fraud_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fraud Detection System\n",
    "![General Design](https://blog.skymind.ai/content/images/2018/04/gates-1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Autoencoder\n",
    "Autoencoder is the unsupervised algorithm in the above picture. While there is a debate as to whether Autoencoder is unsupervised or semi-supervised, I lean towards calling it a semi-supervised algorithm especially for the fraud detection use case. The reason is we need to first know the class of the transactions in order to remove the fraudulant transactions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](process.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup\n",
    "We will be using TensorFlow and Keras. Let's begin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import tensorflow as tf\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Model, load_model\n",
    "from keras.layers import Input, Dense\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping,  TensorBoard\n",
    "from keras import regularizers\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (13.5, 13.5) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "\n",
    "RANDOM_SEED = 42\n",
    "LABELS = [\"Normal\", \"Fraud\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Utility Functions\n",
    "## Plots\n",
    "# Plot Feature Projection [credit: https://www.kaggle.com/shivamb/semi-supervised-classification-using-autoencoders]\n",
    "def tsne_plot(x1, y1, name=\"graph.png\"):\n",
    "    tsne = TSNE(n_components=2, random_state=0)\n",
    "    X_t = tsne.fit_transform(x1)\n",
    "\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    plt.scatter(X_t[np.where(y1 == 0), 0], X_t[np.where(y1 == 0), 1], marker='o', color='g', linewidth='1', alpha=0.8, label='Non Fraud')\n",
    "    plt.scatter(X_t[np.where(y1 == 1), 0], X_t[np.where(y1 == 1), 1], marker='o', color='r', linewidth='1', alpha=0.8, label='Fraud')\n",
    "\n",
    "    plt.legend(loc='best');\n",
    "    plt.savefig(name);\n",
    "    plt.show();\n",
    "    \n",
    "# Plot Keras training history\n",
    "def plot_loss(hist):\n",
    "    plt.plot(hist.history['loss'])\n",
    "    plt.plot(hist.history['val_loss'])\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.show()\n",
    "    \n",
    "## Util methods copied from OCAN package due to failure to install as custom package [credit:https://github.com/PanpanZheng/OCAN]\n",
    "def xavier_init(size): # initialize the weight-matrix W.\n",
    "    in_dim = size[0]\n",
    "    xavier_stddev = 1. / tf.sqrt(in_dim / 2.)\n",
    "    return tf.random_normal(shape=size, stddev=xavier_stddev)\n",
    "\n",
    "def sample_shuffle_uspv(X):\n",
    "    n_samples = len(X)\n",
    "    s = np.arange(n_samples)\n",
    "    np.random.shuffle(s)\n",
    "    return np.array(X[s])\n",
    "\n",
    "def pull_away_loss(g):\n",
    "\n",
    "    Nor = tf.norm(g, axis=1)\n",
    "    Nor_mat = tf.tile(tf.expand_dims(Nor, axis=1),\n",
    "                      [1, tf.shape(g)[1]])\n",
    "    X = tf.divide(g, Nor_mat)\n",
    "    X_X = tf.square(tf.matmul(X, tf.transpose(X)))\n",
    "    mask = tf.subtract(tf.ones_like(X_X),\n",
    "                       tf.diag(\n",
    "                           tf.ones([tf.shape(X_X)[0]]))\n",
    "                       )\n",
    "    pt_loss = tf.divide(tf.reduce_sum(tf.multiply(X_X, mask)),\n",
    "                        tf.multiply(\n",
    "                            tf.cast(tf.shape(X_X)[0], tf.float32),\n",
    "                            tf.cast(tf.shape(X_X)[0]-1, tf.float32)))\n",
    "\n",
    "    return pt_loss\n",
    "\n",
    "def one_hot(x, depth):\n",
    "    x_one_hot = np.zeros((len(x), depth), dtype=np.int32)\n",
    "    x = x.astype(int)\n",
    "    for i in range(x_one_hot.shape[0]):\n",
    "        x_one_hot[i, x[i]] = 1\n",
    "    return x_one_hot\n",
    "\n",
    "def sample_Z(m, n):   # generating the input for G.\n",
    "    return np.random.uniform(-1., 1., size=[m, n])\n",
    "\n",
    "def draw_trend(D_real_prob, D_fake_prob, D_val_prob, fm_loss, f1):\n",
    "\n",
    "    fig = plt.figure()\n",
    "    fig.patch.set_facecolor('w')\n",
    "    # plt.subplot(311)\n",
    "    p1, = plt.plot(D_real_prob, \"-g\")\n",
    "    p2, = plt.plot(D_fake_prob, \"--r\")\n",
    "    p3, = plt.plot(D_val_prob, \":c\")\n",
    "    plt.xlabel(\"# of epoch\")\n",
    "    plt.ylabel(\"probability\")\n",
    "    leg = plt.legend([p1, p2, p3], [r'$p(y|V_B)$', r'$p(y|\\~{V})$', r'$p(y|V_M)$'], loc=1, bbox_to_anchor=(1, 1), borderaxespad=0.)\n",
    "    leg.draw_frame(False)\n",
    "    # plt.legend(frameon=False)\n",
    "\n",
    "    fig = plt.figure()\n",
    "    fig.patch.set_facecolor('w')\n",
    "    # plt.subplot(312)\n",
    "    p4, = plt.plot(fm_loss, \"-b\")\n",
    "    plt.xlabel(\"# of epoch\")\n",
    "    plt.ylabel(\"feature matching loss\")\n",
    "    # plt.legend([p4], [\"d_real_prob\", \"d_fake_prob\", \"d_val_prob\"], loc=1, bbox_to_anchor=(1, 1), borderaxespad=0.)\n",
    "\n",
    "    fig = plt.figure()\n",
    "    fig.patch.set_facecolor('w')\n",
    "    # plt.subplot(313)\n",
    "    p5, = plt.plot(f1, \"-y\")\n",
    "    plt.xlabel(\"# of epoch\")\n",
    "    plt.ylabel(\"F1\")\n",
    "    # plt.legend([p1, p2, p3, p4, p5], [\"d_real_prob\", \"d_fake_prob\", \"d_val_prob\", \"fm_loss\",\"f1\"], loc=1, bbox_to_anchor=(1, 3.5), borderaxespad=0.)\n",
    "    plt.show()\n",
    "\n",
    "## OCAN TF Training Utils\n",
    "def generator(z):\n",
    "    G_h1 = tf.nn.relu(tf.matmul(z, G_W1) + G_b1)\n",
    "    G_logit = tf.nn.tanh(tf.matmul(G_h1, G_W2) + G_b2)\n",
    "    return G_logit\n",
    "\n",
    "\n",
    "def discriminator(x):\n",
    "    D_h1 = tf.nn.relu(tf.matmul(x, D_W1) + D_b1)\n",
    "    D_h2 = tf.nn.relu(tf.matmul(D_h1, D_W2) + D_b2)\n",
    "    D_logit = tf.matmul(D_h2, D_W3) + D_b3\n",
    "    D_prob = tf.nn.softmax(D_logit)\n",
    "    return D_prob, D_logit, D_h2\n",
    "\n",
    "\n",
    "# pre-train net for density estimation.\n",
    "def discriminator_tar(x):\n",
    "    T_h1 = tf.nn.relu(tf.matmul(x, T_W1) + T_b1)\n",
    "    T_h2 = tf.nn.relu(tf.matmul(T_h1, T_W2) + T_b2)\n",
    "    T_logit = tf.matmul(T_h2, T_W3) + T_b3\n",
    "    T_prob = tf.nn.softmax(T_logit)\n",
    "    return T_prob, T_logit, T_h2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the data\n",
    "\n",
    "The dataset can be downloaded from [Kaggle](https://www.kaggle.com/dalpozz/creditcardfraud). It contains data about credit card transactions that occurred during a period of two days, with 492 frauds out of 284,807 transactions.\n",
    "\n",
    "All variables in the dataset are numerical. The data has been transformed using PCA transformation(s) due to privacy reasons. The two features that haven't been changed are Time and Amount. Time contains the seconds elapsed between each transaction and the first transaction in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/ubuntu/Notebooks/Credit-Card-Fraud-Detection-using-Autoencoders-in-Keras'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/creditcardfraud.zip\", compression='infer', header=0, sep=',', quotechar='\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
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       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
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       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
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       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
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       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "31 columns, 2 of which are Time and Amount. The rest are output from the PCA transformation. Let's check for missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x972 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_classes = pd.value_counts(df['Class'], sort = True)\n",
    "count_classes.plot(kind = 'bar', rot=0)\n",
    "plt.title(\"Transaction class distribution\")\n",
    "plt.xticks(range(2), LABELS)\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a highly imbalanced dataset on our hands. Normal transactions overwhelm the fraudulent ones by a large margin. Let's look at the two types of transactions: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "frauds = df[df.Class == 1]\n",
    "normal = df[df.Class == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(492, 31)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frauds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284315, 31)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is highly imbalanced. Use tnse to visualize the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw_data_sample = df[df['Class'] == 0].sample(1000).append(df[df['Class'] == 1]).sample(frac=1).reset_index(drop=True)\n",
    "raw_data_x = raw_data_sample.drop(['Class'], axis = 1)\n",
    "raw_data_x[['Time']]=MinMaxScaler().fit_transform(raw_data_x[['Time']])\n",
    "raw_data_x[['Amount']]=MinMaxScaler().fit_transform(raw_data_x[['Amount']])\n",
    "tsne_plot(raw_data_x, raw_data_sample[\"Class\"].values, \"raw.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How different are the amount of money used in different transaction classes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     492.000000\n",
       "mean      122.211321\n",
       "std       256.683288\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         9.250000\n",
       "75%       105.890000\n",
       "max      2125.870000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frauds.Amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    284315.000000\n",
       "mean         88.291022\n",
       "std         250.105092\n",
       "min           0.000000\n",
       "25%           5.650000\n",
       "50%          22.000000\n",
       "75%          77.050000\n",
       "max       25691.160000\n",
       "Name: Amount, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal.Amount.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a more graphical representation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x972 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n",
    "f.suptitle('Amount per transaction by class')\n",
    "\n",
    "bins = 50\n",
    "\n",
    "ax1.hist(frauds.Amount, bins = bins)\n",
    "ax1.set_title('Fraud')\n",
    "\n",
    "ax2.hist(normal.Amount, bins = bins)\n",
    "ax2.set_title('Normal')\n",
    "\n",
    "plt.xlabel('Amount ($)')\n",
    "plt.ylabel('Number of Transactions')\n",
    "plt.xlim((0, 20000))\n",
    "plt.yscale('log')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do fraudulent transactions occur more often during certain time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x972 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n",
    "f.suptitle('Time of transaction vs Amount by class')\n",
    "\n",
    "ax1.scatter(frauds.Time, frauds.Amount)\n",
    "ax1.set_title('Fraud')\n",
    "\n",
    "ax2.scatter(normal.Time, normal.Amount)\n",
    "ax2.set_title('Normal')\n",
    "\n",
    "plt.xlabel('Time (in Seconds)')\n",
    "plt.ylabel('Amount')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doesn't seem like the time of transaction really matters.\n",
    "\n",
    "\n",
    "# Autoencoders\n",
    "\n",
    "Autoencoders can seem quite bizarre at first. The job of those models is to predict the input, given that same input. Puzzling? Definitely was for me, the first time I heard it.\n",
    "\n",
    "More specifically, let’s take a look at Autoencoder Neural Networks. This autoencoder tries to learn to approximate the following identity function:\n",
    "\n",
    "$$\\textstyle f_{W,b}(x) \\approx x$$\n",
    "\n",
    "While trying to do just that might sound trivial at first, it is important to note that we want to learn a compressed representation of the data, thus find structure. This can be done by limiting the number of hidden units in the model. Those kind of autoencoders are called *undercomplete*.\n",
    "\n",
    "Here's a visual representation of what an Autoencoder might learn:\n",
    "\n",
    "![biycle](https://springml.com/wp-content/uploads/2018/08/screenshot76.jpg)\n",
    "\n",
    "## Reconstruction error\n",
    "\n",
    "We optimize the parameters of our Autoencoder model in such way that a special kind of error - reconstruction error is minimized. In practice, the traditional squared error is often used:\n",
    "\n",
    "$$\\textstyle L(x,x') = ||\\, x - x'||^2$$\n",
    "\n",
    "If you want to learn more about Autoencoders I highly recommend the following videos by Hugo Larochelle:\n",
    "\n",
    "<h2 style=\"background-color: lime; color: green;\">\n",
    "[Autoencoder](https://www.youtube.com/embed/FzS3tMl4Nsc)\n",
    "</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing the data\n",
    "\n",
    "First, \n",
    "Do some scaling of Time and Amount columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data = df.drop(['Time'], axis=1)\n",
    "\n",
    "df[['Time']]=MinMaxScaler().fit_transform(df[['Time']])\n",
    "df.loc[:,'Amount'] = np.log(df['Amount']+1)\n",
    "df['Amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training our Autoencoder is gonna be a bit different from what we are used to. Let's say you have a dataset containing a lot of non fraudulent transactions at hand. You want to detect any anomaly on new transactions. We will create this situation by training our model on the normal transactions, only. Reserving the correct class on the test set will give us a way to evaluate the performance of our model. We will reserve 20% of our data for testing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test = train_test_split(df, test_size=0.2, random_state=RANDOM_SEED)\n",
    "X_train = X_train[X_train.Class == 0] # this will drop all the fraud transactions\n",
    "X_train = X_train.drop(['Class'], axis=1)\n",
    "\n",
    "y_test = X_test['Class']\n",
    "X_test = X_test.drop(['Class'], axis=1)\n",
    "\n",
    "X_train = X_train.values\n",
    "X_test = X_test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(227451, 30)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building the model\n",
    "\n",
    "Our Autoencoder uses 4 fully connected layers with 14, 7, 7 and 29 neurons respectively.  The first two layers are used for our encoder, the last two go for the decoder. Additionally, L1 regularization will be used during training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "class build_and_train_model(object):\n",
    "    \"\"\"build and train an autoencoder model\"\"\"\n",
    "    def __init__(self, input_dim, encoding_dim, activations, nb_epoch, batch_size):\n",
    "        self.input_dim = input_dim\n",
    "        self.encoding_dim = encoding_dim\n",
    "        self.activations = activations\n",
    "        self.nb_epoch = nb_epoch\n",
    "        self.batch_size = batch_size\n",
    "        \n",
    "    def build_model(self):\n",
    "        input_layer = Input(shape=(self.input_dim, ))\n",
    "\n",
    "        encoder = Dense(self.encoding_dim, activation=self.activations[0], \n",
    "                        activity_regularizer=regularizers.l1(10e-5))(input_layer)\n",
    "        encoder = Dense(int(self.encoding_dim / 2), activation=self.activations[1])(encoder)\n",
    "\n",
    "        decoder = Dense(int(self.encoding_dim / 2), activation=self.activations[2])(encoder)\n",
    "        decoder = Dense(self.input_dim, activation=self.activations[3])(decoder)\n",
    "\n",
    "        self.autoencoder = Model(inputs=input_layer, outputs=decoder)\n",
    "        return self.autoencoder\n",
    "\n",
    "    def train_model(self):\n",
    "        self.autoencoder.compile(optimizer='adam', \n",
    "                            loss='mean_squared_error', \n",
    "                            metrics=['accuracy'])\n",
    "\n",
    "        self.checkpointer = ModelCheckpoint(filepath=\"model.h5\",\n",
    "                                       verbose=0,\n",
    "                                       save_best_only=True)\n",
    "        \n",
    "        self.earlystopper = EarlyStopping(monitor='val_loss', mode='min', min_delta=0.005, \n",
    "                                           patience=20, verbose=0,restore_best_weights=True)\n",
    "\n",
    "        self.tensorboard = TensorBoard(log_dir='./logs',\n",
    "                                  histogram_freq=0,\n",
    "                                  write_graph=True,\n",
    "                                  write_images=True)\n",
    "\n",
    "        self.history = autoencoder.fit(X_train, X_train,\n",
    "                            epochs=self.nb_epoch,\n",
    "                            batch_size=self.batch_size,\n",
    "                            shuffle=True,\n",
    "                            validation_data=(X_test, X_test),\n",
    "                            verbose=1,\n",
    "                            callbacks=[self.checkpointer, self.earlystopper, self.tensorboard]).history\n",
    "        return (self.checkpointer, self.tensorboard, self.history)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train our model for 10 epochs with a batch size of 32 samples and save the best performing model to a file. The ModelCheckpoint provided by Keras is really handy for such tasks. Additionally,  the training progress will be exported in a format that TensorBoard understands.\n",
    "\n",
    "Instead of training for a fixed number of epochs, this autoencoder is trained with early-stopping. The training stops when the validation losses fail to decrease for 20 consecutive epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_dim = X_train.shape[1]\n",
    "encoding_dim = 14\n",
    "activations = ['tanh','relu','tanh','relu']\n",
    "nb_epoch = 100\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 227451 samples, validate on 56962 samples\n",
      "Epoch 1/100\n",
      "227451/227451 [==============================] - 9s 41us/step - loss: 0.8054 - acc: 0.5445 - val_loss: 0.7902 - val_acc: 0.6066\n",
      "Epoch 2/100\n",
      "227451/227451 [==============================] - 9s 40us/step - loss: 0.7418 - acc: 0.6192 - val_loss: 0.7710 - val_acc: 0.6225\n",
      "Epoch 3/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7328 - acc: 0.6224 - val_loss: 0.7672 - val_acc: 0.6195\n",
      "Epoch 4/100\n",
      "227451/227451 [==============================] - 9s 40us/step - loss: 0.7296 - acc: 0.6227 - val_loss: 0.7637 - val_acc: 0.6236\n",
      "Epoch 5/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7277 - acc: 0.6238 - val_loss: 0.7649 - val_acc: 0.6232\n",
      "Epoch 6/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7263 - acc: 0.6251 - val_loss: 0.7649 - val_acc: 0.6278\n",
      "Epoch 7/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7255 - acc: 0.6260 - val_loss: 0.7620 - val_acc: 0.6252\n",
      "Epoch 8/100\n",
      "227451/227451 [==============================] - 9s 40us/step - loss: 0.7247 - acc: 0.6265 - val_loss: 0.7619 - val_acc: 0.6279\n",
      "Epoch 9/100\n",
      "227451/227451 [==============================] - 9s 40us/step - loss: 0.7237 - acc: 0.6266 - val_loss: 0.7615 - val_acc: 0.6304\n",
      "Epoch 10/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7231 - acc: 0.6279 - val_loss: 0.7602 - val_acc: 0.6225\n",
      "Epoch 11/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7227 - acc: 0.6286 - val_loss: 0.7604 - val_acc: 0.6323\n",
      "Epoch 12/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7221 - acc: 0.6293 - val_loss: 0.7588 - val_acc: 0.6249\n",
      "Epoch 13/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7217 - acc: 0.6303 - val_loss: 0.7580 - val_acc: 0.6288\n",
      "Epoch 14/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7213 - acc: 0.6310 - val_loss: 0.7582 - val_acc: 0.6334\n",
      "Epoch 15/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7212 - acc: 0.6322 - val_loss: 0.7589 - val_acc: 0.6228\n",
      "Epoch 16/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7206 - acc: 0.6326 - val_loss: 0.7585 - val_acc: 0.6273\n",
      "Epoch 17/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7202 - acc: 0.6324 - val_loss: 0.7570 - val_acc: 0.6359\n",
      "Epoch 18/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7199 - acc: 0.6327 - val_loss: 0.7582 - val_acc: 0.6198\n",
      "Epoch 19/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7199 - acc: 0.6329 - val_loss: 0.7568 - val_acc: 0.6339\n",
      "Epoch 20/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7193 - acc: 0.6334 - val_loss: 0.7572 - val_acc: 0.6347\n",
      "Epoch 21/100\n",
      "227451/227451 [==============================] - 9s 40us/step - loss: 0.7193 - acc: 0.6348 - val_loss: 0.7584 - val_acc: 0.6266\n",
      "Epoch 22/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7191 - acc: 0.6352 - val_loss: 0.7583 - val_acc: 0.6309\n",
      "Epoch 23/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7188 - acc: 0.6352 - val_loss: 0.7547 - val_acc: 0.6379\n",
      "Epoch 24/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7185 - acc: 0.6363 - val_loss: 0.7579 - val_acc: 0.6296\n",
      "Epoch 25/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7183 - acc: 0.6368 - val_loss: 0.7544 - val_acc: 0.6291\n",
      "Epoch 26/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7182 - acc: 0.6360 - val_loss: 0.7539 - val_acc: 0.6328\n",
      "Epoch 27/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7179 - acc: 0.6359 - val_loss: 0.7532 - val_acc: 0.6343\n",
      "Epoch 28/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7177 - acc: 0.6369 - val_loss: 0.7540 - val_acc: 0.6338\n",
      "Epoch 29/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7176 - acc: 0.6360 - val_loss: 0.7523 - val_acc: 0.6344\n",
      "Epoch 30/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7174 - acc: 0.6365 - val_loss: 0.7539 - val_acc: 0.6361\n",
      "Epoch 31/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7172 - acc: 0.6374 - val_loss: 0.7524 - val_acc: 0.6323\n",
      "Epoch 32/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7171 - acc: 0.6363 - val_loss: 0.7521 - val_acc: 0.6375\n",
      "Epoch 33/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7171 - acc: 0.6364 - val_loss: 0.7528 - val_acc: 0.6335\n",
      "Epoch 34/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7171 - acc: 0.6364 - val_loss: 0.7548 - val_acc: 0.6374\n",
      "Epoch 35/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7168 - acc: 0.6368 - val_loss: 0.7526 - val_acc: 0.6336\n",
      "Epoch 36/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7170 - acc: 0.6367 - val_loss: 0.7545 - val_acc: 0.6358\n",
      "Epoch 37/100\n",
      "227451/227451 [==============================] - 9s 40us/step - loss: 0.7171 - acc: 0.6373 - val_loss: 0.7529 - val_acc: 0.6353\n",
      "Epoch 38/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7166 - acc: 0.6375 - val_loss: 0.7521 - val_acc: 0.6309\n",
      "Epoch 39/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7166 - acc: 0.6379 - val_loss: 0.7530 - val_acc: 0.6365\n",
      "Epoch 40/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7165 - acc: 0.6374 - val_loss: 0.7522 - val_acc: 0.6326\n",
      "Epoch 41/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7167 - acc: 0.6376 - val_loss: 0.7541 - val_acc: 0.6379\n",
      "Epoch 42/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7163 - acc: 0.6377 - val_loss: 0.7535 - val_acc: 0.6363\n",
      "Epoch 43/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7165 - acc: 0.6384 - val_loss: 0.7525 - val_acc: 0.6325\n",
      "Epoch 44/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7163 - acc: 0.6384 - val_loss: 0.7529 - val_acc: 0.6354\n",
      "Epoch 45/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7161 - acc: 0.6388 - val_loss: 0.7516 - val_acc: 0.6361\n",
      "Epoch 46/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7163 - acc: 0.6386 - val_loss: 0.7522 - val_acc: 0.6350\n",
      "Epoch 47/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7161 - acc: 0.6384 - val_loss: 0.7520 - val_acc: 0.6403\n",
      "Epoch 48/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7160 - acc: 0.6391 - val_loss: 0.7519 - val_acc: 0.6350\n",
      "Epoch 49/100\n",
      "227451/227451 [==============================] - 9s 39us/step - loss: 0.7158 - acc: 0.6383 - val_loss: 0.7521 - val_acc: 0.6350\n"
     ]
    }
   ],
   "source": [
    "anc_model = build_and_train_model(input_dim, encoding_dim, activations, nb_epoch, batch_size)\n",
    "autoencoder = anc_model.build_model()\n",
    "checkpointer, tensorbord, history = anc_model.train_model()\n",
    "autoencoder = load_model('model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x972 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history['loss'])\n",
    "plt.plot(history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reconstruction error on our training and test data seems to converge nicely. Is it low enough? Let's have a closer look at the error distribution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = autoencoder.predict(X_test) # X_test still has fraud transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = np.mean(np.power(X_test - predictions, 2), axis=1)\n",
    "error_df = pd.DataFrame({'reconstruction_error': mse,\n",
    "                        'true_class': y_test})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>reconstruction_error</th>\n",
       "      <th>true_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>56962.000000</td>\n",
       "      <td>56962.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.743305</td>\n",
       "      <td>0.001720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.295401</td>\n",
       "      <td>0.041443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.057026</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.273553</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.410126</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.635882</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>256.910182</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       reconstruction_error    true_class\n",
       "count          56962.000000  56962.000000\n",
       "mean               0.743305      0.001720\n",
       "std                3.295401      0.041443\n",
       "min                0.057026      0.000000\n",
       "25%                0.273553      0.000000\n",
       "50%                0.410126      0.000000\n",
       "75%                0.635882      0.000000\n",
       "max              256.910182      1.000000"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reconstruction error without fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x972 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "normal_error_df = error_df[(error_df['true_class']== 0) & (error_df['reconstruction_error'] < 10)]\n",
    "_ = ax.hist(normal_error_df.reconstruction_error.values, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For majority of the non-fraud records, the reconstruction error is quite low."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reconstruction error with fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x972 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "fraud_error_df = error_df[error_df['true_class'] == 1]\n",
    "_ = ax.hist(fraud_error_df.reconstruction_error.values, bins=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the fraud classes, the reconstruction error is more spread out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import (confusion_matrix, precision_recall_curve, auc,\n",
    "                             roc_curve, recall_score, classification_report, f1_score,\n",
    "                             precision_recall_fscore_support)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ROC curves are very useful tool for understanding the performance of binary classifiers. However, our case is a bit out of the ordinary. We have a very imbalanced dataset. Nonetheless, let's have a look at our ROC curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x972 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(error_df.true_class, error_df.reconstruction_error)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, label='AUC = %0.4f'% roc_auc)\n",
    "plt.legend(loc='lower right')\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([-0.001, 1])\n",
    "plt.ylim([0, 1.001])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ROC curve plots the true positive rate versus the false positive rate, over different threshold values. Basically, we want the blue line to be as close as possible to the upper left corner. While our results look pretty good, we have to keep in mind of the nature of our dataset. ROC doesn't look very useful for us. Onward...\n",
    "\n",
    "## Precision vs Recall\n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/Precisionrecall.svg/350px-Precisionrecall.svg.png\" />\n",
    "\n",
    "Precision and recall are defined as follows:\n",
    "\n",
    "$$\\text{Precision} = \\frac{\\text{true positives}}{\\text{true positives} + \\text{false positives}}$$\n",
    "\n",
    "$$\\text{Recall} = \\frac{\\text{true positives}}{\\text{true positives} + \\text{false negatives}}$$\n",
    "\n",
    "Let's take an example from Information Retrieval in order to better understand what precision and recall are. Precision measures the relevancy of obtained results. Recall, on the other hand, measures how many relevant results are returned. Both values can take values between 0 and 1. You would love to have a system with both values being equal to 1.\n",
    "\n",
    "Let's return to our example from Information Retrieval. High recall but low precision means many results, most of which has low or no relevancy. When precision is high but recall is low we have the opposite - few returned results with very high relevancy. Ideally, you would want high precision and high recall - many results with that are highly relevant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x972 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, th = precision_recall_curve(error_df.true_class, error_df.reconstruction_error)\n",
    "plt.plot(recall, precision, 'b', label='Precision-Recall curve')\n",
    "plt.title('Recall vs Precision')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate. High scores for both show that the classifier is returning accurate results (high precision), as well as returning a majority of all positive results (high recall)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x972 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(th, precision[1:], 'b', label='Threshold-Precision curve')\n",
    "plt.title('Precision for different threshold values')\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('Precision')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that as the reconstruction error increases our precision rises as well. Let's have a look at the recall:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x972 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(th, recall[1:], 'b', label='Threshold-Recall curve')\n",
    "plt.title('Recall for different threshold values')\n",
    "plt.xlabel('Reconstruction error')\n",
    "plt.ylabel('Recall')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x972 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_f1(precision,recall):\n",
    "    return (2*precision*recall)/(precision+recall)\n",
    "\n",
    "\n",
    "plt.plot(th, get_f1(precision[1:],recall[1:]), 'b', label='F1 curve')\n",
    "plt.title('F1 vs. Threshold')\n",
    "plt.xlabel('Reconstruction Error')\n",
    "plt.ylabel('F1 score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1 = list(get_f1(precision[1:],recall[1:]))\n",
    "index_of_max_f1 =  np.where(f1==max(f1))\n",
    "best_f1_th = th[index_of_max_f1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us say that we care about recall more than the precision. Fbeta score will be used when higher (beta times) weight is given to recall as opposed to precision."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "F{\\beta} = \\dfrac{(1+ß²)* Precision  * Recall}{(ß^² * Precision) + Recall}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us say that finding frauds is 10 times as important than the additional work required in evaluating the false positives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "beta = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L3XXXXbrrrruqnde2bVstWLBAzz77rFasWKG33npLCQkJatu2rQYMGKChQ4d6z83OztaUKVM0f/58Pfjgg6qsrNSkSZPkdDr1wAMPqEmTJlq6dKmWL1+utm3batSoUerWrZvGjx/vU80XXHCBFixYoCeeeEKrVq3Sm2++qeTkZHXu3NnbjMBTx4wZM/TUU0/pkUceUatWrXTzzTcrKyvL53udacSIEXr44YflcDj0s5/9rNZzHnroIfXu3Vuvv/66nn76aVVUVKhly5bq2rWr7r33Xp/vddVVV3mn7t1www01/lHfu3dvff3111q5cqUOHDggh8OhtLQ0/e53vwvZep9A6tevn9544w0988wzeuutt3TkyBElJyerffv2Gj9+vHevqGB97vvuu0/p6el65ZVXNHPmTMXFxal79+6aOXOmevbsedb3T5gwQYmJiXrhhRc0c+ZMtW3bVhMmTFBSUlK9jRwAwFeGySpCAAAAAFGANT8AAAAAogLhBwAAAEBUIPwAAAAAiApR3/DA5XLp+PHjiouLC5u9HAAAAIBoZpqmKioq1KRJE582YvaI+vBz/PhxbdmyxeoyAAAAAPjJ6XQqKSnJ5/OjPvzExcVJcv/g4uPjLa1l06ZNysrKsrQG2AvPBM7EM4Ha8FzgTDwTqE0kPRfl5eXasmWL99/yvor68OOZ6hYfH6+EhASLq5EtaoC98EzgTDwTqA3PBc7EM4HaRNpz4e+yFRoeAAAAAIgKhB8AAAAAUYHwAwAAACAqEH4AAAAARAXCDwAAAICoQPgBAAAAEBUIPwAAAACiAuEHAAAAQFQg/AAAAACICoQfAAAAAFGB8AMAAAAgKhB+AAAAAEQFwg8AAACAqED4AQAAABAVCD8AAAAAogLhBwAAAEBUIPwAAAAAiAqEHwAAAABRgfADAAAAICoQfgAAAABEBcIPAAAAgKhA+AEAAAAQFQg/AAAAAKIC4QcAAABAVCD8AAAAAIgKhB8AAAAAUYHwAwAAACAqEH4AAAAARAXCDwAAAICoQPgBAAAAEBUIP2Hg+/2lGvrARu0+UKrVXxSpvNJldUkAAABA2CH8hIGl6w9Jkh56daf++spOLf73QYsrAgAAAMIP4ScMHDlWKUnac7BckhTjMKwsBwAAAAhLhJ8wcORHd/gpq3BPd2uZEmdlOQAAAEBYIvyEgSPHKqofMBj5AQAAAPxF+AkDRSenvXmZpjWFAAAAAGGM8BMGfiypsroEAAAAIOwRfsIAAz0AAABAwxF+AAAAAEQFwo/NmbUM+zAQBAAAAPiP8GNzJeUuq0sAAAAAIgLhx+a+/aHU6hIAAACAiED4sbkpT2+zugQAAAAgIhB+whDd3wAAAAD/EX7C0Ibtx6wuAQAAAAg7hJ8w0rpZvCTpnY8OWVwJAAAAEH4IP2GkZUqc1SUAAAAAYYvwY2Mu16nFPdf0bqEb+reysBoAAAAgvMVaXQDqVl55ao+fXw9P82542v0nTa0qCQAAAAhbjPzYWElZ9Q1ODcNQxvmNFR9rWFQRAAAAEL4Y+bGxknJXjWPb9pZIkrbuOaEL2yWGuiQAAAAgbDHyY2OlJ8PPH8d0qPHarv1lIa4GAAAACG+EHxvzTHtrFFfza0ptyqAdAAAA4A/Cj42VlldJkhon1PyaTLPGIQAAAAD1IPzYmGfaW+P4U1/TQ3d0Ovkn0g8AAADgD8KPjXkaHjQ6beQnIdb9Z0Z+AAAAAP8QfmystKzmyI9Odrkm+wAAAAD+IfzYmHfk57Tw49nhh5EfAAAAwD+EHxsrKXfJMKT42NPCj8EGpwAAAMC5IPzYWGlZlRrFOeRw1Aw8jPwAAAAA/iH82FhphataswNJMrxrfkg/AAAAgD8IPzZWUuaq3uxAp8IP2QcAAADwD+HHxkrLXdWaHUinNTwIfTkAAABAWCP82FhJLeHH2+qa9AMAAAD4hfBjY6VlLjWOj6l2zDiZfsg+AAAAgH8IPzZWWlHLtDc2+gEAAADOCeHHpiY9vkU7C0sVH1e9zfXh4gpJ0povj1pRFgAAABC2CD82tX1viSQpIa76V9QiOU6S1KxpXMhrAgAAAMIZ4cfmzgw/iY3ca4DSWzeyohwAAAAgbMVaefPjx49r1qxZWrp0qYqLi5WRkaGJEydq4MCB9b4vNzdXe/bsqfW1jh07aunSpcEo1xI1ur0BAAAAOCeWhp9Jkybpq6++0pQpU5SWlqZFixZp0qRJeuqpp9S/f/863zd79myVl5dXO7ZlyxY9+OCDGjRoULDLDjqX61QzgzNHfjxM+r0BAAAAfrEs/OTn52vt2rWaPXu2Bg8eLEnq06ePdu3apRkzZtQbfrp27Vrj2JIlSyRJP//5z4NTcAhV1hN+DHY5BQAAAM6JZXOqli1bpqSkpGpT3AzD0IgRI7Rjxw5t27bN52uVl5dr8eLFys7OVseOHYNRbkhVVJ4WfuKrd3vz/K2iivQDAAAA+MOy8LN161ZlZGTI4aheQmZmpiT3NDZfvf/++yoqKoqIUR9Jqjwt2DSqY9rbE2/WvuYJAAAAQO0sm/ZWVFSkDh061DiekpLifd1XCxYsUGJiooYOHXrO9WzatOmc3xtIBQUFKi6RPF/Nnl3fqcD81vv64WOnXisoKAh5fQg9vmeciWcCteG5wJl4JlCbaH8uLG14YBjGOb12un379mnt2rUaOXKkEhMTz7mWrKwsJSQknPP7A6GgoEDZ2dn64XCZ9PY3kqQunTOUnZnsPaeyytSjSz9XfKyh7Oxsq0pFiHieCcCDZwK14bnAmXgmUJtIei7KysrOafDCsvCTmppa6+jO0aNHJZ0aATqbhQsXyuVyRcyUN0mqrKy74UFsjKFLL2yqE6WuUJcFAAAAhDXL1vxkZGRo+/btcrmq/yPes9bH6XSe9RqmaWrRokXq1KmTLr300qDUaYXTmxnU1uraNKVvdp3Q6i98nxoIAAAARDvLws/gwYNVXFysFStWVDuel5enjh07KiMj46zXWL9+vb7//vuIGvWRqnd7q22T0+8KSyVJTy+h6QEAAADgK8umvfXv31+9e/fW1KlTVVRUpLS0NOXl5amgoEBz5szxnjd27FitX79emzdvrnGNBQsWKDY2VsOHDw9l6UFXWW3kp+bapyM/VkqSUprE6rNtP+qSjKSQ1QYAAACEK8tGfgzD0Jw5c3Tttddq1qxZuvPOO7V582bNnj1bubm5Z33/sWPH9N577+nKK6/UeeedF4KKQ6fKVf/Ij8eOH0r1h+d3hKIkAAAAIOxZ2u2tadOmmjZtmqZNm1bnOS+//HKd792wYUOwSrNURT0NDwAAAACcG/5lbUOnj/zEx9b8in53U/tQlgMAAABEBMKPDZ2+5icmpuaan6u6NwtlOQAAAEBEIPzY0OnhBwAAAEBgEH5syJ/wE1vLyBAAAACAmgg/NuRZ8/OLK1vWeU7MyW+usspUFSNFAAAAwFkRfmzIE35+llN3C+8l07ur8wWJkqS3/n0wJHUBAAAA4YzwY0OeaW8xjvqntB0qrpAklVa4gl4TAAAAEO4IPzb03ieHJUmxZwk/k4anSZLaNIsPek0AAABAuCP82NDWPSWSJMdZws/5LRJCUQ4AAAAQEQg/NhYbY3UFAAAAQOQg/NjY2db8AAAAAPAd4cfGCD8AAABA4BB+bOxsa348vt55PMiVAAAAAOGP8BPGKqrcLa4XrztkcSUAAACA/RF+wlj7lo0kSTkXpVhcCQAAAGB/hB8b6tI+0afzYmIMJTWOUYvk2CBXBAAAAIQ/wo8NNU+KU4fWjXw61zAk0wxyQQAAAEAEIPzYUJXLlMPHb8YwDMIPAAAA4APCjw1VuUyf21w7GPkBAAAAfEL4sSH3yI+Pe/wYkov0AwAAAJwV4ceGXC4pxsdvhn1QAQAAAN8QfmzIn2lvhmHIxcAPAAAAcFaEHxvyK/xIMpn2BgAAAJwV4ceGqlzyY+SHhgcAAACALwg/NuTyo9W1g1bXAAAAgE8IPzbkz7Q3ur0BAAAAviH82JDLj2lv7PMDAAAA+IbwY0PukR/fzjWY9gYAAAD4JNbqAlCTP5uc7jlYph9LKoNcEQAAABD+GPmxIX+mvUlS8fGqIFYDAAAARAbCjw1V+dHtLTMtUU0a8TUCAAAAZ8O0Nxvyp9tbm+bxOlbKyA8AAABwNgwZ2JA/m5wCAAAA8A3hx4Zc/uzzI9HrGgAAAPAB4ceG/FnzYzBABAAAAPiE8GNDTHsDAAAAAo/wY0P+NDzYX1SuPYfKVVnF1DcAAACgPoQfG3Kv+fHt3K92npAk7T5QGsSKAAAAgPBH+LEZ0zTlMn2f9jayX0tJkoNpcgAAAEC9CD8243K5f/c1zDjTGkui4RsAAABwNoQfm6lyuVOMr9PeDDHiAwAAAPiC8GMznvDj68iPp9W1ydAPAAAAUC/Cj82czD5+t7om+gAAAAD1I/zYTFWVZ9qbb+HHc9qPJ6qCVRIAAAAQEQg/NnNq2puv57t/nz7vu+AUBAAAAEQIwo/NeMKMryM/nsYIxYz8AAAAAPUi/NjMqW5vvoWfyzonS5J6ZSYFrSYAAAAgEhB+bMZl+tfqOi7WoYzzG8swaHkNAAAA1IfwYzOehgeEGQAAACCwCD828+4nhyVJ7xUctrgSAAAAILIQfmym8Ei5JOnwjxUWVwIAAABEFsKPzVSenPYW6+cmpwAAAADqF2t1AajOs+YnNsb38LNtb4m0tyRYJQEAAAARgZEfm0lp6s6jbZrHW1wJAAAAEFkIPzbTr1uqJGn4FS19fk/vLsnq1LZRsEoCAAAAIgLhx2ZObvPj8yanksTqIAAAAODsCD824wk//mBLIAAAAODsCD82s3LjEUnSgaPlfr3vXEITAAAAEE0IPzazcmORJOn7wjK/3ne8tEp7D/r3HgAAACCaEH5sKibG93MNw9D+ogrdPvOb4BUEAAAAhDnCj0350/Bg/TfFQawEAAAAiAyEH5vyJ/zkXJQiSUpq7MdwEQAAABBlYq0uALVz+BFLp9xwgRyGtHH7seAVBAAAAIQ5Rn5spnG8+ytp1jTO5/fExTqUmMCoDwAAAFAfwo/NjPtpG0lSVocmFlcCAAAARBbCj83ExLjX+pzLxqVHjlXqjVX7A1wRAAAAEBkIPzbj2azU8DP9lJRXSZKe/9cPgS4JAAAAiAiEH5txnUw/fjR7kyR9sKEoCNUAAAAAkYPwYzPekR8/v5k/jk6XJHVs0yjAFQEAAACRgfBjM57w4/Bz2tsVWam6vGtyECoCAAAAIgPhx2Y8097OpeGBJJkBrAUAAACIJIQfmzk18uP/ew3DkEz3ZqflFa7AFgYAAACEOcKPzbjOsdubx3eFpfr9c9u1ciMNEAAAAIDTEX5sxmzAtLcjP1Z4/3yu0+YAAACASEX4sRnvyM85vLeMqW4AAABAnQg/NmOezC+Oc1j0M21sR/3p1g7u69D5AAAAAKjG0vBz/Phx/eUvf1Hfvn118cUXa+TIkVq+fLlP7zVNU6+99ppGjhyp7t27q2fPnrrxxhv16aefBrnq4PJOezuH97ZuFq+ObRq7r0PfNwAAAKCaWCtvPmnSJH311VeaMmWK0tLStGjRIk2aNElPPfWU+vfvX+97p06dqvfee0933HGHLrnkEpWUlGjTpk0qKSkJUfXB4Yks57pmxztgRPYBAAAAqrEs/OTn52vt2rWaPXu2Bg8eLEnq06ePdu3apRkzZtQbft59910tWrRIr7zyii655BLv8auuuirYZQedy3QHn3Pt9uYZMnIRfgAAAIBqLJv2tmzZMiUlJWngwIHeY4ZhaMSIEdqxY4e2bdtW53v/8Y9/qGfPntWCT6QwTfOcprx5GCff/frK/YEpCAAAAIgQloWfrVu3KiMjQw5H9RIyMzMlSVu2bKn1fRUVFdqwYYMyMzP16KOPKicnR127dtW1116rRYsWBb3uYDPNhrWp9rx335HywBQEAAAARAjLpr0VFRWpQ4cONY6npKR4X6/rfeXl5Vq0aJHatGmjBx98UMnJyXrjjTf0+9//XhUVFbrxxhv9rmfTpk1+vycYfvhhnyRDBQUF5/T+Kpfk+VrP9RqwF75HnIlnArXhucCZeCZQm2h/LixteFDfupamPnaWAAAgAElEQVS6XnO53L2gy8rK9Mwzz6hdu3aSpJycHO3atUtPPPHEOYWfrKwsJSQk+P2+QCooKFCr1q3l2H5Q2dk9zvk6Iwr3aOnHh5WdnR3A6mCFgoICvkdUwzOB2vBc4Ew8E6hNJD0XZWVl5zR4Ydm0t9TU1FpHd44ePSrp1AjQmVJSUmQYhjp16uQNPpI7LPXr10/79u3ToUOHglN0iJzDFj/VGIZBszcAAADgDJaFn4yMDG3fvt07kuPhWevjdDprfV+jRo2Unp5e62vePXIasmjGYu5ubw1NP2xyCgAAAJzJsvAzePBgFRcXa8WKFdWO5+XlqWPHjsrIyKj3vTt27NDu3bu9x0zT1KpVq3TBBReoefPmQas72ExXw7q9SZ5u16QfAAAA4HSWrfnp37+/evfuralTp6qoqEhpaWnKy8tTQUGB5syZ4z1v7NixWr9+vTZv3uw9dvvtt2vx4sW64447NGnSJCUlJWnBggX68ssvNWvWLCs+TsCYali3N518PyM/AAAAQHWWhR/DMDRnzhw9+uijmjVrloqLi5WRkaHZs2crNze33vc2a9ZM8+bN0//8z//oP//zP1VaWiqn06knnnhCgwYNCtEnCA7TlBwNTD8ul1ReaeqNVfv1iytbBagyAAAAILxZ2u2tadOmmjZtmqZNm1bnOS+//HKtx9PS0vTYY48FqzTLuExTDZ33Vl7pXkf1+krCDwAAAOBh2Zof1MFseLc3T+Dpkt4kAAUBAAAAkYHwYzOB6PbWulm8Ms5vzMIfAAAA4DSEH5sxzYY3PJBONj1o+GUAAACAiEH4sRlTZkDCj8TADwAAAHA6wo/NmAFY8yM1vGMcAAAAEGkIPzbjMqUGt3s7eQkXQz8AAACAl6WtrlGTaQZm2tvmXSckSSVlVWqcENPwCwIAAABhjpEfmwnUtDePXQfKAncxAAAAIIwRfmzGDECra0m6fWhbSVJcLGt/AAAAAInwYzumGZAVP2rTPP7kBQNwMQAAACACEH5sJlCtrj2XIPsAAAAAboQfmwnUJqeei9DwDQAAAHAj/NhMoKa9nRr5If0AAAAAEuHHdkxJRgDavRnMewMAAACqIfzYjGmaAR35+e9/fCeTuW8AAAAA4cduArXmx3Fy9Gh/UQXrfgAAAAARfmwnUPv8XJLRVJkXJLqv2eCrAQAAAOGP8GMzpgIz8hMX61DvLsnua5J+AAAAAMKP3QRqzY90at2Pi/QDAAAAKNbqAlDd19+fUNGxyoBcy0HHNwAAAMCLkR+bCVTwkU6tHXIRfgAAAADCTyTzrB2i1TUAAABA+Ilo3vBjbRkAAACALRB+IpjjZPph4AcAAAAg/EQFur0BAAAAhJ+IRrc3AAAA4BRaXdtM62bxAbsW3d4AAACAUwg/NnNeSpxiHYHZ5pRubwAAAMApTHuzGdM0pcBkn9PCT2CuBwAAAIQzwo8NBWjgh/ADAAAAnIbwYzMuV+CuxZofAAAA4BTCj82YOjVi01CeEaQ7Zn7Nuh8AAABEPcKP3ZiSEaBFP82T4iRJZRUmU98AAAAQ9Qg/NmPKDNjIT6/MJI3s19J9XcIPAAAAohzhx2ZMM3DT3gzDUFLjGEmSi/QDAACAKEf4sZkAdrqWdGrdD9kHAAAA0Y7wYzOmFLihH9HxDQAAAPAg/NiMaZoB2+dHOn2vH9IPAAAAohvhx2YCueZHkhyM/AAAAACSCD+2E+iM4glSNDwAAABAtCP82I15ap1OIHgbHrgCdkkAAAAgLBF+bMZUgKe9OZj2BgAAAEiEH9txmWZAW13T8AAAAABwI/zYTZAaHpB9AAAAEO0IPzZjKrCbnJ5qeBDAiwIAAABhiPBjM2aAF/04mPYGAAAASCL82E/ANzml4QEAAAAgEX5sJ9AhhYYHAAAAgBvhx2YC3er6xxNVkqTXVu4P3EUBAACAMET4sRtTMgLY8qBj20aSpD0HywJ2TQAAACAcEX5sJtAjP5dkJKlN83i1TIkP3EUBAACAMET4sRnTNAMafiR362wXa34AAAAQ5Qg/NmMGeJNTSQHtHgcAAACEK8KPzQRjfMZwGLS6BgAAQNQj/NiMaUqOAA/9GAatrgEAAADCj80EY82Pw2DkBwAAAIi1ugBUF4yMsr+oXKXlriBcGQAAAAgfhB+7CULDg5Iyl0rKygN7UQAAACDMMO3NZtz7/AQ2/fTukhzQ6wEAAADhiPBjMy7TVKA7U7c7L0EJcXzVAAAAiG78i9hugjDtLdZhqIqOBwAAAIhyhB+bMU0FfOQnJsZQZZVJu2sAAABENcKPzQRjzU+sw309Fw3fAAAAEMUIPzYTjH1+YmPcvx8srtALS39Q0bGKwN4AAAAACAO0uraZYMxMi41xZ9zx//O1JKlRvEM357YO/I0AAAAAG2Pkx2bc094Ce82Kqurz3c5LiQvsDQAAAIAwQPixmyA0PLioQxNlnN9Yf7glXZK7nTYAAAAQbZj2ZjOmJMMR2PiT1aGpHv+1UweOlrvvQfYBAABAFGLkx2bMIGxy6mGcvDJb/gAAACAaEX5sxgzCJqce3gElwg8AAACiEOHHZoKZSzyhijU/AAAAiEaEH5sxTckRpKEfz+apX353PCjXBwAAAOyM8GMzwdjk1MMz7W3lxqLg3AAAAACwMcKPzQRzQlpSYkwQrw4AAADYG+HHboLY8MAwDI0e2Np9G9b9AAAAIMoQfmzG1Km1OcHguTTZBwAAANHG0k1Ojx8/rlmzZmnp0qUqLi5WRkaGJk6cqIEDB9b7vscff1yzZ8+ucfy8887TmjVrglVuSJimgrbPj3Rqrx/CDwAAAKKNpeFn0qRJ+uqrrzRlyhSlpaVp0aJFmjRpkp566in179//rO9/4YUXlJiY6P17XFxcMMsNOk8gCeLAj4yTY31kHwAAAEQby8JPfn6+1q5dq9mzZ2vw4MGSpD59+mjXrl2aMWOGT+EnKytLycnJwS41ZDyBJJgjP45qe/0E804AAACAvVi25mfZsmVKSkqqNsXNMAyNGDFCO3bs0LZt26wqzXJBXfNz8vf3Pjms0vKqoN0HAAAAsBvLws/WrVuVkZEhh6N6CZmZmZKkLVu2nPUa11xzjbp06aK+ffvqj3/8ow4dOhSUWkMlFNPePBd/4s09+mzbsSDeCAAAALAXy6a9FRUVqUOHDjWOp6SkeF+vywUXXKDJkyerS5cuiouL06effqrnnntO//73v7Vw4ULvNfyxadMmv98TaJ5pb3v37lFBwe7g3OO4Icm938/WrdsVX8LqH7srKCiwugTYDM8EasNzgTPxTKA20f5cWNrwoL7pXfW9Nnz48Gp/v/zyy9WjRw9NmDBB8+bN01133eV3LVlZWUpISPD7fYH00Xr3w9iuXTtlZ7cOyj2yJfW+tET/8X9b1LFTJ2V3Sw3KfRAYBQUFys7OtroM2AjPBGrDc4Ez8UygNpH0XJSVlZ3T4IVl095SU1NrHd05evSoJPk9enPFFVeoZcuW2rBhQ0Dqs4JnDMYR5D4EnmBJu2sAAABEE8vCT0ZGhrZv3y6Xy1XtuGetj9Pp9PuapmnWWEMUTrxhJKiLfk67Hw2vAQAAEEUsSwqDBw9WcXGxVqxYUe14Xl6eOnbsqIyMDL+u9+GHH+rgwYPq3r17IMu0RLCjj2dkiZEfAAAARBPL1vz0799fvXv31tSpU1VUVKS0tDTl5eWpoKBAc+bM8Z43duxYrV+/Xps3b/YeGz58uIYPH66OHTsqNjZWn332mZ5//nmlp6dr9OjRVnycgAjVwI9B+AEAAEAUsiz8GIahOXPm6NFHH9WsWbNUXFysjIwMzZ49W7m5ufW+t1OnTnrllVe0f/9+VVZWqk2bNrrhhht01113hfemp6Fode2+g/t2hB8AAABEEUu7vTVt2lTTpk3TtGnT6jzn5ZdfrnHs0UcfDWZZlvGO/AR54luIlhQBAAAAthK+3QEiUEg2OdXp094Y+gEAAED0IPxEIU/4cZF9AAAAEEUIPzYSsoYHwb08AAAAYEuEHzsJ2bQ3Gh4AAAAg+hB+bCRUDQ9O3Y/0AwAAgOhB+LGRUDU88GxySvYBAABANCH82FCItvmh4QEAAACiCuHHRrzT3oL8rXim1X2z64R+LKkM7s0AAAAAmyD82Ih32luINjl975PDen3l/qDeCwAAALALwo8dBXneW2qTWA28pJkkqaTMFdybAQAAADZB+LGRUyM/wRUTY2jKje2VnBhzqvkBAAAAEOEIPzbiWfNDIAEAAAACj/BjI+apjX4AAAAABBjhx4ZCtckpAAAAEE0IPzbiHfgh+wAAAAABR/ixE0/DA8IPAAAAEHCEHxsJ9ZKf4hNVWrzukO6ZszVEdwQAAACsQ/ixEW+r6xAN/XS+IFGStG3PiZDcDwAAALAS4SeKzbrrQt08oJVc5tnPBQAAAMId4cdGrNjnx+EwZJqSaZKAAAAAENkIPzZixT4/jpNT7Bj9AQAAQKQj/NhQKLu9GSefABfpBwAAABGO8GMj3oYHIRz6iTl5K7IPAAAAIh3hx4ZCOfLjOLnAiJEfAAAARDrCj414l/yEctrbyXsdOFoRupsCAAAAFiD82MipaW+h43K5f5+3vDCEdwUAAABCj/BjRyEc+rmmdwv3H2h1DQAAgAhH+LERK0Z+mjSKUbvzEkI71w4AAACwAOHHhkK5yal0Mvcw8gMAAIAIR/ixESs2OfXcjugDAACASEf4sZFT2Sf06YeBHwAAAEQ6wo8NhXr5Dat9AAAAEA0IPzbibXhgQRph4AcAAACRLtbqAnCKRUt+9P3+Mn2/v0zlFS7Fx5GHAQAAEJn4l66dWDTy0yo1TpL0za4Tob0xAAAAEEKEHxs5NfUstOlnyo3tJUlVLia/AQAAIHIRfmzEEz0cIf5WYk5uLOQi/AAAACCCEX5sxKp2055NVatc1twfAAAACAXCjw2Fes2PwzPyw2Y/AAAAiGCEHxsxTXcICfUmp55pdi5GfgAAABDBCD82FOqRnxiDkR8AAABEPsKPjVi1yal32hsjPwAAAIhghB8bsWqTU0/Dg4de3RniOwMAAAChQ/ixoxCnn5ap8aG9IQAAAGABwo+NeKa9OUI8761RvEO/uLKl4mJDPeYEAAAAhA7hx0asmvYmudcZ0e8AAAAAkYzwYycWpp9Qt9cGAAAAQo3wYyPWj/ww9AMAAIDIRfixISPUva4BAACAKED4sRGr9vnx3t+a2wIAAAAhQfixEe+0NyvW/BjuTU6rXEQgAAAARCbCjw1ZMfATe3Kn04WrD1hwdwAAACD4CD82YlrY8WDoZS0kSUXHK0N/cwAAACAECD82FOpNTiWpeXKcGic4ZDLtDQAAABGK8GMj3oYHFt3fYUhkHwAAAEQqwo+NeHOHRenHkCG2+gEAAECkIvzYkFUjP4YhmTS8BgAAQIQi/NjIqX1+rIk/hkOM/AAAACBiEX5sxMp9fiR3owXW/AAAACBSEX5sxOqGB0XHKvXZ1h9lMvwDAACACET4sSGrRn5iYwz9cLhcW3aXWFMAAAAAEESEHxs5Ne3NmvTz259fIEkqLXdZcn8AAAAgmAg/NmL1tLfzUuLcddDxDQAAABEo1t837Nq1S+vWrdPBgwd1/fXXKy0tTeXl5Tp48KDOO+88xcfHB6PO6GLVPj8n78uSHwAAAEQiv8LPI488orlz56qqqkqGYahHjx7e8HPttdfqN7/5jcaPHx+kUiPfqVbX1tyf8AMAAIBI5vO0t/nz5+v555/XLbfcor///e/VOoI1bdpUubm5+uCDD4JSZLRxWLXPz8khJ8IPAAAAIpHPIz+vvPKKBg8erKlTp+rIkSM1Xs/MzNTHH38c0OKijdWh41TmIv0AAAAg8vg88vPdd98pJyenztebNWtWayiC76ze5NRzW6tDGAAAABAMPoefhIQElZTUvf/L3r17lZycHJCiop1V3d48N951oMyqCgAAAICg8Tn8XHzxxVq2bFmtr5WVlenNN9/UpZdeGrDCopF3xMWi9JPSxD0L8uPNxdYUAAAAAASRz+Hn9ttv14YNG3Tfffdp8+bNkqSDBw9q9erVGjt2rAoLCzVhwoSgFRpNrGp4cH6LBKW1TFBcjGVjTwAAAEDQ+NzwICcnR3/+8581ffp0LVmyRJJ0//33S5Li4uL03//937rkkkuCU2WUsHqTU0lKbRqrknKXhRUAAAAAweHXPj+jRo1Sbm6uli5dqh07dsg0TXXo0EFDhw5V69atg1Vj1PD2GbAw/TSOd6joWKV1BQAAAABB4lP4KS8v18aNG9WyZUt16NBBY8eODXZdUc3KkZ/GCTHae6jcwgoAAACA4PBpzY/D4dD48eO1atWqYNcT1bzT3qzqdS33yE9peZVl9wcAAACCxafwExsbq/POO08mG8AEldX7/EhS4wSHSspY8wMAAIDI43O3tyFDhuhf//qXXK7A/cP4+PHj+stf/qK+ffvq4osv1siRI7V8+XK/rmGapm699VZlZmZq+vTpAavNEjZoeOAe+XERdAEAABBxfA4/N9xwg0pLS3XbbbdpxYoV2r59u/bu3Vvjlz8mTZqkxYsX6ze/+Y2efvppZWRkaNKkScrPz/f5Gq+//rp27Njh133tyg4jP40SYuQypZUbi6wrAgAAAAgCn7u9XXfddTIMQ6Zpav369XWe9/XXX/t0vfz8fK1du1azZ8/W4MGDJUl9+vTRrl27NGPGDPXv3/+s1ygsLNQjjzyi6dOn6+677/btg4QBK9f8ZKYlSpLe/eSwBvRoZlkdAAAAQKD5HH4mTpwY0H+UL1u2TElJSRo4cKD3mGEYGjFihB588EFt27ZNGRkZ9V7jT3/6k3r27Kmrr746YHVZ6VTDA+tq6P6Tpuqanmjp1DsAAAAgGHwOP7/+9a8DeuOtW7cqIyNDDkf1mXeZmZmSpC1bttQbfpYsWaKPPvpI77zzTkDrspJdVtk4HIZcrPkBAABAhPFrk9NAKioqUocOHWocT0lJ8b5el8OHD2v69On67W9/q7Zt2waknk2bNgXkOg1iusdbNm7YoIQ468o4fswhl8tQQUGBdUXAi+8BZ+KZQG14LnAmngnUJtqfC7/Cj8vl0qJFi7Rs2TLt3r1bkpSWlqaf/vSnGj58eI1RnLOpbxpdfa9Nnz5daWlpGjNmjF/3q09WVpYSEhICdr1zsXrzp5KkSy/toUbxMZbVsWDDdpWUuZSdfaFlNcCtoKBA2dnZVpcBG+GZQG14LnAmngnUJpKei7KysnMavPA5/JSWlurOO+/UJ598IsMw1LJlS0nSqlWrlJ+fr7y8PD377LM+B4jU1NRaR3eOHj0q6dQI0JnWrFmjd955Ry+++KKOHTtW7bXy8nIVFxcrMTFRsbGWDWqds1MzzaxdcRPDtDcAAABEIJ+Hap588kl9/PHHuu222/Tvf/9b+fn5ys/P17p16zRhwgStX79eTz75pM83zsjI0Pbt22vsG7RlyxZJktPprPV9W7dulcvl0tixY9WrVy/vL0maP3++evXqpbVr1/pch53YodW15F7zU8U+pwAAAIgwPg+PvPPOOxo6dKjuv//+aseTk5N13333ae/evXr77bd1zz33+HS9wYMH64033tCKFSs0aNAg7/G8vDx17NixzmYHQ4YMUZcuXWocv/XWW3X11Vdr9OjR3qYJ4crqTmsxDsnlYuQHAAAAkcXn8LNv3z5NmDChztd79eql999/3+cb9+/fX71799bUqVNVVFSktLQ05eXlqaCgQHPmzPGeN3bsWK1fv16bN2+WJLVp00Zt2rSp9ZqtW7dW7969fa7BbuzQ6tp9f0Pf7iu1tggAAAAgwHwOP8nJydq5c2edr3///fdKTk72+caGYWjOnDl69NFHNWvWLBUXFysjI0OzZ89Wbm6uz9eJJKemvVmbfo78WGHp/QEAAIBg8Dn85OTk6JVXXtEVV1yhfv36VXvtww8/1KuvvqohQ4b4dfOmTZtq2rRpmjZtWp3nvPzyyz5dyzMyFNY8Iz/WVqGLOjTRjh9KLK4CAAAACCyfw88999yjDz/8UL/85S/VpUsXXXihuw3y1q1b9fXXX6tZs2a6++67g1ZoNLBJszdJp3eeAwAAACKDz+GnXbt2WrBggWbOnKkPPvhAX331lSSpSZMmuvbaazV58mSdf/75QSs0mjhsEH4AAACASOPXZjjnn3++Zs6cKdM0dfjwYZmmqRYtWli+RiVSmKb758jPEwAAAAi8c9oJ1DAMtWjRItC1RD07zTSzUy0AAABAIPi8yem8efM0fvz4Ol+fMGGC5s+fH4iaopdpfZtryR41AAAAAIHmc/hZuHCh0tPT63y9Q4cOWrBgQUCKilambNHrQJK74QEbnQIAACCS+Bx+du7cKafTWefrGRkZ9e4DBN/YZdSlssrUzX/9UmUVLqtLAQAAAALC5/BTWVmp8vLyOl8vLy9XWVlZQIqKVqZpj2YHl3dNkSQVH6/S8dIqi6sBAAAAAsPn8NOhQwetWbOmztc//PBDtW/fPiBFRSu7TDLr0r6J7h6RJklyMfADAACACOFz+Ln22mu1Zs0a/e///m+1EaCKigo99thjWrNmja677rqgFBk1bNLwQJIcJ58MF7udAgAAIEL43Op6/PjxWrVqlZ566im9+uqr6tSpkwzD0Pbt23X06FH17NlTt912WzBrjXim7LPBqeNkCqPpAQAAACKFz+EnLi5Of//73zV37lwtWbJEX3/9tST3dLhf/vKXuvXWWxUXFxe0QqOBe5DFHuknxuEJPxYXAgAAAASIX5ucxsXF6c4779Sdd94ZrHqinm2mvZ2so4qRHwAAAEQIn9f81Ke+LnDwnZ32+XF4Rn7IPgAAAIgQPoef/Px8Pf7449WOzZs3T5deeql69Oihe++9VxUVFQEvMKrYsOHB6i+KtPqLImuLAQAAAALA52lvzz//vFq0aOH9+/bt2/XXv/5VF1xwgdLS0vTOO++oW7duGj9+fDDqjAqm7BN+EuLc6Wfe8kIlxDnUr1uqxRUBAAAADePzyM+OHTuUlZXl/fs777yjhIQEvfHGG3ruued0zTXXKC8vLyhFRgv3tDd7pJ8eP2mqP9ySrqu6p9LuGgAAABHB5/Bz9OhRNWvWzPv3tWvXqk+fPmratKkk6bLLLtPu3bsDX2E0sdGin7hY92hPy9Q4kX0AAAAQCXwOP82aNdPevXslSceOHdMXX3yh7Oxs7+uVlZWqqqoKfIVRxE77/AAAAACRxuc1Pz169ND8+fOVkZGhVatWqaqqSv379/e+vnPnTrVq1SooRUYL00YjPx52mYYHAAAANJTPIz933323XC6X7rnnHi1cuFDDhw9XRkaGJMk0Tb3//vu69NJLg1ZotCBsAAAAAMHh88hPRkaG3nnnHX366adKSkpSr169vK8VFxdr3Lhx6t27d1CKjBZ26vbmYRjucAsAAACEO5/DjySlpqYqNze3xvGUlBSNGzcuYEVFK9NG+/wAAAAAkcbnaW8IPhsu+ZHLNFXlkr749pjVpQAAAAANQvixExuO/FzYLlGStPrzIosrAQAAABqG8GMj7pU19ko//bqlqkVyrMorWfcDAACA8Eb4sRm7jfxIUnysQ2UVLqvLAAAAABqE8GMjpmnPTU4T4hyM/AAAACDsEX5sxK7xIj7OUDkjPwAAAAhzPrW6LikpUX5+vr744gsVFhaqtLRUjRo1UuvWrZWVlaX+/fsrMTEx2LVGPlMybDjvLT6OaW8AAAAIf2cNP4sWLdLDDz+so0eP1rnZZUpKin73u99p5MiRAS8wmtix1bUkJcQ6dKykyuoyAAAAgAapN/wsXbpUDzzwgLp166YHHnhAPXr0UOvWrZWQkKCysjIVFhbqs88+0z/+8Q9NnTpViYmJGjJkSKhqj0g2HPhRfJyhsh/dIz+l5S7tLypX4ZFTv7I6NFXvLskWVwkAAADUr97w8+yzz+qyyy7T3Llz5XBUXx7UqFEjpaenKz09XT/72c80btw4PfPMM4SfBjBtuM+P5G54sPtAmW6e/qWKjlXWeH3Tt8cJPwAAALC9esPPtm3b9Mc//rFG8DmTw+HQ9ddfr+nTpwe0uGhj12lvfbul6lhJlVqmxql1s3i1So1X62buXzP/+b1OlLIeCAAAAPZXb/hJTExUYWGhTxcqLCyk6UFDmbLl0M8VF6XoiotSan3NYRgybdunDgAAADil3iGdfv366e9//7vy8/PrvcjKlSs1d+5cXXnllQEtLtqYsuc+P/UxDPd0PQAAAMDu6h35mTJligoKCvSrX/1K7du3V/fu3dW6dWvFx8ervLxchYWF2rhxo77//nu1bdtW9957b6jqjkimXee9AQAAABGg3vDTqlUrLVy4UE8//bTefvttvfXWWzXOad26tW677Tb98pe/VGpqatAKjRbhln0Y+QEAAEC4OOs+PykpKbr//vt1//3368CBA9q/f79KSkrUuHFjtWrVSi1btgxFnVHBveQnvOKPIbHiBwAAAGHhrOHndC1btiTsBJNNW13XK+wKBgAAQLSqv4c1Qiocl/y4p70x9gMAAAD7C1j4efLJJ9W1a9dAXS4q2XWT0/qEWbkAAACIYgEd+WEEIPrQ8AAAAADhot41P3v37vX5QsXFxQ0uJtq59/kJv7EUsg8AAADCQb3hJzc3N+y6j4Uz05SMMFuFZRgG6QcAAABhod7wExsbq/bt2+vyyy8/64U2bdqkzz//PGCFITzQ6hoAAADhot7w85Of/ESNGzfWgw8+eNYLPfnkk4SfBgrHhgei2xsAAADCRL2TrLp27apvvvlGLpcrVPVEvXCbZsjIDwAAAMJFveHnyiuvVLdu3bR///6zXqhXr16aOHFiwAqLRi6T1tEAAABAsNQ77W3o0KEaOnSoTxfq2bOnevbsGZCiolmYDfzIMKRd+8v0wYYjGtCjmdXlAAAAAKzNzkUAACAASURBVHUKs95ikc1U+IUfZ1qiJGnJuoMWVwIAAADUr97w8/nnn6uoqChUtSAMF8/84spWuiSjqVgWBgAAALurN/yMGjVKq1ev9v79+PHjuvfee7Vt27agFxaNwnWT09gYQ1WuMExuAAAAiCr1hp8zWxiXl5fr7bff1oEDB4JaVLQyTSMsOx7EOAg/AAAAsD/W/NhMGGYfxcQYqqwi/AAAAMDeCD82Eo4NDyTPyI/VVQAAAAD1I/zYiRl+m5xKUqzDUBUjPwAAALC5evf5kaT8/HwdPOhuY1xSUiLDMLR06VJ98803Nc41DEPjx48PeJHRwlS4TnuTKlnzAwAAAJs7a/hZsmSJlixZUu3Ya6+9Vuu5hJ+GMc3wnfZ28GiFZv7ze917Q3urywEAAABqVW/4eemll0JVB8JYtjNJSz8+rA82HCH8AAAAwLbqDT+XXXZZqOqAwnefn75ZqRqWc1zvf3rY6lIAAACAOtHwwEbCddqbJDkMyUXHNwAAANgY4QcB4XAYcpk0PQAAAIB9EX5sJFz3+ZEkh0Oi4RsAAADsjPBjJ2G6z4/kXqvkIv0AAADAxgg/NhLWIz8GIz8AAACwt7Pu84PQMc3w3ORUcq/5MU3JNE0d+//s3Xl8VPW9//H3mZnsOyGAECDAkCBGQYOAYEWWWEpFBLUuP7G2eq0C1d6W2qpX2nq7gFqoGoEqtSrUVr0oSLEoVEtbEZG0VJEl7CAQtpBM9mXm/P4IGTIkQIBkzsnM6/l45CH5nnNmPpN8Tead73IqvTJNKTGO7gUAAAD74N2pzbTfkZ/6wifM+Fy1daZcTkOvPdpfCbF0MQAAANgD70xtpH7aW/tMP8Ozk1R4vFpJcS4dKa7V6s+KVVrhJfwAAADANnhnaiPtedpbz87R+v7NPSRJf9twXKs/K1Ydi4AAAABgI2x4YDftNf004nTWvwgv4QcAAAA2QvixkfY88tOYqyH8eAk/AAAAsA/Cj8200yU/AZyO+hdRR/gBAACAjRB+bKQ9b3jQWMPID2t+AAAAYCeEHxsJvWlvFhcCAAAANMJubzYTAgM//mlvW7+skMtpqLrWp5pan2rqTF3WO17J8XQ7AAAABJ+l70LLy8s1Z84crVixQh6PR263W1OnTtXo0aPPeN2bb76pxYsXa/fu3SorK1NqaqpycnI0ZcoUud3uIFXf+swQmSUWE1U/oPj7FQebHBt/Vaqm3JAe7JIAAAAAa8PPtGnTtGnTJk2fPl3p6el6++23NW3aNM2fP18jRow47XXHjx/XsGHDdO+99yoxMVFffvmlXnzxRd1yyy1asmSJevbsGcRX0XpMSY4QGPrp1SVa//P/esowDEVHOhQZYSgqwqGZf9yjIk+d1eUBAAAgTFkWflavXq01a9YoLy9Pubm5kqShQ4dq3759mjlz5hnDz3333Rfw+eDBgzVgwACNGzdOy5Yt07Rp09q09rZimqEx7c0wDA3PTm7SnpoYoZIKwg8AAACsYdmGBytXrlRCQkLAFDfDMDRx4kTt3LlT27dvP6fHS0lJkSRFRES0ap1oPYlxLnnKCT8AAACwhmXhZ9u2bXK73XI4AkvIysqSJBUUFJz1Mbxer2pqarRz5079z//8jzp27Kgbb7yxTeoNhvqtrq2uou0kxbpUWsEWcAAAALCGZdPeiouLlZGR0aQ9KSnJf/xshg0b5j8vIyNDr776qjp37nxe9WzcuPG8rmtVplNFRceUn3/E6kraRLnHoZJyQ+vX54d0yGtt+fn5VpcAm6FPoDn0C5yKPoHmhHu/sHTDgzPd0LMlN/t85ZVXVFVVpX379umVV17RXXfdpZdffll9+/Y951qys7MVFRV1zte1JnP5BnXs2FE5Od0traOt7K08otVbDqjfJQMVH+O0upx2IT8/Xzk5OVaXARuhT6A59Aucij6B5oRSv6iurj6vwQvLpr0lJyc3O7pTUlIi6eQI0Jn069dPAwcO1Pjx4/XKK6/IMAzNnj271WsNllC5yenpJMbWBx7W/QAAAMAKloUft9utHTt2yOfzBbQ3rPXJzMw8p8eLi4tTnz59tHv37tYq0RKhPB0sMa5+oHH5umPauKvM4moAAAAQbiwLP7m5ufJ4PPrggw8C2pcsWaJevXqd881Ki4uLtWXLlnZ7jx8p9Dc86JISKUl66x9HNOv1vTJD5a6uAAAAaBcsW/MzYsQIDRkyRI899piKi4uVnp6uJUuWKD8/X3PnzvWfN3nyZK1bt05bt271t02YMEETJkxQr169FBMTo927d2vhwoWqqqrSlClTrHg5raJ+2lvopp/unaL1u+n9tPqzYr36fqEOF9eq84lABAAAALQ1y8KPYRiaO3euZs+erTlz5sjj8cjtdisvL0+jRo0647UDBgzQW2+9pQMHDqi6ulqpqam68sorNWfOnHOeLmc7oZt9JEldU6M0pF+iXn2/UBt3lalzSgerSwIAAECYsHS3t/j4eM2YMUMzZsw47TkLFy5s0vbEE0+0ZVmWCfUNDxr07BytuGiHNu4u1+grCD8AAAAIDsvW/KB5jjBIP06Hof494/TF7nKrSwEAAEAYIfzYiGkqPIZ+JGVnxGnfkWoVl7HtNQAAAIKD8GMj9dknPNLPJRnxkqQv9jD6AwAAgOAg/NhMKG913Vjf9BhFuAx9wf1+AAAAECSWbniAQKYZPuEn0uVQVvdYfbLFo+R4l6pqTVXX+HRF33jlZCZaXR4AAABCEOHHRsJoyY8kaXBWol5acVC/f6/QH/o27y0n/AAAAKBNEH7sxKy//1G4uPmaNI0d3EGRLociXYaeWLhbh4trrC4LAAAAIYrwYyOmFFZDP4ZhKCHmZBeMjHCouta0sCIAAACEMjY8sJkwyj5NREUYqqn1WV0GAAAAQhThx0bCacOD5kS4HKquI/wAAACgbRB+bMRUeIefKJehWqa9AQAAoI0QfuzEDJ+bnDYnMoKRHwAAALQdwo+NhP3IT4RDPp9U52X0BwAAAK2P8APbiIyoT36vfXBItYwAAQAAoJURfmzENCVHGI/89OgULZfT0B8/OKSNu8utLgcAAAAhhvBjI6aMsJ73dmVWon43vZ8k6WARNzsFAABA6yL82Ez4Rp96qYkRcjkNHSL8AAAAoJURfmzCNOsX+YfxwI8kyekw1Dk5QoVF1VaXAgAAgBBD+LGJE9kn7MOPJHXpEMW0NwAAALQ6wo9N+MNP2E98kzp3iFQh4QcAAACtjPBjEw13tmHkR7qoQ6RKK70qq/RaXQoAAABCCOHHJsyTQz9hr0uHSElS4XHW/QAAAKD1EH5swj/yY2kV9tAQfjbtrrC4EgAAAIQSwo9dnEg/4XyT0wbpHaMkSUWltRZXAgAAgFBC+LEJH4t+/KIjnXLSMwEAANDKeItpGyfu82NxFXbhcBjyNayDAgAAAFoB4ccmuM9PIIch+XxWVwEAAIBQQvixCWa9BXIYhhj4AQAAQGsi/NgENzkNZBiS10f6AQAAQOsh/NgE094CseYHAAAArc1ldQGoZ4o3+o05HAratDfTNOUz65/PZ5oyzWbafDrxef3xOp+p2jpTtXU+1Xnr/+311V/j9Znyek15ffXXen0nPryNP5d83sBrGh7HrC9KpqSDBx36/OjB+ppk+r8m9Z9LaqhJJ79e1w3qoN4XxQTniwcAANCOEH7sgpGfAA7D0M6Dlfq/vx9Wrbdp0Gj4b63Xp6oanyprfKqs9qmq2quaOlM+05TPJ3lNUz6v/CHjZKgxTwQbq19p8wyjYec/Q0bBYRknOkZDe/2nxsnzTvy3ps7UPz4v1vMPZik5nv+9AQAAGuPdkU34CD8B0pIitGlPhTbtqfC3RbgMRTgNuVyGIpwO/+fRkQ7FRDmUmhihmMgoRUYYchiGHI76EOV0GHI66qfSGUZ9W/1/JcMw/P81HKe2nTw38Lr6x4tw1dfgcp78cDrqn9dpGHI4DDmdqm9rqCPgc51oq38+p8NQZETgTNT8/Hzl5OS0+Ou282ClHnp+m37z1j79ZHKGPzQBAACA8GMbDVOWHLxZlST9+n63Kqt9AeGCN/Jn1/uiGH177EV6YfkBLf/kmK4f2tHqkgAAAGyDDQ9swjS5yWljES6HEuNciolyKsLlIPicgwnDOmpQZoJeXH5Aew5VWV0OAACAbRB+bMK/9IT3+LhADoeh79/cXbHRTs360x7V1HK3WAAAAInwYx9Me0MrSkmI0H/f1F27Cqv00oqDVpcDAABgC4Qfm/DvOkb2QSsZ3C9RE4Z11NI1R/XpVo/V5QAAAFiO8GMTDff5cRB+0Iq+PfYiZXSJ1uz/26fjpbVWlwMAAGApwo9NNOz2RvZBa4qMcOjHt/VURZVXs/9vn39jDQAAgHBE+LEJf/hhzQ9aWc/O0bpjdGetLyjVwaIaq8sBAACwDOHHJvxbXZN90AZ6dYmRJJWU11lcCQAAgHUIPzbh3++A8IM2kBjrlCSVVngtrgQAAMA6hB+bME/cioXsg7aQGOeSxMgPAAAIb4Qfmzg58kP8QetLjK0PP4z8AACAcEb4sQnW/KAtxUU75HBIngpGfgAAQPgi/NjEyd3erK0DockwDCXGugg/AAAgrBF+bMJH+EEbS4h1ysO0NwAAEMYIPzZjsOUB2khSrEuljPwAAIAwRvixCdb8oK0lxbtUVEr4AQAA4YvwYxOs+UFbS+8YpYPHqlVb57O6FAAAAEsQfmyCra7R1np2jpbXJ+0/Wm11KQAAAJYg/NiEr2Ham8V1IHT17BwtSdpzuMriSgAAAKxB+LGLE0M/DtIP2kh6xyg5DGnPIUZ+AABAeCL82ITPP+/N0jIQwiIjHLqoQ6T2HmLkBwAAhCfCj02Y/pEf0g/aTo/O0Ux7AwAAYYvwYxMNW10Dbaln52gdOFatGnZ8AwAAYYjwYxMN0Yc1P2hLPTtFy8eObwAAIEwRfmyC+/wgGHp2qd/xbceBSosrAQAACD7Cj02cnPVG+kHb6dkpWikJLn26tdTqUgAAAIKO8GMT5omJbw6+I2hDDoehwVmJyi/wqM7LOjMAABBeeKttE+x3gGAZcnGiyqt82ri7zOpSAAAAgorwYxNsdY1gudwdrwiXoU82e6wuBQAAIKgIPzbBhgcIluhIpwb2idcnmz1ssQ4AAMIK4ccmGtb8EH4QDEMuTtTBohrtO8KW1wAAIHwQfmzCP/JjbRkIE4P7JUoSU98AAEBYIfzYxMlpb8QftL20pEj16RqjtZtLVF7lVVmllylwAAAg5LmsLgD1Gt54kn0QLEP6Jeq1Dw7p5p9tlCTl5qTo+zf3sLgqAACAtkP4sQk2PECw3Ti8o+JjnDJN6ePNJfp4k0c+nymHg04IAABCE+HHJnys+UGQJcS6NPHqNElSUpxTT7+5T7sKq9Sna4zFlQEAALQN1vzYDGt+YIXLesdLkj7byY1PAQBA6CL82ISPNT+wUFpypC7qEEn4AQAAIY3wYxes+YHFBvSJ1+e7yuT1sesbAAAITYQfm2h4u0n4gVUu6x2v8iqfdh6stLoUAACANkH4sYmTGx6QfmAN1v0AAIBQR/ixixNrfthlGFZJTYxQt45R+mwH4QcAAIQmS7e6Li8v15w5c7RixQp5PB653W5NnTpVo0ePPuN1b775pv76179q69atOnbsmLp06aJrrrlGU6ZMUYcOHYJUfevyL7Mg/MBCA3rH68P/HOd+PwAAICRZOvIzbdo0LVu2TA899JB++9vfyu12a9q0aVq9evUZr3v22WcVHx+v73//+1qwYIHuvvtu/eUvf9HNN98sj8cTpOrbhoNFP7BQelqUKqt9Kq/2Wl0KAABAq7Ns5Gf16tVas2aN8vLylJubK0kaOnSo9u3bp5kzZ2rEiBGnvXbJkiVKTU31fz548GC53W5NnjxZS5cu1eTJk9u8/tbWsNU1YKWEWKckqbTCq4QY7oEMAABCi2UjPytXrlRCQkLAFDfDMDRx4kTt3LlT27dvP+21jYNPg0svvVSSVFhY2PrFBkFD9mGmEayUGFsfeEorGPkBAAChx7Lws23bNrndbjkcgSVkZWVJkgoKCs7p8dauXStJ6tu3b+sUGGQm9/mBDSTE1I/8eCrqLK4EAACg9VkWfoqLi5WUlNSkvaGtuLj4nB7r5z//uTIyMjRu3LhWqzGYToYf0g+sk8DIDwAACGGWTuo/0xv9loaAyspKTZ06VSUlJVq0aJEiIyPPq5aNGzee13WtZeceQ5JTX3yxUYXxlpYCm8nPzw/ac1VUS5JLm7buUpJvZ9CeF+cmmH0C7Qf9AqeiT6A54d4vLAs/ycnJzY7ulJSUSFKzo0Knqqqq0gMPPKBNmzbpd7/7nfr163fe9WRnZysqKuq8r79Qx40i6dN9ys7OVtdU6+qAveTn5ysnJydoz+f1mTL+/JmSUrsqJ6dL0J4XLRfsPoH2gX6BU9En0JxQ6hfV1dXnNXhh2bQ3t9utHTt2yOfzBbQ3rPXJzMw84/XV1dWaMmWKNmzYoN/+9re64oor2qzWYGiY9uZkxwNYyOkwFBftVClrfgAAQAiyLPzk5ubK4/Hogw8+CGhfsmSJevXqJbfbfdpra2pqNGXKFK1fv15z587V4MGD27rcNtew1TVLfmC1xFinjnpqrS4DAACg1Vk27W3EiBEaMmSIHnvsMRUXFys9PV1LlixRfn6+5s6d6z9v8uTJWrdunbZu3epve/DBB/XPf/5TU6dOVWxsrDZs2OA/1qFDB/Xo0SOor6U1sNsb7CInM0HLPj6m9Vs9GpSVaHU5AAAArcay8GMYhubOnavZs2drzpw58ng8crvdysvL06hRo8547YcffihJev755/X8888HHJs4caJmzpzZZnW3FZ//Pj+kH1jrnq911ee7yvXUm3uV991MpSWd3yYiAAAAdmPpbm/x8fGaMWOGZsyYcdpzFi5c2KSt8ShQqDCZ9gabiIpw6LE7euq7eds06097NevePnI66ZgAAKD9s2zNDwKZJ/Z9YL8D2EF6WrQenJiuL3aX69WVhVaXAwAA0CoIPzbh4yansJmRA1P0tcEd9Mbqw1q3xWN1OQAAABeM8GMTDdPeGPmBnXzn+m7q1SVav35zr44U11hdDgAAwAUh/NgEIz+wo6gIhx69I0O1daZm/mmP6rym1SUBAACcN8KPTbDVNewqPS1KD05M16Y9FXrl/YNWlwMAAHDeLN3tDSf5mPYGG7t2YIo+31Wu//v7ER0sqpGrUUdtMhZkBrY0N1bUt1usbhnRqdXrBAAAOBPCj00w7Q12953ru6qkvE67C6sC2pvrsk2aGjWUlNVp3RYP4QcAAAQd4ccm/BseMBERNhUZ4dD/3JlxwY/z2l8PaeGqQtV5Tbm4fxAAAAgi3mrbRMPIj4ORH4S4uOj6HzvlVV6LKwEAAOGG8GMTDTc5Jfsg1MVGOyURfgAAQPARfmyC+/wgXMSdCD8VhB8AABBkhB+bYMMDhIv4mPrwU0b4AQAAQUb4sQnTNGU0uykwEFpi/Wt+fBZXAgAAwg3hxyZ8Jut9EB6Y9gYAAKxC+LEJk/CDMBEXxYYHAADAGoQfm/CZZtMbQwIhKC7aKadDKi6rs7oUAAAQZgg/NsHID8KF02moc0qk9h+ttroUAAAQZgg/NkH4QTjpmhqlA8cIPwAAILgIPzbhdEouvhsIE906RunAsRr//a0AAACCwWV1Aah347A0pRgHrS4DCIpuHaNUVeNTUWmdUhMjrC4HAACECcYabKJDYoR6dLS6CiA4uqZGSRLrfgAAQFARfgAEXbeOkZLEuh8AABBUhB8AQZeWHCmX02DkBwAABBXhB0DQOR2GLuoQqQPHaqwuBQAAhBHCDwBLpKdFad+RKqvLAAAAYYTwA8ASGV1itP9ItaprfVaXAgAAwgThB4Al+lwULZ8p7S5k9AcAAAQH4QeAJXp3jZEk7TxYaXElAAAgXBB+AFiic3KkYqMchB8AABA0hB8AlnA4DPW6KIbwAwAAgobwA8AyvS+K1q6DVfL5TKtLAQAAYYDwA8AyfS6KUWWNT4VF3O8HAAC0PcIPAMs0bHqwg6lvAAAgCAg/ACzTs1O0DIPtrgEAQHAQfgBYJjLCoeR4l455aq0uBQAAhAHCDwBLpSZEqKiU8AMAANoe4QeApTokMPIDAACCg/ADwFIdEiNUVFpndRkAACAMEH4AWCo1MUIl5XWq83KvHwAA0LYIPwAs1SExQqYpHS9j6hsAAGhbhB8AlkpNiJAkFXmY+gYAANoW4QeApTokuiSJTQ8AAECbI/wAsFRqYv3Iz/6j1RZXAgAAQh3hB4ClOiREqF/3WL277pi8PjY9AAAAbYfwA8Byk76SpsKiGq3d7LG6FAAAEMIIPwAsN+ySJHVJidRb/zhsdSkAACCEEX4AWM7pMDRheEdt2lOhLXvLrS4HAACEKMIPAFu4blAHxUU79NY/j1hdCgAACFGEHwC2EBvl1NcGp+qjjSU6dLzG6nIAAEAIIvwAsI0bhnWUYUhLPmL0BwAAtD7CDwDbSEuK1DWXJeu9T4tUVum1uhwAABBiCD8AbGXS1WmqrPFp8sxN+sYTG5VfwPbXAACgdRB+ANiKu1usptzQTV8d1EE1dT6t21JqdUkAACBEuKwuAABONf6qjpKkbfsrtP1AhcXVAACAUMHIDwDb6tstVjsPVsnrM60uBQAAhADCDwDb6tM1RlU1Pu0/Wm11KQAAIAQQfgDYlrtbjCRp+/5KiysBAAChgPADwLZ6pEUr0mVoxwHCDwAAuHCEHwC25XQa6nVRDJseAACAVkH4AWBr7q4x2r6/Uj42PQAAABeI8APA1tzdYlRR7VNhUY3VpQAAgHaO8APA1rIz4iRJ6wu42SkAALgwhB8AtpaeFq3uaVFas6nE6lIAAEA7R/gBYHvDLknS57vK5Cmvs7oUAADQjhF+ANjeVf2T5PNJn2zxWF0KAABoxwg/AGwvMz1GHZMi9DFT3wAAwAUg/ACwPcMwdFX/ROUXlKqqxmt1OQAAoJ0i/ABoF4ZdkqSaOlP57PoGAADOE+EHQLtwaUa8EmKcWvMF634AAMD5cVldAAC0hNNpaMjFifpoY4mefH1Pi64xzZY/fmKsS5O+kqbOKZHnWSEAALA7wg+AduNrg1O1ZV+FtuytaPE1htGy846W1Oovnx7TpKvT9I1rOyk2ynmeVQIAALsi/ABoN/r3jNOL3+/XJo99pKRGL68o1Ot/O6z31xfpruu6KDeng5yOFqYnAABge6z5AQBJaUmR+uGtPfSbKX11UWqknnnrSz2YV6D/7CizujQAANBKCD8A0EhW91g9/R23fnx7D5VVevXjBTv0xMJd2n+02urSAADABSL8AMApDMPQiMtS9ML3++nur3bRhu1luv83W/Xi8gMqq+Q+QwAAtFes+QGA04iKcOjWazsrN6eDXn2/UG9/dESr/lWkyWO66GuDU+V0sh4IAID2hJEfADiLDgkR+t5N3fXctExldInW8+/s15Rnt2r9Vu45BABAe2LpyE95ebnmzJmjFStWyOPxyO12a+rUqRo9evQZr1u/fr0WL16sTZs2afv27aqrq9PWrVuDVDWAcNWna4xm3ttHH2/yaMG7B/T4y7s0KDNB947rqp6do60uDwAAnIWl4WfatGnatGmTpk+frvT0dL399tuaNm2a5s+frxEjRpz2urVr12rdunW65JJL5HK5tHHjxiBWDSCcGYahYZckaVBWgpZ9fFR//OCQpjy7VSMHpKhLh/obpJ56c9XGn8ZGOXTDsI6KdDHwDgBAsFkWflavXq01a9YoLy9Pubm5kqShQ4dq3759mjlz5hnDz5QpUzRt2jRJ0i9+8QvCD4Cgi3Q5dNNXOmn05R30h78WasWnRarzmme/UFJmeqwu6x3fxhUCAIBTWRZ+Vq5cqYSEhIApboZhaOLEiXr88ce1fft2ud3uZq91OPiLKQB7SI53aeqEdE25oZsM48wbIGzaU64fzN+u2rqWhSQAANC6LEsR27Ztk9vtbhJksrKyJEkFBQVWlAUA5+VswUeSnI76c7w+wg8AAFawbOSnuLhYGRkZTdqTkpL8x4PJLlPn8vPzrS4BNkOfCB0HjkuSSwXbtstZfv4BiD6B5tAvcCr6BJoT7v3C0g0PzvSX0pb8FbU1ZWdnKyoqKqjPear8/Hzl5ORYWgPshT4RWjoUVkp/LVDPjN7KuTT5vB6DPoHm0C9wKvoEmhNK/aK6uvq8Bi8sm/aWnJzc7OhOSUmJpJMjQAAQKlwN095auDECAABoXZaFH7fbrR07dsjn8wW0N6z1yczMtKIsAGgzTmd9+KljzQ8AAJawLPzk5ubK4/Hogw8+CGhfsmSJevXqddqd3gCgvWLDAwAArGXZmp8RI0ZoyJAheuyxx1RcXKz09HQtWbJE+fn5mjt3rv+8yZMna926ddq6dau/raioSOvWrZMk7d27V5K0YsUKSVK3bt106aWXBvGVAEDLuJwN094sLgQAgDBlWfgxDENz587V7NmzNWfOHHk8HrndbuXl5WnUqFFnvHbbtm166KGHAtoaPp84caJmzpzZZnUDwPmKjqwfbC8uq7O4EgAAwpOlu73Fx8drxowZmjFjxmnPWbhwYZO2IUOGBIwEAUB7EBftlLtrjP61vVR3jO5sdTkAAIQdy9b8AEA4GpSVoM17y1VayegPAADBRvgBgCAalJkon0/697Yyq0sBACDsEH4AIIj6dY9VfLRT6ws8VpcCAEDYIfwAQBA5nYauyEzQ+oJS+djyGgCAoCL8AECQDcpM0PHSOu0srLS6FAAAwgrhBwCCbFBmgiRp/dZSiysBACC8EH4AIMhSEiLk7hqj9QWEHwAAgonwAwAWuDIrQZv3lGvPoSqrSwEAIGwQfgDAAjcM66i4aKeeeWufvGx8AABAUBB+AMACyfER+s71XbV5b4WWrz1mdTkAVe4oFgAAIABJREFUAIQFwg8AWGTU5SnK6Zugl987qMPFNVaXAwBAyCP8AIBFDMPQdyemy5T03NtfyjSZ/gYAQFsi/ACAhTqnROqb13XR+oJSfbih2OpyAAAIaYQfALDY+Ks6Kqt7rH775/0qLquzuhwAAEIW4QcALOZ0GPrvm7qrotqnF5bvt7ocAABCFuEHAGygZ+do3XptJ324oVifbPaozmuqzmuqts7n/6ip86nOK9Wc+HdN7cmP6kYfdV7WDgEA0ByX1QUAAOp949pO+sfnxfrpq7vOcJZLevvzMz5OpMvQb/87S106RLVugQAAtHOEHwCwiUiXQ0/c3VsfbjiuxmM3RqN/HziwX926dWvSLqP+s2MltXrn46Pae7ia8AMAwCkIPwBgI51TInXbyM6nPZ6f/6Vyck5/vLCoWu98fJSNEwAAaAZrfgAghCTHR0iSistqLa4EAAD7IfwAQAiJjnQoJsqholJGfgAAOBXhBwBCTEq8i2lvAAA0g/ADACEmmfADAECzCD8AEGJS4iNUxJofAACaIPwAQIhJjnepmDU/AAA0wVbXABBiUhJcKq306u4nN5/xvOQ4l66/KlXXDkiRy2mc8VwAAEIB4QcAQsyIy1J06HiNfL4zn7f9QKV+/eY+vfp+oSZenaaxV3ZQTJQzOEUCAGABwg8AhJj0tCh9/+YeZz3PNE19urVUb64+rBeWH9AfPzik8Vd11A3DOiopjl8PAIDQw283AAhThmFocL9EDe6XqM17yvXm3w/rtQ8OafE/Duu6QR1001c6qXNKpNVlAgDQagg/AABd3DNOMyb30t7DVfq/vx/WX9YVafknx3TNpcm6ZUQn9b4oxuoSAQC4YIQfAIBfj07R+v7NPTQ5t4uWfHRUf/nkmP72n2INykzQLSM66dJecTIMNkcAALRPhB8AQBNpSZH6r3FdddvITlq+9piWrjmqH724Q1npsbplRJqu6p8kh4MQBABoXwg/AIDTSohx6baRnTXx6jStzC/S4n8c0c//sEfdOkbp5mvSNOryFEW6uGUcAKB9IPwAAM4qKsKh64d21NcGp+qjjSV6c/VhPfPWl1q4slA3Dk/TuCGpiotmm2wAgL0RfgAALeZ0GLrmsmR95dIkbdhRpjf+dlgvrTioP314SPeP76bcnA5WlwgAwGkxVwEAcM4Mw9Dl7gT96t4+enZaX8VGO7X6P8VWlwUAwBkRfgAAF6Rvt1ilJUXIZ5pWlwIAwBkRfgAAF8zhMOT1EX4AAPZG+AEAXDCnQ/L5rK4CAIAzI/wAAC6YwzCY9gYAsD3CDwDggjkckpeRHwCAzRF+AAAXzGEY8rHmBwBgc4QfAMAFczgMkX0AAHZH+AEAXLD6DQ9IPwAAeyP8AAAuGCM/AID2gPADALhgTkPc5wcAYHuEHwDABXM42PAAAGB/hB8AwAWr3+3N6ioAADgzwg8A4II5HOImpwAA2yP8AAAumNNhcJNTAIDtEX4AABeMkR8AQHtA+AEAXDDW/AAA2gOX1QUAANo/h0OqrvXpww3H2/y5QmmAyWylF7Nrj6Fio6jZY5ERDg3pl6jICP7eCQCEHwDABUuJj1BVjU9Pvr7X6lLClFP6dN9pj3ZPi9KDk9KVnREfxJoAwH4IPwCAC3brtZ10zWXJQRmVMYy2fw5JCsrTtNKTbNy4UdnZ2c0e23u4SvPe2a8f/naHrh+aqru/epHiop2t88QA0M4QfgAAF8zhMNStY5TVZYStg/FS19Tmv/5dU6M0oHe8Xnm/UO98fFRrN3v03RvTNbhfYpCrBADrMQEYAIAQFxPl1P3ju+nX97sVF+3UT17ZpVl/2qPisjqrSwOAoCL8AAAQJi7uEafnpvXVnaM7658bS/SdOVv0wb+Pt9rGCwBgd4QfAADCSITLof83povyvpuprqlReuqNvZrx8i4dOl5jdWkA0OYIPwAAhKGenaP19P1u3X99V23cXa77f7NVS9cckc/HKBCA0EX4AQAgTDkdhiYMT9P872Xpkp5xmr/sgKb/drv2HKqyujQAaBOEHwAAwlznlEj977d6afot3fXlkWpNe65Af/hroQ4dr1FVjdfq8gCg1bDVNQAAkGEYGn1FB+VkJmjesgNatOqQFq06JEmKijCUFOcK/Iiv/2+yv83pb4uJdMgI1g2ZAOAcEH4AAIBfcnyEHrm9p24c1lF7j1SppNyrkvI6lZTV1f+3vE57D1epuKxONXXNrw+KdDUNS8nxJwJSMwEqNoqwBCA4CD8AAKCJi3vG6eKecac9bpqmqmp8JwKR1x+MihuFpIaPfSdCVHWtr9nHcjkN/+hRcrzrNMHp5EdcNGEJwPkh/AAAgHNmGIZiopyKiXKqS4eWXXMyLDUfkupHl7w6cLRCJeV1qqw5U1hynnYqnj9InWiLj3YSlgBIIvwAAIAgiY50KDoyUp1TIlt0fnWt75RgVKdif1g6OR3vYNGJsFTdfFhyOqTExiNJAYHJ2aQtPsYph4OwBIQiwg8AALClqAiHOiVHqlNyy8JSTa1PJRWBo0iNR5YaRpu27a8PS+VVzYclh0NKig0cRWo6snRySl4CYQloNwg/AAAgJERGOJSWFKm0pJaFpdo6nzwV3man4DVu23GwViVldSqran7bb4chJcSeDElnXrfkVEKsS07CEmAJwg8AAAhLES6HUhMdSk2MaNH5dV5TnvJG0+8CQpPX376rsEolZXUqrWw+LBmGlBDjbH4kqdFUPP9HrEtOJ2EJaA2EHwAAgBZwOQ11SIxQhxaGJa/XlKei8TqlOpWUeU9Zu1S/dXhJeZ1KK7wym989vElYSm48He/ElLyGdUuJcby9A06H/zsAAADagNNpKCUhQikJLQxLPlOlFaesVSo7JTyV1+nLI1X6YrdXpRV18p0mLEW6nEp4f5PiY5yKi3YoLtoZ+NHQHtXw75Mf8TEORUWwnThCE+EHAADABpwOQ8nxEUqOb3lYKqts/h5LO/ccVFxivCqqfCqrql/XtP9otcqqvCqv9Mrb/F4Pfg6HFB/tVGxDIIp2KjbaofgYp2KjnCdC1Ym2E+fFxzgDwpSLqXqwIcIPAABAO+R0GP5pb6fKz9+vnJwezV5nmqaqa01VVHnrw1CVt9G/fSqvrG/zf1R6VV7t08GiGv+xitNsK95YVISj6ajTKSNRjQNWXIwjoC0m0sEuemh1hB8AAIAwYhiGoiMNRUc6Wrx+6VRen6nKap8/IJVV1geo8hMhqmHEqeLEsYZzCo/X+INWbd1p5uyd4DCk2KiTgSn2RDA6GaYcTabsnXos0uU4r9eH0EX4AQAAwDlxOgzFx9RPdTtfNbUN4ckXONLUeMSp0bGyKq8OF9ecHJ2qPv0GEQ0iXEaj0aVGI04xTn+Yapi6F9d4+l7D51GMPoUaS8NPeXm55syZoxUrVsjj8cjtdmvq1KkaPXr0Wa/du3evZs6cqU8++UQ+n0+DBg3Sj370I7nd7iBUDgAAgAsRGeFQZIRDKQnnd73PZ6qyxnfK9D2ffxSqLCBMnZzOd9RT6w9W1bVnn74XG+VoNGXPqbioZkacToxONV0n5VRUhMHmETZiafiZNm2aNm3apOnTpys9PV1vv/22pk2bpvnz52vEiBGnve7YsWO64447lJqaqlmzZsnpdGrevHm68847tWTJEnXp0iWIrwIAAADB5nAY/vCRdp6PUec1G61rajx975Qpff5jPhWV1mnvkWp/wPKdJT+5nEbA6JJ/VKlxW5PpeycCVlT959znqfVYFn5Wr16tNWvWKC8vT7m5uZKkoUOHat++fZo5c+YZw8/vfvc7eTweLV68WJ07d5YkDRw4UKNHj9a8efP0s5/9LCivAQAAAO2Xy3n6TSNaon7zCJ/KKk8zda+5DSSqvDp+tM4fuiprzj76FB3p8Ieihh33mhtxij9l6l5DwIqOZOvyBpaFn5UrVyohISFgipthGJo4caIef/xxbd++/bRT2FatWqVhw4b5g48kpaSkaOTIkVq5ciXhBwAAAG2ufvMIp6IjneqYdJ6bR3hNVVQHbhbReBOJxhtINASmkvI6HTxWrbIT59Z5z7J5hEOKi3LK5XCqw5qC5u/91GjHveY2kIgIkc0jLAs/27Ztk9vtlsMR+IXMysqSJBUUFDQbfqqqqrR3716NHTu2ybGsrCz9+c9/1rFjx5Samto2hQMAAACtxOk0lBDrUkLs+Y8+1dQ1mr7XeNTplA0k9h44oqjYCFVUeVVYVOM/VlHtO+vmEVERhn906YarOur6qzqeV71Wsyz8FBcXKyMjo0l7UlKS/3hzSkpKZJqm/7zGkpOT/deea/jZuHHjOZ3fVvLz860uATZDn8Cp6BNoDv0Cp6JPhLc4SXHOhn/Utw1Jl6SiJuf6TKmmTqqskaprpcpaqbrWUFWtGn0Yqqr1qqpWOnZ4r/Lz9wTvxbQiSzc8ONPcw7PNS2zteYvZ2dmKiopq1cc8V/n5+crJybG0BtgLfQKnok+gOfQLnIo+geaEUr+orq4+r8ELyybvJScnNzu6U1JSIknNjuw0tBuG0ey1DW0NI0AAAAAA0MCy8ON2u7Vjxw75TtkfsKCgQJKUmZnZ7HXR0dHq3r27/7xTr+3QoQPrfQAAAAA0YVn4yc3Nlcfj0QcffBDQvmTJEvXq1euMNysdM2aM1qxZoyNHjvjbiouL9eGHH/q3zQYAAACAxixb8zNixAgNGTJEjz32mIqLi5Wenq4lS5YoPz9fc+fO9Z83efJkrVu3Tlu3bvW33XPPPXrnnXd03333aerUqXK5XJo3b55cLpfuv/9+K14OAAAAAJuzLPwYhqG5c+dq9uzZmjNnjjwej9xut/Ly8jRq1KgzXtuxY0f94Q9/0KxZs/Twww/LNE3l5ORo0aJF6tq1a5BeAQAAAID2xNLd3uLj4zVjxgzNmDHjtOcsXLiw2faMjAzNmzevrUoDAAAAEGJC41atAAAAAHAWhB8AAAAAYYHwAwAAACAsEH4AAAAAhAXCDwAAAICwQPgBAAAAEBYIPwAAAADCAuEHAAAAQFgg/AAAAAAIC4QfAAAAAGGB8AMAAAAgLBB+AAAAAIQFwg8AAACAsED4AQAAABAWCD8AAAAAwgLhBwAAAEBYIPwAAAAACAuEHwAAAABhgfADAAAAICwQfgAAAACEBcIPAAAAgLBA+AEAAAAQFgg/AAAAAMIC4QcAAABAWHBZXYDVTNOUJNXU1FhcSb3q6mqrS4DN0CdwKvoEmkO/wKnoE2hOqPSLhvfuDe/lW8owz/WKEFNaWqqCggKrywAAAABwjjIzM5WQkNDi88M+/Ph8PpWXlysiIkKGYVhdDgAAAICzME1TtbW1iouLk8PR8pU8YR9+AAAAAIQHNjwAAAAAEBYIPwAAAADCAuEHAAAAQFgg/AAAAAAIC4QfAAAAAGGB8AMAAAAgLBB+AAAAAIQFwg8AAACAsOCyuoBwVl5erjlz5mjFihXyeDxyu92aOnWqRo8ebXVpCIJPPvlEd911V7PH3n33XfXp08f/+UcffaRnnnlGW7ZsUVxcnHJzczV9+nQlJiYGq1y0ssLCQi1YsEBffPGFtmzZooqKCr366qsaMmRIk3OXLVumF198Ubt27VJKSopuuOEGffe731VUVFTAeUePHtVTTz2lv/3tb6qurlb//v01ffp0XXHFFcF6WbhALe0Xo0aN0v79+5tc/1//9V+aPn16QBv9on37+OOPtXTpUv373/9WYWGhkpKSdNlll+m73/2usrKyAs5t6e8K3n+0by3tE5MnT9a6deuaXD9u3DjNmTMnoC2c+gThx0LTpk3Tpk2bNH36dKWnp+vtt9/WtGnTNH/+fI0YMcLq8hAk06dP15VXXhnQlp6e7v/3J598ovvuu0+jR4/W9773PR0+fFhPP/20CgoK9Nprr8nhYAC3PdqzZ4+WL1+u/v37a+jQofrggw+aPW/p0qV6+OGHdfvtt+vRRx/Vjh079PTTT2v//v0Bv7yqq6t19913q6KiQo8//riSk5P1yiuv6O6779af/vQn9e/fP1gvDRegpf1Ckq688somQadz584Bn9Mv2r8//vGPKi4u1t13360+ffro6NGjWrBggW6++WYtXLhQAwcOlHRuvyt4/9G+tbRPSFJGRoZmzZoVcH1KSkqTxwyrPmHCEn/729/MzMxM8/333/e3+Xw+87bbbjPHjh1rYWUIlrVr15qZmZnmypUrz3jeTTfdZE6YMMH0er3+tn/+859mZmamuXz58rYuE22k8fdz5cqVZmZmprl27dqAc+rq6szhw4eb999/f0D766+/bmZmZpobNmzwty1atMjMzMw0N27c6G+rrq42R40aZd5zzz1t9CrQ2lrSL0zTNEeOHGk+8MADZ308+kX7d/To0SZtJSUl5qBBg8xp06b521r6u4L3H+1fS/vEnXfead5www1nfbxw6xP8ydgiK1euVEJCQsBwomEYmjhxonbu3Knt27dbWB3s4tChQ/r88881YcKEgL/aDR8+XJ07d9Z7771nYXW4EC0ZsduwYYOOHDmiiRMnBrSPHz9eERERAd//VatWKTMzU5dccom/LTIyUtdff73WrFmjsrKy1isebaa1R3LpF+1fampqk7bExET17NlThYWFks7tdwXvP9q/lvSJcxFufYLwY5Ft27bJ7XY3+UXXMFezoKDAirJggRkzZqh///7KycnRd77zHW3cuNF/rKEf9O3bt8l1mZmZ2rZtW9DqRPA1fH9P/f7HxMSoe/fuAd//bdu2KTMzs8ljZGVlyev1aufOnW1bLIJu7dq1uvzyy5Wdna3x48frtddek2maAefQL0JTUVGRtm3b5v/ZcC6/K3j/EZpO7RMNdu3apSuvvFL9+/fXddddp7lz56q2tjbgnHDrE6z5sUhxcbEyMjKatCclJfmPI7QlJCTom9/8pgYPHqzk5GTt2LFDL7zwgm6//XYtWrRIAwYM8PeDhn7RWFJSkjZt2hTsshFEZ/v+N/45UVxcfNrzJOn48eNtVCWscO211yo7O1vdu3dXcXGx3nnnHf3sZz/T7t279eijj/rPo1+EHtM09fjjj8vn8+mee+6RdPafFY1/V/D+I/Q01yckKScnR+PGjVPv3r1VUVGhVatW6dlnn9UXX3yh559/3n9euPUJwo+FDMM4r2MIDf379w9YbDxo0CCNGjVK119/vebMmaOXX37Zf+x0/YF+Eh5a+v3nZ0r4mDFjRsDnubm5+sEPfqCFCxfqm9/8prp16+Y/Rr8ILU8++aRWrVqlX/3qVwG7gkr8rAhXp+sT3/ve9wLOGzlypDp27Kj58+dr/fr1GjRokP9YOPUJpr1ZJDk5udkkXVJSIqn5v94g9KWlpenqq6/Wf/7zH0n1/URq/q8uJSUl9JMQdy7f/7P9TGl4LISuiRMnyufz6bPPPvO30S9Cy5w5c/TSSy/pscce06RJk/ztrfmzgt8r7cvp+sTp3HjjjZLq15Q2CLc+QfixiNvt1o4dO+Tz+QLaG+ZVNjdHG+GhcZ9omLvb3NqegoKCZud3I3S43W5JTb//lZWV2rdvX8D33+12Nzsve+vWrXI6nerdu3fbFgvLNfzsaDxvn34ROp555hnNnz9fP/zhD5vcI+5cflfw/iN0nKlPnM7pfk6EU58g/FgkNzdXHo+nyT0clixZol69evnf9CC8HDlyRGvWrPHv0d+lSxdlZ2dr2bJlAT+UPv74Yx06dEjXXXedVaUiCAYOHKi0tDQtXbo0oP3Pf/6zamtrA77/ubm5Kigo0ObNm/1tNTU1Wr58ua666irFx8cHrW5YY+nSpXI4HLr00kv9bfSL0JCXl6e5c+fqoYce0r333tvk+Ln8ruD9R2g4W584nYbfJwMGDPC3hVufcP70pz/9qdVFhKOePXvq008/1RtvvKGUlBR5PB7l5eXpww8/1C9/+Uv16tXL6hLRxn7wgx9o8+bNKi0t1dGjR/WPf/xDjzzyiEpLS/XUU0/5b1bYo0cPvfTSS9q+fbuSkpKUn5+vn/3sZ+rbt69+/OMfc5PTdmzFihXavn27/vOf/+hf//qX0tPTVVRUpP379ysjI0MOh0MpKSl64YUXdPz4cUVHR+vvf/+7nnzySY0aNUrf+ta3/I+VlZWl999/X8uWLVNaWpoOHz6smTNnauvWrXr66afVqVMnC18pzsXZ+sWf//xnzZs3T1VVVSouLtYXX3zhvzP7t7/9bY0dO9b/WPSL9u+ll17S7NmzNXLkSE2cOFGFhYX+j6KiIqWlpUlq+e8K3n+0fy3pE+vXr9dPf/pTVVdXq6SkRAUFBVqwYIEWLVqksWPH6tvf/rb/8cKtTxjmqftiImjKyso0e/Zsvffee/J4PHK73Zo6darGjBljdWkIghdeeEHLly/X/v37VVlZqeTkZA0ePFgPPPBAkyHmv//973ruuee0ZcsWxcXFacyYMfrhD38YcvNww03DNqKn6tatW8Bf4JYuXaoFCxZo165dSklJ0fjx4/Xggw8qOjo64LojR47oySef1OrVq1VdXa3+/fvrBz/4QcCiVtjf2frFhg0b9Jvf/Ebbt29XcXGxIiIilJWVpVtvvbXJPaEk+kV7N3nyZK1bt67ZY6f+rGjp7wref7RvLekTe/bs0S9+8Qtt2bJFx48fl8PhUK9evXTjjTdq8uTJcjqdAdeFU58g/AAAAAAIC8yXAQAAABAWCD8AAAAAwgLhBwAAAEBYIPwAAAAACAuEHwAAAABhgfADAAAAICwQfgAAOINPPvlEWVlZeuutt6wuBQBwgVxWFwAA4eaTTz7RXXfdFdAWGxurjIwMTZgwQXfeeadcrtD+8ezxePTKK69o8ODBGjJkiNXlaPPmzVq1apUmTpyo9PR0q8tp1uluftrgD3/4AzcuBYCzCO3frgBgY9dff72uueYamaapo0ePaunSpfrVr36lHTt26H//93+tLq9NeTwe5eXladq0abYJP3l5eRo8eHCT8HPllVfqs88+s0Ugvfjii/Wtb32r2WO9e/cOcjUA0P5Y/5McAMJU//79NWHCBP/nd9xxh772ta/pzTff1H//93+rQ4cOFlZnP2VlZYqPjw/68zocDkVFRQX9eZvTuXPngD5zLs709fN6vaqpqVFMTMyFlHfW5wEAq7HmBwBsIjY2VgMGDJBpmtq7d2+T459//rmmTp2qIUOGKDs7W1/96lc1b9481dXVNTl3z549euSRR3TNNdcoOztbV199tR544AFt3Lgx4LxVq1bptttu0+WXX67LL79ct912m1atWtXk8UaNGqXJkydrx44duu+++3T55ZcrJydHDz74oI4cORJwbnFxsX75y19qzJgxuvTSSzVkyBBNmjRJCxYskFQ/7W/06NGSpLy8PGVlZSkrK0ujRo2SJH355ZfKysrSc889p3fffVeTJk3SZZddpp///OeSpMmTJ/vPbazxdY2Zpqk33nhDt9xyi/91jh8/Xs8884wk6bnnntMjjzwiSbrrrrv89fz4xz/219vcmp+Kigr9+te/1pgxY5Sdna3hw4fr4Ycf1v79+wPOa3z94sWL9fWvf13Z2dkaOXKkXnzxxSavozU0fL82bdqke+65Rzk5ObrhhhskSW+99ZaysrK0Zs0aPf/88xozZowuu+wy/eUvf/Fff679ornnAQA7YuQHAGxk3759kqSkpKSA9tWrV2vq1Knq2bOnvv3tbyspKUkbNmzQs88+q82bN+vZZ5/1n/v555/r7rvvVl1dnW6++Wb17dtXJSUlWrdunf79738rOztbUv0akSeeeEK9e/fWAw88IEl6++23NXXqVD3xxBO69dZbA2o4dOiQ7rrrLo0ZM0YPP/ywtmzZotdff11lZWV66aWX/Oc99NBDWr9+vW699Vb169dPlZWV2rlzp9atW6d7771Xffr00SOPPKJf/epXys3NVW5uriQpLi4u4PlWrVqlhQsX6vbbb9dtt9123qMJP/zhD7Vs2TINGDBA999/vxISErRz50699957euihh5Sbm6sjR47o9ddf1/333++fPtajR4/TPmZdXZ3uuece/etf/9JXv/pVfetb39KePXv0xz/+UR999JEWL16sLl26BFzzpz/9SUePHtXNN9+sxMREvfPOO3r66afVpUsXjR8/vkWvpa6uTkVFRU3aDcNQSkpKQNuBAwf0zW9+U2PHjtV1112nioqKgOOzZs1SXV2dvvGNbyguLk69evWSdO794mzPAwC2YgIAgmrt2rVmZmam+dxzz5nHjh0zjx07Zm7ZssX86U9/amZmZpo33XRTwPlVVVXmsGHDzDvuuMOsra0NOPb73//ezMzMNNeuXWuapmn6fD7z61//upmdnW1u3ry5yXN7vV7TNE2zuLjYHDhwoDlmzBiztLTUf7y0tNQcPXq0OXDgQLOkpMTfPnLkSDMzM9Ncvnx5wOM11Lx9+3bTNE3T4/GYmZmZ5k9+8pMzfg327dtnZmZmms8+++xpj/Xv39//uI3deeed5siRI1v0mMuXLzczMzPN6dOn+1/7qV8L0zTNxYsXB3wdG2v4fi1evNjf9vrrr5uZmZnmrFmzAs798MMP/c936vXDhw8P+JpWVFSYQ4YMMb/xjW80ec7mZGZmnvZj4MCBAec2fL/eeOONJo/T8Fqvu+46s6KiIuDY+faL5p4HAOyIaW8AYJHnnntOV111la666irdcMMNeu2113Tddddp3rx5Aed99NFHOnr0qCZNmiSPx6OioiL/xzXXXOM/R6pfuL9t2zZNmjRJ/fr1a/KcDofDf35FRYUmT54cMKISHx+vO++8UxUVFVqzZk3AtZ06ddK4ceMC2oYOHSpJ/ml6UVFRioyM1GeffaYvv/zyQr48GjFihPr06XNBj7Fs2TJJ0o9+9CP/a29w6ufnYuXKlXI4HPrOd74T0H7ttdfq4osv1l//+lf5fL6AYzeLZBT6AAAFz0lEQVTddJMSExP9n8fExGjgwIHavXt3i593wIAB+v3vf9/kY/78+U3OTU5O1qRJk077WLfffnuTNT7n0y/O9jwAYCdMewMAi9x6660aO3asamtrVVBQoAULFqiwsLDJ4vodO3ZIkh599NHTPtbRo0clyf9Gun///md87oZg0rdv3ybHMjMzJZ2cgtege/fuTc5NTk6WVL/OR5IiIyP16KOP6he/+IVGjx4tt9utoUOHasyYMbrqqqvOWNOpMjIyzun85uzZs0dpaWnq2LHjBT9WY19++aU6derUZHqiJLndbm3evFnHjx9Xamqqv725LbSTk5P9X7uWSElJ0bBhw1p0bvfu3eV0Ok97vGGaW2Pn2y/O9DwAYCeEHwCwSM+ePf1vZEeMGKGcnBzdcccd+slPfqI5c+b4zzNNU5L08MMP6+KLL272sTp16hTwuWEYrV7vmd7gNtQo1Y8ojB49WqtXr9a6dev03nvvadGiRRo3blzA6zqbc915zOv1NltXW3wtGr/elgp2QDjb1y86OjoozwMAdsK0NwCwiSuuuEITJkzQu+++q3/961/+9oYRkJiYGA0bNqzZD7fbLenkX/M3bdp0xudqGMXZtm1bk2Pbt28POOd8dOrUSbfccoueeuoprV69Wtdff73effddffbZZ5IuLJydbrTk1BEJqf7rcfjwYf/I2Omcaz09evTQ4cOH5fF4mhzbsWOH4uPjm2xA0B60db8AAKsRfgDARqZMmSKn0xmwe9vVV1+t1NRUvfjii82+6a+qqlJZWZkkqV+/furbt68WL17c7BvYhhGL4cOHKzY2VosWLfJfK9Xfo2XRokWKjY3V8OHDz7n+yspKVVZWBrQ5nU5lZWVJkkpKSiTVb+vd+PNzkZGRofLycn+QkiSfz6eXX365ybkNu6g99dRTTdbgNB69Odd6xowZI5/PpxdeeCGgffXq1dq0aZNGjRp1QWuKrNJW/QIA7IJpbwBgIz179tS4ceO0bNkyrV+/XoMGDVJsbKxmzZqlqVOnauzYsbrpppvUs2dPeTwe7dy5UytXrlReXp6GDBkiwzD0y1/+UnfffbduueUW/1bXHo9Hn376qb7yla9o8uTJSkz8/+3dvUsjQRzG8cdACoUsKYKvG60sjSjb2ghCUskSTKOIjYXYiIh9gqTSSIpVMQQLQS0WfMEoaso0/gV2kggpUomVoMVdEU64u9xdzCEo+/208yuGYZqHmd+MoZWVFaVSKSUSCdm2Lan+pHGlUlEqlVIgEHj3/MvlsmZmZjQxMaHBwUEZhqH7+3sdHh7KNE1ZliWp3rsyMDCgQqGgcDisUCik9vb2hv/3/CqRSGhvb0+Li4uanZ2V3+/X1dVVw2tvsVhM19fXOjk5UaVS0fj4uAzDULlcVqlU0vn5uSRpaGhIPp9POzs7enp6UkdHh0zT1PDwcMM52Lat4+Nj5XI5VatVWZalh4cHHRwcKBQKaXl5+d1r14xarabT09OGYyMjI399nrsZH7UvAOCzIPwAwCezsLCgQqGgbDar/f19SdLY2Jhc19Xu7q7Ozs70+PgowzDU39+vubm5t5MVSYpEInJdV1tbW7q8vNTR0ZGCwaAikYhGR0ff6qanp9XZ2al8Pi/HcSTVT45+fHzZiu7ubsXjcd3e3qpYLOrl5UVdXV2amprS/Pz8T/0h6+vrSqfT2tzc1PPzs/r6+poKP+FwWI7jKJPJKJvNKhgManJyUvF4XLFY7Lf6jY0NWZYl13XlOI58Pp9M01Q0Gn2r6e3tVTqdVi6XUzKZ1Ovrq2zb/mP48fv9yufz2t7e1sXFhW5ubhQIBBSNRrW0tKSenp4WVu/f7u7utLq62nBsbW3tv8OP9DH7AgA+i7ZvrXRtAgAAAMAX8/UuJAMAAABACwg/AAAAADyB8AMAAADAEwg/AAAAADyB8AMAAADAEwg/AAAAADyB8AMAAADAEwg/AAAAADyB8AMAAADAE74DVsHa9iz0XMEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 972x972 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_fbeta(precision,recall, beta):\n",
    "    return ((1+np.square(beta))*precision*recall)/((np.square(beta)*precision)+recall)\n",
    "\n",
    "label='F'+ 'beta' + ' curve'\n",
    "plt.plot(th, get_fbeta(precision[1:],recall[1:],beta), 'b', label=label)\n",
    "plt.title(label + ' vs. Threshold')\n",
    "plt.xlabel('Reconstruction Error')\n",
    "plt.ylabel('F' + str(beta) + ' score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "fbeta = list(get_fbeta(precision[1:],recall[1:], beta))\n",
    "index_of_max_fbeta =  np.where(fbeta==max(fbeta))\n",
    "best_fbeta_th = th[index_of_max_fbeta]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we have the exact opposite situation. As the reconstruction error increases the recall decreases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction\n",
    "\n",
    "Our model is a bit different this time. It doesn't know how to predict new values. But we don't need that. In order to predict whether or not a new/unseen transaction is normal or fraudulent, we'll calculate the reconstruction error from the transaction data itself. If the error is larger than a predefined threshold, we'll mark it as a fraud (since our model should have a low error on normal transactions). Let's pick that value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "#threshold = best_f1_th\n",
    "threshold = best_fbeta_th"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.97467077]\n"
     ]
    }
   ],
   "source": [
    "print(threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And see how well we're dividing the two types of transactions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x972 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups = error_df.groupby('true_class')\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for name, group in groups:\n",
    "    ax.plot(group.index, group.reconstruction_error, marker='o', ms=3.5, linestyle='',\n",
    "            label= \"Fraud\" if name == 1 else \"Normal\")\n",
    "ax.hlines(threshold, ax.get_xlim()[0], ax.get_xlim()[1], colors=\"r\", zorder=100, label='Threshold')\n",
    "ax.legend()\n",
    "plt.title(\"Reconstruction error for different classes\")\n",
    "plt.ylabel(\"Reconstruction error\")\n",
    "plt.xlabel(\"Data point index\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I know, that chart might be a bit deceiving. Let's have a look at the confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQUVcPQCQX9Rr2TnH7Z6exbV3S5jt51lzFunjL0JzPPafo5/U0Md7Z9t++cpVfbZgibZ9F64LsZdUsoSn7q1eTWOGD1LjBvUkSWfOXVDH3k/cdsZ3Xh+vRzq2t/2ckHhZX4Qu17c79+jchViVLFlCNatV0cC+3dW+dfM/+cQA4BwvvPCMGjasp4YN71P16lV06tRvCg5uleOx7du3Uo8endWo0X2qWzdIxYsXV4cO/bRjx54cjw8I8NeLL45Ru3Yt5e9fUQkJiTpw4BdNn/6pdu36Ptvxnp7F9dxzI9W7dzdVr15FKSnX9euvJzRt2idavXqTQz83YC8CHPidxvfXU+/uWUO8iLt7jsdOeHakfH19smyrE3RvtuPOnr+goWMmKDklRY890lFVAyopKSlZR49H68KlS7bjSvv6aMrr43N8r8nTPtL11FS1bNrYti3l+nUNfOqfOn8hVr0e7aTAmtV15epVrVq/Wc9OnKRXXxit/j0fsfuzA4CjvPXWBMXFJejHH3+Rj4/3bY/t37+H+vXrrkOHjioq6rgaNKib67H33FNeu3atVZEiRTRnTqiOHTupe+6poGHD+mvTpiXq3Xu4Nm7cZjve19db69cv1r33VtPChcv0wQefq2RJTwUF3asqVSo57PMCeUWAA79T2b+iuv3uCvPttH+ohSrdU+FPj5v45ntKz8jQyvkfq1xZv1yPK+FZPMf3/vGXSF1NuqYO7Vqp9O+C/5sde3TqtzOaMG6UBvXtYdve+9HOerjnIC1btYEAB+AStWu30smTv0mS9u37Wl5eJXI99o033tOYMS8rLS1Nzz038rYBPmBAb5UrV0Z9+gzX2rWbbdu//PIrHTr0nYYN658lwP/znzdVs2ZVPfRQD0VF/eqATwY4BveAA39w48YNJSen2HVs0rVrSk/PyHX/vh9/1v6fDmnY471VrqyfbqSnK+X69TzNs2LNRklSr26d/vDeyZKk8mXLZNleyqukPIsXl6dn8Ty9DwA4yq34tsfZsxeUlpZm17He3l6SpHPnLmTZfuFCrDIyMnTt2v/+7K5SpbL69euuuXOXKCrqV7m5ualkydz/IgCYRIADv/P1tzv1wMM91ORvj+mhrv01edpHupp0LcdjHxv8tJp16K3G7R/VgFH/0I7wvdmOubXtnorlNfrFf+mB9t314MM91bX/cK3ZtC3b8X+UnJyiTVt36J4K5dX8wYZZ9jVtfL+KuLvr/U/m6rvd3+v8xVgdPR6t16ZM19WkJI0c3O8OfgMAkH9t3vydJGnGjLfVqlVT+ftXUOPG9TV//kwlJV3TjBmzbcd26NBG7u7uioz8VXPmTFd8fJQuXYrUsWN7NHbsk676CIAkbkEBbO6rE6QO7VqpSmV/JV1L1o7wvfp/K9Zo748/K/STaSpRwlOS5O1VUn26d1aDerXlXaqUok/HaNGXq/TM+H/prZeeV4+uf7OdM/p0jCTpjXdmqEpAJf3fK/9U2o0bWrAkTC9Nek/p6enq2bVDrjNt2PqdklNS9MTjveTmlvXvy1UDKum9SS/pnRmf6Jnx/7JtL+NXWnM+eEeN6uf+z7gAUBDt2LFH48a9qtde+4c2b/7Stv3XX0+oTZueOnLkmG1bYGANSdKkSS8qLi5BY8e+ohs3bmj48AF6993X5ePjrbffnm78MwCSCwP8ww8/tPtYi8Wi0aNHO3EaQFo8+/0sP3fvHKLAmtX1wWfztXDZKo0a8ndJ0qB+PbMc107SY490UI9BT+ndmZ+pQ7tWtli/dStLiRKemjvzHRUtWlSS9PBDLdS57zDN+HSeuncOyRbXt6xcs1Fubm7q2eVvOe73LlVSgTWrq1e3TgquVUMXY+M0b/EKPTtxkj6fMUXBtWrc8e8DAPKjS5fitH//T/rmm1369dcTqlWrhp57bqTCwuaqQ4e+iok5J0ny8rp5u4qHR1GFhPRWfHyiJGn58rU6cGCL/vGPp/Thh3OUmHjFZZ8Fdy8CHLiNoQN66+O5ofpu915bgOfE18dbfXt01UdzFunAz4dtq5UUK+YhSeryt7a2+JYkH+9SatuyqVZv3Kro0zGqWa1KtnMejz6lg4ei1LJpY91TsXy2/bsiftDTL7yuj957U62aPWDbHtKmhbo9PlL/N22WFn78nzv+7ACQ3wwd2l8zZrytZs266PDho7btmzdvV3j4Ok2aNEHDhj0nSbr+3+/bbNiwzRbfkpSenq6lS7/SK688pyZNGunrr781+hkAyYUBHhUV5aq3BuxWtEgRlS9bRomX//wKSaX/RvLvj61Qrqwkqaxf6WzH31oR5crVpBzPt3Lt15KkXt065rh/zqJl8ixeLEt8S1LZMn5qdH9d7Qjfpxs3bmQJfwAoyMaPH60jR45niW9JOnToiI4cOa7WrZvatp05c/NK+IULsdnOc/78RUlS6dI+2fYBJvAlTOA2UlPTdOHiJZUp7funx56KOStJKuP3v2PvqxMkSboQeynb8ecv3tyW07lvpKdrzcat8vP1UbtcHqhzMfaSMq1WWa3WbPsyMjKVmZmpzMzs+wCgoPL3ryD3XJ7NUKRIEbm7/++64t69ByVJlSpVzHZspUr3SJIuXsz+ZzNgAgEOSLle4Z45e4HSMzLUttXNqyrp6Rk5ropy7kKsloatk6+PtxrcV8e2vX3r5ipZwlNrN23LsrRh7KV4bdsRrqoBlVSlsn+2832zI1zxiZfVrdPDKlok53+oqvHfJ7pt2rYjy/aYs+f1w48/q1bNarZbYACgMIiMPKbAwBpq0uQPq0I1baRatarrhx8O2rbt3BmhU6d+U5cuIfL3/98zG0qU8NSAAY8pIeGyIiL2G5sd+L18swrKihUrFBoaqhMnTig1NTXb/sjISBdMhbvFp/MW66dDUXqw0f26p0I5JaekaEf4Pn2//6Dq1wnS470flSQlp6SoU5+hat+6uWpUC5B3KS9Fn47RyjWblJySonffmKjixYrZzuvjXUovjBmuN9+dqcdHPq+ej3TQjRvpWhq2TjdupOvl55/OcZ5bt588lsvtJ5I0YnA/7drzgyZOek97D/yk4Fo1dOHiJS1dtU6paTc0btQTjvsFAUAe/P3vPVWlSmVJUtmyfvLw8NCECWMlSadPx2jx4jDbsfXqBavrf1ePat785i11jz/+mFq0eFCS9PHH83TlylVJ0ttvT9PSpZ9p7dpF+vzzUB07Fq17762uESMGKi3thiZP/t+X6TMzM/Xss69qxYo5+vbbMH322UKlpd3QoEF9FBBQSaNGjbf7mQ+Ao1msOf37tWEbNmzQ+PHj1aNHDy1fvly9evVSWlqatm3bprJly6pbt24aM2ZMns5549IJJ02LwmjbjnAtWblWx06cUuKVK3J3c1OVypXUsX1rDen/mO1Kclpamt7+z0f6+fARXYi9pOTkFPn6eqvhfXU0bEAf2y0nf7T5212aG7pMv544KYvFTffXC9bTwwbkuFTg+Yux6tDrCdWvG6xFn9z+S5SRR4/ps/lL9MPBQ7p85YpKliih++oE6cmBfdWkUf2//ovBXcU7oJ2rR0AhsWnTEj30UM63z333Xbg6duxv+3ngwN6aPTv3P+uCglrq9H+XdJWkNm1a6PnnR+mBB+6Xj08pJSRc1q5d32vKlA/000+Hs72+VaumeuWV59S4cX25u7vr4MFDmjr1I61fv/UvfELgf1JSTuX5NfkiwPv06aPmzZtr3Lhxqlu3rlasWKG6desqLi5OAwYM0BNPPKH+/fv/+Yl+hwAHgLwhwAEg7+4kwPPFPeDR0dFq2rSp3NzcZLFYlJ6eLkkqU6aMnn76ac2fP9/FEwIAAACOkS8C3M3NTe7u7rJYLPLz89OFCxds+8qXL6+zZ8+6cDoAAADAcfJFgFepUkXnz5+XJAUHB+vLL79Uenq6MjIytHTpUlWsmH0JIQAAAKAgyhcB3rZtW+3du1eSNGLECIWHh+uBBx5QkyZNtGnTJg0fPtzFEwIAAACOkS++hPlHBw8e1Pr162WxWNSmTRs1b57zN6lvhy9hAkDe8CVMAMi7ArsKijMQ4ACQNwQ4AORdgV0FBQAAALhb5IsnYWZmZio0NFRr167VmTNnlJaWlmW/xWJRRESEi6YDAAAAHCdfBPjUqVP1xRdfqHbt2mratKk8PDxcPRIAAADgFPkiwFevXq1Ro0bp+eefd/UoAAAAgFPli3vA09LS1KxZM1ePAQAAADhdvgjwhx56SD/88IOrxwAAAACcLl/cgvLyyy/rqaeeUtGiRfXQQw/Jx8cn2zH+/v4umAwAAABwrHwR4EWKFFHZsmU1ffp0vf/++zkeExkZaXgqAAAAwPHyRYC/8sor2rlzp3r27KmaNWuqaNGirh4JAAAAcIp8EeA7d+7UhAkTNGDAAFePAgAAADhVvvgSpqenp6pXr+7qMQAAAACnyxcB3rVrV23atMnVYwAAAABOly9uQWnYsKGmTp2qcePGqU2bNjmugvLwww+7YDIAAADAsSxWq9Xq6iGCg4Nvu99iseR5FZQbl078lZEA4K7jHdDO1SMAQIGTknIqz6/JF1fAFyxY4OoRAAAAACNcHuCZmZmqXLmyvL295eXl5epxAAAAAKdy+ZcwrVarQkJCtHfvXlePAgAAADidywPc3d1d5cuXd/UYAAAAgBEuD3BJeuyxx7R48WLlg++DAgAAAE7l8nvAJcnLy0tRUVHq2rWr2rdvr3Llyslisdj2WywWDRo0yIUTAgAAAI7BMoQAAEksQwgAd6LALkO4detWV48AAAAAGJEvArxSpUquHgEAAAAwIl8E+C3R0dH6/vvvlZiYKF9fXzVt2lTVqlVz9VgAAACAw+SLAM/MzNS//vUvLV++PMtKKG5uburbt6/eeOMN1w0HAAAAOFC+CPDZs2dr+fLlGjx4sHr06KFy5copNjZWX331lebPn6/KlStr+PDhrh4TAAAA+MvyRYCvXLlSTzzxhCZMmGDbVrZsWdWuXVsWi0XLli0jwAEAAFAo5IsH8Zw5c0atW7fOcV/r1q119uxZwxMBAAAAzpEvAtzX11fR0dE57jtx4oR8fHwMTwQAAAA4R74I8LZt22rGjBnavn17lu3bt2/XBx98oPbt27toMgAAAMCx8sWTMOPj4zVo0CCdOHFCpUqVUtmyZXXp0iVdvXpV9957rxYuXChfX988nZMnYQJA3vAkTADIuzt5Ema+CHBJun79ulauXKnvv/9ely9flq+vr5o1a6YePXqoWLFieT4fAQ4AeUOAA0DeFegAdzQCHADyhgAHgLy7kwB32TKEgwcPtvtYi8Wi+fPnO3EaAAAAwAyXBfjly5dlsVhue8yNGzd0/PjxPz0OAAAAKChcFuBfffVVrvusVqtWr16tDz74QJJYBQUAAACFRr54Eubvbd++XdOmTdPRo0fVsGFDvffee2rUqJGrxwIAAAAcIt8E+MGDBzV16lTt3btXtWrV0qxZs7jyDQAAgELH5QF+4sQJTZ8+XVu2bFHFihU1efJk9ezZk/u+AQAAUChv2ExpAAAgAElEQVS5LMAvXLigmTNnatWqVSpZsqTGjx+vgQMHysPDw1UjAQAAAE7nsgDv0KGD0tLS1Lp1a40YMUKlSpXSiRO5r90dHBxscDoAAADAOVz2IJ7fB/XtbjexWq2yWCyKjIzM0/l5EA8A5A0P4gGAvCtQD+KZMmWKq94aAAAAcBkeRQ8AkMQVcAC4E3dyBdzNCXMAAAAAyAUBDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAGEeAAAACAQQQ4AAAAYBABDgAAABhEgAMAAAAG2R3gMTExioiIyLLt8OHDevbZZzVkyBCtWrXK4cMBAAAAhU0Rew989913FRcXp9DQUElSYmKihg4dqitXrsjDw0Pff/+9fH191bZtW2fNCgAAABR4dl8B/+WXX9SiRQvbz+vWrdPVq1e1YsUKRUREqG7dupo3b54zZgQAAAAKDbsDPC4uThUrVrT9vGPHDjVo0EB16tRR8eLF1a1bN/36669OGRIAAAAoLOwO8OLFi+vq1auSpMzMTP3www968MEHbftLlChh2w8AAAAgZ3YHeM2aNbV27Vpdu3ZNYWFhSkpKUvPmzW37z5w5o9KlSztlSAAAAKCwsPtLmMOGDdPYsWP14IMPymq1KjAwUE2aNLHtDw8PV+3atZ0yJAAAAFBY2B3gISEhmj17trZu3apSpUppyJAhcnO7eQE9ISFB3t7e6tGjh9MGBQAAAAoDi9Vqtbp6CGe4cemEq0cAgALFO6Cdq0cAgAInJeVUnl9j9xXwnGRmZuq7775TYmKi2rRpwz3gAAAAwJ+wO8CnT5+uiIgILVmyxLZt+PDhCg8Pl9VqVZkyZbR06VJVrlzZKYMCAAAAhYHdq6B88803ql+/vu3nb7/9Vrt379agQYM0efJkpaen67PPPnPKkAAAAEBhYfcV8PPnz6tq1aq2n7dt2yZ/f3+9/PLLkqRTp05p3bp1jp8QAAAAKETsvgKempoqDw8P288RERFZHk1fpUoVxcbGOnY6AAAAoJCxO8ArVqyogwcPSpKOHz+uU6dOZXkSZnx8vIoXL+74CQEAAIBCxO5bUDp16qTZs2frypUrOnLkiEqUKKE2bdrY9kdFRSkgIMApQwIAAACFhd0B/tRTTykmJkbbtm1TyZIlNXnyZPn6+kqSkpKStHXrVg0aNMhpgwIAAACFgUMexJOenq7ExESVKlVKxYoVc8RcfxkP4gGAvOFBPACQd8YfxGM7SZEiKlu2rCNOBQAAABRqeQ7wy5cv6/Dhw7p8+bIyMzOz7e/SpYtDBgMAAAAKI7sD3Gq1asqUKVq8eLHS09NzPY4ABwAAAHJnd4DPmzdPCxYsUKdOndSyZUu99tprGjdunDw9PbVw4UL5+flpzJgxzpwVAAAAKPDsXgd85cqVatGihd5//32FhIRIkho0aKAnnnhCX331leLi4hQdHe20QQEAAIDCwO4AP336tG3dbze3my+7dSuKl5eXevXqpaVLlzphRAAAAKDwsDvAPTw8bI+i9/T0lMViUXx8vG1/+fLldfbsWcdPCAAAABQidge4v7+/Tp8+LelmjAcEBGj37t22/RERESpTpozjJwQAAAAKEbu/hNm0aVNt27ZNEyZMkCR169ZNs2bNUnx8vDIzM7V7924NHjzYaYMCAAAAhYHdAT5s2DA1adJEqampKlasmJ566ildvHhRa9askbu7u3r06KHnnnvOmbMCAAAABZ5DHkWfH/EoegDIGx5FDwB5dyePorf7HnAAAAAAf12ut6DExcXd0Qn5IiYAAACQu1wDvGXLlrJYLHk+YWRk5F8aCAAAACjMcg3w4cOH31GAAwAAAMgdX8IEAEjiS5gAcCf4EiYAAACQz9kd4F9//bVeeeWVXPe/+uqr2rJli0OGAgAAAAoruwN8wYIFSktLy3V/enq65s+f75ChAAAAgMLK7gA/duyY6tatm+v+OnXq6NixYw4ZCgAAACis7A7w5ORkFS1aNNf97u7uSkpKcshQAAAAQGFld4D7+/vrwIEDue7fv3+/Klas6JChAAAAgMLK7gAPCQnRunXrtHr16mz71qxZow0bNigkJMShwwEAAACFjd3rgF+9elW9e/fW6dOnVbt2bdWuXVsWi0WHDx9WZGSkAgICtHz5cnl7ezt7ZruwDjgA5A3rgANA3t3JOuB5ehBPQkKC3n33XW3cuFEpKSmSJE9PT3Xp0kUvvPCCSpcunecBnIUAB4C8IcABIO+cHuC3ZGRk6OLFi7JarapQoYLc3d3z/MbORoADQN4Q4ACQd8YCvCAo4lHJ1SMAAACgkEtPO5Pn1/AoegAAAMAgAhwAAAAwiAAHAAAADCLAAQAAAIMIcAAAAMCgOwrwzMxMJSQkKD093dHzAAAAAIVangL8yJEjGjFihBo0aKCWLVtq7969kqS4uDiNHDlSERERThkSAAAAKCzsDvCjR4+qf//+ioyMVMeOHfX75cPLlCmjhIQEhYWFOWVIAAAAoLAoYu+BM2bMkJ+fn8LCwpSenq41a9Zk2d+iRQtt3LjR4QMCAAAAhYndV8D37dunfv36ydvbWxaLJdt+f39/Xbx40aHDAQAAAIWN3QGekpIiHx+fXPcnJyc7ZCAAAACgMLM7wAMCAnT48OFc90dERKhGjRoOGQoAAAAorOwO8M6dO2vVqlW2lU8k2W5FCQ0N1fbt29WtWzfHTwgAAAAUIhbr75czuY3U1FQNGTJEP//8swIDAxUVFaW6desqISFBZ8+e1YMPPqi5c+fK3d3d2TPbpYhHJVePAAAAgEIuPe1Mnl9jd4BLUlpamubOnas1a9YoOjpaVqtVAQEB6tGjh5588kl5eHjkeQBnIcABAADgbE4P8D+yWq05roiSHxDgAAAAcLY7CfA7ehT9Lfk1vgEAAID8yu4H8axfv96u47p06XLHwwAAAACFnd23oAQHB8tiseiPh//xKnhkZKTjpvsLuAUFAAAAznYnt6DYfQV89uzZ2bZlZGTo9OnTWrJkiby9vfXMM8/keQAAAADgbvKXvoR5y/Xr19WrVy/169dPgwcPdsRcfxlXwAEAAOBsxr+EeUvx4sXVvXt3LVq0yBGnAwAAAAothwS4JHl6eurcuXOOOh0AAABQKDkkwOPj4/Xll1/K39/fEacDAAAACi27v4Q5YsSIHLdfvnxZR48e1fXr1/X22287bDAAAACgMLI7wA8fPpzjg3d8fX3VqlUrDRgwQM2bN3focAAAAEBh45BVUPIjVkEBAACAszltFZSUlBTNnj1b4eHheX4DAAAAAP9jV4B7enpqxowZ+u2335w9DwAAAFCo2b0KSkBAgOLi4pw5CwAAAFDo2R3g/fv314oVK3T16lVnzgMAAAAUanavglKuXDl5eXmpU6dO6t27t6pWrarixYtnO65Lly4OHRAAAAAoTOxeBSU4OPjPT2axKDIy8i8P5QisggIAAABnu5NVUOy+Aj579uw8nxwAAABAVre9An727Fn5+fnleKtJfscVcAAAADibw9cBf/jhh7V58+Y7HggAAABAVrcN8EL6kEwAAADAZexehhAAAADAX0eAAwAAAAb96Soo+/btU0ZGht0n7NGjx18aCAAAACjMbrsKSnBwsCwWi10nslqtrAMOAACAu4pT1gHv27evGjRocEcDAQAAAMjqTwP8gQceULdu3UzMAgAAABR6fAkTAAAAMIgABwAAAAwiwAEAAACDbrsKSkHGKigAAABwtjtZBYUr4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAw404cUxWrL4Ux2N2q30tDM6dnTPn75mwIBe+u7bVYq/FKXE+KP68cBWvfLycwamBYD8oWTJEpo4YawO7N+ihLgjOn/2Z+3Y/pUGD+prO6ZYsWJ6ctjjWrniCx07ukdXLx/T0ajdWrRwloKD73Xh9EDeWaxWq9XVQzhDEY9Krh4Bd6H0tDOKi0vQgQM/q1Gj+3TlSpLuDWyW6/GzP/uPBg/qo5Vh67V9e7gyMjJUvXqAypTx06inxhucHABcw2Kx6JutK9S8+QNasHCZIiL2q0QJT/Xv111NmjTSe1Nn6aWXJysoqKYO/fyddu6M0OYt3+ns2fOqUaOqRo0cpJIlS6jrIwP17fbdrv44uAulp53J82sIcMCBqlevoujo05KkHw9slVfJkrkG+NAn+mv2Z//RkKHPKjR0hckxASDfaNa0sXbuWK0ZM2brn+PfsG0vWrSoDv28XX5+vipbvo78/EorIMBfBw8eyvL62rVrad/3m/TzL1Fq1ryL4emBOwvwIk6YA7hr3Ypve0x4cYx+2P+TLb69vEoqKemas0YDgHzJ29tLknT23Pks22/cuKG4uHgVK+YhSYqPT1B8fEK210dG/qpfDh1RvbpBzh8WcBDuAQdcICiopu69t7rCw/fplZef04Vzvygx/qjiYiM168N3VLJkCVePCABGfL/3RyUkJOqFfz6jXr0eUUCAvwIDa+r/3p6oRo3qa9Jb0277eovFonsqlteFC5cMTQz8dVwBB1wgMLCmJKlvn0fl4VFUk6fMUPTJ39S1S4hGjRykoMCaCunQx8VTAoDzJSZeVs/HhurTT6dq6eJPbduvXLmqPv1GaPXqTbd9/VOjBsvfv6Le/r/pzh4VcBgCHHCBUqVu/pNr+fJl1bFTf23dtkOSFBa2XhaLRUMG91Wnju20cdM3rhwTAIxIunZNhw5Fae2ar7V7zz75lfbV008/oUULZumxXkO1ZeuOHF/XvNkDeu/d13Xwp8Oa8s5Mw1MDd85lAd6hQwdZLBa7j9+06fZ/AwYKkpSU65KkmJhztvi+ZeHCZRoyuK/atGlOgAMo9OrVC9aO7V/pny+8qc9mL7RtX7J0lQ4e2KZPPn5PgcEtlJmZmeV1jRrep9VfzdfZsxfU7dFBSk1NNT06cMdcdg94/fr1s/xfamqqYmJi5Ofnp6CgIPn5+SkmJkZpaWm6//77XTUm4BRnYs5Jks5fuJht37nzFyRJvr4+RmcCAFcY9+wIeXp6avmKtVm2p6Rc1/oNW1WtWoCqVQvIsq9hg3rauGGxLl++qpAOfXT2bNYvcAL5ncuugE+dOtX2/69atUoHDhzQ+vXrVbVqVdv2kydPavjw4WrZsqUrRgSc5udfIpWcnKJK/hWz7atcyV+SFBsbZ3osADDu1p+D7u7ZrwkWKVIky39KUoMGdbVxwxJdvXpNIR366PTpvC8BB7havlgF5bPPPtOzzz6bJb4lqVq1ahozZow+/fTTXF4JFEwpKdcVtmq97rmngrp375Rl36hRgyRJGzZsc8VoAGBUZOSvkqQhg/tm2e7j461Hu3VQfHyCjh8/KelmfG/asFTXriUrpEMfnTz5m+lxAYfIF1/C/O233+Tjk/M/t5cuXVoxMTGGJwLuzIABvVS1SmVJUrmyZeThUVQvvzROknTqdEyWB+68+to7erh9ay1a8KFmfTRXJ0/GqHOn9uraNUQLFi5T+J59LvkMAGDSjJmzNXBgb03+v5dVr15t7d69V35+vnpy2OPy96+oMWNfVkZGhqpUqaSN65eodGkffThrjpo3f0DNmz+Q5VyrVm1QcnKKiz4JYL988STMjh07qk6dOpo+PfsSQuPGjVNUVFSev4TJkzDhCls3L1ObNi1y3Ld9+249/LesSwtWrVpZb02aoL+FtJGPTykdP3FKc+cu1vT3P1M++J8mABhRo0ZVvfrK82rfrpUqVCirlJTrOnjwkGbM/FyrVm2QJLV5qLm2bll+2/PUrNVUp05x0Q5mFdhH0X/55Zd6/fXX1bhxY3Xt2lXlypVTbGys1q5dqwMHDmjSpEnq0ydvayIT4AAAAHC2AhvgkrRkyRLNnDlTcXH/++JZmTJl9Oyzz6pfv355Ph8BDgAAAGcr0AEuSRkZGTp+/LgSExPl6+ure++9V25ud/Y9UQIcAAAAzlbgA9yRCHAAAAA4250EeL5YBWXNmjV/eky3bt0MTAIAAAA4V764Ah4cHJzj9t8/qj4yMjJP5+QKOAAAAJytwF4B//rrr7NtS0hI0NatW7Vp06YsT80EAAAACrJ8cQX8dt577z3Fx8drypQpeXodV8ABAADgbHdyBTxfPIr+dlq2bKlt23gkNwAAAAqHfB/gZ8+ezXIvOAAAAFCQ5Yt7wPfv359tW1pamo4cOaKPP/5YTZo0ccFUAAAAgOPli3vAg4ODs13lvjVWkyZN9J///EflypXL0zm5BxwAAADOVmAfxBMeHp5tm4eHhypXrqwKFSrc0TkJcAAAADhbgQ1wZyDAAQAA4GyFchUUAAAAoDDJF1/ClKS9e/cqNDRUJ06cUGpqarb9mzZtcsFUAAAAgGPliyvgERERGjJkiOLj43X06FFVrlxZpUuX1m+//abMzEzVr1/f1SMCAAAADpEvAvzDDz9Ur169NHfuXEnSP/7xDy1ZskQrV67U9evX1bFjRxdPCAAAADhGvgjwY8eO6W9/+5ttKcKMjAxJN5cnHD16tD766CNXjgcAAAA4TL4I8LS0NHl6esrNzU0+Pj6Ki4uz7atWrZqOHz/uwukAAAAAx8kXAV6pUiXFxsZKkmrUqKENGzbY9m3ZskVlypRx1WgAAACAQ+WLVVCaN2+u8PBwdenSRYMHD9bzzz+vn376SUWLFtWxY8c0duxYV48IAAAAOES+eBBPamqqrl+/Lh8fH0nS2rVrtXbtWlksFrVt21b9+vXL8zl5EA8AAACcrUA+CTMzM1OxsbEqVaqUSpQo4bDzEuAAAABwtgL5JEyr1ap27dppz549rh4FAAAAcDqXB7i7u7vKlSsnNzeXjwIAAAA4Xb6o3h49emjJkiWuHgMAAABwunyxCkrp0qV1+PBhdevWTe3bt1f58uWzHTNgwAAXTAYAAAA4lsu/hCndfOLl7VgsFkVGRubpnHwJEwAAAM52J1/CzBdXwL/++mtXjwAAAAAY4bIr4LNnz1aPHj1Urlw5p5yfK+AAAABwtgK1DOG0adN07tw528+ZmZlq1KiRoqKiXDUSAAAA4HQuC/A/Xni3Wq1KTk5WRkaGiyYCAAAAnC9fLEMIAAAA3C0IcAAAAMAgl66CcurUKXl4eEi6eQ/4rW3u7u7Zjv2zpQoBAACAgsBlq6AEBwfLYrFk2Wa1WnPdxjrgAAAAyG8K1DrgU6ZMcdVbAwAAAC6TL56E6QxcAQcAAICzFah1wAEAAIC7EQEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESA4/+3d+9BVZYJHMd/gAKaXCp1vVBiGCdbUhFveEGDTNEQDF01VBrUdLIklcbVsXA201DRFFvZvACm621RDLK8bbqRBu6qWZtt3jXNVcD7ggic/aPlrCdQocVHd/p+ZpzhvO/zvu9zcObw5T3veQEAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBBDgAAABgEAEOAAAAGESAAwAAAAYR4AAAAIBBDlar1XqvJwEAAAD8UnAGHAAAADCIAAcAAAAMIsABAAAAgwhwAAAAwCACHAAAADCIAAcAAAAMIsABAAAAgwhwAAAAwCACHAAAADCIAMcv3vr162WxWNSqVSudPXu2wvrw8HANGzbsHszsf2exWJSUlHSvpwHgF6T8NbWyf++///69np5NXFycevbsea+ngV+oWvd6AsD94vr161qwYIFmzJhxr6cCAP/3EhIS5O3tbbescePG92YywH2GAAf+o1u3bsrIyNCIESPk4+NT4/svKSmRg4ODnJycanzfAHC/sVgsatmyZZXG8vqIXxouQQH+Y9SoUXJzc9PcuXNvO66goEBTp05Vly5d5Ofnp2effVbJyckqLS21jfn+++9lsVi0dOlSzZ8/X927d5efn59++OEH5eTkyGKx6KOPPtL06dMVGBgof39/vfLKK7p48aIKCgo0ceJEtW/fXl27dlVCQoJKSkrs5pCUlKTIyEi1b99eAQEBGjBggDZt2nRXvi8AUJNOnDghi8Wi1NRUzZs3T0FBQfLz89P58+dVVFSkGTNmKCwsTG3btlWnTp00bNgwffHFF3b72LVrlywWi/76179Wuu+NGzfaLV+7dq169eolPz8/9e7dWxs2bLjrzxO4Hc6AA//h7u6ul156SbNmzdK+ffvk7+9fYUxhYaGGDRumf/7zn4qNjVXz5s2VnZ2td999V6dOndLbb79tNz41NVUWi0VvvvmmJMnDw0OnT5+WJM2ZM0fdunXT7NmzdezYMc2aNUuTJ09WXl6egoKCFBkZqd27d+v9999Xo0aNFB0dbdvvmTNnFBUVpcaNG6ukpEQ5OTmKi4vTtWvXNHDgwLv4XQKAqiktLbU7efDTM9xLly5Vy5YtNW3aNJWVlalevXoqKirStWvXNHr0aDVo0EBFRUXasmWLXnzxRaWlpaljx47Vnsfq1asVHx+vXr16afLkybp48aKSkpJUUlIiZ2fnGnmuQHUR4MBNhg4dqg8++ECJiYlasWJFhfUbNmzQ4cOHlZycrKefflqS1LVrV5WVlSktLU0xMTF2l6888MAD+sMf/lDp26p+fn763e9+Z9vHN998o/Xr1ys2NlYvvwQML38AAA9fSURBVPyyJKlz58767LPPlJWVZRfgM2fOtH1dVlamwMBAXbhwQStXriTAAdwXIiMj7R7XrVtX+/btsz12d3dXcnKyHB3t34y/+URGaWmpunTpopMnT2rlypXVDvCSkhItWLBA/v7+WrBggW15mzZt1KdPHzVt2rRa+wNqCgEO3MTFxUWvvvqqpkyZoh07dqhHjx5263NycuTm5maL73IRERFKS0tTbm6uXYAHBwff8prG7t272z1+7LHHKl3u4+NT4e3XL774QosXL9bBgwdVUFAgq9Vqmz8A3A/mzJlj9yHMn74WhoSEVIhvSfr444+VlpamY8eO6eLFi7blvr6+1Z7DkSNHlJ+fr7Fjx9ot9/b2VqtWrZSfn1/tfQI1gQAHfqJ///5KSUlRYmKigoKC7NZdunRJ9evXr7BNgwYNJMnuh4UkNWzY8JbH8fT0tHtcu3ZtST9epvLT5devX7c93r9/v2JiYtSpUye9+eab+tWvfqVatWpp1apVSk9Pr8IzBIC7r0WLFrf9EGb56+bNsrKyNHHiRD333HMaNWqU6tevL0dHR82bN0+nTp2q9hzKX5Mre91u2LAhAY57hgAHfsLR0VHjx4/Xyy+/rA8//NBunaenp77++usK25w/f962/m7btGmTatWqpeTkZLvrF5cvX37Xjw0ANcXBwaHCsqysLHl7eysxMdFu+bVr1+wel7/23bhxw275hQsX7B6Xvybn5eVVONa5c+eqP2mghnAXFKASISEhatu2rRYsWGD3At+pUydduXJFn376qd34Dz/8UA4ODj/rA0LVVf5Bppt/eOXn52v79u13/dgAcLeVvxtY7ttvv9WBAwfslnl5eUmS/vGPf9gt//Of/2z3uEWLFnr44YeVmZlpt/z48eMV9gmYRIADtxAXF6fTp0/ryJEjtmURERHy9fXV66+/rg8++ECff/65EhISlJqaqt/85je267jvph49euhf//qX4uLitGvXLm3cuFEvvPBCpW+xAsD/kx49eujQoUOaPn26du/erdWrV2vkyJFq0qSJ3bhGjRqpQ4cOWrRokdatW6fPP/9cs2bN0ubNm+3GOTk5ady4cdq3b5/GjRunnTt32v7eQ2WXwACmcAkKcAsBAQEKDg62O6Pi6uqqtLQ0zZ07V8nJybp06ZKaNGmiCRMmaOTIkUbmFRgYqLfeektLlizR6NGj1aRJE0VHRys/P18LFy40MgcAuBsGDRqkvLw8paena82aNfLx8VF8fLw2b96sL7/80m7snDlzNH36dM2ZM0dlZWUKDg7W7NmzK9wJavDgwXJ0dNSSJUs0duxYeXl56dVXX1V2dnaFfQKmOFjLb58AAAAA4K7jEhQAAADAIAIcAAAAMIgABwAAAAwiwAEAAACDCHAAAADAIAIcAAAAMIgAB4D7yPr162WxWJSTk3PbZfeT4OBgDRs2rMb2l5OTI4vFovXr19fYPgHgfsIf4gHwi5aTk6Phw4fbLatbt66aN2+u8PBwDR06VE5OTvdodv+7nJwc5ebmKjo6Wu7u7vd6OgAAEeAAIEl67rnnFBQUJKvVqnPnzmnDhg2aMWOGDh8+rLfeeuuezi08PFx9+/ZV7dq1q71tbm6uFi5cqP79+xPgAHCfIMABQNKTTz6p8PBw2+MXXnhBoaGhWrdunWJjY1W/fv1Kt7tx44bKysrk4uJy1+bm5OT0f30WHgBgj2vAAaAS9erVk7+/v6xWq06dOiVJSkpKksVi0aFDhzRz5kwFBQWpVatW2r9/v227Xbt2KSYmRu3atdNTTz2lsLAwrVq1qtJjrFu3Tr1795afn5969uyptLQ0Wa3WCuNudQ14cXGxFi9erPDwcLVu3VoBAQF6/vnntWLFCknSb3/7Wy1cuFCSFBISIovFIovFoqSkJNs+rly5otmzZ6tnz57y8/NTp06dNGHCBNtzvtkPP/yg2NhYBQQEqG3bthozZoxOnjxZre+r1WrV2rVrNXDgQPn7+8vf319hYWGaP3/+bbcrKyvTokWLFBUVpS5dusjPz089evRQfHy8Lly4UGF8RkaGBgwYoHbt2qlNmzYKCQnRxIkTVVBQYBtz6NAhjRs3Tt26dZOfn5+6dOmiYcOGaceOHdV6TgBQXZwBB4BKWK1WnThxQpL04IMP2q2Li4uTq6urYmJiJEkNGjSQJK1Zs0bx8fFq06aNxowZozp16mjXrl2aNm2aTp48qUmTJtn2kZqaqpkzZ+qJJ57QhAkTVFhYqKVLl+rhhx+u0vyKi4s1YsQI5ebmqmvXrurXr59cXFz03XffacuWLRo6dKgGDRqkq1evauvWrZo8ebLteVgsFkk/xvfgwYN15swZRUZG6vHHH9f58+f1xz/+UQMHDlR6erqaNm0qSbp8+bKioqJ09uxZDR48WD4+PtqzZ4+GDx+uoqKiKn9fX3/9dWVmZqp169YaM2aM3NzcdPToUW3evFmxsbG33O7GjRtaunSpnn32WYWEhKhOnTr66quvlJ6err179yo9PV3Ozs6SpI0bN2rSpElq166dxo0bJ1dXV505c0Z/+ctflJ+fr4ceekgXLlxQdHS0JGnw4MFq0qSJLly4oK+//lpffvmlevToUeXnBADVRYADgKTCwkLb2dFz585pxYoV+vbbb9WmTRt5e3vbjXV3d1dKSopq1frvS+i5c+c0ffp09e3bV4mJibblUVFRmj59ulJTUzVkyBA9+uijunz5st599135+Pho9erVqlOnjiQpMjJSoaGhVZpvWlqacnNzNXr0aE2YMMFuXVlZmSTJ399fFotFW7du1TPPPCMvLy+7cfPnz9epU6e0du1aPfHEE7bl/fv3V1hYmJKSkvTOO+9IkpYsWaLTp09rxowZioyMtD23t99+W8uXL6/SnDdt2qTMzEz169dPCQkJcnT875uw5XO+FWdnZ2VnZ8vV1dW2bMiQIfL399fUqVO1bds29enTR5K0detWPfDAA0pLS7P7P3rttddsX+/du1f5+fmaN2+ebTsAMIVLUABAP15eEhgYqMDAQIWHhys9PV3BwcF67733KoyNjo62CztJ2rx5s4qLizVgwAAVFBTY/QsODlZZWZl2794tScrOzlZhYaGioqJs8S1JjRo1UlhYWJXmm5mZKQ8PD40dO7bCupvD9lasVqsyMzPVvn17NWzY0G6+derUUZs2bZSdnW0bv23bNtWvX18RERF2+xk1alSV5ls+Z0maNGlShTneac4ODg62+C4tLdXly5dVUFCgTp06SZIOHDhgG+vm5qaioiLt2LGj0kt6ysdI0meffaarV69W+TkAQE3gDDgASBo0aJB69+4tBwcH1alTR97e3vL09Kx07E/PiEvSkSNHJEkvvvjiLY+Rl5cnSfr+++8lSY899liFMT4+PlWa74kTJ9SyZcuf/eHPgoICXbx4UdnZ2QoMDKx0zM1RfOrUKT311FMVPgzasGHDKt9d5cSJE2rQoMEtP9B6J5s2bVJKSooOHjyoGzdu2K27dOmS7evRo0drz549Gjt2rDw9PdWhQwcFBQUpNDRU9erVkyR16NBBERERWr9+vTIzM+Xn56fOnTurT58+atGixc+aHwBUFQEOAJKaNWumzp07V2nszZdBlCs/05qQkKCGDRtWut0jjzxiN7Yyt1tXk8qP07lz5yqfxXZwcLjtvqpyzFvt4062bNmi8ePHq1WrVpoyZYoaN24sFxcXlZaWauTIkXZz8Pb21qZNm7R7927t3r1bubm5mjp1qhYsWKCVK1fq0UcflfTj/9WIESO0c+dO/e1vf1NKSoqSk5M1ZcoUDR069GfNEwCqggAHgBpQflb8wQcfvGPIl4f40aNHK5x9Pnr0aJWPd/ToURUXF9s+fFiZWwXvQw89JHd3d129erVKv3g88sgjOn78uEpLS+3Ogp87d05Xrlyp0pybN2+u7du3Ky8vr9pnwTdu3CgXFxctX77c7rKd8ncefsrZ2Vndu3dX9+7dJUk7d+7USy+9pJSUFMXHx9vG+fr6ytfXV6NGjdLly5c1cOBAJSYmKioq6mf/sgAAd8I14ABQA0JDQ+Xs7KykpKRK7wpy5coVFRcXS5K6dOkiV1dXrVy5UoWFhbYxZ8+etV0nfSdhYWG6dOmSfv/731dYd/PZ4Lp160qyv0RD+vHykrCwMB04cECffPJJpcfIz8+3fR0SEqK8vDxlZGTYjVm8eHGV5ls+Z0maPXt2hQ9d3uksupOTkxwcHOy2s1qtWrRoUYWxN99qsNyTTz4p6b/fh4sXL1aYg7u7u7y8vFRYWKjr169X4RkBwM/DGXAAqAGNGjXStGnTNHXqVPXp00f9+vVT06ZNVVBQoO+++07btm3TRx99JC8vL3l4eCg2NlYJCQkaPHiwIiIiVFhYqNWrV8vb21vffPPNHY83fPhwffrpp1q0aJG++uorde3aVc7Ozjp8+LCOHTum1NRUSVLr1q0lSXPmzFFYWJhcXFz0+OOPy9fXV+PHj9fevXv12muvKTQ0VK1bt1bt2rVtt+z79a9/bbsLysiRI5WVlaU33nhDf//739WiRQvl5uZq//79FW7TeCuhoaHasmWLMjIydOLECQUHB8vd3V3Hjx9Xdna2srKybrltr169tHnzZkVHRysiIkIlJSXatm2b3S8w5UaMGKF69eqpffv2aty4sS5fvqwNGzbIwcHB9seWMjIylJaWpmeeeUbNmjVTrVq1tGfPHmVnZys0NLTSy4wAoKYQ4ABQQyIjI+Xt7a1ly5ZpzZo1unLlijw9PdW8eXPFxsba7hcuSTExMapbt65SUlKUmJioxo0bKyYmRm5ubpoyZcodj+Xs7Kxly5Zp2bJlysrK0ty5c+Xi4qJmzZrp+eeft40LCAhQXFycVq9erTfeeEMlJSV65ZVX5OvrKzc3N61atUrLli3TJ598ou3bt8vJyUmNGjVSQECABg4caNuPh4eHVq5cqXfeeUcZGRmyWq3q2LGjli9fftsPnv5UYmKi2rVrpz/96U9677335OjoKC8vL/Xu3fu22/Xt21fXrl1TamqqEhIS5OHhoaeffloTJ05Ux44d7cYOGTJEH3/8sdasWaNLly7J09NTLVu21NSpU213TenYsaMOHjyoHTt26Pz587Z5TJo0ieu/Adx1DlZTn/gBAAAAwDXgAAAAgEkEOAAAAGAQAQ4AAAAYRIADAAAABhHgAAAAgEEEOAAAAGAQAQ4AAAAYRIADAAAABhHgAAAAgEEEOAAAAGDQvwG8FZhNpolU/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = [1 if e > threshold else 0 for e in error_df.reconstruction_error.values]\n",
    "conf_matrix = confusion_matrix(error_df.true_class, y_pred)\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\", cbar=False);\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model seems to catch a lot of the fraudulent cases. Of course, there is a catch (see what I did there?). The number of normal transactions classified as frauds is really high. Is this really a problem? Probably it is. You might want to increase or decrease the value of the threshold, depending on the problem. That one is up to you.\n",
    "\n",
    "# Conclusion\n",
    "\n",
    "We've created a very simple Deep Autoencoder in Keras that can reconstruct what non fraudulent transactions looks like. Initially, I was a bit skeptical about whether or not this whole thing is gonna work out, bit it kinda did. Think about it, we gave a lot of one-class examples (normal transactions) to a model and it learned (somewhat) how to discriminate whether or not new examples belong to that same class. Isn't that cool? Our dataset was kind of magical, though. We really don't know what the original features look like.\n",
    "\n",
    "Keras gave us very clean and easy to use API to build a non-trivial Deep Autoencoder. You can search for TensorFlow implementations and see for yourself how much boilerplate you need in order to train one. Can you apply a similar model to a different problem?\n",
    "\n",
    "# References\n",
    "\n",
    "- [Building Autoencoders in Keras](https://blog.keras.io/building-autoencoders-in-keras.html)\n",
    "- [Stanford tutorial on Autoencoders](http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/)\n",
    "- [Stacked Autoencoders in TensorFlow](http://cmgreen.io/2016/01/04/tensorflow_deep_autoencoder.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:tensorflow_p36]",
   "language": "python",
   "name": "conda-env-tensorflow_p36-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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