370_12_Classification-complete.ipynb

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   "source": [
    "# SI 370: Classification\n",
    "\n",
    "## Objectives:\n",
    "- Be able to perform classifications using the following methods and interpret their results.\n",
    "  - Naive Bayes\n",
    "  - Decision Tree\n",
    "  - Random Forest\n",
    "  \n",
    "Note: some slides from Eytan Adar and Kevyn Collins-Thompson\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOTE: use conda to install the ```pydotplus``` and ```graphviz``` packages before starting this notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide02.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide03.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide04.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide05.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide06.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide07.png)"
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  },
  {
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide08.png)"
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  },
  {
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide09.png)"
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  },
  {
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide10.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide11.png)"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide12.png)"
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  },
  {
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide13.png)"
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  },
  {
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide14.png)"
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  },
  {
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide15.png)"
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  },
  {
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide16.png)"
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  },
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide17.png)"
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  },
  {
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide18.png)"
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  },
  {
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide19.png)"
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  },
  {
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide20.png)"
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  },
  {
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide21.png)"
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  },
  {
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide22.png)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide23.png)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide24.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide25.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide26.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide27.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide28.png)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide29.png)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide30.png)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](resources/classification/Slide31.png)"
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  },
  {
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   "metadata": {},
   "source": [
    "![](resources/classification/Slide32.png)"
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  },
  {
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   "source": [
    "![](resources/classification/Slide33.png)"
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  },
  {
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   "source": [
    "![](resources/classification/Slide43.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification using scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import sklearn as sk\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import sklearn.ensemble as skens\n",
    "import sklearn.metrics as skmetric\n",
    "import sklearn.naive_bayes as sknb\n",
    "import sklearn.tree as sktree\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(style='white', color_codes=True, font_scale=1.3)\n",
    "import sklearn.externals.six as sksix\n",
    "import IPython.display as ipd\n",
    "\n",
    "from sklearn import metrics\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Filter all warnings.\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the usual dataset\n",
    "df_iris = sns.load_dataset('iris')\n",
    "df_iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split into train and test (we'll see a better way in the next block)\n",
    "np.random.seed(200)\n",
    "msk = np.random.rand(len(df_iris)) <= 0.7\n",
    "df_iris_train = df_iris[msk]\n",
    "df_iris_test = df_iris[~msk]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(98, 52)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_iris_train),len(df_iris_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same thing, this time using a scikit_learn function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iris_train,df_iris_test = train_test_split(df_iris, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105, 45)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_iris_train),len(df_iris_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Decision Trees\n",
    "\n",
    "## 1.1 Train a decision tree classifier\n",
    "\n",
    "We will train a decision tree classifier to classify iris species. Here are the specifications:\n",
    "- __Objective__: predict which species an iris instance belongs to.\n",
    "- __Possible classes__: \"setosa\", \"versicolor\", and \"virginica\"\n",
    "- __Features__: all four features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=1,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Build and train the classifier\n",
    "dt_model = sktree.DecisionTreeClassifier(max_depth=1,\n",
    "                                         criterion='entropy')\n",
    "# given first 4 columns, learn the species\n",
    "dt_model.fit(df_iris_train.ix[:,:4],df_iris_train.species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.externals.six import StringIO  \n",
    "from IPython.display import Image  \n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()\n",
    "export_graphviz(dt_model, out_file=dot_data,  \n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To interpret the above plot, please read through the following paragraphs quoted from http://www.thegrimmscientist.com/tutorial-decision-trees/, which has slightly different results than our data set because they used alightly different data.  Their results looks like:\n",
    "\n",
    "![](http://www.thegrimmscientist.com/content/images/2017/06/TreeDepth1.png)\n",
    "\n",
    "> This image needs a bit of explanation. The top node represents the whole dataset. The whole dataset has 150 samples, as seen in the third line of that first node. We'll explain entropy more later, but for now, think of it as a formalization of the amount of uncertainty in the data. The first line on the top node gives us the decision our tree magically found. When that decision \"petal width is less than or equal to 0.80 cm\" is true for a given sample, that sample is assigned to the bottom left node. When it is false, the sample is assigned to the bottom right node.\n",
    "\n",
    ">On these two leaf nodes, the number of samples is shown on the second line. The number of samples broken down by class is on the third line. On the left leaf node, all 50 of the samples in class 1 and none from the other two classes are represented. Given the decision and the data at hand, we are certain to have a sample in class 1 (setosa). Since we are totally certain about that, uncertainty (entropy) is 0.\n",
    "\n",
    ">When our petal width <= 0.80 decider is false, the given sample is assigned to the right node. It works out that all 50 samples from class 2 (versicolor) and all 50 samples from class 3 (virginica) are assigned to this right leaf. Thus, when we're at this leaf given the decision, we're evenly unsure whether we're in class 2 or class 3. The amount of uncertainty this works out to be is 1.0. Again, we'll explain the math and meaning behind that later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1: Train a decision tree with `max_depth=2`. Plot it as a graph.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### YOUR CODE HERE\n",
    "\n",
    "dt_model = sktree.DecisionTreeClassifier(max_depth=2,\n",
    "                                         criterion='entropy')\n",
    "# given first 4 columns, learn the species\n",
    "dt_model.fit(df_iris_train.ix[:,:4],df_iris_train.species)\n",
    "dot_data = StringIO()\n",
    "export_graphviz(dt_model, out_file=dot_data,  \n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Deploying a Decision Tree Classifier\n",
    "\n",
    "With our trained decision tree with `max_depth=2` , we can see how good our fit is by using our testing dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>predicted_label_tree</th>\n",
       "      <th>predicted_rf_tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>virginica</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species  \\\n",
       "123           6.3          2.7           4.9          1.8  virginica   \n",
       "132           6.4          2.8           5.6          2.2  virginica   \n",
       "103           6.3          2.9           5.6          1.8  virginica   \n",
       "29            4.7          3.2           1.6          0.2     setosa   \n",
       "107           7.3          2.9           6.3          1.8  virginica   \n",
       "38            4.4          3.0           1.3          0.2     setosa   \n",
       "101           5.8          2.7           5.1          1.9  virginica   \n",
       "141           6.9          3.1           5.1          2.3  virginica   \n",
       "3             4.6          3.1           1.5          0.2     setosa   \n",
       "106           4.9          2.5           4.5          1.7  virginica   \n",
       "\n",
       "    predicted_label_tree predicted_rf_tree  \n",
       "123           versicolor         virginica  \n",
       "132            virginica         virginica  \n",
       "103            virginica         virginica  \n",
       "29                setosa            setosa  \n",
       "107            virginica         virginica  \n",
       "38                setosa            setosa  \n",
       "101            virginica         virginica  \n",
       "141            virginica         virginica  \n",
       "3                 setosa            setosa  \n",
       "106           versicolor        versicolor  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is testing the model \n",
    "predicted_labels = dt_model.predict(df_iris_test.ix[:,:4])\n",
    "df_iris_test['predicted_label_tree'] = predicted_labels\n",
    "df_iris_test.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can determine our overall accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = accuracy_score(df_iris_test.species, predicted_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 82.22%\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: {0:.2%}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# utility class to compare the predictions versus ground truth\n",
    "def comparePlot(input_frame,real_column,predicted_column):\n",
    "    df_a = input_frame.copy()\n",
    "    df_b = input_frame.copy()\n",
    "    df_a['label_source'] = 'Species'\n",
    "    df_b['label_source'] = 'Classifier'\n",
    "    df_a['label'] = df_a[real_column]\n",
    "    df_b['label'] = df_b[predicted_column].apply(lambda x: 'Predict %s'%x)\n",
    "    df_c = pd.concat((df_a, df_b), axis=0, ignore_index=True)\n",
    "    sns.lmplot(x='sepal_length', y='sepal_width', col='label_source',\n",
    "               hue='label', data=df_c, fit_reg=False, size=4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5+H/ChAk4ceIEvvzyS6Snp2vuejg6OsLb2xsA4O7ujsOHD+Ps2bPIyMhAWFiY1pPW5WndujX8/f0RERGBP/74Azdv3sTy5ctx7do19OvXD71790anTp0wa9YsnDx5EpmZmfj2228RHR1d4x8DTpo0CUqlEiEhITh69CiysrKQlJSEH374AevWrSvzUfL//vc/HDt2DOnp6YiIiMC1a9cwfvx4AMD777+P9PR0zJ8/H9euXcOVK1fw0UcfISkpSfMHbOLEidixYwd27dqFzMxM7Nu3D9u3by/3Y+l33nkHcrkcM2bMwOXLl5GamorZs2fj1KlTcHJyAsdxuHnzJsLCwnD27FncunULhw4dQm5ubpkhHKR20TmvrT6d81ZWVli8eDG2b9+OuXPn4uLFi8jMzMSvv/6K9957DwYGBggNDS3zuqZNm4LneRw6dAiZmZlISUnBvHnzcOXKFc17ampqii1btmDVqlW4efMmrl69iiNHjqBNmzawtLSESCTCmjVr8MMPPyAzMxOXLl3CiRMnXuh8tbe3h6+vL5YsWYIjR44gMzMTO3fuxMqVKzXDYQghVVBnj/jpkaefRE9KSmIjR45krq6urEePHuz//b//x3755Rfm4ODATpw4wRgrfaJ72bJlbNy4cczZ2Zn169eP/fDDD1rrzMrKYh999BHr1q0bc3V1ZcOHD2e///67Znl1n0RXq9Vs7dq1rH///szZ2Zn16tWLhYeHa2ZKYIyxI0eOsCFDhrBOnToxT09PtmDBAiaTyTTLc3Jy2OTJk5mrqyvr2bMnW7FiBZs/f36ZJ9EfP739WH5+PgsLC2Oenp7M1dWVjRgxgp07d06zXCaTsU8++YT17NmTde7cmb355pssNja2yvtWHVlZWWz+/PmsX79+zNnZmbm5ubGQkBB27NgxTZ/Hs0+sWbOGvfnmm6xz587s7bffZn/++afWus6dO8dGjx7NXFxcWLdu3dikSZNYWlqaZrlarWabNm1i/fv3Z506dWIDBw7UPCnOWNkn+5OTk9n48eNZly5dmLu7O3v33XfZ33//rVmel5fH5s2bx3r16sU6derEXn/9dfb999/r4jCR56BzvuGc84wxlpiYyKZOncp69+7NnJ2dWf/+/dmSJUtYbm6ups+z+/LLL7+wgQMHMmdnZ9anTx82Z84c9t1337HOnTuzhw8fMsYYO3bsGAsODtacsx9++CG7efOmZp07d+5kAQEBzMXFhXXv3p3NmTOH3bt3jzGmPfsEY4x98cUXrF+/fprXPvt+FxcXs8jISObt7a05/zdt2lRh/ISQsjjGnvr8jhDyXFlZWfDz88P69evRp0+fug6HEEIIITWAhk8QQgghhBC9R7NPNHIXL17EuHHjKu3z2muvvdDDHYSQ+ofOeUIIeTE0fKKRUygUuHPnTqV9DAwM0KJFi1qKiBCiS3TOE0LIi2m0SbEgCLhz5w6aNWtWZj5ZQkjjR9cAQggh1dFoxxTfuXMHfn5+z71jQghpnOgaQAghpDoabVJMCCGEEEJIVVFSTAghhBBC9B4lxYQQQgghRO9RUkwIIYQQQvQeJcWEEEIIIUTvUVJMCCGEEEL0HiXFhBBCCCFE71FSTAghhBBC9B6VeWqECtMu4MGZWAh5ueAtbNDEMwgm7dzrOixCSC1JTc7F6fg05MmKYGFlDC+fdrDvYFPXYRFCSL1Gd4obmcK0C7h3OBpCwX1wUlMIBfdx73A0CtMu1HVohJBakJqci4Mxl5H/UA6pkQHyH8pxMOYyUpNz6zo0Qgip1ygpbmQenIkFxDxEBlJwHAeRgRQQ86XthJBG73R8GsQ8B0NDHhxX+l3Mczgdn1bXoRFCSL1GSXEjI+TlguMlWm0cL4GQR3eJCNEHebIiGBiItdoMDMTIkxXVUUSEENIwUFLcyPAWNmCCQquNCQrwFjSekBB9YGFlDKVSpdWmVKpgYWVcRxERQkjDQElxI9PEMwhQCVAr5WCMQa2UAyqhtJ0Q0uh5+bSDSmAoKRHAWOl3lcDg5dOurkMjhJB6jZLiRsaknTus/ceDN7UEkxeAN7WEtf94mn2CED1h38EGAUOdYWYuhbxYCTNzKQKGOtPsE4QQ8hx1MiXb119/jdjYWPz222/lLs/Pz0dYWBji4+NhbGyMcePGYezYsbUcZcNl0s6dkmBC9Jh9BxtKggkhpJpqPSlOSkrCunXrYGtrW2Gf+fPnIzc3F9u2bcONGzcwb9482NjYYNCgQbUYKSGEEEII0Re1mhQrlUqEhobCzc0N2dnZ5fa5desWjh49iv3798PBwQFOTk5ITU3Fpk2bKCkmhBBCCCE6Uatjir/99lu0atUK/v7+Ffb566+/YG5uDgcHB02bh4cHrly5AoVCUeHrCCGEEEIIeVG1lhQnJSVh586dCAsLq7RfTk4ObGy0x8I1bdoUarUaubk01y4hhBBCCKl5tZIUl5SUIDQ0FHPmzEHTpk0r7VtcXAxDQ0Ottsc/l5SU6CxGQgghhBCiv2olKf72229hY2OD4ODg5/aVSqVlkt/HPxsZGekkPkIIIYQQot9q5UG7/fv34+7du3BzcwNQ+sCdIAhwc3PD+vXr4eHhoenbrFkz3L17V+v1ubm54Hke1tbWtREuIYQQQgjRM7WSFG/ZsgWCIGh+3r9/P3bt2oUtW7aUmZqtS5cuyMvLQ1paGtq1K63AlJiYiI4dO0IikdRGuIQQQgghRM/USlJsZ2en9bOlpSV4nkfr1q0BAHl5eRCLxTAzM4OdnR369euHefPmISwsDJmZmdiwYQMiIiJqI1RCCCGEEKKH6kWZ52nTpmklvZGRkWjWrBlGjRqFiIgIfPTRRwgICKjDCAkhhBBCSGPGMcZYXQehC1lZWfDz80NcXBxatmxZ1+HUW4VpF/DgTCyEvFzwFjZo4hlEJaJJo0DXgKpJTc7F6fg05MmKYGFlDC+fdlQimhCil+rFnWJSNwrTLuDe4WgIBffBSU0hFNzHvcPRKEy7UNehEUJqQWpyLg7GXEb+QzmkRgbIfyjHwZjLSE2mOeEJIfqHkmI99uBMLCDmITKQguM4iAykgJgvbSeENHqn49Mg5jkYGvLguNLvYp7D6fi0ug6NEEJqHSXFekzIywXHa8/owfESCHl0l4gQfZAnK4KBgVirzcBAjDxZUR1FRAghdYeSYj3GW9iACQqtNiYowFvQeEJC9IGFlTGUSpVWm1KpgoWVcR1FRAghdYeSYj3WxDMIUAlQK+VgjEGtlAMqobSdENLoefm0g0pgKCkRwFjpd5XA4OXTrq5DI4SQWkdJsR4zaecOa//x4E0tweQF4E0tYe0/nmafIERP2HewQcBQZ5iZSyEvVsLMXIqAoc40+wQhRC/VSvEOUn+ZtHOnJJgQPWbfwYaSYEIIAd0pJoQQQgghhO4UE0KqLiE5BzHxqciRFcHWyhhDfezh0cG2rsMihNQiWeIFZO+NhTwnF1JbG7QIDoJVV/rEkTR8dKeYEFIlCck5iIq5hPsPi2FmxOP+w2JExVxCQnJOXYdGCKklssQLSI+KhkJ2H2JTUyhk95EeFQ1ZIhV9Ig0fJcWEkCqJiU8Fz3OQPir0IDXkwfMcYuJT6zo0Qkgtyd4bC47nIZaWFn0SS6XgeB7Ze6noE2n4KCkmhFRJjqwIkmcKPUgMxMilQg+E6A15Ti5EEu2iTyKJBPIcKvpEGj5KigkhVWJrZQzFM4UeFEoVbKjQAyF6Q2prA7VCu+iTWqGA1JZmMCENHyXFhJAqGepjD0FgkD8q9CAvESAIDEN97Os6NEJILWkRHAQmCFDJS4s+qeRyMEFAi2Aq+kQaPkqKCSFV4tHBFhOHusDS3AgFxQIszY0wcagLzT5BiB6x6uqOthPHQ2JlCVVBASRWlmg7cTzNPkEaBZqSjRBSZR4dbCkJJkTPWXV1pySYNEp0p5gQQgghhOg9SooJIYQQQojeo6SYEEIIIYToPUqKCSGEEEKI3qOkmBBCCCGE6D1KigkhhBBCiN6jpJgQQgghhOg9SooJIYQQQojeo6SYEEIIIaQOZGVlwdHRETKZrNJ+Y8aMwYYNG15oG46Ojvjnn39e6LX6hpJiQgghhBCi96jMMyH1TEJyDmLiU5EjK4KtlTGG+thTaWVC9Igs8QKy98ZCnpMLqa0NWgQHUVllPXDkyBGsW7cOmZmZYIyhf//+CA8Ph4GBAQAgNTUVb731FrKystCtWzcsXrwY1tbWAIC4uDh8/fXXyM7Ohr29PRYuXIiOHTvW5e40SHSnmJB6JCE5B1Exl3D/YTHMjHjcf1iMqJhLSEjOqevQCCG1QJZ4AelR0VDI7kNsagqF7D7So6IhS7xQ16ERHSouLsb//d//4ZNPPsGff/6J3bt347fffsOxY8c0fU6cOIGIiAjEx8dDJBJh/vz5AIB//vkHs2fPRmhoKM6ePYtRo0Zh3LhxePjwYV3tToNFSTEh9UhMfCp4noPUkAfHlX7neQ4x8al1HRohpBZk740Fx/MQS6XgOK70O88je29sXYdGdEgqleLnn39G165dkZ+fD5lMBgsLC+TkPLkh8s4778DJyQlGRkaYOXMmjh8/joKCAuzevRuDBg2Cl5cXeJ5HUFAQWrdujcOHD9fhHjVMNHyCkHokR1YEMyPt01JiIEaurKiOIiKE1CZ5Ti7EpqZabSKJBPKc3DqKiNQGjuOwZ88e7N69G1KpFB07doRSqYRardb0sbOz0/y7efPmAIA7d+4gOzsb586d00qCBUFAdnZ27e1AI0FJMSH1iK2VMe4/LIbU8MmpqVCqYGNlXIdREUJqi9TWpnTohFSqaVMrFJDa2tRhVETXTpw4gdjYWOzZswe2tqXPkAQGBmr1yc198h+jW7duAShNjm1sbDB69GjMnTtXs/zGjRt45ZVXaiHyxoWGTxBSjwz1sYcgMMhLBDBW+l0QGIb62Nd1aISQWtAiOAhMEKCSy8EYK/0uCGgRHFTXoREdys7OhlgshqGhIZRKJbZs2YJr165BEARNn+3btyMtLQ0FBQVYsWIFBg0aBBMTEwwZMgQxMTH466+/wBjDmTNnMHjwYFy+fLkO96hhqtad4szMTFy5cgVyubzMsiFDhtRYUIToK48OtsBQF8TEpyJXVgQbmn2CEL1i1dUdmDieZp/QM8HBwbh27Rr8/PwgkUjg7u6OwMBApKWlafr4+fnhww8/xP3799G3b18sXLgQANCtWzcsXLgQCxYsQFZWFmxsbBAeHo6ePXvW1e40WBxjjFWl4969e/Hpp59CpVKVXQnHITk5ucaDexlZWVnw8/NDXFwcWrZsWdfhEEJqGV0DCCGEVEeV7xSvXbsWgwcPxscffwwLC4sX2ti///6LJUuWICEhASYmJpr1PZ6D72mbNm1CZGSkVpuPjw+ioqJeaNuEEEIIIYRUpMpJ8e3bt/Hhhx/CxubFBvur1WpMmDABjo6O2LNnD/777z/MmTMHhoaGmDFjRpn+qampGDZsmNYyiUTyQtsmhBBCCCGkMlVOiu3t7ZGVlYVWrVq90Ibu3r0LJycnhIeHw9zcHG3btoW/vz/Onz9fbv/U1FQEBgaiadOmL7Q98uIK0y7gwZlYCHm54C1s0MQzCCbtaDwbIfokNTkXp+PTkCcrgoWVMbx82sG+A82AQAhpvCqdfSIjI0PzNWzYMCxatAhxcXFIS0vTWpaRkfHcDdna2uKrr76Cubk5ACAlJQVxcXHw9PQst39qaipee+21F9gl8jIK0y7g3uFoCAX3wUlNIRTcx73D0ShMo2pKhOiL1ORcHIy5jPyHckiNDJD/UI6DMZeRmkxz5RJCGq9K7xS/8cYb4DgOAPD4ebwpU6Zo2h63V/dBu8GDB+Pq1atwdnbG2LFjyyy/ffs2CgoK8PPPP2PhwoUQiUTw9/fHtGnTYGhoWOXtkOp7cCYWEPMQGZTOkckZSKGGHA/OxNLdYkL0xOn4NIh5DoaP5ss2NORRAgGn49PobjEhpNGqNCn+4YcfdLLRyMhIPHjwAJ999hmmTZuGjRs3ai1PTS0tadukSRN88803uHHjBiIiIvDgwQOEh4frJCZSSsjLBSfVrqbE8RIIeXSHiBB9kScrgtRI+wFoAwMx8qiyIiGkEauASIg3AAAgAElEQVQ0Ke7evbvm3/v27UNAQECZO7VFRUXYuXOnVt/n6dixIwBg6dKlGD58OK5fv4727dtrlnt7e+PMmTOwsrICADg5OQEAZs6cifnz59MDdzrEW9iUDp0weFJNiQkK8BZ0d4gQfWFhZYz8h3LNnWIAUCpVsKDKioSQRqzSMcUqlQolJSUoKSnBvHnzIJPJND8//kpOTsaqVaueu6Hc3FwcOXJEq+1xIiyTycr0f5wQP2Zvbw+VSoW7d+8+d1vkxTXxDAJUAtTK0mpKaqUcUAml7YQQveDl0w4qgaHkUWXFkhIBKoHBy6ddXYdGCCE6U+md4u3btyMiIgIcx4Exhn79+pXbr2vXrs/dUGZmJqZPn641kf4///wDkUiEdu20L7Tbtm3D5s2bcfToUc345aSkJBgbG6NZs2ZV2jHyYkzauQP+42n2CUL0mH0HGwQMdabZJwgheqXSpHjUqFEwMzODWq3GJ598gnnz5sHMzEyznOM4mJiYVDiDxNO6dOkCFxcXhIaGYsGCBcjLy8OCBQswcuRIvPLKK8jLy4NYLIaZmRm8vb2xYsUKfPbZZ3j33XeRlpaG5cuXY8KECeD5alWmJi/ApJ07JcGE6Dn7DjaUBBNC9EqlGSbP8xgyZAiA0gR40KBBLzz7g1gsxjfffIOIiAiEhIRALBYjKCgIs2bNAgBMmzYNdnZ2iIyMxKuvvor169djxYoVCAoKgrm5OUaNGoVJkya90LYJIYQQQnTB19cX48aNQ0hIiM5fHxoaiqKiIqxevfqFtkUqx7HHc62V4+TJk1VeUe/evWskoJqSlZUFPz8/reEa9YkuC2Q09uIbCck5iIlPRY6sCLZWxhjqYw+PDrZ1HRapZ+r7NQDQXYGMxl54Q5Z4Adl7YyHPyYXU1gYtgoNg1bXxXONIwyKTyWBkZAQjIyOdvz4/Px+MMU3NB1KzKk2KnZycNOOJn52bGIBWW3XmKa4N9fkP4uMCGRDz4HgJmKAAVAKs/ce/dPKqy3XXBwnJOYiKuQSe5yAxEEOhVEEQGCYOdaHEmGipz9cA4EmBDDHPwcBADKVSBZXAEDDU+aUSWF2tt76QJV5AelQ0OJ6HSCKBWqEAEwS0nTieEmNCyEupdPaJuLg4/Prrr4iLi8Pnn3+OZs2aYfXq1Th16hT+/PNPREVFoXXr1oiMjKyteBuFpwtkcBxXWihDzJe21+N11wcx8angeQ5SQx4cV/qd5znExKfWdWiEVMvTBTI4rvS7mOdwOj6tXq63vsjeGwuO5yGWll7jxFIpOJ5H9t7GcY0jVZOQnINP1p7C+xHH8MnaU0hIztHp9mbPno1p06Zpta1duxbBwcHw9fXF1q1bAQBjxoxBeHg4Bg4cCC8vL2RmZuLBgweYMWMG3N3d0bdvX8TExKBjx47IysoCAK3Xh4aGYsmSJZg7dy7c3Nzg6+uLqKgozTZDQ0Mxffp0zc+HDx9GYGAgXF1dERgYiN9//12zbO/evXjzzTfh7OwMDw8PfPTRR3j48KHOjlFjUGlSbGdnp/lat24dlixZggEDBsDa2hrm5ubo06cPFi1aRGNbqknIywXHa8+1XFMFMnS57vogR1YEiYFYq01iIEYuFRUgDUyerAgGz/wu10SBDF2tt76Q5+RC9Mxc9SKJBPKcxnGNI8/3+BPD+w+LYWbE4/7DYkTFXNJpYhwYGIgTJ06gqOjJeXTo0CG8+eabZfru2rULYWFh+O6779CqVSvMnDkTmZmZ+OGHH/DFF1/gu+++g0qlqnBbO3fuhJ2dHWJiYjBs2DCsWrUKKSkpZfqdPXsWH3/8MYYOHYr9+/cjKCgIU6dORWZmJhISErBgwQJMmjQJR44cwZdffolz587prChbY1FpUvy027dvo3nz5mXaX3nllXLnGSYV4y1sSoc1PKWmCmToct31ga2VMRRK7YuJQqmCDRUVIA2MhZUxlM/8LtdEgQxdrbe+kNraQK3QvsapFQpIbRvHNY48X118YtirVy8YGRkhPj4eAJCRkYFr165h0KBBZfp6eXnB09MTLi4uyMjIwMmTJxEREQFnZ2d0794dn376aaXbevXVVzF9+nS89tpr+PDDD2FhYYFLly6V6bd9+3b4+flh7NixaN26NcaPH4+JEyeioKAAUqkUn332Gd58803Y2dnB29sbXl5emorBpHxVToo7dOiA6OhoCIKgaVMoFFizZg1cXV11ElxjpcsCGY29+MZQH3sIAoP8UVEBeYkAQWAY6mNf16ERUi26KpDR2AtvtAgOAhMEqOSl1ziVXA4mCGgR3DiuceT56uITQ57n4e/vrylCdvDgQXTr1q3c2gl2dnaaf1+9ehWGhoZwdHTUtLm5uVW6rVdffVXrZxMTEyiVyjL90tLS0LlzZ622qVOnokOHDnB2doaLiwvWrFmDGTNmIDAwEIcOHYJarX7+zuqxKifFc+bMweHDh+Hr64uJEydiwoQJ8PX1xblz5zB//nxdxtjomLRzh7X/ePCmlmDyAvCmljX2IJwu110feHSwxcShLrA0N0JBsQBLcyN6yI40SI8LZJiZSyEvVsLMXFojD8Ppar31hVVXd7SdOB4SK0uoCgogsbKkh+z0TF19Yvjmm2/ixIkTkMvlOHz4cLlDJwBA8tTwHp7nUcl8BuUqb+rb8tZhYGBQ4TpOnz6NwYMHIzs7Gz179kRkZCQCAgKqFYc+qnIlDHd3d+zfvx8//fQTrl+/Do7j8Pbbb2PUqFGwsWkcF9vapMsCGY29+IZHB1tKgkmjoKsCGY298IZVV3dKgvXYUB97RMVcghyC1ixEuv7EsGvXrrC0tMSPP/6IjIwMDBw48Lmvad++PZRKJa5evQonJycApdV8a0KbNm2QlJSk1TZmzBgEBgbi5MmTGDRoEJYuXapZdvPmzXKHwZInqlUerlWrVppiG4QQQgghtc2jgy0w1AUx8anIlRXBppbmq+c4DgEBAfj666/Ru3dvWFhYPPc1rVu3Rr9+/bBgwQIsWrQIJSUlWLJkiWZ9L+O9995DSEgItm3bBm9vbxw5cgSXL1/G559/jsuXL+P8+fNISkqCVCrF9u3b8c8//8Da2vqlttnYVZoUz5o1C4sXL4apqelzk+GVK1fWaGCEVMfF25exP+UYcgvvwcbEGoOdBsCtuXONrHvH0RTEnkhHsUKAkYRHUJ+2GPW6U42smxBSc3RV1OPmzl24HXsAquJiiI2M0DwoEK1HDK+BiMmLqqtPDAMDA7F+/XoEBgZW+TWff/45Fi5ciNGjR6NJkyYICQnBypUrKx3+UBVubm5YunQpvvnmG0RGRsLe3h7ffvstWrVqhenTp2PevHkYPXo0jIyM4OHhgWnTpmHXrl1lak+QJyot3jFmzBh88803MDc3x5gxYypd0ZYtW2o8uJdR3yfuJzXn4u3L2JC4EwYiMQzFhihRlUCpVuH9riNeOjHecTQFO49dAzhAzAEqBoABIwY4UGJcz9E1QL/oqqjHzZ27kLVzFwCAE4nAHj2o1HLEcEqMyXMVFxfj1KlT6NOnj2as8KVLl/DOO+/gr7/+As9X6wN7omOVvhtPJ7obN2586f/VEKIL+1OOwUAkhuTR/MwSXgIICuxPOfbSSXHsiXSAA3hR6TOpPAcIajViT6RTUkxIPfJ0UQ8AEEulUMnlyN4b+1JJ8e3YAwAA0aPkhROJoBYE3I49QEkxeS6JRIJPP/0UQ4YMwejRo/HgwQMsW7YMAwcOpIS4Hqry7BM9evTArFmzcOjQIRQWFuoyJkKqJbfwHgzF2k/rGooNkVt476XXXawQIH7mUyYxV9pOCKk/dFXUQ1VcDE6k/aeSE4mgKi5+qfUS/SASibB27Vr89ddfCAwMxPjx49GuXTssXry4rkMj5ajyf1M+++wzxMfHY/HixSgqKoKnpyf69+8PPz8/WFlZ6TJGQiplY2KNvOIHmjvFAFCiKoGNycs/UGAk4SEvEcA/lRirWGk7IaT+kNraQCG7r7lTDNRMUQ+xkRFUcrlWYszUaoiNjF5qvUR/uLm54ccff6zrMEgVVPlOcUBAAJYvX47Tp09j48aNaN++PTZu3Ahvb2+EhIToMkZCKjXYaQCUahUUggKMMSgEBZRqFQY7DXjpdQf1aQuw0iETjKkhqNUAe9ROCKk3dFXUo3lQ6QNVakEAU6uhflTA6nE7IaTxqHJS/NjjaihSqRQWFhZgjOH69es1HhghVeXW3Bnvdx0BC6MmKFAWwcKoSY08ZAcAo153wogBDpAa8hDUgNSQp4fsCKmHdFXUo/WI4Wg5YjjEUimYSgWxVEoP2RHSSFU6+8TT1q9fj7Nnz+LChQsQBAFdunRBr1694OXlBWdnZ4hE1c6vdYqePCdEv9E1gBBCSHVUeWDkypUrIRKJMGDAAEyePFlTmYUQQgghhJCGrspJ8e7du3Hy5EmcPHkSw4YNg42NDTw9PeHl5QVPT0962K4S9/7YhfxzB6AuKYbI0AhmPQJh7U0fvTVkCck5iIlPRY6sCLZPVVOqqF2XxUVI/Xfi6DWcPZEOhUIFiUSMnn3aos/rDnUdFnlJ5RULAVBuARFdFRYhhNScKg+feFphYSHOnj2L33//HbGxsRAEAVeuXNFFfC+svnx0eu+PXXhwsnTid3AigJWOyW7Sezglxg1UQnIOomIugec5SAzEUChVEAQG326t8Nv5zDLtA/pLceLuUZ0UFyEVqy/XgBNHr+HEsesAB3AcwB4VgOkzoD0lxg1YecVCVIWFYIyBNzXVKiDS1K8f7sYdr/HCIoSQmlXtgcDp6enYs2cPfvzxR8TGxkIqlSIgIEAXsTUK+edKJ37nRDw4TgROxGu1k4YnJj4VPM9BasiD40q/8zyH2BPp5bb/cj1OU1yE4zhIeAkMRGLsTzlW17tCasHZRwVgxCIRRJwIYpEI4B61kwbr6WIhHMeVFgspLoK6uFirjeN53I49UKYvx/PI3htb17tBaklKSgrOnj1b12GQ56jy8ImFCxfi5MmTuH37Nlq0aAFfX1+8//776NatG8RisS5jbNDUJcWA6Jnjw4lK20mDlCMrgpmR9qkjMRCjWCGgmZVRmXYZ8mEo1p4zuaaKi5D6T6FQ4dnnkDmutJ00XPKcXIhNTbXa1IIKHKdd7UckkUBVXAyJrW2Z9pctLEIajg8//BDjxo1Dz5496zoUUokqJ8WXL1/GW2+9BT8/v0ofsrt8+TIcHR2pJPQjIkMjqJXy0qETjzE1RIY08XtDZWtljPsPiyE1fHL6KJQqGEl4KJSqMu1SmKFEVaKT4iKk/pNIxCgpUQFP5UqMlbaThqu8YiEiXgw8MyBRrVBAbGRU+r2GC4sQQmpWlYdPxMTEYMqUKc+ddeLdd9/FnTt3XjqwxsKsR+kE70wtgDE1mFrQaicNz1AfewgCg7xEAGOl3wWBIahP23LbB7X301lxEVL/9XxUAEalVkPN1FA9KgDTkwrANGjlFQsRGxlD9KgC3tMFRJoHBeqksIg+K0y7gOyti/Dv/yYje+siFKZdqJXt7tixA/3794ezszPefPNNHDtWOgyuoKAAn3zyCbp164aePXti1qxZuHev9NPAMWPG4NatW1iyZAlCQ0MBAGlpaZg4cSI8PDzQs2dPLFmyBHK5XLOdNWvWoE+fPujcuTOGDRuGhIQEzbK///4bY8aMQZcuXeDi4oJRo0YhJSWlVva/savxyYVf4Lm9Rs3aezia9B4OkYEUUKsgMpDSQ3YNnEcHW0wc6gJLcyMUFAuwNDfCxKEuGPW6U7ntb/fw1llxEVL/9XndAX0GtIehoRhqNWBoKKaH7BqB8oqF2E+fgvYfTS1TQKT1iOE6KSyirwrTLuDe4WgIBffBSU0hFNzHvcPROk+Mk5KSEB4ejjlz5uDIkSMYPHgwPv74Y9y7dw/z58/HrVu3sHnzZmzevBmFhYWYPHkyGGNYs2YNmjVrhpkzZ2L+/Pm4f/8+QkJCYGxsjO3bt2PVqlU4fvw4IiIiAAC//vorNm3ahGXLluHQoUPo0qULpk6dCkEQUFBQgA8++ABdunTBgQMHsH37dqjVanz++ec63Xd9UeXhE+TFWXtTEtzYeHSwhUcH2yq3uzV3piRYj/V53YGS4EbIqqt7uYltRW2UBNeMB2diATFferMJAGcghRpyPDgTC5N2ujvGt27dAsdxsLOzg52dHT744AN07NgRMpkMhw8fxokTJ2D7aOz4ypUr0aNHDyQmJsLDwwNisRgmJiYwMzPDli1bAACRkZGQSCRwcHDAokWLMGnSJHz88cfIysqCgYEBWrZsiZYtW2LmzJnw9fUFYwzFxcWYMGECxo0bB5FIhFatWiE4OBirV6/W2X7rE0qKCSGEENJgCHm54KTaDzlyvARCnm4fXOzduze6deuGt956C/b29ujXrx+GDRuGjIwMAIC/v792nIKAjIwMeHh4aLWnpqbCyckJEsmT50y6du0KtVqN9PR0BAYGIiYmBgMGDECnTp3g6+uLYcOGwcDAAE2bNsWwYcPwww8/4OrVq8jIyMCVK1dgbGys033XF5QU16DCtAt4cCYWQl4ueAsbNPEMqvB/rdXpSxqOn879gV+ux0GOfEhhhkHt/fB2D++6DovUktTkXJyOT0OerAgWVsbw8mkH+w4VP0xV3f6k/ru5cxduxx6AqrgYYiMjNA8KROsR9ElhTeItbEqHThg8eXCRCQrwFro9d4yMjLB582YkJiYiPj4ecXFx2LZtGyZPngwDAwPs3bu3zOwj5RU2k0gkZfqpVKWz0TDGYG1tjZiYGJw5cwa///47du/eja1bt+Knn36CRCLB0KFD4eDgAG9vbwQFBSEtLY3uFNeQGh9TrK+qM8aprsZDEd366dwf2HN9HxSsEGJmCAUrxJ7r+/DTuT/qOjRSC1KTc3Ew5jLyH8ohNTJA/kM5DsZcRmpy+Xevqtuf1H83d+5C1s5dUMnl4MRiqORyZO3chZs7d9V1aI1KE88gQCVArSx9cFGtlAMqobRdhy5cuIA1a9bAw8MDs2fPxsGDB9G8eXOkpKRAqVSiuLgYrVu3RuvWrdGkSRMsXboU2dnZZdbTrl07JCcnQ6FQaNouXrwIjuPQpk0bHD9+HNu2bYO3tzc+/fRTHDlyBEqlEmfPnsWxY8dgaGiIDRs2YOzYsejZsydu3bql0/3WJ5QU15CnxzhxHFc61knMl7a/RF/ScPxyPQ4cE0EMA3DgSr8zEX65HlfXoZFacDo+DWKeg+Gj4i2GhjzEPIfT8Wk10p/Uf7djS4syiXgenEgEEc9rtZOaYdLOHdb+48GbWoLJC8CbWsLaf7zOP201NjZGVFQUtmzZgqysLBw/fhy3bt1CQEAAfH19MXfuXCQmJuL69euYNWsWrl+/jjZt2pTGbGKCjIwM3L9/H4GBgeB5HqGhobh+/TrOnDmD8PBwBAQEoGnTpuA4DitWrMAvv/yCrKws/PzzzygqKkLHjh1hYWGB//77DydOnEBWVhZ27NiBrVu3oqSkRKf7ri9o+EQNqc4Yp7oaD0V0S458iGGo1SYCDzny6ygiUpvyZEWQGmnPz25gIEaerKhG+pP6T1VcDO6ZYlacSARVMRVrqmkm7dxrfcihk5MTli9fjm+++QbLly9H06ZNMWPGDPTv3x/du3fH0qVLMXnyZAiCgK5du2Ljxo2accOjR4/GsmXLkJ2djbVr12LDhg2IiIjAW2+9BTMzMwwePBgzZswAAPj4+GD27Nn48ssvcefOHbRs2RKRkZHo1KkTOnTogAsXLuD//u//oFKp4ODggMWLFyM0NBQ3btzQJOHkxdR4Uvzqq6/qZeGO6oxxqqvxUES3pDCDAoUQ48nvvxoCpDCrw6hIbbGwMkb+QzkMnyreolSqYGFV/gMw1e1P6j/xozmKuadKGDK1GmIjKtbUWAwaNAiDBg0q025ubo6lS5dW+LqRI0di5MiRmp8dHBzw/fffV9j/vffew3vvvVemXSQSYcGCBViwYIFWe3BwcFXCJ89RaVJ88uTJKq+od+/eAIDYWP0cAtDEMwj3DkdDDTk4XgImKCoc41SdvqThGNTeD3uu74OKKSECDzUEME6NQe396jo0Ugu8fNrhYMxllECAgYEYSqUKKoHBy6ddjfQn9V/zoEBk7dwFtSCAE4nA1GpNOyGk/qs0KR4/fjw4jntuQQ6O45CcnFyjgTU0Ju3cAf/xVZpRojp9ScPxeJYJmn1CP9l3sEHAUOcqzyZR3f6k/ns8ywTNPkFIw8SxSjLe6jzRaGdn99w+//77L5YsWYKEhASYmJhoqsGUN9wiPz8fYWFhiI+Ph7GxMcaNG4exY8dWOZ6srCz4+fkhLi4OLVu2rPLrCCGNA10DCCGEVEeld4qrkugCpTW/n0etVmPChAlwdHTEnj178N9//2HOnDkwNDTUDC5/2vz585Gbm4tt27bhxo0bmDdvHmxsbMody0MIIYQQQsjLqPKDdjKZDGvWrMHVq1ehfjROijGGkpISZGRk4K+//qr09Xfv3oWTkxPCw8Nhbm6Otm3bwt/fH+fPny/T99atWzh69Cj2798PBwcHODk5ITU1FZs2bdJZUlxRMY3aLrLREIt6XLx9GftTjiG38B5sTKwx2GlAtUsaJyTnICY+FTmyIthaGWOojz0AlGkrr4QyqZryjjEdT23lFdMAUKtDHBpiQQ9Z4gVk742FPCcXUlsbtAgOqnZJ44rWURPrJk/Q8SSkYlWep3jx4sU4dOgQWrRogUuXLqFVq1ZQKpVITk7Ghx9++NzX29ra4quvvoK5uTkAICUlBXFxcfD09CzT96+//oK5uTkcHBw0bR4eHrhy5YrWZNc1paJiGvf+2FWrRTYaYlGPi7cvY0PiTuQVP4CpgTHyih9gQ+JOXLx9ucrrSEjOQVTMJdx/WAwzIx73Hxbj6x8vYPXOi1ptUTGXkJCco8O9abzKO8Z0PLWVV0xj349/IXbnX7VWYKMhFvSQJV5AelQ0FLL7EJuaQiG7j/SoaMgSq37dqmgdN3fueul1kydq4r0ipDGrclJ89uxZfP7551ixYgXatGmDDz74ADExMQgODkZ6enq1Njp48GAEBQWhSZMm5Y4TzsnJgY2N9p2Rpk2bQq1WIze35v84VFRMI//cgVotstEQi3rsTzkGA5EYEr60bKWEl8BAJMb+lGNVXkdMfCp4noP0UREDqSGPYoWAIrmg1cbzHGLiU3W4N41XeceYjqe28opplCgEKORCrRXYaIgFPbL3xoLjeYilpdctsVQKjueRvbfq162K1nE79sBLr5s8URPvFSGNWZWT4sLCQnTo0AEA0LZtW81sE++99x7OnTtXrY1GRkZi8+bNkMvlmDZtWpnlxcXFMDTULoLw+GddVG0R8nLB8RKtNo6XQF1SXG67ropsVBRHfS7qkVt4D4biZ94rsSFyC+9VeR05siJIDLQnvFepGQSVWqtNYiBGLhU2eCHlHWM6ntryZEUweOYYqdUM6md+D3VZYKO8GOp7QQ95Ti5EEu3rlkgigTyn6tetitahKi5+6XWTJ2rivSKkMatyUvzKK68gJ6f0o9ZWrVrh2rVrAEpLF8pksmpttGPHjvD09MTSpUtx6tQpXL9+XWu5VCotk/w+/tlIB5Og8xY2pXMFP4UJCogMjcpt11WRjYriqM9FPWxMrFGieua9UpXAxsS6yuuwtTKGQqnSahOLOPBi7V9PhVIFGyps8ELKO8Z0PLVZWBlD+cwxEok4iJ75PdRlgY3yYqjvBT2ktjZQPzOsTa1QQGpb9etWResQGxm99LrJEzXxXhHSmFU5Kfbx8cGCBQuQlJSEHj164MCBA0hISMCmTZuqNEtFbm4ujhw5otXWvn17ACiTVDdr1gx3794t83qe52FtXfVkq6qaeAYBKgFqpRyMMaiVckAlwKxHYLntuiqyUVEc9bmox2CnAVCqVVAICjDGoBAUUKpVGOw0oMrrGOpjD0FgkJcIYKz0u5GEh7GU12oTBKZ5AI9UT3nHmI6nNi+fdlAJDCWPjlFJiQBDCQ+JlNdq02WBjfJiqO8FPVoEB4EJAlTy0uuWSi4HEwS0CK76dauidTQPCnzpdZMnauK9IrqVlZUFR0dHzY3Hl+Hr64utW7fWQFT6o8pJ8ezZs9GmTRv8/fff6Nu3Lzp27IiQkBD89NNP5Q6BeFZmZiamT5+OrKwsTds///wDkUiEdu20L/hdunRBXl4e0tKejKNLTExEx44dNXXEa5JJO3dY+48Hb2oJJi8Ab2oJa//xsPYeXm67rmaDqCiO+jz7hFtzZ7zfdQQsjJqgQFkEC6MmeL/riGrNPuHRwRYTh7rA0twIBcUCLM2N8NFId0wf4abVNnGoC82W8ILKO8Z0PLU9LqZhZi6FvFgJM3MphozsgqARXbTaAoY662w2iPJi0OX2aoJVV3e0nTgeEitLqAoKILGyRNuJ46s1o0FF62g9YvhLr5s8URPvFdGt5s2b4+TJk2jbtm1dh6KXKi3eURnGGJKTk9G0aVM0bdr0uf1VKhVGjhwJiUSCBQsWIC8vDwsWLECvXr2waNEi5OXlQSwWw8zMDAAwadIkyGQyhIWFITMzE6GhoYiIiEBAQECV4qOJ+wnRb3QNIIToM19fX4wbNw4hISF1HUqDUeU7xUBplblt27ZhwYIFCAsLQ3JysiaJfR6xWIxvvvkG1tbWCAkJwUcffYR+/fph3rx5AIBp06YhIiJC0z8yMhLNmjXDqFGjEBERgY8++qjKCTEhhBBCGq+Lty9j8fEvMeXnT7H4+JfVmgb0RcyePbvMp+Jr165FcHAwCgoK8Mknn6Bbt27o2bMnZs2ahXv3Sh82fzwc4ttvv0X37t0xZcoUCIKAxYsXw8vLC66urnj33Xc1z1Y9O3ziwYMHmDt3Lrp3744ePXpg3rx5KCwsBFD6rNWXX36Jfv36wcXFBWPGjEFKSjGpllIAACAASURBVEq58avVamzevBmvv/46OnfujLfeegtnz57VLB8zZgzCw8MxcOBAeHl5ITMzs8aPYUNQ5eIdaWlpePfdd1FYWIjXXnsNKpUK+/btQ1RUFLZs2QJb2+d/DGtjY4Ovv/663GVbtmzR+tnCwgKrV6+uanhEzzX2whSNff8IqQmNvTBFY9+/qno8P76BSKw1P/77XVHtwlFVFRgYiOnTp6OoqAjGxqUPvh46dAhBQUGYP38+8vLysHnzZojFYnz11VeYPHkydu7cqXn9yZMn8dNPP0GlUmHr1q04fvw41q5dC0tLS6xYsQJz5szBvn37ymx36tSpKCwsxPr168HzPEJDQ7F06VJ89tlnCAsLw5kzZxAeHo7mzZsjKioKY8eOxZEjRzQ1IR5bu3Ytvv/+e4SFhcHJyQm7du3CBx98gIMHD6JVq1YAgF27dmHdunUwMTHRtOmbKt8pjoiIgKOjI44fP469e/di//79+O2339CsWTOtO7yE1LbGXpiise8fITWhsRemaOz7Vx01MT9+dfXq1QtGRkaIj48HAGRkZODatWvo3LkzDh8+jOXLl6NTp05wcnLCypUrkZSUhMTERM3r3333XbRp0wbt2rVDVlYWpFIpWrZsiVdffRWLFi3C/Pnzy2zz+vXr+PPPP/H555/D1dUVnTp1Qnh4OFq3bo2HDx9i3759mDdvHry9vWFvb4+IiAgYGhpi9+7dWuthjGHLli2YPHkyAgIC0LZtW8ydOxdOTk7YvHmzpp+Xlxc8PT3h4uKik2PYEFQ5Kb548SLmzp0LS0tLTZu1tTVCQ0Nx6tQpnQRHSFU09sIUjX3/CKkJjb0wRWPfv+qoifnxq4vnefj7+2tm0Tp48CC6deumGcrg7+8PNzc3uLm5oXfv3hAEARkZGZrXPz1LV0hICORyOby9vTF69GjExsZqVfB9LDU1FYaGhnB0dNS0ubm54YMPPkBGRgZUKhVcXV01ywwNDdG5c2etSQoA4N69e7h//36ZZNfd3V2rb1VmEmvsqjx8wsLCAnl5eWXalUolTExMajQoQqojR1YEMyPtX+XGVJiise8fITVBnpMLsampVltjKkzR2PevOmxMrJH3/9u787goq/2B4x/2RVBAwX2FCxiKouSWhhtlrgltZGioiVq2YCogLpdcULOuqCSKejUKvSWmmaalhfVKvbkUF3FDUbEUTUBF9uH5/cGP0Qk0yBlmgO/79eKFPHPmOd/HwTNfnznnfPNvYXFfsavq7o//dwwfPpzXXnuNgoICvv76a1555RVUKhVmZmZs374dIyMjjfYODg7cunULQGPnrHbt2rF3715++OEHkpKSiIuLIyEhgcTERI3nm5mZAVQ475/Pdz+VSkVpqWbBIUtLy0rP8+e2utjdq7ap8p3id955h7lz53L8+L2PatLS0oiMjGTKlCkUFRWpv4SoSXW9MEVdvz4htKGuF6ao69dXHdrYH//v6N69O/b29mzZsoX09HSefvppOnToQHFxMfn5+bRt25a2bdvSqFEjFi9ezO+//17pebZv387XX3/N4MGDee+999ixYweXL18mJUVzsWD79u0pKioiLe3ep4IHDx7k6aefpk2bNpiZmfHLL7+oHysqKuLkyZMVtnOzsbHB0dFRoy2UzQCQrd80VflO8dKlS8nJyWHMmDGYm5tjbGxMwf9vAJ6amkpkZKS6bXkJaCFqgl9/F2ITkymgBAszEwqLVXWqMEVdvz4htKHF6FFciI1DVVCAsYUFpYWFdaowRV2/vuoo2x+/bG7x9bs3cWrQmJHuvjpbZFfOyMiIoUOHsmLFCvr27YudnR12dnYMHDiQWbNmMW/ePBo2bEhUVBQXL16kXbt2FQqRAeTl5REdHY2dnR3Ozs7s2rULCwsLXFxcKLzvPz7Ozs707duXiIgIIiIiKC0tZdmyZfTu3Rtra2sCAgKIiorC2tpavdCuoKCAUaMq/k5MnDiRmJgYmjdvjru7O//5z384c+YMixYt0unfWW1T5aR4+vTpuoxDiL/Nu2NT8PMk8fs0rmfl4VTHdmeo69cnhDY4dO8GwRPr7O4Mdf36qsureSedJ8GVGTFiBOvWrWPEiBHqY0uWLGHx4sVMmTKFkpISunfvzoYNGx44HSEgIIDr168zZ84csrKycHFxISYmBkdHR40CZ1B2Q/K9994jMDAQCwsLhgwZwqxZswCYMWMGADNnziQ/P59u3boRHx+Pk1PFTw/Gjh1LXl4eixcvJicnh8cee4yNGzdqzFcWj1C8w9DJxv1C1G8yBgghhKiOahXv+OGHHxg3bhx9+/blt99+Y9WqVXz11Ve6ik0IIYQQQogaUeXpE4cOHWLKlCn4+vpy/PhxSktLKSgoUN++HzZsmM6CFIbhQQUkTlxNqfG5XVWNzZBjFqI2qqyABGAQH+tXJzYphCGE+LMqT58ICAhg0KBBTJw4ES8vL3bu3Enr1q2JiYlh79697NhhWHslyken2lVeQMLU1EhjsZfvYEsO3tiHmbEJ5ibmFKmKKC5VMaH7izWWZD4otoGPt+bAzxkGGbPQPRkDtK+8gISRqal6sZfq7l0URcHUxkZjAViH4Ik1mmRWFltJbm7Znr4NGmjE5jhoADf2f6fRVh8xCyEMS5WnT5w5cwZf34rbnYwYMYJLly5pNShheB5UQOKrc/trvLJQVWPbcfCCwcYsRG1UWQEJVX4epfn5ei8qUVlspfn5qPLzKsR2dceXUghDCFFBlZNiKysrsrOzKxy/fPmyFO+oBzKz8rAwM9E4ZmFmQgF3aryyUFVjyy8sMdiYhaiNCjKvY/ynFfWlJSqUPxUL0EdRicpiU0pLKS3R3OPb2MICVX5+hbb1tRCGEOKeKifFgwYNYtmyZWRmZqqroqSmprJgwQIGDhyoswCFYXhQAQlLbClSaRZsqYnKQlWJzcrC1GBjFqI2qqyAhLGpCUbGmm8l+igqUVlsRsbGGJtq/se4tLAQEysrKYQhhKigyknxzJkzUalU9O/fn7y8PIYPH46/vz9WVla8++67uoxRGAC//i6UlCgUFJWgKGXfS0oUhv1jkF4qC1UltlFPdjDYmIWojVqMHoVSUoLq/ws3qQoKMLGyxtjKSuOYPopKVBabsZUVJlbWFWJrPmpEhbb1tRCGEOKeKu8+YWNjw5YtWzh06BCpqank5+djbGzM2LFjsflTPXZR9zysgMQ/rtrrdSeHh8bW2t4gYxaiNqq0gMSEVwH97z5RWWztHxKbrYuz3mMWQhiWKu8+kZmZyezZs3nzzTdxd3cnICCAkydPYmdnx8aNG+nYsaOuY60WWXkuRP0mY4AQQojqqPL0icWLF/PHH39gZ2fHV199xYULF9i4cSM+Pj4sW7ZMlzEKIYQQQujVwIEDcXNzU395eHgwaNAgVq5cSXFxsdb6OXLkCG5ubty9e1fdb3x8/F8+r7i4mE8++URrcTyKwMBAlixZ8sjnWblyJX5+flqIqGqqVbxjw4YNtGnThg8++IC+ffvSu3dvmjZtynPPPafLGEUt9KCiGQ/yqMU0qtufEEL3qlMgQxvFNKQgh9C1kJAQdZKmUqlISUlhxowZGBsb8/rrr+ukz88//xwrK6u/bLdr1y6io6MZM2aMTuKojpUrV2JqWuUU02BU+U5xYWEh9vb2KIrCoUOH6NWrl/oxExOThzxT1DflxTSyb+dja2VK9u18YhOTOXoqs9L2J66msP7YVnLyb2FjZk1O/i3WH9vKiaspOulPCKF75cU0CrOyMbGxoTArmwuxcWQdO/5IbbXRnxB/V4MGDXB0dMTR0ZFmzZoxePBgRowYwb59+3TWp4ODQ5WS4irOhq0RdnZ2tXK9WZWT4n/84x8kJiayZcsWbt++zZNPPklJSQlxcXG4ubnpMkZRyzyomEbi92mVtt95+ptHKqZR3f6EELpXWTGNBxXIqE5bbfQnhDaZmppibl62931oaCgzZ87E39+fHj16cPToUYqLi1myZAl9+vTB29ub4OBgMjIy1M+/efMmU6dOxcvLiyFDhpCSonlD6P7pE6WlpaxevRofHx+8vLwICgoiPT2dI0eOEBYWRk5ODm5ubly5ckXjHD/99BMeHh7k5OSoj127dg13d3dOnz4NwLZt23jqqafo0qUL/v7+/PTTT+q2gYGBREZG8vTTT9OnTx8yMjLYt28fw4YNo3Pnzvj6+rJlyxaN9vdPn/j000/V537hhRf49ddf1Y8lJSXh7+9Ply5dGDRo0EOngPz6668EBgbi5eVFv379WLlyJaX/v096YmIi/v7+hISE0L17d9avX/8Xr1xFVU6K33rrLdatW0dkZCQjR46kdevWvPfee+zZs4e333672h2LuutBxTSuZ+VV2v763ZuPVEyjuv0JIXSvsmIaDyqQUZ222uhP1H5Zx46TEjGPo69NISVinl4+EVCpVBw6dIgdO3Zo1GvYuXMnr776Khs3bsTT05MPP/yQQ4cOER0dzdatW3F0dGTcuHEUFBQAMG3aNHJyckhISCAsLIwNGzY8sM9Vq1bx8ccfM2fOHLZv346dnR2TJ0/Gy8uL8PBw7Ozs+PHHH2nevLnG83r16oW9vT3ffvut+tjXX3+Ni4sL7u7uJCUlsWTJEkJCQti5cyejRo0iODiYM2fOqNt/9tlnzJ8/nzVr1mBtbU1ISAgBAQF8/fXXvP7668yfP1+dYN9v27ZtLFmyhMmTJ7Nz5066du3KpEmTyM3N5ciRI0yZMoWnnnqKL774gilTprB06VK+/PLLCue5cOECY8eOxd3dnc8//5y5c+cSHx/P2rVr1W1SUlJwcHBg27ZtDBs2rAqvoqYqT/jo27cvBw8e5OrVq7i7uwPw8ssv8/rrr+PkJBuei3uaOliTfTsfS/N7v16FxSqcHKwrbe/UoDE5+bewML33hladYhrV7U8IoXuWTZ3KpjJYWqqPPahARnXaaqM/UbuVT5UxMjXVmCpD8ESdzyGPiopi+fLlQNm0UhMTE0aMGMGECRPUbVxcXBgxYgQABQUFfPzxx2zevBkvLy8AIiMj6d+/P3v37uWxxx7j2LFj7N69G2dnZ9zd3Zk6dSqRkZEV+lYUhYSEBKZOncrgwYMBmDdvHrGxsRQUFGBrawuAo6NjhecaGxvzzDPPsHfvXvU6sD179jB8+HAA1q5dy4QJExgyZAgAY8eO5ddff2Xz5s0sXLgQgD59+tC7d2+grHhbcXExzZo1o2XLlrRs2ZJmzZrRtGnFtTyffvopL730knou9syZMzExMSEnJ4f4+HiefPJJgoODAWjfvj3p6emsXbtW/XdY7j//+Q9t2rRh9uzZADg7O3Pjxg2WL1+ufj7A1KlTcXBwqPT1+yvVmgXdqFEjGjVqpP5Zpk2Iyvj1dyE2MZkCysosFxarKClR8OvvUmn7ke6+rD+2FUoKMTcxp0hVVK1iGtXtTwihey1Gj+JCbFxZEQ0LC0oLCx9YIKM6bbXRn6jd7p8qA2BiaYmqoIDft+/QeVIcHBzMyJEjATA3N6dJkyaYmZlptGnZsqX6z5cvX6aoqIigoCB1NWAoS5bT09MxNzfH3NwcZ2dn9WOdO3eutO/s7GyysrI0Hrezs2PWrFlVin3EiBG8/PLL3L59m7t375KcnMz7778PQFpaGsnJyaxZs0bdvri4GE9Pz0qvq2PHjgwfPpzXX3+dVq1aMWDAAEaPHo29vX2Ffs+fP09QUJD6Z1NTU3XMaWlp6r/Pct26dWPTpk2oVJoVadPS0ujSpUuFtrm5uWRmlq0hsra2/tsJMVQzKRaiKh5WTKMyXs07MaE7f3v3ier2J4TQvUoLfTxgN4jqtNVGf6J2K8i8jsmfFnHV1FQZe3t72rZt+9A2FvdN4ylP7DZs2EDjxpqfftra2nLkyBGg7C5wedL85yS73IOOV5WnpyctWrTgwIEDZGdn06VLF1q3bq2Oc/r06QwYMEDjOeVzpf98XUZGRixfvpzx48fz3XffceDAARISElixYoX6LnZV4rawsND4zwKUzZsuLS2tcPxBbe//fn+8f4ckxUInvDs2rVZS6tW80yNVlKtuf0II3XPo3q3KSWl12uryHMLw1aapMm3atMHU1JSsrCy6dSv73SwuLmb69OkEBATg5uZGUVERp06d4rHHHgOosNCunK2tLY0bNyY1NVU9FSM3NxdfX182bdpUIWGszLBhw9i/fz9//PGHeuoElE1F+O233zQS/g8++IAmTZowduzYCuc5f/48CQkJRERE4OHhwRtvvMGECRPYu3dvhaS4Xbt2pKamqvsrLS3lmWeeYfbs2Tg7O/PLL79otD9+/Djt2rXD2Fhz2ZuzszPfffedxrETJ05gY2NT6ZSRv6PKC+2EEEIIIfStxehRKCUlqAoKUBSl7LuBTpVp0KABAQEBLFy4kIMHD3Lx4kUiIiI4fPgwzs7OdOjQgX79+hEREUFKSgqHDh0iJibmged79dVX+eijj0hKSiI9PZ05c+bQpEkTXFxcsLa2Ji8vj/Pnzz+wmMiIESP44Ycf+N///sczzzyjPj5x4kS2bNnC1q1buXz5Mv/+979Zt24d7dq1q/Q8dnZ2bNu2jQ8++ICMjAz++9//cvr0aTw8PCqNOSEhgV27dnHp0iUWL17M3bt38fLyYvz48Rw8eJDY2FguXrzItm3b+PTTTytNxMeMGcOVK1dYuHAhFy5c4JtvviE6OpqXX375ke+il5M7xUInHrUYh6g5UvhE6IoU06gdatvrVNumysycORNjY2NCQ0PJy8vDw8OD9evXqzcp+PDDD5k3bx6BgYHY29szbtw4Fi9eXOm5xo8fT25uLuHh4eTl5eHt7U1MTAzGxsb06tULV1dXnn32WeLj4yvMvwXo0KEDHTp0wN7eXmM6h6+vLxEREaxfv5733nuP1q1bs3TpUp588slK42jcuDGrV6/m/fffZ9OmTdja2uLn50dgYGCFtsOGDVMviMvKysLDw4O1a9dia2uLh4cHK1asYMWKFaxcuZKWLVsSHh7Oiy++WOE8TZs2Ze3atSxdupSEhAQcHR0JCgpi0qRJVXodqsJIMaTdnrXoypUrDBo0iP3799OqVSt9h1OvlBfjMDM20Vg4N6H7i5IYG5jywiempkYaixSD/TxrfWIsY4B+3b9DwP0L3zrUwA4BourkdRLiHpk+IbTuUYtxiJojhU+ErkgxjdpBXich7pGkWGjdoxbjEDVHCp8IXZFiGrWDvE5C3CNJsdA6pwaNKVIVaRyrTjEOUXOaOlhTWKy5F6QUPhHaYNnUidLCQo1jhrpDQH0mr5MQ90hSLLRupLsvxaUqCksKURSFwpLCahXjEDXHr78LJSUKBUUlKErZdyl8IrShNu0QUJ/J6yTEPZIUC60rK8bxInZWjcgtzsPOqpEssjNQ3h2bEuzniX1DK3LzS7BvaFUnFtkJ/XPo3o0OwROxcLBHlZuLhYO9LN4yQPI6CXGPbMkmdOJRi3GImiOFT4SuSDGN2kFeJyHK1Oid4mvXrvHmm2/Ss2dPnnjiCcLCwrh161albRcsWICbm5vGV2RkZE2GK4QQQggh6okau1NcWlrK1KlTsbe3Z9OmTRQVFTF//nxmzZrFmjVrKrRPS0tj2rRpGhs4W1lZ1VS4elOdQgqGUiBDij8IoT3VLaRgCIUXDCEGIYR4VDV2p/jUqVOcPHmSqKgo3N3d8fT0ZPbs2Xz33Xfcvn27Qvu0tDQ8PDxwdHRUf9nY2NRUuHpRXkgh+3Y+tlamZN/OJzYxmaOnMiu0LS+QkZN/Cxsza3Lyb7H+2FZOXK28ZrohxCyEeLjyQgqFWdmY2NhQmJXNhdg4so4d10p7Q4hZCCEMVY0lxS1atGDdunU4OjqqjxkZGQFQ+KftYHJycrhx4wbt27evqfAMQnUKKRhKgQwp/iCE9lS3kIIhFF4whBiEqAkDBw7UmNLp4eHBoEGDWLlyJcXFxVrr58iRI7i5uXH37l11v/Hx8X/5vOLiYj755BOtxXG/u3fv8tlnn+nk3IakxqZP2NvbV6ih/e9//5t27dppJMpQdpcYYMOGDSQlJdGgQQP8/f0JCgrC2LjubpiRmZWHrZXmS/KgQgrX797ExkxzL1l9FMioTsxCiIcryLyOyZ8+EXtYIYXqttcFQ4hBiJoSEhKCn58fACqVipSUFGbMmIGxsTGvv/66Tvr8/PPPqzR9dNeuXURHRzNmzBitx7Bx40YOHDjA888/r/VzGxK9ZZhr165l3759hIeHV3gsLS0NY2NjWrVqxdq1a5kwYQIxMTHExcXpIdKaU51CCoZSIEOKPwihPdUtpGAIhRcMIQYhakqDBg3UUzqbNWvG4MGDGTFiBPv27dNZnw4ODlVKihVF0VkMujy3IdFLUrx69WqWL1/OnDlz8PHxqfD4iy++yKFDh5g0aRJubm74+/szdepUPv74Yz1EW3OqU0jBUApkSPEHIbSnuoUUDKHwgiHEIIQ+mZqaYm5uDkBoaCgzZ87E39+fHj16cPToUYqLi1myZAl9+vTB29ub4OBgMjIy1M+/efMmU6dOxcvLiyFDhpCSork26P7pE6WlpaxevRofHx+8vLwICgoiPT2dI0eOEBYWRk5ODm5ubly5cqVCnGfPnuWVV16ha9eu9OnThwULFlBUdO/m2rZt23jqqafo0qUL/v7+/PTTTwAkJiayatUqTp48iZubGwBFRUV8+OGHDBgwAE9PTwIDAzl9+rT6XD///DP+/v54enri4+PDqlWr1Il1bm4uc+bM4YknnsDDw4OBAwcaTH5X40nxwoULWblyJfPnz3/gLX4jIyPs7Ow0jrm4uPDHH3+gUqkqfU5dUJ1CCoZSIEOKPwihPdUtpGAIhRcMIQZR/6Sdus7mjw4RvXA/mz86RNqpmp+uo1KpOHToEDt27GDgwIHq4zt37uTVV19l48aNeHp68uGHH3Lo0CGio6PZunUrjo6OjBs3joKCAgCmTZtGTk4OCQkJhIWFsWHDhgf2uWrVKj7++GPmzJnD9u3bsbOzY/LkyXh5eREeHo6dnR0//vgjzZs3r/DcGTNm0KpVK7788kuio6PZs2cPCQkJACQlJbFkyRJCQkLYuXMno0aNIjg4mDNnzjB06FDGjx+Pu7s7P/74IwDz589n586dREZGkpiYSLNmzQgKCuL27duoVCreeOMN+vTpw+7du/nnP//JunXr2L9/PwCLFy/m5MmTrFmzhj179jBq1CgWLVqk8R8FvVFq0L/+9S/F3d1dSUxMfGi7pUuXKq+88orGsZiYGGXIkCFV7isjI0NxdXVVMjIy/lasQojaTcYAIeqmc6mZyooF+5VVUQeU2OVJyqqoA8qKBfuVc6mZOu13wIABioeHh9K1a1ela9euSseOHZVOnTopYWFhSmFhoaIoijJr1ixl2LBh6ufk5+crnTp1Uo4fP64+plKplH79+ilffPGFcvbsWcXV1VVJS0tTPx4fH6+4uroqubm56n4//vhjpbS0VOnVq5eyadMmddvs7GwlKipKuXXrlrJt2zalR48eD4y/W7duSlRUlFJSUqIoiqKkpKQoly9fVhRFUV5++WVlzZo1Gu1DQkKU8PBwRVEUJTo6Whk9erSiKIpy69YtpWPHjsrevXvVbQsLC5Unn3xSWb9+vZKdna24uroqmzZtUkpLSxVFUZRjx44p165dUxRFUbZt26akpqZqPNfV1VX5/vvvH/4C1IAaW2h3+vRp1qxZw/jx4+nbty83btxQP2Zvb8/t27exsLCgQYMGDBw4kI0bN7JmzRqGDh3KiRMnWLduHfPnz6+pcIUQQghhgH76/jwmpkaYm5elMObmphRRwk/fn8elo27nsgcHBzNy5Mj/79ecJk2aYGZmptGmZcuW6j9fvnyZoqIigoKC1DtuARQUFJCeno65uTnm5uY4OzurH+vcuXOlfWdnZ5OVlaXxuJ2dHbNmzapS7CEhISxYsIDExET69u3LM888g4eHB1C2lis5OVmjbkRxcTGenp4VzpOeno5KpaJLly7qY+bm5nTu3Jnz589jZ2fHpEmTWLhwIWvXrsXHx4eRI0fStGnZJ8ijR4/mwIEDJCYmkp6ezqlTp4CyqSH6VmNJ8d69eyktLSUuLq7Cgrkvv/ySyZMnM3r0aKZNm0b37t2Jjo5m5cqVxMTE4OTkxIwZM9S/iEIIIYSon3Ky8rC00kxEzcxMyKmBXY/s7e1p27btQ9tYWFio/1w+5XPDhg00bqy5EN7W1pYjR44AZQvZypPmPyfZ5R50vKrGjBnDgAED2L9/P0lJSbz55puMGzeOWbNmoVKpmD59OgMGDNB4Tvlc6fvdf333U6lU6sR2+vTpjB49Wt3XuHHjiIiI4JVXXiEsLIzDhw8zatQonnvuObp06UL//v0f6dq0pcbmFL/11lucOXOm0i9XV1cOHDjAtGnT1O0HDx7Mjh07SE5O5ttvvyUgIKCmQhVCCCGEgbJzsKb4T7seFRersDPAXY/atGmDqakpWVlZtG3blrZt29KiRQuWL1/OmTNncHNzo6ioSH23FKiw0K6cra0tjRs3JjU1VX0sNzeX3r17c/bsWY070X9WWFjIggULKC0tJTAwkLi4ON5++212794NgLOzM7/99ps6xrZt27Jt2za++aas9sH9527Tpg1mZmb88ssv6mNFRUWcPHmSDh06cOPGDebNm0ezZs147bXXiI+P54UXXmD37t3k5uayY8cOoqKieOeddxgyZIh6P2bFAHa4qLE7xUIIIYQQj6pPf2d2J6ZQRAlmZiYUF6tQlSj06e/810+uYQ0aNCAgIICFCxdibm5OmzZt+Oijjzh8+DARERE4OTnRr18/IiIiiIyM5M6dO8TExDzwfK+++iofffQRrVq1ok2bNkRHR9OkSRNcXFxIT08nLy+P8+fPqxPXchYWFpw4cYJLly4xc+ZMFEUhIELWmwAAEbJJREFUKSlJPX1i4sSJhISE4OLiQu/evTlw4ADr1q0jNjYWAGtra27evElGRgatW7cmICCAqKgorK2tad68ObGxsRQUFDBq1Cjs7OzYv38/RUVFBAcHc/v2bY4ePcoTTzyBhYUFVlZWfPvtt7Rq1Yrff/+dRYsWAWjshKEvdbcShhBCCCHqHJeOTgz164RtQ0sK8ouxbWjJUL9OOp9P/HfNnDkTX19fQkNDefbZZ7ly5Qrr16/Hyaks3g8//JB27doRGBjI7NmzGTdu3APPNX78ePz8/AgPD8fPz4/c3FxiYmIwNjamV69euLq68uyzz2rcTS73r3/9C4CAgABeeuklnJycWLBgAQC+vr5ERESwfv16hg4dytatW1m6dKm66NpTTz2FhYUFw4cP5/r168yYMYPBgwczc+ZMnnvuOW7evEl8fDxOTk6YmZkRGxvL5cuXGT16NK+99hre3t6EhIRgZmbG8uXL+eGHHxg6dCgREREMGzaMrl27atwt1xcjxRDuV+vAlStXGDRoEPv376dVq1b6DkcIUcNkDBBCCFEdcqdYCCGEEELUe5IUCyGEEEKIek+SYiGEEEIIUe9JUiyEEEIIIeo9SYqFEEIIIUS9J/sUiwpOXE1h5+lvuH73Jk4NGjPS3Rev5p30HZYQogZlHTvO79t3UJB5HcumTrQYPQqH7t30HZYQQuiM3CkWGk5cTWH9sa3k5N/CxsyanPxbrD+2lRNXK6+wI4Soe7KOHedCbByFWdmY2NhQmJXNhdg4so4d13doQgihM5IUCw07T3+DmbEJFqYWGBkZYWFqgZmxCTtPf6Pv0IQQNeT37TswMjXFxNISIyOjsu+mpvy+fYe+QxNCCJ2RpFhouH73JuYm5hrHzE3MuX73pp4iEkLUtILM6xhbWGgcM7awoCDzup4iEkII3ZOkWGhwatCYIpVm/fEiVRFODRrrKSIhRE2zbOpEaWGhxrHSwkIsmxpmGV0hhNAGSYqFhpHuvhSXqigsKURRFApLCikuVTHS3VffoQkhakiL0aNQSkpQFRSgKErZ95ISWowepe/QhBBCZyQpFhq8mndiQvcXsbNqRG5xHnZWjZjQ/UXZfUKIesShezc6BE/EwsEeVW4uFg72dAieKLtPCCHqNNmSTVTg1byTJMFC1HMO3btJEiyEqFfkTrEQQgghhKj35E6xMAhHT2WS+H0amVl5NHWwxq+/C94dm1baVoqLCFH3VKdYiBQWEULogtwpFnp39FQmsYnJZN/Ox9bKlOzb+cQmJnP0VGaFtlJcRIi6pzrFQqSwiBBCVyQpFnqX+H0apqZGWJqbYmRU9t3U1IjE79MqtJXiIkLUPdUpFiKFRYQQuiJJsdC7zKw8LMxMNI5ZmJlwPSuvQlspLiJE3VOdYiFSWEQIoSuSFAu9a+pgTWGxSuNYYbEKJwfrCm2luIgQdU91ioVIYREhhK5IUiz0zq+/CyUlCgVFJShK2feSEgW//i4V2kpxESHqnuoUC5HCIkIIXZGkWOidd8emBPt5Yt/Qitz8EuwbWhHs51np7hNSXESIuqc6xUKksIgQQldkSzZhELw7Nn3gFmx/JsVFhKh7qlMsRAqLCCF0Qe4UCyGEEEKIek+SYiGEEEIIUe9JUiyEEEIIIeo9SYqFEEIIIUS9J0mxEEIIIYSo9+rs7hMqVVkxiGvXruk5EiHE/Zo1a4apqe6HHhkDhDBMNTUGCFFddfa38saNGwCMGTNGz5EIIe63f/9+WrVqpfN+ZAwQwjDV1BggRHUZKYqi6DsIXSgoKCAlJQVHR0dMTEz0HY4Q4v/V1F0iGQOEMExyp1gYqjqbFAshhBBCCFFVstBOCCGEEELUe5IUCyGEEEKIek+SYiGEEEIIUe9JUiyEEEIIIeo9SYqFEEIIIUS9J0mxEEIIIYSo9yQpFkIIIYQQ9Z4kxbXAvn37cHNz0/gaPny4vsPSmpKSEpYtW0afPn3w9vZm+vTp3LlzR99haUViYmKF16786+eff9Z3eFpx+/ZtQkND6dmzJ3369GHu3LncvXtX32HVOTIO1F4yDghRO0jxjlogJiaGY8eOERUVpT5mamqKvb29HqPSnqioKL766ivef/99bGxsCA0NxcPDQ+N6a6uCgoIKb+xhYWHcuXOHTz75pE5UdZo+fToZGRlERkZSUFBAaGgo3t7eLFiwQN+h1SkyDtReMg4IUTvU/n+J9UBaWhqurq44OjrqOxStu3PnDvHx8axatYqePXsC8O677/L++++jKApGRkZ6jvDRWFpaYmlpqf7522+/5ciRI+zevbtOvBECfP/990RGRuLu7g7AmDFj2Lhxo56jqntkHKi9ZBwQonaQ6RO1wLlz52jfvr2+w9CJo0ePYmpqSt++fdXHfHx8+PLLL2v9G+GflZSU8P777xMUFETr1q31HY7W2Nvbs2vXLnJzc7l16xb79u2jc+fO+g6rzpFxoG6QcUAIwyVJsYErKSkhPT2dw4cPM2TIEAYMGMDcuXPrzFy7S5cu0axZM/bv38/IkSN58sknmTt3Lrm5ufoOTev27t1LZmYmEyZM0HcoWhUZGUlycjKPP/44PXv25NatW/KRqZbJOFB3yDgghOGSpNjAXb58meLiYoyNjfnggw+YP38+P//8M2+//ba+Q9OKvLw8rl+/TmxsLOHh4SxdupRjx44RFham79C07tNPP8XPz49GjRrpOxStSk9Pp0OHDmzevJmNGzeiKAqhoaH6DqtOkXGg7pBxQAgDpgiDl5WVpZSWlqp/Tk5OVlxdXZX09HT9BaUlsbGxiqurq3L69Gn1sf/+97+Kq6urkpmZqcfItOvatWuKq6ur8uuvv+o7FK26dOmS4u7urly8eFF97Ny5c4qrq6uSnJysx8jqHhkHaj8ZB4QwbHKnuBawt7fXmFfn4uICQGZmpr5C0honJycAnJ2d1cfK/3z16lW9xKQLBw8epHnz5nh6euo7FK1KSUnBzMyMtm3bqo+5uLhgaWlJRkaGHiOre2QcqP1kHBDCsElSbOAOHDjA448/rrHfY2pqKsbGxnVi0Y2XlxdQdk3lzp8/j7GxMS1bttRXWFp34sQJvL299R2G1jVt2pTCwkIuXryoPnblyhUKCgpo06aN/gKrY2QcqBtkHBDCsElSbOC6d++OhYUFYWFhnD9/niNHjjB79mz8/f3Vd1dqs7Zt2/LUU08RERFBcnIyycnJREZG8vTTT9OkSRN9h6c1Z86c4R//+Ie+w9C6Ll264OHhwezZszl58iQpKSm8++679OjRg06dOuk7vDpDxoG6QcYBIQybFO+oBc6cOcOSJUv45ZdfMDc3Z/jw4cycORNzc3N9h6YVeXl5REVFsWfPHhRF4emnnyY8PJwGDRroOzSt6d+/P5MnT+all17Sdyhad+PGDaKiovjpp58wMjLCx8eH0NDQOreQSN9kHKj9ZBwQwrBJUiyEEEIIIeo9mT4hhBBCCCHqPUmKhRBCCCFEvSdJsRBCCCGEqPckKRZCCCGEEPWeJMVCCCGEEKLek6RYCCGEEELUe5IUCyGEEEKIek+SYqEXV65cwc3NjYMHD1b5OW5ubiQkJOgwqsolJSVx+vRpvcchRF0j44AQwpBIUizEQ/z2229MmjSJ69ev6zsUIYSeyDggRP0gSbEQDyEFH4UQMg4IUT9IUlxP/fjjjzz33HN06dKFnj178tZbb5GZmQlAbm4uc+fOpU+fPnh5efHSSy9x6NAh9XMTExN54okn2LFjB/3796dr164EBQWRlpambnPnzh3mz5+Pj48PHh4e9OjRgzfffJObN29q7RpOnDhBYGAgnp6e9OvXj1mzZvHHH3+oHw8MDCQqKoq5c+fSs2dPevbsyRtvvKHRJicnhxkzZtCjRw+8vb2ZO3cu06dPJzQ0lCtXrjBo0CAAXnvtNUJDQ9XPy8jIYNKkSXTp0oXevXuzZMkSVCqV1q5NiJog40AZGQeEECBJcb2Uk5PD1KlT6devH7t27SIuLo6LFy8SHh6OoihMnDiRc+fOsXLlShITExk4cCATJ04kKSlJ4xyrV69m8eLFbNmyBYCxY8dy69YtAMLCwjh69CgffPAB+/btY9GiRRw5coTo6GitXMPp06cZN24c3bt354svvmD16tXcuHGDl156ifz8fHW7+Ph4rK2tSUhIYNGiRRw+fJjly5cDZXd/Jk+ezKlTp4iJieGTTz7h5s2bfPXVVwA0b96czz77DIDly5cze/Zs9Xk/+eQTfH192bVrF2+88QYbNmzgiy++0Mq1CVETZByQcUAIoclU3wGImnf16lUKCwtp0qQJLVu2pHXr1qxYsYKcnBwOHz7MiRMnSEpKolmzZgBMmjSJ1NRU4uLi8PHxAaCkpIRFixbh7e0NlL1ZDBgwgJ07dxIYGEjv3r2ZPHkynTp1AqBly5bs2bOHM2fOaOUa1q9fT7du3Xj77bfVx6Kjo+nVqxdff/01o0ePBqB169bqOzsdOnRg2LBhHD58GICjR49y4sQJtm/fzmOPPaa+jsGDBwNgYmKCg4MDAA0bNsTW1lbd1/PPP8/zzz8PwJgxY9i8eTP/+9//8Pf318r1CaFrMg7IOCCE0CRJcT3UsWNHRo4cSWRkpPoNxMfHh+HDh7N582YAnnnmGY3nFBcX07BhQ/XP5ubmdO/eXf2zg4MD7du3V6/ODggIICkpiSVLlnD58mUuXLjAxYsX6dy5s1auITU1lYsXL+Ll5aVxvKSkROPj2w4dOmg8bmtrS3FxMQApKSlYWlqq3wgBLC0t8fT0/Mv+27dvr/Fzo0aNKCwsrPZ1CKEvMg7IOCCE0CRJcT21bNky3njjDQ4ePMjhw4f55z//SXx8PAMHDsTc3LzSjwCNje/NtjExMcHIyEjjcZVKhYmJCaWlpUydOpWUlBRGjBiBr68v7u7uxMXFcfnyZa3EX1paypAhQ3jzzTcrPHb/nRxzc/MHnsPExARFUVAUpcK1/JX7/y7KyWIcUdvIOCDjgBDiHplTXA+dPn2auXPn0rx5cwIDA1m9ejVr1qzh5MmTABQVFXHnzh3atm2r/vrss8/Yvn27+hz5+fmcPXtW/fPNmze5dOkSnTt35tSpU3z33XcsX76cWbNm8eyzz+Lu7s6FCxe09obh6urKuXPnaN26tTpGW1tbFi5cyLlz56p0jscee4zCwkJOnTqlPlZUVKT+ewCq/SYpRG0h40AZGQeEEOUkKa6H7O3t+fLLL5k3bx7nz5/nwoULfPHFFzRq1Ihx48bh4eHB9OnT+fHHH8nIyCAmJoa4uDjatWuncZ7Q0FB++eUXUlNTeeedd3B0dGTYsGE4OjpiamrKnj17yMjI4PTp04SFhXHy5EmKioq0cg0TJkzgwoULzJ49m7Nnz3Ly5EneeustUlNTcXV1rdI5vL29efzxx5k1axZHjx7l3LlzhIeHc+3aNfWbYIMGDQA4e/Ys2dnZWoldCEMg40AZGQeEEOUkKa6HmjZtytq1a7l06RIvvPAC/v7+ZGZmsnHjRmxtbVm/fj3e3t7MmDGDYcOGsWfPHpYuXcrIkSM1zuPv78+0adMYM2YMDRs2VK/wdnJyYtmyZRw+fJihQ4cSHByMSqUiJCSE9PR07ty588jX4OnpyYYNG8jIyOD5558nKCgIGxsbNm/ejL29fZXPs2LFCpydnZk0aRIBAQHY2trStWtXzMzMALCzsyMgIIAVK1YQFhb2yHELYShkHLhHxgEhBICRIhOgRDUlJiYSFhZGcnIyFhYW+g7nb8vOzubYsWP069dP4zp8fX3x8/NjypQpeoxOCMMm44AQoq6RhXai3jIzM2PGjBmMGjWKcePGAfDZZ5+RmZlZYdW9EKJuknFACFFOkmKhdyNHjiQjI+OhbTZt2lSlLZKqw8bGhtjYWFasWIG/vz+KotCpUyc2bNhQYd6kEEK3ZBwQQuibTJ8Qevf777+r9wx9kObNmz90WyUhRO0m44AQQt8kKRZCCCGEEPWe7D4hhBBCCCHqPUmKhRBCCCFEvSdJsRBCCCGEqPckKRZCCCGEEPXe/wFqshj4jddCPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 736.98x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# see the difference\n",
    "comparePlot(df_iris_test,\"species\",\"predicted_label_tree\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Random Forest\n",
    "\n",
    "Let's build a random forest classifier to classify irises into three species.\n",
    "- __Objective__: predict which species an iris instance belongs to.\n",
    "- __Possible classes__: \"setosa\", \"versicolor\", and \"virginica\"\n",
    "- __Features__: all four features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=True, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build a random forest\n",
    "rf_model = skens.RandomForestClassifier(n_estimators=10,oob_score=True, criterion='entropy')\n",
    "rf_model.fit(df_iris_train.ix[:,:4],df_iris_train.species)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"red\">Use the same approach that you did in predicting the Decision Tree Classifier.  Note that you don't need to use .sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# now do the predicting... use the same approach as you did in testing the Decision Tree Classifier above,\n",
    "# but don't include the .sample() line\n",
    "# this is testing the model \n",
    "predicted_labels = rf_model.predict(df_iris_test.ix[:,:4])\n",
    "df_iris_test['predicted_rf_tree'] = predicted_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 736.98x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparePlot(df_iris_test,\"species\",\"predicted_rf_tree\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also check __feature importance__ as dumped by the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0df34f60>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check feature importance\n",
    "feat_importance = rf_model.feature_importances_\n",
    "pd.DataFrame({'Feature Importance':feat_importance},\n",
    "            index=df_iris_train.columns[:4]).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2: What is the most important feature in the model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Insert your answer here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning the model\n",
    "The model paramters are called _hyperparameters_.  Setting them to appropriate values and running the model experiment again can be tedious, so we have a technique called cross-validation to help with this.\n",
    "\n",
    "From http://scikit-learn.org/stable/modules/cross_validation.html:\n",
    "\n",
    "> When evaluating different settings (“hyperparameters”) for estimators, such as the C setting that must be manually set for an SVM, there is still a risk of overfitting on the test set because the parameters can be tweaked until the estimator performs optimally. This way, knowledge about the test set can “leak” into the model and evaluation metrics no longer report on generalization performance. To solve this problem, yet another part of the dataset can be held out as a so-called “validation set”: training proceeds on the training set, after which evaluation is done on the validation set, and when the experiment seems to be successful, final evaluation can be done on the test set.\n",
    "\n",
    "> However, by partitioning the available data into three sets, we drastically reduce the number of samples which can be used for learning the model, and the results can depend on a particular random choice for the pair of (train, validation) sets.\n",
    "\n",
    "> A solution to this problem is a procedure called cross-validation (CV for short). A test set should still be held out for final evaluation, but the validation set is no longer needed when doing CV. In the basic approach, called k-fold CV, the training set is split into k smaller sets (other approaches are described below, but generally follow the same principles). The following procedure is followed for each of the k “folds”:\n",
    "\n",
    "> A model is trained using  of the folds as training data;\n",
    "the resulting model is validated on the remaining part of the data (i.e., it is used as a test set to compute a performance measure such as accuracy).\n",
    "The performance measure reported by k-fold cross-validation is then the average of the values computed in the loop. This approach can be computationally expensive, but does not waste too much data (as is the case when fixing an arbitrary validation set), which is a major advantage in problems such as inverse inference where the number of samples is very small."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(rf_model, df_iris_train.ix[:,:4],df_iris_train.species, cv=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean score and the 95% confidence interval (2 x the standard deviation) of the score estimate are hence given by:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.96 (+/- 0.12)\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But let's say we want to fiddle with the hyperparameters.\n",
    "\n",
    "To do this, we first set up a parameter grid with the lists of paramters we want to try:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "                 'n_estimators': [5, 10, 15, 20, 25],\n",
    "                 'max_depth': [2, 5, 7, 9],\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.grid_search import GridSearchCV\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now run the cross-validation on the classifier. Note: this will take a while (why?)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=True, random_state=None, verbose=0, warm_start=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'n_estimators': [5, 10, 15, 20, 25], 'max_depth': [2, 5, 7, 9]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf = GridSearchCV(rf_model, param_grid, cv=10)\n",
    "grid_clf.fit(df_iris_train.ix[:,:4],df_iris_train.species)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and extract the best model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=2, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=1,\n",
       "            oob_score=True, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or perhaps we're just interested in the best paramters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 2, 'n_estimators': 5}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case we want the details:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[mean: 0.98095, std: 0.03636, params: {'max_depth': 2, 'n_estimators': 5},\n",
       " mean: 0.96190, std: 0.08332, params: {'max_depth': 2, 'n_estimators': 10},\n",
       " mean: 0.96190, std: 0.08332, params: {'max_depth': 2, 'n_estimators': 15},\n",
       " mean: 0.95238, std: 0.08381, params: {'max_depth': 2, 'n_estimators': 20},\n",
       " mean: 0.95238, std: 0.08381, params: {'max_depth': 2, 'n_estimators': 25},\n",
       " mean: 0.96190, std: 0.04454, params: {'max_depth': 5, 'n_estimators': 5},\n",
       " mean: 0.95238, std: 0.08381, params: {'max_depth': 5, 'n_estimators': 10},\n",
       " mean: 0.95238, std: 0.08381, params: {'max_depth': 5, 'n_estimators': 15},\n",
       " mean: 0.96190, std: 0.08332, params: {'max_depth': 5, 'n_estimators': 20},\n",
       " mean: 0.97143, std: 0.04166, params: {'max_depth': 5, 'n_estimators': 25},\n",
       " mean: 0.95238, std: 0.08381, params: {'max_depth': 7, 'n_estimators': 5},\n",
       " mean: 0.94286, std: 0.08332, params: {'max_depth': 7, 'n_estimators': 10},\n",
       " mean: 0.95238, std: 0.08381, params: {'max_depth': 7, 'n_estimators': 15},\n",
       " mean: 0.96190, std: 0.06030, params: {'max_depth': 7, 'n_estimators': 20},\n",
       " mean: 0.95238, std: 0.08381, params: {'max_depth': 7, 'n_estimators': 25},\n",
       " mean: 0.94286, std: 0.08332, params: {'max_depth': 9, 'n_estimators': 5},\n",
       " mean: 0.96190, std: 0.06030, params: {'max_depth': 9, 'n_estimators': 10},\n",
       " mean: 0.95238, std: 0.08381, params: {'max_depth': 9, 'n_estimators': 15},\n",
       " mean: 0.95238, std: 0.08381, params: {'max_depth': 9, 'n_estimators': 20},\n",
       " mean: 0.95238, std: 0.08381, params: {'max_depth': 9, 'n_estimators': 25}]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf.grid_scores_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3: Run the best model from the cross-validation on the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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