2_DENV_Classifiers_2_1024.ipynb

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      "cell_type": "code",
      "execution_count": null,
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        "id": "zDVmCQRIabzw"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pickle"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Load the datasets**"
      ],
      "metadata": {
        "id": "AiTiRLaeav7k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"2-1024_drug_like_MorganFp_no_intermed.csv\")\n",
        "df"
      ],
      "metadata": {
        "id": "EiSSBpUvanOU",
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          "height": 443
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        "outputId": "e2585be1-4846-4ac6-f721-1779b152370b"
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "     molecule_chembl_id                            canonical_smiles  type  \\\n",
              "0          CHEMBL115145       O=C(O)c1cc(Cc2ccc(O)c(C(=O)O)c2)ccc1O  IC50   \n",
              "1          CHEMBL115145       O=C(O)c1cc(Cc2ccc(O)c(C(=O)O)c2)ccc1O  IC50   \n",
              "2          CHEMBL115145       O=C(O)c1cc(Cc2ccc(O)c(C(=O)O)c2)ccc1O  IC50   \n",
              "3          CHEMBL115145       O=C(O)c1cc(Cc2ccc(O)c(C(=O)O)c2)ccc1O  IC50   \n",
              "4          CHEMBL115145       O=C(O)c1cc(Cc2ccc(O)c(C(=O)O)c2)ccc1O  IC50   \n",
              "...                 ...                                         ...   ...   \n",
              "1299       CHEMBL607576  COc1cc(CNc2ccc(C(=O)O)cc2)ccc1OCc1ccccc1Cl  IC50   \n",
              "1300       CHEMBL607863         COc1ccc2c(c1)C(CC(=O)O)CN2Cc1ccccc1  IC50   \n",
              "1301        CHEMBL63883           COc1ccc(NC(=O)c2cc3ccccc3cc2O)cc1  IC50   \n",
              "1302        CHEMBL66953               O=c1c(O)cccc2cc(O)c(O)c(O)c12  IC50   \n",
              "1303         CHEMBL8260        O=c1cc(-c2ccccc2)oc2cc(O)c(O)c(O)c12  IC50   \n",
              "\n",
              "     units   value  activity  Bit_0  Bit_1  Bit_2  Bit_3  ...  Bit_1014  \\\n",
              "0       uM    1.52    active      0      0      0      0  ...         0   \n",
              "1       uM    3.16    active      0      0      0      0  ...         0   \n",
              "2       uM    6.22    active      0      0      0      0  ...         0   \n",
              "3       uM    7.14    active      0      0      0      0  ...         0   \n",
              "4       uM    9.46    active      0      0      0      0  ...         0   \n",
              "...    ...     ...       ...    ...    ...    ...    ...  ...       ...   \n",
              "1299    uM  100.00  Inactive      0      0      0      1  ...         0   \n",
              "1300    uM  100.00  Inactive      0      0      0      1  ...         0   \n",
              "1301    uM  100.00  Inactive      0      0      0      0  ...         0   \n",
              "1302    uM   12.19    active      0      0      0      0  ...         0   \n",
              "1303    uM   35.06  Inactive      0      0      0      0  ...         0   \n",
              "\n",
              "      Bit_1015  Bit_1016  Bit_1017  Bit_1018  Bit_1019  Bit_1020  Bit_1021  \\\n",
              "0            0         0         0         0         0         0         0   \n",
              "1            0         0         0         0         0         0         0   \n",
              "2            0         0         0         0         0         0         0   \n",
              "3            0         0         0         0         0         0         0   \n",
              "4            0         0         0         0         0         0         0   \n",
              "...        ...       ...       ...       ...       ...       ...       ...   \n",
              "1299         0         0         0         0         0         0         0   \n",
              "1300         0         0         0         0         1         0         0   \n",
              "1301         0         0         0         0         0         0         0   \n",
              "1302         0         0         0         0         0         0         1   \n",
              "1303         0         0         0         0         0         0         0   \n",
              "\n",
              "      Bit_1022  Bit_1023  \n",
              "0            0         0  \n",
              "1            0         0  \n",
              "2            0         0  \n",
              "3            0         0  \n",
              "4            0         0  \n",
              "...        ...       ...  \n",
              "1299         0         0  \n",
              "1300         0         0  \n",
              "1301         0         0  \n",
              "1302         0         0  \n",
              "1303         0         0  \n",
              "\n",
              "[1304 rows x 1030 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>molecule_chembl_id</th>\n",
              "      <th>canonical_smiles</th>\n",
              "      <th>type</th>\n",
              "      <th>units</th>\n",
              "      <th>value</th>\n",
              "      <th>activity</th>\n",
              "      <th>Bit_0</th>\n",
              "      <th>Bit_1</th>\n",
              "      <th>Bit_2</th>\n",
              "      <th>Bit_3</th>\n",
              "      <th>...</th>\n",
              "      <th>Bit_1014</th>\n",
              "      <th>Bit_1015</th>\n",
              "      <th>Bit_1016</th>\n",
              "      <th>Bit_1017</th>\n",
              "      <th>Bit_1018</th>\n",
              "      <th>Bit_1019</th>\n",
              "      <th>Bit_1020</th>\n",
              "      <th>Bit_1021</th>\n",
              "      <th>Bit_1022</th>\n",
              "      <th>Bit_1023</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CHEMBL115145</td>\n",
              "      <td>O=C(O)c1cc(Cc2ccc(O)c(C(=O)O)c2)ccc1O</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>1.52</td>\n",
              "      <td>active</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>CHEMBL115145</td>\n",
              "      <td>O=C(O)c1cc(Cc2ccc(O)c(C(=O)O)c2)ccc1O</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>3.16</td>\n",
              "      <td>active</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>CHEMBL115145</td>\n",
              "      <td>O=C(O)c1cc(Cc2ccc(O)c(C(=O)O)c2)ccc1O</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>6.22</td>\n",
              "      <td>active</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CHEMBL115145</td>\n",
              "      <td>O=C(O)c1cc(Cc2ccc(O)c(C(=O)O)c2)ccc1O</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>7.14</td>\n",
              "      <td>active</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CHEMBL115145</td>\n",
              "      <td>O=C(O)c1cc(Cc2ccc(O)c(C(=O)O)c2)ccc1O</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>9.46</td>\n",
              "      <td>active</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1299</th>\n",
              "      <td>CHEMBL607576</td>\n",
              "      <td>COc1cc(CNc2ccc(C(=O)O)cc2)ccc1OCc1ccccc1Cl</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>100.00</td>\n",
              "      <td>Inactive</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>1300</th>\n",
              "      <td>CHEMBL607863</td>\n",
              "      <td>COc1ccc2c(c1)C(CC(=O)O)CN2Cc1ccccc1</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>100.00</td>\n",
              "      <td>Inactive</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1301</th>\n",
              "      <td>CHEMBL63883</td>\n",
              "      <td>COc1ccc(NC(=O)c2cc3ccccc3cc2O)cc1</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
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              "    <tr>\n",
              "      <th>1302</th>\n",
              "      <td>CHEMBL66953</td>\n",
              "      <td>O=c1c(O)cccc2cc(O)c(O)c(O)c12</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>12.19</td>\n",
              "      <td>active</td>\n",
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              "      <th>1303</th>\n",
              "      <td>CHEMBL8260</td>\n",
              "      <td>O=c1cc(-c2ccccc2)oc2cc(O)c(O)c(O)c12</td>\n",
              "      <td>IC50</td>\n",
              "      <td>uM</td>\n",
              "      <td>35.06</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a2038892-c9a7-4f1a-b1eb-9b6c4a7af0cd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['activity'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w686cXXYhPlw",
        "outputId": "ec1d823d-e7e9-4636-eabe-e51eebdbcbe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Inactive    829\n",
              "active      475\n",
              "Name: activity, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Select X and Y variables**"
      ],
      "metadata": {
        "id": "pn-DieNtbwaD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:, 6:]\n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "-TdRiYe4a-GW",
        "outputId": "15bdc06d-8503-4914-d154-1c66ff3ddf55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Bit_0  Bit_1  Bit_2  Bit_3  Bit_4  Bit_5  Bit_6  Bit_7  Bit_8  Bit_9  \\\n",
              "0         0      0      0      0      0      0      0      0      0      0   \n",
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              "3         0      0      0      0      0      0      0      0      0      0   \n",
              "4         0      0      0      0      0      0      0      0      0      0   \n",
              "...     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
              "1299      0      0      0      1      0      0      0      0      0      0   \n",
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              "1302      0      0      0      0      0      0      0      0      0      0   \n",
              "1303      0      0      0      0      0      0      0      0      0      0   \n",
              "\n",
              "      ...  Bit_1014  Bit_1015  Bit_1016  Bit_1017  Bit_1018  Bit_1019  \\\n",
              "0     ...         0         0         0         0         0         0   \n",
              "1     ...         0         0         0         0         0         0   \n",
              "2     ...         0         0         0         0         0         0   \n",
              "3     ...         0         0         0         0         0         0   \n",
              "4     ...         0         0         0         0         0         0   \n",
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              "1299  ...         0         0         0         0         0         0   \n",
              "1300  ...         0         0         0         0         0         1   \n",
              "1301  ...         0         0         0         0         0         0   \n",
              "1302  ...         0         0         0         0         0         0   \n",
              "1303  ...         0         0         0         0         0         0   \n",
              "\n",
              "      Bit_1020  Bit_1021  Bit_1022  Bit_1023  \n",
              "0            0         0         0         0  \n",
              "1            0         0         0         0  \n",
              "2            0         0         0         0  \n",
              "3            0         0         0         0  \n",
              "4            0         0         0         0  \n",
              "...        ...       ...       ...       ...  \n",
              "1299         0         0         0         0  \n",
              "1300         0         0         0         0  \n",
              "1301         0         0         0         0  \n",
              "1302         0         1         0         0  \n",
              "1303         0         0         0         0  \n",
              "\n",
              "[1304 rows x 1024 columns]"
            ],
            "text/html": [
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              "      <th>Bit_4</th>\n",
              "      <th>Bit_5</th>\n",
              "      <th>Bit_6</th>\n",
              "      <th>Bit_7</th>\n",
              "      <th>Bit_8</th>\n",
              "      <th>Bit_9</th>\n",
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              "      <th>Bit_1015</th>\n",
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              "      <th>Bit_1017</th>\n",
              "      <th>Bit_1018</th>\n",
              "      <th>Bit_1019</th>\n",
              "      <th>Bit_1020</th>\n",
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              "      <th>3</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1299</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1300</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1301</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1302</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1303</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>1304 rows × 1024 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7cfb4a6c-6867-4e01-b998-58deb8b95fc0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-7cfb4a6c-6867-4e01-b998-58deb8b95fc0 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-7cfb4a6c-6867-4e01-b998-58deb8b95fc0');\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",
              "\n",
              "\n",
              "<div id=\"df-a6ecdf03-2873-44f8-9f00-87b71861e268\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a6ecdf03-2873-44f8-9f00-87b71861e268')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a6ecdf03-2873-44f8-9f00-87b71861e268 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "class_le = LabelEncoder()\n",
        "y = class_le.fit_transform(df['activity'].values)\n",
        "y[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pLvk38_Va-BX",
        "outputId": "0835c800-ae75-4ed4-f568-038f78c4b32d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Split the data**"
      ],
      "metadata": {
        "id": "ecDAEB0QZrk3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV, cross_val_score\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, roc_auc_score, balanced_accuracy_score, matthews_corrcoef"
      ],
      "metadata": {
        "id": "6kzttr85_lp2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=10, stratify=y)"
      ],
      "metadata": {
        "id": "MFaMlhJka92X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape, X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x2NFmgGzcmH2",
        "outputId": "4684703c-645b-4504-bb20-e45b70648f50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1043, 1024), (261, 1024))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1. Random forest**"
      ],
      "metadata": {
        "id": "-ZclMiiq0mnN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model building**"
      ],
      "metadata": {
        "id": "npoBFzwV4dVp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "dRl4tzPHa-Iy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1024\n",
        "Rf = RandomForestClassifier(n_estimators=400, max_features='log2', max_depth= 50, min_samples_leaf=1, criterion='entropy', random_state=4)"
      ],
      "metadata": {
        "id": "hGneVJfScl6_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2048\n",
        "#Rf = RandomForestClassifier(n_estimators=300, max_depth= 60, min_samples_leaf=2, criterion='entropy', random_state=4)"
      ],
      "metadata": {
        "id": "JVSytTtCOCKi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Rf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "bIvsFmWgcl4M",
        "outputId": "33d98c46-d938-49f0-b40e-3d898d9550c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(criterion='entropy', max_depth=50, max_features='log2',\n",
              "                       n_estimators=400, random_state=4)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=50, max_features=&#x27;log2&#x27;,\n",
              "                       n_estimators=400, random_state=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, max_depth=50, max_features=&#x27;log2&#x27;,\n",
              "                       n_estimators=400, random_state=4)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_train = Rf.predict(X_train)\n",
        "preds_Rf = Rf.predict(X_test)"
      ],
      "metadata": {
        "id": "svvm5cSrcl12"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#labels back\n",
        "class_le.inverse_transform(preds_Rf)[:10]"
      ],
      "metadata": {
        "id": "QLM3kLfTy9-7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e61d7ff-9344-4aaa-9342-d321c62318a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Inactive', 'Inactive', 'Inactive', 'Inactive', 'Inactive',\n",
              "       'Inactive', 'active', 'Inactive', 'Inactive', 'Inactive'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_Rf[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zm3TziOe0FBo",
        "outputId": "aa7c597c-8c0c-437e-d268-5be112066ac4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model evaluation**"
      ],
      "metadata": {
        "id": "XFJXidi7dAMO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Confusion matrix: ','\\n', confusion_matrix(y_test, preds_Rf))\n",
        "print('\\n')\n",
        "print('Classification report', '\\n', classification_report(y_test, preds_Rf))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S91olmXETt_Y",
        "outputId": "9e68504a-78e7-4749-bea9-951cbfed91c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix:  \n",
            " [[159   7]\n",
            " [ 30  65]]\n",
            "\n",
            "\n",
            "Classification report \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.96      0.90       166\n",
            "           1       0.90      0.68      0.78        95\n",
            "\n",
            "    accuracy                           0.86       261\n",
            "   macro avg       0.87      0.82      0.84       261\n",
            "weighted avg       0.86      0.86      0.85       261\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test the model on the testing set\n",
        "Rf_train_accuracy = Rf.score(X_train, y_train)\n",
        "Rf_test_accuracy = Rf.score(X_test, y_test)\n",
        "\n",
        "print(\"Training accuracy:\", Rf_train_accuracy)\n",
        "print(\"Testing accuracy:\", Rf_test_accuracy)"
      ],
      "metadata": {
        "id": "u1owMnMyTd3q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f144fb0-a45f-4936-bc62-b16a35506926"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.9779482262703739\n",
            "Testing accuracy: 0.8582375478927203\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Balanced accuracy:\", balanced_accuracy_score(y_test, preds_Rf))\n",
        "print(\"Matthews correlation coefficient (MCC):\", matthews_corrcoef(y_test, preds_Rf))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VZ2mOJTzLUO",
        "outputId": "959c715b-5edc-46b6-d38d-fcd4dd427e09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Balanced accuracy: 0.8210209258084972\n",
            "Matthews correlation coefficient (MCC): 0.6911654729690635\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Hyperparameters tuning**"
      ],
      "metadata": {
        "id": "uh3JBzhc7MuN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Stratified cross validation will be used to find the best parameters for the classifier"
      ],
      "metadata": {
        "id": "U1zz6kYpB2E9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grid = {\n",
        "    'n_estimators': [100, 200, 300, 400],\n",
        "    'max_features': ['sqrt', 'log2', 200],\n",
        "    'max_depth': [5, 10, 20, 30, 40, 50],\n",
        "    'min_samples_leaf': [1, 2, 3, 4, 5]\n",
        "}\n",
        "\n",
        "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "rf = RandomForestClassifier()\n",
        "\n",
        "grid_search = GridSearchCV(rf, param_grid=grid, cv=skf)\n",
        "\n",
        "grid_search.fit(X, y)\n",
        "\n",
        "best_rf = grid_search.best_estimator_\n",
        "\n",
        "scores_rf = cross_val_score(best_rf, X, y, cv=skf)\n",
        "\n",
        "print('Best parameters: ', grid_search.best_params_)\n",
        "\n",
        "print('Cross-validation scores:', scores_rf)\n",
        "print('Mean score:', np.mean(scores_rf))\n",
        "\n",
        "print('Best score: ', grid_search.best_score_)"
      ],
      "metadata": {
        "id": "ewg_LK1OvAas",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07d5177b-4107-4252-e587-f7cfd39227ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters:  {'max_depth': 50, 'max_features': 'log2', 'min_samples_leaf': 1, 'n_estimators': 400}\n",
            "Cross-validation scores: [0.81992337 0.8467433  0.89655172 0.86590038 0.82692308]\n",
            "Mean score: 0.8512083701738874\n",
            "Best score:  0.8527379899793692\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **ROC curve**"
      ],
      "metadata": {
        "id": "DLaYnz13sniP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_proba_Rf = Rf.predict_proba(X_test)[:,1]\n",
        "fpr_Rf, tpr_Rf, thresholds_Rf = roc_curve(y_test, y_pred_proba_Rf)\n",
        "auc_Rf = roc_auc_score(y_test, y_pred_proba_Rf)\n",
        "\n",
        "plt.rc('font', size=12)\n",
        "plt.plot(fpr_Rf, tpr_Rf, label='Random forest (AUC= %.2f)' % (auc_Rf))\n",
        "plt.plot([0, 1], [0, 1],'k')\n",
        "plt.xlim([0, 1])\n",
        "plt.ylim([0, 1])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC curve')\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "nXRtvt9IyJEk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "fc7dfb31-7eaa-4497-9840-9b635a8a7be3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7d21ccc9f7f0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Learning curve**"
      ],
      "metadata": {
        "id": "-MM6RplS4-Kv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "# Generate the learning curve\n",
        "train_sizes, train_scores, test_scores = learning_curve(\n",
        "    Rf, X, y, cv=10, scoring='accuracy')\n",
        "\n",
        "# Calculate the mean and standard deviation of the training scores\n",
        "train_mean = np.mean(train_scores, axis=1)\n",
        "train_std = np.std(train_scores, axis=1)\n",
        "\n",
        "# Calculate the mean and standard deviation of the test scores\n",
        "test_mean = np.mean(test_scores, axis=1)\n",
        "test_std = np.std(test_scores, axis=1)\n",
        "\n",
        "# Plot\n",
        "plt.rc('font', size=12)\n",
        "plt.plot(train_sizes, train_mean, label='Training score')\n",
        "plt.plot(train_sizes, test_mean, label='Cross-validation score')\n",
        "plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, alpha=0.1)\n",
        "plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, alpha=0.1)\n",
        "plt.xlabel('Number of training samples')\n",
        "plt.ylabel('Accuracy score')\n",
        "plt.title('Learning Curve for Random Forest Classifier')\n",
        "plt.legend(loc='best')"
      ],
      "metadata": {
        "id": "l4U7ItuI5Bn0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Save the model**"
      ],
      "metadata": {
        "id": "a7CsGZwKLEon"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "with open('Rf_model.pkl', 'wb') as f:\n",
        "    pickle.dump(Rf, f)\n",
        "\n",
        "# Load the model from the uploaded file\n",
        "# import pickle\n",
        "# with open('/content/Rf_model.pkl', 'rb') as f:\n",
        "#     model = pickle.load(f)"
      ],
      "metadata": {
        "id": "KkBGo6QVLJIj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. XGBoost models**"
      ],
      "metadata": {
        "id": "IFvBnVfqfGCt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier"
      ],
      "metadata": {
        "id": "Kppniw0x-VAd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1024\n",
        "xgb = XGBClassifier(n_estimators=200, max_depth=9, learning_rate= 0.5, min_child_weight=0)"
      ],
      "metadata": {
        "id": "eFngj0oyO0DS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2048\n",
        "#xgb = XGBClassifier(n_estimators=200, max_depth=9, learning_rate= 0.05)"
      ],
      "metadata": {
        "id": "rBZutaq8Oa-x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "0U62XQeDfQ3i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "345aed6e-2a16-4a3b-86eb-39360ad4f121"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.5, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=9, max_leaves=None,\n",
              "              min_child_weight=0, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=200, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.5, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=9, max_leaves=None,\n",
              "              min_child_weight=0, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=200, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.5, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=9, max_leaves=None,\n",
              "              min_child_weight=0, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=200, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_xgb = xgb.predict(X_test)"
      ],
      "metadata": {
        "id": "frgJnUeffVXj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preds_xgb[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JNDxJhDi4zHh",
        "outputId": "b4aa12f9-1b96-4e2a-dfc3-a0acbfda605d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test,preds_xgb))\n",
        "print('\\n')\n",
        "print(classification_report(y_test,preds_xgb))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6hA3Npq-ZjCt",
        "outputId": "6dfc93dd-5f79-400e-98bd-8428e71505d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[151  15]\n",
            " [ 28  67]]\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.91      0.88       166\n",
            "           1       0.82      0.71      0.76        95\n",
            "\n",
            "    accuracy                           0.84       261\n",
            "   macro avg       0.83      0.81      0.82       261\n",
            "weighted avg       0.83      0.84      0.83       261\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test the model on the testing set\n",
        "xgb_train_accuracy = xgb.score(X_train, y_train)\n",
        "xgb_test_accuracy = xgb.score(X_test, y_test)\n",
        "\n",
        "print(\"Training accuracy:\", xgb_train_accuracy)\n",
        "print(\"Testing accuracy:\", xgb_test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rmh2wMLKo6gr",
        "outputId": "0711d368-2b0d-4ef0-8c66-78857f745e34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.9779482262703739\n",
            "Testing accuracy: 0.8352490421455939\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Balanced accuracy:\", balanced_accuracy_score(y_test, preds_xgb))\n",
        "print(\"Matthews correlation coefficient (MCC):\", matthews_corrcoef(y_test, preds_xgb))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d4FYQEf00LhO",
        "outputId": "3e60b3d5-02dc-4427-8dfc-67f3c83ab48a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Balanced accuracy: 0.8074508560558021\n",
            "Matthews correlation coefficient (MCC): 0.637364847576887\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Hyperparameters tuning**"
      ],
      "metadata": {
        "id": "KVgyJQbE4k14"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# NOTE: This cell takes ~6 hours\n",
        "grid = {\n",
        "    'max_depth': [5, 7, 9],\n",
        "    'min_child_weight': [0, 1, 2, 3],\n",
        "    'learning_rate': [0.01, 0.05, 0.3, 0.5],\n",
        "    'n_estimators': [100, 200, 300]\n",
        "}\n",
        "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=40)\n",
        "\n",
        "xg = XGBClassifier()\n",
        "\n",
        "grid_search = GridSearchCV(xg, param_grid=grid, cv=skf)\n",
        "grid_search.fit(X, y)\n",
        "\n",
        "best_xg = grid_search.best_estimator_\n",
        "\n",
        "scores_xg = cross_val_score(best_xg, X, y, cv=skf)\n",
        "\n",
        "print('Best parameters: ',grid_search.best_params_)\n",
        "print('\\n')\n",
        "print('Cross-validation scores:', scores_xg)\n",
        "print('\\n')\n",
        "print('Mean score:', np.mean(scores_xg))\n",
        "print('\\n')\n",
        "print('Best score: ', grid_search.best_score_)"
      ],
      "metadata": {
        "id": "l4d2yoE2advf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3c8f8f0-9ebc-47ea-8310-f9cbf6472047"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters:  {'learning_rate': 0.3, 'max_depth': 9, 'min_child_weight': 0, 'n_estimators': 300}\n",
            "\n",
            "\n",
            "Cross-validation scores: [0.85823755 0.83141762 0.83524904 0.8697318  0.83846154]\n",
            "\n",
            "\n",
            "Mean score: 0.8466195107574418\n",
            "\n",
            "\n",
            "Best score:  0.8466195107574418\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **ROC curve**"
      ],
      "metadata": {
        "id": "ivKbZDg4_Qb0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_proba_xgb = xgb.predict_proba(X_test)[:,1]\n",
        "fpr_xgb, tpr_xgb, thresholds_xgb = roc_curve(y_test, y_pred_proba_xgb)\n",
        "auc_xgb = roc_auc_score(y_test, y_pred_proba_xgb)\n",
        "\n",
        "plt.plot(fpr_xgb, tpr_xgb, label='XGBoost (AUC= %.2f)' % (auc_xgb))\n",
        "plt.plot([0, 1], [0, 1],'r')\n",
        "plt.xlim([0, 1])\n",
        "plt.ylim([0, 1])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC curve')\n",
        "plt.legend()\n",
        "\n",
        "# Add threshold point\n",
        "# threshold_xgb = 0.34\n",
        "# idx = np.min(np.where(tpr_xgb > threshold_xgb))\n",
        "# plt.scatter(fpr_xgb[idx], tpr_xgb[idx], marker='o', color='black', label='Threshold')"
      ],
      "metadata": {
        "id": "vW_RStAv6AVs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "6068b7c5-2790-4fd7-c619-8f8c28963aa5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7d21833bf3a0>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('XGB_model.pkl', 'wb') as f:\n",
        "    pickle.dump(xgb, f)"
      ],
      "metadata": {
        "id": "jxko_joERdGE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Leraning curve for Rf & XGB**"
      ],
      "metadata": {
        "id": "_D2sJj3VUFTG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import LearningCurveDisplay, ShuffleSplit\n",
        "\n",
        "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 6), sharey=True)\n",
        "\n",
        "common_params = {\n",
        "    \"X\": X,\n",
        "    \"y\": y,\n",
        "    \"train_sizes\": np.linspace(0.1, 1.0, 5),\n",
        "    \"cv\": ShuffleSplit(n_splits=10, test_size=0.2, random_state=0),\n",
        "    \"score_type\": \"both\",\n",
        "    \"line_kw\": {\"marker\": \"o\"},\n",
        "    \"std_display_style\": \"fill_between\",\n",
        "    \"score_name\": \"Accuracy\",\n",
        "}\n",
        "\n",
        "for ax_idx, estimator in enumerate([Rf, xgb]):\n",
        "    LearningCurveDisplay.from_estimator(estimator, **common_params, ax=ax[ax_idx])\n",
        "    handles, label = ax[ax_idx].get_legend_handles_labels()\n",
        "    ax[ax_idx].legend(handles[:2], [\"Training Score\", \"Test Score\"])\n",
        "    ax[ax_idx].set_title(f\"Learning Curve for {estimator.__class__.__name__}\")"
      ],
      "metadata": {
        "id": "SE9BNPvlSa5X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3. K-Nearest Neighbors**"
      ],
      "metadata": {
        "id": "9Z-8IdOdsBkJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "metadata": {
        "id": "lgrYYzXcsDwL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1024\n",
        "knn = KNeighborsClassifier(n_neighbors=3, algorithm ='kd_tree', leaf_size= 20, weights= 'uniform')"
      ],
      "metadata": {
        "id": "3d1Q-75fscCm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2048\n",
        "#knn = KNeighborsClassifier(n_neighbors=3, algorithm ='kd_tree', leaf_size= 10, weights= 'distance')"
      ],
      "metadata": {
        "id": "uXCf4lt-eXxP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "wM38do-pseZe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "897ededa-5297-4ac7-8190-842c51e9ac8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='kd_tree', leaf_size=20, n_neighbors=3)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(algorithm=&#x27;kd_tree&#x27;, leaf_size=20, n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(algorithm=&#x27;kd_tree&#x27;, leaf_size=20, n_neighbors=3)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_knn = knn.predict(X_test)"
      ],
      "metadata": {
        "id": "wDotQybQsjwS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test,pred_knn))\n",
        "print('\\n')\n",
        "print(classification_report(y_test,pred_knn))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rVnpz3OXQeAw",
        "outputId": "7972bd94-c73d-4a6d-af30-9d8edefcde26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[147  19]\n",
            " [ 28  67]]\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.89      0.86       166\n",
            "           1       0.78      0.71      0.74        95\n",
            "\n",
            "    accuracy                           0.82       261\n",
            "   macro avg       0.81      0.80      0.80       261\n",
            "weighted avg       0.82      0.82      0.82       261\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test the model on the testing set\n",
        "knn_train_accuracy = knn.score(X_train, y_train)\n",
        "knn_test_accuracy = knn.score(X_test, y_test)\n",
        "\n",
        "print(\"Training accuracy:\", knn_train_accuracy)\n",
        "print(\"Testing accuracy:\", knn_test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1DQmjhmlpEfa",
        "outputId": "ae1a3ee8-3f44-4b95-de28-69bce79c8d5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.8954937679769894\n",
            "Testing accuracy: 0.8199233716475096\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Balanced accuracy:\", balanced_accuracy_score(y_test, pred_knn))\n",
        "print(\"Matthews correlation coefficient (MCC):\", matthews_corrcoef(y_test, pred_knn))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gt7XVXh60ORM",
        "outputId": "46c69f62-2583-4ec2-d2ba-3d211d5bd15c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Balanced accuracy: 0.7954026632847178\n",
            "Matthews correlation coefficient (MCC): 0.6047724498708196\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Hyperparameters tunning**"
      ],
      "metadata": {
        "id": "diK_Gd4tMBuF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grid = {\n",
        "    'n_neighbors': [3, 5, 7],\n",
        "    'weights': ['uniform', 'distance'],\n",
        "    'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
        "    'leaf_size': [10, 20, 30, 40, 50]\n",
        "}\n",
        "skf = StratifiedKFold(n_splits=10, shuffle=True, random_state=40)\n",
        "\n",
        "kn = KNeighborsClassifier()\n",
        "\n",
        "grid_search = GridSearchCV(kn, param_grid=grid, cv=skf)\n",
        "grid_search.fit(X, y)\n",
        "\n",
        "best_kn = grid_search.best_estimator_\n",
        "\n",
        "scores_kn = cross_val_score(best_kn, X, y, cv=skf)\n",
        "\n",
        "print('Best parameters: ',grid_search.best_params_)\n",
        "print('\\n')\n",
        "print('Cross-validation scores:', scores_kn)\n",
        "print('\\n')\n",
        "print('Mean score:', np.mean(scores_kn))\n",
        "print('\\n')\n",
        "print('Best score: ', grid_search.best_score_)"
      ],
      "metadata": {
        "id": "aUbhI-Ll0De1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1cb1232b-7ac0-40da-841d-aa2e880ae3fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters:  {'algorithm': 'kd_tree', 'leaf_size': 20, 'n_neighbors': 3, 'weights': 'uniform'}\n",
            "\n",
            "\n",
            "Cross-validation scores: [0.80916031 0.80916031 0.84732824 0.79389313 0.82307692 0.82307692\n",
            " 0.83846154 0.85384615 0.81538462 0.83076923]\n",
            "\n",
            "\n",
            "Mean score: 0.8244157369348208\n",
            "\n",
            "\n",
            "Best score:  0.8244157369348208\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **ROC curve**"
      ],
      "metadata": {
        "id": "62C23CQ1OjiG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_proba_knn = knn.predict_proba(X_test)[:,1]\n",
        "fpr_knn, tpr_knn, threshold_knn = roc_curve(y_test, y_pred_proba_knn)\n",
        "auc_knn = roc_auc_score(y_test, y_pred_proba_knn)\n",
        "\n",
        "plt.plot(fpr_knn, tpr_knn, label='KNN (AUC= %.2f)' % (auc_knn))\n",
        "plt.plot([0, 1], [0, 1],'k')\n",
        "plt.xlim([0, 1])\n",
        "plt.ylim([0, 1])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC curve')\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "10WDqOaUOiyN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "9f87f956-7dbe-4ede-b2cd-cbb95d76fdcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7d2183408370>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Qqfnrr7/w8PDg4sWLaDQapkyZwqeffoq5efrvKTe/v02znBRCCCFMmE6neBSbkGaAdEhkyuN/W4XiErM+PsjK3OyprrDU3WGuTz0u7mCNuRG6xfKKUgofHx+GDx9OXFwcpUqVYvPmzbRr185omaRgEkIIIf4vMVlHeHRC2rFBKV1i///vsGgticlZ76BxsLZI0xX23zvFShSxxsnWstCvMBEdHc3777/Phg0bAOjYsSMbN26kRIkSRs0lBZMQQogCLz4x+T/jgf6dRyjlcWiUloexCWRnoEoxeyt98eOazp1iKf9tZyVft1lx4cIFPDw8uHLlCmZmZnz++ed89NFHBl/mJCfkCgohhMiXlFJExic9afV5xp1iKQVSVHxSlp/X3EyDi4PVf7rDUg+QThkfZGVh/C/ygkApxbfffsuoUaPQarWUKVOGLVu28NJLLxk7mp4UTEIIIUyKTqcIj0n4d66gpwZLh/xnvJA2G8tqWFuYpW75+X/x89+usaJ2VgV6fJCpiYyMZOjQofj6+gLQuXNn1q9fj4uLi5GTpSYFkxBCiDzx9LIaTxc//20hytqyGv/KybIawjQEBgbi4eHBtWvXMDc354svvmDcuHEm0QX3X1IwCSGEeC6xCUnpdomZ4rIawjQopfj6668ZO3YsCQkJlC9fHl9fX5o3b27saM8kBZMQQog08mJZjTR3iuXyshrCNDx+/JjBgwezbds2ALp168batWspVqyYkZNlTAomIYQoRDJbVuNJl9mT/03IxvggOyvzpwog01tWQ5iG06dP4+npyc2bN7G0tGTu3LmMHj06X3SVSsEkhBAFgDYp+am7w55uAXqqdSiq8C2rIUyDUorFixczYcIEEhMTqVSpEn5+fjRu3NjY0bJM/h8uhBAm6ullNUL/M19QXiyr4VrEGmsLGR8kns/Dhw955513+P777wHo2bMnq1atwtnZ2bjBskkKJiGEyGNKKR7FJqbfJSbLaogC5LfffsPLy4ugoCCsrKxYuHAh77//fr7ogvsvKZiEEMJAkpJ1hGWyrEZo1JPxQbKshijIdDodCxYs4OOPPyYpKYkqVarg7+9PgwYNjB0tx6RgEkKITGS0rEboU3MJhcfIshpChIWFMWDAAHbv3g2Ap6cnK1euxNHR0cjJno/8FgohCqX0ltX4b3eYLKshRPYcOXKEPn36cPfuXaytrVmyZAnvvvtugWj5lIJJCFGgpCyr8XTxE/qfVqHcXFajmJ2V3DYvCh2dTsfs2bP57LPPSE5Opnr16vj7+1O3bl1jRzMYKZiEEPlCQpKOsGhtOl1iub2sxpOiSJbVECJ9ISEh9O3bl7179wLQt29fli9fjoODg5GTGZYUTEIIo8rKshqh0VoexiRk+TllWQ0h8sbBgwd56623uH//Pra2tnz11Vd4e3sXyD8upGASQhhcRstq/Hfx1Wht1scHybIaQpiG5ORkZsyYwfTp09HpdNSsWRN/f39q1apl7Gi5RgomIUSW/XdZjX9nkk5bFMmyGkIUTMHBwfTt25cDBw4A8M4777B06VLs7e2NnCx3ScEkhECblPzUTNL/Lq0hy2oIIZ62d+9e+vbtS0hICPb29ixfvpx+/foZO1aekE8pIQqwaG1S6rvDZFkNIUQOJCUlMXXqVGbNmoVSijp16uDv70+NGjWMHS3PSMEkRAFy73Ecs3+6zIU7jwmJ0hKbYJhlNZ4eLC3LaghRuNy5c4e33nqLI0eOADB06FAWLVqEra2tkZPlLSmYhCgAdDrFplNBzPnpcppB1Oktq/H0eCFZVkMI8Sw//fQT/fr1Izw8nCJFirBy5Uq8vLyMHcsopGASIp+7GRbDxG0XOHXzIQANKxRlbMdqlHG2lWU1hBA5kpiYyKeffsrcuXMBqF+/Pn5+flStWtXIyYxHPkmFyKeSknWsPnqThXuvok3SYWdlzoRXq9O/eUW5k0wIkWNBQUF4eXnx22+/ATB8+HDmz5+PjY2NkZMZlxRMQuRDl+9HMiHgAhfuRADwUlUXZnWvQ7lidkZOJoTIz77//nu8vb159OgRTk5OrF69mp49exo7lkmQgkmIfCQhSceyg9f4+uA1knQKRxsLPu1ak94Ny8r4IyFEjiUkJPDRRx+xaNEiABo3boyvry+VK1c2cjLTIQWTEPnE+duPmRDwO1cfRAPwSs2SzHizNiUcC3czuRDi+dy8eRMvLy9OnToFwJgxY5gzZw5WVlZGTmZapGASwsTFJSSz4JcrrDl2E50CFwcrpnWrTZc6paRVSQjxXLZv387AgQOJiIigaNGi+Pj40K1bN2PHMklSMAlhwn67Hs5H2y/wT3gsAD3ql2Fy15oUtZe//IQQOafVahk/fjzLli0DoFmzZvj6+lKhQgUjJzNdUjAJYYIi4xP5YvdltpwKAqC0kw2zutehXY0SRk4mhMjvrl27hqenJ+fOnQNgwoQJzJgxA0tLSyMnM21SMAlhYg5cfsDH2//kfmQ8AH2blWdipxoUsZEPMyHE8/Hz8+Pdd98lKiqK4sWLs379erp06WLsWPmCFExCmIiHMQlM++Evdp6/B0DF4nbM7lmXZpWLGzmZECK/i4uL44MPPmDFihUAtGrVii1btlC2bFkjJ8s/pGASwsiUUvxwIZip3//Fw5gEzDQw+KXKfNChGrZWsnitEOL5XLlyBQ8PDy5cuIBGo2HSpElMmzYNCwspAbJD/rWEMKIHkfF8suNP9l16AECNUkWY07Mu9co5GzeYEKJA2LhxI8OGDSMmJgZXV1c2btzIK6+8YuxY+ZIUTEIYgVIK/zO3mfHjJaLik7A01zCiXVXea1sFKwszY8cTQuRzsbGxjBw5kjVr1gDQtm1bNm/eTOnSpY2cLP+SgkmIPBYUHsukHRc4di0cgHrlnJnbsy7VSxUxcjIhREFw8eJFPDw8+Ouvv9BoNHz22WdMnjwZc3Pp4n8eUjAJkUeSdQqf47eY//MV4hKTsbE0Y1zH6gxsVQlzWSxXCGEAPj4+vP/++8TFxVGqVCk2bdpE+/btjR2rQJCCSYg8cC0kigkBFzgX9BiAZpWLMbtHXSq62Bs3mBCiQIiOjmb48OGsX78egA4dOrBx40ZKlixp5GQFhxRMQuSixGQdKw5dZ8n+ayQk63CwtuDjLi/i1bgcZtKqJIQwgD/++AMPDw8uX76MmZkZ06dPZ9KkSZiZyXhIQ5KCSYhc8ufdCD4MuMCl4EgA2tcowczutSntZGvkZEKIgkApxapVqxg1ahTx8fG4ubmxZcsWWrdubexoBZIUTEIYWHxiMl/u+5tvj9wgWacoamfJ1G616FbPTRbLFUIYRGRkJEOHDsXX1xeAzp07s27dOlxdXY2crOCSgkkIAzp96yETAy5wIywGgK51SzO1Wy1cHKyNnEwIUVAEBgbi4eHBtWvXMDc3Z9asWYwfP1664HKZFExCGEC0Nol5ey6z/sQ/KAUlilgz483avFKrlLGjCSEKCKUUy5cv54MPPiAhIYFy5crh6+tLixYtjB2tUJCCSYjndOhqKB9v/4O7j+MA8GxUjo9fexEnW1ksVwhhGBEREQwePJiAgAAAXn/9dXx8fChWrJiRkxUeUjAJkUOPYxP4fNcltp27A0C5YrbM7lGXli+4GDmZEKIgOX36NJ6enty8eRNLS0vmzJnDmDFjZExkHpOCSYgc2PNnMJ9+9xdh0Vo0GvBuUZEPX62OnZX8SgkhDEMpxZIlS/jwww9JTEykYsWK+Pn50aRJE2NHK5Tk012IbAiJimfKzr/46c/7AFRxtWdur7o0rCDN4kIIw3n48CEDBw5k586dAPTo0YPVq1fj7Oxs3GCFmBRMQmSBUort5+4yfddFIuISsTDTMKxNFUa0fwEbS1mfSQhhOCdOnMDT05OgoCCsrKxYsGABw4cPly44I5OCSYhM3H0cx8fb/+DQ1VAAapdxZE7PutRyczJyMiFEQaLT6Vi4cCGTJk0iKSmJKlWq4O/vT4MGDYwdTSAFkxDPpNMpNp38h9k/XSYmIRkrCzPGdKjKkJcqY2Eu850IIQwnLCwMb29vfvzxRwA8PT1ZuXIljo6ORk4mUkjBJEQ6boRG89G2Pzh16yEAjSoUZU6vulRxdTByMiFEQXP06FG8vLy4e/cu1tbWLF68mCFDhkgXnImRgkmIpyQl61h19CaL9l5Fm6TDzsqciZ1q0K9ZBVksVwhhUDqdjjlz5jB58mSSk5OpVq0a/v7+1KtXz9jRRDqkYBLi/y7ei2Titgv8cTcCgJequjCrex3KFbMzcjIhREETEhJCv379+OWXXwDo27cvy5cvx8FBWrFNlRRMotDTJiWz7MA1lv96nSSdwtHGgslda9KrYVlpEhdCGNyvv/7KW2+9RXBwMLa2tixbtox33nlHPm9MnBRMolA7F/SIiQEX+DskGoBOtUox/c1alChiY+RkQoiCJjk5mZkzZzJt2jR0Oh0vvvgiW7dupVatWsaOJrJACiZRKMUmJLHgl6usOXYTpcDFwYrpb9SmS53Sxo4mhCiA7t+/z9tvv82BAwcAeOedd1i6dCn29vZGTiaySgomUegcvxbGR9v/IOhhLAA9GpRh8ms1KWpvZeRkQoiCaN++fbz99tuEhIRgb2/P8uXL6devn7FjiWySgkkUGpHxicz68RK+p28D4OZkw8wedWhXvYSRkwkhCqKkpCSmTZvGzJkzUUpRp04d/P39qVGjhrGjiRwwudn3tFotEydOxM3NDVtbW5o2bcrevXuzdO6+ffto164dLi4uODs706RJEzZs2JDLiUV+sO/iAzouPKQvlvo1q8AvY9tIsSSEyBV3797l5ZdfZsaMGSilGDJkCCdPnpRiKR8zuYLJ29ubhQsX8vbbb7N48WLMzc3p0qULR48ezfC877//nldeeYWEhASmTp3KzJkzsbW1pX///ixatCiP0gtTEx6tZdSWQAavP8ODSC2VXOzxG9KMz9+sjYO1NLAKIQxvz549uLu7c/jwYRwcHNiyZQsrVqzA1tbW2NHEc9AopZSxQ6Q4deoUTZs2Zd68eYwfPx6A+Ph4ateuTYkSJTh+/Pgzz33llVf466+/uHHjBtbW1sCT5tAaNWpgb2/P77//nuUckZGRODk5ERERIdPS51NKKb7//R7TfrjIw5gEzDTwbuvKfNChmiyWK4TIFYmJiUyePJk5c+YA4O7ujr+/P1WrVjVyssIjN7+/TepP7ICAAMzNzRkyZIh+m42NDYMGDeLjjz/m9u3blCtXLt1zIyMjKVq0qL5YArCwsMDFxSXXcwvTcj8ink92/MH+yyEA1ChVhLm96lK3rLNxgwkhCqygoCD69Omj/8N++PDhzJ8/HxsbmaKkoDCpgikwMJBq1aqlqQqbNGkCwPnz559ZMLVt21Y/xfyAAQPQaDRs3ryZM2fO4O/vn+vZhfEppfA9fZtZP14iSpuEpbmGke2rMqxNFawsTK73WQhRQPzwww94e3vz8OFDHB0dWb16Nb169TJ2LGFgJlUwBQcHU7p02nlwUrbdu3fvmedOnjyZmzdvMnPmTGbMmAGAnZ0d27Zt44033sjwdbVaLVqtVv84MjIyJ/GFEf0THsNH2/7gtxvhALiXc2Zur7pUK1nEyMmEEAVVQkICkyZNYuHChQA0atQIPz8/KleubORkIjeYVMEUFxeXqkstRUqTZlxc3DPPtba2plq1avTq1YsePXqQnJzMypUr6du3L3v37qVZs2bPPPeLL75g2rRpz/8GRJ5L1inWHrvJ/F+uEJ+ow8bSjPGvVOedlpUwl8VyhRC55ObNm3h5eXHq1CkAxowZw+zZs9P9DhMFg0kVTLa2tqlaelLEx8fr9z/LiBEjOHHiBOfOncPM7En3i4eHB7Vq1WL06NGcPHnymedOmjSJsWPH6h9HRkY+s+tPmI6rD6KYEHCB87cfA9C8cnFm96xDheIyc64QIvfs2LGDd955h4iICJydnfHx8cm0J0PkfyZVMJUuXZq7d++m2R4cHAyAm5tbuuclJCSwevVqJkyYoC+WACwtLencuTPLli0jISEBK6v0Z3K2traWvwrykYQkHd8cus7SA3+TmKwoYm3Bx6+9iFfjcrJ4pRAi12i1Wj788EOWLl0KQLNmzfD19aVChQpGTibygkkVTO7u7hw8eJDIyMhUA79TWofc3d3TPS88PJykpCSSk5PT7EtMTESn06W7T+Q/F+48ZkLABS7fjwLg5RolmNG9NqWdZH4TIUTuuX79Op6enpw9exaADz/8kJkzZ2JpaWnkZCKvmNStQ7169dKPPUqh1WpZu3YtTZs21XeTBQUFcfnyZf0xJUqUwNnZmR07dpCQkKDfHh0dzQ8//ECNGjVkwrB8Lj4xmS9+usSbXx3j8v0oitlbsdjLnVUDGkmxJITIVf7+/tSvX5+zZ89SvHhxdu3axdy5c6VYKmRMqoWpadOm9O7dm0mTJhESEsILL7zAunXruHXrFqtXr9Yf179/fw4dOkTKnJvm5uaMHz+eTz/9lGbNmtG/f3+Sk5NZvXo1d+7cYePGjcZ6S8IATt4I56Ptf3AzLAaA1+u5MfX1mhR3kG5UIUTuiYuLY+zYsXzzzTcAtGrVii1btlC2bFkjJxPGYFIFE8D69euZPHkyGzZs4NGjR9StW5ddu3bRunXrDM/75JNPqFSpEosXL2batGlotVrq1q1LQEAAPXv2zKP0wpCitUnM+ekyG078A0BJR2tmvFmHjjVLGjmZEKKgu3LlCh4eHly4cAF4cnPQ9OnTsbAwua9NkUdMamkUUyFLoxjfr1dC+Hj7H9yLeHKHZJ8m5fio84s42UoTuBAid23atImhQ4cSExODq6srGzZs4NVXXzV2LJEFhWZpFCEexyYwfddFtp97crdkuWK2zOlRlxYvyBI3QojcFRsby6hRo/RDQNq2bcumTZueeYe2KFykYBImY/cfwXy280/CohPQaOCdFpUY/2o17Kzk/6ZCiNx18eJFPDw8+Ouvv9BoNEyePJnPPvsMc3NZrFs8Id9EwuhCIuP5bOdf7PnrPgAvlHBgbq+6NChf1MjJhBCFgY+PD8OHDyc2NpaSJUuyefNm2rdvb+xYwsRIwSSMRilFwNk7fL7rIpHxSViYaXi/bRWGt38Bawv5q04Ikbuio6MZPnw469evB6BDhw5s3LiRkiXlxhKRlhRMwijuPIrl4x1/cvhqKAB1yjgxp2ddarrJIHshRO77448/8PDw4PLly5iZmTF9+nQ++ugj6YITzyQFk8hTOp1iw4l/mLPnMrEJyVhZmDG2YzUGt6qEhblJzaMqhCiAlFKsXr2akSNHEh8fj5ubG1u2bMl06hohpGASeeZ6aDQTAy5w5p9HADSuWJQ5PetS2dXByMmEEIVBVFQUQ4cOZcuWLQB06tSJ9evX4+rqauRkIj+QgknkuqRkHSuP3ODLfX+TkKTD3sqciZ1r0LdpBczMZLFcIUTuCwwMxMPDg2vXrmFubs7MmTP58MMPUy3YLkRGpGASueqvexFM3HaBP+9GAtC6miuzutembFE7IycTQhQGSimWL1/O2LFj0Wq1lCtXDl9fX1q0aGHsaCKfkYJJ5Ir4xGSWHbjGN4euk6RTONla8lnXmvRoUAaNRlqVhBC5LyIigsGDBxMQEADA66+/ztq1aylevLiRk4n8SAomYXBn/3nEhIDfuR76ZLHczrVLMe2NWpQoYmPkZEKIwuLMmTN4enpy48YNLCwsmDNnDh988IH8wSZyTAomYTCxCUnM+/kKPsdvoRS4OFjz+Ru16FyntLGjCSEKCaUUS5Ys4cMPPyQxMZEKFSrg5+dH06ZNjR1N5HNSMAmDOPp3GB9tv8CdR3EA9GxQlsldX8TZzsrIyYQQhcWjR48YOHAg3333HQDdu3dn9erVFC0qqwaI5ycFk3guEXGJzPrxEn5nbgNQxtmWWT3q0Kaa3KYrhMg7J0+exNPTk3/++QcrKyvmz5/PiBEjpAtOGIwUTCLHfvnrPp9+9ychUVoA+jevwIRONXCwlv9bCSHyhk6nY9GiRXz00UckJSVRuXJl/P39adiwobGjiQJGvtlEtoVFa5n6/V/suhAMQCUXe+b0rEuTSsWMnEwIUZiEh4czYMAAfvzxRwA8PDxYuXIlTk5ORk4mCiIpmESWKaXYef4e0374i0exiZibaXj3pcqM6VAVG0tZf0kIkXeOHTuGl5cXd+7cwdrami+//JKhQ4dKF5zINVIwiSwJjojjkx1/cuByCAAvlnZkbs+61Ckrf8kJIfKOTqdjzpw5TJ48meTkZKpVq4a/vz/16tUzdjRRwEnBJDKk0ym2nA7ii92XidYmYWVuxsj2LzCsbRUsZbFcIUQeCgkJoX///vz8888AvP322yxfvpwiRYoYOZkoDKRgEs90KyyGj7Zf4MSNhwDUL+/M3J51qVpSPpyEEHnr0KFD9OnTh+DgYGxtbVm6dCkDBw6ULjiRZ6RgEmkk6xRrjt5kwd4rxCfqsLU0Z/yr1fFuURFzWSxXCJGHkpOTmTlzJtOmTUOn0/Hiiy/i7+9P7dq1jR1NFDJSMIlUrtyPYsK2C/x++zEALaoUZ3aPupQvLovlCiHy1v379+nbty/79+8HwNvbm2XLlmFvb2/kZKIwkoJJAJCQpOPrX6/x1cFrJCYrilhb8MlrL+LZuJw0eQsh8tz+/ft5++23efDgAXZ2dixfvpz+/fsbO5YoxKRgEvx++zETAi5w5UEUAB1eLMmMN2tTykkWyxVC5K2kpCSmT5/OjBkzUEpRu3Zt/P39efHFF40dTRRyUjAVYnEJySzad5VVR26gU1DM3oqp3Wrxet3S0qokhMhz9+7do0+fPhw+fBiAd999l8WLF2Nra2vkZEJIwVRonbgRzkfbLnArPBaAN9zd+KxrTYo7WBs5mRCiMNqzZw/9+vUjLCwMBwcHVq5cSZ8+fYwdSwg9KZgKIZ9jN5n6w0UASjnaMOPN2nSoWdLIqYQQhVFiYiKfffYZs2fPBsDd3R0/Pz+qVatm5GRCpCYFUyHzx50IZvx4CQDPRuX4pOuLONpYGjmVEKIwun37Nl5eXhw/fhyA999/nwULFmBjI+MnhemRgqkQiU1IYrRvIEk6RefapZjds46MVRJCGMWuXbsYMGAADx8+xNHRkVWrVtG7d29jxxLimWRti0Lk810XuREWQylHG77oIcWSECLvJSQkMG7cOF5//XUePnxIw4YNOXfunBRLwuQ9VwuTVqvl3LlzhISE0LJlS1xcXAyVSxjYnj/vs+XUbTQaWOhZD2c7K2NHEkIUMrdu3cLLy4uTJ08CMHr0aObMmYO1tdxsIkxfjluYlixZQunSpWnVqhU9evTgwoULAISFheHi4sKaNWsMFlI8n/sR8Xy0/cn1GdK6Mi2qSGErhMhb3333HfXr1+fkyZM4OzuzY8cOvvzySymWRL6Ro4Jp7dq1jBkzhk6dOrF69WqUUvp9Li4utG/fHl9fX4OFFDmn0ynGbT3P49hEapdxZFzH6saOJIQoRLRaLaNHj6Z79+48fvyYpk2bEhgYyJtvvmnsaEJkS44KpgULFvDGG2+wefNmXn/99TT7GzZsyF9//fXc4cTzW3X0BseuhWNrac5ir/pYWciwNSFE3rh+/TotW7ZkyZIlAIwfP54jR45QsWJF4wYTIgdy9O157do1Onfu/Mz9xYoVIzw8PMehhGH8eTeCeT9fAeCz12tSxdXByImEEIXF1q1badCgAWfPnqVYsWLs2rWLefPmYWkp05iI/ClHBZOzszNhYWHP3H/x4kVKlSqV41Di+cUmJDHKN5DEZMWrtUri1bicsSMJIQqB+Ph43n//fTw8PIiMjKRly5acP3+e1157zdjRhHguOSqYunTpwsqVK3n8+HGafX/99Rfffvst3bp1e95s4jnM+PESN0JjKOlozewedWUKASFErrt69SrNmjVj+fLlAEyaNIlff/2VcuXkDzaR/+WoYJoxYwbJycnUrl2bTz/9FI1Gw7p16+jbty+NGjWiRIkSfPbZZ4bOKrLo57/us/lkEAALPdwpai9TCAghctfmzZtp2LAhv//+O66uruzZs4dZs2ZhYSHzI4uCIUcFk5ubG2fPnqVTp074+fmhlGLDhg388MMP9OnThxMnTsicTEbyIDKej7b9O4VAyxfkOgghck9sbCzvvvsub7/9NtHR0bRp04bz58/z6quvGjuaEAalUU/PCZBDoaGh6HQ6XF1dMTPL/3dhRUZG4uTkREREBI6OjsaOk2U6naL/mlMcvRZGLTdHtr/fAmsLc2PHEkIUUJcuXcLDw4M///wTjUbD5MmTmTx5srQqCaPJze/vHFU3AwcO1M/UCuDq6krJkiX1xdKpU6cYOHCgYRKKLFtz7CZHr4VhY2nGYq/6UiwJIXLNunXraNSoEX/++SclS5Zk7969TJs2TYolUWDlqGDy8fHh+vXrz9x/8+ZN1q1bl+NQIvv+uhfB3D1PphCY3LUmL5SQKQSEEIYXExODt7c33t7exMbG8vLLL3P+/HlefvllY0cTIlflSv/ZvXv3sLW1zY2nFumIS0hm1JZAEpJ1dKxZkrealDd2JCFEAfTnn3/SqFEj1q1bh5mZGdOnT+fnn3+WaWREoZDlttOdO3eyc+dO/eOVK1eyb9++NMc9fvyYffv20bhxY8MkFJmaufsi10NjKFHEmjk9ZQoBIYRhKaVYvXo1I0eOJD4+Hjc3NzZv3kybNm2MHU2IPJPlgunixYts3boVAI1Gw8mTJzl79myqYzQaDfb29rRu3ZqFCxcaNqlI196LD9h44skUAgs86lFMphAQQhhQVFQUw4YNY/PmzQB06tSJ9evX4+rqauRkQuStHN0lZ2ZmxsaNG3nrrbdyI5PR5Ze75EIi4+m0+AgPYxIY3KoSn3ataexIQogC5Pz583h6enL16lXMzc2ZOXMmH374YYG4G1oUTLn5/Z2j2xl0Op1BQ4js0+kU47b+zsOYBF4s7ciHnaobO5IQooBQSvHNN9/wwQcfoNVqKVu2LL6+vrRs2dLY0YQwGrn/M59ae/wWR/4Ow9rCjCVe7jKFgBDCICIiInj33Xf1QzC6du2Kj48PxYsXN3IyIYwrx+2qP/30Ex07dqR48eJYWFhgbm6e5kfkjov3Ipnz02UAPu1ak6olixg5kRCiIDhz5gwNGjRg69atWFhYsGDBAr7//nsploQghwXTtm3b6Nq1Kw8ePMDLywudTkefPn3w8vLC1taWunXrylpyuSQ+MZnRvk+mEOjwYgn6NpUpBIQQz0cpxZIlS2jRogU3btygQoUKHD16lLFjx8pdt0L8X4665L744guaNGnC0aNHefToEcuXL2fgwIG0b9+eW7du0axZMypVqmTorAKYtfsSf4dE4ypTCAghDODRo0cMGjSIHTt2APDmm2+yZs0aihYtauRkQpiWHLUwXbx4ES8vL8zNzfXT4CcmJgJQsWJF3n//febMmWO4lAKA/ZcesP63fwCY37sexR2sjZxICJGfnTx5kvr167Njxw4sLS1ZvHgx27dvl2JJiHTkqGCys7PDyurJfD/Ozs5YW1sTHBys31+yZElu3rxpmIQCgJCoeCYEXABgYMtKtKkmc6AIIXJGKcWCBQto1aoV//zzD5UrV+b48eOMGjVKWq2FeIYcFUzVq1fn4sWL+sfu7u5s2LCBpKQk4uPj2bx5M+XLy9gaQ9HpFB9uvUB4TAI1ShVhgkwhIITIofDwcLp168b48eNJSkqid+/enDt3jkaNGhk7mhAmLUcFU/fu3dm5cydarRaATz75hF9//RVnZ2dcXV05cuQIH330kUGDFmbrfrvFoauhT6YQ6FMfG0u5A1EIkX3Hjh2jfv367Nq1C2tra5YvX46fnx9OTk7GjiaEycvRTN/pOXLkCNu3b8fc3JzXXnuNdu3aGeJpjcKUZvq+fD+SbsuOkZCkY/obtejfvKJR8wgh8h+dTsfcuXP59NNPSU5OpmrVqvj7++Pu7m7saEIYlMnN9J2el156iZdeekn/OCoqiiJFZH6g5xGfmMyoLYEkJOloX6ME/ZpVMHYkIUQ+ExoaSv/+/dmzZw8Ab731Ft988418PguRTQZfECgkJISPP/5YxjAZwOyfLnP1QTQuDtbM7SVTCAghsufQoUO4u7uzZ88ebGxsWLVqFRs3bpRiSYgcyFYLU0hICOvXr+f69esULVqUnj170rBhQwDu3r3LzJkz8fHxIT4+nrZt2+ZG3kLj4OUQfI7fAmB+77q4yBQCQogsSk5OZtasWUydOhWdTkeNGjXYunUrtWvXNnY0IfKtLBdMly9fpnXr1oSHh5My7Gnu3Lls3LgRjUbD4MGDiY+Pp2fPnnz44Yf6QkpkX2iUlg8DfgfAu0VF2lYvYeREQoj84v79+/Tt25f9+/cDMGDAAL766ivs7e2NnEyI/C3LXXKTJ08mOjqar7/+mj///JMffviBypUrM2bMGLy9vencuTNXrlzB19f3uYolrVbLxIkTcXNzw9bWlqZNm7J3794sn+/n50fz5s2xt7fH2dmZFi1acODAgRznyWtKKSYE/E5YdALVSxbho841jB1JCJFP7N+/H3d3d/bv34+dnR0+Pj74+PhIsSSEAWS5henw4cO89957DB06FICaNWtiYWFB586dGTBgAGvXrjVIIG9vbwICAhgzZgxVq1bFx8eHLl26cPDgQVq1apXhuVOnTmX69On06tULb29vEhMT+fPPP7l7965BsuWFdcdvcfBKKFYyhYAQIouSk5OZPn06n3/+OUopateujZ+fHzVr1jR2NCEKjCwXTOHh4dStWzfVtnr16gFP5mUyhFOnTuHr68u8efMYP348AP3796d27dpMmDCB48ePP/PcEydOMH36dBYsWMAHH3xgkDx57cr9KGb9dBmAjzvXoHopGZgphMjYvXv3eOuttzh06BAAgwcPZvHixdjZ2Rk5mRAFS5a75HQ6HZaWlqm2pTx2cHAwSJiAgADMzc0ZMmSIfpuNjQ2DBg3it99+4/bt288898svv6RUqVKMHj0apRTR0dEGyZRXnp5CoG11Vwa0qGjsSEIIE/fzzz/j7u7OoUOHcHBwYNOmTXz77bdSLAmRC7J1l9yZM2ewsbHRP46KikKj0XD06FEeP36c5vgePXpkK0xgYCDVqlVLM9lUkyZNADh//jzlypVL99z9+/fTokULlixZwowZMwgPD6dUqVJ88sknjBgxIls5jGHOnstceRBFcXsr5vWqJ1MICCGeKSkpicmTJzN79mzgSWu/v78/1apVM3IyIQqubBVMX375JV9++WWa7VOnTk2zTaPRkJycnK0wwcHBlC5dOs32lG337t1L97xHjx4RFhbGsWPHOHDgAFOmTKF8+fKsXbuWkSNHYmlpqR97lR6tVqtf5gWezBSal369EsLaY7cAmNe7Lq5FZAoBIUT6bt++TZ8+fTh27BgA7733HgsXLkz1x6wQwvCyXDAdPHgwN3MAEBcXh7V12mIh5YMgLi4u3fNSut/Cw8Px9fXF09MTgF69elGnTh1mzJiRYcH0xRdfMG3atOeNnyNh0VrGb70AwIDmFWhfo6RRcgghTN+PP/5I//79efjwIY6Ojnz77bd4eHgYO5YQhUKWC6Y2bdrkZg4AbG1tU7X0pIiPj9fvf9Z58GRMVa9evfTbzczM8PT0ZMqUKQQFBT1z9vFJkyYxduxY/ePIyMhndv0ZklKKiQEXCIvWUq2kA5O6vJjrrymEyH8SExOZNGkSCxYsAKBhw4b4+flRpUoVIycTovAw2FpyhlC6dOl0pwAIDg4GwM3NLd3zihUrho2NDc7Ozpibp74Nv0SJJ5M+Pnr06JkFk7W1dbotW7ltw4l/2H85BCsLMxZ7yRQCQoi0bt26hZeXFydPngRg1KhRzJ071yifWUIUZgZfS+55uLu7c/Xq1TRjiFI+KJ61sraZmRnu7u6EhoaSkJCQal/KuCdXV1fDB34OVx9EMfPHSwB81KkGL5Y27KrKQoj877vvvqN+/fqcPHkSZ2dntm/fzuLFi6VYEsIITKpg6tWrF8nJyaxcuVK/TavVsnbtWpo2barvJgsKCuLy5cupzvX09CQ5OZl169bpt8XHx7Np0yZq1qz5zNYpY0iZQkCbpKN1NVe8ZQoBIcRTtFotY8aMoXv37jx+/JgmTZoQGBhosDnvhBDZZ1Jdck2bNqV3795MmjSJkJAQXnjhBdatW8etW7dYvXq1/rj+/ftz6NAh/Zp2AEOHDmXVqlUMHz6cq1evUr58eTZs2MA///zDDz/8YIy380zzfr7C5ftRFLO3Yn7vupiZyRQCQognbty4gYeHB2fPngVg3LhxzJo1CysrKyMnE6JwM6mCCWD9+vVMnjyZDRs28OjRI+rWrcuuXbto3bp1hufZ2tpy4MABJkyYwJo1a4iJicHd3Z0ff/yRV199NY/SZ+7w1VBWH70JwLxedSlRRG4FFkI8ERAQwKBBg4iMjKRYsWL4+Pjw+uuvGzuWEALQqKebaQTw5C45JycnIiIi0kyi+TzCo7V0WnyE0Cgt/ZpV4PM3axvsuYUQ+Vd8fDzjxo3j66+/BqBly5Zs2bIlT+7WFaIgya3vb3iOMUxBQUEMGzaM6tWrU6xYMQ4fPgxAWFgYo0aNIjAw0GAhCwKlFBO3XSA0SssLJRz45DWZQkAIAX///TfNmzfXF0sfffQRBw8elGJJCBOToy65ixcv8tJLL6HT6WjatCnXrl0jKSkJABcXF44ePUpMTEyqcUeF3caTQey7FIKVuRlLZAoBIQSwZcsWhgwZQnR0NC4uLmzYsIFOnToZO5YQIh05KpgmTJiAs7MzJ06cQKPR6Oc6SvHaa6/h5+dnkIAFwbWQKGbsugjAhE7VqekmUwgIUZjFxcUxatQoVq1aBUDr1q3ZvHkzZcqUMXIyIcSz5KhL7vDhw7z33nu4urqmu0hs+fLl052AsjDSJiUzcst5tEk6XqrqwsCWlYwdSQhhRJcuXaJJkyasWrUKjUbD5MmT2b9/vxRLQpi4HLUw6XQ67Ozsnrk/NDRUJlb7v/k/X+FScCRF7SxZ0LueTCEgRCG2fv163nvvPWJjYylZsiQbN26kQ4cOxo4lhMiCHLUwNWjQgB9//DHdfUlJSfj6+tKsWbPnClYQHPk7lG+PPJlCYG6vepRwlCkEhCiMYmJieOeddxgwYACxsbG0b9+e8+fPS7EkRD6So4Jp0qRJ7Nmzh/fee48///wTgAcPHrBv3z5eeeUVLl26xEcffWTQoPnNw5gExvn/DsDbTcvTsWZJIycSQhjDn3/+SePGjfHx8cHMzIzp06fzyy+/UKpUKWNHE0JkQ47nYdqwYQOjR48mIiICpRQajQalFI6Ojixfvpw+ffoYOmueMcQ8DquO3GDGj5eo7GrPjyNfwtZK7ooTojBRSrFmzRpGjhxJXFwcpUuXZvPmzbRt29bY0YQosHJzHqYcz/Tdr18/evTowd69e/n777/R6XRUqVKFV199lSJFihgyY750PyIegI4vlpRiSYhCJioqivfee49NmzYB8Oqrr7J+/fo0dxQLIfKPHBVMKS1K9vb2vPnmmwaOVDCERWsBKO4g6z8JUZj8/vvveHh4cPXqVczNzZkxYwYTJkzAzMyk1joXQmRTjn6Dy5Qpw+jRozl27Jih8xQY4TEJABS3l7sFhSgMlFJ88803NG3alKtXr1K2bFl+/fVXPvroIymWhCgAcvRb3KZNG9asWUPr1q0pX74848eP59SpU4bOlq+FRT8pmFyKSMEkREEXGRmJl5cX7733Hlqtltdee43z58/TqlUrY0cTQhhIjgqmLVu2EBISgq+vL02aNGH58uU0b96cKlWq8PHHH3P+/HkDx8x/wlO65OylS06Iguzs2bM0aNAAf39/LCwsmD9/Pt9//z3Fixc3djQhhAHluJ3Y1taW3r17ExAQQEhICBs3bqROnTosWrSIhg0bUqNGDUPmzFd0OqXvknNxkBYmIQoipRRLly6lRYsWXL9+nQoVKnDkyBHGjRsnXXBCFEAG+a22t7enT58+bNy4kXnz5uHg4MDff/9tiKfOlyLiEknWPZmtoZi0MAlR4Dx69IiePXsyatQoEhISePPNNwkMDJQJe4UowHI8rUCK2NhYvv/+e/z9/dmzZw9arZYqVaowatQoQ+TLl8JjnnTHOdlaYmUhf2kKUZCcOnUKT09Pbt26haWlJfPnz2fkyJHprqsphCg4clQwxcfH8+OPP+Ln58fu3buJjY2lYsWKjBo1Ck9PT+rXr2/onPlKyoBvmVJAiIJDKcWiRYuYOHEiSUlJVK5cGT8/Pxo1amTsaEKIPJCjgsnV1ZXY2Fjc3NwYMmQInp6eNG3a1NDZ8q2UOZhcZEoBIQqEhw8f4u3tzQ8//ABAr169WLVqFU5OTkZOJoTIKzkqmLy9vfH09JRbZp8hXFqYhCgwjh8/jpeXF7dv38ba2ppFixYxbNgw6YITopDJUcG0dOlSQ+coUFKmFJA75ITIv3Q6HfPmzeOTTz4hOTmZqlWr4u/vj7u7u7GjCSGMIEsF0+HDhwFo3bp1qseZSTm+sAmLkRYmIfKz0NBQBgwYwE8//QRAnz59WLFihayTKUQhlqWCqW3btmg0GuLi4rCystI/fpaUteaSk5MNFjQ/CYtKWUdOWpiEyG8OHz5Mnz59uHfvHjY2NixdupRBgwZJF5wQhVyWCqaDBw8CYGVlleqxSJ9+0kqZg0mIfCM5OZkvvviCKVOmoNPpqFGjBv7+/tSpU8fY0YQQJiBLBVObNm0yfCxS049hknXkhMgXHjx4QN++fdm3bx8A/fv356uvvsLBwcHIyYQQpiJHsyq2b9+e/fv3P3P/wYMHad++fY5D5Xf6u+SkhUkIk3fgwAHq1avHvn37sLOzY+3ataxbt06KJSFEKjkqmH799VcePHjwzP0hISEcOnQox6Hys/jEZKK0SYCMYRLClCUnJzNlyhQ6dOjAgwcPqFWrFqdPn8bb29vY0YQQJijHS6NkNADy2rVrhfZukpTxS1bmZjjaPPfKM0KIXHDv3j3efvttfv31VwAGDRrEkiVLsLOzM24wIYTJyvI3+rp161i3bp3+8YwZM/j222/THPf48WMuXLhAly5dDJMwn0kZv1TcwUruqhHCBP3yyy/07duX0NBQ7O3tWbFiBW+//baxYwkhTFyWC6bY2FhCQ0P1j6OiojAzS92jp9FosLe3Z9iwYXz22WeGS5mPhD1VMAkhTEdSUhKfffYZX3zxBQD16tXD39+fatWqGTmZECI/yHLB9N577/Hee+8BUKlSJRYvXky3bt1yLVh+pV94V9aRE8Jk3Llzhz59+nD06FEAhg0bxqJFi7CxsTFyMiFEfpGjQTY3b940dI4CQ9aRE8K07N69m/79+xMeHk6RIkVYtWoVHh4exo4lhMhnslQwBQUFAVC+fPlUjzOTcnxhkjKGyVXukBPCqBITE/nkk0+YN28eAA0aNMDf358qVaoYOZkQIj/KUsFUsWLFVEujpDzOTGFcGkXGMAlhfP/88w9eXl6cOHECgJEjRzJv3jysreUPGSFEzmSpYFqzZg0ajQZLS8tUj0VaKdMKyBgmIYxj586deHt78/jxY5ycnFizZg09evQwdiwhRD6XpYLpvxO5ycRuzxYmY5iEMIqEhAQmTJjA4sWLAWjSpAm+vr5UqlTJyMmEEAVBjmb6fpaEhARiYmIM+ZT5jn4dORnDJESeuXHjBi1bttQXS2PHjuXIkSNSLAkhDCZHBZOvry8ffPBBqm3Tpk3DwcEBZ2dnunfvTnR0tEEC5ic6ndJ3yUnBJETeCAgIoH79+pw5c4aiRYvy/fffs2DBAqyspJVXCGE4OSqYFixYkKol6fjx40ybNo1XX32VDz74gD179jBz5kyDhcwvIuISSdYpAIrJwrtC5Kr4+HiGDx9O7969iYyMpEWLFpw/f57XX3/d2NGEEAVQjuZhun79OgMGDNA/3rx5M6VKlWLHjh1YWFig0+nYtm2bfkbdwiI85kl3nKONBVYWBu3tFEI85e+//8bT05PAwEAAJk6cyOeff66/MUUIIQwtR9/qWq021Qy5v/zyC507d8bC4kn9VbNmTe7cuWOYhPlIyoBvlyLSHSdEbvH19aVBgwYEBgbi4uLCTz/9xOzZs6VYEkLkqhwVTJUqVWLfvn0AnDlzhmvXrtGpUyf9/gcPHuDg4GCYhPlIyhxMLjKlgBAGFxcXx9ChQ+nTpw/R0dG0bt2a8+fPp/rsEUKI3JKjLrmhQ4cyevRoLl68yJ07dyhbtixdu3bV7z927Bi1atUyWMj8QpZFESJ3XL58GQ8PD/744w80Gg2ffPIJU6ZM0bdqCyFEbsvRp83IkSOxsbFh9+7dNGzYkIkTJ2JrawvAw4cPuX//PsOGDTNo0PwgXGb5FsLg1q9fz3vvvUdsbCwlSpRg06ZNdOjQwdixhBCFTI7/PHv33Xd5991302wvVqwYZ86cea5Q+VWYTCkghMHExMQwYsQIfHx8AGjfvj0bN26kdOnSxg0mhCiUnrs9++LFi/zzzz8AVKhQgZo1az53qPwqLCqlhUkKJiGex19//YWHhwcXL17EzMyMKVOm8Mknn2Bubm7saEKIQirHBdPOnTsZO3Yst27dSrW9UqVKLFy4kG7duj1vtnxHP2mlzMEkRI4opVi7di0jRowgLi6O0qVLs3nzZtq2bWvsaEKIQi5Hd8nt3r2bnj17AjBr1ix27NjBjh07mDVrFkopevTowZ49ewwaND/4dwyTtDAJkV3R0dH069ePQYMGERcXxyuvvML58+elWBJCmASNUkpl96TmzZuj1Wo5cuQI9vb2qfbFxMTQqlUrbGxs+O233wwWNC9FRkbi5OREREQEjo6OWT6vzpSfidImcWBcGyq7Fr5pFYTIqd9//x0PDw+uXr2Kubk5n3/+ORMnTsTMTCaAFUJkXU6/v7MiR59GFy5cYMCAAWmKJQB7e3u8vb25cOHCc4fLT+ITk4nSJgHSwiREVimlWLFiBU2bNuXq1auUKVOGX3/9lUmTJkmxJIQwKTkaw2RjY8PDhw+fuf/hw4epZgIvDFLGL1mZm+FoI3PDCJGZyMhIhgwZgp+fHwCvvfYaPj4+uLi4GDmZEEKklaM/4dq3b8/ixYvT7XI7efIkS5YsKXTzpDw9B5NGozFyGiFM27lz52jQoAF+fn5YWFgwb948vv/+eymWhBAmK0dNIXPnzqV58+a0atWKJk2aUL16dQCuXLnCqVOnKFGiBHPmzDFoUFMns3wLkTmlFF999RXjxo0jISGB8uXL4+fnR7NmzYwdTQghMpTjteQuXLjAqFGjePToEX5+fvj5+fHo0SNGjx7N77//TsWKFQ0c1bSFprQwyTpyQqTr8ePH9OrVi5EjR5KQkMAbb7xBYGCgFEtCiHwh2y1MycnJhIaG4uzszKJFi1i0aFFu5Mp3pIVJiGc7deoUnp6e3Lp1C0tLS+bNm8eoUaOk+1oIkW9kuYVJKcXHH39M0aJFKVOmDI6OjnTv3j3Dwd+FScoYJle5Q04IPaUUixYtolWrVty6dYtKlSpx7NgxRo8eLcWSECJfyXILk4+PD7Nnz6Zs2bJ06tSJ69evs3PnTnQ6HTt37szNjPlCyl1y0sIkxBMPHz7E29ubH374AYCePXuyatUqnJ2djRtMCCFyIMsF0/Lly6lfvz5Hjx7F1tYWgNGjR/PVV18RFhZW6O9uCZMxTELoHT9+HC8vL27fvo2VlRWLFi3ivffek1YlIUS+leUuuevXr9O/f399sQTw/vvvo9Pp+Pvvv3MlXH4SJmOYhECn0zF37lxat27N7du3eeGFFzhx4gTvv/++FEtCiHwtyy1Mjx49wtXVNdW2lFal+Ph4w6bKh1LGMLnIGCZRSIWGhjJgwAB++uknAPr06cOKFSsoUqSIkZMJIcTzy9ZdcvIXYvp0OqUfwyQFkyiMjhw5gpeXF/fu3cPGxoYlS5YwePBg+cwQQhQY2ZqH6aOPPqJu3br6n9atWwMwePDgVNvr1q1LvXr1chRIq9UyceJE3NzcsLW1pWnTpuzduzfbz9OxY0c0Gg0jRozIUY7siIhLJFn3ZA3jYvbSJScKD51Ox8yZM2nbti337t2jevXqnDx5knfffVeKJSFEgZLlFqbWrVun+wFYokQJgwby9vYmICCAMWPGULVqVXx8fOjSpQsHDx6kVatWWXqO7du3p7tsS24Jj3nSHedoY4GVhSwYKgqHBw8e0K9fP/0fNP369ePrr7/GwcHByMmEEMLwslww/frrr7kY44lTp07h6+vLvHnzGD9+PAD9+/endu3aTJgwgePHj2f6HPHx8YwbN46JEyfy2Wef5XZk4N8B3y5FpDtOFA4HDhzg7bff5v79+9ja2vL111/j7e1t7FhCCJFrTKo5JCAgAHNzc4YMGaLfZmNjw6BBg/jtt9+4fft2ps8xd+5cdDqdvuDKCylTCrjIlAKigEtOTmbq1Kl06NCB+/fvU6tWLc6cOSPFkhCiwMvR4ru5JTAwkGrVquHo6Jhqe5MmTQA4f/485cqVe+b5QUFBzJ49mzVr1qSa/iC3ybIoojAIDg7mrbfe0rc2Dxw4kKVLl2JnZ2fcYEIIkQdMqmAKDg6mdOnSabanbLt3716G548bN4769evj5eWVrdfVarVotVr948jIyGydnzKlgBRMoqD65Zdf6Nu3L6Ghodjb2/PNN9/Qt29fY8cSQog8Y1JdcnFxcVhbp+3WsrGx0e9/loMHD7Jt2za+/PLLbL/uF198gZOTk/4no1as9ITJlAKigEpKSuKTTz6hU6dOhIaGUrduXc6ePSvFkhCi0DGpgsnW1jZVS0+KlIkxn9XNlpSUxKhRo+jXrx+NGzfO9utOmjSJiIgI/U9Wxko9LSwqpYVJCiZRcNy5c4f27dsza9YslFIMHTqUEydOUL16dWNHE0KIPGdSXXKlS5fm7t27abYHBwcD4Obmlu5569ev58qVK6xYsYJbt26l2hcVFcWtW7coUaLEM8daWFtbp9uylVX6SStlDiZRQOzevZv+/fsTHh5OkSJF+Pbbb/H09DR2LCGEMJrnamG6e/cuW7ZsYfHixdy5cwd4chfNw4cPSU5Ozvbzubu7c/Xq1TRjiE6ePKnfn56goCASExNp2bIllSpV0v/Ak2KqUqVK/PLLL9nOk1X/jmGSFiaRvyUmJjJhwgRee+01wsPDadCgAefOnZNiSQhR6OWoYFJKMXbsWCpVqsTbb7/N2LFjuXr1KgDR0dFUrFiRpUuXZvt5e/XqRXJyMitXrtRv02q1rF27lqZNm+rHFgUFBXH58mX9MV5eXuzYsSPND0CXLl3YsWMHTZs2zclbzZKUu+RcZNC3yMeCgoJo06YN8+bNA2DkyJEcP36cF154wcjJhBDC+HLUJTdv3jwWL17MxIkTefnll+nYsaN+n5OTEz169GDbtm2MGTMmW8/btGlTevfuzaRJkwgJCeGFF15g3bp13Lp1i9WrV+uP69+/P4cOHUKpJ8uR1KhRgxo1aqT7nJUqVeLNN9/M9nvMqvjEZKK0SYC0MIn86/vvv8fb25tHjx7h5OTEmjVr6NGjh7FjCSGEychRwfTtt9/Sv39/Zs2aRXh4eJr9devW1a9Ynl3r169n8uTJbNiwgUePHlG3bl127dqlX7fO1KSMX7I01+BoY1JDwoTIVEJCAhMnTtTfXdq4cWP8/Pz0XdpCCCGeyNE3/O3bt2nRosUz99vb22d7LqMUNjY2zJs3T98tkJ6sLtOS0gKVm/Tjl+ytZbFRka/cvHkTT09PTp8+DcAHH3zA7NmzsbKSrmUhhPivHBVMJUqUyPDW+7Nnz1K+fPkch8pP9OOXisiXjMg/tm3bxqBBg4iIiKBo0aL4+PjQrVs3Y8cSQgiTlaNB3z169OCbb77hxo0b+m0prSu//PILPj4+9O7d2zAJTVzoUy1MQpi6+Ph4RowYQa9evYiIiKB58+acP39eiiUhhMhEjgqmadOmUbp0adzd3enfvz8ajYY5c+bQqlUrOnfuTN26dfn4448NndUkyTpyIr+4du0aLVq04KuvvgJgwoQJHDp0qNC0BgshxPPIUcHk5OTEiRMnmDBhAnfv3sXGxoZDhw7x+PFjpkyZwpEjRwrNgpwpY5hkWRRhynx9fWnQoAGBgYG4uLiwe/du5syZg6WlpbGjCSFEvpDj27psbW359NNP+fTTTw2ZJ9/Rz/ItLUzCBMXFxTFmzBj93GYvvfQSW7ZsoUyZMkZOJoQQ+YtJrSWXH4XJGCZhoq5cuUKzZs1YuXIlGo2GTz/9lAMHDkixJIQQOZCjFqaBAwdmeoxGo0k12WRBFSZjmIQJ2rhxI8OGDSMmJoYSJUqwcePGVBPMCiGEyJ4cFUwHDhxIM+dQcnIywcHBJCcn4+rqir29vUECmjoZwyRMSUxMDCNHjmTt2rUAtGvXjk2bNlG6dGkjJxNCiPwtRwXTrVu30t2emJjIihUr+PLLL9m7d+/z5MoXdDrFQ/0YJimYhHH99ddfeHh4cPHiRTQaDVOmTOHTTz/F3Nzc2NGEECLfM+gYJktLS0aMGMErr7zCiBEjDPnUJikiLpEk3ZPZxIvZS5ecMA6lFGvXrqVx48ZcvHiRUqVKsX//fqZMmSLFkhBCGEiuDPquV68ehw8fzo2nNinhMU+64xxtLLCykPHzIu9FR0fTv39/Bg4cSFxcHB07duT333+nXbt2xo4mhBAFSq58y+/du7dQzMOUMuBbuuOEMVy4cIFGjRqxceNGzMzMmDlzJnv27KFEiRLGjiaEEAVOjsYwTZ8+Pd3tjx8/5vDhw5w7d46PPvrouYLlB+FSMAkjUErx7bffMmrUKLRaLWXKlGHLli289NJLxo4mhBAFVo4KpqlTp6a7vWjRolSpUoVvvvmGd99993ly5Qv6OZhkSgGRRyIjIxk6dCi+vr4AdOnShXXr1uHi4mLkZEIIUbDlqGDS6XSGzpEvhUvBJPJQYGAgHh4eXLt2DQsLC2bNmsW4ceMwM5Pxc0IIkduy/UkbFxfH2LFj+eGHH3IjT74S9v8pBWSWb5GblFJ89dVXNGvWjGvXrlG+fHkOHz7Mhx9+KMWSEELkkWx/2tra2rJixQoePHiQG3nylbCo/09aWUQKJpE7Hj9+TO/evRkxYgQJCQl069aNwMBAmjdvbuxoQghRqOToz9OGDRvy559/GjpLvqNfeFfmYBK54PTp0zRo0IBt27ZhaWnJokWL+O677yhWrJixowkhRKGTo4Lpyy+/xNfXl1WrVpGUlGToTPnGv2OYpIVJGI5Sii+//JKWLVty8+ZNKlWqxLFjxxgzZkyaJYmEEELkjSwP+j58+DAvvvgirq6uDBgwADMzM4YOHcqoUaMoU6YMtra2qY7XaDT8/vvvBg9sSv6dVkBamIRhPHz4kHfeeYfvv/8egJ49e7Jq1SqcnZ2NG0wIIQq5LBdM7dq1Y+PGjfTp04fixYvj4uJC9erVczObSYtPTCZK+6R1TVqYhCH89ttveHl5ERQUhJWVFQsXLuT999+XViUhhDABWS6YlFIo9WTdtF9//TW38uQbKeOXLM01ONrkaHYGIYAn03QsWLCAjz/+mKSkJKpUqYK/vz8NGjQwdjQhhBD/J9/0OaQfv2RvLS0AIsfCwsIYMGAAu3fvBsDT05OVK1fi6Oho5GRCCCGelq1B31IY/Es/fqmIjF8SOXPkyBHc3d3ZvXs3NjY2rFixgi1btkixJIQQJihbBVPfvn0xNzfP0o+FRcFuvAp9qoVJiOzQ6XTMmjWLdu3acffuXapXr87JkycZMmSI/FEihBAmKltVTYcOHahWrVpuZclXUlqYZFkUkR0hISH07duXvXv3AtCvXz++/vprHBwcjJxMCCFERrJVMA0YMIC33nort7LkKyljmFzkDjmRRQcPHuStt97i/v372Nra8tVXX+Ht7S2tSkIIkQ/IQlQ5pJ/lW1qYRCaSk5OZNm0aHTp04P79+9SsWZPTp0/zzjvvSLEkhBD5RMEeaJSLwmQMk8iC4OBg3n77bQ4ePAjAwIEDWbp0KXZ2dkZOJoQQIjukYMqhMBnDJDKxd+9e+vbtS0hICPb29ixfvpx+/foZO5YQQogcyHLBpNPpcjNHviNjmMSzJCUlMXXqVGbNmoVSijp16uDv70+NGjWMHU0IIUQOSQtTDuh0iof6MUxSMIl/3blzh7feeosjR44AMHToUBYtWpRmrUUhhBD5ixRMORARl0iS7skyMcXspUtOPPHTTz/Rr18/wsPDKVKkCCtXrsTLy8vYsYQQQhiA3CWXA+ExT7rjHG0ssLKQf8LCLjExkYkTJ9KlSxfCw8OpX78+586dk2JJCCEKEGlhyoGUAd/SHSeCgoLw8vLit99+A2DEiBHMmzcPGxsbIycTQghhSFIw5UC4FEwC+P777/H29ubRo0c4OTmxevVqevbsaexYQgghcoH0J+WAfg4mmVKgUEpISGDs2LG88cYbPHr0iMaNG3Pu3DkploQQogCTFqYcCJeCqdC6efMmnp6enD59GoAxY8YwZ84crKzk/wtCCFGQScGUA2H/n1JAZvkuXLZv387AgQOJiIigaNGi+Pj40K1bN2PHEkIIkQekSy4H9JNWFpGCqTDQarWMHDmSnj17EhERQbNmzQgMDJRiSQghChEpmHJAf5eczMFU4F27do0WLVqwbNkyACZMmMDhw4epUKGCkZMJIYTIS9IllwP/jmGSFqaCzM/Pj3fffZeoqCiKFy/O+vXr6dKli7FjCSGEMAJpYcqBcFl4t0CLi4tj2LBheHl5ERUVRatWrTh//rwUS0IIUYhJwZRN8YnJRGmTAJmHqSC6cuUKzZo1Y8WKFWg0Gj755BMOHjxI2bJljR1NCCGEEUmXXDaF//8OOUtzDY428s9XkGzcuJFhw4YRExODq6srmzZtomPHjsaOJYQQwgRIC1M26ccv2Vuj0WiMnEYYQmxsLIMGDaJfv37ExMTQrl07fv/9dymWhBBC6EnBlE0yfqlguXjxIk2aNGHNmjVoNBqmTJnC3r17KV26tLGjCSGEMCHSp5RNoSlzMMn4pXxNKYWPjw/Dhw8nLi6OUqVKsWnTJtq3b2/saEIIIUyQFEzZJC1M+V90dDTvv/8+GzZsAKBjx45s2LCBkiVLGjmZEEIIUyVdctkULi1M+dqFCxdo3LgxGzZswMzMjBkzZrBnzx4ploQQQmRIWpiyKVy/jpy0MOUnSim+/fZbRo8eTXx8PG5ubmzZsoXWrVsbO5oQQoh8QAqmbAqTFqZ8JzIykqFDh+Lr6wtA586dWbduHa6urkZOJoQQIr+QLrlsCpMxTPlKYGAgDRs2xNfXF3Nzc+bOncuuXbukWBJCCJEt0sKUTTKGKX9QSrF8+XI++OADEhISKFeuHH5+fjRv3tzY0YQQQuRDUjBlg06neBgjLUymLiIigsGDBxMQEABAt27dWLt2LcWKFTNyMiGEEPmVdMllQ0RcIkk6BTyZ6VuYntOnT1O/fn0CAgKwtLRk4cKFfPfdd1IsCSGEeC7SwpQN4TFPuuMcbSywspBa05QopVi8eDETJkwgMTGRihUr4ufnR5MmTYwdTQghRAEgBVM2pAz4lvFLpuXhw4cMHDiQnTt3AtCjRw9Wr16Ns7OzcYMJIYQoMKSZJBvCpWAyOSdOnKB+/frs3LkTKysrli5dSkBAgBRLQgghDEoKpmxImYNJBnwbn06nY968ebz00ksEBQVRpUoVfvvtN0aMGIFGozF2PCGEEAWMyRVMWq2WiRMn4ubmhq2tLU2bNmXv3r2Znrd9+3Y8PT2pXLkydnZ2VK9enXHjxvH48WODZQuXgskkhIWF0a1bNyZMmEBSUhKenp6cO3eOBg0aGDuaEEKIAsrkCiZvb28WLlzI22+/zeLFizE3N6dLly4cPXo0w/OGDBnCpUuX6Nu3L0uWLKFTp04sW7aM5s2bExcXZ5BsYfplUaRLzliOHj2Ku7s7P/74I9bW1qxYsYItW7bg6Oho7GhCCCEKMJMa9H3q1Cl8fX2ZN28e48ePB6B///7Url2bCRMmcPz48WeeGxAQQNu2bVNta9iwIQMGDGDTpk0MHjz4ufPpJ60sIgVTXtPpdMyZM4fJkyeTnJxMtWrV2Lp1K3Xr1jV2NCGEEIWASbUwBQQEYG5uzpAhQ/TbbGxsGDRoEL/99hu3b99+5rn/LZYAunfvDsClS5cMkk9/l5wsvJunQkJC6Ny5Mx9//DHJycn07duXs2fPSrEkhBAiz5hUwRQYGEi1atXSdK+kzKVz/vz5bD3f/fv3AXBxcTFIvn/HMEkLU1759ddfcXd355dffsHW1pbVq1ezfv16HBwcjB1NCCFEIWJSXXLBwcGULl06zfaUbffu3cvW882ZMwdzc3N69eqV4XFarRatVqt/HBkZme5x4bLwbp5JTk5m5syZTJs2DZ1OR82aNfH396dWrVrGjiaEEKIQMqkWpri4OKyt07be2NjY6Pdn1ebNm1m9ejXjxo2jatWqGR77xRdf4OTkpP8pV65cmmPiE5OJ0iYBMg9Tbrt//z6vvPIKU6ZMQafT8c4773Dq1CkploQQQhiNSRVMtra2qVp6UsTHx+v3Z8WRI0cYNGgQr776KjNnzsz0+EmTJhEREaH/SW+sVPj/75CzNNfgaGNSDXMFyr59+6hXrx4HDhzA3t6e9evXs2bNGuzt7Y0dTQghRCFmUt/8pUuX5u7du2m2BwcHA+Dm5pbpc/z+++9069aN2rVrExAQgIVF5m/R2to63Zatp+nHL9lby8SIuSApKYmpU6cya9YslFLUqVMHf39/atSoYexoQgghhGm1MLm7u3P16tU0Y4hOnjyp35+R69ev06lTJ0qUKMHu3bsNOjBYxi/lnrt37/Lyyy8zc+ZMlFIMGTKEkydPSrEkhBDCZJhUwdSrVy+Sk5NZuXKlfptWq2Xt2rU0bdpUP7YoKCiIy5cvpzo3ZdyLmZkZP//8M66urgbNlrIsioxfMqw9e/bg7u7O4cOHcXBwYMuWLaxYsSLL3a9CCCFEXjCpLrmmTZvSu3dvJk2aREhICC+88ALr1q3j1q1brF69Wn9c//79OXToEEop/bZOnTpx48YNJkyYwNGjR1PNDF6yZEk6duz4XNnCpIXJoBITE5k8eTJz5swBnrQe+vv7ZzpAXwghhDAGkyqYANavX8/kyZPZsGEDjx49om7duuzatYvWrVtneN7vv/8OwNy5c9Psa9OmzXMXTOHSwmQwQUFB9OnTRz9z+/Dhw5k/f77+bkghhBDC1JhcwWRjY8O8efOYN2/eM4/59ddf02x7urUpN4Tr15GTFqbn8cMPP+Dt7c3Dhw9xdHRk9erVmc6TJYQQQhibSY1hMmUyhun5JCQkMG7cOLp168bDhw9p1KgRgYGBUiwJIYTIF0yuhclUyRimnLt58yZeXl6cOnUKgDFjxjBnzhysrOTfUgghRP4gBVMWyRimnNm+fTsDBw4kIiICZ2dnfHx8eOONN4wdSwghhMgW6ZLLAp1O8TBGWpiyQ6vVMnLkSHr27ElERATNmjXj/PnzUiwJIYTIl6RgyoKIuESSdE8GlRe3lxamzFy7do0WLVqwbNkyAD788EMOHz5MhQoVjJxMCCGEyBnpksuC8Jgn3XGONhZYWUiNmRF/f38GDx5MVFQUxYsXZ926dbz22mvGjiWEEEI8F/n2z4KUAd8yfunZ4uLieO+99/D09CQqKopWrVpx/vx5KZaEEEIUCFIwZYGsI5exK1eu0KxZM7755hs0Gg0ff/wxBw8epGzZssaOJoQQQhiEdMllgczB9GybNm1i6NChxMTE4OrqysaNG3nllVeMHUsIIYQwKGlhyoKUKQWkhelfsbGxDB48mL59+xITE0Pbtm05f/68FEtCCCEKJCmYsiBMvyyKtDABXLx4kSZNmrB69Wo0Gg1Tpkxh3759uLm5GTuaEEIIkSukSy4L/p20UlqYfHx8GD58OLGxsZQqVYpNmzbRvn17Y8cSQgghcpW0MGWB3CUH0dHRDBgwgHfeeYfY2Fg6dOjA+fPnpVgSQghRKEjBlAX/jmEqnAXTH3/8QePGjVm/fj1mZmbMmDGDPXv2ULJkSWNHE0IIIfKEdMllQWGdVkApxapVqxg1ahTx8fG4ubmxZcsWWrdubexoQgghRJ6SgikT8YnJRGmTAHApRIO+o6KiGDp0KFu2bAGgU6dOrF+/HldXVyMnE0IIIfKedMllIvz/d8hZmmtwtC0c9WVgYCANGjRgy5YtmJubM3v2bH788UcploQQQhRahaMCeA768Uv21mg0GiOnyV1KKZYvX87YsWPRarWUK1cOX19fWrRoYexoQgghhFFJwZSJwjJ+KSIigsGDBxMQEADA66+/ztq1aylevLiRkwkhhBDGJ11ymQgrBHfInTlzhgYNGhAQEICFhQULFy5k586dUiwJIYQQ/yctTJn4dw6mgtfCpJRiyZIlfPjhhyQmJlKxYkX8/Pxo0qSJsaMJIYQQJkUKpkyEF9CFdx89esTAgQP57rvvAOjevTtr1qzB2dnZqLmEEEIIUyRdcpkI168jV3BamE6cOEH9+vX57rvvsLKyYunSpWzbtk2KJSGEEOIZpGDKREEaw6TT6Zg/fz4vvfQS//zzD1WqVOH48eOMGDGiwN8BKIQQQjwP6ZLLREEZwxQeHs6AAQP48ccfAfDw8GDlypU4OTkZOZkQQghh+qRgykRBGMN09OhR+vTpw507d7C2tubLL79k6NCh0qokhIlQSpGcnExSUpKxowhhkiwtLTE3NzdqBimYMqDTKR7G5N95mHQ6HXPmzGHy5MkkJydTrVo1/P39qVevnrGjCSF4Uig9fvyY0NBQkpOTjR1HCJPm7OxMqVKljPbHvhRMGYiMTyRJp4AnM33nJyEhIfTv35+ff/4ZgLfffpvly5dTpEgRIycTQqS4f/8+jx8/xtHREUdHRywsLKTlV4j/UEoRGxtLSEgIAKVLlzZKDimYMhAe86Q7ztHGAiuL/DM+/tChQ/Tp04fg4GBsbW1ZtmwZ77zzjnwQC2FCkpOTiYiIwNXVFRcXF2PHEcKk2draAk8aA0qUKGGU7rn8UwUYwcPoRCD/jF9KTk5m+vTptG/fnuDgYF588UVOnTrFwIEDpVgSwsQkJiailMLe3t7YUYTIF+zs7IAnvzvGIC1MGchP45fu379P37592b9/PwDe3t4sW7ZMPoyFMHHyx4wQWWPs3xUpmDLwMDZ/3CG3b98++vbty4MHD7Czs2P58uX079/f2LGEEEKIAkO65DIQHmXaLUxJSUlMnjyZV155hQcPHlC7dm3Onj0rxZIQQghhYFIwZSA8NmVZFNNrYbp79y4vv/wyM2bMQCnFu+++y6lTp6hRo4axowkhRL7i7+9PsWLFiI6ONnaUQicxMZFy5crx9ddfGztKpqRgysDDmJQuOdNqYdqzZw/u7u4cPnwYBwcHNm/ezMqVK/V3EQghhLH5+Pig0Wg4c+ZMqu0RERE0adIEGxsb9uzZA8DUqVPRaDSULFmS2NjYNM9VsWJFunbtmmqbRqNBo9GwYMGCLL92epKTk5kyZQojR47EwcEh3f1ubm5oNBp++umndJ/D29s73XNTODg44O3tnWb7gwcPGD9+PDVq1MDOzg57e3saNmzIjBkzePz4cabZc+ru3bt4eHjg7OyMo6Mjb7zxBjdu3MjSuTqdjm+++QZ3d3ccHBwoWbIknTt35vjx46mO8/b21l+j9H7u3r0LPJmQcuzYscycOZP4+HiDv1dDkoIpA6Z2l1xiYiKTJk2ic+fOhIWF4e7uztmzZ+nTp4+xowkhRKYiIyN55ZVXuHDhAjt27KBTp06p9oeEhLB8+fJsPee8efPSLbKy6ocffuDKlSsMGTIk3f0HDhwgODiYihUrsmnTphy/zn+dPn2a2rVr89VXX/HSSy+xcOFCFixYQP369Zk9ezYeHh4Ge62nRUdH065dOw4dOsTHH3/MtGnTCAwMpE2bNoSHh2d6/ocffsh7771HnTp1WLhwIePGjePq1au0adOGU6dO6Y8bOnQoGzZsSPWzfv167OzsqFmzJmXKlNEf+8477xAWFsbmzZtz5T0bjBJpREREKEC1nP6DqjBxlzp5I9zYkVRQUJBq0aKFAhSg3n//fRUXF2fsWEKIHIqLi1MXL14ssL/Ha9euVYA6ffq0UkqpyMhI1axZM2VlZaV27dqV6tgpU6YoQLm7u6uSJUuq2NjYVPsrVKigXnvttVTbUo4H1IIFCzJ87Yx069ZNtWrV6pn7+/fvrxo0aKAWL16s7O3tVXR0dJpjBgwYoOzt7Z/5HPb29mrAgAH6x48ePVJlypRRJUuWVJcuXUpz/P3799Xnn3+eafacmDNnjgLUqVOn9NsuXbqkzM3N1aRJkzI8NzExUdna2qpevXql2n7jxg0FqFGjRmV4/pEjRxSgZs6cmWZf165d1UsvvZTh+Vn5nUn5/o6IiMjwuXJCWpgyYCrTCuzatQt3d3eOHz+Oo6MjW7du5auvvsLGxsaouYQQIiuio6Pp1KkT586dY9u2bbz22mvpHvfZZ5/x4MGDLLcytWzZkvbt2zN37lzi4uKynSs+Pp49e/bQoUOHdPfHxcWxY8cOvLy88PDwIC4ujp07d2b7df5rxYoV3L17l4ULF6Y77rRkyZJ8+umnz/066QkICKBx48Y0btxYv61GjRq8/PLL+Pv7Z3huYmIicXFxlCxZMtX2EiVKYGZmlumwkM2bN6PRaHjrrbfS7OvYsSNHjx7l4cOH2Xg3eUsKpgxEa5+s7eRipEHfCQkJjBs3jtdff52HDx/SqFEjAgMD6dWrl1HyCCFEdsXExNC5c2dOnz7N1q1b04xFetpLL72U7QJo6tSp2Sqynnb27FkSEhJo0KBBuvu///57oqOj8fLyolSpUrRt29Yg3XLff/89tra2Wf4sj46OJiwsLNOfiIiIDJ9Hp9Nx4cIFGjVqlGZfkyZNuH79OlFRUc8839bWlqZNm+Lj48OmTZsICgriwoULeHt7U7Ro0Wd2a8KTYsvf358WLVpQsWLFNPsbNmyIUirNWChTIgVTJizNNTja5v10Vbdu3aJ169YsXLgQgNGjR3P06FEqV66c51mEEHlHKUVsQpLJ/Cilnuv9DBgwgJMnT7J161a6deuW6fFTpkzhwYMHfPPNN1l6/pdeeol27doxb968bLcyXb58GYBKlSqlu3/jxo20aNGCcuXKAeDl5cUvv/xCaGhotl7nvy5dukS1atWwsspa78WIESNwdXXN9OeNN97I8HkePnyIVqtNdy22lG337t3L8Dk2btxI9erV6du3LxUqVKBevXqcO3eOY8eOZfj99PPPPxMeHs7bb7+d7v6Ucy9evJjh6xuTTFyZieL21nk+u+h3333HO++8w+PHj3F2dmbt2rW8+eabeZpBCGEccYnJ1PzsZ2PH0Ls4/VXsrHL+VfHgwQNsbGz0RUdmWrduTbt27Zg7dy7Dhg3L0t2/U6dOpU2bNnzzzTd88MEHWc6WMsi5aNGi6e77+eefWbRokX5bz549GT58OP7+/gwfPjzLr/NfkZGR2VoIfcKECfTt2zfT49J7H09LKSitrdP2mqQM8cis6CxSpAi1atWiefPmvPzyy9y/f5/Zs2fz5ptvcuTIkWeui7h582YsLS2fOZg9JXtYWFiGr29MUjBlIi/HL2m1WiZMmMCSJUsAaNq0KX5+flSoUCHPMgghhCGtWLGCsWPH0qlTJ44cOUL16tUzPSe7BdB/i6zsSq8Vzc/Pj8TEROrXr8+1a9f025s2bcqmTZuyXTA9/Ye3o6Njhl1f/1WzZk1q1qyZrddLT0rxqdVq0+xLuaU/owI1KSmJDh060LZtW5YuXarf3qFDB2rVqsW8efOYM2dOmvOio6PZuXMnr776KsWLF0/3uVOugbGXP8mIFEyZKJ5HUwpcv34dT09Pzp49C8D48eOZNWsWlpaWefL6QgjTYGtpzsXprxo7hp6t5fOtCl+zZk12797Nyy+/TMeOHTl27FimrU2tW7embdu22SqApkyZQtu2bVmxYgXOzs5ZOifly/vRo0eULVs21b6UsUotW7ZM99wbN27ou5FsbGzQarUopdJ84SuliI+PT3WTTo0aNTh//jwJCQlZ6paLiIjIUnejlZUVxYoVe+b+YsWKYW1tTXBwcJp9Kdvc3Nyeef7hw4f5888/9UNFUlStWpUXX3yRY8eOpXved999R2xs7DO74+DJNQCe2UJlCmQMUybyYtLKrVu30qBBA86ePUuxYsXYtWsX8+bNk2JJiEJIo9FgZ2VhMj+G+Iu/SZMmfPfdd4SEhNCxY8csjQGaOnUq9+/fZ8WKFVl6jTZt2tC2bVvmzJmT5bFMKXeo3bx5M9X2mzdvcvz4cUaMGMHWrVtT/fj5+WFlZZVqzqAKFSqQlJTE9evX07zGtWvXSE5OTtVT8PrrrxMXF8e2bduylHP06NGULl06058ePXpk+DxmZmbUqVMn3Qk9T548SeXKlTPsKnzw4AHwZDLP/0pMTCQpKSnd8zZt2oSDg0OGY9hSrsGLL76Y4XswJimYMpGbk1bGx8fz/vvv4+HhQWRkJC1btuT8+fPPvOVWCCHyq5dffpktW7Zw7do1OnXqRGRkZIbHP10AZXUG6JQia+XKlVk6vmHDhlhZWaUpIFJalyZMmECvXr1S/Xh4eNCmTZtUd8t17twZgGXLlqV5ja+++irVMQDDhg2jdOnS+kkf/yskJIQZM2boH0+YMIG9e/dm+pPerOf/1atXL06fPp3qPV+5coUDBw7Qu3fvVMdevnyZoKAg/eNq1aoB4Ovrm+q4c+fOceXKFerXr5/m9UJDQ9m3bx/du3fHzs7umbnOnj2LRqOhefPmmb4HozH4zE4FQMrEV+XG+Ktvfr2WK69x5coVVa9ePf1ElJMmTVKJiYm58lpCCNNT2CauTLFmzRoFqDZt2ujfe8rElaGhoamOPXjwoP4zMr2JK4cPH57mddu0aaM/JysTV3bt2lU1b9481bYaNWood3f3Z56zdOlSBaizZ8/qtw0ePFgBqkuXLmrRokVq0aJFqkuXLgpQgwcPTvMcJ06cUMWKFVO2trbq3XffVd9884365ptv1JAhQ1SRIkXUK6+8kmn2nIiMjFRVqlRRJUqUUHPnzlWLFi1S5cqVU25ubiokJCTVsSnX6WkdO3ZUgOrevbtavny5+uyzz1TRokWVvb29unz5cprXS/m32rNnT4a5unbtmuEEokoZf+JKKZjS8XTBtPXMbYM//6ZNm5SDg4MClKura6b/RxJCFDyFtWBSSqn58+crQHXt2lUlJiY+s2BS6t8CKKsF09NFVlYKpu3btyuNRqOCgoKUUkqdPXtWAWry5MnPPOfWrVsKUB988IF+W3Jyslq8eLGqV6+esrGxUTY2NqpevXpqyZIlKjk5Od3nuXfvnvrggw9UtWrVlI2NjbKzs1MNGzZUM2fOzJUv/BS3b99WvXr1Uo6OjsrBwUF17dpV/f3332mOS69gio2NVdOnT1c1a9ZUtra2ysnJSXXt2lUFBgam+1rNmjVTJUqUUElJSc/M8/jxY2VlZaVWrVqVYW5jF0wapZ5zko0CKDIyEicnJ8qN8Wf9sDa0rV7CIM8bGxvL6NGjWbVqFYB+ErSMBtkJIQqm+Ph4bt68SaVKlWTWfiNKTk6mZs2aeHh48Pnnnxs7TqH05ZdfMnfuXK5fv57hXXpZ+Z1J+f6OiIjA0dHRoDllDFMmDDWG6dKlSzRt2pRVq1ah0Wj47LPP2LdvnxRLQghhRObm5kyfPp2vvvqK6OhoY8cpdBITE1m4cCGffvpplubcMiaZViAThpiHad26dbz//vvExsZSsmRJNm3axMsvv2yAdEIIIZ6Xp6cnnp6exo5RKFlaWqYaWG7KpIUpE8Xsc14wxcTE4O3tjbe3N7Gxsbz88sucP39eiiUhhBAin5GCKQNFbMyxtsjZpG1//PEHjRo1Yt26dZiZmfH555/z888/U6pUKQOnFEIIIURuky65DBS3z/74JaUUq1evZuTIkcTHx+Pm5sbmzZtp06ZNLiQUQgghRF6QgikD2e2Oi4qKYtiwYfoZYDt16sT69etxdXXNjXhCCCGEyCPSJZeB7BRM58+fp2HDhmzevBlzc3Nmz57Njz/+KMWSECJDMrOLEFlj7N8VaWHKQFbukFNK6VfU1mq1lC1bFl9f32cu2CiEEPDk7iCNRkNMTIzJ304thCmIjY0FMNo6q1IwZaCYXcZjmCIiInj33XfZunUrAF27dsXHx0e/ArYQQjyLubk5Tk5OhIaGotVqcXR0xMLCMIvdClGQKKWIjY0lJCQEZ2dnzM1zdjPW85KCKQPFHZ5dxZ45cwZPT09u3LiBhYUFc+bM4YMPPpAPOyFElpUqVQpbW1tCQkIyXYxWiMLO2dnZqHeaS8GUgWLp3CWnlGLp0qWMHz+exMREKlSogJ+fH02bNjVCQiFEfqbRaHB2dsbJyYnk5GSSkpKMHUkIk2RpaWm0lqUUUjBl4L+Dvh89esSgQYPYsWMHAG+++SZr1qyhaNGixognhCggNBoNFhYWWFjIR7IQpsrk7pLTarVMnDgRNzc3bG1tadq0KXv37s3SuXfv3sXDwwNnZ2ccHR154403uHHjRo6zFHtq0PfJkyepX78+O3bswMrKiiVLlrB9+3YploQQQohCwOQKJm9vbxYuXMjbb7/N4sWLMTc3p0uXLhw9ejTD86Kjo2nXrh2HDh3i448/Ztq0aQQGBtKmTRvCw8NzlKW4nTVKKRYsWECrVq34559/qFy5MsePH2fkyJEyXkkIIYQoLJQJOXnypALUvHnz9Nvi4uJUlSpVVPPmzTM8d86cOQpQp06d0m+7dOmSMjc3V5MmTcpWjoiICAWoGzduqK5duypAAap3797q8ePH2XtTQgghhMgTKd/fERERBn9uk2phCggIwNzcnCFDhui32djYMGjQIH777Tdu376d4bmNGzemcePG+m01atTg5Zdfxt/fP0d5WrVqxa5du7C2tmb58uX4+fnh5OSUo+cSQgghRP5lUgVTYGAg1apVw9HRMdX2Jk2aAE9m006PTqfjwoULNGrUKM2+Jk2acP36daKiorKd5969e1StWpUTJ04wbNgw6YITQgghCimTuiUjODiY0qVLp9mesu3evXvpnvfw4UO0Wm2m51avXj3d87VaLVqtVv84IiICeHIX3LJlyyhSpIjMkSKEEEKYuJTvapULy6iYVMEUFxeHtXXauY9sbGz0+591HpCjcwG++OILpk2blmb7d999x3fffZdpbiGEEEKYjvDwcIMPoTGpgsnW1jZVS0+K+Ph4/f5nnQfk6FyASZMmMXbsWP3jx48fU6FCBYKCgmTMkpFFRkZSrlw5bt++naarVuQtuRamQ66F6ZBrYVoiIiIoX748xYoVM/hzm1TBVLp0ae7evZtme3BwMABubm7pnlesWDGsra31x2XnXHjSMpVe65STk5P8ApgIR0dHuRYmQq6F6ZBrYTrkWpgWMzPDD9E2qUHf7u7uXL16Nc14oZMnT+r3p8fMzIw6depw5syZNPtOnjxJ5cqVKVKkiMHzCiGEEKJwMKmCqVevXiQnJ7Ny5Ur9Nq1Wy9q1a2natCnlypUDICgoiMuXL6c59/Tp06mKpitXrnDgwAF69+6dN29ACCGEEAWSSXXJNW3alN69ezNp0iRCQkJ44YUXWLduHbdu3WL16tX64/r378+hQ4dSjYJ///33+fbbb3nttdcYP348lpaWLFy4kJIlSzJu3Lhs5bC2tmbKlCnpdtOJvCXXwnTItTAdci1Mh1wL05Kb10OjcuPeu+cQHx/P5MmT2bhxI48ePaJu3bp8/vnnvPrqq/pj2rZtm6ZgArhz5w4ffPABv/zyCzqdjrZt27Jo0SJeeOGFvH4bQgghhChATK5gEkIIIYQwNSY1hkkIIYQQwhRJwSSEEEIIkQkpmIQQQgghMlGoCiatVsvEiRNxc3PD1taWpk2bsnfv3iyde/fuXTw8PHB2dsbR0ZE33niDGzdu5HLigiun12L79u14enpSuXJl7OzsqF69OuPGjePx48e5H7qAep7fi6d17NgRjUbDiBEjciFl4fC818LPz4/mzZtjb2+Ps7MzLVq04MCBA7mYuOB6nmuxb98+2rVrh4uLC87OzjRp0oQNGzbkcuKCKzo6milTptCpUyeKFSuGRqPBx8cny+c/fvyYIUOG4Orqir29Pe3atePcuXPZD6IKES8vL2VhYaHGjx+vVqxYoZo3b64sLCzUkSNHMjwvKipKVa1aVZUoUULNmTNHLVy4UJUrV06VLVtWhYWF5VH6giWn16J48eKqTp06avLkyerbb79Vo0aNUlZWVqpGjRoqNjY2j9IXLDm9Fk/btm2bsre3V4AaPnx4LqYt2J7nWkyZMkVpNBrVu3dv9c0336ilS5eqoUOHqvXr1+dB8oInp9di586dSqPRqBYtWqilS5eqZcuWqdatWytALVy4MI/SFyw3b95UgCpfvrxq27atAtTatWuzdG5ycrJq0aKFsre3V1OnTlXLli1TNWvWVEWKFFFXr17NVo5CUzCdPHlSAWrevHn6bXFxcapKlSqqefPmGZ47Z84cBahTp07pt126dEmZm5urSZMm5Vrmgup5rsXBgwfTbFu3bp0C1LfffmvoqAXe81yLp4+vWLGimj59uhRMz+F5rsVvv/2mNBqNfCEbyPNci44dOyo3NzcVHx+v35aYmKiqVKmi6tatm2uZC7L4+HgVHByslFLq9OnT2SqY/Pz8FKC2bt2q3xYSEqKcnZ1Vnz59spWj0HTJBQQEYG5uzpAhQ/TbbGxsGDRoEL/99hu3b9/O8NzGjRvTuHFj/bYaNWrw8ssv4+/vn6u5C6LnuRZt27ZNs6179+4AXLp0yeBZC7rnuRYp5s6di06nY/z48bkZtcB7nmvx5ZdfUqpUKUaPHo1Siujo6LyIXGA9z7WIjIykaNGiqSZOtLCwwMXFJcNF4MWzWVtbU6pUqRydGxAQQMmSJenRo4d+m6urKx4eHuzcuROtVpvl5yo0BVNgYCDVqlVLszhikyZNADh//ny65+l0Oi5cuECjRo3S7GvSpAnXr18nKirK4HkLspxei2e5f/8+AC4uLgbJV5g877UICgpi9uzZzJkzR74MntPzXIv9+/fTuHFjlixZgqurK0WKFKF06dIsW7YsNyMXWM9zLdq2bctff/3F5MmTuXbtGtevX+fzzz/nzJkzTJgwITdji3QEBgbSoEGDNIvxNmnShNjYWK5evZrl5zKppVFyU3BwMKVLl06zPWXbvXv30j3v4cOHaLXaTM+tXr26AdMWbDm9Fs8yZ84czM3N6dWrl0HyFSbPey3GjRtH/fr18fLyypV8hUlOr8WjR48ICwvj2LFjHDhwgClTplC+fHnWrl3LyJEjsbS0ZOjQobmavaB5nt+LyZMnc/PmTWbOnMmMGTMAsLOzY9u2bbzxxhu5E1g8U3BwMK1bt06z/elrWadOnSw9V6EpmOLi4tJdW8bGxka//1nnATk6V6Qvp9ciPZs3b2b16tVMmDCBqlWrGixjYfE81+LgwYNs27aNkydP5lq+wiSn1yKl+y08PBxfX188PT2BJwuS16lThxkzZkjBlE3P83thbW1NtWrV6NWrFz169NAvKN+3b1/27t1Ls2bNci23SMuQ3zeFpmCytbVNt68yPj5ev/9Z5wE5OlekL6fX4r+OHDnCoEGDePXVV5k5c6ZBMxYWOb0WSUlJjBo1in79+qUa2ydy7nk/oywtLVO1spqZmeHp6cmUKVMICgqifPnyuZC6YHqez6gRI0Zw4sQJzp07p+8G8vDwoFatWowePVr+wMhjhvq+gUI0hql06dIEBwen2Z6yzc3NLd3zihUrhrW1dY7OFenL6bV42u+//063bt2oXbs2AQEBWFgUmtrfoHJ6LdavX8+VK1cYOnQot27d0v8AREVFcevWLWJjY3Mtd0H0PJ9RNjY2FC9eHHNz81T7SpQoATzpthNZl9NrkZCQwOrVq3nttddSjZmxtLSkc+fOnDlzhoSEhNwJLdJliO+bFIWmYHJ3d+fq1atERkam2p5S7bu7u6d7npmZGXXq1OHMmTNp9p08eZLKlStTpEgRg+ctyHJ6LVJcv36dTp06UaJECXbv3o2Dg0NuRS3wcnotgoKCSExMpGXLllSqVEn/A0+KqUqVKvHLL7/kavaC5nk+o9zd3QkNDU3zZZwy1sbV1dXwgQuwnF6L8PBwkpKSSE5OTrMvMTERnU6X7j6Re9zd3Tl37hw6nS7V9pMnT2JnZ0e1atWy/mTZmoQgHztx4kSaeTXi4+PVCy+8oJo2barf9s8//6hLly6lOnf27NkKUKdPn9Zvu3z5sjI3N1cTJ07M/fAFzPNci+DgYFW5cmXl5uambt68mVeRC6ycXotLly6pHTt2pPkBVJcuXdSOHTvUvXv38vS95HfP83uxaNEiBaiVK1fqt8XFxanKlSurmjVr5n74Aian1yIpKUk5OzuratWqKa1Wq98eFRWlypYtq2rUqJE3b6AAy2gepnv37qlLly6phIQE/TZfX9808zCFhoYqZ2dn5enpma3XLjQFk1JK9e7dW1lYWKgPP/xQrVixQrVo0UJZWFioQ4cO6Y9p06aN+m8dGRkZqapUqaJKlCih5s6dqxYtWqTKlSun3NzcVEhISF6/jQIhp9eiXr16ClATJkxQGzZsSPXzyy+/5PXbKBByei3Sg0xc+Vxyei1iY2NVrVq1lKWlpRo/frxasmSJaty4sTI3N1e7d+/O67dRIOT0WsyYMUMBqn79+mrRokVq/vz56sUXX1SA2rhxY16/jQJj6dKl6vPPP1fvvfeeAlSPHj3U559/rj7//HP1+PFjpZRSAwYMUECqP6aTkpJUs2bNlIODg5o2bZr66quvVK1atVSRIkXU5cuXs5WhUBVMcXFxavz48apUqVLK2tpaNW7cWO3ZsyfVMc/6Yrh9+7bq1auXcnR0VA4ODqpr167q77//zqvoBU5OrwXwzJ82bdrk4TsoOJ7n9+K/pGB6Ps9zLR48eKAGDBigihUrpqytrVXTpk3TnCuy7nmuxaZNm1STJk2Us7OzsrW1VU2bNlUBAQF5Fb1AqlChwjM/+1MKpPQKJqWUevjwoRo0aJAqXry4srOzU23atEnVY5RVGqWUynoHnhBCCCFE4VNoBn0LIYQQQuSUFExCCCGEEJmQgkkIIYQQIhNSMAkhhBBCZEIKJiGEEEKITEjBJIQQQgiRCSmYhBBCCCEyIQWTEEIIIUQmpGASQgDw66+/otFo+PXXX40dJVdpNBqmTp2apWMrVqyIt7d3ruYRQuQPUjAJkc/5+Pig0WjS/fnoo4+MHS9D/81uY2NDtWrVGDFiBA8ePMiTDMePH2fq1Kk8fvw4T14vKypWrJjq38Xe3p4mTZqwfv36HD/n7t27s1woCiHSsjB2ACGEYUyfPp1KlSql2la7dm0jpcmelOzx8fEcPXqU5cuXs3v3bv7880/s7OwM+lpxcXFYWPz70Xf8+HGmTZuGt7c3zs7OqY69cuUKZmbG+bvS3d2dcePGARAcHMyqVasYMGAAWq2Wd999N9vPt3v3br766ispmoTIISmYhCggOnfuTKNGjYwdI0eezj548GCKFy/OwoUL2blzJ3369DHoa9nY2GT5WGtra4O+dnaUKVOGvn376h97e3tTuXJlFi1alKOCSQjxfKRLTogC7p9//uH999+nevXq2NraUrx4cXr37s2tW7cyPffvv/+mZ8+elCpVChsbG8qWLYuXlxcRERGpjtu4cSMNGzbE1taWYsWK4eXlxe3bt3OcuX379gDcvHkTgKSkJD7//HOqVKmCtbU1FStW5OOPP0ar1aY678yZM7z66qu4uLhga2tLpUqVGDhwYKpjnh7DNHXqVD788EMAKlWqpO8CS/m3eXoM05kzZ9BoNKxbty5N3p9//hmNRsOuXbv02+7evcvAgQMpWbIk1tbW1KpVizVr1uT438TV1ZUaNWpw/fr1VNuPHDlC7969KV++PNbW1pQrV44PPviAuLg4/THe3t589dVX+vef8pNCp9Px5ZdfUqtWLWxsbChZsiRDhw7l0aNHOc4rREEjLUxCFBARERGEhYWl2ubi4sLp06c5fvw4Xl5elC1bllu3brF8+XLatm3LxYsXn9nllZCQwKuvvopWq2XkyJGUKlWKu3fvsmvXLh4/foyTkxMAM2fOZPLkyXh4eDB48GBCQ0NZunQprVu3JjAwME03V1akFAXFixcHnrQ6rVu3jl69ejFu3DhOnjzJF198waVLl9ixYwcAISEhvPLKK7i6uvLRRx/h7OzMrVu32L59+zNfp0ePHly9epUtW7awaNEiXFxcgCfFyX81atSIypUr4+/vz4ABA1Lt8/Pzo2jRorz66qsAPHjwgGbNmqHRaBgxYgSurq789NNPDBo0iMjISMaMGZPtf5OkpCTu3LlD0aJFU23funUrsbGxvPfeexQvXpxTp06xdOlS7ty5w9atWwEYOnQo9+7dY+/evWzYsCHNcw8dOhQfHx/eeecdRo0axc2bN1m2bBmBgYEcO3YMS0vLbOcVosBRQoh8be3atQpI90cppWJjY9Oc89tvvylArV+/Xr/t4MGDClAHDx5USikVGBioALV169ZnvvatW7eUubm5mjlzZqrtf/zxh7KwsEiz/VnZ9+3bp0JDQ9Xt27eVr6+vKl68uLK1tVV37txR58+fV4AaPHhwqnPHjx+vAHXgwAGllFI7duxQgDp9+nSGrwmoKVOm6B/PmzdPAermzZtpjq1QoYIaMGCA/vGkSZOUpaWlevjwoX6bVqtVzs7OauDAgfptgwYNUqVLl1ZhYWGpns/Ly0s5OTmle03++7qvvPKKCg0NVaGhoeqPP/5Q/fr1U4AaPnx4qmPTe64vvvhCaTQa9c8//+i3DR8+XKX3kX/kyBEFqE2bNqXavmfPnv+1c7chTb1vHMC/uvZL2aYSmxpmIlOb0jIQ0rJakjZKDctysySfMl+YEiKJFII4MwgtCSPmCxMxEl2SPaGNfBMlEVYkUQ16AgPzaRqV4Lbr/yI2nJvNf/Xn98euD5w3577vc1/nnDcX95Pb+4z9rXhKjrFlorm5Gffu3XO6AMDX19dRZ25uDhMTE4iIiEBAQACGhoYWfZ59BKmvrw/fvn1zW+f69euw2WzIysrC+Pi44woODkZkZCQGBgaWFHtycjJkMhlCQ0Oh1WohFovR09ODkJAQ3LlzBwBQXl7u1Ma+IPr27dsA4BjJunXrFubm5pbU739Lo9Fgbm7OadSqv78fZrMZGo0GAEBEMBgMSE9PBxE5fRe1Wo3p6emffvf5z5XJZJDJZFAqlWhvb0d+fj7OnTvnVG/+//369SvGx8exZcsWEBGePn3qsZ+uri74+/sjJSXFKda4uDiIxeIl/0PGljuekmNsmdi0aZPbRd/fv39HfX09WltbMTIyAiJylC1cizRfeHg4ysvL0djYiI6ODmzbtg179+5FTk6OI5kymUwgIkRGRrp9xlKncpqbmxEVFYUVK1YgKCgI69atc+xO+/DhA7y9vREREeHUJjg4GAEBAfjw4QMAQKVSITMzEzU1NTh//jx27NiBjIwMHDp06I8t3o6NjYVCoUBnZycKCwsB/JiOk0qljnVXY2NjMJvN0Ov10Ov1bp/z+fNnj33Fx8dDp9PBarVieHgYOp0OU1NT+Oeff5zqffz4EdXV1ejt7XVZc/Sz/2tnMpkwPT2NwMDAX46Vsb8BJ0yMLXOlpaVobW3FiRMnsHnzZvj7+8PLywtarRY2m+2nbRsaGpCXl4cbN26gv78fZWVlqK+vx+DgINasWQObzQYvLy/cvXsXAoHApb1YLF5SjIsle/PNX6S8WHl3dzcGBwdx8+ZN9PX1oaCgAA0NDRgcHFxyLJ5oNBrU1dVhfHwcEokEvb29yM7OdhxVYP+mOTk5Lmud7DZs2OCxH6lUiuTkZACAWq2GQqFAWloampqaHKNtVqsVKSkpmJycRGVlJRQKBUQiEUZGRpCXl+fx/9rjDQwMREdHh9tyd+u5GPsbccLE2DLX3d2N3NxcNDQ0OO7Nzs4u+aBGpVIJpVKJ06dP4+HDh0hMTMTly5eh0+kgl8tBRAgPD0dUVNT/JP6wsDDYbDaYTCZER0c77o+OjsJsNiMsLMypfkJCAhISElBXV4erV6/i8OHDuHbtGo4ePer2+Z4SsYU0Gg1qampgMBgQFBSEmZkZaLVaR7lMJoNEIoHVanUkPH9CamoqVCoVzpw5g+LiYohEIrx48QJv3rxBW1sbjhw54qhrn46db7H3lMvlMBqNSExMdJreY4w54zVMjC1zAoHAaRoOAC5evAir1frTdjMzM7BYLE73lEolvL29Hdv59+/fD4FAgJqaGpc+iAgTExO/Hf+ePXsAABcuXHC639jYCOBHIgEAU1NTLjFs3LgRAFyOH5hPJBIBwJITyOjoaCiVSnR2dqKzsxOrV6/G9u3bHeUCgQCZmZkwGAwYHh52aT82NrakftyprKzExMQEWlpaHH0BcHpvIkJTU5NL28XeMysrC1arFbW1tS5tLBbL/9UJ6Iz9m3iEibFlLi0tDe3t7fD390dMTAwePXoEo9Ho2LK/mPv37+P48eM4ePAgoqKiYLFY0N7e7kgIgB+jEzqdDlVVVXj//j0yMjIgkUjw7t079PT04NixY6ioqPit+GNjY5Gbmwu9Xg+z2QyVSoXHjx+jra0NGRkZSEpKAgC0tbXh0qVL2LdvH+RyOb58+YKWlhb4+fk5ki534uLiAACnTp2CVquFUChEenq6I8FwR6PRoLq6Gj4+PigsLHQ5Dfzs2bMYGBhAfHw8ioqKEBMTg8nJSQwNDcFoNGJycvKXvsXu3buxfv16NDY2oqSkBAqFAnK5HBUVFRgZGYGfnx8MBoPb85Ps71lWVga1Wg2BQACtVguVSoXi4mLU19fj2bNn2LVrF4RCIUwmE7q6utDU1IQDBw78UryMLSv/zuY8xtifYt+av9h2+qmpKcrPzyepVEpisZjUajW9evXKZcv8wmMF3r59SwUFBSSXy8nHx4dWrVpFSUlJZDQaXfowGAy0detWEolEJBKJSKFQUElJCb1+/fq3Yrebm5ujmpoaCg8PJ6FQSKGhoVRVVUWzs7OOOkNDQ5SdnU1r166llStXUmBgIKWlpdGTJ0+cnoUFxwoQEdXW1lJISAh5e3s7HTGw8BvZmUwmx9ENDx48cBvz6OgolZSUUGhoKAmFQgoODqadO3eSXq//6bva+01NTXVbduXKFQJAra2tRET08uVLSk5OJrFYTFKplIqKiuj58+dOdYiILBYLlZaWkkwmIy8vL5cjBvR6PcXFxZGvry9JJBJSKpV08uRJ+vTpk8d4GfsbeBEtGMNmjDHGGGNOeA0TY4wxxpgHnDAxxhhjjHnACRNjjDHGmAecMDHGGGOMecAJE2OMMcaYB5wwMcYYY4x5wAkTY4wxxpgHnDAxxhhjjHnACRNjjDHGmAecMDHGGGOMecAJE2OMMcaYB5wwMcYYY4x5wAkTY4wxxpgH/wEF8fs7tBsJgwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Learning curve for KNN**"
      ],
      "metadata": {
        "id": "ELZSiBQrddSM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import LearningCurveDisplay, ShuffleSplit\n",
        "\n",
        "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 6), sharey=True)\n",
        "\n",
        "common_params = {\n",
        "    \"X\": X,\n",
        "    \"y\": y,\n",
        "    \"train_sizes\": np.linspace(0.1, 1.0, 5),\n",
        "    \"cv\": ShuffleSplit(n_splits=5, test_size=0.2, random_state=0),\n",
        "    \"score_type\": \"both\",\n",
        "    \"line_kw\": {\"marker\": \"o\"},\n",
        "    \"std_display_style\": \"fill_between\",\n",
        "    \"score_name\": \"Accuracy\",\n",
        "}\n",
        "\n",
        "for ax_idx, estimator in enumerate([knn]):\n",
        "    LearningCurveDisplay.from_estimator(estimator, **common_params, ax=ax[ax_idx])\n",
        "    handles, label = ax[ax_idx].get_legend_handles_labels()\n",
        "    ax[ax_idx].legend(handles[:2], [\"Training Score\", \"Test Score\"])\n",
        "    ax[ax_idx].set_title(f\"Learning Curve for {estimator.__class__.__name__}\")"
      ],
      "metadata": {
        "id": "v9KHpQffdOL8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model\n",
        "with open('KNN_model.pkl', 'wb') as f:\n",
        "    pickle.dump(knn, f)"
      ],
      "metadata": {
        "id": "rhA9t77ushS3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4. Support vector machines (SVM)**"
      ],
      "metadata": {
        "id": "WnpsVnUnv8ao"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n"
      ],
      "metadata": {
        "id": "lGQFazOEtFIm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#1024\n",
        "svm = SVC(C=5, kernel='rbf', probability=True, gamma='scale', random_state=10)"
      ],
      "metadata": {
        "id": "TQoMw-p0es0Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2048\n",
        "#svm = SVC(C=10, kernel='rbf', probability=True, gamma='scale', random_state=10)"
      ],
      "metadata": {
        "id": "JQgVaonHewQ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svm.fit(X_train,y_train)"
      ],
      "metadata": {
        "id": "DtPKi_4UtFFp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "9988f535-71ea-45ad-8b90-bce725833e1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=5, probability=True, random_state=10)"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=5, probability=True, random_state=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=5, probability=True, random_state=10)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "preds_svm = svm.predict(X_test)"
      ],
      "metadata": {
        "id": "_dgiNh1UtFDQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test,preds_svm))\n",
        "print('\\n')\n",
        "print(classification_report(y_test,preds_svm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h4B_yshjpTne",
        "outputId": "12eb1811-0032-42cf-c1df-ab4db735b452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[152  14]\n",
            " [ 26  69]]\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.92      0.88       166\n",
            "           1       0.83      0.73      0.78        95\n",
            "\n",
            "    accuracy                           0.85       261\n",
            "   macro avg       0.84      0.82      0.83       261\n",
            "weighted avg       0.85      0.85      0.84       261\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test the model on the testing set\n",
        "svm_train_accuracy = svm.score(X_train, y_train)\n",
        "svm_test_accuracy = svm.score(X_test, y_test)\n",
        "\n",
        "print(\"Training accuracy:\", svm_train_accuracy)\n",
        "print(\"Testing accuracy:\", svm_test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4MKfvRnypNmw",
        "outputId": "8c6a82cb-baca-4ed9-8d38-cff2830e017b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.9779482262703739\n",
            "Testing accuracy: 0.8467432950191571\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Balanced accuracy:\", balanced_accuracy_score(y_test, preds_svm))\n",
        "print(\"Matthews correlation coefficient (MCC):\", matthews_corrcoef(y_test, preds_svm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ex2uOoGI0gHn",
        "outputId": "9c931b43-cace-4dac-db96-967c64b895cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Balanced accuracy: 0.8209892200380469\n",
            "Matthews correlation coefficient (MCC): 0.6632652473684647\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Hyperparameter tuning**\n",
        "\n",
        "gamma is not used with linear kernel"
      ],
      "metadata": {
        "id": "z5jf_yvDTZ4B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# NOTE: This cell takes ~ 4 hours to complete in Google Colab.\n",
        "grid = {\n",
        "    'C': [0.1, 1, 5, 10, 15, 20],\n",
        "    'kernel': ['poly', 'rbf', 'sigmoid'],\n",
        "    'gamma': [0.1, 1.0, 10.0, 'scale', 'auto']\n",
        "}\n",
        "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=40)\n",
        "\n",
        "sv = SVC()\n",
        "\n",
        "grid_search = GridSearchCV(sv, param_grid=grid, cv=skf)\n",
        "grid_search.fit(X, y)\n",
        "\n",
        "best_sv = grid_search.best_estimator_\n",
        "\n",
        "scores_sv = cross_val_score(best_sv, X, y, cv=skf)\n",
        "\n",
        "print('Best parameters: ',grid_search.best_params_)\n",
        "print('\\n')\n",
        "print('Cross-validation scores:', scores_sv)\n",
        "print('\\n')\n",
        "print('Mean score:', np.mean(scores_sv))\n",
        "print('\\n')\n",
        "print('Best score: ', grid_search.best_score_)"
      ],
      "metadata": {
        "id": "5s89xRwFOkoN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c3710df-8079-4e41-a4f7-6e6591bb050c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters:  {'C': 0.1, 'gamma': 1.0, 'kernel': 'poly'}\n",
            "\n",
            "\n",
            "Cross-validation scores: [0.84291188 0.83141762 0.8467433  0.86206897 0.83846154]\n",
            "\n",
            "\n",
            "Mean score: 0.844320660182729\n",
            "\n",
            "\n",
            "Best score:  0.844320660182729\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('SVM_model.pkl', 'wb') as f:\n",
        "    pickle.dump(svm, f)"
      ],
      "metadata": {
        "id": "ie7i6wCaE_yR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **ROC curve**"
      ],
      "metadata": {
        "id": "qYs1_xec56i_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_proba_svm = svm.predict_proba(X_test)[:,1]\n",
        "fpr_svm, tpr_svm, thresholds_svm = roc_curve(y_test, y_pred_proba_svm)\n",
        "auc_svm = roc_auc_score(y_test, y_pred_proba_svm)\n",
        "\n",
        "plt.plot(fpr_svm, tpr_svm, label='SVM (AUC= %.2f)' % (auc_svm))\n",
        "plt.plot([0, 1], [0, 1],'r')\n",
        "plt.xlim([0, 1])\n",
        "plt.ylim([0, 1])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC curve')\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "iAXPK_8tSWsb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "39ba0eaf-dde0-46c3-fee6-cc21312bd6ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7d218346b6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **5. Naive Bayes**"
      ],
      "metadata": {
        "id": "3cdvaO_AUxud"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "# var_smoothing=1e-9 is the default. This is used to decrease regularization\n"
      ],
      "metadata": {
        "id": "jHtGEkqrSWp2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1024\n",
        "nb = MultinomialNB(alpha = 0.5)"
      ],
      "metadata": {
        "id": "EhjqBU5khnLu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2048\n",
        "#nb = MultinomialNB(alpha = 0.1)"
      ],
      "metadata": {
        "id": "DBKbChl1hkO5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nb.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "oZO1HtJXASvL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "fa7f2f44-46b1-46c2-f8c1-88ebf9f92593"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB(alpha=0.5)"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB(alpha=0.5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB(alpha=0.5)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_nb = nb.predict(X_test)"
      ],
      "metadata": {
        "id": "j9nzGAiU052L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test,pred_nb))\n",
        "print('\\n')\n",
        "print(classification_report(y_test,pred_nb))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h2ry9ab6pvXn",
        "outputId": "762a5f23-fc46-44df-dfb6-af52fbcc2d0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[156  10]\n",
            " [ 38  57]]\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.94      0.87       166\n",
            "           1       0.85      0.60      0.70        95\n",
            "\n",
            "    accuracy                           0.82       261\n",
            "   macro avg       0.83      0.77      0.79       261\n",
            "weighted avg       0.82      0.82      0.81       261\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test the model on the testing set\n",
        "nb_train_accuracy = nb.score(X_train, y_train)\n",
        "nb_test_accuracy = nb.score(X_test, y_test)\n",
        "\n",
        "print(\"Training accuracy:\", nb_train_accuracy)\n",
        "print(\"Testing accuracy:\", nb_test_accuracy)"
      ],
      "metadata": {
        "id": "3fNUE70XRGLM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2bb2637-8213-4b3b-831a-288b9d512328"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 0.8264621284755513\n",
            "Testing accuracy: 0.8160919540229885\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Balanced accuracy:\", balanced_accuracy_score(y_test, pred_nb))\n",
        "print(\"Matthews correlation coefficient (MCC):\", matthews_corrcoef(y_test, pred_nb))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THmmeA8i0mPZ",
        "outputId": "28654887-c904-4779-dc1f-f9cb79a94b3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Balanced accuracy: 0.7698795180722892\n",
            "Matthews correlation coefficient (MCC): 0.5945351034238081\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('NB_model.pkl', 'wb') as f:\n",
        "    pickle.dump(nb, f)"
      ],
      "metadata": {
        "id": "nFpp-QzrFUC8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Hyperparameters tuning**"
      ],
      "metadata": {
        "id": "iM8egcxPTs8d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grid = {\n",
        "    'alpha': [0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]}\n",
        "\n",
        "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=40)\n",
        "\n",
        "nb = MultinomialNB()\n",
        "\n",
        "grid_search = GridSearchCV(nb, param_grid=grid, cv=skf)\n",
        "grid_search.fit(X, y)\n",
        "\n",
        "best_nb = grid_search.best_estimator_\n",
        "\n",
        "scores_nb = cross_val_score(best_nb, X, y, cv=skf)\n",
        "\n",
        "print('Best parameters: ',grid_search.best_params_)\n",
        "print('\\n')\n",
        "print('Cross-validation scores:', scores_nb)\n",
        "print('\\n')\n",
        "print('Mean score:', np.mean(scores_nb))\n",
        "print('\\n')\n",
        "print('Best score: ', grid_search.best_score_)\n"
      ],
      "metadata": {
        "id": "XVRFMLMoVzIL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25555af0-4825-4e63-d980-7c8e7e72a17a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters:  {'alpha': 0.2}\n",
            "\n",
            "\n",
            "Cross-validation scores: [0.78544061 0.81609195 0.80842912 0.78927203 0.77692308]\n",
            "\n",
            "\n",
            "Mean score: 0.7952313586796345\n",
            "\n",
            "\n",
            "Best score:  0.7952313586796345\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **ROC curve**"
      ],
      "metadata": {
        "id": "i4SxX9ZtTIy2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_proba_nb = nb.predict_proba(X_test)[:,1]\n",
        "fpr_nb, tpr_nb, thresholds_nb = roc_curve(y_test, y_pred_proba_nb)\n",
        "auc_nb = roc_auc_score(y_test, y_pred_proba_nb)\n",
        "\n",
        "plt.plot(fpr_nb, tpr_nb, label='Naive Bayes (AUC= %.2f)' % (auc_nb))\n",
        "plt.plot([0, 1], [0, 1],'r')\n",
        "plt.xlim([0, 1])\n",
        "plt.ylim([0, 1])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC curve')\n",
        "plt.legend()\n",
        "\n",
        "# Add threshold point\n",
        "# threshold = 0.86\n",
        "# idx = np.min(np.where(tpr > threshold))\n",
        "# plt.scatter(fpr[idx], tpr[idx], marker='o', color='black', label='Threshold')\n"
      ],
      "metadata": {
        "id": "ffx6xC7LTIy2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "b7c1bd20-2b13-4053-ae43-530d5a84c650"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7d218344df60>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Learning curves for SCV and Naive Bayes**"
      ],
      "metadata": {
        "id": "dAN4jqvJSGXQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import LearningCurveDisplay, ShuffleSplit\n",
        "\n",
        "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10, 6), sharey=True)\n",
        "\n",
        "common_params = {\n",
        "    \"X\": X,\n",
        "    \"y\": y,\n",
        "    \"train_sizes\": np.linspace(0.1, 1.0, 5),\n",
        "    \"cv\": ShuffleSplit(n_splits=10, test_size=0.2, random_state=0),\n",
        "    \"score_type\": \"both\",\n",
        "    \"n_jobs\": 4,\n",
        "    \"line_kw\": {\"marker\": \"o\"},\n",
        "    \"std_display_style\": \"fill_between\",\n",
        "    \"score_name\": \"Accuracy\",\n",
        "}\n",
        "\n",
        "for ax_idx, estimator in enumerate([nb, svm]):\n",
        "    LearningCurveDisplay.from_estimator(estimator, **common_params, ax=ax[ax_idx])\n",
        "    handles, label = ax[ax_idx].get_legend_handles_labels()\n",
        "    ax[ax_idx].legend(handles[:2], [\"Training Score\", \"Test Score\"])\n",
        "    ax[ax_idx].set_title(f\"Learning Curve for {estimator.__class__.__name__}\")\n"
      ],
      "metadata": {
        "id": "SwA3Wa_5_2FB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **get labels back**"
      ],
      "metadata": {
        "id": "_dWdvu1AWddK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred_nb"
      ],
      "metadata": {
        "id": "HkykEqKmYeoS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_le.inverse_transform(pred_nb)\n"
      ],
      "metadata": {
        "id": "gzhWcvUlWg38"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **ROC curve for all**"
      ],
      "metadata": {
        "id": "uVlDPDB24y-U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.rcParams['font.size'] = 13\n",
        "\n",
        "plt.plot(fpr_Rf, tpr_Rf, label='Rf (AUC= %.2f)' % (auc_Rf))\n",
        "plt.plot(fpr_xgb, tpr_xgb, label='XGBoost (AUC= %.2f)' % (auc_xgb))\n",
        "plt.plot(fpr_svm, tpr_svm, label='SVM (AUC= %.2f)' % (auc_svm))\n",
        "plt.plot(fpr_knn, tpr_knn, label='KNN (AUC= %.2f)' % (auc_knn))\n",
        "plt.plot(fpr_nb, tpr_nb, label='NB (AUC= %.2f)' % (auc_nb))\n",
        "plt.plot([0, 1], [0, 1],'k')\n",
        "plt.xlim([0, 1])\n",
        "plt.ylim([0, 1])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC curves')\n",
        "plt.legend()\n",
        "plt.savefig('ROC curves_1024.pdf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "MfXq76KeFZY3",
        "outputId": "1c8e9516-3421-485e-c400-8727a62cb785"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}