diabetic_DL_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Diabetes Data Set</b> (reference to UCI Machine Learning Repository <a href=\"https://archive.ics.uci.edu/ml/datasets/diabetes\">description</a>)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coding: utf-8\n",
    "import multiprocessing \n",
    "multiprocessing.set_start_method('spawn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tb 2018-09-14 \n",
      "\n",
      "CPython 3.6.4\n",
      "IPython 6.2.1\n",
      "\n",
      "sys 3.6.4 |Anaconda custom (64-bit)| (default, Jan 16 2018, 12:04:33) \n",
      "[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "numpy 1.14.2\n",
      "pandas 0.22.0\n",
      "sklearn 0.19.2\n",
      "torch 0.4.0a0+9692519\n",
      "IPython 6.2.1\n",
      "\n",
      "compiler   : GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)\n",
      "system     : Darwin\n",
      "release    : 17.5.0\n",
      "machine    : x86_64\n",
      "processor  : i386\n",
      "CPU cores  : 24\n",
      "interpreter: 64bit\n",
      "\n",
      "GPU Name: TITAN Xp\n",
      "GPU Memory: 12.0GB\n",
      "CUDA Version: (9, 1, 0)\n",
      "GPU Free/Total Memory: 86%\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 14, 8\n",
    "\n",
    "from IPython.display import display\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=4, threshold=10000, linewidth=100, edgeitems=999, suppress=True)\n",
    "\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', 30)\n",
    "pd.set_option('display.width', 200)\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "pd.set_option('precision', 6)\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler, Normalizer\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import ShuffleSplit, KFold\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import ShuffleSplit, KFold\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score, KFold, RandomizedSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import validation_curve\n",
    "from imblearn.pipeline import Pipeline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import validation_curve\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "from lib.utils import *\n",
    "\n",
    "from lib.classifier import Classifier, torch_weight_init, Stopping, OutlierSampler, exp_decay, to_var, PytorchLogger\n",
    "\n",
    "from lib.model import MLP\n",
    "\n",
    "%load_ext watermark\n",
    "%watermark -a \"tb\" -d -v -m -p sys,numpy,pandas,sklearn,torch,IPython\n",
    "gpu_stat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters\n",
    "HP = HYPERPARAMETERS({\n",
    "    \"GPU\"                 : True ,\n",
    "    \"SEED\"                : 0\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#torch.cuda.is_available = lambda : False\n",
    "\n",
    "# Seed random generators for repro \n",
    "np.random.seed(HP.SEED)  \n",
    "torch.manual_seed(HP.SEED);\n",
    "if torch.cuda.is_available():\n",
    "    torch.cuda.manual_seed(HP.SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://archive.ics.uci.edu/ml/datasets/diabetes\n",
    "dataframe = pd.read_csv(\"data/diabetic_data.csv\")\n",
    "dataframe.drop('encounter_id', axis=1, inplace=True)\n",
    "dataframe.drop('patient_nbr', axis=1, inplace=True)\n",
    "dataframe.drop('payer_code', axis=1, inplace=True)\n",
    "dataframe.drop('weight', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe['readmitted'] = dataframe['readmitted'].apply(lambda x: 1 if x == '<30' else 0)\n",
    "\n",
    "cols = list(dataframe)\n",
    "cols.insert(0, cols.pop(cols.index('readmitted')))\n",
    "dataframe = dataframe.loc[:, cols]\n",
    "\n",
    "feature_names = dataframe.columns[1:]\n",
    "target_names = ['no', 'readmitted']\n",
    "\n",
    "# Transform data form categorial to numeric\n",
    "cat_variables = [i for i in dataframe.columns[1:] if dataframe[i].dtype == 'object']\n",
    "encods = [LabelEncoder() for col in cat_variables]\n",
    "for i, col in enumerate(cat_variables):\n",
    "    dataframe[col] = encods[i].fit_transform(dataframe[col].astype(str))\n",
    "    \n",
    "data = dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dataset statistics:\n",
      "===================\n",
      "number of features:       45\n",
      "number of classes:        2\n",
      "data type:                float32\n",
      "number of train samples:  68183 (pos=7590, neg=60593, size=12MB)\n",
      "number of test samples:   33583 (pos=3767, neg=29816, size=6MB)\n",
      "labels                    ['no', 'readmitted']\n"
     ]
    }
   ],
   "source": [
    "X = np.float32(data.values[:,1:])\n",
    "y = np.int64(data.values[:,0])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, shuffle=True, random_state=HP.SEED)\n",
    "\n",
    "dataset_statistics(X_train, y_train, X_test, y_test, target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      " [[16772 13044]\n",
      " [ 1442  2325]]\n",
      "Accuracy  0.5686508054670518\n",
      "Recall    0.6172020175205734\n",
      "Precision 0.15127854772594182\n",
      "F1        0.24299749163879594\n",
      "ROC AUC   0.5898593935201472\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "    'module'                       : MLP,\n",
    "    'module_H'                     : 25,\n",
    "    'module_D_in'                  : X_train.shape[1] ,\n",
    "    'module_D_out'                 : 2 ,\n",
    "    'module_dropout'               : 0.5,\n",
    "\n",
    "    'module_initialize'            : torch_weight_init ,\n",
    "\n",
    "    'max_epochs'                   : 1000,\n",
    "    'batch_size'                   : 80 ,\n",
    "    'batch_shuffle'                : True,\n",
    "    'validation_ratio'              : 0.33,\n",
    "    'validation_shuffle'            : True,\n",
    "\n",
    "    'optimizer'                    : torch.optim.SGD ,\n",
    "    'optimizer_lr'                 : 0.5 ,\n",
    "    'optimizer_momentum'           : 0.9 ,\n",
    "    'optimizer_weight_decay'       : 5e-4 ,\n",
    "    'optimizer_nesterov'           : True ,\n",
    "\n",
    "    'criterion'                    : nn.CrossEntropyLoss ,\n",
    "    'criterion_size_average'       : True ,\n",
    "\n",
    "    'scheduler'                    : torch.optim.lr_scheduler.LambdaLR ,\n",
    "    'scheduler_lr_lambda'          : exp_decay ,\n",
    "\n",
    "    'stopping'                     : Stopping ,\n",
    "    'stopping_patience'            : 80 ,\n",
    "\n",
    "    'scoring'                      : metrics.f1_score ,\n",
    "\n",
    "    'random_state'                 : 10\n",
    "}\n",
    "\n",
    "pipeline = Pipeline(steps=[\n",
    "                ('scaler',     StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
    "                ('sampler',    SMOTE(random_state=HP.SEED)),\n",
    "                ('classifier', Classifier(**params))\n",
    "                ])\n",
    "\n",
    "y_pred = pipeline.fit(X_train, y_train).predict(X_test)\n",
    "y_prob = pipeline.predict_proba(X_test)\n",
    "\n",
    "print('Confusion Matrix\\n', metrics.confusion_matrix(y_test, y_pred))  # Confusion Matrix\n",
    "print('Accuracy ', metrics.accuracy_score(y_test, y_pred))            # Accuracy (TP + TN) / float(TP + TN + FP + FN)\n",
    "print('Recall   ', metrics.recall_score(y_test, y_pred))              # Recall (Sensitivity)   TP / float(FN + TP)\n",
    "print('Precision', metrics.precision_score(y_test, y_pred))     \n",
    "print('F1       ', metrics.f1_score(y_test, y_pred))     \n",
    "print('ROC AUC  ', metrics.roc_auc_score(y_test, y_pred))     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy score: 0.5899424904707843 0.018041708037915005\n"
     ]
    }
   ],
   "source": [
    "#  Cross-validate score on test set\n",
    "cv = KFold(n_splits=10, shuffle=True, random_state=HP.SEED).split(X_test)\n",
    "scores = cross_val_score(pipeline, X_test, y_test, cv=cv, scoring='accuracy', verbose=0, n_jobs=-1)\n",
    "\n",
    "print(\"Test accuracy score:\", np.mean(scores), np.std(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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k0Lp1WzZtWk+HDp3cx5jnynUfm5ubS3BwMJMmTWTkyHF07dqdNWtWMWHCWCZPfsf9fmRkVG495REJkZKRTtjdt5F3/4NkT5tZ3cURwmtZLGbPRnZ2NgsWzGXx4u8BeOGFp9zd8aed7gU5n9K2N2nSlJ07d9C8ucquXTsA84rgL7+sYNy41zAMgwcfvId//KMPderUPef4qKho9u/fR7Nmzdm2bQsxMbEcOLCf3NwcJk9+h5SUFAYPHoCqtkLTdvP6629RUFDAnXfeTJ8+N7krhZCQ0BJf4BWJp6L7NW+usmXLJjp16sK6dWvo1KlLie3t23dg7drVXH31dezbt5fatWvTq9e19Op1LWAOG/nmm8U8+GB/9uzZzezZ7/LPfz7IyZMn0XWDsLAwQkJCCAwMdL8mO3Zsd5/fTPZKTo4VQohLwcWqg3r06FLhOiglJeWc+uT6628kOzubzMwMQkJCmTZtMr1730hubg4pKSlERUW566jicYWGhlGrVi3eeGMKQUFBrFr1G/7+JeexhoeHk5V1Zh5uWWGW9Tq99dbrPPvsiyxYMJeOHbuUGMnQrl0ca9eupnXrtqxbt5q4uI4lztuuXQe+/PJzCgoK0HWd+PhDNGgQA8CmTRtKJI2tWrVh3rxZFBQU4HA4SEg4ROPGTQkODnH3HEVFRZWIKSsr051MVhaPSIgMP38ArEeOVHNJhBBgXvFp1y6OAQMewN/fn+DgYFJSkqlbt97fOu8DD/TnX/8aw88//0hUVDQ2mw0fHx9CQkLo3/9+goODueyybu6rfmd76aVRTJ06CcMwsFqtjBjxClFR0XzwwTyWLv0fNpudgQMfJzIykrS0VB555H78/QO4774HSlwh8/HxISIiklOn0ir9S/npp59n0qSJzJ07k4YNG3H11dcBZkU1adI0br21H2+99TqDBvXHMAyGDh153nO1bNmK9u078Pjjj2AYBkOGvFT0OrzCuHEjsVpt2Gw2XnrJHBuu6zrJyUk0btykUmMSQoiLqarroJUrfyY0NKJCdVBp9YndbmfIkJcYNux5LBYLLVqotG7dhuHDRzFq1DAsFoXg4BBGjhzHwYP73eeyWCw899xQ9xzSgIBAXnllfIkyduzYmd9//9Xdk/VXXqfPPvuE8PAI7rzzHvz9/XnzzX8xb94c93H9+t3FhAljGTx4IHa7nbFjzZEqs2a9w9VXX0fr1m255ZbbGDx4IGDw8MMDCQkx72F9+HACN9xwc7HXJ4q77rqPp556DF3XGTToSXx9fXn++WFMnToJXdeL6q/h7mP27tV44omKL6ZREcrZGfMlyEhOziKyeSx6vXqc+m1d+Udc4i6VVbgqk8TsPcqKe+3aVYSFhdOqVRs2blzPxx9/wLvvzil136r2449LSUtL5d57/+9vn6umvNdr165C0/bQv/+jF3xsdHRwxbrIvJNRE97fi6mmfKYvJonZ852ug666qhtLlvxUrXXQ+ei6zrPPPsHUqTOx2+2Vdt6a8l4fOnSQTz9dxIgRr1zwsWXVUx7RQwSg16qFpWg1CiGEZ6pbtz6vv/4qVqsVXddLLPN5sf3jH32YMGEMubm5FVp6u6YzDIMff1zGsGHn73ESQghvdroOmjPHh4ICR7XWQedjsVgYMGAQX331+XlXXL2ULV78KY8+OrjSz+sxPUSht9+Ez5pVJB9NhUrMiGuimpKlX0wSs/fwxrg9IWbpISqT9BB5AYnZe3hj3J4Qc7X0EKmqagFmAXFAAfCopmn7i22/ETh9V6UtwFPAS8ANRY+FAXU0TSt9MsBZ9KIbEVpSU9BLmUwthBBCCCGEEGeryjvv3Q74aZrWHRgBvH16g6qqwcBk4BZN07oB8UCUpmlvaJp2taZpVwOJwMMVfTK9aJlCGTYnhBBCCCGEqKiqTIh6AksBNE1bBxRfO7YHsAN4W1XVlUCSpmnJpzeqqnoHcErTtGUVfTK9lnn/IUvyyb9fciGEEEIIIYRXqMpFFUKA4ndNcqmqatM0zQlEAdcAHYBsYKWqqms1TdtbtO/LwD8r+kTR0cHQxFyrPTQvE4qtle6por0gxrNJzN7DG+P2xpiFEEKImqAqE6JMoHgNbylKhgBSgY2app0AUFX1d8zkaK+qqq2B9OLzjcqTnJyFj38IoUD2wcPkXeKTvsrjCRPbLpTEXHM89dRjDBgwiM6dL3M/Nm3aWzRt2oxbb739nP2PHz/G2LEjmTdvIWPHvszo0a+WWAp03bo1rFixnFGjxgHnxl1QUMDy5T9w6623s2TJd4SEhNCzZ6+/VPbTNy0dP/71v3T8+WRkpDN37kyGDx9V7r4ff7yQzp270Lr1mTuJn++9XrXqdxYufA+r1crNN/elb99+JbafOpXGm29OICsrC113MXr0q9Sv34BPP13ETz8tB6B79ysYMGCQ+5jffvuFX375iXHjJgKwYcM6Zs9+Fz8/f7p27U7//o+SmprChx8ucN+3qCIkoRNCXAxVXQedrbQ6qF+/8u/xU1N89NH7XH55d1q2bFUl509MPMLEieNQFIUmTZoyZMhLWCxnBqC5XC6mT5+Kpv1JYaGDAQMGccUVV5Za95x26lQaAwc+yNSp5v34NG0Pb731Gna7D82bt+C554bicBQyefLrjBo1rsI3Ri9LVQ6ZWw3cBKCqajfMIXKnbQbaqqoapaqqDegG/Fm07R/ADxf6ZO4hczKHSIgq1bdvP5Yu/Z/7d4fDwerVK/nHP/qUe+z48a9f8H0R0tJS+e67rwG46aZb/3IyVJXmz5/NHXfcU6F9H3ywf4lk6HycTifTp09hypQZzJgxj2+//YrU1JQS+8ya9S69e9/IzJnzeeyxJ0lIiOfo0USWL1/KnDnvM3fuB2zcuI79+/cBZqNh7twZGIYOmPereOONfzFhwiRmz17A4cMJbN++jcjIKAICAtm6dfMFvhJCCFG1pA6quKSkExw4sL/KkiGA6dOn8Nhjg5k16z0Mw2Dlyt9KbF+2bAlOp5PZs9/njTfe5ujRI+ete8Cs+yZNeg0fH1/3OSZNmsizz77IrFnvERgYxI8/LsXX14+2bduX+Cz8HVXZQ/QV0FtV1TWAAjyiquoQYL+mad+qqvoycHqO0Geapu0s+lkFfrzQJ3MvqiBziIQXGTfOl+++q9w/41tvdTJuXMF5t1999XXMmzeL/Px8/Pz8WLnyNy6/vCv+/v5s3bqZDz6YD0B+fj6jR48vUfncddetLFr0BcePH+P111/Fz88ff38/goNDAPP+AmvW/E5eXgFBQUFMnDiZjz56n/j4Q3zwwXx0XScyMpLbb7+L6dOn8scf5hdo7943cM89/2TixHHY7XZOnDhOamoKI0eOQ1VblhrH8uU/8Nlnn2C324mJiWX48FEcO3aU114bj81mw2q1Mnr0eGw2O2PHvoyu67hcToYOHUnTps3c58nJyWb37j8ZOrQ5APfeeztt27YnMfEInTp1Kdq+i9jYhrzyyr+YOHEc1113PWlpqaxdu5qCgnxOnDjGffc9yE033eo+b3z8IerXjyEkxHxt2rePY/v2bVx77T/c++zYsZ2mTZvx3HNPUrduXZ57bih2u523356O1WoFzMrFx8cHgHbt2nPVVVfzzTeLAbNnKzg4hPr1GxRtj+OPP7YRF9eB3r1vYMGCuXTs2Lncz4wQwjsNGwaffhpYqees7jrot99+wel0llkHPfbYI3+pDjp16lSp9cnChe+xcuVvuFwubr/9Tm6//U4++eTfrFixHKvVSlxcR5588lkWLJjLzp1/kJeXx4gRr7Bp03p+/HEZiqJw3XXXc/fd95V4rb7+ejHXXHMdAAsWzOXo0UTS09PJysqgX7+7+fXXnzlyJIFRo8bTtm075syZwZ49f5Kbm0ujRo0ZOXIsM2e+g81mY+TI4Tz33GDuvff/6NGjp/s5NG2Pu57o1q0HGzasp1eva9zb169fS9OmzRg27DkMw+CFF4aXWffMmDGN22+/k48//sB9juTkk7RrF+fed9Wq3+jT5yauvbY3L774DDfe+Pd77Kqsh0jTNF3TtCc0TeuhaVp3TdP2aJo2RdO0b4u2/1fTtM5F/94sdtxTmqZ9faHPp0dFA2A5KQmREFXJ19eXK6/sxe+//wLAkiXf0rfvHYB5B+kxY/7Fu+/OoWfPq/jll59KPcd7783m0Ucf5513ZtG2bXvA7K3IyMhg4cKFzJr1Hk6nk927d/HQQwNo1KgxjzzymPv41atXcvz4MebNW8js2Qv48celHDhgjrKtU6cuU6bM4M477+Xbb78s9fkzMtJZsGAu7747m9mzFxAUFMQ33yxm48b1qGpLpk2bxUMPDSArK5Pdu3cRGBjE22+/y3PPDSMnJ7vEuXbt2klsbEP37ydOHOexx55k5sz5fPHFp/Trdzfz5n3IH39sJyur5LC4nJxsJk2axuzZs/n3vxeetS2HoKAg9+8BAYHnPPfx48cIDg7hnXdmUbt2HRYt+hCbzUZYWBiGYTBjxjSaN1fd5bvuuutLHB8WFk5BQT4JCfG4XC7Wrl1Nfn4eAI0aNWbHju2lvn5CCFFdqroOmjZtVrl10C+//PKX6qDS6pO9e/ewfv0a5s1byJw5ZvJ14MB+fv75R+bMeZ85c94nMfEIq1evBKBhw8bMmfM+hmGwYsWPzJr1HrNmvcfKlb9y+HB8iefbunUzTZs2L/HaTZkynauuupa1a1czadJUHnigPytWLCcnJ5vg4GCmTZvFnDnvs2vXDpKTT/L440+xefNGXnrpJVq1alMiGQLzpt6nh6yVVk9lZKSTmHiESZOm8X//9zCvvTb+vHXPkiXfERYWRteu3Uuco169+u4RC6tXr3TXUyEhIWRkpJOdXfI5/4qq7CG6uOx29IgI6SESXmXcuIIyr6RVlVtv7cfMme/QqVMXsrKy3FfAoqOjmTZtMv7+ASWu6Jzt0KGDtGplDhtr164DCQnxWCwW7HY7Q4YMwWKxc/LkSZxOZ6nHJyQcIi6uA4qiYLPZaNOmHfHxBwFo3lwFoFat2udt0B87dpTGjZsQEGBe2YyL68TGjet45pkhLFr0IS+++AyBgUE8/vhTdOvWg8TEw4wY8SI2m42HHx5Y4lzp6elERES4fw8JCaVOHfP2af7+/jRu3ASAwMAgCgtLvlfNmrUAoG7duhQWFpbYFhgYSG5ujvv33NySCRJAaGgYPXteBcAVV1zJvHmzAHPM++uvv0pAQAAvvjii1NcAQFEURo9+lcmTXyM4OITY2IaEhoYBYLVasVqt6LpeYjy4EEKcNnkyDB+eU/6Olawq66Bx40bh7+9fZh104MCBv1QHlVafHD6cQKtWbdzfuc8/P5Sff/6JNm3aYbOZzfS4uA4cOnQAwH2B6+DBAyQlneC55wYDkJWVRWJiIrGxjdzPd3b91KKF+ToFBwfRqFHjop9DKCwswNfXr6gHayQBAQHk5eXhdDqx2Wzu3q8vvvjunNeieP1Qej0VSo8ePVEUhY4dO3PkyOHz1j3/+9+3KIrCpk0b2L9/LxMmjOGNN6YwcuQYpk17m//85yNatmyNj8+ZXr+IiEgyMzPOed4L5VG1nF6rtswhEuIiaNq0GXl5OXz22SfcfHNf9+NvvjmBkSPHMmrUOKKKem1LExvbiJ07/wBgz55dAOzfv4/ff/+VadOm8cILw93zXBTF4v75tIYNG7uHKjidTnbu/IMGDWKL9i9/cmXduvWJjz9EXp55lWnbti3ExMSyatVvxMV15J13ZnPNNdexaNGHbN26mcjIKKZOncnDDw9k7tyZJc4VHh5eoufnQiZ3lrVvo0aNSUw8QmZmBg6Hg23btrqvZJ7Wvn0ca9euLophK40bN8UwDF5++UWaNWvO8OGj3EPnzmf9+jVMnvwOr702maNHE+nS5XLAvOpntVolGRJC1DhVWQe9+urr5dZBTZs2/Ut1UGn1ScOGjdi7V0PXdZxOJ88//yQxMbH8+edOnE4nhmGwbdtWYmLMRMhiUYpiaEijRk2YPn0uM2bM46abbqFJk2Ylns+sn870npRVPa1bt5qTJ5MYP/41Bg16ioKCfAzDIDMzk48//oARI0bw5psTzzmueXOVLVs2FZ1jDXFxHUtsb9++g7ue2rd03pFTAAAgAElEQVRvL7Vrm3P+S6t7Zs6cz4wZ85gxYx7NmrVg9OhXiYyMYs2aVYwcOYbJk98hMzODyy7r6j5/dnYWYWHh5w+sgjynhwhzHpFtz24oKABf3/IPEEL8ZTff3JeZM99l8eLv3Y/16XMTgwb1Jzg4mPDwSFJSkks99sUXRzB27Mt88snHhIWF4ePjS4MGMfj7+3PHHXdgsViJjIwiJSWZNm3a4XA4mTXrXXyL/q6vuOJKtm7dzOOPP4LD4eDaa/9x3rlCpQkLC2PAgMd59tnHURQLDRrE8MQTT5OSksyrr77iTgSeeWYIderUYcyYkXz22SdYLJYSwyYA2rRpx+zZ0//CK1g2m83G00+/wJAhz6DrOjff3Jfo6FocOnSQxYs/Y+jQETz99Au88ca/+PrrxQQGBjF27AR+//1Xtm3bQmFhIevWrQHgiSeePieZOi06uhaDBw/E19eX66+/gSZNmgJw4MB+2rZtV+lxCSFEZaiqOmjgwAfx8bGXWQddc801/Prrqguug5o1a35OfdK8uUrXrt0ZPHgguq7Tr99dNG/egmuv/QeDBw/EMAzat4/jqquuZv/+ve5zNW/egi5dLuPJJwdSWOigVas2REeXTAI7duzMn3/udI9aKEurVm1YuHABgwb1x8fHh3r16pOSksyMGdO4//6HeOCBe9m0aSuff/7fEnOVnn76eSZNmuhO7q6+2pyz9MILTzFp0jRuvbUfb731OoMG9ccwDIYOHQmcv+4pTYMGsQwd+hx+fn507NiZ7t3NYXtZWVkEBQUTEBBQbnzlUQzD+NsnqWbG6eVqg58YiN+Xn5O69U/0oolanqimLsdclSRm73Epxj158mvcdtsd7uEIF6omxjxr1jtcccVV51ztO5/o6OC/v+6p5zJq2vtb1WriZ7qqScze41KJ+8SJ48yYMY0JE94sf+dy1MSYv/zycwIDA+nT56YK7V9WPeVRYyHcK83JsDkhxEX06KNP8NVXX1R3MSpNamoKOTk5FU6GhBBC1Dx16tSlWbPm7NnzZ/k7X2IKCvLZsWM7vXvfUCnn86whc6fvRSQLKwghLqLw8Aheeml0dRej0kRGRjFs2MjqLoYQQoi/qfgNTz2Jr68fY8dOqLTzeVgPkSy9LYQQQgghhKg4z0qIpIdICCGEEEIIcQE8KyGSOURCCCGEEEKIC+BZCVFRD5GSXPoyi0IIIYQQQghRnEclREZUFIbFIj1EQgghhBBCiArxqIQIqxUjIlLmEAkhhBBCCCEqxLMSIsxhc7LKnBBCCCGEEKIiPC8hio7GkpUJeXnVXRQhhBBCCCFEDed5CZEsvS2EEEIIIYSoIM9LiGTpbSGEEEIIIUQFeV5C5O4hkqW3hRBCCCGEEGXzvIQoOhqQHiIhhBBCCCFE+TwvIZI5REIIIYQQQogK8ryESOYQCSGEEEIIISrI8xKi0z1Eci8iIYQQQgghRDk8LiEyIiMxAgKxJsRXd1GEEEIIIYQQNZzHJUQoCq7GTbAeOgiGUd2lEUIIIYQQQtRgnpcQAa7GTVByc7AknajuogghhBBCCCFqMM9MiJo0BcB68EA1l0QIIYQQQghRk0lCJIQQQgghhPBakhAJIYQQQgghvJZHJkTOxkUJ0aGD1VwSIYQQQgghRE3mkQmRUasWemCQ9BAJIYQQQgghyuSRCZF76e34g6Dr1V0aIYQQQgghRA3lmQkR5jwiJS8Py4nj1V0UIYQQQgghRA3l0QkRyMIKQgghhBBCiPOThEgIIYQQQgjhtTw3IWosCZEQQgghhBCibJ6bEDWRpbeFEEIIIYQQZfPYhMiIikIPCsZ6SHqIhBBCCCGEEKXz2IQIRcHVpCnW+EOy9LYQQgghhBCiVJ6bEAGuJk1Q8vOxHDta3UURQgghhBBC1EAenhDJwgpCCCGEEEKI8/PshEhWmhNCCCGEEEKUwVbdBahK0kMkhBDiQqiqagFmAXFAAfCopmn7i20fCvwT0IHXNE37qloKKoQQotJ4Rw+RrDQnhBCiYm4H/DRN6w6MAN4+vUFV1TDgWaA7cD0wrVpKKIQQolJ5dEJkREaih4TKvYiEEEJUVE9gKYCmaeuALsW25QAJQGDRP1nCVAghPIBHD5kzl95ugu3PXeBygdVa3SUSQghRs4UAGcV+d6mqatM0zVn0+xHgT8AKvF7Rk0ZHB1deCS8RErN38MaYwTvj9uSYPTshAlyNm2DfthXLsaPoMbHVXRwhhBA1WyZQvNa3FEuGbgTqAo2Lfl+mqupqTdM2lHfS5OSsyi1lDRcdHSwxewFvjBm8M25PiLmshM6jh8xB8XlEMmxOCCFEuVYDNwGoqtoN2FFs2ykgDyjQNC0fSAfCLnoJhRBCVCovSIiaALLSnBBCiAr5CshXVXUNMBV4QVXVIaqq9tU0bSWwEVinqupaYC/wYzWWVQghRCXw/CFzTaSHSAghRMVomqYDT5z18J5i28cCYy9qoYQQQlQpz+8hatIMkKW3hRBCCCGEEOfy+ITIiIiQpbeFEEIIIYQQpfL4hOj00tvW+EOgyy0jhBBCCCGEEGd4fkKEubCCUlCA5djR6i6KEEIIIYQQogapskUVVFW1ALOAOKAAeFTTtP3Ftt/ImYmpW4CnMBO0KZh3BvcFxmma9v3fLYt76e2DB9AbxPzd0wkhhBBCCCE8RFX2EN0O+Gma1h0YAbx9eoOqqsHAZOAWTdO6AfFAFPAgYNc07QrgNqBZZRTEvfS2zCMSQgghhBBCFFOVCVFPYCmApmnrMHt9TuuBebO7t1VVXQkkaZqWDPQBElVV/R8wH/iuMgriXnpb7kUkhBBCCCGEKKYq70MUAmQU+92lqqpN0zQnZm/QNUAHIBtYWXSTuyigOXALcBXwQdH/ZYqODi57h8viAAg4mkBAefteIsqN2QNJzN7DG+P2xpiFEEKImqAqE6JMoHgNbylKhgBSgY2app0AUFX1d8zkKBX4XtM0A/hNVdUWFXmi5OSssncwfIkMCUXX9nKqvH0vAdHRweXH7GEkZu/hjXF7QsyS0AkhhLhUVeWQudXATQCqqnbDHCJ32magraqqUaqq2oBuwJ/AqmLHxAGHK6UkioKrsSy9LYQQQgghhCipKhOir4B8VVXXAFOBF1RVHaKqat+i+UIvA8uA9cCXmqbtxJw3pKiqug6YBzxRWYVxNZGlt4UQQgghhBAlVdmQOU3TdM5NaPYU2/5f4L9nHVMADKiK8rhXmpOlt4UQQgghhBBFvOLGrFDsXkSy9LYQQgghhBCiiPclRLL0thBCCCGEEKKI9yRETaSHSAghhBBCCFGS1yRERmQkemgYVm13dRdFCCGEEEIIUUN4TUKEouDs3AXboYMoJ09Wd2mEEEIIIYQQNYDHJEQVub2Qo1sPAOzr11ZxaYQQQgghhBCXAo9IiPLyoGPHQKZP9ylzP0fX7gDY16+5GMUSQgghhBBC1HAekxAdP25h06ayw3F07Izh44N9/bqLVDIhhBBCCCFETeYRCVFoqPl/RoZS9o5+fjg7dMK2YztKdlbVF0wIIYQQQghRo3lEQmS1QkiIQXp6OQkR5rA5RdexbdxwEUomhBBCCCGEqMk8IiECCAurYELUTeYRCSGEEEIIIUzelxBd3g1DUWQekRBCCCGEEMJzEqLQUIPcXIXCwrL3M0LDcLVqg33zRsrdWQghhBBCCOHRPCYhCgszACo4j6gbSn4+tu1bq7pYQgghhBBCiBrM4xKicleao9gNWtfJDVqFEEIIIYTwZh6XEJ06Vf6+7hu0bpCESAghhBBCCE92+HDZHSa2i1SOKhcWZv5fkR4ivV59XI0a47Pyd5TMDIyQ0CounRBCCCGE8HaGAXl5kJenEBho4OdXcrvDAampClFRBrZSWum6DkuX2ti82cKddzpp3Vr/y2X5+Wcrkyf74udn0KmTi06ddHr0cBIe/pdPCUBaGuzdayUy0iA6WkfXYds2K1u2WPnzTwtHj1o4ckTB5VJ44YUCHn3U4Y7V5YJjxxQaNDBQym/Sl7Bvn4UffrARGmrQrZuLFi10Nm608O67vixfbsMwzn+sByVEp3uIKvbq5f3fQwRNHI/fJ/8m7/GnqrJoQgghhBCihnA6KTXZKMu+fRY+/9zG0qU2YmOha1cfevVyoqo6vr7lH3/woMKzz/qxYcOZJw4KMrj7bgf9+zuIiDBYuNDORx/ZSU62YLUa1K1r0LChTtu2OnFxLpxOmDnTB02zAjBjhg+33ebk8ccLOX7cwubNZsJRUAAOh4JhQN26Oo0b6zRqZBAbqxMbq+PjA6++6suXX9qxWAwMA1avNstlsxn07OnillucuFywfr2VTZus1KkD99xj5447HPj6wurVVpYuNY+55x4HHTvqOBwwf76dKVN8yco6f3vcx8egfn2DU6dgzBg/vvjCzosvFrJ+vZXFi20kJVno0sXFK68U0L27i8JCWLnSyooVNvLzzXMoCvj5gb+/gcUCK1bY2LHDWuJ5goIMsrPNclx2mQuwcj6KUVa6dGkwkpOz+O47GwMH+jNxYj6PPeYo9yAlNZXIDi3R69Yjbd1WsFw6owejo4NJTs6q7mJcVBKz9/DGuD0h5ujo4Au8ludVjEv9/b1QnvCZvlAS87k0zcJ779mpW9fg2mudtG+vn9PcSk5W+OEHG9u2Wdi924qmWTAMaNBAp149g3r1dOrUMZMDqxVSUhSSkxWCgw369nXSqpXZQ3LwoMJ//2vnwAELrVubCUT9+gaJiQoJCRYOHrSgaRb27LGQnGwhKkonJsagfn2d8HCD0FCDkBCzse7jYza4T5xQOHLEwt69FnbuNBvTfn4G+fklv+4iI80yni5v/foGqqrTubOLWrUMPv/cxvDhfuTkKHTs6CI83MDPz2DrVivHj5sviNVq4HIphIYaXHGFk5QUhaNHLRw7pmAYZ57PajW46y4nvXo5mTPHhz/+KL2Rb7Wa7XuX6/xfzZ06uZg8OZ+GDXW2bbOycaOVZctsbNtW8pzh4QYZGQq6DgEBBnb7uSOyWrVykZ+vcOiQhfBwg3vucZCTA8nJFpxOiItz0bGji/btdWrXNpOY1FSFceN8+fRTu/s8YWEGrVu7WLPGTLY6d3Zx4ICl3EXTbDaDa65xcdttDvLzFdatM3ukmjXTefrpQrp1c5VZT3lMQrRypZU77wxg6NAChg+v2HLaQc89if8n/yZj0WcU9r6hiotZeeRL1zt4Y8zgnXF7QsySEJVJEiIvEB0dzPHjWSxfbmPXLguDBhUSetaIfF03G/RJSQpZWQqdO7vO6V1ISVHQNAv79llISLDQubOLG25wuns0UlIUvv3WRnq6gt1uNuCDgw0iIw0iIgyaNdNLDHnKy4Ply21kZSn06OGkcWNzKJKum/MqDh+2kJamkJpqlsnhMHtQcnPNxCM52expePrpQq691lWirEFBwWRlZZ0ztCkrC956y5f58+04nWc2RkXptG5t9lLUrm2wcaOVVaus6Lq5j81m0LSpjtUKx46V3wgGsyEeGmqwbl3FunxiY3Xq1dNJSrJw9KhCYWH5z2GzGfTq5eKeexz06ePE1zeYr7/OY80aK/HxFo4fVzh+3EJOzrnnql3bfK6gIIPJk/O5806ne5vTCcuW2fjoIzuZmQr33efgrrscBAaeOT47G3butPLHHxYyMhTuvddBbKzZdjcM+OEHs9eqWTOdTp1ctG/vIijIvM6v65CUpBAfbyE+3nyvDx+2kJSkcNNNTh5+2IG1lHzq8GGF5ctt+PpCt24umjXTcTiCmTGjgE8+saPr0KePk5tuclJYCIsW2Vm61Iauw4ABDoYOLbigYXcrV1r54QcbPXq46N3bia8vbN5sYcIEX1avtlGnjk7fvk5uucVJrVo6hgG6rpCfb36+8/MV2rTRiYwsO6fxioRoxw4L110XyGOPFTJxYkGFDrTt2E74dVdSeM11ZHz6VRUXs/J4a0UjMXsHb4zbE2KWhKhMkhBVkaNHFT74wE7v3i66dj3TWF++3Mq77/oQG2vw0EMOunZ1oShm42n3bgt5eYq7JyAszKB27XPncpyPrsPx42YjMy1NwWIxG8yJiQHMnKmTmGhe8Y+J0Zk5M59u3VwkJipMmeLD55/bKSg486dSq5bOgAEO7r3Xwbp1Vv7zHzsrV57bsK9bV+feex3s22dh2TJbiSTjbIpiEBenc9VVTlJTFb791l5i+FL9+mYysmePhdzcC/uzvekmBy+8UMiGDVYWL7azZYuVsDAziWnYUCcnRyElReHgQQunTinExuqMHVuArsPPP9v47Tcrx46V7CLq3Nm8qn/VVWbD28fnzLbsbLOX5vhxM+nQdYiONoiKMoiPt/DVVzZWrLBRUKDQs6eT++833+vduy1s327lxAmFmBiDRo10GjXSadZMJyio5HuZmqqQnq6Qng5ZWeb9LB0OBZcLatc2iIkxe3+KD7Er7fNtGJCZCUePmonWjh1WNm+2snWrhRYtdKZNy6dRo0u3zV3e33RamtkbFR1deTEaBpw8aZ6zMgZyeUVCdOSIQufOQdx9t4OZM/MrfHDYrX2wr19L2prNuJo1N2ezuVxU+JuxGnhC4+lCSczewxvj9oSYJSEqk9ckRA4H/Pijjdq1/enU6dyegws5z8qVVlautHHokJl8ZGQo9O1rzpeoW9fgv/+1MXq0n7ux37u3uW3hQjvff28vcb4WLVxYrbB3r+W8Q4jCwgyCgkq2iSwWsFrNhpnTad7PPSNDKZHUFBcQYA4VCgkxmDHDp6hcLn75xUphoUKjRjrt2rmoU8fA6YQvvrCfM9fi8suddO9uJgf16hksWWLj00/t7rkQrVu7uP9+By1a6DidUFCgkJVlNuyTky1s3Wph0yYrDoe5f/36Onff7aBOHYPVq62sXm0lM1OheXOdVq10mjQxr6xHRhqEhJjDoex2c2jY6eRj3z4LL7/sy/r1Z7ICi8Wga1eF5GQX8fEWd5JmsxnUqmXwwAMOnn668JzmVG4uJCZaSExUaNZMd/d2/FVZWWZvVu3aF6896wnf2RfKE2L2ioQoKwuaNg2md28nixblVfhgn2+/IvTRhynsfgX4+GDfuB49KJj0pT+jN4ipwmL/dZ7wobxQErP38Ma4PSFmSYjK5PEJUVKSwqJFdhYutHPihHkpt3dvJ5Mm5VO/fvntDJfLTFa2bbOwerWNZctsJeYoFJ+3YLMZtGyps3OnlaAgg+efL2TFCitr155prHft6mTSpALS0hQ++sjO99/bsNuhbVtzDkNoqEFhIRQWKqSlmUPYkpIU8vLOPKdhmOXSixbx8vExJ+IHB5/pcahVy0DXzf0aNPDj6quzCAkx91+3zsqTT/qRmGghNlZn2LAC7rrLWWKIUnY2/Oc/5nCjLl1c3HefgyZNzn29srPNHpaGDXXat9fLTTSzs83J8P7+5pCn4lfXdd38d6GLChgGfP65jR9+sNG9u4vbbnPStm0QyclZOBzmZyA42JyH81cT4UuFJ3xnXyhPiNkrEiLDgHr1gujUSed//8ut+NEOBxGXx2E9mgiAq2EjrAnxODp0JP3bZTWyp8gTPpQXSmL2Ht4YtyfELAlRmS6phCg9HV55xY9bbnHQp4+r1H2cTti+3cKKFeaQpa1bzVZ+UJDBvfc6iI/3YcUK8/dHHy2kYUNz+V2bzewdOHpU4dgxCydOmInI4cMlh2/Vq6dz881O+vRx0rKlTnS0mcAsXmxj1iwf9u61cuWVTqZNyycmxlwl65dfrHzwgQ99+phDp4onAXl5ZkJT2nyJylLa33FmJmzcaOXKK10lhoJ5igv+7nI4zEzsEs+Y3HF7SDwVcc57XVCA9dBBLOmnMCxWsFpQcnKwJh7BcjgB7HYKbr0dVwv1zDFOJ5bjx1AKC8DhBB87rkZNyl7YzDBQsjJR0tKwZGXiql0XIzr6zGuu6yhpaWC1YAQGUeofmsOBkp1FVIuGnp8QAbRqFUhEhMHq1ReQEAHWvRrWfXtxXN4NIyqK4OeexO+/i8i7/0Gyp86ocR90T2g8XSiJ2Xt4Y9yeELMkRGW6ZBKiwkK4915/Vq+24etr8PXXuXTubHaRGIY5eXrJEhvr1lndQ7hsNoOuXc1leu+910FQEERFBTN9eh5jxviVe3/A0FBzpa+4OHNlsM6dXbRrd+5qZKfpOhw4YKFp0/PvUyHZ2ViPHMYIDDRHhFTkZC6X2Qgu5WJpmX/HTieWwwlYTybhiolFr1cfANv2rfgu/gyf337B1SAGZ1xHnB064arfACMqCj0i8kwDT9ex7tqJz68/47P6d4zAIJzt2uNs1x7Dzx9LSjJKSjKKy4Xh54/h54eSlYU1/hDWhHgwdFyNGuNq2Ai9dl3w9cHw8UUpLMBy9CjWo4ko6ekY/v4YgQGgG1gTDpmN3rQ0XPUb4GrUGL1+A/eM/UA/G7np2VCQj1JYtMJv0dutZGWhZKRjSU9HSUvFkpKCJTMDPTAIPTYWV0wsWCwo2dko2VnotWrj7NAJZ1wHDB9fs2GdeATr4QSs8YewJMSDry+Oyy7HcVk39JgYs5GckoLlxDF3nEpGBkZYGHpYOEZYGIafH4Z/AFgsWE6dMsuSkQFOB4rDgaEo6HXrocfE4qpXzyy7wwmFBVgyMlAy0lEyMzEio3A1iEGvW5egE4k4Vq3B9udOsNnM97RBDIaPD0puHkpeLlit6EFBGEHBZtJk6ODSUXJzsKSloqSloRQUmFn66c+e02l+vhQF/P0xAgLNz2dICEZICIafP0puLkpONkpBIUZwMHp4OPj4mJ+vgwfMC/w2mxm3jy/4+GBYreb/AQEYgUEYAQFgs4PViqEoKIUF7nIr2dnm+bMyQVEwfP0w/PywBfjjsJrjKZW0VPO11su/B5KjbXucnbpg+3Mntl07UPJKjuLSIyNx9LgSR1wHLEknzBiOHC4qR45ZFqez5DFBweixDVGys7GcOIZSeGYxNcPHB8NedPXDoqDk56OcXqu7+HJ9Z/GohKhbt0CysmDXrpy/d8a8PHNu0R/byJo0lfz+AyuhmJXHExpPF0pi9h7eGLcnxCwJUZmM5OQsBg40G9FDhhTSps1fv5liRblcZrvKYjF7SLZutbJunbmscbduLvr1c7hvag5mwvPMM3589pmdzp1dbN1qISrKYPnyXEJCDJ55xo///c+cm9O0qXkDx6uvdtGrl9M9TOy005/ptDTzhownTyqcPGnB5TLntDRoYFC3rk6d2jr+eg5Kfj5GePiZLhzDQElJwZJ80gzCZgOHA9vePVh378KamEjhVVdTcMttnF6SS8nKxL5hHbZtW7Ft34btz12AgREYaDYAARxOFEchlqQTWNLSzsQeEIizRQucbdrh7NQFR6cuGMHBZuPs0EFse/7EtuMPbH/uRMnNRQ8NQ69dG712HfToaPToWgTUiSY3NQMlN8dsyGWkmw3w5JNYDyeUaNQZAYHooaFYjx8zf/fzO9NoO4thsZiTesBsQF9kho8PengElpNJKH+hzWhYrRgRkehR0egREVhOncJyOAFL9pnvPMPXt8zYDKsVvUEMSk42lpSU8+6nh4RihIebSUxGRqnlNRQFIyQU7HYMmw3F5TITyQuMzfDxwdmmLbh0rEcSsJw6VaK86Pp5z2nYbGay6+9vZvhOp/kHeLpMRXdvdSc/FUg8wEwuXDGxYIBSkG8mH0VJluIoRMnLO+/n7HS5jaBgjKAgjKLVJ5T8fMjPx+ooxMgvgMICjJAQXM1VnM1boEdFm+VzuTD8/HDFNkRvEIMlJdlM9n/+CcXpxLBacbVsjVNtWZSQ2VAyM7GvXe3+O3DHERaGERrmTt70iAiM8AiMoCAsx49jPXQAa0ICemgoer16ZoJvGEVJVJb5d67roJtlMoJCMIKD8f3ft96REN14YwA7dlg4ciT7b3fqWI4cJvz6XiiZmaSt3YIe27ASilo5PKHxdKEkZu/hjXF7QsySEJXJSE7O4qGH/Fi61GzY9u3r4LbbnAQEmKubtWxZ+pKxum4OYUtLUwgJgVq1yq+zd+2y8PbbPixZYkPXFRTl9BK9Jd8iHx+D6693EhenU7++zo5tBrPnBdCpk4svv8zlo4/sjBnjR7tWBThcFvbstdOjh5Pp081haiXk5Jg9DKdOYUk/RWhWKrk7dmONP4SSkQ5WG9is5hXynKyi3oMMLKfS3Fd3DYsFIyISIyAQS9LxCjX+9aBgCq+/AevhBGxbN6O4zgzx06NrmQ3tHPNKMwA2O4bdbiYxMbG4GsSiZGdi0zSs+/eWuNJ8NsNqxdWiJXpUNJbkJDOpKtYILvUYRcGIiMDVqAmups3Qa9fBcjgB2769WJKOU3hlLwruvJfCa65DycjA/sdWbDv+wJJ0wuzJSE1DKcgHpwOcLlxqSwqvvpbCq65BKSwwk7Sdf4DLhR4djREZhWGzo+TnoeTlYQQEFPUKNQaLgjUh3uxtSUmGgkIUR6GZbNRvgKt+jJlM5JsNcQzD7E2qW8/MqgsKsB45jOXYUTM4i4WwiCBO5ToxfP3O9GQVtSuN4GCM0FBzGNPZjTLDMD8X4O5BUZKSzPi3bTWfu6jX5fT/Zi+LgfXgfuzr16GkpJi9aJFR6LVq4WrUGCM84sxzuFzmUKuitZkV3YUeFoERFnbu2MmCAixHE7GeOA4WC4bNBj4+7gTLCApGSUkxE59jRwlt15Lkek0ovma6kp0Fum72Rtnt5h9vbi6WnGwzKbFYzKFlAf5mzBVtqBoG5ORgycqE/Hyz1yjIHBqmZGb+P3t3HmdT/cdx/HXuPnd2exQiHfpRlEJpQQsVbaTSokX5tdOifZN20vJr1U6kbKWSipCkkhbKiVLas8w+c/fz++PODJMxBrOZ+34+Hh7j3nPuOZ/vNXXnM9/P9/PFkb0JigLE9toLOz1j+9eLRDAKC+IxRYsXlHk92P7k8svNiu3s55S9sT8AACAASURBVJSxcSPO39YR2bd9PAEsZ3zOtT/iXLWKWPPmRPduU7lx7ISEWEMEcMYZScyb52Lt2rwyPdx3lnfqZNIuv4Sic84nf+wju37BKlIffnjaURpz4kjEcdeHMSshqlDpWtd585zcf793q40P09Ntnn66qHSfl7//NrjuOi/vv+8q7YrmdtvcfXeQoUPDW/0sFYvFF/E/84ybd96JJ13t20dp2NAmEozhDBXRaf8o3Xp5abcvvD8lh6mve1j1T6My12nNWj7xH02T9AAUFDIsdywvcAEAV7ie4IEW4zH2bRMvbercBcfvv+N57x08ixZUmEyUeTO83vgPyympxBo0IJbZALw+HBs3YKz/B6OggNgeexBrsRexJk3iL4rEp7ui++xDpMN/iDVqjPetmfimTML5x+/YTieRLgcROvwIIl0PIbJ/Z2JNm+3QPxKRCM5V3+Ne9jmuL7/ACAaJ7t2GaJu2RNvtS6T9fluXygWDODasx7H+HzKdUbKCdmmZk52RgZ2atltt/L6j6sP/u3ZGIo67Pow5YRKi4cN9TJ/u5quv8mnevArGFYmQefghOH/5mU1LvyK2V8tdv2YVqA/flDtKY04ciTju+jBmJUQVKrOGqKQBwJo1DgKBeJez555zEw7DTTeFaN8+ytVX+9iwwUGHDlFat47RoIHNnDkuNm50MHBgmAcfDBD+8TfWvPQ5i5al8OofvfklO/5b1YPa53LjUR/Tlzl4PvsE1zdfl5Zr2V4vsfQMnP/8jQ1YmKz2duSX9I5sStqTMxq+R5ughZGXh53spyijGbf9fQUHJn3HGY6pOP/6M17G9i+R/ToSPuhg7AYNiKVnkLJPK7Ia7BH/rX2jRvH6vZIavn/vRLorolGc339HrHXr+G/da1F9+O94RyXimCExx10fxlzR59QONl2s2zIy4klQVpZRNQmRy0XhyOtJu+xi/OPH1qlZIhER2T0ZBvTuHS2dDYJ4Cd355ydx993xZMHrjvLgoa9zedIEjKgDNhj82qkpQ766gTfeMHl7WpQiuwPQAYAU8hjKCwzlRY5YtRBjVfy6tssVX6jfaX+MTZtwrvsZxz//EDy+P6E+x9C499E0bN6C7qVTToP5dxHYTQD0IZsr4vGvX4/7q2W4vlqOnZFB8Nh+xFq1LvOalMapRLb84WmLdTBVyukk2rFT1V9XRBJKvUyIttfRZkcETxlIZNwD+Ca/QuHV19SZWSIREak/Dmq7iY+u/4zh93cgd0OYF8Jn0+mTFWXO2QdYxCRu5F7esvvTtsEmOnR20/GIdPplLCZ9xVKcvyRR1Ho40XYm0fYdCO/fGUoaClQRu3FjQsf0JXRM3yq9rohIbamXCVFWVhVWbmiWSEREqkskQurll+CdOY1GsRjzgFhmJsF+J5LT/w7Ch3SPTynZduki6JsiYW70+7HT9tjiQqdSwKm1NQoRkd1avUyIcnKq9rplZomuHRXvuCIiIrIrbJuUG67FN/11Ih32I3jiSYSO7E3kwIPiHbW29bIaDFFEJBHUq9YnJQlRdnYVr+11uSgafjlGJILvtVer9toiIpKQkp58nKSXnyfccX+y3v6AwutuJHJItwqTIRERqXr1LCGKf63yhAgInnIadlISvldfifc3FRER2Umed2aTfOctRJvtQe7E16B4E0QREal59SohSk+vphkiwE5LJzjgFJw/r8X9ycdVfn0REUkMRk42qVcMhyQ/uZOmEmveorZDEhFJaPUqIcrMrL6ECCAw5FwAfJNerpbri4hI/Zf0wgQcebkUXDOKSKcDajscEZGEV68SouqcIQIId+tBpO0+eGfPwsj+904NIiIi21FYSNIzTxBLzyAw9ILajkZERKhnCVFSEvh8drUlRBgGgbPOxQgG8U57vXruISIi9ZZv8is4Nmyg6IKLsFPTajscERGhniVEEJ8lqraECAicfia2y6WyORER2THhMP7/PYqdlETRsEtrOxoRESlW7xKizEybnJzqS4jspk0JHdMX94pvcC9ZXG33ERGR+sU7/XWcv/1K0ZBzsRs1qu1wRESkWL1LiOIzRNXbGbvwiqsBSL7r1vju4cWMTRvxvfgchELVd3MREdkt+Z98HNvloujSK2s7FBER2UK9S4gyMsC2DfLyqu8eka6HEOx/Mu5lX+B5a2b8yVCI9HPPJPX6EfgmvlR9NxcRkd1SdM89KbxyBLE996rtUEREZAv1MCGKz9hkZVVf2RxA/s23Y7tcpNx9B4RCpNx6A+7PPgXAN/XVar23iIjsfnInTqXwhltrOwwREfkXV20HUNVKEqL4OiK74pN3QaxNW4qGXoh/wtOkDxmEZ8F8Ih3+Q6xhQzwfL8RprSJqtq+2+4uISNUzTdMBPAEcAASBiyzLWrPF8X7A7cUPvwQusyyr+j5sRESk2tXbGaLq7DRXonDkKGKpaXgWzCeWkUHOS69SNPRCAHyvaZZIRGQ3dDLgsyyrB3ADMLbkgGmaqcCDwImWZXUHfgbUHUFEZDenhGgX2I0aUXDz7cTS0sl9+gVirfcmdGw/YhkZeF+fApFItccgIiJVqicwB8CyrE+BrlscOxT4FhhrmuYi4G/LstbXfIgiIlKV6l3JXHp6zSVEAIELhhE493xwFb+VPh/Bk08j6cXn8CyYR6jPsTUSh4iIVIk0IGeLx1HTNF2WZUWIzwb1AjoD+cAi0zSXWJb1w/Yu2rhxarUEW5dpzIkhEccMiTnu+jzmepcQZWbWbEIEbE6GigXOGELSi8/hnfKqEiIRkd1LLrDlp76jOBkC2Ah8blnWXwCmaS4knhxtNyFav74aW5/WQY0bp2rMCSARxwyJOe76MOaKErp6VzJX0zNE5Yl0OYjIvibed2djZGfVWhwiIrLDFgPHA5im2Z14iVyJZUBH0zQbmabpAroD39V8iCIiUpXqXUK0eYaoFoMwDAKDh2CEQnjfmlWLgYiIyA6aAQRM0/wEeBgYYZrmSNM0BxSvF7oReA9YCky3LGtFLcYqIiJVoN6VzGVkxL9+842TggJITq6dOIInnULK6Nvwvv0mgXOGlnuOkZ2Fe9FCwj0Px85sULMBiojIVizLigHD//X0qi2OTwGm1GhQIiJSraotIdqZvRyK//4bsLr470ssy7pxR+7boIFNv35h3n3Xzamn+pk0qYhGjWp+i4hYy1aE9++Me9ECjJxs7PSM0mOe2W/imzIRz/wPMcJhwt16kD3zHXA6azxOEREREZFEVp0lczuzl0Nb4EvLso4q/rNDyRCAYcCECQEGDw6zfLmTE07ws3Zt7awnCp3QHyMcxjN3TulznrdmkX7B2XjnziFidiDc9RDcS5fgH/9QrcQoIiIiIpLIqjMh2pm9HA4CWpimOd80zXdM0zR35sZuNzz6aIARI4KsXevg1lt9uzaSnRQ8YQAA3rffij9h2/gfGYttGGS9+yHZ8z4m59XXiTZvgf+h+3B9vnSrazjW/kTynbfinf56TYYuIiIiIpIQqnMN0Q7v5QD8CdxrWdbrpmn2BCYCB2/vRttqozduHDz/PPz5p6t2eqc37grt2+Od/wGN/Q745BP45isYNIjMvr2Lz0mFVydBr15kXn4xzJgBoRBs2BAPfvp0iMXi5/5kwb33xl9Wj3vBb4vGnDgScdyJOGYREZG6oDoTop3Zy2E2EAGwLOtj0zRbmKZpWJZV4SKgivqip6Uls2kTrF9fsPMj2QX+fieS/PBD5EydSdILE/AAWRdfTmTLmPc7EP9V15A8/iHo3LnM68P7dyZwzlCSnnoc1wMPEFzxPd6pk1lfGKvZgdSy+tD/fkcl4pghMcddH8ashE5ERHZX1ZkQLQb6A1Mr2ssByCa+l8OzxJssbAQeME3zAGDd9pKh7UlLs/nll9rrLh46YQDJDz9E8tj7ca38ltARvYgc0GWr8wqvuxH8fhy//YadnIydnEz40J6EDzscDIPggJNJu/BcvO+8BWedBRMm1sJoRERERETql+pMiGYAxxTv5WAA55umORJYY1nWm6ZpluzlADDVsqwVpmneB0w0TfME4jNFQ3c1iIwMm5UrDSIRcNVCk/FIpwOI7tUS18p4Plh45YjyT3S7Kbz62m1ex85sQM6U6WSc1Bf3m2/i+HktsdZ7V0fIIiIiIiIJo9pShJ3Zy8GyrCzghKqMIy0tPsGUmwsNamOrH8MgeHx//E//j/ABXQgffuTOX8vjoej8YbiXfYFv8isU3nhb1cUpIiIiIpKAaq+WrIakp8e/5uTUTuttgMCQc4m2bEXBLXfE+4LvguCJJ0F6Or7JkyAS2f4LRERERERkmxIgIYrPENVmQhRt34FNX3xL+Mheu34xvx+GDMH515945r1f+rTj13U4f1xdwQvB8dOPeGdN3/UYRERERETqCSVEu6OLLgLAN/FlAFxffkHmkT3I7HUYri8+K/81+flkDD6FtGFDcS1fVlORioiIiIjUaQmTEOXm1qOEqEsXwvt3xvP+HDxz3yX99FMwCgsgHCb9nME4fvpxq5ekjL4N5y8/A+CbrA51IiIiIiKQQAlRvZohIr4uyYhGST97MEZeLnmPP03+Aw/j2LiR9DNPw9iwofRc94L5JL0wgUj7DkSb7YF3+htQVFSL0YuIiIiI1A0JkxBlZ9dyIFUseOpA7KQkbMMg75EnCA4cTOCcoRSMuBbX2p/I6H8s/rH34/p0CalXX4btcpH32FMETz8TR25OfD8jEZF6zjTN1qZpnmCaptM0Te1VICIiW0mAhCj+tV6VzAF2egY5k14n5/VZBM8YUvp84Q23UnThxTh/Xkvy/WPIHHAczt9/o/Dqa4kc0IXAmfFzfZMn1VboIiI1wjTNwcBbwKNAQ2CJaZpn125UIiJS19TCVqU1q76WzAGEex6x9ZOGQf69D1Ew6mY8C+bjef89MAwKR1wHQLRtO8LdeuBe9BGOX9cR26tlDUctIlJjRgGHAgsty/rHNM0uwAeAFlKKiEipBJghqr8JUUXsjEyCJ51K3uNPk/fYU+B2lx4LnHk2hm3je+3VCq/hXrKYBl33x/3Jx9UdrohIdYhalpVX8sCyrD+BWC3GIyIidVClEiLTNA8xTXOkaZoe0zTnmqa53jTNvtUdXFVIS0vMhKgiwQEnY/uT8U2ZBLFt/Gxg2ySPvh3nup9JHX4hxqaNNRukiMiuW2ma5uWA2zTNzqZpPgN8VdtBiYhI3VLZGaJHgRXAQKAQOBAYXV1BVaXkZHA6bSVEW7BTUgmcchrOdb+QfNuNYNtbneNeshj3F58Ra9AA519/kjriinLPExGpwy4DWgBFwPNALnBprUYkIiJ1TmUTIodlWXOBE4BplmX9ym6y/sgwICPDJientiOpWwpuvZOI2R7/M0/iH/fAVsf9j4wFIOfl1wj1PALvu7PxvfR8TYcpIrIrHrcs60bLsg62LOtAy7Ku3bKETkREBCqfEBWapnkN0BuYbZrmlcBu86GSlqaSuX+zGzQkZ+pMoi1bkXz/GHwTnio95vp6OZ75HxLqeQSRQ7qR979niGVmknLbjThX/1D2QqEQ/nEPlLsZrIhILetommZKbQchIiJ1W2UToiFAMnCaZVlZxEsQzqq2qKpYerpd79puV4XYHs3Jfn0W0SZNSb3pelIvOg/HTz/if/RhAAqvHFl6Xv4DD2MEAiQ9+ViZa3inTSX5vrtJvm+3qKAUkcQSA9aZprnENM15JX9qOygREalbKlv2th6YaVnWN6ZpnkU8kQpUX1hVKz3dJhAwCATA56vtaOqW2N5tyJn2FqlX/RffmzPiG7ZGo4QP6EL4yF6l5wX7n0x0r5b4pr9BwV33YKekApD0cryMzjv3PfKKiiApqVbGISJSjutrOwAREan7KjtDNBEYYprmIcCdxBemvlhdQVW1RG29XVlRsz3Z784jZ8JLRPdqiWHbFF4zKr4Aq4TDQeCsczAKC/DOmAaAc8W3uJd9ge1wYBQW4Pnw/fJvEAySfPcdeN5+q/oHIyJSzLKsBYAf6A+cAmQUPyciIlKqsgnR3pZljQJOAyZYljUaaFp9YVWtkoRIZXMVMAxCA04ha9FnbPr0S0J9j9/qlMCZZ2M7HPgmvghA0isvAFA4Mv5LWO9bM7a+biBA2tCz8D86jrRh5+H6dEm1DUFEZEumaV4P3AGsA9YCN5umeXOtBiUiInVOZRMil2majYj/hu1t0zSbAbtNbVRJQpSdXcuB7A48HqJt9in3UKx5C0JHH4t7+Ze4PluK942pRPdoTuHI64m2bI33vTlQVLT5BUVFpJ97Bt4P3yd8UFewbdIuOhfHX3/W0GAq5vxuJYTDtR2GiFSfs4GjLMt61LKsR4Cjip8TEREpVdmE6EFgKfC2ZVkrgIXsJvsQAaSnx79qhmjXBc4eCkDaJefjyMslcNY54HIRHHByvGxu3gfxE4uKSD97MJ6P5hE8ti/ZM9+l4LbROP/5m7QLzoFQqML7eN6ahe+5p6ttHJ733qXBUT1IP2sgFBZW231EpFY5LMva4rc0BIBIbQUjIiJ1U6USIsuyXgU6AM+ZptkZ2M+yrNeqNbIqlJamNURVJXT0sUSb7YHz99+wHQ4CQ84FIDjgZKC4bC4SIe2S8/Es+ohg3xPIfX4ieL0UDb+MwCmn4f7iMxr0PJiMfn1IP60/Sf97tMymr+6PF5I27DxSb7wO19JPy9zf885sUkZegbF+/TZj9E5/nYzjj8b90babSfleezV+vQXzST/7dCgo2On3RETqrA9N05xmmmZ/0zT7A68D6jInIiJlVCohMk2zK/AD8BLwAvE2pt2qM7CqlJFRUjKnhGiXuVwEzhwCQKjPMcT23AuAyAFdiLZshee9OaReMRzvnHcIHdmL3GdfBI8n/lrDIG/c44R6H42xYQOub77Cs2gBKXfeQvLdd4BtY/z9N6nDLyxt6JA85o7SZMnx+2+kXnYxSRNfIrNPz62SJQDfpJdJ/e9FuL/4jPTBp5A85k6IlP2FsJGfh+eD94js047gCQPwfLyQjDNOxcjfbbbWEpHKuRr4ADgXGAp8CFxTmwGJiEjdU9mSuUeAwZZlHWRZVhfgVOCx7bymzlBThapVdOFwgn2Pp3DUFmuTDYNg/5NxFOTjmzaV8EFdyXlhEni9ZV+cnEzOlOls/Ol3Nvy+kY3fWETa7oP/sYfx3zuatEsvwvnP3xTcNprgcf3wfPoJnnnx7nUpN16HoyCf4PH9caz/h4yT++Efez+u5csw8vPwPf8sqSMux87MJPfxp4m1bIX/kbFknNQPI2tTaQie997FCAQInnwauc+8QOCkU3EvXULS4+Nr4u0TkZqTTLxsbhBwJdAM8NRuSCIiUtdUNiFKsSxrackDy7I+BXabHX1UMle17CZNyH15CpH9O5d5PnjKaQBEzPbkTHodUra/QXys2R7kTJ9NZO82JI9/CM+iBQT7Hk/R8MsouOFWbMPAP+YumD4d75y3CR3ak9wXJpIz7S1iDRuRfP8YMo/rRaM2LUi94RpijZuQPeMdgqefSdaHiwgMOAX350tJHnNX6T29s6bH4z3pVHC7yRv/P2y3G8+HH1ThuyQidcCrQPPiv+cR/8x7pfbCERGRuqiyCdEm0zRPKnlgmuYpwMbqCanqlZTM5ebWciD1XGT/zmTPeJvsN+dgN2hY6dfF9mhOzoy3ibRpS6RNW/IefRIMg+h/OhI8ZSDuFd/AkCHYHg/5D44HwyB8aE+yPlpC3oPjKRw2nNCRvQgdfhTZs94l2mE/AOy0dPKenEBkXxPfxBdxrvgWIycbz7wPiHTYj6jZPh5AcjLhQ7rj+uYrjI0VfFuHQmXWOolIndfKsqybASzLyrUs6xagbS3HJCIidUxlE6KLgZtM09xgmuYG4EbgkuoLq2qlpcW/ag1R9Qsfdjh2ZoMdfl2seQuyPv6crAWfYmdklj5fMOpmbJcLAgEKrxxJtN2+pcfsRo0InHcBBWMeIOf1WeRMe5PoPu3KXtjtJv+uezFiMVJuvQHPu29jhELx2aEt4z6yF4Zt41n00dbBBYMkPTaehu33Jn3gAIycbfdv93w4F8/sN3d4/DXCtvG9/AKub76q7UhEaoptmmankgemaXYA1GtfRETKcFV00DTN+UDJr8QLiW9s5wAKgKeA3tUaXRUpWUOkkrk6zuWK/9lCbO82FF5/E8lfL6Pwqp1bCx3ufTTBY47D+/57ONesBiB4ctmEKHRUb5LvuQv3R/MInnxa6fOeD94j5abrcf68Nl5Wt2gBGSceS87kaaUNJUo4fvmZtPPOwgiFKDrnfPLveWDrNVS1yLlyBanXXkWkw35kfbSktHGFSD12LfC+aZq/Ef8sa4L2IRIRkX+pMCEivsP3bs/rhaQkW00VdlOFV19LcuNUWL/zXeAK7roHz/wPcf79F+FOB2y1+Wyk0wHEMjPxLJgfL4szDFzffEXakNPB6aTwkkspHHEd/nEP4H/mSTL69SFn8jSiHUt/+UzyvXdhhEJEm+1B0isv4Fr1HbnPv0KsabOdjrsqed+dDYDr++9wL11CuPuhtRyRSLXLBcYBi4nvnbc38UYLIiIipSosmbMsa0FFf2oqyKqQlmarZC6BRdu2o+ii4QBlZoBKOZ2EjuiF8/ffcP64BgD/uAcxbJvcl16lYPR92A0aUnD3/eTfdQ/Ov/8iY9AAnD/GZ5xcX32Jb/obhA/owqYlXxI4dSDuz5eSccIxGJuqZ7mde8liGu7dHM/7cyp1vmfOO6V/9z3/TLXEJFLHPAp8BbQinhx1YTfaVFxERGpGZdcQ7fbS0201VUhwBTfdRu6jT1I0bHi5x8NH9gLA/dGHOL9bifedtwgfeBCho48rc17R8MvJG/sojo0bSR98Ko6//yL5jlvi97h9NCQnk/fkcxReORLnul9IvfwSiMVKX+/8cTWeue/uWoMG2yb5tptwFOST9OxT2z3d8es63N9+TahXHyId9sM7+00cf/+18/cX2T04LMuaC5wATLMs61e2XxkhIiIJJoESovgaIjUJS2A+H8EzhoCv/I7xoeKEyLNgPv7xDwJQOPL6ctfaBM4ZSsH1N+Fc9wsZxx6F55OPCR5zHOGeR8RPMAwKbrqNUK8+eD+Yi//RcQB4p0wis3dP0s8eTNqQQWWSEud3K/G8M7tM8rQtntmzcH+9HAD3wo+2m9x43ovPDgX7nUjR0IswIhF8E1/a7n0qw9i4sXSmTKSOKTRN8xri611nm6Z5JfH22yIiIqUSKCGyiUYNCgpqOxKpq2J7tSTSdh88C+bjnTWDcKcDCB3Td5vnF14ziqJzL8D55x/YDgcFt/2rEsfhIPeJCUSbt8B/392knXsGaVf+F9vtIdytB94P5pJ5RDeS77yVzCN70OCoHqQPPYvUSy6AoqJtBxqJkHzvaOzitU1GLIZ32usVjs377tsAhPoeT3DQYGIpqfhefgEika3OTR59O8m33QTRaIXXdK74lpSrLqVh5/Zk9jxE3eukLhpCfM3QaZZlZQEtgLNqNyQREalrEiYh0uasUhnhI3thBAIYtk3hiOsq7sRmGOTfP5bCK0eSP+aBzfsabcFu2JDcCS+Bw4F3zjuEOx1A1vsLyJ71Lnn3PoQRCOD/3yM41/xAsO8JhA/uhm/WdDJOOR7+Kn/Wx/faq7jWrCZw1rkUjrgO2+3G9/qUbYeZtQn3Jx8TPvAgYs32wE5JJTj4TJx//lFmXRGAa/ky/I89jP+px0m59qryy/qiUVKuupQGvQ8jafJEYk2aYkSjpIy6plKzW9viXLOaBh3bwZRtj0VkR1iW9btlWXdZlvVJ8eNRlmX9VttxiYhI3ZIwCVHJ5qxKiKQioaP6ABDpsB+h40/c/gucTgpuuYPAhRdv85RI10PInfAyBdffRPbb7xPbuw04HAQuvJhNCz4l57mX2bhiNbkvTyZ7+mwCg87A/eUy6NYN56rvy14sEMD/4L3YPh+F147CbtCQUJ9jca38Fud3K8u9v+eDuRjRKMF+m8dTdP4wAPyPjSszE+R/9GEAonu1JGnSyyTffnPZpCgWI3XE5SRNnki40wHkTJrKps+/IXDyqbiXfY5vyqTtv2fb4J3xBs5//obLLsPYsGGnryMiIiKyIxImIdJeRFIZoV59KDr/IvLGPgqOqvvPI3T8iRRee8NW65dirfcm1P/kzZvZer3kPf40BTfeCuvWkXHisbg/XgjE9znKGDgA5x+/U3TRcGJ7NAcgMGgwwDZniTaXy51Q+lx0X5PAKafhXv4lvpeeB8C5+gc877xFuMuBZL33EZF9zfhM0aiRuD5dAsEgKdePxDdlEuEuB5IzY3a8pNDhoOCOMdj+ZJJH34aRtWmn3iPPvA/if9m0iZQ7b9mpa4iIiIjsqIRJiDaXzNVyIFK3eb3k3z+OSNdDai8Gw4iX602ahFFUSPrgU0i+ZRSZRx2K+7NPCfY/mYJrRpWeHjqmL7G0dLzTXy8z22Pk5eJ9fQqeeR8QadOW6L5mmdvk33UfsbR0ksfcieOvP0l6fHy8VPCKkdiNGpHz+iyiLVuT9OJzZA44jkZtmpP08vOEO+5PzmszsNPSS68Va96CgmtvwLFxI8l33Raf4dnOGqQyQ87OwrV8GeGDusKBB+J77VXcixftwptYgwIBvG+8BsFgbUciIiIiOyFhEqKMjPhXzRDJbuOss8iZOhM7yY//mSfjTRoefzq+Jil5i70lfT6CJ52C888/SDv/bFKHDSV90Ek03K8taZddjFFYEC/p+9d6KLtpUwpuvRNHXi6pl12C743XiOzTrrRUMLZHc7I+WkzO8xMpvOgSovu0I3xwN3Jen4WdkblVuEUX/5fIviZJk16m0X5taNSiIQ33a0tGv96kXnI+/ofuw9hY/p5M7oUfYcRi8RbnTz2FbRikXHd1uUmGkZdbbjOIUpEI6QP6knlEN7xTJ1d8LkAoRMr1I8g47igcmGylkQAAIABJREFUf/1Z8bnlSLnjZtIuHUbSM0/u8GtFRESk9iVMQqSmCrI7Ch92ONnvfEDhZVeRNX8xwdPPLL8N+NnnYRsG3jlv45s1Hc+C+UT3bkPB9TexafEXFA37b7nXD5wzlPDB3fAs+ggjHKbwihFlSgXtlFRCJw6g4J4HyVrwKdlvv4/dsGH5wXo85L48maLzLyJ4wgAiB3fDTknB9c3X+GZMI/mBe2jQsyveaVO3atZQUi4X6n00HHwwgQuG4VqzmpTrR5RJaDzvz6Fhp31p2H5vUi86D++USRj5Zbso+6ZOxvPpJ7hWfU/a5ZfQoMeB8RbjodBWIRvZWaSfcSpJLz6He/mXpA05HfLzN58QiVSYJLmWforvhQnx+05+Zdf2ltoJRnYWjj9+r9F7ioiI1DeGvftvzGOvX7/9bSUWLnQycKCf664Lct11W/9gtDtp3DiVyoy5PtGYt8/45x+MYADb44UkX5mStoo4v/+OzD49iTVpyqbPvgaPZ2dDLl80iuPPP/DOmkHyA2MwiooI9jmGvCeeja+dsm0adO6AEQywceWPNG6WwYaffif95BNwf/s1wb7Hk/v0C3jffpPUK4aDx0OscROc634BINzpALJnz4WkJCgqokGPA3Fs2kj2G2/hm/YavkkvY4RCRJu3oPDyqwgd3x8jKwvnX3+QfOuNuNasJnh8f2IZGSS9+grBY/uS+9Jk3IsXkXLLKFyrvicw6AwKbruLWNNmm8cVCJDZpyfONauJdNwf97dfk/XOBztVbrmz39/pg0/B9dlSshYtJbbnXjv8+qrUuHGqftu0bZX6nKpP9P/sxJCIY4bEHHd9GHNFn1MJM0NU0mUuN1ef2VI/2U2aENurJXbTppVOhgCiHfYje8Y75Lw2o+qTIQCnk9iee1F02ZVsWriU0FG98X74PimjRsYPr/oe559/EDqqNzid8bGkppEz821Chx+Fd847ZPbpSdqlw7BTUsmeOotNn3/DpsVfxBtDfPs1qaNGgm2T9Nwz8aYTw/5L5JBu5N8/jk1ffEvh8MtxZGeRetP1NOzcgQa9DiX9zIG41qym8NIryX3+FfIfHB+Pbe4cMo/oFm9gYa0i2qo1vtenkNnjIJIeHYdzzWqwbfzjH8K1+geKLryYglvuAMA3+V9d9gKB+AxWOFz172swiPuTj3EU5JNyyw1Vf30REZEE4artAGpKSclcdrYSIpF/i3TrXiP3ibVqTc7kaWT0Pw7fzOkETzwJ52/xbWFKWp6XsFPTyHn1dVKvuATfzOlEmzQl57UZRP/TEYBou33Je+RJnGt/wjdlEpF99sX/+MPE0jMovOLqzfdstgcFd91D4ZUjSXruaZyrf8Bu1IhYo8ZEuhxIqM+x8RMdDnInvERG/+Nwff8d4UO6k3/PA0T+0wnfpJdJvudOUu6+A+6+g1iDBhi5uURb7EnhTbdhJ/mJNm+Bd+Y08kffC34/7o/mkXbBOTiKS/psr5fCy6+m8PqbKt7fqpJcX3+FUbzGyvvOW3g+eC++BktERER2SMIkRJvbbtdyICKJzukk77Enyezdk9RRI4m2bAVAuFefrc/1esl76nmCJ55EpOshxJq3KHvc5yP3uVfIPPpwUu6+HYD820aX2/TBbtSIwlE3VxianZZO9qx3ca1cQfjQnqWJS+Dc8wn2Pwnv9Ddwf74U9+dLMXJzyRv7KHZKavyc088kefxDeN+dTaTLgaQNG4oRChI8+liMYAjnD6tIHns/xKIU3nDrLidF7qVLACgYeR3+R8aRcuN1bDrsiHjpoIiIiFRawpTMpaXFv6pkTqT2Rdu2o+Dm23Fs3Ih7+ZdE/tOp7PqcLTkchAacsnUyVCy2V0tyn3oe2zCINm9BUQWb5FaGnZFJ+LDDt+7Kl9mAwIUXk/fUc2xatoINv/xNuPfRpceDg88EIOmFCaSdeyaOnGzyHnqE3FffIGfam2TP/YjI3m1Ifvgh/A/eu0sxArg/iydEgfMupGjYf3H+8jP+xx7e5euKiIgkmoSZIXI6ITPTZs0aB4WF4Pdv/zXRaLzBlddb/fGJJJqii4bjefstPEsWx7vL7YJwrz5kz55LrEnTmpshcbvLPIy2bUf4kO64P/sUgMJLLiN4xpDS47E9mpMz420yTupH8kP3gctF4cjrd+7esRjupUuItmxNbI/mFF5/I96Z0/A/9jCBQWcQ27vNjl2vsBDfzGk4/voTo3gaveiSS7eZhIqIiNQnCTNDBHDOOSH++cfB+PGVWzh+zTVeunRJJhCo5sBEEpHDQd7/niFw+pkUnXfBLl8ucnA3Yq1a73pcuyBw1jkAhI7sRcHto7c6HmveguwZbxNt2Yrk++4m6bHxO3Uf5w8WjuxswsVrv+yUVAruugcjGCTlllHbefW/FBaSPmQQqVdfRvJ9d+N/8jH8Tz5G+lmDoKCgUpcwNpW/v5SIiMjuIKESohEjQuy5Z4z//c/D6tUVD/3nnw2mTHGzYYODv/5SmZ1IdYjtuRd5jz9NrHgd0e4uMPgscp99kdwXJoKr/An42J57kT3tLaIt9iRl9G0kPf2/Hb5PyfqhcLcepc8FTzqV0OFH4n3/PTzvvVv2BdvaXqGwkPRzBuNZvIjg8f3JnjqTrPfmU3TO+bi+W0Hq1ZdVvLdSJIL/4Qdp2LHdDo9BRESkrkiohCg5GcaMCRIOG9xwg7fCz/lnnvEQi8UToY0blRCJSCU4nQRPOrW00cK2xFq1jidFzfYg5dYb4eCDyex5MA06d8B/z13bvU1pQtT90M1PGgb59zyI7XKRcvP1UFSE4/ffSDvvLBq23ZPk0bdjbNhQerrjzz9IP/dMPIsWEDy+P7nPvkj4qN5EuhxE/r0PEup+KL5Z00naxrok509ryOh/HMn3jibWqHEl3hwREZG6KWHWEJXo2zfCscdGmDvXxYwZLk49NbLVOVlZ8Oqrm9cHKCESkaoWa9OWnOmzSRsyCNfXX+NIS4NwhOTxDxFt07bM+qN/c3/2KbEGDYi227fM81GzPUUXX4r/iUdJP+9MXJ9/hqMgHzspCf9jD5P03DME+/bDteJbXD9YAAT7nkDuMy+UXRPl8ZA74WUyjz2S5DF34pn/IXaDhsTS0nD+8TvO1T/g/O1XAAKnDiT/vrE0qvq3SEREpEYk1AwRxBtHjRkTwOezefDB8rslvPSSh8JCg/32iwKwYYMSIhGpetF92pH16XIIhdj4/VqyPlhILC2d1FEjca76vtzXOP74Hee6Xwgf0r3c1t2F144i2mwPPB/NA7eLvPH/Y8Oqn8m75wFiqan4pr+B87dfCfY5hrx7HiB3wkvlbshrN2lC7ouTiO25F57Fi/C+NZOkSS/jmf8hRCKEDj+K3GdfJO+p58ttcy4iIrK7SLgZIoBWrWy6do2yeLFzq45zwSA8+6yb1FSbK68MMXx4Ehs2JFzeKCI1ZYukJtZ6b/LG/4/0C84mbdh5ZM2ZH6/1LSqKz+C4XKVd7MKH9Cj3cnZKKrnPvYx37hwKL74Uu3G8nC1w0XACZw/FuWY10X3NcpOgf4t0PpBNy1ZAOIyRlYUjO4tYs2bYaelVMPC6yTRNB/AEcAAQBC6yLGtNOee8DcyyLOupmo9SRESqUkImRAD77BPj449d/Pijg06dYqXPT5vmYv16B5dfHqR16/jzmiESkZoSOnEAhcOG43/2KRp26YARDGIUFRFLTiHc83Ac2dkApR3myhM5uBuRg7ttfcDnI9qx044H5XZjN2lCtEmTHX/t7udkwGdZVg/TNLsDY4GT/nXO3UCDGo9MRESqRcImRO3axZOd1avLJkRTprhxOGwuuihMpHh5kdYQiUhNKrhtNI5//sG9fBnRBg2wMzJx/LoOb3H3ODspicj+nWs5ynqrJzAHwLKsT03T7LrlQdM0BwIx4N1yXisiIrshJURbtN+OxeDbb520axejeXO7dAsOzRCJSI3yesl79sWtnnb8ug7Pwo+ItthTO0ZXnzQgZ4vHUdM0XZZlRUzT7AicBQwEbtuRizZuXHHnwfpIY04MiThmSMxx1+cxV1tCtL06bNM0+wG3Fz/8ErjMsiy7+Fh7YCnQ1LKsatkWtSQhWrNmc0L0yy8GBQUG//lP/FhyMvj9tmaIRKROiO3VksCQc2s7jPouF9jyU99hWVZJO9JzgRbAPKA1EDJN82fLsuZs76Lr1+dVdZx1WuPGqRpzAkjEMUNijrs+jLmihK46uwWU1mEDNxCvwwbANM1U4EHgRMuyugM/Q7xrq2maacXnBqsxNpo3t/H7bX74YfNbsGKFE6A0IQJo2NDWDJGISOJYDBwPULyG6NuSA5ZlXW9ZVjfLso4CXgTGVSYZEhGRuq06E6IyddjAlnXYhxL/kBlrmuYi4G/LstabpmkAzwA3AYXVGBuGEZ8l+uknB9F4d21Wroy/HR07RkvPa9QoPkNU0SauIiJSb8wAAqZpfgI8DIwwTXOkaZoDajkuERGpJtW5hmibddjEZ4N6AZ2BfGCRaZpLiNdmv21Z1temaVb6Rjtb09ixI3z9NRQUpNK2LawpLug78kg/xZ1q2WMPWL4ckpJSSa1DpZP1uY5zWzTmxJGI407EMddFlmXFgOH/enpVOefdUSMBiYhItavOhKiiOuyNwOeWZf0FYJrmQuLJ0dnAb6ZpXgg0A+YCR2zvRjtb09iypQfwsnRpIWlpUb78MpkmTcDhKGD9+vg5qak+wM333+ez9951Y5qoPtRx7iiNOXEk4rjrw5iV0ImIyO6qOhOixUB/YOq/67CBZUBH0zQbAdlAd+BZy7L2KTnBNM2fgWOrMT722Wdzp7mDD47y228OevWKlDmnUaN4ErRxo1FnEiIREREREaka1ZkQzQCOKa7DNoDzTdMcCayxLOtN0zRvBN4rPneqZVkrqjGWcu277+aE6LvvShoqRMuc07ChNmcVEREREamvqi0h2l4dtmVZU4ApFby+dfVEtlnr1jGcTpvVqx2sWFHSUCFW5pzNM0QOIPrvS4iIiIiIyG6sOrvM1XleL7RqZbNmjYOVK7duuQ2bEyLNEImIiIiI1D8JnRAB7LtvlE2bHCxa5MTns2nbVgmRiIiIiEiiSPiEqKSxwm+/OWjfPobrX0WEDRtubqogIiIiIiL1S8InRCWNFaDshqwlShIizRCJiIiIiNQ/CZ8QlcwQwdbrhwD8fvD7bc0QiYiIiIjUQwmfELVrV3FCBPF1RJohEhERERGpfxI+IUpPhyZN4onQv/cgKtGoUXyGyNa+rCIiIiIi9Up1bsy62xg6NMyffxqkppZ/vGFDm1DIIC8P0tJqNjYREREREak+SoiAa68NVXh8y9bbaWmaJhIRERERqS8SvmSuMho2jJfUqbGCiIiIiEj9ooSoEjbPEOntEhERERGpT/QTfiVoc1YRERERkfpJCVElNG6szVlFREREROojJUSVoBkiEREREZH6SQlRJZQkROvXKyESEREREalPlBBVgmaIRERERETqJyVElZCUBMnJttYQiYiIiIjUM0qIKqlRI1szRCIiIiIi9YwSokoqSYhsu7YjERERERGRqqKEqJIaNbIJhw1yc2s7EhERERERqSpKiCqpVasYAB9/7KrlSEREREREpKooIaqkc88NA/D44x6VzYmIiIiI1BNKiCpp331j9O0bZtkyJ0uWOGs7HBERERERqQJKiHbAlVeGAHj0UU8tRyIiIiIiIlVBCdEO6No1Ro8eEebNc7Fihd46EREREZHdnX6q30Els0SPP65ZIhERERGR3Z0Soh3Uu3eU/faLMnOmi19/3fZGrVOmuDjmGD+WpbdYRERERKSu0k/rO8gw4OKLQ8RiBlOnurc6Hg7DTTd5ufLKJL7+2snUqWrTLSIiIiJSVykh2gn9+0dISrKZOtVdpgV3djYMGpTEhAke2reP4nLZfPKJEiIRERERkbpKCdFOSE2Ffv0irF3r4PPPN7+Fd97p5ZNPXJx4Yph33imkc+cYX33lID+/FoMVEREREZFtUkK0kwYPjm/U+tpr8bK5H35wMHmyG9OM8uyzAVJS4LDDIkSjBp99pn2LRERERETqIiVEO+mII6LssUeMWbPcBAIwZoyHWMzg5puDOIvzn0MPjQKweLESIhERERGRukgJ0U5yOmHgwDC5uQZjxnh59103hxwS4bjjoqXnHHyw1hGJiIiIiNRlSoh2wemnRwB4+un4nkS33BLC2KITd0oKWkckIiIiIlKHKSHaBaYZo3Pn+IzQccdF6N49utU5WkckIiIiIlJ3KSHaRZdfHqJFixi33hos97jWEYmIiIiI1F1a3LKLBgyIMGBAZJvHy64jCtVcYCIiIiIisl2aIapmWkckIiIiIlJ3KSGqASXriJYuVdmciIiIiEhdooSoBvTsGV9HNH++KhRFREREROoSJUQ1oEePKCkpNnPmuLDt8s8pKoJLL/XxwQeaRRIRERERqSlKiGqAxwN9+kRYt86BZZX/lr/xhps33nAzZoy3hqMTEREREUlcSohqyHHHxTvRvffe1mVztg3PPusGYOVKJ6tW6Z9FRERERKQm6CfvGtKnTwSnM14292+LFjlZtcpJs2YxAKZN01ojEREREZGaoISohmRmQrduUb780sE//xhljj37rAeAJ58MkJJiM326m1isNqIUEREREUksSohq0HHHRbBto0zjhJ9+Mpg718lBB0U57LAoJ5wQ4ddfHXz2mZoriIiIiIhUNyVENahkHdGWZXPPPefBtg2GDQsBMHBgGIA33lDZnIiIiIhIdVNCVIPatLFp1y7KwoUuNm2KJz2TJ7tp1ixG//7xZKlnzyhNm8Z46y03oVAtBywiIiIiUs8pIaphxx0XobDQYP/9U7j00iTy8w2uvTaEO95kDqcTTj45QlaWwezZLq0lEhERERGpRkqIatgpp8S7zTVubDNiRJAlS/I599xwmXNKyuaGD0+iVasUevRI5uabvVs1Y9iWm2/2ctZZSdvcBFZEREREROK0UKWGdeoU47vv8klPB8c20tH9949x990BPvvMybp1DtaudfDssx4mTXJz8cUhbr9929f/8ENnade6lSsddOyoKSYRERERkW3RDFEtyMzcdjIEYBhw8cVhJkwIMHduIStX5nP//QFSU23Gj/fSpQusWbP1bFFhIYwa5St9/MEHyndFRERERCpSbT8xm6bpAJ4ADgCCwEWWZa3Z4ng/oGSu40vgMsAPvAo0AAqAcyzLWl9dMe4uPB44//wwgweHefhhD4884uWEE5KZOLGQgw/ePAM0dqyHdescnHNOiEmT3Myd6+Lqq9WZQUSksirx2TUCOKP44TuWZd1Z81GKiEhVqs4ZopMBn2VZPYAbgLElB0zTTAUeBE60LKs78DPQCBgGLLMs63BgCnBLNca32/H74eabQzz3HOTmwmmn+XniCTcLFjhZsMDJE094aNkyxujRQQ4+OMqyZQ42bqzcuqPybNoEo0d7yMurwkGIiNRtFX12tQGGAIcCPYBjTdPcv1aiFBGRKlOdCVFPYA6AZVmfAl23OHYo8C0w1jTNRcDflmWttyxrPDCm+JyWwN/VGN9u64ILYOLEIhwOuOMOH4MG+Rk0yE80avDAAwH8fjjmmCi2bTBv3s5v8PrSSx4ee8zLa6+5qzB6EZE6raLPrl+BvpZlRS3LigFuIFDzIYqISFWqzkUmaUDOFo+jpmm6LMuKEJ8N6gV0BvKBRaZpLrEs6wfLsqKmac4DOgHHVOZGjRunVnHodd8ZZ/jp3h0WLoQff4z/6dgRBg/2AzB4MNx9NyxcmMSll+7cPVasKPnqo3FjX8Un14BE/HdOxDFDYo47EcdcR23zs8uyrDCwwTRNg3iVw3LLsn6ozEUT8d9XY04MiThmSMxx1+cxV2dClAts+c45ipMhgI3A55Zl/QVgmuZC4snRDwCWZfU2TbM98DbQdns3Wr8+sWq6GjdOZf36PJKToV+/ssfWF6+4atIE9tormTlzDP74I790n6PKsm1YsiQZcPDxxzHWry+okth3VsmYE0kijhkSc9z1Ycz16IOyos8uTNP0Ac8DeUClf920u//77qj68D29ozTmxJGI464PY67oc6o6S+YWA8cDmKbZnXiJXIllQEfTNBuZpukCugPfmaZ5o2ma5xSfUwBEqzG+es0w4OijI+TkGHz++Y6Xzf36q8GGDfFvj3XrHJXeA0lEZDe3zc+u4pmhWcDXlmVdYlmWPqNEROqB6kyIZgAB0zQ/AR4GRpimOdI0zQHFneNuBN4DlgLTLctaQfy3bkNM0/yIeLe586sxvnrvmGPiv9R8/30X//xj8MwzbsaM8VBQicmeL7+MJ1GNGsWKH2/+Vvn+ewd7753CzJlq6y0i9c42P7uIN1w4EuhnmuZHxX961GawIiKy66rtJ9riBafD//X0qi2OTyHeSW7L1/wN9K2umBLNYYdFSUqyeeEFN0895SYajc/yzJ3r4uWXi2jVyt7ma0sSovPOCzN2rJcvvnDSt2/8l6GTJ7spKDAYN87DSSdFMDR5JCL1xPY+u4DaX1ApIiJVShuz1mNJSdC3b4TCQoMDDohxzz0Bzj8/xPffOzn22GQWLtx2Kd2XXzpwOm2GDg1jGDbLlsXPtW2YPTueR69a5eTjj3e+i52IiIiISG1TQlTPjR8f4Jtv8pkzp5CLLgpz//1Bxo0LUFAAp5+exH33eYhEyr4mHIZvvnHSvn2Mpk1tTDPG8uVOIhH46isHv/3moEOH+GzRhAlqyS0iIiIiuy8lRPVcUhI0a1a2NO7ss8PMnFlIixY248Z56d/fz9q1m+vevv/eQSBgcOCB8aSna9cohYUG33/v4M034wnQDTeEOOCAKO+95+LXX1UzJyIiIiK7JyVECapr1xjz5xdw6qlhli1z0qdPMqtWxb8dSsrjDjooWvw13ljhiy+cvPWWi+Rkm169Ilx4YYhYzODFFzVLJCIiIiK7JyVECSwtDZ56KsC4cQHy8w2uuspHJLK5ocKBB8YToa5d44nRSy+5WbfOwXHHRfD54OSTIzRsGGPiRA9FRbU2DBERERGRnaaESDj77DADB4ZZvtzJU0+5Wb7cQXKyTbt28YSoXbsYaWk2330XT5T6948vOvL54q/NyjKYMUMtuEVERERk96OESAC4++4AjRrFuP9+L6tXO+jSJYqzuIGcw0HpeiK/36Z3781dGM47LwzA1KkqmxMRERGR3Y8SIgGgQQO4//4gwaCBbW9uqFCiZD3RMcdESEra/Pyee9r06BHhk09c/P57+c0VbBtuv93L9OmaRRIRERGRukUJkZTq3z9C//7xGZ9DDimbEJ1wQoTGjWOcf354q9edemp8xmhbZXNffOHgySc9XH21j59/Vkc6EREREak7lBBJGY8+GuCFF4o4+uiyCVHHjjFWrizg0EOjW72mf/8wbrfNtGnll82VlNMFAgajRvmw7XJPK+Prrx3Mm6dNX0VERESkeikhkjKSk+OzQY4d+M5o0AB6946ycqWztHV3iWAQZs1y07RpjCOPjDB/vouZMysunQuF4OyzkzjzzCQWLSqbFH37rYNXX3VVKqkSEREREdkeJURSJU47LV5K9++yufffd5GdbXDaaREeeCCAz2dzyy1ecnK2fa0ZM1z8/bcD2zb47399rF8fL7P74gsH/fv7ufrqJCZMUBMHEREREdl1SoikShx7bAS/P142t+XszdSp8QRp0KAwe+9tM3JkiPXrHdx1l7fc69g2PP20B4fDZtiwEP/84+CKK3x89RWceaafYBDS023uuMPL8uW79u2rWSYRERERUUIkVcLvh+OPj7BunYMvvoh/W23caPDhhy7+858o//lPfE+jSy8N0aFDlFde8fDhh1uvEVq82MmKFU5OPDHC6NFBeveOMG+ei+7dISfH4JFHAjz7bBGRCAwblkR29o7Hatvw3HNu2rdP0f5JIiIiIglOCZFUmYED42VzV16ZxIIFTmbOdBEOGwwatLkznccDjz8ewO22ueoqH5s2lb3GU095ABg+PITDAY89FqBp0xjBIDzwQIDTT49w1FFRRowIsW6dg//+N4kvv3QQ3rr5XbmCQRgxwsuNN/rIyjK46SbvTiVVIiIiIlI/KCGSKnPUUVEuuSTE2rUGgwb5uftuLw6HzWmnRcqc16lTjFGj4uVw1123uevcjz8azJ3romvXKF27xmeUGje2mT27kPnzYejQzVnPddeFOOywCB9+6KJv32T22ef/7d13gFTV2cfx7522FZBXQI0oKugRiUpREQkgRrMqFlSiYBQsUdENEECUIpYIgi0IthAVSxRRFCIltCAo3YglaPQookSCIQgisGV2yn3/uLvuwi5bYAs78/v8AzOzM/c8084895zznEz69Elj48Z9l/Vev96hZ890pk4NccopXlu3bfPx4INlT99LZLm5cNVVaUycGKrrpoiIiIjUKSVEUm18Prj//jCLFuVy2mkxcnIczjknxmGHlV6sk51dwBlnRJk9O8hdd6Xw4IMhBg9OBbzRoZJatHA5++w97+/3w9SpeTz1VB79+hXQokWcxYsDZGWls2LFnlPxtmxxGDo0hS5dMli71s8VV0SYPTuX0aPDtGwZ5/nng3zySdU+CnPnBli3rv5+fMaNS2HJkgCTJoXIy6vr1oiIiIjUHcet/yvL3a1bd9V1G2pV06YNONhjjsdh2TI/J50Up2nTst9j33zj0L17Bjk5xaM6xx0XZ/nyHAJ7Le2pTMx/+UuQO+/0Rntuv72ASAQ++MDP6tV+8vIcWrWKMXJkAT16RHEKD7lkiZ+rrkqnY8cos2bl/XR9eebMCXDDDWkcf3yM5ctzK3Wf/VFTr/OqVX569kzDdb2GP/NMHpdeGq3gXrWnPry/q1sixNy0aQPturxv6qeSgGJOHskYdyLEXF4/pRXlUiN8PujWrfQmriUdc4zLggW5fPX2/8SPAAAa5ElEQVSVj4YNXRo2dDn22HipZKiyrr02QqtWcW64IZXx44unwbVsGefWW8NcfXWk1GN37x6jR48Ic+cGufvuFO68M0xm5r6PsXGjw+9/741kffmlH2t9nHhifP8aXAvCYRg6NJUjjojz299GyMhwGTAgFceBxx7LY9CgNN54I3hQJUQiIiIitUkJkdSpE06Ic8IJ1ZdQdOoUY9GiXP72twDHHRenXbs4hx5a/ijo/feH+fhjP5Mnh3jrrQCjR4fp1StaauSnoABuvjmNnTsdsrKiLFgQYPbsACeeWFD2A9eC3bspN4Fbs8bP6697ezY9/XSI44+P8+9/+xg4MEyfPlGefTbG4sV+tm1zKnyeRERERBJR/V0EIbIPzZu73HxzhHPPjVXqR37z5i7LluUwZEiYHTscsrPT6NMnjW3bijOieJzCvY/89OoV4emn80hJcZkzp+7OKcyaFaBVq8xy2/D++956qj59Ihx5pMunn/pp3TrGsGFeEterV4Ro1OGvf9W5EREREUlOSohEgIwMGD68gOXLc+je3dv76Jxz0lm92s/8+X66d0/n2WdDtGoV46GH8snMhO7do3z2mZ8vv6z4Y/TZZz6mTw+UKjO+v7ZscRg2LJV43OHtt0vv51TkH//wbhs1KszKlTlMn57L66/nkVI4o/Dyy6P4fC5vvBGsnoaJiIiI1DNKiERKOPpol1dfzWPUqDBbtjhcckk6ffumY62Pq66K8OabeT9NUbv4Ym/dTUWjRPE49OuXRnZ2Gj//eSa9e6cxfXqg0nsn7c11vXVBP/zgjWB98EHZCVE8DmvX+mnRIk6zZi5+v7euq2TVv8MOc+naNcbatX6++sphxowAXbqkc+21adR2vZXt2+GWW1JZt652jysiIiLJTQmRyF58Phg0qICZM/No1SrGRRdFeOedXB5/PJ8jjijOErKyogSDLrNmlZ8QLV3q55tvfLRvH6NNmzhvvx0gOzuNjh0zeO65INu3w8cf+5g2LcArrwQrTEReey3AwoUBunSJcsYZUT7/3Mfu3aX/bv16Hzt2OJx+evnFLYo21P3VrzLo3z8Na/0sWBDg73/f98jTvoTDYO3+fa1MnRpk5swgQ4fu191FRERE9osSIpF96NQpxsqVuUyZko8xpQs/NGzobUb76ad+NmxwcF344AMf69fvWY3hhRe86WgPPJDPokW5rF69m5tvLmDbNocRI1I58cQGnHdeBgMHpjF4cGqpfZRK2rzZYdSoVDIzXSZOzKdDhzjxuMO6daXv8/773sf7tNPKT4guvDBKgwYuu3fDlVdGePnlXBzH5YEHUohXod5FQYG32WuXLhlMmBCq8gjTnDne87RoEfV6jycRERGpX/SrQ+QAXHyxN7oyfHgqnTunc/75GWRlZfDtt15StHmzw8KFAU45JUa7dl52cdxxLmPGhFm7NofBg8N06xblxhsLuPVWr9DBggX7HnG6664Udu1y+MMfwjRv7tK+vZfsfPBB6Y9y0fqhikaIMjNhwYIcVq3K4Ykn8vnVr2JccUWUTz/1M3t25YotuC6MHJnCypUB/H6XceNSuPvuyidUmzY5fPCBnyZNvDs89VSocnesopkzAzz1lNZLiYiISDElRCIHICsrSiDgsnRpgG+/9dG5c5Rduxx+97tUYjFvs9h43OG66yKlyng3aeIyYkQB06fnMW5cmJEjw2RmusyfHyhzdOXtt/3MmROkY8cov/mNl4i1a1eUEJU1QuQnPd2ldeuKs5JWrVyOO674oMOGhQkEXMaPTyFaiS2Knn8+yEsvhfj5z2MsX56DMTEmTw4xYEBqpe4/d26g8LgFtGkDf/1rgE2b9m+fz+3boW/fVIYNS9njedy82WHQoFTuvTe10omeiIiIJD4lRCIHoHFj+POf83n44XzWrdvNjBl5XHhhhFWrAkycGOLll4M0bOhy2WUVV1BISfEq123c6OOLL/b8aIbDMHJkKn6/y/jx4Z+Sq6OOcmnSJM6HH+6ZEO3YAdb66dAhtl8b3R57rMvVV0f46iuvOl55VqzwM2pUCk2axHnppTxatnSZNSuXDh1iTJ8e/Ck5LPLJJz5Gj05hy5bihGf27ACO49KjR5Tbb4dYzGHy5KqPEm3a5BXCmD8/yIsvhvjb34rb/sgjIfLzvWPeeWfKHmXVRUREJHkpIRI5QBddFKVfvwiHHAKOA48+Guaww+KMH5/Cli0+rrwyQkZG5R4rK8sbTtl72tzTT4fYsMHHDTdEaNOmeMTHcaB9+zibNvn2SDCKRowqWj9UniFDCkhJcRk1KpXHHguRk1P6b3buhNtuS8VxYMqUfJo394ZkGjeG6dNzOeOMKDNmBBk0qHjE7IIL0pk8OcStt3rX/fe/Dv/4h59OnWI0a+Zy9dVw+OFxXn45yI8/Vr69n33mo0ePdL74wk/v3hGCQZdRo1LYvRu+/NLH1KlBTjghxl13hfn+ex+jRqXs93NzMCoogK1b6ybJW7Wq6gU4REREDhZKiESq2aGHukyalP/T5euuq3x97XPP9fYFKpkQffutw4QJIZo2jXPHHeFS9ylaR/Thh8Uf5/feq9z6ofL87Gcujz2WTyjkFVjo2DGjVBW8u+9O4bvvfAwdWsCZZ+55rMxMePXVPDp0iPH660G6dk1n6NBU0tK8RG358gBPPhli7twAruv8VMY8FIKbboqQk+Nwxx2p5OdToR9/hCuuSOO773zcc08+kyblM2BAAZs3+3jkkRTGjQsRjzuMHFlAdnYBHTrEmDEjyJQpQV58Mchtt6Vy6aVp9O2byqBBqTz3XLBKBSXqWl6eF3+bNpn84hfpjBqVwsqVtZOkvP22n0svTa+VY4mIiNQETaQXqQHdu8cYOzafH390OOGEyv+y/r//gzPOiLFmjZ+tWyEahezsVPLyHB5+OJ9GjUrfp2gd0Ycf+jn/fO//RQUVOnTY/4QI4Ioropx3XpSnngrxpz+FGDw4lbVrfYwfH2bZMj9Tp4Y4+eQYAwcWlHn/Bg1g2rRcevVK5+OP/bRvH+OZZ/JIT4ezz05n/PgQRx3lZVg9ehQvNrr++gLmzAkwc2aQr7/28fzzeRx55L7L1k2YkML33/u4884w2dleAjpoUAFvvhlk8uQgsZhDhw4xLrggiuPAxIn5/PKX6Qwfnlrm4736apCMDJfevSuxAKqORaPe/k1r1gRo1SrGpk0+nnnGz7PPBnnvvRxatKi5DaV27fL2xAoEXEBTEEVEpH7SCJFIDbnppgi33152olCerKworuswd6637mX16gAXXRTh178u+8f53oUVYjHv/8cfH6Nx4/1vf5GGDWH48AJWrMjh5JNjvPxyiCuvTGPIEO+H8KRJ+QTLKdzWqBHMmJHLlCl5zJqVy1FHuRx6qMsTT+QTjTp8/bWP00+PcfjhxT/cMzPhrbdy6d07wkcf+TnvvHRmzgyUWaBh40aHZ58NctRRcbKzi5/vtDQYPz6fWMz7oT56dPHaqxNOiPP44/n06RPhkUfyWbYsh82bd2HtLhYtyiEtzWXs2JQy93c6mLiutx5q/vwgXbpEWbo0F2t3c/vtYVzXYcmSmj3ndd99KfznPz4GDar6+1xERORgoYRI5CBTtI7owQdhwoQQRx8dZ8KE/FJV6ooccgi0bOkVVojF4NFHQ+TkVLwha1UdeaRXLOGiiyKsXBngu+98DBlSsMeapn1p0MBbaxUqUSehW7fYTwlMz56lpxWmpnojOePH57Njh8Mtt6TRqVMGU6YEyc0t/rsxY1IoKHAYNSpM6l4DPr/8ZYwBA8L071/AWWft+Xz07Bll4sR8+vaNYEycQMBb+3TqqXFuu62ALVt8PPFEcYMXLfLTr18qS5ZUfipafj5V3o+pKh5/PMRf/uJV93vhhTxCIa84R9Fmu++8c2DT5r7+2mHEiBRWry79OMuW+XnppRCtW8cYPFgJkYiI1F+OW5O9de1wt27dVddtqFVNmzZAMSe2Tp0y+OorH4GAy+zZuXToUH7SkZ2dyvTpQTp1irJqVYDmzeNMn55Ly5bV//mOx+GJJ0Js2ODw8MPhckeHKhKLwfLlfs46K/bT45T1Wm/Y4PD00yGmTQsSDjs0bRpnwIACTjopTq9e6bRvH2PevNx9Jo1VlZPjvQY7djisXJnDrFkB7rsvBdf1DtC9e5S77w6XmwwuWeLn2mvTCAS8hLVVqziDBnltLumHH6BFiwbs3Fm19/d//uNw5pkZNG7ssmhRLocdVvxauy6cfrrX/s8/313lSoPxuFdK/f77U8jNdXAcl/79I4wY4a1hmz8/wB/+kMLmzQ7z5uXSrl2cpk0baM7cvqmfSgKKOXkkY9yJEHN5/ZRGiEQOQhde6J3hv+uucIXJEBQXVli1KkDnzlEWLqyZZAjA54OBAwt47LEDS4YA/H5vpKiixznuOJeHH/Y2sx00KExensPdd6fSq5e3mP+++8LVlgwBZGR4z31+vsOFF6Zz772pHHaYy+TJeXTtGmXJkgDnnJPO2LEhCsoYHFm/3uGmm9IAOOaYOOvX+5g5M8gDD+xZ2W7bNofTT8/khhuq3saHHkohHHYYOTK8RzIEXvXBbt2i7Nzp8NFHVfua37LF4Yor0hgxIpWUFLjnnnyOPdbl6adDdO2awamnZnLzzWls2uRj2LCCnzYcFhERqa+UEIkchAYPLmDxYrj11spVqOvePUqjRi633FLA66/n0aRJvR/5LVOzZi6jRhWwdu1uhgzxNrK96qoIHTtW7/RAgF69orRtG+O///Vx8skxFizI5bLLokyfnse0ad5aqIkTU+jRI53164uzsR074Jpr0tm50+GPf8xn6dJcvv56N23axFiyxM8PPxQf4623Auzc6TBtmrdxbGV99pmP114L0Lp1bJ9ry84+23tO3nmn8sNDGzY49OiRzooVAc4/P8K77+aQnR1h8eIcbryxgG++8eH3u2Rne2vKhg7VVDkREan/NGWuHkqEYcuqUswVc12qdZSkrlQl7njci7mm4t640WHu3AD9+pXeS2rXLm+z3NdeC5Ka6tK+fYwTT4zz6ac+1qwJMGBAmNGjixOGSZNCjBmTwoQJ+fzmN16ie8EF6axd663PGTIkzPDhlUswrrkmjYULA7zySi7nnVd2MvjDD9C6dSannx5j9uy8Ch/zn//00bt3Gt9/72PYsDC3315Q6nn94Qev4EVZI3qaMlcu9VNJQDEnj2SMOxFi1pQ5kSSQCMlQVfl8NRt3ixYut91W9sa6DRrA44/n88wzXknwVav8TJkSYs2aAFlZUUaN2jO5KSocMXOmN2KzYYPD2rV+OnaMcsgh8PLLwTKn3+1t1So/CxcGOOusKOeeu++RscaNoW3bOGvX+iuslrdypZ+ePdPZts3hoYfyGTasdDJU9JgHOk1SRETkYKOESETkAFx6aZRVq3L4+uvdLFqUw3PP5fHnP+fh2+vb9eijXTp0iLF8uZ///c/hjTe8zOLaayNcdx38738+5s0rf3pbNAr33OOtQypZRnxfunWLEo06rFhRXCVu70kB777rp0+fNMJhePbZ/CptJCwiIpIIlBCJiFSD9HSvZPfFF0dJSyv7by67LEI87jB7doA33giSnu5y4YVR+vf3bn/++fKHX558MsRHH/np1StSqWIbReuIli4NsGWLQ79+qTRvnknfvqm8/bafxYv9XHNNGrEYvPBCHhdffPBvRCsiIlLdlBCJiNSSSy6J4jguf/xjiG++8XHBBVEyM8EY6No1ysqVAT7/vOyv5X/9y8dDD4Vo1izO2LH5lTpehw4x0tNd3norQNeuGcybF+SQQ1zmzw/Su3c6ffp4Vfpeeilvn2uRREREEp0SIhGRWnL44S6dO8fYutX76v31r4unp11/vff/Bx4IsWuvdauRCAwcmEok4vDoo/k0bly544VC0LlzjO+/9xEOw7hx+axbl8OCBTn06ROhZcs4r7ySxznnKBkSEZHkVcXt+kRE5ED07Bll+fIATZvG6dq1OBHJyopyyikx5s8P0rmzn7Fjw3TvHuWTT/y8/nqAf/7Tz1VXRcjKqlryMmxYmCOOiJOdXcCxx3oLiNq1i9OuXeVGmURERBKdEiIRkVp08cURJk0K0bdvhECJb+BAAObMyeXxx0NMnBjixhv3XIh05JFxxoypehLTtm2ctm3DB9psERGRhKWESESkFjVuDO+/n1PmbampMGxYAZdfHmHs2BS2b3c49dQ4bdvG6NYtSqNGtdxYERGRJKCESETkINOypcuUKZrSJiIiUhtUVEFERERERJKWEiIREREREUlaSohERERERCRpKSESEREREZGkpYRIRERERESSlhIiERERERFJWjVWdtsY4wOeAk4FwsBvrbXrS9x+AXBP4cUPgGygIfBy4b8hYIi1dlVNtVFERERERJJbTY4Q9QRSrbWdgOHAo0U3GGMaAA8DF1lrzwS+AZoAQ4DF1tpuwHXAkzXYPhERERERSXI1mRD9ApgPYK1dDZxW4razgHXAo8aYZcAWa+1WYAIwufBvAoB2JhQRERERkRpTY1Pm8Ka9/VjicswYE7DWRvFGg7oDbYHdwDJjzCpr7RcAxpjD8abO/b4yB2ratEG1Nrw+UMzJIRljhuSMOxljFhERORjUZEK0EyjZw/sKkyGAbcA/rLX/BTDGvIuXHH1hjDkZmAbcbq19pzIH2rp1V/W1uh5o2rSBYk4CyRgzJGfciRBzoiR0lVj/ehNwCxAFxlhr59RJQ0VEpNrU5JS5FcCFAMaYM/GmyBVZC/zcGNPEGBMAzgT+ZYw5CZgOXG2tnVeDbRMRESlLeetfDwcGAp2BLGCcMSalTlopIiLVpiYToplAvjFmJd7aoMHGmCHGmEsK1wuNABYAa4AZ1tpPgHFAKjDRGLPUGPNWDbZPRERkb+Wtfz0DWGGtDVtrfwTWA6fUfhNFRKQ61diUOWttHOi/19Wfl7h9Gt7UuJL3uXQ/DuUkylSNqlDMySEZY4bkjDsZYz5Ilbf+de/bdgGNKvGY6qeShGJOHskYdyLHrI1ZRUREipW3/nXv2xoAO2qrYSIiUjOUEImIiBQrb/3re0AXY0yqMaYR0Br4pPabKCIi1clxXbeu2yAiInJQKFFl7hTAAa7HS5DWW2tnFVaZuxnvhOID1to366yxIiJSLZQQiYiIiIhI0tKUORERERERSVpKiEREREREJGnVWNntmlTRTuKJxBgTBKYAxwApwBjgX8ALgIu3oDe7sMx5QjHGNMPbxPc8vF3hXyCBYzbGjAAuAUJ47+93SOCYC9/bL+K9t2PATST462yM6Qg8aK092xjTijJiNcbcA/TAey5+b619r84aLPtN/ZT6KRIw5mTrpyD5+qpk7afq6wjRPncST0DXANustV2AC4AngD8CdxVe5wD7s3/TQa3wC2gykFd4VULHbIw5GzgL6Ax0A44iwWPGW6gesNaeBfwBGEsCx2yMuQN4Fm/zaSgjVmNMe7zXvyPQG3iyLtoq1UL9VIJ+louon0qKfgqSqK9K5n6qviZE5e0knmimA6NLXI4CHfDOygDMA86t7UbVgkeAPwGbCy8nesxZeOV9ZwKzgTkkfsxfAIHCM+kNgQiJHfNXwOUlLpcV6y+AhdZa11r7b7znp2ntNlOqifqpxP0sF1E/lfgxQ3L1VUnbT9XXhKjMncTrqjE1yVq721q7yxjTAHgDuAtwrLVF5QEru1N6vWGMuQ7Yaq1dUOLqhI4ZaIL3g+nXQH/gFbwNIRM55t14UxA+B54BJpHAr3NheeZIiavKinXv77aEeg6SjPqpBP0sg/opkqefgiTqq5K5n6qvCVF5O4knHGPMUcAS4C/W2qlAyXmqibhT+g3AecaYpUBb4CWgWYnbEzHmbcACa22BtdYC+ez5BZOIMQ/Gi/kEvHUWL+LNSy+SiDGXVNbneO/vtkR/DhKZ+qliifg+Vj+VHP0UJHdflTT9VH1NiMrbSTyhGGMOAxYCd1prpxRe/WHhXF7w5msvq4u21RRrbVdrbTdr7dnAR0BfYF4ixwwsB843xjjGmJ8BGcDiBI/5B4rPMm0HgiT4e3svZcW6AsgyxviMMUfj/Yj+vq4aKAdE/VQCf5bVTyVNPwXJ3VclTT9VX4fvZ+KdmVlJ8U7iiWok0BgYbYwpmqM9CJhkjAkBn+FNUUh0Q4FnEjVma+0cY0xX4D28ExXZwNckcMzABGCKMWYZ3tm2kcD7JHbMJZV6T1trY4XPxyqK3wdSP6mfUj+VUDEnaT8Fyd1XJU0/5biuW/FfiYiIiIiIJKD6OmVORERERETkgCkhEhERERGRpKWESEREREREkpYSIhERERERSVpKiEREREREJGkpIRI5SBljrjPGvFDX7RARESmL+ilJFEqIREREREQkaWkfIpEDZIwZDlwJ+IEFwNPAW8DnQBtgI3CNtXa7MeYiYAzeyYgNwC3W2i3GmHOBRwuv3whcDVwO/BaIAkcDi621NxljmgOv4O0SHgcGWmtX11a8IiJSv6ifEimfRohEDoAx5nygA3A60A44EvgNcDLwlLW2Dd7uzvcaY5oBk4Ge1tpTgBXAE8aYFLyOo5+19mRgHdCv8BBH43U4rYELjDFtgBuBOdba04C7gV/USrAiIlLvqJ8SqVigrhsgUs+dC3QE1hZeTsM70fCFtXZp4XUvAlOBhcB71tpvCq//MzACr1P6j7X2IwBr7Qjw5mYD71prtxde/gpoAvwdmGGMaQfMBZ6oufBERKSeUz8lUgGNEIkcGD/wmLW2rbW2LV6nMxZv+kARX+HlvT9vDt5JiQjw09xVY0yjwukG7PU4LuBYa1cAJ+FNe7gKmF194YiISIJRPyVSASVEIgfmbeBaY0ymMSYA/BU4DTDGmLaFf3M9MA9YA5xpjDmm8PqbgSWABZoZY04qvP4OoP++DmiMeQhvrveLwO+A9tUbkoiIJBD1UyIVUEIkcgCstbOBN/E6kU+Aj4B3gO3AfcaYT4FmwBhr7Ra8zmVm4fVnA/2ttfnANcBLxph/4p1VG1/OYR8HehljPgJmAn1rIjYREan/1E+JVExV5kSqWeGZtaXW2mPquCkiIiKlqJ8S2ZNGiEREREREJGlphEhERERERJKWRohERERERCRpKSESEREREZGkpYRIRERERESSlhIiERERERFJWkqIREREREQkaSkhEhERERGRpPX/8RzLy3TOYzoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aaa7e1160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = pipeline.named_steps['classifier']\n",
    "plot_loss_curve(clf.loss_train_, clf.score_train_,  clf.loss_valid_, clf.score_valid_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aad2c0630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# param_name ='classifier__optimizer_lr'\n",
    "# param_range = [  0.01, 0.1, 0.2, 0.3, 0.5, 0.8, 1.0, 1.1, 2.0]\n",
    "\n",
    "param_name ='classifier__module_H'\n",
    "param_range = [  14, 15, 16]\n",
    "\n",
    "# param_name ='classifier__stopping_patience'\n",
    "# param_range = [ 120, 150, 240]\n",
    "\n",
    "# param_name ='classifier__batch_size'\n",
    "# param_range = [ 70, 80, 90]\n",
    "\n",
    "cv = KFold(n_splits=3, shuffle=True, random_state=HP.SEED).split(X)\n",
    "train_scores, test_scores = validation_curve(\n",
    "                 estimator=pipeline,\n",
    "                 X=X,\n",
    "                 y=y,\n",
    "                 param_name=param_name,\n",
    "                 param_range=param_range,\n",
    "                 scoring = 'f1',\n",
    "                 cv=cv, n_jobs=-1)\n",
    "\n",
    "train_mean = np.mean(train_scores, axis=1)\n",
    "train_std = np.std(train_scores, axis=1)\n",
    "test_mean = np.mean(test_scores, axis=1)\n",
    "test_std = np.std(test_scores, axis=1)\n",
    "plt.plot(param_range, train_mean,\n",
    "          color='blue', marker='o',\n",
    "          markersize=5,\n",
    "          label='training accuracy')\n",
    "plt.fill_between(param_range, train_mean + train_std,\n",
    "                  train_mean - train_std, alpha=0.15,\n",
    "                  color='blue')\n",
    "plt.plot(param_range, test_mean,\n",
    "          color='green', linestyle='--',\n",
    "          marker='s', markersize=5,\n",
    "          label='validation accuracy')\n",
    "plt.fill_between(param_range,\n",
    "                  test_mean + test_std,\n",
    "                  test_mean - test_std,\n",
    "                  alpha=0.15, color='green')\n",
    "plt.grid()\n",
    "# plt.xscale('log')\n",
    "plt.legend(loc='lower right')\n",
    "plt.xlabel('Parameter')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([0.8, 1.0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aad2a2860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(y_test, y_pred, y_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.5907919871361794 0.016627844841276936\n",
      "Confusion Matrix\n",
      " [[16772 13044]\n",
      " [ 1442  2325]]\n",
      "Accuracy  0.5686508054670518\n",
      "Recall    0.6172020175205734\n",
      "Precision 0.15127854772594182\n",
      "F1        0.24299749163879594\n",
      "ROC AUC   0.5898593935201472\n"
     ]
    }
   ],
   "source": [
    "cv = KFold(n_splits=10, shuffle=True, random_state=HP.SEED).split(X_train)\n",
    "scores = cross_validate(pipeline, X_train, y_train, cv=cv, return_train_score=True, scoring=\"accuracy\",\n",
    "                        verbose=0, n_jobs=-1)['test_score']\n",
    "\n",
    "print(\"Train score:\", np.mean(scores), np.std(scores))\n",
    "\n",
    "y_pred = pipeline.predict(X_test)\n",
    "y_prob = pipeline.predict_proba(X_test)\n",
    "\n",
    "print('Confusion Matrix\\n', metrics.confusion_matrix(y_test, y_pred))  # Confusion Matrix\n",
    "print('Accuracy ', metrics.accuracy_score(y_test, y_pred))            # Accuracy (TP + TN) / float(TP + TN + FP + FN)\n",
    "print('Recall   ', metrics.recall_score(y_test, y_pred))                # Recall (Sensitivity)   TP / float(FN + TP)\n",
    "print('Precision', metrics.precision_score(y_test, y_pred))     \n",
    "print('F1       ', metrics.f1_score(y_test, y_pred))     \n",
    "print('ROC AUC  ', metrics.roc_auc_score(y_test, y_pred))     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy score: 0.24896758113398185 0.008009805710082604\n"
     ]
    }
   ],
   "source": [
    "#  Cross-validate score \n",
    "cv = KFold(n_splits=10, shuffle=True, random_state=HP.SEED).split(X_train)\n",
    "scores = cross_val_score(pipeline, X_train, y_train, scoring=\"f1\", cv=cv,  verbose=0, n_jobs=-1)\n",
    "\n",
    "print(\"Test accuracy score:\", np.mean(scores), np.std(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aadf993c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_classifier_summary(y_test, y_pred, target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aaa7f2e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter_plots(X_test, y_pred, y_prob, y_test, target_names, figsize=(18,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: weighted average f1-score \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         no       0.93      0.57      0.70     90409\n",
      " readmitted       0.16      0.64      0.25     11357\n",
      "\n",
      "avg / total       0.84      0.58      0.65    101766\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Data points=101766'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Features=45'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Class dist.=0.111599'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'F1 valid=0.253049'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'ACC=0.575762'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'ROC_AUC=0.605560'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'LOG_LOSS=14.652967'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Misclassified=43173'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Data points=[8, 9, 11, 13, 26, 29, 33, 34, 38, 44, 48, 49, 50, 55, 56, 58, 59, 61, 62, 63]'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "classifier_summary_report(X, y, pipeline.predict(X), target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary for model: MLP\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Shape                     Param #   \n",
      "=================================================================\n",
      "fc1.0 (Linear)               ((25, 45),)               1125      \n",
      "_________________________________________________________________\n",
      "fc1.1 (BatchNorm1d)          ((25,), (25,))            50        \n",
      "_________________________________________________________________\n",
      "fc1.2 (ReLU)                 ()                        0         \n",
      "_________________________________________________________________\n",
      "fc2.0 (Linear)               ((25, 45),)               1125      \n",
      "_________________________________________________________________\n",
      "fc2.1 (BatchNorm1d)          ((25,), (25,))            50        \n",
      "_________________________________________________________________\n",
      "fc2.2 (ReLU)                 ()                        0         \n",
      "_________________________________________________________________\n",
      "classifier.0 (Dropout)       ()                        0         \n",
      "_________________________________________________________________\n",
      "classifier.1 (Linear)        ((2, 25), (2,))           52        \n",
      "=================================================================\n",
      "Total params:         2,402\n",
      "Trainable params:     2,402\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "print_model_summary(pipeline.named_steps['classifier'].module_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aadfaaf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aaa7ce588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aaa7d6cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aadf95d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aaa7ceb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa2509710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ab0964400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa2b535f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa2615c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aaa801940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa2b5a208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ab0c4e7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa2db5be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa2509550>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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       "<matplotlib.figure.Figure at 0x1aadf8b780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_layer_stats(pipeline.named_steps['classifier'].module_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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    "plot_model_graph(pipeline.named_steps['classifier'].module_(to_var(torch.from_numpy(X_test).float())), \n",
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