Imbalanced Dataset - Classification.ipynb

{
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   "source": [
    "#Credit Card Fraud Detection\n",
    "\n",
    "#Generic Libraries\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "import matplotlib.patches as mpatches\n",
    "import time\n",
    "\n",
    "# Other Libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.metrics import classification_report_imbalanced\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import KFold, StratifiedKFold\n",
    "\n",
    "# Classifier Libraries\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import collections\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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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",
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       "      <th>V21</th>\n",
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       "      <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",
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       "      <td>0</td>\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>...</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",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\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>...</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",
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       "<p>5 rows × 31 columns</p>\n",
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./Dataset/creditcard.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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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",
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       "      <th>V23</th>\n",
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       "      <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",
       "      <td>count</td>\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>...</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",
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       "    <tr>\n",
       "      <td>mean</td>\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>...</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",
       "      <td>std</td>\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>...</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",
       "      <td>min</td>\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>...</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",
       "      <td>25%</td>\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>...</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",
       "      <td>50%</td>\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>...</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",
       "      <td>75%</td>\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>...</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",
       "      <td>max</td>\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>...</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",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check whether any of the columns have null values so no null values found\n",
    "df.isnull().sum().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
       "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
       "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
       "       'Class'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records: 284807\n",
      "Number of columns: 31\n"
     ]
    }
   ],
   "source": [
    "#Number of records in the dataset\n",
    "print('Number of records:',len(df))\n",
    "print('Number of columns:',len(df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classes found in dataset: [0 1] \n",
      "\n",
      "Number of records with Class 0: 284315\n",
      "Percentage of Class 0(No Frauds):  99.827251436938\n",
      "\n",
      "\n",
      "Number of records with Class 1: 492\n",
      "Percentage of Class 1(Frauds):  0.1727485630620034\n"
     ]
    }
   ],
   "source": [
    "#Class is the target variable \n",
    "#0 - Not Fraud transaction\n",
    "#1 - being Fraud transaction \n",
    "print('Classes found in dataset:',df.Class.unique(),'\\n')\n",
    "\n",
    "#Lets check the class distribution in the dataset\n",
    "print('Number of records with Class 0:',df['Class'].value_counts()[0])\n",
    "print('Percentage of Class 0(No Frauds): ',df['Class'].value_counts()[0]*100/len(df))\n",
    "print('\\n')\n",
    "print('Number of records with Class 1:',df['Class'].value_counts()[1])\n",
    "print('Percentage of Class 1(Frauds): ',df['Class'].value_counts()[1]*100/len(df))\n",
    "\n",
    "#Classes are imbalanced in the dataset so we cant directly consider accuracy as the model performance to evaluate the models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram of the classes \n",
    "\n",
    "\n",
    "sns.countplot(x=\"Class\", data=df)\n",
    "plt.title('Class Distributions 0-No Fraud and 1- Fraud')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Amount and Time of transaction distribution \n",
    "df.columns\n",
    "\n",
    "amount_vals=df['Amount'].values\n",
    "time_vals=df['Time'].values\n",
    "\n",
    "fig,ax=plt.subplots(1,2,figsize=(18,4))\n",
    "\n",
    "sns.distplot(amount_vals,ax=ax[0],color='r')\n",
    "ax[0].set_xlim([min(amount_vals),max(amount_vals)])\n",
    "ax[0].set_title('Transaction Amount Distribution',fontsize=14)\n",
    "\n",
    "sns.distplot(time_vals,ax=ax[1],color='g')\n",
    "ax[1].set_xlim([min(time_vals),max(time_vals)])\n",
    "ax[1].set_title('Transaction Time Distribution',fontsize=14)\n",
    "                \n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <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",
       "      <td>count</td>\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>...</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",
       "      <td>mean</td>\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>...</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",
       "      <td>std</td>\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>...</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",
       "      <td>min</td>\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>...</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",
       "      <td>25%</td>\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>...</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",
       "      <td>50%</td>\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>...</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",
       "      <td>75%</td>\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>...</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",
       "      <td>max</td>\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>...</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",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.919560e-15  5.688174e-16 -8.769071e-15  2.782312e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean  -1.552563e-15  2.010663e-15 -1.694249e-15 -1.927028e-16 -3.137024e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.537294e-16  7.959909e-16  5.367590e-16  4.458112e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   1.453003e-15  1.699104e-15 -3.660161e-16 -1.206049e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#By looking at Standard distirbutions of other features - V1 to V28 which are already normalized\n",
    "#Time and Amount are the only features which are not normalized\n",
    "#Before continuing with modelling the data we have to normalize these two features\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler,RobustScaler\n",
    "\n",
    "#We use robust scaler compared to standard scaler as robust scaler is non prone to outliers\n",
    "rob_scaler=RobustScaler()\n",
    "\n",
    "df['Scaled_Amount']=rob_scaler.fit_transform(df['Amount'].values.reshape(-1,1))\n",
    "df['Scaled_Time']=rob_scaler.fit_transform(df['Time'].values.reshape(-1,1))\n",
    "\n",
    "df.head()\n",
    "\n",
    "df.drop(['Time','Amount'],axis=1,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>...</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>Class</th>\n",
       "      <th>Scaled_Amount</th>\n",
       "      <th>Scaled_Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>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>...</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>0</td>\n",
       "      <td>1.783274</td>\n",
       "      <td>-0.994983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</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>...</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>0</td>\n",
       "      <td>-0.269825</td>\n",
       "      <td>-0.994983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</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>...</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>0</td>\n",
       "      <td>4.983721</td>\n",
       "      <td>-0.994972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</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>...</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>0</td>\n",
       "      <td>1.418291</td>\n",
       "      <td>-0.994972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</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>...</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>0</td>\n",
       "      <td>0.670579</td>\n",
       "      <td>-0.994960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10  ...       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  0.090794  ...  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024 -0.054952  ...  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  0.753074  ...  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Class  Scaled_Amount  Scaled_Time  \n",
       "0 -0.189115  0.133558 -0.021053      0       1.783274    -0.994983  \n",
       "1  0.125895 -0.008983  0.014724      0      -0.269825    -0.994983  \n",
       "2 -0.139097 -0.055353 -0.059752      0       4.983721    -0.994972  \n",
       "3 -0.221929  0.062723  0.061458      0       1.418291    -0.994972  \n",
       "4  0.502292  0.219422  0.215153      0       0.670579    -0.994960  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Class Distribution in Percentage: [99.83  0.17] \n",
      "\n",
      "Test Class Distribution in Percentage: [99.83  0.17]\n"
     ]
    }
   ],
   "source": [
    "#As we already found that classes are imbalanced in the dataset \n",
    "#To make algorithms efficient and not bias towards one class we have to subsample and \n",
    "#create balanced dataset which contains same number of fraud and non fraud transactions\n",
    "\n",
    "#Before subsampling - we have to divide data into test and train \n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "\n",
    "X=df.drop(['Class'],axis=1)\n",
    "y=df['Class']\n",
    "\n",
    "ssf=StratifiedKFold(n_splits=5,random_state=None,shuffle=False)\n",
    "\n",
    "for trainidx,testidx in ssf.split(X,y):\n",
    "    Original_Xtraindata,Original_Xtestdata=X.iloc[trainidx],X.iloc[testidx]\n",
    "    Original_ytraindata,Original_ytestdata=y.iloc[trainidx],y.iloc[testidx]\n",
    "    \n",
    "    \n",
    "#Convertng into numpy array\n",
    "\n",
    "Xtraindata=Original_Xtraindata.values\n",
    "ytraindata=Original_ytraindata.values\n",
    "\n",
    "Xtestdata=Original_Xtestdata.values\n",
    "ytestdata=Original_ytestdata.values\n",
    "\n",
    "\n",
    "trainclass,traincount=np.unique(ytraindata,return_counts=True)\n",
    "print('Train Class Distribution in Percentage:',np.round(traincount*100/len(ytraindata),2),'\\n')\n",
    "\n",
    "testclass,testcount=np.unique(ytestdata,return_counts=True)\n",
    "print('Test Class Distribution in Percentage:',np.round(testcount*100/len(ytestdata),2))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Non Fraud Transactions in new dataframe: 492\n",
      "Number of Fraud Transactions in new dataframe: 492\n"
     ]
    }
   ],
   "source": [
    "#Random undersampling\n",
    "\n",
    "df.head()\n",
    "\n",
    "df=df.sample(frac=1)\n",
    "\n",
    "class_fraud=df.loc[df['Class']==1]\n",
    "len(class_fraud) ## 492 records found for class now we have to have the same number of the class 0 \n",
    "\n",
    "class_non_fraud=df.loc[df['Class']==0][:492]\n",
    "len(class_non_fraud)\n",
    "\n",
    "class_non_fraud.head()\n",
    "class_fraud.head()\n",
    "\n",
    "normal_distributed_df=pd.concat([class_fraud,class_non_fraud])\n",
    "\n",
    "#Shuffle rows in dataframe\n",
    "newdf=normal_distributed_df.sample(frac=1, random_state=42)\n",
    "\n",
    "newdf.head()\n",
    "print('Number of Non Fraud Transactions in new dataframe:',newdf['Class'].value_counts()[0])\n",
    "print('Number of Fraud Transactions in new dataframe:',newdf['Class'].value_counts()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.countplot(x='Class',data=newdf)\n",
    "plt.title('Distribution of Class 0(Non Fraud) and 1(Fraud) ')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now distribution of classes is absolutely fine\n",
    "#Analyzing correlation between the predictors\n",
    "\n",
    "oldcorr=df.corr()\n",
    "newcorr=newdf.corr()\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(24,20))\n",
    "\n",
    "\n",
    "sns.heatmap(oldcorr,ax=ax1,cmap='coolwarm_r', annot_kws={'size':20})\n",
    "ax1.set_title('Correlation of imbalaced dataset',fontsize=14)\n",
    "\n",
    "sns.heatmap(newcorr,ax=ax2,cmap='coolwarm_r', annot_kws={'size':20})\n",
    "ax2.set_title('Correlation of balanced (undersampled) dataset',fontsize=14)\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "#With this we can see the predictors dont even correlate much due to imbalanced class distributions\n",
    "#With undersampled balanced dataset shows the correlation between predictors which will help to \n",
    "#reduce dimensionality and also efficency of the algorithms\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#By looking at the correlation of predictors wrt to the class variables \n",
    "#we see the V17,V14,V12,V10 are negatively correlated in that case the lower the value of the features it will be fraud transactions \n",
    "\n",
    "\n",
    "fig,axes=plt.subplots(ncols=4,figsize=(20,4))\n",
    "\n",
    "sns.boxplot(x='Class',y='V17',data=newdf,ax=axes[0])\n",
    "axes[0].set_title('Negative correlation V17 v/s class',fontsize=10)\n",
    "\n",
    "\n",
    "sns.boxplot(x='Class',y='V14',data=newdf,ax=axes[1])\n",
    "axes[1].set_title('Negative correlation V14 v/s class',fontsize=10)\n",
    "\n",
    "\n",
    "sns.boxplot(x='Class',y='V12',data=newdf,ax=axes[2])\n",
    "axes[2].set_title('Negative correlation V12 v/s class',fontsize=10)\n",
    "\n",
    "\n",
    "sns.boxplot(x='Class',y='V10',data=newdf,ax=axes[3])\n",
    "axes[3].set_title('Negative correlation V10 v/s class',fontsize=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#By looking at the correlation of predictors wrt to the class variables \n",
    "#we see the V2,V4,V11,V19 are positively correlated in that case the higher the value of the features it will be fraud transactions \n",
    "\n",
    "\n",
    "fig,axes=plt.subplots(ncols=4,figsize=(20,4))\n",
    "\n",
    "sns.boxplot(x='Class',y='V2',data=newdf,ax=axes[0])\n",
    "axes[0].set_title('Positive correlation V2 v/s class',fontsize=10)\n",
    "\n",
    "\n",
    "sns.boxplot(x='Class',y='V4',data=newdf,ax=axes[1])\n",
    "axes[1].set_title('Positive correlation V4 v/s class',fontsize=10)\n",
    "\n",
    "\n",
    "sns.boxplot(x='Class',y='V11',data=newdf,ax=axes[2])\n",
    "axes[2].set_title('Positive correlation V11 v/s class',fontsize=10)\n",
    "\n",
    "\n",
    "sns.boxplot(x='Class',y='V19',data=newdf,ax=axes[3])\n",
    "axes[3].set_title('Positive correlation V19 v/s class',fontsize=10)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing using plots - normal distribution we can identify which features have more outliers possible \n",
    "#lets plot the normal distributions of these features and then check for outliers\n",
    "\n",
    "from scipy.stats import norm\n",
    "\n",
    "fig,(ax1,ax2,ax3)=plt.subplots(1,3,figsize=(20,6))\n",
    "\n",
    "v10_vals=newdf['V10'].loc[newdf[\"Class\"]==1].values\n",
    "sns.distplot(v10_vals,fit=norm,ax=ax1,color='#ff2435')\n",
    "ax1.set_title('V10 Distribution\\n(Fraud transactions)')\n",
    "\n",
    "v12_vals=newdf['V12'].loc[newdf[\"Class\"]==1].values\n",
    "sns.distplot(v12_vals,fit=norm,ax=ax2,color='#0aa5d1')\n",
    "ax2.set_title('V12 Distribution\\n(Fraud transactions)')\n",
    "\n",
    "v14_vals=newdf['V14'].loc[newdf[\"Class\"]==1].values\n",
    "sns.distplot(v14_vals,fit=norm,ax=ax3,color='#58e1ab')\n",
    "ax3.set_title('V14 Distribution\\n(Fraud transactions)')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quartile 25: -9.692722964972385 | Quartile 75: -4.282820849486866\n",
      "iqr: 5.409902115485519\n",
      "Cut Off: 8.114853173228278\n",
      "V14 Lower: -17.807576138200663\n",
      "V14 Upper: 3.8320323237414122\n",
      "Feature V14 Outliers for Fraud Cases: 4\n",
      "V10 outliers:[-18.8220867423816, -18.4937733551053, -19.2143254902614, -18.049997689859396]\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "V12 Lower: -17.3430371579634\n",
      "V12 Upper: 5.776973384895937\n",
      "V12 outliers: [-18.4311310279993, -18.683714633344298, -18.553697009645802, -18.047596570821604]\n",
      "Feature V12 Outliers for Fraud Cases: 4\n",
      "Number of Instances after outliers removal: 975\n",
      "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
      "V10 Lower: -14.89885463232024\n",
      "V10 Upper: 4.920334958342141\n",
      "V10 outliers: [-18.9132433348732, -16.7460441053944, -22.1870885620007, -15.2399619587112, -20.949191554361104, -14.9246547735487, -23.2282548357516, -15.124162814494698, -15.2318333653018, -16.6011969664137, -24.5882624372475, -15.563791338730098, -18.2711681738888, -22.1870885620007, -15.563791338730098, -22.1870885620007, -15.1237521803455, -19.836148851696, -15.2399619587112, -16.3035376590131, -14.9246547735487, -16.6496281595399, -15.346098846877501, -17.141513641289198, -16.2556117491401, -24.403184969972802, -22.1870885620007]\n",
      "Feature V10 Outliers for Fraud Cases: 27\n",
      "Number of Instances after outliers removal: 946\n"
     ]
    }
   ],
   "source": [
    "# # -----> V14 Removing Outliers (Highest Negative Correlated with Labels)\n",
    "v14_fraud = newdf['V14'].loc[newdf['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v14_fraud, 25), np.percentile(v14_fraud, 75)\n",
    "print('Quartile 25: {} | Quartile 75: {}'.format(q25, q75))\n",
    "v14_iqr = q75 - q25\n",
    "print('iqr: {}'.format(v14_iqr))\n",
    "\n",
    "v14_cut_off = v14_iqr * 1.5\n",
    "v14_lower, v14_upper = q25 - v14_cut_off, q75 + v14_cut_off\n",
    "print('Cut Off: {}'.format(v14_cut_off))\n",
    "print('V14 Lower: {}'.format(v14_lower))\n",
    "print('V14 Upper: {}'.format(v14_upper))\n",
    "\n",
    "outliers = [x for x in v14_fraud if x < v14_lower or x > v14_upper]\n",
    "print('Feature V14 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "print('V10 outliers:{}'.format(outliers))\n",
    "\n",
    "newdf = newdf.drop(newdf[(newdf['V14'] > v14_upper) | (newdf['V14'] < v14_lower)].index)\n",
    "print('----' * 44)\n",
    "\n",
    "# -----> V12 removing outliers from fraud transactions\n",
    "v12_fraud = newdf['V12'].loc[newdf['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v12_fraud, 25), np.percentile(v12_fraud, 75)\n",
    "v12_iqr = q75 - q25\n",
    "\n",
    "v12_cut_off = v12_iqr * 1.5\n",
    "v12_lower, v12_upper = q25 - v12_cut_off, q75 + v12_cut_off\n",
    "print('V12 Lower: {}'.format(v12_lower))\n",
    "print('V12 Upper: {}'.format(v12_upper))\n",
    "outliers = [x for x in v12_fraud if x < v12_lower or x > v12_upper]\n",
    "print('V12 outliers: {}'.format(outliers))\n",
    "print('Feature V12 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "newdf = newdf.drop(newdf[(newdf['V12'] > v12_upper) | (newdf['V12'] < v12_lower)].index)\n",
    "print('Number of Instances after outliers removal: {}'.format(len(newdf)))\n",
    "print('----' * 44)\n",
    "\n",
    "\n",
    "# Removing outliers V10 Feature\n",
    "v10_fraud = newdf['V10'].loc[newdf['Class'] == 1].values\n",
    "q25, q75 = np.percentile(v10_fraud, 25), np.percentile(v10_fraud, 75)\n",
    "v10_iqr = q75 - q25\n",
    "\n",
    "v10_cut_off = v10_iqr * 1.5\n",
    "v10_lower, v10_upper = q25 - v10_cut_off, q75 + v10_cut_off\n",
    "print('V10 Lower: {}'.format(v10_lower))\n",
    "print('V10 Upper: {}'.format(v10_upper))\n",
    "outliers = [x for x in v10_fraud if x < v10_lower or x > v10_upper]\n",
    "print('V10 outliers: {}'.format(outliers))\n",
    "print('Feature V10 Outliers for Fraud Cases: {}'.format(len(outliers)))\n",
    "newdf = newdf.drop(newdf[(newdf['V10'] > v10_upper) | (newdf['V10'] < v10_lower)].index)\n",
    "print('Number of Instances after outliers removal: {}'.format(len(newdf)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "###Now we can verify the box plots and see the status of outliers by which we can confirm that the outliers have been handled\n",
    "\n",
    "\n",
    "fig,(ax1,ax2,ax3)=plt.subplots(1,3,figsize=(20,5))\n",
    "\n",
    "sns.boxplot(x='Class',y='V10',data=newdf,ax=ax1,palette=['#0aa5d1','#ff2435'])\n",
    "ax1.set_title('Removal of Outliers V10',fontsize=14)\n",
    "\n",
    "sns.boxplot(x='Class',y='V12',data=newdf,ax=ax2,palette=['#0aa5d1','#ff2435'])\n",
    "ax2.set_title('Removal of Outliers V12',fontsize=14)\n",
    "\n",
    "sns.boxplot(x='Class',y='V14',data=newdf,ax=ax3,palette=['#0aa5d1','#ff2435'])\n",
    "ax3.set_title('Removal of Outliers V14',fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time Taken for tSNE:3.2s\n",
      "Time Taken for PCA:0.038s\n",
      "Time Taken for SVD:0.002s\n"
     ]
    }
   ],
   "source": [
    "#Dimension Reductionality and clustering\n",
    "\n",
    "from sklearn.decomposition import PCA,TruncatedSVD\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "newdf.head()\n",
    "\n",
    "X=newdf.drop('Class',axis=1)\n",
    "y=newdf['Class']\n",
    "\n",
    "tstart=time.time()\n",
    "X_tsne=TSNE(n_components=2,random_state=42).fit_transform(X)\n",
    "tend=time.time()\n",
    "\n",
    "print('Time Taken for tSNE:{:.2}s'.format(tend-tstart))\n",
    "\n",
    "tstart=time.time()\n",
    "X_pca=PCA(n_components=2,random_state=42).fit_transform(X)\n",
    "tend=time.time()\n",
    "\n",
    "print('Time Taken for PCA:{:.2}s'.format(tend-tstart))\n",
    "\n",
    "\n",
    "tstart=time.time()\n",
    "X_svd=TruncatedSVD(n_components=2,random_state=42,algorithm='randomized').fit_transform(X)\n",
    "tend=time.time()\n",
    "\n",
    "print('Time Taken for SVD:{:.2}s'.format(tend-tstart))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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22/jmm28YNWoUAJdddhkzZsxg+PDhTJ06lZ49y/8pT3JyMlFRUQBMnjw5f3uLFi1YuNA+33zz5s0cPHgQgCuuuIKRI0fy7LPPYrFYWLBgAffff3+5r18XpKdkEH/wDGHNQ5n86jJW7NKk4UuUMYUW4bA8oTGoJmCGTPz56TD8M3QuX86+BS9v59nZ3Jxc7rvxR/YZooDQQvs2H4GFj23hjXvD6XFzlyq4w6oV2iiIh7qlM21NDkleDUo9raxUtAaluKZPuOfOKWospZQR2ARcAHwG7AeStNZ5lTLjgCgXx94H3AcQGRlJbGxsof3BwcGkpqYW2BLo0diBIud33cZsNnPDDTcwadIk7rzzTlJTU+nWrRvffvstQ4cOZerUqXTv3r3Y+bKyssjJySm23Wq1kp6enr89MTGRyMhIUlNTmTRpElarldTUVCIjI1myZAmpqals2bKFgwcPkpaWRufOnXnwwQd5+OGHsVgszJs3j1GjRjm9ftGfa1VLS0ur9hgqi9xb7eSpezu75yjG4BR41/n0oDzWgGCiz29SZ3+ewq6mPk88/PDDAPI8IUQ1sNlszL3/UwLT4nFVQl4D6TfcyZCpz1VKDB5LDDl+6d8IHNNaD1RKxQAzsD9JbwaGa61zKnqdiAjPriQQEVG29mPGjCk0hHP8+PGMGjWK999/n4YNGxaa71tWY8eO5dZbbyUqKoru3bvnv2HffPPNfP/993Ts2JGuXbvSqlUrADp16sSQIUPo2LEjzZs3p1evXuW+dm13dMcRfhz8CjFxmzBbc5jR5TEOhV9kH5cA7COAfWcpnn7Vmn2GJsz/ZCW3PH2103M/eFNeUsi5bJMvr351nIXXWzB51excq81m48SekwRFBBAYFlSqY+54aQADTiby1uhF/JkZ7blglCIi5wxNW57vuXOKGktrbQU6KqVCgDnAhc6auTj2K+ArgC5duug+ffoU2r9r1y4CA88lgyqjnyh4flfy2jz33HN89dVXeHt7ExgYyOeff86oUaOYMGFCfj9R9Hw+Pj54eXkV2240GvH398/f/tprr3H33XcTHR1N9+7diYuLIzAwkDvvvJMff/yRXr165fcTAQEBtGvXjqFDh9KrVy+aN29O79698+Mqev1LLrmkIj+mCouNjaXov21dIfdWO3ni3la8NxvvF97InybmSnyXvty1ttbOpK115Hmi+PPE5ZdfTkxMTL1+nhCiuswY+CLBy3922yYluDF3/PhSpcXgyafYx4BdQN7T5rtUQu0Ix3tblUpLS8v/e2RkJBkZ54o9tWjRgpUrV7o9fuTIkYwcObLY9qKfCA0ePJjBgwcXa+fr68uyZcucnvuFF17ghRdecHv9ui4jOYNlV9xL65TDAOxr2N6eFHKMSHHLsX/6ihRucTKd+/ieE+ym5KF6aV7BPHLLTD6ZfjPe/j5lvofKZrPZeHn4DFYnRmA1mkFrQnKTeOXhZnQd1MntsdtW7ODx9w+TZY4u3c/UGcdxbYhjrzUSm9GesTvt1ZDHv07hpqU/8sRnt5bn1kQto7VOUkrFAt2BEKWUyTFqKBo47olrHDxoH71TmmSOp1RlP9G3b99i9yb9hBA1z5kDpzk57jNCcJ8UyjV60ePtR6oqLEH5nicq2q/U9OeJ0aNHV2m/KYSwO7rlEIHLZ7v8ACFvu89NN1ZqHB6pMaSUigauAyY5vlfUwdoRomb6YdQEGjmSQgD7Ijra/1KGBMYpc0O+eW5use2//rCx1OfZrqN59PbZWC01r2bOk7dOIzYlyp4UAlCKJK8GPPFlEnvX7XN5XG5WLqPHxZFl9s8/Ll9Z6g0phdGay26i85NCBc8z+0gE6+fI6mR1lVKqoWOkEEopX6Af9g8SfgNucTS7C5hXPREKIYRnbZr5B7+3u5qQxDi37VICI2gyaQKt+rStosiEEELUJGtem4wB7bauUEKnPgya8HClxuGpEUMfA09zrrBDGJVQOyKvlkJtUZPiLU39iNpYJyAtLY2QKxtiufzcvP1LfcO42Fz2n7sFI78u+7XQdDDVIpfhzctyriAWzllEcEPnn7hUx8/Ymmul9bXBtMb5fazdvo3jWcec7ktNT+f2m01AroeiyXK5Z09CApmxrldrKa3a9jqubfGWU2NgsmPKsQGYpbVeqJTaCcxQSr0B/A18U51BCiGEJ+Rk5/Lv/U8RkOt+OeFcZeKa7fNo0CTUbTshhBB1j81mY8b1LxK8zP0UsoQOvbhz7RcYDJW7bliFE0NKqYHAaa31JqVUn7zNTppWuHZEVU8NqKiaFG9p6kfUxjoBsbGx/Pvxj7Q8vC5/28nG3Zjf4d5ynW9U+2RGvXVumc7r3ppLsnfZ/g17+Mbx/qzrXcZb1T/j6W8uZso61/WEvC2ZrFjcx+m+SV/PY8p8J/fvmBp2xe6fWd36BrSq+JLzymblu1eacX638yp0ntr2Oq5t8ZaH1norUOwNSGt9AOhW9REJIUTlsNlsLHhoAgHpCS7b5E0LyLhqkCSFHJRSPsDvgDf255OftNavVFbNUiGEqG6rxy8keNnPJU4ha/PYsEpPCoFnppJdDgxSSh3C/sbdF/sIohClVF7iyWO1I4QoKCs9m0PNu7Gi9S3sjuyEVRlpc3ITjZPKV4wqManwiJZUU9kTewVWAi2RJcfC9t92sSN2d6VNQfP2dp+0URVYgr7r0Vju9f+Ly/ziCM+Jx8uSWe5zaYORHyasL/fxQgghRHXKyczmh0734PP9RLftNIrEPoO4ZfbYqgmsdsgG+mqtOwAdgWuUUt05V7O0JZCIvWapEELUeoe+ngW4X5o+od3ldBnWu0riqfCIIa31c8BzAI4RQ09qrYcppX7EXjtiBlI7QlSC8aN/wvtibzb6tYOYdgBEpBxlyMZP6Hh0NSdCYsp8zrYdIgt938CaTIIxrEzn6Na5dJ/+zR63jP8tzyTJKwSA0Ld3cd/AYAY+2rdM1yvJtQ9cwce//YnN4DxBdKHPWZfH+hhdJKuUIvrsPrys2YS3iOC9d4cBkHw6iRvvWk+OqXwFuPfHl6OwtRBCCFEDzLvvE8J2/FniCmSZQ0Zy5w/PVlVYtYLWWgN51ZnNji+N/QPnOxzbJwNj8cBiNkIIUV1sNhtz/vsJofs2uW2XeOUgbl/wVpWMFgLPrkpW1DNI7Yh6y5Jj4avn5rNsO2Qob8JIY8TgSK59sI9Hzh875U9mHWzI8IsL1805HdSURe1GcDKoeelP5pgW1Tj3NP3uLlztfeAlBibvKP2pGuec4obRgwptO7DpIDO/WM/xBCuX3eDH3j/3cWDbcT6MNYOXd367s16hvLvURmDoWnoP61H6i5bAN8CH66LOsOB4ZLFC2mZrNi+N7+/y2IaR/vjkpp8rPu34WRlsFvrsm0OO0ZsrHztXV37+52vIKccoqzwB5ppXuFsIIYQoSeKxszB7JlBCUsg7kP7v3lc1QdUyjjp0m4ALgM+A/ZSyZqkQQtQW8x6cUOLIUovBzMDvnsfL2+y2nSd5NDGktY4FYh1/r5TaEYsbNSL71CmPnc87MpJrT55028ZoNNK+ffv87+fOnUuLFi08FgPAoUOHGDhwINu3b/foeatDRkomI25dyEmvCPBybCOANxfBpg3TefHboRW+xtw5B7HPUCxCa/Y3bAeqDJlVpbjAdpw3P7qiUOFpgFFvXM/226azKbdpqU7VJjwXb38fMpIzWPTVatZtiGdDRiNshsYAtMtN5d43j9LAkgRexUciaWVg/PfH6DowA79gv9LfQwme+WoIDV+Yz8xNBtJN/hi0jeb6NG+Pu4KImAiXx3n7e/Pp8414/+XV/Ot/PjaDkWYJe7hi3zyaJB/E95WxBDUKZvHEVaxYEceBFG/wKn9iaED/xuU+Vog8db2fWLt2rUfPK4SomFUfz+fscy/hZ3G9wALYVyBrO/lDGkRJXSFntNZWoKNjFcs5wIXOmjk7tiyL2QCsvuACck6f9kzggFdEBL3+/ddtm5CQENq2Pbf63LRp02jevAwfZJbC4cOHue2221i/vuxT80taNKc0C9nUdPVhwY+6fo+1/f601tDGjOXd0a4bRUVg/fRZtuz9B/ZWXWyVOWKoUnjyl/3Sns/X15ctW7a43G+xWDCZat2PstJ88sQ8Tno5f8BfEt+EgUu3knMmkSPLN+AVHEi3hwfRqHWTMl0jPtPo/NWrFO4/qzunvYpj0IBoWlwUyYVXOJ+7aTQZ+WT2ndx2zSyOmyOdtikoIQ3WzFjP69+dId0cCEQXq+RlMxhJcJIUynPKqyHXDV3NU9d7M8BDI6wARr05iFHlOO7CK9rwv9g2/DN/A1s+mo5KOoWxczsufO59Ynq04v9uns5mSzQQnZ8ILI8YywkGPHRb+U8ghIP0E0KIqnJ8x1ESn3keL5v71TtT/cO47XQsRlPFF2uo67TWSUqpWKA7jpqljlFDLmuWlmUxG8CjSaG885W04Iyvry9bt251ud8T/URAQAAGg6Fci9+UtGhOaRayqenqw4Ifdf0ea/v9LRgzCa/xH7ttk/Xp81x35xB8AspXmqO85LfUcvruu+/45ZdfyMrKIj09nfnz5zN48GASExPJzc3ljTfeoG/fvsVGAo0bN460tDTGjh3Lpk2bGDVqFH5+fvTs2bOa78i1Y7uOYbXYiG4bVao5jr/FBdpnhjujNRvuep6ohH34Ojatn/gp6sFHGfTJA6WOKSrIyiFnq8BqjdI2jNqKxegiS+GYDnVN30iufaB0xbxeGHMRT35whMy8KVUuBPnC2MlnyTJXbDW6XKMPb/2iMZt/p/+9V1ToXJ7SYVA3OgwqPAjwuxcX2JNCjp9peYXlxPPtL7dU2RxaIapCafqJwYMH14l+Qoj66o+3p+HnJimUV2/I764RkhRyQynVEMh1JIV8gX7YC0//Rh2uWSr9hBD1x8whbxAwe4rbNokR5xHdpnmVJ4VAEkOlkpmZSceOHQGIiYlhzpw5AKxdu5atW7cSGhqKxWJhzpw5BAUFER8fT/fu3dm8ebPb89599918+umn9O7dm6eeeqrS76Os1sxYzyffHuGEl32qkW/uNm7vlMM9b93g9rgso5sXslI0Tvi3UGFGk7bA5x+xqUdbOt/eq1DzxOOJ/LvxEKGNggstYz7krnb8+bmToslKEZp2igyvQNeJIaWIzj3JdQ/d7PY+8uzfeIC5U7YRjiIhx0aGs6lSjsSIr7eBLJOHpoApxbs/p9N7WDZevt4lt68Gv25KB1NQuZJCQZkJRCUdIMfkw9XXNJXRFKJWK28/MWjQIHenrfH9hBD1XdbhY7jr9TWQcvUt3P6R1BUqQWNgsqPOkAGYpbVeqJTaSR2pWSr9hBD1175VOwmYPaXEpekDb3L//70yyZNYKbiaItC/f39CQ+3zxLXWPP/88/z+++8YDAaOHTvG6dOnXT7sJicnk5SURO/e9hErw4cPZ/HixZV3E2W0aeE/vPB9Glavc/VnMs3+fLvVjyPDp/HqlDtcHtvQksgpr4ZO94WnHkOhnf6H2PHhFDrf3outv27nrXE7iDNFFKgVlELT3L94/ql27Nkcx6JVZwnM9aPYPC0gIbDkaWlPj76wWD0hZ9bM3MDLkxPJMTYpPAqq6AgZpbgx+hSJKeVf+t2ZLJMfKyavK/XIpqqWZivF3DHHz0ppG1oZMFhzuWbnNC4+9ifKUSpAb4LpH78HV/Sl+4t3EdPtgkqOXAjPKm8/ccrNNLWa3k8IIcCnRTSsc7P/tTe4/rlbqy6gWkprvRUoNk+psmqWVgfpJ4Sov/6eOBd/Slia/rwODHnnbtb+5aZTqUSSGKoAf/9z04qmTp3KmTNn2LRpE2azmRYtWpCVlUVISAg2my2/XVaWvTCh1hpVgak3lW3SV9uxGp0Ud1aKFYlNaPXaQoa+OMDp1J+h/QL5+Hfn57300DLX/yFOHGfzon94/LN4rOZGxfYfNTfi0Q+OYzH6gGriqGeTCtpGd9/jXNYjkilLkznjFe723kJyEul4TS+3bQCsFivj/hdHTtF6QI5ER2dzHNkWCPFTDL61FT1uvo33H5gJKSWeukziT7ouBOhO4okkZn34G3sPZhDkb+CGYe3o8J/2JR9YBs38Mom3uNipbfbEnstVzA4AACAASURBVFI0Sj5E312z2NziKqIS/6XDsT8KVY9UQFB6PCyexdalszky7l16PzrQo7EKUR1K00+YTKZa2U8IUV/FbT3Mug9mkX06Ad9mTcg1emG25hRrlxTWjKHPlG50sqi/pJ8Qou6zpDmrgXJOfMcrGPr7hGqdJSKJIQ9JTk4mIiICs9nMb7/9xuHDhwH7yginT58mISGBgIAAFi5cyDXXXENISAjBwcGsWbOGnj17MnXq1Gq+g8L25oa5fnUoxRd/BbJr+Axem3J7fnJo8cRVzF0Ux5lsH7yVjWyTb6FRNWZLFm2Pr8eG4nBYG+IDGhOUlcj5p7dh0hZ0RCM+nrATq9H1iB+L0cvJaB0DW9JCefXu7tz0RAAbF2zhl5928cepQDKK1PoxWXN45eHmpapls3nRVuKdFYl2XFspmDh/WOHzGz1YI8dxn227lW5FtIK2r9zJk+8dIM0cCoRCNiz/9Cz9fpjGg69cReT5JRfSLo07hrfh7/8lo1XxugnD1r+P2WbB25JJaIa9yGOzLedW7HD1a4zJZiH+2ZdJH9YH/9AAj8QpRE1Q1/oJIeqjRc99h+WD9/DV1vxaiRnegWhlwKvAqmQpwY3oMeczqZ0nykT6CSHqpsZXdiFjyY/FtudNIes09oFqLx1S6xJD3pGRHl+G2BOGDRvG9ddfT5cuXejYsSNt2rQBwGw28/LLL3PppZcSExOTvx3g22+/zS8Wd/XVV3skDk9R2ua+gdbEpkQxdsQM4hIVpyx+JHuF4G5lqlyTD5ub9mZL0yuID4zK3x6QlcSNWyZy0b23cGCRr/ODCwVXPKWQZfJjxZT1DBp9FV2u70iX6ztitViZ+e5SflmTQrY20j7CwsgnLqN5x9ItDZpy1n1mNyOneBxnk92vSlJqjqRQS9txOg0o2zQym83Gq+/tIM1cZBl6rfk1MYpfH9tFoGU9L9wdSc8hl1YozO43dWbM4V/5YmmGYxU2MFpzuWz/Qpom7QcKJ4BK+5mWT046f37+C/1fHFKh+ET9JP2EEKIy7Fu9C+sH72Is8juSX3YqiQ2iMd91B5nHzxDWsTX9Rw/Ey9vVShyiukk/IYSoCjmZ2fy7ZjdRnVuyIeI8Gpw+UKjOkAISWnfluus6V2OUdrUuMXTtyZNVfs20tLRi20aOHMnIkSPzvw8PD2ft2rWF2qSm2qcAjR49mtGjRxc7R+fOnfnnn3/yvx87dqxnAvaAtr6JbLK4WYHLkZxZmRxlL/NTymXKV7QZgi7y6VmaTwjTuz7B9z3bwKID5YwYMtMLD+M2mozc8cIAXFdDcu/iK1th/PEfrMrkNBl1YbPiv/BFhHlBcjkvWJBStOEYb3/ev8yH/r1oGyeKJoUc58z7M9UczHNTMvg8Yiftr7yoQqHe8Hg/rv5vFn/+tIl/Zq+laex0/HKL/58pq4xTToqLC1EK1548WeKyu55W3n4iT1n6iby+RQhRdawWK2tHv0+Yiw/OGiTG0bjbhXS69Z4qjkyUR3meJyrar1RlPyGEqH7z/+9Lsr/5Br8s+8OhCokioVk7GhzdidI2LAYzKd2v4uY5b1ZzpHYyvlU49fy4qzA5mS9fjC5bseWiSaE8FqMXS3/YRNPcciT+HEvUd7vmwrIf60bD5g25MuxM4aSQ4379c9O444niI3lueqAHJmvFRw11NscxacEdNGzuvIi3O6fjkkrVTisD777/T8kNS8E3wIerRl5O+L+bK5wUyntFNevdseKBCSGEEBV0at8Jpje/nrDtf7htl7A3rooiEkIIUZP98sy3GD/7MD8ppIGQpGMExe0h8n8TiVk0i94HVzN81Sc1pnSGJIacyEzN4vTRRM6eSMZqsVZ3ONUi8vxGfPlic7wt7qdTlWeZcleOHMvggeHN3E5jy4+nYEJKKS7zP07MJS08Fkue57++havDjmO2Zudfq5nlJB88HUPk+cVH5URdGMWT1xqKJdWMTpJF7u5zR2Zooe+P7TrGD6/+wuSXF3Dw70NuY25zafNSJ+zicFJDqZLZAKuTmkRwbp5tQrO2dLipYtPchBBCCE9YOugJGpze73J/Xo/bpEvLqglICCFEjWW1WEn96ptC2/KemM22XHZPmM5F/TsQ0qRB1QfnRq2YSlZVFfetFiuHDiSTq8zk/WhOp6XT0M9GWFRIpV+/sugyjurJ0/qyVjRRmzmIn4cjci4i3Jvew3rwlmE9H34XZ19dTGtAE5KbzPArvek5+GLefi6Wf3Ibo5URpW1c0/AET392S6XE5OVt5qXvhvLoyWR2//kvoY2CaHWZ+5o/Ax/tS5drT/H66EV4H/mX8LQTtD61mZ2Nu2EeeDXBGdlEphyh9anNLOhwr9NzZBt98lef+Ojhn5h3JBybwZ5NnrT5IH0brOXlb2/DaCqcYLHZbJw+dBZva5a9+HcJVPleGmQkZ2C1WAkMKzykOujKnrBvk8vjsk0++D39DF3v/Q+/3D4W7+2b8M5KxWyzJ84UkNCqC9ct/kQKdooykZVZyq+8fYQQ9cGWeRsI+3dzoZoQRSkgoelFXHd1sdXWRQ0i/UT5ST8hROnFfjiPwLQzLveb9+6swmhKr8Ynhnx8fEhISCAsrPJHNpxLChWkOJNhxDsxnYAGbmru1FBaaxISEvDx8SnX8VZdNR2owWal98DWAPQaeim9hl5KdnoWymQsVrxxwtxhxB+J58zhBE7kWLnvv4MqPb6QRsF0v8leFCzx2Fm+eGU564+ayVUmWvmlcveDHWhyQQQbl+7CL9CLHjd14oOf72BW4/6EpduLG3Y6ugpLegce+n08GlhzwfUur9fUepozB+OZ/tEq5sRFFsrgaBQrkpqQeccMxn59I37B9sTdvvX7eeGVTRw3R0IpkkIALU1nyEzLYtpbS1nzTwYWrWjf1MBdT/dxOiJq86J/mPD5TvYq+8pxMdYT3D8ihp6320f39H99BHN/nkNIwpFix57tOYDBs14lqGEQACP+mJC/b8fSv4nfe4ym3Vpz3qXyiasom4L9hPzSXzYV7SOEqMuSDp8m5b+vYMT94glnG7XkP4s/q6qwRDlIP1F+0k8IUXpzH5qA+etP3baxepfuOa2q1fjEUHR0NHFxcZw5c4asrKwKvynZbDYUkJWeQ1aWFQ14mw2YvY2cTXU9tefMcSvhEf5YLVa0TWM0G0vsWDwRryf4+PgQHR1drmM7NIUjJzwTx9Vhx2kY7s2c7V75K1jlsRmMjPngCO9kWblkQHvSE9PxC/YrNiImT3izcMKbhXMq9gRWi9VlO09LOpnMf0et5KRXo/yC2xtzg9n08WkUp7AZTEAOgVOW8R/vvTTLPleJOi+1k2EOYPYl93M0tLXL6xjQ3PzYDlCO5EzB15rj739mRjP49lge7Gfi2vuvYMzLWznrVWBVDMfKZvl/FmGy5vDYM50Ydcs8jpobgcE+Ku7gcVj28EYe6mcipNO5pNz2lTt5csJJcoxN8qeqHTQ25oUpabxl+IvLb+uKb4gfly/+itVPTsBv7W9452aQGHkeUY/9l+FP3ejyfttefQnIJ62inAr2E3lqyvtvZfD0vVWkjxCirvp9wi+onDPFViArKr51N4ZvnSyjXGs4Z/1EWdXlfgXc35/0E0KU7PiOoxgmlfwhgXf/q6ogmrKr8Ykhs9lMTEwMALGxsVxySfkeHpd8uYrv5pwizhwJ2gaqSAfu4uE5j3duOjGmJHYr+zLrQbnJ3NjRxj1vDXL5y0BF4q0p7nq6D6seWU+KObhC5/HPTeWZL2/By9tMi69X8cZ8iv3MM03+PP/pEWwT4sg0B2CwWWhtOMl7X11Lg8aFa+7sW7+fd15fT7cbAnlx3Gr8LOlc1zKDRz+5uVJ/Ofvf68s46VV8JI02GAsNs001BzHH0pGRvovwSbUXo8y703kd/2tPCjl5zfnlpGDGyiGvxqWKJ9Pkz4e/2fhp+VzOFj2mwCpk9iDt8Slto5ntNGPHdmbK+79z1HxBsfNmmf35cBXcFZzMweXzuOftwXz16d/kGKMLnxOwGky8/r+TeH8zm0RTCDaDEaP3tXS/vhORYSY2H9RYV+bw176ZjHzmSqcjkYSoiIL9RJ668P7rSl2+NyFqApvNxqF3Pyf48QHu26Ho/N7jkhSqBZz1E2VV19976/r9CVHZ/nhvJn4lfJhwtnErbvr4oSqKqGzqRU+28NOVvLHA5kgK6eJJISixiHK2ydeeFHI8XKeYg5m8owETn5xbGSHXGI0uiOSzty/mElMcBpu9ELe7osmuDGydlT8lbOVyx6odTn7m6V5BZJrttXRsBhO7iObWUetJSTi3PPPhLYd54JV97FFRgAKlyDAH8OOhCEbfNL3MsZXF+v1u5lgXuR+bwcRfLfoV2mYxmDgUdqHT9gBt/FJI9ipjITJl4Ii5FIkkpQjITeXXHy/n9Zc7se6Wx1mX4j5JY1MGvt0ewoy3FrMzK9RluzRzEAleYdgM9pFbVqOZPzKimX20EYdMjTlqasSCE40Y+cCfTOw2mp9HfciZA6dJPJHE18/M5clbp/L6yOn8s3x7mW5dCCGE8KRDG/czLXoAYSf/ddvOoowYxzxLuwGdqigyIYQQNdXCMZPwmv4/t23iW3bm5u2zaswqZEXV+cSQzWZj0sKkc8mg8s4rdnH87F2+ZKRkViDCmi/mkhZ8OmcYS2dexsKvO/L6EG8CclNLPhAwWHMZEHGChz44N40oJasMF9eaLJMfr9w3P3/T52+vIdvsfG7mFms06+dsLsMFysbqtspAcSeDmhX6PtvkvpD3gYzKrWOV5hXEmCGz2DDgbkJP7iPLXLrC4jNXZWDUFVuhz2TN5rwz27EdOojPlC+Z1/UOho5czeSdDViXFc3ShCY8PD6B8aN/qtB1hBBCiPLIyc7lz2vvpcGZg7grtWtVRjquW8iAd0ZWVWhCCCFqqLXf/Ioa/z4mF89Kef1JqzGj8AuqmfWFoBZMJauoo9viiPeqvMLVWSY/Ni78hyvu6F5p16gpfAN88A3woc/wy+hxUxYrvl/H6eMpzN5g4axXWLGaNg2z4xnWz5/et/YtVAPogiZmth8r5UUdibi/MyN49a7phDbwYnNyCBStEV7A/Fm7ufTGyvkEr32EhZOJpW/vl1M4gZZr9HLbPqmCU/ZKY5u1Mf0y7LWPjDaLoy6Se2e8wunuE8e6rMAS2xbV+uQmuh9YQqOUI2il0Cg0sLjtXaR5BRVurDWzDjbk0jmbK+3fUAghhHBm5duzCE46DjgvNp23Mllyj/4073ReVYYmhBCihtr30Xe4yjbk9Rtno9pw7d19qzCqsqvzI4aM5sq/xVXLDlb6NWoab38fBjzYh5GvD2Li+B60U3Hk5UOVtmG2ZnPGO5yPV/tyyyNbeW3kdCw5FgCGPnY5PpaMwicsYRlMi9GL5WebMHN/OJkm96NqzqRV3pKadz/VE//cNPs3WruO27G9/bG1RXe4v4CzaY4eZjWamd7l/7AYTMTEl265RJM1h4eevYzA3OSSGxfQ9eBybtryJU1SDmNAY9Q2TNrKv+HtORXUtPjPz5EInDtrV5muI4QQQpSXzWbjx2HvkPPWm27b5f1yP3jWa1UTmBBCiBptzeeLCdq3xeV+hX2RgoF/fFvj69HV7Og8IPqiaJStYlNgSrL7VP1e9rJJ68ZMnD+MGe+15P96ZWHARq7RO3+/VZlYltCEDx/5GYCoC6N46/5IwnMSzp2kLFP8Smi7xxrJrdf8yAcPzSLldNkSGSVp1r4pn77cinYqzj7uxTEyyr/o1DqlaHNyI22Pry+02WSzeDSectGaY6Et2dj8Kq7c+zNmS8lz+7r4n+a8zjF8Pa4TfYKO4VWKY3xy0um9r3gNLg20jN9G87N7XP5bJqaXeHohhBDCI+Y/8jl+s74tccp0Yu+B3PHvbIIiK390rxBCiJpt6dhpJD32OGZbrtP9GvvU45tXfU6DKNe1WmuKOjWVbMPczaz8ZR9Z2VbSszRxqSYybUa0ufKmkgFYdd3Nr2WkZGLJySUoPKjEttEXRfPXq6uwGoosZ+l4+F96NJhHkjPwC/aj2w2d+KZLc4bcu54ss2fr6liNZk4YI5hzFNaNWMY33/+HoAjP/RLX6rKWTJzfkk2L/uHontN06NOZsKgGvDX6FzYmNyDL5GcfCRMRyanBo/DavhkMBhpccyUR50fin5tKujmwxJXwiskr+l3RUUWOa25v0p3uB5dy17p3WNH6Zg6Gt3V67vCcBMZ8YF9WMaJlJA2CTBgTSk5wxSTsdPpGmXfHLU9v4XBYG6fHtoisU29N9Z5SqinwPdAIsAFfaa0/UUqFAjOBFsAh4DatdRkmawohRMVYLVZypkxxN0MdgByjN9dNfqHQ1HghhBD1U05mNskffoK/m9kgCkju1JuAsLKX4qgOdeLpy2qx8uwdM1ibGY39ucOhiu6uXZOyr9JV0+3+Yy8fv/sXO2xN0MpAVO4pRt4QybUP9HZ73MFks8v6P9kmX/b/dZD2/dqy5JVp7PvqJ7K6PFYJ0Z9zwhzBpFeX8cRnt5bpuPgj8UwbF8vuHWc4/8Bamp7ZBUqRfXE3znvgNr6YdpxDpsaAF6w4SDPrOo4YG4PJkD89arehKYfTQ/ly6q2c19m+RGpsbCwfPtuUh96Lw2os6dfQIpSB4OyzJHt7JuOc6Sg83TDtOLdv+pQMkx9zO97P4XDHqmla08A7l++nXJWfWHtu2CzWZ0e7rfEEYLZm08DmvkC5IS/RVSRB5mXNYugjPcp3U6KmsgBjtNablVKBwCal1HJgJLBCa/2OUupZ4FngmWqMUwhRj+xesY2/hj9FaFaS23YWg4mQN1+tFZ/4CiGEqHx/frkU/0z3fUdiwxgGzHq9iiKquDqRGPrupYX2pFBZR2B4gH9uKiOf7Fml16xMmxdvZdGsnfx6MhSLKRqw19E5Zo7kzYU2rLkrGfio68JZASYXw7Ad/zahUSH8OWk5lrdew9Tggsq5iSLW7i9b+33r9/PYKzsw5mhGrJuEX15NIcB/w3KO/b2WpB7PgjHv9aY4YiqwXHyB12CmyZ9vPljLm9Ni8rfFXNIMkz6EtcTPJ4tLMXku45xr9GZz094EZiUSH9CEv5teQbJfeP5+H2smEU0b5CeFtv26w54UKvr/zPH9A13S6HNzB4xeJvasP8B7ky+mp5qFQdsKFfHMK8J2ILytfUOBcwXlJvPM8IY071B4NTdRu2mtTwAnHH9PVUrtAqKAwUAfR7PJQCySGBJCVIHT+0+ybfAoQrNT8vslZ9J9QuixeiZNO7aowuiEEELUVEtemUbO22/gbvxoYlgzbts/Dy9fbzetapY6kRha8neOfQRDFSeFAIZ2tRF9UXTJDStJRkomJ3afILRpKA0ah5T7PDnZucQdOMvEOQpofO6VUfBnqgy8t8RCSvIi7nhxgNPzXH15CHv/pHjyQCla6ePsXmdk2qSdZHQbQ2j6SbxzM8h2tWS6u0Sf1vjllq4QTY4u27Dvd15fT4o5isE7vi6UFMrjn5tGr38XsODie+wbSnjdbU/wKfT930t3kG0q41KFjp+FLusoIzeyzX4sbTvM5f4gWzpwLs7Vi/YAYcXv1/H9iRPpRLeLxmqx8uCjf5LiG86GFv+hx8ElhZsDhxu0Yn/D9gCE5CQyvI83oQ0D6D3s6lr1BirKTinVArgEWA9EOpJGaK1PKKUiXBxzH3AfQGRkJLGxsSVeJy0trVTtaqO6em919b5A7q0mSjxwAq/XRuJ2YnRUBF4TxrA/6RD7Yw9VUWRCCCFqqkMb99uTQiXUpPO/YVCte6apE4mhJOXZGjWFaJvbmi6+fp57UC+LnMxsxj06lxXHgsk2+aL0YdoajvPqR/2JPN/ps5VbE5+ah/fFPiWOurIZTHy+3sSx+2fy1JdDiu2/5an/sH7IDDbkFE6WBeck0SRM88psKzTsCMDR0FYuV/UyWXOwuFraXWtGXJTIgBFdWb1xAxE58SSYgrEanP9bWDGQmZaFb4CP0/0Fnfz3FHtUFNhstDztusJ8y1Ou9xVlVIWnGprM5ahPUA1Jz27NCr/hmUzuaxsZjfYY/1m+g3gve12v2FY3kuoTQrdDywnJTCDT5MfW6MtZfcEgtOP/1U2dDQx57tpKuANR0yilAoCfgf/TWqeoUr6utdZfAV8BdOnSRffp06fEY2JjYylNu9qort5bXb0vkHurSSw5Fn4e/g6+c6aV+It91sfPMnDkULz9S/79QQghRN23/p2pBJTQdySFRnP963dVUUSeUycSQw1J5QiVmBxyxpFAuaR31UyHKuqlkT/xR0Z0/r+gVga262huf3QLHX1O07ihFzf/twvndzmvVOdbusfMoIspdQJi/rEIhv97ikYXRBbabjQZGffjUJZ9vZply+NIzlJYbIpspYhNjXI6ksgZsy2Hd+8LZerkXezOCCbb6ItB22im4nlqTEfaXdUHgMbx/zJ7aR9+fGcJ8+ft5cITG/GyZnEs5Hy2Rl1GtsmXJK8GfPTYXJ7/5vYS7yvpVHJ+XMrVUvQUqI9TCl2jC38eecmA9pi+/g2L0YNZZA9PowzPSeDel65i+4Ht+dv6DenA968cApTTqWT9b7wIgIyUAiuWKcWm5n3Z1OxKTLYcLAZzfqLVaLNwXVQ8I98oW/0nUTsppczYk0JTtdazHZtPKaUaO0YLNQZOV1+EQoj6YOagFwhZUXzFzKIybh9Fw4tiJCkkhBACgFXjF2BeNNttm/jm7bju14kENSx54aaapk4sp3VjH8cUKjcP8uWmDHjnTVnKO7/jQbijKY5Wl7X0/DVLcGDTQf5Ij3J6v7lGb/7Kbcr845HcNfYIg6/+mRlvLcZmc53IyMnOJdmrbNPQtDKwbMpfTvcZDAauub83gwbHcMAWzn5jE+LMjho8pUxeZJoDeGHiaTZbmpLhFYTVaCbX5I1VG4jpVLj+jNViJWvBAm7f+Akdjv3BhSc30W/3LO7541VCMs8A8NuxIKyWwtndLUu28cQtU7np6p8ZPmAG3724gOiLGtunjynFwfCLXMbndJ+Tf4/wnAR69Y/hg4dm8d59M0k7m47JbCTC5uGFlyqaFNI2vC2ZBOck0b/Bcb78vCfhzcILNTmvcwwDIk85nUrWO/AY7fvZawZdfFWb4svZK2VPhCkDnUxx/N8VWcwa356nvxqCwVAn3oaEG8o+NOgbYJfW+sMCu+YDeR+p3AXMq+rYhBD1x8k9xwlcucDNGjJ2KcGNuOnbJ10XHhJCCFGvrPtuJSlPPo13bqbT/Xn9Sof3nya8RcOqC8yD6sSIoZuf6s+RQz8x90g4WhWfpuObm45NKazKhNI2ck3nPv0xWnNLXB2qqSEJiyXFsQqVfbRIN98TvPrVYM/eSCmtX7ILSjN9TikSvMKZsBa+vO5Xmqqz3NgvjBuf6F+omZe3meCcJHBbQqu4Q4dSsdlsTh/sc7Jzef+HM+SWMeFUUFbR2kNac8jUmG9eWcLo8bfkb175zk+Ebvi1WPHI4KxErtnxAzO6PkGm2Z/U+DRCGtkLKS/7+nfenJuL1RANjhlrk/6BDSPnc+35Xvx8JIDVF1xPi4RdmG25+f/ZFZBr8GL1BYMKhWa05TK8fRo796WzOz0Qk7bStXEW6Vk2nv0hC7CPrAo+m8q9g2Zwwlj26X6V5QLrcR558EK6XN+xxLbPfn0bMW8vYf6aVOJ1IKGkMaCrL8NfPTcaKyg8iOtikplztMCnrI5kakBuCi+N70/DmJpz/6JKXA4MB7YppfLmYT4PvAPMUkrdAxwBZPiYEKJS/DPvL7bf9xwhJUwByDF6c/7Hr8my9EIIIfLtff0zwtzMGFHA2cYtuXZw16oLysPqRGLIYDAw5ovbGLIzjhXT/8ZitdHz+oswmY1Yc22c3zWmUAe/a/UeNizbi4+fiX53dmPkvb+T6OV6CdJQXxsf/nwHu//Yy+nDZ2nZpQWNW7lemauy+fq5qL3jRq7JmwM05oPf4OD+H4st335161zAWKYpScvONmH7gJ94+43iU9Y+fORnkr0auziylFxMO5u/15+jt02lV89GBLU3cmLqXMIo/sGeBmISdnP19h/YH9Ee/9DLAHt9gfE/ncXq1aDY9bbaornqvGwGWU6x2NqIHy59it575xKTsAuAhPMupsN7T/KcMjN72k4S0hXRYYrb7+9Kqx6FpxVOfe0XvvgroNh97FVRNepTSBvw1Vc7+X3JPoY90dttjSqDwcDQFwYwtIRzPj7hFryemMOC3T5kmAPsxcdtx3n25S6SFKqHtNZrcP2qv6oqYxFC1D+7lm/lwJB7CLFmu22X1CCaHgsmct6lVT8aXAghRM20N3YHDY5sd9smMeJ8rlr8Ra2eCVEnEkN5oi+K5q7XS14h7MJerbmwV+v87995oQ33v+dkiozDpV3shXTbXN6KNpd7JtaK6Du8Gx/8uhFtKMenWVoz51AYt22PI7hxMJ8/t4jVh73JMAQwtH12mackHTdH8vgLWxj7WCoxHZrSICqUDx6axcKTFUwKgctYssx+rM30Y+1y+K9fAoaUZOeHO/7sFPc7neJ+Z3qnDQxe/imzH5tEz907sBhM7GnUiYNhF9mv5bjeqj/OMH72MO47mcyW5TvwC3yJtn1aYzQZ8SlQwLrroEvchr9wbRqYAqq8cLTZkkVrczzHs/046ybhmeeAsQkAO4/Akof/4sOnY2jX1/U0utIwGAw8+vHN3JeexcEtRwhuGETjVr0rdM6KsuRYWPrSFJK+/QHf1AQsJm+yO/XgqolP0uTC6ltZUAghROX669lPCHOTFMobcdx87JOSFBJCCJFv1cfzSX76WbfzarKN3txxdGGtTgpBHUsMlVfbKy7k8gmb+COz6bmNjlEejXJOM+jR66svOCeCwoO4IvgEq1LL8TCrFFoZWTT5L1Ztz+awqXH+VCqbsthXYQO3JyeweQAAIABJREFUK7EVddYrjNFfJGG0xdPB6yRbchqD8mwxZKe0JgsvUnwiCWW/+6ZA2J6/WNbyP0Rkp5A3ZuWSuNXsiuzEvI735a+SlZVtnzgW0iiYPsMvK3d4Z7VnC6IrmxWlbdhKmPqYa/Lhkta+7NlRiusXGc2UYQ7gzfe3Mb2CiaE83v4+tLm8lUfOVZTVYmX1p79wdOZidE4OIT270veVYS7bTrv0PsK2/0He5EZvSyb+fy5m08WL+T2gIbrPVVwz4TEaRJWcTBNCCFHzZadnMe+eD2iw9Q+37RSQ0KkP1z5wddUEJopRSjUFvgca4RjMrLX+RCkVCswEWgCHgNu01h4u1CiEEMVlpGRy5sXX8C1hCnL6pVfW+qQQSGIo35s/3M5Ho2ez9FAAWWZ/lLbS2fskL4zvX6plzqva6z8M5aEbprNdl2+kw/Z96Rw2NXUyXcuAwZqLzVj2F7dVGdlsia66kuaOuNc0u5pWcRsw2Syumzr+9MtOKbbvwlObyd32Hb9cPAqAxvqsR8JrqNJIJ7Dcx5ssOVxydBVpPiGEpp+kY9waDoS1ZXH7ESUeO2+rkVwvN6ue5f27F03eac1RUyN2/7HXowmdvbE7OLpuF76hQVgtFky+3nS4uQdbf93FykX/YrNpOnaKAKUwmQ30urUz/g0CXJ4vJzuXGZf+l7Ada8lP42xbw/wpU2k4/eVi7Vd9NJ+w7c4fDAxAYNoZWDiD35bMw+vhR/nPmyPw8nafgBNCCFFz2Ww2Zva4n7Bd69y204D6v6cZ9vbIOvGLfS1mAcZorTcrpQKBTUqp5cBIYIXW+h2l1LPAs8Az1RinEKKeWPvFInyzU922SQ2MoN+XdeMtqcKJobqS4Td5mXhq4m2MTs/i+N6ThEU1ICgiuLrDcslgMPD53KH8MfMvli7cT3KG5nBGKaYOORICmy2OOjdORvWUNCLFJaU8vmx6aSQENOanTg8zYPv3BGW5f4kVLVCd5+Lj69gWdTlJfuH4+lq489oZnCCEQFsGfVvbuP/tgWVesvaGvg34+HfK/TPxy02l354fC2070LBtqY5N8QohMDeFVLPzpRK9rZlkm/yK73DEmXiyeAKtPBKOxPPLfx4hbP/fAOStVWZD8find7Mjqjv2tw5YshTQmpiEHWx+azIhpmyiB/Si7yvDCAgrnGBb8vQ3hO1YW7zgeNppMnbuZfLTU2gz5m4uvetKAOJm/EJYKeL1tmTCJ+8xY94Sbtn0PX5BvhW5fSGEENVk47TVhO1a57Lfz3O2dVdGvH9PVYUlXNBanwBOOP6eqpT6f/bOOr6q+v/jz3Nr3WNNd9ekY6B0SiiggkroT0Xs7kbFQtAvFiGlIikhNTqlu2OMdeeNc35/3G1s3NjGenyej8ce2/3UeZ977068zjtOA8HAMCAsZ9g8IBwhDAkEgjJm14/rSXr/Q+zd/SX61qH/nt+rbBWy2ykNj6FqpfA7uDhSt22dijajSKhUKrqP7Uj3sR0B89Oxzb/t5IO/jbbzD+UKFEUNFSuuqJE7tpwFosu+zZnd4xOCky4w9PhcPDLjrZtnZ42wc39zsUYL1jQYkvefkY0Tf1yGk2OXMfvvMcWqUjLixT4s2fQ3UbqiHyw0xmzqqeMY1tefhq2acmKPKy76tCLZnx+tKZuBjbNZegmrn4WDrCcbK8IQoDHpqdO6Mb+8vhJ1fRMPT19Cq5oS418JI6CBf5H3BeCfe5/E58pxiwvzQ7XCzKJQftsUmcHH59Eycs+tgT8cZNXvi+i5/XeCW9TKa05evgZvbL8fvqf3ETdpHwsXDWPsus9QkouuSSuAz5Vj/PPMTEbPfwVZlkmMSMDR1REXb9teTAKBQCCoHBz+aw8Xp72NO/bPm1k6FzrOrPSXpncdkiTVAdoC+wD/HNEIRVFuSpJktYKFJElTgCkA/v7+hIeHl7pdaWlpZbJuZaG67x+IfawOlMf+ZadnkZVxCc1Hk7EdjwJufv6cuHLS7AJTilTUZ1hiYUgo/OVH1IVoVvy4m4TEbBo28mLw0z0LhLmpVCqMBuXOklLb4k7FnXL2GgJQVGoivBuzs/4gBp2YX0CMKOyJIUBg8hUWdnzJysIKJwlh4y876f9E0RMoq1QqVJLtsobWMGocOEcwc/+J49fR7TkxbBT8OTfP/gYxRzkT0L5Q4a2jexxPzXiQ2MeWsjUhAEUyfyfUJj0mlZYUnaflpJw1e3rH8vLUcK5oAnmkfipXND5cuQlbn9nPd+83o2HH+kXal+P//GdVFAI4UrOH+Y98+9Aich8tI614AaVGs/7+V5h4fklemyatcI8mBfDcspKds3si2Quru43cbRvWrWPjR7WJ/fZ/eCTdQEYisXEo3X56h3qdyyZvkkAgEAhKxu6fNxL31DTcC8kJkRDUmK5LvhTH80qGJEmuwDLgOUVRUqQiXk8qijIHmAMQGhqqhIWFlbpt4eHhlMW6lYXqvn8g9rE6UB77t7DfK3huWWm1L/c+JdXNjyEX1pXJQ+OK+gxLNcdQWSv8VU0BLU17E6NTiE2VkJpo8UFLAgYWLl6Pj5vZ88fBSYuzhxMG3zQeGXZn2/DxNPHIMPtxlHdGUWSZO8O6za1JzX7TIibUqHVElhUUSUJr0qNSCoo2RpWWca5Z3Ap4KkispC/25zl4lIZMbtlR9PfYgY2btuI3uTMJvWpBWhp6jQN1gMd1cRjUtoUOrWIgJNiXRfNWEtzTjYfJAECtGDFJasBWZRYFJ8mACkeCFB3dSb3NXok9x49yI/N6EeyH5KR4mP6s1b4Bbq4oUsH3wTu9BkaT9fG+wKYNG9Hk5P3RffQ4xqx06xsO9sOYb7ux6jhcXxyCMbl4JQWdkcggDpfXR+Y9LfAAju7dwsXkq2gdC4ZcGvVGUiNiUdLTAQmVuyvuwTVQaex751W145pAIBBUVmRZ5vKb0/G0IwopgIJEzxXfUqtt3fIzTlAokiRpMYtCCxVF+TunOVqSpMCce4lAIKbiLBQIBNWd4//8h27XFpv9EpBYow7dVs2udpEEpSYMlYfCX9UU0NKy9+LBS7zzxWVklf2Py1cfR1OvLHal+CGrdcXeziPDUlmwshjJkisgn9Dt2LK5ljGNqRPacXbtAbx8HElzrsHc/xzI1JordWlMejpc2USP8ysxXyLC2lYTORnU2O72Ag0xvDilHp1GtC+SfevObOPjf8h7r4rzHtcx3mTuym5M/OJPLqoa573XkiITaIghSuOLLKlBkpBkEwHGeIZ1cWL4tL5MGvMPEdrihX2hyBYhhgXsVRQkFFb/3BbPgMLzb237bjVpr35ntW9jp9e56Vnwgnzizhn4pUXaXC/pjY8Z9f4oAA7F7iZy3GNW5Ubj9GfR5Ntucq0WhP74DpcHPlCozfkxqLRoZIPVvrgBDzB21YfIssyhpbs48+tKnHduMucoykeMiy9NfvuSNvd3trmdqnZcEwgEgspIRlI6K8Z/hmeC/YcXEpDSf5QQhSoZkvnG4RfgtKIoX+XrWgVMAD7L+W39Mb5AIBCUkIOLd3Dj0f/D2cb1f66rQ+e/vqNOaNEiKKoSpVJ+wZ7Cn9MvFP4S8NecA4WKQigKcTpfdqSH3JEodEfciSikKPZflxJRePHyQj0/J7bmi3MN+OGoV54oBGBU69hdfyB76/ZFAg7V7MnJwA72F1UUbmr9eOPnOM7tuYAsy0RfiiYtKc3mlAFP9mRwQJTFe+WuT2ZoYJTdzSXLDjw1cikX1UEF5iuSikhdgPk7kdOuqNTc1Plx7Xo6O5ceLL4oBIXnnZIkFElFeqLt/c1P58l9SXfystoXenWz+Y98n3+Me02ba2VqnMmWbn2v243uQkJz22JLfjSNGtGsT2viQ5oVaTyYD/xaGycFAOcNfzO/81MsaDKG6PGT8ApfbSEKAbilx3HjgUeZ32QMcVdii7x9gUAgEBSdrLQslrUZh8e6P+yOM6i0KM++zJiVH5STZYJi0BV4BOgtSdKRnJ+BmAWhPpIknQf65LwWCASCUuf0K9NtPhQGsyiUENyY+l3sOxJUVUqjKplQ+MuY6Hh7aa9ysFJ2vKK9eawiSeiMWTjLmWSqHKxXxoJbgsEd7oNeky+HvJ2cS7vqD+Ksf1tuetYrfNEcW/RqR9547zCx6iuY1FpQFLwMSbw3tQ7tB7exmPbaTw8yeMsp/llyHHeNlpd6Gxj4ZD/UWg07Bq0i0UYluUSdN4mKZ7Gi8NZG+RGxNgIIKfqkYuClTySgYUCRxuqcHKgz61NuTHkOnbFgeF7zm/tJdPFnV72BefmP9te+j6Y3D6DOF+KXq8wfrN2bsV0LfkbDN3zL6hb345l0w6YNBrWOTu89jslo4kibEQQZnGgcfQgV9gXJwnJTaWQjPgc32x2Tfx98Lh5m7b1P8PD5v0Q5ZIFAIChl/n1zLt43zhR6PM4cMJKxMyaVl1mCYqAoyk5sf3z3lqctAoHg7uPSvnN4R523ex5Jc/Ghy6IvytOscqU07lCEwl/GBPvfQfn4yigK5aDXOJKk87olClnzGpIkHIwZZW6LQeNYNFHoNqJ0fmZRCECSSNR58fwPCVw4cMnq+Ba9m/HqnAcJrOPN8OfvQ+fkgFqjZmJ/N3MIly2KWj0u3/hjpqDizSkGD3TWFKsyW4dHetHp8DrSRz9KfPMuxNdpSVydliS06kbvcaHM/7Aek9ukML5pAp2aOrCi9WTSdO5582VJzf7a9xJdrx2t+7QosLa7vwfDzqwkZdAY0h3dkZEKyD2pLr4E/Pgt9To2ZNV3W9hvrMWKNlP4vO8sDgV3AyhEHiqaJlfYGClnOz7XTnJ0+b4irCgQCASCovLf0l1k/forYP94nObsRe8vniwfowQCgUBQZdj/+zYOh5lTTtg6j2Q4ejDg7Lpq6y0EpVOVTCj8ZcyDT3VhzSun7CYcrtLYELGy84V+VQVklZpP39/LL2uKLjQNf/4+Vm5axHkpuPQMKa6YVATUJj3j22TwyHvFz2we2CSIBxa9brO/brs6eX8fWHWYD2fWwj0tFq1Jzw3PeoRo0/h89n1W57r6uDF2xfvA+wDcOHGNQycP4fvzj/Qb2x2NznyI+3dbDBCSEw6noUHcCaCsUqJbkrud6zuO0XZk0ULgBHcHUReiWT93P1lZBjrc24B2A1pVtEkCQZVh3ZsLMH3+MS6FyPxxdVvTa+l0/BsGlpNlAoFAIKgKpMSmcO2JF3CykhIiP/ouYbj7F55jtSpTqlXJBGVDSIsQ3hhxmenL0siyFXp1JygmkEqxtP3dQCEheldM1nPq2MPHWeG89UJolQdFYfQLvcp8M/cMbcvf/VuyffE+Ym6k0DQ0hNZ9WxZ5fnCLWpyPu0TH25I5pxvUkO+r7qwvevU9o6RGpciFhp8VBZegGiVeQ1B9+N/Ly1l0yh2Tyuwl9/vZRFr9uJAv59+Ps0cpHusFgmpIclQSWV99ia6QY3OGgztjDs/HwcXR7jiBQCAQ3H3snPE3Tnr7+VMzHN3p+cXT5WRRxSGSXVQR+kzqzvLfuzG1SwbNiCiVNb0MSaWyTh5llEi6UlFIiJ50B29B9645iaJL+/0rxfVMGgfO771cauvZQ6PT0HtCV8a8MaBYopA9GvoVfC+O1OzO5sajOFujdeH2KCYS6hU+rjBMkorOTw4o8TqC6sHmubtYcMYbU/7CAorCMTmET59eUXGGCQRVhH1z1lnkr7OG7rHHhSgkEAgEAguOrT5Ayo9z7I5J8gqh/b8LCWlVu5ysqjiEMFSFcPNx48HXB/DJrH64GZILnyCbAHAxpFLfFImzwewl4aePY0hgFMlq19IzrqTJrstYVHLUp9L35O/UiTuFe0Y8nukxZbLNJo4JxZ4z6KkwGimRpZ8XqpTX8wp0L3xQJeXhaZ0LVA3b2Gwc++v2ZXm7/+OqdyO7czMc3PAdObDENiTVa42zu1OJ1xFUD/5afsWyMed/dmeCb5Gr/wkEdyv6VPt5CLM1jijPvcKwmf9XThYJBAKBoKpwfsdpLjwwCbf0eLvjakydTL3O9u8VqgsilKwK4lvLl+/eb87k9y9htFaaPkek+f3jemgdtPjX98/LtWIymlBr1DwycAlyaeYsKqkIUdqiiCKjNWbjm36T2glnaX91K+7ZSbS/vh2A5a2nkOTiV6qb1JqyeXdmn2LP0+g0fL9oOD+8tpptF7WkqcwhJAUqq1UwntkJ1Gvfs6LNuGPqtq3D2NZHmHvCscB3TZFU/NH+WbpeWEOny/+iwjIReJaHH94erpQk2k+WVLT76pUSrCCobkRnOYKVwzeAQe3AjTNRNOrcoHyNEgiqEI0Hd+bcN7YrQzq99goD3n2ovM0SCAQCQRVg7+uz8S7E6zTN2Zthzw8vJ4sqHuExVEVp2LE+nT1irHdKEigK776xG0c3xzxRCECtURN1IZrL6sCSeczkzJVyvJJKuk5X5+v462Nx0aeWiiePVjYwrZ85N0zTqIO4Z5vD5pIcfVjVaiJnAkNLvI38eGQn8kQ3I8e2niMjufjV1JzdnXhx9gOs2nA/W9b1Y0DtIniElSOZKgcOrz+GLNupoFbJWXVItipAGtU6tjUewc4Gg6zOkz29qNfLHEpWlG+m6bbk31laZxzffZ8WA9sV22ZB9cVbl229Q1FQy0YC6ot8VALB7WybuYb5jR9kiXd39jz6JglBjQqIQrnH6CSfWvR+aWRFmCgQCASCSk7kyes4H9pld0yakxct/piNo2vleVBf1giPoSrMpBc7c/DtM2RqXCxDuSSJi+pgxkzay6o/e+HkdiuRaZ5wURIvHUnCXx9DtK6EXjc5NhxPcmfJgp64+3nwyxsr+e2Ye4mqaxnUDqg0Rm54NeC3zm/hnpWAWjaS5FwDpZSrdkmyiXSNM9/vcQBMOP0RzqOdFR56x7rQUBQee6M3OybvIEHnXXqGloBsrQtTZyXi8/UKRnbS8fC7A1GpKk5XlmWZFV9v4vyZBFrfE0j/KYV7MyVp7YfCJTn5Wm13bNGUuh0asKNlN3yO77T5dDqXzGHjaPfsKM6v3YeDpxthk/vh4l2KYZuCasHQPv6c2UjBY3fO3/c4ReHuV70rXwgExeXvSd/gMO8HfHIbUs0PxxJ96+CaGInWpEdBIqF5J/r9+anIKyQQCAQCC46u3M+FsU/gaLD+ID/3Oj/s8Ar86weUq20VjfAYqsLUD63HN6/Wo77JRn4aRSFT68rXz60q0Fy7dS2cDSXLXxFkiOa+FqVX0SxF58HP7/8LwMRPhvG/lwLp7nqjRGs6u+hAMXuJpDj5kOjiX2xRSHO7i+Ht3kyKjKJSY8wXlpepceGHA65s/HnHnZqOby1f/jezM3762DteoyyI1/kw55AbP7y4/I7my7LM5cNXuHr02h17H21btIf7Bm3kq3AHVkcF8tFq6NN/LSe3nrY7T12Id5uLlUpl2Ronur/3KAAjNn5DfOseWJOFcr8VmQ5u9PhwIg27N2Xgp49y76sjhSgksMrgZ3oxOCDKQtCva7rJm9/duagsEFRHos5GoplvmSBUAbzirlBzwY/UW/snnc/vYPyRuaIsvUAgEAgskGWZE1NetykKgfkqP75Oy7tOFAIhDFV5moc1wWbEQc4Nx74IbYFmtUbNqNYG+wsris2QrjbqCBavGUn3QU2Ka65ddp4xMb/Fw8xvOIqTPy5j2ns9qaGPK/5COXbXbRGMQxEqltij45VNDD36E73P/MkDB79lQtwy7vOKpBk36OF6A09Dis25i5aXrHpcYKMAZnwWiraE+1AWLDvjSkpM8cLd1szcwsgBy3nknas89NZlHhy4jK3zdxdrjeSYZN5emGHOv5T7/VQUMrUuTP38GiajbfGnhYOd75Ki0CTxBHI+0SfFPYA682cT1DQEAFcfN8Yf/IlW+9fhPH06CV36k6UxJ5OWgITgxjRf/iuBTYKKtU+CuxOVSsVrPz3IT68EMKJWDAP9bvLOUIm5q0bjFehZ0eYJBJWGmItR/DviZdSK5fE994h94Y8tNO3TCt86IgRTIBAIBNY5ue4wngn278+ytM6Ezn67nCyqXIhQsmpAYXlPEjRevP3wIp54owchzcw3uVOmD+fgkIWcIsTqHJ0xE73W2SJETS0bmPZWJ9QaNS16N6PFNws5oVhfo7gYFQmfswfML64cZ/s/Kxn55PP8eFo2h5UVtfKZJOGtT8DdvxnNnRM4ZHAufI4NPDLjaX7TbJMCSHEn6TXjKRp0bUpqfCoDHj1kc26EXPJQkLpt6zDrjSze+ugkMVqf0k/SbQedIdNmn17jyIF/jtOubzP+nrWDyMgMgoKcGfF0d7yCLcPf/v1pO9PXKzgpOtQmAyaVhptaP95dmsm/axcS5O9Avwdb06hLQ7s2ffnCGmRVzvct973I+a3XOPLz66toPMjL6twPfxzEyMf2kq25rTKYojDQP4opN1Zy7fBlLm45gluQL/1Hd0GtsfSKq9W2LrXa1oUXhpORlM6lPedwD/A0twkExaRp98Y07d64os0QCCol149cYXfYOHwKqRpjyrKRs0sgEAgEghzSohLt9qc6+9Bx43zqdrg7i38Ij6FqwKiJbe0mbFZUarYmBzPlhUNEnLilkjYI0tqco9c6E5h0qYAQ4WhIZ6TLafY88RHzWzzM8ie+453pPWkkl8wzJpeaCecKvHbJSkZesYx3hqmpaYwqliiSoPPm8Sk7GXF/3UKTWatl695TOmMmjaNvCT+5Wz821xzy5uTmhNZk+2LURS4dT59m3Zvw94aR/PVNU17sbaC/b2SJ1tMas2ijvk4Ne2FqioJrTsJuW2zZeI1Rk/bx23FPNsQH8dtxT0ZN3MP8t1YUCBMzGU1s+3olT297nWlbX+KFzdMYdGIeTvo0ZJWGHekhLL1Ug8c/jeSjx5fYDTE7H2dfyz581rZrqGeAJ8t+60QbVQRaYzYq2YCHPpF3R0i88csYwCz69HrxfkLHdrcqCt2Os6cLLQa0FaKQQCAQlAFbJ32Mqx1RKPfsXmtA1/IxSCAQCARVlga9WmGUNDadKqQ+/e9aUQiEx1C1oHmPJjSYfogLUrDdcSlaD77/aDufLRkHQFS8/XCyDlc2452xiJvutXEypFMjKxqf5Hx5f84eYNtfyxjy8kvMsJfYvQiePhpjFh0v/1twGuBz5TitejRg8eQejOm/lAht0eM9E3XeTP89DknjiiJZv8lXyUZe6AvfbMjGkC9PkMaUzfAjP+FotOI1kyM0aXQaOrnHsiM9xLJfkghrWLKKbdEXY1j01TbOXtfjooP+A2oxbFpv7lepqP3eGubtVZGldSnWmoMDbpKQZGJfRiAmnZ1/f0kiwTUQSLX6+UmKzLbU4LwjiKM+DaNaS7bWlTlHYXnvxdzfLJvxsx9nyfB36HJ+Td5cjWyk1Y3d+KdcY16n1zGpcwRKRWF9bCCNPv+XB17rb9UsN63999Tbxf73zDPAk+9XivLFAoFAUNnRZxvwOGa/aowExIc0o/+UvuVjlEAgEAiqJEmRiRj1RlK798dr+xqLYjJZWme6f/h4RZlXKRAeQ9WEX1ePobNThE3vl1z2ptRgzfdbMBlNBPna8BjKET48M2IISLlG24gdNIk+hE/yDQuF1T0lioRFfxeYZ0Fhnj6KwrgDX+GTEVNwWs7v1JxcNr1bOdjfjhVSdR4oKtueH/c4RTFs2n38PqM5o2rH0NU5gn5OF/i/7W9RL+5EQTNzfrd85NYF6Iuf9yPQUNBuJImGciRTPhlcZDtv5+jGEzzy9AGWXfPjhBLCvuwQ3l8h89ZDSzAajeizTRQeRGjJ9mtO7M4KwZQrlFkk01bQGbNQycbcBsvPT5EJTLqc91JnyCRL53orAbeiEOsWwtxLQUy95zN23nDmmqel+u6fGkHTqIO3GnK2s2qbbU+lSRMb2/78FYWn3ym8OpmgYpAk6VdJkmIkSTqRr81bkqSNkiSdz/ltPQ5QIBDcdWydvgyNlbxCuShAYtcBDN8/r0gengKBQCC4+zi79TjzGz/ArtqdONSyF+ojB4mr2xqTdOsBeZJ3TRos/l9eXtG7FeExVE1QqVR88cdD6LMN9B6502apd6Nax2cb4M+1f+DlaMqp2nXbWEkiMOkyvmk3OevfljSdByGJ5/BPi7Sox6QANc7so35gby7qat2R7fUTTxOcfMVqX6aDG7XamcN0Jrzbn30P/s3ZQjyjikqQIYbXZ/UDILhpMM99Pzqvb36Hfbge3lZgvIT5IrRhz2Z5bb61fJn3x2D+mrGZg8eSUUnQo0sNBk8dhc7BdqiePWRZ5tMZp8jQ+hfsUBS2pwXz1sNL2ZkeAnewfIrOs6AHUD7Rp8WNPXS5tBaf9GgMKi2nA0JRDb2f1te3c8m3BRkO7qhlA34pEeg1ulv23i685eb80TphMCqcCO7K8ZBudL2wmh4XVhcYWif+DCeCOxdoS1Bs54TqOLw9nRYuZG+WpZfWoIAogpuGcT76fFHfDkH5Mhf4Hpifr+01YLOiKJ9JkvRazutXK8A2gUBQidjyxXKMH75rd0x8w/ZMCP+mnCwSCMqf45tOcvZQBAG1vegyOhSVSjzPFwiKw/UjVzg++FF89Gl5HkLuKVGQEoXxyefxbtMAt0BvBvRvI/6/EMJQtSL2cgzfvb0JleKPbK8su6JwUR0EBqxV3sY39QZtr21nZu8v0OdL1Nsw+ghDj/2CLl9eHQlQKyaah0hcjLFcqzBc9Sk881wbbjysRWvF20l6YBwOLo4AOLg48sOy0Sz7ciNrdyZzA8skx0VBkk1M7a5n2NQheWvfzpgds1g15RuMK1fglh5HipsfDiNHMfaHZyzGOrs7Mf79wYy/I2ssOb39HBFaf8sQrpy/96T6l8zXz4oHV/urW+h7eknea61soFXkHrLSO+OkTyPVyfxem1QaIrwLJog2qnXYIsHVH7esRFIdvdjVYAgNYo50ghVjAAAgAElEQVQRlHL11lyV5SHIV2U7TxDAl38+xKrvNvH7Pwmk4oSXKoMpD9Ui7OExducJKhZFUbZLklTntuZhQFjO3/OAcIQwJKiinFh7iMOfzUWJuA5+/jSb9hChY7tXtFlVDlmWufnl99irzWeS1LR83/J8LBBUB6IvxvDas5s5rwoCHGFHJn6/Luf9l5vQ8r7mFW2eQFBl2P7aD3jq0wDLW97M3xfQ4evtwuM0H0IYqiYk3kxiypM7iNUFFxQNrOX3sRbalROe0931Bq0amZjt8nDBECxF4bx/GzY0G8uQ43MLTE118cXJ1RGKIQypTHo6qK/y8uyh+Nf3w5T+HZde+gCP5JsAZGucMI0Yw8ifnyswT+egZeybAxmb83rL5i28NRg+Xm2ymUfIGrZy2OTfzqh5LwMvI8tyuanIKfGpLJt7CAi2GYJnUt2ZJ5ItNCY93c+vst4nG4u2PRt5pBwMmSQ7+eb1nQjuRFDK1TzV/pxfG4s1hoYVHk009Nn7GPps4WYJKj3+iqLcBFAU5aYkSX7WBkmSNAWYAuDv7094eHihC6elpRVpXFWkuu5bVd6vlMgElKibuA9vDJirzN24eYyIeTfxrO1XpfetMEp734x6I66vjsBoZ4ymZk1SaujL/D2tzp+boHIiyzIvT93CJXVQgWurGJ0vr3x5iaWtQnD3K3nVW4GguhN3JRbtrq02+93S4rh64CL1OjcqR6sqN0IYqibM+3gjsTor91RFreSVM+56kgq1Csu8PJIEisKpwI70PrsMF31qXpfz+EfofF8Dls5JthQIcl5rjNkYNQ44GDJoc30HXS6upffFLXmlzTs92psO48M4teEo2clpNO7TBlcft0LNVqlV9H+iJ4qyjTnL44jV+RbYrjXcjKlW23Mx6o3s+H4NkRv3onJwoNljg2k97J5CbSkpbz+8iPCkQJTcUDl7SbuLkNC7qAQkX8XJaNtLp07CGQ7U7WOz3y0rgVTH27y3cuwLSrrM5Rot8pozta6AWRS6GNCaS/n6QGFwQDQjXhqNQJAfRVHmAHMAQkNDlbCwsELnhIeHU5RxVZHqum9Vdb8SbySwrX9PtCa9RZ+MRO1da7hKRJXct6JQ2p9bclQSOwc9bZEYNK/fI5BxceGltj17VNXvpKDqcmDlES6pA61e56Vq3Vn27TYe+3hoBVknEFQNzm87xeHBj+KSlWy1P/f84uBmPXLkbkUIQ9WEAxdNpfJp6hU11xIVsOZ8I0nIkpoEZ39c9KnoNY6k3dMTh/gkIjfupbnswklVLYs5deJOMfq/mWRpXXA0pKNRTCT1HJInCuWiUqloMaDtHdk94Mme9Jsic/ngFQC+/2wPBwxWEogpCg93s/1GXT10iX09x+CSlZznxh7xzxKO9xjMuI1flJnn0Mxpf7E1Kchq6Fh+tMYsPOW0WwLYnXDbxYZFjqDbsBuWCOhVOhwM6WTnr5AmSTSMPsJl32YFxgYmXyVL64z6sUk8+dUUuvx7kt0bL6DVqug7tg31Q3sVf38EVZloSZICc7yFAimW36FAUPFkJKWzZvRbeFsRhQBUKByc+Tc1JnYoZ8uqLh4BnsTXbY3P5aNW+1U9wsrXIIGgHDlz6AbgbvPh34XLaeVrkEBQBdk74XW8bYhCYBaFEgIaENzizvLjVleEMFRNUKw+VysakiLTIOYo7lledLy4jVj/hlzyDLR5UtrZcCiP3+9H/Le/4LNnA+wxt/dTO+DUZTLHHBqh1ziiNWXTyniJTicXoFFMuOpTUJBICO3N6BUf3bG9tlCpVNTvUA+A6UtCGDN4JTG6Gnn7oZJNDKsdx7i3H7A6P/5aHIe6DMclXw6lXLy2r+Hfd9vQ/8NHSt1ugFVnnUFr/zP00cfzysSagDdvzU3EkFsFLB819LEkS87o7ZSx99fH0sBLT0SKGi9HE4MeqkPqUS9cMhMLjMut/XXOz75Y99xgR+o2rsGv763npuSF1qTHKz2Ga14NiPWolSdEuWQn0/LGbtJqN2bCrKcBCB3ShtAhbeyuL6jWrAImAJ/l/F5ZseYIBEUn/loc6zuMxTv+mt1xhqSUcrKo+hA6601OD3sUR0NBb9YkrxAGzhRxxILqi4+/7es3AC93cesmENjjysGLeN84Y9PrFECvdqD5V2+Wp1lVAnF0qSa0ryNxLaL487TGLEb/9z21E89hHPMsTa7sosmVXfj5H2VF68nIVpID16/vTsTvK/DJV/0pU+vMoZphRGU7EqiOpbG3TF3TTdTpqXg8+384+bhjSMuk8aAO1G5XryS7apfU+FSmT11FeEoQOBQMrXM0ZTBmaler805uOMyZ4Y/haEUUyiVmwV9QRsJQpsZ2JS6A9+6X6DV+WF6CtNkBZ5g76z/OJTkgIxHsmMWQgSH0nXg/H0/8g38TbF9YTBjkzdBp9xZo25z0Ehlvv4WKW6XgJcCoceBkYKjNteoYbzLo6QdQqVR81bcVAH9M+pYthxyJc8sJiZMk/JOvMeT4rzgZM9De18PuvgqqJ5IkLcacaNpXkqQI4F3MgtAfkiRNBK4BVSaOMO5aHD+8u5H9kU4YJA2NnJKZ+H+taN23ZUWbJign1j7yYaGiEIB/93blYE31olmf1jhu/YPdr85GfeIQslqDpkcYA7952sLbWCCoTvR5rAvfr9lImtbdok+STdw/yfY1mUAggPjL0YBtUSjDwY1Wq3+jcS9xvXY7QhiqJkx4vTdbJ+8iSWevjocl3S+spnbiuXxygNlTpHH0YUKvbuFQrZ7ojNlk6FxBUuGuT2LEuCZcX/J+nhKb6uDJ7x1fJsm5Rt4aV1PAP1li3IEZKOv/JMrNn07rfy22KJSdnsWvLywlYfMu/BOvIGt1uPYJY+SPz1hUFDPqjTzzyBouqkOsHg0ytG688eJ25q0dW6Bdn23g+JipuBkz7drikBBdLNuLg0qRkW0lz1YUuo3tVCBrftMeTZjeo4nV4ZPf7MWuZw+QbuWiookSweCpYy3a731tFPsCfTj72Rycr5/H4OiCuk8/fJvX42N/Vz75NYpUbUHX5obyDT6ffZ9FeN2I2U8j+/Ug7NxyYl2DcdGn4JcagQSkO7rT/92Hi/COCKobiqJYfvHM3GujvdKSeCOByZO3EasLgpyifIeMbhz7JoqPk7Lo+kDZ5yQTVCwnNxzBY89Gm/2558ckn1qMnjaE3Xt2lZtt1YV6HRtSL/zrijZDIChXHFwceXOCH+/OT0KvuXWdKykyk9qnUz+07B6uCgRVnfSENE69PN1u3erszr2EKGQDIQxVE3xr+TJttDfvr5SLPkmRaRVhvljNr6Pk/t3twhrCzv2NWpFJdvTmYqOeTFw8jcj/LiDlk5LCG91fQBTKJdqjFnvr9Sfs3HLcU6PZOXIata+uLnKenuz0LF4YMIdeB36jVv7kyEvOs2j7TsadWlJg/NoftnHxtioOt3NRFUhCRDzeIT55bXt+WIdbWmyh9ug9LfextKgtx3BZFWi1z9cQj6OTZdiYLQIbBfDLV22Z8fY2Dmf6YZTUuBrTGNrCwJNfjLX5/nec0IuOEwrm+AkPD6f7gx1Y9yDE30hg68IDGIwm2nSrR9MePa2uo9FpGHxpA393eJQ6V0/meSElBDSg+/LvcK9hKVgJBFWJXz7cRKzO36LdqNbx/a+XhDBUzVn+xHdof51lNRVfLhIQ36AdfZbPQOdQupUkBeWIAiajSZQzFpQr3cd2ZGH7KJZ+t4OrN/X4eKgZ+VhbmnZvXNGmCQSVmtWTZ9gNIzOodXR67/HyNqvKIIShakBqfCrzP/qXHaeNoPEvcrUqtWy0W43KwZSVJ/94ZCXQ7thy5j2YzbCvJubkNFJQJBWnA2y7tZ4KvIewc8tRAO+oC5xcd5iWg9oXyb6FH/9Ly9PrLWxUAL/I06x/7Tc8RjbNa9+9Jxp7Zd4BkCTiricWEIYSz12jKJftNZ+fVCS774Qvvu3JuGnHCjwdAlCbDHz6VvNirxfSLISvlz1UWuYB4BPszahX+hVprKu3G+MvLCMpMpGrB87jXduPmm3qlKo9AkFFsf+KhNWDhqJwXRvAjdM3CG4aXO52CcqeE2sPoft1VqHj4oMaM/704nKwSHCnHF15gKMfzkFz5TwmF3c87h9E/+mPo1arWPHEt5j+WILD+xNY2/dJEuu1pv13r9G8350VyBAIiktgowCe+77KRFcLBJUCadN6828rfTISAT9+S4OuTa30CgDKpsSSoNxIikpm4kPrWXzBlwhtgF1RRJJNBV6bVFoSnexXt7p9tcant/DBx0e5XqstBrWOM/7tMKm1Zi8dK2SrnQqsE3em8HwMuezeG03NpIvcvnLuWokr193Wbt2G/KhkIyHNC96weTWtU+i8+LZhhD0/rNBxd0pAo0CWz+tEZ8cIXA0puBhSaauOYOm3LWna3XrIWFXAM8iL1sM6CFFIUK0wKTaOsznHX2O2sRytEZQX2elZ/PfC50UaW//1p8rYGkFJCP96FVdHj8fn6HY8km/iHXkW9ayvWBz6OL83H4fj/P/llTlWoeBz6Qjnho3n9MZjFWx59UWSpF8lSYqRJOlEvjZvSZI2SpJ0Pue3V0XaKBAIKjcOWak2+1QohI4TeU7tIYShKs6Pb68nUutX+EAgJDEnWXSuiCNJ7K/Tx9xUxO05mLJQZaSzpPHjzOz1BSvbTMlby+o2ky4UWD+oXYMibglko/nmypbUpcrOKvC6W48g8x82RCqADs5ROLs7FWjr+kR/Utwsw0LArC6bnnmR8fv/VzSjS4CHnwdf/PkQ69cPYcP6wcxc8RABDa3bJRAIKo5W/gabfTX0cdRsVbMcrRGUB9cOX2ZZ7f74Xjxsd5xe44j00ut0fbJ/OVkmKC76bAMx736CWikYeq8Avmf243vpiNV5OpOeA698Uw4W3rXMBW7/x3kN2KwoSkNgc85rgUAgsEqqfx2bfUk+tdDoRLCUPYQwVMXZfU1XpHGSbKLXueV0ubi2gIhzqFYYe+r2LZD4WLYpxZgxqTSYVFr0Gie741Syic6XzF49EhBfp2Wxkn2FhLiRprOdj0ZqUXCt/lN60IQb1kUqRaG+KZLPFj5o0aXRaWi3bBaprgVzCGXqXPH/9QeGfj2lyDYLBILqz2Mvd8PFkPNUSlFuidGKzGMDPYucR01Qddg24nk8km/a7M99HNFo1TwGfvpoudgkuDOO/Lkbl8xEi/aiBOG7nT5Y+gYJAFAUZTuQcFvzMGBezt/zgOHlapRAUIbs+ulf5jcdy1KPzvwePJBV037EqBcexyUh6Bnb+YN8Jo8vR0uqJkI2q+LopcKz47hlJdL31CKCky8TnHyZmvFnOR10Dxk6NwJSrtEich8GlRbF3Rvndz8gKyEZZs6wmrgrxcGTSI86heYx8kqP5r7TSwlJugSYEw/3XfNdsfZtwgtd+P74Ge4782deW65NWRonun36f1zNiMjrU2vUzFw8gtmvrGLrRR3Jalcc5WyCVMlMe6YF7QaG2dxW414tqRu5mR3fribhxEVcawcx4MX7cfF2LZbNAoGg6qPPzGb9nJ1cuZiIf6ALofc1YvW8Q1y9qcfXQ82Ix9oy890mfPPZPo4bA1FUaoIM0UwYWoNBT1W5ImuCQtj54zq8I07bTGaZ2x7f+B4G9WlTvsYJio0hzX4FUrsULYWjoPTwVxTlJoCiKDclSbLpIi9J0hRgCoC/vz/h4eGlbkxaWlqZrFtZqO77B5VnH5Mj4iA1Go/J3YBuOAIKJlb+shCfprVLtHZl2ceywt7+qTt4Y/z1I5TY2LwUIwoS+PjiUtuvXN4XQ6aetKgEFL0BSafDNdAbrWPxilBU1GcohKEqTn3HZI6a3Kz2eWTEMvTYLwQlX0GV4zJtktTUSzhNvYTTFuOzDHr6vDUBfbaBxas34nulYCy9LKn4t9k4FJX96hwj/ptFo9hjJLTuRvbgpwju1ooBj/Qs9lP0+qH1GPfRCJa9aaDt5S246lOQgGivOrT76QPqdmjA1fCIAnOcXB2ZOmMYgTM2s31/NEZZonV9HXVbhxS6PZ2TA/e+NqpYNgoEgupBekIap9YdIjYigR83yyQ4+AA14DKw6zrk3o/EwrrpkUxpn87slQ+RmZaFIUuPu6+otlfdkGWZpSPfx23NEiRsawISEF+/LUM2FO/hh6BiaDakA9ufVaNRTIUPvo3UZh3KwCJBaaAoyhxgDkBoaKgSFhZW6tsIDw+nLNatLFT3/YPKsY8p0clsHtADnbFgSgwF84257ofv6TKpzx2vXxn2sSwpyv6lxadyaPF2FEWh7QPdcff3KBfbts5YTsYbb6OTb6UcSFM74PfN58UKMa+oz1AIQ1WcxyY258Uf4zGpLD/K3meX5XnsgDlEzF4uIXVmOgA6By0PHl7AnIHv43LiAE6GdG6612ZfvX5EetazvYCiAAohyZfMW0qIZ8RP0+5wz8zcM6wd9wxrx9ldZ7m89zwNOjVgUFfbyZgz07J46sHlnFcFAS4gwZlLsOHx7cz6vB21W9cqkT0CgaB6Icsyyyd9A0sW4GjIIFvtwD2BHThcqycx7jnHi9s9JCUVc/5zodt/l6nXvi5Oro6WCwuqPBs/WIL7miWF5uCLa9eLCft+LBebBCXHK9ibdA9/PJIirfbbK3PcdebLZWqbwIJoSZICc7yFAoGYijZIICgpO75abiEKwa3jzqWF60okDAnA1ceNHs8MKrP19ZnZ7Jj5D/GHTuMU5Ee3F0diMphIfvMdtHLBPJQ6UzZRL7xOysguuNeo3A8RhTBUxQkd0oZ3E3Yxa9FNonXmHDke+iSGtjLhGRJG3CYjmpRETCF1aPbKo9wY/0SRMk07ujry7PZPWfXdZhatjSdC64/WlE2oNoIjmb4YNY5mISj/DZMk0TD6KC763Nwbpbefjbs2pnHXxoWO+/XttWZR6DbbknRefPHuDr5fUbol3AUCQdVmzXNzcFxgTi5/JKQbm5uMvpU/7fZjXH4kFYtn7eXNX+uWk6WC8iZm7hK8sR89ZJLUdPmyZA9ABKWLLMts/uRPbv6yBMf4SLK9/PF9eDT9Pnwoz3NZkmWb8yUgzdET16wkwHwpk+wVwj1/fU/9LoVfhwhKlVXABOCznN8rK9YcgaBkrH1tLqavP8de7IVisF3gQlDxXNp3nn2DJuGeHIVLTlv47JmkdeqNt0lvdY6jIYOdX69g4CeVO8+REIaqAb0ndCXsEZkL+y5hyDbSuEvXfFnXnygw9tRH7fA5s9/qOiZHZ4u2oc/ey9BnzVU8NFo1KpWKAysP8cYPUWRqXQqM9U2NpP+phXmvnXt2LdmOFZPLh6+w8rQOtFjezCkKRw2BpMSliJAPgUAAgMloImPefNyAyz5NWdfithN2IbnULsaUovotqHQ4JETb7c9WO+DxwXs07N60nCwSFIU/x3yC6/IF+OS8dslMQv78I5YcOsW4dZ8CYHT3gpQoi7kK5rD5PqfXcu3ABW6YYuh0eRc+Ib7ltwN3KZIkLQbCAF9JkiKAdzELQn9IkjQRuAaMrjgLBYKScfyf/zDN+Ax1IU/O/e7rXE4WCYqLLMvsHv4MXskFzx86UzaeuzbYnRu3fT9wlwtDkiT1B74F1MDPiqJ8VtbbvBtRqVQ06lx4KfjQr17i7NDxFi6M2RpHXGraLo2uc7iVNOueYe3YMERmycfr2Lb5KrrEWEISL9Ik6j80ijmbfrJHIAM/mXiHe1N8Tmw5xfNfXCFTaz3fEpKEIqlJjU0tM2Ho0p5z7P3gN4yXLiH5+NB06lhCx3Yvk20JBIKSs/LztbhlxAOwv07x3bZ9nCu/MKTPNrD6qe/RL1+Oc3o8aR4BuI17gMFfTRbV0woh28sfl8wkq30GtY6wc1vwriUEg8rEtcOXcV7+u0U4mAJ4bPqbs1vH0bhXS3weHI4ywzKhuAQkteqGR4AnhoxsZLWRxKuxQhgqBxRFGWujS2T0F1QLjnw+H+9CRKHEGnUZ/fKIcrJIUFwO/7ELr7grVvtU2PZEBXA/EE58RFylPp+U6VWhJElqYBYwAGgGjJUkqVlZblNgn2Z9WtNoxVzim3TAqNJgVGmIb9qRJqvm4+hmv/x8flQqFePeHsT/wp9i9ORQgqQk1IqJbI0TiV0HcN++JXgGeZXZfqyZuYWHBizh3IVUBvVbyWvTz5GpcbE7x0OfREDDAIv2k+FnmHr/QnoP2EDvARt4dsRCTu84Wyx7dsxey/GwEXhs+hufS0fwPrCZ6PGT+PPh6cVaRyAQlA9JUcn8EG72DlCAGLfg4i2gKEycFloWppUqS7o/jeP8H3FPjUYjG/FMjEA96yuWDHqzok2rdMiyzOG/9rBkxHssHvYOmnbtgIJR0bl/Z9w3VIhClZBDP/2DylyDpgC5r4/9shaAfh89QkJob4txSd41qf/EKJYG3EvMo5ORIyM5Gzac+S0eJinSssS9QCAQFIVt363GZX+43THx9dsxYP/vOLiIvIWVldiTV+z2G1W2q49pZQM7Pv2jlC0qXcraY6gDcEFRlEsAkiQtAYYBp8p4uwI7NO/Xlub9FiDnxNjnPjWOucOyeD2eGQTPDEKW5RI9gTbqjcRdi8cjwAMnV0eSIhPZ8NwsjNu3IRmNyC3b0u2LpwlfcYqfj7qDJhCFVJJ1nuYF7OUDAUa0N5e0z8+JraeZ9vlVsjUhef8NhwwhTP3kEjPfgKbdC88nkJWWRdQr7+J8W7IxBXBe+iunJvSnWZ/WxXovBAJB2fL3zHC0sokkJx9kySySF4eOThE06RpWNsaVEoeX7cHn8Dar3hPum5Zzef9E6nYo3NP0bsCoN7K4x1S8/9tCfr/TFDc/3FNv5buVgPgmHXhgydvlbqOgcEzZBuwVBZb15vO0WqPmoV2zWPfGAuJWbUByccW3TzfCHurFng7D8dCn5c1RAJ+zB1jVdyrjT/xetjsgqDbEXYtj+aydRMdmUbuWK8On9sTNx4ZXu6BaE/71KtJeeQUHG95Cuefonos+rtTeJAKo0bwOlkHIt0gKqIdvpG3ngoxr1oseVBbKWhgKBq7nex0BdMw/QJKkKcAUAH9/f8LtiBNpaWl2+ysbwt6iE3M9keQsFbKkRkLBRdLjlBaPYycv6DQ8b9yRHf8i1/TjkTrmBNc+niYeGZZayOoK3o4maoR45u2fIdtA3M1U0gxqHhipBizXOHjuJNGmm4XanhqdhO6DRzHa6D9z9SQx4eYnjeI7UfZUNZurmr3VAVmW2R4eQdi13SxtP40k5xqF5hO6NdnImEZJjH1pIMu/2kh2lpHOg5qXa8XDtJhk5jcYhWPMdfRuPng8cD8Dv3jcQvg+t2QTrlgmTza/Vjg6798CwtDpjcf47+NfUS5dRPH0ouajI+j53NC7IuRs9bM/4P3flgIimgK4p8aQ0G0gTnVCUPQGGtzfkwEjOt4V70lVxLVOoNVzce7nWneoObz7wJKdXJn0PE7ZKfjm9MfFx7H1vxN45ROFyJmXKw6d33aKhj2F47vAPuvnbOPzFVno1Tme87Hw+0Pb+PTZmrQbKB4U3k3IssyNz77D004ImQTEN2hH7XZ2Kj8LKgVtH+jKoufr2Awnc0iOtzvfvUnlLlhS1sKQtSvtAv8ZiqLMAeYAhIaGKmFhYTYXCw8Px15/ZUPYWzTeH7+YjYlBBdoGHfuNVpF7rI5P823OH6HmKjCPDEtlwUr7T2BUspGVP7fHK9ATWZb54NElbEoIBMnH7jydMYst68IKtX/t63NRffmdzf6Uzv0Ysd3cL74TZU9Vs7mq2Vsd+OX1VbilxrKqzZRiz23vEIW7uwejJx1Ar3EEdMzafZEe7jt5f+4D+RL/lw1/T/kWdRMNPlePA+bEunz/JYv2H+GhHTMLChZKYTmQbvVv+2YVya+8hpdiMjfchIxX97F4w24e2vB5Ke9F5SPj7xXosMw3A+B0YAcjNu8XYlAlJ+psJIkzvsPaFYEEJHmHMGBcdyKOX+Xm+Mk4K3Lef4AE+Fw/CddPWl0797twefsxIQwJ7BJ7OYbpK7IxqAuGA6Vr3Xj72yss79UEnZNDBVknKG+OLNuLZ8J1u2MSa9Sl36qvyskiQWFEnY1k73crMCSnEtKrPR0f6513/lepVHRZ8X1eVbL8KIBbehwyKqv5hjIc3Bnw2gPlsQt3TFlf5UQANfO9DgEqtw+VoFyJOBXBpnj/Ajcw9WOP09KGKKQA9eNOWiTPtoes0jD+0a0s/XQdnz/xB5sSg0Eq/KuvFNGDoN597fNss4ZnaMuimlpsoi/GcGH/BXb/sonf+7zMgh7Psv6d38lOL/r7IxDcTciyzJp96UR4NWTYkZ+YuuUl/i/8dXqf+RMnfWHeh3AoO5A5h9xyRCEzChLbUoP5dtrfZWk6cVdiUc+bY9GuAN77N3H4j10F2huMvjev//bxAK0eNifczk7PIubtD1HnikL5xnluWcnBxTtKwfrKjVOq7ad8TtmpZKVll6M1guJyYOF29rTuj1u67c/R5OyKSqViy8PvoFbMF+23n+ULk1LdQmqUzFBBtWfZrJ0Y1NaFn2SdJxt/213OFgkqik2f/EnEOPuFeFKdvRl77R/8GwaWk1UCe6x+4Sf2teyDds63OC/9lYQnn2Zh3eHEX4vLG1OvY0PaLp0JFDxn5J5PVMhk6Ao+okh38qLhgu9xreThpGUtDB0AGkqSVFeSJB0wBlhVxtsUVBHO77vI9De3oajUt8I4FJn7Ti+16moGt/7p1Lfl8ymMRJ03M3c7syaq6AfeGrL1ajS30+TelsQ36ZDnbk6+36kuvvR6c0yxbC0KJ7aeZsLAxYycdpIlD35G4pNP4xW+Cu89GzB9+iF/1R9C7KWYwhcSCO4yNv+2EwdjNuP3fkazqAO46lPwzIqn45WNjN87HafbwkhuR1GpLRtzjl8bLrugzyw7AeHgL+vRyJaBMrnHxed5waIAACAASURBVLML1hVobzu6M/Etu1kNJUvqOYR6nRsBsH/eFpyzUmyv+8vKkhleBcjwtn1uSHPxwdFVPOGvrOgzs7n01KvoTPb/91TZ5n6Hy7bzP9h7HJTq5sc9D/W8ExMFdxHRMfa/h5FXk8vJEkFFEh8RR9oHH6AupFKV0quvRRi4oGI4vGwP0swZeRW2c/GOPMs/Q18q0HZ1xwnA9jkjs0UorjO+JPvRp3D86BOG3Qynzf0dbYyuPJSpMKQoihF4BtgAnAb+UBTFup+u4K5i9gt/8/iH1zhsDCnQ7pkRg3eGfUFDRqJe7ImyNA8AqRiVqIdvmUV8m555tVAkICGwER3W/Vbq6vC149d5YfolLqqDaH5zP20jdlg85fSMv8a6Ua+V6nYFgqrOx48v4f0VCvee/QudrLfo986I4Z4rG+94/QytKzEXY0tiol1M2fYFccVQ8GJGpVIxZtdsMh58nDQXc+hsips/+onPMGb9rYqJWQn2PaVMafbFsuqA9wRzpWxrFcg0I0aJMLJKzJ6fN+KakVD4wOZm711ZYy89tXWytM40+t/0Mg8VFVR9QkKc7fbXaWQ/jYGgerDz87/QmiyvM/KT7BFI3++mlpNFgsI4MWMBkhW/UQXwOrmXyNMReW2uAd5211J7e9Lz2SG0erQv6Tfj2PTBYmIu2ktbXTko8zOcoihrgbVlvR1B1WHPnwdYdN7HqsyqSIV/JVUoDDk+lyRnP8Cv9A3Ms6WIyWgB9xrujD8wh8iT17my5wy+DYJoFNa8TOyaO2MnGVpzTqZWEebQEWvu8N7Hd7F0zCeMXvSauKkR3PVsW7iHdbGBOGWnopaNbGoyGr3akaDkyzS7uR9dzgVcw5ijbG90/x1tQy0b8Ar2LE2zC9D8gZ6c+Noy309uYt3gAd0s+nRODoz+/VXgVZuVIxsP6sCx980+j9aOep4d25TU9ApHlmW2frGcGwtXQloqmqZN6frhpLxkn/0/epg/zl7GZfUSJCX3Ca9EYveBjJ0zreIMF9jk4u6zXNl5kpi9x3AvZGy2xpGuHz8BgHRfP1i+wKJany3SnL0JO/i3CPUQFIkRU3uy5LHdZGpdLPpq6OPoPWG4lVmC6sS2b1Zh+vlHuzfZ8UGNGbjtF3xqiSpklQXlpvWCQxIgoXDz6GWCmpodGu557F6Wv+pt8VAi97zSbMr9LOj6DN77N5LrD7bn6y8xPjKJkb88X2b7UFLEow9BubPsj3OY003dhqKQ7OxLkpMPnpn2s7pLKIRe3QyMLRMbARp6Fy9cDSCoeU2CmtcsfGAJOBWlJrcer2u2dZfk3Itd12XzWNDyFA8fnSdcVQV3NSuWXQQlBKNay9wub+a1H63ZnX+bjqX32T9pfy0clWLf7dse9zhH4+LlWhrmWqVOaH12dhtokVxXAhKCm9Dv6YF259sSiGu1rUt4mx74HNmW15Z7cZPqWoOBb40rkd0VjSzLLOz2DN4HNpP3rP76KQ5t+Ye4ubNp/2BXs3fVsne5ceIxDv2yAcVkosXY3nnhdoLKQ+KNBFb3exafswcAChWFUl18aPLb13kV+EYteo2/fNbilmH/OiP3ubHb/z0hRCFBkfEK9OSDyX68/1MUadpbR2tffTzT32strsWqOUeW7yP5lVdxKORaoua0x/GtI3KWVSakgACIOGXRbj4XSAS2rJPXpnPQUvvbj7n55HMFwpglIH3UBM4t+hfv/RsLPIDQKEY0838kvEV9wp4fWnY7UgKEMCQod+IzVNaDGHM8dFIcvQsVhgBqpN2grLJ5aI1ZTJjaoYxWLxk61a2TTaxrEL7p9l0Tfc8dYNF9L/JI+DdlbZpAUGk5le4GOgmDxtGc7D6fR6BJrWVjs3Gk69xxub1UtWyynlfoNoIN0bw2q1+p2307YzZ8zuqFf5Hh6I5zVgp6lY6Uus1p9PIkVJo78wxc//YC3I4VTPgvAfG1mtNj6Re417B+623UG9n29UoiV4eDJBEyvBfdpw6pdOE2279dg/eBzRYeIjpjFmefeYu2ozfniWbBLWoR/PXkAvNlWebIsr1E7DiGS5AvnZ8cgLOnpTeAoHxYfd/T+Fw4VCSPn0ydCwPOrMUj4JYnX0ZiBgZXTyhEGJIAk5snQ54ruwdQgupD7NVYLv53Df+6PnQeGcryflms+992om6kUbeRD30eH1rpjo2C0ufYh3PwsSEK5R6zkrxC6PP0oHK1S1A4jaeOJXbCFot2CYiv3cLiwX+nR3tzrfVKtj33DcqVSxBck2ZTx9JqRCdWeXfKm3s712YvACEMCQRmgjwUzltLaaEoSCh4ZcQQ5Wb2KApIjbAy0EyagwfFzxRQNB67R0+jzg3KaPWS0b2lI5dOAYrCwTr30iT6cIGYWGsXy9671nFx9/9Rv0vj8jRVIKg0pKvz3cjbCBPdXX8Qbpn53IJlIyMPfs/6VhNIc/SyOkdj0vN4hyzGvDqsXEoQa3QavOoG0Or0elb3m4bPuYP4XjxMwpNPs/j1EFrP/QKfev6kRCfSoGtT1Bo1afGpxF2Owa9BgIWgcWT5PgyffYzOyjFECg6hTmh9q3ac336KfQ8+h1fcVXIj7TP2bmDRj4sY9d98nN2dCoy/eSaSM//sw8HDldCHepRrueZrv6/EB+sXaB5JkZz45z9aDbnH6tzYSzGsu/cJfCJOoQaygLUffkrgjI/p+mT/MrRaYI0zm48XWRQCyO7et4AoBPDPlBl4x1wsdK5h8jN4NwwWodgCu2QkZ/D2pOXszwjMeYiQRkN5D+993IURL/ataPME5Yzuyjmbff/P3nmGN1W2Afg+Sdq0TXfaUmjZe+8lYNl7yQaZKqgoiANwfuIWUVQUVFBAluwNgqwCMkT2FGhZLVCgeydNcr4fbUrTjKZ7cO7r4qJ553OSM97zvM8QgGi/6nTcsQBHZWG9wUjklUdnQwDL71GkmceLOvTDdh6+/T+8tUkAGO6FcGmujisLN+JtI5GJ8qH1d9viRnraSRQ5I55rjMxgmhbZuINfJ+IUbpo4yiXcZW+dYehtpJW/6V230GQUcxF4uqh59r0eVNPfB0Eg3Ksm2xuOJzVL6mxri+Xj/V/karAU+13iCUWQ5XxhCwJJSnea3d6HTKely5W11Iy+TPuQbRab+2oj+W6qP2Nn9SsURcfD0Aj+fG85O2Ys5s6ZmyZ123u/gc+1f8kaLtkzJpybA0ZyumFnQroO5k/neqxTNWdf+dZcatub3f5tWdljBsnxKZl9LsxeiixbsEVjhkX34/uICo80qYsKj2RZo3Fc7fIMXpG3TepEQH3rPNte+CazTKtJY0WXN/m3UReS336bmJdfZXO5pzn4w/Z8fTe5IinJdvVD61mC/uw1BXX4ZZNvyFmbyKNp081+E4nC59bBc4B9SiEAuYuTyedjv+3FeeeGHPvFeZSn97cv5VY8iSeQF0du4Z/UQBPL0uuyCkyeeZbkuORilEyiONA5WbYmFUlPnjPg9B9UbFKlSGWSyJm4iFgSf1kEWI7bqr53lXuXwjLLzm8/Sfwbb+GsTcpcH8gw4HP2IN5/2w6trPUouQHoJcWQRJHTsGt9emuO46h7/HKCIFAl8jI9L61I/4jIkDMLkNvw0W17czcy0Tx1c0HgF5BT1ILiw8XdmZ9XD+DZWlGotVH859+c+U9/wXXfRjb7ecbd53q3QeyetaqIJJWQyBlBEHoKgnBVEIQQQRAKJZWewWCwaiVk1lbuQPM7B+l6dR2twoIBqHfvBHLD45hjdcRw5ox1YcOfz9CkZ8PCEJn1E77hWN0uGL76FL6dzblWvVjZfQZ6nR5NUirqK8ct7mplfagLgIs2EYWYroh31Gvw3L+Fdc2ezWyjuGY5w6NAuj/83SzKD4PBwI6gF1FfOW61DwB/PV4UrW42Dq9D203u5aqUWOLenMHlPec4u+kftk79mT2frsGgz3t8J5uorLt96QQFtbpZDq59/eBl1DfOWvyeHfRajn66rOBklLALtwq5C9RavlOLzL+3vbGI6JdewcFgO35grFcgbbYtlHb0JWxiMBiY+8pabsorWKyPc/Rk/dx9RSyVRHHj0rcXANm3oQQgpn4b3Mt5FLlMErbRpmjY2nK01eyWxud/fERsZtm5ybMyvTUsKZIsYSz3Hjk4z7IWNpIrmUSRc2DuZhoe/J1a8tWE+DUm1cGFCrE3KR9vuvvspEsFrJj0Ac66ZLTaBMCyi0eOZIszYsQ9LY4u4wo/Vkh+cHF35uVvBvFyxmdtiobVtX/KsZ8ApH7xGbr3h6FQSJe/RPEiCIIcmA90A8KBfwVB2CqKonn0v3yQGJ1on8UQgGjARZtI07BDmUVKg5aAmBvcUae7Yo4YXJm2Qy27HuUVnVbHkV92EXH4LNq7EXif2GNSLyDieWALG5/zRR4UgBL7rSay43P7Er+3eAG3pg1xSbSe5lsE/GoFZH4+u+E46jsXc3TjcUpJt8BZN/pL1NdOWWwjF/VcHDwJVUosckALpH01jYOXkgia2i+zXczdaA7PWUdyWAReDWsS9NYgnFydLI5piYc3HuDy31mr9XEValjNChN24ipg/Vi1oZLFUFFjLRMMPF4rGP+P8atOjxfT3f1i78WgW/ADjjmMH+/mx8iIPZL7mESOfDJhDXuiLSuFjBw+ncDYIpJHovi4c+Ymxz5bjubmbRT+5UgMqIP67n8mbRJcfem87KNiklDCFvu/3IB3xHWbbVIdVVRpXTPzs+ujO1bbWlszCEBMu16M+mJcHqQsGqQ3Q4kiJ+yH31EDSr2G+vdPWG2ncXAmxcULz7h7Vts46DSPX/ZsWQRkUwI56ZLpXCGOXRF+GLKY/zrpkvlgYoUijX9REDg6KxlxdT1r6gzB+551/2YAhUHHXx+soPcX44tGOAkJ67QCQkRRvAEgCMJqYABQoIohlafKqiI4OzUfnscl7bFvuPEl01GX7hLgro2l64QBBSkeZzf9w/XRk3HRJmItp5lRcqc/fsPQZEq+5/Q5dxjOHbbZRkTAv/bjF59ra/biRs4KqQSfSsRFxOKwboXNdqqUWBMlkyAaiJ3+Njda16Fa65oc+nEHUdPfxVGXihug2wqbv/uRppt+oXannC21tr2+CMOC73A2WLcsFWwoC9W1AgjH+uYETs6WSiUKEUelA9UWzCb8hVdx1JmmnzD+RiIQXbcNvTbPQeGo4N6VcPb2fQ21Pud0FerXX5GUQhI5cnrnuXSlUA7PFUMJDksgUTAcnLeN2Onv4GpIS39+nwe9ICOmQ18MUZGg1aJ6qhU9PxmPZ4U8bmRLFCqPdh/OjJVoDcOgESabUgYh99kFI2u1YFwJTwQkKYYkihyXh9a1rEaimgQxaNc3rH/6ZbChGBIFmX0uIhlt/GNvondxo5Iqlae71WBETT82LPqXyJg0qgQ6M2RqB3wrl870kY7OSkZe38TWV+aj+P1nm254CaElN/CZxBNFABCW5XM40Dp7I0EQJgGTAMqVK0dwcHCOAycmJpq0GzsgPv1+YQO5IQ11khLdiKlmdZ2cfWiniKVSgLNd89uLJjGV1JuhOH7yHHY7xgb4oZttLmNhsGHBElwrlSfx1l2cn1Kjeyrned39y3P40EGEL162/5gAAvzgi8mcvfAvN2Jvkai9jeyzSSZjOAOXzh/lvmA7o1RydAKGilr4YrJNGVQOSuu/pwdovnkLuc486GRGNRsXrcS7ZoDF+qxkPx/LEkV+bAFgmPMaOm2qxWq9QkmlRjW4dOcyupDzJFwOxeOVzujobHtcXz9UFX1NjqUs/24SeWfVkgtAxRzXn+0al87shbdOhnL6523oUzVU69eepkPbSgpTC8Q/iCNq5gcos7mnykUDbkd20fr8HpPNFYkSitz2uR3VuAPPLnnTpCypTjOcLvxtVyIEY5saU0u+/aCkGJIocjSuXjhHW4/WniYo6Ln6Y+IfxqG+dtLiRWcsS3VwAUCm12KQO+a4exMQe4Obivqccgzk1MpUhlQ+yfSfh+X7mEoKCkcFgxa9xrJ9B1GHmQeaNm5e1RrepWgFk5CwjKWL1WyPVRTFhcBCgBYtWogdO3bMceDg4GCytju89A92R5ku0GrdP4WTLhm9TE6lmBDq3zuBg8GyEsBF7oTCxQPXz9+l3Us5z28vy2oPR33DuquTJXSzp6KYOa/AZLCGSPoiIdHBBae0ZLszQUU/3ZdKAzuRmEsZjccVW6M5hjbNcV6x0GI7BeCw4AfaTbSe8WdZkwmoLx3NcU7tiOfpOKWj1frr8nKc6vc8bknmiijj95Mw5S36zTVNcR9+/jZHvlyFqDNQb0x3cAN7ztvSSPZrrTAxGAwsbzgGn2snLdYbf5OGN4+gDvRh9TOzcNv+R47jyt/9gJ5vDDUrL8pjkygdzBq7iuMpgTneDJ3Tkhj3Ud+iEaoAWTvyc1zWL0OZ8SiOWLOYFR81Z8jRRWZZLQ0GA2FnbyFXyKnQoOITpzz6+/stKLPGS82CwqDj6DfrGLTwtSKWSsIeDAYDZ9Yd4+rynRgePLDaTitXMvDPucgVphZC/Xd+w/6qHa3+/lkRgOi2Peg1sVt+xS50nqwrWKJE4P7scNsNJk7Gr7o/1/ecQYbIVb8maBSmMSUE4JpfY1Ic0h9SBrkjrtq4dKWQDdeA6pEXaXjvWPoHUWT9bT8uHriSn8MpkTT98T2zt2vjS12Cqy9NB7ctBqkkJMwIBypm+RwIWDcRzAevfd2HctqHJmWh5RrR+tYe+l9YSpPwv60qhQBc9Kl4JjwgesprHFtSMAFFdVodnjfPF8hYhUGm+1passnnnPA+tJ07X/1kNQBjTqju/EfylRCbbaKv3LZYrtPq2PzKfDwvWw6QnZV4d3+6fvmCzTZVW9fE8/VX0cnM99EyXZd+nMvaUV+g1aTvGv9edyRnm3fHdd1S3DYtI2zQaKLPh5CaaNnCRcI6Wk0auz5cxbIm41lWbxQrqj5jVSmUFYPOwMaJ3+OyY22ObWP8qtP53RzWJRJPNNHhUfwyfRM3rsewNybALkv1oPJx3LkQxs9vbeKHaRs4tf1cEUiaPxIexKJa/ztZ92dEQH39FBuHzjJpe2DuZlaX68LF1r0417w7qwJ6cuy3vUUqb3GTEhFps17zwLZlq0TxoNfpWdlhChGjn8Nj93p8bl+w2jaxWQfcfc0TEnn6exJ0dS+Jzjm7B0a16Myzwd+VCsWpZDEkUeT0/uo5Vvx1CJ+r5vGFImu14NlvXwTA1c+TGJkD2xs9h4s2kRa39xMQG4pG4czlCq24WL41o3mcCtRgh5tItcjLJCkzMgJkPNi3rTxHg051C+joSgYNezfn9msz0M/7BnlGRiKABJUPXU+tL0bJJCRM+BeoKQhCVeAuMAIYVRgTufu4s2pjPz6esJqDyZVJD+cssK3BeAaeW4RXqn0LOBlw/+XXOeHwA61GB+VLJoNeb9PlszTjHWFbsWMLZ20SaVaypRnxqV/VYvkfQVPwPrnfaj/j6050i870WPY/vAIeRxa4e/EOwdMXwOl/QZAhtGyN7tw51Pev2VwsyUUDqnVLWX3jNoboGHxvmluAyXUa1rSdyMiTi4m88QDP8l5mu+8SpmiSUlnTbBzqG2exN7mvAET71+DAO79mvODaJrJeG/ru+EbKQCZhlcN/HOeD5Qno5N6MqZ1gd79L92WM/eAmopB+j1kTGkuTX1cyZ8VgnHMRQD+/GAwG/l1+kPCDZ3Dy9eKpqQNM7ntZ0T6KxgnTTQDj36rDu0mOnYWLp4ojP+8icebbeGTcUUXAK/I2j16eyhmP32g65MnYfPRv24DY5dbr1S3qFZ0wEnaz461f8T6x18QK2ppFtBj5yOo459f/jWtKjPW+GeVtv5xSKpRCICmGJIoBuULOuIvL2Td7A/fn/YpjfBQaN2+8x47g2Y9Ho3BMPy2bDHmKne+3I03hRJzCiX11bbt8KUUdYloiKQ6Ww7fWijiNgIg68R5tbuzifMBTJCvdSUwumy9mfb9+Hu2nY/hzxmKSb9+j5oiutBj5dHGLJSGRiSiKOkEQXgV2A3JgsSiK5j6QBYRS5cRna8eTEJvIK0M2EK7wo2r0f2Aw2O0mBemB8x9NmMS6v55n6LIZeZYnISoBAwKyPNvWlE1EwD3hIVq5I456cyuuWK9Aeo7rZFZ+ZsMxvE/ut/lbCqTHsBt77HEWx6jwSG4dvUroc9Pw1MQ/brzrTqY89sQQUJ86YLON+r8TbPFui4s2AZ1MQXzzIHqv/thqVrQnhRvHrnHyhw3o4hIo16EZHV4fgFwuY02nqahvnM3VtWlAoOL0F4md8V6ObeM8KzDmzJJSs2CXKHqS4pMzlEI55bMzJ8zB39SCXRQ5qw/k6ymb+GDJyAKU0jqPbjxkV8fn8b5/DaMqKvj7b3F9/z26vW9uJSfo0szKjDjqNUTefEilplW58fl8vLM8t4zXp1zUc2HW/DKnGIq5G01yTCL+dQJMXIraTOjCHx9WwevRLbOsiPFu5Rj0xsBikljCFvHrNuOFZQVodmQJsRbLDQYDYT+tQG2jrwDEdOhDzaDSoyCUFEMSxUaXmYNh5mCr9XKFnOR6zS1EHLFMqwophEUmcdmY1ydLtjIHXSrtQ3cCEBB3i4C4WzwVupP1zV6hXsucA4eWVhydHBkw76XiFkNCwiqiKO4EdhblnG6erizbO46khCQGDNaT6OhOn0vLMm819r6EOv/xG9ef75vnh/7ZVYeKRSmUmxftwporJ+UNQNozI5BtWIVCNA0frQuobLHfpZ82mC32spPk7EmfDZ8DcHnPOU5O/gT1rXQzcmv2O/Z8V/a2cdGmWxwoDDq8/93Hn21CGXp9C0pV0VkQFAfaFA03j19D6eZClRbVM8vXT/gGpxWLUGVcB6l/bWDd3J9J8/XH99q/QO7O1dhO/Uj88CvcLSgUs2IQZAT8701JKSRhFYPBwLvjNqCTV8r7IFldzjL+Do7wYmaKpkiy3/7Zdxrq+6aZah31GlI+nsXVdvXMsjuKCuuWc1q5Ep+qfmhTNHjfv2b1Hu4aatvaszQRevQqR56fhXfIGQREElQ+qJ4bnxlTTq6Q03nPr+zrOwXv8PSwFAIQ41eNpzbOK/P39dKKMt5+Fz+xag2zMp1Wx6r2kzPXDtaIebovI3d/lWv5ihPpiShRoqlS274MYX7aSF7+qBvPjqgGgGDQpz+EBYEqkVcY/c8cfJLum/RR6lMZePYXeow3S4IkISHxBKCJSyVV6Ua4d83HKdNz0V8ATv+4Ic/zy+RFpZ4xxbirWVRz5aYcHsuWfPYiClFnIqsI+Fw8wqrK/bh9+kZm+eU953A9vCtHedLadUZdyYebJ0K4MmA86lsXiuy7sDSP16NbBM/ZWEQSFA9bp/3C5nJPE9J9CJfa9mZFQC/OrD/GieUHMgKMm34znjFhmUqh3OIWvBP3ROum/wDJSjf8Fi0gaErpCwwsUTTotDqmDV7FKV0+lEJW0CicibwTXeDjZufmiRDU109ZvO/IRQMnvzD3gXL0TXcxy37PBUhu3x0XTxVyBwU6mcLqPVznUPgKr6LgwfX7nOoxBnXIaYzfgltSJLIfvmbzKwsy21WoX5ExNzdTeetqlB99SsDaZYwK20G11jUBSIpO5P5/99Dr9JamkSgGUvwC7WqnF+Q0fd88DuGONxehPnPQ5tpBJ3Og15J3zIJWl3QkxZBEiWbQ5HYorO38iSJyUcfAgAf8trgTPpV86DCyNe1V4Yiy9AtRnXCXhneP4J8QZnEIVVoiZ5YVTCBZCQmJ0oXKSwWiSIyqHCcrdc6TFU1atGUzY3to9mwntPLiWUQXj0rKPgTSF2S+IaczP2etA/B+GMo/QSMIP58ehPrkpFk4GKy7QRgXcIG92wFw5O0FuQ6qbQt7lEvW5nm491gBSFAy2fbGIuTz56JKSb9ORMDr4Q1ujX6RK1/+CljOOppXHLJZl1ka12PGm7S24IooIWHk9w93cFpXMeeGecBRl4o6MOeAtfnl3un0OG/W7jv6MPN1sVs5TxIHjTHpJQBRNZoxcO0sIN1KJq5RO6vzaluXjZAFwe8sQpUaB5h/h7rfF5slE2jQqyld3x1Kk2daI5PJiLh6j2UtJrLPvxWnG3Zig3c7Nk76HoOhbIavKE0EvjTGrCz7cyfJ2RP32V/SoHczs7aJa9YBOVgndx9YKt3EJcWQRInGt6ofb/WSI9ebLvgFg55RtaKoUdOTt34elhlITyaT8cHCZ8CgB1Ekyi2ARKWnzTkSw6ynKZSQkCi7KFVOOOrTF3d76g5jd71R6IXc7e6I1/7L8/we/p4Iz03Kc/+yiggmQfOtoUqN4+AbP3DvSjjq8Ms2FQoCEB1Qh/Yv9wJAduFMruTJifwolwRl7uOXlAZ0Wh3Jvy02KTN+T456Dc53rlvsV1hKSwGIqtmczm+bp6WXkDASEfKAVWdNFfYKvYaG4UdxS42h5a29uGSNRWYvGeENnvaNLhIXo3IN011urd2/ZOX8LZYPX/M+DY7tQDPuZZKHTcB/+a+MvrQSlffj+J0dF75Doou5civew5/uP7+Zb9lLAtqTp6zWOWsSuHbARiarqAT2t3sW9blDyDKeZaqUGJRLFrBuxOcFLqtE7ug8/RkSB43FkE0BasSAgMebr9Hx9f4m/bSaNJYHvYZHzF2b48d1H8ywDbMKUOKiQ1IMSZR4+k7pzMqv6zCkyiM6uIYzskYUG39swOS5luMTqdxdaCC/n+nPHelWweb4/q1KT1AwCQmJgqVX5fQdQQQZpyt15ECtQYD9VgueEaFE3bGdstYWAxe8itMnnxHtXxO9IJfCUJM7xYD7wW3sf3qMzX4iEN2mO/2P/55p1m1Q2KeMsTfwdF5+N2Of6qN65aF3ySf8/G1ck627zGTf8DFSWNdAXM+hjDi1JDPBhYREdrQpGl6YfBhNFktOZVoSHa5tocflZsmFawAAIABJREFUlai08XT9by2TD75DrQfWlctO2kRa3NxjWigI1OMuM34YUFjim1CjXV2iKjWwEuNNoPH00Vb7VmlRnUG/TmPoyrdpPqKDWSyuSk2rEnRiI/G9hhGrrkSMbxUSnxlD97Mb8KtuWeGUlZsnQjiy6C9uHLuWY9viQnSw/YxQ2sgst/+jlXjE3QPMnx/KLauJuVv4roQS1om9F4N+316rMR5liCTMmWtmFbZp9Jd4H7Xtsp7k5MHwLZ+U2ueMpBiSKBUE1gtk2g9D+OKPZ3nl20H4VvWz2X7azJa4pCUCcMW/OfFOpjsbxltBjE8VWk/oXBgiS0hIlAJe+34QjWXhmZ9PVunCmcAOdisnZIhc3Jw/V6Aubw9hTNh2+msv0+nBKZJHTyTB1ReNwokEFzVR5Wvna/yyjAzwir1ns02Cyocxh3/Aw/+x9ai+gn0xBuwNKm1LKRXVJAjRx/yZJQBRTYPK7DPIxSs9nLc1RU+qq7fJjq2xrb3XXm4USHGeFRix5eMiCfgrUfAIgtBTEISrgiCECILwdmHNM/+traSlAcLj1yONg4oDdYcxt+v3JCo9EBFwMKQx4NwiXFNjzMZw1iYw9PR8ul1dx4QjnyDLUIA+3yiOn7eMwMXDpbDEN6PLlm+J9TK91+kFGeKrb9KwT/M8jxt1J5Ijs1eTdvc+NGhMg/kfM3zt+6gDbbvOPAyNYFndkVxu14fYyVO48nQ/ltUaxr0r4Rbbn930D+vHf83myT8SdvZWnuXNC569uwCW7zNx7uWoGVTfat/YQ/9YrXMwpHFxy/H8iieRD/ZM/yVTcWcNZ00CJ5c9zjSqTdGg2JEeD9BaQg0A4ZkhpS6uUFYkxZBEmaROu1os+rIBnT3u4qVP4M/GE4hSPd7FEIBo/5p02rNIykoiIfEE46h04IdNI/nyWSc6u9yiWdhBvJIf5eql01HlXGDyqLxdGbrkLUbE/M2glHOMiDtKwCvjCmz8JxG9s6vJ56jwSJyuXyqSuaMbdWDsvwvxrOSL108/ElWzOQkqH6L9a2J49S1G/b2gzD6D/Kr7W7RYMF5bzv374fDO+yZxtoRsbaxhEGR2K5A0Cmfq/vR5mf2eyzqCIMiB+UAvoB4wUhCEQjH13hLiQpKL2mKdQaYgUenJX3VHAOmZBRvezdgUEEU6XN9C3/OLmXzwHQJjQwHwTwijatQVxtWLYcJn/Yv8HAxoUImhYbtw+vRz4vuMIHXMizQ/vZt+307M85gX/zzD/nq9cFr+C+rzh/E8uI27w8ayeugnNvvpdXp2d3wedcjpzOtbBNQ3z7G/83PotI/jgyXHp7Cs4RjuDhuL88pFOCz6gbMte7Ju9Ow8y51bun82nij/GhbvM8qUeEKPXLXe2dF6djcARRl1Hy6p3DoZyopu01np343llfqi37HNrn7apHSLIa0mjbV93s2MS2iNmI79GbS4dLtSlk47JwkJO6jcpDIfr3ic1live4GTKw8RfS0c/2Y16JURIE5CQuLJRiaT0X5Ea27M+hZ1qKl7QE4WDMmOrnQd0b5Q5Ws4pB3HP5AjE/UFFn+lKFPWFzdi9VqZf9+7Es7BdiNxTzHf6S+w+Uj/bnUyB5p9Mjmz/KkXuvHUC90Kbd6SSMufP+TSwAk4axMzy9I3ZmpQuUkNytWvzPWUqRi++9rErN/SuRlZrQmqti1xUnvSZko/Lqw7yqNP5+CabDn1sF6Qk9RzME9/PpGABgWfXUqiyGgFhIiieANAEITVwADgckFOcvvsbXSOrpmxgKxxunInWt7ei3fyIzxSIjPaizQJ/xtXTZxZ+8Hd1PT/dGBBiporHJUOdJk5GLAcfiE3GAwGzo2fjme2GEsi4LZ5BX947sJt4nP0nj3BbH19fMk+vCNMA2Ib//eKvM2RBTsJmpYe02XT0A9R/3fC5DklIOKyZjEHmtWm0xuF/306uTpBufKQIbNJXVoKx175kprnf7fYN2BwD1IzslZlv5dpFE5oE1PQaXWl1t2oNHFx52muDnkerxyUOlkRAYMgp/7ANgCs6/MOXoe32+wT61ed0Xvm5EfUEoF0Rko8McgVcikTiYSEhEXCz99GHXrGbCFn/FsvyJCL5tlE9I7OCLLCVbH4VfcnoUt/PPduKrAxS5pSqLDytIhA03cnZH7eO+5j1AkPC2m2dATSXZeqzfnAYkaTJ4m63Rrhdmwjh97+BcO5s4gKBej1eEaEkvD6myRktLNni0YA+v80hV0zl7AraCKylCR0AVVQXY8yOZ+N13By32GM3DirgI9IohgIALKm0AoHWmdvJAjCJGASQLly5QgODs7VJKlJGsYMsJ7ZEEDtqWfMgAQUPSah0yZQxcmLsQ7xeCdFoOg8Hl02WzcRAfdG/rmWpbhITEwkODiY1Phkku5FImhSQSZD5uGBR6APqQkpuM58Bmu5/1wAPalsWboWr2qmsYZiHGKQzZ5qde5o9ySCg4MxGERUPaqg62G5bbQy2q7vU6fVkfQwFlGvx9FNhUtG8GzjMdpCm6IlITQMj1FN0Y1qarGNB3DgwAEEwfxpKm+tJvW7t1FozJURciBWf4etC37DpVolnNxNLY41CSkk3Y8CTSqiXIHSxwtXP48cjzcr9hxjaSY3xxcdGori0xesnrPW0Lt7cuXuVc6HXsS1d1V0va2fuwDu5Qr2Oi+u31BSDElISEhIPPFEhkYA1hUmMtFgcffPLfERwV9vovsHIwpTPIZv+ZT1I5xw3LWZ0uu9bh0ZhaMcEgUZ9Xo2wWAwsH70bLzPHCyEWbLMR/rLYL//tuCmdi/UuUoLgY0qM2pneiaeZc2eR33hbxNXEruVlD4+rG42HnXIabyNZTHh6AU5epkCR70mY0yB2Ha9GPbHewV3EBLFia2QHo8LRHEhsBCgRYsWYseOHXM1iU6ro/OAfRjkyszkJdkZMyCB5Vvc6H55G43D/uaBX0N2NJxA1yvHaXz3iFn78C4jGfjGZAsjFQ4Prt/nyJy1pN5/hFej2gTNHIKLuzM6rQ6ZQoZMJuPepTAehUTgWVHN+eX7iD1zCQcvT5rPHMmFkJPEvPoODnot2UMrh1VvSsVJIzHMnJejHDqZgvpXDuBb7XFstRVd38TroHWrC+1zr9DxuZHcOXOTCzNfstou2c2PjtGHbc6/891lpH3zFQpDuqJPBO4E1qPf379x7vp5bJ0bqYmpbKrSA/e4CKv3J2N566izuLhbdiXXtm7LjtcXkrRlO+5RYWaZNhVAvLMn7W/uxVXtBsDBH7ajeXMGymxtw9v3RlmhHJr9B5CladHXb8RTX0ym+lOW4w8GBwfbPMbSjr3Hd/+/e5zu9mKuLaSjfasy6s4ORIPIqk6voT6x13b7tj0Ytf+lAo0tVFy/oaQYkpCQMCHk6H+c+GwZhlQN6illMyiqhER2Kraszm1BgVzUWVxA2FpUPNx3HApZMaRwVDBi4yySY6dz6PAhEkY+j8sfvxXqnGUBvSx9mbN92kJU65YW+nwCEF2npaQUssCOmUsylULZXUnswbF8OdxO7DVb5MtFPYkuatxmvoY+RUu9gU9RsUmVghJbovgJBypm+RwI2I4cmwcUjgrcDCnEyXMIUC4aKB97k+u+Dan74Awy0UCIbyMa3z2Sqa166BbI0Wq9+ejbwnsuRN56xNG5G0h5EIlvi/qkpaSS+tlnKA1pKAHDLtjx/fdoXL3xirpNmswBjZNbZqbAMNIV8kYl69XtfyCfPRWFXmt+yIA69Az3Pr+Pp1mtOQqDjrOrDtDt/eFoklLZMOxjPG0ohQxAm9eHAOBT1Q+dzCFTqZOdNK/HyqYH1+9zbN5mtJGxNHiuN/W6NebCjlPo53yOIovuUATU4ZfZ1udNKn1nO2Zf8JyNuMfZ3igSgOgKtU2UQlF3Irm27xyufp7U79UUR2clz/w8heBalUiaOcPiOKqUWA5+tZ4+syegSUrl4bsf45JNKSQCXn/vBNItsgA4eo9zXYJJWvsrjfq1tHk8TzJXtqcHAc+thbTXo5uEHL7MiXcXoD6532bbyFotGXcoZ2VpaUFSDElISADpvuPLmz6H+vIxjEar2j7VWOnbmSG3/0TpImVzkSi7qAN9iG/bFe+ju8xePPUI2EokLzgV3bXh4qnCyc2ZnstmcLBZHW7PW4Lb3es4GNJKnHtYThRFnKP4+q0ASF6+Ajc72ltzGbSXJGdP2v/6vzz3L6v88cyHuG9fDeT9N5ft2ma1v0fCQ8o1qJqvTEsSJZZ/gZqCIFQF7gIjgFGFMZESnVVrISNeyQ9Z2fJNXjzyISJQ6+E5ol3SlRWZCk+DHs8q5QioG1DgMhoMBlZ1n47HwR3IEXEFUjam30+zv9S5aBJw0aQ7bDoY0nBIjs6872Y/SltHbazzzFCY2IMgk5EUnciG1uNR37pguy3gnKFkCf5yHXIrSiEApwd3uP73Ff6Z9SueB7fjADgAN9cv5bR3JfSVq+KT7XktkKFguXSMtFTbp87Dv/7GcvjxxxgEGdXefxVID0y8buAHuO7fjsKQRjRw3iuQ2vNmcX3NXlx2rrf5sh2/cDErz/2HS7VKuKSax6iy9rsodamcm/oZjfptzkHaJ5fwNTtz/C0tIQDHp8/D59yhHK3Gakwbmx8RSxxS5F0JCQkA1g7/DJ/Lx8xugJ6xd1lTO/9BCyUkSjqDt3xOVIN2JtdAqoMLiunvmGROMmJcetYe17dI5MtO0LT+jL2xiWc0F2n1X7BVVyyR3KX2LioKWymU4uhKux9mEHMvGrfER3b1kYsGkpxsW/sYv8sEF2+imgYRFViX6PI1Seg3kqf/3US11jXzKXnZIfz8bX5vOgH37atzPAdzqnfJFvA2O5p4+4OLSpQeRFHUAa8Cu4ErwFpRFAslrWAlVWqObWJU/nS4sQP31JjMe1idiFPpsmZ89ku6T1BVc8ubgmBlx2l4HdxuEqwd7L+fFsUGggjc27yHv8q3zVEpBOkynVl7mJi70Wi/+8amjCpNPOd6jMbr4Hazdl7Rd3A/d9TqHDJE0pI1Vsfe+OI8vE/usylrvMoHr+/m0m5idwDWP/Mhnns3mVg4ecaEc3fsS7hvX43CYDu6jXviQzz3bcZhUe6sTkTAO/wKD67fz1W/Jwn5nZt57ut9Lt1d0abVWOvutH2+a57nKIlIFkMSEhIAKLettVrnHXGd5PhkXNxdrLaRkCjtuHiqGHtmMdcPXubazn9w8nan48QeqLxd2aHTw7emqXIFILpND3oNb1c8AmdBNECq0g0XTYLZDld0y66o/7XtI19WMB57VOOnaT9/BtVa10STlGrTNSF735Sq9ZCFXsBZm2S1bUz7PozcM6dAYwqUNW6dDOVEp5H4ZOyC5/RCmp96jcKZOt2b5EY8iVKEKIo7gZ2FPc+wUbU5+XuycdJM6yHvxPvIRBGlTs7wk79RLdI0IZpnarp7VtZzNG75apjzXIHKF3LkCt7HdhfomIWBAKhzGc8tfsbb7H9XiZMFV7asiGCS5TA7jlbu88b7u4Oz5VTx57b8i+Pi+TncZ5zoF7oLfZqeVX3fQ3FwD+4WrHwAFGLuwh3nVmFnbK9JzFmZ+aSid/OE6LCcG1ogu+I1O1HNOzP60PdlLru1pBiSkJAAwMHGw1gATq4M5umXexedQBISxUTNoHrUDKpnUtbnq+c4Xq8K175ZgsPd2+g8vFGPGMioT8aUiIVB8Hu/4ZrhMpB9gel65m9SccAJ+xQj9paXRAQgqlIDxp5clFmmVDkR17gd6jPBOfYVAZ8rx83qDAjE+ldH2aE9LacNpWqrGgUqd1nk0Ctfobby0mSNnM61ZEdXXLSJ5u6eg0dmBnCVkMgrbQY1p8GyP7goBpq4lMlEkYlHZqHrPxXfbEoha3hEhRV4SvJj7/3yOPB6GUMh6lHYkVI8r88iAUh08sQjzbLC5uy73+Fjo78IeMz6HzK5jM2NhuH18EYeJSk44t38CGhYqbjFKJHs+3I9ykd37Wqb/Xli6zlkrGs7Z2qJWPsVNGXviCQkJAoFhdKhuEWQkChyHoZGsH36b2x+ZQGeFX0Ze2E5I6MPMebmZnp/Mb7EWIxoTp+1WqfUpZLU5CnAuruOcbGTJjO9zhNUavSC+TGWRNc0gGQnd9ou/sSsXOFrX6QBa4oxGSLO0REMW/WOpBSyE9fz5gq2nLD10qeTKWj053KiqjXJbJfs5I5m3MsMXjY9TzJKSGRFJpPx7arBdHS/C4b0IMCuKVE0uX2AVLllSxNLpN8zDKwd8hHh528XiGx6nR7nfw8VyFhlnagqjSw+o1xTY9GE3GTnO0tNyg8v2In3tZM2x0xw9aPLzMHs/XB5iVAKAXhNebnErEGKkpS4ZJY1Gc8GVTPWurdmRZc3ibj6OB79X5+sJvWD9zIDredEbuNtxbTrTc0OdXMtd2lAUgxJSEgAWHz5y4pfrYo26yUkyhqbXpzHsbpdEL77CoeF3xPacwjLWkxEk1QCTbdz2Llq9O4LRDVsb7YrZkQvKIgN6kfQjUPI3/mA5BHPw7QZaKrVQZEtSwqUHAsi4zHEeZQncdAYnj691cTaK/ZeDEd+24NjcN7dL4yWRM7aRA58uyU/4j5RyHOIrWENa4pI7bCx1H66HmOvrqHV1cPUPbSNAQ//ZtCv08rkzq1E8eDs6sSnK0exb107XmkeR8W0ByQ5eeCYg4tTVoz3R48/13KyZW+OL7Wd2cgeLmw7adO9VSIdvSBDNBhsPqPS5n7Nw9D0QNp3ztwkatr0HF+IdQ2bAhC7J2flXGFvnMS7+yN/90N6flQoMdhLNKfWHEETegv1pWM4aZNQpcTidWg7wW2H8zA0Ar1OT9S38+0ay4AMpr9D4PoV8PpMoprbzsSc4uiKZvxkRu79uiAOpUQiuZJJSEgAEO8VgHf0Hav1HhXKqgGzhIQ5fy/4E8fFposLEVCfO8SG4Z8wavtnxSOYNXTWX8I1CmcaDWhBk2dacXHnaa5tPIRM6Uizib1JeBBD/L1oanZqhE8VXwB6fjyas5v+IfTZl/Cxw7TfFoXthiYA0a26MurgPJOdU71Oz7rhn+K0Yz0Oei3O1oewex6Ae7uPwesD8jla6ebIz7sInbMI13shpClVOHzzKvH143H3NQ3aHVetEeqQ07kaO03uSOXVi7g4eRaej24hIJKidEc5aRJD507MbOdbzQ/fan42RpKQyB9KFyUjZ/Vn5CyY3PZrfnn6U0Y7JOFK7u5pDgYdd19+A+3wf3B0znsGy5RY63F1ipOS5GosAkyehnr+XJtyKQxpHJ+3mf7fv8TRj5filkM8IL0go/3XUzMmyVntIwCxHhVQxT/EIZexhmyRJnOgybGtVGpS5YlVhl+eMRv313qYlbslPGTvtB+RKR1xT3ho11iyN2bQ5/PxADQe0JLrB9twret+i65lIgKtj2ygYpMq+T2EEo2kGJKQeELQpmrZPOFrtAcOAALKbl0Z+NvrODimu464DuwHi+dbfJhGVW0sLcIlnihu/LDMLM2p8bpw2reD1MQPcHJ1KmqxrKKKsJ59I2vq3wa9m9Ggd7MstVXM2ut1eq68MAN3G0ohe18Gopp2xLtdC2KO/Asx0biFXc3Vznt2UhxU+H35MeF7jiMo5NQZ3ZM+g9uatds4bg5uW1fleR5ruNaqUuBjliZ2fbgK3ecfo87YE1fqUtHFxbCl2SiGXllvck00/HQqd0a9gMKgs/t8Sek1iKYD29B04K7COQAJiTzQNKgGFy64E+fszNWKnWgRdiBX/Z10Kayr2AP/t16l04xBeXqpr9O9KQdlDjjkEES/qCkpSiFIt+hQLvjeLpniLl4HIO1f2y5kekGOxzdfZboQuz7VCuxQeMuDOlJ/2jCOjXsXr7ArOQYzzgkdcsr/+iNVmlXL1zilmag7kXjfu4oOc8UQgPOerTjqrWedM5LiqML3y48JmtKX1MRU9n64guhdB8BggIr1UYeZJj0UgOTBY8q8UgjyqRgSBGEO0A/QAqHABFEUYzPq3gGeB/TAVFEUS34YfQmJMkp0eBT7a3dHpU1EZSxc/RubNq+ne8hfeJbzpNc3k1i7/5BZatFkJ3fa/vZRkcssIVGcODy8Z7VOqUvhwbV7VC5BCzRbbjsygx7RINrtPH56zRHc4yNsthGAqAq1IbASqjNHUaYlmS3Gk5y96Lr4PQIaVALSrT2uBl/kerfBeXqZ0MkU+M7+mKen9IWpfa22S45PQbbZepZFa+SkvNAho8/cF3I9bllBk5RKwtzvcLHwguMdcZ017V9CUd4fr8Z1CJoxBCd3FbHVGqMOOZXj721AIGXoOIYtm1E4wktI5INRM7uyfkQwAHvqDSfKrTzt/9uAi0Fj973MM+4+qR+8x6rdRxl9YG6uZfAK8Cal5yAcdq7Jdd8nBRcb2cqy431oO6sHueEUZ9m6xPg8UH36CUFTHj9v5Cr7NoT8g5oTsvsk6jD7gpVbI8Y7EEWHIAYunVGiNqOKA72VwOFGHPUauzYhkms2ImhKXxKjEtjUdBTe96+ZbAQmOXmg8fTFMT4ajU95AiaOos/bQ/IrfqkgvxZDe4B3RFHUCYIwG3gHmCkIQj1gBFAfqADsFQShlihaCFQgIVHGiYuI5d+le0m69wjnHsXzIrm9/XOos2VyEQFVahxb273A2JD0nd4R51fy1/vLid7+F4JWi6uHNx1ObsHVx42o8Ei8Kng/searEk8WaZ4+YEU5opM5oK7sW8QS2Sa+agOrbjsxlernKkBlfPgjm/U6QU5S14EMWv5OZiao64evcHzGPJSXTiMKMtKat6XTvGkZSqHH1O7YgH8C66IOv2K3PKkOKjSdetLmfxOo1rpmju3DToXglMtYHHHu/ig0yag08VbbxFZvjELx5BpaX9z2Ly42soz5XDoGl0DcC399NxdHvcZmlp+sxHd7hmdXvVMwgkpIFDAqL1eW/tCCXQePAQKnK3XkdMUgKmju0v/YPLw0sXaP5fX3Dk4s70erMZ1yLcewDR+yfpQjDtvXo0xLyXV/iceIgNu2P6zWG9fK5ZtWzyyLuhNJyrLl2JP/MO6tmblOWZ9VNgGI8a3CiFs7CjSzXWnGp6ofMT6VbX7/tpRCxu9V3Tv92tvx0nd4379m1k6VGoemYguGH92RH3FLJfl6wxNF8S9RzDzrjwOBGX8PAFaLoqgRRfEmEAK0ys9cEhKlkQ3Pf8vBim3RfPAeip++I+VqCKu92hF29laRyaDX6fG6exUwvWEa//a689hk0tFZSd9vXmDs1bWMubkZl3Je/DXwTYL9W3K8ajv+qNCD/XM22TXvmfXHWNHpDZY1Gc/aUV+YZAyQkCjp+I9L3x3Kahth/Du+ZacSlxq7wcdTMoP2ijyWVS/IqDPr1VyNVfXphpnjWEI7fDyjdn5u8h3U7FCXMcd+Ylj8PwyPO8bo/XPNlEJGhl9cTWStFibjJykfj5Vd/oAFXzNqx+d2KYUA3Py9bMqfnRRHFX0vbyXVz3KAfeM4/iOf7NhCep3B7rbGnVt7SHL2ovPcKXkTSkKiiChfy5+qNb3YubgpC6f7s/jtcvS/tDxXSiEj1xZvy5MMcoWc4Wvfp+e9IwRuWEGKo2ueximL5NZRyx5LL61cSZWW6c8dg8HAzg7P4ZYUZdf4eVUKGWWLqt6UrgeWSEqhbOgrVslzX+Nvrp37NYnxyRj27bHa1u3UIbSakuW2WRQU5Nn2HGC0bwwgXVFkJDyjzAxBECYBkwDKlStHcHCw1QkSExNt1pc0JHkLn5Isc/z9aBT1HRFnv0rm4yHAD+cPRnFq/06uRdYukjSTBr0Bw+wp2FrSW/oOtSlaUpIT8JjYHj3tAXAFErjHpqVrMKSkImhTQZAj83THI9AXQZZ+2425GYEsJgq3ftWB9N2Wf3Zsggzlu17hiFPFCrh4qczmzS8l+ZywRGmT90mh6/vDWX3sPB57N2aWCUB0QB36rzdPh17cNBv6FGnJ87n23hy8H4QC6buNVT98I9c709Xa1uLvem1QXz5uZpad6qgiaNbYfMmqVDkx7tJKHoZGEHLgAu4V1NTr2YRdW3YSFVAb7wxFdoxfNarOep02421nCslOhbqB7K3S0Mwt1pKJeaJKTZ3fv8e9nAeN5s4kfOhYi6lrNQon+nw4MldylDUa9G/FTkdXnO1017ArBpV/DYK2/ED5OhXyJ5yERBHh7udBHR83VgX2wuvRrbwNculcvmRw8VTRuH9LYj/7kPgZM5GJ9ittyxrG+3pBxjoyjhlfrSEununr1OOL9+F972oBzmKd5BHPM3a55FabnT/fXorPmYPoRjS22sYeVzJHvZaNDYfjorFuWexgSEObmIqj0iFvwpZSclQMCYKwF/C3UPWeKIpbMtq8B+iAlcZuFtpbVOaKorgQWAjQokULsWPHjlZlCQ4OxlZ9SUOSt/ApyTKv8O+GV5Rpli/d7KkoZs4D4H6nAYz+66tCl8NgMLCt1xSruxdpMgc6ai6ala/oNh23XlUy5c2KpRtvWLXGjDi/kss7T5M06UPbGSEAA5D6zgf0/Hh0Lo4mZ0ryOWGJ0ibvk4JMJmPUn19wafcwLv66HX1KKgE92tLz5V5FotDNC63HdaL1uE48uH4f0SDiV9M/z66fA/+ax+Yer6G+dCyzLN7Dn3q/zcGvuqUlQe7xq+5vMpaLlyu9b20l6k4kBp0hXwHv2/3+Gad6jkWV8ng3XwAi67WhxivPEnXpBh7VA+j2Ys/MhV+Tga25NmwCLmuXmNy70mQO1Fjza55lKSu4uDvjOHESzE+Pj5LfbEQGBHofXpqZDU9CorRwYllw3pVCgGdMOA9DI/J9Lw2a1p+zlctx/tOFONy8hmAwoHd0Qufuidedy8ifAIWRVq5EaUfA4dzcr4ztZPExQHpIiFtvf45HniTMHRqFM93nTCqCmUoXD0MjSFmwIMcMo/b+xt73rhG/YAfJAAAgAElEQVRdrQnON85arI/zDChxluFFQY6KIVEUu9qqFwRhHNAX6CKKmTn8woGsNtmBgORHIvFEYDAY2PX+CjyjrKd+B1D88zcAkbce8c/PO4g+/C/IZKhqV6fDu6MKLAuYTCYjtkl7fM4EW6wXBYHNTg1JKF+dSq8/T9DUfgDIT/wNvapY7JP9xisC6hvn2P3uEmLXbcXbQhtLY6TO/pL4V/rhXs7+x+3D0AgOfbyc5CvXUfj40PT1EdTt1sju/hJFiyAIQ4FZQF2glSiKJ7PUlfgkBfV7NKV+j6bFLUauKFezfL7HcC/nwdizS7lx7Bo3D13APdCXnsPbFYlSTF3J3sg01qn+VG08zm/nwAdLSDl5BpycCRjem9FvPWPzGIatfJuIWePY++LX6B8+xLV1Mwb8MuWJjC2k1aRx+/h1lO7OVGpaFYD+373I73sO4XPtZL536FMGj5GUQhKlkoh//yM/dgQC8M/8bfSbOzHfsjR5pjVNnmltVr79zV8R580pUVnDCgNbiReyIpB7dzOnuEgAtvR8HXVc4b/GahVOlJv3FR7+noU+V2ni3pVwDrUdjpsmwa72OpkCRBGFjdDGAuA3aiBpn56zmDHO++Xn8ipuqSa/Wcl6AjOBIFEUs+a13QqsEgRhLunBp2sCJ/Izl4REaWFVj5l4BW/N8WGsSEvhjwH/Q/XnOuSiAW9jxXE4vHIpFX7+FkSR238eRe7iTPOXB2Smy8wtzx6dz6oag/G++59J8GkBMlNHe4dfIfHNt9h5L4reX47HOdW+GzA8VgKlLZiPty7V7n4OhjSOL/yT7h+MyCy7dymMmPBI/GsHos720nB63VFujnsF57TkzF2D0D0buTppCgPnv2L3vBJFykVgEPBL1kIpSUHpoFrbWlRrW6u4xcgTPlV8GZoHc3z/muUZvf+bQpCo9LD9zV9JXvQrqpT0HfNDftWp9937+NUOwPvaKbt237MnOxBItxJKdPPFe9pkhv3vyXbNkyi9eFQrT3LOzWySFp+7APm5pe83L3Dmqfr8N2km7vEPCnWu4sTWy392cqskS/WpwO3TN/C+dCxfFpK2+iaofNA1bY2qdlU6vD1CUpZbYN+LX+GdFGl3e4VBR4KzN64p0TZ/swaDnuI/h0+Imj0X1+RoAFIcXXF47gX6FLA3Q2khv1tgPwJKYI8gCADHRVF8SRTFS4IgrAUuk+5i9oq02Jd4Eri48zRewVvteoDIDXqcraQdVepSePTCy8gQMYYWvLRiIf8OGcewP97NlUx6nZ6Iy3fpuX8hhz5ZgWb7DhSaZFwzFvxm7efOZv28eTgZ0sjtRavMhVLIiCY6XQF1/fAVjo15G/Xd/wC4BSQr3XB59VX6fDkerSaNa5Om45ZmuhwTEFEs/IFrQztSq2P9XM8vUbiIongFIOMZkZXMJAXATUEQjEkKjmVvKCEhUXTsmLkEYd4cjBHgRMDrYSi3x7xEyOBRqOzYd09y8jDJYCYAUc07MeLgD09czAaJssdTL/Viy0ezMxWnWdEhQ54RQt/WOrBqL3Mrn4Km6eC21O+7j83jvkLYvgkXOy0uShP5dWm1Nab/8yO5ffQKQq5tjUyxJl+iizcD7+/HyVmZr/HLOi4n/851H7eUaJv1qXInAhtWJrBhZbRvDuTStpPo03TU69MCF/ecHNbKLvlSDImiaNV8QRTFz4DP8jO+hERp49Li7bhi30NKkWGpY43spo0CoFr/OwdbNyBoWv/M8lNrjnDpq8XIw2+j9/DGf8wgur43DIDtry8kZenvuCZHIwLOGf9szisacE4r3J2srNTo3ZpHNx5ypvc41NlSIbtoEuCbL9gUm4Bf01q4JVreMRAQOTV3NbU6lrygwBJWKZQkBUbKclDvsnpsZfW4oHQdm7a8FtnsqWblMkDu4ISueba6AD90Ge11jk44lffDT+2GNklDcmT6Pd3J241Kbs4cPXaksMUvUErT7yZRdChVTlRf9A23JryKU5bNKp2gwOXDD3mw5yheR/602j+yehP6DG5bFKLiqHRg2Or30GlncmDORpJnzUKR622/kkthuMqJCCQPHkOf94dzcedpEsidAirSvwbKhBgcNMnIRH2mZX52XJOjuXf+jt0ZN59U5Dm8LxnJ/htZ+81EwPmN1zM/OyodaDqkaK7Hks6T5zQvIVGI6DX23bzAXPFjL7fmL8tUDO36YDlpX36G2jhWdBhpH59j1YETiGk6vI/vzrQ4Kol+5pF1WtOnWyPWPfslqmxKoawYlizixo6Kj93tLJD24FHBCyhhF/YkKbDUzUJZvpMUGCnLQb3L6rGV1eOC0nNsYWdvcX76i1brk93K4ZAYiTyLEbhu9lTkM+elx2xYOJ+2g22GpixVlJbfTaLoaT68HVXa7eHQJ6tIunoTZaA/bWeMILBRZbRvDGDdAEdcD+5AkSUGjh6BuFZdeGbzF0Uur8JRQbf3hnGtXX2OvfMj6pP7S+S6sLjRCzLq7FlPraB0C/R6PZtw1rcqXo9u2tU/TeaA94MbyEQDBgS0Di5gQ7Fh0OU9rf2TQnyVeqhv5JzJz1JGUYAkRzectQkIQJKLN+q336DrO0MLWswygaQYkpAoQCr3aU/srnU57iwYBBmIIkIOpsaWcH4QBkD8o3iSv/4GZbZ3aRHwOrwjl6MWPpbiTTiG32TLlJ9IOXUWFxt9nQwanCJCbI4vODoWjKASuSanJAVWkJIUSEiUMJwzUjNbe4bpnVWkdOuO68blJuXGtiFLt9F2QtlRDElI2EId6MMzv6RbyyVFJ7L9pe8Q9+7GMTUBffnqqGZ9hFe18sgcFDg4O1K+YUXUgfkPrp8fanWsT61jP7F29GxUaxYXqywlicwU9e17ZSqFID2BS6tV33Cm33O4psbm2N/BkJa5KhcQcbJhgZ/g5kfV1qUzhl9BYDAYOPjdVsJ+34QQH4esVi3afPg81Z+qbdKu3qwp3Bv3InJRb/LGYwDivCviFR1mcx758FH0W/xGwR9AGSRveWwlJCQs0uaFbkRVamBT2aMXZLh8+hkx9dvmabdGkZbK1mm/cOynnSh1KWb1JXUHSLDwt3viQxQ/f4ciMiLf47s1qm2xPPLWI44v3c+l3WcwGMp+6tZSxFZghCAISkEQqiIlKZCQKHZ8qvgSVaWh1eeIU7cuVBv4NGBu3icC3kd3EXLkSmGKKCFR4tCmaNjQfAyum5bjlvAQZVoK6jsX0fzvPR6ev0HTQa1p0KtpsSuFstL7+8nEqivl2E4EtLLc2xHkLypP0SMAUXXbMGijeUiCWh3ro23WBrB+XNk3PrOWZSVrf/UbrxZJps+SiMFgYFXXt0ieORP15eN4h1/Bc/8WLnQewrEl+0zathjZAfX874hRVzL5TmWAV3SY1d/EWN5oXM9COIKyiaQYkpAoQOQKOQOPLyGqZRerNyrvH76ly4xBdP3jE+I9LHnf2EapS0U+fy5R3/+UP2FLCCLgGnc/X/0BavQx9Q/WpmhY2fUtjtTqSNTEl7nVdwSrAnpyYcepvAsrkWsEQXhGEIRwoC2wQxCE3QCiKF4CjEkKdiElKZCQKBG0+vlDUhxdzcqjy9ei19yXCF1/ALButn9h+d7CFVBCooSx79O1qMMvW1z3JX3/PamJuU/MUdi4qt1o+NvnGWGyLSMCqc9OIqFctVyPX1I3KS0R3a4XlbeuZuz533HxVJnVpyam4vqP5ftedqzVp8kUmUqjGJ8quMyeTbf3h+dH7FLNP0v243V4h9m556DXcvuN/6HTmrrYtRzbCbF2PYtjWfvOBSDm6b7UDLLcT8IcSTEkIVHAuPu6M/boAuoc2kZUw3ZoFU7oBTlR1RrjULUK7V9M11xXqBtIv6vb0b/8OlENniJepbZ7DhHwiI+w+UC31q+kIQD52S8RgKiqjWnQu5lJ+dq+7+J5cBtyMf3hIgJekbe5Nmwi9/+TPJaKClEUN4miGCiKolIUxXKiKPbIUveZKIrVRVGsLYqi9UidEhISRUadLg1pc2wjcV0HEeNbleiA2mgmTGbw+dWovF0RZLaXjoJcWlpKPFk82mldaeCsSeDs+qNFK5CdhGw+ioD1F+uo5p0ZsvRNZA3sz/iqlZUet369ICdt0lTGBH9Hg15NLbYJP3+bTWO+xFGvyddcDgZd5vfsFP+IhoOeytd4pZ2QxZsBy+eeW2Ikwd9uZtf/VrD383Uc+G4rm8oF4X10V67miAnqx8jdXxWAtE8OUowhCYlCokbbWtQ4beq/nT27iavajf7zXgJeIjEqgR1Vutr0YTZivJFqHFxMMmLkFNuoNO3g2ItOkNNhxeNAjqIosuOt3/A4tMOiSa+TNolDHy5m+Jr3i1pUCQkJiVJBYKPKjPrTcoDcOqN7EL55hcUMMCDQZIJkti/xhKG3bexq0JVMY1hDWprNeueq6WEAy3dsReqeTTmOp5Ur7VKgJDp74qRJNAnMXZSIQHSb7vRf8zGeFbystts+/TcM33+Nu1hwYQhEwFmbxM6OExl7y1pujrKPmJRosz7l/feRIaInw7Mgl+PrZA70Wvr2E+uql1ekbR0JiRLC/9u77/CoqvSB498zM8kkpBdCC12kCSpdbJEqRVAERVHAhu66P3XtiHUV17KWdVdXsaxdbDS7IAYrgrhIkyagdEwnPTNzfn/MTJgkM5OZJJMpeT/Pkycz95577nsmk5PMe889Jz4tgYpeJ/p1jNUYReHYqVQYzYB9iU1vf76qDCYqVWTlg03ayv9e+BCwT/6Yt3kX/PMRDF4m9q7YsLn5AhRCiAhy4uQh5A0e6fZWsqKx59NlUPdghCVE0CSP8Dz6o8IUS//zhjVjNL47/vwzAffzhQF0P9e+//T/m8DRVt5HtRcmt8cy7RKvZSqNZoomTGfs1k8pbsBUCk5VxsaNSirI6MbMr//lNSn0y/IN6CcfxdiESSE4lkxP2b+tSesNN3ED+rvd7jpxN9WP/Vcy5tyQmtMrXEhiSIgQEr/pB7/Kx5YXEZvZlsrYRACvyRCA0tHnUjpiYiMiDA5rPV1V8boNACy77BFMleX13jKnivKbKDIhhGh5Lsp+isrLr6UwuQMaRX56Z/QNtzJ9Wd2JW4WIdKPuvcTzRM6XXkZ8WkLzBuSj/ucMJrffaW6TvLm9hnDyNPvcjdGxZk5a9BxFCRk1ypXEJlN19fV0eOdVph/+gqE3TkPX+Ehfq97L53DRkvtIbp+C4ZRT642vKC6dgjFTyc3sTak5kfzWXbBccwOFvQb631gXGVfPrLfMT/94HYOfEzBo7KPYfeH5VWoZsv42k5LYZKBmYtLbxN2+Khw7lQsX39eIGlquyBo6IESYi7L4dw+zAU3Ui08T7UNmPb91F8554RYObPydXV8sqfe2s1Bi9DoOCtSh/ZSXlGFa+SmcOavedsUW/IHVYpUhpkII0QCmaJN9me7nriM7O5tzD/452CEJETStkuM4e81bfDLzAeJ+WEm0pYLC5A4kXzWbKQ/Wn4QIpgu/fppFl/ydqM8/JAooj2pFxcgJXPDWnRhc5hPrNbIf3Q6u5LtnPiFvy26SenRk9P9NIDrWXF2m08ldWTXwLFLXraz+H9P5vbhVKmfffWxE0YQFN7Giy1LMlroTc1uVkYRHHmLcdRNrxOD06oAfG9RWiyEKW2o6Y26sf9Jn68HDftdfkN6Z6OICTOWFPsXSkqVlphM9cxY8988m/SxSEpvMhUv+Jv/fN5AkhoQIIYWtu5Dyx26/jvF21UEDuR37kDRuNOfdP5O41HjMCWZ2KEOTD48NptQju/ksZSCx2oovd/LHVhaT93surbtl1F9YCCGEEMKLtE7pXJL9JFaLlYricrerW4Uic1wMFy2+D0vlXazKzub0/DVEm90nLaLNUWT9dZLX+qateIL3Js4l4bvPMWn7hMt5HXox/M1HSGqbXF0usU0Sg1cv4avRV5KUv686iZTf7njGffUS6V1aezzHcX+eTv6fvvGrnXmnjmP863eycecmn8pHdekEv6yus93dRVXntuSLL6Boy6+wYlG9F1+PDjnLt8AjiKXSgs1qrU4mHl36EalNVLfz9TacN1WSQo0giSEhQkRFSTm2Lt3Az8SQNwpInTyec564qnrbsqueJC6CkkJOJm31eWCuxRBFQkZiQOMRQgghRP2UUtOAe4HewBCt9Y8u++YCVwBW4Dqt9WdBCdJHRpMxbJJCrkzRJozRJo9JIV/FxMdwSfYT5Oz5gz3fbyW1Sxu6nXK827IdT+zKjCNfUHAwn33rd9OuXyef5oUZfuVo3rizI8m5e32KKb91F85f9ndaJcbCTt/aMXTeTDZ+tqjOBNkKsBhMNbYrILf3UKY/OAu0ZlG7r0ko/sNtvRrI69KPi798wrdAIsD27M2s/r+HSN72I0ZtI7dTX3redx3Jh3716Xh3SbYKUywGaxVRzpWHlYHCs85h+os3Nm3wLYwkhoQIAZUVVbw9YCZpu352u9/TlQdfbgerKravWpa7L4dvH3kPPljSmFDr1q9M1VeF3PElRmeZguQOWBOSSNu7pUljrK1oUBYx8TEBPYcQQgghfLIJmAI857pRKdUHmA70BdoDK5RSx2utQ3OZL1EtvUtrr6N+XCW3SyG5neeJoN2ZvHkx7w+/ktRd66tnocxP70L8eZMoXvIBKX/spjwqjrJTRzLh5bn2pJAfug3twW9/n0/+nfdiriqr3p7XrgdDX3uYn1/4iLK1P0FMDO2mjuPiW8/HFG3/WD31j2zePe9eDF+twGippCwpA1v7TEypqQy5+zJ6nNbbr1jC2a7vt7Nx/CWkOVZQ1kDa75s5fPmfsZhiMFvKvFeA/fNBTrcTMbRthy4uJjGtNWfuWAHAupeWY62wcOKFZ5DZv3MAW9IySGJIiBCw8oGFpO362WMSxfU+bXdLsHvTecxgvn7mY3Jumku0pZymvo5VfOZ4rIePkO5myC1AbteTIDaG9C2rvbYvv3VXDCcPwKQUNDAx5KzbpgwYPIyKyk/vzPi37rWXs9nc3r8uhBBCiOahtf4FQKk6/yFMBhZqrSuA3UqpncAQ4PvmjVCEmvi0BGZte5tD2w7w2+ptpB3XluNOdSRcnrm2SeaRPOvGcym6dATf/WsZpYdyyTy9P+NmnInBYOD4M/t6PM5kMnHRBw8ADzTq/JHg25v/SaojKQTH/k83aivlUWaPiaGc7gMwtMnAEBVF90vGM25mVvX/69nZ2dUjy8bcfVFA429pJDEkRAg4suRz0vCe6KmdDLIqI0YfLpoV7DlI0V33EW31b2JrX1QazZy78G6WZ3peXSJlz0ay9n/P0sEzSd2/tWZsSe3Qg4YR89Vn9rmVPrffRtfYibHzM3uT+adL0dqGtbKKw59+i7ZUkT5iOFNun8qXDyyk4JU3SMrbT2lsEnrMBM556RYKD+Sz6s4X0JUWhtwxg+7DejYiCiGEEEI0QgfA9arTPse2OpRSc4A5AG3atCE7O7vJgykuLg5IvaEibNvXWbGv6jD7suufMLohbYwZkUkMmZQCX331VcNibEah9HOMn9oPy/l93O4zAxXGKIzWqjr7EpURY0ZrEjukUU7N1z2U2hcowWqjJIaECAWVlT4XLUxsS9+X/sGWWTeQWJJTb/kDy7JJbYKkkGuypnqVicFZfH77S0R7WU3NqK1UlVdx4S/vkP3oIg59+CUAbcZnEb37ALFvPu9XEsiGqncJUUP37oy45bxjG+6cXv3w7QsfIH7RazinQIwrK4Clb7Ay7Q0AnIvK/vLJ23zbsQ8Xb38Xk6luV2mz2Vgx/x0OvfQ25rxDVKS2pe3lFzJq3gXYLDYObN5LQkYSKR2aamo9IYQQIjwppVYAbd3smqe1XurpMDfb3P4DoLVeACwAGDRokM7KympImF5lZ2cTiHpDRaS3D6SNze3tc+8gviS3znbn54jh279i+XX/IvHTd93+slfd8zdG31lzFblQal+gBKuNkhgSIgTEDh0Iu9b7VrYkn/jWiSSW5HgdWaMBqzL5PCFzfVStx5VGM6mrP6t5Lc9DHNg0mz5cR/dRA6qHfX7zzMdUzb/P461lYB9RpLTGaKmkvGN3TrjzavZ9twnDf570es4h82a73X54x0FiFr/ldl/tOBSQtncLbw69hpnrXuDA5r0UHsqn69DjiYmP4Z2pfyPhg7dIc5SPL82j6m/38Ppri4g59BtxZQXYgMK0TqTMupjRf7uk0ZM6CiGEEOFIaz2qAYftAzq6PM8EDjRNREKIQKsaMAy+/qjOdgXkdj2R1l3bYPltr8fPMkf+tQBqJYZE4EhiSIgQMOKBy1mxbAnxJbn13kZVHpfC0cMFUE85BZTGp2LYua1JYrShqDKZKYtLIbq0kFYu9ww7uYu91JzI1z1HEGWt5CDwXbseDPrv39k/9wHi6zmnNrdixv6Pa27TcPg/Xo5RBo7Pcn/v9/9e/xKTtrjd50nKhm94P+ZEYqzlAOxAUZjeiZSc3+q0VwNpu49NIG4AUnJ/h8cfYtFzz9PntX/S/5zBfp1fCCGEaKGWAW8qpR7HPvl0D2BNcEMSQvjqrKdu4Nvh3xFXll9je6XRzEmP3QJA7O5fPB6fnLePwkMFJLVN9lhGNB2ZdTVIrBYrVossqiDs0jqlM2zl6+T2HIx2STW4jvZxPjZNmEC303pTaTR7rM9ZNvHoEVJyfmtUbM66DGjMlnKSCw+6TQpB3aSQRRmJqygiylpZXVfqwR1snjSb+NK6Q0trs7VtX2fbgAtPpcLkeUUxW7znZejrzmtZPwO6OikEYESTmvMbCvejjDxJKMll28V/puhwof9BCCGEEBFKKXWeUmofcArwkVLqMwCt9WbgHWAL8ClwraxIJkT46HBCJ4Z/8zZ5p4ylNCaR8ug4ck84lZ4fvFZ9odQS7XnFOKsyYpZVhJuNjBgKsAOb9/LN/a9SvmM3pnZtaD9yCL+/+C7J29fZV2LqfhInPnQjJ06WUQQtXecB3Zi56XUKDuRzZNchVs++i7TfNlbvV0Bu76FMf+56omPNFHXpQ/qv/3NbV+0ERWOWu/c3l1JqTqAyNhHbiQNIXPWR21XUYiuLfaqr940z62wzGAwk3DmPinvvqhNbSUwyqZ3beKzv5EtH8P2D8/0eNdRUWpUXseqhdzjniauCcn4hhBAi1GitFwOLPeybD8xv3oiEEE0ls39nLv3qKY/7VdYIWPam230FJ5xCjCSGmo0khhrIYrGwcMI8zN+uxGCzUNyhO4P+PY++Y08G7BPT5u8+xLoxfyJO2+xLhG+A4s/eq56TBCBt50/8Pm0muwxRKG2jqFs/+t1/HSdPPSUYzRIhILl9CsntU+i+9W2+eeZj9i7NBoOBrheMZtwVo6qXazR16QweEkO1uU4a7frcW9LHohq2zGdZRkdUx05Ep6Vgwv2S8QAWgwmTzX2CRgNVV/yFwTPOcLt/9LwL+K5NCjvve5KUQ79iMUZRPOB0Rr54B9sP7/R4zozubamYcjGm91/1q01NqWjLjqCdWwghhBBCiFAx/pkbWPb99/bViV0Ut0ol68U7gxRVyySJoQbI25fLquPOIsVlpaeYPRvZPXE6++68h7H3XMybY24jYXwXDLrmB2N3H8QN2la9lHjazp/Yf9FsNj93DtOWPIA5TrKkLZXRZOTM686B686ps6+yrALLwUN+1+nP6J/CNl1J8vsM9gmb2bul3nJFqR1IdXObm1UZSH/mKYZfOdrr8cOvHM3wK0djs9mqk2WA18TQ/97/nvK164jDt9FSgWBu73lEkxBCCCGEEC1FYpskztv0Hp/d/iLFK7JRVivRQ4cw4qE5tO6WEezwWhRJDDXAxwMuIMVaUeeDpQLK5z/Ami7tSFm1DMv46xpUvwJSsj/g7YH7mP7za7KSkajBarGycPAVpG9bG9DzpBza6WW8T/3qu33NOfeR87kG8rr057RX59PtlOPd1rnhg7Wsv/PftNq5CaspGsuw0xn9zI1kdHe3Am5NXz/zMfk33EyaY3oCb0mhQCWNLAYTw2+V1RWEEEIIIYQAiEuNZ8qC64Hrgx1KiyaTT/vpyK+HSM7fB7j/4GjSVn556PkmOVfar/8j++H3mqQuETmyH19K2ra1TbYMvSeNTYzUvn3NU73O53l9hjFzx7sek0Jr3/iK3VMvI23LamIri4kvzSN55VK+GDyNn97/nvLicrfHgT2Ztu+uhzH6MGdllSGK3OMHN/nrazGYiLvnXtr3zmzimoUQQgghhBCi4SQx5Kf963fX+4E5dedPTXa+Q4s+b7K6RGTY/96nQOBvg6pvRI0/CpPakZfRjfKoYysP1FnmfctqdqzyfAva1tsewmSrqrM98egRDk6fzUcZp/BK30vI+/UA7176CFs+X4/NZh/ztO3LTSQW1X/rnQaMtirSt69t0tfXYjAxaN0njLpjWhPWKoQQQgghhBCNJ4khP7Xp07HeD8WGphxrUFXZdHWJyFBZNzniSVO9E531lJgTAP+TUkmFB0k5souYqjK3+531bf9otdv9h7YdIPXwr17bE1NVSvr2tRgL82m18EV2T7iQhelZ/PDKl1SV+v57FIhO0Wiz0OGETgGoWQghhBBCCCEaRxJDfmrfO5P8jG7Ndr744UOa7VzCs/Lict699BHeSj2dpea+LIo9kTfTsnjvsn9QnHu0WWNJPnOoz2Wdc/c0ljNxE1fhf1udMfiSTIpOine7ffPi72rE4auko4c5dNVf0DYbpebEess31etVm9Ug07kJIYQQQgghQpMkhhpgTPZLlJgbsl6Tf4ri0hh5/6yAn0d4Z6m08Hb/i2i18EUSjx7BZLNgtpSTVHSQ2Nef55POZ3Fox8Fmi2fkPZdQmNwe8C2JEYhbzvxNnvgSQ0VULKdcM67O9k/veo2yu+b5ecZjTNrChgdfJOaqOTW261rfnZry9XLWXXD8gCasVQghhBBCCLvuSL8AACAASURBVCGajiSG/HBo2wHWLfyaypIKzs39lsKk9gE7lwZO/+otktomB+wcwjfLH1hIupfl11tVHGXlKRc1WzxxqfGM+OYNck86E5syAr4lajRQHtWKo3HpFCZ4XjI9GMkmG4r4228jPi2hxvaSvGJKHnu80eeL3rGZc564CuPtd1KY1A4AjQr4kvUKKG6VwpRV/wngWYQQQgghhBCi4SQx5IPCQwW8OngOa/uN5NClV7Jh8FgWnjQTW7Q5oOe1WRuzWLhoKkVP/qveMkmFB9nwQWCXj3fVtmd7Zq5dwMhDaxiw8UsMt9xBpdH7+1EBasZsphd8y9SDX2C99iZKzQluy/miLDoOqzJSaYrxmmjyRdRt8xhzd93k2tpXVhJTVdqougGs5hgAzr7/UqYfWcmwX78l7/hBAUsKFSVkkJ/aibJL5nDeka9JSHV/i1xLoJR6VCm1VSm1QSm1WCmV7LJvrlJqp1Jqm1JqbDDjFEIIIYQQoqWSxJAPlp45h7T1q1DanqjRQNrOnzAX5QTsnArY++OOgNUvfBdbXuRTuR2Lvw5wJHXFpcbTrld7xj84i7N2rSL3hFO9lrcUlwAQbY5i0pNzmFb8I52XvkXU3feR/O+nqDJE+Xzudo89yKTKLZxX9jMqa0SD21BhiuHsBy51u6+q1PMS9P6IGjmq+rHBYCChTRIpO9Y1Sd21aeCcnZ9wyeHlTP3vTUSbfX9NI9Ry4AStdX9gOzAXQCnVB5gO9AXOBp5RyjEETgghhBBCCNFsmiQxpJS6WSmllVLpjudKKfWU40rwBqVU2E6wsX7xD6Tt+rnGLSfO760qjlJhsi+/3ZQT1h6rK9ALkgtfaB9/DsaY6ABH4l1y+xR63WSfk6r2+9H5vNPYYXWOO2H8AMbcNZ1Trx5L8fAxbo+vXU+FKRab7VgpXc/oNnf1ObdVnXuBx+P6ThmOVTVunb+8Nt05+5/X1thmqbBg1IEZkacAg0nyG05a68+11hbH09VApuPxZGCh1rpCa70b2AnIbPtCCCGEEEI0s0YvlaOU6giMBn532TwO6OH4Ggr8x/E97Gx7aznxeE7RmC1lVBrNRFkrALAYojDZfF9O3B3nufbfOI99Q48ns3/nRtUnGqegw/Gk7/vF436NPXk0cM7E5gsKsNlsHNlxiNikVtVzUQ26+HRev68faXs2Vicznd9zO/Vl3Mwsr3VOWXw/i3pvIiXnN7f7ne9Ns6WMwuv/yqeH8zn7votpP2IwJR+/7bHeClMMZkt5jd8jBRyNS2fKy7d6PK5970yOnnUOySuXeo27NhsG8tt2I37cGKY8dAVxtW7lapUYS35GN1KO7PKjTkV9KSrnXoNBkroeXA443ygdsCeKnPY5ttWhlJoDzAFo06YN2dnZ9Z6ouLjYp3LhKFLbFqntAmlbuIrktgkhhBCummIN5SeAWwHXT26TgVe11hpYrZRKVkq101o339JNTeDdGQ8Rt/i1estFO5JClVf8BWtZBaY3n2+S88eVF7Lq5qeZ8fkjTVKfaJjxXz7P6h6neRxep4DS8y+l40ldmiUem83GOxfcj/HTD2lVUYRGkXfcyQx/4W6OO7U3537zIkunzCNx7ZeYtAWNwqYMmHMOsHDy3Yx/7iaS26e4r9xgIKYo16c4FJqjjz9Bxa1TOOP/JrB47j3EVJW4LZs6/z6O7vuDshdfIrasEIsxiuKThjP1s8fqvdXqwg8eZNGsZAzL3ie2shgrBgozupCYs9dtEtZqjKbX5+/Q86x+XuvNvPlPlNx6i09tLciahHXPHtL2bPBaTgG5xw0gJj7Gp3ojhVJqBdDWza55WuuljjLzAAvwhvMwN+XdZt601guABQCDBg3SWVlZ9caUnZ2NL+XCUaS2LVLbBdK2cBXJbRNCCCFcNSoxpJSaBOzXWv+sVI3/8TsAe12eO68E10kM+XMluDmv3BQfKSR6YALWgdf5fpDBSErfPhQOuAmjtQo6ZGB52I/j3Yg1mpqvzWF4Zay5Yo5a9CRlO/dgqHX7kSU6huj0NDLaJgd0FENZYSllOQXoKgvG0mJih6fD8Nk4789JAjauyWb30X1ExUTRcf5Uig6MwHLo2K+cM1WRvfAtSEsnsWNrKo6WUnogB5QiKiEOQ3QUUfdfXl1vfe/haOCLz1bQKjWeuIXzKd+527HW1zHWhGTieiSTOCCZxEl/r96eCqz5aY1P7c+4ejh6zilUlVcRG2Uk0WSkrLCU0n2HMVXYJ6e2RMUQnZFGdJyJgyqXg/W9zicnYnv1ISyHDmPQVo/FrMYo2vfrQVlBCVV73I+kctIoMo7r6tfPOBx/72rTWo/ytl8pNQuYCIx0XDAA+9+Fji7FMoEDgYlQCCGEEEII4Um9iSFvV4KBO4Ax7g5zs63RV4Kb88rNa5kTSD280+/jOq1cQur5J/LZ1Y8TNxJMtz3VqDhKktqRlZPdqDp8FY5XxoIRc3FeMfENXGXKl3htNhs/vPIlv3/8HYYYM+U7d5P640p8WQMvZ9wFXLTsfiyVFt5rk0VC8R8eyx5VJozaguvYlrKoVsS6rAJmefi6et/Dqc//h2FT7G2qrKjig2ufpvi7NRhSUjj9qRvpMrC7D5E3nNViT+oYHfP6OF/j8uJySvKKSclMxWDwPJ2a1WJl0eWPEb3wZUwuCSIN5B4/iLGLHqVtz/YAvDrwCtI2fFPjeA1YDCaK+g1n6KPX1TtSqbZw/L3zh1LqbOA24EyttesSc8uAN5VSjwPtsd967FumUAghhBBCCNFk6k0MeboSrJTqB3QFnKOFMoGflFJDiIArwfF/eB8ZUJtzHpeo2GjSMtO5+KMHWfTU842/V2/I8MbWIJpYQ5NCvij6o4glwy8nbc9GnGdpBTUmP/cm7tP3WZj8BTaTmSQvSSEAU/V8wMfEVpViw4AB3yZmLo9qRb9Jx+YLjjZHcf4LN9R7XP7+PJZNvJmEretQ2kZxeidOW/wkXQf38Om8rpwJod1rdrBq9t0kXXUGy8b8GYO22ucxik+n1ayZTHz8KrcJIqPJyLRXbyX3wcv54d8fUnIwh4Su7Tl5xlm06dGuRtmZ617ks7+9Rc7TLxJdWkR5Ujrd//ZXhl8+2u+4W5B/A2ZgueNvxWqt9TVa681KqXeALdhvMbtWay9Dt4QQQgghhBAB0eC8hdZ6I5DhfK6U2gMM0lrnKKWWAX9RSi3EPul0YbjNLxTl5wTSCshv3YVOA7oe29gqrlExlJiTGPHEXxpVhwgvy6bdXWPiaCdfpzI2aisJJb7NEeSJr0khAOPsuhM71+e3n3axftg5pLskplIP72Tz8In89sgjZP11sl/1VZZV8NbAy0jbsY7WgEWfhtElv5BQnANPP86i/CKmvuJ5TqG0zHTGPzS73vONvfsiuPsiv2JsybTWx3nZNx+Y34zhCCGEEEIIIWppkuXq3fgY2IV9+eHngT8H6DwBUVrgfgJdbywGEz0fnVdjREJyt7ZUGewT6zrvo/Nn2e3EG6+vvoVFRLacPX/w4c0vkPjtJ4DviaBAyRs+DpuXKIoSMjDefifnPuN/4vKbsVe4Ha2kgMLb7vC7voWjbyJ9x7p6XzPjO69TdLjQ7/qFEEIIIYQQIpI1WWJIa91Fa53jeKy11tdqrbtrrftprX9sqvM0hz1rd/r9wfzo6eMYPOOMGtsMBgM9P3qT0phEv+rSgFUZOfnSEX5GIcKFzWZj2fXP8kbGSJZF92Z1j9NQ/3wUY7ADc2gz+hR6frEIqzGqRjKzNDqeqPvu56K8rzn7/kv9rjf39xySCjzfVWrSFr58wvel6XP35ZC85gufykZbytm4ZHX9BYUQQgghhBCiBWmK5eojTlI7D0t5e2FKSnC7vfeo/vQ+upYNy9ay44NvKf52DWmO0Q3u5o1xbis67ew685uIyPHW+Lkkf7GE5GAH4kHO6g2MufNCDtiO0O/Xb9ny4Rqi42I5+YLhRMf6Mg22e4UH8+tNuuZv3u1zfbtWbcGofb/1zRQT7XNZIYQQQgghhGgJJDHkRocTOvFFxz6k7d1Sb1lnIqfzhFO9lkton4rh9ZdIt1RUb3N+QHZNEFkNJo6eMZ4Llj3QkNBFiNrx9S/sW7uN/LijvD9hAMmVJT5PKN1UXM9XYTRjtlZ4LGsrKat+nNYpndP/PL5JYsjs34lNyuAxmaOBXhee5XN9yZnpHPGxbGlMIidNPcXnuoUQQgghhBCiJQjUHENh75RXHqQsqv7JoxX2lZmGzvZ825fNZuOHUbOItrj/IO78sF4wagqn7VjFJcsfbdSoDBE6tn25kdc6TmT7iHMpve02jCVFxFTa57Bq7nmEXM9X3G8Y4HnOq9ZnDgpIDNGxZgqHj/W4vzgujT6jT/S5vh5n9iE/rVO95Wwokm69CXNcjM91CyGEEEIIIURLIIkhD3qc3pszN31KfkZXr+U0kHzfXW6XwXZa++ZXJJR4XzocIGnFIr559F1sNt9vjRGhK2fPH/w86QpSD+3wa9LxQNNA2vpV5PQYWCNZ5IyxILUjWbdMCdj5Z6x8nJyu/Wu8JhooNScwav0Sv+sb+NpjVJjcJ3xsKHLb9yTjhf8w5q7pDQtYCCGEEEIIISKYJIa8aN0tg+m7P6Ts4ispbpUKgFUde8mKEtoQe/98RtbzIXr7gkU+nU8Bpmef5I2sGxocswgd2fe+Qly5fRWsYK8y5soZS0zf3hScNZkqo33eHY0it/cwRn71akBH1hgMBmZtf5cBG74gb9hYcvqfRuqz/2Za8Y+kd8nwu77eo/tz+ublHJ14EQXJHbApIzkd+5D2/LOcU7WVmb8tY+gs329PE0IIIYQQQoiWROYYqocp2sTUV27BZruJqrJKjGYTe3/ag81ipcuQ4zCa6l9HSm3/xa9zpn7/Ge9f9QTnP//XhoYtgqCyoootH/+EzWKlz7gBlP64nthgB1WPGZ8/QtEfd7J//R7Su7eldTf/EzMN1b53Jpd+/VST1NW6WwbTF98LQHZ2NuftuqZJ6hVCCCFEYCmlHgXOASqBX4HLtNYFjn1zgSsAK3Cd1vqzoAUqhBARTBJDPjIYDNWjKLoOOc6vY81H8/w+n/nlZ3m3rJJpr9/m97Gi+a148F1yH36c+FL7z3qP0UyKtTIg58rpNZSMc0eT9+yLJBccBNyvcOeJs2zXyWcAkNg6kcTR/QMRqhBCCCFEfZYDc7XWFqXUw8Bc4DalVB9gOtAXaA+sUEodr7W2BjFWIYSISHIrWYBZLVaiq8rqL1iLAlq9/RIrHny36YMSTeq7F5ZTfs9d1UkhALO1AkOAZhZK3/oD+c/+l/TrriH/1HFUmswooNLo24TlCsjtOZhBF58ekPiEEEIIIXyltf5ca21xPF0NZDoeTwYWaq0rtNa7gZ3AkGDEKIQQkU5GDAVY/r48TI24sJH/4MNYbj4PU7T8qELVzocXkNbM00snFeyn4v776Pnf5xi48nEsFVVsW7mJnedfRrSbZeido4TKo1pRMXICU9+Y53XCdCGEEEKIILgceNvxuAP2RJHTPse2OpRSc4A5AG3atCE7O7vJAysuLg5IvaEi0tsH0sZIEOntg+C1UbINAZbYJhGLwYTJZqm/sButKo6y5KonmPrKLU0cmXDn4NYD/PjCJ+R/vQZdVoaKiyNxYD/aDupFRs9MMgd0JdocVeOY+H07ghKrUdvYdvN8fnkoA3W0CEOvXnR9+jF2LniPVpvWYrRaKItNRI06m3H/uhZsEJ+eIElGIYQQQjQrpdQKoK2bXfO01ksdZeYBFuAN52Fuyru9Eqe1XgAsABg0aJDOyspqbMh1ZGdnE4h6Q0Wktw+kjZEg0tsHwWujfEIMsOhYM8VJbUnO39fgOmyL38f235tkhEcAWSotvD3xDhK+XIoRSHPd+eNKcp+DXGCdOZGoWZcx6V/XVP88Ks1xmC3+3y7YFFJy9kDOHvuT/Vs58uVH9H7pGQbPeC4o8QghhBBC1Ka1HuVtv1JqFjARGKm1diZ/9gEdXYplAgcCE6EQQrRskmloBmrY8EYdH1eWT2l+SRNFI9x5f8bfSXYkhbxpVVFE1IJ/svTPT1dv01le/9dpFBvKr5vUomxV7PrTrVgtMi+jEEIIIUKfUups4DZgkta61GXXMmC6UsqslOoK9ADWBCNGIYSIdJIYagZDbru4UTPQlJnjiU1q1WTxiJrKi8sxffi+X8dYX3+FipJyACY8dyN5bboHIjTKzfE+rzbmFF+Wz4al8n+TEEIIIcLCv4EEYLlSar1S6lkArfVm4B1gC/ApcK2sSCaEEIEhiaFmcNypvSk4bbzfxzmTSVWjJmI01TeWRTTU/g2/EePnrWCxFUfZsWoLAIltkjh/y/tYr72R3OMGkNvtRIqnzMTaBL9eFUPPbFBS8fCGXY0+txBCCCFEoGmtj9Nad9Ran+T4usZl33ytdXetdU+t9SfBjFMIISKZzDHUTC5a/g/e7LKd1MM7fT5GAbk9BnLB63MDF5gguVMaNnzPkjpX+IpNjqve1ioxlklPXg1cDYDFYuHT2FcbFdfRuHTajzuN0q8+9PvYDkN6NercQgghhBBCCCFaBhkx1EyMJiNTNrxN7oCs6hEg3kaCFMckk/TUk1yy6XVi4mOaI8QWKy0znbyuJ/pcXgH56V3oOqyHxzJHDxX5fQtYbW3uuoV9L7/vdz1l0XH0HXdyI88uhBBCCCGEEKIlkMRQM4pLjWfmD8+R8fLz5GV0q/7A75ogsikDBVmTmFb4Paf9aZysRNZMRi9+jApTrE9lK41mej95t9efTULbRKyqcT+7Q6vWEfP7Dr+O0UDqfXfJ+0YIIYQQQgghhE/kVrIgGDLjDIbMOIND2w5gqahCRRn5+bWVKKUYOHMkbXu2D3aILU77vh0ZuetLloy8luSd6zFpa/UtYwBWZaQ0NhnLyUMYev/V9Di9t9f6TCYT+SecQvrGb2vU4xelsETHQllBvUWd5yi78DIm3nxeQ84mhBAtXt6hPFZ1PoMoW1X1NqsyMnTbStp2bRvEyIQQQoSLlwdcTvrGb2tsy+k7nNnr/xukiISonySGgsg1AdThwZlBjEQAJLVLYdaWN6ksq+CPXw9TWVbJH1v3kn5cB7qdcrzf9c1Ys4C3up1HysHtNbb/0e1kzEf2k1h8xOvxbbMGkr98mU/nUkBu/9O55NVb/Y5TCCGE3bcdTyG61jaTtvLj8WcysWpbUGISQggRPpxJodoXhdM3f8fLJ10mySERsuR+EyFqiY410+GETnQdfBxDLj2rQUkhsI8auvT3D+iy7C3yT59IwVmTiel5HLO3LSThytlej83tOZiDK37AXFX/amn5rbugbprLxd8/K7eQCSFEA708ZA4G6s7/5xyR+XKPac0flBBCiLDiLikE9r8j6Zu/a+5whPCZjBgSIsBOGDeAE8YNACA7OxuA8Q9fxpL8IgyvvkCUtlSXtSgjJaMmM/39e3m383iv9To/rAx64zF6nXVCgKIXQoiWIXn9N0DdW3+dz1N/29Ss8QghhIg8xcXFxMfHBzsMIeqQxJAQQWAwGJjywl8peeQqNixZTfHhfPqeN5z2vTr4XIcCKkyxkhQSIU0pdT8wGbABR4DZWusDSikF/BMYD5Q6tv8UvEiFEEIIIYRomeS+EyGCKC41nlMuH8XoudPqJIXU4KFej9VA3O23BTA6IZrEo1rr/lrrk4APgbsd28cBPRxfc4D/BCk+IQAoOOk0wP2tZAB5nSUJL4QQonFktJAIVZIYEiJEjXj8LxS3Sq2zXQNHW6WR8vRTjL3nouYPTAg/aK2LXJ7Gcexz9mTgVW23GkhWSrVr9gCFcJi9ZgE23N9KpoHZO95t/qCEEEKElZy+w+tcYAD735GcXt4v+goRTJIYEiJEte3ZnlO/Xkje4JFUmGKxKiO5XfqT8d8FTC/8jlPnjA12iEL4RCk1Xym1F5jBsRFDHYC9LsX2ObYJETSn7v2eKmVCQ/WXRRkZtH1VkCMTQggRDmav/291csj1K6fXUGZvfDW4wQnhhcwxJEQIy+zfmUu/eybYYQjhlVJqBdDWza55WuulWut5wDyl1FzgL8A91B2YAXXv4nHWPwf77Wa0adOmehJ3b4qLi30qF44itW2h0q7kz5+us23rb1vZ+tvWBtcZKm0LBGmbEELUJEvSi3AkiSEhhBCNorUe5WPRN4GPsCeG9gEdXfZlAgc81L8AWAAwaNAgnZWVVe+JsrOz8aVcOIrUtkVqu0DaFq4iuW1CCCGEK7mVTAghRMAopXq4PJ0EOIddLANmKrthQKHW+mCzByiEEEIIIUQLJyOGhBBCBNJDSqme2Jer/w24xrH9Y+xL1e/Evlz9ZcEJTwghhBBCiJZNEkNCCCECRmt9voftGri2mcMRQgghhBBC1CK3kgkhhBBCCCGEEEK0UJIYEkIIIYQQQgghhGihJDEkhBBCCCGEEEII0UJJYkgIIYQQQgghhBCihVL2+T9Dg1LqD+yr1niSDuQ0UzhNQeINvHCLWeINvHCL2dd4O2utWwc6mFDnw98Jp3B7H/gjUtsWqe0CaVu4Cre2yd8J/Po74a9wez/4K9LbB9LGSBDp7YPAttHj34mQSgzVRyn1o9Z6ULDj8JXEG3jhFrPEG3jhFnO4xRsuIvl1jdS2RWq7QNoWriK5bcJ/kf5+iPT2gbQxEkR6+yB4bZRbyYQQQgghhBBCCCFaKEkMCSGEEEIIIYQQQrRQ4ZYYWhDsAPwk8QZeuMUs8QZeuMUcbvGGi0h+XSO1bZHaLpC2hatIbpvwX6S/HyK9fSBtjASR3j4IUhvDao4hIYQQQgghhBBCCNF0wm3EkBBCCCGEEEIIIYRoIpIYEkIIIYQQQgghhGihwiYxpJS6WSmllVLpjudKKfWUUmqnUmqDUmpAsGN0Ukrd74hpvVLqc6VUe8f2kIxZKfWoUmqrI6bFSqlkl31zHfFuU0qNDWacTkqpaUqpzUopm1JqUK19IRevk1LqbEdcO5VStwc7ntqUUi8ppY4opTa5bEtVSi1XSu1wfE8JZoyulFIdlVJfKqV+cbwfrndsD8mYlVIxSqk1SqmfHfHe59jeVSn1gyPet5VS0cGONZyFa//gi1DvQ/wRbv2NP8Ktb/JVS+jDlFJGpdT/lFIfOp5HTNtEw0VS3+sUyX0wRG4/7Kol9MkQ+f2yUmqPUmqjsucNfnRsC8r7NCwSQ0qpjsBo4HeXzeOAHo6vOcB/ghCaJ49qrftrrU8CPgTudmwP1ZiXAydorfsD24G5AEqpPsB0oC9wNvCMUsoYtCiP2QRMAb5y3RjC8eKI42ns74E+wEWOeEPJy9hfN1e3A19orXsAXziehwoLcJPWujcwDLjW8ZqGaswVwAit9YnAScDZSqlhwMPAE45484ErghhjJAi7/sEXYdKH+ONlwqu/8Ue49U2+agl92PXALy7PI6ltogEisO91epnI7YMhcvthVy2hT4aW0S+fpbU+SWvtvKAZlPdpWCSGgCeAWwHXmbInA69qu9VAslKqXVCiq0VrXeTyNI5jcYdkzFrrz7XWFsfT1UCm4/FkYKHWukJrvRvYCQwJRoyutNa/aK23udkVkvE6DAF2aq13aa0rgYXY4w0ZWuuvgLxamycDrzgevwKc26xBeaG1Pqi1/snx+Cj2PxodCNGYHb/3xY6nUY4vDYwA3nNsD5l4w1WY9g++CPk+xB/h1t/4I9z6Jl9Feh+mlMoEJgAvOJ4rIqRtolEiqu91iuQ+GCK3H3YV6X0ytOh+OSjv05BPDCmlJgH7tdY/19rVAdjr8nyfY1tIUErNV0rtBWZwbMRQSMfscDnwieNxOMTrKpTjDeXYvGmjtT4I9j+yQEaQ43FLKdUFOBn4gRCO2TEcdj1wBPtIvV+BApfEbLi8L8JRuP4OOoV7/L4I2d/dhgqXvslXEd6HPYn9IqTN8TyNyGmbaLiW0Pc6hX0f5U6k9cOuIrxPhpbRL2vgc6XUOqXUHMe2oLxPTc1xkvoopVYAbd3smgfcAYxxd5ibbdrNtoDwFrPWeqnWeh4wTyk1F/gLcA9BjLm+eB1l5mEfevmG8zA35UMmXneHudnWbO+JeoRybGFNKRUPvA/coLUusl9MCE1aaytwkrLP47UY6O2uWPNGFX4isH/wRbjH3+KEU9/kq0jtw5RSE4EjWut1Sqks52Y3RcOubaLR5H0QxiKxH3YVqX0ytKh++VSt9QGlVAawXCm1NViBhERiSGs9yt12pVQ/oCvws+MXORP4SSk1BHuGsKNL8UzgQIBDreYpZjfeBD7CnhgKWsz1xauUmgVMBEZqrZ2/YCEbrwdBfU/UI5Rj8+awUqqd1vqg47bHI8EOyJVSKgr7H/w3tNaLHJtDOmYArXWBUiob+33vyUopk+PqR7i8L4IqAvsHX4R7/L4I+d9dX4Vr3+SrCOzDTgUmKaXGAzFAIvYr1ZHQNtE4LaHvdYqYPgoivx92FYF9MrSQfllrfcDx/YhSajH221eD8j4N6VvJtNYbtdYZWusuWusu2DvnAVrrQ8AyYKayGwYUOodcBZtSqofL00mAM/MXkjErpc4GbgMmaa1LXXYtA6YrpcxKqa7YJ81eE4wYfRTK8a4Fejhm0o/GPgnusiDH5ItlwCzH41mAp9EYzc5xn/GLwC9a68dddoVkzEqp1o4rOiilYoFR2O95/xKY6igWMvFGoFDuH3wRrn2IP0Lyd9df4dY3+SqS+zCt9Vytdabjf83pwEqt9QwioG2i0VpC3+sU1n2Uq0jth11Fcp8MLaNfVkrFKaUSnI+x3yW1iWC9T7XWYfMF7AHSHY8V9lUCfgU2AoOCHZ9LnO87fqgbgA+ADqEcM/ZJWPcC6x1fz7rsm+eIdxswLtixOmI6D3uSsAI4DHwWyvG6xDYe+6pvv2K/5SXoMdWK7y3gIFDleH2vwH4v7xfADsf31GDH6RLvEiKwlwAAAOxJREFUadiHj25wee+OD9WYgf7A/xzxbgLudmzvhj1BsRN4FzAHO9Zw/grX/sHHtoV0H+JnW8Kqv/GzbWHVN/nRrhbRhwFZwIeR2Db5avB7ImL6Xpc2RWwf7GhfRPbDtdrYIvpkR5sisl92tOVnx9dmZ/8SrPepcpxcCCGEEEIIIYQQQrQwIX0rmRBCCCGEEEIIIYQIHEkMCSGEEEIIIYQQQrRQkhgSQgghhBBCCCGEaKEkMSSEEEIIIYQQQgjRQkliSAghhBBCCCGEEKKFksSQEEIIIYQQQgghRAsliSEhhBBCCCGEEEKIFur/AR6u8sxboclnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Cluster plots from the components of reduced X to one dimensional\n",
    "\n",
    "fig,(ax1,ax2,ax3)=plt.subplots(1,3,figsize=(20,4))\n",
    "\n",
    "bluepatch=mpatches.Patch(label=\"No Fraud\",color='#0A0AFF')\n",
    "redpatch=mpatches.Patch(label=\"Fraud\",color='#AF0000')\n",
    "\n",
    "ax1.scatter(X_tsne[:,0],X_tsne[:,1],c=(y==0),cmap='coolwarm',label=\"No Fraud\",linewidths=2)\n",
    "ax1.scatter(X_tsne[:,0],X_tsne[:,1],c=(y==1),cmap='coolwarm',label=\"Fraud\",linewidths=2)\n",
    "ax1.grid(True)\n",
    "ax1.set_title('t-SNE',fontsize=14)\n",
    "ax1.legend(handles=[bluepatch,redpatch])\n",
    "\n",
    "\n",
    "ax2.scatter(X_pca[:,0],X_pca[:,1],c=(y==0),cmap='coolwarm',label=\"No Fraud\",linewidths=2)\n",
    "ax2.scatter(X_pca[:,0],X_pca[:,1],c=(y==1),cmap='coolwarm',label=\"Fraud\",linewidths=2)\n",
    "ax2.grid(True)\n",
    "ax2.set_title('PCA',fontsize=14)\n",
    "ax2.legend(handles=[bluepatch,redpatch])\n",
    "\n",
    "\n",
    "ax3.scatter(X_svd[:,0],X_svd[:,1],c=(y==0),cmap='coolwarm',label=\"No Fraud\",linewidths=2)\n",
    "ax3.scatter(X_svd[:,0],X_svd[:,1],c=(y==1),cmap='coolwarm',label=\"Fraud\",linewidths=2)\n",
    "ax3.grid(True)\n",
    "ax3.set_title('SVD',fontsize=14)\n",
    "ax3.legend(handles=[bluepatch,redpatch])\n",
    "\n",
    "plt.show()\n",
    "\n",
    "#With this clustering plots we clearly see the classes separate very well with which we can confirm that the predictive models \n",
    "#will perform better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of records in under sampled records: 946\n"
     ]
    }
   ],
   "source": [
    "#UnderSampling - Classification Algorithms\n",
    "\n",
    "#We will model undersampled data using four classifiers \n",
    "'''\n",
    "Logistic Regression - two class classification\n",
    "SVC - Support Vector Classifier\n",
    "Decision tree classifier - Ensembling to improve performance\n",
    "k-NN - K Nearets Neighbors\n",
    "'''\n",
    "newdf.head()\n",
    "print('Number of records in under sampled records: {}'.format(len(newdf)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier LogisticRegression has training score of  94.0 % accuracy score\n",
      "Classifier KNeighborsClassifier has training score of  93.0 % accuracy score\n",
      "Classifier SVC has training score of  94.0 % accuracy score\n",
      "Classifier DecisionTreeClassifier has training score of  92.0 % accuracy score\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "X=newdf.drop('Class',axis=1)\n",
    "y=newdf['Class']\n",
    "\n",
    "Xtrain,Xtest,ytrain,ytest=train_test_split(X,y,test_size=0.2,random_state=42)\n",
    "\n",
    "X_train=Xtrain.values\n",
    "#print(X_train.shape)\n",
    "X_test=Xtest.values\n",
    "#print(X_test.shape)\n",
    "\n",
    "y_train=ytrain.values\n",
    "#print(ytrain.shape)\n",
    "y_test=ytest.values\n",
    "#print(ytest.shape)\n",
    "\n",
    "classifiers= {\n",
    "    \"LogisiticRegression\": LogisticRegression(),\n",
    "    \"KNearest\": KNeighborsClassifier(),\n",
    "    \"Support Vector Classifier\": SVC(),\n",
    "    \"DecisionTreeClassifier\": DecisionTreeClassifier()\n",
    "}\n",
    "\n",
    "\n",
    "for key,classifier in classifiers.items():\n",
    "    classifier.fit(X_train,y_train)\n",
    "    train_score=cross_val_score(classifier,X_train,y_train,cv=5)\n",
    "    \n",
    "    print('Classifier',classifier.__class__.__name__,\"has training score of \", round(train_score.mean(),2)*100,'% accuracy score')\n",
    "    \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Parameter tuning for different classifiers using Grid Search with cross validation \n",
    "#Pass each classifier different parameters and then grid search will return better estimator of the classifier\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "# Logistic Regression Tuning\n",
    "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n",
    "grid_log_reg.fit(X_train, y_train)\n",
    "log_reg = grid_log_reg.best_estimator_\n",
    "\n",
    "\n",
    "#k-NN Tuning\n",
    "knears_params = {\"n_neighbors\": list(range(2,5,1)), 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']}\n",
    "grid_knears = GridSearchCV(KNeighborsClassifier(), knears_params)\n",
    "grid_knears.fit(X_train, y_train)\n",
    "knears_neighbors = grid_knears.best_estimator_\n",
    "\n",
    "#Support Vector Classifier Tuning\n",
    "svc_params = {'C': [0.5, 0.7, 0.9, 1], 'kernel': ['rbf', 'poly', 'sigmoid', 'linear']}\n",
    "grid_svc = GridSearchCV(SVC(), svc_params)\n",
    "grid_svc.fit(X_train, y_train)\n",
    "svc = grid_svc.best_estimator_\n",
    "\n",
    "# DecisionTree Classifier Tuning\n",
    "tree_params = {\"criterion\": [\"gini\", \"entropy\"], \"max_depth\": list(range(2,4,1)), \n",
    "              \"min_samples_leaf\": list(range(5,7,1))}\n",
    "grid_tree = GridSearchCV(DecisionTreeClassifier(), tree_params)\n",
    "grid_tree.fit(X_train, y_train)\n",
    "tree_clf = grid_tree.best_estimator_\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Cross Validation Score:  94.57%\n",
      "Knears Neighbors Cross Validation Score 93.78%\n",
      "Support Vector Classifier Cross Validation Score 94.57%\n",
      "DecisionTree Classifier Cross Validation Score 93.12%\n"
     ]
    }
   ],
   "source": [
    "# Overfitting Case\n",
    "\n",
    "log_reg_score = cross_val_score(log_reg, X_train, y_train, cv=5)\n",
    "print('Logistic Regression Cross Validation Score: ', round(log_reg_score.mean() * 100, 2).astype(str) + '%')\n",
    "\n",
    "\n",
    "knears_score = cross_val_score(knears_neighbors, X_train, y_train, cv=5)\n",
    "print('Knears Neighbors Cross Validation Score', round(knears_score.mean() * 100, 2).astype(str) + '%')\n",
    "\n",
    "svc_score = cross_val_score(svc, X_train, y_train, cv=5)\n",
    "print('Support Vector Classifier Cross Validation Score', round(svc_score.mean() * 100, 2).astype(str) + '%')\n",
    "\n",
    "tree_score = cross_val_score(tree_clf, X_train, y_train, cv=5)\n",
    "print('DecisionTree Classifier Cross Validation Score', round(tree_score.mean() * 100, 2).astype(str) + '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NearMiss Label Distribution: Counter({0: 492, 1: 492})\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "\n",
    "underSample_X=df.drop(['Class'],axis=1)\n",
    "underSample_y=df['Class']\n",
    "\n",
    "ssf=StratifiedKFold(n_splits=5,random_state=None,shuffle=False)\n",
    "\n",
    "for trainidx,testidx in ssf.split(underSample_X,underSample_y):\n",
    "    underSample_Xtrain,underSample_Xtest=underSample_X.iloc[trainidx],underSample_X.iloc[testidx]\n",
    "    underSample_ytrain,underSample_ytest=underSample_y.iloc[trainidx],underSample_y.iloc[testidx]\n",
    "\n",
    "undersample_Xtrain = underSample_Xtrain.values\n",
    "undersample_Xtest = underSample_Xtest.values\n",
    "undersample_ytrain = underSample_ytrain.values\n",
    "undersample_ytest = underSample_ytest.values \n",
    "\n",
    "undersample_accuracy = []\n",
    "undersample_precision = []\n",
    "undersample_recall = []\n",
    "undersample_f1 = []\n",
    "undersample_auc = []\n",
    "\n",
    "# Implementing NearMiss Technique \n",
    "# Distribution of NearMiss (Just to see how it distributes the labels we won't use these variables)\n",
    "X_nearmiss, y_nearmiss = NearMiss().fit_sample(underSample_X.values, underSample_y.values)\n",
    "print('NearMiss Label Distribution: {}'.format(Counter(y_nearmiss)))\n",
    "# Cross Validating the right way\n",
    "\n",
    "for train, test in ssf.split(undersample_Xtrain, undersample_ytrain):\n",
    "    undersample_pipeline = imbalanced_make_pipeline(NearMiss(sampling_strategy='majority'), log_reg) # SMOTE happens during Cross Validation not before..\n",
    "    undersample_model = undersample_pipeline.fit(undersample_Xtrain[train], undersample_ytrain[train])\n",
    "    undersample_prediction = undersample_model.predict(undersample_Xtrain[test])\n",
    "    \n",
    "    undersample_accuracy.append(undersample_pipeline.score(Xtraindata[test], ytraindata[test]))\n",
    "    undersample_precision.append(precision_score(ytraindata[test], undersample_prediction))\n",
    "    undersample_recall.append(recall_score(ytraindata[test], undersample_prediction))\n",
    "    undersample_f1.append(f1_score(ytraindata[test], undersample_prediction))\n",
    "    undersample_auc.append(roc_auc_score(ytraindata[test], undersample_prediction))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's Plot LogisticRegression Learning Curve\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "from sklearn.model_selection import learning_curve\n",
    "\n",
    "def plot_learning_curve(estimator1, estimator2, estimator3, estimator4, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(20,14), sharey=True)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    # First Estimator\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator1, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax1.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    ax1.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax1.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "             label=\"Training score\")\n",
    "    ax1.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "             label=\"Cross-validation score\")\n",
    "    ax1.set_title(\"Logistic Regression Learning Curve\", fontsize=14)\n",
    "    ax1.set_xlabel('Training size (m)')\n",
    "    ax1.set_ylabel('Score')\n",
    "    ax1.grid(True)\n",
    "    ax1.legend(loc=\"best\")\n",
    "    \n",
    "    # Second Estimator \n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator2, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax2.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    ax2.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax2.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "             label=\"Training score\")\n",
    "    ax2.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "             label=\"Cross-validation score\")\n",
    "    ax2.set_title(\"Knears Neighbors Learning Curve\", fontsize=14)\n",
    "    ax2.set_xlabel('Training size (m)')\n",
    "    ax2.set_ylabel('Score')\n",
    "    ax2.grid(True)\n",
    "    ax2.legend(loc=\"best\")\n",
    "    \n",
    "    # Third Estimator\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator3, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax3.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    ax3.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax3.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "             label=\"Training score\")\n",
    "    ax3.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "             label=\"Cross-validation score\")\n",
    "    ax3.set_title(\"Support Vector Classifier \\n Learning Curve\", fontsize=14)\n",
    "    ax3.set_xlabel('Training size (m)')\n",
    "    ax3.set_ylabel('Score')\n",
    "    ax3.grid(True)\n",
    "    ax3.legend(loc=\"best\")\n",
    "    \n",
    "    # Fourth Estimator\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator4, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    ax4.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"#ff9124\")\n",
    "    ax4.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n",
    "    ax4.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n",
    "             label=\"Training score\")\n",
    "    ax4.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n",
    "             label=\"Cross-validation score\")\n",
    "    ax4.set_title(\"Decision Tree Classifier \\n Learning Curve\", fontsize=14)\n",
    "    ax4.set_xlabel('Training size (m)')\n",
    "    ax4.set_ylabel('Score')\n",
    "    ax4.grid(True)\n",
    "    ax4.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=42)\n",
    "plt=plot_learning_curve(log_reg, knears_neighbors, svc, tree_clf, X_train, y_train, (0.87, 1.01), cv=cv, n_jobs=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "# Create a DataFrame with all the scores and the classifiers names.\n",
    "\n",
    "log_reg_pred = cross_val_predict(log_reg, X_train, y_train, cv=5,\n",
    "                             method=\"decision_function\")\n",
    "\n",
    "knears_pred = cross_val_predict(knears_neighbors, X_train, y_train, cv=5)\n",
    "\n",
    "svc_pred = cross_val_predict(svc, X_train, y_train, cv=5,\n",
    "                             method=\"decision_function\")\n",
    "\n",
    "tree_pred = cross_val_predict(tree_clf, X_train, y_train, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression:  0.9793236241422769\n",
      "KNears Neighbors:  0.9366545301778462\n",
      "Support Vector Classifier:  0.9774191289735331\n",
      "Decision Tree Classifier:  0.9300658171124492\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "print('Logistic Regression: ', roc_auc_score(y_train, log_reg_pred))\n",
    "print('KNears Neighbors: ', roc_auc_score(y_train, knears_pred))\n",
    "print('Support Vector Classifier: ', roc_auc_score(y_train, svc_pred))\n",
    "print('Decision Tree Classifier: ', roc_auc_score(y_train, tree_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_fpr, log_tpr, log_thresold = roc_curve(y_train, log_reg_pred)\n",
    "knear_fpr, knear_tpr, knear_threshold = roc_curve(y_train, knears_pred)\n",
    "svc_fpr, svc_tpr, svc_threshold = roc_curve(y_train, svc_pred)\n",
    "tree_fpr, tree_tpr, tree_threshold = roc_curve(y_train, tree_pred)\n",
    "\n",
    "\n",
    "def graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr):\n",
    "    plt.figure(figsize=(16,8))\n",
    "    plt.title('ROC Curve \\n Top 4 Classifiers', fontsize=18)\n",
    "    plt.plot(log_fpr, log_tpr, label='Logistic Regression Classifier Score: {:.4f}'.format(roc_auc_score(y_train, log_reg_pred)))\n",
    "    plt.plot(knear_fpr, knear_tpr, label='KNears Neighbors Classifier Score: {:.4f}'.format(roc_auc_score(y_train, knears_pred)))\n",
    "    plt.plot(svc_fpr, svc_tpr, label='Support Vector Classifier Score: {:.4f}'.format(roc_auc_score(y_train, svc_pred)))\n",
    "    plt.plot(tree_fpr, tree_tpr, label='Decision Tree Classifier Score: {:.4f}'.format(roc_auc_score(y_train, tree_pred)))\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.axis([-0.01, 1, 0, 1])\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.annotate('Minimum ROC Score of 50% \\n (This is the minimum score to get)', xy=(0.5, 0.5), xytext=(0.6, 0.3),\n",
    "                arrowprops=dict(facecolor='#6E726D', shrink=0.05),\n",
    "                )\n",
    "    plt.legend()\n",
    "    \n",
    "graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def logistic_roc_curve(log_fpr, log_tpr):\n",
    "    plt.figure(figsize=(12,8))\n",
    "    plt.title('Logistic Regression ROC Curve', fontsize=16)\n",
    "    plt.plot(log_fpr, log_tpr, 'b-', linewidth=2)\n",
    "    plt.plot([0, 1], [0, 1], 'r--')\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.axis([-0.01,1,0,1])\n",
    "    \n",
    "    \n",
    "logistic_roc_curve(log_fpr, log_tpr)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precision, recall, threshold = precision_recall_curve(y_train, log_reg_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "Overfitting: \n",
      "\n",
      "Recall Score: 0.88\n",
      "Precision Score: 0.81\n",
      "F1 Score: 0.84\n",
      "Accuracy Score: 0.84\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "How it should be:\n",
      "\n",
      "Accuracy Score: 0.85\n",
      "Precision Score: 0.00\n",
      "Recall Score: 0.12\n",
      "F1 Score: 0.00\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score, precision_score, f1_score, accuracy_score\n",
    "y_pred = log_reg.predict(X_train)\n",
    "\n",
    "# Overfitting Case\n",
    "print('---' * 45)\n",
    "print('Overfitting: \\n')\n",
    "print('Recall Score: {:.2f}'.format(recall_score(y_train, y_pred)))\n",
    "print('Precision Score: {:.2f}'.format(precision_score(y_train, y_pred)))\n",
    "print('F1 Score: {:.2f}'.format(f1_score(y_train, y_pred)))\n",
    "print('Accuracy Score: {:.2f}'.format(accuracy_score(y_train, y_pred)))\n",
    "print('---' * 45)\n",
    "\n",
    "# How it should look like\n",
    "print('---' * 45)\n",
    "print('How it should be:\\n')\n",
    "print(\"Accuracy Score: {:.2f}\".format(np.mean(undersample_accuracy)))\n",
    "print(\"Precision Score: {:.2f}\".format(np.mean(undersample_precision)))\n",
    "print(\"Recall Score: {:.2f}\".format(np.mean(undersample_recall)))\n",
    "print(\"F1 Score: {:.2f}\".format(np.mean(undersample_f1)))\n",
    "print('---' * 45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "undersample_y_score = log_reg.decision_function(Xtestdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.05\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "undersample_average_precision = average_precision_score(ytestdata, undersample_y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      undersample_average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'UnderSampling Precision-Recall curve: \\n Average Precision-Recall Score =0.05')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(ytestdata, undersample_y_score)\n",
    "\n",
    "plt.step(recall, precision, color='#004a93', alpha=0.2,\n",
    "         where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
    "                 color='#48a6ff')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('UnderSampling Precision-Recall curve: \\n Average Precision-Recall Score ={0:0.2f}'.format(\n",
    "          undersample_average_precision), fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of X (train): 227846 | Length of y (train): 227846\n",
      "Length of X (test): 56961 | Length of y (test): 56961\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "accuracy: 0.9492469553763689\n",
      "precision: 0.06071972559133783\n",
      "recall: 0.9137293086660175\n",
      "f1: 0.11217076850807414\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# SMOTE stands for Synthetic Minority Over-sampling Technique. \n",
    "# Unlike Random UnderSampling, SMOTE creates new synthetic points in order to have an equal balance of the classes. \n",
    "# This is another alternative for solving the \"class imbalance problems\".\n",
    "\n",
    "'''\n",
    "SMOTE is better than random undersampling as it retains full information there is no information loss in this method\n",
    "Accuracy is better than random sampling but it takes more time compared to under sampling due to its data size\n",
    "'''\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
    "\n",
    "\n",
    "print('Length of X (train): {} | Length of y (train): {}'.format(len(Xtraindata), len(ytraindata)))\n",
    "print('Length of X (test): {} | Length of y (test): {}'.format(len(Xtestdata), len(ytestdata)))\n",
    "\n",
    "# List to append the score and then find the average\n",
    "accuracy_lst = []\n",
    "precision_lst = []\n",
    "recall_lst = []\n",
    "f1_lst = []\n",
    "auc_lst = []\n",
    "\n",
    "# Classifier with optimal parameters\n",
    "# log_reg_sm = grid_log_reg.best_estimator_\n",
    "log_reg_sm = LogisticRegression()\n",
    "# Parameters\n",
    "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "\n",
    "rand_log_reg = RandomizedSearchCV(LogisticRegression(), log_reg_params, n_iter=4)\n",
    "\n",
    "\n",
    "# Implementing SMOTE Technique \n",
    "# Cross Validating the right way\n",
    "for train, test in ssf.split(Xtraindata, ytraindata):\n",
    "    pipeline = imbalanced_make_pipeline(SMOTE(sampling_strategy='minority'), rand_log_reg) # SMOTE happens during Cross Validation not before..\n",
    "    model = pipeline.fit(Xtraindata[train], ytraindata[train])\n",
    "    best_est = rand_log_reg.best_estimator_\n",
    "    prediction = best_est.predict(Xtraindata[test])\n",
    "    \n",
    "    accuracy_lst.append(pipeline.score(Xtraindata[test], ytraindata[test]))\n",
    "    precision_lst.append(precision_score(ytraindata[test], prediction))\n",
    "    recall_lst.append(recall_score(ytraindata[test], prediction))\n",
    "    f1_lst.append(f1_score(ytraindata[test], prediction))\n",
    "    auc_lst.append(roc_auc_score(ytraindata[test], prediction))\n",
    "    \n",
    "print('---' * 45)\n",
    "print('')\n",
    "print(\"accuracy: {}\".format(np.mean(accuracy_lst)))\n",
    "print(\"precision: {}\".format(np.mean(precision_lst)))\n",
    "print(\"recall: {}\".format(np.mean(recall_lst)))\n",
    "print(\"f1: {}\".format(np.mean(f1_lst)))\n",
    "print('---' * 45)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    No Fraud       1.00      0.99      0.99     56863\n",
      "       Fraud       0.09      0.86      0.17        98\n",
      "\n",
      "    accuracy                           0.99     56961\n",
      "   macro avg       0.55      0.92      0.58     56961\n",
      "weighted avg       1.00      0.99      0.99     56961\n",
      "\n"
     ]
    }
   ],
   "source": [
    "labels = ['No Fraud', 'Fraud']\n",
    "smote_prediction = best_est.predict(Xtestdata)\n",
    "print(classification_report(ytestdata, smote_prediction, target_names=labels))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_score = best_est.decision_function(Xtestdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.70\n"
     ]
    }
   ],
   "source": [
    "average_precision = average_precision_score(ytestdata, y_score)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'OverSampling Precision-Recall curve: \\n Average Precision-Recall Score =0.70')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(ytestdata, y_score)\n",
    "\n",
    "plt.step(recall, precision, color='r', alpha=0.2,\n",
    "         where='post')\n",
    "plt.fill_between(recall, precision, step='post', alpha=0.2,\n",
    "                 color='#F59B00')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('OverSampling Precision-Recall curve: \\n Average Precision-Recall Score ={0:0.2f}'.format(\n",
    "          average_precision), fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SMOTE Technique (OverSampling) After splitting and Cross Validating\n",
    "sm = SMOTE(sampling_strategy='minority', random_state=42)\n",
    "# Xsm_train, ysm_train = sm.fit_sample(X_train, y_train)\n",
    "\n",
    "\n",
    "# This will be the data were we are going to \n",
    "Xsm_train, ysm_train = sm.fit_sample(Xtraindata, ytraindata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting oversample data took :4.9512364864349365 sec\n"
     ]
    }
   ],
   "source": [
    "# We Improve the score by 2% points approximately \n",
    "# Implement GridSearchCV and the other models.\n",
    "\n",
    "# Logistic Regression\n",
    "t0 = time.time()\n",
    "log_reg_sm = grid_log_reg.best_estimator_\n",
    "log_reg_sm.fit(Xsm_train, ysm_train)\n",
    "t1 = time.time()\n",
    "print(\"Fitting oversample data took :{} sec\".format(t1 - t0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x864 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Logistic Regression fitted using SMOTE technique\n",
    "y_pred_log_reg = log_reg_sm.predict(X_test)\n",
    "\n",
    "# Other models fitted with UnderSampling\n",
    "y_pred_knear = knears_neighbors.predict(X_test)\n",
    "y_pred_svc = svc.predict(X_test)\n",
    "y_pred_tree = tree_clf.predict(X_test)\n",
    "\n",
    "\n",
    "log_reg_cf = confusion_matrix(y_test, y_pred_log_reg)\n",
    "kneighbors_cf = confusion_matrix(y_test, y_pred_knear)\n",
    "svc_cf = confusion_matrix(y_test, y_pred_svc)\n",
    "tree_cf = confusion_matrix(y_test, y_pred_tree)\n",
    "\n",
    "fig, ax = plt.subplots(2, 2,figsize=(22,12))\n",
    "\n",
    "\n",
    "sns.heatmap(log_reg_cf, ax=ax[0][0], annot=True, cmap=plt.cm.copper)\n",
    "ax[0, 0].set_title(\"Logistic Regression \\n Confusion Matrix\", fontsize=14)\n",
    "ax[0, 0].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[0, 0].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "sns.heatmap(kneighbors_cf, ax=ax[0][1], annot=True, cmap=plt.cm.copper)\n",
    "ax[0][1].set_title(\"KNearsNeighbors \\n Confusion Matrix\", fontsize=14)\n",
    "ax[0][1].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[0][1].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "sns.heatmap(svc_cf, ax=ax[1][0], annot=True, cmap=plt.cm.copper)\n",
    "ax[1][0].set_title(\"Suppor Vector Classifier \\n Confusion Matrix\", fontsize=14)\n",
    "ax[1][0].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[1][0].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "sns.heatmap(tree_cf, ax=ax[1][1], annot=True, cmap=plt.cm.copper)\n",
    "ax[1][1].set_title(\"DecisionTree Classifier \\n Confusion Matrix\", fontsize=14)\n",
    "ax[1][1].set_xticklabels(['', ''], fontsize=14, rotation=90)\n",
    "ax[1][1].set_yticklabels(['', ''], fontsize=14, rotation=360)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}