notebooks/12_ensembling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensembling\n",
    "\n",
    "*Adapted from Chapter 8 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why are we learning about ensembling?\n",
    "\n",
    "- Very popular method for improving the predictive performance of machine learning models\n",
    "- Provides a foundation for understanding more sophisticated models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lesson objectives\n",
    "\n",
    "Students will be able to:\n",
    "\n",
    "- Define ensembling and its requirements\n",
    "- Identify the two basic methods of ensembling\n",
    "- Decide whether manual ensembling is a useful approach for a given problem\n",
    "- Explain bagging and how it can be applied to decision trees\n",
    "- Explain how out-of-bag error and feature importances are calculated from bagged trees\n",
    "- Explain the difference between bagged trees and Random Forests\n",
    "- Build and tune a Random Forest model in scikit-learn\n",
    "- Decide whether a decision tree or a Random Forest is a better model for a given problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1: Introduction\n",
    "\n",
    "Let's pretend that instead of building a single model to solve a binary classification problem, you created **five independent models**, and each model was correct about 70% of the time. If you combined these models into an \"ensemble\" and used their majority vote as a prediction, how often would the ensemble be correct?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1]\n",
      "[1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 0 0 0]\n",
      "[1 1 1 1 0 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1]\n",
      "[1 1 0 0 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1 0]\n",
      "[0 0 1 0 0 0 1 0 1 0 0 0 1 1 1 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# set a seed for reproducibility\n",
    "np.random.seed(1234)\n",
    "\n",
    "# generate 1000 random numbers (between 0 and 1) for each model, representing 1000 observations\n",
    "mod1 = np.random.rand(1000)\n",
    "mod2 = np.random.rand(1000)\n",
    "mod3 = np.random.rand(1000)\n",
    "mod4 = np.random.rand(1000)\n",
    "mod5 = np.random.rand(1000)\n",
    "\n",
    "# each model independently predicts 1 (the \"correct response\") if random number was at least 0.3\n",
    "preds1 = np.where(mod1 > 0.3, 1, 0)\n",
    "preds2 = np.where(mod2 > 0.3, 1, 0)\n",
    "preds3 = np.where(mod3 > 0.3, 1, 0)\n",
    "preds4 = np.where(mod4 > 0.3, 1, 0)\n",
    "preds5 = np.where(mod5 > 0.3, 1, 0)\n",
    "\n",
    "# print the first 20 predictions from each model\n",
    "print preds1[:20]\n",
    "print preds2[:20]\n",
    "print preds3[:20]\n",
    "print preds4[:20]\n",
    "print preds5[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "# average the predictions and then round to 0 or 1\n",
    "ensemble_preds = np.round((preds1 + preds2 + preds3 + preds4 + preds5)/5.0).astype(int)\n",
    "\n",
    "# print the ensemble's first 20 predictions\n",
    "print ensemble_preds[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.713\n",
      "0.665\n",
      "0.717\n",
      "0.712\n",
      "0.687\n"
     ]
    }
   ],
   "source": [
    "# how accurate was each individual model?\n",
    "print preds1.mean()\n",
    "print preds2.mean()\n",
    "print preds3.mean()\n",
    "print preds4.mean()\n",
    "print preds5.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.841\n"
     ]
    }
   ],
   "source": [
    "# how accurate was the ensemble?\n",
    "print ensemble_preds.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** As you add more models to the voting process, the probability of error decreases, which is known as [Condorcet's Jury Theorem](http://en.wikipedia.org/wiki/Condorcet%27s_jury_theorem)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is ensembling?\n",
    "\n",
    "**Ensemble learning (or \"ensembling\")** is the process of combining several predictive models in order to produce a combined model that is more accurate than any individual model.\n",
    "\n",
    "- **Regression:** take the average of the predictions\n",
    "- **Classification:** take a vote and use the most common prediction, or take the average of the predicted probabilities\n",
    "\n",
    "For ensembling to work well, the models must have the following characteristics:\n",
    "\n",
    "- **Accurate:** they outperform the null model\n",
    "- **Independent:** their predictions are generated using different processes\n",
    "\n",
    "**The big idea:** If you have a collection of individually imperfect (and independent) models, the \"one-off\" mistakes made by each model are probably not going to be made by the rest of the models, and thus the mistakes will be discarded when averaging the models.\n",
    "\n",
    "There are two basic **methods for ensembling:**\n",
    "\n",
    "- Manually ensemble your individual models\n",
    "- Use a model that ensembles for you"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: Manual ensembling\n",
    "\n",
    "What makes a good manual ensemble?\n",
    "\n",
    "- Different types of **models**\n",
    "- Different combinations of **features**\n",
    "- Different **tuning parameters**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Machine learning flowchart](images/crowdflower_ensembling.jpg)\n",
    "\n",
    "*Machine learning flowchart created by the [winner](https://github.com/ChenglongChen/Kaggle_CrowdFlower) of Kaggle's [CrowdFlower competition](https://www.kaggle.com/c/crowdflower-search-relevance)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing manual ensembling with a single model approach\n",
    "\n",
    "**Advantages of manual ensembling:**\n",
    "\n",
    "- Increases predictive accuracy\n",
    "- Easy to get started\n",
    "\n",
    "**Disadvantages of manual ensembling:**\n",
    "\n",
    "- Decreases interpretability\n",
    "- Takes longer to train\n",
    "- Takes longer to predict\n",
    "- More complex to automate and maintain\n",
    "- Small gains in accuracy may not be worth the added complexity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 3: Bagging\n",
    "\n",
    "The primary weakness of **decision trees** is that they don't tend to have the best predictive accuracy. This is partially due to **high variance**, meaning that different splits in the training data can lead to very different trees.\n",
    "\n",
    "**Bagging** is a general purpose procedure for reducing the variance of a machine learning method, but is particularly useful for decision trees. Bagging is short for **bootstrap aggregation**, meaning the aggregation of bootstrap samples.\n",
    "\n",
    "What is a **bootstrap sample**? A random sample with replacement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20]\n",
      "[ 6 12 13  9 10 12  6 16  1 17  2 13  8 14  7 19  6 19 12 11]\n"
     ]
    }
   ],
   "source": [
    "# set a seed for reproducibility\n",
    "np.random.seed(1)\n",
    "\n",
    "# create an array of 1 through 20\n",
    "nums = np.arange(1, 21)\n",
    "print nums\n",
    "\n",
    "# sample that array 20 times with replacement\n",
    "print np.random.choice(a=nums, size=20, replace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How does bagging work (for decision trees)?**\n",
    "\n",
    "1. Grow B trees using B bootstrap samples from the training data.\n",
    "2. Train each tree on its bootstrap sample and make predictions.\n",
    "3. Combine the predictions:\n",
    "    - Average the predictions for **regression trees**\n",
    "    - Take a vote for **classification trees**\n",
    "\n",
    "Notes:\n",
    "\n",
    "- **Each bootstrap sample** should be the same size as the original training set.\n",
    "- **B** should be a large enough value that the error seems to have \"stabilized\".\n",
    "- The trees are **grown deep** so that they have low bias/high variance.\n",
    "\n",
    "Bagging increases predictive accuracy by **reducing the variance**, similar to how cross-validation reduces the variance associated with train/test split (for estimating out-of-sample error) by splitting many times an averaging the results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manually implementing bagged decision trees (with B=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>miles</th>\n",
       "      <th>doors</th>\n",
       "      <th>vtype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22000</td>\n",
       "      <td>2012</td>\n",
       "      <td>13000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14000</td>\n",
       "      <td>2010</td>\n",
       "      <td>30000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13000</td>\n",
       "      <td>2010</td>\n",
       "      <td>73500</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9500</td>\n",
       "      <td>2009</td>\n",
       "      <td>78000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9000</td>\n",
       "      <td>2007</td>\n",
       "      <td>47000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4000</td>\n",
       "      <td>2006</td>\n",
       "      <td>124000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3000</td>\n",
       "      <td>2004</td>\n",
       "      <td>177000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2000</td>\n",
       "      <td>2004</td>\n",
       "      <td>209000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3000</td>\n",
       "      <td>2003</td>\n",
       "      <td>138000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1900</td>\n",
       "      <td>2003</td>\n",
       "      <td>160000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2500</td>\n",
       "      <td>2003</td>\n",
       "      <td>190000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5000</td>\n",
       "      <td>2001</td>\n",
       "      <td>62000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1800</td>\n",
       "      <td>1999</td>\n",
       "      <td>163000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1300</td>\n",
       "      <td>1997</td>\n",
       "      <td>138000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    price  year   miles  doors  vtype\n",
       "0   22000  2012   13000      2      0\n",
       "1   14000  2010   30000      2      0\n",
       "2   13000  2010   73500      4      0\n",
       "3    9500  2009   78000      4      0\n",
       "4    9000  2007   47000      4      0\n",
       "5    4000  2006  124000      2      0\n",
       "6    3000  2004  177000      4      0\n",
       "7    2000  2004  209000      4      1\n",
       "8    3000  2003  138000      2      0\n",
       "9    1900  2003  160000      4      0\n",
       "10   2500  2003  190000      2      1\n",
       "11   5000  2001   62000      4      0\n",
       "12   1800  1999  163000      2      1\n",
       "13   1300  1997  138000      4      0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in and prepare the vehicle training data\n",
    "import pandas as pd\n",
    "url = '../data/vehicles_train.csv'\n",
    "train = pd.read_csv(url)\n",
    "train['vtype'] = train.vtype.map({'car':0, 'truck':1})\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "[array([13,  2, 12,  2,  6,  1,  3, 10, 11,  9,  6,  1,  0,  1]),\n",
       " array([ 9,  0,  0,  9,  3, 13,  4,  0,  0,  4,  1,  7,  3,  2]),\n",
       " array([ 4,  7,  2,  4,  8, 13,  0,  7,  9,  3, 12, 12,  4,  6]),\n",
       " array([ 1,  5,  6, 11,  2,  1, 12,  8,  3, 10,  5,  0, 11,  2]),\n",
       " array([10, 10,  6, 13,  2,  4, 11, 11, 13, 12,  4,  6, 13,  3]),\n",
       " array([10,  0,  6,  4,  7, 11,  6,  7,  1, 11, 10,  5,  7,  9]),\n",
       " array([ 2,  4,  8,  1, 12,  2,  1,  1,  3, 12,  5,  9,  0,  8]),\n",
       " array([11,  1,  6,  3,  3, 11,  5,  9,  7,  9,  2,  3, 11,  3]),\n",
       " array([ 3,  8,  6,  9,  7,  6,  3,  9,  6, 12,  6, 11,  6,  1]),\n",
       " array([13, 10,  3,  4,  3,  1, 13,  0,  5,  8, 13,  6, 11,  8])]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set a seed for reproducibility\n",
    "np.random.seed(123)\n",
    "\n",
    "# create ten bootstrap samples (will be used to select rows from the DataFrame)\n",
    "samples = [np.random.choice(a=14, size=14, replace=True) for _ in range(1, 11)]\n",
    "samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>year</th>\n",
       "      <th>miles</th>\n",
       "      <th>doors</th>\n",
       "      <th>vtype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
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       "      <td>13000</td>\n",
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       "      <td>73500</td>\n",
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       "      <td>1999</td>\n",
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       "      <td>2010</td>\n",
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       "      <td>2</td>\n",
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       "      <th>3</th>\n",
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       "      <td>4</td>\n",
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       "      <td>2500</td>\n",
       "      <td>2003</td>\n",
       "      <td>190000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5000</td>\n",
       "      <td>2001</td>\n",
       "      <td>62000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1900</td>\n",
       "      <td>2003</td>\n",
       "      <td>160000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3000</td>\n",
       "      <td>2004</td>\n",
       "      <td>177000</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14000</td>\n",
       "      <td>2010</td>\n",
       "      <td>30000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22000</td>\n",
       "      <td>2012</td>\n",
       "      <td>13000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14000</td>\n",
       "      <td>2010</td>\n",
       "      <td>30000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    price  year   miles  doors  vtype\n",
       "13   1300  1997  138000      4      0\n",
       "2   13000  2010   73500      4      0\n",
       "12   1800  1999  163000      2      1\n",
       "2   13000  2010   73500      4      0\n",
       "6    3000  2004  177000      4      0\n",
       "1   14000  2010   30000      2      0\n",
       "3    9500  2009   78000      4      0\n",
       "10   2500  2003  190000      2      1\n",
       "11   5000  2001   62000      4      0\n",
       "9    1900  2003  160000      4      0\n",
       "6    3000  2004  177000      4      0\n",
       "1   14000  2010   30000      2      0\n",
       "0   22000  2012   13000      2      0\n",
       "1   14000  2010   30000      2      0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the rows for the first decision tree\n",
    "train.iloc[samples[0], :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>price</th>\n",
       "      <th>year</th>\n",
       "      <th>miles</th>\n",
       "      <th>doors</th>\n",
       "      <th>vtype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3000</td>\n",
       "      <td>2003</td>\n",
       "      <td>130000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6000</td>\n",
       "      <td>2005</td>\n",
       "      <td>82500</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12000</td>\n",
       "      <td>2010</td>\n",
       "      <td>60000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   price  year   miles  doors  vtype\n",
       "0   3000  2003  130000      4      1\n",
       "1   6000  2005   82500      4      0\n",
       "2  12000  2010   60000      2      0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in and prepare the vehicle testing data\n",
    "url = '../data/vehicles_test.csv'\n",
    "test = pd.read_csv(url)\n",
    "test['vtype'] = test.vtype.map({'car':0, 'truck':1})\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1300.,   5000.,  14000.],\n",
       "       [  1300.,   1300.,  13000.],\n",
       "       [  3000.,   3000.,  13000.],\n",
       "       [  4000.,   5000.,  13000.],\n",
       "       [  1300.,   5000.,  13000.],\n",
       "       [  4000.,   5000.,  14000.],\n",
       "       [  4000.,   4000.,  13000.],\n",
       "       [  4000.,   5000.,  13000.],\n",
       "       [  3000.,   5000.,   9500.],\n",
       "       [  4000.,   5000.,   9000.]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# grow each tree deep\n",
    "treereg = DecisionTreeRegressor(max_depth=None, random_state=123)\n",
    "\n",
    "# list for storing predicted price from each tree\n",
    "predictions = []\n",
    "\n",
    "# define testing data\n",
    "X_test = test.iloc[:, 1:]\n",
    "y_test = test.iloc[:, 0]\n",
    "\n",
    "# grow one tree for each bootstrap sample and make predictions on testing data\n",
    "for sample in samples:\n",
    "    X_train = train.iloc[sample, 1:]\n",
    "    y_train = train.iloc[sample, 0]\n",
    "    treereg.fit(X_train, y_train)\n",
    "    y_pred = treereg.predict(X_test)\n",
    "    predictions.append(y_pred)\n",
    "\n",
    "# convert predictions from list to NumPy array\n",
    "predictions = np.array(predictions)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2990.,   4330.,  12450.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average predictions\n",
    "np.mean(predictions, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "998.58232843700307"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate RMSE\n",
    "from sklearn import metrics\n",
    "y_pred = np.mean(predictions, axis=0)\n",
    "np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bagged decision trees in scikit-learn (with B=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# define the training and testing sets\n",
    "X_train = train.iloc[:, 1:]\n",
    "y_train = train.iloc[:, 0]\n",
    "X_test = test.iloc[:, 1:]\n",
    "y_test = test.iloc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# instruct BaggingRegressor to use DecisionTreeRegressor as the \"base estimator\"\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "bagreg = BaggingRegressor(DecisionTreeRegressor(), n_estimators=500, bootstrap=True, oob_score=True, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3344.2,   5395. ,  12902. ])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit and predict\n",
    "bagreg.fit(X_train, y_train)\n",
    "y_pred = bagreg.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "657.80003040437748"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate RMSE\n",
    "np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimating out-of-sample error\n",
    "\n",
    "For bagged models, out-of-sample error can be estimated without using **train/test split** or **cross-validation**!\n",
    "\n",
    "On average, each bagged tree uses about **two-thirds** of the observations. For each tree, the **remaining observations** are called \"out-of-bag\" observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13,  2, 12,  2,  6,  1,  3, 10, 11,  9,  6,  1,  0,  1])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the first bootstrap sample\n",
    "samples[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set([0, 1, 2, 3, 6, 9, 10, 11, 12, 13])\n",
      "set([0, 1, 2, 3, 4, 7, 9, 13])\n",
      "set([0, 2, 3, 4, 6, 7, 8, 9, 12, 13])\n",
      "set([0, 1, 2, 3, 5, 6, 8, 10, 11, 12])\n",
      "set([2, 3, 4, 6, 10, 11, 12, 13])\n",
      "set([0, 1, 4, 5, 6, 7, 9, 10, 11])\n",
      "set([0, 1, 2, 3, 4, 5, 8, 9, 12])\n",
      "set([1, 2, 3, 5, 6, 7, 9, 11])\n",
      "set([1, 3, 6, 7, 8, 9, 11, 12])\n",
      "set([0, 1, 3, 4, 5, 6, 8, 10, 11, 13])\n"
     ]
    }
   ],
   "source": [
    "# show the \"in-bag\" observations for each sample\n",
    "for sample in samples:\n",
    "    print set(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 5, 7, 8]\n",
      "[5, 6, 8, 10, 11, 12]\n",
      "[1, 5, 10, 11]\n",
      "[4, 7, 9, 13]\n",
      "[0, 1, 5, 7, 8, 9]\n",
      "[2, 3, 8, 12, 13]\n",
      "[6, 7, 10, 11, 13]\n",
      "[0, 4, 8, 10, 12, 13]\n",
      "[0, 2, 4, 5, 10, 13]\n",
      "[2, 7, 9, 12]\n"
     ]
    }
   ],
   "source": [
    "# show the \"out-of-bag\" observations for each sample\n",
    "for sample in samples:\n",
    "    print sorted(set(range(14)) - set(sample))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to calculate **\"out-of-bag error\":**\n",
    "\n",
    "1. For every observation in the training data, predict its response value using **only** the trees in which that observation was out-of-bag. Average those predictions (for regression) or take a vote (for classification).\n",
    "2. Compare all predictions to the actual response values in order to compute the out-of-bag error.\n",
    "\n",
    "When B is sufficiently large, the **out-of-bag error** is an accurate estimate of **out-of-sample error**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.76698184959239768"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute the out-of-bag R-squared score (not MSE, unfortunately!) for B=500\n",
    "bagreg.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimating feature importance\n",
    "\n",
    "Bagging increases **predictive accuracy**, but decreases **model interpretability** because it's no longer possible to visualize the tree to understand the importance of each feature.\n",
    "\n",
    "However, we can still obtain an overall summary of **feature importance** from bagged models:\n",
    "\n",
    "- **Bagged regression trees:** calculate the total amount that **MSE** is decreased due to splits over a given feature, averaged over all trees\n",
    "- **Bagged classification trees:** calculate the total amount that **Gini index** is decreased due to splits over a given feature, averaged over all trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 4: Random Forests\n",
    "\n",
    "Random Forests is a **slight variation of bagged trees** that has even better performance:\n",
    "\n",
    "- Exactly like bagging, we create an ensemble of decision trees using bootstrapped samples of the training set.\n",
    "- However, when building each tree, each time a split is considered, a **random sample of m features** is chosen as split candidates from the **full set of p features**. The split is only allowed to use **one of those m features**.\n",
    "    - A new random sample of features is chosen for **every single tree at every single split**.\n",
    "    - For **classification**, m is typically chosen to be the square root of p.\n",
    "    - For **regression**, m is typically chosen to be somewhere between p/3 and p.\n",
    "\n",
    "What's the point?\n",
    "\n",
    "- Suppose there is **one very strong feature** in the data set. When using bagged trees, most of the trees will use that feature as the top split, resulting in an ensemble of similar trees that are **highly correlated**.\n",
    "- Averaging highly correlated quantities does not significantly reduce variance (which is the entire goal of bagging).\n",
    "- By randomly leaving out candidate features from each split, **Random Forests \"decorrelates\" the trees**, such that the averaging process can reduce the variance of the resulting model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 5: Building and tuning decision trees and Random Forests\n",
    "\n",
    "- Major League Baseball player data from 1986-87: [data](https://github.com/justmarkham/DAT8/blob/master/data/hitters.csv), [data dictionary](https://cran.r-project.org/web/packages/ISLR/ISLR.pdf) (page 7)\n",
    "- Each observation represents a player\n",
    "- **Goal:** Predict player salary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read in the data\n",
    "url = '../data/hitters.csv'\n",
    "hitters = pd.read_csv(url)\n",
    "\n",
    "# remove rows with missing values\n",
    "hitters.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>League</th>\n",
       "      <th>Division</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "      <th>NewLeague</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>3449</td>\n",
       "      <td>835</td>\n",
       "      <td>69</td>\n",
       "      <td>321</td>\n",
       "      <td>414</td>\n",
       "      <td>375</td>\n",
       "      <td>N</td>\n",
       "      <td>W</td>\n",
       "      <td>632</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>475.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1624</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>224</td>\n",
       "      <td>266</td>\n",
       "      <td>263</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>880</td>\n",
       "      <td>82</td>\n",
       "      <td>14</td>\n",
       "      <td>480.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5628</td>\n",
       "      <td>1575</td>\n",
       "      <td>225</td>\n",
       "      <td>828</td>\n",
       "      <td>838</td>\n",
       "      <td>354</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>200</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>500.0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>396</td>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>N</td>\n",
       "      <td>E</td>\n",
       "      <td>805</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>91.5</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "      <td>74</td>\n",
       "      <td>51</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>4408</td>\n",
       "      <td>1133</td>\n",
       "      <td>19</td>\n",
       "      <td>501</td>\n",
       "      <td>336</td>\n",
       "      <td>194</td>\n",
       "      <td>A</td>\n",
       "      <td>W</td>\n",
       "      <td>282</td>\n",
       "      <td>421</td>\n",
       "      <td>25</td>\n",
       "      <td>750.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  \\\n",
       "1    315    81      7    24   38     39     14    3449    835      69    321   \n",
       "2    479   130     18    66   72     76      3    1624    457      63    224   \n",
       "3    496   141     20    65   78     37     11    5628   1575     225    828   \n",
       "4    321    87     10    39   42     30      2     396    101      12     48   \n",
       "5    594   169      4    74   51     35     11    4408   1133      19    501   \n",
       "\n",
       "   CRBI  CWalks League Division  PutOuts  Assists  Errors  Salary NewLeague  \n",
       "1   414     375      N        W      632       43      10   475.0         N  \n",
       "2   266     263      A        W      880       82      14   480.0         A  \n",
       "3   838     354      N        E      200       11       3   500.0         N  \n",
       "4    46      33      N        E      805       40       4    91.5         N  \n",
       "5   336     194      A        W      282      421      25   750.0         A  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hitters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>AtBat</th>\n",
       "      <th>Hits</th>\n",
       "      <th>HmRun</th>\n",
       "      <th>Runs</th>\n",
       "      <th>RBI</th>\n",
       "      <th>Walks</th>\n",
       "      <th>Years</th>\n",
       "      <th>CAtBat</th>\n",
       "      <th>CHits</th>\n",
       "      <th>CHmRun</th>\n",
       "      <th>CRuns</th>\n",
       "      <th>CRBI</th>\n",
       "      <th>CWalks</th>\n",
       "      <th>League</th>\n",
       "      <th>Division</th>\n",
       "      <th>PutOuts</th>\n",
       "      <th>Assists</th>\n",
       "      <th>Errors</th>\n",
       "      <th>Salary</th>\n",
       "      <th>NewLeague</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>315</td>\n",
       "      <td>81</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>14</td>\n",
       "      <td>3449</td>\n",
       "      <td>835</td>\n",
       "      <td>69</td>\n",
       "      <td>321</td>\n",
       "      <td>414</td>\n",
       "      <td>375</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>632</td>\n",
       "      <td>43</td>\n",
       "      <td>10</td>\n",
       "      <td>475.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>479</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>66</td>\n",
       "      <td>72</td>\n",
       "      <td>76</td>\n",
       "      <td>3</td>\n",
       "      <td>1624</td>\n",
       "      <td>457</td>\n",
       "      <td>63</td>\n",
       "      <td>224</td>\n",
       "      <td>266</td>\n",
       "      <td>263</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>880</td>\n",
       "      <td>82</td>\n",
       "      <td>14</td>\n",
       "      <td>480.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>141</td>\n",
       "      <td>20</td>\n",
       "      <td>65</td>\n",
       "      <td>78</td>\n",
       "      <td>37</td>\n",
       "      <td>11</td>\n",
       "      <td>5628</td>\n",
       "      <td>1575</td>\n",
       "      <td>225</td>\n",
       "      <td>828</td>\n",
       "      <td>838</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>321</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>39</td>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>396</td>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>805</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>91.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>594</td>\n",
       "      <td>169</td>\n",
       "      <td>4</td>\n",
       "      <td>74</td>\n",
       "      <td>51</td>\n",
       "      <td>35</td>\n",
       "      <td>11</td>\n",
       "      <td>4408</td>\n",
       "      <td>1133</td>\n",
       "      <td>19</td>\n",
       "      <td>501</td>\n",
       "      <td>336</td>\n",
       "      <td>194</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>282</td>\n",
       "      <td>421</td>\n",
       "      <td>25</td>\n",
       "      <td>750.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AtBat  Hits  HmRun  Runs  RBI  Walks  Years  CAtBat  CHits  CHmRun  CRuns  \\\n",
       "1    315    81      7    24   38     39     14    3449    835      69    321   \n",
       "2    479   130     18    66   72     76      3    1624    457      63    224   \n",
       "3    496   141     20    65   78     37     11    5628   1575     225    828   \n",
       "4    321    87     10    39   42     30      2     396    101      12     48   \n",
       "5    594   169      4    74   51     35     11    4408   1133      19    501   \n",
       "\n",
       "   CRBI  CWalks  League  Division  PutOuts  Assists  Errors  Salary  NewLeague  \n",
       "1   414     375       0         0      632       43      10   475.0          0  \n",
       "2   266     263       1         0      880       82      14   480.0          1  \n",
       "3   838     354       0         1      200       11       3   500.0          0  \n",
       "4    46      33       0         1      805       40       4    91.5          0  \n",
       "5   336     194       1         0      282      421      25   750.0          1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encode categorical variables as integers\n",
    "hitters['League'] = pd.factorize(hitters.League)[0]\n",
    "hitters['Division'] = pd.factorize(hitters.Division)[0]\n",
    "hitters['NewLeague'] = pd.factorize(hitters.NewLeague)[0]\n",
    "hitters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/envs/sfdat28/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "# allow plots to appear in the notebook\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115203550>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hhMhQTzNHSBo6s3oDU4CaQGMgG7BRKWWeTVIpNQLoD3wA1AAigQ1KKYcEx5kMtALeBOoD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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113da5b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot of Years versus Hits colored by Salary\n",
    "hitters.plot(kind='scatter', x='Years', y='Hits', c='Salary', xlim=(0, 25), ylim=(0, 250))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AtBat',\n",
       " 'Hits',\n",
       " 'HmRun',\n",
       " 'Runs',\n",
       " 'RBI',\n",
       " 'Walks',\n",
       " 'Years',\n",
       " 'League',\n",
       " 'Division',\n",
       " 'PutOuts',\n",
       " 'Assists',\n",
       " 'Errors',\n",
       " 'NewLeague']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define features: exclude career statistics (which start with \"C\") and the response (Salary)\n",
    "feature_cols = [h for h in hitters.columns if h[0] != 'C' and h != 'Salary']\n",
    "feature_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# define X and y\n",
    "X = hitters[feature_cols]\n",
    "y = hitters.Salary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting salary with a decision tree\n",
    "\n",
    "Find the best **max_depth** for a decision tree using cross-validation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# list of values to try for max_depth\n",
    "max_depth_range = range(1, 21)\n",
    "\n",
    "# list to store the average RMSE for each value of max_depth\n",
    "RMSE_scores = []\n",
    "\n",
    "# use 10-fold cross-validation with each value of max_depth\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "for depth in max_depth_range:\n",
    "    treereg = DecisionTreeRegressor(max_depth=depth, random_state=1)\n",
    "    MSE_scores = cross_val_score(treereg, X, y, cv=10, scoring='mean_squared_error')\n",
    "    RMSE_scores.append(np.mean(np.sqrt(-MSE_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1155e09d0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N3ZcArwL9o6Z9CIlV6jkzCcW2/ZG8U1UF7dqF9UVERKSwpd0zYmZ7AhOBxcCO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\nLmnyeboAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1151e5a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot max_depth (x-axis) versus RMSE (y-axis)\n",
    "plt.plot(max_depth_range, RMSE_scores)\n",
    "plt.xlabel('max_depth')\n",
    "plt.ylabel('RMSE (lower is better)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(340.03416870475201, 2)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the best RMSE and the corresponding max_depth\n",
    "sorted(zip(RMSE_scores, max_depth_range))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\n",
       "           max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, presort=False, random_state=1,\n",
       "           splitter='best')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# max_depth=2 was best, so fit a tree using that parameter\n",
    "treereg = DecisionTreeRegressor(max_depth=2, random_state=1)\n",
    "treereg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/envs/sfdat28/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AtBat</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HmRun</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Runs</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RBI</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Walks</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>League</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Division</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PutOuts</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Assists</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Errors</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NewLeague</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Years</td>\n",
       "      <td>0.488391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hits</td>\n",
       "      <td>0.511609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      feature  importance\n",
       "0       AtBat    0.000000\n",
       "2       HmRun    0.000000\n",
       "3        Runs    0.000000\n",
       "4         RBI    0.000000\n",
       "5       Walks    0.000000\n",
       "7      League    0.000000\n",
       "8    Division    0.000000\n",
       "9     PutOuts    0.000000\n",
       "10    Assists    0.000000\n",
       "11     Errors    0.000000\n",
       "12  NewLeague    0.000000\n",
       "6       Years    0.488391\n",
       "1        Hits    0.511609"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute feature importances\n",
    "pd.DataFrame({'feature':feature_cols, 'importance':treereg.feature_importances_}).sort('importance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting salary with a Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "           verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rfreg = RandomForestRegressor()\n",
    "rfreg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning n_estimators\n",
    "\n",
    "One important tuning parameter is **n_estimators**, which is the number of trees that should be grown. It should be a large enough value that the error seems to have \"stabilized\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# list of values to try for n_estimators\n",
    "estimator_range = range(10, 310, 10)\n",
    "\n",
    "# list to store the average RMSE for each value of n_estimators\n",
    "RMSE_scores = []\n",
    "\n",
    "# use 5-fold cross-validation with each value of n_estimators (WARNING: SLOW!)\n",
    "for estimator in estimator_range:\n",
    "    rfreg = RandomForestRegressor(n_estimators=estimator, random_state=1)\n",
    "    MSE_scores = cross_val_score(rfreg, X, y, cv=5, scoring='mean_squared_error')\n",
    "    RMSE_scores.append(np.mean(np.sqrt(-MSE_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115685c10>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SAABJVZL6lzzCTsjdOmZmZsUruIappJHAHcBWQG/gLlI3z9Ts8QmlDLAzqqqCJUvg7beh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\neZtlzqglAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115669250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot n_estimators (x-axis) versus RMSE (y-axis)\n",
    "plt.plot(estimator_range, RMSE_scores)\n",
    "plt.xlabel('n_estimators')\n",
    "plt.ylabel('RMSE (lower is better)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning max_features\n",
    "\n",
    "The other important tuning parameter is **max_features**, which is the number of features that should be considered at each split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# list of values to try for max_features\n",
    "feature_range = range(1, len(feature_cols)+1)\n",
    "\n",
    "# list to store the average RMSE for each value of max_features\n",
    "RMSE_scores = []\n",
    "\n",
    "# use 10-fold cross-validation with each value of max_features (WARNING: SLOW!)\n",
    "for feature in feature_range:\n",
    "    rfreg = RandomForestRegressor(n_estimators=150, max_features=feature, random_state=1)\n",
    "    MSE_scores = cross_val_score(rfreg, X, y, cv=10, scoring='mean_squared_error')\n",
    "    RMSE_scores.append(np.mean(np.sqrt(-MSE_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10f1fac50>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3EJ5X6ee9WXcocF3sIJowFS835zb6gbwkh6JMhus9ITAv9gJewvsb8ji8uBPY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\nERERERERERGRovr/EMpa06k07ngAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ef10890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot max_features (x-axis) versus RMSE (y-axis)\n",
    "plt.plot(feature_range, RMSE_scores)\n",
    "plt.xlabel('max_features')\n",
    "plt.ylabel('RMSE (lower is better)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# show the best RMSE and the corresponding max_features\n",
    "sorted(zip(RMSE_scores, feature_range))[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting a Random Forest with the best parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# max_features=8 is best and n_estimators=150 is sufficiently large\n",
    "rfreg = RandomForestRegressor(n_estimators=150, max_features=8, oob_score=True, random_state=1)\n",
    "rfreg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# compute feature importances\n",
    "pd.DataFrame({'feature':feature_cols, 'importance':rfreg.feature_importances_}).sort('importance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# compute the out-of-bag R-squared score\n",
    "rfreg.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reducing X to its most important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(263, 13)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the shape of X\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(263, 4)\n",
      "(263, 5)\n",
      "(263, 7)\n"
     ]
    }
   ],
   "source": [
    "# set a threshold for which features to include\n",
    "print rfreg.transform(X, threshold=0.1).shape\n",
    "print rfreg.transform(X, threshold='mean').shape\n",
    "print rfreg.transform(X, threshold='median').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# create a new feature matrix that only includes important features\n",
    "X_important = rfreg.transform(X, threshold='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284.82790842153145"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the RMSE for a Random Forest that only includes important features\n",
    "rfreg = RandomForestRegressor(n_estimators=150, max_features=3, random_state=1)\n",
    "scores = cross_val_score(rfreg, X_important, y, cv=10, scoring='mean_squared_error')\n",
    "np.mean(np.sqrt(-scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "297.64680677115257"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the RMSE for a Random Forest that includes ALL features ( for comparison )\n",
    "rfreg = RandomForestRegressor(n_estimators=150, max_features=3, random_state=1)\n",
    "scores = cross_val_score(rfreg, X, y, cv=10, scoring='mean_squared_error')\n",
    "np.mean(np.sqrt(-scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Random Forests with decision trees\n",
    "\n",
    "**Advantages of Random Forests:**\n",
    "\n",
    "- Performance is competitive with the best supervised learning methods\n",
    "- Provides a more reliable estimate of feature importance\n",
    "- Allows you to estimate out-of-sample error without using train/test split or cross-validation\n",
    "\n",
    "**Disadvantages of Random Forests:**\n",
    "\n",
    "- Less interpretable\n",
    "- Slower to train\n",
    "- Slower to predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Machine learning flowchart](images/driver_ensembling.png)\n",
    "\n",
    "*Machine learning flowchart created by the [second place finisher](http://blog.kaggle.com/2015/04/20/axa-winners-interview-learning-telematic-fingerprints-from-gps-data/) of Kaggle's [Driver Telematics competition](https://www.kaggle.com/c/axa-driver-telematics-analysis)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Plotting the Decision Boundary of four types of trees:\n",
    "#     1. decision trees\n",
    "#     2. random forest\n",
    "#     4. ExtraTreesClassifier(http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html#sklearn.ensemble.ExtraTreesClassifier)\n",
    "#     5. Adaboost (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('DecisionTree with features', [0, 1], 'has a score of', 0.92666666666666664)\n",
      "('RandomForest with 30 estimators with features', [0, 1], 'has a score of', 0.92666666666666664)\n",
      "('ExtraTrees with 30 estimators with features', [0, 1], 'has a score of', 0.92666666666666664)\n",
      "('AdaBoost with 30 estimators with features', [0, 1], 'has a score of', 0.83999999999999997)\n",
      "('DecisionTree with features', [0, 2], 'has a score of', 0.99333333333333329)\n",
      "('RandomForest with 30 estimators with features', [0, 2], 'has a score of', 0.99333333333333329)\n",
      "('ExtraTrees with 30 estimators with features', [0, 2], 'has a score of', 0.99333333333333329)\n",
      "('AdaBoost with 30 estimators with features', [0, 2], 'has a score of', 0.99333333333333329)\n",
      "('DecisionTree with features', [2, 3], 'has a score of', 0.99333333333333329)\n",
      "('RandomForest with 30 estimators with features', [2, 3], 'has a score of', 0.99333333333333329)\n",
      "('ExtraTrees with 30 estimators with features', [2, 3], 'has a score of', 0.99333333333333329)\n",
      "('AdaBoost with 30 estimators with features', [2, 3], 'has a score of', 0.99333333333333329)\n"
     ]
    },
    {
     "data": {
      "image/png": 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FZKzpgXFxHyC+zQm6LXsj8LqIfMYY8/pBKENXyEHbsaEbr5oDgcNNEAB40yQaPv1SRM4A\n/oMab002xuzXOfepsC8TkzmAZ+UbY9qJuaoBICJfQA2Q/tGTNEQkx6ZzqBDPy8dEpB415LoY3Q88\n0rDAGHNAbn4TkWzzyTSmSu7v+4JeTcg9rSujFuzJffA7qDfJJ6UPHpYQkTGoF8Fn0VMJr6FrY9W+\nRkKbE5HHUPuSL6DHYR8qHDLB8rDZGugOxpgP0EZSQpyELnp97L9FpN5uJSwVkYuS44tIgYjcL3q7\nmFdEdorI30XdZ+LV8NfHxRliVdk7bZxKEXkpXg0vIh+IyPtJeQ0WkUdFpNqWaVV8ukn5fU1EbhOR\nLTaPj0Vk9r7UkYjcYdM8SfQAj1rUwC1CHykiT1pVmFf0Stbk1Q4ikiUiP5PYTYjlIvJTEem1j3kK\nfIg29uihISJysYi8JroN5LV18T0RSWibtq7XiMhk0Rvr2kS3G76Z4huGW1557Pf+DjUo69TRROQK\nEVkmIu0iUisiT0nS9oWIPCF66MlIW9ZWm/ddlj5NRN6z+ZVbgW2fYNWl62xd7BaRP0qSG2lcXcwU\nkfki0oZa80foF9r3HhFpsWWekpRGt+1bRLajHitnSEyNmtDWDzREDygKiciZSe8fFhGfrffT0UNz\nDPCExLYWrrdhu6wrEbmkJ22vF+X9hc1/vIg8K+rF8E4c/WgReVF0vGoXkSWSQiUtIgMt33fa79ws\nIl9LEe56EVlh22OTiKwWkTv3peyHANegBpz/QRcF1yQHEB23n7Df1igij5Po9RAJN8225a2iY26V\n6Bg8sCcFMep10YGebRCfbo6I/FZEdtj2sUlEvp4if6eI3CexcXy76BjqTgo3W0TesuNMu4hsE5FH\nLa0ENf42wA/j+tz3e/IN+4vDUSPQFZ4C/hd1q3tURKaiFs+7UJeXNtSd5iURucwY8zKAiOTacJNQ\nd6KVqL/rxehxp135YL+AHnDxAHpxRhG6FTAKteqHpD1nUbe9ecBY9Bz9cuAKdIAqMMY8mJTHNejh\nGn+xaX0LeF5UTdbbfcdIWR5BLdK/j6rkEZHh6GDZjqqnG1CL9idFVywP23AO1BVspi1TGepb/i37\nTVf3skzJiJy0Fn+96I2oqvW3qJX4Wag7Yr7NN/77BtryvYBehXw58AsRWWOMect+QxZqgT0Ctcav\nQl2XzqIzv25EXQ+XAN9G1YT/A8wRkRkmdhWzQYXmN1D+fhPl3YNxE8vTwPPoxUp/F5FFprMfeb6I\nJPtpNxhrsSsiP0T59jZ6AMskVD0/W0ROjmsTBm3Dr9t6eBJd0SDqlvgE6sb2/1B1453Ah/abIm13\nb+37q+iZ//Fq8PjbJ/sKBSnqxBg9G+GnwEVof59mjGkTkfNR6/l7jTFrRaQIrbMfA39FhU2InTTY\nZV2hHgE9aXs9RaR9vYS6O34r8k5EjkXVvdvQcawDXYG+JqqSftOGy0PHq4FoH9yNnrj3GxEpNMZ8\n14a7COXzG/a7HajgdhJ6MuPhjquB540xQRH5B3CHiMwyxsSfrvgKqjV4CPVM+Cy6TZVs63MuOrY8\nhnquTEVdr6eQ+lCnSJuL3ENxN+qR81RSuFdRz5m/oS6M56NuqsOMMfECwaPA9ehV7b9BvU++g3rl\nfA50gYieGVODzldNqEvyZTaNWnTs+AvaNyOaw77c6uoah9ptIfIQO9N7ZjdhGrHncaM3r63EXgIT\nF2YBsCnu949I4bqWFKcENfq73v6OnLn+tb2UeS6Jbl9ftXldFffOibrONBO7kCOSXw2Jrm4X2fif\n6iK/V4lzQ0ui3W7TfDsF7Wl0Ly4/6f0LtgyRi3RuRQ/ymJUU7m5brmN7ycsz0QNKhqMdYg8qsA2L\nC5vqApiH0AE6I+7dXJvm1XHvMlCh59kUPLgs7l0W6uIW7xrnQgeNVcTdQ4C6yoWBH8S9e5yky6Fs\nG2lDVxHxZ/FPtPHj7y44nc63uEVv27NhClE3qteT6uIuG+6GFHVxa1LYXFTIeyjp/WC07/yll+17\nLXHtuy8f20bCXTztceGm2nr5qy33LvTIY0dcmFnE9d8UfbRTXfWm7fW0TtABPkyKexLQcWlJUrkF\nPQdkVdy7n1pejUyK/ztbD4Pjyll9IHhzoJ84fp0Z924Hce6TqPthQhu19TWPpBsku+DjlTbcyT1o\nc+0k3csSl/+3k94/i/b5Mfb3MTbcX5LC/crmf3pceiGSbmhMijOIpLHjYD2fiK2BOHjQVdUAdJJ5\nDivdRR50NTVBRCInpl2GHlLxSi/y6UAnxDMkxald3eBCtHP+M/LC6CruAXTlf3pS+H+a2KoTYqrz\nsewbDDpgRiF6mtgl6EUu7qS6egttfJHz4S9HJd/ypHDv23L1xk1SgPdQSXcnyisPKpBVRgscZ+8h\nInk2vwXoSjb56GWPMeaZuLgBVNMRX18XoofmvBAXzovuQ8ZjNroa+LOJsw8xaiy0CT26ORmPxoVr\nxl4DbYz5d9z7zai0n4qHPwLOiXvORYUR7O8MVGMTj0fQiSm5PD50RRiPc9HJ8p9J/DPoJBTh3762\n776GQbUV5yQ9F0YDGLMeNaC6DW2vA1GhqDfeOqnqqrdtr6cw6KouChEpRle2/wIGxPElMl5Ns2Ma\naB98H2hP4uG76PG9p9hwTejYd9Y+lvNQ4hq03X8Q9+5fwFUiEtm+uxA13o7WpdHZ8kGStviS+Jhp\n62uJDTczKe/kNncNKiw+KiKXxoW7EJ3wk7W4v0W1L5E2+mmb5v0pwgmxfttkf18sevLlYYXDrkB7\nQR66qhyPVupPUAk6GQYd5KuIHWjSYxhj/KKnt/0G2CN6AttrwJPGmO7UoyXE7cvHYaMtb0nS+51J\n+TbZfjCAfUd50u9h6ErxK+jKPhmRugI93GU0qb0Q4sP1BAZdzZahk9PNqIozwShTdO/6Z+gk1S8p\nfvIRy7tS5NNITJABreNUFuelSb9LbB6bU4TdRNyRtRZe0/mkueYuytRMah6uM8Z0tc8eaRsJ5THG\nBERkG53bzm7T2Vh1AtrO5qZI36AHu+xP+z4QWGr2biz4a/TAnuOA7xo9Crc3SFVXvW17vcH2pN8T\n7N9fo3WejEjfakTHtgmoGryrcKAT1GeBd0RkFypQ/MsY8+5+lPuAw24/Xom20bGxeZ+P0RtUz0aF\nnhJUoE82tOzEeytE/dCmGz9GdcXHhDYnIv9Etct/FJHXbFspQW+YTL7/IXK0dqQ/jkJX8QljjjFm\nj+hlTCX29zwR+Te6jXWP6Bk0LwHPmMPAg+oTIwjYfe4CtMIjmozfoKuEVOiN+1EnGGP+ICKvoEf3\nno/uHX5HRM40xqzen7Tj0JUdwP5YjyZbQ0fq6jGgK0vnVXFhl6N7m6nK0Nuz06MdTkReRldbz4jI\nJKNHwxag+6ZN6EUq21D15yzUTStZY3Ug6qun6CrvQ1WmVFbvDnTwu5bU+/nRyfAgte++wjhik+m+\n3JTZqa72oe3tT36RtP6X1EIawI641fB/6KwZimATgDGmUvSmxwuBC+zfW0Tkr8aYw9lg8Cz0four\nUBuJeBh0hd5bYeY51D35V6hG04PW+Vv0gI/GGCMic9GF0gTi7tHoBZLtFlLl83nR8zEuQvvcY6hb\n/IkpBJ6Dik+MIIAaYxjUCCribxroZoUVwVb0LPdewxizHVX53C8i49BG9nVbllSoIPVANTmOfrBR\niQ5M0sO6KjHGdDVY7TOMMWFR16u5wJfRTnsGunK+xBizMBLW1vW+ooLU9zMkq3or0Ml6EokqSuy7\ng82rSH6TiNPqiHprjCHO+rwbbEW/qbYHvO5J+97r4HagYSfHJ1Aty/3AvSLyb2PMS3HB9qWcZ9D3\nba8rbLV/fXvji+i9Dzk95F8ANah7xdbTo+i9AT+J3347zBARUu+is7D8OeCzInIH2h/Oks7ulwn9\n2G5tnQXcZ4yJ95wZ38tyRebCyMF1Feg9IblJWoHIWF4eF86BChBRbYU1Yu1P0jhijPkY1X7cJ+pd\n9H+oUPQYh7C/fSJsBOw+WERqf8YYU4sO3rfb/bfk8PG3YD0PTBd7BW8P88sWkcyk19vRvdrk9/F4\nHSgWvQwokpYTVcu3ooYuBxV2sHgZvd99YjI9qa6eRdV116UIlyMpbkjsZVnmoZ3gf0Rda0LoYBBt\nh/b9XfuRzevAMBH5XFyaOXS+XXEZaih5h8S5RorIhWhnf20/yrAveBfdE03evrkVVVv3pDxvoer/\n76bah4zwuhftu40U7loHGV9HV3u3oWrVRcBDkugaFhmoe1PWA9H2UsLolbwfobeMJt9QmaoPniEp\nTpcTkQERrUHS90f2z9fZn92NUYcMoh49nwVeNca8aIx5If5BvVT6oR5dr6M2M3fGxXegY2n8hBnR\nyCXPZffQw4nV9pXz0W3LiDbgdVQ4+HKKdMPogjQSTlBvo3h83eb/ms0jVduMaN4i/IoIPAe9zx1u\nGgEBPiUik9GyDUGlvXPRgeriuP2UL6HGdWtF5BFUSBiCuosMR93eQPflLgeeE/VDXY4a6VwE3G5S\nn1Q4EXhP9K7vDahK9TJ0/6m7g0QeRq33nxA9D6AcdR88Cfhqiv2mg4VvoEZGy2xdbUSt1GcTqy/Q\nFcUVwOMich56aUgG6oZzhU2jp1e7dqUa/zWqyrsRtd1oRN0YH7D0a9k/yfgRtPM+ZXkQcR9MqHuj\nbkvfQiXx+aIuTMXoRLyNrlWzBwTGmDoR+Tl609+b6ErvKHQg/BhdOewtjVZRP/IngRV277MW3cf8\nNLo1czc9b9/LUUHpXnSrraaPtUXx/T0Zi1Bvjx8Dj1sjzojL5yrUaj4icG9FVfx3iIgH5fVHpvtr\ngBfR922vO9yBLgTWicjf0PFsKGqLMoDYVc//i/LqHdGDblah7ozHEONRO/C0FeY+QF0Mx6LtfonV\n9ByOuAT9lq4Mtz9C2+s1xphLRWQh6h48Bm2nl9n4Udg2Px/4f1aQ2426mI8m9RiU3OaK0O2IccDP\njTEe+/5VVHv5M5t/xH3wIuD+SB0bY9aIyN9RTcwAlMcnoFq1F0zsXpMbRM8deRFtr/mocNuMPcTI\nGOMVkQ3AlSJShnoArbMGswcWB8M1oScPMZezyNOBMvVNdNLPTRFnNOratRvd39uBrn4vTQrXH/Up\n32HTrUAnvciNeyXEuaSglskPoDeltaAMWUScS5oNNxd4L+ldIep3usfmtYrOrimR/O5J8U0hVM2V\nqo5eBbZ2Qbvdxp3SBX0IeptXBbHbvt5MUTYX6lO/zpa/Fu2g30ZVlr3hZSdXULQjlqFGcZG77hei\n+3o70YHwHDrfeDgX9f5ITu/x5DpBzxB4EV3h7kEteM9NTtOGvRzVDrQTO053aIo8mlPk3VWZtgEv\nx/0+nSSXxm7q7k7b7rzots6DxLmYdpdvHP00dHBpQCfFzba9z+hl+y5CB+0mW/4+cyWkc39Pfm5C\nLb/L6ez2+hUbJt5t8zOoa5+PxL7cZV31tO0lxVlLUp+Po/0cFapS9hN0snkSFU69aF98EfhMUrg8\n1E6hDO2D1egE8xWIXhT3eVQDVGXDbLM8HdRXPOrrBx2bPUBWN2Ees3UzAB23n0AFtgbbD4+J56+N\nM5TYDZcNqDA7hKSxtIs214YKvLelKEsOaocWue1zE6nHbAeqsd5C7FbQn5Do/nwsMTfudsu3l0hy\nJ0SFiI8tTw/arYvp2wfTSCONNNJI4wjGJ8JGII000kgjjTTSODBICwJppJFGGmmkcQQjLQikkUYa\naaSRxhGMtCCQRhpppJFGGkcwDiv3QXtG9Pmo1aX30JYmDYss1DvjLdP5iN1eI83jwxJ9ymNI8/kw\nRbov//djn3h8WAkCaKPaq790GocE1wDP7DXU3pHm8eGLvuIxpPl8OCPdl//70SseH26CQDnA008/\nzeTJsTNG7rnnHu6/P/lyp73jkxLvUOTZ03gbN27k2muvhc6XGe0ryqEzj3tTpmSk4+1fvAPA42ha\nR1pfPpzjHay+fDjXwScxXm/i7iuPD5ogICLfRg/s+L0x5mtdBPMCTJ48mZkzY7dHFhQUJPzuKT4p\n8Q5FnvsQr0eqvx7wOSWP97FM6Xh9G6+veBxN60jry5+QeHvlc5rHh0+8fYzbq62ag2IsKCLHAV8k\ndrZyGv8OpLxtAAAgAElEQVSFSPP5vx9pHv/3I83jIw8HXCMgInno0Yq3AvftSxre2gY++MztmFCI\nCXdcxYhLzonS9sz9iE2//zshr5/xt13BqMsviNJ8DU3M/+yXCHraGXvTZYy++qIorW7Jajb++lH8\njc2Mue4Sxt54WZQWaG5lwee/iq+ukZIvfIbxt30+Smtcs4kNv3iEjqoaRl1xARPuvJrI7aGB1jYW\nXfsN2nftYcSlZzPp7usRh8paO3Y08bfHl1NZ1cppp5RwzRem43QqbXdlC6Wb67jl9hc5+aRRXHft\nsWS4nABUVbfyt8eWs728kROPH8GN18/E7VZabW0bZVvqufmLL3LcrOHcdMNMsrKUpQ0N7Tzy2HJK\nN9cxY3oxt9w0m5wcvVunqdnL1m0N3HzbC0ybVsytN88iP2//7inZXz57tu+kcdVG3jntGoZdcCqT\nv3kLjgwtb9vOKtb/9CGaN2yh+Jw5TPn2F3FmugHoqKqhaU0p75x6NUVnHM/U79yOK0fvRvLWNrDu\nJ3+mceUGBp8yi6nfvZ2MfL1czNfQRNP6Mt455QsUnnQsU++9E3d/vZLe39zK+p89RN2ilQw8bhpH\nf+9OMgcNACDgaaN541beOfVqBhw7maPvu4usokGanzfI439fwdLluxk/biC33TybwYNzNU1/iIod\nTdxy+4uMLhnArTfPYmixHpseCIZ46ulVLFy8g1EjC7jlplmMGK7XqIdCYXbuaubWO15i2NB8brlp\nFiWj9E6ScNjwz2fXMHfedoqKcrnlhlmMHat30RhjqKxs5Yt3vsSgQTncdMNMJk7odN9Nr7C/PA75\n/LRs3s47p11Dv6PGcvT37iR31DD9lkCAjb99nMrX55E/fhRHf+8u8saOVFoohGfrDt49/VpyRw9n\n6r130G/iGP3OcJjSB59i14vvkjNiCFO/ewcFU8ZH66CtfDfvnnkd2cWDmfLtLzJgeuzyui1/e46K\nZ14ls3AAU751GwNnxS4pbd9ZzXvn3Ii7IJ/J37yFwhOPjdLK//Ea2x5/AVduNkd97SaKTp0dpdXV\ntXHX3a/idju5+qpjOH72iCht3vztPPfCehwO4arPT2POiaOitIbGDu7+2n8wYcMVlx/NaaeMjtKW\nLN3FP/61hkAgxGWXTuHsM2MXJDY1e/naN9/A6w1wycWTOf/cCewP+mK87qiqZe4Ft+BwZzDpqzdQ\nfPZJUdquV9+n7KF/IA4HE798DcMuiN2t5K2pj43zd13NiIvOitKq31tM6R/+TsgXYPwXP8+oz50f\npfnqm5h/6V0E272MvflzjL7q01Fa7eKVbPrNY/ibWxlz/aWMvf7SuJIGMWxA7/oqQhgapWzeXMfj\nT66gvr6dc88Zz+WXTY2O823tAe79wTvU1LRx1hljufKKaTgcSiuvaOTRx5dTVe3h9FNHc/VVx0TH\n+Y6OAD/8yfvs3NXMKXNKuPaa6dFxvrKqlUcfX0Z5RRMnHj+SG6+fQUaG0mpqPLFxfvZwbro+Ns73\nFQ7G1sCf0Num3heRXjes2kUrqP9oNZXUAlD15oec8frDDLvwdBqWr+P9827BBPWa9eq3F3DqC39k\n5GfPpWl9GXWLVrHL6LXs1e8uwhjDmGsupnVLBe+dcR0hrw+APe9/RDgQZPxtn6dtZxW1C1ewI6S3\neO6Zu4RQh5dJd19PR3Ut7552LYHmVgBqPviYQEsbU7/9RXwNTdQtXEF5YJfS5n2Mv7GFY350N+3t\nAW65/UXqG/Sa8hUrK6lv6OCeu+fg8wW59fYXqd7jYeWqKlauqqKmto1vf/M0AsEQt3/pZXbtagFg\n1eoqKqta+eF9ZxEOG+748itUVrWyanUVq1ZXUbGjiV/87DwAvvw//2FTaW003rbtjdz/m08BcM83\nXmfX7hYy11Szak01m8vqeOjBi3vLmmTsM59DPj/vnn4d7TurqN25jNoPl9G+q5rj/vxDwsEg7511\nA54ten9M7YLltJXv5sTHf44Jh3n/3Jtpq9hNbYXSWjeXc8q/9L6guRfcSuOK9dF4zevLOP2VvwAw\n/5K7aNu2k9ptYWoXrqBh5UbOfvcJABZc8VWq39GbaWsXrqB+yRrOW/RPABZf+008Wyqo3RKkdsFy\naheu4MIVLwLwo5++z1vvbAG0zpevqOS5f1yFwyH8/FfzKK9owpmpPF66bBfPP/sFMlxOfvf7Rfzr\nOb37auWqKj5aspOX/n0NmZkuHvzzR2zb3ohkVLJiJSz+aAcvP38tOTkZPPzoUh7+27JoPS5atIOX\n/n0NBQVZ/P2plZRtrSfs1Ha8cNEOXnjuagoH5fSOq4nYr7687Es/orV0O7Wlfmo/XEb1u4u4qPRN\nHBkZrPj6L9n84FMA1H64jKq3F3JR2du4srNYc+/9NG/YQg0dMH8pVW9+yEVb3iYjP491P/kza3/4\nYDSPyjc+5KKyt8gc2J+Nv3mUprWl1KD3yFS+Po/PbHqD7KFFlP7xaZZ/5SexeK/P59PrXyO3ZDhb\nH/s3jas2sIfmaLwLV79Mv4ljKP/Hayy6+uuxeP+ZxwXLn6f/tEnMnbeN9Rtr8YZ2AsqPJx69jCmT\ni1j00Q7u+eYb0XiLFu/gkYcuZcaxQ1m+spJ16/fQ7tc2vuijHfz5gYs44fiRrFu/hy9/9VVCIT0K\nfvFHO7n/1xdy+mlj2Ly5jrVrq/F49Y6hjz7WsWc/hYH94vGul9+lYdlaqmgAtF7PXfRPCo8/hqp3\nFjL/krvAHmtf9cZ8zv7gKYpOnU3N/KXUL1lDJXVKe2sBZ775N4aedwr1S9cw9/xbMCG9aLD67QWc\n9tKfGHHJOTStLaVucdw4b/vt6Ks+TUvpNt4/64bYOP/eYkwoxLibPofBi973U2tL3oQhjDCc2ro2\nbr3jJTxter/dshWVtLcHuOG6GTQ1e1m9uorG1jIAlq+opLXVxxdvPQ6Px88tt79EY2NsnG9s6uDu\nL52E1xtk9Zpq6ls2AdrP6+ra+ObXTyUQCHH7XS+zu7IlSqve08p93z2TUCjcaZzftbuZn/3o3N6y\nplsc0LsGROQq4DvAbGNMQETmAiu72nMSkZnA8uXLl0f3Q5Z88T6eeuRvzKFfNFxONgweLDQ0GFo9\niWlkZ0FRkdDYZHizpSUhXmYmFA8RmpoNzc2J8dxuGFosNLcYXm9KjJeRAcOGCq2thobGxHguFwwf\nJng8hlcbEuM5nfCnq35EoNWHrzrp4kGHkDduAEGPH2+Vh4b6ZQwcZFcWArnjBvDrm7zcdW9Hp3rK\nHTeAsC9Ix67WxHgRmj9Ex86WzvHG9MeEDe0VzZ3i5YwuwGEl0PlPXxV9v2LFCmbNmgUwyxizolOi\n9I7PqXi8+7W5zLvoDhYRqz9xOphy/Wm0VTVS/maihlIcwuTrTsNb38q211YkxENg8jWn4m/tYOvL\ny0jGUV84mZA/SNnzSxLjARM/ryuXzc8u7hRv4mUzaB48hD1/fbNTvOAt19DWfxB/WyAkd6eLTs5m\nRF6Yv77to64usc7POnUAk4Y6eeT5OoKhxHinzspnxJBMnn+nnurqpQnx5hybT8mwTF5+v4F2bzgh\n3vHT8hg3MovXPmigYufHCfFmTcll4mjVlnzzBzEtV1/z2IZP4HM4EODZvBks8Ncn1F3JeceQP2IQ\n65+chwkmfsuos4+mX8lgNj6zgA+9ifFGnD6F/uOGUPqvRQTafAnxhp0yiYETh7H5+SV80FydEG/o\niRMYNGUEW15airchcfAYMmsMg6ePZuurS3mvtjIhXvZp0/HPnoF57i3CO6sS4pkTZhA86Vhe2ZjD\n8i2rEup8+jgXxx3lZO7KEFsrgwnxxo7I5KSZuSxc0cGK1R8mxBs5LIsTZ/ZnxdoWtla0J8QbNiST\nk48bwOoNLXy0dH5CvCGD3Zx2QuyG4u/cF9N07o3PfTFez7vkTp575aWEuhs4eTjDTprIzrnrad5e\nk5BG/wnFjDh1Mrs+3MTrZaUJ8QrGFDHyzKlULy2lbm1lQrz8MYWMv+IUdn+wjpc+XpEQL39cEZPu\nOJ/db62m6t01CfFyxw1h9Fc/ReGxI5i3azNf+MLZUVpzrZcPHi9j6XY3b6xI5NWgfoa7LurHyi1+\nnnx9YUKd98sRvnRJHuvLA7yyOHFrPjtTaWW7Ajz+6oKEeBku+Opl+WyvDvL8/MRx3umAuy/Lo7I+\nxL/mdiSM1yLwlc/m4XYJhcMGcN4XPhON15O+nAoHTCMgIiPQq1zPMcYE9jUdV05WApMBxJH4NyVN\n6BTPam+QFJdTRmiOXsaTOFqnckZpnSNKfIaQ0EAQQUTIykxVUPTOPkkVLyntTpnGwiTEiy9sL9EX\nfHbmZAGJ9edwORARHFZ1lpCn04EIiNPZKZ44HIiji3gO0ceq6hL4JeBwOqL/J19G63A5wKHPnHAi\nn3FnIAJOgWBSPJdLEFQoTK5zl1Oif4Mh0yWtczz963R25lkkntOVKt6h4zEOB86sTOb4k/qW/RiH\ny0koSRCI0pyOzn3SpbySFHyOpZkqXoyWDInm5+zcl+02lWR0zg9Ly0jB4ww7wqYoZoxXKXkcofUu\nnrOrvr8X9N14nd1nvJJueBVZtDhcnXnldGulO9wp8rO0cDCcIAQAhGznzUgxK0bYnqo/Zrhk7/FS\n9McMy8dUTcrl1KkhEiY+rtMRm5P6Cgdya2AWMBi9Fz1SbCdwmoh8Gcg0Xagj7rnnHgoKdI806Gmn\nxlnNiaEc5tAPEci3N1Ln5YHHA+G48aNfhJYLra1JtH6JtFCoMy0nF5pbuqDlKC0YJywWxNFczYm0\nSDxnbgYOt5OwP5ZoxgCd/JzZLhyZTsK+GM09UGnTpzhxZLkIe2OJuvtnIyI4s1w4c1yE2mO0jP5Z\nOtG5nThzMwi1xfpzRkGmTqBOcOW5CXr8UZqrXyYOl4Oa8oXUli/i4otj7qfNyaqTztgnPsfzGGOo\n6d/K7CYT7dSF03T/NKeogJziAtqrY+UYNHUk4nCQPSiPvOED8exuiNGmDMfhcpJZkEO/kkJaKuqi\ntAGThuHMzMCZmUHBuCE0b90TpfUfPxRXttodDJxYTENpdRxtMO68LByZGWTPHEfHsrIozYwvgUED\ncQWCTB8Dy7fGvnH4QCgscOIIhJk+zs2y0lidDx7gYnChGwgyY3IOi1fHNEYDC1wMGaSTy+Sx2Szf\nEKP1z3cydLA7Svt4bWxV2y/XyYghEVoOi1e1Rml5OQ5GDnWzcu1CVq1dyIfLn47SDhSPIZHPLUP9\ntLbs5iTymUM/5e0QpQ2eNorqpbHKyy7MJ3eY2mUUHlNC1eLNUVrmgFzyRw6Kxtu9YFOU5i7Iod8o\ntYUonDaKXR9siNIy8rPoN2ZwlLbj/XVRgS8j103B2CKlTRlK256WqArbkZdN5jHj8AEZM6YQKq+M\nDSzZmTB9CgDHDguwtd5JyGgVuV0wbYwLMEwb62bL7iAhG83lEiaUaD8fPzqHil0dUWHQ6RTGj9Et\nnHElOWzf0UHATlIOB0ywtLGjctha3o4/oDQRmDhWbVJWr/mQ1WsXsHjpE9Hv3wuf+4TH/sYWah1V\nnBTOZQ79cLhdDJg0HIBBU0bQvH1PVPPjcDkZOFlpAycPp6msmrBVjYnTwaApNt7Rw6jfUE3IF4zS\nBh6rtheDjimhduU2Qh2BKG3Iybo1Ujh7HDUfbiDQajVGDmHIOWoH0rixnsLZxWTlq22UCRu2r1Z1\n79RRTj5cH6KxzdYrMGey/n/USBeD+jmob4lNLCdO1j43bqiLwgIHdc0x2vFHKa2kyMmQAQ72NHam\nDS90MrzQye662Bwwe5IbEaF4oJNRRU521MRosya6cTmFRSsXs/yZ5fzxHw9HaT3oyylxwLYGRCQX\nKEl6/QSwEfiFMWZjijidVE0ANQuWs+Z799P80VL69YOc7Jg45PUampq1zxb0g5ycGM3nNzQ1aZ9N\npvn9hkZLy8+HvNwuaHmQlxejBQJKC4VUoMjPj9GCQd06CIUgNxf65QvfOfWHgEqgvpo2TDCMK9+N\ne0B2NF44GMZf20Y4EMaZ5yZzoNLm/SHAqV9y4q9tJ+wP4czNILMwtscbDoWV5ktBC1uaN4Qzx4W7\nMCeqmUigZbvIKMzG4YhJ3b3ZGugtn7visbeugWVf+Sk178yj3+giiqbHkgz6AlR9VIavsY38UYMY\nMnNslBYKBKlaXIa3wUPeiIEUzx6XQKtesoWOulZyh/an+PjxsToIhahespW2mmZyiwsoPm58TCNA\ngKplFbRVNpJbPIARJ5VgQobafoUEstw0vLcaU1GPGTqI0JknY/IG4A8Eych1MH+tsKMhTGGecOJE\ncBfkkRkIEMzIYNGaJnbUuyjIz2DWlDyy+7vp5wgiIny4ysPOKj/9cp3MmpJHVmaMH5u2d1C+20tu\ntpPZU3PJzootITaXd7Btl5ecLCezpuaQmx2T7bfs6GDLDi/ZmQ5mTsklPzdG683WQF/25bU//hNl\nDzxGZkEuQ0+agCszI0qrWVNBy/Ya3Pk5DDtpQlQwA6hbt4OmrXvIyMtm2IkTyMiNGbfWb9hFY1kV\nGblZDD1xPO68WN9qKK2kobQSV7abocePJ7Mg1kcay6qo37ibjGwnw08ajTOnAHE4MP4Oajw+Wj/a\njsnKoP+nTiA4qJCmNsgKhQlu2UFg+TrCmRk4Tj6OUMkogh1tIE7Km7P4uEJwuITjxzsoKcnG0xHC\n5XRTVhNkzSYfBph+VB5FA904XGH8YTe19X7WlbaCgSmT8hhSGPd9DX7WlrYSDsPkCbkMLcqKfUOT\nnzUbWwmGYPL4XIYVx2jQ862BvuRx5Rvz+fjmbwJQPHssOUUFUZqnsoGaleUAFM0aS15x/xituoma\n5duUNmM0ecN0i8OVE6atLkDVglJMKMyQE8aTPSofd5YuGpr31FAzbwsm4Kf4rGMYcFQRuK3B8K7d\n7Hi7lEC7jyHnTGPg7LH4AmHCQRft/TM46szRZORkUr2+ka2b2/E1d+B051DfGubNFUE8HYbjJjg5\nZpwfHPodzW1hXl/qxdNuOGZsBrMmxtppQ2uY91d6aW4zTClxMXtSjI9tHUpr9BgmjXRxwuQYrcMX\n5v2VPupbwowf7mLO1BjN5w/z3ioftU1hxg51ceq0GO2w3xowxrQBG+LfiUgbUJ+qUXWF1q07mH/x\nnfgbVdKpq4PiIQa3WwgGDbV1MeG8rh6GOA2ZmUIoZKitISqB19bBkCJDVpbSampjq/76enA6DdlZ\nQjisaUZW9vUN4HAacrKVVlMbozX4lZabIxijtIBdhPv94HCYSF3grWyNrvr9vg7EIWQUZCmtyhNd\n9Yd9HYgIbqsx8FV7CHVYmj+EOAS3FRR8e9qiq/6wPwQCmdYYzL+nPbrqV02EkDnY0mraCbbG0yCz\nKLenLElAX/DZGMOHn/0ytQuWA+Ct9+BwOSicqlbjO+eup61SpXVvgwdxOiiaPhqAXfM20rqjLkYT\nYcgsFRQqF5RG9yR1P1gYeoJalFcu2kxTma76fQ0eCBuGzZkEQNXirdRv1H1gb50HE/Az4mSN1zZ3\nDeGldnVaU494g5gr1BJ5calhZYXyvL7V0BGA8+do0JVlPtaUOwFDU6sfr6+Vc87UVe2aze2sLdU9\nwqaWEO3eFs45sQARYcsOLys3qkagsSVEe0eYc+cU4HAI23d7o9qCxpYQno4Q58/pj9Mp7KjysWxd\nGwZoJETrslYuOLk/LlfvdYp91Zc3/PJh1v7gAUB5HOzwMebCGVpfG3ZRs2xbjNbuZcynZyIiNJRW\nUv3x1iitwtPBuItmIQ4HTVurqfqoLErzt3Yw7uLZOJwOmstrqVxUGl31VzS3M/6S43BkOGndWc/u\nBaVg1GxsW9NGxn5mNq5sN57qZmrf3wRhjVj39Pv0u+1iIItQZQ2+N+dHB5Zw7Ttwy1XghD0eB//Z\n4CAY1jp+Y5Xh6sIwDgc0tISYt7Qjagsyf1kr55/cn4ICodUTZOHSxuiqf9GyJs46eSAF+Rm0tQdZ\nsLQxuupfvLyZM+c4GVCQQUdHiA8/bsLn17IsXtHEGScNZNCA2MTUU/QVjxuWr+PDz32FUIfulVe8\ns5ZxF8/CnZ+Nt9HDjnfXRVf9O95dy7iLZpFZkIOvuZ0d764l7A9a2jrGXjSTrAF5+Fu8bHtpBSGv\njnXbX1vOmMtn4h7Vj0BrBzv+vYygNezb9o8FTLr5NPImjCTg8bL5icX4m7VvVTwxj4z8LNxji3Fm\nOpnx2aPI7q/jbF5hFs0dVexu7sAXMPzfvAD1rVrnrywNkuGGySUQDBn+MbeDWrvqr17uI8stTB2d\nQShseHFBBzVNSqtp8pOZIUwb68YYw8uLOqisj9EyXMLMCcqrVxZ72WlX/TVNflxOiWoMXvvIy/Zq\npdU2+XE6SBAU+gIH+9KhXqsfyv78TFQIAF35t1iNZ2vStkA8zeOJCQERRGhtbYmqf4CWlhgtGExN\na29PQbNFa++ICQERRLQ0ofZAguofwN+gHSXUEUxQ/QMErNXp2k2hqBAQjdfoxRhDyBdMUP1rPC8m\nbAj7Qwmqf4BAsxcTMoQD4agQEKP5Ohlq7Sd6xeea+UujQkAEtavVgrq9tiUqBERQt3YnJhzG2+iJ\nCgFR2vqdhIMhfC3tnQyTGjbuIuQPEmjzRYWAKK20imCHn6DXT/2mZNoe/B4fYX8gYVsAwFG6DRoa\nCYZgxbbE79pRB/XNIcLGsLIssc731Aeoq9d3yzckGoPVNQbZU6+83bA1kdbQEqSqLkJLNDBqbg2x\nu8YfjRfPhNa2EDurE43q9hO94rEJh1n/i0cS3rVVNdG+RztJhN8RtNe00FbVmJLmrffQuqshJc3X\n2Ear3Q6qW1ORUEp/SwfN5domatdUEG/Z6W/1RdtL/frKqBAAEGppx7davUECK9YnDiztHbBK58+V\nuzOiQgCANwBrt2n/XbMtkGAQGggaNlco/7aUt0eFAFDN4pbtyvetFR1RIQAgFDJs3qbC37adHVEh\nAHQsLN2aZJS8f+j1eL3p/ieiQgBAyBegoVQN/erX74oKAQBhf5CGjbsBaNi4OyoEAISDIerXqxdE\n3brKqBAAYIJh6leqZ0b92oqoEBChVS9QQb1+2daoEABgQmH2vK3eOf0nDYwKAaD2Q2OP1a2oDTtD\nUSEAtJks2qB8Ld0ZjAoBESzaYIWQylBUCIjgo41K21ETigoBESyxtN11oagQEMHHm3wYY9jTGIoK\nAREsLfVHvUj6Cgf1iGFjzFl7D5WIiKteQjrhxL+paOEUtHBPaCnqt1ua6TrNyDhjUkSMvuuCZozB\n095FhqaLwhh9UuWH0ZV3J7P2aFkNfWV/0ls+p+JxOBDCGJMwOCTQwoZQCpoJhqPCUCda2GBCYcKB\nzvEwRgcpkZR1FA6EVFhKli4BfH7C2Z2FSwB/0GAMnbwCQLeZImGSEbTv4ieBaDxLC3RHS5Fmqnf7\nit7y2IRChNo6e8CELC9S8TLCw1T8Ckfjda7YHqWZghaNF0jRdnyBhL8J8OuA7kvJ/8jf7njVuU11\nRwtG+d81rS+wL+O1v9nT6V2kzlPVa6zOU/HDxkvB42iavhTx7Lt44SFK8yqvnJmdpz6XW9fFXn8n\nEhG2+1L0uUgfTU3rOp6vu3hBHeJT0YIhCBk14OgrHFCNgIjcISKrRaTZPotE5IK9x4xh9HWXdLJo\nz81N/NtTWl5PaDmdDegjtJzszrSmLD+LOzw0ZPg70SL5uHLcalIeh4x+qvZx5mQgSTRXfiYiwuxp\nTiTJYtaV71ar+CwXkpFIc+ZqWo5MZyeLWWdOBg6XA4fbiSMzkebIdkWtcHuLvuBx8bknkz10cMK7\n/uOGICLkDu1PRl7ivmfBmCIcLic5gwtw98tOoPUrKcTpdpE1KI+sAYmMzhs+EFe2m8z+uWQX5ifQ\ncooLcOdn487LIre4IIHWOMDFUk8DVb523GOLE2imcAAUF+F2QRKJftkwZKATp0MYNzxx4MnJclBU\npG1gUkni92VlCsXWIHDMiEQVoDtDGD44IyUtwyVRY8ExwxPTdDmFEcW9VxlD3/DYkZHBqKs+lVje\n3Exyh+oqrP/4xMpzZbvJGzEwJc2ZmUH+SDUIHJBEc7hdUWPB5HgOl5N+o7Wd9Z+QSBOng6ZBmSzY\nuQPPsKSzFpwO3FP1ACPX5HGJNIfAlIkATClKnJQEOGqU9qtJIzNIxpjhyr+SEdmdaJF3JcO7pyWP\nOanS6in6gs9jb7g08YVA//FDgM68Aug/rjjhbzwG2HgDJxWRvEopOErDD5gyopMJfeEMNXUYOGN0\n1EMogrYpRSzYUMrqDzd18lLZvVlVv1NGOXEnyQnHjNEJedJIF5lJrDx6jL4YP9xFVlIXmzJaExpd\n7CInK7GcU0drvFFFTvKzE2mTR7lwOoQRhU765yXSJgx34d6HLb7ucKA1AjuBbwFlKCtvBF4WkWN7\nuu805PTjOfWFP7Lux3/Es26jGgtao7/MTGFwoaHZGvj2y4dca/TndgtFg9WQMBxWWsToLyMjkZaf\nFzP6c7mEIUVq9BcORwwCSaA1NkFzMMRfpIqlnnbseSWc6M7hDoaSHXaSmwMRo3hxCtnD89VALxjG\nlefGPUg7rDiE7BH98NW2EfaHceVl4Lb7/BkZQvaIfHw1nY0FRTRNXwpjQREha3i+pukN4czJILMw\nNkBkDdOyhLxBnNmuqO3APmK/eezKzuLsD55i+T3/S92HH9OvpDBqECgOB2MuOJaqJWV4m9rIHzGI\n4uPGRetu9AXHUr2kjI4GD3nDBlJ8/LhoHZScP52qJWV01LWSV6zGghGUnHsMVUvKaK9tIWdIAUOP\njx3CUnLOFKqWllNdWctDoSqWNjbCO6r+Pb5kDLdNGUNuZTOmuJDwGXOi0uF5x8K8tcLOBsPgfOGE\niYLD0k6fnoWDZnbVuyjIdzFzcl7U1evUmXmEgN3VfvJzncycnBt1EZtxlP6/o8pHXraTmVNyybAC\n4J3lzQsAACAASURBVLSJOTgEyit95GQ5mDklj0y7qpk6PhsRw/ZdPrIyHcyYnEtO1j6vIfabxwDH\n//XHZA4sYNtjz5LVP5fiEyZEDTSHnjgBR4aTlopaMvvl6G/rPlZ83Djd899eS0Z+FkNPGB91ESua\nOQacQvPWPbhyMhl6wviokeHg6SUg0FRWjSvbTfHx46NGhoOmjgQDDZsr6XDDX6li4duvxMo6qIg7\nQ8XkZmfT//zZhAf3hzbImDIOQiECqzZi3C4cc2YRKh4MHW1MKAxxHmGW7wSHU5g91sHQQU48HSFK\nhrg4eUYm68v8gHD0xFyGDHIDYYYUZnLSrAI2lrVhDBw1PpehRVrOwoFuTp7dn/VlHsJhmDQ2hxFD\nVcgb0D+DU47rz7pSD6GQYcKY3P0SBOgDPo+6/AJO/PsvWHXPT0GgaMaYqLFg/shBjDhjCrWrKxAR\nBk8vIc96huQNG8DIs46mdlU5xhgGTy+JCnu5QwsYe/FsqhZvxoQMRbPGkG9PycwZ0p+Sy2dRu2Ar\nYX+AIadOZdAMtS3KHtKfiTfPYde7pTR42njYWcNHr70QLevJ/3ye//vH9ykcNpCaTc1UrFVBoCBH\nuPEsN2+tDODxwowxDk6cokJDbpaD687J4a3lPjztYaaOzuDkqdrestzClWfmMHelj5b2MJNGZjDH\n0twu4cozspm7ykdTa5jxI1yccrTy2OUUPn9GDnNXeWloCTN2WMwg0OEQrjg9h7krvdQ2hxlT7OK0\n6X1rHwAHWCNgjPmPMeZNY8xWY8wWY8z30GnzxJ6m4W9sZu0PHqBx5UYCAd13j+yPhMMqBPj9RGkR\n1Vg4rIcG+f26r9/cElO3GROLFwyqDUBEzWqMCghRWmts7z8Sz+eDP4aq2BJs52lgB3omZ6m/nT/4\nq6LxfHEqpkCjl1BHEBMIE2jyJuz9B5q8hNqDmGCYQLMv6gqjNB+h9gAmGCbYov9HaS0+Qm2W1uxL\n2CsLtvgIeWLxgnH2BMFWP0GPX2mt/k42A71BX/AYoPSBp6h6fT6BVi8NGytpq2qK0ho27aZ1Zz2B\nVi+Nm6vw7Iq5CzZurqSloo5Aq5emsipad8Su4G4qq6Zle63G27InwZWwadsemrfXEGj10rx1T4I9\nQfP2Who3V/Ngazlb2hsTeFy2o5w/b/gI09QKm7Yj60qj8cprYFOlobUDttUYNu6KqfV21gQpq3TQ\n1mGorAlQtiOmJt9dE2BLuRdPe5iq2gCl5bE91trGAJstrbo+wMZtsXj1TUFKLa2mIah2AXZbo6E5\nyKbtXlrbw9Q2Blm/tYPwPnoI9RWPq99ZSNlf/kmg1UvrznoaNu6K0tqqGmnYuJtAqxfP7gbq1u2M\n0fY0U79hN/7WDtoqG6lbG6N11LVQv34X/pYO2qubqF1dEa0Db72H+nU7lbanWWl228zX1Ebt2h34\nmzu4v7aMDbU1iXyur+GBpi0Eqhpp+WBN9PTScHMrgY/XYBqaobqe8OKVUf1vq09YUiHUtzuobRWW\nbTNR1W67N8yazX6aPIYmT5i1Ze3R/X2fP8z6zW00tQRpbg2yocyD1+4zBAJh1m/20NQcpKU1yIay\nNjo6lBYMarzG5iAtnhAbt3hoa0+x7dVD9AWf23fvYd2P/oS3wYO33kPNyu3RLZpgh5+aleX4Gtvw\nNnioWVVO0Orcg74ANSu3423w4Gtso2ZlOcEOHZdC/iBVizfTUdOCt76V6iVbCHh8UVrNgi20Vzbi\nrfNQNXcdvga1kwgHQlS+u4m27bU8WLuZ0urKBB5vXF/KXV/7HTn9Mxk2fSB51sgyHDbMWx+kotZQ\n32pYuClEdWO0jli43s/OmhCNHsPSUj9VDTHNwtJNfnbUhGjyGFaU+amMcwlcvjlAeXWIpjbDqi0B\ndtXGaKu2+NlWpbTVWwNU7InR1mwNsKUyRHObYe32ANur9p3HXeGgGQuKiMOeXJUDdD66rQts/tP/\n0bQmNthGJm5Qg0B/3BwWDOmED2r0Fz8Rh0Ix4722Np3Mo7QwNEWM/trBG3c4VDiO1uGFjg6ows9K\n2nkQvfR5pP37ALCSdqrxYww02cYT6ggkTrYG/HVqDBTyBQk0+xJovlqlle9SoSGZZowhHAjx/6l7\n7zhJrvrc+1tVndPktDO7O5tz0kqrDJIIBr022GBzDRcBtsG8MvEayzaywdhcg23wFWCLYINtkm1M\nuLwCLK1yllab8+7s5Jx6uqdzVzr3j1Nd3T09u9rZWen1/X0+8+npevpUV9dTJz/nOcZctYuVXsJM\nGz1ePR9bnMm5c+R6vFqAVpzNLa4rWGJcKceJE+e4cH95W3Nh2UwckKK84nyuqlIoYUII9EyhSiwm\nbCEx23YKnYHylwjB5Eu92KaFVTSYOtRflkIJmDrYh2WY2IbFxEsDTIji4hwL4XIMoDx/GDJZbAFP\nna6WbhwfFKRztiw8ThWxK4Rk5wcKpNIyQz97NFMlPegdLjA3L7EjZ7NVZkMDY0VmE7LwPHYuWzXv\nPzyhuyLD4+dzVfqCsSmdiekrb/CV4ko5Bjj8sb/ErsiU8dOjFJKy0J480IuouAmJ8+PkZ6V2pMRb\nKZK9k2Snkg7WVzXfnxqcccWlk4f6quaQ08OzpEdlQ3Hq8ABWwbisvFwcnKJ4Sj5LxksnEZmK/DMx\nAydlZ/nQqJf5QpnjmTSccsSCx/t00tkyH/NpiwtDMv/2DmTdZwEgnbG40F8SC+ZIpspYNmdxzhEE\nDozkmUuWG/i5vM3ZC1dHLHilPJ/5q38g01/Or4V4hrlzUhA4e2oEfb5874qJLHNnZGNw7swoxUT5\n2vWKfD97apz8dKoaOyzz/dypYfLj5U6Dnswy8aTkI360n/TA7EU5/rJl85/7D3Lhwii+oIcN18mp\nqPPjNufHys9iXofHj0te+yctzg6X+Sga8MQxWX6PzJicGaro4Jnw5HGJTc5ZnOiv6IxZ5XTxlMXh\nC2XMsuHxY/LZSGZsDpzTq7AnjhWxr0J5XRmveENAUZTtiqKkgSLwVeDXhBDnXiaZG6mewZpjpR76\nYpov8wqxS52zhJXSTyH/ec2Cz73WeZ108NK57EVEPWXR0iLCJENWHsNjtemEaYNY/JzCEmALxCKi\nHGwhVw046asTsqxVA8vlOL0Ix3oqLyv7VK4GM7JF2aBJ52t+i1UwsHQTPZ2vadxYuolZMNz0lWGb\nFmZOx8gVsQ3rsjlWbAGJeXQTcgtE+QJIZW0sGzL52oybSptylClTy1c6a1W9VqW7BHY56a4klsux\nVdTJDo7VHC9VDMVFeC4dq1R+l9PlL57OOaeeqk3nYs7r5fJsxWVvwE7WWncTlxVRslBbnCYy8jmb\nzy7C/yV5NF8eW/S5WV5v8ZUor0s8FOcvzlVxEY5dLLkIlnCwuVpxYsFpQBZn5OvLcdzbK5/LcL0c\nEYinarmac/TMc6nacrJkLjS3aDqJlZ6DRbF0LZbKCkxLjh4tjExeLFpPLSdejVUD54BdQB3w68B3\nFEV5zaUerkqnqtzoJAnKbmQAQWcaLBiQowKVEajA0umLY6kF+bnynAvNmYKO7irgvJay+9PIlmUp\nnnJe2/FWpdOCtbdZc3YC1ILeGktbLeRFURT2bNNqMDXoQVGlsyCqUtUFVf0aiqai+hUUTZENgxLm\n01C9KkIoKB4FUdGTVLwqiveKnQVhmRzbusGkOsENjhsZQKSzAUVRCLXWoXq0qh5hqE0eCzVL57LK\nHmGgKYon4EP1etAC3irlsL8+hC8SQNg2nrAfs8Kj3hsNuMJDXyyAmpK9k5fjWIQC0NFKQEBbHUxV\n3C6fB1obNDxC0NGkMREv/waPBq3NPhTFZmWbl+HJ8nWqKrQ6zoIdzT5Gp8o9AkXBdR1sb/YxNF7+\nDYpzDKCjxUvfSHXLpKP5ip0F4Qo4hmqeZ5qy6PGkm5cVj0rIMZSJdDZWTfkomkrEERJGuhpJDc6U\nT6oorutgpLOxyiESRR4rYYnz1R71JZOaSGcjg/NJ5pxK4uV49q3rxAC01Suwx6uXpbJWumCurjcZ\nTlbrMFa3yfcrWzQGJ6tL7w5H9Nne4mNkvHp0r73F72KDI/nFsVZfzT4EJewKnAVLsay8nBkdYX5B\neV3iI9rVWLPcN9LZ5H5mvn+qCos6YtHYqkbmzlQv6Y2sds65ppWZBet2Y44QNLapg6NPHn1Zjtev\nlw6GsyPyXq7rUHn0eHUfY62zMeGaDg+KUqxaWLS2Q3Lc3a4tLJJZ0y7L/lUtGppavehoTbtM19ns\nwaNVryxa2arh0RRWNGn4vOXVBwAdTSp+3/8lzoIX/UJFeQToFULcvQhW41QlhODEp7/M+S9/ByuT\nJRKBhvqyf39yXpByxILRCDQ0lLH5lMRKor/GxjKWSksNgW1LdX9jA+5Wkul0WUgYCkFTo8RSlsVn\npsZ5ycyhAlHg75Ety6eAjwLdhPgDuggGZbo/ve3PATkvX5zNIUxbivfawq6HtpnR5dC9aaOFPPjb\nIqgelae+bHDz+6E4k0UYthT2tYVdhb+ZMySm26gBD4G2sLtawMobFKZzCN1CDWgE2iJlrGBSnMpi\n6xaqX8PfFq5aTrPUTYeWyzHIjYcOf+wvyfSPEOlsoPOWLa6wKzM2x/gLPeipPOGOejpv2YwvKivt\n7ESC8Rd6KCZzhNrq6Lx1M/6YFD/mpucZe+48xUSWYEuMzls2uysJ8rNpxp49R2EuQ6ApSuctmwk2\nRZgvFvjkI/t5bnpycY4VhW5fjD8otkFzA9Ybb0WsXY9umGRRePgoTCShIQw3bVJZ0RXGbxgkDI3H\nDqWZTKpEQirXbovQuTpETDXJFWz2v5BmcsYgHFTZuzVMZ5v87YWizYGTacanDSkI3BJmZYfEirrN\nSyczjE7pBAMquzeF6HZWC+hGGfP7FHZtDLN2ZXklwVI3HVoKxxfjOTs8zou/fS9Tj72ALxak44YN\nRLtkRWDkiow9c47M2BzeSICOGza46n8zrzP27DnSo3G8IT/t+9ZTt0baAZtFg/Fnz5EanpWCwOvW\nuQp0SzcZe+48qcFpPAEfbXvX0rCxg/ligXufeIxnx+Ww9KXy8h8G1xJ77U6067aTzIJfN9CfPIh5\nthc8HtTrdmDdeiNmPouNxjNDQU5OKGgK7FqtcvM10llQU708fbpIz5CBAmzqDrJjYwjNKyhaXk6d\nz9A7IL0f1q0OsnNL1C2rTvdk6OnPImzBmlUhdm8rY+d6M5zrzWLZgu6VQfZsi7nlGCxt06Gl8rwY\nx7ZpcuQTf8WF+7+Poio0b19J6x654kIIwdTBPuLnxlBQaNraReveNSiKY8h2eID4mVEEgsbNnbRf\ntw5FUfCEbCYPTzB9uB9h2zRvX0XLLd34Q7IROX7gDLMHBrANi+Zr17Hyzh2kUPjD7/+Qp3p6L8rx\nx1SVfXdcw88f+gLx/jTHnp4hF8+h+UIc6TN57IRJtgjbVqrcuc/E75eNnZMDBo8eLZLNCzZ0ebjz\n+gBBn7znpwYNnjxWJJMXrOnQeNN1AUIBWc73jpk8frRAKifobpdYJCixgQmTx44WSGYEq1olFgs7\nW9hPmTx6pMBcWtDVrPKmfUHqIxL7L+8seIlQgcuWPQrLIj8+jZmVw8CVa7WFEFhWedm36fwvl4JL\nrLS+36rASu+rsIrvLH12Ap053WCb4WW1389n4+NccASCO4H3AHdVpLvOE+IDZkfVOcq/w3aHqoVl\nV4HCEmXMFFVNSmHZbs/erjiH/BIh/xacv3TOUvNTmKJqdEBYwh0aFxXnuIqxJI4BCjNz6Ak5TGPk\ndGeduDyFWTDcuV4zp2NXTH2YRQPT6fWbeb1qqsUqGu6IgJnXq9ajWxXpRnJpBsaG2ejp5AsvPsuZ\nmamLc9zRxQcy9VJkki9AvtzrrpweKOjSUKYUhinI6/Lh0w1RZQSjG4K8s4ugbggKepkP0ypjhiko\nVKSrwhaksyxpWyqE893Fq2oYBVfAsZHOknd605XcgBR2mbmyAMysWMxtmxZGrijzv266IjIAYVoY\nOV1Ol+lyeqcynemkG9Vz9E6OsqMtJDmeGON7yOHiB5EVfyXPe4P1fDDfhK2bWOlcec22ZSOyOZm3\nbEPqBZy8bNmQKSoSArJFXOGibUOuINzyK1ewyz4jAvIFy9WCFBysVI7lC5Yrgi4ULWxbbkYkMdvV\niRQKNrYtqhoCVyGWxLNtmOTHpp1yS+ZlYQsUVUHYQr43bQRg5IuVBTZGvuiO/Jm5okynOekyBZl/\nBRiZQrm8FAIjU8QqGkwInZ7xEfZNd/DXjzzJyQt9l+T4tuu2851//RSqpuALeVArlnGnC3L+XwhI\nO0PxfucuZApSBCqATF7e/1JDIFsQFE2JZQsC3ZQiC4Bc0XbFo9m8oNLKIlcs51+ZrpyXc7qg9Mhn\ni2JRb4HlxivtI/A5RVFuVRRltTP39Hlkg+x7L5e2FH3f+hH9//Rjt9bO5aodAiunBvL5ChfAfPXU\nQL5QHvLP5UXV1EChAPPJ0v+CsZTFF8Qof8AgnzPH+O9Tg3xkapgXCmXByQ7gKPAF5xx/09DJx80u\nwk6Roeu4WxbbuiUFgM7DaxctitNlZWtxOlvGdIvClMTiCbsKE7pN0cGEZVOYyrgVvDDke5CiucJU\ntoyZNoXJDELIBkdhMrModiVxNTjOjkzw0gc+5TpIFhNZdyMZM68z9sxZLEddXJzPMfqMFANZRYPR\np8+6FYqeyjP6lFzmZxkmI0+ecSsNI1Ng5MkzUkxp2Yw8dYZkLscXGeXj+R7+4NAzvOXH/85zY6P8\nlRCLcvzXv/rr/H6umXDKqe2zedRfPO4qVvcfhdJUZ96AJ0/bbuZ+/GiB+Vy5IXDgRIZ8QRZ6Tx5M\nM592VOKm4OCpDFlHGX7wVIZEqowdOp0l5cwNHz6TJe6ICk1LcPRsloQjLDt6NstswjFWsaSNcUlk\nuNS4GhwDvPi+PyZ1VloFW0WTsWfPyQoe5OiMIxazdZPx586jZ+Rw+fjzPRTi8tm2DUvuO+HMH4+/\ncIH8jMzMtmkxebCPfLwkMuxjemqOLzLKJ6x+/qT3iMvx5x2OVwK/C3zTucY/u/k1fHPNdfx+vlXm\nZcsm/dwZ9B4pXDNeOoE14Kx2EAJx7CzWqbMYlsXBUY3eWQVQECicGROcGtAx9QLHLhQYqtiGeGCs\nSN+I/H39QzmGRstTA8PjBXoHHcHwSJ6BihUmoxNFzjvOgiMVnwMYnypytvfKxYJXg+ezX/gmIz/e\n775PnB8n2SuH9efOjFYN/ycvTDJ3Xlp5z52fqHL7nO+fLgsJz00RPzns9taSvZPMHhmW/58bY/hw\nP190yuvPjJzgzvu/yTM9vRfl+NO/+TYe/ru/4PEXv0Jzi+zl13WG2Hi9HJ0anrF5/IRJqb8xNCN4\n5KjMuxNzFo8eKbpz9ONxm0cPl0V/jx8tusP4UwmbR49IXuezNo8cLrrGRDPzNg8fklgmb7P/UME1\nMoqnbB46KLF8UfDQSwXyRfnjE2nBgy9VTyNdjXilxYKtwLeR806PIne4eqMQ4vHLPcFC61mAonMf\nisUayD22GFa4DKxQhK8zwSDVSwMHi3lUagUn/815HS0aHCPrqskrv8cq1Co7SssHF8PsghSRne6p\nFfbZRcdVr2jVYEK3pXNe0axxHhSmjTBt2ZteiFkCsYj48DJj2RzHXzyGWGDLl5tOIYQgN5OqEf0V\n4hlswyIfz9SIHIvzOcy8TjGRrRo5ANkYMLJFiskcVsFYlOco8IMF11fieHImzpHkdBXHim7A1AwF\nAxbqliwbZpOypze7wJbUFhCfk6XCxGz1MyAEbiU+M1dbeZcq9MWw0rGZRSr9xY5dZiybY6uoM3fo\nVNUxYQu3Es9NLZjbFOVjNRi4qwZy07VYOV3ysjkuCceEgIGZeBXHAMawrMCshfoAQJuYxat5mMzU\nmgZNJBQ0f4jpVG0vfWZOcrxYA23GsZ9eDJudc7DFno25Za0MuQrlde1odNbhI7sIj7lJh8epZA3m\npptYJN2Y/HxmLL5kjlvrYwRbQjz44AEuXCgvYW1ol9ONQzO1ZeGII20YmbFqMefY6CJYaUfB8Vmr\nxrC0hE3MWTXOtJNzNqYlmExYNa6ks/N21ejf1YhXemqgDznC2ozUMmWB0UumWBANu7cw+L0Hqo75\nfBWvCxrALlabJy8Lm1blUpPvURaW/HdknXsX8B/AJyrS/QLZmvpKrlxA7CHE3XTQVNoz219r5KI6\nc/KLWV2qfg1FUdjQvcie6V5VOgsuste24pEbVau+WpGhoikoHhVFUIOhKjUOhkuIZXNcv2tzjbVv\noCEsjZEaIzXX64sFUb2a/Iwz5FgKb9iPFvDiUxUUTa1aHeAJ+vCGfGh+D1Mek6PmxXm+AJQshkoc\n3/fcE+65ShyHPD5oasTvgWgQ0hW6LkWBxpiKRxPUR9QqBbAC1NdJ7pvrPcwmqxsDDTHNeV0Ec9I1\nxDzucsHFsNwCr9SG2BVn92VzrPl9xDavJXWuQtilIPkFAk0Rt9dfikCTgzVGair8YGPUxRbuRVFK\nF495OJpdGsd/8fzT7nlKHIfRUDoaMFQbtbVRLhmsCL29EV0RNIRNRhaIBZvqBDY6TVHBULXejbqo\njW3miUVqC/W6KFjWxTBFYtGLYzKWbCB1FcrrzUw+8lzVsWAFjwvFgpUcz/dXN7JKz0awOVLzPYFW\nyf9sWL1keV3JcUkc+C8PP8mhr/+Le647f+k6vvtvnybvXFpHQ22jrc3ZJLG9obacbHOOtTbU3u/W\nenmspb42XatzrKWuNp0sNxSaY2qNALEurNS4Gy43XukRgVuBvwOuB14PeIGHFUW5bPurDXe/k/bX\n3+S+9/sgJsWoRCJlZT7IyryEhcPSErgUXi/UO05/oZD8WwyLa5deavIZZItzxHn9OLL1WTV6QI6v\nMUGDFDaj+T14Gyo2uNDKOwGqPs3dTXAh1tGm4qtwBERVCDi7BKoeFV9LqGy9qSr4W2XlqWiqPIeL\nUcacz7mYAoFWWaFeYSyb49jGNez8i4+ieJzGUcDLipvlToC+SID2a9e516f5PS5Wcosr2YiqPokp\nioLH76Xjxg1lzKtJTFXRvB6MzVJsdjGe/4HL4XgSccdNEJJWr6/biWtNqipwwwbFFQq9Zpcfv1e4\n2M5NISLOtsA37Pbhd+YYFQW2rfUR8puYRoHdm7wE/GVsc7eXaFBiuzZ6CFbYlm5Y5aUhYmEaBXas\n9xCusC1d2+Whuc7GNAqYxpKHFpfNMTjOgk2yRFVUuUtkSfS54saNaAGndFMUWvd0E6iXz3rHjRuk\nTbeDNe9Y5VpEd1xfsSWxIve8DzuldnGtzIDL43iC4I41+LdvxCKIZ9810NzonsvesBpl1y7Az55V\nKi2x8j3vbFbZtDaM5Qmxc3sdLY3lhlhLs481GxqwA0G6NzXTXLHtcGOTjzWbm7ECMVZtaKK1rVx2\n1Df4WLelCSsQo2tdE+0dZQrq6rxs2CbTyb8l7yi6bJ63/vHv0rRvp/s+0tVIw0apm2revpJQW9m+\nO9xRT9OWLgCatnQR7ihvSRxqq6N5u3QIbNrW4VpDA4Ta62neuxqAVIu8tMvh+KNAUyhI/+BQFc8v\nPnqYd/36n3PhJekxsa5dZe86zS0iGyIKr98j8+6qVg/7NnldrVkspPD6ayR3HY0a123yuVgkqPC6\nPY5DZJ3GjVvLWCig8PprJK/1EZVbd/go7QQf9Cu8ca9MFw2pvHaXn5JTcsAHb7w24IpFr1a80s6C\ndwohviuEOCuEOIm0rFyFHHK6rPCEgqx6x5sJr12JpskKvCSGURSFUAg8HlxM0xbHgkHcG60oCuEQ\neD3yWDAgPwPQ6ZUFztMLrqPUmgwrGnc5P+Iu5GLb+1ncjGRClHtknrAXxa+BpqCGvVU9ei3sRQ1o\noIEaWoAFHUwFLeSpGl3Qgh7UgOcimLeMlf4vYQEPalBiatAjz3+FcTU4Bmh/4y00Xrdd+sh3NeGv\nLxdi4RUNBFtiaH4Pkc4mAg3lHkK4o55QCVvRSLCpvIdAuK2ecFsdmt8jz1Gxv8C6TqksX8jz3zmv\nX+RyOM4yES0X4C0x6GoCvxfa62FFYzmzNkY1VjQKfF5orPe4y/wA6mMqHW1+fD6FxjoPHe0RVF8Y\n1RcmVh9hRasfv0+hPuZhRUcZi9ZF6WwrY50VWCQWobMtgN+vUBfT6FpRxlTf0iqIq8Vx/Y6NdLz5\ntWh+L8HmmLuUD8BfHya6ssnBou6SMwB/LER0ZTNawEugKUJ0ZZOL+aIBoqsk5m+MEF1Vxrpb5P/L\n4zhHYnUMr8dCC2rYdUHExtVyGVJTHfa2ddh+FeGFupDF2g4IBwSxEKztVPD7FFTVxheAVV1eQkGF\nUEhhVZeHgF+O9Hg8Kiu6QgRDGsGgRmdnyLWR1jSVFSuChEIagYBGZ2cQn1M+qKrCis4g4bCHQEBj\nRWcIn+/Ki/SrwbO/sZ5Vv/EmPJEA3miQuu5W1ypa83mo6251sIDcM6Q0aurVqFvTijcawBP2U9fd\n6tpIqx6V+o0d+OpCeCMB6je043Eajasb5XNysfK6kuOVDU3Ec3m+YosaY6H9jx+ir1/6CSiKwpYu\nldZ6CPthc6dCfUXHcWOXh5Z6hZAfNnRqroIfYO0KjbZ6lZAf1nVo1EfL2JoOD+0NKkGfXHLYGCtj\nq9s8dDRKrLtNpblilGBVq8aKphKm0VJ39avtV3sb4nrkqM3cy32wFGM/f4KXfvdTZPtHsCxIJCHj\nmHPkC4L4nHQbtCxIJiGdKalrBbPxMpZKlcWDRV0wMysNf2xbig9L4sF2vOwhxEco9/z/Fvgt53qm\nhJywiaDwDuRDeLHW6Pl5Z8cyyyY/lkYU5bZRVkp3xYIlzC5YYIGV1ilO59zflx93MBusjOEKCYUt\nKIylsfOmixUnHbGgEPKcJSxrUJiowMbT2DmJ2TnTxa5SLJljI5XhiV/6HeIvHMMqGiR7J12xx7E+\nYgAAIABJREFUoG1aDO4/Tm5qHqtoMt8/xZgjFrQtm8H9J8hOJrGKJqnBaUaePC1/py0Y3H+czHgC\nq2iSHppl+PFT7j3gwGAVz88CDZSFgXB5HM88+LArZH3oKPRPSbXxeAIePGK7dthPHiswMKWiG3L+\n/8mD864S/LkjBQZHCui6IJ40eeqlBLqj2Th6Ok3/SIGiLkjMmzz9UpKCswLg+Nk0vUMSS6ZMnjmY\nIOcIEE/2ZOgZzFMsCuZTFs8eTJJZptlMRSyZY4AXf+uTDH7v/8MqGuSm5xncf9zVcYw/f57khUms\nokF+JsXg/uOuQHTiwAUS5+VWtIXZNIMPH3dFoJMH+5g7O4ZVMCjGMww9chIjK0c8gn2Jq8LxuZ8/\njz6VxtI07MPnUJ4/CpksxOfRfvo4DE5jawFOTAY4cF6QLSikcvDMcYuhCRNbCHoHdQ6fyJPLC3I5\nweFjOQYGdQo5mBhMcOJYgnzOIp+3OHkiyVj/HOSzTA8nOHY0QS5nUShYnD41z9AFic2OJTlyaI5s\n1qRQsDh7Zp6B8xIjn8Uu1Br4LDGWzHPfP/2Io/f8DWamgJHOy2WfYzJ5sn+KiQMXHKzA+PM9pIbk\nNEtqaIbx53sw0gXMbJGJAxdIOv4QqeE5hh8+gT6fw8gUGH/mHInT0h+iOScuWV6XIobK9QnZsLgY\nz3ZM3q942ubfnjGYSsqVHy+ct3nipLMkPWvzb0/kmU4IckU4fMHkccchMJu3+fHTeSYTNrkiHO83\neeKoxAq64EdP55iYs8nrcGrA5NHD8jnVDcGPn84zNiuxs8OWKxY0LcGPns4zMiOxcyMW//l/oVjQ\nDUWOZXwJeFYIceZy0w3/aH/NsVyu+vWysfzLY/kc3E0HUbxuz/+PgDDVw4YagoeQgpW/Q85FlaLU\nGq0vylarmTVqhH1mxvHRztUK+8yMjhCCwyetmqV9VtaQYsGCWbUksJCfYm7iOLnkOHbRIp+eYD55\nmkJBzrvZBRPbtLF1q0YYaBetRR0OlxpXyvHko89jLHBsSw3NIoQgO5Gs2U40PRLHNi3y0/PukrNS\nZCeSmEWDfDyNkanOMPnpFEauSDGZpZjMsYMwCnKE5zYkRUvluD1nwsQURbMsKCpFToexRJGCqdeY\nyRR1wdRMFsMoMDhWjRmmYHI6i7ALjEyUf0MhP0U8fopzF/oQdoGe/qEqji0bxiczCLvA6ES1CY0t\nYHRSnlPYV16QXCnHlq4z9rMnqo8VDTITcn6/yjAIuTog48z9V+4DAVL4WrIKXphOWDbpkbibbikc\n/wC5zKzEc6WpkH7a0Tb0DFR9nwJoZ6QFet8CDQBA37iJHQrQO1abv8bjQF29fK2IQn6KY6dPENez\n9AxNVnEMMBG3ZbpE7fBwCaOuHjvaXHtBlxlXXF7/8KGaYyWOUgMzNdi8g80P1mKldPO9s7VYr8SS\n58cuq7xWsPkBMs3F8vK1N8oNy86N2jW7jZ9zXJMvjJk12PkRmX/7JmqFfT2jsuwanDSrTIEK+SkO\nnjzO5MwkR3vGmJw+VcVx37iJZQnGZi2yheo6YHDSWnR78uXEq+kj8FVgK3Dzy32w0qlq/mwfmQVO\nVaVhfG2REe3SMc9SMadJ1GfnuYcRKqsXi/KwIVSKUeQT8QXn7w7gHcAnkUKjLo8c/l1MiFc6piyy\nnaTiUeXuXI2LYJoCCmUzIjPLYO93SKccRXYPeHz1mHpZhRuNbad7w3tR1AYWbfsp8rzLcBYsxRVx\nrCfmmVnAsSfoc8xEapcwawEviqouiqk+D6pHc3egqwzFI/UBZ+fivJ+eq8JxGz6scBiPKqcEFm5X\nH/b78Xpsgv68uwSoFMFQEMWnEQpkyS6wIA6EAgjNRyCQITmfquK4vwceCNaTyy/geP17CQTrEVqA\nQCBHNm8s+D4/x04f4vjJZ3n+SFlT/UpxDNU8T3imsHW9imevw6En6MPIVjfqShx6Q/4ae9oS956Q\nr8pXoJTuzOw078ucuGyOw8A9FZ/dDQwgOW7Hh4iEsRWBHQnW5CAzEsRSbOQlVefZoF/DKMrXheH1\ne9FNDa8jGF6Ylw8+DzK/ymewxLHXH3PS1SrGSuc8fewJzhx7guNPfdnFXo28nDh9lhwTNXkZKOs8\nKq83VOZ4YZQ+7wnXpisd67cLfIy+ZeXlezWVO1+/l86ONvq5QDRY22iLBAU2JuFgbQUcDoCFSShY\nu9ogHFCwhUkoILGFHH/ir2ExjmPhECgmoUDt9wV8oKgmzx05wOHvH7kqzoKvyoiAoih/D9wJ3CaE\nmHi5z99333088MADPPDAAzz41ON8qmuv+1CpqtxSGKSTYGWlrqpQVxILRqQ+oHwNFVhYCgSrMEfD\nck96hADl1mSpcLjYcNLbKbc6DwO/h3Qku5sO6h3tixb0oIWq21x+Z6thLehFC1dn6NIWxZvWaXgi\nvhpMUeTKAE/M5zxUY1S2f01dIIsy+T6dGmN46HtytYFHxVtfvVe9tyGAoqm0dt/Mttvuce/9Aw88\nwH333cflxHI4fuiZp7jvre9xOQZo2yu3IQ42Rahb21qVtu2aNVL0WBeifkNHFda6pxtVU/FFAjRu\n6azGdnWjejU+8MjPrxrH4pqtKE1hvF7BjZuq029codAcE3g0i31bfFQOC63qUGmqM/GJBPt2eKqq\nj85WlfaGHB6RZNcmlaG+Wo5z+VqOx4e/TVdLHo9IsnNjWRMD0FSvsKq1wN4dO/jtd939qnAM1Tz/\n2/3f4BNKl8tzrLvF1W20Xbu2qg6Nrmwi7NgPt+5dW3YCQ2pGStvXlp6FUoTa6oiubOK3fvbTJXFs\nUd2DHAAUVO6mA62tAf81G2VhuW8nwlfOk6KxDn3vHhQFdq+pVnOHgwpb12moArZu8uH3l6/TH1BZ\nszaEYut0rw3jD2iL5mXp9HsHJY6H+r/D2vURFFtn1aogoXC5XPF4VdZtkNj2nTfzjvd86lXPy794\nZD/3Nm0rN/QiAZqcfNi8fWVVA90T8tO01RELbu2saihoAS/N26RYsHnHCtf+28X2Slvnjx9/etl5\n+YbX7+VfvvOnXDgyj42PrauDdDaVM49HhVu3qwjVz/qVQbpay5WOqsItO0MIxc/qjiAr28p8KArc\ntDOIUP10toVY0+G5LI4He7/NzTuDoAVorA+ypbu6frhxexDVE+CGva/l07//6SvieGG84iMCzkP1\nVuC1QojhpaYPrWjjxu/8Ncc/9WVSB49QFwOv11GQawpNTYJkUq48q6urwFSF5iZBIiGHReti4HOU\n2aqDzSXk9G40Cn6/ws/TSYrAtyi3Jt+PbD1ezKf6g5RFJ6UlK+/ztNIZ0QgFy8JFX2sYfSaHbVh4\non480fJD72sJoSs5bN3CE/EtwIIIhMTCPjyxcsvZ8iWdluXFFs8U3Pfzs3eRT00QjHXgawoibBur\nYKIFPPgalrWH+bI5Brju63+O4vUw/dizxLpbqF/f5mLt128AAYVklujKZho2rihj161F2DaFuQzR\nzka30AFZudimRX42LRXK21fyk/NnKQjBN1kex+9u2EBo5Uryt96CikbBVti8SmE2pTI6Z9EYgd0b\nveTxY6GxarXGlmSBkVmNSNTL+u11ZDQfFkFaVpusTRWYGssTjnhYv72OOVVmTd0/Rmr+8jiOz95F\n/7xBY9MKfK2waWua0eEc/oDGpm11zHuvfM3R1eB43W+/nezQGL33fwd/XZiOG9e7WN3aNorpPKmB\nGXyxICtu2FjGulsoXrOG+f4pfFFpTVxSTUdXNtN27ToSvRN4w346rt/A/75wfskcf4zFe5DFlQ3U\nve12dL8P01Qxm1sYfetvkDs3hupXabh+Hf5gEKHrRCOCG7b7Od1fRFFVdmzy4Qt4SRsQCnnZvTfK\n+XM5QKV7fQgt4MMQssfctUbnxacvxfPXga+QSt5FtjCLP9KJ4lPZtreF3tNzWJZg9fo6grEApVFj\n9QqU5cvlObZpLfu+9TkO/+4nEUD7tWvdyt8XDbLilk3MHBsCBdquWYs3LDsl3nCArtdsZepIPwhB\ny+5ut/L3hnys/qVdjD93HmHZtO1bj78hzI9PnKBg28vOy//zz+8mcb5AYkqOK3g0hV+70cdDh3Uy\nBcHe9V7WtBsIZN3xyzeF2P9SnkzOZvtaH2s7vaV7xxuvDfLE0TyprM3mVT42dJXL8u3dSX5yyfJa\ncpxO3UVTZA6QnZzX7g5gO54ka1Z42bmudoRkufGKNgQURfkq8E7gLUBWUZRS6T4vhLisicq5o2d4\n4k3vx3YmWGZmoa1VEAgo6IZgeqa8/HxmFlpbBMGggmEIpqbL2GwcFEUQCimYpmByqozNzYGqCI44\n4prK1uRG5JqaDyHpei3yofow0AS8oeKzpVbnqGnQnCz3AIUtKIymXfMbI55HEbJ3L4QU/ZXm7Y25\nAgjwN4ewbUFhLOPO3xt6AYTA3xJGCEFqaMj5xovuq4VcRSvf59NTBGMdFCYyWDnHelfXEZYguCLK\nlcTV4Ng2TR67/T3uGvOZRBZh2rRftw4hBEP7j1Nw3HqKiSy2aboVxdAjJ11TmmIii6WbdN6y2cVK\nhiXFRBarYHIIOZG7XI4nE7O0J/IE5tKYv/VOPNg8c0rltLNFaTIDqZzBm28TaAgOn8xxdlABLDIZ\ni1zW4o13tuNVFU4cz9B3Tj57mbRBJm1w+x0dqKrCuVOlOenL43h+doKO1i4G+tOcPSWHCdMpg9S8\nzh2v63AV50uJq8ExwJFP/BXnv/RtQPJRnM+y/q3XoagK04cHmDkx5GIDiSwb3rYPRVWZPjbI9JEB\nFyvMZdjwtn2oHo3ZU8NMviTdCotzWQbiRznYtrR8rDrHK6N0d4dHJmj6xs8JfuQ3sENN9Cf87B8K\nQlDOv3uOwC/fqhH1+xiZs3j6aMHJ9RZPvZQn1hhCC8PknMpLL6TcMuf4IZ29t8WINQRIzRU4+mJp\nT59L8Sz/Hxocoa6tm2xa5/Bzk9iOVujkoRkUn5fmDrmqxl5iQ+Bq8Dz52As8+7YPIxwB7eD+46z7\nlb3S72EqyfCjp9yCd+jh46z95WsINsfIz6YYevi46wky/Ogp1rx5N+H2evLxLL0/OeJ6ggz8/DCr\nf3U3Lw0P19yxK8nLE3MzvPnNa8maM/QezJMtCP75kQKlmapfHNSxBFyzRWp7vrc/4+4m+oTjHHjt\n5gCGKfjhExlSOYk9e7KADVy/NYBlCX76ZKld9fIcP/bSCO/+5Q6EEPzkqRzTCVkHxFNFdEPwumtD\nXM14pacG/l8gBjwJjFf8veMSaaqi7x//w20ElCLtiNwzGWrcmkpYNntxrCelc1RUuwCmM3BNQN7c\nhUtREsh+V+WywQLQtuBzleIiKK9SsLJGjQOeMS8fICtn1Ij3StiRU2URXyE/xXzyNOmpEWmTW7Tw\nqqUlVhdbPLO+6n0g3IqtW24joBRW1pDbE19ZLJvjyUefrzaaAebOjklnwcmk2wgoReL8BLZlk5tJ\nuY0AF7swiWWYFBIZ+ianq9we5wemuKZZTjNcLY7V0QnsmSl0RXB2pPqBm00JZpMZdDvD+aHq+e9U\nymB6JIGdnqWnV1ZcJY7js+NMDk5ipqZJJkqjNZfHsZH1YqamGeirdmrTizajfVOYqWnMVK073svE\nsjm2TZPeb1R7vRUTWbJOQy1+rnqLYj2VJ+OozefOVmNGpuAKAk+e6q/i2MzpbNPksPTlciwW+Wwl\nz6KgY57oQfXYnJuEyjkM04K+UQNQODdsVe9ZYsOFvjyqotDXm0GIMsf53DQTgylURWV8cB6fryTs\nuxTP8v98OoKqqEwOpdxGQCnG+udRFRVVUVFY8ojAsnm+8NV/dRsBIMWbiR7HRvjcePUeK7aoshiu\nchAVgjln58jjR/s4aqXL5bWAxIkx9q2S0wPLzcul3QdXb5OdodPDJgvkKhx21IXnR/SaLcWP9sjr\n6hszSOWEy3GhMM3xC/JEg5Mmhl1a2vryHE8k6rFtwUTcchsBpTg9oLsrjq5WvNJTA69FTtvsRY5z\n/KoQ4oFLJ1kQl2jVLvUxTwuLL05P8ELFspqSe5guYH8u5fYOSq3JfwdKgzn7kG229cAB5AP2RaQF\n7VNIw4qSuKjq+pacH8sGMjViQKB+eg8br/0ggWAb0dh20qmPUNv+3Q3u7NlHica2E4y219j1XoV4\nZThWLoE5hxdNpsB8scifPvc4z1Ge3txDiLtEK/850HtVOQYwFA8GXlBqbZ9tLYrpUYFa5bPP58cX\nimCa/fSe+3YVx9nkHt777o8TCncsieOW1jWEw0FUdRaoXo3g94cIh5fekxBCqIqi3MpyeL4oYZdM\ndFEkZej8yf5f8EyubHxXystrIzE0lsbx71F9dxfybCkKuqJiLyQYELaFh6LTq1p4zTZC2OjFJL3n\n/rGK49npPazZ/GcocIm8/FHk/PEBShyHo+0XvTvL9Jl5RfPyUi8tYxl8eP8veGaslmPFNnjg1Kll\n5eWPOSLBDRukTmERahf5WRf/FYVCht5z36guqxu2k33DR1EU35I4DgRbF/uKVyxe6RGBMHCMcj5b\ncqz/3f+G6q+eE4k6fjKRSO1zF3VGuMOLYF+1JzhXWLCHgOMe9jUxwWAxz9eQuaDUmvxDJ+1rkAOw\nb6ZyIFbmmlKrsyQgW3gtWsiL4q2+1SXBnhbyoiwwAfHWSR3Anm0aQ/3frRGXJCcH6Tn0DdSARvf6\n9xKNdVLZ/vX4FeRtl++jsU7W734/iqaiejW0UPVcsRbxuqsQriCWzXH7626kbuv6qmONWzqlKVRb\nnWtD6mKbV6CoKsHmGMHWWBVWv6GDTz/3JGdnJmt4/qw6Sv/M9LI4XhNtquLYXt0JrY14FZtNK6t/\nV1OdQkudgUdk2LSm+p7XR1VaG200O8vkaK2AaGRkkH//wZfY0O1blGOpP6nmeOu299HZCopdYMPq\n6u8L+BVWtktMubLlg8viWdU0Ntz9zupragi7gsCmrdXCTl9diEhng4N1VWHeSIC/6j/O6fGxGo6/\nrk5x//BpwsAaLp/jZsVf1YOsystBP/aebegiyLp2u+rnezRYucJDUQmwfk11v0pTYU23j6IFJ098\nu4bj2alBfvavn6dzbQOKqizKM8wDj1PieM2G99K1Vt6zFd31roFaKbocN8UrjGXn5Y0fehdKxXIu\nRVNp2NAGwqJxc0dVoayoCo0bHWxjW7W7qaLwlczAohx/jQn+PjtE79jYsvLyvtuv4bv/9mn3K4dP\nzaGhs32lTaRaT821G0xUu8iWLptoqPqe792ooQqdBx/7Zg3H88kxvvrd++lutWmIXh7H3evfy651\nHjQMVjRYroVxKbav8eBVDRRbR13GUuDKeEVHBIQQDwEPgbsudcnRsGszt+//Fic+9SXmDxymrg4C\njq2q16vQ2lohFozhWq56PQptrYKEg6VCOi8lL+ZJnQND0ncdUkzyeuBe4APAP3Jx8cn/aunCQtDl\n8dGOl0RCDhdGwhBz7EYVVSHYGaU4k0MYNp6oz7UVVpQKTLfRIl4X6+2fJJU8SY24RAiSE3chrskS\n8NazceeHMZjD9CQJRdsJxjrIJsdJjw7h8zQRburE11zuCQY6IhRnctgFEy3oqcKWGleDY9Xj4Y7H\nv82hj/xPph97hrruFlp2rXbvT/cv7WLixQsUElliq5ppvWaNm3b1G3Yy+eIF8nMZol1NFNY38Mz/\nfnJxnm2TLyLXky+F4394421YlkF03UYa6lsYfnaI+EgKpaOB0E2b8ZPCqyjcttOHR1MZnVFoisG+\nrTZeNYfPLnLDdi+abTE666EuorBvuwef1yQ+PcL4eC3HQggu9N3F298ax7uzgZbGD4GYZkVLkq4V\nHbQ0tzMzO8nRk2PMZxtpbm7j2h1+ws4Spi3rPWianwuDBsGAwrXb/URCAqmPX3pcDZ73/M09eMJB\nLvz9t/HXhaQFtHOq1j1rUFSV+cEZ/NEgHTeuR3GWPbTsWg2qwnzfFN5IAH1bK88+eOIiHGdgLuPm\n5R8C54HvOp+7GMdffsf/g7c+wvF0kfZQiG5/mELPGCLgJ7d7K3osBrpKV6uXW1U4MywtzXdsjVDX\n4EdBp6PDz803eTh3dh5UD9s2B6lrjjAwOc3wwBEW43iw5y4sc5Y9t3TRd3qWprb/QWO7jqIkaGiS\njaOR/n6SswECwVbWbG6i0bEZD0V9XPOalfSemsUybVZvanL1AVcSV4Pjtttv4DU/vZ+DH/hjQNC+\nbx3BVrkkK9zZSPebdjp6D4XWa9cS6pANl1BHA6vfvJvpQ1IsWNjczPNPP3Tx8no6d0Xl9bc++n5S\nhmBNYzMtG7ogLUhZRcbPpOjrM9HVEB5fgHe9weLRQ0WyBcGeDV62r9MoqkHww2++0c+jB3Nkcjbb\n1vrZuTnA0PQEJ3uOsxjHJ3vuYiKZ5Ndub+Wpox7amz9Mc2yOtvokbS1ydGd4bILBqRimaGFtp5d9\n24MYDgVvuc3P00dyzCYtuld4uWFHEMNpNOXUJdtILxqvpo/AFUVudJLn3/UJdx/zeBy8bQKvV8Gy\nBLOzuHt8x+Pg8Qh8PgdznAUBzuuX3kMA4J+Ad1e8V5EPVT1y8GYxkdEPknP8WdsKoorK1HR5x8FE\nUl4LSCe74lTW3XFQj+dRvCpex562OJVz5+3tOQvVq+GN+ekbLM3lLn7V6dEhIn65Zk2jjmCs3VXa\nasU6FzOSRRRNdRsYejyH6Wyla+uW3KdgGY2BqxEH3v+njP/8CQCm5zJ4Q37Xo3z8+R7XXGQmkZXL\njpzVAZMHel0HsmIiy1BBzjlfjOfvAn9QcfxyOP7WgRPc/4E3Izrb+enZJv7TXF0S9LJrTuWmnQ0U\nTJWTx3OcdLaITaRB94a5/nVtqDZcOJ/mZH8cECRSAt3r45Y3r2Bk9NICouNDKSZHw07/rBlPZBWb\n1nWRVxRSqRgzqTBCwNikRcEWvOZNUmQ4OZbl0MkxbFuQmIdnTJvX3tmJpr3Sg4AXj95//A9O/cX9\nABTnMmDbrHrdDgCSvVNlQeBcBtuy6H7jLkAaA00d6pMrR+YynEvLaZZL5eVvUJuXFWCxCRYV+MxP\nHuXLv/cerm+vI9riY+cHbkFzhJXpuQL7vz+EKNrEEzovnPVgWrIgTh3McNuNgvp6D3PzJi8dTGAY\nssF18IhBx8ow2dSl8/HU+DDTI3l0xxUyl9G47vY9hCI+inmT+dkCet5Ezxc4fXCC6273EY750Ysm\np16aIJ+VZceZQxOEIl6iC5YHv5oxf7aPF97zR+6W4iOPnWbdr+2TXhCpHCNPnHENwkYfl5gvEkDP\nFBh9/LTrCXEiIe/ZlZbXi/Fcj8paNURo2yr8ioc1d64j6uzrEr6piZn5aaZSBbAFP3uuwMScbFQ/\ncqhIfVhl9UqwbMEvnssyPiPL8qeP5KiPqGRSpe2VF7/i6dlJDpyJMDQhf/tsso5VK1bQ0SrrgMPn\nI0wmDMBiNmkRCansWB9wv+P8kLwv8XmLcFBlzyZntcXS550Xjf//SoXLjJ77v+82AqBsCQxS4Fe5\ne60tylgmW24EALQ64q6LyTRAztBUDkPVI2dhbeTgTeVgzl7ga0CPkeOzs+Pk8+VGwAQ6x8hyNinJ\ny0yPMDX0JLPTz7nuUXpcOr9ZeaNGvKfHpYZB0S4tIPKI6mFAfa4ghYSG5Vb0ZSyPsAW2aWMkqzEj\nWajape/VjpkXjrqNgFJMHZbiwXw8XeMeN31kAGELivM5d6/zUgQGpMDsYv7yvVwBx/Nz/P6PnqZg\nBnj0pMyAJUHQgWOTzMzBQP80Tz1xkFTyjCsUGh7MMzWcJptIcepQtUvr+FCGif44UV+pAbY4x1Oj\n3qpB2pmJHEPnJkjPznLq0GSVIDY+nWfgjIMdnMSu0IMk4gX6TkksPVurV3ilQwjBiU99uepYamiW\n/KwUe04fqRaLZkbnXCHh1JGBqntQl5D56mIcA5ygdqW2BqRYnON+I8U9//pDEDarbt/oNgJ6ekZ4\n9sBx1AadqfgsTxy/wFz8rMtxNi8YmlJQvGHO9llOI0BGPm9zvidNe9ul83EuFXYbAQBG0WL4gnxe\nRvsTFPPlgsw0bIZ6JDY2MO82AgAs02bw/JJcn696nP3CN91GAICRLTJ3Rs7xz54YrnIJNfM68VPS\nsi9+aqTKGKrFkFXTlZbXC3leAwhs/vBb3wegflOD2wjo6Rnh4UcO4q3PMD4zxQNPHeV8/ymXY8uG\nZ0/J8rFv1HAbASDrnOdP5GltLkkRL3LFarPbCCjFCydkHTAVNx3BaTUmhGA2abqNAPd3n8xjXWWt\n13/5EYHCZG2hVar8rUVGOUvH7AVYBz52KUE+JPJuS/E/gD+u+EwWucXwC1Sv7vwCZaOKe5DDT6Wt\nLUPAXYUcw34dC42vqRMcsx0xogmeH30Qs1ipbFeJRDexZuNvA/WIRdSfwhQIIYhGLiIuUT5KXctu\nAoEFghJbgC2qrIfLJ5Uq3UUrfAHCEihXvvfQsmIxjs2CgRCixjUOwNINhG0virWZHm5o6+BDUxMI\noBv4FaCkob9ijgdH6BubIlcMMjzwz8wnzrrf+Zd/Vk8uW6nSl05h0dh2brj1j4i0NGMYtQW0go9V\na7cSq9tOar6aY0X5KOs37UPTWmoUwqoSJBaNouvJmnOqip9YtA7TqHUYU/ETi9bXHH81wjYM9Hjt\n9Ro5nSAsymXp2EKsAx/X1jXzofnZRTkG2fu/ido146VYlOOZKabmE1wTCzA3l+Kud3+WBx886Hyi\n7P5W+T4a28767g8Q9igYC1Y3AeRyFm0rV9HavovpyVqOV2+4Hp+/BVl1laPUMNALtftDFJ1ji2GL\nHXs1I79YXs4tzqPEig5W3TnpwMcef4wPFVNLLq/vQebnLyKtEdcjef4ecFc6zujkJF172pbEccT/\nHlQrSi67CMcFm5UtzaxdtZP+4Voh4I5NuwkHmpBrF6rTabZBIV/LWUEXKKZBMV9byRXkX/2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5E+LnUjsl5PANnA9dGrXAdsQTzmv41eNRubzYCixAoOo8yCva007/xlGtulbDaIYkGLEeRUpWSS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1PnbtCEAhh9dr46kfa+HgS1Fi8QJr13rZuVUYiS6XzE++o5lDR6Kms2Bxn99hl/mJd7Rw6KUV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vT8ejfJkMAiwwF6HhLbK6lwgu4Hd5EIiJWx6GiQ7gd3HalZDinHnYZY/SJJugn4iKIoC2c5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\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rHeHaoDbV7aj423nW9uJrM2NVchRP+X16tnYc6BXuHSOry9TTJdZrVYU9e2Bg4NIw7h14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\nfa7Vxnw8mMqxvPlz/0LL/z6mH0e7Rph963J9EYj3hzjy0g793UQ6hmi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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116408250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import clone\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.ensemble import (RandomForestClassifier, ExtraTreesClassifier,\n",
    "                              AdaBoostClassifier)\n",
    "from sklearn.externals.six.moves import xrange\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Parameters\n",
    "n_classes = 3\n",
    "n_estimators = 30\n",
    "plot_colors = \"ryb\"\n",
    "cmap = plt.cm.RdYlBu\n",
    "plot_step = 0.02  # fine step width for decision surface contours\n",
    "plot_step_coarser = 0.5  # step widths for coarse classifier guesses\n",
    "RANDOM_SEED = 13  # fix the seed on each iteration\n",
    "\n",
    "# Load data\n",
    "iris = load_iris()\n",
    "\n",
    "plot_idx = 1\n",
    "\n",
    "models = [DecisionTreeClassifier(max_depth=None),\n",
    "          RandomForestClassifier(n_estimators=n_estimators),\n",
    "          ExtraTreesClassifier(n_estimators=n_estimators),\n",
    "          AdaBoostClassifier(DecisionTreeClassifier(max_depth=3),\n",
    "                             n_estimators=n_estimators)]\n",
    "\n",
    "for pair in ([0, 1], [0, 2], [2, 3]):\n",
    "    for model in models:\n",
    "        # We only take the two corresponding features\n",
    "        X = iris.data[:, pair]\n",
    "        y = iris.target\n",
    "\n",
    "        # Shuffle\n",
    "        idx = np.arange(X.shape[0])\n",
    "        np.random.seed(RANDOM_SEED)\n",
    "        np.random.shuffle(idx)\n",
    "        X = X[idx]\n",
    "        y = y[idx]\n",
    "\n",
    "        # Standardize\n",
    "        mean = X.mean(axis=0)\n",
    "        std = X.std(axis=0)\n",
    "        X = (X - mean) / std\n",
    "\n",
    "        # Train\n",
    "        clf = clone(model)\n",
    "        clf = model.fit(X, y)\n",
    "\n",
    "        scores = clf.score(X, y)\n",
    "        # Create a title for each column and the console by using str() and\n",
    "        # slicing away useless parts of the string\n",
    "        model_title = str(type(model)).split(\".\")[-1][:-2][:-len(\"Classifier\")]\n",
    "        model_details = model_title\n",
    "        if hasattr(model, \"estimators_\"):\n",
    "            model_details += \" with {} estimators\".format(len(model.estimators_))\n",
    "        print( model_details + \" with features\", pair, \"has a score of\", scores )\n",
    "\n",
    "        plt.subplot(3, 4, plot_idx)\n",
    "        if plot_idx <= len(models):\n",
    "            # Add a title at the top of each column\n",
    "            plt.title(model_title)\n",
    "\n",
    "        # Now plot the decision boundary using a fine mesh as input to a\n",
    "        # filled contour plot\n",
    "        x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "        y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n",
    "                             np.arange(y_min, y_max, plot_step))\n",
    "\n",
    "        # Plot either a single DecisionTreeClassifier or alpha blend the\n",
    "        # decision surfaces of the ensemble of classifiers\n",
    "        if isinstance(model, DecisionTreeClassifier):\n",
    "            Z = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "            Z = Z.reshape(xx.shape)\n",
    "            cs = plt.contourf(xx, yy, Z, cmap=cmap)\n",
    "        else:\n",
    "            # Choose alpha blend level with respect to the number of estimators\n",
    "            # that are in use (noting that AdaBoost can use fewer estimators\n",
    "            # than its maximum if it achieves a good enough fit early on)\n",
    "            estimator_alpha = 1.0 / len(model.estimators_)\n",
    "            for tree in model.estimators_:\n",
    "                Z = tree.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "                Z = Z.reshape(xx.shape)\n",
    "                cs = plt.contourf(xx, yy, Z, alpha=estimator_alpha, cmap=cmap)\n",
    "\n",
    "        # Build a coarser grid to plot a set of ensemble classifications\n",
    "        # to show how these are different to what we see in the decision\n",
    "        # surfaces. These points are regularly space and do not have a black outline\n",
    "        xx_coarser, yy_coarser = np.meshgrid(np.arange(x_min, x_max, plot_step_coarser),\n",
    "                                             np.arange(y_min, y_max, plot_step_coarser))\n",
    "        Z_points_coarser = model.predict(np.c_[xx_coarser.ravel(), yy_coarser.ravel()]).reshape(xx_coarser.shape)\n",
    "        cs_points = plt.scatter(xx_coarser, yy_coarser, s=15, c=Z_points_coarser, cmap=cmap, edgecolors=\"none\")\n",
    "\n",
    "        # Plot the training points, these are clustered together and have a\n",
    "        # black outline\n",
    "        for i, c in zip(xrange(n_classes), plot_colors):\n",
    "            idx = np.where(y == i)\n",
    "            plt.scatter(X[idx, 0], X[idx, 1], c=c, label=iris.target_names[i],\n",
    "                        cmap=cmap)\n",
    "\n",
    "        plot_idx += 1  # move on to the next plot in sequence\n",
    "\n",
    "plt.suptitle(\"Classifiers on feature subsets of the Iris dataset\")\n",
    "plt.axis(\"tight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# VotingClassifier is a manual ensembler\n",
    "# It will take in several classifier models and have them duke it out\n",
    "# You can either have them vote hard or soft\n",
    "# A hard vote is like a ballot system, each model gets a set number of votes\n",
    "# A soft vote includes models combing their predict_proba methods together\n",
    "\n",
    "\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Breast Cancer Detection Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>Uniformity of Cell Shape</th>\n",
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       "      <th>Bare Nuclei</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1016277</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1017023</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  Clump Thickness  Uniformity of Cell Size  \\\n",
       "0  1000025                5                        1   \n",
       "1  1002945                5                        4   \n",
       "2  1015425                3                        1   \n",
       "3  1016277                6                        8   \n",
       "4  1017023                4                        1   \n",
       "\n",
       "   Uniformity of Cell Shape  Marginal Adhesion  Single Epithelial Cell Size  \\\n",
       "0                         1                  1                            2   \n",
       "1                         4                  5                            7   \n",
       "2                         1                  1                            2   \n",
       "3                         8                  1                            3   \n",
       "4                         1                  3                            2   \n",
       "\n",
       "  Bare Nuclei  Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
       "0           1                3                1        1      2  \n",
       "1          10                3                2        1      2  \n",
       "2           2                3                1        1      2  \n",
       "3           4                3                7        1      2  \n",
       "4           1                3                1        1      2  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)\n",
    "breast_cancer.columns = ['ID', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']\n",
    "breast_cancer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>ID</th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1057013</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1096800</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>?</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>1183246</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1184840</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>1193683</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  Clump Thickness  Uniformity of Cell Size  \\\n",
       "23   1057013                8                        4   \n",
       "40   1096800                6                        6   \n",
       "139  1183246                1                        1   \n",
       "145  1184840                1                        1   \n",
       "158  1193683                1                        1   \n",
       "\n",
       "     Uniformity of Cell Shape  Marginal Adhesion  Single Epithelial Cell Size  \\\n",
       "23                          5                  1                            2   \n",
       "40                          6                  9                            6   \n",
       "139                         1                  1                            1   \n",
       "145                         3                  1                            2   \n",
       "158                         2                  1                            3   \n",
       "\n",
       "    Bare Nuclei  Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
       "23            ?                7                3        1      4  \n",
       "40            ?                7                8        1      2  \n",
       "139           ?                2                1        1      2  \n",
       "145           ?                2                1        1      2  \n",
       "158           ?                1                1        1      2  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# They have ? where values are missing\n",
    "breast_cancer[breast_cancer['Bare Nuclei']=='?'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                             0\n",
       "Clump Thickness                0\n",
       "Uniformity of Cell Size        0\n",
       "Uniformity of Cell Shape       0\n",
       "Marginal Adhesion              0\n",
       "Single Epithelial Cell Size    0\n",
       "Bare Nuclei                    0\n",
       "Bland Chromatin                0\n",
       "Normal Nucleoli                0\n",
       "Mitoses                        0\n",
       "Class                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer.replace(to_replace='?', value=np.nan, inplace=True)\n",
    "# replace ? with null values\n",
    "\n",
    "breast_cancer.dropna(inplace=True)\n",
    "# drop said null values\n",
    "\n",
    "breast_cancer.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(683, 11)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.650073\n",
       "4    0.349927\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer['Class'].value_counts(normalize=True)\n",
    "# score to beat is 65%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "X = breast_cancer.drop(['ID', 'Class'], axis=1)\n",
    "y = breast_cancer.Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rf 0.964941876183\n",
      "\n",
      "decision 0.935593403623\n",
      "\n"
     ]
    }
   ],
   "source": [
    "models = [('rf', RandomForestClassifier(n_estimators=10)),\n",
    "          ('decision', DecisionTreeClassifier(max_depth=3))\n",
    "         ] \n",
    "# try a few models by themselves\n",
    "for index, model in models:\n",
    "    print index, np.mean(cross_val_score(model, X, y, cv=5, scoring='accuracy'))\n",
    "    print\n",
    "# each one can beat the null accuracy by itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94295755609624232"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vc1 = VotingClassifier(models, voting='hard')\n",
    "np.mean(cross_val_score(vc1, X, y, cv=5, scoring='accuracy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9414328196809949"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vc2 = VotingClassifier(models, voting='soft')\n",
    "np.mean(cross_val_score(vc2, X, y, cv=5, scoring='accuracy'))\n",
    "# Neither of these are better than a single random forest!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94443903757772374"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vc3 = VotingClassifier(models, voting='soft', weights=[2, 1])\n",
    "np.mean(cross_val_score(vc1, X, y, cv=5, scoring='accuracy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.grid_search import GridSearchCV\n",
    "params = {'voting':['soft', 'hard'], 'weights':[[1, 1], [2, 1], [1, 2]], 'rf__n_estimators':range(10,50,10), 'decision__max_depth':[None, 1, 2]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "grid = GridSearchCV(vc3, params, cv=5, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=VotingClassifier(estimators=[('rf', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            o...it=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best'))],\n",
       "         voting='soft', weights=[2, 1]),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'voting': ['soft', 'hard'], 'weights': [[1, 1], [2, 1], [1, 2]], 'rf__n_estimators': [10, 20, 30, 40], 'decision__max_depth': [None, 1, 2]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# WARNING SUPER SLOWWWWWWW\n",
    "# WARNING SUPER SLOWWWWWWW\n",
    "grid.fit(X, y)  # WARNING SUPER SLOWWWWWWW # WARNING SUPER SLOWWWWWWW # WARNING SUPER SLOWWWWWWW \n",
    "# WARNING SUPER SLOWWWWWWW\n",
    "# WARNING SUPER SLOWWWWWWW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96925329428989748"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_\n",
    "\n",
    "# Ah there it is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'decision__max_depth': None,\n",
       " 'rf__n_estimators': 40,\n",
       " 'voting': 'hard',\n",
       " 'weights': [2, 1]}"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A1WpzBdZMHiGspubbtdYz4sRdey6/Umos8LvIedKwBoI/qLV+K85+8W7MDW0/WXGPFZl1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1240a8f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn import datasets\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "# Loading some example data\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:, [0, 2]]\n",
    "y = iris.target\n",
    "\n",
    "# Training classifiers\n",
    "clf1 = DecisionTreeClassifier(max_depth=4)\n",
    "clf2 = KNeighborsClassifier(n_neighbors=7)\n",
    "clf3 = SVC(kernel='rbf', probability=True)\n",
    "eclf = VotingClassifier(estimators=[('dt', clf1), ('knn', clf2),\n",
    "                                    ('svc', clf3)],\n",
    "                        voting='soft', weights=[2, 1, 2])\n",
    "\n",
    "clf1.fit(X, y)\n",
    "clf2.fit(X, y)\n",
    "clf3.fit(X, y)\n",
    "eclf.fit(X, y)\n",
    "\n",
    "# Plotting decision regions\n",
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                     np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "f, axarr = plt.subplots(2, 2, sharex='col', sharey='row', figsize=(10, 8))\n",
    "\n",
    "for idx, clf, tt in zip(product([0, 1], [0, 1]),\n",
    "                        [clf1, clf2, clf3, eclf],\n",
    "                        ['Decision Tree (depth=4)', 'KNN (k=7)',\n",
    "                         'Kernel SVM', 'Soft Voting']):\n",
    "\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    axarr[idx[0], idx[1]].contourf(xx, yy, Z, alpha=0.4)\n",
    "    axarr[idx[0], idx[1]].scatter(X[:, 0], X[:, 1], c=y, alpha=0.8)\n",
    "    axarr[idx[0], idx[1]].set_title(tt)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [sfdat28]",
   "language": "python",
   "name": "Python [sfdat28]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}