Metrics.ipynb

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      "name": "Metrics.ipynb",
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        "id": "5_v7PQb9iY7M",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>-1.359807</td>\n",
              "      <td>-0.072781</td>\n",
              "      <td>2.536347</td>\n",
              "      <td>1.378155</td>\n",
              "      <td>-0.338321</td>\n",
              "      <td>0.462388</td>\n",
              "      <td>0.239599</td>\n",
              "      <td>0.098698</td>\n",
              "      <td>0.363787</td>\n",
              "      <td>0.090794</td>\n",
              "      <td>-0.551600</td>\n",
              "      <td>-0.617801</td>\n",
              "      <td>-0.991390</td>\n",
              "      <td>-0.311169</td>\n",
              "      <td>1.468177</td>\n",
              "      <td>-0.470401</td>\n",
              "      <td>0.207971</td>\n",
              "      <td>0.025791</td>\n",
              "      <td>0.403993</td>\n",
              "      <td>0.251412</td>\n",
              "      <td>-0.018307</td>\n",
              "      <td>0.277838</td>\n",
              "      <td>-0.110474</td>\n",
              "      <td>0.066928</td>\n",
              "      <td>0.128539</td>\n",
              "      <td>-0.189115</td>\n",
              "      <td>0.133558</td>\n",
              "      <td>-0.021053</td>\n",
              "      <td>149.62</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1.191857</td>\n",
              "      <td>0.266151</td>\n",
              "      <td>0.166480</td>\n",
              "      <td>0.448154</td>\n",
              "      <td>0.060018</td>\n",
              "      <td>-0.082361</td>\n",
              "      <td>-0.078803</td>\n",
              "      <td>0.085102</td>\n",
              "      <td>-0.255425</td>\n",
              "      <td>-0.166974</td>\n",
              "      <td>1.612727</td>\n",
              "      <td>1.065235</td>\n",
              "      <td>0.489095</td>\n",
              "      <td>-0.143772</td>\n",
              "      <td>0.635558</td>\n",
              "      <td>0.463917</td>\n",
              "      <td>-0.114805</td>\n",
              "      <td>-0.183361</td>\n",
              "      <td>-0.145783</td>\n",
              "      <td>-0.069083</td>\n",
              "      <td>-0.225775</td>\n",
              "      <td>-0.638672</td>\n",
              "      <td>0.101288</td>\n",
              "      <td>-0.339846</td>\n",
              "      <td>0.167170</td>\n",
              "      <td>0.125895</td>\n",
              "      <td>-0.008983</td>\n",
              "      <td>0.014724</td>\n",
              "      <td>2.69</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>-1.358354</td>\n",
              "      <td>-1.340163</td>\n",
              "      <td>1.773209</td>\n",
              "      <td>0.379780</td>\n",
              "      <td>-0.503198</td>\n",
              "      <td>1.800499</td>\n",
              "      <td>0.791461</td>\n",
              "      <td>0.247676</td>\n",
              "      <td>-1.514654</td>\n",
              "      <td>0.207643</td>\n",
              "      <td>0.624501</td>\n",
              "      <td>0.066084</td>\n",
              "      <td>0.717293</td>\n",
              "      <td>-0.165946</td>\n",
              "      <td>2.345865</td>\n",
              "      <td>-2.890083</td>\n",
              "      <td>1.109969</td>\n",
              "      <td>-0.121359</td>\n",
              "      <td>-2.261857</td>\n",
              "      <td>0.524980</td>\n",
              "      <td>0.247998</td>\n",
              "      <td>0.771679</td>\n",
              "      <td>0.909412</td>\n",
              "      <td>-0.689281</td>\n",
              "      <td>-0.327642</td>\n",
              "      <td>-0.139097</td>\n",
              "      <td>-0.055353</td>\n",
              "      <td>-0.059752</td>\n",
              "      <td>378.66</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>-0.966272</td>\n",
              "      <td>-0.185226</td>\n",
              "      <td>1.792993</td>\n",
              "      <td>-0.863291</td>\n",
              "      <td>-0.010309</td>\n",
              "      <td>1.247203</td>\n",
              "      <td>0.237609</td>\n",
              "      <td>0.377436</td>\n",
              "      <td>-1.387024</td>\n",
              "      <td>-0.054952</td>\n",
              "      <td>-0.226487</td>\n",
              "      <td>0.178228</td>\n",
              "      <td>0.507757</td>\n",
              "      <td>-0.287924</td>\n",
              "      <td>-0.631418</td>\n",
              "      <td>-1.059647</td>\n",
              "      <td>-0.684093</td>\n",
              "      <td>1.965775</td>\n",
              "      <td>-1.232622</td>\n",
              "      <td>-0.208038</td>\n",
              "      <td>-0.108300</td>\n",
              "      <td>0.005274</td>\n",
              "      <td>-0.190321</td>\n",
              "      <td>-1.175575</td>\n",
              "      <td>0.647376</td>\n",
              "      <td>-0.221929</td>\n",
              "      <td>0.062723</td>\n",
              "      <td>0.061458</td>\n",
              "      <td>123.50</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>-1.158233</td>\n",
              "      <td>0.877737</td>\n",
              "      <td>1.548718</td>\n",
              "      <td>0.403034</td>\n",
              "      <td>-0.407193</td>\n",
              "      <td>0.095921</td>\n",
              "      <td>0.592941</td>\n",
              "      <td>-0.270533</td>\n",
              "      <td>0.817739</td>\n",
              "      <td>0.753074</td>\n",
              "      <td>-0.822843</td>\n",
              "      <td>0.538196</td>\n",
              "      <td>1.345852</td>\n",
              "      <td>-1.119670</td>\n",
              "      <td>0.175121</td>\n",
              "      <td>-0.451449</td>\n",
              "      <td>-0.237033</td>\n",
              "      <td>-0.038195</td>\n",
              "      <td>0.803487</td>\n",
              "      <td>0.408542</td>\n",
              "      <td>-0.009431</td>\n",
              "      <td>0.798278</td>\n",
              "      <td>-0.137458</td>\n",
              "      <td>0.141267</td>\n",
              "      <td>-0.206010</td>\n",
              "      <td>0.502292</td>\n",
              "      <td>0.219422</td>\n",
              "      <td>0.215153</td>\n",
              "      <td>69.99</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79995</th>\n",
              "      <td>58284</td>\n",
              "      <td>1.216664</td>\n",
              "      <td>0.249684</td>\n",
              "      <td>0.174210</td>\n",
              "      <td>0.510376</td>\n",
              "      <td>-0.179497</td>\n",
              "      <td>-0.560982</td>\n",
              "      <td>-0.021601</td>\n",
              "      <td>-0.028357</td>\n",
              "      <td>-0.179274</td>\n",
              "      <td>-0.112919</td>\n",
              "      <td>1.309070</td>\n",
              "      <td>0.722206</td>\n",
              "      <td>0.024770</td>\n",
              "      <td>-0.073315</td>\n",
              "      <td>0.438072</td>\n",
              "      <td>0.780292</td>\n",
              "      <td>-0.365029</td>\n",
              "      <td>0.326964</td>\n",
              "      <td>0.212787</td>\n",
              "      <td>-0.049519</td>\n",
              "      <td>-0.253330</td>\n",
              "      <td>-0.797699</td>\n",
              "      <td>0.069440</td>\n",
              "      <td>-0.041220</td>\n",
              "      <td>0.220108</td>\n",
              "      <td>0.096814</td>\n",
              "      <td>-0.030399</td>\n",
              "      <td>0.018715</td>\n",
              "      <td>11.99</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79996</th>\n",
              "      <td>58284</td>\n",
              "      <td>-0.690760</td>\n",
              "      <td>1.331928</td>\n",
              "      <td>0.913395</td>\n",
              "      <td>0.111679</td>\n",
              "      <td>-0.344744</td>\n",
              "      <td>-1.041187</td>\n",
              "      <td>0.340727</td>\n",
              "      <td>0.379708</td>\n",
              "      <td>-0.492570</td>\n",
              "      <td>-0.725864</td>\n",
              "      <td>-0.440533</td>\n",
              "      <td>-0.175328</td>\n",
              "      <td>-0.299903</td>\n",
              "      <td>-0.017137</td>\n",
              "      <td>0.907764</td>\n",
              "      <td>0.527965</td>\n",
              "      <td>0.204175</td>\n",
              "      <td>-0.039421</td>\n",
              "      <td>-0.094320</td>\n",
              "      <td>-0.012731</td>\n",
              "      <td>-0.223222</td>\n",
              "      <td>-0.739941</td>\n",
              "      <td>0.060943</td>\n",
              "      <td>0.319461</td>\n",
              "      <td>-0.150171</td>\n",
              "      <td>0.075715</td>\n",
              "      <td>0.115052</td>\n",
              "      <td>0.034766</td>\n",
              "      <td>9.99</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79997</th>\n",
              "      <td>58285</td>\n",
              "      <td>-2.287270</td>\n",
              "      <td>1.121568</td>\n",
              "      <td>1.163585</td>\n",
              "      <td>0.948471</td>\n",
              "      <td>-1.490482</td>\n",
              "      <td>1.046850</td>\n",
              "      <td>-0.699580</td>\n",
              "      <td>1.584645</td>\n",
              "      <td>-0.717062</td>\n",
              "      <td>-0.954794</td>\n",
              "      <td>0.501476</td>\n",
              "      <td>1.175644</td>\n",
              "      <td>0.378114</td>\n",
              "      <td>0.756961</td>\n",
              "      <td>0.358987</td>\n",
              "      <td>0.301812</td>\n",
              "      <td>0.269831</td>\n",
              "      <td>0.586977</td>\n",
              "      <td>0.571730</td>\n",
              "      <td>-0.323816</td>\n",
              "      <td>0.243795</td>\n",
              "      <td>0.398433</td>\n",
              "      <td>-0.048050</td>\n",
              "      <td>-0.282601</td>\n",
              "      <td>0.233288</td>\n",
              "      <td>-0.252045</td>\n",
              "      <td>-0.409044</td>\n",
              "      <td>-0.253114</td>\n",
              "      <td>102.08</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79998</th>\n",
              "      <td>58286</td>\n",
              "      <td>-1.350941</td>\n",
              "      <td>0.233411</td>\n",
              "      <td>0.950824</td>\n",
              "      <td>1.023505</td>\n",
              "      <td>0.415847</td>\n",
              "      <td>-0.505863</td>\n",
              "      <td>0.711381</td>\n",
              "      <td>-0.070386</td>\n",
              "      <td>-0.193938</td>\n",
              "      <td>0.208942</td>\n",
              "      <td>-0.406974</td>\n",
              "      <td>-0.549137</td>\n",
              "      <td>-0.888598</td>\n",
              "      <td>0.415234</td>\n",
              "      <td>1.472159</td>\n",
              "      <td>-0.525311</td>\n",
              "      <td>0.088762</td>\n",
              "      <td>-0.239321</td>\n",
              "      <td>0.136668</td>\n",
              "      <td>-0.313279</td>\n",
              "      <td>0.026327</td>\n",
              "      <td>0.595887</td>\n",
              "      <td>0.461400</td>\n",
              "      <td>0.066594</td>\n",
              "      <td>-0.158632</td>\n",
              "      <td>-0.273392</td>\n",
              "      <td>0.350422</td>\n",
              "      <td>0.140851</td>\n",
              "      <td>67.50</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79999</th>\n",
              "      <td>58287</td>\n",
              "      <td>-2.007004</td>\n",
              "      <td>0.736137</td>\n",
              "      <td>0.051847</td>\n",
              "      <td>-2.120136</td>\n",
              "      <td>-0.034212</td>\n",
              "      <td>-0.335378</td>\n",
              "      <td>1.294663</td>\n",
              "      <td>-2.431624</td>\n",
              "      <td>1.773854</td>\n",
              "      <td>0.220403</td>\n",
              "      <td>0.899449</td>\n",
              "      <td>0.600870</td>\n",
              "      <td>-1.000116</td>\n",
              "      <td>-0.283793</td>\n",
              "      <td>-0.521240</td>\n",
              "      <td>-0.791677</td>\n",
              "      <td>-0.717891</td>\n",
              "      <td>0.364789</td>\n",
              "      <td>0.634716</td>\n",
              "      <td>-0.841477</td>\n",
              "      <td>1.375444</td>\n",
              "      <td>0.209020</td>\n",
              "      <td>0.174004</td>\n",
              "      <td>0.007307</td>\n",
              "      <td>-0.082919</td>\n",
              "      <td>-0.962304</td>\n",
              "      <td>-0.950551</td>\n",
              "      <td>-0.591748</td>\n",
              "      <td>117.13</td>\n",
              "      <td>0.0</td>\n",
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              "<p>80000 rows × 31 columns</p>\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0815d634-705a-46be-9d65-01752558ac5a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0815d634-705a-46be-9d65-01752558ac5a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        Time        V1        V2        V3  ...       V27       V28  Amount  Class\n",
              "0          0 -1.359807 -0.072781  2.536347  ...  0.133558 -0.021053  149.62    0.0\n",
              "1          0  1.191857  0.266151  0.166480  ... -0.008983  0.014724    2.69    0.0\n",
              "2          1 -1.358354 -1.340163  1.773209  ... -0.055353 -0.059752  378.66    0.0\n",
              "3          1 -0.966272 -0.185226  1.792993  ...  0.062723  0.061458  123.50    0.0\n",
              "4          2 -1.158233  0.877737  1.548718  ...  0.219422  0.215153   69.99    0.0\n",
              "...      ...       ...       ...       ...  ...       ...       ...     ...    ...\n",
              "79995  58284  1.216664  0.249684  0.174210  ... -0.030399  0.018715   11.99    0.0\n",
              "79996  58284 -0.690760  1.331928  0.913395  ...  0.115052  0.034766    9.99    0.0\n",
              "79997  58285 -2.287270  1.121568  1.163585  ... -0.409044 -0.253114  102.08    0.0\n",
              "79998  58286 -1.350941  0.233411  0.950824  ...  0.350422  0.140851   67.50    0.0\n",
              "79999  58287 -2.007004  0.736137  0.051847  ... -0.950551 -0.591748  117.13    0.0\n",
              "\n",
              "[80000 rows x 31 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pylab as plt\n",
        "\n",
        "df  = pd.read_csv(\"/content/creditcard.csv\")[:80_000]\n",
        "df.head(80000)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns=['Time', 'Amount', 'Class']).values\n",
        "y = df['Class'].values\n",
        "f\"Shapes of X={X.shape} y={y.shape}, #Fraud Cases={y.sum()}\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "smP_VXFUQjzZ",
        "outputId": "d54e9455-a95b-4ba8-9330-fee158828865"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Shapes of X=(80000, 28) y=(80000,), #Fraud Cases=196.0'"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "mod = LogisticRegression(max_iter=1000)\n",
        "mod.fit(X, y).predict(X).sum()"
      ],
      "metadata": {
        "id": "Pe3a64YbnOwK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96e735b9-82a0-4ddb-ce4f-990806f4c991"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "151.0"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "#class_weight : dict or 'balanced', default=None\n",
        "#Weights associated with classes in the form {class_label: weight}.\n",
        "#If not given, all classes are supposed to have weight one.\n",
        "mod = LogisticRegression(class_weight={0: 1, 1: 2}, max_iter=1000)\n",
        "mod.fit(X, y).predict(X).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YvDUSk0CQu9J",
        "outputId": "d96aa8c9-cbac-40f2-9366-86d2126d3dfc"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "171.0"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "\n",
        "#GridSearchCV implements a \"fit\" and a \"score\" method.\n",
        "#It also implements \"score_samples\", \"predict\", \"predict_proba\",\n",
        "#\"decision_function\", \"transform\" and \"inverse_transform\" if they are\n",
        "#implemented in the estimator used.\n",
        "grid = GridSearchCV(\n",
        "    estimator = LogisticRegression(max_iter=1000),\n",
        "    param_grid={'class_weight': [{0: 1, 1: v} for v in range(1, 4)]}, #setting to loop over\n",
        "    cv=4,\n",
        "    n_jobs=-1 #this grid search appear a paralel\n",
        ")\n",
        "grid.fit(X, y)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ARbn9rOjSEIG",
        "outputId": "ffbab32f-298a-412d-d889-2c96e81a5f7a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=4, estimator=LogisticRegression(max_iter=1000), n_jobs=-1,\n",
              "             param_grid={'class_weight': [{0: 1, 1: 1}, {0: 1, 1: 2},\n",
              "                                          {0: 1, 1: 3}]})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Where the this scores comes from even the gris doesnt have any metris is \n",
        "lr = LogisticRegression()\n",
        "??lr.score #this shows the implementation of it\n",
        "#it says unless we pecify score for LogisticRegression just gioing to be accuracy"
      ],
      "metadata": {
        "id": "7xFoeeByU56u"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid.cv_results_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LxUVdVLjUOe9",
        "outputId": "23a18ff8-6ddb-440a-ec12-8125cd6eb051"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mean_fit_time': array([1.8960197 , 1.55637306, 1.5070166 ]),\n",
              " 'mean_score_time': array([0.00518435, 0.00526911, 0.00432384]),\n",
              " 'mean_test_score': array([0.9974125, 0.996575 , 0.995875 ]),\n",
              " 'param_class_weight': masked_array(data=[{0: 1, 1: 1}, {0: 1, 1: 2}, {0: 1, 1: 3}],\n",
              "              mask=[False, False, False],\n",
              "        fill_value='?',\n",
              "             dtype=object),\n",
              " 'params': [{'class_weight': {0: 1, 1: 1}},\n",
              "  {'class_weight': {0: 1, 1: 2}},\n",
              "  {'class_weight': {0: 1, 1: 3}}],\n",
              " 'rank_test_score': array([1, 2, 3], dtype=int32),\n",
              " 'split0_test_score': array([0.99405, 0.99025, 0.9873 ]),\n",
              " 'split1_test_score': array([0.99835, 0.9984 , 0.99845]),\n",
              " 'split2_test_score': array([0.99945, 0.9996 , 0.9996 ]),\n",
              " 'split3_test_score': array([0.9978 , 0.99805, 0.99815]),\n",
              " 'std_fit_time': array([0.17419669, 0.46529582, 0.1569929 ]),\n",
              " 'std_score_time': array([0.00022984, 0.00052024, 0.00066938]),\n",
              " 'std_test_score': array([0.0020302 , 0.0036967 , 0.00498027])}"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(grid.cv_results_)\n",
        "#this score the grid serch use to choose best model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259
        },
        "id": "EiqTAL9xUdU8",
        "outputId": "c534b096-b425-4c43-d11e-3c761673afe1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "        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>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_class_weight</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.896020</td>\n",
              "      <td>0.174197</td>\n",
              "      <td>0.005184</td>\n",
              "      <td>0.000230</td>\n",
              "      <td>{0: 1, 1: 1}</td>\n",
              "      <td>{'class_weight': {0: 1, 1: 1}}</td>\n",
              "      <td>0.99405</td>\n",
              "      <td>0.99835</td>\n",
              "      <td>0.99945</td>\n",
              "      <td>0.99780</td>\n",
              "      <td>0.997413</td>\n",
              "      <td>0.002030</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.556373</td>\n",
              "      <td>0.465296</td>\n",
              "      <td>0.005269</td>\n",
              "      <td>0.000520</td>\n",
              "      <td>{0: 1, 1: 2}</td>\n",
              "      <td>{'class_weight': {0: 1, 1: 2}}</td>\n",
              "      <td>0.99025</td>\n",
              "      <td>0.99840</td>\n",
              "      <td>0.99960</td>\n",
              "      <td>0.99805</td>\n",
              "      <td>0.996575</td>\n",
              "      <td>0.003697</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.507017</td>\n",
              "      <td>0.156993</td>\n",
              "      <td>0.004324</td>\n",
              "      <td>0.000669</td>\n",
              "      <td>{0: 1, 1: 3}</td>\n",
              "      <td>{'class_weight': {0: 1, 1: 3}}</td>\n",
              "      <td>0.98730</td>\n",
              "      <td>0.99845</td>\n",
              "      <td>0.99960</td>\n",
              "      <td>0.99815</td>\n",
              "      <td>0.995875</td>\n",
              "      <td>0.004980</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-de2474ad-1dd7-4100-a0da-6dd0a40e1a90')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-de2474ad-1dd7-4100-a0da-6dd0a40e1a90 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-de2474ad-1dd7-4100-a0da-6dd0a40e1a90');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   mean_fit_time  std_fit_time  ...  std_test_score  rank_test_score\n",
              "0       1.896020      0.174197  ...        0.002030                1\n",
              "1       1.556373      0.465296  ...        0.003697                2\n",
              "2       1.507017      0.156993  ...        0.004980                3\n",
              "\n",
              "[3 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#With make_scorer: "
      ],
      "metadata": {
        "id": "2Bmv0iR1pNO1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score, recall_score, make_scorer\n",
        "#recall_score gives that did I get all the froud cases\n",
        "#precision_score gives me that I predict froud ho accurate am i \n",
        "recall_score(y, grid.predict(X))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hHo8YrKOWdJX",
        "outputId": "7d4a2091-3dc6-4e1a-b76e-b7804042d5c4"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5918367346938775"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid = GridSearchCV(\n",
        "    estimator=LogisticRegression(max_iter=1000),\n",
        "    param_grid={'class_weight': [{0: 1, 1: v} for v in np.linspace(1, 20, 30)]},\n",
        "    scoring={'precision': make_scorer(precision_score), 'recall': make_scorer(recall_score)},\n",
        "    refit='precision',\n",
        "    return_train_score=True,\n",
        "    cv=10,\n",
        "    n_jobs=-1\n",
        ")\n",
        "grid.fit(X, y);"
      ],
      "metadata": {
        "id": "O0emish0XZpm"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 4))\n",
        "df_results = pd.DataFrame(grid.cv_results_)\n",
        "for score in ['mean_test_recall', 'mean_test_precision']:\n",
        "    plt.plot([_[1] for _ in df_results['param_class_weight']], \n",
        "             df_results[score], \n",
        "             label=score)\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "67gTAIOndDg0",
        "outputId": "992d70ee-272c-4f12-b08a-47722fe842e2"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Tm7YQ3enMjlFZAfvWwc4VsHM5ZH0EX77srYuK9y4o9faH5h4j1SOUNCsKyadjwz+8myTU1EJEpF55RaV8sGk/Szbt5+PN+RSVVhAZHsaF53Vi1oVeME5J0EilLUJ4m5r20mO+612dPpTtBeady73wvOUt/7ZtoWdazZXmXqO9YdtFmiiF5NOxbiF07OP9b1hERADw+Yz1uwt5f+N+3t+4jy9yCzCDrnFt+crw7kwe2IWL+iYQ01Z/clo856BTH29K/Za3rCS/5krzzhWw7Cn45HHAeW2ak8eoXbM0SfrEaqji/ZD1IVz0/9SVjoi0esWlFXyyJc+76W5THnlFpTjnDS37/y7tz+SBXRjSIw6nz0uJSYBB070JoOwI5K6uudpcu11z+hy48HsQGR26mkVQSG64zNfBfDDsulBXIiISEtvziv2heD8rsw5SXmnERbXh4v6JXDKwCxP7J9K5vdqcyilERkOfCd4EXrvm/eu9q8yb34L3HoGVf4BJ90GquluV0HFmjdgR5RlIT0+3jIyMUJdxornT4FgB3L481JWIiJwTpRWVrMw6WN1FW/aBIwD069KeSwZ14ZIBXRjVuyNtwnXTnQTRjmXwzoOQs9IbFOXSB2HAlfoWVxqFc261maXXtU7/PWuIghzvK6FL7g91JSIijWp/4TGWbNrPexv2s3RrPiVllbRtE8bY8ztzy0V9mDygC7066WtwaUS9x8Gtb8PGf8C7D8OL3/J6ybjsEUi+MNTVSSuikNwQ6171Hod8PbR1iIgEmc9nrM057HXRtmk/63K9kdp6xEfxtZE9uWRgF8adn0C7yGY+8IY0L87BoK9C/yvg8/nwwf/A3Mth4HSY8qA35LZII1NIboh1C6FHmn9UIxGR5q3gaDkf+2+6+3BTHgdKyghzkJbckZ9MG8AlA7swoGusbrqT0Atv493IN/wbsOJ38MmTsOlCGHmj12Y5rnuoK5QWTCH5VA5sgz1r4PL/CnUlItKClZRWkH2ghOz8I/7HEnYcPEJphS+oP6e8wsemfUVU+owO0RFM9N90d3G/RDrGRAb1Z4kETWQMXHw3jJoDHz0Gq56DL16CsbfD+Lu8gUtEgkwh+VSqm1pcE9o6RKTZqzMIHzhC1oES8opKj9s2MbYtKZ2j6dAuIqg1OAeTBnjBeGRyR8LDdLVYmpGYBLjiUW/k2yX/BR//GjL+5AXoC27ViH4SVOrd4mTM4HdjoF0nuOXNUFcjIs1AVRDeceAIWfmnDsJ9OsfQu3M0KQkx9Enwz3eO0cAbIg2xew28+yBs/wA6JMMlD8DQ6yBMPa5Iw6h3izO1PxPyNsJXfh3qSkSkCakdhHf4rw6fLAhP6p+oICwSbD1S4abXYdv7Xrdxr/6bN6LfpQ9D3ymhrk6aOX1Cn8y6heDCYdDVoa5ERM6x0w3CKZ2jq4NwSucYUhIUhEXOmfMvgT6TvL/b7z8Cf/k6nDcJLn0IeowMaWnSfOnTuz5m3i/beROhfWKoqxGRRlBfEM4+UML+U1wRrgrCvTvH0F5BWCT0wsJg+PUw+CrImAsf/hKenQRDr4V+U73mGB2SIbYbhKlLQzm1Bn2yO+emAU8C4cBzZvZorfXJwPNAB/8295rZG/519wG3ApXAv5vZW8ErvxHlfgaHsr2bAUSk2TqTIDxRQVik+WrTFsZ8D1K/BUuf8rqOW7ewZn1YBMQneYG5Y29/eO5dE6Lbd1ObZgEaEJKdc+HAM8BlQA6wyjm32MwyAza7H3jJzP7XOTcYeANI8c/PAIYAPYB3nXP9zawy2C8k6NYthPBIr+NyEWnSzqRphIKwSAsXFQ9THvAudhXsgsM74NAOOLyzZtr0LyjZf/x+4ZE1Ibp6SgkI0V0VoluJhvxFGA1sNbPtAM65F4GrgcCQbECcfz4e2O2fvxp40cxKgSzn3Fb/8ZYHofbG4/PB+leh72XQrkOoqxERTr/7NDWNEBEAIqIgoZ831aXsCBTk+IPzaYTo2B5e043Ybl5wDnyM7QZt47w+F6XZashfi57AroDnOUDtwdMfAt52zn0fiAEuDdh3Ra19e9b+Ac6524DbAJKTkxtSd+PauRyK9sBQDUMtci4FIwjrZjkROS2R0d4w1/UNdV1fiC7aC7mroXgflB85cb827SC2q9d8o/qxdqjuBtGdFKabqGD9JZkJzDOzXzvnxgLznXNDG7qzmT0LPAteP8lBqunMrXsFIqJhwBWhrkSkxTmTATXUfZqIhMypQrQZlBZ5YbloDxTtg+K9Xogu2ust35cJ25ZAaeGJ+4dF1ITnrkMh6QJv6txXzTpCrCF/ZXKBXgHPk/zLAt0KTAMws+XOuSggoYH7Ni2V5ZD5uheQI2NCXY1Is3QmA2ooCItIs+QcRMV5U31NOqqUldQE5+oQvdcL1oW53v1Qq//kbRsVDz3Ta0JzzzTvqrOcMw35C7QK6Oec64MXcGcA36q1zU5gCjDPOTcIiALygMXAX51zj+PduNcPWBmk2htH1odw5IDXZYxIC2NmbN1fzAeb8vh4az4FR8qCenyfwd7CYwrCIiJ1iYyBzud7U118PsjfDDmrvCl3NXz0SzCft75zX39o9ofnLkMgXJ+hjeWUZ9bMKpxzdwJv4XXvNtfM1jvnHgEyzGwx8CPgD865H+LdxHezeeNdr3fOvYR3k18FcEeT79li3avQNh76XnrqbUWagZLSCpZuzeeDzXl8uCmP3MNHAejftT09OrQL+s8b2C1WbYRFRM5EWBh0GehNaTd6y0qLYPfn/uCcAVvfhbULvHUR0dA9tSY0J10Acd1DV38L47ws23Skp6dbRkZGaH54+TF4rB8M+ip87XehqUHkLAVeLf5g835WZR2irNJHTGQ44/smMGlAFyYOSKRnIwRkERFpZGbejYNVoTlnFez9Air93wzGJflDc7rXXKPz+RCdoPbN9XDOrTaz9LrW6fJOoK3veo3q1auFNDMnu1p88/gUJg1IJL13JyLb6ENSRKRZc84bBKVjbxh2nbesohT2flnTTCNnFWQuqtknPBJiu3td18X1hPie/kf/87ie6mWjDgrJgdYthOjO3vjvIk3Yqa4W3zG5r64Wi4i0Fm3a1lw95nvesuL93ujBh3dCYQ4U5Ho3B+5cAUW7wVdR6xjtIK6HP0An1QrSPbz5qPhWFaQVkquUlcDmf8GImWoEL03SKa8W908kPUVXi0VEBGjfBQZMq3udr9IL0YW5Xh/QhbsD5nNh+wderxtVNwxWiWxfE5w79IJ4/1Q1H9u9RWWolvNKztamN73OwNWrhTQxG/cW8thbm/hoc76uFouIyNkLC/du8Ivr7r/6XIfKCq/f58Ldx1+JLsjxpj1rvN7AArlw/9XoXgFBOgnik2vmm1H3ugrJVdYt9IaYTB4b6kpEADhQXMpv3t3MXz/dSWxUBLPH9WbygC66WiwiIo0vvI0XbDv04sSBlv3KSrzwXLDTPyrhLijY5c3vXOFlq9qdmrXrdPxV6KownXSBF7CbEIVk8PolPHLQu2FPd39KiJVV+Pjz8myefG8LR8oquWlsCndN6UfHmMhQlyYiIlIjMubkoxH6Kr2r0Yf9wTkwTB/Y6o1CWF7ibXvN/8GIGeeu9gZQSAYvGN/6lvfVgkiImBnvbdjPf72xgaz8Eib2T+SB6YPo2yU21KWJiIicvrBwf3OLpLrXm8HRQ15wjut5bmtrAIXkQC2osbk0Lxv3FvLzf2zgk635nJ8Yw5/mXMDkAV1CXZaIiEjjcc7req6JDretVCgSQrXbHT/01cHMGtObiHA1+xEREQklhWSREFC7YxERkaZNIVnkHFK7YxERkeZBIVnkHNm0t4if/zOTj7eo3bGIiEhTp5As0sgOlpTx+Dub1O5YRESkGVFIFmkkancsIiLSfCkkiwSZ2h2LiIg0fwrJIkHi8xkfbs7j2Y+2s3z7AbU7FhERacYUkkXOUklpBQs/y2He0my255fQNa4tD351MDeo3bGIiEizpZAscoZyDh3hz8t3sGDlToqOVTAiKZ4nZ6RyxdDuRLZROBYREWnOFJJFToOZkbHjEHM/yeKt9XtxznHF0G7MGd+HtOQOOOdCXaKIiIgEgUKySAOUVlTyzy/2MHdpFutyC4lvF8FtF5/PTWN706NDu1CXJyIiIkGmkCxyEvnFpbywYifzV+wgv7iUvl3a81/XDOWakT2JjtSvj4iISEvVoL/yzrlpwJNAOPCcmT1aa/1vgMn+p9FAFzPr4F9XCXzpX7fTzK4KRuEijWn97gL+tDSbxWt2U1bpY9KARG4Z34cJ/RLUpEJERKQVOGVIds6FA88AlwE5wCrn3GIzy6zaxsx+GLD994GRAYc4amapwStZpHFU+ox3N+xj7idZfJp1kHYR4Xzzgl7MHpdC3y7tQ12eiIiInEMNuZI8GthqZtsBnHMvAlcDmfVsPxN4MDjliTS+wmPlvLRqF88vz2bXwaP07NCO+64YyIwLkomPjgh1eSIiIhICDQnJPYFdAc9zgAvr2tA51xvoA7wfsDjKOZcBVACPmtmiOva7DbgNIDk5uWGVi5ylrPwSnl+WzcsZuygpq+SClI7cd8UgLh/clTbq31hERKRVC/adRzOAV8ysMmBZbzPLdc6dB7zvnPvSzLYF7mRmzwLPAqSnp1uQaxKp5vMZH23JY96ybD7YlEdEuOMrw7pzy0V9GJ7UIdTliYiISBPRkJCcC/QKeJ7kX1aXGcAdgQvMLNf/uN059wFee+VtJ+4q0niKjpWzcHUOf16+g+35JSS0b8sPLu3Hty5MpktsVKjLExERkSamISF5FdDPOdcHLxzPAL5VeyPn3ECgI7A8YFlH4IiZlTrnEoDxwC+DUbhIQ1Q1qXhldQ7FpRWk9urAE99M5cphGhVPRERE6nfKkGxmFc65O4G38LqAm2tm651zjwAZZrbYv+kM4EUzC2wuMQj4P+ecDwjDa5Nc3w1/IkFRX5OK2eNSGJncMdTliYiISDPgjs+0oZeenm4ZGRmhLkOaobqaVNwwJllNKkRERKROzrnVZpZe1zoNGSbNXl1NKp6ckcoVQ9WkQkRERM6MQrI0S3U1qZg+vAezx6WQ2ku9VIiIiMjZUUiWZqV2k4rEWPVSISIiIsGnkCzNQsHRcn77/hYWrNylJhUiIiLS6BSSpUmr9BkvZ+zil29t4vCRMr46ogdzxvdRkwoRERFpVArJ0mSt3nGQhxZn8mVuAaNTOvHgVYMZ0iM+1GWJiIhIK6CQLE3O/sJjPPrmRl79PJducVE8OSOVq0b0wDkX6tJERESklVBIliajtKKSPy3N5un3tlBeadwx+Xxun9SXmLZ6m4qIiMi5pfQhTcKSTft55O+ZZOWXcOmgLjwwfTC9O8eEuiwRERFppRSSJaSy80v42T8yeW/jfs5LiOFPcy5g8oAuoS5LREREWjmFZAmJktIKfrtkK3/8OIuIcMd/XDmQm8f1UXduIiIi0iQoJMs5ZWa8vmY3//PmBvYVlvL1tJ7cO20gXeI0EIiIiIg0HQrJcs6syy3gocXrydhxiGE94/ndrFGM6t0x1GWJiIiInEAhWRrdwZIyHnt7EwtW7qRTdCS/uHYY14/qRViYunQTERGRpkkhWRpNRaWPFz7dya/f3kRJWSU3j0vhB5f2J75dRKhLExERETkphWRpFCuzDvKfr69j494ixvftzINfHUL/rrGhLktERESkQRSSJeheXLmTny5aR7e4KH5/QxpTh3TTaHkiIiLSrCgkS9CYGb95dwtPvbeFi/sn8rtZabTXaHkiIiLSDCnBSFCUV/r46Wtf8lJGDtePSuK/vz6MiHD1eSwiIiLNk0KynLWS0gru+OtnfLApj3+/pC8/vKy/mleIiIhIs6aQLGclr6iUW+atYv3uAv77mmF868LkUJckIiIictYa9H24c26ac26Tc26rc+7eOtb/xjm3xj9tds4dDlg32zm3xT/NDmbxElrb84r5+v8uZcv+Iv5wU7oCsoiIiLQYp7yS7JwLB54BLgNygFXOucVmllm1jZn9MGD77wMj/fOdgAeBdMCA1f59DwX1Vcg599nOQ9w6bxXOOV68bSypvTqEuiQRERGRoGnIleTRwFYz225mZcCLwNUn2eNAJ9kAAB/eSURBVH4msMA/PxV4x8wO+oPxO8C0sylYQu+dzH186w8riGsXwavfG6eALCIiIi1OQ0JyT2BXwPMc/7ITOOd6A32A909nX+fcbc65DOdcRl5eXkPqlhD5y4odfGd+BgO6xrLwe+NISYgJdUkiIiIiQRfsPrpmAK+YWeXp7GRmz5pZupmlJyYmBrkkCQYz47G3NnH/onVMGtCFBbeNIaF921CXJSIiItIoGhKSc4FeAc+T/MvqMoOaphanu680UeWVPn788hf8dslWZo7uxbM3jiI6Uh2jiIiISMvVkJC8CujnnOvjnIvEC8KLa2/knBsIdASWByx+C7jcOdfROdcRuNy/TJqJ4tIKbpm3ioWf5fDDS/vz39cMo40GCREREZEW7pSXA82swjl3J164DQfmmtl659wjQIaZVQXmGcCLZmYB+x50zv0ML2gDPGJmB4P7EqSx7C88xpx5q9i4t4hfXjucb1zQ69Q7iYiIiLQALiDTNgnp6emWkZER6jJava37i5k9dyWHjpTxzKw0Jg/oEuqSRERERILKObfazNLrWqeGpXKCjOyDfPvPGbQJc7x42xiGJ6mLNxEREWldFJLlOP9at5e7XvycHh3a8fyc0SR3jg51SSIiIiLnnEKyVHt+WTYP/X09qb068MfZF9ApJjLUJYmIiIiEhEKy4PMZv3xrE7//cBuXDurK0zNH0i4yPNRliYiIiISMQnIrV1bh4yevrGXRmt3MujCZh68aoi7eREREpNVTSG7FCo+V872/rGbp1gPcPXUAt086H+dcqMsSERERCTmF5FZqX+ExZs9dydb9xfz6+hFcOyop1CWJiIiINBkKya3Qln1FzJ67koKj5cy9+QIu7p8Y6pJEREREmhSF5FZmZdZBvv38KtpGhPO374xlaM/4UJckIiIi0uQoJLcib3y5hx/8bQ1JHb0+kHt1Uh/IIiIiInVRSG4l5n6Sxc/+mUlackeeuymdjuoDWURERKReCsktnM9n/M+bG/jDx1lMHdKVJ2eMJCpCfSCLiIiInIxCcgtWWlHJj1/+gr+v3c1NY3vz4FeHEB6mLt5ERERETkUhuYUqOFrOd+ZnsGL7Qe6ZNpDvTjxPfSCLiIiINJBCcgu0p+AoN89dxfb8Yp74ZipfG9kz1CWJiIiINCsKyS3Mpr1F3PynlRQdq2DenNGM75sQ6pJEREREmh2F5BZk+bYD3DY/g3YR4bz0nbEM7hEX6pJEREREmiWF5BZi8drd/PiltSR3jmbenAtI6qg+kEVERETOlEJyC/Dcx9v5+T83MDqlE8/eNIoO0eoDWURERORsKCQ3Yz6f8fN/bmDu0iyuHNaNx7+Rqj6QRURERIJAIbmZOlZeyY9eWss/v9zDzeNSeGD6YPWBLCIiIhIkYQ3ZyDk3zTm3yTm31Tl3bz3bfMM5l+mcW++c+2vA8krn3Br/tDhYhbdmBUfKuWnuSv755R5+euUgHvyqArKIiIhIMJ3ySrJzLhx4BrgMyAFWOecWm1lmwDb9gPuA8WZ2yDnXJeAQR80sNch1t1q5h49y89yVZB8o4ckZqVydqj6QRURERIKtIc0tRgNbzWw7gHPuReBqIDNgm38DnjGzQwBmtj/YhbZ2uw4eYd6ybF5atQuA528Zzbjz1QeyiIiISGNoSEjuCewKeJ4DXFhrm/4AzrmlQDjwkJn9y78uyjmXAVQAj5rZoto/wDl3G3AbQHJy8mm9gJbMzPg06yBzP8ni3Q37CHOOK4d159+n9KNvl/ahLk9ERESkxQrWjXttgH7AJCAJ+Mg5N8zMDgO9zSzXOXce8L5z7ksz2xa4s5k9CzwLkJ6ebkGqqdkqrajk72v3MPeTLDL3FNIxOoLvTTqfG8ek0C0+KtTliYiIiLR4DQnJuUCvgOdJ/mWBcoBPzawcyHLObcYLzavMLBfAzLY75z4ARgLbkBPsLzrGCyt28sKnO8gvLqN/1/Y8+vVhfG1kT3XtJiIiInIONSQkrwL6Oef64IXjGcC3am2zCJgJ/Mk5l4DX/GK7c64jcMTMSv3LxwO/DFr1LcS63ALmLs3iH2v3UFbpY8rALswZ34fxfTvjnHqtEBERETnXThmSzazCOXcn8BZee+O5ZrbeOfcIkGFmi/3rLnfOZQKVwN1mdsA5Nw74P+ecD6+7uUcDe8VozSp9xjuZe5m7NJuVWQeJjgxn5uhezB6XwnmJam8sIiIiEkrOrGk1AU5PT7eMjIxQl9FoCo+V89KqXcxblk3OoaMkdWzHzeNSuD69F/HtIkJdnoiIiEir4ZxbbWbpda3TiHvnSFZ+CfOWZvHK6hxKyioZ3acT939lMJcN7qqBQERERESaGIXkRmRmLN16gD8tzeL9TfuJCAvjqyN6MGd8CkN7xoe6PBERERGph0JyIzEzbpm3iiWb8khoH8ldU/ox68LeJMa2DXVpIiIiInIKCsmNZPWOQyzZlMftk87nrkv70baNunATERERaS7CQl1ASzVvWTZxUW2485K+CsgiIiIizYxCciPYW3CMN9ft5ZsX9CI6UhfrRURERJobheRG8MKnO/CZceOYlFCXIiIiIiJnQCE5yI6VV/LXT3cyZWBXkjtHh7ocERERETkDCslB9s8v9nCgpIybx6WEuhQREREROUMKyUFkZsxblk3fLu0Z37dzqMsRERERkTOkkBxEn+08zJe5Bcwel4JzGkVPREREpLlSSA6i55dlExvVhq+P7BnqUkRERETkLCgkB8m+wmO88eUevpHei5i26vZNREREpDlTSA6SFz7dSaUZN43tHepSREREROQsKSQHQWlFJX/9dAeXDOhC784xoS5HRERERM6SQnIQvPHlHvKLy5itbt9EREREWgSF5CCYtzSb8xNjmNAvIdSliIiIiEgQKCSfpc93HmJtjrp9ExEREWlJFJLP0rxl2cS2bcPX05JCXYqIiIiIBIlC8lnY7+/27br0JNqr2zcRERGRFkMh+Sy88OlOKnzG7LEpoS5FRERERIKoQSHZOTfNObfJObfVOXdvPdt8wzmX6Zxb75z7a8Dy2c65Lf5pdrAKD7WyCh8vfLqTSf0TSUlQt28iIiIiLckp2wg458KBZ4DLgBxglXNusZllBmzTD7gPGG9mh5xzXfzLOwEPAumAAav9+x4K/ks5t7xu30rV7ZuIiEiIlZeXk5OTw7Fjx0JdijRRUVFRJCUlERER0eB9GtKQdjSw1cy2AzjnXgSuBjIDtvk34Jmq8Gtm+/3LpwLvmNlB/77vANOABQ2usImatyyb8xJiuLhfYqhLERERadVycnKIjY0lJUU9TcmJzIwDBw6Qk5NDnz59GrxfQ5pb9AR2BTzP8S8L1B/o75xb6pxb4Zybdhr74py7zTmX4ZzLyMvLa3DxobJm12HW7DrMTWN7ExamX0YREZFQOnbsGJ07d1ZAljo55+jcufNpf9MQrBv32gD9gEnATOAPzrkODd3ZzJ41s3QzS09MbPpXZp9flk37tm24dpS6fRMREWkKFJDlZM7k/dGQkJwL9Ap4nuRfFigHWGxm5WaWBWzGC80N2bdZ2V90jH98sZvrRiURG9Xwdi0iIiIi0nw0JCSvAvo55/o45yKBGcDiWtsswruKjHMuAa/5xXbgLeBy51xH51xH4HL/smZrwae7KK80bhrbO9SliIiIiEgjOWVINrMK4E68cLsBeMnM1jvnHnHOXeXf7C3ggHMuE1gC3G1mB/w37P0ML2ivAh6puomvOfK6fdvBxP6JnJfYPtTliIiIiADwxBNPcOTIkTPad9GiRWRmZp56w0bQvr2Xp7Kzsxk6dGhIaqhPg4aJM7M3gDdqLfvPgHkD/p9/qr3vXGDu2ZXZNLy5bg/7i0r5xXUpoS5FRERE6vDw39eTubswqMcc3COOB786JKjHDLYnnniCG264gejo6NPed9GiRUyfPp3Bgwc3aPuKigratGn5Iw1rxL3T8PyybPokxDBR3b6JiIiIX3Z2NgMHDuTmm2+mf//+zJo1i3fffZfx48fTr18/Vq5cSUlJCbfccgujR49m5MiRvP7669X7TpgwgbS0NNLS0li2bBkAH3zwAZMmTeK6665j4MCBzJo1C++a5Imeeuopdu/ezeTJk5k8eTIAb7/9NmPHjiUtLY3rr7+e4uJiAO69914GDx7M8OHD+fGPf8yyZctYvHgxd999N6mpqWzbtq3OnzFp0iR+8IMfkJ6ezpNPPsnq1auZOHEio0aNYurUqezZsweArVu3cumllzJixAjS0tLYtm0bxcXFTJkyhbS0NIYNG1b92ps8M2tS06hRo6wpWrvrkPW+5x8295PtoS5FREREAmRmZob052dlZVl4eLh98cUXVllZaWlpaTZnzhzz+Xy2aNEiu/rqq+2+++6z+fPnm5nZoUOHrF+/flZcXGwlJSV29OhRMzPbvHmzVeWgJUuWWFxcnO3atcsqKyttzJgx9vHHH9dbQ+/evS0vL8/MzPLy8mzChAlWXFxsZmaPPvqoPfzww5afn2/9+/c3n89XXYeZ2ezZs+3ll18+6WucOHGife973zMzs7KyMhs7dqzt37/fzMxefPFFmzNnjpmZjR492l599VUzMzt69KiVlJRYeXm5FRQUVNd2/vnnV9cQExNTfQ6HDBnSsBN+hup6nwAZVk8mbfnXyoNk3rJsYiLDuU7dvomIiEgtffr0YdiwYQAMGTKEKVOm4Jxj2LBhZGdnk5OTw+LFi3nssccAr2/nnTt30qNHD+68807WrFlDeHg4mzdvrj7m6NGjSUryckdqairZ2dlcdNFFp6xlxYoVZGZmMn78eADKysoYO3Ys8fHxREVFceuttzJ9+nSmT59+Wq/xm9/8JgCbNm1i3bp1XHbZZQBUVlbSvXt3ioqKyM3N5ZprrgG8Ue7AGxHxP/7jP/joo48ICwsjNzeXffv20a1bt9P6+eeaQnID5BeX8o+1e5gxupe6fRMREZETtG3btno+LCys+nlYWBgVFRWEh4ezcOFCBgwYcNx+Dz30EF27dmXt2rX4fL7qYFn7mOHh4VRUVDSoFjPjsssuY8GCEwc4XrlyJe+99x6vvPIKv/3tb3n//fcb/BpjYmKqjz9kyBCWL19+3PqioqI693vhhRfIy8tj9erVREREkJKS0iyGEFeb5AZY8OlOyip93DQ2JdSliIiISDM0depUnn766ep2xZ9//jkABQUFdO/enbCwMObPn09lZeUZHT82NrY6pI4ZM4alS5eydetWAEpKSti8eTPFxcUUFBRw5ZVX8pvf/Ia1a9eesG9DDBgwgLy8vOqQXF5ezvr164mNjSUpKYlFixYBUFpaypEjRygoKKBLly5ERESwZMkSduzYcUav8VxTSD6F8koff/l0BxP6JdC3i7p9ExERkdP3wAMPUF5ezvDhwxkyZAgPPPAAALfffjvPP/88I0aMYOPGjdVXa0/XbbfdxrRp05g8eTKJiYnMmzePmTNnMnz4cMaOHcvGjRspKipi+vTpDB8+nIsuuojHH38cgBkzZvCrX/2KkSNH1nvjXqDIyEheeeUV7rnnHkaMGEFqamr1DYfz58/nqaeeYvjw4YwbN469e/cya9YsMjIyGDZsGH/+858ZOHDgGb3Gc81V/Y+mqUhPT7eMjIxQl1Ht72t38/0FnzP35nQuGdg11OWIiIhILRs2bGDQoEGhLkOauLreJ8651WaWXtf2upJ8Cs8vy6Z352gm9e8S6lJERERE5BzRjXsnsS63gIwdh3hg+mDCwlyoyxEREZFW7pprriErK+u4Zb/4xS+YOnVqUI5/xx13sHTp0uOW3XXXXcyZMycox29OFJJPYt6ybKIjw7k+Xd2+iYiISOi99tprjXr8Z555plGP35youUU9DhSXsnjtbq5NSyJO3b6JiIiItCoKyfV4cdUuyip8zB7XO9SliIiIiMg5ppBch/JKH/OXV3X7FhvqckRERETkHFNIrsPb6/ext/AYszV4iIiIiEirpJBch3nLskjuFM3kger2TURERJq+J554giNHjpzRvosWLSIzMzPIFTXMlVdeyeHDh+td/+1vfztktal3i1rW5RawKvsQ939lEOHq9k1ERKR5efNe2PtlcI/ZbRhc8WhwjxlkTzzxBDfccAPR0dGnve+iRYuYPn06gwcPPqsaKioqaNPm9KLlG2+8cdL1zz333NmUdFZ0JbmW55dl0y4inOvTe4W6FBEREWkGsrOzGThwIDfffDP9+/dn1qxZvPvuu4wfP55+/fqxcuVKSkpKuOWWWxg9ejQjR47k9ddfr953woQJpKWlkZaWVj288wcffMCkSZO47rrrGDhwILNmzaK+UZKfeuopdu/ezeTJk5k8eTIAb7/9NmPHjiUtLY3rr7+e4uJiAO69914GDx7M8OHD+fGPf8yyZctYvHgxd999N6mpqfUOSz1p0iTuuusuUlNTGTp0KCtXrgTgoYce4sYbb2T8+PHceOON5OXlce2113LBBRdwwQUXVPe5XFxczJw5cxg2bBjDhw9n4cKFAKSkpJCfn09JSQlf+cpXGDFiBEOHDuVvf/tb9c+tGol5wYIFDBs2jKFDh3LPPfdU19a+fXt++tOfMmLECMaMGcO+ffvO/B8zkJk1qWnUqFEWKgeKS63fT9+w/3j1i5DVICIiIqcnMzMzpD8/KyvLwsPD7YsvvrDKykpLS0uzOXPmmM/ns0WLFtnVV19t9913n82fP9/MzA4dOmT9+vWz4uJiKykpsaNHj5qZ2ebNm60qBy1ZssTi4uJs165dVllZaWPGjLGPP/643hp69+5teXl5ZmaWl5dnEyZMsOLiYjMze/TRR+3hhx+2/Px869+/v/l8vuo6zMxmz55tL7/88klf48SJE+3b3/62mZl9+OGHNmTIEDMze/DBBy0tLc2OHDliZmYzZ86srnPHjh02cOBAMzP7yU9+YnfddVf18Q4ePHhc3a+88kr18c3MDh8+XP1zV61aZbm5udarVy/bv3+/lZeX2+TJk+21114zMzPAFi9ebGZmd999t/3sZz+r8zXU9T4BMqyeTKrmFgFeXLXT3+1bSqhLERERkWakT58+DBs2DIAhQ4YwZcoUnHMMGzaM7OxscnJyWLx4MY899hgAx44dY+fOnfTo0YM777yTNWvWEB4ezubNm6uPOXr0aJKSvAHNUlNTyc7O5qKLLjplLStWrCAzM5Px48cDUFZWxtixY4mPjycqKopbb72V6dOnM3369NN6jTNnzgTg4osvprCwsLot8VVXXUW7du0AePfdd49rQ1xYWEhxcTHvvvsuL774YvXyjh07HnfsYcOG8aMf/Yh77rmH6dOnM2HChOPWr1q1ikmTJpGYmAjArFmz+Oijj/ja175GZGRk9WsZNWoU77zzzmm9rvooJPtVVPr4y/IdjO/bmf5d1e2biIiINFzbtm2r58PCwqqfh4WFUVFRQXh4OAsXLmTAgAHH7ffQQw/RtWtX1q5di8/nIyoqqs5jhoeHU1FR0aBazIzLLruMBQsWnLBu5cqVvPfee7zyyiv89re/5f3332/wa3TO1fk8JiamepnP52PFihXHvY6G6N+/P5999hlvvPEG999/P1OmTOE///M/G7RvREREdS2nc55OpUFtkp1z05xzm5xzW51z99ax/mbnXJ5zbo1/+nbAusqA5YuDUnUjeCdzH7sL1O2biIiIBN/UqVN5+umnq9sVf/755wAUFBTQvXt3wsLCmD9/PpWVlWd0/NjYWIqKigAYM2YMS5cuZevWrQCUlJSwefNmiouLKSgo4Morr+Q3v/kNa9euPWHfk6lqJ/zJJ58QHx9PfHz8CdtcfvnlPP3009XP16xZA8Bll1123JDXhw4dOm6/3bt3Ex0dzQ033MDdd9/NZ599dtz60aNH8+GHH5Kfn09lZSULFixg4sSJp6z5bJwyJDvnwoFngCuAwcBM51xdtz/+zcxS/VPgrYhHA5ZfFZyyg+9Py7JJ6tiOKYO6hroUERERaWEeeOABysvLGT58OEOGDOGBBx4A4Pbbb+f5559nxIgRbNy48birsqfjtttuY9q0aUyePJnExETmzZvHzJkzGT58OGPHjmXjxo0UFRUxffp0hg8fzkUXXcTjjz8OwIwZM/jVr37FyJEj671xDyAqKoqRI0fy3e9+lz/+8Y91bvPUU0+RkZHB8OHDGTx4ML///e8BuP/++zl06BBDhw5lxIgRLFmy5Lj9vvzyS0aPHk1qaioPP/ww999//3Hru3fvzqOPPsrkyZMZMWIEo0aN4uqrrz6jc9VQrup/NPVu4NxY4CEzm+p/fh+Amf1PwDY3A+lmdmcd+xebWfuGFpSenm5VdzGeKxWVPn7+zw2c36U9N47RMNQiIiLNyYYNGxg0aFCoy2jRJk2axGOPPUZ6enqoSzljdb1PnHOrzazOF9WQNsk9gV0Bz3OAC+vY7lrn3MXAZuCHZla1T5RzLgOoAB41s0UN+JnnVJvwMB66akioyxARERGRJiJYN+79HVhgZqXOue8AzwOX+Nf1NrNc59x5wPvOuS/N7Lhr+c6524DbAJKTk4NUkoiIiEjLcs0115CVlXXcsl/84hdMnTo1KMe/4447qvs2rnLXXXfxwQcfBOX4zUlDQnIuEDiyRpJ/WTUzOxDw9DnglwHrcv2P251zHwAjgW219n8WeBa85hYNL19ERETE69Ghdu8LLdFrr73WqMcPvLmuJTlV8+K6NKR3i1VAP+dcH+dcJDADOK6XCudc94CnVwEb/Ms7Oufa+ucTgPFAaAbgFhERkRYpKiqKAwcOnFEQkpbPzDhw4MBpd0t3yivJZlbhnLsTeAsIB+aa2Xrn3CN4o5QsBv7dOXcVXrvjg8DN/t0HAf/nnPPhBfJHzUwhWURERIImKSmJnJwc8vLyQl2KNFFRUVHVA7M01Cl7tzjXQtG7hYiIiIi0Pifr3aJBg4mIiIiIiLQmCskiIiIiIrUoJIuIiIiI1NLk2iQ75/KAHaGuo5lKAPJDXUQzpvN3dnT+zo7O39nR+Ts7On9nT+fw7ITq/PU2s8S6VjS5kCxnzjmXUV/jczk1nb+zo/N3dnT+zo7O39nR+Tt7OodnpymePzW3EBERERGpRSFZRERERKQWheSW5dlQF9DM6fydHZ2/s6Pzd3Z0/s6Ozt/Z0zk8O03u/KlNsoiIiIhILbqSLCIiIiJSi0KyiIiIiEgtCsnNjHOul3NuiXMu0zm33jl3Vx3bTHLOFTjn1vin/wxFrU2Vcy7bOfel/9xk1LHeOeeecs5tdc594ZxLC0WdTZFzbkDA+2qNc67QOfeDWtvo/RfAOTfXObffObcuYFkn59w7zrkt/seO9ew727/NFufc7HNXddNRz/n7lXNuo//38zXnXId69j3p73prUM/5e8g5lxvwO3plPftOc85t8n8W3nvuqm466jl/fws4d9nOuTX17Kv3Xz2Zpbl8BqpNcjPjnOsOdDezz5xzscBq4GtmlhmwzSTgx2Y2PURlNmnOuWwg3czq7LTc/wfj+8CVwIXAk2Z24bmrsHlwzoUDucCFZrYjYPkk9P6r5py7GCgG/mxmQ/3LfgkcNLNH/eGjo5ndU2u/TkAGkA4Y3u/6KDM7dE5fQIjVc/4uB943swrn3C8Aap8//3bZnOR3vTWo5/w9BBSb2WMn2S8c2AxcBuQAq4CZgX9rWoO6zl+t9b8GCszskTrWZaP3X52ZBbiZZvAZqCvJzYyZ7TGzz/zzRcAGoGdoq2pxrsb7QDQzWwF08P+iy/GmANsCA7KcyMw+Ag7WWnw18Lx//nm8Pxq1TQXeMbOD/j8K7wDTGq3QJqqu82dmb5tZhf/pCiDpnBfWTNTz/muI0cBWM9tuZmXAi3jv21blZOfPOeeAbwALzmlRzchJMkuz+AxUSG7GnHMpwEjg0zpWj3XOrXXOvemcG3JOC2v6DHjbObfaOXdbHet7ArsCnueg/4jUZQb1/3HQ++/kuprZHv/8XqBrHdvofdgwtwBv1rPuVL/rrdmd/uYqc+v5qlvvv1ObAOwzsy31rNf7L0CtzNIsPgMVkpsp51x7YCHwAzMrrLX6M7yxyEcATwOLznV9TdxFZpYGXAHc4f86TU6Dcy4SuAp4uY7Vev+dBvPavKnd2xlwzv0UqABeqGcT/a7X7X+B84FUYA/w69CW02zN5ORXkfX+8ztZZmnKn4EKyc2Qcy4C7832gpm9Wnu9mRWaWbF//g0gwjmXcI7LbLLMLNf/uB94De9rxUC5QK+A50n+ZVLjCuAzM9tXe4Xefw2yr6oJj/9xfx3b6H14Es65m4HpwCyr5+aaBvyut0pmts/MKs3MB/yBus+L3n8n4ZxrA3wd+Ft92+j956knszSLz0CF5GbG3wbqj8AGM3u8nm26+bfDOTca79/5wLmrsulyzsX4bx7AORcDXA6sq7XZYuAm5xmDd1PGHiRQvVdQ9P5rkMVA1Z3as4HX69jmLeBy51xH/9fhl/uXtXrOuWnAT4CrzOxIPds05He9Vap1j8U11H1eVgH9nHN9/N8czcB734rnUmCjmeXUtVLvP89JMkvz+Aw0M03NaAIuwvta4gtgjX+6Evgu8F3/NncC64G1eDe1jAt13U1lAs7zn5e1/nP0U//ywPPngGeAbcCXeHcnh7z2pjIBMXihNz5gmd5/9Z+vBXhfaZfjtam7FegMvAdsAd4FOvm3TQeeC9j3FmCrf5oT6tfShM7fVry2ilWfgb/3b9sDeMM/X+fvemub6jl/8/2fbV/ghZXutc+f//mVeD1cbNP5qzl//uXzqj7zArbV++/E81dfZmkWn4HqAk5EREREpBY1txARERERqeX/t1vHAgAAAACD/K33j6EokmQAABhJBgCAkWQAABhJBgCAkWQAABhJBgCACSA+LYa2i6/DAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#we will create our own metric like f1 to find the best between recall and precision\n",
        "#find the min of recall and precision\n",
        "def min_recall_precision(y_true, y_pred):\n",
        "    recall = recall_score(y_true, y_pred)\n",
        "    precision = precision_score(y_true, y_pred)\n",
        "    return min(recall, precision)\n",
        "\n",
        "grid = GridSearchCV(\n",
        "    estimator=LogisticRegression(max_iter=1000),\n",
        "    param_grid={'class_weight': [{0: 1, 1: v} for v in np.linspace(1, 20, 30)]},\n",
        "    scoring={'precision': make_scorer(precision_score),\n",
        "             'recall': make_scorer(recall_score), \n",
        "             'min_both': make_scorer(min_recall_precision)},\n",
        "    refit='min_both',\n",
        "    return_train_score=True,\n",
        "    cv=10,\n",
        "    n_jobs=-1\n",
        ")\n",
        "grid.fit(X, y);"
      ],
      "metadata": {
        "id": "gfl1PhGgmvV5"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 4))\n",
        "df_results = pd.DataFrame(grid.cv_results_)\n",
        "for score in ['mean_test_recall', 'mean_test_precision', 'mean_test_min_both']:\n",
        "    plt.plot([_[1] for _ in df_results['param_class_weight']], \n",
        "             df_results[score], \n",
        "             label=score)\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "XounAmxHRtXj",
        "outputId": "d09d4a24-f9e0-4eb5-c68e-63a8867fed18"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Without make_scorer"
      ],
      "metadata": {
        "id": "WvVhZgtrrYIc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "s = make_scorer(min_recall_precision)\n",
        "??s\n",
        "#Signature:      s(estimator, X, y_true, sample_weight=None)\n",
        "#sample_weight=None ---> this says hey this row more important the other rows pay attantion\n",
        "#Type:           _PredictScorer\n",
        "#like F(y_true, y_pred) ==make_scorer==> s(est, X, y, w)\n",
        "#we can do all this without make_scorer using its signature"
      ],
      "metadata": {
        "id": "3knTY4bdUc3t"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def min_recall_precision(est, X, y_true, sample_weight=None):\n",
        "    y_pred = est.predict(X)\n",
        "    recall = recall_score(y_true, y_pred)\n",
        "    precision = precision_score(y_true, y_pred)\n",
        "    return min(recall, precision)\n",
        "\n",
        "grid = GridSearchCV(\n",
        "    estimator=LogisticRegression(max_iter=1000),\n",
        "    param_grid={'class_weight': [{0: 1, 1: v} for v in np.linspace(1, 20, 30)]},\n",
        "    scoring={'precision': make_scorer(precision_score), \n",
        "             'recall': make_scorer(recall_score),\n",
        "             'min_both': min_recall_precision},\n",
        "    refit='min_both',\n",
        "    return_train_score=True,\n",
        "    cv=10,\n",
        "    n_jobs=-1\n",
        ")\n",
        "grid.fit(X, y, sample_weight= np.log(1 + df['Amount'])); #take  log for no to over fit"
      ],
      "metadata": {
        "id": "YF_pbT1dUN05"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 4))\n",
        "df_results = pd.DataFrame(grid.cv_results_)\n",
        "for score in ['mean_test_recall', 'mean_test_precision', 'mean_test_min_both']:\n",
        "    plt.plot([_[1] for _ in df_results['param_class_weight']], \n",
        "             df_results[score], \n",
        "             label=score)\n",
        "plt.legend();\n",
        "#Balance is changed , sample weight definetly effects the algorithm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "yeiWICr0uD7C",
        "outputId": "037f9e5a-e294-4cdd-901c-4addcb876de1"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 4))\n",
        "df_results = pd.DataFrame(grid.cv_results_)\n",
        "for score in ['mean_train_recall', 'mean_train_precision', 'mean_test_min_both']:\n",
        "    plt.scatter(x=[_[1] for _ in df_results['param_class_weight']], \n",
        "                y=df_results[score.replace('test', 'train')], \n",
        "                label=score)\n",
        "plt.legend();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "fdSYrkoLvZYw",
        "outputId": "ad3ee228-6c1c-48b0-85d1-6760a801c9c9"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Using Outlier Detection Models\n",
        "In this data set froud is a outliner that it is rare and not ordinary. But when we write froud detection algorithm might not corralate with froud. we will try to replace LogisticRegression with a out llne detector and adopt this in the metric to check if its the case"
      ],
      "metadata": {
        "id": "EuEE2jj7umHf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "from sklearn.ensemble import IsolationForest #outline detector algorithm\n",
        "mod = IsolationForest().fit(X) #when we say fit it just need data set\n",
        "mod.predict(X)\n",
        "Counter(mod.predict(X)) #it shows that -1 is a outliner"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGaDGIu5SDVC",
        "outputId": "19909bb2-4c8c-4b54-b036-631685af9cfb"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({-1: 3223, 1: 76777})"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.where(mod.predict(X) == -1, 1, 0)#if predict -1 I want 1 else 0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "STGDXqyB1ffG",
        "outputId": "ae6f1105-aa4b-4032-c6ef-86750c5159ed"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def outlier_precision(mod, X, y): #own costum metric\n",
        "    preds = mod.predict(X)\n",
        "    return precision_score(y, np.where(preds == -1, 1, 0))\n",
        "\n",
        "def outlier_recall(mod, X, y):  #own costum metric\n",
        "    preds = mod.predict(X)\n",
        "    return recall_score(y, np.where(preds == -1, 1, 0))\n",
        "\n",
        "grid = GridSearchCV(\n",
        "    estimator=IsolationForest(),\n",
        "    param_grid={'contamination': np.linspace(0.001, 0.02, 10)}, #contamination as a hyperprameter that I want to tune\n",
        "    scoring={'precision': outlier_precision, \n",
        "             'recall': outlier_recall},\n",
        "    refit='precision',\n",
        "    cv=5,\n",
        "    n_jobs=-1\n",
        ")\n",
        "grid.fit(X, y);\n",
        "\n",
        "plt.figure(figsize=(12, 4))\n",
        "df_results = pd.DataFrame(grid.cv_results_)\n",
        "for score in ['mean_test_recall', 'mean_test_precision']:\n",
        "    plt.plot(df_results['param_contamination'], \n",
        "             df_results[score], \n",
        "             label=score)\n",
        "plt.legend();\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "L6_fSI2B2Si4",
        "outputId": "7155be6c-aeb3-4f85-bbd4-eba2ef6d044e"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}