Classification_on_imbalanced_data.ipynb

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      "name": "Classification_on_imbalanced_data.ipynb",
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      "authorship_tag": "ABX9TyNlFUkTUjTewq5Q110bPddk",
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    {
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/wenxuan0923/My-notes/blob/master/Classification_on_imbalanced_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OJyEw5r0RJT-",
        "colab_type": "text"
      },
      "source": [
        "## Credit Card Fraud detection\n",
        "\n",
        "In this note I will explore common techniques dealing with highly imbalanced dataset, and train a Deep neural network model for fraud detection. The following methods are covered here.\n",
        "\n",
        "- Under-sampling methods: **NearMiss**\n",
        "- Over-sampling methods: **SMOTE**\n",
        "- Class weighting: `class_weight` in Keras model.fit\n",
        "\n",
        "The dataset being used in this note come from Kaggle: <a target='_blank' href='https://www.kaggle.com/mlg-ulb/creditcardfraud'>Credit Card Fraud Detection</a>."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXBvYDqltMpD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "3c0d7a76-8cdb-4ea8-bb70-a21e56c0b734"
      },
      "source": [
        "# Set up\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "sns.set_style(\"whitegrid\")\n",
        "from collections import Counter\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRS4qdh-PmLA",
        "colab_type": "code",
        "outputId": "9079156c-6d33-4b53-cb1b-335ea1fe652c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        }
      },
      "source": [
        "# Load data\n",
        "data = pd.read_csv('creditcard.csv')\n",
        "print('Data shape: ', data.shape)\n",
        "display(data.head())"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data shape:  (284807, 31)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <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",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</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>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</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",
              "    </tr>\n",
              "    <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>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</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",
              "    </tr>\n",
              "    <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>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</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",
              "    </tr>\n",
              "    <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",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0   0.0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62      0\n",
              "1   0.0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69      0\n",
              "2   1.0 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66      0\n",
              "3   1.0 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50      0\n",
              "4   2.0 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99      0\n",
              "\n",
              "[5 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UMyZTMqkQ6Bh",
        "colab_type": "text"
      },
      "source": [
        "####According to the intro page of the dataset:\n",
        "\n",
        "The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is **highly unbalanced**, the positive class (frauds) account for 0.172% of all transactions.\n",
        "\n",
        "It contains **only numerical input** variables which are the **result of a PCA transformation**. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. \n",
        "\n",
        "> PCA assumes that the dataset is centered around the origin, which implies these V features have been scaled beforehand.\n",
        "\n",
        "Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LfN0mmA1QSJi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "13334718-eec1-40ed-bcc7-7ebf33ab11e0"
      },
      "source": [
        "display(data.describe())"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "  <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>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <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>count</th>\n",
              "      <td>284807.000000</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>2.848070e+05</td>\n",
              "      <td>284807.000000</td>\n",
              "      <td>284807.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94813.859575</td>\n",
              "      <td>3.919560e-15</td>\n",
              "      <td>5.688174e-16</td>\n",
              "      <td>-8.769071e-15</td>\n",
              "      <td>2.782312e-15</td>\n",
              "      <td>-1.552563e-15</td>\n",
              "      <td>2.010663e-15</td>\n",
              "      <td>-1.694249e-15</td>\n",
              "      <td>-1.927028e-16</td>\n",
              "      <td>-3.137024e-15</td>\n",
              "      <td>1.768627e-15</td>\n",
              "      <td>9.170318e-16</td>\n",
              "      <td>-1.810658e-15</td>\n",
              "      <td>1.693438e-15</td>\n",
              "      <td>1.479045e-15</td>\n",
              "      <td>3.482336e-15</td>\n",
              "      <td>1.392007e-15</td>\n",
              "      <td>-7.528491e-16</td>\n",
              "      <td>4.328772e-16</td>\n",
              "      <td>9.049732e-16</td>\n",
              "      <td>5.085503e-16</td>\n",
              "      <td>1.537294e-16</td>\n",
              "      <td>7.959909e-16</td>\n",
              "      <td>5.367590e-16</td>\n",
              "      <td>4.458112e-15</td>\n",
              "      <td>1.453003e-15</td>\n",
              "      <td>1.699104e-15</td>\n",
              "      <td>-3.660161e-16</td>\n",
              "      <td>-1.206049e-16</td>\n",
              "      <td>88.349619</td>\n",
              "      <td>0.001727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47488.145955</td>\n",
              "      <td>1.958696e+00</td>\n",
              "      <td>1.651309e+00</td>\n",
              "      <td>1.516255e+00</td>\n",
              "      <td>1.415869e+00</td>\n",
              "      <td>1.380247e+00</td>\n",
              "      <td>1.332271e+00</td>\n",
              "      <td>1.237094e+00</td>\n",
              "      <td>1.194353e+00</td>\n",
              "      <td>1.098632e+00</td>\n",
              "      <td>1.088850e+00</td>\n",
              "      <td>1.020713e+00</td>\n",
              "      <td>9.992014e-01</td>\n",
              "      <td>9.952742e-01</td>\n",
              "      <td>9.585956e-01</td>\n",
              "      <td>9.153160e-01</td>\n",
              "      <td>8.762529e-01</td>\n",
              "      <td>8.493371e-01</td>\n",
              "      <td>8.381762e-01</td>\n",
              "      <td>8.140405e-01</td>\n",
              "      <td>7.709250e-01</td>\n",
              "      <td>7.345240e-01</td>\n",
              "      <td>7.257016e-01</td>\n",
              "      <td>6.244603e-01</td>\n",
              "      <td>6.056471e-01</td>\n",
              "      <td>5.212781e-01</td>\n",
              "      <td>4.822270e-01</td>\n",
              "      <td>4.036325e-01</td>\n",
              "      <td>3.300833e-01</td>\n",
              "      <td>250.120109</td>\n",
              "      <td>0.041527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-5.640751e+01</td>\n",
              "      <td>-7.271573e+01</td>\n",
              "      <td>-4.832559e+01</td>\n",
              "      <td>-5.683171e+00</td>\n",
              "      <td>-1.137433e+02</td>\n",
              "      <td>-2.616051e+01</td>\n",
              "      <td>-4.355724e+01</td>\n",
              "      <td>-7.321672e+01</td>\n",
              "      <td>-1.343407e+01</td>\n",
              "      <td>-2.458826e+01</td>\n",
              "      <td>-4.797473e+00</td>\n",
              "      <td>-1.868371e+01</td>\n",
              "      <td>-5.791881e+00</td>\n",
              "      <td>-1.921433e+01</td>\n",
              "      <td>-4.498945e+00</td>\n",
              "      <td>-1.412985e+01</td>\n",
              "      <td>-2.516280e+01</td>\n",
              "      <td>-9.498746e+00</td>\n",
              "      <td>-7.213527e+00</td>\n",
              "      <td>-5.449772e+01</td>\n",
              "      <td>-3.483038e+01</td>\n",
              "      <td>-1.093314e+01</td>\n",
              "      <td>-4.480774e+01</td>\n",
              "      <td>-2.836627e+00</td>\n",
              "      <td>-1.029540e+01</td>\n",
              "      <td>-2.604551e+00</td>\n",
              "      <td>-2.256568e+01</td>\n",
              "      <td>-1.543008e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>54201.500000</td>\n",
              "      <td>-9.203734e-01</td>\n",
              "      <td>-5.985499e-01</td>\n",
              "      <td>-8.903648e-01</td>\n",
              "      <td>-8.486401e-01</td>\n",
              "      <td>-6.915971e-01</td>\n",
              "      <td>-7.682956e-01</td>\n",
              "      <td>-5.540759e-01</td>\n",
              "      <td>-2.086297e-01</td>\n",
              "      <td>-6.430976e-01</td>\n",
              "      <td>-5.354257e-01</td>\n",
              "      <td>-7.624942e-01</td>\n",
              "      <td>-4.055715e-01</td>\n",
              "      <td>-6.485393e-01</td>\n",
              "      <td>-4.255740e-01</td>\n",
              "      <td>-5.828843e-01</td>\n",
              "      <td>-4.680368e-01</td>\n",
              "      <td>-4.837483e-01</td>\n",
              "      <td>-4.988498e-01</td>\n",
              "      <td>-4.562989e-01</td>\n",
              "      <td>-2.117214e-01</td>\n",
              "      <td>-2.283949e-01</td>\n",
              "      <td>-5.423504e-01</td>\n",
              "      <td>-1.618463e-01</td>\n",
              "      <td>-3.545861e-01</td>\n",
              "      <td>-3.171451e-01</td>\n",
              "      <td>-3.269839e-01</td>\n",
              "      <td>-7.083953e-02</td>\n",
              "      <td>-5.295979e-02</td>\n",
              "      <td>5.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>84692.000000</td>\n",
              "      <td>1.810880e-02</td>\n",
              "      <td>6.548556e-02</td>\n",
              "      <td>1.798463e-01</td>\n",
              "      <td>-1.984653e-02</td>\n",
              "      <td>-5.433583e-02</td>\n",
              "      <td>-2.741871e-01</td>\n",
              "      <td>4.010308e-02</td>\n",
              "      <td>2.235804e-02</td>\n",
              "      <td>-5.142873e-02</td>\n",
              "      <td>-9.291738e-02</td>\n",
              "      <td>-3.275735e-02</td>\n",
              "      <td>1.400326e-01</td>\n",
              "      <td>-1.356806e-02</td>\n",
              "      <td>5.060132e-02</td>\n",
              "      <td>4.807155e-02</td>\n",
              "      <td>6.641332e-02</td>\n",
              "      <td>-6.567575e-02</td>\n",
              "      <td>-3.636312e-03</td>\n",
              "      <td>3.734823e-03</td>\n",
              "      <td>-6.248109e-02</td>\n",
              "      <td>-2.945017e-02</td>\n",
              "      <td>6.781943e-03</td>\n",
              "      <td>-1.119293e-02</td>\n",
              "      <td>4.097606e-02</td>\n",
              "      <td>1.659350e-02</td>\n",
              "      <td>-5.213911e-02</td>\n",
              "      <td>1.342146e-03</td>\n",
              "      <td>1.124383e-02</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>139320.500000</td>\n",
              "      <td>1.315642e+00</td>\n",
              "      <td>8.037239e-01</td>\n",
              "      <td>1.027196e+00</td>\n",
              "      <td>7.433413e-01</td>\n",
              "      <td>6.119264e-01</td>\n",
              "      <td>3.985649e-01</td>\n",
              "      <td>5.704361e-01</td>\n",
              "      <td>3.273459e-01</td>\n",
              "      <td>5.971390e-01</td>\n",
              "      <td>4.539234e-01</td>\n",
              "      <td>7.395934e-01</td>\n",
              "      <td>6.182380e-01</td>\n",
              "      <td>6.625050e-01</td>\n",
              "      <td>4.931498e-01</td>\n",
              "      <td>6.488208e-01</td>\n",
              "      <td>5.232963e-01</td>\n",
              "      <td>3.996750e-01</td>\n",
              "      <td>5.008067e-01</td>\n",
              "      <td>4.589494e-01</td>\n",
              "      <td>1.330408e-01</td>\n",
              "      <td>1.863772e-01</td>\n",
              "      <td>5.285536e-01</td>\n",
              "      <td>1.476421e-01</td>\n",
              "      <td>4.395266e-01</td>\n",
              "      <td>3.507156e-01</td>\n",
              "      <td>2.409522e-01</td>\n",
              "      <td>9.104512e-02</td>\n",
              "      <td>7.827995e-02</td>\n",
              "      <td>77.165000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>172792.000000</td>\n",
              "      <td>2.454930e+00</td>\n",
              "      <td>2.205773e+01</td>\n",
              "      <td>9.382558e+00</td>\n",
              "      <td>1.687534e+01</td>\n",
              "      <td>3.480167e+01</td>\n",
              "      <td>7.330163e+01</td>\n",
              "      <td>1.205895e+02</td>\n",
              "      <td>2.000721e+01</td>\n",
              "      <td>1.559499e+01</td>\n",
              "      <td>2.374514e+01</td>\n",
              "      <td>1.201891e+01</td>\n",
              "      <td>7.848392e+00</td>\n",
              "      <td>7.126883e+00</td>\n",
              "      <td>1.052677e+01</td>\n",
              "      <td>8.877742e+00</td>\n",
              "      <td>1.731511e+01</td>\n",
              "      <td>9.253526e+00</td>\n",
              "      <td>5.041069e+00</td>\n",
              "      <td>5.591971e+00</td>\n",
              "      <td>3.942090e+01</td>\n",
              "      <td>2.720284e+01</td>\n",
              "      <td>1.050309e+01</td>\n",
              "      <td>2.252841e+01</td>\n",
              "      <td>4.584549e+00</td>\n",
              "      <td>7.519589e+00</td>\n",
              "      <td>3.517346e+00</td>\n",
              "      <td>3.161220e+01</td>\n",
              "      <td>3.384781e+01</td>\n",
              "      <td>25691.160000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Time            V1  ...         Amount          Class\n",
              "count  284807.000000  2.848070e+05  ...  284807.000000  284807.000000\n",
              "mean    94813.859575  3.919560e-15  ...      88.349619       0.001727\n",
              "std     47488.145955  1.958696e+00  ...     250.120109       0.041527\n",
              "min         0.000000 -5.640751e+01  ...       0.000000       0.000000\n",
              "25%     54201.500000 -9.203734e-01  ...       5.600000       0.000000\n",
              "50%     84692.000000  1.810880e-02  ...      22.000000       0.000000\n",
              "75%    139320.500000  1.315642e+00  ...      77.165000       0.000000\n",
              "max    172792.000000  2.454930e+00  ...   25691.160000       1.000000\n",
              "\n",
              "[8 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gehyZQqWSdGO",
        "colab_type": "text"
      },
      "source": [
        "## Target Variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t2SaawLVQOMM",
        "colab_type": "code",
        "outputId": "02ad2de1-119d-4b61-f306-fc8de51a29e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "ax = sns.countplot('Class', data=data)\n",
        "ax.set_title('Count Plot of Target Variable \\n', fontsize=17)\n",
        "plt.show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rP7bU5uoawlj",
        "colab_type": "code",
        "outputId": "1989872c-5a71-4ccb-851d-62d0c71441e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "# Percentage of target variable\n",
        "data.Class.value_counts()/len(data)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.998273\n",
              "1    0.001727\n",
              "Name: Class, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_ECtDjJQ2za",
        "colab_type": "text"
      },
      "source": [
        "Positive class (frauds) account for only 0.172% of all transactions.\n",
        "\n",
        "## Distribution of features "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zQewVvZZnSh",
        "colab_type": "code",
        "outputId": "5d6c5110-f195-481d-89ed-71076e253242",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Distribution of all features in the dataset\n",
        "cols = list(data.columns)\n",
        "cols.remove('Class')  # remove the target variable\n",
        "num_rows = int(np.ceil(len(cols)/3))\n",
        "fig, axs = plt.subplots(num_rows, 3, figsize=(15, 3*num_rows))\n",
        "axs = axs.ravel()\n",
        "for ax in axs[len(cols):]:\n",
        "    ax.set_visible(False)\n",
        "for i, col in enumerate(cols):\n",
        "  sns.distplot(data[col], ax=axs[i])\n",
        "  axs[i].set_xlabel('')\n",
        "  axs[i].set_title(col, fontsize=15)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x2160 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ULbs3ch8dLK3",
        "colab_type": "text"
      },
      "source": [
        "From the distribution of features, we can see that:\n",
        "- All the **V** features have been scaled since they are the outputs of PCA\n",
        "- A lot of the features are **highly skewed**, especially the Amount columns\n",
        "- We need to scale the **Time** and **Amount** columns using some techniques like log transform.\n",
        "\n",
        "Before we proceed with further analysis, let's process the data first such that they are have the same scale and less skewed. Besides, we want to get ready the train, test and validation sets for model fitting and evaluation.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kU9r9nWj0PCc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clean_df = data.copy()\n",
        "clean_df['Amount'] = np.log1p(clean_df['Amount'])\n",
        "clean_df.drop('Time', axis=1, inplace=True)\n",
        "features = clean_df.columns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S6D_PHMEtNcO",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "**Overall**: 64% Train, 16% Validation, 20% test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "njl6ek6FrXuC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df, test_df = train_test_split(clean_df, test_size=0.2)\n",
        "train_df, val_df = train_test_split(train_df, test_size=0.2)\n",
        "x_train, y_train = train_df.drop('Class', axis=1), train_df['Class']\n",
        "x_val, y_val = val_df.drop('Class', axis=1), val_df['Class']\n",
        "x_test, y_test = test_df.drop('Class', axis=1), test_df['Class']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ansVdX_3fl-",
        "colab_type": "text"
      },
      "source": [
        "Clip the extreme values base on quantile of training set:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1V-mjyr3eIL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df = train_df.clip(train_df.quantile(0.05), train_df.quantile(0.95), axis=1)\n",
        "test_df = test_df.clip(train_df.quantile(0.05), train_df.quantile(0.95), axis=1)\n",
        "val_df = val_df.clip(train_df.quantile(0.05), train_df.quantile(0.95), axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ySv1HkCZrYmG",
        "colab_type": "text"
      },
      "source": [
        "The **StandardScaler** is only fit using the training data to ensure the model is not peeking at the validation or test sets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUfHoOp1t6YT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler = StandardScaler()   # Standardize features to mean 0 variance 1\n",
        "x_train = scaler.fit_transform(x_train)\n",
        "x_val = scaler.transform(x_val)\n",
        "x_test = scaler.transform(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JVYPETl5Uhyb",
        "colab_type": "text"
      },
      "source": [
        "There are mainly two ways to address the problem of imbalanced dataset:\n",
        "\n",
        "1. **Undersampling**: Randomly delete examples from the majority class. \n",
        "\n",
        "2. **Oversampling**: Randomly creates synthetic data from the minority class.\n",
        "\n",
        "Importantly, these techniques can only be applied to the training dataset to reach a better fit of the models. The resampling is not applied to the test or evaluation set used to evaluate the performance of a model.\n",
        "\n",
        "Now let's try start with Undersampling method.\n",
        "\n",
        "## Undersampling: NearMiss\n",
        "\n",
        "Random undersampling involves randomly deleting data from the majority class to achieve a more balanced dataset. However, it usually lead to information loss at the same time. Thus, instead of using random undersampling method, I will perform **under-sampling based on NearMiss methods**:\n",
        "\n",
        "> When instances of two different classes are very close to each other, we remove the instances of the majority class to increase the spaces between the two classes. This helps in the classification process. To preserve as much infomation as possible while deleting data from the majority class. Here is the basic intuition:<br>\n",
        "1. Find the distances between all instances of the majority class and the instances of the minority class. <br>\n",
        "2. $n$ instances of the majority class that have the smallest distances to those in the minority class are selected. If there are $k$ instances in the minority class, the nearst method will result in the deletion of $k*n$ instances of the majority class.\n",
        "\n",
        "The **imbalanced-learn** toolbox provides methods to under-sample (over-sample) a dataset. Check their <a target='_blank' href='https://imbalanced-learn.readthedocs.io/en/stable/api.html#module-imblearn.under_sampling'>official documents</a> for details.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gdLD_v6tsEq",
        "colab_type": "text"
      },
      "source": [
        "NearMiss implements **3 different types of heuristic** which can be selected with the parameter `version`.\n",
        "\n",
        "> Note: NearMiss heuristic rules are based on **nearest neighbors algorithm**. It's important to scale the data beforehand. \n",
        "\n",
        "\n",
        "**Version 1**: The model selects samples of the majority class for which **average distances** to the $k$ **closest instances** of the minority class is **smallest**.\n",
        "\n",
        "**Version 2**: The model selects samples of the majority class for which **average distances** to the $k$ **farthest instances** of the minority class is **smallest**.\n",
        "\n",
        "**Version 3**: First, for each minority class instance, their $M$ nearest-neighbors will be stored. Then, the majority class instances are selected for which the **average distance** to the $M$ nearest-neighbors is the **largest**.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjPEasopzOMQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "620e7973-94ef-4f0f-d5cf-9af3bd7cfb12"
      },
      "source": [
        "from imblearn.under_sampling import NearMiss \n",
        "nm = NearMiss(sampling_strategy='majority', version=2)\n",
        "x_nm, y_nm = nm.fit_resample(x_train, y_train)\n",
        "\n",
        "# Check the new class distribution\n",
        "print('Before Under-Sampling: ', Counter(y_train))\n",
        "print('After Under-Sampling: ', Counter(y_nm))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before Under-Sampling:  Counter({0: 181952, 1: 324})\n",
            "After Under-Sampling:  Counter({0: 324, 1: 324})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SCpNtYkgAlDo",
        "colab_type": "text"
      },
      "source": [
        "## DNN model for fraud detection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5UJyDSn5GfX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.callbacks import EarlyStopping"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wYtr22q4UhHk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "METRICS = [\n",
        "      keras.metrics.Precision(name='precision'),\n",
        "      keras.metrics.Recall(name='recall'),\n",
        "      keras.metrics.AUC(name='auc'),\n",
        "]\n",
        "\n",
        "def make_model(metrics = METRICS, output_bias=None):\n",
        "  if output_bias is not None:\n",
        "    output_bias = tf.keras.initializers.Constant(output_bias)\n",
        "  model = Sequential()\n",
        "  model.add(Dense(16, activation='relu',\n",
        "          input_shape=(x_train.shape[-1],)))\n",
        "  model.add(Dropout(0.5))\n",
        "  model.add(Dense(1, activation='sigmoid',\n",
        "                  bias_initializer=output_bias))\n",
        "  model.compile(\n",
        "      optimizer=keras.optimizers.Adam(lr=1e-3),\n",
        "      loss='BinaryCrossentropy',\n",
        "      metrics=metrics)\n",
        "\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qtla3jCUhOS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "EPOCHS = 100\n",
        "BATCH_SIZE = 2048\n",
        "\n",
        "early_stopping = EarlyStopping(\n",
        "    monitor='val_auc', \n",
        "    verbose=1,\n",
        "    patience=5,\n",
        "    mode='max',\n",
        "    restore_best_weights=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5U6pKeT9UhMV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b1a33cb5-3926-48b8-bf80-4ec9d6ecd7eb"
      },
      "source": [
        "nm_model = make_model()\n",
        "nm_history = nm_model.fit(\n",
        "    x_nm, y_nm,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    callbacks = [early_stopping],\n",
        "    validation_data=(x_val, y_val))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1/1 [==============================] - 1s 631ms/step - loss: 1.0936 - precision: 0.6236 - recall: 0.5062 - auc: 0.6090 - val_loss: 0.8007 - val_precision: 0.0012 - val_recall: 0.4359 - val_auc: 0.4460\n",
            "Epoch 2/100\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 1.1432 - precision: 0.5872 - recall: 0.4259 - auc: 0.5847 - val_loss: 0.8029 - val_precision: 0.0014 - val_recall: 0.4744 - val_auc: 0.4738\n",
            "Epoch 3/100\n",
            "1/1 [==============================] - 0s 202ms/step - loss: 1.0696 - precision: 0.6375 - recall: 0.4938 - auc: 0.6384 - val_loss: 0.8053 - val_precision: 0.0015 - val_recall: 0.5385 - val_auc: 0.5020\n",
            "Epoch 4/100\n",
            "1/1 [==============================] - 0s 200ms/step - loss: 0.9628 - precision: 0.6431 - recall: 0.5340 - auc: 0.6575 - val_loss: 0.8076 - val_precision: 0.0016 - val_recall: 0.5769 - val_auc: 0.5317\n",
            "Epoch 5/100\n",
            "1/1 [==============================] - 0s 195ms/step - loss: 0.8508 - precision: 0.7126 - recall: 0.5586 - auc: 0.7153 - val_loss: 0.8099 - val_precision: 0.0017 - val_recall: 0.5897 - val_auc: 0.5618\n",
            "Epoch 6/100\n",
            "1/1 [==============================] - 0s 207ms/step - loss: 0.8910 - precision: 0.6866 - recall: 0.5679 - auc: 0.6960 - val_loss: 0.8121 - val_precision: 0.0018 - val_recall: 0.6410 - val_auc: 0.5914\n",
            "Epoch 7/100\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.8417 - precision: 0.7008 - recall: 0.5710 - auc: 0.7153 - val_loss: 0.8141 - val_precision: 0.0020 - val_recall: 0.7051 - val_auc: 0.6188\n",
            "Epoch 8/100\n",
            "1/1 [==============================] - 0s 195ms/step - loss: 0.8402 - precision: 0.6740 - recall: 0.5679 - auc: 0.7178 - val_loss: 0.8161 - val_precision: 0.0020 - val_recall: 0.7308 - val_auc: 0.6440\n",
            "Epoch 9/100\n",
            "1/1 [==============================] - 0s 202ms/step - loss: 0.8359 - precision: 0.7197 - recall: 0.5309 - auc: 0.7228 - val_loss: 0.8183 - val_precision: 0.0021 - val_recall: 0.7564 - val_auc: 0.6672\n",
            "Epoch 10/100\n",
            "1/1 [==============================] - 0s 196ms/step - loss: 0.7682 - precision: 0.7413 - recall: 0.5926 - auc: 0.7585 - val_loss: 0.8204 - val_precision: 0.0022 - val_recall: 0.7821 - val_auc: 0.6884\n",
            "Epoch 11/100\n",
            "1/1 [==============================] - 0s 220ms/step - loss: 0.7269 - precision: 0.7500 - recall: 0.6389 - auc: 0.7749 - val_loss: 0.8226 - val_precision: 0.0021 - val_recall: 0.7821 - val_auc: 0.7058\n",
            "Epoch 12/100\n",
            "1/1 [==============================] - 0s 197ms/step - loss: 0.6793 - precision: 0.7643 - recall: 0.6204 - auc: 0.7848 - val_loss: 0.8248 - val_precision: 0.0022 - val_recall: 0.7949 - val_auc: 0.7208\n",
            "Epoch 13/100\n",
            "1/1 [==============================] - 0s 198ms/step - loss: 0.8223 - precision: 0.7231 - recall: 0.5802 - auc: 0.7341 - val_loss: 0.8271 - val_precision: 0.0022 - val_recall: 0.7949 - val_auc: 0.7350\n",
            "Epoch 14/100\n",
            "1/1 [==============================] - 0s 200ms/step - loss: 0.6760 - precision: 0.7626 - recall: 0.6049 - auc: 0.8034 - val_loss: 0.8295 - val_precision: 0.0022 - val_recall: 0.7949 - val_auc: 0.7466\n",
            "Epoch 15/100\n",
            "1/1 [==============================] - 0s 205ms/step - loss: 0.7543 - precision: 0.7609 - recall: 0.6481 - auc: 0.7900 - val_loss: 0.8318 - val_precision: 0.0022 - val_recall: 0.8077 - val_auc: 0.7578\n",
            "Epoch 16/100\n",
            "1/1 [==============================] - 0s 207ms/step - loss: 0.6967 - precision: 0.7536 - recall: 0.6420 - auc: 0.7921 - val_loss: 0.8342 - val_precision: 0.0022 - val_recall: 0.8205 - val_auc: 0.7675\n",
            "Epoch 17/100\n",
            "1/1 [==============================] - 0s 196ms/step - loss: 0.5986 - precision: 0.7790 - recall: 0.6636 - auc: 0.8341 - val_loss: 0.8365 - val_precision: 0.0022 - val_recall: 0.8333 - val_auc: 0.7757\n",
            "Epoch 18/100\n",
            "1/1 [==============================] - 0s 197ms/step - loss: 0.7450 - precision: 0.7336 - recall: 0.6204 - auc: 0.7814 - val_loss: 0.8389 - val_precision: 0.0022 - val_recall: 0.8333 - val_auc: 0.7837\n",
            "Epoch 19/100\n",
            "1/1 [==============================] - 0s 264ms/step - loss: 0.6211 - precision: 0.7978 - recall: 0.6698 - auc: 0.8320 - val_loss: 0.8414 - val_precision: 0.0022 - val_recall: 0.8462 - val_auc: 0.7910\n",
            "Epoch 20/100\n",
            "1/1 [==============================] - 0s 202ms/step - loss: 0.6665 - precision: 0.7448 - recall: 0.6667 - auc: 0.8077 - val_loss: 0.8439 - val_precision: 0.0023 - val_recall: 0.8590 - val_auc: 0.7975\n",
            "Epoch 21/100\n",
            "1/1 [==============================] - 0s 206ms/step - loss: 0.7048 - precision: 0.7614 - recall: 0.6698 - auc: 0.8138 - val_loss: 0.8465 - val_precision: 0.0023 - val_recall: 0.8590 - val_auc: 0.8042\n",
            "Epoch 22/100\n",
            "1/1 [==============================] - 0s 216ms/step - loss: 0.5816 - precision: 0.8156 - recall: 0.7099 - auc: 0.8360 - val_loss: 0.8490 - val_precision: 0.0023 - val_recall: 0.8590 - val_auc: 0.8098\n",
            "Epoch 23/100\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.6346 - precision: 0.7986 - recall: 0.7099 - auc: 0.8392 - val_loss: 0.8514 - val_precision: 0.0023 - val_recall: 0.8718 - val_auc: 0.8157\n",
            "Epoch 24/100\n",
            "1/1 [==============================] - 0s 211ms/step - loss: 0.6111 - precision: 0.7619 - recall: 0.6914 - auc: 0.8345 - val_loss: 0.8537 - val_precision: 0.0023 - val_recall: 0.8718 - val_auc: 0.8206\n",
            "Epoch 25/100\n",
            "1/1 [==============================] - 0s 212ms/step - loss: 0.5717 - precision: 0.8078 - recall: 0.7654 - auc: 0.8654 - val_loss: 0.8559 - val_precision: 0.0023 - val_recall: 0.8718 - val_auc: 0.8257\n",
            "Epoch 26/100\n",
            "1/1 [==============================] - 0s 208ms/step - loss: 0.6159 - precision: 0.7736 - recall: 0.7068 - auc: 0.8340 - val_loss: 0.8580 - val_precision: 0.0022 - val_recall: 0.8718 - val_auc: 0.8299\n",
            "Epoch 27/100\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.5732 - precision: 0.8082 - recall: 0.7284 - auc: 0.8614 - val_loss: 0.8601 - val_precision: 0.0023 - val_recall: 0.8846 - val_auc: 0.8344\n",
            "Epoch 28/100\n",
            "1/1 [==============================] - 0s 335ms/step - loss: 0.5714 - precision: 0.8194 - recall: 0.7284 - auc: 0.8670 - val_loss: 0.8623 - val_precision: 0.0023 - val_recall: 0.8846 - val_auc: 0.8379\n",
            "Epoch 29/100\n",
            "1/1 [==============================] - 0s 258ms/step - loss: 0.5417 - precision: 0.8149 - recall: 0.7747 - auc: 0.8653 - val_loss: 0.8643 - val_precision: 0.0023 - val_recall: 0.8846 - val_auc: 0.8412\n",
            "Epoch 30/100\n",
            "1/1 [==============================] - 0s 212ms/step - loss: 0.4873 - precision: 0.8477 - recall: 0.7901 - auc: 0.8875 - val_loss: 0.8662 - val_precision: 0.0023 - val_recall: 0.8846 - val_auc: 0.8446\n",
            "Epoch 31/100\n",
            "1/1 [==============================] - 0s 214ms/step - loss: 0.5143 - precision: 0.8058 - recall: 0.7685 - auc: 0.8686 - val_loss: 0.8682 - val_precision: 0.0022 - val_recall: 0.8846 - val_auc: 0.8474\n",
            "Epoch 32/100\n",
            "1/1 [==============================] - 0s 220ms/step - loss: 0.5575 - precision: 0.8062 - recall: 0.7191 - auc: 0.8600 - val_loss: 0.8702 - val_precision: 0.0022 - val_recall: 0.8846 - val_auc: 0.8504\n",
            "Epoch 33/100\n",
            "1/1 [==============================] - 0s 224ms/step - loss: 0.5249 - precision: 0.8322 - recall: 0.7654 - auc: 0.8769 - val_loss: 0.8721 - val_precision: 0.0022 - val_recall: 0.8846 - val_auc: 0.8523\n",
            "Epoch 34/100\n",
            "1/1 [==============================] - 0s 223ms/step - loss: 0.4559 - precision: 0.8224 - recall: 0.8148 - auc: 0.8878 - val_loss: 0.8740 - val_precision: 0.0022 - val_recall: 0.8846 - val_auc: 0.8550\n",
            "Epoch 35/100\n",
            "1/1 [==============================] - 0s 203ms/step - loss: 0.5105 - precision: 0.8441 - recall: 0.7685 - auc: 0.8893 - val_loss: 0.8759 - val_precision: 0.0023 - val_recall: 0.8974 - val_auc: 0.8570\n",
            "Epoch 36/100\n",
            "1/1 [==============================] - 0s 203ms/step - loss: 0.4903 - precision: 0.8571 - recall: 0.7963 - auc: 0.8958 - val_loss: 0.8777 - val_precision: 0.0022 - val_recall: 0.8974 - val_auc: 0.8589\n",
            "Epoch 37/100\n",
            "1/1 [==============================] - 0s 313ms/step - loss: 0.4987 - precision: 0.8365 - recall: 0.8210 - auc: 0.8921 - val_loss: 0.8795 - val_precision: 0.0023 - val_recall: 0.9103 - val_auc: 0.8609\n",
            "Epoch 38/100\n",
            "1/1 [==============================] - 0s 252ms/step - loss: 0.4877 - precision: 0.8367 - recall: 0.7593 - auc: 0.8940 - val_loss: 0.8813 - val_precision: 0.0023 - val_recall: 0.9103 - val_auc: 0.8624\n",
            "Epoch 39/100\n",
            "1/1 [==============================] - 0s 205ms/step - loss: 0.4811 - precision: 0.8669 - recall: 0.7840 - auc: 0.9057 - val_loss: 0.8830 - val_precision: 0.0023 - val_recall: 0.9103 - val_auc: 0.8644\n",
            "Epoch 40/100\n",
            "1/1 [==============================] - 0s 204ms/step - loss: 0.4710 - precision: 0.8511 - recall: 0.8117 - auc: 0.8991 - val_loss: 0.8846 - val_precision: 0.0023 - val_recall: 0.9103 - val_auc: 0.8661\n",
            "Epoch 41/100\n",
            "1/1 [==============================] - 0s 205ms/step - loss: 0.4398 - precision: 0.8494 - recall: 0.8179 - auc: 0.9031 - val_loss: 0.8862 - val_precision: 0.0022 - val_recall: 0.9103 - val_auc: 0.8670\n",
            "Epoch 42/100\n",
            "1/1 [==============================] - 0s 222ms/step - loss: 0.5307 - precision: 0.8411 - recall: 0.7840 - auc: 0.8848 - val_loss: 0.8878 - val_precision: 0.0022 - val_recall: 0.9103 - val_auc: 0.8684\n",
            "Epoch 43/100\n",
            "1/1 [==============================] - 0s 225ms/step - loss: 0.4573 - precision: 0.8729 - recall: 0.8056 - auc: 0.9072 - val_loss: 0.8893 - val_precision: 0.0022 - val_recall: 0.9103 - val_auc: 0.8696\n",
            "Epoch 44/100\n",
            "1/1 [==============================] - 0s 202ms/step - loss: 0.4512 - precision: 0.8462 - recall: 0.8148 - auc: 0.9025 - val_loss: 0.8908 - val_precision: 0.0022 - val_recall: 0.9103 - val_auc: 0.8705\n",
            "Epoch 45/100\n",
            "1/1 [==============================] - 0s 205ms/step - loss: 0.4723 - precision: 0.8698 - recall: 0.8457 - auc: 0.9067 - val_loss: 0.8922 - val_precision: 0.0022 - val_recall: 0.9103 - val_auc: 0.8717\n",
            "Epoch 46/100\n",
            "1/1 [==============================] - 0s 217ms/step - loss: 0.4323 - precision: 0.8632 - recall: 0.8179 - auc: 0.9148 - val_loss: 0.8937 - val_precision: 0.0022 - val_recall: 0.9103 - val_auc: 0.8727\n",
            "Epoch 47/100\n",
            "1/1 [==============================] - 0s 217ms/step - loss: 0.4805 - precision: 0.8571 - recall: 0.8333 - auc: 0.9014 - val_loss: 0.8952 - val_precision: 0.0022 - val_recall: 0.9103 - val_auc: 0.8736\n",
            "Epoch 48/100\n",
            "1/1 [==============================] - 0s 208ms/step - loss: 0.4469 - precision: 0.8656 - recall: 0.8148 - auc: 0.9082 - val_loss: 0.8967 - val_precision: 0.0022 - val_recall: 0.9103 - val_auc: 0.8747\n",
            "Epoch 49/100\n",
            "1/1 [==============================] - 0s 208ms/step - loss: 0.4804 - precision: 0.8553 - recall: 0.8395 - auc: 0.8994 - val_loss: 0.8981 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8753\n",
            "Epoch 50/100\n",
            "1/1 [==============================] - 0s 206ms/step - loss: 0.4522 - precision: 0.8833 - recall: 0.8179 - auc: 0.9088 - val_loss: 0.8996 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8764\n",
            "Epoch 51/100\n",
            "1/1 [==============================] - 0s 235ms/step - loss: 0.3758 - precision: 0.8758 - recall: 0.8704 - auc: 0.9300 - val_loss: 0.9010 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8770\n",
            "Epoch 52/100\n",
            "1/1 [==============================] - 0s 208ms/step - loss: 0.4724 - precision: 0.8498 - recall: 0.8210 - auc: 0.8992 - val_loss: 0.9024 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8778\n",
            "Epoch 53/100\n",
            "1/1 [==============================] - 0s 210ms/step - loss: 0.4320 - precision: 0.8766 - recall: 0.8333 - auc: 0.9155 - val_loss: 0.9038 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8786\n",
            "Epoch 54/100\n",
            "1/1 [==============================] - 0s 214ms/step - loss: 0.4228 - precision: 0.8812 - recall: 0.8241 - auc: 0.9161 - val_loss: 0.9052 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8793\n",
            "Epoch 55/100\n",
            "1/1 [==============================] - 0s 227ms/step - loss: 0.4159 - precision: 0.8795 - recall: 0.8333 - auc: 0.9170 - val_loss: 0.9067 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8800\n",
            "Epoch 56/100\n",
            "1/1 [==============================] - 0s 224ms/step - loss: 0.3706 - precision: 0.8703 - recall: 0.8488 - auc: 0.9245 - val_loss: 0.9082 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8808\n",
            "Epoch 57/100\n",
            "1/1 [==============================] - 0s 214ms/step - loss: 0.4307 - precision: 0.8694 - recall: 0.8426 - auc: 0.9123 - val_loss: 0.9097 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8811\n",
            "Epoch 58/100\n",
            "1/1 [==============================] - 0s 210ms/step - loss: 0.3827 - precision: 0.8762 - recall: 0.8519 - auc: 0.9240 - val_loss: 0.9112 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8819\n",
            "Epoch 59/100\n",
            "1/1 [==============================] - 0s 218ms/step - loss: 0.3736 - precision: 0.8770 - recall: 0.8364 - auc: 0.9276 - val_loss: 0.9127 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8823\n",
            "Epoch 60/100\n",
            "1/1 [==============================] - 0s 217ms/step - loss: 0.4235 - precision: 0.8808 - recall: 0.8210 - auc: 0.9126 - val_loss: 0.9143 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8830\n",
            "Epoch 61/100\n",
            "1/1 [==============================] - 0s 216ms/step - loss: 0.3638 - precision: 0.8777 - recall: 0.8642 - auc: 0.9253 - val_loss: 0.9159 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8832\n",
            "Epoch 62/100\n",
            "1/1 [==============================] - 0s 214ms/step - loss: 0.3800 - precision: 0.8842 - recall: 0.8488 - auc: 0.9252 - val_loss: 0.9176 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8843\n",
            "Epoch 63/100\n",
            "1/1 [==============================] - 0s 236ms/step - loss: 0.3979 - precision: 0.8782 - recall: 0.8457 - auc: 0.9186 - val_loss: 0.9193 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8851\n",
            "Epoch 64/100\n",
            "1/1 [==============================] - 0s 257ms/step - loss: 0.3494 - precision: 0.8900 - recall: 0.8488 - auc: 0.9343 - val_loss: 0.9211 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8856\n",
            "Epoch 65/100\n",
            "1/1 [==============================] - 0s 253ms/step - loss: 0.3859 - precision: 0.8951 - recall: 0.8426 - auc: 0.9240 - val_loss: 0.9228 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8861\n",
            "Epoch 66/100\n",
            "1/1 [==============================] - 0s 222ms/step - loss: 0.3592 - precision: 0.8822 - recall: 0.8549 - auc: 0.9299 - val_loss: 0.9247 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8868\n",
            "Epoch 67/100\n",
            "1/1 [==============================] - 0s 200ms/step - loss: 0.3868 - precision: 0.8808 - recall: 0.8210 - auc: 0.9227 - val_loss: 0.9266 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8875\n",
            "Epoch 68/100\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.3286 - precision: 0.8997 - recall: 0.8858 - auc: 0.9362 - val_loss: 0.9285 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8881\n",
            "Epoch 69/100\n",
            "1/1 [==============================] - 0s 216ms/step - loss: 0.3708 - precision: 0.8958 - recall: 0.8488 - auc: 0.9264 - val_loss: 0.9304 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8888\n",
            "Epoch 70/100\n",
            "1/1 [==============================] - 0s 214ms/step - loss: 0.3394 - precision: 0.8816 - recall: 0.8735 - auc: 0.9322 - val_loss: 0.9324 - val_precision: 0.0022 - val_recall: 0.9231 - val_auc: 0.8894\n",
            "Epoch 71/100\n",
            "1/1 [==============================] - 0s 205ms/step - loss: 0.3405 - precision: 0.9019 - recall: 0.8796 - auc: 0.9362 - val_loss: 0.9343 - val_precision: 0.0022 - val_recall: 0.9359 - val_auc: 0.8897\n",
            "Epoch 72/100\n",
            "1/1 [==============================] - 0s 202ms/step - loss: 0.3618 - precision: 0.8902 - recall: 0.9012 - auc: 0.9259 - val_loss: 0.9362 - val_precision: 0.0022 - val_recall: 0.9359 - val_auc: 0.8902\n",
            "Epoch 73/100\n",
            "1/1 [==============================] - 0s 213ms/step - loss: 0.3468 - precision: 0.8862 - recall: 0.8889 - auc: 0.9298 - val_loss: 0.9380 - val_precision: 0.0022 - val_recall: 0.9359 - val_auc: 0.8902\n",
            "Epoch 74/100\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.3393 - precision: 0.8825 - recall: 0.8580 - auc: 0.9313 - val_loss: 0.9398 - val_precision: 0.0022 - val_recall: 0.9359 - val_auc: 0.8905\n",
            "Epoch 75/100\n",
            "1/1 [==============================] - 0s 207ms/step - loss: 0.3055 - precision: 0.9051 - recall: 0.8827 - auc: 0.9422 - val_loss: 0.9416 - val_precision: 0.0022 - val_recall: 0.9359 - val_auc: 0.8909\n",
            "Epoch 76/100\n",
            "1/1 [==============================] - 0s 196ms/step - loss: 0.3619 - precision: 0.8875 - recall: 0.8519 - auc: 0.9255 - val_loss: 0.9434 - val_precision: 0.0022 - val_recall: 0.9359 - val_auc: 0.8910\n",
            "Epoch 77/100\n",
            "1/1 [==============================] - 0s 207ms/step - loss: 0.3054 - precision: 0.8852 - recall: 0.9043 - auc: 0.9392 - val_loss: 0.9453 - val_precision: 0.0022 - val_recall: 0.9359 - val_auc: 0.8911\n",
            "Epoch 78/100\n",
            "1/1 [==============================] - 0s 223ms/step - loss: 0.3360 - precision: 0.9082 - recall: 0.8858 - auc: 0.9346 - val_loss: 0.9471 - val_precision: 0.0022 - val_recall: 0.9359 - val_auc: 0.8913\n",
            "Epoch 79/100\n",
            "1/1 [==============================] - 0s 204ms/step - loss: 0.3115 - precision: 0.8827 - recall: 0.8827 - auc: 0.9371 - val_loss: 0.9490 - val_precision: 0.0022 - val_recall: 0.9359 - val_auc: 0.8915\n",
            "Epoch 80/100\n",
            "1/1 [==============================] - 0s 201ms/step - loss: 0.2721 - precision: 0.9021 - recall: 0.9105 - auc: 0.9477 - val_loss: 0.9509 - val_precision: 0.0022 - val_recall: 0.9359 - val_auc: 0.8916\n",
            "Epoch 81/100\n",
            "1/1 [==============================] - 0s 204ms/step - loss: 0.2908 - precision: 0.8963 - recall: 0.9074 - auc: 0.9409 - val_loss: 0.9528 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8916\n",
            "Epoch 82/100\n",
            "1/1 [==============================] - 0s 209ms/step - loss: 0.3141 - precision: 0.8859 - recall: 0.9105 - auc: 0.9343 - val_loss: 0.9547 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8917\n",
            "Epoch 83/100\n",
            "1/1 [==============================] - 0s 365ms/step - loss: 0.2946 - precision: 0.9037 - recall: 0.8981 - auc: 0.9442 - val_loss: 0.9566 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8917\n",
            "Epoch 84/100\n",
            "1/1 [==============================] - 0s 395ms/step - loss: 0.3178 - precision: 0.8951 - recall: 0.8951 - auc: 0.9357 - val_loss: 0.9584 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8918\n",
            "Epoch 85/100\n",
            "1/1 [==============================] - 0s 360ms/step - loss: 0.3035 - precision: 0.9148 - recall: 0.8951 - auc: 0.9409 - val_loss: 0.9604 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8919\n",
            "Epoch 86/100\n",
            "1/1 [==============================] - 0s 332ms/step - loss: 0.3055 - precision: 0.8949 - recall: 0.9198 - auc: 0.9371 - val_loss: 0.9623 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8920\n",
            "Epoch 87/100\n",
            "1/1 [==============================] - 0s 328ms/step - loss: 0.3148 - precision: 0.8810 - recall: 0.9136 - auc: 0.9358 - val_loss: 0.9643 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8923\n",
            "Epoch 88/100\n",
            "1/1 [==============================] - 0s 358ms/step - loss: 0.2560 - precision: 0.9185 - recall: 0.9043 - auc: 0.9528 - val_loss: 0.9662 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8927\n",
            "Epoch 89/100\n",
            "1/1 [==============================] - 0s 334ms/step - loss: 0.2745 - precision: 0.8943 - recall: 0.9136 - auc: 0.9434 - val_loss: 0.9681 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8927\n",
            "Epoch 90/100\n",
            "1/1 [==============================] - 0s 348ms/step - loss: 0.2925 - precision: 0.9102 - recall: 0.9074 - auc: 0.9420 - val_loss: 0.9701 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8930\n",
            "Epoch 91/100\n",
            "1/1 [==============================] - 0s 353ms/step - loss: 0.3003 - precision: 0.8869 - recall: 0.8951 - auc: 0.9394 - val_loss: 0.9721 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8927\n",
            "Epoch 92/100\n",
            "1/1 [==============================] - 0s 337ms/step - loss: 0.2853 - precision: 0.8902 - recall: 0.9012 - auc: 0.9429 - val_loss: 0.9742 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8925\n",
            "Epoch 93/100\n",
            "1/1 [==============================] - 0s 343ms/step - loss: 0.2726 - precision: 0.9125 - recall: 0.9012 - auc: 0.9456 - val_loss: 0.9764 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8927\n",
            "Epoch 94/100\n",
            "1/1 [==============================] - 0s 354ms/step - loss: 0.2856 - precision: 0.8735 - recall: 0.8951 - auc: 0.9414 - val_loss: 0.9785 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8922\n",
            "Epoch 95/100\n",
            "1/1 [==============================] - ETA: 0s - loss: 0.3103 - precision: 0.8882 - recall: 0.9321 - auc: 0.9369Restoring model weights from the end of the best epoch.\n",
            "1/1 [==============================] - 1s 554ms/step - loss: 0.3103 - precision: 0.8882 - recall: 0.9321 - auc: 0.9369 - val_loss: 0.9806 - val_precision: 0.0021 - val_recall: 0.9359 - val_auc: 0.8925\n",
            "Epoch 00095: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PkZbBR0UhKf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_metrics(history):\n",
        "  metrics =  ['loss', 'auc', 'precision', 'recall']\n",
        "  f = plt.figure(figsize=(12, 6))\n",
        "  for n, metric in enumerate(metrics):\n",
        "    name = metric.replace(\"_\",\" \").capitalize()\n",
        "    ax = f.add_subplot(2, 2, n+1)\n",
        "    ax.plot(history.epoch, history.history[metric], label='Train')\n",
        "    ax.plot(history.epoch, history.history['val_'+metric],\n",
        "             linestyle=\"--\", label='Val')\n",
        "    ax.set_xlabel('Epoch')\n",
        "    ax.set_ylabel(name)\n",
        "    plt.suptitle('Training history', y=1.05, fontsize=17)\n",
        "    plt.legend()\n",
        "    plt.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SXrwh7u-UhCu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "632c0b1b-71fb-45ee-c810-72fe905d781a"
      },
      "source": [
        "plot_metrics(nm_history)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FG7fNFveNJfc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_roc(model):\n",
        "  train_predictions = model.predict(x_train, batch_size=BATCH_SIZE)\n",
        "  test_predictions = model.predict(x_test, batch_size=BATCH_SIZE)\n",
        "  plt.figure(figsize=(8, 5))\n",
        "  fp, tp, _ = sklearn.metrics.roc_curve(y_train, train_predictions)\n",
        "  plt.plot(fp, tp, label='Train Baseline', linewidth=2)\n",
        "  fp, tp, _ = sklearn.metrics.roc_curve(y_test, test_predictions)\n",
        "  print('AUC for test set: ', sklearn.metrics.auc(fp, tp), '\\n')\n",
        "  plt.plot(fp, tp, label='Test Baseline', linewidth=2, linestyle='--')\n",
        "  plt.legend(loc='lower right')\n",
        "  plt.title('ROC curve', fontsize=17)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zM1t2wnZ9Aww",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "1c41762f-96e4-48bf-fa60-7484a4216653"
      },
      "source": [
        "plot_roc(nm_model)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC for test set:  0.8746539988434066 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CqMhhH9CB7-D",
        "colab_type": "text"
      },
      "source": [
        "## Oversampling: SMOTE\n",
        "\n",
        "SMOTE aims to balance class distribution by randomly increasing minority class examples by replicating them. SMOTE synthesizes new minority instances between existing minority instances. It generates the **virtual** training records by linear interpolation using the k-nearest neighbors for each example in the minority class.\n",
        "\n",
        "The basic intuition of SMOTE Algorithm is:<br><br>\n",
        "\n",
        "1. Set the sampling rate $N = \\frac{\\text{number of instances in majority class}}{\\text{number of instances in minority class}}$. <br><br>\n",
        "2. For every data point $x$ in the minority class, $N$ random samples in its k-nearest neighbors (in the minority set) are selected.\n",
        "Let's denote the set of these $N$ samples $K$.<br>\n",
        "3. For each sample $x_k \\in K$, a new sample is synthesized using the formula: <br><br>\n",
        "$$x' = x + rand(0, 1)*|x - x_k|$$\n",
        "\n",
        "This way, $N$ virtual data is created for every data point in the minority class, which lead to a balanced dataset.\n",
        "\n",
        "Similarly, an implementation of the SMOTE method is available in the imblearn pakage, check \n",
        "<a href='https://imbalanced-learn.readthedocs.io/en/stable/generated/imblearn.over_sampling.SMOTE.html#imblearn.over_sampling.SMOTE'>imblearn.over_sampling.SMOTE</a> for more details."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0HeJRrC0CIRh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "852ae0fd-9841-4e2a-fd74-4cf95fe7afd6"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE \n",
        "sm = SMOTE(random_state=42)\n",
        "x_sm, y_sm = sm.fit_resample(x_train, y_train)\n",
        "\n",
        "# Check the new class distribution\n",
        "print('Before Under-Sampling: ', Counter(y_train))\n",
        "print('After Under-Sampling: ', Counter(y_sm))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Before Under-Sampling:  Counter({0: 181952, 1: 324})\n",
            "After Under-Sampling:  Counter({0: 181952, 1: 181952})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nbQ_RECFCIT3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        },
        "outputId": "b6fc5c7a-8115-4855-e103-0e8a95313b1d"
      },
      "source": [
        "sm_model = make_model()\n",
        "sm_history = sm_model.fit(\n",
        "    x_sm, y_sm,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    callbacks = [early_stopping],\n",
        "    validation_data=(x_val, y_val))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "178/178 [==============================] - 2s 13ms/step - loss: 0.7872 - precision: 0.5005 - recall: 0.9022 - auc: 0.8119 - val_loss: 0.6169 - val_precision: 0.0044 - val_recall: 0.9231 - val_auc: 0.9202\n",
            "Epoch 2/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.3795 - precision: 0.7320 - recall: 0.9409 - auc: 0.9432 - val_loss: 0.2728 - val_precision: 0.0218 - val_recall: 0.8974 - val_auc: 0.9456\n",
            "Epoch 3/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.2774 - precision: 0.8101 - recall: 0.9430 - auc: 0.9645 - val_loss: 0.1651 - val_precision: 0.0403 - val_recall: 0.8974 - val_auc: 0.9684\n",
            "Epoch 4/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.2287 - precision: 0.8444 - recall: 0.9489 - auc: 0.9742 - val_loss: 0.1197 - val_precision: 0.0486 - val_recall: 0.8974 - val_auc: 0.9788\n",
            "Epoch 5/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.1979 - precision: 0.8671 - recall: 0.9520 - auc: 0.9796 - val_loss: 0.0976 - val_precision: 0.0526 - val_recall: 0.9103 - val_auc: 0.9827\n",
            "Epoch 6/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.1796 - precision: 0.8781 - recall: 0.9547 - auc: 0.9826 - val_loss: 0.0861 - val_precision: 0.0541 - val_recall: 0.9103 - val_auc: 0.9854\n",
            "Epoch 7/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.1638 - precision: 0.8887 - recall: 0.9587 - auc: 0.9852 - val_loss: 0.0804 - val_precision: 0.0557 - val_recall: 0.9231 - val_auc: 0.9868\n",
            "Epoch 8/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.1530 - precision: 0.8944 - recall: 0.9627 - auc: 0.9870 - val_loss: 0.0759 - val_precision: 0.0561 - val_recall: 0.9231 - val_auc: 0.9877\n",
            "Epoch 9/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.1443 - precision: 0.8999 - recall: 0.9652 - auc: 0.9883 - val_loss: 0.0723 - val_precision: 0.0584 - val_recall: 0.9359 - val_auc: 0.9877\n",
            "Epoch 10/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.1371 - precision: 0.9037 - recall: 0.9675 - auc: 0.9894 - val_loss: 0.0685 - val_precision: 0.0601 - val_recall: 0.9359 - val_auc: 0.9881\n",
            "Epoch 11/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.1302 - precision: 0.9086 - recall: 0.9692 - auc: 0.9903 - val_loss: 0.0662 - val_precision: 0.0603 - val_recall: 0.9231 - val_auc: 0.9886\n",
            "Epoch 12/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.1252 - precision: 0.9104 - recall: 0.9705 - auc: 0.9911 - val_loss: 0.0625 - val_precision: 0.0632 - val_recall: 0.9231 - val_auc: 0.9887\n",
            "Epoch 13/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.1192 - precision: 0.9135 - recall: 0.9715 - auc: 0.9919 - val_loss: 0.0598 - val_precision: 0.0662 - val_recall: 0.9231 - val_auc: 0.9887\n",
            "Epoch 14/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.1142 - precision: 0.9165 - recall: 0.9723 - auc: 0.9926 - val_loss: 0.0573 - val_precision: 0.0677 - val_recall: 0.9103 - val_auc: 0.9887\n",
            "Epoch 15/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.1105 - precision: 0.9178 - recall: 0.9730 - auc: 0.9931 - val_loss: 0.0549 - val_precision: 0.0702 - val_recall: 0.9103 - val_auc: 0.9878\n",
            "Epoch 16/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.1067 - precision: 0.9192 - recall: 0.9750 - auc: 0.9936 - val_loss: 0.0525 - val_precision: 0.0724 - val_recall: 0.9103 - val_auc: 0.9882\n",
            "Epoch 17/100\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.1035 - precision: 0.9209 - recall: 0.9759 - auc: 0.9939 - val_loss: 0.0498 - val_precision: 0.0758 - val_recall: 0.9103 - val_auc: 0.9833\n",
            "Epoch 18/100\n",
            "177/178 [============================>.] - ETA: 0s - loss: 0.1001 - precision: 0.9219 - recall: 0.9770 - auc: 0.9943Restoring model weights from the end of the best epoch.\n",
            "178/178 [==============================] - 2s 10ms/step - loss: 0.1002 - precision: 0.9219 - recall: 0.9770 - auc: 0.9943 - val_loss: 0.0478 - val_precision: 0.0796 - val_recall: 0.9103 - val_auc: 0.9828\n",
            "Epoch 00018: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NmOq2YpACIkW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "572fc441-8d9f-483f-c351-b88f90f34397"
      },
      "source": [
        "plot_metrics(sm_history)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sgodkr5vMlITdy533PQlRS4KUnOAh2eshqeVzQpt2r2evdRM97sMeSXGKMw07rG0lNsVdgpWc4GFojyQWrtG8ZBGRFjfddBOJiYmUlJQwbtw4zjzzTO644w4aGxtJTk7m17/+NQMHDmTBggXMmjWLRx55hPvvv59NmzZRVlbGpk2buPzyy7nsssui3RWR+BEMgr8JmmohMQ28SdC4C6rb1hQDsgohMQ1f3Q7qt2+ipmIVq4L11Db5qW1sZps3nx3NXppqqgju2kxto4/aJj+NTU00NftYEyxgR5OblMat5DaV4/c109zcjBs/Hvx8FBhJI4kMdpUxzLUBD348BPC4Anjx86L/ZHwuLycnlDLBu4pUt48Uj59kd4Bkt48X8q4lJdHLybVvMLL2YxJcPhLxkYAPl8fLB8c+Smqih9HL7iJ/079xB5oINjfi9bgJpOZRc+V/nWTnte/gXvUueyYGBSGjCL4zz/n6zLdg3Yeh9tA6+aPgyrecz7OmQ/mn7DWxqP9kuMy5KMXDJ8D2L/duHzwFZjzjfL5nNNSU7/2zH3kunPeo8/muQdBY43wOzU8tGPBVGP6Ys+yOPnviIpSwTfo2nH4bNDfAH48GbzIkpOx5P/oiGHsJNFTDmzdDQvLe6ww8FQrHO/83Vr7jLPMmO8cP+KDncMjqC3WVsOZ9CPhDL5/zKj4JcothxwZY8eqe5cGA8z76AsgbjLupGiqWQc9h4Im7FGMvcdn7kfnJvFKyk4ZmP8kJnmiHIyJx7qXFZTy/aMNeywKBAG734Vc7umBCP84bX3hI22zevJnnnnsOj8fDrl27ePrpp/F6vXz44Yfcc8893H///ftss2bNGp544gl27drF9OnTmTFjBgkJCYcdt0i3FAw6f7B6vM4fyRVLobHaeTXVQnM99D8GCkbDzo3wwd3QVAfNdU5bcx2c8EMYPIXghoUEn7sImmpx+epxBZ1RnjlH38vS9BPouXkeF315/T4hXOP9OXOaRnKq7788mPhHJrVpP7fxdj4NDuYCz1zuSvjLPtv/JP/PpOcM4qu7FvONrQ86f/u3upv4n195B7IKGfHlAgYt2/d3xS9u/BlJGbm4/r0A/vs0BICAFzxJ4Elg+mWzwJMA770Nyyucz54k8CRCYhoXTAgVpm4YCcn14EmgqnoXObk98CSmk5sWCqboeEjNDR01lKCk5e0JZPAUyB24p93lgozee9pHnO0kI623zxmwp33MBVC3be/2vCF72ifMdBKZ1vJH7vk88UrwNbIniYLaYD45rdsJ7p3A9Qs9RjYYcOL3NTj/L3yNzucWzXWwag746p3/Z756Z3lCitOn6o3wwkz28bX7YPxMqFrTfvt5f3USrMrV8PbP9m3vMw7yBpNePh9e/jkkpELBGOg7zmkbOhWSM/fdrhuLzwSrVzIvLNvJpxt2cMzAHtEOR0QkJkybNg2Px7noVFNTw4033si6detwuVw0Nze3u83JJ59MYmIiubm55Obmsn37dgoKCiIZtkhkBPzg9jh/5No3nFGIhp3Oe2O180ewme6MIjzzLWdZQyiJaqyBU2+Gk34MtVvgr1P22f26CbdQUtSTwNYvOfXTF2lyJdHoSqY+mEhtMImnXvqct5shs34DV7pGU08i9SRRH0yinkTeXRBkffAL+iemsjzh+t23rqUkekhJ8JCfNY4ZGfn0IZU59b1pqK2mT6+83be/3V18Cuk5vcisNwQ3T3aqfwaD4HaD28tdA091/kje0Qu2TwGXB9xe52fi9vK1gtHOCNqga+Hki0Pte9ZJTs9xkplTf+a83AnOvts65SbntT+Tvu28gIqSEnLaPndnwv8c+N8xtO1+HfO9A7cff92B20+84cDtp/50n0U1JSV7vpzxy/1vm5gK5zyw//aMArih1b6CQScJc4V+zjlF8P35oeQslJi5PE7yBNBrhNPe5t+OlFDCOuA4uGnDXv/uuNy7R+Lqeo2Db/wFNi6BTUtg0WPgewh+tNz5v1PyOmxcvCfxyuyze9vuJi4TrBG9knG5YOGaSiVYIhJ1540v3Ge0KRoPGm59vPvuu4/Jkyfz4IMPUlZWtt9b/1oXxPB4PPh8vrDHKdLpmuqckaT0ns73D+6BqrXOaFL1JqgugxHnwtl/BFzw3EVOotXCnQDHBp0Ey5sEbg++zELqs9PYRSo7AymUbO/PJ68uY+uOavIzb6Os1suGei+7AsnUk0TNB6k0f9Dy8O2HSPS4yU5NICc1cff7lNQEslML2ZV6EjmpifRPTSA7NZGc1AS+H1ovwdORke+T9v9Q9szBkD94/5tm93de+5Pec8/PsT3epA7EJ53C5XJuF2zhTYJeB3gQbELKgds9Cc5rP3wpeTD8RGeUD8Dvg62lkNnX+b5xMXz4R+e2QoD0fCicCBc86STbvibwdo8K33GZYGUkeTD5GXy8VvOwRETaU1NTQ36+Uwjo5ZdfjnI0Ikegqc5Jknz1zi14AHPugPLPnFumdpZBww4YfDpc8qLTvmiWc5U/s49ze9iA45xbz3Bu7Np5ydtsbfRS0ZjAxvpENtYEqNjZSMWsj6nYWU/Fzmupbtj3YkNWyiYKMpOpyz6Ogv7JjMhKpmdGEtkprRKpNCdhSknwxNwz5EQOiccLBaP2fJ/yCzj5J848rU1LnJGupl17RjKf+SZUrnFGuHofBUkZkNUfhp7htNs3wd8YGv30OvtPz99zC2bFUufd7Q2t44HkrFa3jEZOXCZYABOKcnh5yUZ8/gDeDl3tERGJH1dddRU33XQTDz/8MCeffHK0wxE5uJZCDwAfPQRfvg1bLdRscpb1HA4/mO983rzMKUSQ1c+Z+5TZxyl0gFMZb+sVH1G2s5kNlXWUVdU7r/l1lL3xHmWVdTS3KeXtdkHPjCQKMpMpzkvj2IE9KMhKoSAriYLMFAqykinITCYlUfO+Jc4lpEC/ic6rrWFnwdr/QNliWB66sDfotD0J1r9ucEaTWxtxDlzwhPP58bOciyWtHX0xnPtQ5/ahA+I2wZpYlMtT89dTWlHDqL5Z0Q5HRCQqrr322naXjx07lrfeemv39x/96EcATJ48mcmTJ7e77euvvx6mKEXa2FIKG+Y771tDr+Y6uHGdc1vU9i+hvsqpfpY32Emksp1CBcFgkMqvPb47cdpQVUfZ9jo2fFlP2T/fo6yqnkbf3uXB89IT6ZuTyog+mYzL9zKyuC+9s5LJz0qmd1YyPdOTdLFW5Ei1ml9Hw06nUIe7Vapy2avO3LFAszMn0t8MKTl72s971Gn3h9oDzZATnedyxW2CNanYGS78eE2lEiwREZFYU1fpjEBtLXHet5TAt55yJst//nen0l5CKuQNhYGnQE/jzO3wJMBZ97Cr0cen63dQWlHNhrV1oWTqfcqq6qlr8u91qOzUBPrlpDI0P4PThudTmJNCYU4K/XJS6ZuTQmrinj+XnLlLepiqSFglZzmv1vIOMDcQYMjp4YvnEMVtgtU7y/nluWhdJf9zgn5RioiIRFVT3Z5J9EuegNdajZAmpDkJVH2lk2BN+jaMv9yZn+F2EwwGKauqZ/HnW1i8ropF66qwFdW03MmXkeylX04qRT3SOHFIz1AClUq/3BT6ZqeQkaxHC4hI54nbBAuc2wT/8+U2gsGgJpKKiIhEw86NsPAvsPhxmH6XU4Gs32Q4/XZn3lSvYZBZuFdJ78bUfJZvqmbx0rUsXlfF4vVVbK1pBCA9ycvY/tmc8ZUhjB+Qw5jCLLJTu0dlMhHpGuI+wXr5k42s3V5HcV5atMMRERGJHxsWwoKHYfkrQNCZ4N7ywNaexnmFbK1pZMn6Kpasq2Lxuio+37iTptA8qf65qZwwOI9xA3KYMCCHofkZeNy6aCoi0RPXCdakYmdi3MK1lUqwREREwi0YdIpQBIPwz+uc0atjvgeTrnbKoQP+QJAvt9Q4I1Oh17rtdQAketyM6pvJ5ccOYPyAXMYNyKZXRvKBjigiEnFxnWAN6plOTmoCC9dUcsGEftEOR0REpHuqq6RHyRMwZw5c9W/n+Tbnz4KsQucz8NmGHTy9YB1vLKugJvQMqbz0RMYPyOHiyf0ZPyCHkX2ySE5QqXMRiW1xnWC5XC4mFOWyUA8cFpE4c+mll3L11Vdz4okn7l72+OOPs2bNGm677bZ21//JT37C6NGjIxmmdHVbSp3bAD/7O7189U61v9ptTlLVazh1TT5e+3g9Ty9Yz9KNO0lJ8HDmmN4cN6gH4wfk0D83VXOkRaTLiesEC2BSUS7vrNjMlpoG3WYgInHjrLPOYvbs2XslWLNnz+bHP/5xFKOSbmX7KnhoMniTYcwFrO45lYHHngXAF5treHr+Ov6xZCM1jT6G5qdz+zkjOXdsXzJV0U9Euri4T7AmFDnzsBatreKro3tHORoRkciYOnUq9957L01NTSQmJlJWVsaWLVt4/fXX+c1vfkNjYyNTp07luuuui3ao0lU01cJnz8KuLXDqT6HHIDjnQRg6HdJ6ULNsBa9+upGn56/n47WVJHrcfHV0ARcfM4AJA3I0UiUi3UbcJ1ij+maRkuDh4zWVSrBEJHoeO3Ovr4kBP4w+z3neT1MdPP3Nfbc5+iIYezHUbofnL9u77Yp/HfBw2dnZjBkzhnnz5jFlyhRmz57N9OnT+c53vkN2djZ+v5+ZM2dSWlrKsGHDjrR30p3t2AAf/xmW/A0adkL/Y+HkG8HtgbGXsH57Hc/MK+XZBevY2RCgf24qN08fxvnjC+mRnhTt6EVEOl3cJ1gJHjdj+2drHpaIxJ0zzzyT2bNnM2XKFP71r39xxx138MYbb/D888/j8/nYunUrq1atUoIl+7fsH/DSVc7nEWfDMd+Hwon4AkHmLK/g6QXrmfflVlzA5MJUvnf6KE4YnIdbZdRFpBuL+wQLnOdh3T/nS2oamvU0dxGJjjYjTk319aSkpDhfElMPPCKV1uOgI1btOe200/jNb37D8uXLaWhoICsri1mzZvHiiy+SlZXFTTfdRGNj4yHvV+JI/2OcUdSTfgLZ/dhc3cBz767kuYXrKd/ZQH5mEtd9ZQgXTurHjk1rGT60Z7QjFhEJOyVYOAlWIAhL1u/gZP3yF5E4kZaWxuTJk/npT3/KmWeeSW1tLSkpKWRkZLBt2zbmzZvHpEmToh2mxKKS18FMh8w+BM76Ix+u2s5T/1zMOyWb8QeCnDgkj198bSRThvfC63EDsGNTlGMWEYkQJVjA2P7ZeNwuFq6pVIIlInHlrLPO4gc/+AF33303gwYNYsSIEUyfPp2CggLGjRsX7fAk1gSDMOdX8J/fw9fu45Oe53DD85+xelstOakJXHVCMTMm9acoLy3akYqIRI0SLCAtycuoPpmahyUicWfKlClYa3d/v/POO9td78knn4xUSBKrgkF466cw/yEYdxn/TjqDa/4yn54ZSdz7raOZNqpADwEWEUEJ1m4TinJ5av46Gn1+krw6QYiIiOwWCMC/fgSLH4fJ3+PZ3O/xs6c/YVTfLGbNnEieqgGKiOzmjnYAsWJiUS6NvgDLNu6MdigiIiKxZfuX8PnzBE+4gXs8V3Dzy8s4aWhPnv32MUquRETaUIIVMjH0wOGFa6uiHImIxItgMBjtECIiXvrZLQUCzntPg++7H3HTjnO5b85Kvjm+kL9cNoG0JN0IIyLSlhKskB7pSQzsmcbCNZqHJSLhl5yczPbt27t98hEMBtm+fTvJycnRDkUOVXM9PHMBLHyUuiYfV/9zK39ftIFrvzKYu84fQ4JHf0KIiLRHl55amVSUyxvLKggEgnoIooiEVWFhIWVlZWzdurXd9ubmZhISuv5z+Zqbm8nIyKCwsDDaocihaKyBZ2fA2g/YNXAaF/95Pks37uRX547ikmMGRDs6EZGYpgSrlYlFuTy3cANfbKlhWEFmtMMRkW4sISGB4uLi/baXlJQwfPjwCEYUHiUlJQfsp8Sg+ip4+puwcQnbzrif8/9bSPnOGv50yXjOGFkQ7ehERGKexvdbmVScC2geloiIxClfI/ztbNj0KWtPe4hpcwrYUd/MM98+RsmViEgHaQSrlcKcFPIzk1i4ppJLdQuEiEi3ZIyZBtwHeIBHrbV3tmkfAMwCegKVwCXW2rJQmx9YGlsOKw4AACAASURBVFp1vbX27IgFHgneJDhqBp839OLCt9PJSfXw9ysnMahnerQjExHpMuJzBMvf3O5il8vFxKJcFq6t7PYTz0VE4pExxgM8CEwHRgAzjDEj2qz2e+AJa+0Y4HbgN63a6q21R4de3Se52rEeNnwMwIuJZ/ONd1Ip6pHGy98/TsmViMghCusI1sGuEobWuQC4FQgCn1lrLwpnTLz5UwYufx1Gfd5u86TiXF7/vJyNO+opzEkNaygiIhJxk4CV1trVAMaY54BzgBWt1hkBXB/6PBd4JaIRRtr2VfC3swm6XPxpzAv89p3VnDA4j4cvGUdGctcvtCIiEmlhG8HqyFVCY8wQ4GbgeGvtSOCH4Ypnt5wBJNWsg8o17TZPGNAyD0vl2kVEuqG+wIZW38tCy1r7DPhG6PPXgQxjTI/Q92RjzCJjzHxjzLnhDTUCNq+AWdMI+up5KP92fvvOas49ug+zZk5UciUicpjCOYLVkauE3wYetNZWAVhrt4QxHsfgKc77qnch96p9mk1BBhnJXj5eU8XXx6qssIhIHPo/4AFjzExgHrAR8IfaBlhrNxpjBgJzjDFLrbWrDrSzxsZGSkpKjiighoaGI95HW8mVpfR7/38JuhO4KeVWXvo8ifNHZXHFmCRWfWk79VgQnj5Eg/oRO7pDH0D9iCWd1YdwJljtXSWc3GadoQDGmP/i3EZ4q7X2zTDGBLkDaUrrS+LKd2HivgmWx+1iwoAcFmkES0SkO9oI9Gv1vTC0bDdr7SZCI1jGmHTgPGvtjlDbxtD7amPMe8BY4IAJVlJS0hGX3A9L2f7XHsKfnMl1CbfyxqYUbv3aCGYeH76S+t3p0QPqR2zoDn0A9SOWHKgPixcv7vB+ol1F0AsMAU7BOcnNM8aMbjmRtaczrgTm9ZxIj1VvYZd9Dp59b4EYkOZjrt3Fgk+WkZnsOaJjhZOuFMQO9SN2dIc+gPoRRguBIcaYYpzE6kJgr7m/xpg8oNJaG8C5jX1WaHkOUGetbQytczxwVySD7xSBALjdlB33K35k3+WzyhQevOhovjq6d7QjExHpFsKZYB30KiHOqNYCa20zsMYY8wVOwrVwfzvtjCuBa7afRc/hxzN86GBI3LeQxZkplTy+5CN2JuYxeXjsPveju18p6ErUj9jRHfoA8dGPQ7ka2FmstT5jzDXAWzh3Tsyy1i43xtwOLLLWvoZz0e83xpggzi2CPwhtPhx4xBgTwJnDfKe1dsU+B4lla+bBWz+j9PTHuey5NTQ0p/Hk/0xg8sAeB91UREQ6JpwJ1kGvEuJUZpoBPBa6GjgUWB3GmABo6DEKDvCHy5jCLBK9bhaurdSDFUVEuhlr7WxgdptlP2/1+UXgxXa2+xAYHfYAw6VsETw7g9qU3lz1xBI8ybm8+L3jGJqfEe3IRES6lbBVEbTW+oCWq4QlwPMtVwmNMS3PDnkL2G6MWYFTCvfH1trt4YppL7u2Qsk/221K8no4ujCbhWurIhKKiIhIWG1eAU+dhz+lB2dsu5607Hz+8X0lVyIi4RDWOVgduEoYxHnWyPVE2qdPw79/AdeXQua+951PKMrhz/NWU9fkIzUx2lPVREREDlPlanjyXEhI4cPjZ7HxH5t56Ruj6Z2VEu3IRES6pbCNYMW81uXa2zGxOBdfIMin6/dbb0NERCT2JaRC3lC49BU+qcnC5YJhBRq5EhEJl/hNsPJHQnoBrPx3u83jB+TgcqHbBEVEpGuqrwK/DzIKYObr0GsYpRXVDMhNJS1Jd2aIiIRL/CZYLpczirVqrnMCaiMzOYFhBZks1POwRESkq2nYCU+cA698d6/FJeU1DCvIjFJQIiLxIX4TLIDBX4GGHbB5abvNk4pyWLK+Cp8/EOHAREREDlNTHTxzIWxeDqMv2L24rsnH2u21DO+tBEtEJJziO8EaMhV+uBT6jG23eWJxLnVNflaUV0c4MBERkcPga4LnL4P1H8E3/gxDz9jdZCtqCAZhWG/NvxIRCaf4TrCS0iG7/36bJxblAvDxGt0mKCIiXcDrP4KV78DX7oVR5+3VVFpRA8AIjWCJiIRVfCdYAJs+hb9fCrX7Pn4rPzOZ/rmpmoclIiJdw4QrYPpdMH7mPk0l5dWkJ3npm63y7CIi4aQEK+CDktdg9dx2mycW5bJobRXBYDDCgYmIiHRAMAjrPnI+F06Ayd9pd7XS8hpMQQZutyuCwYmIxB8lWH3GQkoOrGz/eViTinPYXtvE6m21EQ5MRESkA+b9Hh6btt/HjgAEg0FKKqoZrvlXIiJhpwTL7YFBX3FOTIF9qwVOCM3DWqh5WCIiEmsWPAJzfwVHzYCBX9nvaht31FPT4FOJdhGRCFCCBc7zsGq3wOZl+zQNzEsjLz2RjzUPS0REYsmnz8IbP4FhZ8HZD4B7/6f0knKnwIVKtIuIhJ8SLHBGsPJHOw9mbMPlcjFhgDMPS0REJCZUroHXroGBp8D5s8DjPeDqpaHHjZgC3SIoIhJuB/6NHC8yCuB7H+y3eUJRDm8ur2BzdQP5mckRDExERKQducVwwRNQfDJ4kw66eklFNQN6pJKepNO+iEi4aQSrNV+T82pjUrGehyUiIjFg/QJY8x/n87Aznec5dkBpeQ3DNHolIhIRSrBabF4Bvy1yHtDYxojemaQmelikeVgiIhItFUvh6W/CmzdBwN/hzeqafKzZXqv5VyIiEaIEq0WPweByt1vm1utxM65/Dh9rHpaIiERBYs16ePLrzojVjGedCrgd9MXmXQSDqIKgiEiEKMFq4U2EgSc7CVY7DxWeWJRLaUU1O+uboxCciIjErR0b6P/etc656dJXILv/IW1eEipwMUIjWCIiEaEEq7XBp8GO9bB95T5NE4tzCAZhyXqNYomISAQt+ivu5lq49B/Qc+ghb15aXk1aoofCnJQwBCciIm0pwWpt0GnOezu3CY7tl4PX7dIDh0VEJLK+8nPWnj4Leh91WJuXlNcwrHcmbrerkwMTEZH2KMFqLWcAnHGHU/a2jZRED6P6ZrFQhS5ERCSS3G6aMg7ttsAWwWCQkopqVRAUEYkgJVhtHXcN5I9ot2lScS6fbdhJQ3PHqzeJiIhEy6adDdQ0+FRBUEQkgpRgteVvdm4R3FK6T9PEolya/AGWbtwZhcBEREQOTckmp8DF8N4awRIRiRQlWG0FfPDcxbDkb/s0TRiQA+iBwyIi0jWUVjgJllGJdhGRiOlQgmWMSTPGuEOfhxpjzjbGJIQ3tChJSIEBx7db6CInLZEhvdI1D0tERLqEkvIa+uemkp7kjXYoIiJxo6MjWPOAZGNMX+Bt4FLg8XAFFXWDp8C2L6Bq3T5NE4tzWbyuCn9g32dliYiIxJKSimrdHigiEmEdTbBc1to64BvAQ9babwIjwxdWlA0OlWtf9e4+TROLcqhp8GEraiIclIiItNb67orQd7cxJjWaMcWS+iY/a7fVMky3B4qIRFSHEyxjzLHAxcC/Qss84QkpBuQNhax+sO6jfZomFuUC6DZBEZHoexdonVClAvve3x2nvthcQyCIKgiKiERYRxOsHwI3Ay9ba5cbYwYCc8MXVpS5XHDFG/D1P+3TVJiTSp+sZCVYIiLRl2yt3dXyJfRZI1ghJeWqICgiEg0dmvVqrX0feB+cWzCAbdba68IZWNRl99tv04SiXBas2U4wGMTlckUwKBERaaXWGDPOWrsEwBgzHqiPckwxo7SihrRED/1ylHOKiERShxIsY8wzwHcBP7AQyDTG3Get/V04g4uqYBDevAmyB8Cx39+raWJxLq99tokNlfX076ETl4hIlPwQeMEYswlwAQXAt6IbUuxYUV6NKcjA7daFQBGRSOroLYIjrLXVwLnAG0AxTiXB7svlgoql8Nmz+zRNCs3D+li3CYqIRI21diEwDPgezkXA4dbaxdGNKjYEg0FKy6s1/0pEJAo6+mCMhNBzr84FHrDWNhtjun+d8sGnwbu3Q81myMjfvXhIr3SyUhJYtLaS88cXRjFAEZH4ZYy5rM2iccYYrLVPHGS7acB9OMWaHrXW3tmmfQAwC+gJVAKXWGvLQm2XA7eEVv2VtXbfp9LHgE07G6hu8DFMCZaISMR1dATrEWAtkAbMC518qg+2kTFmmjHGGmNWGmNuOsB65xljgsaYCR2MJzIGT3HeV83Za7Hb7WLCgByNYImIRNfEVq8TgVuBsw+0gTHGAzwITAdGADOMMSParPZ74Alr7RjgduA3oW1zgV8Ak4FJwC+MMTmd1ZnOVBoqcDFCBS5ERCKuo0Uu/gj8sdWidcaYUw+0TauT2OlAGbDQGPOatXZFm/UygP8FFhxK4BGRPxrSesLKf8PRM/Zqmlicy7ulW9i2q5G89KQoBSgiEr+stde2/m6MyQaeO8hmk4CV1trVoW2eA84BWp+bRgDXhz7PBV4JfZ4KvGOtrQxt+w4wDdj3XvIoa6kgaPQMLBGRiOtokYssnKt2J4UWvY9zVW/nATbryEkM4JfAb4EfdzzsCHG7YUz786Vbnoe1aG0V00YVRDIqERFpXy0w8CDr9AU2tPpehjMi1dpnwDdwbiP8OpBhjOmxn237HknA4VJSUUP/3FTSkzo6E0BERDpLR3/zzgKWAReEvl8KPIZzAtqfg57EjDHjgH7W2n8ZY2IvwQKYeke7i0f3zSLJ62bh2kolWCIiUWCM+SfQMh/YAwwHnu+EXf8f8IAxZiYwD9iIU0X3sDQ2NlJSUnJEATU0NBzSPj5bu41+WQlHfNzOdKh9iFXqR+zoDn0A9SOWdFYfOppgDbLWntfq+23GmE+P5MCh52ndDcw8lO2icaIiGMTdVE0gKWuvxUN7JDKvZBMlg6JTAlf/kWOH+hE7ukMfQP3ooN+3+uzDSbIOVqZ9I9D6QYeFoWW7WWs3EbqAaIxJB86z1u4wxmwETmmz7XsHCzIpKYnhw4cfbLUDKikp6fA+6pv8bKpZzXkTixg+fOgRHbczHUofYpn6ETu6Qx9A/YglB+rD4sUdL1Lb0QSr3hhzgrX2AwBjzPEc/GGOBzuJZQCjgPeMMeA8v+Q1Y8zZ1tpF+9tppE9UADxzIdRXwpVv77X4lA1uHnpvFf0HDiEtCrdhdPf/yF2J+hE7ukMfID76cSgnq/ZYa983xowFLgK+CawBXjrIZguBIcaYYpxz0oWh7XczxuQBldbaAHAzzl0cAG8Bv25V2OKMUHtM+WJzDYEgDFeBCxGRqOhoVvBd4InQXCyAKuDyg2xzwJOYtXYnkNfy3RjzHvB/B0quoqb3GJj3O6ivgpQ9BaMmFOXiD6zkk/U7OGFI3gF2ICIincUYMxSYEXptA/4OuKy1Byy+BGCt9RljrsFJljzALGvtcmPM7cAia+1rOKNUvwk9jmQe8IPQtpXGmF/inN8Abm8peBFLSiucAhd6BpaISHR0tIrgZ8BRxpjM0PdqY8wPgc8PsE1HTmJdw6DT4P3fwur3YOTXdy8e1z8btwtmLytXgiUiEjmlwH+As6y1KwGMMT/q6MbW2tnA7DbLft7q84vAi/vZdhZ7RrRiUkl5DWmJHvrlpEY7FBGRuHRI97VZa1s/++p64N6DrH/Ak1ib5accSiwR1Xc8JGc55dpbJVgZyQlcNLk/T81fT1GPVK4+aVAUgxQRiRvfwLkrYq4x5k2c0uzRmQwbg0rKqzEFGbjd+pGIiERDRx803J74+c3t8cLAU2HlHAgG92q67exRnDm6N7+eXcozC9ZHKUARkfhhrX3FWnshMAznOVU/BHoZYx42xpwR3eiiKxgMUlJezTDdHigiEjVHUpkhePBVupFjvgejz4dgAFye3Ys9bhf3fOto6pv9/OyVpaQmejh3bEw+FkVEpFux1tYCzwDPhApPfBO4EXj7gBt2Y+U7G6hu8Gn+lYhIFB0wwTLG1NB+IuUCUsISUazqf8x+mxK9bh66eBwzH/uYG174jNRED2eM1LOxREQixVpbBfw59IpbJeWhAhcFqiAoIhItB0ywrLX6Dd3allIo/wyO2vcxK8kJHh69fCIXP7qAa575hFkzJ6rwhYiIRFRpRQ0ARgmWiEjUHMkcrPjz6dPw6g+gcVe7zelJXv52xUQG9kzj208sYtHamKveKyIi3diK8mr65aaQkZwQ7VBEROKWEqxDMfg0CDTD2v/sd5Xs1ESevHIyBVnJXPHYQpZt3BnBAEVEJJ6VllczrEDzr0REokkJ1qHofywkpDrl2g+gZ0YST101mcyUBC6b9TErt9REKEAREYlXDc1+1myrVYELEZEoU4J1KLxJUHzSQRMsgL7ZKTx11WTcLhcXP7qADZV1EQhQRETi1RebawgEVeBCRCTalGAdqsFToHoTVJcfdNXivDSevmoyjb4AFz06n4qdDREIUERE4tHuCoIawRIRiSolWIfqqBlw41rI7N2h1U1BBn+7YhKVu5q45K8L2L6rMbzxiYhIXCopryE10UP/3NRohyIiEteUYB2qpHRITDukTY7ql81fZ05kQ2Udl836mOqG5jAFJyIi8aqkvBpTkIHb7Yp2KCIicU0J1uH48h147Exo7vgtf8cM7MEjl47ni801/M9jC6lr8oUxQBERiSfBYJDSihpVEBQRiQFKsA5HMADrPoD1Hx3SZqeYXtx34ViWrK/i6icW09DsD1OAIiIST8p3NrCzvpkRvVXgQkQk2pRgHY6iE8CT2KFqgm19dXRv7jr/KD5YuY1rn/2EZn8gDAGKiEg8Ka1wClwMU4ELEZGoU4J1OBLTnGdirXz3sDY/f3wht509kndWbObHL3xGIBDs5ABFRCSelJQ7z1s0KtEuIhJ1SrAO1+ApsLUEdm48rM0vP66IH081vPLpJm55dRnBoJIsERE5PCXl1RTmpJCZnBDtUERE4p432gF0WUPOgLX/gcaaw97FD04dzK5GHw+/t4r0JC83Tx+Gy6XqTyIicmhKyqv1/CsRkRihBOtw9RoGF79wxLv5yVRDbaOPP89bTXqSl+tOG9IJwYmISLxoaPazZlstZ47u2PMZRUQkvJRgHaldWyAlFzyH96N0uVzc+rWR7Gr0cfc7X5CW5OXKE4o7OUgREemuvthcQyCIRrBERGKE5mAdiZXvwu+HwMZFR7Qbt9vFXeeNYfqoAn75+gr+3yvLqNjZ8WdsiYhI/CoNFbhQBUERkdigBOtI9B0PLg988tQR78rrcXPvhUdzyTH9efbj9Zz0u7nc+tpyNlcr0RIRkf0rqagmJcHDgNzUaIciIiIowToyKdlwzPfgkyfhwweOeHdJXg+/Onc0c//vFM49ug9Pzl/HSXfN5bZ/LmdLjRItERHZV0l5NaYgA7dbRZJERGKBEqwjdfrtMOJcePtnsPTFTtllv9xU7jr/KObccDJfO6oPT3y0jhN/O5dfvr6CrTWNnXIMERHp+oLBIKUVNZp/JSISQ1Tk4ki5PfD1R6BpF3iTO3XXA3qk8ftvHsU1pw7m/jkreey/a3h6wTouPWYA3zl5UKceS0REup6K6gZ21DUzvLceMCwiEiuUYHWGhGS4+EVoeYZVUx0kdt698EV5afzhgqO45iuDuf/dL/nrB2t4av56zhyazs39GumRntRpxxIRka6jpcCFRrBERGKHbhHsLC3J1bKX4IEJULW20w9RnJfG3d86mneuP5mpI/N5aflOTrxrLne+UUplbVOnH09ERGLbivJqAEyBRrBERGKFEqzOlj8KmmrhyW9A7bawHGJQz3TuvXAsj5xbyJTh+TwybxUn/nYOd71ZSpUSLRGRuFFaUUNhTgqZyQnRDkVEREKUYHW2ngYueh6qN8LT33SSrTDpl5XIH2eM5e0fnsSpw3rx8PurOOG3c/jdW6XsqFOiJSLS3ZWUVzOsQLcHiojEEiVY4dB/Mpz/GJR/Cs9fDn5fWA83JD+DBy4ax1s/PIlTTC8enLuKE347lz+8bdlZ1xzWY4uISHQ0NPtZvXUXI1TgQkQkpqjIRbgM+yqcdQ/UVDiVBiNgaH4GD148jmsrqrnv319y/5yVPP7ftZwxsoBpowo4cUgeyQmRiUVERMLry827CARhmApciIjEFCVY4TR+5p7PtdshrUdEDjusIJOHLxlPSXk1f/nPat5eUcFLS8pITfRwqunF1FEFnGp6kqF79kVEuqySCqfAhSoIiojEFiVYkbB9FTw6BU65GSZfHbHDDu+dyd0XHE2TL8D81dt5c3kFby/fzL+WlpPocXP84B5MG1XAlOH5KvUuItLFlJRXk5LgoX9u5z0WREREjlxYEyxjzDTgPsADPGqtvbNN+/XAVYAP2Ar8j7V2XThjiorsAdD/WHjjJ5DeC0aeG9HDJ3rdnDS0JycN7ckvzxnFJ+ureHNZBW8ur2DuS0txu5YyqTiXqSMLmDqygD7ZKRGNT0QkkjpwbuoP/A3IDq1zk7V2tjGmCCgBbGjV+dba70Ys8DZKy2swBRl43K5ohSAiIu0IW5ELY4wHeBCYDowAZhhjRrRZ7RNggrV2DPAicFe44okqjxfO/yv0mwT/+Das/SB6obhdTCjK5ZazRvCfn5zKv647gWtOHUxlbRO3/XMFx905h3Me+ICH3lvJqq27ohaniEg4dPDcdAvwvLV2LHAh8FCrtlXW2qNDr6glV8FgkJKKaoarwIWISMwJ5wjWJGCltXY1gDHmOeAcYEXLCtbaua3Wnw9cEsZ4oishBWY8B7OmwbMXwVX/hp5DoxqSy+ViZJ8sRvbJ4vozDKu27uKt5RW8tXwzd71puetNy5Be6Uwb5YxsjeyTiculK6Ui0qUd9NwEBIGWiU1ZwKaIRtgBm6sb2VHXrPlXIiIxKJwJVl9gQ6vvZcDkA6x/JfBGGOOJvtRcuOQl+M8fILtftKPZx6Ce6Xz/lMF8/5TBbNpRz9vLndsIH5y7kvvnrKQwJ4XTR+RzdL9sRvTOZGDPdN2aIiJdTUfOTbcCbxtjrgXSgCmt2oqNMZ8A1cAt1tr/HOyAjY2NlJSUHFHQDQ0Ne+3j47I6AFIaKykpqT+ifUdK2z50VepH7OgOfQD1I5Z0Vh9iosiFMeYSYAJw8sHWDceJKuIGfwdWrsXdVAPBAIGkrMPaTbj7MTkXJp+YzY6JGSzYUMuH6+t46qN1PPbftQAkeVwU5SQyKDeRgblJDMpNpCgnkWRvx+88jfq/RSdRP2JHd+gDqB9RNgN43Fr7B2PMscCTxphRQDnQ31q73RgzHnjFGDPSWlt9oJ0lJSUxfPjwIwqopKRkr33M3bwSqGDqMaPJ7CIVYdv2oatSP2JHd+gDqB+x5EB9WLx4cYf3E84EayPQepimMLRsL8aYKcDPgJOttY0H22k4TlRREQjAo6eBJwEue9W5hfAQRbIfx46FHwLN/gCrtu5i+cZqVpRXs2JTNR+s38nsL2oAcLugOC+NkX2yGNEnk5F9MhnRO3O/VQpj4t+iE6gfsaM79AHiox+HcrLqRB05N10JTAOw1n5kjEkG8qy1W4DG0PLFxphVwFBgUdijbqOkvIa+2SldJrkSka6tubmZsrIyGhoaDrhOF7ygtpfm5mbWrFlDYWEhCQmH//s1nAnWQmCIMaYY5+R1IXBR6xWMMWOBR4BpoRNX/HC74fj/hRdmwov/Axc86RTDiHEJHjfDCjIZVpDJeaFlwWCQjTvqWb7JSbhWlFezeF0Vr322Z9pCfmaSk3T1DiVdfTLpl6PSwiIScQc9NwHrgdOAx40xw4FkYKsxpidQaa31G2MGAkOA1ZELfY/SchW4EJHIKSsrIyMjg6Kiov3Ox6+vryclpWtXoq6rq6Ouro6ysjKKi4sPez9h+4veWuszxlwDvIVT5naWtXa5MeZ2YJG19jXgd0A68IIxBmC9tfbscMUUc0aeC7vugjd+DLNvgLPuhS5YRMLlclGYk0phTipTRxbsXr6jrml3wrViUzXLN1Xz/hdb8QeCAGQkeemf5WVMiY8hvdIZkp/OkF4Z5GcmqZiGiIRFB89NNwB/Mcb8CKfgxUxrbdAYcxJwuzGmGQgA37XWVka6Dw3NflZvq2XaqIKDrywi0gkaGhoOmFx1Fy6Xix49erB169Yj2k9Yh0ystbOB2W2W/bzV5yn7bBRvJl8NNeXwwd3QYwgcd020I+o02amJHDc4j+MG5+1e1tDs54vNNbsTriWrN/PGsnKerWvevU5GkpfB+elO0tUrg8G90hncK52+2Sm4VVRDRI5QB85NK4Dj29nuJeClsAd4ECu37MIfCDKsQBUERSRyunty1aIz+hn796TFg9N+Dp5EGPn1aEcSdskJHsYUZjOmMBuAkhIPw4YNY3ttE19u3sXKLTV8uWUXX27exZzSrTy/qGz3tikJHgb3chKvwaHRriG90umXm6pqhiISN1aUOzU1dIugiMSLqqoqZs6cCcC2bdtwu93k5uYC8MILL5CYmLjfbZcuXcqrr77KLbfcEolQASVYscHlglNvdj4318P8h2D8FU5Z9zjgcrnIS08iLz2JYwf12KutqraJlVudhOvLLTWs3LKLD1dt5x+f7JmTnuh1M6inM8pVnJdGn6xkemen0Dsrmd5ZyWRoEriI/H/27js+rupM+Phv+qh3S8aSu30sgw0uWJQQUwzYsRMSSAglgLOwpBCSlxQw2Ww2YUN504AYXkKKacsuNZQYE4cQWBOcgDGh2fLBRS5j2eptJE2f9497JY1kSZYtaWY0er6fz3xuOffeeY5H1tEz59xzU8iOQ224HVamFGQkOhQhhIiLvLw8XnjhBQDWrl1Leno61157bXd5KBTCbu8/rZk3bx7z5s2LS5xdJMFKNlWb4NXbYNPPYdFqOP0GyClNdFQJk5fh5NSMfE6d2jvZbPUF2V3rZWetl121XnbWtPHP/U2s/6CaaLT3NTJddibmuCnJcXNCTholZuIlSZgQYiyqPNSKKsmWnnshxLi2Zs0anE4nlZWVLFy4kJUrV3L77bfj9/txrf62tgAAIABJREFUu93ccccdTJ8+nbfeeot169bx4IMPsnbtWqqrq/F4PFRXV3PNNddw9dVXj3hskmAlm9kXwtf/AW/eC289CG//FuZ/EVb+/Limck9V2W4HCybnsWByXq/9gVCEmlYfh1t9HGrxcai501i2dHK4xceOw23Ue/1DSsJOyHUzIdtNkdm7VpDpxGEb+jO+hBBipEWjUXYcbu01oZAQQsTTs1s9PPXOgSP2RyIRrNbj+zvp0sVlXLLo2DsUampqeOKJJ7DZbHi9Xh5//HHsdjubN2/m7rvvZu3atUecU1VVxaOPPorX62XFihVcfvnlw5qSvT+SYCWjCeXwuV/DOd+HzfdB7Xawu42yloOQMymx8SUxp91KWX46ZfkDTwF/vEkYQF66o3s4Y1GWsSzMchJsbeOwpbZ7nyRjQojRUNPqp6kjSPlEmeBCCCGWL1+OzWYDoK2tjVtuuYV9+/ZhsVgIBoP9nrN06VKcTif5+fnk5+fT0NBAScnIfmklCVYyy50Mn/opRKPGfVrtDbB2EZSdCp+4CaLyDebxOJYkrLbNT73XT525rPf6qW8LUO/1876nmfo2P+2BsHHS5t5TeuamO7p7vwqzXBRmOinIcJKb7iQv3UleusNYz3CQl+7E7bCNZrWFECmg8rAxwcWcEpngQgiRGJcsKu23tykRz8GKfb97772XiooK7r//fjwez4BD/2InxLDZbIRCoRGPSxKssaBrukiHG879N/j7/fDY55iapyB6K5R/Bqzyx/lIGkoS1qUzEOYf720jp6SsJxEzk7Cu14eeZuq9Abz+gf8Tux1W8tK7EjCHud6zzM9w9tqXl+4ky22XqeuFGEcqzRkE50gPlhBC9NLW1kZxcTEAzz33XEJjkQRrLHFmwBk3wpLr4f0nsL72M3h6NdzwNhSpREc3bqU5bZRkOSjvcz9Yf/yhMC0dQZo6gjS2B2juCNDUEaSpo2e9a1l5uJVmczvSz3BFAKsFctKMnjBj6SDX3I5dz+lK1MxjstwOuUFeiDFox6E2JuWmkZMmE/MIIUSs6667jjVr1vDAAw+wdOnShMYiCdZYZHfBomvY415EeWZbT3K14WZjxsHFXwaXDB9JRi67jQnZNiZku4d8TiQSpc0Xoqkj0PNqN5Kyls6gkYR1GolYY3uAPXXtNHUEaPMN3FtmsRgTheSawxRz0xzkpDkIdbZRVgUZLjsZLjuZLlvMes/L2GfDZZeeUyHiqfJQqzz/Sggxrt1444397l+wYAEbN27s3r7pppsAqKiooKKiot9z169fPyoxSoI1llltMOV0Yz0cgoad8PaD8MbPjV6uiq9CRmFiYxTDZrVayEl3kJPuYCpDf+5NKByh1ReiuSNAc2fQ7DkLdCdkLeb+rl6yvQ3ttLT76dyzF38oMqT3cNgsRrLltJPltvdKzDJddrLdDrLTHGS77ebS3E7rKctw2sbN0+GFGA5fMMye+naZQVAIIZKcJFipwmaHq54Dz1Z4827jOVqb74PP/x7mrEx0dCIB7DYr+RlO8jMGfrp5X5WVlZSXlxMKR2j3h/EGQrT7Q3j95tLXs94eCPfsN8vaAyFaOoNUN3fi9YVo8wV7JgEZgNVCTPJlJl7u3klYtttOlttBhqsnkTOSOAcZLhsZTrkXTaS+XbVewpGozCAohBBJThKsVFO6CL74X1D3MWz+FUxaZOx/91HjuVplS6DsNGOZN7VnAg0hYthtVnLSreSkD/8+j1A4QpsvRKsvSGtn1zLYz3aoe39VfXv3/qMlaF0ynMZwRqclQv6rDWQ47WS6u4Y0GslY15DHrmGOaQ4bLoeVNIeNNKcNt71n6XZacdqs0rsmkkbPBBcyRFAIIZKZJFipqmg2XHRfz3Z6AWROgA+ehnfWGfsyS+Cb/wRnOrTVQFoe2Ife2yHEUNhtVvIynOQdQ09arNgELbanzOsPG+tdPWhmb1p1XSNWl5N2fwhPUydef9DojfOHCAxx6GMXqwXcDhtpDhtuhw23w9o7ETP3p5lJmttpHJvmsJFulqd17eu7jFm3yzPTxBBUHmrD7bAytWDoQ4WFEELEnyRY48WclcYrEobaSjjwD2isMpIrgD9+E3a/BpMWQllFzyujILFxi3HvWBO0rmGO/QmEIj1DHgMhfMEIvmCYzmAYXyCMLxSmMxAxts1XZ8z+7n1BI2Gr9wa6j+k09x9rEgfGvWxuMynrSuaioQB5bzTjshuJ3VCWrsHKHTbc9p6lJHVjz47DrajiLJkBVAghkpwkWOON1QYlJxmvWIuvhfwZcOAt4zlbb95jJFjX/tko3/ESFMyEwtkyrFCMWU67Faf9+HvThiIciXYnYd2J1wBLXzBMR8y+ru2OQJjGlhCRCDR3BPAFI/hD4V5LXyhMdIDp+4fCbrXgslu7e+FcZvJl7LN274tddq3npDm4bMlkMl3ShMRLNBql8lCrTHAhhBBjgLSOwjD7AuMFEOyE6n9CxJzmO+gznrcVDoArB/ImQ85kOOlimPd5iESM43NKIaMIrPLNuBi/bFZL92yKwzFYTxwYf3AHw9EjEq/Ypb+/xCwYxh/qvezvvMb2AH4zketadp3jsFpZPDWfU8pyh1VHMXSNnWGaOoLMKZH7r4QQ489VV13F9ddfz1lnndW97+GHH6aqqoof//jH/R5/8803M2/evHiG2U0SLHEkRxpMOaNn2+6Cr202ereq/wnN+6GpCtoOG+Xew/C7c411mwtyJkFOGZz2NVArwO+Fg1shtwyyJxnXE0IMi8ViwWm34LRbyRr6Y9WGLRqNEo5EZYhhnO1pDADIDIJCiHFp1apVbNiwoVeCtWHDBr73ve8lMKqBSYIljs5igcJZxmvBl44sd+fA5U9Ai8dIvlo80HLA6PEC456vRz/Tc3xmidHbtexHQCE07YUdG8CdDa7snmXhbHBlQjQqwxKFSBIWiwW7Tf4/xltVk/H7dE6JJFhCiPHnwgsv5J577iEQCOB0OvF4PNTW1rJ+/XruvPNO/H4/F154Id/85jcTHSogCZYYCc4Mo6dqIBPmwDV/NBOwA0by1XLA6CnzAYc+gI23Hnne6g0w9Uz48Gl44YbeyZcrCz59LxTMgANvw85XzLIscGYaMU1bakzi0dEIwQ5jnzMTbMOfelwIIeKpqsnPpNy0EXl0ghBCDNtDRz5j1TZ7JZz5dQh0wONfOPKcU66ABVdCewM8dXXvsi+/NOjb5ebmMn/+fDZt2sSyZcvYsGEDK1as4Ctf+Qq5ubmEw2FWr17Njh07mDNnznBqNiIkwRKjz5UF0z7Zf1llpTG74S17wdcK/tae5QTz/pPC2XDa13uX+VrBav74HnwXNv0M6HPH/03bjQRry+/htZ/07Lc5wZEO33of0nLh7d/C9hfMBCyjJxG74CfGpCBVb0DjHuMch9tIDJ2ZMPk043rtDdh8jcZQSEeacY4QQoygqqYAc0ryEh2GEEIkzMqVK9mwYQPLli3jpZde4vbbb+fll1/mqaeeIhQKUVdXx+7duyXBEgIwEpK0POPVnxNOMV4DOe2rsOR6CHjB32b0VvnbjOd+AajlkFUMgXbjmECHse4wp6i3WIzp61urzWPaIeSDC+8wyt9/At77r97v6cqBW/cb6xu+w+xtz8ELZpnNCXnT4Btvm+XfM5JAR1rPK28qnH+bUf7uY9BeayZwacYysximLzXK67QxTLKrzOE2lpLICTEu+IJhDrQE+fQCGR4ohEgS/fQ4hTs7jRVn+uA9UhkFR+2x6s95553HnXfeybZt2/D5fOTk5LBu3TqeeeYZcnJyWLNmDX6//5ivOxokwRKpwWo1hgi6+/kDpGSe8RrIqdcZr4EsvxPOudWYTTHYYcyy2DXDIsDCazjsmkFJQY5RFuoEe1pPuTvXiCvYCd5aYxlo7ynf+jAcfKf3e5ad1pNgPXU11O3oXT79HLj6eWP915+A9npj6KPNabxmnmf0wAE8+SUIBXrK7S6Y+ome++n+8mOjN9DmJL+xBbxToWQ+lC0xZojc9zejPt0JYrpx313XM9SEEKNqV62XSBTmTJQZBIUQ41dGRgYVFRV8//vfZ+XKlbS3t5OWlkZWVhb19fVs2rSJJUuWJDpMQBIsIY5uoMSty4xzaAqUUDLQlNrn/tvg17/uL8aEIF3JW7Cz96QeK34KHQ1mmXlMzqSe8pnLjPJw0LhOyG9Ml9/F12IMqQwHel5d5dGo8dyzsPGNTzHA+8Dp3zASrGAHPPLpI2P+5M1Gvby18KsFvZMvR5oxpHP+pcZMky/fYiR1dpcxy6TdBXM/C2WnGud/+HRP4mdzgd0JpacaE6H4WowevO5yc5leaPTkRcJGHWzyq0ykrh2H2wCZQVAIIVatWsUNN9zAL3/5S2bMmMHcuXNZsWIFJSUlLFy4MNHhdZO/SoRINIulJwHpb5hkV0/WQJb9aPDya/44+Hv/e62RpERC6G3vo6aVGUkOGDGtfikmuTN78SbON8ptDlh4TUxy2GEMr7Sb84YHO6Bmm5HAhQI9ywlzjQSraR9s/P6RcX3+ISPBOvguPPbZI8uveNp4btvHf4InrgCL1UjObE5mYYXMp43r6z/Ba7cfmaAtvwvyp0HVJvjo2Z6ev65XxVcgPR8OvW/E0HVu12vGOca+loPQXhdzfbOXMLO4Z+ipxSqzYIphqTzUistmYWpBRqJDEUKIhFq2bBla6+7tu+66q9/jHnvssXiF1C9JsIQQRgJgcxBxZBj3q3WxOYzhhANJy4Pldwxcnj8dbnxn4PJJC+GWfT09b13L7BOM8oknw5XPmomZ3+yl80PxiUZ5wSw459/MMqO8rb6GvIwCo9yRZlwrHDASu4AXOhshGjHKm/eDfrmnPByASNCY5Sg9H3b+Gf76kyPjvrnKSKi2/Bb+dveR5T+oNcr/tAbe/g1Yu4ZvOoxJVL693Tju1f+EnRt7J2/p+fCFh43yt34DNR/29PzZnEbv4+lfN8o//rNx/15Xz5/NTNInVxjlDbuNJM9uXtvqMHr+XOZQM3kEwpiw43ArU3Kd2KzyWQkhxFggCZYQInGsNmMmx4Gk58OsZQOXF82GpTf32nW4spK8/OnGxvSlg/cALvjSkc92i0R6ko6Kr8EpV/ZOwMIB41EBACdfYQxn7CqPmMM0bWYP4KwLIC2/9/DMWBmFkF3aUxbyG8M5u9R8aCRRXQlmyA+5k3sSrM2/gr1v9L5m8Tz42t+M9Wevg+p3e5eXnQbXbjTW768wZsi0OYyX1QEzzoVLfmuUP3qR8ZiDruTQ5jBmBP1kcj7YMVXtONTG4hPkAe1CCDFWSIIlhBCxrNaedVem8RpI0WzjNZBZ5xuvgZz2NeM1kM+sPXJfJNKz/oVHjF657h5Af09yB8bw0fa6nt7BSMhI6ros/hfw1vSUhQNQFDO9bU6ZMcFJV89eOGgkkiKulp9Uwil5wUSHIYQQYogkwRJCiLEkNgHMKDBeAzna/XunfXXw8ovuG3pcYtTc/rl5VFZWJjoMIcQ4F41GsYyDYeXRaPToBx2F9eiHCCGEEEIIIcYrt9tNQ0PDiCQfySwajdLQ0IDb7R7WdaQHSwghhBBCCDGg0tJSPB4PdXV1Ax4TDAZxOBxxjGrkBYNBsrKyKC0tHdZ1JMESQgghhBBCDMjhcDBt2rRBj6msrKR8oGeCjhGVlZVHredQyBBBIYQQQgghhBghkmAJIYQQQgghxAiRBEsIIYQQQgghRohlrM0GsnXr1jpgX6LjEEIIMWxTFi1aVJToIEabtFtCCJEShtxmjbkESwghhBBCCCGSlQwRFEIIIYQQQogRIgmWEEIIIYQQQowQSbCEEEIIIYQQYoRIgiWEEEIIIYQQI0QSLCGEEEIIIYQYIfZEBzCalFLLgXsBG/A7rfVdfcpdwKPAIqAB+KLWem+84xyIUqoMI75iIAr8Rmt9b59jzgZeAKrMXX/QWt8WzziHQim1F2gDwkBIa724T7kF47P6FNABrNZavxvnMAellFLAkzG7pgM/1FrfE3PM2STh56GUWgesAmq11ieZ+/Ix6jMV2AtcqrVu6ufca4AfmJs/0Vo/Eo+Y+4mjvzr8DPg0EAB2A1/WWjf3c+5eBvn5i6cB6vEj4F+BOvOw72utN/Rz7qC/0+JpgHo8CSjzkFygWWt9Sj/n7iVJPo9kMtbbLJB2S9qtkZEKbZYZi7RbSdJuxbvNStkESyllA+4Hzgc8wBal1Ita6+0xh10LNGmtZyqlLgP+L/DF+Ec7oBDwHa31u0qpLGCrUuqVPnUAeENrvSoB8R2rc7TW9QOUrQBmma8K4AFzmTS01ho4Bbp/vg4Cz/VzaDJ+Hg8D92H84dNlDfCq1voupdQac/uW2JPMBu0/gMUYfyxtNf8fHdGoxcHDHFmHV4BbtdYhpdT/BW6lTx1iDPbzF08Pc2Q9AO7WWv98oJOG+Dstnh6mTz201t2/P5VSvwBaBjk/WT6PpJAibRZIuyXt1sh4mLHfZoG0W8nUbj1MHNusVB4iuATYpbXeo7UOAE8AF/U55iKg65uNZ4DzzG+kkoLW+lDXt2Fa6zagEpiU2KhGzUXAo1rrqNb6H0CuUmpiooMaxHnAbq31mHh4qNZ6E9DYZ3fsz/8jwGf7OfVC4BWtdaPZQL0CLB+1QAfRXx201n/WWofMzX8ApXEP7BgN8FkMxVB+p8XNYPUwf49eCvxPXIMa28Z8mwXSbkm7NTJSoc0CabdIonYr3m1WKidYk4ADMdsejvwl332M+cPeAhTEJbpjpJSaCiwA3uqn+HSl1PtKqZeVUifGN7IhiwJ/VkptVUpd30/5UD6vZHIZA/9HHAufB0Cx1vqQuX4YY0hPX2Ppc/kX4OUByo7285cMvqGU+kAptU4plddP+Vj6LM4CarTWOwcoHwufR7ylVJsF0m4lobHebqVamwXSbiWLEW+zUjnBShlKqUzgWeD/aK1b+xS/C0zRWp8MrAWej3d8Q/QJrfVCjCEVNyilPpnogI6XUsoJfAZ4up/isfJ59KK1jmL8AhmTlFL/hjE06fEBDkn2n78HgBkYQ3kOAb9IbDjDdjmDfxOY7J+HGCZpt5JLqrVbY73NAmm3ksyIt1mpnGAdBMpitkvNff0eo5SyAzkYNw4nDaWUA6ORelxr/Ye+5VrrVq2111zfADiUUoVxDvOotNYHzWUtxvjvJX0OGcrnlSxWAO9qrWv6FoyVz8NU0zWcxVzW9nNM0n8uSqnVGDeuXmk2ukcYws9fQmmta7TWYa11BPgt/ceX9J8FdP8uvZjeN9b3kuyfR4KkRJsF0m7FJ7pjlgrtVkq0WSDtVjziG6rRarNSOcHaAsxSSk0zv7m5DHixzzEvAteY658H/jrQD3oimGNCfw9Uaq1/OcAxJV1j8JVSSzA+06RqcJVSGebNziilMoALgI/6HPYicLVSyqKUOg1oiRkKkGwG/KZjLHweMWJ//q/BmEWqr43ABUqpPLP7/wJzX1IwZye6GfiM1rpjgGOG8vOXUH3u2/gc/cc3lN9pyWAZsENr7emvcCx8Hgky5tsskHZL2q1RNebbLJB2i+Rrt0alzUrZWQTN2Vm+gfEfywas01pvU0rdBryjtX4RoxF4TCm1C+PGt8sSF3G/zgSuAj5USr1n7vs+MBlAa/1rjEb2a0qpENAJXJZsDS7GOOnnlFJg/Mz9t9b6T0qpr0J3PTZgTHW7C2O62y8nKNZBmf+5zge+ErMvth5J+Xkopf4HOBsoVEp5MGZZugt4Sil1LbAP4wZPlFKLga9qra/TWjcqpf4T45ckwG1a6+O50XXYBqjDrYALeMX8+fqH1vqrSqkTMKaD/RQD/PwloArAgPU4Wyl1CsaQl72YP1+x9Rjod1oCqoAZ2xH10Fr/nn7u80jmzyNZpEibBdJuJZ2x2G6lQptlxibtVpK0W/FusyzRaLL9ThNCCCGEEEKIsSmVhwgKIYQQQgghRFxJgiWEEEIIIYQQI0QSLCGEEEIIIYQYIZJgCSGEEEIIIcQIkQRLCCGEEEIIIUZIyk7TLkQyUEqFgQ9jdj2htb5rhK49FVivtT5pJK4nhBBifJM2S4iRIQmWEKOrU2t9SqKDEEIIIYZA2iwhRoAkWEIkgFJqL/AUsALjwY5XaK13md/wrQMKgTrgy1rr/UqpYuDXwHTzEl8DqgGbUuq3wBnAQeAirXVnHKsihBAixUmbJcSxkXuwhBhdaUqp92JeX4wpa9FazwPuA+4x960FHtFazwceB35l7v8V8L9a65OBhUDXk9BnAfdrrU8EmoFLRrk+QgghUpe0WUKMAOnBEmJ0DTbc4n9ilneb66cDF5vrjwE/NdfPBa4G0FqHgRalVB5QpbV+zzxmKzB15EIXQggxzkibJcQIkB4sIRInOsD6sfDHrIeRL02EEEKMDmmzhBgiSbCESJwvxiz/bq5vBi4z168E3jDXX8UYw45SyqaUyolXkEIIIQTSZgkxZPLNgRCjK00p9V7M9p+01mvM9Tyl1AcY3+hdbu67EXhIKfU9zBuGzf3fAn6jlLoW41u/rwGHRj16IYQQ44m0WUKMAEs0ery9vEKI42XOyLRYa12f4FCEEEKIQUmbJcSxkSGCQgghhBBCCDFCpAdLCCGEEEIIIUaI9GAJIYQQQgghxAiRBEsIIYQQQgghRogkWEIIIYQQQggxQiTBEkIIIYQQQogRIgmWEEIIIYQQQowQSbCEEEIIIYQQYoRIgiWEEEIIIYQQI0QSLCGEEEIIIYQYIZJgCSGEEEIIIcQIkQRLCCGEEEIIIUaIPdEBCCGEEPGklFoO3AvYgN9pre/qUz4FWAcUAY3Al7TWHrPsp8BKjC8oXwG+pbWOxjF8IYQQSU56sIQQQowbSikbcD+wApgLXK6UmtvnsJ8Dj2qt5wO3AXea554BnAnMB04CTgWWxil0IYQQY8SY68F67733oi6Xa1jX8Pv9DPcaySAV6pEKdQCpRzJJhTrA+KhHR0dH/aJFi4riHNISYJfWeg+AUuoJ4CJge8wxc4Fvm+uvAc+b61HADTgBC+AAao72htJuGVKhDiD1SCapUAeQeiSTkWqzxlyC5XK5KC8vH9Y1Kisrh32NZJAK9UiFOoDUI5mkQh1gfNRj69at++IcDsAk4EDMtgeo6HPM+8DFGMMIPwdkKaUKtNZ/V0q9BhzCSLDu01pXxiFmotGxPwoxFeoAUo9kkgp1AKlHMjlKHYbcZo25BEsIIYQYZd8F7lNKrQY2AQeBsFJqJlAOlJrHvaKUOktr/cZgF5MvBg2pUAeQeiSTVKgDSD2SyVG+FBzydSTBEkIIMZ4cBMpitkvNfd201tUYPVgopTKBS7TWzUqpfwX+obX2mmUvA6cDgyZYQgghxheZ5EIIIcR4sgWYpZSappRyApcBL8YeoJQqVEp1tY+3YswoCLAfWKqUsiulHBgTXMRliKAQQoixQ3qwhBBCjBta65BS6hvARoxp2tdprbcppW4D3tFavwicDdyplIpiDBG8wTz9GeBc4EOMCS/+pLX+4/HEEQwG8Xg8+Hy+IR9fWTk2czm3201paenRDxRCiBQhCZYQQiRINBolEoVwJEokGiUciRKORolEojT7wtS2+Yx9fV/RKKGwcU4oYhzftQz3sy9kXj8a817RKMZ7mjFEzfeNmPsj0Z712PMiUXDZrXzptCnkpDkS/U94XLTWG4ANffb9MGb9GYxkqu95YeArIxGDx+MhKyuLqVOnYrFYjnp8Z2cnaWlpI/HWcRWNRmloaMDj8SQ6FCHEOBGORKlr83O41cfhFh81rT6sVgtXLpmM1Xr037cjQRIsIUTSiZpJQiAUIRiOEAhFCJjLYDjaZztmGY6w70Ab/2zdTyAUNo6NOT/YtQxHCISi3YlErwQnQr/7I5GYhMRMZMIRzKSk9/5IhO5EqCspisRs91z7aP8SiZhk7+jSnTY+OauIeaU5iQ5lzPL5fENOrsYyi8VCQUEBdXV1iQ5FCJEC2v0hDrf6qGnxGQmUuX7ITKQOt/qoa/Mf0b4WZbm4eMEkMlzxSX0kwRJiHAmGI3QEwviCYToCYToDYTqDIToDETqDYfbu97IrUE3Y7PUIRyLm0ugxCcf0hhjbMeXdy0j38bHJzJEJTu8kqW/iNDxH/jHnsFlw2qw47FZjabNitYLNYsFqsWC1Wox1qwWbud9isWDr3g8Oq9U41txvLMFmNY/tZ7/VYsFu7bm+zdrnvcxje5VbLNTV1jDphInG+5tldlvv68Vet+8+e9d723rey4id7vp2r1t6r1ti/l0s3ft76ilGxnj5txwv9RRCHJ9oNEqrL0SD109De4D6Nj/1Xj81rf5eiVNNi482f+iI87Pcdkqy3ZTkuJldnEVJjpvibHf3vuJsNwUZzrj1XoEkWEIkTFcvjT8UwR8MEwhH8AcjxnYobO6PWQ+Fe5cHjaQoNmHqWQ/RGYzQGQh1H9MZCBM6epcJUHtM9XDYjATAbrWaS0t3UuC0G4lMV2LjsllxO6xku+04YvY5bNaeY+1WnLaec3v29T7GYR7Td//+qj3MnTO793E265j7I6+y0kd5+ZREhyFSVFNTE6tXrwagvr4eq9VKfn4+AE8//TROp3PAcz/88ENeeOEFfvCDH8QjVCHEGBQMR2hsD1DXZiRNDV4jaWrwBqj3Boz1dmO7wRsgED7yi1Wb1cKELBfF2W5mFmXyiZmFRuKU4+qVQKU7ky+dSb6IhEgi0aiRAHUEwrT7Q8YyEKIzZrsjEKbqQDOZB3fSEeg5psMfc2zASHp8wd6J05DynUHYrRbSnDbSHDbSnTbc5jLdaSc/w1hPc9iMY5w20mPXu8vsxtJh48D+vcyeOR2b1dqdKMUmTH0TqXh+GzRUgXoHxdnuRIchRFLLy8vjhRdeAGDt2rWkp6dz7bXXdpeHQiHs9v7/RJg3bx7z5s2LS5xCiOQUjRr3OW3LNyy9AAAgAElEQVQ71ErloVZ2HGpjz+FGOjfUUO8N0NIZ7Pc8p91KYYaTwiwXRZku5pRkU5jpojDTSUGmk8JMFwUZXdsubEn4d8ZQSIIlUkZXMhSbCLX7Q7T7jeSma+k9Yjt0RALV7u9JkIaeBDXislu7E5wMl7FMd9rITXd2JzQuhxWX3YrLbjOWjgHW7YMf29VrM5LsbU5mTsga0WsKIcaGNWvW4HQ6qaysZOHChaxcuZLbb78dv9+P2+3mjjvuYPr06bz11lusW7eOBx98kLVr11JdXY3H46G6upprrrmGq6++OtFVEUKMoFA4wp76drZXG8nUdjOpqvcGuo+ZlJtGngtUSRZnmklSV8LUlSwVZjrJdNnH3IiS4yEJlkhqkUiUls4g9V4/dd1dy0Y3c31bgIZ2P3VeY7xuQ7sfX3Bo9+5YLJBhJj8Zrp5kqDDTyWRXOplOO+kuW3d5usNGusvefU73fjOZ8uzdzSknlWMf4YRHCJH6nt3q4al3Dgx6TCQSwWod+u+XSxeXccmiY58avaamhieeeAKbzYbX6+Xxxx/HbrezefNm7r77btauXXvEOVVVVTz66KN4vV5WrFjB5ZdfjsMxNmeYFGK8a/UF2XGozUikqo1kSte0dd8b7bRZmV2SyblzJlA+MZu5E7OZMzGbnDQHlZWVlJeXJ7gGyUESLBF34UiUhnYjQXr3YAeVnR4zaTJvbIy5wbGxPdDvfUM2q4X8DCcFGU6KslxML8ygMNNJbrqTjO6kKSaBiulRynAZPUkj+Q1Kk9MqyZUQYsxbvnw5NpsNgLa2Nm655Rb27duHxWIhGOx/yM/SpUtxOp3k5+eTn59PQ0MDJSUl8QxbCHGMotEoB5s7zV6pNrYfamH7oVYONHZ2H5Of4WTuxGyuOX0Kc0/IZu7EHKYXZYz46JlUJAmWGBGxSVO910+dmSD1rPf0PDW0B4j2ypkOA8a43KJMo0u5JMfNSZOMcbld3cpFMet56fGdDUYIIUbLJYtKj9rbFK/nYMW+x7333ktFRQX3338/Ho9nwKF/sRNi2Gw2QqEjZ/kSQoyOUDhCqy9ES2eQ5g7j3qfuV0eQZnO9uSNIa2eQ5s5A97bf7JWyWGBaQQbzJ+Vy2amTmTsxm/KJ2RRnu8bFcL7RIAmWGJLaVh/bDrWyq8ZLbZuvO2Gqi+lp6u9eJbfDao6/dVGWn86CyXkUZfbc3OhtOMSiE2dTmOUia5yMyxVCiLGgra2N4uJiAJ577rkERyNEaotEorT5QjR2BGjqCNDcEaCx3Uiamsz1lpjkqCuB6m/a8lgZThs5aQ5y0p3kpNmZXphpbjuYnJ9O+cRs5pRkxe35UOOF/GuKXiKRKHsb2tl+qJVt1cZre3Ur9V5/9zFdSVNRVu+kqSjLSKQKzWVRlosM5+BD8Sorm5lelBmPqgkhhDgG1113HWvWrOGBBx5g6dKliQ5HiDEjGo3S2hmizuujqSNIU7uRJA20Xt/WSZt/z4CTatmsFvLSHeSkOchNd1KS7UYVZ5Gd5iC3e7+xzElzdm9nux047TKcLxEkwRrH/KEwHx/2sv1QS3cyVXmolY5AGDCmAJ9VnMXZqoi5E7M58YRs5pRkk50mPU1CCJEqbrzxxn73L1iwgI0bN3Zv33TTTQBUVFRQUVHR77nr168fpSiFSA7t/pDx0NtWH7WtfmpafdS0+qlp81Hbtd7q6x5+15fTbiU/3UluuoO8dCdzSrKJ5lmZPqmYvAwneeb+rvXcdCfZbvm7a6yRBGucaOkMsr26lW3Vxk2M26tb2VXr7Z5AIsNpY+4J2Vy6uIy5E7OZe0I2s4ozcdltCY5cCCGEEGJ0BUIRDrf4qGnzdSdNta2+PgmUH28/Q/LSnTZKst1MyHaxcHIuxdluJmS7Kcx0kp/h7JUw9TfJljH7nopXVUUcSIKVojxNHbyu63hzVz0fHmzB09QzK0xRlosTT8jmvPIJzJ2Yw4knZDM5P10mjRBCCCFESvKHwlQ3+/A0deBp6uRgU2f3uqepk5o2X58JuIzepuJsF8VZbspLslk620VxttvYl+3ufmXK/UuiD/mJSBH+UJgtVU28rmt5/eM6dtV6AePBb6dMzuWKisndPVMTstwJjlYIIYQQYuT4gmGqmzu7E6buRKrZWK9p9fc63ma1MDHHzaTcNM6cWUhpXhqTctMoznF3J1W56Q4ZmieOiyRYY1iNN8jWf+zjdV3H5t31dATCOG1WlkzL57JTyzhbTWBGUYb8chBCCCHEmBcIRdjb0M7OGi87a9v4564aWl9rxNPUSV1b7wTKbrUwMddNaW46n5xVRGleupFE5aVRmpdGSbZbnl8pRo0kWGNI/71UByjNS+PihZM4e/YETp9RIFNtCiGEEGLM8gXD7KlrZ2dtG7tqvd0J1d6GDsLmveMWCxRn2JlR4uZcNaE7cepKpIqz3djk1geRIPKXeJLrupeqv16qcyY7+eInT2JGUab0UgkhhBBiTOkMhNldZyRPRhLlZVetl30N7d1TltusFqYUpDNrQiYrTprIrOJMZk7IZEZRJlW7Pqa8vDyxlRCiH6OaYCmllgP3Ajbgd1rru/qUTwYeAXLNY9ZorTeMZkzJbqB7qfrrpaqsrGTmhKwERyyEEGKsueqqq7j++us566yzuvc9/PDDVFVV8eMf/7jf42+++WbmzZsXzzBFiohEouyq8/KBp4WdNW3srDWSKk9TZ/fEEg6bhWmFGcydmM1nTj6BWcWZzJqQxdTCdJnRWIw5o5ZgKaVswP3A+YAH2KKUelFrvT3msB8AT2mtH1BKzQU2AFNHK6ZkFo1Gefbdg/zkpe00dwT73EtVJL1UQgghRsyqVavYsGFDrwRrw4YNfO9730tgVCJV1LX5ee9AM//c38R7B5r5wNPSPb2502ZlelEGp5Tl8YVFZcyakMms4kymFGTgkHuiRIoYzR6sJcAurfUeAKXUE8BFQGyCFQWyzfUcoHoU40lanqYOvv/cR2z6uI7FU/L46tIZci+VEEKIUXPhhRdyzz33EAgEcDqdeDweamtrWb9+PXfeeSd+v58LL7yQb37zm4kOVSQ5XzDMRwdbjITqQDPv7W/mYLPxaBi71cKciVl8dsEJnFKWxyllOUwtyJDJJUTKG82/4CcBB2K2PUBFn2N+BPxZKXUjkAEsO9pF/X4/lZWVwwrM5/MN+xojIRKNsn5HKw+92wjA1ysKWKmysVoa2b+n8ajnJ0s9hiMV6gBSj2SSCnUAqce489DKI/ed+FlY8q8Q6MD53xeDtc8wqVOugAVXQnsDPHV177IvvzTo2+Xm5jJ//nw2bdrEsmXL2LBhAytWrOArX/kKubm5hMNhVq9ezY4dO5gzZ84wKydSRSQSpaqhnX/ub+a9A0bv1I5DbYTMG6a6Hg3z5TOnckpZLidNysHtkOF9YvxJdBfJ5cDDWutfKKVOBx5TSp2ktY4MdILL5Rr2DY3GE7MTe1Pk7jova579gC17mzhrViF3XjyP0rz0Y7pGMtRjuFKhDiD1SCapUAcYH/XYunVrnKMRsVauXMmGDRtYtmwZL730Erfffjsvv/wyTz31FKFQiLq6Onbv3i0J1jjW2B4wEqn9Ru/U+weaafUZQ/0yXXbml+Zw/Sens2ByHieX5chzNoUwjWaCdRAoi9kuNffFuhZYDqC1/rtSyg0UArWjGFdChcIRfvtGFXf/5WPcdis/+/x8Pr+oVO6vEkKI8WywHidnOoErniMtLa3/8oyCo/ZY9ee8887jzjvvZNu2bfh8PnJycli3bh3PPPMMOTk5rFmzBr/ff/QLiZRxqKWTt6saeauqkberGrsn2rJaQJVks3L+CSwoy+WUybnMKMqUadCFGMBoJlhbgFlKqWkYidVlwBV9jtkPnAc8rJQqB9xA3SjGlFDbq1u5+dn3+ehgK8tPLOG2z54o3/YIIYRIiIyMDCoqKvj+97/PypUraW9vJy0tjaysLOrr69m0aRNLlixJdJhilESjUfbWt/ckVHsbONBo3DuV5bKzeGoeFy+cxKLJecwrzSHdmehBT0KMHaP2v0VrHVJKfQPYiDEF+zqt9Tal1G3AO1rrF4HvAL9VSt2EMeHFaq11dLRiShR/KMx9f93FA6/vJjfdyQNXLmTFvImJDksIIcQ4t2rVKm644QZ++ctfMmPGDObOncuKFSsoKSlh4cKFiQ5PjKBIJMrOWi9vVzXwVlUjm3fW0thZBUB+hpMlU/P58hnTWDItn/KJ2dI7JcQwjOrXEeYzrTb02ffDmPXtwJmjGUOibd3XxC3PfsCuWi8XL5zED1fNJTfdmeiwhBBCCJYtW4bWunv7rrvu6ve4xx57LF4hiRESCkfYfqi1u4dqy95GmjuCAJRku5lfksb5p0ylYlq+PApGiBEm/b2jpCMQ4mcbNQ9v3svEbDcPfflUzlETEh2WEEIIIVJQNBrlfU8Lb+6q562qRrbubaQ9EAZgakE6F8wtZsm0Aiqm5VOal8aOHTsoL5+S4KiFSE2SYI2CN3fVs+YPH3CgsZOrTpvCLSvmkCnPtBJCCCHECKtt8/Hcuwd56p0D7K5rB0AVZ3HxwlKWTMtnybR8irPlfm8h4kn+6h9BLZ1B7txQyRNbDjCtMIMnrz+NiukFiQ5LCCGEECkkGI7wuq7jqXcO8NcdtYQjURZPyeOnl8xg2dxi8jPkVgQhEkkSrBHyyvYafvD8h9S1+fnK0unctGy2PFxPCCGSkFJqOXAvxgRMv9Na39WnfAqwDigCGoEvaa09Ztlk4HcYjyGJAp/SWu89njii0ei4uO8lGk25uasSZletl6e3HuAP7x6krs1PYaaL686axhcWlTFzQmaiwxNCmCTBGqYGr58f/XE7f3y/mjklWfz26sXML81NdFhCCCH6oZSyAfcD5wMeYItS6kVz0qUuPwce1Vo/opQ6F7gTuMosexS4XWv9ilIqE4gcTxxut5uGhgYKCgpSOsmKRqM0NDTgdrvx+XyJDmdM8vpDbPjgEE++c4Ct+5qwWS2cO2cCly4u42xVhMNmTXSIQog+JMEaBl8wzEX3v0lNq49vnz+bry6dgdMuv+iEECKJLQF2aa33ACilngAuAmITrLnAt83114DnzWPnAnat9SsAWmvv8QZRWlqKx+Ohrm5oj34MBoM4HI7jfbuEcrvdlJaWsmvXrkSHMmZEo1G27mviyS0HeOnDQ3QEwkwvyuDWFXP43MJJ8gxNIZKcJFjD8Njf9+Fp6uSxa5dw1qyiRIcjhBDi6CYBB2K2PUBFn2PeBy7GGEb4OSBLKVUAzAaalVJ/AKYBfwHWaK3DxxqEw+Fg2rRpQz6+srKS8vLyY30bMcbUtvn4gzlhxZ66djKcNj49/wQuPbWUhZPzUrq3U4hUIgnWcfL6Qzzwv7s5a1ahJFdCCJFavgvcp5RaDWwCDgJhjDbzLGABsB94ElgN/H6wi/n9fiorK4cVkM/nG/Y1Ei0V6gAjX49QJMoWTwcbd7ax5WAHkSicOMHNTWcUcdbUDNIcVuioYceOmhF7T0iNzyMV6gBSj2QyUnWQBOs4PfS3KhrbA3znApXoUIQQQgzdQYwJKrqUmvu6aa2rMXqwMO+zukRr3ayU8gDvxQwvfB44jaMkWC6Xa9i9T6nQg5UKdYCRq8euWi9Pv3OAZ9+tpt7rpyjLxfWfnMEXFpcyo2j0J6xIhc8jFeoAUo9kMlgdtm7dOuTrSIJ1HFo6gvzmjT0sKy/mlDKZ0EIIIcaQLcAspdQ0jMTqMuCK2AOUUoVAo9Y6AtyKMaNg17m5SqkirXUdcC7wTtwiF2Neuz/ES/1MWPFFc8IKu0xYIURKkATrOPzmjd20+UJ854LZiQ5FCCHEMdBah5RS3wA2YkzTvk5rvU0pdRvwjtb6ReBs4E6lVBRjiOAN5rlhpdR3gVeVUhZgK/DbRNRDjB3RaJR39zfz1JYDrP+gmvZAmOmFGaxZMYeLZcIKIVKSJFjHqN7r56E397Jq/kTKJ2YnOhwhhBDHSGu9AdjQZ98PY9afAZ4Z4NxXgPmjGqBICfVeP8+9e5An3znArlovaQ4bq+ZP5IunlrFoikxYIUQqkwTrGD3w+m58wTA3nS+9V0IIIYToEY5E2fRxHU9uOcBfKmsIRaIsmJzLXRfPY9XJJ5Dpkj+7hBgP5H/6MTjU0slj/9jHxQvjcwOqEEIIIZLf/oYOnnrnAM9s9XC41Ud+hpPVZ0zl0lPLmF2clejwhBBxJgnWMVj7111Eo1G+dd6sRIcihBBCiATyBcP86aPDPLnlAH/f04DVAp+cXcR/fHou55UX47TLhBVCjFeSYA3R/oYOntpygMuXTKYsPz3R4QghhBAiAXY1+Pnv5z/ihfcO0uoLMTk/ne9eMJtLFpUyMSct0eEJIZKAJFhDdM+rH2OzWvjGuTMTHYoQQggh4qSpPcDf9zTw5q56Nu9uoKq+HZfdyoqTSrj01DJOm1aA1SoTVgghekiCNQS7att4/p8HufYT0yjOlulUhRBCiFTVEQjxdlUjm3cbSdX2Q61Eo5DhtFExvYBPzUjj+gsXkpPuSHSoQogkJQnWENz9yk7SHDa+unRGokMRQgghxAgKhCK872k2eqh2NfDPA00Ew1EcNgsLJ+dx07LZnDmzgPmluThsViorKyW5EkIMShKso/joYAsvfXiIG8+dSUGmK9HhCCGEEGIYIpEolYdb2byrgTd31/N2VSMdgTAWC5x0Qg7/8olpnDmjkFOn5pPmtCU6XCHEGCQJ1lHc/crHZLvtXHfW9ESHIoQQQohjFI1G2dvQwebdRg/V3/c00NgeAGB6UQaXLCzlzJkFnDa9gNx0Z4KjFUKkAkmwBvHu/iZe3VHL9y5U5KTJcAAhhBBiLGj3h/jbrnpe17Vs+rieg82dAJRkuzlbFXHmjELOmFkgs/4JIUaFJFiD+PlGTWGm8bBAIYQQQiSvvfXt/HVHLa/pWt7a00ggHCHTZefMmQV8del0zphZyPTCDCwWmfFPCDG6JMEawGZzOtZ/XzWXDJf8MwkhhBDJJBCK8M7eRv66o5a/6lr21LUDxrC/q0+fwrlzJrB4ar488FcIEXejmjkopZYD9wI24Hda67v6OeZS4EdAFHhfa33FaMY0FNFolJ//WVOS7ebKismJDkcIIYQQQG2bj9d1Ha/tqOWNnfV4/SGcNisV0/O56jQjqZpSkJHoMIUQ49yoJVhKKRtwP3A+4AG2KKVe1FpvjzlmFnArcKbWukkpNWG04jkWr+s63t3fzO2fOwm3Q2YQEkIIIRIhEony4cGW7qF/H3haACjOdvHpkydyjprAmTMLZaSJECKpjOZvpCXALq31HgCl1BPARcD2mGP+Fbhfa90EoLWuHcV4hiQSMXqvJuenc+niskSHI4QQQowrrb4gf9tZz1931PK6rqPe68digQVluXz3gtmcM2cCcydmy71UQoikNZoJ1iTgQMy2B6joc8xsAKXUmxjDCH+ktf7TKMZ0VH/adpht1a384gsn47DJuG0hhBAiHhq8fn7xysc8teUAoUiUbLedpWoC584pYunsCeRnyBTqQoixIdF96nZgFnA2UApsUkrN01o3D3SC3++nsrJyWG/q8/n6vUY4EuXO9R7Kchwod+uw32e0DVSPsSQV6gBSj2SSCnUAqYcYPwKhCI9s3suvXt1JRzDM5UvK+MzJk1g4ORe7fNEphBiDRjPBOgjEjrErNffF8gBvaa2DQJVS6mOMhGvLQBd1uVyUl5cPK7DKysp+r/GHdz0caAly/xULOenEicN6j3gYqB5jSSrUAaQeySQV6gDjox5bt26NczQimUSjUV6trOX2DZVU1bezdHYR/76qnJkTshIdmhBCDMtoJlhbgFlKqWkYidVlQN8ZAp8HLgceUkoVYgwZ3DOKMQ0oGI5wz192MndiNitOKklECEIIIcS48HFNG/+5fjtv7KxnelEGD335VM5JjnmuhBBi2IaUYCmlzsSYSn2KeY4FiGqtpw90jtY6pJT6BrAR4/6qdVrrbUqp24B3tNYvmmUXKKW2A2Hge1rrhuFU6Hg9/Y6H/Y0drFu9GKtVbpwVQgghRlpTe4C7//Ixj7+1nwynjR+umstVp0+Re56FECllqD1YvwduArZiJEJDorXeAGzos++HMetR4NvmK2F8wTBr/7qTBZNz5Rs0IYQQYoQFwxGe397C/zz5Gl5/iCsrpnDT+bNl4gohREoaaoLVorV+eVQjSaD/fms/h1p8/OILJ8u0r0IIIcQIek3X8pP129ld184nZhby76vmokrkPishROoaaoL1mlLqZ8AfAH/XTq31u6MSVRx1BEL8v9d3ccaMAs6YWZjocIQQ40QwGMTj8eDz+QYsT4XZ94LBIFVVVZSWluJwOBIdjoijXbVefvLSdl7XdUwtSOc/zi1m9fmL5ItMIUTKG2qC1fX8qsUx+6LAuSMbTvw99OZe6r0BHrxKJToUIcQ44vF4yMrKYurUqf3+wdnZ2UlaWloCIhtZHR0ddHR04PF4mDZtWqLDEXHQ0hHknlc/5rG/7yPNYePfPlXONWdMZfdOLcmVEGJcGFKCpbU+Z7QDSYSWziAP/u9uzp0zgUVT8hIdjhBiHPH5fAMmV6nEYrFQUFBAXV1dokMRoywUjvA/b+/nl698TEtnkC+eOpnvXDCbwkxXokMTQoi4GuosgjnAfwCfNHf9L3Cb1rpltAKLh9+/sYdWX4hvnz870aEIIcahVE+uuoyXeo5nf9tZz3+u346uaeO06fn8cNWJzD0hO9FhCSFEQgx1iOA64CPgUnP7KuAh4OLRCCoeGtsD/P5vVXxqXgknTcpJdDhCCBE3TU1NrF69GoD6+nqsViv5+fkAPP300zidA8/s9uGHH/LCCy/wgx/8IB6hDkgp1YYxVL2vrseIyF/3oygUjrCz1suHB1vY+NFhXt1Ry+T8dH79pUVceGKxJNVCiHFtqAnWDK31JTHbP1ZKvTcaAcXLr/93N53BsPReCSHGnby8PF544QUA1q5dS3p6Otdee213eSgUwm7vv3mYN28e8+bNi0ucg9FayzR0cRIKR9hd184HnmY+OtjCBwdbqDzUii8YASAnzcEty+fwL5+YistuS3C0QgiReENNsDqVUp/QWv8Nuh883Dl6YY2uho4Qj2z28NlTJjFzgrTRQgixZs0anE4nlZWVLFy4kJUrV3L77bfj9/txu93ccccdTJ8+nbfeeot169bx4IMPsnbtWqqrq/F4PFRXV3PNNddw9dVXxyVepVT+YOVa68a4BJJiwpEoe+q8fOBp4cODxmt7dSudQeMRmBlOGydOyuHKiinML81h3qQcphZkYLVKj5UQQnQZaoL1NeAR814sC9AIrB6toEbbkx80E45E+T/LpPdKCJF4z2718NQ7B3rti0QiWK3W477mpYvLuGRR6TGdU1NTwxNPPIHNZsPr9fL4449jt9vZvHkzd999N2vXrj3inKqqKh599FG8Xi8rVqzg8ssvj9d07Fsxhgj295d9FJg+0IlKqeXAvYAN+J3W+q4+5VMwhsYXYbR3X9Jae2LKs4HtwPNa628Msx4JE45Eqao3hvl94Gnho4MtbKtupSNgJFNpDhsnTcrmsiVlZjKVy7TCDGySTAkhxKCGOovge8DJZqOC1rp1VKMaRQcaO3h5ZyuXnjqZyQXpiQ5HCCGSxvLly7HZjCFebW1t3HLLLezbtw+LxUIwGOz3nKVLl+J0OsnPzyc/P5+GhgZKSkpGPVat9XHN+a6UsgH3A+cDHmCLUupFrfX2mMN+DjyqtX5EKXUucCfGvcdd/hPYdHyRJ1Y4EuXuVz7m7b2NbDvYQruZTLkdVk48IYdLF5cxb1IO80tzmF6UKcmUEEIch0ETLKXUl7TW/6WU+naf/QBorX85irGNigc37caChRvPnZnoUIQQAoBLFpUe0duUiOdgxb7fvffeS0VFBffffz8ej2fAoX+xE2LYbDZCodCox9mXUioPmAW4u/ZprQdKgJYAu7TWe8xznwAuwuiR6jIX6Gr3XgOej3mvRUAx8Cd6PxtyTHirqoH7XtvFiSdk8/lFpZw0KYf5pbnMKMrAbjv+HlMhhBA9jtaDlWEuU+ZGJVWcxdcqCpiYM/Yf4CmEEKOlra2N4uJiAJ577rkERzMwpdR1wLeAUuA94DTg78C5A5wyCYgdj+kBKvoc8z7GLLn3Ap8DspRSBUAT8AvgS8CyEapCXL25qx6b1cIT159GljsuQzmFEGLcGTTB0lo/aC5/HJ9wRt9Vp0+lsnLMzs8hhBBxcd1117FmzRoeeOABli5dmuhwBvMt4FTgH1rrc5RSc4A7hnnN7wL3KaVWYwwFPAiEga8DG7TWnq6RHEPh9/uprKwcVkA+n2/Y1wB45YODqEInnqpdw77WsRqpOiSa1CN5pEIdQOqRTEaqDkN90PBPgZ9gzBz4J2A+cJPW+r+GHYEQQoiEufHGG/vdv2DBAjZu3Ni9fdNNNwFQUVFBRUVFv+euX79+lKIclE9r7VNKoZRyaa13qMGzn4NAWcx2qbmvm9a6GvM5j0qpTOASrXWzUup04Cyl1NeBTMCplPJqrdcMFqDL5aK8vPw4qtajsrJy2Ndo7giws3EP3zx3FuXl8Z/kaSTqkAykHskjFeoAUo9kMlgdtm7dOuTrDHUWwQu01jcrpT4H7MVoeDYBkmAJIYRIJI9SKhfjPqlXlFJNwL5Bjt8CzFJKTcNIrC4Drog9QClVCDRqrSPArRgzCqK1vjLmmNXA4qMlV8lk8+4GolE4a1ZhokMRQoiUNtQ7WrsSsZXA01rrllGKRwghhBgyrfXntNbNWusfAf8O/B747CDHh4BvABuBSuAprfU2pdRtSqn/396dx0dV3X0c/0x2lrAvssnuAZRFQFAB64IKoiC4gAsq4lp9Wh+tVq2t1mrV1kdbq7ZVS0FFEbdqNW51qWt4tiIAACAASURBVMZdFFEMxwREBBEIu5A98/xxbshkmclAMmu+79drXnPnnnvv/E5mMmd+c889Z6q32ZGANcZ8jRvQ4tYIViFqcgsKaZ2ZxvBe7WIdiohIUgv3DNYLxpgVuC6ClxpjOgPFkQtLRESkYcaYQ4Hl1tqd1tr/etOJHAx8GGwfa20OkFNr3W8Clp8Cngr1vNba+cD8fY88+nLzCzm0X0fSNVqgiEhEhfUp63WBOBzXHaIM2IUb1lZERCSW/gr8GPD4R2+dBFizeTdrtuxm/ICOsQ5FRCTpNTQP1tHW2jeMMTMC1gVu8kykAhMREQmDz1rrr3pgra00xoTbO6PZeKdgEwDjB3aOcSSSUCrKobIM0r2pbcqKwV9Zcxufr4HyFEjPqj5eqv49Jfk19C7/CfAGcFI9ZX6UYImISGytMsb8jOqzVj8FVsUwnrj0bkEh3dpm0b9zq4Y3FgFYvwwengaZ2XDFMrfu8Zmw6q2a23U5EH76nluePwXWfVKzvNdYmPuqW37oGDjiahh8YkRDF4m1hubButG7nxOdcEREJBpmz57NRRddxIQJE/asmz9/Pt988w2//W3dqQ9nz57NNddcw9ChQ6MZZjguAe4BbsD98Pc6cFFMI4ozFZV+3i3YzHFDuuLz+WIdjiSCXYWw6Ex3Zmr8FdXrR54D/Y6quW2rgFEpx1wIO2v9Jt+mu7v3+yEtE565CC74D3QdEpnYReJAuPNg/R74g7V2m/e4PXCVtfaGSAYnIiKRceKJJ5KTk1MjwcrJyeHqq6+OYVR7z1q7ETfUugTx5brtbC8qY7yGZ5dwVJTB4nNh1yaY8xL0GFlddtApofcdHuJf0eeD0xbAAz+BRWfAhW9Cyw5NE7NInAl3KKHJVckVgLV2K3BCZEISEZFIO/7443nrrbcoLS0FYO3atWzcuJEXXniBGTNmMGXKFO65554YR9kwY8wBxpjXjTFfeo+HGWP041+A3IJCAMYNUIIlYVgyH77Nhal/qZlcNYU23WDmo7Dje3hqjrsmSyQJhXulYaoxJtNaWwJgjGkBZEYuLBGRZuafU2o8zKisgKGnuC43pbth4Wl19xlxJhx8FuzaDIvPqVk258WQT9euXTuGDRvG22+/zcSJE8nJyWHy5MlcfPHFtGvXjoqKCs477zxWrFjBoEGDGlu7SHoQuBr4O4C1dpkx5jHglphGFUfeyd/E4G5t6NRazbaEYdQcaNsLzKTIHL/XGJhyF7x5K2z/Djr0jczziMRQuGewFgKvG2PmGmPmAq8BCxrayRgzyRhjjTEFxpigs90bY04xxviNMaPDjEdERBppypQp5OS46aBefPFFpkyZwksvvcT06dM5+eSTyc/PZ+XKlTGOskEtrbUf1Vqnn8U9u0vL+fTbbUxQ90BpyPdLYecPbpS/SCVXVUbOhss+UnIlSSusM1jW2juMMZ8DE71Vv7PWvhJqH2NMKnAfcCywFvjYGPO8tfarWttlAz8nxKSQIiJJr9YZp9KiIlq08IY+zmgZ+oxUq44NnrGqzzHHHMNtt93G8uXLKS4upm3btsybN4+nnnqKtm3bcu2111JSUrLXx42yQmNMf9wAFxhjTgXWxzak+PHRN1sorahkvLoHSijb17mz5J0O2KfPkn2S1QYqK+D1m2HwVOg5KjrPKxIFezOdex7wsrX2F8A7XmIUyhigwFq7ylpbCiyi/smJfwfcARTvRSwiItJIrVq1YuzYsVx//fVMmTKFXbt20aJFC7KzsyksLOTtt9+OdYjhuAzXPXCQMWYdcAVuZEEBcvMLyUhLYUxfDSYgQZQVwRNnQdlumHJndJ+7eDssf8Y9/84fovvcIhEUVoJljLkQeAqvjzvQA/hXA7v1AL4LeLzWWxd43JFAL2ttlH4uERGRQCeeeCIrVqxgypQpDBo0iCFDhjB58mSuuuoqRo5s4gvcI8D7EW8i0BkYhJu/cXxso4ofuQWFjO7dnqz01FiHIvHI74d/XwHffwYzHoAug6P7/C07wKzHXKL1xGwoj/sz5iJhCXeQi8twZ6Q+BLDW5htjujTmiY0xKcBdwHl7s19JSQl5eXmNeWqKi4sbfYx4kAz1SIY6gOoRTxKlDmVlZRQVFQUt9/v9Icubyrhx41i6dCkARUVF3HjjjXW2KSoq4oEHHtizvDeq6lFWVtakr4sxpg2ubeoBPAf8x3t8FbAMd+1ws7ZxZzErftjJNZNMrEORePXpAli2CI68HgZNaXj7SNhvKJx8Pzx5Hrx4lRu9UPO1SYILN8EqsdaWGuM+pI0xaXj93UNYB/QKeNzTW1clGzgIeMs77n7A88aYqdbaWtOAV8vMzGTw4Mb9wpKXl9foY8SDZKhHMtQBVI94kih1yMvLq77Gqh5FgddgJbCqeqSnp9d5XZYsWdKYQz8CbAXeBy4EfgX4gOnW2qWNOXCyeK9gMwATBnSOcSQSt4ZMg92bYdz/xjaOA6fDD1/Ce3+BcT+HTgNjG49II4WbYP3XGHM90MIYcyzwU+DfDezzMTDQGNMXl1jNAs6sKrTWbgf2XHVrjHkL+EWo5EpERMTTz1o7FMAY8xBuYIv9rbW6ntfzTn4h7Vqmc2D3NrEOReLNju/xVZRCi/Yw4apYR+Mc9SsYdrqSK0kK4Q5y8UtgE/AFcDGQA4ScyNFaWw5cDryCGyBjsbV2uTHmZmPM1H0PWUREhLKqBWttBbBWyVU1v99PbsEmxvXvREqKultJgJKd8MgMer4bdPac2EhJgc5ed9ZlT8LWb2Mbj0gjNHgGyxtufbm1dhBuQsewWWtzcMlY4LrfBNn2yL05tohIovP7/fiawbUGfn9DPcr3yXBjzA5v2YfrYbHDW/Zba5v1aZuVm35kw44Sxmv+KwlUWQnPXgKFls1H/InWsY6nPrs2u2ux2u0Pc1+BjFaxjkhkrzWYYFlrK7zJgve31q6JRlAiIskuKyuLzZs307Fjx6ROsvx+P5s3byYrK6tJj2ut1bB4IbyTXwig+a+kprf/ACtegONvY3e7MbGOpn6tOsKp82DhqfDcZXDqPzXohSSccK/Bag8sN8Z8BOyqWmmtVVc/EZF90LNnT9auXcumTZvqLS8rKyM9PT3KUTW9srIysrOz6dmzZ6xDaVZy8wvp07ElvTq0jHUoEi9WvAhv3QbDz4RDL4UVK2IdUXADJ8LEm+A/N7pRBuPlOjGRMIWbYP06olGIiDQz6enp9O3bN2h5ooyG2JC8vLyQ9ZSmV1ZRyQerNjN9ZI+GN5bmo+NAGHoanHh3YpwRGvdz+OELeP13MOgk6HxArCMSCVvIBMsYkwVcAgzADXDxD2/wChEREYlDS7/bxq7SCnUPFKesCNKyXIJyykOxjiZ8Pp+bE+ugU5RcScJpaBTBBcBoXHI1Gfi/iEckIiIi++yd/EJSfHBYfyVYzV5FOTx+BrxwRawj2TcZLWHQCW553adQtC228YiEqaEEa4i19mxr7d+BU4EJUYhJRERE9lFu/iaG9WxH2xaJfw2fNNJ/boRVb0KP0bGOpHF2b4EFJ8EzF0JlRayjEWlQQwlW4Dwj6hooIiISx3YUl/H52u1M0PDs8vkieP9eGHMxjJwd62gap2UHOPa3kP8qvPG7WEcj0qCGBrnQPCMiIiIJ4v2Vm6mo9DNO1181b+uWwPM/gz4T4PhbYx1N0xg91w16kXs3dD0Ihp4a64hEggqZYGmeERERkcTxbkEhLTNSGbl/+1iHIrFUvB06HQCnLYDUJOkq6vPB5D/CxhXw3OXQbXisIxIJqqEugiIiIpIgcvMLGdu3Axlpat6bJb/f3fc/Gi5+203am0zSMmDmI3DEVdChX6yjEQkq3HmwREREJFbCuLB/3bYiVhXu4qxDe0chIGkSK3JgzXs117XsBOO9Uf8+ehC2fVuzvE0PN1EwwPv3w87vq8s2Weh3JBx2GaQkaZLdugsccTUAmVu/hlcX1iwP/Pt9/BBsXV2zPNTfD6B9XzhkrlvOvRt2b65Z3nkQHHy2W37rDijdWbN8v+Ew7DS3bF8CM3nv6idJQQmWiIhIPPvXZfTYvB4OfCbkZrn5mwA0wEWi8Pth2SL46jlIb1m9vmP/6gRhxYvw3Yc19+s2vDpBWP4sbPiyusyXAvsfGtm440XxDrp/+FvYvb7m+tp/vzUf1CwP9fcD6H14dYK17EnY+k3N8oHHVidYSx+FXYU1yw+c4RKs4u3w1Pkw4wEYfNK+1VESlhIsERGReNayPdmfPw471kObbkE3yy3YTJfsTAZ2aR3F4GSf+Xxw6nyoLIO0zPq3OedfoY9xwWtNHlbCyGrDN5MWMnjw4ODbzH429DEa+vv99L3Q5Vd8EbwsNRO6DIZnL4EO/aHrkNDHkqSSpOePRURE6meMmWSMscaYAmPMtfWU9zbGvG6MWWaMecsY09NbP8IY874xZrlXNjMqAY+ag89fAZ89GnSTyko/7xYUMn5AJ3w+X1TCkn1UvB0WnwNbv3Xd+IIlV5LY0rNg5qOQ0QoWneHm8pJmQwmWiIg0G8aYVOA+YDIwBDjDGFP7p+U7gYettcOAm4HbvPW7gXOstQcCk4A/GWPaRTzojv35seshsGR+0Guxvlq/gy27Shmv7oHxrbICnr7QdV3bvjbW0Uiktenukqwd37vughWaUra5UIIlIiLNyRigwFq7ylpbCiwCptXaZgjwhrf8ZlW5tfZra22+t/w9sBHoHI2gt/WfDjvWQsF/6i3PLXDXgYzX/Ffx7Y1bIP8VmHwH9BkX62gkGnqNgSl3QfeDXbdQaRaUYImISHPSA/gu4PFab12gz4EZ3vJ0INsYU2O8a2PMGCADWBmhOGvY2eMImPhb6Dai3vJ3CwoxXbPp0iYrGuHIvvjyaci9C0ad5ybNleZj5GyYeCOkpEJFWayjkSjQIBciIiI1/QK41xhzHvA2sA7Y0zfPGNMNeAQ411pb2dDBSkpKyMvLa1RAxaXl5HU8HtZuAWpey1FaUcmHqzZzwgHZjX6eSCouLo7r+MK1T/XwV9L7zbug0zC+7TsXVqyITHB7IRlej0SrQ+ZWS893r2XdYbdS3LG6Z3Ki1SOYZKhHU9VBCZaIiDQn64BeAY97euv28Lr/zQAwxrQGTrHWbvMetwFeBH5lra01/nP9MjMzQ490Foa8vDx3jK+eg9JdMOLMPWW5+YWUVqxm2ljD4EFdGvU8kbSnDglun+sx4FUoK2Jwq/joxpkMr0fC1WFXF/gwnb4f3gAXvQXZXYEErEcQyVCPUHVYsmRJ2MdRF0EREWlOPgYGGmP6GmMygFnA84EbGGM6GWOq2sfrgHne+gzgWdwAGE9FMeZqny2E/9xUo5vROwWbSE/1MbZfh5iEJCFUlLnJaEt+dKPJxUlyJTHSqiOc8RgUb4PFs6G8JNYRSYQowRIRkWbDWlsOXA68AuQBi621y40xNxtjpnqbHQlYY8zXQFfgVm/96cARwHnGmKXerf6LoiJl9Pnw4wawL+1ZlZtfyMj929MyQ51S4s7L18Fbv4dVb8U6EokX+w2Fk+93E0jn/MJNOC1JR5/GIiLSrFhrc4CcWut+E7D8FFDnDJW19lEg+GRU0TDwWGjTEz6ZB0OmsmVXKcu/38FVxx4Q07CkHksWwMcPwmGXw+ATYx2NxJMDp8MPX8CG5Rr0IkkpwRIREUkUKaluFLo3b4HNK3l3bQsAzX8Vb9Z8CC9eBf2PdqM/itR21A3uPkWdyZKRXlUREZFEMnI2dB4MP24gN7+QNllpDOsZ+fmOJUyVlfDCFdC2J5w6D1L1W7bUIyXF3bavo9d/r4Bta2IdkTShiP7XG2MmAX8GUoGHrLW31yq/ErgAKAc2Aedba7+NZEwiIiIJLXs/uOwD/H4/uY+/yeH9O5GaoglM40ZKCpyxCMqLoUX7WEcj8a6siBabv4RFZ8L5r0JGy1hHJE0gYmewjDGpwH3AZGAIcIYxZkitzT4DRltrh+H6u/8hUvGIiIgkk29/KCRl+2p1D4wXfj/k/dudwWrfGzqbWEckiaDTANYddjP88CU8d5kGvUgSkewiOAYosNaustaWAouAaYEbWGvftNbu9h5+gJuPRERERBrQ+vFp3Jn+d8YPUIIVFz64H544G756NtaRSILZ1e1wmHgjLH8Gcu+OdTjSBCKZYPUAvgt4vNZbF8xc4KUQ5SIiIuL5b9rhjE1ZQe9KXbsRcyvfgFdvgMEnwZDpsY5GEtG4K+CgU+CLJzU/VhKIiysvjTFnA6OBnzS0bUlJCXl5eY16vuLi4kYfIx4kQz2SoQ6gesSTZKgDqB4SWnlFJX/ePJZpKf8kbckCmHx7wztJZGxZBU/Ogc6D4OS/aVQ42Tc+H0y9F/wVkJYZ62ikkSKZYK0DegU87umtq8EYMxH4FfATa22DKXtmZiaDBw9uVGB5eXmNPkY8SIZ6JEMdQPWIJ8lQB2ge9ViyZEmUo0key9ZtZ01JSzYMOI4enz8Gx/xGF8fHQmUlLD7HfTme9Rhkto51RJLIqv6HS3fDW7fBhKughUYITUSR/JnlY2CgMaavMSYDmAU8H7iBMeZg4O/AVGvtxgjGIiIikjRy8wvx+aDN+IugeDsUvBbrkJqnlBSYdAectgA69I11NJIsNix31/Q9cyFUVsQ6GtkHEUuwrLXlwOXAK0AesNhau9wYc7MxZqq32R+B1sCTxpilxpjngxxOREREPLn5hRzUvS3Z5ki4+G0YPLXBfaSJFea7+z7joF+DVziIhK/XITD5Dsh/Fd64JdbRyD6I6DVY1tocIKfWut8ELE+M5POLiIgkmx9Lyvl0zVYuPKKf65rWbXisQ2p+8l6AxbPh9Edg8ImxjkaS0ei5sH4Z5N4F+x3kBsCQhKErMUVERBLIR99sprzSz4TA4dlzroFXfhW7oJqRzG0r4dmLodsIGKDfiSVCfD444U7odSi8+msoK451RLIX4mIUQREREQnPO/mFZKWnMLJ3++qVpT/CV8/BkddCZnbsgotHZcWQnuUtF4G/sma5LwXSW4RXvnMDPXOvgYxWMGth9XFFIiEtA2Y+4v6/07Pc4BfUmojYl1r9PmywfFfd50hJqx61MFS53w9lu+spT3dx+v34yovqHqOqvLISyovq7p+aAanpYZRXQHk9SWZqJqSmQUU5VNQzVl5aFqSk1l0fYUqwREREEkhufiGH9OlAVnrAl4ZRc2DpQjeHzujzYxdcPPH74aVfwtcvwxXL3LrHZ8Gqt2pu1/UguPRdtzz/RFj3Sc3yXmNh7qtu+eGppBVthDk50KZ7RMMXAaB1F6CLW37wKNi0omZ5/2Ng9jNu+b4xsP27muVDpsHpD7vlu4ZA8baa5SPOgpPvd8u37w+V5TXLx1wMJ/zBzc31+3re8xOucqOY7t7MoKePqlt+zI0w4UrYvgb+XE935hPuhDEXwsav4G/j6paf/FcYcSas/RjmHV+3/PRHYMhU93+9sJ5ulLOfhf5H110fYUqwREREEsQP24vJ3/gjp43uWbOg52joOhQ+nueSLZ8vNgHGk48fgo/+DsPPqF438ty6X7ZaBnS1HHMR/FhrwJDsbtXLh13Oml0Z9Ok1punjFWnIYZdD0Zaa69rtX7084Uoo2VmzvOOA6uWjrq97FqjLkOrliTfVPYNbdY1nShoce3PdmHoe4u7TW7Jh+OV07dKlZnlvL2nKalf//lX/S6271l/ebYS7b7d//eVV8XcaUH95h/5110WBEiwREZEEkVtQCMD4AZ1rFvh8MHoOvHglrFviEq7mbHUuvHwtHDAJpt1fvf6gGaH3Gz4zdPnI2RRp4myJlZGzQ5c3dPZ67MWhyw//n+BlqWkw7ufByzNasmXQ2XQNNn9ji3ah92/dOXR5m+6hy9v3CV0eZUqwREREEsS7BYV0ap3BoP3quc5q2OmwuQBadapb1pxsW+Mm/23fF2Y84OaqEhGJIiVYIiIiCcDv95NbUMi4AZ1ISamnC2BmNky6LfqBxZvUDOh+MEy6HbLaxjoaEWmGlGCJiIgkALthJ5t2ljBuQANnqFa/60bjam5DiPv97vqR7P3g7KdjHY2INGM6by4iIpIAcvPd9VcTBjaQYL32a3j5epdwNCe5d8PCU72hqkVEYkcJloiISALILSikf+dWdGvbIvSGo8+HQgvfvhedwOLB16/A6zdDi/bVc1aJiMSIEiwREZE4V1rh58NVW5gwsHPDGx84AzLbwpJ/Rj6weFCYD09fAPsNhan3aoh6EYk5JVgiIiJxbsWmYorKKhq+/gogoyWMOAO+eg52FUY+uFgq3g6Pn+EGtpj1mKu7iEiMKcESERGJc59+X0Rqio9D+3UIb4dRc9wIeptsZAOLtR3roaIETn8Y2vWKdTQiIoBGERQRkb3l96sbVpQtXV/Ewb3akZ2VHt4OXQbBlXmQGub2iarLILh8CaRlxDoSEZE9lGCJiCQSvx8qSqG8xM175PO5bmC7NkF5MZSXul/0y0thwDGu/LuPYMNyt095sbvHD0de64758UNuQITA8oxWcOYTrvxfl0H+q155ERx0Kkz/a8z+BM3N9t1l5G8u4Wcj9t+7HVPTobLCdaNrGeaZr0Tx5dOw7lOY+FslVyISd5RgiUjzUDVHTmUF+CugogzSMt2tvAR2fA+V5W59ZZm779DPfTHdtRnWfVKzrKIM+h8FbbpDYQHYHFdWWVG93ei5rtvSmg9h6UJ3/MDnOP42V573b/jw7zWO33f3j9AjB9p0g/fuhf/e4eKsKKmu0y9Xu1HT3rsH3v1z3TrfsMl9+Vz2hEuiAqW3qk6wtnwD3y91o6+lZUJalrumpUq34e7LelqWK+9+cJO/PBLceysLqfSHMTx7bX4/PHQMtO0FMx+JTHCxsH6ZS/q7DXf/0yIicUYJloiEx+/3EotS71YGKWnQqqMr/+FLd/YjsLx1F/clCOCzhe7sR3lp9TbdhsMBx0NFObx0jUseKsq8RKIMBk2Bg89yv8A/Mt1LUCpdglRZDmMvhkMugO3r3BfJygoGlpfCc3633cQbYcyFsDEP7j+0bp2m3QcHnw3rP4d/HFu3/LT5cOB0+OFzeOz0uuVnPeUSrI1fubmH9vC5hOSAyS6B2rEOvn4ZUtIhNc393VLS3d+r6m/rr3TJS2prSEmjNK2YLJ93mWzXITDirOqEMDXDS4IyXfnQ06DbCC8BynDr0zIhJdWVH/UrmHCVV55Vswzg+FvdLZixFwUvk4h7p6CQFuk+hvVst3c7+nzQZzy8f7+7VqlNt8gEGE27CmHRme6Hj5mP6OyViMQlJViS2Kom0vT53Jf08mLA76337jNauy+15SVQuqtmGUCLdu7LcOlu90U+cP/KCvcFOjUdftwEO9a656k6i1FZBn0muONsWO5uVesry922h8x1+xf8x3XVqix3x60sd1+qJ93m9l/6GKzOrVmemgGnPOjK3/4jfPO2V+aVt+wIZy125S9fD2ver/rDuLu2PWHmo275+f9xiUTg362zgVO8MxtPzqHfd5/Ba77qRKrnITBroSu/52DY+k3Nv7+ZAmc85pYfOdl1Uws09PTq+HN+AWW1JgAdNcclWL4UN+JZWqb7W6V6SULJDrddShpktfMSkzRISfGSuy6uPL0FDJgIKans2L6TDh06uQSiy2BX3rITHHFNzX1T0qD7SFfeoR+c/NeABMiLodswV95jFFzwRs2y1DRo3dX7O0yG69Z6Zek1kxeAg2a4WzBDprpbgHV5ebTJ9o7f/2h3C2a/oe4WTLJ1D2tm3i0oZPh+LUhP3YdxqUbNgff+Ap89Cj+5uumDi6aKMlh8rvucmfOS+wFHRCQOKcFqbiorXBJSVuxda1EM6S3dL5t+P6x8o3p9eQmUFUGXIdD7MLfPW7+v/oLv9+4HHgdmEhRtc2chAsv8lTD8DBh8ojvL8K9La3TT6rNrJ3A9DD7JnWVYfK6XeFS4MxCV5XDCH93+377vziJUdbOqep4zFrkvuAWvweOz6tb53H9D3yNcN6yn59Ytv/BN6DHSdaN64Yq65Zd/Ap0GwueP1zpL4blyhbv/6nn47+11y0ec6b50r3wT3r8XfKkBiUIqHHer+9K/aYVLoHwBCUBm6+rjVJS5sz8pad4ZiJbuGpwqmdku4ari80GLgC/WLTtCduAv2D5oFTCnTnY3StpsI7N9Ry+ByIBOB1SXH3oplOysLktNh/Z9qsun/829h6oSpJR0aB1w/Ms/8crSXfJUlaSAq/81K+v+7apktILZzwQvb9kBpt0LwIa8PDoMHlyzvHVnOPpXwfdv1cm9TsFktYWeo4KXV9VLJAIO6t6WQ7v6923njv2h35Hw6QKYcGXd5D+RrP/cddU96R73mS0iEqeUYCWa0l1QtBWKttJyw+fgz3dfrPsf5cpfvQG2rdmzDUXbYcDRcJJ3fcbtvaF0Z81jHjx7z5dTFp5at0/72EtdgoUfPvhr9Zd/X4prrNv1cglWZTms+cCt86VW3xdv9w7kd4lbVVlKBhUZvuprPdJbuDMOe/b3zjZUnSXI7upirToDUXWcDv1deedBcOzNgM8b4cy779DPlXcbAZP/4MXiqx4Fra03tO/+h8GJd9faP6U6CRk0xSVaVfVPTXdJRMsOwDbXFW3oaQFnOdK9JMlLgo79HRx3S/DR14692Ys/iKOud7eg5dcFLwOYeFPo8km/d2dNaicnVcZeHHr/ARNDl7ftEbpcJEqMMZOAPwOpwEPW2ttrlfcG5gGdgS3A2dbatV7ZucAN3qa3WGsXRDre+84aSV5e3r4fYNQcePJc9wPawHq6wiaKnqPhZ5+5XgUiInFMCVY8qKyo/lVxyQI3b0nxtuokqW3P6m5cDx0LG5cD0Ltq/z4TqhOs7z5yZ5JatIc2PaHLgbDfsOrn+sk1gD/gWows6DjAlfl8cP6rrk971XUaaVnVCUJ6C/h1rS5ggVp1giuWBS9v2xPmLg4POAAADjlJREFUvlpj1Xd5eQw+wPtC374PnB7iu0qHfjDp9yHK+8K4nwcv7zTA3YLpMsjdgunY392CadXJ3YJJ0bRzIrFmjEkF7gOOBdYCHxtjnrfWfhWw2Z3Aw9baBcaYo4HbgNnGmA7AjcBoXD/cJd6+W6Nbi700aAqcuTh0N9N4tuZD2LLSnWVWciUiCUAJVrT98AWs/QQ2F0Dh11CY79b/fKm7X/EifPuuu96kRXt3fVBgt6/x/+uuY2nRnm837aC3GV6zm1etBKaOcT8LXd7rkL2vk4hI4hgDFFhrVwEYYxYB04DABGsIcKW3/CbwL2/5eOA1a+0Wb9/XgEnA41GIe9+lprtrHRPR9nXwxNmum/CB090PfSIicU4JVlMrL4Etq6qTp8J82Loa5uS4s1Qf/wOW/LP6zFH3EdDJVE/cOeux6utS6jPstD2Lu/PyYL8g3blERKQ+PYDvAh6vBcbW2uZzYAauG+F0INsY0zHIvg32fS0pKWlcFz+guLi4ccfw++n85QNUZLRhizmjUbHsq72tg6+8mN5vXEpGyY+snvAnSgtWRy64vdDo1yJOJEM9kqEOoHrEk6aqgxKsfVFZCTvXu7NQW1a6OXAmXOWGq373Hnjzlupts7u7bmlVEz1OuNKdhWrbq/4uY6GSKxERiYZfAPcaY84D3gbWARX7erDMzEwGB7u2MUx5eXmNPgaffQ9rXqLrSb+OyaAse1UHvx+evQS25sHMhfQfPCWywe2FJnkt4kAy1CMZ6gCqRzwJVYclS5aEfZyIfpsP40LiTOBhYBSwGZhprV0dyZjC5vfDjxtdArV5pRuFqV0v14XvqbluPp8qaS1g6KkuwRo0xV1L1GmAO0MVOMobQLv9o1gJERGpZR3QK+BxT2/dHtba73FnsDDGtAZOsdZuM8asA46ste9bkQy2SY0+Hx6fCfalOtMCxJ0178OyRXDk9W4UWRGRBBKxBCvMC4nnAluttQOMMbOAO4CZkYqpXru3uAQqez+XQG3Mg2cvhs2rao62N+MhV95xgGukOvZzyx36Q5se1Wejug5xNxERiUcfAwONMX1xidUsoMYY/caYTsAWa20lcB1uREGAV4DfG2Pae4+P88oTw8Bj3eBHn8yL/wSr9+Fw7gvQe1ysIxER2WuRPIMVzoXE04CbvOWncF0yfNbafZzwIwxF2+j+wU2QW+i6+BVvc+sn3uS67mW1c5OS9jrUjRjXwRs5rmoo784m9Eh2IiISt6y15caYy3HJUiowz1q73BhzM/CJtfZ53Fmq24wxflwXwcu8fbcYY36HS9IAbq4a8CIhpKTCqHPhzVvdxOmtO8PyZ93AS4HSsuAYb86/z5+AH2qNDpvZBo78pVv+9GE38m2gVp1cewrw0YPuOmSgy5YtsKaD+1HysJ+68vfudV3uq1SUueSvz3joO6HxdRYRiYFIJljhXEi8Zxuv0dsOdAQKIxZVRitaFC6DLgPhoBleAjUAug135W26hZ7QVEREEpq1NgfIqbXuNwHLT+F+9Ktv33lUn9FKPCPPgU8fqZ4EfPW7sPSxmttkZlcnWKvedJOoB8ruWp1g5b8KBW/ULO/YvzrBsjlumHWgvb8SVqW49rYqwfrqOdiwvOb+q3Phwtc1YqCIJKyEG1GhSUZjmvgYWVlZ1SsqgXXb3C2BaLSW+KF6xI9kqAOoHhIh2fvB/35R/XjKne4WzPS/uVswMx8N/Xyzn92zaOu7ePyC10LvLyKSgCKZYDV4IXHANmuNMWlAW9xgF0HFzWhMcSAZ6pEMdQDVI54kQx2gedRjb0ZkEhERSRSRTLAavJAYeB44F3gfOBV4I6LXX4mIiIiIiERQPRMxNQ1rbTlQdSFxHrC46kJiY0zV8EX/ADoaYwqAK4FrIxWPiIiIiIhIpEX0GqwwLiQuBk6LZAwiIiIiIiLRErEzWCIiIiIiIs2NEiwREREREZEm4vP7E2tMiSVLlmwCvo11HCIi0mi9R40a1TnWQUSa2i0RkaQQdpuVcAmWiIiIiIhIvFIXQRERERERkSaiBEtERERERKSJKMESERERERFpIkqwREREREREmogSLBERERERkSaSFusAIskYMwn4M5AKPGStvb1WeSbwMDAK2AzMtNaujnacwRhjeuHi6wr4gQestX+utc2RwHPAN96qZ6y1N0czznAYY1YDO4EKoNxaO7pWuQ/3Wp0A7AbOs9Z+GuUwQzLGGOCJgFX9gN9Ya/8UsM2RxOHrYYyZB5wIbLTWHuSt64CrTx9gNXC6tXZrPfueC9zgPbzFWrsgGjHXE0d9dfgjcBJQCqwE5lhrt9Wz72pCvP+iKUg9bgIuBDZ5m11vrc2pZ9+Qn2nRFKQeTwDG26QdsM1aO6KefVcTJ69HPEn0NgvUbqndahrJ0GZ5sajdipN2K9ptVtImWMaYVOA+4FhgLfCxMeZ5a+1XAZvNBbZaawcYY2YBdwAzox9tUOXAVdbaT40x2cASY8xrteoA8I619sQYxLe3jrLWFgYpmwwM9G5jgb9693HDWmuBEbDn/bUOeLaeTePx9ZgP3Iv74lPlWuB1a+3txphrvce/DNzJa9BuBEbjviwt8f6P6jRqUTCfunV4DbjOWltujLkDuI5adQgQ6v0XTfOpWw+Au621dwbbKczPtGiaT616WGv3fH4aY/4P2B5i/3h5PeJCkrRZoHZL7VbTmE/it1mgdiue2q35RLHNSuYugmOAAmvtKmttKbAImFZrm2lA1S8bTwHHeL9IxQVr7fqqX8OstTuBPKBHbKOKmGnAw9Zav7X2A6CdMaZbrIMK4RhgpbU2ISYPtda+DWyptTrw/b8AOLmeXY8HXrPWbvEaqNeASRELNIT66mCtfdVaW+49/ADoGfXA9lKQ1yIc4XymRU2oenifo6cDj0c1qMSW8G0WqN1Su9U0kqHNArVbxFG7Fe02K5kTrB7AdwGP11L3Q37PNt6bfTvQMSrR7SVjTB/gYODDeooPM8Z8box5yRhzYHQjC5sfeNUYs8QYc1E95eG8XvFkFsH/ERPh9QDoaq1d7y3/gOvSU1sivS7nAy8FKWvo/RcPLjfGLDPGzDPGtK+nPJFeiwnABmttfpDyRHg9oi2p2ixQuxWHEr3dSrY2C9RuxYsmb7OSOcFKGsaY1sDTwBXW2h21ij8FeltrhwN/Af4V7fjCNN5aOxLXpeIyY8wRsQ5oXxljMoCpwJP1FCfK61GDtdaP+wBJSMaYX+G6Ji0Mskm8v//+CvTHdeVZD/xfbMNptDMI/UtgvL8e0khqt+JLsrVbid5mgdqtONPkbVYyJ1jrgF4Bj3t66+rdxhiTBrTFXTgcN4wx6bhGaqG19pna5dbaHdbaH73lHCDdGNMpymE2yFq7zrvfiOv/PabWJuG8XvFiMvCptXZD7YJEeT08G6q6s3j3G+vZJu5fF2PMebgLV8/yGt06wnj/xZS1doO1tsJaWwk8SP3xxf1rAXs+S2dQ88L6GuL99YiRpGizQO1WdKLba8nQbiVFmwVqt6IRX7gi1WYlc4L1MTDQGNPX++VmFvB8rW2eB871lk8F3gj2Ro8Fr0/oP4A8a+1dQbbZr6oPvjFmDO41jasG1xjTyrvYGWNMK+A44Mtamz0PnGOM8RljDgW2B3QFiDdBf+lIhNcjQOD7/1zcKFK1vQIcZ4xp753+P85bFxe80YmuAaZaa3cH2Sac919M1bpuYzr1xxfOZ1o8mAissNaura8wEV6PGEn4NgvUbqndiqiEb7NA7Rbx125FpM1K2lEEvdFZLsf9Y6UC86y1y40xNwOfWGufxzUCjxhjCnAXvs2KXcT1GgfMBr4wxiz11l0P7A9grf0brpG91BhTDhQBs+KtwcX1k37WGAPuPfeYtfZlY8wlsKceObihbgtww93OiVGsIXn/XMcCFwesC6xHXL4expjHgSOBTsaYtbhRlm4HFhtj5gLf4i7wxBgzGrjEWnuBtXaLMeZ3uA9JgJuttftyoWujBanDdUAm8Jr3/vrAWnuJMaY7bjjYEwjy/otBFYCg9TjSGDMC1+VlNd77K7AewT7TYlAFvNjq1MNa+w/quc4jnl+PeJEkbRao3Yo7idhuJUOb5cWmditO2q1ot1k+vz/ePtNEREREREQSUzJ3ERQREREREYkqJVgiIiIiIiJNRAmWiIiIiIhIE1GCJSIiIiIi0kSUYImIiIiIiDSRpB2mXSQeGGMqgC8CVi2y1t7eRMfuA7xgrT2oKY4nIiLNm9oskaahBEsksoqstSNiHYSIiEgY1GaJNAElWCIxYIxZDSwGJuMmdjzTWlvg/cI3D+gEbALmWGvXGGO6An8D+nmHuBT4Hkg1xjwIHA6sA6ZZa4uiWBUREUlyarNE9o6uwRKJrBbGmKUBt5kBZduttUOBe4E/eev+Aiyw1g4DFgL3eOvvAf5rrR0OjASqZkIfCNxnrT0Q2AacEuH6iIhI8lKbJdIEdAZLJLJCdbd4POD+bm/5MGCGt/wI8Adv+WjgHABrbQWw3RjTHvjGWrvU22YJ0KfpQhcRkWZGbZZIE9AZLJHY8QdZ3hslAcsV6EcTERGJDLVZImFSgiUSOzMD7t/3lt8DZnnLZwHveMuv4/qwY4xJNca0jVaQIiIiqM0SCZt+ORCJrBbGmKUBj1+21l7rLbc3xizD/aJ3hrfuf4B/GmOuxrtg2Fv/c+ABY8xc3K9+lwLrIx69iIg0J2qzRJqAz+/f17O8IrKvvBGZRltrC2McioiISEhqs0T2jroIioiIiIiINBGdwRIREREREWkiOoMlIiIiIiLSRJRgiYiIiIiINBElWCIiIiIiIk1ECZaIiIiIiEgTUYIlIiIiIiLSRJRgiYiIiIiINJH/B5ySUnkPtfzFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4FdsNh7cCInB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "26e2679c-57ba-406b-a7ff-8a538fb1bf77"
      },
      "source": [
        "plot_roc(sm_model)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC for test set:  0.9668662376330472 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zTtRXeUKJeTR",
        "colab_type": "text"
      },
      "source": [
        "## Class Weight\n",
        "\n",
        "Instead of performing any over-sampling or under-sampling techiniques to create a balanced dataset, one can simply put more weight on the instances in the minority class by setting `class_weight` when training DNN model.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MtpM4FHCIf5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "ebd709f0-d5e2-4b90-b83c-8685f48f68d0"
      },
      "source": [
        "# Scaling by total/2 helps keep the loss to a similar magnitude.\n",
        "# The sum of the weights of all examples stays the same.\n",
        "neg, pos = np.bincount(data['Class'])\n",
        "total = len(data)\n",
        "weight_0 = (1 / neg)*(total)/2.0 \n",
        "weight_1 = (1 / pos)*(total)/2.0\n",
        "\n",
        "class_weight = {0: weight_0, 1: weight_1}\n",
        "\n",
        "print('Weight for class 0: {:.2f}'.format(weight_0))\n",
        "print('Weight for class 1: {:.2f}'.format(weight_1))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Weight for class 0: 0.50\n",
            "Weight for class 1: 289.44\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VSRJeGYyCIah",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8ca1934c-3e10-4698-95be-d31b994745f1"
      },
      "source": [
        "cw_model = make_model()\n",
        "cw_history = cw_model.fit(\n",
        "    x_train, y_train,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    callbacks = [early_stopping],\n",
        "    class_weight=class_weight,\n",
        "    validation_data=(x_val, y_val))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "90/90 [==============================] - 1s 15ms/step - loss: 1.0163 - precision: 0.0019 - recall: 0.8184 - auc: 0.7205 - val_loss: 1.2118 - val_precision: 0.0017 - val_recall: 1.0000 - val_auc: 0.9059\n",
            "Epoch 2/100\n",
            "90/90 [==============================] - 1s 13ms/step - loss: 0.6587 - precision: 0.0020 - recall: 0.9383 - auc: 0.8166 - val_loss: 0.9211 - val_precision: 0.0020 - val_recall: 0.9872 - val_auc: 0.9362\n",
            "Epoch 3/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.5324 - precision: 0.0025 - recall: 0.9136 - auc: 0.8779 - val_loss: 0.7333 - val_precision: 0.0032 - val_recall: 0.9487 - val_auc: 0.9466\n",
            "Epoch 4/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.4457 - precision: 0.0035 - recall: 0.9290 - auc: 0.9205 - val_loss: 0.5916 - val_precision: 0.0068 - val_recall: 0.9359 - val_auc: 0.9518\n",
            "Epoch 5/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.3905 - precision: 0.0047 - recall: 0.9074 - auc: 0.9288 - val_loss: 0.4919 - val_precision: 0.0121 - val_recall: 0.8974 - val_auc: 0.9536\n",
            "Epoch 6/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.3568 - precision: 0.0060 - recall: 0.9012 - auc: 0.9318 - val_loss: 0.4193 - val_precision: 0.0216 - val_recall: 0.8974 - val_auc: 0.9590\n",
            "Epoch 7/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.3302 - precision: 0.0075 - recall: 0.8889 - auc: 0.9354 - val_loss: 0.3631 - val_precision: 0.0313 - val_recall: 0.8974 - val_auc: 0.9619\n",
            "Epoch 8/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.2994 - precision: 0.0094 - recall: 0.9043 - auc: 0.9490 - val_loss: 0.3206 - val_precision: 0.0418 - val_recall: 0.8846 - val_auc: 0.9666\n",
            "Epoch 9/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.2855 - precision: 0.0123 - recall: 0.8981 - auc: 0.9502 - val_loss: 0.2883 - val_precision: 0.0463 - val_recall: 0.8846 - val_auc: 0.9708\n",
            "Epoch 10/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.2676 - precision: 0.0161 - recall: 0.8796 - auc: 0.9559 - val_loss: 0.2544 - val_precision: 0.0520 - val_recall: 0.8846 - val_auc: 0.9732\n",
            "Epoch 11/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.2764 - precision: 0.0198 - recall: 0.8889 - auc: 0.9472 - val_loss: 0.2379 - val_precision: 0.0538 - val_recall: 0.8974 - val_auc: 0.9772\n",
            "Epoch 12/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.2486 - precision: 0.0224 - recall: 0.8920 - auc: 0.9584 - val_loss: 0.2228 - val_precision: 0.0553 - val_recall: 0.9103 - val_auc: 0.9794\n",
            "Epoch 13/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.2314 - precision: 0.0249 - recall: 0.9043 - auc: 0.9663 - val_loss: 0.2017 - val_precision: 0.0598 - val_recall: 0.9103 - val_auc: 0.9817\n",
            "Epoch 14/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.2266 - precision: 0.0281 - recall: 0.9012 - auc: 0.9639 - val_loss: 0.1898 - val_precision: 0.0614 - val_recall: 0.9103 - val_auc: 0.9832\n",
            "Epoch 15/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.2217 - precision: 0.0311 - recall: 0.9167 - auc: 0.9646 - val_loss: 0.1796 - val_precision: 0.0612 - val_recall: 0.9103 - val_auc: 0.9840\n",
            "Epoch 16/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.2071 - precision: 0.0319 - recall: 0.9105 - auc: 0.9719 - val_loss: 0.1697 - val_precision: 0.0616 - val_recall: 0.9103 - val_auc: 0.9840\n",
            "Epoch 17/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.2182 - precision: 0.0329 - recall: 0.8889 - auc: 0.9640 - val_loss: 0.1650 - val_precision: 0.0617 - val_recall: 0.9103 - val_auc: 0.9864\n",
            "Epoch 18/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.2050 - precision: 0.0351 - recall: 0.9167 - auc: 0.9708 - val_loss: 0.1533 - val_precision: 0.0651 - val_recall: 0.9103 - val_auc: 0.9863\n",
            "Epoch 19/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.2199 - precision: 0.0364 - recall: 0.8981 - auc: 0.9651 - val_loss: 0.1517 - val_precision: 0.0642 - val_recall: 0.9103 - val_auc: 0.9873\n",
            "Epoch 20/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.1959 - precision: 0.0391 - recall: 0.9167 - auc: 0.9716 - val_loss: 0.1461 - val_precision: 0.0649 - val_recall: 0.9103 - val_auc: 0.9883\n",
            "Epoch 21/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.2000 - precision: 0.0391 - recall: 0.9074 - auc: 0.9696 - val_loss: 0.1410 - val_precision: 0.0660 - val_recall: 0.9103 - val_auc: 0.9893\n",
            "Epoch 22/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.1911 - precision: 0.0417 - recall: 0.9136 - auc: 0.9726 - val_loss: 0.1376 - val_precision: 0.0660 - val_recall: 0.9103 - val_auc: 0.9900\n",
            "Epoch 23/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.1794 - precision: 0.0420 - recall: 0.9105 - auc: 0.9785 - val_loss: 0.1316 - val_precision: 0.0670 - val_recall: 0.9103 - val_auc: 0.9900\n",
            "Epoch 24/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.1803 - precision: 0.0442 - recall: 0.9228 - auc: 0.9764 - val_loss: 0.1284 - val_precision: 0.0671 - val_recall: 0.9103 - val_auc: 0.9903\n",
            "Epoch 25/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.1808 - precision: 0.0445 - recall: 0.9136 - auc: 0.9793 - val_loss: 0.1245 - val_precision: 0.0668 - val_recall: 0.9103 - val_auc: 0.9901\n",
            "Epoch 26/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.1949 - precision: 0.0431 - recall: 0.8981 - auc: 0.9711 - val_loss: 0.1226 - val_precision: 0.0673 - val_recall: 0.9103 - val_auc: 0.9902\n",
            "Epoch 27/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.1843 - precision: 0.0460 - recall: 0.9105 - auc: 0.9738 - val_loss: 0.1231 - val_precision: 0.0660 - val_recall: 0.9103 - val_auc: 0.9907\n",
            "Epoch 28/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.1755 - precision: 0.0445 - recall: 0.9198 - auc: 0.9784 - val_loss: 0.1216 - val_precision: 0.0674 - val_recall: 0.9103 - val_auc: 0.9912\n",
            "Epoch 29/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.1666 - precision: 0.0447 - recall: 0.9105 - auc: 0.9825 - val_loss: 0.1166 - val_precision: 0.0697 - val_recall: 0.9103 - val_auc: 0.9911\n",
            "Epoch 30/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.1633 - precision: 0.0473 - recall: 0.9167 - auc: 0.9818 - val_loss: 0.1146 - val_precision: 0.0679 - val_recall: 0.9103 - val_auc: 0.9912\n",
            "Epoch 31/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.1697 - precision: 0.0475 - recall: 0.9012 - auc: 0.9803 - val_loss: 0.1130 - val_precision: 0.0684 - val_recall: 0.9103 - val_auc: 0.9915\n",
            "Epoch 32/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.1645 - precision: 0.0479 - recall: 0.9259 - auc: 0.9807 - val_loss: 0.1130 - val_precision: 0.0670 - val_recall: 0.9103 - val_auc: 0.9920\n",
            "Epoch 33/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.1611 - precision: 0.0469 - recall: 0.9198 - auc: 0.9821 - val_loss: 0.1095 - val_precision: 0.0693 - val_recall: 0.9103 - val_auc: 0.9922\n",
            "Epoch 34/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.1521 - precision: 0.0497 - recall: 0.9290 - auc: 0.9844 - val_loss: 0.1082 - val_precision: 0.0678 - val_recall: 0.9103 - val_auc: 0.9921\n",
            "Epoch 35/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.1569 - precision: 0.0507 - recall: 0.9167 - auc: 0.9839 - val_loss: 0.1024 - val_precision: 0.0720 - val_recall: 0.9103 - val_auc: 0.9918\n",
            "Epoch 36/100\n",
            "90/90 [==============================] - 1s 12ms/step - loss: 0.1677 - precision: 0.0485 - recall: 0.8981 - auc: 0.9802 - val_loss: 0.1033 - val_precision: 0.0722 - val_recall: 0.9103 - val_auc: 0.9920\n",
            "Epoch 37/100\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.1628 - precision: 0.0552 - recall: 0.9198 - auc: 0.9804 - val_loss: 0.0994 - val_precision: 0.0769 - val_recall: 0.9103 - val_auc: 0.9919\n",
            "Epoch 38/100\n",
            "90/90 [==============================] - ETA: 0s - loss: 0.1722 - precision: 0.0524 - recall: 0.9105 - auc: 0.9785Restoring model weights from the end of the best epoch.\n",
            "90/90 [==============================] - 1s 11ms/step - loss: 0.1722 - precision: 0.0524 - recall: 0.9105 - auc: 0.9785 - val_loss: 0.1027 - val_precision: 0.0729 - val_recall: 0.9103 - val_auc: 0.9922\n",
            "Epoch 00038: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EeaQRYVrCIYs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "e0dc9627-5b97-4d6e-a059-e8a0afae69b9"
      },
      "source": [
        "plot_metrics(cw_history)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0DDC-S0FCIW6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "ae983415-786e-4fdd-caef-15e85b60383e"
      },
      "source": [
        "plot_roc(cw_model)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC for test set:  0.9745853261124398 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bAlhSxbnMMul",
        "colab_type": "text"
      },
      "source": [
        "According to the introduction of the dataset on Kaggle:\n",
        "> \"Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.\"\n",
        " \n",
        "Base on the final AUC(Area Under the Curve) on the test set: **Class Weight > SMOTE > NearMiss** in this example."
      ]
    }
  ]
}