OVER02. Model building with naive sample and SMOTE.ipynb

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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</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>176518</td>\n",
       "      <td>122804.0</td>\n",
       "      <td>1.867633</td>\n",
       "      <td>0.067490</td>\n",
       "      <td>-2.083760</td>\n",
       "      <td>2.949168</td>\n",
       "      <td>2.798819</td>\n",
       "      <td>4.513581</td>\n",
       "      <td>-0.459531</td>\n",
       "      <td>1.013802</td>\n",
       "      <td>-0.603922</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064481</td>\n",
       "      <td>0.346401</td>\n",
       "      <td>0.037249</td>\n",
       "      <td>0.720629</td>\n",
       "      <td>0.339727</td>\n",
       "      <td>0.209881</td>\n",
       "      <td>-0.010506</td>\n",
       "      <td>-0.061317</td>\n",
       "      <td>19.32</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>283094</td>\n",
       "      <td>171370.0</td>\n",
       "      <td>-10.243984</td>\n",
       "      <td>8.517532</td>\n",
       "      <td>-6.591644</td>\n",
       "      <td>-2.460064</td>\n",
       "      <td>-3.135762</td>\n",
       "      <td>-1.112216</td>\n",
       "      <td>-2.164932</td>\n",
       "      <td>3.746472</td>\n",
       "      <td>4.803544</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.996068</td>\n",
       "      <td>-1.425127</td>\n",
       "      <td>0.660345</td>\n",
       "      <td>-1.235856</td>\n",
       "      <td>1.492283</td>\n",
       "      <td>0.295328</td>\n",
       "      <td>2.177236</td>\n",
       "      <td>1.240884</td>\n",
       "      <td>10.76</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>45955</td>\n",
       "      <td>42579.0</td>\n",
       "      <td>-3.529237</td>\n",
       "      <td>-1.085031</td>\n",
       "      <td>0.599323</td>\n",
       "      <td>-1.531427</td>\n",
       "      <td>-1.084952</td>\n",
       "      <td>-0.020062</td>\n",
       "      <td>1.592441</td>\n",
       "      <td>-0.500454</td>\n",
       "      <td>-0.928957</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.538717</td>\n",
       "      <td>0.445493</td>\n",
       "      <td>0.360510</td>\n",
       "      <td>0.307602</td>\n",
       "      <td>0.745300</td>\n",
       "      <td>-0.319171</td>\n",
       "      <td>0.674855</td>\n",
       "      <td>0.267995</td>\n",
       "      <td>372.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1582</td>\n",
       "      <td>1233.0</td>\n",
       "      <td>-0.956865</td>\n",
       "      <td>1.483801</td>\n",
       "      <td>1.601873</td>\n",
       "      <td>0.884115</td>\n",
       "      <td>-0.094897</td>\n",
       "      <td>-0.015144</td>\n",
       "      <td>0.305496</td>\n",
       "      <td>0.443680</td>\n",
       "      <td>-0.567253</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.238321</td>\n",
       "      <td>-0.462552</td>\n",
       "      <td>-0.268929</td>\n",
       "      <td>-0.094198</td>\n",
       "      <td>0.524294</td>\n",
       "      <td>-0.364814</td>\n",
       "      <td>0.227555</td>\n",
       "      <td>0.078699</td>\n",
       "      <td>1.49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>216856</td>\n",
       "      <td>140662.0</td>\n",
       "      <td>-0.348561</td>\n",
       "      <td>0.813080</td>\n",
       "      <td>0.801079</td>\n",
       "      <td>-0.417471</td>\n",
       "      <td>0.132750</td>\n",
       "      <td>0.186855</td>\n",
       "      <td>0.167475</td>\n",
       "      <td>0.046369</td>\n",
       "      <td>0.260304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088899</td>\n",
       "      <td>0.104370</td>\n",
       "      <td>0.302994</td>\n",
       "      <td>0.753871</td>\n",
       "      <td>-1.752069</td>\n",
       "      <td>-0.259744</td>\n",
       "      <td>-0.274304</td>\n",
       "      <td>0.266956</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "            Time         V1        V2        V3        V4        V5        V6  \\\n",
       "176518  122804.0   1.867633  0.067490 -2.083760  2.949168  2.798819  4.513581   \n",
       "283094  171370.0 -10.243984  8.517532 -6.591644 -2.460064 -3.135762 -1.112216   \n",
       "45955    42579.0  -3.529237 -1.085031  0.599323 -1.531427 -1.084952 -0.020062   \n",
       "1582      1233.0  -0.956865  1.483801  1.601873  0.884115 -0.094897 -0.015144   \n",
       "216856  140662.0  -0.348561  0.813080  0.801079 -0.417471  0.132750  0.186855   \n",
       "\n",
       "              V7        V8        V9  ...       V21       V22       V23  \\\n",
       "176518 -0.459531  1.013802 -0.603922  ...  0.064481  0.346401  0.037249   \n",
       "283094 -2.164932  3.746472  4.803544  ... -0.996068 -1.425127  0.660345   \n",
       "45955   1.592441 -0.500454 -0.928957  ... -0.538717  0.445493  0.360510   \n",
       "1582    0.305496  0.443680 -0.567253  ... -0.238321 -0.462552 -0.268929   \n",
       "216856  0.167475  0.046369  0.260304  ...  0.088899  0.104370  0.302994   \n",
       "\n",
       "             V24       V25       V26       V27       V28  Amount  Class  \n",
       "176518  0.720629  0.339727  0.209881 -0.010506 -0.061317   19.32      0  \n",
       "283094 -1.235856  1.492283  0.295328  2.177236  1.240884   10.76      0  \n",
       "45955   0.307602  0.745300 -0.319171  0.674855  0.267995  372.62      0  \n",
       "1582   -0.094198  0.524294 -0.364814  0.227555  0.078699    1.49      0  \n",
       "216856  0.753871 -1.752069 -0.259744 -0.274304  0.266956    4.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import sklearn\n",
    "import imblearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# reading in CSV\n",
    "df = pd.read_csv('creditcard.csv')\n",
    "df.sample(5)"
   ]
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   "execution_count": 2,
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      "Time      284807 non-null float64\n",
      "V1        284807 non-null float64\n",
      "V2        284807 non-null float64\n",
      "V3        284807 non-null float64\n",
      "V4        284807 non-null float64\n",
      "V5        284807 non-null float64\n",
      "V6        284807 non-null float64\n",
      "V7        284807 non-null float64\n",
      "V8        284807 non-null float64\n",
      "V9        284807 non-null float64\n",
      "V10       284807 non-null float64\n",
      "V11       284807 non-null float64\n",
      "V12       284807 non-null float64\n",
      "V13       284807 non-null float64\n",
      "V14       284807 non-null float64\n",
      "V15       284807 non-null float64\n",
      "V16       284807 non-null float64\n",
      "V17       284807 non-null float64\n",
      "V18       284807 non-null float64\n",
      "V19       284807 non-null float64\n",
      "V20       284807 non-null float64\n",
      "V21       284807 non-null float64\n",
      "V22       284807 non-null float64\n",
      "V23       284807 non-null float64\n",
      "V24       284807 non-null float64\n",
      "V25       284807 non-null float64\n",
      "V26       284807 non-null float64\n",
      "V27       284807 non-null float64\n",
      "V28       284807 non-null float64\n",
      "Amount    284807 non-null float64\n",
      "Class     284807 non-null int64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'No Fraud (0) vs. Fraud (1)')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# taking a closer look at the class variable\n",
    "sns.countplot('Class', data = df)\n",
    "plt.title('No Fraud (0) vs. Fraud (1)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(['Class'], axis=1)\n",
    "y = df['Class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "492"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.sum()\n",
    "\n",
    "# Only 492 positives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 284315), (1, 284315)]\n"
     ]
    }
   ],
   "source": [
    "# imports \n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "# random oversampling\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_01, y_01 = ros.fit_resample(x, y)\n",
    "\n",
    "# using Counter to display results of naive oversampling\n",
    "from collections import Counter\n",
    "print(sorted(Counter(y_01).items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_01[0:50,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284315"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_01.sum()\n",
    "\n",
    "# There are indeed 284315 values after resampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import xgboost as xgb \n",
    "from sklearn import metrics\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.over_sampling import ADASYN\n",
    "import math\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "class modelpipeline:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def run_model(self, df, testratio, standardize, sampletype, modelname, text, CV):\n",
    "        df = df.drop('Time', axis=1)\n",
    "        if standardize == True:\n",
    "            df = self.standardize(df)\n",
    "        if sampletype == 'smote':\n",
    "            X_train, X_test, y_train, y_test = sampling.smote_oversample(df, testratio)\n",
    "        elif sampletype == 'adasyn':\n",
    "            X_train, X_test, y_train, y_test = sampling.adasyn_oversample(df, testratio)\n",
    "        else:\n",
    "            X_train, X_test, y_train, y_test = sampling.naive_oversample(df, testratio)\n",
    "        store = self.build_model(X_train, X_test, y_train, y_test, text, modelname, CV)\n",
    "        # test model with all actual fraud results\n",
    "        store['actual_accuracy'] = evaluate.actual_acc(df, store['model'])\n",
    "        return store\n",
    "    \n",
    "    def build_model(self, X_train, X_test, y_train, y_test, text, modelname, CV):\n",
    "        if modelname == 'LogisticRegression':\n",
    "            if CV == True:\n",
    "                param_grid = dict(C=[0.8,1,1.2], max_iter=[300], solver=['liblinear'])\n",
    "                LogRegression = LogisticRegression()\n",
    "                model = GridSearchCV(LogRegression, param_grid, cv=5, scoring='f1', verbose=10)\n",
    "                model.fit(X_train,y_train)\n",
    "                print(\"Best f1 score: \" + str(model.best_score_))\n",
    "                print(\"Best parameters: \" + str(model.best_params_))\n",
    "            else:\n",
    "                model = LogisticRegression(max_iter=300, C=0.8, solver='liblinear')\n",
    "                model.fit(X_train,y_train)\n",
    "        elif modelname == 'XGBoost':\n",
    "            if CV == True:\n",
    "                end_value = math.ceil(math.sqrt(X_train.shape[1]))\n",
    "                start_value = end_value - 2       \n",
    "                # treedepth = list(range(start_value, end_value+1, 2))\n",
    "                param_grid = dict(n_estimators=[100], max_depth=[end_value])\n",
    "                GradientBoost = GradientBoostingClassifier()\n",
    "                model = GridSearchCV(GradientBoost, param_grid, cv=5, scoring='f1', verbose=10)\n",
    "                model.fit(X_train,y_train)\n",
    "                print(\"Best f1 score: \" + str(model.best_score_))\n",
    "                print(\"Best parameters: \" + str(model.best_params_))\n",
    "                \n",
    "                # Testing out xgb.cv (incomplete)\n",
    "                # model = xgb.XGBClassifier(seed=42, nthread=1, max_depth=start_value, n_estimators=100, random_state=42)\n",
    "                # xgb_param = dict(n_estimators=100, max_depth=end_value)\n",
    "                # xgtrain = xgb.DMatrix(X_train, label=y_train)\n",
    "                # model = xgb.cv(params=xgb_param, dtrain=xgtrain, nfold=5, metrics='auc')\n",
    "                # model.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], verbose=5)\n",
    "                \n",
    "                # USING kfold library to do kfold testing on XGBoost:\n",
    "                # cross_val_score using kfold does not fit the model, so nothing can be predicted\n",
    "                # it's just to see the results but the model has to be fitted later on\n",
    "                # kfold = KFold(n_splits=3, random_state=42)\n",
    "                # print(kfold)\n",
    "                # scores = cross_val_score(model, X_train, y_train, cv=kfold)\n",
    "                # print(\"CV Accuracy: %.2f%% (%.2f%%)\" % (scores.mean()*100, scores.std()*100))\n",
    "            else:\n",
    "                model = xgb.XGBClassifier(seed=42, nthread=1, max_depth=math.ceil(math.sqrt(X_train.shape[1])),\n",
    "                                          n_estimators=100, random_state=42)\n",
    "                model.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], verbose=5)\n",
    "        elif modelname == 'RandomForest':\n",
    "            if CV == True:\n",
    "                start_value = math.ceil(math.sqrt(X_train.shape[1]))\n",
    "                end_value = start_value + 11         \n",
    "                treedepth = list(range(start_value, end_value, 5))\n",
    "                param_grid = dict(random_state=[42], max_depth=treedepth, n_estimators=[100,150])\n",
    "                RFC = RandomForestClassifier()\n",
    "                model = GridSearchCV(RFC, param_grid, cv=5, scoring='f1', verbose=10)\n",
    "                model.fit(X_train,y_train)\n",
    "                print(\"Best f1 score: \" + str(model.best_score_))\n",
    "                print(\"Best parameters: \" + str(model.best_params_))\n",
    "            else:\n",
    "                treedepth = math.ceil(math.sqrt(X_train.shape[1]))\n",
    "                model = RandomForestClassifier(random_state=42, max_depth=treedepth, n_estimators=150)\n",
    "                model.fit(X_train,y_train)\n",
    "        else:\n",
    "            # Parameters based on gridsearchcv of modelname = logistic regresion\n",
    "            # Leave parameter blank for modelname to run this instance of logistic regression\n",
    "            model = LogisticRegression(C=0.8, max_iter=300, solver='liblinear')\n",
    "            model.fit(X_train,y_train)\n",
    "        \n",
    "        y_predict = model.predict(X_test)\n",
    "        results = evaluate.model_results(y_test, y_predict, text)\n",
    "        store = {\"model\": model, \"X_train\": X_train, \"X_test\": X_test, \"y_train\": y_train, \n",
    "                 \"y_test\": y_test, \"results\": results}\n",
    "        print(\"Model fitting and results are complete!\")\n",
    "        return store\n",
    "    \n",
    "    def standardize(self, df):\n",
    "        # Variables already standardized except for Amount\n",
    "        # columns = df.columns.values.tolist()\n",
    "        # columns.remove('Class')\n",
    "        for column in ['Amount']:\n",
    "            df[column] = (df[column] - df[column].mean()) / df[column].std()\n",
    "        return df\n",
    "\n",
    "class sampling:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    @staticmethod\n",
    "    def naive_oversample(df, testratio):\n",
    "        X = df.drop(['Class'], axis=1)\n",
    "        y = df['Class']\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testratio, random_state=42)\n",
    "        ros = RandomOverSampler(random_state=42)\n",
    "        X_train, y_train = ros.fit_resample(X_train, y_train)\n",
    "        # train test split keeps X_test and y_test as pd series, oversampler converts X_train, y_train to numpy\n",
    "        # Convert all to numpy array for XGBoost to not have bugs\n",
    "        X_test = X_test.values\n",
    "        y_test = y_test.values\n",
    "        print(\"Oversampling is complete!\")\n",
    "        return X_train, X_test, y_train, y_test\n",
    "    \n",
    "    @staticmethod\n",
    "    def smote_oversample(df, testratio):\n",
    "        X = df.drop(['Class'], axis=1)\n",
    "        y = df['Class']\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testratio, random_state=42)\n",
    "        X_train, y_train = SMOTE().fit_resample(X_train, y_train)\n",
    "        # train test split keeps X_test and y_test as pd series, oversampler converts X_train, y_train to numpy\n",
    "        # Convert all to numpy array for XGBoost to not have bugs\n",
    "        X_test = X_test.values\n",
    "        y_test = y_test.values\n",
    "        print(\"Number of Xs and Ys for SMOTE:\")\n",
    "        print(sorted(Counter(y_train).items()))\n",
    "        print(\"Oversampling is complete!\")\n",
    "        return X_train, X_test, y_train, y_test\n",
    "    \n",
    "    @staticmethod\n",
    "    def adasyn_oversample(df, testratio):\n",
    "        X = df.drop(['Class'], axis=1)\n",
    "        y = df['Class']\n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=testratio, random_state=42)\n",
    "        X_train, y_train = ADASYN().fit_resample(X_train, y_train)\n",
    "        # train test split keeps X_test and y_test as pd series, oversampler converts X_train, y_train to numpy\n",
    "        # Convert all to numpy array for XGBoost to not have bugs\n",
    "        X_test = X_test.values\n",
    "        y_test = y_test.values\n",
    "        print(\"Number of Xs and Ys for ADASYN:\")\n",
    "        print(sorted(Counter(y_train).items()))\n",
    "        print(\"Oversampling is complete!\")\n",
    "        return X_train, X_test, y_train, y_test\n",
    "\n",
    "\n",
    "\n",
    "class evaluate:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    @staticmethod\n",
    "    def model_results(y_test, y_predict, text):\n",
    "        cm = metrics.confusion_matrix(y_test, y_predict)\n",
    "        print(cm)\n",
    "        RFC_CM = pd.DataFrame(cm, ['Actual 0', 'Actual 1'], ['Predict 0', 'Predict 1'])\n",
    "        sns.heatmap(RFC_CM, annot=True, annot_kws={\"size\": 16}, cmap='Greens', linewidths=1, fmt='g')# font size\n",
    "        sns.set(font_scale=1.4)#for label size\n",
    "        plt.title(\"Confusion Matrix for \" + text)\n",
    "\n",
    "        # fix for mpl bug that cuts off top/bottom of seaborn viz\n",
    "        b, t = plt.ylim() \n",
    "        b += 0.5 \n",
    "        t -= 0.5 \n",
    "        plt.ylim(b, t) \n",
    "        plt.show() \n",
    "\n",
    "        accuracy = metrics.accuracy_score(y_test, y_predict)\n",
    "        print('Accuracy: ' + str(accuracy))\n",
    "        sensitivity = cm[1][1] / (cm[1][1] + cm[1][0])\n",
    "        recall = sensitivity\n",
    "        print('Sensitivity: ' + str(sensitivity))\n",
    "        specificity = cm[0][0] / (cm[0][0] + cm[0][1])\n",
    "        print('Specificity: ' + str(specificity))\n",
    "        precision = cm[1][1] / (cm[1][1] + cm[0][1])\n",
    "        print('Precision: ' + str(precision))\n",
    "        f1 = 2 * (recall * precision)/(recall + precision)\n",
    "        print('f1 score: ' + str(f1))\n",
    "        auc = evaluate.ROC(y_test, y_predict, text)\n",
    "        results = {\"accuracy\": accuracy, \"sensitivity\": sensitivity, \"specificity\": specificity, \n",
    "                   \"precision\": precision, \"f1\": f1, \"auc\": auc}\n",
    "        print(\"Model classification metrics have finished calculating!\")\n",
    "        return results\n",
    "    \n",
    "    @staticmethod\n",
    "    def ROC(y_test, y_predict, text):\n",
    "        # IMPORTANT: first argument is true values, second argument is predicted probabilities\n",
    "        auc = metrics.roc_auc_score(y_test, y_predict)\n",
    "        print(\"AUC value is: \" + str(auc))\n",
    "        fpr, tpr, thresholds = metrics.roc_curve(y_test, y_predict)\n",
    "        plt.plot(fpr, tpr)\n",
    "        plt.xlim([0.0, 1.0])\n",
    "        plt.ylim([0.0, 1.0])\n",
    "        plt.title('ROC curve for ' + text)\n",
    "        plt.xlabel('False Positive Rate (1 - Specificity)')\n",
    "        plt.ylabel('True Positive Rate (Sensitivity)')\n",
    "        plt.grid(True)\n",
    "        return auc\n",
    "\n",
    "    @staticmethod\n",
    "    def actual_acc(df, model):\n",
    "        allpositive = df[df['Class'] == 1].copy()\n",
    "        x_positive = allpositive.drop(['Class'], axis=1)\n",
    "        y_positive = allpositive['Class']\n",
    "        y_pospredict = model.predict(x_positive)\n",
    "        accuracy_positive = metrics.accuracy_score(y_positive, y_pospredict)\n",
    "        print(\"Accuracy with all fraud results is \" + str(accuracy_positive * 100) + \"%\")\n",
    "        return accuracy_positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelpipeline = modelpipeline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n",
      "[[55487  1377]\n",
      " [    8    90]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.975685544749131\n",
      "Sensitivity: 0.9183673469387755\n",
      "Specificity: 0.9757843275182893\n",
      "Precision: 0.06134969325153374\n",
      "f1 score: 0.11501597444089458\n",
      "AUC value is: 0.9470758372285324\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 91.869918699187%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = modelpipeline.run_model(df, 0.2, False, 'naive', None, 'fraud detection with naive sampling only', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model': LogisticRegression(C=0.8, class_weight=None, dual=False, fit_intercept=True,\n",
       "                    intercept_scaling=1, l1_ratio=None, max_iter=300,\n",
       "                    multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                    random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
       "                    warm_start=False),\n",
       " 'X_train': array([[ 1.95504092e+00, -3.80782711e-01, -3.15012853e-01, ...,\n",
       "          4.51682478e-02, -4.71447917e-02,  9.99000000e+00],\n",
       "        [-4.00975239e-01, -6.26942769e-01,  1.55533881e+00, ...,\n",
       "         -3.70468822e-01, -1.44791686e-01,  4.59000000e+01],\n",
       "        [ 7.25090164e-02,  8.20565650e-01, -5.61350916e-01, ...,\n",
       "          2.06394866e-01,  7.02877702e-02,  1.19900000e+01],\n",
       "        ...,\n",
       "        [-6.39191253e-01, -8.55947886e-02,  1.26545196e+00, ...,\n",
       "         -6.19624216e-02, -6.26865318e-02,  2.90180000e+02],\n",
       "        [-7.96525366e-02,  3.22201046e+00, -3.72420139e+00, ...,\n",
       "          6.68496566e-01,  5.95609829e-01,  1.00000000e+00],\n",
       "        [-1.55421637e+00,  1.69422929e+00, -9.03333638e-01, ...,\n",
       "          3.95200622e-01,  2.76930321e-02,  3.60000000e+02]]),\n",
       " 'X_test': array([[-1.65265066e+01,  8.58497180e+00, -1.86498532e+01, ...,\n",
       "         -2.01857525e+00, -1.04280417e+00,  3.64190000e+02],\n",
       "        [ 3.39812064e-01, -2.74374524e+00, -1.34069511e-01, ...,\n",
       "          4.09958027e-02,  1.02037825e-01,  5.20120000e+02],\n",
       "        [ 1.39959027e+00, -5.90701288e-01,  1.68618940e-01, ...,\n",
       "          1.14086454e-02,  4.63414166e-03,  3.10000000e+01],\n",
       "        ...,\n",
       "        [ 4.04414982e-02, -1.09736789e-01, -1.26642984e+00, ...,\n",
       "          3.57844730e-01,  3.54379443e-01,  7.63000000e+00],\n",
       "        [-4.95047586e-01,  9.91480934e-01,  1.67158401e+00, ...,\n",
       "          4.05292111e-02, -2.69775457e-01,  3.59000000e+00],\n",
       "        [-1.59048567e+00,  9.92415466e-01, -5.12841392e-01, ...,\n",
       "          1.25749856e-01, -6.07225509e-01,  9.06310000e+02]]),\n",
       " 'y_train': array([0, 0, 0, ..., 1, 1, 1], dtype=int64),\n",
       " 'y_test': array([1, 0, 0, ..., 0, 0, 0], dtype=int64),\n",
       " 'results': {'accuracy': 0.975685544749131,\n",
       "  'sensitivity': 0.9183673469387755,\n",
       "  'specificity': 0.9757843275182893,\n",
       "  'precision': 0.06134969325153374,\n",
       "  'f1': 0.11501597444089458,\n",
       "  'auc': 0.9470758372285324},\n",
       " 'actual_accuracy': 0.9186991869918699}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(results['X_test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n",
      "[[55487  1377]\n",
      " [    8    90]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.975685544749131\n",
      "Sensitivity: 0.9183673469387755\n",
      "Specificity: 0.9757843275182893\n",
      "Precision: 0.06134969325153374\n",
      "f1 score: 0.11501597444089458\n",
      "AUC value is: 0.9470758372285324\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 91.869918699187%\n",
      "Number of Xs and Ys for SMOTE:\n",
      "[(0, 227451), (1, 227451)]\n",
      "Oversampling is complete!\n",
      "[[55408  1456]\n",
      " [    8    90]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9742986552438467\n",
      "Sensitivity: 0.9183673469387755\n",
      "Specificity: 0.9743950478334271\n",
      "Precision: 0.05821474773609314\n",
      "f1 score: 0.1094890510948905\n",
      "AUC value is: 0.9463811973861013\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 91.869918699187%\n",
      "Number of Xs and Ys for ADASYN:\n",
      "[(0, 227451), (1, 227459)]\n",
      "Oversampling is complete!\n",
      "[[51674  5190]\n",
      " [    5    93]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9087988483550438\n",
      "Sensitivity: 0.9489795918367347\n",
      "Specificity: 0.9087296004501969\n",
      "Precision: 0.017603634298693924\n",
      "f1 score: 0.034566065787028435\n",
      "AUC value is: 0.9288545961434659\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 95.9349593495935%\n"
     ]
    },
    {
     "data": {
      "image/png": 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CQ0OZUChkYrG43H1NSEhgQqGQpaWlccv+/vtvFhwczFJTUxljjK1YsYKtWLFCbr2bN2/KJfuyY7J0gnHlyhUmFArZzJkzuWUSiYTZ2dlxx4Es0RgzZozc9r/55hvWqVMnlpWVxRhTLlkpe26eO3cus7OzY4wx9urVK2ZjY8OWLVsmV2bx4sWVJisFBQVs3Lhxcn/UMMbY5s2bmVAo5OpImWP+999/l/tDljHGiouL2YABAypNVpT5jsoe+8oew7Jr4P3797llKSkpTCgUss2bNzPGGJdI3Lp1iyvz8uVL1qNHD7lkZdasWeyHH36Q+7wjR47IJd2yOOPi4rgyUqmUubi4sKlTp8qtK/vDqKJkpbTi4mI2adIk5ujoyJ2rla2Dis4bst9wda5b1f39ySh1G8jY2Bi7du3Crl27EBsbi169eqFx48ZYsmQJpk2bJtcXgjFWZUdD2fvsdROZpqZmpc2ZVbl27RqKioowYMAAueWzZ8/G9u3boaFRvbtdQqFQ7rWnpyd0dHTkmpyTk5Ph5eUFoKRDMWMM/fr1Q3FxMfdf//79IZFIqjUi5cKFC+jSpYtC3xwvLy+IxWK5Zr+ycVZH2XW9vb0RGxsLiUSCv//+G8ePH+dGeinTBFt2H1xdXaGtrc0ts7e3R9OmTatcz9bWFqamptyyFi1awNbWlnt99epV5OXlKdS1m5sbNDQ08Mcff5S7bWXXu3TpElxdXeVuR3bo0AFJSUlwdHSsct+vXbuGwsJCDB06VG55jx490KxZM1y4cEFueffu3bl/N2zYEAYGBsjNza1w+3379uWaeK9fv46ioiJMnDgR9+/fh1gsRlZWFm7cuAEPD48qYy3N3t4ePB6Pe92yZUsAJf3JymNmZoa4uDg4OzsjLS0N586dQ1xcHK5evQqJRAKJRIL8/Hxcv34d/fr1k9v2Rx99JPeb7NixI3755Re0adMGjx8/RnJyMmJiYvDPP/9wx56xsTEsLS3x1VdfYeHChUhMTEROTg4WLVrE1aGTkxOaN2+Offv2ASj5ncpuQZVWus4BcMdlRbeZ7ezsoKOjg//9738IDg7GH3/8gebNm+PLL7/kOi4vWrQIixYtQk5ODm7evIlDhw4hPj4eABR+P6U/v0mTJgCArl27css0NDRgYGDA9TeTGTZsmNzrgQMHori4GFevXi037vKUt++y/b527Rry8/MxcOBAuTJlj+WyBAIBYmJiMGzYMGRkZODSpUvYuXMnkpKSAFS+/2WP+QsXLkBTUxNubm5cGU1NTQwaNKjSGJT5jspS5hiWady4Mdq3b8+9lm2zdNzNmjWDtbU1V0ZXVxd9+vSR+8zQ0FBMmjQJz58/x7Vr17Bv3z7uulJUVCRXtvQ5+uHDh3j27Bn69+8vV6aq76a0lStX4uzZs4iIiEDz5s2rXQeVqc51q7q/PxmlOthqaWnJddayt7fHZ599Bn9/f/z4449yH968eXM8ffq00u3J7oXJOiVaWFjg+vXrVa7TokWLct+T9QUwMTGpemeUUHY7Ojo6GDBgABISEjB9+nQcOXIEQMlJt/Tnlz2ZyIhEIqU/Ozs7G1ZWVhXG9PLlywrjrI6y6xYUFGDFihXYt28fCgsL0aJFC3Tt2hVaWlrVHlv//PlzGBoaKiwvnYRUtF55vdhNTU2RlpYG4E1dT58+vdxtVFTXyq6XlZX1Th1SZReY8r4bExMThYt/6YQOKLlQVVbfbm5u2LJlCx48eIAzZ85wfW+0tLRw/vx5FBcXQ0tLCz179qxW3GX7l8mSCalUWuE6hw4dwrp16/D06VMYGBigU6dO3P4wxpCdnQ3GGIyMjOTW4/P5Cst+/PFHREdHIzMzEyYmJujcuTMaNmyI/Px8ACV912JiYhAdHY1jx45hz5490NTUhKurK5YvXw5zc3PweDz4+voiOjoaOTk5OHToEHg8nsIJvbw6r2xfLSws8PPPP+OHH37Arl27sGXLFujo6MDHxwfz58+HQCDA48ePsWzZMvzxxx/Q0tJCu3btKhyRUbZfHlByjimtdHInY2ZmJvdaVodlk5rKlP2eeTwed7zJ+q6U/W6U+T2cOXMGq1atwr1796CrqwsrKyvus8oez5Ud88+fP4e+vr7CH7xVnTuU+Y7KU9UxXFnMpcsoe877888/8c033+Dq1avQ1taGUCjkLtZl60mWyMq2Dyh+N1XVi0xcXBy2bduG5cuXw8HBQe49ZeugMtW5blX39yfzVmMt+Xw+Vq9ejaFDh2L+/PlISEhAgwYNAAAeHh7YtGkTnjx5wmVvZR0+fBja2tpwcXEBALi6uuLkyZO4ceMGbGxsFMo/ffoU/fv3x8SJEzFv3jyF9/X19QFAoaNYeno6Hjx4gK5du3I//rIVIuvkW5Vhw4Zh7969uHfvHg4dOoTevXtzB6fs82NiYqCnp6ewrrIHFFCSwT979kxheXp6OgCU+4OoCUFBQUhMTERoaCicnZ25k6ezs7NC2bJ1mJOTI/fa0NBQrhOoTFZWFvcXe3kqW09GVtfBwcFo165dudsoj7Lr6enpyX2ezOnTp9GuXbsqR/00btwYAJCRkaHQepWeni7XSvQ2unfvjsaNG+PMmTM4f/48HB0doaurC2tra1y4cAE5OTlwcnJSuPjVtEuXLmHevHkYO3YsJk6cyF1Iv/32W1y+fBlASZ1qaGgoHM9SqVTuAnvw4EGsXLkS8+bNg4+PD3dCnjlzptx3YWxszLVg/PPPPzh+/DjWr1+PpUuXIjo6GkDJMPLIyEj89ttvSExMRP/+/bnv/l107NgR69atg0QiwY0bN7B3715s27YNFhYWGD9+PCZPngw+n49du3bBysoKWlpa+Pvvv7F//36FbZWXiCij7HEp+63U1GgvWWtBRkYGOnTowC0ve14t69GjR5g6dSr69euH9evXo3nz5uDxeIiLi8Pp06erFYORkRGys7NRVFQk12Jf3m+yrMq+o4kTJyqUV+YYVpahoSEePHigsLx03Dk5OZgwYQIsLS2RmJiINm3aQENDA6dOnapyAILsN1H2t6RMvZw+fRorVqzAqFGjMHLkSLn3aqoOauO69dajgVq1agU/Pz88fvwYGzdu5JaPHTsWenp6WLBgQbnN2UePHsX+/fvx2WefcSOJPv74YzRp0gQrV65UWIcxhtWrV4PH48Hb27vcWGxsbMDn83Hs2DG55XFxcZg2bRoYY9xn/ffff9z7f/75p8KFtiJOTk6wsLDAL7/8gsuXL3O3gABwmWpmZia6dOnC/Zebm4u1a9dyX5gyHBwccPPmTYXe0QcOHICRkRHatGmj9Laq4/Lly3BwcICHhwd3obt58ybEYrFcdq2rqytXhwAURgm4uLjg999/l6vbu3fvVjm6xMXFBTdu3JArl5GRgWvXrnGvbW1tIRAIkJaWJlfXurq6CA4O5k4YmpqacttWdj17e3ucPn2a+4seAFJSUjBp0iRuPyu7rSj7nIMHD8otv3jxIlJTUxWaQKtLU1MTvXr1wqlTp3Dz5k04OTkBKDk+z549i+TkZLi7u1e4fnVviVbk6tWrkEqlmDZtGneCKy4u5m6nSaVSCAQC2Nvb48iRI3LNyadOnZJr8r58+TJ0dHQwadIk7qSck5ODy5cvc4nx1atX0bNnT25URNu2beHn5wdHR0e5llwzMzP06tUL27dvx9WrVxVuAb2N2NhYuLm5oaCgAJqamujatSuWL18OHR0dPH36FFlZWXj48CG8vb3RuXNnrlXg1KlTXF3UhLLntyNHjqBBgwZyt5DehZWVFfT09HD06FG55VVdSG/duoWCggJMmDABLVq04JKx33//HUD19r9nz56QSqU4fPiw3PITJ05Uul5V3xGgeOwrcwwry8XFBWlpabh06RK3rKCgQC5Z++eff/D8+XPuOWKyeJQ5Tlq3bo1mzZrJjS4DoDDapqz79+9j1qxZsLe3x//93/8pvK9sHVR13qiN69Y7PcVq8uTJ2LdvH3744Qd4eXmhRYsWMDExQUREBPz9/eHl5YWxY8eiffv2ePXqFU6cOIF9+/ZhwIAB3DNWgJK/ZoODg+Hv7w9fX1+MHj0abdu2RXp6OrZv344rV65g2bJlcvcMSzMyMsJnn32GmJgY8Pl89OzZE3fu3MHmzZsxZcoUNGzYEG5ubli9ejUWL16MyZMnIysrC5GRkTAwMFBqX2XNyZs2bYK+vj769u3LvScUCuHl5YUlS5bg8ePHsLW1xaNHj/Ddd9/BxMSkWg/oGT9+PA4cOIBx48Zh+vTpMDIywr59+3D+/HkEBQUpXIRriq2tLRISErBt2za0a9cOd+/eRXR0NHg8nlwC6e7ujsOHD6NLly7o0KEDfv31V4VbeDNmzMCxY8fw+eefw8/PD3l5eQgPD+da3yry+eefY9euXZgwYQICAgLA5/OxYcMGuTKGhoaYPHkyIiMj8eLFCzg7OyMzMxORkZHIz89H586dAYBr4fr999/RpEkTdOjQQan1pk+fjhEjRmDChAkYN24ciouLERUVhfbt23N9ovT19fHXX3/h7NmzCv1YDAwM4Ofnh8jISPD5fHh4eODJkycIDw9HmzZt4Ovr+xbfjry+ffti3rx5EAgE3IXKycmJa10ofb+/LFkrw7Fjx+Dp6VlpS1dlZC2g33zzDXx9fZGdnY2ff/4Zf/31F4CS+88CgQCzZ8/GZ599Bj8/P4waNQoikYirGxlbW1vEx8cjKCgI7u7uSE9Px5YtW5CRkcHdMunUqRMaNmyIuXPnYvr06TA1NcX169eRnJyscGvvk08+gb+/PywsLMptGawuJycnhISEYOrUqRg9ejQEAgEOHTrE9e8wNjZGs2bN8PPPP8Pc3Bz6+vpITk7Gtm3buLqoCcePH8eyZcvg5uaG8+fPIz4+HgEBATX24DddXV1MmjQJ3333HRo2bAhHR0dcvHgRv/zyC4CKL1jW1tbQ0tJCSEgIxo0bh6KiIuzZs4e7CFdn/x0dHdG3b18sXboUmZmZaNOmDfbs2VNuq0VpVX1HgOKxr+wxrIyhQ4di69atCAwMxKxZs2BkZIQff/wRL1684D63bdu20NPTQ3R0NDQ1NcHn83HkyBGuj1VV9TRv3jzMnj0bc+bMwdChQ3H//n1s2rSpwvJZWVmYMmUK+Hw+pk2bhhs3bsglRBYWFkrXQVXnjdq4br1TstKgQQMsWrQI06ZNw6pVq7B+/XoAJVnWvn378NNPPyEuLg6pqalo1KgROnbsiNDQUIUOXEBJZrpz507ExMQgNjYW6enp0NfX557IV9UDaObNm4cmTZogPj4eW7duRbNmzTB37lzuqbetWrXC2rVrERUVhRkzZqBVq1aYN28e1wlOGV5eXoiOjsagQYMUDuKVK1dyP6z169fD0NAQ/fv3x8yZM5U+4AFw+xASEoJvv/0WBQUFsLS0RGRkpELnqpo0f/58FBYWIiIiAoWFhWjevDmmTp2Khw8f4tixY1xfiIULF0IqlSI8PBwaGhpwd3dHUFCQ3AWjRYsWiIuLQ3BwMBYsWAA9PT1MnjxZobWhLH19ffz8889YtWoVli1bBoFAgBEjRiAlJUWuL0pgYCBMTU0RFxeHn376Cfr6+nB0dMTs2bO5JvEOHTpg2LBhiIuLw4MHD/DDDz8otV6nTp2wbds2rFu3Dl9++SUaNmwIFxcXzJs3j2txmjJlCpYuXYrp06eXu08BAQEwMTHBtm3bsGfPHhgYGGDgwIGYPXt2uf0Vqqt3797Q1NSEnZ0dd/+3W7duEAgEaN++fYUdCoGS23q9evVCWFgYHj9+jOXLl79VDI6OjliyZAliYmJw7NgxmJiYoEePHhg3bhxmzJiBS5cuwcPDA926dcPmzZsRGhqK2bNnw8zMDF999RWCgoK4bXl5eeHx48fYvXs3duzYATMzM/Tp0wdjx47F4sWLcffuXVhZWSEmJgYhISFYs2YNsrOz0axZM8yaNQuTJk1SqB8tLS14e3vXSEuSlZUVNm7ciKioKCxcuBCFhYWwtLREVFQUevToAQBYv349VqxYgf/7v//jvoeoqCh8++23uHTpEsaNG/fOccycORNXrlxBQEAAmjZtisWLF2P06NHvvN3SpkyZAqlUiu3bt+Onn36CnaQLvGsAACAASURBVJ0d5s2bh5UrV1Z4a7FVq1YICQlBZGQkAgIC0LhxY9ja2mLr1q0YO3YsLl26hE6dOikdQ3h4ONatW4dNmzbh1atX6Nu3L6ZNm4Z169ZVuI4y31F5x74yx7Ay+Hw+YmJiEBwcjJCQEEgkEnz00Ufo2LEj18dRV1eXOybmzJnDXRO3bdsGPz8/XLp0qdJz/KBBg6ChoYGoqCgEBASgdevWWLNmDSZPnlxu+Xv37nGtSuUdf/7+/ggICFCqDqo6b9TGdYvHqtt7khBC6rATJ07A398fJ06cqLNPFq6OJ0+ewMPDA0FBQfjkk0/e2+cUFxdj37596Nmzp1y9bdu2DStXrsT58+fL7ZNHSG2oe5OZqMirV6+Qk5MDQ0PDarWElBUdHY2kpKRKW2wKCgqwevVqHDlyBPn5+XB1dcWSJUtqbDQTIero+PHjuHnzJnbu3ImPPvrog0hUapOWlhZiY2OxdetWTJkyBYaGhrh37x7Cw8Ph4+NDiQpRKbVOVk6cOIGDBw/i3LlzcqMTjIyM4OrqikGDBimMk69MXFwcQkNDq+zwtnTpUly5cgUREREQCARYtmwZAgICqnVLihAi77///sOPP/4IW1vbcjsTkqpt2LAB69atQ1BQEF68eMGNpPHz81N1aETNqeVtoLNnz2LlypV48OAB7Ozs0KVLFzRr1gwNGzZEdnY20tLScPnyZfz1118QCoWYN28eN8y6PCKRCEuXLsX58+dhbm4OAwODChOPtLQ0uLm54fvvv+cSoZSUFHh6eirVN4cQQghRN2rXshIUFIRff/0V48aNw5AhQxQetFSaSCTCjh078OWXX2LgwIH46quvyi33559/olGjRjhw4ACioqKQkpJS4TavXLkCqVTKdfoCSjqomZub4+LFi5SsEEIIIWWoXbKira2NX3/9VamHZpmZmSEgIADjx4/nRjqVx93dvdJnW5QmEolgYGCg8CRJU1NTpKamKrUNQgghRJ2oXbIimzK9OnR1dfHll1/WyOfn5eXJPWNCRiAQVHsOHkIIIUQdqF2yUpqPjw98fX0xdOjQGnkktzK0tbUVJqwCSib7qu4j0rOyXkEqVbsuRwqMjXWRmanck4g/dFQXb3xodcEYg0TKUFQsRWGxFMXFEhQVs5J/SyQoLGYoLpagUCJFUZEURRIpioqlKCqWQEtLCy9zClBQLEGxhKGoWPL6vdf/SaQoLJJ/T/KW5xZNDR74WhrQ0tIEX5MHAV8TWpoa4GtpQKBV8n+515qa4PN54GtqgK+lCf7rMnxNDfD5JdsoKasJLS0e+Fqa3HY0NXjVmr5AQ4MHQ8N3f9YRqX1qnay0bNkSwcHBCA4OhoeHB4YPH15pR9qaYG5ujuzsbBQUFMg90TU9Pb3Sh3mVRypllKy8RvXwBtXFG++7LqSMoahIioJiCYqKpCgslqCwSPo6oSj5d+Hri39hkQSFrxONwiIJl3TIlhdx70teb7Mk0eC2USTF2+6N7OIu4GtyF34BvyRZEPBLLv662lrlvP/69et/C0pvo9QyPl/2ngY0a2hKB2Uwpvxke6R+U+tkJSwsDC9evMDBgwexd+9eTJw4Eebm5vDy8oKvr2+Fszy/C9ncMBcuXICrqyuAktFAaWlpCrNhEkKqR9b6UFgkhfhFPtKf571JAl4nCAVFr5OAipIGLkEonXxIyiQWJe8VS97uQqnB44HP10ADrZLWBC4x4GugAV8Teg0FEPDfJBiC1+Ua8DW4lgX5ZOJNa0MDvvwyLS0NmJnq49mzl1UHRkgdpdbJClDyiPfRo0dj9OjRuH//PhITE3H8+HFER0fD3t4eI0eOxIABAxSmLK+OZ8+eQUdHB40aNYKZmRkGDx6MpUuXYuXKlWjUqBGWLVuGHj161NiEZITUJVLGuNsNhUVlL/rltDaU2wKhfIvF2/6hraWpIZ8ElGo5aNSQDyNuWZn3q2qBKJNwCPglt0EIIcpT+2SltIyMDGRmZiIjIwOampqQSCRYuHAhwsLCEBYWBmtr67fabq9evbh5GICSSaNWrlyJgIAAMMbg6upa4bBoQt6HYom0khYFSZlbG2+WVdQCUVQsqbDFoqj47WYd5vHAJQHyLQolyxq9vm1R9jYEX0sTDbQ0YGSog4L8ogqShlItEK8TD41q9H0ghNQutXwoXGn//PMP9u/fjwMHDiAtLQ2tW7fG8OHD4e3tDSMjI4jFYkyaNAn5+fkK03OrWmZmDvVPANCkiV69b+JmjMld3AtL9V/gbkPI+kSUug0ha22QJRYamhp4kVNQKmlQbIF4l86TWpo85W9DvG5FKP1+RX0mymuB0NKsXufJsj6E46KmUF2U0NDgwdi4ZmapJrVLrVtWPvnkE9y6dQva2toYOHAghg8fzvUpkTEyMoK7uztiY2NVEyRRGYlUqnDrobzbEAXc7YvSiUH5/R0q6zPxtkpf8Bs20IKmBo9LHBrrChT7O/A15G5JlJtUVLBMQ4NaHwghtU+tkxXGGJYtW4bBgwdDV7fibLtfv37VmiOIvB+MMRRLmEJrQ1ZeMdKfvVRIAsq2UnC3NqrqM/F6u+86dLN0a4Ps39oCTeg3ElQ64qKk46XisrLlG7zu+1C69YH+giaEfIjUOlkZM2YMevfuXW6iIhKJcODAAUyePBlWVlYqiK5+qC9DN0vfZih7y0FPR1B+a0NFtyjkWiNK+kfwSyUm1HmSEEJqltolK1JpSXM7YwwLFy7E1q1bYWBgoFDu3LlziIiIwOTJk2s7xDpt96kHSL6Z+s5DN3k8VHjLQWHoZqnEoLxOkibGjZD3qqDU8x/kEw2+FnWeJISQ+kztkpXRo0fj2rVrAEoSlrFjx1ZYtnPnzrUVVr1x/rYIOg200MnSSIlhmqVbIzTlnilR3SdPVoZufRBCyIdN7ZKVoKAgHD58GIwxREVFwdvbGxYWFnJlNDQ0oK+vj48++khFUdZNRcVSZGbn4+NebTCsVxtVh0MIIURNqF2y0q5dO/j7+wMA/vvvP0ydOhUtW7ZUcVT1Q/rzPDAAZoYNqyxLCCGE1BS1S1akUik0Xs9dsWLFCm5ZRTRqcZ6Luk4kzgUAmBlVb8JFQggh5F2oXbJibW2NuLg4dOvWDZ06daq03wSPx8Pt27drMbq6jUtWDClZIYQQUnvULlmZMWMG10dlxowZNdbJUx2kiXOh30gAHW21O2wIIYSokNpddWT9VQBgwoQJaNSokQqjqV9E4lyYU38VQgghtUytO2S4uLhg7ty5SE5OhppPkaSUtKw86q9CCCGk1ql1sjJp0iTcvn0bkyZNQu/evfHtt9/i7t27qg6rTsrNL8aLV4Uwp2SFEEJILVPrZMXf3x+JiYnYvXs3Bg8ejISEBHh7e2PYsGGIjY1FRkaGqkOsM0RZNBKIEEKIaqh1siJjbW2NBQsWICkpCbGxsbC2tsbatWvRt29fVYdWZ9CwZUIIIaqidh1sK1JYWIiTJ08iMTERv//+O7S0tODh4aHqsOoMUVYeeABMDaiDLSGEkNql1smKVCrFH3/8gYSEBBw/fhyvXr1C9+7dsWjRIgwaNKjc2ZjVlUicC+PG2uBrUWMcIYSQ2qXWyYqLiwueP3+OFi1aYNy4cfDy8kLz5s1VHVadlCbOpVtAhBBCVEKtk5X+/fvDy8sL3bp1U3UodRpjDKKsXPS0aKrqUAghhKghtU5Wvv76a1WHUC+8yC1CXoEEZkbUX4UQQkjtU7tkpW/fvoiMjETnzp2rHO3D4/Fw8uTJ2gmsDpONBKJnrBBCCFEFtUtWnJ2doa+vDwBwcnKiuYGUkEbDlgkhhKiQ2iUrq1at4v69evXqSssWFxe/73DqBZE4F1qaPBjra6s6FEIIIWpIrcehenh44Pbt2+W+d/nyZbi4uNRyRHVTmjgXpoY60NCgVihCCCG1T+1aVjZu3Ii8vDwAwNOnT7F161aYm5srlLt+/TqkUmlth1cnibLyYEazLRNCCFERtUtWpFIpNmzYAKCkA+3evXsVymhoaEBPTw+zZs2q7fDqHKmUIT0rF7btjFUdCiGEEDWldsnK1KlTMXXqVACAlZUVfv75Z3rOSiXEL/JRLGHUuZYQQojKqF2yUtqJEydgamqq6jDqtLQsGrZMCCFEtdQuWfnuu+8wYsQImJubY9euXZWW5fF4CAwMrKXI6iaRuKR/D/VZIYQQoipql6xs2LABrq6uMDc35/quVISSlZKRQNoCTeg3Eqg6FEIIIWpK7ZKVu3fvlvtvUj7R6wkM6eF5hBBCVEWtn7NSnoyMDPz555/VGrYslUoRHh4OV1dX2NraYsKECUhJSamwfHp6OmbPng1HR0c4Ojpi5syZSEtLq4nwa1yaOJf6qxBCCFEptU5W8vLysGTJEmzbtg0AcOzYMfTt2xfDhw/H0KFDIRKJlNpOVFQU4uPjERQUhO3bt0NTUxMTJ05EQUFBueUDAwORmpqKLVu2ICYmBmlpaZg2bVqN7VdNKSqWIjM7n/qrEEIIUSm1TlZCQkKwf/9+NGrUCACwdu1aCIVChIWFQSKRICQkpMptFBYWYsuWLfD390efPn1gZWWF0NBQZGRk4PDhwwrlxWIxrl69Cj8/P1hbW6NTp07w8/PD7du3kZmZWeP7+C7Sn+eBgUYCEUIIUS21TlaOHz+OOXPmwNvbG3///TdSUlLg5+eHAQMGYMaMGUhOTq5yG3fu3EFubi6cnJy4Zbq6uujUqRMuXbqkUF5HRwc6OjrYt28fcnJy8OrVKxw6dAitW7eGgYFBje7fuxLRBIaEEELqALXrYFtaZmYmhEIhACA5ORlaWlro1asXAMDExAS5ublVbkN2q8jMzExuuampKVJTUxXKa2trY9WqVVi2bBns7e3B4/FgYmKCbdu2QVNT8113qUZxyYohJSuEEEJUR62TlaZNmyIlJQVOTk44ceIEunTpAl1dXQDApUuXyp0zqCzZPEMCgfzQXoFAgMLCQoXyjDHcvn0btra28PPzg0QiQVhYGKZPn45ffvkFenp6SsdvbKyrdNm38Ty3CAZ6DdCqheF7/Zya0KSJ8vX2oaO6eIPq4g2qC1KfqXWy8vHHH2Pt2rU4evQoLl68iBUrVgAAgoKC8Msvv8Df37/KbWhrawMo6btSOmEpLCyEjo5ii0RiYiLi4uKQlJTEJSYbNmyAm5sbduzYgYkTJyodf2ZmDqRSpnT56kpJfQHTxtp49uzle/uMmtCkiV6dj7G2UF28QXXxBtVFCQ0N3nv/I4+8H2qdrPj7+0NLSwuXL1/GggUL4OvrCwC4desWJkyYAD8/vyq30bRpUwAlw5FlrTKy1+3bt1cof/nyZbRq1UquBaVx48Zo06ZNpcOdVUEkzoUNTWBICCFExdQ6WQHATWpY2i+//KL0+lZWVtDV1cWFCxfQtm1bAEBOTg5u376NUaNGKZQ3NzfHo0ePkJeXh4YNS4YE5+bm4smTJxg8ePBb7kXNyysoRvarQhoJRAghROXUPlnJycnB2bNnkZubC8YUb6l4eXlVur5AIMCYMWMQGhoKExMTNG/eHCEhITAzM4OnpyckEgnEYjH09PSgra0NLy8vbN68GbNnz8asWbMAAGFhYeDz+VzLTl0gej2BoSl1riWEEKJiap2s/PHHHwgICKhw1A+Px6syWQFKHvImkUiwZMkS5OXloXv37ti0aRMEAgGePHkCDw8PrFq1Cj4+PjA1NcXPP/+MNWvWYNy4cQCA7t27Iz4+Ho0bN67J3XsnaWLZbMv0QDhCCCGqxWPlNSeoCR8fHxQXF2PhwoVo2rQpNDQUHzvTsmVLFUSmnPfZwXZ/8kMcSH6I7+f2AV+rbg2pLos6D75BdfEG1cUbVBclqINt/aXWLSt///03wsLC4OzsrOpQ6hyROBfGjbXrfKJCCCHkw6fWT7A1NTUt91kopOQ2ED25lhBCSF2g1snKmDFjsHHjRrx8Sc2jpTHGIMrKhTl1riWEEFIHqP1toKdPn8LV1RVt27blhhLL8Hg8bkZmdfIitwh5BRKYUedaQgghdYBaJyspKSnc3EDkDRE3EohaVgghhKieWicrW7duVXUIdVIazbZMCCGkDlHrZEWmsLAQN27cgEgkQq9evZCXl6fUJIYfKlFWLrQ0eTDW11Z1KIQQQgglK/Hx8QgLC0N2djZ4PB527dqFdevWAQAiIyMV+rGoA5E4D6aGOtDQ4Kk6FEIIIUS9RwPt27cPy5cvx4ABAxAdHc09bt/b2xtXrlxBZGSkiiNUDZE4F2aG6pekEUIIqZvUOlnZtGkTPv30U3z99dfo1asXt3zIkCGYMWMGjhw5osLoVEMqZRBl5VF/FUIIIXWGWicrKSkpcHNzK/c9a2trPHv2rJYjUj3xi3wUS6Q0EogQQkidodbJiomJCf76669y37t//z5MTExqOSLVS3s92zLdBiKEEFJXqHWyMnjwYKxfvx4HDhxAXl4egJIHwV27dg3R0dEYNGiQiiOsfSJxST1QywohhJC6Qq1HAwUGBuL+/fv48ssvweOVjHwZPXo08vPz4eDggMDAQBVHWPvSxLnQFmhCv5FA1aEQQgghANQ8WREIBIiOjsaZM2dw7tw5ZGVlQU9PD46OjujduzeXwKgT0esJDNVx3wkhhNRNap2syPTs2RM9e/ZUdRh1Qpo4F+2aNVZ1GIQQQghHLfusSKVSHDt2DLdv3+aWPXr0CDNnzsSQIUMwZ84cPHz4UIURqkZRsRSZL/Kpcy0hhJA6Re2Slfz8fIwePRqBgYE4ffo0ACAnJwdjxozBiRMn0Lx5c9y5cwcjR45EamqqiqOtXc+e54Ex6lxLCCGkblG7ZCU2NhZ//fUX1qxZg3HjxgEomdAwPT0dX331Fb7//nvs378frVq1woYNG1QbbC0T0QSGhBBC6iC1S1YOHz6MCRMmYMiQIWjQoAEA4Pjx49DV1YWvry8AgM/nY8SIEVzLi7p484wVSlYIIYTUHWqXrDx69Ai2trbc61evXuHu3buwt7eHltab/sbNmzdHRkaGKkJUGZE4F/o6fOhoU79rQgghdYfaJStASQdbmatXr0IikcDR0VGuTHZ2NnR01KuFIU1McwIRQgipe9QuWWnbti1u3brFvf7tt9/A4/Hg4uIiVy4pKQlt2rSp7fBUSvaMFUIIIaQuUbv2fi8vL4SFhcHY2BiMMezevRudO3eGUCjkyuzZswcHDhzA3LlzVRhp7corKEb2q0IaCUQIIaTOUbtkZdSoUbh9+zaWL18OxhgsLCwQHBzMve/u7o7U1FQ4ODhgzJgxKoy0domocy0hhJA6Su2SFU1NTaxatQqBgYHIzMyEpaUl+Hw+976npyfatWsHLy8vuQ63H7q018OWzY3ogXCEEELqFvW5GpfRtGlTNG3aVGH5ggULVBCN6onEeeABMKWn1xJCCKlj1K6D7bfffovc3NxqrZOTkyN3q+hDJBLnwrixNvhamqoOhRBCCJGjdslKUVERPD09sXHjxiofp5+Wlobw8HAMGDAARUVFtRShaqTRSCBCCCF1lNrdBvq///s/9O/fHytXrkRYWBi6dOmCLl26oHnz5mjYsCFevHiBtLQ0XL58Gffu3YOVlRXWrl0LZ2dnVYf+3jDGIMrKQ08Lmm2ZEEJI3aN2yQoA9OjRA/v27cPJkydx8OBBJCYmQiwWc+83adIEvXr1whdffIHevXurMNLa8TK3CHkFxTCjzrWEEELqILVMVmTc3Nzg5uYGoGQ25hcvXsDQ0FBudJA6SKMJDAkhhNRhatdnpSLa2towNTV9q0RFKpUiPDwcrq6usLW1xYQJE5CSklJh+aKiIoSEhMDV1RV2dnYYM2YM7ty58y7hvxOabZkQQkhdRslKDYiKikJ8fDyCgoKwfft2aGpqYuLEiSgoKCi3/LJly7Bz505888032L17N4yMjDBp0iS8ePGiliMvkZaVC00NHkz0tVXy+YQQQkhlKFl5R4WFhdiyZQv8/f3Rp08fWFlZITQ0FBkZGTh8+LBC+cePH2PXrl0ICgpC37590a5dO6xYsQINGjTAjRs3VLAHJc9YMTVsCA0Nnko+nxBCCKkMJSvv6M6dO8jNzYWTkxO3TFdXF506dcKlS5cUyicnJ6NRo0ZcXxkA0NPTw2+//YZevXrVSsxlicS5NCcQIYSQOouSlXckEokAAGZmZnLLTU1Ny32Oy7///ovmzZsjKSkJw4cPh4uLCyZPnowHDx7USrxlSaUlw5apvwohhJC6Sq1HA8mIRCKcPXsW6enp8Pb2Rnp6OoRCoVKdbfPy8gAAAoFAbrlAIEBhYaFC+ZycHDx9+hRhYWGYN28eDAwM8P3332PUqFFISEiAiYmJ0nEbG+sqXbYiInEuiiVStG9phCZN9N55e6pSn2OvaVQXb1BdvEF1QeoztU9W1q5di9jYWBQXF4PH48HFxQVr1qyBWCxGbGwsjIyMKl1fW7ukU2phYaFcwlJYWAgdHcXWCj6fj5ycHKxduxaWlpYAgHXr1qFPnz7YvXs3pkyZonTsmZk5kEqZ0uXLc/thJgCgEZ+HZ89evtO2VKVJE716G3tNo7p4g+riDaqLEhoavBr5I4/UPrW+DbRlyxZs2bIFAQEBSEhIAGMlF/5p06bh2bNn+O6776rchmwyxPT0dLnl6enpCreGAMDc3Bw8Hg8dOnTglmlra6NFixZ48uTJu+zOWxGJS1qGqM8KIYSQukqtk5X4+Hj4+flhypQpaN26Nbfc0dERgYGBSEpKqnIbVlZW0NXVxYULF7hlOTk5uH37Nnr06KFQ3t7eHowx3Lp1i1uWn5+Px48fo2XLlu+0P29DJM6FtkAT+o0EVRcmhBBCVECtbwOlpaWhW7du5b7XqlUruUfwV0QgEGDMmDEIDQ2FiYkJmjdvjpCQEJiZmcHT0xMSiQRisRh6enrQ1taGvb09evbsifnz5+Prr7+GoaEhwsPDwePx4OPjU9O7WKW0rJIJDHk8GrZMCCGkblLrlhULCwtcvny53PeuX78OCwsLpbYTGBiITz75BEuWLMGnn34Kxhg2bdoEgUCA1NRU9OrVC4mJiVz5yMhIODk5ISAgAL6+vnjx4gV++uknGBsb18h+VYdInAszQ5oTiBBCSN2l1i0rn3zyCcLCwqCtrQ0PDw8AwMuXL5GQkIBNmzYp3dlVU1MTc+fOxdy5cxXea968Of766y+5ZY0aNcLSpUuxdOnSd9+Jd1BULEVGdj6crc1VGgchhBBSGbVOViZOnIinT58iPDwc4eHhAIDx48cDAIYNG4bJkyerMrz37tnzPDBGcwIRQgip29Q6WeHxeFi6dCnGjRuHc+fOISsrC3p6eujRo4fcaJ0PlWwCQxoJRAghpC5T62QlMjISw4cPR6tWrdCqVSu59x4/fowtW7ao/FbN+5SW9Xq2ZeqzQgghpA5Tu2Tl8ePH3L+joqLQvn17WFtbK5Q7evQodu/e/UEnKyJxLvR1+NDRrvpJvYQQQoiqqF2ysmLFCpw6dQoAwBjD7Nmzyy3HGIOrq2tthlbr0sQ0JxAhhJC6T+2SleXLl+PMmTNgjGHRokXw8/OTeyAcAGhoaEBfXx/Ozs6qCbKWiMS56NKu9odLE0IIIdWhdsmKmZkZvL29AQD//fcfhg8fDnNz9Ru6m1dQjOxXhdS5lhBCSJ2n1g+F8/f35xIVxhikUimkUimKi4vx8uVLnDx5UsURvj8irnMtJSuEEELqNrVrWSntyZMnWLJkCS5cuACJRFJumTt37tRyVLXjzQSGNBKIEEJI3abWycrq1atx7do1jBw5EleuXEHDhg1hZ2eH5ORk3L9/H5GRkaoO8b0RiXPBA2BKw5YJIYTUcWp9G+jixYsIDAzE4sWL4evrC21tbcybNw+7d+9G9+7dcezYMVWH+N6kZeXCSF8bfC1NVYdCCCGEVEqtk5VXr17BysoKANC+fXv8+eefAAAtLS18+umnOH/+vCrDe69E4ly6BUQIIaReUOtkxdTUFOnp6QCA1q1bIzs7G8+ePQMAGBgYIDMzU5XhvTeMMXrGCiGEkHpDrZOVPn36IDw8HBcuXICZmRksLCywZcsWZGdnY/fu3TAzM1N1iO/Fy9wi5BUUU7JCCCGkXlDrZCUwMBCGhoaIiIgAAMyePRs//vgjnJyckJiYyM3A/KFJowkMCSGE1CNqPRrI0NAQO3fu5G4FDRkyBObm5rh27RpsbGzQo0cPFUf4fshmW6aWFUIIIfWBWicrMqampty/7e3tYW9vDwA4ceIEPDw8VBXWe5OWlQtNDR5M9LVVHQohhBBSJbVMVu7fv4/9+/cDKGlNkY0IkklJSUFQUBCSk5M/yIfCicR5MDVsCA0NnqpDIYQQQqqkdsnKuXPn4Ofnh8LCQgBAbGwsYmNjYW9vj6KiIkRGRiImJgaFhYXw9PRUcbTvR8mwZboFRAghpH5Quw62GzZsgIWFBRITE3H69Gk4ODggNDQUYrEYI0eORHR0NJo1a4bNmzcjPDxc1eHWOCljEGXRsGVCCCH1h9olK3fv3sWkSZPQtm1bNGnSBHPnzsX169cxc+ZM3Lt3DzNnzsSBAwfg4uKi6lDfC/GLfBRLpNSyQgghpN5Qu9tAOTk5aNmyJfe6bdu2KC4uxsOHD7Fz506F/isfGtkEhmY0JxAhhJB6Qu1aViQSCbS03uRoAoEAADB37twPPlEB3jxjhW4DEUIIqS/ULlmpSPv27VUdQq0QiXPRQKCJxo0Eqg6FEEIIUQolK6/xeOoxjDctKxfmhjpqs7+EEELqP7XrswIAO3fuxOnTpwGUTOrH4/EQHx+PJk2ayJXj8XgIDAxURYjvjUicizZN9VUdBiGEEKI0tUxW9u7dq7Bs165dCss+tGSlqFiKjOx8OFubqzoUQgghRGlql6zcvXtX1SGozLPneWCMOtcSQgipX6jPihoR0WzLhBBC6iFKVtRIWtbrYcv0Qv+ImgAAIABJREFUjBVCCCH1CCUrakQkzoO+Dh862nxVh0IIIYQojZKVGiCVShEeHg5XV1fY2tpiwoQJSElJUWrdgwcPwtLSUuny70IkzqX+KoQQQuodSlZqQFRUFOLj4xEUFITt27dDU1MTEydOREFBQaXrPX36FMuXL6+lKEtuA5kZUrJCCCGkfqFk5bXU1FRcu3YNubm5yM/PV3q9wsJCbNmyBf7+/ujTpw+srKwQGhqKjIwMHD58uML1pFIp5s2bB2tr65oIv0p5BcXIzimEmRH1VyGEEFK/qH2ykpSUhEGDBsHd3R2jRo3Cw4cPMWvWLCxZsgRSqbTK9e/cuYPc3Fw4OTlxy3R1ddGpUydcunSpwvW+//57FBUVYcqUKTWyH1VJzyqZwJBGAhFCCKlv1DpZOXXqFKZPnw4LCwssWbIEjDEAgKOjI3bv3o1NmzZVuQ2RSAQAMDMzk1tuamqK1NTUcte5ceMGtmzZgjVr1kBTU/Md90I5NIEhIYSQ+krtHgpXWnh4OAYMGIDQ0FBIJBKu/8j48ePx/Plz7NmzB35+fpVuIy+vpMVCNnuzjEAgQGFhoUL53NxczJ07F3PnzkXr1q25ZOdtGBvrKl32ZcF/4PGATh1M0YBfOwlSbWrSRE/VIdQZVBdvUF28QXVB6jO1Tlbu37+PgICAct9zcnJCbGxsldvQ1tYGUNJ3pXTCUlhYCB0dxVaMoKAgtG7dGiNHjny7oEvJzMyBVMqUKvvPkywY6WnjxfPcd/7cuqZJEz08e/ZS1WHUCVQXb1BdvEF1UUJDg1etP/JI3aHWyYq+vj7++++/ct978uQJ9PSq/kukadOmAID09HTo6r75EaSnp6N9+/YK5Xfv3g2BQICuXbsCACQSCQBg2LBh+Pjjj/H1119Xez+UIRLnwpw61xJCCKmH1DpZ8fDwQEREBIRCIZc88Hg8PH36FBs3boS7u3uV27CysoKuri4uXLiAtm3bAgBycnJw+/ZtjBo1SqH80aNH5V5fv34d8+bNw4YNGyAUCmtgrxQxxpAmzoOztVnVhQkhhJA6Rq2TlTlz5uDGjRsYO3YsDA0NAQCzZs1CWloaWrRogS+++KLKbQgEAowZMwahoaEwMTFB8+bNERISAjMzM3h6ekIikUAsFkNPTw/a2tpo1aqV3PppaWkAAAsLCxgbG9f8TgJ4mVuEvIJi6lxLCCGkXlLrZEVfXx/bt2/H/v37ce7cOWRlZUFPTw+ff/45fHx80LChcrdNAgMDIZFIsGTJEuTl5aF79+7YtGkTBAIBnjx5Ag8PD6xatQo+Pj7veY/Kl0YTGBJCCKnHeEw2XlcNnT17Fs7OzqoO460p28H29I3/EJN4F6unOsPU4MPrt0KdB9+guniD6uINqosS1MG2/lLr56yMHz8effv2xbp16/DgwQNVh/PeiMR50NTgwVi/gapDIYQQQqpNrZOVrVu3onfv3tixYweGDBkCX19fbN26FWKxWNWh1SiROBemhg2hqaHWXzchhJB6Sq2vXg4ODvj666+RnJyMqKgotGrVCuvWrUPv3r0xdepU/Prrr6oOsUbQBIaEEELqM7XuYCujpaUFd3d3uLu7IycnB2FhYYiPj8epU6dw584dVYf3TqSMQSTOQ5c272ekESGEEPK+UbLy2pUrV5CQkIAjR44gMzMTdnZ28Pb2VnVY70z8Ih/FEinNtkwIIaTeUutk5e7du0hISEBCQgJSU1PRrFkzjBgxAl5eXmjZsqWqw6sRIjHNtkwIIaR+U+tkxcvLC7q6uhgwYAC8vLzg4OCg6pBqHM22TAghpL5T62QlJCQE/fr1Q4MGH+6QXpE4Fw0EmmjcSFB1YUIIIaQOUrtk5fHjxzAzM4NAIICNjQ3S09MrLd+iRYtaiuz9SMvKhbmhDng8nqpDIYQQQt6K2iUrnp6eiIuLQ7du3dC/f/8qL+L1fTSQSJyLNk31VR0GIYQQ8tbULllZuXIlWrduzf37Q25xKJZIkZGdD2drc1WHQgghhLw1tUtWSg9HdnJyQpMmTcDn8xXK5eXl4fbt27UZWo179jwPjFHnWkIIIfWbWj/B1sPDA7du3Sr3vatXr2LixIm1HFHN4kYC0dNrCSGE1GNq17Iyd+5cpKamAgAYY1i2bBl0dRVn4UxJSYGBgUFth1ejZM9YoQfCEUIIqc/UrmXlo48+gobG/7d351FNHnsfwL8BiShBRWRTqTsgIGsAUZBCFa3VCmiPorjvCFhFq/VVxBarvi6IggpauCporfutRa/brbdi0WJd2kq9WBUUIayCYYfM+wcvT00JShAIJL/POZxDJpMnv2cSyC8z88yoQU1NDTwej/v99R8NDQ3Y2dlhx44dig73nWQXlEK7swa0NOsPcxFCCCHthcr1rNTtAVT3e1hYGCwsLBQcVcsQFZTSfBVCCCHtnsolK6+7evWqokNoUdmFpbSBISGEkHZP5ZKVadOmITQ0FIMGDcK0adPeWJfH4yE+Pr6VImteZRXVKBJX0nwVQggh7Z7KJStqamoyf1c2OYW0gSEhhBDloHLJyuHDh2X+rmxoA0NCCCHKQnm7FuRQWlrK/X7x4kXExcUhIyNDgRG9O1FBKXgA9LvRMBAhhJD2TaWTlYyMDIwZMwbR0dEAgMjISAQFBWHLli2YMGEC7t69q+AImy67sBTdu2iCr6Gu6FAIIYSQd6LSycq2bdtQVVUFNzc31NTUID4+HqNHj8ZPP/0EoVCInTt3KjrEJhMVlMGQJtcSQghRAiqdrNy8eRPLly+HnZ0d7t69i5cvX2Lq1KnQ0dHB1KlT8euvvyo6xCZhjEFUUAp9mq9CCCFECah0slJRUYHu3bsDAK5fv45OnTrB3t4eALgVbtujV2VVKK2ohiHtCUQIIUQJqHSy0r9/fyQnJ6Oqqgrnz5+Hs7MzOnSovUDq7Nmz6Nevn4IjbBoRXQlECCFEiah0sjJ//nzs378fQ4cOxbNnzzB79mwAwKRJk3DhwgXMmzdPwRE2Td1lyzRnhRBCiDJQuXVWXvfhhx9CT08Pd+7cgZOTE6ysrAAADg4OWLp0KVxdXRUcYdOICsqgrsaDbldNRYdCCCGEvDOVTlYAQCgUQigUQiwWIzs7G926dcOqVasUHdY7ERWUQl+nE9SVeIVeQgghqkPlk5WbN29iy5YtSE1N5crMzc0RHByMYcOGKTCypssuLIUBTa4lhBCiJFT6q3dKSgrmzp2LkpISLFmyBOvXr8eiRYsgFouxYMEC3L59W9Ehyk3C2P+vsULJCiGEEOWg0j0rERERsLW1RVxcHHcVEAAEBARg9uzZiIyMRFxcnAIjlF9BcTmqayS02zIhhBClodI9K7/++itmzJghlagAgLq6OqZPn4779+836jgSiQS7du2Cq6srrK2tMWfOHKSnpzdYPyMjA4GBgXB2doajoyPmzZuHtLS0dzqXOqIC2m2ZEEKIclHpZEUgEKCqqkrmfZWVlY0+TlRUFI4ePYqwsDAcO3YM6urqmDt3LioqKurVFYvFmDVrFsrLyxEbG4v4+HhoaWlhxowZyM/Pb/K51BEV0horhBBClItKJyt2dnaIiYmBWCyWKheLxYiJiYFQKHzrMSorKxEbG4uAgAC4ubnBzMwM4eHhyMvLw/nz5+vVv3btGkQiEXbs2IHBgwfDxMQEW7duRVlZGa5cufLO55RdUIqOfHV01eK/87EIIYSQtkCl56wEBwfDx8cHH3zwAdzc3NCjRw/k5eXh2rVrqKqqwpYtW956jNTUVJSWlmLo0KFcmUAggLm5OVJSUuDl5SVVvy5B0tbWlipnjOHly5fvfE6igjIY6HRqt1sFEEIIIX+n0slKnz59cOzYMURGRiIpKQlFRUXo2rUrnJ2dERAQgIEDB771GCKRCABgYGAgVa6vr4+srKx69Y2MjGBkZCRVdvDgQVRUVMDNze0dzub/4ykoRV8j7bdXJIQQQtoJlU5WAGDgwIHYuXNnkx9fVlY7oZXPlx524fP5jZr3cv78eezcuROzZs2CqampXM+tqyuQul1VLUFeURncHYyhp6daCYuqne+bUFv8hdriL9QWpD1TyWQlLS0NCQkJePHiBfr06YMpU6ZgwIABTTqWpmbtkvaVlZVSCUtlZSU6d37zJNdDhw5h06ZN8PLywmeffSb3c+fniyGRMO52Vn4JJAzo0rEDcnNfyX289kpPT1ulzvdNqC3+Qm3xF2qLWmpqvHpf8kj7oHLJyu3btzFr1izU1NRAR0cH169fxzfffIMdO3Zg1KhRch+vbkgnJycHAsFffwQ5OTkNDiNJJBJs3LgR8fHxWLBgAZYvX94sc0yyabdlQgghSkjlrgbas2cP+vfvj0uXLiEpKQlJSUkQCoWNmkwri5mZGQQCAW7dusWVicViPHjwAI6OjjIfExoaiiNHjiAkJATBwcHNNhm2bo0VWhCOEEKIMlG5ZOW3336Dv78/evXqBQDQ0dHBypUrkZmZiZycHLmPx+fz4efnh/DwcFy+fBl//PEHli1bBgMDA3h6eqKmpga5ubkoLy8HAFy8eBHHjh3DggUL4OnpidzcXO6npKTknc4tu6AU2p01oKWp8U7HIYQQQtoSlUtWxGIxdHV1pcr69u0LxhgKCwubdMygoCB88sknCAkJga+vLxhjOHDgAPh8PrKysuDi4oLExEQAwD//+U8AwL59++Di4iL1ExMT807nJioopSEgQgghSkfl5qzU1NRATU06R+vYsSMAoLq6uknHVFdXx4oVK7BixYp69/Xu3RsPHz7kbkdGRjbpORoju7AUQ/rpvr0iIYQQ0o6oXM+KsiqrqEaRuJLmqxBCCFE6KtezAtReqfPs2TPudk1NDYDaBd66dOkiVdfY2LhVY2uqnELawJAQQohyUslkZdmyZTLLlyxZUq8sNTW1pcNpFtwGhjqUrBBCCFEuKpesbNq0SdEhtIi6NVb0dWgYiBBCiHJRuWTF29tb0SG0CFFBKXS7dARfQ13RoRBCCCHNiibYKonsgjK6bJkQQohSomRFCTDGaI0VQgghSouSFSXwqqwKpRXVMKTJtYQQQpQQJStKQEQbGBJCCFFilKwogborgQxpQThCCCFKSOWuBvo7kUiEPXv2ICkpCTk5OTh69CjOnTsHCwsLjBs3TtHhNYqooAzqajzodtVUdCiEEEJIs1PpnpUnT55gwoQJuHjxIqytrVFVVQUAyM/Px8qVK3Hx4kUFR9g4ooJS6Ot0grqaSr+chBBClJRK96xs2bIFRkZGOHz4MDQ1NfH9998DAP73f/8X5eXlOHDgADw9PRUc5duJCktp5VpCCCFKS6W/it+8eRPz58+HQCAAj8eTum/SpEl49OiRgiJrPAljEBWW0QaGhBBClJZKJytqamr1kpQ6ZWVlUGsHwyqFxRWoqpbQlUCEEEKUVtv/NG5BDg4OiI6OxqtXr7gyHo+HmpoaJCQkQCgUKjC6xsn+/w0MaY0VQgghykql56ysXLkSU6ZMgaenJxwdHcHj8bB//348evQImZmZOHLkiKJDfCtaY4UQQoiyU+melQEDBuDkyZMYPnw4bt++DXV1dSQnJ6N///44duwYzMzMFB3iW2UXlKKjhjq6CfiKDoUQQghpESrdswIA7733HrZt26boMJpMVFA7ubahuTeEEEJIe6fSycqLFy/eWqdnz56tEEnTiQpK0ddIW9FhEEIIIS1GpZMVDw+Pt/ZIpKamtlI08quRSJBbVAYncwNFh0IIIYS0GJVOVr788st6ZSUlJUhOTsb9+/exceNGBUTVePnFFWAMMKTJtYQQQpSYSicrn3zyiczyWbNmISQkBP/617/g7u7eylE1Xu7LMgB0JRAhhBDlptJXA73JmDFjcPXqVUWH8UZ5L8sBgFavJYQQotQoWWlAWloaJBKJosN4o7yiMgg6aUBLU0PRoRBCCCEtRqWHgSIiIuqVSSQSvHjxAhcuXMDo0aMVEFXj5b0so/kqhBBClJ5KJyt79+6VWS4QCPDhhx9i9erVrRyRfHKLymkIiBBCiNJT6WTl/v374PPb78qvxSWVsB6gq+gwCCGEkBal0nNWPvroI5w/f17RYbwTA9rAkBBCiJJT6WSlsLAQXbp0UXQY74TmrBBCCFF2Kp2seHt7Y+/evXjy5ImiQ2kyfR2as0IIIUS5qfSclbS0NNy9exdjx46FhoYGunfvLnU/j8fDv//977ceRyKRIDIyEsePH0dxcTHs7e2xfv169OnTR2b9wsJChIWF4ccffwRjDGPGjMHq1auhpaUlV/w6go7ga6jL9RhCCCGkvVHpZMXIyAjjx49/5+NERUXh6NGj2Lx5MwwMDLB9+3bMnTsX33//PTp27FivflBQEMrLyxEXFwexWIw1a9YgJCQE27dvl+t5e3TTfOfYCSGEkLZOpZOVTZs2vfMxKisrERsbixUrVsDNzQ0AEB4eDhcXF5w/fx5eXl5S9X/55RfcunUL33//PQYOHAgACAsLw+zZsxEcHCzXLs89utEQECGEEOWncnNWPvjgA/z+++/NdrzU1FSUlpZi6NChXJlAIIC5uTlSUlLq1U9JSYGuri6XqACAvb09eDyezPpvoteVkhVCCCHKT+V6VjIzM1FRUdFsxxOJRAAAAwMDqXJ9fX1kZWXVq5+TkwNDQ0OpMj6fDx0dHWRnZ8v13L30taCmxpMzYuVE7fAXaou/UFv8hdqC2qA9U7lkpbmVldXufPz3xeX4fD4qKytl1pe1EB2fz5c7iRJaNH7ISNnp6goUHUKbQW3xF2qLv1BbkPZM5YaBgNqrfJqLpmbtJNe/JyaVlZXo3Ln+Giiampoyk5iG6hNCCCGqTiV7VgICAqCh8fadihtz6bKRkRGA2uEdgeCvby45OTlS81LqGBoaIicnR6qssrIShYWF9YaHCCGEEKKiyYqFhQV0dZtnTx0zMzMIBALcunUL/fv3BwCIxWI8ePAAU6dOrVffwcEB27Ztw+PHj7n6dRNrhUJhs8RECCGEKBOVTFYWLVoEOzu7ZjkWn8+Hn58fwsPD0aNHD/Tu3Rvbt2+HgYEBPD09UVNTg4KCAmhra0NTUxPW1taws7NDcHAwNmzYgPLycoSEhGDChAn1JukSQgghREXnrDS3oKAgfPLJJwgJCYGvry8YYzhw4AD4fD6ysrLg4uKCxMREALVDS5GRkTA2NsbMmTMRGBiIYcOGITQ0VLEnQQghhLRRPMYYU3QQrcnMzAxHjhxptp4VQgghhLQsletZ8fb2Ro8ePRQdBiGEEEIaSeV6VgghhBDSvqhczwohhBBC2hdKVtogiUSCXbt2wdXVFdbW1pgzZw7S09MbrF9YWIjg4GA4OjrCwcEB69atQ0lJSStG3HLkbYuMjAwEBgbC2dkZjo6OmDdvHtLS0lox4pYjb1u87rvvvoOpqWmj67d18rZFVVUVtm/fDldXV9jY2MDPzw+pqamtGHHLkbctcnJysGzZMjg5OcHJyQlLly6Ve6uP9iA6Ohq+vr5vrFNRUYENGzbA2dkZtra2CAoKQl5eXitFSORByUobFBUVhaNHjyIsLAzHjh2Duro65s6d2+By/EFBQcjIyEBcXBwiIyNx48YNhISEtHLULUOethCLxZg1axbKy8sRGxuL+Ph4aGlpYcaMGcjPz1dA9M1L3vdFnczMTGzYsKGVomwd8rZFaGgojh8/ji+//BInT55E9+7dMW/ePBQXF7dy5M2vKf8vsrKyEBsbi7i4OGRnZ2Px4sWtHHXLSkhIQHh4+FvrrV+/HklJSdi9ezcOHjyI58+fIzAwsBUiJHJjpE2pqKhgNjY2LD4+nit79eoVs7a2ZqdPn65X//bt28zExISlpaVxZTdu3GCmpqYsMzOzVWJuKfK2xblz55i5uTkrLi6WOoa1tTU7duxYq8TcUuRtizo1NTXM19eXzZgxg5mYmLCnT5+2RrgtSt62yMjIYCYmJuzSpUtcWXFxMXN3d2c//vhjq8TcUuRti/z8fGZiYsKuXLnClV2+fJmZmJiwvLy8Vom5JWVnZ7OFCxcyGxsbNmbMGDZlypQG62ZlZTEzMzP2ww8/cGVPnz5lJiYm7Oeff26NcIkcqGeljUlNTUVpaSmGDh3KlQkEApibm3Mr3b4uJSUFurq6Ukv729vbg8fjyazfnsjbFnZ2doiJiYG2trZUOWMML1++bPF4W5K8bVFn3759qKqqwsKFC1sjzFYhb1tcv34dWlpacHd358q0tbVx9epVuLi4tErMLUXetujcuTM6d+6MM2fOQCwWo6SkBOfOnUPfvn3RrVu31gy9Rfz+++/Q0tLCP//5T1hbW7+x7i+//AKJRAJHR0eurE+fPjA0NMTPP//c0qESOankCrZtmUgkAoB6q9nq6+sjKyurXv2cnJx6ewrx+Xzo6Oi0+3FoedvCyMiI26upzsGDB1FRUQE3N7eWC7QVyNsWAHD//n3ExsbixIkT3OOVgbxt8fTpU/Tu3Rs//PAD9u7di6ysLJibm2P16tUYMGBAq8TcUuRtC01NTWzatAmhoaEQCoXg8Xjo0aMH4uPjoa6u3ioxtyQPDw94eHg0qq5IJEK3bt3QqVMnqfI3/U0RxaGelTamrKwMQG3C8To+ny9zt+aysrJ6devqv20uQ1snb1v83fnz57Fz507MmjULpqamLRJja5G3LUpLS7FixQqsWLECffv2bY0QW428bSEWi5GZmYmdO3ciKCgIe/fuhYaGBqZOndruJ1PK2xaMMTx48ADW1tZISEjAwYMHYWxsDH9/f7x69apVYm4rysrKZG5o29j/L6R1UbLSxmhqagJAvT+WyspKdO7cWWZ9WX9YDdVvT+Rti9cdOnQIy5cvx8cff4zPPvusxWJsLfK2RVhYGPr27YspU6a0SnytSd620NDQgFgsxrZt2zBixAhYWVlhx44dAICTJ0+2fMAtSN62SExMREJCArZt2wZ7e3s4OjpyvU3ffvttq8TcVmhqaqKqqqpeuTL871RGNAzUxtQNY+Tk5EAgEHDlOTk5UvNS6hgaGiInJ0eqrLKyEoWFhfWGh9obedsCqL2Mc+PGjYiPj8eCBQuwfPly8Hi8Vom3JcnbFidPngSfz4etrS0AoKamBgAwYcIEfPzxx/jiiy9aIeqW0ZS/ER6Ph0GDBnFlmpqaMDY2xvPnz1s+4BYkb1vcvn0bffr0kZrX1bVrV/Tr109pLmtvLENDQxQVFaGiogIdO3bkymUNrRPFo56VNsbMzAwCgQC3bt3iysRiMR48eCA1EayOg4MDcnNz8fjxY66sbmKdUChs+YBbkLxtAdReonrkyBGEhIQgODhYKRIVQP62uHjxIs6dO4czZ87gzJkzCAsLAwDs3bsXS5cubbW4W4K8bSEUCsEYw2+//caVlZeX49mzZ3jvvfdaJeaWIm9bGBoaIiMjgxs+AmqHDJ8/f650w4VvY29vDwBSbZeeno7s7Gw4ODgoKizSAPVQ2u63TVFXV0dpaSkOHDiAfv36obKyEuvXr0dNTQ3WrVsHAMjPz4e6ujo6dOgAAwMD3LhxA4mJiTA3N0d6ejrWrl0LDw8PeHl5Kfhs3o28bXHx4kVs3boVCxcuhLe3N0pLS7kfoP64fnsib1t069ZN6ufly5c4ffo0/P39601Cbm/kbYuePXvil19+wZkzZ2BqaoqysjKEhYXhxYsXCAsLa9dd/vK2hbGxMb755hvcu3cPAwcORF5eHjZs2ID8/HyEhYVxw0rK4PLlyygqKsKkSZO4stzcXAC1/wsEAgGePHmCo0ePwtTUFIWFhfj888/Rt29f+Pv7Kyps0hAFXzpNZKiurmZbt25lzs7OzMbGhs2dO5dlZGQwxhh79uwZMzExYSdPnuTq5+XlscDAQGZjY8McHR3ZunXrWFlZmaLCb1bytMWSJUuYiYmJzJ8dO3Yo8jSahbzvi9clJycrzTorjMnfFmKxmIWGhjInJydmZWXFZs6cyR4+fKio8JuVvG3x6NEjtnDhQubk5MScnJyYv78/V1+ZrFq1qt46KyYmJmzXrl3c7ZKSEvY///M/TCgUMnt7e/bpp5+y/Pz81g6VNAJtZEgIIYSQNo3mrBBCCCGkTaNkhRBCCCFtGiUrhBBCCGnTKFkhhBBCSJtGyQohhBBC2jRKVghpY5TtAj1lOx9VQq8daSsoWSHNbvXq1TA1NW3w5+jRo40+1qlTp2BqatoqS4HLitXa2hrjx49HTEwMJBJJsz7f388tOzsbixYtwrNnz6RiCg8Pb9bnlaWh18zW1hYfffQRIiMjuSX75bF3717ExMQ0W5y//fYbRo4cKbUCa53U1FRYWlo2+3vl0aNHCA4OhouLCywtLTF8+HAEBgbizp07zfo88ti9ezdMTU1RXV0NoHbV2sDAQNjY2MDOzg4//fST3O8dDw8PrFixgrt9/PhxfPXVV41+fHl5OTw9PXHv3r3GnwghjUR7A5EW0b17d+zevVvmfX369GnlaBrPy8sLn3zyCXe7rKwMly5dwvbt2/Hq1SsEBwc323O5ubkhISGB24fkxo0b+Pe//43PP/+cq5OQkICePXs223O+iazX7OXLl0hMTMTu3btRUVEh9/nv3LkTixYtapb4KioqsGrVKixbtgydOnWSuu+///0v5s+fL3NjuneRlpaGyZMnw9zcHJ999hn09PSQm5uLY8eOYdq0aYiKioK7u3uzPmdjTJw4Ec7OzujQofZf+OnTp3Hx4kWsWrUKgwcPxpAhQ+R+70REREjtL7R3717Y2dk1+vGampoIDg7GZ599hrNnzyrVarhE8ShZIS1CQ0OjXe5NZGhoWC9uV1dXpKenIyEhAUFBQTK3lW8KXV1d6OrqvrFOa7ZhQ6/ZyJEjkZmZiRMnTjRrsiavo0ePorq6GmPHjuXKKisrceTIEezcuVNqM7rmEhcXB4FAgNjYWKntGsaMGQMfHx/s2LFDIcnb35/JAAARoklEQVRKz549pRKRoqIiAMD06dO596e8750hQ4a8c1yjR4/G7t27cfjwYcyfP/+dj0dIHRoGIgpTU1ODmJgYjBs3DlZWVrCxscGUKVPw008/NfiYiooKhIWF4f3334elpSVGjRqFXbt2cd3hQO0H2LZt27g6H330EU6fPv1OsVpaWqKkpIT7UKioqMCePXswZswYDBkyBKNGjUJ0dLTUUMmzZ88QEBCAoUOHwsrKCt7e3jh37hx3/+vDQLt37+Z6VDw9Pbkejrqu/IqKCgiFQmzcuLFebKNGjZLqvj958iTGjx8PS0tLjBgxAtu3b0dlZeU7nf/r37jrHD9+HD4+PrCxsYGVlRUmTJiAxMREAMDz589hamoKANi3bx88PDy4x/3yyy+YPn06bGxs4ODggODgYIhEojc+f2VlJb7++muMHz9eanPK//znP9i1axcWLVok1QbNJT8/X+ZmmHw+HytXrpTqhVu9ejV8fX1x6tQpuLu7w8bGBjNmzMCDBw+kHltUVIT169dj+PDhGDJkCHx8fHDt2jWpOowxHD58GB999BGsrKzwwQcfICoqint/vT4MNH36dO79YmlpienTpwOoP4SYl5eHzz//HMOGDYOtrS2mTJmC5ORk7v7Xh4FMTU2RmZmJ7777DqampkhLS4OpqSkSEhKk4iwuLoaVlRUOHTrElY0fPx4HDx5ERUVF4xuakLegZIW0mOrq6no/r8/72L59OyIjIzFx4kRER0cjNDQUhYWFCAoKQklJicxjhoWF4eLFi1i6dCn279+Pjz/+GHv27MH+/fu5OoGBgYiPj4evry+ioqIgFAqxevXqev9o5fHkyRNoaWlBV1cXjDEsWrQIMTEx8PLyQmRkJDw9PREREYG1a9cCACQSCRYsWMBtEBcVFYX33nsPwcHB3K7Yr5s4cSI3XBIeHo6JEydK3d+xY0eMGTMG58+fl2rDu3fvIiMjAxMmTAAAHDhwAGvWrIGtrS2ioqIwffp0HDp0CCtXrmzUeb7+WlVWViI7OxsxMTFISkqS2hgzISEB69atg5ubG/bs2YPNmzejQ4cOWLFiBTIzM6Gvr8+1t5eXFyIiIgAAt2/fxowZMwAAW7duxZo1a3Dnzh34+flBLBY3GFdycjJycnLw4YcfSpUPGTIEV69exaJFi6Curt6oc5SHh4cHsrOzMXnyZCQkJODPP//k7nNzc+POpU5aWhq2bt2KxYsXY/PmzSgsLMT06dORnZ0NoDbpmjVrFi5cuIDFixcjIiICxsbGWLRoEa5evcodJzw8HBs3boSzszN2796NyZMnY+/evTLnoKxduxY+Pj4AgMOHD3PvwdeVlZVh6tSp+PHHHxEYGIiIiAh069YN8+fPR2pqar36CQkJ0NPTg4uLCxISEjBo0CDY2NjgzJkzUvXOnTsHiUSC8ePHc2VjxoxBbm7uG790ECIvGgYiLUIkEsHCwqJe+fz587lvb1lZWQgKCsLs2bO5+zt16oSgoCCkpqbK7MZOSUmBm5sbvL29AQDOzs4QCATQ0dEBUDvv44cffsCWLVu4D1c3NzdIJBLs3LkTEydOfOtYel0vDWMMeXl5+O6773D16lUsWLAAPB4P165dw40bN+o9R6dOnbB7927MnDkTPXr0wOPHj7Ft2zaMHDkSADB06FAYGBjI/Kbes2dPbi6PhYWFzLkGXl5eOH78OG7evAlnZ2cAtR8Wenp6GDZsGMRiMSIjI+Hj44MvvviCi8vQ0BArVqzAnTt3YGtr2+B5N/Sa9erVC0uXLsW8efO4svT0dMyYMQNLly7lyt577z1MnDgRKSkpmDBhAvf6GRoackMM27ZtQ69evfD1119zwypCoRAffvghEhISsHDhQpmxJScnQ0tLC/3795cqNzAwaPB8msPkyZORm5uLAwcOcG2qo6MDZ2dnTJ48GUOHDpWq/+rVKxw6dAhOTk4AAFtbW4waNQr/+Mc/sHr1apw9exYPHjzA4cOH4ejoCKA2IZo7dy62bNkCDw8PvHr1CrGxsZgyZQqXeLi5uaGkpATJycn1Jnqbmppy7xc7OztuHsvrTp8+jfT0dHz77bewtrYGUPu34+Pjg6SkJAwePFiqvlAoBJ/Ph46ODvc6Tpo0CWvXrsXjx4+51+HUqVPw8PDg/v6A2jlpXbt2RXJyMt5//335G50QGShZIS1CV1cX0dHR9cr19fW53+u+Jb58+RJPnz7F06dPceXKFQBocKLk8OHDcfjwYeTk5MDNzQ2urq5SyU7dtzkPDw+poaGRI0fi22+/xf3797kPCVn27duHffv2SZVpamrC19cXAQEBAIBbt25BTU1Nau4EUJtM7N69G7du3cL06dNhamqKdevW4caNG3B1dYWzszPWrFnT4HO/jb29PYyNjXHu3Dk4OzujpqYGiYmJmDBhAtTV1XHnzh2UlZVh5MiRUufu7u4ONTU1JCUlvTFZef01E4vFOHDgAH799VeEhITU+9CpOw+xWIwnT54gPT2da/uGhpzKy8tx9+5dzJw5E2pqalyMRkZGsLCwwPXr1xtMVp4/f45evXrJTPTkVVVVJfWBz+PxpOaj/F1AQABmzZqFpKQkJCcn49atW0hMTERiYiLmzJmDVatWcXWNjIy4RAWoTaZsbW1x69YtALXvTx0dHdjZ2dV7f4aGhiIzMxOPHz9GVVUVRo8eLRXHsmXLmnzOKSkpMDIy4hIVoHaO0nfffdfoY4wdOxZfffUVzp49i2XLliEtLQ2//vorAgMD69Xt2bMnnj9/3uR4Cfk7SlZIi+jQocNbJ+z9/vvv+PLLL3Hnzh1oamrCxMQERkZGABpe32HVqlXo2bMnzp49iy+++AKMMQwePBhr166FUChEYWEhAMDBwUHm4982N2LixInw9fUFUPshpqWlhd69e0tNqi0qKkKXLl3qfcDp6ekBqB3H5/F4iIuLQ3R0NC5duoRTp05BXV0drq6u2LBhA3cFkDx4PB4mTJiAQ4cOYf369bh58yby8/O53p26c/f392/Suf/9NRMKhZgxYwYCAgJw8OBB2Nvbc/c9e/YMoaGhSEpKQocOHTBgwABujkpDioqKIJFIEBcXh7i4uHr39+3bt8HHisXielcANdW6deuk5jD16tVLaghGFoFAgNGjR3MJxOPHj7F27VrExsbC29sbJiYmAGT39Ojq6iIzMxNA7WtUWFgoswcLqH2N6l7HHj16yH9yDSgsLHzrZO630dLSwtixY3H27Fl8+umnOHXqFPT19eHi4lKvbqdOnfDq1at3ej5CXkfJClEIsViMOXPmwNTUFImJiejXrx/U1NRw7do1/Otf/2rwcRoaGpgzZw7mzJmD/Px8XLt2DXv27IG/vz+uX78ObW1taGpqIj4+Xubje/fu/ca49PT03ppkde3aFcXFxaisrJRKWHJycgCA6xLX1dXFmjVrsGbNGjx+/BiXL1/Gnj17sH79epm9To1RN0cmKSkJFy5cgJmZGZckdOnSBQCwZcsWDBgwoN5jX++qbwwNDQ1s3rwZ48ePx6pVq/D999+jY8eOkEgkmD9/PjQ0NHDixAmYmZmhQ4cOePToEc6ePdvg8QQCAXg8HqZPn46PP/643v1v6t3Q0dHBixcv5Iq/IQEBAZg2bdpbnzc7Oxve3t716gNA//79sWbNGkycOBGPHj3ikpW6RON1ubm5XKKgra0NY2PjBtc/6devH4qLiwEABQUFUvfl5OTgzz//fGPvWEO0tbVlrj9z//598Pl8mJmZNeo4kyZNwokTJ3D79m2cP38e3t7eMucKFRUVcW1CSHOgCbZEIR4/foyXL19i2rRpGDBgANTUat+KdVdFyFqArby8HKNHj8aBAwcA1CYDPj4+8PX1RVFREcRiMZycnFBeXo6qqioMGTKE+0lPT8fOnTtlLiYmL0dHR0gkEu7Klzp1H9T29va4c+cOhg0bhvv37wOo/XBbsGABnJycuG/Zf1fXBm9ibGwMOzs7XLhwAVeuXJGa9GptbQ0+n4/s7GypcxcIBNiyZYvU5NDG6tOnDxYsWIBnz55xi7sVFhbiyZMn8Pb2hqWlJTdHQtZr9/o5aWlpwcLCAn/++adUfKampoiKiqp3RczrevXqBZFI1CwL8/Xu3bve88uip6cHTU1NHDlyROaE70ePHgGA1IdyRkYG/vjjD+62SCTC3bt3ubktTk5OyM7ORrdu3aRiSElJQVRUFNTU1GBlZQUNDQ1cunRJ6vkSEhKwePHiJq0qKxQKkZmZid9//50rq6qqwrJly3Dw4EGZj5H1frS1tcWgQYMQERGBrKwsbu7Y6xhjEIlErbY+EFEN1LNCFKJ///7Q1tZGdHQ01NXVoaGhgQsXLnBXG8hKKjQ1NWFhYcH9Ux88eDCeP3+OuLg4DB06FN27d8eIESPg6OiIgIAALFy4EIMGDcKDBw8QGRkJW1vbZvkHOmLECDg5OSE0NBQikQiDBw/Gzz//jK+//hrjxo2DmZkZKioq0KlTJ6xYsQL+/v7Q19fHvXv3cP369QaHaep6Ri5dugRPT0+89957Mut5eXlhw4YNAIBx48Zx5To6Opg/fz4iIyNRXFwMZ2dn5OfnIzIyEuXl5bC0tGzS+c6fPx9nzpzB/v374eXlBWNjY/Tq1QtHjhyBoaEhunTpguvXr3O9Wa+/dl26dMG9e/eQkpICoVCI4OBgzJs3D0FBQVzvyuHDh5GSkoJZs2Y1GIOLiwuio6Pxxx9/wNzcvEnnIS91dXWEhoZiyZIl8PHxwdSpUzFo0CBUVVXh5s2bSEhIgK+vLwYOHMg9hsfjwd/fH59++ik6dOiAqKgodOnSBTNnzgQAeHt7IyEhAbNnz8aCBQvQu3dv3Lx5E/v374e3tzc6d+6Mzp07Y8aMGYiLi4OGhgaGDRuG1NRUfP3111i4cGGThsN8fHxw+PBh+Pv7IzAwEHp6evjmm29QUFCAOXPmyHxMly5d8PDhQ/z0009wcnLikpdJkyZh06ZNsLe3R79+/eo97uHDhxCLxXB1dZU7TkIaxAhpZqtWrWKurq5vrZecnMx8fHyYlZUVc3Z2ZnPmzGEpKSnMzs6OffXVV4wxxk6ePMlMTEzY06dPGWOMlZSUsE2bNjF3d3dmYWHBhg8fzkJCQtjLly+545aWlrLNmzczNzc3ZmFhwdzd3dnmzZuZWCx+YzwmJiZsx44djTrHuucYMWIEs7CwYJ6eniw6OppVV1dzddLT01lQUBAbNmwYVycmJoZJJBKZ51ZaWsrmzJnDLCwsWEhISIMxFRcXsyFDhrB58+bJjO3o0aNs3LhxzMLCgjk7O7NPP/2Upaenv/F83vaaXblyhZmYmLDFixczxhhLTU1lfn5+zMbGhjk6OrKpU6eyH374gY0dO5YtWbKEe9yhQ4eYnZ0dc3BwYBUVFYyx2tfdz8+PWVtbM3t7e+bn58du3Ljxxviqq6vZ8OHDWVRUVIN1/t6ezSU1NZUtX76cvf/++8zS0pLZ2NiwyZMnsxMnTnCvJWN/teGJEyeYi4sLs7W1Zf7+/iwjI0PqePn5+Wzt2rVs2LBhzNLSknl6erI9e/awqqoqro5EImGxsbFs1KhR3HsnLi6Oe75du3YxExMT7jF/v81Y/fdOdnY2Cw4OZo6OjszGxob5+fmxe/fucfe7u7uz4OBg7vaFCxeYk5MTs7GxYc+ePePKnz59ykxMTNiJEydktld0dDRzdnaWioWQd8VjjHaqIoS0fQcPHsTBgwdx+fLlRg2ZtbbVq1fjxo0b+M9//qPoUFrUoUOHEBERgR9//BGdO3eWuk8ikWDUqFHw8/OTukqPkHfV9v7iCSFEhilTpqBDhw5yXW5Lms+ZM2ewdetWhIeHw8/Pr16iAoCbx1V3RR0hzYWSFUJIu9CxY0ds3boVERERzTJRmsjn4cOHiI+Px4gRI2TOuyovL0d4eDi2bt1KmxiSZkfDQIQQQghp06hnhRBCCCFtGiUrhBBCCGnTKFkhhBBCSJtGyQohhBBC2jRKVgghhBDSplGyQgghhJA27f8A2RIA4FzqDVkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for samplingtypes in ['naive','smote','adasyn']:\n",
    "    results = modelpipeline.run_model(df, 0.2, True, samplingtypes, 'LogisticRegression', 'fraud detection with ' + samplingtypes + ' sampling and standardization', False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n",
      "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n",
      "[CV] C=0.8, max_iter=300, solver=liblinear ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.8, max_iter=300, solver=liblinear, score=0.946, total=   8.9s\n",
      "[CV] C=0.8, max_iter=300, solver=liblinear ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    8.8s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.8, max_iter=300, solver=liblinear, score=0.945, total=   9.9s\n",
      "[CV] C=0.8, max_iter=300, solver=liblinear ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   18.8s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.8, max_iter=300, solver=liblinear, score=0.944, total=  10.5s\n",
      "[CV] C=0.8, max_iter=300, solver=liblinear ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   29.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.8, max_iter=300, solver=liblinear, score=0.943, total=   9.2s\n",
      "[CV] C=0.8, max_iter=300, solver=liblinear ...........................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:   38.5s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  C=0.8, max_iter=300, solver=liblinear, score=0.947, total=   9.8s\n",
      "[CV] C=1, max_iter=300, solver=liblinear .............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:   48.4s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . C=1, max_iter=300, solver=liblinear, score=0.946, total=   9.0s\n",
      "[CV] C=1, max_iter=300, solver=liblinear .............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:   57.3s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . C=1, max_iter=300, solver=liblinear, score=0.945, total=  10.5s\n",
      "[CV] C=1, max_iter=300, solver=liblinear .............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:  1.1min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . C=1, max_iter=300, solver=liblinear, score=0.944, total=  10.1s\n",
      "[CV] C=1, max_iter=300, solver=liblinear .............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:  1.3min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . C=1, max_iter=300, solver=liblinear, score=0.943, total=   9.7s\n",
      "[CV] C=1, max_iter=300, solver=liblinear .............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:  1.5min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . C=1, max_iter=300, solver=liblinear, score=0.947, total=  10.0s\n",
      "[CV] C=1.2, max_iter=300, solver=liblinear ...........................\n",
      "[CV]  C=1.2, max_iter=300, solver=liblinear, score=0.946, total=   9.0s\n",
      "[CV] C=1.2, max_iter=300, solver=liblinear ...........................\n",
      "[CV]  C=1.2, max_iter=300, solver=liblinear, score=0.945, total=  10.2s\n",
      "[CV] C=1.2, max_iter=300, solver=liblinear ...........................\n",
      "[CV]  C=1.2, max_iter=300, solver=liblinear, score=0.944, total=  10.0s\n",
      "[CV] C=1.2, max_iter=300, solver=liblinear ...........................\n",
      "[CV]  C=1.2, max_iter=300, solver=liblinear, score=0.943, total=   9.4s\n",
      "[CV] C=1.2, max_iter=300, solver=liblinear ...........................\n",
      "[CV]  C=1.2, max_iter=300, solver=liblinear, score=0.947, total=   9.6s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:  2.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best f1 score: 0.9451771361511645\n",
      "Best parameters: {'C': 1, 'max_iter': 300, 'solver': 'liblinear'}\n",
      "[[55487  1377]\n",
      " [    8    90]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.975685544749131\n",
      "Sensitivity: 0.9183673469387755\n",
      "Specificity: 0.9757843275182893\n",
      "Precision: 0.06134969325153374\n",
      "f1 score: 0.11501597444089458\n",
      "AUC value is: 0.9470758372285324\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 91.869918699187%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = modelpipeline.run_model(df, 0.2, True, \"naive\", 'LogisticRegression', 'fraud detection with naive sampling, CV 5 fold and standardization', True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
      "             estimator=LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
      "                                          fit_intercept=True,\n",
      "                                          intercept_scaling=1, l1_ratio=None,\n",
      "                                          max_iter=100, multi_class='warn',\n",
      "                                          n_jobs=None, penalty='l2',\n",
      "                                          random_state=None, solver='warn',\n",
      "                                          tol=0.0001, verbose=0,\n",
      "                                          warm_start=False),\n",
      "             iid='warn', n_jobs=None,\n",
      "             param_grid={'C': [0.8, 1, 1.2], 'max_iter': [300],\n",
      "                         'solver': ['liblinear']},\n",
      "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
      "             scoring='f1', verbose=10)\n",
      "0.9451771361511645\n",
      "{'C': 1, 'max_iter': 300, 'solver': 'liblinear'}\n",
      "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
      "                   intercept_scaling=1, l1_ratio=None, max_iter=300,\n",
      "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
      "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
      "                   warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(results['model'])\n",
    "print(results['model'].best_score_)\n",
    "print(results['model'].best_params_)\n",
    "print(results['model'].best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n",
      "[[56752   112]\n",
      " [   11    87]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9978406657069625\n",
      "Sensitivity: 0.8877551020408163\n",
      "Specificity: 0.998030388294879\n",
      "Precision: 0.4371859296482412\n",
      "f1 score: 0.5858585858585859\n",
      "AUC value is: 0.9428927451678476\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 93.29268292682927%\n",
      "Number of Xs and Ys for SMOTE:\n",
      "[(0, 227451), (1, 227451)]\n",
      "Oversampling is complete!\n",
      "[[56668   196]\n",
      " [   11    87]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9963659983848882\n",
      "Sensitivity: 0.8877551020408163\n",
      "Specificity: 0.9965531795160383\n",
      "Precision: 0.30742049469964666\n",
      "f1 score: 0.4566929133858268\n",
      "AUC value is: 0.9421541407784273\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 92.07317073170732%\n",
      "Number of Xs and Ys for ADASYN:\n",
      "[(0, 227451), (1, 227459)]\n",
      "Oversampling is complete!\n",
      "[[54959  1905]\n",
      " [    8    90]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9664162072960921\n",
      "Sensitivity: 0.9183673469387755\n",
      "Specificity: 0.9664990151941475\n",
      "Precision: 0.045112781954887216\n",
      "f1 score: 0.08600095556617296\n",
      "AUC value is: 0.9424331810664615\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 96.7479674796748%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test out different sampling and get best AUC before doing CV\n",
    "\n",
    "for samplingtypes in ['naive','smote','adasyn']:\n",
    "    text = 'fraud detection with random forest and ' + str(samplingtypes) + ' sampling'\n",
    "    results = modelpipeline.run_model(df, 0.2, True, samplingtypes, 'RandomForest', text, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n",
      "Fitting 5 folds for each of 2 candidates, totalling 10 fits\n",
      "[CV] max_depth=6, n_estimators=150, random_state=42 ..................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_depth=6, n_estimators=150, random_state=42, score=0.968, total= 3.4min\n",
      "[CV] max_depth=6, n_estimators=150, random_state=42 ..................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  3.4min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_depth=6, n_estimators=150, random_state=42, score=0.971, total= 4.6min\n",
      "[CV] max_depth=6, n_estimators=150, random_state=42 ..................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:  8.0min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_depth=6, n_estimators=150, random_state=42, score=0.971, total= 9.7min\n",
      "[CV] max_depth=6, n_estimators=150, random_state=42 ..................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed: 17.7min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_depth=6, n_estimators=150, random_state=42, score=0.968, total= 9.1min\n",
      "[CV] max_depth=6, n_estimators=150, random_state=42 ..................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed: 26.8min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_depth=6, n_estimators=150, random_state=42, score=0.969, total= 8.8min\n",
      "[CV] max_depth=11, n_estimators=150, random_state=42 .................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed: 35.6min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_depth=11, n_estimators=150, random_state=42, score=0.998, total=15.7min\n",
      "[CV] max_depth=11, n_estimators=150, random_state=42 .................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed: 51.3min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_depth=11, n_estimators=150, random_state=42, score=0.998, total=14.7min\n",
      "[CV] max_depth=11, n_estimators=150, random_state=42 .................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed: 66.1min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_depth=11, n_estimators=150, random_state=42, score=0.999, total=13.8min\n",
      "[CV] max_depth=11, n_estimators=150, random_state=42 .................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed: 79.9min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_depth=11, n_estimators=150, random_state=42, score=0.999, total=13.3min\n",
      "[CV] max_depth=11, n_estimators=150, random_state=42 .................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed: 93.2min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  max_depth=11, n_estimators=150, random_state=42, score=0.998, total=13.4min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed: 106.7min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best f1 score: 0.9984507724359305\n",
      "Best parameters: {'max_depth': 11, 'n_estimators': 150, 'random_state': 42}\n",
      "[[56842    22]\n",
      " [   16    82]]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9993328885923949\n",
      "Sensitivity: 0.8367346938775511\n",
      "Specificity: 0.999613111986494\n",
      "Precision: 0.7884615384615384\n",
      "f1 score: 0.8118811881188118\n",
      "AUC value is: 0.9181739029320225\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 96.54471544715447%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Do CV on naive sampling model as that is best model based on AUC\n",
    "text = 'Fraud detection with CV random forest and naive sampling'\n",
    "results = modelpipeline.run_model(df, 0.2, True, 'naive', 'RandomForest', text, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
      "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
      "                                              criterion='gini', max_depth=None,\n",
      "                                              max_features='auto',\n",
      "                                              max_leaf_nodes=None,\n",
      "                                              min_impurity_decrease=0.0,\n",
      "                                              min_impurity_split=None,\n",
      "                                              min_samples_leaf=1,\n",
      "                                              min_samples_split=2,\n",
      "                                              min_weight_fraction_leaf=0.0,\n",
      "                                              n_estimators='warn', n_jobs=None,\n",
      "                                              oob_score=False,\n",
      "                                              random_state=None, verbose=0,\n",
      "                                              warm_start=False),\n",
      "             iid='warn', n_jobs=None,\n",
      "             param_grid={'max_depth': [6, 11], 'n_estimators': [150],\n",
      "                         'random_state': [42]},\n",
      "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
      "             scoring='f1', verbose=10)\n",
      "0.9984507724359305\n",
      "{'max_depth': 11, 'n_estimators': 150, 'random_state': 42}\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "                       max_depth=11, max_features='auto', max_leaf_nodes=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=1, min_samples_split=2,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=150,\n",
      "                       n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
      "                       warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "print(results['model'])\n",
    "print(results['model'].best_score_)\n",
    "print(results['model'].best_params_)\n",
    "print(results['model'].best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n",
      "[0]\tvalidation_0-error:0.024755\tvalidation_1-error:0.017169\n",
      "[5]\tvalidation_0-error:0.015894\tvalidation_1-error:0.007145\n",
      "[10]\tvalidation_0-error:0.00619\tvalidation_1-error:0.005302\n",
      "[15]\tvalidation_0-error:0.004482\tvalidation_1-error:0.004336\n",
      "[20]\tvalidation_0-error:0.002957\tvalidation_1-error:0.003687\n",
      "[25]\tvalidation_0-error:0.001473\tvalidation_1-error:0.003125\n",
      "[30]\tvalidation_0-error:0.00122\tvalidation_1-error:0.002879\n",
      "[35]\tvalidation_0-error:0.000925\tvalidation_1-error:0.00223\n",
      "[40]\tvalidation_0-error:0.000774\tvalidation_1-error:0.001843\n",
      "[45]\tvalidation_0-error:0.00064\tvalidation_1-error:0.00151\n",
      "[50]\tvalidation_0-error:0.000541\tvalidation_1-error:0.001387\n",
      "[55]\tvalidation_0-error:0.000464\tvalidation_1-error:0.001159\n",
      "[60]\tvalidation_0-error:0.000409\tvalidation_1-error:0.001053\n",
      "[65]\tvalidation_0-error:0.000376\tvalidation_1-error:0.000913\n",
      "[70]\tvalidation_0-error:0.000303\tvalidation_1-error:0.000755\n",
      "[75]\tvalidation_0-error:0.000262\tvalidation_1-error:0.000685\n",
      "[80]\tvalidation_0-error:0.000237\tvalidation_1-error:0.000632\n",
      "[85]\tvalidation_0-error:0.000191\tvalidation_1-error:0.000562\n",
      "[90]\tvalidation_0-error:0.000158\tvalidation_1-error:0.000527\n",
      "[95]\tvalidation_0-error:0.000123\tvalidation_1-error:0.000509\n",
      "[99]\tvalidation_0-error:0.00011\tvalidation_1-error:0.000509\n",
      "[[56850    14]\n",
      " [   15    83]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9994908886626171\n",
      "Sensitivity: 0.8469387755102041\n",
      "Specificity: 0.9997537985368599\n",
      "Precision: 0.8556701030927835\n",
      "f1 score: 0.8512820512820514\n",
      "AUC value is: 0.923346287023532\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Number of Xs and Ys for SMOTE:\n",
      "[(0, 227451), (1, 227451)]\n",
      "Oversampling is complete!\n",
      "[0]\tvalidation_0-error:0.031482\tvalidation_1-error:0.026386\n",
      "[5]\tvalidation_0-error:0.024287\tvalidation_1-error:0.013465\n",
      "[10]\tvalidation_0-error:0.021205\tvalidation_1-error:0.012447\n",
      "[15]\tvalidation_0-error:0.018444\tvalidation_1-error:0.010428\n",
      "[20]\tvalidation_0-error:0.016799\tvalidation_1-error:0.009059\n",
      "[25]\tvalidation_0-error:0.015271\tvalidation_1-error:0.00876\n",
      "[30]\tvalidation_0-error:0.013645\tvalidation_1-error:0.008181\n",
      "[35]\tvalidation_0-error:0.011136\tvalidation_1-error:0.007496\n",
      "[40]\tvalidation_0-error:0.010079\tvalidation_1-error:0.006847\n",
      "[45]\tvalidation_0-error:0.00936\tvalidation_1-error:0.006443\n",
      "[50]\tvalidation_0-error:0.008277\tvalidation_1-error:0.005881\n",
      "[55]\tvalidation_0-error:0.007854\tvalidation_1-error:0.005723\n",
      "[60]\tvalidation_0-error:0.00665\tvalidation_1-error:0.005249\n",
      "[65]\tvalidation_0-error:0.005377\tvalidation_1-error:0.00474\n",
      "[70]\tvalidation_0-error:0.004423\tvalidation_1-error:0.004143\n",
      "[75]\tvalidation_0-error:0.003713\tvalidation_1-error:0.003915\n",
      "[80]\tvalidation_0-error:0.002963\tvalidation_1-error:0.003616\n",
      "[85]\tvalidation_0-error:0.002543\tvalidation_1-error:0.003529\n",
      "[90]\tvalidation_0-error:0.002047\tvalidation_1-error:0.003353\n",
      "[95]\tvalidation_0-error:0.001855\tvalidation_1-error:0.003178\n",
      "[99]\tvalidation_0-error:0.001752\tvalidation_1-error:0.003002\n",
      "[[56705   159]\n",
      " [   12    86]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9969979986657772\n",
      "Sensitivity: 0.8775510204081632\n",
      "Specificity: 0.99720385481148\n",
      "Precision: 0.3510204081632653\n",
      "f1 score: 0.5014577259475218\n",
      "AUC value is: 0.9373774376098216\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Number of Xs and Ys for ADASYN:\n",
      "[(0, 227451), (1, 227455)]\n",
      "Oversampling is complete!\n",
      "[0]\tvalidation_0-error:0.071942\tvalidation_1-error:0.095924\n",
      "[5]\tvalidation_0-error:0.048399\tvalidation_1-error:0.051964\n",
      "[10]\tvalidation_0-error:0.035451\tvalidation_1-error:0.042291\n",
      "[15]\tvalidation_0-error:0.028584\tvalidation_1-error:0.035023\n",
      "[20]\tvalidation_0-error:0.023732\tvalidation_1-error:0.028738\n",
      "[25]\tvalidation_0-error:0.019571\tvalidation_1-error:0.025175\n",
      "[30]\tvalidation_0-error:0.016201\tvalidation_1-error:0.022612\n",
      "[35]\tvalidation_0-error:0.013385\tvalidation_1-error:0.020224\n",
      "[40]\tvalidation_0-error:0.011508\tvalidation_1-error:0.01889\n",
      "[45]\tvalidation_0-error:0.010079\tvalidation_1-error:0.017064\n",
      "[50]\tvalidation_0-error:0.00896\tvalidation_1-error:0.015642\n",
      "[55]\tvalidation_0-error:0.007846\tvalidation_1-error:0.014027\n",
      "[60]\tvalidation_0-error:0.0065\tvalidation_1-error:0.012377\n",
      "[65]\tvalidation_0-error:0.00588\tvalidation_1-error:0.011253\n",
      "[70]\tvalidation_0-error:0.005298\tvalidation_1-error:0.010288\n",
      "[75]\tvalidation_0-error:0.004425\tvalidation_1-error:0.00883\n",
      "[80]\tvalidation_0-error:0.003985\tvalidation_1-error:0.008409\n",
      "[85]\tvalidation_0-error:0.003396\tvalidation_1-error:0.007163\n",
      "[90]\tvalidation_0-error:0.00286\tvalidation_1-error:0.006443\n",
      "[95]\tvalidation_0-error:0.002625\tvalidation_1-error:0.005899\n",
      "[99]\tvalidation_0-error:0.002499\tvalidation_1-error:0.005706\n",
      "[[56550   314]\n",
      " [   11    87]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9942944419086408\n",
      "Sensitivity: 0.8877551020408163\n",
      "Specificity: 0.994478052898143\n",
      "Precision: 0.2169576059850374\n",
      "f1 score: 0.3486973947895792\n",
      "AUC value is: 0.9411165774694797\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n"
     ]
    },
    {
     "data": {
      "image/png": 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FpTt27Bh69+4NJSXJBjxTU1PuM2VhYSHRDWFubo7vv/8eAoEAAJCdnY2EhAQUFhbKrJHX5e7uDl9fX+zcuRP+/v7c+dR1584d9OjRQ2p7VVWVVCtY3c9pfd89/fr1w4cffsg9X758OVq3bo3ExERufzc3N3h7eyM6OhpxcXFITExs9Nq6cuUKSktLMWnSJBgbGwMAOnXqhJSUFBQWFjZYhnVdu3ZN7uupbdu2iI6O5p7fu3cPq1atwqNHj6Cnp4eVK1dyLYDiv4+enh5mzJgh873F/vrrLwwcOBARERHctm7dusHZ2RmnTp2Cg4MDMjIycP78ecTFxcHLywtAzd9z4MCBuH37NrdfVlYWRo0axXXDivMdEBCAM2fOoHXr1jhz5gzMzMwwbtw4Lp/6+vq4evUqGGPo2LEjunXrhl27dmH48OEAalr0Ll26hIkTJ3LHbep1AdSMD+nduzf8/PwAAC4uLtDW1uZajG7fvg0jIyMsXLgQHTt2BAC4urriypUrEtcOUNNasHLlSm4syaFDh5CRkYG0tDR06tQJQM3ncunSpdzfCKiJQ+vXr4eTkxOAmu8uLy8vrFu3TqKVsSFPnz5FcnIyOnfuDKCm5cvLywtHjhxBhw4dsGHDBhQWFmLHjh1c37+dnR369+8v1/HlCvgtW7bE6tWrAdR84SYkJODy5cuYO3cu+vTpI5GWMdbo4CXx6+ILXVlZWeZIf3lduHABFRUV6Nevn8T2KVOmvNDxxMFCzNvbG/Pnz8fevXsxYcIEPH78GBkZGVzTXmZmJhhj6Nu3r0QTkpeXF6KiopCZmcl9EBtz6tQpWFlZSY1V8PX1xezZs/H333/D3NxcZj5f5hz9/Pzg5+eH8vJy3L59G//88w+uXr2Kqqoqmc17jZ2Dm5sbF+yBmh83sn70PL+fjY0NF+yBmiBnY2PD/Wg7f/48SkpKpMra3d0dSkpK+OOPP2BnZyd1bHn3O3PmDNzc3CS6lj744AOkp6fLde4XLlxAeXk5Bg0aJLG9e/fuaN26NU6dOsUFfKDmy0xMQ0MDLVq0QHFxcb3H79OnD9eMevHiRVRUVCAoKAhTpkxBQUEBBAIBLl26hODgYLnyK+bg4MB9SQO1P8qfPn3aaMB/9OgRMjMzIRAIcPjwYeTl5XHBUqy+69vBwUHqfA8dOsRVAOp+9wA1XQ9//vknVq5cieDgYCQmJqK0tBSXL1/G+PHjJb57mjdvDnd3dxw+fBiAfNeWra0tNDU18X//93/48MMP0bNnTzg4ODQaXJ/XlOup7mcAAHedlJSUQENDAxcvXsTEiRMl/j4ffvhho0EkJCQEAFBaWop//vkH//zzDy5fvgwAXB5OnToFZWVluLu7c/spKytjwIABWLlyJbftyy+/BFDz/X/79m1kZWVxd/yIj+Xq6orNmzcjICAA3t7e6NmzJzw8PODp6ckdZ8iQIZg9ezZu376NDh06ICUlBYaGhnBzc6u3TOS5LlxdXbFhwwbk5+ejd+/ecHNzw5gxY7jXu3Tpgs2bN4MxhuzsbGRlZeHmzZv43//+J/X36NChg8TAUUNDQ+jq6nLBHgAX5J8+fco9btWqFRfsAcDY2Bh2dnZSPyga0rx5cy7YA+CCuvjcMzMzYWtrKzHQz8zMTOZ3nixyBXwVFRWJ0d0ODg4YNWoUQkND8fPPP0v8cczMzLiae33EfYPiX8empqa4ePFio/u0adNG5mvivlEDA4PGT0YOzx9HU1MT/fr1Q2pqKiZMmIBff/0VALgahvj966sF5+Xlyf3eT5484QK6rDw9e/as3nw2xfP7lpWVYcGCBdi5cyfKy8vRpk0b2NnZQUVFpcn3Gz9+/FhmX2zdQF7ffuJ+8ef3y83NBVBb1hMmTJB5jPrKWt79Hj169FIDacT9k7L+NgYGBlK3ntX9UQQASkpKDZa3u7s71q5di1u3buH48ePcWAQVFRWcPHkSlZWVUFFRkVmbbsjz423ErQONtY4xxjB79mw8ffoUcXFxmDx5MmbOnInExESJAGVmZiYxxkMsKSmJ+zFw6NAhrFq1SuL15797gJofT/r6+pgxYwYOHToEa2trMMZgaGgodXwDAwPumpHn2jI3N8emTZvw448/Ytu2bVi7di00NTXh7++PmTNnQk1NrcHyEGvK9STrMwDUlP2TJ0/AGJMIQEDNYMbntz3v0aNHmD9/Pg4cOADGGNq1a8e1Sonz8PjxY+jq6kpV0p6/VrOzsxEREYE//vgDKioq6NSpk9S16u3tjbi4OCQlJSEuLg5RUVEwNjbG559/zt1WO2DAAK5cwsLCsGfPHvj6+kqM96ivTBq6LmbOnAlTU1Ps2rUL33zzDRhj6NKlC+bMmcOd888//4zVq1fj4cOHMDAwgKWlJTQ0NFBaWipxrOfHkQHS10fdz7bY8z9ygZofrI3Fw7rq+yyIz72goEDmd6SBgYHE2J76vNB9RKqqqli8eDEGDRqEmTNnIjU1Fc2aNQNQ00SWkJCAu3fv1ttMv2/fPqirq3PNM25ubjhy5AguXboEa2trqfT37t2Dl5cXgoKCMH36dKnXdXV1AUBi8AVQM0Dn1q1bsLOz4/5Az3+BiQcONmbw4MHYsWMH/vrrL+zduxe9evXigpr4/RMTE6GjoyO1b2OBrq7mzZvj/v37Utvz8/MBQO5BTU0VGRmJtLQ0REVFwcXFBZqamgBqmsae93wZFhYWSjzX09OT+eF79OgRV3OUpaH9xMRlvWTJEolf3HWPIYu8++no6Ei8n9ixY8fQqVOnRkfjN2/eHADw4MEDqVaU/Px82NjYNLh/Y7p164bmzZvj+PHjOHnyJJycnKCtrQ0LCwucOnUKhYWFcHZ25v5+r1tiYiKOHDmChQsXom/fvggPD8fy5cvx008/SXT1eXh4YPXq1RLdYgDQtWtX7vH169flfl/xfrdv34arqysEAkG91414gJ+811aXLl2wYsUKVFVV4dKlS9ixYwc2btwIU1NTidsPG9KU66khenp6UFJSksq3+MdAQ6ZOnYpbt24hMTERtra2UFNTQ0lJCbZs2cKl0dfXx5MnT1BRUSFxF0/da6C6uhrBwcFQVVXFtm3bYG5uDhUVFfz999/YtWuXxHt6eXnBy8sLJSUlyMzMRGJiIiIiImBpaQkrKytoaGhg4MCB2LdvH5ydnXH//v1XcqeFqqoqxo4di7Fjx+Lhw4c4evQoVq5ciQkTJiAjIwO//fYbFi5ciOnTp8Pf35/7sTRp0iSp611WMJeHrO+N+/fvv9KR+CYmJnj48KHUdlnbZHnhUfrt2rVDSEgIsrOzsWbNGm77yJEjoaOjg1mzZslsgtm/fz927dqFUaNGcU2FH330EQwNDbFw4UKpfRhjWLx4MQQCQb3N4tbW1lBVVcWBAwckticlJeHzzz8HY4x7r3///Zd7/c8//5QKVvVxdnaGqakpNm/ejLNnz8LX15d7zdHREUBNoVtZWXH/iouLsWzZMu4LRR6Ojo64fPmy1Cjd3bt3Q19fHx06dJD7WE1x9uxZODo6wtPTk/tyunz5MgoKCiR+WWtra0uUIVAzoURdrq6u+P333yXK9vr1642O+nZ1dcWlS5ck0j148AAXLlzgntvY2EBNTQ25ubkSZa2trY0lS5bg1q1bACBVY5B3PwcHBxw7dkziV39WVhbGjRvHnefzfeOy3mfPnj0S20+fPo2cnByp5tumUlZWRs+ePXH06FFcvnwZzs7OAGo+nydOnEBGRgY8PDzq3b+hvDfVpUuXsGLFCnz44YcICAgAAIwbNw7du3dHdHQ0N7IdqPleaNmyJWbMmCHVygHUXOfPj6hvyPnz5wEA7du3h6amJiwtLfHrr79KdNc8ffoU6enpXJnLc22tW7cO7u7uKCsrg7KyMuzs7DB//nxoampyNTV5ylDe66kxampqcHBwwK+//irRLXL06FFUVFQ0moe+ffuie/fuXMvE0aNHAdTWGHv06IHq6mrs27dPYt9Dhw5xjx89eoTbt2/Dz88PlpaWXGuA+FjiCsDkyZO5FjQNDQ24u7tzFbS6tdwhQ4YgKysLK1euhJ2dHden/qJKS0vRr18/JCQkAKipVfv7++OTTz7BkydPUFhYiLNnz0JTUxPjxo3jgr14e1PHeNXnzp07Ej9a8/LycOHCBe4afRUcHR1x8eJFrsUTqPnBWvc7siEvNVNIcHAwdu7ciR9//BG+vr5o06YNDAwMEBsbi9DQUPj6+mLkyJHo3LkzioqKcOjQIezcuRP9+vWTGPyho6ODJUuWIDQ0FAEBAQgMDETHjh2Rn5+PX375BefOnUNERIRE30Zd+vr6GDVqFBITE6GqqooePXrg2rVr+OmnnzB+/Hjuw7d48WLMmTMHwcHBePToEeLi4mTe3iOLQCDAoEGDkJCQAF1dXYmxC0KhEL6+vpg7dy6ys7NhY2ODO3fu4Pvvv4eBgYHMJpj6jBkzBrt378bo0aMxYcIE6OvrY+fOnTh58iQiIyOlAtmrYmNjg9TUVGzcuBGdOnXC9evXsXr1aggEAokfYR4eHti3bx+srKzwwQcf4LfffpPqjpk4cSIOHDiATz/9FCEhISgpKUFMTAzXClSfTz/9FNu2bcPYsWMRFhYGVVVVqSZePT09BAcHIy4uDk+fPoWLiwsePnyIuLg4lJaWwtLSEgC4lpbff/8dhoaG+OCDD+Tab8KECRg6dCjGjh2L0aNHo7KyEvHx8ejcuTM3RkRXVxc3btzAiRMnJPrsgJrbxUJCQhAXFwdVVVV4enri7t27iImJQYcOHbjA+DL69OmD6dOnQ01Njeu7c3Z25vq66/bHPk/c0nHgwAF4e3s32OLSkGfPnmHKlCkwMjLiBkECNcFwyZIl+OijjzB16lTs2LEDmpqaMDAwQFxcHMLDwzFw4EB8/PHHsLKygqqqKq5fv46dO3fi5s2bcHd3l2iqFg9MFKuursaVK1cQFxcHkUjEnevUqVMRFBSEMWPGYOTIkaioqMCaNWtQVlbGDaSV59pydnbG8uXL8dlnnyEwMBBqamrYu3cvSktLuYFR8pShvNeTPKZMmYJRo0YhJCQEw4cPR15eHvf5aoi1tTX27t0LCwsLmJiY4Ny5c0hISJDIg5OTE/r06YN58+bh4cOH6NChA7Zv3879AAZqAmjr1q2xadMmmJiYQFdXFxkZGdi4cSMAcLdLOzs7Y968efj222/Rp08flJSUICEhAXp6ehItG9bW1twtyZGRkU0qC1nU1dVhYWGB+Ph4KCkpoUuXLrh79y4SExPh7OwMfX192NjYIDk5GZGRkfDw8EB+fj7Wrl2LBw8eyGzCfxECgQATJkzA5MmToaKigvj4eOjq6r7SCeNGjRqFpKQkBAUFcQMdV61ahcrKSrlaJl4q4Ddr1gxffvklPv/8cyxatIgb5OHo6IidO3di/fr1SEpKQk5ODrS0tNClSxdERUXJHFHo6uqKrVu3IjExEevWrUN+fj50dXXRtWtXJCUlyRwRXdf06dNhaGiI5ORkbNiwAa1bt8a0adO4wm7Xrh2WLVuG+Ph4TJw4Ee3atcP06dORnJws9/n6+vpi9erVGDBggFRf3sKFC7mLZeXKldDT04OXlxcmTZokd78fAO4cli9fju+++w5lZWUQiUQSo2hfB/H907GxsSgvL4eZmRk+++wz3L59GwcOHOD6hmfPno3q6mrExMRASUkJHh4eiIyMlOgbb9OmDZKSkrBkyRLMmjULOjo6CA4Olqr1Pk9XVxebNm3CokWLEBERATU1NQwdOhRZWVkSffPh4eEwMjJCUlIS1q9fD11dXTg5OWHKlClc89kHH3yAwYMHIykpCbdu3cKPP/4o135du3bFxo0bsWLFCsyYMQMaGhpwdXXF9OnTuZra+PHjMW/ePEyYMEHmOYWFhcHAwAAbN27E9u3b0aJFC/Tv3x9Tpkx5JV8uvXr1grKyMmxtbbk+P3t7e6ipqaFz584Nztzl4uKCnj17Ijo6GtnZ2RKT3DTFV199hZycHGzYsEGqG8vU1BTz5s3DtGnTsGDBAixYsABAzWjiPXv2YPPmzdi/fz82bNiA0tJSGBsbw9HREREREVLXeUFBgcTdHaqqqjAyMsJHH32E8PBwLiqXEugAACAASURBVOi5uLggMTERsbGxmD59Ojdhz3fffcf94Jbn2jI3N8eaNWsQHx+P2bNno7y8HCKRCPHx8ejevbvcZSjv9SQPe3t7/PTTT4iKisKUKVNgbGyMr7/+utFguXjxYnz77bfcNNDt27dHREQE0tLScPbsWS5dTEwMVqxYgYSEBBQVFaFPnz74/PPPsWLFCi7NypUrsWDBAnz11Vfc5yw+Ph7fffcdzpw5g9GjR2PYsGGoqqrC5s2bkZKSAhUVFTg4OGDhwoVcV5eYu7s7srOzJe7AeRmRkZGIiYnBxo0buW4cT09PfPHFFwBqvruzs7ORkpKCLVu2wNjYGL1798bIkSMxZ84cXL9+Xeb4jqYwNDTExIkTsXTpUhQVFcHFxQWzZs1qdKxFU+jq6mL9+vVYsGABZs+eDS0tLQQGBuLYsWNydeMJWFNHZBFCCCEvwd/fH127dn0lNXw+mDVrFo4fP47ff//9tb7PuXPn8OTJE4lWvIqKCri7u3Nj6hrCv8m/61FUVITCwkLo6ek1qcYsy+rVq5Gent5g7b6srAyLFy/Gr7/+itLSUri5uWHu3Lmv7E4AQgh5nxQVFSExMRF//vknbty4gaVLlyo6S2+d/Px8TJ48GePHj4eTkxOKi4uxdetWFBYW4v/+7/8a3Z/XAf/QoUPYs2cPMjMzJUak6uvrw83NDQMGDJA5yUdDkpKSEBUV1eh9i/PmzcO5c+cQGxsLNTU1REREICwsrEldAIQQQmqoq6tjy5YtKC8vx/z582XeLUMa1r9/f8ybNw+bNm3Czz//DFVVVdja2iIpKUmuAd28bNI/ceIEFi5ciFu3bsHW1hZWVlZo3bo1NDQ08OTJE+Tm5uLs2bO4ceMGhEIhpk+fXu8MTGJ5eXmYN28eTp48CRMTE7Ro0aLe4J2bmwt3d3f88MMP3A+KrKwseHt7yzWegBBCCOEb3tXwIyMj8dtvv2H06NHw8fGROZmBWF5eHrZs2YIZM2agf//++Prrr+tN++eff0JLSwu7d+9GfHx8g2sQnzt3DtXV1dwgHaBm0J+JiQlOnz5NAZ8QQshbh3cBX11dHb/99ptcIw6NjY0RFhaGMWPGSEwDKYuHh0eD9yfXlZeXJ3NteiMjI+Tk5Mh1DEIIIYRPeBfwp02b1uR9tLW1mzzXdUNKSkpk3uOqpqbW5HnlCSGEED7gXcCvy9/fHwEBARg0aBA32cWboK6uLnMWq/Ly8iZPWfroURGqq3k3TOKNa9lSGw8fyjer4buOyqIWlUWtN1UW5ZXVKC6tQHFpZc3/ZVUo4h5XoqS0ouZ5WSWKSytRVFqJkrIK1DfaSwBAo5kKNNRVoNlMFZrqKtBSr3mu1UwFmuqq0GxWs63msTI01FXRTFVJ5mQxSkoC6Om9mslwiCReB/y2bdtiyZIlWLJkCTw9PTFkyJBGB+e9CiYmJnjy5AnKysokZofLz89vcFITWaqrGQX8/1A51KKyqEVlUaspZVFVXY2ikkoUllSgqLQChSU1/57fViTeXlqzvaKy/qlk1VSVoK2hCm11VWhp1PwzbKEBbQ0VaKmrQvu/bdr//dNSr9mupNT0+ecZQ5MX5iIvh9cBPzo6Gk+fPsWePXuwY8cOBAUFwcTEBL6+vggICKh39byXJZ57W7zMK1AzSj83N5ebN58QQl4FxhhKyqqQ+7AId3KecgG6bgCvG9DFwbukrLLeYyorCWqC8X+B2aC5BtqbiAO2yn/Buk7g1lCFtoYKVFVez9TdhB94HfCBmqkEAwMDERgYiJs3byItLQ0HDx7E6tWr4eDggGHDhqFfv35yT1NZn/v370NTUxNaWlowNjbGwIEDMW/ePCxcuBBaWlqIiIhA9+7d5V53mBDy/qmorELhfzVsrnZdWiERxItKKiW2FZVUorqBmq5GMxVo/xektTVVYdJS87lgrcLVyMXb1NWUX3jVN/Lu4n3Ar+vBgwd4+PAhHjx4AGVlZVRVVWH27NmIjo5GdHQ0LCwsXvjYPXv2RGhoKMLCwgCAm4M6LCwMjDG4ubk1eNsfIeTdUVVdjaLSShTVaSJvqJlcvK28oeZyFaWaZnL1mtp0awMtrnatpa6KVkY6YJVVtTXw/5rMlV/hCofk/cbLiXfq+t///oddu3Zh9+7dyM3NRfv27TFkyBD4+flBX18fBQUFGDduHEpLS5GWlqbo7Ep5+LCQ+igBGBrq4P79Z4rOBi9QWdR63WXBGENpeZXMfm6u1l0qva24geZyJYFAqlm8vmZyLXUV7rmaasPN5fS5qKGkJEDLltqKzsY7idc1/I8//hhXrlyBuro6+vfvjyFDhkitJ66vrw8PDw+sW7dOMZkkhLwRFZXVXE1aVp+2rJp3UUkFqhr4wa3RTFliMJqRnsZ/zeMqEk3kXD+3ugrUm6lAiZrLyVuI1wGfMYaIiAgMHDgQ2tr1/+Lr27dvk+fUJ4QoRnU1Q3FZTUB+WFSBuzlPnuvvltUHXomyiqp6j6mirFTbz62hilb6mpKjyWX0c2uqq0BFmZrLyfuD1wF/xIgR6NWrl8xgn5eXh927dyM4OPil1zEmhDQdYwxlFVUybwWrb4R5UUnN/d/11bkFAkBLvXbUuJ5OM7Qx0uZuEat7O1jdmreaiux7ugkhtXgX8Kurawa9MMYwe/ZsbNiwAS1atJBKl5mZidjYWAQHB7/pLBLyzqmsqn7udrCaQC11i1hpZZ1m8wpUVtXfXK6uplynX1sFBs3Vpfq5TU10UVlewT3XoOZyQl4b3gX8wMBAXLhwAUBN0B85cmS9aS0tLd9Utgh5K1Qz9t/saJKjyQvFA9Keux1MPGitrLyh5nIBV7vWUleFsb4mOon7uNVl17y1NFTlai6ngWqEvDm8C/iRkZHYt28fGGOIj4+Hn58fTE1NJdIoKSlBV1cXH374oYJyScjrxRhDeUW1jNHlsm8HKxTfQlba8BSomnWawptrq8H0v1vDtOsMUqsN5DVpm6nSPd2EvAt4F/A7deqE0NBQAMC///6Lzz77DG3btlVwrgh5cZVV1RKjxm/lFeLf3KcybwerrYFXorKq/nu6m6kqSwRpfV11iWlP6/Zxi7drNlN5oSlQCSHvBt4F/Orqaij9N9HEggULuG31UaJJKcgbUjMFaiXXRC7PRCxFpRUoKau/uVxZqba5XFtdBUYtNKDVSlcyWKs/VwNXV4WqCn3uCSFNw7uAb2FhgaSkJNjb26Nr164NNiUKBAJcvXr1DeaOvCvKKqrq9GXXNolLButKiQFrxaUNT4EqXkhES0MVuppqMG2pKbOf26xVc1SUlkOLpkAlhLxBvAv4EydO5PrsJ06cSF+GpEHiFcPkuR2ssM62pqwYZmakXdvP/dyKYdwgtSasGEYD1QghisDrqXWLioqgpfV2r4tMU+vWaCzIiVcME/dhS9S2pWZRqx153pQVw55fcERc+37TK4ZRwK9FZVGLyqIGTa37+vCuhl+Xq6sr+vbtC19fX7i6ulJt/y1Rd8UwcZAW/P0QufefScyiVje4F5VWNjIFau2KYVoaqjDW06z3djBaMYwQQqTxOuCPGzcOaWlp2Lt3LwwNDTFo0CB89NFHNLPeG1JdzeoMSquUXubzuZq3uPZdXtG0FcPqq4HTimGEEPLq8LpJX+zPP//Enj17sG/fPuTn50MoFMLPzw8+Pj4wMDBQdPYaxIcmffGKYeKJVySmQpWobUtuk2fFMJlBWsaKYe3M9FBWXNboimHvA2q6rUVlUYvKogY16b8+b0XAF2OM4dSpU9xyuQBw5coVBeeqYa864FdUVsuYRU3G7WD/jTx/kRXDagesyVgx7L+m86auGEZfZrWoLGpRWdSisqhBAf/14XWTfl3l5eU4cuQI0tLS8Pvvv0NFRQWenp6KztYLE68YJjVXuaxbxOpMxvJSK4Y9188trn3TimGEEPLu43XAr66uxh9//IHU1FQcPHgQRUVF6NatG7788ksMGDCgwSVz+WL/6Tu4/e8zqVp5k1YM024GM0PthmdSU1eFmiqtGEYIIUQ2Xgd8V1dXPH78GG3atMHo0aPh6+sLMzMzRWerSQ6euYvSskro6ahzK4bJuh1Mq07NnFYMI4QQ8qrxOuB7eXnB19cX9vb2is7KSxnZT4RuIiNFZ4MQQsh7jNcB/5tvvlF0Fl4JamYnhBCiaLwL+H369EFcXBwsLS3Rp0+fBtMKBAIcOXLkzWTsJVDzPCGEEEXjXcB3cXGBrq4uAMDZ2fmdqB2/A6dACCHkLce7gL9o0SLu8eLFixtMW1lZ/8QwfEJrkBNCCFE0Xt+A7enpWe/yt2fPnoWrq+sbztGLoRo+IYQQReNdDX/NmjUoKSkBANy7dw8bNmyAiYmJVLqLFy+iurr+Odv55F3oliCEEPJ2413Ar66uxqpVqwDUBModO3ZIpVFSUoKOjg4mT578prP3QmjQHiGEEEXjXcD/7LPP8NlnnwEAzM3NsWnTprf+PnzqwieEEKJovAv4dR06dAhGRm//hDXUpE8IIUTReBfwv//+ewwdOhQmJibYtm1bg2kFAgHCw8PfUM5eHDXpE0IIUTTeBfxVq1bBzc0NJiYmXF9+fd6WgC/g9b0QhBBC3ge8C/jXr1+X+fhtRjV8QgghivbW1T0fPHiAP//8s0m35FVXVyMmJgZubm6wsbHB2LFjkZWVVW/6/Px8TJkyBU5OTnBycsKkSZOQm5v7wnmmgE8IIUTReB3wS0pKMHfuXGzcuBEAcODAAfTp0wdDhgzBoEGDkJeXJ9dx4uPjkZycjMjISPzyyy9QVlZGUFAQysrKZKYPDw9HTk4O1q5di8TEROTm5uLzzz9/4fOgeE8IIUTReB3wly9fjl27dkFLSwsAsGzZMgiFQkRHR6OqqgrLly9v9Bjl5eVYu3YtQkND0bt3b5ibmyMqKgoPHjzAvn37pNIXFBTg/PnzCAkJgYWFBbp27YqQkBBcvXoVDx8+fKHzoBo+IYQQReN1wD948CCmTp0KPz8//P3338jKykJISAj69euHiRMnIiMjo9FjXLt2DcXFxXB2dua2aWtro2vXrjhz5oxUek1NTWhqamLnzp0oLCxEUVER9u7di/bt26NFixYvdB4U7wkhhCga7wbt1fXw4UMIhUIAQEZGBlRUVNCzZ08AgIGBAYqLixs9hrjZ39jYWGK7kZERcnJypNKrq6tj0aJFiIiIgIODAwQCAQwMDLBx40YoKyu/0HnQffiEEEIUjdcBv1WrVsjKyoKzszMOHToEKysraGtrAwDOnDkjc47954nn5VdTU5PYrqamhvLycqn0jDFcvXoVNjY2CAkJQVVVFaKjozFhwgRs3rwZOjo6TT4PAwNtGBpqN3m/d42hYdPL7l1FZVGLyqIWlQV5nXgd8D/66CMsW7YM+/fvx+nTp7FgwQIAQGRkJDZv3ozQ0NBGj6Gurg6gpi+/btAvLy+HpqamVPq0tDQkJSUhPT2dC+6rVq2Cu7s7tmzZgqCgoCafx6NHRVADa/J+7xJDQx3cv/9M0dngBSqLWlQWtagsaigpCdCyJVWQXgdeB/zQ0FCoqKjg7NmzmDVrFgICAgAAV65cwdixYxESEtLoMVq1agWg5lY7ceuA+Hnnzp2l0p89exbt2rWTqMk3b94cHTp0aPBWvobQoD1CCCGKxuuAD4BbSKeuzZs3y72/ubk5tLW1cerUKXTs2BEAUFhYiKtXr2L48OFS6U1MTHDnzh2UlJRAQ0MDAFBcXIy7d+9i4MCBL3QOFO8JIYQoGu8DfmFhIU6cOIHi4mIwJt0s7uvr2+D+ampqGDFiBKKiomBgYAAzMzMsX74cxsbG8Pb2RlVVFQoKCqCjowN1dXX4+vrip59+wpQpU7jld6Ojo6Gqqsq1MDQV1fAJIYQoGq8D/h9//IGwsLB6R+MLBIJGAz5QM5FOVVUV5s6di5KSEnTr1g0JCQlQU1PD3bt34enpiUWLFsHf3x9GRkbYtGkTli5ditGjRwMAunXrhuTkZDRv3vyFzoNG6RNCCFE0AZNVbeYJf39/VFZWYvbs2WjVqhWUlKSnDWjbtq0Ccia/oMj9mD2iG5prqTWe+B1GA5JqUVnUorKoRWVRgwbtvT68ruH//fffiI6OhouLi6Kz8lKogk8IIUTReD3TnpGRkcx75d821IdPCCFE0Xgd8EeMGIE1a9bg2bO3u5lLieI9IYQQBeN9k/69e/fg5uaGjh07crfJiQkEAm4lPT6jQXuEEEIUjdcBPysri5tL/21GTfqEEEIUjdcBf8OGDYrOwitB8Z4QQoii8Trgi5WXl+PSpUvIy8tDz549UVJSItfCOXyhRJ34hBBCFIz3AT85ORnR0dF48uQJBAIBtm3bhhUrVgAA4uLipPr1+Yhq+IQQQhSN16P0d+7cifnz56Nfv35YvXo1N7Wun58fzp07h7i4OAXnUD7Uh08IIUTReF3DT0hIwCeffIJ58+ahqqqK2+7j44Pc3FwkJydj+vTpCsyhfGiUPiGEEEXjdQ0/KysL7u7uMl+zsLDA/fv333COmo5q94QQQviA1wHfwMAAN27ckPnazZs3YWBg8IZz1HQU7wkhhPABrwP+wIEDsXLlSuzevRslJSUAaprHL1y4gNWrV2PAgAEKzqEcKOITQgjhAV734YeHh+PmzZuYMWMG1w8eGBiI0tJSODo6Ijw8XME5bJyMBf4IIYSQN47XAV9NTQ2rV6/G8ePHkZmZiUePHkFHRwdOTk7o1avXWzEYTgD+55EQQsi7j9cBX6xHjx7o0aOHorPxQmjQHiGEED7gZYNzdXU1Dhw4gKtXr3Lb7ty5g0mTJsHHxwdTp07F7du3FZhD+dEke4QQQviAdwG/tLQUgYGBCA8Px7FjxwAAhYWFGDFiBA4dOgQzMzNcu3YNw4YNQ05OjoJzKweq4RNCCOEB3gX8devW4caNG1i6dClGjx4NoGYRnfz8fHz99df44YcfsGvXLrRr1w6rVq1SbGblQDV8QgghfMC7gL9v3z6MHTsWPj4+aNasGQDg4MGD0NbWRkBAAABAVVUVQ4cO5VoA+ExAEZ8QQggP8C7g37lzBzY2NtzzoqIiXL9+HQ4ODlBRqR1jaGZmhgcPHigii01Cg/YIIYTwAe8CPlAzaE/s/PnzqKqqgpOTk0SaJ0+eQFNT801nrcl4WcCEEELeO7yLRx07dsSVK1e454cPH4ZAIICrq6tEuvT0dHTo0OFNZ6/pqIJPCCGEB3h3H76vry+io6PRsmVLMMaQkpICS0tLCIVCLs327duxe/duTJs2TYE5lQ+16BNCCOED3gX84cOH4+rVq5g/fz4YYzA1NcWSJUu41z08PJCTkwNHR0eMGDFCgTmVD/XhE0II4QPeBXxlZWUsWrQI4eHhePjwIUQiEVRVVbnXvb290alTJ/j6+koM4uOrt2H6X0IIIe8+3kbMVq1aoVWrVlLbZ82apYDcvDglui2PEEIID/Bu0N53332H4uLiJu1TWFgo0ezPJ1TBJ4QQwge8C/gVFRXw9vbGmjVrGp06Nzc3FzExMejXrx8qKireUA6bhlbLI4QQwge8a9L/6quv4OXlhYULFyI6OhpWVlawsrKCmZkZNDQ08PTpU+Tm5uLs2bP466+/YG5ujmXLlsHFxUXRWZeJaviEEEL4gHcBHwC6d++OnTt34siRI9izZw/S0tJQUFDAvW5oaIiePXviiy++QK9evRSY08bRKH1CCCF8wMuAL+bu7g53d3cANavoPX36FHp6ehKj9vmORukTQgjhA9714ddHXV0dRkZGLxTsq6urERMTAzc3N9jY2GDs2LHIysqqN31FRQWWL18ONzc32NraYsSIEbh27doL5VvprSlhQggh77L3IhzFx8cjOTkZkZGR+OWXX6CsrIygoCCUlZXJTB8REYGtW7fi22+/RUpKCvT19TFu3Dg8ffq06W9ONXxCCCE88M4H/PLycqxduxahoaHo3bs3zM3NERUVhQcPHmDfvn1S6bOzs7Ft2zZERkaiT58+6NSpExYsWIBmzZrh0qVLTX7/d76ACSGEvBXe+Xh07do1FBcXw9nZmdumra2Nrl274syZM1LpMzIyoKWlxY0dAAAdHR0cPnwYPXv2bPL707w7hBBC+OCdD/h5eXkAAGNjY4ntRkZGMu/z/+eff2BmZob09HQMGTIErq6uCA4Oxq1bt17o/WnQHiGEED7g9Sh9sby8PJw4cQL5+fnw8/NDfn4+hEKhXAP4SkpKAABqamoS29XU1FBeXi6VvrCwEPfu3UN0dDSmT5+OFi1a4IcffsDw4cORmpoKAwODJuVdTU0FhoY6TdrnXUXlUIvKohaVRS0qC/I68T7gL1u2DOvWrUNlZSUEAgFcXV2xdOlSFBQUYN26ddDX129wf3V1dQA1ffl1g355eTk0NTWl0quqqqKwsBDLli2DSCQCAKxYsQK9e/dGSkoKxo8f36T8V1ZW4f79Z03a511kaKhD5fAfKotaVBa1qCxqKCkJ0LKltqKz8U7idZP+2rVrsXbtWoSFhSE1NRWMMQDA559/jvv37+P7779v9BjiBXjy8/Mltufn50s18wOAiYkJBAIBPvjgA26buro62rRpg7t37zb5HGjxHEIIIXzA64CfnJyMkJAQjB8/Hu3bt+e2Ozk5ITw8HOnp6Y0ew9zcHNra2jh16hS3rbCwEFevXkX37t2l0js4OIAxhitXrnDbSktLkZ2djbZt2zb5HCjcE0II4QNeN+nn5ubC3t5e5mvt2rWTmG63PmpqahgxYgSioqJgYGAAMzMzLF++HMbGxvD29kZVVRUKCgqgo6MDdXV1ODg4oEePHpg5cya++eYb6OnpISYmBgKBAP7+/k0+B6rhE0II4QNe1/BNTU1x9uxZma9dvHgRpqamch0nPDwcH3/8MebOnYtPPvkEjDEkJCRATU0NOTk56NmzJ9LS0rj0cXFxcHZ2RlhYGAICAvD06VOsX78eLVu2bPI50Gp5hBBC+IDXNfyPP/4Y0dHRUFdXh6enJwDg2bNnSE1NRUJCgtwD6JSVlTFt2jRMmzZN6jUzMzPcuHFDYpuWlhbmzZuHefPmvfQ50F15hBBC+IDXAT8oKAj37t1DTEwMYmJiAABjxowBAAwePBjBwcGKzJ5c6D58QgghfMDrgC8QCDBv3jyMHj0amZmZePToEXR0dNC9e3eJUfR8RvGeEEIIH/A64MfFxWHIkCFo164d2rVrJ/FadnY21q5d+0qa3V8nGrRHCCGED3gX8LOzs7nH8fHx6Ny5MywsLKTS7d+/HykpKbwP+BTuCSGE8AHvAv6CBQtw9OhRAABjDFOmTJGZjjEGNze3N5m1F6LE6/sgCCGEvC94F/Dnz5+P48ePgzGGL7/8EiEhIRKT7gCAkpISdHV14eLiophMNgnV8QkhhCge7wK+sbEx/Pz8AAD//vsvhgwZAhMTEwXn6sUp0ag9QgghPMDrBufQ0FAu2DPGUF1djerqalRWVuLZs2c4cuSIgnPYOBqzRwghhA94V8Ov6+7du5g7dy5OnTqFqqoqmWmuXbv2hnPVNAKK+IQQQniA1wF/8eLFuHDhAoYNG4Zz585BQ0MDtra2yMjIwM2bNxEXF6foLDZKifrwCSGE8ACvm/RPnz6N8PBwzJkzBwEBAVBXV8f06dORkpKCbt264cCBA4rOYuMo3hNCCOEBXgf8oqIimJubAwA6d+6MP//8EwCgoqKCTz75BCdPnlRk9uRCE+8QQgjhA14HfCMjI+Tn5wMA2rdvjydPnuD+/fsAgBYtWuDhw4eKzJ5cKNwTQgjhA14H/N69eyMmJganTp2CsbExTE1NsXbtWjx58gQpKSkwNjZWdBYbRbflEUII4QNeB/zw8HDo6ekhNjYWADBlyhT8/PPPcHZ2RlpaGrdyHp/RanmEEEL4gNej9PX09LB161auWd/HxwcmJia4cOECrK2t0b17dwXnsHECXv+kIoQQ8r7gdcAXMzIy4h47ODjAwcEBAHDo0CF4enoqKltyofo9IYQQPuBlwL958yZ27doFoKZWLx6pL5aVlYXIyEhkZGTwfuIdGqVPCCGED3gX8DMzMxESEoLy8nIAwLp167Bu3To4ODigoqICcXFxSExMRHl5Oby9vRWc28ZRFz4hhBA+4F0P86pVq2Bqaoq0tDQcO3YMjo6OiIqKQkFBAYYNG4bVq1ejdevW+OmnnxATE6Po7DaKZtojhBDCB7wL+NevX8e4cePQsWNHGBoaYtq0abh48SImTZqEv/76C5MmTcLu3bvh6uqq6KzKhWr4hBBC+IB3TfqFhYVo27Yt97xjx46orKzE7du3sXXrVqn+fL6j2/IIIYTwAe9q+FVVVVBRqf0doqamBgCYNm3aWxfsAQr4hBBC+IF3Ab8+nTt3VnQWXggN0ieEEMIHb03Af1trygKK+IQQQniAd334ALB161YcO3YMAMAYg0AgQHJyMgwNDSXSCQQChIeHKyKLcqO59AkhhPABLwP+jh07pLZt27ZNatvbEPAp3BNCCOED3gX869evKzoLr9Tb2hVBCCHk3fLW9OG/rZSohAkhhPAAhaPXjOr3hBBC+IAC/mtGTfqEEEL44L0I+NXV1YiJiYGbmxtsbGwwduxYZGVlybXvnj17IBKJ5E7/PMF7UcKEEEL47r0IR/Hx8UhOTkZkZCR++eUXKCsrIygoCGVlZQ3ud+/ePcyfP/+l3ptq+IQQQvjgrQn4OTk5uHDhAoqLi1FaWir3fuXl5Vi7di1CQ0PRu3dvmJubIyoqCg8ePMC+ffvq3a+6uhrTp0+HhYXFS+WbVssjhBDCB7wP+Onp6RgwYAA8PDwwfPhw3L59G5MnT8bcuXNRXV3d6P7Xrl1DcXExnJ2duW3a2tro2rUrzpw5U+9+P/zwAyoqKjB+/PiXyj9V8AkhhPABrwP+0aNHMWHCBJiammLu3LlgyNxPAAAAIABJREFUjAEAnJyckJKSgoSEhEaPkZeXBwAwNjaW2G5kZIScnByZ+1y6dAlr167F0qVLoays/FLnQDPtEUII4QPeTbxTV0xMDPr164eoqChUVVVx/eljxozB48ePsX37doSEhDR4jJKSEgC1q+6Jqampoby8XCp9cXExpk2bhmnTpqF9+/bcD4YXpaurDkNDnZc6xruCyqEWlUUtKotaVBbkdeJ1wL958ybCwsJkvubs7Ix169Y1egx1dXUANX35dYN+eXk5NDU1pdJHRkaiffv2GDZs2Itl+jmFRWW4f//ZKznW28zQUIfK4T9UFrWoLGpRWdRQUhKgZUttRWfjncTrgK+rq4t///1X5mt3796Fjk7jv4ZbtWoFAMjPz4e2du2HKD8/X+aSuykpKVBTU4OdnR0AoKqqCgAwePBgfPTRR/jmm2+adA40Sp8QQggf8Drge3p6IjY2FkKhkAvAAoEA9+7dw5o1a+Dh4dHoMczNzaGtrY1Tp06hY8eOAIDCwkJcvXoVw4cPl0q/f/9+iecXL17E9OnTsWrVKgiFwiafA/XhE0II4QNeB/ypU6fi0qVLGDlyJPT09AAAkydPRm5uLtq0aYMvvvii0WOoqalhxIgRiIqKgoGBAczMzLB8+XIYGxvD29sbVVVVKCgogI6ODtTV1dGuXTuJ/XNzcwEApqamaNmyZZPPgdejIgkhhLw3eB3wdXV18csvv2DXrl3IzMzEo0ePoKOjg08//RT+/v7Q0NCQ6zjh4eGoqqrC3LlzUVJSgm7duiEhIQFqamq4e/cuPD09sWjRIvj7+7/yc6AKPiGEED4QMPG9bjx04sQJuLi4KDobL+XkxbvoaNpc0dlQOBqQVIvKohaVRS0qixo0aO/14XWL85gxY9CnTx+sWLECt27dUnR2XggN2iOEEMIHvA74GzZsQK9evbBlyxb4+PggICAAGzZsQEFBgaKzJjclJQr4hBBCFI/XTfpilZWV+P3337F3714cOXIEFRUV6NmzJ/z8/NCvXz9FZ69BZ//8F22NaDINaq6sRWVRi8qiFpVFDWrSf314PWhPTEVFBR4eHvDw8EBhYSGio6ORnJyMo0eP4tq1a4rOXoPotjxCCCF88FYEfAA4d+4cUlNT8euvv+Lhw4ewtbWFn5+forMlBwr4hBBCFI/XAf/69etITU1FamoqcnJy0Lp1awwdOhS+vr5o27atorMnFyVej5IghBDyvuB1wPf19YW2tjb69esHX19fODo6KjpLTSagGj4hhBAe4HXAX758Ofr27YtmzZopOisvjEbpE0II4QPeBfzs7GwYGxtDTU0N1tbWyM/PbzB9mzZt3lDOXgyN2SOEEMIHvAv43t7eSEpKgr29Pby8vBqduIbvo/Rp4h1CCCF8wLuAv3DhQrRv3557/LYHzLc8+4QQQt4RvAv4dW+1c3Z2hqGhIVRVVaXSlZSU4OrVq28yay+EBukTQgjhA17HI09PT1y5ckXma+fPn0dQUNAbzlHTCWjQHiGEEB7gXQ1/2rRpyMnJAQAwxhAREQFtbelpFrOystCiRYs3nb0m4/UvKkIIIe8N3sWjDz/8EEpKSlBSUoJAIOAe1/2nqqoKe3t7rFixQtHZbRTV8AkhhPAB72r44jnzxY8jIyNhYWGh4Fy9uLd90CEhhJB3A+8Cfl2HDx9WdBZeGlXwCSGE8AHvAn5gYCAiIiLwwQcfIDAwsMG0AoEAGzdufEM5ezFUwyeEEMIHvAv4SnVWm1F6B1aeoXBPCCGED3gX8Dds2CDz8duK5tInhBDCB29FFbq4uJh7vH//fiQmJuLOnTsKzFFTUMAnhBCieLwO+Hfu3EH//v2xevVqAEBcXBzCw8OxZMkSDB48GBcuXFBwDhv3DvRKEEIIeQfwOhwtW7YMFRUV6N27N6qqqrBx40b069cPJ06cgIODA6KjoxWdxUbRoD1CCCF8wOuAf/LkSXzxxRewt7fHhQsX8PjxYwwfPhx6enoYPnw4Ll++rOgsNoq68AkhhPABrwN+WVkZ9PX1AQAZGRnQ0NBAt27dAICbiY/v3oIsEkIIeQ/wOuB37NgRmZmZqKiowL59+/D/7d17WIzp/wfw93QSFdpEIWczUTofDKXVEmtZFXuRrRCFVNaWZf1IdrP4OiQViq1vNKx13rXxddr1XZHdrGV3tb5ZVNJJmzLppLl/f3T1ZLaJJjVN9XldV9dl7rnnmc/zmTGfee7nnucWCoVQU6v9YcGpU6cwePDgNo7w9drDlxJCCCEdn1IXfF9fX+zduxejR49GdnY25s+fDwCYOXMmzp49i4ULF7ZxhK9H5Z4QQogyULrf4b/s3Xffhb6+Pm7evAl7e3uYmZkBAGxtbbFs2TI4Ojq2cYSvx+PxwBhr6zAIIYR0cjzWTqqRWCyGWCxGz549oamp2dbhNFlRkRgSSbtIcavS19dBYeGztg5DKVAu6lEu6lEuaqmo8KCn13BJdPLmlPoIH6idqb9582akp6dzbSNHjkRwcDDGjBnThpERQggh7YdSF/y0tDQsWLAA/fr1w9KlS9GrVy/k5+cjOTkZfn5+SExM5GbtE0IIIaRxSj2k7+XlBQBISEjgZucDQE1NDebPnw9VVVUkJCS0VXhNQkP6tWi4sh7loh7loh7lohYN6bcepZ6l/9tvv8Hb21uq2AOAqqoqvLy8cPv27SZtRyKRYOfOnXB0dIS5uTl8fHyQmZnZaP+srCwEBgZCKBTCzs4OCxcuREZGxhvtCyGEENKWlLrga2tro7q6WuZ9VVVVTd5OTEwMDh06hPDwcBw+fBiqqqpYsGABKisrG/QVi8WYN28eKioqEB8fj6SkJGhpacHb2xtFRUXN3hdCCCGkLSl1wbeyskJcXBzEYrFUu1gsRlxcHGxsbF67jaqqKsTHxyMgIABOTk4wNjZGREQEnjx5gjNnzjTof/nyZeTn52P79u0YMWIE+Hw+tmzZgvLycly8eLHF9o0QQghRJKWetBccHAx3d3e88847cHJyQq9evfDkyRNcvnwZ1dXV2Lx582u3kZ6ejufPn2P06NFcm7a2NkaOHIm0tDS4urpK9a/7kqGjoyPVzhjD06dPW2bHCCGEEAVT6oI/cOBAHD58GNHR0UhJSUFJSQl69OgBoVCIgIAADBs27LXbyM/PBwD06dNHqr13797Izc1t0N/Q0BCGhoZSbYmJiaisrISTk9Mb7A0hhBDSdpS64APAsGHD3mgZ3PLycgCAhoaGVLuGhkaT5gGcOXMGO3bswLx58yAQCOR+fpptWk9fX+f1nToJykU9ykU9ygVpTUpZ8DMyMiASifD48WMMHDgQs2fPxtChQ5u1rbqr8lVVVUkV/aqqKnTr1u2Vj92/fz82btwIV1dXfPLJJ816fvpZXi36yVE9ykU9ykU9ykUt+lle61G6gn/jxg3MmzcPNTU10NXVxZUrV/DVV19h+/btmDhxotzbqxueLygogLZ2/ZuooKCg0VMCEokEGzZsQFJSEvz8/PDxxx/TqneEEELaNaWbpb9r1y4MGTIE58+fR0pKClJSUmBjY9OkCXqyGBsbQ1tbGz/99BPXJhaLcefOHdjZ2cl8TFhYGA4ePIjQ0FAEBwdTsSeEENLuKV3B//333+Hv749+/foBAHR1dbFixQrk5OSgoKBA7u1paGjA09MTERERuHDhAv78808sX74cffr0gYuLC2pqalBYWIiKigoAwLlz53D48GH4+fnBxcUFhYWF3F9ZWVmL7ishhBCiKEpX8MViMfT09KTaBg0aBMYYiouLm7XNoKAgfPDBBwgNDYWHhwcYY9i3bx80NDSQm5sLBwcHJCcnAwC++eYbAMCePXvg4OAg9RcXF/dmO0cIIYS0EaU7h19TUwMVFenvIV26dAEAvHjxolnbVFVVRUhICEJCQhrc179/f9y9e5e7HR0d3aznIIQQQpSZ0h3hE0IIIaTlKd0RPlA7gz47O5u7XVNTA6D2Ijrdu3eX6mtkZKTQ2AghhJD2SCkL/vLly2W2L126tEFbenp6a4dDCCGEtHtKV/A3btzY1iEQQgghHY7SFXw3N7e2DoEQQgjpcGjSHiGEENIJUMEnhBBCOgEq+IQQQkgnQAWfEEII6QSo4BNCCCGdgNLN0v+n/Px87Nq1CykpKSgoKMChQ4dw+vRpmJiYYOrUqW0dHiGEENIuKPUR/oMHDzB9+nScO3cO5ubmqK6uBgAUFRVhxYoVOHfuXBtHSAghhLQPSn2Ev3nzZhgaGuLAgQPQ1NTEd999BwD417/+hYqKCuzbtw8uLi5tHCUhhBCi/JT6CP/69evw9fWFtrY2eDye1H0zZ87EvXv32igyQgghpH1R6oKvoqLSoNDXKS8vb7CMLiGEEEJkU+qKaWtri9jYWDx79oxr4/F4qKmpgUgkgo2NTRtGRwghhLQfSn0Of8WKFZg9ezZcXFxgZ2cHHo+HvXv34t69e8jJycHBgwfbOkRCCCGkXVDqI/yhQ4fi2LFjGDt2LG7cuAFVVVWkpqZiyJAhOHz4MIyNjds6REIIIaRdUOojfAAYMGAAtm7d2tZhEEIIIe2aUhf8x48fv7ZP3759FRAJIYQQ0r4pdcF3dnZudJZ+nfT0dAVFQwghhLRfSl3wP//88wZtZWVlSE1Nxe3bt7Fhw4Y2iIoQQghpf3iMMdbWQTRHaGgoqqqqsGnTprYO5ZWKisSQSNpliluUvr4OCgufvb5jJ0C5qEe5qEe5qKWiwoOennZbh9EhKfUs/VeZPHkyLl261NZhEEIIIe1Cuy34GRkZkEgkbR0GIYQQ0i4o9Tn8yMjIBm0SiQSPHz/G2bNnMWnSpDaIihBCCGl/lLrg7969W2a7trY23n33XaxatUrBERFCCCHtk1IX/Nu3b0NDQ6OtwyCEEELaPaU+h//ee+/hzJkzbR0GIYQQ0u4pdcEvLi5G9+7d2zoMQgghpN1T6oLv5uaG3bt348GDB20dCiGEENKuKfU5/IyMDPz666+YMmUK1NXV8dZbb0ndz+Px8P333zdpWxKJBNHR0Thy5AhKS0thbW2NdevWYeDAgTL7FxcXIzw8HD/++CMYY5g8eTJWrVoFLS2tN94vQgghRNGUuuAbGhpi2rRpLbKtmJgYHDp0CJs2bUKfPn2wbds2LFiwAN999x26dOnSoH9QUBAqKiqQkJAAsViM1atXIzQ0FNu2bWuReAghhBBFareX1pVHVVUV7O3tERISgg8//BAAIBaL4eDggLCwMLi6ukr1/+WXX+Dh4YHvvvsOw4YNAwBcu3YN8+fPx6VLl+RaoY8urVuLLhtaj3JRj3JRj3JRiy6t23qU7hz+O++8gz/++KNFt5meno7nz59j9OjRXJu2tjZGjhyJtLS0Bv3T0tKgp6fHFXsAsLa2Bo/Hk9mfEEIIUXZKN6Sfk5ODysrKFt1mfn4+AKBPnz5S7b1790Zubm6D/gUFBTAwMJBq09DQgK6uLvLy8uR6bhWVVy/v25lQLupRLupRLupRLigHrUnpCn5rKC8vB4AGF/HR0NBAVVWVzP6yLvijoaEh95cRXV2a5FeHhunqUS7qUS7qUS5Ia1K6IX2gdvZ9S9LU1ASABsW9qqoK3bp1k9lf1heBxvoTQgghyk4pj/ADAgKgrq7+2n5N/VmeoaEhgNqhem3t+m/QBQUFUufp6xgYGKCgoECqraqqCsXFxQ2G+gkhhJD2QCkLvomJCfT09Fpse8bGxtDW1sZPP/2EIUOGAKidpX/nzh3MmTOnQX9bW1ts3boV9+/f5/rXTdazsbFpsbgIIYQQRVHKgr948WJYWVm12PY0NDTg6emJiIgI9OrVC/3798e2bdvQp08fuLi4oKamBn///Td0dHSgqakJc3NzWFlZITg4GOvXr0dFRQVCQ0Mxffr0BhP/CCGEkPZAKc/ht4agoCB88MEHCA0NhYeHBxhj2LdvHzQ0NJCbmwsHBwckJycDqD1VEB0dDSMjI8ydOxeBgYEYM2YMwsLC2nYnCCGEkGZSugvvGBsb4+DBgy16hE8IIYR0dkp3hO/m5oZevXq1dRiEEEJIh6J0R/iEEEIIaXlKd4RPCCGEkJZHBb+ZJBIJdu7cCUdHR5ibm8PHxweZmZmN9i8uLkZwcDDs7Oxga2uLtWvXoqysTIERtx55c5GVlYXAwEAIhULY2dlh4cKFyMjIUGDErUfeXLzs22+/hUAgaHJ/ZSdvLqqrq7Ft2zY4OjrCwsICnp6eSE9PV2DErUfeXBQUFGD58uWwt7eHvb09li1bJvdlvduD2NhYeHh4vLJPZWUl1q9fD6FQCEtLSwQFBeHJkycKirBjoYLfTHXL7YaHh+Pw4cNQVVXFggULGr30blBQELKyspCQkIDo6GhcvXoVoaGhCo66dciTC7FYjHnz5qGiogLx8fFISkqClpYWvL29UVRU1AbRtyx53xd1cnJysH79egVFqRjy5iIsLAxHjhzB559/jmPHjuGtt97CwoULUVpaquDIW15zPi9yc3MRHx+PhIQE5OXlYcmSJQqOunWJRCJERES8tt+6deuQkpKCqKgoJCYm4tGjRwgMDFRAhB0QI3KrrKxkFhYWLCkpiWt79uwZMzc3ZydOnGjQ/8aNG4zP57OMjAyu7erVq0wgELCcnByFxNxa5M3F6dOn2ciRI1lpaanUNszNzdnhw4cVEnNrkTcXdWpqapiHhwfz9vZmfD6fPXz4UBHhtip5c5GVlcX4fD47f/4811ZaWsrGjx/PfvzxR4XE3FrkzUVRURHj8/ns4sWLXNuFCxcYn89nT548UUjMrSkvL48tWrSIWVhYsMmTJ7PZs2c32jc3N5cZGxuzH374gWt7+PAh4/P57Oeff1ZEuB0KHeE3Ay23W0/eXFhZWSEuLg46OjpS7YwxPH36tNXjbU3y5qLOnj17UF1djUWLFikiTIWQNxdXrlyBlpYWxo8fz7Xp6Ojg0qVLcHBwUEjMrUXeXHTr1g3dunXDyZMnIRaLUVZWhtOnT2PQoEHo2bOnIkNvFX/88Qe0tLTwzTffwNzc/JV9f/nlF0gkEtjZ2XFtAwcOhIGBAX7++efWDrXDUcor7Sm7tlxuV9nImwtDQ0NubYM6iYmJqKyshJOTU+sFqgDy5gIAbt++jfj4eBw9epR7fEcgby4ePnyI/v3744cffsDu3buRm5uLkSNHYtWqVRg6dKhCYm4t8uZCU1MTGzduRFhYGGxsbMDj8dCrVy8kJSVBVVVVITG3JmdnZzg7Ozepb35+Pnr27ImuXbtKtb/q/xRpHB3hN0NbLrerbOTNxT+dOXMGO3bswLx58yAQCFolRkWRNxfPnz9HSEgIQkJCMGjQIEWEqDDy5kIsFiMnJwc7duxAUFAQdu/eDXV1dcyZM6fdT9CSNxeMMdy5cwfm5uYQiURITEyEkZER/P398ezZM4XErCzKy8tlLqTW1M8XIo0KfjPQcrv15M3Fy/bv34+PP/4Y77//Pj755JNWi1FR5M1FeHg4Bg0ahNmzZyskPkWSNxfq6uoQi8XYunUrxo0bBzMzM2zfvh0AcOzYsdYPuBXJm4vk5GSIRCJs3boV1tbWsLOz40Y9vv76a4XErCw0NTVRXV3doL0jfHa2BRrSbwZabreevLkAan+itGHDBiQlJcHPzw8ff/wxeDyeQuJtTfLm4tixY9DQ0IClpSUAoKamBgAwffp0vP/++/jss88UEHXraM7/ER6Ph+HDh3NtmpqaMDIywqNHj1o/4FYkby5u3LiBgQMHSs1z6dGjBwYPHtxhfrLZVAYGBigpKUFlZSW6dOnCtcs6TUpej47wm+Hl5Xbr1C23+/Lkkjq2trYoLCzE/fv3ubaOstyuvLkAan9+dfDgQYSGhiI4OLhDFHtA/lycO3cOp0+fxsmTJ3Hy5EmEh4cDAHbv3o1ly5YpLO7WIG8ubGxswBjD77//zrVVVFQgOzsbAwYMUEjMrUXeXBgYGCArK4s7FQDUnv559OhRhzv18zrW1tYAIJW7zMxM5OXlwdbWtq3CardUw2gJOLmpqqri+fPn2LdvHwYPHoyqqiqsW7cONTU1WLt2LQCgqKgIqqqqUFNTQ58+fXD16lUkJydj5MiRyMzMxJo1a+Ds7AxXV9c23ps3I28uzp07hy1btmDRokVwc3PD8+fPuT+g4XnO9kTeXPTs2VPq7+nTpzhx4gT8/f0bTGxsb+TNRd++ffHLL7/g5MmTEAgEKC8vR3h4OB4/fozw8PB2PXwrby6MjIzw1Vdf4datWxg2bBiePHmC9evXo6ioCOHh4dwpgo7gwoULKCkpwcyZM7m2wsJCALWfBdra2njw4AEOHToEgUCA4uJifPrppxg0aBD8/f3bKuz2q41/FthuvXjxgm3ZsoUJhUJmYWHBFixYwLKyshhjjGVnZzM+n8+OHTvG9X/y5AkLDAxkFhYWzM7Ojq1du5aVl5e3VfgtSp5cLF26lPH5fJl/27dvb8vdaBHyvi9elpqa2mF+h8+Y/LkQi8UsLCyM2dvbMzMzMzZ37lx29+7dtgq/Rcmbi3v37rFFixYxe3t7Zm9vz/z9/bn+HcnKlSsb/A6fz+eznTt3crfLysrY//3f/zEbGxtmbW3NPvroI1ZUVKToUDsEWjyHEEII6QToHD4hhBDSCVDBJ4QQQjoBKviEEEJIJ0AFnxBCCOkEqOATQgghnQAVfELk1NF+2NLR9qczodeOyIMKfie0atUqCASCRv8OHTrU5G0dP34cAoFAIZf8lBWrubk5pk2bhri4OEgkkhZ9vn/uW15eHhYvXozs7GypmCIiIlr0eWVp7DWztLTEe++9h+joaO7SvPLYvXs34uLiWizO33//HRMmTJC6Slyd9PR0mJqatvh75d69ewgODoaDgwNMTU0xduxYBAYG4ubNmy36PPKIioqCQCDAixcvANReWS8wMBAWFhawsrLCtWvX5H7vODs7IyQkhLt95MgRfPHFF01+fEVFBVxcXHDr1q2m7wjpUOha+p3UW2+9haioKJn3DRw4UMHRNJ2rqys++OAD7nZ5eTnOnz+Pbdu24dmzZwgODm6x53JycoJIJOKu2X316lV8//33+PTTT7k+IpEIffv2bbHnfBVZr9nTp0+RnJyMqKgoVFZWyr3/O3bswOLFi1skvsrKSqxcuRLLly9vsJzp//73P/j6+spcCOVNZGRkYNasWRg5ciQ++eQT6Ovro7CwEIcPH8aHH36ImJgYjB8/vkWfsylmzJgBoVAINbXaj9gTJ07g3LlzWLlyJUaMGIFRo0bJ/d6JjIyUuhb/7t27YWVl1eTHa2pqIjg4GJ988glOnTrVoa7YR5qGCn4npa6u3i6v429gYNAgbkdHR2RmZkIkEiEoKEjmcprNoaenBz09vVf2UWQOG3vNJkyYgJycHBw9erRFv/DI69ChQ3jx4gWmTJnCtVVVVeHgwYPYsWOH1OInLSUhIQHa2tqIj4+Xuizz5MmT4e7uju3bt7dJwe/bt69UMS8pKQEAeHl5ce9Ped87o0aNeuO4Jk2ahKioKBw4cAC+vr5vvD3SvtCQPmlUTU0N4uLiMHXqVJiZmcHCwgKzZ8/GtWvXGn1MZWUlwsPD8fbbb8PU1BQTJ07Ezp07uaFNoLYIbN26levz3nvv4cSJE28Uq6mpKcrKyrgP1srKSuzatQuTJ0/GqFGjMHHiRMTGxkoNe2dnZyMgIACjR4+GmZkZ3NzccPr0ae7+l4f0o6KiuCN7FxcX7ki7bli2srISNjY22LBhQ4PYJk6cKDUUe+zYMUybNg2mpqYYN24ctm3b9sZre7985FfnyJEjcHd3h4WFBczMzDB9+nQkJycDAB49egSBQAAA2LNnD5ydnbnH/fLLL/Dy8oKFhQVsbW0RHByM/Pz8Vz5/VVUVvvzyS0ybNk1qMaT//ve/2LlzJxYvXiyVg5ZSVFQkc/ElDQ0NrFixQmo0aNWqVfDw8MDx48cxfvx4WFhYwNvbG3fu3JF6bElJCdatW4exY8di1KhRcHd3x+XLl6X6MMZw4MABvPfeezAzM8M777yDmJgY7v318pC+l5cX934xNTWFl5cXgIang548eYJPP/0UY8aMgaWlJWbPno3U1FTu/peH9AUCAXJycvDtt99CIBAgIyMDAoEAIpFIKs7S0lKYmZlh//79XNu0adOQmJiIysrKpieadAhU8DuxFy9eNPh7+Tz4tm3bEB0djRkzZiA2NhZhYWEoLi5GUFAQysrKZG4zPDwc586dw7Jly7B37168//772LVrF/bu3cv1CQwMRFJSEjw8PBATEwMbGxusWrWqwYeVPB48eAAtLS3o6emBMYbFixcjLi4Orq6uiI6OhouLCyIjI7FmzRoAtUv0+vn5cQuSxMTEYMCAAQgODuZWMnzZjBkzuKHviIgIzJgxQ+r+Ll26YPLkyThz5oxUDn/99VdkZWVh+vTpAIB9+/Zh9erVsLS0RExMDLy8vLB//36sWLGiSfv58mtVVVWFvLw8xMXFISUlRWohJpFIhLVr18LJyQm7du3Cpk2boKamhpCQEOTk5KB3795cvl1dXREZGQmgdmlWb29vAMCWLVuwevVq3Lx5E56enhCLxY3GlZqaioKCArz77rtS7aNGjcKlS5ewePFiqKqqNmkf5eHs7Iy8vDzMmjULIpEIf/31F3efk5MTty91MjIysGXLFixZsgSbNm1CcXExvLy8kJeXB6D2i8u8efNw9uxZLFmyBJGRkTAyMsLixYtx6dIlbjsRERHYsGEDhEIhoqKiMGvWLOzevVvmOfk1a9bA3d0dAHDgwAHuPfiy8vJyzJkzBz/++CMCAwMRGRmJnj17wtfXF+np6Q36i0Qi6Ovrw8HBASKRCMOHD4eFhQVOnjwp1e/06dOQSCSYNm0a1zZ58mQUFha+8os76ZhoSL+Tys/Ph4mJSYN2X19f7igiNzcXQUFBmD8CexBcAAANMUlEQVR/Pnd/165dERQUhPT0dJlDkmlpaXBycoKbmxsAQCgUQltbG7q6ugBqz4P/8MMP2Lx5M1egnJycIJFIsGPHDsyYMeO15xbrRgsYY3jy5Am+/fZbXLp0CX5+fuDxeLh8+TKuXr3a4Dm6du2KqKgozJ07F7169cL9+/exdetWTJgwAQAwevRo9OnTR+YRY9++fbm5DSYmJjLPvbq6uuLIkSO4fv06hEIhgNoPXH19fYwZMwZisRjR0dFwd3fn1rp3cnKCgYEBQkJCcPPmTVhaWja63429Zv369cOyZcuwcOFCri0zMxPe3t5Sy+wOGDAAM2bMQFpaGqZPn869fgYGBtxw8datW9GvXz98+eWX3BC5jY0N3n33XYhEIixatEhmbKmpqdDS0sKQIUOk2vv06dPo/rSEWbNmobCwEPv27eNyqqurC6FQiFmzZmH06NFS/Z89e4b9+/fD3t4eAGBpaYmJEyfi3//+N1atWoVTp07hzp07OHDgALd0rbOzMxYsWIDNmzfD2dkZz549Q3x8PGbPns0VbycnJ5SVlSE1NbXB5FGBQMC9X6ysrLjz+i87ceIEMjMz8fXXX8Pc3BxA7f8dd3d3pKSkYMSIEVL9bWxsoKGhAV1dXe51nDlzJtasWYP79+9zr8Px48fh7OzM/f8Daufo9OjRA6mpqXj77bflTzppt6jgd1J6enqIjY1t0N67d2/u33VHK0+fPsXDhw/x8OFDXLx4EQAanXw1duxYHDhwAAUFBXBycoKjo6PUF4a6owpnZ2epYf4JEybg66+/xu3bt2WuEV5nz5492LNnj1SbpqYmPDw8EBAQAKB27WwVFRWpc8lAbUGOiorCTz/9BC8vLwgEAqxduxZXr16Fo6MjhEIhVq9e3ehzv461tTWMjIxw+vRpCIVC1NTUIDk5GdOnT4eqqipu3ryJ8vJyTJgwQWrfx48fDxUVFaSkpLyy4L/8monFYuzbtw+//fYbQkNDG3xw1+2HWCzGgwcPkJmZyeW+sdMHFRUV+PXXXzF37lyoqKhwMRoaGsLExARXrlxptOA/evQI/fr1k/llSV7V1dVSRZPH471y2eSAgADMmzcPKSkpSE1NxU8//YTk5GQkJyfDx8cHK1eu5PoaGhpyxR6o/UJiaWnJrbd+7do16OrqwsrKqsH7MywsDDk5Obh//z6qq6sxadIkqTiWL1/e7H1OS0uDoaEhV+yB2jkb3377bZO3MWXKFHzxxRc4deoUli9fjoyMDPz2228IDAxs0Ldv37549OhRs+Ml7RMV/E5KTU3ttZOA/vjjD3z++ee4efMmNDU1wefzuXXaG/v978qVK9G3b1+cOnUKn332GRhjGDFiBNasWQMbGxsUFxcDAGxtbWU+/nXnimfMmAEPDw8AtYVAS0sL/fv3l5qoV1JSgu7duzcoEvr6+gBqz2vyeDwkJCQgNjYW58+fx/Hjx6GqqgpHR0esX7+em5kvDx6Ph+nTp2P//v1Yt24drl+/jqKiIm6UoW7fG1vH+3X7/s/XzMbGBt7e3ggICEBiYiKsra25+7KzsxEWFoaUlBSoqalh6NCh3Dn7xpSUlEAikSAhIQEJCQkN7h80aFCjjxWLxQ1m5jfX2rVrpeZ09OvXT2o4XRZtbW1MmjSJK8L379/HmjVrEB8fDzc3N/D5fACyRxz09PSQk5MDoPY1Ki4uljmSAtS+RnWvY69eveTfuUYUFxe/doLo62hpaWHKlCk4deoUPvroIxw/fhy9e/eGg4NDg75du3bFs2fP3uj5SPtDBZ/IJBaL4ePjA4FAgOTkZAwePBgqKiq4fPky/vOf/zT6OHV1dfj4+MDHxwdFRUW4fPkydu3aBX9/f1y5cgU6OjrQ1NREUlKSzMf379//lXHp6+u/9otKjx49UFpaiqqqKqmiX1BQAADc8Kaenh5Wr16N1atX4/79+7hw4QJ27dqFdevWyRz9aIq6OQMpKSk4e/YsjI2NuULbvXt3AMDmzZsxdOjQBo99edi1KdTV1bFp0yZMmzYNK1euxHfffYcuXbpAIpHA19cX6urqOHr0KIyNjaGmpoZ79+7h1KlTjW5PW1sbPB4PXl5eeP/99xvc/6qjbF1dXTx+/Fiu+BsTEBCADz/88LXPm5eXBzc3twb9AWDIkCFYvXo1ZsyYgXv37nEFv65Yv6ywsJArtjo6OjAyMmr09/GDBw9GaWkpAODvv/+Wuq+goAB//fXXK0dpGqOjoyPz+gS3b9+GhoYGjI2Nm7SdmTNn4ujRo7hx4wbOnDkDNzc3mXMnSkpKuJyQzoMm7RGZ7t+/j6dPn+LDDz/E0KFDoaJS+1apm60s6yI3FRUVmDRpEvbt2wegtqC6u7vDw8MDJSUlEIvFsLe3R0VFBaqrqzFq1CjuLzMzEzt27JB5wRZ52dnZQSKRcDPS69QVO2tra9y8eRNjxozB7du3AdQWCD8/P9jb23NHe/9Ul4NXMTIygpWVFc6ePYuLFy9KTaQzNzeHhoYG8vLypPZdW1sbmzdvlppw1lQDBw6En58fsrOzuQvoFBcX48GDB3Bzc4OpqSl3zljWa/fyPmlpacHExAR//fWXVHwCgQAxMTENZqq/rF+/fsjPz2+Rix/179+/wfPLoq+vD01NTRw8eFDmJNJ79+4BgFRhy8rKwp9//sndzs/Px6+//sqd67e3t0deXh569uwpFUNaWhpiYmKgoqICMzMzqKur4/z581LPJxKJsGTJkmZd/c7GxgY5OTn4448/uLbq6mosX74ciYmJMh8j6/1oaWmJ4cOHIzIyErm5udxcmpcxxpCfn6+w60cQ5UFH+ESmIUOGQEdHB7GxsVBVVYW6ujrOnj3LzQKWVZg1NTVhYmLCfTCOGDECjx49QkJCAkaPHo233noL48aNg52dHQICArBo0SIMHz4cd+7cQXR0NCwtLVvkQ2jcuHGwt7dHWFgY8vPzMWLECPz888/48ssvMXXqVBgbG6OyshJdu3ZFSEgI/P390bt3b9y6dQtXrlxpdMi97gj9/PnzcHFxwYABA2T2c3V1xfr16wEAU6dO5dp1dXXh6+uL6OholJaWQigUoqioCNHR0aioqICpqWmz9tfX1xcnT57E3r174erqCiMjI/Tr1w8HDx6EgYEBunfvjitXrnCjKi+/dt27d8etW7eQlpYGGxsbBAcHY+HChQgKCuKO8g8cOIC0tDTMmzev0RgcHBwQGxuLP//8EyNHjmzWfshLVVUVYWFhWLp0Kdzd3TFnzhwMHz4c1dXVuH79OkQiETw8PDBs2DDuMTweD/7+/vjoo4+gpqaGmJgYdO/eHXPnzgUAuLm5QSQSYf78+fDz80P//v1x/fp17N27F25ubujWrRu6desGb29vJCQkQF1dHWPGjEF6ejq+/PJLLFq0qFmnNtzd3XHgwAH4+/sjMDAQ+vr6+Oqrr/D333/Dx8dH5mO6d++Ou3fv4tq1a7C3t+e+AMycORMbN26EtbU1Bg8e3OBxd+/ehVgshqOjo9xxknaOkU5n5cqVzNHR8bX9UlNTmbu7OzMzM2NCoZD5+PiwtLQ0ZmVlxb744gvGGGPHjh1jfD6fPXz4kDHGWFlZGdu4cSMbP348MzExYWPHjmWhoaHs6dOn3HafP3/ONm3axJycnJiJiQkbP34827RpExOLxa+Mh8/ns+3btzdpH+ueY9y4cczExIS5uLiw2NhY9uLFC65PZmYmCwoKYmPGjOH6xMXFMYlEInPfnj9/znx8fJiJiQkLDQ1tNKbS0lI2atQotnDhQpmxHTp0iE2dOpWZmJgwoVDIPvroI5aZmfnK/Xnda3bx4kXG5/PZkiVLGGOMpaenM09PT2ZhYcHs7OzYnDlz2A8//MCmTJnCli5dyj1u//79zMrKitna2rLKykrGWO3r7unpyczNzZm1tTXz9PRkV69efWV8L168YGPHjmUxMTGN9vlnPltKeno6+/jjj9nbb7/NTE1NmYWFBZs1axY7evQo91oyVp/Do0ePMgcHB2Zpacn8/f1ZVlaW1PaKiorYmjVr2JgxY5ipqSlzcXFhu3btYtXV1VwfiUTC4uPj2cSJE7n3TkJCAvd8O3fuZHw+n3vMP28z1vC9k5eXx4KDg5mdnR2zsLBgnp6e7NatW9z948ePZ8HBwdzts2fPMnt7e2ZhYcGys7O59ocPHzI+n8+OHj0qM1+xsbFMKBRKxUI6Bx5jtPoCIeTNJSYmIjExERcuXGjS6Q9FW7VqFa5evYr//ve/bR1Kq9q/fz8iIyPx448/olu3blL3SSQSTJw4EZ6enlK/niGdg/L9rySEtEuzZ8+GmpqaXD8lIy3n5MmT2LJlCyIiIuDp6dmg2APg5rXU/dKFdC5U8AkhLaJLly7YsmULIiMjW2TyJZHP3bt3kZSUhHHjxsmch1JRUYGIiAhs2bKFFs7ppGhInxBCCOkE6AifEEII6QSo4BNCCCGdABV8QgghpBOggk8IIYR0AlTwCSGEkE6ACj4hhBDSCfw/0UgAZ0p/aqEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for samplingtypes in ['naive','smote','adasyn']:\n",
    "    text = 'fraud detection with XGBoost and ' + str(samplingtypes) + ' sampling'\n",
    "    results_xg = modelpipeline.run_model(df, 0.2, True, samplingtypes, 'XGBoost', text, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model': XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "               colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "               learning_rate=0.1, max_delta_step=0, max_depth=6,\n",
       "               min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
       "               nthread=1, objective='binary:logistic', random_state=42,\n",
       "               reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=42,\n",
       "               silent=None, subsample=1, verbosity=1),\n",
       " 'X_train': array([[ 0.99813402, -0.23059452, -0.20775717, ...,  0.11190439,\n",
       "         -0.14282697, -0.31328796],\n",
       "        [-0.20471542, -0.37966421,  1.02577654, ..., -0.91783696,\n",
       "         -0.43865201, -0.16971694],\n",
       "        [ 0.03701903,  0.49691842, -0.37022197, ...,  0.51134353,\n",
       "          0.21293952, -0.3052918 ],\n",
       "        ...,\n",
       "        [ 0.96071009,  0.75565574, -1.4057755 , ..., -0.02153576,\n",
       "          0.04447385, -0.32973272],\n",
       "        [ 0.95962621,  0.73195307, -1.20441125, ...,  0.03409568,\n",
       "          0.08232703, -0.3405045 ],\n",
       "        [ 0.96767316,  0.63487022, -1.15801489, ...,  0.05949449,\n",
       "          0.07902666, -0.33546532]]),\n",
       " 'X_test': array([[ -8.43750548,   5.19889008, -12.29994501, ...,  -5.00102265,\n",
       "          -3.15921552,   1.10283168],\n",
       "        [  0.17348894,  -1.66155816,  -0.08842148, ...,   0.10156715,\n",
       "           0.30912753,   1.72625217],\n",
       "        [  0.71455214,  -0.35771708,   0.11120751, ...,   0.02826493,\n",
       "           0.01403931,  -0.22928832],\n",
       "        ...,\n",
       "        [  0.02064716,  -0.06645444,  -0.83523539, ...,   0.88656076,\n",
       "           1.07360621,  -0.32272343],\n",
       "        [ -0.25274348,   0.60042135,   1.10244253, ...,   0.10041117,\n",
       "          -0.81729517,  -0.33887567],\n",
       "        [ -0.8120126 ,   0.60098729,  -0.33822899, ...,   0.31154542,\n",
       "          -1.83961314,   3.27027036]]),\n",
       " 'y_train': array([0, 0, 0, ..., 1, 1, 1], dtype=int64),\n",
       " 'y_test': array([1, 0, 0, ..., 0, 0, 0], dtype=int64),\n",
       " 'results': {'accuracy': 0.9942944419086408,\n",
       "  'sensitivity': 0.8877551020408163,\n",
       "  'specificity': 0.994478052898143,\n",
       "  'precision': 0.2169576059850374,\n",
       "  'f1': 0.3486973947895792,\n",
       "  'auc': 0.9411165774694797}}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_xg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Oversampling is complete!\n",
      "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] max_depth=6, n_estimators=100 ...................................\n",
      "[CV] ....... max_depth=6, n_estimators=100, score=1.000, total=30.0min\n",
      "[CV] max_depth=6, n_estimators=100 ...................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed: 30.0min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....... max_depth=6, n_estimators=100, score=1.000, total=13.2min\n",
      "[CV] max_depth=6, n_estimators=100 ...................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed: 43.2min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....... max_depth=6, n_estimators=100, score=1.000, total=11.6min\n",
      "[CV] max_depth=6, n_estimators=100 ...................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed: 54.8min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....... max_depth=6, n_estimators=100, score=1.000, total=10.9min\n",
      "[CV] max_depth=6, n_estimators=100 ...................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed: 65.7min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ....... max_depth=6, n_estimators=100, score=1.000, total=10.8min\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed: 76.5min remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed: 76.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best f1 score: 0.9996593862747869\n",
      "Best parameters: {'max_depth': 6, 'n_estimators': 100}\n",
      "[[56835    29]\n",
      " [   12    86]]\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9992802219023208\n",
      "Sensitivity: 0.8775510204081632\n",
      "Specificity: 0.999490011254924\n",
      "Precision: 0.7478260869565218\n",
      "f1 score: 0.8075117370892018\n",
      "AUC value is: 0.9385205158315435\n",
      "Model classification metrics have finished calculating!\n",
      "Model fitting and results are complete!\n",
      "Accuracy with all fraud results is 97.5609756097561%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = modelpipeline.run_model(df, 0.2, True, 'naive', 'XGBoost', \"XGBoost CV with naive sampling\", True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model': GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "              estimator=GradientBoostingClassifier(criterion='friedman_mse',\n",
       "                                                   init=None, learning_rate=0.1,\n",
       "                                                   loss='deviance', max_depth=3,\n",
       "                                                   max_features=None,\n",
       "                                                   max_leaf_nodes=None,\n",
       "                                                   min_impurity_decrease=0.0,\n",
       "                                                   min_impurity_split=None,\n",
       "                                                   min_samples_leaf=1,\n",
       "                                                   min_samples_split=2,\n",
       "                                                   min_weight_fraction_leaf=0.0,\n",
       "                                                   n_estimators=100,\n",
       "                                                   n_iter_no_change=None,\n",
       "                                                   presort='auto',\n",
       "                                                   random_state=None,\n",
       "                                                   subsample=1.0, tol=0.0001,\n",
       "                                                   validation_fraction=0.1,\n",
       "                                                   verbose=0, warm_start=False),\n",
       "              iid='warn', n_jobs=None,\n",
       "              param_grid={'max_depth': [6], 'n_estimators': [100]},\n",
       "              pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "              scoring='f1', verbose=10),\n",
       " 'X_train': array([[ 1.95504092, -0.38078271, -0.31501285, ...,  0.04516825,\n",
       "         -0.04714479, -0.31328796],\n",
       "        [-0.40097524, -0.62694277,  1.55533881, ..., -0.37046882,\n",
       "         -0.14479169, -0.16971694],\n",
       "        [ 0.07250902,  0.82056565, -0.56135092, ...,  0.20639487,\n",
       "          0.07028777, -0.3052918 ],\n",
       "        ...,\n",
       "        [-0.63919125, -0.08559479,  1.26545196, ..., -0.06196242,\n",
       "         -0.06268653,  0.80693384],\n",
       "        [-0.07965254,  3.22201046, -3.72420139, ...,  0.66849657,\n",
       "          0.59560983, -0.34923069],\n",
       "        [-1.55421637,  1.69422929, -0.90333364, ...,  0.39520062,\n",
       "          0.02769303,  1.08607973]]),\n",
       " 'X_test': array([[-1.65265066e+01,  8.58497180e+00, -1.86498532e+01, ...,\n",
       "         -2.01857525e+00, -1.04280417e+00,  1.10283168e+00],\n",
       "        [ 3.39812064e-01, -2.74374524e+00, -1.34069511e-01, ...,\n",
       "          4.09958027e-02,  1.02037825e-01,  1.72625217e+00],\n",
       "        [ 1.39959027e+00, -5.90701288e-01,  1.68618940e-01, ...,\n",
       "          1.14086454e-02,  4.63414166e-03, -2.29288318e-01],\n",
       "        ...,\n",
       "        [ 4.04414982e-02, -1.09736789e-01, -1.26642984e+00, ...,\n",
       "          3.57844730e-01,  3.54379443e-01, -3.22723429e-01],\n",
       "        [-4.95047586e-01,  9.91480934e-01,  1.67158401e+00, ...,\n",
       "          4.05292111e-02, -2.69775457e-01, -3.38875669e-01],\n",
       "        [-1.59048567e+00,  9.92415466e-01, -5.12841392e-01, ...,\n",
       "          1.25749856e-01, -6.07225509e-01,  3.27027036e+00]]),\n",
       " 'y_train': array([0, 0, 0, ..., 1, 1, 1], dtype=int64),\n",
       " 'y_test': array([1, 0, 0, ..., 0, 0, 0], dtype=int64),\n",
       " 'results': {'accuracy': 0.9992802219023208,\n",
       "  'sensitivity': 0.8775510204081632,\n",
       "  'specificity': 0.999490011254924,\n",
       "  'precision': 0.7478260869565218,\n",
       "  'f1': 0.8075117370892018,\n",
       "  'auc': 0.9385205158315435},\n",
       " 'actual_accuracy': 0.975609756097561}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <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>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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try to get square root of number of feature for max depth of XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Xs and Ys for ADASYN:\n",
      "[(0, 284315), (1, 284298)]\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import ADASYN\n",
    "X = df.drop(['Class'], axis=1)\n",
    "y = df['Class']\n",
    "X_adasyn, y_adasyn = ADASYN().fit_resample(X, y)\n",
    "print(\"Number of Xs and Ys for ADASYN:\")\n",
    "print(sorted(Counter(y_adasyn).items()))\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_adasyn, y_adasyn, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "a = math.ceil(math.sqrt(X_train.shape[1]))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test standardization function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    2.848070e+05\n",
       "mean    -2.316949e-17\n",
       "std      1.000000e+00\n",
       "min     -2.879850e+01\n",
       "25%     -4.698909e-01\n",
       "50%      9.245335e-03\n",
       "75%      6.716927e-01\n",
       "max      1.253349e+00\n",
       "Name: V1, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = df.columns.values.tolist()\n",
    "columns.remove('Class')\n",
    "print(columns)\n",
    "\n",
    "test = df['V1']\n",
    "\n",
    "test = (test - test.mean())/test.std()\n",
    "\n",
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
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       "      <th>V3</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0.082637</td>\n",
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       "      <td>...</td>\n",
       "      <td>-0.024923</td>\n",
       "      <td>0.382854</td>\n",
       "      <td>-0.176911</td>\n",
       "      <td>0.110507</td>\n",
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       "      <td>0.330891</td>\n",
       "      <td>-0.063781</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>-1.996580</td>\n",
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       "      <td>-0.307376</td>\n",
       "      <td>-0.880075</td>\n",
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       "      <td>0.261069</td>\n",
       "      <td>-0.022256</td>\n",
       "      <td>0.044607</td>\n",
       "      <td>-0.342474</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>-1.996558</td>\n",
       "      <td>-0.693499</td>\n",
       "      <td>-0.811576</td>\n",
       "      <td>1.169466</td>\n",
       "      <td>0.268231</td>\n",
       "      <td>-0.364571</td>\n",
       "      <td>1.351451</td>\n",
       "      <td>0.639775</td>\n",
       "      <td>0.207372</td>\n",
       "      <td>-1.378673</td>\n",
       "      <td>...</td>\n",
       "      <td>0.337631</td>\n",
       "      <td>1.063356</td>\n",
       "      <td>1.456317</td>\n",
       "      <td>-1.138090</td>\n",
       "      <td>-0.628536</td>\n",
       "      <td>-0.288446</td>\n",
       "      <td>-0.137137</td>\n",
       "      <td>-0.181021</td>\n",
       "      <td>1.160684</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>-1.996558</td>\n",
       "      <td>-0.493324</td>\n",
       "      <td>-0.112169</td>\n",
       "      <td>1.182514</td>\n",
       "      <td>-0.609726</td>\n",
       "      <td>-0.007469</td>\n",
       "      <td>0.936148</td>\n",
       "      <td>0.192070</td>\n",
       "      <td>0.316017</td>\n",
       "      <td>-1.262501</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.147443</td>\n",
       "      <td>0.007267</td>\n",
       "      <td>-0.304776</td>\n",
       "      <td>-1.941024</td>\n",
       "      <td>1.241902</td>\n",
       "      <td>-0.460217</td>\n",
       "      <td>0.155396</td>\n",
       "      <td>0.186188</td>\n",
       "      <td>0.140534</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>-1.996537</td>\n",
       "      <td>-0.591329</td>\n",
       "      <td>0.531540</td>\n",
       "      <td>1.021410</td>\n",
       "      <td>0.284655</td>\n",
       "      <td>-0.295015</td>\n",
       "      <td>0.071998</td>\n",
       "      <td>0.479301</td>\n",
       "      <td>-0.226510</td>\n",
       "      <td>0.744325</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012839</td>\n",
       "      <td>1.100009</td>\n",
       "      <td>-0.220123</td>\n",
       "      <td>0.233250</td>\n",
       "      <td>-0.395201</td>\n",
       "      <td>1.041609</td>\n",
       "      <td>0.543619</td>\n",
       "      <td>0.651815</td>\n",
       "      <td>-0.073403</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>-1.996537</td>\n",
       "      <td>-0.217474</td>\n",
       "      <td>0.581674</td>\n",
       "      <td>0.752584</td>\n",
       "      <td>-0.118833</td>\n",
       "      <td>0.305008</td>\n",
       "      <td>-0.022313</td>\n",
       "      <td>0.384935</td>\n",
       "      <td>0.217954</td>\n",
       "      <td>-0.517618</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.283522</td>\n",
       "      <td>-0.771426</td>\n",
       "      <td>-0.042273</td>\n",
       "      <td>-0.613272</td>\n",
       "      <td>-0.446583</td>\n",
       "      <td>0.219637</td>\n",
       "      <td>0.628899</td>\n",
       "      <td>0.245636</td>\n",
       "      <td>-0.338556</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>-1.996495</td>\n",
       "      <td>0.627794</td>\n",
       "      <td>0.085389</td>\n",
       "      <td>0.029923</td>\n",
       "      <td>0.849382</td>\n",
       "      <td>0.139019</td>\n",
       "      <td>0.204694</td>\n",
       "      <td>-0.004170</td>\n",
       "      <td>0.067997</td>\n",
       "      <td>0.423217</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.228333</td>\n",
       "      <td>-0.373032</td>\n",
       "      <td>-0.246779</td>\n",
       "      <td>-1.287970</td>\n",
       "      <td>1.439034</td>\n",
       "      <td>-0.533435</td>\n",
       "      <td>0.085492</td>\n",
       "      <td>0.015656</td>\n",
       "      <td>-0.333278</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>-1.996432</td>\n",
       "      <td>-0.328928</td>\n",
       "      <td>0.858691</td>\n",
       "      <td>0.708575</td>\n",
       "      <td>-0.347630</td>\n",
       "      <td>0.687510</td>\n",
       "      <td>0.321345</td>\n",
       "      <td>0.905858</td>\n",
       "      <td>-3.188224</td>\n",
       "      <td>0.560128</td>\n",
       "      <td>...</td>\n",
       "      <td>2.645884</td>\n",
       "      <td>-1.399273</td>\n",
       "      <td>0.092085</td>\n",
       "      <td>-1.072752</td>\n",
       "      <td>-0.796632</td>\n",
       "      <td>-0.107075</td>\n",
       "      <td>-2.990148</td>\n",
       "      <td>-3.288077</td>\n",
       "      <td>-0.190107</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>-1.996432</td>\n",
       "      <td>-0.456572</td>\n",
       "      <td>0.173291</td>\n",
       "      <td>-0.074652</td>\n",
       "      <td>-0.191774</td>\n",
       "      <td>1.934146</td>\n",
       "      <td>2.793589</td>\n",
       "      <td>0.299205</td>\n",
       "      <td>0.712590</td>\n",
       "      <td>-0.356851</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.099963</td>\n",
       "      <td>-0.369424</td>\n",
       "      <td>-0.327055</td>\n",
       "      <td>1.670266</td>\n",
       "      <td>0.715942</td>\n",
       "      <td>-0.796632</td>\n",
       "      <td>0.029104</td>\n",
       "      <td>0.431419</td>\n",
       "      <td>0.019392</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>-1.996390</td>\n",
       "      <td>-0.172697</td>\n",
       "      <td>0.678004</td>\n",
       "      <td>0.688780</td>\n",
       "      <td>-0.156926</td>\n",
       "      <td>0.361791</td>\n",
       "      <td>-0.185218</td>\n",
       "      <td>0.526705</td>\n",
       "      <td>0.058223</td>\n",
       "      <td>-0.670586</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.336155</td>\n",
       "      <td>-0.873297</td>\n",
       "      <td>-0.193438</td>\n",
       "      <td>-0.635766</td>\n",
       "      <td>-0.133773</td>\n",
       "      <td>0.195341</td>\n",
       "      <td>0.610009</td>\n",
       "      <td>0.251681</td>\n",
       "      <td>-0.338516</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Time        V1        V2        V3        V4        V5        V6  \\\n",
       "0 -1.996580 -0.694241 -0.044075  1.672771  0.973364 -0.245116  0.347067   \n",
       "1 -1.996580  0.608495  0.161176  0.109797  0.316522  0.043483 -0.061820   \n",
       "2 -1.996558 -0.693499 -0.811576  1.169466  0.268231 -0.364571  1.351451   \n",
       "3 -1.996558 -0.493324 -0.112169  1.182514 -0.609726 -0.007469  0.936148   \n",
       "4 -1.996537 -0.591329  0.531540  1.021410  0.284655 -0.295015  0.071998   \n",
       "5 -1.996537 -0.217474  0.581674  0.752584 -0.118833  0.305008 -0.022313   \n",
       "6 -1.996495  0.627794  0.085389  0.029923  0.849382  0.139019  0.204694   \n",
       "7 -1.996432 -0.328928  0.858691  0.708575 -0.347630  0.687510  0.321345   \n",
       "8 -1.996432 -0.456572  0.173291 -0.074652 -0.191774  1.934146  2.793589   \n",
       "9 -1.996390 -0.172697  0.678004  0.688780 -0.156926  0.361791 -0.185218   \n",
       "\n",
       "         V7        V8        V9  ...       V21       V22       V23       V24  \\\n",
       "0  0.193679  0.082637  0.331127  ... -0.024923  0.382854 -0.176911  0.110507   \n",
       "1 -0.063700  0.071253 -0.232494  ... -0.307376 -0.880075  0.162201 -0.561130   \n",
       "2  0.639775  0.207372 -1.378673  ...  0.337631  1.063356  1.456317 -1.138090   \n",
       "3  0.192070  0.316017 -1.262501  ... -0.147443  0.007267 -0.304776 -1.941024   \n",
       "4  0.479301 -0.226510  0.744325  ... -0.012839  1.100009 -0.220123  0.233250   \n",
       "5  0.384935  0.217954 -0.517618  ... -0.283522 -0.771426 -0.042273 -0.613272   \n",
       "6 -0.004170  0.067997  0.423217  ... -0.228333 -0.373032 -0.246779 -1.287970   \n",
       "7  0.905858 -3.188224  0.560128  ...  2.645884 -1.399273  0.092085 -1.072752   \n",
       "8  0.299205  0.712590 -0.356851  ... -0.099963 -0.369424 -0.327055  1.670266   \n",
       "9  0.526705  0.058223 -0.670586  ... -0.336155 -0.873297 -0.193438 -0.635766   \n",
       "\n",
       "        V25       V26       V27       V28    Amount  Class  \n",
       "0  0.246585 -0.392170  0.330891 -0.063781  0.244964      0  \n",
       "1  0.320693  0.261069 -0.022256  0.044607 -0.342474      0  \n",
       "2 -0.628536 -0.288446 -0.137137 -0.181021  1.160684      0  \n",
       "3  1.241902 -0.460217  0.155396  0.186188  0.140534      0  \n",
       "4 -0.395201  1.041609  0.543619  0.651815 -0.073403      0  \n",
       "5 -0.446583  0.219637  0.628899  0.245636 -0.338556      0  \n",
       "6  1.439034 -0.533435  0.085492  0.015656 -0.333278      0  \n",
       "7 -0.796632 -0.107075 -2.990148 -3.288077 -0.190107      0  \n",
       "8  0.715942 -0.796632  0.029104  0.431419  0.019392      0  \n",
       "9 -0.133773  0.195341  0.610009  0.251681 -0.338516      0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test if finding actual accuracy with all fraud points work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "allpositive = df[df['Class'] == 1].copy()\n",
    "x_positive = allpositive.drop(['Class'], axis=1)\n",
    "y_positive = allpositive['Class']\n",
    "y_pospredict = store[model].predict(x_positive)\n",
    "accuracy_positive = metrics.accuracy_score(y_positive, y_pospredict)\n",
    "print(\"Accuracy with all fraud results is \" + str(accuracy_positive * 100) + \"%\")\n",
    "store['actual_accuracy'] = accuracy_positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 284315), (1, 284315)]\n"
     ]
    }
   ],
   "source": [
    "# importing SMOTE\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "# applying SMOTE to our data and checking the class counts\n",
    "X_resampled2, y_resampled2 = SMOTE().fit_resample(x, y)\n",
    "print(sorted(Counter(y_resampled2).items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing ADASYN\n",
    "from imblearn.over_sampling import ADASYN\n",
    "\n",
    "# applying ADASYN\n",
    "X_resampled3, y_resampled3 = ADASYN().fit_resample(x, y)\n",
    "print(sorted(Counter(y_resampled3).items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# BorderlineSMOTE\n",
    "from imblearn.over_sampling import BorderlineSMOTE\n",
    "\n",
    "X_resampled4, y_resampled4 = BorderlineSMOTE().fit_resample(x, y)\n",
    "print(sorted(Counter(y_resampled4).items()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def model_train(X, Y):\n",
    "#     # Define search parameter values\n",
    "#     depth = [5,10,15,20]\n",
    "#     tree_amt = [50,100,150]\n",
    "#     RFC = RandomForestClassifier(max_depth=5, random_state=42, n_estimators=50)\n",
    "\n",
    "#     # Create a parameter grid: map the parameter names to search values\n",
    "#     param_grid = dict(max_depth=depth, n_estimators=tree_amt)\n",
    "#     print(param_grid)\n",
    "\n",
    "#     # Instantiate and fit the grid\n",
    "#     grid = GridSearchCV(RFC, param_grid, cv=10, scoring='accuracy')\n",
    "#     grid.fit(X,Y)\n",
    "    \n",
    "#     return grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# naive_sampling_model = model_train(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#     def model_normalnaive(self, df, testratio, text, modelname):\n",
    "#         df = df.drop('Time', axis=1)\n",
    "#         return self.initialize_model(df, testratio, text, \"naive\", modelname)\n",
    "\n",
    "#     def model_w_standardize(self, df, testratio, text, modelname):\n",
    "#         df = df.drop('Time', axis=1)\n",
    "#         df = self.standardize(df)\n",
    "#         return self.initialize_model(df, testratio, text, \"naive\", modelname)\n",
    "\n",
    "#     def model_smote(self, df, testratio, text, modelname):\n",
    "#         df = df.drop('Time', axis=1)\n",
    "#         df = self.standardize(df)\n",
    "#         return self.initialize_model(df, testratio, text, \"smote\", modelname)\n",
    "    \n",
    "#     def model_adasyn(self, df, testratio, text, modelname):\n",
    "#         df = df.drop('Time', axis=1)\n",
    "#         df = self.standardize(df)\n",
    "#         return self.initialize_model(df, testratio, text, \"adasyn\", modelname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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