PHjsFqtePTRR3HttddeTreJiFTr/MWzABfQatH5J4MD4ndw9eaTyC97q3R1dbW3YN22bduwa9cu1NfXw8/Pr1vJRUtLC8LDw7Fo0SJER0ejoKAAL730EqxWKwYNGoRFixZhypQpePnll7F161YsXrwYf//7333uzAgi6t16w8GMvk7uBLQ3J7xdTl4KCwvbJSsFBQVobW1FUFAQbrzxRjz55JMYM2YMRo8ejZCQkC53oE+fPrj33nu9l4cMGQK73Q6Hw4H6+no0Njbi9ttvh5+fHyZMmIAvvvgC27dvx6RJk7r3SImox/l6dVY5Sd/Q3W43iouLYTAYfPI59eWYkRLQ6upqfPjhh3jggQfaHZ8jqj25E141vIZdTl6k3UVWqxWjR4/G3LlzMWbMGAwfPhz+/pc9gNNBQ0MDjhw5gunTp+PEiROw2Wzw8/v30pwBAwbgxIkT7X6nrKwMZWVl3ssBAQHo169fj/WpJ+h0Ouj1eqW7oTrSc8LnpiMtxExLSwvy8/Nx9dVXy9LX3hAvra2tOHPmDFpbW7v9OBkzHckZM3q93vvj5+fX7rIIgYGBCAwMhF6vh06nQ0hICAIDA7t1H92JmfOnqfLz89G/f3/Fpqm6nHV88MEHGDt2LAYOHCisMx6PB2+++SauuuoqDB8+HIcPH+5waqXJZEJdXV2765YtW4aFCxd6L8+fPx+LFy8W1s/L5WvfMnpSd0brehM1x4zb7UZjYyNMJhMMBoN3pECOPvtivDidTjidTu+iS7fbDYPB0O0pcsbMxckRM7m5ucjNzQUAXHPNNfjuu++QkpKClJQUoe2aTCaMHz8eFovlsp7Lrv7O+Y/PZDJh8+bNsjy+i+nywYyieTwevPPOOygqKsJLL72EPn36YM2aNcjNzcVLL73kvd3SpUsRGBiIuXPneq/TwsiLyWTyLuKif9Pr9QgJCUFtbS23219A7TGzd+9e7N27t9111113Ha677jphbfpyvHz66afYsGFDu+smTpyI2bNnd/k+GDMdyRkz549MZGVlIT09XfULaLsTM3I8vq5Oe/XcfM8V8Hg8+Nvf/oajR4/ij3/8I/r06QMAiI+Px+rVq9Ha2uqdOjp27BimTJnS7vdjYmIQExPjvVxRUaG6NzaPx6O6PqlJS0sLn58LqD1mrrnmGkRHR3vn2k0mE4xGoyx99sV4mTx5MsaOHYuysjK88sorePbZZzFo0KBuPU7GzKXJETN6vR6BgYFoaWmBx+NBnz59oNfrVf2adCdm1PT4ulznRaRly5bh0KFDWLhwIYKCgrzXDxs2DAaDAZ9//jmampqwadMmnDp1CqmpqQr2loiAjtVZzWazqr9hqp3ZbIbFYkFERAQAICIiwud2VfammOnu2hMtUUNhQ8VHXk6fPo2vvvoKBoMBDz/8sPf6O++8E7Nnz8bzzz+PjIwMrFixAhaLBfPnz0dwcLCCPSYiifQmRldOGpLX6XQYNmwYdDod3G63z324+3LMnD+tUl9f77MndavhNVTNmpeeVFFRoXQXOggODsa5c+eU7obq6PV6hIWFoaqqStVDq0pgzHTky/GSl5fXrmYHgG7X7GDMdCRnzPTEayg3tcVMZGRkl26n+MgLERGxSJ0v6C2voabqvBARkTjS9II0JK/2XSrU0YVTRNKCZF+hpuMImLwQEamIGtYT0JVRw4JWEdR0HAGTFyIioh7kqwmomqbFVLFVmoiIege32428vDxvJWHSDqPRCLPZ7K18r+S0GJMXIiKShdvtRlVVFfLy8lBVVdXuOATSDjVMi3HaiIiIZHH+mons7GwA6t9KrAVq2P0jN468EBGRLOx2OyZPngyg7TiEWbNmwW63K9yrnif31Jjc7blcLqxdu1bRc7SYvBARkSyMRmO79RK+ejyAXMmE2+2G0+n0JhEul0tom1J71dXVKCkpQXV1tWLTfpw2IiLqxeSeclDDeglRzq+DAkD48QByb13Oz8/H7t27vQlLZmYmRo0aheuvv15Ie51h8kJE1Eudv4BWroJjvrqNGJA/mVBi67JOpxN6/13F5IWIqJe6cAEtF89eGSmZqK6uxocffogHHngA0dHRwtqTuypzUlISBg8ejOrqatTU1GDmzJlCH19nmLwQEfVSdrsdVqtV8YJjvkJKJKRplaCgIFmn4uRoR/qZNm0aoqOjWeeFiIjkZTQaERYW5tNnKbndbuTm5sqysFRa0NrU1IS4uDg0NTWxjo0gTF6IiHoxX15AC8ibvDgcDqxevdpbwyY7OxsOh0N4u3JTQ5VkThsREZFPunDnT0tLi9DdP2o6+0cEuXdTdYbJCxER+SSHw4H8/HyYTCZkZWXB4/EIXZQs9wJaufFUaSIiIsHsdjv69++PzZs3Iz09HX369PGpBbRyU9PIEte8EBGRbORcLyEtSE5JSUFYWJjPVvSVi3SqtBoWeTN5ISIiWShxqrTRaERKSgqTlh6khkXenDYiIiJZOBwO5ObmoqysDJmZmfD392dhPLosHHkhIlIRNWxDFSUxMREjR45ESUkJRo4cicmTJyMxMVHpbpEGMXkhIlIBqcDZ+dMqvpbAFBYWYvPmzaiqqsLmzZuRnZ2NwsJCpbtFGsRpIyIiFZC2oTY0NGD//v1oaGjAyJEjfWpKJTExEW63G2vXrsX111+P+Ph4mEwmpbuleXKfDK4GHHkhIlKBuLg4jBs3DoMHD8ahQ4cwePBgxMXFKd2tHlVQUIAtW7agubkZW7ZswZo1a1BQUKB0tzTPl6caL4XJCxGRCmzYsAFLlizBRx99BAD46KOPsGHDBoV71fP0ej3CwsKg1+uh0+mU7o4QciUT0lTj+RVv5TrDSelkidNGREQqkJ6ejvHjx6OsrAyvvPIKHn/8cQwaNEjpbvWopKQkREREoKCgADNmzEBUVJRPTXNI5fOrq6uxdu1aRERECD15We6Kt+cfD5CXlwer1apYrRcmL0REKmA2m2E2mwEAYWFhiImJ8V72FUajEaGhoYiNjUVoaKjPPT4pmWhsbERJSQmysrJw4403Cksm5K54y+MBiIjookwmE9LS0nx2IavZbMa0adN8LnEB2hYkW61WlJWVYcOGDbjhhhuEbgWX+ywlNR0PwOSFiEhFzGYz7rrrLqW7IYyvnvsDtC1I3rNnD2pra1FVVYUNGzagubkZ119/vdB25XpO1XTwJBfsEhGpiBoWQ9Ll83g88Pf3R1hYGPz9fXN8QA3HAzB5ISJSkZKSErzwwgsoKSlRuitC+HJylpSUhDlz5mDixImoq6vDxIkTkZSUpHS3epzT6cSqVavgdDoV6wOTFyIiFSguLsYPP/yArVu34tChQ9i6dSuKi4uV7laPUuJgRjk5nU6cOXMGJ0+e9P4o+QHf05xOJ8rLy1FUVIScnBwUFRUp9vh0Ho/Ho0jLAtXW1iIgIEDpbrTj7++P5uZmpbuhOjqdDkajEW63Gz4YileEMdORL8fLf/zHf2Dz5s3trhs/fjw+/vjjLt+H2mNm9+7d+P7771FaWop+/frB398fI0aMwIgRI4S1KWfM/OlPf8JXX32F1tZW1NfXIzAwENOmTcP8+fOFtnsluhMzK1aswLp169pdN2nSJMyZM6fH+tPVz26fTF4qKiqU7kIHwcHBOHfunNLdUB2pYFVVVRVaWlqU7o6qMGY68uV4KS4uxqlTp+BwOLB48WI899xzmDBhQreq7Ko9ZtxuN06ePIklS5bgmWee8dZ5Ebl2Qs6YqaysRE1NDY4dO4YFCxZg4cKFuO666xAeHi603SvRnZiRCuJVVFQgIyMD8+bNg81m69GdY5GRkV26nW+uJiIi0pi4uDjExcXBYDDAaDQiKSnJ544H8HXh4eEIDw9HXV0d6urqYLFYVJ24dNf5tYiAtkRDqS3vTF6IiFTEarViypQpsFqtSnelxzkcDuzcuRNGoxHffPMN+vTpo1iRMxGkCrQGgwF2ux0GgwFut9unqggD6qhFxOSFiKgTcp/Ya7Va8ec//1l4O0qw2+0ICQnBnj17cMstt/jc8QDnV6CdNGkSduzYgfr6ep9JziRqqEXE3UakKLfbjdzcXJ/acUC+QTr07vTp01i7di1Onz7NOO0hBoNB6S4IYbfbMWvWLKSlpaG4uBhpaWmw2+1Kd6vHqWG7O0deSFFS8hIdHY3AwEClu0PkJX2LbmhoQElJCb766iuMHDnS575Fy0maNmpqavLJaSNp8bHL5cLp06e965d8jcvlwtq1axEfH6/Y42PyQoqQ5oYbGhoAtP0x6PV6n/xDJ22SzqmpqKjAnj17MGbMGNhsNqW7pWmJiYnweDzIycnBjTfeiJiYGJ86w0l6X6urq2v3X195Xzt/t1FJSQlKS0thMBgUWbTL5IUUIX2r1el0MJlMyMrKQlJSks98AyPtKyws9J4QDADbt29Hc3Oz8BiVe42NnAoLC/Hdd98BAL777juYTCafGnnJz8/H7t27UV9fj6qqKnzxxRcYM2aM8LON5JKVlYWcnBzv5YyMDKSlpSmy/oXJCylCOp20oaEBmzdvRnp6OkJCQpTuFpGXFKPV1dU4ffo00tPTER0dLaw96Vu7y+VCXl4erFarogffiZCYmAh/f3/k5+dj/PjxiIyM9KmRF6CtKF5LS4tP1iJKT0/H+PHjUVZWhldeeQXPPvssBg0apEhfmLyQIqS54cDAQKSkpCAsLAx6vV7pbhF5nX+C7rRp0xAdHS00kZBGI5ubm1FWVobMzEykpKT4zKgE0Dbykpubi9jYWOzcuRP+/v4+NfIyePBgxMXF4ejRo1izZg1SU1MxePBgpbvVY6Q6L01NTQBY54V6MaPRiJSUFJ/8lkK+QTpBVzRpjY30rfbWW29FYmKi8HblZLfbYbVakZ2djcmTJ8NkMvnUyNK+ffuQm5uLyspKuFwufPPNNwCA1NRUhXvWM6TRwaamJlgsFjQ1NSm2pofJCxFphrQ7LTY21udG6qQ1NqdPn0ZhYSHWr18PnU7nM6MSQFsiGBYWhuTkZJ+bEgOAI0eOYNeuXWhoaEBjYyMOHjyIAQMG+EzyIq3pkZKYzMxMjBo1SpE1PazzQkSaoURdILlqWsTFxWHcuHGIi4vDmTNnvMcFiCZ3zQ5pJMvXEhcAsNlsSEpKwsCBAwEAAwcO9LkdajqdTukuAODICxFphLSYFWjbWt/S0iL8UD+p3by8PCQmJgptKzs7Gzk5OTh79iyamprwySefoLW1Fffcc4+wNt1uN6qqqnx2gbDcgoKCYDabvVMpISEhCAoKUrpbPSYpKQmDBw9GdXU1ampqMHPmTKGL2DvD5IWINMHhcCA/P9+7td7j8Qhd7Hn+7h+gLWESmSyFh4cjLi4Ofn5+8PPzQ1RUlPBD/c4vZ5+dne1Ti2eVIH24Hz9+HJs3b8a0adNw9dVXK92tHiP3IvbOMHkhIk2w2+3o37+/d2t9nz59ZNn9IxH94Z6Xl4fNmzejsbERfn5+OHjwIBITEzF58mQh7QEdF9CGhYUJa6s3kD7Y+/Xrh4kTJ6Jfv36yfLjLXRtIrkXsneGaFyLSBGmxp7S13mw2C32jls6pkZKHyZMnCz2nZu7cuXj33Xcxd+5ctLS0YO7cuZg7d66w9oC259RgMODMmTOylbKXe42N0+nE//7v/8LpdApvSzoPq6mpCVFRUd7dOKLb643nb3HkhYg0Q86t9ecPkcuxO0Yqu15XVwedToe6ujq4XC6hU0dutxs1NTU4cuQIampqhCeEF66xkbZKi2rT6XSiuLgYX3/9NWw2G8LDw2EymYTVJpF7tC4/Px979uxBY2MjSkpK8Pnnnyu2+0duTF6IiDoh1xD5H/7wB2zfvh0ejwdGoxGvv/46vv/+e7z//vvC2szPz8eOHTsAQJZtrw6HA7m5ud4ifKKL1GVlZWHjxo0wGo1YunQpPB6P0HL2UlVml8sl21Scx+MRev9qxeSFiEgFXnrpJZw6dQq7du3Cn/70J8yfPx/Tpk1Tuls9ym63IyQkBEuWLMGcOXMQFRUldKQnPT0dQ4cOxbJly/DrX//aO/IiijSK1NTUhOLiYuG74aQFwmfOnMGBAwdw6623on///sLaUxOueSEiUoGwsDDEx8cjICAATU1NCAgIEP6tPSkpCbfeeisA4NZbb0VSUpLQ9gCgqakJVVVV3hLzIpnNZkRFRQEAoqKiYLFYZCln39jYiPLycu+hnqIYjUaYzWb4+/vD6XTC399flnVLTqcTq1atkmUd0aVw5IWISAVWrlyJDRs24OzZs/B4PFi5ciVaWlrwi1/8QlibRqMRUVFRSEtLEz4KArRNU23btg1A25ROUFAQhg8fLnSqymQyYcqUKbIcAOl0OuFyuXDq1CmcPn0ap06d8p4HJLI9qTbQ2bNnvYvZRbZXUVGBnJwcDBkyBDabTZHzjZi8EBGpQH5+Pg4ePIjm5mb4+fnh5MmTyM/PF96u2WwWtgbkYvR6vfcgVjnWa5jNZtx3332yLPLOyspCTk4O3G43Tp48iaVLl2LKlCnCnl+pPaBt5G7ZsmVC1/Sc3x4AZGRkCG2vM0xeiIhUYObMmejfvz+KiorwxRdf4KabbsLMmTOV7laPSkpKgtVq9VZn7du3r09V9B03bhyGDBmCI0eO4KWXXsKMGTMwcuRIYe2lp6dj/PjxqKioQEZGBubNmyf0OAK52+sMkxciIhUYOXIkhg4dim+//RZffPEFbr75ZqEffEowGo3o27cv4uLi0LdvX0WmG0TasmULcnJyUF9fD51Oh88++wxut1vYyMSFU1KRkZFCn1O52+sMkxciIhUoKCjA7t27ceTIEeh0OuTm5iIxMdHnanb48sGMEydOxPXXX49jx45hwYIF+PnPf47rrrtOeLsmkwlpaWmyrOsBAIPBgEGDBsFgMMjS3sVwtxERkQrU1dXh3LlzqK+vBwDU19ejrq5OeLu+fqq0nCeRFxcXY8uWLThw4ACCg4Nx4MABFBcXC29XLlJF37q6OjQ0NKCurk6xir5MXoiIVODAgQPYsWOHd9HuwYMHceDAAaFtut1u2UvLy5ksSRV9c3NzUVVVBafTKbRd6SyqcePGISQkBOPGjUNiYqKw9pxOJ8rLy1FUVIScnBwUFRUJ3b6cn5+PFStW4PPPP/dW9JVjUfnFcNqIyIfIfUAb9ZyDBw/i0KFD3vonpaWlOHjwoNA2pQq7cpaWd7lcWLt2LeLj44XHqNwnkRcWFiIvL897Evl3330HnU4ntIKw3Lt/dDqdsPvuDiYvRD5C7nNjegu5EkJpt9GPP/6Ir776CqNHj/a53UZOpxOlpaUoKSlBaWkp3G630LOG5D6JPDExEVarFWVlZdi0aRNSU1OFjrzIvftHTRV9mbwQ+YjzD4XLzs4GAKHfMnsLaZojMTFR6AeftNvoq6++QmZmJsaOHSt8t1FSUhIiIiJk+yA6f6QgIyMDAISOFBiNRu8iVpPJhMDAQCHtSOQeeZF794/0ZcjtdiM2NhahoaGKfTli8kLkI+x2O6xWq/dAOGnkhS6P2+2G2+32fhC5XC6hI1nSbiNpqigvLw/Dhg0TOo1jNBoRFBTU7r8ipaenIyEhAQsWLMDChQsRHx8vyw6ZoKAg4W0A/z6YsaioCF9++SXGjx+Pq666Sni7cu82MpvNmDZtmqJb3Zm8EPkIo9GIsLAwJCcnIywsjInLFTp/JAtoG80SOZJVU1ODs2fPemuE1NfXo6amRkhb5zMYDIiOjpZl26vRaITBYIDL5YLBYBCeYEvJZ11dHVwuF1paWoQmoFJ7TU1NCAkJQVNTE9xut/C/RbmrJMt10npnFE9e1q5di5ycHBw/fhypqan47W9/6/23oqIiLF26FMePH4fVasWjjz6Ka6+9VsHeEqmbGt5UfIX0LdrlcnlHs0QelLh9+3Zs2LABDQ0N8PPzQ15eHmJjYzFx4kRhbbrdbu927Lq6OpjNZqEftLm5ucjJyUFraysyMzMRGhqKlJQUpKamCmnP4XBg9+7dqK6uxpdffgm9Xi80AZW7XH9vpvhW6fDwcMyePRuTJk1qd31zczMWLVqE1NRUrFy5EnfccQcWL16s6CmWRNR7SCf2yjWaFRsbi9jYWFgsFhgMBlgsFsTGxgprD2jbbSTnttfjx4+joKAAdXV1KCgowN69e3H8+HFh7dntdqSlpaGoqAhpaWmYNWsW7Ha7sPbS09Px4osvYt68eQCAefPmIT09XVh7Erlr9aiB4snL6NGjMWrUKISEhLS7ft++fWhsbMTtt98Og8GACRMmwGKxYPv27Qr1lIiUVllZiT/+8Y+orKyUrU25iqrdddddeO211/CLX/wCBoMBv/jFL2T5xt7S0iLLoYUAMHnyZMycORPV1dWYOXMmnnvuOUyePFlom0ajETabTZZpVLPZDIvFArfbjR07dsDtdgtdFyIVjZN2GVZVVflcrZ5LUXza6FJOnDgBm80GP79/51cDBgzAiRMnFOwVESmpuroaa9euxdSpUxEaGqp0d3qUtHPEYrFAr9fDYrEIXxCZlJSEkJAQfPfdd97FtCIVFxdj//79aGlpwf79++FyuZCcnIzw8HAh7UnTRgDw9ddfC582klRVVaGiogJVVVVC25F7XZZErh14nVFt8lJfX99h5bTJZLpoueyysjKUlZV5LwcEBKBfv37C+9gdOp0Oer1e6W6ojvSc8LnpiDHTXmVlJUpLSwHA+9++ffsK++CTuN1uHDx4EEOGDBH6Rn3q1ClUVFSgpKQEBoMBJSUlSEhIgNVq7fJ9dDdm9Ho9AgICEB4ejoCAAOj1eqEx169fPyQlJSE4ONhbMyQyMlJYm0OHDkVISIh3d5PVaoXRaBTWXmFhIUpKSlBQUAA/Pz8UFBRg4MCBwmq9DB06FFdffTVcLheysrKQnp6OsLCwbj2+7sSMtAOvoaEBOp0ODQ0NCAwMVCSBUW3yEhgY2CFRqauru+g+/WXLlmHhwoXey/Pnz8fixYuF97G7uPvj0i6cNqQ2ao8Zt9uNffv2YdiwYcL7mpGRgbVr1wIAFi1aBACYNm0aXnjhBaHtOp1O/Pjjj7jhhhuEjoQ888wzWLduHZqbm+F0OvHiiy9i6tSpePfdd7t1P915HXJzc7Fz505cddVV2LlzJwAgJSWlW+11R0ZGBtasWYOgoCAsW7YMer1e6Gvodru9U4zS+iWRu41+97vfeZ9HnU6HJUuWYNOmTdiwYYOQ9iROpxMmkwmxsbGXFaNdfT5yc3ORm5sLoG0wYfPmzUhJSREaM5ei2uQlPj4eq1evRmtrq3fq6NixY5gyZUqH2z7yyCOYMWOG93JAQIDw4bruMplM3noR9G96vR4hISGora2VZc5dS7QQM5WVlVi+fDkef/xx4SMgc+bMwXXXXYdFixbh+eefR79+/dC3b19hf+vSt8yqqioUFBTg6NGjsFgswj740tLSEBQUhEOHDiE7OxtjxoxBWlpatx5fd2MmNjbWWzo/LS0NYWFhQt8758yZg0GDBuHZZ5/FU089hauvvlroa/jDDz9g586dCA8Px7/+9S8YDAaMGDECN9xwg5D2Xn31VZSUlCA/Px+vvvoqfve732Hq1KnCP4+cTic8Hg+qqqq8x0t0VXdiJjY2FtHR0R1Genry8XV1R5/iyUtLSwtaWlrQ2tqK1tZWuN1u+Pn5YdiwYTAYDPj8888xffp0bN++HadOnbrolrqYmBjExMR4L1dUVKjug9Dj8aiuT2oixQH9m9pjRvpWW1xcjMrKSuj1eqHfakNDQzFkyBBMmzYNQ4YM8a55EfUc7d+/H3l5eWhsbERxcTEyMzNx4403CltP4OfnB4PB4P3waWpqgp+fX7ceX3djRvryIK190ev1QmMuMDAQNpsNQ4YMgc1mQ0REBIxGo7A2pc8V6f89Hg9aW1uFtZeQkICEhARERUXhs88+w8SJE5GQkCD877i+vh4FBQWYMGFCt6sIdydm9Ho9AgMDodfrZYuZS1E8efnkk0/w8ccfey9v27YNaWlpeOKJJ/D8888jIyMDK1asgMViwfz58xEcHKxgb4lIIh3qBwCZmZkICAjA8OHDhVaEDQ8PxwsvvCDL7hjpnJqKigrs3r0bo0ePFnpuzLfffouNGzeitrYWQNtizG+//VZonRdA3tpADocDubm5GDx4MLZu3Qp/f3+hC0yTkpLQr18/fPLJJ7j99tsREhIiy1RsVFQUbr75ZkRFRQltRxodrKmpQUlJCWpqaoTX6gHUUU9K8eRlzpw5mDNnzkX/LSEhAf/93/8tc4+I6HJ4PB6lu9CjpHNqmpubERsbi507d6K5uVnYm3Z5eTnOnj2LxsZGAG1TAeXl5ULaOp+cJ5Hb7XaEhIRgyZIlmDNnDqKiooS2aTQaYbFYMGvWLO8uLpHkTiby8/OxZ88eb8xkZmbKcjK4GiievBBRz3E6nd65aDm22VqtVtTU1GDmzJno27ev8A8/t9uN3NxcxMbGCv8gkrvCbmRkJPr27YuzZ88CAPz9/REZGSmsPYnL5cLatWsRHx8vy6iEwWBAbGysLMcRAG0JTEpKiiyjdVIyUVlZiYMHD+LTTz/FxIkThSYTvvaloauYvBD5CLfbjdLSUvzf//0fkpOT0a9fP6FrUIxGI6KjozFt2jRER0fLkrhUVFRg9erVuPvuu71TACIfn/QjR4Xd6OhoREdHexcJBwcHIzo6Wlh7QFuyW1paipKSEpSWlsJgMAhNeh0OB3bu3Iny8nJ89dVX6NOnj0+dfC6dT1VWVuZ9TkWeTyVtN6+urvZ+iRAdM2rB5IXIR+Tm5mLTpk2oqqrC6tWrYTabhZ4bA8g7952fn49t27Zh9+7d8PPzQ1BQkPA1NnI6fPgwjh49ivr6egBtH4SHDx8W2mZWVhays7NRWlqK119/HZMnTxZa1TcxMRF1dXX46KOPcM8992DgwIGynYQsh++//x7r1q3zLrrOzc2F1WoVtm7p/ARbri8RaqH48QBE1DOOHz+OQ4cOAQAOHTok/NwYuTU1NaGhoQGVlZVoaGhAQ0NDt7eFdofcpdftdjsGDRoEi8UCALBYLELP4QGAcePGYfr06SgvL8f06dMxbtw4oe3t27cPX3/9NWpqavD1119j1apV2Ldvn9A25TRmzBjv8xgUFIRx48ZhzJgxwtuV6wgLNeHIC5GPmD59OgYNGoTnn38ejz32GOLj44V/q5VzsafBYPC2YTQaERAQIHTdhNyl16XjAc6cOQOgrciZ6HVLW7ZsQVZWFoKCgrBmzRq43W6hIy9HjhyBw+FAY2MjHA4HioqKEBoaKnR0UE6jRo3CiBEj4HA4sG7dOsyaNUuWkUk5/w7VgskLkY8wm82Ij4/HNddcg/j4eO83eFGktRl5eXmwWq0wmUxC16AkJSUhLCwMe/bswYwZM4QWjAPkX7AbEhKCqKgonDp1CkBbTRTRlael84xefPFFPProo8JHemw2GwYMGIC8vDwMGDAAFotF6PZzuUnxb7VaMWTIEO9xBKJIu5tcLpf371D02iy1YPJC5EPCwsLwn//5n0I/ZCXnj0xkZ2cDgNCRCaPRiKioKAwfPhxRUVHCRyUuTMSk5EyUDz/8EHv37vUWVXM4HPjwww9xxx13CGvTaDQiKCgIzc3NCAoKEv6hFxQUhL59+3p/goODERQUJLRNJVgsFjz99NPCv0AodTCjGjB5IfIhci6gtdvtsFqt3lEJ0R/uQFsCMWvWLFkXecq1nuCRRx7Bd999hyNHjiAnJwdjx47F/fffL7RNh8OBbdu2ISoqCps2bRJaxwZoGz0LDAzEt99+ixkzZsBms/nkKIFcf4dyjw6qCRfsEgkkHR0vcqGnUoxGozeJMJlMslX2TElJkfUDT67k5ZZbbsGzzz6L8ePHw+PxYPz48bjllluEtmm32/Gzn/0Mzc3N+NnPfiZ82kjiiwnL+eT6uzcajTCbze3+DuV4btXwvsbkhUgAuXeqKKU37nIQpaSkBLt370ZxcTEAoLi4GCUlJULblKrBVlVVoaamRniM5ubm4ssvv4Tb7caXX36JDz74wHtKsS+Q/u5PnjyJ9957DydPnpTt7767ZxpdDjW9r3HaiEiA3jIXrYYzTnzFU089hd27d6O1tRU6nQ7/+7//i4KCAnz22WfC2pTqvPj7+2PZsmXC67xcuJ1fr9cjIiJC6G4jOasy5+fnY/fu3XA6nd7Xbvz48bLUIpLqA4mkpvc1Ji9EAvTmuWi6PH/4wx9w+PBh5OTkYPXq1Zg6dSp+9atfCW1z4sSJsFgsWLx4MX7+859j6NChQtuTtvMvWLBA1u38ubm5iI6OFj460dTU5N0BJLUtuhaRtNsIaDvqQeSOPzW9rzF5IRJA7p0qJI5cNTTWrl2Lb775BmVlZQCAnJwcxMbGYvjw4cLaLC4uxoEDBxAWFoYDBw4gNDQU4eHhwtozm80ICgrCmTNnEBQUJMt2/vM/2FtaWoR+uJ9fi6iystJ7nShyj4So6X2NyQuRQFwTol1y19AoLi5GRUUF6urqAAB1dXXe9S+i2O12REREoLKyEtOnT5flLKWzZ8/C4/Hg7NmzKC8v9y72FsHhcCA/Px8mkwlZWVnweDxCP9wHDhyIiIgIHDx4EMuXL8ewYcMwcOBAIW0Byo2EqOF9jckLkUBcE6Jdcn+r3bhxI6qrq72X3W43Nm7cKKQtidFoRN++fREXFyfLqeBffvklvv76awQGBuIf//gHjEYj0tLScM899whpz263o3///ti8eTPS09PRp08foY9xw4YNyMnJQVNTE+Lj4/HZZ5+hrq5O2DoiuQ8PPb9dpd/XuNtIxdSwHY2uDF/DniWtX5Dj+bTb7Zg1axbGjRuH3NxcjBs3TuhW4nfffRcLFixASkoKACAlJQXvvvuusPYkcn6LTkhI8B7OmJiYiOTkZCQkJAhrz2g0IiwsDCkpKQgLCxO+nT89PR0vvvgi/t//+3+Ij4/H//t//w/p6enC2lOKGt7XmLyomBoChK6M3K+hr8eMnMmLVEPDYDCgvLy83XoGEcLCwhASEoKIiAgAQEREhM9NAQwbNgy33HILgoODccstt+Cuu+7CsGHDhLYpZ20gs9nsPfIgLS0NNptNeCVooG09z9q1a73re0RTw/sMkxeVunChmdPp9NkPJF8k1UM4/zWU4/Xz5WTJ7Xbj9OnT2LFjB06fPi38b8LpdKK8vBw1NTUICwtDTU0NnE6nsPb0ej1CQ0MRGRkJAIiMjBS+tVduhYWF2LVrF4KCgrBr1y5kZ2ejsLBQ6W71OLPZjLvuukt44iK9z1RXV6OkpATV1dVC/yaUel+7GK55USm5z42hnqVEPYQLE16g4+6Anm5PzoMZpVL2Bw4cQGZmJsxms9DnNCsrCzk5Od7LGRkZSEtLE7Z+4YknnsDBgwfh8XgAAJ9++ikOHDiA9evXC2lPCXFxcUhOTsbatWuRnJyMuLg4hIaGKt2tHifXDjWproyUQGRmZmLUqFHC6sqwzgv9JCXOjaGeo8QuAIfDgdzcXJSVlSEzMxP+/v5C31jkTrDj4uJw7bXX4tNPP8W1116L+Ph4oR986enpGD9+PE6cOIEFCxZg4cKFQte83H///diyZQsKCgpw6NAhJCYmCj/bSG4bNmzA+vXrERYWhuXLl8PPz09oQig3JU551ul0wu77QqzzQj9JWmgm5wpy6jlK7AKw2+0ICQnBkiVLMGfOHERFRQltU+4Ee8OGDd4k6aOPPoLBYBD6wWc2m2E2m72jWBEREUKnAfbs2YMffvgBtbW1AICTJ09iz549eOCBB4S1Kbf09HQMGTIEGRkZmDdvHiIjI2U9ZFM0uUcmkpKSMHjwYFRXV6OmpgYzZ84Uut1dqd1NF8PkRcXUsB2Nrozcr6HBYEBsbKzQwlgS6U2suLjYu7hVpIkTJyImJgYvv/wyHnzwQfTv31/oyIs0t19fXw+bzYb6+no4nU5hj3PcuHHw8/PD7t27ceDAAdhsNowbN05IW+dzOp3IyspCenq68NfQbDZ7k8CIiAjhRerkJo1MlJaW4o9//CNeeOEF4buppJ9p06YhOjpatoMZDx8+jMTERMWSFy7YJfIRDocDOTk5iIuL85aYdzgcQtuU1mdI/xWpuLgY+/btg8Viwb59+7BlyxahRdyysrLw4osvIiMjA0DbmpesrCxh7X3zzTfIzs5GUVERAKCoqAjffPONsPYkLpcLOTk5su1U0el0cDqdsk53yEVK4l0uF/bu3est1y9Hu3IWjZM7Zi6GIy/kJdcis95EzudU7mkc6dyWuLg4NDU1wel0Cj9XpW/fvjh06BCmTp2KiIgI4TU7xo8fj4qKCu80h81mE9Zea2ur9+f8yyI5nU6cOHECRUVFOHHihNBqt1J7FRUVAOD9r+g25TyY8dSpUzh79ix+/PFHNDU14ccff0RMTAysVqvQduV6n5FGI89/DUW/fpfC5IW8pG2vSg4F+ho5n1O510kpsSPOaDTCZrPJVrPDbDbDZDLJUrNj+PDhqKqqQnFxMQoLCxETEyP0XCOgbXRpzZo1OHr0KN58803cdtttQhfPnr+DSxrREr1gV86DGV977TXk5OSgtbUVHo8HL7/8Mn744Qf8+c9/FtquVOclPj5e6N+G9Pq1traipqYGb731Fn72s58psuCayQsBkH+brVLk+oYi92mvEjnX2Ejn4nz44Yd44IEHhJeXl86psdlsWL9+vfBzaiRSzQ7R4uPjcc0118DtdqOwsBAWiwXx8fFC2xw1ahTOnj2LTZs2YezYsRg1apTQ9uResCv3wYy/+c1vMHv2bOzduxeLFi3C008/jSlTpghpC/j3+8z5dV5EjrhKo5GHDh3Ck08+ib/85S8YMWKEkLZ+CpMXAtB76srINRKipnoIohiNRu8HjxxDx3KfUyO3devW4dtvv/UezHjw4EGsW7cOt956q7A2P/roI2RmZkKn0+Gjjz5CZWUl5s+fL6w9uRfsyn0wY0lJCXJzc3HixAnodDr8+OOPGDp0KOLi4oS0J3edF+nvLTAwEDqdDoGBgdxtRMry9boyco+EqKkegijScxoXF+etvCnyOb3wnBq5qs9WVlbi/fffx0MPPYTw8HBh7YwfPx56vR75+fmorq6GzWbD+PHjhbUHADExMYiMjERJSQkiIyMRExMjtD23243a2loUFxejtrbWe9aQyL9DORPeI0eOYNeuXXA6nfD398fRo0dx5MgRpKamCmtTzoXPubm52LVrF1wuF8LDw7FhwwY0NzcLfXyXwuSFAPh+XRm5R0LUVA9BFKXWvKSkpKCqqgotLS3C2gHakpaamhoUFxdj3bp1uOGGGzB06FBhCcwXX3yBHTt2oL6+HgBw+PBhfPHFF7jjjjuEtAcAt912G6Kjo/H73/8ev/zlL4V/COXm5mLTpk2oqqrC6tWrYTabkZKSIqxduRPefv364ZprrkFpaSl27dqFmJgY9OvXT1h7ctd5OX78OPLz89Hc3AwAOHToEGw2G5MXUpYv15VRaiRE7ufUl3c3AfLuHHn//fexbt067+XFixdj0qRJeOqpp4S0d/DgQVRWVnov19fX4+DBg0LakhQXF6OoqAhDhgxBUVERYmNjhY4uHT9+HIcOHQLQ9sGn1+sREREh9MNPzoS3tLQUhw4dQk1NDVpaWlBSUoLS0lJh7cld52X69OlIS0vD0aNHvQmv6IM1L4XJC/UKvWEkRDq4cO3atYiIiPAuoBU9jSPX8ylNOeTm5sJkMnmnAES1+9BDD2HWrFkoLi7G4sWL8dxzz2Ho0KFC2gKAuXPnYsuWLThw4ABKSkoQGxuLuXPnCmsP+HdV5oKCAqSnp6N///5C25s+fToGDRqE559/Ho899hji4+N9qsKuzWZDUlISSkpKsGfPHgwYMEDo9nqJXF+SpB14dXV1CAoKgtVqVWSbNMDkhXoZXx5dcjgc2LlzJ0pKSvDVV1+hT58+PrVIWO7Fl+Hh4e1GIeLi4oSOSmzatAnff/99u+MBNm3ahEceeURYmxK51k1IC3WDgoJ8ssJuUFAQgoODERwcDH9/fwQHByMoKEh4u3LXeamrq4PNZkNdXZ3QqtOdYfJC5CMSExPh7++PPXv2YMyYMbKcG1NZWYl//OMfmD9/vvBCXHa7HdHR0fj444/xH//xHwgODpZl9Cw0NBSTJk0SfvpxbGwsCgsL0dDQgMbGRhiNRsTGxgptMz8/Hzt27IDb7Ra+UwVoi5eSkhLU1dWhpKQEOp0OoaGhQpNCOacaBw4ciIiICOzbtw+ffPIJUlJSMHDgQGHtyX0QpNwnrXfGJ5MXo9GIgIAApbvRjpSFU3vSNz6TySRLiXkt6W7M/Pjjj9i5cydqa2uxc+dOmEwmjBgxQlgdBqfT6a0mevbsWQQHB3uHlUVwu92oqalBUVGR97mRo3ZOcHAwXnzxRaFtAMCaNWvarXlpbGzEmjVr8Ne//rXL99HdmAkKCkJAQAD8/f0REBDgHTkQZenSpfjqq6/g5+eHl19+GQAwdepUoduzXS4XcnNzMWDAAOHJ/Jdffol169ahsbER0dHRWLt2LXQ6HebMmSOkvd27d2P37t0AgICAAHz77bfd/pvvTszMnj0bU6dOxenTp/GXv/wFTz75JAYOHMiRl54iZaNqEhwcjHPnzindDdXR6/UwGo3eAlKiaekIhO7GjM1mg9vtxr/+9S+MHDnSW4lWVNytXLkS69atQ2trK/77v/8b/v7+SEtLwz333COkvby8POzevRs2mw1r166FXq/3qWmxxx57DFu3bsXBgwdx+vRpREdH45e//GW3Xr/uxsygQYPg7++PDRs24KabbkJCQoLQ96l7770XycnJ3jVEcXFxCA0NFdamtE4KAE6fPi18ndSECRNw4403oqysDK+88goee+wxDBo0SNjjs9lsiImJ6bARQWTMmEwmREVFYfz48YiKioLH4+nRx9fVgQefTF6ILiT38Or57cqZLBkMBoSEhMhyqnRCQoJ3W+g111wDs9ks9ATd86eNpkyZItu0kVynLh87dgyHDx9GTU0NAKCmpgbHjh0T1h7QNkptMBhQW1sLg8Eg/PkMDw+H1WpFXV0drFYrBgwYILQ9uddJSSOPTU1NAIDIyEihMXNhIiZXfS65qk53hskL9Qpy13lRIlnKz8/Htm3bALTNTQcFBWH48OHC1jCkpKQgIiICe/fuxaxZs9CvXz+hj0/6oC0qKoLBYBA+VC0tTiwrK8P//d//4aqrrsKgQYOEtTt8+HDvFEdxcTGio6OFn2104UGJUVFRwg9JBODdgSPHYZ5yFqmT/u6bmppgsVjQ1NQEt9st27Egah9N7klMXqhXkLvOixLHA0gnO588eRJOpxN6vd77DVAEo9GIfv364Y477kC/fv2EJxPSB4M0PSb6g+/LL79ETk4O3G43Tp48iddffx1TpkwRNi32wQcfoKCgwFsArLi4GB988AEeeOABIe0BbUluVlYWzp07h7/+9a9IT08X+o3a4XAgNzcXdrsdW7duhb+/v/BikXIWqTv/7z4lJQVbtmyRZWrTl3dRXgqTF+oV5B5eVcPxAHIsgJZz+Pj8gxk3bNggfAogISEBSUlJqKqqwv79+5GYmCh0WuzBBx/E1q1bsW/fPhQWFiIxMREPPvigsPYAYNy4cTAajXjppZdw2223YeTIkULbk+rKLFmyBHPmzEFUVJQsoxJyFalTw999b+GndAeI5CTX8KrRaITZbG53cKHoNqWplP79+8NsNqNPnz6yrH2Ri91uR3p6OoC2021nzZoFu90urL1hw4Zh9uzZGDNmDNxuN8aMGSO0muiOHTvw3Xff4eTJkwDa6rzs2LFDWHsAsGXLFnz22WfQ6XT47LPPsGXLFqHtAW0jhFVVVUJHBZUi/d0bDAYUFxfLso6ot+LIC/Uqcg+vyjkXnZSUhIiICBQUFGDGjBmyfKuVk9xTAAUFBdi9ezfOnDkDoC25CA0NFbaGyM/PD35+fh2uE2nixIkIDw/Hiy++iPvvv1/4Ghu512XJTZralE4Gr6ur8x4+ST2LIy/d4Ha7kZeXp7pt2KReciYvRqMRoaGhiI2NRWhoqE++aUpTAHI8LmmxZV1dHZqbm1FXVyd0tCA3Nxfl5eVoaGgAADQ0NCA3N1dYe0DbuprDhw+jf//+OHz4MIqLi4W2B7SVR5CSTzmmNqUidXK8bzscDqxevdp7Jta6devgcDiEt9sbceSli9xuN6qqqmTdZkvUXUajEVdffbVssSnXNuLz28vMzMRNN92EwMBA4e0BbcXinE4nGhsbhbazYMEC5OXlYfXq1Th27BgGDBiABQsWCG1TWoPicDgwZcoU4WcbSaODP/74I2699Vbho4Nyn4eVmJgIq9WKiooKZGRkYObMmbKcbdQbceSlixwOB7KzswG07RxhNk1q5HQ6sWXLFjidTlnac7lcyMnJgcvlkq29r7/+Wpb2mpqa0NjYiMbGRrS2tqKxsVHoyMvSpUuRkZHhre1y7NgxLF26VFh7wL+3n58+fVqW9RlGoxFBQUFwu90ICgoSPjrocDiQlZUFoG2aavXq1ULfuwsLC5GdnY3t27cDALZv347CwkJh7fVmTF66yG63Y/LkyQCAyZMnC10oSL5DzqlGt9uNs2fP4tChQzh79iycTqfQdi+sEVJeXi40aXI6nd71J2fOnBHeXmFhIfbu3dsumRD5QSQdWOjv3zYg7u/vj4iICGHtAW3PaVlZGc6dO4eysjLhSa80DWcwGLyH+omMUbkXedvtdsyaNQujR49GSUkJRo8ezc8KQTht1EXSYsHk5GROGdFPUqJIXW5uLjZt2gSgrUaJ2WxGSkoKUlNThbR3/iFtGRkZACD0kLasrCxs3LgRRqMRS5cuhcfjkeVQOL1eD39/f+ELhP38/KDT6aDX69Hc3Ay9Xi98wa50Fk9zczPeeecdTJo0SVgdG+DfB0E2NTUhMzMTAQEBQhfsyr3I2+l0oqamBuXl5aiurkZ5eTliYmKEHjzZWzF56YbeWAjI18hVrl+JInXHjx/HoUOHAACHDh2CXq9HRESEsOQlPT0dQ4YMQUZGBubNmyf8FOv09HQMHToUy5Ytw69//WuEh4cLbW/w4MFwuVyorKzEyZMnkZKSgsGDBwtrT6rMKh1WqtPphI/YXXjEg8g6Nhcjx4JdOeu8vP/++97FukFBQXj11VcxadIkPPXUU0Lb7Y2YvFCvIk3jJCYm+lyRuunTp8Nut+OVV17Bf/3XfyEmJkboh7vZbIbNZkNaWhpsNpvwBbvS2UlTpkxBQkKC8AW7KSkpGDZsGM6cOYOKigrcf//9Qhe0LlmyBKdOncLatWvxzjvv4OGHH8bcuXOFtQd0POJBdPKSlJQEq9WKmpoazJw5E3379vWpUeyHHnoIs2bNQnFxsffwyaFDhyrdLZ/E5IV6hfOncYC2hZ8idx0ocWCa2WzGoEGDcMcddwg9g+fCNuU8oM1sNuO+++6T5Vv0+a/h9ddfL3xnjMViQVhYGE6cOAF/f38kJSXBYrEIaw9oe4yRkZEYNmwYIiMjZVmw27dvX8TFxaFv376yxKicwsPD200RxcXFccpIECYv1CsoMY0DyH9gmhpOe/U1cr2G+fn53qJ4FosFBQUFsNlswgu4mc1mTJs2TbZEojccIhgaGopJkyYhNDRU6a74LCYv1CsodeYI10lpn5yvoU6nQ2hoKFJTU2X74FOq6rQvCw8P5zoXwZi8UK8gTQFIb5zcMUZqk5SUhMGDB6O6utq7JiQ6OlrpbhGpEpMX6lV6w7c+0qbzE+xp06YhOjqaCTbRJTB5ISJSESbYRD+NFXaJiHoxHjhLWsTkhYiolzr/wNmqqiomMKQZTF6IiFREzpEQHjhLWsXkhYhIBdxuN5xOp6wjITxwlrSKC3aJiFRAiUKKRqPRe4SEHFWgiXoKkxciIhVQupAiExfSEiYvREQqoMR5WFK73JpNWsM1L0REKsKREKKfxuSFiIiINIXJCxGRCiix24hIq7jmhYhIBZTYbUSkVUxeiIhUQKndRkRaxOSFiEgFlNptRKRFmkhenE4n3n77bezevRuBgYGYPXs2pk6dqnS3iIh6HHcbEf00TSQvy5YtQ0tLC95//32UlZXhD3/4A2JjY5GUlKR014iIehTrrhD9NNXvNmpoaMC2bdtw3333ISgoCImJiUhLS8P69euV7hoREREpQPXJy8mTJwEA8fHx3usGDhyIoqIipbpEREREClL9tFFDQwMCAwPbXWcymVBfX++9XFZWhrKyMu/lgIAA9OvXT7Y+doVOp4Ner1e6G6ojPSd8bjpizHTEeOkcY6YjxkzntBozqk9e+vTp0y5RAQCXy9UuoVm2bBkWLlzovTx//nwsXrxYtj52FRfgXVpISIjSXVAlxszFMV4ujTFzcYyZS9NizKg+eenfvz8AoLi4GHFxcQCAY8eOwWazeW/zyCOPYMaMGd7LAQEBqKqqkrejP8FkMsHlcindDdXR6/UICQlBbW0tWlpalO6OqjBmOmK8dI4x0xFjpnNqi5mu1jZSffLSp08fjBkzBsuXL8dvfvMblJeXY8OGDXjmmWe8t4mJiUFMTIz3ckVFheqC1OPxqK5PatLS0sLn5wKMmUtjvFwcY+bSGDMXp9WYUX3yArSNrGRkZODBBx9EUFAQ7r33Xm4lJCIi6qU0kbyYzWY8++yzSneDiIiIVED1W6WJiIiIzsfkhYiIiDSFyQsRERFpCpMXIiIi0hSdx+PxKN0J6r3KysqwbNkyPPLII+22uxNdDOOFuosx45s48kKKKisrw8KFC9sd70B0KYwX6i7GjG9i8kJERESawuSFiIiINIXJCykqJiYGCxYs4Fw0dQnjhbqLMeObuGCXiIiINIUjL0RERKQpTF6IiIhIUzRxMCP5njfeeAObN2+Gv/+/Q/Dtt99GVFSUgr0iNVm7di1ycnJw/PhxpKam4re//a3334qKirB06VIcP34cVqsVjz76KK699loFe0tq0FnMzJ07F9XV1fDza/vOHhUVhbfffluprtIVYvJCirntttvwwAMPKN0NUqnw8HDMnj0be/fuxblz57zXNzc3Y9GiRZgyZQpefvllbN26FYsXL8bf//53mM1mBXtMSrtUzEh+//vfIyUlRYGeUU/jtBERqdLo0aMxatQohISEtLt+3759aGxsxO233w6DwYAJEybAYrFg+/btCvWU1OJSMUO+hyMvpJjs7GxkZ2cjMjIS06dPxy233KJ0l0gDTpw4AZvN5h3+B4ABAwbgxIkTCvaKtOCNN96Ax+NBfHw87rvvPgwZMkTpLtFlYvJCipg+fToefvhhmEwmHDx4EK+88gpMJhNGjx6tdNdI5err62EymdpdZzKZUFdXp1CPSAuefPJJJCYmAgA2bNiAhQsXYunSpYiOjla4Z3Q5OG1EikhMTERISAj0ej2GDRuGW2+9Fdu2bVO6W6QBgYGBHRKVuro6BAYGKtQj0oIhQ4YgICAAAQEBmDp1KgYOHIjc3Fylu0WXickLqYJOpwPrJVJXxMfHo6ioCK2trd7rjh07hvj4eAV7RVrj5+fH9xwNY/JCiti6dSvq6urQ2tqKgwcPIjMzE6NGjVK6W6QiLS0tcLvdaG1tRWtrK9xuN5qbmzFs2DAYDAZ8/vnnaGpqwqZNm3Dq1CmkpqYq3WVS2KVi5syZMzhw4ACamprQ1NSE7Oxs/Pjjjxg+fLjSXabLxOMBSBHPPvus99tzZGQkpk2bhvT0dKW7RSqyYsUKfPzxx+2uS0tLwxNPPIHjx48jIyMDx48fh8ViwaOPPoqhQ4cq1FNSi0vFzKxZs/Daa6+hrKwM/v7+iIuLw3333Ydhw4Yp1FO6UkxeiIiISFM4bURERESawuSFiIiINIXJCxEREWkKkxciIiLSFCYvREREpClMXoiIiEhTmLwQERGRpjB5ISIiIk1h8kJERESawuSFqBd48cUXodPpLvqzaNEiAEBCQoL3OqPRiOjoaKSlpeGNN96Ay+Vqd3/ffvstdDoddu3a1aGtXbt2QafT4dtvv213vcvlwuLFi5GcnIygoCAEBQUhJSUFb7/9Nurr6zvcz1tvvQWdTocHH3ywQ7s/9XP8+HF88MEH0Ol0qKioaHe/NTU1+P3vf4+rr74aAQEBiIyMxKxZsy56wvDNN98MnU6HF1544aL/Nm3atEs+50Qkjr/SHSAieQQGBiInJ6fD9XFxcd7/v/POO/HUU0+hpaUFp0+fxsaNG/Hcc8/hnXfeQU5ODmJjYy+r7bNnz2LixIk4evQonnjiCYwbNw5+fn7YuXMn/vSnP6GiogILFixo9zsrVqwAAKxevRp//etfERgYiBEjRuC7777z3mb37t14/PHH8f7772Pw4MHe62NiYi7aj9OnT+Omm25CeXk55s+fjxtuuAGnTp3C0qVLkZqaik8//RQzZ87s8HtvvfUWnnzySYSFhV3W4yeinsXkhaiX8PPz+8mTuy0WS7vb3H777XjooYcwduxYPPTQQ/jmm28uq+3HH38chw8fxnfffYfk5GTv9RMnTsSvf/3rDqMehYWF2LlzJyZPnozs7Gx8+eWXmD17NkJCQtr1r6GhAQAwdOhQXH/99T/Zj8ceewyFhYXIzc1tdyjfrFmzkJaWhgceeACHDx+GxWLx/tvIkSOxf/9+vPHGG1i4cOFlPX4i6lmcNiKiTg0fPhyPP/441q9fj0OHDnX790+cOIFVq1bhV7/6VbvERRIcHIybb7653XXLly+HTqfDsmXLYLVavaMwV+LEiRNYvXr1RU8TNhgMWLRoEWpra/E///M/7f4tMjISjz32GN58801UV1dfcT+I6MoxeSHqRZqbmzv8dOVg+UmTJgEAduzY0e02N2/ejNbWVkydOrXLv7Ny5UqMGzcONpsNd999N7KyslBVVdXtts+3adMmeDyei04LAcBNN92Evn37dlirAwBPP/00mpqa8MYbb1xRH4ioZzB5IeolXC4XDAZDh59Nmzb95O9K62JOnTrV7XZPnjzZ7j5+Sm5uLgoKCjBnzhwAwL333gu3241Vq1Z1u+2L9cNms13yNjabDSUlJR2uj46OxqOPPoo333wTNTU1V9QPIrpyTF6IeonAwED88MMPHX5SUlJ+8nel0RmdTtftdrv7u8uXL4fBYMBdd90FALjhhhtw1VVX9cjUUVdcqp+//e1v0djYiDfffFOWfhDRpTF5Ieol/Pz8cP3113f4CQ4O/snflUYjrFYrAMDfv22tf0tLS4fbStcZDAYA8O5QOnHixE+209raik8++QQ333wz/Pz8UF1djerqasycORObN29GcXFxFx7pxfXv3x8AUFRUdMnbFBUVeW93IYvFgkceeQRvvPEGamtrL7sfRHTlmLwQ0U/Kzs4GAKSmpgIAoqKiAFx8GqmsrAxA21QL0LaWxM/PD1lZWT/ZzsaNG1FaWopvvvkGYWFh3p8///nP8Hg8WLly5WU/hptuugk6nQ5ffPHFRf99y5YtqK6u7rB4+HzPPPMM6uvr8dZbb112P4joyjF5IaJO7d27F++88w4mT56Mq666CgBw1VVXISYmBmvWrOlw+zVr1iAmJgaDBg0C0LbW5a677sLf/vY37Nu3r8PtnU6nd93N8uXLYTKZsH79emzcuLHdz4gRI65o6ig+Ph6zZs3CP//5T+zfv7/dvzU3N+P5559HSEgI5s6de8n7iImJwS9/+Uu8/vrrHH0hUhDrvBD1Eq2trRfdLRQVFYXExEQAQHl5OXbs2IHW1lacOXMGOTk5+J//+R/ExcXhvffe8/6On58fFi5ciF/+8pfQ6/W47bbbALQlLh988AHefffddmtH3n77bTgcDowbNw7/9V//hfHjxwNoW5y7dOlSzJ07F6NGjcLq1atxxx13YOLEiR36+Z//+Z94/PHHceDAAVx77bWX9Ry888472L9/P26++Wb8/ve/x/XXX4/Tp0/jrbfews6dO/Hpp5+2q/FyMb/73e+wbNky7NmzB/369busfhDRFfIQkc9bsGCBB8BFfx544AGPx+Px2Gw273X+/v6eyMhIz0033eR54403PE6n86L3u3LlSs8NN9zgCQwM9AQGBnquv/56z4oVKy56W6fT6fnjH//oGTp0qPf2I0aM8Lz66qsep9Pp+de//uUB4Fm/fv1Ff//s2bMeo9Ho+f3vf++9buPGjR4Anh9++KHD7d9//30PAM+ZM2faXV9dXe353e9+5xk0aJDHaDR6IiIiPDNnzvTs2rWrw33cdNNNnltvvbXD9fPmzfMAuOi/EZF4Oo+nC0UeiIiIiFSCa16IiIhIU5i8EBERkaYweSEiIiJNYfJCREREmsLkhYiIiDSFyQsRERFpCpMXIiIi0hQmL0RERKQpTF6IiIhIU5i8EBERkaYweSEiIiJN+f+FZ90yjC6i8gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8781209519300)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    p9.ggplot(wages, p9.aes(\"EDUCATION\", \"WAGE\"))\n",
    "        + p9.geom_point(shape='+', alpha=0.4)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "honest-softball",
   "metadata": {},
   "source": [
    "Experimenting with different models (SVM with linear and radial basis function (rbf) kernels, and a RandomForestRegressor)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "august-channel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model = svm.SVR(kernel='linear')\n",
    "# model = svm.SVR(kernel='rbf', C=10)\n",
    "model = ensemble.RandomForestRegressor()\n",
    "model.fit(X=wages.loc[:,['EXPERIENCE']], y=wages['WAGE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "religious-drunk",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.linspace(0,20,100).reshape(-1,1)\n",
    "y_hat = model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "outside-california",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8781208428894)>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = pd.DataFrame({'X': X.reshape(-1), 'y_hat': y_hat}) \n",
    "(\n",
    "    p9.ggplot(wages, p9.aes(\"EDUCATION\", \"WAGE\"))\n",
    "        + p9.geom_point(shape='+', alpha=0.4)\n",
    "        + p9.geom_line(p9.aes(x='X', y='y_hat'), data=predictions, color='red')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nutritional-cocktail",
   "metadata": {},
   "source": [
    "The fundamental question in machine learning is, 'how wiggly do I make my line?'. One way to assess this is to use a 'holdout' set. We fit the model on some proportion of the available data, and 'hold out' the rest to evaluate how well the model generalises to data it's never previously seen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "republican-eclipse",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "wages_train, wages_test = train_test_split(wages, test_size=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "subtle-asset",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X=wages_train.loc[:,['EXPERIENCE']], y=wages_train['WAGE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cultural-brooks",
   "metadata": {},
   "source": [
    "Let's plot the predictions based on the model derived from the training set against the predictions from the full dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "persistent-contract",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ikkqlgpeXV0+LJSIiIroshyeynDt3DmPHjsXHH39sv89sNsPf3x8BAQH2H51Oh/z8fFEqS0RERORw8PLPf/4TlZWVuOuuu9r87q9//Ss++ugj/Pvf/8bIkSOxZs0aQStJREREZOPwsNH69evx0EMPQals+yfz5s1rtbzrH//4B1577TVhakhERETUgsM9L7m5uW3Wn8tkMgwfPhzu7u72+yIjI5GbmytcDYmIiIha6NKE3UsXJsnlchw7dqzVfRaLpVcnjyIiIiLncrjnJTIyEocPH+70uEOHDiEyMrIndSIiIiJql8PBy7x58/D222+jrKys3WPOnTuHt99+G/PnzxekckRERM7GzRNdj8PBy9NPPw2FQoH4+HisXbsWubm5aGpqgslkwtmzZ/HBBx/g2muvhVqtxp/+9Ccx60xERHRFGI3GVpsn6vV6BjEuwOE5L76+vkhJScHixYvx8MMPQyaTtfq91WrFmDFjsG7dOsHSJxMRETlTZmYm0tPTAQBJSUkAgLi4OMTFxTmzWn1elybsRkdH49dff8WePXuwa9culJSUwGq1IiQkBNOmTcOkSZPEqicREdEVFxMTA51OZ988UavVCroHkdFoRGZmJmJiYri3URd0a3uAyZMnY/LkyZf9XU1NDb799lssWbKkRxUjIiJyNrVaDa1WCwDQarWCbfhoY5tPExUVxeClCxye89KRxsZG/Pe//8XChQuh0+nw4IMPClEsERGR09l2fha6x0Wv16Ourg4AUFdXx7k0XdDt4MVqtWLHjh24//77ERQUhNtuuw2//vorfv/737fJ/UJERAQ0X7RTU1NFuVDr9Xp8++230Ov1gpYrRvCSmZmJ9evXY/PmzSgsLMTmzZuRmZkpWPm9XZeHjQ4fPox169bhq6++QllZGby8vJCYmIhvvvkG69atw5QpU8SoJxERSZzRaERtbS1SU1Oh1Wrh5uYGtVrd46DA1oNRUVGBlJQUDBs2DBEREYIP8QgpKioKOp0OFRUVWLNmDRYsWICIiAhnV0syHA5eVqxYgS+++AJnzpyBSqVCYmIi7rjjDsybNw/19fX4+uuvxawnEZFkcVJms8zMTGRkZECr1WLr1q2wWq2CrNzZunUrUlJS7LfXrFmDhIQE3HrrrT2tsmhycnKQnp4OvV6PixcvYteuXZg4cSJXMTnI4eDlxRdfhEwmw6xZs/Dxxx8jKCjI/ruGhgZRKkdEJGVGoxFGoxF1dXVIT0+HTqeDj49Pnw1gYmJiEBISgt27dyMxMdHe89JTiYmJmDJlir0XY+nSpS7fixEWFgZvb28UFhZiw4YNGDFiBMLCwpxdLclweM7L448/juDgYPz0008YMmQI7r//fuzYsYP7GBERtcM2r8GWHyQpKUky8xrEyCp7uZU7QgQvnp6eCAoKgr+/PwDA39/fpYeMACA5ORmvvvoqPvvsMwDAZ599huTkZCfXSjoc7nn5xz/+gTfeeAMpKSn44osv8P333+PTTz9FYGAg5s2bB5lM1iZxHRFRXxYTE4OoqCjU1dXZ84T4+Pg4u1odEru3SK1WIz4+XpTeJ61Wi4SEBHuA5MpsvUWlpaV45ZVXsHTpUkRHRzu7WpLRpdVGMpkMM2bMwEcffYSysjJ88803GD9+PD7//HNYrVbce++9WLFiBfLy8sSqLxGRZKjVanh6erbqbXD1ISOxe4vEDF48PT1x6623unyvC/C/3qLg4GD4+PggODhYEvV2FQ4HL6tWrcKuXbtQX18PoPkFePPNN2P9+vUoKyvDhx9+iMjISKxYsYLRIxFRC2IstRVLTEwMFi5ciFmzZgEAZs2ahZiYGCfXqveSUm+RK5FZHZy0olKpYLFYoFQqMWrUKEycOBGTJk3CxIkTERgYaD+upKQEX331FZ544gnRKt2ZiooKUcpVKBTw8fFBVVUVzGazKI/hbF5eXrh48aKzqyEatqG09YX2A1yjDcVaIcU2lD4x29A2b6kzDs95qa6uxoEDB7Bv3z7s27cP//rXv/Dmm29CJpMhKioKkyZNwuTJkzFhwgSnBi5ERH2FmEuwbb1FRK7I4eBFq9VixowZmDFjBoDmDLsZGRnYt28f9u/fj59//hmffvopgObIqaysTJwaExERAO6LQ31XtzZmBJon79qSCyUmJmLv3r349ttvsXnzZtGGbYiIqJltRRAA+79CZKslkoIuBy9msxlHjhzBvn37sHfvXuzbtw9lZWWIjIzE+PHjsXr1akyYMEGMuhIR0W8yMzORnp4OAPaVQUJkqyWSAoeDl+effx779u3Dr7/+CpPJhNGjR2PChAm4/fbbMWHCBOh0OjHrSURELcTExECn09nzx0hhGTaRUBwOXlatWgWtVov77rsPv//97zF48GAx60VERB1Qq9Xw8fFBXFxcn95ygPomh/O82Ha93Lx5M4YOHYqwsDDcdttteOedd3D48OFeveSNiKgnxEi1D0grf8yVINZ5JtfjcPDyyCOP4PPPP0dubi6Kiorw5ptvIiQkBF988QUmTpyIfv36Yfr06Xj22WexefNmMetMRCQJRqMRer0eVVVVSE9PR1VVFS+sIhIreGFQ5Hq6tdooODgYt9xyC2655RYAQH19Pfbu3Yt33nkHf//73wEAJpPJobKamprw/vvvIz09HRcvXoS/vz9uvfVWTJs2DQCQn5+P1atX4+zZs9DpdHj44YcxfPjw7lSbiOiKajmpFmieWMtJtcJruR8T0Lz6SqiVV0aj0R586nQ6+9wi9nY5V7eXSldXV9sT1u3btw+HDx9GfX095HJ5l4ILs9kMX19frFy5EoGBgcjKysKKFSug0+kQHR2NlStXYvbs2Xj55Zexd+9erFq1Ch988AH3gCAilyfFjRmlSMwgkau6XJPDwUtOTk6rYCUrKwsWiwUeHh649tpr8cQTT2DixImYMGECvL29Ha6Am5sb7rjjDvvtYcOGISYmBpmZmaivr0djYyNuuukmyOVyTJ8+HT/++CP279+PmTNndu2ZEtEVI2bmVymxfUM3Go0oLCyESqWSzPmQUhvagsTq6mp8+umnuOeee1ptW9PTssVc1SWl8+xKHA5ebKuLdDodJkyYgCVLlmDixIkYNWoUlMpud+C00dDQgDNnzmDevHkoKChAREQE5PL/Tc0ZOHAgCgoKWv1NaWkpSktL7bc1Gg0GDBggWJ1sFApFq397I5lM1qufH9vwyjCbzcjIyMBVV10laF2k2n4WiwXl5eWwWCwO1Z1t2PUybT9yubzV7Z5yd3eH2WyGTCaDt7e3w73+nbVhy6GujIwMhISESGbVmCu8Dx2OOj755BNMmjQJgwYNEq0yVqsVb7/9NgYPHoxRo0bh1KlTbXba1Gq1MBgMre5bu3Ytli9fbr+9bNkyrFq1SrR6dqVnSYqk8ObpKbahOGwfyCqVClqt1v6v0PWRSvvp9Xro9Xr7RE/buXHkAsg2dFxqaipSU1MBAEOGDMEvv/yC+Ph4xMfHC1K+VqvFlClTEBQU1KXz0NGxLeus1Wqxe/duQet8JTjzfejwrtJis1qteO+995Cfn48VK1bAzc0NGzZsQGpqKlasWGE/bvXq1XB3d8eSJUvs913Jnhdvb2/U1tb22qXhWq3WPumtN2Ibiuvo0aM4evRoq/uuvvpqXH311YKUL7X2++abb5CcnNzqvhkzZmDRokUd/h3bsGta9mJs3boViYmJTu/F6KwNXbHOjhLzfejonDDhxnt6wGq14v3330dubi7+9re/wc3NDQAQHh6O9evXw2Kx2IeO8vLyMHv27FZ/HxwcjODgYPvtiooKUT/YzGazJD44u8Nqtfba59YS21AcQ4YMwcCBA9tMUBW6PlJpv1mzZmHSpEkoLS3FK6+8gr/85S+Ijo7utO5sw65RKBT24R2r1Qo3NzcoFAqnvkY6a0NXrHNXOfN96HCeFzGtXbsW2dnZWL58OTw8POz3jxw5EiqVCj/88AOampqwa9cunDt3DuPHj3dibYmoPWq1Gp6ensz8+htPT08EBQXBz88PAODn5+fyKyWl3obu7u7OrkKXMNFg9zi95+X8+fPYsmULVCoV7r//fvv9t9xyCxYtWoTnnnsOa9aswbp16xAUFIRly5bBy8vLiTUmos7YPpD7OtvQgEwmw8iRIyGTyWA0GiVxoZJSG7Ycgqmvrxc0z4vYpHSeXYnLzHkRUkVFhSjlKhQK+Pj4oKqqSlJde13h5eWFixcvOrsaomEbSpvU2i89Pb1V/hHAsRwhbMOu6e55FhPbsHv8/f0dOs7pPS9ERL0Vk9RdGVI+z8zz0j0MXoiIRGIburANDUht/ohUXDpEJMbSbqG1HOqybT3A14fjGLwQEYmM8xquDClNfuW+Vz3D4IWIiHoFKQWJUh7qcgUusVSaiIhci9FoRHp6uj07MAnLtiTdlkVeCkNdroTBCxER2RmNRuj1elRVVSE9PR1VVVUMYEQkpaEuV8JhIyIisuNcjLa4Isj1sOeFiIjsYmJisHDhQsyaNQtA8xYHMTExTq6VY8Qa6hJzCK2urg6bNm3q1XvKiYHBCxER2Ul5LoYYQYZtOTPQHGi03CW8p+Xq9XpUV1ejqKgI1dXVHJ7rAg4bERFJlJjDGVKai9EyZwoAQbcHaDmMlpSUBECY7L0ZGRlIS0uzByybN2/GuHHjcM011/Sswn0EgxciIgkyGo32SbU6nc7eQyJUsCGlZcdiztOJiYmBTqezL2cWsidKJpMJUk5fxOCFiEiCxOoRkCJbzpTq6mp8+umnuOeeexAYGChI2Wq1WpQdtmNjYzF06FBUV1ejpqYGCxYsEKzOfQGDFyIiCRKzR0BqbD1OtiEYDw8PQc+FGL1QLbeOmDt3LgIDA/ts+3UHJ+wSEUnQpT0Cnp6ekrj4GY1GpKamCj6pVq/Xo6mpCWFhYWhqauLk116OwQsRkURJaVKtjRjBS2ZmJtavX28fPktKSkJmZqZg5YuJmYy7h8NGRER0RVy6IshsNgsyyViK+wSJuUKqL2DwQkREV0RmZiYyMjKg1WqxdetWWK1WQSYZt5w/IvTEWrEwk3HPMHghIqIrIiYmBiEhIdi9ezcSExPh5ubm8hNrxSLF3iJXwjkvRETUhhhzMWyTjOPj4yU1yVgMtkzGYizD7gsYvBARkZ3Yu0qr1WrEx8fzQv0bKU66dgUcNiIiIjvbXAyTyYTS0lJs3rwZ8fHxkhmOob6BPS9ERCKT0nLYqKgozJo1C2PHjkVRURHGjh2LqKgoZ1eLqBUGL0REIhF7CEYMOTk5SEpKwu7du1FVVYXdu3cjJyfH2dUiaoXDRkREIrENwTQ0NOD48eNoaGjA2LFjXXoIJioqCjqdDgUFBdi0aROuueaaPt/zIubu3dQ97HkhIhJJWFgYJk+ejKFDhyI7OxtDhw5FWFiYs6vVoaysLGzYsAF79uyByWTCnj17kJWV5exqOZWUhv36CgYvREQiSU5OxquvvorPP/8cAPD5558jOTnZybXqnEwmg0KhgI+PDxQKhbOr4zAxgoxLs+Dq9XrBy2dg1HUcNiIiEkliYiKmTJmC0tJSvPLKK3j00UcRHR3t7Gp1KDY2FkOHDkV5eTmysrIwf/58hISEOLtaHbKl2q+ursamTZvg5+cn2C7NLTPh2vZOEiITbsvtAdLT06HT6ZjrpQsYvBARicTT0xOenp4AAB8fHwQHB9tvuypbmn2j0YjQ0FD069fP5S+otgCjsbERRUVF2Lp1K6699lpB5hbFxMRAp9PZs+BqtVrBgyKA2wN0FYMXIiKRabVaJCQkQKvVOrsqDvP09MTcuXNdPtgC/jfJuLS0FMnJyRgzZoxgk4xtWYGFzoLL7QF6hsELEZHIPD09ceuttzq7Gl0ipX2CsrKycOTIEdTW1qKqqgrJyckwmUy45pprBClfjHMhxc0kXQkn7BIRiYyTMsVntVqhVCrh4+MDpVI638u5PUD3MHghIhJZUVERnn/+eRQVFTm7Kg6TUsAVGxuLxYsXY8aMGTAYDJgxYwZiY2OdXS2H6PV6fPvtt9Dr9c6uiqQweCEiEklhYSEOHTqEvXv3Ijs7G3v37kVhYaGzq9UhKWYF1uv1KC8vR3Fxsf3H1YMBvV6PsrIy5OfnIyUlBfn5+S5fZ1cis1qtVmdXQmi1tbXQaDSClyuTyeyz8HvhaQMAKJVKmEwmZ1dDNGxDaZNa+/3ud7/D7t27W903ZcoUfPXVVx3+nTPbMC0tDWlpaTCZTCgpKcGAAQNw7bXXYvTo0YKUL0YbvvTSS9iyZQssFgvq6+vh7u6OuXPnYtmyZYKU3x2dteG6deuwbdu2VvfNnDkTixcvFrtqPSbm+9DRa3evDF4qKipEKdeWtKmqqgpms1mUx3A2Ly8vXLx40dnVEA3bUNqk1n6FhYU4d+4cMjMzsWrVKjz77LOYPn16p1l2ndmGtvwj5eXlePXVV/H0008jJCREsDkZYrRhZWUlampqkJeXhxdeeAHLly/H1VdfDV9fX0HK747O2lCv16Ourg4VFRVYs2YNli5dioiICEms7hLzfejv7+/QcdKZ1UREJDFhYWEICwuDSqWCWq1GbGysy28PIEW+vr7w9fWFwWCAwWBAUFCQUwMXR7TMAQQ0X7SlELi4CgYvREQi0+l0mD17NnQ6nbOr0qmWm0mq1Wps377d5TeTtPUWqVQqxMTEQKVSwWg0SmIFjxRzALkCBi9ERBB352CdTofXXntN0DLFYkueVl5ejiNHjuD66693+e0BWmarnTlzJg4cOID6+nqXDrhspJgDyBVwtRH1KUajEampqS6/eoKuLKPRiPPnz2PTpk04f/48Xx+/UalUzq6CQ2JiYrBw4UIkJCSgsLAQCQkJiImJcXa1HCKlJemuhMEL9SkMXuhyMjMzsWXLFhQVFWHLli3IzMx0dpWcJjMzE+vXr8f27dvR1NSE7du3u/z5UKvV8PT0hEqlwvnz5+1zjKSgrq4OmzZtsu9cTY7hsBH1CbYx8YaGBgDNHxgKhUIyH3AkrqioKCiVShw5cgQTJ05ERESEs6vkNC33CUpJScG1114r2D5BYrG9vw0GQ6t/Xfn93XK1UVFREUpKSqBSqThp10EMXqhPsI2Jy2QyaLVabN26FbGxsZIYEyfx5eTk4NdffwUA7N+/HyaTSdDXhpjzaYSWk5OD9PR0e0/AL7/8AplM5tLvlYyMDKSlpaG+vh5VVVX48ccfMXHiRMH2NhLD1q1bkZKSYr+9Zs0aJCQkcP6Lgxi8UJ9gm4TY0NCA3bt3IzExEd7e3s6uFrmImJgY+Pn54fz580hMTERgYKAg5dp6BOrq6pCeng6dTufyG/DZel4qKiqQkZGBKVOmSKInSiaTwWw2SyYHUGJiIqZMmYLS0lK88sor+Mtf/oLo6GhnV0syGLxQn2DbvdXd3R3x8fHw8fGBQqFwdrXIRajVagQGBmLu3LkIDAwULLiw9fiZTCaUlpZi8+bNiI+Pd+leDFvPi8lkQmhoKA4ePCh4T5TQhg4dirCwMOTm5mLDhg0YP348hg4d6uxqdciW56WpqQkA87x0FYMX6lPUajXi4+Ml8+2Mrhzb7r5Cajl/5JVXXsENN9zg8vNHbL2UdXV1SEpKwqxZs+Dj4+PsanXo2LFjSE1NRWVlJerq6rB9+3YAwPjx451cs/bZeuWampoQFBSEpqYml5+n40oYvBCRZNhWi4WGhkqi58zWi3H+/Hnk5ORgx44dLj9/xNZLaQvmXH2YCwDOnDmDw4cPo6GhAY2NjTh58iQGDhzo0sGLbZ6OLYjZvHkzxo0b59LzdFwJl0oTkWSIudRdjHwbYWFhmDx5MsLCwlBeXm7fLkAoYuYIsQUvrh64AEBERARiY2MxaNAgAMCgQYMkM0+Huoc9L0QkCbaJr0DzUnez2WzvIRCq/PT0dERFRQlWZlJSElJSUnDhwgU0NTXh66+/hsViwe23397jso1GI6qqquwTgbVaraDnQ0o8PDzg6elpH3bx9vaGh4eHs6vVodjYWAwdOhTV1dWoqanBggULBJso3hcweCEiScjMzERGRoZ9qbvVakVcXJwgQzCXBkZCBQG+vr4ICwuDXC6HXC5HQECAYBsGtkyJn5SUBACCnQ+psQUCZ8+exe7duzF37lxcddVVzq5Wh1oOzwk9UbwvYPBCRJIQExODkJAQ+1J3Nzc3wVcFAc2BgFBBQHp6Onbv3o3GxkbI5XKcPHkSUVFRmDVrVo/LjomJgU6ns0+qtfW89EW2IGDAgAGYMWMGBgwYIOi5EDNPjxgTxfsCznkhIklQq9Xw8fGxL3X39PQU7EISExNjDyhmzZol2L44S5YswYcffoglS5bAbDZjyZIlWLJkiSBlq9VqqFQqlJeX2zOzCn3BFmM+jV6vx3/+8x/o9XrByjQajdDr9WhqakJAQIB95Y5QZbfc90qv13N7ERfAnhcikgyxlrrbAiOhV9fY0r8bDAbIZDIYDAbU1dUJMnRkNBpRU1ODM2fOoKamptUwRE/LFSuxnl6vR2FhIX766SdERETA19cXWq22x/lNWvacAcL2nmVkZODAgQMoKirCDz/8AI1Gg1GjRnFVkJMxeCEigjjd93/961+xf/9+WK1WqNVqvPnmm/j111/x8ccf97hs20UVADZv3gy1Wo3Ro0f3+KIqZmK9rVu34ueff4Zarcbq1athtVoFSYl/JXPTWK1Wh4+VnT8PmcXS88fUaGB18Vw7VxqDFyIikaxYsQLnzp3D4cOH8dJLL2HZsmWYO3euKI8l1LJbWyBQXl6OV199FYsXL0ZISIggZScmJmLEiBFYu3YtHnvsMXvPS0/ZepyamppQWFgo6Kqr2NhY+Pn54cSJE7jhhhsQEBDgUNnyzZvhd/PNgtTBKpejav9+WFw8weGVxOCFiEgktuGWY8eOoampCRqNRrAege5eVB3V1NSEqqoqe/p6IXh6eiIgIAAAEBAQAH9/f8HKBoDGxkaUlZWhsbFRsDLVajWUSiX0ej2USqXDQ1yytDQ0jRmDi//8Z4fHGQwG7Ny5E9OmTWt3ebf33XdD88MPqH/yyS7Xv7di8EJEJJIvv/wSycnJuHDhAqxWK7788kuYzWb8v//3/3pctlqtRkBAABISEhAQECDYvjgZGRk4cuQIDAYDgOahHiF3aNZqtZg9e7YgPS42er0edXV1OHfuHM6fP49z587Z9w4Somxbnp4LFy5ArVY7NE9Hlp2NpuHDYWknWZ6tzhV6PTZkZCBsyhRE+PldttzGBQug+f57Bi8tMHghIhJJRkYGTp48CZPJBLlcjuLiYmRkZAhWvqenZ4/ni1yO1WqFQqEQZQNTT09P3HnnnYJOut66dStSUlJgNBpRXFyM1atXY/bs2YKcG1vZPj4+WLt2LQA4NE9HlpUFcwfJCG3l2qxZs6bdchtvvBHal16CIjsb5iFDuvlMehcGL0REIlmwYAFCQkKQn5+PH3/8EVOnTsWCBQucXa0OSTHz6+TJkzFs2DCcOXMGK1aswPz58zF27FhByk5MTMSwYcOwZs0aLF26FP7+/p33GpnNkJ0+DXMHifISExMxZcoUVFRU2Mtub0sDy6BBMMXGQrNhAwxPP92Tp9NrMHihXiUvT46Cgva/Kcrlcnh5ARcvKmGxdJzmaPhwE/z9HV9ZQHSpsWPHYsSIEdi5cyd+/PFHTJs2rdOLalGRHLfdpkJDQ8/nxiiVwGef1WLIEMd7OFpOdg0LC0P//v1dPvndnj17kJKSgvr6eshkMnz//fcwGo2C9Lx4enra5+b4+/sjKCio07+R5+dD1tjYYfBy6bCWv79/h0NRjQsWQPPllzD86U8A90Ri8EK9y513eiMvT4HOe7o7Hq9uagJuvrkR774rXCIt6nuysrKQlpaGM2fOQCaTITU1FVFRUR3OH9m+XQ2DQYZly+p6/PhvveWOjRvVGDKkvst/K6WNGWfMmIFrrrkGeXl5eOGFF3D33Xfj6quvFqx8rVaLhIQEh+fpKE+fhtXLC5bg4E6PValUiI6Ohkql6vC4xvnzoV2xAorMTFSHDceJE44N5ykUth8rZDJAfsl3Nn9/C3Q66X1JY/BCvYbVCuTnK7BuXS2mTbv8CgnbOH5VVXWH4+1vvumOlBTX/9Cmjm3bpsLBgx1fFBw1cWITEhK6tvLGYDDg4sWLqK9vDh7q6+vtE2Hbs3evCtddZ8Gtt3a+YqaztPU5OQokJ6vx1FPdD16EZtsZPDQ0VLD5NIWFhUhPT0dNTQ28vLxw4sQJ+Pn5CbaPVFcpTp2CdciQDntIbMkADQYDGhoaYDAYOsySbImIQNPo0Sj/fCfm/DwBOTnCXL4HDDAjPb1KkLKuJAYv1GtUVMjQ2ChDaGjPk0KFhlpQXMzdM6TMagWeesoTQUEWBAT07JtlWZkcGzdq8OuvXfuQP3HiBA4cOIDKykqYTCacPHkSJ06cwJQpU9qt8/79Krz2WufDPC3T1vv5+dmHd1pe/GbMMOIf/3BHZaUMvr5dOwdi7OdjNBpRW1uL1NRUaLVa+/5UPS0/KioKOp0OpaWl2LdvHyZPnowogXKi6PV65OfnIyUlBcOGDbPPeeloiMcevHQgIyMDaWlpMBqN9uy948aN67BXLmvyvZj37k0IH2/Bjh0V6GwxldUKWCyA2fy/n5ZOnlTihhv64+JFGby8pNX7wuCFeo3i4uZvcQMG9HwFw4ABZpSUyGE2w4EhKGkSc7M5V5CZqUBpqQI//VSDAQN6FtDm5soxdqwvcnLkiIpyvKyTJ08iOzvbniulpKQEJ0+ebPf47GwFKirkuP6Rq+D3//I7LNtqtSLIasVrVitk77wDoDlRXctkdde5adHf8zx27lRh4cKu7cdTV1eHTZs2ITw8XNANMMXYGTwnJwfp6en2ncF/+eUXyGQywbIC21YFrVmzBkDnq40UZ87AOn9+p2V3JbHgqVMK3LzuIcSbkvDBM55Qe45woPz/DRtdzrBhJgDNr++4OOG227gSGLxQr1FUJIefnwXt5HnqktBQC8xmGcrK5D2+8Lkio9GIqqoq+941th2Je1MQs2OHGsOHmxxuv46CuUGDLBg0yIzt29WIimpwuA621UanT5/Gli1bMGHChA5XG+3dq8Lg/mUIHtQPtX/9b4dlZ2dn4+TJk8jLy8PAgQOhVCpx1VVXYUiLb/zu772H6zP2ITl5vMPBiz3/SEUFioqKUFJSYt/4safE2hm8Zc/Lrl27MH78eMF6Xrq82shqheLUKZiHDu2wXNuqrvLycnuiwfYyGR87psCiRf0wfnwTvix5CbKt18AwpvPgpTOenkBQkBk5OQoGL0TOUlQkR0iIMIFGcLAFMpkVRUW9M3hpuZFdUlISAAi2kZ2r2LFDjZkRJ6B99j2Hjlc3NcHnzBl4tjN5ctbQJ5GcHImHHnI8eLGtNtqyZQs2b96MSZMmdbjaaO9eJaY3bYP5rrvQNHFih2WHjhkDWXExNr/6KuY++qg9w25Ti2DA6uGBG2Z+jCe3TYLF0nay5uV0Jf9IV9kSvAHNk2Dd3d17XCYgbs9LV1cbycrKIK+txb7GUbgz3qfNUM2lLJYQ6PUfYN8+T8jbaaALF+SYP78Rb7+th/XfN8Dtww9heP55QVYdDRpkQW6u9LqXGbxQr1FcrEBoqDDfHtRqICio9857iYmJgU6ns29iZ+t56S1qamT49VclXtb8GYrRdZ1uamexWGBtaoJnTQ2sxcWAStXqQiK/cAELjv0F/278Dno9Op1rYGNbbWQbKkpPT8fIkSMvO6/BYgH275bjPcMWWBb+vdOy1Wo1PDw87P9ermfENGoUEka/iYo0FTIylLj6alOn5dryjxQUFOCFF17A8uXLERMT48CzdVx7afC7y7YfU35+PjZu3IgpU6Zg8ODBgpXfldVGytOnYVWpsCM7HB4eVvzxjx1P0DaZzCguLkZISAiUyssHEVotcP31RsjlgHH+fGiffx7Ko0dhGjWqW8+npaio5p4XqWHwQr1GYaFwPS9A89BRUZH03tSOUKvV8PHxQVxcnH3/nd5k504VPFWNGO92BLVfpQEaTYfHp6en23uigMv0QhkMmBw7Bio0YfduNebMcWwIpqamBhcuXLDnH6mvr0dNTc1ljz15UoGqi2pMGN8IBAUBFy92Wr5KpUJgYGCHy2y9H78d8fekIvnHcFx9deff1G2vBZVKhbq6OqhUKkEn7NbV1cFgMKCurg5ms1mQ4UpbuU1NTfD29kZTUxOMRqNg9e5KJmPFqVMwDxqEjOMKTJhgdHC4TgfA/NtPxyzBwTCNHQvtn/4kSLbdIRX34/vqhB6Xc6U5PXjZtGkTUlJScPbsWYwfPx5/+tOf7L/Lz8/H6tWrcfbsWeh0Ojz88MMYPny4E2tLrqy4WI4Jlj3wfOSDdo+RyWSAWg0Po7HTre3DFK+hqMi1M4v2hFhLYV3Bjh1qzNTsgumO2zoNXIC2PVFtNk/08ID1zltx3ed7sWPHeIeDl/379yM5ORkNDQ2Qy+VIT09HaGgoZsyY0ebYvbsViFFkw+e2qQ6VbVtmCzQvybYFAZdesI0zZ2JWv2+QvN4fT/61856D1NRUpKamoqamBhaLBZs3b0Z9fT3Gjx/vUL06kpmZibS0NFRXV2Pjxo1QKBSCDFe2HOqypfEXaqirqxSnTsE8eDDS0+V47LHOe7q6o+6ZZ6D57rselyOvqsKwnR/gdc/rYLVKK/ed04MXX19fLFq0CEePHsXFFt80TCYTVq5cidmzZ+Pll1/G3r17sWrVKnzwwQeCbUBGvUtxkQJRWZ9BPqUG5naSQ8lksuaLmVLZYfCiTE9HZNVBnOx/o1jVJZFYLEBKkgyv1HyFhnuWOvQ3jvRENdx3H+a8uxbLt0yA9Q3HPuhDQ0MRGhqKixcv4ty5cwgKCkJoaOhlj92/UY9p1p9hnDsXjvQXZGRk4MCBA/ZlthqNBqNGjWo7JKVQYPrtPnjln6GoKj0Hn+COg7mzZ8/i6NGjaGxshMFgQFZWFgYNGiRI8BITE4P+/fvjzTffxMKFC+Hn5ydI70hXUu13R1dW5ilOn8aF2MnI2yTDyJHiTII1TZgA04QJPS+ooQFXDZyLmloFKitl8POTznJppwcvE35rgNzc3FbBy7Fjx9DY2IibbroJcrkc06dPx48//oj9+/dj5syZzqouuaj6eqC8Qo5IZEP/97WwtHOBUCgU0Pj4wNDJpnCadesQsfwkthUtFKvK1A2VlZVYs2YNFi9ejH79+l32mGPHFDhfpcGMSfp2d/S9nM56oiwREbhumgEP7nTDiRP1GDGi8wvTrbfeijlz5uDQoUP44x//iP/3//4fpk2b1uY4sxnYn94Pd8VegLWd53U5ZrPZvsFhR8H4yKcmo9/7tdj7Wjrm/ePaDsucNWsWxo0bh7S0NOzfvx8LFizA9OnTHa5TZ9RqNSIiIgQdqrSl2i8pKcGBAwfwwAMPCPYlt6sr8xSnTiHj2iehUFgxZEjnPS9OTVng5oaIOE/IUpsn7fr5idNTJAaXnY1YUFCAiIiIVpPmBg4ciIKCAifWilxVSUnz6yS0/0VY2llu2BXm6GhEVB5FcZHLvkX6pOrqamzatAnV1dXtHpO8yYx4WSr6P7RA8Mf3e3QBrsZR7PjesWEjT09PBAUFISgoCAqFAkFBQZe9qB5Ps6CmSYtxd4U5XJfY2FgkJiYCaO55WLx4MWJjYy97rMJbixmD85CywdicuawDhYWF2LNnD44fPw6z2Yzjx4+jsLDQ4Xp1JDMzEz/99BMA4KeffsL69euRmZkpSNkAUFVVhYqKClRVCZcxNjMz074iLykpqcM6y2proSgrQ7pxKIYMscKRxVRGoxHp6ekwGruWh0coinFXI1xzXnIrjpze89Ke+vr6NjO7tVrtZVNrl5aWorS01H5bo9FgwIABgtfJlspa6C3iXYlMJpPk8ystVUKtMME/TgeDsv2XtcNtOGQIIpCP6ho5DAYFvLyErK24pNqGnamsrERJSQkA2P/t379/mxTwKd/VYbbnHphn3dml82A0GnHy5EkMGzas/RTtCQmY7fM1kr9LxJMvdt5Dcu7cOXu+FJVKhaKiIkRGRkKn07U67sDnBRgpO4/+ixIAhcKhNlQoFNBoNPD19YVGo4FCobD/XE7CvTosXzYAqpSdsMy8rt1yBwwYALVaDS8vL3h5eSE2NhYDBgwQ5DU1YsQIeHt721cx6XQ6qNXqHpedk5ODoqIiZGVlQS6X24e6hMj1MmLECISEhGDr1q1ITEy097xcrs6KM2dglclwrDwUcXHWDp+XbXuAhoYGyGQyNDQ0wN3d/Yr3vljGjcPg908iL2ecw+3gCtdClw1e3N3d2wQqBoPhsnkB1q5di+XLl9tvL1u2DKtWrRKtbt7e3qKV7QqkuPKkqgoIUxdDM+5aaDpZFgs40IY+Pgj3MwAXAL3eB+HhAlX0CnF2GxqNRhw7dgwjR44UrC5r1qzBpk2bAAArV64EAMydOxfPP/+8/ZiK8xb8WtgPbz3YHz6/5eZwlF6vx+nTpzFmzJgOhxzm3ReIV/8RBkuDGX7BHT+3p59+Gtu2bYPJZIJer8eLL76IOXPm4MMPP2x13C878zB9cBF8Qv+XeKyz85aamoqDBw9i8ODBOHjwIJRKJeLj4xEfH3/Z4xfe74NHlgE5D72P0RHtfz42lpVBUVuLQKsVyWYzFH/4A/r16wefS/ObBAYCX3wBBAR0WM+WjEYjKisrATT3StnmF/X0NfLnP/8ZBw8eBNAcvL/66qvYtWsXkpOTe1SujVarhVarRWhoaMfDUcXFQEQEMk9rcdttaDvxuwXbxGhb+bt37+6w/UQzcyYGm/+LwhOjO6zv5TjzWuiywUt4eDjWr18Pi8ViHzrKy8vD7Nmz2xz74IMPYn6LVMwajUbQbkMbhUIBb29v1NbWdjhfQsq0Wq090ZOUZGdrEN6UC/1VV6Gpg7bvSht6DfaDR60RJ040IjhYOmPBV6oNs7PlOHny8t+89Ho9kpKOY9asEIfmHlx1lQXDh3fcHosXL8bVV1+NlStX4rnnnsOAAQPQv3//Vu/1DS+fgS8GYvDvx3fpM8BoNKKsrAxZWVnIzc1FUFBQuxfUq5aOQf9/1GDD80dx02sd59lISEiAh4cHsrOzkZSUhIkTJyIhIaFV3UzVeuwujcYdv9Pb73ekDUNDQ+1p9m05SNRqdbvPW60G4kZ4YEPYcgyd/ku75Srq6uBWX4/z589jw4YNuPHGG+EbEQHDJT3h6nXrYF24EPrvvgM62RHZ5tChQzh48CB8fX3x3//+FyqVCqNHj8aYMWMc+vv2/P3vf0dRUREyMjLw97//HX/+858xZ84cwa4Der0eVqsVVVVV9q0eLsf96FFYBl6F4/utWLHChKqq9nelDw0NRWBgIOrq6uy9Os2bxl7hTRJVKkT7V2Hf8UaHH1vMa6GjAZTTgxez2Qyz2QyLxQKLxQKj0Qi5XI6RI0dCpVLhhx9+wLx587B//36cO3fusjPeg4ODEdxidUlFRYWowYWtzr2R1WqV5HMryqpHuCkHxpEjYXGg/o60oWVwNMJOnEdhYT9JnZMr0YZWK3D33V4oL5fDzc16ye+ssFjUuHhxHg4d8uy0a9lqBc6fl+PWWxvx3HMGBAdfPldPv379MGzYMMydOxfDhg2zT9ht+Vx3fKPH9aEnYA0c3qVzcPz4cfz6668oLCzE5s2bce2117Y/edfLA9dHncb2742Y/0rHjyGXy6FSqewXvKamJsjl8lZ1O772EPRYgDH/F22/35E2tF1AYmNj4e3tbQ+2Ovq7GTPNeO2fE/Gv9PZXDjVPiWl+/Nr6V/HBFm8oFMo2q6t8PJfik7NzMfy551D30ksd1tXG9jlv+3/za8XS49drZGQkIiMjERAQgO+//x4zZsxAZGSkYO+D+vp6ZGVlYfr06R1mBZZlZ+NkwCQ0NsowcmTHz0uhUMDd3R0KhcLehgqFwimfNQOHqZCzzxsmU12Xlks781ro9ODl66+/xldffWW/vW/fPiQkJODxxx/Hc889hzVr1mDdunUICgrCsmXL4CWlyQd0xRSfacQkdRkskTcIVqZp8GCEWwtQVHS1YGX2FtnZCpw+rcShQ5WIjPxfsHH48GEcOXIEjY2NOHHiBIYPH97pTrkAcOiQEs8/r8W4cT547DEDHg/7Bl6lOW2O08rleN7dHfWffmq/CNqYmyzYXvIEXvpjxxsaXk5UVBSUSiXS0tIwYcKETpfZJtzth2deiIbX2PFQyNufADu1ogJjDAZYzGaYACh37YJnWhr6txjSOlh0B+J8RqN/UNdXx3Q1V88jj9Rj6NCOexHz8wuQn58Ps9mM6upq9O/fH4MGDUJEROux0x071Ji6IQlf/vsWTB/5JRpvv73Tx7fNn/n6669x0003tQq6hBAQEIBp06YhoAtDWZ0xGo2oqalBUVERampq2s2nAwDKU6dwZPwfoNOZERjoUJ5Bl8i3FDExEHW73VFWZoBOJ43l0k4PXhYvXozFixdf9neRkZF4/fXXr3CNSIqKS5QICRM2y5I5Ohrh9dkoLhotWJm9xcaNasTGmloFLjadJf+7nDFjTNiypQbr12vwt+dV+LxiJq7yLr3Mkc27Jjc/RuvHabBoUAlfTH2gyw+PnJwcpKamIjQ0FAcPHoTJZOrwgjLtdz6oWe6D0PNHoZC1/3zr6w0wmc2A1QorAJkZUOqVcDf/Lz1+jVmD++ZVd73S6Poy2379rLjppo5XtRiNvjAaPVFeXo5XX/0cf/zj0wgJ8YVa3frvFiwwYvBgdyx8eT3e+ONTuGdIGkyjO36vqNVqBAUFYeHChfYVWB3XBUhOVmPDBjWqqtpf+We12nrulcjNvRsHDwbCzc0NMlnbv7njjgbMn+/4yh5bPh0A2Lx5c/v5dBoaIM/PR0bcVQ4to3cloYlDoXjZhLyDVdDd2N/Z1XGI04MXop6yWoHCmn4Imdh59tCuMA8ejAjzBiTlSWe+S1fo9Xr7WHtXc2Js3KjBwoWNbe637ZRbXV2NmpoaLFiwAIGBjmUplsuBW25pxG3r78LnJTNROu+eNsdYLFbU1tbA27sf5PK2gerd4XXw69o8XQAOZNi9hK+vFRt+rEVZWcdL6det+w4nTpzAxYsXYajTQ6v1xJhx4y75wmbApEkyXBqMOaKurg6bNm1CeHi44JO0VSoVQkND2916QCYDHn+8HhERZjz20Cs4fdN/8ML3Sii9Ol4frJLLMcqrHypO5aPeaIXZ3LYds2uC8fVWf2zYoEFDgwyzZzciLq7992FpaSnOnTsHq7UeZnMOrNYLCAoa2Go6AQCcOKHEK694YN48Y7e/57QXnCtycyGzWJBRpsPosSYA0lnxJx8ahYHyfJzdVYLxDF6IrowLF2RosGgwIF64rmIAsISFIUxZiqICCeXMdpDRaERJSQm+++47xMXF2ZfHOnIBPH1agcxMJebNq23zu5Zd6nPnzkVgYGCXLqqK48fhs30D7trxGMxx9W3qXFFRga+//hq33XabfchBiIt2d/Z6Gju286A2N7cI9fVpKCoqQl1dAfz8wjFuXGiXvvm3R6/Xo6SkBEVFRSgpKYHRaIRWq+1xcjbbjuMNDQ0oKyvDli1bMHbs2HZ7om66yYjQoEbcffNNeH+WF2SdBGFmKGDtJMWYHGZMCz2FlX/uj8RFqk43wkxOPoxDhw6htLQUJ09uwqBBczF//vw22zAUF8sxapQP0tKUiI937EtJbGwsdDqdPRjv37//ZV8filOnYPbzx/Fsd9z9f3pIKXiBTIZovwvIO9rxJpKuhMELSV5xehUAPwRNjhS2YIUCoQNMKCl2g9lcj96UOiU1NRW7du1CVVUV1q9fD09PT8THxzuUAn7jRjWGDzchKqr9TTC7O47v8eabMF5/PcyX+duMjAzs27cPaWlpkMvl8PDwuHz3vQs5deoUcnNzUV/fHIjV1NTg1KlTgpS9detWJCUloaSkBG+++SZUKpUg+/lERUVBp9MhNzcXn3/+OW6//fZO86WMmSDD7iNNOHXkckN9rSmVcvj6eKK+/iLkcjOUCmubXhBdzgFEvPsC5H8rQX3lo2h46CFYO4hgfv31V2zbts0+MTo1NRU6na5N8BISYsHUqU346iuNw8GLWq1GYGBgp8G44vRpFERMQGWaHCNGmAB0vqeWKxk0yIzcTMdWjbkCBi9XgLygAPLyckHKMg0d2rw/OtmVHiiBH/rDbcRAwcseEK1GU4EC5eVy6HTC7VjtbGfPnkV2djYAIDs7GwqFAn5+fg4GLxrMm9d2yKinFKdOQb1xI2o2b77s75uamtDQ0IDKykr7RocdLVvtiktTwAu103ZMTAxKS0tx/vx51NTUICgoCDExMQLUGJg8eTLUajVWrFiBBx98ENHR0fDz8+txuceOHUNqairKy8tRU1Njz4jb2WsjQCdHQGLnKWUVCgV8fPqhqqqD1Thjr0P1bdOh+fZbeLz6Ktw//BDmwYPbLfOZ2lo8dvEimpqaUFlfD9/qavhs3Yp++/a1OfYey+/w+FcPYdV9uVDHDHRonpwjwbjy1Cmk9Z8GDw8rBg6U3mdF5Cgv7DroBZle32Gg6CoYvIjNakX/66+H/LfETD1leOQRGFok5COg5FgtwjwqAEU3Jjt0InhEfyAFKCrqXcHLvHnzEB0djeeeew6PPPIIwsPD22S0vpzcXDmOH1di7dqOl1F0Z78W97feQtPkyTC1k/NDpVLZy1Kr1dBoNO3Ox+gq21AJ0JwCXoidjoH/7blT/tuXF5lMJtieO3v27MHWrVvh4eGBDRs2QKPRCNLzcubMGRw+fBgGgwGNjY3IzMxEdHS0IBszdolCgcbf/Q6NN90Ezfr1kJ8/3+6harMZ/cxmnD9/Hj9//z1uSkyEOjgYxku7S61WzM8+gj8cNmLP1DdwS/AemMaMgbWT177FYrGvvGq5ZU1Lqn37kB77BwwfbkI7h7i0gVN0OPO+J+S/boI5YZqzq9MpBi8ik1VWorHSgKIf9sHcw1TV7mvWIOjoTmEq1osU5zQhNKC+8wO7QRkzCDp5GYqK3OHCoxNd5unpifDwcAwZMgTh4eEIujR7ajs2bdJg6FATrrrq8t+YbSnP6+rqutSLIc/Lg+a771Czfn27x8TGxsLHxwdHjhzB/PnzO0wk11VdnbDrKG9vbwQEBODcuXMAmjOHC5WVNDExEZGRkXjxxRfx8MMPOxyAdiYiIgKxsbEoKytDeno6Bg4cKOgOzV2m0Ti0DBsADIWF2HXuHGb9/vcwhrW/T9SN7mr8K/cDzLntMyjT0iDrYF8h82/5xyqrquDu4QGlSgW5XA7FJRGKcfZsHC2J+23ISHoGDZGjEW4oSz4FfwYvJM87iyHIRuECIfLL/w3Pa/6O31uFXRIsdUVlaoReK875MA0ejHDLWRTntt9lLVU+Pj74v//7vy5dqDduVGPevPY/6Fv2YACO92J4vP02TGPGwPTbLvOXo1arERAQgFGjRiEgIECwHgxb2bYLvy1TrRA+/fRTHD161J6TJjMzE59++iluvvnmHpetVqvh4eEBk8kEDw8Pwert4eEBLy8vNDY2on///ujfvz88PDw6/0MXEBQUhKeeeqrTYPx3v2vAvHn9kP/+HdB1Ehilp6e3ek0DaPc1fWyMN35/g3QmvbYUEmKBRtGEvAMXIHwftvAYvIjsQkYZCjEL33xTg/Dwnq39/+jNJuz8ejweLy6GJTRUoBpKm+zCBRTWB+DqYeIkLzRHRSEch1CSGQxAnA/ww4eVWLzYGyYBvrC5uQGrVplx002dH9vVSbUFBXIcParCO+/8lvK8sRHyS9KJD/fxwVUTJ8JgMGDnzp2YNm0a+vXrB/lvPQ+XIysvh+brr1G7bl2nQblWq8XChQsF6WG4lO18CLnk+MEHH8Qvv/yCM2fOICUlBZMmTcJdd90lSNmZmZnYt28fAgICsGvXLnh6egoy3GVb7p6fn4+dO3di/vz5GNzBfBNX4uhresyY5hxF336rwWOPddxre2mvXHtB4sWLMpw9q5BcjhcbhQIYqKtDbqYZY0wmoIMNbl2Ba9euF8g6YoRaZsTkyU09fi1Mv1GDz7++Btb0HwAGLwAA5bFjKMAkDLhaA0CEDw1PT4RqK3E6V7wPpK8+lSFGm48nx1x+EzmLxYLa2lp4e3u3O95uk1kTiqVLZ2H3bk+89JIeHWQy77JNmzSIjjZh6FAz5MXF6LdgARRnz7Z7/BAAWLHCobKbxo5F07RpnR6nVqsRHx+PqqoqwdOSi5Hp9Prrr8fUqVPx2WefITk5GVOmTMH1118vSNkxMTFwd3fH7t27cd1119mXuwvJ2Rt8dpWjc61ksubel6++0mDp0voOY+ZLe+Xa6/E7cUIBudzaaQbj7tb5Shg4VIEzxRFQnDhx2RV/roTBi8iys5W4yrsUSmXPvynGX2NGPTyQufMChgqXBV/STGknUYZbEBpRLdpjhOqM+PmcOEsITSZg0wYFXle8jrlVJ1v9zmK1wvrbvi8VFRXwb/RvHm/v4JP2xtxcXBc6Hrcmf4Y5c/rjo49qBVv5YBsyUpSWoN+CBTCHh6Pm669xuTXkTU1NOHPmDKKjox2aVGvR6XrlUGhRURGKiopQWFgIACgsLERRUREGDRrU47JtaeurqqpQU1Nj3+uppxdA227Her0eRqMRGzduxNSpU6/8hN0usM21Ki8vx0cffYTHH38cISEhHZ6LRYsa8fLLHjhyRInRozsPODra0wgAjh9XYvBgs8NfGLo7P0xMUTEKZB8Yhf6JMzvsebF6eQFlZVewZm0xeBFZVnF/DB1QDaDnwYuPjxVX9StF6iElhva4tN6h7NcSAEBIiHg9IyGRchTt9gbQ88Ril9q3W46L9Spc93wUan/fehVZV8babWQ1NRizZAlSM6/GnZ57cN11Pnjkkfoe98AYjcDhwyq8/vTZ5sAlLAy1n38OtDMXQgFgSHQ0AKD3rNHquieffBJpaWmwWCyQyWT4z3/+g6ysLHz//fc9LtuW50WpVGLt2rWC5Xk5e/Ysjh49au/Zys7ORkREhGDBi9FotG/F0Nn2AI7KyMhAWloa9Hq9/fxOmTKlwxxALXO+OBK82HL1tOf4cWWXJut2d36YmAYNMmOr3zjUvrGuw+PkajWcvcsggxeRnawagIRxwuXEuOaqSvx6Mhh3CFbi5RUWyvHRR26wCHDl8fe34uGH60UZQi0+cREqhRmBgeJtJhYS44ELyd6oq6sQPMXOxvcqMEvxC9zvXdAmL2lMTAyioqJQV1fn8CoYa79+aPrxR3gtWYLNmwfipZv24sftwuQVWZhYjYl/ng3zgAEdBi70P3/9619x6tQppKSkYP369ZgzZw4eeughQcqeMWMGgoKCsGrVKtx9990ICwuz9770xLx585CQkICCggK88MILeOSRRwTLTQP8L3gJDAzstDfDUU1NTfaeDNtjOJID6He/a8TTT2s7/GyyWMxoaJChoGAa0tK8oFKpoFDIIZe3DrxSUlR44IEGh+vcnfe32AYNsiC/RAPDxGnoqMNUqKCzJxi8iMhqqMfJpsF4OO6sYGXGj5fjvUMjILt4sbnrTiQff+yG777TYNSons8i/eQTNQIDLbjtNmETm8lqa1F8ToWQ4EZR8yoMGNU8976kEBgsYJdXUxOwcV8QXh+bDKv32Da/vzT1vcOrSdRq6NesgUfIy3h+dTz+dP/9grxW1D/8AKsuCLVffNErEyWKMfdg06ZN2L59O0pLmzPPpqSkIDQ0FKNGjepx2YWFhThx4gR8fHxw4sQJnD17FnFxcfD19e1RubbcNBUVFSgvL4eHh4dgK7tswyRA875MZrNZkC0eWuYAqvwtp5Yjw5Vz5jRi925Vh3tUVVfXoqamGoAWx49XAAD69WtehdXSuHEmJCY6/hnX7fe3iKKizDCZZCgokHeYQdsVMHgRUXlaKapxLQZPqOr8YAeNnuODnHd0qNy/HT6zev4B2J6kJDUefLABjz7a8/wpL7/sgTfe8MDNNzdCqWzO6aFKTe1xufKzZ5Evj0RIpLjfAvrHR8AN9Sg5cgGDh/Y8g6nNvm8qUGeKRMKfO/5W261VMDIZDMuWwTxwIDQd5E7pCtPYsdCvWtVrAxcxMuwWFhaioqICBkPz8lmDwWCf/9JTMTEx8PPzQ2VlJebNm9funjtdpdfrUVdXhwsXLsBqteLChQvQ6/WCBDCZmZnIyMiAVqvF1q1bYbVaBRkqGTRoEPz8/HDy5El88cUXGDlypEPzitzdgbff1nd4jNFoRlWV6ZLVRmao1R0nanSUGKvcuisoyAIPDytycxUMXvqyU7/UQoMGRF4tXA/J0DgltLI6HNlWg4RZghXbSl6eHKdOKTFrljBvzocfrseHH7rh2281uNtvI7weeKA5/bQAb9azYR8hJEy8ISMAsA4IRrisECUZ1cDtwgUvG9+vxMx+56CdMKLD43qyCqbx9tsdTvDVl4mVYffnn39GdXW1/bbRaMTPP//c43KB5tdF//79ERYWhv79+wvWO7Jx40akpKTAaDTC3d0d//73v1FcXIzbBXgdxcTEICQkBLt370ZiYiLc3NwEuWgnJycjJSUFTU1NCA8Px/fffw+DwdDj+T9A9zbt7Gr5zpzn0pJMBlx1lQmLF3c8/OjlZUVt231ZrygGLyLKPmbBELezUCiFS/mjVALX+OXicKoaCYKV2lpSkhqDBpkRHd3xJFhHu9n797fiwQfq8eYLTXiw5n7UP/1H1D/xhCCrS87e6o3RoSJntJTJEKatRPFp4SbsGmvqsTE7Bq/d9Wvnx7rQUkpnE2OyJ9B8UfX29sZbb71lX6kihA8//BDHjx/Hpk2bkJqaivj4ePzlL38RpGxAnG/tkZGRiIuLQ2VlJY4fP46oqChERkYKUrYtEIiPj4ePj49gbZiYmIgpU6agoqICa9aswdKlS52bFbgLXO39/dlnF1FQ0PE4vEolByBMpujuYvAioqwcN8T4ngMEzlc4ZkgVDqUHC1pmS9u2qTFrVucXaqPRiPT0dERFRXX8pmtsxJNn/4IPq17Hv+7bjkVPCjf5r6hIjvnzxU8KFepvQHGhcMulf3njKAzW6zHjL8M7Pdbh89xFrvah6QgxJnsCzRdVlUqFsrKyVvMnesrHxwfe3t72DRP9/PwEnZQpxrd225BLbm4uUlJScP3112PkyJGClS9Grh7bPB2tVouEhAREREQImoG5rq4OmzZtQnh4uODvFbHe390VHGxBcHDHQ0acsNvLZZX5YXZMruDlxk+U4Z/7YmBq0EPpJmwTVlcDv/yiwlP3FsHjhTcgu0zaV7PZDIvFAq+mJkzLz4fXkSPQaDTtvqCVqamQl5TgobuX4dWUCbipqarDmeyOslqB4mIFQkPFH5sNCbVgf4ZwF8sfvwFmDcyEZ0D7+6+0zAMBNH+ACjG5sWX5Yn1oihEYGY1GnD9/HgcOHEB8fDz8/PwEOx96vR41NTXw8fFBTU2NYMMwCoUC/fr1g79/8xcYf39/l/jg70hOTg7S09NRU1MDDw8PHD58GB4eHi4ztNERT09PQYaKWjIajaiurkZRURGqq6vtwW1PX3div797OwYvIrFagZMXw/GHmDOCl331DQGoe9UTp5PPIOYGYTPtbtsmh6enFdO3vwjNgT0wXeYbV21NDWp+G8f3BHAhIwP9+vdH/3aWaZqHDMHFTz7B/3Pvh/fjZfj6aw3uvLPnK48qK2Wor5ddmeBlsAaF+3u2isPGnHoMGy5MxuuPl3d4nJh5IC790ATarn7obrliJd6ypcM/ceIENm/eLFg6fKA5Z0pKSgoAYM2aNYLkSwGAxx9/HCdPnoTV2jwv65tvvsGJEyewY8eOHpctlrCwMHh7e6OwsBCbNm1CXFwcwjrY5NCViBE0Z2Rk4MCBAwCAzZs3Q61WY/To0R3mkHGEK+Z5kRIGLyIpLbKi1uKFIfFugpftP8wfUfI8HN52ETECZ9rdvFmOGZP08PzhW1z8179gnNV2VrDZaIT6twuUbQa+2ccHFzv5sOgHKx56qB5vvumBRYsaezxft6ioeVx2wADxh40GxPZHoSUE6vdeg9xd06OydvynCo3yCbjuzo6HD8TMA5GZmYnU1FSUlpZi8+bNUCqVgnxwivmBHBYWhuHDh+Obb77B8OHDER4eLkheE+B/OzS/8MILWL58uWB5Te666y7s2bMHWVlZyM7ORlRUlGB7G4nFNvnVYrHAx8cHX3zxBa677jrBezSEdCWz1coEygTtinlepITBi0hO7a+BGzwRem2AKOVf638GqWneEPJj0GRq7nn5R8I2WAICYLzuusseZ/uGbhtv78qHxAMPNOD9993x+OOeiIzsWY9Jbq4cvr6WK7JyN/iaQBihwYfvKeEp79kqrG9q52HmtRXw9Ow4COrJee6MbYLqq6++isWLFyMgIECQssX8QE5OTkZSUhIA4PPPPxcsoyzQPNzQcl6KUPMljhw5gkOHDqH2t6UZxcXFOHLkCO655x5ByheDFCe/ihk0x8bGQqfToaamBgsWLBBsSbqY7+++gMGLSLIP1yMGWZCFDRCl/DEx1Xj/0BBByzx4UImLF4F5v65Aw333XXbPmpa6M1nQ29uK11/X4z//ccP58z3PLHf33Y5ntOyJsEEKjB3bhLWVT/W4LLk38NKf6gB0ngEUEG8ppUqlQmhoqEPJvBxl+zBuampCYWGhoGP4M2bMQHBwMF5++WXce++9CAkJEaznRa/Xo76+HhEREaivrxcsr8nkyZMhl8uRlpaGEydOICIiApMnTxagxs30ej22bt2KxMREwQIu2+RXq9VqD+qEnPwqBlvQXFJSgr/97W94/vnnBV0hFRgYiLlz5yIwMFCU+WGnTp1ymQm7UsHgRSSnMoFh2rOAUpyx4tETFTi1KxxVVRfg4yNMnpNt29SYNLQCvmdOo/IO8TYgWLDAiAULhN8nSExqNbBpU42zqyEY2zfVsLAw+1wPIcfbbXM8bP8KobCwEMeOHUNQUBCOHTuG3NxcQTLKAuLNedm+fTv27NljT1KXn5+P7du34+abb+5x2UDzfKWUlBRMmTJF8ABDJpNBr9cLNkwiJluQXFdXh6NHj9onvwpZvlhzUcRsw95MxKTqfVtWvidigipEKz9mpg7uMCAtuePskF2RlKTGvKbv0LhgAawCXBC6y7YKxrZPSV8nxvmIiYnBrN/mM82aNQsLFy4UZJ6H0WiEXq9HU1MTwsLC7HvOCCEmJgZz5syBt7c35syZI1idgeahkqVLlwIAli5disTEREHKtVgs9p+Wt4Wg1+tRUFCA/Px8FBQUoKysDHp9zz8P9Ho9ysrKUFHR/PlVUVEhSLk2tuXuQr6ez507hxMnTuD06dNoamrC6dOnce7cOcHKF+M9KPZ57u3Y8yICqxXIqgjEn6YaRHsM+ZBBuEaWhtQdPphxS88nfeTkyJGTo8R8vI76d9cKUMPuc7W8B84mxvkQK2uo2Cso1Go1IiIiBH9deHp6IiIiQvAcIaNGjUJVVRUKCwuRk5OD4OBgQfY1App7izZs2IDc3Fy8/fbb8PLyEqTHqGUvFCBsTxQgTq6eN954wz7J2Gq14uWXX8ahQ4fw2muvCVK+GHlebOfZYrGgpqYG77zzjstPjHYlDF5EUFIiR61JiyEjRezYUipxbcBppB4RZvw8KUmDIf1LMegqP1Q6cameWEt4xSZWXhMx80CI0RVum3tQXV2NTz/9FPfccw8CAwMFKdu2L05ERAR27Ngh2L44NmLkCAkPD8eQIUNgNBqRk5ODoKAghIeHC1L2uHHjcOHCBezatQuTJk1CfHw8dDpdj8sVc8KuWBsz/v73v8eiRYtw9OhRrFy5Ek899RRmz54tRJVFy/NiO8/Z2dl44okn8I9//AOjR48WpM59AYMXEWRlyeEOA8Ku7g8xE9ePGVqDD/YPwAcf9Lwb+qt1KswzfAPzQw8JULPuu3SPGUDYuRhiEaN3RIp5IFpO2AWE3SlXrH1xxLRt2zbs3LnTPufl5MmT2LZtG264oec5Dj7//HNs3rwZMpkMn3/+Ob755htcd911WLZsWY/KFXPCrlgbMxYVFSE1NRUFBQWQyWQ4ffo0RowYIUh+GrHyvNheu+7u7pDJZHB3d3f517MrYfAiguwjTYhBDqyDIkV9nPHTZIg7dAJff93zbmiP+nLc6fEdLDdvApw41yQmJgY6ne6SHVxd+w0tVm+RFPNA2HqLjEYjwsLC7P8XammpGPvi2FRWVuLjjz/GfffdJ8gkYACYMmUKFAoFMjIyUF1djYiICEyZMkWQsoODg+Hv74+ioiL4+/vD29sbwcE93zbE1ma1tbUoLCxEbW0tfH19BVtKL0YAeubMGRw+fBh6vR5KpRK5ubk4c+YMxo8f3+OyLyXUBObU1FQcPnwYdXV18PX1RXJyMkwmkyh17o0YvIjg1BEjhuMEzOFTRX0c7zFR2N04Dk2+Pf8wVJaeQMO9dwAajVODF7F3cBWDWL1FUswDcSXmvAi9Lw4A+yaE27Ztw5gxYzBixAhBApgff/wRBw4cQH19PQDg1KlT+PHHHwVZbXTjjTciMDAQzzzzDB544AEMGzZMkKXjqampSE1NhV6vR1VVFdavX4+pU6cKclEVKwAdMGAAhgwZgpKSEhw+fBjBwcEYMECYNBVi5Xk5e/YsMjIyYPptC5bs7GxEREQweHEQgxcRZJ1S4RZtPsTOnmaKj4dh2TLIfvtg7ImmCRPQcP/9cIWFeq60RbwjxO4tEut8iDFPR+zeIrF2lf7444+xbds2AMCqVaswc+ZMPPnkkz0u9+TJk6isrLTfrq+vx8mTJ3tcLtC8dDw/Px/Dhg1Dfn4+Lly4IMjS8bNnz+Lo0aP24FDoi6oYAWhJSQmys7NRU1MDs9mMoqIilJSUCFK2WHle5s2bh4SEBOTm5toDUCE3wOztGLwIzGoFskv6YWhElfgPplKh/g9/EP9xqENS7C2ybXK4adMm+Pn52b9N9rTuYvYW2YYyUlNTodVq7UMOQpR/3333YcyYMVi1ahWeffZZjBgxQoAaA0uWLMGePXtw4sQJFBUVITQ0FEuWLBGkbFuW5KysLCQmJgqWJdl2US0oKMBzzz2HRx55RLAl6WKJiIhAbGwsioqKcOTIEQwcOFDQrMBifIGwzS0yGAzw8PCATqdjnpcuYJ4XgRUVyaE3ajB0MHOU9CW2DzcpBC5A8/DOli1bUFRUhC1btmD9+vXIzMx0drU6lJmZia1btwJoXmYqZJ19fX3tkzvDwsIEm/Oya9cuHDhwAMXFxQCatwfYtWuXIGXbCJ1EztPTE0FBQfDz84OHh4ckMux6eHjAy8sLXl5eUCqV8PLygoeHh2Dli5nnxWAwICIiAgaDgXleuoA9LwLLylLAQ16PkBFe6Pm+yUTiiIqKglKpxJEjRzBx4kT4+/tDK+AwZ2VlJf79739j2bJlgizfBZp7GgIDA/HVV1/hd7/7Hby8vAQNFvv164eZM2cKtuUAAISGhiInJwcNDQ1obGyEWq1GaKgwO8HbVsEYjUZBV8FUVlaipqYGRUVFMBgMKCoqQv/+/QUL6MQY+hs0aBD8/Pxw7NgxfP3114iPj8egQYMEKdtoNKKqqsq+4aNtWLinrz2x8+n0dr0yeFGr1dBoerbz7+XIZDIEBACNjf3bPcZoBOIUR+EWMwRqLy/B6yA227eW3sr2LVWr1Qqaut6VONKGp0+fxsGDB1FbW4uDBw9Cq9Vi9OjRPc4zodfrodfrceHCBZw+fRoXLlxAcHCwIN/cjUYjampqkJ+fb3+OQua98fLywosvvihIWTYbNmxoNeelsbERGzZswD//+c8O/86RNvTw8IBGo4FSqYRGo4FGo7H3QPTE6tWrsWXLFgCAXC7Hyy+/jDlz5vR4CbZNXV0dUlNTMXDgQMEC5o0bN2Lbtm1obGxEYGAgNm3aBJlMhsWLF/e47LS0NKSlpUGj0WDnzp0A4NB7pbM2XLRoEebMmYPz58/jH//4B5544gkMGjTI5Xu5ANf4HO2VwYttqZ/QFAoF/vMfNWpq9O2n+DYace09N6Mu6F2YLvZs92Fn8PLywkUJ1ttRCoXCvgeKkKtVxJj82l2OtGFERASMRiP++9//YuzYsfastT1t+y+//BIpKSkwmUywWCx4/fXXMXPmTNx+++09KhcA0tPTkZaWhoiICGzatAkKhcLl89488sgj2Lt3L06ePInz588jMDAQDzzwQKfn2ZE2jI6OhlKpRHJyMqZOnYoBAwYI0oZ33HEH5s6di8LCwlZzgIT4XLDNWwKA8+fPCzZvafr06bj22mtRWlqKV155BY888giio6MFqXNERAS8vb3bTMgXog21Wi0CAgIwZcoUBAQEwGq1SuLzV6zPUQAOdzz0yuBFTLNmAVVVpnYbTJF9Cj7IwwWBdjQl1ydWt3LL8sUIjFQqFby9vQXdVToyMhJxcXHQ6/UoKSnBkCFDBNvdt+Ww0ezZswUfNhJjh+a8vDycOnUKNTXNm3rW1NQgLy9PkLLVajVUKhVqa2uhUqkEq7Ovry98fX3R1NQEg8EAnU4n2JCRWEnqbJNfbckR/f39BTsfarXa3kOk1WoF7xkRI7NzX8DgRWDy/HxYtFpY/f2dXRW6QsTK89JyewBbYCTU6p2MjAzs27cPQPPYu4eHB0aNGtXj+RLx8fEYOXIkSkpKcPToUSxcuFCw4MV2sc7Pzxf0Yg00By5nzpzBd999h8GDByM6OlqQ8keNGmUfJiksLERgYKBgexvp9fpWm/p5eHgIcnFt2XNtW7EjVKJBsZLU2erc1NSEoKAg+4agQm+l4exeVfofBi9dNXs2POvr2x3nk587B0tkJCCBbeRJGGLleREz4VtTUxP0ej2Ki4uh1+uhUCjs31p7wtbjNGDAANx88832oQwh2C5QtiEvvV4vWA/Xxo0b8dNPP6G4uBhvvvkmZs+eLchQ1yeffIKsrCx7IrLCwkJ88sknuOeee3pc9tatW7F161ZcvHgR//znP6HRaASZ8Gl73ZlMJsTExGDv3r2or68X5HUnVpK6lu+V+Ph47NmzR/DkiK48PNkXMXjpqsmTYdLrYelgkpKph99eSVrE6la+ktsDCD3pToyu8JYbMyYnJwu6MWNkZCSioqJw/PhxREVFCdZbdO+992Lv3r04duwYcnJyEBUVhXvvvVeQsidPngy1Wo0VK1bgxhtvRHR0NPz8/Hpcru11V15ejldffRWLFy9GSEiIADVuJkaSOilupUE9w+Clq559Fg0CpyYn6ROjW/nSXgUhM/fahl1CQkLg6ekJNzc3Qee+iEHMjRlHjhwJg8GAb7/9FhMnThQs0+mBAwfwyy+/2Oe8FBcX48CBA4L0vOzZs8e+MeP3338Pd3d3QZfaNjU1oaqqSpAeObHZ3itGoxGFhYX2nZ+p92LwQiQAMbuVxQiMYmNj4efnh6ysLMyfP1+w7KxiEnNjxqysLPvOwQcOHEC/fv16PP8HaF5qLJfL29wnhBkzZsDX1xcvvvgi7rrrLgwaNEiQHDUZGRk4cuSIfSfsrVu3YuLEiYKcD7HYhhRtdTYYDPD09HT51zR1HzPs9nJiZIakK0usXp1+/fohNDQU/fr1k8wHvW3IQei62lbWmEwmGAwGwXobUlNTUVZWhoaGBgBAQ0MDUlNTBSm7sLAQp06dQkhICE6dOoU9e/agsLBQkLKtVisUCoUou3fbktQJ+ZmUmZmJ9evX2/en2rZtm8tnjKaeYc9LLyb2El6SNrVajauuukqU14MYy45t5W7evBlTp06Fu7u7YOUCzQnk9Ho9GhuFy439wgsvID09HevXr0deXh4GDhyIF154QZCybXsbZWZmYvbs2YL1nsXGxmLo0KEoLy/H6dOnccMNNwg250Ws/amioqKg0+lQUVGBNWvWYMGCBYLubUSuh8FLLybWEl7qHfR6Pfbs2YPJkycLnruirq4OKSkpmDJliqBl19XV4aeffsI111wjaPDS1NSExsZGWCwWNDY2Ctbzsnr1amRkZNgDory8PKxevRqJiYk9Ltu2dPz8+fOCLh23BRN1dXUwGo3w8PAQLMAVK89LTk4O0tPT7ed5//79MJlM/KzrxRi89GJiLeGlK0uMJHVGoxEXLlxAdnY2Lly4YB826mn5er0edXV1rfKPCLUCS6/Xo7y8HABQXl4Os9ksWNk5OTn25HF5eXnIycnBlClTelyubXNDs9kMk8kEpVIpyIogoPl8lJaW4uLFiygtLYVKpRI0z4vBYIBKpYLBYHD5PC+21Ub5+fn48ccfsXTpUgwePLjH5ZLrYvDSi9kmOMbFxQmW3IyuHDGT1KWmptp3N964cSM8PT0RHx+P8ePH96hcMTeb27p1K37++Weo1WqsXr0aVqtV0NU1CoUCSqVS0DkecrkcMpkMCoUCJpMJCoVCsAm7tv18TCYT3nvvPSgUCsyYMaPH+WlsE3ZtPVCbN2/GuHHjBJmwK9aka71ej5qaGpSVlaG6uhplZWUIDg4WLDMwuR4GL70ckytdGWL0joiZpO7s2bPIzs4GAGRnZ0OhUMDPz6/HwUtiYiKmTJlin3uwdOlSweYeJCYmYsSIEVi7di0ee+wx+Pr6Crax39ChQxEfH4/i4mLEx8dj6NChgpRry/pq28hOJpMJNlE1MjISQ4YMsW/D4OnpKVh+GjE32xMjz8vHH39sn6zr4eGBv//975g5cyaefPJJQcon18PghUgAtlVdUVFRggUvYibemjdvHmJiYvDKK6/gD3/4A4KDgwUJBGx7zGi1WiQkJCAiIkKwuRi2i/Ps2bMRGRkp6JyX+Ph4+4TPu+66S7AJqq+++irOnTuHTZs24b333sP999+PJUuWCFJ2fHw8/Pz87NswCJXN2DZht7q6GjU1NViwYAECAwMFqLF47rvvPixcuLDNZpLUezF4Ieoh29AOAPu/QswfETNJnaenJ6Kjo3HzzTcLto/PpeWLsdmcp6cn7rzzTkG/tQPN5zogIADXXHONoDlvgoKC4OPjg4KCAiiVSsTGxiIoKEiQstVqNfz9/TFy5EjBNyK0Pf+wsDD079/f5YecbZtJ2oSFhXHIqJdj8ELUQ2Kv6hJrUzjuZtuaGOc5IyMDaWlpKC8vR1BQELKyshARESFYwjdPT0/MnTtX8OATkOZmhP369cPMmTMFSdZHro3BC1EPib2qi/OWrgyxzrNMJkO/fv0wfvx4wS+qVyKzs5T4+vpynksfweCFqIe4qovaI8X5I0RSwOCFSABS/JZK4rPNH1Gr1Zg7dy4CAwMZ3BIJgMELEZHIGNwSCYsbMxIRSRQ3XqW+isELEZEEtdx4taqqCnq9nkEM9RkcNiIiEpnYGZi58Sr1NQxeiIhEIub+VNx4lfoyBi9ERCIRc38qtVpt39JBqN21iaSCwQsRkUjE3J8KkGYWXCIhMHghIhKJmPtT2crnHBfqi7jaiIhIZOwhIRIWgxciIiKSFAYvREQiMRqN0Ov1rfKxMBcLUc9xzgsRkUjEXG1E1JcxeCEiEonYq42I+ioGL0REIhF7tRFRXyWJ4EWv1+Pdd99FWloa3N3dsWjRIsyZM8fZ1SIicghXGxEJSxLBy9q1a2E2m/Hxxx+jtLQUf/3rXxEaGorY2FhnV42IqFPMx0IkLJdfbdTQ0IB9+/bhzjvvhIeHB6KiopCQkIAdO3Y4u2pERETkBC4fvBQXFwMAwsPD7fcNGjQI+fn5zqoSEREROZHLDxs1NDTA3d291X1arRb19fX226WlpSgtLbXf1mg0GDBggOB1USgUrf7tjWQyWa9+fmxDaesL7QewDXsDtqG4XD54cXNzaxWoAEBdXV2rgGbt2rVYvny5/fayZcuwatUq0erk7e0tWtmuoC9MKmQbSltvbz+AbdgbsA3F4/LBS0hICACgsLAQYWFhAIC8vDxERETYj3nwwQcxf/58+22NRoOqqirB66JQKODt7Y3a2lqYzWbBy3cFWq0WdXV1zq6GaNiG0tYX2g9gG/YGbMPucTQPkssHL25ubpg4cSK++OIL/P73v0dZWRmSk5Px9NNP248JDg5GcHCw/XZFRYWobwqz2dxr33RWq7XXPreW2IbS1pvbD2Ab9gZsQ3G5fPACNPesrFmzBvfeey88PDxwxx13cNkhERFRHyWJ4MXT0xN/+ctfnF0NIiIicgEuv1SaiIiIqCUGL0RERCQpDF6IiIhIUhi8EBERkaTIrFar1dmVkIrS0lKsXbsWDz74YKul2SQdbENpY/tJH9tQ+lyhDdnz0gWlpaVYvnx5q60ISFrYhtLG9pM+tqH0uUIbMnghIiIiSWHwQkRERJLC4KULgoOD8cILL3CcVsLYhtLG9pM+tqH0uUIbcsIuERERSQp7XoiIiEhSGLwQERGRpEhiY0ZXoNfr8e677yItLQ3u7u5YtGgR5syZ4+xqkYPeeust7N69G0rl/17y7777LgICApxYK+rIpk2bkJKSgrNnz2L8+PH405/+ZP9dfn4+Vq9ejbNnz0Kn0+Hhhx/G8OHDnVhbupyO2nDJkiWorq6GXN78HTogIADvvvuus6pKl9HU1IT3338f6enpuHjxIvz9/XHrrbdi2rRpAJz7PmTw4qC1a9fCbDbj448/RmlpKf76178iNDQUsbGxzq4aOejGG2/EPffc4+xqkIN8fX2xaNEiHD16FBcvXrTfbzKZsHLlSsyePRsvv/wy9u7di1WrVuGDDz6Ap6enE2tMl2qvDW2eeeYZxMfHO6Fm5Aiz2QxfX1+sXLkSgYGByMrKwooVK6DT6RAdHe3U9yGHjRzQ0NCAffv24c4774SHhweioqKQkJCAHTt2OLtqRL3WhAkTMG7cOHh7e7e6/9ixY2hsbMRNN90ElUqF6dOnIygoCPv373dSTak97bUhSYObmxvuuOMO6HQ6yOVyDBs2DDExMcjMzHT6+5A9Lw4oLi4GAISHh9vvGzRoEH744Qcn1Yi6IykpCUlJSfD398e8efNw/fXXO7tK1A0FBQWIiIiwDzcAwMCBA1FQUODEWlF3vPXWW7BarQgPD8edd96JYcOGObtK1IGGhgacOXMG8+bNc/r7kMGLAxoaGuDu7t7qPq1Wi/r6eifViLpq3rx5uP/++6HVanHy5Em88sor0Gq1mDBhgrOrRl1UX18PrVbb6j6tVguDweCkGlF3PPHEE4iKigIAJCcnY/ny5Vi9ejUCAwOdXDO6HKvVirfffhuDBw/GqFGjcOrUKae+Dzls5AA3N7c2gUpdXV2bgIZcV1RUFLy9vaFQKDBy5EjccMMN2Ldvn7OrRd3g7u7e5gPSYDDw/Sgxw4YNg0ajgUajwZw5czBo0CCkpqY6u1p0GVarFe+99x4uXLiAp59+GjKZzOnvQwYvDggJCQEAFBYW2u/Ly8tDRESEs6pEPSSTycD8jNIUHh6O/Px8WCwW+315eXmthnVJeuRyOd+TLshqteL9999Hbm4uXnzxRbi5uQFw/vuQwYsD3NzcMHHiRHzxxRcwGAzIy8tDcnIyZsyY4eyqkYP27t0Lg8EAi8WCkydPYvPmzRg3bpyzq0UdMJvNMBqNsFgssFgsMBqNMJlMGDlyJFQqFX744Qc0NTVh165dOHfuHMaPH+/sKtMl2mvD8vJynDhxAk1NTWhqakJSUhJOnz6NUaNGObvKdIm1a9ciOzsby5cvh4eHh/1+Z78PuT2Ag/R6PdasWYO0tDR4eHgwz4vE/OUvf7F/S/D398fcuXORmJjo7GpRB9atW4evvvqq1X0JCQl4/PHHcfbsWaxZswZnz55FUFAQHn74YYwYMcJJNaX2tNeGCxcuxBtvvIHS0lIolUqEhYXhzjvvxMiRI51UU7qc8+fPY8mSJVCpVFAoFPb7b7nlFixatMip70MGL0RERCQpHDYiIiIiSWHwQkRERJLC4IWIiIgkhcELERERSQqDFyIiIpIUBi9EREQkKQxeiIiISFIYvBAREZGkMHghIiIiSWHwQtQHvPjii5DJZJf9WblyJQAgMjLSfp9arUZgYCASEhLw1ltvoa6urlV5O3fuhEwmw+HDh9s81uHDhyGTybBz585W99fV1WHVqlWIi4uDh4cHPDw8EB8fj3fffbfNru0A8M4770Amk+Hee+9t87id/Zw9exaffPIJZDIZKioqWpVbU1ODZ555BldddRU0Gg38/f2xcOHCy+5oPG3aNMhkMjz//POX/d3cuXPbPedEJB6lsytARFeGu7s7UlJS2twfFhZm//8tt9yCJ598EmazGefPn8fPP/+MZ599Fu+99x5SUlIQGhrarce+cOECZsyYgdzcXDz++OOYPHky5HI5Dh48iJdeegkVFRV44YUXWv3NunXrAADr16/HP//5T7i7u2P06NH45Zdf7MekpaXh0Ucfxccff4yhQ4fa7w8ODr5sPc6fP4+pU6eirKwMy5Ytw5gxY3Du3DmsXr0a48ePxzfffIMFCxa0+bt33nkHTzzxBHx8fLr1/IlIWAxeiPoIuVze6U7aQUFBrY656aabcN9992HSpEm47777sH379m499qOPPopTp07hl19+QVxcnP3+GTNm4LHHHmvT65GTk4ODBw9i1qxZSEpKwsaNG7Fo0SJ4e3u3ql9DQwMAYMSIEbjmmms6rccjjzyCnJwcpKamttoEcOHChUhISMA999yDU6dOISgoyP67sWPH4vjx43jrrbewfPnybj1/IhIWh42IqEOjRo3Co48+ih07diA7O7vLf19QUIBvv/0WDz30UKvAxcbLywvTpk1rdd8XX3wBmUyGtWvXQqfT2XtheqKgoADr16+/7O7FKpUKK1euRG1tLf71r3+1+p2/vz8eeeQRvP3226iuru5xPYio5xi8EPUhJpOpzY8jG8vPnDkTAHDgwIEuP+bu3bthsVgwZ84ch//myy+/xOTJkxEREYHbbrsNW7duRVVVVZcfu6Vdu3bBarVedlgIAKZOnYr+/fu3masDAE899RSamprw1ltv9agORCQMBi9EfURdXR1UKlWbn127dnX6t7Z5MefOnevy4xYXF7cqozOpqanIysrC4sWLAQB33HEHjEYjvv322y4/9uXqERER0e4xERERKCoqanN/YGAgHn74Ybz99tuoqanpUT2IqOcYvBD1Ee7u7jh06FCbn/j4+E7/1tY7I5PJuvy4Xf3bL774AiqVCrfeeisAYMyYMRg8eLAgQ0eOaK+ef/rTn9DY2Ii33377itSDiNrH4IWoj5DL5bjmmmva/Hh5eXX6t7beCJ1OBwBQKpvn+pvN5jbH2u5TqVQAYF+hVFBQ0OnjWCwWfP3115g2bRrkcjmqq6tRXV2NBQsWYPfu3SgsLHTgmV5eSEgIACA/P7/dY/Lz8+3HXSooKAgPPvgg3nrrLdTW1na7HkTUcwxeiKhTSUlJAIDx48cDAAICAgBcfhiptLQUQPNQC9A8l0Qul2Pr1q2dPs7PP/+MkpISbN++HT4+Pvaf1157DVarFV9++WW3n8PUqVMhk8nw448/Xvb3e/bsQXV1dZvJwy09/fTTqK+vxzvvvNPtehBRzzF4IaIOHT16FO+99x5mzZqFwYMHAwAGDx6M4OBgbNiwoc3xGzZsQHBwMKKjowE0z3W59dZb8f777+PYsWNtjtfr9fZ5N1988QW0Wi127NiBn3/+udXP6NGjezR0FB4ejoULF+Kzzz7D8ePHW/3OZDLhueeeg7e3N5YsWdJuGcHBwXjggQfw5ptvsveFyImY54Woj7BYLJddLRQQEICoqCgAQFlZGQ4cOACLxYLy8nKkpKTgX//6F8LCwvDRRx/Z/0Yul2P58uV44IEHoFAocOONNwJoDlw++eQTfPjhh63mjrz77rvIzMzE5MmT8Yc//AFTpkwB0Dw5d/Xq1ViyZAnGjRuH9evX4+abb8aMGTPa1PP//u//8Oijj+LEiRMYPnx4t87Be++9h+PHj2PatGl45plncM011+D8+fN45513cPDgQXzzzTetcrxczp///GesXbsWR44cwYABA7pVDyLqISsR9XovvPCCFcBlf+655x6r1Wq1RkRE2O9TKpVWf39/69SpU61vvfWWVa/XX7bcL7/80jpmzBiru7u71d3d3XrNNddY161bd9lj9Xq99W9/+5t1xIgR9uNHjx5t/fvf/27V6/XW//73v1YA1h07dlz27y9cuGBVq9XWZ555xn7fzz//bAVgPXToUJvjP/74YysAa3l5eav7q6urrX/+85+t0dHRVrVabfXz87MuWLDAevjw4TZlTJ061XrDDTe0uX/p0qVWAJf9HRGJT2a1OpDkgYiIiMhFcM4LERERSQqDFyIiIpIUBi9EREQkKQxeiIiISFIYvBAREZGkMHghIiIiSWHwQkRERJLC4IWIiIgkhcELERERSQqDFyIiIpIUBi9EREQkKf8fkuRBrTPe0lgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8781208465767)>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_train = model.predict(X)\n",
    "predictions_train = pd.DataFrame({'X': X.reshape(-1), 'y_hat': y_hat_train}) \n",
    "(\n",
    "    p9.ggplot(wages, p9.aes(\"EDUCATION\", \"WAGE\"))\n",
    "        + p9.geom_point(shape='+', alpha=0.4)\n",
    "        + p9.geom_line(p9.aes(x='X', y='y_hat'), data=predictions, color='red')\n",
    "        + p9.geom_line(p9.aes(x='X', y='y_hat'), data=predictions_train, color='blue')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "compatible-stereo",
   "metadata": {},
   "source": [
    "Now assess the model performance (coefficient of determination R^2 of the prediction) on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "blessed-layout",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.006124658203792932"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=wages_test.loc[:,['EXPERIENCE']], y=wages_test['WAGE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "municipal-pencil",
   "metadata": {},
   "source": [
    "Since this is negative, it means that our model performance is worse than if we just predicted the mean value of `WAGE` for every sample! Compare this to the model performance if evaluated on the dataset used to train it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "prerequisite-sailing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1321728891784656"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X=wages_train.loc[:,['EXPERIENCE']], y=wages_train['WAGE'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "attached-touch",
   "metadata": {},
   "source": [
    "This is an example of 'overfitting' the training set - the model has learned to approximate the noise in our training set, rather than learning to extrapolate or interpolate in a way that generalises to samples it has not seen before."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "better-beach",
   "metadata": {},
   "source": [
    "### Extending to multivariate models\n",
    "\n",
    "As we did with the linear regression, we can extend the SVM and RandomForestRegressor to work with more than one predictor variable. Let's see how good our model is when we include `EXPERIENCE` alongside `EDUCATION`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dressed-witch",
   "metadata": {},
   "source": [
    "Fit our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "large-culture",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ensemble.RandomForestRegressor()\n",
    "model.fit(X=wages_train.loc[:,['EXPERIENCE', 'EDUCATION']], y=wages_train['WAGE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "generic-gospel",
   "metadata": {},
   "outputs": [],
   "source": [
    "ed_min, ed_max = wages.EDUCATION.min(), wages.EDUCATION.max()\n",
    "ex_min, ex_max = wages.EXPERIENCE.min(), wages.EXPERIENCE.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "hearing-routine",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = np.mgrid[ex_min - 1:ex_max + 1:100j, ed_min - 1:ed_max + 1:100j]\n",
    "X = grid.reshape((2, -1)).T\n",
    "prediction = model.predict(X)\n",
    "prediction = np.reshape(prediction, (100, 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "selected-builder",
   "metadata": {},
   "source": [
    "Display the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "martial-jumping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fc88d503550>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x324 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(6, 4.5))\n",
    "ax = fig.add_subplot()\n",
    "ax.set_ylabel('EDUCATION')\n",
    "ax.set_xlabel('EXPERIENCE')\n",
    "wage_min, wage_max = wages.WAGE.min(), wages.WAGE.max()\n",
    "wages_plot = ax.imshow(\n",
    "    np.rot90(prediction),\n",
    "    vmin=wage_min,\n",
    "    vmax=wage_max,\n",
    "    aspect='auto',\n",
    "    extent=(ex_min - 1, ex_max, ed_min -1, ed_max + 1),\n",
    "    cmap=plt.cm.viridis)\n",
    "\n",
    "fig.colorbar(wages_plot, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quarterly-sequence",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "preceding-affair",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "We will work with the built-in `sklearn.datasets` diabetes dataset.\n",
    "\n",
    "This dataset comprises 442 diabetes patients, each with ten baseline predictors: age, sex, body mass index, average blood pressure, and six blood serum measurements. The response variable is measure of disease progression one year after baseline. (Note that it is not neccessary to transform any of these variables prior to training the model - everything is all ready to go straight into `sklearn`.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "sixth-stationery",
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019908</td>\n",
       "      <td>-0.017646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068330</td>\n",
       "      <td>-0.092204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>-0.025930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>-0.009362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2  0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "\n",
       "         s4        s5        s6  \n",
       "0 -0.002592  0.019908 -0.017646  \n",
       "1 -0.039493 -0.068330 -0.092204  \n",
       "2 -0.002592  0.002864 -0.025930  \n",
       "3  0.034309  0.022692 -0.009362  \n",
       "4 -0.002592 -0.031991 -0.046641  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "diabetes_dataset = datasets.load_diabetes()\n",
    "predictors = pd.DataFrame(data=diabetes_dataset.data, columns=diabetes_dataset.feature_names)\n",
    "response = diabetes_dataset.target\n",
    "\n",
    "predictors.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prescribed-confusion",
   "metadata": {},
   "source": [
    "### Exercise 1a\n",
    "\n",
    "Using sklearn, fit a multivariate linear regression using all 442 samples to predict the response variable. Use all 10 predictors, and also score your model using predictions on all 442 samples.\n",
    "\n",
    "### Exercise 1b\n",
    "\n",
    "Do a test-train split, and run _Exercise 1a_ again, this time fitting your model with the training set, and scoring it both with the test set and with the training set.\n",
    "\n",
    "### Exercise 1c\n",
    "\n",
    "As with Exercise 1b, but this time try using the SVM ('linear' and 'rbf') and RandomForestRegressor instead. How do your results compare? Try just using one or two predictors \n",
    "\n",
    "### Exercise 1d (more technical)\n",
    "\n",
    "If you're feeling so inclined, dive into the documentation of [RandomForestRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html) and try tweaking some of the _hyperparameters_ to see how this affects your model score on the training and test datasets ('n_estimators' and 'max_depth' are good places to start). If you want do delve deeper this [blog post](https://towardsdatascience.com/hyperparameter-tuning-the-random-forest-in-python-using-scikit-learn-28d2aa77dd74) is a nice introduction to the concepts of _cross-validation_ and _hyperparameter tuning_ (more on this next week though!).\n",
    "\n",
    "### Exercise 2\n",
    "\n",
    "As a bit of pre-reading for next week, have a read through the [Kaggle Titanic](https://www.kaggle.com/c/titanic) prediction competition blurb (watch the video too). The [discussion forum](https://www.kaggle.com/c/titanic/discussion) is full of interesting beginner material too."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "utility-crawford",
   "metadata": {},
   "source": [
    "## Additional material\n",
    "\n",
    "### Titanic\n",
    "\n",
    "https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv\n",
    "\n",
    "https://www.openml.org/d/40945\n",
    "\n",
    "https://www.openml.org/data/get_csv/16826755/phpMYEkMl\n",
    "\n",
    "http://bit.ly/1juuXIr \n",
    "    "
   ]
  }
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