00 - Tutorial 3 - Feature Engineering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "hide_input": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>html, body{overflow-y: visible !important} .CodeMirror{min-width:105% !important;} .rise-enabled .CodeMirror, .rise-enabled .output_subarea{font-size:140%; line-height:1.2; overflow: visible;} .output_subarea pre{width:110%}</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Global imports and settings\n",
    "from preamble import *\n",
    "%matplotlib inline\n",
    "plt.rcParams['savefig.dpi'] = 120 # Use 300 for PDF, 100 for slides\n",
    "#InteractiveShell.ast_node_interactivity = \"all\"\n",
    "HTML('''<style>html, body{overflow-y: visible !important} .CodeMirror{min-width:105% !important;} .rise-enabled .CodeMirror, .rise-enabled .output_subarea{font-size:140%; line-height:1.2; overflow: visible;} .output_subarea pre{width:110%}</style>''') # For slides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Representing Data and Engineering Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Categorical Variables\n",
    "In scikit-learn, all input features have to be numeric.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### One-Hot-Encoding (Dummy variables)\n",
    "Convert a feature with c categories to $c$ dummy variables:  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Load some data with categorical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>gender</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>occupation</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass   education   gender  hours-per-week  \\\n",
       "0   39          State-gov   Bachelors     Male              40   \n",
       "1   50   Self-emp-not-inc   Bachelors     Male              13   \n",
       "2   38            Private     HS-grad     Male              40   \n",
       "3   53            Private        11th     Male              40   \n",
       "4   28            Private   Bachelors   Female              40   \n",
       "\n",
       "           occupation  income  \n",
       "0        Adm-clerical   <=50K  \n",
       "1     Exec-managerial   <=50K  \n",
       "2   Handlers-cleaners   <=50K  \n",
       "3   Handlers-cleaners   <=50K  \n",
       "4      Prof-specialty   <=50K  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "# The file has no headers naming the columns, so we pass header=None\n",
    "# and provide the column names explicitly in \"names\"\n",
    "data = pd.read_csv(\n",
    "    os.path.join(mglearn.datasets.DATA_FOLDER, \"adult.data\"), header=None, index_col=False,\n",
    "    names=['age', 'workclass', 'fnlwgt', 'education',  'education-num',\n",
    "           'marital-status', 'occupation', 'relationship', 'race', 'gender',\n",
    "           'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',\n",
    "           'income'])\n",
    "# For illustration purposes, we only select some of the columns:\n",
    "data = data[['age', 'workclass', 'education', 'gender', 'hours-per-week',\n",
    "             'occupation', 'income']]\n",
    "# IPython.display allows nice output formatting within the Jupyter notebook\n",
    "display(data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "##### Sanity checking string-encoded categorical data\n",
    "\n",
    "It might be that some people specified gender as “male” and some as “man” by mistake\n",
    "\n",
    "Check the contents of a column by using the value_counts function of a pandas Series:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Male      21790\n",
      " Female    10771\n",
      "Name: gender, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.gender.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Encoding data in pandas (`get_dummies`)  \n",
    "\n",
    "Automatically transforms all columns that have object type (like strings) or are categorical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original features:\n",
      " ['age', 'workclass', 'education', 'gender', 'hours-per-week', 'occupation', 'income'] \n",
      "\n",
      "Features after get_dummies:\n",
      " ['age', 'hours-per-week', 'workclass_ ?', 'workclass_ Federal-gov', 'workclass_ Local-gov', 'workclass_ Never-worked', 'workclass_ Private', 'workclass_ Self-emp-inc', 'workclass_ Self-emp-not-inc', 'workclass_ State-gov', 'workclass_ Without-pay', 'education_ 10th', 'education_ 11th', 'education_ 12th', 'education_ 1st-4th', 'education_ 5th-6th', 'education_ 7th-8th', 'education_ 9th', 'education_ Assoc-acdm', 'education_ Assoc-voc', 'education_ Bachelors', 'education_ Doctorate', 'education_ HS-grad', 'education_ Masters', 'education_ Preschool', 'education_ Prof-school', 'education_ Some-college', 'gender_ Female', 'gender_ Male', 'occupation_ ?', 'occupation_ Adm-clerical', 'occupation_ Armed-Forces', 'occupation_ Craft-repair', 'occupation_ Exec-managerial', 'occupation_ Farming-fishing', 'occupation_ Handlers-cleaners', 'occupation_ Machine-op-inspct', 'occupation_ Other-service', 'occupation_ Priv-house-serv', 'occupation_ Prof-specialty', 'occupation_ Protective-serv', 'occupation_ Sales', 'occupation_ Tech-support', 'occupation_ Transport-moving', 'income_ <=50K', 'income_ >50K']\n"
     ]
    }
   ],
   "source": [
    "print(\"Original features:\\n\", list(data.columns), \"\\n\") \n",
    "data_dummies = pd.get_dummies(data)\n",
    "print(\"Features after get_dummies:\\n\", list(data_dummies.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>workclass_ ?</th>\n",
       "      <th>workclass_ Federal-gov</th>\n",
       "      <th>...</th>\n",
       "      <th>occupation_ Tech-support</th>\n",
       "      <th>occupation_ Transport-moving</th>\n",
       "      <th>income_ &lt;=50K</th>\n",
       "      <th>income_ &gt;50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  hours-per-week  workclass_ ?  workclass_ Federal-gov      ...       \\\n",
       "0   39              40             0                       0      ...        \n",
       "1   50              13             0                       0      ...        \n",
       "\n",
       "   occupation_ Tech-support  occupation_ Transport-moving  income_ <=50K  \\\n",
       "0                         0                             0              1   \n",
       "1                         0                             0              1   \n",
       "\n",
       "   income_ >50K  \n",
       "0             0  \n",
       "1             0  \n",
       "\n",
       "[2 rows x 46 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_dummies.head(n=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Next step: Convert the data_dummies DataFrame into a NumPy array, and then train a machine learning model on it. \n",
    "\n",
    "Be careful to separate the target variable (which is now encoded in two income columns) from the data before training a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X.shape: (32561, 44)  y.shape: (32561,)\n"
     ]
    }
   ],
   "source": [
    "# Get only the columns containing features\n",
    "# that is all columns from 'age' to 'occupation_ Transport-moving'\n",
    "# This range contains all the features but not the target\n",
    "\n",
    "features = data_dummies.ix[:, 'age':'occupation_ Transport-moving']\n",
    "# extract NumPy arrays\n",
    "X = features.values\n",
    "y = data_dummies['income_ >50K'].values\n",
    "print(\"X.shape: {}  y.shape: {}\".format(X.shape, y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now the data is represented in a way that scikit-learn can work with, and we can\n",
    "proceed as usual:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.81\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)\n",
    "print(\"Test score: {:.2f}\".format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Integers can encode categories\n",
    "\n",
    "Categorical variables are sometimes pre-encoded as integers (e.g. 0=green, 1=blue. They should not be encoded as a single continuous variable.\n",
    "\n",
    "To get around this, you need to convert numeric columns in the DataFrame to strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Categorical Feature</th>\n",
       "      <th>Integer Feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>socks</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fox</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>socks</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>box</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Categorical Feature  Integer Feature\n",
       "0               socks                0\n",
       "1                 fox                1\n",
       "2               socks                2\n",
       "3                 box                1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a dataframe with an integer feature and a categorical string feature\n",
    "demo_df = pd.DataFrame({'Integer Feature': [0, 1, 2, 1],\n",
    "                        'Categorical Feature': ['socks', 'fox', 'socks', 'box']})\n",
    "display(demo_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Integer Feature</th>\n",
       "      <th>Categorical Feature_box</th>\n",
       "      <th>Categorical Feature_fox</th>\n",
       "      <th>Categorical Feature_socks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Integer Feature  Categorical Feature_box  Categorical Feature_fox  \\\n",
       "0                0                        0                        0   \n",
       "1                1                        0                        1   \n",
       "2                2                        0                        0   \n",
       "3                1                        1                        0   \n",
       "\n",
       "   Categorical Feature_socks  \n",
       "0                          1  \n",
       "1                          0  \n",
       "2                          1  \n",
       "3                          0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(pd.get_dummies(demo_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Integer Feature_0</th>\n",
       "      <th>Integer Feature_1</th>\n",
       "      <th>Integer Feature_2</th>\n",
       "      <th>Categorical Feature_box</th>\n",
       "      <th>Categorical Feature_fox</th>\n",
       "      <th>Categorical Feature_socks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Integer Feature_0  Integer Feature_1  Integer Feature_2  \\\n",
       "0                  1                  0                  0   \n",
       "1                  0                  1                  0   \n",
       "2                  0                  0                  1   \n",
       "3                  0                  1                  0   \n",
       "\n",
       "   Categorical Feature_box  Categorical Feature_fox  Categorical Feature_socks  \n",
       "0                        0                        0                          1  \n",
       "1                        0                        1                          0  \n",
       "2                        0                        0                          1  \n",
       "3                        1                        0                          0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demo_df['Integer Feature'] = demo_df['Integer Feature'].astype(str)\n",
    "display(pd.get_dummies(demo_df, columns=['Integer Feature', 'Categorical Feature']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Binning (Discretization)\n",
    "\n",
    "The best way to represent data depends not only on the semantics of the data, but also on the kind of model you are using. \n",
    "\n",
    "E.g. Linear models can learn better models by using different feature representations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1165354e0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDIgMCBSID4+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\nMDc1CiUlRU9GCg==\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoYAAAG8CAYAAABDg2QvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAASdAAAEnQB3mYfeAAAIABJREFUeJzs3Xl8U1X6+PHPaZqWTTahKQplFUUU+cqMy1QEBGRYRBZF\nxw3UUbS4MG5UrVWIYuuKPyjqjCOuM6OyiAoCikW0IjojosUNUKgitOxLgTZtzu+PNCFps+9Jn/fr\nlRfk3pvck6TNffqcc56jtNYIIYQQQgiREusGCCGEEEKI+CCBoRBCCCGEACQwFEIIIYQQdSQwFEII\nIYQQgASGQgghhBCijgSGQgghhBACkMBQCCGEEELUkcBQCCGEEEIAEhgKIYQQQog6EhgKIYQQQghA\nAkMhhBBCCFFHAkMhhBBCCAFIYCiEEEIIIeqkxroB8U4p1QoYAPwKVMe4OUIIIYQQ3qQBnYCPtdb7\nA32wBIa+DQAWx7oRQgghhBABuBh4J9AHSWDo268Ab7/9Nj169Ih1W4QQQgghPNq0aRNjxoyBuvgl\nUBIY+lYN0KNHD3r37h3rtgghhBBC+COo4W8y+UQIIYQQQgASGAohhBBCiDoSGAohhBBCCEACQyGE\nEEIIUUcCQyGEEEIIAcis5LDQWnPw4EEOHDiAxWJBax3rJgkRUykpKaSnp2MymUhJkb8/hRAiUUhg\nGKKamhq2bdvG4cOHAUhNTSUlJQWlVIxbJkRsaK2prq7myJEjVFVVkZWVJcGhEEIkCAkMQ7R3714O\nHz5Mq1atyMjIIDVV3lIhtNZUVFSwZ88eysvL6dChQ6ybJIQQwg8SxYTo0KFDGAwGOnToIFlCIeoo\npcjIyGD//v1UVVXFujlCiCiyWCyUlJSwZ88e2rZtS3Z2NkajMdbNEn6SwDBEWmtSU1MlKBSiHqUU\nBoMBq9Ua66YIIaLAYrFQUFBAUVER5eXlju2ZmZnk5OSQm5srAWICkMBQCBEx8geTEI2DxWJhzJgx\nLF26tMHvfXl5Ofn5+axdu5ZFixZJcBjnZES4EEIIIUJSUFDA0qVLARpU5rDfX7JkCYWFhVFvmwiM\nBIYiYA899FBEM0HBPP+WLVtQSvHSSy9FplFCCCHcslgsFBUV+fzeVkpRVFSExWKJUstEMCQwFCJE\n//rXv5g1a1asmyGEEFH18stbGD/+MwYPfp7y8nKfNXy11uzYsYOSkpIotVAEQwJDEXfy8vI4cuRI\nQI/p3LkzR44c4eqrr45QqzyTwFAI0Rh99FEFf/lLFpdc0jqgx+3ZsydCLRLhIJNPRNxJTU0NuB6k\nUoomTZpEqEXhc/ToUdLS0qTgsxAi4Vmtmq5dm9OuXceAHte2bdsItUiEg1ydhFeffvopf/zjH2nS\npAndu3fn+eef93jsa6+9Rr9+/WjatClt27bl8ssv59dff21w3Nq1axkxYgRt2rShefPm9OnTh2ee\necax390Yww8++IDzzjuP1q1b06JFC04++WTuu+8+x35PYww/+ugj+vfvT/PmzWndujUXX3wx33//\nvcsx9vNt2rSJSZMm0bp1a1q1asW1117rWNHGk4EDB7JkyRK2bt2KUgqlFF26dAFg1apVKKX4z3/+\nQ15eHieeeCLNmjXjwIEDAOzbt4+pU6fSqVMn0tPT6dGjB4WFhQ3Ku1itVmbNmkXv3r1p0qQJJpOJ\nyZMns3fvXq9tE0KISNIaUlIgOzsbk8nk1xjDzMxMsrOzo9RCEQzJGAqPvv32Wy688ELat2/PQw89\nRE1NDQ8++CAmk6nBsY888ggPPPAAEyZM4K9//Ss7d+5k9uzZnH/++axbt47WrW1dDR988AGjRo2i\nQ4cO3H777WRmZvL999/z3nvvcfvtt7ttx4YNGxg1ahR9+vRhxowZpKens2nTJp/jVD788EOGDx9O\nt27deOihhzhy5AizZ88mOzubr776yhHA2U2YMIGuXbvy6KOP8tVXX/HCCy+QkZHhdRbd/fffz/79\n+/ntt994+umnAWjRooXLMWazmbS0NO666y6qqqpIS0vj8OHDDBgwgG3btjF58mSysrL47LPPuPfe\ne9m+fbtL1/TkyZN56aWXuPbaa7ntttv45ZdfmDNnDuvWraOkpERKPwghYsJq1aSkKIxGI1OmTCE/\nP9/r8VprpkyZIt9Z8U5rLTcvN6A3oEtLS7U7mzdv1ps3b3a7L9GNGTNGN2nSRG/dutWx7bvvvtMG\ng0HbfnRstmzZog0Gg37kkUdcHv/tt9/q1NRUx/aamhrdtWtX3blzZ713716XY61Wq+P/Dz74oMvz\nP/300xrQO3fu9NjWX375RQN63rx5jm19+/bVGRkZevfu3Y5t69ev1ykpKfqaa65pcL7rrrvO5TnH\njh2rjz/+eI/ntBs5cqTu3Llzg+3FxcUa0N26ddOHDx922Wc2m3Xz5s31Tz/95LI9NzdXGwwGXVZW\nprXW+pNPPtGAfv31112OW7Zsmdvt8SaZfz+EaOwuv3yNXr/e9l1eXV2tR44cqQGtlNKA42a/P3Lk\nSF1dXR3jVie/0tJS+3vfWwcR90hXsnCrtraW5cuXM2bMGLKyshzbe/XqxbBhw1yOXbhwIVarlQkT\nJrBr1y7HLTMzk5NOOoni4mIA1q1bxy+//MLUqVMdGUQ7b10Q9mMXL17s9yoa27dv5+uvv2bSpEku\n41n69OnD0KFDHfW2nN10000u9/v378/u3bsdXb/BmjhxIk2bNnXZ9tZbb9G/f3/atGnj8p4NGTKE\n2tpaVq9e7TiuVatWDB061OW4fv360aJFC8d7K4QQ0WbPGAIYjUYWLVqE2Wxu0KtkMpkwm81S3DpB\nSFdyhGVnf8TevdUxbUObNmmUlFwQ0GN27tzJkSNHOOmkkxrsO/nkk10Cq40bN6K1dnss4Pgi2Lx5\nMwCnnXZaQG257LLLeOGFF/jrX/9Kbm4ugwcPZty4cVxyySUeJ3Fs3brV0db6evXqxfLly6msrKR5\n8+aO7c4BMECbNm0A2Lt3Ly1btgyozc66du3aYNvGjRv55ptvaN++vdvHVFRUOI7bv38/GRkZXo8T\nQohos1pxBIZg+67Py8tj2rRplJSU8NJL39K+fTtmzrxEAsIEIoFhhAUakCUiq9WKUor3338fg8HQ\nYH/9MXeBatq0KatXr6a4uJglS5awbNky3njjDS644AJWrFjh9pzB8PQ82kdtLl/qZwvB9p4NHTqU\ne+65x+1jevbs6TguIyOD119/3e1xngJLIYSINK017jp7jEYjAwcOZN26E2jRIlWCwgQjgaFwq337\n9jRt2pSNGzc22Pfjjz+63O/evTtaa7p27eoIaNzp3r07AKWlpQwZMiSg9qSkpDB48GAGDx7MU089\nxcyZM7n//vspLi52+1ydO3d221aAH374gXbt2rlkC0MRzCow3bt359ChQz7fh+7du/Phhx+SnZ3t\nNsAUQohYqZ8xrC8lRWG1hvaHtYg+GWMo3DIYDAwbNoy3336bsrIyx/bvv/+e5cuXuxw7btw4DAYD\n06dPd7tG5u7duwE488wz6dq1K7NmzWLfvn0NjvPEXTHUvn37AlBVVeX2MR06dKBv3768/PLLLucq\nLS1lxYoVjBgxwuP5AtW8eXP2798f0GMmTJjAmjVrGryXYCtjU1NT4ziutrYWs9nc4LiampoG76MQ\nQkSL1eo+Y2iXkmILHkVikYyh8Gj69OksW7aM/v37k5OTQ01NDbNnz6Z379588803juO6d+/Oww8/\nzL333suWLVsYM2YMxx13HL/88guLFi3ixhtv5K677iIlJYVnn32Wiy66iL59+3LttdfSoUMHfvjh\nBzZs2OA2SAKYMWMGq1evZuTIkXTu3JmKigrmzp1Lx44dOe+88zy2//HHH2f48OGce+65XH/99Y5y\nNa1ateKhhx4K2/vUr18/3njjDe644w7++Mc/0qJFCy666CKvj7n77rt55513GDVqFJMmTaJfv35U\nVlby7bffMn/+fLZs2UK7du0YMGAAkydP5tFHH+Xrr7/mwgsvxGg0snHjRt566y2eeeYZLrnkkrC9\nFiGE8JetjqFkDJONBIbCoz59+rB8+XLuuOMO8vPz6dixI9OnT2f79u0ugSFAbm4uPXv25Omnn2b6\n9OkAdOrUiQsvvJDRo0c7jhs2bBjFxcVMnz6dJ598EqvVSvfu3bnhhhs8tmP06NFs2bKFF198kV27\ndjkCpunTp9OqVSuPjxsyZAjLli3jwQcfJD8/H6PRyIABAygsLHQ7ISRYOTk5fP3118ybN4+nn36a\nzp07+wwMmzVrxscff8zMmTN56623eOWVV2jZsiU9e/Zs8Lqee+45+vXrx/PPP899991HamoqXbp0\n4aqrrpJCsUKImLHNSva8PyVFUVMjKcNEo0IdWJ/slFK9gdLS0lJ69+7dYP/PP/8MQLdu3aLcMiHi\nn/x+CJG8Ro36lNmz/4+uXd2P137++c1UVVm57Tb3FStEZGzYsMFe/eM0rfWGQB8vYwyFEEIIETB/\nMobSlZx4JDAUQgghRMBsk098jTGMYoNEWEhgKIQQQoiA2SafeN5vm5UsGcNEI4GhEEIIIQLmvCSe\nO9KVnJgkMBRCCCFEwKxWfNQxlK7kRCSBoRBCCCECprWvjKF0JSeipAsMlVJ/VErNUUptUEpVKqXK\nlFJvKqU8r9UmhBBCiIDIknjJKRkLXE8DsoG3gG+ATOAW4Cul1Dla69JYNk4IIYRIBlr7WhJPupIT\nUTIGhk8BV2itq+0blFJvAN8CucBVsWqYEEIIkSx8ZwylKzkRJV1gqLX+zM22jUqpDUCvGDRJCCGE\nSDq2Ooae9yslXcmJKOnGGLqjbBU4TcCuWLdFCCGESAa2Ooa+MoZRbJAIi6TLGHpwJXAikO/tIKVU\nBtC+3ubukWqUEEIIkahkSbzklPQZQ6XUKUARsAZ42cfhOUBpvdviiDYwjr300ksopdiyZYtj28CB\nAxk4cGDM2iSC06VLFyZNmhTrZgghkoh/S+JJYJhokjpjqJTKBJYA+4FLtNa1Ph4yF9tsZmfdacTB\noRBCiMbDYrFQUlLCnj17aNu2LdnZ2RiNRrfH+rckXoQaKiImaQNDpVQr4H2gNdBfa/27r8dorSuA\ninrPE5kGJqgVK1bEugkiCD/++CMp3r7BhRCNmsVioaCggKKiIsrLyx3bMzMzycnJITc3t0GAKBnD\n5JSUgaFSqgnwLtATGKK1/i7GTUoaaWlpsW6CV5WVlTRv3tzv448ePUpaWlrUgqZA2xcu6enpUT+n\nECIxWCwWxowZw9KlSxsEeuXl5eTn57N27VoWLVrkEhz6zhhKYJiIki6FoJQyAG8A5wKXaq3XxLhJ\nSaX+GMNVq1ahlOLNN9/kkUceoWPHjjRp0oTBgwezadOmBo9fu3Ytf/7zn2nVqhXNmjVjwIABlJSU\nuByzdetWcnJyOPnkk2natCnHH388l156qctYRzg2BvLjjz8mJyeHjIwMOnbs6LHt9rb+5z//IS8v\njxNPPJFmzZpx4MABAPbt28fUqVPp1KkT6enp9OjRg8LCQqz1+kJ2797N1VdfTcuWLWndujUTJ05k\n/fr1KKV46aWXHMdNmjSJFi1asHnzZkaMGMFxxx3HlVdeGdB7cfDgQaZOnUqXLl1IT08nIyODoUOH\n8tVXXzmO2bhxI+PHjyczM5MmTZrQsWNHLr/8cvbv3+84xt0Yw59//plLL72Utm3b0qxZM8455xyW\nLFni9j3z9/MVQiSegoICli5dCtiKVjuz31+yZAmFhYUu+2yTT2RWcrJJxozhk8BobBnDtkopl4LW\nWuvXYtKqJFdQUEBKSgp33XUX+/fv57HHHuPKK69k7dq1jmM++ugjhg8fTr9+/XjwwQdJSUlh3rx5\nXHDBBXzyySecddZZAHz55Zd89tlnXH755XTs2JEtW7bw7LPPMnDgQL777juaNWvmcu6cnBzat29P\nfn4+lZWVPttqNptJS0vjrrvuoqqqirS0NA4fPsyAAQPYtm0bkydPJisri88++4x7772X7du3M2vW\nLACsVisXXXQRX3zxBTfffDOnnHIKixcvZuLEiW7PVVNTw7BhwzjvvPN44oknHG3397246aabmD9/\nPrfccgunnnoqu3fv5tNPP+X777/nzDPPpLq6mmHDhlFVVcWtt95KZmYm27Zt47333mPfvn20atXK\nbbvKy8v505/+xOHDh7nttts4/vjjefnllxk9ejTz589n7NixAX++QojEY7FYKCoqQinVICh0ppSi\nqKiIadOmObKGVit+rHwiGcOEo7VOqhuwCtCebkE8X29Al5aWanc2b96sN2/e7HZfops3b54G9C+/\n/OLYNmDAAD1gwADH/eLiYg3oXr166aqqKsf2Z555RgP622+/1VprbbVa9UknnaSHDRumrVar47jD\nhw/rrl276qFDh7psq2/NmjUa0K+88kqD9p133nm6pqbG5+uxt7Vbt24NzmE2m3Xz5s31Tz/95LI9\nNzdXGwwGXVZWprXWesGCBRrQs2bNchxTW1urL7jgAg3oefPmObZPnDhRAzo3N9flOQN5L1q1aqWn\nTJni8TWtW7dOA/qtt97y+to7d+6sJ06c6Lg/depUDehPPvnEse3gwYO6a9euukuXLrq2tlZr7f/n\n60ky/34IkQzsv+P+3oqLix2PPe20Zbq21urxuVeuLNdTpvwvCq9COCstLbV/Xr11EHFU0nUla60H\naq2Vp1us25esrr32Wpfxh/379wds3ZUAX3/9NRs3buSKK65g9+7d7Nq1i127dlFZWcngwYNZvXq1\no8u2adOmjuexWCzs3r2bHj160Lp1a5cuVLsbbrgBg8Hgd1snTpzocg6At956i/79+9OmTRtH23bt\n2sWQIUOora1l9erVACxbtgyj0cgNN9zgeGxKSgpTpkzxeL6bb77Z5X4g70Xr1q1Zu3Ytv//ufu6U\nPSO4fPlyDh8+7Pd7sHTpUs466yzOO+88x7YWLVpw4403smXLFr77znVYrq/PVwiRmPbs2RP08b4z\nhtKVnIiSsSs5vrzyCrz6qvdjsrLgn/88dn/LFnAKPDz6xz+gS5dj96+/HsrKGh539dVwzTX+tDZo\nWVlZLvfbtGkDwN69ewHbODjAY5crwP79+2nTpg1Hjhzh0UcfZd68eWzbts2le8N53Jxd165dA2qr\nu+M3btzIN998Q/v29eub21RU2Carb926lQ4dOjTozu7Ro4fbx6WmpjYY9xjIe/HYY48xceJEOnXq\nRL9+/RgxYgTXXHMN3bp1c7yWO+64g6eeeorXX3+d/v37M3r0aK666iqP3cj213H22Wc32N6rVy/H\n/tNOO82x3dfnK4RITG3btg36eK1lVnIyksAw0q65JvCgrEsX+OCDwM/lHFxGmaeMnT2os2fAHn/8\ncfr27ev22BYtWgBw6623Mm/ePKZOncq5555Lq1atUEpx+eWXN5gIAjTI/vni7nir1crQoUO55557\n3D6mZ8+eAZ3DLj09vcGM50DeiwkTJtC/f38WLVrEihUrePzxxyksLGThwoUMHz4cgCeffJJJkyax\nePFiVqxYwW233cajjz7K559/7nUyTiB8fb5CiMSUnZ2NyWSivLwCW++je0opTCYT2dnZLtu8SUlR\nyFdE4pHAUERF9+62lQVbtmzJkCFDvB47f/58Jk6cyJNPPunYdvToUfbt2xfR9h06dMhn2zp37kxx\ncTGHDx92yRoGMkM3kPcCoEOHDuTk5JCTk0NFRQVnnnkmjzzyiCMwBDj99NM5/fTTycvL47PPPiM7\nO5vnnnuOhx9+2OPr+PHHHxts/+GHHxz7hRDJz2g0MmXKFPLzva4Yi9aaKVOmeCx27Y6tK1kiw0ST\ndGMMRXzq168f3bt354knnuDQoUMN9u/cudPxf4PB0CATNXv2bGprfS1cE7wJEyawZs0ali9f3mDf\nvn37qKmpAWDYsGFYLBb+8Y9/OPZbrVaKior8Ppe/70VtbW2DrvOMjAxOOOEEqqqqADhw4ICjbXan\nn346KSkpjmPcGTFiBF988QVr1hyr5lRZWcnf//53unTpwqmnnur36xFCJLbc3FxOO20Q0DALaL8/\ncuRIpk2bFtDzSldyYpKMoYiKlJQUXnjhBYYPH07v3r259tprOfHEE9m2bRvFxcW0bNmSd999F4BR\no0bx6quv0qpVK0499VTWrFnDhx9+yPHHHx+x9t1999288847jBo1ikmTJtGvXz8qKyv59ttvmT9/\nPlu2bKFdu3aMGTOGs846izvvvJNNmzZxyimn8M477zgGZPuzUo6/78XBgwfp2LEjl1xyCWeccQYt\nWrTgww8/5Msvv3RkUz/66CNuueUWLr30Unr27ElNTQ2vvvoqBoOB8ePHe2xDbm4u//73vxk+fDi3\n3XYbbdu25eWXX+aXX35hwYIFskqKEI2I0Wjk2mvn8PHHL/DFF/9mx44djn0mk4kpU6a4lKnxly0w\nDHdrRaRJYCiiZuDAgaxZswaz2cycOXM4dOgQmZmZnH322UyePNlx3DPPPIPBYOD111/n6NGjZGdn\n8+GHHzJs2LCIta1Zs2Z8/PHHzJw5k7feeotXXnmFli1b0rNnT6ZPn+6YyGEwGFiyZAm33347L7/8\nMikpKYwePZoHHniA/v3706RJE7/O58970axZM3JyclixYgULFy7EarXSo0cP5s6d65jpfMYZZzBs\n2DDeffddtm3bRrNmzTjjjDN4//33Oeecczye32Qy8dlnnzFt2jRmz57N0aNH6dOnD++++y4jR44M\n8d0UQiQaq9XAmDG3Mn9+ISUlJdx1Vwl33HEml146JOCA0E66khOTksHj3imlegOlpaWl9O7du8F+\ne7kO+yxR0Ti9/fbbjB07lk8//dRlcHZjJ78fQiSGmTO/JyurGVddZRtffPHFJTzxRB9OOuk4j4/p\n3Xs5GzZ4/oP9q6/28vTTP/Hqqw0rIIjI2bBhg72qxGla6w2BPl4yhkIE6MiRIy4zm2tra5k9ezYt\nW7bkzDPPjGHLhBAiONXVVtLSjg0hMRgUtbXeE0e+EkuBdCVbLBZKSkrYs2cPbdu2JTs7O+hMpQiN\nBIZCBOjWW2/lyJEjnHvuuVRVVbFw4UI+++wzZs6cGXDpHCGEiAfBBIa++NOVbLFYKCgooKioiPLy\ncsf2zMxMcnJyyM3NlQAxyiQwFCJAF1xwAU8++STvvfceR48epUePHsyePZtbbrkl1k0TQoigWCxW\njMZjgaE/q5b4U8fQW2BosVgYM2YMS5cubfBc5eXl5Ofns3btWhYtWiTBYRRJYChEgK644gquuOKK\nWDdDCCHCJjIZQ+9dyQUFBSxduhRo2C1tv79kyRIKCwvJy8sLqS3Cf1KTQgghhGjkot2VbLFYKCoq\n8pl1VEpRVFSExWIJqS3CfxIYCiEiRqoeCJEYLBYdockn7o8pKSmhvLzc53NordmxYwclJSVejxPh\nI4FhiJRS1NTUyAVQiHq01tTW1kqxbCESQHW1FaPxWPYuHKuWeOtKti8K4K9AjxfBk2/sELVo0YLa\n2lq2b9/eYGkyIRorrTUVFRXU1taSnp4e6+YIIXwIpivZ9+QTz13Jbdu2Dah9gR4vgieTT0LUpk0b\nDh8+zP79+9m/fz+pqamkpKT4tTSaEMnInimsra2ladOmmEymWDdJCOFD5OoYuj8mOzsbk8lERUWF\n1+dRSmEymWThgCiSjGGIUlNTycrK4sQTT+S4444jNTVVgkLRqCmlSEtLo3Xr1mRlZUlXshBxxmKx\nsGrVKhYuXMiqVauwWCxYLA0Dw0h2JRuNRqZMmeLXGMMpU6ZIuZookoxhGCilaNmyJS1btox1U4QQ\nQgi3vBWTbt58BPCUY1tKSmS7kgFyc3NZu3YtS5YsQSnlEiTa748cOZJp06b5eGUinORPeSGEECLJ\n2YtJ5+fnU1FR4bKvvLyczZtfZPLkvzjKwoSvjqHn5zAajSxatAiz2dxgyInJZMJsNktx6xiQjKEQ\nQoiwkTVv45M/xaQ//PB9RzHpaBS4BltwmJeXx7Rp0+TnJk5IYCiEECJksuZt/HIuJu1rokdRURHT\npk3za0k835NPfK+VbGc0Ghk4cKBfx4rIksBQCCFESGTN2/hmLybti3MxaYOhbcS7kkV8kjGGQggh\nQhLImrci+oIpJh2OOoZK+c46ivgjgaEQQoigyZq38S+YYtLhqmMoq4IlHgkMhRBCBE3WvI1/9mLS\n/gTvmZmZZGdnR3xJvGTlrkZkopHAUAghRNBkzdv4F0wx6fDMSvZ/8kmis1gsmM1mOnXqxKBBgxg/\nfjyDBg0iKysLs9mcUAGiBIZCCCGCJmveJobc3FxGjhzpdp89k+hcTNpXYKi19qPAdeOYfOKrRmR+\nfj5jx45NmOBQAkMhhBBBC6abUkSfvZj03Xfnk5bmGpy7Kybta0k8rW0ZQW8aS1dysk2+knI1Qggh\ngmbvpszPz/d6nKx5G33uio3feOPdfPfdIO66C6/FpH0tiWe1+pMxjExXcjwVUQ+mRmS8/w5IYCiE\nECIkzmve1idr3kaft2Lj48ZdR7Nmwxk48Dyvz+GrK9lq9TdjGL7AMB6LqAdTIzLeC3lLV7IQQoiQ\n2LspH3hgOqmpbVz2yZq30eVrvNvcuTNZsybX53g3f8YYpqT4M8bQ/7Z7E6/j+JJx8pUEhkIIIUJm\nNBq55577OOOMf1FcXEzHjnkUFxdTVlZGXl6eBIVR4s94t99+K/E53s02xtDzfqvVVsDam3B2Jcfr\nOL5knHwlXclCCCHCwmrVpKba1rxt2bIq7rvMko2/490A5syZ43W8W0oKPscY+soYKqU4dKiGjz6q\n8HpcfU2apPCnP7Vz3I/ncXz2yVcVFRU+22YymRJi8pUEhkIIIcJCa99ZJBE5/o53A1v36+TJk3n+\n+efdBlEGg8Ji8Zwy9PezvuKKLBYu/M2vNtnNn/8bGzcO57jjbO2K53F8yTj5SgJDIYQQYeHPTFUR\nOYGOX5s3bx4VFRVux3/6nnziO2MIMHPm6QG1CeCbb/Zz9KiV446z3Y/3cXzOk6/qZzUTcfKVjDEU\nQggRFv6iwWXnAAAgAElEQVTUthOR06pV64Af42lcnq8Zxf4GhsFIT0+hqqrWcT/ex/HZJ1+ZzWZM\nJpPLvkScfCW/wkIIIcLCn9UwROT07XsOqamtA/oM7OPy6s/m9T0rOXLDBtLTDVRVHevGToQi6kaj\nkby8PMrKyiguLmbBggUJO/lKAkMhhBBh4U9tOxE5SqVywgljfE48ceY8Ls9ZuLqSg2HLGB4LDINZ\n6zlWjEbb5Ktx48YxcODAhAoI7eRXWAghRFhIxjC2amqsdO58pcc1kb2pPy4vthlD165kcF3ruf7P\nmLu1nkXwJDAUQggRFv7UthORU1OjSUuzjXe77rrrAnps/XF5sR1jaKC62nVGdLKN44tnMitZCCFE\nWPizGoaInJoaTWqqwmg08txzz/Hee+81WCWkPk/19cKxJF6w0tJcu5Lt7OP4pk2bFjdrJScjCQyF\nEEKEhdQxjK3aWk1qqi1aMxqN3HLLLUHX19O6hp9//oKFC39yG3xFcthA/TGG9dnH8YnIkK5kIYQQ\nYRHJ7kXhW02NFYPh2PsfzLg8i8WC2Wzmxhv/yOuvX8/48eMZNGgQWVlZmM1mx+zlSGYM3Y0xFNEj\nGUMhhEgSFoslpl1szhlDpWQySrTZu5Lt7OPyCgsLKSoqYseOHY59JpOJKVOmuCwfZ7FYGDNmTN2a\nxK6fW3l5Ofn5+axdu5ZFixZFtJh5/XI18WbSpC/49NNdpKcb/H5ME13NoNr1qLZteXxNTgRbFzoJ\nDIUQIsFZLBYKCgooKipyWTosMzOTnJwccnNzoxIg2oIF2/9tkxfA4P+1U4SofmAIgY3LKygoqAsK\nAVzHF9pLxdgLYl9zzR0RzhjGb2B44EANS5b05+STj/N+YE0NlJTA4sXwzTdwwQVw2bDoNDIEEhgK\nIUQCc87y1M/g1M/yRDo4tK18YmtDSootUHTu2hSRVVNjdYwxrM/XuDyLxUJRUVGDJd3qsxfEvuyy\nWyJax7D+rOR4Yhsy4ceBq1fD8uVw6aXw+OMJ81eSjDEUQogE5pzlqX9Br5/liTTnrmRfs1pF+Nkm\nnwQXrJWUlFBeXu5XEekdO3bw7LNLIjbRyDYrOX7HGLodS7tlC/zrX67bLrgACgvh3HMTJigECQyF\nECJhOWd5vPG07Fm4OY8781UHT4RfTU3wGdr6Ba59KSvbyQ03dAvqXL7E+xhDxx9AO3fCvHkwbhzc\nd58t+Atg1Zl4JV3JQgiRoOxZHl+clz2LZJkPW1ey7f/2MYbJINaTevzlboyhv+oXuPblllv6MnDg\niV6PCfZ9S09P4dChmoDaEzWVlQzY8ykdcp+Dowdg5EgoKoIOHWLdsrCRwFAIIRJUoFmeQI8PlPMs\nZPsYw0QWL5N6/OVtjKEv2dnZmEwmKioqfI4xdFcQ21mo71tclqvRGu68E0pL6Vh1Cvum3EPTAX1i\n3aqIkK5kIYRIUIFmeQI9PlDOte0SvSvZPqknPz+/weoh9kk9Y8eOjXj3fCBCyRgajUamTJni1xhD\ndwWx7cLxvsVFV7LW4NxGpWDyZFi2jJfaXUJNtx6xa1uEScZQCCESVDizPOHgmjFM7MkngUzqycvL\ni3r73All8gnYCmKvXbuWJUuWNJidbL9fvyB2feF439LTU/jhh4O8//52v9qtlGLQoPYB1RX0aPNm\nePttWLEC7r8fzj//2L6TT8ZisbBr1zqWLdvGSSd1iNthBaGQjKEQQiSocGV5wsVqdZ2VnKhjDONt\nUo+/Qpl8AscKYpvNZkwmk8s+k8mE2Wz2WvYoXO/b//1fa7p0acbKlRV+3XJzv+Hzz0MYJlFeDi+8\nABdfDA8+CJ07w/z5LkGhfUWYTp068b//TeXGG69wuyJMMpCMoRBCJLBwZHnCRWvdoI5hIoq3ST3+\nCmWMoV0gBbHrC9f71rFjMx5//Ay/2/y3v33NkSMBjkk8cMBWY3DxYjh0CC66CP7xD8jIaHBoPNUK\njQYJDIUQIoEFuuxZJDnXMUzkMYbxNqnHX6GMMazPV0Fsd2L1vjVtauDo0QADw5UroawMpk+H7t29\nHpqIwwpCIYGhEEIkuFCyPOHkXPg3kcvVxNukHn+FMzAMRqzetyZNDJ4zhlYrfPGFbezglVce2z52\nrF/PHeiKMNH6IyySJDAUQogkEUyWJ5xcM4aJ25Xs76QesJVgifSkHn/ZJp/EbupArCZDuc0Y/vCD\nrZv4ww+hd29bEeogJOqwglCE9BOklPpIKTXYy/5BSqmPQjmHEEKIxGBb+cT2/0ReEs/fST1AVCb1\n+KumxhrTtaljNRmqadO6jOG2bfDcczBqlG0pupNPts0wnjXLdXZxABJ1WEEoQv3TYiBg8rI/AxgQ\n4jkCopRqoZSarpRappTao5TSSqlJ0WyDEEI0RraVT5JjSbzc3FxGjhwJ0GDCgf1+q1ZnR2VSj79i\n3ZUM/r1v4Z4M1SKliov+dSfcfjs0bQovvWRbqm7MGGjePKTnTtRhBaEIR1eyt9/8HsDBMJwjEO2A\nfKAMWI8teBVCiKS0du1uv4oBn3FGa1q1imxmq+Hkk4ieLqKcJ/XMmjWb3buPFWs2mUzcdFMOixb9\nIW6yhRAfgWFUJkNVVdnWJU61hTDGVi14+7y/cZM52zbGdvXqsI2xjbdaodEQcGColJoITHTalKeU\nusHNoa2BPsDSINsWrO1AB631DqXUH4Avo3x+IYSIir17q7n44hLGjvW+Zm1p6QEuv7wTU6ZEdrUG\n18kniTvG0M4+qef886+nsPAdrr++vSPgMBhSeeutFbFuootwlKsJh4hMhqqthbVrbeMG//tfePZZ\n6NkTgNRUK/PXzOeRTpeHfelCe/d4fn6+1+OiVSs0GoLJGDYD2jvdPw6o/3ehBiqB54AZwTUtOFrr\nKmCHzwOFECLB1dRoevY8jmef7ef1uOee2xyVJcaSpVxNfQaDkRNO6Me4cX9w2e7HEMSoqq3VGI2x\nDwztQp4MpTV8951tnGBxMZxxhq0I9SOPOLKFFouFJ564gf/+d2XEagzGU63QaAg4MNRaPws8C6CU\n+gW4XWv9TrgbJoQQwjur1b+VLqI1EcQ2+SQ5lsRz5pwJjWc1NZomTeK/nT799pstGFy6FE44wTZW\n8I47bOMH6ykoKOC//10JRK7GYDzVCo2GkMYYaq27hqsh8UAplYFrNhTAe+VLIYSIEX8DlmgFhrbJ\nJ8fOmchjDJ05Z0I9sVgsMa0hCfExxjAsVq+GNm3g9ddt/3oQTI1BIKjPybl7/NRTn+Hhh7tgMrVL\nyrWSQwoMlVJZ/hyntS4L5TxRlAM8GOtGCCGEP6zWY4GYN9ELDJ0zhok/xtDOWwBusVgoKCigqKgo\n7OPbvHEXiMbLGEO/HTkCH3wAlZXwl78c237FFX4F2oHWGJw8eTJLly4N6XMyGo00b96HceOGxFW3\nfTiFOit5C95nJdsZQjxPtMwF3qq3rTuwOAZtEUIIr2pr4ytj6ByoJtMYQ08ZQ61ror6GrrdA9NRT\nL2HSpKlhOU/E1NRASYmtq/jbb+GCC1yKTwcSaAdaM3DevHlh+Zz8ySAnslADw+toGBgagC7ANUAF\nUBTiOaJGa12Brc0O9X+IhBAiXngaY1g/26J1pxhkDJOnK9lTxnDXrjf4/vvoraFrsVi8BqI7dsxh\n377vuPzyZfHVvak1rF9vm1G8ejX84Q8wYQI88YSt7EwdX6+vfgAXTM3AcHxOiTLmNFgh5UG11i9p\nrV+ud3tRa50P9MY2g7lVWFoqhBDCRf0LlMViwWw206lTJwYNGsT48eMZNGgQt912LsXFz2KxWCLc\nnuRYEq8+dxkii8XCnj3v+kwe2Me3heO9LygoYOlS74HoV199RGFhYcjnCpvffoPhw+H55+Hss20T\nSgoL4dxzXYJC8O/12QM4OFZjMBwJnEA+J+ef82QUsQ5yrXUlMA/4W6TOIYQQjZlz160925Kfn09F\nhUvHB/v27WLVqjmMHTs2osGh1scC1UReEq8+dxmikpISamv3+bX8m30N3VA4T7TwLnyBaFB273at\n49OhA7z5pq3u4J//DOnpbh/m7+tzDuACWbrQl0A/p2TuTYz0yMkUIDPC52hAKXWLUioPW1c3wEVK\nqby6m2QwhRBJwXmMobdsi33Ej3O2JRKStY6hu4xhtNfQtU+08B0EhScQDUhlJSxcCBMnwqRJsHfv\nsX0GA7Rs6fMp/H199QM4f5bgC0QyrHUcqogEhkqplkqpUcDdwLpInMOHuwAzcHPd/XF1982A57nv\nQgiRQOxjDIPJtkSqPa5rJUfkNFHnvAa0XbTX0I12IOqTxQIffWRbn3jMGNi0Ce67D959F4J4rcG+\nPnuNQbPZjMlkcjnGZDJx3XXXuXu4R2VlZbHLtsaJkAJDpZRVKVVb/wbsBd4BDmMrARNVWusuWmvl\n4bYl2u0RQohIsAdiwWZbws01Y5g8Ywxthbtdt2VnZ5Oa2tqvYDwzMzPkNXSjHYh6tG4dPPCArVt4\n5Uq48kpYvhzuuQdOPjnopw3l9dlrDJaVlVFcXMyCBQsoLi6mrKyM5557LqBxiH/729/IysrCbDY3\n2gAx1FnJM2g4K1ljCww3Ayu01jUhnkMIIYQb9jGG8ZJNcg6gkr0r2Wg00r79aLZvf8XHY8Ozhq59\nokVFRYXPYs4mkynkQNSjb76B88+3BYdpaWF72nC8Pk9L8Pmz1rGzSJUaShShzkp+SGs9vd5thtZ6\nttZ6qQSFQggROfYxhvGSTXLucm0MS+KdeOIVDBs2AvA8vi1ca+j6O9EiXIEoO3bAP/4BCxa4bp84\nEYYODWtQCJF9fd7GIXo6B0R+TG68CtsYQ6VUhlLqrLpbRrieVwghhHv2MYb+lu0IV7emJ86Ztcaw\nJF5ampFXXnnT4/g2s9kc1oyTPxMtQgpEDxywzSC+8kq46SZbOrp//5DaHIhIvT5v4xC9ifSY3HgV\ncmColBqslPovsB1YU3fbrpT6r1JqSKjPL4QQwj17Jivq2SQf7YHkG2PoLmNoNKagVKrH8W15eXlh\nfa/tAU779teELxCtqrKNEbz5Zrj0UlvdwRkzbCuTXH89ZEQvz+NrIkkogbbzOMSnn37ar8dEekxu\nvAp1reSx2JaQKwceA36q23UycDXwvlJqgtZ6UUitFEII0YBzHcPc3FzWrl3LkiVLUEq5BIn2++Hq\n1vSkMZWrAUhNVdTU2NKinsa3hZvRaKRdu7+wfv0LPtcS9umrr+Duu+G88+DGG6Fv35hXbrYHcNOm\nTQv99Xl4/qysrIAe09hK2IQ6+eRhoBTor7U+6LxDKTUT+LTuGAkMhRAizJzrGNqzLYWFhRQVFbFj\nxw7HcW3bticrayyLFs2O6EB62+ST5CtX4yljmJqqsFhiE/wGFYj++it06nTs/mmnwbJlEIeTKyIZ\naMfLmNx4FWpg2A3IrR8UAmitDyil/gk8GuI5hBBCuFF/rWRP2ZYmTXoxd+7WiM+utE0+sf0/mbqS\nPWUMjcYUR8Ywbv3+O7zzDrz3HphMtqXpUusu/WGeQJIo4maGd5wKNTD8AfA2AMHEse5lIYQQYeR5\n7JtrtmXdur1RmSGs9bGModY1rF9fgsVicOkKtFgsEekijCRvGcOamsi/r/XfM58FP/bvt61JvHgx\n1NTARRfBSy9Bu3YRb2sisI/J9VXCRmvNTTfdFPc/n+EWamB4D/AfpdQXWuvFzjvqxh9OBi4L8RxC\niChKxAt3Y+U8xtCbaK1bbLWC1hbMZjNFRc9QWbnbsc9kMtG7d29KS0td1nLOzMwkJyeH3NzcuP05\n85YxtFgilzG0WCwUFBRQVFREeXm5Y7vB0Aaz+W8N37Nly2DRIti6FUaMgIIC6NIlYu1LZLm5uXz+\n+eeOZSQ9KSn53LEuc2MRamB4K7ATWKiU+h3YVLe9B3ACtmzhbUqp25weo7XWF4d4XiFEmHm6CCXC\nhbux8pTJqi9agaHFUs38+VPZvPkTwLVd5eXlLj9XztvjvZhwLDKGFouFMWPGsHTp0galW2pr97p/\nz8rLYcoUOP30mE8iiXdGo5E//vGPPgPDDz5YRmFhIXl5eVFqWeyFWq6mD5AOlAE1QJe6W03dtibA\n6W5uQog4Yr8I5efnu2Rz4NiFe+zYsY2unle8q611HWPoSbQCw4ULi+qCQmi4KJZ7iVBM2POs5JSI\nBYYFBQWOoMXTOLgG79nEidCnjwSFfrBYLDz33HMxX188HoW68kkXrXXXAG/dwtV4IUR4eLsIJcKF\nu7GKp4yhxWJh2bKXqZ8p9Fc8X4C19jSWU7F69U7ef3+741ZZGdyCXxaLhVWrVrFw4UI+/PBD5syZ\nI0FLBMXL+uLxKKTAUCl1vlKqvZf97ZRS54dyDiFE+MlFKDnE0xjDkpIS9u/fhb+Zwvri+QJstbpP\nwl16aSe2bTvCypUVrFxZwYMPbuCjjyoaHuiFxWIbk9mpUycGDRrE+PHjGTp0qM8ZsxDf71m8i5f1\nxeNRqGMMi7EVsv6Xh/2D6/YZQjyPECIMPI0j9IfzRSgahXyFb/GUMQzXhTMeL8CeMoZ//nMmf/5z\npuP+Aw+UUl3t/2QUb+MIAxGP71m8k1qGnoUaGPr6SU4HakM8hxAiDOQilHziaYxhuC6c8XgB9pQx\nrC8tLSWgwNCfcYT+iMf3LN5JLUPPAg4MlVJZ2CaY2J3iobu4NbZyNVuDa5oQIpzkIpR8AskYhqsQ\ns6dyRtnZ2bRq1Y79+3cTTHdyPF+APWUM6wskMLRUV1M0axaKYDvf4/s9i3eB1DKM5Pri8SiYjOG1\nwIPYfpY1cH/drT6FLVs4OejWCSHCwmKxUFRU1GAN3UDIRSj+RHOMoT/ljIYMuYYFC54K6vnj+QLs\nb8YwPd13YDhr6gq6rv+A6i3LKQ8x+x7P71ki8Gd98fPPHxbR9cXjUTCB4ZvY1kdWdf//f8An9Y7R\nQCXwtdY6sIFMQoiws8/AC4VchOJPtMYYehuG4FyH8OKLH6e09H/8+OPHAfwRYsuZjRw5Mm4vwCFn\nDGtq4NVX4Z13OGd9Je3/ehnzq7rBjJyQ2hXP71ki8La+uMlkol270Tz11MON7jsv4MBQa/098D2A\nUupaYLXW+pdwN0wIET6hjAu0X+DlIhR/amujExj6W84oPf1krr56NlovdnuhPe200ygtLXX5I6VN\nm/accsolLFo0K24vwIGMMTx40E25GoPBti7x3LlcN3g93933Z85etQpmBN+mwYMHx21B8ETiaX3x\n7OxsJk36ipSUUKdiJJ6QXrHW+uVwNUQIETmhjAts27Y9U6feyrRp0+QiFGes1shPPvF3GIJSipUr\nX+UPf5jEvfe6v9C6Wyv5yJEeLFu2M65/tpzXgPYm3WCl9cY1cNscuOoqOOss2w6l4Mor696/9YD/\nkx/cUUqxYcOGQF+G8KL++uIQvcLw8SakwFAp9aIfh2mt9fWhnEcIEZpAZuBlZGTw2muvMWnSx/Tq\n1YGHH76Es8/OiGJrhb+iMcbQ32EIWmv279/J1q3rgNPdXmih4QV45cpyrJFbbjgsvL7PWsP//geL\nF3Px8tX81PY0ME+Cfv0aHFpTo0lLsz2Rv5Mf3J9SSkdFgwSGwbmAhhOqDECHun93YhtrKISIoUBm\n4N1yyy0MGTKEli1r6NChLamp8ZvJaeyiMcYw0GEIR4/uD+j4lBSF1RrfF1+3GcONG+Htt+GDD7D0\n6EFJ9+683/9yfq9owot9+2J0E0kePVpLkybHyvp6m/zgDykdFVmNNTCMxJJ4WUAz4DbgILYi10KI\nGMvNzWXkyJEADS5y9vv1xxEqFVppGxFZ0RhjGOgwhGbNWgd0fCIEhg0yhs89BzNmYMnKwnzWWXRa\nuJBBd93FY0/l8Npr15GVlYXZbG6wStDRo1bS0489kX3yg9lsxmQyBdwuKR0VWbYyTw3H1Cb7UtQh\nBYaeaK0tWus5wApgTiTOIYQIjLeLkMlkwmw2Owaz2zMktsAwRg0WPkVjjKF9GII/Sya2bNmebt3O\nDOj5U1KI767kAwdov/0H19c/eTKWF19kzCuvkP/II1RUuC6DZ5+pPXbsWJfgsH7GEI5NfigrK+OD\nDz6gVatWPpuklCIzM1NKR0VYamrD3xvbHwnJHRlGJDB0sh6QtZKFiBPOF6Hi4mIWLFhAcXExZWVl\n5OXlOSYAWCwao1GhVPxncxozf8cYhrLSjX0Ygj/r9g4ceBVGY1pAzx+XGcOqKli+HG6+GSZMwPT7\n967vs1J+z9QuLCx0elprg8DQzmg0MmTIEO68806fzZPSUdHh7g8q2/CNGDUoSiL98oYChyN8DiFE\ngOwTAMaNG8fAgQMbXGDsmQ3JGMY3f8cYhsrfYQgXXnhjwN1scRMYWq3w+edw770wYgSsWQM33gjv\nv8/XZ1zs8rqdZ2p7o5SiqKjIkTW0/V55v+zeeeedHH98e6/HZGRkcMcdd/j5wkSw3AWGWof2h1Yi\nCCkwVErle7g9pZT6LzAO+Gd4miqEiBZ7YJiSoiQwjGP+jjEMlb/DEJRKDbg9Me9KPnAACgth2DD4\nz39s/y5bBg89BP/3f6BsvwPOWSL7TG1/sqj22cNg+71KT3efMbR78skn2b17p9djKioqeOqp4FaY\nEf5zFxhWVVVTWbmehQsXsmrVqgbjSJNBqLOSH/KwfS+wGbgJ+EeI5xBCRNnRo1aaNjXI5JM45+8Y\nw3DwVgjYnnG2ZVMCe96YZwybNIFeveDWW6FZM7eHWK2us5IDnQ1sP/7oUavXjGEgNSOLioqktmiE\nOQeG9iUh58yZQ0VFBePH245xXhIyWT6LUAtcJ3lPuxCN05Ejti4v6UqOb/6OMQwnT/UJbe0JPFCN\nWmC4bx8sXQrvvAOPPQZZWbbtaWkwerTXh9bPGAY6G9h+vLvJJ84CqRkpdQwjzz4r2d8lIZNlJZrG\nt9aLEMLhn//8he++O9Bg+65dVXVjDONk/Jdwy2rVpKbGz9/ntgAqjrqSjx6FlSth8WL49VcYPtzW\nbWwPCv1UP2MYyKolLVq04Ky6FVC8TT6B4DORIjLsGcNAJhrl5eVFvZ3hFpbAUCk1ABgJdK7btBVY\norX+OBzPL4SIjLlzN/HAA6e61Faz69nzOB5//EfJGMaxQMYYHjlSy6xZP4X1/L16tWTYsEzH/bjo\nSq6ttU0cWbwYvvoKBg2ydROfdlrgjatTP2MYyKolhw4dYsKECSxatKhujKHnQD7YTKSIjNRURVVV\ndaPr3g91Sbw04N/AGEAB++p2tQbuVEotAv6itU6+0ZlCJAGtYfjwTI8D4mXySXwLpOv26af7UlFR\nFbZzHzxooaDgB5fAMJhZ0mEPDGfOtE0oufhiKCgAg/fJHv6onzEE11VLfLFnk7p2vdprxjCQpStN\nJpPUMYwwg0Hx/fdfNrru/VAzhg8CY4EngCe11uUASqkM4E7gbiAfeCDE8wghIsDdBc+ZTD6Jb4GM\nMRwz5sSwnnvXrioWL/7dZVtwGcMQupLLymD3btvsYbsHwn+5qZ8xBFvW8M033yQjI4PKSu8rv9qz\nSQ89NMHr5JNAlq6UOoaRZzAoDhzY5/tAJ8nQvR/q4JQrgJe11vfYg0IArXWF1noa8ApwdYjnEEJE\niK/AQqk4X5WikYtWHUN3DIaGmT5ff2i4E3DGcPdueOUVuOQSuPtu2Om9tEs4eHpdX3zxhc+gEI5l\nk+6/fz7t26d7PTaYpStFZBgMimbNfK9E4ywZuvdDDQw7AGu97F8LZHrZL4SIIV+Bha9xNSK2olXH\n0J2UFPfFfwOdJe1XYHj4sG3M4HXXwTXXQGUlzJoFb7wBF14YYMsD5+l1BZod+vvfT+Gaa7p4Pca5\nZmRGRobLvoyMDJelK0VkGQyKrl3P9HtJyGRZpjDUruTfgIHAcx72D6g7RggRh2yZENdtFovFUafu\n9993Y7EMj03jhE/BlIdx/nzr1yEMhPtVIQLPGNoyjx52btsGTzwB334LgwfDXXfZag5GeeUJTxnD\nSE4W0Vq7/aNM/lCLHtvvlqHRde+HGhi+DExXSu0DngY2ARo4CZgKXIptHKIQIk7ZL3j2Aq5FRUUu\ng62LizP4299uSaoCrskikDGGnj7fYAv0ugvogqmraBtj6CHYadECLrsMnnwy+gUbnXgaOxmJySLe\nauZVVFQkXc28eGb/48d5olH9XhT7/WTq3g/1N20mtnGENwLfAUeBqrr/T67bNzPEcwghIsx+McrP\nz6eiosJl3969O8nPz2fs2LFJufxTIrBYLKxatarBMlz+jjH09vnaC/QG+vmmpBCWjGFKiiLzyO/w\n9NMwcqStm9iuVSs455yYBoXguT6jfbKIP0vj+ZtNCqRmnois1NQUamu1S/d+u3au3fvOS0ImS6Ae\n0m+b1rpWaz0J6AvcD7xQd7sf6Ku1vlZrLUPXhYhz3i5Gtk4AuRjFgsViwWw206lTJwYNGsT48eMZ\nNGgQWVlZmM1mqqur/QoMIxFsuOtKtloD6OXduRNefJEOOX9hyvaX4IQT4N//hubN/W5DJDkH4z//\n/AW1te6D5nBOFnFeEs8b+yxn+UMtspx/xu1LQq5d+yN/+MMsFixYQHFxMWVlZeTl5SVNUAgcG8cg\nN/c3oDegS0tLtRDJplev93V1dbU2mUxaKaWxRYFub0opnZmZqaurq2Pd7EahurpajxgxwvHe1/8s\nAN2jx/n6zTd/8fk8kfh8rVar7t17mcs2s3mDfv31rZ4fdPCg1vPna3311VqPHq3188/r3/+3WV94\n4cd+nTMaqqur9YwZM7TJZHJ5f1q3bq9nzJjh9v2prq7WZrNZZ2ZmujwmMzNTm81mv9/T4uJir59R\n/VtxcXGYX71w9sILP+snnvjBZVtZWaUeNix+fl7dKS0ttf+M9NZBxD2yJJ4QjZyszxqf/Mnybdq0\nmrffnsullz7m8Xki9fnaxlbVfw4fGcP8fOjQwVZr8KSTbI/5/QhW668+zxcN3sb37du3y+P4Pns2\naeI9CP8AACAASURBVNq0aSFN7JEl8eKL+6x47CoBRIsEhkI0cnIxij/OXYr1g0JXivfffwmL5RGP\nAUgkP1+ttcss582b99Ot2wBbn/L//gfp6dCnz7EHPPVUg+ewlasJqIkRE8iQCndr4hqNxpD+aJIl\n8eKLp+ESyR4Yxs/q60KImJCLUfyxZ/m8B4UAmr17d1JSUuLxiEh9vhaLhZ07/+Uy/vHll69j6g19\nMPfsiWXePNu6xT54nZUcRfEwvs8+y7kx1cyLZwaDoqbG3QSrGDUoSiQwFKKRk4tR/Alnli8Sn6+9\ny3XXrlcbzHLefaSS/M2bGVtWhuW003w+V9jXSg6Sv8G4c5d7uEVilrMInmQMhRCNklyM4k84s3xR\nL6lS96+/s5zjpSs5XoZUyJJ48SM11dMYwxg1KEqS/OUJIfzh7WIEcjGKNn+zfABt2rT3meULS7BR\nF72Fu8s1XrqS42VIhXPNPJPJ5LIvGWvmxTP3q/u4+45MLiEHhkqpYUqpN5VS/1VKbVZK/Vzvtjkc\nDRVCRI63i1Hr1u3kYhRl/mb5ANq2be3X8wUVbFit8PnnkJsLl1wCWoe9yzVeupLjaUiFfZZzWVkZ\nxcXFyV0zL455npUcowZFSUizkpVSdwMFQDnwBfBtOBolEk+41l8VseOu5MaiRfsZNWogl13WNdbN\na3Ryc3N57bXX+Omnn7wet3nzRo+zZJ0FVFLl++/h7bdh5Uo4/XQYMwYefhiUCnuXa7x0JduD8Xha\nEzfUWc4iNFKuJji3Ax8BI7TWUoK9EQr3+qsi9pwvRl9/XYrBIJ9frOzbt8/nMfYu22nTpvn1u+Yx\n2Ni2Dd55B5YsgYwMuPhiuP12aNbM5bBwd7nGS1cy4HVNXNuQiuRaE1d457krOUYNipJQA8M2wHwJ\nChsnb8Vg7euvymLviS0lxVcdPREpJSUlDWb8uhOWwuNawyOP2NYlfvllOP54j4fau1wrKiq8/mwo\npTCZTD67XOOlKxmOdbkXFhZSVFTEjh07HPvatm3P3/52q98BuEh8TZoYePvtbWzefMix7cCBGjp2\nbBrDVkVeqIHhF8DJ4WiISDyBrL/qq5tLxCelaLC6hYiOiM2SPXoUPvzQtvLIyXVf30rB3Ll+PTzc\nXa7x0pVs567L/dVXd5GTM5KhQ0+MdfNEFPXv346VKwc0+A5s3z49Ng2KklCHUOYA45RSV4SjMSJx\nxEMxWBF5SsVPNqexCWuXbW0tfPIJ3HUXjBwJX38NTYPPeoSzpEo8dSU7s3e5jxs3jg4d/kBaWlqs\nmySiLCVF0a1bC7p3d721bJncGeNQM4Zv1D3Hq0qpZ4HfgPql7rXW+owQzyPijKyv2zhIxjB2Qu6y\n1RpKS22TSFatgjPPtE0iKSwEgyGkttm7XE888a8YDCtculxNJhNTpkzxu8s1nrqSPWkMq10IYRdq\nYLgH2A1sDENbRAKJl2KwIrIkMIydkLpsV66EJ56ATp1sk0juvhuaNAl7+9q1+wvr17/AsGH/YNSo\n4zjzzE4BVyRQirjqSnanMax2IYRdSIGh1npgmNohEky8FIMVkSWTT2LLeZasfVasnX3W7MiRI5l2\nxx2uD+zdG/79b2jtu8ZhKJRSGI1Gjj/+/xg27FR6924V1HNEymWXrWHfvtCGsYwf31EyhqJRCTVj\nKBqpcM9MFPFJMoaxZe+yvf76PBYs+CeHD+927DNlZDBl8GCmGY0Y77wTnn322AMzM6PSPvvvfk2N\nldTU+Kr6q7Vm3bp9fPTRgKCf49tv9/Ovf5WRnm6QwFA0GiEHhkopA3AVMBLoXLd5K/Ae8LrWuv6Y\nQ5EE4rEYrAg/mXwSe0ajkUsvvZ2MjAmMGr6PPSUltP3mG7L37MF4+um2ruJevWLSNnvWsqZGk5oa\nX5FTTY2maVMDHTs2832wBwcO1HD0qJW0tBTpShaNRqgrn7QClgN/BA4CP9ftGgqMB25WSg3TWh8I\nqZUiLnkrBuvSzSXFYBOWZAzjgNa0/LmU8d+8w7lffQdnnWWbXXzWWcR6bS57AeBQA8NIDFeorrYF\ndKFo0iSFo0dradEiVTKGotEI9VvlEaAfcCvQXmt9ptb6TCADuAX4Q90xUaWUSldKFSqlfldKHVFK\nrVVKDY12O5KdLPae/CQwjANVVXT9+C1+6XAGvP8+FBTYClHHwYKtBoOtBmE8diWHJzA0cPRoLVon\n/zJoQtiF2pU8FpirtXapjFq3EsqzSqlewCXYAsdoeqnuvLOwzZieBCxVSg3SWn8a5bYktYDWXxVx\nxZ8sjUw+ibKdO+Hdd+HCC6FjR9u2Jk1Yc1k+G384AOnxVVg3JYWwZAwjwWJpGBgGuqa7LTC0YrUm\n/zJoQtiFGhgeD/zoZf8PQFSnoyqlzgIuB+7WWj9Rt+0VoBR4DPhTNNvTWMhi74lHa98lOBKhlEjC\nO3gQVqyw1Rs8cABGjYLmzV0Oqa3VGAzxF5mEqys5EqqrrRiNtsAw2DXd7V3JkjEUjUmogeEmYDTg\naS2l0cDmEM8RqEuwFdn+u32D1vqoUuqfwEylVCet9a9RbpMQMVc/W3LOOX/ymQWpP3ZUhEl1Naxe\nbQsGf/rJliF88EHo0cPt4fEcGFqtmtpaHbddyaGs6Z6ebutKloyhCDTbnMhCDQznAnOUUkuxddv+\nVLf9ZOA2bJNQbgnxHIH6P+AnNxNevqj7ty/gNjBUSmUA7ett7h7e5gkRXZ6yJSZTJnAhFssFHr/g\nZIxhBLz2GsybB3/6E0yaBP36+Yw64nEMH9gyzraMoTWkjGEkahnaA8NQ1nS3j6GUjGHjFWy2OZGF\nWuB6bl0wlQsMq7fbAszQWj/b8JER1QHY7ma7fdsJXh6bAzwY9hYJESPesiUVFeVo/Qpjx+72OElI\nAsMwqKx07RoeOhQuuwwCuJjEc8YwnF3J4czKWCya1FSrY013X/VWi4qKPC7jp7VkDBujULLNiSzk\nP0G11g8BHYErgfvqblcCHbXW00N9/iA0BarcbD/qtN+TucBp9W4Xh7V1QnhhsVhYtWoVCxcuZNWq\nVVgsoa3aEEi2xB2ZfBKkHTvg73+31Rj8f//PdZ/JFFBQCPEdGFqttsAwlPZpXYPZbKZTp04MGjSI\n8ePHM2jQILKysjCbzUH9HlRXW9m79xvKy8t9/gw7r+nujtUqGcPGKNTvz0QVlpVPtNa7gP+E47nC\n4AjgbupeE6f9bmmtK4AK522RXK5JCLtIdFdYLJaQsyUy+SQABw7YysksXgxHjsDo0fDCC9C+/uiU\nwMXj5A5w7UoONjC0WCz8+ut08vO/DGtWprraitV6KKC21F/T3WKxUFm5nl9/reXLL3dz8snDkioz\nJDwLx/dnogooY6iUylJKZdW/7+sW/mZ7tR1bd3J99m2/R7EtcSvcmSkRPHt3RX5+PhUVLn+XOC6M\nY8eObfAZ+foMS0pKQs6WyOQTH6qqYPlyuPlmmDABtm+Hhx+GRYvg2mvDEhRC/GYMlarl008/5sCB\nEj7++OOgvkcKCgo4dOhLILxZGYvFSvPmga3dbF/T3WKxODKYW7dOY82a+7juuotCymCKxBKO78+E\npbX2+wZYsc34Tat33+stkHOEegMeB2qAlvW234dtBfpOAT5fb0CXlpbqZFBdXa1nzJihTSaTrns/\nNKAzMzP1jBkzdHV1dayb2OjMmDHD5bPwdDObzVpr359hZWWlLi4u1nfffbdfz2u/LViwoEHb5szZ\nqOfM2RjttyRxlJdr/eCDWq9bp7XVGrHTPPXUj/rvf98csecPlP1nMC2tTUjfI9XV1Q1+jt3dlFI6\nMzMzoO+nDz7YoadMWatNJpNWSvn9/NXV1XrEiBGO7fWPA/TIkSPluzLJLViwIOTvz1gpLS21t6u3\nDiaOCuhgW6HoiYCqd9/rLZiGBXsDzq57Q+5y2paOrdD150E8X9IEhvKFF3/sF0Z/L1yVlZU+P8O0\ntLSAvtDst+Li4gbtmzt3k549O/DAsLq6WhcXF+sFCxbo4uLixP+Zslq13rBB60cf1Xr//qif/rHH\nftAvvvhz1M/rjvP3CIT2PVJcXBzyz6gnS5b8ru+88+uA//AK9HiRnCL5sxlpoQaGAY0x1Fq/5O1+\nPNBar1VKvQU8WjdjehO2ALULcH0s2xZroZRtEMFbv/7/t3fncVbW5f/HX9eZOcMmIqAMapjmkluW\n5pKOyZCWCZkstrunZuD3m2j9GIvQnK8FZWXpJFpiapa5ACJgagYuo2KaS6KmYopmMOAgosDMmZnP\n74/73MOZM2e5zzZnmffz8eABZ5n7/syZ4dzXuT6f6/q8S1PTqwkfe/vtp3qsKUzGOW+64vzzz0/7\nM2xvb89ofGZGbW0tdXV1CR7zFt4HVXGtHd56CxYtgiVLYPRor5ikCLuPlNJUcuz7iHft2SbT95H4\nNX3pZPJ8v11NJnu69+d1ZdJTXV0dtbW1tLS0pP1dSPb+Wa4K0hjLzGrMbEj6ZxbMaXh9FU8Ffg2E\ngS845x4q4piKKvYNLxX/DU9raPLn739vZeDAKk4/ffdef446KrMgY/78+XkviHLOMW3atJzb1WS7\nVrLkbNgAt9zirRmcPh2GDoWbboLrr/cKSvpxYJjv9xF/TV9QmTzf3xIvkz3d+/W6MukhHA4zbdq0\nQL8Lyd4/y1VOVclm9lXgCOfc9Jj7LgF+4P3TFgOnOucyKw3LkXNuK/C96B9h20LadGLf8LTFXX50\ndcGee25HXd2OvR6LRHbP6FgbN27M06h6Z0tSPSeIishIX345PPIITJgAP/85jBmT8umF2A0h0TFL\npcF1vt9H/KxMumNmk5WJ3RIv6J7uhcxgSvnJJNtcSXJ9p7kI6M4MmtlReA2i7wV+CXweL0iUItMb\nXvF0djpCSf6n+RfGIBmYHXbYIa/j2mmnUey++1kp24AEzRiWZUa6s9PLDsaaOhWWLoXzz08ZFMZW\nrear716qYy5bdg3OdWR8zHzL9/uIn5VJJ5usjD+VHH+++vp6Jk+eTH19fa/jFTKDKeUnk2xzJck1\nMNwTeC7m9teBNcAk59z/A5qAKTmeQ/JAb3jF09WVfBowk+mKyZMn52U8hx56OieeOJcnn3yFPfY4\nNeWbmtfgOv0xy2YKzjn45z+9ljKf+5zXdzDW8OFpt7goxJR5umM++GATV1xxTtED6kK8jzQ0NLDd\ndocDvfvG+rezycokCgzTyeSD2ujRoytqXZkk5mebV69ezbJly7jzzjtZtmwZq1evZubMmRUXFELu\ngeEAtu0oAvA54B637aPtC3i7okiR6Q2veNLtmtDQ0MCECROARA3Vt10Yr7rqqkA/w3QOOGAso0d/\nkqqq6rS7OQQtPin5jPQbb3g7kIwfD1ddBQcf7BWUfP3rGR+qELshBDnmU089UPQdFgrxPhIOhxkz\nZhaXXXZZXrMykYjLODDsz+vKJLV02eZKkmtg+G/gOAAzOxTYC/hLzOO1QJ+uL5TE9IZXPF1dpAzA\nUk1XbLfdSKZMuYgFCxYwePDgQD/DZMyMQYNGsu++h9HR4QJt8xV0KrkkM9IbN8KNN8LJJ8OMGbDj\njvCnP3lb1U2YAAMHpj9GnEJMmQc9JhR/Gr5Q7yNVVdX84Af5zcp4awwz/xCV6oNaLhlMkXKR65Z4\n1wK/MrP98TKDbwGLYx6vA1bmeA7Jk/66kLbYUq0x9CVbHP/UUzsxdOig7gtjqp9hOs45PvrRkxk2\nbBAdHZujAWvqrwl6jpJs7dDSAps3w5VXwofyM3FRiCKuoMeE0igMi/0d9DLaub+PhELG3//eSnV1\niKFDP87Qod79zz2XfV7h3//+gMMOG57x1/kf1ObMmUNTUxNr1qzpfqy2tpZp06apTY1UtJwCQ+fc\nVWa2FRgPPAXMcc5tATCzEcBoYG7Oo5S80BtecaRaYxjPn67w/etfq9i6tbPH48l+hgMGDKCtra3X\nMWMv1oMGfYMhQ6ro7PQamabPfAXLGPqZpFmzZqV8XkEy0pEINDfDfffBZZdBdfRtbe+9vT95VIgp\n85Kfho8T+zv44x9fyZYt73Q/lu37yNe/vhtNTavyPtaDD848MITgVcwilSjXjCHOud8Cv01wfytw\naK7Hl/zSG17fCzJlm8zAgVW8+27PqcNkP8PDDz+cX/ziFymD/pNPfoLttqumo6MrUMYwFLLADa5T\nZzO9zFLeMtLOwTPPwMKFXnuZww/3mk+n+4ZyVIgp85Kchk/D/x3897+PY//936ap6VnmzRuX9fvI\njBn7FmCUuYv/oCbSH+QcGMYzLwUxDq8w5RHn3KZ8n0Nypze8vpNujWEqgwZV9cgYxkr0M0wX9G/d\n2smQIdV0duZ3jaE/nmTZzMGDR3DxxRfknpFetQruugvuvRf23BMmToTvf7/Pmk7X1dWx446jWL9+\nHfG7fsQyM0KhHQJNmZfkNHxA4XCYESMOZqedhun9RKRC5Nrg+nLgKOfcuOhtA+4DPoOXIlhtZsc6\n5/I/RyBSJoKsMUxm4MAqtmxJHBgmkyrob2vrYsiQ6rwXn8Se2w9Ov/nNP1Bb28Euu+zIv/61CzNn\nHpHBd5HAued6awZPOgluvx223z6342UhHA5z6qnn8stf/l/K5znnGDHixEBBcFGn4XNUV7cjt966\nmhNOGF20MRSiybhIf5ZrxnAKcFfM7ZOBY/GaWj+LV5xyKd7WdFJAenPMTF++XpmsMYznZQy78jYW\nL2PorzFM27Iv4wIXXzgcZrfdDuOQQ3Zg9OiBrFz5emYH2LQJ2tth5Mht9/3qVzBoUMZjybdzz72Q\nW255gJaWx1IWcb322pcDHzNIYVhd3edKrjDs1FM/zKmnfrgo5664fblFSkSuC3J2BV6NuT0ZeME5\n9xPn3FLgGqA+x3NICoXYgaGSFeP1ym2NYSjjjGEq/lRy0IxhKJRZxjCWv79vTU2I9vYAwW17O/z1\nr96uI1OmeI2oY5VAUAhQXR3msMN+nHY3BLPgn7vT7bBw0EHn8vOf31xygU4kEmH58uXMnz+f5cuX\n99n7TcXsyy1SgnLNGHbgrSX0p5GPBW6KeXwt0HuDWMkL/81x6dKlvapL/TfHFStWVOSWPdko1utV\nqDWGqcyf/xYbNvS+KK5b18bgwVXRNYbB2tUELT6JFygw7OqCp57y1g0+9hgcdRSccQZ88pPp05lF\n0tXlqK7OfxFXqsKw0057ioEDa/L8nWSv2Nm6itiXW6RE5RoYPg+cYma3AJOAkcCSmMc/DKzP8RyS\nhN4cM1Os1yuXNYaDBlWxcuV7XHfda2mfO2bMIE44YWcAvvOdZ5g+fZ9ez7nssgOprg5Fq5Lzv8Yw\nlh8YDhhQRVtbXHD75ptw661w//2w777eusFLLoEy+AAT+7qlWs9ZVWXdr0E6v/71K/zznxtj7tku\n+gduueVZVqxoJRwubMV1UMX+QBrbEDxdsU5TU5NacIlkKNfA8DLgbrYFf83OuWUxj08A/p7jOSSB\nUnpzfOqpDdx1139SPmfYsDAXXfTRgpw/iGK+XrmsMdxzz+0444zdA00nX3jhs92B4dCh1Vx4Ye/A\nEGD9+jY6OoKuMcw+MPS/74QZw/Xrvarib38bttsuuxMUSZDXDaC62ohEuqiqqkr73BtueJ1f/eoT\nVFcnPvDZZ+/BvvsOzXSoBVHsD6SFaDIuItvk2uD6fjM7BPgs8C7wZ/8xMxsOPETP4hTJk1zeHDdv\n7uj1vFDIGDgw/QUskcWL32bTpg6OPjr5qoHp058pamBYzItJrn0Mzz9/r0DPvfbaYMX/fiYrWMYw\nu+IT8DKGNVvfZ4d77+f8t/4KfLr7sciBB9K8cSOt991XdsVSQX+e4XCIjo5gr117exef/vSOOe+D\nXWil8IG03BqCi5SbfDS4fgF4IcH9G4DpuR5fEsv2zfHee9dwxhl/Z9ddey7kX7XqfV566fPU1ma+\nf6xzcOSRI5kyJfm2Yz/84fMZHzefinkxyWWNYSFUV1tGxSddmRZFt7XBsmWc9cTv2esf67ETPs/N\nQ07gBIq/Ni0fgqzNhG0Zw6ASBYWl1m2gFLJ15dgQXKSc5KXBtZl9Cq+p9SjgN865V8xsMLAv8LJz\nLvsNLyWhbN8cN26M8K1vfYRLLz2gx+MTJjzMBx/0ziQG0dXlSrVOoFsxLya5rDHMhB9YeD+P5D+Q\nbRnD/O2VTFcXrFjh7UTy5JNwzDH8ZfeJHPXtz/CJg4fz2t0PFX1tWr4E2UoQvIxhJoFhrFINoEsh\nW1fODcFFykFOlyszqzGz+UAzcDnwv8CY6MNdeM2uv5PTCCUh/80x/V63xujRo7vfHNvbu6ip6f1j\nz2TaK55zpZURSyTb1ysfclljmI1kBQ9+a5HFixfy7rtP097enp/ik87ObU2nx4/3diW55BJWDd2L\nqupQ9xrDTNamlbKgGcNw2LL6P1XKrVhKIVvnNwRP94GlFBuCi5SDXPMYjcAXgG8DH8Xb7QQA59xW\n4HbgpBzPIQlk++YYiXQlrG70pxezEXQxfrZr1fKhmBeTXNYYZiM+MIzv3fiVr3yJVav+HyeeeBDP\nPXd9ygAjYWD41lvw7rvbbldVwYIF8ItfwNixUO1NRPjfdzgcoq2tvXttWir+2rRS7j8X9OdZXZ1d\nxrCUA+hifsCK1dDQwIQJE7rPFX9uIH/7cov0M7kGhl8DrnHOXQckmjN4EfhIjueQJBoaGvjsZ08A\nEq1PSvzmmCxjmO1FDIJdKL0pyawOnzfFupj09RrDzk7XXd2aKvvU2rqOZ565LmX2qXsqecMGuOUW\n+PKX4YIL4O23ez6xuveqFD9Araoy3n9/JWvXrg0UmPtr00pV0Ax5JhlD/3WJLe5IpVgBdKlk69I1\nBPebjCtbKJK5XAPDUcA/UzzeCQzO8RySRDgc5vrr/8wee5zV681x6NCRCd8cIxFHONz7opPttBcE\nyxjmsoNGvhTrYpKPNYaZ7DARmzHMKfu0ZQu7Pv1XTnvo/+DrX4fWVrjiCrjjDth//7Rjjh1HR8e7\naZ7dUylXkgZdU5vNhy2/uKOUA+hSydb5DcFXr17NsmXLuPPOO1m2bBmrV69m5syZCgpFspRr8cmb\neAUmydTRc8s8ybsq9t77dBYvnttdvfjEE+0MG3YAF1/8sV7Pbm/vYvvte//Y/abH2cis7Ulx1yKm\n2l2iFPdKzqQIwQ8m/IAs69Yizz4LN98MTz/N8N0PZ9H+X2X/307KeCcSr8Clg8bGRtasuSqjry3l\nStLg7WqMSGTb656swrijo4vqau+TQykUd6Tjf8CaM2cOTU1NrFmzpvux2tpapk2b1qdNpVM1GReR\nzOUaGP4RuNDM7gRejt7nAMzsHODLQEOO55AU/Knh2DfH999/nbff3prw+anWGMZexDIRNGOYcduT\nAurLi0m2awyzreL1A8OsW4ts3QqTJsGcObx0bwtv/a0lq+3pIpEIF110Cg8/fF/grymHSlJvKjn9\n87yCrq60wf3//u93u5d3lEJxRxDF+IAlIn0j18DwcuBTeI2sX8QLCn9pZiOADwFLgV/meA5JIVGg\nFwol3982dVVyX2QM+59s1xhmu8OEHxhmnX064oju+3LZ+eSFF27mhReCB4XQ95Wk2fQJTNcOyFdd\nbWzZ0p42uH/kkccIhy8Ayq8Vi7J1IpUnp5VPzrl24PPAmcBrwEvAAOA54AzgROdc+r28JJBE68wS\nrRlMFRgWqyrZrLQyhn0pmzWGuRQh+IFhPrJPqX6XUolEIqxadWfGO3kcd9zn8742LfH/m56V2lOm\nTGHcuHHstttuNDY2plzDGbxdTYgbbrgybXB/3333sGbNn6NfUxrFHSLSf2WdMTSzMLAf0Oqc+wPw\nh7yNSnpINRU1ceKZVFVN6PH8VFme+IyhnzF5/fV/8NRTH2bs2C9mfLEJtoNGf84YZr7GMJtpYD8I\n8wPDurqjcs4+ZZsxbG5upq0teMZy+PDhjBw5mWuv/Xnegp1k/29qa2vZfvvteeWVV7JqtO1csKUB\nZp3cccf1gdZ4vv32QiKRuYTDYRoaGlixYgVLlizp9bX+bbViEZFCySVj2AU8BUzO01gkgXTNbufO\n/QkPPzyjR4YjdcbQRRfF98yY3H33RTQ0nBwoYxIvaMawn8aFWa0xzHYa2DnXHRiGw2GmffObgbJP\n5513XsIgKNslAJmOf+7cuey99+mEQnnZjCnt/5tXXnkFyK5PYFdXsCWX69c/x4YN6wK9/u3trd0V\nxmrFIiLFlHVgGJ0ifgNv6lgKJMg6s7feau5xEUtV6NHe3kUo1JnXnRWCZgyzmZKsBNmsMcxmGrgq\nBJ1P/oOu9z/ozlA2XHpp0tYisa655pqEHwiyDegzHf+oUaNyWs4QL9X/myBS9QkMGuh3dGzK6Jyx\nwbRasYhIseTax/Aq4NxosYnkWdB1ZtDzIpZujeFtt12d150VlDFMLZs1hhntMLHjjtT9/e/M33wZ\ndv3vYOPG7sAwNvs0atSopMdpaWlJ+IEg259bXV0dNTXDM9ohw9/DOVfB/98kl6pPYNAG10OGDMvo\nnImCab+4Y/LkydTX1ysgFJGCyzUwrALagFVm9lsz+4GZXRj3Z3oextkvBW12Cz0vYqkCw61b21m4\ncF5ed1ZQxjC1bNYYZlSEsMsuhHffnW8Om0HbL66ibURtj/P52afzzjsv5XGg9weCbH9u4XCYD31o\nUkZFFLn00owV/P9NeommxIM2uN5990MYNmzHQP/XBg0aWfQKYxERyD0wvALYHxgGfBNv7+QrEvyR\nLGS7zizVLiP/+c/TtLYGW/cUdGcFZQxTy7aPYcodJqJ/TzjuOGY8+SR86UtsDQ+ho8P12isZvCza\n3LlzM/5AkMvP7UMf+hrHHZd4y8ZEO2RUV+cnY5jPps+JsnhBf54DB9Zw/PGnB/q/duCBX1I2UERK\nQq6B4R4B/miv5Cxl227ELHmWZ/PmjRkdM8hFNlgfw/4cGGbXxzBlEcLo0V4RwtKl3QGFPxWbHN4v\nQgAAIABJREFUKDDMdqu1XPpPOlfFTTfdFriIoqoqP2sM89H0OXaKO17QBtfV1SHGjz8v7fZxRxxx\nHIcffmbOYxYRyYecSgCdc2/kayDSW9Bmt2CMHr2t3Uiq4pNQaGhGYwhykQ2280n/nUrOeI1hVxc8\n+STcdRfhHXYIvMOEn3FLFBhmm33OJaDv6nIMGFCT8fhzFfz/TXKp+gQGbXA9YECIefPeZM89Z3LI\nIWN48cXb2bLlne7Hw+GhHHDAVxk69CsMHjwwq3GKiORbfnpDSEH468xmzZqV5pk9L2KxQVj8zg5D\nhuzLjjuO4p13Uk8nZ7KzQpC+bv07YxhwjeG//gV33QX33w/77w8nnQSf/jQQbIeJVBnD7LPPZB3Q\nx44j6PjzkTEM/v+mNz9DWl9/fNI+gUEbXJ9zzkeoq9sRgI6OS7jxxqHcdtt1bNrkZe3b29/jjTcW\ncuihI5g+/QcZj1VEpBByCgzN7N9E90ZOwgFbgbeAZcC1zrkNuZyzvwnS7Hbffcf2uIiFQkYk0k5j\nY2Ov5r41NSM49NCP8eijPdvUxMtkZ4Ugfd36c8Yw5VT722/DokWwZAmMGAETJ8K0aTBkSMbnSRUY\nZrvVmteYPOOhACQcRyrV1aG8ZAwh2P+bESM+TGfnB2zcuL77sdraWoYP/wJXXz0n6e9+0AbXw4aF\nOfLIkSn3vN6wYR2//e1s3n77n+pNKCIlIdc1hg8C7wO7A5uAp6N/NkXvex94ARgF/Bj4p5ntkeM5\n+5V0zW6/+MXpnH12U48LSldXhEWLLkzYp7C9vZVHH32wu3VJkKKAdJQxTC3pGsOXX4bzz4cBA2De\nPLjxRpg0KaugELYFhh0dvQOybLday+Xnlmlg6GUM87NvYpAm0V//+u388Y//6NUncL/9zkzZaDto\ng2tfJntei4gUW65TyQuBE4GxzrmHYx8ws7HAncAPnXOLzKweWAL8BPhqjuftV/x2I4nWaV13Xe9l\nnn/+89W8/vojQPLmvi0tLRx77LGsXLmSNWvWdN9fW1vLtGnTmDFjRuDsRZALZaqCmGLYsqWTRx/d\nlikKh0N8+tPpW4tko7PTEYq0wf3NcOyx2+Yh99kH5s/P23lSZQwhWBYt/gNBZ2cHb7/9JPPnr066\nNjCZzDOG+WtwDan/34TDYb71racYOnQgn/50fY+vq6pK/buaSZV5bE/FdJnapqamjP7fiYgUQq6B\n4WXAVfFBIYBz7kEzawIuBxY555ab2bXAaTmes99KtE4rEuli8ODqmNsR7r77BryGJqkvRCtXrmTV\nqlU88cQT3HXXv9i4cQDXXvuNjC9MzqVfjJ+qhU4x/PWva7n00pUcdZS3Bmzhwv/wxBPHsvPOg/J3\nkq4uWLGCC9Zcz+iz3oTPjIWjjso6I5hOusDQz6LNmTOHpqamlB8I/H2Gr7zyKlpb13HPPd7zRo8e\nzdSpU2loaEj7exJ0LZ4vX8Un8ZKtb9y8uYNBg6p63e8te0h+vKANriG7Pa/TrcUUESmkXAPDvYFU\nawZbgb1ibr8IFOaq2E/5ex/7mpub2bBhXdqv8y9ETzzxBPX19axbtyfPP/9eVtmKIAFALm1PCqGz\n03H88aP58Y8/BsDLL2/KT7bKOXjxRVi4EP72NzjoIJYPPpRP/rGJ2l23y/34KaQLDCF9Fg1IuSbO\n3zJxxYoVadfEZTeV3He/I1u2dPb4UOULhUgZoAZtcA3ZV4OLiBRLroHha8DpZnadc25L7ANmNhg4\nM/oc3y5A+qhFeti8uSNpBuP99zvYZZdtrS6yuRBFIhFeeulxnn/+LZYvX5fRdCEEzximysL0tfjp\nwLxMY954I9x2G4we7VUUX3ABDB7ME/XLCdUUfnrQD6yCBGSpqoQzWRM3c+bMlOfJZGo+n8UnQWzZ\n0pkwY5jPqeRsq8FFRIol18DwUuBW4CUzuxFYFb1/L7wp412BrwGYWRVwCpB+Kw3ptmbNVvbf/y/s\nvXfy/oOf//wnuv+d6YVl8eLFTJ06tXu6a/78zKYLoXwzhrFjzst2bB/9KNx8s1ddHMMLJHI7dBCx\nfQyrq7NbK1nMNXH5LD4JIllgGAqlntIO2uAasq8GFxEpllwbXN9uZpvxCkriUwfPA9Occ4ujtw04\njtRTzxLnvfciHHPMTixcGOyCUVdXx/DhO7Fhw3pSdxKCAQMGcMMNN+Q0XQjJM4axPRRbW9+ire1j\ngb6HvtDVRY+sWuCM4ZYt8MAD3lRxXR2cGbNjxac+lfBLMp1SzVaQqeR0irkmrlBrDJPZvLmTwYOz\nyxgGzYQG7amYSXsoEZFCyrnBtXNuCbDEzHYGPhy9+w3n3H/jntcBaKeUDCXLaiQTDoeZMuWb/O53\ns9M+t62tDUg9XXjaaafxrW99K+X0cnzG0C9ciO+hePTRP+Z//mda4ExkIWU0ldzRAY8+6jWffvZZ\nGDcOpk/3mlBnca5CyUdgWMw1cdmuMXzssXe499416Z8Y5403NmeVMcy0qCabanARkWLJ284n0UDw\nv2mfKBnZurWTgQODB4YAp5/+Hf7yl4d5663mpBeimpoaIpFI2undW2+9lVtvvbW7ajVRUBebMUxV\nuLB+fUtGmchC8qaSYwPDuPVtzsFzz3nB4IMPwqGHwpQp8NOfQlVmP49s90rOVD4Cw2Kuics2Y3jb\nbW8yZEg1H//4sIy+bt68QwmHe0d4XsYw+dcFbXDty6QaXESk2HIODM1sN+D7wDhgJ2Cic+4hM9sR\nmAXc4Jx7Otfz9Fdbt3YxcGDPi1f8Nnfx2byamhrGjfsp++zzt4QXovHjxzNv3ryMxuFPLz/22GPc\nddddcQ21t2VQ8lm4UEjx29R5GcOYaOChh+CPf/SKSGbM8JpQ53CuvlhjmI/AsJhr4rLNGG7d2snE\nibsyduxOeRmHVyiVOmOYabvLINXgIiKlIKfLlZntj7fTyVeAfwPDiAabzrn1wNHA+TmOsV+LnUqO\nRCI0NjYyZswYxo0bx5QpUxg3bhy77bYbjY2NRCIRwA/Sqpk5cyarV69mt93m9NjZYcKECVmP5557\n7uHHP/5xj/v8jGFs4UIqfuGCP95i6FF8sn49deubewYlY8fCtdfC+PE5BYX+ucpljWG2O6Qke04m\nsq1K3rq1iwED8hd5p59Kzn5pgF8NPnnyZOrr6xUUikjJyfXd9KfAu8A+eBXH8e+WS4BP53iOfs2f\nSvanaBNtc+dn8yZNmkQkEumxL3E4HGbIkIN6XIhynf674ooregR1fsbQL1wIElT4hQvFEtq6hQNf\n/RuccQaceSYjI610tHcW5FzltMYQvDVx/oeHfGyZGFSvrG1AbW2ZL7dIJV3xSSYNrkVEyk2ugeEx\nwDXOuXUkLoFdjdeyRrLkZwwzmaJNt7+tP12Y7fZv77//Pg8++GCP85tZ6TfzjURg2TK44AK+eP23\n2bF1NVx8Mdx9Nw/sNYlCdUrpyzWGHR1dOQeGsfsMjxzZc3rW32c4yBrRTH+//MA2U4mWW+QiSMaw\nADsnioiUhFzXGIaAzSke3wloy/Ec/drWrZ2Ew11ceWXw3nKf//y3UmY8grbQSOXRRx/luOOOA7b1\ndcs0E/n888/3zTor5+Dyy72g8Igj4Gtf488fnUZbOxz+0b2B/O/TG6sv1xi2tbWzcuVjrFu3nuXL\n12T92vpr4k488dt84Qu/49BDwwwaNIxdd/0E778f5oc/fDHtMbZuzSwDW11ttLVlHp1nU6CVSrri\nk77KAIuIFEOugeE/gAnAb+IfMLNq4KvA4zmeo1/burWLt99+JqPecs89t4KurpEpn5uqhUam/L5u\nQQsXfJdccgmQeUPtgIPaVhFj5q0V/N73utcLdj31ao/i4kIGhs5lnj3LVCQS4cknf8dNN93Ou+96\nmwvdeGPur+0BBwzn2mtPyyqTd9JJmU0W5FJ8ks/AMF3xSSYNrkVEyk2ugeFPgMVmdg3eDigAtWZ2\nHF6l8n6o+CSwRNXGW7Z00tGxKaPjvPfehrSBYaoWGkEcddRR3f/2L5TZZiIzbaidVEsL3H03LFoE\nU6fC8cdve+yQQ3o8NX66NejOJ+kqwovBX3/66KNLiV/mm+trW10dYvz4nTMeT3NzM//9byvLlwd/\njbItPmlrC158EuTnF2wqWRlDEalMOX3udc7dA5yBV5X8t+jdfwDuAw4BTnPOPZTLOfqDVNXGS5de\nzfbbb5/R8YYPH9Gd8UiVufOnC1evXs3999/PgIDVt9tttx1jx47tvh17oUxVuJBM/BrJjGzaBHfc\nAaecAuec40Wp113XMyhMINOdT4JWhBdD7PrT+KW+Ob22Gcr1Ncp2S7wgGcNMxhas+CTjYYqIlIWc\n396cczcDY4ApwAy8TOGXgTHOuT/levxKl67a+IEHruK++65n1KhRgdrAjB49mk9+8sjuC1uQwodw\nOMxxxx1HQ0NDoDF/97vf7ZFlib1QxhYu1NbWBjpe7PgDtbFpb4f77oNp07ym02+8AZde6jWjPvts\nCHDeTHY+yaQivK+VSougfLxG2Ta4TteuJtOxFbJdjYhIqcvL517n3AfOuYXOuZ855+Y45+5wzm0C\nMM25pBSk2vjpp5dx4IEHBu4tV1MT7q5KzqRC9Qc/+AHjx49P+Zzx48fz/e9/v8d98VNrsZnIZcuW\n8aMf/SjQ+QO3sVm8GB5+GM46C+69Fy66CPbaK9A5wAsUXn75cZ5++l6WL19OJBJJOZWcSUV4XyuV\nFkH5eI2yXeeZbieSTMeWvvgk8wbXIiLlomATImZWY2bnAv8q1DnKXdBsDxgrV67sDtrS9ZaL7WMY\nv8NHKuFwmIULFybM9o0aVcsuu5zBwoULE2yJl3hqzW/me+CBBwY6v69HG5uXXoI77+z5hMmTobER\nPvnJjK7QsdOJc+eezjXXTO2eTnzoobls3dqe8GtKISOXTCm0CMrXaxTbriYSibB8+XLmz5/fHbyn\nOm4+xxYKkTJjmOmWeCIi5SSr4hMzqwG+COwJbAAWO+fejj42GK/g5AJgNLAqP0OtPH62Jz3H2rVr\nmT59OkceeWTa/VZDobbujEemPe2Sbd116KGf4phjHk5YRJBuMX7G++92dcHcubBkCYwcCSefnNHX\nJ5JqD+e1a9eyZs1VrF+/km984y89vsegPyM/I3fYYU3ssMMnejy2cWPhgsVi7m3sy/Q1am5upr6+\nvtfj1dUh2traaWxspKmpqccxU1VXp8qWZjO2qqpRabfEU2AoIpUq48DQzHYBluMFhf674xYz+yLQ\nDvwRr6n1E8D/APPzMtIKlGn25r333gu032psxtDb+i3zi5if7fNFIl1JsyjpFuPX1dUxcOAI2to2\npO7DCNQOHEjdLbd4exTPmwc75Wf/2yDTic8++7deezhn+jP6znd25gtfOLLHffncri1eMfc29uUr\na+lcB3feeQGvvfZwwuA9m+rqbMYWCtWmCQzV4FpEKlc2V6zLgT3wtsP7Al7w9z5wHbAYeBMY55z7\nlHPuTpdLg7wMmdnOZjbbzJaZ2SYzc2ZW31fnz1S22Z50+63GB4b52Kc3VaVmuoxhOBxmv/2+lH4d\nHDBt6lTCCxZ4W9XlKSjMZMo+fqoz05/RHnvUstNOA3r82X77wrWzyefextnKV9byzjuv5rXXHgby\nt5Yzm7GlKz7RlngiUsmyCQw/C9zgnLvYObfUOdcETMULFh8CjnbOPZjyCIXzUbzK6F2BfxZpDIEF\n3ZrOrzYOmu0JhbZtiZfJGsPUYyDpgnyvgXPqrz/ooNM56qjP+keLO3bMGsnZs3McaW9BCzSgd4FG\noX5G+VSsvY19+XiNIpEIixf/nt7brfc+Rmzw3tHRRXV18rexbMYWbOeTlIcTESlb2by91dJ7NxP/\n9jznXIF2nA3kKWCkc24f4BdFHEcghcr2mOU/Y5jqwpq2fYdz7LHlLW752Cdp/MhHGD50hx4PZ7L/\nbjZymeoshYxcOqlaBBX6tfXPn+tr1NzczIYNybZc73mMNWvWsO++V/Lxj9/HwQffz/Dhyb+vbMaW\nrvhEDa5FpJJlU3xSBWyNu8+/vTG34eTGb5FTTlJtTeffzjTb423p5f3bW2OY71H3lDRj+NZbXm/B\npUuZ2DKItq9MZuYvZ7Hrrf/h5pvvIxJ5j8bGTxV895BcpzobGhp44IFmHnzw3rz9jPItWdFQX+3M\nkuvvcabB+09+sgeTJnl7daf74JPp2NTgWkT6s2zf3nY3s0P8P8BB0fv3jr0/5nFJohDZnp7tatJf\nOHPVI2O4YQP84Q/wpS/B9OkwbBjcfDNNBzfQcsRnYdAgzKoZM+ZQdt55bMI1kvkWdDoREk91hsNh\nfv3rW9h//3OKkpHLRLr1p4U8by6/x5kG76NG7Ug4HCIcDgVq4J7J2GL//ySiBtciUsmy3Su5Mfon\n3m/ibhve3FD+drgvIDMbBcRXPOxZ6PPmO9tTiOKTVHpkDBctgvffh1/+Ej70oZgx/bvHuscBA0Js\n2tRR0HH5gu/hnHyqMxSq5sADz+QPf2gqub2SS0Uuv8eFrq7OZGzpdz5Rg2sRqVzZBIZn5n0UCZhZ\nCKgJ+PS2PFU/TwUuycNxshLfIiZb8VPJBQkMOzqguRnWr8e54dsyKKefnvDpZtsKYjo7HTU1yXca\nKYQg04l7731M0qlOP0uUr59RJcvmNQoavOe6ljPI2GIbbScbgzKGIlKpMg4MnXM3FmIgCRwDLAv4\n3P2Al/Jwzt8At8fdtydwVx6O3WdiA59s+xgm5Bw8+6y3bvDBB+HQQ+Hkk+nqei9tBiV+N5YBA6qI\nRPqsk1H3dOKcOXMSNgivrz+FPfb4OgDLly/vlVFSU+PCK8R622yEQtDergbXItI/ZTuV3BdeInh2\n8r/5OKFzrgVoib2vHKsPYzOGeWlX8/rrcNddzN96O1xzoNd8esYMGDAAAOceSHuhLHbGEHpOJ37t\nazey//7GZz6zJ3V1dSxa9B9+9asrGDPmjoQ7bnzuc99SwUGBpQveY3f3KaR0xSdqcC0ilaxkA0Pn\n3Brg98UeRznK2xrDtWth6lSoqYGTTuKMgRfy2NzJvZ4W5EIZ20Knq8vbDaSjo+8yhrHC4TA77/xJ\njjlmF+rra4lEIsyefTZPPvlA0h037rvvEXbfvWirDPqNYldXQ/o1hmpwLSKVrGQDQ8le1lviffAB\nDB68bWX9jjvCVVfBLrsA8F7jvQm/LMiFMrbpdmenK2pgCD0zqbNnz+bJJx8Aku+48cgj9/HBB3sA\nR/XpOPurYq7lDFaV3IcDEhHpQxUXGJqZv9HtAdG/TzWzowGcc/9XnFH1rYymkiMRePhhb93gCy94\nrWb8lh5VVd1BYSpBM4Z+kOWvMezrqeRYfsDsb5e3rYA+MTPjpZfuIBK5SlXIFS7YzifKGIpIZaq4\nwJDebXTOivl3PwkM00wlOwf/+AcsXOhVFn/qU/D1r8Nhh2XVuTdoxjC2UnrgwKqSyBj62+Wl45xj\ny5Z3aG5uVlVyhUu380mQLSBFRMpVxQWGzrl+/5YdX+jRHRiuWuUFg/fdB3vt5RWRzJzZXUSSrewy\nhsWeSvYCgFy2y5PKFKT4RBlDEalUFRcYSqI1htEHHngAPvxhuOMOGDo0b+cLkjE065kxrKkJEYkU\nfyo51+3ypPIEaXCtwFBEKpUCwwoU+mATX2xrhkVb6dqpblvG8NxzC3K+IBnDUCg2YwjV1amzMoXm\nTyUH3XEDjMGDR2S844aUn/R7JatdjYhULtXWVYq2Nm+KeOpUQl/5Mrt0roePfazvt8RLInZ62w/K\nitkj0s8Y+jtupN84x3HQQV9R4Uk/4GXckz+ujKGIVDIFhuWsqwsefxwuvhhOOAEeeQTOPhv7y1+4\nKnwi7LFHnwSGkL4ReNYtdAoktlq7oaGBCRMmAL2/D//24Ycfx2GHndGnY5TiSFd8onY1IlLJ9PZW\nrl54AY4/Hm69FT73Obj3XrjsMjjkELBt2bhSWSifKGNYTH7xCWzbcaOxsZFav1VPVG1tLY2Njfzw\nh/MIh4Nu3S3lLP1UcnnuiCQiEoQCw3Lxzjs9b++5p1dhfOWVMG4cxE1xxu6VnK8gzAvuslsX6BWf\nlE7GMH4M/o4bq1ev5k9/WsonPtHIsmXLWL16NTNnziQUqlaWqJ9IX3yijKGIVC4Vn5SyjRth6VJY\ntAg6Orzm035rmQEDArWZyWdg6F8wq6szP55XfOL9u6uLEsgYJn5dwuEwRx89lnnzhlBff0z3/aUQ\nzErfiM0YRiKRXlvzlUoWXkSkEBQYlpq2Nq+tzF13wRtvwPjx8JOfwO67Z3W4fAY06XaESCW+t2Kx\nMy6pLu5VVb0zRqUw/S19w98Rp7Gxkaamph4N0EePHs1uu01k4sSZKY4gIlK+FBiWiscfhwUL4Mkn\nYexYmDoVDjoo5y0W8hnQeLuXZDeVHFt8EnRMibI1+aoKThWcJg4MVYnaX3R1Rbj77gt5441Heq0l\nXLt2LWvWzGXWrFWMHbtEVeoiUnEUGJaKZ56BCRPg8suhOj8/lkgkwjPPNPPqq2+xfHlrzoFVurVX\nqcRnDLu6Ovjgg2eZP/+DXkFfJBJh9uzZCbM1U6dOpaGhgXA4nFPgmGo6O1nGsNhZTukbf/7z1bzx\nxiNA7zW1/u3HHrufOXPmMHOmMociUlkUGJaK887L26EikQjr1v2RMWPO6A6sFizoHVhlKl21Zipm\n3tdGIhEef/w6brzxDjZsWMeUKd7j/tguuugivvSlL7F06dKE2ZpZs2bx+OOPc9hhhzF37tyUgWMq\nqabYkweGyhhWukgkwsKF8wADkv+umxlNTU3MmDFDWUMRqSjKgVSYSCTCxIkTWb/+ZlpaWno85gdW\nkyZNIhKJZHzsXDKGodC2sT3++DVs2LA+4dgOPvhgli5dCiTP1ixdupQf/ehHOX1/qaazEwWGKj7p\nH5qbm9mwYR2pgkLwfhfXrFlDc3Nz3wxMRKSPKDCsMLNnz04bWC1ZsoQ5c+ZkfOzcik+MRYt+0z22\n+AuvP7aXX3458DFz+f4yLz4pfiW1FF5ra2tBny8iUuoUGFaQSCRCU1NT2ua7/jRYplnDdDtCpOJc\nhPvvv6lPGgMH+f4yLz7RGsP+YMSIEQV9vohIqdOlroI0Nzezdu3atE2os50Gy2WN4erVz/Dee+uz\nbpCdiXTfXyQSobX1Gf7yl7tYvnx5rwBSawz7r7q6OmprawN9uBo9ejR1dXV9NDIRkb6hwLCCFHoa\nLLblTKa2bn03q6/LRUtLC8uXL2f+/PksX76czZs309jYyJgxY3j22emcddbXGDduHLvtthuNjY3d\nAWKitZRqV9M/hMNhpk2bFujD1bRp01R4IiIVR1XJFaTQ02C5FJ8MHrxDVl+Xi/POO48NGzZ0366p\nqaG9vb3X8/yilRUrVrBgwQLC4XCvwKAUmnJL32hoaGDFihUsWbIEM+vxu+DfnjBhAjNmzCjiKEVE\nCkOXugpS6GmwXIpP9tjjELbffsc+WWPoe/fdnlnKREEhBCta0c4n/Uc4HGbBggU0NjZSW1vb47Ha\n2loaGxu7P0CIiFQaBYYVpNDTYLkUn4TDNYwbd0qgNYb77LMPQK8gMtOgMtP1jLFFK/Hn0hrD/iUc\nDjNz5kxWr17NsmXLuPPOO1m2bBmrV69m5syZCgpFpGIpMKwwDQ0NTJgwAUgeWGU7DZZbg2s4/vjz\nAo3t6aefTpqtufTSSxkz5qiEx8hVqqIVrTHsn8LhMPX19UyePJn6+noFhCJS8RQYVphCToPlUnwS\nChmhUDULFizggAPOYeTInZKObfDgwd3Zmvnz7+WAA37Una255JJL+Oxnf8455zT0+v6GDx+e1dji\ntba24lxHj8KVSKRdawxFRKTiqfikAvnTYDNmzMh6L+FE8rFXcjgcZt99z+CPf/wJra3/TDm2cDjM\n0UePZeRIL2uzTRVnnfVdmpouo7m5mVNPfYCbbvoM69at4ytf+UrW359v8eLFvPzyAsaN27ZGcejQ\nHamvP4VzzvmpskYiIlKxFBhWMH8aLF9y2/mE7mxjZ6djwICaQGMLheh1Tn9a1//+Ro5s5+ijx9Lc\n/FB2g4sxYMAAbrjhhl73b9r0DnfffSWTJr2iwgMREalYmhyTwHIpPgmFDL8WJJPt5RJNX8fvQlJT\nE6KtrYu6ujpGjRqV1fh8bW1tSR7JbTtBERGRcqDAUALLtfjEDwwz6QmYPDDcFljW1IRob+8iHA5z\n5pnnZTE2ix6npmDbCYqIiJQDBYYSmL/GMBKJ9CjMcK4j0Nf6AV4mPQFTTSX7Bgyoor3de9LZZ09n\np52OBJJXPg8YMKDH/bW1tZx11lm0t7cXbDtBERGRcqDAUAIz6+S6637GmDFjGDduHFOmTGHcuHG8\n/PKpPbaUS/y18RnD/E4l+4FhZ2cVRx75k5RV2a2trb160/ltdILKdDtBERGRcqDiEwnEyxL+P956\nq7lXJq6zc0OvLeXimfXMGOYeGPacSm5r6wRgy5ZOhgwZkLYqO77wpdDbCYqIiJQDBYYSyOzZs3nr\nLW/6NNl0q1+YMXPmzF6PhUJkWXwSZCp5W8Zwy5ZOBg6sAjKryva3E2xpaUk5nWxm1NbWZrydoIiI\nSDnQVLKkFYlEaGpqArIvzDCz7oArP8UndK91XLPmQR599CEikQhbtnQyaFBVsIPHKPR2giIiIuVA\ngaGk1dzczNq1a/FbtiSTqjAjNpBraXmQxx57KFBlb6LAsKMjwjXX/LR7rePDD1/Mued+kd122415\n866gpia7Zoux2wn2ltt2giIiIuVAgaGklWmhRfzzI5EId999FZdfXs+4ceN48cVLmTjxeHbbbbe0\nRSvxU8mRSITHH7+YX//6clpaWno8d+3atdxyy89YvPiirNrJxG4nWF3dc3u9IUNG8rWvfVfNrUVE\npKJpjaGklUthRiQSYeLEiSxdupT4qei1a9cGKlqJnd6dPXs2LS2PAb3XOvq3X331oaRwP3dFAAAT\nW0lEQVRrHdPxtxO8/fZDuOKKgWza9C4jRozgnnu256ijahUUiohIRVPGUNLyCzOCNH8ePXp0j8KM\n2bNnR4NCiJ+K9gO5oLuJBF3rCLk3oa6u9vZpnjx5MvX19ZhVB66kFhERKVcKDCWtbAsz/EAuX7uJ\nBF3rCLk3oa6qsh7b/8VXQouIiFQiBYYSSGxhRrIdReILM/xALtfdRPzj57rWMRO9A8PgldQiIiLl\nSpc6CSS2MCPZjiLx6wTzHcj1ZRPq+MAwk91aREREypWKTyQwvzAj1Y4isfIdyPlrHb3p5OTy0YQ6\nUcYwaFNuERGRcqXAUDIWdEeRfO8m4q91nDVrVsrn5aMJtdYYiohIf6SpZCmYQuwm0tDQwIgRnwKC\nr3XMhtYYiohIf6RLnRRUNkUrqYTDYfbb7zIuvviSwGsdsxEKJQoMlTEUEZHKpsBQCiqbopX0qvje\n977P6tWrWbZsGXfeeSfLli1j9erVzJw5My9NqDWVLCIi/ZHWGErBZVq0ko4fpAVd65iNqqqeezR3\ndqr4REREKp8CQ+kz+Qrk+mK9X01NiIsuepbtt/cC10cfXc+3vvWRwp5URESkyBQYStnpi/V+V199\nMG+/vbX79vTpe3PQQTsU9JwiIiLFpsBQyk5frPfbeedB7LzzoIKeQ0REpNSo+ERKXny7G7WOERER\nKQxdXqXsqHWMiIhIYSgwlLKj1jEiIiKFocBQyo6mkkVERApDl1cpO8713kVFREREcqfAUEREREQA\nBYYiIiIiEqXAUEqepo1FRET6hgJDKXlmPXsZxvc1FBERkfxQYCglLxQyurqKPQoREZHKpy3xpOSF\nQl6LmqqqvplSjkQiNDc309rayogRI6irqyMcDvfJuUVERIpJgaGUPC9jWPjp40gkwuzZs2lqamLt\n2rXd948ePZqpU6fS0NCgAFFERCpaRU0lm9mxZjbPzF42s81m9pqZ/c7Mdi722CR7fTGVHIlEmDhx\nIrNmzaKlpaXHY2vXrmXWrFlMmjSJSCRS2IGIiIgUUUUFhsAcoB5YAPwvcCvwZeBpMxtdxHFJDvyp\n5EKaPXs2S5cuBXoXt/i3lyxZwpw5cwo6DhERkWKqtMDwQmAv59wM59zvnHPfB74A1ALnF3dokq1C\nTyVHIhGamprStsUxM5qampQ1FBGRilVRgaFz7iHnXFf8fUArsF9xRiW5KvRUcnNzM2vXrk3bBsc5\nx5o1a2hubi7cYERERIqoogLDRMxsO2A7YH2xxyLZKfRUcmtra0GfLyIiUi76Q1XyBUAN8Od0TzSz\nUcBOcXfvWYhBSXCFnkoeMWJEQZ8vIiJSLko2MDSzEF5AF0SbSzAPaGbHAJcAtznn/hbgOFOjz5cS\nUuip5Lq6Ompra2lpaUk5nWxm1NbWUldXV7jBiIiIFFEpTyUfA2wJ+Oej8V9sZvviVSc/D5wd8Jy/\nAQ6M+3NSLt+E5K7QU8nhcJhp06YFWmM4bdo09TIUEZGKVbIZQ+Al4MyAz/1v7A0zGwPcB2wExjvn\nNgU5iHOuBejRxC5dpaoUXvxUciF+Jg0NDaxYsYIlS5ZgZj2CRP/2hAkTmDFjRt7PLSIiUipKNjB0\nzq0Bfp/p15nZSLygcABwrHPuv2m+REpcXzS4DofDLFiwgDlz5tDU1MSaNWu6H6utrWXatGnMmDFD\n2UIREaloJRsYZsPMhgBLgV2Bcc65V4o8JMmDvmhwDV5wOHPmTGbMmKG9kkVEpF+qqMAQuAU4HJgH\n7Gdmsb0L33fOLSzOsCQXfbVXsi8cDlNfX99n5xMRESkVlRYYfiL691nRP7HeABQYlqG+mEoWERGR\nCgsMnXO7F3sMkn9mvfcvFhERkfwr5XY1IkDPjKFzDhWKi4iIFIYCQyl5scUnXV0oMBQRESkQBYZS\n8mKLT7q6HKGQIkMREZFCqKg1hlJ5IpEI69c/zb33vsaaNR/i0EM/pcBQRESkQBQYSkmKRCLMnj2b\npqYm1q5dy0MPeffX1tYSCh1PJFKv3oIiIiJ5psBQSk4kEmHixIksXbq01/Z3LS0tOHcTkya9w4IF\nCxQcioiI5JHWGErJmT17NkuXLgV6t6nxby9ZsoQ5c+b0+dhEREQqmQJDKSmRSISmpqZemcJ4ZkZT\nUxORSKSPRiYiIlL5FBhKSWlubmbt2rVpG1o751izZg3Nzc19NDIREZHKp8BQSkpra2tBny8iIiLJ\nKTCUkjJixIiCPl9ERESSU2AoJaWuro7a2tpAawxHjx5NXV1dH41MRESk8ikwlJISDoeZNm1aoDWG\n06ZNU7saERGRPFJgKCWnoaGBCRMmAPTKHPq3J0yYwIwZM/p8bCIiIpVMgaGUnHA4zIIFC2hsbKS2\ntrbHY7W1tTQ2Nqq5tYiISAFYuim7/s7MDgCef/755znggAOKPZx+JxKJ0NzcTGtrKyNGjKCurk4B\noYiISBIrV67kwAMPBDjQObcy06/XlnhS0sLhMPX19cUehoiISL+gqWQRERERARQYioiIiEiUAkMR\nERERARQYioiIiEiUAkMRERERARQYioiIiEiUAkMRERERARQYioiIiEiUGlynVwPw6quvFnscIiIi\nIinFxCs12Xy9tsRLw8y+CNxV7HGIiIiIZOAk59yiTL9IgWEaZjYMGAu8CbQX6DR74gWfJwGrCnSO\n/kavaX7p9cw/vab5p9c0v/R65l9fvKY1wBjgQefcxky/WFPJaURf1Iwj7kyYmf/PVdlseC296TXN\nL72e+afXNP/0muaXXs/868PX9Olsv1DFJyIiIiICKDAUERERkSgFhiIiIiICKDAsFeuAH0X/lvzQ\na5pfej3zT69p/uk1zS+9nvlX8q+pqpJFREREBFDGUERERESiFBiKiIiICKDAUERERESiFBiKiIiI\nCKDAUERERESiFBiWIDM7xswWmdmbZrbVzNaY2V/MrK7YYytHZnasmc0zs5fNbLOZvWZmvzOznYs9\ntnJlZjub2WwzW2Zmm8zMmVl9scdVDsxsgJnNMbO3zWyLma0ws88We1zlysy2M7MfRd8jW6O/i2cU\ne1zlyswOM7OrzWylmX1gZqvN7DYz26fYYytXZnaAmd0evfZsNrP1ZvaQmZ1Y7LElosCwNO0DdAFz\ngWnAFcBo4CEz+3wxB1am5gD1wALgf4FbgS8DT5vZ6CKOq5x9FJgB7Ar8s8hjKTe/By4EbgG+A3QC\nS83s6GIOqoztCMwC9gOeLfJYKsEMYArwAN7v53XAMcA/zOzAYg6sjH0YGArciPeaNkbvX2Rm5xZt\nVEmoj2GZMLPBwGvAM845BYcZMLNjgEecc11x9z0IXO6cm1m0wZUpMxsKhJ1zrWZ2MnA7MM45t7y4\nIyttZnY4sAL4nnPuiuh9A4HngRbn3FHFHF85MrMBwHDn3BozOxT4O3Cmc+73xR1ZeTKzo4AnnXPt\nMfftjfcB8A7n3ClFG1wFMbMq4ClgoHNu32KPJ5YyhmXCObcZr1P6DsUeS7lxzj0UGxT69wGteFkG\nyZBzbpNzrrXY4yhDJ+NlCK/z73DObQWuB440szHFGli5cs61OefWFHsclcI592hsUBi97xVgJXq/\nzBvnXCfwJiV4Ta8u9gAkOTPbHqjBmyo5DTgQ+HFRB1UhzGw7YDtgfbHHIv3KwcDLzrn34u5/Ivr3\nJ/AuFiIlw8wMqMULDiVLZjYEGAQMA74InAD8uaiDSkCBYWm7DTg++u924Fq2rU2Q3FyAF3SX3H9K\nqWg7A/9NcL9/3y59OBaRoL6Bt554VrEHUuZ+Dnwr+u8uYD5wfvGGk5gCwwIzsxBeABJEm+u56LMB\n7xdpDHB69Dj9+meW4+vpH+MY4BLgNufc3/I5vnKUj9dUAhsEtCW4f2vM4yIlw8z2BZqAx/CKJyR7\nVwJ34H0A/DJQRfD33j7Tr4OMPnIMsCzgc/cDXvJvOOee8f9tZn8A/oFX0XhyHsdXbrJ+PaH7TW4B\n3mL/s/M7tLKV02sqGdkCDEhw/8CYx0VKQrRrwxJgI3BydF2cZMk59xLb3j9vMrP7gLvN7IhS+sCt\nwLDwXgLODPjcRFNMADjn2s1sEdBgZoOcc/31ApL16xld2H8f3pvceOfcpjyPrVzl5XdUAvkv3pRc\nPL+n5tt9OBaRpMxsGHAPXnHEp51z+t3MvzvwlojtA/yryGPppsCwwKLVcr/P0+EGAYbXD6lfBobZ\nvp5mNhIvKBwAHOucU4ATleffUUntGWCcmW0fV4ByRMzjIkUVbaF0N17Acpxz7oUiD6lS+UtHhhV1\nFHHUrqYEmdmoBPftgNd09E3nXEvfj6p8RSvBluJlasZHWy+IFMMdeOuKupvaRvvwnQmscM6pIlmK\nKtpf78/AkcCXnHOPFXlIZS/JNT2M121kC1BSgbcyhqXpHjN7C68RbguwG96FYxfgK8UcWJm6BTgc\nmAfsZ2axvbjed84tLM6wypuZ+Y3BD4j+faq/e4dz7v+KM6rS5pxbYWa3Az+JXixexSss2x34ZjHH\nVs7M7Hy8KU+/qvtEM/tQ9N9XOec2FmdkZenneK1U7gZGmFmPhtbOuT8UZVTl7dpo+7mHgP/g7WT2\nDWBf4CLn3PvFHFw87XxSgsxsGvBVvF+aHYANwOPAz5xzDxdzbOXIzF7H25IokTecc7v33Wgqh5kl\nffNwzllfjqWcRKfpGoFTgOHAc8APnXP3FnVgZSzN//E9nHOv991oypuZLQfGJntc/7czZ2Zfxfvg\n9zFgJLAJb9eTq5xzi4o5tkQUGIqIiIgIoDWGIiIiIhKlwFBEREREAAWGIiIiIhKlwFBEREREAAWG\nIiIiIhKlwFBEREREAAWGIiIiIhKlwFBEREREAAWGIiIiIhKlwFBEpI+Y2d5mdp+ZbTQzZ2YTiz0m\nEZFYCgxFpCyY2RnRYOrQYo8FwMwGm9mlZlafwZfdiLdf6g+AU4EnS2RcIiIAVBd7ACIiZWowcEn0\n38vTPdnMBgFHApc7564ulXGJiMRSxlBEpG/sFP373aKOIktmVm1mNcUeh4gUlgJDESlbZvZ7M3vf\nzHY1s4XRf68zsyvMrCrmebtHp6G/a2bTzewNM9tiZg+a2YFxx1xuZsuTnOt1/3jAuuhDl0SP7czs\n0iTjvBR4I3rzZ9Hnvh7z+K5mNs/M1ppZm5mtNLOz4o5RY2aXmdlT0TWKH5jZw2Y2Lvb7TDWuIN9b\ngtfrAjNbBbQB+0cfH2BmPzKzV6PjfdPMfmpmAxJ9/yJSPjSVLCLlrgq4F1gBfBc4DrgIWAVcE/fc\n04ChQBMwEPgO8Dcz+5hzbm0G51wHfDt6/AXA/Oj9zyV5/ny8TOEvgT8BS4H3AcysFngccMDV0WOf\nAFxvZts7566MHmN74Ozo1/82+n18E7jXzA53zj2TxbjSORPvdboOLzBsNbMQsAg4Onr/i3jrJqcD\n+wAqqBEpYwoMRaTcDQT+7JxrjN6ea2b/wAua4gPDvYC9nXP/ATCzv+AFlDOAC4Oe0Dn3gZndET3+\nc865P6R5/nNm9h5eYPiPuOdfjhfcfsw5907M9/An4FIzu9Y5twXYAOzunGv3v9DMfgu8BPwP8M1M\nxxXAh4C9nHN+FhIzOwUv+B7rnHsk5v7no+M+yjn3aI7nFZEi0VSyiFSCuXG3HwY+kuB5C/2gEMA5\n9wReYDi+gGNLyswMmALcHb25o/8HLws6DDgkOtZOPyg0s5CZjcD7cP+k/5wCuDM2KIz6El6W8KW4\n8f4t+vg4RKRsKWMoIuVua4LgZQMwPMFzX0lw38vAl/M+qmB2AnYAzo3+SWSU/w8zOx1vmnxfIBzz\nnH8XaHyJjrs3sB/b1jLGG5XkfhEpAwoMRaTcdeb5eA6wBPdXJbgvV/6szR/wehwm8hx0T+H+HlgI\n/AxowfveLwb2DHi+TL+3LQnuCwH/JPnU+5sBxyIiJUiBoYj0J3snuG8f4PWY2xtIPA394bjbLg/j\nWQdsAqqcc39N89yTgdeAyc657nOb2Y8yGFfQ7y2VVcDHgQdixyEilUFrDEWkP5loZrv6N8zscOAI\n4J6Y56wC9jWznWKe93GgLu5Ym6N/75DtYJxzncCdwJT4tjnR8+4Uc9PPjFrM40fgNc0OOq6g31sq\ntwG7AuckGO8gMxuSwbFEpMQoYygi/cmrwCNmdg0wALgAeAf4acxz5uFNk95rZtfjrZk7D1iJ1zIG\nAOfcFjN7AfiKmb0MtALPO+eez3BMDXgFGyuiVcYvACPwCkqOi/4bYDEwGVhgZkuAPaLjegHYLuC4\nAn1vadyMtyZzbrSHYjPeVPS+0fuPpwBb/YlI31DGUET6k5uAq4Dz8fYrXgl8xjn3X/8JzrkX8fod\nDgN+AXwRb1/jfyQ43tnAf9jWn/DkTAcU7Z94OHADXuB3NV5/xRF4bXR8vwe+jzeN+2u8AOwUEgdh\nCceV4feWbLxdeL0KG/D6F16BtwXfYcCv8Ip5RKRMmZaIiEili+4I8m/ge865K4o7GhGR0qWMoYiI\niIgACgxFREREJEqBoYiIiIgAWmMoIiIiIlHKGIqIiIgIoMBQRERERKIUGIqIiIgIoMBQRERERKIU\nGIqIiIgIoMBQRERERKIUGIqIiIgIoMBQRERERKIUGIqIiIgIoMBQRERERKIUGIqIiIgIAP8f6zR5\nMih90zsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1163897f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "X, y = mglearn.datasets.make_wave(n_samples=100)\n",
    "line = np.linspace(-3, 3, 1000, endpoint=False).reshape(-1, 1)\n",
    "\n",
    "reg = DecisionTreeRegressor(min_samples_split=3).fit(X, y)\n",
    "plt.plot(line, reg.predict(line), label=\"decision tree\")\n",
    "\n",
    "reg = LinearRegression().fit(X, y)\n",
    "plt.plot(line, reg.predict(line), label=\"linear regression\")\n",
    "\n",
    "plt.plot(X[:, 0], y, 'o', c='k')\n",
    "plt.ylabel(\"Regression output\")\n",
    "plt.xlabel(\"Input feature\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Make linear models more powerful by splitting up a feature into multiple artificial features:\n",
    "- Partition the feature values into a fixed number of bins  \n",
    "- A data point will then be represented by which bin it falls into."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bins: [-3.  -2.4 -1.8 -1.2 -0.6  0.   0.6  1.2  1.8  2.4  3. ]\n"
     ]
    }
   ],
   "source": [
    "bins = np.linspace(-3, 3, 11)\n",
    "print(\"bins: {}\".format(bins))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Numpy's `digitize` maps each value to its corresponding bin. E.g. the first sample goes to bin nr. 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Data points:\n",
      " [[-0.753]\n",
      " [ 2.704]\n",
      " [ 1.392]\n",
      " [ 0.592]\n",
      " [-2.064]]\n",
      "\n",
      "Bin membership for data points:\n",
      " [[ 4]\n",
      " [10]\n",
      " [ 8]\n",
      " [ 6]\n",
      " [ 2]]\n"
     ]
    }
   ],
   "source": [
    "which_bin = np.digitize(X, bins=bins)\n",
    "print(\"\\nData points:\\n\", X[:5])\n",
    "print(\"\\nBin membership for data points:\\n\", which_bin[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Encoding data  with scikit-learn (`OneHotEncoder`)\n",
    "- Scikit-learn offers a convenient `OneHotEncoder`\n",
    "- Call `fit` to compute the internal parameters of the transformation\n",
    "- Call `transform` to produce the transformed data. You can also run `fit_transform` to do both at once. \n",
    "\n",
    "!! Test data should _never_ be used to compute the preprocessing, because information about the test data will _leak_ into the training data, invalidating your model evaluation. Use a nested loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "# transform using the OneHotEncoder.\n",
    "encoder = OneHotEncoder(sparse=False)\n",
    "# encoder.fit finds the unique values that appear in which_bin\n",
    "encoder.fit(which_bin)\n",
    "# transform creates the one-hot encoding\n",
    "X_binned = encoder.transform(which_bin)\n",
    "print(X_binned[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_binned.shape: (100, 10)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_binned.shape: {}\".format(X_binned.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now we build a new linear regression model and a new decision tree model on the one-hot-encoded data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1165f8668>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDIgMCBSID4+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\nIC9JbmZvIDQxIDAgUiA+PgpzdGFydHhyZWYKMTI1MTQKJSVFT0YK\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoYAAAG8CAYAAABDg2QvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a8cd470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "line_binned = encoder.transform(np.digitize(line, bins=bins))\n",
    "\n",
    "reg = LinearRegression().fit(X_binned, y)\n",
    "plt.plot(line, reg.predict(line_binned), label='linear regression binned')\n",
    "\n",
    "reg = DecisionTreeRegressor(min_samples_split=3).fit(X_binned, y)\n",
    "plt.plot(line, reg.predict(line_binned), label='decision tree binned')\n",
    "plt.plot(X[:, 0], y, 'o', c='k')\n",
    "plt.vlines(bins, -3, 3, linewidth=1, alpha=.2)\n",
    "plt.legend(loc=\"best\")\n",
    "plt.ylabel(\"Regression output\")\n",
    "plt.xlabel(\"Input feature\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Interaction features\n",
    "\n",
    "Another way to enrich a feature representation, particularly for linear models, is adding interaction features and polynomial features of the original data.\n",
    "\n",
    "For instance: our linear model learned a constant value for each bin in the wave dataset. If we want a sloped linear model we need to allow interaction with another feature, e.g., the original feature. So let's add the original feature (the x-axis in the plot) back in. This leads to an 11-dimensional dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 11)\n"
     ]
    }
   ],
   "source": [
    "X_combined = np.hstack([X, X_binned])\n",
    "print(X_combined.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116367d68>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDIgMCBSID4+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\nMTI4MDkKJSVFT0YK\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoYAAAG8CAYAAABDg2QvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERERkRg1hiIiIiICwP8HvxznniKO2DQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116389358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "reg = LinearRegression().fit(X_combined, y)\n",
    "\n",
    "line_combined = np.hstack([line, line_binned])\n",
    "plt.plot(line, reg.predict(line_combined), label='linear regression combined')\n",
    "\n",
    "for bin in bins:\n",
    "    plt.plot([bin, bin], [-3, 3], ':', c='k')\n",
    "plt.legend(loc=\"best\")\n",
    "plt.ylabel(\"Regression output\")\n",
    "plt.xlabel(\"Input feature\")\n",
    "plt.plot(X[:, 0], y, 'o', c='k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Product features\n",
    "\n",
    "If we want a different slope per bin, we need a new _interaction feature_ (or _product feature_) that indicates in which bin a data point is in __and__ where it lies on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 20)\n"
     ]
    }
   ],
   "source": [
    "X_product = np.hstack([X_binned, X * X_binned])\n",
    "print(X_product.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11a8cde48>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDIgMCBSID4+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\nUiAvSW5mbyA0MCAwIFIgPj4Kc3RhcnR4cmVmCjEyMTM2CiUlRU9GCg==\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoYAAAG8CAYAAABDg2QvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1163b1e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = LinearRegression().fit(X_product, y)\n",
    "\n",
    "line_product = np.hstack([line_binned, line * line_binned])\n",
    "plt.plot(line, reg.predict(line_product), label='linear regression product')\n",
    "\n",
    "for bin in bins:\n",
    "    plt.plot([bin, bin], [-3, 3], ':', c='k')\n",
    "\n",
    "plt.plot(X[:, 0], y, 'o', c='k')\n",
    "plt.ylabel(\"Regression output\")\n",
    "plt.xlabel(\"Input feature\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Polynomials\n",
    "\n",
    "We can also make linear models behave more flexibly by adding polynomials of the original continuous features. \n",
    "\n",
    "For a given feature x, we might want to consider $x^2$, $x^3$, $x^4$, and so on. In scikit-learn, this is implemented in `PolynomialFeatures` in the preprocessing module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "# include polynomials up to x ** 10:\n",
    "# the default \"include_bias=True\" adds a features that's constantly 1\n",
    "poly = PolynomialFeatures(degree=10, include_bias=False)\n",
    "poly.fit(X)\n",
    "X_poly = poly.transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Using a degree of 10 yields 10 features, with the original value raised to the n-th power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_poly.shape: (100, 10)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_poly.shape: {}\".format(X_poly.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entries of X:\n",
      "[[-0.753]\n",
      " [ 2.704]\n",
      " [ 1.392]\n",
      " [ 0.592]\n",
      " [-2.064]]\n",
      "Entries of X_poly:\n",
      "[[   -0.753     0.567    -0.427     0.321    -0.242     0.182    -0.137\n",
      "      0.103    -0.078     0.058]\n",
      " [    2.704     7.313    19.777    53.482   144.632   391.125  1057.714\n",
      "   2860.36   7735.232 20918.278]\n",
      " [    1.392     1.938     2.697     3.754     5.226     7.274    10.125\n",
      "     14.094    19.618    27.307]\n",
      " [    0.592     0.35      0.207     0.123     0.073     0.043     0.025\n",
      "      0.015     0.009     0.005]\n",
      " [   -2.064     4.26     -8.791    18.144   -37.448    77.289  -159.516\n",
      "    329.222  -679.478  1402.367]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Entries of X:\\n{}\".format(X[:5]))\n",
    "print(\"Entries of X_poly:\\n{}\".format(X_poly[:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polynomial feature names:\n",
      "['x0', 'x0^2', 'x0^3', 'x0^4', 'x0^5', 'x0^6', 'x0^7', 'x0^8', 'x0^9', 'x0^10']\n"
     ]
    }
   ],
   "source": [
    "print(\"Polynomial feature names:\\n{}\".format(poly.get_feature_names()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Using polynomial features together with a linear regression model yields _polynomial regression_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1163f8ef0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDIgMCBSID4+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\nIDAgUiA+PgpzdGFydHhyZWYKMTMzMjMKJSVFT0YK\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoYAAAG8CAYAAABDg2QvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116570978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = LinearRegression().fit(X_poly, y)\n",
    "\n",
    "line_poly = poly.transform(line)\n",
    "plt.plot(line, reg.predict(line_poly), label='polynomial linear regression')\n",
    "plt.plot(X[:, 0], y, 'o', c='k')\n",
    "plt.ylabel(\"Regression output\")\n",
    "plt.xlabel(\"Input feature\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "As a comparison, here is a kernel SVM model learned on the original data, without any transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x116418160>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDIgMCBSID4+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\nPgpzdGFydHhyZWYKMTUzNDkKJSVFT0YK\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoYAAAG8CAYAAABDg2QvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiIiIgIoMRQRERGRCCWGIiIiIgLA/wdbiPlgj3Zr8wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116418278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "for gamma in [1, 10]:\n",
    "    svr = SVR(gamma=gamma).fit(X, y)\n",
    "    plt.plot(line, svr.predict(line), label='SVR gamma={}'.format(gamma))\n",
    "\n",
    "plt.plot(X[:, 0], y, 'o', c='k')\n",
    "plt.ylabel(\"Regression output\")\n",
    "plt.xlabel(\"Input feature\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Non-linear transformations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are other transformations that often prove useful for transforming certain features.\n",
    "\n",
    "For instance, `log` or `exp` are very useful to better scale your data. This is useful for models that are sensitive to feature scales, such as linear models, SVMs and neural networks.\n",
    "\n",
    "The functions log and exp can help by adjusting the relative scales, tranforming them to more Gaussian-like value distributions.\n",
    "\n",
    "Here we generate some data that has a very non-normal (Poisson) distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "rnd = np.random.RandomState(0)\n",
    "X_org = rnd.normal(size=(1000, 3))\n",
    "w = rnd.normal(size=3)\n",
    "\n",
    "X = rnd.poisson(10 * np.exp(X_org))\n",
    "y = np.dot(X_org, w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of feature appearances:\n",
      "[28 38 68 48 61 59 45 56 37 40 35 34 36 26 23 26 27 21 23 23 18 21 10  9\n",
      " 17  9  7 14 12  7  3  8  4  5  5  3  4  2  4  1  1  3  2  5  3  8  2  5\n",
      "  2  1  2  3  3  2  2  3  3  0  1  2  1  0  0  3  1  0  0  0  1  3  0  1\n",
      "  0  2  0  1  1  0  0  0  0  1  0  0  2  2  0  1  1  0  0  0  0  1  1  0\n",
      "  0  0  0  0  0  0  1  0  0  0  0  0  1  1  0  0  1  0  0  0  0  0  0  0\n",
      "  1  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1]\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of feature appearances:\\n{}\".format(np.bincount(X[:, 0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x116441400>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDIgMCBSID4+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\nIC9JbmZvIDQxIDAgUiA+PgpzdGFydHhyZWYKMTM0MjYKJSVFT0YK\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoMAAAG8CAYAAAClqq9rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n5Jr3KQ9/YWYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a8cd2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.bincount(X[:, 0])\n",
    "plt.bar(range(len(bins)), bins, color='b')\n",
    "plt.ylabel(\"Number of appearances\")\n",
    "plt.xlabel(\"Value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "This is something most linear models can’t handle very well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.622\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "score = Ridge().fit(X_train, y_train).score(X_test, y_test)\n",
    "print(\"Test score: {:.3f}\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Applying a logarithmic transformation can help to create a more normal (Gaussian) distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We actually compute log(x+1) to avoid the occurrence of log(0)\n",
    "X_train_log = np.log(X_train + 1)\n",
    "X_test_log = np.log(X_test + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDIgMCBSID4+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\nIFIgL0luZm8gNDEgMCBSID4+CnN0YXJ0eHJlZgoxMDQ2NAolJUVPRgo=\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAo0AAAG8CAYAAAC7Y5/YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAASdAAAEnQB3mYfeAAAIABJREFUeJzt3Xm4LFV97vHvK8hhFlDQI3AdQBSNCkpQjON1RiMOqAlx\nwDgGjbPXBOMsRKMRRUVDNChoHBCDqBGHCCIxQlRwwCiCKIhHwRwFZVT43T+qtjRN713n7Op9unvv\n7+d5+undq1bVWl32KV5rVdVKVSFJkiQt5EaT7oAkSZKmn6FRkiRJnQyNkiRJ6mRolCRJUidDoyRJ\nkjoZGiVJktTJ0ChJkqROhkZJkiR1MjRKkiSpk6FRkiRJnQyNkiRJ6mRolCRJUidDoyRJkjptPOkO\nzLIkNwHuB1wAXD3h7kiSJC1kE2Bn4MtVdcn6rmxo7Od+wCcn3QlJkqT1sB9wwvquNNWhMcmWwMuA\newB7A9sCT6uq94+ouztwGHBvmrN+nwFeXFUXj6j7dOClwG1ozhIeXlXvWEQXLwA4/vjj2XXXXRex\nuiRJ0oZxzjnn8OhHPxra/LK+pjo0AjcDXgWcD3wLuP+oSkl2Ak4BLgEOBrakCYV3TrJ3VV09UPfZ\nwHuA44C3AvcBDk+yeVW9aT37dzXArrvuyp3udKf1XFWSJGkiFnVJ3bSHxjXA6qr6eZK9gP+ep97B\nwBbA3avqfIAkpwNfAA4EjmzLNgMOAT5TVfu36/5zkhsBr0xyZFX9asm+jSRJ0oya6runq+qqqvr5\nOlR9HPDpucDYrvtF4GzgCQP1HgDcFDhiaP130YTOR/TrsSRJ0vI01aFxXSTZEdgB+PqIxacDew58\nnvt7uO43gGuH6kqSJKk17cPT62J1+75mxLI1wHZJVlXVVW3da6rqosFKVXV1kv8FbjlfI0l2ALYf\nKt5l8d2WJEmaHcshNG7Wvl81YtmVA3Wuat/nu/jzyoFtjXIQ8OrFdFCSJGnWLYfQeEX7vmrEsk2H\n6lxB82DLUTYdqDfKEcCxQ2W74HMaJUnSCrAcQuPcsPTqEctWA2vboem5uhsl2WFwiDrJJjQ3yPxs\nvkba+tcb1k7Sp9+SJEkzY+ZvhKmqC4GLgb1GLN4bOHPg89zfw3X3otkXZyJJkqQbmPnQ2DoOeGSS\nnecKkjwQ2I3rDyl/CVgL/NXQ+n8FXE4zi4wkSZKGTP3wdJLnAdtw3Z3Nf9rOAAPwjnbC7UOBxwMn\nJXk7zYwwLwO+Axw1t62quiLJK4F3JTkW+BzNjDBPAl5RVWs3xHeSJEmaNVMfGmmmA7zVwOfHti+A\nDwKXVNUFSe5HMy3gG7lu7umXDFzPCEBVHZHkd8BLgEfRzL/4IuDtS/otJEmSZtjUh8aquvU61jsL\neOg61v1n4J97dEuSJGlFWS7XNEqSJGkJGRolSZLUydAoSZKkTlN/TaMkSYOWYl6FqvFvU1puPNMo\nSZKkToZGSZIkdTI0SpIkqZOhUZIkSZ0MjZIkSepkaJQkSVInQ6MkSZI6GRolSZLUydAoSZKkToZG\nSZIkdTI0SpIkqZOhUZIkSZ0MjZIkSepkaJQkSVInQ6MkSZI6GRolSZLUydAoSZKkToZGSZIkdTI0\nSpIkqZOhUZIkSZ0MjZIkSepkaJQkSVInQ6MkSZI6GRolSZLUydAoSZKkToZGSZIkdTI0SpIkqZOh\nUZIkSZ0MjZIkSepkaJQkSVInQ6MkSZI6bTzpDkiSpIUl491e1Xi3p5XBM42SJEnqZGiUJElSJ4en\nJUkas3EPJ0vTwDONkiRJ6mRolCRJUidDoyRJkjoZGiVJktTJ0ChJkqROhkZJkiR1MjRKkiSpk6FR\nkiRJnQyNkiRJ6mRolCRJUidDoyRJkjoZGiVJktTJ0ChJkqROhkZJkiR1MjRKkiSpk6FRkiRJnQyN\nkiRJ6mRolCRJUqdlERqT3C7JR5L8NMnlSb6f5FVJNh+qt3uSE5P8NsnaJMck2X5S/ZYkSZoVG0+6\nA30l2Rk4HbgEeCewFtgHeC1wd2C/tt5OwCltvYOBLYGXAndOsndVXb3hey9JkjQbZj40Ak8GtgHu\nXVVntWVHJrkR8JQk21bVr2iC4hbA3avqfIAkpwNfAA4EjtzgPZckSZoRy2F4euv2/RdD5WuAa4G5\nM4iPAz49FxgBquqLwNnAE5a6k5IkSbNsOYTGk9v39yXZI8nOSZ4I/BVweFVdlmRHYAfg6yPWPx3Y\nc8N0VZIkaTbN/PB0VZ2Y5JU0w8+PGlh0SFX9Xfv36vZ9zYhNrAG2S7Kqqq6ar50kOwDDN83ssshu\nS5IkzZSZD42tH9Pc5HIc8L/AI4CDk/y8qt4JbNbWGxUKr2zfN5tn+ZyDgFePpbeSJEkzZuZDY5I/\no7mJZbeq+mlb/In2Rpg3JfkwcEVbvmrEJjZt368YsWzQEcCxQ2W7AJ9c/15LkiTNlpkPjTRnAM8Y\nCIxzTqC5K3pP4H/astXc0Gpg7UJD0wBVdRFw0WBZksX0V5IkaeYshxthbg5sNKL8xu37xlV1IXAx\nsNeIensDZy5R3yRJkpaF5RAazwb2TLLbUPmf0zxy59vt5+OAR7YPAwcgyQOB3bjhsLMkSZIG9Bqe\nTrIVsE1VXTBQdkvgOTTXDx5XVaf362KnNwMPB76S5J00N8I8si17b1X9rK13KPB44KQkb6eZEeZl\nwHeAo5a4j5IkSTOt7zWNRwK3Ae4JkGRr4GvATjRn+V6Q5GFVdXLPduZVVackuRfwGprrG28KnAe8\nAviHgXoXJLkf8FbgjTQP/f4M8JKu6xklSZJWur6h8d7APw18fhJwS+BewFnAfwB/x3UP4F4S7dnM\nfdeh3lnAQ5eyL5IkSctR32sabwZcOPD5UcCpVfW1qvoNcDRw155tSJIkacL6hsZfA7cASLIZcB/g\n8wPLfw9s3rMNSZIkTVjf4emvAgcl+T7wMJoHZQ8+7Ho3rn8mUpIkSTOob2h8Oc2ZxePaz//YXjdI\nko1o7lY+sWcbkiRJmrBeobGqzklye+COwCVV9eOBxZsDzwO+1acNSZIkTV7vaQSr6neMCIbtjTDO\nyyxJkrQM9J4RJsnWSf4myeeSnJFk77Z8uyQvTrJr/25KkiRpkvrOCLMT8GVgZ+CHwB1oZlqhqtYm\neTZwK+AFPfspSZKkCeo7PP1mYCtgD+Ci9jXoeJop/SRJkjTD+g5PPwQ4vKq+B9SI5T+iOQspSZKk\nGdY3NG4GXLzA8q16bl+SJElToG9o/B5w3wWWPxo4o2cbkiRJmrC+ofFtwJ8leTlwk7ltJtk1yTHA\nPsBhPduQJEnShPV9uPcHk9wKeANwSFt8IhDgWuDgqjq+XxclSZI0aeN4uPch7VnFxwG70py9PBf4\nRFX9qO/2JUmSNHm9QyNAVZ2Pw9CSJEnLVq9rGpPcLclBCyw/KMkefdqQJEnS5PW9EeYQ4EELLP+/\nNNc7SpIkaYb1DY13B76ywPKvAHv1bEOSJEkT1jc0bgX8foHl13Ldo3gkSZI0o/qGxh/STCU4n4fR\nTCUoSZKkGdY3NL4PeESStybZZq4wyTZJDqMJje/r2YYkSZImrO8jdw4H9gBeCDw/yc/a8lvSBNJj\n8FE8kiRJM6/vjDAFPC3J0TQP975tu+iTwHFVdXK/7kmSJGkajOvh3icBJ41jW5IkaWkl499m1fi3\nqenS95pGSZIkrQB9Z4RJkmcnOT3JL5NcM+K10CN5JEmSNAP6Dk//A/Bi4Ezgg8CvevdIkiRJU6dv\naHwqzQ0vTxhHZyRJkjSd+l7TuBnwxXF0RJIkSdOrb2j8D+CPx9ERSZIkTa++ofEg4J5JDk5y03F0\nSJIkSdOnb2j8Ac0DvV8PXJTksiSXDr0u6d9NSZIkTVLfG2GOA3ycpyRJ0jLXdxrBA8fUD0mSJE0x\nZ4SRJElSp7HMPZ1kJ2BP4CaMCKJVdfQ42pEkSdJk9AqNSTYFPgA8jiYsFjA3DfrgtY6GRkmSpBnW\nd3j6UOCxwCuA+9MExqcCDwE+C3wLuGvPNiRJkjRhfUPj/sBRVfUm4Ky27MKq+mJVPRL4NfDcnm1I\nkiRpwvqGxh2A09u/r2jftxhYfhzNmUhJkiTNsL6h8RfATQGq6nLgV8DtB5ZvDWzasw1JkiRNWN+7\np08D7g28qf38KeBlSdbQBNIXAV/r2YYkSZImrO+ZxsOBHyVZ1X5+Jc11jMfQ3FV9CfD8nm1IkiRp\nwvrOCHMqcOrA5wuS7A7cGbgG+H5V/b5fFyVJkjRpiz7TmGTzJJ9I8heD5VV1bVV9q6q+a2CUJEla\nHhYdGtsbXx4EbD6+7kiSJGka9b2m8VRgn3F0RJIkSdOrb2h8HnCfJG9o55+WJEnSMtQ3NH4L2An4\nW+AnSa5KcunQ65L+3ZQkSdIk9X1O43FAjaMjkiRJml59H7lz4Jj6IUmSpCnWd3hakiRJK0Df4WkA\n2ptg9gRuwoggWlVHj6MdSZIkTUav0JhkU5rpAh9HExYLSLt48FpHQ6MkSdIM6zs8fSjwWOAVwP1p\nAuNTgYcAn6W5u/quPduQJEnShPUNjfsDR1XVm4Cz2rILq+qLVfVI4NfAc3u2IUmSpAnrGxp3AE5v\n/76ifd9iYPlxNGcil1ySuyU5IcnaJJcn+W6S5w/V2T3JiUl+29Y7Jsn2G6J/kiRJs6zvjTC/AG4K\nzVzUSX4F3B74VLt8a2DTnm10SvKQts0zgNcDvwV2oXnw+FydnYBTgEuAg4EtgZcCd06yd1VdvdT9\nlCRJmlV9Q+NpwL2BN7WfPwW8LMkamrOYLwK+1rONBSXZmuZGm88A+1fVtfNUPZjmLOjdq+r8dt3T\ngS8ABwJHLmU/JUmSZlnf4enDgR8lWdV+fiXNdYzH0NxVfQnw/HnWHZcDgJsDr6iqa5NskWTU93oc\n8Om5wAhQVV8EzgaesMR9lCRJmmm9QmNVnVpVL6iqq9rPFwC70zyz8S7A7lX1g/7dXNCDgEuBHZP8\ngGZo+tIk724fCUSSHWmuv/z6iPVPb/srSZKkeYzl4d6D2uHhb417uwu4Hc33+CTwPuBvaR7/89fA\nNsCfA6vbumtGrL8G2C7JqrnwO0qSHYDhm2Z26dVzSZKkGdE7NLbXFB4EPIDmbN6zq+r0JNvRXCt4\nQlWd07edBWwJbA68p6rmhsI/kWQT4NlJXgVs1paPCoVXtu+bzbN8zkHAq8fQX0mSpJnTa3i6vSP5\nDOB1NHcq34UmxFFVa4Fn05zxW0pzj/r58FD5v7bv+wzUWcUNzd3dfcWIZYOOAP5o6LXfevVUkiRp\nRvU90/hmYCtgD+Ci9jXoeOCRPdvo8jPgTjSP/xk015dtuW5YejU3tBpYu9DQNEBV3eD7JZmntiRJ\n0vLS9+7phwCHV9X3uP5c03N+BOzcs40u32jfdxwqv2X7fnFVXQhcDOw1Yv29gTOXqG+SJEnLQt/Q\nuBlNGJvPVj23vy4+1r4/faj8GcDvgZPbz8cBj0zyhxCb5IHAbsCxS9xHSZKkmdZ3ePp7wH2Bf5pn\n+aNprnlcMlV1RpJ/Af4yycbAl2nunn488PdV9bO26qFt2UlJ3k5z7eXLgO8ARy1lHyVJkmZd39D4\nNuADSb7NdWfrbpRkV5o7jfeheaj2UnsOcD7wNOAxwE+AF1XV2+YqVNUFSe4HvBV4I3A1zSwyL+m6\nnlGSJGml6xUaq+qDSW4FvAE4pC0+EQhwLXBwVR3fr4vr1I/fAa9tXwvVOwt46FL3R5Ikabnp/ZzG\nqjokyTE0ZxR3pblO8lzgE1X1o77blyRJ0uSNZUaYdj7nw8axLUmSJE2fsYTGJH8E7Avcui06Dzix\nqr4zju1LkiRpsnqFxiSraO6cfjLXXccIzRD1G5N8CHhGVV3dq5eSJEmaqL7PaXwT8BTg3cDuNFPy\nrWr/fg/wJOAferYhSZKkCes7PP0k4Jiqet5Q+Q+A5ybZuq3zwp7tSJIkaYL6nmm8MfC1BZZ/lTFd\nNylJXZLxvyRJjb6h8XMs/NzDhwGf79mGJEmSJqzvWcBXAh9L8gngXcA5bfntgOcCtwKemGS7wZWq\nam3PdiVJkrQB9Q2N/9O+3xnYb2jZ3MDO90ast1HPdiVJkrQB9Q2NrwNqHB2RJEnS9Oo79/RrxtQP\nSZIkTbGx3dmcJMD27ceLq8ozkJIkSctE37unSXLHJB8HLgXWtK9Lk3y8nV5QkiRJM67vNIL3AT5L\nEz4/CZzdLro98Cjg4UkeVlVf6dVLSZIkTVTf4enDgIuA+1XVBYMLkuwMnAK8Ffjjnu1IkiRpgvoO\nT98JOGI4MAK0Ze9u60iSJGmG9Q2NPwFWLbB8E+AGgVKSJEmzpW9ofB3w/CR7DC9Isifw18BrerYh\nSZKkCet7TeM9gV8A30jyVa4/jeA+wHeBfZLsM7BOVdULerYrSZKkDahvaHzewN9/0r4G3bl9DSrA\n0ChJkjRD+s4I0/s5j5IkSZp+hj5JkiR1MjRKkiSp0zimEXx4ki8k+d8kv09yzfBrHB2VJGmpJON9\nSctRr9CY5HHAp4GbAx9pt/fh9u8rgG/TPJZHkiRJM6zvmca/BU4H9gRe3Zb9S1X9BfBHwGrgvJ5t\nSJIkacL6hsY7Ah+pqmuA37dlNwaoqh8DRwAv79mGJEmSJqxvaLwcuBqgqn4NXEVzdnHOL4Db9GxD\nkiRJE9Y3NP6A5mzjnDOBJyfZOMmmwAHA+T3bkCRJ0oT1DY3/BuyXZFX7+RDg/sCvgYuB+wBv7NmG\nJEmSJqzvjDBvAd4y8PnTSe4PPBa4BvhMVZ3Uq4eSJEmauL5zT99AVX0F+Mq4tytJkqTJcUYYSZIk\ndTI0SpIkqZOhUZIkSZ0MjZIkSeq0XqExyfOT7LZUnZEkSdJ0Wt8zjYcBe819SHJNkgPG2yVJkiRN\nm/UNjb8Cbj7wOWPsiyRJkqbU+j6n8WTgNUn2AC5py56S5J4LrFNV9YLFdE6SJEnTYX1D40HA24CH\nADsA1f79kAXWKcDQKEmSNMPWa3i6qi6qqgOqanVVbUQzPP2kqrrRAq+NlqbrkiRJ2lD6PnLnacBX\nx9ERSZIkTa9ec09X1Qfm/k5yR+BW7cefVNX3+mxbkiRJ06NXaARIsh/wVuDWQ+XnAS+uqhP6tiFJ\nkqTJ6jU8nWRf4Lj248HAY9rXwTTXO34iycN69VCSJEkT1/dM4yuBbwP3qarLBspPSPJO4FTg1cCJ\nPduRJEnSBPW9EeYuwAeGAiMAbdn72zqSJEmaYX3PNF4JbLfA8u3aOpKkGZExz/VVNd7tSZqMvmca\nvwS8IMk+wwuS3AN4PvDFnm1IkiRpwvqeafx/wH8BpyY5HfhBW357YG/gIuDlPduQJEnShPU601hV\n59Fcs3g4sC3wxPa1LfB24K5V9eOefZQkSdKE9X5OY1VdBLyofUmSJGkZ6ntNoyRJklYAQ6MkSZI6\nGRolSZLUydAoSZKkTssyNCZ5RZJK8t0Ry3ZPcmKS3yZZm+SYJNtPop+SJEmzYtGhMcnmSb6R5Dnj\n7FBfSXYCDgZuMLVhu+wUYNe2zluARwBfSLLJhuynJEnSLFn0I3eq6vIktwGmbYKotwBfAzYCbja0\n7GBgC+DuVXU+QPtQ8i8ABwJHbrhuSlJ/457yT5Lm03d4+kTgoePoyDgkuS+wP/DCeao8Dvj0XGAE\nqKovAmcDT1j6HkqSJM2mvg/3fj1wbJJjgH8CzgOuGK5UVWt7ttMpyUbAO4D3VtV3MvR/v5PsCOwA\nfH3E6qcD+3Zsfwdg+NrHXRbdYUmSpBnSNzSe1b7fEThggXob9WxnXTwHuBXwoHmWr27f14xYtgbY\nLsmqqrpqnvUPAl7dr4uSJEmzqW9ofB1TcE1jkpu2fXl9VV08T7XN2vdRofDKgTrzhcYjgGOHynYB\nPrkeXZUkSZpJvUJjVb1mTP3o6w3AWprh6fnMDZuvGrFs06E6N9DOsX3RYNnwELgkSdJy1fdM4/Uk\nuQnw26q6Zpzb7WjzdsCzaG5+ueVAkNsUuHGSWwOXct2w9GpuaDWwdoGhaUmSpBWt98O9k+zVPiz7\ncuB/gfu15TdL8skk9+/bRocdab7H4TQ34sy97gHs1v79qqq6ELgY2GvENvYGzlzifkqSJM2sXmca\nk9wL+BJwIfBB4Blzy6rql+2Zx2cDJ/dpp8N3gceMKH8DsBXwAuDctuw44KlJdq6qCwCSPJAmXB62\nhH2UJEmaaX2Hpw8F/ge4J01Ae8bQ8pOAp/ZsY0FV9Uvg+OHyJC9slw8uOxR4PHBSkrcDWwIvA74D\nHLWU/ZQkSZplfYen/xg4qr0WcNRd1BcCt+jZxti0ZxfvR3Pm8Y3A/wP+HXiw1zNKkiTNr++Zxt+x\ncPDcEfhtzzYWparuP0/5WUzRLDaSJEmzoO+Zxq/RTNt3A0m2AJ4GfLlnG5IkSZqwvqHx1cBeST4D\nPLwtu2uSZwDfoJl27/U925AkSdKE9X2492lJ9gXeDRzdFv9j+34usG9VfbtPG5K0nDgngJarcf+2\na+LzzWlY74d7V9WXgNsn2RPYlebs5bnAN6r8n1ySJGk5GNuMMFV1BnDGuLYnSZKk6dE7NCZZBTwT\n2Be4dVv8Y5pH2by3qq7s24YkSZImq9eNMEl2opl+73DgrjTT9F3c/n04cGZbR5IkSTOs793T7wJu\nBTyhqnasqvu1rx2BJwL/p60jSZKkGdZ3ePqBwGFV9fHhBVV1bJK7AX/dsw1JkiRNWN/Q+BvgogWW\n/7ytI0mStM58hM/06Ts8fRRwYJLNhxck2ZJmRpj39WxDkiRJE7ZeZxqTPHao6AzgEcD3k3wAOKct\nvx3wFGAt4MO9JUmSZtz6Dk9/HChg7qTx4N+vGFF/J+DDwMcW1TtJkiRNhfUNjQ9Ykl5IkiRpqq1X\naKyqLy9VRyRJkjS9+t4II0mSpBVgHNMI3hv4S+C2wLZcd43jnKqqu/ZtR5IkSZPTKzQmeTHwZuBK\n4Ac0d0tLkiRpmel7pvFlwH8Cf1pVl4yhP5IkSZpCfa9p3Bz4kIFRkiRpeesbGk8C7jyOjkiSJGl6\n9Q2Nfw08MMlLk2w3jg5JkiRp+vQKjVV1AfBPwBuBi5NcluTSoZdD15IkSTOu793Tr6OZPvBC4OuA\nAVGaIhl+AFZPVePdniRpdvS9e/o5wGeAR1fVtWPojyRJkqZQ32saNwE+Y2CUJEla3vqGxk8D9xlH\nRyRJkjS9+g5Pvxb4aJIjgPcB5wPXDFeqKmeKkaQVatzX1kqajL6h8Qft+x7Asxeot1HPdiRJkjRB\nfUPj6wDvp5QkSVrmeoXGqnrNmPohSZKkKdb3RhhJkiStAH0f7v2qdahWVfX6Pu1IkiRpsvpe0/ia\nBZYVkPbd0ChJkjTD+s49faPhF00Q3QU4jGZqwR3G0E9JkiRN0Nivaayqa6vqvKp6KfBD4B3jbkOS\nJEkb1lLfCHMKsO8StyFJkqQlttShcS/AeaklSZJmXN+7p58yz6JtgPsCjwXe26cNSZIkTV7fu6ff\nv8CyXwJvpJk1RpIkSTOsb2i8zYiyAn5VVb/puW1JkiRNib7TCP5kXB2RJEnS9HIaQUmSJHVa7zON\nSb69nqtUVd11fduRJEnS9FjM8PRamusWu9wCuP061pUkSdIUW+/QWFX3X2h5klsALweeDVwDHLOo\nnkmSJGlq9L17+g+S3Bz4G+BZwI2BDwKHVNW542pDkiRJk9E7NA6cWRwMi2+oqh/13bYkSZKmw6JD\nYxsW/wZ4Jk1YPIYmLJ43pr5JkiRpSizm7unVXBcWNwaOphmGNixKkiQtU4s503gusAo4EzgUOA/Y\nNsm2861QVd9cXPckSZI0DRYTGjdt3/cEPtZRNzSP3NloEe1IkiRpSiwmND5t7L2QJEnSVFvMcxo/\nsBQdkSRJ0vRy7mlJkiR1MjRKkiSpk6FRkiRJnZZFaEzyx0nemeSsJJclOT/Jx5LsNqLu7klOTPLb\nJGuTHJNk+0n0W5IkaVaMbe7pCXs58CfAscC3gVsAzwO+meSeVfVdgCQ7AacAlwAHA1sCLwXunGTv\nqrp6Ep2XJEmadsslNL4VOGAw9CX5KPAdmtlrntQWHwxsAdy9qs5v650OfAE4EDhyA/ZZkiRpZiyL\n4emq+urwWcKq+iFwFrD7QPHjgE/PBca23heBs4EnbIi+SpIkzaJlERpHSRLg5sAv2887AjsAXx9R\n/XSaGW4kSZI0wnIZnh7lL4AdgVe1n1e372tG1F0DbJdkVVVdNWpjSXYAhm+Y2WUcHZUkSZp2yzI0\nJrkD8C7gv4C5GWw2a99HhcIrB+qMDI3AQcCrx9VHSZKkWbLsQmOSWwCfoblDev+quqZddEX7vmrE\napsO1RnlCJq7swftAnxykV2VJEmaGcsqNCa5CfBZYBvgPlX1s4HFc8PSq2+wYlO2dr6haYCqugi4\naKi9fh2WJEmaEcsmNCbZFPgUsBvwoKr63uDyqrowycXAXiNW3xs4c+l7uXjjzqdV492eJEla3pbF\n3dNJNgI+CuwDPL6q/mueqscBj0yy88C6D6QJmsNDz5IkSWotlzON/wg8iuZM43ZJnjS4sKo+2P55\nKPB44KQkb6eZEeZlNA8BP2rDdVeSJGm2LJfQuEf7/qfta9gHAarqgiT3o5lB5o3A1TQ3zbxkoesZ\nJUmSVrplERqr6v7rUfcs4KFL1xtJkqTlZ1lc0yhJkqSlZWiUJElSJ0OjJEmSOhkaJUmS1MnQKEmS\npE6GRkmSJHUyNEqSJKmToVGSJEmdDI2SJEnqZGiUJElSJ0OjJEmSOhkaJUmS1MnQKEmSpE4bT7oD\nkiRJSy0Z/zarxr/NaeaZRkmSJHUyNEqSJKmToVGSJEmdDI2SJEnqZGiUJElSJ0OjJEmSOvnIHUmS\npEUY92N8pv0RPp5plCRJUidDoyRJkjoZGiVJktTJ0ChJkqROhkZJkiR1MjRKkiSpk6FRkiRJnQyN\nkiRJ6mRolCRJUidDoyRJkjoZGiVJktTJ0ChJkqROhkZJkiR1MjRKkiSpk6FRkiRJnQyNkiRJ6mRo\nlCRJUidDoyRJkjoZGiVJktTJ0ChJkqROhkZJkiR1MjRKkiSpk6FRkiRJnQyNkiRJ6mRolCRJUidD\noyRJkjpCkexEAAANMUlEQVQZGiVJktTJ0ChJkqROhkZJkiR1MjRKkiSpk6FRkiRJnQyNkiRJ6mRo\nlCRJUidDoyRJkjoZGiVJktTJ0ChJkqROKy40JlmV5E1JfpbkiiSnJXnwpPslSZI0zVZcaATeD7wY\n+BDwAuAa4N+T3HuSnZIkSZpmG0+6AxtSkr2BPwNeVlVvacuOBr4L/ANwrwl2T5IkaWqttDON+9Oc\nWTxyrqCqrgTeB+yTZOdJdUySJGmarbTQuCdwdlVdOlR+evu+xwbujyRJ0kxYUcPTwGpgzYjyubJb\nzrdikh2A7YeK7wBwzjnnjKVzG9JZZ026B5pFK/F3sxK/s6TJWOrjzUBe2WQx66eqxtebKZfkXOAH\nVbXvUPltgXOBF1XV2+ZZ9zXAq5e8k5IkSUtrv6o6YX1XWmlnGq8AVo0o33Rg+XyOAI4dKtsS2I3m\nRpqre/dufrsAnwT2owm3uj73z8LcPwtz/yzM/bMw908399HCNuT+2QTYGfjyYlZeaaFxDbDjiPLV\n7fvP5luxqi4CLhqx6LQx9GtBSeb+PLeqHCwb4v5ZmPtnYe6fhbl/Fub+6eY+WtgE9s8Zi11xpd0I\ncyawW5Kth8rvMbBckiRJQ1ZaaPw4sBHwrLmCJKuApwGnVdUFk+qYJEnSNFtRw9NVdVqSY4G/b++G\nPgd4KnBr4OmT7JskSdI0W1GhsfUU4PXAk4FtgW8Dj6yqUybaq4VdDLy2fdcNuX8W5v5ZmPtnYe6f\nhbl/urmPFjYz+2dFPXJHkiRJi7PSrmmUJEnSIhgaJUmS1MnQKEmSpE6GRkmSJHUyNEqSJKmToXED\nSLIqyZuS/CzJFUlOS/LgdVx3myRHJrk4yWVJTkpyt3nq3ivJqUkuT/LzJIcn2XK832b8Frt/kjww\nyb8kObv9zj9K8t4kq0fUPTlJjXiduDTfanx67J8D5/nOleQWI+rP5O8Heu2j+X4XleR361h3Fn5D\nWyZ5bZITk6xt+33geqy/rI9DffbPSjgO9dw/y/441HP/zNQxaCU+p3ES3g/sD7wN+CFwIPDvSR5Q\nVafOt1KSGwGfAe4KvBn4JXAQcHKSu1fVDwfq7gH8B/A/wIuBnYCXArcDHj7+rzRW72cR+wd4E7Ad\ncGy73m2B5wGPTLJHVf18qP5Pgb8dKpt3vvEp8n4Wt3/mvAo4b6js14MfZvz3A4vfR4cA7x0q2wJ4\nD/D5EfVn9Td0M5rfwfnAt4D7r+uKK+Q4tOj9w8o4DvXZP3OW83Goz/6ZrWNQVflawhewN1DASwfK\nNqWZjearHes+oV13/4Gy7YFfAf86VPff2x/O1gNlz2jXf8ik98MS7Z/7AjcaUVbAG4bKTwa+O+nv\nu4H3z4HtunutQzsz+fvpu4/m2d6T2u0dsBx+Q23fVwG3aP/eq/1+B67juivhONRn/6yE41Cf/bPs\nj0N99s8825vaY5DD00tvf+Aa4Mi5gqq6EngfsE+SnTvW/QXwiYF1LwY+BuyXZt5skmwNPBj4YFVd\nOrD+0cBvaQ7602rR+6eqTqmqa4fLgLXA7qPWSbLxtA91DOnz+/mDJFsl2WieZbP8+4Ex7aMBBwCX\nAZ8ctXAGf0NU1VV1wzNe62rZH4f67J+VcBzq+fv5g+V6HBrX/hkwtccgQ+PS2xM4e+gfAcDp7fse\nHet+c/iA1K67ObBb+/nONJcafH2wUlVdDZzZbmda9dk/N9D+Q9qSZght2G40/xB/014r8/okN17f\nDm9g49g/JwGXApcnOSHJ7YaWz/LvB8b4G0qyPc1/uI6vqstGVJnF31BfK+E4NFbL8Dg0Dsv9ODQW\n034M8prGpbcaWDOifK7slh3rjpoTe3Dd77T1BsuH696nu5sT02f/jPJCYBPgo0Pl59IctL5Dc73I\n/sDf0fwDfOJ6trEh9dk/l9Nc6zd3sL47zXVCX01yt6q6YKCNwW0OtzPNvx8Y72/oiTTHxQ+NWDar\nv6G+VsJxaNyW23Goj5VyHBqXqT4GGRqX3mbAVSPKrxxY3nfduff56i7UxqT12T/Xk+S+wKuBj1XV\nlwaXVdXTh6ofk+RI4JlJDquqr61HnzekRe+fqvoYzRDinOOTfI4mALwCeM7QNmbx9wNj/A3RDAtd\nDHxheMEM/4b6WgnHobFZpsehRVtBx6FxmepjkMPTS+8Kmotkh206sLzvunPv89VdqI1J67N//iDJ\nHYB/A75Lc+H0uvjH9v1B61h/Esayf+ZUcyfxaVz/O8/y7wfG9xu6LbAP8NGq+v06tj0Lv6G+VsJx\naCyW8XForJbpcai3WTgGGRqX3hquO+0+aK5soVvl13XdNUPlw3Wn+XEOffYPAO2NDp8HLgH2rarf\nrGPbc8Mi261j/UnovX9GuIDrf+dZ/v3A+PbRAe37qGGh+czCb6ivlXAc6m2ZH4eWwnI7Do3D1B+D\nDI1L70xgt/bOsEH3GFi+0Lp3a5+TNrzu5cDZ7efvAr+nudX/D5JsQnMTwEJtTFqf/UOSm9IcqFcB\nD62qUdfDzOe27fvF67HOhtZr/8zjtlz/O8/y7wfGt48OAM5dzyGeWfgN9bUSjkO9rIDj0FJYbseh\ncZj6Y5Chcel9HNgIeNZcQfuIiqcBp81dBJxkdZI7DN0F9XHg5sBjB9a9GfB44FNVdRVAVV0CfBF4\nUpKtBtZ/Ms0dfMcuxRcbk0XvnyRb0DzXa0ea/2f/Q0ZIsvXcY0EGykJzATHA58b4fcatz/7Zfnhj\nSfaluRD9DzMIzPjvB/r9G5urvyfN41H+dVQDM/4bWmcr+Di0TlbwcWidrPDjUKflcAzyRpglVlWn\nJTkW+PskO9A8cPipwK2BwYta/74tvw3w47bs48DXgKOS3JHrZmLYiOZC60GvAL4KfLm9MHYn4CXA\n56tqaqeo6rl/PkTzYOd/AXZPMvhMtN9W1fHt33cDPpzkw+32NwMeA/wJcGRVfXMJvtpY9Nw/X01y\nBs0jLC6h2Q9/STOccehQUzP5+4He+2jOX7Tv8w0LzexvaE6S5wHbcN3d5H+aZKf273e0/9Fekcch\n6LV/lv1xCHrtnxVxHOqxf+bMxjFokk8WXykvmot430xzzcaVNM83e+hQnffTPAH+1kPl29JMMfRL\nmmczncw8T9YH7g38J80FwxcB7wS2mvT3X6r9Q/OPruZ5/Xig3m1o7t47r903l9EcwJ4NZNLffwn3\nzxuAM2im6roa+AlwBHDz5fT76bOP2vIb0UzN9Y0Ftj/Tv6H2Oyz07+XWHftoJRyHFrV/VtBxaLH7\nZ0Uch3r++5qZY1DazkiSJEnz8ppGSZIkdTI0SpIkqZOhUZIkSZ0MjZIkSepkaJQkSVInQ6MkSZI6\nGRolSZLUydAoSZKkToZGSZIkdTI0StKUSfLTJO+ddD8kaZChUZIWKckJSS5PstUCdT6U5OokN92Q\nfZOkcTM0StLifQjYDHjMqIVJNgf2A06sqv/dkB2TpHEzNErS4p0A/AY4YJ7l+wFb0IRLSZpphkZJ\nWqSqugL4BPDAJDuMqHIATag8ASDJy5N8NcnaJFck+XqSkWcpByV5Q5Lfjyh/RpJKstNQ+SOSnJrk\nsiSXJvlUkt0X9SUlqWVolKR+PgRsDDxhsDDJdsBDgX9rwyXAC4BvAq8EDgYK+ESSh42rM0kOBD4F\n/Bp4OXAIcBfg1CT/Z1ztSFp5Np50ByRpxn0JWENzVvGdA+WPB27M9YemdxkIkCR5F3Am8CLgxL4d\nSbI18HbgPVV10ED50cD3gb8BDppndUlakGcaJamHqroG+AiwT5JbDyw6APgF8B8Dda8ASGNbYGvg\nVOBuY+rOQ9ttfjjJzeZewO+A/wYeMKZ2JK1AhkZJ6m/ubOIBAO01hvcBPtKGStryRyU5DbgCWAtc\nDDwTuMmY+nG79v2UdtuDrwcCo667lKR14vC0JPVUVd9I8n3gz4FD2/cwMDSd5AHA8cDJwF8BP6c5\nA/gMYP+uJuYp32jo89yJgANoguKw33W0I0nzMjRK0nh8CHh9krvQhLYfVtV/Dyx/HHA58LCqunqu\nMMkz12HbvwI2SrJlVf12oPxWQ/XObd9/UVVfWu9vIEkLcHhaksZj7qzi64A9uOGzGa8BrmXguJvk\ntsCj1mHbc2HwvgPrbgk8ZajeZ2ke8fOKJDc4KZBk+3VoS5JGStV8ox6SpPWR5D+Be7Ufb1dV5wws\newjwOeDLwIeBWwDPpbnz+k5VtfFA3Z/SzCLzjPbzJsCPaO7GfgvNcPXTgcuAuwM7V9VP27pPAY4C\nzqK5QeeXNGckHwGcXFUvXJIvL2nZ80yjJI3P3NnF0wcDI0BVfR54FrAjzWNxngi8lOaZigtqh7Mf\nDfwYeAPwPODdwHtG1D0aeDDNNZMvB95G8wzJbwJHL+I7SRLgmUZJkiStA880SpIkqZOhUZIkSZ0M\njZIkSepkaJQkSVInQ6MkSZI6GRolSZLUydAoSZKkToZGSZIkdTI0SpIkqZOhUZIkSZ0MjZIkSepk\naJQkSVInQ6MkSZI6GRolSZLUydAoSZKkTv8fOMDdAvIJl+EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a8cd860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.log(X_train_log[:, 0] + 1), bins=25, color='b')\n",
    "plt.ylabel(\"Number of appearances\")\n",
    "plt.xlabel(\"Value\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "And our Ridge regressor now performs a lot better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.875\n"
     ]
    }
   ],
   "source": [
    "score = Ridge().fit(X_train_log, y_train).score(X_test_log, y_test)\n",
    "print(\"Test score: {:.3f}\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding the transformation that works best for each combination of dataset and model is somewhat of an art."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Automatic Feature Selection\n",
    "\n",
    "When adding new features, or with high-dimensional datasets in general, it can be a good idea to reduce the number of features to only the most useful ones, and discard the rest.\n",
    "- Simpler models that generalize better\n",
    "- Help algorithms that are sensitive to the number of features (e.g. kNN)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Univariate statistics (ANOVA)\n",
    "\n",
    "We want to keep the features for which there is statistically significant relationship between it and the target.\n",
    "In the case of classification, this is also known as analysis of variance (ANOVA). These test consider each feature individually (they are univariate), and are completely independent of the model that you might want to apply afterwards.\n",
    "The result will be a p-value for each feature (lower is better). \n",
    "\n",
    "In scikit-learn\":\n",
    "- `SelectKBest` will only keep the $k$ features with the lowest p values. \n",
    "- `SelectPercentile` selects a fixed percentage of features. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "To test these methods, we'll take the `breast_cancer` dataset, and add 50 random noise features. The feature selector should be able to remove at least these noise features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape: (284, 80)\n",
      "X_train_selected.shape: (284, 40)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.feature_selection import SelectPercentile\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "# get deterministic random numbers\n",
    "rng = np.random.RandomState(42)\n",
    "noise = rng.normal(size=(len(cancer.data), 50))\n",
    "# add noise features to the data\n",
    "# the first 30 features are from the dataset, the next 50 are noise\n",
    "X_w_noise = np.hstack([cancer.data, noise])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_w_noise, cancer.target, random_state=0, test_size=.5)\n",
    "# use f_classif (the default) and SelectPercentile to select 50% of features:\n",
    "select = SelectPercentile(percentile=50)\n",
    "select.fit(X_train, y_train)\n",
    "# transform training set:\n",
    "X_train_selected = select.transform(X_train)\n",
    "\n",
    "print(\"X_train.shape: {}\".format(X_train.shape))\n",
    "print(\"X_train_selected.shape: {}\".format(X_train_selected.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can retrieve which features were selected with `get_support`, and visualize the selected (black) and removed (white) features. `SelectPercentile` removed most of the noise features, but not perfectly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11f07cb00>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDIgMCBSID4+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\nPj4Kc3RhcnR4cmVmCjkyMzQKJSVFT0YK\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABh8AAABgCAYAAAANb2NuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nK8APAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d596940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = select.get_support()\n",
    "# visualize the mask. black is True, white is False\n",
    "plt.matshow(mask.reshape(1, -1), cmap='gray_r')\n",
    "plt.xlabel(\"Sample index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "As usual, we need to check how the transformation affects the performance of our learning algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score with all features: 0.930\n",
      "Score with only selected features: 0.940\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# transform test data:\n",
    "X_test_selected = select.transform(X_test)\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "print(\"Score with all features: {:.3f}\".format(lr.score(X_test, y_test)))\n",
    "lr.fit(X_train_selected, y_train)\n",
    "print(\"Score with only selected features: {:.3f}\".format(\n",
    "        lr.score(X_test_selected, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Model-based Feature Selection\n",
    "Model-based feature selection uses a supervised machine learning model to judge the importance of each feature, and keeps only the most important ones. Compared to ANOVA, they consider all features together, and are thus able to capture interactions: a feature may be more (or less) informative in combination with others.\n",
    "\n",
    "The supervised model that is used for feature selection doesn’t need to be the same model that is used for the final supervised modeling, it only needs to be able to measure the (perceived) importance for each feature:\n",
    "\n",
    "- Decision tree–based models return a `feature_importances_` attribute\n",
    "- Linear models return coefficients, whose absolute values also reflect feature importance "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "In scikit-learn, we can do this using `SelectFromModel`. It requires a model and a threshold. Threshold='median' means that the median observed feature importance will be the threshold, which will remove 50% of the features.\n",
    "\n",
    "We've seen before how RandomForests return good estimates of feature importance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "select = SelectFromModel(\n",
    "    RandomForestClassifier(n_estimators=100, random_state=42),\n",
    "    threshold=\"median\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape: (284, 80)\n",
      "X_train_l1.shape: (284, 40)\n"
     ]
    }
   ],
   "source": [
    "select.fit(X_train, y_train)\n",
    "X_train_l1 = select.transform(X_train)\n",
    "print(\"X_train.shape: {}\".format(X_train.shape))\n",
    "print(\"X_train_l1.shape: {}\".format(X_train_l1.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11f0dc5c0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDIgMCBSID4+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\nCnN0YXJ0eHJlZgo5MjMyCiUlRU9GCg==\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABh8AAABgCAYAAAANb2NuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIkqa9MPkiSJEmSJEmSpL76X5et98mRkldFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f246fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = select.get_support()\n",
    "# visualize the mask. black is True, white is False\n",
    "plt.matshow(mask.reshape(1, -1), cmap='gray_r')\n",
    "plt.xlabel(\"Sample index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "All but two of the original features were selected, and most of the noise features removed. Our linear model trained on the selected features also performs quite a bit better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.951\n"
     ]
    }
   ],
   "source": [
    "X_test_l1 = select.transform(X_test)\n",
    "score = LogisticRegression().fit(X_train_l1, y_train).score(X_test_l1, y_test)\n",
    "print(\"Test score: {:.3f}\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Iterative feature selection\n",
    "\n",
    "Instead of building a model to remove many features at once, we can also just ask it to remove the worst feature, then retrain, remove another feature, etc. This is known as _recursive feature elimination_ (RFE).\n",
    "\n",
    "Vice versa, we couls also ask it to iteratively add one feature at a time. This is called _forward selection_.\n",
    "\n",
    "In both cases, we need to define beforehand how many features to select. When this is unknown, one often considers this as an additional hyperparameter of the whole process (pipeline) that needs to be optimized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11fb1e320>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDIgMCBSID4+\nCmVuZG9iago4IDAgb2JqCjw8IC9YT2JqZWN0IDcgMCBSIC9Gb250IDMgMCBSIC9QYXR0ZXJuIDUg\nMCBSIC9TaGFkaW5nIDYgMCBSCi9Qcm9jU2V0IFsgL1BERiAvVGV4dCAvSW1hZ2VCIC9JbWFnZUMg\nL0ltYWdlSSBdIC9FeHRHU3RhdGUgNCAwIFIgPj4KZW5kb2JqCjEwIDAgb2JqCjw8IC9SZXNvdXJj\nZXMgOCAwIFIgL0Fubm90cyBbIF0gL1BhcmVudCAyIDAgUiAvVHlwZSAvUGFnZSAvQ29udGVudHMg\nOSAwIFIKL01lZGlhQm94IFsgMCAwIDk0MS45ODEyNSA1NS45MDM3NSBdCi9Hcm91cCA8PCAvVHlw\nZSAvR3JvdXAgL0NTIC9EZXZpY2VSR0IgL1MgL1RyYW5zcGFyZW5jeSA+PiA+PgplbmRvYmoKOSAw\nIG9iago8PCAvTGVuZ3RoIDExIDAgUiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJyt\nlk1zGjEMhu/+FTq2hwjLH2v7GCYt094oTHvI9MAASWGAljJp+vOr3cDa6zXpdqaHDNnXsvRoV5ZF\nsBWjW4LHE0jY8t8z3MNX/l0BwQRGd+tfm+X602QMy5OQrO9FMITBk7L8tEuerMUgtbMsyvThmxAH\nwRHYfMJOH4XQHk2zRUn0jjybsVtNqDJ1l6haodRntXWQihznQRyh4NwHhR6IkCr4uYYvcIDRrapz\nJs6ZkpwZUXRzPvJujQbq3NmDKflf7mH0geDuO0zFFI4X55JzrgOwXTdEvSKMQVnlL6EVyaG+vAMx\njnpMODHWFq2Oxm1uz2I8h9F7ApIwfxCGA3myFe/gj0YE85V4I9/CfAvv5pddDawg/n5Vjy6qGV5c\nSPkSdRggmYBaNnYRkMqEqnKoeoRRzQjjQkqYqMMIVSW5rjNCVSbUzqPvEUY1I4wLKWGiDiPUTqHN\nCXWZ0ASJpl+DrZoXYbvQqcKoDixDb5ByQlMmrKQqnJKoZoRxISVM1GGENlh0OaEtEzrShZMS1Yww\nLqSEiTqM0EnXPylVmdArWzgpUc0I40JKmKjDCD35/klxLWG3IqxEVxlZd6aAyjXttbafLfY/dmvY\nHFbr3+Uz1l4EfC7Jns+MOd9IUcxRj3whSbjhlg7cAUA5Jgzcx7Mc+MX40GDJl2aJpsYQjHEUEiU1\n10L7D+/ny2O7+Pw0WxxON/vN4enEt4LgW+EKtvJYeZ1hR/Fv2IYbuhuCrf4zdoXGUo7diq83ea4f\ntlEKK2vPeFcaaBvOoKKQh2vFQeH40ymdvo1XwtUTjcvDteKQcFz8FCitmTqcgo8vU1ZnIOjOG8XZ\nqDjviFlxZtpfmZnY/B/mrtQ67QNXfU/FHy7qIAgKZW5kc3RyZWFtCmVuZG9iagoxMSAwIG9iago2\nNTkKZW5kb2JqCjE3IDAgb2JqCjw8IC9MZW5ndGggNjggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4K\nc3RyZWFtCnicMzM2UzBQsDACEqamhgrmRpYKKYZcQD6IlcsFE8sBs8wszIEsIwuQlhwuQwtjMG1i\nbKRgZmIGZFkgMSC60gBy+BKRCmVuZHN0cmVhbQplbmRvYmoKMTggMCBvYmoKPDwgL0xlbmd0aCA0\nOSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzNrRQMFAwNDAHkkaGQJaRiUKKIRdI\nAMTM5YIJ5oBZBkAaojgHriaHKw0AxugNJgplbmRzdHJlYW0KZW5kb2JqCjE5IDAgb2JqCjw8IC9M\nZW5ndGggMzcgL0JCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9TdWJ0eXBlIC9Gb3JtCi9G\naWx0ZXIgL0ZsYXRlRGVjb2RlIC9UeXBlIC9YT2JqZWN0ID4+CnN0cmVhbQp4nOMyNDBTMDY1Vcjl\nMjc2ArNywCwjcyMgCySLYEFk0wABXwoKCmVuZHN0cmVhbQplbmRvYmoKMjAgMCBvYmoKPDwgL0xl\nbmd0aCAyMzYgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTVBLbkQhDNtzilzgSSQh\nAc5D1VXn/tuxw1TtKoYYf0gP6bJVHutTYnWJ7PKlTZfKMnkVqOVP2/9RDAJu/9DIQbS3jJ1i5hLW\nxcIkPOU0Ixsn1ywfjztPG2aFxsSN450uGWCfFgE1W5XNgTltOjdAupAat6qz3mRQDCLqQs0Hky6c\np9GXiDmeqGBKdya1kBtcPtWhA3FavQq5Y4uTb8QcWaHAYdBMcdZfAdaoybJZyCBJhiHOfaN7lAqN\nqMp5KxXCD5OhEfWG1aAGlbmFoqnlkvwd2gIwBbaMdekMSoGqAMHfKqd9vwEkjV1TCmVuZHN0cmVh\nbQplbmRvYmoKMjEgMCBvYmoKPDwgL0xlbmd0aCAzMTcgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4K\nc3RyZWFtCnicNVJLckMxCNu/U3CBzpi/fZ50smruv62EJyuwLUBCLi9Z0kt+1CXbpcPkVx/3JbFC\nPo/tmsxSxfcWsxTPLa9HzxG3LQoEURM9+DInFSLUz9ToOnhhlz4DrxBOKRZ4B5MABq/hX3iUToPA\nOxsy3hGTkRoQJMGaS4tNSJQ9Sfwr5fWklTR0fiYrc/l7cqkUaqPJCBUgWLnYB6QrKR4kEz2JSLJy\nvTdWiN6QV5LHZyUmGRDdJrFNtMDj3JW0hJmYQgXmWIDVdLO6+hxMWOOwhPEqYRbVg02eNamEZrSO\nY2TDePfCTImFhsMSUJt9lQmql4/T3AkjpkdNdu3Csls27yFEo/kzLJTBxygkAYdOYyQK0rCAEYE5\nvbCKveYLORbAiGWdmiwMbWglu3qOhcDQnLOlYcbXntfz/gdFW3ujCmVuZHN0cmVhbQplbmRvYmoK\nMjIgMCBvYmoKPDwgL0xlbmd0aCAyNDggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnic\nLVE5kgNBCMvnFXpCc9PvscuR9//pCsoBg4ZDIDotcVDGTxCWK97yyFW04e+ZGMF3waHfynUbFjkQ\nFUjSGFRNqF28Hr0HdhxmAvOkNSyDGesDP2MKN3pxeEzG2e11GTUEe9drT2ZQMisXccnEBVN12MiZ\nw0+mjAvtXM8NyLkR1mUYpJuVxoyEI00hUkih6iapM0GQBKOrUaONHMV+6csjnWFVI2oM+1xL29dz\nE84aNDsWqzw5pUdXnMvJxQsrB/28zcBFVBqrPBAScL/bQ/2c7OQ33tK5s8X0+F5zsrwwFVjx5rUb\nkE21+Dcv4vg94+v5/AOopVsWCmVuZHN0cmVhbQplbmRvYmoKMjMgMCBvYmoKPDwgL0xlbmd0aCAz\nMDQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPZI7ksMwDEN7nYIXyIz4k+TzZCeV\n9/7tPjLJVoBJiQAoL3WZsqY8IGkmCf/R4eFiO+V32J7NzMC1RC8TyynPoSvE3EX5spmNurI6xarD\nMJ1b9Kici4ZNk5rnKksZtwuew7WJ55Z9xA83NKgHdY1Lwg3d1WhZCs1wdf87vUfZdzU8F5tU6tQX\njxdRFeb5IU+ih+lK4nw8KCFcezBGFhLkU9FAjrNcrfJeQvYOtxqywkFqSeezJzzYdXpPLm4XzRAP\nZLlU+E5R7O3QM77sSgk9ErbhWO59O5qx6RqbOOx+70bWyoyuaCF+yFcn6yVg3FMmRRJkTrZYbovV\nnu6hKKZzhnMZIOrZioZS5mJXq38MO28sL9ksyJTMCzJGp02eOHjIfo2a9HmV53j9AWzzczsKZW5k\nc3RyZWFtCmVuZG9iagoyNCAwIG9iago8PCAvTGVuZ3RoIDE3IC9GaWx0ZXIgL0ZsYXRlRGVjb2Rl\nID4+CnN0cmVhbQp4nDM2tFAwgMMUQy4AGpQC7AplbmRzdHJlYW0KZW5kb2JqCjI1IDAgb2JqCjw8\nIC9MZW5ndGggMTYxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWQSxLDIAxD95xC\nR/BHBnyedLpK77+tIU2zgKexQAZ3JwSptQUT0QUvbUu6Cz5bCc7GeOg2bjUS5AR1gFak42iUUn25\nxWmVdPFoNnMrC60THWYOepSjGaAQOhXe7aLkcqbuzvlHcPVf9Uex7pzNxMBk5Q6EZvUp7nybHVFd\n3WR/0mNu1mt/FfaqsLSspeWE285dM6AE7qkc7f0FqXM6hAplbmRzdHJlYW0KZW5kb2JqCjI2IDAg\nb2JqCjw8IC9MZW5ndGggODcgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNU25EcAw\nCOuZghHMo9jsk0vl7N8G7LhBOn0glBtr5AGC4Z1vIfimLxmEdQhPKrslOmyhhrMKkonhVzZ4Va6K\n9rWSiexspjHYoGX60c63Sc8Hpd4bmAplbmRzdHJlYW0KZW5kb2JqCjI3IDAgb2JqCjw8IC9MZW5n\ndGggMzM4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVSS5JbQQjbv1PoAq5q/s15\nnJrV5P7bCOysoIEWEpAWOMjESwxRjXLFH3mC8TqBv+vlafw+3oXUgqci/cC1aRvvx5o1UbA0YinM\nPvb9KCHHU+PfEOi5SBNmZDJyIBmI+7U+f9abTDn8BqRpc/ooSXoQLdjdGnZ8WZBB0pMaluzkh3Ut\nsLoITZgbayIZObUyNc/HnuEynhgjQdUsIEmfuE8VjEgzHjtnLXmQ4XiqFy9+vY3XMo+pl1UFMrYJ\n5mA7mQmnKCIQv6AkuYm7aOoojmbGmtuFhpIi9909nJz0ur+cRAVeCeEs1hKOGXrKMic7DUqgauUE\nmGG99oVxmjZKuFPT7V2xr99nJmHc5rCzUjINznFwL5vMESR73TFhEx6HmPfuEYzEvPldbBFcucy5\nJtOP/SjaSB8U1+dcTZmtKOEfquSJFdf4//zez88/kDd9sQplbmRzdHJlYW0KZW5kb2JqCjI4IDAg\nb2JqCjw8IC9MZW5ndGggNDUgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzK3UDBQ\nsDQBEoYWJgrmZgYKKYZclhBWLhdMLAfMAtGWcAoingYAn30MtQplbmRzdHJlYW0KZW5kb2JqCjI5\nIDAgb2JqCjw8IC9MZW5ndGggNjggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzK3\nUDBQsDQBEoYWJgrmZgYKKYZcQL6piblCLhdIDMTKAbMMgLQlnIKIW0I0QZSCWBClZiZmEEk4AyKX\nBgDJtBXlCmVuZHN0cmVhbQplbmRvYmoKMzAgMCBvYmoKPDwgL0xlbmd0aCAzMzggL0ZpbHRlciAv\nRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNVI5rt1ADOt9Cl0ggHbNnOcFqX7u34aUXwpDtFaKmo4W\nlWn5ZSFVLZMuv+1JbYkb8vfJCokTklcl2qUMkVD5PIVUv2fLvL7WnBEgS5UKk5OSxyUL/gyX3i4c\n52NrP48jdz16YFWMhBIByxQTo2tZOrvDmo38PKYBP+IRcq5YtxxjFUgNunHaFe9D83nIGiBmmJaK\nCl1WiRZ+QfGgR61991hUWCDR7RxJcIyNUJGAdoHaSAw5sxa7qC/6WZSYCXTtiyLuosASScycYl06\n+g8+dCyovzbjy6+OSvpIK2tM2nejSWnMIpOul0VvN299PbhA8y7Kf17NIEFT1ihpfNCqnWMomhll\nhXccmgw0xxyHzBM8hzMSlPR9KH5fSya6KJE/Dg2hf18eo4ycBm8Bc9GftooDF/HZYa8cYIXSxZrk\nfUAqE3pg+v/X+Hn+/AMctoBUCmVuZHN0cmVhbQplbmRvYmoKMzEgMCBvYmoKPDwgL0xlbmd0aCA5\nMCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxNjUESwCAIA++8Ik9QRND/dHrS/1+r\n1A69wE4CiRZFgvQ1aksw7rgyFWtQKZiUl8BVMFwL2u6iyv4ySUydhtN7twODsvFxg9JJ+/ZxegCr\n/XoG3Q/SHCJYCmVuZHN0cmVhbQplbmRvYmoKMzIgMCBvYmoKPDwgL0xlbmd0aCAyMTAgL0ZpbHRl\nciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNVDLDUMxCLtnChaoFAKBZJ5WvXX/a23QO2ER/0JY\nyJQIeanJzinpSz46TA+2Lr+xIgutdSXsypognivvoZmysdHY4mBwGiZegBY3YOhpjRo1dOGCpi6V\nQoHFJfCZfHV76L5PGXhqGXJ2BBFDyWAJaroWTVi0PJ+QTgHi/37D7i3koZLzyp4b+Ruc7fA7s27h\nJ2p2ItFyFTLUszTHGAgTRR48eUWmcOKz1nfVNBLUZgtOlgGuTj+MDgBgIl5ZgOyuRDlL0o6ln2+8\nx/cPQABTtAplbmRzdHJlYW0KZW5kb2JqCjMzIDAgb2JqCjw8IC9MZW5ndGggMjQ1IC9GaWx0ZXIg\nL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEVQu41DMQzrPQUXCGD9LHued0iV2789SkZwhSFaP5Ja\nEpiIwEsMsZRv4kdGQT0LvxeF4jPEzxeFQc6EpECc9RkQmXiG2kZu6HZwzrzDM4w5AhfFWnCm05n2\nXNjknAcnEM5tlPGMQrpJVBVxVJ9xTPGqss+N14GltWyz05HsIY2ES0klJpd+Uyr/tClbKujaRROw\nSOSBk0004Sw/Q5JizKCUUfcwtY70cbKRR3XQydmcOS2Z2e6n7Ux8D1gmmVHlKZ3nMj4nqfNcTn3u\nsx3R5KKlVfuc/d6RlvIitduh1elXJVGZjdWnkLg8/4yf8f4DjqBZPgplbmRzdHJlYW0KZW5kb2Jq\nCjM0IDAgb2JqCjw8IC9MZW5ndGggMjI3IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4\nnDVPO7IDIQzrOYUukBmMbWDPs5lUL/dvn2SyDRL+SPL0REcmXubICKzZ8bYWGYgZ+BZT8a897cOE\n6j24hwjl4kKYYSScNeu4m6fjxb9d5TPWwbsNvmKWFwS2MJP1lcWZy3bBWBoncU6yG2PXRGxjXevp\nFNYRTCgDIZ3tMCXIHBUpfbKjjDk6TuSJ52KqxS6/72F9waYxosIcVwVP0GRQlj3vJqAdF/Tf1Y3f\nSTSLXgIykWBhnSTmzllO+NVrR8dRiyIxJ6QZ5DIR0pyuYgqhCcU6OwoqFQWX6nPK3T7/aF1bTQpl\nbmRzdHJlYW0KZW5kb2JqCjM1IDAgb2JqCjw8IC9MZW5ndGggMjQ3IC9GaWx0ZXIgL0ZsYXRlRGVj\nb2RlID4+CnN0cmVhbQp4nE1Ru21EMQzr3xRc4ADra3meC1Jd9m9DyQiQwiChLymnJRb2xksM4Qdb\nD77kkVVDfx4/MewzLD3J5NQ/5rnJVBS+FaqbmFAXYuH9aAS8FnQvIivKB9+PZQxzzvfgoxCXYCY0\nYKxvSSYX1bwzZMKJoY7DQZtUGHdNFCyuFc0zyO1WN7I6syBseCUT4sYARATZF5DNYKOMsZWQxXIe\nqAqSBVpg1+kbUYuCK5TWCXSi1sS6zOCr5/Z2N0Mv8uCounh9DOtLsMLopXssfK5CH8z0TDt3SSO9\n8KYTEWYPBVKZnZGVOj1ifbdA/59lK/j7yc/z/QsVKFwqCmVuZHN0cmVhbQplbmRvYmoKMzYgMCBv\nYmoKPDwgL0xlbmd0aCA4MCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFjLsNwDAI\nRHumYAR+JmafKJWzfxsgStxwT7p7uDoSMlPeYYaHBJ4MLIZT8QaZo2A1uEZSjZ3so7BuX3WB5npT\nq/X3BypPdnZxPc3LGfQKZW5kc3RyZWFtCmVuZG9iagozNyAwIG9iago8PCAvTGVuZ3RoIDI1NSAv\nRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkUuSAyAIRPeegiOA/OQ8mZpVcv/tNJhM\nNnaXqP2ESiOmEiznFHkw/cjyzWS26bUcq52NAooiFMzkKvRYgdWdKeLMtUS19bEyctzpHYPiDeeu\nnFSyuFHGOqo6FTim58r6qu78uCzKviOHMgVs1jkONnDltmGME6PNVneH+0SQp5Opo+J2kGz4g5PG\nvsrVFbhONvvqJRgHgn6hCUzyTaB1hkDj5il6cgn28XG780Cwt7wJpGwI5MgQjA5Bu06uf3Hr/N7/\nOsOd59oMV4538TtMa7vjLzHJirmARe4U1PM9F63rDB3vyZljctN9Q+dcsMvdQabP/B/r9w9QimaI\nCmVuZHN0cmVhbQplbmRvYmoKMTUgMCBvYmoKPDwgL0Jhc2VGb250IC9EZWphVnVTYW5zIC9GaXJz\ndENoYXIgMAovRW5jb2RpbmcgPDwgL1R5cGUgL0VuY29kaW5nCi9EaWZmZXJlbmNlcyBbIDMyIC9z\ncGFjZSA0NiAvcGVyaW9kIDQ4IC96ZXJvIC9vbmUgL3R3byAvdGhyZWUgL2ZvdXIgL2ZpdmUgL3Np\neCAvc2V2ZW4KODMgL1MgOTcgL2EgMTAwIC9kIC9lIDEwNSAvaSAxMDggL2wgL20gL24gMTEyIC9w\nIDEyMCAveCBdCj4+Ci9DaGFyUHJvY3MgMTYgMCBSIC9OYW1lIC9EZWphVnVTYW5zIC9Gb250TWF0\ncml4IFsgMC4wMDEgMCAwIDAuMDAxIDAgMCBdCi9Gb250RGVzY3JpcHRvciAxNCAwIFIgL0xhc3RD\naGFyIDI1NSAvVHlwZSAvRm9udCAvU3VidHlwZSAvVHlwZTMKL0ZvbnRCQm94IFsgLTEwMjEgLTQ2\nMyAxNzk0IDEyMzMgXSAvV2lkdGhzIDEzIDAgUiA+PgplbmRvYmoKMTQgMCBvYmoKPDwgL0ZvbnRO\nYW1lIC9EZWphVnVTYW5zIC9DYXBIZWlnaHQgMCAvTWF4V2lkdGggMTM0MiAvRmxhZ3MgMzIKL0l0\nYWxpY0FuZ2xlIDAgL1N0ZW1WIDAgL1R5cGUgL0ZvbnREZXNjcmlwdG9yIC9EZXNjZW50IC0yMzYK\nL0ZvbnRCQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvWEhlaWdodCAwIC9Bc2NlbnQgOTI5\nID4+CmVuZG9iagoxMyAwIG9iagpbIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAw\nIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwCjYwMCA2MDAgNjAwIDYwMCA2MDAg\nNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgMzE4IDQwMSA0NjAgODM4IDYzNgo5\nNTAgNzgwIDI3NSAzOTAgMzkwIDUwMCA4MzggMzE4IDM2MSAzMTggMzM3IDYzNiA2MzYgNjM2IDYz\nNiA2MzYgNjM2IDYzNiA2MzYKNjM2IDYzNiAzMzcgMzM3IDgzOCA4MzggODM4IDUzMSAxMDAwIDY4\nNCA2ODYgNjk4IDc3MCA2MzIgNTc1IDc3NSA3NTIgMjk1CjI5NSA2NTYgNTU3IDg2MyA3NDggNzg3\nIDYwMyA3ODcgNjk1IDYzNSA2MTEgNzMyIDY4NCA5ODkgNjg1IDYxMSA2ODUgMzkwIDMzNwozOTAg\nODM4IDUwMCA1MDAgNjEzIDYzNSA1NTAgNjM1IDYxNSAzNTIgNjM1IDYzNCAyNzggMjc4IDU3OSAy\nNzggOTc0IDYzNCA2MTIKNjM1IDYzNSA0MTEgNTIxIDM5MiA2MzQgNTkyIDgxOCA1OTIgNTkyIDUy\nNSA2MzYgMzM3IDYzNiA4MzggNjAwIDYzNiA2MDAgMzE4CjM1MiA1MTggMTAwMCA1MDAgNTAwIDUw\nMCAxMzQyIDYzNSA0MDAgMTA3MCA2MDAgNjg1IDYwMCA2MDAgMzE4IDMxOCA1MTggNTE4CjU5MCA1\nMDAgMTAwMCA1MDAgMTAwMCA1MjEgNDAwIDEwMjMgNjAwIDUyNSA2MTEgMzE4IDQwMSA2MzYgNjM2\nIDYzNiA2MzYgMzM3CjUwMCA1MDAgMTAwMCA0NzEgNjEyIDgzOCAzNjEgMTAwMCA1MDAgNTAwIDgz\nOCA0MDEgNDAxIDUwMCA2MzYgNjM2IDMxOCA1MDAKNDAxIDQ3MSA2MTIgOTY5IDk2OSA5NjkgNTMx\nIDY4NCA2ODQgNjg0IDY4NCA2ODQgNjg0IDk3NCA2OTggNjMyIDYzMiA2MzIgNjMyCjI5NSAyOTUg\nMjk1IDI5NSA3NzUgNzQ4IDc4NyA3ODcgNzg3IDc4NyA3ODcgODM4IDc4NyA3MzIgNzMyIDczMiA3\nMzIgNjExIDYwNQo2MzAgNjEzIDYxMyA2MTMgNjEzIDYxMyA2MTMgOTgyIDU1MCA2MTUgNjE1IDYx\nNSA2MTUgMjc4IDI3OCAyNzggMjc4IDYxMiA2MzQKNjEyIDYxMiA2MTIgNjEyIDYxMiA4MzggNjEy\nIDYzNCA2MzQgNjM0IDYzNCA1OTIgNjM1IDU5MiBdCmVuZG9iagoxNiAwIG9iago8PCAvZm91ciAz\nMSAwIFIgL3BlcmlvZCAxOCAwIFIgL2ZpdmUgMzUgMCBSIC90d28gMjIgMCBSIC9TIDI3IDAgUgov\nYSAyMyAwIFIgL3NwYWNlIDI0IDAgUiAvbSAzNyAwIFIgL24gMjUgMCBSIC94IDI2IDAgUiAvc2V2\nZW4gMTcgMCBSCi9pIDI5IDAgUiAvdGhyZWUgMzAgMCBSIC9zaXggMjEgMCBSIC9lIDMzIDAgUiAv\nemVybyAzMiAwIFIgL2QgMzQgMCBSCi9wIDIwIDAgUiAvb25lIDM2IDAgUiAvbCAyOCAwIFIgPj4K\nZW5kb2JqCjMgMCBvYmoKPDwgL0YxIDE1IDAgUiA+PgplbmRvYmoKNCAwIG9iago8PCAvQTEgPDwg\nL1R5cGUgL0V4dEdTdGF0ZSAvQ0EgMCAvY2EgMSA+PgovQTIgPDwgL1R5cGUgL0V4dEdTdGF0ZSAv\nQ0EgMSAvY2EgMSA+PiA+PgplbmRvYmoKNSAwIG9iago8PCA+PgplbmRvYmoKNiAwIG9iago8PCA+\nPgplbmRvYmoKNyAwIG9iago8PCAvRGVqYVZ1U2Fucy1taW51cyAxOSAwIFIgL0kxIDEyIDAgUiA+\nPgplbmRvYmoKMTIgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL1dpZHRoIDE0ODkgL0xl\nbmd0aCAzOCAwIFIgL0hlaWdodCAxOQovRGVjb2RlUGFybXMgPDwgL0NvbHVtbnMgMTQ4OSAvUHJl\nZGljdG9yIDEwIC9Db2xvcnMgMyA+PiAvVHlwZSAvWE9iamVjdAovU3VidHlwZSAvSW1hZ2UgL0Nv\nbG9yU3BhY2UgL0RldmljZVJHQiAvQml0c1BlckNvbXBvbmVudCA4ID4+CnN0cmVhbQp4nO3YQQ6D\nMAwAQdL//zk99AfpyhR15o6CBHakXRdwk733wVNrrfxN4F5ns3B9MQ7HJwIfw5fR8JYYXhHzN/sj\ndqAN/yOGh8h3bz1iBx57yt/ymjwMAAAA4E9oLgAAAAA9zQUAAACgp7kAAAAA9DQXAAAAgJ7mAgAA\nANDTXAAAAAB6mgsAAABAT3MBAAAA6GkuAAAAAD3NBQAAAKCnuQAAAAD0NBcAAACAnuYCAAAA0NNc\nAAAAAHqaCwAAAEBPcwEAAADoaS4AAAAAvTcwBiEjCmVuZHN0cmVhbQplbmRvYmoKMzggMCBvYmoK\nMjA4CmVuZG9iagoyIDAgb2JqCjw8IC9Db3VudCAxIC9UeXBlIC9QYWdlcyAvS2lkcyBbIDEwIDAg\nUiBdID4+CmVuZG9iagozOSAwIG9iago8PCAvUHJvZHVjZXIgKG1hdHBsb3RsaWIgcGRmIGJhY2tl\nbmQpCi9DcmVhdG9yIChtYXRwbG90bGliIDIuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcpCi9D\ncmVhdGlvbkRhdGUgKEQ6MjAxODAyMjYwMzA1NDMrMDInMDAnKSA+PgplbmRvYmoKeHJlZgowIDQw\nCjAwMDAwMDAwMDAgNjU1MzUgZiAKMDAwMDAwMDAxNiAwMDAwMCBuIAowMDAwMDA5MDE3IDAwMDAw\nIG4gCjAwMDAwMDgzMjkgMDAwMDAgbiAKMDAwMDAwODM2MSAwMDAwMCBuIAowMDAwMDA4NDYwIDAw\nMDAwIG4gCjAwMDAwMDg0ODEgMDAwMDAgbiAKMDAwMDAwODUwMiAwMDAwMCBuIAowMDAwMDAwMDY1\nIDAwMDAwIG4gCjAwMDAwMDAzOTYgMDAwMDAgbiAKMDAwMDAwMDIwOCAwMDAwMCBuIAowMDAwMDAx\nMTMwIDAwMDAwIG4gCjAwMDAwMDg1NTkgMDAwMDAgbiAKMDAwMDAwNzAyMiAwMDAwMCBuIAowMDAw\nMDA2ODIyIDAwMDAwIG4gCjAwMDAwMDY0MDIgMDAwMDAgbiAKMDAwMDAwODA3NSAwMDAwMCBuIAow\nMDAwMDAxMTUwIDAwMDAwIG4gCjAwMDAwMDEyOTAgMDAwMDAgbiAKMDAwMDAwMTQxMSAwMDAwMCBu\nIAowMDAwMDAxNTgxIDAwMDAwIG4gCjAwMDAwMDE4OTAgMDAwMDAgbiAKMDAwMDAwMjI4MCAwMDAw\nMCBuIAowMDAwMDAyNjAxIDAwMDAwIG4gCjAwMDAwMDI5NzggMDAwMDAgbiAKMDAwMDAwMzA2NyAw\nMDAwMCBuIAowMDAwMDAzMzAxIDAwMDAwIG4gCjAwMDAwMDM0NjAgMDAwMDAgbiAKMDAwMDAwMzg3\nMSAwMDAwMCBuIAowMDAwMDAzOTg4IDAwMDAwIG4gCjAwMDAwMDQxMjggMDAwMDAgbiAKMDAwMDAw\nNDUzOSAwMDAwMCBuIAowMDAwMDA0NzAxIDAwMDAwIG4gCjAwMDAwMDQ5ODQgMDAwMDAgbiAKMDAw\nMDAwNTMwMiAwMDAwMCBuIAowMDAwMDA1NjAyIDAwMDAwIG4gCjAwMDAwMDU5MjIgMDAwMDAgbiAK\nMDAwMDAwNjA3NCAwMDAwMCBuIAowMDAwMDA4OTk3IDAwMDAwIG4gCjAwMDAwMDkwNzcgMDAwMDAg\nbiAKdHJhaWxlcgo8PCAvU2l6ZSA0MCAvUm9vdCAxIDAgUiAvSW5mbyAzOSAwIFIgPj4Kc3RhcnR4\ncmVmCjkyMjUKJSVFT0YK\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABh8AAABgCAYAAAANb2NuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11650fac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "select = RFE(RandomForestClassifier(n_estimators=100, random_state=42),\n",
    "             n_features_to_select=40)\n",
    "\n",
    "select.fit(X_train, y_train)\n",
    "# visualize the selected features:\n",
    "mask = select.get_support()\n",
    "plt.matshow(mask.reshape(1, -1), cmap='gray_r')\n",
    "plt.xlabel(\"Sample index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.951\n"
     ]
    }
   ],
   "source": [
    "X_train_rfe = select.transform(X_train)\n",
    "X_test_rfe = select.transform(X_test)\n",
    "\n",
    "score = LogisticRegression().fit(X_train_rfe, y_train).score(X_test_rfe, y_test)\n",
    "print(\"Test score: {:.3f}\".format(score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.951\n"
     ]
    }
   ],
   "source": [
    "print(\"Test score: {:.3f}\".format(select.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Automatic feature selection can be helpful when:\n",
    "\n",
    "- You expect some inputs to be uninformative, and your model does not select features internally (as tree-based models do)\n",
    "- You need to speed up prediction without loosing much accuracy\n",
    "- You want a more interpretable model (with fewer variables)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Scaling\n",
    "\n",
    "When the features have different scales (their values range between very different minimum and maximum values), it makes sense to scale them to the same range. Otherwise, one feature will overpower the others, expecially when raised to the $n$th power.\n",
    "\n",
    "- We can rescale features between 0 and 1 using MinMaxScaler. \n",
    "- Remember to `fit_transform` the training data, then `transform` the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "boston = load_boston()\n",
    "X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, \n",
    "                                                    random_state=0)\n",
    "\n",
    "# rescale data:\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Several scaling techniques are available:\n",
    "- `StandardScaler` rescales all features to mean=0 and variance=1\n",
    "    - Does not ensure and min/max value\n",
    "- `RobustScaler` uses the median and quartiles\n",
    "    - Median m: half of the values < m, half > m\n",
    "    - Lower Quartile lq: 1/4 of values < lq\n",
    "    - Upper Quartile uq: 1/4 of values > uq\n",
    "    - Ignores _outliers_, brings all features to same scale\n",
    "- `MinMaxScaler` brings all feature values between 0 and 1\n",
    "- `Normalizer` scales data such that the feature vector has Euclidean length 1\n",
    "    - Projects data to the unit circle\n",
    "    - Used when only the direction/angle of the data matters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDIgMCBSID4+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\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABZMAAAMQCAYAAABSfvu8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAMCq/w+H+X78o2DpdQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fb20b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_scaling()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Applying scaling transformations\n",
    "- Lets apply a scaling transformation _manually_, then use it to train a learning algorithm\n",
    "- First, split the data in training and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target,\n",
    "                                                    random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- Next, we `fit` the preprocessor on the __training data__\n",
    "    - This computes the necessary transformation parameters\n",
    "    - For `MinMaxScaler`, these are the min/max values for every feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler(copy=True, feature_range=(0, 1))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* After fitting, we can `transform` the training and test data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "per-feature minimum before scaling:\n",
      " [  6.981   9.71   43.79  143.5     0.053   0.019   0.      0.      0.106\n",
      "   0.05    0.115   0.36    0.757   6.802   0.002   0.002   0.      0.\n",
      "   0.01    0.001   7.93   12.02   50.41  185.2     0.071   0.027   0.\n",
      "   0.      0.157   0.055]\n",
      "per-feature maximum before scaling:\n",
      " [  28.11    39.28   188.5   2501.       0.163    0.287    0.427    0.201\n",
      "    0.304    0.096    2.873    4.885   21.98   542.2      0.031    0.135\n",
      "    0.396    0.053    0.061    0.03    36.04    49.54   251.2   4254.\n",
      "    0.223    0.938    1.17     0.291    0.577    0.149]\n",
      "per-feature minimum after scaling:\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0.]\n",
      "per-feature maximum after scaling:\n",
      " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "# transform training data\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "# print dataset properties before and after scaling\n",
    "print(\"per-feature minimum before scaling:\\n {}\".format(X_train.min(axis=0)))\n",
    "print(\"per-feature maximum before scaling:\\n {}\".format(X_train.max(axis=0)))\n",
    "print(\"per-feature minimum after scaling:\\n {}\".format(\n",
    "        X_train_scaled.min(axis=0)))\n",
    "print(\"per-feature maximum after scaling:\\n {}\".format(\n",
    "        X_train_scaled.max(axis=0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "per-feature minimum after scaling:\n",
      "[ 0.034  0.023  0.031  0.011  0.141  0.044  0.     0.     0.154 -0.006\n",
      " -0.001  0.006  0.004  0.001  0.039  0.011  0.     0.    -0.032  0.007\n",
      "  0.027  0.058  0.02   0.009  0.109  0.026  0.     0.    -0.    -0.002]\n",
      "per-feature maximum after scaling:\n",
      "[0.958 0.815 0.956 0.894 0.811 1.22  0.88  0.933 0.932 1.037 0.427 0.498\n",
      " 0.441 0.284 0.487 0.739 0.767 0.629 1.337 0.391 0.896 0.793 0.849 0.745\n",
      " 0.915 1.132 1.07  0.924 1.205 1.631]\n"
     ]
    }
   ],
   "source": [
    "# transform test data\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "# print test data properties after scaling\n",
    "print(\"per-feature minimum after scaling:\\n{}\".format(X_test_scaled.min(axis=0)))\n",
    "print(\"per-feature maximum after scaling:\\n{}\".format(X_test_scaled.max(axis=0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* After scaling the test data, the values are not exactly between 0 and 1\n",
    "* This is correct: we used the min/max values from the training data only\n",
    "* We are still interested in how well our preprocessing+learning model generalizes from the training to the test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "- Remember to `fit` and `transform` on the training data, then `transform` the test data\n",
    "\n",
    "* 2nd figure: `fit` on training set, `transform` on training and test set\n",
    "* 3rd figure: `fit` and `transform` on the training data\n",
    "    * Test data points nowhere near same training data points\n",
    "    * Trained model will have a hard time generalizing correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": "JVBERi0xLjQKJazcIKu6CjEgMCBvYmoKPDwgL1R5cGUgL0NhdGFsb2cgL1BhZ2VzIDIgMCBSID4+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\nbmZvIDU1IDAgUiA+PgpzdGFydHhyZWYKMTc3NjAKJSVFT0YK\n",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABgoAAAHVCAYAAAAgmKCwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkY2ZrgbVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fe1fd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "# make synthetic data\n",
    "X, _ = make_blobs(n_samples=50, centers=5, random_state=4, cluster_std=2)\n",
    "# split it into training and test set\n",
    "X_train, X_test = train_test_split(X, random_state=5, test_size=.1)\n",
    "\n",
    "# plot the training and test set\n",
    "fig, axes = plt.subplots(1, 3, figsize=(13, 4))\n",
    "axes[0].scatter(X_train[:, 0], X_train[:, 1],\n",
    "                c=mglearn.cm2(0), label=\"Training set\", s=60)\n",
    "axes[0].scatter(X_test[:, 0], X_test[:, 1], marker='^',\n",
    "                c=mglearn.cm2(1), label=\"Test set\", s=60)\n",
    "axes[0].legend(loc='upper left')\n",
    "axes[0].set_title(\"Original Data\")\n",
    "\n",
    "# scale the data using MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# visualize the properly scaled data\n",
    "axes[1].scatter(X_train_scaled[:, 0], X_train_scaled[:, 1],\n",
    "                c=mglearn.cm2(0), label=\"Training set\", s=60)\n",
    "axes[1].scatter(X_test_scaled[:, 0], X_test_scaled[:, 1], marker='^',\n",
    "                c=mglearn.cm2(1), label=\"Test set\", s=60)\n",
    "axes[1].set_title(\"Scaled Data\")\n",
    "\n",
    "# rescale the test set separately\n",
    "# so that test set min is 0 and test set max is 1\n",
    "# DO NOT DO THIS! For illustration purposes only\n",
    "test_scaler = MinMaxScaler()\n",
    "test_scaler.fit(X_test)\n",
    "X_test_scaled_badly = test_scaler.transform(X_test)\n",
    "\n",
    "# visualize wrongly scaled data\n",
    "axes[2].scatter(X_train_scaled[:, 0], X_train_scaled[:, 1],\n",
    "                c=mglearn.cm2(0), label=\"training set\", s=60)\n",
    "axes[2].scatter(X_test_scaled_badly[:, 0], X_test_scaled_badly[:, 1],\n",
    "                marker='^', c=mglearn.cm2(1), label=\"test set\", s=60)\n",
    "axes[2].set_title(\"Improperly Scaled Data\")\n",
    "\n",
    "for ax in axes:\n",
    "    ax.set_xlabel(\"Feature 0\")\n",
    "    ax.set_ylabel(\"Feature 1\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Note: you can fit and transform the training together with `fit_transform`\n",
    "* To transform the test data, you always need to `fit` on the training data and `transform` the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "# calling fit and transform in sequence (using method chaining)\n",
    "X_scaled = scaler.fit(X).transform(X)\n",
    "# same result, but more efficient computation\n",
    "X_scaled_d = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### How great is the effect of scaling?\n",
    "* First, we train the (linear) SVM without scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy: 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target,\n",
    "                                                    random_state=0)\n",
    "\n",
    "svm = LinearSVC()\n",
    "svm.fit(X_train, y_train)\n",
    "print(\"Test set accuracy: {:.2f}\".format(svm.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* With scaling, we get a much better model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaled test set accuracy: 0.97\n"
     ]
    }
   ],
   "source": [
    "# preprocessing using 0-1 scaling\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# learning an SVM on the scaled training data\n",
    "svm.fit(X_train_scaled, y_train)\n",
    "# scoring on the scaled test set\n",
    "print(\"Scaled test set accuracy: {:.2f}\".format(svm.score(X_test_scaled, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Scaling for polynomial regression\n",
    "After scaling, we extract polynomial features and interactions up to a degree of 2. Note how we `fit` the `PolynomialFeatures` only on the training data and then apply it (`transform`) on both the training and test data.\n",
    "\n",
    "`PolynomialFeatures` will add a new features for each possible interaction (product) of up to 2 input features, including the products of a feature with itself (the squares). Hence, $\\frac{13!}{11!  2!}$ + 13 + 13 features total.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "boston = load_boston()\n",
    "X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target,\n",
    "                                                    random_state=0)\n",
    "\n",
    "# rescale data:\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape: (379, 13)\n",
      "X_train_poly.shape: (379, 105)\n"
     ]
    }
   ],
   "source": [
    "poly = PolynomialFeatures(degree=2).fit(X_train_scaled)\n",
    "X_train_poly = poly.transform(X_train_scaled)\n",
    "X_test_poly = poly.transform(X_test_scaled)\n",
    "print(\"X_train.shape: {}\".format(X_train.shape))\n",
    "print(\"X_train_poly.shape: {}\".format(X_train_poly.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The exact correspondence between input and output features can be found using the get_feature_names method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polynomial feature names:\n",
      "['1', 'x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10', 'x11', 'x12', 'x0^2', 'x0 x1', 'x0 x2', 'x0 x3', 'x0 x4', 'x0 x5', 'x0 x6', 'x0 x7', 'x0 x8', 'x0 x9', 'x0 x10', 'x0 x11', 'x0 x12', 'x1^2', 'x1 x2', 'x1 x3', 'x1 x4', 'x1 x5', 'x1 x6', 'x1 x7', 'x1 x8', 'x1 x9', 'x1 x10', 'x1 x11', 'x1 x12', 'x2^2', 'x2 x3', 'x2 x4', 'x2 x5', 'x2 x6', 'x2 x7', 'x2 x8', 'x2 x9', 'x2 x10', 'x2 x11', 'x2 x12', 'x3^2', 'x3 x4', 'x3 x5', 'x3 x6', 'x3 x7', 'x3 x8', 'x3 x9', 'x3 x10', 'x3 x11', 'x3 x12', 'x4^2', 'x4 x5', 'x4 x6', 'x4 x7', 'x4 x8', 'x4 x9', 'x4 x10', 'x4 x11', 'x4 x12', 'x5^2', 'x5 x6', 'x5 x7', 'x5 x8', 'x5 x9', 'x5 x10', 'x5 x11', 'x5 x12', 'x6^2', 'x6 x7', 'x6 x8', 'x6 x9', 'x6 x10', 'x6 x11', 'x6 x12', 'x7^2', 'x7 x8', 'x7 x9', 'x7 x10', 'x7 x11', 'x7 x12', 'x8^2', 'x8 x9', 'x8 x10', 'x8 x11', 'x8 x12', 'x9^2', 'x9 x10', 'x9 x11', 'x9 x12', 'x10^2', 'x10 x11', 'x10 x12', 'x11^2', 'x11 x12', 'x12^2']\n"
     ]
    }
   ],
   "source": [
    "print(\"Polynomial feature names:\\n{}\".format(poly.get_feature_names()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let’s compare the performance of a linear model (Ridge regression) on the data with and without interactions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score without interactions: 0.621\n",
      "Score with interactions: 0.753\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge = Ridge().fit(X_train_scaled, y_train)\n",
    "print(\"Score without interactions: {:.3f}\".format(\n",
    "        ridge.score(X_test_scaled, y_test)))\n",
    "ridge = Ridge().fit(X_train_poly, y_train)\n",
    "print(\"Score with interactions: {:.3f}\".format(\n",
    "        ridge.score(X_test_poly, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Clearly, the interactions and polynomial features gave us a good boost in performance when using Ridge. When using a more complex model like a random forest, the story is a bit different, though:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score without interactions: 0.795\n",
      "Score with interactions: 0.767\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf = RandomForestRegressor(n_estimators=100).fit(X_train_scaled, y_train)\n",
    "print(\"Score without interactions: {:.3f}\".format(\n",
    "        rf.score(X_test_scaled, y_test)))\n",
    "rf = RandomForestRegressor(n_estimators=100).fit(X_train_poly, y_train)\n",
    "print(\"Score with interactions: {:.3f}\".format(rf.score(X_test_poly, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "The random forest does not benefit from the interaction features, in fact, performance decreases...\n",
    "\n",
    "Adding polynomials is typically good for linear models, but not a cure-for-all. Always evaluate the performance of models when adding preprocessing steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* What if we want the cross-validated evaluation?\n",
    "    - Apply scaling on every fold independently?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Building Pipelines\n",
    "* In scikit-learn, a `pipeline` combines multiple processing _steps_ in a single estimator\n",
    "* All but the last step should be transformer (have a `transform` method)\n",
    "    * The last step can be a transformer too (e.g. Scaler+PCA)\n",
    "* It has a `fit`, `predict`, and `score` method, just like any other learning algorithm\n",
    "* Pipelines are built as a list of steps, which are (name, algorithm) tuples\n",
    "    * The name can be anything you want, but can't contain `'__'`\n",
    "    * We use `'__'` to refer to the hyperparameters, e.g. `svm__C`\n",
    "* Let's build, train, and score a `MinMaxScaler` + `LinearSVC` pipeline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.97\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "pipe = Pipeline([(\"scaler\", MinMaxScaler()), (\"svm\", LinearSVC())])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target,\n",
    "                                                    random_state=1)\n",
    "pipe.fit(X_train, y_train)\n",
    "print(\"Test score: {:.2f}\".format(pipe.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Now with cross-validation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores: [0.984 0.953 0.979]\n",
      "Average cross-validation score: 0.97\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(pipe, cancer.data, cancer.target)\n",
    "print(\"Cross-validation scores: {}\".format(scores))\n",
    "print(\"Average cross-validation score: {:.2f}\".format(scores.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* We can retrieve the trained SVM by querying the right step indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM component: LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0)\n"
     ]
    }
   ],
   "source": [
    "pipe.fit(X_train, y_train)\n",
    "print(\"SVM component: {}\".format(pipe.steps[1][1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Or we can use the `named_steps` dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM component: LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0)\n"
     ]
    }
   ],
   "source": [
    "print(\"SVM component: {}\".format(pipe.named_steps['svm']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* When you don't need specific names for specific steps, you can use `make_pipeline`\n",
    "    * Assigns names to steps automatically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline steps:\n",
      "[('minmaxscaler', MinMaxScaler(copy=True, feature_range=(0, 1))), ('linearsvc', LinearSVC(C=100, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0))]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "# standard syntax\n",
    "pipe_long = Pipeline([(\"scaler\", MinMaxScaler()), (\"svm\", LinearSVC(C=100))])\n",
    "# abbreviated syntax\n",
    "pipe_short = make_pipeline(MinMaxScaler(), LinearSVC(C=100))\n",
    "print(\"Pipeline steps:\\n{}\".format(pipe_short.steps))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Visualization of a pipeline `fit` and `predict`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![pipeline_illustration](images/07_pipelines.png)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}