Ch08-baggboost-lab-b.ipynb

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   "source": [
    "\n",
    "# Chapter 8\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98b86be9",
   "metadata": {},
   "source": [
    "# Lab: Tree-Based Methods"
   ]
  },
  {
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib.pyplot import subplots\n",
    "import sklearn.model_selection as skm\n",
    "from ISLP import load_data, confusion_table\n",
    "from ISLP.models import ModelSpec as MS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "747b056a",
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   "source": [
    "from sklearn.tree import (DecisionTreeClassifier as DTC,\n",
    "                          DecisionTreeRegressor as DTR,\n",
    "                          plot_tree,\n",
    "                          export_text)\n",
    "from sklearn.metrics import (accuracy_score,\n",
    "                             log_loss)\n",
    "from sklearn.ensemble import \\\n",
    "     (RandomForestRegressor as RF,\n",
    "      GradientBoostingRegressor as GBR)\n",
    "from ISLP.bart import BART\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4feef94d",
   "metadata": {},
   "source": [
    "## Fitting Classification Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b977dbf4",
   "metadata": {},
   "source": [
    "We first use classification trees to analyze the  `Carseats`  data set.\n",
    "In these data, `Sales` is a continuous variable, and so we begin\n",
    "by recoding it as a binary variable. We use the `where()` \n",
    "function to create a variable, called `High`, which takes on a\n",
    "value of `Yes` if the `Sales` variable exceeds 8, and takes\n",
    "on a value of `No` otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Carseats = load_data('Carseats')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a29167fd",
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "# Code the Y variable\n",
    "High = np.where(Carseats.Sales > 8,\n",
    "                \"Yes\",\n",
    "                \"No\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9887c34b",
   "metadata": {},
   "source": [
    "We now use `DecisionTreeClassifier()`  to fit a classification tree in\n",
    "order to predict `High` using all variables but `Sales`.\n",
    "To do so, we must form a model matrix as we did when fitting regression\n",
    "models.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9fc6c8b9",
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   "source": [
    "model = MS(Carseats.columns.drop('Sales'), intercept=False) # no intercept for a tree\n",
    "D = model.fit_transform(Carseats)\n",
    "feature_names = list(D.columns)\n",
    "X = np.asarray(D)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86912d40",
   "metadata": {},
   "source": [
    "We have converted `D` from a data frame to an array `X`, which is needed in some of the analysis below. We also need the `feature_names` for annotating our plots later.\n",
    "\n",
    "There are several options needed to specify the  classifier,\n",
    "such as `max_depth` (how deep to grow the tree), `min_samples_split`\n",
    "(minimum number of observations in a node to be eligible for splitting)\n",
    "and `criterion` (whether to use Gini or cross-entropy as the split criterion).\n",
    "We also set `random_state` for reproducibility; ties in the split criterion are broken at random."
   ]
  },
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   "id": "533f0949",
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       "DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DTC(criterion='entropy',\n",
    "          max_depth=3,\n",
    "          random_state=0)        \n",
    "clf.fit(X, High)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "118bae59",
   "metadata": {},
   "source": [
    "In our discussion of qualitative features in Section 3.3,\n",
    "we noted that for a linear regression model such a feature could be\n",
    "represented by including a matrix of dummy variables (one-hot-encoding) in the model\n",
    "matrix, using the formula notation of `statsmodels`.\n",
    "As mentioned in Section 8.1, there is a more\n",
    "natural way to handle qualitative features when building a decision\n",
    "tree, that does not require such dummy variables; each split amounts to partitioning the levels into two groups.\n",
    "However, \n",
    "the `sklearn` implementation of decision trees does not take\n",
    "advantage of this approach; instead it simply treats the one-hot-encoded levels as separate variables."
   ]
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    {
     "data": {
      "text/plain": [
       "0.79"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(High, clf.predict(X))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcbbe99a",
   "metadata": {},
   "source": [
    "With only the default arguments, the training error rate is\n",
    "21%.\n",
    "For classification trees, we can\n",
    "access the value of the **deviance** using `log_loss()`,\n",
    "\\begin{equation*}\n",
    "\\begin{split}\n",
    "-2 \\sum_m \\sum_k n_{mk} \\log \\hat{p}_{mk},\n",
    "\\end{split}\n",
    "\\end{equation*}\n",
    "where $n_{mk}$ is the number of observations in the $m$th terminal\n",
    "node that belong to the $k$th class."
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "id": "2fe92cb1",
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    "execution": {}
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    {
     "data": {
      "text/plain": [
       "0.47106470626493574"
      ]
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resid_dev = log_loss(High, clf.predict_proba(X))\n",
    "resid_dev\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e47c068c",
   "metadata": {},
   "source": [
    "This is closely related to the *entropy*, defined in (8.7).\n",
    "A small deviance indicates a\n",
    "tree that provides a good fit to the (training) data.\n",
    " \n",
    "One of the most attractive properties of trees is that they can\n",
    "be graphically displayed. Here we use the `plot()`  function\n",
    "to display the tree structure."
   ]
  },
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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = subplots(figsize=(12,12))[1]\n",
    "plot_tree(clf,\n",
    "          feature_names=feature_names,\n",
    "          ax=ax);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d089b674",
   "metadata": {},
   "source": [
    "The most important indicator of `Sales` appears to be `ShelveLoc`.\n",
    "\n",
    "We can see a text representation of the tree using\n",
    "`export_text()`, which displays the split\n",
    "criterion (e.g. `Price <= 92.5`) for each branch.\n",
    "For leaf nodes it shows the overall prediction  \n",
    "(`Yes` or `No`). \n",
    " We can also see the number of observations in that\n",
    "leaf that take on values of `Yes` and `No` by specifying  `show_weights=True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "38ec5f13",
   "metadata": {
    "execution": {}
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- ShelveLoc[Good] <= 0.50\n",
      "|   |--- Price <= 92.50\n",
      "|   |   |--- Income <= 57.00\n",
      "|   |   |   |--- weights: [7.00, 3.00] class: No\n",
      "|   |   |--- Income >  57.00\n",
      "|   |   |   |--- weights: [7.00, 29.00] class: Yes\n",
      "|   |--- Price >  92.50\n",
      "|   |   |--- Advertising <= 13.50\n",
      "|   |   |   |--- weights: [183.00, 41.00] class: No\n",
      "|   |   |--- Advertising >  13.50\n",
      "|   |   |   |--- weights: [20.00, 25.00] class: Yes\n",
      "|--- ShelveLoc[Good] >  0.50\n",
      "|   |--- Price <= 135.00\n",
      "|   |   |--- US[Yes] <= 0.50\n",
      "|   |   |   |--- weights: [6.00, 11.00] class: Yes\n",
      "|   |   |--- US[Yes] >  0.50\n",
      "|   |   |   |--- weights: [2.00, 49.00] class: Yes\n",
      "|   |--- Price >  135.00\n",
      "|   |   |--- Income <= 46.00\n",
      "|   |   |   |--- weights: [6.00, 0.00] class: No\n",
      "|   |   |--- Income >  46.00\n",
      "|   |   |   |--- weights: [5.00, 6.00] class: Yes\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(export_text(clf,\n",
    "                  feature_names=feature_names,\n",
    "                  show_weights=True))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f6a131b",
   "metadata": {},
   "source": [
    "In order to properly evaluate the performance of a classification tree\n",
    "on these data, we must estimate the test error rather than simply\n",
    "computing the training error. We split the observations into a\n",
    "training set and a test set, build the tree using the training set,\n",
    "and evaluate its performance on the test data. This pattern is\n",
    "similar to that in Chapter 6, with the linear models\n",
    "replaced here by decision trees --- the code for validation\n",
    "is almost identical. This approach leads to correct predictions\n",
    "for 68.5% of the locations in the test data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3959f39a",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.685])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation = skm.ShuffleSplit(n_splits=1,\n",
    "                              test_size=200,\n",
    "                              random_state=0)\n",
    "results = skm.cross_validate(clf,\n",
    "                             D,\n",
    "                             High,\n",
    "                             cv=validation)\n",
    "results['test_score']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e226364",
   "metadata": {},
   "source": [
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "710f158a",
   "metadata": {},
   "source": [
    "## Pruning a tree\n",
    "Next, we consider whether pruning the tree might lead to improved\n",
    "classification performance. We first split the data into a training and\n",
    "test set. We will use cross-validation to prune the tree on the training\n",
    "set, and then evaluate the performance of the pruned tree on the test\n",
    "set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "201c4690",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "(X_train,\n",
    " X_test,\n",
    " High_train,\n",
    " High_test) = skm.train_test_split(X,\n",
    "                                   High,\n",
    "                                   test_size=0.5,\n",
    "                                   random_state=0)\n",
    "                                   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eed4457",
   "metadata": {},
   "source": [
    "We first refit the full tree on the training set; here we do not set a `max_depth` parameter, since we will learn that through cross-validation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a8dc5c3a",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.735"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DTC(criterion='entropy', random_state=0)\n",
    "clf.fit(X_train, High_train)\n",
    "accuracy_score(High_test, clf.predict(X_test))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78654680",
   "metadata": {},
   "source": [
    "Next we use the `cost_complexity_pruning_path()` method of\n",
    "`clf` to extract cost-complexity values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0cbe0d28",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "ccp_path = clf.cost_complexity_pruning_path(X_train, High_train)\n",
    "kfold = skm.KFold(10,\n",
    "                  random_state=1,\n",
    "                  shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.01622556, 0.0171946 , 0.0180482 , 0.0180482 ,\n",
       "       0.01991688, 0.02012073, 0.02070855, 0.02193427, 0.0219518 ,\n",
       "       0.02220877, 0.02274806, 0.02417233, 0.02588672, 0.02714959,\n",
       "       0.02735525, 0.02900052, 0.02906078, 0.03209543, 0.04499252,\n",
       "       0.06236632, 0.10024835])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccp_path.ccp_alphas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "854bd2fe",
   "metadata": {},
   "source": [
    "This yields a set of impurities and $\\alpha$ values\n",
    "from which we can extract an optimal one by cross-validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`refit=True`: After finding the best hyperparameters, `GridSearchCV` refits the model with the entire dataset using the best parameters found during the search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea649080",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.685"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = skm.GridSearchCV(clf,\n",
    "                        {'ccp_alpha': ccp_path.ccp_alphas},\n",
    "                        refit=True, # Refit the best estimator with the entire dataset\n",
    "                        cv=kfold,\n",
    "                        scoring='accuracy')\n",
    "grid.fit(X_train, High_train)\n",
    "grid.best_score_\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b8e7965",
   "metadata": {},
   "source": [
    "Let’s take a look at the pruned true."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e005da14",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = subplots(figsize=(12, 12))[1]\n",
    "best_ = grid.best_estimator_\n",
    "plot_tree(best_,\n",
    "          feature_names=feature_names,\n",
    "          ax=ax);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "923e44d2",
   "metadata": {},
   "source": [
    "This is quite a bushy tree. We could count the leaves, or query\n",
    "`best_` instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "23324f7e",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_.tree_.n_leaves\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0d9bb88",
   "metadata": {},
   "source": [
    "The tree with 30 terminal\n",
    "nodes results in the lowest cross-validation error rate, with an accuracy of\n",
    "68.5%. How well does this pruned tree perform on the test data set? Once\n",
    "again, we apply the `predict()`  function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6dcd8b37",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.72\n"
     ]
    },
    {
     "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>Truth</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Predicted</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>94</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>24</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Truth      No  Yes\n",
       "Predicted         \n",
       "No         94   32\n",
       "Yes        24   50"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(accuracy_score(High_test,\n",
    "                     best_.predict(X_test)))\n",
    "confusion = confusion_table(best_.predict(X_test),\n",
    "                            High_test)\n",
    "confusion\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42c39ca1",
   "metadata": {},
   "source": [
    "Now 72.0% of the test observations are correctly classified, which is slightly worse than the error for the full tree (with 35 leaves). So cross-validation has not helped us much here; it only pruned off 5 leaves, at a cost of a slightly worse error. These results would change if we were to change the random number seeds above; even though cross-validation gives an unbiased approach to model selection, it does have variance.\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94ac4462",
   "metadata": {},
   "source": [
    "## Fitting Regression Trees\n",
    "Here we fit a regression tree to the  `Boston`  data set. The\n",
    "steps are similar to those for classification trees."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0459d3a9",
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "Boston = load_data(\"Boston\")\n",
    "model = MS(Boston.columns.drop('medv'), intercept=False)\n",
    "D = model.fit_transform(Boston)\n",
    "feature_names = list(D.columns)\n",
    "X = np.asarray(D)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b09cc1f",
   "metadata": {},
   "source": [
    "First, we split the data into training and test sets, and fit the tree\n",
    "to the training data. Here we use 30% of the data for the test set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "34bf2864",
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "(X_train,\n",
    " X_test,\n",
    " y_train,\n",
    " y_test) = skm.train_test_split(X,\n",
    "                                Boston['medv'],\n",
    "                                test_size=0.3,\n",
    "                                random_state=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc5ddbb2",
   "metadata": {},
   "source": [
    "Having formed  our training  and test data sets, we fit the regression tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd0dfd8a",
   "metadata": {
    "execution": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = DTR(max_depth=3)\n",
    "reg.fit(X_train, y_train)\n",
    "ax = subplots(figsize=(12,12))[1]\n",
    "plot_tree(reg,\n",
    "          feature_names=feature_names,\n",
    "          ax=ax);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62207ca5",
   "metadata": {},
   "source": [
    "The variable `lstat` measures the percentage of individuals with\n",
    "lower socioeconomic status. The tree indicates that lower\n",
    "values of `lstat` correspond to more expensive houses.\n",
    "The tree predicts a median house price of $12,042 for small-sized homes (`rm < 6.8`), in\n",
    "suburbs in which residents have low socioeconomic status (`lstat  > 14.4`) and the crime-rate is moderate (`crim > 5.8`)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "361d7f84",
   "metadata": {},
   "source": [
    "### Pruing the regression tree\n",
    "Now we use the cross-validation function to see whether pruning\n",
    "the tree will improve performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "33fb7786",
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "ccp_path = reg.cost_complexity_pruning_path(X_train, y_train)\n",
    "kfold = skm.KFold(5,\n",
    "                  shuffle=True,\n",
    "                  random_state=10)\n",
    "grid = skm.GridSearchCV(reg,\n",
    "                        {'ccp_alpha': ccp_path.ccp_alphas},\n",
    "                        refit=True,\n",
    "                        cv=kfold,\n",
    "                        scoring='neg_mean_squared_error')\n",
    "G = grid.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7218495",
   "metadata": {},
   "source": [
    "In keeping with the cross-validation results, we use the pruned tree\n",
    "to make predictions on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a386755b",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.06985754975404"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_ = grid.best_estimator_\n",
    "np.mean((y_test - best_.predict(X_test))**2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fe01e33",
   "metadata": {},
   "source": [
    "In other words, the test set MSE associated with the regression tree\n",
    "is 28.07.  The square root of\n",
    "the MSE is therefore around\n",
    "5.30,\n",
    "indicating that this model leads to test predictions that are within around\n",
    "$5300\n",
    "of the true median home value for the suburb.\n",
    "\n",
    "Let’s plot the best tree to see how interpretable it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3d646928",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = subplots(figsize=(12,12))[1]\n",
    "plot_tree(G.best_estimator_,\n",
    "          feature_names=feature_names,\n",
    "          ax=ax);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "074e230c",
   "metadata": {},
   "source": [
    " \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa0e4849",
   "metadata": {},
   "source": [
    "## Bagging and Random Forests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b17ca840",
   "metadata": {},
   "source": [
    "Here we apply bagging and random forests to the `Boston` data, using\n",
    "the `RandomForestRegressor()` from the `sklearn.ensemble` package. Recall\n",
    "that bagging is simply a special case of a random forest with\n",
    "$m=p$. Therefore, the `RandomForestRegressor()`  function can be used to\n",
    "perform both bagging and random forests. We start with bagging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "275feef7",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(max_features=12, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=12, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(max_features=12, random_state=0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_boston = RF(max_features=X_train.shape[1], random_state=0)\n",
    "bag_boston.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e84da6",
   "metadata": {},
   "source": [
    "The argument `max_features` indicates that all 12 predictors should\n",
    "be considered for each split of the tree --- in other words, that\n",
    "bagging should be done.  How well does this bagged model perform on\n",
    "the test set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "01dbbef3",
   "metadata": {
    "execution": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.634700151315787"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = subplots(figsize=(8,8))[1]\n",
    "y_hat_bag = bag_boston.predict(X_test)\n",
    "ax.scatter(y_hat_bag, y_test)\n",
    "np.mean((y_test - y_hat_bag)**2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "885a3843",
   "metadata": {},
   "source": [
    "The test set MSE associated with the bagged regression tree is\n",
    "14.63, about half that obtained using an optimally-pruned single\n",
    "tree.  We could change the number of trees grown from the default of\n",
    "100 by\n",
    "using the `n_estimators` argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b75cc90e",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.605662565263161"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bag_boston = RF(max_features=X_train.shape[1],\n",
    "                n_estimators=500,\n",
    "                random_state=0).fit(X_train, y_train)\n",
    "y_hat_bag = bag_boston.predict(X_test)\n",
    "np.mean((y_test - y_hat_bag)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37d5fe61",
   "metadata": {},
   "source": [
    "There is not much change. Bagging and random forests cannot overfit by\n",
    "increasing the number of trees, but can underfit if the number is too small.\n",
    "\n",
    "Growing a random forest proceeds in exactly the same way, except that\n",
    "we use a smaller value of the `max_features` argument. By default,\n",
    "`RandomForestRegressor()`  uses $p$ variables when building a random\n",
    "forest of regression trees (i.e. it defaults to bagging), and `RandomForestClassifier()` uses\n",
    "$\\sqrt{p}$ variables when building a\n",
    "random forest of classification trees. Here we use `max_features=6`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bf9a5ed4",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.04276446710527"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF_boston = RF(max_features=6,\n",
    "               random_state=0).fit(X_train, y_train)\n",
    "y_hat_RF = RF_boston.predict(X_test)\n",
    "np.mean((y_test - y_hat_RF)**2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb2c3917",
   "metadata": {},
   "source": [
    "### Variable (Feature) Importance\n",
    "The test set MSE is 20.04;\n",
    "this indicates that random forests did somewhat worse than bagging\n",
    "in this case. Extracting the `feature_importances_` values from the fitted model, we can view the\n",
    "importance of each variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "71316e9a",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 0
   },
   "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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lstat</th>\n",
       "      <td>0.356203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rm</th>\n",
       "      <td>0.332163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ptratio</th>\n",
       "      <td>0.067270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crim</th>\n",
       "      <td>0.055404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indus</th>\n",
       "      <td>0.053851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>0.041582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nox</th>\n",
       "      <td>0.035225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tax</th>\n",
       "      <td>0.025355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.021506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rad</th>\n",
       "      <td>0.004784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chas</th>\n",
       "      <td>0.004203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zn</th>\n",
       "      <td>0.002454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         importance\n",
       "lstat      0.356203\n",
       "rm         0.332163\n",
       "ptratio    0.067270\n",
       "crim       0.055404\n",
       "indus      0.053851\n",
       "dis        0.041582\n",
       "nox        0.035225\n",
       "tax        0.025355\n",
       "age        0.021506\n",
       "rad        0.004784\n",
       "chas       0.004203\n",
       "zn         0.002454"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_imp = pd.DataFrame(\n",
    "    {'importance':RF_boston.feature_importances_},\n",
    "    index=feature_names)\n",
    "feature_imp.sort_values(by='importance', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3cd6a28",
   "metadata": {},
   "source": [
    " This\n",
    "is a relative measure of the total decrease in node impurity that results from\n",
    "splits over that variable, averaged over all trees (this was plotted in Figure 8.9 for a model fit to the `Heart` data). \n",
    "\n",
    "The results indicate that across all of the trees considered in the\n",
    "random forest, the wealth level of the community (`lstat`) and the\n",
    "house size (`rm`) are by far the two most important variables.\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "270fc0fb",
   "metadata": {},
   "source": [
    "## Gradient Boosting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bd28754",
   "metadata": {},
   "source": [
    "Here we use `GradientBoostingRegressor()` from `sklearn.ensemble`\n",
    "to fit boosted regression trees to the `Boston` data\n",
    "set. For classification we would  use `GradientBoostingClassifier()`.\n",
    "The argument `n_estimators=5000`\n",
    "indicates that we want 5000 trees, and the option\n",
    "`max_depth=3` limits the depth of each tree. The\n",
    "argument `learning_rate` is the $\\lambda$\n",
    "mentioned earlier in the description of boosting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0bcc5ff1",
   "metadata": {
    "execution": {}
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor(learning_rate=0.001, n_estimators=5000,\n",
       "                          random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor(learning_rate=0.001, n_estimators=5000,\n",
       "                          random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingRegressor(learning_rate=0.001, n_estimators=5000,\n",
       "                          random_state=0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boost_boston = GBR(n_estimators=5000,\n",
    "                   learning_rate=0.001,\n",
    "                   max_depth=3,\n",
    "                   random_state=0)\n",
    "boost_boston.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e8553ec",
   "metadata": {},
   "source": [
    "We can see how the training error decreases with the `train_score_` attribute.\n",
    "To get an idea of how the test error decreases we can use the\n",
    "`staged_predict()` method to get the predicted values along the path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "060f47eb",
   "metadata": {
    "execution": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oJ8JmZeXC5vaffKrRFwAAwO8QNitLS1OJHIrVMR3ctN/uagAAAPwaYbOy8HAda9RKklS4eZvNxQAAAPg3wmYVClJNV3roDsImAABAfRA2qxB0tgmbsVmETQAAgPogbFYhuke6JKlF/jbl5NhcDAAAgB8jbFYhorNp2UzXVu3caXMxAAAAfoywWZV2ZWGT5Y8AAADqjrBZlbZtVSKH4pWrrA0H7K4GAADAbxE2qxIRoew4s/zRiXVbbS4GAADAfxE2q5HX3EwScmz9weZKAAAA/BdhsxolZ5mwGf0zYRMAAKCuCJvVCO92tiSpyZGtspgjBAAAUCeEzWok9DVhs83JrTpyxOZiAAAA/BRhsxoRXU03erq2asdPJTZXAwAA4J8Im9Vp00YnHSGK0gllrvzZ7moAAAD8EmGzOqGhOhCTJkk6uorljwAAAOqCsFmD3GQzbvPkZmakAwAA1AVhswYnTy1/FL6Tlk0AAIC6IGzWIKyzadlMPEjLJgAAQF0QNmuQ2M+EzRbHt6qoyOZiAAAA/BBhswaN+plu9Lb6Ubt+OmlzNQAAAP6HsFmDoFYtle+IUKhOat/iHXaXAwAA4HcImzUJClJmTDtJUs4KJgkBAAC4irB5BjlJp5Y/2sQkIQAAAFcRNs+gKM2EzbCdhE0AAABXETbPINS5/NF+wiYAAICrCJtnkNCvvSSpRd4WWZbNxQAAAPgZwuYZJA8yLZup1m4d3JlnczUAAAD+hbB5BhEtm+hIUCNJ0r5vmJEOAADgCsJmLfwcY7rSc5YzbhMAAMAVhM1ayE42YbN40xabKwEAAPAvhM1aKGpzavmjHYRNAAAAVxA2ayG0i2nZTGD5IwAAAJcQNmsh/hwTNlvmbRHrHwEAANQeYbMWmg9upxI5FGfl6sSOLLvLAQAA8BuEzVpo1CxcuxxtJEmZCxi3CQAAUFuEzVpwOKSfY01Xeu5ywiYAAEBtETZr6UhyR0lS8cbvba4EAADAfxA2a6nwrA6SpIjtm22uBAAAwH8QNmsptJtp2Wx8gLAJAABQW4TNWoo/14TN5BM7pbw8m6sBAADwD4TNWmrVq4kOqrEkqXgzi7sDAADUBmGzllJTpe8dpnXzyCK60gEAAGqDsFlLwcHS3lgzSejYCmakAwAA1AZh0wXZzUzLprWJlk0AAIDaIGy6oKidCZtROwmbAAAAtUHYdEF4d9ON3ujwVunkSZurAQAA8H2ETRc07dNaxxWp0JJCaft2u8sBAADweYRNF6S3D9IWmT3SGbcJAABwZoRNF7RtK30vZqQDAADUFmHTBeHh0r4EM0kobyUtmwAAAGdC2HTR8VTTshm0hbAJAABwJoRNFwV1Ni2bcT9vlizL5moAAAB8G2HTRXF9ztZJBSuiIFfau9fucgAAAHwaYdNFbTuFa5vamQcbN9pbDAAAgI8jbLooPV3aqM6SpJINhE0AAICaEDZd1KaNtNlhwubx5YRNAACAmhA2XRQSIh1MMWGzeC1hEwAAoCaEzTooSjdhM3L7RmakAwAA1ICwWQeR3c9WkUIUln9U2rPH7nIAAAB8FmGzDtp2DCvdI10bNthbDAAAgA8jbNZBerq0QV3MA8ImAABAtQibdVA+bJasI2wCAABUh7BZB6mp0g8hZpJQ0WrCJgAAQHUIm3UQFCQda2NaNkO2bZaKi22uCAAAwDcRNusoqstZOqEIBReckLZvt7scAAAAn0TYrKO2ZwdrkzqZB+vX21sMAACAjyJs1lF6urReXc0DwiYAAECVCJt1dPbZ0lp1Nw/WrbO3GAAAAB9F2Kyj9HRpnbpJkkrWrLW5GgAAAN9E2KyjlBTpp2gTNh0//SgdO2ZzRQAAAL6HsFlHDoeUeHZT7VOKHJYlbdxod0kAAAA+h7BZD+W70hm3CQAAcDrCZj2kpzNJCAAAoCaEzXo4++xyLZtrmSQEAABQGWGzHk7rRrcsewsCAADwMYTNemjfXvpeHVSkECknR9q92+6SAAAAfAphsx4aNZISk8K0WR3NE4zbBAAAqICwWU8dOpSbJMS4TQAAgAoIm/XUoQPLHwEAAFSHsFlPhE0AAIDqETbrqULY/OEH6cQJewsCAADwIYTNeurQQcpUig6oiVRSwraVAAAA5RA266l1aykiwsFOQgAAAFUgbNZTUJBZb5NxmwAAAKcjbLoBk4QAAACqRth0g9PCJttWAgAASCJsukWHDtImddJJBUuHDkl799pdEgAAgE8gbLpBhw5SgSK0Lbi9eYKudAAAAEmETbc4+2xzXl3MuE0AAIDyCJtuEBVllkBikhAAAEBFhE036dixXNhcu9beYgAAAHwEYdNNOnRQ2cLu338vFRTYWxAAAIAPIGy6SYcO0s9qoaOhiVJxsbR5s90lAQAA2I6w6SYdOkiSQxuDGbcJAADgRNh0ExM2peX5hE0AAAAnwqabJCVJCQnSWiYJAQAAlCJsuonDUWmSEC2bAAAAhE136tBB2qjOshwOaf9+KSvL7pIAAABsRdh0o44dpROK0r6YdPMErZsAACDAETbdyDlJaFMw4zYBAAAkwqZbOcPmwrwe5mL1attqAQAA8AWETTdKS5NCQ6UlRb3NEytW2FsQAACAzQibbhQaKqWnSyt1Kmz+8IOUm2tvUQAAADYibLpZ587SQTVVbkIr88SqVfYWBAAAYCPCppt17mzOP8T3MRcrV9pXDAAAgM0Im27mDJvLik+FTcZtAgCAAEbYdDNn2Pz8IJOEAAAACJtu1q6dmSi0MP9U2Ny2TcrOtrUmAAAAuxA23Sw0VGrfXjqsxjqe3MY8ySQhAAAQoAibHtCliznvSmKSEAAACGyETQ9wjttcG8okIQAAENgImx7gDJsLjp4at0nLJgAACFCETQ9whs2P95wKmz/+KB05Yl9BAAAANiFsekDbtlJ4uLT3RKKKWp1lnqR1EwAABCDCpgcEB0sdOpjr/alMEgIAAIGLsOkhzq70LbFMEgIAAIGLsOkhzuWPlhQxSQgAAAQuwqaHOFs252T1Mhfbt0uHDtlXEAAAgA0Imx7iDJsrtiXIatfOPGAnIQAAEGAImx6SliZFRkr5+dKx9ozbBAAAgYmw6SFBQVLHjuZ6Z5NT4zYJmwAAIMAQNj3I2ZW+OpjljwAAQGAibHqQM2x+lXNqktDOndLBg/YVBAAA4GUuh82ff/5Zv/71r9W4cWNFRkaqa9euWlGue9iyLI0fP17NmjVTZGSkMjIytHXrVrcW7S+cyx+t+CFOOvts84DWTQAAEEBcCptHjhzRwIEDFRoaqjlz5mjTpk16/vnnlZiYWHrPs88+q4kTJ2ry5MlaunSpoqOjNWTIEOXn57u9eF9XurD7FqmkF5OEAABA4Alx5eZnnnlGqampeuutt0qfS0tLK722LEsvvvii/vznP+uqq66SJL3zzjtKTk7WzJkzdf3115/2mQUFBSooKCh9nJub6/If4atatZKio6W8POlA695K1nu0bAIAgIDiUsvmrFmz1KdPH1177bVKSkpSz5499cYbb5S+vn37dmVmZiojI6P0ufj4ePXr10+LFy+u8jMnTJig+Pj40iM1NbWOf4rvCQqSOnUy15ujaNkEAACBx6Ww+dNPP+m1115Tenq6Pv/8c9155526++679fbbb0uSMjMzJUnJyckV3pecnFz6WmXjxo1TTk5O6bF79+66/B0+q2tXc150oqfkcEi7d0v799tbFAAAgJe41I1eUlKiPn366KmnnpIk9ezZUxs2bNDkyZM1evToOhUQHh6u8PDwOr3XHzjD5ootsVL79tL335uu9EsvtbcwAAAAL3CpZbNZs2bq5OwXPqVjx47atWuXJCklJUWSlJWVVeGerKys0tcCTbdu5rxunaQ+dKUDAIDA4lLYHDhwoLZs2VLhuR9++EGtW7eWZCYLpaSkaP78+aWv5+bmaunSperfv78byvU/zpbNn36SCrqc2kmISUIAACBAuBQ277vvPi1ZskRPPfWUtm3bpvfee0//+Mc/NGbMGEmSw+HQvffeq7/+9a+aNWuW1q9fr9/85jdq3ry5rr76ak/U7/OaNpVSUiTLkrYlnGrZXL7c3qIAAAC8xKUxm3379tWMGTM0btw4Pfnkk0pLS9OLL76oUaNGld7z0EMPKS8vT7fffruys7N13nnn6bPPPlNERITbi/cX3bpJmZnSsqKe6hwcLO3dK+3ZI7VsaXdpAAAAHuWwLMuyu4jycnNzFR8fr5ycHMXFxdldjls8+KD03HPSXXdJExf2klavlqZPl6691u7SAAAAXOZKXmNvdC9wjttct07SgAHmQTXrjgIAADQkhE0vKD8j3Tr31ESpRYvsKwgAAMBLCJte0LGjFBwsHTkiZaadCpurVkkBuF88AAAILIRNLwgPN+u5S9KanDQpOVkqKjKBEwAAoAEjbHpJaVf6eofUn650AAAQGAibXlJhkpAzbDJJCAAANHCETS9xtmyuX6+KM9J9a+UpAAAAtyJseokzbG7eLBV27S2FhEj79kmn9pUHAABoiAibXpKaKiUkSCdPSpu2R0o9e5oXGLcJAAAaMMKmlzgcUo8e5nrNGrG4OwAACAiETS9yhs3Vq8UkIQAAEBAIm15UoWXTGTbXrJGOH7enIAAAAA8jbHqRc5jmmjWS1TJVat7cDOJcscLWugAAADyFsOlFHTtKYWFSbq60fYeDcZsAAKDBI2x6UWio1KWLua7Qlc6MdAAA0EARNr2s2klCLO4OAAAaIMKml5Uft6levUy/+oED0k8/2VkWAACARxA2vaxCy2Z4uNS7t3mCcZsAAKABImx6Wffu5vzzz6ZBk3GbAACgISNsellsrNSunbleu1ZlM9K/+862mgAAADyFsGkD57jN1aslnXeeebB+vZSdbVdJAAAAHkHYtEGFnYSSk6Wzzzaz0RcutLEqAAAA9yNs2qDCJCFJGjzYnL/91o5yAAAAPIawaQNnN/qWLae2RXeGzW++sa0mAAAATyBs2iAlRUpKkkpKzFBNDRpkXlixQsrLs7U2AAAAdyJs2sDhqLS4e+vWUmqqdPKktGSJnaUBAAC4FWHTJhUmCTkcjNsEAAANEmHTJhWWP5IYtwkAABokwqZNnC2b69ZJxcUqC5uLF0uFhXaVBQAA4FaETZu0aydFR0snTkg//CCpfXupaVMpP99MFAIAAGgACJs2CQ6WunUz16tXq+K4TbrSAQBAA0HYtFHv3ua8cuWpJ5xLIBE2AQBAA0HYtFGfPuZc2mvubNn87rtTAzkBAAD8G2HTRs6wuWrVqWzZrZsUFyfl5pqZQwAAAH6OsGmjDh2kqCjp2LFTk4SCg6XzzjMv0pUOAAAaAMKmjYKDpV69zPVpXemETQAA0AAQNm1W7bjNb76RLMuWmgAAANyFsGmz08Jm795SZKR08KD0/fe21QUAAOAOhE2bOcPm6tXSyZOSwsKkc881T9KVDgAA/Bxh02bp6VJsrNlJaPPmU08ybhMAADQQhE2bBQWVLe7OuE0AANDQEDZ9gLMrffnyU0+ce64UEiLt2SPt3GlbXQAAAPVF2PQBp00SioqS+vY113SlAwAAP0bY9AHOsLl2rVRYeOpJxm0CAIAGgLDpA846S0pMNEFzw4ZTTw4aZM6ETQAA4McImz7A4aiiK33gQPPC1q3Svn221QYAAFAfhE0fcVrYTEiQunc3199+a0dJAAAA9UbY9BGnhU2JcZsAAMDvETZ9hDNsrl8v5eefetIZNmnZBAAAfoqw6SNSU6WmTc2WlevWnXrSOUlo/Xrp8GHbagMAAKgrwqaPqHKSUFKS1L692UXou+9sqw0AAKCuCJs+hHGbAACgoSFs+pDTtq2UCJsAAMCvETZ9iHOHyk2bpGPHTj3pDJsrV5Z7EgAAwD8QNn1Is2ZmolBJSbmu9FatpNatpeJiadEiW+sDAABwFWHTx5xzjjkvXVruyQsuMOf5871dDgAAQL0QNn1Mv37mXCFsDhlizp995vV6AAAA6oOw6WOqDJsXX2zWRlq3Ttq715a6AAAA6oKw6WN695aCg02m3LPn1JNNmpTNHvr8c9tqAwAAcBVh08dER0tdupjrZcvKvTB0qDnTlQ4AAPwIYdMHVdmV7gyb8+aZPS0BAAD8AGHTB1UZNvv2lRITpSNHKq36DgAA4LsImz7IGTZXrDDLa0qSQkKkjAxzTVc6AADwE4RNH9ShgxQbK+XlSRs3lnuBcZsAAMDPEDZ9UHBw2eTzKtfbXL5cOnjQ63UBAAC4irDpo6oct9mihdS1q2RZ0hdf2FIXAACAKwibPqrKsCnRlQ4AAPwKYdNHOcPmxo1STk65F8qHzZISr9cFAADgCsKmj0pJkdLSTI95hcXdBw40K79nZZntKwEAAHwYYdOHDRhgzosWlXsyPFy68EJzTVc6AADwcYRNH9a/vzlXCJsS4zYBAIDfIGz6MGfL5pIllYZnOsPmd99JublerwsAAKC2CJs+rGtXMzwzN1fatKncC2edJaWnmz3Sv/zStvoAAADOhLDpw0JCpHPOMdd0pQMAAH9E2PRxzq70xYsrvVA+bFqWV2sCAACoLcKmj6tyRroknX++FBYm7dwpbdni9boAAABqg7Dp484915x/+KHSdujR0dLgweaarnQAAOCjCJs+rlEjqUMHc71kSaUXGbcJAAB8HGHTD1Tble4Mm19/LR0/7s2SAAAAaoWw6QeqXdy9UycpNVUqKGAJJAAA4JMIm37A2bK5fLlUVFTuBYdDuvJKc/3xx16vCwAA4EwIm36gQwcpIcH0lK9dW+lFZ9j85JNK2wwBAADYj7DpB4KCyrrSFy6s9OIFF0hxcVJWlrRsmbdLAwAAqBFh0084Vzn69ttKL4SFSZdeaq7pSgcAAD6GsOknnGHzm2+q2DDoqqvMmbAJAAB8DGHTT/TpI0VEmIXdv/++0ouXXmo2Ut+8Wdq61Zb6AAAAqkLY9BNhYWW7CX3zTaUXExLM9pWSNGuWN8sCAACoEWHTj1Q7blOiKx0AAPgkwqYfcYbNBQuqGLfpXALpu+8qbaIOAABgH8KmHzn3XDM0c88eaefOSi+2bi11727W2vzf/2ypDwAAoDLCph+JjpZ69zbXp43blOhKBwAAPoew6WdqNW7z88+lEye8VhMAAEB1CJt+pvx6m6fp2VNKTTX7Ws6f79W6AAAAqkLY9DMDB0oOh/TDD1JmZqUXHY6yiUJ0pQMAAB9A2PQziYlS167musau9FmzpOJir9UFAABQFcKmH6px3Ob550vx8dL+/dKiRV6tCwAAoDLCph+qcdxmWFhZ6+b06V6rCQAAoCqETT80aJA5r1snHTlSxQ0jR5rzRx+ZdTcBAABsQtj0QykpUnq62UXou++quCEjQ4qLk/bulRYv9np9AAAAToRNP1XjuM3w8LJZ6R995LWaAAAAKiNs+qkax21K0rBh5vzhh1VspA4AAOAdhE0/5Ry3uWKFlJdXxQ1DhkhRUWYT9dWrvVobAACAE2HTT7VpI7VsKZ08Wc0KR1FR0qWXmusPP/RmaQAAAKUIm37K4ZB+8Qtz/eWX1dw0fLg5f/ABXekAAMAWhE0/dtFF5lztNui//KUUESFt3SqtWeOtsgAAAEoRNv2YM2yuXCllZ1dxQ2ysCZySNG2at8oCAAAoRdj0Yy1bSmefbdZtX7Cgmpuuu86cp0+nKx0AAHgdYdPPnbEr/bLLpOhoaccOaelSb5UFAAAgibDp984YNqOiyvZKnzrVKzUBAAA4ETb93AUXmJnpmzZJ+/ZVc9MNN5jz9OlScbG3SgMAACBs+rvGjaUePcx1tUsgXXKJlJgoZWZKX3/tpcoAAAAImw3CGbvSw8KkESPMNV3pAADAiwibDUD5sFnthHNnV/qHH0oFBV6pCwAAgLDZAAwaJIWGSrt2ST/+WM1NgwdLzZubBTk/+8yb5QEAgABG2GwAoqOlc88119WO2wwOlq6/3ly/+65X6gIAACBsNhBnHLcpSaNGmfMnn0jHjnm8JgAAAMJmA+EMm19+aXYUqlLPnlK7dlJ+vvTpp16rDQAABC7CZgNxzjmmO/3gQWn9+mpucjikkSPN9fvve602AAAQuAibDURYmJkDJJ2hK925V/rs2dKRIx6vCwAABDbCZgNSq3Gb3bpJXbtKhYVmRyEAAAAPImw2IBdeaM7ffCMVFdVw429+Y87vvOPxmgAAQGAjbDYg3btLTZqYieZLltRw469+JQUFSYsWSVu3eq0+AAAQeAibDUhQkNkGXZLmzKnhxubNy278z388XhcAAAhchM0GZuhQcz7jJkHlu9KrXSsJAACgfgibDYyzwXL1aikzs4Ybr7pKio2VduyQFi70RmkAACAA1StsPv3003I4HLr33ntLn8vPz9eYMWPUuHFjxcTEaPjw4crKyqpvnail5GSpd29zPXduDTdGRZWtufn22x6vCwAABKY6h83ly5fr9ddfV7du3So8f9999+mTTz7RBx98oAULFmjv3r0aNmxYvQtF7dW6K330aHN+/33p6FGP1gQAAAJTncLmsWPHNGrUKL3xxhtKTEwsfT4nJ0f/+te/9MILL+jCCy9U79699dZbb2nRokVaUuP0aLiTM2zOnSsVF9dw43nnSe3bS3l57CgEAAA8ok5hc8yYMbr88suVkZFR4fmVK1eqqKiowvMdOnRQq1attHjx4io/q6CgQLm5uRUO1M+550rx8dKhQ9LKlTXc6HBIv/2tuX7jDa/UBgAAAovLYXPatGlatWqVJkyYcNprmZmZCgsLU0JCQoXnk5OTlVnNbJUJEyYoPj6+9EhNTXW1JFQSEiI5836tZqWHhkrLlknr1nm8NgAAEFhcCpu7d+/WPffco3fffVcRERFuKWDcuHHKyckpPXbv3u2Wzw10tR63mZRkZqZLtG4CAAC3cylsrly5Uvv371evXr0UEhKikJAQLViwQBMnTlRISIiSk5NVWFio7OzsCu/LyspSSkpKlZ8ZHh6uuLi4Cgfqb8gQc166VDp8+Aw333abOf/nP9KJEx6tCwAABBaXwuZFF12k9evXa82aNaVHnz59NGrUqNLr0NBQzZ8/v/Q9W7Zs0a5du9S/f3+3F4/qpaZKnTub9dq/+OIMN2dkSK1bS9nZ0ocfeqM8AAAQIFwKm7GxserSpUuFIzo6Wo0bN1aXLl0UHx+vW2+9Vffff7+++uorrVy5UjfffLP69++vc88911N/A6pR6670oCDp1lvN9T/+4dGaAABAYHH7DkJ///vf9ctf/lLDhw/X4MGDlZKSoo8++sjdX4NaKB82LesMN99yixQcLH37rbR5s8drAwAAgcFhWWeMIV6Vm5ur+Ph45eTkMH6zngoKpEaNpOPHpTVrpO7dz/CGq6+WPv5Yuuce6cUXPV8gAADwS67kNfZGb8DCw6ULLzTX//tfLd5wxx3mPGWKWegdAACgngibDdwvf2nOn3xSi5svuUQ66ywpJ0eaOtWjdQEAgMBA2GzgnGFz6VJp//4z3BwUJN15p7meNKkWAz0BAABqRths4Fq0kHr1MrmxVl3pN98sRUSYQZ7VbDEKAABQW4TNAHDlleZcq670xo2lG24w16+95rGaAABAYCBsBoArrjDnuXOl/PxavOH3vzfn6dNr0fcOAABQPcJmAOjZ03Sn5+VJX31Vizf06SOdc45UWCi9+abH6wMAAA0XYTMAOBwuzkqXylo3J0+Wios9UhcAAGj4CJsBwjlu89NPaznJfORIsyL8zp21nFkEAABwOsJmgLjwQikqStq9W1q7thZviIyUfvtbc81uQgAAoI4ImwEiIkK6+GJzPWtWLd80dqzZL/2rr8xSSAAAAC4ibAYQ56z0Wo/bTE013emS9Pe/e6QmAADQsBE2A8gvf2kmC61YIe3dW8s33XefOU+dKu3b57HaAABAw0TYDCDJyWZFI8lMFKqVvn2l886TioqkV17xWG0AAKBhImwGmKuuMucZM1x40/33m/Nrr5nFOgEAAGqJsBlghg0z5/nzpZycWr7pyiultm2lI0ekKVM8VRoAAGiACJsBpn17qVMn0yte6+Uzg4PLxm7+/e8s8g4AAGqNsBmAnK2bH33kwptuuklKTJR+/FH6+GNPlAUAABogwmYAcobNOXOk48dr+abo6LItLJ99tpbbEAEAgEBH2AxAPXpIrVuboDl3rgtvHDtWCg+Xli6VFizwVHkAAKABIWwGIIejjl3pKSnSzTeb6+eec3tdAACg4SFsBihn2PzkE6mw0IU33n+/Sav/+5+0aZNHagMAAA0HYTNA9e9vFnnPzpa+/tqFN6anS9dcY66ffdYDlQEAgIaEsBmggoOlq6821y4t8C5JDz9szv/5j7RjhxurAgAADQ1hM4A5u9JnzHBx6cxzzpEuucS8acIEj9QGAAAaBsJmAPvFL8zSmVlZ0jffuPjmP//ZnN98k9ZNAABQLcJmAAsNLWvdfP99F988aJCUkSGdPCn95S9urw0AADQMhM0Ad9115vzhhyY3uuTJJ8357belLVvcWhcAAGgYCJsB7he/kJo0kQ4elL76ysU39+8vXXGFGbv56KMeqQ8AAPg3wmaACwmRhg831y53pUvS3/5m1t384ANp5Uq31gYAAPwfYROlXekffeTiAu+S1LWrNGqUuf7jH91aFwAA8H+ETWjwYLPA+5Ej0vz5dfiAJ54ws43mzpW+/NLt9QEAAP9F2ISCg6URI8x1nbrSzzpL+t3vzPW4cZJlua02AADg3wibkFTWlT5zplRQUIcP+POfpehoadky8yEAAAAibOKUgQOlFi2knBzp88/r8AHJydK995rrP/3JxS2JAABAQ0XYhCQpKEi69lpzPX16HT/kwQelRo2kzZulf//bbbUBAAD/RdhEqZEjzfnjj6UTJ+rwAfHxZsymJD32WB374wEAQENC2ESpc8+VWrWSjh2T5syp44eMGWP643ftkiZPdmt9AADA/xA2UcrhKGvdfO+9On5IZKRp1ZSkv/5VOnrULbUBAAD/RNhEBc712T/9VMrOruOH3HyzdPbZZg/MF15wV2kAAMAPETZRQffuUpcuZrjlBx/U8UNCQkyrpiQ995x04IDb6gMAAP6FsIkKHA7pxhvNdb0mlA8fLvXubQaATpjgltoAAID/IWziNL/6lQmd334r7dhRxw8JCpKeespcT5pkJgwBAICAQ9jEaVq2lC680Fz/5z/1+KCLL5Z+8QupsFB6/HF3lAYAAPwMYRNVKt+VXuetzh2Osi70t9+W1q51S20AAMB/EDZRpWHDzCpGP/wgLV9ejw/q18+sp1RSIt19dz2SKwAA8EeETVQpNla65hpzXe+dJ//v/0xy/eabekxxBwAA/oiwiWo5u9KnTZOKiurxQa1aSY88Yq4feEDKy6t3bQAAwD8QNlGtjAwpOdmszf755/X8sAcflFq3lnbvlp55xi31AQAA30fYRLVCQqQbbjDX77xTzw+LjCzbTejZZ6Xt2+v5gQAAwB8QNlGj0aPN+eOPTQtnvVxzjVlTqaBA+sMf6l0bAADwfYRN1KhHD6lXL7NUZr3W3JTMUkgvvSQFB0szZkhz57qjRAAA4MMImzijW28153/9yw0rF3XpIo0ZY67HjJHy8+v5gQAAwJcRNnFGv/qVFBEhbdhQzzU3nZ58UmreXNq2rWxLSwAA0CARNnFGCQnSiBHm+l//csMHxsdLEyea66efljZvdsOHAgAAX0TYRK04u9KnTnXTMpnDhkm//KVZwPN3vzM7DAEAgAaHsIlaOf98qW1b6ehRN20C5HBIkyZJ0dHSt99KU6a44UMBAICvIWyiVhwO6ZZbzLVbutIls7PQk0+a6wcekPbvd9MHAwAAX0HYRK3ddJMUFCQtXCh9/72bPvTuu836SkeOmMAJAAAaFMImaq15c+myy8z1m2+66UNDQqR//MM0nf7736y9CQBAA0PYhEucE4XeftvM7XGLvn2lu+4y17ffLh075qYPBgAAdiNswiWXXy4lJ5vhlZ984sYP/tvfpDZtpJ07pXHj3PjBAADAToRNuCQ01IzdlKTJk934wTEx0htvmOtXXpG+/NKNHw4AAOxC2ITLfvc7M8Ry3jyzCZDbZGSYbnTJJNrsbDd+OAAAsANhEy5LS5OGDjXXr7/u5g9//nmzoOfu3WXjOAEAgN8ibKJO7rjDnN96S8rPd+MHx8SYWelBQdJ//uOmFeQBAIBdCJuok8svl1JTpUOHPJAH+/cvmyR0xx3Svn1u/gIAAOAthE3USXBw2fBKt04Ucho/XurVSzp82GxdZFke+BIAAOBphE3U2a23mjXZFy2S1q1z84eHhZnu9IgI6bPPPJRoAQCApxE2UWfNmklXX22uJ03ywBd06iQ9/bS5fuABadMmD3wJAADwJMIm6mXsWHP+97/N+E23u+su6eKLpePHpeHD2V0IAAA/Q9hEvQweLPXoIZ04UbYmu1sFBUnvviu1aCF9/70ZKMr4TQAA/AZhE/XicEj33GOuJ01y437p5TVtKr3/vhkgOnWqNHGiB74EAAB4AmET9Xb99SYP7tkjzZjhoS8ZONAs+C6Z8ZsLF3roiwAAgDsRNlFvERFli7y/9JIHv+iuu0yyPXlSuvZa1t8EAMAPEDbhFnfeKYWGmmWQVqzw0Jc4HNI//yl16SJlZkrDhkkFBR76MgAA4A6ETbhFs2bSddeZa4+2bkZHm776xERpyRLpvvs8+GUAAKC+CJtwG+dEofff93APd7t2Zq0lSXrtNenVVz34ZQAAoD4Im3CbPn2kAQPMjHSPb/hz+eVlC77fc4+0YIGHvxAAANQFYRNu5WzdnDxZys/38Jc99FDZhKFhw8w6nAAAwKcQNuFWw4ZJqanS/v3StGke/jKHQ3rzTalfP+nwYenSS83EIQAA4DMIm3CrkBBpzBhz/dJLXtjsJzJS+uQTM45zxw7pl79kS0sAAHwIYRNud9ttUlSUtGaN9MUXXvjCpk2lOXOkJk2klSulkSNN1zoAALAdYRNu16iRCZyS9NRTXvrSdu2kTz81LZ1z5piFP9lDHQAA2xE24RF/+INZ5P3rr81C717Rr58ZKBoUZBZ//+tfvfTFAACgOoRNeERqqvSb35jrCRO8+MVXXim98oq5Hj9emjLFi18OAAAqI2zCYx5+2DQyfvqptHatF7/4zjulRx4x17fdJn3+uRe/HAAAlEfYhMekp0vXXmuuneuve83f/iaNGmUmCl1zjTRvnpcLAAAAEmETHjZunDlPny5t2+bFLw4KMmtwXn65dOKEdMUV0uzZXiwAAABIhE14WPfuJu+VlEjPPuvlLw8Lkz76SLr6aqmgwJxnzvRyEQAABDbCJjzuj3805ylTpJ9/9vKXh4WZZtWRI82m7ddeK33wgZeLAAAgcBE24XEDBkjnn2+y3vPP21BAaKj07rvSr39txnBef715DAAAPI6wCa9wtm6+/rp08KANBYSEmKbVW24xffo33ii99ZYNhQAAEFgIm/CKiy+WeveWjh+XJk60qYjgYOmNN8p2F7rlFpN+AQCAxxA24RUOR1nr5ssvS7m5NhUSFCRNmiTdc495fMcd0gsv2FQMAAANH2ETXnP11VKHDlJ2tjR5so2FOBzS3/8uPfSQefyHP5jwWVJiY1EAADRMhE14TVBQ2cY+L7xglr+0jcNhVpp3rsc0caJ0661ScbGNRQEA0PAQNuFVv/qV1KqVlJXlA/NzHA7pwQfNzPTgYDOB6MYbzbR5AADgFoRNeFVoaFnv9TPPSPn59tYjySTg9983xU2davr78/LsrgoAgAaBsAmvu+UWqUULadcu6bXX7K7mlOHDze5CkZFmW8sLL5T277e7KgAA/B5hE14XGSk98YS5/utfpZwce+spddll0hdfSImJ0rJlZjX6LVvsrgoAAL9G2IQtRo+WOnaUDh+2Yc/0mgwYIC1eLKWlST/+KPXrJ82da3dVAAD4LcImbBESIk2YYK7//ndp715766mgfXtpyRITPHNypEsvlV55xSwEDwAAXELYhG2uvNLkuRMnyrrVfUZSkvTll9JNN5n1N++6S7rtNqmgwO7KAADwK4RN2MbhMDPSJelf/5K+/97eek4THi69+aYpMijIFHn++T7WDAsAgG8jbMJW551nWjiLi6U//cnuaqrgcJi1mmbPNhOHli41m7x/+63dlQEA4BcIm7DdU0+ZhsOPPjJDJX3SkCHS8uVSly5SZqb0i1+YHYjY4hIAgBoRNmG7zp3N0EjJNCL67Dyctm3NTPVf/9o0xY4bJ111lZlSDwAAqkTYhE94/HEpIsL0Ts+ebXc1NYiJkd55R3rjDTOm89NPpe7dpa++srsyAAB8EmETPiE1Vbr7bnP9yCOm4dBnORzSb39rWjnT06U9e6SLLpIefVQ6edLu6gAA8CmETfiMRx6REhKkDRuk//zH7mpqoWdPadUqs/+mZZntkAYNkrZts7syAAB8BmETPiMx0QyDlEwjYX6+vfXUSkyMWRJp6lQpLs7McOre3Wz67rODTwEA8B7CJnzKXXdJLVpIu3dLkybZXY0Lrr9eWr/ezFI/flz6/e+loUOlXbvsrgwAAFsRNuFTIiOlJ58013/7m5SdbWs5rmnVSvriC+mll8xsp7lzzVJJr73GEkkAgIBF2ITP+c1vpE6dpCNHpL/8xe5qXBQUZGY6rVlj9uI8etS0cg4aJG3ebHd1AAB4HWETPickRHr+eXM9caKfZrT27aVvvjF/QEyMtGiR1KOHGYx64oTd1QEA4DWETfikoUPNNpYnT0r33OOnc22Cg80g1I0bpcsukwoLzYz17t1NdzsAAAGAsAmf9cILUliYNG+e9PHHdldTD61amcXfP/xQat5c2rpVuvhiM6no55/trg4AAI8ibMJntW0rPfCAub7vPj/vfXY4pGHDpE2bzJjOoCDp/fdNd/uECX6yzhMAAK4jbMKn/fGPZimkHTuk556zuxo3iI83s9VXrJD695fy8swf2amTNHOmn44XAACgeoRN+LTo6LKQ+dRT0vbt9tbjNj17SgsXmn3Wmzc3f9g110iDB5ttMAEAaCAIm/B5111n1krPz5fGjm1AjX9BQdKNN0pbtpjWzchIE0AHDJCGDzfPAwDg5wib8HkOh/Tqq1JoqDR7tjRjht0VuVlMjFnBfutW6dZbTQj96COpc2fpzjulzEy7KwQAoM4Im/ALHTpIDz9sru+5x6yV3uC0aCH985/SunXSFVdIxcXS5MlSu3bSY4810D8aANDQETbhN/74R+mss6Q9e8za6A1W587SrFlmUfh+/cwkoiefNKFz0iSpqMjuCgEAqDXCJvxGZKTZZlwyG/MsWWJvPR43aJCZLPTf/0rp6dL+/WbQaqdO0nvvmRXvAQDwcS6FzQkTJqhv376KjY1VUlKSrr76am2pNIkhPz9fY8aMUePGjRUTE6Phw4crKyvLrUUjcF1yidk73bKk3/7WbMrToDkcZrLQxo1m4GpysrRtmzRqlAmgr7wiHT9ud5UAAFTLpbC5YMECjRkzRkuWLNG8efNUVFSkSy65RHl5eaX33Hffffrkk0/0wQcfaMGCBdq7d6+GDRvm9sIRuF54QWra1OSvZ56xuxovCQ01k4W2bTNbXjZtahYfvesus0PRE09Ihw7ZXSUAAKdxWFbdF5I5cOCAkpKStGDBAg0ePFg5OTlq2rSp3nvvPY0YMUKS9P3336tjx45avHixzj333NM+o6CgQAUFBaWPc3NzlZqaqpycHMXFxdW1NDRwU6dKv/qV2c5yzRqpY0e7K/KyEyekKVPMIqQ//WSei4oyzb333y+1bm1reQCAhi03N1fx8fG1ymv1GrOZk5MjSWrUqJEkaeXKlSoqKlJGRkbpPR06dFCrVq20uJqFqidMmKD4+PjSIzU1tT4lIUBcf7102WWmG/2mmwJw+GJkpGnp3LJFmjbNLBJ//LgZzNq2rfTrX5tZ7QAA2KzOYbOkpET33nuvBg4cqC5dukiSMjMzFRYWpoSEhAr3JicnK7OatQLHjRunnJyc0mP37t11LQkBxOEwqwLFx0vLlgVQd3plISFm1fuVK6W5c6WMDLNk0rvvSt27m9Xw//tfZrADAGxT57A5ZswYbdiwQdOmTatXAeHh4YqLi6twALWRmiq9/LK5fvxxafVqW8uxl8MhXXyxNG+e2Xd95EizOPzXX0vXXiu1aWPW6tyzx+5KAQABpk5hc+zYsfr000/11VdfqWXLlqXPp6SkqLCwUNnZ2RXuz8rKUkpKSr0KBary619Lw4aZbvQbbzRbWga83r2l9983+63/8Y9mMtHevWatztatpcsvN9sw0doJAPACl8KmZVkaO3asZsyYoS+//FJpaWkVXu/du7dCQ0M1f/780ue2bNmiXbt2qX///u6pGCjH2Z2elGRmp48fb3dFPqRVK7MN5p49Zlzn+edLJSVmz89hw6SWLaUHHpA2b7a7UgBAA+bSbPTf//73eu+99/Txxx+rffv2pc/Hx8crMjJSknTnnXdq9uzZmjJliuLi4nTXXXdJkhYtWlSr73BldhPgNGuWdNVVJnwuWGDWQ0cVtmyR3nxTevttqfz6t717SzfcYMZ/luutAACgKq7kNZfCpsPhqPL5t956SzfddJMks6j7H/7wB02dOlUFBQUaMmSIXn311Vp3oxM2UVe33mpyVFqatHatFBtrd0U+rKhImjNH+te/pP/9z0wqkkxaHzTIBM8RI6QmTeytEwDgkzwWNr2BsIm6ys2VunWTdu40y02+8YbdFfmJ/fvNjPWpU6WFC8ueDwkxk46uv166+mqJfx8BAKcQNhGwFiwwq/1YlpkjM3Kk3RX5mV27zD+4qVMrTu+PiJCGDJGuvFK64goz6QgAELAImwhof/qT9NRTpht91SqpXTu7K/JTzgXjp041104Oh1lE/uKLzWb1AwdK4eH21QkA8DrCJgLayZPShRdK334r9eolLVpEFqoXyzKDYGfNkmbOPH1B08hIafBgEzwvvljq0sUEUgBAg0XYRMDbs0fq0UM6dEgaO7Zs8Xe4QWam9MUXZgH5uXPN4/JSUkzovPhis6NRs2b21AkA8BjCJiAz2fqyy8z1f/8rDR9ubz0NkmWZBU7nzjXhc8EC6cSJivd06SJdcIHpbh80SGrRwpZSAQDuQ9gETnn4YenZZ80e6qtWSWedZXdFDVxBgRm34Ayfq1aZQFpemzbSgAFlR9euZuY7AMBvEDaBU4qKTKPaokVS9+7Sd99J0dF2VxVADh6UvvrKLKm0cKG0Zo3Zxai86GizqPw550j9+kl9+5rdjxj3CQA+i7AJlLNnj8ky+/dL115rVvYhx9jk6FFp6VKT/r/7TlqyxCyQWlmTJmZ2V69eUp8+5gds3ZofDgB8BGETqGThQjNDvajILIs0bpzdFUGS2bno+++lZcvMsWSJtGGDWVKgsrg4s2p/9+7m6NzZHPHx3q8bAAIcYROowj/+If3ud6Zx7JNPpMsvt7siVCk/X1q3ziyxtHKlOdavN/+lUJWWLU3o7NjRHB06SO3bS0lJtIQCgIcQNoFq3HmnNHmyaSRbutTkEviBwkLTArp2rQmi69ebWfB79lT/nrg4KS3NTEhq3dqcnUfr1lJiImEUAOqIsAlUo7DQLP347bfS2WebwJmQYHdVqLPsbGnTJhM8N282x5Yt0o4dp8+Cryw2tmL4rHzduDFhFACqQdgEarB/v5lzsnu3WYdz1iwpONjuquBW+fnSjz9KO3ea4LljR8Xr/fvP/BnR0VW3iDqvmzYljAIIWIRN4AxWrpTOO89kkocflp5+2u6K4FXHj0u7dp0eQp3X+/ad+TPCw81uSSkpUnJy2XVVz0VFefbvAQAvI2wCtfDee9KoUeZ68mQzeQiQZP4rZNeu6ltG9+49czd9ebGxpwfS5GSzlWdKStk5KYkF7gH4BcImUEuPPy498YQUFCTNnCldcYXdFcEvFBZKP/8sZWWZIzOz4lH+ucrbd9bE4TDd887g6TyaNjXnJk0qHomJjAEBYAvCJlBLliX99rfSm29KkZFms5t+/eyuCg2GZZmF7LOyTNe8M4Q6H2dmlp337zfrjrrC4ZAaNTJhtHwILf+4aVMz2alRIxNOExIIqADqjbAJuKCoSLrySumzz8z/b160SEpPt7sqBJziYrO95759JnhmZZmz8zh4sOKRnV3374qPN8HTGUCrOxISTj/Cwtzx1wLwc4RNwEXHjknnny+tWiW1bWsCZ1KS3VUBNSgqkg4flg4ckA4dMmdnEHVel3/uyBHzf+j1FRlpQmf5MFpVMI2Pr/qIjGQWP9AAEDaBOsjMlPr3N/M/+vY1XerR0XZXBbhRYaFpET182ITPI0cqXmdnV7x2HkeOVL2HfV2EhFQfRGt7REURWAGbETaBOtqyRRo40DQU/fKX0owZTA4GJJlu/qNHqw6iVT3Oyal45OZKJSXuqSUkxOwQ5QyfiYmmK8I5NrVRI/N6XJxZCSA29vTr0FD31AIEKMImUA+LFkkXXWRWv7n9drMsEo0oQD1ZlunGrxxCXT3cFVgjIsqCpzOIOq8rP67pvpgYs5wFEGBcyWu02QCVDBhg1uAcPlz6xz+k1FTpz3+2uyrAzzkcZS2LLVvW7TMsS8rLOz2AHj5sJlEdPmy6JQ4fNq2wubnmXP5wLkWVn2+OAwfq/7fFxJjgWZsJVgkJpuXV2QrLGFYEAFo2gWpMmiSNHWuu33pLuukmW8sB4A4nT5YFz9zcsqP849q8lpNjPqu+wsPLgmn5canlH9f0WlwcS1nBFrRsAm4wZozZRObZZ6XbbjMbvlx6qd1VAaiXkJCylsf6sCypoKBi+Cw/6cr5uPxY1vL3HDpkwmpBQdkGAHUVG1sWPGNjTUtrdHTZtbNF2XlU9ZzzeSZfwQNo2QRqUFIi3Xij6VYPD5c++US6+GK7qwLg95xjWA8dKptQVX5iVW2u8/PdX1dQkAmdzuAZFWWCqzPA1vU6KspMyiLINhi0bAJuEhRkutDz8qSPPzaLv3/6qZlABAB1Vn4Ma10VFFQMoc5W1rw8E2Tz8k4fs3r0qHmtquck81/YziED7hYUVBZeK5+res4ZdiMizNjWiIjTr6t7LTyciVs+hJZNoBYKC82EoU8/Nf9bNnu2dMEFdlcFAG5SUiIdP14xfJ44YQJr+cMZYp3n2ly7Y2xrXYSHuxZQ63pf5feEhwdECy5LHwEeUFAgXXONNGeO+Y/tOXOkwYPtrgoAfFxBgQmux4+XHc5A6ryufM7LK3tPfr65dq4gUP66/OMTJ9y3NFZ9eSrInuk+Lw5VIGwCHpKfL111lTR3runl+d//zDaXAAAfUFRUdSh19drV9ziX1LKbw2EWiz73XI9/FWM2AQ+JiJBmzjRjN7/4wsxOnzFDGjLE7soAAAoNNUd9xsLWhWWZ8VbuDrG1uS4oqFhHeLh3//ZaIGwCLoqMlGbNkkaMMGM3r7xSmj7dtHgCAAKQw2FCXni4WYbKm0pKTNB1hs/Gjb37/bXAVC2gDiIjTYvm8OFlk4emTrW7KgBAwAkKMt1uiYlSs2ZSWJjdFZ2GsAnUUViYNG2a9OtfS8XF0q9+Jf3973ZXBQCAbyFsAvUQEiK9/bZ0113m8f33Sw884DsTIgEAsBthE6inoCDppZekZ54xj59/3rR2lh+zDQBAoCJsAm7gcEgPPSS9845p7Zw6VbrsMs9swgEAgD8hbAJudOONZu3NmBjpyy/Nou9799pdFQAA9iFsAm52ySXSggVScrK0dq00YIC0fr3dVQEAYA/CJuABvXqZTRzS06WdO6X+/c1SSQAABBrCJuAhZ50lLV4sXXih2eZ32DDpySeZqQ4ACCyETcCDGjeWPv9cuvtu8/ixx6SRI6Vjx+ytCwAAbyFsAh4WEmKWRvrXv8yWvR9+aMZxbt9ud2UAAHgeYRPwkltukb7+2kwcWr9e6tvXzFgHAKAhI2wCXjRggLRihdS7t3TokHTxxWYcZ3Gx3ZUBAOAZhE3Ay1q2lL791rR0lpSYcZyXXCJlZtpdGQAA7kfYBGwQGWnGcL7zjhQVZbrTe/SgWx0A0PAQNgEb3Xij6Vbv0kXKypIyMqTHH6dbHQDQcBA2AZt17CgtXSr99reSZUlPPCFdcAGz1QEADQNhE/ABUVHSG29I//63FBsrLVwode8uTZliAigAAP6KsAn4kF//2uynPnCgdPSodPPN0ogR0oEDdlcGAEDdEDYBH5OWJi1YID31lFkQ/qOPpE6dpA8+sLsyAABcR9gEfFBwsDRunBnL2bWrdPCg2eZyxAgzkQgAAH9B2AR8WK9eZrb6+PGmlfPDD00r5z//adboBADA1xE2AR8XFmZmqC9fbiYNHT4s3XabNGiQtG6d3dUBAFAzwibgJ3r0MIHz+eelmBhp0SLT8nn//WYyEQAAvoiwCfiR0FATLjdvNuM3i4ulv//drNX53/+yTBIAwPcQNgE/1LKlmZ0+e7Z01lnSzz9L114rXXqp9MMPdlcHAEAZwibgxy69VNqwwUwgCguTPv9c6txZGjuWtTkBAL6BsAn4uchIM4Fo/Xrp8sulkyelSZOktm2lCROkEyfsrhAAEMgIm0ADcfbZ0qefSvPnSz17mklDf/yj1L699M47LJUEALAHYRNoYC680KzN+e9/S61aSbt3S6NHS717myAKAIA3ETaBBigoyOyz/v330tNPS3Fx0po1UkaGdPHF0rff2l0hACBQEDaBBiwyUnr4YenHH6W77jK7EH3xhTR4sPSLX0hffcVySQAAzyJsAgGgSRNp4kRp61bpd78z63V+/bXpcj//fBNACZ0AAE8gbAIBpE0bafJk09I5ZoxZLunbb03X+sCB0mefEToBAO5F2AQCUGqq9Mor0k8/SXffLUVESIsXm3U7zzlHmj7dLKEEAEB9ETaBANaihfTSSyZ03n+/GeO5YoV03XVSu3bSiy+y7zoAoH4ImwDUrJn0/PPSjh3SY4+ZMZ47d0r33WdaQR9+WNqzx+4qAQD+iLAJoFRSkvT449KuXdLrr5uF4nNypGefNeM9R4yQvvyScZ0AgNojbAI4TWSkdPvt0ubN0qxZ0gUXSMXF0ocfShddJHXqJL38sgmiAADUhLAJoFpBQdIVV5j1ONevl+68U4qJMYvF3323GfN5xx3S2rV2VwoA8FWETQC10qWL9Oqr0s8/m5nsHTtKeXmmu71HD6lPH2nSJOnIEbsrBQD4EsImAJfExZk1OjduNOM3R4wwi8SvXCmNHWsmG91wg1mzs6jI7moBAHZzWJZvDfXPzc1VfHy8cnJyFBcXZ3c5AGrh4EHp3XelN9+U1q0re75xY2nYMGnkSDPuMyTEthIBAG7kSl4jbAJwG8uSVq82oXP6dOnAgbLXmjSRhg83wXPwYIInAPgzwiYA2508KX3zjQmdH35oWj+dkpLKguegQVJwsH11AgBcR9gE4FNOnpS+/roseB4+XPZacrIZ9zlypNmfneAJAL6PsAnAZxUVmaWUpk+XPvqo4uz1Zs1M8Lz2WhM8g5jCCAA+ibAJwC8UFpoZ7dOnSzNmSNnZZa8lJUmXXipdfrl0ySVSfLxtZQIAKiFsAvA7hYXSF1+Y4DlzZsXdiUJCTEvn5ZdLl11mdjByOGwrFQACHmETgF8rLJQWLpRmz5b+9z+zY1F5rVtLQ4earTMvuEBq2tSWMgEgYBE2ATQoP/1UFjy/+koqKKj4eteu0oUXmmPwYCkhwZYyASBgEDYBNFjHj5txnvPnm3P5ReQlM6moV6+y8HneeVJ0tD21AkBDRdgEEDAOHDDLKn35pTl++KHi66GhUu/eJnSed540YADd7gBQX4RNAAHr559NV7uz9XPXrtPvad++LHyed57Uti0TjgDAFYRNAJDZPnPHDjPZyHls2nT6fUlJ0jnnSH37lp0bN/Z6uQDgNwibAFCNQ4ekRYtM8PzuO2n5cjP7vbKzzqoYQHv0kGJivF4uAPgkwiYA1FJ+vrR6tQmdy5aZc+Vxn5LpZk9Pl3r2NEePHuaclOT1kgHAdoRNAKiHI0ekFSsqBtC9e6u+t3nziuGzZ08pLY0xoAAaNsImALhZVpa0Zo1pBXWet24140Iri4srC59du0qdO5tdj/ifNAANBWETALzg2DFp7dqy8Ll6tbRhQ9VjQCWpVSsTPDt3lrp0MeeOHVkHFID/IWwCgE2KiqTNm8taQDduNAF0376q73c4pDZtysJn585maaZ27aTERG9WDgC1R9gEAB9z5EhZ8Ny4sezYv7/69zRubEJnenrFc7t2UqNG3qsdACojbAKAnzhwoGL43LjRjAWtriXUqVGj6oMoa4QC8DTCJgD4uWPHpB9/lLZtM+Gz/Lm6mfFOiYkmdLZubcaJVj6aNGG2PID6IWwCQAOWl2eCqDN8lg+iP/985vdHRp4eQMsH05YtpfBwz/8dAPwXYRMAAlRenvTTTyaM7tpV8di5U8rMrN3npKSUBc/mzc3RokXFc1wcLaRAoCJsAgCqVFAg7dlzeggt//jEidp9VnR01UE0JcXsrJScbM6NGknBwZ79uwB4lyt5LcRLNQEAfEB4uNS2rTmqYllm/3hnCN2713TN791b8To727Sibt1qjpoEBUlNm1YMoNWdk5KkiAi3/9kAbETLJgDAZXl5ZsZ85SD6889mOaesLHM+dMj1z46Lq10wbdpUio+n1RSwAy2bAACPio4uW2qpJkVF0sGDZeHzTOeiIik31xzbtp25DodDSkgwyz01amSO8teVHzuvCamA9xA2AQAeExoqNWtmjjOxLCknp3bBNCtLOnrUvOfIEXO4wuEwS0RVFUgTEsxr5c/lr2NjzdAAALVD2AQA+ARnK2VCgtmy80wKC03IPHRIOny47Cj/uKrXjh0zIdX5nKuCgkzLaFVB9EzPxcebpaeYxY9AQtgEAPilsDAzfjM52bX3nSmkZmeb17OzK14fOWJm85eU1K011SkkpCx4Oo+aHlf1WlhY3b4bsANhEwAQUOoaUiUpP//0AFpVKK3quZwcE1RPnjTjWA8erPvfEBFRMXzGxkoxMWVH5cdVPVf+cWho3WsBzoSwCQBALUVEmHVEU1Jcf69lmS78nJyyIzu76uvqHh89aj4rP98cWVnu+bvCwuoWUmt6jgALJ8ImAABe4HCYUBYba3ZmqoviYhM4K4fRY8fM88eOVTwqP1f5cUGB+dzCwrqPYa1OeHjdQmpsrFntIDKy+oOVBPwLYRMAAD8RHFw22ah16/p/XlGR6wH1TPcUFprPLigwR13WWj2T0NCaw6i7j7AwJnXVB2ETAIAAFRpqZssnJrrvMwsLTw+ndQmxJ05UPJwhVjIh2bkmqzc4HKalNiKi7Fz+2lPnys+Fhvpn6CVsAgAAtwkLK1uz1J2Ki8041coh1JOHc49FyyobJ2unqkJv5fNrr0mdO9tbZ2WETQAA4POCg81Yzuho73yfZZnW1BMnTMgsKCgLnM7rM51dube69xQVVazpTKHX7kBcFcImAABAJc5WxPBwe+soKXEtoLZta2+9VSFsAgAA+KigoLKJSv6K3V0BAADgMYRNAAAAeAxhEwAAAB5D2AQAAIDHEDYBAADgMYRNAAAAeAxhEwAAAB5D2AQAAIDHEDYBAADgMYRNAAAAeAxhEwAAAB7jsbA5adIktWnTRhEREerXr5+WLVvmqa8CAACAj/JI2Hz//fd1//3367HHHtOqVavUvXt3DRkyRPv37/fE1wEAAMBHeSRsvvDCC7rtttt08803q1OnTpo8ebKioqL05ptveuLrAAAA4KPcHjYLCwu1cuVKZWRklH1JUJAyMjK0ePHi0+4vKChQbm5uhQMAAAANg9vD5sGDB1VcXKzk5OQKzycnJyszM/O0+ydMmKD4+PjSIzU11d0lAQAAwCa2z0YfN26ccnJySo/du3fbXRIAAADcJMTdH9ikSRMFBwcrKyurwvNZWVlKSUk57f7w8HCFh4e7uwwAAAD4ALe3bIaFhal3796aP39+6XMlJSWaP3+++vfv7+6vAwAAgA9ze8umJN1///0aPXq0+vTpo3POOUcvvvii8vLydPPNN3vi6wAAAOCjPBI2r7vuOh04cEDjx49XZmamevTooc8+++y0SUMAAABo2ByWZVl2F1Febm6u4uPjlZOTo7i4OLvLAQAAQCWu5DXbZ6MDAACg4SJsAgAAwGM8MmazPpy9+uwkBAAA4JucOa02ozF9LmwePXpUkthJCAAAwMcdPXpU8fHxNd7jcxOESkpKtHfvXsXGxsrhcHjlO3Nzc5Wamqrdu3czKckP8fv5P35D/8dv6P/4Df2bt38/y7J09OhRNW/eXEFBNY/K9LmWzaCgILVs2dKW746Li+NfMD/G7+f/+A39H7+h/+M39G/e/P3O1KLpxAQhAAAAeAxhEwAAAB5D2JQUHh6uxx57TOHh4XaXgjrg9/N//Ib+j9/Q//Eb+jdf/v18boIQAAAAGg5aNgEAAOAxhE0AAAB4DGETAAAAHkPYBAAAgMcQNgEAAOAxAR82J02apDZt2igiIkL9+vXTsmXL7C4pIH3zzTe64oor1Lx5czkcDs2cObPC65Zlafz48WrWrJkiIyOVkZGhrVu3Vrjn8OHDGjVqlOLi4pSQkKBbb71Vx44dq3DPunXrNGjQIEVERCg1NVXPPvusp/+0gDFhwgT17dtXsbGxSkpK0tVXX60tW7ZUuCc/P19jxoxR48aNFRMTo+HDhysrK6vCPbt27dLll1+uqKgoJSUl6cEHH9TJkycr3PP111+rV69eCg8PV7t27TRlyhRP/3kN3muvvaZu3bqV7j7Sv39/zZkzp/R1fjv/8/TTT8vhcOjee+8tfY7f0bc9/vjjcjgcFY4OHTqUvu63v58VwKZNm2aFhYVZb775prVx40brtttusxISEqysrCy7Sws4s2fPtv70pz9ZH330kSXJmjFjRoXXn376aSs+Pt6aOXOmtXbtWuvKK6+00tLSrBMnTpTeM3ToUKt79+7WkiVLrG+//dZq166ddcMNN5S+npOTYyUnJ1ujRo2yNmzYYE2dOtWKjIy0Xn/9dW/9mQ3akCFDrLfeesvasGGDtWbNGuuyyy6zWrVqZR07dqz0njvuuMNKTU215s+fb61YscI699xzrQEDBpS+fvLkSatLly5WRkaGtXr1amv27NlWkyZNrHHjxpXe89NPP1lRUVHW/fffb23atMl6+eWXreDgYOuzzz7z6t/b0MyaNcv63//+Z/3www/Wli1brD/+8Y9WaGiotWHDBsuy+O38zbJly6w2bdpY3bp1s+65557S5/kdfdtjjz1mde7c2dq3b1/pceDAgdLX/fX3C+iwec4551hjxowpfVxcXGw1b97cmjBhgo1VoXLYLCkpsVJSUqz/+7//K30uOzvbCg8Pt6ZOnWpZlmVt2rTJkmQtX7689J45c+ZYDofD+vnnny3LsqxXX33VSkxMtAoKCkrvefjhh6327dt7+C8KTPv377ckWQsWLLAsy/xmoaGh1gcffFB6z+bNmy1J1uLFiy3LMv/RERQUZGVmZpbe89prr1lxcXGlv9tDDz1kde7cucJ3XXfdddaQIUM8/ScFnMTEROuf//wnv52fOXr0qJWenm7NmzfPOv/880vDJr+j73vssces7t27V/maP/9+AduNXlhYqJUrVyojI6P0uaCgIGVkZGjx4sU2VobKtm/frszMzAq/VXx8vPr161f6Wy1evFgJCQnq06dP6T0ZGRkKCgrS0qVLS+8ZPHiwwsLCSu8ZMmSItmzZoiNHjnjprwkcOTk5kqRGjRpJklauXKmioqIKv2OHDh3UqlWrCr9j165dlZycXHrPkCFDlJubq40bN5beU/4znPfw7637FBcXa9q0acrLy1P//v357fzMmDFjdPnll5/2z5rf0T9s3bpVzZs311lnnaVRo0Zp165dkvz79wvYsHnw4EEVFxdX+EEkKTk5WZmZmTZVhao4f4+afqvMzEwlJSVVeD0kJESNGjWqcE9Vn1H+O+AeJSUluvfeezVw4EB16dJFkvlnHBYWpoSEhAr3Vv4dz/QbVXdPbm6uTpw44Yk/J2CsX79eMTExCg8P1x133KEZM2aoU6dO/HZ+ZNq0aVq1apUmTJhw2mv8jr6vX79+mjJlij777DO99tpr2r59uwYNGqSjR4/69e8X4pFPBRDQxowZow0bNmjhwoV2lwIXtG/fXmvWrFFOTo7++9//avTo0VqwYIHdZaGWdu/erXvuuUfz5s1TRESE3eWgDi699NLS627duqlfv35q3bq1pk+frsjISBsrq5+Abdls0qSJgoODT5vFlZWVpZSUFJuqQlWcv0dNv1VKSor2799f4fWTJ0/q8OHDFe6p6jPKfwfqb+zYsfr000/11VdfqWXLlqXPp6SkqLCwUNnZ2RXur/w7nuk3qu6euLg4v/4fY18QFhamdu3aqXfv3powYYK6d++ul156id/OT6xcuVL79+9Xr169FBISopCQEC1YsEATJ05USEiIkpOT+R39TEJCgs4++2xt27bNr/89DNiwGRYWpt69e2v+/Pmlz5WUlGj+/Pnq37+/jZWhsrS0NKWkpFT4rXJzc7V06dLS36p///7Kzs7WypUrS+/58ssvVVJSon79+pXe880336ioqKj0nnnz5ql9+/ZKTEz00l/TcFmWpbFjx2rGjBn68ssvlZaWVuH13r17KzQ0tMLvuGXLFu3atavC77h+/foK/+Ewb948xcXFqVOnTqX3lP8M5z38e+t+JSUlKigo4LfzExdddJHWr1+vNWvWlB59+vTRqFGjSq/5Hf3LsWPH9OOPP6pZs2b+/e+hx6Ye+YFp06ZZ4eHh1pQpU6xNmzZZt99+u5WQkFBhFhe84+jRo9bq1aut1atXW5KsF154wVq9erW1c+dOy7LM0kcJCQnWxx9/bK1bt8666qqrqlz6qGfPntbSpUuthQsXWunp6RWWPsrOzraSk5OtG2+80dqwYYM1bdo0KyoqiqWP3OTOO++04uPjra+//rrCsh3Hjx8vveeOO+6wWrVqZX355ZfWihUrrP79+1v9+/cvfd25bMcll1xirVmzxvrss8+spk2bVrlsx4MPPmht3rzZmjRpEsuuuMEjjzxiLViwwNq+fbu1bt0665FHHrEcDoc1d+5cy7L47fxV+dnolsXv6Ov+8Ic/WF9//bW1fft267vvvrMyMjKsJk2aWPv377csy39/v4AOm5ZlWS+//LLVqlUrKywszDrnnHOsJUuW2F1SQPrqq68sSacdo0ePtizLLH/06KOPWsnJyVZ4eLh10UUXWVu2bKnwGYcOHbJuuOEGKyYmxoqLi7Nuvvlm6+jRoxXuWbt2rXXeeedZ4eHhVosWLaynn37aW39ig1fV7yfJeuutt0rvOXHihPX73//eSkxMtKKioqxrrrnG2rdvX4XP2bFjh3XppZdakZGRVpMmTaw//OEPVlFRUYV7vvrqK6tHjx5WWFiYddZZZ1X4DtTNLbfcYrVu3doKCwuzmjZtal100UWlQdOy+O38VeWwye/o26677jqrWbNmVlhYmNWiRQvruuuus7Zt21b6ur/+fg7LsizPtZsCAAAgkAXsmE0AAAB4HmETAAAAHkPYBAAAgMcQNgEAAOAxhE0AAAB4DGETAAAAHkPYBAAAgMcQNgEAAOAxhE0AAAB4DGETAAAAHkPYBAAAgMf8P93xNU122rUTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_error = np.zeros_like(boost_boston.train_score_)\n",
    "for idx, y_ in enumerate(boost_boston.staged_predict(X_test)):\n",
    "   test_error[idx] = np.mean((y_test - y_)**2)\n",
    "\n",
    "plot_idx = np.arange(boost_boston.train_score_.shape[0])\n",
    "ax = subplots(figsize=(8,8))[1]\n",
    "ax.plot(plot_idx,\n",
    "        boost_boston.train_score_,\n",
    "        'b',\n",
    "        label='Training')\n",
    "ax.plot(plot_idx,\n",
    "        test_error,\n",
    "        'r',\n",
    "        label='Test')\n",
    "ax.legend();\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ebd0944",
   "metadata": {},
   "source": [
    "We now use the boosted model to predict `medv` on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "43505dad",
   "metadata": {
    "execution": {}
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.481405918831591"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_boost = boost_boston.predict(X_test);\n",
    "np.mean((y_test - y_hat_boost)**2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d561719",
   "metadata": {},
   "source": [
    "## Bayesian Additive Regression Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f68c0d8f",
   "metadata": {},
   "source": [
    "In this section we demonstrate a  `Python` implementation of BART found in the\n",
    "`ISLP.bart` package. We fit a  model\n",
    "to the `Boston` housing data set. This `BART()` estimator is\n",
    "designed for quantitative outcome variables, though other implementations are available for\n",
    "fitting logistic and probit models to categorical outcomes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ndraw=15`: This parameter  represents the total number of iterations or draws from the posterior distribution after the burn-in period. These draws are used to approximate the posterior distribution of the model parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fab69c8",
   "metadata": {},
   "source": [
    "The code below is running into error with islp 0.3.19. Update it using the following command in the terminal with the islp virtual environment activated: \n",
    "```\n",
    "pip install -upgrade ISLP==0.3.20\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "58f6e11f",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "BART(burnin=5, ndraw=15, random_state=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bart_boston = BART(random_state=0, burnin=5, ndraw=15) #num_trees=200, max_states=100\n",
    "# ndraw: number of iterations or samples to draw from the posterior distribution after the burn-in \n",
    "bart_boston.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26e4d574",
   "metadata": {},
   "source": [
    "On this data set, with this split into test and training, we see that the test error of BART is similar to that of  random forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a5b1296f",
   "metadata": {
    "execution": {},
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.145009458109232"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_test = bart_boston.predict(X_test.astype(np.float32))\n",
    "np.mean((y_test - yhat_test)**2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92a60f97",
   "metadata": {},
   "source": [
    "We can check how many times each variable appeared in the collection of trees.\n",
    "This gives a summary similar to the variable importance plot for boosting and random forests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "36c52755",
   "metadata": {
    "execution": {},
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "crim       26.933333\n",
       "zn         27.866667\n",
       "indus      26.466667\n",
       "chas       22.466667\n",
       "nox        26.600000\n",
       "rm         29.800000\n",
       "age        22.733333\n",
       "dis        26.466667\n",
       "rad        23.666667\n",
       "tax        24.133333\n",
       "ptratio    24.266667\n",
       "lstat      31.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "var_inclusion = pd.Series(bart_boston.variable_inclusion_.mean(0),\n",
    "                               index=D.columns)\n",
    "var_inclusion\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab69e5b5",
   "metadata": {},
   "source": [
    "    \n",
    "  \n",
    " \n",
    "\n"
   ]
  }
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