eLGB Regression Example

eLGB Regression Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:39.152526Z",
     "start_time": "2018-10-25T22:13:37.503680Z"
    }
   },
   "outputs": [],
   "source": [
    "%config Completer.use_jedi = False\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import lightgbm as lgb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:39.238695Z",
     "start_time": "2018-10-25T22:13:39.160165Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['x1', 'x4', 'x6']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_regression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "X, y, coef = make_regression(\n",
    "    n_samples=10_000,\n",
    "    n_features=8,\n",
    "    n_informative=5,\n",
    "    bias=0.1,\n",
    "    noise=0.5,\n",
    "    coef=True,\n",
    "    random_state=123,\n",
    ")\n",
    "\n",
    "X = pd.DataFrame(X, columns=[\"x\" + str(i) for i in range(X.shape[1])])\n",
    "\n",
    "# print uninformatives\n",
    "print(X.loc[:, coef == 0].columns.tolist())\n",
    "\n",
    "# Make some non-linear terms. Leave one non-informative term.\n",
    "y = y + 345 + (5 * (X.x1 ** 2)) - (20 * np.sin(2 * X.x4))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approximate coefs for x1 and x4\n",
    "coef[1] = 5\n",
    "coef[4] = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Dataset object for LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_train = lgb.Dataset(X_train, y_train)\n",
    "lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:39.243104Z",
     "start_time": "2018-10-25T22:13:39.240578Z"
    }
   },
   "outputs": [],
   "source": [
    "params_lgb = {\n",
    "    \"n_estimators\": 5000,\n",
    "    \"learning_rate\": 0.1,\n",
    "    \"verbose\": -1,\n",
    "    \"random_state\": 123,\n",
    "    \"metric\": [\"l1\", \"l2\"],\n",
    "}\n",
    "\n",
    "# Replicate EBM (first order term) functionality.\n",
    "params_elgb = {\n",
    "    \"boost_from_mean\": True, # Test with boost from mean first\n",
    "    \"n_estimators\": 50_000, # lots of estimators - might be overkill but makes the point\n",
    "    \"learning_rate\": 0.01, # low learning rate\n",
    "    \"num_leaves\": 4, # shallow trees\n",
    "    \"interaction_constraints\": [[i] for i in range(X.shape[1])], # single feature trees\n",
    "    \"verbose\": -1,\n",
    "    \"random_state\": 123,\n",
    "    \"metric\": [\"l1\", \"l2\"],\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training - Compare LGB defaults to Explainable LGB (eLGB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\bb5361\\lightgbm\\python-package\\lightgbm\\engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
      "  _log_warning(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[250]\ttraining's l1: 4.46869\ttraining's l2: 33.3311\tvalid_1's l1: 9.29008\tvalid_1's l2: 163.353\n",
      "[500]\ttraining's l1: 2.81718\ttraining's l2: 13.4778\tvalid_1's l1: 8.85636\tvalid_1's l2: 150.128\n",
      "[750]\ttraining's l1: 1.90876\ttraining's l2: 6.3368\tvalid_1's l1: 8.68759\tvalid_1's l2: 144.42\n",
      "[1000]\ttraining's l1: 1.35365\ttraining's l2: 3.27852\tvalid_1's l1: 8.61329\tvalid_1's l2: 142.245\n",
      "[1250]\ttraining's l1: 0.96428\ttraining's l2: 1.71232\tvalid_1's l1: 8.56932\tvalid_1's l2: 140.948\n",
      "[1500]\ttraining's l1: 0.698101\ttraining's l2: 0.925314\tvalid_1's l1: 8.53898\tvalid_1's l2: 140.121\n",
      "[1750]\ttraining's l1: 0.517159\ttraining's l2: 0.517885\tvalid_1's l1: 8.52532\tvalid_1's l2: 139.603\n",
      "[2000]\ttraining's l1: 0.385929\ttraining's l2: 0.300864\tvalid_1's l1: 8.51551\tvalid_1's l2: 139.302\n",
      "[2250]\ttraining's l1: 0.285225\ttraining's l2: 0.168914\tvalid_1's l1: 8.50502\tvalid_1's l2: 138.984\n",
      "Early stopping, best iteration is:\n",
      "[2184]\ttraining's l1: 0.307943\ttraining's l2: 0.195442\tvalid_1's l1: 8.50444\tvalid_1's l2: 138.978\n"
     ]
    }
   ],
   "source": [
    "gbm = lgb.train(\n",
    "    params_lgb,\n",
    "    lgb_train,\n",
    "    valid_sets=[lgb_train, lgb_test],\n",
    "    verbose_eval=250,\n",
    "    early_stopping_rounds=100,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-25T22:13:39.336630Z",
     "start_time": "2018-10-25T22:13:39.246006Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 50 rounds\n",
      "[1000]\ttraining's l1: 20.0694\ttraining's l2: 688.878\tvalid_1's l1: 21.2094\tvalid_1's l2: 777.485\n",
      "[2000]\ttraining's l1: 7.88412\ttraining's l2: 115.028\tvalid_1's l1: 8.63363\tvalid_1's l2: 139.96\n",
      "[3000]\ttraining's l1: 4.73158\ttraining's l2: 43.2975\tvalid_1's l1: 5.27556\tvalid_1's l2: 55.5311\n",
      "[4000]\ttraining's l1: 3.92957\ttraining's l2: 30.7148\tvalid_1's l1: 4.30182\tvalid_1's l2: 38.8624\n",
      "[5000]\ttraining's l1: 3.63311\ttraining's l2: 26.7862\tvalid_1's l1: 3.94838\tvalid_1's l2: 33.6286\n",
      "[6000]\ttraining's l1: 3.41178\ttraining's l2: 24.2429\tvalid_1's l1: 3.70743\tvalid_1's l2: 30.5394\n",
      "[7000]\ttraining's l1: 3.20753\ttraining's l2: 22.1258\tvalid_1's l1: 3.50051\tvalid_1's l2: 28.2498\n",
      "[8000]\ttraining's l1: 3.03988\ttraining's l2: 20.5643\tvalid_1's l1: 3.34473\tvalid_1's l2: 26.5917\n",
      "[9000]\ttraining's l1: 2.89661\ttraining's l2: 19.3154\tvalid_1's l1: 3.20932\tvalid_1's l2: 25.2456\n",
      "[10000]\ttraining's l1: 2.77887\ttraining's l2: 18.3478\tvalid_1's l1: 3.09806\tvalid_1's l2: 24.1915\n",
      "[11000]\ttraining's l1: 2.67936\ttraining's l2: 17.5588\tvalid_1's l1: 3.00331\tvalid_1's l2: 23.326\n",
      "[12000]\ttraining's l1: 2.59691\ttraining's l2: 16.9202\tvalid_1's l1: 2.92552\tvalid_1's l2: 22.6379\n",
      "[13000]\ttraining's l1: 2.52426\ttraining's l2: 16.3664\tvalid_1's l1: 2.85333\tvalid_1's l2: 22.0522\n",
      "[14000]\ttraining's l1: 2.45848\ttraining's l2: 15.8905\tvalid_1's l1: 2.78649\tvalid_1's l2: 21.525\n",
      "[15000]\ttraining's l1: 2.40306\ttraining's l2: 15.4882\tvalid_1's l1: 2.72991\tvalid_1's l2: 21.0991\n",
      "[16000]\ttraining's l1: 2.35284\ttraining's l2: 15.1372\tvalid_1's l1: 2.68418\tvalid_1's l2: 20.7658\n",
      "[17000]\ttraining's l1: 2.30865\ttraining's l2: 14.8278\tvalid_1's l1: 2.64335\tvalid_1's l2: 20.4755\n",
      "[18000]\ttraining's l1: 2.2668\ttraining's l2: 14.5461\tvalid_1's l1: 2.6058\tvalid_1's l2: 20.2104\n",
      "[19000]\ttraining's l1: 2.22791\ttraining's l2: 14.2845\tvalid_1's l1: 2.56838\tvalid_1's l2: 19.9527\n",
      "[20000]\ttraining's l1: 2.19433\ttraining's l2: 14.0539\tvalid_1's l1: 2.53513\tvalid_1's l2: 19.7187\n",
      "[21000]\ttraining's l1: 2.16393\ttraining's l2: 13.8454\tvalid_1's l1: 2.50312\tvalid_1's l2: 19.5042\n",
      "[22000]\ttraining's l1: 2.13656\ttraining's l2: 13.6538\tvalid_1's l1: 2.47774\tvalid_1's l2: 19.3212\n",
      "[23000]\ttraining's l1: 2.10972\ttraining's l2: 13.4766\tvalid_1's l1: 2.45175\tvalid_1's l2: 19.1237\n",
      "[24000]\ttraining's l1: 2.08421\ttraining's l2: 13.3057\tvalid_1's l1: 2.425\tvalid_1's l2: 18.9438\n",
      "[25000]\ttraining's l1: 2.06067\ttraining's l2: 13.1425\tvalid_1's l1: 2.40086\tvalid_1's l2: 18.7939\n",
      "[26000]\ttraining's l1: 2.04122\ttraining's l2: 13.0025\tvalid_1's l1: 2.38246\tvalid_1's l2: 18.6672\n",
      "[27000]\ttraining's l1: 2.02339\ttraining's l2: 12.8706\tvalid_1's l1: 2.36546\tvalid_1's l2: 18.5526\n",
      "[28000]\ttraining's l1: 2.00728\ttraining's l2: 12.7553\tvalid_1's l1: 2.35018\tvalid_1's l2: 18.4557\n",
      "[29000]\ttraining's l1: 1.9932\ttraining's l2: 12.6522\tvalid_1's l1: 2.33706\tvalid_1's l2: 18.3704\n",
      "[30000]\ttraining's l1: 1.97984\ttraining's l2: 12.5526\tvalid_1's l1: 2.32503\tvalid_1's l2: 18.3019\n",
      "[31000]\ttraining's l1: 1.96679\ttraining's l2: 12.4614\tvalid_1's l1: 2.3137\tvalid_1's l2: 18.2358\n",
      "[32000]\ttraining's l1: 1.95441\ttraining's l2: 12.3739\tvalid_1's l1: 2.3042\tvalid_1's l2: 18.1741\n",
      "[33000]\ttraining's l1: 1.94268\ttraining's l2: 12.2931\tvalid_1's l1: 2.29445\tvalid_1's l2: 18.1169\n",
      "[34000]\ttraining's l1: 1.93279\ttraining's l2: 12.2151\tvalid_1's l1: 2.28684\tvalid_1's l2: 18.071\n",
      "Early stopping, best iteration is:\n",
      "[34069]\ttraining's l1: 1.93203\ttraining's l2: 12.2096\tvalid_1's l1: 2.28621\tvalid_1's l2: 18.0677\n"
     ]
    }
   ],
   "source": [
    "gbm = lgb.train(\n",
    "    params_elgb,\n",
    "    lgb_train,\n",
    "    valid_sets=[lgb_train, lgb_test],\n",
    "    verbose_eval=1000,\n",
    "    early_stopping_rounds=50,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Plots and Feature Importance Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "\n",
    "\n",
    "def _is_split_node(tree):\n",
    "    return 'split_index' in tree.keys()\n",
    "\n",
    "def create_node_record(\n",
    "    tree,\n",
    "    node_depth=1,\n",
    "    tree_index=None,\n",
    "    feature_names=None,\n",
    "    parent_node=None,\n",
    "    parent_split_feature=None,\n",
    "    parent_threshold=None,\n",
    "    parent_left=None,\n",
    "    parent_right=None,\n",
    "    child_type=None,\n",
    "):\n",
    "\n",
    "    def _get_split_feature(tree):\n",
    "        if _is_split_node(tree):\n",
    "            feature_name = tree['split_feature']\n",
    "        else:\n",
    "            feature_name = None\n",
    "        return feature_name\n",
    "\n",
    "    def _is_single_node_tree(tree):\n",
    "        return set(tree.keys()) == {'leaf_value'}\n",
    "\n",
    "    # Create the node record, and populate universal data members\n",
    "    node = OrderedDict()\n",
    "    node['tree_index'] = tree_index\n",
    "    node['split_feature'] = _get_split_feature(tree)\n",
    "    node['value'] = None\n",
    "    node['parent_left'] = parent_left\n",
    "    node['parent_right'] = parent_right\n",
    "    node['child_type'] = child_type\n",
    "\n",
    "    # Update values to reflect node type (leaf or split)\n",
    "    if _is_split_node(tree):\n",
    "        node['value'] = tree['internal_value']\n",
    "        node['child_type'] = child_type\n",
    "        node['split_feature'] = _get_split_feature(tree)\n",
    "\n",
    "        if child_type == 'right':\n",
    "            node['parent_left'] = parent_threshold\n",
    "        elif child_type == 'left':\n",
    "            node['parent_right'] = parent_threshold\n",
    "\n",
    "        node['leaf'] = False\n",
    "\n",
    "    else:\n",
    "        node['value'] = tree['leaf_value']\n",
    "        node['leaf'] = True\n",
    "\n",
    "        if not _is_single_node_tree(tree):\n",
    "            node['split_feature'] = parent_split_feature\n",
    "\n",
    "            if child_type == 'right':\n",
    "                node['parent_left'] = parent_threshold\n",
    "            elif child_type == 'left':\n",
    "                node['parent_right'] = parent_threshold\n",
    "    return node\n",
    "\n",
    "def tree_dict_to_node_list(\n",
    "    tree, node_depth=1,\n",
    "    tree_index=None,\n",
    "    feature_names=None,\n",
    "    parent_node=None,\n",
    "    parent_split_feature=None,\n",
    "    parent_threshold=None, \n",
    "    parent_left=None,\n",
    "    parent_right=None,\n",
    "    child_type=None,\n",
    "    ):\n",
    "\n",
    "    node = create_node_record(\n",
    "        tree,\n",
    "        node_depth=node_depth,\n",
    "        tree_index=tree_index,\n",
    "        feature_names=feature_names,\n",
    "        parent_node=parent_node,\n",
    "        parent_split_feature=parent_split_feature,\n",
    "        parent_threshold=parent_threshold,\n",
    "        parent_left=parent_left,\n",
    "        parent_right=parent_right,\n",
    "        child_type=child_type,\n",
    "    )\n",
    "\n",
    "    res = [node]\n",
    "\n",
    "\n",
    "    if _is_split_node(tree):\n",
    "\n",
    "        subtree_list = tree_dict_to_node_list(\n",
    "            tree['left_child'],\n",
    "            node_depth=node_depth + 1,\n",
    "            tree_index=tree_index,\n",
    "            feature_names=feature_names,\n",
    "            parent_threshold=tree['threshold'],\n",
    "            parent_split_feature=node['split_feature'],\n",
    "            parent_left=node['parent_left'],\n",
    "            parent_right=node['parent_right'],\n",
    "            child_type='left',\n",
    "        )\n",
    "\n",
    "        res.extend(subtree_list)\n",
    "\n",
    "        subtree_list = tree_dict_to_node_list(\n",
    "            tree['right_child'],\n",
    "            node_depth=node_depth + 1,\n",
    "            tree_index=tree_index,\n",
    "            feature_names=feature_names,\n",
    "            parent_threshold=tree['threshold'],\n",
    "            parent_split_feature=node['split_feature'],\n",
    "            parent_left=node['parent_left'],\n",
    "            parent_right=node['parent_right'],\n",
    "            child_type='right',\n",
    "        )\n",
    "\n",
    "        res.extend(subtree_list)\n",
    "    return res\n",
    "\n",
    "\n",
    "def make_model_list(model_dict, X):\n",
    "    model_list = []\n",
    "\n",
    "    mins = X.min(axis=0).rename({col: i for i, col in enumerate(X.columns)}).to_dict()\n",
    "    maxs = X.max(axis=0).rename({col: i for i, col in enumerate(X.columns)}).to_dict()\n",
    "\n",
    "    for tree in model_dict['tree_info']:\n",
    "        model_list.extend(tree_dict_to_node_list(\n",
    "            tree['tree_structure'],\n",
    "            tree_index=tree['tree_index'],\n",
    "            parent_left=mins[tree['tree_structure']['split_feature']],\n",
    "            parent_right=maxs[tree['tree_structure']['split_feature']],\n",
    "        ))\n",
    "    return model_list\n",
    "\n",
    "\n",
    "def calculate_feature_importances(model_list, gbm, show_plot=True, remove_y_mean=False,):\n",
    "    df = pd.DataFrame(model_list)\n",
    "\n",
    "    n_features = gbm.num_feature()\n",
    "    feature_names = gbm.feature_name()\n",
    "\n",
    "    feature_importance = []\n",
    "\n",
    "    for n, feature_name in enumerate(feature_names):\n",
    "        dfx = df.loc[lambda x: x.split_feature == n]\n",
    "        dfx = (\n",
    "            dfx\n",
    "            .assign(parent_left_ = np.where(dfx.parent_left < dfx.parent_right, dfx.parent_left, dfx.parent_right))\n",
    "            .assign(parent_right_ = np.where(dfx.parent_left < dfx.parent_right, dfx.parent_right, dfx.parent_left))\n",
    "        )\n",
    "\n",
    "        if isinstance(remove_y_mean, float) and dfx.tree_index.eq(0).any():\n",
    "            dfx.loc[lambda x: x.tree_index == 0, 'value'] = dfx.loc[lambda x: x.tree_index == 0, 'value'] - remove_y_mean\n",
    "\n",
    "        a = np.unique(np.concatenate([dfx.parent_left.unique(), dfx.parent_right.unique()]))\n",
    "        a.sort()\n",
    "        X_ = X.iloc[:, n]\n",
    "\n",
    "        x_values = {}\n",
    "\n",
    "        pi = pd.IntervalIndex.from_arrays(dfx.parent_left, dfx.parent_right)\n",
    "        x_values = np.vectorize(lambda x: dfx.value.loc[pi.contains(x)].sum(axis=0))(a)\n",
    "        feature_importance.append(np.vectorize(lambda x: dfx.value.loc[pi.contains(x)].sum(axis=0))(X_))\n",
    "\n",
    "        df_plot = pd.DataFrame(x_values, index=a).sum(axis=1).iloc[1:]\n",
    "        if show_plot:\n",
    "            (\n",
    "                pd.concat([\n",
    "                    df_plot.shift(0).to_frame('value').assign(order=0),\n",
    "                    df_plot.shift(-1).to_frame('value').assign(order=1)\n",
    "                ], axis=0)\n",
    "                .rename_axis(feature_name)\n",
    "                .sort_values([feature_name, 'order'])\n",
    "            ).value.plot()\n",
    "            plt.show()\n",
    "\n",
    "    return feature_importance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot First Trees\n",
    "\n",
    "We can see here that tree-0 containts the intercept/mean of the target and is thus heavily biased. Compare to tree-1. This is unfavourable when calculating feature importance as we shall see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>EBM_FI</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x0</th>\n",
       "      <td>36.571615</td>\n",
       "      <td>24.483835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>6.382724</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>39.116323</td>\n",
       "      <td>26.159495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x3</th>\n",
       "      <td>75.503260</td>\n",
       "      <td>50.329661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x4</th>\n",
       "      <td>20.831752</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x5</th>\n",
       "      <td>1057.607595</td>\n",
       "      <td>89.961696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x6</th>\n",
       "      <td>0.204210</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x7</th>\n",
       "      <td>124.870875</td>\n",
       "      <td>82.295561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         EBM_FI       coef\n",
       "x0    36.571615  24.483835\n",
       "x1     6.382724   5.000000\n",
       "x2    39.116323  26.159495\n",
       "x3    75.503260  50.329661\n",
       "x4    20.831752  20.000000\n",
       "x5  1057.607595  89.961696\n",
       "x6     0.204210   0.000000\n",
       "x7   124.870875  82.295561"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dict = gbm.dump_model()\n",
    "model_list = make_model_list(model_dict, X)\n",
    "feature_importance = calculate_feature_importances(model_list, gbm, show_plot=True, remove_y_mean=False)\n",
    "FI = pd.DataFrame(np.mean(np.abs(feature_importance), axis=1), index=X.columns, columns=['EBM_FI']).assign(coef=coef)\n",
    "FI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgb.plot_tree(gbm, tree_index=0, figsize=(12, 8))\n",
    "lgb.plot_tree(gbm, tree_index=1, figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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irs5Zxpyk9VXP10k6tUR545LGJWlkZKThgQEA0JpudOkckXS57Y22Xydpm6QDXSgXAFAl77DMG2zPSdoi6X7bB7PXL7M9IUkRcU7SbZIOSjom6VsRcTRftQEArco7SmefpH11Xj8l6bqq5xOSJvKUBQCN7Jk8of3TJ8uuRmGGL1uhOz54ReHvy522APre/umTLNbeBNa0BTAQhtes0N5bt5RdjZ5GCx8AEkHgA0AiCHwASASBDwCJIPABIBEEPgAkgsAHgEQQ+ACQCAIfABJB4ANAIgh8AEgEgQ8AiSDwASARBD4AJCLvilcftX3U9su2R5bY7ye2f2R72vZUnjIBAO3JOx/+U5I+LGl3E/u+NyJO5ywPANCmvEscHpMk28XUBgDQMd3qww9JD9p+zPaOpXa0vcP2lO2phYWFLlUPAAbfsi182w9JurTOptsjYn+T5bwnIk7ZfpOkQ7afjoiH6+0YEeOSxiVpZGQkmnx/AMAylg38iLg6byERcSr783nb+yRtllQ38AEAndHxLh3bb7B9ceWxpA9o8WIvAKCL8g7LvMH2nKQtku63fTB7/TLbE9lub5Z02PYTkn4o6f6I+E6ecgEArcs7SmefpH11Xj8l6brs8bOSrsxTDgAgP+60BYBEEPgAkAgCHwASQeADQCIIfABIBIEPAIkg8AEgEQQ+ACSCwAeARBD4AJAIAh8AEkHgA0AiCHwASASBDwCJIPABIBEEPgAkIu+KV1+2/bTtJ23vs31Jg/222n7G9qztnXnKBAC0J28L/5Ckt0XEOyT9WNLnanewfYGkuyRdK2lY0k22h3OWCwBoUa7Aj4gHI+Jc9vRRSevq7LZZ0mxEPBsRL0m6T9JYnnIBAK0rsg//E5IeqPP6WknPVT2fy16ry/YO21O2pxYWFgqsHgCkbdlFzG0/JOnSOptuj4j92T63Szon6Rv13qLOa9GovIgYlzQuSSMjIw33AwC0ZtnAj4irl9pu+xZJ10t6f0TUC+g5Seurnq+TdKqVSgIA8ss7SmerpM9K+lBEvNhgtyOSLre90fbrJG2TdCBPuQCA1uXtw79T0sWSDtmetr1LkmxfZntCkrKLurdJOijpmKRvRcTRnOUCAFq0bJfOUiLirQ1ePyXpuqrnE5Im8pQFAMiHO20BIBEEPgAkgsAHgEQQ+ACQCAIfABKRa5QOAHTDzPxZ3bj7kSW3D69Z0cUa9ScCH0BPG9vUcOqt84bXrGhqv9QR+AB62vbRIW0fHSq7GgOBPnwASASBDwCJIPABIBEEPgAkgsAHgEQQ+ACQCAIfABJB4ANAIlx/GdreYHtB0k/LrkcbVkk6XXYlcuIzlK/f6y/1/2fox/q/JSJW19vQ04Hfr2xPRcRI2fXIg89Qvn6vv9T/n6Hf61+LLh0ASASBDwCJIPA7Y7zsChSAz1C+fq+/1P+fod/r/wr04QNAImjhA0AiCHwASASB3yG2v2z7adtP2t5n+5Ky69Qq2x+1fdT2y7b7Zmia7a22n7E9a3tn2fVple17bD9v+6my69IO2+ttf8/2sez/z5+WXadW2b7I9g9tP5F9hr8qu05FIPA755Ckt0XEOyT9WNLnSq5PO56S9GFJD5ddkWbZvkDSXZKulTQs6Sbbw+XWqmVfl7S17ErkcE7Sn0fE70h6t6TP9OG/wf9Jel9EXClpk6Sttt9dbpXyI/A7JCIejIhz2dNHJa0rsz7tiIhjEfFM2fVo0WZJsxHxbES8JOk+SWMl16klEfGwpDNl16NdETEfEY9nj38h6ZikvlpwNhb9Mnt6YfbT9yNcCPzu+ISkB8quRCLWSnqu6vmc+ixsBontDZLeKWmy5Kq0zPYFtqclPS/pUET03WeoxSLmOdh+SNKldTbdHhH7s31u1+Ip7je6WbdmNfMZ+ozrvNb3LbN+ZPuNkv5V0p9FxNmy69OqiPi1pE3Z9bd9tt8WEX15XaWCwM8hIq5earvtWyRdL+n90aM3PCz3GfrQnKT1Vc/XSTpVUl2SZftCLYb9NyLi38quTx4R8T+2v6/F6yp9Hfh06XSI7a2SPivpQxHxYtn1ScgRSZfb3mj7dZK2STpQcp2SYtuSvibpWET8Xdn1aYft1ZWRdbZfL+lqSU+XWqkCEPidc6ekiyUdsj1te1fZFWqV7Rtsz0naIul+2wfLrtNysgvlt0k6qMWLhd+KiKPl1qo1tr8p6RFJv217zvaflF2nFr1H0h9Jel/2f3/a9nVlV6pFayR9z/aTWmxEHIqIfy+5TrkxtQIAJIIWPgAkgsAHgEQQ+ACQCAIfABJB4ANAIgh8oA22h2w/mM0IOZNNIQD0NIZlAm3I7rz8m4g4lE0h8DI32KHX0cIHlmD7d7M1DS6y/YZsbvR3SHptRBySpIj4JWGPfkALH1iG7b+WdJGk12txrp5jkj4p6SVJGyU9JGlnNtkW0LMIfGAZ2Zw8RyT9r6Tfk3SDFueKeaekE5L2SpqIiK+VVkmgCXTpAMtbKemNWpwb6SIttvL/M1tk5Zykb0t6V3nVA5pD4APLG5f0l1pc0+Bvtdja/y3bq7Pt75M0U1LdgKYxHz6wBNsfk3QuIvZk6+X+h6Q/kPQXkr6bTQX8mKR/KrGaQFPowweARNClAwCJIPABIBEEPgAkgsAHgEQQ+ACQCAIfABJB4ANAIv4fb3zPQZ4STf4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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esAfctimpNYb/Bupt7YA9fUntMfw3mK0dSaPA8F8Hi9s7C3pn/ZLUpje1XcAkWmjvLGZfX9KocOa/TmzvSBplhv+A+rV4bO9IGnW2fQbUr8Vje0fSqHPmPwS2eCSNG8N/ldzBI2mS2PZZJXfwSJokzvzXwPaOpEnhzF+SOsiZf4+l+vpgb1/SZHHm32Opvj7Y25c0WZz5L2JfX1IXOPOXpA4y/CWpgya+7bPcIu5iLupK6oqBZv5J/kOSZ5KcSvK5JFt7rh1McjbJ80ne0zP+riRfaa79VpIMUsNKllvEXcxFXUldMejM/2NV9e8AknwE+PfAB5NMA/uB64CtwONJ3lFVl4CPAweAJ4HPAnuBRwesY1ku4krS6w0086+q3in1DwPVPN4HPFxVr1XVOeAssCfJFLCpqp6oqgI+Bdw2SA2SpLUbuOef5BBwB/D/gJ9uhrcxP7NfMNeMfbd5vHhckrSBVpz5J3k8ybN9fu0DqKr7qmoH8BDw4YW39flWtcz4Uj/7QJLZJLMXLlxY+XcjSVqVFWf+VXXrKr/XEeAzwEeZn9Hv6Lm2HXipGd/eZ3ypn30YOAwwMzOz5D8SkqS1GXS3z+6epz8HfLV5fBzYn+TNSXYBu4Gnquo88GqSm5pdPncAxwapQZK0doP2/O9P8uPA94C/Aj4IUFWnkxwFzgAXgbubnT4AHwI+CVzN/C6fdd3pI0l6o4HCv6r+5TLXDgGH+ozPAtcP8nMlSYPxeAdJ6qCJP95heqvHNUjSYhMf/h9973VtlyBJI8e2jyR1kOEvSR1k+EtSBxn+ktRBhr8kdZDhL0kdZPhLUgcZ/pLUQZn/QK3Rl+QC84fHjZtrgL9uu4gBWH+7xr1+GP/fw7jX//erasviwbEJ/3GVZLaqZtqu43JZf7vGvX4Y/9/DuNe/FNs+ktRBhr8kdZDhv/4Ot13AgKy/XeNeP4z/72Hc6+/Lnr8kdZAzf0nqIMNfkjrI8N8AST6W5KtJnkny6SQ/2nZNa5Hk9iSnk3wvydhseUuyN8nzSc4mubftetYiySeSvJzk2bZruRxJdiT5QpLnmr87v9J2TWuR5KokTyX586b+32i7pmEz/DfGCeD6qvpHwP8GDrZcz1o9C7wf+FLbhaxWkiuA3wH+BTAN/GKS6XarWpNPAnvbLmIAF4Ffq6qfAG4C7h6zP//XgHdX1T8GbgD2Jrmp3ZKGy/DfAFX1uaq62Dx9EtjeZj1rVVXPVdXzbdexRnuAs1X1tar6O+BhYF/LNa1aVX0J+FbbdVyuqjpfVX/WPH4VeA7Y1m5Vq1fzvtM8vbL5NVG7Ywz/jffLwKNtF9EB24AXe57PMUbhM0mS7ATeCZxsuZQ1SXJFklPAy8CJqhqr+lcy8R/gvlGSPA68vc+l+6rqWPOa+5j/7/BDG1nbaqym/jGTPmMTNXMbB0neAvwh8KtV9Urb9axFVV0CbmjW6D6d5PqqGss1mH4M/yGpqluXu57kTuBngVtqBG+uWKn+MTQH7Oh5vh14qaVaOinJlcwH/0NV9Udt13O5qurbSb7I/BrMxIS/bZ8NkGQvcA/wc1X1N23X0xFfBnYn2ZXkh4D9wPGWa+qMJAF+H3iuqh5ou561SrJlYVdekquBW4GvtlrUkBn+G+O3gR8BTiQ5leS/tV3QWiR5X5I54GbgM0kea7umlTQL7B8GHmN+sfFoVZ1ut6rVS/IHwBPAjyeZS3JX2zWt0U8CvwS8u/k7fyrJz7Rd1BpMAV9I8gzzE4kTVfXHLdc0VB7vIEkd5MxfkjrI8JekDjL8JamDDH9J6iDDX5I6yPCXBpDkp3u2Mp5K8rdJbmu7LmklbvWUhiTJZuAssN2b+TTqnPlLq5TknzSfyXBVkh9uznm/vuclPw88avBrHDjzl9YgyX8ErgKuBuaq6j/3XPsfwAOTdieoJpPhL61Bc07Ql4G/Bf5pc/IjSaaAZ4CtVfXdFkuUVsW2j7Q2m4G3MH9W01U9478AfNrg17hw5i+tQZLjzH8q2C5gqqo+3Iw/CRysqi+0WZ+0Wp7nL61SkjuAi1V1pPmM4P+V5N3A15j/7ID/2WqB0ho485ekDrLnL0kdZPhLUgcZ/pLUQYa/JHWQ4S9JHWT4S1IHGf6S1EH/HwBlkY8kv86LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>EBM_FI</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x0</th>\n",
       "      <td>36.571615</td>\n",
       "      <td>24.483835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>6.382724</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>39.116323</td>\n",
       "      <td>26.159495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x3</th>\n",
       "      <td>75.503260</td>\n",
       "      <td>50.329661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x4</th>\n",
       "      <td>20.831752</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x5</th>\n",
       "      <td>135.343726</td>\n",
       "      <td>89.961696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x6</th>\n",
       "      <td>0.204210</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x7</th>\n",
       "      <td>124.870875</td>\n",
       "      <td>82.295561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        EBM_FI       coef\n",
       "x0   36.571615  24.483835\n",
       "x1    6.382724   5.000000\n",
       "x2   39.116323  26.159495\n",
       "x3   75.503260  50.329661\n",
       "x4   20.831752  20.000000\n",
       "x5  135.343726  89.961696\n",
       "x6    0.204210   0.000000\n",
       "x7  124.870875  82.295561"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_dict = gbm.dump_model()\n",
    "model_list = make_model_list(model_dict, X)\n",
    "feature_importance = calculate_feature_importances(model_list, gbm, show_plot=True, remove_y_mean=y_train.mean())\n",
    "FI = pd.DataFrame(np.mean(np.abs(feature_importance), axis=1), index=X.columns, columns=['EBM_FI']).assign(coef=coef)\n",
    "FI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Edit Target Variable (- target mean).\n",
    "\n",
    "This should mimic the proposials mentioned by @shiyu1994 on [github](https://github.com/microsoft/LightGBM/pull/4235#issuecomment-828914811)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_train = lgb.Dataset(X_train, (y_train - y_train.mean()))\n",
    "lgb_test = lgb.Dataset(X_test, (y_test - y_train.mean()), reference=lgb_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\bb5361\\lightgbm\\python-package\\lightgbm\\engine.py:148: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
      "  _log_warning(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 25 rounds\n",
      "[1000]\ttraining's l1: 20.0694\ttraining's l2: 688.878\tvalid_1's l1: 21.2094\tvalid_1's l2: 777.485\n",
      "[2000]\ttraining's l1: 7.88412\ttraining's l2: 115.028\tvalid_1's l1: 8.63363\tvalid_1's l2: 139.96\n",
      "[3000]\ttraining's l1: 4.73314\ttraining's l2: 43.3071\tvalid_1's l1: 5.27794\tvalid_1's l2: 55.5393\n",
      "[4000]\ttraining's l1: 3.92555\ttraining's l2: 30.6694\tvalid_1's l1: 4.29884\tvalid_1's l2: 38.8162\n",
      "[5000]\ttraining's l1: 3.62976\ttraining's l2: 26.7527\tvalid_1's l1: 3.94617\tvalid_1's l2: 33.5896\n",
      "[6000]\ttraining's l1: 3.41128\ttraining's l2: 24.2481\tvalid_1's l1: 3.70836\tvalid_1's l2: 30.5441\n",
      "[7000]\ttraining's l1: 3.20546\ttraining's l2: 22.1129\tvalid_1's l1: 3.49931\tvalid_1's l2: 28.2243\n",
      "[8000]\ttraining's l1: 3.03793\ttraining's l2: 20.5541\tvalid_1's l1: 3.3429\tvalid_1's l2: 26.5781\n",
      "[9000]\ttraining's l1: 2.89575\ttraining's l2: 19.3194\tvalid_1's l1: 3.21058\tvalid_1's l2: 25.2542\n",
      "[10000]\ttraining's l1: 2.77859\ttraining's l2: 18.3516\tvalid_1's l1: 3.09941\tvalid_1's l2: 24.203\n",
      "[11000]\ttraining's l1: 2.67929\ttraining's l2: 17.5636\tvalid_1's l1: 3.00481\tvalid_1's l2: 23.3444\n",
      "[12000]\ttraining's l1: 2.59731\ttraining's l2: 16.9214\tvalid_1's l1: 2.92719\tvalid_1's l2: 22.6409\n",
      "[13000]\ttraining's l1: 2.52526\ttraining's l2: 16.3711\tvalid_1's l1: 2.85455\tvalid_1's l2: 22.0677\n",
      "[14000]\ttraining's l1: 2.46105\ttraining's l2: 15.9021\tvalid_1's l1: 2.7909\tvalid_1's l2: 21.5662\n",
      "[15000]\ttraining's l1: 2.4053\ttraining's l2: 15.5026\tvalid_1's l1: 2.73467\tvalid_1's l2: 21.1511\n",
      "[16000]\ttraining's l1: 2.35528\ttraining's l2: 15.1511\tvalid_1's l1: 2.68964\tvalid_1's l2: 20.8242\n",
      "[17000]\ttraining's l1: 2.31008\ttraining's l2: 14.8351\tvalid_1's l1: 2.64775\tvalid_1's l2: 20.5191\n",
      "[18000]\ttraining's l1: 2.26635\ttraining's l2: 14.5411\tvalid_1's l1: 2.60833\tvalid_1's l2: 20.2359\n",
      "[19000]\ttraining's l1: 2.22874\ttraining's l2: 14.2889\tvalid_1's l1: 2.57355\tvalid_1's l2: 20.0017\n",
      "[20000]\ttraining's l1: 2.19476\ttraining's l2: 14.0569\tvalid_1's l1: 2.53907\tvalid_1's l2: 19.7657\n",
      "[21000]\ttraining's l1: 2.16477\ttraining's l2: 13.8497\tvalid_1's l1: 2.50818\tvalid_1's l2: 19.544\n",
      "[22000]\ttraining's l1: 2.1374\ttraining's l2: 13.6571\tvalid_1's l1: 2.48067\tvalid_1's l2: 19.3465\n",
      "[23000]\ttraining's l1: 2.11069\ttraining's l2: 13.4733\tvalid_1's l1: 2.45467\tvalid_1's l2: 19.1506\n",
      "[24000]\ttraining's l1: 2.08561\ttraining's l2: 13.3033\tvalid_1's l1: 2.42902\tvalid_1's l2: 18.9712\n",
      "[25000]\ttraining's l1: 2.0629\ttraining's l2: 13.1462\tvalid_1's l1: 2.40645\tvalid_1's l2: 18.8207\n",
      "[26000]\ttraining's l1: 2.04285\ttraining's l2: 13.0057\tvalid_1's l1: 2.38705\tvalid_1's l2: 18.6922\n",
      "[27000]\ttraining's l1: 2.02473\ttraining's l2: 12.8791\tvalid_1's l1: 2.36898\tvalid_1's l2: 18.5861\n",
      "[28000]\ttraining's l1: 2.00851\ttraining's l2: 12.7621\tvalid_1's l1: 2.35237\tvalid_1's l2: 18.4767\n",
      "Early stopping, best iteration is:\n",
      "[28690]\ttraining's l1: 1.99793\ttraining's l2: 12.6843\tvalid_1's l1: 2.34167\tvalid_1's l2: 18.4097\n"
     ]
    }
   ],
   "source": [
    "params_elgb = {\n",
    "    \"boost_from_mean\": False,\n",
    "    \"n_estimators\": 50000, # lots of estimators\n",
    "    \"learning_rate\": 0.01, # low learning rate\n",
    "    \"num_leaves\": 4, # shallow trees\n",
    "    \"interaction_constraints\": [[i] for i in range(X.shape[1])], # single feature trees\n",
    "    \"verbose\": -1,\n",
    "    \"random_state\": 123,\n",
    "    \"metric\": [\"l1\", \"l2\"],\n",
    "}\n",
    "\n",
    "gbm = lgb.train(\n",
    "    params_elgb,\n",
    "    lgb_train,\n",
    "    valid_sets=[lgb_train, lgb_test],\n",
    "    verbose_eval=1000,\n",
    "    early_stopping_rounds=25,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replot First Trees\n",
    "\n",
    "We can see now that the leaves in tree-0 are behaving much better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VQRAEQTgFeRR0fX09e/fuZe3atcyYMYNFixYxb948TCbTZdmutLS0UwaHF4vL5WL37t0MDAyg1WqVADMlJYWUlJSTlpd7z+7YsYOamhpUKhWLFi1CpVKxZ88etm3bxtq1a5XBOFqt9qJuv9/vp7a2ltLSUj777DP+9a9/KbfNmDHjrNfjdDqpr6/nwIEDdHZ2YjAY0Gg0GI1GSktLKS0txe12M3r0aJKTk0+6f319PT6fj4KCgmEPcvLy8oiPj0eSJBwOB4cPH6a0tBSbzaa0QOvu7ubQoUMcOnSIsWPHEh4eflmDVTheimA0Gpk7dy4Wi4Xu7m4++ugjsrKy0Ov1TJ06leDg4PPKpItgVRAEQRBOw+Vy8d577/HOO+8QHR3NL3/5SxISEq7ovqmDS/zk3wcHCSqV6qT6x1MtJ9dW9vf38/777yvLyn8/VU2iPBDtN7/5DTNmzOCJJ57AYDCgUqnIz8/H6/WydOlSCgoKmDFjhnLa/MS61pEyMDDAu+++iyRJdHV1nfd6qqqqaGlpwWq1smnTJgwGA7W1tWzatIlHH30USZIoLy/nX//6F0uWLFH2Qd6vQ4cOMXr0aH784x9jNptPWr+8vNfr5fDhwzidTpYsWcLvf/97jEYjmzdvZtWqVbz66qsArF27FqPRyMSJE897n0aSTqdj0qRJZGdnU1dXxzvvvENtbS3Z2dnnnV0dkekGBEEQBOFa9fLLL7NlyxaMRiN//etfSUhIuOhZwAvldDqpra3lv//7vxk7dixz5szhiSeeYOfOnbhcLuD4afH+/n4++ugjHnjgARITE0lMTOTHP/4xGzZswGazIUkSs2fPJjExkVmzZinrf+mll5TlKysrh90Gt9vNF198gcvlIiwsbEigEhoaSkREBABlZWU0Nzcrt7322mv86le/4tVXXz0poL4QBoOBH/zgBzz11FPMnz//vNcTERHBlClTuPvuuzGZTAQFBZGamsoNN9ygBJpNTU18/vnnJx00dHV18cknn/Dkk09SUFDAf/3Xf7Fy5Up27dpFa2vrkMdRq9XExMSwfPly5s2bh8FgAGDWrFkUFBQoy33xxRccPnz4vPfnYlCpVBgMBn7/+98zbtw4ysrKeO655/D5fOe1PpFZFQRBEIRheL1eenp62LhxI2FhYcyaNYv09HQ0Gs1lH319KpIk4XQ62b9/PyUlJej1eh555BFlBqLf/va3PPvss6SnpwPw0Ucf8f777xMREcG3v/1tfD4fn3/+OX19fdjtdlasWMFDDz2EzWajs7OT559/Hjh+2nzp0qUAStB5Io/Hw9GjR3G73YSGhmKxWJTbdDodRqMROB6s5ufnK7fJrbFGug5YrVYrj3khWXGz2YwkSQQFBSlZ5eDgYEJCQpRlUlNTmTdv3pDXid/vp6amhp6eHnp7e3E4HLz//vuYTCbMZjOxsbE8/PDDZGRkEBYWhlqtJjIyErVajUajUZrt63S6IT1MZ82axYQJE857fy4GlUqFWq0mLi6OBQsWsHXrVrZv3051dTVJSUmEhoae0/pEsCoIgiAIw3C73VRVVXH06FGWLl3K9ddfrwQ7V7LOzk7279/P7t27eeihh5g9ezbd3d3s3r2bn/70p3zlK18hPDwcvV7P3r17KSoq4oEHHuDRRx/F4/Hw+uuv09bWhlqtZsWKFdx///0AVFRUKMHqlClTePzxx0+7HR6Ph+LiYlwuF1qtVskMAmg0GvR6PXByZjUqKoqgoKAhy19JhhsMFggElKxhWFgYOTk53HTTTScFq8eOHVMy2z6fj/379w9Zb2ZmJnq9nqSkJEJCQoZ9vfl8Pvx+v1IjOm/ePCZPnjzSu3nBVCoVISEhzJgxg87OTtauXcuhQ4cwm80iWBUEQRCEkWCz2Vi1ahVhYWFMmTKF2bNnX+5NOiuHDh1i165dHD16lMWLFwOQkJBAXl4ecHx0eXx8PGPHjsVisXDjjTcyceJErFYrAMHBwRw9ehSv13tB2+FwOFi1atUZlztw4MCQ0/J33nnnBT3u5eByuejp6UGSJAoKCrj++uuZO3fukGW8Xi+7du2iv79/2HU4HA6efPJJVCoVixcvZsyYMcMu19/fj8PhIDg4mNmzZ7NgwQJyc3NHepdGTE5ODq2trYwaNYpVq1aRmZlJTEzMOZ2dEMGqIAiCIAyjv7+frVu3Mnv2bDIyMi50zvNLZsOGDZSXl1NVVUVSUpLydznz9/rrr5OcnMzs2bP5/ve/TyAQwOv1UlFRwZYtW+jt7b1cm37VamtrY9++fQDcfffdzJ8//6TMcFhYGM8//zw/+clPCAQCygC02tpaSkpK2LFjB59//jm7d+8mNjb2lMFqSUkJlZWVhISE8OSTT5KdnT2kBOFKFB8fz9KlS/nzn/9MZ2enMoHC2RLBqiAIgiCcQJIk3G439fX1rFixgvDw8Cu2TvVEJSUltLS0YLFYeOKJJ4ZdZubMmQQFBWEymSguLqayspL29nbS0tJGbHIDrVZLTk7OKQdgycaNGzdsi6erhTyT2IcffsgTTzzB9OnTiYuLO+ngRq1WY7FYlNrdQCCAxWIhLi6O9PR0cnNz0Wq1SunJiQKBAK2trXzwwQd0d3fz2GOPMWbMGKW+9UpmNBrJzMxU6nUHBgbOqbeuCFYFQRAE4QSSJOH1eunu7iYqKuqyzxB0Lurr6+np6WHUqFE89thjp5yf3e/309fXx5YtW2hsbESn07F06dIR63Sg1WopKCigsbFRqemUByX5/X6lzCA/P/+CgtVjx44N23NWo9GccvrXkSBJEi6Xi4qKCsrKyujo6OC//uu/SElJUepxT0etVmMymTCZTCQnJ5OXl0dnZyd/+ctf6OzsHLKsz+fD6XQqWdXU1FQefPDBcz6dfrmEhIQQGxuLx+PBbreLYFUQBEEQLpTP58Pj8eD3+wkJCbmkU6mOFK/XS21tLWlpaUNGj8scDgefffYZv/rVr7jzzjv57ne/S1xc3LDLno+QkBDmzJnD5s2bGRgYoKenR+kcYLfbaW9vByA9PZ3o6Ojzfpw77riDoqKik/6ekJBAQ0PDea93uLZZg3um+nw+jh07xj//+U8yMjL46KOPiIyMHLaH7XB/G7w+OD7K//7776eurm5I8C5JEn19fZSXl/OnP/2JH/7wh+Tn5ysTUgz3WFcaeYYrOD5wUR5kdrauvnefIAiCIFxk8mj10NBQuru7L/lsURfiBz/4Ae+88w6HDx/mgw8+4N577yUqKorg4GBsNhvr169n9OjRmEwmvve979Hd3Y3RaCQ6OnpEA52QkBBmz55NaGgofX19NDc3K8Gq0+lUsocTJ05UWmmdj08//XTYwWAX2vrK6/XyxRdf8Mwzz5CRkcFDDz3EpEmTUKlU2Gw26urq+Oyzz7jnnntIS0tTBqjJ9+3r62PDhg0sXLiQyMhIXC4Xb775JocPHyY5OZkFCxYwduxY5TmXR/dPnTp1SJuvuro6ysrKOHz4ML/85S9JSUkZMpp+YGCA2tpajh49yooVK67IYNXtdtPR0YEkSRiNxnPuqiGCVUEQBEE4gUqlQqfTkZycTF1dHT09PZd7k4Zwu904nU7lusfjwel0otfrmTx5Mrt372b37t18/PHH1NbWKnWNAwMDzJ8/H7PZjFqtJioqir6+PjweD/39/UpbJDheI+lwOAgNDcXhcNDR0aE8ntPppLu7G4vFcsrgKDg4mLi4OJYsWYJGo6GwsJCsrCzgeKlCRUUFt956KxkZGUOCl507d2K325Vg90zBlzzz1dnq7+8/6eDD5XLR1dWF1WpVHs/pdPKHP/yB4uJiGhoaUKvVTJw4kaamJnbu3Mknn3xCVVUVx44dIzw8fEj5hNxa6rrrrlPqSX0+H0VFRezZswedTsfRo0f5xje+QW5uLkajEb/fT0VFBcnJyVitVmV62H/84x8cPnyYzs5OamtrCQkJGRKIezweYmNjGTdu3Dk9D5eS3W6nqqoKq9WK2Ww+qzKJwUSwKgiCIAjDCAkJITs7m6qqKjo6OvD7/SPeqP58NTc3U1paetL1iRMnkpGRQXZ2NgkJCVRUVLB3716lz6nFYuFrX/sa0dHROBwOZsyYQVNTEz09PTQ0NBAaGkp4eDgREREYjUbsdjsNDQ20t7cPmSWpqamJ/fv3k5CQQFZW1rBN9uWazJtuuony8nKqq6spLi4mLCyM2tpaent7ufPOO0lISBgymv3gwYO0t7djNptHtF2Y1+vl2LFjdHR0nFQe0Nrayv79+0lMTCQjIwO9Xo/H42Hv3r3Y7Xa8Xi8HDx5Utm/16tW8+eabAOzevXvYx7NYLNx9991Dnhu/34/L5aKyspLCwkLi4+PRarUkJiYSFBRES0sLmZmZWK1WvF4v27ZtY+XKlVRUVACwffv2YR/r5ptv5sYbb7wis6qSJNHT08ORI0dITk7GbDafe120JEmnuwiCIAjCl1JbW5v0yiuvSDExMdJ//ud/St3d3Zd7kxQ/+tGPJGDIZfTo0crtNptNKi4ulp555hnJYrFIkydPln784x9LdXV1ksfjkSRJkgYGBqSioiIpOTlZmj17tvTiiy9KbW1t0osvvig9+eST0o9+9COprq5OSk9PP+mxBl9KS0vPuL3Hjh2T/vKXv0iANHfuXOnnP/+5tHPnTikQCJy07O9//3vpmWeekX79618Pe/v5amxslFQq1Wn3BZD27dsnSZIkuVwuadOmTVJ2drZ0yy23SNu3b5cCgYA0ZcqUM64DkKKioiSfz6c8vt/vl9ra2qRVq1ZJ9913n7LcLbfcIv3sZz+TVq1aJdlsNsnr9UqSJEmdnZ1SUFDQWT3W1772tRF7nkaa2+2W3nvvPSkhIUF6+umnpbKystMtPmw8qpJOP+/uyE3KKwiCIAhXEa/XS1dXF4sWLSI1NZUFCxbwve9973JvFgDd3d0nlSbIGTo4nsHz+/04HA76+voIDg5Gr9crc9nLA348Hg9tbW1oNBpCQ0MxGAzY7XZ8Ph8qlQqTyURLS8tpJwhITEw8Y6bM6/Xicrno6OhAr9cTEhKCXq9Hp9OdlA3s7e0lEAgomdmRyhb6fD4aGhqGHeg0WEJCAjqdThnt39XVhUajwWw2o9PpaG5uxu12n/HxgoKCSE5OHjIoKxAI4HK5cDqdyuQAYWFhaLVatFotISEhqNVqpWNCfX39GbcXjreGioqKOotn4dL76KOP+PTTT/noo4/YsGED6enpp5udbNh/tghWBUEQBGEYcrulH/3oRxw5ckRp6p6ZmTliI+YF4VoVCARoamriN7/5DaWlpYSHh/Pqq69iMplO111j2GD1yu4iKwiCIAiXiVqtRqPRcOONN2KxWCgvL2fr1q309vZe8FSkgnAt8/v9DAwMsG/fPg4ePIjb7ebmm28+U6B6SmKAlSAIgiCcgkqlYt68eXR0dODz+XjqqadISEhg0qRJxMXFXZEDWgThcpIkCYfDQUVFBd/73vdIT09nyZIl3Hvvvee9TlEGIAiCIAhn0NvbS2lpKc899xyNjY0sX76cZcuWMW3atMu9aYJwRTl27BibN2/mv//7v9FoNDz11FPMmjWLmJgY4IyTFgx7o8isCoIgCMIZGI1GsrKyeOCBB3jjjTc4dOiQMsf59OnThx0oJAhfJn6/n0OHDrF69WqKi4vRaDR87Wtfo6CgQJnZ63yJYFUQBEEQzkCtVhMREcHtt99Oe3s7a9asYdOmTQQFBREXF0dcXBxhYWFX5bSsgnAh5N6x3d3drF+/XplRbObMmdx9990YjcYLfl+IMgBBEARBOEvyd+aOHTtYs2YNL774InPmzOHuu+/mhhtuOOfZlAThatfT08P+/fv529/+xqpVq7j99ttZtmwZd9xxB3DG0/4nEq2rBEEQBGEkOJ1OOjo62LlzJ3/84x/p6+sjMjKS++67j9tuuw2DwaBMsykI1xpJkvB6vXzyySf861//4tixYwQCAR555BHmzJlDcnKyMoWuCFYFQRAE4TLxeDw0Nzezdu1aCgsLqa2tRavVMn/+fPLy8sjIyCAtLQ045y9sQbhiNTc3U1dXR0lJCevXr6evrw+r1cqECRO46aabSE5OPl3T/zMRwaogCIIgjCQ5w7Rr1y42b97MW2+9hdFoZPr06cyYMYM5c+ZgNpsJCQlBo9GIoFW4Kvl8PtxuNz09PRw8eJB9+/axceNGWlpauOGGG5g3bx433XQTer3+Ql/jIlgVBEEQhJEmf4+63W7q6up48cUX2b59O/X19WRmZvL4448za9YssrKyRGmAcFVqaWlh3759vPjii5SUlBAaGsqUKVP4yU9+QnZ29vme8h+OCFYFQRCEkRUIBFi5ciUffPABR44cUf6+dOlS5s+fz5IlS4DjA5K2bNnCv/71LwC+//3vs2zZMiIiIrDZbBQWFrJ7927+9a9/MXPmTGbPnk1BQQG5ubnKOtvb2/nkk0945ZVXmD9/PvPnz6egoIDly5fz+OOPc/vtt1/anT9BIBDA6/XS1dVFfX09ZWVlfPjhhxw+fJiIiAgyMzO54YYbWLhwIRaLBZ1Od1m3VxBOxefz4XA42LRpE5s3b6a0tJSamhpGjRrFokWLGDduHNnZ2URGRqLVagkKChqphxZ9VgVBEISRl5qaSn5+Pn19faxfv56xY8cSGxtLQkKCskx0dDRxcXH4fD7mz59PamoqGo0Gh8PBunXrcDgcmEwmbr75ZvR6PV988QW1tbVERUURGRlJXV0dzc3NREdHs2TJEkaPHo1Wq2Xfvn0cOXIEm812SfZVkiR2796N2+1GrVYTEhKCTqdDq9Wi0WjQ6XSEhISQkJBAaGgoarWaxMREWltb6erq4sMPP+TYsWOkpaWRkpLC6NGjiYmJES2vhMsuEAjQ2dlJVVUVdXV1VFVVUVJSQn9/P0ajkXnz5jFz5kwmTJhAYmIikZGRl2zbxLtDEARBOG9qtZoZM2YQHx9PUlISX3zxBaNHj2bSpElDsqLp6em43W4mT57Mj370I6Kjo4Hjpxfffvttpk2bxqxZs3jsscfYv38/zz//PJs3b2b+/PlYLBaKiopoaGhgxYoVLFmyBK/XS0NDA++//z4+n++S7vPGjRvp6OggEAhgNpsxmUyEhoYSGhqK0WjEZDIRFhamnCpNTExk79697Nmzh02bNrF+/XpGjx5NQUEBS5YsIS8vj7CwMLRaLQaDAa1Wi0qlEvWtwkUlSZJSi+rxeHA6nRw+fJjPP/+c4uJiDh8+jE6nY+rUqUyZMoV58+aRk5NzWUpZRLAqCIIgXLCUlBTmz5/PD3/4Q372s58xbdo0xowZo0yx2NzcjN1uZ9myZcTExKDVauns7GTXrl1s2rSJm2++mZkzZ6JSqZg8eTLR0dEUFhaydu1a8vPzWbt2LUVFRdx5552oVCp0Oh2pqancf//9/PrXv76k+9rc3MyWLVuorKxEo9Hg9/vx+/2cqqxOp9Oh1+vR6/VKkN7U1ER5eTl/+ctfMBqNZGRkMH78eL7xjW8wceJEQkJCLuUuCV9CkiTR2NjIrl27OHjwIDt37mTfvn3Ex8eTl5fHE088wd13343FYkGr1V7WbRXBqiAIgnDBVCoVJpOJqVOnEhwcTEVFBYcPH1aC1erqalpbWxk/fjzBwcGoVCq6u7tZu3YtHo+H5557jpdeeklZX2NjI3a7nb/85S88/vjjTJw4kebmZubNm8dzzz3H+PHjiY+PJzIykm3btimPcylMmjSJ8vJyKisr8Xg8pwxSZR6PB6/Xi91up6urCzh+yjUQCAAQGxuLy+Vi586dbNu2jczMTHJycsjNzWXKlCmMGjUKrVYrBmcJ502SJPx+P1VVVRQXF1NaWkpxcTFlZWUEBQURGRlJfn4+Dz30ELm5uURFRWE0GgkPDycoKOiyZ/lFsCoIgiCMiJCQENLT08nPz6e6upqtW7cya9YsADo6Oujp6SEmJkb54rPb7Rw8eBC/309eXh5jx449aZ1qtRqdTkdubi6tra2UlZXx7rvvsmfPHlJTU5k2bRrjx49Ho9Fcsv3Mzs4mIiICSZLOGKgCynJycKpWqwkKCiIiIoJZs2YxZcoU1Go1drud6upqenp6qKyspKmpieLiYlJTU4mKiiIqKor4+HgSExMxGAxigJZwSl6vl4GBARobG2lpaaGzs5P29nZqampoa2vD4XDg9XoZN24cycnJJCQkkJqaSl5eHnFxcSPRgmpEiWBVEARBGBE6nY74+HjmzZvH+++/z6ZNm/jOd76DSqXC4XDg9/uxWCzK8k6nk6NHjwLwla98hQcffPCUA42ys7MJBALU1tayadMmNmzYQGRkJB0dHSQnJ1/SU5VZWVlERESgUqnOKlg9UWhoKFarlezsbL797W8za9YsdDodAwMDHDt2jE2bNlFYWEhZWRnbt29Hq9WSmJhIeno6Y8eOZfLkycTFxREeHo5Wq0Wr1RIcHExwcLAyKvtKCjSEi8vv9+Pz+fD5fHg8HjweDw6Hg46ODvbu3UtpaSnV1dVUV1djt9uJjIwkPT2dKVOmsHDhQsaMGYPZbL6iM/ciWBUEQRBGjFqt5qtf/SpFRUVUVlaydetWQkNDSUlJUeo1h9PR0UFzczPJycnD3h4ZGcl1113HrFmzKC0tZfPmzWzZsoXnn3+eoKAgli9fzrhx4y7WbilUKhUxMTEkJCQQExNDS0vLWd8PjmdZr7vuOu677z6WLFmC2WxWltHr9YwbN07ZD7fbTXV1Ndu2bePQoUMcOXKE999/n4GBASIiIkhMTGTChAlMnDiRrKws0tPTSU9PH/mdFq5ora2tSqu0L774gkOHDlFXV0dTUxMajYa8vDxGjx7NokWLmDt3LllZWRiNxqvqgEb0WRUEQRBGjCRJuFwu/vznP7N27VrKy8uZOXMmDz74IDNnzhwycKiuro6//OUv/Pa3v2XZsmUsW7aMm2++GYPBgNPppKWlhd27d/PVr36VX/3qV1RUVPDKK68QFBSkZI4OHTrEc889xzPPPMN999130fbL4XBgs9koLS2lsLCQTZs2ceDAAfr6+s7YjSAoKIjg4GDi4uL41re+xS233EJ8fDwmk+m0/SkDgQAejwe73Y7L5cLpdNLX18fhw4epra2lsbFRCUr8fj8ajYaIiAhGjx5NcnIySUlJpKenk5aWRnh4OGFhYSP9tAiXiNvtpr+/X2kr1dTURE1NDaWlpXR2duJyuQgEAiQmJpKQkEBcXBxpaWmMHz+e8PBwQkND0ev1SvmIWq2+UoNV0WdVEARBuLhUKhV6vZ6cnByqqqpYv349ixYtwmAwnDTC3Ww2M2fOHP7whz9QUlKC3++nrq6OkJAQ3G43Wq2WrKwsVCoVjY2NHDx4kP379zN+/HjMZrMy/3hmZuaQDOWFkAei9PT00N3dTUtLC01NTXR0dNDZ2UlzczNOp5NAIEBsbCy9vb1nXKc8unrixIksXryYrKyssypZkPu4ys+bvG1Wq5VRo0bR1tZGY2MjTU1NdHd309vbS39/P21tbXR3d3PkyBGMRiPR0dGEh4djNpuxWCxYrVaMRiNGoxGLxYLRaBzpxu7COQoEAvh8Pnp7e+nt7cVut9PX10dXVxc9PT309/fT09NDa2sr/f39SrspnU5HRkYGRqMRq9VKcnIysbGxREdHEx8fT1paGhqN5oo+xX82RLAqCIIgjLisrCzy8/OVU9sREREnLRMWFkZ+fj6xsbE0NzdTU1PDqlWrlDZPkyZN4o477lDqWO12O/v37ycyMlLJSqrVar7yla+QmZl5XtspB4CDa/76+vqorq6msrKSoqIiDh48SGtrKw6HA4PBQEFBgTLoqbS09JTrDgoKwmg0MmHCBO644w4WLlx4QY3UVSoVwcHBQ073y9vf1tZGQ0MDR44coaSkhJqaGqqqqmhpacHn8yk9XFNTU0lNTSU2NlbJvskDtkJCQggKClKeV/l3+bp8Ec5NIBBQBtjJbc78fr8SoPr9fmVAVF1dHfX19bS1tdHc3ExVVRWNjY309PTgdDoJDg4mMjKSuLg40tPTGTdu3JBMutyj91ojygAEQRCEESdJEocPH+aZZ57hZz/7Genp6SdlP+Xvn8bGRrZu3crGjRt54403uP/++1mxYgVTp05VWlI9/PDDFBYWsnr1at577z3efPNNWlpaeOyxx3j44YcJDQ09ry9pSZKor6+ntLSUI0eOsG/fPrZs2UJfXx8qlYqEhARmzZpFfn4+ubm5TJs2DaPRiMfj4cCBA8yZM0cZ5S+TBzolJibygx/8gLvvvlvJAl+MQOJU3+N+v5/e3l7Kysqor6+noaGBsrIyysrKaGxspL29HbfbjUqlwmAwYLValWA2JiaGmJgYUlNTSU5OJiIigvDwcMLDw6/JYOhikrOknZ2d1NXV0dDQQGtrKy0tLdTU1NDQ0EBXVxc2m41AIEBwcDAWi4WkpCRycnLIyMggKSmJtLQ0cnJyzjiY8Cr//wy78SJYFQRBEEacy+WirKyMV155hRdeeEHp13giSZLwer309/crpz0jIiKwWCyEhYUpLalqa2sZGBggMzMTm81GV1cXXq+X6OhooqOjzyrj5/P56O/v59ChQ1RUVFBVVcWhQ4doamoiEAgQEhJCamoqGRkZZGZmkpSURFxcHAaDQcn2GgwG1Go1kiRx9OhR7rzzTsrLy/F6vcDxQCEnJ4dbbrmFBQsWkJeXh9VqvSyn2OWsq8vlUkaJu1wu5eJ0OmltbaW5uRmbzUZvby9dXV10dXXR19en/E80Gg1BQUFoNBr0er2S2TaZTERGRhIREaGUFQye0SssLEz5u7yOq5WcFZVP0Q8MDOBwOOjr66Ovr08JSNvb2+np6aGvr4/e3l7a29sZGBjA6/UqmfvQ0FAMBgNms5nIyEgsFotSopGYmEhMTIyS6dbr9cp0vlqtFr1er2S6r1GiZlUQBEG4NOTejqNHjyY0NPSUgYpKpUKr1RIREUFERARpaWnDLpeamqr8LgeopyMHwT09PTQ0NNDe3q7UnNbU1NDb24vD4cDlcpGcnExMTAyxsbGkpqaSmZlJQkIC0dHRmEymU253aGgoY8aMoaqqCp/Ph8FgYPz48SxevJjZs2eTn58/YrW050MuG5CzuoPJp6BtNhvd3d1K0NXd3U1XV5cSqNpsNvr7+3G5XLjdbrxeL36/n76+PhwOB52dnUPKBYKCgggNDUWn0ynBVmhoqBJshYSEoNFohrTaOvH3wX8DhpQknGi4097DTdQw+BQ8/F+7pxN/yhf5utfrVepD5ZZQ8nMhB/xer1dZv/z8yKf55aBUDjQNBgPh4eHKtLxysCoH+vJ1MQnEUCJYFQRBEC6YnK0bGBgAoLS0lO7ubmVGq4ttcLDg8XiGjJ7esWMHR44cobq6mvr6enQ6ndIEferUqUydOpXs7Gzi4+PPKfsXGhrKhAkT2LRpEzqdjrS0NO69917uuusuDAbDFX06Vq1Wo9VqldP9w5G7EbS0tGCz2ejp6aGzs5PW1lZ6enro6emho6ODtrY2ent76evro6enR6nRlOs0A4EAGo0GnU6nDIyTs4VyUKvT6U76mxywydt64qlvlUpFWFjYSc+z0+lUglKZnNWUOzd4PB4GBgaUIFQOPk/MQssdGAYGBnC73cooepVKpfwuZ4/Dw8OJjY0lJiaG8PBwrFYrsbGxWK1W5Xp8fLxSGyycPVEGIAiCIFyw9957j48//pg33ngDgJtvvplly5bx9a9/Hbj4dXQdHR00Njayf/9+tm/fTlFREbW1tTgcDpKTk8nJySEnJ4cpU6Ywe/ZspaH+idt2Lttpt9spLi7mueeeY+nSpdxyyy1D+pxeycHq2ThdfDDcbXKAKo9el0ew9/T0YLfb6e/vp7u7m/7+fiX4GxgYGHIZXKIgH/jIwePgFmFyiUN/f/9J2xEaGjpkggT4v/Zher0eOD6BhVzaIXdckNs7ydeHy4RaLBbCw8MxGAxKgCpPH3yi0/3/r/bXxkUkygAEQRCEi8NkMhEeHg7A448/zo033kh+fv6IfylLkoTT6aSmpobq6moOHz7MwYMHaWtro7+/H0mSSE9PZ/bs2dx9993KSOmwsDAl+BjcSeBC6PV68vLy+P/+v/8Pq9WK1Wq9poKQcw225ABWDu4Gn2KXLz6fT8m2nuoyOCMLDMnUnvh4J2ZQgSHZz1P9Xc6MDncZfNuJJQ5yEDz4+pmeK+HCicyqIAiCcMGOHTtGSUkJmzZt4rbbbiM3N/eUp5fPlvz91N3dTWdnJ93d3UqLJrn3aU9PDw6HA41GQ1hYGHFxcWRlZZGYmKj0mbRYLBel/k8OoCRJUgIcQRAuiOgGIAiCIFy55MBPHjkt1xkeOXKEI0eOUF5eTnFxMaWlpUiShMlkYvTo0cyaNYu8vDwyMzPJzs6+kmfnEQTh9ESwKgiCIFy5vF4vvb297Nixg0OHDnHkyBF27dpFa2sroaGhREdHU1BQwKxZs5g0aRJZWVnKZAMnBqciWBWEq5IIVgVBEIQrQyAQwOl00tDQQFFREZWVldTW1lJaWkpfX9+QJvUFBQUkJycTFRWl1JzKfSjlPqyCIFwTRLAqCFcDl8vFzp07aWpqwuPxXO7NEa5hVquVgoICUlJSLnq9pdwqqKuri8bGRjo6Oujs7KShoYGGhgalf6XH41Ha/8TFxZGZmUlubi7R0dFXfDuoLzu5jMPtdp/Uo9Tr9eL1enG5XMP2Nx3upzx4yuv1Dju4Su4Ve6LB08OeSO4AoVarh+3tOnjglHzRarVoNBql/daJ10XZyYgS3QAE4WrgcDh49dVX2bJlizJwRBBG2sDAAGPGjOHZZ58lMTFxxIJVOagY3PdUPr3f1tZGSUkJ27dv5/DhwzQ1NeFyuYiJiSEnJ4dx48YxdepUZsyYgclkOu2UksLFN/h/eeIo/cG/D77u8/mUWZ4cDge9vb3KIDiHw0FPT4/SokrubTq4x6nb7R5ygf/rmzo4YJVfX3J7q8F0Ot1JrasApScqoASack9X+TK4z6vc2spoNBIWFobBYFAa+BsMBqWtlTw71+AuAsP9rlKplG0Swe25EcGqIFyhxowZQ15eHrNnz77cmyJcg15++eVhe1SOhEAgQH19PRUVFezatYuNGzdSUVGBzWZDq9UyefJkpk+fTk5ODhMnTiQ/Px+NRqN8gYsv8iuDnCVtbGxU5q5va2ujpaWF7u5upTvD4EkD+vr6htxfptPpCAsLw2q1EhYWNqSnqTxZQFxcnHJdvg2G75sKx7Ojw80w5nA48Pl8JwW3Pp8Ph8MBMGTqWTl4HhgYwG6309nZqUyn6nQ6hwTfg8mv07CwMEwmE1arlbi4OKKiorBarURFRSnXLRYLUVFRJCQknHZGN2F4IlgVhCuU1WolJyeHBQsWXO5NEa5B77777ogEq5Ik0dHRQUVFBdXV1Rw9epTi4mJsNhter5eQkBCl72lSUhKjRo0iMjKSsLAwJVslT5kpgtRLR56dqrW1ldbWViX4bG1tpa2tTbne2dk5ZJpVON5TV846mkwmEhMTlcxkWFgYRqNRmWZVzkJqtVqCg4PRaDRK/9IT+5vKf5MzkXJAd6q+qcCws6OdmIWF/ytRGNy79cSLfD/5d/mnXJbg9XqVaWadTidOp5P+/n6cTqcykUF/fz92u52Ojg4KCwvp7+/H7/ejVqvRaDRotVplYoHIyEji4uKIjo7GYrEQERFBXFwcMTEx6PX6SzLz29VCPBOCcIXSarUYjUaioqIu96YI1yCdTnfO95G/8Ds6OpRMW3t7OzU1NTQ2NtLZ2alMTRkREUF4eDiJiYmMGTOGlJQUEhISSE5OHpJFFS4eOSA98bS8fL2vr0+pHT7x1LxcZyoHV/Ip87CwMCwWi3KwIU8GIU+6YDQaCQ0NVTKkchZ1JCZhuJzkINbhcAyZaWtw4Gq325XZuxwOh3Jdrsd2u93Y7XbcbjddXV309/fT2tqq1MHq9XoiIyOJjIxUZswKDw/HZDIpB3bh4eHodLovXWZWBKuCIAjCsOTgVO556vP5cLlcHDhwgJKSEo4ePUpRURE1NTWEhIQQFRXF+PHjWbJkCfn5+aSlpREbG3u5d+OadmJd6eBZorxeLzabjWPHjtHU1ERTUxPV1dVUVVUp2dRAIKCcTrdarSQlJZGUlER0dDTR0dEkJyeTmJioZAONRuOX8kBDrjc1mUzDlh6cjsPhoL+/XxlcKE9o0draSl1dHTU1NUom2+fzoVKpMBgMxMbGkpWVRUpKCvHx8SQlJTF69GgiIiKUgHXwRT4YuBb/PyJYFQRBEIbl9Xrp6elh165dfPHFFxQXF7N79256e3uJiIggKSmJqVOn8uMf/5iCggISEhLQarXX9Jfmlcjr9dLc3ExtbS2HDh3i4MGDVFZWUldXR2trK5IkKfWio0aNYtKkSUpQmpmZSUpKCqGhoUrGe/D/bbjrwrkJDQ0lNDSUmJgYxowZA/zfQYb80+/343K5qK2tpba2lsbGRmprazl69CgfffSRMlubSqXCarUSHx9PVlYWkyZNIjc3l/T0dFJTU5U632uNCFYFQRAEJEnC4XDQ2NjIoUOHKCsro6amhsrKSvr7+5W+p3fccQcFBQUkJSURERGB0WjEarViMBiUNj7CxSEPEpL/N3V1dVRWVlJVVUVvby8DAwOoVCpiY2NJT09n8uTJSkbOYrFgMpmUwEk+TS//fqpWT8KFO5tgX26XlZmZSUJCglKOIZcdyCUc1dXVSk1xZ2cnq1at4u2330aj0WA0GsnMzCQ1NZWkpCQyMjIYO3bsNVE2IIJVQRCELymPx0N7e7uSLZX7ntbX1yu1dlqtlpycHOLj40lISCArK4v8/HwiIyMJDQ293LtwTfP7/fT399Pe3o7NZlNqhCsqKpRMmzzAJywsjJiYGKKjo0lNTSU2Npbo6GgSExOJi4tTJlEQrkxymYFcC3wir9fLwMAAo0aNorW1lY6ODpqbm6mpqaGjo4O+vj6cTid1dXV0dnZSXl5OYWEhhw8fVgZwWSwWYmNjMRqNV13duAhWBUEQvqQcDgclJSXs2rWL0tJSmpubcTgcJCUlMW7cOGbOnMmkSZOYPn06YWFhYnTyRTT4dLA8+tzpdFJVVcXu3bs5evQo5eXllJSU4PP5iIqKIjExkfHjxzNu3DgyMjJITU0lJSXlMu+JcDHIkxDk5uaSm5s75La2tjYaGxs5duwYhYWFHDlyhKNHj9LQ0IBKpSIrK0uZXGPWrFlkZmZiMpmUdZ6u28KVQsxgJQhXmK6uLh555BEAbrzxRh544IHLu0HCNemuu+7i7bffVjKnEyZMYOzYsUybNo38/HyCg4NP+hK7kr/MrnaSJOF0Ojl8+DBr165l3759HDlyhObmZsxmM5mZmeTk5DB16lRmzZpFbGwsJpNJ+f8MvghfLvJAyMEXl8tFV1cX27Zt48CBAxw9epTDhw/T0dGB1WolIyODadOmcdNNNzFu3DgsFsuVUgYiZrASBEEQ/k9KSgp33XUXS5cuxWq1YjQaMZvN6HQ6EfhcAi6Xi+rqavbu3UtZWRnV1dXU1dWh0WiIioripptuYvTo0WRlZSkj8cPDw5X2RSLTLcDJg+AApa/rwoULmTp1Kna7nd7eXiorK5XuEF988QXbtm0jNjaW5ORkcnNzmTFjBhkZGVdc1wfxShcEQfiSMhgMjB07lilTpoipTS8BuQ1YU1MTbW1tNDc3c/jwYcrKyujs7MThcGA0Ghk9ejSZmZmkp6eTm5tLUlKSMnGCIJwNuZ1VXFwccXFxSp9Y+XVVU1NDeXk5x44dw+l0UlNTQ29vL62trYwePZqEhATi4uJITk5Gq9Ve9gFaIlgVBEH4klKpVFfdQIurzeAZkeQG/Rs3bmTbtm0cPHiQmpoaJat13XXXMX/+fCZMmHBekzYIwqnIA7iSk5NJTk4GjneXaGxsZM+ePRQVFbF3715Wr15NREQE2dnZzJkzh9tuu42oqChlRi25NOhSE8GqIAiCIFxEVVVVbNmyha1bt7Ju3Tr8fj95eXksXryYW2+9lfz8fEJCQpRpRsXBg3ApqFQqkpKSSEhI4NZbb1Xaoq1Zs4bdu3fz4osv8sILLzBjxgxmzpzJ0qVLKSgoEMGqIAiCcG1wOp10d3dTWVnJzp07Wbp0KRkZGRw5coQXX3yR2NhYxo0bx/Lly4mMjFQGd0iSxMDAADU1NRQVFfHPf/4TgPnz5zNt2jTy8vIwmUxXymCQU+ru7qauro41a9bw+eefMzAwgE6n45577mHGjBkkJCQQHR1NbGwsYWFhV/z+CNceOeiUT/EHBQWRkZHBnXfeyYIFC2hubmbHjh2Ul5ezfv161q9fz+zZs5k/fz7Z2dkkJCRcsm0VwaogCIIw4ioqKigpKWH//v3s3buX9PR0oqOjCQoKQqVScejQIdra2khNTWXu3LlDamb37dtHZ2cnnZ2dGAwG3G43dXV1qFQq+vv7mT17NiEhIZe9ju5Efr8fh8NBeXm5MpBl9+7dDAwMkJiYSFZWFtOnT2fq1KlKr0tBuFKoVCqMRiNGo5GUlBTy8vIwm83ExMQo9a179+7F5XJRWVlJTk4OY8aMUdpgXUwiWBUEQRBG3I4dO3j33Xf5/PPPATh69Cjjx49n8uTJvPTSS/y///f/2Lp1K1arlRkzZqDVapEkCa/Xy6uvvkpWVhbTpk3je9/7Hl1dXbzxxhts27aNLVu2MHr0aOLi4q6IYHVwf1Sn00llZSUvv/wy+/bto62tjezsbL7zne8wbdo0MjIyxCl+4aoQFBREaGgo119/Pddffz1tbW0cOHCAl156ibfeeguNRkN+fj7f//73ycnJwWKxKN0pLsZrXJx3EARBEEbcww8/zDPPPKNcT01NJTs7G4D4+HhCQ0Npa2vjH//4B263G4C+vj62b9/O+vXrsVgsTJs2DYCIiAhuuukmFi9ezLp169i0aRNNTU2XfqdOwePxsHHjRp555hmuv/56Dh48yI033shbb73Fli1buPPOO0lPT7/cmykI5y06OprFixfz8ccf88EHHygHkTfddBOPPfYY//znP3E4HBft8UVmVRAEQRhxwcHBQ/qAyoOHBl+H4xlJWW9vL9u3b8ftdhMaGjqk12NkZCRxcXH4/X527NhBdnY2GRkZl2hvhjcwMEBVVRV///vfOXLkCH19fdx5553ccsstpKamEh0drYzqFxlV4WomdxMICgpizJgxREdHM2nSJFavXs3Bgwd57bXX2L59O9/85jfJyckhPDx8RB9fBKuCIAjCFaG/v5/du3fj9XoJDg4eUscqB6+SJFFcXExHR8dl3FLo7OyktraW9evXs3//frRaLWPGjOGmm25izpw5hIaGXtQA1efz4Xa7sdlstLS0EB8ff8kGvPT29tLd3U1XVxcNDQ0kJiYSFRVFREQEBoNBBObXOLPZjMlkIjk5GUmSUKlUlJaWUlJSwurVq3E6nUqvVhiZAzURrAqCoBg8Vd9w1Go1QUFBBAcHXxVfSIFAgEAggMfjAY5vf3BwsDLI51zXJUmSMnc7DM02DNdy6MT53iVJUp5DtVo97Ahw+THkbQ8EAkp/w1Pd51rR09PDxo0bleda/iIE0Ol0hISEAFBSUkJnZ+cl3z75/+n1eikqKmLt2rW88sorzJs3jzvvvJOFCxcSGxt7SbbF4XDQ1tbG7t27+fTTT7n11lu58847L+pjyj1jy8rKKCwsZPfu3bz77rusWLGC2bNnM336dMaOHTvs/fx+v3IZ/Pq/HK265PejnNXXaDTKNp2Lwe9v+SKvT36fn+p+Pp8Pr9c77O3yZ5R8/zMtP5herwcufiZfpVIRHBzM9ddfz4QJEzhw4ACvv/46L730EtXV1SxdupTbb7+dkJCQIe/j83XtfuoJgnDOXC4X5eXlREdHExkZedLlhhtu4LXXXlM+pK90jY2NvPXWW8TGxhIZGcmSJUtYvXo1gUDgnNdVV1fHhg0b+MUvfqGsb+LEiTz99NNs3bqV7u7uk+7j8XhobW3lzTffZO7cueTk5PC1r32NVatW0dLSMuzjBAIBPv74Y5566ikWLVpEbGwsTzzxBGvWrKGuru6ct/tqEh4ezuLFi9FoNNjtdnp6ek5aRqVSceeddyr1r5eaz+fjhRde4Mc//jHvvPMOTz75JP/zP//DnXfeSUxMzCXbjm3btvHv//7vPPTQQ7z//vtUV1df9MccGBhg+/bt3HvvvZSVlfHzn/+czs5O7rjjDhobG3n11VeHvV9raysffPABjz76KJGRkXzzm9/kz3/+M/v377/o2zycLVu28N3vflf5XPvd735HcXHxOa9HkiT6+vr45JNPlH2LjIzkxRdfpKio6JT36+np4ZVXXhn2MzYyMpIf/vCHbN68WVm+o6PjtMvLl8zMTLq7u4eU1lwKJpOJOXPm8Mc//pFXX32V2tpa/v3f/52vf/3rdHV1KQf3F0JkVgVBUPT09FBZWYnT6Rw2IDWbzSPaFLq/v5+2tjaOHDnCzJkziYyMHJH1AmzevJmtW7eyceNGfvnLX6JWq6mpqeHFF1/EbrezYMEC4uLizrgev9/P0aNHefXVVzlw4AD9/f08++yzxMfHo9Fo6O/v5xe/+AX33nsvM2fOJCcnBzie+SorK+P9999n3bp1LFq0iOTkZAYGBnjppZeoqKhg9uzZzJkzR3msxsZGNm/ezKuvvkprays9PT3Y7XbWrFnDgQMHmDp1Kt/97nfJzMy8JjOsZrOZuXPn8vnnnyunmi0WCwBut1vJ+E+fPv2s/ncjKRAI0NzczGuvvcYnn3xCRkYGd9xxB0uXLiU8PPySn22YNGkSXq9X6UN7KQ4gXS4X27dvp6enh5CQEGJiYggJCWHKlClkZmYqA+VkgUCAgYEBGhoaSEpK4tFHH2X+/Pn09fVRUlLCunXr+OlPf0pubi6hoaEXffudTicffvghb775JpGRkfz6178GYPXq1VRUVLB06VKWL19+Vv9Ht9tNV1cXf/3rX9m6dSsVFRUMDAwA8Prrr1NbW8vy5cu54YYbTlpffX09TU1NyvInys7OHjIgr7m5mZaWllMuD5CRkcHixYsvS89e+QyTXq9n3rx5qFQqdu3axbZt23j++ed54IEHyMnJuaApnUWwKgiCoqenh/r6ehYvXjzs6blZs2aRlpZ2QY8RCATo7e2lvb0du92Ow+HA4XCMWDZAkiT6+/vZvn07GzZs4PDhw7z55ptotVo++ugjXnrpJeLj48nMzMRsNp/xSzIQCLB//362bdtGSUkJoaGh3HTTTSQnJ+P1ejl69Ch79uwhMjISnU6nBKsdHR0cPnyYdevWcfDgQb73ve8xc+ZMKisrKSwsVE7t5+XlYbVacTgctLa2Ul5eTnh4OBaLBY/HQ0dHByUlJdTV1eF2u5k2bRppaWlXfLDq9XqHnLaUT70GBQXh8/mGZLfdbjd+vx+DwUBBQQFxcXF0dXVx7NgxEhMT0Wq19PT00NfXR0pKCmPHjiUiIuKS7k9TUxMHDhxg7dq1mM1mpk6dysKFCxkzZswl3Q5ZXFwco0ePvqSP6XK5lL6xGo1GOeUcGxs7bPmD3++nra1NmaM+ISGBnJwcjhw5QlFREdu2bePjjz8mOTn5ogerbrebzs5OPv74Y3bv3s2KFStYvnw5AH/+85+prq5GrVYzb948DAbDGdui9fX1UVNTQ3d3N1arlZycHBITE9mzZw9lZWVIkoRer2fx4sXDBqsajYbFixcPGYQoGzt2LNHR0cp1m82GwWBg/vz5SinMYF1dXaSnpzNr1iy0Wu1l+2xQq9UkJiYyZ84cdDodLS0tbN26lezsbKWmG86vREEEq4JwjZHryrxe70nZFnmE9uCaVJVKhVarRaVS0dbWRmVlJX//+98JDw9X5o0ffDmf7YH/q/n0er2Ul5ezYcMGVCoVOTk5zJo1C7PZfGE7/r8CgQDV1dWsXbuWvXv3YjKZSEhIQKvVYrVa8fl8rFq1iuuvv57Y2NgzBt8+n49PPvmEtrY24P+mKNTr9ej1evLy8lCr1Xz44YeoVCruv/9+4Hhf0d27d3PgwAHgeNYwPj4ev9+PSqVix44d+Hw+pk2bxty5c2lubqa5uRm9Xs/rr7+O2Wymo6ODLVu28I1vfAO3201zc7NSI3ilN5Tv6+vDZrMp1x0OB319fVgsFmw225AsXEdHByaTCYPBwKRJk5g5cyaNjY1s2LCB3NxcIiIiqK6upr29nWXLllFQUIDRaDzpMQfXlMrdB+Qv7vPNesrvp88//5z33nuPY8eO8cYbb1BQUHDJ6lPPh1zzGwgElNOwg+ulVSrVkPfm4JrLwTXSKpUKl8uFJEl0d3ezdu1a4Pj7Qs706XS6YQMk+WBu9uzZyv8rLCyMKVOmsHnzZnp7e3nhhRe44447hpRQyPXdcsZuJDLWvb29lJWV8fbbbwNgMBiUAUAajYZDhw5hs9l4+OGHyc7OJiws7LTra25upqioiFtuuYWJEyfi9XopKSlh3rx5SJJERUUFdrud3/zmN8p95Of7yJEjJCcn8/jjj2OxWJTP1lO9Vp1OJ5MmTeLBBx8kPj7+pNs3btyIx+MhJSXlihhLkJycjMViYfTo0axYsYI333yTzs5OfvjDH57355YIVgXhGuPxeNizZw9PPPEEFRUVQ2576KGH+OEPf0hWVpaS2YqMjOTzzz8nJCSEAwcO8Mc//pE///nP3HfffeTm5pKUlMSECROIiYlR2vCcCznTWVJSwtGjRykvL2f27NncfffdREZGKqetRupD1u/3U1hYSH9//2mXq6yspLa29pwzxX6/n507dzJx4kRMJpPyOPPnz2fevHnKcoWFhSfVrYWEhAz5smltbeXTTz/luuuuIzw8nPHjxzNx4kSsVisqlYrY2FiWLVtGcHCwkhnasGHDJa9JOx8/+tGPeP3115XrTz31FGvWrOHTTz9l4sSJtLa2Asefz+nTp/Pqq69y3333YTab+Y//+A8KCwvZtm0bY8aMUQ4Cbr75ZlasWHHaQMLn83HfffeRkZHBpEmTmDNnjhIQnK81a9bw9ttvc+zYMf70pz9x3XXXXZLT1hfC6XQqr6/f/OY3REREMGnSJO655x6mTJmCXq8nEAhgt9tZv3497777LmvWrAHgW9/6FgsXLmTq1KlYrVamT59ORUXFkGz4b37zG/7whz8AcODAgWFriOXM4qmylAaDgTvuuOOkNkd//etfaW9vx2w2873vfW9Eno/29nZKSkpOu4zX6+XgwYMkJSWdMVjNy8tjzJgxQwZSZWdnKwcBixYt4sknnxwSxEuSRFdXFx999BGFhYU888wzfPe73yUtLY2UlBQyMzOJj48/6bEWL14McMqM6eHDh7Fardxwww2n3eZLyWAwMGbMGF577TV++tOfsnr1aiIiInjooYeGzSafiQhWBeEaExwczJgxY/j2t7/N6tWrWb16NQBf+9rXmDNnDgaDgZ/97GccOnSIqKgoJk+eTGRkJMeOHaOlpUU5ffvZZ5+xe/duQkNDsVgsfOtb36KgoIDU1NSz2o6enh46Oztpamri8OHDpKWlkZuby+TJk4mOjiY8PBytVnteH1ynEwgEqKqqwul0nna5srIyxowZw/XXX3/a5YKDg/nqV7+qZPY8Hg+/+MUvuOeee4iNjaW+vl6Z43727NnK/eTpCQc7sddoe3s7n332Gb/85S8xm80YjUYlcJcvg5+fpKQkli1bdsVnVeF4wLNo0aIhf4uKikKtVvPHP/5R6dAgmzRp0pCeqpMnTyYlJYUpU6YAkJaWRkJCAuHh4WcMPA8dOkRhYSGbN2/m9ddfJz8/n5ycHLKyssjJySEkJOSsgtdAIIDb7eatt97C4/EwZ84cZs2ahV6vv2LLMCRJwm63s337dgoLC4mPj+fFF19kYGCAtrY2fvrTn/LrX/+a7OxsJEnirbfeYtWqVWRnZ/PSSy/h9Xr5xz/+oUx3e9999/Gf//mf9Pf309raymOPPQbAsmXLlO4Dp6ofPvH1KxsYGMDj8aDT6ZgyZcpJgb/8GXQ2o9/PVk9PD7W1taddxuv1sm/fPhYsWEBUVNRplx08Wt/lctHW1sbRo0eZOHEiN9xwA9OmTaOgoGDIffx+P8eOHcNut+N2u/H5fPzrX//CYDAQGhpKdHQ0Tz31FGPGjBly5uBUn5GBQIDW1lZiY2OJiIi4ol6T8v8+Ly+PxYsXs3fvXlauXMmtt96K1Wo95/pVEawKwjUmKCiI6OhoFi5ciNvtpqGhgaKiIqWdSiAQIDc3l7i4OCVrqtfraWxsVE51AyeNPI+Li8Pn82Eymc4qU7Vnzx7a2tqU0/8Wi4XU1NSLPjDG7/dTUlKC3W4/7XLV1dVnNbo+KCiIqVOnMmvWLFQqFY2NjezcuROr1UpUVBRtbW1MnTqVSZMmDcnSyu1nZMN9+TqdTmprawkEAkr932CD226ZTCaysrJYsmTJFTHN6JlMnjyZyZMnD3vbsmXLTnk/lUqFTqcjJiaGmJiYYVshnY58urq9vR1AGVhXWVnJ6NGjaWhoIC4uDovFQnh4uPIlP9zr2ePxUFdXR1FREQUFBcyZM+eSD+w6H21tbRw8eJC9e/fy/e9/nylTptDT08PevXvZvn07R44cISIiApPJRHl5OW1tbSxatIhbb70Vr9fLr3/9azo7O9HpdNx3331KZm/wmZoxY8Zw2223ndf2yV0eYmNjycnJOemMTUpKCmaz+YzZzXPR2dlJaWnpaZfxer0UFxef8UD3RLt27aK8vJyKigrUajXJyckkJCScFIT7/X7Ky8uV8gn5ukyv15OTk4NeryctLQ2DwXDaxw0EAtTX1xMZGXlJO1GcLZVKpWTmu7u7efPNNykvLyc/P18Eq4IgHJeamsry5cuJi4vjq1/9Kn/961/xer1kZ2eze/duvvnNb5KYmKgsf+zYMZqbm4d8iAyue/v73/9Oa2sr0dHRQzKIp/KrX/0Kl8vFPffcw0MPPXRefQzPh9frZe3atWfMypSXl1NZWXnG9anVatLS0nj66afZs2cPr732GuvXr1cy1iEhITzxxBNkZGQM+XKaPHkyLS0tSo2fw+HAbrcP+WKWa/JOxePxYLPZkCSJ0aNHM2vWLGbOnHlVBKtXikAgwOHDhzl8+DBw/OBj1qxZTJs2jUmTJnH99dcrpSiDg1aVSkVfXx8bNmygt7eXCRMmcNNNN13OXTlrR44c4YsvvuDYsWPKlLVWq1UZYb5v3z5SUlIYN24cVquVhQsXMm7cOMxmM4FAALVafVbvjfNVU1ODTqdT+rKeOGjodAcz56u2tnZIO6jhyJnVc5029LnnnmPv3r1K7W9MTAxOpxOtVquUR6hUKnw+H0VFRXg8npM+ZwOBAC6Xi3//93/HYrGwaNEicnNzT/u48oH55MmThy0fuFLMnDmT3t5eVq1axSeffEJMTIzS5eNsiWBVEK5hkZGRyuh0gDfffJOtW7eybdu2IaNNAR577DEeffRRpR6yra2N1tZWqqqqeOedd9i1axe1tbV89tlnXHfddWfMrP7973+nsbGR6upqfvjDH7JgwQLGjBlz1c6RbjabSUtLY9asWeTm5rJq1Sqqq6txuVwsW7aM//7v/2bJkiVK+63rrrtuyICUxsZGGhsbh0wRmp2dzYMPPnjKLEN3dzc7d+7E7/ezYsUKVqxYgclkuvg7e40ZXGsZCATYtWsXu3fvJigoCIPBoNRnjh8/nmnTpikHAz09PXz88cfK//xqee4/++wzysrKqKysHNIOTn4e/v73v5OUlMS8efP40Y9+hCRJOBwOZeBjb2/vRd2+d955h5iYGL75zW9eNc/p6bz33nuUl5ezfft2nnnmGdasWcO6deuIjIzk008/JSsri9DQUMLCwvjVr37Ff/zHfygTJTQ3N1NdXU1RURFbt25l06ZN7Nixg+jo6DMGqz6fj0OHDjFnzhysVusl2ttzp9FoSE9PZ/ny5axZs4abbrrpnDtZiGBVuKqVl5dz4MABPv74Y+VvRqORW265hblz52IwGHA4HGzZsoWPP/6Yvr4+oqKieO655wgPD8fn89Hb28v+/ftZvXo1er2ezMxM5s6dS1ZW1pAs2NatW9mzZw9VVVXceeed5Obm8vnnn7Njxw5+//vfXxGjME+k0WiIjo7mscceY+XKlbS3t+NwOIbdVq1WOyRoCg4OJiIiguTkZNLT01m5ciVHjhxh06ZNPP/882d87NjYWGUkfmpqKh6Ph+LiYrZt20ZeXh6pqakYjcbzGrR1OkFBQUyYMIHS0tLTDrLKzMw8q8FVgUCAtrY2NmzYQF9fH2PHjiUpKYnIyEi2bt3KmjVrGBgY4MCBAyQkJCiDrJKSkli4cCFer5ddu3axadMmamtrueGGG5QDApPJRF5e3rD/j46ODvbu3cuf/vQnnn32WRYtWkRCQsIV+Tq72ng8HmW0ucvlYseOHZSWlmK1WklMTGTSpEmMGTMGu91OeXk5999/P5GRkVfNc19UVERTUxMxMTH89re/HXYZ+XWn1WqVdkvt7e1MmTLlgvphno7L5eLgwYMkJiaSnZ3NqFGjgIs/2xJAdHQ0Y8eOPe0gq+DgYPLz88958JzFYiE3N5fw8HDa2tp45513aG1txWazsXLlSh599FGSk5NRqVRDssiSJKHRaIiIiCAzM5MpU6ZgNpuprKxUzgScSl9fH01NTUyaNAmTyXRFvzZVKhVms5m8vDxef/11bDYbAwMDw5Y+nYoIVoWrms/nw263U11dzaFDh9DpdKSnp7N48WKlTYg8feiuXbsIBALk5+crrZ2am5uVUeEdHR3Kh/TAwAAmk4mYmBjUajXNzc10dXVhs9no7u7G4XBQW1vLjh07WL16Nb///e8v59NwSkFBQYSFhTFv3jzWrFlDe3u7UuckD3A6Fbk1k8ViITk5GZvNhtfrZdu2bWf12CEhIYSEhGCxWIiOjqaqqoqBgQHsdjudnZ14vV7MZrNSO6fVas94erulpeWUH+ITJkwgIiKCoKAg8vPzaWhoOG2wmp6eTkpKyhn3IxAIUFpayqZNmzAajSxYsED5kh0YGFBGUBcVFZGamqoEqyaTidGjR6PX64mIiGDv3r14PB5aWlqQJGnIgcCJI4btdjtlZWVUV1ej1+tZvnw5ycnJZ6xhO1d2u53i4mLCwsJGfKDb5SBnq04cvDXccoOXb2hooK6uDpVKpdRvV1VVoVaraW9vJyQk5KoY1CaTexjHxcVx8803n3JAmM/nUzpM9Pb2EhERQVZW1kUJVt1uNz09PZSWlpKVlUVWVtY5nwo+lT179gz7XtfpdMpZoKioKHJyck4brGo0GsaNG3fOwapGo8FqtWIwGLj55pvZsGEDra2t+Hw+Dh8+fMoaWJVKRVhYGGFhYcTExJCamkpDQwOvvvrqGetme3t7qa6uJicnZ0Rrey+W0NBQEhISsNvt2O12XC6XCFaFL4/c3FzS09MZN24cd955J5GRkSxevJivfOUryjJhYWHceuutbN68mQkTJrBixQqsVitdXV1s2bKFTZs2sWLFCv7nf/6H7u5udu3axT333ENGRgbTpk0jNDSUlStXsnTpUubPn6+0K/rHP/7B0aNHL+Pen5mcPYqKiiIkJAS1Wo3b7eaTTz4hKSkJg8GgHJH7/X7lS3zwiHSZ3D/Q5XKd01G8/IGcn59Pfn4+Pp+PgwcPsm7dOuD4QI2JEycSFxd3xmB1y5YtPPDAA8Pe9tlnnzFv3jyCg4OZNm0aO3bsoLm5+ZTrysnJUYJOOB6Uyhd5NiK5zuz9999ny5YtTJkyRTl9lZ+fP6Sub9u2bURGRvLEE08ofzMajeTl5ZGXl8d3vvMd2tvb2bt3rzJoa/78+UNOh8m1a9XV1axZswatVstbb72lZPUG98UciXZfNTU1/Md//McVnZU5H+fa2kteXj4dvnHjRjZt2oRKpVJmrjrTgL0ridyb1OfzUV9fT0ZGxpBgOxAIoFKpcDqdbNy4kVdeeYW77rqLBx54gLi4uBE/cJEkCZvNRlVVFY2NjSxbtozo6OghNfGDe+Keq8cee2zY6VITEhKUaWjj4uKYNGmS0md1OFqtlunTpysHhfL2+Xy+IT1f5dfFiZ+XwcHBTJ48echB5YkHosPdT37/abVabrvtNsrLy0lKSjrldkqSREdHB0VFRXz7298+p6DvctFoNMrAXIfDgdPpPKeDFRGsCle9kJAQJk6cyA9+8AM++OAD3n33XR5++GFiY2PRaDR4PB6OHTvG3LlzGTVqlPKG+eKLL9i8eTMHDx7kH//4BxqNBoPBoGRltmzZgtlsJiMjg5/97GfMnDmTtLQ0VCoVJpOJ22+/nZaWlos6EOFCdXd3U1tby759+/je977H559/zptvvsnvfvc7Ro8ezdy5c5Xs4vvvv09RUREtLS088sgj5OTkDPnQDQsLU0okLoR8mj4/Px+bzUZDQwOffPIJK1asGDLgazi33XYbS5cuHfY2eVuDgoIYP378GWvhTiwDOHLkCB9//DEvv/wyb7zxBhMmTFAmEXjjjTew2+1KG6XhzJ8//7RtsOrr6/n00095+umnmT17No888ogy+EXW399PTU0Nn3zyCUuWLCE7O3vITE3ymQS5N+vZZIZPJzExkVtuuYWCgoJrZtCW3+/n3/7t387YZ/dEcuDg9/uxWq1MmjSJ8PBwVq1aRW5u7hlbGV1JfvzjH/PWW29RWFjIu+++ywMPPEBMTAxBQUHYbDY+/fRTcnJysFgsPPTQQ7hcLkwmE7GxsRflwMVms1FWVkZVVRVPP/20krmVJwj54IMPKCgo4Oabbz6v9W/evHnYA5TBgWJUVBR5eXmnXY9Go6GgoEDJrHq9Xvbs2cOTTz7JqFGjeOyxx5T37MGDB9m4cSOZmZnMmzdP6Y1sMpmU91JISAj33nuv8tpxuVz87W9/o7i4mLS0NJYsWcL48eOVx5fvP3369JN6zw7mdDrx+/2YTCZMJtMV1bLqVPx+v3LAJ591OxfnHKz29/djs9loaWk517sKwhDjxo075xfscOQj3ry8PHbt2kVpaSnFxcVYLBYlWD106BDp6elER0crb+wDBw5QU1NDc3MzX//615X1yW+odevWkZmZSU5ODjNmzOC3v/0t06dPZ968eYwZMwaz2cwdd9xx2gDmcmpoaGDjxo2sX7+eZ555BpVKpbRqcrvdbNmyhZCQEIxGIxaLhYqKCr744gvKy8tpbm7mvvvuY9KkScpo1mPHjuF2u5k4ceIFfaHJGYjg4GCl357BYDiro+wT62pPtf60tDTmz5+Pz+ejvLyc1tZWNBoNPT09BAcHs2TJEvLz84cEIG+99RaffvopHR0dvPDCC7zwwgtMmTIFjUbDI488wqpVq+jr6+Po0aNkZ2fT2tqqNLYHmDhx4kl9FZ1OJ+3t7ZSVlfHJJ59QV1fHwoULueeee5TR17KGhgb27t3La6+9RmNjI9u3b8dsNg/JiMmZnrvuumtEan0NBgO5ubnMnTv3mikD8Pl8Z3Uae3CGTKvVkpqaqvQCLigoID4+no6ODnbt2kVra+s5B78Xm8vlGpLtla+HhYUxadIktm/fzueff867775LYWEher0elUqFx+Ph/vvvJzY2lkAgwOjRoyktLWVgYIDu7m68Xq8y41UgEKCvrw+DwYDdbh9ypsJut9PR0XHaWt5AIEB/fz9vv/02u3fv5tixY2zZsmXI7X19fcyZM2fIe3H9+vX09fURGhrKkiVLzvh5M9xsZicym82MGjWKW265ha1bt+JwOJQ4xuv1kpaWxnXXXUd6erry3rLb7Tz//PNUVFTQ1taGz+dj5cqVqFQqent7KSkp4dNPP8Xv9zN58mSSk5Opra3F5XKRkpLCrFmzmDp1qnIgLQ+I2rNnD3v37mX//v1897vfZeLEiYSHhyuj+5OTk087nXBdXR0DAwPk5OSc98yCl5rD4aCuro7Q0FDCw8PPuXThnD+d5COkHTt2nOtdBWGIUaNGjUiwKktNTVUG7WzZsoVJkyYREhLCwMAA1dXVTJ48eUi2raSkhKamJvx+/0kHX3LdYVhYGHq9nvnz5/OPf/xDqbVpampi9OjRpKamDjkyvhL4/X6amprYtm0ba9euZePGjXzjG99g7NixQ0aMHjhwAJ1Oh1ar5YYbbsBgMCjB0bp167BYLAwMDCjF8X19fej1+tOenjpXOp0OnU43ovO8q9VqLBYLs2fPxuv1EggE2LRpE2q1mrq6OsaOHcuyZctIT08fkjnu6+ujv79fmWVGbn0VHBzMrbfeqkyYUFxcTHNzMw0NDRw7doysrCwlEze400FxcTHt7e243W7q6upoa2sjLCyMJUuWMHv2bKVOV3bw4EHWrFmj1MAeOnRo2P3TarU8+eSTIzKDkkajISoqiuTk5KuqJvNU5Ol8z5QlVqlUGI1G5UAtPj6eUaNGkZ2drRx06HQ6jh07Rnp6ujIHvFxWcyWorKxk9+7dyvWqqip27drFwoULSUpKYty4cRw+fJj29nY++eQT9Ho9VquVhIQEkpKSiIiIwOFwcN111yn7V1NTQ0REBPHx8fh8PqKionA6nZSUlNDa2jqkJ2h1dTXr168nNjaWadOmDRt8eDwejh49yvr16zlw4IByQHai5cuXD+laUF5ersxgtWTJkhF5vkJCQoiNjeWWW26hr68Pr9fL+vXrgeNdU/Lz81myZMmQHtKBQICuri58Ph8ul4vu7u4h6zObzdTU1LB582a6u7sZNWoUAwMDJCUlkZeXx9KlS0lKShrSDs1isWA0GqmpqaGiooLU1FSCg4NJTk5Gp9Nht9tJT08/bWa1qakJSZLIysq6Yl6PZ9LX10d5eTkxMTEnffadFflI/RSXk2zfvl36/ve/LwFScHCwuIjLOV2CgoIktVotAVJVVdVwL7EL8sknn0h33323BEj79++Xuru7pYqKCunRRx+VWltbhyybnZ0tAVJGRoYUCASkQCAw7Do9Ho9UXl4uPfTQQ1Jubq4UFBQkqVQq6cknn5S2bNki+f3+Ed2Hzs5O6bbbbpNuu+026bXXXjvn+/f390u//e1vpbS0tCHP/f79+6U//vGPJ/1PYmNjpZqaGqmlpUXau3ev9Nvf/lbSaDRScHCwNHbsWOnxxx+XVq5cKdXW1kpOp3NE9/Viq62tlV577TUpJCRECg4OlmbNmiW99957ks/nO2nZoqIi6bnnnpNMJpP02WefSZ2dnUNuLy4ult544w3poYceknQ6nRQcHCyNHz9eeumll6SjR49K/f39Q5Zfvny5lJmZKT322GPSW2+9Je3bt09qamo65bZ+5StfOav3kMFgOOmxzsedd94pjR07Vnr33Xclj8dzweu7EgQCAcntdkvR0dESMOSiVqul4OBgSaPRSHq9XiooKJAefPBBadWqVVJvb++wr4mGhgbpqaeekkaNGiX9+c9/lrxe72XYq+H95Cc/Oem1kZeXp9xus9mkQ4cOSc8++6wUFRUlTZs2TfrpT38q1dXVKf9vl8slFRUVSenp6dL1118v/fa3v5Xa2tqk3/72t9L3v/996Sc/+YlUV1cnZWVlnfY1efTo0WG3saOjQ/r2t78tRUVFnfb+n3zyidTe3q7c7+WXX5aeffZZ6Te/+c0pP5cvxLp166QHH3xQefwXXnhBOnDgwEnLuVwuafPmzVJubq506623Sjt27FC2x2azSYWFhdJ3vvMdKT4+XgoODpbMZrP005/+VPrwww+lsrKyk9bn9/ultrY26b333pO+9rWvKY9/6623Sr/4xS+k999/X7LZbGd8nf3tb3+TPvjggxF5Li6FQCAgbdy4UZoyZYp01113SYWFhadbfNh4VCX9b6HvqWLZE/+wY8cOPvroI/74xz9SUVFxVRT2CleO0tJStmzZwrPPPktVVdWI99xsbGxkz549fPWrX+X3v/89KSkpaLVa9Ho9kydPHpKNGj16NOXl5SQnJ7Np0yaSk5OHPdqTBjVsbmlp4dixY6xatYq1a9cyYcIE7rjjDu67774RO8Lt6urikUceAeDGG2885YCiU5EkCbfbjcvlGtJf0mQy4fP5ThplqlarMRqNyinRwcsEBQWh0WgICgoiODh4RAb1XEp+vx+fz6c0+Q4ODlZGdp+4Hz6fD6/Xi8vlwmAwnDSJgc/nU9bndruB48+PTqdTZgcbvM7+/n78fj8ajUaZzep0g0jsdvsZR7HD8ezM2Uw5eiZ33XUXR44c4dlnn+WWW265pjKrSUlJtLe3D5m2Njs7m/z8fBYuXMjcuXMxm83Ka0He9xOfU/mszOLFi5k/fz5f/epXr5iJAZxOJy6Xa8jfgoKClNISebCg/JqW38tarVZ5rUr/2w3BbrejVquVMhuPx6N8duh0Ovr7+4d8lpzIbDYPm80OBALK1KqnizUMBoPy+QLHSxqk/81i63S6Ef/M8Xq9eDwe5X2s1+uV9+lg0v+WlTidTuW9Hvy/gy/l59ftditncOTWVPJUrCc+J3Lg5fP58Hg8yvtd7oQif86e6dS+2+1W2o5dDQ4ePMiHH37I7373O1atWsWkSZNOdzZt2B2/oCIli8UyIqeihC8Pk8l0UV8zERERynzvq1atIj4+ntzcXB5++OGT3ti33norn332mVIfuGTJEqX1kd1up7CwUJmW8Qc/+AE//elPiY6OVk4djh07lsLCQlatWsV999130fbpXMkfmMOVWMjB2qmo1eozLnO2/uM//oNjx46d9fJPP/30OTeKPhP5C+Bs6juD/7eO9lQH4PLtOp3urOqtzqaObrCRbkv1ZaZSqYiJiVFOh48bN474+HhiYmJISEggPj5eOcA4Ha1WS1JSEnPmzKG1tZWPP/6YadOmYbFYLvuAtNDQ0NN+lsoHRqd7Tcv14yeecj7x/X+6U9Kno1arldZM52Iky8OGIx+gnGm7VCoVGo1mSG25bPDze7bkIPRsau9PZ6R7U18skiThdDpZu3YthYWFTJgwgby8vPP6rLv6K+oFYRC9Xk9sbCyzZ8/mD3/4A729vSQnJw87CGDu3LlK3dCWLVvQ6/VERkYSFBSk1C8ajUaCg4P5xz/+wa233srEiROJjIxk2rRpREVF0dfXx6ZNm4D/m+9a/nK42rKQI02emeVsnZglEoTzoVKpmDhxIkajkaysLKZNm8a0adMwGo3nHCAEBQVhNBqZN28eH374IYcOHWLfvn3Mnj2b0NDQq2IUtiBcDnIGubi4mP3799PT08OCBQuIjo4+r7M4IlgVrjmhoaHccMMNvPbaayQnJzNx4sRhl5s4cSJbtmzB6XTyz3/+kzfeeAM4/mWnVqtZv3496enpSlG9PN/81KlTiY2NRa/XM336dBISEgAoLCxU2t5kZmYOacQ9uMB+sGs5mH355Zcv9yYIX0JBQUH87W9/w2g0jliz9LvvvpvW1lbeffddnnnmGVauXKkMiLmW38OCcD7k8pL+/n5+8YtfUF9fz7Rp0/jud7973l1HRLAqXHPkvqvz589X2tAMx2Kx8Pjjj3PjjTfywQcf8D//8z8kJiZy/fXX8+CDD5KVlUVISIgSrMoTDbz22mu89NJLPProoyxcuJB7770XgNWrV7Nt2zYqKyuVaU7j4+NJSUlh1KhRpKWlkZiYSEJCAomJieJLThAuApVKRXR09Ii+v0JCQnjggQcYM2YMjz/+OA8//DAPPvggN9100xU9J7sgXA4ul4u9e/fy8ssvc/DgQX70ox+xePHiC5qxTASrwjVHHmRhtVqJi4s7ZaNrtVqN1WolJyeHkJAQCgoKCAsLIzExkYyMDGXGp6ioKP7yl7+Ql5dHIBAgPDycuLg4xo4dS0pKilJfZTabCQ4OVgYjOBwOOjo6qK6upri4GKPRSGhoKAaDgfDwcCIiIoiIiCAqKorY2FhiYmKwWCynHcggCMKpneoMxkisMyIigoKCAv7t3/6NlStX8sEHH1BfX8/9999PfHz8NdGnVhAuRCAQoLu7m/fff59du3ZRWVnJo48+yuzZs0lKSrqgshnx7hKuOT6fj5aWFqKjo4mJiTntQBetVovValX6ZA7HaDTyzW9+U7keHx8/7LJRUVGYzWbly21gYICBgQE6Ozupq6sbMs2eRqMhJiaGuLg4EhMTSU5OJiEhgZiYGHQ6Hd3d3QQFBdHb20tfX58yWl8QhMsjJCSEhIQEvvKVr1BSUkJ5eTkbNmwgJSWFOXPmEBkZqTTeF4QvG4/HQ29vL7t372bdunU0NDSQnJzMV77yFdLS0s55wOmJRLAqXPUG92KD4y2ANm3axLhx44ZMp3mxJSQkYLFYTmrRItfvDOb1emlsbKSpqYnCwkIAZdYYOB5EG41G4uPjmThxIjNnzrz4OyAIwmlptVqSk5P53e9+x2uvvca7777Lt7/9bX7xi1+wYMECcnJyTqpTF4RrlfxdJ0kS7e3t7Ny5k3/7t3/DarUyf/58nnrqKeLj40fkvSCCVeGq193dzV/+8hdee+01bDYbZrOZu+++m2XLll3S+bxTUlKIiIg4p9P48ojJwVQqFXq9ntTUVMaNG3fFTucqCF9WJpOJb33rWyxYsIBXXnmFX/3qV7z11ltMnTqVJ598kuTkZFEWIFzzJEmira2N//mf/2HLli0cOHCA+fPn8/DDDzN+/Pjzbnk2HPFuEq56ct+67u5u4uLimD17NjfeeCPh4eEXtRei3Ey7t7eXjo4Ojh49OmSu+HOhUqkwGAzKlI/V1dWYzWYMBsNV0/hZEL4M5F6ZYWFhpKamct999xETE8Phw4cpKSnh5z//OTfeeCPjxo0jNTX1qumJKQhny+fz0dbWRklJCR9++CGVlZVotVoeeOABbrjhBsaOHTvi378iWBWuelqtlvT0dCZPnkxOTg433HADkydPvuBTD/LsL/IMMC6XC4/Ho1x3OBz09PTQ2dlJY2MjLS0ttLS0oFarzzq7KjedjouLIyMjg2nTpjF+/HjeeecdgGtiViFBuBapVCqMRiPTp08nMjKSzz77jK1bt7Jr1y4AbDYb+fn5JCUlER0dPWSGJkG42sjlbJ2dncpMjjt27GDr1q0kJyczfvx4br31ViZMmHBRXuciWBWuegaDgeXLl7N8+fJzut9w0/8N/pvL5aKzs5Pm5mYqKyupqqqiubmZ5uZmKioqaGhowO12I0kSQUFBJCYm4vV6CQsLo7+//4yPL8+sFBkZyaOPPso999xDbGwsvb29SrAqCMKVTaVSMWrUKDIyMvjqV7/Km2++ycsvv8zq1auJjIzknnvu4cEHHyQqKmrIzEyiplW40g3+PvT5fPT397Ny5UrefvttKisr8fv9PPLII9xzzz1kZWVd1LOAIlgVvrS8Xi82m4329nZqa2upqalRsqM1NTXU1dUxMDCA3+9HpVJhtVqJjIwkOjqaG264gaSkJGJjY4mLiyMtLY3Q0FBWr17NX//6V/bu3TvsY8pT9KnVar7yla+wYMEC5s6dS3R09JBJBARBuLqo1Wqio6N55JFHWLFiBbt27WLbtm38+c9/5p///CdTp05l9uzZ3HTTTcTExFz26VoF4UwkScJms7FlyxZ27NjBhg0b6OrqYtasWdx+++0sWrSIpKQk9Hr9Ra/RFsGqcM1yuVzY7Xb6+vro6Oigo6ODnp4eenp66OjooLu7G4fDgdPppL+/n6CgIGX+99jYWFJSUpSeqLGxsZhMJmWe6/DwcMxmM2FhYRgMBqXHqtyK6osvvhjSAUAuDYiJiSE7O5vFixczceJE0tPTSUxMRKPRiEyLIFzFVCoVQUFBhIWFkZSUxKxZs0hKSmLs2LHs3r2brq4u3nvvPfbs2cP48ePJzs4mIyOD9PR0pQ5WEC4nOZPa3NxMXV0dpaWlfPHFFzQ1NeHxeBg1ahTTp08nNzeXtLQ0UlNTL1m7NhGsClctv9+P1+vF4/HgdruVelL5us1mw2az0d3dTUNDA+3t7dhsNnp6emhvb6evr0+pGQ0LCyMuLo7IyEilSX9iYiLR0dFER0eTnJyMRqM5Y+bTaDQSGxurBKeSJKFWq7FYLERGRpKTk8O0adN44IEHsFqtYsSwIFxj5AGfaWlpynTPERER7Nq1i8OHD7N9+3aampqoqakhNzcXh8OBxWLBYDAQGho6pFRAEC4Fj8eDy+Wiv78fm83GkSNHKCsr4+DBg5SVlWGxWMjIyGDmzJncfvvtmM3mSz6eQnxTCles4WpKB9/W399Pa2srjY2NVFVV0dDQQHNzs3K9sbERr9erfHkkJSWRkJBAQkICkydPJjMzU7mekZGBWq2+4CNEs9lMUlISgUAAtVqNJEnodDqmTZvGk08+SW5uLjExMRf0GIIgXB2CgoIwGo3cc8893HHHHdhsNjZv3sybb77JBx98wJ/+9Cfi4uK45ZZblMGV2dnZQ9YhMq7CSDvxu7W1tZWysjJ27NjBe++9R11dHXq9njFjxvDUU09x/fXXk5CQcFk7W4hgVbgiycFoe3s7ra2tw9aUdnZ24vV6kSSJkJAQYmNjiYqKIiEhgWnTpik1pXJdqUajISgoSLkEBwcrv49UrajFYiE1NRW/3092djbz58/npptuYsqUKRgMBpFJFYQvqaCgIKxWK8uWLWPBggW0tbVRXV3NunXr2Lp1K++//z6BQIDU1FTmz5/PhAkTyM3NJTMz83JvunCNaWlpoaKigv3797Nu3Tqqq6txuVyEhYVx3XXX8fTTTzN69GjS0tLQ6/XodLrLXmMtvjmFy8Zut9PU1KS0f+rs7KS/v5++vj7a29vp6emhv78fp9OJy+VS6kl1Oh3Z2dlMnDgRk8mE2WwmOjpaqSmV60zDw8MxGAzKBS5+lsJkMpGVlcUDDzzA9OnTGTNmDFlZWVitVpEhEYQvMbmmVa/XKxeLxYLZbGbSpEm0trbS1tZGc3MzBw4c4MiRIxiNRpKSkpTpmOWzQGIwpnA2JEnC6/XS2tpKe3s7TU1NVFVVUVVVRUdHBzabjeDgYGbMmEF8fDwZGRlkZmaSlpZGREQEJpPpcu+CQgSrwmVTWFiITqejvb2d5uZmpY60r6+PlpYW3G630t7JbDaTnJxMdHQ0UVFRxMTEkJSURExMjHIdLv8ps9DQUFJSUrj33nuVbOrl3iZBEK48oaGhhIaGEhcXx3XXXUdXVxeNjY3s3r2brVu3Ul9fT3d3N1988QUpKSmkpKSQnp5Od3c30dHRSn1raGgoer1+RM8QCVcfSZIIBAI4nU4GBgZwu904nU5sNhuVlZU0NDRQV1fHsWPH6OjoQK1WExUVxYQJE5g0aRLZ2dmMGjWKoKCgK/I766oJVk9Xv3iiK+GJPt32nu/2nWqdJ67vXJ6rM23T2T7m+XjggQeA46fH5IxkQkICo0eP5sYbbyQrK4vExETi4uIu6bSpF0Kr1RIREcH8+fMv96YIgnAViYiIICIigvz8fB5++GEaGxupqKhg+/btbN++ndWrV9Pc3AzA2LFjyc3NJScnhwkTJlBQUIDZbD5tTeGV8L0ojIzhvpd9Ph8Oh4OioiIOHTrE0aNHOXTokNKZJjw8nIyMDK6//nqmTp3K6NGjGTVq1IiM1bgUrppgFY7PAf/DH/6QdevWnXRbUFAQ/+///T+WLFlyxdT4rFu3jtdee41du3ahVqt54YUXmDlzJikpKee8LkmSWL9+Pa+//jrbtm1DrVbz/PPPM2vWLNLS0oYs6/V6aW9v5+abb6azs/O063366ad54IEHCAsLG/Z2ue3TkSNHKCwsZPfu3cq+Xag///nP5OTkEBkZSVhY2JCaUvmUv3xdEAThy0Q+SJ84cSKPPvoodrudrq4uCgsLKSwspLq6mn/+85/87ne/Q6fTER0dTUJCAnl5eeTl5ZGamkpycjKJiYmXe1eEETZ4sppDhw5RVlZGY2MjjY2NDAwMYDKZiImJITMzkzvuuINx48YRGxtLeHg4Op0OrVZ7Vt1triRXTbAqSRJ1dXXU19fT2Nh40u3BwcEkJycrtYkj8Xitra3K9Jnjx48/6/vabDZ2797N3//+dyIiIrjnnnuQJIm3336blpYWZs6cybRp0856fT09PezevZvXXnsNs9msrO+9996jpaWFWbNmMWPGDGV5j8dDQ0MDDQ0NdHV1nXK9CxYsUAYeDbf/hYWF7Nixg6qqKnJyckhLS8NisRAaGnrW2346+fn5ZGdnExoaelFnvhAEQbhayFku+YBdbmVltVqJiorCbDaTlZVFZ2cnXV1dyvfUwMAAXq+XiooKjhw5QmhoKCaTicjISKX+UO4ZHRsbi8ViwWQyic/eK5Df78flctHb20tzczPd3d309fVhs9loamqis7OTnp4eent78fl8ypTdaWlpSttFuQ2jPNA4LCzsso7mv1BXVbBaWVmJ2WwmNzf3pCOC4OBgcnNzMZvNF/Q4brdb6TfW0tJCfX09Op3urINVj8dDc3MzH3zwAZ988gk///nPueOOOwgEAkyePBmbzYbH42HcuHGEhIScMf0uF0fL6/vxj3/MvffeiyRJTJ06lc7OTjweDwUFBcr6vF4vLS0tpKenKz0/B/P7/djtdhYvXkxmZuZJH1Zer5euri42bdrEtm3bsNlsTJgwgQkTJhAZGTliRddRUVGEh4ePyLoEQRCuZcHBwRiNRoxGI6NGjQKOn/rt7e2lvLycxsZGmpqaOHr0KEeOHFFGeKvVaiIjI7FYLFitVhITE0lPTyc+Pp7o6GiMRiNarZbg4GA0Gg0ajUa5Lv8NRBnBSJBP3/t8viF9wr1eLz6fT7kuB6rt7e1UVFQoE9p0dnZSU1OD2+1GrVYTGhpKcnIyo0aNIjk5mbS0NEaNGkViYiJ6vf4y7+3IuuTB6vnUUwIEAgF27drFY489xoQJE046bT1SdaANDQ0cPnyY9evXK6O5k5KSznp9bW1t7N+/n7/+9a/A8TokuZWRWq1m+/bt9PX1sXTpUnJzc8/Yykhe31/+8hfg+NF1amoqgUCAoKAgdu7cSU9PDzfffDM5OTloNBp8Ph9dXV28+eabxMbGYjQah6yzu7ub9evXk5mZSVxc3EnPR2dnJ3/84x/5/e9/z0MPPcS3vvWtk3r/CYIgCJdXcHAwERERQ86sySPA5VmIysvLKS0tpb6+nrKyMj7++GPsdjuSJCn9p1NSUpSMa2pqKqmpqcTExBATE0N8fDxBQUHn/N39ZQpuz+W5kSSJ7u5uOjo6aG5upqamhoaGBqUTRFVVFU1NTTidTiRJQq/Xk5aWRlJSEqmpqSxatIjMzEySk5Mv6QxSl9slD1bLy8vZtGkTv/rVr4b8XafTUVhYyA9+8AM+/fRT5e///u//zsKFC5XT6CtXriQzM5MVK1aQkZFBRkaGMvPQ+XC5XHR3d1NaWsqOHTuUKcSeeuopIiIilCPMs1VfX09lZeVpl3E4HBQXF5OdnX3GdctF9qfjdDopLi5m1KhRaDQarFYrd955JyEhIcPWezocDvbu3cv8+fOxWq1DbistLWXLli28/PLL/Od//idz5swhPT39tI8vCIIgXDk0Gg2JiYnExsYyYcIEJXPn9/uVs39yz+rGxkZaWlro7u6mpqaGDz74AKfTCaCUIchlA+Hh4URGRhIdHa20B4yKilLKE+RSgy+b/v5+pe1ia2srXV1d9Pb20tPTQ2trK93d3fT29tLd3U1zc7NSshEIBNDr9URHR2OxWIiIiODmm28mLi6OmJgYEhMTSU5OPinTrdFolDEdXxaXPFiNiIhgzJgx3HDDDfz973/H5/ORkJDA7NmzCQoKYvLkybhcLurr65XMJkBdXR19fX04nU7cbjeSJCmjJ0eNGsWKFSvIyso6q3+e1+vFbrcPKUgODw9nxowZREdHK29GrVZ7zkcscuHz6cjB6q233joi65OD1eXLlystTE7MpsrsdjsOh4O4uLghwawkSTidTrZv386qVavo7e3FbrezevVqPB4PY8eOZezYsUo/U0EQBOHKc6qaV/i/DKDFYiEpKUmZXlP+vHc4HEqQZbfblQAsEAgQCAQYGBigvr6e2tpaZV3BwcGEhYUREhKCTqdTWmlptVp0Oh16vV65Tb5dp9MpAdfgn8P9TX6ME1tzqVQqVCoVOp3upO9pj8ejbPPgfZdPvQNK4O7z+ZSLfH3waXm5NFD+OTAwoFx3u90MDAworaJcLhd2u115DHmkvdxjNzo6mpiYGKX3t9FoVGqHQ0NDMRgMSu9do9GoBP/yOr7MLnmwGhUVxaRJkzAajXz++ec0NDQoTZHlOszo6Ghqa2u56667MBqNdHR0UFFRobzwenp62LVrl7LO0aNHEx4eTkxMDEaj8YzZyv7+fg4ePEhbWxttbW3odDoyMzPJycm54BmGWlpaqKurO+0ycnsJ+QV9Oq2trWdcn9PppLCw8KzWJxdlD5fV7erqYv/+/Xz++efA8axzUVERZWVllJaWYrPZmDRpEqNGjRLzV18C8qQJpaWll3tThGtQX1/f5d4E4RKTAx6TyXTapEN/fz/d3d1KsqSlpYXe3l56e3vp7Oyko6ND6Ynd39+vZAnlixyw6vV6wsLChlw3Go2EhISg1WqVUelyYDs4qJUvKpVqSCA7eF+CgoKG7WQzMDCAz+cbEqz6/X58Ph9utxtAqQ11u914PB4l8BxcRyoHoA6HQwlK5aBeDlztdjsqlQq1Wq10sgkNDVUC0aioKKxWK2azGYvFQkxMjHI2ODIyUglGhdO7LAOsTCYTEyZM4L//+7/56U9/yvbt23n88cdZtGgR+/fvR6VS8Z3vfEdZ3mazsWPHDvx+/7DrKysr43vf+x6JiYlMnjyZ2NjY0z5+UVERixcv5uc//zkrVqwgJydnxPZt//79bN68+bTL9PX1sXXr1rMKLg8cOMDGjRtPu0x/fz9bt27F4/GccX319fXU19czY8aMk7oAFBYW0traqlz/+te/zsDAAFu3buWhhx7i7bff5rHHHuOb3/wm+fn5Z3ysy2W4+iGVSnVWPWPPpvboUn2w7N69m88++4xnnnnmkjye8OUzduzYy70JwhVIHsiVkpLCxIkTT7lcIBDA5XIpMxH29vbS1dWFzWZTMrM2m03J1HZ1ddHV1UV/f/+QLOXAwMCQwPJyUalUhISEKBc52zk40xkfH094eDhhYWEYjUasVquSDbVarcTFxREWFiam1h5hl+3ZVKvV5OTkKEd3Ho+HuXPn8txzz3HDDTcMWTYrK4tf/vKXPP3000iShNvtprOzk4qKCnbu3ElhYSGHDh1i/fr1Sp3O6UyYMIGtW7fS0NDAmjVr2LRpE7NnzyYvL++af4G1trbS0NCgFM0PdmKwCscz4YP7wpaUlLBx48YrOlgNBALYbDaKioooLi6mu7ub559/nj179vDOO++wYcMGpk+fzmOPPUZqauqQDIPH46G3t5e9e/fywgsv0NbWhsVi4fvf/z7Tp08nKipqxFp3nUpYWBhf//rXWbhwoZIFEISLITIykoKCgquq36Jw5ZCDO3nQbyAQwO/3D8myDr7Ip+Ll3wdfBp9ul393uVzA8bN8J2ZKA4EAPp+PgYGBk7ZLp9MpZRCDtzUoKEj5/NZoNEpQKmd15cFKgy9qtVq5yOuQf5ezqYOXCQ4OFpnSi+CyRmYmk4k5c+bQ19fH9u3baWhowO/3n3SKWavVKkXccLzmND4+nri4OJKSksjJycFsNrN7925uvPHGMz6uwWBg7NixREREEB0djdPppKuri7Vr1xITE0N0dDSxsbHD1sKcSWxsLMnJydTX159ymZCQENLS0s6qvjYmJuas1peamnraQFuSJFpaWggJCSE5OXnYZffu3XtSD1u5V5+soqKC/fv3n3G7Lye73c7777/Ptm3bqK2tRaPR0NjYSGRkJFOmTMHlcrFp0yZyc3OZO3fukLZkR48e5eDBgwQFBbFs2f/f3n3Ht1mei///yJYly7Js2fLee48ktrPIDglhBAK0gZaGA3TQdYCec77lwJf2lFK6e4C2rLaUUqCMMBNCIAnZezpOPOK9t2XJki1b6/n9we95vnZ2iBM75H6/XnollqVHt4alS/d93dd1s5IjfeDAAXp6eigqKqKoqOgL5TOfL7VaTX5+PsnJyZNitkH48tJqtYSHh4tgVfhCRgd0F2t0rujoEzAmzUAmtxc93QqlWq1WgsfRY/Xx8VFKNY5uPjM6Z1aYnCYsWJUTo+fOnYvFYmHHjh0ASh6MyWQ643X9/PyUnYjp6elkZmYiSRK/+c1vsNls57xttVqtXD85OZn+/n4qKys5evQoCQkJDA4O4na7CQsLG5MIfj7kmmdnCy4DAwOZOnXqef1hxMfHk5mZedbjBQQEMG3atHMer66uDr1eT2xs7Bl/39/fP+Y8jUYzpl5bT08PjY2N5xz3RBoeHubIkSN89NFHWK1W4uPj6evrIysri6ioKBITE3nhhRfYuHEjMTExSrDqcDg4cuQIH374IXfeeSff+9730Gg0tLa28stf/pLjx4/jdrtJSUkhMjLyko1frVaLrjOCIFxVTp4JFYTRJvTrtCRJhIaGjik7tWfPHnbs2HFK7uDJSwajxcTEcN9997F48WIiIiIuaAw6nY6YmBgWL17MY489xuzZs7Farfzv//4va9eupby8/KxdoE6WlZXFtGnTznqZ4OBgFi5cOCa4PNP9y8zMPGvOEHyeX7RgwYJzdiLZs2cPHo/njKWoZs+efc6asmlpaZSUlJz1MhMtMjKS559/XnktaLVapQmDwWBQZug//vhjjh07plzv6NGj7Nixg6NHj7Jo0SL0ej06nY74+Hjuv/9+qqur2bZt26SfWRYEQRCEL5MJC1a9Xi/V1dUcPXoUtVrNb3/7WzQaDRs2bOCNN96grKxM2VDV2trKSy+9xA9+8APeffdd6uvrxxxLrVYTFBTEvHnzLnrGKyEhgWuvvZb/+Z//IScnh4aGBvbt23fe14+Pjyc1NfWslwkICCA/P1/5Fmmz2dizZw/FxcX86le/4vjx48plY2NjSUtLO+fxCgoKzvit1OPxYLPZlITw0+2eBFiyZMmYQLarqwu73T7mMjk5OcyfP/+s47lS7dixg5qaGnx8fDCZTEqahp+fH6mpqWg0Gmpqas654U0QBEEQhPEzIXPuDoeDrq4unn32WWbPnk1JSYnSkclut9PQ0MCmTZuIiYkhODiYgYEBjhw5wrZt22hra6O8vJzly5dTWFiIr68vQ0NDtLS0UFBQcEqR+wsll9PQ6/WoVKoLzk00mUxkZmYyd+5c9uzZo+yA9Hg8SJJEeno6M2bMIDExUQmGurq6eOGFFygvL0ej0eB2u5VdunId2blz57J3714GBwfp6+tT8ndSU1OZOXPmmOOdbGRkhIaGBuLj4wkJCTljflFeXh65ubnU1NTQ1NREc3MzRqNRCVhTUlLIzc1VWv192ezfv5/6+nql/qz8vPv4+GAwGPDx8aGzs3PMbOzl4Ha76ejooKysjMHBwct628KXS1RUFPPmzZvoYQhXIXnVUK4AIOenOp3OU+qdjoyMKE0M5LzU8cpZ9fPzUzZGndxa1tfX95QasHKd2NGfCcLld9mDVbvdTkdHBwcOHOAvf/kLGRkZzJ49m4CAAOWF1dPTw6effkpBQQH5+fm4XC4cDgcWi4VPP/2UiooKNBoNkZGR6PV6pcB/QUEBwcHB4zJOlUpFRETEBacVBAcHk5aWxooVK2hpaaGnp4fDhw/j9XoJCQlh9uzZXHvttWMqFlitVj777DNcLhdVVVVj0iKCgoKU47W1tdHb2zvmeCUlJacc72TDw8NUV1eTlZV11mA+NTWVoqIi2tracDgcHDt2TNltGRERwcyZMyksLLyg9rNXkrq6Onp6eoiLi8Pj8ShvTvKOV5VKxeDgID09PZd1XB6Ph8bGRlavXo3ZbEar1V7W2xeubPIObKfTyZQpU0SwKlw0OVCUd+TLO/XlWqajKwCMrg7gcrmwWq0MDAwou/2HhoYYGhoaU9vU4XAoP3s8HkZGRpTjyORjn65iikajwdfXd8wEjhysyjv+/fz8lKYFo+u9BgYGjqkQIHfmGn3M0bv/5fPk0+gGBvLtiyD34l32YHXz5s2sWbOGl156CYB///d/Z926dbz//vvKE2o2m9m4cSMbN27k2Wef5eabb+bJJ59k7ty5PPfccxw8eJDHHnuM2tpaCgsLyc7OZvr06ZOms1JsbCwPPfQQSUlJ/PnPf+aXv/wlKpWKF154gcWLF5+SM5qWlsYrr7zCV77yFe655x7uuuuuMb+Pjo7mRz/6ESkpKTz33HP89re/RaVS8dxzz7Fo0SLS09PPOh6Hw0FZWRnXXXfdGTtbqVQq9Ho9K1asYObMmWzZsoWHH34Yi8VCXFwcv/jFL1i6dCnh4eFjNlx9mcycOZOhoSGGh4fp6+vDZDKdklqRk5PD9ddff1nH5fF46OjoYNu2bajV6jHVCwThfHR1dVFdXa3srhaEiyGnlpnNZjo6Omhra8NsNtPX10dnZyc9PT1K96v+/n76+/ux2WwMDg6esZa1PNOp0+kICgpSGgfIQaR8Gs3Hx4egoKBTgkGz2Yzb7R5zW3K1AbkMltPpxOFwKDVf5eD4TPz9/QkKCiI0NBSj0ajUXZUrFYWGhhIeHk5sbKzSnjYkJERUGBgnlz1YXbhwISUlJfzXf/2Xcp5er0ej0XDo0KFTSvXIrclUKhW33norCxcuVGqvBQcHK10u5HJXk+UbjEqlYsmSJRQXFyt9lqOioggICDhljIGBgcyaNYv9+/cr3+JOZ/HixRQVFSlLwfLM8rnuc0REBA888MA5LysHrImJiaxcuZKFCxfi8XhQq9WEhYWh1+u/1EshCxYsoL29nYMHD9La2kpQUBBqtRqv18vg4CBer5eUlJQJzdldsGABP/vZzybs9oUrj9fr5dNPP+WFF16Y6KEIk5wkSbjdbiXg7OnpUdqS9/T00NvbS1dXF319fcpSvRwU6nQ6AgMDCQ0NVYK6uLg4pbWo3MFKbi8q1ziVN7KOXrof3aZ09Gm00WlaJ5PjiHNt1JZniOV/PR6P0p1KPskzwUNDQwwODipBt9zRqq6ujsOHD2O327Hb7UiSpMyuajQaTCYTYWFhSqnMhIQEpYNVZGQkcXFxSmqCcGaXPViVO2NER0ef8rtz5ULK5aYuVmNjI6+//vp5Xz4hIYFVq1Zd0G2oVCrlvp6L3DLubPf/Qo53Mj8/P2UH/LnIdeg0Gs24PNYTQe5hPfoNa2RkBI1Gg9frHdPpy+VyMTQ0hE6nIzs7m9zcXFpaWti6datSc1eSJKqrq0lLS2PKlClkZWVN1F07a+kxQTgdj8dDaGjoOauFCFcPOeiyWCz09vbS29urlI20Wq2YzWYlKHO5XGOCu5CQEOX1JM82ysGo3GY0ICBAWUqXg1S5paper1dmTdVqNVqtFo1GowSoE2V0cwKXy6Wc5KBUblYwNDSkpCmMjIwo7VcdDgdDQ0NYrdYxAa/X60WlUmE2m+nv76empga1Wq1UpzGZTAQHByudsiIiIpT2rHLnrC/rBNGFuCqLmrW3tytpCOdjxowZFxysChPH6XRSUVGhBKVOp5MTJ06QlZWFxWKhoaFBuWxPTw91dXXk5eWRkpLCtGnTaGtrY//+/eTl5WGxWPD19aWmpobp06czffp0EhISJuquCYIgnBd5tlAOquQNTS6Xi+7ubmX5vrGxkcbGRrq7u+nr66Onp0fp1CTvDYmLi1OWu6Ojo4mIiMBoNBISEqIsefv5+V3RQZU8eysH11+E1+tVgn+LxYLZbKarq4vu7m56e3vp7u6mubmZ7u5uHA6HsnFM/gIQERFBcnKyMvsaFxdHbGzsmA1fo4P7q2k29qoMVmfNmkVdXd1ED0O4RJqbm5k+fbryc0tLC1OmTKGqqoq33nqL//mf/1F+9/e//51t27ZRU1NDQEAAy5YtY9q0aWzcuJF77rmHzs5OwsPDeeaZZ7jvvvvGbH4TBEGYrJxOJwMDAxw9epSDBw9SW1tLQ0MDlZWVdHd34/F48PPzIy4ujuzsbDIzM4mPjyc5OZmUlBSioqK+1HsULgWVSjWm2+aZOJ1O+vv7aW9vp76+nubmZtrb22lsbGTt2rW0trbicDhQqVSEhoaSkZFBYmIi6enpTJ8+nfz8/MvS+nsyuSqD1Sv5259wbomJiactL5WUlMR3v/tdbrvttjHnjy5PptPpiI2N5Stf+Qrz58/H5XKhVquJjo4mICDgvFrkCoIgXC6SJGGz2WhtbaWhoYHjx49TXl5OS0sL3d3dDA4OYjQalVnRkpISkpKSiImJITIykpCQEKVck7yZSZ7JE33uL8z5PlZ+fn6EhYURHBxMamrqmDJeIyMjDAwM0NfXR2trK/X19bS3t9PT08P69et55ZVXlNzfuLg4CgsLycjIICkpidTUVEJDQ7+Uz9lVGawKk8PevXvp6ekhMDCQkJAQjEajksd0MbRaLXl5eaf93bnKkck5u6GhoRdds1cQBGG8SZJEf38/PT09dHd3K7Nz8sYnu92O2+0mMDAQo9GoLOOP3uQjB6nBwcHK5mTh8pGX8NVq9Wlnrp1OJ3a7nZSUFDIyMujp6aG/v5++vj7a29vp6+vD4XDgdrspKyujqqoKg8FAREQE8fHxxMbGEh4eTkREBLGxsROeDzweRLAqTJhPP/2UhIQEwsLCSEhIIDExUUnOH12kWf7/6Qo9C4IgfFnJu9lHRkaUWbeRkRFqa2s5ceIE1dXVlJeXU1VVhcfjwd/fn8TERAoKCkhJSSE5OZmCggKio6MvehJAuHxGT5iM7mApSRJ2u53a2lrq6+uprKyktLSU48eP09/fz8jICDExMeTl5ZGamkpWVhYlJSXo9fpJWznpfIlXrzBhjhw5wtatW+np6cHhcACfV4sICwsjNTWV1NRU4uLiiImJITU1lfT0dIxG41WVpyMIwtXN4/Fw+PBh9u3bx/79+9m+fTtdXV3odDqioqKYM2cOX//618nLyyMtLe0LVYwRrhyBgYFMmTJlTL3t4eFh2traKCsr47PPPmPPnj18/PHHmM1mTCYT06dPZ+rUqcyZM4cFCxZccGfOyUAEq8KEefrpp4mKimJkZIS+vj6am5vp7+/HarXS1dVFZ2cnx48fx2KxMDw8jEajUUqjREVFER8fr5SXSkhIICEhQanxB5Pnm6Pb7aa5uZm4uDhRPkgQhLOSS+UdO3aMY8eOsXPnTlpbW9HpdISFhfHVr36VKVOmkJiYSFRUFIGBgUqt0isxCBHO35meW61WS3x8POHh4UyfPp2hoSHMZjPNzc0cPHiQEydOsGHDBt58802ioqIoLi4mPz+fadOmUVBQcEWkCYhgVZgwSUlJpKSk4PF4sNvtJCcnY7PZsNvtY8p/WK1WpQuKXIJlcHBQWQpRq9UEBAQQFhaG0WhUGiuEhIQoBakNBgNGo5HAwEBlZvZy/XEODQ2xevVqoqKilBykiIiISf/mIAjCpSdJEi6Xi87OTlpbW6mqqqKyspKuri4sFgt+fn7MnDmT2NhY4uLiSE9PJzU1lbCwMFGDUwA+z4GVl/nllvMOh4P4+HgiIiLIzs6mra2NpqYmWltbqauro7Ozk9LSUnJzc0lLSyM+Pp6EhASltfhkI4JVYcL5+voqBZBPR27t19DQQGdnJ52dnTQ0NNDY2EhnZyddXV309vbidruVhPWTawNGRkaSlJREVFQUJpNJ2e06up/z6J7OMD7BrJxj9Pe//52AgADmzJnDjTfeSHFxMQaDQblNQRCuLh6PB7fbzdDQEL29vZSWlrJv3z42btxIT08PJpOJxMREli5dypIlS4iLiyMkJGSihy1cIeTKNrGxsSxYsIChoSG6u7v55JNP2LlzJ9XV1Wzbto2NGzcyZ84cSkpKuOaaa5TKN/Jn5GQxeUYiCGfg4+NDcHDwmByd0TweDwMDA7S0tNDW1kZbWxu1tbXU1tZy8OBB2tvbaW1tRZIkfH19lU0IaWlpp8xWxMTEEB4ePm6buOSOKA0NDXg8Ho4ePcrf//53li5dykMPPUROTs55dxcTBOHLo6+vj5qaGt58803eeecdBgcHCQ4O5sYbb+RrX/samZmZREZGTvQwhS8JnU5HYmIi999/P/fffz9Wq5XGxkb+9a9/sX79elavXo0kSdx6663ceuutFBYWkpycPNHDVohgVZj0zjXD6evrS1BQEBkZGSQnJ4/ZNetyuRgZGcFqtdLR0aH0u+7q6qK/v5+GhgYOHz5MT0+P0h1Ep9MRHh6u5ABFRkaSkJCg/Gwymc47N8xms9He3q70z4bPk+F37NhBRUUFeXl5zJkzh1WrVmE0GifVN1lBOB9er5fVq1ezZs0aKisrlfOXLl3KokWLWLp0KQC7d+9m27ZtrF69GoAHH3yQm266idDQUCwWizKz+PbbbzNr1izmzJnDlClTyM7OVo7Z3d3N+vXr+ctf/sLChQtZsGABU6ZM4atf/So//OEPuf322y/vnb9Acveot99+m127dnHixAnUajUrVqyguLiY3NxcYmJiCA0NRavVTsrlWOHKdPJryWAwkJGRwYMPPsiqVatobGzk0KFDbNy4kZ/85CeEhIQwdepUvv3tb5OUlERgYOAEjfxz4pNRuOKpVCplGf90Neu8Xi8jIyPK5i2LxUJPT4+SD2uxWOju7mZgYIChoSGln3NbWxtdXV1UVVWh0WgwmUwYjUblJPdzlusZBgcHo9frldJbKpUKu91Od3e3EqjK4+nv78dsNjM4OIjVasXhcFBUVERqaioJCQlXfOtC4eoSExNDSkoK7e3tbN26lezsbIxG45iObyEhIYSEhGC1Wpk3bx7R0dH4+voyNDTEZ599htVqRavVMnfuXIKCgjh48CDNzc1KfdCWlhba29sxGo3MmjWL1NRUfH19OXToEEeOHKG3t3cCH4EzkySJwcFBmpqaqK2tpbS0lLKyMpxOJ9nZ2eTm5lJcXExGRgZxcXGiY5RwWfj6+hIQEEBAQABRUVFERkZiMpnQ6/WUl5fT09PDsWPHePnll5k6dSrp6elkZGRgNBon5LNJBKvCl56Pj4/S7zkmJuaU38sbHFpbW+nq6qKjo2NMTqycmO50OvF6vfj6+hIWFqbkxMozr4mJiURGRhIeHk5gYCBqtZr29naamppQqVSnBKwAHR0ddHV1sWPHDm677TauvfZaFixYQEREBAEBAaKmrDDp+fj4MHfuXKVV55EjRygsLGTWrFkUFhYql8vIyMDtdrNt2zZ+9rOfERkZiUqlorOzk9dff53p06czZ84cHnroIfbv388vfvELNm3axIIFCzAajZSWltLa2srNN9/MTTfdhNPppKWlhXfeeQe32z2Bj8DpSZKEJEk4HA5qa2vZsmULW7duZdeuXWRmZnLttdeyePFi5syZo/SlF4SJ4OvrqzTMmT17NmVlZezevZsPP/yQV199lcOHDzNjxgxuuukm8vLy0Ov1l727mQhWBYHP298lJyefkqMjf+A4nU6l7V17ezs1NTU0NDTQ2trKvn37aGxsxO12o1Kp0Gg0xMfHk5KSAqDs6HW5XGMCVvg8aJUDV3kpNSwsjO9///vcddddREVFXZ4HQBAuUmJiIkuWLOGRRx7h8ccfp6SkhKysLCXvsq2tDbvdzi233EJUVBQajYbe3l527drFpk2buOmmm5gzZw4A06dPJzIyktLSUtatW0deXh7r1q2jtLSUr371q6hUKvz9/UlKSuLee+/lf//3fyfyrp+R2Wzmvffe44knnsDpdJKQkMCTTz7JHXfcgcFgEF9GhUkpPz+f/Px8vvnNb7Jt2zb++te/8vbbb/PnP/+Z733ve9xzzz1kZGTg5+d32cYkglXhqne2b4fyjKhWqyUlJYX4+HicTifDw8NKTqzT6WRoaIiOjg56e3uVNog9PT00NzfT2tqKx+M5JVA9mcfjweFw0NnZybPPPsumTZuU3L1zXVcQJppKpcJgMDB9+nTUajW1tbVUVFQowapczaOgoECZlenv7+fTTz/F5XLxi1/8ghdeeEE5XmNjI1arlZdffpkHH3yQqVOn0tbWxrJly3jssceUzkwmk4mNGzcSGxs7UXd9DEmSaGtrY/369Xz22WccOnSIJUuWMG/ePKZNm6bURr0SalsKVyf5dalWq5k+fTqJiYlUVVWxZcsWPvjgA44dO8bs2bNZuXIlGRkZl+VLlwhWBeEc5CU6OZXgZJIk4fV6MZvNWK1WrFarspFr+/btNDQ0KLOn5yLP4ra2tmKz2RgYGKC9vR2DwcDg4OB43zVBGFc6nY6UlBRyc3NpaGhg+/btzJ49G0DJE4+KilI+DG02G4cPH8btdpOSkjJmM9WMGTOAz//+tFotOTk5tLe38/rrr/Pee+9x6NAhkpKSKCkpIS8v77LO8pzJyMgIzc3NfPDBBxw+fJiOjg5mzpzJjTfeyJQpU0hKShKl6oQrhkqlIigoCIPBQFBQEAEBAQwNDVFTU8P+/ftxOp185StfITU19ZJ3ThPBqiBcJJVKha+vr1LTVSYHnlu2bLngmVFJkrBarRw4cIBDhw4RFxeH1WrF5XJht9vR6/XKbQvCZKHVaomLi2Px4sW8//77WK1Wvve976FSqbDZbLhcLkJDQ5XLDw0Ncfz4cQDuvPNO7rvvvjMGczk5OUiSRGNjI5999hl2u52IiAhWrVpFbGwsRqNxQgNWp9NJT08PW7Zs4fnnnycoKIipU6fy4x//mLS0tMsyNvmLs8vlUtKO5I5+l5pcN3ZkZITh4WF8fX3RaDT4+/tf9vxGYXypVCplP0ZGRgYvvvgi27Zt4y9/+QtarZZbb72V1NRU/P39lcuPNxGsCsIlIkkSFouFlpaWL3R9X19fJEnC4/HQ1NQEwIEDB/jzn//Mf/zHf0yKmSRBOJmPjw933HEHZWVl1NbWsnnzZgICAkhKSiIiIuKM1+vp6aG9vZ34+PjT/j4sLIz58+czd+5cKioq2Lx5M1u3buXnP/85KpWKFStWUFBQcKnu1jnt2rWLNWvW8Mc//pF77rmHr33ta8yfP/+ylqPzeDyYzWb27NnDvn37sFgsPPfcc5fltjs7O6moqOD999/nrbfeIjk5mcWLF/P1r3+dzMxMJZARrlwqlYq4uDh+/vOfU1FRwTvvvMMTTzxBaWkpt9xyC9/4xjcu2ZcSEawKwiUit4t1uVyn/b2csybPhgBoNBoCAwOVVrSRkZGEhIRgNpt5++23KSgoYNWqVZftA3BkZIT29nbuuuuu06Yy5Obmcv3113PbbbdNis0iHR0dHDx4kF//+td4PB7y8/P52te+xoIFCy54fDabjY6ODo4ePcrTTz9NVlYWWVlZFBUVMX/+/FNmAPv7+9m2bRu//vWvz3pcrVbLe++9R3BwMGq1WvlSU15ezt69e3nvvfeYOXMm06ZNIzc3l6lTp17w4zDRUlNTWbhwISMjI/z0pz9l1qxZ3H333adsYIyPj+fHP/4xf/zjHzl27Bi7du3ipptuQq/XMzQ0RGdnJ/v27eO2227jqaeeora2lmeeeYaUlBTCw8NZuHAht956K7/+9a9JTU2dkGBVkiT+9re/sX79eurr63nooYe49957iY+Pv+wzihaLhb/85S+89957dHV1XbY83uPHj7N69Wq2bdvGr371K+6++266u7tpbm7mxRdf5Cc/+QnR0dFjriPn+re2ttLS0kJ9fT0Wi4VHH3103B+zvXv38umnn7J+/XoAvve97zFjxgyysrLG5fiffvopW7duVTYEnuu9pqqqihMnTtDU1MQPf/jD015+ZGSEzs5Ovva1r532vTc7O5sbb7zxsr73jn5eUlJSuPfee9Fqtaxbt46XXnqJ5uZmHn744UvyuhfBqiBcIr29vdhstjEpAHJN2KCgIEwmEwaDAb1eT1BQkFK31WAwEB0dTXR0NCEhIQQEBHD8+HH8/f0JDAy8rBtJBgcHaWlpYd++fad9wwwKCkKn043bG9Pw8LASJKakpFxQIeoTJ05w6NAhNm/erMzOORwO3nnnHQICAsjOzj5jS9/RJElieHiYQ4cOcfToUQ4cOEBSUhJ6vZ62tjbq6+sJCAggKysLo9GoXM9sNlNXV8e+ffvOeOyQkBAKCgqULyryprotW7awZ88e5X77+vqye/duJd95ypQpV0yuo0qlIiAggJycHGpra9m4cSPz589XahCPFhwczMKFC3n++ecpLy/Hx8eHzs5O/P39GRkZwdfXl7i4OFQqFQ0NDezbt4/Dhw+Tn5+PyWRSAv64uDglNeZyGhoa4sSJE3zyySfY7Xby8/O56aabSEtLm5CZRLmqSVtbG93d3Zc8j1BWWVlJWVkZjY2NSi3Orq4uQkNDlZzjk3V1dVFbW0t3dzfbtm2jrq5u3AMcp9PJ0aNH+eCDD+jo6CApKQmAbdu2YbFYGB4eprCw8AvfrsfjobW1lQ0bNrBlyxba2tpOm/I1MjKCzWajp6eHHTt20NjYSEtLC1arlR/84AenPfbQ0NBZ33sDAgImbLZa/huPi4tj6dKl9PX1UV5ezqZNm1iyZAlZWVnn9V57IUSwKgiXSG9vLw6HQ2kS4Ovri5+fHwEBASQnJ5OZman0bk5PTyclJQWj0XjKB7o8wzQR3a0sFgv19fVj8gxHS05OvuiZP0mScLvdOJ1OLBYLra2tHDhwgNDQ0PMKVuVUic2bN7N27Vp27NjB/v370Wg0rF27locffhiNRsN3vvMdAgMDzxn0SZJEX18fH330EZs2baKmpoaysjI6OjpYv349zz33HBqNhm9961tKPV34fBnbarUSGhp62pkOl8tFeno6K1euVMbhcDjo7u7mH//4B/v27aOgoICXXnqJiooKnnjiCSUIyszMvOLq7mZkZDBlyhS0Wi1TpkzBZDKdcpnAwECmTJlCeHg4jY2NVFdX869//QutVotWq6W4uJhXXnlFeYxtNhs7d+5UGgz4+vqiVqtZuXIl6enpZx2P2+3G6/UqXxjh4nLrPB4PfX19vP/+++zdu5ebb76ZO+64gwULFnzhY16s4OBgvv71r/PTn/6U7u7uy3a7R44coampCV9fXyVAjYmJISYmRilHdrLW1lb27t0LoMwEJyQkjNuYvF4vNpuNN998k1dffZUbb7yRJ598EoBbbrmF0tJS+vv7yc3N/UIzgV6vF4fDwc6dO/nwww+pq6s7Y5pLf38/TU1NHD58mB/84AdKQGsymc64n8FqtVJXV3fGyyQnJzNt2rQJzQX29fWlqKgIrVbL2rVreeqpp3j33Xf5t3/7NwICAsY1VU0Eq4Jwifj5+TF79mwSExPJysoiLS2NqKgo5c189JvMZN180NbWxvHjx6mqqhoTmMnGo5i5x+Ph8OHDfPLJJ8DnqQU33XTTmO5HZ+P1eqmoqOCf//wne/fuJTg4mJSUFDQaDRERETidTv74xz+SlZWFXq8nMTHxrMdzu91s2LCBTz/9lOPHjyuz2QaDgePHj+NyufjTn/6k1MDNz88HPv9wKSoqUjbEnfy47Ny5k4GBARITE5WAubu7m08++YQNGzYwMjKCTqcjLi4O+Lwd4u7du3nmmWeYP38+xcXFZ835nGySkpKYOXMmS5cuZfbs2acdu1qtJjIyks2bN7Nt2zY+++wzXn31Ve644w5uvfVWZsyYMabWcEREBPfeey/vvfce//rXv+jo6ODf//3f+c53vnPKl7yTVVdX09bWhk6no6SkBI1Gc1H3r6Wlhe3bt/PrX/+aRx55hOXLl1NUVHRRx7xSffDBB1RWViq1pc/HzJkzlYoPr7/+Ol1dXeM6pr6+PkpLS3n66afxer3K+wJ8nopz+PBh2traWLFiBRkZGed8/ZzMbDZTVlbGv/3bv+HxeM562bVr19Lc3MzQ0NB5H7+9vZ2ysjIqKiowGAyX5L13vOTm5ipfMB999FEMBgM33ngj06ZNG7fbEMGqIFwiubm5pKam4nQ6lbJXfn5+SqByud5oXC4XZWVl/OEPf6C5uXnM72699VbuvfdebrnlFuXbe3BwMH/729/QaDRUVFTwxhtvsHPnTpYvX05qaipRUVFkZWURGhr6hT7w5faTtbW1SqewrKwsbrjhBoxGIyEhIQQGBp73sT0eD5WVldjtduU8+Y1cfowlSaKpqYnW1tZzBqsej4fdu3fT398/5vyQkBBl2V+SJI4ePUpiYqISrE6fPh2v16vMmp78/DY2NuLxeFi0aJHyO7PZzO7du8d0YJJ33spLfB6Ph23btpGYmHhFBasjIyN4PB4iIiKIj48/7TK9nLMdFRXFsmXLmDlzJt/5zneIiIjAZDIRGBioPFY//vGPGRwcxGQycdtttzFnzhycTicxMTH4+/uf8++pqamJDRs28NlnnxEfH09+fj55eXnMmDGD5OTk8165kHPMN2/ezAcffMC0adO4/fbbSU5OnjTBw5l4PB4GBgbYvn07W7du5fjx40ybNo3rr7+ejIwMpcPf0NAQdXV1HD58mL/+9a8AFBYWMmfOHJYtW0ZQUBCvvvoqf/vb34DPX9vw+ZfbuXPnArBq1Sruv//+M45l9N/n+TxuZWVlSorNbbfddtqZ+tHMZjPV1dVjltBPvh2Xy0VFRQUJCQkXFKwODAzQ1NREaWnpeZUlXL58OSMjI5jNZp5++ulzVoex2WxUVFTw5ptvKnnc8ntvdnb2F37vvVTkjVe33norW7ZsYcuWLTidTnJyctBqtePyd/GFg1W5hIjY4SdciLa2Nsxm80QP47IICgq6bGVjzkbOLzIajRw7dkwpFVRSUoKfnx8+Pj6Eh4fT1taGXq8nISEBPz8/+vr6lI5d7e3tqNVqYmNjMZlMyk7fpKSkM6YInMzhcDA4OEh/fz/Nzc14vV58fHyIjIwkKiqKhIQEDAbDBb8Je71empqacDgcZ71cc3MzbW1t53W8srIyBgYGxpzv5+c3JqCpqKggIyND+TkkJOS0x5MkiYGBAXx8fNBqtWOCtoGBAcrKyk75QNVqtcqSv9frZc+ePdxwww3nHPtk0tXVhdlsJiMjQ2nPeDry/ZXbPY5+TEdLS0tT/i/ndF8IOeXi+PHjNDY20tHRQX19PbW1tWRmZhIdHU1ERARxcXFK0f4zaWtro7y8nJaWFpYvX05iYuIF5VdPBJfLhdVqZfv27fT29qLX60lOTsbHx4d169ZhtVqZP3++0tp27969HD16lISEBDweDx0dHaxbt46QkBDmzJmDyWQiMzMT+HyDlcPhQKPRKOed78rI+RoYGFC+bJ9p0+rJl29tbT3rZeRgdf78+Rc0lpqaGiwWy2nbd5+OvDpwvgFxb28vHR0dynuvnLttMplISkpS3nvPFbBfTjqdjtjYWK6//npee+01Kisrqa6uJi8vb2KDVfnb/mSK7oXJr7W1ldra2okexlVFrVaTnZ3ND3/4Q0JDQ2lsbMRut3PTTTcxc+ZMdDodjzzyCPv37yc+Pp6FCxcSGBjIsWPHxizN7dq1a8xx/+d//ofrrruO4uLi88r5amtro7W1lebmZsrKyli0aBEFBQWn7BC/UF6vl+rq6nMusZ04cYK6urrzOt6JEyfGNGHwer2nzIYcO3ZsTAB1JnJHo/Dw8FO+vNjtdqqrq5Vjy7N2ox9Lr9fLjh076OnpOedtTaSRkRGcTidOpxP4fNNNT08PM2fOnJB869ORayLbbDaOHj1KWVkZq1evJj4+npKSEmbMmMGiRYtISkpS6oNqNJpTllxLS0upqalBkiS+8Y1vEBAQMOlnVR0OB42NjfzlL3/hzjvv5K677iIxMZGDBw9y1113YbFYSE5OJjg4mA0bNrBhwwbMZjM7duzA6XTy1FNP8Ze//AU/Pz8KCwtZvnw5y5cvB2D37t1YrVbCw8P529/+dmnqbKrVSlOW8zl+f38/9fX1Z72My+XiwIED3H333ec1BkmScDgclJaWEhsby4IFC5TVgfHU3NxMZ2en8vPu3bvH/P6xxx7j+uuvp6SkZFLVsFWr1Xz1q19lw4YNdHR0sG3bNnJycsYl1/4Lv4O4XC5++MMfXvQAhKuPaB06MbKzs/nOd75DQUEBd955J48//jhNTU385Cc/4aOPPuKb3/zmmFzLgwcPUllZecbjPfHEExw6dIiHH36Ya6655py3/61vfYvh4WFWrVrFr3/963FrN+lyuXjttdcYGRk56+UOHTp0zk048PkM6je+8Q3Wrl1LS0uLsuHqiy7Bezwe9u3bR15e3ikpCHFxcdx1113885//VDaZ9fX1jfus1OWwbt061qxZw2uvvQbATTfdxPLly8+4wWaiyK+50bPZLS0ttLe3s3btWrxeLykpKVxzzTXMmTOH66+/nsjIyDEb89atW4fX62XmzJnn9ZqaDJqamtixYwcbNmzgj3/8ozLuOXPmoNPpqK6uZsuWLeTn5xMcHMzMmTPR6/XKa9FgMGCz2XjllVd4+OGHL3hm+2LNmDGD6dOnA+cXrFZUVLB69eqzXmZkZISNGzeesopyJoODg/zud7/j9ttvJykp6YJyUC/EwYMHqaioOOPvf/nLX3Lo0CEeeeQRrrnmmkkTrKpUKsLCwpg3bx67du3i/fffP2sqyIW44GA1Pz+f6OhobrzxxnEZgHD1utxvdlc7lUpFaGio8iHl9XpZt24dbW1tPPnkk5hMpjHfgO+44w6WLFmCxWIBPp+p6Ovro7W1lY0bN3Ls2DHa2trYuXMns2fPPucb5k9/+lM6OzsZGRnhj3/8I9OnTyc5OfmiS3GNrlN7Ppc9F19fX6677jr27t1LS0sLXq+XxsbGU5Z5b7jhBpYtW3Zet1lTU8PUqVPHlLqCzzcMXXfddbz++uu43W6GhoZoaGgYk1rh6+vLvffeS2pq6nndx4liMBgIDg7G6/Xy/e9/X2kxOlk+SM/F4/EoG2VaW1v59NNP2bdvHy+99BI5OTnk5uaSk5PD9OnTKS8vJyYmhvz8/Cvm/tXV1bFr1y4kSWLVqlVjWke3trbS3d2NSqXigQce4Ktf/SojIyNIkkR3dzf79++nvLwcmLjJhi+yoeh8xnq+96e1tZUTJ04wd+5cYmNjldajl8LKlStZvHjxKe+9bW1tyntve3s7O3bsUNoZTwby85OZmamU8XM4HPj6+l506b0LDlaDg4MJDg6e9G+cgiCcyt/fn4iICObNm8fhw4fp6uri0KFDREVFnVJmJCEhYUwpGavVisVioaurS6n/arVa2bt373m94RcVFWE2m2lvb6eyshKbzUZ9fT2tra1ER0cTGhqqLL1eCB8fH+Li4mhvbz/r7GpYWNh55Xj5+vqSm5urpEP09vayZs0apYC5LCsr64z5lTJ5U0VoaCh6vf6UtKmgoCDy8/O5/vrraWpqwsfHhw8++ACtVqvM9vj4+DB9+vTzzg0+G7PZzNatWy/6OKdTV1eH0+kkKSmJoKAgBgYGOHHiBCdOnLgkt3ehqqqqzjtf3uFwMDIyQldXF16vl/b2dhoaGqiqqqKmpoampibCw8PRarW43e7TbqibbNrb25WAMzY2dkxKilx/NDIyEvh8xl/OWT98+PAVuRoWGBhIdHQ0HR0dZ7yMj48PMTEx55XOWFtby/vvv09kZCT19fVoNBoGBgbGPDbDw8P885//5MYbb7yozZDx8fFjOrlZrVasVitdXV1KeUOLxcK+ffsm5XMTGRmJ0WhkYGAAu92OTqe7/MGqIAhXLrVajclk4vvf/z6PPvqo0rTAbrefM5CTv6gmJiYyffp0goODWbNmDXv27DmvN0z5+klJSeTn53P8+HElCX/OnDlkZWUpO8B9fX3PO03A19eX4uJitm7deta8zszMzPP6ku3j40Nqairf+c53mD17Nlu3buXll19maGhImUnRaDQkJiYqZabOxGazUVtbS0lJyWk3YOn1ejIzM3nsscfYtm0be/bs4a9//StGo5Hu7m58fHzw9/cnMzNzXAq8t7S0XPL2m8XFxdTW1k663PS+vr4LKo80era+ubmZlpYWtm7dqjRzSE9PZ2BgAJfLdUU0bOjo6KCqqgqAX/3qV6Snp58xl9DlcnHixAm2b9/OkSNH+NGPfnTem4kmi6ioKKZMmXLWYFWj0VBSUnJeDSWOHz/Os88+e9bLDAwMcN9997F79+5xrdwhv3cmJCQo7yVr1qxhx44dkzJYNRqNBAUF4fF4sFqtGI3Gi97fJIJVQbjK+Pj4EB8fr3SVGR4e5rXXXuM73/nOGfuyn85tt91GUFDQeZUNOpnBYFDqLLrdbo4cOcIbb7wBfF7yq7i4eEz5prNRq9UsWbKEY8eOnTVYLSoquqB2nOnp6aSlpXHzzTfzhz/8gePHj/PBBx+gVqv5xje+wYIFC84Z/Pb09LB161a++93vnrGji0qlYurUqUyZMoUHHngAt9vNnj17WLt2LXFxcSxfvpySkhJReUUYN83NzWddadi4cSOvvPIKBw8eZNOmTcTGxvLpp59e5lFenMTERObPn6+0WD0drVbL0qVLJ0XVlguxYsUKpXvbZGwUcnLXxgmtBiAIwpXHarUqy4F33nknu3fvZsOGDbz66qtMmzYNtVqt5BJ/9tlnVFdX09vby2233UZycvKY0isBAQHKLOuFvBmdXFvRx8eH7OxsIiMjsdlsSnmdJUuWjCkIfya+vr5kZ2efc3YkISFhTH5sbW0tO3bsYPXq1Tz++ONkZmaO+dAa/Sbb2NjIn//8Z44ePco111yj9H4/2/0eHh7G5XIREBBw1s5Zo29ncHCQ5uZmHn30UbxeL0uWLOHuu+9WdqRfrNTUVH7yk59c9HGuRFu2bGHz5s1UV1ef1+VHt8RNTU0lJyeHnJwcSkpK+K//+i9CQkIIDg6+YirizJ49m3vuuYd//OMf7Nq1i4CAAKZOnYpOp8Nms1FTU0N3dzfLli3j2Wef5cCBAwQFBREdHT2pdpyfr9H5+WciV0qRvwi6XC6OHz/OM888Q3x8PCtXrlRKL61YsYLs7Owx1+/v72flypVKcGY0Glm9ejU5OTkXNfYtW7Zw4sQJuru7lRq+o9975RKDM2bMmJTPi9lsxmKxoFarCQ0NHZdOViJYFYSrhMVi4ejRo+zfv5+cnBxSU1Mxm8188sknNDU1ceDAAeUDWKfTUVVVxe7du6mvr8fj8XDdddeRmpqqLG+ZzWb8/Pwuuo6eSqVSlrnsdjvd3d3YbLbzfoPz8fEhISGBrKwsurq6GBgYYGBgAI1Gg8PhwMfHh6ysLFJSUggPD1eut2PHDtasWcNnn31Gfn4+3/zmN8cEq3a7HZvNRm9vLx9//DGtra2Eh4ezcOFCcnJyzllX02w2Mzw8TGxsrFLP9nQkScJisdDf309jYyO7d+/G4XAwa9YsJT1ivGZPAgMDlSYGV5uamppzPmdyGaLAwEAiIyOJiIggOjqajIwMMjIySEtLo6CggMTERFQqFVardVKlAHi9XpxO55hSaMPDw2g0GlJTU5k1a5YSrA4NDVFVVYW/vz92u13JZ4fPl7OHh4cxGAx4PB7sdvuYfPCRkRHcbjcqlYqBgQElZUJucarX68/rcZEkCZfLNWYmzuVy4efnd8p7Snd3t7Kkn5GRMWaD2OkYDAaSkpLIy8ujtrYWp9Op5IF7PB5MJhO5ubkkJiYqXzgcDgfvvPMOGzZsICYmhoCAAPLy8oDP83hPTvvp7OwcU7pKo9GwcOHC0953l8t1yoYsSZKw2WyndKiqqqpi165d1NbW4vV6Wbp0KampqUpOsdlsRq1WK2ObbNrb2zGbzZhMJgICAsblb0QEq4LwJSdJEk6nkxMnTvDOO+/w1ltvceLECXx8fDhy5IhyuS1btuDv709UVBTp6el0dHRQW1vL0aNH2bt3LwMDAyxbtoy5c+eiVqupra3Fz8+PKVOmjNtYAwMDCQwMvKC2jXKwev311+PxeNi0aRN1dXWo1Wp6enoICgpi5cqVFBQUjCkJ9f777/Ppp5/idDr585//zLXXXktGRobyAdLa2kpTUxNHjx7l17/+NbfeeivXXnst1113HSEhIecM0Jubm7HZbOTn5581N3BwcJC6ujrKy8vZvXs3b7/9Ng888AA33njjKbO9wqWhUqnQaDTKDGJKSgqzZs1i1qxZLF26lIiICOUDV5IkcnJyaGpqorKyEo/HM25l2C6W2+2mtbVV6Ygm/5yYmEhsbCwFBQUEBgayY8cOtmzZgsfjwc/PD41Gww9/+EOWLl0KwNSpUzGbzQwODtLR0aHUF9VoNEoHOqvVqtQ4lgNZl8tFTU2N0uDjbKkrIyMjDA0NYbfblfF6PB66uroIDAxEp9ONuX51dbWSivCDH/zgnMFqUFAQaWlp3HHHHbz00ksMDQ1RU1Oj/D43N5dbb71V+eIBn69sPPPMMzgcDnp6ejCbzTz88MMX/dza7XYGBgZoaWkZc77H46G2tpaEhASCgoKU+9TR0UFdXR1Hjx5l37599Pf3c/311zN//nzlvdfX15epU6dOitedTJIkJEmiurqa7u5u0tPTxzQ4GZeDn+EkCMIEGxwclN566y0pKSlJevDBBy/4+na7XXrqqaek5ORkycfHRwIkPz8/ad++fdKzzz4rARIgqVQqydfXV4qKipIaGhqk1tZWadeuXdJvfvMbSaVSST4+PlJeXp700EMPSW+++aZUX18v2e12yev1Sl6vd/zv+AVyu91SbW2t9OKLL0r+/v6Sn5+fNGfOHOnNN9+URkZGThnjgQMHpEceeUTS6XTS2rVrpZ6eHkmSJGlgYEAyGAzS8uXLpZ/+9KfSm2++KZWVlUkWi0Vyu93nfX9fe+016fXXX5c8Hs8ZL7927VpJp9NJ3/nOd6Q//elP0kcffSSVl5dLTqfzrNe70Mfl3XfflWbNmiUtXbr0oo93pXr33Xelu+66S1Kr1RIg+fj4SL6+vhIgJSQkSHfccYf09NNPS5WVldLQ0JDkcrlO+3x7vV5p/fr10s033yzl5eVJFRUVktPpnMB79v9UVlZKfn5+yt+0/LdeW1sreb1eaWRkRKqpqZF+9atfSddcc40UHBwsffvb35b2798v9fX1Kffz+PHj0v333y+FhIRIP/3pT6Wqqirpww8/lH784x9L3/72t6XDhw9LP/7xjyU/Pz/l8Rx9e35+ftJjjz121rH+9re/VS57uus//PDDYy6/Y8cO6bHHHpMee+wxqaOj47weD6/XK7lcLumjjz6SVq1apRz7iSeekPbt26c8v7Lh4WHpk08+kTIyMqTly5dLW7ZsOevfYEdHh/KeCkgRERGS2+0+5XJz58497WM1+v7ef//9yuV7enqk3bt3S7/73e8kHx8fycfHR8rJyZEefPBB6c0335Tq6uom1XuvzOPxSJ2dndINN9wgzZo1S3rhhRckl8t1oYc5bTwqZlYF4UvO39+fFStWMHXqVIaHh5XzMzMziYqKOmWTkEajITIyEh8fHwIDA4mJiWHKlCnK8mhoaChGo1HZ4TlZvtn7+voSExPDjTfeSFJSEpIkYTQaSUpKOu2yYkZGBvfeey+LFi1iypQpygymTqfj3XffxWQyERQURGBgIAaDAa1We0HLWQsXLkSlUp11VmH69Ol88MEHxMbGYjAY0Ol0BAQEXJE5glcCSZLweDwEBwcrOahFRUVKJQqj0UhYWNg5Nw0WFRWRl5dHa2srzz//PI8//jhGo3HCn7O4uDjWrl17yvnR0dGoVCqlZfIdd9zBokWLsNvtREVFERcXh06nU8afmJjIAw88wMqVK4mPjycmJobQ0FCys7NxuVzExcVx3333sWjRojOO5VyrI1/5ylfOuuFRLqcly83NVZbBz9Ta+GTyfZ4xYwYJCQncddddwOebJ8PCwk75e/bz86O4uJiXXnoJvV5/yhhOFhISwvr168ekAZzu7/13v/udUjP1TEanGMivz5iYGAoKCpAkCb1er7xGg4OD0Wq1E/56O5nL5eKll16it7eXlJQUlixZMm4pTCpJOmvZg8lXE0EQrjJDQ0N89NFHPPzww9xyyy08/fTTEz2kL2Tz5s0X1DJ04cKF41r+5Wrl8Xj48MMP+f3vf4/BYLjidnWPl40bN7J161b27NlDcnIymZmZZGZmUlhYSFxc3HnX95Vnet5++23WrFlDRUUFzz33HFlZWeNSC1cQrkQ2m43GxkYeeugh/P39mTdvHj/60Y9OO1FwDqe9sJhZFQThsvjNb35zSo/rs1m3bp0IVoVxExcXx6JFi7jxxhspKir6wqsCcvWG2bNn4/V6WbNmDWvWrAE+nym/0KYWgnCl83g8tLa28tFHH7F//34effRRli1bNq6VMsRflSAIl4Xcd/18XSklgYQrQ2ZmJhkZGeNW9zE+Pp6FCxfy2GOP8fvf/56uri7sdruySUkQrhbHjh3jww8/5KmnnuI//uM/uP3220lLSxvX2xDBqiAIl4UIPq8uQ0NDmM1mamtr2bVrFzfccAOpqamUl5fz+9//nqioKAoLC1mxYgVhYWFKbpv0/+88b2hooLS0lNdeew2AxYsXM3PmTPLy8ggKCrrgXLjxLp6uUqkIDQ1l5cqVVFdX09DQwFNPPYXD4WDp0qXn3K0uCFc6t9vN7t27+fvf/059fT1z5szha1/7GrGxseP+9yaCVUEQBGHc1dbWcuzYMQ4cOMC+fftISUkZUwKqrKyMrq4ukpOTmT9//pgvMwcOHKC3t5fe3l70ej0jIyM0Njbi4+ODzWZj3rx56HS6Ce/eo9VqSU5O5vrrr2f9+vVUVlayZs0aTCYTGRkZY4JwQfiykP7/8n5NTU2sWbOGhoYGjEYj119/PWlpaZek9rD4KxIEQRDG3Y4dO/jrX//KM888w969e6msrGRgYICSkhL++Mc/EhMTw86dO1m9ejVOpxP4/EPQ7Xbz/PPPU1paSlpaGqtXr+bll18mNTWVHTt28Oyzz9LT06NcZ6KpVCq++tWvcs899zB//nzeeOMN/va3v3Hw4EEcDgder3dS9m8XhAslSZLSeKK2tpY33niD559/ntjYWL72ta9x//334+vre0mqFIiZVUEQBGHc3X///WRmZrJt2zbg81JEmZmZAMTExKDX6+nq6uKVV17hV7/6FYGBgQwMDHDw4EE2bNjA9OnTmTlzJgAmk4mbbroJnU7Hv//7v7Np0yYWLlx4Stm1iXTNNdeQk5NDfn4+jzzyCLt372bKlCn85Cc/ITMzU6TBCFc8SZJoamriT3/6E9u2baOuro7HH3/8lOYKl4IIVgVBEIRxp1arx+yMP7nmrPzB5vF4lPOsVis7d+5kZGSEgIAADAaDcrmwsDCioqLweDzs3LmTzMzMSRWs+vr6EhwczMKFC3nyySfZvn07lZWV/OhHP+LOO+9k1qxZZGZmimoBwhXH6/XS3t7Ovn37eOWVV+jt7SUpKYl77rmHG264gejo6Ev+uhZ/NYIgCMKkYLPZ2LNnDy6XC7VaPWY2MiAggKCgICRJ4ujRoxdUs/dy8fPzIzo6miVLlqDRaNBqtezdu5dNmzYxMDBAZ2cnqampxMbGisYPwqTn9Xrp6+ujoaGB8vJydu7cSWNjI/n5+cyYMYMbbriBlJSUy/I6FsGqIAiCMClYLBY2btyo5HlKkqR8EGq1WqVX/LFjx+jt7Z3IoZ6RSqUiKiqKlStXcu211/LBBx/wq1/9ih07dhATE8O//du/8ZWvfAWj0Yifn58y2ywCV2Gy8Hq9eDweRkZGOHDgAC+//DKlpaWYzWbuv/9+Vq1aRVpaGn5+fpdtTCJYFQRBECYFo9HI0qVL+eyzz7Db7VgsllNaa6pUKu68804l/3UyCw0N5e6772bhwoVs3LiRDRs28N///d+8+OKLLFu2jOuuu45FixaJQFWYVEpLS9m2bRurV6/m6NGjzJgxg2984xvcdtttZGRkfJGuVBdNBKuCIAjCpBAcHMyCBQvYvn07VqsVs9msBKsjIyMMDw8DMGvWLKKjoydyqOckf5ir1WpiYmJYtmwZeXl5XHfddWzbto3S0lIOHjzIu+++y9y5c8nPzyclJQW9Xj/BIxeuNi6Xi+7ubioqKvjkk0+orKzEZrMREBDAz372MwoKCkhMTCQuLu4Ld367WCJYFQRBEMady+XC5XIpP3s8HtxuN76+vrjd7jHdzEZGRvB4PAQGBjJt2jSio6Pp6+ujurpa+YC0WCwMDAyQmJhIfn4+JpNpIu7WBVOpVPj7+5OYmEhsbCzp6emo1Wr279/PiRMnKC0tZWhoiJaWFjIzM0lJSSEmJobAwEAl7UEQxpvL5WJoaIi2tjba29tpamri+PHjHDlyBK/XS2RkJAUFBdxyyy3ExcUREBAwoeMVwaogCMJVQpIkPB7PZcmTHBgYoL+/X/l5cHCQgYEBQkJCsFgsjIyMKL/r6ekhKCiIwMBAiouLueaaa2htbWXjxo3k5uYSFhZGfX093d3dLF++nKlTp2IwGC7Z2C8VtVpNREQE9913HzfffDO1tbWsXr2a9957j7Vr1xIYGMh1113H7bffTlZWFtHR0fj4+Ci1K0W6gPBFyTngXq8Xr9dLf38/9fX1vP3222zcuJGOjg58fX1ZsWIFt9xyC3l5eSQmJk70sBUiWBUEQbhK9Pf3s337dmbMmIFWq70knWZkjz76KP/85z+Vnx9++GE+/vhj1q1bx7Rp0+js7AQ+n3GdPXs2zz//PKtWrSI4OJgnn3yS0tJStm/fTk5ODiqVilWrVrF8+XJWrFjxpVgqN5lMhISEMHXqVB5++GEOHTrE7t27ef/993nrrbcwGo0kJiZy3XXXccMNN5CYmHjFzCYLk8/Q0BAdHR2sXbuWLVu2cOLECdra2oiKimLp0qXMnj2b+fPnExYWNmbj32QhglVBEISrRG1tLT/72c+IiYkhOTmZhIQE0tPTKSgoICgoCK1WO2639c1vfpNrr712zHnh4eH4+Pjw5z//+ZQOVMXFxcrMYXh4OMXFxSQkJFBSUgJAcnIycXFxhISEfClmGFUqFb6+vvj4+BAaGkpRURGJiYlcc801VFRU0NTURFtbG5988gnbt28nJiaGhIQE8vPzyc3NJSoqCqPROOmCCmFysNvtdHd3c/z4cSorK2lqaqKxsRGLxUJYWBjz5s0jKSmJgoIC4uLiCA8PJyIiYkI2T50PEawKgiBcJeTC/PX19fT399PU1ERtbS1tbW1ERkYSEhKC0WgkJCQEg8GARqP5wsHQ9OnTmT59+ml/d8stt5x1jFqtlqioKKKioigoKPhCt3+lUKlUSnpAREQE2dnZZGZmcuLECSorKzl48CANDQ3U1NTQ3t6u5BfGxcURFRWFyWQiNDSUwMBAAgICLms5IWFy8Hg8DA8PY7PZ6Ovrw2Kx0NvbS2trK+Xl5bS2tmK1WnE4HCQkJFBYWEhmZiZ5eXlkZmbi4+MzKQPU0USwKgiCcJUoLCzkX//6F9u2bePgwYMcO3aMTz75BJvNRnR0NMnJycyYMYM5c+aQl5dHZGSksiQo50xO9g+1K51KpSI5OZnk5GSuu+46vF4vBw4cUE6bN2/mX//6FxqNhvDwcGbPns3cuXPJy8sjJSUFo9E45rkSdVy/PCRJUv4dfRocHKStrY2ysjK2bt3KgQMHlAA1LCyMkpISFi1axOzZs5k3b96kXOY/FxGsCoIgXCX8/PyIjIzk9ttvZ8WKFbhcLhwOBwcOHFCWC9etW8dzzz2HXq8nKiqKKVOmcM0111BUVERycvIpdU+FS8vHx4fi4mKmTZvGt771LdxuNy0tLVRUVHDs2DH27NnDo48+yvDwMGq1mri4OKZOnUp6ejrp6enMmDGD2NhYMeP6JSBJEn19fVRVVVFfX091dTUHDx7kxIkTWCwW3G43ycnJFBcXc+edd5KXl8fMmTPRarWo1Wp8fX0vaZ76pSSCVUEQhKuEPNPm4+ODn58fWq0WnU5HcXExqampLFiwgJ6eHhoaGmhra6Onp4fe3l7+8Y9/8P7772MymUhMTCQrK4uUlBTi4uKIj49Xji2Mr9G1WmWSJJGYmIjRaCQrK4v58+fT2tpKe3s7HR0ddHV10dXVRWNjI5988gmvv/46cXFxREZGEhERQWxsrJKjGBISQmBg4ETdPeEMhoeHsVqtdHd309LSQkdHBz09PXR1ddHU1ITNZkOSJLRaLeHh4WRkZGAymYiJiSE2NpawsDCMRqNy+jKsiIhgVRAE4Sol50tGRkYSGRkJfB4MdXV1UV9fT0NDAxUVFRw6dIi+vj66u7tpbW2lqamJ5ORkEhMTycjIIDQ0VCk9ZTAYlFJLwvhTqVTo9Xr0ej0xMTFIkqTs9G5paaG6uprq6mra2tqU56unp4fg4GCMRiNhYWFER0cTGRlJeHg4JpMJvV6Pv7+/8uVFp9Oh1WrRaDRX7EzcZCbXHB4ZGcHhcOBwOJSmF3Lntr6+Pjo7O2ltbcVsNmO1WrFYLNjtdgwGgxKcZmdnk5qaqnxxDA4O/lL+7YlgVRAEQVDIve2joqKYPXs2kiRht9s5fvw45eXl7N27l82bN/Phhx/i8XgICwtj4cKFTJs2jZycHKZNm0ZgYOBp81y/jB+iE00OXtPS0khLS2PhwoUAOJ1ObDYbZWVlyiatxsZG9u3bR2dnJ263W/mikpWVRVxcHHFxcUq+bFRUFOHh4crM68nP4eme06v5+ZXzSU/3/9G5pvD5zKnZbKajo4O6ujqam5vp6OigubmZ8vJyOjo6GBkZwcfHh7CwMNLT00lMTKSkpISSkhKlKYZOp7u8d3ICiWBVEARBOKvAwEBKSkqYNm0aX//613G5XDQ1NVFXV8fBgwfZuXMnmzdvxmq1otFoKC4uJj8/n5ycHIqKisjKyhqzlC1cen5+foSEhDBnzhxmzZqlFIP3eDz09fXR3t5Oc3MzNTU11NTU0NDQwJ49e2hra1O6i/n5+REREUFSUtKYNILIyEhCQ0MJDQ1VgtrJWvLocvJ4PPT09NDd3U1/f78yOyov43d2dtLQ0EBXVxfDw8N4vV58fHyUShAxMTHccccdSopNQkKC0hhCbg4hn662x1q8ewiCIAhnNDpvUq1WI0kSOp2OlJQUwsLCSElJYe7cuXR3d9PT00NTUxO9vb2Ul5dz5MgR3n33XeLi4oiNjSUmJoaUlBTy8vIwGAzjWtdVGGt0NYCTN1f5+/srTQcKCgqwWCwMDg4yNDSEzWajt7cXq9WK1WrFZrPhcDiUskilpaW4XC58fX3RaDTodDoCAgKU1ASj0UhgYOAp5/v7++Pv709gYCCBgYFoNBr8/PyUclsTvflHDubl5XiXy4XT6cRutzM4OIjD4VCW6YeGhpTle5vNxsDAAIODg2Mu63Q6cblcqNVqtFotfn5+Sn54QEAABoOB0NBQZfZafqyMRqOSUqPX69HpdFddYHo6IlgVhCtIX18fx48fn+hhCFcQr9dLS0sLDodjXFqUyh+cctARExNDYWEhDocDs9lMdXU1ZWVlnDhxgtbWVjo7O2lvb8dkMhEWFkZsbCxdXV1ERkZiNBoxGAwYjUb0ej1arVZ8MF8GWq0WrVZLaGioskEOUNpx9vT0YDab6evro6Ojg46ODvr6+jCbzfT29tLf38/w8DCDg4N0dnbi8XhQq9X4+/tjMBgICAhQbkOn0yn5sFqtFoPBoDSg0Gg0yvM+ere63CxBPgFj/i+TGyuczOPxjFmKh/8XjMr/l++r2+1WZpw9Ho8ShDqdTpxOpxKwj4yMKDmmw8PDSlA7NDTE4OCgcp4cxMsbGI1GIyaTSckXjomJUWamw8PDiYyMRK1Wi9f9OahOfkJPctZfCoJw6Q0NDfHRRx/x3//936jVagoLCyd6SMIVRJIk2tvbqampoaioiE8++eSy3fbg4CANDQ1s2rSJo0ePUlVVxdGjR3G73cTFxZGSkkJRUZFS1zU6OlpZThZ5rpOXx+PBbrfT399PW1sbXV1dSjDb0dFBf38/AwMDWK1WzGYzFosFm83G4OAgbrf7lONJkoSPjw9qtRq9Xk9gYKASzPr7+wMoge9oPj4+GAyGU3a7DwwM4PF4xlzW6XTicDiQJEmZ9RwZGVGK5bvdbtxu92lfbz4+Puh0OoKCgggNDVVmj4OCgggPD1cqK4SFhSmVF+Sd+CIQvWCnfbBEsCoIk9zIyAgHDx7kH//4B729vSL3T7gg8gYPj8dDYWEhjz/++GW9bY/Hg8vlUv612+0cOXKEsrIyKioqOHDgAD09Pej1eiIjI5kyZQqzZ8+msLCQ5ORkwsLCLtt4hfMzuiD96FnK0f+e/Hv5/1arVZmZHR4eZmBgQJmVlANKu92uzGzKrx3559G8Xi82mw0Y+6VGp9OdEiSq1WplxlOtViszn3JgPHqmd3TaQkhICMHBwWO+RI1utCBvJBz97+jNhSePTTgnEawKwpVI3hBRV1fH8PDwRA9HuIKFhoZO6My8vNza29urzMR1dnbS3NxMZ2cnfX199Pf3Y7VaCQwMJCQkhOjoaHJzc0lKSlLK84jZqiuTPKs5MjKilG9yuVxjluLl8+RleTnYlf8/+ljAKQEsfB6YntxCVE4ZGJ1e4OvrqwSvJ//s6+ur5JtqNJovRa3SK4QIVgVBEITJZXRd1/r6esrKyjhy5AgWiwWn04m/vz8ZGRkkJSURHx9PRkYGISEhGAwG9Ho9wcHBItdVEL48RLAqCIIgTG6SJDE8PExtbS3V1dUcOHCAHTt2UFtbi9lsxt/fn6KiIqZOnUphYSFz584lPj5eKecj6n4KwhVNBKuCIAjC5CZ/JsnLwPKysNyN6dChQ+zdu5fq6mq6urpwu92kpaWRk5NDdnY2RUVFlJSUoNfrRX63IFx5RLAqCIIgXJkcDgeDg4N0d3fT3t5Ob28v3d3dNDY20tnZyeDgoJK/GBMTo5ySk5PJyMhQ6lmK2dbJSf5yIue0Op1OJZfV6XSeNmdVrot6Mrlu6+hSV/LmJ39/f6XNsEajUWqgynWEhQknglVBEAThy0Eun1RTU8Phw4epqamhqamJxsZGHA4HISEhhIeHEx8fT25uLvHx8UrAGhQUpOz41mg04zYmp9NJZ2cnJpNJqR16NZA3R7lcLmXDlNvtVmbGR8+Sy6fRHbXkeqdyOamhoSFl97/H42F4ePiUYFW+7uk2nWo0GmWTlEwOVgMCAlCpVEqgqtPplIB1dHMCeUPW6PqvckArb8iS/z+6woDYiHXRRLAqCIIgfHm5XC76+/vZs2cPZWVllJeXs2vXLjo7O9HpdERERDBt2jTmzJnD1KlTSUtLIyIiYlz63EuSRGdnJ88//zy33347CQkJGI3GL3y8iXa62OBM58nF881mM11dXfT29jIwMIDNZqO/v5/+/n5sNpvSBctisTA0NKR0hLLb7acEo5fj8Rp9f+RgNigoSGlkoNPpCA0NJTg4mKCgIIKDg5WfR9dZjYiIUNrPynVhTx7/6e7PlfaauExEsCoIgiB8ecn1PEfP7jmdTpqbmykvL6eiooLDhw9z/PhxXC4XGo2G2NhYZs6cSUFBATk5OUybNu0LVRdwuVyUlZWxZMkSdDodN910k3KCKy8wkR9HubRYd3c33d3ddHR00NXVRX9/vxKc9vX14XQ6x3SJ8vf3R6/XExQUpMxk6/V6QkJCCAoKUrpaBQQEKLObchOA0U0B5Hqo8uzlaL6+vgQGBp4y9qGhoVOCX3nmV56ldTqdSheu4eFhJf1gdDeq4eFhrFbrmHaqckcruT2t3NBALn1lMpkIDw9XOldFR0cTERFBWFgYUVFRJCUlERkZib+//4S2l53ERLAqCIIgXF0kScJutyu1XTs6Omhra6Ojo4Oenh56enoYHBxEkiS0Wi2xsbGkpKQQGxtLdHQ0SUlJxMbGKkvDZ9LR0cHu3btZuXIlKpWK5ORkcnJyWLx4MbfffjthYWFotdrLeM/PTpIk3G43fX19dHZ2Kp2m5MdJDtLkk9vtRpKkMUvsct6nRqNRetvLfe3lAHN0wX0/Pz90Op1yDHnpfPTyu9yuVM45Pbn96mgqlUpZeh/N5XKNCVSBU9IORqcejK7zKufKymkLcuDqcrmUAHd0ByybzYbdbmdwcJDBwUGlG5ac+iBfTw5qg4ODMRgMBAYGYjQaCQ8PV2ZrQ0NDiYmJwWQyodFoTrm/V4nT/pFdHQk1giAIwlVJpVJhMBgwGAwkJycry9atra3KjGtpaSlNTU10dnZSW1tLfX09UVFRxMTEkJGRQVZWFoGBgeh0OmX5Vw6wZD09PTQ2Niobf2pra2lvb6e7u5vIyEhycnKIjY0lNDT0st13eTJq9Myhw+FQ+tsPDQ3R1tZGY2Mjvb29WCwWpVmDPJvo4+ODVqtVAtHQ0FBMJhPBwcEEBwcTEREx5uewsDCCg4PPGdxfSn5+fpfldrxeLxaLBYvFwsDAABaLha6uLuXxk4P/7u5uJehvamrC4/Gg1WoxGAxKq9bg4GBMJhPJycnExMRgMBjQ6XTKSZ51llvOXmkz9RdLzKwKgiAIV72BgQG6u7s5cuQIO3bs4PDhw1RXV9Pb24vJZCI9PZ3s7GymT5/O4sWLiYyMRK/XK0HDRx99xHvvvcfrr7+Oy+VSjit3QVq+fDm33XYbt99++yVpw3mmfFJJkqipqaGxsZGGhgYqKyupqKigqamJjo4OhoaGUKvVhIWFER0dTUpKCklJSURFRY35OSgoiICAgHEb79XG6XQyODhIXV0dLS0tygz/6J/b29txOp1oNBrCwsJITEwkPz+fjIwMEhMTSU1NJScn57Q1heFLE8CKNABBEARBOB15aVje0e50OhkYGKCmpoYDBw5QXV1NfX09VVVV+Pj4EBkZSWJiIrNmzWL69OmsX7+e9evXU1VVdcqxVSoV/v7+hIeHs2TJEh555BFiYmKUWbLxGr/D4aC2tpajR49SV1dHXV0dR44coa+vD7fbjZ+fH1FRUaSmphIbG0tcXJwyc2wwGJTl99FL76P//yUJhiaE/MVBTg+Q0xFGV0oYGRmhoaGBuro62traaG1tpampiaamJgYGBnA6ncoKQVJSEhkZGcycOZPs7GyMRuO4VraYQCJYFQRBEITzIW8wslgsSl3Xvr4+Wltbqauro7+/X6ntqtVqaWhooLm5GavVesZjarVaEhMTWbBgAfPmzWP69Omkp6d/ofHZ7Xb6+/upq6ujpqaGzs5Ouru7lRxcuc+9VqtVdquHhIRgNBoJDQ1VNj6ZTCZCQ0Ov5hzJSUFOHxmdVmC1WrFYLMpmtu7ubtra2hgcHFTyaPV6vbKBKyYmhszMTJKSkggLCyMkJGSi79YXIYJVQRAEQfiiPB4PDoeDsrIyampqqK2t5dixY5SVldHb24vNZjvnMdRqNTqdjqVLl3L99dezaNEiYmJi8PPzO22wKH9Gy/mmdrsdm81GT08PbW1tHD16lBMnTtDZ2YnFYsHX15fIyEji4uJITEwkOzubnJwcZeOO2IF+5fF6vdhsNjo7Ozl27Bg1NTU0NzfT0NBAW1sbTqeTgIAAIiMjlec7Pj6euLg4DAYDer1eyX29AurAimBVEARBEMaTJEm89NJLvP7662zduvW8rqNSqZAkCaPRyA033MCTTz5JdHQ0Go1GCSRGfza73W7q6uooLy9n27ZtbN26ldbWVgYGBggODuaaa66hoKCA3NxcioqKSEpK+rIsCQtn4fF4aG1tpaysjMrKSkpLS9myZQtmsxk/Pz8iIyNZtGgRs2bNIi8vj4KCgtNWpJhkwasIVgVBEARhPEmSxMMPP8yGDRs4fvw4Ho/nvK/r4+ODXq8nKSmJr3zlK9x8881MmTIF+LwUVl1dHRs2bGDTpk10dHTgcDgwmUwUFxeTk5NDVlYWU6ZMQa/XK+Wg5HqkkywAES4BOQdWLr8ll9Nqamqivr6eY8eOsXfvXhoaGnC73YSEhLBgwQJmz55Nbm7umM1ak4gIVgVBEARhPEmSxM0338yhQ4fo7Ow87a78s1GpVAQEBJCVlUVeXh5TpkzBbrfT1dVFT08PVqtV6b4VFRVFfHw8CQkJREREEB4eTmRkpNj8JIxhtVrp7++ns7NT2aDV0dGhzMZrNBpMJhNJSUkUFhaSl5dHZGQkQUFBEz10EMGqIAiCIIwfr9fLyMgIxcXF1NfXn9KnfnQAeT5BrL+/P+np6QwODqJSqdDpdGRkZDB//nwKCwvJzMwkKipq3O+H8OXW399PU1MThw8fZvPmzVRXV2O32/Hz82PatGlcc801pKenEx8fj8lkIiAgYCJn50WwKgiCIAjjZXh4mIaGBhYsWEB3d/eY38lln3x8fJSd3qPbkZ6Jr68v6enp3HHHHdx6660UFhZeyrsgXIVaW1uprKxk27ZtvPXWW7S3t2M0GikuLuZb3/oWM2bMICwsbEywehkDVxGsCoIgCMJ4MZvNfPTRRzzxxBOMjIyg0+kIDg4mJCSEwMBApdtVUFAQer2egIAAAgMDqa+v5/Dhw5SXl9Pa2kpubi6zZ89m9uzZzJs3D39/f2UHt9goJYy30fmtdrudgwcPcuTIEXbu3El5eTmxsbFMnTqVG2+8kZtuuulyz7KKYFUQBEEQxovD4aCmpob9+/fj9XqVuqY6nQ6tVotGo0Gr1aLVarFarbS3t3Pw4EEqKirweDwEBASQnp5OYWEhKSkpxMfHk5SUJHJQhctCjv/a29tpb2+ntraW0tJS6urqGBwcRKPRKE0vUlNTSUhIAC75LKsIVgVBEAThcpE7YnV1dVFRUcGRI0dYs2YN/f39SuvWm2++mYyMjMvWz14QzsTr9dLU1MTGjRvZu3cv+/btQ6PRsHjxYoqLiykpKSE6OhqtVnsp6/WKYFUQBEEQLrXRhfybmpp49NFHOXDgAFarlfnz5/Of//mfZGdnExERMcEjFYTTs9lsNDU18bvf/Y5NmzYxPDxMdnY2v//978nOzlYqB1yCWVYRrAqCIAjCpSRvptq/fz+bN2/m5ZdfJigoiCVLlrBo0SKmTJlCcHAwfn5+opuUMGl5vV7cbjdWq5Xa2lr27dvHe++9R2NjI7fccgvXXnst119/PX5+fuMdsIpgVRAEQRAuFY/Hg91u5/3332f//v3U1dXh6+vL9ddfz9SpU0lPTycyMnKihykIF8RisdDS0sKhQ4dYu3YtNpuN8PBwpk+fzsqVKwkNDT1tZ6wv6LTBqnq8ji4IgiAIVyuXy4XNZqOiooI33niDzs5OgoKCuOuuu/ja176GwWDAx8fnko5B7mjkcDhwOBx4PB6io6Mv6W3KXC4XTqeTgYEBpfC8Xq8nKCgIjUZzye+7cOkYjUaCg4PJzMxEp9PxxhtvcOzYMU6cOEFKSgpTp04lPDx8PAPWU4iZVUEQBEG4CJIk0d7ezv79+3nggQcIDAzknnvu4fbbbyctLe2yjcPtdtPX18eePXvYt28fFouF559//pLfriRJtLW1UVlZyXvvvcebb75JcnIy1157LV//+tfJysrC39//lOuczaXYcX7ybV7obYznmM93LGe6zYmsFtHZ2cmWLVt45plnqKmp4bHHHmPp0qXk5OSMx7hEGoAgCIIgjCdJkjhw4ACvvfYamzdvxmQy8eSTT5Kenk5oaOhl3eXf3d3N008/zerVq+nt7SUpKYkjR45c8tstLS3ltddeY+vWrbz00ksEBATQ2tpKVVUV+/bt48knnyQ2NnbMdRwOB93d3bz33nu88cYbmM1mQkJC+OlPf8r8+fMvSetPp9PJoUOH2LVrFzk5Odxwww0XdH2v18vg4CC7du3i008/Ze3atQA89NBDzJ8/n/z8/PM+VlVVFRs3buSPf/wjb7zxBsXFxacd78aNG3nmmWeor68nICCAJ554gjlz5mAymS5o7OPJ7XYzODhIW1sb//f//l9qa2tJT0/n29/+NkuXLr3YXGyRBiAIgiAI48XpdNLV1cXrr79OTU0N8fHx3HHHHeTk5BAUFIRafXk/YgMCArjmmmt4+eWXsVgsOByOy3K7x48fp6qqir6+PpKSkggMDFSWjuUGCaMNDQ1htVqx2+1MmTIFj8dDU1MTnZ2d/OMf/8Dr9VJYWEhSUtJFjcvr9TI0NERbWxvl5eUcOnSIlpYW6uvrAS4oWHW5XFitVtauXcuWLVs4cuQIdXV1ALz77ruYzWYcDgclJSXnnF1sampi9+7dvPvuu9TW1p7Sphc+r3165MgRXn75ZVJSUsjKysLr9fLPf/6TgYEBioqKyMnJuYBHY/yo1WoMBgPJycmsWrWK999/n66uLv71r3+RkZFBTEwMOp1ufG9zXI8mCIIgCFcBr9eLzWZj//79fPzxx6Snp3Pttddy1113TVhf9cDAQG644QYCAgIu6+0eOXKE5uZmfHx8CAoKQqVSER4eTnh4ONOmTTvl8jabDbvdjkqlYsGCBSxcuJC6ujq2b9/Offfdh9FoxM/P76KDVY/HQ1tbG8eOHeP999/nX//6l/K7oqKiCzrW4OAgra2tbN26lfLychwOB3Fxccp5FosFlUpFcXHxWZf0R0ZGOHToEJs3b2bbtm2nvZzT6aS2tpZ3331XSasoKSnBYrFQUlKCx+PB4/GQmpqKRqOZkNeaj48P/v7+3HrrrbhcLtavX8+nn37Kddddx4IFC4iOjh7XahciWBUEQRCEC+RwOKisrOTBBx8kNzeXb33rW9xyyy1XZfepjz/+mKqqKlJSUs7r8p2dnYSEhJCWlqY8VqmpqcpM9N///ndCQ0MveJn+ZA6Hg9deew2Anp6eizpWQ0MD27dv5+6776a4uBiXy0VZWRnXXnstkiRRWlpKe3s7jz766BmP4fF4OHHiBD//+c85evToGS/X3NzMli1bePnllwGIjo4mKSkJs9kMwIcffogkSRQXF5Obm3tR9+tiqFQqJEli5cqVxMXFYbFY+D//5//w1FNPce211xIWFjZutyWCVUEQBEG4QJs2bWLNmjX4+Pjw2GOPkZOTM+kDVY/Hg8ViYdOmTXz66accOXKEGTNmcNttt5GdnU18fDzw+SxidXU1e/fu5emnnwaguLiYRYsWsWLFCoxGIy+++CLPPPMMAI2NjQC0tLSQlZUFwP33389//Md/nHYcmZmZ+Pr6jnmsnE4ndrsdgDvuuINZs2aNuc6hQ4fYsWMHAHfddRfh4eHnvL96vZ4HH3wQgBdffJGNGzeez8N0WtnZ2SQlJaHRaJTNYnKwLUkSCxcu5P777z9j1YPh4WHMZjN79uzBZrOd9bbq6upoaWkZc97Jr6v+/n7Ky8vHa1PTFybf/4KCAn7+85+zatUqXn/9dbq6upTHfjyIYFUQBEEQzpMkSfT19XH06FGqqqpYunQpaWlpBAcHT+pA1el00tfXx7p16/B6veTk5BAfH49Go+Gdd95h3rx5LFu2jLCwMHbu3MmuXbuoqKhg+fLluN1uOjs7+fDDDzEYDCxbtozc3FzuuOMOAJ5//nl6e3vR6/XKeQUFBWccy8mVAWw2Gy0tLRw8eBCAkpISUlNTx1zG4XDQ19cHfB50nw9fX19ldk+v15/Xdc42ZnncTqeT/v5+mpubSU9Pp6SkhLlz5zJz5swzXr+rq4vjx4+Tn59/zjSNjo4Oent7z3oZs9lMeXk5X/nKVy78zowzlUpFQEAAycnJLF++XMnpbWhoIDExcVzKlolgVRAEQRAuQENDAydOnMBsNnP77bcTEhJy2TdTXaihoSGampp44403uPfee1mwYAHx8fGUlpZyxx134PF4yM7OxmQysXfvXiUP88UXX8TpdPKHP/yBF198kcDAQObMmcP8+fOZP38+gFJ9IDQ0lMcff/y8g3ZJknC73Zw4cYLDhw+zc+dOABISEk5ZQvb39yc0NBRgwjt/NTY2UlNTw/Hjx4mLi2PFihVMnTqVmJiY017earXS3t5OY2Mj3/ve9zAYDGc9vrzZ7GzMZjNlZWXnLKd1ufj6+hIQEMDtt9/O7t27aWxspLS0lISEhHE5/uT+6xIEQRCESWbt2rX09PSQmZnJddddN6lnVGXNzc3s3r2bzZs388ILLyhL2DNnzsTf35/a2lp27NjBtGnTCA4OpqSkBL1erwSIQUFB2O123njjDX7605+eMTC7EB6Ph46ODr7xjW9w4sQJ5fx9+/YRERExptxVUVGRsilqoh/vVatWsX//fuXnoKAguru7Wbp06Wnzdt966y2CgoJYuXLleY1906ZN7Nu376yX6ejoYPv27ZMmWIXPA9YpU6ZQUlLCkSNHeOedd7jxxhvH5cuFCFYFQRAE4QIcOHAAg8HA9OnTJzxwOl/V1dVs3boVgGXLlqHRaJTfNTY20trayvDwMA8++CB33303LpcLr9dLR0cHO3fuvCT1Wn19fYmOjmbdunV0dHTQ0tLC9u3b+fDDD2lvb8dut3PDDTegUqkm1eP897//ncrKSvbv38/vf/97Nm3axO7du/nb3/7Gq6++SnJyMjqdjpGRET777DOlnFNISMhED/2SU6lUFBUV0dfXx7Zt23C73fj5+V308yeCVUEQBEE4D5Ik4fV6aW9vJy8vj/j4+EkVRJ1Ne3s75eXlACxduvS0gZNcaD40NJTOzk6ampo4duwYJpNp3OtmwueBjZ+fH6mpqURGRhIbG4uPjw/r1q3jwIEDSimuySYtLQ2j0YjJZKKmpoZt27bR1dXF4OAgGzduZOXKleh0OoaHh3nttdcIDg4mKCiI4OBgANra2sYc79VXX2VgYEC5r3FxcURERNDd3X3GMRgMBlJSUibl6y8mJobQ0FD6+/ux2+34+flddHMMEawKgiAIwnnyer309/ej0WjOa0f6ZNHT06MUw3/ggQfIyMg44/Ls0NAQFRUV7Nixg8bGRr7//e8TGRl5SccXGBiIVqtFkiR0Oh3V1dWMjIxc0tv8orRaLbGxsZhMJmw2GxUVFfT39+N0Otm0aRPXXXcd0dHRjIyM8NZbb+H1es96vL/85S94PB4lWM3IyKCuru6swarJZKKwsHBSBqthYWEYjUYcDgdWqxWDwSCCVUEQBEG4HOSi7vLS5sm72q8Ura2tREREnLFl5+bNm3nllVc4ePAgW7duJSYmhvXr11/ycanVapKTk8ekKExmGo3mkrSGLSoqorGxkQMHDpzxMjExMcybN29SBqtarVZ5DoeGhs67esPZiGBVEARBEM6DSqVCp9Ph7+/P8PAwFotlood03hYuXEh/fz/PPvssmzdvxs/Pj5KSEgICArBarVRWVtLR0cGtt97K7373O0pLSwkPDyc6OvqyVTqQJInBwUG8Xi/z58/nzjvvvCy3K3M6nRw5coRf/OIXJCcnc8899zB16lRUKhVlZWXs3LmTpKQkZs+ejdFoRKVSERgYqJRm8vf35/bbb1c2pYWGhnL06NFTNkGtWrVqTFOAV155hcWLFys/p6amEhcXd9axhoSETHiN1TMZGBjAZrOhUqkICQm56FlVEMGqIAiCIJwXeaNPZGQkIyMjtLe3T/SQxvB6vTgcDmXZ2ev1Mjg4iE6nIzk5menTp/Pss8+ybds2uru72bFjBxqNhqGhIWJiYsjIyADAz88PtVqNJEkMDw9jt9txOBzK7TgcDpxOJz4+PpjNZmXmzOPx0N/fT3Bw8Fl3gMvF+cPCwkhNTUWv1yuB6v79+0lPT6ewsJB58+Yp12ltbaWhoQGAqVOnEhgYeN6Pi91uZ3h4eMx58peN0fVxh4aG+Pvf/86+ffuor69Hq9UydepUAPr6+igtLWXnzp1oNBry8vIIDw+nq6sLl8tFZGSkshNeHptarSYvL++U8ZxcZzUlJWVM5YPIyEiys7MpKSnhwIEDDAwMYDablS9Hubm5FBYWKk0cJpvOzk7ldWAwGMbly44IVgVBEAThPKlUKlJSUhgcHKS+vh6v1ztpdqs7nU5OnDiBy+Ua83Nubi6RkZHk5OQQExNDZWUlhw8fxul0otfrCQwM5P7772fFihXA58FgT08PFouF1tZW/Pz88Hg8BAUFKQFxT08PDoeD2tpaJRB0Op2UlZURFxen5C2ezoYNGxgcHCQ2NpZFixYRERGhBM379+/nmmuuYfr06eTk5CjXaWxsZMOGDQCkp6efV7Dqdrtpb2+nu7v7lLqlvb29HDt2jISEBKKiotBqtYyMjPDhhx8qG4M++eQTfvOb36BSqfB4PFitVj7++GPi4+MZHh4mJyeHpqYmdDodhYWF3HzzzeMy2xkWFkZhYSHLly+nsbGR5uZm9Ho9drsdk8nE4sWLmTt3LlFRURd1O+NNnkFuamqit7eXmJgYAgICROkqQRAEQbjcFixYwDvvvMOOHTuw2+0EBgZOimC1oaFBqUUKnwcNRUVF1NTUkJqaSmFhIbt37+btt99m7dq1lJWVceedd3L//feTnJyszDLed999DA4O8uabb/LGG2/wjW98g3nz5uHr60t/fz86nY6nnnqKP/zhD2Nuv6Ojg4ULFwLwyCOP8Mtf/vK047zhhhv4xz/+wWOPPaact3DhQm677TYWLVpEYWHhuFQf6OrqIikp6bS1SN977z3ee+894PNSZMXFxRiNRl5//XV+8IMfkJWVxX/+538qz2tRUREmk4nQ0FD+8Y9/8Nvf/ha9Xs9DDz3Ef/3Xf5GdnU1mZua4vQ7y8vLIyMggPT2dxx9/nKqqKgIDA3nhhRdYsmTJpN3c5/F42LVrF83NzSxdunRculcBqM5RUHbyVJsVBEEQhAkmSRItLS385je/YcuWLXz729/m29/+9gUtS18qw8PDY4rry7KyspSd9i6XC4vFwsDAAMPDw0oJJo1Go8yADQ8P09vbi8ViUWZInU4ng4ODeDweTCYT/f399PT0nHEsERERREdHn/Z3drsds9lMf3+/cl5gYCDBwcHodDp0Ot0pQc7g4CA2mw34fCf8+eRBulwuKioqznm59PR0AgIClLSJtrY2tFotERERBAQEKDOrcstaq9WK2+3Gx8eHsLAwAgIC0Gq1573hrqamhqGhIeXntLS007aD9Xq9DAwM0NnZycjICD4+PsTFxY3b0vp4c7lcvPfeezz11FOYTCaeeOIJpkyZcqEB62mjfRGsCoIgCMJ5kisCvPzyy3z00Ue4XC6efvppEhISJkXAKggTYXh4mO7ubh555BE6OzspKiriv//7vwkJCbnQ2ebTXnh85mcFQRAE4SqgUqnw9/enqKiImTNnsmfPHg4dOkRXV9e4lOgRhCuNXHv46NGjbNy4kbS0NGbPnk1oaOi4pUWImVVBEARBuECSJFFbW8uDDz5IRUUF3/3ud7n77ruJiYmZ6KEJwmVlsVj4+OOPeeyxx0hJSeEXv/gFJSUlX3RjlUgDEARBEITxIEkSDoeD6upqHnjgAdxuN1lZWfzf//t/SUxMnJQ5hYIwnrxeLz09PfzpT39i9+7ddHV18be//Y3s7OwxJcEukEgDEARBEITxIKcDZGZmsmLFCqKjo6mpqeHVV1+lurqagYGBiR6iIFwyIyMjdHd38+abb3Lw4EF8fX1ZsWIFOTk5GAyGca+OIYJVQRAEQfgCfHx88Pf351vf+hZLly4lICCA5557jq1bt9LQ0MDQ0BCSJJ22dJIgXGnk17K8merIkSM8++yz9Pf3M23aNH7wgx8QFBQ0LnVVTybSAARBEAThIkiShNvtpqGhgZ/97GesX7+eOXPmcPPNN3PPPfegVqsnRR1WQbgYcrz40Ucf8dFHH/H2229TVFTEY489prTuBS72tS5yVgVBEAThUpBLWrW0tPDPf/6T/fv309nZSX5+Pg899BCpqamEhIRM9DAF4QsZGhqio6OD//3f/+XAgQP4+voyZcoUvvvd75KUlITBYBivBgCnDVZFBrggCIIgXCSVSoVWqyUtLY1ly5ZhMBg4cuQINTU1vPnmmxQWFpKTk0N2djb+/v7j1tlHEC4VSZJwOp3U1dVRW1vLkSNHKC0tJTo6mqysLObNm0dOTs5lWTkQM6uCIAiCMM76+vqorKzkt7/9LQcOHCArK4vZs2fz9a9/ndjYWHQ6HX5+fiJoFSYdOa1F3kS1Zs0aduzYwa5du8jKyuL73/8+M2fOJCEh4VLcvEgDEARBEITLQf5sdbvdvPnmm3z44Yfs3r0bu93Ovffey0033cSMGTMICgqa4JEKwljDw8NUVVWxdu1a/vznP+PxeMjKyuL222/n/vvvR6vV4uPjc6lmU0WwKgiCIAiXi/z52tvbS09PD42NjXz66afs2LEDPz8/YmJiWLx4McuWLSMqKkq0axUmzMjICGazmfXr17N9+3bq6+sxm83MnDmTxYsXk56eTmxsLJGRkahUqku57C9yVgVBEAThcpE/0MPDwwkNDSUqKgpfX180Gg0NDQ309/ezefNm+vr6SE5OJjExkdTUVOVyooKAcCnJRf0bGxtpbW2lrq6OQ4cOYTabCQoKIjc3l6VLl1JcXExkZCQajWbCxipmVgVBEAThMpEkicHBQfbv38+2bdv44IMPaGtrIz4+nvz8fG6++WZmzZpFYGAgarUajUYjSl8J40KSJLxeLy6XC7fbzfDwMAcPHuSTTz7h8OHDlJeXYzKZWLp0KXPnzuXaa68lNDT0cr/2RBqAIAiCIEw0+XPX6/Xi9XrZvHkzmzdvZvv27Rw4cIDo6GgKCgooKipSZrb8/f0neNTClc7r9dLS0sKuXbuUL0vHjh0jJSWFqVOnsnTpUr761a+i0+nGzOyLYFUQBEEQrlLy56/FYsFisdDT00NlZSV79+6loaGBrq4uvF4vOTk5yqmoqIjY2Fj8/PwmePTCZOf1euns7OTYsWOcOHGCo0ePcvz4cbxeL8HBwSQmJlJSUkJ2djYRERGEhoYSFhZ2KTdPnQ8RrAqCIAjCZOVyuejq6uLQoUPU1tbS0NBAfX09LpcLvV5PaGgoqampJCcnExYWRkhICFFRUYSHh6PRaEQZrKuYJEl4PB66u7vp6emhv7+f3t5e6uvraWxspLe3F4vFgkqlIj4+noSEBDIyMigpKSEmJgadTjfRd0EmglVBEARBuBJIksTQ0BDHjh1j/fr1HDx4kMrKSvr6+ggPDyctLY2srCxmzJjB9OnTCQ4ORqvVolar8fX1xdfXd6JnyIRLRJIkJTh1u914PB5cLhdDQ0Ps37+fI0eOUFVVRUVFBS0tLRgMBmUWdfny5UyZMoWQkJDJ+uVGBKuCIAiCcCUY/dks57YODg5y7Ngxtm7dSllZGZWVlVRVVaHVaklKSiIjI4OioiKKi4tJS0tTmg8IXy5Op5Oenh5qa2vZt28fx44do7q6mhMnTmC320lISCAtLY1p06axcOFCCgoKCAkJwc/Pb0zZqUn6RUYEq4IgCIJwJZJn0gYHB7FYLNjtdmw2G+3t7ZSVldHZ2Ul3dzft7e2MjIzg7+9PcHAwqampZGRkEBsbS0xMDElJSURERKBWi8qVk53X66W3t5fW1lY6OztpbW2lvLxcKXtmt9tRq9VERkYSFhZGdHQ0hYWFxMTEEBwcjMFgICQkBL1erwSqVwARrAqCIAjCl4XH48Fms1FVVUVbWxvt7e00NDTQ3t7OwMAAw8PD6HQ6DAYDgYGBBAcHEx0dTVRU1JjzgoOD0ev1BAQEYDAYRPrAZSKXkhoaGsLhcGC327FYLAwMDChfRrq6uujs7MRqtWKz2bBYLDgcDnx9fTEYDMTExJCQkEB0dDTx8fHk5ORgNBqv5A14IlgVBEEQhC8zSZLo7u6mvr6empoajh49yuHDh2lra6O3txeXy0VAQACRkZHExcWRnp5OWloacXFxxMTEkJiYiL+/v1K6aHTu6+h/YdIuI08KcmwlB6TyvyefRkZGaG9vp7Ozk5aWFqqqqqivr6e9vZ3W1lYGBgbw8fEhODiYmJgYioqKyM/PJzU1lfT0dBITE79sz4MIVgVBEAThy07egCP/3+v14nQ6sdvtVFdXU1lZSVNTE83NzVRVVVFTU4PD4cDr9eLj40NMTAyRkZFER0eTnJxMSkoKUVFRSoArdzMSqQRn5vV6cbvddHV10dHRQXd3N21tbTQ0NNDU1ERHR4cSkLrdbgD8/PyUIDQ+Pp6UlBRyc3NJT0/HaDSi0+mUnNMrIPf0ixLBqiAIgiBcjbxer5LzOjg4yPDwMMPDwwwODjIwMEB/fz99fX10dnYq+ZDysrPVasXtdqNSqdBqtWi1WvR6PQaDAZPJRGhoKEFBQQQFBWEymTAYDEpaQVBQEAaDAZ1ON2bG9koiz4AODQ0xMDCgPIaDg4NYrVYsFgs2mw2r1Upvby/9/f3YbDZsNhvDw8OMjIzg9XpRqVQYjUYl7UJ+/OR2vGFhYQQHBxMQEIC/vz86nY7AwEACAgKUKg9XARGsCoIgCILw/8gzr3Jg2t3dTXd3t9KooKuri56eHmw2G4ODgzgcDoaHh1GpVPj5+SnBq0ajwc/PD51Oh5+f35hgS6/X4+/vr5y0Wq1SXsvPz29MqS21Wq0EZj4+PkqAJl/mZCdvHJIkCbfbzcmxjcfjwePxAP+vuoJc8snj8Sg/u91upRyUx+PB6XQqAaecVyrnmDocDuX3LpdL+f/w8LByDK1Wi06nU3KHIyIiCAsLw2g0EhoaqmyOMhqNGI1G0VpXBKuCIAiCIHwR8ixiR0eHMvtqNpuVn81mM2azmc7OTiwWCy6XS8nVlE8qlYrAwEBCQkLQ6XRotVoMBgP+/v5oNBo0Go0yI6vRaJRAGECj0eDv7z8mkFOpVOj1+jEbwiRJwm634/V6x4zf6XTicDiU/7tcLkZGRrDb7QwPD+N0OpXZ06GhISXolDc8yYHu6CV4tVqtzIiGhIRgMpmIiYkhIiJC+TkqKoqoqChCQkIIDg6+HE/VlU4Eq4IgCIIgXLjRObAn/3u68xwOBzabjf7+fmWZ3GazYTab6e/vV2ZoBwYGlBnKkZGRMWkK8nlyzu3w8PCYMXm9XgYGBk4Zq16vP2WGUp7tlWdz5Z+DgoLQ6XRKsHzyTHBISAihoaEYDAYMBgOhoaFKWSi9Xq/M9p6cPzr63y9pbumlIoJVQRAEQRAuPXlJ3eVyKUvr8s+jl97l5fLRS/PyST4PGPN/mbzkfzI5L/bkWdiTqxr4+vqiVqvx8fFRTienIMiBrZyeMPr/JweqwrgQwaogCIIgCIIwaZ02WJ2UjWEFQRAEQRAEAUSwKgiCIAiCIExiIlgVBEEQBEEQJq1ztZ9ovSyjEARBEARBEITTOFewuueyjEIQBEEQBEEQTuNc1QAEQRAEQRAEYcKInFVBEARBEARh0hLBqiAIgiAIgjBpiWBVEARBEARBmLREsCoIgiAIgiBMWiJYFQRBEARBECYtEawKgiAIgiAIk9b/B4WEktiuVfpkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgb.plot_tree(gbm, tree_index=0, figsize=(12, 8))\n",
    "lgb.plot_tree(gbm, tree_index=1, figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate Feature Importances and Plot Featurewise Explainations\n",
    "\n",
    "You can see we recover the functions of most of the variables really well.\n",
    "\n",
    "Note to self: small inflections in the linear features in (-0.5, 0.5) are likely due to `noise=0.5` - test this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7eFBLkqZNa8k/yXXA65q3rwT+uqq2J9kC3Avc35z7RlW9r604JmH5E7o+nStp2rW5h+8/H7xO8nHgfw2dfrCqtrd17S7Y3pE0S1pv+yQJ8HbgjW1fq21unyhpXkxitc8bgMer6q+Gjm1N8p0kf57kDSt9McnOJItJFg8cONB+pIcxar4+uJJH0uxZV+Wf5DbgpBGnrqqqG5vXFwFfHDq3H9hcVT9IcibwJ0lOr6oXZdWq2gXsAlhYWKj1xHokDlfh296RNOvWlfyr6tzVzid5CfArwJlD33keeL55vTfJg8BrgcX1xDJOo0YsgxW+pPnRds//XOC+qto3OJBkI/BUVR1K8hrgNOChluM4Ylb4kuZZ28n/Ql7Y8gH4eeBDSQ4Ch4D3VdVTLcchSRrSavKvqn854tgNwA1tXleStDpn+0hSD839eIeVVu6sxnX7kubd3Ff+K63NX42reiTNu7mv/MGVO5K03NxX/pKkFzP5S1IPmfwlqYdM/pLUQyZ/Seohk78k9ZDJX5J6yOQvST1k8pekHpr7J3y3vdoZPZK03Nwn/w/+8uldhyBJU8e2jyT1kMlfknpoXck/yduS3J3kR0kWlp27IskDSe5P8ktDx89Mcmdz7j8kyXpikCQdufVW/ncBvwJ8dfhgkm0s7d97OnAe8HtJjmlOfwrYydLG7ac15yVJE7Su5F9V91bV/SNOnQ9cW1XPV9XDwAPAWUk2ASdU1derqoA/BN66nhgkSUeurZ7/ycD3ht7va46d3LxefnykJDuTLCZZPHDgQCuBSlIfHXapZ5LbgJNGnLqqqm5c6WsjjtUqx0eqql3ALoCFhYUVPydJOjKHTf5Vde5R/N59wKlD708BHmuOnzLiuCRpgtp6yOsm4JoknwBezdKN3W9W1aEkzyb5WWAP8C7gP67lF+7du/fJJN9tKd42nAg82XUQR2jWYp61eMGYJ2HW4oV2Y/7bow6uK/knuYCl5L0R+K9Jbq+qX6qqu5NcD9wDHAQurapDzdf+FfA54GXAnzZ/DquqNq4n1klLslhVC4f/5PSYtZhnLV4w5kmYtXihm5jXlfyrajewe4VzVwNXjzi+CJyxnutKktbHJ3wlqYdM/u3Z1XUAR2HWYp61eMGYJ2HW4oUOYs7Ss1aSpD6x8pekHjL5S1IPmfxblOS3ktyX5I4ku5O8suuYVrPalNZpk+S8ZmLsA0ku7zqew0ny2SRPJLmr61jWIsmpSf4syb3N34lf6zqmw0lyXJJvJvmLJuZ/03VMa5HkmCTfSfKlSV7X5N+uW4EzquqngL8Erug4nsMZOaV12jQTYn8XeBOwDbiomSQ7zT7HbE2wPQi8v6r+HvCzwKUz8L/x88Abq+qnge3Aec0DpdPu14B7J31Rk3+LqurLVXWwefsNXjjaYuqsMqV12pwFPFBVD1XVD4FrWZokO7Wq6qvAU13HsVZVtb+qvt28fpal5LTiEMZpUEv+d/P2pc2fqV7RkuQU4J8Cn5n0tU3+k/Nu1vg0sw5rpamxakGSLcDPsDSSZao1LZTbgSeAW6tq2mP+d8C/Bn406QvP/QbubVvL1NMkV7H0z+gvTDK2UY5ySuu0OaLpsDp6SV4B3AD8elU903U8h9OMkdne3F/bneSMqprK+yxJ3gw8UVV7k/zCpK9v8l+nw009TXIx8GbgnJqChyqOckrrtFlpaqzGKMlLWUr8X6iqP+46niNRVX+d5H+wdJ9lKpM/cDbwliT/BDgOOCHJH1XVOyZxcds+LUpyHvAB4C1V9VzX8cyRbwGnJdma5FiWtgy9qeOY5kqzt/YfAPdW1Se6jmctkmwcrKhL8jLgXOC+ToNaRVVdUVWnVNUWlv4O//dJJX4w+bftd4DjgVuT3J7k97sOaDVJLkiyD/g5lqa03tJ1TKM0N9EvA25h6Ubk9VV1d7dRrS7JF4GvA69Lsi/JJV3HdBhnA+8E3tj83b29qVCn2Sbgz5LcwVKBcGtVTXT55CxxvIMk9ZCVvyT1kMlfknrI5C9JPWTyl6QeMvlLUg+Z/KV1SnJxkr9q/lzcdTzSWrjUU1qHJBuARWCBpRETe4Ezq+rpTgOTDsPKX1qjJP+g2ZvhuCQ/keRu4FKWHiZ6qkn4tzJbo5vVU872kdaoqr6V5Cbg3wIvA/4I+L84YVQzyMpfOjIfAn6RpTbPx3DCqGaUyV86MhuAV7A0s+k4nDCqGeUNX+kING2fa4GtLA0S+w2WbvL+/eYj32bphu/M7NqlfrLnL61RkncBB6vqmmYf4f/J0l6xH2ZpiiTAh0z8mgVW/pLUQ/b8JamHTP6S1EMmf0nqIZO/JPWQyV+SesjkL0k9ZPKXpB76fxaNbKEfPGB+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_dict = gbm.dump_model()\n",
    "model_list = make_model_list(model_dict, X)\n",
    "feature_importance = calculate_feature_importances(model_list, gbm, show_plot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show Feature Importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EBM_FI</th>\n",
       "      <th>coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x0</th>\n",
       "      <td>36.543692</td>\n",
       "      <td>24.483835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>6.370000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>39.087931</td>\n",
       "      <td>26.159495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x3</th>\n",
       "      <td>75.490736</td>\n",
       "      <td>50.329661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x4</th>\n",
       "      <td>20.812964</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x5</th>\n",
       "      <td>135.344171</td>\n",
       "      <td>89.961696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x6</th>\n",
       "      <td>0.178805</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x7</th>\n",
       "      <td>124.866644</td>\n",
       "      <td>82.295561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        EBM_FI       coef\n",
       "x0   36.543692  24.483835\n",
       "x1    6.370000   5.000000\n",
       "x2   39.087931  26.159495\n",
       "x3   75.490736  50.329661\n",
       "x4   20.812964  20.000000\n",
       "x5  135.344171  89.961696\n",
       "x6    0.178805   0.000000\n",
       "x7  124.866644  82.295561"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FI = pd.DataFrame(np.mean(np.abs(feature_importance), axis=1), index=X.columns, columns=['EBM_FI']).assign(coef=coef)\n",
    "FI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare to Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR-scores   - l1: 12.29, l2: 14.67\n",
      "eLGB-scores - l1:  2.32, l2: 17.1\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></th>\n",
       "      <th>EBM_FI</th>\n",
       "      <th>coef</th>\n",
       "      <th>LRCoef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>x0</th>\n",
       "      <td>36.543692</td>\n",
       "      <td>24.483835</td>\n",
       "      <td>24.601528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x1</th>\n",
       "      <td>6.370000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.032809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x2</th>\n",
       "      <td>39.087931</td>\n",
       "      <td>26.159495</td>\n",
       "      <td>26.124502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x3</th>\n",
       "      <td>75.490736</td>\n",
       "      <td>50.329661</td>\n",
       "      <td>50.388726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x4</th>\n",
       "      <td>20.812964</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>-5.342284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x5</th>\n",
       "      <td>135.344171</td>\n",
       "      <td>89.961696</td>\n",
       "      <td>89.880649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x6</th>\n",
       "      <td>0.178805</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.051978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x7</th>\n",
       "      <td>124.866644</td>\n",
       "      <td>82.295561</td>\n",
       "      <td>82.296390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        EBM_FI       coef     LRCoef\n",
       "x0   36.543692  24.483835  24.601528\n",
       "x1    6.370000   5.000000   0.032809\n",
       "x2   39.087931  26.159495  26.124502\n",
       "x3   75.490736  50.329661  50.388726\n",
       "x4   20.812964  20.000000  -5.342284\n",
       "x5  135.344171  89.961696  89.880649\n",
       "x6    0.178805   0.000000  -0.051978\n",
       "x7  124.866644  82.295561  82.296390"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "pred = lr.predict(X_test)\n",
    "print(f'LR-scores   - l1: {mean_absolute_error(y_test, pred):.2f}, l2: {np.sqrt(mean_squared_error(y_test, pred)):.2f}')\n",
    "print('eLGB-scores - l1:  2.32, l2: 17.1')\n",
    "\n",
    "FI.assign(LRCoef=lr.coef_)"
   ]
  }
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